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.

Base stock system for patient vs impatient customers with varying demand distribution

Science.gov (United States)

Fathima, Dowlath; Uduman, P. Sheik

2013-09-01

An optimal Base-Stock inventory policy for Patient and Impatient Customers using finite-horizon models is examined. The Base stock system for Patient and Impatient customers is a different type of inventory policy. In case of the model I, Base stock for Patient customer case is evaluated using the Truncated Exponential Distribution. The model II involves the study of Base-stock inventory policies for Impatient customer. A study on these systems reveals that the Customers wait until the arrival of the next order or the customers leaves the system which leads to lost sale. In both the models demand during the period [0, t] is taken to be a random variable. In this paper, Truncated Exponential Distribution satisfies the Base stock policy for the patient customer as a continuous model. So far the Base stock for Impatient Customers leaded to a discrete case but, in this paper we have modeled this condition into a continuous case. We justify this approach mathematically and also numerically.

Distributed control system for demand response by servers

Science.gov (United States)

Hall, Joseph Edward

Within the broad topical designation of smart grid, research in demand response, or demand-side management, focuses on investigating possibilities for electrically powered devices to adapt their power consumption patterns to better match generation and more efficiently integrate intermittent renewable energy sources, especially wind. Devices such as battery chargers, heating and cooling systems, and computers can be controlled to change the time, duration, and magnitude of their power consumption while still meeting workload constraints such as deadlines and rate of throughput. This thesis presents a system by which a computer server, or multiple servers in a data center, can estimate the power imbalance on the electrical grid and use that information to dynamically change the power consumption as a service to the grid. Implementation on a testbed demonstrates the system with a hypothetical but realistic usage case scenario of an online video streaming service in which there are workloads with deadlines (high-priority) and workloads without deadlines (low-priority). The testbed is implemented with real servers, estimates the power imbalance from the grid frequency with real-time measurements of the live outlet, and uses a distributed, real-time algorithm to dynamically adjust the power consumption of the servers based on the frequency estimate and the throughput of video transcoder workloads. Analysis of the system explains and justifies multiple design choices, compares the significance of the system in relation to similar publications in the literature, and explores the potential impact of the system.

Machine Learning for Identifying Demand Patterns of Home Energy Management Systems with Dynamic Electricity Pricing

Directory of Open Access Journals (Sweden)

Derck Koolen

2017-11-01

Full Text Available Energy management plays a crucial role in providing necessary system flexibility to deal with the ongoing integration of volatile and intermittent energy sources. Demand Response (DR programs enhance demand flexibility by communicating energy market price volatility to the end-consumer. In such environments, home energy management systems assist the use of flexible end-appliances, based upon the individual consumer’s personal preferences and beliefs. However, with the latter heterogeneously distributed, not all dynamic pricing schemes are equally adequate for the individual needs of households. We conduct one of the first large scale natural experiments, with multiple dynamic pricing schemes for end consumers, allowing us to analyze different demand behavior in relation with household attributes. We apply a spectral relaxation clustering approach to show distinct groups of households within the two most used dynamic pricing schemes: Time-Of-Use and Real-Time Pricing. The results indicate that a more effective design of smart home energy management systems can lead to a better fit between customer and electricity tariff in order to reduce costs, enhance predictability and stability of load and allow for more optimal use of demand flexibility by such systems.

Smart microgrid hierarchical frequency control ancillary service provision based on virtual inertia concept: An integrated demand response and droop controlled distributed generation framework

International Nuclear Information System (INIS)

Rezaei, Navid; Kalantar, Mohsen

2015-01-01

Highlights: • Detailed formulation of the microgrid static and dynamic securities based on droop control and virtual inertia concepts. • Constructing a novel objective function using frequency excursion and rate of change of frequency profiles. • Ensuring the microgrid security subject to the microgrid economic and environmental policies. • Coordinated management of demand response and droop controlled distributed generation resources. • Precise scheduling of day-ahead hierarchical frequency control ancillary services using a scenario based stochastic programming. - Abstract: Low inertia stack, high penetration levels of renewable energy source and great ratio of power deviations in a small power delivery system put microgrid frequency at risk of instability. On the basis of the close coupling between the microgrid frequency and system security requirements, procurement of adequate ancillary services from cost-effective and environmental friendly resources is a great challenge requests an efficient energy management system. Motivated by this need, this paper presents a novel energy management system that is aimed to coordinately manage the demand response and distributed generation resources. The proposed approach is carried out by constructing a hierarchical frequency control structure in which the frequency dependent control functions of the microgrid components are modeled comprehensively. On the basis of the derived modeling, both the static and dynamic frequency securities of an islanded microgrid are provided in primary and secondary control levels. Besides, to cope with the low inertia stack of islanded microgrids, novel virtual inertia concept is devised based on the precise modeling of droop controlled distributed generation resources. The proposed approach is applied to typical test microgrid. Energy and hierarchical reserve resource are scheduled precisely using a scenario-based stochastic programming methodology. Moreover, analyzing the

Demand management in Multi-Stage Distribution Chain

NARCIS (Netherlands)

de Kok, T.; Janssen, F.B.S.L.P.

1996-01-01

In this paper we discuss demand management problems in a multi-stage distribution chain.We focus on distribution chains where demand processes have high variability due to a few large customer orders.We give a possible explanation, and suggest two simple procedures that help to smooth demand.It is

Complexity Dynamic Character Analysis of Retailers Based on the Share of Stochastic Demand and Service

Directory of Open Access Journals (Sweden)

Junhai Ma

2017-01-01

Full Text Available Apart from the price fluctuation, the retailers’ service level becomes another key factor that affects the market demand. This paper depicts a modified price and demand game model based on the stochastic demand and the retailer’s service level which influences the market demand decided by customers’ preference, while the market demand is stochastic in this model. We explore how the price adjustment speed affects the stability of the supply chain system with respect to service level and stochastic demand. The dynamic behavior of the system is researched by simulation and the stability domain and the bifurcation phenomenon are shown clearly. The largest Lyapunov exponent and the chaotic attractor are also given to confirm the chaotic characteristic of the system. The simulation results indicate that relatively small price adjustment speed may maintain the system at stable state. With the price adjustment speed gradually increasing, the price system gets unstable and finally becomes chaotic. This chaotic phenomenon will perturb the product market and this phenomenon should be controlled to keep the system stay in the stable region. So the chaos control is done and the chaos can be controlled completely. The conclusion makes significant contribution to the system referring to the price fluctuation based on the service level and stochastic demand.

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...

The Distributed Geothermal Market Demand Model (dGeo): Documentation

Energy Technology Data Exchange (ETDEWEB)

McCabe, Kevin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Mooney, Meghan E [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Sigrin, Benjamin O [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Gleason, Michael [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Liu, Xiaobing [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2017-11-06

The National Renewable Energy Laboratory (NREL) developed the Distributed Geothermal Market Demand Model (dGeo) as a tool to explore the potential role of geothermal distributed energy resources (DERs) in meeting thermal energy demands in the United States. The dGeo model simulates the potential for deployment of geothermal DERs in the residential and commercial sectors of the continental United States for two specific technologies: ground-source heat pumps (GHP) and geothermal direct use (DU) for district heating. To quantify the opportunity space for these technologies, dGeo leverages a highly resolved geospatial database and robust bottom-up, agent-based modeling framework. This design is consistent with others in the family of Distributed Generation Market Demand models (dGen; Sigrin et al. 2016), including the Distributed Solar Market Demand (dSolar) and Distributed Wind Market Demand (dWind) models. dGeo is intended to serve as a long-term scenario-modeling tool. It has the capability to simulate the technical potential, economic potential, market potential, and technology deployment of GHP and DU through the year 2050 under a variety of user-defined input scenarios. Through these capabilities, dGeo can provide substantial analytical value to various stakeholders interested in exploring the effects of various techno-economic, macroeconomic, financial, and policy factors related to the opportunity for GHP and DU in the United States. This report documents the dGeo modeling design, methodology, assumptions, and capabilities.

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...

Real-time Distributed Economic Dispatch forDistributed Generation Based on Multi-Agent System

DEFF Research Database (Denmark)

Luo, Kui; Wu, Qiuwei; Nielsen, Arne Hejde

2015-01-01

The distributed economic dispatch for distributed generation is formulated as a optimization problem with equality and inequality constraints. An effective distributed approach based on multi-agent system is proposed for solving the economic dispatch problem in this paper. The proposed approach...... consists of two stages. In the first stage, an adjacency average allocation algorithm is proposed to ensure the generation-demand equality. In the second stage, a local replicator dynamics algorithm is applied to achieve nash equilibrium for the power dispatch game. The approach is implemented in a fully...

Towards a dynamic assessment of raw materials criticality: Linking agent-based demand — With material flow supply modelling approaches

International Nuclear Information System (INIS)

Knoeri, Christof; Wäger, Patrick A.; Stamp, Anna; Althaus, Hans-Joerg; Weil, Marcel

2013-01-01

Emerging technologies such as information and communication-, photovoltaic- or battery technologies are expected to increase significantly the demand for scarce metals in the near future. The recently developed methods to evaluate the criticality of mineral raw materials typically provide a ‘snapshot’ of the criticality of a certain material at one point in time by using static indicators both for supply risk and for the impacts of supply restrictions. While allowing for insights into the mechanisms behind the criticality of raw materials, these methods cannot account for dynamic changes in products and/or activities over time. In this paper we propose a conceptual framework intended to overcome these limitations by including the dynamic interactions between different possible demand and supply configurations. The framework integrates an agent-based behaviour model, where demand emerges from individual agent decisions and interaction, into a dynamic material flow model, representing the materials' stocks and flows. Within the framework, the environmental implications of substitution decisions are evaluated by applying life-cycle assessment methodology. The approach makes a first step towards a dynamic criticality assessment and will enhance the understanding of industrial substitution decisions and environmental implications related to critical metals. We discuss the potential and limitation of such an approach in contrast to state-of-the-art methods and how it might lead to criticality assessments tailored to the specific circumstances of single industrial sectors or individual companies. - Highlights: ► Current criticality assessment methods provide a ‘snapshot’ at one point in time. ► They do not account for dynamic interactions between demand and supply. ► We propose a conceptual framework to overcomes these limitations. ► The framework integrates an agent-based behaviour model with a dynamic material flow model. ► The approach proposed makes

Demand Response in Low Voltage Distribution Networks with High PV Penetration

DEFF Research Database (Denmark)

Nainar, Karthikeyan; Pokhrel, Basanta Raj; Pillai, Jayakrishnan Radhakrishna

2017-01-01

the required flexibility from the electricity market through an aggregator. The optimum demand response enables consumption of maximum renewable energy within the network constraints. Simulation studies are conducted using Matlab and DigSilent Power factory software on a Danish low-voltage distribution system......In this paper, application of demand response to accommodate maximum PV power in a low-voltage distribution network is discussed. A centralized control based on model predictive control method is proposed for the computation of optimal demand response on an hourly basis. The proposed method uses PV...

Load kick-back effects due to activation of demand response in view of distribution grid operation

DEFF Research Database (Denmark)

Han, Xue; Sossan, Fabrizio; Bindner, Henrik W.

2014-01-01

. The paper has shown how aggregated consumption dynamics introduce new peaks in the system due to the synchronous behaviors of a portfolio of homogeneous DSRs, which is instructed by the flexibility management system. This dynamic effect is recognized as load kick-back effect. The impact of load kick......-back effects onto the distribution grid is analysed in this paper by establishing scenarios based on the estimation of DSR penetration levels from the system operator. The results indicate some risks that the activation of demand response may create critical peaks in the local grid due to kick-back effects....

Optimized maritime emergency resource allocation under dynamic demand.

Directory of Open Access Journals (Sweden)

Wenfen Zhang

Full Text Available Emergency resource is important for people evacuation and property rescue when accident occurs. The relief efforts could be promoted by a reasonable emergency resource allocation schedule in advance. As the marine environment is complicated and changeful, the place, type, severity of maritime accident is uncertain and stochastic, bringing about dynamic demand of emergency resource. Considering dynamic demand, how to make a reasonable emergency resource allocation schedule is challenging. The key problem is to determine the optimal stock of emergency resource for supplier centers to improve relief efforts. This paper studies the dynamic demand, and which is defined as a set. Then a maritime emergency resource allocation model with uncertain data is presented. Afterwards, a robust approach is developed and used to make sure that the resource allocation schedule performs well with dynamic demand. Finally, a case study shows that the proposed methodology is feasible in maritime emergency resource allocation. The findings could help emergency manager to schedule the emergency resource allocation more flexibly in terms of dynamic demand.

Predicting U.S. food demand in the 20th century: a new look at system dynamics

Science.gov (United States)

Moorthy, Mukund; Cellier, Francois E.; LaFrance, Jeffrey T.

1998-08-01

The paper describes a new methodology for predicting the behavior of macroeconomic variables. The approach is based on System Dynamics and Fuzzy Inductive Reasoning. A four- layer pseudo-hierarchical model is proposed. The bottom layer makes predications about population dynamics, age distributions among the populace, as well as demographics. The second layer makes predications about the general state of the economy, including such variables as inflation and unemployment. The third layer makes predictions about the demand for certain goods or services, such as milk products, used cars, mobile telephones, or internet services. The fourth and top layer makes predictions about the supply of such goods and services, both in terms of their prices. Each layer can be influenced by control variables the values of which are only determined at higher levels. In this sense, the model is not strictly hierarchical. For example, the demand for goods at level three depends on the prices of these goods, which are only determined at level four. Yet, the prices are themselves influenced by the expected demand. The methodology is exemplified by means of a macroeconomic model that makes predictions about US food demand during the 20th century.

Modeling workforce demand in North Dakota: a System Dynamics approach

OpenAIRE

Muminova, Adiba

2015-01-01

This study investigates the dynamics behind the workforce demand and attempts to predict the potential effects of future changes in oil prices on workforce demand in North Dakota. The study attempts to join System Dynamics and Input-Output models in order to overcome shortcomings in both of the approaches and gain a more complete understanding of the issue of workforce demand. A system dynamics simulation of workforce demand within different economic sector...

INCREASING RETURNS TO SCALE, DYNAMICS OF INDUSTRIAL STRUCTURE AND SIZE DISTRIBUTION OF FIRMS

Institute of Scientific and Technical Information of China (English)

Ying FAN; Menghui LI; Zengru DI

2006-01-01

A multi-agent model is presented to discuss the market dynamics and the size distribution of firms.The model emphasizes the effects of increasing returns to scale and gives the description of the born and death of adaptive producers. The evolution of market structure and its behavior under the technological shocks are investigated. Its dynamical results are in good agreement with some empirical "stylized facts" of industrial evolution. With the diversity of demand and adaptive growth strategies of firms, the firm size in the generalized model obeys the power-law distribution. Three factors mainly determine the competitive dynamics and the skewed size distributions of firms: 1. Self-reinforcing mechanism; 2. Adaptive firm growing strategies; 3. Demand diversity or widespread heterogeneity in the technological capabilities of firms.

Demand Management Based on Model Predictive Control Techniques

Directory of Open Access Journals (Sweden)

Yasser A. Davizón

2014-01-01

Full Text Available Demand management (DM is the process that helps companies to sell the right product to the right customer, at the right time, and for the right price. Therefore the challenge for any company is to determine how much to sell, at what price, and to which market segment while maximizing its profits. DM also helps managers efficiently allocate undifferentiated units of capacity to the available demand with the goal of maximizing revenue. This paper introduces control system approach to demand management with dynamic pricing (DP using the model predictive control (MPC technique. In addition, we present a proper dynamical system analogy based on active suspension and a stability analysis is provided via the Lyapunov direct method.

Constrained consumption shifting management in the distributed energy resources scheduling considering demand response

International Nuclear Information System (INIS)

Faria, Pedro; Vale, Zita; Baptista, Jose

2015-01-01

Highlights: • Consumption reduction and/or shift to several periods before and after. • Optimization problem for scheduling of demand response and distributed generation. • Minimization of the Virtual Power Player operation (remuneration) costs. • Demand response can be efficient to meet distributed generation shortages. • Consumers benefit with the remuneration of the participation in demand response. - Abstract: Demand response concept has been gaining increasing importance while the success of several recent implementations makes this resource benefits unquestionable. This happens in a power systems operation environment that also considers an intensive use of distributed generation. However, more adequate approaches and models are needed in order to address the small size consumers and producers aggregation, while taking into account these resources goals. The present paper focuses on the demand response programs and distributed generation resources management by a Virtual Power Player that optimally aims to minimize its operation costs taking the consumption shifting constraints into account. The impact of the consumption shifting in the distributed generation resources schedule is also considered. The methodology is applied to three scenarios based on 218 consumers and 4 types of distributed generation, in a time frame of 96 periods

Definition of Distribution Network Tariffs Considering Distribution Generation and Demand Response

DEFF Research Database (Denmark)

Soares, Tiago; Faria, Pedro; Vale, Zita

2014-01-01

The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits for the wh......The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits...... the determination of topological distribution factors, and consequent application of the MW-mile method. The application of the proposed tariffs definition methodology is illustrated in a distribution network with 33 buses, 66 DG units, and 32 consumers with DR capacity...

Providing frequency regulation reserve services using demand response scheduling

International Nuclear Information System (INIS)

Motalleb, Mahdi; Thornton, Matsu; Reihani, Ehsan; Ghorbani, Reza

2016-01-01

Highlights: • Proposing a market model for contingency reserve services using demand response. • Considering transient limitations of grid frequency for inverter-based generations. • Price-sensitive scheduling of residential batteries and water heaters using dynamic programming. • Calculating the profits of both generation companies and demand response aggregators. - Abstract: During power grid contingencies, frequency regulation is a primary concern. Historically, frequency regulation during contingency events has been the sole responsibility of the power utility. We present a practical method of using distributed demand response scheduling to provide frequency regulation during contingency events. This paper discusses the implementation of a control system model for the use of distributed energy storage systems such as battery banks and electric water heaters as a source of ancillary services. We present an algorithm which handles the optimization of demand response scheduling for normal operation and during contingency events. We use dynamic programming as an optimization tool. A price signal is developed using optimal power flow calculations to determine the locational marginal price of electricity, while sensor data for water usage is also collected. Using these inputs to dynamic programming, the optimal control signals are given as output. We assume a market model in which distributed demand response resources are sold as a commodity on the open market and profits from demand response aggregators as brokers of distributed demand response resources can be calculated. In considering control decisions for regulation of transient changes in frequency, we focus on IEEE standard 1547 in order to prevent the safety shut-off of inverter-based generation and further exacerbation of frequency droop. This method is applied to IEEE case 118 as a demonstration of the method in practice.

Smart Demand for Improving Short-term Voltage Control on Distribution Networks

DEFF Research Database (Denmark)

Garcia-Valle, Rodrigo; P. Da Silva, Luiz C.; Xu, Zhao

2009-01-01

customer integration to aid power system performance is almost inevitable. This study introduces a new type of smart demand side technology, denoted demand as voltage controlled reserve (DVR), to improve short-term voltage control, where customers are expected to play a more dynamic role to improve voltage...... control. The technology can be provided by thermostatically controlled loads as well as other types of load. This technology is proven to be effective in case of distribution systems with a large composition of induction motors, where the voltage presents a slow recovery characteristic due to deceleration...... of the motors during faults. This study presents detailed models, discussion and simulation tests to demonstrate the technical viability and effectiveness of the DVR technology for short-term voltage control....

Dynamic temperature dependence patterns in future energy demand models in the context of climate change

International Nuclear Information System (INIS)

Hekkenberg, M.; Moll, H.C.; Uiterkamp, A.J.M. Schoot

2009-01-01

Energy demand depends on outdoor temperature in a 'u' shaped fashion. Various studies have used this temperature dependence to investigate the effects of climate change on energy demand. Such studies contain implicit or explicit assumptions to describe expected socio-economic changes that may affect future energy demand. This paper critically analyzes these implicit or explicit assumptions and their possible effect on the studies' outcomes. First we analyze the interaction between the socio-economic structure and the temperature dependence pattern (TDP) of energy demand. We find that socio-economic changes may alter the TDP in various ways. Next we investigate how current studies manage these dynamics in socio-economic structure. We find that many studies systematically misrepresent the possible effect of socio-economic changes on the TDP of energy demand. Finally, we assess the consequences of these misrepresentations in an energy demand model based on temperature dependence and climate scenarios. Our model results indicate that expected socio-economic dynamics generally lead to an underestimation of future energy demand in models that misrepresent such dynamics. We conclude that future energy demand models should improve the incorporation of socio-economic dynamics. We propose dynamically modeling several key parameters and using direct meteorological data instead of degree days. (author)

Towards a dynamic assessment of raw materials criticality: linking agent-based demand--with material flow supply modelling approaches.

Science.gov (United States)

Knoeri, Christof; Wäger, Patrick A; Stamp, Anna; Althaus, Hans-Joerg; Weil, Marcel

2013-09-01

Emerging technologies such as information and communication-, photovoltaic- or battery technologies are expected to increase significantly the demand for scarce metals in the near future. The recently developed methods to evaluate the criticality of mineral raw materials typically provide a 'snapshot' of the criticality of a certain material at one point in time by using static indicators both for supply risk and for the impacts of supply restrictions. While allowing for insights into the mechanisms behind the criticality of raw materials, these methods cannot account for dynamic changes in products and/or activities over time. In this paper we propose a conceptual framework intended to overcome these limitations by including the dynamic interactions between different possible demand and supply configurations. The framework integrates an agent-based behaviour model, where demand emerges from individual agent decisions and interaction, into a dynamic material flow model, representing the materials' stocks and flows. Within the framework, the environmental implications of substitution decisions are evaluated by applying life-cycle assessment methodology. The approach makes a first step towards a dynamic criticality assessment and will enhance the understanding of industrial substitution decisions and environmental implications related to critical metals. We discuss the potential and limitation of such an approach in contrast to state-of-the-art methods and how it might lead to criticality assessments tailored to the specific circumstances of single industrial sectors or individual companies. Copyright © 2013 Elsevier B.V. All rights reserved.

On the relation between the mean and variance of delay in dynamic queues with random capacity and demand

DEFF Research Database (Denmark)

Fosgerau, Mogens

2010-01-01

This paper investigates the distribution of delays during a repeatedly occurring demand peak in a congested facility with random capacity and demand, such as an airport or an urban road. Congestion is described in the form of a dynamic queue using the Vickrey bottleneck model and assuming Nash...

Emergency material allocation with time-varying supply-demand based on dynamic optimization method for river chemical spills.

Science.gov (United States)

Liu, Jie; Guo, Liang; Jiang, Jiping; Jiang, Dexun; Wang, Peng

2018-04-13

Aiming to minimize the damage caused by river chemical spills, efficient emergency material allocation is critical for an actual emergency rescue decision-making in a quick response. In this study, an emergency material allocation framework based on time-varying supply-demand constraint is developed to allocate emergency material, minimize the emergency response time, and satisfy the dynamic emergency material requirements in post-accident phases dealing with river chemical spills. In this study, the theoretically critical emergency response time is firstly obtained for the emergency material allocation system to select a series of appropriate emergency material warehouses as potential supportive centers. Then, an enumeration method is applied to identify the practically critical emergency response time, the optimum emergency material allocation and replenishment scheme. Finally, the developed framework is applied to a computational experiment based on south-to-north water transfer project in China. The results illustrate that the proposed methodology is a simple and flexible tool for appropriately allocating emergency material to satisfy time-dynamic demands during emergency decision-making. Therefore, the decision-makers can identify an appropriate emergency material allocation scheme in a balance between time-effective and cost-effective objectives under the different emergency pollution conditions.

Multi-Model Prediction for Demand Forecast in Water Distribution Networks

Directory of Open Access Journals (Sweden)

Rodrigo Lopez Farias

2018-03-01

Full Text Available This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+ for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction is forecasted and the pattern mode estimated using a Nearest Neighbor (NN classifier and a Calendar. The patterns are updated via a simple Moving Average scheme. The NN classifier and the Calendar are executed simultaneously every period and the most suited model for prediction is selected using a probabilistic approach. The proposed solution for water demand forecast is compared against Radial Basis Function Artificial Neural Networks (RBF-ANN, the statistical Autoregressive Integrated Moving Average (ARIMA, and Double Seasonal Holt-Winters (DSHW approaches, providing the best results when applied to real demand of the Barcelona Water Distribution Network. QMMP+ has demonstrated that the special modelling treatment of water consumption patterns improves the forecasting accuracy.

Declarative Event-Based Workflow as Distributed Dynamic Condition Response Graphs

DEFF Research Database (Denmark)

Hildebrandt, Thomas; Mukkamala, Raghava Rao

2010-01-01

We present Dynamic Condition Response Graphs (DCR Graphs) as a declarative, event-based process model inspired by the workflow language employed by our industrial partner and conservatively generalizing prime event structures. A dynamic condition response graph is a directed graph with nodes repr...... exemplify the use of distributed DCR Graphs on a simple workflow taken from a field study at a Danish hospital, pointing out their flexibility compared to imperative workflow models. Finally we provide a mapping from DCR Graphs to Buchi-automata....

Dynamic inventory rationing strategies for inventory systems with two demand classes, Poisson demand and backordering

NARCIS (Netherlands)

Teunter, Ruud H.; Haneveld, Willem K. Klein

2008-01-01

We study inventory systems with two demand classes (critical and non-critical), Poisson demand and backordering. We analyze dynamic rationing strategies where the number of items reserved for critical demand depends on the remaining time until the next order arrives. Different from results in the

Demand Response Programs Design and Use Considering Intensive Penetration of Distributed Generation

Directory of Open Access Journals (Sweden)

Pedro Faria

2015-06-01

Full Text Available Further improvements in demand response programs implementation are needed in order to take full advantage of this resource, namely for the participation in energy and reserve market products, requiring adequate aggregation and remuneration of small size resources. The present paper focuses on SPIDER, a demand response simulation that has been improved in order to simulate demand response, including realistic power system simulation. For illustration of the simulator’s capabilities, the present paper is proposes a methodology focusing on the aggregation of consumers and generators, providing adequate tolls for the demand response program’s adoption by evolved players. The methodology proposed in the present paper focuses on a Virtual Power Player that manages and aggregates the available demand response and distributed generation resources in order to satisfy the required electrical energy demand and reserve. The aggregation of resources is addressed by the use of clustering algorithms, and operation costs for the VPP are minimized. The presented case study is based on a set of 32 consumers and 66 distributed generation units, running on 180 distinct operation scenarios.

Electricity demand savings from distributed solar photovoltaics

International Nuclear Information System (INIS)

Glassmire, John; Komor, Paul; Lilienthal, Peter

2012-01-01

Due largely to recent dramatic cost reductions, photovoltaics (PVs) are poised to make a significant contribution to electricity supply. In particular, distributed applications of PV on rooftops, brownfields, and other similar applications – hold great technical potential. In order for this potential to be realized, however, PV must be “cost-effective”—that is, it must be sufficiently financially appealing to attract large amounts of investment capital. Electricity costs for most commercial and industrial end-users come in two forms: consumption (kWh) and demand (kW). Although rates vary, for a typical larger commercial or industrial user, demand charges account for about ∼40% of total electricity costs. This paper uses a case study of PV on a large university campus to reveal that even very large PV installations will often provide very small demand reductions. As a result, it will be very difficult for PV to demonstrate cost-effectiveness for large commercial customers, even if PV costs continue to drop. If policymakers would like PV to play a significant role in electricity generation – for economic development, carbon reduction, or other reasons – then rate structures will need significant adjustment, or improved distributed storage technologies will be needed. - Highlights: ► Demand charges typically account for ∼40% of total electricity costs for larger electricity users. ► Distributed photovoltaic (PV) systems provide minimal demand charge reductions. ► As a result, PVs are not a financially viable alternative to centralized electricity. ► Electricity rate structures will need changes for PV to be a major electricity source.

Blockchain Based Decentralized Management of Demand Response Programs in Smart Energy Grids

Science.gov (United States)

Pop, Claudia; Cioara, Tudor; Antal, Marcel; Anghel, Ionut; Salomie, Ioan; Bertoncini, Massimo

2018-01-01

In this paper, we investigate the use of decentralized blockchain mechanisms for delivering transparent, secure, reliable, and timely energy flexibility, under the form of adaptation of energy demand profiles of Distributed Energy Prosumers, to all the stakeholders involved in the flexibility markets (Distribution System Operators primarily, retailers, aggregators, etc.). In our approach, a blockchain based distributed ledger stores in a tamper proof manner the energy prosumption information collected from Internet of Things smart metering devices, while self-enforcing smart contracts programmatically define the expected energy flexibility at the level of each prosumer, the associated rewards or penalties, and the rules for balancing the energy demand with the energy production at grid level. Consensus based validation will be used for demand response programs validation and to activate the appropriate financial settlement for the flexibility providers. The approach was validated using a prototype implemented in an Ethereum platform using energy consumption and production traces of several buildings from literature data sets. The results show that our blockchain based distributed demand side management can be used for matching energy demand and production at smart grid level, the demand response signal being followed with high accuracy, while the amount of energy flexibility needed for convergence is reduced. PMID:29315250

Blockchain Based Decentralized Management of Demand Response Programs in Smart Energy Grids.

Science.gov (United States)

Pop, Claudia; Cioara, Tudor; Antal, Marcel; Anghel, Ionut; Salomie, Ioan; Bertoncini, Massimo

2018-01-09

In this paper, we investigate the use of decentralized blockchain mechanisms for delivering transparent, secure, reliable, and timely energy flexibility, under the form of adaptation of energy demand profiles of Distributed Energy Prosumers, to all the stakeholders involved in the flexibility markets (Distribution System Operators primarily, retailers, aggregators, etc.). In our approach, a blockchain based distributed ledger stores in a tamper proof manner the energy prosumption information collected from Internet of Things smart metering devices, while self-enforcing smart contracts programmatically define the expected energy flexibility at the level of each prosumer, the associated rewards or penalties, and the rules for balancing the energy demand with the energy production at grid level. Consensus based validation will be used for demand response programs validation and to activate the appropriate financial settlement for the flexibility providers. The approach was validated using a prototype implemented in an Ethereum platform using energy consumption and production traces of several buildings from literature data sets. The results show that our blockchain based distributed demand side management can be used for matching energy demand and production at smart grid level, the demand response signal being followed with high accuracy, while the amount of energy flexibility needed for convergence is reduced.

Blockchain Based Decentralized Management of Demand Response Programs in Smart Energy Grids

Directory of Open Access Journals (Sweden)

Claudia Pop

2018-01-01

Full Text Available In this paper, we investigate the use of decentralized blockchain mechanisms for delivering transparent, secure, reliable, and timely energy flexibility, under the form of adaptation of energy demand profiles of Distributed Energy Prosumers, to all the stakeholders involved in the flexibility markets (Distribution System Operators primarily, retailers, aggregators, etc.. In our approach, a blockchain based distributed ledger stores in a tamper proof manner the energy prosumption information collected from Internet of Things smart metering devices, while self-enforcing smart contracts programmatically define the expected energy flexibility at the level of each prosumer, the associated rewards or penalties, and the rules for balancing the energy demand with the energy production at grid level. Consensus based validation will be used for demand response programs validation and to activate the appropriate financial settlement for the flexibility providers. The approach was validated using a prototype implemented in an Ethereum platform using energy consumption and production traces of several buildings from literature data sets. The results show that our blockchain based distributed demand side management can be used for matching energy demand and production at smart grid level, the demand response signal being followed with high accuracy, while the amount of energy flexibility needed for convergence is reduced.

Dynamic Pricing in Cloud Manufacturing Systems under Combined Effects of Consumer Structure, Negotiation, and Demand

Directory of Open Access Journals (Sweden)

Wei Peng

2017-01-01

Full Text Available In this study, we proposed a game-theory based framework to model the dynamic pricing process in the cloud manufacturing (CMfg system. We considered a service provider (SP, a broker agent (BA, and a dynamic service demander (SD population that is composed of price takers and bargainers in this study. The pricing processes under linear demand and constant elasticity demand were modeled, respectively. The combined effects of SD population structure, negotiation, and demand forms on the SP’s and the BA’s equilibrium prices and expected revenues were examined. We found that the SP’s optimal wholesale price, the BA’s optimal reservation price, and posted price all increase with the proportion of price takers under linear demand but decrease with it under constant elasticity demand. We also found that the BA’s optimal reservation price increases with bargainers’ power no matter under what kind of demand. Through analyzing the participants’ revenues, we showed that a dynamic SD population with a high ratio of price takers would benefit the SP and the BA.

Demand response scheme based on lottery-like rebates

KAUST Repository

Schwartz, Galina A.; Tembine, Hamidou; Amin, Saurabh; Sastry, S. Shankar

2014-01-01

In this paper, we develop a novel mechanism for reducing volatility of residential demand for electricity. We construct a reward-based (rebate) mechanism that provides consumers with incentives to shift their demand to off-peak time. In contrast to most other mechanisms proposed in the literature, the key feature of our mechanism is its modest requirements on user preferences, i.e., it does not require exact knowledge of user responsiveness to rewards for shifting their demand from the peak to the off-peak time. Specifically, our mechanism utilizes a probabilistic reward structure for users who shift their demand to the off-peak time, and is robust to incomplete information about user demand and/or risk preferences. We approach the problem from the public good perspective, and demonstrate that the mechanism can be implemented via lottery-like schemes. Our mechanism permits to reduce the distribution losses, and thus improve efficiency of electricity distribution. Finally, the mechanism can be readily incorporated into the emerging demand response schemes (e.g., the time-of-day pricing, and critical peak pricing schemes), and has security and privacy-preserving properties.

Demand response scheme based on lottery-like rebates

KAUST Repository

Schwartz, Galina A.

2014-08-24

In this paper, we develop a novel mechanism for reducing volatility of residential demand for electricity. We construct a reward-based (rebate) mechanism that provides consumers with incentives to shift their demand to off-peak time. In contrast to most other mechanisms proposed in the literature, the key feature of our mechanism is its modest requirements on user preferences, i.e., it does not require exact knowledge of user responsiveness to rewards for shifting their demand from the peak to the off-peak time. Specifically, our mechanism utilizes a probabilistic reward structure for users who shift their demand to the off-peak time, and is robust to incomplete information about user demand and/or risk preferences. We approach the problem from the public good perspective, and demonstrate that the mechanism can be implemented via lottery-like schemes. Our mechanism permits to reduce the distribution losses, and thus improve efficiency of electricity distribution. Finally, the mechanism can be readily incorporated into the emerging demand response schemes (e.g., the time-of-day pricing, and critical peak pricing schemes), and has security and privacy-preserving properties.

Feasibility of solid oxide fuel cell dynamic hydrogen coproduction to meet building demand

Science.gov (United States)

Shaffer, Brendan; Brouwer, Jacob

2014-02-01

A dynamic internal reforming-solid oxide fuel cell system model is developed and used to simulate the coproduction of electricity and hydrogen while meeting the measured dynamic load of a typical southern California commercial building. The simulated direct internal reforming-solid oxide fuel cell (DIR-SOFC) system is controlled to become an electrical load following device that well follows the measured building load data (3-s resolution). The feasibility of the DIR-SOFC system to meet the dynamic building demand while co-producing hydrogen is demonstrated. The resulting thermal responses of the system to the electrical load dynamics as well as those dynamics associated with the filling of a hydrogen collection tank are investigated. The DIR-SOFC system model also allows for resolution of the fuel cell species and temperature distributions during these dynamics since thermal gradients are a concern for DIR-SOFC.

Market-based demand forecasting promotes informed strategic financial planning.

Science.gov (United States)

Beech, A J

2001-11-01

Market-based demand forecasting is a method of estimating future demand for a healthcare organization's services by using a broad range of data that describe the nature of demand within the organization's service area. Such data include the primary and secondary service areas, the service-area populations by various demographic groupings, discharge utilization rates, market size, and market share by service line and organizationwide. Based on observable market dynamics, strategic planners can make a variety of explicit assumptions about future trends regarding these data to develop scenarios describing potential future demand. Financial planners then can evaluate each scenario to determine its potential effect on selected financial and operational measures, such as operating margin, days cash on hand, and debt-service coverage, and develop a strategic financial plan that covers a range of contingencies.

Impact of Demand Side Management in Active Distribution Networks

DEFF Research Database (Denmark)

Ponnaganti, Pavani; Bak-Jensen, Birgitte; Pillai, Jayakrishnan Radhakrishna

2017-01-01

Demand Side Management (DSM) is an efficient flexible program which helps distribution network operators to meet the future critical peak demand. It is executed in cases of not only technical issues like voltage sag or swell, transformer burdening, cable congestions, but also to increase the degree...... of visibility in the electricity markets. The aim of this paper is to find the optimal flexible demands that can be shifted to another time in order to operate the active distribution system within secure operating limits. A simple mechanism is proposed for finding the flexibility of the loads where electric...

Dynamic energy-demand models. A comparison

International Nuclear Information System (INIS)

Yi, Feng

2000-01-01

This paper compares two second-generation dynamic energy demand models, a translog (TL) and a general Leontief (GL), in the study of price elasticities and factor substitutions of nine Swedish manufacturing industries: food, textiles, wood, paper, printing, chemicals, non-metallic minerals, base metals and machinery. Several model specifications are tested with likelihood ratio test. There is a disagreement on short-run adjustments; the TL model accepts putty-putty production technology of immediate adjustments, implying equal short- and long-run price elasticities of factors, while the GL model rejects immediate adjustments, giving out short-run elasticities quite different from the long-run. The two models also disagree in substitutability in many cases. 21 refs

A Distributed Intelligent Automated Demand Response Building Management System

Energy Technology Data Exchange (ETDEWEB)

Auslander, David [Univ. of California, Berkeley, CA (United States); Culler, David [Univ. of California, Berkeley, CA (United States); Wright, Paul [Univ. of California, Berkeley, CA (United States); Lu, Yan [Siemens Corporate Research Inc., Princeton, NJ (United States); Piette, Mary [Univ. of California, Berkeley, CA (United States)

2013-03-31

The goal of the 2.5 year Distributed Intelligent Automated Demand Response (DIADR) project was to reduce peak electricity load of Sutardja Dai Hall at UC Berkeley by 30% while maintaining a healthy, comfortable, and productive environment for the occupants. We sought to bring together both central and distributed control to provide “deep” demand response1 at the appliance level of the building as well as typical lighting and HVAC applications. This project brought together Siemens Corporate Research and Siemens Building Technology (the building has a Siemens Apogee Building Automation System (BAS)), Lawrence Berkeley National Laboratory (leveraging their Open Automated Demand Response (openADR), Auto-­Demand Response, and building modeling expertise), and UC Berkeley (related demand response research including distributed wireless control, and grid-­to-­building gateway development). Sutardja Dai Hall houses the Center for Information Technology Research in the Interest of Society (CITRIS), which fosters collaboration among industry and faculty and students of four UC campuses (Berkeley, Davis, Merced, and Santa Cruz). The 141,000 square foot building, occupied in 2009, includes typical office spaces and a nanofabrication laboratory. Heating is provided by a district heating system (steam from campus as a byproduct of the campus cogeneration plant); cooling is provided by one of two chillers: a more typical electric centrifugal compressor chiller designed for the cool months (Nov-­ March) and a steam absorption chiller for use in the warm months (April-­October). Lighting in the open office areas is provided by direct-­indirect luminaries with Building Management System-­based scheduling for open areas, and occupancy sensors for private office areas. For the purposes of this project, we focused on the office portion of the building. Annual energy consumption is approximately 8053 MWh; the office portion is estimated as 1924 MWh. The maximum peak load

Smart Demand Response Based on Smart Homes

Directory of Open Access Journals (Sweden)

Jingang Lai

2015-01-01

Full Text Available Smart homes (SHs are crucial parts for demand response management (DRM of smart grid (SG. The aim of SHs based demand response (DR is to provide a flexible two-way energy feedback whilst (or shortly after the consumption occurs. It can potentially persuade end-users to achieve energy saving and cooperate with the electricity producer or supplier to maintain balance between the electricity supply and demand through the method of peak shaving and valley filling. However, existing solutions are challenged by the lack of consideration between the wide application of fiber power cable to the home (FPCTTH and related users’ behaviors. Based on the new network infrastructure, the design and development of smart DR systems based on SHs are related with not only functionalities as security, convenience, and comfort, but also energy savings. A new multirouting protocol based on Kruskal’s algorithm is designed for the reliability and safety of the SHs distribution network. The benefits of FPCTTH-based SHs are summarized at the end of the paper.

A New Approach to Site Demand-Based Level Inventory Optimization

Science.gov (United States)

2016-06-01

Note: If probability distributions are estimated based on mean and variance , use ˆ qix  and 2ˆ( )qi to generate these. q in , number of...TO SITE DEMAND-BASED LEVEL INVENTORY OPTIMIZATION by Tacettin Ersoz June 2016 Thesis Advisor: Javier Salmeron Second Reader: Emily...DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE A NEW APPROACH TO SITE DEMAND-BASED LEVEL INVENTORY OPTIMIZATION 5. FUNDING NUMBERS 6

Dynamical Models For Prices With Distributed Delays

Directory of Open Access Journals (Sweden)

Mircea Gabriela

2015-06-01

Full Text Available In the present paper we study some models for the price dynamics of a single commodity market. The quantities of supplied and demanded are regarded as a function of time. Nonlinearities in both supply and demand functions are considered. The inventory and the level of inventory are taken into consideration. Due to the fact that the consumer behavior affects commodity demand, and the behavior is influenced not only by the instantaneous price, but also by the weighted past prices, the distributed time delay is introduced. The following kernels are taken into consideration: demand price weak kernel and demand price Dirac kernel. Only one positive equilibrium point is found and its stability analysis is presented. When the demand price kernel is weak, under some conditions of the parameters, the equilibrium point is locally asymptotically stable. When the demand price kernel is Dirac, the existence of the local oscillations is investigated. A change in local stability of the equilibrium point, from stable to unstable, implies a Hopf bifurcation. A family of periodic orbits bifurcates from the positive equilibrium point when the time delay passes through a critical value. The last part contains some numerical simulations to illustrate the effectiveness of our results and conclusions.

An EMD-ANN based prediction methodology for DR driven smart household load demand

NARCIS (Netherlands)

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

2015-01-01

This study proposes a model for the prediction of smart household load demand influenced by a dynamic pricing demand response (DR) program. Price-based DR programs have a considerable impact on household demand pattern due to the expected choice of customers or their home energy management systems

Distributed Generation Market Demand Model (dGen): Documentation

Energy Technology Data Exchange (ETDEWEB)

Sigrin, Benjamin [National Renewable Energy Lab. (NREL), Golden, CO (United States); Gleason, Michael [National Renewable Energy Lab. (NREL), Golden, CO (United States); Preus, Robert [National Renewable Energy Lab. (NREL), Golden, CO (United States); Baring-Gould, Ian [National Renewable Energy Lab. (NREL), Golden, CO (United States); Margolis, Robert [National Renewable Energy Lab. (NREL), Golden, CO (United States)

2016-02-01

The Distributed Generation Market Demand model (dGen) is a geospatially rich, bottom-up, market-penetration model that simulates the potential adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the continental United States through 2050. The National Renewable Energy Laboratory (NREL) developed dGen to analyze the key factors that will affect future market demand for distributed solar, wind, storage, and other DER technologies in the United States. The new model builds off, extends, and replaces NREL's SolarDS model (Denholm et al. 2009a), which simulates the market penetration of distributed PV only. Unlike the SolarDS model, dGen can model various DER technologies under one platform--it currently can simulate the adoption of distributed solar (the dSolar module) and distributed wind (the dWind module) and link with the ReEDS capacity expansion model (Appendix C). The underlying algorithms and datasets in dGen, which improve the representation of customer decision making as well as the spatial resolution of analyses (Figure ES-1), also are improvements over SolarDS.

Demand dynamic bio-girdling in heart failure: improved efficacy of dynamic cardiomyoplasty by LD contraction during aortic out-flow.

Science.gov (United States)

Carraro, U; Rigatelli, G; Rossini, K; Barbiero, M; Rigatelli, G

2003-03-01

The value of dynamic cardiomyoplasty has been brought into question by the disappointing results produced by slow contraction-relaxation cycle and possibly degeneration of the latissimus dorsi muscle (LD) secondary to temporary tenotomy and chronic daily electrical stimulation. Objective of our study is to determine whether daily periods of rest introduced by demand stimulation in the continuous contraction protocol produce systolic assistance and improve clinical results. Twelve dynamic cardiomyoplasty patients (mean age 58.2 +/- 5.8 years, M/F=11/1, sinus rhythm/atrial fibrillation=11/1) with dilated myocardiopathy were enrolled in an unrandomized trial of Demand Dynamic Heart Bio-Girdling in a public regional teaching hospital. Periods of LD inactivity, each lasting several hours, were introduced daily on a heart rate-based demand regime. To avoid full transformation of LD, fewer impulses per day were delivered, daily providing the LD with long periods of rest (Demand light stimulation). The contractile properties were measured by transcutaneous non-invasive LD tensiomyogram interrogation (LD tensiomyogram). Bio-Girdle activation was synchronized to heart beat by combining tensiomyogram and echocardiography. Clinical, echocardiographic and hemodynamic records, as well as aortic flow measurements by Doppler aortic flow wire were taken during the follow-up. Mean duration of the demand stimulation follow-up was 40.2+13.8 months. At five years, "Demand stimulation" shows: 1) no operative death; 2) 83% actuarial survival; 3) highly significant 47.4% decrease of the NYHA class (from 3.17 +/- 0.38 to 1.67 +/- 0.77, p=0.0001); 4) 41.6% improvement of LVEF (from 22.6 +/- 4.38 to 32.0 +/- 7.0, p=0.001); 5) 7.5 +/- 3.0% increase in aortic flow velocity peak in assisted vs. unassisted beats, and 6) preservation of LD from slowness (TFF value 33 +/- 7.86 at follow-up versus 15.8 +/- 11.1 Hz just before switching from continuous to demand stimulation, p=0.0001) and muscle

Location Model for Distribution Centers for Fulfilling Electronic Orders of Fresh Foods under Uncertain Demand

Directory of Open Access Journals (Sweden)

Hao Zhang

2017-01-01

Full Text Available The problem of locating distribution centers for delivering fresh food as a part of electronic commerce is a strategic decision problem for enterprises. This paper establishes a model for locating distribution centers that considers the uncertainty of customer demands for fresh goods in terms of time-sensitiveness and freshness. Based on the methodology of robust optimization in dealing with uncertain problems, this paper optimizes the location model in discrete demand probabilistic scenarios. In this paper, an improved fruit fly optimization algorithm is proposed to solve the distribution center location problem. An example is given to show that the proposed model and algorithm are robust and can effectively handle the complications caused by uncertain demand. The model proposed in this paper proves valuable both theoretically and practically in the selection of locations of distribution centers.

