Optimal strategies for real-time sparse actuator compensation in RFX-mod MHD control operations

Energy Technology Data Exchange (ETDEWEB)

Pigatto, L., E-mail: leonardo.pigatto@igi.cnr.it [Consorzio RFX, Corso Stati Uniti 4, 35127 Padova (Italy); University of Padova, Padova (Italy); Bettini, P. [Consorzio RFX, Corso Stati Uniti 4, 35127 Padova (Italy); University of Padova, Padova (Italy); Bolzonella, T.; Marchiori, G. [Consorzio RFX, Corso Stati Uniti 4, 35127 Padova (Italy); Villone, F. [CREATE, DIEI, Università di Cassino e del Lazio Meridionale, Cassino (Italy)

2015-10-15

Highlights: • Sparse missing actuator compensation is solved with a new real-time strategy. • Testing is carried out with a dynamical model to prove feasibility and limits. • Dedicated experiments have been run to validate simulated results. - Abstract: In many devices aiming at magnetic confinement of fusion relevant plasmas, feedback control of MHD instabilities by means of active coils is nowadays mandatory to ensure the robustness of high performance operational scenarios. Actuators involved in the control loop are often coupled in the sensor measurements and an optimal strategy for decoupling can be limited by the need of reducing as much as possible the cycle time of the control loop itself. It is also important to stress the fact that the problem is intrinsically 3D, involving different non-axisymmetric contributions. The baseline situation in RFX-mod is documented, where the identity matrix is chosen to represent the simplest case of mutual coupling matrix. The problem of missing or broken actuators is introduced and tackled with dedicated compensation strategies. A detailed description is given for a possible compensation concept which can be applied in real-time operation thanks to its implementation strategy, yielding very promising results in terms of local field reconstruction.

Emergency strategy optimization for the environmental control system in manned spacecraft

Science.gov (United States)

Li, Guoxiang; Pang, Liping; Liu, Meng; Fang, Yufeng; Zhang, Helin

2018-02-01

It is very important for a manned environmental control system (ECS) to be able to reconfigure its operation strategy in emergency conditions. In this article, a multi-objective optimization is established to design the optimal emergency strategy for an ECS in an insufficient power supply condition. The maximum ECS lifetime and the minimum power consumption are chosen as the optimization objectives. Some adjustable key variables are chosen as the optimization variables, which finally represent the reconfigured emergency strategy. The non-dominated sorting genetic algorithm-II is adopted to solve this multi-objective optimization problem. Optimization processes are conducted at four different carbon dioxide partial pressure control levels. The study results show that the Pareto-optimal frontiers obtained from this multi-objective optimization can represent the relationship between the lifetime and the power consumption of the ECS. Hence, the preferred emergency operation strategy can be recommended for situations when there is suddenly insufficient power.

Multiobjective optimization of strategies for operation and testing of low-demand safety instrumented systems using a genetic algorithm and fault trees

International Nuclear Information System (INIS)

Longhi, Antonio Eduardo Bier; Pessoa, Artur Alves; Garcia, Pauli Adriano de Almada

2015-01-01

Since low-demand safety instrumented systems (SISs) do not operate continuously, their failures are often only detected when the system is demanded or tested. The conduction of tests, besides adding costs, can raise risks of failure on demand during their execution and also increase the frequency of spurious activation. Additionally, it is often necessary to interrupt production to carry out tests. In light of this scenario, this paper presents a model to optimize strategies for operation and testing of these systems, applying modeling by fault trees associated with optimization by a genetic algorithm. Its main differences are: (i) ability to represent four modes of operation and test them for each SIS subsystem; (ii) ability to represent a SIS that executes more than one safety instrumented function; (iii) ability to keep track of the down-time generated in the production system; and (iv) alteration of a genetic selection mechanism that permits identification of more efficient solutions with smaller influence on the optimization parameters. These aspects are presented by applying this model in three case studies. The results obtained show the applicability of the proposed approach and its potential to help make more informed decisions. - Highlights: • Models the integrity and cost related to operation and testing of low-demand SISs. • Keeps track of the production down-time generated by SIS tests and repairs. • Allows multiobjective optimization to identify operation and testing strategies. • Enables integrated assessment of an SIS that executes more than one SIF. • Allows altering the selection mechanism to identify the most efficient strategies

Optimized Power Dispatch Strategy for Offshore Wind Farms

DEFF Research Database (Denmark)

Hou, Peng; Hu, Weihao; Zhang, Baohua

2016-01-01

which are related to electrical system topology. This paper proposed an optimized power dispatch strategy (OPD) for minimizing the levelized production cost (LPC) of a wind farm. Particle swarm optimization (PSO) is employed to obtain final solution for the optimization problem. Both regular shape......Maximizing the power production of offshore wind farms using proper control strategy has become an important issue for wind farm operators. However, the power transmitted to the onshore substation (OS) is not only related to the power production of each wind turbine (WT) but also the power losses...... and irregular shape wind farm are chosen for the case study. The proposed dispatch strategy is compared with two other control strategies. The simulation results show the effectiveness of the proposed strategy....

Control strategy for power management, efficiency-optimization and operating-safety of a 5-kW solid oxide fuel cell system

International Nuclear Information System (INIS)

Zhang, Lin; Jiang, Jianhua; Cheng, Huan; Deng, Zhonghua; Li, Xi

2015-01-01

Highlights: • Efficiency optimization associated with simultaneous power and thermal management. • Fast load tracing, fuel starvation, high efficiency and operating safety are considered. • Open loop pre-conditioning current strategy is proposed for load step-up transients. • Feedback control scheme is proposed for load step-up transients. - Abstract: The slow power tracking, operating safety, especially the fuel exhaustion, and high efficiency considerations are the key issues for integrated solid oxide fuel cell (SOFC) systems during power step up transients, resulting in the relatively poor dynamic capabilities and make the transient load following very challenging and must be enhanced. To this end, this paper first focus on addressing the efficiency optimization associated with simultaneous power and thermal management of a 5-kW SOFC system. Particularly, a traverse optimization process including cubic convolution interpolation algorithm are proposed to obtain optimal operating points (OOPs) with the maximum efficiency. Then this paper investigate the current implications on system step-up transient performance, then a two stage pre-conditioning current strategy and a feedback power reference control scheme is proposed for load step-up transients to balance fast load following and fuel starvation, after that safe thermal transient is validated. Simulation results show the efficacy of the control design by demonstrating the fast load following ability while maintaining the safe operation, thus safe; efficient and fast load transition can be achieved

Parameter Optimization and Operating Strategy of a TEG System for Railway Vehicles

Science.gov (United States)

Heghmanns, A.; Wilbrecht, S.; Beitelschmidt, M.; Geradts, K.

2016-03-01

A thermoelectric generator (TEG) system demonstrator for diesel electric locomotives with the objective of reducing the mechanical load on the thermoelectric modules (TEM) is developed and constructed to validate a one-dimensional thermo-fluid flow simulation model. The model is in good agreement with the measurements and basis for the optimization of the TEG's geometry by a genetic multi objective algorithm. The best solution has a maximum power output of approx. 2.7 kW and does not exceed the maximum back pressure of the diesel engine nor the maximum TEM hot side temperature. To maximize the reduction of the fuel consumption, an operating strategy regarding the system power output for the TEG system is developed. Finally, the potential consumption reduction in passenger and freight traffic operating modes is estimated under realistic driving conditions by means of a power train and lateral dynamics model. The fuel savings are between 0.5% and 0.7%, depending on the driving style.

Optimal operation of hybrid-SITs under a SBO accident

International Nuclear Information System (INIS)

Jeon, In Seop; Heo, Sun; Kang, Hyun Gook

2016-01-01

Highlights: • Operation strategy of hybrid-SIT (H-SIT) in station blackout (SBO) is developed. • There are five main factors which have to be carefully treated in the development of the operation strategy. • Optimal value of each main factor is investigated analytically and then through thermal-hydraulic analysis using computer code. • The optimum operation strategy is suggested based on the optimal value of the main factors. - Abstract: A hybrid safety injection tank (H-SIT) is designed to enhance the capability of pressurized water reactors against high-pressure accidents which might be caused by the combined accidents accompanied by station blackout (SBO), and is suggested as a useful alternative to electricity-driven motor injection pumps. The main purpose of the H-SIT is to provide coolant to the core so that core safety can be maintained for a longer period. As H-SITs have a limited inventory, their efficient use in cooling down the core is paramount to maximize the available time for long-term cooling component restoration. Therefore, an optimum operation strategy must be developed to support the operators for the most efficient H-SIT use. In this study, the main factors which have to be carefully treated in the development of an operation strategy are first identified. Then the optimal value of each main factor is investigated analytically, a process useful to get the basis of the global optimum points. Based on these analytical optimum points, a thermal-hydraulic analysis using MARS code is performed to get more accurate values and to verify the results of the analytical study. The available time for long-term cooling component restoration is also estimated. Finally, an integrated optimum operation strategy for H-SITs in SBO is suggested.

Memory and Energy Optimization Strategies for Multithreaded Operating System on the Resource-Constrained Wireless Sensor Node

Directory of Open Access Journals (Sweden)

Xing Liu

2014-12-01

Full Text Available Memory and energy optimization strategies are essential for the resource-constrained wireless sensor network (WSN nodes. In this article, a new memory-optimized and energy-optimized multithreaded WSN operating system (OS LiveOS is designed and implemented. Memory cost of LiveOS is optimized by using the stack-shifting hybrid scheduling approach. Different from the traditional multithreaded OS in which thread stacks are allocated statically by the pre-reservation, thread stacks in LiveOS are allocated dynamically by using the stack-shifting technique. As a result, memory waste problems caused by the static pre-reservation can be avoided. In addition to the stack-shifting dynamic allocation approach, the hybrid scheduling mechanism which can decrease both the thread scheduling overhead and the thread stack number is also implemented in LiveOS. With these mechanisms, the stack memory cost of LiveOS can be reduced more than 50% if compared to that of a traditional multithreaded OS. Not is memory cost optimized, but also the energy cost is optimized in LiveOS, and this is achieved by using the multi-core “context aware” and multi-core “power-off/wakeup” energy conservation approaches. By using these approaches, energy cost of LiveOS can be reduced more than 30% when compared to the single-core WSN system. Memory and energy optimization strategies in LiveOS not only prolong the lifetime of WSN nodes, but also make the multithreaded OS feasible to run on the memory-constrained WSN nodes.

Optimal robust control strategy of a solid oxide fuel cell system

Science.gov (United States)

Wu, Xiaojuan; Gao, Danhui

2018-01-01

Optimal control can ensure system safe operation with a high efficiency. However, only a few papers discuss optimal control strategies for solid oxide fuel cell (SOFC) systems. Moreover, the existed methods ignore the impact of parameter uncertainty on system instantaneous performance. In real SOFC systems, several parameters may vary with the variation of operation conditions and can not be identified exactly, such as load current. Therefore, a robust optimal control strategy is proposed, which involves three parts: a SOFC model with parameter uncertainty, a robust optimizer and robust controllers. During the model building process, boundaries of the uncertain parameter are extracted based on Monte Carlo algorithm. To achieve the maximum efficiency, a two-space particle swarm optimization approach is employed to obtain optimal operating points, which are used as the set points of the controllers. To ensure the SOFC safe operation, two feed-forward controllers and a higher-order robust sliding mode controller are presented to control fuel utilization ratio, air excess ratio and stack temperature afterwards. The results show the proposed optimal robust control method can maintain the SOFC system safe operation with a maximum efficiency under load and uncertainty variations.

Mixed integer evolution strategies for parameter optimization.

Science.gov (United States)

Li, Rui; Emmerich, Michael T M; Eggermont, Jeroen; Bäck, Thomas; Schütz, M; Dijkstra, J; Reiber, J H C

2013-01-01

Evolution strategies (ESs) are powerful probabilistic search and optimization algorithms gleaned from biological evolution theory. They have been successfully applied to a wide range of real world applications. The modern ESs are mainly designed for solving continuous parameter optimization problems. Their ability to adapt the parameters of the multivariate normal distribution used for mutation during the optimization run makes them well suited for this domain. In this article we describe and study mixed integer evolution strategies (MIES), which are natural extensions of ES for mixed integer optimization problems. MIES can deal with parameter vectors consisting not only of continuous variables but also with nominal discrete and integer variables. Following the design principles of the canonical evolution strategies, they use specialized mutation operators tailored for the aforementioned mixed parameter classes. For each type of variable, the choice of mutation operators is governed by a natural metric for this variable type, maximal entropy, and symmetry considerations. All distributions used for mutation can be controlled in their shape by means of scaling parameters, allowing self-adaptation to be implemented. After introducing and motivating the conceptual design of the MIES, we study the optimality of the self-adaptation of step sizes and mutation rates on a generalized (weighted) sphere model. Moreover, we prove global convergence of the MIES on a very general class of problems. The remainder of the article is devoted to performance studies on artificial landscapes (barrier functions and mixed integer NK landscapes), and a case study in the optimization of medical image analysis systems. In addition, we show that with proper constraint handling techniques, MIES can also be applied to classical mixed integer nonlinear programming problems.

Optimal energy management strategy for battery powered electric vehicles

International Nuclear Information System (INIS)

Xi, Jiaqi; Li, Mian; Xu, Min

2014-01-01

Highlights: • The power usage for battery-powered electrical vehicles with in-wheel motors is maximized. • The battery and motor dynamics are examined emphasized on the power conversion and utilization. • The optimal control strategy is derived and verified by simulations. • An analytic expression of the optimal operating point is obtained. - Abstract: Due to limited energy density of batteries, energy management has always played a critical role in improving the overall energy efficiency of electric vehicles. In this paper, a key issue within the energy management problem will be carefully tackled, i.e., maximizing the power usage of batteries for battery-powered electrical vehicles with in-wheel motors. To this end, the battery and motor dynamics will be thoroughly examined with particular emphasis on the power conversion and power utilization. The optimal control strategy will then be derived based on the analysis. One significant contribution of this work is that an analytic expression for the optimal operating point in terms of the component and environment parameters can be obtained. Owing to this finding, the derived control strategy is also rendered a simple structure for real-time implementation. Simulation results demonstrate that the proposed strategy works both adaptively and robustly under different driving scenarios

Development of performance model and optimization strategy for standalone operation of CPV-hydrogen system utilizing multi-junction solar cell

KAUST Repository

Burhan, Muhammad; Shahzad, Muhammad Wakil; Ng, Kim Choon

2017-01-01

Despite highest energy potential, solar energy is only available during diurnal period with varying intensity. Therefore, owing to solar intermittency, solar energy systems need to operate in standalone configuration for steady power supply which requires reliable and sustainable energy storage. Hydrogen production has proved to be the most reliable and sustainable energy storage option for medium and long term operation. However, at the first priority, solar energy must be captured with high efficiency, in order to reduce the overall size of the system and energy storage. Multi-junction solar cells (MJCs) provide highest energy efficiency among all of the photovoltaic technologies and the concentrated photovoltaic (CPV) system concept makes their use cost effective. However, literature is lacking the performance model and optimization strategy for standalone operation of the CPV-hydrogen system. In addition, there is no commercial tool available that can analyze CPV performance, utilizing multi-junction solar cell. This paper proposes the performance model for the CPV-hydrogen systems and the multi-objective optimization strategy for its standalone operation and techno-economic analysis, using micro genetic algorithm (micro-GA). The electrolytic hydrogen production with compression storage and fuel cell, is used as energy storage system. The CPV model is verified for the experimental data of InGaP/InGaAs/Ge triple junction solar cell. An optimal CPV system design is provided for uninterrupted power supply, even under seasonal weather variations. Such approach can be easily integrated with commercial tools and the presented performance data can be used for the design of individual components of the system.

Development of performance model and optimization strategy for standalone operation of CPV-hydrogen system utilizing multi-junction solar cell

KAUST Repository

Burhan, Muhammad

2017-09-16

Despite highest energy potential, solar energy is only available during diurnal period with varying intensity. Therefore, owing to solar intermittency, solar energy systems need to operate in standalone configuration for steady power supply which requires reliable and sustainable energy storage. Hydrogen production has proved to be the most reliable and sustainable energy storage option for medium and long term operation. However, at the first priority, solar energy must be captured with high efficiency, in order to reduce the overall size of the system and energy storage. Multi-junction solar cells (MJCs) provide highest energy efficiency among all of the photovoltaic technologies and the concentrated photovoltaic (CPV) system concept makes their use cost effective. However, literature is lacking the performance model and optimization strategy for standalone operation of the CPV-hydrogen system. In addition, there is no commercial tool available that can analyze CPV performance, utilizing multi-junction solar cell. This paper proposes the performance model for the CPV-hydrogen systems and the multi-objective optimization strategy for its standalone operation and techno-economic analysis, using micro genetic algorithm (micro-GA). The electrolytic hydrogen production with compression storage and fuel cell, is used as energy storage system. The CPV model is verified for the experimental data of InGaP/InGaAs/Ge triple junction solar cell. An optimal CPV system design is provided for uninterrupted power supply, even under seasonal weather variations. Such approach can be easily integrated with commercial tools and the presented performance data can be used for the design of individual components of the system.

Optimal sizing and operation of energy storage systems considering long term assessment

Directory of Open Access Journals (Sweden)

Gerardo Guerra

2018-01-01

Full Text Available This paper proposes a procedure for estimating the optimal sizing of Photovoltaic Generators and Energy Storage units when they are operated from the utility’s perspective. The goal is to explore the potential improvement on the overall operating conditions of the distribution system to which the Generators and Storage units will be connected. Optimization is conducted by means of a General Parallel Genetic Algorithm that seeks to maximize the technical benefits for the distribution system. The paper proposes an operation strategy for Energy Storage units based on the daily variation of load and generation; the operation strategy is optimized for an evaluation period of one year using hourly power curves. The construction of the yearly Storage operation curve results in a high-dimension optimization problem; as a result, different day-classification methods are applied in order to reduce the dimension of the optimization. Results show that the proposed approach is capable of producing significant improvements in system operating conditions and that the best performance is obtained when the day-classification is based on the similarity among daily power curves.

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

Directory of Open Access Journals (Sweden)

Fei Wang

2017-07-01

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

Optimal GENCO bidding strategy

Science.gov (United States)

Gao, Feng

Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming. The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed. A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed

Long-term damage management strategies for optimizing steam generator performance

International Nuclear Information System (INIS)

Egan, G.R.; Besuner, P.M.; Fox, J.H.; Merrick, E.A.

1991-01-01

Minimizing long-term impact of steam generator operating, maintenance, outage, and replacement costs is the goal of all pressurized water reactor utilities. Recent research results have led to deterministic controls that may be implemented to optimize steam generator performance and to minimize damage accumulation. The real dilemma that utilities encounter is the decision process that needs to be made in the face of uncertain data. Some of these decisions involve the frequency and extent of steam generator eddy current tube inspections; the definition of operating conditions to minimize the rate of corrosion reactions (T (hot) , T (cold) ; and the imposition of strict water quality management guidelines. With finite resources, how can a utility decide which damage management strategy provides the most return for its investment? Aptech Engineering Services, Inc. (APTECH) developed a damage management strategy that starts from a deterministic analysis of a current problem- primary water stress corrosion cracking (PWSCC). The strategy involves a probabilistic treatment that results in long-term performance optimization. By optimization, we refer to minimizing the total cost of operating the steam generator. This total includes the present value costs of operations, maintenance, outages, and replacements. An example of the application of this methodology is presented. (author)

Using genetic algorithms to determine near-optimal pricing, investment and operating strategies in the electric power industry

Science.gov (United States)

Wu, Dongjun

Network industries have technologies characterized by a spatial hierarchy, the "network," with capital-intensive interconnections and time-dependent, capacity-limited flows of products and services through the network to customers. This dissertation studies service pricing, investment and business operating strategies for the electric power network. First-best solutions for a variety of pricing and investment problems have been studied. The evaluation of genetic algorithms (GA, which are methods based on the idea of natural evolution) as a primary means of solving complicated network problems, both w.r.t. pricing: as well as w.r.t. investment and other operating decisions, has been conducted. New constraint-handling techniques in GAs have been studied and tested. The actual application of such constraint-handling techniques in solving practical non-linear optimization problems has been tested on several complex network design problems with encouraging initial results. Genetic algorithms provide solutions that are feasible and close to optimal when the optimal solution is know; in some instances, the near-optimal solutions for small problems by the proposed GA approach can only be tested by pushing the limits of currently available non-linear optimization software. The performance is far better than several commercially available GA programs, which are generally inadequate in solving any of the problems studied in this dissertation, primarily because of their poor handling of constraints. Genetic algorithms, if carefully designed, seem very promising in solving difficult problems which are intractable by traditional analytic methods.

Control strategies for wind farm power optimization: LES study

Science.gov (United States)

Ciri, Umberto; Rotea, Mario; Leonardi, Stefano

2017-11-01

Turbines in wind farms operate in off-design conditions as wake interactions occur for particular wind directions. Advanced wind farm control strategies aim at coordinating and adjusting turbine operations to mitigate power losses in such conditions. Coordination is achieved by controlling on upstream turbines either the wake intensity, through the blade pitch angle or the generator torque, or the wake direction, through yaw misalignment. Downstream turbines can be adapted to work in waked conditions and limit power losses, using the blade pitch angle or the generator torque. As wind conditions in wind farm operations may change significantly, it is difficult to determine and parameterize the variations of the coordinated optimal settings. An alternative is model-free control and optimization of wind farms, which does not require any parameterization and can track the optimal settings as conditions vary. In this work, we employ a model-free optimization algorithm, extremum-seeking control, to find the optimal set-points of generator torque, blade pitch and yaw angle for a three-turbine configuration. Large-Eddy Simulations are used to provide a virtual environment to evaluate the performance of the control strategies under realistic, unsteady incoming wind. This work was supported by the National Science Foundation, Grants No. 1243482 (the WINDINSPIRE project) and IIP 1362033 (I/UCRC WindSTAR). TACC is acknowledged for providing computational time.

An Equivalent Emission Minimization Strategy for Causal Optimal Control of Diesel Engines

Directory of Open Access Journals (Sweden)

Stephan Zentner

2014-02-01

Full Text Available One of the main challenges during the development of operating strategies for modern diesel engines is the reduction of the CO2 emissions, while complying with ever more stringent limits for the pollutant emissions. The inherent trade-off between the emissions of CO2 and pollutants renders a simultaneous reduction difficult. Therefore, an optimal operating strategy is sought that yields minimal CO2 emissions, while holding the cumulative pollutant emissions at the allowed level. Such an operating strategy can be obtained offline by solving a constrained optimal control problem. However, the final-value constraint on the cumulated pollutant emissions prevents this approach from being adopted for causal control. This paper proposes a framework for causal optimal control of diesel engines. The optimization problem can be solved online when the constrained minimization of the CO2 emissions is reformulated as an unconstrained minimization of the CO2 emissions and the weighted pollutant emissions (i.e., equivalent emissions. However, the weighting factors are not known a priori. A method for the online calculation of these weighting factors is proposed. It is based on the Hamilton–Jacobi–Bellman (HJB equation and a physically motivated approximation of the optimal cost-to-go. A case study shows that the causal control strategy defined by the online calculation of the equivalence factor and the minimization of the equivalent emissions is only slightly inferior to the non-causal offline optimization, while being applicable to online control.

Modelling and optimal operation of a small-scale integrated energy based district heating and cooling system

International Nuclear Information System (INIS)

Jing, Z.X.; Jiang, X.S.; Wu, Q.H.; Tang, W.H.; Hua, B.

2014-01-01

This paper presents a comprehensive model of a small-scale integrated energy based district heating and cooling (DHC) system located in a residential area of hot-summer and cold-winter zone, which makes joint use of wind energy, solar energy, natural gas and electric energy. The model includes an off-grid wind turbine generator, heat producers, chillers, a water supply network and terminal loads. This research also investigates an optimal operating strategy based on Group Search Optimizer (GSO), through which the daily running cost of the system is optimized in both the heating and cooling modes. The strategy can be used to find the optimal number of operating chillers, optimal outlet water temperature set points of boilers and optimal water flow set points of pumps, taking into account cost functions and various operating constraints. In order to verify the model and the optimal operating strategy, performance tests have been undertaken using MATLAB. The simulation results prove the validity of the model and show that the strategy is able to minimize the system operation cost. The proposed system is evaluated in comparison with a conventional separation production (SP) system. The feasibility of investment for the DHC system is also discussed. The comparative results demonstrate the investment feasibility, the significant energy saving and the cost reduction, achieved in daily operation in an environment, where there are varying heating loads, cooling loads, wind speeds, solar radiations and electricity prices. - Highlights: • A model of a small-scale integrated energy based DHC system is presented. • An off-grid wind generator used for water heating is embedded in the model. • An optimal control strategy is studied to optimize the running cost of the system. • The designed system is proved to be energy efficient and cost effective in operation

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

DEFF Research Database (Denmark)

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

2011-01-01

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

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

DEFF Research Database (Denmark)

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

2011-01-01

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

Optimal day-ahead operational planning of microgrids

International Nuclear Information System (INIS)

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

2016-01-01

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

Service Operations Optimization: Recent Development in Supply Chain Management

Directory of Open Access Journals (Sweden)

Bin Shen

2015-01-01

Full Text Available Services are the key of success in operation management. Designing the effective strategies by optimization techniques is the fundamental and important condition for performance increase in service operations (SOs management. In this paper, we mainly focus on investigating SOs optimization in the areas of supply chain management, which create the greatest business values. Specifically, we study the recent development of SOs optimization associated with supply chain by categorizing them into four different industries (i.e., e-commerce industry, consumer service industry, public sector, and fashion industry and four various SOs features (i.e., advertising, channel coordination, pricing, and inventory. Moreover, we conduct the technical review on the stylish industries/topics and typical optimization models. The classical optimization approaches for SOs management in supply chain are presented. The managerial implications of SOs in supply chain are discussed.

