WorldWideScience

Sample records for optimization based day-ahead

  1. Optimal day-ahead operational planning of microgrids

    International Nuclear Information System (INIS)

    Hosseinnezhad, Vahid; Rafiee, Mansour; Ahmadian, Mohammad; Siano, Pierluigi

    2016-01-01

    Highlights: • A new multi-objective model for optimal day-ahead operational planning of microgrids is proposed. • A new concept called seamlessness is introduced to control the sustainability of microgrid. • A new method is developed to manage the load and renewable energy resources estimation errors. • A new solution based on a combination of numerical and evolutionary approaches is proposed. - Abstract: Providing a cost-efficient, eco-friendly and sustainable energy is one of the main issues in modern societies. In response to this demand, new features of microgrid technology have provided huge potentials while distributing electricity more effectively, economically and securely. Accordingly, this paper presents a new multi-objective generation management model for optimal day-ahead operational planning of medium voltage microgrids. The proposed model optimizes both pollutant emission and operating cost of a microgrid by using multi-objective optimization. Besides, a seamlessness-selective algorithm is integrated into the model, which can be adopted to achieve the desired self-sufficiency level for microgrids along a specified planning horizon. Furthermore, the model is characterized by a reserve-assessment strategy developed to handle the load and renewable energy resources estimation errors. The introduced model is solved using a combination of numerical and evolutionary methods of species-based quantum particle swarm optimization to find the optimal scheduling scheme and minos-based optimal power flow to optimize the operating cost and emission. In addition, the suggested solution approach also incorporates an efficient mechanism for considering energy storage systems and coding the candidate solutions in the evolutionary algorithm. The proposed model is implemented on a test microgrid and is investigated through simulations to study the different aspects of the problem. The results show significant improvements and benefits which are obtained by

  2. Hybrid Particle Swarm Optimization based Day-Ahead Self-Scheduling for Thermal Generator in Competitive Electricity Market

    DEFF Research Database (Denmark)

    Pindoriya, Naran M.; Singh, S.N.; Østergaard, Jacob

    2009-01-01

    in day-ahead energy market subject to operational constraints and 2) at the same time, to minimize the risk due to uncertainty in price forecast. Therefore, it is a conflicting bi-objective optimization problem which has both binary and continuous optimization variables considered as constrained mixed......This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead self-scheduling for thermal power producer in competitive electricity market. The objective functions considered to model the self-scheduling problem are 1) to maximize the profit from selling energy...... integer nonlinear programming. To demonstrate the effectiveness of the proposed method for self-scheduling in a day-ahead energy market, the locational margin price (LMP) forecast uncertainty in PJM electricity market is considered. An adaptive wavelet neural network (AWNN) is used to forecast the day...

  3. Day-ahead optimal dispatch for wind integrated power system considering zonal reserve requirements

    International Nuclear Information System (INIS)

    Liu, Fan; Bie, Zhaohong; Liu, Shiyu; Ding, Tao

    2017-01-01

    Highlights: • Analyzing zonal reserve requirements for wind integrated power system. • Modeling day-ahead optimal dispatch solved by chance constrained programming theory. • Determining optimal zonal reserve demand with minimum confidence interval. • Analyzing numerical results on test and large-scale real-life power systems. - Abstract: Large-scale integration of renewable power presents a great challenge for day-ahead dispatch to manage renewable resources while provide available reserve for system security. Considering zonal reserve is an effective way to ensure reserve deliverability when network congested, a random day-ahead dispatch optimization of wind integrated power system for a least operational cost is modeled including zonal reserve requirements and N − 1 security constraints. The random model is transformed into a deterministic one based on the theory of chance constrained programming and a determination method of optimal zonal reserve demand is proposed using the minimum confidence interval. After solving the deterministic model, the stochastic simulation is conducted to verify the validity of solution. Numerical tests and results on the IEEE 39 bus system and a large-scale real-life power system demonstrate the optimal day-ahead dispatch scheme is available and the proposed method is effective for improving reserve deliverability and reducing load shedding after large-capacity power outage.

  4. A Unified Trading Model Based on Robust Optimization for Day-Ahead and Real-Time Markets with Wind Power Integration

    DEFF Research Database (Denmark)

    Jiang, Yuewen; Chen, Meisen; You, Shi

    2017-01-01

    In a conventional electricity market, trading is conducted based on power forecasts in the day-ahead market, while the power imbalance is regulated in the real-time market, which is a separate trading scheme. With large-scale wind power connected into the power grid, power forecast errors increase...... in the day-ahead market which lowers the economic efficiency of the separate trading scheme. This paper proposes a robust unified trading model that includes the forecasts of real-time prices and imbalance power into the day-ahead trading scheme. The model is developed based on robust optimization in view...... of the undefined probability distribution of clearing prices of the real-time market. For the model to be used efficiently, an improved quantum-behaved particle swarm algorithm (IQPSO) is presented in the paper based on an in-depth analysis of the limitations of the static character of quantum-behaved particle...

  5. A Unified Trading Model Based on Robust Optimization for Day-Ahead and Real-Time Markets with Wind Power Integration

    Directory of Open Access Journals (Sweden)

    Yuewen Jiang

    2017-04-01

    Full Text Available In a conventional electricity market, trading is conducted based on power forecasts in the day-ahead market, while the power imbalance is regulated in the real-time market, which is a separate trading scheme. With large-scale wind power connected into the power grid, power forecast errors increase in the day-ahead market which lowers the economic efficiency of the separate trading scheme. This paper proposes a robust unified trading model that includes the forecasts of real-time prices and imbalance power into the day-ahead trading scheme. The model is developed based on robust optimization in view of the undefined probability distribution of clearing prices of the real-time market. For the model to be used efficiently, an improved quantum-behaved particle swarm algorithm (IQPSO is presented in the paper based on an in-depth analysis of the limitations of the static character of quantum-behaved particle swarm algorithm (QPSO. Finally, the impacts of associated parameters on the separate trading and unified trading model are analyzed to verify the superiority of the proposed model and algorithm.

  6. Aggregators’ Optimal Bidding Strategy in Sequential Day-Ahead and Intraday Electricity Spot Markets

    Directory of Open Access Journals (Sweden)

    Xiaolin Ayón

    2017-04-01

    Full Text Available This paper proposes a probabilistic optimization method that produces optimal bidding curves to be submitted by an aggregator to the day-ahead electricity market and the intraday market, considering the flexible demand of his customers (based in time dependent resources such as batteries and shiftable demand and taking into account the possible imbalance costs as well as the uncertainty of forecasts (market prices, demand, and renewable energy sources (RES generation. The optimization strategy aims to minimize the total cost of the traded energy over a whole day, taking into account the intertemporal constraints. The proposed formulation leads to the solution of different linear optimization problems, following the natural temporal sequence of electricity spot markets. Intertemporal constraints regarding time dependent resources are fulfilled through a scheduling process performed after the day-ahead market clearing. Each of the different problems is of moderate dimension and requires short computation times. The benefits of the proposed strategy are assessed comparing the payments done by an aggregator over a sample period of one year following different deterministic and probabilistic strategies. Results show that probabilistic strategy reports better benefits for aggregators participating in power markets.

  7. Optimal operation and forecasting policy for pump storage plants in day-ahead markets

    International Nuclear Information System (INIS)

    Muche, Thomas

    2014-01-01

    Highlights: • We investigate unit commitment deploying stochastic and deterministic approaches. • We consider day-ahead markets, its forecast and weekly price based unit commitment. • Stochastic and deterministic unit commitment are identical for the first planning day. • Unit commitment and bidding policy can be based on the deterministic approach. • Robust forecasting models should be estimated based on the whole planning horizon. - Abstract: Pump storage plants are an important electricity storage technology at present. Investments in this technology are expected to increase. The necessary investment valuation often includes expected cash flows from future price-based unit commitment policies. A price-based unit commitment policy has to consider market price uncertainty and the information revealing nature of electricity markets. For this environment stochastic programming models are suggested to derive the optimal unit commitment policy. For the considered day-ahead price electricity market stochastic and deterministic unit commitment policies are comparable suggesting an application of easier implementable deterministic models. In order to identify suitable unit commitment and forecasting policies, deterministic unit commitment models are applied to actual day-ahead electricity prices of a whole year. As a result, a robust forecasting model should consider the unit commitment planning period. This robust forecasting models result in expected cash flows similar to realized ones allowing a reliable investment valuation

  8. Optimal scheduling for electric heat booster under day-ahead electricity and heat pricing

    DEFF Research Database (Denmark)

    Cai, Hanmin; You, Shi; Bindner, Henrik W.

    2017-01-01

    Multi-energy system (MES) operation calls for active management of flexible resources across energy sectors to improve efficiency and meet challenging environmental targets. Electric heat booster, a solution for Domestic Hot Water (DHW) preparation under Low-Temperature-District-Heating (LTDH......) context, is identified as one of aforementioned flexible resources for electricity and heat sectors. This paper extends the concept of optimal load scheduling under day-ahead pricing from electricity sector only to both electricity and heat sectors. A case study constructing day-ahead energy prices...

  9. Optimal Resources Planning of Residential Complex Energy System in a Day-ahead Market Based on Invasive Weed Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    P. Αhmadi

    2017-10-01

    Full Text Available This paper deals with optimal resources planning in a residential complex energy system, including FC (fuel cell, PV (Photovoltaic panels and the battery. A day-ahead energy management system (EMS based on invasive weed optimization (IWO algorithm is defined for managing different resources to determine an optimal operation schedule for the energy resources at each time interval to minimize the operation cost of a smart residential complex energy system. Moreover, in this paper the impacts of the sell to grid and purchase from grid are also considered. All practical constraints of the each energy resources and utility policies are taken into account. Moreover, sensitivity analysis are conducted on electricity prices and sell to grid factor (SGF, in order to improve understanding the impact of key parameters on residential CHP systems economy. It is shown that proposed system can meet all electrical and thermal demands with economic point of view. Also enhancement of electricity price leads to substantial growth in utilization of proposed CHP system.

  10. Energy and Reserve under Distributed Energy Resources Management-Day-Ahead, Hour-Ahead and Real-Time

    DEFF Research Database (Denmark)

    Soares, Tiago; Silva, Marco; Sousa, Tiago

    2017-01-01

    and evaluates a generic model for day-ahead, intraday (hour-ahead) and real-time scheduling, considering the joint optimization of energy and reserve in the scope of the virtual power player concept. The model aims to minimize the operation costs in the point of view of one aggregator agent taking into account...

  11. Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey

    Directory of Open Access Journals (Sweden)

    Mustafa Akpinar

    2017-06-01

    Full Text Available The increase of energy consumption in the world is reflected in the consumption of natural gas. However, this increment requires additional investment. This effect leads imbalances in terms of demand forecasting, such as applying penalties in the case of error rates occurring beyond the acceptable limits. As the forecasting errors increase, penalties increase exponentially. Therefore, the optimal use of natural gas as a scarce resource is important. There are various demand forecast ranges for natural gas and the most difficult range among these demands is the day-ahead forecasting, since it is hard to implement and makes predictions with low error rates. The objective of this study is stabilizing gas tractions on day-ahead demand forecasting using low-consuming subscriber data for minimizing error using univariate artificial bee colony-based artificial neural networks (ANN-ABC. For this purpose, households and low-consuming commercial users’ four-year consumption data between the years of 2011–2014 are gathered in daily periods. Previous consumption values are used to forecast day-ahead consumption values with sliding window technique and other independent variables are not taken into account. Dataset is divided into two parts. First, three-year daily consumption values are used with a seven day window for training the networks, while the last year is used for the day-ahead demand forecasting. Results show that ANN-ABC is a strong, stable, and effective method with a low error rate of 14.9 mean absolute percentage error (MAPE for training utilizing MAPE with a univariate sliding window technique.

  12. Agent-Based Modeling of Day-Ahead Real Time Pricing in a Pool-Based Electricity Market

    Directory of Open Access Journals (Sweden)

    Sh. Yousefi

    2011-09-01

    Full Text Available In this paper, an agent-based structure of the electricity retail market is presented based on which day-ahead (DA energy procurement for customers is modeled. Here, we focus on operation of only one Retail Energy Provider (REP agent who purchases energy from DA pool-based wholesale market and offers DA real time tariffs to a group of its customers. As a model of customer response to the offered real time prices, an hourly acceptance function is proposed in order to represent the hourly changes in the customer’s effective demand according to the prices. Here, Q-learning (QL approach is applied in day-ahead real time pricing for the customers enabling the REP agent to discover which price yields the most benefit through a trial-and-error search. Numerical studies are presented based on New England day-ahead market data which include comparing the results of RTP based on QL approach with that of genetic-based pricing.

  13. Collaborative Optimal Pricing and Day-Ahead and Intra-Day Integrative Dispatch of the Active Distribution Network with Multi-Type Active Loads

    Directory of Open Access Journals (Sweden)

    Chong Chen

    2018-04-01

    Full Text Available In order to better handle the new features that emerge at both ends of supply and demand, new measures are constantly being introduced, such as demand-side management (DSM and prediction of uncertain output and load. However, the existing DSM strategies, like real-time price (RTP, and dispatch methods are optimized separately, and response models of active loads, such as the interruptible load (IL, are still imperfect, which make it difficult for the active distribution network (ADN to achieve global optimal operation. Therefore, to better manage active loads, the response characteristics including both the response time and the responsibility and compensation model of IL for cluster users, and the real-time demand response model for price based load, were analyzed and established. Then, a collaborative optimization strategy of RTP and optimal dispatch of ADN was proposed, which can realize an economical operation based on mutual benefit and win-win mode of supply and demand sides. Finally, the day-ahead and intra-day integrative dispatch model using different time-scale prediction data was established, which can achieve longer-term optimization while reducing the impact of prediction errors on the dispatch results. With numerical simulations, the effectiveness and superiority of the proposed strategy were verified.

  14. Powernext Day-AheadTM statistics - June 30, 2006

    International Nuclear Information System (INIS)

    2006-01-01

    The introduction of a power exchange in France is a direct response to the opening up of the European electricity markets. Powernext SA is a Multilateral Trading Facility in charge of managing an optional and anonymous organised exchange offering: - Day-ahead contracts for the management of volume risk on Powernext Day-Ahead TM since 21 November 2001, - Medium term contracts for the management of price risk on Powernext Futures TM since 18 June 2004. This document presents in a series of tables and graphics the June 30, 2006 update of Powernext Day-Ahead TM statistics: daily traded volumes and base-load prices from November 2001 to June 2006, monthly overview from June 2005 to June 2006 (volumes and prices), weekly overview from March to June 2006 (volumes and prices), daily and hourly overview and market resilience for June 2006, power consumption in May and June 2006 (average consumption, average forecasted consumption and average price on Powernext Day-Ahead TM ), power consumption on the French hub from July 2005 to May 2006 and Powernext Day-Ahead TM prices, transfer capacities in June 2006 (auction results for France-Germany, France-Belgium, France-UK, France-Spain and France-Italy, and daily capacity allocation for France-Switzerland), temperature variations in France from January 2005 to June 2006 and base-load Powernext Day-Ahead TM prices, and balancing mechanism for April, May and June 2006 (half-hourly imbalance settlement prices). (J.S.)

  15. Coordination of bidding strategies in day-ahead energy and spinning reserve markets

    International Nuclear Information System (INIS)

    Fushuan Wen; David, A.K.

    2002-01-01

    In this paper, the problem of building optimally coordinated bidding strategies for competitive suppliers in day-ahead energy and spinning reserve markets is addressed. It is assumed that each supplier bids 24 linear energy supply functions and 24 linear spinning reserve supply functions, one for each hour, into the energy and spinning reserve markets, respectively, and each market is cleared separately and simultaneously for all the 24 delivery hours. Each supplier makes decisions on unit commitment and chooses the coefficients in the linear energy and spinning reserve supply functions to maximise total benefits, subject to expectations about how rival suppliers will bid in both markets. Two different bidding schemes have been suggested for each hour, and based on them an overall coordinated bidding strategy in the day-ahead energy and spinning reserve market is then developed. Stochastic optimisation models are first developed to describe these two different bidding schemes and a genetic algorithm (GA) is then used to build the optimally coordinated bidding strategies for each scheme and to develop an overall bidding strategy for the day-ahead energy and spinning reserve markets. A numerical example is utilised to illustrate the essential features of the method. (Author)

  16. Can a wind farm with CAES survive in the day-ahead market?

    International Nuclear Information System (INIS)

    Mauch, Brandon; Carvalho, Pedro M.S.; Apt, Jay

    2012-01-01

    We investigate the economic viability of coupling a wind farm with compressed air energy storage (CAES) to participate in the day-ahead electricity market at a time when renewable portfolio standards are not binding and wind competes freely in the marketplace. In our model, the CAES is used to reduce the risk of committing uncertain quantities of wind energy and to shift dispatch of wind generation to high price periods. Other sources of revenue (capacity markets, ancillary services, price arbitrage) are not included in the analysis. We present a model to calculate profit maximizing day-ahead dispatch schedules based on wind forecasts. Annual profits are determined with dispatch schedules and actual wind generation values. We find that annual income for the modeled wind–CAES system would not cover annualized capital costs using market prices from the years 2006 to 2009. We also estimate market prices with a carbon price of $20 and $50 per tonne CO 2 and find that revenue would still not cover the capital costs. The implied cost per tonne of avoided CO 2 to make a wind–CAES profitable from trading on the day-ahead market is roughly $100, with large variability due to electric power prices. - Highlights: ► We modeled a wind farm participating in the day-ahead electricity market. ► We calculated optimal day-ahead market offers based on wind forecasts. ► Revenue is then calculated using measured wind power. ► We find that revenue is insufficient to cover capital costs at current market prices.

  17. Incentive-based demand response programs designed by asset-light retail electricity providers for the day-ahead market

    DEFF Research Database (Denmark)

    Fotouhi Ghazvini, Mohammad Ali; Faria, Pedro; Ramos, Sergio

    2015-01-01

    how a REP with light physical assets, such as DG (distributed generation) units and ESS (energy storage systems), can survive in a competitive retail market. The paper discusses the effective risk management strategies for the REPs to deal with the uncertainties of the DAM (day-ahead market) and how...... to hedge the financial losses in the market. A two-stage stochastic programming problem is formulated. It aims to establish the financial incentive-based DR programs and the optimal dispatch of the DG units and ESSs. The uncertainty of the forecasted day-ahead load demand and electricity price is also...... taken into account with a scenario-based approach. The principal advantage of this model for REPs is reducing the risk of financial losses in DAMs, and the main benefit for the whole system is market power mitigation by virtually increasing the price elasticity of demand and reducing the peak demand....

  18. Deriving Optimal End of Day Storage for Pumped-Storage Power Plants in the Joint Energy and Reserve Day-Ahead Scheduling

    Directory of Open Access Journals (Sweden)

    Manuel Chazarra

    2017-06-01

    Full Text Available This paper presents a new methodology to maximise the income and derive the optimal end of day storage of closed-loop and daily-cycle pumped-storage hydropower plants. The plants participate in the day-ahead energy market as a price-taker and in the secondary regulation reserve market as a price-maker, in the context of the Iberian electricity system. The real-time use of the committed reserves is considered in the model formulation. The operation of the plants with the proposed methodology is compared to the ones that use an end of day storage of an empty reservoir or half of the storage capacity. Results show that the proposed methodology increases the maximum theoretical income in all the plants analysed both if they only participate in the day-ahead energy market and if they also participate in the secondary regulation service. It is also shown that the increase in the maximum theoretical income strongly depends on the size of the plant. In addition, it is proven that the end of day storages change notably in the new reserve-driven strategies of pumped-storage hydropower plants and that the proposed methodology is even more recommended if the secondary regulation service is considered.

  19. Co-optimization of Energy and Demand-Side Reserves in Day-Ahead Electricity Markets

    Science.gov (United States)

    Surender Reddy, S.; Abhyankar, A. R.; Bijwe, P. R.

    2015-04-01

    This paper presents a new multi-objective day-ahead market clearing (DAMC) mechanism with demand-side reserves/demand response (DR) offers, considering realistic voltage-dependent load modeling. The paper proposes objectives such as social welfare maximization (SWM) including demand-side reserves, and load served error (LSE) minimization. In this paper, energy and demand-side reserves are cleared simultaneously through co-optimization process. The paper clearly brings out the unsuitability of conventional SWM for DAMC in the presence of voltage-dependent loads, due to reduction of load served (LS). Under such circumstances multi-objective DAMC with DR offers is essential. Multi-objective Strength Pareto Evolutionary Algorithm 2+ (SPEA 2+) has been used to solve the optimization problem. The effectiveness of the proposed scheme is confirmed with results obtained from IEEE 30 bus system.

  20. Energy and Reserve under Distributed Energy Resources Management—Day-Ahead, Hour-Ahead and Real-Time

    Directory of Open Access Journals (Sweden)

    Tiago Soares

    2017-11-01

    Full Text Available The increasing penetration of distributed energy resources based on renewable energy sources in distribution systems leads to a more complex management of power systems. Consequently, ancillary services become even more important to maintain the system security and reliability. This paper proposes and evaluates a generic model for day-ahead, intraday (hour-ahead and real-time scheduling, considering the joint optimization of energy and reserve in the scope of the virtual power player concept. The model aims to minimize the operation costs in the point of view of one aggregator agent taking into account the balance of the distribution system. For each scheduling stage, previous scheduling results and updated forecasts are considered. An illustrative test case of a distribution network with 33 buses, considering a large penetration of distribution energy resources allows demonstrating the benefits of the proposed model.

  1. Optimal bidding in Turkey day ahead electricity market for wind energy and pumped storage hydro power plant

    Directory of Open Access Journals (Sweden)

    Ceyhun Yıldız

    2016-10-01

    Full Text Available In electrical grid; when the demand power increases energy prices increase, when the demand decreases energy prices decrease. For this reason; to increase the total daily income, it is required to shift generations to the hours that high demand power values occurred. Wind Power Plants (WPP have unstable and uncontrollable generation characteristic. For this reason, energy storage systems are needed to shift the generations of WPPs in time scale. In this study, four wind power plants (WPP which are tied to the Turkish interconnected grid and a pumped hydro storage power plant (PSPP that meets the energy storage requirement of these power plants are investigated in Turkey day ahead energy market. An optimization algorithm is developed using linear programming technique to maximize the day ahead market bids of these plants which are going to generate power together. When incomes and generations of the plants that are operated with optimization strategy is analyzed, it is seen that annual income increased by 2.737% compared with WPPs ‘s alone operation and generations are substantially shifted to the high demand power occurred hours.

  2. Hourly Electricity Prices in Day-Ahead Markets

    NARCIS (Netherlands)

    R. Huisman (Ronald); C. Huurman; R.J. Mahieu (Ronald)

    2007-01-01

    textabstractThis paper focuses on the characteristics of hourly electricity prices in day-ahead markets. In these markets, quotes for day-ahead delivery of electricity are submitted simultaneously for all hours in the next day. The same information set is used for quoting all hours of the day. The

  3. Day-Ahead Anticipation of Complex Network Vulnerability

    Science.gov (United States)

    Stefanov, S. Z.; Wang, Paul P.

    2017-11-01

    In this paper, a day-ahead anticipation of complex network vulnerability for an intentional threat of an attack or a shock is carried out. An ecological observer is introduced for that reason, which is a watch in the intentional multiverse, tiled by cells; dynamics of the intentional threat for a day-ahead is characterized by a space-time cell; spreading of the intentional threat is derived from its energy; duration of the intentional threat is found by the self-assembling of a space-time cell; the lower bound of probability is assessed to anticipate for a day-ahead the intentional threat; it is indicated that this vulnerability anticipation for a day-ahead is right when the intentional threat leads to dimension doubling of the complex network.

  4. Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme

    International Nuclear Information System (INIS)

    Shayeghi, H.; Ghasemi, A.

    2013-01-01

    Highlights: • Presenting a hybrid CGSA-LSSVM scheme for price forecasting. • Considering uncertainties for filtering in input data and feature selection to improve efficiency. • Using DWT input featured LSSVM approach to classify next-week prices. • Used three real markets to illustrate performance of the proposed price forecasting model. - Abstract: At the present time, day-ahead electricity market is closely associated with other commodity markets such as fuel market and emission market. Under such an environment, day-ahead electricity price forecasting has become necessary for power producers and consumers in the current deregulated electricity markets. Seeking for more accurate price forecasting techniques, this paper proposes a new combination of a Feature Selection (FS) technique based mutual information (MI) technique and Wavelet Transform (WT) in this study. Moreover, in this paper a new modified version of Gravitational Search Algorithm (GSA) optimization based chaos theory, namely Chaotic Gravitational Search Algorithm (CGSA) is developed to find the optimal parameters of Least Square Support Vector Machine (LSSVM) to predict electricity prices. The performance and price forecast accuracy of the proposed technique is assessed by means of real data from Iran’s, Ontario’s and Spain’s price markets. The simulation results from numerical tables and figures in different cases show that the proposed technique increases electricity price market forecasting accuracy than the other classical and heretical methods in the scientific researches

  5. Hydrothermal self-scheduling problem in a day-ahead electricity market

    International Nuclear Information System (INIS)

    Bisanovic, Smajo; Dlakic, Muris; Hajro, Mensur

    2008-01-01

    This paper addresses the self-scheduling problem of determining the unit commitment status for power generation companies before submitting the hourly bids in a day-ahead market. The hydrothermal model is formulated as a deterministic optimization problem where expected profit is maximized using the 0/1 mixed-integer linear programming technique. This approach allows precise modelling of non-convex variable cost functions and non-linear start-up cost functions of thermal units, non-concave power-discharge characteristics of hydro units, ramp rate limits of thermal units and minimum up and down time constraints for both hydro and thermal units. Model incorporates long-term bilateral contracts with contracted power and price patterns, as well as forecasted market hourly prices for day-ahead auction. Solution is achieved using the homogeneous interior point method for linear programming as state of the art technique, with a branch and bound optimizer for integer programming. The effectiveness of the proposed model in optimizing the generation schedule is demonstrated through the case studies and their analysis. (author)

  6. Stochastic coordination of joint wind and photovoltaic systems with energy storage in day-ahead market

    International Nuclear Information System (INIS)

    Gomes, I.L.R.; Pousinho, H.M.I.; Melício, R.; Mendes, V.M.F.

    2017-01-01

    This paper presents an optimal bid submission in a day-ahead electricity market for the problem of joint operation of wind with photovoltaic power systems having an energy storage device. Uncertainty not only due to the electricity market price, but also due to wind and photovoltaic powers is one of the main characteristics of this submission. The problem is formulated as a two-stage stochastic programming problem. The optimal bids and the energy flow in the batteries are the first-stage variables and the energy deviation is the second stage variable of the problem. Energy storage is a way to harness renewable energy conversion, allowing the store and discharge of energy at conveniently market prices. A case study with data from the Iberian day-ahead electricity market is presented and a comparison between joint and disjoint operations is discussed. - • Joint wind and PV systems with energy storage. • Electricity markets. • Stochastic optimization. • Day-ahead market.

  7. Two-stage stochastic day-ahead optimal resource scheduling in a distribution network with intensive use of distributed energy resources

    DEFF Research Database (Denmark)

    Sousa, Tiago; Ghazvini, Mohammad Ali Fotouhi; Morais, Hugo

    2015-01-01

    The integration of renewable sources and electric vehicles will introduce new uncertainties to the optimal resource scheduling, namely at the distribution level. These uncertainties are mainly originated by the power generated by renewables sources and by the electric vehicles charge requirements....... This paper proposes a two-state stochastic programming approach to solve the day-ahead optimal resource scheduling problem. The case study considers a 33-bus distribution network with 66 distributed generation units and 1000 electric vehicles....

  8. A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price

    Directory of Open Access Journals (Sweden)

    Zhilong Wang

    2014-01-01

    Full Text Available In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO, the backpropagation artificial neural network (BPANN, and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.

  9. Incentive-based demand response programs designed by asset-light retail electricity providers for the day-ahead market

    International Nuclear Information System (INIS)

    Fotouhi Ghazvini, Mohammad Ali; Faria, Pedro; Ramos, Sergio; Morais, Hugo; Vale, Zita

    2015-01-01

    Following the deregulation experience of retail electricity markets in most countries, the majority of the new entrants of the liberalized retail market were pure REP (retail electricity providers). These entities were subject to financial risks because of the unexpected price variations, price spikes, volatile loads and the potential for market power exertion by GENCO (generation companies). A REP can manage the market risks by employing the DR (demand response) programs and using its' generation and storage assets at the distribution network to serve the customers. The proposed model suggests how a REP with light physical assets, such as DG (distributed generation) units and ESS (energy storage systems), can survive in a competitive retail market. The paper discusses the effective risk management strategies for the REPs to deal with the uncertainties of the DAM (day-ahead market) and how to hedge the financial losses in the market. A two-stage stochastic programming problem is formulated. It aims to establish the financial incentive-based DR programs and the optimal dispatch of the DG units and ESSs. The uncertainty of the forecasted day-ahead load demand and electricity price is also taken into account with a scenario-based approach. The principal advantage of this model for REPs is reducing the risk of financial losses in DAMs, and the main benefit for the whole system is market power mitigation by virtually increasing the price elasticity of demand and reducing the peak demand. - Highlights: • Asset-light electricity retail providers subject to financial risks. • Incentive-based demand response program to manage the financial risks. • Maximizing the payoff of electricity retail providers in day-ahead market. • Mixed integer nonlinear programming to manage the risks

  10. Day-ahead tariffs for the alleviation of distribution grid congestion from electric vehicles

    DEFF Research Database (Denmark)

    O'Connell, Niamh; Wu, Qiuwei; Østergaard, Jacob

    2012-01-01

    An economically efficient day-ahead tariff (DT) is proposed with the purpose of preventing the distribution grid congestion resulting from electric vehicle (EV) charging scheduled on a dayahead basis. The DT concept developed herein is derived from the locational marginal price (LMP), in particular...... the congestion cost component of the LMP. A step-wise congestion management structure has been developed whereby the distribution system operator (DSO) predicts congestion for the coming day and publishes DTs prior to the clearing of the day-ahead market. EV fleet operators (FOs) optimize their EV charging...... schedules with respect to the predicted day-ahead prices and the published DTs, thereby avoiding congestion while still minimizing the charging cost. A Danish 400V distribution network is used to carry out case studies to illustrate the effectiveness of the developed concept for the prevention...

  11. Incorporating price-responsive customers in day-ahead scheduling of smart distribution networks

    International Nuclear Information System (INIS)

    Mazidi, Mohammadreza; Monsef, Hassan; Siano, Pierluigi

    2016-01-01

    Highlights: • Proposing a model for incorporating price-responsive customers in day-ahead scheduling of smart distribution networks; this model provides a win–win situation. • Introducing a risk management model based on a bi-level information-gap decision theory and recasting it into its equivalent single-level robust optimization problem using Karush–Kuhn–Tucker optimality conditions. • Utilizing mixed-integer linear programing formulation that is efficiently solved by commercial optimization software. - Abstract: Demand response and real-time pricing of electricity are key factors in a smart grid as they can increase economic efficiency and technical performances of power grids. This paper focuses on incorporating price-responsive customers in day-ahead scheduling of smart distribution networks under a dynamic pricing environment. A novel method is proposed and formulated as a tractable mixed integer linear programming optimization problem whose objective is to find hourly sale prices offered to customers, transactions (purchase/sale) with the wholesale market, commitment of distribution generation units, dispatch of battery energy storage systems and planning of interruptible loads in a way that the profit of the distribution network operator is maximized while customers’ benefit is guaranteed. To hedge distribution network operator against financial risk arising from uncertainty of wholesale market prices, a risk management model based on a bi-level information-gap decision theory is proposed. The proposed bi-level problem is solved by recasting it into its equivalent single-level robust optimization problem using Karush–Kuhn–Tucker optimality conditions. Performance of the proposed model is verified by applying it to a modified version of the IEEE 33-bus distribution test network. Numerical results demonstrate the effectiveness and efficiency of the proposed method.

  12. How Does Pricing of Day-ahead Electricity Market Affect Put Option Pricing?

    Directory of Open Access Journals (Sweden)

    H. Raouf Sheybani

    2016-09-01

    Full Text Available In this paper, impacts of day-ahead market pricing on behavior of producers and consumers in option and day-ahead markets and on option pricing are studied. To this end, two comprehensive equilibrium models for joint put option and day-ahead markets under pay-as-bid and uniform pricing in day-ahead market are presented, respectively. Interaction between put option and day-ahead markets, uncertainty in fuel price, day-ahead market pricing, and elasticity of consumers to strike price, premium price, and day-ahead price are taken into account in these models. By applying the presented models to a test system impact of day-ahead market pricing on equilibrium of joint put option and day-ahead markets are studied.

  13. Powernext Day-AheadTM. Powernext futuresTM. Activity report - 2004

    International Nuclear Information System (INIS)

    2004-01-01

    The introduction of a power exchange in France is a direct response to the opening up of the European electricity markets. Powernext SA is a Multilateral Trading Facility in charge of managing the French power exchange through an optional and anonymous organised exchange offering: - Day-ahead contracts for the management of volume risk on Powernext Day-Ahead TM since 21 November 2001, - Medium term contracts for the management of price risk on Powernext Futures TM since 18 June 2004. This document is the 2004 activity report of Powernext SA, it presents the key figures of the power market and of Powernext in 2004: - Increasing volumes: Powernext Day-Ahead TM 's traded volumes increased by 89%, from 7.48 to 14.18 TWh. Powernext Futures TM kicks off to a promising debut with 12.86 TWh traded in less than 7 months. - Less volatile prices: During 2004, the base price averaged 28.13 euro/MWh, and the peak prices averaged 33.71 euro/MWh. Compared to 2003, these prices decreased by an average of 3.7% on base-load and 10.9% on peak-load. In comparison to the two previous years, the daily volatility has noticeably settled down with 27% on base-load and 37% on peak-load. - Increasing liquidity: 10 new members joined Powernext Day-Ahead TM in 2004. The activity level of the members remains very high as 89% of them trade on an actual daily basis during 2004. The market resiliency stays strong. In December, an additional market 50 MW order on each hour resulted in a balance price variation of only 0.16 euro/MWh, or 0.53% of this balance price. For a 100 MW order, the resiliency is 0.32 euro/MWh, or 1.07% of the balance price. Thus, in 2004, Powernext Day-Ahead TM consolidates its role as a short term reference price. Moreover, in 2004, Powernext launched a futures market, Powernext Futures TM . This new market segment proposes contracts tradable up to 2 years ahead of delivery

  14. Powernext Day-AheadTM statistics April 30, 2005

    International Nuclear Information System (INIS)

    2005-04-01

    The introduction of a power exchange in France is a direct response to the opening up of the European electricity markets. Powernext SA is a Multilateral Trading Facility in charge of managing an optional and anonymous organised exchange offering: - Day-ahead contracts for the management of volume risk on Powernext Day-Ahead TM since 21 November 2001, - Medium term contracts for the management of price risk on Powernext Futures TM since 18 June 2004. This document presents in a series of tables and graphics the April 30, 2005 update of Powernext Day-Ahead TM statistics: traded volumes and average prices from November 2001 to April 2005, monthly overview from April 2004 to April 2005 (volumes, prices and price spreads), weekly overview from January to April 2005, daily and hourly overview and market resilience for April 2005, power consumption in March and April 2005 (average consumption, average forecasted consumption and average price on Powernext Day-Ahead TM ), power consumption on the French hub from January to April 2005 and Powernext Day-Ahead TM prices, transfer capacities in April 2005 (daily capacity allocations for France-Germany, France-Switzerland and France-Spain, daily and monthly capacity allocations for France-Belgium, auction on the France-UK Interconnector, daily and yearly capacity allocation for France-Italy), temperature variations in France from November 2004 to April 2005 and average prices on Powernext Day-Ahead TM , and balancing mechanism for March-April 2005 (half-hourly imbalance settlement prices). (J.S.)

  15. Optimal operational strategies for a day-ahead electricity market in the presence of market power using multi-objective evolutionary algorithms

    Science.gov (United States)

    Rodrigo, Deepal

    2007-12-01

    This dissertation introduces a novel approach for optimally operating a day-ahead electricity market not only by economically dispatching the generation resources but also by minimizing the influences of market manipulation attempts by the individual generator-owning companies while ensuring that the power system constraints are not violated. Since economic operation of the market conflicts with the individual profit maximization tactics such as market manipulation by generator-owning companies, a methodology that is capable of simultaneously optimizing these two competing objectives has to be selected. Although numerous previous studies have been undertaken on the economic operation of day-ahead markets and other independent studies have been conducted on the mitigation of market power, the operation of a day-ahead electricity market considering these two conflicting objectives simultaneously has not been undertaken previously. These facts provided the incentive and the novelty for this study. A literature survey revealed that many of the traditional solution algorithms convert multi-objective functions into either a single-objective function using weighting schemas or undertake optimization of one function at a time. Hence, these approaches do not truly optimize the multi-objectives concurrently. Due to these inherent deficiencies of the traditional algorithms, the use of alternative non-traditional solution algorithms for such problems has become popular and widely used. Of these, multi-objective evolutionary algorithms (MOEA) have received wide acceptance due to their solution quality and robustness. In the present research, three distinct algorithms were considered: a non-dominated sorting genetic algorithm II (NSGA II), a multi-objective tabu search algorithm (MOTS) and a hybrid of multi-objective tabu search and genetic algorithm (MOTS/GA). The accuracy and quality of the results from these algorithms for applications similar to the problem investigated here

  16. Day-Ahead Self-Scheduling of Thermal Generator in Competitive Electricity Market Using Hybrid PSO

    DEFF Research Database (Denmark)

    Pindoriya, N.M.; Singh, Sri Niwas; Østergaard, Jacob

    2009-01-01

    in day-ahead energy market subject to operational constraints and 2) at the same time, to minimize the risk due to uncertainty in price forecast. Therefore, it is a conflicting biobjective optimization problem which has both binary and continuous optimization variables considered as constrained mixed......This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead selfscheduling for thermal power producer in competitive electricity market. The objective functions considered to model the selfscheduling problem are: 1) to maximize the profit from selling energy...... integer nonlinear programming. To demonstrate the effectiveness of the proposed method for self-scheduling in a dayahead energy market, the locational margin price (LMP) forecast uncertainty in PJM electricity market is considered. An adaptive wavelet neural network (AWNN) is used to forecast the dayahead...

  17. Optimal day-ahead wind-thermal unit commitment considering statistical and predicted features of wind speeds

    International Nuclear Information System (INIS)

    Sun, Yanan; Dong, Jizhe; Ding, Lijuan

    2017-01-01

    Highlights: • A day–ahead wind–thermal unit commitment model is presented. • Wind speed transfer matrix is formed to depict the sequential wind features. • Spinning reserve setting considering wind power accuracy and variation is proposed. • Verified study is performed to check the correctness of the program. - Abstract: The increasing penetration of intermittent wind power affects the secure operation of power systems and leads to a requirement of robust and economic generation scheduling. This paper presents an optimal day–ahead wind–thermal generation scheduling method that considers the statistical and predicted features of wind speeds. In this method, the statistical analysis of historical wind data, which represents the local wind regime, is first implemented. Then, according to the statistical results and the predicted wind power, the spinning reserve requirements for the scheduling period are calculated. Based on the calculated spinning reserve requirements, the wind–thermal generation scheduling is finally conducted. To validate the program, a verified study is performed on a test system. Then, numerical studies to demonstrate the effectiveness of the proposed method are conducted.

  18. Powernext Day-AheadTM products and market organization

    International Nuclear Information System (INIS)

    2004-06-01

    The introduction of a power exchange in France is a direct response to the opening up of the European electricity markets. Powernext SA is a Multilateral Trading Facility in charge of managing the French power exchange through an optional and anonymous organised exchange offering: - Day-ahead contracts for the management of volume risk on Powernext Day-Ahead TM since 21 November 2001, - Medium term contracts for the management of price risk on Powernext Futures TM since 18 June 2004. This document presents the principle of the trading of hourly contracts on Powernext Day-Ahead TM , the accessibility of the market, the SAPRI trading platform operated by Nord Pool, the Scandinavian power exchange, the validation of the auction results, the collaboration with LCH.Clearnet SA to secure and facilitate the transactions, and the delivery guarantee implemented by RTE (the French energy transport network). (J.S.)

  19. How to sell renewable electricity. Interactions of the intraday and day-ahead market under uncertainty

    Energy Technology Data Exchange (ETDEWEB)

    Knaut, Andreas; Obermueller, Frank

    2016-04-15

    Uncertainty about renewable production increases the importance of sequential short-term trading in electricity markets. We consider a two-stage market where conventional and renewable producers compete in order to satisfy the demand of consumers. The trading in the first stage takes place under uncertainty about production levels of renewable producers, which can be associated with trading in the day-ahead market. In the second stage, which we consider as the intraday market, uncertainty about the production levels is resolved. Our model is able to capture different levels of flexibility for conventional producers as well as different levels of competition for renewable producers. We find that it is optimal for renewable producers to sell less than the expected production in the day-ahead market. In situations with high renewable production it is even profitable for renewable producers to withhold quantities in the intraday market. However, for an increasing number of renewable producers, the optimal quantity tends towards the expected production level. More competition as well as a more flexible power plant fleet lead to an increase in overall welfare, which can even be further increased by delaying the gate-closure of the day-ahead market or by improving the quality of renewable production forecasts.

  20. New bidding strategy formulation for day-ahead energy and reserve markets based on evolutionary programming

    International Nuclear Information System (INIS)

    Attaviriyanupap, Pathom; Kita, Hiroyuki; Tanaka, Eiichi; Hasegawa, Jun

    2005-01-01

    In this paper, a new bidding strategy for a day-ahead market is formulated. The proposed algorithm is developed from the viewpoint of a generation company wishing to maximize a profit as a participant in the deregulated power and reserve markets. Separate power and reserve markets are considered, both are operated by clearing price auction system. The optimal bidding parameters for both markets are determined by solving an optimization problem that takes unit commitment constraints such as generating limits and unit minimum up/down time constraints into account. This is a non-convex and non-differentiable which is difficult to solve by traditional optimization techniques. In this paper, evolutionary programming is used to solve the problem. The algorithm is applied to both single-sided and double-sided auctions, numerical simulations are carried out to demonstrate the performance of the proposed scheme compared with those obtained from a sequential quadratic programming. (author)

  1. Price forecasting of day-ahead electricity markets using a hybrid forecast method

    International Nuclear Information System (INIS)

    Shafie-khah, M.; Moghaddam, M. Parsa; Sheikh-El-Eslami, M.K.

    2011-01-01

    Research highlights: → A hybrid method is proposed to forecast the day-ahead prices in electricity market. → The method combines Wavelet-ARIMA and RBFN network models. → PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. → One of the merits of the proposed method is lower need to the input data. → The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.

  2. Price forecasting of day-ahead electricity markets using a hybrid forecast method

    Energy Technology Data Exchange (ETDEWEB)

    Shafie-khah, M., E-mail: miadreza@gmail.co [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Moghaddam, M. Parsa, E-mail: parsa@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Sheikh-El-Eslami, M.K., E-mail: aleslam@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of)

    2011-05-15

    Research highlights: {yields} A hybrid method is proposed to forecast the day-ahead prices in electricity market. {yields} The method combines Wavelet-ARIMA and RBFN network models. {yields} PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. {yields} One of the merits of the proposed method is lower need to the input data. {yields} The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.

  3. Real-time versus day-ahead market power in a hydro-based electricity market

    OpenAIRE

    Tangerås, Thomas P.; Mauritzen, Johannes

    2014-01-01

    We analyse in a theoretical framework the link between real-time and day-ahead market performance in a hydro-based and imperfectly competitive wholesale electricity market. Theoretical predictions of the model are tested on data from the Nordic power exchange, Nord Pool Spot (NPS).We reject the hypothesis that prices at NPS were at their competitive levels throughout the period under examination. The empirical approach uses equilibrium prices and quantities and does not rely on bid data nor o...

  4. Forecasting Nord Pool day-ahead prices with an autoregressive model

    International Nuclear Information System (INIS)

    Kristiansen, Tarjei

    2012-01-01

    This paper presents a model to forecast Nord Pool hourly day-ahead prices. The model is based on but reduced in terms of estimation parameters (from 24 sets to 1) and modified to include Nordic demand and Danish wind power as exogenous variables. We model prices across all hours in the analysis period rather than across each single hour of 24 hours. By applying three model variants on Nord Pool data, we achieve a weekly mean absolute percentage error (WMAE) of around 6–7% and an hourly mean absolute percentage error (MAPE) ranging from 8% to 11%. Out of sample results yields a WMAE and an hourly MAPE of around 5%. The models enable analysts and traders to forecast hourly day-ahead prices accurately. Moreover, the models are relatively straightforward and user-friendly to implement. They can be set up in any trading organization. - Highlights: ► Forecasting Nord Pool day-ahead prices with an autoregressive model. ► The model is based on but with the set of parameters reduced from 24 to 1. ► The model includes Nordic demand and Danish wind power as exogenous variables. ► Hourly mean absolute percentage error ranges from 8% to 11%. ► Out of sample results yields a WMAE and an hourly MAPE of around 5%.

  5. Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

    DEFF Research Database (Denmark)

    Soares, João; Valle, Zita; Morais, Hugo

    2013-01-01

    This paper presents a decision support Tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy ressource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application...... of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network...... constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance...

  6. What day-ahead reserves are needed in electric grids with high levels of wind power?

    International Nuclear Information System (INIS)

    Mauch, Brandon; Apt, Jay; Jaramillo, Paulina; Carvalho, Pedro M S

    2013-01-01

    Day-ahead load and wind power forecasts provide useful information for operational decision making, but they are imperfect and forecast errors must be offset with operational reserves and balancing of (real time) energy. Procurement of these reserves is of great operational and financial importance in integrating large-scale wind power. We present a probabilistic method to determine net load forecast uncertainty for day-ahead wind and load forecasts. Our analysis uses data from two different electric grids in the US with similar levels of installed wind capacity but with large differences in wind and load forecast accuracy, due to geographic characteristics. We demonstrate that the day-ahead capacity requirements can be computed based on forecasts of wind and load. For 95% day-ahead reliability, this required capacity ranges from 2100 to 5700 MW for ERCOT, and 1900 to 4500 MW for MISO (with 10 GW of installed wind capacity), depending on the wind and load forecast values. We also show that for each MW of additional wind power capacity for ERCOT, 0.16–0.30 MW of dispatchable capacity will be used to compensate for wind uncertainty based on day-ahead forecasts. For MISO (with its more accurate forecasts), the requirement is 0.07–0.13 MW of dispatchable capacity for each MW of additional wind capacity. (letter)

  7. Day-ahead wind speed forecasting using f-ARIMA models

    International Nuclear Information System (INIS)

    Kavasseri, Rajesh G.; Seetharaman, Krithika

    2009-01-01

    With the integration of wind energy into electricity grids, it is becoming increasingly important to obtain accurate wind speed/power forecasts. Accurate wind speed forecasts are necessary to schedule dispatchable generation and tariffs in the day-ahead electricity market. This paper examines the use of fractional-ARIMA or f-ARIMA models to model, and forecast wind speeds on the day-ahead (24 h) and two-day-ahead (48 h) horizons. The models are applied to wind speed records obtained from four potential wind generation sites in North Dakota. The forecasted wind speeds are used in conjunction with the power curve of an operational (NEG MICON, 750 kW) turbine to obtain corresponding forecasts of wind power production. The forecast errors in wind speed/power are analyzed and compared with the persistence model. Results indicate that significant improvements in forecasting accuracy are obtained with the proposed models compared to the persistence method. (author)

  8. Day-ahead resource scheduling of a renewable energy based virtual power plant

    International Nuclear Information System (INIS)

    Zamani, Ali Ghahgharaee; Zakariazadeh, Alireza; Jadid, Shahram

    2016-01-01

    Highlights: • Simultaneous energy and reserve scheduling of a VPP. • Aggregate uncertainties of electricity prices, renewable generation and load demand. • Develop a stochastic scheduling model using the point estimate method. - Abstract: The evolution of energy markets is accelerating in the direction of a greater reliance upon distributed energy resources (DERs). To manage this increasing two-way complexity, virtual power plants (VPPs) are being deployed today all over the world. In this paper, a probabilistic model for optimal day ahead scheduling of electrical and thermal energy resources in a VPP is proposed where participation of energy storage systems and demand response programs (DRPs) are also taken into account. In the proposed model, energy and reserve is simultaneously scheduled considering the uncertainties of market prices, electrical demand and intermittent renewable power generation. The Point Estimate Method (PEM) is applied in order to model the uncertainties of VPP’s scheduling problem. Moreover, the optimal reserve scheduling of VPP is presented which efficiently decreases VPP’s risk facing the unexpected fluctuations of uncertain parameters at the power delivery time. The results demonstrated that implementation of demand response programs (DRPs) would decrease total operation costs of VPP as well as its dependency on the upstream network.

  9. Optimal Day-ahead Charging Scheduling of Electric Vehicles through an Aggregative Game Model

    DEFF Research Database (Denmark)

    Liu, Zhaoxi; Wu, Qiuwei; Huang, Shaojun

    2017-01-01

    The electric vehicle (EV) market has been growing rapidly around the world. With large scale deployment of EVs in power systems, both the grid and EV owners will benefit if the flexible demand of EV charging is properly managed through the electricity market. When EV charging demand is considerable...... in a grid, it will impact spot prices in the electricity market and consequently influence the charging scheduling itself. The interaction between the spot prices and the EV demand needs to be considered in the EV charging scheduling, otherwise it will lead to a higher charging cost. A day-ahead EV charging...... scheduling based on an aggregative game model is proposed in this paper. The impacts of the EV demand on the electricity prices are formulated with the game model in the scheduling considering possible actions of other EVs. The existence and uniqueness of the pure strategy Nash equilibrium are proved...

  10. Day-ahead distributed energy resource scheduling using differential search algorithm

    DEFF Research Database (Denmark)

    Soares, J.; Lobo, C.; Silva, M.

    2015-01-01

    The number of dispersed energy resources is growing every day, such as the use of more distributed generators. This paper deals with energy resource scheduling model in future smart grids. The methodology can be used by virtual power players (VPPs) considering day-ahead time horizon. This method...... considers that energy resources are managed by a VPP which establishes contracts with their owners. The full AC power flow calculation included in the model takes into account network constraints. This paper presents an application of differential search algorithm (DSA) for solving the day-ahead scheduling...

  11. Sequential bidding in day-ahead auctions for spot energy and power systems reserve

    International Nuclear Information System (INIS)

    Swider, Derk J.

    2005-01-01

    In this paper a novel approach for sequential bidding on day-ahead auction markets for spot energy and power systems reserve is presented. For the spot market a relatively simple method is considered as a competitive market is assumed. For the reserve market one bidder is assumed to behave strategically and the behavior of the competitors is summarized in a probability distribution of the market price. This results in a method for sequential bidding, where the bidding prices and capacities on the spot and reserve markets are calculated by maximizing a stochastic non-linear objective function of expected profit. With an exemplary application is shown that the trading sequence leads to increasing bidding capacities and prices in the reverse rank number of the markets. Hence, the consideration of a defined trading sequence greatly influences the mathematical representation of the optimal bidding behavior under price uncertainty in day-ahead auctions for spot energy and power systems reserve. (Author)

  12. Day-ahead economic optimisation of energy storage

    NARCIS (Netherlands)

    Lampropoulos, I.; Garoufalis, P.; Bosch, van den P.P.J.; Groot, de R.J.W.; Kling, W.L.

    2014-01-01

    This article addresses the day-ahead economic optimisation of energy storage systems within the setting of electricity spot markets. The case study is about a lithium-ion battery system integrated in a low voltage distribution grid with residential customers and photovoltaic generation in the

  13. Everything you need to know to operate on Day-Ahead{sup TM}; Tout ce que vous devez savoir pour intervenir sur Powernext Day-Ahead{sup TM}

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2005-05-15

    The introduction of a power exchange in France is a direct response to the opening up of the European electricity markets. Powernext SA is a Multilateral Trading Facility in charge of managing the French power exchange through an optional and anonymous organised exchange offering: - Day-ahead contracts for the management of volume risk on Powernext Day-Ahead{sup TM} since 21 November 2001, - Medium term contracts for the management of price risk on Powernext Futures{sup TM} since 18 June 2004. This document is the user's guide of Powernext Day-Ahead{sup TM}. It presents: 1 - the power exchange in France (market model, Powernext's regulatory environment, general market operations, 2 - Powernext Day-Ahead{sup TM} members (membership, agreement, start-up notification, tariffs, standing obligations, membership termination), 3 - Powernext Day-Ahead{sup TM} products (specifications, management), 4 - trading (connections, system flow chart, ElWeb client installation and daily connections, portfolio-management, single bidding, type of order, submitting, importing, saving, sending, modifying or canceling an order form, transmission problems, block bidding block bid characteristics, sending, saving, transmitting, modifying and canceling a block bid, price calculations, blind auction procedure, example, taking into account block bids, rounding off rules, consulting and saving the results, sample documents, auction validation), 5 - clearing (LCH.Clearnet SA, legal framework, clearing agreement, PP-DPES agreement, market security, initial margin, daily adjustments, additional margin calls, trade-related financial flows, net financial position, value-added tax, settlement statements, general idea, characteristics and transmission method of settlement statements sample Documents, cash calls, settlement, default, sample cash call documents, financial Reports), 6 - delivery (balance responsible entity, file characteristics and transmission, imbalance settlement

  14. Multi-objective parallel particle swarm optimization for day-ahead Vehicle-to-Grid scheduling

    DEFF Research Database (Denmark)

    Soares, Joao; Vale, Zita; Canizes, Bruno

    2013-01-01

    This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle-To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming...... to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow...

  15. Everything you need to know to operate on Day-AheadTM

    International Nuclear Information System (INIS)

    2005-05-01

    The introduction of a power exchange in France is a direct response to the opening up of the European electricity markets. Powernext SA is a Multilateral Trading Facility in charge of managing the French power exchange through an optional and anonymous organised exchange offering: - Day-ahead contracts for the management of volume risk on Powernext Day-Ahead TM since 21 November 2001, - Medium term contracts for the management of price risk on Powernext Futures TM since 18 June 2004. This document is the user's guide of Powernext Day-Ahead TM . It presents: 1 - the power exchange in France (market model, Powernext's regulatory environment, general market operations, 2 - Powernext Day-Ahead TM members (membership, agreement, start-up notification, tariffs, standing obligations, membership termination), 3 - Powernext Day-Ahead TM products (specifications, management), 4 - trading (connections, system flow chart, ElWeb client installation and daily connections, portfolio-management, single bidding, type of order, submitting, importing, saving, sending, modifying or canceling an order form, transmission problems, block bidding block bid characteristics, sending, saving, transmitting, modifying and canceling a block bid, price calculations, blind auction procedure, example, taking into account block bids, rounding off rules, consulting and saving the results, sample documents, auction validation), 5 - clearing (LCH.Clearnet SA, legal framework, clearing agreement, PP-DPES agreement, market security, initial margin, daily adjustments, additional margin calls, trade-related financial flows, net financial position, value-added tax, settlement statements, general idea, characteristics and transmission method of settlement statements sample Documents, cash calls, settlement, default, sample cash call documents, financial Reports), 6 - delivery (balance responsible entity, file characteristics and transmission, imbalance settlement, interconnection access), contacts and

  16. The impact of renewable energies on EEX day-ahead electricity prices

    International Nuclear Information System (INIS)

    Paraschiv, Florentina; Erni, David; Pietsch, Ralf

    2014-01-01

    In this paper, we analyze the impact of renewable energies, wind and photovoltaic, on the formation of day-ahead electricity prices at EEX. We give an overview of the policy decisions concerning the promotion of renewable energy sources in Germany and discuss their consequences on day-ahead prices. An analysis of electricity spot prices reveals that the introduction of renewable energies enhances extreme price changes. In the frame of a dynamic fundamental model, we show that there has been a continuous electricity price adaption process to market fundamentals. Furthermore, the fundamental drivers of prices differ among hours with different load profiles. Our results imply that renewable energies decrease market spot prices and have implications on the traditional fuel mix for electricity production. However, the prices for the final consumers increased overall because they must pay in addition the feed-in tariffs for the promotion of renewable energy. - Highlights: • We analyze the impact of renewable energies on the day-ahead electricity prices at EEX. • We discuss the impact of renewables on day-ahead prices. • We show a continuous electricity price adaption process to market fundamentals. • Renewable energies decrease market spot prices and shift the merit order curve. • The prices for the final consumers however increased because of feed-in tariffs

  17. Comparative analysis of features of Polish and Lithuanian Day-ahead electricity market prices

    International Nuclear Information System (INIS)

    Bobinaite, Viktorija; Juozapaviciene, Aldona; Staniewski, Marcin; Szczepankowski, Piotr

    2013-01-01

    The goal of this article is to better understand the processes of electricity market price formation in Poland and Lithuania through an analysis of the features (volatility and spikes) of Lithuanian and Polish day-ahead electricity market prices and to assess how acquired electricity price features could affect the achievement of the main goals of the national energy policy. The following indicators have been calculated to determine electricity market price volatility: the oscillation coefficient, the coefficient of variation, an adjusted coefficient of variation, the standard deviation indicator, the daily velocity indicator (based on the overall average price) and the daily velocity indicator (based on the daily average price). Critical values for electricity market price have been calculated to evaluate price spikes. This analysis reveals that electricity market-price volatility is moderate in Poland and high in Lithuania. Electricity price spikes have been an observable phenomenon both in Lithuanian and in Polish day-ahead electricity markets, but they are more common in Lithuania, encompassing 3.15% of the time period analysed in Poland and 4.68% of the time period analysed in Lithuania. Volatile, spiking and increasing electricity prices in day-ahead electricity markets in Lithuania and Poland create preconditions and substantiate the relevance of implementation of the national energy policies and measures. - Highlights: • Moderate and seasonal volatility. • spiking market price and. • stable average price

  18. Optimal Bidding Strategies for Wind Power Producers in the Day-ahead Electricity Market

    Directory of Open Access Journals (Sweden)

    Xiaolin Liu

    2012-11-01

    Full Text Available Wind Power Producers (WPPs seek to maximize profit and minimize the imbalance costs when bidding into the day-ahead market, but uncertainties in the hourly available wind and forecasting errors make the bidding risky. This paper assumes that hourly wind power output given by the forecast follows a normal distribution, and proposes three different bidding strategies, i.e., the expected profit-maximization strategy (EPS, the chance-constrained programming-based strategy (CPS and the multi-objective bidding strategy (ECPS. Analytical solutions under the three strategies are obtained. Comparisons among the three strategies are conducted on a hypothetical wind farm which follows the Spanish market rules. Results show that bid under the EPS is highly dependent on market clearing price, imbalance prices, and also the mean value and standard deviation of wind forecast, and that bid under the CPS is largely driven by risk parameters and the mean value and standard deviation of the wind forecast. The ECPS combining both EPS and CPS tends to choose a compromise bid. Furthermore, the ECPS can effectively control the tradeoff between expected profit and target profit for WPPs operating in volatile electricity markets.

  19. Day Ahead Real Time Pricing and Critical Peak Pricing Based Power Scheduling for Smart Homes with Different Duty Cycles

    Directory of Open Access Journals (Sweden)

    Nadeem Javaid

    2018-06-01

    Full Text Available In this paper, we propose a demand side management (DSM scheme in the residential area for electricity cost and peak to average ratio (PAR alleviation with maximum users’ satisfaction. For this purpose, we implement state-of-the-art algorithms: enhanced differential evolution (EDE and teacher learning-based optimization (TLBO. Furthermore, we propose a hybrid technique (HT having the best features of both aforementioned algorithms. We consider a system model for single smart home as well as for a community (multiple homes and each home consists of multiple appliances with different priorities. The priority is assigned (to each appliance by electricity consumers and then the proposed scheme finds an optimal solution according to the assigned priorities. Day-ahead real time pricing (DA-RTP and critical peak pricing (CPP are used for electricity cost calculation. To validate our proposed scheme, simulations are carried out and results show that our proposed scheme efficiently achieves the aforementioned objectives. However, when we perform a comparison with existing schemes, HT outperforms other state-of-the-art schemes (TLBO and EDE in terms of electricity cost and PAR reduction while minimizing the average waiting time.