Real-Time Procurement Strategies of a Proactive Distribution Company with Aggregator-Based Demand Response

DEFF Research Database (Denmark)

Zhang, Chunyu; Wang, Qi; Wang, Jianhui

2016-01-01

and inelastic demand components. A one-leader multi-follower bilevel model is proposed to derive the procurement strategies, i.e., the upper-level problem intends to maximize the profit of the proactive distribution company, while the lower-level expresses the profit maximization per rational aggregator....... The proposed model is then transformed into a solvable mathematical program with equilibrium constraints through a primal-dual approach. A modified 33-bus distribution network is utilized to demonstrate the effectiveness of the proposed model....

A dynamic re-partitioning strategy based on the distribution of key in Spark

Science.gov (United States)

Zhang, Tianyu; Lian, Xin

2018-05-01

Spark is a memory-based distributed data processing framework, has the ability of processing massive data and becomes a focus in Big Data. But the performance of Spark Shuffle depends on the distribution of data. The naive Hash partition function of Spark can not guarantee load balancing when data is skewed. The time of job is affected by the node which has more data to process. In order to handle this problem, dynamic sampling is used. In the process of task execution, histogram is used to count the key frequency distribution of each node, and then generate the global key frequency distribution. After analyzing the distribution of key, load balance of data partition is achieved. Results show that the Dynamic Re-Partitioning function is better than the default Hash partition, Fine Partition and the Balanced-Schedule strategy, it can reduce the execution time of the task and improve the efficiency of the whole cluster.

Dynamically Authorized Role-Based Access Control for Grid Applications

Institute of Scientific and Technical Information of China (English)

YAO Hanbing; HU Heping; LU Zhengding; LI Ruixuan

2006-01-01

Grid computing is concerned with the sharing and coordinated use of diverse resources in distributed "virtual organizations". The heterogeneous, dynamic and multi-domain nature of these environments makes challenging security issues that demand new technical approaches. Despite the recent advances in access control approaches applicable to Grid computing, there remain issues that impede the development of effective access control models for Grid applications. Among them there are the lack of context-based models for access control, and reliance on identity or capability-based access control schemes. An access control scheme that resolve these issues is presented, and a dynamically authorized role-based access control (D-RBAC) model extending the RBAC with context constraints is proposed. The D-RABC mechanisms dynamically grant permissions to users based on a set of contextual information collected from the system and user's environments, while retaining the advantages of RBAC model. The implementation architecture of D-RBAC for the Grid application is also described.

Characterising Wildlife Trade Market Supply-Demand Dynamics

Science.gov (United States)

Rowcliffe, M.; Cowlishaw, G.; Alexander, J. S.; Ntiamoa-Baidu, Y.; Brenya, A.; Milner-Gulland, E. J.

2016-01-01

The trade in wildlife products can represent an important source of income for poor people, but also threaten wildlife locally, regionally and internationally. Bushmeat provides livelihoods for hunters, traders and sellers, protein to rural and urban consumers, and has depleted the populations of many tropical forest species. Management interventions can be targeted towards the consumers or suppliers of wildlife products. There has been a general assumption in the bushmeat literature that the urban trade is driven by consumer demand with hunters simply fulfilling this demand. Using the urban bushmeat trade in the city of Kumasi, Ghana, as a case study, we use a range of datasets to explore the processes driving the urban bushmeat trade. We characterise the nature of supply and demand by explicitly considering three market attributes: resource condition, hunter behaviour, and consumer behaviour. Our results suggest that bushmeat resources around Kumasi are becoming increasingly depleted and are unable to meet demand, that hunters move in and out of the trade independently of price signals generated by the market, and that, for the Kumasi bushmeat system, consumption levels are driven not by consumer choice but by shortfalls in supply and consequent price responses. Together, these results indicate that supply-side processes dominate the urban bushmeat trade in Kumasi. This suggests that future management interventions should focus on changing hunter behaviour, although complementary interventions targeting consumer demand are also likely to be necessary in the long term. Our approach represents a structured and repeatable method to assessing market dynamics in information-poor systems. The findings serve as a caution against assuming that wildlife markets are demand driven, and highlight the value of characterising market dynamics to inform appropriate management. PMID:27632169

Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs

International Nuclear Information System (INIS)

Nwulu, Nnamdi I.; Xia, Xiaohua

2015-01-01

Highlights: • In this work, a game theory based DR program is integrated into the DEED problem. • Objectives are to minimize fuel and emissions costs and maximize the DR benefit. • Optimal generator output, customer load and customer incentive are determined. • Developed model is tested with two different scenarios. • Model provides superior results than independent optimization of DR or DEED. - Abstract: The dynamic economic emission dispatch (DEED) of electric power generation is a multi-objective mathematical optimization problem with two objective functions. The first objective is to minimize all the fuel costs of the generators in the power system, whilst the second objective seeks to minimize the emissions cost. Both objective functions are subject to constraints such as load demand constraint, ramp rate constraint, amongst other constraints. In this work, we integrate a game theory based demand response program into the DEED problem. The game theory based demand response program determines the optimal hourly incentive to be offered to customers who sign up for load curtailment. The game theory model has in built mechanisms to ensure that the incentive offered the customers is greater than the cost of interruption while simultaneously being beneficial to the utility. The combined DEED and game theoretic demand response model presented in this work, minimizes fuel and emissions costs and simultaneously determines the optimal incentive and load curtailment customers have to perform for maximal power system relief. The developed model is tested on two test systems with industrial customers and obtained results indicate the practical benefits of the proposed model

Exact Fill Rates for the (R, S Inventory Control with Discrete Distributed Demands for the Backordering Case

Directory of Open Access Journals (Sweden)

Eugenia BABILONI

2012-01-01

Full Text Available The fill rate is usually computed by using the traditional approach, which calculates it as the complement of the quotient between the expected unfulfilled demand and the expected demand per replenishment cycle, instead of directly the expected fraction of fulfilled demand. Furthermore the available methods to estimate the fill rate apply only under specific demand conditions. This paper shows the research gap regarding the estimation procedures to compute the fill rate and suggests: (i a new exact procedure to compute the traditional approximation for any discrete demand distribution; and (ii a new method to compute the fill rate directly as the fraction of fulfilled demand for any discrete demand distribution. Simulation results show that the latter methods outperform the traditional approach, which underestimates the simulated fill rate, over different demand patterns. This paper focuses on the traditional periodic review, base stock system when backlogged demands are allowed.

Impact of peak electricity demand in distribution grids: a stress test

NARCIS (Netherlands)

Hoogsteen, Gerwin; Molderink, Albert; Hurink, Johann L.; Smit, Gerardus Johannes Maria; Schuring, Friso; Kootstra, Ben

2015-01-01

The number of (hybrid) electric vehicles is growing, leading to a higher demand for electricity in distribution grids. To investigate the effects of the expected peak demand on distribution grids, a stress test with 15 electric vehicles in a single street is conducted and described in this paper.

Habit Formation in Natural Cheese Consumption An Approach Based on Dynamic Demand Analysis

OpenAIRE

WAKABAYASHI, Katsufumi

2010-01-01

In expectation of growing cheese consumption, natural cheese production is being increased to reduce surplus milk and create high added value in raw milk. Other studies found positive trends in cheese consumption. However, those studies neither clarified recent trends, nor distinguished natural cheese from processed cheese. The purpose of this paper is to discuss the structure of natural cheese consumption, focusing on habit formation. We test structural changes in cheese demand using dynamic...

An Optimal and Distributed Demand Response Strategy for Energy Internet Management

Directory of Open Access Journals (Sweden)

Qian Liu

2018-01-01

Full Text Available This study proposes a new model of demand response management for a future smart grid that consists of smart microgrids. The microgrids have energy storage units, responsive loads, controllable distributed generation units, and renewable energy resources. They can buy energy from the utility company when the power generation in themselves cannot satisfy the load demand, and sell extra power generation to the utility company. The goal is to optimize the operation schedule of microgrids to minimize the microgrids’ payments and the utility company’s operation cost. A parallel distributed optimization algorithm based on games theory is developed to solve the optimization problem, in which microgrids only need to send their aggregated purchasing/selling energy to the utility company, thus avoid infringing its privacy. Microgrids can update their operation schedule simultaneously. A case study is implemented, and the simulation results show that the proposed method is effective and efficient.

Shape Distributions of Nonlinear Dynamical Systems for Video-Based Inference.

Science.gov (United States)

Venkataraman, Vinay; Turaga, Pavan

2016-12-01

This paper presents a shape-theoretic framework for dynamical analysis of nonlinear dynamical systems which appear frequently in several video-based inference tasks. Traditional approaches to dynamical modeling have included linear and nonlinear methods with their respective drawbacks. A novel approach we propose is the use of descriptors of the shape of the dynamical attractor as a feature representation of nature of dynamics. The proposed framework has two main advantages over traditional approaches: a) representation of the dynamical system is derived directly from the observational data, without any inherent assumptions, and b) the proposed features show stability under different time-series lengths where traditional dynamical invariants fail. We illustrate our idea using nonlinear dynamical models such as Lorenz and Rossler systems, where our feature representations (shape distribution) support our hypothesis that the local shape of the reconstructed phase space can be used as a discriminative feature. Our experimental analyses on these models also indicate that the proposed framework show stability for different time-series lengths, which is useful when the available number of samples are small/variable. The specific applications of interest in this paper are: 1) activity recognition using motion capture and RGBD sensors, 2) activity quality assessment for applications in stroke rehabilitation, and 3) dynamical scene classification. We provide experimental validation through action and gesture recognition experiments on motion capture and Kinect datasets. In all these scenarios, we show experimental evidence of the favorable properties of the proposed representation.

Including dynamic CO2 intensity with demand response

International Nuclear Information System (INIS)

Stoll, Pia; Brandt, Nils; Nordström, Lars

2014-01-01

Hourly demand response tariffs with the intention of reducing or shifting loads during peak demand hours are being intensively discussed among policy-makers, researchers and executives of future electricity systems. Demand response rates have still low customer acceptance, apparently because the consumption habits requires stronger incentive to change than any proposed financial incentive. An hourly CO 2 intensity signal could give customers an extra environmental motivation to shift or reduce loads during peak hours, as it would enable co-optimisation of electricity consumption costs and carbon emissions reductions. In this study, we calculated the hourly dynamic CO 2 signal and applied the calculation to hourly electricity market data in Great Britain, Ontario and Sweden. This provided a novel understanding of the relationships between hourly electricity generation mix composition, electricity price and electricity mix CO 2 intensity. Load shifts from high-price hours resulted in carbon emission reductions for electricity generation mixes where price and CO 2 intensity were positively correlated. The reduction can be further improved if the shift is optimised using both price and CO 2 intensity. The analysis also indicated that an hourly CO 2 intensity signal can help avoid carbon emissions increases for mixes with a negative correlation between electricity price and CO 2 intensity. - Highlights: • We present a formula for calculating hybrid dynamic CO 2 intensity of electricity generation mixes. • We apply the dynamic CO 2 Intensity on hourly electricity market prices and generation units for Great Britain, Ontario and Sweden. • We calculate the spearman correlation between hourly electricity market price and dynamic CO 2 intensity for Great Britain, Ontario and Sweden. • We calculate carbon footprint of shifting 1 kWh load daily from on-peak hours to off-peak hours using the dynamic CO 2 intensity. • We conclude that using dynamic CO 2 intensity for

The Demand for Divisia Money in the United States: A Dynamic Flexible Demand System.

OpenAIRE

Serletis, Apostolos

1991-01-01

This paper applies the Anderson and Blundell (1982) approach to the analysis of the demand for money and attempts to establish the nature of the relationship between Divisia money, defined from narrow to broad, and the "nested like assets" at different levels of aggregation. This is achieved by conducting the analysis within a microtheoretical framework--utilizing the demand system approach--and by estimating a sequence of nested dynamic specifications and performing tests of the nested struc...

A System Dynamics Modeling of Water Supply and Demand in Las Vegas Valley

Science.gov (United States)

Parajuli, R.; Kalra, A.; Mastino, L.; Velotta, M.; Ahmad, S.

2017-12-01

The rise in population and change in climate have posed the uncertainties in the balance between supply and demand of water. The current study deals with the water management issues in Las Vegas Valley (LVV) using Stella, a system dynamics modeling software, to model the feedback based relationship between supply and demand parameters. Population parameters were obtained from Center for Business and Economic Research while historical water demand and conservation practices were modeled as per the information provided by local authorities. The water surface elevation of Lake Mead, which is the prime source of water supply to the region, was modeled as the supply side whereas the water demand in LVV was modeled as the demand side. The study was done from the period of 1989 to 2049 with 1989 to 2012 as the historical one and the period from 2013 to 2049 as the future period. This study utilizes Coupled Model Intercomparison Project data sets (2013-2049) (CMIP3&5) to model different future climatic scenarios. The model simulates the past dynamics of supply and demand, and then forecasts the future water budget for the forecasted future population and future climatic conditions. The results can be utilized by the water authorities in understanding the future water status and hence plan suitable conservation policies to allocate future water budget and achieve sustainable water management.

Dynamic modelling of a PV pumping system with special consideration on water demand

International Nuclear Information System (INIS)

Campana, Pietro Elia; Li, Hailong; Yan, Jinyue

2013-01-01

Highlights: ► Evaluation of water demand and solar energy is essential for PV pumping system. ► The design for a PV water pumping system has been optimized based on dynamic simulations. ► It is important to conduct dynamic simulations to check the matching between water demand and water supply. ► AC pump driven by the fixed PV array is the most cost-effective solution. - Abstract: The exploitation of solar energy in remote areas through photovoltaic (PV) systems is an attractive solution for water pumping for irrigation systems. The design of a photovoltaic water pumping system (PVWPS) strictly depends on the estimation of the crop water requirements and land use since the water demand varies during the watering season and the solar irradiation changes time by time. It is of significance to conduct dynamic simulations in order to achieve the successful and optimal design. The aim of this paper is to develop a dynamic modelling tool for the design of a of photovoltaic water pumping system by combining the models of the water demand, the solar PV power and the pumping system, which can be used to validate the design procedure in terms of matching between water demand and water supply. Both alternate current (AC) and direct current (DC) pumps and both fixed and two-axis tracking PV array were analyzed. The tool has been applied in a case study. Results show that it has the ability to do rapid design and optimization of PV water pumping system by reducing the power peak and selecting the proper devices from both technical and economic viewpoints. Among the different alternatives considered in this study, the AC fixed system represented the best cost effective solution

Fast demand response in support of the active distribution network

NARCIS (Netherlands)

MacDougall, P.; Heskes, P.; Crolla, P.; Burt, G.; Warmer, C.

2013-01-01

Demand side management has traditionally been investigated for "normal" operation services such as balancing and congestion management. However they potentially could be utilized for Distributed Network Operator (DNO) services. This paper investigates and validates the use of a supply and demand

Distribution network design under demand uncertainty using genetic algorithm and Monte Carlo simulation approach: a case study in pharmaceutical industry

Science.gov (United States)

Izadi, Arman; Kimiagari, Ali Mohammad

2014-05-01

Distribution network design as a strategic decision has long-term effect on tactical and operational supply chain management. In this research, the location-allocation problem is studied under demand uncertainty. The purposes of this study were to specify the optimal number and location of distribution centers and to determine the allocation of customer demands to distribution centers. The main feature of this research is solving the model with unknown demand function which is suitable with the real-world problems. To consider the uncertainty, a set of possible scenarios for customer demands is created based on the Monte Carlo simulation. The coefficient of variation of costs is mentioned as a measure of risk and the most stable structure for firm's distribution network is defined based on the concept of robust optimization. The best structure is identified using genetic algorithms and 14 % reduction in total supply chain costs is the outcome. Moreover, it imposes the least cost variation created by fluctuation in customer demands (such as epidemic diseases outbreak in some areas of the country) to the logistical system. It is noteworthy that this research is done in one of the largest pharmaceutical distribution firms in Iran.

Distribution of Video-on-Demand Service over Cable Television Networks

Directory of Open Access Journals (Sweden)

L. T. Jordanova

2009-06-01

Full Text Available The paper deals with investigations carried out to increase the effectiveness of video-on-demand (VoD systems when cable television networks are used. A comparative analysis of the existing VoD architectures is made with respect to the equipment cost and the traffic load over the central transport network. Through statistical studies the main characteristics of a particular hybrid VoD are determined (such as twenty-four-hour distribution of the video traffic, average inter-arrival time of the VoD requests, average duration of video streams demanded, movies distribution according to the subscribers’ preferences. An algorithm for the videocontent flexible distribution among the distribution hubs is suggested. Thus a higher effectiveness of the system is achieved without significantly increasing the equipment cost.

Dynamic Characteristics Analysis and Stabilization of PV-Based Multiple Microgrid Clusters

DEFF Research Database (Denmark)

Zhao, Zhuoli; Yang, Ping; Wang, Yuewu

2018-01-01

-based multiple microgrid clusters. A detailed small-signal model for PV-based microgrid clusters considering local adaptive dynamic droop control mechanism of the voltage-source PV system is developed. The complete dynamic model is then used to access and compare the dynamic characteristics of the single...... microgrid and interconnected microgrids. In order to enhance system stability of the PV microgrid clusters, a tie-line flow and stabilization strategy is proposed to suppress the introduced interarea and local oscillations. Robustly selecting of the key control parameters is transformed to a multiobjective......As the penetration of PV generation increases, there is a growing operational demand on PV systems to participate in microgrid frequency regulation. It is expected that future distribution systems will consist of multiple microgrid clusters. However, interconnecting PV microgrids may lead to system...

Fuel demand in Brazil in a dynamic panel data approach

International Nuclear Information System (INIS)

Santos, Gervásio F.

2013-01-01

The purpose of this paper is to evaluate the sensitivity of fuel consumers regarding price and income, taking recent changes in the Brazilian fuel market into account. In this market, new market rules, energy policy towards fuel diversification and introduction of flex-fuel engines have determined fuel competition among gasoline, ethanol and compressed natural gas. Using a dynamic panel data model, demand equations for these three fuels are econometrically estimated to obtain consumer adjustment coefficients, price, cross-price and income elasticities in the short and long-run. In addition, the effect of the introduction of flex-fuel engines in the market and the rationality of consumers towards efficiency constraints of the engines were tested. Apart from considerable competition, results show that the dynamics of the Brazilian fuel market revolves around ethanol instead of gasoline. While demands for gasoline and natural gas are inelastic to price, demand for ethanol is elastic in Brazil. Furthermore, after the introduction of the flex-fuel technology the sensitivity of consumers to fuel prices changed, and ethanol consumers take efficiency constrains into account when ethanol prices reach the threshold of 70% of gasoline prices. - Highlights: ► Fuel demand in Brazil is evaluated, considering the changes in the fuel market. ► A dynamic panel data model is used to fit demand equations for fuels. ► Adjustment coefficients, price, cross-price and income elasticities are estimated. ► The impact of flex-fuel technology on the consumer behavior is tested. ► The results showed that the dynamic of the fuel market revolves around ethanol. ► The flex-fuel technology increased the competition among fuels

Dynamics of electricity efficiency in commercial air-distribution systems in Sweden

Energy Technology Data Exchange (ETDEWEB)

Christiansson, Lena

1996-04-01

This paper illustrates the long-term potential for reducing future electricity demand for air-distribution in commercial buildings in Sweden. The objective has been to develop a general quantitative scenario-based framework to describe some possible paths for electricity demand for air distribution and to analyze how governmental and utility-sponsored policy measures can affect electricity demand. The focus is on improved electricity efficiency, i.e. a reduction of electricity demand for the same level of services. The results suggest that higher electricity prices will not be very effective in reducing electricity demand, whereas significant electricity savings can be reached by implementing various policy programs, particularly standards. 56 refs, 4 figs, 5 tabs

Demonstrating demand response from water distribution system through pump scheduling

International Nuclear Information System (INIS)

Menke, Ruben; Abraham, Edo; Parpas, Panos; Stoianov, Ivan

2016-01-01

Highlights: • Water distribution systems can profitably provide demand response energy. • STOR and FFR are financially viable under a wide range of operating conditions. • Viability depends on the pump utilisation and peak price of the electricity tariff. • Total GHG emissions caused by the provision of reserve energy are <300 gCO_2/kW h. • These are lower than those from the major reserve energy provision technologies. - Abstract: Significant changes in the power generation mix are posing new challenges for the balancing systems of the grid. Many of these challenges are in the secondary electricity grid regulation services and could be met through demand response (DR) services. We explore the opportunities for a water distribution system (WDS) to provide balancing services with demand response through pump scheduling and evaluate the associated benefits. Using a benchmark network and demand response mechanisms available in the UK, these benefits are assessed in terms of reduced green house gas (GHG) emissions from the grid due to the displacement of more polluting power sources and additional revenues for water utilities. The optimal pump scheduling problem is formulated as a mixed-integer optimisation problem and solved using a branch and bound algorithm. This new formulation finds the optimal level of power capacity to commit to the provision of demand response for a range of reserve energy provision and frequency response schemes offered in the UK. For the first time we show that DR from WDS can offer financial benefits to WDS operators while providing response energy to the grid with less greenhouse gas emissions than competing reserve energy technologies. Using a Monte Carlo simulation based on data from 2014, we demonstrate that the cost of providing the storage energy is less than the financial compensation available for the equivalent energy supply. The GHG emissions from the demand response provision from a WDS are also shown to be smaller than

Demand forecasting for automotive sector in Malaysia by system dynamics approach

International Nuclear Information System (INIS)

Zulkepli, Jafri; Abidin, Norhaslinda Zainal; Fong, Chan Hwa

2015-01-01

In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars

Demand forecasting for automotive sector in Malaysia by system dynamics approach

Energy Technology Data Exchange (ETDEWEB)

Zulkepli, Jafri, E-mail: zhjafri@uum.edu.my; Abidin, Norhaslinda Zainal, E-mail: nhaslinda@uum.edu.my [School of Quantitative Sciences, Universiti Utara Malaysia, Sintok, Kedah (Malaysia); Fong, Chan Hwa, E-mail: hfchan7623@yahoo.com [SWM Environment Sdn. Bhd.Level 17, Menara LGB, Taman Tun Dr. Ismail Kuala Lumpur (Malaysia)

2015-12-11

In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.

Assessing the benefits of residential demand response in a real time distribution energy market

International Nuclear Information System (INIS)

Siano, Pierluigi; Sarno, Debora

2016-01-01

Highlights: • A new probabilistic methodology, integrating DR in a distribution energy market is proposed. • The method can alleviate distribution network congestions. • This method based on D-LMPs allows cost savings for end-user customers. • Innovative thermal and shiftable loads Real Time control algorithms are also presented. - Abstract: In the field of electricity distribution networks and with the advent of smart grids and microgrids, the use of Distribution Locational Marginal Price (D-LMPs) in a Real Time (RT) distribution market managed by a Distribution System Operator (DSO) is discussed in presence of empowered residential end-users that are able to bid for energy by a demand aggregator while following Demand Response (DR) initiatives. Each customer is provided by a transactive controller, which reads the locational market signals and answers with a bid taking into account the user preferences about some appliances involved in DR activities and controlled by smart plugs-in. In particular, Heating Ventilation and Air Conditioning (HVAC) appliances and shiftable loads are controlled so that their consumption profile can be modified according to the price of energy. In order to assess the effectiveness of the proposed method in terms of energy and cost saving, an innovative probabilistic methodology for evaluating the impact of residential DR choices considering uncertainties related to load demand, user preferences, environmental conditions, house thermal behavior and wholesale market trends has been proposed. The uncertainties related to the stochastic variations of the variables involved are modeled by using the Monte Carlo Simulation (MCS) method. The combination of MCS and RT distribution market simulation based on D-LMPs are used to assess the operation and impact of the DR method over one month. Simulations results on an 84-buses distribution network confirmed that the proposed method allows saving costs for residential end-users and making

Modified Normal Demand Distributions in (R,S)-Inventory Models

NARCIS (Netherlands)

Strijbosch, L.W.G.; Moors, J.J.A.

2003-01-01

To model demand, the normal distribution is by far the most popular; the disadvantage that it takes negative values is taken for granted.This paper proposes two modi.cations of the normal distribution, both taking non-negative values only.Safety factors and order-up-to-levels for the familiar (R,

Geospatial Analysis of the Building Heat Demand and Distribution Losses in a District Heating Network

Directory of Open Access Journals (Sweden)

Tobias Törnros

2016-11-01

Full Text Available The district heating (DH demand of various systems has been simulated in several studies. Most studies focus on the temporal aspects rather than the spatial component. In this study, the DH demand for a medium-sized DH network in a city in southern Germany is simulated and analyzed in a spatially explicit approach. Initially, buildings are geo-located and attributes obtained from various sources including building type, ground area, and number of stories are merged. Thereafter, the annual primary energy demand for heating and domestic hot water is calculated for individual buildings. Subsequently, the energy demand is aggregated on the segment level of an existing DH network and the water flow is routed through the system. The simulation results show that the distribution losses are overall the highest at the end segments (given in percentage terms. However, centrally located pipes with a low throughflow are also simulated to have high losses. The spatial analyses are not only useful when addressing the current demand. Based on a scenario taking into account the refurbishment of buildings and a decentralization of energy production, the future demand was also addressed. Due to lower demand, the distribution losses given in percentage increase under such conditions.

Construction of a fuel demand function portraying inter-fuel substitution, a system dynamics approach

International Nuclear Information System (INIS)

Abada, Ibrahim; Briat, Vincent; Massol, Olivier

2011-04-01

Most of the recent numerical market equilibrium models of natural gas markets use imperfect competition assumptions. These models are typically embedded with an oversimplified representation of the demand side, usually a single-variable affine function, that does not capture any dynamic adjustment to past prices. To remedy this, we report an effort to construct an enhanced functional specification using the system dynamics-based model of Moxnes (1987, 1990). Thanks to a vintage representation of capital stock, this putty-clay model captures the effect of both past and current energy prices on fuel consumption. Using a re-calibrated version of this model, we first confirm the pertinence of this modeling framework to represent inter-fuel substitutions at different fuel prices in the industrial sector. Building on these findings, a dynamic functional specification of the demand function for natural gas is then proposed and calibrated. (authors)

A distributed algorithm for demand-side management: Selling back to the grid.

Science.gov (United States)

Latifi, Milad; Khalili, Azam; Rastegarnia, Amir; Zandi, Sajad; Bazzi, Wael M

2017-11-01

Demand side energy consumption scheduling is a well-known issue in the smart grid research area. However, there is lack of a comprehensive method to manage the demand side and consumer behavior in order to obtain an optimum solution. The method needs to address several aspects, including the scale-free requirement and distributed nature of the problem, consideration of renewable resources, allowing consumers to sell electricity back to the main grid, and adaptivity to a local change in the solution point. In addition, the model should allow compensation to consumers and ensurance of certain satisfaction levels. To tackle these issues, this paper proposes a novel autonomous demand side management technique which minimizes consumer utility costs and maximizes consumer comfort levels in a fully distributed manner. The technique uses a new logarithmic cost function and allows consumers to sell excess electricity (e.g. from renewable resources) back to the grid in order to reduce their electric utility bill. To develop the proposed scheme, we first formulate the problem as a constrained convex minimization problem. Then, it is converted to an unconstrained version using the segmentation-based penalty method. At each consumer location, we deploy an adaptive diffusion approach to obtain the solution in a distributed fashion. The use of adaptive diffusion makes it possible for consumers to find the optimum energy consumption schedule with a small number of information exchanges. Moreover, the proposed method is able to track drifts resulting from changes in the price parameters and consumer preferences. Simulations and numerical results show that our framework can reduce the total load demand peaks, lower the consumer utility bill, and improve the consumer comfort level.

DYNAMIC ABC STORAGE POLICY IN ERRATIC DEMAND ENVIRONMENTS

Directory of Open Access Journals (Sweden)

Benjamin Pierre

2003-01-01

Full Text Available The paper describes a dynamic variant of the traditional ABC storage policy. The variant is to be used in manual order picking warehouses where SKUs experience a rather unstable demand. Its objective is to reduce the order picking time. It mainly consists in shifting items between the storage areas A, B and C according to the way their daily number of order lines changes. A few case studies based on computer simulations have shown that this variant can yield interesting time savings if its parameters are chosen with care. One of those case studies is presented in this paper. Because of the pioneering nature of this variant, some ideas for further research are also sketched.

Dynamic Subsidy Method for Congestion Management in Distribution Networks

DEFF Research Database (Denmark)

Huang, Shaojun; Wu, Qiuwei

2016-01-01

Dynamic subsidy (DS) is a locational price paid by the distribution system operator (DSO) to its customers in order to shift energy consumption to designated hours and nodes. It is promising for demand side management and congestion management. This paper proposes a new DS method for congestion...... management in distribution networks, including the market mechanism, the mathematical formulation through a two-level optimization, and the method solving the optimization by tightening the constraints and linearization. Case studies were conducted with a one node system and the Bus 4 distribution network...... of the Roy Billinton Test System (RBTS) with high penetration of electric vehicles (EVs) and heat pumps (HPs). The case studies demonstrate the efficacy of the DS method for congestion management in distribution networks. Studies in this paper show that the DS method offers the customers a fair opportunity...

Agent-based Decision Support System for the Third Generation Distributed Dynamic Decision-making (DDD-III) Simulator

National Research Council Canada - National Science Library

Meirina, Candra; Ruan, Sui; Yu, Feili; Zhu, Liang; Pattipati, Krishna R; Kleinman, David L

2004-01-01

...) based on the third-generation distributed dynamic decision-making (DDD-III) simulator and contingency theory to increase the organizational cognitive capacity and to facilitate the processes of adaptation...

Responsiveness of residential electricity demand to dynamic tariffs: Experiences from a large field test in the Netherlands

OpenAIRE

Klaassen, EAM; Kobus, C.B.A.; Frunt, J; Slootweg, JG

2016-01-01

To efficiently facilitate the energy transition it is essential to evaluate the potential of demand response in practice. Based on the results of a Dutch smart grid pilot, this paper assesses the potential of both manual and semi-automated demand response in residential areas. To stimulate demand response, a dynamic tariff and smart appliances were used. The participating households were informed about the tariff day-ahead through a home energy management system, connected to a display instal...

Prediction Models for Dynamic Demand Response

Energy Technology Data Exchange (ETDEWEB)

Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

2015-11-02

As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D²R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D²R, which we address in this paper. Our first contribution is the formal definition of D²R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D²R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D²R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D²R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D²R. Also, prediction models require just few days’ worth of data indicating that small amounts of

A distributed algorithm for demand-side management: Selling back to the grid

Directory of Open Access Journals (Sweden)

Milad Latifi

2017-11-01

Full Text Available Demand side energy consumption scheduling is a well-known issue in the smart grid research area. However, there is lack of a comprehensive method to manage the demand side and consumer behavior in order to obtain an optimum solution. The method needs to address several aspects, including the scale-free requirement and distributed nature of the problem, consideration of renewable resources, allowing consumers to sell electricity back to the main grid, and adaptivity to a local change in the solution point. In addition, the model should allow compensation to consumers and ensurance of certain satisfaction levels. To tackle these issues, this paper proposes a novel autonomous demand side management technique which minimizes consumer utility costs and maximizes consumer comfort levels in a fully distributed manner. The technique uses a new logarithmic cost function and allows consumers to sell excess electricity (e.g. from renewable resources back to the grid in order to reduce their electric utility bill. To develop the proposed scheme, we first formulate the problem as a constrained convex minimization problem. Then, it is converted to an unconstrained version using the segmentation-based penalty method. At each consumer location, we deploy an adaptive diffusion approach to obtain the solution in a distributed fashion. The use of adaptive diffusion makes it possible for consumers to find the optimum energy consumption schedule with a small number of information exchanges. Moreover, the proposed method is able to track drifts resulting from changes in the price parameters and consumer preferences. Simulations and numerical results show that our framework can reduce the total load demand peaks, lower the consumer utility bill, and improve the consumer comfort level. Keywords: Energy, Systems engineering, Electrical engineering

FPGA-based distributed computing microarchitecture for complex physical dynamics investigation.

Science.gov (United States)

Borgese, Gianluca; Pace, Calogero; Pantano, Pietro; Bilotta, Eleonora

2013-09-01

In this paper, we present a distributed computing system, called DCMARK, aimed at solving partial differential equations at the basis of many investigation fields, such as solid state physics, nuclear physics, and plasma physics. This distributed architecture is based on the cellular neural network paradigm, which allows us to divide the differential equation system solving into many parallel integration operations to be executed by a custom multiprocessor system. We push the number of processors to the limit of one processor for each equation. In order to test the present idea, we choose to implement DCMARK on a single FPGA, designing the single processor in order to minimize its hardware requirements and to obtain a large number of easily interconnected processors. This approach is particularly suited to study the properties of 1-, 2- and 3-D locally interconnected dynamical systems. In order to test the computing platform, we implement a 200 cells, Korteweg-de Vries (KdV) equation solver and perform a comparison between simulations conducted on a high performance PC and on our system. Since our distributed architecture takes a constant computing time to solve the equation system, independently of the number of dynamical elements (cells) of the CNN array, it allows us to reduce the elaboration time more than other similar systems in the literature. To ensure a high level of reconfigurability, we design a compact system on programmable chip managed by a softcore processor, which controls the fast data/control communication between our system and a PC Host. An intuitively graphical user interface allows us to change the calculation parameters and plot the results.

Process-based distributed modeling approach for analysis of sediment dynamics in a river basin

Directory of Open Access Journals (Sweden)

M. A. Kabir

2011-04-01

Full Text Available Modeling of sediment dynamics for developing best management practices of reducing soil erosion and of sediment control has become essential for sustainable management of watersheds. Precise estimation of sediment dynamics is very important since soils are a major component of enormous environmental processes and sediment transport controls lake and river pollution extensively. Different hydrological processes govern sediment dynamics in a river basin, which are highly variable in spatial and temporal scales. This paper presents a process-based distributed modeling approach for analysis of sediment dynamics at river basin scale by integrating sediment processes (soil erosion, sediment transport and deposition with an existing process-based distributed hydrological model. In this modeling approach, the watershed is divided into an array of homogeneous grids to capture the catchment spatial heterogeneity. Hillslope and river sediment dynamic processes have been modeled separately and linked to each other consistently. Water flow and sediment transport at different land grids and river nodes are modeled using one dimensional kinematic wave approximation of Saint-Venant equations. The mechanics of sediment dynamics are integrated into the model using representative physical equations after a comprehensive review. The model has been tested on river basins in two different hydro climatic areas, the Abukuma River Basin, Japan and Latrobe River Basin, Australia. Sediment transport and deposition are modeled using Govers transport capacity equation. All spatial datasets, such as, Digital Elevation Model (DEM, land use and soil classification data, etc., have been prepared using raster "Geographic Information System (GIS" tools. The results of relevant statistical checks (Nash-Sutcliffe efficiency and R–squared value indicate that the model simulates basin hydrology and its associated sediment dynamics reasonably well. This paper presents the

Directed graph based carbon flow tracing for demand side carbon obligation allocation

DEFF Research Database (Denmark)

Sun, Tao; Feng, Donghan; Ding, Teng

2016-01-01

In order to achieve carbon emission abatement, some researchers and policy makers have cast their focus on demand side carbon abatement potentials. This paper addresses the problem of carbon flow calculation in power systems and carbon obligation allocation at demand side. A directed graph based...... method for tracing carbon flow is proposed. In a lossy network, matrices such as carbon losses, net carbon intensity (NCI) and footprint carbon intensity (FCI) are obtained with the proposed method and used to allocate carbon obligation at demand side. Case studies based on realistic distribution...... and transmission systems are provided to demonstrate the effectiveness of the proposed method....

Real-time Trading Strategies for Proactive Distribution Company with Distributed Generation and Demand Response

DEFF Research Database (Denmark)

Wang, Qi

Distributed energy resources (DERs), such as distributed generation (DG) and demand response (DR), have been recognized worldwide as valuable resources. High integration of DG and DR in the distribution network inspires a potential deregulated environment for the distribution company (DISCO...... in the presented DL market and transact with TL real-time market. A one-leader multi-follower-type bi-level model is proposed to indicate the PDISCO's trading strategies. To participate in the TL real-time market, a methodology is presented to derive continuous bidding/offering strategies for a PDISCO. A bi...

Optimal reconfiguration-based dynamic tariff for congestion management and line loss reduction in distribution networks

DEFF Research Database (Denmark)

Huang, Shaojun; Wu, Qiuwei; Cheng, Lin

2016-01-01

This paper presents an optimal reconfiguration-based dynamic tariff (DT) method for congestion management and line loss reduction in distribution networks with high penetration of electric vehicles. In the proposed DT concept, feeder reconfiguration (FR) is employed through mixed integer programm...

Dynamic peak demand pricing under uncertainty in an agent-based retail energy market

NARCIS (Netherlands)

M. Ansarin (Mohammad); W. Ketter (Wolfgang); J. Collins (John)

2016-01-01

textabstractFor a transition to a sustainable energy future, smart grids must adapt to the mass introduction of renewable energy sources and their inherent unpredictability. The Power TAC competition is a simulation of distribution grid market dynamics with autonomous retail broker agents. It seeks

BrainBrowser: distributed, web-based neurological data visualization

Directory of Open Access Journals (Sweden)

Tarek eSherif

2015-01-01

Full Text Available Recent years have seen massive, distributed datasets become the norm in neuroimaging research, and the methodologies used analyze them have, in response, become more collaborative and exploratory. Tools and infrastructure are continuously being developed and deployed to facilitate research in this context: grid computation platforms to process the data, distributed data stores to house and share them, high-speed networks to move them around and collaborative, often web-based, platforms to provide access to and sometimes manage the entire system. BrainBrowser is a lightweight, high-performance JavaScript visualization library built to provide easy-to-use, powerful, on-demand visualization of remote datasets in this new research environment. BrainBrowser leverages modern Web technologies, such as WebGL, HTML5 and Web Workers, to visualize 3D surface and volumetric neuroimaging data in any modern web browser without requiring any browser plugins. It is thus trivial to integrate BrainBrowser into any web-based platform. BrainBrowser is simple enough to produce a basic web-based visualization in a few lines of code, while at the same time being robust enough to create full-featured visualization applications. BrainBrowser can dynamically load the data required for a given visualization, so no network bandwidth needs to be waisted on data that will not be used. BrainBrowser's integration into the standardized web platform also allows users to consider using 3D data visualization in novel ways, such as for data distribution, data sharing and dynamic online publications. BrainBrowser is already being used in two major online platforms, CBRAIN and LORIS, and has been used to make the 1TB MACACC dataset openly accessible.

Future Opportunities and Challenges with Using Demand Response as a Resource in Distribution System Operation and Planning Activities

Energy Technology Data Exchange (ETDEWEB)

Cappers, Peter [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); MacDonald, Jason [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Page, Janie [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Potter, Jennifer [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Stewart, Emma [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2016-01-01

This scoping study focuses on identifying the ability for current and future demand response opportunities to contribute to distribution system management. To do so, this scoping study will identify the needs of a distribution system to operate efficiently, safely and reliably; summarize both benefits and challenges for the operation of the distribution system with high penetration levels of distributed energy resources; define a suite of services based on those changing operational needs that could be provided by resources; identify existing demand response opportunities sponsored by distribution utilities and/or aggregators of retail customers; assess the extent to which distribution system services can be provided via DR opportunities both in their current form and with alterations to their design; and provide a qualitative assessment of coordination issues that bulk power and distribution system providers of DR opportunities will need to address.

Distributed Energy Systems Integration and Demand Optimization for Autonomous Operations and Electric Grid Transactions

Energy Technology Data Exchange (ETDEWEB)

Ghatikar, Girish [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Greenlots, San Francisco, CA (United States); Mashayekh, Salman [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Stadler, Michael [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Center for Energy and Innovation Technologies (Austria); Yin, Rongxin [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Liu, Zhenhua [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2015-11-29

Distributed power systems in the U.S. and globally are evolving to provide reliable and clean energy to consumers. In California, existing regulations require significant increases in renewable generation, as well as identification of customer-side distributed energy resources (DER) controls, communication technologies, and standards for interconnection with the electric grid systems. As DER deployment expands, customer-side DER control and optimization will be critical for system flexibility and demand response (DR) participation, which improves the economic viability of DER systems. Current DER systems integration and communication challenges include leveraging the existing DER and DR technology and systems infrastructure, and enabling optimized cost, energy and carbon choices for customers to deploy interoperable grid transactions and renewable energy systems at scale. Our paper presents a cost-effective solution to these challenges by exploring communication technologies and information models for DER system integration and interoperability. This system uses open standards and optimization models for resource planning based on dynamic-pricing notifications and autonomous operations within various domains of the smart grid energy system. It identifies architectures and customer engagement strategies in dynamic DR pricing transactions to generate feedback information models for load flexibility, load profiles, and participation schedules. The models are tested at a real site in California—Fort Hunter Liggett (FHL). Furthermore, our results for FHL show that the model fits within the existing and new DR business models and networked systems for transactive energy concepts. Integrated energy systems, communication networks, and modeling tools that coordinate supply-side networks and DER will enable electric grid system operators to use DER for grid transactions in an integrated system.

Dynamic Demand-Capacity Balancing for Air Traffic Management Using Constraint-Based Local Search: First Results

Directory of Open Access Journals (Sweden)

Farshid Hassani Bijarbooneh

2009-10-01

Full Text Available Using constraint-based local search, we effectively model and efficiently solve the problem of balancing the traffic demands on portions of the European airspace while ensuring that their capacity constraints are satisfied. The traffic demand of a portion of airspace is the hourly number of flights planned to enter it, and its capacity is the upper bound on this number under which air-traffic controllers can work. Currently, the only form of demand-capacity balancing we allow is ground holding, that is the changing of the take-off times of not yet airborne flights. Experiments with projected European flight plans of the year 2030 show that already this first form of demand-capacity balancing is feasible without incurring too much total delay and that it can lead to a significantly better demand-capacity balance.

Modelling and short-term forecasting of daily peak power demand in Victoria using two-dimensional wavelet based SDP models

International Nuclear Information System (INIS)

Truong, Nguyen-Vu; Wang, Liuping; Wong, Peter K.C.

2008-01-01

Power demand forecasting is of vital importance to the management and planning of power system operations which include generation, transmission, distribution, as well as system's security analysis and economic pricing processes. This paper concerns the modeling and short-term forecast of daily peak power demand in the state of Victoria, Australia. In this study, a two-dimensional wavelet based state dependent parameter (SDP) modelling approach is used to produce a compact mathematical model for this complex nonlinear dynamic system. In this approach, a nonlinear system is expressed by a set of linear regressive input and output terms (state variables) multiplied by the respective state dependent parameters that carry the nonlinearities in the form of 2-D wavelet series expansions. This model is identified based on historical data, descriptively representing the relationship and interaction between various components which affect the peak power demand of a certain day. The identified model has been used to forecast daily peak power demand in the state of Victoria, Australia in the time period from the 9th of August 2007 to the 24th of August 2007. With a MAPE (mean absolute prediction error) of 1.9%, it has clearly implied the effectiveness of the identified model. (author)

Automated leak localization performance without detailed demand distribution data

NARCIS (Netherlands)

Moors, Janneke; Scholten, L.; van der Hoek, J.P.; den Besten, J.

2018-01-01

Automatic leak localization has been suggested to reduce the time and personnel efforts needed to localize
(small) leaks. Yet, the available methods require a detailed demand distribution model for successful
calibration and good leak localization performance. The main aim of this work was

Distributed demand-side management optimisation for multi-residential users with energy production and storage strategies

Directory of Open Access Journals (Sweden)

Emmanuel Chifuel Manasseh

2014-12-01

Full Text Available This study considers load control in a multi-residential setup where energy scheduler (ES devices installed in smart meters are employed for demand-side management (DSM. Several residential end-users share the same energy source and each residential user has non-adjustable loads and adjustable loads. In addition, residential users may have storage devices and renewable energy sources such as wind turbines or solar as well as dispatchable generators. The ES devices exchange information automatically by executing an iterative distributed algorithm to locate the optimal energy schedule for each end-user. This will reduce the total energy cost and the peak-to-average ratio (PAR in energy demand in the electric power distribution. Users possessing storage devices and dispatchable generators strategically utilise their resources to minimise the total energy cost together with the PAR. Simulation results are provided to evaluate the performance of the proposed game theoretic-based distributed DSM technique.