A two-level strategy to realize life-cycle production optimization in an operational setting

NARCIS (Netherlands)

Essen, van G.M.; Hof, Van den P.M.J.; Jansen, J.D.

2012-01-01

We present a two-level strategy to improve robustness against uncertainty and model errors in life-cycle flooding optimization. At the upper level, a physics-based large-scale reservoir model is used to determine optimal life-cycle injection and production profiles. At the lower level these profiles

A two-level strategy to realize life-cycle production optimization in an operational setting

NARCIS (Netherlands)

Essen, van G.M.; Hof, Van den P.M.J.; Jansen, J.D.

2013-01-01

We present a two-level strategy to improve robustness against uncertainty and model errors in life-cycle flooding optimization. At the upper level, a physics-based large-scale reservoir model is used to determine optimal life-cycle injection and production profiles. At the lower level these profiles

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

International Nuclear Information System (INIS)

Connolly, D.; Lund, H.; Finn, P.; Mathiesen, B.V.; Leahy, M.

2011-01-01

In this paper, three practical operation strategies (24Optimal, 24Prognostic, and 24Hsitrocial) are compared to the optimum profit feasible for a PHES facility with a 360 MW pump, 300 MW turbine, and a 2 GWh storage utilising price arbitrage on 13 electricity spot markets. The results indicate that almost all (∼97%) of the profits can be obtained by a PHES facility when it is optimised using the 24Optimal strategy developed, which optimises the energy storage based on the day-ahead electricity prices. However, to maximise profits with the 24Optimal strategy, the day-ahead electricity prices must be the actual prices which the PHES facility is charged or the PHES operator must have very accurate price predictions. Otherwise, the predicted profit could be significantly reduced and even become a loss. Finally, using the 24Optimal strategy, the PHES profit can surpass the annual investment repayments required. However, over the 5-year period investigated (2005-2009) the annual profit from the PHES facility varied by more than 50% on five out of six electricity markets considered. Considering the 40-year lifetime of PHES, even with low investment costs, a low interest rate, and a suitable electricity market, PHES is a risky investment without a more predictable profit. - Highlights: → Electricity generators typically operate on a market, including energy storage. → This paper assesses how energy storage can maximise its profits on a market. → Four operating strategies are assessed on 13 markets using a case study.→ One operating strategy achieves 97% of the profits feasible.→ However, the profit varies a lot depending on the market and capital costs.

Optimal control of operation efficiency of belt conveyor systems

International Nuclear Information System (INIS)

Zhang, Shirong; Xia, Xiaohua

2010-01-01

The improvement of the energy efficiency of belt conveyor systems can be achieved at equipment or operation levels. Switching control and variable speed control are proposed in literature to improve energy efficiency of belt conveyors. The current implementations mostly focus on lower level control loops or an individual belt conveyor without operational considerations at the system level. In this paper, an optimal switching control and a variable speed drive (VSD) based optimal control are proposed to improve the energy efficiency of belt conveyor systems at the operational level, where time-of-use (TOU) tariff, ramp rate of belt speed and other system constraints are considered. A coal conveying system in a coal-fired power plant is taken as a case study, where great saving of energy cost is achieved by the two optimal control strategies. Moreover, considerable energy saving resulting from VSD based optimal control is also proved by the case study.

Optimal control of operation efficiency of belt conveyor systems

Energy Technology Data Exchange (ETDEWEB)

Zhang, Shirong [Department of Automation, Wuhan University, Wuhan 430072 (China); Xia, Xiaohua [Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002 (South Africa)

2010-06-15

The improvement of the energy efficiency of belt conveyor systems can be achieved at equipment or operation levels. Switching control and variable speed control are proposed in literature to improve energy efficiency of belt conveyors. The current implementations mostly focus on lower level control loops or an individual belt conveyor without operational considerations at the system level. In this paper, an optimal switching control and a variable speed drive (VSD) based optimal control are proposed to improve the energy efficiency of belt conveyor systems at the operational level, where time-of-use (TOU) tariff, ramp rate of belt speed and other system constraints are considered. A coal conveying system in a coal-fired power plant is taken as a case study, where great saving of energy cost is achieved by the two optimal control strategies. Moreover, considerable energy saving resulting from VSD based optimal control is also proved by the case study. (author)

Modelling and operation strategies of DLR's large scale thermocline test facility (TESIS)

Science.gov (United States)

Odenthal, Christian; Breidenbach, Nils; Bauer, Thomas

2017-06-01

In this work an overview of the TESIS:store thermocline test facility and its current construction status will be given. Based on this, the TESIS:store facility using sensible solid filler material is modelled with a fully transient model, implemented in MATLAB®. Results in terms of the impact of filler site and operation strategies will be presented. While low porosity and small particle diameters for the filler material are beneficial, operation strategy is one key element with potential for optimization. It is shown that plant operators have to ponder between utilization and exergetic efficiency. Different durations of the charging and discharging period enable further potential for optimizations.

Achieving clean and efficient engine operation up to full load by combining optimized RCCI and dual-fuel diesel-gasoline combustion strategies

International Nuclear Information System (INIS)

Benajes, Jesús; García, Antonio; Monsalve-Serrano, Javier; Boronat, Vicente

2017-01-01

Highlights: • Optimized dual-fuel strategy to cover the whole engine load-speed map. • EURO VI NOx levels up to 14 bar IMEP with fully and highly premixed RCCI strategies. • Dual-fuel provides up to 7% higher efficiency than CDC if urea consumption is considered. - Abstract: This experimental work investigates the capabilities of the reactivity controlled compression ignition combustion concept to be operated in the whole engine map and discusses its benefits when compared to conventional diesel combustion. The experiments were conducted using a single-cylinder medium-duty diesel engine fueled with regular gasoline and diesel fuels. The main modification on the stock engine architecture was the addition of a port fuel injector in the intake manifold. In addition, with the aim of extending the reactivity controlled compression ignition operating range towards higher loads, the piston bowl volume was increased to reduce the compression ratio of the engine from 17.5:1 (stock) down to 15.3:1. To allow the dual-fuel operation over the whole engine map without exceeding the mechanical limitations of the engine, an optimized dual-fuel combustion strategy is proposed in this research. The combustion strategy changes as the engine load increases, starting from a fully premixed reactivity controlled compression ignition combustion up to around 8 bar IMEP, then switching to a highly premixed reactivity controlled compression ignition combustion up to 15 bar IMEP, and finally moving to a mainly diffusive dual-fuel combustion to reach the full load operation. The engine mapping results obtained using this combustion strategy show that reactivity controlled compression ignition combustion allows fulfilling the EURO VI NOx limit up to 14 bar IMEP. Ultra-low soot emissions are also achieved when the fully premixed combustion is promoted, however, the soot levels rise notably as the combustion strategy moves to a less premixed pattern. Finally, the direct comparison of

An optimal inspection strategy for randomly failing equipment

International Nuclear Information System (INIS)

Chelbi, Anis; Ait-Kadi, Daoud

1999-01-01

This paper addresses the problem of generating optimal inspection strategies for randomly failing equipment where imminent failure is not obvious and can only be detected through inspection. Inspections are carried out following a condition-based procedure. The equipment is replaced if it has failed or if it shows imminent signs of failure. The latter state is indicated by measuring certain predetermined control parameters during inspection. Costs are associated with inspection, idle time and preventive or corrective actions. An optimal inspection strategy is defined as the inspection sequence minimizing the expected total cost per time unit over an infinite span. A mathematical model and a numerical algorithm are developed to generate an optimal inspection sequence. As a practical example, the model is applied to provide a machine tool operator with a time sequence for inspecting the cutting tool. The tool life time distribution and the trend of one control parameter defining its actual condition are supposed to be known

Optimization of Operations Resources via Discrete Event Simulation Modeling

Science.gov (United States)

Joshi, B.; Morris, D.; White, N.; Unal, R.

1996-01-01

The resource levels required for operation and support of reusable launch vehicles are typically defined through discrete event simulation modeling. Minimizing these resources constitutes an optimization problem involving discrete variables and simulation. Conventional approaches to solve such optimization problems involving integer valued decision variables are the pattern search and statistical methods. However, in a simulation environment that is characterized by search spaces of unknown topology and stochastic measures, these optimization approaches often prove inadequate. In this paper, we have explored the applicability of genetic algorithms to the simulation domain. Genetic algorithms provide a robust search strategy that does not require continuity and differentiability of the problem domain. The genetic algorithm successfully minimized the operation and support activities for a space vehicle, through a discrete event simulation model. The practical issues associated with simulation optimization, such as stochastic variables and constraints, were also taken into consideration.

A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy

Directory of Open Access Journals (Sweden)

Guohua Wu

2014-01-01

Full Text Available Although Particle Swarm Optimization (PSO has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSO-IVL is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSO-IVL. The effectiveness of the PSO-IVL stresses a usefulness of augmenting evolutionary algorithms by problem-oriented domain knowledge.

Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

Directory of Open Access Journals (Sweden)

Tinggui Chen

2014-01-01

Full Text Available Artificial bee colony (ABC algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA, artificial colony optimization (ACO, and particle swarm optimization (PSO. However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.

Optimal operation of a micro-combined cooling, heating and power system driven by a gas engine

International Nuclear Information System (INIS)

Kong, X.Q.; Wang, R.Z.; Li, Y.; Huang, X.H.

2009-01-01

The objective of this paper is to investigate the problem of energy management and optimal operation of cogeneration system for micro-combined cooling, heating and power production (CCHP). The energy system mainly consists of a gas engine, an adsorption chiller, a gas boiler, a heat exchanger and an electric chiller. On the basis of an earlier experimental research of the micro-CCHP system, a non-linear-programming cost-minimization optimization model is presented to determine the optimum operational strategies for the system. It is shown that energy management and optimal operation of the micro-CCHP system is dependent upon load conditions to be satisfied and energy cost. In view of energy cost, it would not be optimal to operate the gas engine when the electric-to-gas cost ratio (EGCR) is very low. With higher EGCR, the optimum operational strategy of the micro-CCHP system is independent of energy cost

Optimal management strategies in variable environments: Stochastic optimal control methods

Science.gov (United States)

Williams, B.K.

1985-01-01

Dynamic optimization was used to investigate the optimal defoliation of salt desert shrubs in north-western Utah. Management was formulated in the context of optimal stochastic control theory, with objective functions composed of discounted or time-averaged biomass yields. Climatic variability and community patterns of salt desert shrublands make the application of stochastic optimal control both feasible and necessary. A primary production model was used to simulate shrub responses and harvest yields under a variety of climatic regimes and defoliation patterns. The simulation results then were used in an optimization model to determine optimal defoliation strategies. The latter model encodes an algorithm for finite state, finite action, infinite discrete time horizon Markov decision processes. Three questions were addressed: (i) What effect do changes in weather patterns have on optimal management strategies? (ii) What effect does the discounting of future returns have? (iii) How do the optimal strategies perform relative to certain fixed defoliation strategies? An analysis was performed for the three shrub species, winterfat (Ceratoides lanata), shadscale (Atriplex confertifolia) and big sagebrush (Artemisia tridentata). In general, the results indicate substantial differences among species in optimal control strategies, which are associated with differences in physiological and morphological characteristics. Optimal policies for big sagebrush varied less with variation in climate, reserve levels and discount rates than did either shadscale or winterfat. This was attributed primarily to the overwintering of photosynthetically active tissue and to metabolic activity early in the growing season. Optimal defoliation of shadscale and winterfat generally was more responsive to differences in plant vigor and climate, reflecting the sensitivity of these species to utilization and replenishment of carbohydrate reserves. Similarities could be seen in the influence of both

Nuclear Power Plant Outage Optimization Strategy. 2016 Edition

International Nuclear Information System (INIS)

2016-10-01

This publication is an update of IAEA-TECDOC-1315, Nuclear Power Plant Outage Optimisation Strategy, which was published in 2002, and aims to communicate good outage management practices in a manner that can be used by operators and utilities in Member States. Nuclear power plant outage management is a key factor for safe and economic nuclear power plant performance. This publication discusses plant outage strategy and how this strategy is actually implemented. The main areas that are important for outage optimization that were identified by the utilities and government organizations participating in this report are: 1) organization and management; 2) outage planning and preparation; 3) outage execution; 4) safety outage review; and 5) counter measures to avoid the extension of outages and to facilitate the work in forced outages. Good outage management practices cover many different areas of work and this publication aims to communicate these good practices in a way that they can be used effectively by operators and utilities

Diffusion Strategy-Based Distributed Operation of Microgrids Using Multiagent System

Directory of Open Access Journals (Sweden)

Van-Hai Bui

2017-07-01

Full Text Available In distributed operation, each unit is operated by its local controller instead of using a centralized controller, which allows the action to be based on local information rather than global information. Most of the distributed solutions have implemented the consensus method, however, convergence time of the consensus method is quite long, while diffusion strategy includes a stochastic gradient term and can reach convergence much faster compared with consensus method. Therefore, in this paper, a diffusion strategy-based distributed operation of microgrids (MGs is proposed using multiagent system for both normal and emergency operation modes. In normal operation, the MG system is operated by a central controller instead of the distributed controller to minimize the operation cost. If any event (fault occurs in the system, MG system can be divided into two parts to isolate the faulty region. In this case, the MG system is changed to emergency operation mode. The normal part is rescheduled by the central controller while the isolated part schedules its resources in a distributed manner. The isolated part carries out distributed communication using diffusion between neighboring agents for optimal operation of this part. The proposed method enables peer-to-peer communication among the agents without the necessity of a centralized controller, and simultaneously performs resource optimization. Simulation results show that the system can be operated in an economic way in both normal operation and emergency operation modes.

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

International Nuclear Information System (INIS)

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

2015-01-01

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

Optimization and Model Validation of Operation Control Strategies for a Novel Dual-Motor Coupling-Propulsion Pure Electric Vehicle

Directory of Open Access Journals (Sweden)

Jianjun Hu

2018-03-01

Full Text Available The strict operational condition of driving motors for vehicles propels the development of more complicated configurations in pure electric vehicles (PEVs. Multi-power-source powertrain configurations are one of the efficient technologies to reduce the manufacturing difficulty of driving motors. However, most of the existing studies are predominantly focused on optimal designs of powertrains and power distribution between the engine and motor of hybrid electric vehicles, which are not appropriate for PEVs. This paper proposes a novel dual-motor coupling-propulsion powertrain system that improves the dynamic and economic performance of the powertrain system in PEVs. The proposed powertrain system can realize both the single-motor driving mode and dual-motor coupling driving mode. The driving modes are divided and a power distribution strategy for the different driving modes based on an optimal system efficiency rule is employed, which enhances the performance of the proposed system. Further, a mode-switching strategy that ensures driving comfort by preventing jerk during mode switching is incorporated into the system. The results of comparative evaluations that were conducted using a dual-motor electric vehicle model implemented in MATLAB/Simulink, indicate that the mileage and dynamic performance of the proposed powertrain system are significantly better than those of the traditional single-motor powertrain system.

Optimization of fuel-cell tram operation based on two dimension dynamic programming

Science.gov (United States)

Zhang, Wenbin; Lu, Xuecheng; Zhao, Jingsong; Li, Jianqiu

2018-02-01

This paper proposes an optimal control strategy based on the two-dimension dynamic programming (2DDP) algorithm targeting at minimizing operation energy consumption for a fuel-cell tram. The energy consumption model with the tram dynamics is firstly deduced. Optimal control problem are analyzed and the 2DDP strategy is applied to solve the problem. The optimal tram speed profiles are obtained for each interstation which consist of three stages: accelerate to the set speed with the maximum traction power, dynamically adjust to maintain a uniform speed and decelerate to zero speed with the maximum braking power at a suitable timing. The optimal control curves of all the interstations are connected with the parking time to form the optimal control method of the whole line. The optimized speed profiles are also simplified for drivers to follow.

Optimal strategy for selling on group-buying website

Directory of Open Access Journals (Sweden)

Xuan Jiang

2014-09-01

Full Text Available Purpose: The purpose of this paper is to help business marketers with offline channels to make decisions on whether to sell through Group-buying (GB websites and how to set online price with the coordination of maximum deal size on GB websites. Design/methodology/approach: Considering the deal structure of GB websites especially for the service fee and minimum deal size limit required by GB websites, advertising effect of selling on GB websites, and interaction between online and offline markets, an analytical model is built to derive optimal online price and maximum deal size for sellers selling through GB website. This paper aims to answer four research questions: (1 How to make a decision on maximum deal size with coordination of the deal price? (2 Will selling on GB websites always be better than staying with offline channel only? (3 What kind of products is more appropriate to sell on GB website? (4How could GB website operator induce sellers to offer deep discount in GB deals? Findings and Originality/value: This paper obtains optimal strategies for sellers selling on GB website and finds that: Even if a seller has sufficient capacity, he/she may still set a maximum deal size on the GB deal to take advantage of Advertisement with Limited Availability (ALA effect; Selling through GB website may not bring a higher profit than selling only through offline channel when a GB site only has a small consumer base and/or if there is a big overlap between the online and offline markets; Low margin products are more suitable for being sold online with ALA strategies (LP-ALA or HP-ALA than high margin ones; A GB site operator could set a small minimum deal size to induce deep discounts from the sellers selling through GB deals. Research limitations/implications: The present study assumed that the demand function is determinate and linear. It will be interesting to study how stochastic demand and a more general demand function affect the optimal

Operational strategies for nitrogen removal in granular sequencing batch reactor

International Nuclear Information System (INIS)

Chen, Fang-yuan; Liu, Yong-Qiang; Tay, Joo-Hwa; Ning, Ping

2011-01-01

This study investigated the effects of different operational strategies for nitrogen removal by aerobic granules with mean granule sizes of 1.5 mm and 0.7 mm in a sequencing batch reactor (SBR). With an alternating anoxic/oxic (AO) operation mode without control of dissolve oxygen (DO), the granular sludge with different size achieved the total inorganic nitrogen (TIN) removal efficiencies of 67.8-71.5%. While under the AO condition with DO controlled at 2 mg/l at the oxic phase, the TIN removal efficiency was improved up to 75.0-80.4%. A novel operational strategy of alternating anoxic/oxic combined with the step-feeding mode was developed for nitrogen removal by aerobic granules. It was found that nitrogen removal efficiencies could be further improved to 93.0-95.9% with the novel strategy. Obviously, the alternating anoxic/oxic strategy combined with step-feeding is the optimal way for TIN removal by granular sludge, which is independent of granule size.

Optimal Bidding Strategy for Renewable Microgrid with Active Network Management

Directory of Open Access Journals (Sweden)

Seung Wan Kim

2016-01-01

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

Development of an evaluation method for optimization of maintenance strategy in commercial plant

International Nuclear Information System (INIS)

Ito, Satoshi; Shiraishi, Natsuki; Yuki, Kazuhisa; Hashizume, Hidetoshi

2006-01-01

In this study, a new simulation method is developed for optimization of maintenance strategy in NPP as a multiple-objective optimization problem (MOP). The result of operation is evaluated as the average of the following three measures in 3,000 trials: Cost of Electricity (COE) as economic risk, Frequency of unplanned shutdown as plant reliability, and Unavailability of Regular Service System (RSS) and Engineering Safety Features (ESF) as safety measures. The following maintenance parameters are considered to evaluate several risk in plant operation by changing maintenance strategy: planned outage cycle, surveillance cycle, major inspection cycle, and surveillance cycle depending on the value of Fussel-Vesely importance measure. By using the Decision-Making method based on AHP, there are individual tendencies depending on individual decision-maker. Therefore this study could be useful for resolving the problem of maintenance optimization as a MOP. (author)

Optimal generator bidding strategies for power and ancillary services

Science.gov (United States)

Morinec, Allen G.

generator operating point in the P-Q plane. Four computer programs were developed to automatically perform the market auction simulations using the equal incremental cost rule. The software calculates the payoffs for the two competing competitors, dispatches six generators, and allocates ancillary services for 64 combinations of bidding strategies, three levels of system demand, and three different types of competitors. Matrix Game theory was utilized to calculate Nash Equilibrium solutions and mixed-strategy Nash solutions as the optimal generator bidding strategies. A method to incorporate ancillary services into the generation bidding strategy, to assure an adequate supply of ancillary services, and to allocate these necessary resources to the on-line units was devised. The optimal generator bid strategy in a power auction was shown to be the Nash Equilibrium solution found in two-player variable-sum matrix games.

Optimal energy management strategy for self-reconfigurable batteries

International Nuclear Information System (INIS)

Bouchhima, Nejmeddine; Schnierle, Marc; Schulte, Sascha; Birke, Kai Peter

2017-01-01

This paper proposes a novel energy management strategy for multi-cell high voltage batteries where the current through each cell can be controlled, called self-reconfigurable batteries. An optimized control strategy further enhances the energy efficiency gained by the hardware architecture of those batteries. Currently, achieving cell equalization by using the active balancing circuits is considered as the best way to optimize the energy efficiency of the battery pack. This study demonstrates that optimizing the energy efficiency of self-reconfigurable batteries is no more strongly correlated to the cell balancing. According to the features of this novel battery architecture, the energy management strategy is formulated as nonlinear dynamic optimization problem. To solve this optimal control, an optimization algorithm that generates the optimal discharge policy for a given driving cycle is developed based on dynamic programming and code vectorization. The simulation results show that the designed energy management strategy maximizes the system efficiency across the battery lifetime over conventional approaches. Furthermore, the present energy management strategy can be implemented online due to the reduced complexity of the optimization algorithm. - Highlights: • The energy efficiency of self-reconfigurable batteries is maximized. • The energy management strategy for the battery is formulated as optimal control problem. • Developing an optimization algorithm using dynamic programming techniques and code vectorization. • Simulation studies are conducted to validate the proposed optimal strategy.

Optimal economic and environment operation of micro-grid power systems

International Nuclear Information System (INIS)

Elsied, Moataz; Oukaour, Amrane; Gualous, Hamid; Lo Brutto, Ottavio A.

2016-01-01

Highlights: • Real-time energy management system for Micro-Grid power systems is introduced. • The management system considered cost objective function and emission constraints. • The optimization problem is solved using Binary Particle Swarm Algorithm. • Advanced real-time interface libraries are used to run the optimization code. - Abstract: In this paper, an advanced real-time energy management system is proposed in order to optimize micro-grid performance in a real-time operation. The proposed strategy of the management system capitalizes on the power of binary particle swarm optimization algorithm to minimize the energy cost and carbon dioxide and pollutant emissions while maximizing the power of the available renewable energy resources. Advanced real-time interface libraries are used to run the optimization code. The simulation results are considered for three different scenarios considering the complexity of the proposed problem. The proposed management system along with its control system is experimentally tested to validate the simulation results obtained from the optimization algorithm. The experimental results highlight the effectiveness of the proposed management system for micro-grids operation.

Geometric Process-Based Maintenance and Optimization Strategy for the Energy Storage Batteries

Directory of Open Access Journals (Sweden)

Yan Li

2016-01-01

Full Text Available Renewable energy is critical for improving energy structure and reducing environment pollution. But its strong fluctuation and randomness have a serious effect on the stability of the microgrid without the coordination of the energy storage batteries. The main factors that influence the development of the energy storage system are the lack of valid operation and maintenance management as well as the cost control. By analyzing the typical characteristics of the energy storage batteries in their life cycle, the geometric process-based model including the deteriorating system and the improving system is firstly built for describing the operation process, the preventive maintenance process, and the corrective maintenance process. In addition, this paper proposes an optimized management strategy, which aims to minimize the long-run average cost of the energy storage batteries by defining the time interval of the detection and preventive maintenance process as well as the optimal corrective maintenance times, subjected to the state of health and the reliability conditions. The simulation is taken under the built model by applying the proposed energy storage batteries’ optimized management strategy, which verifies the effectiveness and applicability of the management strategy, denoting its obvious practicality on the current application.

Impact of Fluctuating Energy Prices on the Operation Strategy of a Trigeneration System

Directory of Open Access Journals (Sweden)

Dražen Balić

2015-09-01

The optimization method is based on two criteria – energy and economic criterion, which were applied hierarchically. Therefore, two optimal operation strategies are introduced. A mixed integer non-linear programming model provides energy and cost savings up to 32% and 28% respectively in comparison with conventional system. In addition, optimal capacity of trigeneration system is explored.