  20. Application of a Gradient Descent Continuous Actor-Critic Algorithm for Double-Side Day-Ahead Electricity Market Modeling

    Directory of Open Access Journals (Sweden)

    Huiru Zhao

    2016-09-01

    Full Text Available An important goal of China’s electric power system reform is to create a double-side day-ahead wholesale electricity market in the future, where the suppliers (represented by GenCOs and demanders (represented by DisCOs compete simultaneously with each other in one market. Therefore, modeling and simulating the dynamic bidding process and the equilibrium in the double-side day-ahead electricity market scientifically is not only important to some developed countries, but also to China to provide a bidding decision-making tool to help GenCOs and DisCOs obtain more profits in market competition. Meanwhile, it can also provide an economic analysis tool to help government officials design the proper market mechanisms and policies. The traditional dynamic game model and table-based reinforcement learning algorithm have already been employed in the day-ahead electricity market modeling. However, those models are based on some assumptions, such as taking the probability distribution function of market clearing price (MCP and each rival’s bidding strategy as common knowledge (in dynamic game market models, and assuming the discrete state and action sets of every agent (in table-based reinforcement learning market models, which are no longer applicable in a realistic situation. In this paper, a modified reinforcement learning method, called gradient descent continuous Actor-Critic (GDCAC algorithm was employed in the double-side day-ahead electricity market modeling and simulation. This algorithm can not only get rid of the abovementioned unrealistic assumptions, but also cope with the Markov decision-making process with continuous state and action sets just like the real electricity market. Meanwhile, the time complexity of our proposed model is only O(n. The simulation result of employing the proposed model in the double-side day-ahead electricity market shows the superiority of our approach in terms of participant’s profit or social welfare

  1. The impact of wind power on APX day-ahead electricity prices in the Netherlands VVM-Intermittency project

    Energy Technology Data Exchange (ETDEWEB)

    Nieuwenhout, F.D.J. [ECN Policy Studies, Amsterdam (Netherlands); Brand, A.J. [ECN Wind Energy, Petten (Netherlands)

    2013-02-15

    A detailed analysis was conducted to assess to what extent availability of wind energy has influenced day-ahead electricity prices in the Netherlands over the period 2006-2009. With a meteorological model, time series of day-ahead wind forecasts were generated, and these were compared with APX-ENDEX day-ahead market prices. Wind energy contributes to only 4% of electricity generation in the Netherlands, but was found to depress average day-ahead market prices by about 5%. With the help of the bid curves on the APX-ENDEX day-ahead market for 2009, a model was developed to assess the impact of increasing levels of wind generation on power prices in the Netherlands. One of the main findings is that the future impact on prices will be less than in the past. With an increase of installed wind capacity from 2200 MW to 6000 MW, average day-ahead prices are expected to be depressed by an additional 6% in case no additional conventional generation is assumed. Taking into account existing government policy on wind and ongoing investments in new conventional power plants, prices in 2016 will be only 3% lower.

  2. Day-Ahead Energy Planning with 100% Electric Vehicle Penetration in the Nordic Region by 2050

    Directory of Open Access Journals (Sweden)

    Zhaoxi Liu

    2014-03-01

    Full Text Available This paper presents the day-ahead energy planning of passenger cars with 100% electric vehicle (EV penetration in the Nordic region by 2050. EVs will play an important role in the future energy systems which can both reduce the greenhouse gas (GHG emissions from the transport sector and provide the demand side flexibility required by smart grids. On the other hand, the EVs will increase the electricity consumption. In order to quantify the electricity consumption increase due to the 100% EV penetration in the Nordic region to facilitate the power system planning studies, the day-ahead energy planning of EVs has been investigated with different EV charging scenarios. Five EV charging scenarios have been considered in the energy planning analysis which are: uncontrolled charging all day, uncontrolled charging at home, timed charging, spot price based charging all day and spot price based charging at home. The demand profiles of the five charging analysis show that timed charging is the least favorable charging option and the spot priced based EV charging might induce high peak demands. The EV charging demand will have a considerable share of the energy consumption in the future Nordic power system.

  3. Powernext Day-Ahead. Powernext Futures. Powernext Carbon. Powernext Weather. 2006 activity assessment; Powernext Day-Ahead. Powernext Futures. Powernext Carbon. Powernext Weather. Bilan d'activite 2006

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2006-07-01

    Powernext SA is a Multilateral Trading Facility which organizes and warrants the transactions on the European power exchange and CO{sub 2} exchange markets. This activity report presents the highlights of the market and of Powernext in 2006: market conditions (prices on the electricity market, prices on the CO{sub 2} emission allowances market, weather conditions, institutional aspects of the CO{sub 2} market, power generation and consumption, situation at the borders, fuel prices), traded volumes on Powernext (Powernext Day-Ahead{sup TM} in the case of day-ahead contracts, Powernext Futures{sup TM} in the case of medium-term contracts, and Powernext Carbon in the case of CO{sub 2}), new active members, and Powernext liquidity on the power market. (J.S.)

  4. MASCEM: EPEX SPOT Day-Ahead market integration and simulation

    DEFF Research Database (Denmark)

    Santos, Gabriel; Fernandes, Ricardo; Pinto, Tiago

    2015-01-01

    . It is crucial to MASCEM to have the ability to simulate as many market models and player types as possible, thus enhancing the ability to recreate the electricity markets reality in its maximum possible extent. This paper presents the EPEX Spot Day-Ahead market integration in MASCEM. EPEX Spot SE's mission...

  5. Two-Stage Optimal Scheduling of Electric Vehicle Charging based on Transactive Control

    DEFF Research Database (Denmark)

    Liu, Zhaoxi; Wu, Qiuwei; Ma, Kang

    2018-01-01

    In this paper, a two-stage optimal charging scheme based on transactive control is proposed for the aggregator to manage day-ahead electricity procurement and real-time EV charging management in order to minimize its total operating cost. The day-ahead electricity procurement considers both the day......-ahead energy cost and expected real-time operation cost. In the real-time charging management, the cost of employing the charging flexibility from the EV owners is explicitly modelled. The aggregator uses a transactive market to manage the real-time charging demand to provide the regulating power. A model...... predictive control (MPC) based method is proposed for the aggregator to clear the transactive market. The realtime charging decisions of the EVs are determined by the clearing of the proposed transactive market according to the realtime requests and preferences of the EV owners. As such, the aggregators...

  6. Inexact stochastic risk-aversion optimal day-ahead dispatch model for electricity system management with wind power under uncertainty

    International Nuclear Information System (INIS)

    Ji, Ling; Huang, Guo-He; Huang, Lu-Cheng; Xie, Yu-Lei; Niu, Dong-Xiao

    2016-01-01

    High penetration of wind power generation and deregulated electricity market brings a great challenge to the electricity system operators. It is crucial to make optimal strategy among various generation units and spinning reserve for supporting the system safety operation. By integrating interval two-stage programming and stochastic robust programming, this paper proposes a novel robust model for day-ahead dispatch and risk-aversion management under uncertainties. In the proposed model, the uncertainties are expressed as interval values with different scenario probability. The proposed method requires low computation, and still retains the complete information. A case study is to validate the effectiveness of this approach. Facing the uncertainties of future demand and electricity price, the system operators need to make optimal dispatch strategy for thermal power units and wind turbine, and arrange proper spinning reserve and flexible demand response program to mitigate wind power forecasting error. The optimal strategies provide the system operators with better trade-off between the maximum benefits and the minimum system risk. In additional, two different market rules are compared. The results show that extra financial penalty for the wind power dispatch deviation is another efficient way to enhance the risk consciousness of decision makers and lead to more conservative strategy. - Highlights: • An inexact two-stage stochastic robust programming model for electricity system with wind power penetration. • Uncertainties expressed as discrete intervals and probability distributions. • Demand response program was introduced to adjust the deviation in real-time market. • Financial penalty for imbalance risk from wind power generation was evaluated.

  7. Powernext Day-Ahead. Powernext Futures. Powernext Carbon. Powernext Weather. Activity assessment first-half 2006; Powernext Day-Ahead. Powernext Futures. Powernext Carbon. Powernext Weather. Bilan d'activite premier semestre 2006

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2006-07-01

    Powernext SA is a Multilateral Trading Facility which organizes and warrants the transactions on the European power exchange and CO{sub 2} exchange markets. This activity report presents the highlights of the market and of Powernext in the first half of 2006: market conditions, prices and traded volumes on Powernext (Powernext Day-Ahead{sup TM} in the case of day-ahead contracts, Powernext Futures{sup TM} in the case of medium-term contracts, and Powernext Carbon in the case of CO{sub 2}), new active members, and liquidity on the power market. (J.S.)

  8. The effect of contingency analysis on the nodal prices in the day-ahead market

    International Nuclear Information System (INIS)

    Murphy, Frederic H.; Mudrageda, Murthy; Soyster, Allen L.; Saric, Andrija T.; Stankovic, Aleksandar M.

    2010-01-01

    We look at the effect of modeling branch-outage contingencies on locational marginal prices. To model contingencies in the day-ahead auction, we formulate a two-stage stochastic program. Rather than follow the current practice of including a list of possible contingencies that must be satisfied, we incorporate a larger set of contingencies in the model and allow contingencies to result in load reductions/outages at a cost. The model can be used and interpreted in two ways. One is to look at the tradeoff between reliability and outage costs. Another is to consider the load losses resulting from a contingency to be consumer offers of load reductions in response to line outages as part of the day-ahead auction. In analyzing the model structure, we find that the prices in the model closer in definition to those currently used in the day-ahead auction do not maximize expected surplus because the day-ahead auction produces prices that assume shortages will never occur. This raises issues with the design of auctions with important stochastic elements in the market. We present results for a 68-node grid with 86 branches (lines and transformers) to illustrate how prices and expected values change as the costs of outages are varied.

  9. Day-ahead resource scheduling including demand response for electric vehicles

    DEFF Research Database (Denmark)

    Soares, Joao; Morais, Hugo; Sousa, Tiago

    2014-01-01

    Summary form only given. The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering...

  10. Feedback, competition and stochasticity in a day ahead electricity market

    International Nuclear Information System (INIS)

    Giabardo, Paolo; Zugno, Marco; Pinson, Pierre; Madsen, Henrik

    2010-01-01

    Major recent changes in electricity markets relate to the process for their deregulation, along with increasing participation of renewable (stochastic) generation e.g. wind power. Our general objective is to model how feedback, competition and stochasticity (on the production side) interact in electricity markets, and eventually assess what their effects are on both the participants and the society. For this, day ahead electricity markets are modeled as dynamic closed loop systems, in which the feedback signal is the market price. In parallel, the Cournot competition model is considered. Mixed portfolios with significant share of renewable energy are based on stochastic threshold cost functions. Regarding trading strategies, it is assumed that generators are looking at optimizing their individual profits. The point of view of the society is addressed by analyzing market behavior and stability. The performed simulations show the beneficial effects of employing long term bidding strategies for both generators and society. Sensitivity analyses are performed in order to evaluate the effects of demand elasticity. It is shown that increase in demand elasticity reduces the possibility for the generators to exploit their market power. Furthermore, the results suggest that introduction of wind power generation in the market is beneficial both for the generators and the society.

  11. Feedback, competition and stochasticity in a day ahead electricity market

    Energy Technology Data Exchange (ETDEWEB)

    Giabardo, Paolo; Zugno, Marco; Pinson, Pierre; Madsen, Henrik [DTU Informatics, Technical University of Denmark, Richard Petersens Plads 305, DK-2800 Kgs. Lyngby (Denmark)

    2010-03-15

    Major recent changes in electricity markets relate to the process for their deregulation, along with increasing participation of renewable (stochastic) generation e.g. wind power. Our general objective is to model how feedback, competition and stochasticity (on the production side) interact in electricity markets, and eventually assess what their effects are on both the participants and the society. For this, day ahead electricity markets are modeled as dynamic closed loop systems, in which the feedback signal is the market price. In parallel, the Cournot competition model is considered. Mixed portfolios with significant share of renewable energy are based on stochastic threshold cost functions. Regarding trading strategies, it is assumed that generators are looking at optimizing their individual profits. The point of view of the society is addressed by analyzing market behavior and stability. The performed simulations show the beneficial effects of employing long term bidding strategies for both generators and society. Sensitivity analyses are performed in order to evaluate the effects of demand elasticity. It is shown that increase in demand elasticity reduces the possibility for the generators to exploit their market power. Furthermore, the results suggest that introduction of wind power generation in the market is beneficial both for the generators and the society. (author)

  12. Powernext Day-Ahead. Powernext Futures. Powernext Carbon. Powernext Weather. 2006 activity assessment

    International Nuclear Information System (INIS)

    2006-01-01

    Powernext SA is a Multilateral Trading Facility which organizes and warrants the transactions on the European power exchange and CO 2 exchange markets. This activity report presents the highlights of the market and of Powernext in 2006: market conditions (prices on the electricity market, prices on the CO 2 emission allowances market, weather conditions, institutional aspects of the CO 2 market, power generation and consumption, situation at the borders, fuel prices), traded volumes on Powernext (Powernext Day-Ahead TM in the case of day-ahead contracts, Powernext Futures TM in the case of medium-term contracts, and Powernext Carbon in the case of CO 2 ), new active members, and Powernext liquidity on the power market. (J.S.)

  13. Demand Response from Day-Ahead Hourly Pricing for Large Customers

    International Nuclear Information System (INIS)

    Hopper, Nicole; Goldman, Charles; Neenan, Bernie

    2006-01-01

    Day-ahead default-service RTP for large customers not only improves the linkage between wholesale and retail markets, but also promotes the development of retail competition. The default service sets a standard for competitive alternatives and its structure shapes the types of retail market products that develop. (author)

  14. Stochastic Optimization of Economic Dispatch for Microgrid Based on Approximate Dynamic Programming

    DEFF Research Database (Denmark)

    Shuai, Hang; Fang, Jiakun; Ai, Xiaomeng

    2018-01-01

    slope updating strategy is employed for the proposed method. With sufficient information extracted from these scenarios and embedded in the PLF function, the proposed ADPED algorithm can not only be used in day-ahead scheduling but also the intra-day optimization process. The algorithm can make full use......-ahead and intra-day operation under uncertainty....

  15. Scenario-based stochastic optimal operation of wind, photovoltaic, pump-storage hybrid system in frequency- based pricing

    International Nuclear Information System (INIS)

    Zare Oskouei, Morteza; Sadeghi Yazdankhah, Ahmad

    2015-01-01

    Highlights: • Two-stage objective function is proposed for optimization problem. • Hourly-based optimal contractual agreement is calculated. • Scenario-based stochastic optimization problem is solved. • Improvement of system frequency by utilizing PSH unit. - Abstract: This paper proposes the operating strategy of a micro grid connected wind farm, photovoltaic and pump-storage hybrid system. The strategy consists of two stages. In the first stage, the optimal hourly contractual agreement is determined. The second stage corresponds to maximizing its profit by adapting energy management strategy of wind and photovoltaic in coordination with optimum operating schedule of storage device under frequency based pricing for a day ahead electricity market. The pump-storage hydro plant is utilized to minimize unscheduled interchange flow and maximize the system benefit by participating in frequency control based on energy price. Because of uncertainties in power generation of renewable sources and market prices, generation scheduling is modeled by a stochastic optimization problem. Uncertainties of parameters are modeled by scenario generation and scenario reduction method. A powerful optimization algorithm is proposed using by General Algebraic Modeling System (GAMS)/CPLEX. In order to verify the efficiency of the method, the algorithm is applied to various scenarios with different wind and photovoltaic power productions in a day ahead electricity market. The numerical results demonstrate the effectiveness of the proposed approach.

  16. Customer Strategies for Responding to Day-Ahead Market HourlyElectricity Pricing

    Energy Technology Data Exchange (ETDEWEB)

    Goldman, Chuck; Hopper, Nicole; Bharvirkar, Ranjit; Neenan,Bernie; Boisvert, Dick; Cappers, Peter; Pratt, Donna; Butkins, Kim

    2005-08-25

    Real-time pricing (RTP) has been advocated as an economically efficient means to send price signals to customers to promote demand response (DR) (Borenstein 2002, Borenstein 2005, Ruff 2002). However, limited information exists that can be used to judge how effectively RTP actually induces DR, particularly in the context of restructured electricity markets. This report describes the second phase of a study of how large, non-residential customers' adapted to default-service day-ahead hourly pricing. The customers are located in upstate New York and served under Niagara Mohawk, A National Grid Company (NMPC)'s SC-3A rate class. The SC-3A tariff is a type of RTP that provides firm, day-ahead notice of hourly varying prices indexed to New York Independent System Operator (NYISO) day-ahead market prices. The study was funded by the California Energy Commission (CEC)'s PIER program through the Demand Response Research Center (DRRC). NMPC's is the first and longest-running default-service RTP tariff implemented in the context of retail competition. The mix of NMPC's large customers exposed to day-ahead hourly prices is roughly 30% industrial, 25% commercial and 45% institutional. They have faced periods of high prices during the study period (2000-2004), thereby providing an opportunity to assess their response to volatile hourly prices. The nature of the SC-3A default service attracted competitive retailers offering a wide array of pricing and hedging options, and customers could also participate in demand response programs implemented by NYISO. The first phase of this study examined SC-3A customers' satisfaction, hedging choices and price response through in-depth customer market research and a Constant Elasticity of Substitution (CES) demand model (Goldman et al. 2004). This second phase was undertaken to answer questions that remained unresolved and to quantify price response to a higher level of granularity. We accomplished these

  17. Powernext Day-Ahead. Powernext Futures. Powernext Carbon. Powernext Weather. Activity assessment first-half 2006

    International Nuclear Information System (INIS)

    2006-01-01

    Powernext SA is a Multilateral Trading Facility which organizes and warrants the transactions on the European power exchange and CO 2 exchange markets. This activity report presents the highlights of the market and of Powernext in the first half of 2006: market conditions, prices and traded volumes on Powernext (Powernext Day-Ahead TM in the case of day-ahead contracts, Powernext Futures TM in the case of medium-term contracts, and Powernext Carbon in the case of CO 2 ), new active members, and liquidity on the power market. (J.S.)

  18. Predictive densities for day-ahead electricity prices using time-adaptive quantile regression

    DEFF Research Database (Denmark)

    Jónsson, Tryggvi; Pinson, Pierre; Madsen, Henrik

    2014-01-01

    A large part of the decision-making problems actors of the power system are facing on a daily basis requires scenarios for day-ahead electricity market prices. These scenarios are most likely to be generated based on marginal predictive densities for such prices, then enhanced with a temporal...... dependence structure. A semi-parametric methodology for generating such densities is presented: it includes: (i) a time-adaptive quantile regression model for the 5%–95% quantiles; and (ii) a description of the distribution tails with exponential distributions. The forecasting skill of the proposed model...

  19. Assessment of the Impact of Stochastic Day-Ahead SCUC on Economic and Reliability Metrics at Multiple Timescales: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Wu, H.; Ela, E.; Krad, I.; Florita, A.; Zhang, J.; Hodge, B. M.; Ibanez, E.; Gao, W.

    2015-03-01

    This paper incorporates the stochastic day-ahead security-constrained unit commitment (DASCUC) within a multi-timescale, multi-scheduling application with commitment, dispatch, and automatic generation control. The stochastic DASCUC is solved using a progressive hedging algorithm with constrained ordinal optimization to accelerate the individual scenario solution. Sensitivity studies are performed in the RTS-96 system, and the results show how this new scheduling application would impact costs and reliability with a closer representation of timescales of system operations in practice.

  20. Day-ahead stochastic economic dispatch of wind integrated power system considering demand response of residential hybrid energy system

    International Nuclear Information System (INIS)

    Jiang, Yibo; Xu, Jian; Sun, Yuanzhang; Wei, Congying; Wang, Jing; Ke, Deping; Li, Xiong; Yang, Jun; Peng, Xiaotao; Tang, Bowen

    2017-01-01

    Highlights: • Improving the utilization of wind power by the demand response of residential hybrid energy system. • An optimal scheduling of home energy management system integrating micro-CHP. • The scattered response capability of consumers is aggregated by demand bidding curve. • A stochastic day-ahead economic dispatch model considering demand response and wind power. - Abstract: As the installed capacity of wind power is growing, the stochastic variability of wind power leads to the mismatch of demand and generated power. Employing the regulating capability of demand to improve the utilization of wind power has become a new research direction. Meanwhile, the micro combined heat and power (micro-CHP) allows residential consumers to choose whether generating electricity by themselves or purchasing from the utility company, which forms a residential hybrid energy system. However, the impact of the demand response with hybrid energy system contained micro-CHP on the large-scale wind power utilization has not been analyzed quantitatively. This paper proposes an operation optimization model of the residential hybrid energy system based on price response, integrating micro-CHP and smart appliances intelligently. Moreover, a novel load aggregation method is adopted to centralize scattered response capability of residential load. At the power grid level, a day-ahead stochastic economic dispatch model considering demand response and wind power is constructed. Furthermore, simulation is conducted respectively on the modified 6-bus system and IEEE 118-bus system. The results show that with the method proposed, the wind power curtailment of the system decreases by 78% in 6-bus system. In the meantime, the energy costs of residential consumers and the operating costs of the power system reduced by 10.7% and 11.7% in 118-bus system, respectively.

  1. Robust optimization-based DC optimal power flow for managing wind generation uncertainty

    Science.gov (United States)

    Boonchuay, Chanwit; Tomsovic, Kevin; Li, Fangxing; Ongsakul, Weerakorn

    2012-11-01

    Integrating wind generation into the wider grid causes a number of challenges to traditional power system operation. Given the relatively large wind forecast errors, congestion management tools based on optimal power flow (OPF) need to be improved. In this paper, a robust optimization (RO)-based DCOPF is proposed to determine the optimal generation dispatch and locational marginal prices (LMPs) for a day-ahead competitive electricity market considering the risk of dispatch cost variation. The basic concept is to use the dispatch to hedge against the possibility of reduced or increased wind generation. The proposed RO-based DCOPF is compared with a stochastic non-linear programming (SNP) approach on a modified PJM 5-bus system. Primary test results show that the proposed DCOPF model can provide lower dispatch cost than the SNP approach.

  2. Risk premiums in the German day-ahead Electricity Market

    International Nuclear Information System (INIS)

    Viehmann, Johannes

    2011-01-01

    This paper conducts an empirical analysis of risk premiums in the German day-ahead Electricity Wholesale Market. We compare hourly price data of the European Energy Exchange (EEX) auction and of the continuous over-the-counter (OTC) market which takes place prior to the EEX auction. Data provided by the Energy Exchange Austria (EXAA) has been used as a snapshot of the OTC market two hours prior to the EEX auction. Ex post analysis found market participants are willing to pay both significant positive and negative premiums for hourly contracts. The largest positive premiums were paid for high demand evening peak hours on weekdays during winter months. By contrast, night hours on weekends featuring lowest demand levels display negative premiums. Additionally, ex ante analysis found a strong positive correlation between the expected tightness of the system and positive premiums. For this purpose, a tightness factor has been introduced that includes expectations of fundamental factors such as power plant availability, wind power production and demand. Hence, findings by can be supported that power traders in liberalised markets behave like risk-averse rational economic agents. - Research highlights: →Analysis of hourly risk premiums in the German day-ahead Electricity Wholesale Market. →Market participants are willing to pay both significant positive and negative premiums for hourly contracts. →A strong correlation exists between the expected tightness of the power system and premiums.

  3. Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets

    OpenAIRE

    Ziel, Florian; Steinert, Rick; Husmann, Sven

    2015-01-01

    In our paper we analyze the relationship between the day-ahead electricity price of the Energy Exchange Austria (EXAA) and other day-ahead electricity prices in Europe. We focus on markets, which settle their prices after the EXAA, which enables traders to include the EXAA price into their calculations. For each market we employ econometric models to incorporate the EXAA price and compare them with their counterparts without the price of the Austrian exchange. By employing a forecasting study...

  4. Day-Ahead Energy Planning with 100% Electric Vehicle Penetration in the Nordic Region by 2050

    DEFF Research Database (Denmark)

    Liu, Zhaoxi; Wu, Qiuwei; Nielsen, Arne Hejde

    2014-01-01

    This paper presents the day-ahead energy planning of passenger cars with 100% electric vehicle (EV) penetration in the Nordic region by 2050. EVs will play an important role in the future energy systems which can both reduce the greenhouse gas (GHG) emission from the transport sector and provide...... demand side flexibility required by the smart grids. On the other hand, the EVs will increase the electricity consumption. In order to quantify the electricity consumption increase due to the 100% EV penetration in the Nordic region to facilitate the power system planning studies, the day-ahead energy...

  5. Optimal Day-Ahead Scheduling of a Hybrid Electric Grid Using Weather Forecasts

    Science.gov (United States)

    2013-12-01

    with 214 turbines [22]. In July 2011, the DoD declared that a complete study of 217 wind farm projects proposed in 35 states and Puerto Rico found...14. SUBJECT TERMS Hybrid electric grid , Microgrid , Hybrid renewable energy system , energy management center, optimization, Day...electric grid. In the case of a hybrid electric grid (HEG), or hybrid renewable energy system (HRES) where the microgrid can be connected to the commercial

  6. Revisiting short-term price and volatility dynamics in day-ahead electricity markets with rising wind power

    International Nuclear Information System (INIS)

    Li, Yuanjing

    2015-01-01

    This paper revisits the short-term price and volatility dynamics in day-ahead electricity markets in consideration of an increasing share of wind power, using an example of the Nord Pool day-ahead market and the Danish wind generation. To do so, a GARCH process is applied, and market coupling and the counterbalance effect of hydropower in the Scandinavian countries are additionally accounted for. As results, we found that wind generation weakly dampens spot prices with an elasticity of 0.008 and also reduces price volatility with an elasticity of 0.02 in the Nordic day-ahead market. The results shed lights on the importance of market coupling and interactions between wind power and hydropower in the Nordic system through cross-border exchanges, which play an essential role in price stabilization. Additionally, an EGARCH specification confirms an asymmetric influence of the price innovations, whereby negative shocks produce larger volatility in the Nordic spot market. While considering heavy tails in error distributions can improve model fits significantly, the EGARCH model outperforms the GARCH model on forecast evaluations. (author)

  7. Simulation of regional day-ahead PV power forecast scenarios

    DEFF Research Database (Denmark)

    Nuno, Edgar; Koivisto, Matti Juhani; Cutululis, Nicolaos Antonio

    2017-01-01

    Uncertainty associated with Photovoltaic (PV) generation can have a significant impact on real-time planning and operation of power systems. This obstacle is commonly handled using multiple forecast realizations, obtained using for example forecast ensembles and/or probabilistic forecasts, often...... at the expense of a high computational burden. Alternatively, some power system applications may require realistic forecasts rather than actual estimates; able to capture the uncertainty of weatherdriven generation. To this end, we propose a novel methodology to generate day-ahead forecast scenarios of regional...... PV production matching the spatio-temporal characteristics while preserving the statistical properties of actual records....

  8. A SOM clustering pattern sequence-based next symbol prediction method for day-ahead direct electricity load and price forecasting

    International Nuclear Information System (INIS)

    Jin, Cheng Hao; Pok, Gouchol; Lee, Yongmi; Park, Hyun-Woo; Kim, Kwang Deuk; Yun, Unil; Ryu, Keun Ho

    2015-01-01

    Highlights: • A novel pattern sequence-based direct time series forecasting method was proposed. • Due to the use of SOM’s topology preserving property, only SOM can be applied. • SCPSNSP only deals with the cluster patterns not each specific time series value. • SCPSNSP performs better than recently developed forecasting algorithms. - Abstract: In this paper, we propose a new day-ahead direct time series forecasting method for competitive electricity markets based on clustering and next symbol prediction. In the clustering step, pattern sequence and their topology relations are obtained from self organizing map time series clustering. In the next symbol prediction step, with each cluster label in the pattern sequence represented as a pair of its topologically identical coordinates, artificial neural network is used to predict the topological coordinates of next day by training the relationship between previous daily pattern sequence and its next day pattern. According to the obtained topology relations, the nearest nonzero hits pattern is assigned to next day so that the whole time series values can be directly forecasted from the assigned cluster pattern. The proposed method was evaluated on Spanish, Australian and New York electricity markets and compared with PSF and some of the most recently published forecasting methods. Experimental results show that the proposed method outperforms the best forecasting methods at least 3.64%

  9. A regime-switching copula approach to modeling day-ahead prices in coupled electricity markets

    DEFF Research Database (Denmark)

    Pircalabu, Anca; Benth, Fred Espen

    2017-01-01

    significant evidence of tail dependence in all pairs of interconnected areas we consider. As a first application of the proposed model, we consider the pricing of financial transmission rights, and highlight how the choice of marginal distributions and copula impacts prices. As a second application we......The recent price coupling of many European electricity markets has triggered a fundamental change in the interaction of day-ahead prices, challenging additionally the modeling of the joint behavior of prices in interconnected markets. In this paper we propose a regime-switching AR–GARCH copula...... to model pairs of day-ahead electricity prices in coupled European markets. While capturing key stylized facts empirically substantiated in the literature, this model easily allows us to 1) deviate from the assumption of normal margins and 2) include a more detailed description of the dependence between...

  10. Merit-order effects of renewable energy and price divergence in California’s day-ahead and real-time electricity markets

    International Nuclear Information System (INIS)

    Woo, C.K.; Moore, J.; Schneiderman, B.; Ho, T.; Olson, A.; Alagappan, L.; Chawla, K.; Toyama, N.; Zarnikau, J.

    2016-01-01

    We answer two policy questions: (1) what are the estimated merit-order effects of renewable energy in the California Independent System Operator’s (CAISO’s) day-ahead market (DAM) and real-time market (RTM)? and (2) what causes the hourly DAM and RTM prices to systematically diverge? The first question is timely and relevant because if the merit-order effect estimates are small, California’s renewable energy development is of limited help in cutting electricity consumers’ bills but also has a lesser adverse impact on the state’s investment incentive for natural-gas-fired generation. The second question is related to the efficient market hypothesis under which the hourly RTM and DAM prices tend to converge. Using a sample of about 21,000 hourly observations of CAISO market prices and their fundamental drivers during 12/12/2012–04/30/2015, we document statistically significant estimates (p-value≤0.01) for the DAM and RTM merit-order effects. This finding lends support to California’s adopted procurement process to provide sufficient investment incentives for natural-gas-fired generation. We document that the RTM-DAM price divergence partly depends on the CASIO’s day-ahead forecast errors for system loads and renewable energy. This finding suggests that improving the performance of the CAISO’s day-ahead forecasts can enhance trading efficiency in California’s DAM and RTM electricity markets. - Highlights: •Estimate the day-ahead and real-time merit-order effects of renewable energy in California. •Document statistically significant merit-order effects of solar and wind energy. •Document the difference between the day-ahead and real-time prices. •Attribute the price differences to forecast errors for load, solar and wind energy. •Discuss the evidence’s implications for California’s energy policy.

  11. A Modified Feature Selection and Artificial Neural Network-Based Day-Ahead Load Forecasting Model for a Smart Grid

    OpenAIRE

    Ahmad, Ashfaq; Javaid, Nadeem; Alrajeh, Nabil; Khan, Zahoor; Qasim, Umar; Khan, Abid

    2015-01-01

    In the operation of a smart grid (SG), day-ahead load forecasting (DLF) is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly challenging due to the non-linear characteristics of load time series in SGs. In the literature, DLF models do exist; however, these models trade off between execution time and forecast accuracy. The newly-proposed DLF model will be able to accurately ...

  12. Security-constrained self-scheduling of generation companies in day-ahead electricity markets considering financial risk

    International Nuclear Information System (INIS)

    Amjady, Nima; Vahidinasab, Vahid

    2013-01-01

    Highlights: ► A security-constrained self-scheduling is presented. ► The proposed framework takes into account the uncertainty of the predicted market prices. ► We model the risk and profit tradeoff of a GENCO based on an efficient multi-objective model. ► Unit commitment and inter-temporal constraints of generators are considered in an MIP model. ► Simulation results are presented on the IEEE 30-bus and IEEE 118-bus test systems. - Abstract: In this paper, a new security-constrained self-scheduling framework incorporating the transmission flow limits in both steady state conditions and post-contingent states is presented to produce efficient bidding strategy for generation companies (GENCOs) in day-ahead electricity markets. Moreover, the proposed framework takes into account the uncertainty of the predicted market prices and models the risk and profit tradeoff of a GENCO based on an efficient multi-objective model. Furthermore, unit commitment and inter-temporal constraints of generators are considered in the suggested model converting it to a mixed-integer programming (MIP) optimization problem. Sensitivity of the proposed framework with respect to both the level of the market prices and adopted risk level is also evaluated in the paper. Simulation results are presented on the IEEE 30-bus and IEEE 118-bus test systems illustrating the performance of the proposed self-scheduling model.

  13. Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model

    Directory of Open Access Journals (Sweden)

    José R. Andrade

    2017-10-01

    Full Text Available Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 €/MWh for day-ahead market and a maximum value of 2.53 €/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.

  14. Congestion management of distribution networks with day-ahead dynamic grid tariffs

    DEFF Research Database (Denmark)

    Huang, Shaojun; Wu, Qiuwei

    vehicles (EV) and heat pumps (HP), will be largely deployed in electrical distribution networks. Congestion management will be important in the future active distribution networks. In the IDE4L project, work package 5 is dedicated to develop different kinds of congestion management methods. Demand response...... (DR) is one of the important methods. In this report, as one task of work package 5, the day-ahead dynamic tariff (DADT) method for congestion management in distribution networks is presented. The dynamic tariff (DT) can motivate the flexible demands (EV and HP) to shift their energy consumption...

  15. Customer response to day-ahead market hourly pricing: Choices and performance

    International Nuclear Information System (INIS)

    Hopper, Nicole; Goldman, Charles; Bharvirkar, Ranjit; Neenan, Bernie

    2006-01-01

    Real-time pricing (RTP) has been advocated to address extreme price volatility and market power in electricity markets. This study of Niagara Mohawk Power Corporation's largest customers analyzes their choices and performance in response to day-ahead, default-service RTP. Overall price response is modest: 119 customers are estimated to reduce their peak demand by about 10% at high prices. Manufacturing customers are most responsive with a price elasticity of 0.16, followed by government/education customers (0.11), while commercial/retail, healthcare and public works customers are, at present, relatively unresponsive. Within market segments, individual customer response varies significantly. (author)

  16. Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed

    Directory of Open Access Journals (Sweden)

    Emanuele Ogliari

    2018-06-01

    Full Text Available An accurate forecast of the exploitable energy from Renewable Energy Sources is extremely important for the stability issues of the electric grid and the reliability of the bidding markets. This paper presents a comparison among different forecasting methods of the photovoltaic output power introducing a new method that mixes some peculiarities of the others: the Physical Hybrid Artificial Neural Network and the five parameters model estimated by the Social Network Optimization. In particular, the day-ahead forecasts evaluated against real data measured for two years in an existing photovoltaic plant located in Milan, Italy, are compared by means both new and the most common error indicators. Results reported in this work show the best forecasting capability of the new “mixed method” which scored the best forecast skill and Enveloped Mean Absolute Error on a yearly basis (47% and 24.67%, respectively.

  17. Hourly price elasticity pattern of electricity demand in the German day-ahead market

    OpenAIRE

    Knaut, Andreas; Paulus, Simon

    2016-01-01

    System security in electricity markets relies crucially on the interaction between demand and supply over time. However, research on electricity markets has been mainly focusing on the supply side arguing that demand is rather inelastic. Assuming perfectly inelastic demand might lead to delusive statements regarding the price formation in electricity markets. In this article we quantify the short-run price elasticity of electricity demand in the German day-ahead market and show that demand is...

  18. Pricing Energy and Ancillary Services in a Day-Ahead Market for a Price-Taker Hydro Generating Company Using a Risk-Constrained Approach

    Directory of Open Access Journals (Sweden)

    Perica Ilak

    2014-04-01

    Full Text Available This paper analyzes a price-taker hydro generating company which participates simultaneously in day-ahead energy and ancillary services markets. An approach for deriving marginal cost curves for energy and ancillary services is proposed, taking into consideration price uncertainty and opportunity cost of water, which can later be used to determine hourly bid curves. The proposed approach combines an hourly conditional value-at-risk, probability of occurrence of automatic generation control states and an opportunity cost of water to determine energy and ancillary services marginal cost curves. The proposed approach is in a linear constraint form and is easy to implement in optimization problems. A stochastic model of the hydro-economic river basin is presented, based on the actual Vinodol hydropower system in Croatia, with a complex three-dimensional relationship between the power produced, the discharged water, and the head of associated reservoir.

  19. A Comparison of the Performance of Advanced Statistical Techniques for the Refinement of Day-ahead and Longer NWP-based Wind Power Forecasts

    Science.gov (United States)

    Zack, J. W.

    2015-12-01

    Predictions from Numerical Weather Prediction (NWP) models are the foundation for wind power forecasts for day-ahead and longer forecast horizons. The NWP models directly produce three-dimensional wind forecasts on their respective computational grids. These can be interpolated to the location and time of interest. However, these direct predictions typically contain significant systematic errors ("biases"). This is due to a variety of factors including the limited space-time resolution of the NWP models and shortcomings in the model's representation of physical processes. It has become common practice to attempt to improve the raw NWP forecasts by statistically adjusting them through a procedure that is widely known as Model Output Statistics (MOS). The challenge is to identify complex patterns of systematic errors and then use this knowledge to adjust the NWP predictions. The MOS-based improvements are the basis for much of the value added by commercial wind power forecast providers. There are an enormous number of statistical approaches that can be used to generate the MOS adjustments to the raw NWP forecasts. In order to obtain insight into the potential value of some of the newer and more sophisticated statistical techniques often referred to as "machine learning methods" a MOS-method comparison experiment has been performed for wind power generation facilities in 6 wind resource areas of California. The underlying NWP models that provided the raw forecasts were the two primary operational models of the US National Weather Service: the GFS and NAM models. The focus was on 1- and 2-day ahead forecasts of the hourly wind-based generation. The statistical methods evaluated included: (1) screening multiple linear regression, which served as a baseline method, (2) artificial neural networks, (3) a decision-tree approach called random forests, (4) gradient boosted regression based upon an decision-tree algorithm, (5) support vector regression and (6) analog ensemble

  20. Optimal offering and operating strategies for wind-storage systems with linear decision rules

    DEFF Research Database (Denmark)

    Ding, Huajie; Pinson, Pierre; Hu, Zechun

    2016-01-01

    The participation of wind farm-energy storage systems (WF-ESS) in electricity markets calls for an integrated view of day-ahead offering strategies and real-time operation policies. Such an integrated strategy is proposed here by co-optimizing offering at the day-ahead stage and operation policy...... to be used at the balancing stage. Linear decision rules are seen as a natural approach to model and optimize the real-time operation policy. These allow enhancing profits from balancing markets based on updated information on prices and wind power generation. Our integrated strategies for WF...

  1. Multiple time-scale optimization scheduling for islanded microgrids including PV, wind turbine, diesel generator and batteries

    DEFF Research Database (Denmark)

    Xiao, Zhao xia; Nan, Jiakai; Guerrero, Josep M.

    2017-01-01

    A multiple time-scale optimization scheduling including day ahead and short time for an islanded microgrid is presented. In this paper, the microgrid under study includes photovoltaics (PV), wind turbine (WT), diesel generator (DG), batteries, and shiftable loads. The study considers the maximum...... efficiency operation area for the diesel engine and the cost of the battery charge/discharge cycle losses. The day-ahead generation scheduling takes into account the minimum operational cost and the maximum load satisfaction as the objective function. Short-term optimal dispatch is based on minimizing...

  2. 4th Optimization Day

    CERN Document Server

    Eberhard, Andrew; Ralph, Daniel; Glover, Barney M

    1999-01-01

    Although the monograph Progress in Optimization I: Contributions from Aus­ tralasia grew from the idea of publishing a proceedings of the Fourth Optimiza­ tion Day, held in July 1997 at the Royal Melbourne Institute of Technology, the focus soon changed to a refereed volume in optimization. The intention is to publish a similar book annually, following each Optimization Day. The idea of having an annual Optimization Day was conceived by Barney Glover; the first of these Optimization Days was held in 1994 at the University of Ballarat. Barney hoped that such a yearly event would bring together the many, but widely dispersed, researchers in Australia who were publishing in optimization and related areas such as control. The first Optimization Day event was followed by similar conferences at The University of New South Wales (1995), The University of Melbourne (1996), the Royal Melbourne Institute of Technology (1997), and The University of Western Australia (1998). The 1999 conference will return to Ballarat ...

  3. Day-Ahead Coordination of Vehicle-to-Grid Operation and Wind Power in Security Constraints Unit Commitment (SCUC

    Directory of Open Access Journals (Sweden)

    Mohammad Javad Abdollahi

    2015-08-01

    Full Text Available In this paper security constraints unit commitment (SCUC in the presence of wind power resources and electrical vehicles to grid is presented. SCUC operation prepare an optimal time table for generation unit commitment in order to maximize security, minimize operation cost and satisfy the constraints of networks and units in a period of time, as one of the most important research interest in power systems. Today, the relationship between power network and energy storage systems is interested for many researchers and network operators. Using Electrical Vehicles (PEVs and wind power for energy production is one of the newest proposed methods for replacing fossil fuels.One of the effective strategies for analyzing of the effects of Vehicle 2 Grid (V2G and wind power in optimal operation of generation is running of SCUC for power systems that are equipped with V2G and wind power resources. In this paper, game theory method is employed for deterministic solution of day-ahead unit commitment with considering security constraints in the simultaneous presence of V2G and wind power units. This problem for two scenarios of grid-controlled mode and consumer-controlled mode in three different days with light, medium and heavy load profiles is analyzed. Simulation results show the effectiveness of the presence of V2G and wind power for decreasing of generation cost and improving operation indices of power systems.

  4. A Modified Feature Selection and Artificial Neural Network-Based Day-Ahead Load Forecasting Model for a Smart Grid

    Directory of Open Access Journals (Sweden)

    Ashfaq Ahmad

    2015-12-01

    Full Text Available In the operation of a smart grid (SG, day-ahead load forecasting (DLF is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly challenging due to the non-linear characteristics of load time series in SGs. In the literature, DLF models do exist; however, these models trade off between execution time and forecast accuracy. The newly-proposed DLF model will be able to accurately predict the load of the next day with a fair enough execution time. Our proposed model consists of three modules; the data preparation module, feature selection and the forecast module. The first module makes the historical load curve compatible with the feature selection module. The second module removes redundant and irrelevant features from the input data. The third module, which consists of an artificial neural network (ANN, predicts future load on the basis of selected features. Moreover, the forecast module uses a sigmoid function for activation and a multi-variate auto-regressive model for weight updating during the training process. Simulations are conducted in MATLAB to validate the performance of our newly-proposed DLF model in terms of accuracy and execution time. Results show that our proposed modified feature selection and modified ANN (m(FS + ANN-based model for SGs is able to capture the non-linearity(ies in the history load curve with 97 . 11 % accuracy. Moreover, this accuracy is achieved at the cost of a fair enough execution time, i.e., we have decreased the average execution time of the existing FS + ANN-based model by 38 . 50 % .

  5. A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

    Directory of Open Access Journals (Sweden)

    Fanrong Kong

    2017-09-01

    Full Text Available To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost. Although the next-day electricity prices can be obtained in a day-ahead power market, a driving plan is not easily made in advance. Although PHEV owners can input a next-day plan into a charging system, e.g., aggregators, day-ahead, it is a very trivial task to do everyday. Moreover, the driving plan may not be very accurate. To address this problem, in this paper, we analyze energy demands according to a PHEV owner’s historical driving records and build a personalized statistic driving model. Based on the model and the electricity spot prices, a rolling optimization strategy is proposed to help make a charging decision in the current time slot. On one hand, by employing a heuristic algorithm, the schedule is made according to the situations in the following time slots. On the other hand, however, after the current time slot, the schedule will be remade according to the next tens of time slots. Hence, the schedule is made by a dynamic rolling optimization, but it only decides the charging decision in the current time slot. In this way, the fluctuation of electricity prices and driving routine are both involved in the scheduling. Moreover, it is not necessary for PHEV owners to input a day-ahead driving plan. By the optimization simulation, the results demonstrate that the proposed method is feasible to help owners save charging costs and also meet requirements for driving.

  6. Optimal household appliances scheduling under day-ahead pricing and load-shaping demand response strategies

    NARCIS (Netherlands)

    Paterakis, N.G.; Erdinç, O.; Bakirtzis, A.G.; Catalao, J.P.S.

    2015-01-01

    In this paper, a detailed home energy management system structure is developed to determine the optimal dayahead appliance scheduling of a smart household under hourly pricing and peak power-limiting (hard and soft power limitation)-based demand response strategies. All types of controllable assets

  7. Modeling the transient security constraints of natural gas network in day-ahead power system scheduling

    DEFF Research Database (Denmark)

    Yang, Jingwei; Zhang, Ning; Kang, Chongqing

    2017-01-01

    The rapid deployment of gas-fired generating units makes the power system more vulnerable to failures in the natural gas system. To reduce the risk of gas system failure and to guarantee the security of power system operation, it is necessary to take the security constraints of natural gas...... accurately, they are hard to be embedded into the power system scheduling model, which consists of algebraic equations and inequations. This paper addresses this dilemma by proposing an algebraic transient model of natural gas network which is similar to the branch-node model of power network. Based...... pipelines into account in the day-ahead power generation scheduling model. However, the minute- and hour-level dynamic characteristics of gas systems prevents an accurate decision-making simply with the steady-state gas flow model. Although the partial differential equations depict the dynamics of gas flow...

  8. Day-Ahead Scheduling Considering Demand Response as a Frequency Control Resource

    Directory of Open Access Journals (Sweden)

    Yu-Qing Bao

    2017-01-01

    Full Text Available The development of advanced metering technologies makes demand response (DR able to provide fast response services, e.g., primary frequency control. It is recognized that DR can contribute to the primary frequency control like thermal generators. This paper proposes a day-ahead scheduling method that considers DR as a frequency control resource, so that the DR resources can be dispatched properly with other resources. In the proposed method, the objective of frequency control is realized by defining a frequency limit equation under a supposed contingency. The frequency response model is used to model the dynamics of system frequency. The nonlinear frequency limit equation is transformed to a linear arithmetic equation by piecewise linearization, so that the problem can be solved by mixed integer linear programming (MILP. Finally, the proposed method is verified on numerical examples.

  9. Thermal energy storage for electricity-driven space heating in a day-ahead electricity market

    DEFF Research Database (Denmark)

    Pensini, Alessandro

    2012-01-01

    Thermal Energy Storage (TES) in a space heating (SH) application was investigated. The study aimed to determine the economic benefits of introducing TES into an electricity-driven SH system under a day-ahead electricity market. The performance of the TES was assessed by comparing the cost...... of electricity in a system with a TES unit to the case where no storage is in use and the entire heat requirement is fulfilled by purchasing electricity according to the actual load. The study had two goals: 1. Determining how the size – in terms of electricity input (Pmax) and energy capacity (Emax...

  10. Generators' bidding behavior in the NYISO day-ahead wholesale electricity market

    International Nuclear Information System (INIS)

    Zhang, Ning

    2009-01-01

    This paper proposes a statistical and econometric model to analyze the generators' bidding behavior in the NYISO day-ahead wholesale electricity market. The generator level bidding data show very strong persistence in generators' grouping choices over time. Using dynamic random effect ordered probit model, we find that persistence is characterized by positive state dependence and unobserved heterogeneity and state dependence is more important than unobserved heterogeneity. The finding of true state dependence suggests a scope for economic policy intervention. If NYISO can implement an effective policy to switch generators from higher price groups to lower price groups, the effect is likely to be lasting. As a result, the market price can be lowered in the long-run. Generators' offered capacity is estimated by a two-stage sample selection model. The estimated results show that generators in higher-priced groups tend to withhold their capacity strategically to push up market prices. It further confirms the importance of an effective policy to turn generators into lower price groups in order to mitigate unexpected price spikes. The simulated market prices based on our estimated aggregate supply curve can replicate most volatility of actual DA market prices. Applying our models to different demand assumptions, we find that demand conditions can affect market prices significantly. It validates the importance of introducing demand side management during the restructure of electricity industry. (author)

  11. Breastfeeding: Planning Ahead

    Medline Plus

    Full Text Available ... your life Partner resources Subscribe To receive Breastfeeding email updates Enter email Submit Planning ahead From choosing the crib to ... Breastfeeding: The #First31 Days Subscribe To receive Breastfeeding email updates Enter email Submit All material contained on ...

  12. Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System

    Directory of Open Access Journals (Sweden)

    Bo Lin

    2017-10-01

    Full Text Available This paper investigates how to develop a learning-based demand response approach for electric water heater in a smart home that can minimize the energy cost of the water heater while meeting the comfort requirements of energy consumers. First, a learning-based, data-driven model of an electric water heater is developed by using a nonlinear autoregressive network with external input (NARX using neural network. The model is updated daily so that it can more accurately capture the actual thermal dynamic characteristics of the water heater especially in real-life conditions. Then, an optimization problem, based on the NARX water heater model, is formulated to optimize energy management of the water heater in a day-ahead, dynamic electricity price framework. A genetic algorithm is proposed in order to solve the optimization problem more efficiently. MATLAB (R2016a is used to evaluate the proposed learning-based demand response approach through a computational experiment strategy. The proposed approach is compared with conventional method for operation of an electric water heater. Cost saving and benefits of the proposed water heater energy management strategy are explored.

  13. 5th Optimization Day

    CERN Document Server

    Mees, Alistair; Fisher, Mike; Jennings, Les

    2000-01-01

    'Optimization Day' (OD) has been a series of annual mini-conferences in Australia since 1994. The purpose of this series of events is to gather researchers in optimization and its related areas from Australia and their collaborators, in order to exchange new developments of optimization theories, methods and their applications. The first four OD mini-conferences were held in The Uni­ versity of Ballarat (1994), The University of New South Wales (1995), The University of Melbourne (1996) and Royal Melbourne Institute of Technology (1997), respectively. They were all on the eastern coast of Australia. The fifth mini-conference Optimization Days was held at the Centre for Ap­ plied Dynamics and Optimization (CADO), Department of Mathematics and Statistics, The University of Western Australia, Perth, from 29 to 30 June 1998. This is the first time the OD mini-conference has been held at the west­ ern coast of Australia. This fifth OD preceded the International Conference on Optimization: Techniques and Applica...

  14. A day-ahead market for electricity in Alberta : is there a case to be made?

    International Nuclear Information System (INIS)

    Fronimos, P.

    2006-01-01

    Following the success of the introduction of competition in several heavily regulated industries in the 1980s and 1990s, traditional utilities were broken up and markets were introduced for their respective services. Some of these markets evolved into complex structures with multiple sub-markets for installed capacity, energy, transmission and for all types of reserves. Electricity submarkets now operate in a similar manner in different timeframes comprising two settlement systems: the Day Ahead (DA) market, operating a day ahead of the actual generation and consumption of electricity and one that operates in real time, the Real-Time (RT) or Balancing market. This paper explored the case for a binding DA market for electricity in Alberta, by looking at the challenges facing the marketplace and examining how using a DA market would affect them. This paper hypothesized that a correctly designed and implemented, financially binding DA market would enhance the fidelity of the price signal in Alberta by providing an information rich environment for participants and the system operator. The paper defined and discussed both types of settlement systems. It then discussed the DA market in Alberta in terms of risk management, reliability, price fidelity and demand response. It was noted that some of the benefits of using a DA market could also be achieved by improvements to the existing market. Alberta faces several challenges, such as merit order instability and price volatility which are likely due to market design and operations rather than the inherent inability of a power exchange to address them. 31 refs., 1 tab., 3 figs

  15. Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

    Directory of Open Access Journals (Sweden)

    Fei Wang

    2017-12-01

    Full Text Available Accurate solar photovoltaic (PV power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN and support vector machines (SVM are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

  16. A Bi-Level Optimization Approach to Charging Load Regulation of Electric Vehicle Fast Charging Stations Based on a Battery Energy Storage System

    Directory of Open Access Journals (Sweden)

    Yan Bao

    2018-01-01

    Full Text Available Fast charging stations enable the high-powered rapid recharging of electric vehicles. However, these stations also face challenges due to power fluctuations, high peak loads, and low load factors, affecting the reliable and economic operation of charging stations and distribution networks. This paper introduces a battery energy storage system (BESS for charging load control, which is a more user-friendly approach and is more robust to perturbations. With the goals of peak-shaving, total electricity cost reduction, and minimization of variation in the state-of-charge (SOC range, a BESS-based bi-level optimization strategy for the charging load regulation of fast charging stations is proposed in this paper. At the first level, a day-ahead optimization strategy generates the optimal planned load curve and the deviation band to be used as a reference for ensuring multiple control objectives through linear programming, and even for avoiding control failure caused by insufficient BESS energy. Based on this day-ahead optimal plan, at a second level, real-time rolling optimization converts the control process to a multistage decision-making problem. The predictive control-based real-time rolling optimization strategy in the proposed model was used to achieve the above control objectives and maintain battery life. Finally, through a horizontal comparison of two control approaches in each case study, and a longitudinal comparison of the control robustness against different degrees of load disturbances in three cases, the results indicated that the proposed control strategy was able to significantly improve the charging load characteristics, even with large disturbances. Meanwhile, the proposed approach ensures the least amount of variation in the range of battery SOC and reduces the total electricity cost, which will be of a considerable benefit to station operators.

  17. Electricity Portfolio Management: Optimal Peak / Off-Peak Allocations

    OpenAIRE

    Huisman, Ronald; Mahieu, Ronald; Schlichter, Felix

    2007-01-01

    textabstractElectricity purchasers manage a portfolio of contracts in order to purchase the expected future electricity consumption profile of a company or a pool of clients. This paper proposes a mean-variance framework to address the concept of structuring the portfolio and focuses on how to allocate optimal positions in peak and off-peak forward contracts. It is shown that the optimal allocations are based on the difference in risk premiums per unit of day-ahead risk as a measure of relati...

  18. Evaluating the effectiveness of mixed-integer linear programming for day-ahead hydro-thermal self-scheduling considering price uncertainty and forced outage rate

    International Nuclear Information System (INIS)

    Esmaeily, Ali; Ahmadi, Abdollah; Raeisi, Fatima; Ahmadi, Mohammad Reza; Esmaeel Nezhad, Ali; Janghorbani, Mohammadreza

    2017-01-01

    A new optimization framework based on MILP model is introduced in the paper for the problem of stochastic self-scheduling of hydrothermal units known as HTSS Problem implemented in a joint energy and reserve electricity market with day-ahead mechanism. The proposed MILP framework includes some practical constraints such as the cost due to valve-loading effect, the limit due to DRR and also multi-POZs, which have been less investigated in electricity market models. For the sake of more accuracy, for hydro generating units’ model, multi performance curves are also used. The problem proposed in this paper is formulated using a model on the basis of a stochastic optimization technique while the objective function is maximizing the expected profit utilizing MILP technique. The suggested stochastic self-scheduling model employs the price forecast error in order to take into account the uncertainty due to price. Besides, LMCS is combined with roulette wheel mechanism so that the scenarios corresponding to the non-spinning reserve price and spinning reserve price as well as the energy price at each hour of the scheduling are generated. Finally, the IEEE 118-bus power system is used to indicate the performance and the efficiency of the suggested technique. - Highlights: • Characterizing the uncertainties of price and FOR of units. • Replacing the fixed ramping rate constraints with the dynamic ones. • Proposing linearized model for the valve-point effects of thermal units. • Taking into consideration the multi-POZs relating to the thermal units. • Taking into consideration the multi-performance curves of hydroelectric units.

  19. A Game Theoretical Approach Based Bidding Strategy Optimization for Power Producers in Power Markets with Renewable Electricity

    Directory of Open Access Journals (Sweden)

    Yi Tang

    2017-05-01

    Full Text Available In a competitive electricity market with substantial involvement of renewable electricity, maximizing profits by optimizing bidding strategies is crucial to different power producers including conventional power plants and renewable ones. This paper proposes a game-theoretic bidding optimization method based on bi-level programming, where power producers are at the upper level and utility companies are at the lower level. The competition among the multiple power producers is formulated as a non-cooperative game in which bidding curves are their strategies, while uniform clearing pricing is considered for utility companies represented by an independent system operator. Consequently, based on the formulated game model, the bidding strategies for power producers are optimized for the day-ahead market and the intraday market with considering the properties of renewable energy; and the clearing pricing for the utility companies, with respect to the power quantity from different power producers, is optimized simultaneously. Furthermore, a distributed algorithm is provided to search the solution of the generalized Nash equilibrium. Finally, simulation results were performed and discussed to verify the feasibility and effectiveness of the proposed non-cooperative game-based bi-level optimization approach.

  20. One-Step-Ahead Predictive Control for Hydroturbine Governor

    Directory of Open Access Journals (Sweden)

    Zhihuai Xiao

    2015-01-01

    Full Text Available The hydroturbine generator regulating system can be considered as one system synthetically integrating water, machine, and electricity. It is a complex and nonlinear system, and its configuration and parameters are time-dependent. A one-step-ahead predictive control based on on-line trained neural networks (NNs for hydroturbine governor with variation in gate position is described in this paper. The proposed control algorithm consists of a one-step-ahead neuropredictor that tracks the dynamic characteristics of the plant and predicts its output and a neurocontroller to generate the optimal control signal. The weights of two NNs, initially trained off-line, are updated on-line according to the scalar error. The proposed controller can thus track operating conditions in real-time and produce the optimal control signal over the wide operating range. Only the inputs and outputs of the generator are measured and there is no need to determine the other states of the generator. Simulations have been performed with varying operating conditions and different disturbances to compare the performance of the proposed controller with that of a conventional PID controller and validate the feasibility of the proposed approach.

  1. Influence of feasibility constrains on the bidding strategy selection in a day-ahead electricity market session

    International Nuclear Information System (INIS)

    Borghetti, Alberto; Massucco, Stefano; Silvestro, Federico

    2009-01-01

    Large part of liberalized electricity markets, including the Italian one, features an auction mechanism, called day-ahead energy market, which matches producers' and buyers' simple bids, consisting of energy quantity and price pairs. The match is achieved by a merit-order economic dispatch procedure independently applied for each of the hours of the following day. Power plants operation should, however, take into account several technical constraints, such as maximum and minimum production bounds, ramp constraints and minimum up and downs times, as well as no-load and startup costs. The presence of these constraints forces to adjust the scheduling provided by the market in order to obtain a feasible scheduling. The paper presents an analysis of the possibility and the limits of taking into account the power plants technical constraints in the bidding strategy selection procedure of generating companies (Gencos). The analysis is carried out by using a computer procedure based both on a simple static game-theory approach and on a cost-minimization unit-commitment algorithm. For illustrative purposes, we present the results obtained for a system with three Gencos, each owning several power plants, trying to model the bidding behaviour of every generator in the system. This approach, although complex from the computational point of view, allows an analysis of both price and quantity bidding strategies and appears to be applicable to markets having different rules and features. (author)

  2. Optimal energy exchange of an industrial cogeneration in a day-ahead electricity market

    International Nuclear Information System (INIS)

    Yusta, J.M.; De Oliveira-De Jesus, P.M.; Khodr, H.M.

    2008-01-01

    This paper addresses an optimal strategy for the daily energy exchange of a 22-MW combined-cycle cogeneration plant of an industrial factory operating in a liberalized electricity market. The optimization problem is formulated as a Mixed-Integer Linear Programming Problem (MILP) that maximizes the profit from energy exchange of the cogeneration, and is subject to the technical constraints and the industrial demand profile. The integer variables are associated with export or import of electricity whereas the real variables relate to the power output of gas and steam turbines, and to the electricity purchased from or sold to the market. The proposal is applied to a real cogeneration plant in Spain where the detailed cost function of the process is obtained. The problem is solved using a large-scale commercial package and the results are discussed and compared with different predefined scheduling strategies. (author)

  3. Remedial transactions curtailment via optimization

    Directory of Open Access Journals (Sweden)

    Maksimović Viktor

    2009-01-01

    Full Text Available The new method developed in this paper is aiming at transmission congestion management (CM. The new, Optimal Transactions Management method (OTM, is based on linear programming (LP, DC load flow (DCLF and linear security constraints. The OTM method is embedded in Available Transfer Capabilities (ATCs and Power Transfer Distribution Factors (PTDFs definitions' environment. Well-suited for both preventive and corrective modes of operation, the OTM method aids transmission system operator in running a congested power system network, where congestions are due to transactions. Potential congestion threat is solved by finding the 'culprit' transaction and its optimal reduction. Besides the proposed downsizing of scheduled and/or committed transactions, controls of the OTM method also include redispatching of generation and load levels. The task is to establish a system state without constraint violations. To ensure the feasible network solution, both DC and AC power flows are used. The common 5 nodes/7 lines Ward&Hale sample power system is used to clarify the OTM method. Besides, six other power system networks including the real-life power system network of Serbia, Macedonia and Montenegro (part of the South East Europe - SEE grid are used to test remedial potentials and CPU-time performances of the method. The 24-hour daily demand diagram is used with all test networks to study the effects of transactions as they are being superimposed to the regional grid. The remedial, transactions-curtailing OTM method is found well suited for market-related analyses precluding the hour-ahead, the day-ahead dispatch, as well as the real-time generation dispatch. It could also suit for the novel, Day Ahead Congestion Forecast (DACF procedure used in power markets. .