A Study on Grid-Square Statistics Based Estimation of Regional Electricity Demand and Regional Potential Capacity of Distributed Generators

Science.gov (United States)

Kato, Takeyoshi; Sugimoto, Hiroyuki; Suzuoki, Yasuo

We established a procedure for estimating regional electricity demand and regional potential capacity of distributed generators (DGs) by using a grid square statistics data set. A photovoltaic power system (PV system) for residential use and a co-generation system (CGS) for both residential and commercial use were taken into account. As an example, the result regarding Aichi prefecture was presented in this paper. The statistical data of the number of households by family-type and the number of employees by business category for about 4000 grid-square with 1km × 1km area was used to estimate the floor space or the electricity demand distribution. The rooftop area available for installing PV systems was also estimated with the grid-square statistics data set. Considering the relation between a capacity of existing CGS and a scale-index of building where CGS is installed, the potential capacity of CGS was estimated for three business categories, i.e. hotel, hospital, store. In some regions, the potential capacity of PV systems was estimated to be about 10,000kW/km2, which corresponds to the density of the existing area with intensive installation of PV systems. Finally, we discussed the ratio of regional potential capacity of DGs to regional maximum electricity demand for deducing the appropriate capacity of DGs in the model of future electricity distribution system.

Equilibrium Model of Discrete Dynamic Supply Chain Network with Random Demand and Advertisement Strategy

Directory of Open Access Journals (Sweden)

Guitao Zhang

2014-01-01

Full Text Available The advertisement can increase the consumers demand; therefore it is one of the most important marketing strategies in the operations management of enterprises. This paper aims to analyze the impact of advertising investment on a discrete dynamic supply chain network which consists of suppliers, manufactures, retailers, and demand markets associated at different tiers under random demand. The impact of advertising investment will last several planning periods besides the current period due to delay effect. Based on noncooperative game theory, variational inequality, and Lagrange dual theory, the optimal economic behaviors of the suppliers, the manufactures, the retailers, and the consumers in the demand markets are modeled. In turn, the supply chain network equilibrium model is proposed and computed by modified project contraction algorithm with fixed step. The effectiveness of the model is illustrated by numerical examples, and managerial insights are obtained through the analysis of advertising investment in multiple periods and advertising delay effect among different periods.

Distributed Demand Side Management with Battery Storage for Smart Home Energy Scheduling

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Omowunmi Mary Longe

2017-01-01

Full Text Available The role of Demand Side Management (DSM with Distributed Energy Storage (DES has been gaining attention in recent studies due to the impact of the latter on energy management in the smart grid. In this work, an Energy Scheduling and Distributed Storage (ESDS algorithm is proposed to be installed into the smart meters of Time-of-Use (TOU pricing consumers possessing in-home energy storage devices. Source of energy supply to the smart home appliances was optimized between the utility grid and the DES device depending on energy tariff and consumer demand satisfaction information. This is to minimize consumer energy expenditure and maximize demand satisfaction simultaneously. The ESDS algorithm was found to offer consumer-friendly and utility-friendly enhancements to the DSM program such as energy, financial, and investment savings, reduced/eliminated consumer dissatisfaction even at peak periods, Peak-to-Average-Ratio (PAR demand reduction, grid energy sustainability, socio-economic benefits, and other associated benefits such as environmental-friendliness.

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

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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.

Cloud-based multi-agent architecture for effective planning and scheduling of distributed manufacturing

DEFF Research Database (Denmark)

Mishra, Nishikant; Singh, Akshit; Kumari, Sushma

2016-01-01

at distinct locations are being assembled in a plant to develop the final product. In this complex scenario, manufacturing firms have to be responsive enough to cope with the fluctuating demand of customers. To accomplish it, there is a need to develop an integrated, dynamic and autonomous system....... In this article, a self-reactive cloud-based multi-agent architecture for distributed manufacturing system is developed. The proposed architecture will assist manufacturing industry to establish real-time information exchange between the autonomous agents, clients, suppliers and manufacturing unit. The mechanism...

Aggregated Demand Modelling Including Distributed Generation, Storage and Demand Response

OpenAIRE

Marzooghi, Hesamoddin; Hill, David J.; Verbic, Gregor

2014-01-01

It is anticipated that penetration of renewable energy sources (RESs) in power systems will increase further in the next decades mainly due to environmental issues. In the long term of several decades, which we refer to in terms of the future grid (FG), balancing between supply and demand will become dependent on demand actions including demand response (DR) and energy storage. So far, FG feasibility studies have not considered these new demand-side developments for modelling future demand. I...

Solving a Location, Allocation, and Capacity Planning Problem with Dynamic Demand and Response Time Service Level

Directory of Open Access Journals (Sweden)

Carrie Ka Yuk Lin

2014-01-01

Full Text Available Logistic systems with uncertain demand, travel time, and on-site processing time are studied here where sequential trip travel is allowed. The relationship between three levels of decisions: facility location, demand allocation, and resource capacity (number of service units, satisfying the response time requirement, is analysed. The problem is formulated as a stochastic mixed integer program. A simulation-based hybrid heuristic is developed to solve the dynamic problem under different response time service level. An initial solution is obtained from solving static location-allocation models, followed by iterative improvement of the three levels of decisions by ejection, reinsertion procedure with memory of feasible and infeasible service regions. Results indicate that a higher response time service level could be achieved by allocating a given resource under an appropriate decentralized policy. Given a response time requirement, the general trend is that the minimum total capacity initially decreases with more facilities. During this stage, variability in travel time has more impact on capacity than variability in demand arrivals. Thereafter, the total capacity remains stable and then gradually increases. When service level requirement is high, the dynamic dispatch based on first-come-first-serve rule requires smaller capacity than the one by nearest-neighbour rule.

Distributed generation, storage, demand response and energy efficiency as alternatives to grid capacity enhancement

International Nuclear Information System (INIS)

Poudineh, Rahmatallah; Jamasb, Tooraj

2014-01-01

The need for investment in capital intensive electricity networks is on the rise in many countries. A major advantage of distributed resources is their potential for deferring investments in distribution network capacity. However, utilizing the full benefits of these resources requires addressing several technical, economic and regulatory challenges. A significant barrier pertains to the lack of an efficient market mechanism that enables this concept and also is consistent with business model of distribution companies under an unbundled power sector paradigm. This paper proposes a market-oriented approach termed as “contract for deferral scheme” (CDS). The scheme outlines how an economically efficient portfolio of distributed generation, storage, demand response and energy efficiency can be integrated as network resources to reduce the need for grid capacity and defer demand driven network investments. - Highlights: • The paper explores a practical framework for smart electricity distribution grids. • The aim is to defer large capital investments in the network by utilizing and incentivising distributed generation, demand response, energy efficiency and storage as network resources. • The paper discusses a possible new market model that enables integration of distributed resources as alternative to grid capacity enhancement

Demand-Based Optimal Design of Storage Tank with Inerter System

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Shiming Zhang

2017-01-01

Full Text Available A parameter optimal design method for a tank with an inerter system is proposed in this study based on the requirements of tank vibration control to improve the effectiveness and efficiency of vibration control. Moreover, a response indicator and a cost control indicator are selected based on the control targets for liquid storage tanks for simultaneously minimizing the dynamic response and controlling costs. These indicators are reformulated through a random vibration analysis under virtual excitation. The problem is then transformed from a multiobjective optimization problem to a single-objective nonlinear problem using the ε-constraint method, which is consistent with the demand-based method. White noise excitation can be used to design the tank with the inerter system under seismic excitation to simplify the calculation. Subsequently, a MATLAB-based calculation program is compiled, and several optimization cases are examined under different excitation conditions. The effectiveness of the demand-based method is proven through a time history analysis. The results show that specific vibration control requirements can be met at the lowest cost with a simultaneous reduction in base shears and overturning base moments.

Agent-Based Modeling of Mitochondria Links Sub-Cellular Dynamics to Cellular Homeostasis and Heterogeneity.

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Giovanni Dalmasso

Full Text Available Mitochondria are semi-autonomous organelles that supply energy for cellular biochemistry through oxidative phosphorylation. Within a cell, hundreds of mobile mitochondria undergo fusion and fission events to form a dynamic network. These morphological and mobility dynamics are essential for maintaining mitochondrial functional homeostasis, and alterations both impact and reflect cellular stress states. Mitochondrial homeostasis is further dependent on production (biogenesis and the removal of damaged mitochondria by selective autophagy (mitophagy. While mitochondrial function, dynamics, biogenesis and mitophagy are highly-integrated processes, it is not fully understood how systemic control in the cell is established to maintain homeostasis, or respond to bioenergetic demands. Here we used agent-based modeling (ABM to integrate molecular and imaging knowledge sets, and simulate population dynamics of mitochondria and their response to environmental energy demand. Using high-dimensional parameter searches we integrated experimentally-measured rates of mitochondrial biogenesis and mitophagy, and using sensitivity analysis we identified parameter influences on population homeostasis. By studying the dynamics of cellular subpopulations with distinct mitochondrial masses, our approach uncovered system properties of mitochondrial populations: (1 mitochondrial fusion and fission activities rapidly establish mitochondrial sub-population homeostasis, and total cellular levels of mitochondria alter fusion and fission activities and subpopulation distributions; (2 restricting the directionality of mitochondrial mobility does not alter morphology subpopulation distributions, but increases network transmission dynamics; and (3 maintaining mitochondrial mass homeostasis and responding to bioenergetic stress requires the integration of mitochondrial dynamics with the cellular bioenergetic state. Finally, (4 our model suggests sources of, and stress conditions

Shenzhen International Low Carbon City in Development: Practice of Low Carbon Planning Technology Strategy Based on Dynamic Demands

Institute of Scientific and Technical Information of China (English)

Yu; Han; Li; Caige

2016-01-01

Targeted at the dynamic demands in the rapid urban construction, the planning technology strategy of the Shenzhen International Low Carbon City studies the fl exible index model based on carbon emission evaluation, and adopts rolling development and micro-circulation construction mode to achieve quick returns with small investment. Meanwhile, it also evaluates the application of low carbon technology and gives feedback in time, so as to constantly optimize and complete the low carbon city planning. In detail, it involves industrial planning, ecological restoration, transport planning, energy resource planning, architectural design, etc., for which appropriate approaches are selected according to the principle of rolling development of unit cells and based on different requirements of different stages. The quick-response and fl exible technology system can help the low carbon city to choose an appropriate technology strategy in line with its own characteristics in the start-up stage and rapid development, thus realizing the sustainable leap-forward development and providing reference for other similar regions.

Shenzhen International Low Carbon City in Development: Practice of Low Carbon Planning Technology Strategy Based on Dynamic Demands

Institute of Scientific and Technical Information of China (English)

Yu Han; Li Caige

2016-01-01

Targeted at the dynamic demands in the rapid urban construction,the planning technology strategy of the Shenzhen International Low Carbon City studies the flexible index model based on carbon emission evaluation,and adopts rolling development and micro-circulation construction mode to achieve quick returns with small investment.Meanwhile,it also evaluates the application of low carbon technology and gives feedback in time,so as to constantly optimize and complete the low carbon city planning.In detail,it involves industrial planning,ecological restoration,transport planning,energy resource planning,architectural design,etc.,for which appropriate approaches are selected according to the principle of rolling development of unit cells and based on different requirements of different stages.The quick-response and flexible technology system can help the low carbon city to choose an appropriate technology strategy in line with its own characteristics in the start-up stage and rapid development,thus realizing the sustainable leap-forward development and providing reference for other similar regions.

Research on strategy and optimization method of PRT empty vehicles resource allocation based on traffic demand forecast

Science.gov (United States)

Xiang, Yu; Tao, Cheng

2018-05-01

During the operation of the personal rapid transit system(PRT), the empty vehicle resources is distributed unevenly because of different passenger demand. In order to maintain the balance between supply and demand, and to meet the passenger needs of the ride, PRT empty vehicle resource allocation model is constructed based on the future demand forecasted by historical demand in this paper. The improved genetic algorithm is implied in distribution of the empty vehicle which can reduce the customers waiting time and improve the operation efficiency of the PRT system so that all passengers can take the PRT vehicles in the shortest time. The experimental result shows that the improved genetic algorithm can allocate the empty vehicle from the system level optimally, and realize the distribution of the empty vehicle resources reasonably in the system.

A Column Generation Approach to the Capacitated Vehicle Routing Problem with Stochastic Demands

DEFF Research Database (Denmark)

Christiansen, Christian Holk; Lysgaard, Jens

. The CVRPSD can be formulated as a Set Partitioning Problem. We show that, under the above assumptions on demands, the associated column generation subproblem can be solved using a dynamic programming scheme which is similar to that used in the case of deterministic demands. To evaluate the potential of our......In this article we introduce a new exact solution approach to the Capacitated Vehicle Routing Problem with Stochastic Demands (CVRPSD). In particular, we consider the case where all customer demands are distributed independently and where each customer's demand follows a Poisson distribution...

Market concentration and technological innovation in a dynamic model of growth and distribution

Directory of Open Access Journals (Sweden)

Gilberto Tadeu Lima

2000-12-01

Full Text Available This paper develops a post Keynesian macromodel of growth and distribution in which endogenous technological innovation plays a pivotal role. The innovationrate is made quadratic in market concentration, to capture a plausible neo-Schumpeterian non-linear influence of market structure on firms' propensity to innovate. Concentration is endogenous, though, since under neo-Schumpeterian competition the relation between market structure and technical change cuts both ways. Investment will then be non-linear in concentration, and the effect of changes in concentration on capacity utilisation, growth and distribution will depend on the level of concentration. Demand also plays a role, with capacity utilisation andgrowth rising with the wage share. The dynamic stability properties of the system will depend on the direction and relative strength of the technological innovation effects with respect to the demand ones, and on the relative bargaining power of workers and capitalists.

A Dynamic Market Mechanism for Markets with Shiftable Demand Response

DEFF Research Database (Denmark)

Hansen, Jacob; Knudsen, Jesper Viese; Kiani, Arman

2014-01-01

renewables, this mechanism accommodates both consumers with a shiftable Demand Response and an adjustable Demand Response. The overall market mechanism is evaluated in a Day Ahead Market and is shown in a numerical example to result in a reduction of the cost of electricity for the consumer, as well......In this paper, we propose a dynamic market mechanism that converges to the desired market equilibrium. Both locational marginal prices and the schedules for generation and consumption are determined through a negotiation process between the key market players. In addition to incorporating...

CRISP. Market-oriented online supply-demand matching

International Nuclear Information System (INIS)

Kamphuis, I.G.; Kester, J.C.P.; Carlsson, P; Akkermans, H.

2004-04-01

Current power distribution systems are operated in a top-down manner. Power production control and price formation take place on a central level on the basis of relatively static data from a data collection and dispatching network with a limited scope and granularity. When incorporating a more considerable fraction of small-scale producers on the basis of, for instance, renewable energy, operation of the distribution grid requires more data to be collected from a more extensive information and data communication network. Furthermore, increased local flows, in the form of two-way communication with distributed computation techniques, enable a more dynamic adaptation in power supply and demand patterns paving the way to a flexible way of embedding of ill-predictable supply of some types of renewable energy sources. DSM-programs have been in use in the utility sector for years now. In this document, first, current Demand Side Management (DSM) and Demand Response Resource (DRR) techniques are discussed; then, supply side management especially in a DG (Distributed Generation) context is treated. A framework of novel concepts and possible technology directions is presented subsequently and some preliminary scenarios are shown to illustrate these concepts. An overview of more flexible supply and demand matching schemes is given essentially based on four distinct types of SDM clusters. It appears, that it is possible to fulfil requirements for these distributed environments in terms of needed information and communication technology, ICT, if these are paralleled with the expected future penetration of ever-smaller scale data-exchange networks at power customer sites. Agent technology using algorithms from micro-economic market theory offers a promising possibility for managing the complexity of price formation and supply demand matching in these fine-grained bottom-up control distribution networks. Implication of these technical developments in terms of market and business

Chicago's water market: Dynamics of demand, prices and scarcity rents

Science.gov (United States)

Ipe, V.C.; Bhagwat, S.B.

2002-01-01

Chicago and its suburbs are experiencing an increasing demand for water from a growing population and economy and may experience water scarcity in the near future. The Chicago metropolitan area has nearly depleted its groundwater resources to a point where interstate conflicts with Wisconsin could accompany an increased reliance on those sources. Further, the withdrawals from Lake Michigan is limited by the Supreme Court decree. The growing demand and indications of possible scarcity suggest a need to reexamine the pricing policies and the dynamics of demand. The study analyses the demand for water and develops estimates of scarcity rents for water in Chicago. The price and income elasticities computed at the means are -0.002 and 0.0002 respectively. The estimated scarcity rents ranges from $0.98 to $1.17 per thousand gallons. The results indicate that the current prices do not fully account for the scarcity rents and suggest a current rate with in the range $1.53 to $1.72 per thousand gallons.

Dynamic Vehicle Scheduling for Working Service Network with Dual Demands

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Bing Li

2017-01-01

Full Text Available This study aims to develop some models to aid in making decisions on the combined fleet size and vehicle assignment in working service network where the demands include two types (minimum demands and maximum demands, and vehicles themselves can act like a facility to provide services when they are stationary at one location. This type of problem is named as the dynamic working vehicle scheduling with dual demands (DWVS-DD and formulated as a mixed integer programming (MIP. Instead of a large integer program, the problem is decomposed into small local problems that are guided by preset control parameters. The approach for preset control parameters is given. By introducing them into the MIP formulation, the model is reformulated as a piecewise form. Further, a piecewise method by updating preset control parameters is proposed for solving the reformulated model. Numerical experiments show that the proposed method produces better solution within reasonable computing time.

New Product Development Based on Demand

OpenAIRE

Davis-Krook, Shelby

2015-01-01

The purpose of this thesis was to determine how to develop a new product based on demand within a target market for an international company. Specifically looking at developing a new product line in an already developed brand, Alpha Performance. The research I have conducted in the following topics may help Alpha Performance if they choose to use my findings to create a one of a kind woman’s clothing line based on the demands of the Finnish market: target market research, product demand rese...

Fission fragment distributions within dynamical approach

Energy Technology Data Exchange (ETDEWEB)

Mazurek, K. [Institute of Nuclear, Physics Polish Academy of Sciences, Krakow (Poland); Nadtochy, P.N. [Omsk State Technical University, Omsk (Russian Federation); Ryabov, E.G.; Adeev, G.D. [Omsk State University, Physics Department, Omsk (Russian Federation)

2017-04-15

The review covers recent developments and achievements in the dynamical description of fission process at high excitation energy. It is shown that the dynamical approach based on multidimensional Langevin equations combined with the statistical description of nuclear decay by particles evaporation is capable of fairly well describing the formation of fission fragment mass-energy, charge, and angular distributions of fission fragments in coincidence with the pre- and post-scission particle emission. The final yields of fission and evaporation residues channels products could be obtained. The detailed description of fission dynamics allows studying different stages of fission process, indicating the most important ingredients governing fission process and studying in detail such fundamental nuclear properties as nuclear viscosity and fission timescale. The tasks and perspectives of multidimensional dynamical approach are also discussed. (orig.)

The impact of small scale cogeneration on the gas demand at distribution level

International Nuclear Information System (INIS)

Vandewalle, J.; D’haeseleer, W.

2014-01-01

Highlights: • Impact on the gas network of a massive implementation of cogeneration. • Distributed energy resources in a smart grid environment. • Optimisation of cogeneration scheduling. - Abstract: Smart grids are often regarded as an important step towards the future energy system. Combined heat and power (CHP) or cogeneration has several advantages in the context of the smart grid, which include the efficient use of primary energy and the reduction of electrical losses through transmission. However, the role of the gas network is often overlooked in this context. Therefore, this work presents an analysis of the impact of a massive implementation of small scale (micro) cogeneration units on the gas demand at distribution level. This work shows that using generic information in the simulations overestimates the impact of CHP. Furthermore, the importance of the thermal storage tank capacity on the impact on the gas demand is shown. Larger storage tanks lead to lower gas demand peaks and hence a lower impact on the gas distribution network. It is also shown that the use of an economically led controller leads to similar results compared to classical heat led control. Finally, it results that a low sell back tariff for electricity increases the impact of cogeneration on the gas demand peak

Dynamic pricing based on a cloud computing framework to support the integration of renewable energy sources

Directory of Open Access Journals (Sweden)

Rajeev Thankappan Nair

2014-12-01

Full Text Available Integration of renewable energy sources into the electric grid in the domestic sector results in bidirectional energy flow from the supply side of the consumer to the grid. Traditional pricing methods are difficult to implement in such a situation of bidirectional energy flow and they face operational challenges on the application of price-based demand side management programme because of the intermittent characteristics of renewable energy sources. In this study, a dynamic pricing method using real-time data based on a cloud computing framework is proposed to address the aforementioned issues. The case study indicates that the dynamic pricing captures the variation of energy flow in the household. The dynamic renewable factor introduced in the model supports consumer oriented pricing. A new method is presented in this study to determine the appropriate level of photovoltaic (PV penetration in the distribution system based on voltage stability aspect. The load flow study result for the electric grid in Kerala, India, indicates that the overvoltage caused by various PV penetration levels up to 33% is within the voltage limits defined for distribution feeders. The result justifies the selected level of penetration.

Distributed Renewable Generation and Storage System Sizing Based on Smart Dispatch of Microgrids

Directory of Open Access Journals (Sweden)

Raji Atia

2016-03-01

Full Text Available This paper considers the contribution of independent owners (IOs operating within microgrids (MGs toward green power generation in deregulated energy markets. An optimization scheme is introduced for sizing distributed renewable generation (DRG and a distributed energy storage system (DESS based on a novel energy management system (EMS that accounts for demand response (DR, DESS dispatch and performance degradation, dynamic pricing environments, power distribution loss and irregular renewable generation. The proposed EMS utilizes an iterative Newton-Raphson linear programming algorithm that schedules resources in order to minimize the objective function, to deal with the complicated nonlinear nature of the problem and to enable efficient long-term assessments. The EMS is used to evaluate candidate solutions that are generated by a genetic algorithm (GA to determine the optimal combination of DRG and DESS. A case study for IEEE 34-bus distribution MG in Okinawa, Japan, is used for testing the algorithm and analyzing the potential for IO/MG investments and their strategies.

Demand Response Design and Use Based on Network Locational Marginal Prices

DEFF Research Database (Denmark)

Morais, Hugo; Faria, Pedro; Vale, Zita

2014-01-01

Power systems have been experiencing huge changes mainly due to the substantial increase of distributed generation (DG) and the operation in competitive environments. Virtual Power Players (VPP) can aggregate several players, namely a diversity of energy resources, including distributed generation...... (DG) based on several technologies, electric storage systems (ESS) and demand response (DR). Energy resources management gains an increasing relevance in this competitive context. This makes the DR use more interesting and flexible, giving place to a wide range of new opportunities. This paper...... proposes a methodology to support VPPs in the DR programs’ management, considering all the existing energy resources (generation and storage units) and the distribution network. The proposed method is based on locational marginal prices (LMP) values. The evaluation of the impact of using DR specific...

Equal Power Distribution and Dynamic Subcarrier Assignment in OFDM Using Minimum Channel Gain Flow with Robust Optimization Uncertain Demand

OpenAIRE

Tun, F. A. Hla Myo; Phyo, S. B. Aye Thandar; Naing, T. C. Zaw Min

2010-01-01

In this paper, the minimum channel gain flow with uncertainty in the demand vector is examined. The approach is based on a transformation of uncertainty in the demand vector to uncertainty in the gain vector. OFDM systems are known to overcome the impairment of the wireless channel by splitting the given system bandwidth into parallel sub-carriers, on which data-symbols can be transmitted simultaneously. This enables the possibility of enhancing the system's performance by deploying adaptive ...

Optimal real time cost-benefit based demand response with intermittent resources

International Nuclear Information System (INIS)

Zareen, N.; Mustafa, M.W.; Sultana, U.; Nadia, R.; Khattak, M.A.

2015-01-01

Ever-increasing price of conventional energy resources and related environmental concern enforced to explore alternative energy sources. Inherent uncertainty of power generation and demand being strongly influenced by the electricity market has posed severe challenges for DRPs (Demand Response Programs). Definitely, the success of such uncertain energy systems under new market structures is critically decided by the advancement of innovative technical and financial tools. Recent exponential growth of DG (distributed generations) demanded both the grid reliability and financial cost–benefits analysis for deregulated electricity market stakeholders. Based on the SGT (signaling game theory), the paper presents a novel user-aware demand-management approach where the price are colligated with grid condition uncertainties to manage the peak residential loads. The degree of information disturbances are considered as a key factor for evaluating electricity bidding mechanisms in the presence of independent multi-generation resources and price-elastic demand. A correlation between the cost–benefit price and variable reliability of grid is established under uncertain generation and demand conditions. Impacts of the strategies on load shape, benefit of customers and the reduction of energy consumption are inspected and compared with Time-of-Used based DRPs. Simulation results show that the proposed DRP can significantly reduce or even eliminate peak-hour energy consumption, leading to a substantial raise of revenues with 18% increase in the load reduction and a considerable improvement in system reliability is evidenced. - Highlights: • Proposed an optimal real time cost-benefit based demand response model. • Used signaling game theory for the information disturbances in deregulated market. • Introduced a correlation between the cost–benefit price and variable grid reliability. • Derive robust bidding strategies for utility/customers successful participation. �

Why the long hours? Job demands and social exchange dynamics.

Science.gov (United States)

Genin, Emilie; Haines, Victor Y; Pelletier, David; Rousseau, Vincent; Marchand, Alain

2016-11-22

This study investigates the determinants of long working hours from the perspectives of the demand-control model [Karasek, 1979] and social exchange theory [Blau, 1964; Goulder, 1960]. These two theoretical perspectives are tested to understand why individuals work longer (or shorter) hours. The hypotheses are tested with a representative sample of 1,604 employed Canadians. In line with Karasek's model, the results support that high job demands are positively associated with longer work hours. The social exchange perspective would predict a positive association between skill discretion and work hours. This hypothesis was supported for individuals with a higher education degree. Finally, the results support a positive association between active jobs and longer work hours. Our research suggests that job demands and social exchange dynamics need to be considered together in the explanation of longer (or shorter) work hours.

Distributed generation and demand response dispatch for a virtual power player energy and reserve provision

DEFF Research Database (Denmark)

Faria, Pedro; Soares, Tiago; Vale, Zita

2014-01-01

Recent changes in the operation and planning of power systems have been motivated by the introduction of Distributed Generation (DG) and Demand Response (DR) in the competitive electricity markets’ environment, with deep concerns at the efficiency level. In this context, grid operators, market...... proposes a methodology which considers the joint dispatch of demand response and distributed generation in the context of a distribution network operated by a virtual power player. The resources’ participation can be performed in both energy and reserve contexts. This methodology contemplates...

Distributed Optimisation Algorithm for Demand Side Management in a Grid-Connected Smart Microgrid

Directory of Open Access Journals (Sweden)

Omowunmi Mary Longe

2017-06-01

Full Text Available The contributions of Distributed Energy Generation (DEG and Distributed Energy Storage (DES for Demand Side Management (DSM purposes in a smart macrogrid or microgrid cannot be over-emphasised. However, standalone DEG and DES can lead to under-utilisation of energy generation by consumers and financial investments; in grid-connection mode, though, DEG and DES can offer arbitrage opportunities for consumers and utility provider(s. A grid-connected smart microgrid comprising heterogeneous (active and passive smart consumers, electric vehicles and a large-scale centralised energy storage is considered in this paper. Efficient energy management by each smart entity is carried out by the proposed Microgrid Energy Management Distributed Optimisation Algorithm (MEM-DOA installed distributively within the network according to consumer type. Each smart consumer optimises its energy consumption and trading for comfort (demand satisfaction and profit. The proposed model was observed to yield better consumer satisfaction, higher financial savings, and reduced Peak-to-Average-Ratio (PAR demand on the utility grid. Other associated benefits of the model include reduced investment on peaker plants, grid reliability and environmental benefits. The MEM-DOA also offered participating smart consumers energy and tariff incentives so that passive smart consumers do not benefit more than active smart consumers, as was the case with some previous energy management algorithms.

The dynamic model on the impact of biodiesel blend mandate (B5) on Malaysian palm oil domestic demand: A preliminary finding

Science.gov (United States)

Abidin, Norhaslinda Zainal; Applanaidu, Shri-Dewi; Sapiri, Hasimah

2014-12-01

Over the last ten years, world biofuels production has increased dramatically. The biodiesel demand is driven by the increases in fossil fuel prices, government policy mandates, income from gross domestic product and population growth. In the European Union, biofuel consumption is mostly driven by blending mandates in both France and Germany. In the case of Malaysia, biodiesel has started to be exported since 2006. The B5 of 5% blend of palm oil based biodiesel into diesel in all government vehicles was implemented in February 2009 and it is expected to be implemented nationwide in the nearest time. How will the blend mandate will project growth in the domestic demand of palm oil in Malaysia? To analyze this issue, a system dynamics model was constructed to evaluate the impact of blend mandate implementation on the palm oil domestic demand influence. The base run of simulation analysis indicates that the trend of domestic demand will increase until 2030 in parallel with the implementation of 5 percent of biodiesel mandate. Finally, this study depicts that system dynamics is a useful tool to gain insight and to experiment with the impact of changes in blend mandate implementation on the future growth of Malaysian palm oil domestic demand sector.

An EOQ Model with Two-Parameter Weibull Distribution Deterioration and Price-Dependent Demand

Science.gov (United States)

Mukhopadhyay, Sushanta; Mukherjee, R. N.; Chaudhuri, K. S.

2005-01-01

An inventory replenishment policy is developed for a deteriorating item and price-dependent demand. The rate of deterioration is taken to be time-proportional and the time to deterioration is assumed to follow a two-parameter Weibull distribution. A power law form of the price dependence of demand is considered. The model is solved analytically…

The rise of AGILE demand response : enabler and foundation for change

NARCIS (Netherlands)

Babar, M.; Nguyen, H.P.; Cuk, V.; Kamphuis, I.G.; Bongearts, M.; Hanzelka, Z.

The distributed resources – distributed generations, storage, electric vehicles and smart appliances – have fueled many disruptions in the conventional grid dynamics. Increasing tough and competitive situations at demand-side have led the stakeholders of power system to reform, because people are

Evaluating Outdoor Water Use Demand under Changing Climatic and Demographic Conditions: An Agent-based Modeling Approach

Science.gov (United States)

Kanta, L.; Berglund, E. Z.; Soh, M. H.

2017-12-01

Outdoor water-use for landscape and irrigation constitutes a significant end-use in total residential water demand. In periods of water shortages, utilities may reduce garden demands by implementing irrigation system audits, rebate programs, local ordinances, and voluntary or mandatory water-use restrictions. Because utilities do not typically record outdoor and indoor water-uses separately, the effects of policies for reducing garden demands cannot be readily calculated. The volume of water required to meet garden demands depends on the housing density, lawn size, type of vegetation, climatic conditions, efficiency of garden irrigation systems, and consumer water-use behaviors. Many existing outdoor demand estimation methods are deterministic and do not include consumer responses to conservation campaigns. In addition, mandatory restrictions may have a substantial impact on reducing outdoor demands, but the effectiveness of mandatory restrictions depends on the timing and the frequency of restrictions, in addition to the distribution of housing density and consumer types within a community. This research investigates a garden end-use model by coupling an agent-based modeling approach and a mechanistic-stochastic water demand model to create a methodology for estimating garden demand and evaluating demand reduction policies. The garden demand model is developed for two water utilities, using a diverse data sets, including residential customer billing records, outdoor conservation programs, frequency and type of mandatory water-use restrictions, lot size distribution, population growth, and climatic data. A set of garden irrigation parameter values, which are based on the efficiency of irrigation systems and irrigation habits of consumers, are determined for a set of conservation ordinances and restrictions. The model parameters are then validated using customer water usage data from the participating water utilities. A sensitivity analysis is conducted for garden

Distributed Consensus-Based Robust Adaptive Formation Control for Nonholonomic Mobile Robots with Partial Known Dynamics

Directory of Open Access Journals (Sweden)

Zhaoxia Peng

2014-01-01

Full Text Available This paper investigates the distributed consensus-based robust adaptive formation control for nonholonomic mobile robots with partially known dynamics. Firstly, multirobot formation control problem has been converted into a state consensus problem. Secondly, the practical control strategies, which incorporate the distributed kinematic controllers and the robust adaptive torque controllers, are designed for solving the formation control problem. Thirdly, the specified reference trajectory for the geometric centroid of the formation is assumed as the trajectory of a virtual leader, whose information is available to only a subset of the followers. Finally, numerical results are provided to illustrate the effectiveness of the proposed control approaches.

Multiagent Systems Based Modeling and Implementation of Dynamic Energy Management of Smart Microgrid Using MACSimJX

Science.gov (United States)

Raju, Leo; Milton, R. S.; Mahadevan, Senthilkumaran

2016-01-01

The objective of this paper is implementation of multiagent system (MAS) for the advanced distributed energy management and demand side management of a solar microgrid. Initially, Java agent development environment (JADE) frame work is used to implement MAS based dynamic energy management of solar microgrid. Due to unstable nature of MATLAB, when dealing with multithreading environment, MAS operating in JADE is linked with the MATLAB using a middle ware called Multiagent Control Using Simulink with Jade Extension (MACSimJX). MACSimJX allows the solar microgrid components designed with MATLAB to be controlled by the corresponding agents of MAS. The microgrid environment variables are captured through sensors and given to agents through MATLAB/Simulink and after the agent operations in JADE, the results are given to the actuators through MATLAB for the implementation of dynamic operation in solar microgrid. MAS operating in JADE maximizes operational efficiency of solar microgrid by decentralized approach and increase in runtime efficiency due to JADE. Autonomous demand side management is implemented for optimizing the power exchange between main grid and microgrid with intermittent nature of solar power, randomness of load, and variation of noncritical load and grid price. These dynamics are considered for every time step and complex environment simulation is designed to emulate the distributed microgrid operations and evaluate the impact of agent operations. PMID:27127802

Multiagent Systems Based Modeling and Implementation of Dynamic Energy Management of Smart Microgrid Using MACSimJX.

Science.gov (United States)

Raju, Leo; Milton, R S; Mahadevan, Senthilkumaran

The objective of this paper is implementation of multiagent system (MAS) for the advanced distributed energy management and demand side management of a solar microgrid. Initially, Java agent development environment (JADE) frame work is used to implement MAS based dynamic energy management of solar microgrid. Due to unstable nature of MATLAB, when dealing with multithreading environment, MAS operating in JADE is linked with the MATLAB using a middle ware called Multiagent Control Using Simulink with Jade Extension (MACSimJX). MACSimJX allows the solar microgrid components designed with MATLAB to be controlled by the corresponding agents of MAS. The microgrid environment variables are captured through sensors and given to agents through MATLAB/Simulink and after the agent operations in JADE, the results are given to the actuators through MATLAB for the implementation of dynamic operation in solar microgrid. MAS operating in JADE maximizes operational efficiency of solar microgrid by decentralized approach and increase in runtime efficiency due to JADE. Autonomous demand side management is implemented for optimizing the power exchange between main grid and microgrid with intermittent nature of solar power, randomness of load, and variation of noncritical load and grid price. These dynamics are considered for every time step and complex environment simulation is designed to emulate the distributed microgrid operations and evaluate the impact of agent operations.

Flexible Demand Management under Time-Varying Prices

Science.gov (United States)

Liang, Yong

optimization problem when the objective is to minimize the expected total cost and discomfort, then since the decision maker is likely to be risk-averse, and she wants to protect herself from price spikes, we study the robust optimization problem to address the risk-aversion of the decision maker. We conduct numerical studies to evaluate the price of robustness. Next, we present a detailed model that manages multiple types of flexible demand in the absence of knowledge regarding the distributions of related stochastic processes. Specifically, we consider the case in which time-varying prices with general structures are offered to users, and an energy management system for each household makes optimal energy usage, storage, and trading decisions according to the preferences of users. Because of the uncertainties associated with electricity prices, local generation, and the arrival processes of demand, we formulate a stochastic dynamic programming model, and outline a novel and tractable ADP approach to overcome the curses of dimensionality. Then, we perform numerical studies, whose results demonstrate the effectiveness of the ADP approach. At last, we propose another approximation approach based on Q-learning. In addition, we also develop another decentralization-based heuristic. Both the Q-learning approach and the heuristic make necessary assumptions on the knowledge of information, and each of them has unique advantages. We conduct numerical studies on a testing problem. The simulation results show that both the Q-learning and the decentralization based heuristic approaches work well. Lastly, we conclude the paper with some discussions on future extension directions.

Meeting rural demand: a case for combining community-based distribution and social marketing of injectable contraceptives in Tigray, Ethiopia.

Science.gov (United States)

Prata, Ndola; Weidert, Karen; Fraser, Ashley; Gessessew, Amanuel

2013-01-01

In Sub-Saharan Africa, policy changes have begun to pave the way for community distribution of injectable contraceptives but sustaining such efforts remains challenging. Combining social marketing with community-based distribution provides an opportunity to recover some program costs and compensate workers with proceeds from contraceptive sales. This paper proposes a model for increasing access to injectable contraceptives in rural settings by using community-based distributers as social marketing agents and incorporating financing systems to improve sustainability. This intervention was implemented in three districts of the Central Zone of Tigray, Ethiopia and program data has been collected from November 2011 through October 2012. A total of 137 Community Based Reproductive Health Agents (CBRHAs) were trained to provide injectable contraceptives and were provided with a loan of 25 injectable contraceptives from a drug revolving fund, created with project funds. The price of a single dose credited to a CBRHA was 3 birr ($0.17) and they provide injections to women for 5 birr ($0.29), determined with willingness-to-pay data. Social marketing was used to create awareness and generate demand. Both quantitative and qualitative methods were used to examine important feasibility aspects of the intervention. Forty-four percent of CBRHAs were providing family planning methods at the time of the training and 96% believed providing injectable contraceptives would improve their services. By October 2012, 137 CBRHAs had successfully completed training and provided 2541 injections. Of total injections, 47% were provided to new users of injectable contraceptives. Approximately 31% of injections were given for free to the poorest women, including adolescents. Insights gained from the first year of implementation of the model provide a framework for further expansion in Tigray, Ethiopia. Our experience highlights how program planners can tailor interventions to match family

Evaluating price-based demand response in practice – with application to the EcoGrid EU Experiment

DEFF Research Database (Denmark)

Le Ray, Guillaume; Larsen, Emil Mahler; Pinson, Pierre

2016-01-01

users is exploited in the power system, e.g. for system balancing. However, very few real-world experiments have been carried out and price-based demand response has consistently been found difficult to assess and quantify. It is our aim here to describe an approach to do so, as motivated by the large......Increased emphasis is placed today on various types of demand response, motivated by the integration of renewable energy generation and efficiency improvements in electricity markets. Some advocated for the development of price-based approaches, where the conditional dynamic elasticity of final...

Different Optimal Control Strategies for Exploitation of Demand Response in the Smart Grid

DEFF Research Database (Denmark)

Zong, Yi; Bindner, Henrik W.; Gehrke, Oliver

2012-01-01

To achieve a Danish energy supply based on 100% renewable energy from combinations of wind, biomass, wave and solar power in 2050 and to cover 50% of the Danish electricity consumption by wind power in 2025, it requires coordinated management of large numbers of distributed and demand response...... resources, intermittent renewable energy resources in the Smart Grid. This paper presents different optimal control (Genetic Algorithm-based and Model Predictive Control-based) algorithms that schedule controlled loads in the industrial and residential sectors, based on dynamic price and weather forecast......, considering users’ comfort settings to meet an optimization objective, such as maximum profit or minimum energy consumption. It is demonstrated in this work that the GA-based and MPC-based optimal control strategies are able to achieve load shifting for grid reliability and energy savings, including demand...

Robustness of the Drinking Water Distribution Network under Changing Future Demand

NARCIS (Netherlands)

Agudelo-Vera, C.; Blokker, M.; Vreeburg, J.; Bongard, T.; Hillegers, S.; Van der Hoek, J.P.

2014-01-01

A methodology to determine the robustness of the drinking water distribution system is proposed. The performance of three networks under ten future demand scenarios was tested, using head loss and residence time as indicators. The scenarios consider technological and demographic changes. Daily

[Method for optimal sensor placement in water distribution systems with nodal demand uncertainties].

Science.gov (United States)

Liu, Shu-Ming; Wu, Xue; Ouyang, Le-Yan

2013-08-01

The notion of identification fitness was proposed for optimizing sensor placement in water distribution systems. Nondominated Sorting Genetic Algorithm II was used to find the Pareto front between minimum overlap of possible detection times of two events and the best probability of detection, taking nodal demand uncertainties into account. This methodology was applied to an example network. The solutions show that the probability of detection and the number of possible locations are not remarkably affected by nodal demand uncertainties, but the sources identification accuracy declines with nodal demand uncertainties.

POLYANA-A tool for the calculation of molecular radial distribution functions based on Molecular Dynamics trajectories

Science.gov (United States)

Dimitroulis, Christos; Raptis, Theophanes; Raptis, Vasilios

2015-12-01

We present an application for the calculation of radial distribution functions for molecular centres of mass, based on trajectories generated by molecular simulation methods (Molecular Dynamics, Monte Carlo). When designing this application, the emphasis was placed on ease of use as well as ease of further development. In its current version, the program can read trajectories generated by the well-known DL_POLY package, but it can be easily extended to handle other formats. It is also very easy to 'hack' the program so it can compute intermolecular radial distribution functions for groups of interaction sites rather than whole molecules.

Distributed Coordination of Fractional Dynamical Systems with Exogenous Disturbances

Directory of Open Access Journals (Sweden)

Hongyong Yang

2014-01-01

Full Text Available Distributed coordination of fractional multiagent systems with external disturbances is studied. The state observer of fractional dynamical system is presented, and an adaptive pinning controller is designed for a little part of agents in multiagent systems without disturbances. This adaptive pinning controller with the state observer can ensure multiple agents' states reaching an expected reference tracking. Based on disturbance observers, the controllers are composited with the pinning controller and the state observer. By applying the stability theory of fractional order dynamical systems, the distributed coordination of fractional multiagent systems with external disturbances can be reached asymptotically.

Maintained benefits and improved survival of dynamic cardiomyoplasty by activity-rest stimulation: 5-year results of the Italian trial on "demand" dynamic cardiomyoplasty.

Science.gov (United States)

Rigatelli, Gianluca; Barbiero, Mario; Rigatelli, Giorgio; Riccardi, Roberto; Cobelli, Franco; Cotogni, Angelo; Bandello, Attilio; Carraro, Ugo

2003-01-01

Latissimus dorsi (LD) muscular degeneration caused by continuous electrical stimulation has been the main cause of the poor results of dynamic cardiomyoplasty (DCMP) and its exclusion from the recent international guidelines on heart failure. To avoid full transformation of the LD and to improve results, a new stimulation protocol was developed; fewer impulses per day are delivered, providing the LD wrap with daily periods of rest ("demand" stimulation), based on a heart rate cut-off. The aim of this work is to report the results at 5 years of follow-up of the Italian Trial of Demand Dynamic Cardiomyoplasty and to discuss their impact on the destiny of this type of cardiac assistance. Twelve patients with dilated myocardiopathy (M/F=11/1, mean age 58.2+/-5.8 years, sinus rhythm/atrial fibrillation=11/1) were submitted during the period 1993-1996 to DCMP and at different intervals to demand protocol. Clinical, echocardiographic, mechanographic and cardiac invasive assessments were scheduled before initiating the demand protocol and during the follow-up at 0, 6 and every 12 months. The mean duration of follow-up was 40.2+/-13.8 months (range 18-64). There were no perioperative deaths. The demand stimulation protocol showed a decrease in 5 years in New York Health Association (NYHA) class (3.17+/-0.38-1.67+/-0.77, P=0.0001), an improvement of left ventricular ejection fraction (22.6+/-4.38-32.0+/-7.0, Pactuarial survival of 83.3% (one patient was switched to heart transplantation programme due to clinical worsening and another one died of massive pulmonary embolism). Demand DCMP maintains over time LD muscular properties, enhances clinical benefits and improves survival of DCMP, thus reopening the debate whether this type of treatment should be considered in patients with end-stage heart failure.

From hydrological regimes to water use regimes: influence of the type of habitat on drinking water demand dynamics in alpine tourist resorts.

Science.gov (United States)

Calianno, Martin

2017-04-01

In the last decades, integrated water resources management studies produced integrated models that focus mainly on the assessment of water resources and water stress in the future. In some cases, socioeconomic development results to cause more impacts on the evolution of water systems than climate (Reynard et al., 2014). There is thus a need to develop demand-side approaches in the observation and modeling of human-influenced hydrological systems (Grouillet et al., 2015). We define the notion of water use cycle to differentiate water volumes that are withdrawn from the hydrological system and that circulate through anthropic hydro-systems along various steps: withdrawals, distribution, demands, consumption, restitution (Calianno et al., submitted). To address the spatial distribution and the temporal dynamics of the water use cycle, we define the concepts of water use basins and water use regimes (Calianno et al., submitted). The assessment of the temporal variability of water demands is important at thin time steps in touristic areas, where water resource regimes and water demands are highly variable. This is the case for are alpine ski resorts, where the high touristic season (winter) takes place during the low flow period in nival and glacio-nival basins. In this work, a monitoring of drinking water demands was undergone, at high temporal resolution, on different types of buildings in the ski resort of Megève (France). A dataset was created, from which a typology of water demand regimes was extracted. The analysis of these temporal signatures highlighted the factors influencing the volumes and the dynamics of drinking water demand. The main factors are the type of habitat (single family, collective, house, apartment blocks), the presence of a garden or an infrastructure linked to high standing chalets (pool, spa), the proportion of permanent and temporary habitat, the presence of snow in the ski resort. Also, temporalities linked to weekends and weekly tourism

Demand management concept and tool in a dynamic context

NARCIS (Netherlands)

Liu, Ankun

2008-01-01

ChainScope B.V. is a startup company that develops supply chain optimization and planning software. The optimization software is currently based on the assumption of stationary demand. However, in real-life situation future demand is forecasted through a combination of time series analysis and human

Effects of dynamic-demand-control appliances on the power grid frequency

Science.gov (United States)

Tchuisseu, E. B. Tchawou; Gomila, D.; Brunner, D.; Colet, P.