Multi-objective optimization for the maximization of the operating share of cogeneration system in District Heating Network

International Nuclear Information System (INIS)

Franco, Alessandro; Versace, Michele

2017-01-01

Highlights: • Combined Heat and Power plants and civil/residential energy uses. • CHP plant supported by auxiliary boilers and thermal energy storage. • Definition of optimal operational strategies for cogeneration plants for District Heating. • Optimal-sized Thermal Energy Storage and a hybrid operational strategy. • Maximization of cogeneration share and reduction of time of operation of auxiliary boilers. - Abstract: The aim of the paper is to define optimal operational strategies for Combined Heat and Power plants connected to civil/residential District Heating Networks. The role of a reduced number of design variables, including a Thermal Energy Storage system and a hybrid operational strategy dependent on the storage level, is considered. The basic principle is to reach maximum efficiency of the system operation through the utilization of an optimal-sized Thermal Energy Storage. Objective functions of both energetic and combined energetic and economic can be considered. In particular, First and Second Law Efficiency, thermal losses of the storage, number of starts and stops of the combined heat and power unit are considered. Constraints are imposed to nullify the waste of heat and to operate the unit at its maximum efficiency for the highest possible number of consecutive operating hours, until the thermal tank cannot store more energy. The methodology is applied to a detailed case study: a medium size district heating system, in an urban context in the northern Italy, powered by a combined heat and power plant supported by conventional auxiliary boilers. The issues involving this type of thermal loads are also widely investigated in the paper. An increase of Second Law Efficiency of the system of 26% (from 0.35 to 0.44) can be evidenced, while the First Law Efficiency shifts from about 0.74 to 0.84. The optimization strategy permits of combining the economic benefit of cogeneration with the idea of reducing the energy waste and exergy losses.

Cost related sensitivity analysis for optimal operation of a grid-parallel PEM fuel cell power plant

Science.gov (United States)

El-Sharkh, M. Y.; Tanrioven, M.; Rahman, A.; Alam, M. S.

Fuel cell power plants (FCPP) as a combined source of heat, power and hydrogen (CHP&H) can be considered as a potential option to supply both thermal and electrical loads. Hydrogen produced from the FCPP can be stored for future use of the FCPP or can be sold for profit. In such a system, tariff rates for purchasing or selling electricity, the fuel cost for the FCPP/thermal load, and hydrogen selling price are the main factors that affect the operational strategy. This paper presents a hybrid evolutionary programming and Hill-Climbing based approach to evaluate the impact of change of the above mentioned cost parameters on the optimal operational strategy of the FCPP. The optimal operational strategy of the FCPP for different tariffs is achieved through the estimation of the following: hourly generated power, the amount of thermal power recovered, power trade with the local grid, and the quantity of hydrogen that can be produced. Results show the importance of optimizing system cost parameters in order to minimize overall operating cost.

Research of Ant Colony Optimized Adaptive Control Strategy for Hybrid Electric Vehicle

Directory of Open Access Journals (Sweden)

Linhui Li

2014-01-01

Full Text Available Energy management control strategy of hybrid electric vehicle has a great influence on the vehicle fuel consumption with electric motors adding to the traditional vehicle power system. As vehicle real driving cycles seem to be uncertain, the dynamic driving cycles will have an impact on control strategy’s energy-saving effect. In order to better adapt the dynamic driving cycles, control strategy should have the ability to recognize the real-time driving cycle and adaptively adjust to the corresponding off-line optimal control parameters. In this paper, four types of representative driving cycles are constructed based on the actual vehicle operating data, and a fuzzy driving cycle recognition algorithm is proposed for online recognizing the type of actual driving cycle. Then, based on the equivalent fuel consumption minimization strategy, an ant colony optimization algorithm is utilized to search the optimal control parameters “charge and discharge equivalent factors” for each type of representative driving cycle. At last, the simulation experiments are conducted to verify the accuracy of the proposed fuzzy recognition algorithm and the validity of the designed control strategy optimization method.

Synthesis of Optimal Strategies Using HyTech

DEFF Research Database (Denmark)

Bouyer, Patricia; Cassez, Franck; Larsen, Kim Guldstrand

2005-01-01

Priced timed (game) automata extend timed (game) automata with costs on both locations and transitions. The problem of synthesizing an optimal winning strategy for a priced timed game under some hypotheses has been shown decidable in [P. Bouyer, F. Cassez, E. Fleury, and K.G. Larsen. Optimal...... strategies in priced timed game automata. Research Report BRICS RS-04-4, Denmark, Feb. 2004. Available at http://www.brics.dk/RS/04/4/]. In this paper, we present an algorithm for computing the optimal cost and for synthesizing an optimal strategy in case there exists one. We also describe the implementation...

Optimization of power system operation

CERN Document Server

Zhu, Jizhong

2015-01-01

This book applies the latest applications of new technologies topower system operation and analysis, including new and importantareas that are not covered in the previous edition. Optimization of Power System Operation covers both traditional andmodern technologies, including power flow analysis, steady-statesecurity region analysis, security constrained economic dispatch,multi-area system economic dispatch, unit commitment, optimal powerflow, smart grid operation, optimal load shed, optimalreconfiguration of distribution network, power system uncertaintyanalysis, power system sensitivity analysis, analytic hierarchicalprocess, neural network, fuzzy theory, genetic algorithm,evolutionary programming, and particle swarm optimization, amongothers. New topics such as the wheeling model, multi-areawheeling, the total transfer capability computation in multipleareas, are also addressed. The new edition of this book continues to provide engineers andac demics with a complete picture of the optimization of techn...

Production of sugar and alcohol: financial and operational strategies

Directory of Open Access Journals (Sweden)

Celma de Oliveira Ribeiro

2014-12-01

Full Text Available This article proposes the construction of an optimization model to define the product portfolio of a sugarcane mill, taking into account operational and financial aspects. It is considered that the revenue earned by a producer comes from the sale of sugar and alcohol in the physical market and the results obtained through hedging in the derivatives market of sugar. Employing CVaR (Conditional Value-at-Risk, as the risk measure, the model allows the construction of an efficient frontier and, according to the producer's risk tolerance, defines the optimal strategy of production (production mix and activity in the derivatives market (hedge ratio. Through the model the article also seeks to analyze the advantage of using the options market in the construction of financial hedging strategies in agricultural commodities markets.

Optimization of maintenance strategies in case of data uncertainties; Optimierung von Instandhaltungsstrategien bei unscharfen Eingangsdaten

Energy Technology Data Exchange (ETDEWEB)

Aha, Ulrich

2013-07-01

Maintenance strategies are aimed to keep a technical facility functioning in spite of damaging processes (wear, corrosion, fatigue) with simultaneous control of these processes. The project optimization of maintenance strategies in case of data uncertainties is aimed to optimize maintenance measures like preventive measures (lubrication etc.), inspections and replacements to keep the facility/plant operating including the minimization of financial costs. The report covers the following topics: modeling assumptions, model development and optimization procedure, results for a conventional power plant and an oxyfuel plant.

Optimal Control Strategy Search Using a Simplest 3-D PWR Xenon Oscillation Simulator

International Nuclear Information System (INIS)

Yoichiro, Shimazu

2004-01-01

Power spatial oscillations due to the transient xenon spatial distribution are well known as xenon oscillation in large PWRs. When the reactor size becomes larger than the current design, then even radial oscillations can be also divergent. Even if the radial oscillation is convergent, when some control rods malfunction occurs, it is necessary to suppress the oscillation in as short time as possible. In such cases, optimal control strategy is required. Generally speaking the optimality search based on the modern control theory requires a lot of calculation for the evaluation of state variables. In the case of control rod malfunctions the xenon oscillation could be three dimensional. In such case, direct core calculations would be inevitable. From this point of view a very simple model, only four point reactor model, has been developed and verified. In this paper, an example of a procedure and the results for optimal control strategy search are presented. It is shown that we have only one optimal strategy within a half cycle of the oscillation with fixed control strength. It is also shown that a 3-D xenon oscillation introduced by a control rod malfunction can not be controlled by only one control step as can be done for axial oscillations. They might be quite strong limitations to the operators. Thus it is recommended that a strategy generator, which is quick in analyzing and easy to use, might be installed in a monitoring system or operator guiding system. (author)

An Optimal Portfolio and Capital Management Strategy for Basel III Compliant Commercial Banks

Directory of Open Access Journals (Sweden)

Grant E. Muller

2014-01-01

Full Text Available We model a Basel III compliant commercial bank that operates in a financial market consisting of a treasury security, a marketable security, and a loan and we regard the interest rate in the market as being stochastic. We find the investment strategy that maximizes an expected utility of the bank’s asset portfolio at a future date. This entails obtaining formulas for the optimal amounts of bank capital invested in different assets. Based on the optimal investment strategy, we derive a model for the Capital Adequacy Ratio (CAR, which the Basel Committee on Banking Supervision (BCBS introduced as a measure against banks’ susceptibility to failure. Furthermore, we consider the optimal investment strategy subject to a constant CAR at the minimum prescribed level. We derive a formula for the bank’s asset portfolio at constant (minimum CAR value and present numerical simulations on different scenarios. Under the optimal investment strategy, the CAR is above the minimum prescribed level. The value of the asset portfolio is improved if the CAR is at its (constant minimum value.

Are the Economically Optimal Harvesting Strategies of Uneven-Aged Pinus nigra Stands Always Sustainable and Stabilizing?

Directory of Open Access Journals (Sweden)

Carmen Fullana-Belda

2013-10-01

Full Text Available Traditional uneven-aged forest management seeks a balance between equilibrium stand structure and economic profitability, which often leads to harvesting strategies concentrated in the larger diameter classes. The sustainability (i.e., population persistence over time and influence of such economically optimal strategies on the equilibrium position of a stand (given by the stable diameter distribution have not been sufficiently investigated in prior forest literature. This article therefore proposes a discrete optimal control model to analyze the sustainability and stability of the economically optimal harvesting strategies of uneven-aged Pinus nigra stands. For this model, we rely on an objective function that integrates financial data of harvesting operations with a projection matrix model that can describe the population dynamics. The model solution reveals the optimal management schedules for a wide variety of scenarios. To measure the distance between the stable diameter distribution and the economically optimal harvesting strategy distribution, the model uses Keyfitz’s delta, which returns high values for all the scenarios and, thus, suggests that those economically optimal harvesting strategies have an unstabilizing influence on the equilibrium positions. Moreover, the economically optimal harvesting strategies were unsustainable for all the scenarios.

Energy evaluation of optimal control strategies for central VWV chiller systems

International Nuclear Information System (INIS)

Jin Xinqiao; Du Zhimin; Xiao Xiaokun

2007-01-01

Under various conditions, the actual load of the heating, ventilation and air conditioning (HVAC) systems is less than it is originally designed in most operation periods. To save energy and to optimize the controls for chilling systems, the performance of variable water volume (VWV) systems and characteristics of control systems are analyzed, and three strategies are presented and tested based on simulation in this paper. Energy evaluation for the three strategies shows that they can save energy to some extent, and there is potential remained. To minimize the energy consumption of chilling system, the setpoints of controls of supply chilled water temperature and supply head of secondary pump should be optimized simultaneously

Multi-objective optimal operation of smart reconfigurable distribution grids

Directory of Open Access Journals (Sweden)

Abdollah Kavousi-Fard

2016-02-01

Full Text Available Reconfiguration is a valuable technique that can support the distribution grid from different aspects such as operation cost and loss reduction, reliability improvement, and voltage stability enhancement. An intelligent and efficient optimization framework, however, is required to reach the desired efficiency through the reconfiguration strategy. This paper proposes a new multi-objective optimization model to make use of the reconfiguration strategy for minimizing the power losses, improving the voltage profile, and enhancing the load balance in distribution grids. The proposed model employs the min-max fuzzy approach to find the most satisfying solution from a set of nondominated solutions in the problem space. Due to the high complexity and the discrete nature of the proposed model, a new optimization method based on harmony search (HS algorithm is further proposed. Moreover, a new modification method is suggested to increase the harmony memory diversity in the improvisation stage and increase the convergence ability of the algorithm. The feasibility and satisfying performance of the proposed model are examined on the IEEE 32-bus distribution system.

Operating cycle optimization for a Magnus effect-based airborne wind energy system

International Nuclear Information System (INIS)

Milutinović, Milan; Čorić, Mirko; Deur, Joško

2015-01-01

Highlights: • Operating cycle of a Magnus effect-based AWE system has been optimized. • The cycle trajectory should be vertical and far from the ground based generator. • Vertical trajectory provides high pulling force that drives the generator. • Large distance from the generator is required for the feasibility of the cycle. - Abstract: The paper presents a control variables optimization study for an airborne wind energy production system. The system comprises an airborne module in the form of a buoyant, rotating cylinder, whose rotation in a wind stream induces the Magnus effect-based aerodynamic lift. Through a tether, the airborne module first drives the generator fixed on the ground, and then the generator becomes a motor that lowers the airborne module. The optimization is aimed at maximizing the average power produced at the generator during a continuously repeatable operating cycle. The control variables are the generator-side rope force and the cylinder rotation speed. The optimization is based on a multi-phase problem formulation, where operation is divided into ascending and descending phases, with free boundary conditions and free cycle duration. The presented simulation results show that significant power increase can be achieved by using the obtained optimal operating cycle instead of the initial, empirically based operation control strategy. A brief analysis is also given to provide a physical interpretation of the optimal cycle results

Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model

Directory of Open Access Journals (Sweden)

Shuai Su

2016-02-01

Full Text Available Increasing attention is being paid to the energy efficiency in metro systems to reduce the operational cost and to advocate the sustainability of railway systems. Classical research has studied the energy-efficient operational strategy and the energy-efficient system design separately to reduce the traction energy consumption. This paper aims to combine the operational strategies and the system design by analyzing how the infrastructure and vehicle parameters of metro systems influence the operational traction energy consumption. Firstly, a solution approach to the optimal train control model is introduced, which is used to design the Optimal Train Control Simulator(OTCS. Then, based on the OTCS, the performance of some important energy-efficient system design strategies is investigated to reduce the trains’ traction energy consumption, including reduction of the train mass, improvement of the kinematic resistance, the design of the energy-saving gradient, increasing the maximum traction and braking forces, introducing regenerative braking and timetable optimization. As for these energy-efficient strategies, the performances are finally evaluated using the OTCS with the practical operational data of the Beijing Yizhuang metro line. The proposed approach gives an example to quantitatively analyze the energy reduction of different strategies in the system design procedure, which may help the decision makers to have an overview of the energy-efficient performances and then to make decisions by balancing the costs and the benefits.

Operations Strategy in practice

DEFF Research Database (Denmark)

Rytter, Niels Gorm; Koch, Christian; Boer, Harry

2005-01-01

In this paper, we describe and illustrate a new, action-based longitudinal case study approach, which aims at helping scholars narrow the gap between the theory and practice of Operations Strategy (OS). First, we elaborate on the need for new research methods for studying OS in practice. Then, we...... and disadvantages of the method. Finally, we draw conclusions on its potential for operations strategy and operations management studies.......In this paper, we describe and illustrate a new, action-based longitudinal case study approach, which aims at helping scholars narrow the gap between the theory and practice of Operations Strategy (OS). First, we elaborate on the need for new research methods for studying OS in practice. Then, we...

Operation optimization of a distributed energy system considering energy costs and exergy efficiency

International Nuclear Information System (INIS)

Di Somma, M.; Yan, B.; Bianco, N.; Graditi, G.; Luh, P.B.; Mongibello, L.; Naso, V.

2015-01-01

Highlights: • Operation optimization model of a Distributed Energy System (DES). • Multi-objective strategy to optimize energy cost and exergy efficiency. • Exergy analysis in building energy supply systems. - Abstract: With the growing demand of energy on a worldwide scale, improving the efficiency of energy resource use has become one of the key challenges. Application of exergy principles in the context of building energy supply systems can achieve rational use of energy resources by taking into account the different quality levels of energy resources as well as those of building demands. This paper is on the operation optimization of a Distributed Energy System (DES). The model involves multiple energy devices that convert a set of primary energy carriers with different energy quality levels to meet given time-varying user demands at different energy quality levels. By promoting the usage of low-temperature energy sources to satisfy low-quality thermal energy demands, the waste of high-quality energy resources can be reduced, thereby improving the overall exergy efficiency. To consider the economic factor as well, a multi-objective linear programming problem is formulated. The Pareto frontier, including the best possible trade-offs between the economic and exergetic objectives, is obtained by minimizing a weighted sum of the total energy cost and total primary exergy input using branch-and-cut. The operation strategies of the DES under different weights for the two objectives are discussed. The operators of DESs can choose the operation strategy from the Pareto frontier based on costs, essential in the short run, and sustainability, crucial in the long run. The contribution of each energy device in reducing energy costs and the total exergy input is also analyzed. In addition, results show that the energy cost can be much reduced and the overall exergy efficiency can be significantly improved by the optimized operation of the DES as compared with the

Neuro-optimal operation of a variable air volume HVAC and R system

International Nuclear Information System (INIS)

Ning Min; Zaheeruddin, M.

2010-01-01

Low operational efficiency especially under partial load conditions and poor control are some reasons for high energy consumption of heating, ventilation, air conditioning and refrigeration (HVAC and R) systems. To improve energy efficiency, HVAC and R systems should be efficiently operated to maintain a desired indoor environment under dynamic ambient and indoor conditions. This study proposes a neural network based optimal supervisory operation strategy to find the optimal set points for chilled water supply temperature, discharge air temperature and VAV system fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. Simulation results show that compared to the conventional night reset operation scheme, the optimal operation scheme saves around 10% energy under full load condition and 19% energy under partial load conditions.

Turbine Control Strategies for Wind Farm Power Optimization

DEFF Research Database (Denmark)

Mirzaei, Mahmood; Göçmen Bozkurt, Tuhfe; Giebel, Gregor

2015-01-01

In recent decades there has been increasing interest in green energies, of which wind energy is the most important one. In order to improve the competitiveness of the wind power plants, there are ongoing researches to decrease cost per energy unit and increase the efficiency of wind turbines...... and wind farms. One way of achieving these goals is to optimize the power generated by a wind farm. One optimization method is to choose appropriate operating points for the individual wind turbines in the farm. We have made three models of a wind farm based on three difference control strategies...... the generated power by changing the power reference of the individual wind turbines. We use the optimization setup to compare power production of the wind farm models. This paper shows that for the most frequent wind velocities (below and around the rated values), the generated powers of the wind farms...

An advanced Lithium-ion battery optimal charging strategy based on a coupled thermoelectric model

International Nuclear Information System (INIS)

Liu, Kailong; Li, Kang; Yang, Zhile; Zhang, Cheng; Deng, Jing

2017-01-01

Lithium-ion batteries are widely adopted as the power supplies for electric vehicles. A key but challenging issue is to achieve optimal battery charging, while taking into account of various constraints for safe, efficient and reliable operation. In this paper, a triple-objective function is first formulated for battery charging based on a coupled thermoelectric model. An advanced optimal charging strategy is then proposed to develop the optimal constant-current-constant-voltage (CCCV) charge current profile, which gives the best trade-off among three conflicting but important objectives for battery management. To be specific, a coupled thermoelectric battery model is first presented. Then, a specific triple-objective function consisting of three objectives, namely charging time, energy loss, and temperature rise (both the interior and surface), is proposed. Heuristic methods such as Teaching-learning-based-optimization (TLBO) and particle swarm optimization (PSO) are applied to optimize the triple-objective function, and their optimization performances are compared. The impacts of the weights for different terms in the objective function are then assessed. Experimental results show that the proposed optimal charging strategy is capable of offering desirable effective optimal charging current profiles and a proper trade-off among the conflicting objectives. Further, the proposed optimal charging strategy can be easily extended to other battery types.

Optimization of Key Parameters of Energy Management Strategy for Hybrid Electric Vehicle Using DIRECT Algorithm

Directory of Open Access Journals (Sweden)

Jingxian Hao

2016-11-01

Full Text Available The rule-based logic threshold control strategy has been frequently used in energy management strategies for hybrid electric vehicles (HEVs owing to its convenience in adjusting parameters, real-time performance, stability, and robustness. However, the logic threshold control parameters cannot usually ensure the best vehicle performance at different driving cycles and conditions. For this reason, the optimization of key parameters is important to improve the fuel economy, dynamic performance, and drivability. In principle, this is a multiparameter nonlinear optimization problem. The logic threshold energy management strategy for an all-wheel-drive HEV is comprehensively analyzed and developed in this study. Seven key parameters to be optimized are extracted. The optimization model of key parameters is proposed from the perspective of fuel economy. The global optimization method, DIRECT algorithm, which has good real-time performance, low computational burden, rapid convergence, is selected to optimize the extracted key parameters globally. The results show that with the optimized parameters, the engine operates more at the high efficiency range resulting into a fuel savings of 7% compared with non-optimized parameters. The proposed method can provide guidance for calibrating the parameters of the vehicle energy management strategy from the perspective of fuel economy.

Conditions for characterizing the structure of optimal strategies in infinite-horizon dynamic programs

International Nuclear Information System (INIS)

Porteus, E.

1982-01-01

The study of infinite-horizon nonstationary dynamic programs using the operator approach is continued. The point of view here differs slightly from that taken by others, in that Denardo's local income function is not used as a starting point. Infinite-horizon values are defined as limits of finite-horizon values, as the horizons get long. Two important conditions of an earlier paper are weakened, yet the optimality equations, the optimality criterion, and the existence of optimal ''structured'' strategies are still obtained

Optimization of startup and shutdown operation of simulated moving bed chromatographic processes.

Science.gov (United States)

Li, Suzhou; Kawajiri, Yoshiaki; Raisch, Jörg; Seidel-Morgenstern, Andreas

2011-06-24

This paper presents new multistage optimal startup and shutdown strategies for simulated moving bed (SMB) chromatographic processes. The proposed concept allows to adjust transient operating conditions stage-wise, and provides capability to improve transient performance and to fulfill product quality specifications simultaneously. A specially tailored decomposition algorithm is developed to ensure computational tractability of the resulting dynamic optimization problems. By examining the transient operation of a literature separation example characterized by nonlinear competitive isotherm, the feasibility of the solution approach is demonstrated, and the performance of the conventional and multistage optimal transient regimes is evaluated systematically. The quantitative results clearly show that the optimal operating policies not only allow to significantly reduce both duration of the transient phase and desorbent consumption, but also enable on-spec production even during startup and shutdown periods. With the aid of the developed transient procedures, short-term separation campaigns with small batch sizes can be performed more flexibly and efficiently by SMB chromatography. Copyright © 2011 Elsevier B.V. All rights reserved.

Resilient Grid Operational Strategies

Energy Technology Data Exchange (ETDEWEB)

Pasqualini, Donatella [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-03-01

Extreme weather-related disturbances, such as hurricanes, are a leading cause of grid outages historically. Although physical asset hardening is perhaps the most common way to mitigate the impacts of severe weather, operational strategies may be deployed to limit the extent of societal and economic losses associated with weather-related physical damage.1 The purpose of this study is to examine bulk power-system operational strategies that can be deployed to mitigate the impact of severe weather disruptions caused by hurricanes, thereby increasing grid resilience to maintain continuity of critical infrastructure during extreme weather. To estimate the impacts of resilient grid operational strategies, Los Alamos National Laboratory (LANL) developed a framework for hurricane probabilistic risk analysis (PRA). The probabilistic nature of this framework allows us to estimate the probability distribution of likely impacts, as opposed to the worst-case impacts. The project scope does not include strategies that are not operations related, such as transmission system hardening (e.g., undergrounding, transmission tower reinforcement and substation flood protection) and solutions in the distribution network.

Operation strategy of industrial crystallization for the production of 2,3,4,4'-tetrahydroxybenzophenon

Energy Technology Data Exchange (ETDEWEB)

Kim, Do Yeon; Yang, Dae Ryook [Korea University, Seoul (Korea, Republic of)

2015-07-15

To improve the filterability of hydroxybenzophenone crystal, a cooling strategy for the cooling crystallization process is investigated by examining the solubility and growth kinetics of hydroxybenzophenone. The operating strategy is divided into two steps. The first step is to generate the seed by dissolving the raw material and by changing operating conditions. The second step is to grow seeds to the product with desired crystal size distribution. For each part, an operating strategy has been proposed based on the solid-liquid phase equilibrium data in a ternary system and growth kinetic experimental results. The strategy for the first step is experimentally determined under various operating conditions, and the second one is determined by theoretical modeling and model-based optimization. The average crystal size resulting from the proposed strategy has been improved and the filterability has been enhanced compared to an existing strategy used in the industry.

Operation strategy of industrial crystallization for the production of 2,3,4,4'-tetrahydroxybenzophenon

International Nuclear Information System (INIS)

Kim, Do Yeon; Yang, Dae Ryook

2015-01-01

To improve the filterability of hydroxybenzophenone crystal, a cooling strategy for the cooling crystallization process is investigated by examining the solubility and growth kinetics of hydroxybenzophenone. The operating strategy is divided into two steps. The first step is to generate the seed by dissolving the raw material and by changing operating conditions. The second step is to grow seeds to the product with desired crystal size distribution. For each part, an operating strategy has been proposed based on the solid-liquid phase equilibrium data in a ternary system and growth kinetic experimental results. The strategy for the first step is experimentally determined under various operating conditions, and the second one is determined by theoretical modeling and model-based optimization. The average crystal size resulting from the proposed strategy has been improved and the filterability has been enhanced compared to an existing strategy used in the industry

Long-run savings and investment strategy optimization.