  4. Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market

    Energy Technology Data Exchange (ETDEWEB)

    Mandal, Paras; Senjyu, Tomonobu [Department of Electrical and Electronics, University of the Ryukyus, 1 Senbaru, Nagakami Nishihara, Okinawa 903-0213 (Japan); Funabashi, Toshihisa [Meidensha Corporation, Tokyo 103-8515 (Japan)

    2006-09-15

    In daily power markets, forecasting electricity prices and loads are the most essential task and the basis for any decision making. An approach to predict the market behaviors is to use the historical prices, loads and other required information to forecast the future prices and loads. This paper introduces an approach for several hour ahead (1-6h) electricity price and load forecasting using an artificial intelligence method, such as a neural network model, which uses publicly available data from the NEMMCO web site to forecast electricity prices and loads for the Victorian electricity market. An approach of selection of similar days is proposed according to which the load and price curves are forecasted by using the information of the days being similar to that of the forecast day. A Euclidean norm with weighted factors is used for the selection of the similar days. Two different ANN models, one for one to six hour ahead load forecasting and another for one to six hour ahead price forecasting have been proposed. The MAPE (mean absolute percentage error) results show a clear increasing trend with the increase in hour ahead load and price forecasting. The sample average of MAPEs for one hour ahead price forecasts is 9.75%. This figure increases to only 20.03% for six hour ahead predictions. Similarly, the one to six hour ahead load forecast errors (MAPE) range from 0.56% to 1.30% only. MAPE results show that several hour ahead electricity prices and loads in the deregulated Victorian market can be forecasted with reasonable accuracy. (author)

  5. Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market

    International Nuclear Information System (INIS)

    Mandal, Paras; Senjyu, Tomonobu; Funabashi, Toshihisa

    2006-01-01

    In daily power markets, forecasting electricity prices and loads are the most essential task and the basis for any decision making. An approach to predict the market behaviors is to use the historical prices, loads and other required information to forecast the future prices and loads. This paper introduces an approach for several hour ahead (1-6 h) electricity price and load forecasting using an artificial intelligence method, such as a neural network model, which uses publicly available data from the NEMMCO web site to forecast electricity prices and loads for the Victorian electricity market. An approach of selection of similar days is proposed according to which the load and price curves are forecasted by using the information of the days being similar to that of the forecast day. A Euclidean norm with weighted factors is used for the selection of the similar days. Two different ANN models, one for one to six hour ahead load forecasting and another for one to six hour ahead price forecasting have been proposed. The MAPE (mean absolute percentage error) results show a clear increasing trend with the increase in hour ahead load and price forecasting. The sample average of MAPEs for one hour ahead price forecasts is 9.75%. This figure increases to only 20.03% for six hour ahead predictions. Similarly, the one to six hour ahead load forecast errors (MAPE) range from 0.56% to 1.30% only. MAPE results show that several hour ahead electricity prices and loads in the deregulated Victorian market can be forecasted with reasonable accuracy

  6. Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid

    Directory of Open Access Journals (Sweden)

    Nadeem Javaid

    2017-10-01

    Full Text Available In a smart grid, several optimization techniques have been developed to schedule load in the residential area. Most of these techniques aim at minimizing the energy consumption cost and the comfort of electricity consumer. Conversely, maintaining a balance between two conflicting objectives: energy consumption cost and user comfort is still a challenging task. Therefore, in this paper, we aim to minimize the electricity cost and user discomfort while taking into account the peak energy consumption. In this regard, we implement and analyse the performance of a traditional dynamic programming (DP technique and two heuristic optimization techniques: genetic algorithm (GA and binary particle swarm optimization (BPSO for residential load management. Based on these techniques, we propose a hybrid scheme named GAPSO for residential load scheduling, so as to optimize the desired objective function. In order to alleviate the complexity of the problem, the multi dimensional knapsack is used to ensure that the load of electricity consumer will not escalate during peak hours. The proposed model is evaluated based on two pricing schemes: day-ahead and critical peak pricing for single and multiple days. Furthermore, feasible regions are calculated and analysed to develop a relationship between power consumption, electricity cost, and user discomfort. The simulation results are compared with GA, BPSO and DP, and validate that the proposed hybrid scheme reflects substantial savings in electricity bills with minimum user discomfort. Moreover, results also show a phenomenal reduction in peak power consumption.

  7. Look-ahead procedures for Lanczos-type product methods based on three-term recurrences

    Energy Technology Data Exchange (ETDEWEB)

    Gutknecht, M.H.; Ressel, K.J. [Swiss Center for Scientific Computing, Zuerich (Switzerland)

    1996-12-31

    Lanczos-type product methods for the solution of large sparse non-Hermitian linear systems either square the Lanczos process or combine it with a local minimization of the residual. They inherit from the underlying Lanczos process the danger of breakdown. For various Lanczos-type product methods that are based on the Lanczos three-term recurrence, look-ahead versions are presented, which avoid such breakdowns or near breakdowns with a small computational overhead. Different look-ahead strategies are discussed and their efficiency is demonstrated in several numerical examples.

  8. Seismic prediction ahead of tunnel constructions

    Science.gov (United States)

    Jetschny, S.; Bohlen, T.; Nil, D. D.; Giese, R.

    2007-12-01

    To increase safety and efficiency of tunnel constructions, online seismic exploration ahead of a tunnel can become a valuable tool. Within the \\it OnSite project founded by the BMBF (German Ministry of Education and Research) within \\it GeoTechnologien a new forward looking seismic imaging technique is developed to e.g. determine weak and water bearing zones ahead of the constructions. Our approach is based on the excitation and registration of \\it tunnel surface waves. These waves are excited at the tunnel face behind the cutter head of a tunnel boring machine and travel into drilling direction. Arriving at the front face they generate body waves (mainly S-waves) propagating further ahead. Reflected S-waves are back- converted into tunnel surface waves. For a theoretical description of the conversion process and for finding optimal acquisition geometries it is of importance to study the propagation characteristics of tunnel surface waves. 3D seismic finite difference modeling and analytic solutions of the wave equation in cylindric coordinates revealed that at higher frequencies, i.e. if the tunnel diameter is significantly larger than the wavelength of S-waves, these surface waves can be regarded as Rayleigh-waves circulating the tunnel. For smaller frequencies, i.e. when the S-wavelength approaches the tunnel diameter, the propagation characteristics of these surface waves are then similar to S- waves. Field measurements performed by the GeoForschungsZentrum Potsdam, Germany at the Gotthard Base Tunnel (Switzerland) show both effects, i.e. the propagation of Rayleigh- and body-wave like waves along the tunnel. To enhance our understanding of the excitation and propagation characteristics of tunnel surface waves the transition of Rayleigh to tube-waves waves is investigated both analytically and by numerical simulations.

  9. Demand side management in a day-ahead wholesale market: A comparison of industrial & social welfare approaches

    International Nuclear Information System (INIS)

    Jiang, Bo; Farid, Amro M.; Youcef-Toumi, Kamal

    2015-01-01

    Highlights: • We compare two demand side management in a day-ahead electricity wholesale market. • We develop and reconcile social welfare & industrial DSM mathematical models. • We show the industrial netload has an additional forecast quantity of baseline. • We analytically and numerically show the model equivalence with accurate baseline. • We numerically demonstrate the baseline errors lead to higher and costlier dispatch. - Abstract: The intermittent nature of renewable energy has been discussed in the context of the operational challenges that it brings to electrical grid reliability. Demand side management (DSM) with its ability to allow customers to adjust electricity consumption in response to market signals has often been recognized as an efficient way to mitigate the variable effects of renewable energy as well as to increase system efficiency and reduce system costs. However, the academic & industrial literature have taken divergent approaches to DSM implementation. While the popular approach among academia adopts a social welfare maximization formulation, the industrial practice compensates customers according to their load reduction from a predefined electricity consumption baseline that would have occurred without DSM. This paper rigorously compares these two different approaches in a day-ahead wholesale market context analytically and in a test case using the same system configuration and mathematical formalism. The comparison of the two models showed that a proper reconciliation of the two models might make them mitigate the stochastic netload in fundamentally the same way, but only under very specific conditions which are rarely met in practice. While the social welfare model uses a stochastic net load composed of two terms, the industrial DSM model uses a stochastic net load composed of three terms including the additional baseline term. DSM participants are likely to manipulate the baseline in order to receive greater financial

  10. On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?

    Directory of Open Access Journals (Sweden)

    Ana-Maria Fuertes

    2016-09-01

    Full Text Available This paper investigates the information content of the ex post overnight return for one-day-ahead equity Value-at-Risk (VaR forecasting. To do so, we deploy a univariate VaR modeling approach that constructs the forecast at market open and, accordingly, exploits the available overnight close-to-open price variation. The benchmark is the bivariate VaR modeling approach proposed by Ahoniemi et al. that constructs the forecast at the market close instead and, accordingly, it models separately the daytime and overnight return processes and their covariance. For a small cap portfolio, the bivariate VaR approach affords superior predictive ability than the ex post overnight VaR approach whereas for a large cap portfolio the results are reversed. The contrast indicates that price discovery at the market open is less efficient for small capitalization, thinly traded stocks.

  11. On the importance of the long-term seasonal component in day-ahead electricity price forecasting

    International Nuclear Information System (INIS)

    Nowotarski, Jakub; Weron, Rafał

    2016-01-01

    In day-ahead electricity price forecasting (EPF) the daily and weekly seasonalities are always taken into account, but the long-term seasonal component (LTSC) is believed to add unnecessary complexity to the already parameter-rich models and is generally ignored. Conducting an extensive empirical study involving state-of-the-art time series models we show that (i) decomposing a series of electricity prices into a LTSC and a stochastic component, (ii) modeling them independently and (iii) combining their forecasts can bring – contrary to a common belief – an accuracy gain compared to an approach in which a given time series model is calibrated to the prices themselves. - Highlights: • A new class of Seasonal Component AutoRegressive (SCAR) models is introduced. • Electricity prices are decomposed into a trend-seasonal and a stochastic component. • Both components are modeled independently, their forecasts are combined. • Significant accuracy gains can be achieved compared to commonly used approaches.

  12. Energy flow modeling and optimal operation analysis of the micro energy grid based on energy hub

    International Nuclear Information System (INIS)

    Ma, Tengfei; Wu, Junyong; Hao, Liangliang

    2017-01-01

    Highlights: • Design a novel architecture for energy hub integrating power hub, cooling hub and heating hub. • The micro energy grid based on energy hub is introduced and its advantages are discussed. • Propose a generic modeling method for the energy flow of micro energy grid. • Propose an optimal operation model for micro energy grid with considering demand response. • The roles of renewable energy, energy storage devices and demand response are discussed separately. - Abstract: The energy security and environmental problems impel people to explore a more efficient, environment friendly and economical energy utilization pattern. In this paper, the coordinated operation and optimal dispatch strategies for multiple energy system are studied at the whole Micro Energy Grid level. To augment the operation flexibility of energy hub, the innovation sub-energy hub structure including power hub, heating hub and cooling hub is put forward. Basing on it, a generic energy hub architecture integrating renewable energy, combined cooling heating and power, and energy storage devices is developed. Moreover, a generic modeling method for the energy flow of micro energy grid is proposed. To minimize the daily operation cost, a day-ahead dynamic optimal operation model is formulated as a mixed integer linear programming optimization problem with considering the demand response. Case studies are undertaken on a community Micro Energy Grid in four different scenarios on a typical summer day and the roles of renewable energy, energy storage devices and demand response are discussed separately. Numerical simulation results indicate that the proposed energy flow modeling and optimal operation method are universal and effective over the entire energy dispatching horizon.

  13. Evidence-Based Recommendations for Optimizing Light in Day-to-Day Spaceflight Operations

    Science.gov (United States)

    Whitmire, Alexandra; Leveton, Lauren; Barger, Laura; Clark, Toni; Bollweg, Laura; Ohnesorge, Kristine; Brainard, George

    2015-01-01

    NASA Behavioral Health and Performance Element (BHP) personnel have previously reported on efforts to transition evidence-based recommendations for a flexible lighting system on the International Space Station (ISS). Based on these recommendations, beginning in 2016 the ISS will replace the current fluorescent-based lights with an LED-based system to optimize visual performance, facilitate circadian alignment, promote sleep, and hasten schedule shifting. Additional efforts related to lighting countermeasures in spaceflight operations have also been underway. As an example, a recent BHP research study led by investigators at Harvard Medical School and Brigham and Women's Hospital, evaluated the acceptability, feasibility, and effectiveness of blue-enriched light exposure during exercise breaks for flight controllers working the overnight shift in the Mission Control Center (MCC) at NASA Johnson Space Center. This effort, along with published laboratory studies that have demonstrated the effectiveness of appropriately timed light for promoting alertness, served as an impetus for new light options, and educational protocols for flight controllers. In addition, a separate set of guidelines related to the light emitted from electronic devices, were provided to the Astronaut Office this past year. These guidelines were based on an assessment led by NASA's Lighting Environment Test Facility that included measuring the spectral power distribution, irradiance, and radiance of light emitted from ISS-grade laptops and I-Pads, as well as Android devices. Evaluations were conducted with and without the use of off-the-shelf screen filters as well as a software application that touts minimizing the short-wave length of the visible light spectrum. This presentation will focus on the transition for operations process related to lighting countermeasures in the MCC, as well as the evidence to support recommendations for optimal use of laptops, I-Pads, and Android devices during all

  14. PSO-MISMO modeling strategy for multistep-ahead time series prediction.

    Science.gov (United States)

    Bao, Yukun; Xiong, Tao; Hu, Zhongyi

    2014-05-01

    Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.

  15. Risk averse optimal operation of a virtual power plant using two stage stochastic programming

    International Nuclear Information System (INIS)

    Tajeddini, Mohammad Amin; Rahimi-Kian, Ashkan; Soroudi, Alireza

    2014-01-01

    VPP (Virtual Power Plant) is defined as a cluster of energy conversion/storage units which are centrally operated in order to improve the technical and economic performance. This paper addresses the optimal operation of a VPP considering the risk factors affecting its daily operation profits. The optimal operation is modelled in both day ahead and balancing markets as a two-stage stochastic mixed integer linear programming in order to maximize a GenCo (generation companies) expected profit. Furthermore, the CVaR (Conditional Value at Risk) is used as a risk measure technique in order to control the risk of low profit scenarios. The uncertain parameters, including the PV power output, wind power output and day-ahead market prices are modelled through scenarios. The proposed model is successfully applied to a real case study to show its applicability and the results are presented and thoroughly discussed. - Highlights: • Virtual power plant modelling considering a set of energy generating and conversion units. • Uncertainty modelling using two stage stochastic programming technique. • Risk modelling using conditional value at risk. • Flexible operation of renewable energy resources. • Electricity price uncertainty in day ahead energy markets

  16. Probability-Weighted LMP and RCP for Day-Ahead Energy Markets using Stochastic Security-Constrained Unit Commitment: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Ela, E.; O' Malley, M.

    2012-06-01

    Variable renewable generation resources are increasing their penetration on electric power grids. These resources have weather-driven fuel sources that vary on different time scales and are difficult to predict in advance. These characteristics create challenges for system operators managing the load balance on different timescales. Research is looking into new operational techniques and strategies that show great promise on facilitating greater integration of variable resources. Stochastic Security-Constrained Unit Commitment models are one strategy that has been discussed in literature and shows great benefit. However, it is rarely used outside the research community due to its computational limits and difficulties integrating with electricity markets. This paper discusses how it can be integrated into day-ahead energy markets and especially on what pricing schemes should be used to ensure an efficient and fair market.

  17. Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

    Directory of Open Access Journals (Sweden)

    Dehua Zheng

    2017-12-01

    Full Text Available The power generated by wind generators is usually associated with uncertainties, due to the intermittency of wind speed and other weather variables. This creates a big challenge for transmission system operators (TSOs and distribution system operators (DSOs in terms of connecting, controlling and managing power networks with high-penetration wind energy. Hence, in these power networks, accurate wind power forecasts are essential for their reliable and efficient operation. They support TSOs and DSOs in enhancing the control and management of the power network. In this paper, a novel two-stage hybrid approach based on the combination of the Hilbert-Huang transform (HHT, genetic algorithm (GA and artificial neural network (ANN is proposed for day-ahead wind power forecasting. The approach is composed of two stages. The first stage utilizes numerical weather prediction (NWP meteorological information to predict wind speed at the exact site of the wind farm. The second stage maps actual wind speed vs. power characteristics recorded by SCADA. Then, the wind speed forecast in the first stage for the future day is fed to the second stage to predict the future day’s wind power. Comparative selection of input-data parameter sets for the forecasting model and impact analysis of input-data dependency on forecasting accuracy have also been studied. The proposed approach achieves significant forecasting accuracy improvement compared with three other artificial intelligence-based forecasting approaches and a benchmark model using the smart persistence method.

  18. Breastfeeding: Planning Ahead

    Medline Plus

    Full Text Available ... and jobs View all pages in this section Home It's Only Natural Planning ahead It's Only Natural Planning ahead Breastfeeding and baby basics Making breastfeeding work for you Addressing breastfeeding myths Overcoming challenges Finding ...

  19. On the effectiveness of the anti-gaming policy between the day-ahead and real-time electricity markets in The Netherlands

    Energy Technology Data Exchange (ETDEWEB)

    Boogert, A. [Essent Energy Trading (Netherlands); Dupont, D. [University of Twente (Netherlands). School of Business, Public Administration and Technology

    2005-09-01

    In the paper, we study the linkage between two related markets for electricity in The Netherlands: the day-ahead market and the real-time market. The Dutch regulator wants to prevent trading across these two markets and has set up a dual pricing system for this purpose. In this paper, we test the effectiveness of this policy by studying the ex post profitability of trading strategies spanning the two markets over various time segments. Our results show that profits generated by these strategies are rarely positive on average and always characterized by very large potential losses, which dwarf the mean profit when the latter is positive. (author)

  20. On the effectiveness of the anti-gaming policy between the day-ahead and real-time electricity markets in The Netherlands

    International Nuclear Information System (INIS)

    Boogert, A.; Dupont, D.

    2005-01-01

    In the paper, we study the linkage between two related markets for electricity in The Netherlands: the day-ahead market and the real-time market. The Dutch regulator wants to prevent trading across these two markets and has set up a dual pricing system for this purpose. In this paper, we test the effectiveness of this policy by studying the ex post profitability of trading strategies spanning the two markets over various time segments. Our results show that profits generated by these strategies are rarely positive on average and always characterized by very large potential losses, which dwarf the mean profit when the latter is positive. (author)

  1. Optimal operation strategy of battery energy storage system to real-time electricity price in Denmark

    DEFF Research Database (Denmark)

    Hu, Weihao; Chen, Zhe; Bak-Jensen, Birgitte

    2010-01-01

    markets in some ways, is chosen as the studied power system in this paper. Two kinds of BESS, based on polysulfide-bromine (PSB) and vanadium redox (VRB) battery technologies, are studies in the paper. Simulation results show, that the proposed optimal operation strategy is an effective measure to achieve......Since the hourly spot market price is available one day ahead, the price could be transferred to the consumers and they may have some motivations to install an energy storage system in order to save their energy costs. This paper presents an optimal operation strategy for a battery energy storage...

  2. Modeling and analysis of a decentralized electricity market: An integrated simulation/optimization approach

    International Nuclear Information System (INIS)

    Sarıca, Kemal; Kumbaroğlu, Gürkan; Or, Ilhan

    2012-01-01

    In this study, a model is developed to investigate the implications of an hourly day-ahead competitive power market on generator profits, electricity prices, availability and supply security. An integrated simulation/optimization approach is employed integrating a multi-agent simulation model with two alternative optimization models. The simulation model represents interactions between power generator, system operator, power user and power transmitter agents while the network flow optimization model oversees and optimizes the electricity flows, dispatches generators based on two alternative approaches used in the modeling of the underlying transmission network: a linear minimum cost network flow model and a non-linear alternating current optimal power flow model. Supply, demand, transmission, capacity and other technological constraints are thereby enforced. The transmission network, on which the scenario analyses are carried out, includes 30 bus, 41 lines, 9 generators, and 21 power users. The scenarios examined in the analysis cover various settings of transmission line capacities/fees, and hourly learning algorithms. Results provide insight into key behavioral and structural aspects of a decentralized electricity market under network constraints and reveal the importance of using an AC network instead of a simplified linear network flow approach. -- Highlights: ► An agent-based simulation model with an AC transmission environment with a day-ahead market. ► Physical network parameters have dramatic effects over price levels and stability. ► Due to AC nature of transmission network, adaptive agents have more local market power than minimal cost network flow. ► Behavior of the generators has significant effect over market price formation, as pointed out by bidding strategies. ► Transmission line capacity and fee policies are found to be very effective in price formation in the market.

  3. A comparison of the economic benefits of centralized and distributed model predictive control strategies for optimal and sub-optimal mine dewatering system designs

    International Nuclear Information System (INIS)

    Romero, Alberto; Millar, Dean; Carvalho, Monica; Maestre, José M.; Camacho, Eduardo F.

    2015-01-01

    Mine dewatering can represent up to 5% of the total energy demand of a mine, and is one of the mine systems that aim to guarantee safe operating conditions. As mines go deeper, dewatering pumping heads become bigger, potentially involving several lift stages. Greater depth does not only mean greater dewatering cost, but more complex systems that require more sophisticated control systems, especially if mine operators wish to gain benefits from demand response incentives that are becoming a routine part of electricity tariffs. This work explores a two stage economic optimization procedure of an underground mine dewatering system, comprising two lifting stages, each one including a pump station and a water reservoir. First, the system design is optimized considering hourly characteristic dewatering demands for twelve days, one day representing each month of the year to account for seasonal dewatering demand variations. This design optimization minimizes the annualized cost of the system, and therefore includes the investment costs in underground reservoirs. Reservoir size, as well as an hourly pumping operation plan are calculated for specific operating environments, defined by characteristic hourly electricity prices and water inflows (seepage and water use from production activities), at best known through historical observations for the previous year. There is no guarantee that the system design will remain optimal when it faces the water inflows and market determined electricity prices of the year ahead, or subsequent years ahead, because these remain unknown at design time. Consequently, the dewatering optimized system design is adopted subsequently as part of a Model Predictive Control (MPC) strategy that adaptively maintains optimality during the operations phase. Centralized, distributed and non-centralized MPC strategies are explored. Results show that the system can be reliably controlled using any of these control strategies proposed. Under the operating

  4. Ophthalmic public health; the way ahead.

    Science.gov (United States)

    Heidary, F; Rahimi, A; Gharebaghi, R

    2012-01-01

    Visual sciences have been progressing quickly in recent decades through globalization phenomenon. An enormous change has taken place in ocular health issues, however, there are various problems facing ophthalmic public health worldwide. In the previous years, the World Health Organization and the International Agency for the Prevention of Blindness in partnership launched the global initiative to eradicate avoidable blindness by the year 2020, VISION 2020 the Right to Sight. It has concentrated on the prevention of blindness disability and recognized a health issue-sight as a human right. In view of challenges ahead of visual sciences, close collaboration between international agencies at the global level to implement new strategies and monitor the progress will be mandatory. In these circumstances non-governmental organizations should not be neglected. World Sight Day 2012 would be a great opportunity to be a focus on importance of visual impairment as an important public health issue and discovering new challenges ahead.

  5. Path-Wise Test Data Generation Based on Heuristic Look-Ahead Methods

    Directory of Open Access Journals (Sweden)

    Ying Xing

    2014-01-01

    Full Text Available Path-wise test data generation is generally considered an important problem in the automation of software testing. In essence, it is a constraint optimization problem, which is often solved by search methods such as backtracking algorithms. In this paper, the backtracking algorithm branch and bound and state space search in artificial intelligence are introduced to tackle the problem of path-wise test data generation. The former is utilized to explore the space of potential solutions and the latter is adopted to construct the search tree dynamically. Heuristics are employed in the look-ahead stage of the search. Dynamic variable ordering is presented with a heuristic rule to break ties, values of a variable are determined by the monotonicity analysis on branching conditions, and maintaining path consistency is achieved through analysis on the result of interval arithmetic. An optimization method is also proposed to reduce the search space. The results of empirical experiments show that the search is conducted in a basically backtrack-free manner, which ensures both test data generation with promising performance and its excellence over some currently existing static and dynamic methods in terms of coverage. The results also demonstrate that the proposed method is applicable in engineering.

  6. Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy and Improved Combined Cooling-Heating-Power Strategy Based Two-Time Scale Multi-Objective Optimization Model for Stand-Alone Microgrid Operation

    Directory of Open Access Journals (Sweden)

    Fei Wang

    2017-11-01

    Full Text Available The optimal dispatching model for a stand-alone microgrid (MG is of great importance to its operation reliability and economy. This paper aims at addressing the difficulties in improving the operational economy and maintaining the power balance under uncertain load demand and renewable generation, which could be even worse in such abnormal conditions as storms or abnormally low or high temperatures. A new two-time scale multi-objective optimization model, including day-ahead cursory scheduling and real-time scheduling for finer adjustments, is proposed to optimize the operational cost, load shedding compensation and environmental benefit of stand-alone MG through controllable load (CL and multi-distributed generations (DGs. The main novelty of the proposed model is that the synergetic response of CL and energy storage system (ESS in real-time scheduling offset the operation uncertainty quickly. And the improved dispatch strategy for combined cooling-heating-power (CCHP enhanced the system economy while the comfort is guaranteed. An improved algorithm, Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy (SIP-CO-PSO-ERS algorithm with strong searching capability and fast convergence speed, was presented to deal with the problem brought by the increased errors between actual renewable generation and load and prior predictions. Four typical scenarios are designed according to the combinations of day types (work day or weekend and weather categories (sunny or rainy to verify the performance of the presented dispatch strategy. The simulation results show that the proposed two-time scale model and SIP-CO-PSO-ERS algorithm exhibit better performance in adaptability, convergence speed and search ability than conventional methods for the stand-alone MG’s operation.

  7. Energy and ancillary service dispatch through dynamic optimal power flow

    International Nuclear Information System (INIS)

    Costa, A.L.; Costa, A. Simoes

    2007-01-01

    This paper presents an approach based on dynamic optimal power flow (DOPF) to clear both energy and spinning reserve day-ahead markets. A competitive environment is assumed, where agents can offer active power for both demand supply and ancillary services. The DOPF jointly determines the optimal solutions for both energy dispatch and reserve allocation. A non-linear representation for the electrical network is employed, which is able to take transmission losses and power flow limits into account. An attractive feature of the proposed approach is that the final optimal solution will automatically meet physical constraints such as generating limits and ramp rate restrictions. In addition, the proposed framework allows the definition of multiple zones in the network for each time interval, in order to ensure a more adequate distribution of reserves throughout the power system. (author)

  8. Short time ahead wind power production forecast

    International Nuclear Information System (INIS)

    Sapronova, Alla; Meissner, Catherine; Mana, Matteo

    2016-01-01

    An accurate prediction of wind power output is crucial for efficient coordination of cooperative energy production from different sources. Long-time ahead prediction (from 6 to 24 hours) of wind power for onshore parks can be achieved by using a coupled model that would bridge the mesoscale weather prediction data and computational fluid dynamics. When a forecast for shorter time horizon (less than one hour ahead) is anticipated, an accuracy of a predictive model that utilizes hourly weather data is decreasing. That is because the higher frequency fluctuations of the wind speed are lost when data is averaged over an hour. Since the wind speed can vary up to 50% in magnitude over a period of 5 minutes, the higher frequency variations of wind speed and direction have to be taken into account for an accurate short-term ahead energy production forecast. In this work a new model for wind power production forecast 5- to 30-minutes ahead is presented. The model is based on machine learning techniques and categorization approach and using the historical park production time series and hourly numerical weather forecast. (paper)

  9. Short time ahead wind power production forecast

    Science.gov (United States)

    Sapronova, Alla; Meissner, Catherine; Mana, Matteo

    2016-09-01

    An accurate prediction of wind power output is crucial for efficient coordination of cooperative energy production from different sources. Long-time ahead prediction (from 6 to 24 hours) of wind power for onshore parks can be achieved by using a coupled model that would bridge the mesoscale weather prediction data and computational fluid dynamics. When a forecast for shorter time horizon (less than one hour ahead) is anticipated, an accuracy of a predictive model that utilizes hourly weather data is decreasing. That is because the higher frequency fluctuations of the wind speed are lost when data is averaged over an hour. Since the wind speed can vary up to 50% in magnitude over a period of 5 minutes, the higher frequency variations of wind speed and direction have to be taken into account for an accurate short-term ahead energy production forecast. In this work a new model for wind power production forecast 5- to 30-minutes ahead is presented. The model is based on machine learning techniques and categorization approach and using the historical park production time series and hourly numerical weather forecast.

  10. A dynamic optimization-based architecture for polygeneration microgrids with tri-generation, renewables, storage systems and electrical vehicles

    International Nuclear Information System (INIS)

    Bracco, Stefano; Delfino, Federico; Pampararo, Fabio; Robba, Michela; Rossi, Mansueto

    2015-01-01

    Highlights: • We describe two national special projects on smart grid. • We developed dynamic decision model based on a MPC architecture. • We developed an optimization model for microgrids, for a specific case study. - Abstract: An overall architecture, or Energy Management System (EMS), based on a dynamic optimization model to minimize operating costs and CO 2 emissions is formalized and applied to the University of Genova Savona Campus test-bed facilities consisting of a Smart Polygeneration Microgrid (SPM) and a Sustainable Energy Building (SEB) connected to such microgrid. The electric grid is a three phase low voltage distribution system, connecting many different technologies: three cogeneration micro gas turbines fed by natural gas, a photovoltaic field, three cogeneration Concentrating Solar Powered (CSP) systems (equipped with Stirling engines), an absorption chiller equipped with a storage tank, two types of electrical storage based on batteries technology (long term Na–Ni and short term Li-Ion ion), two electric vehicles charging stations, other electrical devices (inverters and smart metering systems), etc. The EMS can be used both for microgrids approximated as single bus bar (or one node) and for microgrids in which all buses are taken into account. The optimal operation of the microgrid is based on a central controller that receives forecasts and data from a SCADA system and that can schedule all dispatchable plants in the day ahead or in real time through an approach based on Model Predictive Control (MPC). The architecture is tested and applied to the case study of the Savona Campus

  11. Stochastic optimal generation bid to electricity markets with emissions risk constraints.

    Science.gov (United States)

    Heredia, F-Javier; Cifuentes-Rubiano, Julián; Corchero, Cristina

    2018-02-01

    There are many factors that influence the day-ahead market bidding strategies of a generation company (GenCo) within the framework of the current energy market. Environmental policy issues are giving rise to emission limitation that are becoming more and more important for fossil-fueled power plants, and these must be considered in their management. This work investigates the influence of the emissions reduction plan and the incorporation of the medium-term derivative commitments in the optimal generation bidding strategy for the day-ahead electricity market. Two different technologies have been considered: the high-emission technology of thermal coal units and the low-emission technology of combined cycle gas turbine units. The Iberian Electricity Market (MIBEL) and the Spanish National Emissions Reduction Plan (NERP) defines the environmental framework for dealing with the day-ahead market bidding strategies. To address emission limitations, we have extended some of the standard risk management methodologies developed for financial markets, such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), thus leading to the new concept of Conditional Emission at Risk (CEaR). This study offers electricity generation utilities a mathematical model for determining the unit's optimal generation bid to the wholesale electricity market such that it maximizes the long-term profits of the utility while allowing it to abide by the Iberian Electricity Market rules as well as the environmental restrictions set by the Spanish National Emissions Reduction Plan. We analyze the economic implications for a GenCo that includes the environmental restrictions of this National Plan as well as the NERP's effects on the expected profits and the optimal generation bid. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Optimal trading strategy for GenCo in LMP-based and bilateral ...

    African Journals Online (AJOL)

    GenCo) in multi-market environment including day-ahead spot and long term bilateral contract markets using self-organising hierarchical particle swarm optimisation with time-varying acceleration coefficients (SPSO-TVAC). The proposed trading ...

  13. Not All Large Customers are Made Alike: Disaggregating Response to Default-Service Day-Ahead Market Pricing

    International Nuclear Information System (INIS)

    Hopper, Nicole; Goldman, Charles; Neenan, Bernie

    2006-01-01

    For decades, policymakers and program designers have gone on the assumption that large customers, particularly industrial facilities, are the best candidates for realtime pricing (RTP). This assumption is based partly on practical considerations (large customers can provide potentially large load reductions) but also on the premise that businesses focused on production cost minimization are most likely to participate and respond to opportunities for bill savings. Yet few studies have examined the actual price response of large industrial and commercial customers in a disaggregated fashion, nor have factors such as the impacts of demand response (DR) enabling technologies, simultaneous emergency DR program participation and price response barriers been fully elucidated. This second-phase case study of Niagara Mohawk Power Corporation (NMPC)'s large customer RTP tariff addresses these information needs. The results demonstrate the extreme diversity of large customers' response to hourly varying prices. While two-thirds exhibit some price response, about 20 percent of customers provide 75-80 percent of the aggregate load reductions. Manufacturing customers are most price-responsive as a group, followed by government/education customers, while other sectors are largely unresponsive. However, individual customer response varies widely. Currently, enabling technologies do not appear to enhance hourly price response; customers report using them for other purposes. The New York Independent System Operator (NYISO)'s emergency DR programs enhance price response, in part by signaling to customers that day-ahead prices are high. In sum, large customers do currently provide moderate price response, but there is significant room for improvement through targeted programs that help customers develop and implement automated load-response strategies

  14. Optimal recharging strategy for battery-switch stations for electric vehicles in France

    International Nuclear Information System (INIS)

    Armstrong, M.; El Hajj Moussa, C.; Adnot, J.; Galli, A.; Riviere, P.

    2013-01-01

    Most papers that study the recharging of electric vehicles focus on charging the batteries at home and at the work-place. The alternative is for owners to exchange the battery at a specially equipped battery switch station (BSS). This paper studies strategies for the BSS to buy and sell the electricity through the day-ahead market. We determine what the optimal strategies would have been for a large fleet of EVs in 2010 and 2011, for the V2G and the G2V cases. These give the amount that the BSS should offer to buy or sell each hour of the day. Given the size of the fleet, the quantities of electricity bought and sold will displace the market equilibrium. Using the aggregate offers to buy and the bids to sell on the day-ahead market, we compute what the new prices and volumes transacted would be. While buying electricity for the G2V case incurs a cost, it would have been possible to generate revenue in the V2G case, if the arrivals of the EVs had been evenly spaced during the day. Finally, we compare the total cost of implementing the strategies with the cost of buying the same quantity of electricity from EDF. - Highlights: • Optimal strategies for buying/selling electricity through day-ahead auction market. • Given fleet size power bought and sold would change market price and volume. • New prices computed using aggregate offers to buy/sell power in 2010 and 2011. • Timing of arrival of EVs critical in V2G case. If evenly spaced BSS makes money. • Strategies are very robust even when French and German markets were coupled Nov. 2010

  15. A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response

    International Nuclear Information System (INIS)

    Ju, Liwei; Tan, Zhongfu; Yuan, Jinyun; Tan, Qingkun; Li, Huanhuan; Dong, Fugui

    2016-01-01

    Highlights: • Our research focuses on Virtual Power Plant (VPP). • Virtual Power Plant consists of WPP, PV, CGT, ESSs and DRPs. • Robust optimization theory is introduced to analyze uncertainties. • A bi-level stochastic scheduling optimization model is proposed for VPP. • Models are built to measure the impacts of ESSs and DERPs on VPP operation. - Abstract: To reduce the uncertain influence of wind power and solar photovoltaic power on virtual power plant (VPP) operation, robust optimization theory (ROT) is introduced to build a stochastic scheduling model for VPP considering the uncertainty, price-based demand response (PBDR) and incentive-based demand response (IBDR). First, the VPP components are described including the wind power plant (WPP), photovoltaic generators (PV), convention gas turbine (CGT), energy storage systems (ESSs) and demand resource providers (DRPs). Then, a scenario generation and reduction frame is proposed for analyzing and simulating output stochastics based on the interval method and the Kantorovich distance. Second, a bi-level robust scheduling model is proposed with a double robust coefficient for WPP and PV. In the upper layer model, the maximum VPP operation income is taken as the optimization objective for building the scheduling model with the day-ahead prediction output of WPP and PV. In the lower layer model, the day-ahead scheduling scheme is revised with the actual output of the WPP and PV under the objectives of the minimum system net load and the minimum system operation cost. Finally, the independent micro-grid in a coastal island in eastern China is used for the simulation analysis. The results illustrate that the model can overcome the influence of uncertainty on VPP operations and reduce the system power shortage cost by connecting the day-ahead scheduling with the real-time scheduling. ROT could provide a flexible decision tool for decision makers, effectively addressing system uncertainties. ESSs could

  16. Finding diversity for building one-day ahead Hydrological Ensemble Prediction System based on artificial neural network stacks

    Science.gov (United States)

    Brochero, Darwin; Anctil, Francois; Gagné, Christian; López, Karol

    2013-04-01

    In this study, we addressed the application of Artificial Neural Networks (ANN) in the context of Hydrological Ensemble Prediction Systems (HEPS). Such systems have become popular in the past years as a tool to include the forecast uncertainty in the decision making process. HEPS considers fundamentally the uncertainty cascade model [4] for uncertainty representation. Analogously, the machine learning community has proposed models of multiple classifier systems that take into account the variability in datasets, input space, model structures, and parametric configuration [3]. This approach is based primarily on the well-known "no free lunch theorem" [1]. Consequently, we propose a framework based on two separate but complementary topics: data stratification and input variable selection (IVS). Thus, we promote an ANN prediction stack in which each predictor is trained based on input spaces defined by the IVS application on different stratified sub-samples. All this, added to the inherent variability of classical ANN optimization, leads us to our ultimate goal: diversity in the prediction, defined as the complementarity of the individual predictors. The stratification application on the 12 basins used in this study, which originate from the second and third workshop of the MOPEX project [2], shows that the informativeness of the data is far more important than the quantity used for ANN training. Additionally, the input space variability leads to ANN stacks that outperform an ANN stack model trained with 100% of the available information but with a random selection of dataset used in the early stopping method (scenario R100P). The results show that from a deterministic view, the main advantage focuses on the efficient selection of the training information, which is an equally important concept for the calibration of conceptual hydrological models. On the other hand, the diversity achieved is reflected in a substantial improvement in the scores that define the

  17. Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2009-01-01

    With the introduction of restructuring into the electric power industry, the price of electricity has become the focus of all activities in the power market. Electricity price forecast is key information for electricity market managers and participants. However, electricity price is a complex signal due to its non-linear, non-stationary, and time variant behavior. In spite of performed research in this area, more accurate and robust price forecast methods are still required. In this paper, a new forecast strategy is proposed for day-ahead price forecasting of electricity markets. Our forecast strategy is composed of a new two stage feature selection technique and cascaded neural networks. The proposed feature selection technique comprises modified Relief algorithm for the first stage and correlation analysis for the second stage. The modified Relief algorithm selects candidate inputs with maximum relevancy with the target variable. Then among the selected candidates, the correlation analysis eliminates redundant inputs. Selected features by the two stage feature selection technique are used for the forecast engine, which is composed of 24 consecutive forecasters. Each of these 24 forecasters is a neural network allocated to predict the price of 1 h of the next day. The whole proposed forecast strategy is examined on the Spanish and Australia's National Electricity Markets Management Company (NEMMCO) and compared with some of the most recent price forecast methods.

  18. Micro-generation dispatch in a smart residential multi-carrier energy system considering demand forecast error

    International Nuclear Information System (INIS)

    Sanjari, M.J.; Karami, H.; Gooi, H.B.

    2016-01-01

    Highlights: • Combination of day-ahead and hour-ahead optimizations to design online controller. • Investigating the effect of load forecast error on the system operating cost. • Proposing effective method for hour-ahead resource re-dispatch. • Using the HSS algorithm as a powerful and effective optimization method. • Combining long-term and short-term strategies for optimal dispatch of resources. - Abstract: This paper deals with a residential hybrid thermal/electrical grid-connected home energy system incorporating real data for the load demand. A day-ahead scheduling (DAS) algorithm for dispatching different resources has been developed in previous studies to determine the optimal operation scheduling for the distributed energy resources at each time interval so that the operational cost of a smart house is minimized. However, demand of houses may be changed in each hour and cannot be exactly predicted one day ahead. System complexity caused by nonlinear dynamics of the fuel cell, as a combined heat and power device, and battery charging and discharging time make it difficult to find the optimal operating point of the system by using the optimization algorithms quickly in online applications. In this paper, the demand forecast error is studied and a near-optimal dispatch strategy by using artificial neural network (ANN) is proposed for the residential energy system when the demand changes are known one hour ahead with respect to the predicted day-ahead values. The day-ahead and hour-ahead optimizations are combined and ANN training inputs are adjusted according to the problem such that the economic dispatch of different energy resources can be achieved by the proposed method compared with previous studies. Using the model of the fuel cell extracted from experimental measurement and real data for the load demand makes the results more applicable in real residential energy systems.

  19. Investigation by CRE into record high electricity prices on Powernext Day-ahead Auction in October and November 2007. Analysis report

    International Nuclear Information System (INIS)

    2008-01-01

    In October and November 2007, electricity prices hit record highs on the Powernext Day-Ahead Auction trading platform. While, during the first nine months of the year, prices for delivery between 6 pm and 8 pm averaged euros 36 /MWh, rising to a maximum of euros 118 /MWh, they spiked at: - euros 1,236 /MWh for delivery on Monday, 29 October 2007 between 6 pm and 7 pm; - euros 2,500 /MWh for delivery on Monday, 12 November 2007 between 8 pm and 9 pm; - euros 1,762 /MWh for delivery on Thursday, 15 November 2007 between 6 pm and 7 pm. Day ahead prices of electricity have a major effect on the procurement costs of suppliers, and consequently on the formation of selling prices to end consumers. Article 28 of French Act No.2000-108 of 10 February 2000 stipulates that CRE 'shall monitor, for electricity and natural gas, all transactions made between suppliers, brokers and producers, all transactions made on the organised markets and cross-border trading. It shall ensure that bids made by suppliers, brokers and producers are consistent with their financial and technical requirements'. Article 33 of the same Act specifies that, 'In performing the tasks entrusted to it, the French Energy Regulatory Commission (CRE) may gather any information it requires from the Ministers for the Economy and for Energy, from public electricity transmission and distribution system operators, from operators of infrastructures for the natural gas transmission and distribution networks and operators of liquefied natural gas facilities, as well as from any other company involved in the electricity and natural gas market. It may also call upon any person whose evidence it deems necessary for the purposes of its investigations'. In this context, and in application of its duty to monitor the electricity wholesale markets, CRE has undertaken an investigation to analyse the mechanisms underlying the formation of such high prices. To this end, CRE gathered information relative to decisions taken by

  20. Seismic prediction ahead of tunnel construction using Rayleigh-waves

    OpenAIRE

    Jetschny, Stefan; De Nil, Denise; Bohlen, Thomas

    2008-01-01

    To increase safety and efficiency of tunnel constructions, online seismic exploration ahead of a tunnel can become a valuable tool. We developed a new forward looking seismic imaging technique e.g. to determine weak and water bearing zones ahead of the constructions. Our approach is based on the excitation and registration of tunnel surface-waves. These waves are excited at the tunnel face behind the cutter head of a tunnel boring machine and travel into drilling direction. Arriving at the fr...

  1. Design and Optimization of Gadolinium Based Contrast Agents for Magnetic Resonance Imaging

    International Nuclear Information System (INIS)

    Pereira, G.A.; Geraldes, C.F.G.C.; University of Coimbra

    2007-01-01

    The role of Gd 3+ chelates as contrast agents in Magnetic Resonance Imaging is discussed. The theory describing the different contributions to paramagnetic relaxation relevant to the understanding of the molecular parameters determining the relativity of those Gd 3+ chelates, is presented. The experimental techniques used to obtain those parameters are also described. Then, the various approaches taken to optimize those parameters, leading to maximum relativity (efficiency) of the contrast agents, are also illustrated with relevant examples taken from the literature. The various types of Gd 3+ -based agents, besides non-specific and hepatobiliary agents, are also discussed, namely blood pool, targeting, responsive and paramagnetic chemical shift saturation transfer (PARACEST) agents. Finally, a perspective is presented of some of the challenges lying ahead in the optimization of MRI contrast agents to be useful in Molecular Imaging. (author)

  2. Multi-step ahead nonlinear identification of Lorenz's chaotic system using radial basis neural network with learning by clustering and particle swarm optimization

    International Nuclear Information System (INIS)

    Guerra, Fabio A.; Coelho, Leandro dos S.

    2008-01-01

    An important problem in engineering is the identification of nonlinear systems, among them radial basis function neural networks (RBF-NN) using Gaussian activation functions models, which have received particular attention due to their potential to approximate nonlinear behavior. Several design methods have been proposed for choosing the centers and spread of Gaussian functions and training the RBF-NN. The selection of RBF-NN parameters such as centers, spreads, and weights can be understood as a system identification problem. This paper presents a hybrid training approach based on clustering methods (k-means and c-means) to tune the centers of Gaussian functions used in the hidden layer of RBF-NNs. This design also uses particle swarm optimization (PSO) for centers (local clustering search method) and spread tuning, and the Penrose-Moore pseudoinverse for the adjustment of RBF-NN weight outputs. Simulations involving this RBF-NN design to identify Lorenz's chaotic system indicate that the performance of the proposed method is superior to that of the conventional RBF-NN trained for k-means and the Penrose-Moore pseudoinverse for multi-step ahead forecasting

  3. AHEAD. Advate in HaEmophilia A outcome Database.

    Science.gov (United States)

    Oldenburg, J; Kurnik, K; Huth-Kühne, A; Zimmermann, R; Abraham, I; Klamroth, R

    2010-11-01

    The clinical picture of haemophilia A patients is often characterised by recurrent bleedings, in particular joint bleeds. Thus far, long-term data on the outcome of haemophilia A patients are scarce as regards the development of target joints, joint replacement, lost days from school or work due to bleedings, and the quality of life, as most previous studies were limited to the aspects of safety and efficacy. The Baxter-initiated AHEAD (Advate in HaEmophilia A outcome Database) study is a multi-centre, prospective, non-interventional observational study of haemophilia A patients. All patients with a residual FVIII activity of £5% who are being treated with ADVATE are eligible. There are no limitations in terms of patient age or treatment regimen. AHEAD is scientifically supported by a renowned interdisciplinary steering board and is intended to yield data on 500 patients in up to 30 haemophilia centres, collected during a period of four years. The large patient population has been chosen in order to ensure a valid database. The objective of the study is to record haemophilia-related arthropathies, which will be defined based on imaging techniques (e. g. MRI, X-ray, ultrasound) and the judgment of the attending physician. In addition, extensive data will be collected on joint replacement surgeries, pseudotumour development, bleeding-related pain, quality of life (age-related questionnaires: Haem-A-QoL, Haemo-QoL, SF10, SF12v2), risk factors (diabetes mellitus, arterial hypertension, nicotine abuse), blood group, gene mutation, physical activity, and on the efficacy and safety of Advate. The patient data will be entered into an electronic CRF system at the centres. Plausibility checks during data entry, regular monitoring visits, and the option of auditing all serve to ensure a high data quality for AHEAD. The first patient was enrolled in the study in early June 2010; recruitment is planned to continue until the end of 2011. The Ethics Committee of the University

  4. Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

    Directory of Open Access Journals (Sweden)

    Yi Xiao

    2013-02-01

    Full Text Available Stock e-exchange prices forecasting is an important financial problem that is receiving increasing attention. This study proposes a novel three-stage nonlinear ensemble model. In the proposed model, three different types of neural-network based models, i.e. Elman network, generalized regression neural network (GRNN and wavelet neural network (WNN are constructed by three non-overlapping training sets and are further optimized by improved particle swarm optimization (IPSO. Finally, a neural-network-based nonlinear meta-model is generated by learning three neural-network based models through support vector machines (SVM neural network. The superiority of the proposed approach lies in its flexibility to account for potentially complex nonlinear relationships. Three daily stock indices time series are used for validating the forecasting model. Empirical results suggest the ensemble ANNs-PSO-GA approach can significantly improve the prediction performance over other individual models and linear combination models listed in this study.

  5. Determination of the Prosumer's Optimal Bids

    Science.gov (United States)

    Ferruzzi, Gabriella; Rossi, Federico; Russo, Angela

    2015-12-01

    This paper considers a microgrid connected with a medium-voltage (MV) distribution network. It is assumed that the microgrid, which is managed by a prosumer, operates in a competitive environment and participates in the day-ahead market. Then, as the first step of the short-term management problem, the prosumer must determine the bids to be submitted to the market. The offer strategy is based on the application of an optimization model, which is solved for different hourly price profiles of energy exchanged with the main grid. The proposed procedure is applied to a microgrid and four different its configurations were analyzed. The configurations consider the presence of thermoelectric units that only produce electricity, a boiler or/and cogeneration power plants for the thermal loads, and an electric storage system. The numerical results confirmed the numerous theoretical considerations that have been made.

  6. A modelling breakthrough for market design analysis to test massive intermittent generation integration in markets results of selected OPTIMATE studies

    DEFF Research Database (Denmark)

    Beaude, Francois; Atayi, A.; Bourmaud, J.-Y.

    2013-01-01

    The OPTIMATE1 platform focuses on electricity system and market designs modelling in order to assess current and innovative designs in Europe. The current paper describes the results of the first validation studies' conducted with the tool. These studies deal with day-ahead market rules, load...... flexibility, cross-border management and intermittent renewable support schemes with a view to better integrating large amounts of renewable energy in Europe. Market and system designs were assessed based on economic efficiency, security of supply2 and environmental impact3 indicators. These results give...

  7. Day ahead forecast of wind power through optimal application of multivariate analyzing methods

    Energy Technology Data Exchange (ETDEWEB)

    Arnoldt, Alexander; Bretschneider, Peter [Fraunhofer Institute for Optronics, System Technology, and Image Exploitation - Application Centre System Technology (IOSB-AST), Ilmenau (Germany). Energy Systems Group

    2011-07-01

    This paper presents two algorithms in identifying input models for artificial neural networks. The algorithms are based on an entropy analysis and an eigenvalue analysis of the correlation matrix. The resulting input models are used for investigating a feed forward and a recurrent artificial neural network structure to simulate a 24 hour forecast of wind power production. The limitation of the forecast error distribution is investigated through successful implementation of hybridization of single forecast models. Errors of the best forecast model stay between a normalized root mean square error from 3.5% to 6.1%. (orig.)

  8. Online multistep-ahead inundation depth forecasts by recurrent NARX networks

    Directory of Open Access Journals (Sweden)

    H.-Y. Shen

    2013-03-01

    Full Text Available Various types of artificial neural networks (ANNs have been successfully applied in hydrological fields, but relatively scant on multistep-ahead flood inundation forecasting, which is very difficult to achieve, especially when dealing with forecasts without regular observed data. This study proposes a recurrent configuration of nonlinear autoregressive with exogenous inputs (NARX network, called R-NARX, to forecast multistep-ahead inundation depths in an inundation area. The proposed R-NARX is constructed based on the recurrent neural network (RNN, which is commonly used for modeling nonlinear dynamical systems. The models were trained and tested based on a large number of inundation data generated by a well validated two-dimensional simulation model at thirteen inundation-prone sites in Yilan County, Taiwan. We demonstrate that the R-NARX model can effectively inhibit error growth and accumulation when being applied to online multistep-ahead inundation forecasts over a long lasting forecast period. For comparison, a feedforward time-delay and an online feedback configuration of NARX networks (T-NARX and O-NARX were performed. The results show that (1 T-NARX networks cannot make online forecasts due to unavailable inputs in the constructed networks even though they provide the best performances for reference only; and (2 R-NARX networks consistently outperform O-NARX networks and can be adequately applied to online multistep-ahead forecasts of inundation depths in the study area during typhoon events.

  9. Linear Look-ahead in Conjunctive Cells: An Entorhinal Mechanism for Vector-Based Navigation

    Directory of Open Access Journals (Sweden)

    John L Kubie

    2012-04-01

    Full Text Available The crisp organization of the firing bumps of entorhinal grid cells and conjunctive cells leads to the notion that the entorhinal cortex may compute linear navigation routes. Specifically, we propose a process, termed linear look-ahead, by which a stationary animal could compute a series of locations in the direction it is facing. We speculate that this computation could be achieved through learned patterns of connection strengths among entorhinal neurons. This paper has three sections. First, we describe the minimal grid cell properties that will be built into our network. Specifically, the network relies of rigid modules of neurons, where all members have identical grid scale and orientation, but differ in spatial phase. Additionally, these neurons must be densely interconnected with synapses that are modifiable early in the animal’s life. Second, we investigate whether plasticity during short bouts of locomotion could induce patterns of connections amongst grid cells or conjunctive cells. Finally, we run a simulation to test whether the learned connection patterns can exhibit linear look-ahead. Our results are straightforward. A simulated 30-minute walk produces weak strengthening of synapses between grid cells that do not support linear look-ahead. Similar training in a conjunctive-cell module produces a small subset of very strong connections between cells. These strong pairs have three properties: The pre- and post-synaptic cells have similar heading direction. The cell pairs have neighboring grid bumps. Finally, the spatial offset of firing bumps of the cell pair is in the direction of the common heading preference. Such a module can produce strong and accurate linear look ahead starting in any location and extending in any direction. We speculate that this process may: 1. compute linear paths to goals; 2. update grid cell firing during navigation; and 3. stabilize the rigid modules of grid cells and conjunctive cells.

  10. Breastfeeding: Planning Ahead

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  11. Design method for marine direct drive volume control ahead actuator

    Directory of Open Access Journals (Sweden)

    WANG Haiyang

    2018-02-01

    Full Text Available [Objectives] In order to reduce the size, weight and auxiliary system configuration of marine ahead actuators, this paper proposes a kind of direct drive volume control electro-hydraulic servo ahead actuator. [Methods] The protruding and indenting control of the servo oil cylinder are realized through the forward and reverse of the bidirectional working gear pump, and the flow matching valve implements the self-locking of the ahead actuator in the target position. The mathematical model of the ahead actuator is established, and an integral separation fuzzy PID controller designed. On this basis, using AMESim software to build a simulation model of the ahead actuator, and combined with testing, this paper completes an analysis of the control strategy research and dynamic and static performance of the ahead actuator. [Results] The experimental results agree well with the simulation results and verify the feasibility of the ahead actuator's design. [Conclusions] The research results of this paper can provide valuable references for the integration and miniaturization design of marine ahead actuators.

  12. Customer response to day-ahead wholesale market electricity prices: Case study of RTP program experience in New York

    Energy Technology Data Exchange (ETDEWEB)

    Goldman, C.; Hopper, N.; Sezgen, O.; Moezzi, M.; Bharvirkar, R.; Neenan, B.; Boisvert, R.; Cappers, P.; Pratt, D.

    2004-07-01

    There is growing interest in policies, programs and tariffs that encourage customer loads to provide demand response (DR) to help discipline wholesale electricity markets. Proposals at the retail level range from eliminating fixed rate tariffs as the default service for some or all customer groups to reinstituting utility-sponsored load management programs with market-based inducements to curtail. Alternative rate designs include time-of-use (TOU), day-ahead real-time pricing (RTP), critical peak pricing, and even pricing usage at real-time market balancing prices. Some Independent System Operators (ISOs) have implemented their own DR programs whereby load curtailment capabilities are treated as a system resource and are paid an equivalent value. The resulting load reductions from these tariffs and programs provide a variety of benefits, including limiting the ability of suppliers to increase spot and long-term market-clearing prices above competitive levels (Neenan et al., 2002; Boren stein, 2002; Ruff, 2002). Unfortunately, there is little information in the public domain to characterize and quantify how customers actually respond to these alternative dynamic pricing schemes. A few empirical studies of large customer RTP response have shown modest results for most customers, with a few very price-responsive customers providing most of the aggregate response (Herriges et al., 1993; Schwarz et al., 2002). However, these studies examined response to voluntary, two-part RTP programs implemented by utilities in states without retail competition.1 Furthermore, the researchers had limited information on customer characteristics so they were unable to identify the drivers to price response. In the absence of a compelling characterization of why customers join RTP programs and how they respond to prices, many initiatives to modernize retail electricity rates seem to be stymied.

  13. Daily River Flow Forecasting with Hybrid Support Vector Machine – Particle Swarm Optimization

    Science.gov (United States)

    Zaini, N.; Malek, M. A.; Yusoff, M.; Mardi, N. H.; Norhisham, S.

    2018-04-01

    The application of artificial intelligence techniques for river flow forecasting can further improve the management of water resources and flood prevention. This study concerns the development of support vector machine (SVM) based model and its hybridization with particle swarm optimization (PSO) to forecast short term daily river flow at Upper Bertam Catchment located in Cameron Highland, Malaysia. Ten years duration of historical rainfall, antecedent river flow data and various meteorology parameters data from 2003 to 2012 are used in this study. Four SVM based models are proposed which are SVM1, SVM2, SVM-PSO1 and SVM-PSO2 to forecast 1 to 7 day ahead of river flow. SVM1 and SVM-PSO1 are the models with historical rainfall and antecedent river flow as its input, while SVM2 and SVM-PSO2 are the models with historical rainfall, antecedent river flow data and additional meteorological parameters as input. The performances of the proposed model are measured in term of RMSE and R2 . It is found that, SVM2 outperformed SVM1 and SVM-PSO2 outperformed SVM-PSO1 which meant the additional meteorology parameters used as input to the proposed models significantly affect the model performances. Hybrid models SVM-PSO1 and SVM-PSO2 yield higher performances as compared to SVM1 and SVM2. It is found that hybrid models are more effective in forecasting river flow at 1 to 7 day ahead at the study area.

  14. Breastfeeding: Planning Ahead

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  20. Simultaneous day-ahead forecasting of electricity price and load in smart grids

    International Nuclear Information System (INIS)

    Shayeghi, H.; Ghasemi, A.; Moradzadeh, M.; Nooshyar, M.

    2015-01-01

    Highlights: • This paper presents a novel MIMO-based support vector machine for forecasting. • Considered uncertainties for better simulation for filtering in input data. • Used LSSVM technique for learning. • Proposed a new modification for standard artificial bee colony algorithm to optimize LSSVM engine. - Abstract: In smart grids, customers are promoted to change their energy consumption patterns by electricity prices. In fact, in this environment, the electricity price and load consumption are highly corrected such that the market participants will have complex model in their decisions to maximize their profit. Although the available forecasting mythologies perform well in electricity market by way of little or no load and price interdependencies, but cannot capture load and price dynamics if they exist. To overcome this shortage, a Multi-Input Multi-Output (MIMO) model is presented which can consider the correlation between electricity price and load. The proposed model consists of three components known as a Wavelet Packet Transform (WPT) to make valuable subsets, Generalized Mutual Information (GMI) to select best input candidate and Least Squares Support Vector Machine (LSSVM) based on MIMO model, called LSSVM-MIMO, to make simultaneous load and price forecasts. Moreover, the LSSVM-MIMO parameters are optimized by a novel Quasi-Oppositional Artificial Bee Colony (QOABC) algorithm. Some forecasting indices based on error factor are considered to evaluate the forecasting accuracy. Simulations carried out on New York Independent System Operator, New South Wales (NSW) and PJM electricity markets data, and showing that the proposed hybrid algorithm has good potential for simultaneous forecasting of electricity price and load

  1. Breastfeeding: Planning Ahead

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    Full Text Available ... activities In your community Funding opportunities Internships and jobs View all pages in this section Back to section menu It's Only Natural Planning ahead Breastfeeding and baby basics Making breastfeeding work for you Addressing breastfeeding myths Breastfeeding myths in ...

  2. Straight Ahead in Microgravity

    Science.gov (United States)

    Wood, S. J.; Vanya, R. D.; Clement, G.

    2014-01-01

    This joint ESA-NASA study will address adaptive changes in spatial orientation related to the subjective straight ahead, and the use of a vibrotactile sensory aid to reduce perceptual errors. The study will be conducted before and after long-duration expeditions to the International Space Station (ISS) to examine how spatial processing of target location is altered following exposure to microgravity. This project specifically addresses the sensorimotor research gap "What are the changes in sensorimotor function over the course of a mission?" Six ISS crewmembers will be requested to participate in three preflight sessions (between 120 and 60 days prior to launch) and then three postflight sessions on R+0/1 day, R+4 +/-2 days, and R+8 +/-2 days. The three specific aims include: (a) fixation of actual and imagined target locations at different distances; (b) directed eye and arm movements along different spatial reference frames; and (c) the vestibulo-ocular reflex during translation motion with fixation targets at different distances. These measures will be compared between upright and tilted conditions. Measures will then be compared with and without a vibrotactile sensory aid that indicates how far one has tilted relative to the straight-ahead direction. The flight study was been approved by the medical review boards and will be implemented in the upcoming Informed Crew Briefings to solicit flight subject participation. Preliminary data has been recorded on 6 subjects during parabolic flight to examine the spatial coding of eye movements during roll tilt relative to perceived orientations while free-floating during the microgravity phase of parabolic flight or during head tilt in normal gravity. Binocular videographic recordings obtained in darkness allowed us to quantify the mean deviations in gaze trajectories along both horizontal and vertical coordinates relative to the aircraft and head orientations. During some parabolas, a vibrotactile sensory aid provided

  3. Optimal Sizing of Energy Storage Systems for the Energy Procurement Problem in Multi-Period Markets under Uncertainties

    Directory of Open Access Journals (Sweden)

    Ryusuke Konishi

    2018-01-01

    Full Text Available In deregulated electricity markets, minimizing the procurement costs of electricity is a critical problem for procurement agencies (PAs. However, uncertainty is inevitable for PAs and includes multiple factors such as market prices, photovoltaic system (PV output and demand. This study focuses on settlements in multi-period markets (a day-ahead market and a real-time market and the installation of energy storage systems (ESSs. ESSs can be utilized for time arbitrage in the day-ahead market and to reduce the purchasing/selling of electricity in the real-time market. However, the high costs of an ESS mean the size of the system needs to be minimized. In addition, when determining the size of an ESS, it is important to identify the size appropriate for each role. Therefore, we employ the concept of a “slow” and a “fast” ESS to quantify the size of a system’s role, based on the values associated with the various uncertainties. Because the problem includes nonlinearity and non-convexity, we solve it within a realistic computational burden by reformulating the problem using reasonable assumptions. Therefore, this study identifies the optimal sizes of ESSs and procurement, taking into account the uncertainties of prices in multi-period markets, PV output and demand.