2017-08-01

Power grid frequency control is a demanding task requiring expensive idle power plants to adapt the supply to the fluctuating demand. An alternative approach is controlling the demand side in such a way that certain appliances modify their operation to adapt to the power availability. This is especially important to achieve a high penetration of renewable energy sources. A number of methods to manage the demand side have been proposed. In this work we focus on dynamic demand control (DDC), where smart appliances can delay their switchings depending on the frequency of the system. We introduce a simple model to study the effects of DDC on the frequency of the power grid. The model includes the power plant equations, a stochastic model for the demand that reproduces, adjusting a single parameter, the statistical properties of frequency fluctuations measured experimentally, and a generic DDC protocol. We find that DDC can reduce small and medium-size fluctuations but it can also increase the probability of observing large frequency peaks due to the necessity of recovering pending task. We also conclude that a deployment of DDC around 30-40% already allows a significant reduction of the fluctuations while keeping the number of pending tasks low.

Density prediction and dimensionality reduction of mid-term electricity demand in China: A new semiparametric-based additive model

International Nuclear Information System (INIS)

Shao, Zhen; Yang, Shan-Lin; Gao, Fei

2014-01-01

Highlights: • A new stationary time series smoothing-based semiparametric model is established. • A novel semiparametric additive model based on piecewise smooth is proposed. • We model the uncertainty of data distribution for mid-term electricity forecasting. • We provide efficient long horizon simulation and extraction for external variables. • We provide stable and accurate density predictions for mid-term electricity demand. - Abstract: Accurate mid-term electricity demand forecasting is critical for efficient electric planning, budgeting and operating decisions. Mid-term electricity demand forecasting is notoriously complicated, since the demand is subject to a range of external drivers, such as climate change, economic development, which will exhibit monthly, seasonal, and annual complex variations. Conventional models are based on the assumption that original data is stable and normally distributed, which is generally insignificant in explaining actual demand pattern. This paper proposes a new semiparametric additive model that, in addition to considering the uncertainty of the data distribution, includes practical discussions covering the applications of the external variables. To effectively detach the multi-dimensional volatility of mid-term demand, a novel piecewise smooth method which allows reduction of the data dimensionality is developed. Besides, a semi-parametric procedure that makes use of bootstrap algorithm for density forecast and model estimation is presented. Two typical cases in China are presented to verify the effectiveness of the proposed methodology. The results suggest that both meteorological and economic variables play a critical role in mid-term electricity consumption prediction in China, while the extracted economic factor is adequate to reveal the potentially complex relationship between electricity consumption and economic fluctuation. Overall, the proposed model can be easily applied to mid-term demand forecasting, and

Stress Distribution in Graded Cellular Materials Under Dynamic Compression

Directory of Open Access Journals (Sweden)

Peng Wang

Full Text Available Abstract Dynamic compression behaviors of density-homogeneous and density-graded irregular honeycombs are investigated using cell-based finite element models under a constant-velocity impact scenario. A method based on the cross-sectional engineering stress is developed to obtain the one-dimensional stress distribution along the loading direction in a cellular specimen. The cross-sectional engineering stress is contributed by two parts: the node-transitive stress and the contact-induced stress, which are caused by the nodal force and the contact of cell walls, respectively. It is found that the contact-induced stress is dominant for the significantly enhanced stress behind the shock front. The stress enhancement and the compaction wave propagation can be observed through the stress distributions in honeycombs under high-velocity compression. The single and double compaction wave modes are observed directly from the stress distributions. Theoretical analysis of the compaction wave propagation in the density-graded honeycombs based on the R-PH (rigid-plastic hardening idealization is carried out and verified by the numerical simulations. It is found that stress distribution in cellular materials and the compaction wave propagation characteristics under dynamic compression can be approximately predicted by the R-PH shock model.

Natural gas demand forecast system based on the application of artificial neural networks

International Nuclear Information System (INIS)

Sanfeliu, J.M.; Doumanian, J.E.

1997-01-01

Gas Natural BAN, as a distribution gas company since 1993 in the north and west area of Buenos Aires Argentina, with 1,000,000 customers, had to develop a gas demand forecast system which should comply with the following basic requirements: Be able to do reliable forecasts with short historical information (2 years); Distinguish demands in areas of different characteristics, i.e. mainly residential, mainly industrial; Self-learning capability. To accomplish above goals, Gas Natural BAN chose in view of its own necessities, an artificial intelligence application (neural networks). 'SANDRA', the gas demand forecast system for gas distribution used by Gas Natural BAN, has the following features: Daily gas demand forecast, Hourly gas demand forecast and Breakdown of both forecast for each of the 3 basic zones in which the distribution area of Gas Natural BAN is divided. (au)

Security Services Lifecycle Management in on-demand infrastructure services

NARCIS (Netherlands)

Demchenko, Y.; de Laat, C.; Lopez, D.R.; García-Espín, J.A.; Qiu, J.; Zhao, G.; Rong, C.

2010-01-01

Modern e-Science and high technology industry require high-performance and complicated network and computer infrastructure to support distributed collaborating groups of researchers and applications that should be provisioned on-demand. The effective use and management of the dynamically provisioned

Window-Based Popularity Caching for IPTV On-Demand Services

OpenAIRE

Hsuan Chiu; Chi-He Chang; Chao-Wei Tseng; Chi-Shi Liu

2011-01-01

In recent years, many telecommunication companies have regarded IP network as a new delivery platform for providing TV services because IP network is equipped with two-way and high-speed communication abilities which are appropriate to provide on-demand services and linear TV programs. However, in this IPTV system, the requests of VOD (video on demand) are usually aggregated in a short period intensively and user preferences are fluctuated dynamically. Moreover, the VOD content is updated fre...

Medium-term electric power demand forecasting based on economic-electricity transmission model

Science.gov (United States)

Li, Wenfeng; Bao, Fangmin; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Mao, Yubin; Wang, Jiangbo; Liu, Junhui

2018-06-01

Electric demand forecasting is a basic work to ensure the safe operation of power system. Based on the theories of experimental economics and econometrics, this paper introduces Prognoz Platform 7.2 intelligent adaptive modeling platform, and constructs the economic electricity transmission model that considers the economic development scenarios and the dynamic adjustment of industrial structure to predict the region's annual electricity demand, and the accurate prediction of the whole society's electricity consumption is realized. Firstly, based on the theories of experimental economics and econometrics, this dissertation attempts to find the economic indicator variables that drive the most economical growth of electricity consumption and availability, and build an annual regional macroeconomic forecast model that takes into account the dynamic adjustment of industrial structure. Secondly, it innovatively put forward the economic electricity directed conduction theory and constructed the economic power transfer function to realize the group forecast of the primary industry + rural residents living electricity consumption, urban residents living electricity, the second industry electricity consumption, the tertiary industry electricity consumption; By comparing with the actual value of economy and electricity in Henan province in 2016, the validity of EETM model is proved, and the electricity consumption of the whole province from 2017 to 2018 is predicted finally.

Dynamically Switching among Bundled and Single Tickets with Time-Dependent Demand Rates

Directory of Open Access Journals (Sweden)

Serhan Duran

2012-01-01

Full Text Available The most important market segmentation in sports and entertainment industry is the competition between customers that buy bundled and single tickets. A common selling practice is starting the selling season with bundled ticket sales and switching to selling single tickets later on. The aim of this practice is to increase the number of customers that buy bundles, which in return increases the load factor of the events with low demand. In this paper, we investigate the effect of time dependent demand on dynamic switching times from bundled to single ticket sales and the potential revenue gain over the case where the demand rate of events is assumed to be constant with time.

Optimising building net energy demand with dynamic BIPV shading

International Nuclear Information System (INIS)

Jayathissa, P.; Luzzatto, M.; Schmidli, J.; Hofer, J.; Nagy, Z.; Schlueter, A.

2017-01-01

Highlights: •Coupled analysis of PV generation and building energy using adaptive BIPV shading. •20–80% net energy saving compared to an equivalent static system. •The system can in some cases compensate for the entire heating/cooling/lighting load. •High resolution radiation simulation including impacts of module self shading. -- Abstract: The utilisation of a dynamic photovoltaic system for adaptive shading can improve building energy performance by controlling solar heat gains and natural lighting, while simultaneously generating electricity on site. This paper firstly presents an integrated simulation framework to couple photovoltaic electricity generation to building energy savings through adaptive shading. A high-resolution radiance and photovoltaic model calculates the photovoltaic electricity yield while taking into account partial shading between modules. The remaining solar irradiation that penetrates the window is used in a resistance-capacitance building thermal model. A simulation of all possible dynamic configurations is conducted for each hourly time step, of which the most energy efficient configuration is chosen. We then utilise this framework to determine the optimal orientation of the photovoltaic panels to maximise the electricity generation while minimising the building’s heating, lighting and cooling demand. An existing adaptive photovoltaic facade was used as a case study for evaluation. Our results report a 20–80% net energy saving compared to an equivalent static photovoltaic shading system depending on the efficiency of the heating and cooling system. In some cases the Adaptive Solar Facade can almost compensate for the entire energy demand of the office space behind it. The control of photovoltaic production on the facade, simultaneously with the building energy demand, opens up new methods of building management as the facade can control both the production and consumption of electricity.

Operation Optimization in a Smart Micro-Grid in the Presence of Distributed Generation and Demand Response

Directory of Open Access Journals (Sweden)

Yongli Wang

2018-03-01

Full Text Available With the application of distributed generation and the development of smart grid technology, micro-grid, an economic and stable power grid, tends to play an important role in the demand side management. Because micro-grid technology and demand response have been widely applied, what Demand Response actions can realize the economic operation of micro-grid has become an important issue for utilities. In this proposed work, operation optimization modeling for micro-grid is done considering distributed generation, environmental factors and demand response. The main contribution of this model is to optimize the cost in the context of considering demand response and system operation. The presented optimization model can reduce the operation cost of micro-grid without bringing discomfort to the users, thus increasing the consumption of clean energy effectively. Then, to solve this operational optimization problem, genetic algorithm is used to implement objective function and DR scheduling strategy. In addition, to validate the proposed model, it is employed on a smart micro-grid from Tianjin. The obtained numerical results clearly indicate the impact of demand response on economic operation of micro-grid and development of distributed generation. Besides, a sensitivity analysis on the natural gas price is implemented according to the situation of China, and the result shows that the natural gas price has a great influence on the operation cost of the micro-grid and effect of demand response.

Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems.

Science.gov (United States)

Munera, Eduardo; Poza-Lujan, Jose-Luis; Posadas-Yagüe, Juan-Luis; Simó-Ten, José-Enrique; Noguera, Juan Fco Blanes

2015-07-24

The inclusion of embedded sensors into a networked system provides useful information for many applications. A Distributed Control System (DCS) is one of the clearest examples where processing and communications are constrained by the client's requirements and the capacity of the system. An embedded sensor with advanced processing and communications capabilities supplies high level information, abstracting from the data acquisition process and objects recognition mechanisms. The implementation of an embedded sensor/actuator as a Smart Resource permits clients to access sensor information through distributed network services. Smart resources can offer sensor services as well as computing, communications and peripheral access by implementing a self-aware based adaptation mechanism which adapts the execution profile to the context. On the other hand, information integrity must be ensured when computing processes are dynamically adapted. Therefore, the processing must be adapted to perform tasks in a certain lapse of time but always ensuring a minimum process quality. In the same way, communications must try to reduce the data traffic without excluding relevant information. The main objective of the paper is to present a dynamic configuration mechanism to adapt the sensor processing and communication to the client's requirements in the DCS. This paper describes an implementation of a smart resource based on a Red, Green, Blue, and Depth (RGBD) sensor in order to test the dynamic configuration mechanism presented.

Distribution Locational Marginal Pricing through Quadratic Programming for Congestion Management in Distribution Networks

DEFF Research Database (Denmark)

Huang, Shaojun; Wu, Qiuwei; Oren, Shmuel S.

2015-01-01

) calculates dynamic tariffs and publishes them to the aggregators, who make the optimal energy plans for the flexible demands. The DLMP through QP instead of linear programing as studied in previous literatures solves the multiple solution issue of the ag- gregator optimization which may cause......This paper presents the distribution locational mar- ginal pricing (DLMP) method through quadratic programming (QP) designed to alleviate the congestion that might occur in a distribution network with high penetration of flexible demands. In the DLMP method, the distribution system operator (DSO...

Reliability-Based Marginal Cost Pricing Problem Case with Both Demand Uncertainty and Travelers’ Perception Errors

Directory of Open Access Journals (Sweden)

Shaopeng Zhong

2013-01-01

Full Text Available Focusing on the first-best marginal cost pricing (MCP in a stochastic network with both travel demand uncertainty and stochastic perception errors within the travelers’ route choice decision processes, this paper develops a perceived risk-based stochastic network marginal cost pricing (PRSN-MCP model. Numerical examples based on an integrated method combining the moment analysis approach, the fitting distribution method, and the reliability measures are also provided to demonstrate the importance and properties of the proposed model. The main finding is that ignoring the effect of travel time reliability and travelers’ perception errors may significantly reduce the performance of the first-best MCP tolls, especially under high travelers’ confidence and network congestion levels. The analysis result could also enhance our understanding of (1 the effect of stochastic perception error (SPE on the perceived travel time distribution and the components of road toll; (2 the effect of road toll on the actual travel time distribution and its reliability measures; (3 the effect of road toll on the total network travel time distribution and its statistics; and (4 the effect of travel demand level and the value of reliability (VoR level on the components of road toll.

Coordinated control of micro-grid based on distributed moving horizon control.

Science.gov (United States)

Ma, Miaomiao; Shao, Liyang; Liu, Xiangjie

2018-05-01

This paper proposed the distributed moving horizon coordinated control scheme for the power balance and economic dispatch problems of micro-grid based on distributed generation. We design the power coordinated controller for each subsystem via moving horizon control by minimizing a suitable objective function. The objective function of distributed moving horizon coordinated controller is chosen based on the principle that wind power subsystem has the priority to generate electricity while photovoltaic power generation coordinates with wind power subsystem and the battery is only activated to meet the load demand when necessary. The simulation results illustrate that the proposed distributed moving horizon coordinated controller can allocate the output power of two generation subsystems reasonably under varying environment conditions, which not only can satisfy the load demand but also limit excessive fluctuations of output power to protect the power generation equipment. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Dynamic shared state maintenance in distributed virtual environments

Science.gov (United States)

Hamza-Lup, Felix George

Advances in computer networks and rendering systems facilitate the creation of distributed collaborative environments in which the distribution of information at remote locations allows efficient communication. Particularly challenging are distributed interactive Virtual Environments (VE) that allow knowledge sharing through 3D information. The purpose of this work is to address the problem of latency in distributed interactive VE and to develop a conceptual model for consistency maintenance in these environments based on the participant interaction model. An area that needs to be explored is the relationship between the dynamic shared state and the interaction with the virtual entities present in the shared scene. Mixed Reality (MR) and VR environments must bring the human participant interaction into the loop through a wide range of electronic motion sensors, and haptic devices. Part of the work presented here defines a novel criterion for categorization of distributed interactive VE and introduces, as well as analyzes, an adaptive synchronization algorithm for consistency maintenance in such environments. As part of the work, a distributed interactive Augmented Reality (AR) testbed and the algorithm implementation details are presented. Currently the testbed is part of several research efforts at the Optical Diagnostics and Applications Laboratory including 3D visualization applications using custom built head-mounted displays (HMDs) with optical motion tracking and a medical training prototype for endotracheal intubation and medical prognostics. An objective method using quaternion calculus is applied for the algorithm assessment. In spite of significant network latency, results show that the dynamic shared state can be maintained consistent at multiple remotely located sites. In further consideration of the latency problems and in the light of the current trends in interactive distributed VE applications, we propose a hybrid distributed system architecture for

Dynamic Strategic Planning in a Professional Knowledge-Based Organization

Science.gov (United States)

Olivarius, Niels de Fine; Kousgaard, Marius Brostrom; Reventlow, Susanne; Quelle, Dan Grevelund; Tulinius, Charlotte

2010-01-01

Professional, knowledge-based institutions have a particular form of organization and culture that makes special demands on the strategic planning supervised by research administrators and managers. A model for dynamic strategic planning based on a pragmatic utilization of the multitude of strategy models was used in a small university-affiliated…

Active Probing Feedback based Self Configurable Intelligent Distributed Antenna System

DEFF Research Database (Denmark)

Kumar, Ambuj

collectively as Place Time Coverage & Capacity (PTC2). The dissertation proves through the concept of the PTC2 that the network performance can severely be degraded by the excessive and unrealistic site demands, the network management inefficiency, and the consequence of the accumulation of subscribers...... challenge through a viable solution that is based on injecting intelligence and services in parallel layers through a Distributed Antenna Systems (DAS) network. This approach would enable the remote sites to acquire intelligence and a resource pool at the same time, thereby managing the network dynamics...... promptly and aptly to absorb the PTC2 wobble. An Active Probing Management System (APMS) is proposed as a supporting architecture, to assist the intelligent system to keep a check on the variations at each and every site by either deploying the additional antenna or by utilising the service antenna...

A distributed dynamic model of a monolith hydrogen membrane reactor

International Nuclear Information System (INIS)

Michelsen, Finn Are; Wilhelmsen, Øivind; Zhao, Lei; Aasen, Knut Ingvar

2013-01-01

Highlights: ► We model a rigorous distributed dynamic model for a HMR unit. ► The model includes enough complexity for steady-state and dynamic analysis. ► Simulations show that the model is non-linear within the normal operating range. ► The model is useful for studying and handling disturbances such as inlet changes and membrane leakage. - Abstract: This paper describes a distributed mechanistic dynamic model of a hydrogen membrane reformer unit (HMR) used for methane steam reforming. The model is based on a square channel monolith structure concept, where air flows adjacent to a mix of natural gas and water distributed in a chess pattern of channels. Combustion of hydrogen gives energy to the endothermic steam reforming reactions. The model is used for both steady state and dynamic analyses. It therefore needs to be computationally attractive, but still include enough complexity to study the important steady state and dynamic features of the process. Steady-state analysis of the model gives optimum for the steam to carbon and steam to oxygen ratios, where the conversion of methane is 92% and the hydrogen used as energy for the endothermic reactions is 28% at the nominal optimum. The dynamic analysis shows that non-linear control schemes may be necessary for satisfactory control performance

The role of technology, organisation, and demand in growth and income distribution

OpenAIRE

Tommaso Ciarli; Andre' Lorentz; Maria Savona; Marco Valente

2012-01-01

The paper proposes a model that explains cross-country growth divergences over time for different aspects of structural change. The model formalises the links between production technology, firm organisation (functional composition of employment) on the supply side and the endogenous evolution of income distribution and consumption patterns on the demand side. Wage distribution is the main channel between the organisation of firms and consumption patterns, and firm selection is the main trigg...

Reliability evaluation of microgrid considering incentive-based demand response

Science.gov (United States)

Huang, Ting-Cheng; Zhang, Yong-Jun

2017-07-01

Incentive-based demand response (IBDR) can guide customers to adjust their behaviour of electricity and curtail load actively. Meanwhile, distributed generation (DG) and energy storage system (ESS) can provide time for the implementation of IBDR. The paper focus on the reliability evaluation of microgrid considering IBDR. Firstly, the mechanism of IBDR and its impact on power supply reliability are analysed. Secondly, the IBDR dispatch model considering customer’s comprehensive assessment and the customer response model are developed. Thirdly, the reliability evaluation method considering IBDR based on Monte Carlo simulation is proposed. Finally, the validity of the above models and method is studied through numerical tests on modified RBTS Bus6 test system. Simulation results demonstrated that IBDR can improve the reliability of microgrid.

Demand response with locational dynamic pricing to support the integration of renewables

International Nuclear Information System (INIS)

Dupont, B.; De Jonghe, C.; Olmos, L.; Belmans, R.

2014-01-01

Electricity production from centralised and decentralised renewable energy resources in Europe is gaining significance, resulting in operational challenges in the electricity system. Although these challenges add to the locational and time dependency of the underlying cost of operating the system, this variability in time and location is not reflected in residential tariff schemes. Consequently, residential users are not incentivised to react to varying system conditions and to help the integration of renewable energy resources. Therefore, this paper provides a theoretical framework for designing a locational dynamic pricing scheme. This can be used to assess existing tariff structures for consumption and injection, and can serve as a theoretical background for developing new tariff schemes. Starting from the underlying costs, this paper shows that the potential for locational dynamic pricing depends on the locational and time dependency of its cost drivers. When converting costs into tariffs, the tariff design should be determined. This includes the advance notice of sending tariffs to users, and the length of price blocks and price patterns. This tariff design should find a balance between tariff principles related to costs, practicality and social acceptability on the one hand, and the resulting demand response incentive on the other. - Highlights: • The integration of renewables affects the locational and time dependency of costs. • Locational dynamic pricing reflects cost variability and allows demand response. • A theoretical framework for designing and assessing tariff schemes is proposed. • Tariff variability depends on the locational and time dependency of its cost drivers. • The tariff design should consider the resulting demand response incentive

Effects of the infectious period distribution on predicted transitions in childhood disease dynamics.

Science.gov (United States)

Krylova, Olga; Earn, David J D

2013-07-06

The population dynamics of infectious diseases occasionally undergo rapid qualitative changes, such as transitions from annual to biennial cycles or to irregular dynamics. Previous work, based on the standard seasonally forced 'susceptible-exposed-infectious-removed' (SEIR) model has found that transitions in the dynamics of many childhood diseases result from bifurcations induced by slow changes in birth and vaccination rates. However, the standard SEIR formulation assumes that the stage durations (latent and infectious periods) are exponentially distributed, whereas real distributions are narrower and centred around the mean. Much recent work has indicated that realistically distributed stage durations strongly affect the dynamical structure of seasonally forced epidemic models. We investigate whether inferences drawn from previous analyses of transitions in patterns of measles dynamics are robust to the shapes of the stage duration distributions. As an illustrative example, we analyse measles dynamics in New York City from 1928 to 1972. We find that with a fixed mean infectious period in the susceptible-infectious-removed (SIR) model, the dynamical structure and predicted transitions vary substantially as a function of the shape of the infectious period distribution. By contrast, with fixed mean latent and infectious periods in the SEIR model, the shapes of the stage duration distributions have a less dramatic effect on model dynamical structure and predicted transitions. All these results can be understood more easily by considering the distribution of the disease generation time as opposed to the distributions of individual disease stages. Numerical bifurcation analysis reveals that for a given mean generation time the dynamics of the SIR and SEIR models for measles are nearly equivalent and are insensitive to the shapes of the disease stage distributions.

From Walras’ auctioneer to continuous time double auctions: a general dynamic theory of supply and demand

Science.gov (United States)

Donier, J.; Bouchaud, J.-P.

2016-12-01

In standard Walrasian auctions, the price of a good is defined as the point where the supply and demand curves intersect. Since both curves are generically regular, the response to small perturbations is linearly small. However, a crucial ingredient is absent of the theory, namely transactions themselves. What happens after they occur? To answer the question, we develop a dynamic theory for supply and demand based on agents with heterogeneous beliefs. When the inter-auction time is infinitely long, the Walrasian mechanism is recovered. When transactions are allowed to happen in continuous time, a peculiar property emerges: close to the price, supply and demand vanish quadratically, which we empirically confirm on the Bitcoin. This explains why price impact in financial markets is universally observed to behave as the square root of the excess volume. The consequences are important, as they imply that the very fact of clearing the market makes prices hypersensitive to small fluctuations.

Optimal Operation of Radial Distribution Systems Using Extended Dynamic Programming

DEFF Research Database (Denmark)

Lopez, Juan Camilo; Vergara, Pedro P.; Lyra, Christiano

2018-01-01

An extended dynamic programming (EDP) approach is developed to optimize the ac steady-state operation of radial electrical distribution systems (EDS). Based on the optimality principle of the recursive Hamilton-Jacobi-Bellman equations, the proposed EDP approach determines the optimal operation o...... approach is illustrated using real-scale systems and comparisons with commercial programming solvers. Finally, generalizations to consider other EDS operation problems are also discussed.......An extended dynamic programming (EDP) approach is developed to optimize the ac steady-state operation of radial electrical distribution systems (EDS). Based on the optimality principle of the recursive Hamilton-Jacobi-Bellman equations, the proposed EDP approach determines the optimal operation...... of the EDS by setting the values of the controllable variables at each time period. A suitable definition for the stages of the problem makes it possible to represent the optimal ac power flow of radial EDS as a dynamic programming problem, wherein the 'curse of dimensionality' is a minor concern, since...

Distributed dynamic simulations of networked control and building performance applications.

Science.gov (United States)

Yahiaoui, Azzedine

2018-02-01

The use of computer-based automation and control systems for smart sustainable buildings, often so-called Automated Buildings (ABs), has become an effective way to automatically control, optimize, and supervise a wide range of building performance applications over a network while achieving the minimum energy consumption possible, and in doing so generally refers to Building Automation and Control Systems (BACS) architecture. Instead of costly and time-consuming experiments, this paper focuses on using distributed dynamic simulations to analyze the real-time performance of network-based building control systems in ABs and improve the functions of the BACS technology. The paper also presents the development and design of a distributed dynamic simulation environment with the capability of representing the BACS architecture in simulation by run-time coupling two or more different software tools over a network. The application and capability of this new dynamic simulation environment are demonstrated by an experimental design in this paper.

Better Water Demand and Pipe Description Improve the Distribution Network Modeling Results

Science.gov (United States)

Distribution system modeling simplifies pipe network in skeletonization and simulates the flow and water quality by using generalized water demand patterns. While widely used, the approach has not been examined fully on how it impacts the modeling fidelity. This study intends to ...

Ice Storage Air-Conditioning System Simulation with Dynamic Electricity Pricing: A Demand Response Study

Directory of Open Access Journals (Sweden)

Chi-Chun Lo

2016-02-01

Full Text Available This paper presents an optimal dispatch model of an ice storage air-conditioning system for participants to quickly and accurately perform energy saving and demand response, and to avoid the over contact with electricity price peak. The schedule planning for an ice storage air-conditioning system of demand response is mainly to transfer energy consumption from the peak load to the partial-peak or off-peak load. Least Squares Regression (LSR is used to obtain the polynomial function for the cooling capacity and the cost of power consumption with a real ice storage air-conditioning system. Based on the dynamic electricity pricing, the requirements of cooling loads, and all technical constraints, the dispatch model of the ice-storage air-conditioning system is formulated to minimize the operation cost. The Improved Ripple Bee Swarm Optimization (IRBSO algorithm is proposed to solve the dispatch model of the ice storage air-conditioning system in a daily schedule on summer. Simulation results indicate that reasonable solutions provide a practical and flexible framework allowing the demand response of ice storage air-conditioning systems to demonstrate the optimization of its energy savings and operational efficiency and offering greater energy efficiency.

Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks

Directory of Open Access Journals (Sweden)

Bishnu P. Bhattarai

2017-01-01

Full Text Available This paper presents a multi-timescale control strategy to deploy electric vehicle (EV demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator’s perspectives. A hierarchical control architecture (HCA comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating multi-time scale controls that work from a day-ahead scheduling up to real-time adaptive control. The performance of the developed method is investigated with high EV penetration in a typical residential distribution grid. The simulation results demonstrate that HCA efficiently utilizes demand flexibility stemming from EVs to solve grid unbalancing and congestions with simultaneous maximization of economic benefits to the participating actors. This is ensured by enabling EV participation in day-ahead, balancing, and regulation markets. For the given network configuration and pricing structure, HCA ensures the EV owners to get paid up to five times the cost they were paying without control.

Fostering Residential Demand Response through Dynamic Pricing Schemes: A Behavioural Review of Smart Grid Pilots in Europe

Directory of Open Access Journals (Sweden)

Kris Kessels

2016-09-01

Full Text Available Many smart grid projects make use of dynamic pricing schemes aimed to motivate consumers to shift and/or decrease energy use. Based upon existing literature and analyses of current smart grid projects, this survey paper presents key lessons on how to encourage households to adjust energy end use by means of dynamic tariffs. The paper identifies four key hypotheses related to fostering demand response through dynamic tariff schemes and examines whether these hypotheses can be accepted or rejected based on a review of published findings from a range of European pilot projects. We conclude that dynamic pricing schemes have the power to adjust energy consumption behavior within households. In order to work effectively, the dynamic tariff should be simple to understand for the end users, with timely notifications of price changes, a considerable effect on their energy bill and, if the tariff is more complex, the burden for the consumer could be eased by introducing automated control. Although sometimes the mere introduction of a dynamic tariff has proven to be effective, often the success of the pricing scheme depends also on other factors influencing the behavior of end users. An important condition to make dynamic tariffs work is that the end users should be engaged with them.

Observer-based distributed adaptive fault-tolerant containment control of multi-agent systems with general linear dynamics.

Science.gov (United States)

Ye, Dan; Chen, Mengmeng; Li, Kui

2017-11-01

In this paper, we consider the distributed containment control problem of multi-agent systems with actuator bias faults based on observer method. The objective is to drive the followers into the convex hull spanned by the dynamic leaders, where the input is unknown but bounded. By constructing an observer to estimate the states and bias faults, an effective distributed adaptive fault-tolerant controller is developed. Different from the traditional method, an auxiliary controller gain is designed to deal with the unknown inputs and bias faults together. Moreover, the coupling gain can be adjusted online through the adaptive mechanism without using the global information. Furthermore, the proposed control protocol can guarantee that all the signals of the closed-loop systems are bounded and all the followers converge to the convex hull with bounded residual errors formed by the dynamic leaders. Finally, a decoupled linearized longitudinal motion model of the F-18 aircraft is used to demonstrate the effectiveness. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Dynamic Behaviour of a Population of Controlled-by-price Demand Side Resources

DEFF Research Database (Denmark)

Sossan, Fabrizio; Han, Xue; Bindner, Henrik W.

2014-01-01

It is described that controlling or shedding by price the power consumption of a population of thermostatic loads introduces in the aggregate consumption dynamic effects th at cannot be disregarded if electrical flexible demand is meant to supply power system services. It is shown that inducing...

Demand and supply in Russian gas market

International Nuclear Information System (INIS)

Milovidov, K.N.

1997-01-01

The big volume of gas supplies for current and future energy and natural gas balances in Russia is important to understand the likely future dynamics of demand for gas. The path of future demand in Russia is uncertain and the range of possible scenarios is wide. For creating the new gas consumption structure, more deep diversification and development of the gas distribution systems, large investments and considerable periods of time are necessary. The factors usually studied in detail in the conditions of market economy can not be used here as a basis for strategic planning due to several reasons. (R.P.)

DataBase on Demand

International Nuclear Information System (INIS)

Aparicio, R Gaspar; Gomez, D; Wojcik, D; Coz, I Coterillo

2012-01-01

At CERN a number of key database applications are running on user-managed MySQL database services. The database on demand project was born out of an idea to provide the CERN user community with an environment to develop and run database services outside of the actual centralised Oracle based database services. The Database on Demand (DBoD) empowers the user to perform certain actions that had been traditionally done by database administrators, DBA's, providing an enterprise platform for database applications. It also allows the CERN user community to run different database engines, e.g. presently open community version of MySQL and single instance Oracle database server. This article describes a technology approach to face this challenge, a service level agreement, the SLA that the project provides, and an evolution of possible scenarios.

Congestion management of electric distribution networks through market based methods

DEFF Research Database (Denmark)

Huang, Shaojun

 EVs and HPs. Market-based congestion management methods are the focus of the thesis. They handle the potential congestion at the energy planning stage; therefore, the aggregators can optimally plan the energy consumption and have the least impact on the customers. After reviewing and identifying...... the shortcomings of the existing methods, the thesis fully studies and improves the dynamic tariff (DT) method, and proposes two  new market-based  congestion management methods,  namely the  dynamic subsidy (DS) method and the flexible demand swap method. The thesis improves the DT method from four aspects......Rapidly increasing share of intermittent renewable energy production poses a great challenge of the management and operation of the modern power systems. Deployment of a large number of flexible demands, such as electrical vehicles (EVs) and heat pumps (HPs), is believed to be a promising solution...

Data Driven Approach for High Resolution Population Distribution and Dynamics Models

Energy Technology Data Exchange (ETDEWEB)

Bhaduri, Budhendra L [ORNL; Bright, Eddie A [ORNL; Rose, Amy N [ORNL; Liu, Cheng [ORNL; Urban, Marie L [ORNL; Stewart, Robert N [ORNL

2014-01-01

High resolution population distribution data are vital for successfully addressing critical issues ranging from energy and socio-environmental research to public health to human security. Commonly available population data from Census is constrained both in space and time and does not capture population dynamics as functions of space and time. This imposes a significant limitation on the fidelity of event-based simulation models with sensitive space-time resolution. This paper describes ongoing development of high-resolution population distribution and dynamics models, at Oak Ridge National Laboratory, through spatial data integration and modeling with behavioral or activity-based mobility datasets for representing temporal dynamics of population. The model is resolved at 1 km resolution globally and describes the U.S. population for nighttime and daytime at 90m. Integration of such population data provides the opportunity to develop simulations and applications in critical infrastructure management from local to global scales.

The optimization model for multi-type customers assisting wind power consumptive considering uncertainty and demand response based on robust stochastic theory

International Nuclear Information System (INIS)

Tan, Zhongfu; Ju, Liwei; Reed, Brent; Rao, Rao; Peng, Daoxin; Li, Huanhuan; Pan, Ge

2015-01-01

Highlights: • Our research focuses on demand response behaviors of multi-type customers. • A wind power simulation method is proposed based on the Brownian motion theory. • Demand response revenue functions are proposed for multi-type customers. • A robust stochastic optimization model is proposed for wind power consumptive. • Models are built to measure the impacts of demand response on wind power consumptive. - Abstract: In order to relieve the influence of wind power uncertainty on power system operation, demand response and robust stochastic theory are introduced to build a stochastic scheduling optimization model. Firstly, this paper presents a simulation method for wind power considering external environment based on Brownian motion theory. Secondly, price-based demand response and incentive-based demand response are introduced to build demand response model. Thirdly, the paper constructs the demand response revenue functions for electric vehicle customers, business customers, industry customers and residential customers. Furthermore, robust stochastic optimization theory is introduced to build a wind power consumption stochastic optimization model. Finally, simulation analysis is taken in the IEEE 36 nodes 10 units system connected with 650 MW wind farms. The results show the robust stochastic optimization theory is better to overcome wind power uncertainty. Demand response can improve system wind power consumption capability. Besides, price-based demand response could transform customers’ load demand distribution, but its load curtailment capacity is not as obvious as incentive-based demand response. Since price-based demand response cannot transfer customer’s load demand as the same as incentive-based demand response, the comprehensive optimization effect will reach best when incentive-based demand response and price-based demand response are both introduced.

Dynamic radial distribution function from inelastic neutron scattering

International Nuclear Information System (INIS)

McQueeney, R.J.

1998-01-01

A real-space, local dynamic structure function g(r,ω) is defined from the dynamic structure function S(Q,ω), which can be measured using inelastic neutron scattering. At any particular frequency ω, S(Q,ω) contains Q-dependent intensity oscillations which reflect the spatial distribution and relative displacement directions for the atoms vibrating at that frequency. Information about local and dynamic atomic correlations is obtained from the Fourier transform of these oscillations g(r,ω) at the particular frequency. g(r,ω) can be formulated such that the elastic and frequency-summed limits correspond to the average and instantaneous radial distribution function, respectively, and is thus called the dynamic radial distribution function. As an example, the dynamic radial distribution function is calculated for fcc nickel in a model which considers only the harmonic atomic displacements due to phonons. The results of these calculations demonstrate that the magnitude of the atomic correlations can be quantified and g(r,ω) is a well-defined correlation function. This leads to a simple prescription for investigating local lattice dynamics. copyright 1998 The American Physical Society

Optimal transport on supply-demand networks.

Science.gov (United States)

Chen, Yu-Han; Wang, Bing-Hong; Zhao, Li-Chao; Zhou, Changsong; Zhou, Tao

2010-06-01

In the literature, transport networks are usually treated as homogeneous networks, that is, every node has the same function, simultaneously providing and requiring resources. However, some real networks, such as power grids and supply chain networks, show a far different scenario in which nodes are classified into two categories: supply nodes provide some kinds of services, while demand nodes require them. In this paper, we propose a general transport model for these supply-demand networks, associated with a criterion to quantify their transport capacities. In a supply-demand network with heterogeneous degree distribution, its transport capacity strongly depends on the locations of supply nodes. We therefore design a simulated annealing algorithm to find the near optimal configuration of supply nodes, which remarkably enhances the transport capacity compared with a random configuration and outperforms the degree target algorithm, the betweenness target algorithm, and the greedy method. This work provides a start point for systematically analyzing and optimizing transport dynamics on supply-demand networks.

Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network

NARCIS (Netherlands)

Paudel, S.; Elmtiri, M.; Kling, W.L.; Corre, le O.; Lacarriere, B.

2014-01-01

This paper presents the building heating demand prediction model with occupancy profile and operational heating power level characteristics in short time horizon (a couple of days) using artificial neural network. In addition, novel pseudo dynamic transitional model is introduced, which consider

Coupling Agent-Based and Groundwater Modeling to Explore Demand Management Strategies for Shared Resources

Science.gov (United States)

Al-Amin, S.

2015-12-01

Municipal water demands in growing population centers in the arid southwest US are typically met through increased groundwater withdrawals. Hydro-climatic uncertainties attributed to climate change and land use conversions may also alter demands and impact the replenishment of groundwater supply. Groundwater aquifers are not necessarily confined within municipal and management boundaries, and multiple diverse agencies may manage a shared resource in a decentralized approach, based on individual concerns and resources. The interactions among water managers, consumers, and the environment influence the performance of local management strategies and regional groundwater resources. This research couples an agent-based modeling (ABM) framework and a groundwater model to analyze the effects of different management approaches on shared groundwater resources. The ABM captures the dynamic interactions between household-level consumers and policy makers to simulate water demands under climate change and population growth uncertainties. The groundwater model is used to analyze the relative effects of management approaches on reducing demands and replenishing groundwater resources. The framework is applied for municipalities located in the Verde River Basin, Arizona that withdraw groundwater from the Verde Formation-Basin Fill-Carbonate aquifer system. Insights gained through this simulation study can be used to guide groundwater policy-making under changing hydro-climatic scenarios for a long-term planning horizon.

Electricity demand and spot price forecasting using evolutionary computation combined with chaotic nonlinear dynamic model

International Nuclear Information System (INIS)

Unsihuay-Vila, C.; Zambroni de Souza, A.C.; Marangon-Lima, J.W.; Balestrassi, P.P.

2010-01-01

This paper proposes a new hybrid approach based on nonlinear chaotic dynamics and evolutionary strategy to forecast electricity loads and prices. The main idea is to develop a new training or identification stage in a nonlinear chaotic dynamic based predictor. In the training stage five optimal parameters for a chaotic based predictor are searched through an optimization model based on evolutionary strategy. The objective function of the optimization model is the mismatch minimization between the multi-step-ahead forecasting of predictor and observed data such as it is done in identification problems. The first contribution of this paper is that the proposed approach is capable of capturing the complex dynamic of demand and price time series considered resulting in a more accuracy forecasting. The second contribution is that the proposed approach run on-line manner, i.e. the optimal set of parameters and prediction is executed automatically which can be used to prediction in real-time, it is an advantage in comparison with other models, where the choice of their input parameters are carried out off-line, following qualitative/experience-based recipes. A case study of load and price forecasting is presented using data from New England, Alberta, and Spain. A comparison with other methods such as autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) is shown. The results show that the proposed approach provides a more accurate and effective forecasting than ARIMA and ANN methods. (author)

Measurement based scenario analysis of short-range distribution system planning

DEFF Research Database (Denmark)

Chen, Peiyuan; Bak-Jensen, Birgitte; Chen, Zhe

2009-01-01

This paper focuses on short-range distribution system planning using a probabilistic approach. Empirical probabilistic distributions of load demand and distributed generations are derived from the historical measurement data and incorporated into the system planning. Simulations with various...... feasible scenarios are performed based on a local distribution system at Støvring in Denmark. Simulation results provide more accurate and insightful information for the decision-maker when using the probabilistic analysis than using the worst-case analysis, so that a better planning can be achieved....

Dynamic modeling method of the bolted joint with uneven distribution of joint surface pressure

Science.gov (United States)

Li, Shichao; Gao, Hongli; Liu, Qi; Liu, Bokai

2018-03-01

The dynamic characteristics of the bolted joints have a significant influence on the dynamic characteristics of the machine tool. Therefore, establishing a reasonable bolted joint dynamics model is helpful to improve the accuracy of machine tool dynamics model. Because the pressure distribution on the joint surface is uneven under the concentrated force of bolts, a dynamic modeling method based on the uneven pressure distribution of the joint surface is presented in this paper to improve the dynamic modeling accuracy of the machine tool. The analytic formulas between the normal, tangential stiffness per unit area and the surface pressure on the joint surface can be deduced based on the Hertz contact theory, and the pressure distribution on the joint surface can be obtained by the finite element software. Futhermore, the normal and tangential stiffness distribution on the joint surface can be obtained by the analytic formula and the pressure distribution on the joint surface, and assigning it into the finite element model of the joint. Qualitatively compared the theoretical mode shapes and the experimental mode shapes, as well as quantitatively compared the theoretical modal frequencies and the experimental modal frequencies. The comparison results show that the relative error between the first four-order theoretical modal frequencies and the first four-order experimental modal frequencies is 0.2% to 4.2%. Besides, the first four-order theoretical mode shapes and the first four-order experimental mode shapes are similar and one-to-one correspondence. Therefore, the validity of the theoretical model is verified. The dynamic modeling method proposed in this paper can provide a theoretical basis for the accurate dynamic modeling of the bolted joint in machine tools.

Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach

Directory of Open Access Journals (Sweden)

Rui Xue

2015-01-01

Full Text Available Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM filter algorithm-based model to predict short-term passenger demand. After aggregated in 15 min interval, passenger demand data collected from a busy bus route over four months were used to generate time series. Considering that passenger demand exhibits various characteristics in different time scales, three time series were developed, named weekly, daily, and 15 min time series. After the correlation, periodicity, and stationarity analyses, time series models were constructed. Particularly, the heteroscedasticity of time series was explored to achieve better prediction performance. Finally, IMM filter algorithm was applied to combine individual forecasting models with dynamically predicted passenger demand for next interval. Different error indices were adopted for the analyses of individual and hybrid models. The performance comparison indicates that hybrid model forecasts are superior to individual ones in accuracy. Findings of this study are of theoretical and practical significance in bus scheduling.

Ontology-based composition and matching for dynamic service coordination

OpenAIRE

Pahl, Claus; Gacitua-Decar, Veronica; Wang, MingXue; Yapa Bandara, Kosala

2011-01-01

Service engineering needs to address integration problems allowing services to collaborate and coordinate. The need to address dynamic automated changes - caused by on-demand environments and changing requirements - can be addressed through service coordination based on ontology-based composition and matching techniques. Our solution to composition and matching utilises a service coordination space that acts as a passive infrastructure for collaboration. We discuss the information models an...

Survey of Models on Demand, Customer Base-Line and Demand Response and Their Relationships in the Power Market

OpenAIRE

Heshmati, Almas

2012-01-01

The increasing use of demand-side management as a tool to reliably meet electricity demand at peak time has stimulated interest among researchers, consumers and producer organizations, managers, regulators and policymakers, This research reviews the growing literature on models used to study demand, consumer baseline (CBL) and demand response in the electricity market. After characterizing the general demand models, it reviews consumer baseline based on which further study the demand response...

Dynamic Power Tariff for Congestion Management in Distribution Networks

DEFF Research Database (Denmark)

Huang, Shaojun; Wu, Qiuwei; Shahidehpour, Mohammad

2018-01-01

This paper proposes dynamic power tariff (DPT), a new concept for congestion management in distribution networks with high penetration of electric vehicles (EVs), and heat pumps (HPs). The DPT concept is proposed to overcome a drawback of the dynamic tariff (DT) method, i.e., DPT can replace...... the price sensitivity parameter in the DT method, which is relatively unrealistic in practice. Based on the control theory, a control model with two control loops, i.e., the power flow control and voltage control, is established to analyze the congestion management process by the DPT method. Furthermore...

Income dynamics with a stationary double Pareto distribution.

Science.gov (United States)

Toda, Alexis Akira

2011-04-01

Once controlled for the trend, the distribution of personal income appears to be double Pareto, a distribution that obeys the power law exactly in both the upper and the lower tails. I propose a model of income dynamics with a stationary distribution that is consistent with this fact. Using US male wage data for 1970-1993, I estimate the power law exponent in two ways--(i) from each cross section, assuming that the distribution has converged to the stationary distribution, and (ii) from a panel directly estimating the parameters of the income dynamics model--and obtain the same value of 8.4.

Hardware-in-the-Loop Simulation of Distributed Intelligent Energy Management System for Microgrids

Directory of Open Access Journals (Sweden)

Dong-Jun Won

2013-07-01

Full Text Available Microgrids are autonomous low-voltage power distribution systems that contain multiple distributed energy resources (DERs and smart loads that can provide power system operation flexibility. To effectively control and coordinate multiple DERs and loads of microgrids, this paper proposes a distributed intelligent management system that employs a multi-agent-based control system so that delicate decision-making functions can be distributed to local intelligent agents. This paper presents the development of a hardware-in-the-loop simulation (HILS system for distributed intelligent management system for microgrids and its promising application to an emergency demand response program. In the developed HILS system, intelligent agents are developed using microcontrollers and ZigBee wireless communication technology. Power system dynamic models are implemented in real-time simulation environments using the Opal-RT system. This paper presents key features of the data communication and management schemes based on multi-agent concepts. The performance of the developed system is tested for emergency demand response program applications.