Science.gov (United States)

Gerrard, Russell; Guillén, Montserrat; Nielsen, Jens Perch; Pérez-Marín, Ana M

2014-01-01

We focus on automatic strategies to optimize life cycle savings and investment. Classical optimal savings theory establishes that, given the level of risk aversion, a saver would keep the same relative amount invested in risky assets at any given time. We show that, when optimizing lifecycle investment, performance and risk assessment have to take into account the investor's risk aversion and the maximum amount the investor could lose, simultaneously. When risk aversion and maximum possible loss are considered jointly, an optimal savings strategy is obtained, which follows from constant rather than relative absolute risk aversion. This result is fundamental to prove that if risk aversion and the maximum possible loss are both high, then holding a constant amount invested in the risky asset is optimal for a standard lifetime saving/pension process and outperforms some other simple strategies. Performance comparisons are based on downside risk-adjusted equivalence that is used in our illustration.

Long-Run Savings and Investment Strategy Optimization

Directory of Open Access Journals (Sweden)

Russell Gerrard

2014-01-01

Full Text Available We focus on automatic strategies to optimize life cycle savings and investment. Classical optimal savings theory establishes that, given the level of risk aversion, a saver would keep the same relative amount invested in risky assets at any given time. We show that, when optimizing lifecycle investment, performance and risk assessment have to take into account the investor’s risk aversion and the maximum amount the investor could lose, simultaneously. When risk aversion and maximum possible loss are considered jointly, an optimal savings strategy is obtained, which follows from constant rather than relative absolute risk aversion. This result is fundamental to prove that if risk aversion and the maximum possible loss are both high, then holding a constant amount invested in the risky asset is optimal for a standard lifetime saving/pension process and outperforms some other simple strategies. Performance comparisons are based on downside risk-adjusted equivalence that is used in our illustration.

Optimization of PHEV Power Split Gear Ratio to Minimize Fuel Consumption and Operation Cost

Science.gov (United States)

Li, Yanhe

A Plug-in Hybrid Electric Vehicle (PHEV) is a vehicle powered by a combination of an internal combustion engine and an electric motor with a battery pack. The battery pack can be charged by plugging the vehicle to the electric grid and from using excess engine power. The research activity performed in this thesis focused on the development of an innovative optimization approach of PHEV Power Split Device (PSD) gear ratio with the aim to minimize the vehicle operation costs. Three research activity lines have been followed: • Activity 1: The PHEV control strategy optimization by using the Dynamic Programming (DP) and the development of PHEV rule-based control strategy based on the DP results. • Activity 2: The PHEV rule-based control strategy parameter optimization by using the Non-dominated Sorting Genetic Algorithm (NSGA-II). • Activity 3: The comprehensive analysis of the single mode PHEV architecture to offer the innovative approach to optimize the PHEV PSD gear ratio.

Emission operational strategy for combined cooling, heating, and power systems

International Nuclear Information System (INIS)

Fumo, Nelson; Mago, Pedro J.; Chamra, Louay M.

2009-01-01

Integrated Energy Systems (IES), as technology that use thermal activated components to recover waste heat, are energy systems that offer key solution to global warming and energy security through high overall energy efficiency and better fuel use. Combined Cooling, Heating, and Power (CCHP) Systems are IES that use recovered thermal energy from the prime mover to produce heating and cooling for the building. The CCHP operational strategy is critical and it has to be considered in a well designed system since it defines the ultimate goal for the benefits expected from the system. One of the most common operational strategies is the cost-oriented strategy, which allows the system to operate at the lowest cost. A primary energy strategy (PES) optimizes energy consumption instead of cost. However, as a result of the worldwide concern about global warming, projects that target reduction of greenhouse gas (GHG) emissions have gained a lot of interest. Therefore, for a CCHP system, an emission strategy (ES) would be an operational strategy oriented to minimize emission of pollutants. In this study, the use of an ES is proposed for CCHP systems targeted to reduce emission of pollutants. The primary energy consumption (PEC) reduction and carbon dioxide (CO 2 ) emission reduction obtained using the proposed ES are compared with results obtained from the use of a PES. Results show that lower emission of CO 2 is achieved with the ES when compared with the PES, which prove the advantage of the ES for the design of CCHP systems targeted to emissions reduction.

Operations Strategy with Paper Boats

Science.gov (United States)

Sumukadas, Narendar

2010-01-01

When participants in introductory business courses encounter the term "operations strategy," it is not easy for them to appreciate what operations strategy is about, or how it fits with overall business strategy. This game breaks down highfalutin jargon into experiences that participants can readily relate to. While working in teams to make paper…

Emergency Load Shedding Strategy Based on Sensitivity Analysis of Relay Operation Margin against Cascading Events

DEFF Research Database (Denmark)

Liu, Zhou; Chen, Zhe; Sun, Haishun Sun

2012-01-01

the runtime emergent states of related system component. Based on sensitivity analysis between the relay operation margin and power system state variables, an optimal load shedding strategy is applied to adjust the emergent states timely before the unwanted relay operation. Load dynamics is also taken...... into account to compensate load shedding amount calculation. And the multi-agent technology is applied for the whole strategy implementation. A test system is built in real time digital simulator (RTDS) and has demonstrated the effectiveness of the proposed strategy.......In order to prevent long term voltage instability and induced cascading events, a load shedding strategy based on the sensitivity of relay operation margin to load powers is discussed and proposed in this paper. The operation margin of critical impedance backup relay is defined to identify...

AI techniques for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands

Science.gov (United States)

Tsai, Wen-Ping; Chang, Fi-John; Chang, Li-Chiu; Herricks, Edwin E.

2015-11-01

Flow regime is the key driver of the riverine ecology. This study proposes a novel hybrid methodology based on artificial intelligence (AI) techniques for quantifying riverine ecosystems requirements and delivering suitable flow regimes that sustain river and floodplain ecology through optimizing reservoir operation. This approach addresses issues to better fit riverine ecosystem requirements with existing human demands. We first explored and characterized the relationship between flow regimes and fish communities through a hybrid artificial neural network (ANN). Then the non-dominated sorting genetic algorithm II (NSGA-II) was established for river flow management over the Shihmen Reservoir in northern Taiwan. The ecosystem requirement took the form of maximizing fish diversity, which could be estimated by the hybrid ANN. The human requirement was to provide a higher satisfaction degree of water supply. The results demonstrated that the proposed methodology could offer a number of diversified alternative strategies for reservoir operation and improve reservoir operational strategies producing downstream flows that could meet both human and ecosystem needs. Applications that make this methodology attractive to water resources managers benefit from the wide spread of Pareto-front (optimal) solutions allowing decision makers to easily determine the best compromise through the trade-off between reservoir operational strategies for human and ecosystem needs.

Optimal Planning and Operation Management of a Ship Electrical Power System with Energy Storage System

DEFF Research Database (Denmark)

Anvari-Moghaddam, Amjad; Dragicevic, Tomislav; Meng, Lexuan

2016-01-01

Next generation power management at all scales is highly relying on the efficient scheduling and operation of different energy sources to maximize efficiency and utility. The ability to schedule and modulate the energy storage options within energy systems can also lead to more efficient use...... of the generating units. This optimal planning and operation management strategy becomes increasingly important for off-grid systems that operate independently of the main utility, such as microgrids or power systems on marine vessels. This work extends the principles of optimal planning and economic dispatch...... for the proposed plan is derived based on the solution from a mixed-integer nonlinear programming (MINLP) problem. Simulation results showed that including well-sized energy storage options together with optimal operation management of generating units can improve the economic operation of the test system while...

VALORAGUA: A model for the optimal operating strategy of mixed hydrothermal generating systems

International Nuclear Information System (INIS)

1992-01-01

To provide assistance to its developing Member States in carrying out integrated power system expansion analysis, the International Atomic Energy Agency (IAEA) has developed the computer model called WASP (Wien Automatic System Planning Package). The WASP model has proven to be very useful for this purpose and is accepted worldwide as a sound tool for electricity planning. Notwithstanding its many advantages, certain shortcomings of the methodology have been noticed, in particular with regard to representation of hydroelectric power plants. In order to overcome these shortcomings, the IAEA decided to acquire the computer model called VALORAGUA, developed by the Electricidade de Portugal (EDP), for optimizing the operating strategy of a mixed hydro-thermal power system. This program, when used together with WASP, would allow economic optimization of hydro-thermal power systems with a large hydro component. The objective of the present document is to assist in the use of the VALORAGUA model and its auxiliary codes, as well as to clarify the interconnection between VALORAGUA and the WASP-III model. This report is organized into five main chapters. The first chapter serves as an introduction to all remaining chapters. Chapter 2 defines the input data needed for every component of the electric power system. Chapter 3 presents the output variables of the model within the standard output tables that can be produced by VALORAGUA. Chapter 4 describes in detail all the input data needed by each program. It also includes the list of computer input data corresponding to the example described in Chapter 5, which is used to illustrate the execution of the VALORAGUA modules. Description of how to prepare the hydro data for the WASP-III model from the results obtained with the VALORAGUA model is given in Appendix A. Some auxiliary programs of the VALORAGUA model system, developed by EDP to help the user with the input data preparation, are described in Appendix B. Refs, figs and

Quantum behaved Particle Swarm Optimization with Differential Mutation operator applied to WWER-1000 in-core fuel management optimization

International Nuclear Information System (INIS)

Jamalipour, Mostafa; Sayareh, Reza; Gharib, Morteza; Khoshahval, Farrokh; Karimi, Mahmood Reza

2013-01-01

Highlights: ► A new method called QPSO-DM is applied to BNPP in-core fuel management optimization. ► It is found that QPSO-DM performs better than PSO and QPSO. ► This method provides a permissible arrangement for optimum loading pattern. - Abstract: This paper presents a new method using Quantum Particle Swarm Optimization with Differential Mutation operator (QPSO-DM) for optimizing WWER-1000 core fuel management. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have shown good performance on in-core fuel management optimization (ICFMO). The objective of this paper is to show that QPSO-DM performs very well and is comparable to PSO and Quantum Particle Swarm Optimization (QPSO). Most of the strategies for ICFMO are based on maximizing multiplication factor (k eff ) to increase cycle length and minimizing power peaking factor (P q ) in order to improve fuel integrity. PSO, QPSO and QPSO-DM have been implemented to fulfill these requirements for the first operating cycle of WWER-1000 Bushehr Nuclear Power Plant (BNPP). The results show that QPSO-DM performs better than the others. A program has been written in MATLAB to map PSO, QPSO and QPSO-DM for loading pattern optimization. WIMS and CITATION have been used to simulate reactor core for neutronic calculations

Impact of Demand Response Programs on Optimal Operation of Multi-Microgrid System

Directory of Open Access Journals (Sweden)

Anh-Duc Nguyen

2018-06-01

Full Text Available The increased penetration of renewables is beneficial for power systems but it poses several challenges, i.e., uncertainty in power supply, power quality issues, and other technical problems. Backup generators or storage system have been proposed to solve this problem but there are limitations remaining due to high installation and maintenance cost. Furthermore, peak load is also an issue in the power distribution system. Due to the adjustable characteristics of loads, strategies on demand side such as demand response (DR are more appropriate in order to deal with these challenges. Therefore, this paper studies how DR programs influence the operation of the multi-microgrid (MMG. The implementation is executed based on a hierarchical energy management system (HiEMS including microgrid EMSs (MG-EMSs responsible for local optimization in each MG and community EMS (C-EMS responsible for community optimization in the MMG. Mixed integer linear programming (MILP-based mathematical models are built for MMG optimal operation. Five scenarios consisting of single DR programs and DR groups are tested in an MMG test system to evaluate their impact on MMG operation. Among the five scenarios, some DR programs apply curtailing strategies, resulting in a study about the influence of base load value and curtailable load percentage on the amount of curtailed load and shifted load as well as the operation cost of the MMG. Furthermore, the impact of DR programs on the amount of external and internal trading power in the MMG is also examined. In summary, each individual DR program or group could be handy in certain situations depending on the interest of the MMG such as external trading, self-sufficiency or operation cost minimization.

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

International Nuclear Information System (INIS)

Zare Oskouei, Morteza; Sadeghi Yazdankhah, Ahmad

2015-01-01

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

Core design and operation optimization methods based on time-dependent perturbation theory

International Nuclear Information System (INIS)

Greenspan, E.

1983-08-01

A general approach for the optimization of nuclear reactor core design and operation is outlined; it is based on two cornerstones: a newly developed time-dependent (or burnup-dependent) perturbation theory for nonlinear problems and a succesive iteration technique. The resulting approach is capable of handling realistic reactor models using computational methods of any degree of sophistication desired, while accounting for all the constraints imposed. Three general optimization strategies, different in the way for handling the constraints, are formulated. (author)

Optimal Spatial Harvesting Strategy and Symmetry-Breaking

International Nuclear Information System (INIS)

Kurata, Kazuhiro; Shi Junping

2008-01-01

A reaction-diffusion model with logistic growth and constant effort harvesting is considered. By minimizing an intrinsic biological energy function, we obtain an optimal spatial harvesting strategy which will benefit the population the most. The symmetry properties of the optimal strategy are also discussed, and related symmetry preserving and symmetry breaking phenomena are shown with several typical examples of habitats

Optimal production of renewable hydrogen based on an efficient energy management strategy

International Nuclear Information System (INIS)

Ziogou, Chrysovalantou; Ipsakis, Dimitris; Seferlis, Panos; Bezergianni, Stella; Papadopoulou, Simira; Voutetakis, Spyros

2013-01-01

This work presents the development of a flexible energy management strategy (EMS) for a renewable hydrogen production unit through water electrolysis with solar power. The electricity flow of the unit is controlled by a smart microgrid and the overall unattended operation is achieved by a supervisory control system. The proposed approach formalizes the knowledge regarding the system operation using a finite-state machine (FSM) which is subsequently combined with a propositional-based logic to describe the transitions among various process states. The operating rules for the integrated system are derived by taking into account both the operating constraints and the interaction effects among the individual subsystems in a systematic way. Optimal control system parameter values are obtained so that a system performance criterion incorporating efficient and economic operation is satisfied. The resulted EMS has been deployed to the industrial automation system that monitors and controls a small-scale experimental solar hydrogen production unit. The overall performance of the proposed EMS in the experimental unit has been evaluated over short-term and long-term operating periods resulting in smooth and efficient hydrogen production. - Highlights: • Development of an energy management strategy based on a finite-state machine and propositional-based reasoning. • Deployment of the energy-aware algorithm to an autonomous renewable hydrogen production unit. • Supervisory control of the electricity flow by a smart microgrid using an industrial automation system. • Unattended operation and remote monitoring incorporating subsystem interactions in a systematic way. • Optimal hydrogen production regardless of the weather conditions through water electrolysis with solar power

Optimal Operation of Network-Connected Combined Heat and Powers for Customer Profit Maximization

Directory of Open Access Journals (Sweden)

Da Xie

2016-06-01

Full Text Available Network-connected combined heat and powers (CHPs, owned by a community, can export surplus heat and electricity to corresponding heat and electric networks after community loads are satisfied. This paper proposes a new optimization model for network-connected CHP operation. Both CHPs’ overall efficiency and heat to electricity ratio (HTER are assumed to vary with loading levels. Based on different energy flow scenarios where heat and electricity are exported to the network from the community or imported, four profit models are established accordingly. They reflect the different relationships between CHP energy supply and community load demand across time. A discrete optimization model is then developed to maximize the profit for the community. The models are derived from the intervals determined by the daily operation modes of CHP and real-time buying and selling prices of heat, electricity and natural gas. By demonstrating the proposed models on a 1 MW network-connected CHP, results show that the community profits are maximized in energy markets. Thus, the proposed optimization approach can help customers to devise optimal CHP operating strategies for maximizing benefits.

What is the optimal myocardial preservation strategy at re-operation for aortic valve replacement in the presence of a patent internal thoracic artery?

Science.gov (United States)

Park, Chan B; Suri, Rakesh M; Burkhart, Harold M; Greason, Kevin L; Dearani, Joseph A; Schaff, Hartzell V; Sundt, Thoralf M

2011-06-01

The optimal myocardial preservation strategy at re-operation for aortic valve replacement (AVR) after prior coronary artery bypass grafting (CABG) in the presence of a patent internal thoracic artery (ITA) remains undefined. Patients undergoing AVR after prior CABG at our institution between 1 January 1996 and 31 December 2007 were identified; operative notes and outcomes were reviewed. Of 628 patients with prior CABG undergoing AVR with or without concomitant procedures, 427 patients had a patent ITA. In 390, management of the ITA was detailed in the operative note, including 251 in whom it was clamped and 139 in whom it was left uncontrolled. Groups were demographically similar, although re-operative CABG was more frequent in the clamped group (42% vs 23%, poptimal perfusion temperature when the ITA was left uncontrolled. Efforts to control the patent ITA at re-operation for AVR after prior CABG increase risk of injury and may actually increase operative mortality rate compared with leaving this critical graft open and perfusing the heart. Copyright © 2010 European Association for Cardio-Thoracic Surgery. All rights reserved.

Optimal Constant DC Link Voltage Operation of aWave Energy Converter

Directory of Open Access Journals (Sweden)

Mats Leijon

2013-04-01

Full Text Available This article proposes a simple and reliable damping strategy for wave powerfarm operation of small-scale point-absorber converters. The strategy is based on passiverectification onto a constant DC-link, making it very suitable for grid integration of the farm.A complete model of the system has been developed in Matlab Simulink, and uses real sitedata as input. The optimal constant DC-voltage is evaluated as a function of the significantwave height and energy period of the waves. The total energy output of the WEC is derivedfor one year of experimental site data. The energy output is compared for two cases, onewhere the optimal DC-voltage is determined and held constant at half-hour basis throughoutthe year, and one where a selected value of the DC-voltage is kept constant throughout theyear regardless of sea state.

Monthly Optimal Reservoirs Operation for Multicrop Deficit Irrigation under Fuzzy Stochastic Uncertainties

Directory of Open Access Journals (Sweden)

Liudong Zhang

2014-01-01

Full Text Available An uncertain monthly reservoirs operation and multicrop deficit irrigation model was proposed under conjunctive use of underground and surface water for water resources optimization management. The objective is to maximize the total crop yield of the entire irrigation districts. Meanwhile, ecological water remained for the downstream demand. Because of the shortage of water resources, the monthly crop water production function was adopted for multiperiod deficit irrigation management. The model reflects the characteristics of water resources repetitive transformation in typical inland rivers irrigation system. The model was used as an example for water resources optimization management in Shiyang River Basin, China. Uncertainties in reservoir management shown as fuzzy probability were treated through chance-constraint parameter for decision makers. Necessity of dominance (ND was used to analyse the advantages of the method. The optimization results including reservoirs real-time operation policy, deficit irrigation management, and the available water resource allocation could be used to provide decision support for local irrigation management. Besides, the strategies obtained could help with the risk analysis of reservoirs operation stochastically.

A characteristic study of CCF modeling techniques and optimization of CCF defense strategies

International Nuclear Information System (INIS)

Kim, Min Chull

2000-02-01

Common Cause Failures (CCFs ) are among the major contributors to risk and core damage frequency (CDF ) from operating nuclear power plants (NPPs ). Our study on CCF focused on the following aspects : 1) a characteristic study on the CCF modeling techniques and 2) development of the optimal CCF defense strategy. Firstly, the characteristics of CCF modeling techniques were studied through sensitivity study of CCF occurrence probability upon system redundancy. The modeling techniques considered in this study include those most widely used worldwide, i.e., beta factor, MGL, alpha factor, and binomial failure rate models. We found that MGL and alpha factor models are essentially identical in terms of the CCF probability. Secondly, in the study for CCF defense, the various methods identified in the previous studies for defending against CCF were classified into five different categories. Based on these categories, we developed a generic method by which the optimal CCF defense strategy can be selected. The method is not only qualitative but also quantitative in nature: the selection of the optimal strategy among candidates is based on the use of analytic hierarchical process (AHP). We applied this method to two motor-driven valves for containment sump isolation in Ulchin 3 and 4 nuclear power plants. The result indicates that the method for developing an optimal CCF defense strategy is effective

Framework for Combined Diagnostics, Prognostics and Optimal Operation of a Subsea Gas Compression System

OpenAIRE

Verheyleweghen, Adriaen; Jaeschke, Johannes

2017-01-01

The efficient and safe operation of subsea gas and oil production systems sets strict requirements to equipment reliability to avoid unplanned breakdowns and costly maintenance interventions. Because of this, condition monitoring is employed to assess the status of the system in real-time. However, the condition of the system is usually not considered explicitly when finding the optimal operation strategy. Instead, operational constraints on flow rates, pressures etc., based on worst-case sce...

Optimal reactor strategy for commercializing fast breeder reactors

International Nuclear Information System (INIS)

Yamaji, Kenji; Nagano, Koji

1988-01-01

In this paper, a fuel cycle optimization model developed for analyzing the condition of selecting fast breeder reactors in the optimal reactor strategy is described. By dividing the period of planning, 1966-2055, into nine ten-year periods, the model was formulated as a compact linear programming model. With the model, the best mix of reactor types as well as the optimal timing of reprocessing spent fuel from LWRs to minimize the total cost were found. The results of the analysis are summarized as follows. Fast breeder reactors could be introduced in the optimal strategy when they can economically compete with LWRs with 30 year storage of spent fuel. In order that fast breeder reactors monopolize the new reactor market after the achievement of their technical availability, their capital cost should be less than 0.9 times as much as that of LWRs. When a certain amount of reprocessing commitment is assumed, the condition of employing fast breeder reactors in the optimal strategy is mitigated. In the optimal strategy, reprocessing is done just to meet plutonium demand, and the storage of spent fuel is selected to adjust the mismatch of plutonium production and utilization. The price hike of uranium ore facilitates the commercial adoption of fast breeder reactors. (Kako, I.)

Optimal control of anthracnose using mixed strategies.

Science.gov (United States)

Fotsa Mbogne, David Jaures; Thron, Christopher

2015-11-01

In this paper we propose and study a spatial diffusion model for the control of anthracnose disease in a bounded domain. The model is a generalization of the one previously developed in [15]. We use the model to simulate two different types of control strategies against anthracnose disease. Strategies that employ chemical fungicides are modeled using a continuous control function; while strategies that rely on cultivational practices (such as pruning and removal of mummified fruits) are modeled with a control function which is discrete in time (though not in space). For comparative purposes, we perform our analyses for a spatially-averaged model as well as the space-dependent diffusion model. Under weak smoothness conditions on parameters we demonstrate the well-posedness of both models by verifying existence and uniqueness of the solution for the growth inhibition rate for given initial conditions. We also show that the set [0, 1] is positively invariant. We first study control by impulsive strategies, then analyze the simultaneous use of mixed continuous and pulse strategies. In each case we specify a cost functional to be minimized, and we demonstrate the existence of optimal control strategies. In the case of pulse-only strategies, we provide explicit algorithms for finding the optimal control strategies for both the spatially-averaged model and the space-dependent model. We verify the algorithms for both models via simulation, and discuss properties of the optimal solutions. Copyright © 2015 Elsevier Inc. All rights reserved.

Optimal operation of batch membrane processes

CERN Document Server

Paulen, Radoslav

2016-01-01

This study concentrates on a general optimization of a particular class of membrane separation processes: those involving batch diafiltration. Existing practices are explained and operational improvements based on optimal control theory are suggested. The first part of the book introduces the theory of membrane processes, optimal control and dynamic optimization. Separation problems are defined and mathematical models of batch membrane processes derived. The control theory focuses on problems of dynamic optimization from a chemical-engineering point of view. Analytical and numerical methods that can be exploited to treat problems of optimal control for membrane processes are described. The second part of the text builds on this theoretical basis to establish solutions for membrane models of increasing complexity. Each chapter starts with a derivation of optimal operation and continues with case studies exemplifying various aspects of the control problems under consideration. The authors work their way from th...

Noise-dependent optimal strategies for quantum metrology

Science.gov (United States)

Huang, Zixin; Macchiavello, Chiara; Maccone, Lorenzo

2018-03-01

For phase estimation using qubits, we show that for some noise channels, the optimal entanglement-assisted strategy depends on the noise level. We note that there is a nontrivial crossover between the parallel-entangled strategy and the ancilla-assisted strategy: in the former the probes are all entangled; in the latter the probes are entangled with a noiseless ancilla but not among themselves. The transition can be explained by the fact that separable states are more robust against noise and therefore are optimal in the high-noise limit, but they are in turn outperformed by ancilla-assisted ones.