  4. Forecasting Day-Ahead Electricity Prices : Utilizing Hourly Prices

    NARCIS (Netherlands)

    E. Raviv (Eran); K.E. Bouwman (Kees); D.J.C. van Dijk (Dick)

    2013-01-01

    textabstractThe daily average price of electricity represents the price of electricity to be delivered over the full next day and serves as a key reference price in the electricity market. It is an aggregate that equals the average of hourly prices for delivery during each of the 24 individual

  5. Optimal operation method coping with uncertainty in multi-area small power systems

    Directory of Open Access Journals (Sweden)

    Shota Tobaru

    2017-07-01

    Full Text Available Japan contains a vast number of isolated islands. Majority of these islands are poweredby diesel generators (DGs, which are operationally not economical. Therefore, the introduction of renewableenergy systems (RESs into these area is very much vital. However, the variability of RESs asa result of weather condition as well as load demand , battery energy storage system (BESS is broughtinto play. Demand response (DR programs have also been so attractive in the energy management systemsfor the past decades. Among them, the real-time pricing (RTP has been one of the most effectivedemand response program being utilized. This program encourages the customer to increase or reducethe load consumption by varying the electricity price. Also, due to the increase in power transactionmarket, Japan electric power exchange (JEPX has established spot (day-ahead, intraday hour-ahead,and forward market programs. This paper utilizes day-ahead and hour-ahead markets, since these marketscan make it possible to deal with uncertainty related to generated power fluctuations. Therefore,this paper presents the optimal operation method coping with the uncertainties of RESs in multi-areasmall power systems. The proposed method enables flexibility to correspond to the forecasting error byproviding two kinds of power markets among multi-area small power systems and trading the shortageand surplus powers. Furthermore, it accomplishes a stable power supply and demand by RTP. Thus, theproposed method was able to reduce operational cost for multi-area small power systems. The processof creating operational plan for RTP, power trading at the markets and the unit commitment of DGs arealso presented in this paper. Simulation results corroborate the merit of the proposed program.

  6. Breastfeeding: Planning Ahead

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  7. Perioperative solutions for rapid recovery joint arthroplasty: get ahead and stay ahead.

    Science.gov (United States)

    Sculco, Peter K; Pagnano, Mark W

    2015-04-01

    Rapid recovery after total joint arthroplasty requires patients to get ahead and stay ahead or the four impediments to early rehabilitation and discharge: volume depletion, blood loss, pain, and nausea. Adequate volume resuscitation starts before entering the operating room and focuses on intravenous fluids rather than red blood cell transfusion. Tranexamic acid limits blood loss and reduces the need for most other blood management systems. Rapid recovery pain management focuses on minimizing parenteral opioids. A short-acting spinal with a peri-articular local anesthetic injection is reliable, reproducible, and safe. Patients at risk for post-operative nausea are treated with anti-emetic medications and perioperative dexamethasone. These interventions reflect a transition from the sick-patient model to the well-patient model and make rapid recovery joint arthroplasty a reality in 2015. Copyright © 2015 Elsevier Inc. All rights reserved.

  8. A Control-Based Multidimensional Approach to the Role of Optimism in the Use of Dementia Day Care Services.

    Science.gov (United States)

    Contador, Israel; Fernández-Calvo, Bernardino; Palenzuela, David L; Campos, Francisco Ramos; Rivera-Navarro, Jesús; de Lucena, Virginia Menezes

    2015-11-01

    We examined whether grounded optimism and external locus of control are associated with admission to dementia day care centers (DCCs). A total of 130 informal caregivers were recruited from the Alzheimer's Association in Salamanca (northwest Spain). All caregivers completed an assessment protocol that included the Battery of Generalized Expectancies of Control Scales (BEEGC-20, acronym in Spanish) as well as depression and burden measures. The decision of the care setting at baseline assessment (own home vs DCC) was considered the main outcome measure in the logistic regression analyses. Grounded optimism was a preventive factor for admission (odds ratio [OR]: 0.34 and confidence interval [CI]: 0.15-0.75), whereas external locus of control (OR: 2.75, CI: 1.25-6.03) increased the probabilities of using DCCs. Depression mediated the relationship between optimism and DCCs, but this effect was not consistent for burden. Grounded optimism promotes the extension of care at home for patients with dementia. © The Author(s) 2013.

  9. Optimal planning and operation of aggregated distributed energy resources with market participation

    International Nuclear Information System (INIS)

    Calvillo, C.F.; Sánchez-Miralles, A.; Villar, J.; Martín, F.

    2016-01-01

    Highlights: • Price-maker optimization model for planning and operation of aggregated DER. • 3 Case studies are proposed, considering different electricity pricing scenarios. • Analysis of benefits and effect on electricity prices produced by DER aggregation. • Results showed considerable benefits even for relatively small aggregations. • Results suggest that the impact on prices should not be overlooked. - Abstract: This paper analyzes the optimal planning and operation of aggregated distributed energy resources (DER) with participation in the electricity market. Aggregators manage their portfolio of resources in order to obtain the maximum benefit from the grid, while participating in the day-ahead wholesale electricity market. The goal of this paper is to propose a model for aggregated DER systems planning, considering its participation in the electricity market and its impact on the market price. The results are the optimal planning and management of DER systems, and the appropriate energy transactions for the aggregator in the wholesale day-ahead market according to the size of its aggregated resources. A price-maker approach based on representing the market competitors with residual demand curves is followed, and the impact on the price is assessed to help in the decision of using price-maker or price-taker approaches depending on the size of the aggregated resources. A deterministic programming problem with two case studies (the average scenario and the most likely scenario from the stochastic ones), and a stochastic one with a case study to account for the market uncertainty are described. For both models, market scenarios have been built from historical data of the Spanish system. The results suggest that when the aggregated resources have enough size to follow a price-maker approach and the uncertainty of the markets is considered in the planning process, the DER systems can achieve up to 50% extra economic benefits, depending on the market

  10. Visual straight-ahead preference in saccadic eye movements.

    Science.gov (United States)

    Camors, Damien; Trotter, Yves; Pouget, Pierre; Gilardeau, Sophie; Durand, Jean-Baptiste

    2016-03-15

    Ocular saccades bringing the gaze toward the straight-ahead direction (centripetal) exhibit higher dynamics than those steering the gaze away (centrifugal). This is generally explained by oculomotor determinants: centripetal saccades are more efficient because they pull the eyes back toward their primary orbital position. However, visual determinants might also be invoked: elements located straight-ahead trigger saccades more efficiently because they receive a privileged visual processing. Here, we addressed this issue by using both pro- and anti-saccade tasks in order to dissociate the centripetal/centrifugal directions of the saccades, from the straight-ahead/eccentric locations of the visual elements triggering those saccades. Twenty participants underwent alternating blocks of pro- and anti-saccades during which eye movements were recorded binocularly at 1 kHz. The results confirm that centripetal saccades are always executed faster than centrifugal ones, irrespective of whether the visual elements have straight-ahead or eccentric locations. However, by contrast, saccades triggered by elements located straight-ahead are consistently initiated more rapidly than those evoked by eccentric elements, irrespective of their centripetal or centrifugal direction. Importantly, this double dissociation reveals that the higher dynamics of centripetal pro-saccades stem from both oculomotor and visual determinants, which act respectively on the execution and initiation of ocular saccades.

  11. Parallel Harmony Search Based Distributed Energy Resource Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Ceylan, Oguzhan [ORNL; Liu, Guodong [ORNL; Tomsovic, Kevin [University of Tennessee, Knoxville (UTK)

    2015-01-01

    This paper presents a harmony search based parallel optimization algorithm to minimize voltage deviations in three phase unbalanced electrical distribution systems and to maximize active power outputs of distributed energy resources (DR). The main contribution is to reduce the adverse impacts on voltage profile during a day as photovoltaics (PVs) output or electrical vehicles (EVs) charging changes throughout a day. The IEEE 123- bus distribution test system is modified by adding DRs and EVs under different load profiles. The simulation results show that by using parallel computing techniques, heuristic methods may be used as an alternative optimization tool in electrical power distribution systems operation.

  12. Modified Chaos Particle Swarm Optimization-Based Optimized Operation Model for Stand-Alone CCHP Microgrid

    Directory of Open Access Journals (Sweden)

    Fei Wang

    2017-07-01

    Full Text Available The optimized dispatch of different distributed generations (DGs in stand-alone microgrid (MG is of great significance to the operation’s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL and combined cooling-heating-power (CCHP model of micro-gas turbine (MT, a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV, wind turbine (WT, fuel cell (FC, diesel engine (DE, MT and energy storage (ES. Four typical scenarios were designed according to different day types (work day or weekend and weather conditions (sunny or rainy in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers’ comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO to propose modified chaos particle swarm optimization (MCPSO whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.

  13. An immediate go-ahead for nuclear plants

    International Nuclear Information System (INIS)

    Stubbe, C.

    1986-01-01

    In the meantime a wealth of court decisions have been reached regarding an immediate go-ahead for nuclear power plants. For all that, it is still not clear - and varying decisions have been made in judgement - according to which criteria the judicial balance is to be made between the interests of the plaintiff in bringing about a stay of the proceedings and the interests of the planner in going ahead with them. The author advocates an examination of the main issue already during suspension proceedings until the court can form a preliminary opinion on the chances of success of the main issue. He also advocates in the Administrative Court Ordinance regulations regarding go-ahead as determined in the commission draft to the Administrative Court of 1978. (orig.) [de

  14. Exploiting Flexibility in Coupled Electricity and Natural Gas Markets: A Price-Based Approach

    DEFF Research Database (Denmark)

    Ordoudis, Christos; Delikaraoglou, Stefanos; Pinson, Pierre

    2017-01-01

    Natural gas-fired power plants (NGFPPs) are considered a highly flexible component of the energy system and can facilitate the large-scale integration of intermittent renewable generation. Therefore, it is necessary to improve the coordination between electric power and natural gas systems....... Considering a market-based coupling of these systems, we introduce a decision support tool that increases market efficiency in the current setup where day-ahead and balancing markets are cleared sequentially. The proposed approach relies on the optimal adjustment of natural gas price to modify the scheduling...

  15. Management of a stage-structured insect pest: an application of approximate optimization.

    Science.gov (United States)

    Hackett, Sean C; Bonsall, Michael B

    2018-06-01

    Ecological decision problems frequently require the optimization of a sequence of actions over time where actions may have both immediate and downstream effects. Dynamic programming can solve such problems only if the dimensionality is sufficiently low. Approximate dynamic programming (ADP) provides a suite of methods applicable to problems of arbitrary complexity at the expense of guaranteed optimality. The most easily generalized method is the look-ahead policy: a brute-force algorithm that identifies reasonable actions by constructing and solving a series of temporally truncated approximations of the full problem over a defined planning horizon. We develop and apply this approach to a pest management problem inspired by the Mediterranean fruit fly, Ceratitis capitata. The model aims to minimize the cumulative costs of management actions and medfly-induced losses over a single 16-week season. The medfly population is stage-structured and grows continuously while management decisions are made at discrete, weekly intervals. For each week, the model chooses between inaction, insecticide application, or one of six sterile insect release ratios. Look-ahead policy performance is evaluated over a range of planning horizons, two levels of crop susceptibility to medfly and three levels of pesticide persistence. In all cases, the actions proposed by the look-ahead policy are contrasted to those of a myopic policy that minimizes costs over only the current week. We find that look-ahead policies always out-performed a myopic policy and decision quality is sensitive to the temporal distribution of costs relative to the planning horizon: it is beneficial to extend the planning horizon when it excludes pertinent costs. However, longer planning horizons may reduce decision quality when major costs are resolved imminently. ADP methods such as the look-ahead-policy-based approach developed here render questions intractable to dynamic programming amenable to inference but should be

  16. Optimal coupling of heat and electricity systems: A stochastic hierarchical approach

    DEFF Research Database (Denmark)

    Mitridati, Lesia Marie-Jeanne Mariane; Pinson, Pierre

    2016-01-01

    modelled using a finite set of scenarios. This model takes advantage of existing market structures and provides a decision-making tool for heat system operators. The proposed model is implemented in a case study and results are discussed to show the benefits and applicability of this approach....... penetration of CHPs and wind. The objective of this optimization problem is to minimize the heat production cost, subject to constraints describing day-ahead electricity market clearing scenarios. Uncertainties concerning wind power production, electricity demand and rival participants offers are efficiently...

  17. Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices

    OpenAIRE

    Raviv, Eran; Bouwman, Kees E.; van Dijk, Dick

    2013-01-01

    This discussion paper led to a publication in 'Energy Economics' , 2015, 50, 227-239. The daily average price of electricity represents the price of electricity to be delivered over the full next day and serves as a key reference price in the electricity market. It is an aggregate that equals the average of hourly prices for delivery during each of the 24 individual hours. This paper demonstrates that the disaggregated hourly prices contain useful predictive information for the daily average ...

  18. Big Data and Dementia: Charting the Route Ahead for Research, Ethics, and Policy.

    Science.gov (United States)

    Ienca, Marcello; Vayena, Effy; Blasimme, Alessandro

    2018-01-01

    Emerging trends in pervasive computing and medical informatics are creating the possibility for large-scale collection, sharing, aggregation and analysis of unprecedented volumes of data, a phenomenon commonly known as big data. In this contribution, we review the existing scientific literature on big data approaches to dementia, as well as commercially available mobile-based applications in this domain. Our analysis suggests that big data approaches to dementia research and care hold promise for improving current preventive and predictive models, casting light on the etiology of the disease, enabling earlier diagnosis, optimizing resource allocation, and delivering more tailored treatments to patients with specific disease trajectories. Such promissory outlook, however, has not materialized yet, and raises a number of technical, scientific, ethical, and regulatory challenges. This paper provides an assessment of these challenges and charts the route ahead for research, ethics, and policy.

  19. Value-Creation Potential from Multi-Market Trading for a Hydropower Producer

    Directory of Open Access Journals (Sweden)

    Marte Fodstad

    2017-12-01

    Full Text Available We study a hydropower producer’s potential for value-creation from multi-market trading given the price variations in the markets and the flexibility provided through access to hydro reservoirs. We use a perfect foresight optimization model for a price-taking hydropower producer co-optimizing his trades in the day-ahead, intra-day and balancing markets. The model is used on real market data from Norway, Sweden and Germany. The study shows a theoretical potential for added value when selling energy in multiple markets relative to optimal day-ahead sale. Most of this value is achievable also when the perfect foresight is limited to the period from day-ahead bidding until operation. Flexible production plants achieve the largest relative added values for multi-market sales, and has the largest benefit from a long horizon with perfect foresight.

  20. Uncertainty Management of Dynamic Tariff Method for Congestion Management in Distribution Networks

    DEFF Research Database (Denmark)

    Huang, Shaojun; Wu, Qiuwei; Cheng, Lin

    2016-01-01

    The dynamic tariff (DT) method is designed for the distribution system operator (DSO) to alleviate congestions that might occur in a distribution network with high penetration of distributed energy resources (DERs). Uncertainty management is required for the decentralized DT method because the DT...... is de- termined based on optimal day-ahead energy planning with forecasted parameters such as day-ahead energy prices and en- ergy needs which might be different from the parameters used by aggregators. The uncertainty management is to quantify and mitigate the risk of the congestion when employing...

  1. Short-Term Wind Speed Forecasting Using Support Vector Regression Optimized by Cuckoo Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Jianzhou Wang

    2015-01-01

    Full Text Available This paper develops an effectively intelligent model to forecast short-term wind speed series. A hybrid forecasting technique is proposed based on recurrence plot (RP and optimized support vector regression (SVR. Wind caused by the interaction of meteorological systems makes itself extremely unsteady and difficult to forecast. To understand the wind system, the wind speed series is analyzed using RP. Then, the SVR model is employed to forecast wind speed, in which the input variables are selected by RP, and two crucial parameters, including the penalties factor and gamma of the kernel function RBF, are optimized by various optimization algorithms. Those optimized algorithms are genetic algorithm (GA, particle swarm optimization algorithm (PSO, and cuckoo optimization algorithm (COA. Finally, the optimized SVR models, including COA-SVR, PSO-SVR, and GA-SVR, are evaluated based on some criteria and a hypothesis test. The experimental results show that (1 analysis of RP reveals that wind speed has short-term predictability on a short-term time scale, (2 the performance of the COA-SVR model is superior to that of the PSO-SVR and GA-SVR methods, especially for the jumping samplings, and (3 the COA-SVR method is statistically robust in multi-step-ahead prediction and can be applied to practical wind farm applications.

  2. Practical operation strategies for pumped hydroelectric energy storage (PHES) utilising electricity price arbitrage

    DEFF Research Database (Denmark)

    Connolly, David; Lund, Henrik; Finn, P.

    2011-01-01

    In this paper, three practical operation strategies (24Optimal, 24Prognostic, and 24Hsitrocial) are compared to the optimum profit feasible for a PHES facility with a 360 MW pump, 300 MW turbine, and a 2 GWh storage utilising price arbitrage on 13 electricity spot markets. The results indicate...... that almost all (not, vert, similar97%) of the profits can be obtained by a PHES facility when it is optimised using the 24Optimal strategy developed, which optimises the energy storage based on the day-ahead electricity prices. However, to maximise profits with the 24Optimal strategy, the day......-ahead electricity prices must be the actual prices which the PHES facility is charged or the PHES operator must have very accurate price predictions. Otherwise, the predicted profit could be significantly reduced and even become a loss. Finally, using the 24Optimal strategy, the PHES profit can surpass the annual...

  3. Simulation-based Strategies for Smart Demand Response

    Directory of Open Access Journals (Sweden)

    Ines Leobner

    2018-03-01

    Full Text Available Demand Response can be seen as one effective way to harmonize demand and supply in order to achieve high self-coverage of energy consumption by means of renewable energy sources. This paper presents two different simulation-based concepts to integrate demand-response strategies into energy management systems in the customer domain of the Smart Grid. The first approach is a Model Predictive Control of the heating and cooling system of a low-energy office building. The second concept aims at industrial Demand Side Management by integrating energy use optimization into industrial automation systems. Both approaches are targeted at day-ahead planning. Furthermore, insights gained into the implications of the concepts onto the design of the model, simulation and optimization will be discussed. While both approaches share a similar architecture, different modelling and simulation approaches were required by the use cases.

  4. Optimal Operation of Electric Vehicles in Competitive Electricity Markets and Its Impact on Distribution Power Systems

    DEFF Research Database (Denmark)

    Hu, Weihao; Chen, Zhe; Bak-Jensen, Birgitte

    2011-01-01

    represent the future of electricity markets in some ways, is chosen as the studied power system in this paper. The impact of the optimal operation strategy for electric vehicles together with the optimal load response to spot market price on the distribution power system with high wind power penetrations...... are also discussed in the paper. Simulation results show that the proposed optimal operation strategy is an effective measure to achieve minimum energy costs of the PEV. The optimal operation strategy of the PEV and the optimal load response may have significant effects on the distribution power system......Since the hourly spot market price is available one day ahead in Denmark, the electricity price could be transferred to the consumers and they may make some optimal charge and discharge schedules for their electric vehicles in order to minimize their energy costs. This paper presents an optimal...

  5. Big Data and Dementia: Charting the Route Ahead for Research, Ethics, and Policy

    Directory of Open Access Journals (Sweden)

    Marcello Ienca

    2018-02-01

    Full Text Available Emerging trends in pervasive computing and medical informatics are creating the possibility for large-scale collection, sharing, aggregation and analysis of unprecedented volumes of data, a phenomenon commonly known as big data. In this contribution, we review the existing scientific literature on big data approaches to dementia, as well as commercially available mobile-based applications in this domain. Our analysis suggests that big data approaches to dementia research and care hold promise for improving current preventive and predictive models, casting light on the etiology of the disease, enabling earlier diagnosis, optimizing resource allocation, and delivering more tailored treatments to patients with specific disease trajectories. Such promissory outlook, however, has not materialized yet, and raises a number of technical, scientific, ethical, and regulatory challenges. This paper provides an assessment of these challenges and charts the route ahead for research, ethics, and policy.

  6. Planning Ahead: Advanced Heart Failure

    Science.gov (United States)

    ... Venous Thromboembolism Aortic Aneurysm More Planning Ahead: Advanced Heart Failure Updated:May 9,2017 An important part of ... Care This content was last reviewed May 2017. Heart Failure • Home • About Heart Failure • Causes and Risks for ...

  7. Day-Ahead Probabilistic Model for Scheduling the Operation of a Wind Pumped-Storage Hybrid Power Station: Overcoming Forecasting Errors to Ensure Reliability of Supply to the Grid

    Directory of Open Access Journals (Sweden)

    Jakub Jurasz

    2018-06-01

    Full Text Available Variable renewable energy sources (VRES, such as solarphotovoltaic (PV and wind turbines (WT, are starting to play a significant role in several energy systems around the globe. To overcome the problem of their non-dispatchable and stochastic nature, several approaches have been proposed so far. This paper describes a novel mathematical model for scheduling the operation of a wind-powered pumped-storage hydroelectricity (PSH hybrid for 25 to 48 h ahead. The model is based on mathematical programming and wind speed forecasts for the next 1 to 24 h, along with predicted upper reservoir occupancy for the 24th hour ahead. The results indicate that by coupling a 2-MW conventional wind turbine with a PSH of energy storing capacity equal to 54 MWh it is possible to significantly reduce the intraday energy generation coefficient of variation from 31% for pure wind turbine to 1.15% for a wind-powered PSH The scheduling errors calculated based on mean absolute percentage error (MAPE are significantly smaller for such a coupling than those seen for wind generation forecasts, at 2.39% and 27%, respectively. This is even stronger emphasized by the fact that, those for wind generation were calculated for forecasts made for the next 1 to 24 h, while those for scheduled generation were calculated for forecasts made for the next 25 to 48 h. The results clearly show that the proposed scheduling approach ensures the high reliability of the WT–PSH energy source.

  8. Stochastic Optimization Model to STudy the Operational Impacts of High Wind Penetrations in Ireland

    DEFF Research Database (Denmark)

    Meibom, Peter; Barth, R.; Hasche, B.

    2011-01-01

    A stochastic mixed integer linear optimization scheduling model minimizing system operation costs and treating load and wind power production as stochastic inputs is presented. The schedules are updated in a rolling manner as more up-to-date information becomes available. This is a fundamental...... change relative to day-ahead unit commitment approaches. The need for reserves dependent on forecast horizon and share of wind power has been estimated with a statistical model combining load and wind power forecast errors with scenarios of forced outages. The model is used to study operational impacts...

  9. Present-day Problems and Methods of Optimization in Mechatronics

    Directory of Open Access Journals (Sweden)

    Tarnowski Wojciech

    2017-06-01

    Full Text Available It is justified that design is an inverse problem, and the optimization is a paradigm. Classes of design problems are proposed and typical obstacles are recognized. Peculiarities of the mechatronic designing are specified as a proof of a particle importance of optimization in the mechatronic design. Two main obstacles of optimization are discussed: a complexity of mathematical models and an uncertainty of the value system, in concrete case. Then a set of non-standard approaches and methods are presented and discussed, illustrated by examples: a fuzzy description, a constraint-based iterative optimization, AHP ranking method and a few MADM functions in Matlab.

  10. A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting.

    Science.gov (United States)

    Ben Taieb, Souhaib; Atiya, Amir F

    2016-01-01

    Multistep-ahead forecasts can either be produced recursively by iterating a one-step-ahead time series model or directly by estimating a separate model for each forecast horizon. In addition, there are other strategies; some of them combine aspects of both aforementioned concepts. In this paper, we present a comprehensive investigation into the bias and variance behavior of multistep-ahead forecasting strategies. We provide a detailed review of the different multistep-ahead strategies. Subsequently, we perform a theoretical study that derives the bias and variance for a number of forecasting strategies. Finally, we conduct a Monte Carlo experimental study that compares and evaluates the bias and variance performance of the different strategies. From the theoretical and the simulation studies, we analyze the effect of different factors, such as the forecast horizon and the time series length, on the bias and variance components, and on the different multistep-ahead strategies. Several lessons are learned, and recommendations are given concerning the advantages, disadvantages, and best conditions of use of each strategy.

  11. Day-ahead load forecast using random forest and expert input selection

    International Nuclear Information System (INIS)

    Lahouar, A.; Ben Hadj Slama, J.

    2015-01-01

    Highlights: • A model based on random forests for short term load forecast is proposed. • An expert feature selection is added to refine inputs. • Special attention is paid to customers behavior, load profile and special holidays. • The model is flexible and able to handle complex load signal. • A technical comparison is performed to assess the forecast accuracy. - Abstract: The electrical load forecast is getting more and more important in recent years due to the electricity market deregulation and integration of renewable resources. To overcome the incoming challenges and ensure accurate power prediction for different time horizons, sophisticated intelligent methods are elaborated. Utilization of intelligent forecast algorithms is among main characteristics of smart grids, and is an efficient tool to face uncertainty. Several crucial tasks of power operators such as load dispatch rely on the short term forecast, thus it should be as accurate as possible. To this end, this paper proposes a short term load predictor, able to forecast the next 24 h of load. Using random forest, characterized by immunity to parameter variations and internal cross validation, the model is constructed following an online learning process. The inputs are refined by expert feature selection using a set of if–then rules, in order to include the own user specifications about the country weather or market, and to generalize the forecast ability. The proposed approach is tested through a real historical set from the Tunisian Power Company, and the simulation shows accurate and satisfactory results for one day in advance, with an average error exceeding rarely 2.3%. The model is validated for regular working days and weekends, and special attention is paid to moving holidays, following non Gregorian calendar

  12. Dynamic Price Vector Formation Model-Based Automatic Demand Response Strategy for PV-Assisted EV Charging Stations

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Qifang; Wang, Fei; Hodge, Bri-Mathias; Zhang, Jianhua; Li, Zhigang; Shafie-Khah, Miadreza; Catalao, Joao P. S.

    2017-11-01

    A real-time price (RTP)-based automatic demand response (ADR) strategy for PV-assisted electric vehicle (EV) Charging Station (PVCS) without vehicle to grid is proposed. The charging process is modeled as a dynamic linear program instead of the normal day-ahead and real-time regulation strategy, to capture the advantages of both global and real-time optimization. Different from conventional price forecasting algorithms, a dynamic price vector formation model is proposed based on a clustering algorithm to form an RTP vector for a particular day. A dynamic feasible energy demand region (DFEDR) model considering grid voltage profiles is designed to calculate the lower and upper bounds. A deduction method is proposed to deal with the unknown information of future intervals, such as the actual stochastic arrival and departure times of EVs, which make the DFEDR model suitable for global optimization. Finally, both the comparative cases articulate the advantages of the developed methods and the validity in reducing electricity costs, mitigating peak charging demand, and improving PV self-consumption of the proposed strategy are verified through simulation scenarios.

  13. A Frequency-Based Assignment Model under Day-to-Day Information Evolution of Oversaturated Conditions on a Feeder Bus Service

    Directory of Open Access Journals (Sweden)

    Silin Zhang

    2017-02-01

    Full Text Available Day-to-day information is increasingly being implemented in transit networks worldwide. Feeder bus service (FBS plays a vital role in a public transit network by providing feeder access to hubs and rails. As a feeder service, a space-time path for frequent passengers is decided by its dynamic strategy procedure, in which a day-to-day information self-learning mechanism is identified and analyzed from our survey data. We formulate a frequency-based assignment model considering day-to-day evolution under oversaturated conditions, which takes into account the residual capacity of bus and the comfort feelings of sitting or standing. The core of our proposed model is to allocate the passengers on each segment belonging to their own paths according to multi-utilities transformed from the time values and parametric demands, such as frequency, bus capacity, seat comfort, and stop layout. The assignment method, albeit general, allows us to formulate an equivalent optimization problem in terms of interaction between the FBS’ operation and frequent passengers’ rational behaviors. Finally, a real application case is generated to test the ability of the modeling framework capturing the theoretical consequents, serving the passengers’ dynamic externalities.

  14. A Smart Grid Framework for Optimally Integrating Supply-Side, Demand-Side and Transmission Line Management Systems

    Directory of Open Access Journals (Sweden)

    Chukwuka Monyei

    2018-04-01

    Full Text Available A coordinated centralized energy management system (ConCEMS is presented in this paper that seeks to integrate for optimal grid operation—the supply side energy management system (SSEMS, home energy management system (HEMS and transmission line management system (TLMS. ConCEMS in ensuring the optimal operation of an IEEE 30-bus electricity network harmonizes the individual objective function of SSEMS, HEMS and TLMS to evolve an optimal dispatch of participating demand response (DR loads that does not violate transmission line ampacity limits (TLMS constraint while minimizing consumer cost (HEMS constraint and supply side operations cost (SSEMS constraint. An externally constrained genetic algorithm (ExC-GA that is influenced by feedback from TLMS is also presented that intelligently varies the dispatch time of participating DR loads to meet the individual objective functions. Hypothetical day ahead dynamic pricing schemes (Price1, Price2 and Price3 have also been adopted alongside an existing time of use (Price0 pricing scheme for comparison and discussion while a dynamic thermal line rating (DTLR algorithm has also been incorporated to dynamically compute power limits based on real time associated data.

  15. Simulation-based optimization parametric optimization techniques and reinforcement learning

    CERN Document Server

    Gosavi, Abhijit

    2003-01-01

    Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: *An accessible introduction to reinforcement learning and parametric-optimization techniques. *A step-by-step description of several algorithms of simulation-based optimization. *A clear and simple introduction to the methodology of neural networks. *A gentle introduction to converg...

  16. Combined scheduling of electricity and heat in a microgrid with volatile wind power

    DEFF Research Database (Denmark)

    Xu, Lizhong; Yang, Guang Ya; Xu, Zhao

    2011-01-01

    An optimization model is developed for scheduling electricity and heat production in a microgrid under a day-ahead market environment considering the operation constraints and the volatility of wind power generation. The model optimizes the total operation costs from energy and heating consumption...... into a mixed-integer programming (MIP) problem. Numerical simulations present the efficacy of the proposed model for day-ahead scheduling of a microgrid with wind penetration under the deregulated environment. © 2011 State Grid Electrtic Resarch Institute Press....

  17. A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models

    KAUST Repository

    El Gharamti, Mohamad

    2015-11-26

    The ensemble Kalman filter (EnKF) recursively integrates field data into simulation models to obtain a better characterization of the model’s state and parameters. These are generally estimated following a state-parameters joint augmentation strategy. In this study, we introduce a new smoothing-based joint EnKF scheme, in which we introduce a one-step-ahead smoothing of the state before updating the parameters. Numerical experiments are performed with a two-dimensional synthetic subsurface contaminant transport model. The improved performance of the proposed joint EnKF scheme compared to the standard joint EnKF compensates for the modest increase in the computational cost.

  18. Survival, Look-Ahead Bias and the Persistence in Hedge Fund Performance

    NARCIS (Netherlands)

    G. Baquero; J.R. ter Horst (Jenke); M.J.C.M. Verbeek (Marno)

    2005-01-01

    textabstractWe analyze the performance persistence in hedge funds taking into account look-ahead bias (multi-period sampling bias). We model liquidation of hedge funds by analyzing how it depends upon historical performance. Next, we use a weighting procedure that eliminates look-ahead bias in

  19. Full STEAM Ahead: From Earth to Ploonoids

    Science.gov (United States)

    Runyon, C. R.; Hall, C.; Blackman, C. L.; Royle, M.; Williams, M. N.

    2015-12-01

    What the heck is a plunoid, you ask? The NASA Solar System Exploration Research Virtual Institute's Education/Public Engagement (EPE) program,from two SSERVI teams (SEEED at Brown/MIT and CLASS at University of Central Florida), is moving full STEAM ahead, engaging the public in the exciting discoveries being made around small bodies, including PLanetary mOONs and asterOIDS (i.e ploonoids). The team has incorporated the arts, from visual representations, storytelling, and music into every facet of the program, to stimulate an affective and personal connection to the content. This past year, the SSERVI STEAM team has participated in numerous public science events, including International Observe the Moon Night, two Astronomy Nights at a local baseball venue, Dark Skies at the US and Canadian National Parks, and Space Day at Camp Happy Days, a camp for children with cancer. Through these events, the team reached over 10000 members of the general public, showcasing current NASA SSERVI research, dispelling myths about our landing and exploring the moon, demonstrating the excitement of STEM through hands-on interactive displays, and providing an outlet for creativity by having multiple ways of representing and explaining scientific information through the arts. Join us on our "ed"venture through the solar system ploonoids.

  20. Need yellow fever vaccine? Plan ahead

    Science.gov (United States)

    ... Submit What's this? Submit Button Past Emails Need yellow fever vaccine? Plan ahead. Language: English (US) Español (Spanish) ... none were from the United States). What is yellow fever? Yellow fever is caused by a virus that ...

  1. 33 CFR 164.76 - Towline and terminal gear for towing alongside and pushing ahead.

    Science.gov (United States)

    2010-07-01

    ... towing alongside and pushing ahead. 164.76 Section 164.76 Navigation and Navigable Waters COAST GUARD... Towline and terminal gear for towing alongside and pushing ahead. The owner, master, or operator of each vessel towing alongside or pushing ahead shall ensure that the face wires, spring lines, and push gear...

  2. Ensuring the Reliable Operation of the Power Grid: State-Based and Distributed Approaches to Scheduling Energy and Contingency Reserves

    Science.gov (United States)

    Prada, Jose Fernando

    Keeping a contingency reserve in power systems is necessary to preserve the security of real-time operations. This work studies two different approaches to the optimal allocation of energy and reserves in the day-ahead generation scheduling process. Part I presents a stochastic security-constrained unit commitment model to co-optimize energy and the locational reserves required to respond to a set of uncertain generation contingencies, using a novel state-based formulation. The model is applied in an offer-based electricity market to allocate contingency reserves throughout the power grid, in order to comply with the N-1 security criterion under transmission congestion. The objective is to minimize expected dispatch and reserve costs, together with post contingency corrective redispatch costs, modeling the probability of generation failure and associated post contingency states. The characteristics of the scheduling problem are exploited to formulate a computationally efficient method, consistent with established operational practices. We simulated the distribution of locational contingency reserves on the IEEE RTS96 system and compared the results with the conventional deterministic method. We found that assigning locational spinning reserves can guarantee an N-1 secure dispatch accounting for transmission congestion at a reasonable extra cost. The simulations also showed little value of allocating downward reserves but sizable operating savings from co-optimizing locational nonspinning reserves. Overall, the results indicate the computational tractability of the proposed method. Part II presents a distributed generation scheduling model to optimally allocate energy and spinning reserves among competing generators in a day-ahead market. The model is based on the coordination between individual generators and a market entity. The proposed method uses forecasting, augmented pricing and locational signals to induce efficient commitment of generators based on firm

  3. Learning to stay ahead of time

    DEFF Research Database (Denmark)

    Staunæs, Dorthe; Raffnsøe, Sverre

    2014-01-01

    In the context of an ongoing change, management is required to take the form of a leadership that must be reignited over and over again. The article examines a new art of leadership that may be viewed as an attempt to keep up with these challenges and stay ahead of time. It emerges from...... a pilgrimage leadership learning laboratory on the road to Santiago de la Compostela. This moving lab creates situations of extraordinary intensity that border on hyperreality and force the leader to find him/herself anew on the verge of him/herself. Conceived as pilgrimage, leadership moves ahead of time...... as it reaches into and anticipates a future still unknown. In this setting, anticipatory affects and the virtual take up a predominant role. As it emerges here, leadership distinguishes itself not only from leadership in the traditional sense, but also from management and governmentality....

  4. Small Signal Stability Improvement of Power Systems Using Optimal Load Responses in Competitive Electricity Markets

    DEFF Research Database (Denmark)

    Hu, Weihao; Su, Chi; Chen, Zhe

    2011-01-01

    Since the hourly spot market price is available one day ahead in Denmark, the price could be transferred to the consumers and they may shift some of their loads from high price periods to the low price periods in order to save their energy costs. The optimal load response to an electricity price...... price is proposed. A 17-bus power system with high wind power penetrations, which resembles the Eastern Danish power system, is chosen as the study case. Simulation results show that the optimal load response to electricity prices is an effective measure to improve the small signal stability of power...... for demand side management generates different load profiles and may provide an opportunity to improve the small signal stability of power systems with high wind power penetrations. In this paper, the idea of power system small signal stability improvement by using optimal load response to the electricity...

  5. Extending market activities for a distribution company in hourly-ahead energy and reserve markets - Part I: Problem formulation

    International Nuclear Information System (INIS)

    Mashhour, M.; Golkar, M.A.; Moghaddas-Tafreshi, S.M.

    2011-01-01

    This work presents a novel hourly-ahead profit model for an active distribution company (DISCO), a DISCO with high capacity level of connected DGs that can make selling proposals for the markets, in a pool-based system. The presented model engages DSICO in both energy producing and reserve providing activities. DISCO's earnings from reserve market include the remuneration both for real-time generation and ready-for-service capacity. To achieve the optimal decisions for an active DISCO in the energy and reserve markets, a two-stage optimization model and associated mathematical formulations have been developed. The first subproblem extracts a single operating profile (a lumped financial model) of the whole distribution system, including DGs and ILs, at the connecting point to the upstream network. The second one determines the optimal values of decision variables (power and reserve commodities) to maximize the DISCO's profit, in case such variables are accepted in the markets. In other words, it aims to optimally allocate the DISCO's generating capability for proposing into the energy and reserve markets, from the DISCO's perspective. It also proposes a profit-based network reconfiguration methodology for a multi-substation multi-feeder DISCO to increase DISCO's ability to gain more benefits from the market activities. It conducts DISCO's generating capabilities towards the proper substations to reap more probable benefits. It also introduces fast, simple, and straightforward algorithms for recognition and removal of configurations having loop and/or islanding parts in. Simulation results are given at the second part of the present work.

  6. A Refined Teaching-Learning Based Optimization Algorithm for Dynamic Economic Dispatch of Integrated Multiple Fuel and Wind Power Plants

    Directory of Open Access Journals (Sweden)

    Umamaheswari Krishnasamy

    2014-01-01

    Full Text Available Dynamic economic dispatch problem (DEDP for a multiple fuel power plant is a nonlinear and nonsmooth optimization problem when valve-point effects, multifuel effects, and ramp-rate limits are considered. Additionally wind energy is also integrated with the DEDP to supply the load for effective utilization of the renewable energy. Since the wind power may not be predicted, a radial basis function network (RBFN is presented to forecast a one-hour-ahead wind power to plan and ensure a reliable power supply. In this paper, a refined teaching-learning based optimization (TLBO is applied to minimize the overall cost of operation of wind-thermal power system. The TLBO is refined by integrating the sequential quadratic programming (SQP method to fine-tune the better solutions whenever discovered by the former method. To demonstrate the effectiveness of the proposed hybrid TLBO-SQP method, a standard DEDP and one practical DEDP with wind power forecasted are tested based on the practical information of wind speed. Simulation results validate the proposed methodology which is reasonable by ensuring quality solution throughout the scheduling horizon for secure operation of the system.

  7. Adaptive Portfolio Optimization for Multiple Electricity Markets Participation

    DEFF Research Database (Denmark)

    Pinto, Tiago; Morais, Hugo; Sousa, Tiago M.

    2016-01-01

    as the most suitable solution to facilitate the small players' participation in electric power negotiations while improving energy efficiency. The opportunity for players' participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types......, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology......, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market...

  8. Day Care Centers for Seniors in Singapore: Looking Back and Looking Ahead.

    Science.gov (United States)

    Liu, Germaine; Yap, Philip; Wong, Gabriel H Z; Wei, Heng Xiao; Hua, Ee Chye

    2015-07-01

    The burden of care for frail elderly persons on families and the society is ever real as our population ages. Given the dual-income nature of many working families, day care centers offer a strong alternative to nursing homes for families wishing to provide custodial care and meaningful engagement for seniors while continuing to uphold their filial duties. Recognizing this, several initiatives, such as SPICE (Singapore Programme for Integrated Care for the Elderly) and Weekend Respite Care, have been launched to enhance the services of Singapore's day care centers. This article traces the evolution of this process, distills current challenges, and offers policy recommendations to improve Singapore's day care services for seniors. Copyright © 2015 AMDA - The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.

  9. A new cellular automata model of traffic flow with negative exponential weighted look-ahead potential

    Science.gov (United States)

    Ma, Xiao; Zheng, Wei-Fan; Jiang, Bao-Shan; Zhang, Ji-Ye

    2016-10-01

    With the development of traffic systems, some issues such as traffic jams become more and more serious. Efficient traffic flow theory is needed to guide the overall controlling, organizing and management of traffic systems. On the basis of the cellular automata model and the traffic flow model with look-ahead potential, a new cellular automata traffic flow model with negative exponential weighted look-ahead potential is presented in this paper. By introducing the negative exponential weighting coefficient into the look-ahead potential and endowing the potential of vehicles closer to the driver with a greater coefficient, the modeling process is more suitable for the driver’s random decision-making process which is based on the traffic environment that the driver is facing. The fundamental diagrams for different weighting parameters are obtained by using numerical simulations which show that the negative exponential weighting coefficient has an obvious effect on high density traffic flux. The complex high density non-linear traffic behavior is also reproduced by numerical simulations. Project supported by the National Natural Science Foundation of China (Grant Nos. 11572264, 11172247, 11402214, and 61373009).

  10. A Day-to-Day Route Choice Model Based on Reinforcement Learning

    Directory of Open Access Journals (Sweden)

    Fangfang Wei

    2014-01-01

    Full Text Available Day-to-day traffic dynamics are generated by individual traveler’s route choice and route adjustment behaviors, which are appropriate to be researched by using agent-based model and learning theory. In this paper, we propose a day-to-day route choice model based on reinforcement learning and multiagent simulation. Travelers’ memory, learning rate, and experience cognition are taken into account. Then the model is verified and analyzed. Results show that the network flow can converge to user equilibrium (UE if travelers can remember all the travel time they have experienced, but which is not necessarily the case under limited memory; learning rate can strengthen the flow fluctuation, but memory leads to the contrary side; moreover, high learning rate results in the cyclical oscillation during the process of flow evolution. Finally, both the scenarios of link capacity degradation and random link capacity are used to illustrate the model’s applications. Analyses and applications of our model demonstrate the model is reasonable and useful for studying the day-to-day traffic dynamics.

  11. Modelling the day to day wind variability offshore central Chile at about 30 deg. south

    International Nuclear Information System (INIS)

    Rutllant, J.

    1994-07-01

    Cycles of strengthening and relaxation of the winds offshore 30 degrees S at central Chile, are related to the propagation of coastal-lows, a year-round phenomenon occurring with periodicities of about one in five days. Simple physical modelling of the day to day variability of the alongshore wind component at a coastal strip extending offshore up to the Rossby deformation radius of these wave perturbations, is presented in terms of the relevant horizontal pressure gradients and the ageostrophic components arising from the coastal-low propagation. The results of 5-day composites of 8 wind-events each, at the winter and summer halves of the annual cycle, respectively; lead to a good agreement between the observed phase-lag of the winds with respect to the pressure forcing field, stressing the importance of the ageostrophic wind components at the extremes of the pressure wave perturbation associated with the passage of coastal-lows over the Point Lengua de Vaca (30 15 S) area. A possible contribution of the upwelling-favorable wind enhancement at the time of the pressure rise and subsequent fall, ahead of the coastal-low, is postulated through an upwelling-front low-level jet, that would be carried onshore and closer to the surface by the combination of the enhanced coastal upwelling, the coastal depression of the subsidence inversion base and the coastal ageostrophic wind components during the passage of the leading edge of the coastal lows. (author). 26 refs, 5 figs, 1 tab

  12. Cooperative Optimal Operation of Wind-Storage Facilities

    DEFF Research Database (Denmark)

    Farashbashi-Astaneh, Seyed-Mostafa; Hu, Weihao; Chen, Zhe

    2014-01-01

    investment cost. We suggest benefitting the storage unit as a regulation service provider beside its normal operation for mitigating wind power imbalances. This idea comes from the fact that storage units have a fast ramping capability which is necessary to meet close to real-time regulation needs......As the penetration of wind power increases in power systems across the world, wind forecast errors become an emerging problem. Storage units are reliable tools to be used in cooperation with wind farms to mitigate imbalance penalties. Nevertheless they are not still economically viable due to huge....... In this paper a framework is proposed to formulate the optimal design of storage unit’s operation under different scenarios. These scenarios include whether the wind farm is actually generating more or less than the scheduled level submitted to day-ahead market. The results emphasize that the deployment...

  13. Transactive-Market-Based Operation of Distributed Electrical Energy Storage with Grid Constraints

    Directory of Open Access Journals (Sweden)

    M. Nazif Faqiry

    2017-11-01

    Full Text Available In a transactive energy market, distributed energy resources (DERs such as dispatchable distributed generators (DGs, electrical energy storages (EESs, distribution-scale load aggregators (LAs, and renewable energy sources (RESs have to earn their share of supply or demand through a bidding process. In such a market, the distribution system operator (DSO may optimally schedule these resources, first in a forward market, i.e., day-ahead, and in a real-time market later on, while maintaining a reliable and economic distribution grid. In this paper, an efficient day-ahead scheduling of these resources, in the presence of interaction with wholesale market at the locational marginal price (LMP, is studied. Due to inclusion of EES units with integer constraints, a detailed mixed integer linear programming (MILP formulation that incorporates simplified DistFlow equations to account for grid constraints is proposed. Convex quadratic line and transformer apparent power flow constraints have been linearized using an outer approximation. The proposed model schedules DERs based on distribution locational marginal price (DLMP, which is obtained as the Lagrange multiplier of the real power balance constraint at each distribution bus while maintaining physical grid constraints such as line limits, transformer limits, and bus voltage magnitudes. Case studies are performed on a modified IEEE 13-bus system with high DER penetration. Simulation results show the validity and efficiency of the proposed model.

  14. Impact of optimal load response to real-time electricity price on power system constraints in Denmark

    DEFF Research Database (Denmark)

    Hu, Weihao; Chen, Zhe; Bak-Jensen, Birgitte

    2010-01-01

    Since the hourly spot market price is available one day ahead in Denmark, the price could be transferred to the consumers and they may shift their loads from high price periods to the low price periods in order to save their energy costs. The optimal load response to a real-time electricity price...... and may represent the future of electricity markets in some ways, is chosen as the studied power system in this paper. A distribution system where wind power capacity is 126% of maximum loads is chosen as the study case. This paper presents a nonlinear load optimization method to real-time power price...... for demand side management in order to save the energy costs as much as possible. Simulation results show that the optimal load response to a real-time electricity price has some good impacts on power system constraints in a distribution system with high wind power penetrations....

  15. A look-ahead variant of the Lanczos algorithm and its application to the quasi-minimal residual method for non-Hermitian linear systems. Ph.D. Thesis - Massachusetts Inst. of Technology, Aug. 1991

    Science.gov (United States)

    Nachtigal, Noel M.

    1991-01-01

    The Lanczos algorithm can be used both for eigenvalue problems and to solve linear systems. However, when applied to non-Hermitian matrices, the classical Lanczos algorithm is susceptible to breakdowns and potential instabilities. In addition, the biconjugate gradient (BCG) algorithm, which is the natural generalization of the conjugate gradient algorithm to non-Hermitian linear systems, has a second source of breakdowns, independent of the Lanczos breakdowns. Here, we present two new results. We propose an implementation of a look-ahead variant of the Lanczos algorithm which overcomes the breakdowns by skipping over those steps where a breakdown or a near-breakdown would occur. The new algorithm can handle look-ahead steps of any length and requires the same number of matrix-vector products and inner products per step as the classical Lanczos algorithm without look-ahead. Based on the proposed look-ahead Lanczos algorithm, we then present a novel BCG-like approach, the quasi-minimal residual (QMR) method, which avoids the second source of breakdowns in the BCG algorithm. We present details of the new method and discuss some of its properties. In particular, we discuss the relationship between QMR and BCG, showing how one can recover the BCG iterates, when they exist, from the QMR iterates. We also present convergence results for QMR, showing the connection between QMR and the generalized minimal residual (GMRES) algorithm, the optimal method in this class of methods. Finally, we give some numerical examples, both for eigenvalue computations and for non-Hermitian linear systems.

  16. Essays on the integration of renewables in electricity markets

    International Nuclear Information System (INIS)

    Knaut, Andreas

    2017-01-01

    The thesis sheds light onto the integration of renewable energy generation into electricity markets based on five articles. The first article is concerned with the optimal strategies of renewable producers selling electricity in sequential markets. A model is developed in which renewable generators trade their production in two sequential markets, which can be regarded as the day-ahead and intraday markets. Trading in the first market takes place under uncertainty about the final production level of renewable generation. The results show that it might be optimal for renewable producers to sell less than the expected quantity in the day-ahead market. The second article focuses on the high variability in production from renewable electricity and its effect on prices. A model for the allocation of hourly and quarter-hourly electricity generation is developed, assuming that the participation in the market for quarter-hourly products is restricted. Restricted participation in the market for quarter-hourly products may have caused welfare losses of about EUR 96 million in 2015. In the third article, the hourly price elasticity of demand for electricity in the German day-ahead market is empirically estimated. The results indicate a high level of variation of price elasticity of demand throughout the day ranging from -0.02 to -0.13 depending on the time of the day in the German day-ahead market in 2015. The fourth article is concerned with the tariff design in retail markets for electricity. It focuses on the inefficiency from time-invariant pricing in combination with an increasing share of renewable energies. The last article finally takes a closer look at the balancing power market and the impact of different market designs on efficiency and competition. Based on a developed model, it shows that shorter tender frequencies could lower the costs of balancing power procurement by up to 15 %. While market concentration decreases in many markets with shorter provision

  17. Essays on the integration of renewables in electricity markets

    Energy Technology Data Exchange (ETDEWEB)

    Knaut, Andreas

    2017-07-06

    The thesis sheds light onto the integration of renewable energy generation into electricity markets based on five articles. The first article is concerned with the optimal strategies of renewable producers selling electricity in sequential markets. A model is developed in which renewable generators trade their production in two sequential markets, which can be regarded as the day-ahead and intraday markets. Trading in the first market takes place under uncertainty about the final production level of renewable generation. The results show that it might be optimal for renewable producers to sell less than the expected quantity in the day-ahead market. The second article focuses on the high variability in production from renewable electricity and its effect on prices. A model for the allocation of hourly and quarter-hourly electricity generation is developed, assuming that the participation in the market for quarter-hourly products is restricted. Restricted participation in the market for quarter-hourly products may have caused welfare losses of about EUR 96 million in 2015. In the third article, the hourly price elasticity of demand for electricity in the German day-ahead market is empirically estimated. The results indicate a high level of variation of price elasticity of demand throughout the day ranging from -0.02 to -0.13 depending on the time of the day in the German day-ahead market in 2015. The fourth article is concerned with the tariff design in retail markets for electricity. It focuses on the inefficiency from time-invariant pricing in combination with an increasing share of renewable energies. The last article finally takes a closer look at the balancing power market and the impact of different market designs on efficiency and competition. Based on a developed model, it shows that shorter tender frequencies could lower the costs of balancing power procurement by up to 15 %. While market concentration decreases in many markets with shorter provision

  18. Optimal Electric Vehicle Scheduling: A Co-Optimized System and Customer Perspective

    Science.gov (United States)

    Maigha

    Electric vehicles provide a two pronged solution to the problems faced by the electricity and transportation sectors. They provide a green, highly efficient alternative to the internal combustion engine vehicles, thus reducing our dependence on fossil fuels. Secondly, they bear the potential of supporting the grid as energy storage devices while incentivising the customers through their participation in energy markets. Despite these advantages, widespread adoption of electric vehicles faces socio-technical and economic bottleneck. This dissertation seeks to provide solutions that balance system and customer objectives under present technological capabilities. The research uses electric vehicles as controllable loads and resources. The idea is to provide the customers with required tools to make an informed decision while considering the system conditions. First, a genetic algorithm based optimal charging strategy to reduce the impact of aggregated electric vehicle load has been presented. A Monte Carlo based solution strategy studies change in the solution under different objective functions. This day-ahead scheduling is then extended to real-time coordination using a moving-horizon approach. Further, battery degradation costs have been explored with vehicle-to-grid implementations, thus accounting for customer net-revenue and vehicle utility for grid support. A Pareto front, thus obtained, provides the nexus between customer and system desired operating points. Finally, we propose a transactive business model for a smart airport parking facility. This model identifies various revenue streams and satisfaction indices that benefit the parking lot owner and the customer, thus adding value to the electric vehicle.

  19. How Far Ahead Does the Central Bank of the Republic of Turkey Look?1

    Directory of Open Access Journals (Sweden)

    Bulut Umit

    2016-01-01

    Full Text Available In monetary economics literature, there is an agreement that monetary policy has a lagged effect on inflation. As a result of this agreement, monetary policy reaction functions that include expected inflation, instead of current or lagged inflation, are established. On the other hand, there is uncertainty about how much time monetary policy needs to affect inflation. The purpose of this paper is to estimate empirically how far ahead the Central Bank of the Republic of Turkey looks. In other words, the paper examines whether the CBRT takes into consideration 12-month ahead inflation expectations or 24-month ahead inflation expectations while steering interest rates. According to the results of the paper, the CBRT considers 12-month ahead inflation expectations while steering interest rates.

  20. Ramsey prices in the Italian electricity market

    International Nuclear Information System (INIS)

    Bigerna, Simona; Bollino, Carlo Andrea

    2016-01-01

    In this paper, we derive optimal zonal prices in the Italian day-ahead electricity market using estimation of a complete system of hourly demand in 2010–2011. In Italy, the hourly equilibrium price for all buyers is computed as a uniform average of supply zonal prices, resulting from market splitting due to line congestion. We model ex-ante individual bids expressed by heterogeneous consumers, which are distinguished by geographical zones. Using empirical estimations, we compute demand elasticity values and new zonal prices, according to a Ramsey optimal scheme. This is a new approach in the wholesale electricity market literature, as previous studies have discussed the relative merit of zonal prices, considering only the issue of line congestion. Our results show that the optimal pricing scheme can improve welfare in the day-ahead Italian electricity market, with respect to both the existing uniform price scheme and the proposal to charge the existing supply zonal prices to the demand side. - Highlights: • We model and estimate the demand of heterogeneous buyers in the electricity market. • Transmission line congestion creates welfare distortions in the market. • We derive optimal Ramsey prices in the Italian day-ahead electricity market. • We compare optimal prices with historical ones showing how to improve welfare.

  1. Optimal Load Response to Time-of-Use Power Price for Demand Side Management in Denmark

    DEFF Research Database (Denmark)

    Hu, Weihao; Chen, Zhe; Bak-Jensen, Birgitte

    2010-01-01

    -of-use power price for demand side management in order to save the energy costs as much as possible. 3 typical different kinds of loads (industrial load, residential load and commercial load) in Denmark are chosen as study cases. The energy costs decrease up to 9.6% with optimal load response to time......-of-use power price for different loads. Simulation results show that the optimal load response to time-of-use power price for demand side management generates different load profiles and reduces the load peaks. This kind of load patterns may also have significant effects on the power system normal operation.......Since the hourly spot market price is available one day ahead in Denmark, the price could be transferred to the consumers and they may shift their loads from high price periods to the low price periods in order to save their energy costs. This paper presents a load optimization method to time...

  2. Effect of driving experience on anticipatory look-ahead fixations in real curve driving.

    Science.gov (United States)

    Lehtonen, Esko; Lappi, Otto; Koirikivi, Iivo; Summala, Heikki

    2014-09-01

    Anticipatory skills are a potential factor for novice drivers' curve accidents. Behavioural data show that steering and speed regulation are affected by forward planning of the trajectory. When approaching a curve, the relevant visual information for online steering control and for planning is located at different eccentricities, creating a need to disengage the gaze from the guidance of steering to anticipatory look-ahead fixations over curves. With experience, peripheral vision can be increasingly used in the visual guidance of steering. This could leave experienced drivers more gaze time to invest on look-ahead fixations over curves, facilitating the trajectory planning. Eighteen drivers (nine novices, nine experienced) drove an instrumented vehicle on a rural road four times in both directions. Their eye movements were analyzed in six curves. The trajectory of the car was modelled and divided to approach, entry and exit phases. Experienced drivers spent less time on the road-ahead and more time on the look-ahead fixations over the curves. Look-ahead fixations were also more common in the approach than in the entry phase of the curve. The results suggest that with experience drivers allocate greater part of their visual attention to trajectory planning. Copyright © 2014 Elsevier Ltd. All rights reserved.

  3. The optimal time of day for training during Ramadan: A review study

    Directory of Open Access Journals (Sweden)

    Hamdi Chtourou

    2014-04-01

    Full Text Available Literature concerning the effects of Ramadan fasting on sports performance presents conflicting results. In this context, some studies reported a significant impairment of sports performance during the month of Ramadan. However, other studies suggested that Ramadan fasting has no significant effect on physical performance.  The discrepancies between the studies could be explained by time-of-day variations in testing. In this regard, recent studies reported that Ramadan negatively affects the afternoon sports performance; however, the morning and the evening (after breaking the fast performances were not affected by fasting. This suggests that the optimal time of day for training during Ramadan is the morning or the evening. Therefore, coaches should schedule the training sessions in the morning or evening during the month of Ramadan. However, further studies should investigate the effect of training at a specific time of day on sports performance during Ramadan.

  4. The Optimal Time of Day for Training during Ramadan: A Review Study

    Directory of Open Access Journals (Sweden)

    Hamdi Chtourou

    2014-03-01

    Full Text Available Literature concerning the effects of Ramadan fasting on sports performance presents conflicting results. In this context, some studies reported a significant impairment of sports performance during the month of Ramadan. However, other studies suggested that Ramadan fasting has no significant effect on physical performance. The discrepancies between the studies could be explained by time-of-day variations in testing. In this regard, recent studies reported that Ramadan negatively affects the afternoon sports performance; however, the morning and the evening (after breaking the fast performances were not affected by fasting. This suggests that the optimal time of day for training during Ramadan is the morning or the evening. Therefore, coaches should schedule the training sessions in the morning or evening during the month of Ramadan. However, further studies should investigate the effect of training at a specific time of day on sports performance during Ramadan.

  5. A red-letter day !

    CERN Multimedia

    2008-01-01

    Today is a red-letter day for the LHC and CERN as a beam of protons has travelled around the LHC ring for the very first time! The start of LHC operation marks the end of a long period in which you have given your all, and this first particle beam circulating in the accelerator now paves the way for discoveries that will open up a whole new field of knowledge. The history of the LHC began in 1984 with a debate on the possible objectives of a future accelerator, based on the state of our knowledge at that time. The CERN Council then approved the single-stage construction of the LHC in 1996, giving the go-ahead for the work that has now reached completion. For the past twelve years, physicists, engineers and technicians from CERN and its associated institutes have been engaged in constructing the three pillars of the LHC: the accelerator (including the upgrade of the existing accelerator chain), the four experiments, and the computing ...

  6. A mid-term, market-based power systems planning model

    International Nuclear Information System (INIS)

    Koltsaklis, Nikolaos E.; Dagoumas, Athanasios S.; Georgiadis, Michael C.; Papaioannou, George; Dikaiakos, Christos

    2016-01-01

    Highlights: • A mid-term Energy Planning along with a Unit Commitment model is developed. • The model identifies the optimum interconnection capacity. • Electricity interconnections affect the power mix and the day-ahead spot price. • Renewables’ penetration has impacts on the power reserves and the CO_2 emissions. • Energy policy and fuel pricing can have significant impacts on the power mix. - Abstract: This paper presents a generic Mixed Integer Linear Programming (MILP) model that integrates a Mid-term Energy Planning (MEP) model, which implements generation and transmission system planning at a yearly level, with a Unit Commitment (UC) model, which performs the simulation of the Day-Ahead Electricity Market. The applicability of the proposed model is illustrated in a case study of the Greek interconnected power system. The aim is to evaluate a critical project in the Ten Year Network Development Plan (TYNDP) of the Independent Power Transmission System Operator S.A. (ADMIE), namely the electric interconnection of the Crete Island with the mainland electric system. The proposed modeling framework identifies the implementation (or not) of the interconnection of the Crete Island with the mainland electric system, as well as the optimum interconnection capacity. It also quantifies the effects on the Day-Ahead electricity market and on the energy mix. The paper demonstrates that the model can provide useful insights into the strategic and challenging decisions to be determined by investors and/or policy makers at a national and/or regional level, by providing the optimal energy roadmap and management, as well as clear price signals on critical energy projects under real operating and design constraints.