Demanding Dynamics - Demand articulation of intermediary organisations in emerging pharmaceutical innovations

NARCIS (Netherlands)

Boon, W.P.C.

2008-01-01

User involvement in emerging technological fields is carried by so-called demand articulation processes. These demand articulation processes are interactive learning processes in which stakeholders try to address what they perceive as important characteristics of, and to unravel preferences for an

Development of Extended Period Pressure-Dependent Demand Water Distribution Models

Energy Technology Data Exchange (ETDEWEB)

Judi, David R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Mcpherson, Timothy N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2015-03-20

Los Alamos National Laboratory (LANL) has used modeling and simulation of water distribution systems for N-1 contingency analyses to assess criticality of water system assets. Critical components considered in these analyses include pumps, tanks, and supply sources, in addition to critical pipes or aqueducts. A contingency represents the complete removal of the asset from system operation. For each contingency, an extended period simulation (EPS) is run using EPANET. An EPS simulates water system behavior over a time period, typically at least 24 hours. It assesses the ability of a system to respond and recover from asset disruption through distributed storage in tanks throughout the system. Contingencies of concern are identified as those in which some portion of the water system has unmet delivery requirements. A delivery requirement is defined as an aggregation of water demands within a service area, similar to an electric power demand. The metric used to identify areas of unmet delivery requirement in these studies is a pressure threshold of 15 pounds per square inch (psi). This pressure threshold is used because it is below the required pressure for fire protection. Any location in the model with pressure that drops below this threshold at any time during an EPS is considered to have unmet service requirements and is used to determine cascading consequences. The outage area for a contingency is the aggregation of all service areas with a pressure below the threshold at any time during the EPS.

Regional Differences in Demand for Coal as A Basis for Development of A Product Distribution Model for Mining Companies in the Individual Customers Segment

Science.gov (United States)

Magda, Roman; Bogacz, Paweł; Franik, Tadeusz; Celej, Maciej; Migza, Marcin

2014-10-01

The article presents a proposal of methodology based on the process of relationship marketing, serving to determine the level of demand for coal in the individual customer segment, as well as fuel distribution model for this customer group in Poland developed on the basis of this methodology. It also includes selected results of tests carried out using the proposed methods. These proposals have been defined on the basis of market capacity indicators, which can be determined for the district level based on data from the Polish Central Statistical Office. The study also included the use of linear programming, based on the cost of coal logistics, data concerning railway, road and storage infrastructure present on the Polish market and taking into account the legal aspects. The presented results may provide a basis for mining companies to develop a system of coal distribution management in the locations with the highest demand values.

A simulation-based approach to capturing auto-correlated demand parameter uncertainty in inventory management

NARCIS (Netherlands)

Akçay, A.E.; Biller, B.; Tayur, S.

2012-01-01

We consider a repeated newsvendor setting where the parameters of the demand distribution are unknown, and we study the problem of setting inventory targets using only a limited amount of historical demand data. We assume that the demand process is autocorrelated and represented by an

Prediction-based dynamic load-sharing heuristics

Science.gov (United States)

Goswami, Kumar K.; Devarakonda, Murthy; Iyer, Ravishankar K.

1993-01-01

The authors present dynamic load-sharing heuristics that use predicted resource requirements of processes to manage workloads in a distributed system. A previously developed statistical pattern-recognition method is employed for resource prediction. While nonprediction-based heuristics depend on a rapidly changing system status, the new heuristics depend on slowly changing program resource usage patterns. Furthermore, prediction-based heuristics can be more effective since they use future requirements rather than just the current system state. Four prediction-based heuristics, two centralized and two distributed, are presented. Using trace driven simulations, they are compared against random scheduling and two effective nonprediction based heuristics. Results show that the prediction-based centralized heuristics achieve up to 30 percent better response times than the nonprediction centralized heuristic, and that the prediction-based distributed heuristics achieve up to 50 percent improvements relative to their nonprediction counterpart.

Coordinating a multi-retailer decentralized distribution system with random demand based on buyback and compensation contracts

Directory of Open Access Journals (Sweden)

Jinyu Ren

2015-01-01

Full Text Available Purpose: The purpose of this paper is to set up the coordinating mechanism for a decentralized distribution system consisting of a manufacturer and multiple independent retailers by means of contracts. It is in the two-stage supply chain system that all retailers sell an identical product made by the manufacturer and determine their order quantities which directly affect the expected profit of the supply chain with random demand. Design/methodology/approach: First comparison of the optimal order quantities in the centralized and decentralized system shows that the supply chain needs coordination. Then the coordination model is given based on buyback cost and compensation benefit. Finally the coordination mechanism is set up in which the manufacturer as the leader uses a buyback policy to incentive these retailers and the retailers pay profit returns to compensate the manufacturer. Findings: The results of a numerical example show that the perfect supply chain coordination and the flexible allocation of the profit can be achieved in the multi-retailer supply chain by the buyback and compensation contracts. Research limitations: The results based on assumptions might not completely hold in practice and the paper only focuses on studying a single product in two-stage supply chain. Practical implications: The coordination mechanism is applicable to a realistic supply chain under a private information setting and the research results is the foundation of further developing the coordination mechanism for a realistic multi-stage supply chain system with more products. Originality/value: This paper focused on studying the coordination mechanism for a decentralized multi-retailer supply chain by the joint application of the buyback and compensation contracts. Furthermore the perfect supply chain coordination and the flexible allocation of the profit are achieved.

Challenges of using model predictive control for active demand side management

DEFF Research Database (Denmark)

Zong, Yi; You, Shi; Hu, Junjie

2015-01-01

When there is a high penetration of renewables in the power system, it requires coordinated management of large numbers of distributed and demand response resources, intermittent resources to maintain the grid reliability and improve operational economics. This paper presents a hierarchical...... and dynamic power price signals....

Distributed Dynamic Condition Response Structures

DEFF Research Database (Denmark)

Hildebrandt, Thomas; Mukkamala, Raghava Rao

We present distributed dynamic condition response structures as a declarative process model inspired by the workflow language employed by our industrial partner and conservatively generalizing labelled event structures. The model adds to event structures the possibility to 1) finitely specify...... as a labelled transition system. Exploration of the relationship between dynamic condition response structures and traditional models for concurrency, application to more complex scenarios, and further extensions of the model is left to future work....

Creating hourly distributions at national level for various energy demands and renewable energy supplies

DEFF Research Database (Denmark)

Connolly, David; Drysdale, Dave; Hansen, Kenneth

2015-01-01

being recorded over longer time horizons, for example over one day. In this paper, a methodology is presented for creating hourly distributions for energy systems analysis tools. On the demand side, hourly distributions are developed for electricity, heating, cooling, and transport while the supply side...... includes wind, solar (photovoltaic and thermal), and wave power. Distributions are not created for dispatchable plants, such as coal, gas, and nuclear thermal plants, since their output is usually determined by the energy modelling tool rather than by a dependent resource. The methodologies are purposely...

Joint Real-Time Energy and Demand-Response Management using a Hybrid Coalitional-Noncooperative Game

Energy Technology Data Exchange (ETDEWEB)

He, Fulin; Gu, Yi; Hao, Jun; Zhang, Jun Jason; Wei, Jiaolong; Zhang, Yingchen

2015-11-11

In order to model the interactions among utility companies, building demands and renewable energy generators (REGs), a hybrid coalitional-noncooperative game framework has been proposed. We formulate a dynamic non-cooperative game to study the energy dispatch within multiple utility companies, while we take a coalitional perspective on REGs and buildings demands through a hedonic coalition formation game approach. In this case, building demands request different power supply from REGs, then the building demands can be organized into an ultimate coalition structure through a distributed hedonic shift algorithm. At the same time, utility companies can also obtain a stable power generation profile. In addition, the interactive progress among the utility companies and building demands which cannot be supplied by REGs is implemented by distributed game theoretic algorithms. Numerical results illustrate that the proposed hybrid coalitional-noncooperative game scheme reduces the cost of both building demands and utility companies compared with the initial scene.

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

Particle swarm optimization of driving torque demand decision based on fuel economy for plug-in hybrid electric vehicle

International Nuclear Information System (INIS)

Shen, Peihong; Zhao, Zhiguo; Zhan, Xiaowen; Li, Jingwei

2017-01-01

In this paper, an energy management strategy based on logic threshold is proposed for a plug-in hybrid electric vehicle. The plug-in hybrid electric vehicle powertrain model is established using MATLAB/Simulink based on experimental tests of the power components, which is validated by the comparison with the verified simulation model which is built in the AVL Cruise. The influence of the driving torque demand decision on the fuel economy of plug-in hybrid electric vehicle is studied using a simulation. The optimization method for the driving torque demand decision, which refers to the relationship between the accelerator pedal opening and driving torque demand, from the perspective of fuel economy is formulated. The dynamically changing inertia weight particle swarm optimization is used to optimize the decision parameters. The simulation results show that the optimized driving torque demand decision can improve the PHEV fuel economy by 15.8% and 14.5% in the fuel economy test driving cycle of new European driving cycle and worldwide harmonized light vehicles test respectively, using the same rule-based energy management strategy. The proposed optimization method provides a theoretical guide for calibrating the parameters of driving torque demand decision to improve the fuel economy of the real plug-in hybrid electric vehicle. - Highlights: • The influence of the driving torque demand decision on the fuel economy is studied. • The optimization method for the driving torque demand decision is formulated. • An improved particle swarm optimization is utilized to optimize the parameters. • Fuel economy is improved by using the optimized driving torque demand decision.

Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons

OpenAIRE

Guillermo Gallego; Garrett van Ryzin

1994-01-01

In many industries, managers face the problem of selling a given stock of items by a deadline. We investigate the problem of dynamically pricing such inventories when demand is price sensitive and stochastic and the firm's objective is to maximize expected revenues. Examples that fit this framework include retailers selling fashion and seasonal goods and the travel and leisure industry, which markets space such as seats on airline flights, cabins on vacation cruises, and rooms in hotels that ...

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.

Assessing the ability of potential evapotranspiration models in capturing dynamics of evaporative demand across various biomes and climatic regimes with ChinaFLUX measurements

Science.gov (United States)

Zheng, Han; Yu, Guirui; Wang, Qiufeng; Zhu, Xianjin; Yan, Junhua; Wang, Huimin; Shi, Peili; Zhao, Fenghua; Li, Yingnian; Zhao, Liang; Zhang, Junhui; Wang, Yanfen

2017-08-01

Estimates of atmospheric evaporative demand have been widely required for a variety of hydrological analyses, with potential evapotranspiration (PET) being an important measure representing evaporative demand of actual vegetated surfaces under given metrological conditions. In this study, we assessed the ability of various PET models in capturing long-term (typically 2003-2011) dynamics of evaporative demand at eight ecosystems across various biomes and climatic regimes in China. Prior to assessing PET dynamics, we first examined the reasonability of fourteen PET models in representing the magnitudes of evaporative demand using eddy-covariance actual evapotranspiration (AET) as an indicator. Results showed that the robustness of the fourteen PET models differed somewhat across the sites, and only three PET models could produce reasonable magnitudes of evaporative demand (i.e., PET ≥ AET on average) for all eight sites, including the: (i) Penman; (ii) Priestly-Taylor and (iii) Linacre models. Then, we assessed the ability of these three PET models in capturing dynamics of evaporative demand by comparing the annual and seasonal trends in PET against the equivalent trends in AET and precipitation (P) for particular sites. Results indicated that nearly all the three PET models could faithfully reproduce the dynamics in evaporative demand for the energy-limited conditions at both annual and seasonal scales, while only the Penman and Linacre models could represent dynamics in evaporative demand for the water-limited conditions. However, the Linacre model was unable to reproduce the seasonal switches between water- and energy-limited states for some sites. Our findings demonstrated that the choice of PET models would be essential for the evaporative demand analyses and other related hydrological analyses at different temporal and spatial scales.

Research on Generating Method of Embedded Software Test Document Based on Dynamic Model

Science.gov (United States)

Qu, MingCheng; Wu, XiangHu; Tao, YongChao; Liu, Ying

2018-03-01

This paper provides a dynamic model-based test document generation method for embedded software that provides automatic generation of two documents: test requirements specification documentation and configuration item test documentation. This method enables dynamic test requirements to be implemented in dynamic models, enabling dynamic test demand tracking to be easily generated; able to automatically generate standardized, standardized test requirements and test documentation, improved document-related content inconsistency and lack of integrity And other issues, improve the efficiency.

Optimal dynamic pricing and replenishment policy for perishable items with inventory-level-dependent demand

Science.gov (United States)

Lu, Lihao; Zhang, Jianxiong; Tang, Wansheng

2016-04-01

An inventory system for perishable items with limited replenishment capacity is introduced in this paper. The demand rate depends on the stock quantity displayed in the store as well as the sales price. With the goal to realise profit maximisation, an optimisation problem is addressed to seek for the optimal joint dynamic pricing and replenishment policy which is obtained by solving the optimisation problem with Pontryagin's maximum principle. A joint mixed policy, in which the sales price is a static decision variable and the replenishment rate remains to be a dynamic decision variable, is presented to compare with the joint dynamic policy. Numerical results demonstrate the advantages of the joint dynamic one, and further show the effects of different system parameters on the optimal joint dynamic policy and the maximal total profit.

Simulation-Based Dynamic Passenger Flow Assignment Modelling for a Schedule-Based Transit Network

Directory of Open Access Journals (Sweden)

Xiangming Yao

2017-01-01

Full Text Available The online operation management and the offline policy evaluation in complex transit networks require an effective dynamic traffic assignment (DTA method that can capture the temporal-spatial nature of traffic flows. The objective of this work is to propose a simulation-based dynamic passenger assignment framework and models for such applications in the context of schedule-based rail transit systems. In the simulation framework, travellers are regarded as individual agents who are able to obtain complete information on the current traffic conditions. A combined route selection model integrated with pretrip route selection and entrip route switch is established for achieving the dynamic network flow equilibrium status. The train agent is operated strictly with the timetable and its capacity limitation is considered. A continuous time-driven simulator based on the proposed framework and models is developed, whose performance is illustrated through a large-scale network of Beijing subway. The results indicate that more than 0.8 million individual passengers and thousands of trains can be simulated simultaneously at a speed ten times faster than real time. This study provides an efficient approach to analyze the dynamic demand-supply relationship for large schedule-based transit networks.

An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and Demand

Science.gov (United States)

Gupta, Saurabh; Black-Schaffer, W. Stephen; Crawford, James M.; Gross, David; Karcher, Donald S.; Kaufman, Jill; Knapman, Doug; Prystowsky, Michael B.; Wheeler, Thomas M.; Bean, Sarah; Kumar, Paramhans; Sharma, Raghav; Chamoli, Vaibhav; Ghai, Vikrant; Gogia, Vineet; Weintraub, Sally; Cohen, Michael B.

2015-01-01

Effective physician workforce management requires that the various organizations comprising the House of Medicine be able to assess their current and future workforce supply. This information has direct relevance to funding of graduate medical education. We describe a dynamic modeling tool that examines how individual factors and practice variables can be used to measure and forecast the supply and demand for existing and new physician services. The system we describe, while built to analyze the pathologist workforce, is sufficiently broad and robust for use in any medical specialty. Our design provides a computer-based software model populated with data from surveys and best estimates by specialty experts about current and new activities in the scope of practice. The model describes the steps needed and data required for analysis of supply and demand. Our modeling tool allows educators and policy makers, in addition to physician specialty organizations, to assess how various factors may affect demand (and supply) of current and emerging services. Examples of factors evaluated include types of professional services (3 categories with 16 subcategories), service locations, elements related to the Patient Protection and Affordable Care Act, new technologies, aging population, and changing roles in capitated, value-based, and team-based systems of care. The model also helps identify where physicians in a given specialty will likely need to assume new roles, develop new expertise, and become more efficient in practice to accommodate new value-based payment models. PMID:28725751

An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and Demand

Directory of Open Access Journals (Sweden)

Saurabh Gupta BPharm

2015-10-01

Full Text Available Effective physician workforce management requires that the various organizations comprising the House of Medicine be able to assess their current and future workforce supply. This information has direct relevance to funding of graduate medical education. We describe a dynamic modeling tool that examines how individual factors and practice variables can be used to measure and forecast the supply and demand for existing and new physician services. The system we describe, while built to analyze the pathologist workforce, is sufficiently broad and robust for use in any medical specialty. Our design provides a computer-based software model populated with data from surveys and best estimates by specialty experts about current and new activities in the scope of practice. The model describes the steps needed and data required for analysis of supply and demand. Our modeling tool allows educators and policy makers, in addition to physician specialty organizations, to assess how various factors may affect demand (and supply of current and emerging services. Examples of factors evaluated include types of professional services (3 categories with 16 subcategories, service locations, elements related to the Patient Protection and Affordable Care Act, new technologies, aging population, and changing roles in capitated, value-based, and team-based systems of care. The model also helps identify where physicians in a given specialty will likely need to assume new roles, develop new expertise, and become more efficient in practice to accommodate new value-based payment models.

An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and Demand.

Science.gov (United States)

Gupta, Saurabh; Black-Schaffer, W Stephen; Crawford, James M; Gross, David; Karcher, Donald S; Kaufman, Jill; Knapman, Doug; Prystowsky, Michael B; Wheeler, Thomas M; Bean, Sarah; Kumar, Paramhans; Sharma, Raghav; Chamoli, Vaibhav; Ghai, Vikrant; Gogia, Vineet; Weintraub, Sally; Cohen, Michael B; Robboy, Stanley J

2015-01-01

Effective physician workforce management requires that the various organizations comprising the House of Medicine be able to assess their current and future workforce supply. This information has direct relevance to funding of graduate medical education. We describe a dynamic modeling tool that examines how individual factors and practice variables can be used to measure and forecast the supply and demand for existing and new physician services. The system we describe, while built to analyze the pathologist workforce, is sufficiently broad and robust for use in any medical specialty. Our design provides a computer-based software model populated with data from surveys and best estimates by specialty experts about current and new activities in the scope of practice. The model describes the steps needed and data required for analysis of supply and demand. Our modeling tool allows educators and policy makers, in addition to physician specialty organizations, to assess how various factors may affect demand (and supply) of current and emerging services. Examples of factors evaluated include types of professional services (3 categories with 16 subcategories), service locations, elements related to the Patient Protection and Affordable Care Act, new technologies, aging population, and changing roles in capitated, value-based, and team-based systems of care. The model also helps identify where physicians in a given specialty will likely need to assume new roles, develop new expertise, and become more efficient in practice to accommodate new value-based payment models.

An Adaptive SPARQL Engine with Dynamic Partitioning for Distributed RDF Repositories

KAUST Repository

Ibrahim, Yasser E.

2012-07-01

The tremendous increase in the semantic data is driving the demand for efficient query engines. RDF data being generated at an unprecedented rate introduces a storage, indexing, and querying challenge. Due to the size of the data and the federated nature of the semantic web, it is in many cases impractical to assume a central repository, and more attention is being given to distributed RDF stores. This work is motivated by two major drawbacks of current solutions: 1) pre-processing part is very expensive and takes prohibitively long time for large datasets, and 2) current distributed systems assume that a static partitioning of the data should perform well for all kinds of queries, and do not consider fluctuations in the queryload. In this paper we propose PHD-Store, an in-memory SPARQL engine for distributed RDF repositories. Our system does not assume any particular initial placement of the data and does not require pre-processing before running the first query. It analyzes incoming queries and adjusts data placement dynamically in such a way that communication among repositories is minimized for future queries. To achieve this flexibility, frequent query patterns are detected, and data are redistributed through a Propagating Hash Distribution (PHD) algorithm to ensure optimal placement for frequent query patterns. Our experiments with large RDF graphs verify that PHD-Store scales well and executes complex queries more efficiently than existing systems.

Distribution transformer lifetime analysis in the presence of demand response and rooftop PV integration

Directory of Open Access Journals (Sweden)

Behi Behnaz

2017-01-01

Full Text Available Many distribution transformers have already exceeded half of their expected service life of 35 years in the infrastructure of Western Power, the electric distribution company supplying southwest of Western Australia, Australia. Therefore, it is anticipated that a high investment on transformer replacement happens in the near future. However, high renewable integration and demand response (DR are promising resources to defer the investment on infrastructure upgrade and extend the lifetime of transformers. This paper investigates the impact of rooftop photovoltaic (PV integration and customer engagement through DR on the lifetime of transformers in electric distribution networks. To this aim, first, a time series modelling of load, DR and PV is utilised for each year over a planning period. This load model is applied to a typical distribution transformer for which the hot-spot temperature rise is modelled based on the relevant standard. Using this calculation platform, the loss of life and the actual age of distribution transformer are obtained. Then, various scenarios including different levels of PV penetration and DR contribution are examined, and their impacts on the age of transformer are reported. Finally, the equivalent loss of net present value of distribution transformer is formulated and discussed. This formulation gives major benefits to the distribution network planners for analysing the contribution of PV and DR on lifetime extension of the distribution transformer. In addition, the provided model can be utilised in optimal investment analysis to find the best time for the transformer replacement and the associated cost considering PV penetration and DR. The simulation results show that integration of PV and DR within a feeder can significantly extend the lifetime of transformers.

Dynamic Consensus Algorithm based Distributed Voltage Harmonic Compensation in Islanded Microgrids

DEFF Research Database (Denmark)

Meng, Lexuan; Tang, Fen; Firoozabadi, Mehdi Savaghebi

2015-01-01

generators can be employed as compensators to enhance the power quality on consumer side. However, conventional centralized control is facing obstacles because of the distributed fashion of generation and consumption. Accordingly, this paper proposes a consensus algorithm based distributed hierarchical...

The dynamics of sectoral electricity demand for a panel of US states: New evidence on the consumption–growth nexus

International Nuclear Information System (INIS)

Saunoris, James W.; Sheridan, Brandon J.

2013-01-01

In this paper, we use a panel of the 48 contiguous US states over the period 1970–2009 to examine the dynamics of electricity demand in addressing the four hypotheses set forth in the literature: growth, conservation, neutrality, and feedback. In doing so we provide both short-run and long-run elasticity estimates for electricity demand. Recent developments in nonstationary panel estimation techniques allow for heterogeneity in the coefficients while examining the direction of causality among electricity consumption, electricity prices, and income growth. In addition to the full sample, we also disaggregate the sample into three sectors: commercial, industrial, and residential. The short-run results provide evidence in favor of the growth hypothesis for the aggregate sample, as well as for the industrial sector. For the residential and commercial sectors, the conservation hypothesis is supported. Long-run results favor the conservation hypothesis. To ascertain differences in electricity demand relating to electricity intensity we also examine states based on their efficiency in electricity consumption. Overall, the results yield in favor of the growth hypothesis for low intensity states and conservation hypothesis for high intensity states. - Highlights: • We use dynamic panel techniques to model electricity demand by sector for US states. • The conservation hypothesis is supported in the long run; short-run results are mixed. • The conservation hypothesis is supported in the high-electricity-intensity subsample. • The growth hypothesis is supported in the low-electricity-intensity subsample. • Policies aimed at energy conservation should be long-run in nature

Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Boyang Qu

2017-12-01

Full Text Available The intermittency of wind power and the large-scale integration of electric vehicles (EVs bring new challenges to the reliability and economy of power system dispatching. In this paper, a novel multi-objective dynamic economic emission dispatch (DEED model is proposed considering the EVs and uncertainties of wind power. The total fuel cost and pollutant emission are considered as the optimization objectives, and the vehicle to grid (V2G power and the conventional generator output power are set as the decision variables. The stochastic wind power is derived by Weibull probability distribution function. Under the premise of meeting the system energy and user’s travel demand, the charging and discharging behavior of the EVs are dynamically managed. Moreover, we propose a two-step dynamic constraint processing strategy for decision variables based on penalty function, and, on this basis, the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D algorithm is improved. The proposed model and approach are verified by the 10-generator system. The results demonstrate that the proposed DEED model and the improved MOEA/D algorithm are effective and reasonable.

The rise of AGILE demand response: enabler and foundation for change

OpenAIRE

Babar, M.; Nguyen, H.P.; Cuk, V.; Kamphuis, I.G.; Bongearts, M.; Hanzelka, Z.

2016-01-01

The distributed resources – distributed generations, storage, electric vehicles and smart appliances – have fueled many disruptions in the conventional grid dynamics. Increasing tough and competitive situations at demand-side have led the stakeholders of power system to reform, because people are more informed and can make decisions that has given rise to turbulent and volatile electricity markets. In such highly diversified and open market environment, the integrated control strategy has exp...

Distributed Dynamic State Estimation with Extended Kalman Filter

Energy Technology Data Exchange (ETDEWEB)

Du, Pengwei; Huang, Zhenyu; Sun, Yannan; Diao, Ruisheng; Kalsi, Karanjit; Anderson, Kevin K.; Li, Yulan; Lee, Barry

2011-08-04

Increasing complexity associated with large-scale renewable resources and novel smart-grid technologies necessitates real-time monitoring and control. Our previous work applied the extended Kalman filter (EKF) with the use of phasor measurement data (PMU) for dynamic state estimation. However, high computation complexity creates significant challenges for real-time applications. In this paper, the problem of distributed dynamic state estimation is investigated. One domain decomposition method is proposed to utilize decentralized computing resources. The performance of distributed dynamic state estimation is tested on a 16-machine, 68-bus test system.

Dynamic Placement of Virtual Machines with Both Deterministic and Stochastic Demands for Green Cloud Computing

Directory of Open Access Journals (Sweden)

Wenying Yue

2014-01-01

Full Text Available Cloud computing has come to be a significant commercial infrastructure offering utility-oriented IT services to users worldwide. However, data centers hosting cloud applications consume huge amounts of energy, leading to high operational cost and greenhouse gas emission. Therefore, green cloud computing solutions are needed not only to achieve high level service performance but also to minimize energy consumption. This paper studies the dynamic placement of virtual machines (VMs with deterministic and stochastic demands. In order to ensure a quick response to VM requests and improve the energy efficiency, a two-phase optimization strategy has been proposed, in which VMs are deployed in runtime and consolidated into servers periodically. Based on an improved multidimensional space partition model, a modified energy efficient algorithm with balanced resource utilization (MEAGLE and a live migration algorithm based on the basic set (LMABBS are, respectively, developed for each phase. Experimental results have shown that under different VMs’ stochastic demand variations, MEAGLE guarantees the availability of stochastic resources with a defined probability and reduces the number of required servers by 2.49% to 20.40% compared with the benchmark algorithms. Also, the difference between the LMABBS solution and Gurobi solution is fairly small, but LMABBS significantly excels in computational efficiency.

Distributed neural network control for adaptive synchronization of uncertain dynamical multiagent systems.

Science.gov (United States)

Peng, Zhouhua; Wang, Dan; Zhang, Hongwei; Sun, Gang

2014-08-01

This paper addresses the leader-follower synchronization problem of uncertain dynamical multiagent systems with nonlinear dynamics. Distributed adaptive synchronization controllers are proposed based on the state information of neighboring agents. The control design is developed for both undirected and directed communication topologies without requiring the accurate model of each agent. This result is further extended to the output feedback case where a neighborhood observer is proposed based on relative output information of neighboring agents. Then, distributed observer-based synchronization controllers are derived and a parameter-dependent Riccati inequality is employed to prove the stability. This design has a favorable decouple property between the observer and the controller designs for nonlinear multiagent systems. For both cases, the developed controllers guarantee that the state of each agent synchronizes to that of the leader with bounded residual errors. Two illustrative examples validate the efficacy of the proposed methods.

Customer based distributed generation: Economics and policy issues

International Nuclear Information System (INIS)

Maribu, Karl Magnus

2005-01-01

This paper presents a model for finding distributed generation systems that maximizes the economic benefits for buildings with electricity, heat and cooling loads. Important factors for profitability are identified using simulated data for a standard health care and office building in California. Under the assumed time of use (TOU) prices demand charges are critical factors for the profitability. Systems with lower reliability than promised can infer large losses to the developer. The outage risk is a diversifiable risk hence demands charges should not be a barrier to distributed generation adoption in a well functioning market. In a variety of natural gas and electricity price scenarios the optimal decision is to install distributed generation units with heat recovery and absorption cooling. The benefit maximizing solution reduces building carbon emissions in most scenarios. Low natural gas price scenarios have the highest carbon emissions. An introduction of a carbon tax can further reduce emissions. Small photovoltaic systems gets profitable at prices around 2.4 $/W and larger systems from prices around 1.8-2 $/W if they are analyzed independently from gas fueled generators. In competition with natural gas fueled equipment both the break-even cost and the installed capacity is reduced in both buildings. It is possible to find a profitable solution for real discount rates up to 20 percent under the base case solution for the health care building. High discount rates favor small less capital intensive base load generation systems with heat recovery. (Author)

Electrical power distribution control methods, electrical energy demand monitoring methods, and power management devices

Science.gov (United States)

Chassin, David P [Pasco, WA; Donnelly, Matthew K [Kennewick, WA; Dagle, Jeffery E [Richland, WA

2011-12-06

Electrical power distribution control methods, electrical energy demand monitoring methods, and power management devices are described. In one aspect, an electrical power distribution control method includes providing electrical energy from an electrical power distribution system, applying the electrical energy to a load, providing a plurality of different values for a threshold at a plurality of moments in time and corresponding to an electrical characteristic of the electrical energy, and adjusting an amount of the electrical energy applied to the load responsive to an electrical characteristic of the electrical energy triggering one of the values of the threshold at the respective moment in time.

Development of a model for activated sludge aeration systems: linking air supply, distribution, and demand.

Science.gov (United States)

Schraa, Oliver; Rieger, Leiv; Alex, Jens

2017-02-01

During the design of a water resource recovery facility, it is becoming industry practice to use simulation software to assist with process design. Aeration is one of the key components of the activated sludge process, and is one of the most important aspects of modelling wastewater treatment systems. However, aeration systems are typically not modelled in detail in most wastewater treatment process modelling studies. A comprehensive dynamic aeration system model has been developed that captures both air supply and demand. The model includes sub-models for blowers, pipes, fittings, and valves. An extended diffuser model predicts both oxygen transfer efficiency within an aeration basin and pressure drop across the diffusers. The aeration system model allows engineers to analyse aeration systems as a whole to determine biological air requirements, blower performance, air distribution, control valve impacts, controller design and tuning, and energy costs. This enables engineers to trouble-shoot the entire aeration system including process, equipment and controls. It also allows much more realistic design of these highly complex systems.

A population-based longitudinal study on the implication of demographic changes on blood donation and transfusion demand.

Science.gov (United States)

Greinacher, Andreas; Weitmann, Kerstin; Schönborn, Linda; Alpen, Ulf; Gloger, Doris; Stangenberg, Wolfgang; Stüpmann, Kerstin; Greger, Nico; Kiefel, Volker; Hoffmann, Wolfgang

2017-06-13

Transfusion safety includes the risk of transmission of pathogens, appropriate transfusion thresholds, and sufficient blood supply. All industrialized countries experience major ongoing demographic changes resulting from low birth rates and aging of the baby boom generation. Little evidence exists about whether future blood supply and demand correlate with these demographic changes. The ≥50% decline in birth rate in the eastern part of Germany after 1990 facilitates systematic study of the effects of pronounced demographic changes on blood donation and demand. In this prospective, 10-year longitudinal study, we enrolled all whole blood donors and all patients receiving red blood cell transfusions in the state of Mecklenburg-West Pomerania. We compared projections made in 2005 based on the projected demographic changes with: (1) number and age distribution of blood donors and transfusion recipients in 2015 and (2) blood demand within specific age and patient groups. Blood donation rates closely followed the demographic changes, showing a decrease of -18% (vs projected -23%). In contrast, 2015 transfusion rates were -21.3% lower than projected. We conclude that although changes in demography are highly predictive for the blood supply, transfusion demand is strongly influenced by changes in medical practice. Given ongoing pronounced demographic change, regular monitoring of the donor/recipient age distributions and associated impact on blood demand/supply relationships is required to allow strategic planning to prevent blood shortages or overproduction.

Simulation of annual electric lighting demand using various occupancy profiles

DEFF Research Database (Denmark)

Iversen, Anne; Andersen, Philip Hvidthøft Delff; Svendsen, Svend

2013-01-01

This paper describes an investigation of the effect on electric lighting demand of applying occupancy models of various resolution to climate-based daylight modelling. The lighting demand was evaluated for a building zone with the occupant always present, with occupancy corresponding to absence...... factors, based on an estimated annual mean occupancy, based on estimated 1-hour mean occupancy, and based on 2-min occupancy intervals. The results showed little difference in the annual electric lighting demand when the same occupancy profile was used every day, as opposed to when profiles were used...... where occupancy varied every day. Furthermore, the results showed that annual electric lighting demand was evaluated slightly conservatively when a mean absence factor was applied as opposed to using dynamic occupancy profiles....

The energy demand in the Netherlands

International Nuclear Information System (INIS)

Stoffers, M.J.

1992-01-01

Based on three scenarios for the global and economic developments the CPB (Dutch Central Planning Bureau) made projections of the Dutch energy demand to the year 2015. Factors of interest are the development of the energy prices, sectoral analysis of the economic growth and the government policy. The scenarios are Balanced Growth, characterized by a strong economic growth, sustainable economic development, and a dynamic technological development, the Global Shift scenario, characterized by a very dynamic technological development, and the European Renaissance scenario with a less dynamic development. 2 ills., 5 tabs., 2 refs

Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks

DEFF Research Database (Denmark)

Bhattarai, Bishnu P.; Myers, Kurt S.; Bak-Jensen, Birgitte

2017-01-01

, and distribution system operator’s perspectives. A hierarchical control architecture (HCA) comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating multi-time scale controls that work from a day-ahead scheduling up to real-time...... adaptive control. The performance of the developed method is investigated with high EV penetration in a typical residential distribution grid. The simulation results demonstrate that HCA efficiently utilizes demand flexibility stemming from EVs to solve grid unbalancing and congestions with simultaneous...... maximization of economic benefits to the participating actors. This is ensured by enabling EV participation in day-ahead, balancing, and regulation markets. For the given network configuration and pricing structure, HCA ensures the EV owners to get paid up to five times the cost they were paying without...

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....

Domestic and outbound tourism demand in Australia: a System-of-Equations Approach

OpenAIRE

George Athanasopoulos; Minfeng Deng; Gang Li; Haiyan Song

2013-01-01

This study uses a system-of-equations approach to model the substitution relationship between Australian domestic and outbound tourism demand. A new price variable based on relative ratios of purchasing power parity index is developed for the substitution analysis. Short-run demand elasticities are calculated based on the estimated dynamic almost ideal demand system. The empirical results reveal significant substitution relationships between Australian domestic tourism and outbound travel to ...

Dynamic Pricing

DEFF Research Database (Denmark)

Sharifi, Reza; Anvari-Moghaddam, Amjad; Fathi, S. Hamid

2017-01-01

Dynamic pricing scheme, also known as real-time pricing (RTP), can be more efficient and technically beneficial than the other price-based schemes (such as flat-rate or time-of-use (TOU) pricing) for enabling demand response (DR) actions. Over the past few years, advantages of RTP-based schemes h...... of dynamic pricing can lead to increased willingness of consumers to participate in DR programs which in turn improve the operation of liberalized electricity markets.......Dynamic pricing scheme, also known as real-time pricing (RTP), can be more efficient and technically beneficial than the other price-based schemes (such as flat-rate or time-of-use (TOU) pricing) for enabling demand response (DR) actions. Over the past few years, advantages of RTP-based schemes...

Evaluation of automated residential demand response with flat and dynamic pricing

International Nuclear Information System (INIS)

Swisher, Joel; Wang, Kitty; Stewart, Stewart

2005-01-01

This paper reviews the performance of two recent automated load management programs for residential customers of electric utilities in two American states. Both pilot programs have been run with about 200 participant houses each, and both programs have control populations of similar customers without the technology or program treatment. In both cases, the technology used in the pilot is GoodWatts, an advanced, two-way, real-time, comprehensive home energy management system. The purpose of each pilot is to determine the household kW reduction in coincident peak electric load from the energy management technology. Nevada Power has conducted a pilot program for Air-Conditioning Load Management (ACLM), in which customers are sent an electronic curtailment signal for three-hour intervals during times of maximum peak demand. The participating customers receive an annual incentive payment, but otherwise they are on a conventional utility tariff. In California, three major utilities are jointly conducting a pilot demonstration of an Automated Demand Response System (ADRS). Customers are on a time-of-use (ToU) tariff, which includes a critical peak pricing (CPP) element. During times of maximum peak demand, customers are sent an electronic price signal that is three times higher than the normal on-peak price. Houses with the automated GoodWatts technology reduced their demand in both the ACLM and the ADRS programs by about 50% consistently across the summer curtailment or super peak events, relative to homes without the technology or any load management program or tariff in place. The absolute savings were greater in the ACLM program, due to the higher baseline air conditioning loads in the hotter Las Vegas climate. The results suggest that either automated technology or dynamic pricing can deliver significant demand response in low-consumption houses. However, for high-consumption houses, automated technology can reduce load by a greater absolute kWh difference. Targeting

Dynamics of final sectoral energy demand and aggregate energy intensity

International Nuclear Information System (INIS)

Lescaroux, Francois

2011-01-01

This paper proposes a regional and sectoral model of global final energy demand. For the main end-use sectors of consumption (industrial, commercial and public services, residential and road transportation), per-capita demand is expressed as an S-shaped function of per-capita income. Other variables intervene as well, like energy prices, temperatures and technological trends. This model is applied on a panel of 101 countries and 3 aggregates (covering the whole world) and it explains fairly well past variations in sectoral, final consumption since the beginning of the 2000s. Further, the model is used to analyze the dynamics of final energy demand, by sector and in total. The main conclusion concerns the pattern of change for aggregate energy intensity. The simulations performed show that there is no a priori reason for it to exhibit a bell-shape, as reported in the literature. Depending on initial conditions, the weight of basic needs in total consumption and the availability of modern commercial energy resources, various forms might emerge. - Research Highlights: → The residential sector accounts for most of final energy consumption at low income levels. → Its share drops at the benefit of the industrial, services and road transportation sectors in turn. → Sectoral shares' pattern is affected by changes in geographic, sociologic and economic factors. → Final energy intensity may show various shapes and does not exhibit necessarily a bell-shape.

Joint energy demand and thermal comfort optimization in photovoltaic-equipped interconnected microgrids

International Nuclear Information System (INIS)

Baldi, Simone; Karagevrekis, Athanasios; Michailidis, Iakovos T.; Kosmatopoulos, Elias B.

2015-01-01

Highlights: • Energy efficient operation of photovoltaic-equipped interconnected microgrids. • Optimized energy demand for a block of heterogeneous buildings with different sizes. • Multiobjective optimization: matching demand and supply taking into account thermal comfort. • Intelligent control mechanism for heating, ventilating, and air conditioning units. • Optimization of energy consumption and thermal comfort at the aggregate microgrid level. - Abstract: Electrical smart microgrids equipped with small-scale renewable-energy generation systems are emerging progressively as an alternative or an enhancement to the central electrical grid: due to the intermittent nature of the renewable energy sources, appropriate algorithms are required to integrate these two typologies of grids and, in particular, to perform efficiently dynamic energy demand and distributed generation management, while guaranteeing satisfactory thermal comfort for the occupants. This paper presents a novel control algorithm for joint energy demand and thermal comfort optimization in photovoltaic-equipped interconnected microgrids. Energy demand shaping is achieved via an intelligent control mechanism for heating, ventilating, and air conditioning units. The intelligent control mechanism takes into account the available solar energy, the building dynamics and the thermal comfort of the buildings’ occupants. The control design is accomplished in a simulation-based fashion using an energy simulation model, developed in EnergyPlus, of an interconnected microgrid. Rather than focusing only on how each building behaves individually, the optimization algorithm employs a central controller that allows interaction among the buildings of the microgrid. The control objective is to optimize the aggregate microgrid performance. Simulation results demonstrate that the optimization algorithm efficiently integrates the microgrid with the photovoltaic system that provides free electric energy: in

Hardware-in-the-Loop Co-simulation of Distribution Grid for Demand Response

Energy Technology Data Exchange (ETDEWEB)

Rotger-Griful, Sergi; Chatzivasileiadis, Spyros; Jacobsen, Rune H.; Stewart, Emma M.; Domingo, Javier M.; Wetter, Michael

2016-06-20

In modern power systems, co-simulation is proposed as an enabler for analyzing the interactions between disparate systems. This paper introduces the co-simulation platform Virtual Grid Integration Laboratory (VirGIL) including Hardware-in-the-Loop testing, and demonstrates its potential to assess demand response strategies. VirGIL is based on a modular architecture using the Functional Mock-up Interface industrial standard to integrate new simulators. VirGIL combines state-of-the-art simulators in power systems, communications, buildings, and control. In this work, VirGIL is extended with a Hardware-in-the-Loop component to control the ventilation system of a real 12-story building in Denmark. VirGIL capabilities are illustrated in three scenarios: load following, primary reserves and load following aggregation. Experimental results show that the system can track one minute changing signals and it can provide primary reserves for up-regulation. Furthermore, the potential of aggregating several ventilation systems is evaluated considering the impact at distribution grid level and the communications protocol effect.

Dynamic Protective Control Strategy for Distributed Generation System with Fixed-speed Wind Turbines

Institute of Scientific and Technical Information of China (English)

2012-01-01

The characteristics of induction generator based fixed-speed wind turbines （FSWT） are investigated. The impacts of different execution time in protective operations are studied under different fauit duration and various wind velocity situations, e.g. , FSWT stabilities of load shedding in distribution systems. Based on this research, a dynamic protective control strategy for a distributed generation system （DGS） with FSWT is proposed. Finally, simulation results demonstrate the effectiveness of the strategy.

Dynamics of biofilm formation in a model drinking water distribution system

DEFF Research Database (Denmark)

Boe-Hansen, Rasmus; Albrechtsen, Hans-Jørgen; Arvin, Erik

2002-01-01

The dynamics of biofilm formation in non-chlorinated groundwater-based drinking water was studied in a model distribution system. The formation of biofilm was closely monitored for a period of 522 days by total bacterial counts (AODC), heterotrophic plate counts (R2A media), and ATP content...

Improving long-term care provision: towards demand-based care by means of modularity

Directory of Open Access Journals (Sweden)

Meijboom Bert

2010-09-01

Full Text Available Abstract Background As in most fields of health care, societal and political changes encourage suppliers of long-term care to put their clients at the center of care and service provision and become more responsive towards client needs and requirements. However, the diverse, multiple and dynamic nature of demand for long-term care complicates the movement towards demand-based care provision. This paper aims to advance long-term care practice and, to that end, examines the application of modularity. This concept is recognized in a wide range of product and service settings for its ability to design demand-based products and processes. Methods Starting from the basic dimensions of modularity, we use qualitative research to explore the use and application of modularity principles in the current working practices and processes of four organizations in the field of long-term care for the elderly. In-depth semi-structured interviews were conducted with 38 key informants and triangulated with document research and observation. Data was analyzed thematically by means of coding and subsequent exploration of patterns. Data analysis was facilitated by qualitative analysis software. Results Our data suggest that a modular setup of supply is employed in the arrangement of care and service supply and assists providers of long-term care in providing their clients with choice options and variation. In addition, modularization of the needs assessment and package specification process allows the case organizations to manage client involvement but still provide customized packages of care and services. Conclusion The adequate setup of an organization's supply and its specification phase activities are indispensible for long-term care providers who aim to do better in terms of quality and efficiency. Moreover, long-term care providers could benefit from joint provision of care and services by means of modular working teams. Based upon our findings, we are able to

Improving long-term care provision: towards demand-based care by means of modularity

Science.gov (United States)

2010-01-01

Background As in most fields of health care, societal and political changes encourage suppliers of long-term care to put their clients at the center of care and service provision and become more responsive towards client needs and requirements. However, the diverse, multiple and dynamic nature of demand for long-term care complicates the movement towards demand-based care provision. This paper aims to advance long-term care practice and, to that end, examines the application of modularity. This concept is recognized in a wide range of product and service settings for its ability to design demand-based products and processes. Methods Starting from the basic dimensions of modularity, we use qualitative research to explore the use and application of modularity principles in the current working practices and processes of four organizations in the field of long-term care for the elderly. In-depth semi-structured interviews were conducted with 38 key informants and triangulated with document research and observation. Data was analyzed thematically by means of coding and subsequent exploration of patterns. Data analysis was facilitated by qualitative analysis software. Results Our data suggest that a modular setup of supply is employed in the arrangement of care and service supply and assists providers of long-term care in providing their clients with choice options and variation. In addition, modularization of the needs assessment and package specification process allows the case organizations to manage client involvement but still provide customized packages of care and services. Conclusion The adequate setup of an organization's supply and its specification phase activities are indispensible for long-term care providers who aim to do better in terms of quality and efficiency. Moreover, long-term care providers could benefit from joint provision of care and services by means of modular working teams. Based upon our findings, we are able to elaborate on how to further

Economic information from Smart Meter: Nexus Between Demand Profile and Electricity Retail Price Between Demand Profile and Electricity Retail Price

OpenAIRE

Yu, Yang; Liu, Guangyi; Zhu, Wendong; Wang, Fei; Shu, Bin; Zhang, Kai; Rajagopal, Ram; Astier, Nicolas

2016-01-01

In this paper, we demonstrate that a consumer's marginal system impact is only determined by their demand profile rather than their demand level. Demand profile clustering is identical to cluster consumers according to their marginal impacts on system costs. A profile-based uniform-rate price is economically efficient as real-time pricing. We develop a criteria system to evaluate the economic efficiency of an implemented retail price scheme in a distribution system by comparing profile cluste...