Testing of Strategies for the Acceleration of the Cost Optimization

Energy Technology Data Exchange (ETDEWEB)

Ponciroli, Roberto [Argonne National Lab. (ANL), Argonne, IL (United States); Vilim, Richard B. [Argonne National Lab. (ANL), Argonne, IL (United States)

2017-08-31

The general problem addressed in the Nuclear-Renewable Hybrid Energy System (N-R HES) project is finding the optimum economical dispatch (ED) and capacity planning solutions for the hybrid energy systems. In the present test-problem configuration, the N-R HES unit is composed of three electrical power-generating components, i.e. the Balance of Plant (BOP), the Secondary Energy Source (SES), and the Energy Storage (ES). In addition, there is an Industrial Process (IP), which is devoted to hydrogen generation. At this preliminary stage, the goal is to find the power outputs of each one of the N-R HES unit components (BOP, SES, ES) and the IP hydrogen production level that maximizes the unit profit by simultaneously satisfying individual component operational constraints. The optimization problem is meant to be solved in the Risk Analysis Virtual Environment (RAVEN) framework. The dynamic response of the N-R HES unit components is simulated by using dedicated object-oriented models written in the Modelica modeling language. Though this code coupling provides for very accurate predictions, the ensuing optimization problem is characterized by a very large number of solution variables. To ease the computational burden and to improve the path to a converged solution, a method to better estimate the initial guess for the optimization problem solution was developed. The proposed approach led to the definition of a suitable Monte Carlo-based optimization algorithm (called the preconditioner), which provides an initial guess for the optimal N-R HES power dispatch and the optimal installed capacity for each one of the unit components. The preconditioner samples a set of stochastic power scenarios for each one of the N-R HES unit components, and then for each of them the corresponding value of a suitably defined cost function is evaluated. After having simulated a sufficient number of power histories, the configuration which ensures the highest profit is selected as the optimal

Optimal Inspection and Maintenance Strategies for Structural Systems

DEFF Research Database (Denmark)

Sommer, A. M.

The aim of this thesis is to give an overview of conventional and optimal reliability-based inspection and maintenance strategies and to examine for specific structures how the cost can be reduced and/or the safety can be improved by using optimal reliability-based inspection strategies....... For structures with several almost similar components it is suggested that individual inspection strategies should be determined for each component or a group of components based on the reliability of the actual component. The benefit of this procedure is assessed in connection with the structures considered....... Furthermore, in relation to the calculations performed the intention is to modify an existing program for determination of optimal inspection strategies. The main purpose of inspection and maintenance of structural systems is to prevent or delay damage or deterioration to protect people, environment...

Advanced operation strategy for feed-and-bleed operation in an OPR1000

International Nuclear Information System (INIS)

Kim, Bo Gyung; Yoon, Ho Joon; Kim, Jaewhan; Kang, Hyun Gook

2016-01-01

Highlights: • Advanced operating strategy covers all necessary conditions for F&B operation. • Advanced operating strategy identifies the urgency of F&B operation. • An advanced operating strategy for F&B operation is developed using a decision tree. • Human error probability is re-estimated based on a thermohydraulic analysis and K-HRA method. • An advanced operation strategy provides indications under various plant situations. - Abstract: When the secondary side is unavailable in a pressurized water reactor (PWR), heat from the core will accumulate in the primary side causing core damage. In this situation a heat removal mechanism called feed-and-bleed operation (F&B operation) must be used, which is a process of directly cooling the primary reactor cooling system (RCS). However, conventional operation strategy in emergency operating procedures (EOPs) does not cover all possible conditions to initiate F&B operation. If the EOP informs on the urgency of F&B operation, operators will be able to more clearly make decisions regarding F&B operation initiation. In order to cover all possible scenarios for F&B operation and systematically inform its urgency, an advanced operating strategy using a decision tree is developed in this study. The plant condition can be classified according to failure of secondary side, RCS pressure condition, injectable inventory to RCS, and remaining core inventory. RCS pressure, core level, and RCS temperature are representative indicators which provide information regarding the initiation of F&B operation. Indicators can be selected based on their detectability and quantification, and a decision tree is developed according to combinations of indicators. To estimate the effects of the advanced operation strategy, human error probability (HEP) of F&B operation is re-estimated based on a thermohydraulic analysis. The available time for operators to initiate F&B operation is also re-estimated to obtain more realistic data. This

Quantum Strategies and Local Operations

Science.gov (United States)

Gutoski, Gus

2010-02-01

This thesis is divided into two parts. In Part I we introduce a new formalism for quantum strategies, which specify the actions of one party in any multi-party interaction involving the exchange of multiple quantum messages among the parties. This formalism associates with each strategy a single positive semidefinite operator acting only upon the tensor product of the input and output message spaces for the strategy. We establish three fundamental properties of this new representation for quantum strategies and we list several applications, including a quantum version of von Neumann's celebrated 1928 Min-Max Theorem for zero-sum games and an efficient algorithm for computing the value of such a game. In Part II we establish several properties of a class of quantum operations that can be implemented locally with shared quantum entanglement or classical randomness. In particular, we establish the existence of a ball of local operations with shared randomness lying within the space spanned by the no-signaling operations and centred at the completely noisy channel. The existence of this ball is employed to prove that the weak membership problem for local operations with shared entanglement is strongly NP-hard. We also provide characterizations of local operations in terms of linear functionals that are positive and "completely" positive on a certain cone of Hermitian operators, under a natural notion of complete positivity appropriate to that cone. We end the thesis with a discussion of the properties of no-signaling quantum operations.

Optimal synthesis and operation of advanced energy supply systems for standard and domotic home

International Nuclear Information System (INIS)

Buoro, Dario; Casisi, Melchiorre; Pinamonti, Piero; Reini, Mauro

2012-01-01

Highlights: ► Definition of an optimization model for a home energy supply system. ► Optimization of the energy supply system for standard and domotic home. ► Strong improvement can be achieved adopting the optimal system in standard and domotic home. ► The improvements are consistent if supply side and demand side strategies are applied together. ► Solutions with internal combustion engines are less sensible to market price of electricity and gas. - Abstract: The paper deals with the optimization of an advanced energy supply systems for two dwellings: a standard home and an advanced domotic home, where some demand side energy saving strategies have been implemented. In both cases the optimal synthesis, design and operation of the whole energy supply system have been obtained and a sensitivity analysis has been performed, by introducing different economic constraints. The optimization model is based on a Mixed Integer Linear Program (MILP) and includes different kinds of small-scale cogenerators, geothermal heat pumps, boilers, heat storages, solar thermal and photovoltaic panels. In addition, absorption machines, supplied with cogenerated heat, can be used instead of conventional electrical chiller to face the cooling demand. The aim of the analysis is to address the question if advanced demand strategies and supply strategies have to be regarded as alternatives, or if they have to be simultaneously applied, in order to obtain the maximum energy and economic benefit.

Optimal Bidding of a Microgrid Based on Probabilistic Analysis of Island Operation

Directory of Open Access Journals (Sweden)

Siyoung Lee

2016-10-01

Full Text Available Island operation of a microgrid increases operation survivability and reliability when there is a large accident in a main grid. However, because a microgrid typically has limited generation capability, a microgrid operator (MGO has to take the risk of island operation into account in its market participation and generation scheduling to ensure efficient operation. In this paper, a microgrid islanding event is interpreted as a trade suspension of a contract, and a set of islanding rules is presented in the form of a market rule. The risk of island operation is evaluated by modeling the microgrid islanding stochastically using an islanding probability function, which is defined in the form of a conditional probability to reflect the influence of outside conditions. An optimal bidding strategy is obtained for the MGO by formulating and solving an optimization problem to minimize the expected operating cost. The effectiveness of the proposed method was investigated by numerical simulations in which the proposed method and two other methods were applied to the same microgrid. Numerical sensitivity analyses of the coefficients of the islanding probability function were conducted to determine how an MGO copes with changes in outside conditions.

Following an Optimal Batch Bioreactor Operations Model

DEFF Research Database (Denmark)

Ibarra-Junquera, V.; Jørgensen, Sten Bay; Virgen-Ortíz, J.J.

2012-01-01

The problem of following an optimal batch operation model for a bioreactor in the presence of uncertainties is studied. The optimal batch bioreactor operation model (OBBOM) refers to the bioreactor trajectory for nominal cultivation to be optimal. A multiple-variable dynamic optimization of fed...... as the master system which includes the optimal cultivation trajectory for the feed flow rate and the substrate concentration. The “real” bioreactor, the one with unknown dynamics and perturbations, is considered as the slave system. Finally, the controller is designed such that the real bioreactor...

Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs

Directory of Open Access Journals (Sweden)

Jiajun Liu

2017-10-01

Full Text Available Energy storage systems (ESS play an important role in the performance of mining vehicles. A hybrid ESS combining both batteries (BTs and supercapacitors (SCs is one of the most promising solutions. As a case study, this paper discusses the optimal hybrid ESS sizing and energy management strategy (EMS of 14-ton underground load-haul-dump vehicles (LHDs. Three novel contributions are added to the relevant literature. First, a multi-objective optimization is formulated regarding energy consumption and the total cost of a hybrid ESS, which are the key factors of LHDs, and a battery capacity degradation model is used. During the process, dynamic programming (DP-based EMS is employed to obtain the optimal energy consumption and hybrid ESS power profiles. Second, a 10-year life cycle cost model of a hybrid ESS for LHDs is established to calculate the total cost, including capital cost, operating cost, and replacement cost. According to the optimization results, three solutions chosen from the Pareto front are compared comprehensively, and the optimal one is selected. Finally, the optimal and battery-only options are compared quantitatively using the same objectives, and the hybrid ESS is found to be a more economical and efficient option.

A quantum particle swarm optimizer with chaotic mutation operator

International Nuclear Information System (INIS)

Coelho, Leandro dos Santos

2008-01-01

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents a novel Quantum-behaved PSO (QPSO) using chaotic mutation operator. The application of chaotic sequences based on chaotic Zaslavskii map instead of random sequences in QPSO is a powerful strategy to diversify the QPSO population and improve the QPSO's performance in preventing premature convergence to local minima. The simulation results demonstrate good performance of the QPSO in solving a well-studied continuous optimization problem of mechanical engineering design

Optimal Advance Selling Strategy under Price Commitment

OpenAIRE

Chenhang Zeng

2012-01-01

This paper considers a two-period model with experienced consumers and inexperienced consumers. The retailer determines both advance selling price and regular selling price at the beginning of the first period. I show that advance selling weekly dominates no advance selling, and the optimal advance selling price may be at a discount, at a premium or at the regular selling price. To help the retailer choose the optimal pricing strategy, conditions for each possible advance selling strategy to ...

The topography of the environment alters the optimal search strategy for active particles

Science.gov (United States)

Volpe, Giorgio; Volpe, Giovanni

2017-10-01

In environments with scarce resources, adopting the right search strategy can make the difference between succeeding and failing, even between life and death. At different scales, this applies to molecular encounters in the cell cytoplasm, to animals looking for food or mates in natural landscapes, to rescuers during search and rescue operations in disaster zones, and to genetic computer algorithms exploring parameter spaces. When looking for sparse targets in a homogeneous environment, a combination of ballistic and diffusive steps is considered optimal; in particular, more ballistic Lévy flights with exponent α≤1 are generally believed to optimize the search process. However, most search spaces present complex topographies. What is the best search strategy in these more realistic scenarios? Here, we show that the topography of the environment significantly alters the optimal search strategy toward less ballistic and more Brownian strategies. We consider an active particle performing a blind cruise search for nonregenerating sparse targets in a 2D space with steps drawn from a Lévy distribution with the exponent varying from α=1 to α=2 (Brownian). We show that, when boundaries, barriers, and obstacles are present, the optimal search strategy depends on the topography of the environment, with α assuming intermediate values in the whole range under consideration. We interpret these findings using simple scaling arguments and discuss their robustness to varying searcher's size. Our results are relevant for search problems at different length scales from animal and human foraging to microswimmers' taxis to biochemical rates of reaction.

Tank Waste Remediation System optimized processing strategy

International Nuclear Information System (INIS)

Slaathaug, E.J.; Boldt, A.L.; Boomer, K.D.; Galbraith, J.D.; Leach, C.E.; Waldo, T.L.

1996-03-01

This report provides an alternative strategy evolved from the current Hanford Site Tank Waste Remediation System (TWRS) programmatic baseline for accomplishing the treatment and disposal of the Hanford Site tank wastes. This optimized processing strategy performs the major elements of the TWRS Program, but modifies the deployment of selected treatment technologies to reduce the program cost. The present program for development of waste retrieval, pretreatment, and vitrification technologies continues, but the optimized processing strategy reuses a single facility to accomplish the separations/low-activity waste (LAW) vitrification and the high-level waste (HLW) vitrification processes sequentially, thereby eliminating the need for a separate HLW vitrification facility

Optimal Strategy and Business Models

DEFF Research Database (Denmark)

Johnson, Peter; Foss, Nicolai Juul

2016-01-01

This study picks up on earlier suggestions that control theory may further the study of strategy. Strategy can be formally interpreted as an idealized path optimizing heterogeneous resource deployment to produce maximum financial gain. Using standard matrix methods to describe the firm Hamiltonia...... variable of firm path, suggesting in turn that the firm's business model is the codification of the application of investment resources used to control the strategic path of value realization....

Optimal design and operation of solid oxide fuel cell systems for small-scale stationary applications

Science.gov (United States)

Braun, Robert Joseph

The advent of maturing fuel cell technologies presents an opportunity to achieve significant improvements in energy conversion efficiencies at many scales; thereby, simultaneously extending our finite resources and reducing "harmful" energy-related emissions to levels well below that of near-future regulatory standards. However, before realization of the advantages of fuel cells can take place, systems-level design issues regarding their application must be addressed. Using modeling and simulation, the present work offers optimal system design and operation strategies for stationary solid oxide fuel cell systems applied to single-family detached dwellings. A one-dimensional, steady-state finite-difference model of a solid oxide fuel cell (SOFC) is generated and verified against other mathematical SOFC models in the literature. Fuel cell system balance-of-plant components and costs are also modeled and used to provide an estimate of system capital and life cycle costs. The models are used to evaluate optimal cell-stack power output, the impact of cell operating and design parameters, fuel type, thermal energy recovery, system process design, and operating strategy on overall system energetic and economic performance. Optimal cell design voltage, fuel utilization, and operating temperature parameters are found using minimization of the life cycle costs. System design evaluations reveal that hydrogen-fueled SOFC systems demonstrate lower system efficiencies than methane-fueled systems. The use of recycled cell exhaust gases in process design in the stack periphery are found to produce the highest system electric and cogeneration efficiencies while achieving the lowest capital costs. Annual simulations reveal that efficiencies of 45% electric (LHV basis), 85% cogenerative, and simple economic paybacks of 5--8 years are feasible for 1--2 kW SOFC systems in residential-scale applications. Design guidelines that offer additional suggestions related to fuel cell

Optimal Deterministic Investment Strategies for Insurers

Directory of Open Access Journals (Sweden)

Ulrich Rieder

2013-11-01

Full Text Available We consider an insurance company whose risk reserve is given by a Brownian motion with drift and which is able to invest the money into a Black–Scholes financial market. As optimization criteria, we treat mean-variance problems, problems with other risk measures, exponential utility and the probability of ruin. Following recent research, we assume that investment strategies have to be deterministic. This leads to deterministic control problems, which are quite easy to solve. Moreover, it turns out that there are some interesting links between the optimal investment strategies of these problems. Finally, we also show that this approach works in the Lévy process framework.

Optimization Under Uncertainty for Wake Steering Strategies: Preprint

Energy Technology Data Exchange (ETDEWEB)

Quick, Julian [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Annoni, Jennifer [National Renewable Energy Laboratory (NREL), Golden, CO (United States); King, Ryan N [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dykes, Katherine L [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Fleming, Paul A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ning, Andrew [Brigham Young University

2017-05-01

Wind turbines in a wind power plant experience significant power losses because of aerodynamic interactions between turbines. One control strategy to reduce these losses is known as 'wake steering,' in which upstream turbines are yawed to direct wakes away from downstream turbines. Previous wake steering research has assumed perfect information, however, there can be significant uncertainty in many aspects of the problem, including wind inflow and various turbine measurements. Uncertainty has significant implications for performance of wake steering strategies. Consequently, the authors formulate and solve an optimization under uncertainty (OUU) problem for finding optimal wake steering strategies in the presence of yaw angle uncertainty. The OUU wake steering strategy is demonstrated on a two-turbine test case and on the utility-scale, offshore Princess Amalia Wind Farm. When we accounted for yaw angle uncertainty in the Princess Amalia Wind Farm case, inflow-direction-specific OUU solutions produced between 0% and 1.4% more power than the deterministically optimized steering strategies, resulting in an overall annual average improvement of 0.2%. More importantly, the deterministic optimization is expected to perform worse and with more downside risk than the OUU result when realistic uncertainty is taken into account. Additionally, the OUU solution produces fewer extreme yaw situations than the deterministic solution.

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

Science.gov (United States)

Rodrigo, Deepal

2007-12-01

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

Reconfiguration strategies for electrical devices for operation within feasibility margins

Energy Technology Data Exchange (ETDEWEB)

Gandor, Malin; Blank, Marita [Oldenburg Univ. (Germany); Lehnhoff, Sebastian [OFFIS - Institut fuer Informatik, Oldenburg (Germany)

2012-07-01

Due to a transition of the energy system towards a sustainable energy supply by the integration of renewable, decentralized energy resources, new challenges regarding a safe and secure energy supply appear. With renewable energy resources the power fee-in will be highly volatile. Furthermore, due to a correlated feed-in depending on e.g. weather phenomena or control strategies on the demand side, the system might be operated beyond feasible boundaries. However, the satisfaction of viable operational boundaries must be guaranteed. In this paper a method is presented that allows the utilization of degrees of freedom in form of decentralized flexible electric consumers and products in order to optimize an operational state with regard to its feasibility. First results are presented in a case study. (orig.)

Performance analysis of supply and return fans for HVAC systems under different operating strategies of economizer dampers

Energy Technology Data Exchange (ETDEWEB)

Nassif, Nabil [Florida Solar Energy Center, A Research Institute of the University of Center Florida, 1679 Clearlake Road, Cocoa, FL 32922 (United States)

2010-07-15

HVAC systems and associated equipment consume a relatively large fraction of total building energy consumption, a significant portion of which is attributed to fan operation. The operation of economizer dampers when installed can cause high energy consumption in fans if they are not functioning in proper and optimal manner. This will mainly be due to the potential high pressure drops through those dampers and associated high total pressures that should be developed by supply and/or return fans. It is then necessary to ensure that a proper strategy to operate optimally the economizer dampers is implemented with minimum fan energy use. The paper examines several operation strategies of the economizer dampers and investigates their effects on the performance of both the supply and return fans in HVAC system. It also discusses a new operating strategy for economizer dampers that can lead to lower fan energy use. The strategies are evaluated by simulations for a typically existing HVAC system. Several factors such as the building locations, system characteristics, resistance in the duct where the dampers are installed, supply air temperature and economizer control, and minimum ventilation requirements are also considered during the evaluations. The results show that the way of the economizer dampers been controlled has a significant effect on fan performance and its energy use. The proposed strategy if properly implemented can provide fan energy saving in the range of 5-30%, depending mainly on the number of hours when the system operates in the free cooling mode, damper characteristics, and minimum outdoor air. (author)

Strategi Persaingan Operator Telekomunikasi Seluler [The Competition Strategy Of Mobile Telecommunication Operators

Directory of Open Access Journals (Sweden)

Azwar Aziz

2015-06-01

Full Text Available Jumlah pelanggan ketiga operator telekomunikasi seluler pada akhir tahun 2013, yaitu PT. Telkomsel Tbk, PT. XL Axiata Tbk, dan PT. Indosat Tbk, sebanyak 251,285 juta. Jumlah pelanggan telekomunikasi seluler ini telah melebihi jumlah penduduk Indonesia  sebesar 242,013 juta pada akhir tahun 2013. Ketiga operator telekomunikasi seluler tersebut mendominasi pangsa pasar seluler di Indonesia dan sangat menentukan tingkat harga, kualitas layanan dan strategi persaingan lainnya. Persaingan di telekomunikasi seluler saat ini sudah masuk ke masa jenuh untuk suara dan SMS, tetapi sudah beralih ke layanan data atau menggunaan internet. Para operator telekomunikasi seluler berlomba-lomba untuk meningkatkan kualitas layanan internet kepada pelanggan yang menggunakan prabayar dan pascabayar untuk suara dan SMS.Kajian ini menggunakan metodologi penelitian kualitatif, dengan melakukan observasi atau pengamatan langsung ke lapangan kepada Telkomsel, XL Axiata dan Indosat.Teknik analisis data menggunakan teknik analisis deskritif. Berdasarkan hasil analisis  diperoleh bahwa strategi persaingan ketiga operator tersebut dari aspekcore competence, time based competition, disiplin nilai sama-sama mengimplementasikannya. Dari aspek expeditionary marketing sangat tergantung pada luas jaringan yang dimiliki, aspek standar produk, memiliki sertifikat ISO yang lebih baik dan kompetensi manajemen fokus pada sumber daya manusia dengan belajar ke luar negeri. *****The number of subscribers of three mobile telecommunication operators by the end of 2013, namely PT. Telkomsel Tbk,  PT. XL Axiata Tbk,  and PT. Indosat Tbk is 251.285 million. The number of mobile telecommunication subscribers has exceeded the population of Indonesia amounted to 242.013 million at the end of 2013. Those  operators dominate the market in Indonesia  and determine the level of price, quality of service and other competitive strategies. Competition in the mobile telecommunications currently

A Regional Time-of-Use Electricity Price Based Optimal Charging Strategy for Electrical Vehicles

Directory of Open Access Journals (Sweden)

Jun Yang

2016-08-01

Full Text Available With the popularization of electric vehicles (EVs, the out-of-order charging behaviors of large numbers of EVs will bring new challenges to the safe and economic operation of power systems. This paper studies an optimal charging strategy for EVs. For that a typical urban zone is divided into four regions, a regional time-of-use (RTOU electricity price model is proposed to guide EVs when and where to charge considering spatial and temporal characteristics. In light of the elastic coefficient, the user response to the RTOU electricity price is analyzed, and also a bilayer optimization charging strategy including regional-layer and node-layer models is suggested to schedule the EVs. On the one hand, the regional layer model is designed to coordinate the EVs located in different time and space. On the other hand, the node layer model is built to schedule the EVs to charge in certain nodes. According to the simulations of an IEEE 33-bus distribution network, the performance of the proposed optimal charging strategy is verified. The results demonstrate that the proposed bilayer optimization strategy can effectively decrease the charging cost of users, mitigate the peak-valley load difference and the network loss. Besides, the RTOU electricity price shows better performance than the time-of-use (TOU electricity price.

Switching strategies to optimize search

International Nuclear Information System (INIS)

Shlesinger, Michael F

2016-01-01

Search strategies are explored when the search time is fixed, success is probabilistic and the estimate for success can diminish with time if there is not a successful result. Under the time constraint the problem is to find the optimal time to switch a search strategy or search location. Several variables are taken into account, including cost, gain, rate of success if a target is present and the probability that a target is present. (paper: interdisciplinary statistical mechanics)

Design principles and operating principles: the yin and yang of optimal functioning.

Science.gov (United States)

Voit, Eberhard O

2003-03-01

Metabolic engineering has as a goal the improvement of yield of desired products from microorganisms and cell lines. This goal has traditionally been approached with experimental biotechnological methods, but it is becoming increasingly popular to precede the experimental phase by a mathematical modeling step that allows objective pre-screening of possible improvement strategies. The models are either linear and represent the stoichiometry and flux distribution in pathways or they are non-linear and account for the full kinetic behavior of the pathway, which is often significantly effected by regulatory signals. Linear flux analysis is simpler and requires less input information than a full kinetic analysis, and the question arises whether the consideration of non-linearities is really necessary for devising optimal strategies for yield improvements. The article analyzes this question with a generic, representative pathway. It shows that flux split ratios, which are the key criterion for linear flux analysis, are essentially sufficient for unregulated, but not for regulated branch points. The interrelationships between regulatory design on one hand and optimal patterns of operation on the other suggest the investigation of operating principles that complement design principles, like a user's manual complements the hardwiring of electronic equipment.

Integrated Life Cycle Management: A Strategy for Plants to Extend Operating Lifetimes Safely with High Operational Reliability

International Nuclear Information System (INIS)

Esselman, Thomas; Bruck, Paul; Mengers, Charles

2012-01-01

Nuclear plant operators are studying the possibility of extending their existing generating facilities operating lifetime to 60 years and beyond. Many nuclear plants have been granted licenses to operate their facilities beyond the original 40 year term; however, in order to optimize the long term operating strategies, plant decision-makers need a consistent approach to support their options. This paper proposes a standard methodology to support effective decision-making for the long-term management of selected station assets. Methods detailed are intended to be used by nuclear plant site management, equipment reliability personnel, long term planners, capital asset planners, license renewal staff, and others that intend to look at operation between the current time and the end of operation. This methodology, named Integrated Life Cycle Management (ILCM), will provide a technical basis to assist decision makers regarding the timing of large capital investments required to get to the end of operation safely and with high plant reliability. ILCM seeks to identify end of life cycle failure probabilities for individual plant large capital assets and attendant costs associated with their refurbishment or replacement. It will provide a standard basis for evaluation of replacement and refurbishment options for these components. ILCM will also develop methods to integrate the individual assets over the entire plant thus assisting nuclear plant decision-makers in their facility long term operating strategies. (author)

Developing an Integrated Design Strategy for Chip Layout Optimization

NARCIS (Netherlands)

Wits, Wessel Willems; Jauregui Becker, Juan Manuel; van Vliet, Frank Edward; te Riele, G.J.