  7. Pay-as-bid based reactive power market

    International Nuclear Information System (INIS)

    Amjady, N.; Rabiee, A.; Shayanfar, H.A.

    2010-01-01

    In energy market clearing, the offers are stacked in increasing order and the offer that intersects demand curve, determines the market clearing price (MCP). In reactive power market, the location of reactive power compensator is so important. A low cost reactive producer may not essentially be favorable if it is far from the consumer. Likewise, a high cost local reactive compensator at a heavily loaded demand center of network could be inevitably an alternative required to produce reactive power to maintain the integrity of power system. Given the background, this paper presents a day-ahead reactive power market based on pay-as-bid (PAB) mechanism. Generators expected payment function (EPF) is used to construct a bidding framework. Then, total payment function (TPF) of generators is used as the objective function of optimal power flow (OPF) problem to clear the PAB based market. The CIGRE-32 bus test system is used to examine the effectiveness of the proposed reactive power market.

  8. Pay-as-bid based reactive power market

    Energy Technology Data Exchange (ETDEWEB)

    Amjady, N. [Department of Electrical Engineering, Semnan University, Semnan (Iran, Islamic Republic of); Rabiee, A., E-mail: Rabiee@iust.ac.i [Center of Excellence for Power System Automation and Operation, Department of Electrical Engineering, Iran University of Science and Technology, Tehran (Iran, Islamic Republic of); Shayanfar, H.A. [Center of Excellence for Power System Automation and Operation, Department of Electrical Engineering, Iran University of Science and Technology, Tehran (Iran, Islamic Republic of)

    2010-02-15

    In energy market clearing, the offers are stacked in increasing order and the offer that intersects demand curve, determines the market clearing price (MCP). In reactive power market, the location of reactive power compensator is so important. A low cost reactive producer may not essentially be favorable if it is far from the consumer. Likewise, a high cost local reactive compensator at a heavily loaded demand center of network could be inevitably an alternative required to produce reactive power to maintain the integrity of power system. Given the background, this paper presents a day-ahead reactive power market based on pay-as-bid (PAB) mechanism. Generators expected payment function (EPF) is used to construct a bidding framework. Then, total payment function (TPF) of generators is used as the objective function of optimal power flow (OPF) problem to clear the PAB based market. The CIGRE-32 bus test system is used to examine the effectiveness of the proposed reactive power market.

  9. Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey

    Directory of Open Access Journals (Sweden)

    Antimo Barbato

    2014-09-01

    Full Text Available The residential sector is currently one of the major contributors to the global energy balance. However, the energy demand of residential users has been so far largely uncontrollable and inelastic with respect to the power grid conditions. With the massive introduction of renewable energy sources and the large variations in energy flows, also the residential sector is required to provide some flexibility in energy use so as to contribute to the stability and efficiency of the electric system. To address this issue, demand management mechanisms can be used to optimally manage the energy resources of customers and their energy demand profiles. A very promising technique is represented by demand-side management (DSM, which consists in a proactive method aimed at making users energy-efficient in the long term. In this paper, we survey the most relevant studies on optimization methods for DSM of residential consumers. Specifically, we review the related literature according to three axes defining contrasting characteristics of the schemes proposed: DSM for individual users versus DSM for cooperative consumers, deterministic DSM versus stochastic DSM and day-ahead DSM versus real-time DSM. Based on this classification, we provide a big picture of the key features of different approaches and techniques and discuss future research directions.

  10. Optimal truss and frame design from projected homogenization-based topology optimization

    DEFF Research Database (Denmark)

    Larsen, S. D.; Sigmund, O.; Groen, J. P.

    2018-01-01

    In this article, we propose a novel method to obtain a near-optimal frame structure, based on the solution of a homogenization-based topology optimization model. The presented approach exploits the equivalence between Michell’s problem of least-weight trusses and a compliance minimization problem...... using optimal rank-2 laminates in the low volume fraction limit. In a fully automated procedure, a discrete structure is extracted from the homogenization-based continuum model. This near-optimal structure is post-optimized as a frame, where the bending stiffness is continuously decreased, to allow...

  11. Optimal Offering and Operating Strategy for a Large Wind-Storage System as a Price Maker

    DEFF Research Database (Denmark)

    Ding, Huajie; Pinson, Pierre; Hu, Zechun

    2017-01-01

    Wind farms and energy storage systems are playing increasingly more important roles in power systems, which makes their offering non-negligible in some markets. From the perspective of wind farm-energy storage systems (WF-ESS), this paper proposes an integrated strategy of day-ahead offering...... and real-time operation policies to maximize their overall profit. As participants with large capacity in electricity markets can influence cleared prices by strategic offering, a large scaled WFESS is assumed to be a price maker in day-ahead markets. Correspondingly, the strategy considers influence...

  12. Ensemble Kalman filtering with one-step-ahead smoothing

    KAUST Repository

    Raboudi, Naila F.; Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim

    2018-01-01

    error statistics. This limits their representativeness of the background error covariances and, thus, their performance. This work explores the efficiency of the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem to enhance

  13. One-Day Prediction of Biometeorological Conditions in a Mediterranean Urban Environment Using Artificial Neural Networks Modeling

    Directory of Open Access Journals (Sweden)

    K. P. Moustris

    2013-01-01

    Full Text Available The present study, deals with the 24-hour prognosis of the outdoor biometeorological conditions in an urban monitoring site within the Greater Athens area, Greece. For this purpose, artificial neural networks (ANNs modelling techniques are applied in order to predict the maximum and the minimum value of the physiologically equivalent temperature (PET one day ahead as well as the persistence of the hours with extreme human biometeorological conditions. The findings of the analysis showed that extreme heat stress appears to be 10.0% of the examined hours within the warm period of the year, against extreme cold stress for 22.8% of the hours during the cold period of the year. Finally, human thermal comfort sensation accounts for 81.8% of the hours during the year. Concerning the PET prognosis, ANNs have a remarkable forecasting ability to predict the extreme daily PET values one day ahead, as well as the persistence of extreme conditions during the day, at a significant statistical level of .

  14. How uncertain are day-ahead wind forecasts?

    Energy Technology Data Exchange (ETDEWEB)

    Grimit, E. [3TIER Environmental Forecast Group, Seattle, WA (United States)

    2006-07-01

    Recent advances in the combination of weather forecast ensembles with Bayesian statistical techniques have helped to address uncertainties in wind forecasting. Weather forecast ensembles are a collection of numerical weather predictions. The combination of several equally-skilled forecasts typically results in a consensus forecast with greater accuracy. The distribution of forecasts also provides an estimate of forecast inaccuracy. However, weather forecast ensembles tend to be under-dispersive, and not all forecast uncertainties can be taken into account. In order to address these issues, a multi-variate linear regression approach was used to correct the forecast bias for each ensemble member separately. Bayesian model averaging was used to provide a predictive probability density function to allow for multi-modal probability distributions. A test location in eastern Canada was used to demonstrate the approach. Results of the test showed that the method improved wind forecasts and generated reliable prediction intervals. Prediction intervals were much shorter than comparable intervals based on a single forecast or on historical observations alone. It was concluded that the approach will provide economic benefits to both wind energy developers and investors. refs., tabs., figs.

  15. Looking ahead

    CERN Multimedia

    2010-01-01

    As I write this, I’m on my way to India for a meeting of the funding agencies for large colliders (FALC), and then on to Korea where I’ll be discussing Korea’s role in our increasingly globalized field. It’s a fitting start to 2010, and to this message, in which I’d like to take a look forward to what we can expect in the year ahead. Enlargement is certainly an issue we’ll be hearing more about this year, with fact-finding missions to the five states that have applied for membership, several countries expressing an interest in associate status and Council due to reach a conclusion on the recommendations of the working group on enlargement. Twelve months from now, CERN will be a different organisation to what it is today. It will have evolved into a form well adapted to carry Europe’s particle-physics banner, coordinating Europe’s involvement in future projects outside the European region while continuing to welcome ...

  16. Reliability-based optimization of engineering structures

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard

    2008-01-01

    The theoretical basis for reliability-based structural optimization within the framework of Bayesian statistical decision theory is briefly described. Reliability-based cost benefit problems are formulated and exemplitied with structural optimization. The basic reliability-based optimization...... problems are generalized to the following extensions: interactive optimization, inspection and repair costs, systematic reconstruction, re-assessment of existing structures. Illustrative examples are presented including a simple introductory example, a decision problem related to bridge re...

  17. Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

    Energy Technology Data Exchange (ETDEWEB)

    Man, Jun [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Zhang, Jiangjiang [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Li, Weixuan [Pacific Northwest National Laboratory, Richland Washington USA; Zeng, Lingzao [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Wu, Laosheng [Department of Environmental Sciences, University of California, Riverside California USA

    2016-10-01

    The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.

  18. Wind up with continuous intra-day electricity markets? The integration of large-share wind power generation in Denmark

    International Nuclear Information System (INIS)

    Karanfil, Fatih; Li, Yuanjing

    2015-01-01

    This paper suggests an innovative idea to examine the functionality of an electricity intra-day market by testing causality among its fundamental components. As fluctuations of poorly predicted wind power generation are challenging the stability of the current electricity system, an intra-day market design can play an important role in managing wind forecast errors. Using Danish and Nordic data, it investigates the main drivers of the price difference between the intra-day and day-ahead markets, and causality between wind forecast errors and their counterparts. Our results show that the wind and conventional generation forecast errors significantly cause the intra-day price to differ from the day-ahead price, and that the relative intra-day price decreases with the unexpected amount of wind generation. Cross-border electricity exchanges are found to be important to handle wind forecast errors. Additionally, some zonal differences with respect to both causality and impulse responses are detected. This paper provides the first evidence on the persuasive functioning of the intra-day market in the case of Denmark, whereby intermittent production deviations are effectively reduced, and wind forecast errors are jointly handled through the responses from demand, conventional generation, and intra-day international electricity trade. (authors)

  19. Day-ahead residential load forecasting with artificial neural network using smart meter data

    NARCIS (Netherlands)

    Asare-Bediako, B.; Kling, W.L.; Ribeiro, P.F.

    2013-01-01

    Load forecasting is an important operational procedure for the electric industry particularly in a liberalized, deregulated environment. It enables the prediction of utilization of assets, provides input for load/supply balancing and supports optimal energy utilization. Current residential load

  20. Energy Optimization in Dyehouse | Jeetah | University of Mauritius ...

    African Journals Online (AJOL)

    ... that the initial investment on the paint, whose shell life is 2 years, would be recuperated by the 11th month. The positive net present value (2411 MUR) and high internal rate of return (80%) obtained suggested that the project should go ahead. Keywords: Insulation paint, steam consumption, energy optimization, dyehouse ...

  1. Further ahead a communication skills course for business English : teacher's guide

    CERN Document Server

    Jones-Macziola, Sarah

    1998-01-01

    Further Ahead is a Business English course at lower-intermediate level. To meet the demand for BEC Preliminary Exam we have added a CD-ROM to the Learner's Book that provides a walk and talk through the exam and practice material. Further Ahead Learner's Book is at the right language level for students who are preparing for BEC Preliminary. The Practice Test with answer key and audio has been specially written for this book by Tricia Aspinall and Jake Allsop, two very experienced test writers.

  2. Further ahead a communication skills course for business English : learner's book

    CERN Document Server

    Jones-Macziola, Sarah

    1998-01-01

    Further Ahead is a Business English course at lower-intermediate level. To meet the demand for BEC Preliminary Exam we have added a CD-ROM to the Learner's Book that provides a walk and talk through the exam and practice material. Further Ahead Learner's Book is at the right language level for students who are preparing for BEC Preliminary. The Practice Test with answer key and audio has been specially written for this book by Tricia Aspinall and Jake Allsop, two very experienced test writers.

  3. Optimizing distance-based methods for large data sets

    Science.gov (United States)

    Scholl, Tobias; Brenner, Thomas

    2015-10-01

    Distance-based methods for measuring spatial concentration of industries have received an increasing popularity in the spatial econometrics community. However, a limiting factor for using these methods is their computational complexity since both their memory requirements and running times are in {{O}}(n^2). In this paper, we present an algorithm with constant memory requirements and shorter running time, enabling distance-based methods to deal with large data sets. We discuss three recent distance-based methods in spatial econometrics: the D&O-Index by Duranton and Overman (Rev Econ Stud 72(4):1077-1106, 2005), the M-function by Marcon and Puech (J Econ Geogr 10(5):745-762, 2010) and the Cluster-Index by Scholl and Brenner (Reg Stud (ahead-of-print):1-15, 2014). Finally, we present an alternative calculation for the latter index that allows the use of data sets with millions of firms.

  4. Imaging disturbance zones ahead of a tunnel by elastic full-waveform inversion: Adjoint gradient based inversion vs. parameter space reduction using a level-set method

    Directory of Open Access Journals (Sweden)

    Andre Lamert

    2018-03-01

    Full Text Available We present and compare two flexible and effective methodologies to predict disturbance zones ahead of underground tunnels by using elastic full-waveform inversion. One methodology uses a linearized, iterative approach based on misfit gradients computed with the adjoint method while the other uses iterative, gradient-free unscented Kalman filtering in conjunction with a level-set representation. Whereas the former does not involve a priori assumptions on the distribution of elastic properties ahead of the tunnel, the latter introduces a massive reduction in the number of explicit model parameters to be inverted for by focusing on the geometric form of potential disturbances and their average elastic properties. Both imaging methodologies are validated through successful reconstructions of simple disturbances. As an application, we consider an elastic multiple disturbance scenario. By using identical synthetic time-domain seismograms as test data, we obtain satisfactory, albeit different, reconstruction results from the two inversion methodologies. The computational costs of both approaches are of the same order of magnitude, with the gradient-based approach showing a slight advantage. The model parameter space reduction approach compensates for this by additionally providing a posteriori estimates of model parameter uncertainty. Keywords: Tunnel seismics, Full waveform inversion, Seismic waves, Level-set method, Adjoint method, Kalman filter

  5. Regression-based season-ahead drought prediction for southern Peru conditioned on large-scale climate variables

    Science.gov (United States)

    Mortensen, Eric; Wu, Shu; Notaro, Michael; Vavrus, Stephen; Montgomery, Rob; De Piérola, José; Sánchez, Carlos; Block, Paul

    2018-01-01

    Located at a complex topographic, climatic, and hydrologic crossroads, southern Peru is a semiarid region that exhibits high spatiotemporal variability in precipitation. The economic viability of the region hinges on this water, yet southern Peru is prone to water scarcity caused by seasonal meteorological drought. Meteorological droughts in this region are often triggered during El Niño episodes; however, other large-scale climate mechanisms also play a noteworthy role in controlling the region's hydrologic cycle. An extensive season-ahead precipitation prediction model is developed to help bolster the existing capacity of stakeholders to plan for and mitigate deleterious impacts of drought. In addition to existing climate indices, large-scale climatic variables, such as sea surface temperature, are investigated to identify potential drought predictors. A principal component regression framework is applied to 11 potential predictors to produce an ensemble forecast of regional January-March precipitation totals. Model hindcasts of 51 years, compared to climatology and another model conditioned solely on an El Niño-Southern Oscillation index, achieve notable skill and perform better for several metrics, including ranked probability skill score and a hit-miss statistic. The information provided by the developed model and ancillary modeling efforts, such as extending the lead time of and spatially disaggregating precipitation predictions to the local level as well as forecasting the number of wet-dry days per rainy season, may further assist regional stakeholders and policymakers in preparing for drought.

  6. Lifecycle-Based Swarm Optimization Method for Numerical Optimization

    Directory of Open Access Journals (Sweden)

    Hai Shen

    2014-01-01

    Full Text Available Bioinspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biological lifecycle, this paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO. Biological lifecycle includes four stages: birth, growth, reproduction, and death. With this process, even though individual organism died, the species will not perish. Furthermore, species will have stronger ability of adaptation to the environment and achieve perfect evolution. LSO simulates Biological lifecycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection, and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on unconstrained benchmark optimization problems and mechanical design optimization problems. Unconstrained benchmark problems include both unimodal and multimodal cases the demonstration of the optimal performance and stability, and the mechanical design problem was tested for algorithm practicability. The results demonstrate remarkable performance of the LSO algorithm on all chosen benchmark functions when compared to several successful optimization techniques.

  7. A class-based search for the in-core fuel management optimization of a pressurized water reactor

    International Nuclear Information System (INIS)

    Alvarenga de Moura Meneses, Anderson; Rancoita, Paola; Schirru, Roberto; Gambardella, Luca Maria

    2010-01-01

    The In-Core Fuel Management Optimization (ICFMO) is a prominent problem in nuclear engineering, with high complexity and studied for more than 40 years. Besides manual optimization and knowledge-based methods, optimization metaheuristics such as Genetic Algorithms, Ant Colony Optimization and Particle Swarm Optimization have yielded outstanding results for the ICFMO. In the present article, the Class-Based Search (CBS) is presented for application to the ICFMO. It is a novel metaheuristic approach that performs the search based on the main nuclear characteristics of the fuel assemblies, such as reactivity. The CBS is then compared to the one of the state-of-art algorithms applied to the ICFMO, the Particle Swarm Optimization. Experiments were performed for the optimization of Angra 1 Nuclear Power Plant, located at the Southeast of Brazil. The CBS presented noticeable performance, providing Loading Patterns that yield a higher average of Effective Full Power Days in the simulation of Angra 1 NPP operation, according to our methodology.

  8. A class-based search for the in-core fuel management optimization of a pressurized water reactor

    Energy Technology Data Exchange (ETDEWEB)

    Alvarenga de Moura Meneses, Anderson, E-mail: ameneses@lmp.ufrj.b [Federal University of Rio de Janeiro, COPPE, Nuclear Engineering Program, CP 68509, CEP 21.941-972, Rio de Janeiro, RJ (Brazil); Rancoita, Paola [IDSIA (Dalle Molle Institute for Artificial Intelligence), Galleria 2, 6982 Manno-Lugano, TI (Switzerland); Mathematics Department, Universita degli Studi di Milano (Italy); Schirru, Roberto [Federal University of Rio de Janeiro, COPPE, Nuclear Engineering Program, CP 68509, CEP 21.941-972, Rio de Janeiro, RJ (Brazil); Gambardella, Luca Maria [IDSIA (Dalle Molle Institute for Artificial Intelligence), Galleria 2, 6982 Manno-Lugano, TI (Switzerland)

    2010-11-15

    The In-Core Fuel Management Optimization (ICFMO) is a prominent problem in nuclear engineering, with high complexity and studied for more than 40 years. Besides manual optimization and knowledge-based methods, optimization metaheuristics such as Genetic Algorithms, Ant Colony Optimization and Particle Swarm Optimization have yielded outstanding results for the ICFMO. In the present article, the Class-Based Search (CBS) is presented for application to the ICFMO. It is a novel metaheuristic approach that performs the search based on the main nuclear characteristics of the fuel assemblies, such as reactivity. The CBS is then compared to the one of the state-of-art algorithms applied to the ICFMO, the Particle Swarm Optimization. Experiments were performed for the optimization of Angra 1 Nuclear Power Plant, located at the Southeast of Brazil. The CBS presented noticeable performance, providing Loading Patterns that yield a higher average of Effective Full Power Days in the simulation of Angra 1 NPP operation, according to our methodology.

  9. Outpatient vaginal hysterectomy: optimizing perioperative management for same-day discharge.

    Science.gov (United States)

    Zakaria, Mark A; Levy, Barbara S

    2012-12-01

    To present tactics for optimizing outpatient vaginal hysterectomy and describe perioperative outcomes in a large consecutive case series. This is a descriptive study and review of clinical outcomes in 1,071 patients selected to undergo vaginal hysterectomy for benign indications from 2000 to 2010. The setting is a single-surgeon private practice in a community hospital. Outcome measures include length of hospital stay, estimated blood loss, operative time, uterine weight, and perioperative complications, including hospital readmissions and emergency room visits. One thousand seventy-one of 1,162 cases (92%, 95% confidence interval [CI] 90.5-93.7) were total vaginal hysterectomies, of which 1,029 (96%, 95% CI 94.9-97.3) were discharged the same day after surgery. The median operative time was 34 minutes (range 17-210 minutes), and estimated blood loss was 45 mL (range 5-800 mL). The median patient age was 46 years (range 27-86 years), and median uterine weight was 160 g (range 25-1,380 g). One hundred ninety-three patients (18%, 95% CI 15.8-20.5) were nulliparous and 218 (20%, 95% CI 18-22.9) had prior pelvic surgery. Five patients (0.5%, 95% CI 0.2-1.1) required readmission or emergency room evaluation within the first 30 days. Vaginal hysterectomy can be successfully adopted as a same-day discharge procedure. In this population, regardless of previous pelvic surgery or nulliparity, good perioperative outcomes have been achieved.

  10. Homogeneous nucleation ahead of the solid-liquid interface during rapid solidification of binary alloys

    International Nuclear Information System (INIS)

    Smith, P.M.; Elmer, J.W.

    1996-01-01

    In recent rapid solidification experiments on Al-5%Be alloys, a Liquid Phase Nucleation (LPN) model was developed to explain the formation of periodic arrays of randomly-oriented Be-rich particles in an Al-rich matrix. In the LPN model, Be droplets were assumed to nucleate in the liquid ahead of the solid-liquid interface, but no justification for this was given. Here the authors present a model which considers the geometric constraints (imposed by proximity to the interface) on the number of solute atoms available to form a nucleus. Calculations based on this model predict that nucleation of second-phase particles can be most likely a short distance ahead of the interface in immiscible binary systems such as Al-Be. As part of the nucleation calculations, a semi-empirical method of calculating solid-liquid surface tensions in binary systems was developed, and is presented in the Appendix

  11. Classifiers based on optimal decision rules

    KAUST Repository

    Amin, Talha

    2013-11-25

    Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

  12. Classifiers based on optimal decision rules

    KAUST Repository

    Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

    2013-01-01

    Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

  13. Agent-Based Optimization

    CERN Document Server

    Jędrzejowicz, Piotr; Kacprzyk, Janusz

    2013-01-01

    This volume presents a collection of original research works by leading specialists focusing on novel and promising approaches in which the multi-agent system paradigm is used to support, enhance or replace traditional approaches to solving difficult optimization problems. The editors have invited several well-known specialists to present their solutions, tools, and models falling under the common denominator of the agent-based optimization. The book consists of eight chapters covering examples of application of the multi-agent paradigm and respective customized tools to solve  difficult optimization problems arising in different areas such as machine learning, scheduling, transportation and, more generally, distributed and cooperative problem solving.

  14. Performance-based shape optimization of continuum structures

    International Nuclear Information System (INIS)

    Liang Qingquan

    2010-01-01

    This paper presents a performance-based optimization (PBO) method for optimal shape design of continuum structures with stiffness constraints. Performance-based design concepts are incorporated in the shape optimization theory to achieve optimal designs. In the PBO method, the traditional shape optimization problem of minimizing the weight of a continuum structure with displacement or mean compliance constraints is transformed to the problem of maximizing the performance of the structure. The optimal shape of a continuum structure is obtained by gradually eliminating inefficient finite elements from the structure until its performance is maximized. Performance indices are employed to monitor the performance of optimized shapes in an optimization process. Performance-based optimality criteria are incorporated in the PBO method to identify the optimum from the optimization process. The PBO method is used to produce optimal shapes of plane stress continuum structures and plates in bending. Benchmark numerical results are provided to demonstrate the effectiveness of the PBO method for generating the maximum stiffness shape design of continuum structures. It is shown that the PBO method developed overcomes the limitations of traditional shape optimization methods in optimal design of continuum structures. Performance-based optimality criteria presented can be incorporated in any shape and topology optimization methods to obtain optimal designs of continuum structures.

  15. Disseminating NASA-based science through NASA's Universe of Learning: Girls STEAM Ahead

    Science.gov (United States)

    Marcucci, E.; Meinke, B. K.; Smith, D. A.; Ryer, H.; Slivinski, C.; Kenney, J.; Arcand, K.; Cominsky, L.

    2017-12-01

    The Girls STEAM Ahead with NASA (GSAWN) initiative partners the NASA's Universe of Learning (UoL) resources with public libraries to provide NASA-themed activities for girls and their families. The program expands upon the legacy program, NASA Science4Girls and Their Families, in celebration of National Women's History Month. Program resources include hands-on activities for engaging girls, such as coding experiences and use of remote telescopes, complementary exhibits, and professional development for library partner staff. The science-institute-embedded partners in NASA's UoL are uniquely poised to foster collaboration between scientists with content expertise and educators with pedagogy expertise. The thematic topics related to NASA Astrophysics enable audiences to experience the full range of NASA scientific and technical disciplines and the different career skills each requires. For example, an activity may focus on understanding exoplanets, methods of their detection, and characteristics that can be determined remotely. The events focus on engaging underserved and underrepresented audiences in Science, Technology, Engineering, and Mathematics (STEM) via use of research-based best practices, collaborations with libraries, partnerships with local and national organizations (e.g. National Girls Collaborative Project or NGCP), and remote engagement of audiences. NASA's UoL collaborated with another NASA STEM Activation partner, NASA@ My Library, to announce GSAWN to their extensive STAR_Net network of libraries. This partnership between NASA SMD-funded Science learning and literacy teams has included NASA@ My Library hosting a professional development webinar featuring a GSAWN activity, a newsletter and blog post about the program, and plans for future exhibit development. This presentation will provide an overview of the program's progress to engage girls and their families through the development and dissemination of NASA-based science programming.

  16. Practical mathematical optimization basic optimization theory and gradient-based algorithms

    CERN Document Server

    Snyman, Jan A

    2018-01-01

    This textbook presents a wide range of tools for a course in mathematical optimization for upper undergraduate and graduate students in mathematics, engineering, computer science, and other applied sciences. Basic optimization principles are presented with emphasis on gradient-based numerical optimization strategies and algorithms for solving both smooth and noisy discontinuous optimization problems. Attention is also paid to the difficulties of expense of function evaluations and the existence of multiple minima that often unnecessarily inhibit the use of gradient-based methods. This second edition addresses further advancements of gradient-only optimization strategies to handle discontinuities in objective functions. New chapters discuss the construction of surrogate models as well as new gradient-only solution strategies and numerical optimization using Python. A special Python module is electronically available (via springerlink) that makes the new algorithms featured in the text easily accessible and dir...

  17. Mixed integer non-linear programming and Artificial Neural Network based approach to ancillary services dispatch in competitive electricity markets

    International Nuclear Information System (INIS)

    Canizes, Bruno; Soares, João; Faria, Pedro; Vale, Zita

    2013-01-01

    Highlights: • Ancillary services market management. • Ancillary services requirements forecast based on Artificial Neural Network. • Ancillary services clearing mechanisms without complex bids and with complex bids. - Abstract: Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids

  18. Picture book support for preparing children ahead of and during day surgery.

    Science.gov (United States)

    Nilsson, Elisabeth; Svensson, Gunnar; Frisman, Gunilla Hollman

    2016-10-07

    Aim To develop and evaluate the use of a specific picture book aiming to prepare children for anaesthesia and surgery. Methods An intervention comparing two different information methods before ear, nose and throat day surgery was performed. The intervention involved using a specific information sheet and a specific picture book. Parents (n=104) of children aged 2-12 years completed open-ended questions that were analysed with qualitative content analysis. They were divided into two groups: one group received routine information and one received routine information and the intervention. Findings The picture sheet and picture book were valuable aids to prepare small children for anaesthesia and surgery by explaining the procedures that would take place. The parents expressed that knowledge of the procedures made them and the child feel secure. Conclusion Peri-operative information through pictures supports children and their parents during day surgery and may be helpful in future healthcare visits.

  19. Risk Based Optimal Fatigue Testing

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Faber, M.H.; Kroon, I.B.

    1992-01-01

    Optimal fatigue life testing of materials is considered. Based on minimization of the total expected costs of a mechanical component a strategy is suggested to determine the optimal stress range levels for which additional experiments are to be performed together with an optimal value...

  20. Autonomous power networks based power system

    International Nuclear Information System (INIS)

    Jokic, A.; Van den Bosch, P.P.J.

    2006-01-01

    This paper presented the concept of autonomous networks to cope with this increased complexity in power systems while enhancing market-based operation. The operation of future power systems will be more challenging and demanding than present systems because of increased uncertainties, less inertia in the system, replacement of centralized coordinating activities by decentralized parties and the reliance on dynamic markets for both power balancing and system reliability. An autonomous network includes the aggregation of networked producers and consumers in a relatively small area with respect to the overall system. The operation of an autonomous network is coordinated and controlled with one central unit acting as an interface between internal producers/consumers and the rest of the power system. In this study, the power balance problem and system reliability through provision of ancillary services was formulated as an optimization problem for the overall autonomous networks based power system. This paper described the simulation of an optimal autonomous network dispatching in day ahead markets, based on predicted spot prices for real power, and two ancillary services. It was concluded that large changes occur in a power systems structure and operation, most of them adding to the uncertainty and complexity of the system. The introduced concept of an autonomous power network-based power system was shown to be a realistic and consistent approach to formulate and operate a market-based dispatch of both power and ancillary services. 9 refs., 4 figs

  1. Logic-based methods for optimization combining optimization and constraint satisfaction

    CERN Document Server

    Hooker, John

    2011-01-01

    A pioneering look at the fundamental role of logic in optimization and constraint satisfaction While recent efforts to combine optimization and constraint satisfaction have received considerable attention, little has been said about using logic in optimization as the key to unifying the two fields. Logic-Based Methods for Optimization develops for the first time a comprehensive conceptual framework for integrating optimization and constraint satisfaction, then goes a step further and shows how extending logical inference to optimization allows for more powerful as well as flexible

  2. Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

    Directory of Open Access Journals (Sweden)

    Qing Zhu

    2014-01-01

    Full Text Available As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations.

  3. Adaptive Portfolio Optimization for Multiple Electricity Markets Participation.

    Science.gov (United States)

    Pinto, Tiago; Morais, Hugo; Sousa, Tiago M; Sousa, Tiago; Vale, Zita; Praca, Isabel; Faia, Ricardo; Pires, Eduardo Jose Solteiro

    2016-08-01

    The increase of distributed energy resources, mainly based on renewable sources, requires new solutions that are able to deal with this type of resources' particular characteristics (namely, the renewable energy sources intermittent nature). The smart grid concept is increasing its consensus as the most suitable solution to facilitate the small players' participation in electric power negotiations while improving energy efficiency. The opportunity for players' participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market, in different contexts. The price forecasts are performed using artificial neural networks, providing a specific database with the expected prices in the different market types, at each time. This database is then used as input by an evolutionary particle swarm optimization process, which originates the most advantage participation portfolio for the market player. The proposed approach is tested and validated with simulations performed in multiagent simulator of competitive electricity markets, using real electricity markets data from the Iberian operator-MIBEL.

  4. A novel two-stage stochastic programming model for uncertainty characterization in short-term optimal strategy for a distribution company

    International Nuclear Information System (INIS)

    Ahmadi, Abdollah; Charwand, Mansour; Siano, Pierluigi; Nezhad, Ali Esmaeel; Sarno, Debora; Gitizadeh, Mohsen; Raeisi, Fatima

    2016-01-01

    In order to supply the demands of the end users in a competitive market, a distribution company purchases energy from the wholesale market while other options would be in access in the case of possessing distributed generation units and interruptible loads. In this regard, this study presents a two-stage stochastic programming model for a distribution company energy acquisition market model to manage the involvement of different electric energy resources characterized by uncertainties with the minimum cost. In particular, the distribution company operations planning over a day-ahead horizon is modeled as a stochastic mathematical optimization, with the objective of minimizing costs. By this, distribution company decisions on grid purchase, owned distributed generation units and interruptible load scheduling are determined. Then, these decisions are considered as boundary constraints to a second step, which deals with distribution company's operations in the hour-ahead market with the objective of minimizing the short-term cost. The uncertainties in spot market prices and wind speed are modeled by means of probability distribution functions of their forecast errors and the roulette wheel mechanism and lattice Monte Carlo simulation are used to generate scenarios. Numerical results show the capability of the proposed method. - Highlights: • Proposing a new a stochastic-based two-stage operations framework in retail competitive markets. • Proposing a Mixed Integer Non-Linear stochastic programming. • Employing roulette wheel mechanism and Lattice Monte Carlo Simulation.

  5. ISG hybrid powertrain: a rule-based driver model incorporating look-ahead information

    Science.gov (United States)

    Shen, Shuiwen; Zhang, Junzhi; Chen, Xiaojiang; Zhong, Qing-Chang; Thornton, Roger

    2010-03-01

    According to European regulations, if the amount of regenerative braking is determined by the travel of the brake pedal, more stringent standards must be applied, otherwise it may adversely affect the existing vehicle safety system. The use of engine or vehicle speed to derive regenerative braking is one way to avoid strict design standards, but this introduces discontinuity in powertrain torque when the driver releases the acceleration pedal or applies the brake pedal. This is shown to cause oscillations in the pedal input and powertrain torque when a conventional driver model is adopted. Look-ahead information, together with other predicted vehicle states, are adopted to control the vehicle speed, in particular, during deceleration, and to improve the driver model so that oscillations can be avoided. The improved driver model makes analysis and validation of the control strategy for an integrated starter generator (ISG) hybrid powertrain possible.

  6. Further ahead a communication skills course for business English : home study book

    CERN Document Server

    Jones-Macziola, Sarah

    1999-01-01

    Further Ahead is a Business English course at lower-intermediate level. To meet the demand for BEC Preliminary Exam we have added a CD-ROM to the Learner's Book that provides a walk and talk through the exam and practice material. Further Ahead Learner's Book is at the right language level for students who are preparing for BEC Preliminary. The Practice Test with answer key and audio has been specially written for this book by Tricia Aspinall and Jake Allsop, two very experienced test writers.

  7. Key action items for the stem cell field: looking ahead to 2014.

    Science.gov (United States)

    Knoepfler, Paul S

    2013-12-01

    The stem cell field is at a critical juncture in late 2013. We find ourselves buoyed by building momentum for both transformative basic science discoveries and clinical translation of stem cells. Cellular reprogramming has given the field exciting new avenues as well. The overall prospect of novel stem cell-based therapies becoming a reality for patients in the coming years has never seemed higher. At the same time, we face serious challenges. Some of these challenges, such as stem cell tourism, are familiar to us, although even those are evolving in ways that require adaptability and action by the stem cell field. Other new challenges are also emerging, including an urgent need for formal physician training in stem cells, regulatory compliance balanced with innovation and U.S. Food and Drug Administration reform, and savvy educational outreach. Looking ahead to 2014, both the challenges and opportunities for the stem cell field require a proactive, thoughtful approach to maximize the potential for a positive impact from stem cell advances. In this study, I discuss the key action items for the field as we look ahead to the coming year and beyond.

  8. Capacity expansion of stochastic power generation under two-stage electricity markets

    DEFF Research Database (Denmark)

    Pineda, Salvador; Morales González, Juan Miguel

    2016-01-01

    are first formulated from the standpoint of a social planner to characterize a perfectly competitive market. We investigate the effect of two paradigmatic market designs on generation expansion planning: a day-ahead market that is cleared following a conventional cost merit-order principle, and an ideal...... of stochastic power generating units. This framework includes the explicit representation of a day-ahead and a balancing market-clearing mechanisms to properly capture the impact of forecast errors of power production on the short-term operation of a power system. The proposed generation expansion problems...... market-clearing procedure that determines day-ahead dispatch decisions accounting for their impact on balancing operation costs. Furthermore, we reformulate the proposed models to determine the optimal expansion decisions that maximize the profit of a collusion of stochastic power producers in order...

  9. Multi-step ahead forecasts for electricity prices using NARX: A new approach, a critical analysis of one-step ahead forecasts

    International Nuclear Information System (INIS)

    Andalib, Arash; Atry, Farid

    2009-01-01

    The prediction of electricity prices is very important to participants of deregulated markets. Among many properties, a successful prediction tool should be able to capture long-term dependencies in market's historical data. A nonlinear autoregressive model with exogenous inputs (NARX) has proven to enjoy a superior performance to capture such dependencies than other learning machines. However, it is not examined for electricity price forecasting so far. In this paper, we have employed a NARX network for forecasting electricity prices. Our prediction model is then compared with two currently used methods, namely the multivariate adaptive regression splines (MARS) and wavelet neural network. All the models are built on the reconstructed state space of market's historical data, which either improves the results or decreases the complexity of learning algorithms. Here, we also criticize the one-step ahead forecasts for electricity price that may suffer a one-term delay and we explain why the mean square error criterion does not guarantee a functional prediction result in this case. To tackle the problem, we pursue multi-step ahead predictions. Results for the Ontario electricity market are presented

  10. Ahead of the game protocol: a multi-component, community sport-based program targeting prevention, promotion and early intervention for mental health among adolescent males.

    Science.gov (United States)

    Vella, Stewart A; Swann, Christian; Batterham, Marijka; Boydell, Katherine M; Eckermann, Simon; Fogarty, Andrea; Hurley, Diarmuid; Liddle, Sarah K; Lonsdale, Chris; Miller, Andrew; Noetel, Michael; Okely, Anthony D; Sanders, Taren; Telenta, Joanne; Deane, Frank P

    2018-03-21

    There is a recognised need for targeted community-wide mental health strategies and interventions aimed specifically at prevention and early intervention in promoting mental health. Young males are a high need group who hold particularly negative attitudes towards mental health services, and these views are detrimental for early intervention and help-seeking. Organised sports provide a promising context to deliver community-wide mental health strategies and interventions to adolescent males. The aim of the Ahead of the Game program is to test the effectiveness of a multi-component, community-sport based program targeting prevention, promotion and early intervention for mental health among adolescent males. The Ahead of the Game program will be implemented within a sample drawn from community sporting clubs and evaluated using a sample drawn from a matched control community. Four programs are proposed, including two targeting adolescents, one for parents, and one for sports coaches. One adolescent program aims to increase mental health literacy, intentions to seek and/or provide help for mental health, and to decrease stigmatising attitudes. The second adolescent program aims to increase resilience. The goal of the parent program is to increase parental mental health literacy and confidence to provide help. The coach program is intended to increase coaches' supportive behaviours (e.g., autonomy supportive behaviours), and in turn facilitate high-quality motivation and wellbeing among adolescents. Programs will be complemented by a messaging campaign aimed at adolescents to enhance mental health literacy. The effects of the program on adolescent males' psychological distress and wellbeing will also be explored. Organised sports represent a potentially engaging avenue to promote mental health and prevent the onset of mental health problems among adolescent males. The community-based design, with samples drawn from an intervention and a matched control community

  11. Set-Based Discrete Particle Swarm Optimization Based on Decomposition for Permutation-Based Multiobjective Combinatorial Optimization Problems.

    Science.gov (United States)

    Yu, Xue; Chen, Wei-Neng; Gu, Tianlong; Zhang, Huaxiang; Yuan, Huaqiang; Kwong, Sam; Zhang, Jun

    2017-08-07

    This paper studies a specific class of multiobjective combinatorial optimization problems (MOCOPs), namely the permutation-based MOCOPs. Many commonly seen MOCOPs, e.g., multiobjective traveling salesman problem (MOTSP), multiobjective project scheduling problem (MOPSP), belong to this problem class and they can be very different. However, as the permutation-based MOCOPs share the inherent similarity that the structure of their search space is usually in the shape of a permutation tree, this paper proposes a generic multiobjective set-based particle swarm optimization methodology based on decomposition, termed MS-PSO/D. In order to coordinate with the property of permutation-based MOCOPs, MS-PSO/D utilizes an element-based representation and a constructive approach. Through this, feasible solutions under constraints can be generated step by step following the permutation-tree-shaped structure. And problem-related heuristic information is introduced in the constructive approach for efficiency. In order to address the multiobjective optimization issues, the decomposition strategy is employed, in which the problem is converted into multiple single-objective subproblems according to a set of weight vectors. Besides, a flexible mechanism for diversity control is provided in MS-PSO/D. Extensive experiments have been conducted to study MS-PSO/D on two permutation-based MOCOPs, namely the MOTSP and the MOPSP. Experimental results validate that the proposed methodology is promising.

  12. Optimal Threshold Determination for Discriminating Driving Anger Intensity Based on EEG Wavelet Features and ROC Curve Analysis

    Directory of Open Access Journals (Sweden)

    Ping Wan

    2016-08-01

    Full Text Available Driving anger, called “road rage”, has become increasingly common nowadays, affecting road safety. A few researches focused on how to identify driving anger, however, there is still a gap in driving anger grading, especially in real traffic environment, which is beneficial to take corresponding intervening measures according to different anger intensity. This study proposes a method for discriminating driving anger states with different intensity based on Electroencephalogram (EEG spectral features. First, thirty drivers were recruited to conduct on-road experiments on a busy route in Wuhan, China where anger could be inducted by various road events, e.g., vehicles weaving/cutting in line, jaywalking/cyclist crossing, traffic congestion and waiting red light if they want to complete the experiments ahead of basic time for extra paid. Subsequently, significance analysis was used to select relative energy spectrum of β band (β% and relative energy spectrum of θ band (θ% for discriminating the different driving anger states. Finally, according to receiver operating characteristic (ROC curve analysis, the optimal thresholds (best cut-off points of β% and θ% for identifying none anger state (i.e., neutral were determined to be 0.2183 ≤ θ% < 1, 0 < β% < 0.2586; low anger state is 0.1539 ≤ θ% < 0.2183, 0.2586 ≤ β% < 0.3269; moderate anger state is 0.1216 ≤ θ% < 0.1539, 0.3269 ≤ β% < 0.3674; high anger state is 0 < θ% < 0.1216, 0.3674 ≤ β% < 1. Moreover, the discrimination performances of verification indicate that, the overall accuracy (Acc of the optimal thresholds of β% for discriminating the four driving anger states is 80.21%, while 75.20% for that of θ%. The results can provide theoretical foundation for developing driving anger detection or warning devices based on the relevant optimal thresholds.

  13. Optimal Bidding Strategy of Generation Companies (GenCos in Energy and Spinning Reserve Markets Using Linear Programming

    Directory of Open Access Journals (Sweden)

    Hassan Barati

    2011-10-01

    Full Text Available In this paper a new bidding strategy become modeling to day-ahead markets. The proposed algorithm is related to the point of view of a generation company (Genco that its end is maximized its benefit as a participant in sale markets of active power and spinning reserve. In this method, hourly forecasted energy price (FEP and forecasted reserve price (FRP is used as a reference to model the possible and probable price strategies of Gencos. A bi-level optimization problem That first level, is used to maximize the individual Genco’s payoffs for obtaining the optimal offered quantity of Gencos. The second one, uses the results of the upper sub-problem and minimizes the consumer’s payment with regard to the technical and network constraints, which leads to the awarded generation of the Gencos. In this paper use of the game theory in exist optimization model. The paper proposes a linear programming approach. A six bus system is employed to illustrate the application of the proposed method and to show its high precision and capabilities.

  14. Genetic algorithm based separation cascade optimization

    International Nuclear Information System (INIS)

    Mahendra, A.K.; Sanyal, A.; Gouthaman, G.; Bera, T.K.

    2008-01-01

    The conventional separation cascade design procedure does not give an optimum design because of squaring-off, variation of flow rates and separation factor of the element with respect to stage location. Multi-component isotope separation further complicates the design procedure. Cascade design can be stated as a constrained multi-objective optimization. Cascade's expectation from the separating element is multi-objective i.e. overall separation factor, cut, optimum feed and separative power. Decision maker may aspire for more comprehensive multi-objective goals where optimization of cascade is coupled with the exploration of separating element optimization vector space. In real life there are many issues which make it important to understand the decision maker's perception of cost-quality-speed trade-off and consistency of preferences. Genetic algorithm (GA) is one such evolutionary technique that can be used for cascade design optimization. This paper addresses various issues involved in the GA based multi-objective optimization of the separation cascade. Reference point based optimization methodology with GA based Pareto optimality concept for separation cascade was found pragmatic and promising. This method should be explored, tested, examined and further developed for binary as well as multi-component separations. (author)

  15. Reliability-Based Optimization in Structural Engineering

    DEFF Research Database (Denmark)

    Enevoldsen, I.; Sørensen, John Dalsgaard

    1994-01-01

    In this paper reliability-based optimization problems in structural engineering are formulated on the basis of the classical decision theory. Several formulations are presented: Reliability-based optimal design of structural systems with component or systems reliability constraints, reliability...

  16. Tackling optimization challenges in industrial load control and full-duplex radios

    Science.gov (United States)

    Gholian, Armen

    In price-based demand response programs in smart grid, utilities set the price in accordance with the grid operating conditions and consumers respond to price signals by conducting optimal load control to minimize their energy expenditure while satisfying their energy needs. Industrial sector consumes a large portion of world electricity and addressing optimal load control of energy-intensive industrial complexes, such as steel industry and oil-refinery, is of practical importance. Formulating a general industrial complex and addressing issues in optimal industrial load control in smart grid is the focus of the second part of this dissertation. Several industrial load details are considered in the proposed formulation, including those that do not appear in residential or commercial load control problems. Operation under different smart pricing scenarios, namely, day-ahead pricing, time-of-use pricing, peak pricing, inclining block rates, and critical peak pricing are considered. The use of behind-the-meter renewable generation and energy storage is also considered. The formulated optimization problem is originally nonlinear and nonconvex and thus hard to solve. However, it is then reformulated into a tractable linear mixed-integer program. The performance of the design is assessed through various simulations for an oil refinery and a steel mini-mill. In the third part of this dissertation, a novel all-analog RF interference canceler is proposed. Radio self-interference cancellation (SIC) is the fundamental enabler for full-duplex radios. While SIC methods based on baseband digital signal processing and/or beamforming are inadequate, an all-analog method is useful to drastically reduce the self-interference as the first stage of SIC. It is shown that a uniform architecture with uniformly distributed RF attenuators has a performance highly dependent on the carrier frequency. It is also shown that a new architecture with the attenuators distributed in a clustered

  17. New approach to bidding strategies of generating companies in day ahead energy market

    International Nuclear Information System (INIS)

    Soleymani, S.; Ranjbar, A.M.; Shirani, A.R.

    2008-01-01

    In the restructured power systems, generating companies (Genco) are responsible for selling their product in the energy market. In this condition, the question is how much and for what price must each Genco generate to maximize its profit. Therefore, this paper intends to propose a rational method to answer this question. In the proposed methodology, the hourly forecasted market clearing price (FMCP) is used as a reference to model the possible and probable price strategies of Gencos. The forecasted price is the basis of the bidding strategies of each Genco, which can be achieved by solving a bi-level optimization problem using GAMS (general algebraic modeling system) language. The first level, called upper sub-problem is used to maximize the individual Genco's payoffs for obtaining the optimal offered quantity of Gencos. The second one, hereafter called the lower sub-problem uses the results of the upper sub-problem and minimizes the consumer's payment with regard to the technical and network constraints, which leads to the awarded generation of the Gencos. Similar to the other game problems, the Nash equilibrium strategies are the optimum bidding strategies of Gencos. A six bus system is employed to illustrate the application of the proposed method and to show its high precision and capabilities. (author)

  18. Optimal Bidding Strategy for Renewable Microgrid with Active Network Management

    Directory of Open Access Journals (Sweden)

    Seung Wan Kim

    2016-01-01

    Full Text Available Active Network Management (ANM enables a microgrid to optimally dispatch the active/reactive power of its Renewable Distributed Generation (RDG and Battery Energy Storage System (BESS units in real time. Thus, a microgrid with high penetration of RDGs can handle their uncertainties and variabilities to achieve the stable operation using ANM. However, the actual power flow in the line connecting the main grid and microgrid may deviate significantly from the day-ahead bids if the bids are determined without consideration of the real-time adjustment through ANM, which will lead to a substantial imbalance cost. Therefore, this study proposes a formulation for obtaining an optimal bidding which reflects the change of power flow in the connecting line by real-time adjustment using ANM. The proposed formulation maximizes the expected profit of the microgrid considering various network and physical constraints. The effectiveness of the proposed bidding strategy is verified through the simulations with a 33-bus test microgrid. The simulation results show that the proposed bidding strategy improves the expected operating profit by reducing the imbalance cost to a greater degree compared to the basic bidding strategy without consideration of ANM.

  19. The Ontology of Knowledge Based Optimization

    OpenAIRE

    Nasution, Mahyuddin K. M.

    2012-01-01

    Optimization has been becoming a central of studies in mathematic and has many areas with different applications. However, many themes of optimization came from different area have not ties closing to origin concepts. This paper is to address some variants of optimization problems using ontology in order to building basic of knowledge about optimization, and then using it to enhance strategy to achieve knowledge based optimization.

  20. An experimental and computational investigation of electrical resistivity imaging for prediction ahead of tunnel boring machines

    Science.gov (United States)

    Schaeffer, Kevin P.

    Tunnel boring machines (TBMs) are routinely used for the excavation of tunnels across a range of ground conditions, from hard rock to soft ground. In complex ground conditions and in urban environments, the TBM susceptible to damage due to uncertainty of what lies ahead of the tunnel face. The research presented here explores the application of electrical resistivity theory for use in the TBM tunneling environment to detect changing conditions ahead of the machine. Electrical resistivity offers a real-time and continuous imaging solution to increase the resolution of information along the tunnel alignment and may even unveil previously unknown geologic or man-made features ahead of the TBM. The studies presented herein, break down the tunneling environment and the electrical system to understand how its fundamental parameters can be isolated and tested, identifying how they influence the ability to predict changes ahead of the tunnel face. A proof-of-concept, scaled experimental model was constructed in order assess the ability of the model to predict a metal pipe (or rod) ahead of face as the TBM excavates through a saturated sand. The model shows that a prediction of up to three tunnel diameters could be achieved, but the unique presence of the pipe (or rod) could not be concluded with certainty. Full scale finite element models were developed in order evaluate the various influences on the ability to detect changing conditions ahead of the face. Results show that TBM/tunnel geometry, TBM type, and electrode geometry can drastically influence prediction ahead of the face by tens of meters. In certain conditions (i.e., small TBM diameter, low cover depth, large material contrasts), changes can be detected over 100 meters in front of the TBM. Various electrode arrays were considered and show that in order to better detect more finite differences (e.g., boulder, lens, pipe), the use of individual cutting tools as electrodes is highly advantageous to increase spatial

  1. Cybersecurity:The Road Ahead for Defense Acquisition

    Science.gov (United States)

    2016-06-01

    contested environment. Cybersecurity being treated as key “leader business ” is criti- cal to the overall cybersecurity posture of our DoD acquisi... Cybersecurity The Road Ahead for Defense Acquisition Steve Mills n Steve Monks Mills and Monks are professors of Program Management at the Defense...not only on adequate funding for leaps in technology but also on honing that technology to protect the capability against cybersecurity threats. The

  2. PRODUCT OPTIMIZATION METHOD BASED ON ANALYSIS OF OPTIMAL VALUES OF THEIR CHARACTERISTICS

    Directory of Open Access Journals (Sweden)

    Constantin D. STANESCU

    2016-05-01

    Full Text Available The paper presents an original method of optimizing products based on the analysis of optimal values of their characteristics . Optimization method comprises statistical model and analytical model . With this original method can easily and quickly obtain optimal product or material .

  3. Subjective Straight Ahead Orientation in Microgravity

    Science.gov (United States)

    Clement, G.; Reschke, M. F.; Wood, S. J.

    2015-01-01

    This joint ESA NASA study will address adaptive changes in spatial orientation related to the subjective straight ahead and the use of a vibrotactile sensory aid to reduce perceptual errors. The study will be conducted before and after long-duration expeditions to the International Space Station (ISS) to examine how spatial processing of target location is altered following exposure to microgravity. This study addresses the sensorimotor research gap to "determine the changes in sensorimotor function over the course of a mission and during recovery after landing."

  4. Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors

    Science.gov (United States)

    Bhagat, Nikunj A.; Venkatakrishnan, Anusha; Abibullaev, Berdakh; Artz, Edward J.; Yozbatiran, Nuray; Blank, Amy A.; French, James; Karmonik, Christof; Grossman, Robert G.; O'Malley, Marcia K.; Francisco, Gerard E.; Contreras-Vidal, Jose L.

    2016-01-01

    This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG)-based brain machine interface (BMI). Intent was inferred from movement related cortical potentials (MRCPs) measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II), to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: (1) an adaptive time window was used for extracting features during BMI calibration; (2) training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and (3) BMI predictions were gated by residual electromyography (EMG) activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR) = 62.7 ± 21.4% on day 4 and 67.1 ± 14.6% on day 5. The overall false positive rate (FPR) across subjects was 27.74 ± 37.46% on day 4 and 27.5 ± 35.64% on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10%). On average, motor intent was detected −367 ± 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration. PMID:27065787

  5. Design and optimization of an EEG-based brain machine interface (BMI to an upper-limb exoskeleton for stroke survivors

    Directory of Open Access Journals (Sweden)

    Nikunj Arunkumar Bhagat

    2016-03-01

    Full Text Available This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG-based brain machine interface (BMI. Intent was inferred from movement related cortical potentials (MRCPs measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II, to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: 1 an adaptive time window was used for extracting features during BMI calibration; 2 training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and 3 BMI predictions were gated by residual electromyography (EMG activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR = 62.7 +/- 21.4 % on day 4 and 67.1 +/- 14.6 % on day 5. The overall false positive rate (FPR across subjects was 27.74 +/- 37.46 % on day 4 and 27.5 +/- 35.64 % on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10 %. On average, motor intent was detected -367 +/- 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration.

  6. Powernext Day-Ahead. Powernext Futures. Activity report - 2004; Powernext Day-Ahead. Powernext Futures. Bilan statistique 2004

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2004-07-01

    Powernext SA is a Multilateral Trading Facility which organizes and warrants the transactions on the European power exchange market. This activity report presents the highlights of the market and of Powernext in 2004: market conditions (more reasonable and less volatile prices, steadier market conditions (climate conditions, power consumption, correlation between French and German prices), increasing liquidity, start-up of Powernext Futures{sup TM} for medium-term contracts and introduction of futures price curve, promising volumes to start, and liquidity of the futures market. (J.S)

  7. Extending market activities for a distribution company in hourly-ahead energy and reserve markets-Part II: Numerical results

    International Nuclear Information System (INIS)

    Mashhour, M.; Golkar, M.A.; Moghaddas-Tafreshi, S.M.

    2011-01-01

    The present work is to show the application and implementation of the algorithms and models proposed in part I. It also represents the simulation results of (a) extracting a lumped financial model (the aggregated model) of the distribution system with distributed generations (DGs) and interruptible loads (ILs), (b) distribution company's (DISCO's) process of decision-making, based on the created financial model, on allocating its generating capability for internal usage and proposing to the hourly-ahead energy and reserve markets, and (c) a profit-based network reconfiguration methodology that increases the DISCO's technical ability and directs its financial affairs towards more profitable transactions in the upcoming markets. The function of the algorithms used for detecting unfeasible configurations, namely loop path and/or isolated part in the network are shown and well exemplified. Influential factors in DISCO's generating capability and in the coefficients of DISCO's internal cost function (ICF) are investigated. The present study substantiates the ICF-based optimization method by comparing the relevant results with the results obtained based on the use of total cost function (TCF). Several scenarios on market prices of energy and reserve and on the contingency probability factor pertaining to the real-time generation in reserve market are considered. Simulation results indicate that getting more economical benefits, DISCO may necessarily play different roles in the market and change the network configuration, at different hours.

  8. Coverage-based constraints for IMRT optimization

    Science.gov (United States)

    Mescher, H.; Ulrich, S.; Bangert, M.

    2017-09-01

    Radiation therapy treatment planning requires an incorporation of uncertainties in order to guarantee an adequate irradiation of the tumor volumes. In current clinical practice, uncertainties are accounted for implicitly with an expansion of the target volume according to generic margin recipes. Alternatively, it is possible to account for uncertainties by explicit minimization of objectives that describe worst-case treatment scenarios, the expectation value of the treatment or the coverage probability of the target volumes during treatment planning. In this note we show that approaches relying on objectives to induce a specific coverage of the clinical target volumes are inevitably sensitive to variation of the relative weighting of the objectives. To address this issue, we introduce coverage-based constraints for intensity-modulated radiation therapy (IMRT) treatment planning. Our implementation follows the concept of coverage-optimized planning that considers explicit error scenarios to calculate and optimize patient-specific probabilities q(\\hat{d}, \\hat{v}) of covering a specific target volume fraction \\hat{v} with a certain dose \\hat{d} . Using a constraint-based reformulation of coverage-based objectives we eliminate the trade-off between coverage and competing objectives during treatment planning. In-depth convergence tests including 324 treatment plan optimizations demonstrate the reliability of coverage-based constraints for varying levels of probability, dose and volume. General clinical applicability of coverage-based constraints is demonstrated for two cases. A sensitivity analysis regarding penalty variations within this planing study based on IMRT treatment planning using (1) coverage-based constraints, (2) coverage-based objectives, (3) probabilistic optimization, (4) robust optimization and (5) conventional margins illustrates the potential benefit of coverage-based constraints that do not require tedious adjustment of target volume objectives.

  9. Introduction to Retail Payments : Mapping Out the Road Ahead

    NARCIS (Netherlands)

    Bolt, Wilko; Mester, Loretta J.

    2017-01-01

    This article introduces four papers that were presented at a payments conference “Retail Payments: Mapping Out the Road Ahead,” which was held at De Nederlandsche Bank in Amsterdam in April of 2016. These papers focus on various behavioral aspects of consumers and merchants in their choice and

  10. Look-Ahead Energy Management of a Grid-Connected Residential PV System with Energy Storage under Time-Based Rate Programs

    Directory of Open Access Journals (Sweden)

    Kyeon Hur

    2012-04-01

    Full Text Available This paper presents look-ahead energy management system for a grid-connected residential photovoltaic (PV system with battery under critical peak pricing for electricity, enabling effective and proactive participation of consumers in the Smart Grid’s demand response. In the proposed system, the PV is the primary energy source with the battery for storing (or retrieving excessive (or stored energy to pursue the lowest possible electricity bill but it is grid-tied to secure electric power delivery. Premise energy management scheme with an accurate yet practical load forecasting capability based on a Kalman filter is designed to increase the predictability in controlling the power flows among these power system components and the controllable electric appliances in the premise. The case studies with various operating scenarios demonstrate the validity of the proposed system and significant cost savings through operating the energy management scheme.

  11. Sharing wind power forecasts in electricity markets: A numerical analysis

    DEFF Research Database (Denmark)

    Exizidis, Lazaros; Pinson, Pierre; Kazempour, Jalal

    2016-01-01

    In an electricity pool with significant share of wind power, all generators including conventional and wind power units are generally scheduled in a day-ahead market based on wind power forecasts. Then, a real-time market is cleared given the updated wind power forecast and fixed day......-ahead decisions to adjust power imbalances. This sequential market-clearing process may cope with serious operational challenges such as severe power shortage in real-time due to erroneous wind power forecasts in day-ahead market. To overcome such situations, several solutions can be considered such as adding...... flexible resources to the system. In this paper, we address another potential solution based on information sharing in which market players share their own wind power forecasts with others in day-ahead market. This solution may improve the functioning of sequential market-clearing process through making...

  12. Optimal electricity market for wind power

    International Nuclear Information System (INIS)

    Holttinen, H.

    2005-01-01

    This paper is about electricity market operation when looking from the wind power producers' point of view. The focus in on market time horizons: how many hours there is between the closing and delivering the bids. The case is for the Nordic countries, the Nordpool electricity market and the Danish wind power production. Real data from year 2001 was used to study the benefits of a more flexible market to wind power producer. As a result of reduced regulating market costs from better hourly predictions to the market, wind power producer would gain up to 8% more if the time between market bids and delivery was shortened from the day ahead Elspot market (hourly bids by noon for 12-36 h ahead). An after sales market where surplus or deficit production could be traded 2 h before delivery could benefit the producer almost as much, gaining 7%

  13. Coordinated Volt/Var Control in Distribution Systems with Distributed Generations Based on Joint Active and Reactive Powers Dispatch

    Directory of Open Access Journals (Sweden)

    Abouzar Samimi

    2016-01-01

    Full Text Available One of the most significant control schemes in optimal operation of distribution networks is Volt/Var control (VVC. Owing to the radial structure of distribution systems and distribution lines with a small X/R ratio, the active power scheduling affects the VVC issue. A Distribution System Operator (DSO procures its active and reactive power requirements from Distributed Generations (DGs along with the wholesale electricity market. This paper proposes a new operational scheduling method based on a joint day-ahead active/reactive power market at the distribution level. To this end, based on the capability curve, a generic reactive power cost model for DGs is developed. The joint active/reactive power dispatch model presented in this paper motivates DGs to actively participate not only in the energy markets, but also in the VVC scheme through a competitive market. The proposed method which will be performed in an offline manner aims to optimally determine (i the scheduled active and reactive power values of generation units; (ii reactive power values of switched capacitor banks; and (iii tap positions of transformers for the next day. The joint active/reactive power dispatch model for daily VVC is modeled in GAMS and solved with the DICOPT solver. Finally, the plausibility of the proposed scheduling framework is examined on a typical 22-bus distribution test network over a 24-h period.