Strategy-making for a proactive distribution company in the real-time market with demand response

DEFF Research Database (Denmark)

Zhang, Chunyu; Wang, Qi; Wang, Jianhui

2016-01-01

This paper proposes a methodology to optimize the trading strategies of a proactive distribution company (PDISCO) in the real-time market by mobilizing the demand response. Each distribution-level demand is considered as an elastic one. To capture the interrelation between the PDISCO and the real......-time market, a bi-level model is presented for the PDISCO to render continuous offers and bids strategically. The upper level problem expresses the PDISCO's profit maximization, while the lower-level problem minimizes the operation cost of the transmission-level real-time market. To solve the proposed model......, a primal-dual approach is used to translate this bi-level model into a single-level mathematical program with equilibrium constraints. Results of case studies are reported to show the effectiveness of the proposed model. (C) 2016 Elsevier Ltd. All rights reserved....

Multi-agent based distributed control architecture for microgrid energy management and optimization

International Nuclear Information System (INIS)

Basir Khan, M. Reyasudin; Jidin, Razali; Pasupuleti, Jagadeesh

2016-01-01

Highlights: • A new multi-agent based distributed control architecture for energy management. • Multi-agent coordination based on non-cooperative game theory. • A microgrid model comprised of renewable energy generation systems. • Performance comparison of distributed with conventional centralized control. - Abstract: Most energy management systems are based on a centralized controller that is difficult to satisfy criteria such as fault tolerance and adaptability. Therefore, a new multi-agent based distributed energy management system architecture is proposed in this paper. The distributed generation system is composed of several distributed energy resources and a group of loads. A multi-agent system based decentralized control architecture was developed in order to provide control for the complex energy management of the distributed generation system. Then, non-cooperative game theory was used for the multi-agent coordination in the system. The distributed generation system was assessed by simulation under renewable resource fluctuations, seasonal load demand and grid disturbances. The simulation results show that the implementation of the new energy management system proved to provide more robust and high performance controls than conventional centralized energy management systems.

Dynamic Pricing and Supply Coordination with Reimbursement Contract under Random Yield and Demand

Directory of Open Access Journals (Sweden)

Guo Li

2013-01-01

Full Text Available This paper investigates the dynamic pricing and supply chain coordination in a decentralized system that consists of one supplier and one manufacturer, in which both the market demand and production yield are stochastic. We show that the centralized expected profit is jointly concave in the production quantity and order quantity when the price is ex-ante selected. We also derive the equilibrium strategies in the decentralized system and prove that the entire profit of supply chain is inevitably lower than that under centralized system. Based on this, we propose a reimbursement contract to coordinate the decentralized supply chain so as to achieve the maximized profit. It is worth mentioning that, under reimbursement contract, the equilibrium production and order quantities are irrelevant to the manufacturer's risk sharing coefficient but are only determined by the supplier’s risk sharing coefficient.

A multi-period distribution network design model under demand uncertainty

Science.gov (United States)

Tabrizi, Babak H.; Razmi, Jafar

2013-05-01

Supply chain management is taken into account as an inseparable component in satisfying customers' requirements. This paper deals with the distribution network design (DND) problem which is a critical issue in achieving supply chain accomplishments. A capable DND can guarantee the success of the entire network performance. However, there are many factors that can cause fluctuations in input data determining market treatment, with respect to short-term planning, on the one hand. On the other hand, network performance may be threatened by the changes that take place within practicing periods, with respect to long-term planning. Thus, in order to bring both kinds of changes under control, we considered a new multi-period, multi-commodity, multi-source DND problem in circumstances where the network encounters uncertain demands. The fuzzy logic is applied here as an efficient tool for controlling the potential customers' demand risk. The defuzzifying framework leads the practitioners and decision-makers to interact with the solution procedure continuously. The fuzzy model is then validated by a sensitivity analysis test, and a typical problem is solved in order to illustrate the implementation steps. Finally, the formulation is tested by some different-sized problems to show its total performance.

Distributed Dynamic Traffic Modeling and Implementation Oriented Different Levels of Induced Travelers

Directory of Open Access Journals (Sweden)

Yan Liu

2015-01-01

Full Text Available In order to respond to the variable state of traffic network in time, a distributed dynamic traffic assignment strategy is proposed which can improve the intelligent traffic management. The proposed dynamic assignment method is based on utility theory and is oriented to different levels of induced users. A distributed model based on the marginal utility is developed which combines the advantages of both decentralized paradigm and traveler preference, so as to provide efficient and robust dynamic traffic assignment solutions under uncertain network conditions. Then, the solution algorithm including subroute update and subroute calculation is proposed. To testify the effectiveness of the proposed model in optimizing traffic network operation and minimizing traveler’s cost on different induced levels, a sequence numerical experiment is conducted. In the experiment, there are two test environments: one is in different network load conditions and the other is in different deployment coverage of local agents. The numerical results show that the proposed model not only can improve the running efficiency of road network but also can significantly decrease the average travel time.

Dynamical parton distributions of the nucleon and very small-x physics

International Nuclear Information System (INIS)

Glueck, M.; Jimenez-Delgado, P.; Reya, E.

2008-01-01

Utilizing recent DIS measurements (F 2,L ) and data on dilepton and high-E T jet production we determine the dynamical parton distributions of the nucleon generated radiatively from valence-like positive input distributions at optimally chosen low resolution scales. These are compared with 'standard' distributions generated from positive input distributions at some fixed and higher resolution scale. It is shown that up to the next-to-leading order NLO(MS, DIS) of perturbative QCD considered in this paper, the uncertainties of the dynamical distributions are, as expected, smaller than those of their standard counterparts. This holds true in particular in the presently unexplored extremely small-x region relevant for evaluating ultrahigh energy cross sections in astrophysical applications. It is noted that our new dynamical distributions are compatible, within the presently determined uncertainties, with previously determined dynamical parton distributions. (orig.)

Assessing the potential of residential HVAC systems for demand-side management

NARCIS (Netherlands)

van der Klauw, Thijs; Hoogsteen, Gerwin; Gerards, Marco Egbertus Theodorus; Hurink, Johann L.; Feng, Xianyong; Hebner, Robert E.

This paper investigates the potential of residential heating, ventilation and air conditioning systems to contribute to dynamic demand-side management. Thermal models for seven houses in Austin, Texas are developed with the goal of using them in a planning based demand-side management methodology.

Market integration of flexible demand and DG-RES supply. A new approach for demand response

International Nuclear Information System (INIS)

Warmer, C.J.; Hommelberg, M.P.F.; Kamphuis, I.G.; Kok, J.K.

2007-06-01

In this paper we discuss the shortcomings of traditional Demand Response programs in an environment in which a large amount of distributed generation is available. An innovative approach is given in which true Customer Site Integration is obtained in the spirit of the liberalized electricity market, by making use of the load flexibility of underlying processes of production and consumption devices. The approach is based on distributed control mechanisms and incorporates new market models for distribution and aggregation costs, load losses, and network constraints

Distributed multi-agent based coordinated power management and control strategy for microgrids with distributed energy resources

International Nuclear Information System (INIS)

Rahman, M.S.; Oo, A.M.T.

2017-01-01

Highlights: • Agent-based energy management and control scheme is designed for power sharing. • Distributed agent communication topology is formed by the graph theory. • Proposed scheme is capable of dynamically adapt to the change in system conditions. • Multi-agent coordination is achieved through information exchange. • Proposed power sharing strategy ensures the reliability of energy supply. - Abstract: In this paper, a distributed peer-to-peer multi-agent framework is proposed for managing the power sharing in microgrids with power electronic inverter-interfaced distributed energy resources (DERs). Recently, the introduction of electric vehicles (EVs) has gained much popularity by offering vehicle-to-home (V2H) technologies to support the sustainable operation of microgrids. Since microgrids often exhibit volatile characteristics due to natural intermittency and uncertainty, it is necessary to maintain the balancing of generation and demand through the proper management of power sharing. Therefore, the main purpose of this paper is to design an agent-based control framework to ensure the coordinated power management within the microgrids through effective utilization of EVs. The required agent communication framework is adhered to the graph theory where the control agents interact with each other using local as well as neighboring information and their distributed coordination effectively steers the proportional sharing of real and reactive powers among the inverter-interfaced EVs to maintain the stability of microgrids. The well known Ziegler-Nichols method is used to tune the proportional-integral (PI) controller of the inner current control loop within each individual control agent to perform necessary shared control tasks. A microgrid with solar photovoltaic (PV) and V2H systems is chosen to illustrate the results and it is seen that the proposed scheme improves the system performance in a smarter way through information exchange. Furthermore

Efficient graph-based dynamic load-balancing for parallel large-scale agent-based traffic simulation

NARCIS (Netherlands)

Xu, Y.; Cai, W.; Aydt, H.; Lees, M.; Tolk, A.; Diallo, S.Y.; Ryzhov, I.O.; Yilmaz, L.; Buckley, S.; Miller, J.A.

2014-01-01

One of the issues of parallelizing large-scale agent-based traffic simulations is partitioning and load-balancing. Traffic simulations are dynamic applications where the distribution of workload in the spatial domain constantly changes. Dynamic load-balancing at run-time has shown better efficiency

Generating spatiotemporal joint torque patterns from dynamical synchronization of distributed pattern generators

Directory of Open Access Journals (Sweden)

Alex Pitti

2009-10-01

Full Text Available Pattern generators found in the spinal cords are no more seen as simple rhythmic oscillators for motion control. Indeed, they achieve flexible and dynamical coordination in interaction with the body and the environment dynamics to rise motor synergies. Discovering the mechanisms underlying the control of motor synergies constitute an important research question not only for neuroscience but also for robotics: the motors coordination of high dimensional robotic systems is still a drawback and new control methods based on biological solutions may reduce their overall complexity. We propose to model the flexible combination of motor synergies in embodied systems via partial phase synchronization of distributed chaotic systems; for specific coupling strength, chaotic systems are able to phase synchronize their dynamics to the resonant frequencies of one external force. We take advantage of this property to explore and exploit the intrinsic dynamics of one specified embodied system. In two experiments with bipedal walkers, we show how motor synergies emerge when the controllers phase synchronize to the body’s dynamics, entraining it to its intrinsic behavioral patterns. This stage is characterized by directed information flow from the sensors to the motors exhibiting the optimal situation when the body dynamics drive the controllers (mutual entrainment. Based on our results, we discuss the relevance of our findings for modeling the modular control of distributed pattern generators exhibited in the spinal cords, and for exploring the motor synergies in robots.

Flexible investment under uncertainty in smart distribution networks with demand side response: Assessment framework and practical implementation

International Nuclear Information System (INIS)

Schachter, Jonathan A.; Mancarella, Pierluigi; Moriarty, John; Shaw, Rita

2016-01-01

Classical deterministic models applied to investment valuation in distribution networks may not be adequate for a range of real-world decision-making scenarios as they effectively ignore the uncertainty found in the most important variables driving network planning (e.g., load growth). As greater uncertainty is expected from growing distributed energy resources in distribution networks, there is an increasing risk of investing in too much or too little network capacity and hence causing the stranding and inefficient use of network assets; these costs are then passed on to the end-user. An alternative emerging solution in the context of smart grid development is to release untapped network capacity through Demand-Side Response (DSR). However, to date there is no approach able to quantify the value of ‘smart’ DSR solutions against ‘conventional’ asset-heavy investments. On these premises, this paper presents a general real options framework and a novel probabilistic tool for the economic assessment of DSR for smart distribution network planning under uncertainty, which allows the modeling and comparison of multiple investment strategies, including DSR and capacity reinforcements, based on different cost and risk metrics. In particular the model provides an explicit quantification of the economic value of DSR against alternative investment strategies. Through sensitivity analysis it is able to indicate the maximum price payable for DSR service such that DSR remains economically optimal against these alternatives. The proposed model thus provides Regulators with clear insights for overseeing DSR contractual arrangements. Further it highlights that differences exist in the economic perspective of the regulated DNO business and of customers. Our proposed model is therefore capable of highlighting instances where a particular investment strategy is favorable to the DNO but not to its customers, or vice-versa, and thus aspects of the regulatory framework which may

Real-Time Congestion Management in Distribution Networks by Flexible Demand Swap

DEFF Research Database (Denmark)

Huang, Shaojun; Wu, Qiuwei

2017-01-01

In addition to the day-ahead congestion management in distribution networks, the real-time congestion management is very important because many unforeseen events can occur at the real operation time, e.g. loss of generation of distributed energy resources (DERs) or inaccurate forecast of energy...... pumps (HPs) for real time congestion management. The swap method can maintain the power balance of the system and avoid the imbalance cost of activating the flexibility service. An algorithm for forming swaps through optimal power flow (OPF) and mixed integer linear programming (MILP) is proposed...... consumption or production. Flexibility service from demand will be a good option to solve the real-time congestions if the cost of activating the flexibility service is fully addressed. This paper proposes a new method, namely “swap”, to employ the flexibility service from electric vehicles (EVs) and heat...

Correlations between Energy and Displacement Demands for Performance-Based Seismic Engineering

Science.gov (United States)

Mollaioli, Fabrizio; Bruno, Silvia; Decanini, Luis; Saragoni, Rodolfo

2011-01-01

(that can be considered as parameters representative of the amplitude, frequency content and duration of earthquake ground motions) and displacement-based response measures that are well correlated to structural and non-structural damage. For the purpose of quantifying the EDPs to be related to the energy measures, for comprehensive range of ground motion and structural characteristics, both simplified and more accurate numerical models will be used in this study for the estimation of local and global displacement and energy demands. Parametric linear and nonlinear time-history analyses will be performed on elastic and inelastic SDOF and MDOF systems, in order to assume information on the seismic response of a wide range of current structures. Hysteretic models typical of frame force/displacement behavior will be assumed for the local inelastic cyclic response of the systems. A wide range of vibration periods will be taken into account so as to define displacement, interstory drift and energy spectra for MDOF systems. Various scalar measures related to the deformation demand will be used in this research. These include the spectral displacements, the peak roof drift ratio, and the peak interstory drift ratio. A total of about 900 recorded ground motions covering a broad variety of condition in terms of frequency content, duration and amplitude will be used as input in the dynamic analyses. The records are obtained from 40 earthquakes and grouped as a function of magnitude of the event, source-to-site condition and site soil condition. In addition, in the data-set of records a considerable number of near-fault signals is included, in recognition of the particular significance of pulse-like time histories in causing large seismic demands to the structures.

A revival of the autoregressive distributed lag model in estimating energy demand relationships

Energy Technology Data Exchange (ETDEWEB)

Bentzen, J.; Engsted, T.

1999-07-01

The findings in the recent energy economics literature that energy economic variables are non-stationary, have led to an implicit or explicit dismissal of the standard autoregressive distribution lag (ARDL) model in estimating energy demand relationships. However, Pesaran and Shin (1997) show that the ARDL model remains valid when the underlying variables are non-stationary, provided the variables are co-integrated. In this paper we use the ARDL approach to estimate a demand relationship for Danish residential energy consumption, and the ARDL estimates are compared to the estimates obtained using co-integration techniques and error-correction models (ECM's). It turns out that both quantitatively and qualitatively, the ARDL approach and the co-integration/ECM approach give very similar results. (au)

A revival of the autoregressive distributed lag model in estimating energy demand relationships

Energy Technology Data Exchange (ETDEWEB)

Bentzen, J; Engsted, T

1999-07-01

The findings in the recent energy economics literature that energy economic variables are non-stationary, have led to an implicit or explicit dismissal of the standard autoregressive distribution lag (ARDL) model in estimating energy demand relationships. However, Pesaran and Shin (1997) show that the ARDL model remains valid when the underlying variables are non-stationary, provided the variables are co-integrated. In this paper we use the ARDL approach to estimate a demand relationship for Danish residential energy consumption, and the ARDL estimates are compared to the estimates obtained using co-integration techniques and error-correction models (ECM's). It turns out that both quantitatively and qualitatively, the ARDL approach and the co-integration/ECM approach give very similar results. (au)

The futility of utility: how market dynamics marginalize Adam Smith

Science.gov (United States)

McCauley, Joseph L.

2000-10-01

Economic theorizing is based on the postulated, nonempiric notion of utility. Economists assume that prices, dynamics, and market equilibria are supposed to be derived from utility. The results are supposed to represent mathematically the stabilizing action of Adam Smith's invisible hand. In deterministic excess demand dynamics I show the following. A utility function generally does not exist mathematically due to nonintegrable dynamics when production/investment are accounted for, resolving Mirowski's thesis. Price as a function of demand does not exist mathematically either. All equilibria are unstable. I then explain how deterministic chaos can be distinguished from random noise at short times. In the generalization to liquid markets and finance theory described by stochastic excess demand dynamics, I also show the following. Market price distributions cannot be rescaled to describe price movements as ‘equilibrium’ fluctuations about a systematic drift in price. Utility maximization does not describe equilibrium. Maximization of the Gibbs entropy of the observed price distribution of an asset would describe equilibrium, if equilibrium could be achieved, but equilibrium does not describe real, liquid markets (stocks, bonds, foreign exchange). There are three inconsistent definitions of equilibrium used in economics and finance, only one of which is correct. Prices in unregulated free markets are unstable against both noise and rising or falling expectations: Adam Smith's stabilizing invisible hand does not exist, either in mathematical models of liquid market data, or in real market data.

Agent-Based Architectures and Algorithms for Energy Management in Smart Grids. Application to Smart Power Generation and Residential Demand Response

International Nuclear Information System (INIS)

Roche, Robin

2012-01-01

Due to the convergence of several profound trends in the energy sector, smart grids are emerging as the main paradigm for the modernization of the electric grid. Smart grids hold many promises, including the ability to integrate large shares of distributed and intermittent renewable energy sources, energy storage and electric vehicles, as well as the promise to give consumers more control on their energy consumption. Such goals are expected to be achieved through the use of multiple technologies, and especially of information and communication technologies, supported by intelligent algorithms. These changes are transforming power grids into even more complex systems, that require suitable tools to model, simulate and control their behaviors. In this dissertation, properties of multi-agent systems are used to enable a new systemic approach to energy management, and allow for agent-based architectures and algorithms to be defined. This new approach helps tackle the complexity of a cyber-physical system such as the smart grid by enabling the simultaneous consideration of multiple aspects such as power systems, the communication infrastructure, energy markets, and consumer behaviors. The approach is tested in two applications: a 'smart' energy management system for a gas turbine power plant, and a residential demand response system. An energy management system for gas turbine power plants is designed with the objective to minimize operational costs and emissions, in the smart power generation paradigm. A gas turbine model based on actual data is proposed, and used to run simulations with a simulator specifically developed for this problem. A meta-heuristic achieves dynamic dispatch among gas turbines according to their individual characteristics. Results show that the system is capable of operating the system properly while reducing costs and emissions. The computing and communication requirements of the system, resulting from the selected architecture, are

Retail Demand Response in Southwest Power Pool

Energy Technology Data Exchange (ETDEWEB)

Bharvirkar, Ranjit; Heffner, Grayson; Goldman, Charles

2009-01-30

among SPP members. For these entities, investment in DR is often driven by the need to reduce summer peak demand that is used to set demand charges for each distribution cooperative. o About 65-70percent of the interruptible/curtailable tariffs and DLC programs are routinely triggered based on market conditions, not just for system emergencies. Approximately, 53percent of the DR resources are available with less than two hours advance notice and 447 MW can be dispatched with less than thirty minutes notice. o Most legacy DR programs offered a reservation payment ($/kW) for participation; incentive payment levels ranged from $0.40 to $8.30/kW-month for interruptible rate tariffs and $0.30 to $4.60/kW-month for DLC programs. A few interruptible programs offered incentive payments which were explicitly linkedto actual load reductions during events; payments ranged from 2 to 40 cents/kWh for load curtailed.

Evaluating Water Demand Using Agent-Based Modeling

Science.gov (United States)

Lowry, T. S.

2004-12-01

The supply and demand of water resources are functions of complex, inter-related systems including hydrology, climate, demographics, economics, and policy. To assess the safety and sustainability of water resources, planners often rely on complex numerical models that relate some or all of these systems using mathematical abstractions. The accuracy of these models relies on how well the abstractions capture the true nature of the systems interactions. Typically, these abstractions are based on analyses of observations and/or experiments that account only for the statistical mean behavior of each system. This limits the approach in two important ways: 1) It cannot capture cross-system disruptive events, such as major drought, significant policy change, or terrorist attack, and 2) it cannot resolve sub-system level responses. To overcome these limitations, we are developing an agent-based water resources model that includes the systems of hydrology, climate, demographics, economics, and policy, to examine water demand during normal and extraordinary conditions. Agent-based modeling (ABM) develops functional relationships between systems by modeling the interaction between individuals (agents), who behave according to a probabilistic set of rules. ABM is a "bottom-up" modeling approach in that it defines macro-system behavior by modeling the micro-behavior of individual agents. While each agent's behavior is often simple and predictable, the aggregate behavior of all agents in each system can be complex, unpredictable, and different than behaviors observed in mean-behavior models. Furthermore, the ABM approach creates a virtual laboratory where the effects of policy changes and/or extraordinary events can be simulated. Our model, which is based on the demographics and hydrology of the Middle Rio Grande Basin in the state of New Mexico, includes agent groups of residential, agricultural, and industrial users. Each agent within each group determines its water usage

Topology Identification of General Dynamical Network with Distributed Time Delays

International Nuclear Information System (INIS)

Zhao-Yan, Wu; Xin-Chu, Fu

2009-01-01

General dynamical networks with distributed time delays are studied. The topology of the networks are viewed as unknown parameters, which need to be identified. Some auxiliary systems (also called the network estimators) are designed to achieve this goal. Both linear feedback control and adaptive strategy are applied in designing these network estimators. Based on linear matrix inequalities and the Lyapunov function method, the sufficient condition for the achievement of topology identification is obtained. This method can also better monitor the switching topology of dynamical networks. Illustrative examples are provided to show the effectiveness of this method. (general)

Implementation of Parallel Dynamic Simulation on Shared-Memory vs. Distributed-Memory Environments

Energy Technology Data Exchange (ETDEWEB)

Jin, Shuangshuang; Chen, Yousu; Wu, Di; Diao, Ruisheng; Huang, Zhenyu

2015-12-09

Power system dynamic simulation computes the system response to a sequence of large disturbance, such as sudden changes in generation or load, or a network short circuit followed by protective branch switching operation. It consists of a large set of differential and algebraic equations, which is computational intensive and challenging to solve using single-processor based dynamic simulation solution. High-performance computing (HPC) based parallel computing is a very promising technology to speed up the computation and facilitate the simulation process. This paper presents two different parallel implementations of power grid dynamic simulation using Open Multi-processing (OpenMP) on shared-memory platform, and Message Passing Interface (MPI) on distributed-memory clusters, respectively. The difference of the parallel simulation algorithms and architectures of the two HPC technologies are illustrated, and their performances for running parallel dynamic simulation are compared and demonstrated.

Building stock dynamics and its impacts on materials and energy demand in China

International Nuclear Information System (INIS)

Hong, Lixuan; Zhou, Nan; Feng, Wei; Khanna, Nina; Fridley, David; Zhao, Yongqiang; Sandholt, Kaare

2016-01-01

China hosts a large amount of building stocks, which is nearly 50 billion square meters. Moreover, annual new construction is growing fast, representing half of the world's total. The trend is expected to continue through the year 2050. Impressive demand for new residential and commercial construction, relative shorter average building lifetime, and higher material intensities have driven massive domestic production of energy intensive building materials such as cement and steel. This paper developed a bottom-up building stock turnover model to project the growths, retrofits and retirements of China's residential and commercial building floor space from 2010 to 2050. It also applied typical material intensities and energy intensities to estimate building materials demand and energy consumed to produce these building materials. By conducting scenario analyses of building lifetime, it identified significant potentials of building materials and energy demand conservation. This study underscored the importance of addressing building material efficiency, improving building lifetime and quality, and promoting compact urban development to reduce energy and environment consequences in China. - Highlights: •Growths of China's building floorspace were projected from 2010 to 2050. •A building stock turnover model was built to reflect annual building stock dynamics. •Building related materials and energy demand were projected.

Holidays in lights: Tracking cultural patterns in demand for energy services

Science.gov (United States)

Román, Miguel O.; Stokes, Eleanor C.

2015-06-01

Successful climate change mitigation will involve not only technological innovation, but also innovation in how we understand the societal and individual behaviors that shape the demand for energy services. Traditionally, individual energy behaviors have been described as a function of utility optimization and behavioral economics, with price restructuring as the dominant policy lever. Previous research at the macro-level has identified economic activity, power generation and technology, and economic role as significant factors that shape energy use. However, most demand models lack basic contextual information on how dominant social phenomenon, the changing demographics of cities, and the sociocultural setting within which people operate, affect energy decisions and use patterns. Here we use high-quality Suomi-NPP VIIRS nighttime environmental products to: (1) observe aggregate human behavior through variations in energy service demand patterns during the Christmas and New Year's season and the Holy Month of Ramadan and (2) demonstrate that patterns in energy behaviors closely track sociocultural boundaries at the country, city, and district level. These findings indicate that energy decision making and demand is a sociocultural process as well as an economic process, often involving a combination of individual price-based incentives and societal-level factors. While nighttime satellite imagery has been used to map regional energy infrastructure distribution, tracking daily dynamic lighting demand at three major scales of urbanization is novel. This methodology can enrich research on the relative importance of drivers of energy demand and conservation behaviors at fine scales. Our initial results demonstrate the importance of seating energy demand frameworks in a social context.

Holiday in Lights: Tracking Cultural Patterns in Demand for Energy Services

Science.gov (United States)

Roman, Miguel O.; Stokes, Eleanor C.

2015-01-01

Successful climate change mitigation will involve not only technological innovation, but also innovation in how we understand the societal and individual behaviors that shape the demand for energy services. Traditionally, individual energy behaviors have been described as a function of utility optimization and behavioral economics, with price restructuring as the dominant policy lever. Previous research at the macro-level has identified economic activity, power generation and technology, and economic role as significant factors that shape energy use. However, most demand models lack basic contextual information on how dominant social phenomenon, the changing demographics of cities, and the sociocultural setting within which people operate, affect energy decisions and use patterns. Here we use high-quality Suomi-NPP VIIRS nighttime environmental products to: (1) observe aggregate human behavior through variations in energy service demand patterns during the Christmas and New Year's season and the Holy Month of Ramadan and (2) demonstrate that patterns in energy behaviors closely track sociocultural boundaries at the country, city, and district level. These findings indicate that energy decision making and demand is a sociocultural process as well as an economic process, often involving a combination of individual price-based incentives and societal-level factors. While nighttime satellite imagery has been used to map regional energy infrastructure distribution, tracking daily dynamic lighting demand at three major scales of urbanization is novel. This methodology can enrich research on the relative importance of drivers of energy demand and conservation behaviors at fine scales. Our initial results demonstrate the importance of seating energy demand frameworks in a social context.

Derivation of confidence intervals of service measures in a base-stock inventory control system with low-frequent demand

DEFF Research Database (Denmark)

Larsen, Christian

We explore a base-stock system with backlogging where the demand process is a compound renewal process and the compound element is a delayed geometric distribution. For this setting it is proven in [4] that the long-run average service measures order fill rate (OFR) and volume fill rate (VFR) are...

Derivation of confidence intervals of service measures in a base-stock inventory control system with low-frequent demand

DEFF Research Database (Denmark)

Larsen, Christian

2011-01-01

We explore a base-stock system with backlogging where the demand process is a compound renewal process and the compound element is a delayed geometric distribution. For this setting it holds that the long-run average service measures order fill rate (OFR) and volume fill rate (VFR) are equal in v...

Long-run gasoline demand for passenger cars: the role of income distribution

International Nuclear Information System (INIS)

Storchmann, Karl

2005-01-01

It is commonly agreed that the level of income and prices are crucial determinants of the consumption of motor gasoline. The respective long run price and income elasticities are regularly calculated using cross sectional models. Despite the acknowledgement of the role of income distribution, it plays no role in intercountry cross sectional models. This is due to a lack of appropriate data. This paper shows that the omission of distributional characteristics provides misleading elasticities. Using available distributional measures this paper is referring to an income threshold, which is crucial to the acquisition of an automobile. It is shown that on the one hand, in poor countries an unequal income distribution is needed to enable at least some people to buy automobiles. On the other hand, in wealthy countries an unequal income distribution would exclude some people from acquiring automobiles. Hence, depending on the income level, inequality has a diverging impact on the ability to buy durable goods. The second part of this paper develops a pooled 90-country model to examine this approach empirically. It could be shown that distribution variables are highly significant to explain the demand for automobiles and motor gasoline. Moreover, the consideration of the distribution of income leads to a considerable decrease in income elasticity values. This is mainly due to the positive correlation between income level and income equality within the sample

Analysis of stationary fuel cell dynamic ramping capabilities and ultra capacitor energy storage using high resolution demand data

Science.gov (United States)

Meacham, James R.; Jabbari, Faryar; Brouwer, Jacob; Mauzey, Josh L.; Samuelsen, G. Scott

Current high temperature fuel cell (HTFC) systems used for stationary power applications (in the 200-300 kW size range) have very limited dynamic load following capability or are simply base load devices. Considering the economics of existing electric utility rate structures, there is little incentive to increase HTFC ramping capability beyond 1 kWs -1 (0.4% s -1). However, in order to ease concerns about grid instabilities from utility companies and increase market adoption, HTFC systems will have to increase their ramping abilities, and will likely have to incorporate electrical energy storage (EES). Because batteries have low power densities and limited lifetimes in highly cyclic applications, ultra capacitors may be the EES medium of choice. The current analyses show that, because ultra capacitors have a very low energy storage density, their integration with HTFC systems may not be feasible unless the fuel cell has a ramp rate approaching 10 kWs -1 (4% s -1) when using a worst-case design analysis. This requirement for fast dynamic load response characteristics can be reduced to 1 kWs -1 by utilizing high resolution demand data to properly size ultra capacitor systems and through demand management techniques that reduce load volatility.

Impact of onsite solar generation on system load demand forecast

International Nuclear Information System (INIS)

Kaur, Amanpreet; Pedro, Hugo T.C.; Coimbra, Carlos F.M.

2013-01-01

Highlights: • We showed the impact onsite solar generation on system demand load forecast. • Forecast performance degrades by 9% and 3% for 1 h and 15 min forecast horizons. • Error distribution for onsite case is best characterized as t-distribution. • Relation between error, solar penetration and solar variability is characterized. - Abstract: Net energy metering tariffs have encouraged the growth of solar PV in the distribution grid. The additional variability associated with weather-dependent renewable energy creates new challenges for power system operators that must maintain and operate ancillary services to balance the grid. To deal with these issues power operators mostly rely on demand load forecasts. Electric load forecast has been used in power industry for a long time and there are several well established load forecasting models. But the performance of these models for future scenario of high renewable energy penetration is unclear. In this work, the impact of onsite solar power generation on the demand load forecast is analyzed for a community that meets between 10% and 15% of its annual power demand and 3–54% of its daily power demand from a solar power plant. Short-Term Load Forecasts (STLF) using persistence, machine learning and regression-based forecasting models are presented for two cases: (1) high solar penetration and (2) no penetration. Results show that for 1-h and 15-min forecasts the accuracy of the models drops by 9% and 3% with high solar penetration. Statistical analysis of the forecast errors demonstrate that the error distribution is best characterized as a t-distribution for the high penetration scenario. Analysis of the error distribution as a function of daily solar penetration for different levels of variability revealed that the solar power variability drives the forecast error magnitude whereas increasing penetration level has a much smaller contribution. This work concludes that the demand forecast error distribution

Singular multiparameter dynamic equations with distributional ...

African Journals Online (AJOL)

Singular multiparameter dynamic equations with distributional potentials on time scales. ... In this paper, we consider both singular single and several multiparameter ... multiple function which is of one sign and nonzero on the given time scale.

A model of the demand for Islamic banks debt-based financing instrument

Science.gov (United States)

Jusoh, Mansor; Khalid, Norlin

2013-04-01

This paper presents a theoretical analysis of the demand for debt-based financing instruments of the Islamic banks. Debt-based financing, such as through baibithamanajil and al-murabahah, is by far the most prominent of the Islamic bank financing and yet it has been largely ignored in Islamic economics literature. Most studies instead have been focusing on equity-based financing of al-mudharabah and al-musyarakah. Islamic bank offers debt-based financing through various instruments derived under the principle of exchange (ukud al-mu'awadhat) or more specifically, the contract of deferred sale. Under such arrangement, Islamic debt is created when goods are purchased and the payments are deferred. Thus, unlike debt of the conventional bank which is a form of financial loan contract to facilitate demand for liquid assets, this Islamic debt is created in response to the demand to purchase goods by deferred payment. In this paper we set an analytical framework that is based on an infinitely lived representative agent model (ILRA model) to analyze the demand for goods to be purchased by deferred payment. The resulting demand will then be used to derive the demand for Islamic debt. We also investigate theoretically, factors that may have an impact on the demand for Islamic debt.

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...

A Distributed Dynamic Super Peer Selection Method Based on Evolutionary Game for Heterogeneous P2P Streaming Systems

Directory of Open Access Journals (Sweden)

Jing Chen

2013-01-01

Full Text Available Due to high efficiency and good scalability, hierarchical hybrid P2P architecture has drawn more and more attention in P2P streaming research and application fields recently. The problem about super peer selection, which is the key problem in hybrid heterogeneous P2P architecture, is becoming highly challenging because super peers must be selected from a huge and dynamically changing network. A distributed super peer selection (SPS algorithm for hybrid heterogeneous P2P streaming system based on evolutionary game is proposed in this paper. The super peer selection procedure is modeled based on evolutionary game framework firstly, and its evolutionarily stable strategies are analyzed. Then a distributed Q-learning algorithm (ESS-SPS according to the mixed strategies by analysis is proposed for the peers to converge to the ESSs based on its own payoff history. Compared to the traditional randomly super peer selection scheme, experiments results show that the proposed ESS-SPS algorithm achieves better performance in terms of social welfare and average upload rate of super peers and keeps the upload capacity of the P2P streaming system increasing steadily with the number of peers increasing.

DEVELOPING GIS-BASED DEMAND-RESPONSIVE TRANSIT SYSTEM IN TEHRAN CITY

Directory of Open Access Journals (Sweden)

H. Faroqi

2015-12-01

Full Text Available Create, maintain and development of public transport network in metropolitan are important problems in the field of urban transport management. In public transport, maximize the efficient use of public fleet capacity has been considered. Concepts and technologies of GIS have provided suitable way for management and optimization of the public transports systems. In demand-responsive public transportation system, firstly fellow traveller groups have been established for applicants based on spatial concepts and tools of GIS, second for each group according to its’ members and their paths, a public vehicle has been allocated to them then based on dynamic routing, the fellow passenger group has been gathered from their origins and has been moved to their destinations through optimal route. The suggested system has been implemented based on network data and commuting trips statistics of 1 to 6 districts in Tehran city. Evaluation performed on the results show the 34% increase using of Taxi capacity, 13% increase using of Van capacity and 10% increase using of Bus capacity in comparison between current public transport system and suggested public transportation system has been improved.

Application of a distributed network in computational fluid dynamic simulations

Science.gov (United States)

Deshpande, Manish; Feng, Jinzhang; Merkle, Charles L.; Deshpande, Ashish

1994-01-01

A general-purpose 3-D, incompressible Navier-Stokes algorithm is implemented on a network of concurrently operating workstations using parallel virtual machine (PVM) and compared with its performance on a CRAY Y-MP and on an Intel iPSC/860. The problem is relatively computationally intensive, and has a communication structure based primarily on nearest-neighbor communication, making it ideally suited to message passing. Such problems are frequently encountered in computational fluid dynamics (CDF), and their solution is increasingly in demand. The communication structure is explicitly coded in the implementation to fully exploit the regularity in message passing in order to produce a near-optimal solution. Results are presented for various grid sizes using up to eight processors.

Multi-agent based controller for islanding operation of active distribution networks with distributed generation

DEFF Research Database (Denmark)

Cha, Seung-Tae; Wu, Qiuwei; Østergaard, Jacob

2011-01-01

-bus system was used to investigate the dynamic and steady state performance of the active distribution system during islanding operation. Case studies have been carried out using the Real-Time Digital Simulator (RTDS) based simulation platform. Case study results show that the proposed multi......The increasing amount of distributed generation (DG) in today’s highly complex restructured power networks gives more options for distribution system operators (DSOs) under contingency conditions. A low voltage distribution network with a large amount of DG can be operated as an islanded system...... if the distribution system is disconnected from the main grid due to the contingency. In order to successfully operate distribution systems under islanding mode, the possibility of small power islands within the distribution system needs to be considered. The control and management of these small power islands...

Energy demand forecasting method based on international statistical data

International Nuclear Information System (INIS)

Glanc, Z.; Kerner, A.

1997-01-01

Poland is in a transition phase from a centrally planned to a market economy; data collected under former economic conditions do not reflect a market economy. Final energy demand forecasts are based on the assumption that the economic transformation in Poland will gradually lead the Polish economy, technologies and modes of energy use, to the same conditions as mature market economy countries. The starting point has a significant influence on the future energy demand and supply structure: final energy consumption per capita in 1992 was almost half the average of OECD countries; energy intensity, based on Purchasing Power Parities (PPP) and referred to GDP, is more than 3 times higher in Poland. A method of final energy demand forecasting based on regression analysis is described in this paper. The input data are: output of macroeconomic and population growth forecast; time series 1970-1992 of OECD countries concerning both macroeconomic characteristics and energy consumption; and energy balance of Poland for the base year of the forecast horizon. (author). 1 ref., 19 figs, 4 tabs

Energy demand forecasting method based on international statistical data

Energy Technology Data Exchange (ETDEWEB)

Glanc, Z; Kerner, A [Energy Information Centre, Warsaw (Poland)

1997-09-01

Poland is in a transition phase from a centrally planned to a market economy; data collected under former economic conditions do not reflect a market economy. Final energy demand forecasts are based on the assumption that the economic transformation in Poland will gradually lead the Polish economy, technologies and modes of energy use, to the same conditions as mature market economy countries. The starting point has a significant influence on the future energy demand and supply structure: final energy consumption per capita in 1992 was almost half the average of OECD countries; energy intensity, based on Purchasing Power Parities (PPP) and referred to GDP, is more than 3 times higher in Poland. A method of final energy demand forecasting based on regression analysis is described in this paper. The input data are: output of macroeconomic and population growth forecast; time series 1970-1992 of OECD countries concerning both macroeconomic characteristics and energy consumption; and energy balance of Poland for the base year of the forecast horizon. (author). 1 ref., 19 figs, 4 tabs.

Sliding mode-based lateral vehicle dynamics control using tyre force measurements

Science.gov (United States)

Kunnappillil Madhusudhanan, Anil; Corno, Matteo; Holweg, Edward

2015-11-01

In this work, a lateral vehicle dynamics control based on tyre force measurements is proposed. Most of the lateral vehicle dynamics control schemes are based on yaw rate whereas tyre forces are the most important variables in vehicle dynamics as tyres are the only contact points between the vehicle and road. In the proposed method, active front steering is employed to uniformly distribute the required lateral force among the front left and right tyres. The force distribution is quantified through the tyre utilisation coefficients. In order to address the nonlinearities and uncertainties of the vehicle model, a gain scheduling sliding-mode control technique is used. In addition to stabilising the lateral dynamics, the proposed controller is able to maintain maximum lateral acceleration. The proposed method is tested and validated on a multi-body vehicle simulator.

[Optimization of urban green space spatial arrangement based on supply-demand analysis: a case study in Nanjing City, China].

Science.gov (United States)

Gui, Kun-Peng; Xu, Jian-Gang; Zhang, Xiang

2013-05-01

Urban green space has the functions of ecological and social services, and the two services levels are decided by the supply-demand relationship. However, the supply-demand of green space not only involves in quantity, but also refers to spatial distribution. Therefore, only greening indicators can not wholly reflect the true levels of green space services. Based on the supply-demand analysis and supported by the ArcGIS, this paper examined the ecological and social services levels of the urban green spaces in Nanjing City by using the evenness indicator and the rate the people could enjoy the public green space in their accessible area. Accordingly, the ecological and social services levels of the green space in the City were investigated. The results showed that in the east of Nanjing City, green spaces were rich, but high accessible ones were lack, which resulted in a moderate social service level. In the center of the City, green spaces were lack and distributed unevenly, resulting in the low levels of ecological and social services. In Hexi area, due to the shortage in ecological green space and its uneven distribution, the green spaces had a high level social service but a low level ecological service. In the southern and northern areas of the City, green spaces were in deficiency, uneven distribution, and lack in high accessible.

The development of demand elasticity model for demand response in the retail market environment

NARCIS (Netherlands)

Babar, M.; Nguyen, P.H.; Kamphuis, I.G.

2015-01-01

In the context of liberalized energy market, increase in distributed generation, storage and demand response has expanded the price elasticity of demand, thus causing the addition of uncertainty to the supply-demand chain of power system. In order to cope with the challenges of demand uncertainty

Revenue Improvement Through Demand-Dependent Pricing of Network Services

National Research Council Canada - National Science Library

Sanders, David

2000-01-01

... of the expectation of rewards based upon variable demands. This work shows that revenue improvement can occur in this network environment when a dynamic pricing policy is applied as opposed to optimal static pricing...

Mapping Multi-Cropped Land Use to Estimate Water Demand Using the California Pesticide Reporting Database

Science.gov (United States)

Henson, W.; Baillie, M. N.; Martin, D.

2017-12-01

Detailed and dynamic land-use data is one of the biggest data deficiencies facing food and water security issues. Better land-use data results in improved integrated hydrologic models that are needed to look at the feedback between land and water use, specifically for adequately representing changes and dynamics in rainfall-runoff, urban and agricultural water demands, and surface fluxes of water (e.g., evapotranspiration, runoff, and infiltration). Currently, land-use data typically are compiled from annual (e.g., Crop Scape) or multi-year composites if mapped at all. While this approach provides information about interannual land-use practices, it does not capture the dynamic changes in highly developed agricultural lands prevalent in California agriculture such as (1) dynamic land-use changes from high frequency multi-crop rotations and (2) uncertainty in sub-annual crop distribution, planting times, and cropped areas. California has collected spatially distributed data for agricultural pesticide use since 1974 through the California Pesticide Information Portal (CalPIP). A method leveraging the CalPIP database has been developed to provide vital information about dynamic agricultural land use (e.g., crop distribution and planting times) and water demand issues in Salinas Valley, California, along the central coast. This 7 billion dollar/year agricultural area produces up to 50% of U.S. lettuce and broccoli. Therefore, effective and sustainable water resource development in the area must balance the needs of this essential industry, other beneficial uses, and the environment. This new tool provides a way to provide more dynamic crop data in hydrologic models. While the current application focuses on the Salinas Valley, the methods are extensible to all of California and other states with similar pesticide reporting. The improvements in representing variability in crop patterns and associated water demands increase our understanding of land-use change and

CyberWalk : a web-based distributed virtual walkthrough environment.

OpenAIRE

Chim, J.; Lau, R. W. H.; Leong, H. V.; Si, A.

2003-01-01

A distributed virtual walkthrough environment allows users connected to the geometry server to walk through a specific place of interest, without having to travel physically. This place of interest may be a virtual museum, virtual library or virtual university. There are two basic approaches to distribute the virtual environment from the geometry server to the clients, complete replication and on-demand transmission. Although the on-demand transmission approach saves waiting time and optimize...

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.

Design of distributed PID-type dynamic matrix controller for fractional-order systems

Science.gov (United States)

Wang, Dawei; Zhang, Ridong

2018-01-01

With the continuous requirements for product quality and safety operation in industrial production, it is difficult to describe the complex large-scale processes with integer-order differential equations. However, the fractional differential equations may precisely represent the intrinsic characteristics of such systems. In this paper, a distributed PID-type dynamic matrix control method based on fractional-order systems is proposed. First, the high-order approximate model of integer order is obtained by utilising the Oustaloup method. Then, the step response model vectors of the plant is obtained on the basis of the high-order model, and the online optimisation for multivariable processes is transformed into the optimisation of each small-scale subsystem that is regarded as a sub-plant controlled in the distributed framework. Furthermore, the PID operator is introduced into the performance index of each subsystem and the fractional-order PID-type dynamic matrix controller is designed based on Nash optimisation strategy. The information exchange among the subsystems is realised through the distributed control structure so as to complete the optimisation task of the whole large-scale system. Finally, the control performance of the designed controller in this paper is verified by an example.

The Dynamic Response Process to Conflicting Institutional Demands in MNC Subsidiaries

DEFF Research Database (Denmark)

Holm, Alison E.; Decreton, Benoit; Nell, Phillip Christopher

2017-01-01

How do subsidiary managers react when their headquarters managers make requests that conflict with the local environment in which the subsidiary operates? Using data from a subsidiary based in Sub-Saharan Africa and headquartered in Europe, we show that subsidiary managers need more time than...... usually expected to react to headquarters demands. Subsidiary managers sometimes postpone or test headquarters demands before deciding how to respond to them. In addition, subsidiary managers can implement headquarters demands in ways that do not fit the expectations from the headquarters or local actors...... (e.g. customers, suppliers), thus resulting in additional delays. Headquarters managers must be aware that implementation can take longer than they anticipate, particularly for subsidiaries located in environments that differ substantially from the environment of the headquarters....