2011-01-01

This paper presents an integrated design strategy for chip layout optimization. The strategy couples both electric and thermal aspects during the conceptual design phase to improve chip performances; thermal management being one of the major topics. The layout of the chip circuitry is optimized

Optimizing integrated airport surface and terminal airspace operations under uncertainty

Science.gov (United States)

Bosson, Christabelle S.

In airports and surrounding terminal airspaces, the integration of surface, arrival and departure scheduling and routing have the potential to improve the operations efficiency. Moreover, because both the airport surface and the terminal airspace are often altered by random perturbations, the consideration of uncertainty in flight schedules is crucial to improve the design of robust flight schedules. Previous research mainly focused on independently solving arrival scheduling problems, departure scheduling problems and surface management scheduling problems and most of the developed models are deterministic. This dissertation presents an alternate method to model the integrated operations by using a machine job-shop scheduling formulation. A multistage stochastic programming approach is chosen to formulate the problem in the presence of uncertainty and candidate solutions are obtained by solving sample average approximation problems with finite sample size. The developed mixed-integer-linear-programming algorithm-based scheduler is capable of computing optimal aircraft schedules and routings that reflect the integration of air and ground operations. The assembled methodology is applied to a Los Angeles case study. To show the benefits of integrated operations over First-Come-First-Served, a preliminary proof-of-concept is conducted for a set of fourteen aircraft evolving under deterministic conditions in a model of the Los Angeles International Airport surface and surrounding terminal areas. Using historical data, a representative 30-minute traffic schedule and aircraft mix scenario is constructed. The results of the Los Angeles application show that the integration of air and ground operations and the use of a time-based separation strategy enable both significant surface and air time savings. The solution computed by the optimization provides a more efficient routing and scheduling than the First-Come-First-Served solution. Additionally, a data driven analysis is

Optimal Stochastic Advertising Strategies for the U.S. Beef Industry

OpenAIRE

Kun C. Lee; Stanley Schraufnagel; Earl O. Heady

1982-01-01

An important decision variable in the promotional strategy for the beef sector is the optimal level of advertising expenditures over time. Optimal stochastic and deterministic advertising expenditures are derived for the U.S. beef industry for the period `1966 through 1980. They are compared with historical levels and gains realized by optimal advertising strategies are measured. Finally, the optimal advertising expenditures in the future are forecasted.

An Overview of Optimizing Strategies for Flotation Banks

Directory of Open Access Journals (Sweden)

Miguel Maldonado

2012-10-01

Full Text Available A flotation bank is a serial arrangement of cells. How to optimally operate a bank remains a challenge. This article reviews three reported strategies: air profiling, mass-pull (froth velocity profiling and Peak Air Recovery (PAR profiling. These are all ways of manipulating the recovery profile down a bank, which may be the property being exploited. Mathematical analysis has shown that a flat cell-by-cell recovery profile maximizes the separation of two floatable minerals for a given target bank recovery when the relative floatability is constant down the bank. Available bank survey data are analyzed with respect to recovery profiling. Possible variations on recovery profile to minimize entrainment are discussed.

Factors affecting the optimal performance of a high-yield pulping operation

Energy Technology Data Exchange (ETDEWEB)

Broderick, G [Noranda Technology Centre, Pointe-Claire, PQ (Canada); Paris, J [Ecole Polytechnique, Montreal, PQ (Canada); Valada, J L [Quebec Univ., Trois-Rivieres, PQ (Canada)

1995-06-01

Strategies for operating a chemical-mechanical pulp mill were investigated from data based on process models from some one hundred pilot scale pulping runs. Optimal values for 55 process and pulp quality variables have been calculated by applying a genetic algorithm search to a fuzzy model of the overall system. Best pulp quality was achieved and maintained when the chemical pretreatment was conducted at moderately low temperatures using a high SO{sub 2} concentration, which produced high sulphonation and high yield at the same time. By characterizing the quality of the pulp at the fibre level, optimization results were said to be more easily transferable to other high yield pulping systems. 19 refs., 6 tabs.

Optimization strategies for complex engineering applications

Energy Technology Data Exchange (ETDEWEB)

Eldred, M.S.

1998-02-01

LDRD research activities have focused on increasing the robustness and efficiency of optimization studies for computationally complex engineering problems. Engineering applications can be characterized by extreme computational expense, lack of gradient information, discrete parameters, non-converging simulations, and nonsmooth, multimodal, and discontinuous response variations. Guided by these challenges, the LDRD research activities have developed application-specific techniques, fundamental optimization algorithms, multilevel hybrid and sequential approximate optimization strategies, parallel processing approaches, and automatic differentiation and adjoint augmentation methods. This report surveys these activities and summarizes the key findings and recommendations.

Group search optimiser-based optimal bidding strategies with no Karush-Kuhn-Tucker optimality conditions

Science.gov (United States)

Yadav, Naresh Kumar; Kumar, Mukesh; Gupta, S. K.

2017-03-01

General strategic bidding procedure has been formulated in the literature as a bi-level searching problem, in which the offer curve tends to minimise the market clearing function and to maximise the profit. Computationally, this is complex and hence, the researchers have adopted Karush-Kuhn-Tucker (KKT) optimality conditions to transform the model into a single-level maximisation problem. However, the profit maximisation problem with KKT optimality conditions poses great challenge to the classical optimisation algorithms. The problem has become more complex after the inclusion of transmission constraints. This paper simplifies the profit maximisation problem as a minimisation function, in which the transmission constraints, the operating limits and the ISO market clearing functions are considered with no KKT optimality conditions. The derived function is solved using group search optimiser (GSO), a robust population-based optimisation algorithm. Experimental investigation is carried out on IEEE 14 as well as IEEE 30 bus systems and the performance is compared against differential evolution-based strategic bidding, genetic algorithm-based strategic bidding and particle swarm optimisation-based strategic bidding methods. The simulation results demonstrate that the obtained profit maximisation through GSO-based bidding strategies is higher than the other three methods.

NSGA-II Algorithm with a Local Search Strategy for Multiobjective Optimal Design of Dry-Type Air-Core Reactor

Directory of Open Access Journals (Sweden)

Chengfen Zhang

2015-01-01

Full Text Available Dry-type air-core reactor is now widely applied in electrical power distribution systems, for which the optimization design is a crucial issue. In the optimization design problem of dry-type air-core reactor, the objectives of minimizing the production cost and minimizing the operation cost are both important. In this paper, a multiobjective optimal model is established considering simultaneously the two objectives of minimizing the production cost and minimizing the operation cost. To solve the multi-objective optimization problem, a memetic evolutionary algorithm is proposed, which combines elitist nondominated sorting genetic algorithm version II (NSGA-II with a local search strategy based on the covariance matrix adaptation evolution strategy (CMA-ES. NSGA-II can provide decision maker with flexible choices among the different trade-off solutions, while the local-search strategy, which is applied to nondominated individuals randomly selected from the current population in a given generation and quantity, can accelerate the convergence speed. Furthermore, another modification is that an external archive is set in the proposed algorithm for increasing the evolutionary efficiency. The proposed algorithm is tested on a dry-type air-core reactor made of rectangular cross-section litz-wire. Simulation results show that the proposed algorithm has high efficiency and it converges to a better Pareto front.

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

DEFF Research Database (Denmark)

Ding, Huajie; Pinson, Pierre; Hu, Zechun

2017-01-01

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

Asymptotic estimation of reactor fueling optimal strategy

International Nuclear Information System (INIS)

Simonov, V.D.

1985-01-01

The problem of improving the technical-economic factors of operating. and designed nuclear power plant blocks by developino. internal fuel cycle strategy (reactor fueling regime optimization), taking into account energy system structural peculiarities altogether, is considered. It is shown, that in search of asymptotic solutions of reactor fueling planning tasks the model of fuel energy potential (FEP) is the most ssuitable and effective. FEP represents energy which may be produced from the fuel in a reactor with real dimensions and power, but with hypothetical fresh fuel supply, regime, providing smilar burnup of all the fuel, passing through the reactor, and continuous overloading of infinitely small fuel portion under fule power, and infinitely rapid mixing of fuel in the reactor core volume. Reactor fuel run with such a standard fuel cycle may serve as FEP quantitative measure. Assessment results of optimal WWER-440 reactor fresh fuel supply periodicity are given as an example. The conclusion is drawn that with fuel enrichment x=3.3% the run which is 300 days, is economically justified, taking into account that the cost of one energy unit production is > 3 cop/KW/h

Identification of strategy parameters for particle swarm optimizer through Taguchi method

Institute of Scientific and Technical Information of China (English)

KHOSLA Arun; KUMAR Shakti; AGGARWAL K.K.

2006-01-01

Particle swarm optimization (PSO), like other evolutionary algorithms is a population-based stochastic algorithm inspired from the metaphor of social interaction in birds, insects, wasps, etc. It has been used for finding promising solutions in complex search space through the interaction of particles in a swarm. It is a well recognized fact that the performance of evolutionary algorithms to a great extent depends on the choice of appropriate strategy/operating parameters like population size,crossover rate, mutation rate, crossover operator, etc. Generally, these parameters are selected through hit and trial process, which is very unsystematic and requires rigorous experimentation. This paper proposes a systematic based on Taguchi method reasoning scheme for rapidly identifying the strategy parameters for the PSO algorithm. The Taguchi method is a robust design approach using fractional factorial design to study a large number of parameters with small number of experiments. Computer simulations have been performed on two benchmark functions-Rosenbrock function and Griewank function-to validate the approach.

Optimal mission planning of GEO on-orbit refueling in mixed strategy

Science.gov (United States)

Chen, Xiao-qian; Yu, Jing

2017-04-01

The mission planning of GEO on-orbit refueling (OOR) in Mixed strategy is studied in this paper. Specifically, one SSc will be launched to an orbital slot near the depot when multiple GEO satellites are reaching their end of lives. The SSc replenishes fuel from the depot and then extends the lifespan of the target satellites via refueling. In the mixed scenario, only some of the target satellites could be served by the SSc, and the remaining ones will be fueled by Pseudo SScs (the target satellite which has already been refueled by the SSc and now has sufficient fuel for its operation as well as the fuel to refuel other target satellites is called Pseudo SSc here). The mission sequences and fuel mass of the SSc and Pseudo SScs, the dry mass of the SSc are used as design variables, whereas the economic benefit of the whole mission is used as design objective. The economic cost and benefit models are stated first, and then a mathematical optimization model is proposed. A comprehensive solution method involving enumeration, particle swarm optimization and modification is developed. Numerical examples are carried out to demonstrate the effectiveness of the model and solution method. Economic efficiencies of different OOR strategies are compared and discussed. The mixed strategy would perform better than the other strategies only when the target satellites satisfy some conditions. This paper presents an available mixed strategy scheme for users and analyzes its advantages and disadvantages by comparing with some other OOR strategies, providing helpful references to decision makers. The best strategy in practical applications depends on the specific demands and user preference.

Multi-Objective Optimization of Start-up Strategy for Pumped Storage Units

Directory of Open Access Journals (Sweden)

Jinjiao Hou

2018-05-01

Full Text Available This paper proposes a multi-objective optimization method for the start-up strategy of pumped storage units (PSU for the first time. In the multi-objective optimization method, the speed rise time and the overshoot during the process of the start-up are taken as the objectives. A precise simulation platform is built for simulating the transient process of start-up, and for calculating the objectives based on the process. The Multi-objective Particle Swarm Optimization algorithm (MOPSO is adopted to optimize the widely applied start-up strategies based on one-stage direct guide vane control (DGVC, and two-stage DGVC. Based on the Pareto Front obtained, a multi-objective decision-making method based on the relative objective proximity is used to sort the solutions in the Pareto Front. Start-up strategy optimization for a PSU of a pumped storage power station in Jiangxi Province in China is conducted in experiments. The results show that: (1 compared with the single objective optimization, the proposed multi-objective optimization of start-up strategy not only greatly shortens the speed rise time and the speed overshoot, but also makes the speed curve quickly stabilize; (2 multi-objective optimization of strategy based on two-stage DGVC achieves better solution for a quick and smooth start-up of PSU than that of the strategy based on one-stage DGVC.

Optimal operation for 3 control parameters of Texaco coal-water slurry gasifier with MO-3LM-CDE algorithms

Energy Technology Data Exchange (ETDEWEB)

Cao, Cuiwen; Zhang, Yakun; Gu, Xingsheng [Ministry of Education, East China Univ. of Science and Technology, Shanghai (China). Key Lab. of Advanced Control and Optimization for Chemical Processes

2013-07-01

Optimizing operation parameters for Texaco coal-water slurry gasifier with the consideration of multiple objectives is a complicated nonlinear constrained problem concerning 3 BP neural networks. In this paper, multi-objective 3-layer mixed cultural differential evolution (MO-3LM-CDE) algorithms which comprise of 4 multi-objective strategies and a 3LM-CDE algorithm are firstly presented. Then they are tested in 6 benchmark functions. Finally, the MO-3LM-CDE algorithms are applied to optimize 3 control parameters of the Texaco coal-water slurry gasifier in methanol production of a real-world chemical plant. The simulation results show that multi-objective optimal results are better than the respective single-objective optimal operations.

Optimal Corridor Selection for a Road Space Management Strategy: Methodology and Tool

Directory of Open Access Journals (Sweden)

Sushant Sharma

2017-01-01

Full Text Available Nationwide, there is a growing realization that there are valuable benefits to using the existing roadway facilities to their full potential rather than expanding capacity in a traditional way. Currently, state DOTs are looking for cost-effective transportation solutions to mitigate the growing congestion and increasing funding gaps. Innovative road space management strategies like narrowing of multiple lanes (three or more and shoulder width to add a lane enhance the utilization while eliminating the costs associated with constructing new lanes. Although this strategy (among many generally leads to better mobility, identifying optimal corridors is a challenge and may affect the benefits. Further, there is a likelihood that added capacity may provide localized benefits, at the expense of system level performance measures (travel time and crashes because of the relocation of traffic operational bottlenecks. This paper develops a novel transportation programming and investment decision method to identify optimal corridors for adding capacity in the network by leveraging lane widths. The methodology explicitly takes into consideration the system level benefits and safety. The programming compares two conflicting objectives of system travel time and safety benefits to find an optimal solution.

Optimal hysteretic control for a BMAP/SM/1/N queue with two operation modes

Directory of Open Access Journals (Sweden)

Alexander N. Dudin

2000-01-01

Full Text Available We consider BMAP/SM/1 type queueing system with finite buffer of size N. The system has two operation modes, which are characterized by the matrix generating function of BMAP-input, the kernel of the semi-Markovian service process, and utilization cost. An algorithm for determining the optimal hysteresis strategy is presented.

Operator assisted optimization of sludge dewatering

DEFF Research Database (Denmark)

Grüttner, Henrik

1991-01-01

by the operator. By graphical presentation and an advisory service these data are used to support the operator in his dewatering operations and to secure a running optimization of the sludge dewatering. Evaluations show that this system is a useful tool for data collection and presentation and that the data...

A strategy for optimizing item-pool management

NARCIS (Netherlands)

Ariel, A.; van der Linden, Willem J.; Veldkamp, Bernard P.

2006-01-01

Item-pool management requires a balancing act between the input of new items into the pool and the output of tests assembled from it. A strategy for optimizing item-pool management is presented that is based on the idea of a periodic update of an optimal blueprint for the item pool to tune item

Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.

Science.gov (United States)

Nair, Govind; Jungreuthmayer, Christian; Zanghellini, Jürgen

2017-02-01

Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives. To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock. PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.

Mean-variance Optimal Reinsurance-investment Strategy in Continuous Time

OpenAIRE

Daheng Peng; Fang Zhang

2017-01-01

In this paper, Lagrange method is used to solve the continuous-time mean-variance reinsurance-investment problem. Proportional reinsurance, multiple risky assets and risk-free asset are considered synthetically in the optimal strategy for insurers. By solving the backward stochastic differential equation for the Lagrange multiplier, we get the mean-variance optimal reinsurance-investment strategy and its effective frontier in explicit forms.

Intelligent fault recognition strategy based on adaptive optimized multiple centers

Science.gov (United States)

Zheng, Bo; Li, Yan-Feng; Huang, Hong-Zhong

2018-06-01

For the recognition principle based optimized single center, one important issue is that the data with nonlinear separatrix cannot be recognized accurately. In order to solve this problem, a novel recognition strategy based on adaptive optimized multiple centers is proposed in this paper. This strategy recognizes the data sets with nonlinear separatrix by the multiple centers. Meanwhile, the priority levels are introduced into the multi-objective optimization, including recognition accuracy, the quantity of optimized centers, and distance relationship. According to the characteristics of various data, the priority levels are adjusted to ensure the quantity of optimized centers adaptively and to keep the original accuracy. The proposed method is compared with other methods, including support vector machine (SVM), neural network, and Bayesian classifier. The results demonstrate that the proposed strategy has the same or even better recognition ability on different distribution characteristics of data.

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

Directory of Open Access Journals (Sweden)

Yi Tang

2017-05-01

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

Optimal fuel inventory strategies

International Nuclear Information System (INIS)

Caspary, P.J.; Hollibaugh, J.B.; Licklider, P.L.; Patel, K.P.

1990-01-01

In an effort to maintain their competitive edge, most utilities are reevaluating many of their conventional practices and policies in an effort to further minimize customer revenue requirements without sacrificing system reliability. Over the past several years, Illinois Power has been rethinking its traditional fuel inventory strategies, recognizing that coal supplies are competitive and plentiful and that carrying charges on inventory are expensive. To help the Company achieve one of its strategic corporate goals, an optimal fuel inventory study was performed for its five major coal-fired generating stations. The purpose of this paper is to briefly describe Illinois Power's system and past practices concerning coal inventories, highlight the analytical process behind the optimal fuel inventory study, and discuss some of the recent experiences affecting coal deliveries and economic dispatch

Growth or reproduction: emergence of an evolutionary optimal strategy

International Nuclear Information System (INIS)

Grilli, J; Suweis, S; Maritan, A

2013-01-01

Modern ecology has re-emphasized the need for a quantitative understanding of the original ‘survival of the fittest theme’ based on analysis of the intricate trade-offs between competing evolutionary strategies that characterize the evolution of life. This is key to the understanding of species coexistence and ecosystem diversity under the omnipresent constraint of limited resources. In this work we propose an agent-based model replicating a community of interacting individuals, e.g. plants in a forest, where all are competing for the same finite amount of resources and each competitor is characterized by a specific growth–reproduction strategy. We show that such an evolution dynamics drives the system towards a stationary state characterized by an emergent optimal strategy, which in turn depends on the amount of available resources the ecosystem can rely on. We find that the share of resources used by individuals is power-law distributed with an exponent directly related to the optimal strategy. The model can be further generalized to devise optimal strategies in social and economical interacting systems dynamics. (paper)

Operation optimization of distributed generation using artificial intelligent techniques

Directory of Open Access Journals (Sweden)

Mahmoud H. Elkazaz

2016-06-01

Full Text Available Future smart grids will require an observable, controllable and flexible network architecture for reliable and efficient energy delivery. The use of artificial intelligence and advanced communication technologies is essential in building a fully automated system. This paper introduces a new technique for online optimal operation of distributed generation (DG resources, i.e. a hybrid fuel cell (FC and photovoltaic (PV system for residential applications. The proposed technique aims to minimize the total daily operating cost of a group of residential homes by managing the operation of embedded DG units remotely from a control centre. The target is formed as an objective function that is solved using genetic algorithm (GA optimization technique. The optimal settings of the DG units obtained from the optimization process are sent to each DG unit through a fully automated system. The results show that the proposed technique succeeded in defining the optimal operating points of the DGs that affect directly the total operating cost of the entire system.

Issues and Strategies in Solving Multidisciplinary Optimization Problems

Science.gov (United States)

Patnaik, Surya

2013-01-01

Optimization research at NASA Glenn Research Center has addressed the design of structures, aircraft and airbreathing propulsion engines. The accumulated multidisciplinary design activity is collected under a testbed entitled COMETBOARDS. Several issues were encountered during the solution of the problems. Four issues and the strategies adapted for their resolution are discussed. This is followed by a discussion on analytical methods that is limited to structural design application. An optimization process can lead to an inefficient local solution. This deficiency was encountered during design of an engine component. The limitation was overcome through an augmentation of animation into optimization. Optimum solutions obtained were infeasible for aircraft and airbreathing propulsion engine problems. Alleviation of this deficiency required a cascading of multiple algorithms. Profile optimization of a beam produced an irregular shape. Engineering intuition restored the regular shape for the beam. The solution obtained for a cylindrical shell by a subproblem strategy converged to a design that can be difficult to manufacture. Resolution of this issue remains a challenge. The issues and resolutions are illustrated through a set of problems: Design of an engine component, Synthesis of a subsonic aircraft, Operation optimization of a supersonic engine, Design of a wave-rotor-topping device, Profile optimization of a cantilever beam, and Design of a cylindrical shell. This chapter provides a cursory account of the issues. Cited references provide detailed discussion on the topics. Design of a structure can also be generated by traditional method and the stochastic design concept. Merits and limitations of the three methods (traditional method, optimization method and stochastic concept) are illustrated. In the traditional method, the constraints are manipulated to obtain the design and weight is back calculated. In design optimization, the weight of a structure becomes the

Reservoir Operating Rule Optimization for California's Sacramento Valley

Directory of Open Access Journals (Sweden)

Timothy Nelson

2016-03-01

Full Text Available doi: http://dx.doi.org/10.15447/sfews.2016v14iss1art6Reservoir operating rules for water resource systems are typically developed by combining intuition, professional discussion, and simulation modeling. This paper describes a joint optimization–simulation approach to develop preliminary economically-based operating rules for major reservoirs in California’s Sacramento Valley, based on optimized results from CALVIN, a hydro-economic optimization model. We infer strategic operating rules from the optimization model results, including storage allocation rules to balance storage among multiple reservoirs, and reservoir release rules to determine monthly release for individual reservoirs. Results show the potential utility of considering previous year type on water availability and various system and sub-system storage conditions, in addition to normal consideration of local reservoir storage, season, and current inflows. We create a simple simulation to further refine and test the derived operating rules. Optimization model results show particular insights for balancing the allocation of water storage among Shasta, Trinity, and Oroville reservoirs over drawdown and refill seasons, as well as some insights for release rules at major reservoirs in the Sacramento Valley. We also discuss the applicability and limitations of developing reservoir operation rules from optimization model results.

Mean-variance Optimal Reinsurance-investment Strategy in Continuous Time

Directory of Open Access Journals (Sweden)

Daheng Peng

2017-10-01

Full Text Available In this paper, Lagrange method is used to solve the continuous-time mean-variance reinsurance-investment problem. Proportional reinsurance, multiple risky assets and risk-free asset are considered synthetically in the optimal strategy for insurers. By solving the backward stochastic differential equation for the Lagrange multiplier, we get the mean-variance optimal reinsurance-investment strategy and its effective frontier in explicit forms.

Optimal intermittent search strategies

International Nuclear Information System (INIS)

Rojo, F; Budde, C E; Wio, H S

2009-01-01

We study the search kinetics of a single fixed target by a set of searchers performing an intermittent random walk, jumping between different internal states. Exploiting concepts of multi-state and continuous-time random walks we have calculated the survival probability of a target up to time t, and have 'optimized' (minimized) it with regard to the transition probability among internal states. Our model shows that intermittent strategies always improve target detection, even for simple diffusion states of motion

Particle Swarm Optimization With Interswarm Interactive Learning Strategy.

Science.gov (United States)

Qin, Quande; Cheng, Shi; Zhang, Qingyu; Li, Li; Shi, Yuhui

2016-10-01

The learning strategy in the canonical particle swarm optimization (PSO) algorithm is often blamed for being the primary reason for loss of diversity. Population diversity maintenance is crucial for preventing particles from being stuck into local optima. In this paper, we present an improved PSO algorithm with an interswarm interactive learning strategy (IILPSO) by overcoming the drawbacks of the canonical PSO algorithm's learning strategy. IILPSO is inspired by the phenomenon in human society that the interactive learning behavior takes place among different groups. Particles in IILPSO are divided into two swarms. The interswarm interactive learning (IIL) behavior is triggered when the best particle's fitness value of both the swarms does not improve for a certain number of iterations. According to the best particle's fitness value of each swarm, the softmax method and roulette method are used to determine the roles of the two swarms as the learning swarm and the learned swarm. In addition, the velocity mutation operator and global best vibration strategy are used to improve the algorithm's global search capability. The IIL strategy is applied to PSO with global star and local ring structures, which are termed as IILPSO-G and IILPSO-L algorithm, respectively. Numerical experiments are conducted to compare the proposed algorithms with eight popular PSO variants. From the experimental results, IILPSO demonstrates the good performance in terms of solution accuracy, convergence speed, and reliability. Finally, the variations of the population diversity in the entire search process provide an explanation why IILPSO performs effectively.