  14. Research on the Method of Traffic Organization and Optimization Based on Dynamic Traffic Flow Model

    Directory of Open Access Journals (Sweden)

    Shu-bin Li

    2017-01-01

    Full Text Available The modern transportation system is becoming sluggish by traffic jams, so much so that it can harm the economic and society in our country. One of the reasons is the surging vehicles day by day. Another reason is the shortage of the traffic supply seriously. But the most important reason is that the traffic organization and optimization hardly met the conditions of modern transport development. In this paper, the practical method of the traffic organization and optimization used in regional area is explored by the dynamic traffic network analysis method. Firstly, the operational states of the regional traffic network are obtained by simulation method based on the self-developed traffic simulation software DynaCHINA, in which the improved traffic flow simulation model was proposed in order to be more suitable for actual domestic urban transport situation. Then the appropriated optimization model and algorithm were proposed according to different optimized content and organization goals, and the traffic simulation processes more suitable to regional optimization were designed exactly. Finally, a regional network in Tai’an city was selected as an example. The simulation results show that the proposed method is effective and feasible. It can provide strong scientific and technological support for the traffic management department.

  15. Real time optimization of solar powered direct contact membrane distillation based on multivariable extremum seeking

    KAUST Repository

    Karam, Ayman M.; Laleg-Kirati, Taous-Meriem

    2015-01-01

    This paper presents a real time optimization scheme for a solar powered direct contact membrane distillation (DCMD) water desalination system. The sun and weather conditions vary and are inconsistent throughout the day. Therefore, the solar powered DCMD feed inlet temperature is never constant, which influences the distilled water flux. The problem of DCMD process optimization has not been studied enough. In this work, the response of the process under various feed inlet temperatures is investigated, which demonstrates the need for an optimal controller. To address this issue, we propose a multivariable Newton-based extremum seeking controller which optimizes the inlet feed and permeate mass flow rates as the feed inlet temperature varies. Results are presented and discussed for a realistic temperature profile.

  16. Real time optimization of solar powered direct contact membrane distillation based on multivariable extremum seeking

    KAUST Repository

    Karam, Ayman M.

    2015-09-21

    This paper presents a real time optimization scheme for a solar powered direct contact membrane distillation (DCMD) water desalination system. The sun and weather conditions vary and are inconsistent throughout the day. Therefore, the solar powered DCMD feed inlet temperature is never constant, which influences the distilled water flux. The problem of DCMD process optimization has not been studied enough. In this work, the response of the process under various feed inlet temperatures is investigated, which demonstrates the need for an optimal controller. To address this issue, we propose a multivariable Newton-based extremum seeking controller which optimizes the inlet feed and permeate mass flow rates as the feed inlet temperature varies. Results are presented and discussed for a realistic temperature profile.

  17. A hybrid bird mating optimizer algorithm with teaching-learning-based optimization for global numerical optimization

    Directory of Open Access Journals (Sweden)

    Qingyang Zhang

    2015-02-01

    Full Text Available Bird Mating Optimizer (BMO is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO, which is established by combining the advantages of Teaching-learning-based optimization (TLBO and Bird Mating Optimizer (BMO. The performance of TLBMO is evaluated on 23 benchmark functions, and compared with seven state-of-the-art approaches, namely BMO, TLBO, Artificial Bee Bolony (ABC, Particle Swarm Optimization (PSO, Fast Evolution Programming (FEP, Differential Evolution (DE, Group Search Optimization (GSO. Experimental results indicate that the proposed method performs better than other existing algorithms for global numerical optimization.

  18. Pixel-based OPC optimization based on conjugate gradients.

    Science.gov (United States)

    Ma, Xu; Arce, Gonzalo R

    2011-01-31

    Optical proximity correction (OPC) methods are resolution enhancement techniques (RET) used extensively in the semiconductor industry to improve the resolution and pattern fidelity of optical lithography. In pixel-based OPC (PBOPC), the mask is divided into small pixels, each of which is modified during the optimization process. Two critical issues in PBOPC are the required computational complexity of the optimization process, and the manufacturability of the optimized mask. Most current OPC optimization methods apply the steepest descent (SD) algorithm to improve image fidelity augmented by regularization penalties to reduce the complexity of the mask. Although simple to implement, the SD algorithm converges slowly. The existing regularization penalties, however, fall short in meeting the mask rule check (MRC) requirements often used in semiconductor manufacturing. This paper focuses on developing OPC optimization algorithms based on the conjugate gradient (CG) method which exhibits much faster convergence than the SD algorithm. The imaging formation process is represented by the Fourier series expansion model which approximates the partially coherent system as a sum of coherent systems. In order to obtain more desirable manufacturability properties of the mask pattern, a MRC penalty is proposed to enlarge the linear size of the sub-resolution assistant features (SRAFs), as well as the distances between the SRAFs and the main body of the mask. Finally, a projection method is developed to further reduce the complexity of the optimized mask pattern.

  19. Development of GPT-based optimization algorithm

    International Nuclear Information System (INIS)

    White, J.R.; Chapman, D.M.; Biswas, D.

    1985-01-01

    The University of Lowell and Westinghouse Electric Corporation are involved in a joint effort to evaluate the potential benefits of generalized/depletion perturbation theory (GPT/DTP) methods for a variety of light water reactor (LWR) physics applications. One part of that work has focused on the development of a GPT-based optimization algorithm for the overall design, analysis, and optimization of LWR reload cores. The use of GPT sensitivity data in formulating the fuel management optimization problem is conceptually straightforward; it is the actual execution of the concept that is challenging. Thus, the purpose of this paper is to address some of the major difficulties, to outline our approach to these problems, and to present some illustrative examples of an efficient GTP-based optimization scheme

  20. Bare-Bones Teaching-Learning-Based Optimization

    Directory of Open Access Journals (Sweden)

    Feng Zou

    2014-01-01

    Full Text Available Teaching-learning-based optimization (TLBO algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

  1. FPGA fabric specific optimization for RLT design

    International Nuclear Information System (INIS)

    Perwaiz, A.; Khan, S.A.

    2010-01-01

    This paper proposes a technique custom to the optimization requirements suited for a particular family of Field Programmable Gate Arrays (FPGAs). As FPGAs have introduced re configurable black boxes there is a need to perform optimization across FPGAs slice fabric in order to achieve optimum performance. Though the Register Transfer Level (RTL) Hardware Descriptive Language (HDL) code should be technology independent but in many design instances it is imperative to understand the target technology especially once the target device embeds dedicated arithmetic blocks. No matter what the degree of optimization of the algorithm is, the configuration of target device plays an important role as far as the device utilization and path delays are concerned Index Terms: Field Programmable Gate Arrays (FPGA), Compression Tree, Bit Width Reduction, Look Ahead Pipelining. (author)

  2. Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game

    Directory of Open Access Journals (Sweden)

    Yajing Gao

    2018-04-01

    Full Text Available The openness of the electricity retail market results in the power retailers facing fierce competition in the market. This article aims to analyze the electricity purchase optimization decision-making of each power retailer with the background of the big data era. First, in order to guide the power retailer to make a purchase of electricity, this paper considers the users’ historical electricity consumption data and a comprehensive consideration of multiple factors, then uses the wavelet neural network (WNN model based on “meteorological similarity day (MSD” to forecast the user load demand. Second, in order to guide the quotation of the power retailer, this paper considers the multiple factors affecting the electricity price to cluster the sample set, and establishes a Genetic algorithm- back propagation (GA-BP neural network model based on fuzzy clustering (FC to predict the short-term market clearing price (MCP. Thirdly, based on Sealed-bid Auction (SA in game theory, a Bayesian Game Model (BGM of the power retailer’s bidding strategy is constructed, and the optimal bidding strategy is obtained by obtaining the Bayesian Nash Equilibrium (BNE under different probability distributions. Finally, a practical example is proposed to prove that the model and method can provide an effective reference for the decision-making optimization of the sales company.

  3. South African energy - the way ahead

    International Nuclear Information System (INIS)

    1986-01-01

    South African energy - the way ahead was the theme of a conference organised by the South African National Committee of the World Energy Conference (SANCWEC). Papers were presented on the following topics: energy and coal; trends in world energy consumption; the role of nuclear energy in the supply of electricity in South Africa; synfuel; the government in energy affairs; the impact of energy on economic growth in South Africa; future energy demands, and the future of energy in South Africa. Separate abstracts were prepared for two of the papers presented. The remaining papers were considered outside the subject scope of INIS

  4. Long range personalized cancer treatment strategies incorporating evolutionary dynamics.

    Science.gov (United States)

    Yeang, Chen-Hsiang; Beckman, Robert A

    2016-10-22

    Current cancer precision medicine strategies match therapies to static consensus molecular properties of an individual's cancer, thus determining the next therapeutic maneuver. These strategies typically maintain a constant treatment while the cancer is not worsening. However, cancers feature complicated sub-clonal structure and dynamic evolution. We have recently shown, in a comprehensive simulation of two non-cross resistant therapies across a broad parameter space representing realistic tumors, that substantial improvement in cure rates and median survival can be obtained utilizing dynamic precision medicine strategies. These dynamic strategies explicitly consider intratumoral heterogeneity and evolutionary dynamics, including predicted future drug resistance states, and reevaluate optimal therapy every 45 days. However, the optimization is performed in single 45 day steps ("single-step optimization"). Herein we evaluate analogous strategies that think multiple therapeutic maneuvers ahead, considering potential outcomes at 5 steps ahead ("multi-step optimization") or 40 steps ahead ("adaptive long term optimization (ALTO)") when recommending the optimal therapy in each 45 day block, in simulations involving both 2 and 3 non-cross resistant therapies. We also evaluate an ALTO approach for situations where simultaneous combination therapy is not feasible ("Adaptive long term optimization: serial monotherapy only (ALTO-SMO)"). Simulations utilize populations of 764,000 and 1,700,000 virtual patients for 2 and 3 drug cases, respectively. Each virtual patient represents a unique clinical presentation including sizes of major and minor tumor subclones, growth rates, evolution rates, and drug sensitivities. While multi-step optimization and ALTO provide no significant average survival benefit, cure rates are significantly increased by ALTO. Furthermore, in the subset of individual virtual patients demonstrating clinically significant difference in outcome between

  5. Interactive Reliability-Based Optimal Design

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Thoft-Christensen, Palle; Siemaszko, A.

    1994-01-01

    Interactive design/optimization of large, complex structural systems is considered. The objective function is assumed to model the expected costs. The constraints are reliability-based and/or related to deterministic code requirements. Solution of this optimization problem is divided in four main...... tasks, namely finite element analyses, sensitivity analyses, reliability analyses and application of an optimization algorithm. In the paper it is shown how these four tasks can be linked effectively and how existing information on design variables, Lagrange multipliers and the Hessian matrix can...

  6. Gas load forecasting based on optimized fuzzy c-mean clustering analysis of selecting similar days

    Directory of Open Access Journals (Sweden)

    Qiu Jing

    2017-08-01

    Full Text Available Traditional fuzzy c-means (FCM clustering in short term load forecasting method is easy to fall into local optimum and is sensitive to the initial cluster center.In this paper,we propose to use global search feature of particle swarm optimization (PSO algorithm to avoid these shortcomings,and to use FCM optimization to select similar date of forecast as training sample of support vector machines.This will not only strengthen the data rule of training samples,but also ensure the consistency of data characteristics.Experimental results show that the prediction accuracy of this prediction model is better than that of BP neural network and support vector machine (SVM algorithms.

  7. Interface of Science, Technology and Security: Areas of Most Concern, Now and Ahead

    Science.gov (United States)

    2017-03-28

    Ph.D. Co-director, Center for International Security and Cooperation, Stanford University Director Emeritus, Los Alamos National Laboratory...either modest, primitive sea-based civilizations, like the Orang Laut of the Malayan peninsula or the Uros of Lake Titicaca in the Andes mountains, or...areas of MosT concern, noW and ahead F Ig U R e 1 C h in a’ s O rg an iz at io n al S tr u ct u re f o r L if e S ci en ce /B io te ch n o lo g

  8. Effects of Risk Aversion on Market Outcomes: A Stochastic Two-Stage Equilibrium Model

    DEFF Research Database (Denmark)

    Kazempour, Jalal; Pinson, Pierre

    2016-01-01

    This paper evaluates how different risk preferences of electricity producers alter the market-clearing outcomes. Toward this goal, we propose a stochastic equilibrium model for electricity markets with two settlements, i.e., day-ahead and balancing, in which a number of conventional and stochastic...... by its optimality conditions, resulting in a mixed complementarity problem. Numerical results from a case study based on the IEEE one-area reliability test system are derived and discussed....

  9. Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns

    International Nuclear Information System (INIS)

    Chou, Jui-Sheng; Ngo, Ngoc-Tri

    2016-01-01

    Highlights: • This study develops a novel time-series sliding window forecast system. • The system integrates metaheuristics, machine learning and time-series models. • Site experiment of smart grid infrastructure is installed to retrieve real-time data. • The proposed system accurately predicts energy consumption in residential buildings. • The forecasting system can help users minimize their electricity usage. - Abstract: Smart grids are a promising solution to the rapidly growing power demand because they can considerably increase building energy efficiency. This study developed a novel time-series sliding window metaheuristic optimization-based machine learning system for predicting real-time building energy consumption data collected by a smart grid. The proposed system integrates a seasonal autoregressive integrated moving average (SARIMA) model and metaheuristic firefly algorithm-based least squares support vector regression (MetaFA-LSSVR) model. Specifically, the proposed system fits the SARIMA model to linear data components in the first stage, and the MetaFA-LSSVR model captures nonlinear data components in the second stage. Real-time data retrieved from an experimental smart grid installed in a building were used to evaluate the efficacy and effectiveness of the proposed system. A k-week sliding window approach is proposed for employing historical data as input for the novel time-series forecasting system. The prediction system yielded high and reliable accuracy rates in 1-day-ahead predictions of building energy consumption, with a total error rate of 1.181% and mean absolute error of 0.026 kW h. Notably, the system demonstrates an improved accuracy rate in the range of 36.8–113.2% relative to those of the linear forecasting model (i.e., SARIMA) and nonlinear forecasting models (i.e., LSSVR and MetaFA-LSSVR). Therefore, end users can further apply the forecasted information to enhance efficiency of energy usage in their buildings, especially

  10. 4. S.F.R.P. days on the optimization of radiation protection in the electronuclear, industrial and medical areas

    International Nuclear Information System (INIS)

    2006-01-01

    These days are dedicated to the implementation of the radiation protection optimization in the activities of the electronuclear sector, of the industrial sector, the medical sector, the laboratories and the centers of research and the university sector. All the aspects of the practical application of the radiation protection optimization of the workers, the public and the patients will be approached. The oral communications and posters concern the following subjects: foundations of the optimization principle, new statutory context, transmission of ALARA principle, operational dosimetry, conception, operating and maintenance of the installations, the construction sites of dismantling, industrial radiology, radioactive waste management. (N.C.)

  11. Availability Based Tariff and its impact On different Industry Players-A Review

    Science.gov (United States)

    Holmukhe, R. M.; Pawar, Yogini; Desai, R. S.; Hasarmani, T. S.

    2010-10-01

    ABT is a performance-based tariff for the supply of electricity by generators owned and controlled by the central government. It is also a new system of scheduling and dispatch, which requires both generators and beneficiaries to commit to day-ahead schedules. It is a system of rewards and penalties seeking to enforce day ahead pre-committed schedules, though variations are permitted if notified One and one half hours in advance. The order emphasizes prompt payment of dues. ABT (Availability Based Tariff) along with the Electricity Act of 2003 is perhaps the most significant and definitive step taken in the Indian power sector so far to bring more efficiency and focus to this vital infrastructure. The ABT mechanism is based on financial principles. ABT scheme is for unscheduled interchange of power. The paper reviews ABT issues, its components, clauses, mechanism, benefits and the impact of grid on different players like generation utilities, grid operator, consumers involved in power generation, transmission and distribution. While the proposed tariff structure has wide implications for each player, this deals exclusively with the technology challenges/opportunities thrown up by ABT.

  12. A New Approach to Optimal Allocation of Reactive Power Ancillary Service in Distribution Systems in the Presence of Distributed Energy Resources

    Directory of Open Access Journals (Sweden)

    Abouzar Samimi

    2015-11-01

    Full Text Available One of the most important Distribution System Operators (DSO schemes addresses the Volt/Var control (VVC problem. Developing a cost-based reactive power dispatch model for distribution systems, in which the reactive powers are appropriately priced, can motivate Distributed Energy Resources (DERs to participate actively in VVC. In this paper, new reactive power cost models for DERs, including synchronous machine-based DGs and wind turbines (WTs, are formulated based on their capability curves. To address VVC in the context of competitive electricity markets in distribution systems, first, in a day-ahead active power market, the initial active power dispatch of generation units is estimated considering environmental and economic aspects. Based on the results of the initial active power dispatch, the proposed VVC model is executed to optimally allocate reactive power support among all providers. Another novelty of this paper lies in the pricing scheme that rewards transformers and capacitors for tap and step changing, respectively, while incorporating the reactive power dispatch model. A Benders decomposition algorithm is employed as a solution method to solve the proposed reactive power dispatch, which is a mixed integer non-linear programming (MINLP problem. Finally, a typical 22-bus distribution network is used to verify the efficiency of the proposed method.

  13. Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators

    Directory of Open Access Journals (Sweden)

    Saber Talari

    2017-11-01

    Full Text Available Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind generators based on Wavelet transform, bivariate Auto-Regressive Integrated Moving Average (ARIMA method and Radial Basis Function Neural Network (RBFN. To this end, a weighted time series for wind dominated power systems is calculated and added to a bivariate ARIMA model along with the price time series. Moreover, RBFN is applied as a tool to correct the estimation error, and particle swarm optimization (PSO is used to optimize the structure and adapt the RBFN to the particular training set. This method is evaluated on the Spanish electricity market, which shows the efficiency of this approach. This method has less error compared with other methods especially when it considers the effects of large-scale wind generators.

  14. Forecasting short-term power prices in the Ontario Electricity Market (OEM) with a fuzzy logic based inference system

    International Nuclear Information System (INIS)

    Arciniegas, Alvaro I.; Arciniegas Rueda, Ismael E.

    2008-01-01

    The Ontario Electricity Market (OEM), which opened in May 2002, is relatively new and is still under change. In addition, the bidding strategies of the participants are such that the relationships between price and fundamentals are non-linear and dynamic. The lack of market maturity and high complexity hinders the use of traditional statistical methodologies (e.g., regression analysis) for price forecasting. Therefore, a flexible model is needed to achieve good forecasting in OEM. This paper uses a Takagi-Sugeno-Kang (TSK) fuzzy inference system in forecasting the one-day-ahead real-time peak price of the OEM. The forecasting results of TSK are compared with those obtained by traditional statistical and neural network based forecasting. The comparison suggests that TSK has considerable value in forecasting one-day-ahead peak price in OEM. (author)

  15. CFD-based optimization in plastics extrusion

    Science.gov (United States)

    Eusterholz, Sebastian; Elgeti, Stefanie

    2018-05-01

    This paper presents novel ideas in numerical design of mixing elements in single-screw extruders. The actual design process is reformulated as a shape optimization problem, given some functional, but possibly inefficient initial design. Thereby automatic optimization can be incorporated and the design process is advanced, beyond the simulation-supported, but still experience-based approach. This paper proposes concepts to extend a method which has been developed and validated for die design to the design of mixing-elements. For simplicity, it focuses on single-phase flows only. The developed method conducts forward-simulations to predict the quasi-steady melt behavior in the relevant part of the extruder. The result of each simulation is used in a black-box optimization procedure based on an efficient low-order parameterization of the geometry. To minimize user interaction, an objective function is formulated that quantifies the products' quality based on the forward simulation. This paper covers two aspects: (1) It reviews the set-up of the optimization framework as discussed in [1], and (2) it details the necessary extensions for the optimization of mixing elements in single-screw extruders. It concludes with a presentation of first advances in the unsteady flow simulation of a metering and mixing section with the SSMUM [2] using the Carreau material model.

  16. Multi-time scale energy management of wind farms based on comprehensive evaluation technology

    Science.gov (United States)

    Xu, Y. P.; Huang, Y. H.; Liu, Z. J.; Wang, Y. F.; Li, Z. Y.; Guo, L.

    2017-11-01

    A novel energy management of wind farms is proposed in this paper. Firstly, a novel comprehensive evaluation system is proposed to quantify economic properties of each wind farm to make the energy management more economical and reasonable. Then, a combination of multi time-scale schedule method is proposed to develop a novel energy management. The day-ahead schedule optimizes unit commitment of thermal power generators. The intraday schedule is established to optimize power generation plan for all thermal power generating units, hydroelectric generating sets and wind power plants. At last, the power generation plan can be timely revised in the process of on-line schedule. The paper concludes with simulations conducted on a real provincial integrated energy system in northeast China. Simulation results have validated the proposed model and corresponding solving algorithms.

  17. Reliability Based Optimization of Fire Protection

    DEFF Research Database (Denmark)

    Thoft-Christensen, Palle

    fire protection (PFP) of firewalls and structural members. The paper is partly based on research performed within the EU supported research project B/E-4359 "Optimized Fire Safety of Offshore Structures" and partly on research supported by the Danish Technical Research Council (see Thoft-Christensen [1......]). Special emphasis is put on the optimization software developed within the project.......It is well known that fire is one of the major risks of serious damage or total loss of several types of structures such as nuclear installations, buildings, offshore platforms/topsides etc. This paper presents a methodology and software for reliability based optimization of the layout of passive...

  18. Optimal design of RTCs in digital circuit fault self-repair based on global signal optimization

    Institute of Scientific and Technical Information of China (English)

    Zhang Junbin; Cai Jinyan; Meng Yafeng

    2016-01-01

    Since digital circuits have been widely and thoroughly applied in various fields, electronic systems are increasingly more complicated and require greater reliability. Faults may occur in elec-tronic systems in complicated environments. If immediate field repairs are not made on the faults, elec-tronic systems will not run normally, and this will lead to serious losses. The traditional method for improving system reliability based on redundant fault-tolerant technique has been unable to meet the requirements. Therefore, on the basis of (evolvable hardware)-based and (reparation balance technology)-based electronic circuit fault self-repair strategy proposed in our preliminary work, the optimal design of rectification circuits (RTCs) in electronic circuit fault self-repair based on global sig-nal optimization is deeply researched in this paper. First of all, the basic theory of RTC optimal design based on global signal optimization is proposed. Secondly, relevant considerations and suitable ranges are analyzed. Then, the basic flow of RTC optimal design is researched. Eventually, a typical circuit is selected for simulation verification, and detailed simulated analysis is made on five circumstances that occur during RTC evolution. The simulation results prove that compared with the conventional design method based RTC, the global signal optimization design method based RTC is lower in hardware cost, faster in circuit evolution, higher in convergent precision, and higher in circuit evolution success rate. Therefore, the global signal optimization based RTC optimal design method applied in the elec-tronic circuit fault self-repair technology is proven to be feasible, effective, and advantageous.

  19. A Novel Consensus-Based Particle Swarm Optimization-Assisted Trust-Tech Methodology for Large-Scale Global Optimization.

    Science.gov (United States)

    Zhang, Yong-Feng; Chiang, Hsiao-Dong

    2017-09-01

    A novel three-stage methodology, termed the "consensus-based particle swarm optimization (PSO)-assisted Trust-Tech methodology," to find global optimal solutions for nonlinear optimization problems is presented. It is composed of Trust-Tech methods, consensus-based PSO, and local optimization methods that are integrated to compute a set of high-quality local optimal solutions that can contain the global optimal solution. The proposed methodology compares very favorably with several recently developed PSO algorithms based on a set of small-dimension benchmark optimization problems and 20 large-dimension test functions from the CEC 2010 competition. The analytical basis for the proposed methodology is also provided. Experimental results demonstrate that the proposed methodology can rapidly obtain high-quality optimal solutions that can contain the global optimal solution. The scalability of the proposed methodology is promising.

  20. A Fast Reactive Power Optimization in Distribution Network Based on Large Random Matrix Theory and Data Analysis

    Directory of Open Access Journals (Sweden)

    Wanxing Sheng

    2016-05-01

    Full Text Available In this paper, a reactive power optimization method based on historical data is investigated to solve the dynamic reactive power optimization problem in distribution network. In order to reflect the variation of loads, network loads are represented in a form of random matrix. Load similarity (LS is defined to measure the degree of similarity between the loads in different days and the calculation method of the load similarity of load random matrix (LRM is presented. By calculating the load similarity between the forecasting random matrix and the random matrix of historical load, the historical reactive power optimization dispatching scheme that most matches the forecasting load can be found for reactive power control usage. The differences of daily load curves between working days and weekends in different seasons are considered in the proposed method. The proposed method is tested on a standard 14 nodes distribution network with three different types of load. The computational result demonstrates that the proposed method for reactive power optimization is fast, feasible and effective in distribution network.

  1. Residential Consumption Scheduling Based on Dynamic User Profiling

    Science.gov (United States)

    Mangiatordi, Federica; Pallotti, Emiliano; Del Vecchio, Paolo; Capodiferro, Licia

    Deployment of household appliances and of electric vehicles raises the electricity demand in the residential areas and the impact of the building's electrical power. The variations of electricity consumption across the day, may affect both the design of the electrical generation facilities and the electricity bill, mainly when a dynamic pricing is applied. This paper focuses on an energy management system able to control the day-ahead electricity demand in a residential area, taking into account both the variability of the energy production costs and the profiling of the users. The user's behavior is dynamically profiled on the basis of the tasks performed during the previous days and of the tasks foreseen for the current day. Depending on the size and on the flexibility in time of the user tasks, home inhabitants are grouped in, one over N, energy profiles, using a k-means algorithm. For a fixed energy generation cost, each energy profile is associated to a different hourly energy cost. The goal is to identify any bad user profile and to make it pay a highest bill. A bad profile example is when a user applies a lot of consumption tasks and low flexibility in task reallocation time. The proposed energy management system automatically schedules the tasks, solving a multi-objective optimization problem based on an MPSO strategy. The goals, when identifying bad users profiles, are to reduce the peak to average ratio in energy demand, and to minimize the energy costs, promoting virtuous behaviors.

  2. Day-ahead price forecasting in restructured power systems using artificial neural networks

    International Nuclear Information System (INIS)

    Vahidinasab, V.; Jadid, S.; Kazemi, A.

    2008-01-01

    Over the past 15 years most electricity supply companies around the world have been restructured from monopoly utilities to deregulated competitive electricity markets. Market participants in the restructured electricity markets find short-term electricity price forecasting (STPF) crucial in formulating their risk management strategies. They need to know future electricity prices as their profitability depends on them. This research project classifies and compares different techniques of electricity price forecasting in the literature and selects artificial neural networks (ANN) as a suitable method for price forecasting. To perform this task, market knowledge should be used to optimize the selection of input data for an electricity price forecasting tool. Then sensitivity analysis is used in this research to aid in the selection of the optimum inputs of the ANN and fuzzy c-mean (FCM) algorithm is used for daily load pattern clustering. Finally, ANN with a modified Levenberg-Marquardt (LM) learning algorithm are implemented for forecasting prices in Pennsylvania-New Jersey-Maryland (PJM) market. The forecasting results were compared with the previous works and showed that the results are reasonable and accurate. (author)

  3. Challenges ahead for mass spectrometry and proteomics applications in epigenetics.

    Science.gov (United States)

    Kessler, Benedikt M

    2010-02-01

    Inheritance of biological information to future generations depends on the replication of DNA and the Mendelian principle of distribution of genes. In addition, external and environmental factors can influence traits that can be propagated to offspring, but the molecular details of this are only beginning to be understood. The discoveries of DNA methylation and post-translational modifications on chromatin and histones provided entry points for regulating gene expression, an area now defined as epigenetics and epigenomics. Mass spectrometry turned out to be instrumental in uncovering molecular details involved in these processes. The central role of histone post-translational modifications in epigenetics related biological processes has revitalized mass spectrometry based investigations. In this special report, current approaches and future challenges that lay ahead due to the enormous complexity are discussed.

  4. Political Skill as Moderator of Personality--Job Performance Relationships in Socioanalytic Theory: Test of the Getting Ahead Motive in Automobile Sales

    Science.gov (United States)

    Blickle, Gerhard; Wendel, Stephanie; Ferris, Gerald R.

    2010-01-01

    Based on the socioanalytic perspective of performance prediction ([Hogan, 1991] and [Hogan and Shelton, 1998]), this study tests whether the motive to get ahead produces greater performance when interactively combined with social effectiveness. Specifically, we investigated whether interactions of the five-factor model constructs of extraversion…

  5. Reliability Based Optimization of Structural Systems

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard

    1987-01-01

    The optimization problem to design structural systems such that the reliability is satisfactory during the whole lifetime of the structure is considered in this paper. Some of the quantities modelling the loads and the strength of the structure are modelled as random variables. The reliability...... is estimated using first. order reliability methods ( FORM ). The design problem is formulated as the optimization problem to minimize a given cost function such that the reliability of the single elements satisfies given requirements or such that the systems reliability satisfies a given requirement....... For these optimization problems it is described how a sensitivity analysis can be performed. Next, new optimization procedures to solve the optimization problems are presented. Two of these procedures solve the system reliability based optimization problem sequentially using quasi-analytical derivatives. Finally...

  6. Volunteers: the key that opens the doors for the Open Days

    CERN Multimedia

    Antonella Del Rosso

    2013-01-01

    2013: the year that CERN opens its doors to the public. 2013 is also the approximate number of volunteers needed to ensure that these Open Days (JPO) go ahead smoothly. Whatever your personnel status and function, you, the volunteers, are the key without which the Laboratory’s doors could not really open. Sign up now!   1,500 of you volunteered for the LHC2008 open days to mark the inauguration of the LHC. This year, with roughly 20% more visitors expected across the CERN sites over the two days, the organisers envisage closer to 2,000 volunteers. “We will be holding a wide variety of activities across the Laboratory’s various sites,” explains Virginie Blondeau, the member of the Open Days organising team in charge of recruiting and training volunteers. “As well as guides for the experiments, we will also need volunteers to welcome and direct visitors, to help with logistics and to man the sales points, etc.” The volunteers will rec...

  7. An online learning approach to dynamic pricing for demand response

    OpenAIRE

    Jia, Liyan; Tong, Lang; Zhao, Qing

    2014-01-01

    In this paper, the problem of optimal dynamic pricing for retail electricity with an unknown demand model is considered. Under the day-ahead dynamic pricing (a.k.a. real time pricing) mechanism, a retailer obtains electricity in a twosettlement wholesale market and serves its customers in real time. Without knowledge on the aggregated demand function of its customers, the retailer aims to maximize its retail surplus by sequentially adjusting its price based on the behavior of its customers in...

  8. A Case Study of Control and Improved Simplified Swarm Optimization for Economic Dispatch of a Stand-Alone Modular Microgrid

    Directory of Open Access Journals (Sweden)

    Xianyong Zhang

    2018-03-01

    Full Text Available Due to the complex configuration and control framework, the conventional microgrid is not cost-effective for engineering applications with small or medium capacity. A stand-alone modular microgrid with separated AC bus and decentralized control strategy is proposed in this paper. Each module is a self-powered system, which consists of wind and solar power, a storage battery, load and three-port converter. The modules are interconnected by three-port converters to form the microgrid. Characteristics, operation principle, control of the modular microgrid and the three-port converter are analyzed in detail. Distributed storage batteries enable power exchanges among modules to enhance economic returns. Economic dispatch of the stand-alone modular microgrid is a mixed-integer programming problem. A day-ahead operation optimization model including fuel cost, battery operation cost, and power transmission cost is established. Because there are so many constraints, it is difficult to produce a feasible solution and even more difficult to have an improved solution. An improved simplified swarm optimization (iSSO method is therefore proposed. The iSSO scheme designs the new update mechanism and survival of the fittest policy. The experimental results from the demonstration project on DongAo Island reflect the effectiveness of the stand-alone modular microgrid and the economic dispatch strategy based on the iSSO method.

  9. Integrated Reliability-Based Optimal Design of Structures

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Thoft-Christensen, Palle

    1987-01-01

    In conventional optimal design of structural systems the weight or the initial cost of the structure is usually used as objective function. Further, the constraints require that the stresses and/or strains at some critical points have to be less than some given values. Finally, all variables......-based optimal design is discussed. Next, an optimal inspection and repair strategy for existing structural systems is presented. An optimization problem is formulated , where the objective is to minimize the expected total future cost of inspection and repair subject to the constraint that the reliability...... value. The reliability can be measured from an element and/or a systems point of view. A number of methods to solve reliability-based optimization problems has been suggested, see e.g. Frangopol [I]. Murotsu et al. (2], Thoft-Christensen & Sørensen (3] and Sørensen (4). For structures where...

  10. The role of co-located storage for wind power producers in conventional electricity markets

    KAUST Repository

    Bitar, E.; Rajagopal, R.; Khargonekar, P.; Poolla, K.

    2011-01-01

    In this paper we study the problem of optimizing contract offerings for an independent wind power producer (WPP) participating in conventional day-ahead forward electricity markets for energy. As wind power is an inherently variable source of energy

  11. An Improved Real-Coded Population-Based Extremal Optimization Method for Continuous Unconstrained Optimization Problems

    Directory of Open Access Journals (Sweden)

    Guo-Qiang Zeng

    2014-01-01

    Full Text Available As a novel evolutionary optimization method, extremal optimization (EO has been successfully applied to a variety of combinatorial optimization problems. However, the applications of EO in continuous optimization problems are relatively rare. This paper proposes an improved real-coded population-based EO method (IRPEO for continuous unconstrained optimization problems. The key operations of IRPEO include generation of real-coded random initial population, evaluation of individual and population fitness, selection of bad elements according to power-law probability distribution, generation of new population based on uniform random mutation, and updating the population by accepting the new population unconditionally. The experimental results on 10 benchmark test functions with the dimension N=30 have shown that IRPEO is competitive or even better than the recently reported various genetic algorithm (GA versions with different mutation operations in terms of simplicity, effectiveness, and efficiency. Furthermore, the superiority of IRPEO to other evolutionary algorithms such as original population-based EO, particle swarm optimization (PSO, and the hybrid PSO-EO is also demonstrated by the experimental results on some benchmark functions.

  12. Optimal perturbations for nonlinear systems using graph-based optimal transport

    Science.gov (United States)

    Grover, Piyush; Elamvazhuthi, Karthik

    2018-06-01

    We formulate and solve a class of finite-time transport and mixing problems in the set-oriented framework. The aim is to obtain optimal discrete-time perturbations in nonlinear dynamical systems to transport a specified initial measure on the phase space to a final measure in finite time. The measure is propagated under system dynamics in between the perturbations via the associated transfer operator. Each perturbation is described by a deterministic map in the measure space that implements a version of Monge-Kantorovich optimal transport with quadratic cost. Hence, the optimal solution minimizes a sum of quadratic costs on phase space transport due to the perturbations applied at specified times. The action of the transport map is approximated by a continuous pseudo-time flow on a graph, resulting in a tractable convex optimization problem. This problem is solved via state-of-the-art solvers to global optimality. We apply this algorithm to a problem of transport between measures supported on two disjoint almost-invariant sets in a chaotic fluid system, and to a finite-time optimal mixing problem by choosing the final measure to be uniform. In both cases, the optimal perturbations are found to exploit the phase space structures, such as lobe dynamics, leading to efficient global transport. As the time-horizon of the problem is increased, the optimal perturbations become increasingly localized. Hence, by combining the transfer operator approach with ideas from the theory of optimal mass transportation, we obtain a discrete-time graph-based algorithm for optimal transport and mixing in nonlinear systems.

  13. The Optimal Wavelengths for Light Absorption Spectroscopy Measurements Based on Genetic Algorithm-Particle Swarm Optimization

    Science.gov (United States)

    Tang, Ge; Wei, Biao; Wu, Decao; Feng, Peng; Liu, Juan; Tang, Yuan; Xiong, Shuangfei; Zhang, Zheng

    2018-03-01

    To select the optimal wavelengths in the light extinction spectroscopy measurement, genetic algorithm-particle swarm optimization (GAPSO) based on genetic algorithm (GA) and particle swarm optimization (PSO) is adopted. The change of the optimal wavelength positions in different feature size parameters and distribution parameters is evaluated. Moreover, the Monte Carlo method based on random probability is used to identify the number of optimal wavelengths, and good inversion effects of the particle size distribution are obtained. The method proved to have the advantage of resisting noise. In order to verify the feasibility of the algorithm, spectra with bands ranging from 200 to 1000 nm are computed. Based on this, the measured data of standard particles are used to verify the algorithm.

  14. One-day-ahead streamflow forecasting via super-ensembles of several neural network architectures based on the Multi-Level Diversity Model

    Science.gov (United States)

    Brochero, Darwin; Hajji, Islem; Pina, Jasson; Plana, Queralt; Sylvain, Jean-Daniel; Vergeynst, Jenna; Anctil, Francois

    2015-04-01

    Theories about generalization error with ensembles are mainly based on the diversity concept, which promotes resorting to many members of different properties to support mutually agreeable decisions. Kuncheva (2004) proposed the Multi Level Diversity Model (MLDM) to promote diversity in model ensembles, combining different data subsets, input subsets, models, parameters, and including a combiner level in order to optimize the final ensemble. This work tests the hypothesis about the minimisation of the generalization error with ensembles of Neural Network (NN) structures. We used the MLDM to evaluate two different scenarios: (i) ensembles from a same NN architecture, and (ii) a super-ensemble built by a combination of sub-ensembles of many NN architectures. The time series used correspond to the 12 basins of the MOdel Parameter Estimation eXperiment (MOPEX) project that were used by Duan et al. (2006) and Vos (2013) as benchmark. Six architectures are evaluated: FeedForward NN (FFNN) trained with the Levenberg Marquardt algorithm (Hagan et al., 1996), FFNN trained with SCE (Duan et al., 1993), Recurrent NN trained with a complex method (Weins et al., 2008), Dynamic NARX NN (Leontaritis and Billings, 1985), Echo State Network (ESN), and leak integrator neuron (L-ESN) (Lukosevicius and Jaeger, 2009). Each architecture performs separately an Input Variable Selection (IVS) according to a forward stepwise selection (Anctil et al., 2009) using mean square error as objective function. Post-processing by Predictor Stepwise Selection (PSS) of the super-ensemble has been done following the method proposed by Brochero et al. (2011). IVS results showed that the lagged stream flow, lagged precipitation, and Standardized Precipitation Index (SPI) (McKee et al., 1993) were the most relevant variables. They were respectively selected as one of the firsts three selected variables in 66, 45, and 28 of the 72 scenarios. A relationship between aridity index (Arora, 2002) and NN

  15. Validity of calendar day-based definitions for community-onset bloodstream infections.

    Science.gov (United States)

    Laupland, Kevin B; Gregson, Daniel B; Church, Deirdre L

    2015-04-02

    Community-onset (CO) bloodstream infections (BSI) are those BSI where the blood culture is drawn culture draw or hospital admission are not always available. We evaluated the validity of using 2- or 3- calendar day based definitions for CO-BSI by comparing to a "gold standard" 48-hour definition. Among the population-based cohort of 14,106 episodes of BSI studied, 10,543 were classified as CO based on "gold standard" 48-hour criteria. When 2-day and 3-day definitions were applied, 10,396 and 10,707 CO-BSI episodes were ascertained, respectively. All but 147 (1.4%) true CO-BSI cases were included by using the 2-day definition. When the 3-day definition was applied, all cases of CO-BSI were identified but and additional 164 (1.5%) cases of hospital-onset HO-BSI were also included. Thus the sensitivity and specificity of the 2-day definition was 98.6% and 100% and for the 3-day definition was 100% and 98.5%, respectively. Overall, only 311 (2.2%) cases were potentially miss-classifiable using either the 2- or 3-calendar day based definitions. Use of either a 2- or 3-day definition is highly accurate for classifying CO-BSI.

  16. The way ahead through European collaboration

    International Nuclear Information System (INIS)

    Vaughan, R.D.

    1987-01-01

    The paper on the ''Way ahead through European Collaboration'' was presented to the seminar on ''European commercial fast reactor programme'', London, 1987. A description is given of the world energy consumption, world nuclear energy consumption, and uranium resources. It is suggested that the fast reactor is likely to be developed first in Western Europe, and more particularly in the European Economic Community. Collaboration in Europe has taken a positive step forward with the decision of the European Fast Reactor Utilities Group to open a dialogue with the design and construction companies, working together. The companies are invited to prepare jointly a new design for a demonstration fast reactor to be ordered in the early 1990's. (U.K.)

  17. 16 Days of Activism against Gender-Based Violence | IDRC ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    2017-11-25

    Nov 25, 2017 ... The International Day for the Elimination of Violence against Women on November 25 ... against gender-based violence and culminates with Human Rights Day ... to Canada's strong voice in the fight against sexual violence.

  18. Optimal policy for value-based decision-making.

    Science.gov (United States)

    Tajima, Satohiro; Drugowitsch, Jan; Pouget, Alexandre

    2016-08-18

    For decades now, normative theories of perceptual decisions, and their implementation as drift diffusion models, have driven and significantly improved our understanding of human and animal behaviour and the underlying neural processes. While similar processes seem to govern value-based decisions, we still lack the theoretical understanding of why this ought to be the case. Here, we show that, similar to perceptual decisions, drift diffusion models implement the optimal strategy for value-based decisions. Such optimal decisions require the models' decision boundaries to collapse over time, and to depend on the a priori knowledge about reward contingencies. Diffusion models only implement the optimal strategy under specific task assumptions, and cease to be optimal once we start relaxing these assumptions, by, for example, using non-linear utility functions. Our findings thus provide the much-needed theory for value-based decisions, explain the apparent similarity to perceptual decisions, and predict conditions under which this similarity should break down.

  19. High Predictive Skill of Global Surface Temperature a Year Ahead

    Science.gov (United States)

    Folland, C. K.; Colman, A.; Kennedy, J. J.; Knight, J.; Parker, D. E.; Stott, P.; Smith, D. M.; Boucher, O.

    2011-12-01

    We discuss the high skill of real-time forecasts of global surface temperature a year ahead issued by the UK Met Office, and their scientific background. Although this is a forecasting and not a formal attribution study, we show that the main instrumental global annual surface temperature data sets since 1891 are structured consistently with a set of five physical forcing factors except during and just after the second World War. Reconstructions use a multiple application of cross validated linear regression to minimise artificial skill allowing time-varying uncertainties in the contribution of each forcing factor to global temperature to be assessed. Mean cross validated reconstructions for the data sets have total correlations in the range 0.93-0.95,interannual correlations in the range 0.72-0.75 and root mean squared errors near 0.06oC, consistent with observational uncertainties.Three transient runs of the HadCM3 coupled model for 1888-2002 demonstrate quite similar reconstruction skill from similar forcing factors defined appropriately for the model, showing that skilful use of our technique is not confined to observations. The observed reconstructions show that the Atlantic Multidecadal Oscillation (AMO) likely contributed to the re-commencement of global warming between 1976 and 2010 and to global cooling observed immediately beforehand in 1965-1976. The slowing of global warming in the last decade is likely to be largely due to a phase-delayed response to the downturn in the solar cycle since 2001-2, with no net ENSO contribution. The much reduced trend in 2001-10 is similar in size to other weak decadal temperature trends observed since global warming resumed in the 1970s. The causes of variations in decadal trends can be mostly explained by variations in the strength of the forcing factors. Eleven real-time forecasts of global mean surface temperature for the year ahead for 2000-2010, based on broadly similar methods, provide an independent test of the

  20. Smoothing-based compressed state Kalman filter for joint state-parameter estimation: Applications in reservoir characterization and CO2 storage monitoring

    Science.gov (United States)

    Li, Y. J.; Kokkinaki, Amalia; Darve, Eric F.; Kitanidis, Peter K.

    2017-08-01

    The operation of most engineered hydrogeological systems relies on simulating physical processes using numerical models with uncertain parameters and initial conditions. Predictions by such uncertain models can be greatly improved by Kalman-filter techniques that sequentially assimilate monitoring data. Each assimilation constitutes a nonlinear optimization, which is solved by linearizing an objective function about the model prediction and applying a linear correction to this prediction. However, if model parameters and initial conditions are uncertain, the optimization problem becomes strongly nonlinear and a linear correction may yield unphysical results. In this paper, we investigate the utility of one-step ahead smoothing, a variant of the traditional filtering process, to eliminate nonphysical results and reduce estimation artifacts caused by nonlinearities. We present the smoothing-based compressed state Kalman filter (sCSKF), an algorithm that combines one step ahead smoothing, in which current observations are used to correct the state and parameters one step back in time, with a nonensemble covariance compression scheme, that reduces the computational cost by efficiently exploring the high-dimensional state and parameter space. Numerical experiments show that when model parameters are uncertain and the states exhibit hyperbolic behavior with sharp fronts, as in CO2 storage applications, one-step ahead smoothing reduces overshooting errors and, by design, gives physically consistent state and parameter estimates. We compared sCSKF with commonly used data assimilation methods and showed that for the same computational cost, combining one step ahead smoothing and nonensemble compression is advantageous for real-time characterization and monitoring of large-scale hydrogeological systems with sharp moving fronts.

  1. Control and EMS of a Grid-Connected Microgrid with Economical Analysis

    Directory of Open Access Journals (Sweden)

    Mohamed El-Hendawi

    2018-01-01

    Full Text Available Recently, significant development has occurred in the field of microgrid and renewable energy systems (RESs. Integrating microgrids and renewable energy sources facilitates a sustainable energy future. This paper proposes a control algorithm and an optimal energy management system (EMS for a grid-connected microgrid to minimize its operating cost. The microgrid includes photovoltaic (PV, wind turbine (WT, and energy storage systems (ESS. The interior search algorithm (ISA optimization technique determines the optimal hour-by-hour scheduling for the microgrid system, while it meets the required load demand based on 24-h ahead forecast data. The control system consists of three stages: EMS, supervisory control and local control. EMS is responsible for providing the control system with the optimum day-ahead scheduling power flow between the microgrid (MG sources, batteries, loads and the main grid based on an economic analysis. The supervisory control stage is responsible for compensating the mismatch between the scheduled power and the real microgrid power. In addition, this paper presents the local control design to regulate the local power, current and DC voltage of the microgrid. For verification, the proposed model was applied on a real case study in Oshawa (Ontario, Canada with various load conditions.

  2. GPU-Monte Carlo based fast IMRT plan optimization

    Directory of Open Access Journals (Sweden)

    Yongbao Li

    2014-03-01

    Full Text Available Purpose: Intensity-modulated radiation treatment (IMRT plan optimization needs pre-calculated beamlet dose distribution. Pencil-beam or superposition/convolution type algorithms are typically used because of high computation speed. However, inaccurate beamlet dose distributions, particularly in cases with high levels of inhomogeneity, may mislead optimization, hindering the resulting plan quality. It is desire to use Monte Carlo (MC methods for beamlet dose calculations. Yet, the long computational time from repeated dose calculations for a number of beamlets prevents this application. It is our objective to integrate a GPU-based MC dose engine in lung IMRT optimization using a novel two-steps workflow.Methods: A GPU-based MC code gDPM is used. Each particle is tagged with an index of a beamlet where the source particle is from. Deposit dose are stored separately for beamlets based on the index. Due to limited GPU memory size, a pyramid space is allocated for each beamlet, and dose outside the space is neglected. A two-steps optimization workflow is proposed for fast MC-based optimization. At first step, a rough dose calculation is conducted with only a few number of particle per beamlet. Plan optimization is followed to get an approximated fluence map. In the second step, more accurate beamlet doses are calculated, where sampled number of particles for a beamlet is proportional to the intensity determined previously. A second-round optimization is conducted, yielding the final result.Results: For a lung case with 5317 beamlets, 105 particles per beamlet in the first round, and 108 particles per beam in the second round are enough to get a good plan quality. The total simulation time is 96.4 sec.Conclusion: A fast GPU-based MC dose calculation method along with a novel two-step optimization workflow are developed. The high efficiency allows the use of MC for IMRT optimizations.--------------------------------Cite this article as: Li Y, Tian Z

  3. Location based Network Optimizations for Mobile Wireless Networks

    DEFF Research Database (Denmark)

    Nielsen, Jimmy Jessen

    selection in Wi-Fi networks and predictive handover optimization in heterogeneous wireless networks. The investigations in this work have indicated that location based network optimizations are beneficial compared to typical link measurement based approaches. Especially the knowledge of geographical...

  4. Ensemble Kalman filtering with one-step-ahead smoothing

    KAUST Repository

    Raboudi, Naila F.

    2018-01-11

    The ensemble Kalman filter (EnKF) is widely used for sequential data assimilation. It operates as a succession of forecast and analysis steps. In realistic large-scale applications, EnKFs are implemented with small ensembles and poorly known model error statistics. This limits their representativeness of the background error covariances and, thus, their performance. This work explores the efficiency of the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem to enhance the data assimilation performance of EnKFs. Filtering with OSA smoothing introduces an updated step with future observations, conditioning the ensemble sampling with more information. This should provide an improved background ensemble in the analysis step, which may help to mitigate the suboptimal character of EnKF-based methods. Here, the authors demonstrate the efficiency of a stochastic EnKF with OSA smoothing for state estimation. They then introduce a deterministic-like EnKF-OSA based on the singular evolutive interpolated ensemble Kalman (SEIK) filter. The authors show that the proposed SEIK-OSA outperforms both SEIK, as it efficiently exploits the data twice, and the stochastic EnKF-OSA, as it avoids observational error undersampling. They present extensive assimilation results from numerical experiments conducted with the Lorenz-96 model to demonstrate SEIK-OSA’s capabilities.

  5. EUD-based biological optimization for carbon ion therapy

    International Nuclear Information System (INIS)

    Brüningk, Sarah C.; Kamp, Florian; Wilkens, Jan J.

    2015-01-01

    Purpose: Treatment planning for carbon ion therapy requires an accurate modeling of the biological response of each tissue to estimate the clinical outcome of a treatment. The relative biological effectiveness (RBE) accounts for this biological response on a cellular level but does not refer to the actual impact on the organ as a whole. For photon therapy, the concept of equivalent uniform dose (EUD) represents a simple model to take the organ response into account, yet so far no formulation of EUD has been reported that is suitable to carbon ion therapy. The authors introduce the concept of an equivalent uniform effect (EUE) that is directly applicable to both ion and photon therapies and exemplarily implemented it as a basis for biological treatment plan optimization for carbon ion therapy. Methods: In addition to a classical EUD concept, which calculates a generalized mean over the RBE-weighted dose distribution, the authors propose the EUE to simplify the optimization process of carbon ion therapy plans. The EUE is defined as the biologically equivalent uniform effect that yields the same probability of injury as the inhomogeneous effect distribution in an organ. Its mathematical formulation is based on the generalized mean effect using an effect-volume parameter to account for different organ architectures and is thus independent of a reference radiation. For both EUD concepts, quadratic and logistic objective functions are implemented into a research treatment planning system. A flexible implementation allows choosing for each structure between biological effect constraints per voxel and EUD constraints per structure. Exemplary treatment plans are calculated for a head-and-neck patient for multiple combinations of objective functions and optimization parameters. Results: Treatment plans optimized using an EUE-based objective function were comparable to those optimized with an RBE-weighted EUD-based approach. In agreement with previous results from photon

  6. Dependence of Parking Pricing on Land Use and Time of Day

    OpenAIRE

    Zong, Fang; He, Yanan; Yuan, Yixin

    2015-01-01

    A key strategy of sustainable transportation, parking pricing can directly contribute to decreased greenhouse gas emissions and air pollution. This paper describes an optimal structure of parking rates in terms of parking locations and time of day. A two-level parking model based on game theory is established using parking survey data collected in Beijing in 2014. The model was estimated based on Stackelberg game and the Nash equilibrium. Using the two-level parking model, the optimal structu...

  7. IAEA Mission Sees Significant Progress in Georgia’s Regulatory Framework, Challenges Ahead

    International Nuclear Information System (INIS)

    2018-01-01

    An International Atomic Energy Agency (IAEA) team of experts said Georgia has made significant progress in strengthening its regulatory framework for nuclear and radiation safety. The team also pointed to challenges ahead as Georgia seeks to achieve further progress. The Integrated Regulatory Review Service (IRRS) team concluded a 10-day mission on 28 February to assess the regulatory safety framework in Georgia. The mission was conducted at the request of the Government and hosted by the Agency of Nuclear and Radiation Safety (ANRS), which is responsible for regulatory oversight in the country. IRRS missions are designed to strengthen the effectiveness of the national safety regulatory infrastructure, while recognizing the responsibility of each State to ensure nuclear and radiation safety. Georgia uses radioactive sources in medicine and industry and operates radioactive waste management facilities. It has decommissioned its only research reactor and has no nuclear power plants. In recent years, the Government and ANRS, with assistance from the IAEA, introduced new safety regulations and increased the number of regulatory inspections.

  8. Biogeography-Based Optimization with Orthogonal Crossover

    Directory of Open Access Journals (Sweden)

    Quanxi Feng

    2013-01-01

    Full Text Available Biogeography-based optimization (BBO is a new biogeography inspired, population-based algorithm, which mainly uses migration operator to share information among solutions. Similar to crossover operator in genetic algorithm, migration operator is a probabilistic operator and only generates the vertex of a hyperrectangle defined by the emigration and immigration vectors. Therefore, the exploration ability of BBO may be limited. Orthogonal crossover operator with quantization technique (QOX is based on orthogonal design and can generate representative solution in solution space. In this paper, a BBO variant is presented through embedding the QOX operator in BBO algorithm. Additionally, a modified migration equation is used to improve the population diversity. Several experiments are conducted on 23 benchmark functions. Experimental results show that the proposed algorithm is capable of locating the optimal or closed-to-optimal solution. Comparisons with other variants of BBO algorithms and state-of-the-art orthogonal-based evolutionary algorithms demonstrate that our proposed algorithm possesses faster global convergence rate, high-precision solution, and stronger robustness. Finally, the analysis result of the performance of QOX indicates that QOX plays a key role in the proposed algorithm.

  9. Virtual power plant mid-term dispatch optimization

    International Nuclear Information System (INIS)

    Pandžić, Hrvoje; Kuzle, Igor; Capuder, Tomislav

    2013-01-01

    Highlights: ► Mid-term virtual power plant dispatching. ► Linear modeling. ► Mixed-integer linear programming applied to mid-term dispatch scheduling. ► Operation profit maximization combining bilateral contracts and the day-ahead market. -- Abstract: Wind power plants incur practically zero marginal costs during their operation. However, variable and uncertain nature of wind results in significant problems when trying to satisfy the contracted quantities of delivered electricity. For this reason, wind power plants and other non-dispatchable power sources are combined with dispatchable power sources forming a virtual power plant. This paper considers a weekly self-scheduling of a virtual power plant composed of intermittent renewable sources, storage system and a conventional power plant. On the one hand, the virtual power plant needs to fulfill its long-term bilateral contracts, while, on the other hand, it acts in the market trying to maximize its overall profit. The optimal dispatch problem is formulated as a mixed-integer linear programming model which maximizes the weekly virtual power plant profit subject to the long-term bilateral contracts and technical constraints. The self-scheduling procedure is based on stochastic programming. The uncertainty of the wind power and solar power generation is settled by using pumped hydro storage in order to provide flexible operation, as well as by having a conventional power plant as a backup. The efficiency of the proposed model is rendered through a realistic case study and analysis of the results is provided. Additionally, the impact of different storage capacities and turbine/pump capacities of pumped storage are analyzed.

  10. Getting ahead communication skills for business English : learner's book

    CERN Document Server

    Jones-Macziola, Sarah

    1994-01-01

    Getting Ahead is a course for students at the pre-intermediate level who want to improve their English for business and professional purposes. It is suitable both for students who are preparing for work and those who are in employment already. At the same time as drawing on the learner's own experience, the course provides activities which give the less experienced learner the opportunity to participate effectively in meaningful communication. The main units are topic-based and focus on such themes as describing a company, welcoming visitors and dealing with problems. All four skills – listening, speaking, reading and writing – are developed. There are controlled practice tasks and meaningful communication activities, and the course as a whole provides a clear structural progression. The Home Study Book provides out-of-class activities which review and expand on what has been done in class. These activities are keyed at the back of the book to aid self-study. The Home Study CD contains all the listening a...

  11. Getting ahead communication skills for business English : teacher's guide

    CERN Document Server

    Jones-Macziola, Sarah

    1993-01-01

    Getting Ahead is a course for students at the pre-intermediate level who want to improve their English for business and professional purposes. It is suitable both for students who are preparing for work and those who are in employment already. At the same time as drawing on the learner's own experience, the course provides activities which give the less experienced learner the opportunity to participate effectively in meaningful communication. The main units are topic-based and focus on such themes as describing a company, welcoming visitors and dealing with problems. All four skills – listening, speaking, reading and writing – are developed. There are controlled practice tasks and meaningful communication activities, and the course as a whole provides a clear structural progression. The Home Study Book provides out-of-class activities which review and expand on what has been done in class. These activities are keyed at the back of the book to aid self-study. The Home Study CD contains all the listening a...

  12. CONNECTION OF TURN AHEAD AND TURN BACK WITH MOTORIC ABILITIES OF THE FIFTH GRADE STUDENTS

    Directory of Open Access Journals (Sweden)

    Jovica Petković

    2006-06-01

    Full Text Available The research is done for the purpose of determination and defining of the level of connection between some motoric abilities with success in realization of programmed contents from the area of gymnastics (turn ahead and turn back. The research is done on the sample of fifty one students from the fifth grade of Elementary School, on ten motoric tests and on two specific motoric assignments – turn ahead and turn back. The results of this research clearly point that there exist the multitude of statistically important coefficients of correlation between treated motoric abilities and applied motoric assignments.

  13. Segment-based dose optimization using a genetic algorithm

    International Nuclear Information System (INIS)

    Cotrutz, Cristian; Xing Lei

    2003-01-01

    Intensity modulated radiation therapy (IMRT) inverse planning is conventionally done in two steps. Firstly, the intensity maps of the treatment beams are optimized using a dose optimization algorithm. Each of them is then decomposed into a number of segments using a leaf-sequencing algorithm for delivery. An alternative approach is to pre-assign a fixed number of field apertures and optimize directly the shapes and weights of the apertures. While the latter approach has the advantage of eliminating the leaf-sequencing step, the optimization of aperture shapes is less straightforward than that of beamlet-based optimization because of the complex dependence of the dose on the field shapes, and their weights. In this work we report a genetic algorithm for segment-based optimization. Different from a gradient iterative approach or simulated annealing, the algorithm finds the optimum solution from a population of candidate plans. In this technique, each solution is encoded using three chromosomes: one for the position of the left-bank leaves of each segment, the second for the position of the right-bank and the third for the weights of the segments defined by the first two chromosomes. The convergence towards the optimum is realized by crossover and mutation operators that ensure proper exchange of information between the three chromosomes of all the solutions in the population. The algorithm is applied to a phantom and a prostate case and the results are compared with those obtained using beamlet-based optimization. The main conclusion drawn from this study is that the genetic optimization of segment shapes and weights can produce highly conformal dose distribution. In addition, our study also confirms previous findings that fewer segments are generally needed to generate plans that are comparable with the plans obtained using beamlet-based optimization. Thus the technique may have useful applications in facilitating IMRT treatment planning

  14. Real-time integration of optimal generation scheduling with MPC for the energy management of a renewable hydrogen-based microgrid

    International Nuclear Information System (INIS)

    Petrollese, Mario; Valverde, Luis; Cocco, Daniele; Cau, Giorgio; Guerra, José

    2016-01-01

    Highlights: • Energy management strategy for a renewable hydrogen-based microgrid. • Integration of optimal generation scheduling with a model predictive control. • Experimental tests are carried out simulating typical summer and winter days. • Effective improvement in performance and reduction in microgrid operating cost are achieved. - Abstract: This paper presents a novel control strategy for the optimal management of microgrids with high penetration of renewable energy sources and different energy storage systems. The control strategy is based on the integration of optimal generation scheduling with a model predictive control in order to achieve both long and short-term optimal planning. In particular, long-term optimization of the various microgrid components is obtained by the adoption of an optimal generation scheduling, in which a statistical approach is used to take into account weather and load forecasting uncertainties. The real-time management of the microgrid is instead entrusted to a model predictive controller, which has the important feature of using the results obtained by the optimal generation scheduling. The proposed control strategy was tested in a laboratory-scale microgrid present at the University of Seville, which is composed of an electronic power source that emulates a photovoltaic system, a battery bank and a hydrogen production and storage system. Two different experimental tests that simulate a summer and a winter day were carried out over a 24-h period to verify the reliability and performance enhancement of the control system. Results show an effective improvement in performance in terms of reduction of the microgrid operating cost and greater involvement of the hydrogen storage system for the maintenance of a spinning reserve in batteries.