A study on the multiple dynamic wavelength distribution for gigabit capable passive optical networks

Directory of Open Access Journals (Sweden)

Gustavo Adolfo Puerto Leguizamón

2014-04-01

Full Text Available This paper presents a data traffic based study aiming at evaluating the impact of dynamic wavelength allocation on a Gigabit capable Passive Optical Network (GPON. In Passive Optical Networks (PON, an Optical Line Terminal (OLT feeds different PONs in such a way that a given wavelength channel is evenly distributed between the Optical Network Units (ONU at each PON. However, PONs do not specify any kind of dynamic behavior on the way the wavelengths are allocated in the network, a completely static distribution is implemented instead. In thispaper we evaluate the network performance in terms of packet losses and throughput for a number of ONUs being out-of-profile while featuring a given percentage of traffic in excess for a fixed wavelength distribution and for multiple dynamic wavelength allocation. Results show that for a multichannel operation with four wavelengths, the network throughput increases up to a rough value of 19% while the packet losses drop from 22 % to 1.8 % as compared with a static wavelength distribution.

High-efficient and high-content cytotoxic recording via dynamic and continuous cell-based impedance biosensor technology.

Science.gov (United States)

Hu, Ning; Fang, Jiaru; Zou, Ling; Wan, Hao; Pan, Yuxiang; Su, Kaiqi; Zhang, Xi; Wang, Ping

2016-10-01

Cell-based bioassays were effective method to assess the compound toxicity by cell viability, and the traditional label-based methods missed much information of cell growth due to endpoint detection, while the higher throughputs were demanded to obtain dynamic information. Cell-based biosensor methods can dynamically and continuously monitor with cell viability, however, the dynamic information was often ignored or seldom utilized in the toxin and drug assessment. Here, we reported a high-efficient and high-content cytotoxic recording method via dynamic and continuous cell-based impedance biosensor technology. The dynamic cell viability, inhibition ratio and growth rate were derived from the dynamic response curves from the cell-based impedance biosensor. The results showed that the biosensors has the dose-dependent manners to diarrhetic shellfish toxin, okadiac acid based on the analysis of the dynamic cell viability and cell growth status. Moreover, the throughputs of dynamic cytotoxicity were compared between cell-based biosensor methods and label-based endpoint methods. This cell-based impedance biosensor can provide a flexible, cost and label-efficient platform of cell viability assessment in the shellfish toxin screening fields.

Simulating Residential Demand in Singapore through Five Decades of Demographic Change

Science.gov (United States)

Davis, N. R.; Fernández, J.

2011-12-01

Singapore's rapid and well-documented development over the last half-century provides an ideal case for studying urban metabolism. Extensive data [1, 2] facilitate the modeling of historical dynamics of population and resource consumption. This paper presents an agent-based population model that simulates key demographic factors - number, size, and relative income of households - through fifty years of development in Singapore. This is the first step in a broader study linking demographic factors to residential demand for urban land, materials, water, and energy. Previous studies of the resource demands of housing stock have accounted for demographics by modifying the important population driver with a single, aggregated "lifestyle" term [3, 4]. However, demographic changes that result from development can influence the nature of the residential sector, and warrant a closer look. Increasing levels of education and affluence coupled with decreasing birth rates have yielded an aging population and changing family structures in Singapore [5]. These factors all contribute to an increasingly resource-intense residential sector. Singaporeans' elevated per capita income and life expectancy have created demand for larger household area, which means a growing percentage of available land must be dedicated to residential use [6]. While the majority of Singapore's housing is public - a strategy designed to maximize land use efficiency - residents are increasingly seeking private alternatives [7]. In the private sector, lower density housing puts even greater pressure on the finite supply of undeveloped land. Agent-based modeling is used to study the selected aspects of demography. The population is disaggregated into historical time-series distributions of age, family size, education, and income. We propose a simplified methodology correlating average education level with birth rate, and income to categorize households and establish housing unit demand. Aggregated lifestyle

Multi-objective PSO based optimal placement of solar power DG in radial distribution system

Directory of Open Access Journals (Sweden)

Mahesh Kumar

2017-06-01

Full Text Available Ever increasing trend of electricity demand, fossil fuel depletion and environmental issues request the integration of renewable energy into the distribution system. The optimal planning of renewable distributed generation (DG is much essential for ensuring maximum benefits. Hence, this paper proposes the optimal placement of probabilistic based solar power DG into the distribution system. The two objective functions such as power loss reduction and voltage stability index improvement are optimized. The power balance and voltage limits are kept as constraints of the problem. The non-sorting pare to-front based multi-objective particle swarm optimization (MOPSO technique is proposed on standard IEEE 33 radial distribution test system.

Advanced model for expansion of natural gas distribution networks based on geographic information systems

Energy Technology Data Exchange (ETDEWEB)

Ramirez-Rosado, I.J.; Fernandez-Jimenez, L.A.; Garcia-Garrido, E.; Zorzano-Santamaria, P.; Zorzano-Alba, E. [La Rioja Univ., La Rioja (Spain). Dept. of Electrical Engineering; Miranda, V.; Montneiro, C. [Porto Univ., Porto (Portugal). Faculty of Engineering]|[Inst. de Engenharia de Sistemas e Computadores do Porto, Porto (Portugal)

2005-07-01

An advanced geographic information system (GIS) model of natural gas distribution networks was presented. The raster-based model was developed to evaluate costs associated with the expansion of electrical networks due to increased demand in the La Rioja region of Spain. The model was also used to evaluate costs associated with maintenance and amortization of the already existing distribution network. Expansion costs of the distribution network were modelled in various demand scenarios. The model also considered a variety of technical factors associated with pipeline length and topography. Soil and slope data from previous pipeline projects were used to estimate real costs per unit length of pipeline. It was concluded that results obtained by the model will be used by planners to select zones where expansion is economically feasible. 4 refs., 5 figs.

Capturing well-being in activity pattern models within activity-based travel demand models.

Science.gov (United States)

2013-04-01

The activity-based approach which is based on the premise that the demand for travel is derived : from the demand for activities, currently constitutes the state of the art in metropolitan travel : demand forecasting and particularly in a form known ...

Time-of-use based electricity demand response for sustainable manufacturing systems

International Nuclear Information System (INIS)

Wang, Yong; Li, Lin

2013-01-01

As required by the Energy Policy Act of 2005, utility companies across the U.S. are offering TOU (time-of-use) based electricity demand response programs. The TOU rate gives consumers opportunities to manage their electricity bill by shifting use from on-peak periods to mid-peak and off-peak periods. Reducing the amount of electricity needed during the peak load times makes it possible for the power grid to meet consumers' needs without building more costly backup infrastructures and help reduce GHG (greenhouse gas) emissions. Previous research on the applications of TOU and other electricity demand response programs has been mainly focused on residential and commercial buildings while largely neglected industrial manufacturing systems. This paper proposes a systems approach for TOU based electricity demand response for sustainable manufacturing systems under the production target constraint. Key features of this approach include: (i) the electricity related costs including both consumption and demand are integrated into production system modeling; (ii) energy-efficient and demand-responsive production scheduling problems are formulated and the solution technique is provided; and (iii) the effects of various factors on the near-optimal scheduling solutions are examined. The research outcome is expected to enhance the energy efficiency, electricity demand responsiveness, and cost effectiveness of modern manufacturing systems. - Highlights: • We propose a TOU based demand response approach for manufacturing systems. • Both electricity consumption and demand are integrated into the system modeling. • Energy-efficient and demand-responsive production scheduling problems are formulated. • The meta-heuristic solution technique is provided. • The effects of various factors on the scheduling solutions are examined

WATER DEMAND PREDICTION USING ARTIFICIAL NEURAL ...

African Journals Online (AJOL)

This paper presents Hourly water demand prediction at the demand nodes of a water distribution network using NeuNet Pro 2.3 neural network software and the monitoring and control of water distribution using supervisory control. The case study is the Laminga Water Treatment Plant and its water distribution network, Jos.

Estimating Reduced Consumption for Dynamic Demand Response

Energy Technology Data Exchange (ETDEWEB)

Chelmis, Charalampos [Univ. of Southern California, Los Angeles, CA (United States); Aman, Saima [Univ. of Southern California, Los Angeles, CA (United States); Saeed, Muhammad Rizwan [Univ. of Southern California, Los Angeles, CA (United States); Frincu, Marc [Univ. of Southern California, Los Angeles, CA (United States); Prasanna, Viktor K. [Univ. of Southern California, Los Angeles, CA (United States)

2015-01-30

Growing demand is straining our existing electricity generation facilities and requires active participation of the utility and the consumers to achieve energy sustainability. One of the most effective and widely used ways to achieve this goal in the smart grid is demand response (DR), whereby consumers reduce their electricity consumption in response to a request sent from the utility whenever it anticipates a peak in demand. To successfully plan and implement demand response, the utility requires reliable estimate of reduced consumption during DR. This also helps in optimal selection of consumers and curtailment strategies during DR. While much work has been done on predicting normal consumption, reduced consumption prediction is an open problem that is under-studied. In this paper, we introduce and formalize the problem of reduced consumption prediction, and discuss the challenges associated with it. We also describe computational methods that use historical DR data as well as pre-DR conditions to make such predictions. Our experiments are conducted in the real-world setting of a university campus microgrid, and our preliminary results set the foundation for more detailed modeling.

Optimal Dynamic Pricing for Perishable Assets with Nonhomogeneous Demand

OpenAIRE

Wen Zhao; Yu-Sheng Zheng

2000-01-01

We consider a dynamic pricing model for selling a given stock of a perishable product over a finite time horizon. Customers, whose reservation price distribution changes over time, arrive according to a nonhomogeneous Poisson process. We show that at any given time, the optimal price decreases with inventory. We also identify a sufficient condition under which the optimal price decreases over time for a given inventory level. This sufficient condition requires that the willingness of a custom...

Managing distributed dynamic systems with spatial grasp technology

CERN Document Server

Sapaty, Peter Simon

2017-01-01

The book describes a novel ideology and supporting information technology for integral management of both civil and defence-orientated large, distributed dynamic systems. The approach is based on a high-level Spatial Grasp Language, SGL, expressing solutions in physical, virtual, executive and combined environments in the form of active self-evolving and self-propagating patterns spatially matching the systems to be created, modified and controlled. The communicating interpreters of SGL can be installed in key system points, which may be in large numbers (up to millions and billions) and represent equipped humans, robots, laptops, smartphones, smart sensors, etc. Operating under gestalt-inspired scenarios in SGL initially injected from any points, these systems can be effectively converted into goal-driven spatial machines (rather than computers as dealing with physical matter too) capable of responding to numerous challenges caused by growing world dynamics in the 21st century. Including numerous practical e...

CLIPS based decision support system for water distribution networks

Directory of Open Access Journals (Sweden)

K. Sandeep

2011-10-01

Full Text Available The difficulty in knowledge representation of a water distribution network (WDN problem has contributed to the limited use of artificial intelligence (AI based expert systems (ES in the management of these networks. This paper presents a design of a Decision Support System (DSS that facilitates "on-demand'' knowledge generation by utilizing results of simulation runs of a suitably calibrated and validated hydraulic model of an existing aged WDN corresponding to emergent or even hypothetical but likely scenarios. The DSS augments the capability of a conventional expert system by integrating together the hydraulic modelling features with heuristics based knowledge of experts under a common, rules based, expert shell named CLIPS (C Language Integrated Production System. In contrast to previous ES, the knowledge base of the DSS has been designed to be dynamic by superimposing CLIPS on Structured Query Language (SQL. The proposed ES has an inbuilt calibration module that enables calibration of an existing (aged WDN for the unknown, and unobservable, Hazen-Williams C-values. In addition, the daily run and simulation modules of the proposed ES further enable the CLIPS inference engine to evaluate the network performance for any emergent or suggested test scenarios. An additional feature of the proposed design is that the DSS integrates computational platforms such as MATLAB, open source Geographical Information System (GIS, and a relational database management system (RDBMS working under the umbrella of the Microsoft Visual Studio based common user interface. The paper also discusses implementation of the proposed framework on a case study and clearly demonstrates the utility of the application as an able aide for effective management of the study network.

Static and dynamic factors in an information-based multi-asset artificial stock market

Science.gov (United States)

Ponta, Linda; Pastore, Stefano; Cincotti, Silvano

2018-02-01

An information-based multi-asset artificial stock market characterized by different types of stocks and populated by heterogeneous agents is presented. In the market, agents trade risky assets in exchange for cash. Beside the amount of cash and of stocks owned, each agent is characterized by sentiments and agents share their sentiments by means of interactions that are determined by sparsely connected networks. A central market maker (clearing house mechanism) determines the price processes for each stock at the intersection of the demand and the supply curves. Single stock price processes exhibit volatility clustering and fat-tailed distribution of returns whereas multivariate price process exhibits both static and dynamic stylized facts, i.e., the presence of static factors and common trends. Static factors are studied making reference to the cross-correlation of returns of different stocks. The common trends are investigated considering the variance-covariance matrix of prices. Results point out that the probability distribution of eigenvalues of the cross-correlation matrix of returns shows the presence of sectors, similar to those observed on real empirical data. As regarding the dynamic factors, the variance-covariance matrix of prices point out a limited number of assets prices series that are independent integrated processes, in close agreement with the empirical evidence of asset price time series of real stock markets. These results remarks the crucial dependence of statistical properties of multi-assets stock market on the agents' interaction structure.

Age distribution dynamics with stochastic jumps in mortality.

Science.gov (United States)

Calabrese, Salvatore; Porporato, Amilcare; Laio, Francesco; D'Odorico, Paolo; Ridolfi, Luca

2017-11-01

While deterministic age distribution models have been extensively studied and applied in various disciplines, little work has been devoted to understanding the role of stochasticity in birth and mortality terms. In this paper, we analyse a stochastic M'Kendrick-von Foerster equation in which jumps in mortality represent intense losses of population due to external events. We present explicit solutions for the probability density functions of the age distribution and the total population and for the temporal dynamics of their moments. We also derive the dynamics of the mean age of the population and its harmonic mean. The framework is then used to calculate the age distribution of salt in the soil root zone, where the accumulation of salt by atmospheric deposition is counteracted by plant uptake and by jump losses due to percolation events.

An Event-Based Approach to Distributed Diagnosis of Continuous Systems

Science.gov (United States)

Daigle, Matthew; Roychoudhurry, Indranil; Biswas, Gautam; Koutsoukos, Xenofon

2010-01-01

Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diagnosers each generate globally correct diagnosis results locally, without a centralized coordinator, and by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.

Pushing the network harder `Dynamic Ratings`

Energy Technology Data Exchange (ETDEWEB)

Liondas, V.; Howatt, C.; Norrie, P. [Prospect Electricity, Blacktown, NSW (Australia)

1995-12-31

The demand for electricity in the area serviced by Prospect Electricity, is increasing, necessitating an increase in power transfer through the distribution system. Satisfying this demand generally requires more electrical infrastructure, but this is becoming less feasible due to economic constraints and environmental considerations. This paper discusses an approach to the dynamic (or real time) rating of different network elements. Dynamic rating is taken to mean that rating which is determined essentially in real time using known temperature constraints for the relevant elements, together with the prevailing ambient or environmental conditions. The purpose of dynamic rating is to achieve greater system utilization, thus allowing significant economic benefits, particularly from deferment of capital expenditure and greater operational flexibility. A number of technologies are being developed to do this for overhead lines, underground cables and transformers. The dynamic rating of cables has proved to be the most intractable part of the dynamic rating project. Work done to date, however, using finite element techniques together with the proposals to further develop point and distributed temperature sensing using fibre optic methods gives some confidence to the future success of this development. (author). 2 tabs., 4 figs., 4 refs.

Dynamic provisioning for community services

CERN Document Server

Qi, Li

2013-01-01

Dynamic Provisioning for Community Services outlines a dynamic provisioning and maintenance mechanism in a running distributed system, e.g. the grid, which can be used to maximize the utilization of computing resources and user demands. The book includes a complete and reliable maintenance system solution for the large-scale distributed system and an interoperation mechanism for the grid middleware deployed in the United States, Europe, and China. The experiments and evaluations have all been practically implemented for ChinaGrid, and the best practices established can help readers to construc

Simple future weather files for estimating heating and cooling demand

DEFF Research Database (Denmark)

Cox, Rimante Andrasiunaite; Drews, Martin; Rode, Carsten

2015-01-01

useful estimates of future energy demand of a building. Experimental results based on both the degree-day method and dynamic simulations suggest that this is indeed the case. Specifically, heating demand estimates were found to be within a few per cent of one another, while estimates of cooling demand...... were slightly more varied. This variation was primarily due to the very few hours of cooling that were required in the region examined. Errors were found to be most likely when the air temperatures were close to the heating or cooling balance points, where the energy demand was modest and even...... relatively large errors might thus result in only modest absolute errors in energy demand....

Distributed model based control of multi unit evaporation systems

International Nuclear Information System (INIS)

Yudi Samyudia

2006-01-01

In this paper, we present a new approach to the analysis and design of distributed control systems for multi-unit plants. The approach is established after treating the effect of recycled dynamics as a gap metric uncertainty from which a distributed controller can be designed sequentially for each unit to tackle the uncertainty. We then use a single effect multi-unit evaporation system to illustrate how the proposed method is used to analyze different control strategies and to systematically achieve a better closed-loop performance using a distributed model-based controller

Molecular dynamics equation designed for realizing arbitrary density: Application to sampling method utilizing the Tsallis generalized distribution

International Nuclear Information System (INIS)

Fukuda, Ikuo; Nakamura, Haruki

2010-01-01

Several molecular dynamics techniques applying the Tsallis generalized distribution are presented. We have developed a deterministic dynamics to generate an arbitrary smooth density function ρ. It creates a measure-preserving flow with respect to the measure ρdω and realizes the density ρ under the assumption of the ergodicity. It can thus be used to investigate physical systems that obey such distribution density. Using this technique, the Tsallis distribution density based on a full energy function form along with the Tsallis index q ≥ 1 can be created. From the fact that an effective support of the Tsallis distribution in the phase space is broad, compared with that of the conventional Boltzmann-Gibbs (BG) distribution, and the fact that the corresponding energy-surface deformation does not change energy minimum points, the dynamics enhances the physical state sampling, in particular for a rugged energy surface spanned by a complicated system. Other feature of the Tsallis distribution is that it provides more degree of the nonlinearity, compared with the case of the BG distribution, in the deterministic dynamics equation, which is very useful to effectively gain the ergodicity of the dynamical system constructed according to the scheme. Combining such methods with the reconstruction technique of the BG distribution, we can obtain the information consistent with the BG ensemble and create the corresponding free energy surface. We demonstrate several sampling results obtained from the systems typical for benchmark tests in MD and from biomolecular systems.

Dynamic multicast routing scheme in WDM optical network

Science.gov (United States)

Zhu, Yonghua; Dong, Zhiling; Yao, Hong; Yang, Jianyong; Liu, Yibin

2007-11-01

During the information era, the Internet and the service of World Wide Web develop rapidly. Therefore, the wider and wider bandwidth is required with the lower and lower cost. The demand of operation turns out to be diversified. Data, images, videos and other special transmission demands share the challenge and opportunity with the service providers. Simultaneously, the electrical equipment has approached their limit. So the optical communication based on the wavelength division multiplexing (WDM) and the optical cross-connects (OXCs) shows great potentials and brilliant future to build an optical network based on the unique technical advantage and multi-wavelength characteristic. In this paper, we propose a multi-layered graph model with inter-path between layers to solve the problem of multicast routing wavelength assignment (RWA) contemporarily by employing an efficient graph theoretic formulation. And at the same time, an efficient dynamic multicast algorithm named Distributed Message Copying Multicast (DMCM) mechanism is also proposed. The multicast tree with minimum hops can be constructed dynamically according to this proposed scheme.

Composition-dependent trap distributions in CdSe and InP quantum dots probed using photoluminescence blinking dynamics.

Science.gov (United States)

Chung, Heejae; Cho, Kyung-Sang; Koh, Weon-Kyu; Kim, Dongho; Kim, Jiwon

2016-07-21

Although Group II-VI quantum dots (QDs) have attracted much attention due to their wide range of applications in QD-based devices, the presence of toxic ions in II-VI QDs raises environmental concerns. To fulfill the demands of nontoxic QDs, synthetic routes for III-V QDs have been developed. However, only a few comparative analyses on optical properties of III-V QDs have been performed. In this study, the composition-related energetic trap distributions have been explored by using three different types of core/multishell QDs: CdSe-CdS (CdSe/CdS/ZnS), InP-ZnSe (InP/ZnSe/ZnS), and InP-GaP (InP/GaP/ZnS). It was shown that CdSe-CdS QDs have much larger trap densities than InP-shell QDs at higher energy states (at least 1Eg (band gap energy) above the lowest conduction band edge) based on probability density plots and Auger ionization efficiencies which are determined by analyses of photoluminescence blinking dynamics. This result suggests that the composition of encapsulated QDs is closely associated with the charge trapping processes, and also provides an insight into the development of more environmentally friendly QD-based devices.

Demand forecast model based on CRM

Science.gov (United States)

Cai, Yuancui; Chen, Lichao

2006-11-01

With interiorizing day by day management thought that regarding customer as the centre, forecasting customer demand becomes more and more important. In the demand forecast of customer relationship management, the traditional forecast methods have very great limitation because much uncertainty of the demand, these all require new modeling to meet the demands of development. In this paper, the notion is that forecasting the demand according to characteristics of the potential customer, then modeling by it. The model first depicts customer adopting uniform multiple indexes. Secondly, the model acquires characteristic customers on the basis of data warehouse and the technology of data mining. The last, there get the most similar characteristic customer by their comparing and forecast the demands of new customer by the most similar characteristic customer.

Scaling architecture-on-demand based optical networks

NARCIS (Netherlands)

Meyer, Hugo; Sancho, Jose Carlos; Mrdakovic, Milica; Peng, Shuping; Simeonidou, Dimitra; Miao, Wang; Calabretta, Nicola

2016-01-01

This paper analyzes methodologies that allow scaling properly Architecture-On-Demand (AoD) based optical networks. As Data Centers and HPC systems are growing in size and complexity, optical networks seem to be the way to scale the bandwidth of current network infrastructures. To scale the number of

Modelling flow dynamics in water distribution networks using ...

African Journals Online (AJOL)

One such approach is the Artificial Neural Networks (ANNs) technique. The advantage of ANNs is that they are robust and can be used to model complex linear and non-linear systems without making implicit assumptions. ANNs can be trained to forecast flow dynamics in a water distribution network. Such flow dynamics ...

Prediction-based Dynamic Energy Management in Wireless Sensor Networks

Science.gov (United States)

Wang, Xue; Ma, Jun-Jie; Wang, Sheng; Bi, Dao-Wei

2007-01-01

Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a target state, which was adopted to awaken wireless sensor nodes so that their sleep time was prolonged. With the distributed computing capability of nodes, an optimization approach of distributed genetic algorithm and simulated annealing was proposed to minimize the energy consumption of measurement. Considering the application of target tracking, we implemented target position prediction, node sleep scheduling and optimal sensing node selection. Moreover, a routing scheme of forwarding nodes was presented to achieve extra energy conservation. Experimental results of target tracking verified that energy-efficiency is enhanced by prediction-based dynamic energy management.

Prediction-based Dynamic Energy Management in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Dao-Wei Bi

2007-03-01

Full Text Available Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a target state, which was adopted to awaken wireless sensor nodes so that their sleep time was prolonged. With the distributed computing capability of nodes, an optimization approach of distributed genetic algorithm and simulated annealing was proposed to minimize the energy consumption of measurement. Considering the application of target tracking, we implemented target position prediction, node sleep scheduling and optimal sensing node selection. Moreover, a routing scheme of forwarding nodes was presented to achieve extra energy conservation. Experimental results of target tracking verified that energy-efficiency is enhanced by prediction-based dynamic energy management.

A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain

Directory of Open Access Journals (Sweden)

Francesca Gagliardi

2017-07-01

Full Text Available This paper proposes a short-term water demand forecasting method based on the use of the Markov chain. This method provides estimates of future demands by calculating probabilities that the future demand value will fall within pre-assigned intervals covering the expected total variability. More specifically, two models based on homogeneous and non-homogeneous Markov chains were developed and presented. These models, together with two benchmark models (based on artificial neural network and naïve methods, were applied to three real-life case studies for the purpose of forecasting the respective water demands from 1 to 24 h ahead. The results obtained show that the model based on a homogeneous Markov chain provides more accurate short-term forecasts than the one based on a non-homogeneous Markov chain, which is in line with the artificial neural network model. Both Markov chain models enable probabilistic information regarding the stochastic demand forecast to be easily obtained.

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-07-26

In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution system operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.

Distribution network topology identification based on synchrophasor

Directory of Open Access Journals (Sweden)

Stefania Conti

2018-03-01

Full Text Available A distribution system upgrade moving towards Smart Grid implementation is necessary to face the proliferation of distributed generators and electric vehicles, in order to satisfy the increasing demand for high quality, efficient, secure, reliable energy supply. This perspective requires taking into account system vulnerability to cyber attacks. An effective attack could destroy stored information about network structure, historical data and so on. Countermeasures and network applications could be made impracticable since most of them are based on the knowledge of network topology. Usually, the location of each link between nodes in a network is known. Therefore, the methods used for topology identification determine if a link is open or closed. When no information on the location of the network links is available, these methods become totally unfeasible. This paper presents a method to identify the network topology using only nodal measures obtained by means of phasor measurement units.

Characterizing single-molecule FRET dynamics with probability distribution analysis.

Science.gov (United States)

Santoso, Yusdi; Torella, Joseph P; Kapanidis, Achillefs N

2010-07-12

Probability distribution analysis (PDA) is a recently developed statistical tool for predicting the shapes of single-molecule fluorescence resonance energy transfer (smFRET) histograms, which allows the identification of single or multiple static molecular species within a single histogram. We used a generalized PDA method to predict the shapes of FRET histograms for molecules interconverting dynamically between multiple states. This method is tested on a series of model systems, including both static DNA fragments and dynamic DNA hairpins. By fitting the shape of this expected distribution to experimental data, the timescale of hairpin conformational fluctuations can be recovered, in good agreement with earlier published results obtained using different techniques. This method is also applied to studying the conformational fluctuations in the unliganded Klenow fragment (KF) of Escherichia coli DNA polymerase I, which allows both confirmation of the consistency of a simple, two-state kinetic model with the observed smFRET distribution of unliganded KF and extraction of a millisecond fluctuation timescale, in good agreement with rates reported elsewhere. We expect this method to be useful in extracting rates from processes exhibiting dynamic FRET, and in hypothesis-testing models of conformational dynamics against experimental data.

Responses of Cloud Type Distributions to the Large-Scale Dynamical Circulation: Water Budget-Related Dynamical Phase Space and Dynamical Regimes

Science.gov (United States)

Wong, Sun; Del Genio, Anthony; Wang, Tao; Kahn, Brian; Fetzer, Eric J.; L'Ecuyer, Tristan S.

2015-01-01

Goals: Water budget-related dynamical phase space; Connect large-scale dynamical conditions to atmospheric water budget (including precipitation); Connect atmospheric water budget to cloud type distributions.

Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-08-23

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operator can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.

Day-Ahead Congestion Management in Distribution Systems through Household Demand Response and Distribution Congestion Prices

DEFF Research Database (Denmark)

Liu, Weijia; Wu, Qiuwei; Wen, Fushuan

2014-01-01

into balancing power might challenge the operation of electric distribution systems and cause congestions. This paper presents a distribution congestion price (DCP) based market mechanism to alleviate possible distribution system congestions. By employing the loca- tional marginal pricing (LMP) model...... is proposed. Finally, a practical Danish 60kV/10.5kV distribution system is employed as the test case to verify the proposed method for mitigating congestion....

A Scalable Distribution Network Risk Evaluation Framework via Symbolic Dynamics

Science.gov (United States)

Yuan, Kai; Liu, Jian; Liu, Kaipei; Tan, Tianyuan

2015-01-01

Background Evaluations of electric power distribution network risks must address the problems of incomplete information and changing dynamics. A risk evaluation framework should be adaptable to a specific situation and an evolving understanding of risk. Methods This study investigates the use of symbolic dynamics to abstract raw data. After introducing symbolic dynamics operators, Kolmogorov-Sinai entropy and Kullback-Leibler relative entropy are used to quantitatively evaluate relationships between risk sub-factors and main factors. For layered risk indicators, where the factors are categorized into four main factors – device, structure, load and special operation – a merging algorithm using operators to calculate the risk factors is discussed. Finally, an example from the Sanya Power Company is given to demonstrate the feasibility of the proposed method. Conclusion Distribution networks are exposed and can be affected by many things. The topology and the operating mode of a distribution network are dynamic, so the faults and their consequences are probabilistic. PMID:25789859

Delivery of video-on-demand services using local storages within passive optical networks.

Science.gov (United States)

Abeywickrama, Sandu; Wong, Elaine

2013-01-28

At present, distributed storage systems have been widely studied to alleviate Internet traffic build-up caused by high-bandwidth, on-demand applications. Distributed storage arrays located locally within the passive optical network were previously proposed to deliver Video-on-Demand services. As an added feature, a popularity-aware caching algorithm was also proposed to dynamically maintain the most popular videos in the storage arrays of such local storages. In this paper, we present a new dynamic bandwidth allocation algorithm to improve Video-on-Demand services over passive optical networks using local storages. The algorithm exploits the use of standard control packets to reduce the time taken for the initial request communication between the customer and the central office, and to maintain the set of popular movies in the local storage. We conduct packet level simulations to perform a comparative analysis of the Quality-of-Service attributes between two passive optical networks, namely the conventional passive optical network and one that is equipped with a local storage. Results from our analysis highlight that strategic placement of a local storage inside the network enables the services to be delivered with improved Quality-of-Service to the customer. We further formulate power consumption models of both architectures to examine the trade-off between enhanced Quality-of-Service performance versus the increased power requirement from implementing a local storage within the network.

A Masters Programme in Telecommunications Management--Demand-Based Curriculum Design

Science.gov (United States)

Gharaibeh, Khaled M.; Kaylani, Hazem; Murphy, Noel; Brennan, Conor; Itradat, Awni; Al-Bataineh, Mohammed; Aloqlah, Mohammed; Salhieh, Loay; Altarazi, Safwan; Rawashdeh, Nathir; del Carmen Bas Cerdá, María; Conchado Peiró, Andrea; Al-Zoubi, Asem; Harb, Bassam; Bany Salameh, Haythem

2015-01-01

This paper presents a curriculum design approach for a Masters Programme in Telecommunications Management based on demand data obtained from surveying the needs of potential students of the proposed programme. Through online surveys disseminated at telecom companies in Jordan, it was possible to measure the demand for such a programme and to…

Dynamics of assembly production flow

Science.gov (United States)

Ezaki, Takahiro; Yanagisawa, Daichi; Nishinari, Katsuhiro

2015-06-01

Despite recent developments in management theory, maintaining a manufacturing schedule remains difficult because of production delays and fluctuations in demand and supply of materials. The response of manufacturing systems to such disruptions to dynamic behavior has been rarely studied. To capture these responses, we investigate a process that models the assembly of parts into end products. The complete assembly process is represented by a directed tree, where the smallest parts are injected at leaves and the end products are removed at the root. A discrete assembly process, represented by a node on the network, integrates parts, which are then sent to the next downstream node as a single part. The model exhibits some intriguing phenomena, including overstock cascade, phase transition in terms of demand and supply fluctuations, nonmonotonic distribution of stockout in the network, and the formation of a stockout path and stockout chains. Surprisingly, these rich phenomena result from only the nature of distributed assembly processes. From a physical perspective, these phenomena provide insight into delay dynamics and inventory distributions in large-scale manufacturing systems.

Temperature Effect on Energy Demand

Energy Technology Data Exchange (ETDEWEB)

Kim, Young Duk [Korea Energy Economics Institute, Euiwang (Korea)

1999-03-01

We provide various estimates of temperature effect for accommodating seasonality in energy demand, particularly natural gas demand. We exploit temperature response and monthly temperature distribution to estimate the temperature effect on natural gas demand. Both local and global smoothed temperature responses are estimated from empirical relationship between hourly temperature and hourly energy consumption data during the sample period (1990 - 1996). Monthly temperature distribution estimates are obtained by kernel density estimation from temperature dispersion within a month. We integrate temperature response and monthly temperature density over all the temperatures in the sample period to estimate temperature effect on energy demand. Then, estimates of temperature effect are compared between global and local smoothing methods. (author). 15 refs., 14 figs., 2 tabs.

World energy use in 2010: over 5% growth. Energy markets have combined crisis recovery and strong industry dynamism. Enerdata analyses the trends in energy demand, based on its 2010 data for G20 countries. May 5, 2011

International Nuclear Information System (INIS)

2011-01-01

Energy markets have combined crisis recovery and strong industry dynamism. Analysis of the energy consumption in 2010 of major countries by Enerdata, based on our global energy database. Energy consumption in the G20 soared by more than 5% in 2010, after the slight decrease of 2009. This strong increase is the result of two converging trends. On the one-hand, industrialized countries, which experienced sharp decreases in energy demand in 2009, recovered firmly in 2010, almost coming back to historical trends. Oil, gas, coal, and electricity markets followed the same trend. On the other hand, China and India, which showed no signs of slowing down in 2009, continued their intense demand for all forms of energy. (authors)

DNA breathing dynamics: analytic results for distribution functions of relevant Brownian functionals.

Science.gov (United States)

Bandyopadhyay, Malay; Gupta, Shamik; Segal, Dvira

2011-03-01

We investigate DNA breathing dynamics by suggesting and examining several Brownian functionals associated with bubble lifetime and reactivity. Bubble dynamics is described as an overdamped random walk in the number of broken base pairs. The walk takes place on the Poland-Scheraga free-energy landscape. We suggest several probability distribution functions that characterize the breathing process, and adopt the recently studied backward Fokker-Planck method and the path decomposition method as elegant and flexible tools for deriving these distributions. In particular, for a bubble of an initial size x₀, we derive analytical expressions for (i) the distribution P(t{f}|x₀) of the first-passage time t{f}, characterizing the bubble lifetime, (ii) the distribution P(A|x₀) of the area A until the first-passage time, providing information about the effective reactivity of the bubble to processes within the DNA, (iii) the distribution P(M) of the maximum bubble size M attained before the first-passage time, and (iv) the joint probability distribution P(M,t{m}) of the maximum bubble size M and the time t{m} of its occurrence before the first-passage time. These distributions are analyzed in the limit of small and large bubble sizes. We supplement our analytical predictions with direct numericalsimulations of the related Langevin equation, and obtain a very good agreement in the appropriate limits. The nontrivial scaling behavior of the various quantities analyzed here can, in principle, be explored experimentally.

OECD (Organization of Economic Cooperation and Development) oil demand

International Nuclear Information System (INIS)

Huntington, H.G.

1993-01-01

Econometric response surfaces for nine different world oil models are estimated for aggregate oil demand with in the developed countries of the Organization of Economic Cooperation and Development (OECD). The estimates are based upon scenario results reported for the 1989-2010 period in a recent model comparison study. The response surface approach provides a parsimonious summary of model responses. It enables one to estimate long-run price elasticities directly rather than to infer such responses from 20-year cross-scenario results. It also shows more directly the significant effect of initial demand conditions (in 1988) on future oil demand growth. Due to the dynamic nature of the oil demand response, past prices exert a strongly positive effect on future oil demands in some models, but little or even negative effect in other models. On the basis of this finding, we urge demand modellers to be much more explicit about what their systems reveal about the extent of disequilibrium embedded in their model's starting oil demand conditions. (author)

Automotive fuel consumption in Brazil. Applying static and dynamic systems of demand equations

Energy Technology Data Exchange (ETDEWEB)

Iootty, Mariana [IE-UFRJ (Institute of Economics - Federal University of Rio de Janeiro), Energy Economics Group (Brazil); UFRRJ (Federal Rural University of Rio de Janeiro) (Brazil); Pinto, Helder Jr. [IE-UFRJ (Institute of Economics - Federal University of Rio de Janeiro), Energy Economics Group (Brazil); Ebeling, Francisco [Brazilian Petroleum Institute (Brazil)

2009-12-15

This paper aims to investigate and explain the performance of the Brazilian demand for automotive fuels in the period 1970-2005. It estimates the price and income elasticities for all the available fuels in the automotive sector in the country: gasoline, compressed natural gas (CNG), ethanol and diesel. The analysis of the expenditure allocation process among these fuels is carried out through the estimation of a linear approximation of an Almost Ideal Demand System (AIDS) model. Two estimation methods were implemented: the static (through a seemingly unrelated regression) and a dynamic (through a vector error correction model). Specification tests support the use of the latter. The empirical analysis suggests a high substitutability between gasoline and ethanol; being this relation higher than the one observed between gasoline and CNG. The study shows that gasoline, ethanol and diesel are normal goods, and with the exception of ethanol, they are expenditure elastic. CNG was estimated as an inferior good. (author)

Automotive fuel consumption in Brazil. Applying static and dynamic systems of demand equations

International Nuclear Information System (INIS)

Iootty, Mariana; Pinto, Helder Jr.; Ebeling, Francisco

2009-01-01

This paper aims to investigate and explain the performance of the Brazilian demand for automotive fuels in the period 1970-2005. It estimates the price and income elasticities for all the available fuels in the automotive sector in the country: gasoline, compressed natural gas (CNG), ethanol and diesel. The analysis of the expenditure allocation process among these fuels is carried out through the estimation of a linear approximation of an Almost Ideal Demand System (AIDS) model. Two estimation methods were implemented: the static (through a seemingly unrelated regression) and a dynamic (through a vector error correction model). Specification tests support the use of the latter. The empirical analysis suggests a high substitutability between gasoline and ethanol; being this relation higher than the one observed between gasoline and CNG. The study shows that gasoline, ethanol and diesel are normal goods, and with the exception of ethanol, they are expenditure elastic. CNG was estimated as an inferior good. (author)

Demand side management program evaluation based on industrial and commercial field data

International Nuclear Information System (INIS)

Eissa, M.M.

2011-01-01

Demand Response is increasingly viewed as an important tool for use by the electric utility industry in meeting the growing demand for electricity. There are two basic categories of demand response options: time varying retail tariffs and incentive Demand Response Programs. is applying the time varying retail tariffs program, which is not suitable according to the studied load curves captured from the industrial and commercial sectors. Different statistical studies on daily load curves for consumers connected to 22 kV lines are classified. The load curve criteria used for classification is based on peak ratio and night ratio. The data considered here is a set of 120 annual load curves corresponding to the electric power consumption (the western area in the King Saudi Arabia (KSA)) of many clients in winter and some months in the summer (peak period). The study is based on real data from several Saudi customer sectors in many geographical areas with larger commercial and industrial customers. The study proved that the suitable Demand Response for the ESC is the incentive program. - Highlights: → Study helps in selecting the proper demand side program. → A credit will be given for the customers during summer months. → Reduction in the electric bill. → Monthly bill credit is decreased based on customers' peak load reduction. → Guide for applying the proper demand side program suitable for the utility and customers.

Multiagent Based Distributed Control for State-of-Charge Balance of Distributed Energy Storage in DC microgrids

DEFF Research Database (Denmark)

Li, Chendan; Dragicevic, Tomislav; Garcia Plaza, Manuel

2014-01-01

In this paper, a distributed multiagent based algorithm is proposed to achieve SoC balance for DES in the DC microgrid by means of voltage scheduling. Reference voltage given is adjusted instead of droop gain. Dynamic average consensus algorithm is explored in each agent to get the required...

Base Stock Policy in a Join-Type Production Line with Advanced Demand Information

Science.gov (United States)

Hiraiwa, Mikihiko; Tsubouchi, Satoshi; Nakade, Koichi

Production control such as the base stock policy, the kanban policy and the constant work-in-process policy in a serial production line has been studied by many researchers. Production lines, however, usually have fork-type, join-type or network-type figures. In addition, in most previous studies on production control, a finished product is required at the same time as arrival of demand at the system. Demand information is, however, informed before due date in practice. In this paper a join-type (assembly) production line under base stock control with advanced demand information in discrete time is analyzed. The recursive equations for the work-in-process are derived. The heuristic algorithm for finding appropriate base stock levels of all machines at short time is proposed and the effect of advanced demand information is examined by simulation with the proposed algorithm. It is shown that the inventory cost can decreases with little backlogs by using the appropriate amount of demand information and setting appropriate base stock levels.

Age-Related Changes in Dynamic Postural Control and Attentional Demands are Minimally Affected by Local Muscle Fatigue

Science.gov (United States)

Remaud, Anthony; Thuong-Cong, Cécile; Bilodeau, Martin

2016-01-01

Normal aging results in alterations in the visual, vestibular and somtaosensory systems, which in turn modify the control of balance. Muscle fatigue may exacerbate these age-related changes in sensory and motor functions, and also increase the attentional demands associated with dynamic postural control. The purpose of this study was to investigate the effect of aging on dynamic postural control and posture-related attentional demands before and after a plantar flexor fatigue protocol. Participants (young adults: n = 15; healthy seniors: n = 13) performed a dynamic postural task along the antero-posterior (AP) and the medio-lateral (ML) axes, with and without the addition of a simple reaction time (RT) task. The dynamic postural task consisted in following a moving circle on a computer screen with the representation of the center of pressure (COP). This protocol was repeated before and after a fatigue task where ankle plantar flexor muscles were targeted. The mean COP-target distance and the mean COP velocity were calculated for each trial. Cross-correlation analyses between the COP and target displacements were also performed. RTs were recorded during dual-task trials. Results showed that while young adults adopted an anticipatory control mode to move their COP as close as possible to the target center, seniors adopted a reactive control mode, lagging behind the target center. This resulted in longer COP-target distance and higher COP velocity in the latter group. Concurrently, RT increased more in seniors when switching from static stance to dynamic postural conditions, suggesting potential alterations in the central nervous system (CNS) functions. Finally, plantar flexor muscle fatigue and dual-tasking had only minor effects on dynamic postural control of both young adults and seniors. Future studies should investigate why the fatigue-induced changes in quiet standing postural control do not seem to transfer to dynamic balance tasks. PMID:26834626

Towards understanding the robustness of energy distribution networks based on macroscopic and microscopic evaluations

International Nuclear Information System (INIS)

Liu Jiming; Shi Benyun

2012-01-01

Supply disruptions on one node of a distribution network may spread to other nodes, and potentially bring various social and economic impacts. To understand the performance of a distribution network in the face of supply disruptions, it would be helpful for policy makers to quantitatively evaluate the robustness of the network, i.e., its ability of maintaining a supply–demand balance on individual nodes. In this paper, we first define a notion of network entropy to macroscopically characterize distribution robustness with respect to the dynamics of energy flows. Further, we look into how microscopic evaluation based on a failure spreading model helps us determine the extent to which disruptions on one node may affect the others. We take the natural gas distribution network in the USA as an example to demonstrate the introduced concepts and methods. Specifically, the proposed macroscopic and microscopic evaluations provide us a means of precisely identifying transmission bottlenecks in the U.S. interstate pipeline network, ranking the effects of supply disruptions on individual nodes, and planning geographically advantageous locations for natural gas storage. These findings can offer policy makers, planners, and network managers with further insights into emergency planning as well as possible design improvement. - Highlights: ► This paper evaluates distribution robustness by defining a notion of network entropy. ► The disruption impacts on individual node are evaluated by a failure spreading model. ► The robustness of the U.S. natural gas distribution network is studied. ► Results reveal pipeline bottlenecks, the node rank, and potential storage locations. ► Possible strategies for mitigating the impacts of supply disruptions are discussed.

2015 California Demand Response Potential Study - Charting California’s Demand Response Future. Interim Report on Phase 1 Results

Energy Technology Data Exchange (ETDEWEB)

Alstone, Peter; Potter, Jennifer; Piette, Mary Ann; Schwartz, Peter; Berger, Michael A.; Dunn, Laurel N.; Smith, Sarah J.; Sohn, Michael D.; Aghajanzadeh, Arian; Stensson, Sofia; Szinai, Julia

2016-04-01

Demand response (DR) is an important resource for keeping the electricity grid stable and efficient; deferring upgrades to generation, transmission, and distribution systems; and providing other customer economic benefits. This study estimates the potential size and cost of the available DR resource for California’s three investor-owned utilities (IOUs), as the California Public Utilities Commission (CPUC) evaluates how to enhance the role of DR in meeting California’s resource planning needs and operational requirements. As the state forges a clean energy future, the contributions of wind and solar electricity from centralized and distributed generation will fundamentally change the power grid’s operational dynamics. This transition requires careful planning to ensure sufficient capacity is available with the right characteristics – flexibility and fast response – to meet reliability needs. Illustrated is a snapshot of how net load (the difference between demand and intermittent renewables) is expected to shift. Increasing contributions from renewable generation introduces steeper ramps and a shift, into the evening, of the hours that drive capacity needs. These hours of peak capacity need are indicated by the black dots on the plots. Ultimately this study quantifies the ability and the cost of using DR resources to help meet the capacity need at these forecasted critical hours in the state.