A Power System Optimal Dispatch Strategy Considering the Flow of Carbon Emissions and Large Consumers

Directory of Open Access Journals (Sweden)

Jun Yang

2015-08-01

Full Text Available The carbon emissions trading market and direct power purchases by large consumers are two promising directions of power system development. To trace the carbon emission flow in the power grid, the theory of carbon emission flow is improved by allocating power loss to the load side. Based on the improved carbon emission flow theory, an optimal dispatch model is proposed to optimize the cost of both large consumers and the power grid, which will benefit from the carbon emissions trading market. Moreover, to better simulate reality, the direct purchase of power by large consumers is also considered in this paper. The OPF (optimal power flow method is applied to solve the problem. To evaluate our proposed optimal dispatch strategy, an IEEE 30-bus system is used to test the performance. The effects of the price of carbon emissions and the price of electricity from normal generators and low-carbon generators with regards to the optimal dispatch are analyzed. The simulation results indicate that the proposed strategy can significantly reduce both the operation cost of the power grid and the power utilization cost of large consumers.

Evaluation of an Extremum Seeking Control Based Optimization and Sequencing Strategy for a Chilled-water Plant

OpenAIRE

Zhao, Zhongfan; Li, Yaoyu; Mu, Baojie; Salsbury, Timothy I.; House, John M.

2016-01-01

Chilled-water plants with multiple chillers account for a significant fraction of energy use in large commercial buildings. Real-time optimization and sequencing of such plants is thus critical for building energy efficiency. Due to the cost and complexity associated with calibrating a chiller plant model to field operation, model-free control has become an attractive solution. Recently, Mu et al. (2015) proposed a model-free real-time optimization and sequencing strategy based on extremum se...

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...

Operational radiation protection: A guide to optimization

International Nuclear Information System (INIS)

1990-01-01

The purpose of this publication is to provide practical guidance on the application of the dose limitation system contained in the Basic Safety Standards for Radiation Protection to operational situations both in large nuclear installations and in much smaller facilities. It is anticipated that this Guide will be useful to both the management and radiation protection staff of operations in which there is a potential for occupational radiation exposures and to the competent authorities with responsibilities for providing a programme of regulatory control. Contents: Dose limitation system; Optimization and its practical application to operational radiation protection; Major elements of an effective operational radiation protection programme; Review of selected parts of the basic safety standards with special reference to operational radiation protection; Optimization of radiation protection; Techniques for the systematic appraisal of operational radiation protection programmes. Refs and figs

Impact of Battery Energy Storage System Operation Strategy on Power System: An Urban Railway Load Case under a Time-of-Use Tariff

Directory of Open Access Journals (Sweden)

Hyeongig Kim

2017-01-01

Full Text Available Customer-owned battery energy storage systems (BESS have been used to reduce electricity costs of energy storage owners (ESOs under a time-of-use (TOU tariff in Korea. However, the current TOU tariff can unintentionally induce customer’s electricity usage to have a negative impact on power systems. This paper verifies the impact of different BESS operation strategies on power systems under a TOU tariff by analyzing the TOU tariff structure and the customer’s load pattern. First, several BESS operation strategies of ESO are proposed to reduce the electricity cost. In addition, a degradation cost calculation method for lithium ion batteries is considered for the ESO to determine the optimal BESS operation strategy that maximizes both electricity cost and annual investment cost. The optimal BESS operation strategy that maximizes ESO’s net benefit is illustrated by simulation using an urban railway load data from Namgwangju Station, Korea. The results show that BESS connected to urban railway loads can negative impact power system operation. This is due to the high BESS degradation costs and lack of incentive of differential rates in TOU tariff that can effectively induce proper demand response.

Computing Optimal Stochastic Portfolio Execution Strategies: A Parametric Approach Using Simulations

Science.gov (United States)

Moazeni, Somayeh; Coleman, Thomas F.; Li, Yuying

2010-09-01

Computing optimal stochastic portfolio execution strategies under appropriate risk consideration presents great computational challenge. We investigate a parametric approach for computing optimal stochastic strategies using Monte Carlo simulations. This approach allows reduction in computational complexity by computing coefficients for a parametric representation of a stochastic dynamic strategy based on static optimization. Using this technique, constraints can be similarly handled using appropriate penalty functions. We illustrate the proposed approach to minimize the expected execution cost and Conditional Value-at-Risk (CVaR).

The Improvement of Particle Swarm Optimization: a Case Study of Optimal Operation in Goupitan Reservoir

Science.gov (United States)

Li, Haichen; Qin, Tao; Wang, Weiping; Lei, Xiaohui; Wu, Wenhui

2018-02-01

Due to the weakness in holding diversity and reaching global optimum, the standard particle swarm optimization has not performed well in reservoir optimal operation. To solve this problem, this paper introduces downhill simplex method to work together with the standard particle swarm optimization. The application of this approach in Goupitan reservoir optimal operation proves that the improved method had better accuracy and higher reliability with small investment.

Minimizing the health and climate impacts of emissions from heavy-duty public transportation bus fleets through operational optimization.

Science.gov (United States)

Gouge, Brian; Dowlatabadi, Hadi; Ries, Francis J

2013-04-16

In contrast to capital control strategies (i.e., investments in new technology), the potential of operational control strategies (e.g., vehicle scheduling optimization) to reduce the health and climate impacts of the emissions from public transportation bus fleets has not been widely considered. This case study demonstrates that heterogeneity in the emission levels of different bus technologies and the exposure potential of bus routes can be exploited though optimization (e.g., how vehicles are assigned to routes) to minimize these impacts as well as operating costs. The magnitude of the benefits of the optimization depend on the specific transit system and region. Health impacts were found to be particularly sensitive to different vehicle assignments and ranged from worst to best case assignment by more than a factor of 2, suggesting there is significant potential to reduce health impacts. Trade-offs between climate, health, and cost objectives were also found. Transit agencies that do not consider these objectives in an integrated framework and, for example, optimize for costs and/or climate impacts alone, risk inadvertently increasing health impacts by as much as 49%. Cost-benefit analysis was used to evaluate trade-offs between objectives, but large uncertainties make identifying an optimal solution challenging.

Optimal intermittent search strategies

Energy Technology Data Exchange (ETDEWEB)

Rojo, F; Budde, C E [FaMAF, Universidad Nacional de Cordoba, Ciudad Universitaria, X5000HUA Cordoba (Argentina); Wio, H S [Instituto de Fisica de Cantabria, Universidad de Cantabria and CSIC E-39005 Santander (Spain)

2009-03-27

We study the search kinetics of a single fixed target by a set of searchers performing an intermittent random walk, jumping between different internal states. Exploiting concepts of multi-state and continuous-time random walks we have calculated the survival probability of a target up to time t, and have 'optimized' (minimized) it with regard to the transition probability among internal states. Our model shows that intermittent strategies always improve target detection, even for simple diffusion states of motion.

Dispositional optimism and coping strategies in patients with a kidney transplant.

Science.gov (United States)

Costa-Requena, Gemma; Cantarell-Aixendri, M Carmen; Parramon-Puig, Gemma; Serón-Micas, Daniel

2014-01-01

 Dispositional optimism is a personal resource that determines the coping style and adaptive response to chronic diseases. The aim of this study was to assess the correlations between dispositional optimism and coping strategies in patients with recent kidney transplantation and evaluate the differences in the use of coping strategies in accordance with the level of dispositional optimism.  Patients who were hospitalised in the nephrology department were selected consecutively after kidney transplantation was performed. The evaluation instruments were the Life Orientation Test-Revised, and the Coping Strategies Inventory. The data were analysed with central tendency measures, correlation analyses and means were compared using Student’s t-test.   66 patients with a kidney transplant participated in the study. The coping styles that characterised patients with a recent kidney transplantation were Social withdrawal and Problem avoidance. Correlations between dispositional optimism and coping strategies were significant in a positive direction in Problem-solving (p<.05) and Cognitive restructuring (p<.01), and inversely with Self-criticism (p<.05). Differences in dispositional optimism created significant differences in the Self-Criticism dimension (t=2.58; p<.01).  Dispositional optimism scores provide differences in coping responses after kidney transplantation. Moreover, coping strategies may influence the patient’s perception of emotional wellbeing after kidney transplantation.

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.

Optimized green operation of LTE networks in the presence of multiple electricity providers

KAUST Repository

Ghazzai, Hakim; Yaacoub, Elias E.; Alouini, Mohamed-Slim; Abu-Dayya, Adnan A.

2012-01-01

Energy efficiency aspects in cellular networks can significantly contribute to the reduction of greenhouse gas emissions and help to save the environment. The base station (BS) sleeping strategy has become a well-known technique to achieve energy savings by switching off redundant BSs mainly for lightly loaded networks. Besides, introducing renewable energies as alternative power sources becomes a real challenge to network operators. In this paper, we propose a method that reduces the energy consumption of BSs by not only shutting down underutilized BSs but also by optimizing the amounts of energy procured from different retailers (Renewable energy and electricity retailers). We formulate an optimization problem that leads to the maximization of the profit of a Long-Term Evolution (LTE) cellular operator, and at the same time to the minimization of CO2 emissions in green wireless cellular networks without affecting the desired Quality of Service. © 2012 IEEE.

Optimized green operation of LTE networks in the presence of multiple electricity providers

KAUST Repository

Ghazzai, Hakim

2012-12-01

Energy efficiency aspects in cellular networks can significantly contribute to the reduction of greenhouse gas emissions and help to save the environment. The base station (BS) sleeping strategy has become a well-known technique to achieve energy savings by switching off redundant BSs mainly for lightly loaded networks. Besides, introducing renewable energies as alternative power sources becomes a real challenge to network operators. In this paper, we propose a method that reduces the energy consumption of BSs by not only shutting down underutilized BSs but also by optimizing the amounts of energy procured from different retailers (Renewable energy and electricity retailers). We formulate an optimization problem that leads to the maximization of the profit of a Long-Term Evolution (LTE) cellular operator, and at the same time to the minimization of CO2 emissions in green wireless cellular networks without affecting the desired Quality of Service. © 2012 IEEE.

Improved quantum-behaved particle swarm optimization with local search strategy

Directory of Open Access Journals (Sweden)

Maolong Xi

2017-03-01

Full Text Available Quantum-behaved particle swarm optimization, which was motivated by analysis of particle swarm optimization and quantum system, has shown compared performance in finding the optimal solutions for many optimization problems to other evolutionary algorithms. To address the problem of premature, a local search strategy is proposed to improve the performance of quantum-behaved particle swarm optimization. In proposed local search strategy, a super particle is presented which is a collection body of randomly selected particles’ dimension information in the swarm. The selected probability of particles in swarm is different and determined by their fitness values. To minimization problems, the fitness value of one particle is smaller; the selected probability is more and will contribute more information in constructing the super particle. In addition, in order to investigate the influence on algorithm performance with different local search space, four methods of computing the local search radius are applied in local search strategy and propose four variants of local search quantum-behaved particle swarm optimization. Empirical studies on a suite of well-known benchmark functions are undertaken in order to make an overall performance comparison among the proposed methods and other quantum-behaved particle swarm optimization. The simulation results show that the proposed quantum-behaved particle swarm optimization variants have better advantages over the original quantum-behaved particle swarm optimization.

Validation of optimization strategies using the linear structured production chains

Science.gov (United States)

Kusiak, Jan; Morkisz, Paweł; Oprocha, Piotr; Pietrucha, Wojciech; Sztangret, Łukasz

2017-06-01

Different optimization strategies applied to sequence of several stages of production chains were validated in this paper. Two benchmark problems described by ordinary differential equations (ODEs) were considered. A water tank and a passive CR-RC filter were used as the exemplary objects described by the first and the second order differential equations, respectively. Considered in the work optimization problems serve as the validators of strategies elaborated by the Authors. However, the main goal of research is selection of the best strategy for optimization of two real metallurgical processes which will be investigated in an on-going projects. The first problem will be the oxidizing roasting process of zinc sulphide concentrate where the sulphur from the input concentrate should be eliminated and the minimal concentration of sulphide sulphur in the roasted products has to be achieved. Second problem will be the lead refining process consisting of three stages: roasting to the oxide, oxide reduction to metal and the oxidizing refining. Strategies, which appear the most effective in considered benchmark problems will be candidates for optimization of the mentioned above industrial processes.

Optimization for a fuel cell/battery/capacity tram with equivalent consumption minimization strategy

International Nuclear Information System (INIS)

Zhang, Wenbin; Li, Jianqiu; Xu, Liangfei; Ouyang, Minggao

2017-01-01

Highlights: • The hybridization of the fuel cell with the energy storage systems is realized for the tram. • A protype tram is tested based on an operation mode switching method. • An equivalent consumption minimization strategy is proposed and verified for optimization. - Abstract: This paper describes a hybrid tram powered by a Proton Exchange Membrane (PEM) fuel cell (FC) stack supported by an energy storage system (ESS) composed of a Li-ion battery (LB) pack and an ultra-capacitor (UC) pack. This configuration allows the tram to operate without grid connection. The hybrid tram with its full load is tested in the CRRC Qingdao Sifang Co.; Ltd. It firstly works on the operation mode switching method (OPMS) without energy regenerative and proper power management. Therefore, an equivalent consumption minimization strategy (ECMS) aimed at minimizing the hydrogen consumption is proposed to improve the characteristics of the tram. The results show that the proposed control system enhances drivability and economy, and is effective for application to this hybrid system.

Multi-Agent System-Based Microgrid Operation Strategy for Demand Response

Directory of Open Access Journals (Sweden)

Hee-Jun Cha

2015-12-01

Full Text Available The microgrid and demand response (DR are important technologies for future power grids. Among the variety of microgrid operations, the multi-agent system (MAS has attracted considerable attention. In a microgrid with MAS, the agents installed on the microgrid components operate optimally by communicating with each other. This paper proposes an operation algorithm for the individual agents of a test microgrid that consists of a battery energy storage system (BESS and an intelligent load. A microgrid central controller to manage the microgrid can exchange information with each agent. The BESS agent performs scheduling for maximum benefit in response to the electricity price and BESS state of charge (SOC through a fuzzy system. The intelligent load agent assumes that the industrial load performs scheduling for maximum benefit by calculating the hourly production cost. The agent operation algorithm includes a scheduling algorithm using day-ahead pricing in the DR program and a real-time operation algorithm for emergency situations using emergency demand response (EDR. The proposed algorithm and operation strategy were implemented both by a hardware-in-the-loop simulation test using OPAL-RT and an actual hardware test by connecting a new distribution simulator.

The Optimal Operation Criteria for a Gas Turbine Cogeneration System

Directory of Open Access Journals (Sweden)

Atsushi Akisawa

2009-04-01

Full Text Available The study demonstrated the optimal operation criteria of a gas turbine cogeneration system based on the analytical solution of a linear programming model. The optimal operation criteria gave the combination of equipment to supply electricity and steam with the minimum energy cost using the energy prices and the performance of equipment. By the comparison with a detailed optimization result of an existing cogeneration plant, it was shown that the optimal operation criteria successfully provided a direction for the system operation under the condition where the electric power output of the gas turbine was less than the capacity

Optimal Protection Coordination for Microgrid under Different Operating Modes

Directory of Open Access Journals (Sweden)

Ming-Ta Yang

2013-01-01

Full Text Available Significant consequences result when a microgrid is connected to a distribution system. This study discusses the impacts of bolted three-phase faults and bolted single line-to-ground faults on the protection coordination of a distribution system connected by a microgrid which operates in utility-only mode or in grid-connected mode. The power system simulation software is used to build the test system. The linear programming method is applied to optimize the coordination of relays, and the relays coordination simulation software is used to verify if the coordination time intervals (CTIs of the primary/backup relay pairs are adequate. In addition, this study also proposes a relays protection coordination strategy when the microgrid operates in islanding mode during a utility power outage. Because conventional CO/LCO relays are not capable of detecting high impedance fault, intelligent electrical device (IED combined with wavelet transformer and neural network is proposed to accurately detect high impedance fault and identify the fault phase.

Optimization of Multipurpose Reservoir Operation with Application Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Elahe Fallah Mehdipour

2012-12-01

Full Text Available Optimal operation of multipurpose reservoirs is one of the complex and sometimes nonlinear problems in the field of multi-objective optimization. Evolutionary algorithms are optimization tools that search decision space using simulation of natural biological evolution and present a set of points as the optimum solutions of problem. In this research, application of multi-objective particle swarm optimization (MOPSO in optimal operation of Bazoft reservoir with different objectives, including generating hydropower energy, supplying downstream demands (drinking, industry and agriculture, recreation and flood control have been considered. In this regard, solution sets of the MOPSO algorithm in bi-combination of objectives and compromise programming (CP using different weighting and power coefficients have been first compared that the MOPSO algorithm in all combinations of objectives is more capable than the CP to find solution with appropriate distribution and these solutions have dominated the CP solutions. Then, ending points of solution set from the MOPSO algorithm and nonlinear programming (NLP results have been compared. Results showed that the MOPSO algorithm with 0.3 percent difference from the NLP results has more capability to present optimum solutions in the ending points of solution set.

Optimal strategy for a single-qubit gate and the trade-off between opposite types of decoherence

International Nuclear Information System (INIS)

Alicki, Robert; Horodecki, Michal; Horodecki, Ryszard; Horodecki, Pawel; Jacak, Lucjan; Machnikowski, Pawel

2004-01-01

We study reliable quantum-information processing (QIP) under two different types of environment. The first type is Markovian exponential decay, and the appropriate elementary strategy of protection of qubit is to apply fast gates. The second one is strongly non-Markovian and occurs solely during operations on the qubit. The best strategy is then to work with slow gates. If the two types are both present, one has to optimize the speed of gate. We show that such a trade-off is present for a single-qubit operation in a semiconductor quantum dot implementation of QIP, where recombination of exciton (qubit) is Markovian, while phonon dressing gives rise to the non-Markovian contribution

Optimal integration strategies for a syngas fuelled SOFC and gas turbine hybrid

Science.gov (United States)

Zhao, Yingru; Sadhukhan, Jhuma; Lanzini, Andrea; Brandon, Nigel; Shah, Nilay

This article aims to develop a thermodynamic modelling and optimization framework for a thorough understanding of the optimal integration of fuel cell, gas turbine and other components in an ambient pressure SOFC-GT hybrid power plant. This method is based on the coupling of a syngas-fed SOFC model and an associated irreversible GT model, with an optimization algorithm developed using MATLAB to efficiently explore the range of possible operating conditions. Energy and entropy balance analysis has been carried out for the entire system to observe the irreversibility distribution within the plant and the contribution of different components. Based on the methodology developed, a comprehensive parametric analysis has been performed to explore the optimum system behavior, and predict the sensitivity of system performance to the variations in major design and operating parameters. The current density, operating temperature, fuel utilization and temperature gradient of the fuel cell, as well as the isentropic efficiencies and temperature ratio of the gas turbine cycle, together with three parameters related to the heat transfer between subsystems are all set to be controllable variables. Other factors affecting the hybrid efficiency have been further simulated and analysed. The model developed is able to predict the performance characteristics of a wide range of hybrid systems potentially sizing from 2000 to 2500 W m -2 with efficiencies varying between 50% and 60%. The analysis enables us to identify the system design tradeoffs, and therefore to determine better integration strategies for advanced SOFC-GT systems.

Schroedinger operators and evolutionary strategies

International Nuclear Information System (INIS)

Asselmeyer, T.

1997-01-01

First we introduce a simple model for the description of evolutionary algorithms, which is based on 2nd order partial differential equations for the distribution function of the individuals. Then we turn to the properties of Boltzmann's and Darwin's strategy. the next chapter is dedicated to the mathematical properties of Schroedinger operators. Both statements on the spectral density and their reproducibility during the simulation are summarized. The remaining of this chapter are dedicated to the analysis of the kernel as well as the dependence of the Schroedinger operator on the potential. As conclusion from the results of this chapter we obtain the classification of the strategies in dependence of the fitness. We obtain the classification of the evolutionary strategies, which are described by a 2nd order partial differential equation, in relation to their solution behaviour. Thereafter we are employed with the variation of the mutation distribution

Optimizing the HLT Buffer Strategy with Monte Carlo Simulations

CERN Document Server

AUTHOR|(CDS)2266763

2017-01-01

This project aims to optimize the strategy of utilizing the disk buffer for the High Level Trigger (HLT) of the LHCb experiment with the help of Monte-Carlo simulations. A method is developed, which simulates the Event Filter Farm (EFF) -- a computing cluster for the High Level Trigger -- as a compound of nodes with different performance properties. In this way, the behavior of the computing farm can be analyzed at a deeper level than before. It is demonstrated that the current operating strategy might be improved when data taking is reaching a mid-year scheduled stop or the year-end technical stop. The processing time of the buffered data can be lowered by distributing the detector data according to the processing power of the nodes instead of the relative disk size as long as the occupancy level of the buffer is low enough. Moreover, this ensures that data taken and stored on the buffer at the same time is processed by different nodes nearly simultaneously, which reduces load on the infrastructure.

Examination of the optimal operation of building scale combined heat and power systems under disparate climate and GHG emissions rates

International Nuclear Information System (INIS)

Howard, B.; Modi, V.

2017-01-01

Highlights: • CHP attributable reductions, not viable by electric generation alone, are defined. • Simplified operating strategy heuristics are optimal under specific circumstances. • Phosphoric acid fuel cells yield the largest reductions except in the extremes. • Changes in baseline emissions affect the optimal system capacity and operating hours. - Abstract: This work aims to elucidate notions concerning the ideal operation and greenhouse gas (GHG) emissions benefits of combined heat and power (CHP) systems by investigating how various metrics change as a function of the GHG emissions from the underlying electricity source, building use type and climate. Additionally, a new term entitled “CHP Attributable” reductions is introduced to quantify the benefits from the simultaneous use of thermal and electric energy, removing benefits achieved solely from fuel switching and generating electricity more efficiently. The GHG emission benefits from implementing internal combustion engine, microturbines, and phosphoric acid (PA) fuel cell based CHP systems were evaluated through an optimization approach considering energy demands of prototypical hospital, office, and residential buildings in varied climates. To explore the effect of electric GHG emissions rates, the ideal operation of the CHP systems was evaluated under three scenarios: “High” GHG emissions rates, “Low” GHG emissions rates, and “Current” GHG emissions rate for a specific location. The analysis finds that PA fuel cells achieve the highest GHG emission reductions in most cases considered, though there are exceptions. Common heuristics, such as electric load following and thermal load following, are the optimal operating strategy under specific conditions. The optimal CHP capacity and operating hours both vary as a function of building type, climate and GHG emissions rates from grid electricity. GHG emissions reductions can be as high as 49% considering a PA fuel cell for a

A comparative study of imbalance reduction strategies for virtual power plant operation

International Nuclear Information System (INIS)

Zapata, J.; Vandewalle, J.; D'haeseleer, W.

2014-01-01

The penetration of a large amount of distributed generation (DG) technologies with intermittent output, such as photovoltaic installations and wind turbines, yields an important challenge to the electric grid. It is believed that aggregating them with controllable technologies such as cogeneration devices (CHP) can help to balance fluctuations of renewable energy. This work evaluates the ability of a virtual power plant (VPP) to reduce the imbalance error of renewable generators. The study is undertaken in a VPP that consists of several cogeneration devices and photovoltaic (PV) installations. The virtual power plant operator bids electricity into the day-ahead market using the forecast for solar irradiation and for the thermal demand. During the actual day, the imbalance due to deviations between the forecasted electricity delivered and the real output has to be settled in the balancing market. Thus, in order to compensate these errors and possible economic drawbacks, the operation of the CHP is adjusted periodically in a so called reschedule. Two different rescheduling strategies are compared against a ‘reference scenario’ in which the imbalance error is settled in the market. The first one (‘forced strategy’) aims at reducing the imbalance error every time step regardless of the imbalance prices. The second (‘economic strategy’) considers the imbalance prices and takes only action if it is economically appropriate and thus intends to reduce the total operational cost. The results show that the rescheduling technique is able to reduce the imbalance error (up to 90% depending on the season and the strategy). Additionally, the total operational cost is estimated. However, the nowadays imbalance prices only lead to a minor financial advantage that is unlikely to motivate real life operators to perform a rescheduling strategy. - Highlights: • The VPP is dispatched by a day-ahead optimization followed by a rescheduling. • A forced rescheduling strategy

Two-objective on-line optimization of supervisory control strategy

Energy Technology Data Exchange (ETDEWEB)

Nassif, N.; Kajl, S.; Sabourin, R. [Ecole de Technologie Superieure, Montreal (Canada)

2004-09-01

The set points of supervisory control strategy are optimized with respect to energy use and thermal comfort for existing HVAC systems. The set point values of zone temperatures, supply duct static pressure, and supply air temperature are the problem variables, while energy use and thermal comfort are the objective functions. The HVAC system model includes all the individual component models developed and validated against the monitored data of an existing VAV system. It serves to calculate energy use during the optimization process, whereas the actual energy use is determined by using monitoring data and the appropriate validated component models. A comparison, done for one summer week, of actual and optimal energy use shows that the on-line implementation of a genetic algorithm optimization program to determine the optimal set points of supervisory control strategy could save energy by 19.5%, while satisfying the minimum zone airflow rates and the thermal comfort. The results also indicate that the application of the two-objective optimization problem can help control daily energy use or daily building thermal comfort, thus saving more energy than the application of the one-objective optimization problem. (Author)

UMTS network planning, optimization, and inter-operation with GSM

CERN Document Server

Rahnema, Moe

2008-01-01

UMTS Network Planning, Optimization, and Inter-Operation with GSM is an accessible, one-stop reference to help engineers effectively reduce the time and costs involved in UMTS deployment and optimization. Rahnema includes detailed coverage from both a theoretical and practical perspective on the planning and optimization aspects of UMTS, and a number of other new techniques to help operators get the most out of their networks. Provides an end-to-end perspective, from network design to optimizationIncorporates the hands-on experiences of numerous researchersSingle

Optimal Pricing Strategy in Marketing Research Consulting.