  15. Into the new electricity age with Optimal integration of decentralized energy resources - The FENIX Project

    Energy Technology Data Exchange (ETDEWEB)

    Cech, Heinz; Fuchs, Erich; Heher, Anton; Ilo, Albana; Sezi, Tevfik; Trimmel, Johann; Werner, Thomas; Marti-Rodriguez, Juan

    2010-09-15

    Decentralized Energy Resources (DERs) will play a significant role in future energy scenarios. Today, the 'plug and forget' connection principle for renewable energy resources has the goal to maximize the active power transfer, without using their real capabilities. Other DERs based on fossil fuels are only activated in emergency situations. This paper describes the results of a demonstration project, where DERs installed in a large distribution area are utilized for participating in the day ahead energy market, frequency and voltage support for the transmission system, voltage support at specific distribution locations and feeders, and stability support in emergency situations.

  16. A coordinated dispatch model for electricity and heat in a Microgrid via particle swarm optimization

    DEFF Research Database (Denmark)

    Xu, Lizhong; Yang, Guangya; Xu, Zhao

    2013-01-01

    This paper develops a coordinated electricity and heat dispatching model for Microgrid under day-ahead environment. In addition to operational constraints, network loss and physical limits are addressed in this model, which are always ignored in previous work. As an important component of Microgrid...

  17. Optimal design of the heat pipe using TLBO (teaching–learning-based optimization) algorithm

    International Nuclear Information System (INIS)

    Rao, R.V.; More, K.C.

    2015-01-01

    Heat pipe is a highly efficient and reliable heat transfer component. It is a closed container designed to transfer a large amount of heat in system. Since the heat pipe operates on a closed two-phase cycle, the heat transfer capacity is greater than for solid conductors. Also, the thermal response time is less than with solid conductors. The three major elemental parts of the rotating heat pipe are: a cylindrical evaporator, a truncated cone condenser, and a fixed amount of working fluid. In this paper, a recently proposed new stochastic advanced optimization algorithm called TLBO (Teaching–Learning-Based Optimization) algorithm is used for single objective as well as multi-objective design optimization of heat pipe. It is easy to implement, does not make use of derivatives and it can be applied to unconstrained or constrained problems. Two examples of heat pipe are presented in this paper. The results of application of TLBO algorithm for the design optimization of heat pipe are compared with the NPGA (Niched Pareto Genetic Algorithm), GEM (Grenade Explosion Method) and GEO (Generalized External optimization). It is found that the TLBO algorithm has produced better results as compared to those obtained by using NPGA, GEM and GEO algorithms. - Highlights: • The TLBO (Teaching–Learning-Based Optimization) algorithm is used for the design and optimization of a heat pipe. • Two examples of heat pipe design and optimization are presented. • The TLBO algorithm is proved better than the other optimization algorithms in terms of results and the convergence

  18. Hierarchical Energy Management of Microgrids including Storage and Demand Response

    Directory of Open Access Journals (Sweden)

    Songli Fan

    2018-05-01

    Full Text Available Battery energy storage (BES and demand response (DR are considered to be promising technologies to cope with the uncertainty of renewable energy sources (RES and the load in the microgrid (MG. Considering the distinct prediction accuracies of the RES and load at different timescales, it is essential to incorporate the multi-timescale characteristics of BES and DR in MG energy management. Under this background, a hierarchical energy management framework is put forward for an MG including multi-timescale BES and DR to optimize operation with the uncertainty of RES as well as load. This framework comprises three stages of scheduling: day-ahead scheduling (DAS, hour-ahead scheduling (HAS, and real-time scheduling (RTS. In DAS, a scenario-based stochastic optimization model is established to minimize the expected operating cost of MG, while ensuring its safe operation. The HAS is utilized to bridge DAS and RTS. In RTS, a control strategy is proposed to eliminate the imbalanced power owing to the fluctuations of RES and load. Then, a decomposition-based algorithm is adopted to settle the models in DAS and HAS. Simulation results on a seven-bus MG validate the effectiveness of the proposed methodology.

  19. Characteristics of high-latitude precursor flows ahead of dipolarization fronts

    Science.gov (United States)

    Li, Jia-Zheng; Zhou, Xu-Zhi; Runov, Andrei; Angelopoulos, Vassilis; Liu, Jiang; Pan, Dong-Xiao; Zong, Qiu-Gang

    2017-05-01

    Dipolarization fronts (DFs), earthward propagating structures in the magnetotail current sheet characterized by sharp enhancements of northward magnetic field, are capable of converting electromagnetic energy into particle kinetic energy. The ions previously accelerated and reflected at the DFs can contribute to plasma flows ahead of the fronts, which have been identified as DF precursor flows in both the near-equatorial plasma sheet and far from it, near the plasma sheet boundary. Using observations from the THEMIS (Time History of Events and Macroscale Interactions during Substorms) spacecraft, we show that the earthward particle and energy flux enhancements ahead of DFs are statistically larger farther away from the neutral sheet (at high latitudes) than in the near-equatorial region. High-latitude particle and energy fluxes on the DF dawnside are found to be significantly greater than those on the duskside, which is opposite to the dawn-dusk asymmetries previously found near the equatorial region. Using forward and backward tracing test-particle simulations, we then explain and reproduce the observed latitude-dependent characteristics of DF precursor flows, providing a better understanding of ion dynamics associated with dipolarization fronts.

  20. Career perspective: Alf O. Brubakk-looking back to see ahead.

    Science.gov (United States)

    Brubakk, Alf O

    2015-01-01

    The following describes my professional life up till today, but it also describes what I think lies ahead. I have led an interesting professional life and been lucky enough to be at the centre of some of the important development in modern medicine and diving, namely ultrasound in cardiology and the mechanisms of decompression. I therefore should be able to see some of the most challenging and exciting problems ahead. Ultrasound in cardiology has developed from simply listening to the Doppler signal to determine the velocity of blood flow to the complicated description of images presented today. Diving, in addition to being an important commercial and environmental activity, exposes the individual to intermittent hyperoxia and pressure reductions. These challenges evoke the production of radical oxygen species (ROS) and microparticles (MP) that also are central to many pathophysiologic mechanisms that are involved in a number of severe human diseases. Thus, diving can be regarded as an important model of disease and allows us to study their effects on healthy young individuals. The future thus points towards an integration of environmental physiology with detailed physiological and pathophysiological mechanisms and makes diving physiology a potentially very important field of study.

  1. Keeping a Step Ahead: formative phase of a workplace intervention trial to prevent obesity.

    Science.gov (United States)

    Zapka, Jane; Lemon, Stephenie C; Estabrook, Barbara B; Jolicoeur, Denise G

    2007-11-01

    Ecological interventions hold promise for promoting overweight and obesity prevention in worksites. Given the paucity of evaluative research in the hospital worksite setting, considerable formative work is required for successful implementation and evaluation. This paper describes the formative phases of Step Ahead, a site-randomized controlled trial of a multilevel intervention that promotes physical activity and healthy eating in six hospitals in central Massachusetts. The purpose of the formative research phase was to increase the feasibility, effectiveness, and likelihood of sustainability of the intervention. The Step Ahead ecological intervention approach targets change at the organization, interpersonal work environment, and individual levels. The intervention was developed using fundamental steps of intervention mapping and important tenets of participatory research. Formative research methods were used to engage leadership support and assistance and to develop an intervention plan that is both theoretically and practically grounded. This report uses observational data, program minutes and reports, and process tracking data. Leadership involvement (key informant interviews and advisory boards), employee focus groups and advisory boards, and quantitative environmental assessments cultivated participation and support. Determining multiple foci of change and designing measurable objectives and generic assessment tools to document progress are complex challenges encountered in planning phases. Multilevel trials in diverse organizations require flexibility and balance of theory application and practice-based perspectives to affect impact and outcome objectives. Formative research is an essential component.

  2. Optimal control, investment and utilization schemes for energy storage under uncertainty

    Science.gov (United States)

    Mirhosseini, Niloufar Sadat

    Energy storage has the potential to offer new means for added flexibility on the electricity systems. This flexibility can be used in a number of ways, including adding value towards asset management, power quality and reliability, integration of renewable resources and energy bill savings for the end users. However, uncertainty about system states and volatility in system dynamics can complicate the question of when to invest in energy storage and how best to manage and utilize it. This work proposes models to address different problems associated with energy storage within a microgrid, including optimal control, investment, and utilization. Electric load, renewable resources output, storage technology cost and electricity day-ahead and spot prices are the factors that bring uncertainty to the problem. A number of analytical methodologies have been adopted to develop the aforementioned models. Model Predictive Control and discretized dynamic programming, along with a new decomposition algorithm are used to develop optimal control schemes for energy storage for two different levels of renewable penetration. Real option theory and Monte Carlo simulation, coupled with an optimal control approach, are used to obtain optimal incremental investment decisions, considering multiple sources of uncertainty. Two stage stochastic programming is used to develop a novel and holistic methodology, including utilization of energy storage within a microgrid, in order to optimally interact with energy market. Energy storage can contribute in terms of value generation and risk reduction for the microgrid. The integration of the models developed here are the basis for a framework which extends from long term investments in storage capacity to short term operational control (charge/discharge) of storage within a microgrid. In particular, the following practical goals are achieved: (i) optimal investment on storage capacity over time to maximize savings during normal and emergency

  3. Optimization-based topology identification of complex networks

    International Nuclear Information System (INIS)

    Tang Sheng-Xue; Chen Li; He Yi-Gang

    2011-01-01

    In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization-based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method. (general)

  4. A Novel Optimal Control Method for Impulsive-Correction Projectile Based on Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Ruisheng Sun

    2016-01-01

    Full Text Available This paper presents a new parametric optimization approach based on a modified particle swarm optimization (PSO to design a class of impulsive-correction projectiles with discrete, flexible-time interval, and finite-energy control. In terms of optimal control theory, the task is described as the formulation of minimum working number of impulses and minimum control error, which involves reference model linearization, boundary conditions, and discontinuous objective function. These result in difficulties in finding the global optimum solution by directly utilizing any other optimization approaches, for example, Hp-adaptive pseudospectral method. Consequently, PSO mechanism is employed for optimal setting of impulsive control by considering the time intervals between two neighboring lateral impulses as design variables, which makes the briefness of the optimization process. A modification on basic PSO algorithm is developed to improve the convergence speed of this optimization through linearly decreasing the inertial weight. In addition, a suboptimal control and guidance law based on PSO technique are put forward for the real-time consideration of the online design in practice. Finally, a simulation case coupled with a nonlinear flight dynamic model is applied to validate the modified PSO control algorithm. The results of comparative study illustrate that the proposed optimal control algorithm has a good performance in obtaining the optimal control efficiently and accurately and provides a reference approach to handling such impulsive-correction problem.

  5. Conducting an acute intense interval exercise session during the Ramadan fasting month: what is the optimal time of the day?

    Science.gov (United States)

    Aziz, Abdul Rashid; Chia, Michael Yong Hwa; Low, Chee Yong; Slater, Gary John; Png, Weileen; Teh, Kong Chuan

    2012-10-01

    This study examines the effects of Ramadan fasting on performance during an intense exercise session performed at three different times of the day, i.e., 08:00, 18:00, and 21:00 h. The purpose was to determine the optimal time of the day to perform an acute high-intensity interval exercise during the Ramadan fasting month. After familiarization, nine trained athletes performed six 30-s Wingate anaerobic test (WAnT) cycle bouts followed by a time-to-exhaustion (T(exh)) cycle on six separate randomized and counterbalanced occasions. The three time-of-day nonfasting (control, CON) exercise sessions were performed before the Ramadan month, and the three corresponding time-of-day Ramadan fasting (RAM) exercise sessions were performed during the Ramadan month. Note that the 21:00 h session during Ramadan month was conducted in the nonfasted state after the breaking of the day's fast. Total work (TW) completed during the six WAnT bouts was significantly lower during RAM compared to CON for the 08:00 and 18:00 h (p effect size [d] = .55 [small] and .39 [small], respectively) sessions, but not for the 21:00 h (p = .03, d = .18 [trivial]) session. The T(exh) cycle duration was significantly shorter during RAM than CON in the 18:00 (p Ramadan fasting had a small to moderate, negative impact on quality of performance during an acute high-intensity exercise session, particularly during the period of the daytime fast. The optimal time to conduct an acute high-intensity exercise session during the Ramadan fasting month is in the evening, after the breaking of the day's fast.

  6. Economic-environmental active and reactive power scheduling of modern distribution systems in presence of wind generations: A distribution market-based approach

    International Nuclear Information System (INIS)

    Samimi, Abouzar; Kazemi, Ahad; Siano, Pierluigi

    2015-01-01

    Highlights: • A new market-based approach is proposed to schedule active and reactive powers. • Multi-component reactive power bidding structures for DERs is introduced. • A new economical/environmental operational scheduling method is proposed. • At distribution level, a reactive power market is developed in presence of DERs. - Abstract: Distribution System Operator (DSO) is responsible for active and reactive power scheduling in a distribution system. DSO purchases its active and reactive power requirements from Distributed Energy Resources (DERs) as well as the wholesale electricity market. In this paper, a new economical/environmental operational scheduling method based on sequential day-ahead active and reactive power markets at distribution level is proposed to dispatch active and reactive powers in distribution systems with high penetration of DERs. In the proposed model, after day-ahead active power market was cleared the participants submit their reactive power bids and then the reactive power market will be settled. At distribution level, developing a Var market, in which DERs like synchronous machine-based Distributed Generation (DG) units and Wind Turbines (WTs) could offer their reactive power prices, DERs are motivated to actively participate in the Volt/VAr Control (VVC) problem. To achieve this purpose, based on the capability curves of considered DERs, innovative multi-component reactive power bidding structures for DERs are introduced. Moreover, the effect of reactive power market clearing on the active power scheduling is explicitly considered into the proposed model by rescheduling of active power by usage of energy-balance service bids. On the other hand, environmental concerns that arise from the operation of fossil fuel fired electric generators are included in the proposed model by employing CO_2 emission penalty cost. The suggested reactive power market is cleared through a mixed-integer nonlinear optimization program. The

  7. New secure bilateral transaction determination and study of pattern under contingencies and UPFC in competitive hybrid electricity markets

    International Nuclear Information System (INIS)

    Kumar, A.; Chanana, S.

    2009-01-01

    In the competitive electricity environment, the flexibility of power transactions is expected to drastically increase among the trading partners and can compromise the system security and reliability. These transactions are to be evaluated ahead of their scheduling in a day-ahead and hour-ahead market to avoid congestion and ensure their feasibility with respect to the system operating conditions. The security of the transactions has become essential in the new environment for better planning and management of competitive electricity markets. This paper proposes a new method of secure bilateral transaction determination using AC distribution factors based on the full Jacobian sensitivity and considering the impact of slack bus for pool and bilateral coordinated markets. The secure bilateral transactions have also been determined considering critical line outage contingencies cases. The bilateral transaction matrix pattern has also been determined in the presence of unified power flow controller (UPFC). The optimal location of UPFC has been determined using mixed integer non-linear programming approach. The proposed technique has been applied on IEEE 24-bus reliability test system (RTS). (author)

  8. Design Optimization of Mechanical Components Using an Enhanced Teaching-Learning Based Optimization Algorithm with Differential Operator

    Directory of Open Access Journals (Sweden)

    B. Thamaraikannan

    2014-01-01

    Full Text Available This paper studies in detail the background and implementation of a teaching-learning based optimization (TLBO algorithm with differential operator for optimization task of a few mechanical components, which are essential for most of the mechanical engineering applications. Like most of the other heuristic techniques, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. A differential operator is incorporated into the TLBO for effective search of better solutions. To validate the effectiveness of the proposed method, three typical optimization problems are considered in this research: firstly, to optimize the weight in a belt-pulley drive, secondly, to optimize the volume in a closed coil helical spring, and finally to optimize the weight in a hollow shaft. have been demonstrated. Simulation result on the optimization (mechanical components problems reveals the ability of the proposed methodology to find better optimal solutions compared to other optimization algorithms.

  9. Bi-objective optimization for multi-modal transportation routing planning problem based on Pareto optimality

    Directory of Open Access Journals (Sweden)

    Yan Sun

    2015-09-01

    Full Text Available Purpose: The purpose of study is to solve the multi-modal transportation routing planning problem that aims to select an optimal route to move a consignment of goods from its origin to its destination through the multi-modal transportation network. And the optimization is from two viewpoints including cost and time. Design/methodology/approach: In this study, a bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. Minimizing the total transportation cost and the total transportation time are set as the optimization objectives of the model. In order to balance the benefit between the two objectives, Pareto optimality is utilized to solve the model by gaining its Pareto frontier. The Pareto frontier of the model can provide the multi-modal transportation operator (MTO and customers with better decision support and it is gained by the normalized normal constraint method. Then, an experimental case study is designed to verify the feasibility of the model and Pareto optimality by using the mathematical programming software Lingo. Finally, the sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case. Findings: The calculation results indicate that the proposed model and Pareto optimality have good performance in dealing with the bi-objective optimization. The sensitivity analysis also shows the influence of the variation of the demand and supply on the multi-modal transportation organization clearly. Therefore, this method can be further promoted to the practice. Originality/value: A bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. The Pareto frontier based sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case.

  10. Reliability-Based Optimization of Series Systems of Parallel Systems

    DEFF Research Database (Denmark)

    Enevoldsen, I.; Sørensen, John Dalsgaard

    1993-01-01

    Reliability-based design of structural systems is considered. In particular, systems where the reliability model is a series system of parallel systems are treated. A sensitivity analysis for this class of problems is presented. Optimization problems with series systems of parallel systems...... optimization of series systems of parallel systems, but it is also efficient in reliability-based optimization of series systems in general....

  11. Commercial Optimization of a 100 kg/day PEM based Hydrogen Generator For Energy and Industrial Applications

    International Nuclear Information System (INIS)

    Moulthrop, L.; Anderson, E.; Chow, O.; Friedland, R.; Maloney, T.; Schiller, M.

    2006-01-01

    Commercial hydrogen generators using PEM water electrolysis are well proven, serving industrial applications worldwide in over 50 countries. Now, market and environmental requirements are converging to demand larger on-site hydrogen generators. North American liquid H 2 shortages, increasing trucking costs, developing economies with no liquid infrastructure, utilities, and forklift fuel cell fueling applications are all working to increase market demand for commercial on-site H 2 generation. These commercial applications may be satisfied by a 100 kg H 2 /day module; this platform can be the pathway towards a 500 kg H 2 /day generator desired for small fore-court hydrogen vehicle fueling stations. This paper discusses the steps necessary and activities already underway to develop a 100 to 500 kg H 2 /day PEM hydrogen generator platform to meet commercial market cost targets and approach US DoE transportation fueling cost targets. (authors)

  12. Wind offering in energy and reserve markets

    Science.gov (United States)

    Soares, T.; Pinson, P.; Morais, H.

    2016-09-01

    The increasing penetration of wind generation in power systems to fulfil the ambitious European targets will make wind power producers to play an even more important role in the future power system. Wind power producers are being incentivized to participate in reserve markets to increase their revenue, since currently wind turbine/farm technologies allow them to provide ancillary services. Thus, wind power producers are to develop offering strategies for participation in both energy and reserve markets, accounting for market rules, while ensuring optimal revenue. We consider a proportional offering strategy to optimally decide upon participation in both markets by maximizing expected revenue from day-ahead decisions while accounting for estimated regulation costs for failing to provide the services. An evaluation of considering the same proportional splitting of energy and reserve in both day- ahead and balancing market is performed. A set of numerical examples illustrate the behavior of such strategy. An important conclusion is that the optimal split of the available wind power between energy and reserve strongly depends upon prices and penalties on both market trading floors.

  13. Strategic wind power trading considering rival wind power production

    DEFF Research Database (Denmark)

    Exizidis, Lazaros; Kazempour, Jalal; Pinson, Pierre

    2016-01-01

    In an electricity market with high share of wind power, it is expected that wind power producers may exercise market power. However, wind producers have to cope with wind’s uncertain nature in order to optimally offer their generation, whereas in a market with more than one wind producers, uncert...... depending on the rival’s wind generation, given that its own expected generation is not high. Finally, as anticipated, expected system cost is higher when both wind power producers are expected to have low wind power generation......In an electricity market with high share of wind power, it is expected that wind power producers may exercise market power. However, wind producers have to cope with wind’s uncertain nature in order to optimally offer their generation, whereas in a market with more than one wind producers......, uncertainty of rival wind power generation should also be considered. Under this context, this paper addresses the impact of rival wind producers on the offering strategy and profits of a pricemaker wind producer. A stochastic day-ahead market setup is considered, which optimizes the day-ahead schedules...

  14. A Framework for Constrained Optimization Problems Based on a Modified Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Biwei Tang

    2016-01-01

    Full Text Available This paper develops a particle swarm optimization (PSO based framework for constrained optimization problems (COPs. Aiming at enhancing the performance of PSO, a modified PSO algorithm, named SASPSO 2011, is proposed by adding a newly developed self-adaptive strategy to the standard particle swarm optimization 2011 (SPSO 2011 algorithm. Since the convergence of PSO is of great importance and significantly influences the performance of PSO, this paper first theoretically investigates the convergence of SASPSO 2011. Then, a parameter selection principle guaranteeing the convergence of SASPSO 2011 is provided. Subsequently, a SASPSO 2011-based framework is established to solve COPs. Attempting to increase the diversity of solutions and decrease optimization difficulties, the adaptive relaxation method, which is combined with the feasibility-based rule, is applied to handle constraints of COPs and evaluate candidate solutions in the developed framework. Finally, the proposed method is verified through 4 benchmark test functions and 2 real-world engineering problems against six PSO variants and some well-known methods proposed in the literature. Simulation results confirm that the proposed method is highly competitive in terms of the solution quality and can be considered as a vital alternative to solve COPs.

  15. Optimization of DNA Sensor Model Based Nanostructured Graphene Using Particle Swarm Optimization Technique

    Directory of Open Access Journals (Sweden)

    Hediyeh Karimi

    2013-01-01

    Full Text Available It has been predicted that the nanomaterials of graphene will be among the candidate materials for postsilicon electronics due to their astonishing properties such as high carrier mobility, thermal conductivity, and biocompatibility. Graphene is a semimetal zero gap nanomaterial with demonstrated ability to be employed as an excellent candidate for DNA sensing. Graphene-based DNA sensors have been used to detect the DNA adsorption to examine a DNA concentration in an analyte solution. In particular, there is an essential need for developing the cost-effective DNA sensors holding the fact that it is suitable for the diagnosis of genetic or pathogenic diseases. In this paper, particle swarm optimization technique is employed to optimize the analytical model of a graphene-based DNA sensor which is used for electrical detection of DNA molecules. The results are reported for 5 different concentrations, covering a range from 0.01 nM to 500 nM. The comparison of the optimized model with the experimental data shows an accuracy of more than 95% which verifies that the optimized model is reliable for being used in any application of the graphene-based DNA sensor.

  16. A Data-driven Approach for Forecasting Next-day River Discharge

    Science.gov (United States)

    Sharif, H. O.; Billah, K. S.

    2017-12-01

    This study focuses on evaluating the performance of the Soil and Water Assessment Tool (SWAT) eco-hydrological model, a simple Auto-Regressive with eXogenous input (ARX) model, and a Gene expression programming (GEP)-based model in one-day-ahead forecasting of discharge of a subtropical basin (the upper Kentucky River Basin). The three models were calibrated with daily flow at the US Geological Survey (USGS) stream gauging station not affected by flow regulation for the period of 2002-2005. The calibrated models were then validated at the same gauging station as well as another USGS gauge 88 km downstream for the period of 2008-2010. The results suggest that simple models outperform a sophisticated hydrological model with GEP having the advantage of being able to generate functional relationships that allow scientific investigation of the complex nonlinear interrelationships among input variables. Unlike SWAT, GEP, and to some extent, ARX are less sensitive to the length of the calibration time series and do not require a spin-up period.

  17. Reliability-Based Structural Optimization of Wave Energy Converters

    DEFF Research Database (Denmark)

    Ambühl, Simon; Kramer, Morten; Sørensen, John Dalsgaard

    2014-01-01

    More and more wave energy converter (WEC) concepts are reaching prototype level. Once the prototype level is reached, the next step in order to further decrease the levelized cost of energy (LCOE) is optimizing the overall system with a focus on structural and maintenance (inspection) costs......, as well as on the harvested power from the waves. The target of a fully-developed WEC technology is not maximizing its power output, but minimizing the resulting LCOE. This paper presents a methodology to optimize the structural design of WECs based on a reliability-based optimization problem...

  18. Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique

    Directory of Open Access Journals (Sweden)

    Po-Chen Cheng

    2015-06-01

    Full Text Available In this paper, an asymmetrical fuzzy-logic-control (FLC-based maximum power point tracking (MPPT algorithm for photovoltaic (PV systems is presented. Two membership function (MF design methodologies that can improve the effectiveness of the proposed asymmetrical FLC-based MPPT methods are then proposed. The first method can quickly determine the input MF setting values via the power–voltage (P–V curve of solar cells under standard test conditions (STC. The second method uses the particle swarm optimization (PSO technique to optimize the input MF setting values. Because the PSO approach must target and optimize a cost function, a cost function design methodology that meets the performance requirements of practical photovoltaic generation systems (PGSs is also proposed. According to the simulated and experimental results, the proposed asymmetrical FLC-based MPPT method has the highest fitness value, therefore, it can successfully address the tracking speed/tracking accuracy dilemma compared with the traditional perturb and observe (P&O and symmetrical FLC-based MPPT algorithms. Compared to the conventional FLC-based MPPT method, the obtained optimal asymmetrical FLC-based MPPT can improve the transient time and the MPPT tracking accuracy by 25.8% and 0.98% under STC, respectively.

  19. Waste disposal in Europe - Looking ahead

    International Nuclear Information System (INIS)

    Verkerk, B.

    1985-01-01

    In this introductory paper a short outline is given of the Commission's programme on management and disposal of radioactive waste, followed by a discussion of the programme structure. This leads to the very important aspect of evaluation of results obtained and the communication of the achievements to the outer world. The important role of the media in this respect is stressed. Looking ahead, an important part of the Third Five years programme, the development of demonstration facilities, is projected against the problem of acceptability. Thinking about the consequences of entering the demonstration stage with respect to future research it turns out to be a broad field of work opens up, when the achievements reached in the radioactive waste area, could be transferred to problems of other toxic wastes and fusion wastes

  20. NASA’s Universe of Learning: Girls STEAM Ahead

    Science.gov (United States)

    Marcucci, Emma; Meinke, Bonnie K.; Smith, Denise A.; Ryer, Holly; Slivinski, Carolyn; Kenney, Jessica; Arcand, Kimberly K.; Cominsky, Lynn R.; Girls STEAM Ahead with NASA Team

    2017-10-01

    NASA Science Mission Directorate’s Universe of Learning (UoL) program enables scientists and engineers to more effectively engage with learners of all ages. The Girls STEAM Ahead with NASA education program within UoL, expands upon the former program, NASA Science4Girls and Their Families, in celebration of National Women’s History Month. The initiative partners the NASA’s Universe of Learning science education program resources with public libraries to provide NASA-themed activities for girls and their families, including hands-on activities for engaging girls, complementary exhibits, and professional development for library partner staff. The science-institute-embedded partners in NASA’s UoL are uniquely poised to foster collaboration between scientists with content expertise and educators with pedagogy expertise. The thematic topics related to NASA Astrophysics enable audiences to experience the full range of NASA scientific and technical disciplines and the different career skills each requires. The events focus on engaging underserved and underrepresented audiences in Science, Technology, Engineering, and Mathematics (STEM) via use of research-based best practices, collaborations with libraries, partnerships with local and national organizations (e.g. National Girls Collaborative Project or NGCP), and remote engagement of audiences. This presentation will provide an overview of the program progress related to engaging girls and their families in NASA-based science programming.

  1. Enterprise Risk Management: The Way Ahead for DRDC within the DND Enterprise

    Science.gov (United States)

    2010-03-01

    Management – Integrated Framework, COSO Executive Summary, Sept 2004 2. Institute of Risk Management (IRM) Risk Management Standard, 2002 3...... Risk Management The Way Ahead for DRDC within the DND Enterprise R.G. Dickinson Dr. B.W. Taylor DRDC CORA Defence R&D Canada – CORA

  2. Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT

    International Nuclear Information System (INIS)

    Anbazhagan, S.; Kumarappan, N.

    2014-01-01

    Highlights: • We presented DCT input featured FFNN model for forecasting in Spain market. • The key factors impacting electricity price forecasting are historical prices. • Past 42 days were trained and the next 7 days were forecasted. • The proposed approach has a simple and better NN structure. • The DCT-FFNN mode is effective and less computation time than the recent models. - Abstract: In a deregulated market, a number of factors determined the outcome of electricity price and displays a perplexed and maverick fluctuation. Both power producers and consumers needs single compact and robust price forecasting tool in order to maximize their profits and utilities. In order to achieve the helter–skelter kind of electricity price, one dimensional discrete cosine transforms (DCT) input featured feed-forward neural network (FFNN) is modeled (DCT-FFNN). The proposed FFNN is a single compact and robust architecture (without hybridizing the various hard and soft computing models). It has been predicted that the DCT-FFNN model is close to the state of the art can be achieved with less computation time. The proposed DCT-FFNN approach is compared with 17 other recent approaches to estimate the market clearing prices of mainland Spain. Finally, the accuracy of the price forecasting is also applied to the electricity market of New York in year 2010 that shows the effectiveness of the proposed DCT-FFNN approach

  3. Reflecting on the Present and Looking Ahead: A Response to Shuler

    Science.gov (United States)

    Tobias, Evan S.

    2014-01-01

    In considering how policy work might forward arts education, it is helpful to reflect on the present state of music and arts education while looking ahead at future challenges and possibilities. This response to Shuler's (2001) set of predictions related to music education and policy in the twenty-first century addresses such work in the…

  4. A comparison of physically and radiobiologically based optimization for IMRT

    International Nuclear Information System (INIS)

    Jones, Lois; Hoban, Peter

    2002-01-01

    Many optimization techniques for intensity modulated radiotherapy have now been developed. The majority of these techniques including all the commercial systems that are available are based on physical dose methods of assessment. Some techniques have also been based on radiobiological models. None of the radiobiological optimization techniques however have assessed the clinically realistic situation of considering both tumor and normal cells within the target volume. This study considers a ratio-based fluence optimizing technique to compare a dose-based optimization method described previously and two biologically based models. The biologically based methods use the values of equivalent uniform dose calculated for the tumor cells and integral biological effective dose for normal cells. The first biologically based method includes only tumor cells in the target volume while the second considers both tumor and normal cells in the target volume. All three methods achieve good conformation to the target volume. The biologically based optimization without the normal tissue in the target volume shows a high dose region in the center of the target volume while this is reduced when the normal tissues are also considered in the target volume. This effect occurs because the normal tissues in the target volume require the optimization to reduce the dose and therefore limit the maximum dose to that volume

  5. Integration of wind generation forecasts. Volume 2

    International Nuclear Information System (INIS)

    Ahlstrom, M.; Zavadil, B.; Jones, L.

    2005-01-01

    WindLogics is a company that specializes in atmospheric modelling, visualization and fine-scale forecasting systems for the wind power industry. A background of the organization was presented. The complexities of wind modelling were discussed. Issues concerning location and terrain, shear, diurnal and interannual variability were reviewed. It was suggested that wind power producers should aim to be mainstream, and that variability should be considered as intrinsic to fuel supply. Various utility operating impacts were outlined. Details of an Xcel NSP wind integration study were presented, as well as a studies conducted in New York state and Colorado. It was concluded that regulations and load following impacts with wind energy integration are modest. Overall impacts are dominated by costs incurred to accommodate wind generation variability and uncertainty in the day-ahead time frame. Cost impacts can be reduced with adjustments to operating strategies, improvements in wind forecasting and access to real-time markets. Details of WindLogic's wind energy forecast system were presented, as well as examples of day ahead and hour ahead forecasts and wind speed and power forecasts. Screenshots of control room integration, EMS integration and simulations were presented. Details of a utility-scale wind energy forecasting system funded by Xcel Renewable Development Fund (RDF) were also presented. The goal of the system was to optimize the way that wind forecast information is integrated into the control room environment. Project components were outlined. It was concluded that accurate day-ahead forecasting can lead to significant asset optimization. It was recommended that wind plants share data, and aim to resolve issues concerning grid codes and instrumentation. refs., tabs., figs

  6. An optimized clarithromycin-free 14-day triple therapy for Helicobacter pylori eradication achieves high cure rates in Uruguay.

    Science.gov (United States)

    Dacoll, Cristina; Sánchez-Delgado, Jordi; Balter, Henia; Pazos, Ximena; Di Pace, María; Sandoya, Gabriela; Cohen, Henry; Calvet, Xavier

    Strong acid inhibition increases cure rates with triple therapy and 14-day are more effective than 7-day treatments. The combination of amoxicillin plus metronidazole at full doses has been shown to overcome metronidazole resistance and to achieve good eradication rates even in patients harboring resistant strains. No previous studies have been reported in Latin-America with this optimized triple-therapy scheme. The aim of the present study was to assess the eradication rate and tolerance of a new first-line treatment regimen associating strong acid inhibition, amoxicillin and metronidazole. Patients from the Clínica de Gastroenterología of the Hospital de Clínicas (Montevideo, Uruguay) were included. Hp status was mainly assessed by at least one of the following: histologyor urea breath test (UBT). A 14-day treatment was prescribed comprising esomeprazole 40mg twice a day plus amoxicillin 1g and metronidazole 500mg, both three times a day. H. pylori cure was assessed by UBT. Forty-one patients were enrolled. Mean age was 53.3±13 years and 17.1% of patients were male. Main indications for treatment were: functional dyspepsia (27.5%), gastritis (45%), gastric or duodenal erosions (20%), gastric ulcer (5%) and intestinal metaplasia (2.5%). H. pylori eradication was achieved in 33 of the 37 patients who returned for follow-up. Eradication rates were 80.5% (95% CI: 68.4-92.6) by intention-to-treat (ITT) analysis and 89.2% (95% CI; 79.2-99.2) per protocol (PP). No major side effects were reported; 26 patients (65.8%) complained of mild side effects (nausea, diarrhea and headache). Cure rates of this triple therapy including esomeprazole, amoxicillin and metronidazole were 81% per ITT and the treatment was well tolerated. These optimal results with a simple clarithromycin-free triple therapy are better than described for standard triple therapy but there is still room for improvement to reach the desired target of 90% per ITT. Copyright © 2017 Elsevier España, S

  7. Performance-based Pareto optimal design

    NARCIS (Netherlands)

    Sariyildiz, I.S.; Bittermann, M.S.; Ciftcioglu, O.

    2008-01-01

    A novel approach for performance-based design is presented, where Pareto optimality is pursued. Design requirements may contain linguistic information, which is difficult to bring into computation or make consistent their impartial estimations from case to case. Fuzzy logic and soft computing are

  8. An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems

    Directory of Open Access Journals (Sweden)

    Vivek Patel

    2012-08-01

    Full Text Available Nature inspired population based algorithms is a research field which simulates different natural phenomena to solve a wide range of problems. Researchers have proposed several algorithms considering different natural phenomena. Teaching-Learning-based optimization (TLBO is one of the recently proposed population based algorithm which simulates the teaching-learning process of the class room. This algorithm does not require any algorithm-specific control parameters. In this paper, elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated. The effects of common controlling parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 35 constrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. The proposed algorithm can be applied to various optimization problems of the industrial environment.

  9. Optimization of ACC system spacing policy on curved highway

    Science.gov (United States)

    Ma, Jun; Qian, Kun; Gong, Zaiyan

    2017-05-01

    The paper optimizes the original spacing policy when adopting VTH (Variable Time Headway), proposes to introduce the road curve curvature K to the spacing policy to cope with following the wrong vehicle or failing to follow the vehicle owing to the radar limitation of curve in ACC system. By utilizing MATLAB/Simulink, automobile longitudinal dynamics model is established. At last, the paper sets up such three common cases as the vehicle ahead runs at a uniform velocity, an accelerated velocity and hits the brake suddenly, simulates these cases on the curve with different curvature, analyzes the curve spacing policy in the perspective of safety and vehicle following efficiency and draws the conclusion whether the optimization scheme is effective or not.

  10. Wet tropospheric delays forecast based on Vienna Mapping Function time series analysis

    Science.gov (United States)

    Rzepecka, Zofia; Kalita, Jakub

    2016-04-01

    It is well known that the dry part of the zenith tropospheric delay (ZTD) is much easier to model than the wet part (ZTW). The aim of the research is applying stochastic modeling and prediction of ZTW using time series analysis tools. Application of time series analysis enables closer understanding of ZTW behavior as well as short-term prediction of future ZTW values. The ZTW data used for the studies were obtained from the GGOS service hold by Vienna technical University. The resolution of the data is six hours. ZTW for the years 2010 -2013 were adopted for the study. The International GNSS Service (IGS) permanent stations LAMA and GOPE, located in mid-latitudes, were admitted for the investigations. Initially the seasonal part was separated and modeled using periodic signals and frequency analysis. The prominent annual and semi-annual signals were removed using sines and consines functions. The autocorrelation of the resulting signal is significant for several days (20-30 samples). The residuals of this fitting were further analyzed and modeled with ARIMA processes. For both the stations optimal ARMA processes based on several criterions were obtained. On this basis predicted ZTW values were computed for one day ahead, leaving the white process residuals. Accuracy of the prediction can be estimated at about 3 cm.

  11. Design of SVC Controller Based on Improved Biogeography-Based Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Feifei Dong

    2014-01-01

    Full Text Available Considering that common subsynchronous resonance controllers cannot adapt to the characteristics of the time-varying and nonlinear behavior of a power system, the cosine migration model, the improved migration operator, and the mutative scale of chaos and Cauchy mutation strategy are introduced into an improved biogeography-based optimization (IBBO algorithm in order to design an optimal subsynchronous damping controller based on the mechanism of suppressing SSR by static var compensator (SVC. The effectiveness of the improved controller is verified by eigenvalue analysis and electromagnetic simulations. The simulation results of Jinjie plant indicate that the subsynchronous damping controller optimized by the IBBO algorithm can remarkably improve the damping of torsional modes and thus effectively depress SSR, and ensure the safety and stability of units and power grid operation. Moreover, the IBBO algorithm has the merits of a faster searching speed and higher searching accuracy in seeking the optimal control parameters over traditional algorithms, such as BBO algorithm, PSO algorithm, and GA algorithm.

  12. Getting ahead communication skills for business English : home study book

    CERN Document Server

    Jones-Macziola, Sarah

    1993-01-01

    Getting Ahead is a course for students at the pre-intermediate level who want to improve their English for business and professional purposes. It is suitable both for students who are preparing for work and those who are in employment already. At the same time as drawing on the learner's own experience, the course provides activities which give the less experienced learner the opportunity to participate effectively in meaningful communication. The main units are topic-based and focus on such themes as describing a company, welcoming visitors and dealing with problems. All four skills – listening, speaking, reading and writing – are developed. There are controlled practice tasks and meaningful communication activities, and the course as a whole provides a clear structural progression. The Home Study Book provides out-of-class activities which review and expand on what has been done in class. These activities are keyed at the back of the book to aid self-study. The Home Study CD contains all the listening a...

  13. 77 FR 19280 - Increasing Market and Planning Efficiency Through Improved Software; Notice of Technical...

    Science.gov (United States)

    2012-03-30

    ... concerns that current system data quality might not allow for an AC optimal power flow model to be properly... Market and Planning Efficiency Through Improved Software; Notice of Technical Conference: Increasing Real-Time and Day- Ahead Market Efficiency Through Improved Software Take notice that Commission staff will...

  14. Interactive Reliability-Based Optimization of Structural Systems

    DEFF Research Database (Denmark)

    Pedersen, Claus

    In order to introduce the basic concepts within the field of reliability-based structural optimization problems, this chapter is devoted to a brief outline of the basic theories. Therefore, this chapter is of a more formal nature and used as a basis for the remaining parts of the thesis. In section...... 2.2 a general non-linear optimization problem and corresponding terminology are presented whereupon optimality conditions and the standard form of an iterative optimization algorithm are outlined. Subsequently, the special properties and characteristics concerning structural optimization problems...... are treated in section 2.3. With respect to the reliability evalutation, the basic theory behind a reliability analysis and estimation of probability of failure by the First-Order Reliability Method (FORM) and the iterative Rackwitz-Fiessler (RF) algorithm are considered in section 2.5 in which...

  15. An integrated reliability-based design optimization of offshore towers

    International Nuclear Information System (INIS)

    Karadeniz, Halil; Togan, Vedat; Vrouwenvelder, Ton

    2009-01-01

    After recognizing the uncertainty in the parameters such as material, loading, geometry and so on in contrast with the conventional optimization, the reliability-based design optimization (RBDO) concept has become more meaningful to perform an economical design implementation, which includes a reliability analysis and an optimization algorithm. RBDO procedures include structural analysis, reliability analysis and sensitivity analysis both for optimization and for reliability. The efficiency of the RBDO system depends on the mentioned numerical algorithms. In this work, an integrated algorithms system is proposed to implement the RBDO of the offshore towers, which are subjected to the extreme wave loading. The numerical strategies interacting with each other to fulfill the RBDO of towers are as follows: (a) a structural analysis program, SAPOS, (b) an optimization program, SQP and (c) a reliability analysis program based on FORM. A demonstration of an example tripod tower under the reliability constraints based on limit states of the critical stress, buckling and the natural frequency is presented.

  16. An integrated reliability-based design optimization of offshore towers

    Energy Technology Data Exchange (ETDEWEB)

    Karadeniz, Halil [Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft (Netherlands)], E-mail: h.karadeniz@tudelft.nl; Togan, Vedat [Department of Civil Engineering, Karadeniz Technical University, Trabzon (Turkey); Vrouwenvelder, Ton [Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft (Netherlands)

    2009-10-15

    After recognizing the uncertainty in the parameters such as material, loading, geometry and so on in contrast with the conventional optimization, the reliability-based design optimization (RBDO) concept has become more meaningful to perform an economical design implementation, which includes a reliability analysis and an optimization algorithm. RBDO procedures include structural analysis, reliability analysis and sensitivity analysis both for optimization and for reliability. The efficiency of the RBDO system depends on the mentioned numerical algorithms. In this work, an integrated algorithms system is proposed to implement the RBDO of the offshore towers, which are subjected to the extreme wave loading. The numerical strategies interacting with each other to fulfill the RBDO of towers are as follows: (a) a structural analysis program, SAPOS, (b) an optimization program, SQP and (c) a reliability analysis program based on FORM. A demonstration of an example tripod tower under the reliability constraints based on limit states of the critical stress, buckling and the natural frequency is presented.

  17. Simulation-based optimization of thermal systems

    International Nuclear Information System (INIS)

    Jaluria, Yogesh

    2009-01-01

    This paper considers the design and optimization of thermal systems on the basis of the mathematical and numerical modeling of the system. Many complexities are often encountered in practical thermal processes and systems, making the modeling challenging and involved. These include property variations, complicated regions, combined transport mechanisms, chemical reactions, and intricate boundary conditions. The paper briefly presents approaches that may be used to accurately simulate these systems. Validation of the numerical model is a particularly critical aspect and is discussed. It is important to couple the modeling with the system performance, design, control and optimization. This aspect, which has often been ignored in the literature, is considered in this paper. Design of thermal systems based on concurrent simulation and experimentation is also discussed in terms of dynamic data-driven optimization methods. Optimization of the system and of the operating conditions is needed to minimize costs and improve product quality and system performance. Different optimization strategies that are currently used for thermal systems are outlined, focusing on new and emerging strategies. Of particular interest is multi-objective optimization, since most thermal systems involve several important objective functions, such as heat transfer rate and pressure in electronic cooling systems. A few practical thermal systems are considered in greater detail to illustrate these approaches and to present typical simulation, design and optimization results

  18. High-resolution temperature-based optimization for hyperthermia treatment planning

    International Nuclear Information System (INIS)

    Kok, H P; Haaren, P M A van; Kamer, J B Van de; Wiersma, J; Dijk, J D P Van; Crezee, J

    2005-01-01

    In regional hyperthermia, optimization techniques are valuable in order to obtain amplitude/phase settings for the applicators to achieve maximal tumour heating without toxicity to normal tissue. We implemented a temperature-based optimization technique and maximized tumour temperature with constraints on normal tissue temperature to prevent hot spots. E-field distributions are the primary input for the optimization method. Due to computer limitations we are restricted to a resolution of 1 x 1 x 1 cm 3 for E-field calculations, too low for reliable treatment planning. A major problem is the fact that hot spots at low-resolution (LR) do not always correspond to hot spots at high-resolution (HR), and vice versa. Thus, HR temperature-based optimization is necessary for adequate treatment planning and satisfactory results cannot be obtained with LR strategies. To obtain HR power density (PD) distributions from LR E-field calculations, a quasi-static zooming technique has been developed earlier at the UMC Utrecht. However, quasi-static zooming does not preserve phase information and therefore it does not provide the HR E-field information required for direct HR optimization. We combined quasi-static zooming with the optimization method to obtain a millimetre resolution temperature-based optimization strategy. First we performed a LR (1 cm) optimization and used the obtained settings to calculate the HR (2 mm) PD and corresponding HR temperature distribution. Next, we performed a HR optimization using an estimation of the new HR temperature distribution based on previous calculations. This estimation is based on the assumption that the HR and LR temperature distributions, though strongly different, respond in a similar way to amplitude/phase steering. To verify the newly obtained settings, we calculate the corresponding HR temperature distribution. This method was applied to several clinical situations and found to work very well. Deviations of this estimation method for

  19. GA based CNC turning center exploitation process parameters optimization

    Directory of Open Access Journals (Sweden)

    Z. Car

    2009-01-01

    Full Text Available This paper presents machining parameters (turning process optimization based on the use of artificial intelligence. To obtain greater efficiency and productivity of the machine tool, optimal cutting parameters have to be obtained. In order to find optimal cutting parameters, the genetic algorithm (GA has been used as an optimal solution finder. Optimization has to yield minimum machining time and minimum production cost, while considering technological and material constrains.

  20. Process optimization of friction stir welding based on thermal models

    DEFF Research Database (Denmark)

    Larsen, Anders Astrup

    2010-01-01

    This thesis investigates how to apply optimization methods to numerical models of a friction stir welding process. The work is intended as a proof-of-concept using different methods that are applicable to models of high complexity, possibly with high computational cost, and without the possibility...... information of the high-fidelity model. The optimization schemes are applied to stationary thermal models of differing complexity of the friction stir welding process. The optimization problems considered are based on optimizing the temperature field in the workpiece by finding optimal translational speed....... Also an optimization problem based on a microstructure model is solved, allowing the hardness distribution in the plate to be optimized. The use of purely thermal models represents a simplification of the real process; nonetheless, it shows the applicability of the optimization methods considered...

  1. OPF-Based Optimal Location of Two Systems Two Terminal HVDC to Power System Optimal Operation

    Directory of Open Access Journals (Sweden)

    Mehdi Abolfazli

    2013-04-01

    Full Text Available In this paper a suitable mathematical model of the two terminal HVDC system is provided for optimal power flow (OPF and optimal location based on OPF such power injection model. The ability of voltage source converter (VSC-based HVDC to independently control active and reactive power is well represented by the model. The model is used to develop an OPF-based optimal location algorithm of two systems two terminal HVDC to minimize the total fuel cost and active power losses as objective function. The optimization framework is modeled as non-linear programming (NLP and solved by Matlab and GAMS softwares. The proposed algorithm is implemented on the IEEE 14- and 30-bus test systems. The simulation results show ability of two systems two terminal HVDC in improving the power system operation. Furthermore, two systems two terminal HVDC is compared by PST and OUPFC in the power system operation from economical and technical aspects.

  2. Novel Verification Method for Timing Optimization Based on DPSO

    Directory of Open Access Journals (Sweden)

    Chuandong Chen

    2018-01-01

    Full Text Available Timing optimization for logic circuits is one of the key steps in logic synthesis. Extant research data are mainly proposed based on various intelligence algorithms. Hence, they are neither comparable with timing optimization data collected by the mainstream electronic design automation (EDA tool nor able to verify the superiority of intelligence algorithms to the EDA tool in terms of optimization ability. To address these shortcomings, a novel verification method is proposed in this study. First, a discrete particle swarm optimization (DPSO algorithm was applied to optimize the timing of the mixed polarity Reed-Muller (MPRM logic circuit. Second, the Design Compiler (DC algorithm was used to optimize the timing of the same MPRM logic circuit through special settings and constraints. Finally, the timing optimization results of the two algorithms were compared based on MCNC benchmark circuits. The timing optimization results obtained using DPSO are compared with those obtained from DC, and DPSO demonstrates an average reduction of 9.7% in the timing delays of critical paths for a number of MCNC benchmark circuits. The proposed verification method directly ascertains whether the intelligence algorithm has a better timing optimization ability than DC.

  3. Reliability-based performance simulation for optimized pavement maintenance

    International Nuclear Information System (INIS)

    Chou, Jui-Sheng; Le, Thanh-Son

    2011-01-01

    Roadway pavement maintenance is essential for driver safety and highway infrastructure efficiency. However, regular preventive maintenance and rehabilitation (M and R) activities are extremely costly. Unfortunately, the funds available for the M and R of highway pavement are often given lower priority compared to other national development policies, therefore, available funds must be allocated wisely. Maintenance strategies are typically implemented by optimizing only the cost whilst the reliability of facility performance is neglected. This study proposes a novel algorithm using multi-objective particle swarm optimization (MOPSO) technique to evaluate the cost-reliability tradeoff in a flexible maintenance strategy based on non-dominant solutions. Moreover, a probabilistic model for regression parameters is employed to assess reliability-based performance. A numerical example of a highway pavement project is illustrated to demonstrate the efficacy of the proposed MOPSO algorithms. The analytical results show that the proposed approach can help decision makers to optimize roadway maintenance plans. - Highlights: →A novel algorithm using multi-objective particle swarm optimization technique. → Evaluation of the cost-reliability tradeoff in a flexible maintenance strategy. → A probabilistic model for regression parameters is employed to assess reliability-based performance. → The proposed approach can help decision makers to optimize roadway maintenance plans.

  4. Reliability-based performance simulation for optimized pavement maintenance

    Energy Technology Data Exchange (ETDEWEB)

    Chou, Jui-Sheng, E-mail: jschou@mail.ntust.edu.tw [Department of Construction Engineering, National Taiwan University of Science and Technology (Taiwan Tech), 43 Sec. 4, Keelung Rd., Taipei 106, Taiwan (China); Le, Thanh-Son [Department of Construction Engineering, National Taiwan University of Science and Technology (Taiwan Tech), 43 Sec. 4, Keelung Rd., Taipei 106, Taiwan (China)

    2011-10-15

    Roadway pavement maintenance is essential for driver safety and highway infrastructure efficiency. However, regular preventive maintenance and rehabilitation (M and R) activities are extremely costly. Unfortunately, the funds available for the M and R of highway pavement are often given lower priority compared to other national development policies, therefore, available funds must be allocated wisely. Maintenance strategies are typically implemented by optimizing only the cost whilst the reliability of facility performance is neglected. This study proposes a novel algorithm using multi-objective particle swarm optimization (MOPSO) technique to evaluate the cost-reliability tradeoff in a flexible maintenance strategy based on non-dominant solutions. Moreover, a probabilistic model for regression parameters is employed to assess reliability-based performance. A numerical example of a highway pavement project is illustrated to demonstrate the efficacy of the proposed MOPSO algorithms. The analytical results show that the proposed approach can help decision makers to optimize roadway maintenance plans. - Highlights: > A novel algorithm using multi-objective particle swarm optimization technique. > Evaluation of the cost-reliability tradeoff in a flexible maintenance strategy. > A probabilistic model for regression parameters is employed to assess reliability-based performance. > The proposed approach can help decision makers to optimize roadway maintenance plans.

  5. Multi-objective optimization of Stirling engine systems using Front-based Yin-Yang-Pair Optimization

    International Nuclear Information System (INIS)

    Punnathanam, Varun; Kotecha, Prakash

    2017-01-01

    Highlights: • Efficient multi-objective optimization algorithm F-YYPO demonstrated. • Three Stirling engine applications with a total of eight cases. • Improvements in the objective function values of up to 30%. • Superior to the popularly used gamultiobj of MATLAB. • F-YYPO has extremely low time complexity. - Abstract: In this work, we demonstrate the performance of Front-based Yin-Yang-Pair Optimization (F-YYPO) to solve multi-objective problems related to Stirling engine systems. The performance of F-YYPO is compared with that of (i) a recently proposed multi-objective optimization algorithm (Multi-Objective Grey Wolf Optimizer) and (ii) an algorithm popularly employed in literature due to its easy accessibility (MATLAB’s inbuilt multi-objective Genetic Algorithm function: gamultiobj). We consider three Stirling engine based optimization problems: (i) the solar-dish Stirling engine system which considers objectives of output power, thermal efficiency and rate of entropy generation; (ii) Stirling engine thermal model which considers the associated irreversibility of the cycle with objectives of output power, thermal efficiency and pressure drop; and finally (iii) an experimentally validated polytropic finite speed thermodynamics based Stirling engine model also with objectives of output power and pressure drop. We observe F-YYPO to be significantly more effective as compared to its competitors in solving the problems, while requiring only a fraction of the computational time required by the other algorithms.

  6. Trust regions in Kriging-based optimization with expected improvement

    Science.gov (United States)

    Regis, Rommel G.

    2016-06-01

    The Kriging-based Efficient Global Optimization (EGO) method works well on many expensive black-box optimization problems. However, it does not seem to perform well on problems with steep and narrow global minimum basins and on high-dimensional problems. This article develops a new Kriging-based optimization method called TRIKE (Trust Region Implementation in Kriging-based optimization with Expected improvement) that implements a trust-region-like approach where each iterate is obtained by maximizing an Expected Improvement (EI) function within some trust region. This trust region is adjusted depending on the ratio of the actual improvement to the EI. This article also develops the Kriging-based CYCLONE (CYClic Local search in OptimizatioN using Expected improvement) method that uses a cyclic pattern to determine the search regions where the EI is maximized. TRIKE and CYCLONE are compared with EGO on 28 test problems with up to 32 dimensions and on a 36-dimensional groundwater bioremediation application in appendices supplied as an online supplement available at http://dx.doi.org/10.1080/0305215X.2015.1082350. The results show that both algorithms yield substantial improvements over EGO and they are competitive with a radial basis function method.

  7. Extremum-Seeking Control and Applications A Numerical Optimization-Based Approach

    CERN Document Server

    Zhang, Chunlei

    2012-01-01

    Extremum seeking control tracks a varying maximum or minimum in a performance function such as a cost. It attempts to determine the optimal performance of a control system as it operates, thereby reducing downtime and the need for system analysis. Extremum Seeking Control and Applications is divided into two parts. In the first, the authors review existing analog optimization based extremum seeking control including gradient, perturbation and sliding mode based control designs. They then propose a novel numerical optimization based extremum seeking control based on optimization algorithms and state regulation. This control design is developed for simple linear time-invariant systems and then extended for a class of feedback linearizable nonlinear systems. The two main optimization algorithms – line search and trust region methods – are analyzed for robustness. Finite-time and asymptotic state regulators are put forward for linear and nonlinear systems respectively. Further design flexibility is achieved u...

  8. Sizing optimization of skeletal structures using teaching-learning based optimization

    Directory of Open Access Journals (Sweden)

    Vedat Toğan

    2017-03-01

    Full Text Available Teaching Learning Based Optimization (TLBO is one of the non-traditional techniques to simulate natural phenomena into a numerical algorithm. TLBO mimics teaching learning process occurring between a teacher and students in a classroom. A parameter named as teaching factor, TF, seems to be the only tuning parameter in TLBO. Although the value of the teaching factor, TF, is determined by an equation, the value of 1 or 2 has been used by the researchers for TF. This study intends to explore the effect of the variation of teaching factor TF on the performances of TLBO. This effect is demonstrated in solving structural optimization problems including truss and frame structures under the stress and displacement constraints. The results indicate that the variation of TF in the TLBO process does not change the results obtained at the end of the optimization procedure when the computational cost of TLBO is ignored.

  9. Global stability-based design optimization of truss structures using ...

    Indian Academy of Sciences (India)

    Furthermore, a pure pareto-ranking based multi-objective optimization model is employed for the design optimization of the truss structure with multiple objectives. The computational performance of the optimization model is increased by implementing an island model into its evolutionary search mechanism. The proposed ...

  10. Purely data-driven approaches to trading of renewable energy generation

    DEFF Research Database (Denmark)

    Mazzi, Nicolo; Pinson, Pierre

    2016-01-01

    could readily learn from market data and deduce how to offer strategically in order to maximize expected market revenues. Our analysis shows that a direct reinforcement learning algorithm can track the nominal level of the optimal quantile forecast to trade in the day-ahead market, while yielding higher...

  11. Nitrogen and phosphorus changes and optimal drainage time of flooded paddy field based on environmental factors

    Directory of Open Access Journals (Sweden)

    Meng-hua Xiao

    2013-04-01

    Full Text Available While many controlled irrigation and drainage techniques have been adopted in China, the environmental effects of these techniques require further investigation. This study was conducted to examine the changes of nitrogen and phosphorus of a flooded paddy water system after fertilizer application and at each growth stage so as to obtain the optimal drainage time at each growth stage. Four treatments with different water level management methods at each growth stage were conducted under the condition of ten-day continuous flooding. Results show that the ammonia nitrogen (NH+4-N concentration reached the peak value once the fertilizer was applied, and then decreased to a relatively low level seven to ten days later, and that the nitrate nitrogen (NO-3-N concentration gradually rose to its peak value, which appeared later in subsurface water than in surface water. Continuous flooding could effectively reduce the concentrations of NH+4-N, NO-3-N , and total phosphorus (TP in surface water. However, the paddy water disturbance, in the process of soil surface adsorption and nitrification, caused NH+4-N to be released and increased the concentrations of NH+4-N and NO-3-N in surface water. A multi-objective controlled drainage model based on environmental factors was established in order to obtain the optimal drainage time at each growth stage and better guide the drainage practices of farmers. The optimal times for surface drainage are the fourth, sixth, fifth, and sixth days after flooding at the tillering, jointing-booting, heading-flowering, and milking stages, respectively.

  12. Automatic Multi-Level Thresholding Segmentation Based on Multi-Objective Optimization

    Directory of Open Access Journals (Sweden)

    L. DJEROU,

    2012-01-01

    Full Text Available In this paper, we present a new multi-level image thresholding technique, called Automatic Threshold based on Multi-objective Optimization "ATMO" that combines the flexibility of multi-objective fitness functions with the power of a Binary Particle Swarm Optimization algorithm "BPSO", for searching the "optimum" number of the thresholds and simultaneously the optimal thresholds of three criteria: the between-class variances criterion, the minimum error criterion and the entropy criterion. Some examples of test images are presented to compare our segmentation method, based on the multi-objective optimization approach with Otsu’s, Kapur’s and Kittler’s methods. Our experimental results show that the thresholding method based on multi-objective optimization is more efficient than the classical Otsu’s, Kapur’s and Kittler’s methods.

  13. Discounted cost model for condition-based maintenance optimization

    International Nuclear Information System (INIS)

    Weide, J.A.M. van der; Pandey, M.D.; Noortwijk, J.M. van

    2010-01-01

    This paper presents methods to evaluate the reliability and optimize the maintenance of engineering systems that are damaged by shocks or transients arriving randomly in time and overall degradation is modeled as a cumulative stochastic point process. The paper presents a conceptually clear and comprehensive derivation of formulas for computing the discounted cost associated with a maintenance policy combining both condition-based and age-based criteria for preventive maintenance. The proposed discounted cost model provides a more realistic basis for optimizing the maintenance policies than those based on the asymptotic, non-discounted cost rate criterion.

  14. Evaluating the Subjective Straight Ahead Before and After Spaceflight

    Science.gov (United States)

    Campbell, D. J.; Wood, S. J.; Reschke, M. F.; Clement, G.

    2017-01-01

    This joint European Space Agency (ESA) - NASA study will address adaptive changes in spatial orientation related to the subjective straight ahead and the use of a vibrotactile sensory aid to reduce perceptual errors. The study will be conducted before and after long duration expeditions to the International Space Station (ISS) to examine how spatial processing of target location is altered following exposure to microgravity. This study addresses the sensorimotor research gap to "determine the changes in sensorimotor function over the course of a mission and during recovery after landing."

  15. CONNECTION OF TURN AHEAD AND TURN BACK WITH MOTORIC ABILITIES OF THE FOURTH GRADE OF HIGH SCHOOL

    Directory of Open Access Journals (Sweden)

    Jovica Petković

    2008-08-01

    Full Text Available The research is done for the purpose of determination and defining of the level of connection between some motoric abilities with success in realization of programmed contents from the area of gymnastics (turn ahead and turn back. The research is done on the sample of fifty students from the fourth grade of High School, on ten motoric tests and on two specific motoric assignments – turn ahead and turn back. The results of this research clearly point that there exist the multitude of statistically important coefficients of correlation between treated motoric abilities and applied motoric assignments.