Market modeling for assessment of demand side programs using the marginal cost

International Nuclear Information System (INIS)

Papastamatiou, Panagiotis; Psarras, John

2000-01-01

Demand side management is nowadays considered as a functional step in the energy planning process. The criteria proposed for the assessment of the demand side programs (DSPs) are usually based on the balance between the marginal supply cost and the mean DSP cost. These criteria could not support the allotting of the invested capital to incentives for the consumers and advertising. This paper presents a methodology to support the utility planning at this point with more reliability. It proposes the expansion of the assessment criteria with the use of the marginal cost of the DSP. For the calculation of the DSP marginal cost, a dynamic model is developed and it is used for the simulation of the penetration of a DS Program. Using the 'least-cost' criterion as the decision rule for the simulation, the planner has a distribution of the available investment capital throughout the whole planning period. The use of the 'most-value' criterion supports the separation of the invested capital between incentives for the consumers and supportive expenses, e.g. advertising, marketing cost, etc. (Author)

2025 California Demand Response Potential Study - Charting California’s Demand Response Future. Final Report on Phase 2 Results

Energy Technology Data Exchange (ETDEWEB)

Alstone, Peter [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Potter, Jennifer [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Piette, Mary Ann [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Schwartz, Peter [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Berger, Michael A. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Dunn, Laurel N. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Smith, Sarah J. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sohn, Michael D. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Aghajanzadeh, Aruab [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Stensson, Sofia [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Szinai, Julie [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Walter, Travis [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); McKenzie, Lucy [Energy and Environmental Economics, Inc. (E3), San Francisco, CA (United States); Lavin, Luke [Energy and Environmental Economics, Inc. (E3), San Francisco, CA (United States); Schneiderman, Brendan [Energy and Environmental Economics, Inc. (E3), San Francisco, CA (United States); Mileva, Ana [Energy and Environmental Economics, Inc. (E3), San Francisco, CA (United States); Cutter, Eric [Energy and Environmental Economics, Inc. (E3), San Francisco, CA (United States); Olson, Arne [Energy and Environmental Economics, Inc. (E3), San Francisco, CA (United States); Bode, Josh [Nexant, Inc., Nashville, TN (United States); Ciccone, Adriana [Nexant, Inc., Nashville, TN (United States); Jain, Ankit [Nexant, Inc., Nashville, TN (United States)

2017-03-01

California’s legislative and regulatory goals for renewable energy are changing the power grid’s dynamics. Increased variable generation resource penetration connected to the bulk power system, as well as, distributed energy resources (DERs) connected to the distribution system affect the grid’s reliable operation over many different time scales (e.g., days to hours to minutes to seconds). As the state continues this transition, it will require careful planning to ensure resources with the right characteristics are available to meet changing grid management needs. Demand response (DR) has the potential to provide important resources for keeping the electricity grid stable and efficient, to defer upgrades to generation, transmission and distribution systems, and to deliver customer economic benefits. This study estimates the potential size and cost of future DR resources for California’s three investor-owned utilities (IOUs): Pacific Gas and Electric Company (PG&E), Southern California Edison Company (SCE), and San Diego Gas & Electric Company (SDG&E). Our goal is to provide data-driven insights as the California Public Utilities Commission (CPUC) evaluates how to enhance DR’s role in meeting California’s resource planning needs and operational requirements. We address two fundamental questions: 1. What cost-competitive DR service types will meet California’s future grid needs as it moves towards clean energy and advanced infrastructure? 2. What is the size and cost of the expected resource base for the DR service types?

Alcohol demand and risk preference.

Science.gov (United States)

Dave, Dhaval; Saffer, Henry

2008-12-01

Both economists and psychologists have studied the concept of risk preference. Economists categorize individuals as more or less risk-tolerant based on the marginal utility of income. Psychologists categorize individuals' propensity towards risk based on harm avoidance, novelty seeking and reward dependence traits. The two concepts of risk are related, although the instruments used for empirical measurement are quite different. Psychologists have found risk preference to be an important determinant of alcohol consumption; however economists have not included risk preference in studies of alcohol demand. This is the first study to examine the effect of risk preference on alcohol consumption in the context of a demand function. The specifications employ multiple waves from the Panel Study of Income Dynamics (PSID) and the Health and Retirement Study (HRS), which permit the estimation of age-specific models based on nationally representative samples. Both of these data sets include a unique and consistent survey instrument designed to directly measure risk preference in accordance with the economist's definition. This study estimates the direct impact of risk preference on alcohol demand and also explores how risk preference affects the price elasticity of demand. The empirical results indicate that risk preference has a significant negative effect on alcohol consumption, with the prevalence and consumption among risk-tolerant individuals being 6-8% higher. Furthermore, the tax elasticity is similar across both risk-averse and risk-tolerant individuals. This suggests that tax policies are as equally effective in deterring alcohol consumption among those who have a higher versus a lower propensity for alcohol use.

The effects of moderate fatigue on dynamic balance control and attentional demands

Directory of Open Access Journals (Sweden)

Teasdale Normand

2006-09-01

Full Text Available Abstract Background During daily activities, the active control of balance often is a task per se (for example, when standing in a moving bus. Other constraints like fatigue can add to the complexity of this balance task. In the present experiment, we examined how moderate fatigue induced by fast walking on a treadmill challenged dynamic balance control. We also examined if the attentional demands for performing the balance task varied with fatigue. Methods Subjects (n = 10 performed simultaneously a dynamic balance control task and a probe reaction time task (RT (serving as an indicator of attentional demands before and after three periods of moderate fatigue (fast walking on a treadmill. For the balance control task, the real-time displacement of the centre of pressure (CP was provided on a monitor placed in front of the subject, at eye level. Subjects were asked to keep their CP within a target (moving box moving upward and downward on the monitor. The tracking performance was measured (time spent outside the moving box and the CP behavior analyzed (mean CP speed and mean frequency of the CP velocity. Results Moderate fatigue led to an immediate decrement of the performance on the balance control task; increase of the percentage of time spent outside the box and increase of the mean CP speed. Across the three fatigue periods, subjects improved their tracking performance and reduced their mean CP speed. This was achieved by increasing their frequency of actions; mean frequency of the CP velocity were higher for the fatigue periods than for the no fatigue periods. Fatigue also induced an increase in the attentional demands suggesting that more cognitive resources had to be allocated to the balance task with than without fatigue. Conclusion Fatigue induced by fast walking had an initial negative impact on the control of balance. Nonetheless, subjects were able to compensate the effect of the moderate fatigue by increasing the frequency of

Coal demand prediction based on a support vector machine model

Energy Technology Data Exchange (ETDEWEB)

Jia, Cun-liang; Wu, Hai-shan; Gong, Dun-wei [China University of Mining & Technology, Xuzhou (China). School of Information and Electronic Engineering

2007-01-15

A forecasting model for coal demand of China using a support vector regression was constructed. With the selected embedding dimension, the output vectors and input vectors were constructed based on the coal demand of China from 1980 to 2002. After compared with lineal kernel and Sigmoid kernel, a radial basis function(RBF) was adopted as the kernel function. By analyzing the relationship between the error margin of prediction and the model parameters, the proper parameters were chosen. The support vector machines (SVM) model with multi-input and single output was proposed. Compared the predictor based on RBF neural networks with test datasets, the results show that the SVM predictor has higher precision and greater generalization ability. In the end, the coal demand from 2003 to 2006 is accurately forecasted. l0 refs., 2 figs., 4 tabs.

Demand-side management and demand response in the Ontario energy sectors

International Nuclear Information System (INIS)

2004-01-01

A directive from the former Minister of Energy was received by the Ontario Energy Board (OEB), directing the Board to consult with stakeholders on options for the delivery of demand-side management (DSM) and demand response (DR) activities within the electricity sector, including the role of local distribution companies in such activities. The implementation costs were to be balanced with the benefits to both consumers and the entire system. The scope of the review was expanded by the Board to include the role of gas distribution companies in DSM. A consultation process was implemented and stakeholders were invited to participate. A series of recommendations was made, including: (1) a hybrid framework utilizing market-based and public-policy approaches should deliver DSM and DR activities in Ontario's energy markets, (2) DSM and DR activities should come under the responsibility of a central agency, (3) DSM and DR activities should be coordinated through cooperation between the Ministry of Energy, the Independent Electricity Market Operator (IMO) and the Ontario Energy Board, (4) regulatory mechanisms to induce gas distributors, electricity transmitters and electricity distributors to reduce distribution system losses should be put in place, (5) all electricity consumers should fund electricity DSM and some retail DR initiatives through a transparent, non-bypassable consumption charge, and (6) the Board should design, develop and deliver information to consumers regarding energy conservation, energy efficiency, load management, and cleaner sources of energy. refs., 4 figs

Sensor-based demand controlled ventilation

Energy Technology Data Exchange (ETDEWEB)

De Almeida, A.T. [Universidade de Coimbra (Portugal). Dep. Eng. Electrotecnica; Fisk, W.J. [Lawrence Berkeley National Lab., CA (United States)

1997-07-01

In most buildings, occupancy and indoor pollutant emission rates vary with time. With sensor-based demand-controlled ventilation (SBDCV), the rate of ventilation (i.e., rate of outside air supply) also varies with time to compensate for the changes in pollutant generation. In other words, SBDCV involves the application of sensing, feedback and control to modulate ventilation. Compared to ventilation without feedback, SBDCV offers two potential advantages: (1) better control of indoor pollutant concentrations; and (2) lower energy use and peak energy demand. SBDCV has the potential to improve indoor air quality by increasing the rate of ventilation when indoor pollutant generation rates are high and occupants are present. SBDCV can also save energy by decreasing the rate of ventilation when indoor pollutant generation rates are low or occupants are absent. After providing background information on indoor air quality and ventilation, this report provides a relatively comprehensive discussion of SBDCV. Topics covered in the report include basic principles of SBDCV, sensor technologies, technologies for controlling air flow rates, case studies of SBDCV, application of SBDCV to laboratory buildings, and research needs. SBDCV appears to be an increasingly attractive technology option. Based on the review of literature and theoretical considerations, the application of SBDCV has the potential to be cost-effective in applications with the following characteristics: (a) a single or small number of dominant pollutants, so that ventilation sufficient to control the concentration of the dominant pollutants provides effective control of all other pollutants; (b) large buildings or rooms with unpredictable temporally variable occupancy or pollutant emission; and (c) climates with high heating or cooling loads or locations with expensive energy.

Dynamic models for distributed generation resources

Energy Technology Data Exchange (ETDEWEB)

Morched, A.S. [BPR Energie, Sherbrooke, PQ (Canada)

2010-07-01

Distributed resources can impact the performance of host power systems during both normal and abnormal system conditions. This PowerPoint presentation discussed the use of dynamic models for identifying potential interaction problems between interconnected systems. The models were designed to simulate steady state behaviour as well as transient responses to system disturbances. The distributed generators included directly coupled and electronically coupled generators. The directly coupled generator was driven by wind turbines. Simplified models of grid-side inverters, electronically coupled wind generators and doubly-fed induction generators (DFIGs) were presented. The responses of DFIGs to wind variations were evaluated. Synchronous machine and electronically coupled generator responses were compared. The system model components included load models, generators, protection systems, and system equivalents. Frequency responses to islanding events were reviewed. The study demonstrated that accurate simulations are needed to predict the impact of distributed generation resources on the performance of host systems. Advances in distributed generation technology have outpaced the development of models needed for integration studies. tabs., figs.

Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices.

Science.gov (United States)

Liu, Wei; Kulin, Merima; Kazaz, Tarik; Shahid, Adnan; Moerman, Ingrid; De Poorter, Eli

2017-09-12

Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant. Identifying concurrent technologies is an important step towards efficient spectrum sharing. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals' modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI's probability distribution. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. Experimental evaluation indicates that more than 92% accuracy is achieved with the appropriate configuration. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access.

Distributed and dynamic intracellular organization of extracellular information.

Science.gov (United States)

Granados, Alejandro A; Pietsch, Julian M J; Cepeda-Humerez, Sarah A; Farquhar, Iseabail L; Tkačik, Gašper; Swain, Peter S

2018-06-05

Although cells respond specifically to environments, how environmental identity is encoded intracellularly is not understood. Here, we study this organization of information in budding yeast by estimating the mutual information between environmental transitions and the dynamics of nuclear translocation for 10 transcription factors. Our method of estimation is general, scalable, and based on decoding from single cells. The dynamics of the transcription factors are necessary to encode the highest amounts of extracellular information, and we show that information is transduced through two channels: Generalists (Msn2/4, Tod6 and Dot6, Maf1, and Sfp1) can encode the nature of multiple stresses, but only if stress is high; specialists (Hog1, Yap1, and Mig1/2) encode one particular stress, but do so more quickly and for a wider range of magnitudes. In particular, Dot6 encodes almost as much information as Msn2, the master regulator of the environmental stress response. Each transcription factor reports differently, and it is only their collective behavior that distinguishes between multiple environmental states. Changes in the dynamics of the localization of transcription factors thus constitute a precise, distributed internal representation of extracellular change. We predict that such multidimensional representations are common in cellular decision-making.

Dynamics of Biofilm Regrowth in Drinking Water Distribution Systems.

Science.gov (United States)

Douterelo, I; Husband, S; Loza, V; Boxall, J

2016-07-15

The majority of biomass within water distribution systems is in the form of attached biofilm. This is known to be central to drinking water quality degradation following treatment, yet little understanding of the dynamics of these highly heterogeneous communities exists. This paper presents original information on such dynamics, with findings demonstrating patterns of material accumulation, seasonality, and influential factors. Rigorous flushing operations repeated over a 1-year period on an operational chlorinated system in the United Kingdom are presented here. Intensive monitoring and sampling were undertaken, including time-series turbidity and detailed microbial analysis using 16S rRNA Illumina MiSeq sequencing. The results show that bacterial dynamics were influenced by differences in the supplied water and by the material remaining attached to the pipe wall following flushing. Turbidity, metals, and phosphate were the main factors correlated with the distribution of bacteria in the samples. Coupled with the lack of inhibition of biofilm development due to residual chlorine, this suggests that limiting inorganic nutrients, rather than organic carbon, might be a viable component in treatment strategies to manage biofilms. The research also showed that repeat flushing exerted beneficial selective pressure, giving another reason for flushing being a viable advantageous biofilm management option. This work advances our understanding of microbiological processes in drinking water distribution systems and helps inform strategies to optimize asset performance. This research provides novel information regarding the dynamics of biofilm formation in real drinking water distribution systems made of different materials. This new knowledge on microbiological process in water supply systems can be used to optimize the performance of the distribution network and to guarantee safe and good-quality drinking water to consumers. Copyright © 2016 Douterelo et al.

An Airline-Based Multilevel Analysis of Airfare Elasticity for Passenger Demand

Science.gov (United States)

Castelli, Lorenzo; Ukovich, Walter; Pesenti, Raffaele

2003-01-01

Price elasticity of passenger demand for a specific airline is estimated. The main drivers affecting passenger demand for air transportation are identified. First, an Ordinary Least Squares regression analysis is performed. Then, a multilevel analysis-based methodology to investigate the pattern of variation of price elasticity of demand among the various routes of the airline under study is proposed. The experienced daily passenger demands on each fare-class are grouped for each considered route. 9 routes were studied for the months of February and May in years from 1999 to 2002, and two fare-classes were defined (business and economy). The analysis has revealed that the airfare elasticity of passenger demand significantly varies among the different routes of the airline.

Modeling and forecasting of electrical power demands for capacity planning

Energy Technology Data Exchange (ETDEWEB)

Al-Shobaki, Salman [Department of Industrial Engineering, Hashemite University, Zarka 13115 (Jordan); Mohsen, Mousa [Department of Mechanical Engineering, Hashemite University, Zarka 13115 (Jordan)

2008-11-15

This paper describes the development of forecasting models to predict future generation and electrical power consumption in Jordan. This is critical to production cost since power is generated by burning expensive imported oil. Currently, the National Electric Power Company (NEPCO) is using regression models that only accounts for trend dynamics in their planning of loads and demand levels. The models are simplistic and are based on generated energy historical levels. They produce results on yearly bases and do not account for monthly variability in demand levels. The paper presents two models, one based on the generated energy data and the other is based on the consumed energy data. The models account for trend, monthly seasonality, and cycle dynamics. Both models are compared to NEPCO's model and indicate that NEPCO is producing energy at levels higher than needed (5.25%) thus increasing the loss in generated energy. The developed models also show a 13% difference between the generated energy and the consumed energy that is lost due to transmission line and in-house consumption. (author)

Modeling and forecasting of electrical power demands for capacity planning

International Nuclear Information System (INIS)

Al-Shobaki, Salman; Mohsen, Mousa

2008-01-01

This paper describes the development of forecasting models to predict future generation and electrical power consumption in Jordan. This is critical to production cost since power is generated by burning expensive imported oil. Currently, the National Electric Power Company (NEPCO) is using regression models that only accounts for trend dynamics in their planning of loads and demand levels. The models are simplistic and are based on generated energy historical levels. They produce results on yearly bases and do not account for monthly variability in demand levels. The paper presents two models, one based on the generated energy data and the other is based on the consumed energy data. The models account for trend, monthly seasonality, and cycle dynamics. Both models are compared to NEPCO's model and indicate that NEPCO is producing energy at levels higher than needed (5.25%) thus increasing the loss in generated energy. The developed models also show a 13% difference between the generated energy and the consumed energy that is lost due to transmission line and in-house consumption

CUMULVS: Collaborative infrastructure for developing distributed simulations

Energy Technology Data Exchange (ETDEWEB)

Kohl, J.A.; Papadopoulos, P.M.; Geist, G.A. II

1997-03-01

The CUMULVS software environment provides remote collaboration among scientists by allowing them to dynamically attach to, view, and steer a running simulation. Users can interactively examine intermediate results on demand, saving effort for long-running applications gone awry. In addition, it provides fault tolerance to distributed applications via user-directed checkpointing, heterogeneous task migration and automatic restart. This talk describes CUMULVS and how this tool benefits scientists developing large distributed applications.

The Dynamics of Wealth Inequality and the Effect of Income Distribution.

Science.gov (United States)

Berman, Yonatan; Ben-Jacob, Eshel; Shapira, Yoash

2016-01-01

The rapid increase of wealth inequality in the past few decades is one of the most disturbing social and economic issues of our time. Studying its origin and underlying mechanisms is essential for policy aiming to control and even reverse this trend. In that context, controlling the distribution of income, using income tax or other macroeconomic policy instruments, is generally perceived as effective for regulating the wealth distribution. We provide a theoretical tool, based on the realistic modeling of wealth inequality dynamics, to describe the effects of personal savings and income distribution on wealth inequality. Our theoretical approach incorporates coupled equations, solved using iterated maps to model the dynamics of wealth and income inequality. Notably, using the appropriate historical parameter values we were able to capture the historical dynamics of wealth inequality in the United States during the course of the 20th century. It is found that the effect of personal savings on wealth inequality is substantial, and its major decrease in the past 30 years can be associated with the current wealth inequality surge. In addition, the effect of increasing income tax, though naturally contributing to lowering income inequality, might contribute to a mild increase in wealth inequality and vice versa. Plausible changes in income tax are found to have an insignificant effect on wealth inequality, in practice. In addition, controlling the income inequality, by progressive taxation, for example, is found to have a very small effect on wealth inequality in the short run. The results imply, therefore, that controlling income inequality is an impractical tool for regulating wealth inequality.

Distributed optimization-based control of multi-agent networks in complex environments

CERN Document Server

Zhu, Minghui

2015-01-01

This book offers a concise and in-depth exposition of specific algorithmic solutions for distributed optimization based control of multi-agent networks and their performance analysis. It synthesizes and analyzes distributed strategies for three collaborative tasks: distributed cooperative optimization, mobile sensor deployment and multi-vehicle formation control. The book integrates miscellaneous ideas and tools from dynamic systems, control theory, graph theory, optimization, game theory and Markov chains to address the particular challenges introduced by such complexities in the environment as topological dynamics, environmental uncertainties, and potential cyber-attack by human adversaries. The book is written for first- or second-year graduate students in a variety of engineering disciplines, including control, robotics, decision-making, optimization and algorithms and with backgrounds in aerospace engineering, computer science, electrical engineering, mechanical engineering and operations research. Resea...

Reliability–based economic model predictive control for generalised flow–based networks including actuators’ health–aware capabilities

Directory of Open Access Journals (Sweden)

Grosso Juan M.

2016-09-01

Full Text Available This paper proposes a reliability-based economic model predictive control (MPC strategy for the management of generalised flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamical allocation of safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuators’ availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the case study considered.

Aging based maintenance and reinvestment scheduling of electric distribution

Energy Technology Data Exchange (ETDEWEB)

Korpijarvi, J.

2012-07-01

The maintenance of electric distribution network is a topical question for distribution system operators because of increasing significance of failure costs. In this dissertation the maintenance practices of the distribution system operators are analyzed and a theory for scheduling maintenance activities and reinvestment of distribution components is created. The scheduling is based on the deterioration of components and the increasing failure rates due to aging. The dynamic programming algorithm is used as a solving method to maintenance problem which is caused by the increasing failure rates of the network. The other impacts of network maintenance like environmental and regulation reasons are not included to the scope of this thesis. Further the tree trimming of the corridors and the major disturbance of the network are not included to the problem optimized in this thesis. For optimizing, four dynamic programming models are presented and the models are tested. Programming is made in VBA-language to the computer. For testing two different kinds of test networks are used. Because electric distribution system operators want to operate with bigger component groups, optimal timing for component groups is also analyzed. A maintenance software package is created to apply the presented theories in practice. An overview of the program is presented (orig.)

Attentional Demand of a Virtual Reality-Based Reaching Task in Nondisabled Older Adults

Science.gov (United States)

Chen, Yi-An; Chung, Yu-Chen; Proffitt, Rachel; Wade, Eric; Winstein, Carolee

2015-01-01

Attention during exercise is known to affect performance; however, the attentional demand inherent to virtual reality (VR)-based exercise is not well understood. We used a dual-task paradigm to compare the attentional demands of VR-based and non-VR-based (conventional, real-world) exercise: 22 non-disabled older adults performed a primary reaching task to virtual and real targets in a counterbalanced block order while verbally responding to an unanticipated auditory tone in one third of the trials. The attentional demand of the primary reaching task was inferred from the voice response time (VRT) to the auditory tone. Participants' engagement level and task experience were also obtained using questionnaires. The virtual target condition was more attention demanding (significantly longer VRT) than the real target condition. Secondary analyses revealed a significant interaction between engagement level and target condition on attentional demand. For participants who were highly engaged, attentional demand was high and independent of target condition. However, for those who were less engaged, attentional demand was low and depended on target condition (i.e., virtual > real). These findings add important knowledge to the growing body of research pertaining to the development and application of technology-enhanced exercise for elders and for rehabilitation purposes. PMID:27004233

Attentional Demand of a Virtual Reality-Based Reaching Task in Nondisabled Older Adults.

Science.gov (United States)

Chen, Yi-An; Chung, Yu-Chen; Proffitt, Rachel; Wade, Eric; Winstein, Carolee

2015-12-01

Attention during exercise is known to affect performance; however, the attentional demand inherent to virtual reality (VR)-based exercise is not well understood. We used a dual-task paradigm to compare the attentional demands of VR-based and non-VR-based (conventional, real-world) exercise: 22 non-disabled older adults performed a primary reaching task to virtual and real targets in a counterbalanced block order while verbally responding to an unanticipated auditory tone in one third of the trials. The attentional demand of the primary reaching task was inferred from the voice response time (VRT) to the auditory tone. Participants' engagement level and task experience were also obtained using questionnaires. The virtual target condition was more attention demanding (significantly longer VRT) than the real target condition. Secondary analyses revealed a significant interaction between engagement level and target condition on attentional demand. For participants who were highly engaged, attentional demand was high and independent of target condition. However, for those who were less engaged, attentional demand was low and depended on target condition (i.e., virtual > real). These findings add important knowledge to the growing body of research pertaining to the development and application of technology-enhanced exercise for elders and for rehabilitation purposes.

Towards Internet QoS provisioning based on generic distributed QoS adaptive routing engine.

Science.gov (United States)

Haikal, Amira Y; Badawy, M; Ali, Hesham A

2014-01-01

Increasing efficiency and quality demands of modern Internet technologies drive today's network engineers to seek to provide quality of service (QoS). Internet QoS provisioning gives rise to several challenging issues. This paper introduces a generic distributed QoS adaptive routing engine (DQARE) architecture based on OSPFxQoS. The innovation of the proposed work in this paper is its undependability on the used QoS architectures and, moreover, splitting of the control strategy from data forwarding mechanisms, so we guarantee a set of absolute stable mechanisms on top of which Internet QoS can be built. DQARE architecture is furnished with three relevant traffic control schemes, namely, service differentiation, QoS routing, and traffic engineering. The main objective of this paper is to (i) provide a general configuration guideline for service differentiation, (ii) formalize the theoretical properties of different QoS routing algorithms and then introduce a QoS routing algorithm (QOPRA) based on dynamic programming technique, and (iii) propose QoS multipath forwarding (QMPF) model for paths diversity exploitation. NS2-based simulations proved the DQARE superiority in terms of delay, packet delivery ratio, throughput, and control overhead. Moreover, extensive simulations are used to compare the proposed QOPRA algorithm and QMPF model with their counterparts in the literature.

Towards Internet QoS Provisioning Based on Generic Distributed QoS Adaptive Routing Engine

Directory of Open Access Journals (Sweden)

Amira Y. Haikal

2014-01-01

Full Text Available Increasing efficiency and quality demands of modern Internet technologies drive today’s network engineers to seek to provide quality of service (QoS. Internet QoS provisioning gives rise to several challenging issues. This paper introduces a generic distributed QoS adaptive routing engine (DQARE architecture based on OSPFxQoS. The innovation of the proposed work in this paper is its undependability on the used QoS architectures and, moreover, splitting of the control strategy from data forwarding mechanisms, so we guarantee a set of absolute stable mechanisms on top of which Internet QoS can be built. DQARE architecture is furnished with three relevant traffic control schemes, namely, service differentiation, QoS routing, and traffic engineering. The main objective of this paper is to (i provide a general configuration guideline for service differentiation, (ii formalize the theoretical properties of different QoS routing algorithms and then introduce a QoS routing algorithm (QOPRA based on dynamic programming technique, and (iii propose QoS multipath forwarding (QMPF model for paths diversity exploitation. NS2-based simulations proved the DQARE superiority in terms of delay, packet delivery ratio, throughput, and control overhead. Moreover, extensive simulations are used to compare the proposed QOPRA algorithm and QMPF model with their counterparts in the literature.

Advanced smartgrids for distribution system operators

CERN Document Server

Boillot, Marc

2014-01-01

The dynamic of the Energy Transition is engaged in many region of the World. This is a real challenge for electric systems and a paradigm shift for existing distribution networks. With the help of "advanced" smart technologies, the Distribution System Operators will have a central role to integrate massively renewable generation, electric vehicle and demand response programs. Many projects are on-going to develop and assess advanced smart grids solutions, with already some lessons learnt. In the end, the Smart Grid is a mean for Distribution System Operators to ensure the quality and the secu

The effect of gas double-dynamic on mass distribution in solid-state fermentation.

Science.gov (United States)

Chen, Hong-Zhang; Zhao, Zhi-Min; Li, Hong-Qiang

2014-05-10

The mass distribution regularity in substrate of solid-state fermentation (SSF) has rarely been reported due to the heterogeneity of solid medium and the lack of suitable instrument and method, which limited the comprehensive analysis and enhancement of the SSF performance. In this work, the distributions of water, biomass, and fermentation product in different medium depths of SSF were determined using near-infrared spectroscopy (NIRS) and the developed models. Based on the mass distribution regularity, the effects of gas double-dynamic on heat transfer, microbial growth and metabolism, and product distribution gradient were systematically investigated. Results indicated that the maximum temperature of substrate and the maximum carbon dioxide evolution rate (CER) were 39.5°C and 2.48mg/(hg) under static aeration solid-state fermentation (SASSF) and 33.9°C and 5.38mg/(hg) under gas double-dynamic solid-state fermentation (GDSSF), respectively, with the environmental temperature for fermentation of 30±1°C. The fermentation production (cellulase activity) ratios of the upper, middle, and lower levels were 1:0.90:0.78 at seventh day under SASSF and 1:0.95:0.89 at fifth day under GDSSF. Therefore, combined with NIRS analysis, gas double-dynamic could effectively strengthen the solid-state fermentation performance due to the enhancement of heat transfer, the stimulation of microbial metabolism and the increase of the homogeneity of fermentation products. Copyright © 2014 Elsevier Inc. All rights reserved.

Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings.

Science.gov (United States)

Mahapatra, Chinmaya; Moharana, Akshaya Kumar; Leung, Victor C M

2017-12-05

Around the globe, innovation with integrating information and communication technologies (ICT) with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Cities today faces multifarious challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation. In this paper, a new method named as Home Energy Management as a Service (HEMaaS) is proposed which is based on neural network based Q -learning algorithm. Although several attempts have been made in the past to address similar problems, the models developed do not cater to maximize the user convenience and robustness of the system. In this paper, authors have proposed an advanced Neural Fitted Q -learning method which is self-learning and adaptive. The proposed method provides an agile, flexible and energy efficient decision making system for home energy management. A typical Canadian residential dwelling model has been used in this paper to test the proposed method. Based on analysis, it was found that the proposed method offers a fast and viable solution to reduce the demand and conserve energy during peak period. It also helps reducing the carbon footprint of residential dwellings. Once adopted, city blocks with significant residential dwellings can significantly reduce the total energy consumption by reducing or shifting their energy demand during peak period. This would definitely help local power distribution companies to optimize their resources and keep the tariff low due to curtailment of peak demand.

Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings

Directory of Open Access Journals (Sweden)

Chinmaya Mahapatra

2017-12-01

Full Text Available Around the globe, innovation with integrating information and communication technologies (ICT with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Cities today faces multifarious challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation. In this paper, a new method named as Home Energy Management as a Service (HEMaaS is proposed which is based on neural network based Q-learning algorithm. Although several attempts have been made in the past to address similar problems, the models developed do not cater to maximize the user convenience and robustness of the system. In this paper, authors have proposed an advanced Neural Fitted Q-learning method which is self-learning and adaptive. The proposed method provides an agile, flexible and energy efficient decision making system for home energy management. A typical Canadian residential dwelling model has been used in this paper to test the proposed method. Based on analysis, it was found that the proposed method offers a fast and viable solution to reduce the demand and conserve energy during peak period. It also helps reducing the carbon footprint of residential dwellings. Once adopted, city blocks with significant residential dwellings can significantly reduce the total energy consumption by reducing or shifting their energy demand during peak period. This would definitely help local power distribution companies to optimize their resources and keep the tariff low due to curtailment of peak demand.

Dynamic Evolution Model Based on Social Network Services

Science.gov (United States)

Xiong, Xi; Gou, Zhi-Jian; Zhang, Shi-Bin; Zhao, Wen

2013-11-01

Based on the analysis of evolutionary characteristics of public opinion in social networking services (SNS), in the paper we propose a dynamic evolution model, in which opinions are coupled with topology. This model shows the clustering phenomenon of opinions in dynamic network evolution. The simulation results show that the model can fit the data from a social network site. The dynamic evolution of networks accelerates the opinion, separation and aggregation. The scale and the number of clusters are influenced by confidence limit and rewiring probability. Dynamic changes of the topology reduce the number of isolated nodes, while the increased confidence limit allows nodes to communicate more sufficiently. The two effects make the distribution of opinion more neutral. The dynamic evolution of networks generates central clusters with high connectivity and high betweenness, which make it difficult to control public opinions in SNS.

An Autonomous Mobile Agent-Based Distributed Learning Architecture-A Proposal and Analytical Analysis

Directory of Open Access Journals (Sweden)

I. Ahmed M. J. SADIIG

2005-10-01

Full Text Available An Autonomous Mobile Agent-Based Distributed Learning Architecture-A Proposal and Analytical Analysis Dr. I. Ahmed M. J. SADIIG Department of Electrical & Computer EngineeringInternational Islamic University GombakKuala Lumpur-MALAYSIA ABSTRACT The traditional learning paradigm invoving face-to-face interaction with students is shifting to highly data-intensive electronic learning with the advances in Information and Communication Technology. An important component of the e-learning process is the delivery of the learning contents to their intended audience over a network. A distributed learning system is dependent on the network for the efficient delivery of its contents to the user. However, as the demand of information provision and utilization increases on the Internet, the current information service provision and utilization methods are becoming increasingly inefficient. Although new technologies have been employed for efficient learning methodologies within the context of an e-learning environment, the overall efficiency of the learning system is dependent on the mode of distribution and utilization of its learning contents. It is therefore imperative to employ new techniques to meet the service demands of current and future e-learning systems. In this paper, an architecture based on autonomous mobile agents creating a Faded Information Field is proposed. Unlike the centralized information distribution in a conventional e-learning system, the information is decentralized in the proposed architecture resulting in increased efficiency of the overall system for distribution and utilization of system learning contents efficiently and fairly. This architecture holds the potential to address the heterogeneous user requirements as well as the changing conditions of the underlying network.

Coordinated Demand Response and Distributed Generation Management in Residential Smart Microgrids

DEFF Research Database (Denmark)

Anvari-Moghaddam, Amjad; Mokhtari, Ghassem; Guerrero, Josep M.

2016-01-01

potentials to increase the functionality of a typical demand-side management (DSM) strategy, and typical implementation of building-level DERs by integrating them into a cohesive, networked package that fully utilizes smart energy-efficient end-use devices, advanced building control/automation systems......Nowadays with the emerging of small-scale integrated energy systems (IESs) in form of residential smart microgrids (SMGs), a large portion of energy can be saved through coordinated scheduling of smart household devices and management of distributed energy resources (DERs). There are significant......, and an integrated communications architecture to efficiently manage energy and comfort at the end-use location. By the aid of such technologies, residential consumers have also the capability to mitigate their energy costs and satisfy their own requirements paying less attention to the configuration of the energy...

Modeling Relief Demands in an Emergency Supply Chain System under Large-Scale Disasters Based on a Queuing Network

Science.gov (United States)

He, Xinhua

2014-01-01

This paper presents a multiple-rescue model for an emergency supply chain system under uncertainties in large-scale affected area of disasters. The proposed methodology takes into consideration that the rescue demands caused by a large-scale disaster are scattered in several locations; the servers are arranged in multiple echelons (resource depots, distribution centers, and rescue center sites) located in different places but are coordinated within one emergency supply chain system; depending on the types of rescue demands, one or more distinct servers dispatch emergency resources in different vehicle routes, and emergency rescue services queue in multiple rescue-demand locations. This emergency system is modeled as a minimal queuing response time model of location and allocation. A solution to this complex mathematical problem is developed based on genetic algorithm. Finally, a case study of an emergency supply chain system operating in Shanghai is discussed. The results demonstrate the robustness and applicability of the proposed model. PMID:24688367

Modeling Relief Demands in an Emergency Supply Chain System under Large-Scale Disasters Based on a Queuing Network

Directory of Open Access Journals (Sweden)

Xinhua He

2014-01-01

Full Text Available This paper presents a multiple-rescue model for an emergency supply chain system under uncertainties in large-scale affected area of disasters. The proposed methodology takes into consideration that the rescue demands caused by a large-scale disaster are scattered in several locations; the servers are arranged in multiple echelons (resource depots, distribution centers, and rescue center sites located in different places but are coordinated within one emergency supply chain system; depending on the types of rescue demands, one or more distinct servers dispatch emergency resources in different vehicle routes, and emergency rescue services queue in multiple rescue-demand locations. This emergency system is modeled as a minimal queuing response time model of location and allocation. A solution to this complex mathematical problem is developed based on genetic algorithm. Finally, a case study of an emergency supply chain system operating in Shanghai is discussed. The results demonstrate the robustness and applicability of the proposed model.

Modeling relief demands in an emergency supply chain system under large-scale disasters based on a queuing network.

Science.gov (United States)

He, Xinhua; Hu, Wenfa

2014-01-01

This paper presents a multiple-rescue model for an emergency supply chain system under uncertainties in large-scale affected area of disasters. The proposed methodology takes into consideration that the rescue demands caused by a large-scale disaster are scattered in several locations; the servers are arranged in multiple echelons (resource depots, distribution centers, and rescue center sites) located in different places but are coordinated within one emergency supply chain system; depending on the types of rescue demands, one or more distinct servers dispatch emergency resources in different vehicle routes, and emergency rescue services queue in multiple rescue-demand locations. This emergency system is modeled as a minimal queuing response time model of location and allocation. A solution to this complex mathematical problem is developed based on genetic algorithm. Finally, a case study of an emergency supply chain system operating in Shanghai is discussed. The results demonstrate the robustness and applicability of the proposed model.

Outlook of the world steel cycle based on the stock and flow dynamics.

Science.gov (United States)

Hatayama, Hiroki; Daigo, Ichiro; Matsuno, Yasunari; Adachi, Yoshihiro

2010-08-15

We present a comprehensive analysis of steel use in the future compiled using dynamic material flow analysis (MFA). A dynamic MFA for 42 countries depicted the global in-use stock and flow up to the end of 2005. On the basis of the transition of steel stock for 2005, the growth of future steel stock was then estimated considering the economic growth for every country. Future steel demand was estimated using dynamic analysis under the new concept of "stocks drive flows". The significant results follow. World steel stock reached 12.7 billion t in 2005, and has doubled in the last 25 years. The world stock in 2005 mainly consisted of construction (60%) and vehicles (10%). Stock in these end uses will reach 55 billion t in 2050, driven by a 10-fold increase in Asia. Steel demand will reach 1.8 billion t in 2025, then slightly decrease, and rise again by replacement of buildings. The forecast of demand clearly represents the industrial shift; at first the increase is dominated by construction, and then, after 2025, demand for construction decreases and demand for vehicles increases instead. This study thus provides the dynamic mechanism of steel stock and flow toward the future, which contributes to the design of sustainable steel use.

A supply and demand based volatility model for energy prices

International Nuclear Information System (INIS)

Kanamura, Takashi

2009-01-01

This paper proposes a new volatility model for energy prices using the supply-demand relationship, which we call a supply and demand based volatility model. We show that the supply curve shape in the model determines the characteristics of the volatility in energy prices. It is found that the inverse Box-Cox transformation supply curve reflecting energy markets causes the inverse leverage effect, i.e., positive correlation between energy prices and volatility. The model is also used to show that an existing (G)ARCH-M model has the foundations on the supply-demand relationship. Additionally, we conduct the empirical studies analyzing the volatility in the U.S. natural gas prices. (author)

A supply and demand based volatility model for energy prices

Energy Technology Data Exchange (ETDEWEB)

Kanamura, Takashi [J-POWER, 15-1, Ginza 6-Chome, Chuo-ku, Tokyo 104-8165 (Japan)

2009-09-15

This paper proposes a new volatility model for energy prices using the supply-demand relationship, which we call a supply and demand based volatility model. We show that the supply curve shape in the model determines the characteristics of the volatility in energy prices. It is found that the inverse Box-Cox transformation supply curve reflecting energy markets causes the inverse leverage effect, i.e., positive correlation between energy prices and volatility. The model is also used to show that an existing (G)ARCH-M model has the foundations on the supply-demand relationship. Additionally, we conduct the empirical studies analyzing the volatility in the U.S. natural gas prices. (author)

WISDOM: A GIS-based supply demand mapping tool for woodfuel management

International Nuclear Information System (INIS)

Masera, Omar; Ghilardi, Adrian; Drigo, Rudi; Angel Trossero, Miguel

2006-01-01

In this paper, it is argued that adequately assessing the implications of the current patterns of woodfuel production and use, and the sustainable potentials of woodfuel resources, requires a holistic view and a better knowledge of the spatial patterns of woodfuel supply and demand. There is a need to conduct multi-scale spatially explicit analyses of woodfuel supply and demand that are able to articulate local heterogeneity at the regional and national levels. Studies that provide full-country coverage and are based on consistent integration of data at lower geographical scales are woefully lacking. This paper describes the Woodfuel Integrated Supply/Demand Overview Mapping model (WISDOM). This is a GIS-based tool, aimed at analyzing woodfuel demand and supply spatial patterns from a new perspective that includes: (a) the assembling of existing but dispersed information into single data sets, (b) a modular integration of these data sets, based on the analysis of key variables associated with woodfuel demand and supply patterns, and (c) a multiple-scale and spatially explicit representation of the results, in order to rank or highlight areas in which several criteria of interest coincide. The final objective of WISDOM is to assess the sustainability of woodfuel as a renewable and widespread energy source, while supporting strategic planning and policy formulation. Three case studies for Mexico, Slovenia, and Senegal illustrate the practical implementation and innovative results of using WISDOM. (author)

Multipoint dynamically reconfigure adaptive distributed fiber optic acoustic emission sensor (FAESense) system for condition based maintenance

Science.gov (United States)

Mendoza, Edgar; Prohaska, John; Kempen, Connie; Esterkin, Yan; Sun, Sunjian; Krishnaswamy, Sridhar

2010-09-01

This paper describes preliminary results obtained under a Navy SBIR contract by Redondo Optics Inc. (ROI), in collaboration with Northwestern University towards the development and demonstration of a next generation, stand-alone and fully integrated, dynamically reconfigurable, adaptive fiber optic acoustic emission sensor (FAESense™) system for the in-situ unattended detection and localization of shock events, impact damage, cracks, voids, and delaminations in new and aging critical infrastructures found in ships, submarines, aircraft, and in next generation weapon systems. ROI's FAESense™ system is based on the integration of proven state-of-the-art technologies: 1) distributed array of in-line fiber Bragg gratings (FBGs) sensors sensitive to strain, vibration, and acoustic emissions, 2) adaptive spectral demodulation of FBG sensor dynamic signals using two-wave mixing interferometry on photorefractive semiconductors, and 3) integration of all the sensor system passive and active optoelectronic components within a 0.5-cm x 1-cm photonic integrated circuit microchip. The adaptive TWM demodulation methodology allows the measurement of dynamic high frequnency acoustic emission events, while compensating for passive quasi-static strain and temperature drifts. It features a compact, low power, environmentally robust 1-inch x 1-inch x 4-inch small form factor (SFF) package with no moving parts. The FAESense™ interrogation system is microprocessor-controlled using high data rate signal processing electronics for the FBG sensors calibration, temperature compensation and the detection and analysis of acoustic emission signals. Its miniaturized package, low power operation, state-of-the-art data communications, and low cost makes it a very attractive solution for a large number of applications in naval and maritime industries, aerospace, civil structures, the oil and chemical industry, and for homeland security applications.

Real-time electricity pricing mechanism in China based on system dynamics

International Nuclear Information System (INIS)

He, Yongxiu; Zhang, Jixiang

2015-01-01

Highlights: • The system dynamics is used to research the real-time electricity pricing mechanism. • Four kinds of the real-time electricity pricing models are carried out and simulated. • It analysed the electricity price, the user satisfaction and the social benefits under the different models. • Market pricing is the trend of the real-time electricity pricing mechanism. • Initial development path of the real-time price mechanism for China is designed between 2015 and 2030. - Abstract: As an important means of demand-side response, the reasonable formulation of the electricity price mechanism will have an important impact on the balance between the supply and demand of electric power. With the introduction of Chinese intelligence apparatus and the rapid development of smart grids, real-time electricity pricing, as the frontier electricity pricing mechanism in the smart grid, will have great significance on the promotion of energy conservation and the improvement of the total social surplus. From the perspective of system dynamics, this paper studies different real-time electricity pricing mechanisms based on load structure, cost structure and bidding and analyses the situation of user satisfaction and the total social surplus under different pricing mechanisms. Finally, through the comparative analysis of examples under different real-time pricing scenarios, this paper aims to explore and design the future dynamic real-time electricity pricing mechanism in China, predicts the dynamic real-time pricing level and provides a reference for real-time electricity price promotion in the future

Experimental validation of a distributed algorithm for dynamic spectrum access in local area networks

DEFF Research Database (Denmark)

Tonelli, Oscar; Berardinelli, Gilberto; Tavares, Fernando Menezes Leitão

2013-01-01

Next generation wireless networks aim at a significant improvement of the spectral efficiency in order to meet the dramatic increase in data service demand. In local area scenarios user-deployed base stations are expected to take place, thus making the centralized planning of frequency resources...... activities with the Autonomous Component Carrier Selection (ACCS) algorithm, a distributed solution for interference management among small neighboring cells. A preliminary evaluation of the algorithm performance is provided considering its live execution on a software defined radio network testbed...

Adaptive Dynamic Process Scheduling on Distributed Memory Parallel Computers

Directory of Open Access Journals (Sweden)

Wei Shu

1994-01-01

Full Text Available One of the challenges in programming distributed memory parallel machines is deciding how to allocate work to processors. This problem is particularly important for computations with unpredictable dynamic behaviors or irregular structures. We present a scheme for dynamic scheduling of medium-grained processes that is useful in this context. The adaptive contracting within neighborhood (ACWN is a dynamic, distributed, load-dependent, and scalable scheme. It deals with dynamic and unpredictable creation of processes and adapts to different systems. The scheme is described and contrasted with two other schemes that have been proposed in this context, namely the randomized allocation and the gradient model. The performance of the three schemes on an Intel iPSC/2 hypercube is presented and analyzed. The experimental results show that even though the ACWN algorithm incurs somewhat larger overhead than the randomized allocation, it achieves better performance in most cases due to its adaptiveness. Its feature of quickly spreading the work helps it outperform the gradient model in performance and scalability.

Distributed continuous energy scheduling for dynamic virtual power plants

International Nuclear Information System (INIS)

Niesse, Astrid

2015-01-01

This thesis presents DynaSCOPE as distributed control method for continuous energy scheduling for dynamic virtual power plants (DVPP). DVPPs aggregate the flexibility of distributed energy units to address current energy markets. As an extension of the Virtual Power Plant concept they show high dynamics in aggregation and operation of energy units. Whereas operation schedules are set up for all energy units in a day-ahead planning procedure, incidents may render these schedules infeasible during execution, like deviation from prognoses or outages. Thus, a continuous scheduling process is needed to ensure product fulfillment. With DynaSCOPE, software agents representing single energy units solve this problem in a completely distributed heuristic approach. Using a stepped concept, several damping mechanisms are applied to allow minimum disturbance while continuously trying to fulfill the product as contracted at the market.

Web-based Distributed Medical Information System for Chronic Viral Hepatitis

Science.gov (United States)

Yang, Ying; Qin, Tuan-fa; Jiang, Jian-ning; Lu, Hui; Ma, Zong-e.; Meng, Hong-chang

2008-11-01

To make a long-term dynamic monitoring to the chronically ill, especially patients of HBV A, we build a distributed Medical Information System for Chronic Viral Hepatitis (MISCHV). The Web-based system architecture and its function are described, and the extensive application and important role are also presented.