  16. Optimal depth-based regional frequency analysis

    Science.gov (United States)

    Wazneh, H.; Chebana, F.; Ouarda, T. B. M. J.

    2013-06-01

    Classical methods of regional frequency analysis (RFA) of hydrological variables face two drawbacks: (1) the restriction to a particular region which can lead to a loss of some information and (2) the definition of a region that generates a border effect. To reduce the impact of these drawbacks on regional modeling performance, an iterative method was proposed recently, based on the statistical notion of the depth function and a weight function φ. This depth-based RFA (DBRFA) approach was shown to be superior to traditional approaches in terms of flexibility, generality and performance. The main difficulty of the DBRFA approach is the optimal choice of the weight function ϕ (e.g., φ minimizing estimation errors). In order to avoid a subjective choice and naïve selection procedures of φ, the aim of the present paper is to propose an algorithm-based procedure to optimize the DBRFA and automate the choice of ϕ according to objective performance criteria. This procedure is applied to estimate flood quantiles in three different regions in North America. One of the findings from the application is that the optimal weight function depends on the considered region and can also quantify the region's homogeneity. By comparing the DBRFA to the canonical correlation analysis (CCA) method, results show that the DBRFA approach leads to better performances both in terms of relative bias and mean square error.

  17. Recent Progress on Data-Based Optimization for Mineral Processing Plants

    Directory of Open Access Journals (Sweden)

    Jinliang Ding

    2017-04-01

    Full Text Available In the globalized market environment, increasingly significant economic and environmental factors within complex industrial plants impose importance on the optimization of global production indices; such optimization includes improvements in production efficiency, product quality, and yield, along with reductions of energy and resource usage. This paper briefly overviews recent progress in data-driven hybrid intelligence optimization methods and technologies in improving the performance of global production indices in mineral processing. First, we provide the problem description. Next, we summarize recent progress in data-based optimization for mineral processing plants. This optimization consists of four layers: optimization of the target values for monthly global production indices, optimization of the target values for daily global production indices, optimization of the target values for operational indices, and automation systems for unit processes. We briefly overview recent progress in each of the different layers. Finally, we point out opportunities for future works in data-based optimization for mineral processing plants.

  18. Methodology for tomographic imaging ahead of mining using the shearer as a seismic source

    Energy Technology Data Exchange (ETDEWEB)

    King, A.; Luo, X. [CSIRO Exploration and Mining, Kenmore, Qld. (Australia)

    2009-03-15

    Poor rock conditions in a coal long wall panel can result in roof collapse when a problematic zone is mined, significantly interrupting mine production. The ability to image rock conditions (stress and degree of fracturing) ahead of the face gives the miners the ability to respond proactively to such problems. This method uses the energy from mining machinery, in this case a coal shearer, to produce an image of the rock velocity ahead of the mining face without interrupting mining. Data from an experiment illustrates the concept. Geophones installed in gate-road roofs record the noise generated by the shearer after it has traversed the panel ahead of the mining face. A generalized crosscorrelation of the signals from pairs of sensors determines relative arrival times from the continuous seismic noise produced by the shearer. These relative times can then be inverted for a velocity structure. The crosscorrelations, performed in the frequency domain, are weighted by a confidence value derived from the spectral coherence between the traces. This produces stable crosscorrelation lags in the presence of noise. The errors in the time-domain data are propagated through to the relative traveltimes and then to the final tomographic velocity image, yielding an estimate of the uncertainty in velocity at each point. This velocity image can then be used to infer information about the stress and fracture state of the rock, providing advance warning of potentially hazardous zones.

  19. Elitism set based particle swarm optimization and its application

    Directory of Open Access Journals (Sweden)

    Yanxia Sun

    2017-01-01

    Full Text Available Topology plays an important role for Particle Swarm Optimization (PSO to achieve good optimization performance. It is difficult to find one topology structure for the particles to achieve better optimization performance than the others since the optimization performance not only depends on the searching abilities of the particles, also depends on the type of the optimization problems. Three elitist set based PSO algorithm without using explicit topology structure is proposed in this paper. An elitist set, which is based on the individual best experience, is used to communicate among the particles. Moreover, to avoid the premature of the particles, different statistical methods have been used in these three proposed methods. The performance of the proposed PSOs is compared with the results of the standard PSO 2011 and several PSO with different topologies, and the simulation results and comparisons demonstrate that the proposed PSO with adaptive probabilistic preference can achieve good optimization performance.

  20. A Bilevel Model for Participation of a Storage System in Energy and Reserve Markets

    DEFF Research Database (Denmark)

    Nasrolahpour, Ehsan; Kazempour, Jalal; Zareipour, Hamidreza

    2017-01-01

    We develop a decision-making tool based on a bilevel complementarity model for a merchant price-maker energy storage system to determine the most beneficial trading actions in pool-based markets, including day-ahead (as joint energy and reserve markets) and balancing settlements. The uncertainty...... of net load deviation in real-time is incorporated into the model using a set of scenarios generated from the available forecast in the day-ahead. The objective of this energy storage system is to maximize its expected profit. The day-ahead products of energy storage system include energy as well...... system into clearing process of multiple markets and enables such a facility to possibly affect the outcomes of those markets to its own benefit through strategic price and quantity offers. The validity of the proposed approach is evaluated using a numerical study....

  1. Group search optimiser-based optimal bidding strategies with no Karush-Kuhn-Tucker optimality conditions

    Science.gov (United States)

    Yadav, Naresh Kumar; Kumar, Mukesh; Gupta, S. K.

    2017-03-01

    General strategic bidding procedure has been formulated in the literature as a bi-level searching problem, in which the offer curve tends to minimise the market clearing function and to maximise the profit. Computationally, this is complex and hence, the researchers have adopted Karush-Kuhn-Tucker (KKT) optimality conditions to transform the model into a single-level maximisation problem. However, the profit maximisation problem with KKT optimality conditions poses great challenge to the classical optimisation algorithms. The problem has become more complex after the inclusion of transmission constraints. This paper simplifies the profit maximisation problem as a minimisation function, in which the transmission constraints, the operating limits and the ISO market clearing functions are considered with no KKT optimality conditions. The derived function is solved using group search optimiser (GSO), a robust population-based optimisation algorithm. Experimental investigation is carried out on IEEE 14 as well as IEEE 30 bus systems and the performance is compared against differential evolution-based strategic bidding, genetic algorithm-based strategic bidding and particle swarm optimisation-based strategic bidding methods. The simulation results demonstrate that the obtained profit maximisation through GSO-based bidding strategies is higher than the other three methods.

  2. North American NGL exciting times are just ahead

    International Nuclear Information System (INIS)

    Gist, R.L.; Otto, K.W.

    1998-01-01

    Although it is a fairly conservative consulting company, Purvin and Gertz believes that exciting times are just ahead for the North American NGL industry. In the United States, NGL production will be rising rapidly, particularly in the Gulf of Mexico offshore from Louisiana. Demand will also be increasing in many sectors, with the largest increases as petrochemical feedstocks. Similarly, in Canada natural gas export pipelines will result in higher gas and NGL production. Petrochemicals will also play a large role in rapidly rising demand for NGL. Lastly, in Mexico quickly growing demand for natural gas to produce electrical power will push up NGL production

  3. Optimizing block-based maintenance under random machine usage

    NARCIS (Netherlands)

    de Jonge, Bram; Jakobsons, Edgars

    Existing studies on maintenance optimization generally assume that machines are either used continuously, or that times until failure do not depend on the actual usage. In practice, however, these assumptions are often not realistic. In this paper, we consider block-based maintenance optimization

  4. Weight loss strategies associated with BMI in overweight adults with type 2 diabetes at entry into the Look AHEAD (Action for Health in Diabetes) trial.

    Science.gov (United States)

    Raynor, Hollie A; Jeffery, Robert W; Ruggiero, Andrea M; Clark, Jeanne M; Delahanty, Linda M

    2008-07-01

    Intentional weight loss is recommended for those with type 2 diabetes, but the strategies patients attempt and their effectiveness for weight management are unknown. In this investigation we describe intentional weight loss strategies used and those related to BMI in a diverse sample of overweight participants with type 2 diabetes at enrollment in the Look AHEAD (Action for Health in Diabetes) clinical trial. This was a cross-sectional study of baseline weight loss strategies, including self-weighing frequency, eating patterns, and weight control practices, reported in 3,063 women and 2,082 men aged 45-74 years with BMI > or =25 kg/m(2). Less than half (41.4%) of participants self-weighed > or =1/week. Participants ate breakfast 6.0 +/- 1.8 days/week, ate 5.0 +/- 3.1 meals/snacks per day, and ate 1.9 +/- 2.7 fast food meals/week. The three most common weight control practices (increasing fruits and vegetables, cutting out sweets, and eating less high-carbohydrate foods) were reported by approximately 60% of participants for > or =20 weeks over the previous year. Adjusted models showed that self-weighing less than once per week (B = 0.83), more fast food meals consumed per week (B = 0.14), and fewer breakfast meals consumed per week (B = -0.19) were associated (P < 0.05) with a higher BMI (R(2) = 0.24). Regular self-weighing and breakfast consumption, along with infrequent consumption of fast food, were related to lower BMI in the Look AHEAD study population.

  5. Rapid Optimal Generation Algorithm for Terrain Following Trajectory Based on Optimal Control

    Institute of Scientific and Technical Information of China (English)

    杨剑影; 张海; 谢邦荣; 尹健

    2004-01-01

    Based on the optimal control theory, a 3-dimensionnal direct generation algorithm is proposed for anti-ground low altitude penetration tasks under complex terrain. By optimizing the terrain following(TF) objective function,terrain coordinate system, missile dynamic model and control vector, the TF issue is turning into the improved optimal control problem whose mathmatical model is simple and need not solve the second order terrain derivative. Simulation results prove that this method is reasonable and feasible. The TF precision is in the scope from 0.3 m to 3.0 m,and the planning time is less than 30 min. This method have the strongpionts such as rapidness, precision and has great application value.

  6. Surrogate-Based Optimization of Biogeochemical Transport Models

    Science.gov (United States)

    Prieß, Malte; Slawig, Thomas

    2010-09-01

    First approaches towards a surrogate-based optimization method for a one-dimensional marine biogeochemical model of NPZD type are presented. The model, developed by Oschlies and Garcon [1], simulates the distribution of nitrogen, phytoplankton, zooplankton and detritus in a water column and is driven by ocean circulation data. A key issue is to minimize the misfit between the model output and given observational data. Our aim is to reduce the overall optimization cost avoiding expensive function and derivative evaluations by using a surrogate model replacing the high-fidelity model in focus. This in particular becomes important for more complex three-dimensional models. We analyse a coarsening in the discretization of the model equations as one way to create such a surrogate. Here the numerical stability crucially depends upon the discrete stepsize in time and space and the biochemical terms. We show that for given model parameters the level of grid coarsening can be choosen accordingly yielding a stable and satisfactory surrogate. As one example of a surrogate-based optimization method we present results of the Aggressive Space Mapping technique (developed by John W. Bandler [2, 3]) applied to the optimization of this one-dimensional biogeochemical transport model.

  7. Impact of Inter- and Intra-Regional Coordination in Markets With a Large Renewable Component

    DEFF Research Database (Denmark)

    Delikaraoglou, Stefanos; Morales González, Juan Miguel; Pinson, Pierre

    2016-01-01

    counterproductive or inefficient under uncertain supply, e.g., from weather-driven renewable power generation. In the absence of a specific target model for the common balancing market in Europe, we introduce a framework to compare different coordination schemes and market organizations. The proposed models......The establishment of the single European day-ahead market has accomplished a crucial step towards the spatial integration of the European power system. However, this new arrangement does not consider any intra-regional coordination of day-ahead and balancing markets and thus may become...... are formulated as stochastic equilibrium problems and compared against an optimal market setup. The simulation results reveal significant efficiency loss in case of partial coordination and diversity of market structure among regional power systems....

  8. Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch

    KAUST Repository

    Xie, Le; Gu, Yingzhong; Zhu, Xinxin; Genton, Marc G.

    2014-01-01

    forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24

  9. A modified teaching–learning based optimization for multi-objective optimal power flow problem

    International Nuclear Information System (INIS)

    Shabanpour-Haghighi, Amin; Seifi, Ali Reza; Niknam, Taher

    2014-01-01

    Highlights: • A new modified teaching–learning based algorithm is proposed. • A self-adaptive wavelet mutation strategy is used to enhance the performance. • To avoid reaching a large repository size, a fuzzy clustering technique is used. • An efficiently smart population selection is utilized. • Simulations show the superiority of this algorithm compared with other ones. - Abstract: In this paper, a modified teaching–learning based optimization algorithm is analyzed to solve the multi-objective optimal power flow problem considering the total fuel cost and total emission of the units. The modified phase of the optimization algorithm utilizes a self-adapting wavelet mutation strategy. Moreover, a fuzzy clustering technique is proposed to avoid extremely large repository size besides a smart population selection for the next iteration. These techniques make the algorithm searching a larger space to find the optimal solutions while speed of the convergence remains good. The IEEE 30-Bus and 57-Bus systems are used to illustrate performance of the proposed algorithm and results are compared with those in literatures. It is verified that the proposed approach has better performance over other techniques

  10. Sharing wind power forecasts in electricity markets: A numerical analysis

    International Nuclear Information System (INIS)

    Exizidis, Lazaros; Kazempour, S. Jalal; Pinson, Pierre; Greve, Zacharie de; Vallée, François

    2016-01-01

    Highlights: • Information sharing among different agents can be beneficial for electricity markets. • System cost decreases by sharing wind power forecasts between different agents. • Market power of wind producer may increase by sharing forecasts with market operator. • Extensive out-of-sample analysis is employed to draw reliable conclusions. - Abstract: In an electricity pool with significant share of wind power, all generators including conventional and wind power units are generally scheduled in a day-ahead market based on wind power forecasts. Then, a real-time market is cleared given the updated wind power forecast and fixed day-ahead decisions to adjust power imbalances. This sequential market-clearing process may cope with serious operational challenges such as severe power shortage in real-time due to erroneous wind power forecasts in day-ahead market. To overcome such situations, several solutions can be considered such as adding flexible resources to the system. In this paper, we address another potential solution based on information sharing in which market players share their own wind power forecasts with others in day-ahead market. This solution may improve the functioning of sequential market-clearing process through making more informed day-ahead schedules, which reduces the need for balancing resources in real-time operation. This paper numerically evaluates the potential value of sharing forecasts for the whole system in terms of system cost reduction. Besides, its impact on each market player’s profit is analyzed. The framework of this study is based on a stochastic two-stage market setup and complementarity modeling, which allows us to gain further insights into information sharing impacts.

  11. Reliability-Based Structural Optimization of Wave Energy Converters

    Directory of Open Access Journals (Sweden)

    Simon Ambühl

    2014-12-01

    Full Text Available More and more wave energy converter (WEC concepts are reaching prototypelevel. Once the prototype level is reached, the next step in order to further decrease thelevelized cost of energy (LCOE is optimizing the overall system with a focus on structuraland maintenance (inspection costs, as well as on the harvested power from the waves.The target of a fully-developed WEC technology is not maximizing its power output,but minimizing the resulting LCOE. This paper presents a methodology to optimize thestructural design of WECs based on a reliability-based optimization problem and the intentto maximize the investor’s benefits by maximizing the difference between income (e.g., fromselling electricity and the expected expenses (e.g., structural building costs or failure costs.Furthermore, different development levels, like prototype or commercial devices, may havedifferent main objectives and will be located at different locations, as well as receive varioussubsidies. These points should be accounted for when performing structural optimizationsof WECs. An illustrative example on the gravity-based foundation of the Wavestar deviceis performed showing how structural design can be optimized taking target reliability levelsand different structural failure modes due to extreme loads into account.

  12. A Shear-Wave Seismic System to Look Ahead of a Tunnel Boring Machine

    NARCIS (Netherlands)

    Bharadwaj, Pawan; Drijkoningen, G.G.; Mulder, W.A.; Tscharner, Thomas; Jenneskens, Rob

    2016-01-01

    The Earth’s properties, composition and structure ahead of a tunnel boring machine (TBM) should be mapped for hazard assessment during excavation. We study the use of seismic-exploration techniques for this purpose. We focus on a seismic system for soft soils, where shear waves are better and easier

  13. Empty tracks optimization based on Z-Map model

    Science.gov (United States)

    Liu, Le; Yan, Guangrong; Wang, Zaijun; Zang, Genao

    2017-12-01

    For parts with many features, there are more empty tracks during machining. If these tracks are not optimized, the machining efficiency will be seriously affected. In this paper, the characteristics of the empty tracks are studied in detail. Combining with the existing optimization algorithm, a new tracks optimization method based on Z-Map model is proposed. In this method, the tool tracks are divided into the unit processing section, and then the Z-Map model simulation technique is used to analyze the order constraint between the unit segments. The empty stroke optimization problem is transformed into the TSP with sequential constraints, and then through the genetic algorithm solves the established TSP problem. This kind of optimization method can not only optimize the simple structural parts, but also optimize the complex structural parts, so as to effectively plan the empty tracks and greatly improve the processing efficiency.

  14. Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    Na Tian

    2015-01-01

    Full Text Available A study on pareto-ranking based quantum-behaved particle swarm optimization (QPSO for multiobjective optimization problems is presented in this paper. During the iteration, an external repository is maintained to remember the nondominated solutions, from which the global best position is chosen. The comparison between different elitist selection strategies (preference order, sigma value, and random selection is performed on four benchmark functions and two metrics. The results demonstrate that QPSO with preference order has comparative performance with sigma value according to different number of objectives. Finally, QPSO with sigma value is applied to solve multiobjective flexible job-shop scheduling problems.

  15. Optimization of microgrids based on controller designing for ...

    African Journals Online (AJOL)

    The power quality of microgrid during islanded operation is strongly related with the controller performance of DGs. Therefore a new optimal control strategy for distributed generation based inverter to connect to the generalized microgrid is proposed. This work shows developing optimal control algorithms for the DG ...

  16. Alaska Satellite Facility: The Quest to Stay Ahead of the Big Data Wave

    Science.gov (United States)

    Labelle-Hamer, A. L.; Nicoll, J.; Munk, S.

    2014-12-01

    Big Data is getting bigger. Fast enough is getting faster. The number and type of products produced is growing. The ideas on how to handle the day-to-day management of data and data systems need to scale with the data and the demand. We have seen the effects of rapid growth spurts at the Alaska Satellite Facility (ASF) and anticipate we are not done yet. Looking back, ASF was conceived in 1982 to be a single-purpose imaging radar receiving station supporting a science team focused on geophysical processes. The primary construction at the University of Alaska Fairbanks (UAF) was completed in 1988 and full operational status achieved in 1991. The expected supports were estimated at 10 minutes per day and quickly grew to 70 minutes per day. In 1994, a Memorandum of Agreement (MOA) between NASA and UAF formed the ASF Distributed Active Archive Center (DAAC) complementing, the existing agreement for ASF. The demand for the use of ASF as a receiving station and as a data center grew as fast as, and at times faster, than the capabilities. Looking forward, as demand drives the system larger just adding on more of the same often complicates rather than simplifies the system. A growing percentage of efforts and resources spent on dealing with problems that originate from a legacy system can creep up on an organization. This in turn limits the ability to keep the overall sustaining costs under control and leads to a crisis. Such growth means more-of-the-same philosophy has to shift into change-or-die philosophy in order to boot strap up to the next level. In this talk, we review how ASF has faced this several times in the past as the volume and demand of data grew along with the technology to acquire and disseminate it. We will look at what is coming for ASF as a data center and what we think are the next steps to stay ahead of the Big Data wave.

  17. Optimal decentralized valley-filling charging strategy for electric vehicles

    International Nuclear Information System (INIS)

    Zhang, Kangkang; Xu, Liangfei; Ouyang, Minggao; Wang, Hewu; Lu, Languang; Li, Jianqiu; Li, Zhe

    2014-01-01

    Highlights: • An implementable charging strategy is developed for electric vehicles connected to a grid. • A two-dimensional pricing scheme is proposed to coordinate charging behaviors. • The strategy effectively works in decentralized way but achieves the systematic valley filling. • The strategy allows device-level charging autonomy, and does not require a bidirectional communication/control network. • The strategy can self-correct when confronted with adverse factors. - Abstract: Uncoordinated charging load of electric vehicles (EVs) increases the peak load of the power grid, thereby increasing the cost of electricity generation. The valley-filling charging scenario offers a cheaper alternative. This study proposes a novel decentralized valley-filling charging strategy, in which a day-ahead pricing scheme is designed by solving a minimum-cost optimization problem. The pricing scheme can be broadcasted to EV owners, and the individual charging behaviors can be indirectly coordinated. EV owners respond to the pricing scheme by autonomously optimizing their individual charge patterns. This device-level response induces a valley-filling effect in the grid at the system level. The proposed strategy offers three advantages: coordination (by the valley-filling effect), practicality (no requirement for a bidirectional communication/control network between the grid and EV owners), and autonomy (user control of EV charge patterns). The proposed strategy is validated in simulations of typical scenarios in Beijing, China. According to the results, the strategy (1) effectively achieves the valley-filling charging effect at 28% less generation cost than the uncoordinated charging strategy, (2) is robust to several potential affecters of the valley-filling effect, such as (system-level) inaccurate parameter estimation and (device-level) response capability and willingness (which cause less than 2% deviation in the minimal generation cost), and (3) is compatible with

  18. Radiation protection optimization using a knowledge based methodology

    International Nuclear Information System (INIS)

    Reyes-Jimenez, J.; Tsoukalas, L.H.

    1991-01-01

    This paper presents a knowledge based methodology for radiological planning and radiation protection optimization. The cost-benefit methodology described on International Commission of Radiation Protection Report No. 37 is employed within a knowledge based framework for the purpose of optimizing radiation protection and plan maintenance activities while optimizing radiation protection. 1, 2 The methodology is demonstrated through an application to a heating ventilation and air conditioning (HVAC) system. HVAC is used to reduce radioactivity concentration levels in selected contaminated multi-compartment models at nuclear power plants when higher than normal radiation levels are detected. The overall objective is to reduce personnel exposure resulting from airborne radioactivity, when routine or maintenance access is required in contaminated areas. 2 figs, 15 refs

  19. MOS-Based Multiuser Multiapplication Cross-Layer Optimization for Mobile Multimedia Communication

    Directory of Open Access Journals (Sweden)

    Shoaib Khan

    2007-01-01

    Full Text Available We propose a cross-layer optimization strategy that jointly optimizes the application layer, the data-link layer, and the physical layer of a wireless protocol stack using an application-oriented objective function. The cross-layer optimization framework provides efficient allocation of wireless network resources across multiple types of applications run by different users to maximize network resource usage and user perceived quality of service. We define a novel optimization scheme based on the mean opinion score (MOS as the unifying metric over different application classes. Our experiments, applied to scenarios where users simultaneously run three types of applications, namely voice communication, streaming video and file download, confirm that MOS-based optimization leads to significant improvement in terms of user perceived quality when compared to conventional throughput-based optimization.

  20. Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis

    Directory of Open Access Journals (Sweden)

    Sen Zhang

    2015-01-01

    Full Text Available One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO, inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm.

  1. Optimal depth-based regional frequency analysis

    Directory of Open Access Journals (Sweden)

    H. Wazneh

    2013-06-01

    Full Text Available Classical methods of regional frequency analysis (RFA of hydrological variables face two drawbacks: (1 the restriction to a particular region which can lead to a loss of some information and (2 the definition of a region that generates a border effect. To reduce the impact of these drawbacks on regional modeling performance, an iterative method was proposed recently, based on the statistical notion of the depth function and a weight function φ. This depth-based RFA (DBRFA approach was shown to be superior to traditional approaches in terms of flexibility, generality and performance. The main difficulty of the DBRFA approach is the optimal choice of the weight function ϕ (e.g., φ minimizing estimation errors. In order to avoid a subjective choice and naïve selection procedures of φ, the aim of the present paper is to propose an algorithm-based procedure to optimize the DBRFA and automate the choice of ϕ according to objective performance criteria. This procedure is applied to estimate flood quantiles in three different regions in North America. One of the findings from the application is that the optimal weight function depends on the considered region and can also quantify the region's homogeneity. By comparing the DBRFA to the canonical correlation analysis (CCA method, results show that the DBRFA approach leads to better performances both in terms of relative bias and mean square error.

  2. A model based on stochastic dynamic programming for determining China's optimal strategic petroleum reserve policy

    International Nuclear Information System (INIS)

    Zhang Xiaobing; Fan Ying; Wei Yiming

    2009-01-01

    China's Strategic Petroleum Reserve (SPR) is currently being prepared. But how large the optimal stockpile size for China should be, what the best acquisition strategies are, how to release the reserve if a disruption occurs, and other related issues still need to be studied in detail. In this paper, we develop a stochastic dynamic programming model based on a total potential cost function of establishing SPRs to evaluate the optimal SPR policy for China. Using this model, empirical results are presented for the optimal size of China's SPR and the best acquisition and drawdown strategies for a few specific cases. The results show that with comprehensive consideration, the optimal SPR size for China is around 320 million barrels. This size is equivalent to about 90 days of net oil import amount in 2006 and should be reached in the year 2017, three years earlier than the national goal, which implies that the need for China to fill the SPR is probably more pressing; the best stockpile release action in a disruption is related to the disruption levels and expected continuation probabilities. The information provided by the results will be useful for decision makers.

  3. Topology Optimization of Passive Micromixers Based on Lagrangian Mapping Method

    Directory of Open Access Journals (Sweden)

    Yuchen Guo

    2018-03-01

    Full Text Available This paper presents an optimization-based design method of passive micromixers for immiscible fluids, which means that the Peclet number infinitely large. Based on topology optimization method, an optimization model is constructed to find the optimal layout of the passive micromixers. Being different from the topology optimization methods with Eulerian description of the convection-diffusion dynamics, this proposed method considers the extreme case, where the mixing is dominated completely by the convection with negligible diffusion. In this method, the mixing dynamics is modeled by the mapping method, a Lagrangian description that can deal with the case with convection-dominance. Several numerical examples have been presented to demonstrate the validity of the proposed method.

  4. Surrogate Based Uni/Multi-Objective Optimization and Distribution Estimation Methods

    Science.gov (United States)

    Gong, W.; Duan, Q.; Huo, X.

    2017-12-01

    Parameter calibration has been demonstrated as an effective way to improve the performance of dynamic models, such as hydrological models, land surface models, weather and climate models etc. Traditional optimization algorithms usually cost a huge number of model evaluations, making dynamic model calibration very difficult, or even computationally prohibitive. With the help of a serious of recently developed adaptive surrogate-modelling based optimization methods: uni-objective optimization method ASMO, multi-objective optimization method MO-ASMO, and probability distribution estimation method ASMO-PODE, the number of model evaluations can be significantly reduced to several hundreds, making it possible to calibrate very expensive dynamic models, such as regional high resolution land surface models, weather forecast models such as WRF, and intermediate complexity earth system models such as LOVECLIM. This presentation provides a brief introduction to the common framework of adaptive surrogate-based optimization algorithms of ASMO, MO-ASMO and ASMO-PODE, a case study of Common Land Model (CoLM) calibration in Heihe river basin in Northwest China, and an outlook of the potential applications of the surrogate-based optimization methods.

  5. A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems

    Directory of Open Access Journals (Sweden)

    R. Venkata Rao

    2014-01-01

    Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.

  6. Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch

    KAUST Repository

    Xie, Le

    2014-01-01

    We propose a novel statistical wind power forecast framework, which leverages the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind farms. Critical assessment of the performance of spatio-temporal wind power forecast is performed using realistic wind farm data from West Texas. It is shown that spatio-temporal wind forecast models are numerically efficient approaches to improving forecast quality. By reducing uncertainties in near-term wind power forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24-bus system. Numerical simulation suggests that the overall generation cost can be reduced by up to 6% using a robust look-ahead dispatch coupled with spatio-temporal wind forecast as compared with persistent wind forecast models. © 2013 IEEE.

  7. Attention is allocated closely ahead of the target during smooth pursuit eye movements: Evidence from EEG frequency tagging.

    Science.gov (United States)

    Chen, Jing; Valsecchi, Matteo; Gegenfurtner, Karl R

    2017-07-28

    It is under debate whether attention during smooth pursuit is centered right on the pursuit target or allocated preferentially ahead of it. Attentional deployment was previously probed using a secondary task, which might have altered attention allocation and led to inconsistent findings. We measured frequency-tagged steady-state visual evoked potentials (SSVEP) to measure attention allocation in the absence of any secondary probing task. The observers pursued a moving dot while stimuli flickering at different frequencies were presented at various locations ahead or behind the pursuit target. We observed a significant increase in EEG power at the flicker frequency of the stimulus in front of the pursuit target, compared to the frequency of the stimulus behind. When testing many different locations, we found that the enhancement was detectable up to about 1.5° ahead during pursuit, but vanished by 3.5°. In a control condition using attentional cueing during fixation, we did observe an enhanced EEG response to stimuli at this eccentricity, indicating that the focus of attention during pursuit is narrower than allowed for by the resolution of the attentional system. In a third experiment, we ruled out the possibility that the SSVEP enhancement was a byproduct of the catch-up saccades occurring during pursuit. Overall, we showed that attention is on average allocated ahead of the pursuit target during smooth pursuit. EEG frequency tagging seems to be a powerful technique that allows for the investigation of attention/perception implicitly when an overt task would be confounding. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. A comparative study on stress and compliance based structural topology optimization

    Science.gov (United States)

    Hailu Shimels, G.; Dereje Engida, W.; Fakhruldin Mohd, H.

    2017-10-01

    Most of structural topology optimization problems have been formulated and solved to either minimize compliance or weight of a structure under volume or stress constraints, respectively. Even if, a lot of researches are conducted on these two formulation techniques separately, there is no clear comparative study between the two approaches. This paper intends to compare these formulation techniques, so that an end user or designer can choose the best one based on the problems they have. Benchmark problems under the same boundary and loading conditions are defined, solved and results are compared based on these formulations. Simulation results shows that the two formulation techniques are dependent on the type of loading and boundary conditions defined. Maximum stress induced in the design domain is higher when the design domains are formulated using compliance based formulations. Optimal layouts from compliance minimization formulation has complex layout than stress based ones which may lead the manufacturing of the optimal layouts to be challenging. Optimal layouts from compliance based formulations are dependent on the material to be distributed. On the other hand, optimal layouts from stress based formulation are dependent on the type of material used to define the design domain. High computational time for stress based topology optimization is still a challenge because of the definition of stress constraints at element level. Results also shows that adjustment of convergence criterions can be an alternative solution to minimize the maximum stress developed in optimal layouts. Therefore, a designer or end user should choose a method of formulation based on the design domain defined and boundary conditions considered.

  9. Open Source Software The Challenge Ahead

    CERN Multimedia

    CERN. Geneva

    2007-01-01

    The open source community has done amazingly well in terms of challenging the historical epicenter of computing - the supercomputer and data center - and driving change there. Linux now represents a healthy and growing share of infrastructure in large organisations globally. Apache and other infrastructural components have established the new de facto standard for software in the back office: freedom. It would be easy to declare victory. But the real challenge lies ahead - taking free software to the mass market, to your grandparents, to your nieces and nephews, to your friends. This is the next wave, and if we are to be successful we need to articulate the audacious goals clearly and loudly - because that's how the community process works best. Speaker Bio: Mark Shuttleworth founded the Ubuntu Project in early 2004. Ubuntu is an enterprise Linux distribution that is freely available worldwide and has both desktop and enterprise server editions. Mark studied finance and information technology at the Universit...

  10. Practical operation strategies for pumped hydroelectric energy storage (PHES) utilising electricity price arbitrage

    International Nuclear Information System (INIS)

    Connolly, D.; Lund, H.; Finn, P.; Mathiesen, B.V.; Leahy, M.

    2011-01-01

    In this paper, three practical operation strategies (24Optimal, 24Prognostic, and 24Hsitrocial) are compared to the optimum profit feasible for a PHES facility with a 360 MW pump, 300 MW turbine, and a 2 GWh storage utilising price arbitrage on 13 electricity spot markets. The results indicate that almost all (∼97%) of the profits can be obtained by a PHES facility when it is optimised using the 24Optimal strategy developed, which optimises the energy storage based on the day-ahead electricity prices. However, to maximise profits with the 24Optimal strategy, the day-ahead electricity prices must be the actual prices which the PHES facility is charged or the PHES operator must have very accurate price predictions. Otherwise, the predicted profit could be significantly reduced and even become a loss. Finally, using the 24Optimal strategy, the PHES profit can surpass the annual investment repayments required. However, over the 5-year period investigated (2005-2009) the annual profit from the PHES facility varied by more than 50% on five out of six electricity markets considered. Considering the 40-year lifetime of PHES, even with low investment costs, a low interest rate, and a suitable electricity market, PHES is a risky investment without a more predictable profit. - Highlights: → Electricity generators typically operate on a market, including energy storage. → This paper assesses how energy storage can maximise its profits on a market. → Four operating strategies are assessed on 13 markets using a case study.→ One operating strategy achieves 97% of the profits feasible.→ However, the profit varies a lot depending on the market and capital costs.

  11. Optimal design of planar slider-crank mechanism using teaching-learning-based optimization algorithm

    International Nuclear Information System (INIS)

    Chaudhary, Kailash; Chaudhary, Himanshu

    2015-01-01

    In this paper, a two stage optimization technique is presented for optimum design of planar slider-crank mechanism. The slider crank mechanism needs to be dynamically balanced to reduce vibrations and noise in the engine and to improve the vehicle performance. For dynamic balancing, minimization of the shaking force and the shaking moment is achieved by finding optimum mass distribution of crank and connecting rod using the equipemental system of point-masses in the first stage of the optimization. In the second stage, their shapes are synthesized systematically by closed parametric curve, i.e., cubic B-spline curve corresponding to the optimum inertial parameters found in the first stage. The multi-objective optimization problem to minimize both the shaking force and the shaking moment is solved using Teaching-learning-based optimization algorithm (TLBO) and its computational performance is compared with Genetic algorithm (GA).

  12. Optimal design of planar slider-crank mechanism using teaching-learning-based optimization algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Chaudhary, Kailash; Chaudhary, Himanshu [Malaviya National Institute of Technology, Jaipur (Malaysia)

    2015-11-15

    In this paper, a two stage optimization technique is presented for optimum design of planar slider-crank mechanism. The slider crank mechanism needs to be dynamically balanced to reduce vibrations and noise in the engine and to improve the vehicle performance. For dynamic balancing, minimization of the shaking force and the shaking moment is achieved by finding optimum mass distribution of crank and connecting rod using the equipemental system of point-masses in the first stage of the optimization. In the second stage, their shapes are synthesized systematically by closed parametric curve, i.e., cubic B-spline curve corresponding to the optimum inertial parameters found in the first stage. The multi-objective optimization problem to minimize both the shaking force and the shaking moment is solved using Teaching-learning-based optimization algorithm (TLBO) and its computational performance is compared with Genetic algorithm (GA).

  13. A New Framework for Reactive Power Market Considering Power System Security

    Directory of Open Access Journals (Sweden)

    A. Rabiee

    2009-09-01

    Full Text Available This paper presents a new framework for the day-ahead reactive power market based on the uniform auction price. Voltage stability and security have been considered in the proposed framework. Total Payment Function (TPF is suggested as the objective function of the Optimal Power Flow (OPF used to clear the reactive power market. Overload, voltage drop and voltage stability margin (VSM are included in the constraints of the OPF. Another advantage of the proposed method is the exclusion of Lost Opportunity Cost (LOC concerns from the reactive power market. The effectiveness of the proposed reactive power market is studied based on the CIGRÉ-32 bus test system.

  14. Topology optimization based on the harmony search method

    International Nuclear Information System (INIS)

    Lee, Seung-Min; Han, Seog-Young

    2017-01-01

    A new topology optimization scheme based on a Harmony search (HS) as a metaheuristic method was proposed and applied to static stiffness topology optimization problems. To apply the HS to topology optimization, the variables in HS were transformed to those in topology optimization. Compliance was used as an objective function, and harmony memory was defined as the set of the optimized topology. Also, a parametric study for Harmony memory considering rate (HMCR), Pitch adjusting rate (PAR), and Bandwidth (BW) was performed to find the appropriate range for topology optimization. Various techniques were employed such as a filtering scheme, simple average scheme and harmony rate. To provide a robust optimized topology, the concept of the harmony rate update rule was also implemented. Numerical examples are provided to verify the effectiveness of the HS by comparing the optimal layouts of the HS with those of Bidirectional evolutionary structural optimization (BESO) and Artificial bee colony algorithm (ABCA). The following conclu- sions could be made: (1) The proposed topology scheme is very effective for static stiffness topology optimization problems in terms of stability, robustness and convergence rate. (2) The suggested method provides a symmetric optimized topology despite the fact that the HS is a stochastic method like the ABCA. (3) The proposed scheme is applicable and practical in manufacturing since it produces a solid-void design of the optimized topology. (4) The suggested method appears to be very effective for large scale problems like topology optimization.

  15. Topology optimization based on the harmony search method

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Seung-Min; Han, Seog-Young [Hanyang University, Seoul (Korea, Republic of)

    2017-06-15

    A new topology optimization scheme based on a Harmony search (HS) as a metaheuristic method was proposed and applied to static stiffness topology optimization problems. To apply the HS to topology optimization, the variables in HS were transformed to those in topology optimization. Compliance was used as an objective function, and harmony memory was defined as the set of the optimized topology. Also, a parametric study for Harmony memory considering rate (HMCR), Pitch adjusting rate (PAR), and Bandwidth (BW) was performed to find the appropriate range for topology optimization. Various techniques were employed such as a filtering scheme, simple average scheme and harmony rate. To provide a robust optimized topology, the concept of the harmony rate update rule was also implemented. Numerical examples are provided to verify the effectiveness of the HS by comparing the optimal layouts of the HS with those of Bidirectional evolutionary structural optimization (BESO) and Artificial bee colony algorithm (ABCA). The following conclu- sions could be made: (1) The proposed topology scheme is very effective for static stiffness topology optimization problems in terms of stability, robustness and convergence rate. (2) The suggested method provides a symmetric optimized topology despite the fact that the HS is a stochastic method like the ABCA. (3) The proposed scheme is applicable and practical in manufacturing since it produces a solid-void design of the optimized topology. (4) The suggested method appears to be very effective for large scale problems like topology optimization.

  16. Reliability-Based Optimization of Wind Turbines

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Tarp-Johansen, N.J.

    2004-01-01

    Reliability-based optimization of the main tower and monopile foundation of an offshore wind turbine is considered. Different formulations are considered of the objective function including benefits and building and failure costs of the wind turbine. Also different reconstruction policies in case...

  17. Genetic-evolution-based optimization methods for engineering design

    Science.gov (United States)

    Rao, S. S.; Pan, T. S.; Dhingra, A. K.; Venkayya, V. B.; Kumar, V.

    1990-01-01

    This paper presents the applicability of a biological model, based on genetic evolution, for engineering design optimization. Algorithms embodying the ideas of reproduction, crossover, and mutation are developed and applied to solve different types of structural optimization problems. Both continuous and discrete variable optimization problems are solved. A two-bay truss for maximum fundamental frequency is considered to demonstrate the continuous variable case. The selection of locations of actuators in an actively controlled structure, for minimum energy dissipation, is considered to illustrate the discrete variable case.

  18. Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting.

    Science.gov (United States)

    Alamaniotis, Miltiadis; Bargiotas, Dimitrios; Tsoukalas, Lefteri H

    2016-01-01

    Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.

  19. APPROACH ON INTELLIGENT OPTIMIZATION DESIGN BASED ON COMPOUND KNOWLEDGE

    Institute of Scientific and Technical Information of China (English)

    Yao Jianchu; Zhou Ji; Yu Jun

    2003-01-01

    A concept of an intelligent optimal design approach is proposed, which is organized by a kind of compound knowledge model. The compound knowledge consists of modularized quantitative knowledge, inclusive experience knowledge and case-based sample knowledge. By using this compound knowledge model, the abundant quantity information of mathematical programming and the symbolic knowledge of artificial intelligence can be united together in this model. The intelligent optimal design model based on such a compound knowledge and the automatically generated decomposition principles based on it are also presented. Practically, it is applied to the production planning, process schedule and optimization of production process of a refining & chemical work and a great profit is achieved. Specially, the methods and principles are adaptable not only to continuous process industry, but also to discrete manufacturing one.

  20. Optimal Model-Based Control in HVAC Systems

    DEFF Research Database (Denmark)

    Komareji, Mohammad; Stoustrup, Jakob; Rasmussen, Henrik

    2008-01-01

    is developed. Then the optimal control structure is designed and implemented. The HVAC system is splitted into two subsystems. By selecting the right set-points and appropriate cost functions for each subsystem controller the optimal control strategy is respected to gaurantee the minimum thermal and electrical......This paper presents optimal model-based control of a heating, ventilating, and air-conditioning (HVAC) system. This HVAC system is made of two heat exchangers: an air-to-air heat exchanger (a rotary wheel heat recovery) and a water-to- air heat exchanger. First dynamic model of the HVAC system...... energy consumption. Finally, the controller is applied to control the mentioned HVAC system and the results show that the expected goals are fulfilled....

  1. Development of a VVER-1000 core loading pattern optimization program based on perturbation theory

    International Nuclear Information System (INIS)

    Hosseini, Mohammad; Vosoughi, Naser

    2012-01-01

    Highlights: ► We use perturbation theory to find an optimum fuel loading pattern in a VVER-1000. ► We provide a software for in-core fuel management optimization. ► We consider two objectives for our method (perturbation theory). ► We show that perturbation theory method is very fast and accurate for optimization. - Abstract: In-core nuclear fuel management is one of the most important concerns in the design of nuclear reactors. Two main goals in core fuel loading pattern design optimization are maximizing the core effective multiplication factor in order to extract the maximum energy, and keeping the local power peaking factor lower than a predetermined value to maintain the fuel integrity. Because of the numerous possible patterns of fuel assemblies in the reactor core, finding the best configuration is so important and challenging. Different techniques for optimization of fuel loading pattern in the reactor core have been introduced by now. In this study, a software is programmed in C language to find an order of the fuel loading pattern of a VVER-1000 reactor core using the perturbation theory. Our optimization method is based on minimizing the radial power peaking factor. The optimization process launches by considering an initial loading pattern and the specifications of the fuel assemblies which are given as the input of the software. The results on a typical VVER-1000 reactor reveal that the method could reach to a pattern with an allowed radial power peaking factor and increases the cycle length 1.1 days, as well.

  2. Think Warm Thoughts: Plan Ahead for Summertime Information Literacy Programs! The College Connection

    Science.gov (United States)

    Kasowitz-Scheer, Abby

    2009-01-01

    It's winter! While it is frosty outside, one can at least think warm thoughts by starting now to plan ahead for summer information literacy programs. This article is designed to provide some ideas for planning next summer's reading, sleuthing, and research programs. It features a variety of programs organized by academic librarians this past…

  3. Defining a region of optimization based on engine usage data

    Science.gov (United States)

    Jiang, Li; Lee, Donghoon; Yilmaz, Hakan; Stefanopoulou, Anna

    2015-08-04

    Methods and systems for engine control optimization are provided. One or more operating conditions of a vehicle engine are detected. A value for each of a plurality of engine control parameters is determined based on the detected one or more operating conditions of the vehicle engine. A range of the most commonly detected operating conditions of the vehicle engine is identified and a region of optimization is defined based on the range of the most commonly detected operating conditions of the vehicle engine. The engine control optimization routine is initiated when the one or more operating conditions of the vehicle engine are within the defined region of optimization.

  4. Reliability-based trajectory optimization using nonintrusive polynomial chaos for Mars entry mission

    Science.gov (United States)

    Huang, Yuechen; Li, Haiyang

    2018-06-01

    This paper presents the reliability-based sequential optimization (RBSO) method to settle the trajectory optimization problem with parametric uncertainties in entry dynamics for Mars entry mission. First, the deterministic entry trajectory optimization model is reviewed, and then the reliability-based optimization model is formulated. In addition, the modified sequential optimization method, in which the nonintrusive polynomial chaos expansion (PCE) method and the most probable point (MPP) searching method are employed, is proposed to solve the reliability-based optimization problem efficiently. The nonintrusive PCE method contributes to the transformation between the stochastic optimization (SO) and the deterministic optimization (DO) and to the approximation of trajectory solution efficiently. The MPP method, which is used for assessing the reliability of constraints satisfaction only up to the necessary level, is employed to further improve the computational efficiency. The cycle including SO, reliability assessment and constraints update is repeated in the RBSO until the reliability requirements of constraints satisfaction are satisfied. Finally, the RBSO is compared with the traditional DO and the traditional sequential optimization based on Monte Carlo (MC) simulation in a specific Mars entry mission to demonstrate the effectiveness and the efficiency of the proposed method.

  5. A systematic optimization for graphene-based supercapacitors

    Science.gov (United States)

    Deuk Lee, Sung; Lee, Han Sung; Kim, Jin Young; Jeong, Jaesik; Kahng, Yung Ho

    2017-08-01

    Increasing the energy-storage density for supercapacitors is critical for their applications. Many researchers have attempted to identify optimal candidate component materials to achieve this goal, but investigations into systematically optimizing their mixing rate for maximizing the performance of each candidate material have been insufficient, which hinders the progress in their technology. In this study, we employ a statistically systematic method to determine the optimum mixing ratio of three components that constitute graphene-based supercapacitor electrodes: reduced graphene oxide (rGO), acetylene black (AB), and polyvinylidene fluoride (PVDF). By using the extreme-vertices design, the optimized proportion is determined to be (rGO: AB: PVDF  =  0.95: 0.00: 0.05). The corresponding energy-storage density increases by a factor of 2 compared with that of non-optimized electrodes. Electrochemical and microscopic analyses are performed to determine the reason for the performance improvements.

  6. An Improved Ensemble of Random Vector Functional Link Networks Based on Particle Swarm Optimization with Double Optimization Strategy.

    Science.gov (United States)

    Ling, Qing-Hua; Song, Yu-Qing; Han, Fei; Yang, Dan; Huang, De-Shuang

    2016-01-01

    For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system.

  7. Baseline Predictors of Missed Visits in the Look AHEAD Study

    Science.gov (United States)

    Fitzpatrick, Stephanie L.; Jeffery, Robert; Johnson, Karen C.; Roche, Cathy C.; Van Dorsten, Brent; Gee, Molly; Johnson, Ruby Ann; Charleston, Jeanne; Dotson, Kathy; Walkup, Michael P.; Hill-Briggs, Felicia; Brancati, Frederick L.

    2013-01-01

    Objective To identify baseline attributes associated with consecutively missed data collection visits during the first 48 months of Look AHEAD—a randomized, controlled trial in 5145 overweight/obese adults with type 2 diabetes designed to determine the long-term health benefits of weight loss achieved by lifestyle change. Design and Methods The analyzed sample consisted of 5016 participants who were alive at month 48 and enrolled at Look AHEAD sites. Demographic, baseline behavior, psychosocial factors, and treatment randomization were included as predictors of missed consecutive visits in proportional hazard models. Results In multivariate Cox proportional hazard models, baseline attributes of participants who missed consecutive visits (n=222) included: younger age ( Hazard Ratio [HR] 1.18 per 5 years younger; 95% Confidence Interval 1.05, 1.30), higher depression score (HR 1.04; 1.01, 1.06), non-married status (HR 1.37; 1.04, 1.82), never self-weighing prior to enrollment (HR 2.01; 1.25, 3.23), and randomization to minimal vs. intensive lifestyle intervention (HR 1.46; 1.11, 1.91). Conclusions Younger age, symptoms of depression, non-married status, never self-weighing, and randomization to minimal intervention were associated with a higher likelihood of missing consecutive data collection visits, even in a high-retention trial like Look AHEAD. Whether modifications to screening or retention efforts targeted to these attributes might enhance long-term retention in behavioral trials requires further investigation. PMID:23996977

  8. Risk-based optimization of land reclamation

    International Nuclear Information System (INIS)

    Lendering, K.T.; Jonkman, S.N.; Gelder, P.H.A.J.M. van; Peters, D.J.

    2015-01-01

    Large-scale land reclamations are generally constructed by means of a landfill well above mean sea level. This can be costly in areas where good quality fill material is scarce. An alternative to save materials and costs is a ‘polder terminal’. The quay wall acts as a flood defense and the terminal level is well below the level of the quay wall. Compared with a conventional terminal, the costs are lower, but an additional flood risk is introduced. In this paper, a risk-based optimization is developed for a conventional and a polder terminal. It considers the investment and residual flood risk. The method takes into account both the quay wall and terminal level, which determine the probability and damage of flooding. The optimal quay wall level is found by solving a Lambert function numerically. The terminal level is bounded by engineering boundary conditions, i.e. piping and uplift of the cover layer of the terminal yard. It is found that, for a representative case study, the saving of reclamation costs for a polder terminal is larger than the increase of flood risk. The model is applicable to other cases of land reclamation and to similar optimization problems in flood risk management. - Highlights: • A polder terminal can be an attractive alternative for a conventional terminal. • A polder terminal is feasible at locations with high reclamation cost. • A risk-based approach is required to determine the optimal protection levels. • The depth of the polder terminal yard is bounded by uplifting of the cover layer. • This paper can support decisions regarding alternatives for port expansions.

  9. Caffeine Enhances Memory Performance in Young Adults during Their Non-optimal Time of Day.

    Science.gov (United States)

    Sherman, Stephanie M; Buckley, Timothy P; Baena, Elsa; Ryan, Lee

    2016-01-01

    Many college students struggle to perform well on exams in the early morning. Although students drink caffeinated beverages to feel more awake, it is unclear whether these actually improve performance. After consuming coffee (caffeinated or decaffeinated), college-age adults completed implicit and explicit memory tasks in the early morning and late afternoon (Experiment 1). During the morning, participants ingesting caffeine demonstrated a striking improvement in explicit memory, but not implicit memory. Caffeine did not alter memory performance in the afternoon. In Experiment 2, participants engaged in cardiovascular exercise in order to examine whether increases in physiological arousal similarly improved memory. Despite clear increases in physiological arousal, exercise did not improve memory performance compared to a stretching control condition. These results suggest that caffeine has a specific benefit for memory during students' non-optimal time of day - early morning. These findings have real-world implications for students taking morning exams.

  10. Maximum length scale in density based topology optimization

    DEFF Research Database (Denmark)

    Lazarov, Boyan Stefanov; Wang, Fengwen

    2017-01-01

    The focus of this work is on two new techniques for imposing maximum length scale in topology optimization. Restrictions on the maximum length scale provide designers with full control over the optimized structure and open possibilities to tailor the optimized design for broader range...... of manufacturing processes by fulfilling the associated technological constraints. One of the proposed methods is based on combination of several filters and builds on top of the classical density filtering which can be viewed as a low pass filter applied to the design parametrization. The main idea...

  11. Optimal Scheduling of a Battery Energy Storage System with Electric Vehicles’ Auxiliary for a Distribution Network with Renewable Energy Integration

    Directory of Open Access Journals (Sweden)

    Yuqing Yang

    2015-09-01

    Full Text Available With global conventional energy depletion, as well as environmental pollution, utilizing renewable energy for power supply is the only way for human beings to survive. Currently, distributed generation incorporated into a distribution network has become the new trend, with the advantages of controllability, flexibility and tremendous potential. However, the fluctuation of distributed energy resources (DERs is still the main concern for accurate deployment. Thus, a battery energy storage system (BESS has to be involved to mitigate the bad effects of DERs’ integration. In this paper, optimal scheduling strategies for BESS operation have been proposed, to assist with consuming the renewable energy, reduce the active power loss, alleviate the voltage fluctuation and minimize the electricity cost. Besides, the electric vehicles (EVs considered as the auxiliary technique are also introduced to attenuate the DERs’ influence. Moreover, both day-ahead and real-time operation scheduling strategies were presented under the consideration with the constraints of BESS and the EVs’ operation, and the optimization was tackled by a fuzzy mathematical method and an improved particle swarm optimization (IPSO algorithm. Furthermore, the test system for the proposed strategies is a real distribution network with renewable energy integration. After simulation, the proposed scheduling strategies have been verified to be extremely effective for the enhancement of the distribution network characteristics.

  12. Optimal Management Of Renewable-Based Mgs An Intelligent Approach Through The Evolutionary Algorithm

    Directory of Open Access Journals (Sweden)

    Mehdi Nafar

    2015-08-01

    Full Text Available Abstract- This article proposes a probabilistic frame built on Scenario fabrication to considerate the uncertainties in the finest action managing of Micro Grids MGs. The MG contains different recoverable energy resources such as Wind Turbine WT Micro Turbine MT Photovoltaic PV Fuel Cell FC and one battery as the storing device. The advised frame is based on scenario generation and Roulette wheel mechanism to produce different circumstances for handling the uncertainties of altered factors. It habits typical spreading role as a probability scattering function of random factors. The uncertainties which are measured in this paper are grid bid alterations cargo request calculating error and PV and WT yield power productions. It is well-intentioned to asset that solving the MG difficult for 24 hours of a day by considering diverse uncertainties and different constraints needs one powerful optimization method that can converge fast when it doesnt fall in local optimal topic. Simultaneously single Group Search Optimization GSO system is presented to vision the total search space globally. The GSO algorithm is instigated from group active of beasts. Also the GSO procedure one change is similarly planned for this algorithm. The planned context and way is applied o one test grid-connected MG as a typical grid.

  13. A QFD-based optimization method for a scalable product platform

    Science.gov (United States)

    Luo, Xinggang; Tang, Jiafu; Kwong, C. K.

    2010-02-01

    In order to incorporate the customer into the early phase of the product development cycle and to better satisfy customers' requirements, this article adopts quality function deployment (QFD) for optimal design of a scalable product platform. A five-step QFD-based method is proposed to determine the optimal values for platform engineering characteristics (ECs) and non-platform ECs of the products within a product family. First of all, the houses of quality (HoQs) for all product variants are developed and a QFD-based optimization approach is used to determine the optimal ECs for each product variant. Sensitivity analysis is performed for each EC with respect to overall customer satisfaction (OCS). Based on the obtained sensitivity indices of ECs, a mathematical model is established to simultaneously optimize the values of the platform and the non-platform ECs. Finally, by comparing and analysing the optimal solutions with different number of platform ECs, the ECs with which the worst OCS loss can be avoided are selected as platform ECs. An illustrative example is used to demonstrate the feasibility of this method. A comparison between the proposed method and a two-step approach is conducted on the example. The comparison shows that, as a kind of single-stage approach, the proposed method yields better average degree of customer satisfaction due to the simultaneous optimization of platform and non-platform ECs.

  14. Tomographic imaging of rock conditions ahead of mining using the shearer as a seismic source - A feasibility study

    Energy Technology Data Exchange (ETDEWEB)

    Luo, X.; King, A.; Van de Werken, M. [CSIRO, Brisbane, Qld. (Australia)

    2009-11-15

    Roof falls due to poor rock conditions in a coal longwall panel may threaten miner's life and cause significant interruption to mine production. There has been a requirement for technologies that are capable of imaging the rock conditions in longwall coal mining, ahead of the working face and without any interruption to production. A feasibility study was carried out to investigate the characteristics of seismic signals generated by the continuous coal cutter (shearer) and recorded by geophone arrays deployed ahead of the working face, for the purpose of seismic tomographic imaging of roof strata condition before mining. Two experiments were conducted at a coal mine using two arrays of geophones. The experiments have demonstrated that the longwall shearer generates strong and low-frequency (similar to 40 Hz) seismic energy that can be adequately detected by geophones deployed in shallow boreholes along the roadways as far as 300 m from the face. Using noise filtering and signal cross correlation techniques, the seismic arrival times associated with the shearer cutting can be reliably determined. It has proved the concept that velocity variations ahead of the face can be mapped out using tomographic techniques while mining is in progress.

  15. Optimal diabatic dynamics of Majorana-based quantum gates

    Science.gov (United States)

    Rahmani, Armin; Seradjeh, Babak; Franz, Marcel

    2017-08-01

    In topological quantum computing, unitary operations on qubits are performed by adiabatic braiding of non-Abelian quasiparticles, such as Majorana zero modes, and are protected from local environmental perturbations. In the adiabatic regime, with timescales set by the inverse gap of the system, the errors can be made arbitrarily small by performing the process more slowly. To enhance the performance of quantum information processing with Majorana zero modes, we apply the theory of optimal control to the diabatic dynamics of Majorana-based qubits. While we sacrifice complete topological protection, we impose constraints on the optimal protocol to take advantage of the nonlocal nature of topological information and increase the robustness of our gates. By using the Pontryagin's maximum principle, we show that robust equivalent gates to perfect adiabatic braiding can be implemented in finite times through optimal pulses. In our implementation, modifications to the device Hamiltonian are avoided. Focusing on thermally isolated systems, we study the effects of calibration errors and external white and 1 /f (pink) noise on Majorana-based gates. While a noise-induced antiadiabatic behavior, where a slower process creates more diabatic excitations, prohibits indefinite enhancement of the robustness of the adiabatic scheme, our fast optimal protocols exhibit remarkable stability to noise and have the potential to significantly enhance the practical performance of Majorana-based information processing.

  16. Shape signature based on Ricci flow and optimal mass transportation

    Science.gov (United States)

    Luo, Wei; Su, Zengyu; Zhang, Min; Zeng, Wei; Dai, Junfei; Gu, Xianfeng

    2014-11-01

    A shape signature based on surface Ricci flow and optimal mass transportation is introduced for the purpose of surface comparison. First, the surface is conformally mapped onto plane by Ricci flow, which induces a measure on the planar domain. Second, the unique optimal mass transport map is computed that transports the new measure to the canonical measure on the plane. The map is obtained by a convex optimization process. This optimal transport map encodes all the information of the Riemannian metric on the surface. The shape signature consists of the optimal transport map, together with the mean curvature, which can fully recover the original surface. The discrete theories of surface Ricci flow and optimal mass transportation are explained thoroughly. The algorithms are given in detail. The signature is tested on human facial surfaces with different expressions accquired by structured light 3-D scanner based on phase-shifting method. The experimental results demonstrate the efficiency and efficacy of the method.

  17. Fast N-Gram Language Model Look-Ahead for Decoders With Static Pronunciation Prefix Trees

    NARCIS (Netherlands)

    Huijbregts, M.A.H.; Ordelman, Roeland J.F.; de Jong, Franciska M.G.

    2008-01-01

    Decoders that make use of token-passing restrict their search space by various types of token pruning. With use of the Language Model Look-Ahead (LMLA) technique it is possible to increase the number of tokens that can be pruned without loss of decoding precision. Unfortunately, for token passing

  18. Looking ahead of a tunnel boring machine with 2-D SH full waveform inversion

    NARCIS (Netherlands)

    Pisupati, P.B.; Mulder, W.A.; Drijkoningen, G.G.; Reijnen, R.

    2015-01-01

    In the near-surface with unconsolidated soils, shear properties can be well imaged, sometimes better than P-wave properties. To facilitate ground prediction ahead of a tunnel boring machine (TBM), active ‘surveys’ with shear-wave vibrators are carried out during boring. In such surveys, only a few

  19. Optimizing Groundwater Monitoring Networks Using Integrated Statistical and Geostatistical Approaches

    Directory of Open Access Journals (Sweden)

    Jay Krishna Thakur

    2015-08-01

    Full Text Available The aim of this work is to investigate new approaches using methods based on statistics and geo-statistics for spatio-temporal optimization of groundwater monitoring networks. The formulated and integrated methods were tested with the groundwater quality data set of Bitterfeld/Wolfen, Germany. Spatially, the monitoring network was optimized using geo-statistical methods. Temporal optimization of the monitoring network was carried out using Sen’s method (1968. For geostatistical network optimization, a geostatistical spatio-temporal algorithm was used to identify redundant wells in 2- and 2.5-D Quaternary and Tertiary aquifers. Influences of interpolation block width, dimension, contaminant association, groundwater flow direction and aquifer homogeneity on statistical and geostatistical methods for monitoring network optimization were analysed. The integrated approach shows 37% and 28% redundancies in the monitoring network in Quaternary aquifer and Tertiary aquifer respectively. The geostatistical method also recommends 41 and 22 new monitoring wells in the Quaternary and Tertiary aquifers respectively. In temporal optimization, an overall optimized sampling interval was recommended in terms of lower quartile (238 days, median quartile (317 days and upper quartile (401 days in the research area of Bitterfeld/Wolfen. Demonstrated methods for improving groundwater monitoring network can be used in real monitoring network optimization with due consideration given to influencing factors.

  20. Reliability-Based Optimization of Series Systems of Parallel Systems

    DEFF Research Database (Denmark)

    Enevoldsen, I.; Sørensen, John Dalsgaard

    Reliability-based design of structural systems is considered. Especially systems where the reliability model is a series system of parallel systems are analysed. A sensitivity analysis for this class of problems is presented. Direct and sequential optimization procedures to solve the optimization...