OpenAIRE

Chang, Chun-Hao; Lee, Chi-Wen Jevons

1994-01-01

This paper studies the optimal pricing scheme for a monopolistic marketing research consultant who sells high-cost proprietary marketing information to her oligopolistic clients in the manufacturing industry. In designing an optimal pricing strategy, the consultant needs to fully consider the behavior of her clients, the behavior of the existing and potential competitors to her clients, and the behavior of her clients' customers. The authors show how the environment uncertainty, the capabilit...

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

International Nuclear Information System (INIS)

Ma, Tengfei; Wu, Junyong; Hao, Liangliang

2017-01-01

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

Bayesian optimization analysis of containment-venting operation in a boiling water reactor severe accident

International Nuclear Information System (INIS)

Zheng, Xiaoyu; Ishikawa, Jun; Sugiyama, Tomoyuki; Maryyama, Yu

2017-01-01

Containment venting is one of several essential measures to protect the integrity of the final barrier of a nuclear reactor during severe accidents, by which the uncontrollable release of fission products can be avoided. The authors seek to develop an optimization approach to venting operations, from a simulation-based perspective, using an integrated severe accident code, THALES2/KICHE. The effectiveness of the containment-venting strategies needs to be verified via numerical simulations based on various settings of the venting conditions. The number of iterations, however, needs to be controlled to avoid cumbersome computational burden of integrated codes. Bayesian optimization is an efficient global optimization approach. By using a Gaussian process regression, a surrogate model of the “black-box” code is constructed. It can be updated simultaneously whenever new simulation results are acquired. With predictions via the surrogate model, upcoming locations of the most probable optimum can be revealed. The sampling procedure is adaptive. Compared with the case of pure random searches, the number of code queries is largely reduced for the optimum finding. One typical severe accident scenario of a boiling water reactor is chosen as an example. The research demonstrates the applicability of the Bayesian optimization approach to the design and establishment of containment-venting strategies during severe accidents

Bayesian optimization analysis of containment-venting operation in a boiling water reactor severe accident

Energy Technology Data Exchange (ETDEWEB)

Zheng, Xiaoyu; Ishikawa, Jun; Sugiyama, Tomoyuki; Maryyama, Yu [Nuclear Safety Research Center, Japan Atomic Energy Agency, Ibaraki (Japan)

2017-03-15

Containment venting is one of several essential measures to protect the integrity of the final barrier of a nuclear reactor during severe accidents, by which the uncontrollable release of fission products can be avoided. The authors seek to develop an optimization approach to venting operations, from a simulation-based perspective, using an integrated severe accident code, THALES2/KICHE. The effectiveness of the containment-venting strategies needs to be verified via numerical simulations based on various settings of the venting conditions. The number of iterations, however, needs to be controlled to avoid cumbersome computational burden of integrated codes. Bayesian optimization is an efficient global optimization approach. By using a Gaussian process regression, a surrogate model of the “black-box” code is constructed. It can be updated simultaneously whenever new simulation results are acquired. With predictions via the surrogate model, upcoming locations of the most probable optimum can be revealed. The sampling procedure is adaptive. Compared with the case of pure random searches, the number of code queries is largely reduced for the optimum finding. One typical severe accident scenario of a boiling water reactor is chosen as an example. The research demonstrates the applicability of the Bayesian optimization approach to the design and establishment of containment-venting strategies during severe accidents.

Evaluation of optimization strategies and the effect of initial conditions on IMAT optimization using a leaf position optimization algorithm

International Nuclear Information System (INIS)

Oliver, Mike; Jensen, Michael; Chen, Jeff; Wong, Eugene

2009-01-01

Intensity-modulated arc therapy (IMAT) is a rotational variant of intensity-modulated radiation therapy (IMRT) that can be implemented with or without angular dose rate variation. The purpose of this study is to assess optimization strategies and initial conditions using a leaf position optimization (LPO) algorithm altered for variable dose rate IMAT. A concave planning target volume (PTV) with a central cylindrical organ at risk (OAR) was used in this study. The initial IMAT arcs were approximated by multiple static beams at 5 deg. angular increments where multi-leaf collimator (MLC) leaf positions were determined from the beam's eye view to irradiate the PTV but avoid the OAR. For the optimization strategy, two arcs with arc ranges of 280 deg. and 150 deg. were employed and plans were created using LPO alone, variable dose rate optimization (VDRO) alone, simultaneous LPO and VDRO and sequential combinations of these strategies. To assess the MLC initialization effect, three single 360 deg. arc plans with different initial MLC configurations were generated using the simultaneous LPO and VDRO. The effect of changing optimization degrees of freedom was investigated by employing 3 deg., 5 deg. and 10 deg. angular sampling intervals for the two 280 deg., two 150 deg. and single arc plans using LPO and VDRO. The objective function value, a conformity index, a dose homogeneity index, mean dose to OAR and normal tissues were computed and used to evaluate the treatment plans. This study shows that the best optimization strategy for a concave target is to use simultaneous MLC LPO and VDRO. We found that the optimization result is sensitive to the choice of initial MLC aperture shapes suggesting that an LPO-based IMAT plan may not be able to overcome local minima for this geometry. In conclusion, simultaneous MLC leaf position and VDRO are needed with the most appropriate initial conditions (MLC positions, arc ranges and number of arcs) for IMAT.

Integrated approach to optimize operation and maintenance costs for operating nuclear power plants

International Nuclear Information System (INIS)

2006-06-01

In the context of increasingly open electricity markets and the 'unbundling' of generating companies from former utility monopolies, an area of major concern is the economic performance of the existing fleet of nuclear power plants. Nuclear power, inevitably, must compete directly with other electricity generation sources. Coping with this competitive pressure is a challenge that the nuclear industry should meet if the nuclear option is to remain a viable one. This competitive environment has significant implications for nuclear plant operations, including, among others, the need for the more cost effective management of plant activities, and the greater use of analytical tools to balance the costs and benefits of proposed activities, in order to optimize operation and maintenance costs, and thus insure the economic competitiveness of existing nuclear power plants. In the framework of the activities on Nuclear Economic Performance Information System (NEPIS), the IAEA embarked in developing guidance on optimization of operation and maintenance costs for nuclear power plants. The report was prepared building on the fundamental that optimization of operation and maintenance costs of a nuclear power plant is a key component of a broader integrated business strategic planning process, having as overall result achievement of organization's business objectives. It provides advice on optimization of O and M costs in the framework of strategic business planning, with additional details on operational planning and controlling. This TECDOC was elaborated in 2004-2005 in the framework of the IAEA's programme on Nuclear Power Plant Operating Performance and Life Cycle Management, with the support of two consultants meetings and one technical meeting and based on contributions provided by participants. It can serve as a useful reference for the management and operation staff within utilities, nuclear power plant operators and regulators and other organizations involved in

Generating optimized stochastic power management strategies for electric car components

Energy Technology Data Exchange (ETDEWEB)

Fruth, Matthias [TraceTronic GmbH, Dresden (Germany); Bastian, Steve [Technische Univ. Dresden (Germany)

2012-11-01

With the increasing prevalence of electric vehicles, reducing the power consumption of car components becomes a necessity. For the example of a novel traffic-light assistance system, which makes speed recommendations based on the expected length of red-light phases, power-management strategies are used to control under which conditions radio communication, positioning systems and other components are switched to low-power (e.g. sleep) or high-power (e.g. idle/busy) states. We apply dynamic power management, an optimization technique well-known from other domains, in order to compute energy-optimal power-management strategies, sometimes resulting in these strategies being stochastic. On the example of the traffic-light assistant, we present a MATLAB/Simulink-implemented framework for the generation, simulation and formal analysis of optimized power-management strategies, which is based on this technique. We study capabilities and limitations of this approach and sketch further applications in the automotive domain. (orig.)

Operations Optimization of Hybrid Energy Systems under Variable Markets

Energy Technology Data Exchange (ETDEWEB)

Chen, Jun; Garcia, Humberto E.

2016-07-01

Hybrid energy systems (HES) have been proposed to be an important element to enable increasing penetration of clean energy. This paper investigates the operations flexibility of HES, and develops a methodology for operations optimization to maximize its economic value based on predicted renewable generation and market information. The proposed operations optimizer allows systematic control of energy conversion for maximal economic value, and is illustrated by numerical results.

Optimal decentralized valley-filling charging strategy for electric vehicles

International Nuclear Information System (INIS)

Zhang, Kangkang; Xu, Liangfei; Ouyang, Minggao; Wang, Hewu; Lu, Languang; Li, Jianqiu; Li, Zhe

2014-01-01

Highlights: • An implementable charging strategy is developed for electric vehicles connected to a grid. • A two-dimensional pricing scheme is proposed to coordinate charging behaviors. • The strategy effectively works in decentralized way but achieves the systematic valley filling. • The strategy allows device-level charging autonomy, and does not require a bidirectional communication/control network. • The strategy can self-correct when confronted with adverse factors. - Abstract: Uncoordinated charging load of electric vehicles (EVs) increases the peak load of the power grid, thereby increasing the cost of electricity generation. The valley-filling charging scenario offers a cheaper alternative. This study proposes a novel decentralized valley-filling charging strategy, in which a day-ahead pricing scheme is designed by solving a minimum-cost optimization problem. The pricing scheme can be broadcasted to EV owners, and the individual charging behaviors can be indirectly coordinated. EV owners respond to the pricing scheme by autonomously optimizing their individual charge patterns. This device-level response induces a valley-filling effect in the grid at the system level. The proposed strategy offers three advantages: coordination (by the valley-filling effect), practicality (no requirement for a bidirectional communication/control network between the grid and EV owners), and autonomy (user control of EV charge patterns). The proposed strategy is validated in simulations of typical scenarios in Beijing, China. According to the results, the strategy (1) effectively achieves the valley-filling charging effect at 28% less generation cost than the uncoordinated charging strategy, (2) is robust to several potential affecters of the valley-filling effect, such as (system-level) inaccurate parameter estimation and (device-level) response capability and willingness (which cause less than 2% deviation in the minimal generation cost), and (3) is compatible with

Activity-Based Information Integrating the operations strategy

Directory of Open Access Journals (Sweden)

José Augusto da Rocha de Araujo

2005-12-01

Full Text Available In the globalized world, companies seek for new operations strategies to ensure world corporate success. This article analyzes how the cost management models – both traditional and activity-based, aid the planning and management of corporate globalized operations. The efficacy of the models application depends on their alignment with the competitive strategy. Companies must evaluate the nature of the competition and its competitive priorities; they should then define the necessary and sufficient dependence level on costs information. In this article, three dependence levels are presented: operational, decision support and strategic control. The result of the research shows the importance of alignment between the cost management model and the competitive strategy for corporate success, and confirms the adequacy of the activity-based costing model as a supporting tool for decision taking in a global strategy. Case studies in world class companies in Brazil are presented.

Stochastic Optimal Regulation Service Strategy for a Wind Farm Participating in the Electricity Market

DEFF Research Database (Denmark)

Zhang, Baohua; Hu, Weihao; Chen, Zhe

2015-01-01

in the stochastic optimization to deal with the uncertainty of the up regulation price and the up regulation activation of the power system.The Danish short-term electricity market and a wind farm in western Denmark are chosen to evaluate the effect of the proposed strategy. Simulation results showthe proposed......As modern wind farmshave the ability to provideregulation service for the power system, wind power plant operators may be motivated to participate in the regulating market to maximize their profit.In this paper, anoptimal regulation servicestrategy for a wind farm to participate...... strategy can increase the revenue of wind farms by leavinga certain amount of wind powerfor regulation service....

An optimization strategy for the control of small capacity heat pump integrated air-conditioning system

International Nuclear Information System (INIS)

Gao, Jiajia; Huang, Gongsheng; Xu, Xinhua

2016-01-01

Highlights: • An optimization strategy for a small-scale air-conditioning system is developed. • The optimization strategy aims at optimizing the overall system energy consumption. • The strategy may guarantee the robust control of the space air temperature. • The performance of the optimization strategy was tested on a simulation platform. - Abstract: This paper studies the optimization of a small-scale central air-conditioning system, in which the cooling is provided by a ground source heat pump (GSHP) equipped with an on/off capacity control. The optimization strategy aims to optimize the overall system energy consumption and simultaneously guarantee the robustness of the space air temperature control without violating the allowed GSHP maximum start-ups number per hour specified by customers. The set-point of the chilled water return temperature and the width of the water temperature control band are used as the decision variables for the optimization. The performance of the proposed strategy was tested on a simulation platform. Results show that the optimization strategy can save the energy consumption by 9.59% in a typical spring day and 2.97% in a typical summer day. Meanwhile it is able to enhance the space air temperature control robustness when compared with a basic control strategy without optimization.

Coordinating decentralized optimization of truck and shovel mining operations

Energy Technology Data Exchange (ETDEWEB)

Cheng, R.; Fraser Forbes, J. [Alberta Univ., Edmonton, AB (Canada). Dept. of Chemical and Materials Engineering; San Yip, W. [Suncor Energy, Fort McMurray, AB (Canada)

2006-07-01

Canada's oil sands contain the largest known reserve of oil in the world. Oil sands mining uses 3 functional processes, ore hauling, overburden removal and mechanical maintenance. The industry relies mainly on truck-and-shovel technology in its open-pit mining operations which contributes greatly to the overall mining operation cost. Coordination between operating units is crucial for achieving an enterprise-wide optimal operation level. Some of the challenges facing the industry include multiple or conflicting objectives such as minimizing the use of raw materials and energy while maximizing production. The large sets of constraints that define the feasible domain pose as challenge, as does the uncertainty in system parameters. One solution lies in assigning truck resources to various activities. This fully decentralized approach would treat the optimization of ore production, waste removal and equipment maintenance independently. It was emphasized that mine-wide optimal operation can only be achieved by coordinating ore hauling and overburden removal processes. For that reason, this presentation proposed a coordination approach for a decentralized optimization system. The approach is based on the Dantzig-Wolfe decomposition and auction-based methods that have been previously used to decompose large-scale optimization problems. The treatment of discrete variables and coordinator design was described and the method was illustrated with a simple truck and shovel mining simulation study. The approach can be applied to a wide range of applications such as coordinating decentralized optimal control systems and scheduling. 16 refs., 3 tabs., 2 figs.

Optimization of European call options considering physical delivery network and reservoir operation rules

Science.gov (United States)

Cheng, Wei-Chen; Hsu, Nien-Sheng; Cheng, Wen-Ming; Yeh, William W.-G.

2011-10-01

This paper develops alternative strategies for European call options for water purchase under hydrological uncertainties that can be used by water resources managers for decision making. Each alternative strategy maximizes its own objective over a selected sequence of future hydrology that is characterized by exceedance probability. Water trade provides flexibility and enhances water distribution system reliability. However, water trade between two parties in a regional water distribution system involves many issues, such as delivery network, reservoir operation rules, storage space, demand, water availability, uncertainty, and any existing contracts. An option is a security giving the right to buy or sell an asset; in our case, the asset is water. We extend a flow path-based water distribution model to include reservoir operation rules. The model simultaneously considers both the physical distribution network as well as the relationships between water sellers and buyers. We first test the model extension. Then we apply the proposed optimization model for European call options to the Tainan water distribution system in southern Taiwan. The formulation lends itself to a mixed integer linear programming model. We use the weighing method to formulate a composite function for a multiobjective problem. The proposed methodology provides water resources managers with an overall picture of water trade strategies and the consequence of each strategy. The results from the case study indicate that the strategy associated with a streamflow exceedence probability of 50% or smaller should be adopted as the reference strategy for the Tainan water distribution system.

Optimizing urology group partnerships: collaboration strategies and compensation best practices.

Science.gov (United States)

Jacoby, Dana L; Maller, Bruce S; Peltier, Lisa R

2014-10-01

Market forces in health care have created substantial regulatory, legislative, and reimbursement changes that have had a significant impact on urology group practices. To maintain viability, many urology groups have merged into larger integrated entities. Although group operations vary considerably, the majority of groups have struggled with the development of a strong culture, effective decision-making, and consensus-building around shared resources, income, and expense. Creating a sustainable business model requires urology group leaders to allocate appropriate time and resources to address these issues in a proactive manner. This article outlines collaboration strategies for creating an effective culture, governance, and leadership, and provides practical suggestions for optimizing the performance of the urology group practice.

Rule Optimization monthly reservoir operation Salvajina

International Nuclear Information System (INIS)

Sandoval Garcia, Maria Clemencia; Santacruz Salazar, Santiago; Ramirez Callejas, Carlos A

2007-01-01

In the present study a model was designed for the optimization of the rule for monthly operation of the Salvajina dam (Colombia) based in the technology) of dynamic programming. The model maximizes the benefits for electric power generation, ensuring at the same time flood regulation in winter and pollution relief during the summer. For the optimization of the rule of operation, it was necessary to define the levels and volumes of reserve and holding required for the control of flood zones in the Cauca river and to provide an effluent minimal flow and assure a daily flow at the Juanchito station (located 141 km downstream from the dam) of the Cauca river, 90 % of the time during the most critical summer periods.

Artificial root foraging optimizer algorithm with hybrid strategies

Directory of Open Access Journals (Sweden)

Yang Liu

2017-02-01

Full Text Available In this work, a new plant-inspired optimization algorithm namely the hybrid artificial root foraging optimizion (HARFO is proposed, which mimics the iterative root foraging behaviors for complex optimization. In HARFO model, two innovative strategies were developed: one is the root-to-root communication strategy, which enables the individual exchange information with each other in different efficient topologies that can essentially improve the exploration ability; the other is co-evolution strategy, which can structure the hierarchical spatial population driven by evolutionary pressure of multiple sub-populations that ensure the diversity of root population to be well maintained. The proposed algorithm is benchmarked against four classical evolutionary algorithms on well-designed test function suites including both classical and composition test functions. Through the rigorous performance analysis that of all these tests highlight the significant performance improvement, and the comparative results show the superiority of the proposed algorithm.

A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model

International Nuclear Information System (INIS)

Zhang, Zili; Gao, Chao; Liu, Yuxin; Qian, Tao

2014-01-01

Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP. (paper)

Optimal waste-to-energy strategy assisted by GIS For sustainable solid waste management

International Nuclear Information System (INIS)

Tan, S T; Hashim, H; Lee, C T; Lim, J S; Kanniah, K D

2014-01-01

Municipal solid waste (MSW) management has become more complex and costly with the rapid socio-economic development and increased volume of waste. Planning a sustainable regional waste management strategy is a critical step for the decision maker. There is a great potential for MSW to be used for the generation of renewable energy through waste incineration or landfilling with gas capture system. However, due to high processing cost and cost of resource transportation and distribution throughout the waste collection station and power plant, MSW is mostly disposed in the landfill. This paper presents an optimization model incorporated with GIS data inputs for MSW management. The model can design the multi-period waste-to-energy (WTE) strategy to illustrate the economic potential and tradeoffs for MSW management under different scenarios. The model is capable of predicting the optimal generation, capacity, type of WTE conversion technology and location for the operation and construction of new WTE power plants to satisfy the increased energy demand by 2025 in the most profitable way. Iskandar Malaysia region was chosen as the model city for this study

Optimal waste-to-energy strategy assisted by GIS For sustainable solid waste management

Science.gov (United States)

Tan, S. T.; Hashim, H.

2014-02-01

Municipal solid waste (MSW) management has become more complex and costly with the rapid socio-economic development and increased volume of waste. Planning a sustainable regional waste management strategy is a critical step for the decision maker. There is a great potential for MSW to be used for the generation of renewable energy through waste incineration or landfilling with gas capture system. However, due to high processing cost and cost of resource transportation and distribution throughout the waste collection station and power plant, MSW is mostly disposed in the landfill. This paper presents an optimization model incorporated with GIS data inputs for MSW management. The model can design the multi-period waste-to-energy (WTE) strategy to illustrate the economic potential and tradeoffs for MSW management under different scenarios. The model is capable of predicting the optimal generation, capacity, type of WTE conversion technology and location for the operation and construction of new WTE power plants to satisfy the increased energy demand by 2025 in the most profitable way. Iskandar Malaysia region was chosen as the model city for this study.

SIMULTANEOUS SCHEDULING AND OPERATIONAL OPTIMIZATION OF MULTIPRODUCT, CYCLIC CONTINUOUS PLANTS

Directory of Open Access Journals (Sweden)

A. Alle

2002-03-01

Full Text Available The problems of scheduling and optimization of operational conditions in multistage, multiproduct continuous plants with intermediate storage are simultaneously addressed. An MINLP model, called TSPFLOW, which is based on the TSP formulation for product sequencing, is proposed to schedule the operation of such plants. TSPFLOW yields a one-order-of-magnitude CPU time reduction as well as the solution of instances larger than those formerly reported (Pinto and Grossmann, 1994. Secondly, processing rates and yields are introduced as additional optimization variables in order to state the simultaneous problem of scheduling with operational optimization. Results show that trade-offs are very complex and that the development of a straightforward (rule of thumb method to optimally schedule the operation is less effective than the proposed approach.

SIMULTANEOUS SCHEDULING AND OPERATIONAL OPTIMIZATION OF MULTIPRODUCT, CYCLIC CONTINUOUS PLANTS

Directory of Open Access Journals (Sweden)

Alle A.

2002-01-01

Full Text Available The problems of scheduling and optimization of operational conditions in multistage, multiproduct continuous plants with intermediate storage are simultaneously addressed. An MINLP model, called TSPFLOW, which is based on the TSP formulation for product sequencing, is proposed to schedule the operation of such plants. TSPFLOW yields a one-order-of-magnitude CPU time reduction as well as the solution of instances larger than those formerly reported (Pinto and Grossmann, 1994. Secondly, processing rates and yields are introduced as additional optimization variables in order to state the simultaneous problem of scheduling with operational optimization. Results show that trade-offs are very complex and that the development of a straightforward (rule of thumb method to optimally schedule the operation is less effective than the proposed approach.

Optimized Strategies for Detecting Extrasolar Space Weather

Science.gov (United States)

Hallinan, Gregg

2018-06-01

Fully understanding the implications of space weather for the young solar system, as well as the wider population of planet-hosting stars, requires remote sensing of space weather in other stellar systems. Solar coronal mass ejections can be accompanied by bright radio bursts at low frequencies (typically measurement of the magnetic field strength of the planet, informing on whether the atmosphere of the planet can survive the intense magnetic activity of its host star. However, both stellar and planetary radio emission are highly variable and optimal strategies for detection of these emissions requires the capability to monitor 1000s of nearby stellar/planetary systems simultaneously. I will discuss optimized strategies for both ground and space-based experiments to take advantage of the highly variable nature of the radio emissions powered by extrasolar space weather to enable detection of stellar CMEs and planetary magnetospheres.

Optimal Energy Management of Multi-Microgrids with Sequentially Coordinated Operations

Directory of Open Access Journals (Sweden)

Nah-Oak Song

2015-08-01

Full Text Available We propose an optimal electric energy management of a cooperative multi-microgrid community with sequentially coordinated operations. The sequentially coordinated operations are suggested to distribute computational burden and yet to make the optimal 24 energy management of multi-microgrids possible. The sequential operations are mathematically modeled to find the optimal operation conditions and illustrated with physical interpretation of how to achieve optimal energy management in the cooperative multi-microgrid community. This global electric energy optimization of the cooperative community is realized by the ancillary internal trading between the microgrids in the cooperative community which reduces the extra cost from unnecessary external trading by adjusting the electric energy production amounts of combined heat and power (CHP generators and amounts of both internal and external electric energy trading of the cooperative community. A simulation study is also conducted to validate the proposed mathematical energy management models.

Development of Long-term Cooling Operation Strategy with H-SIT

International Nuclear Information System (INIS)

Jeon, In Seop; Kang, Hyun Gook

2016-01-01

In the current nuclear power plants (NPPs), most of the critical safety functions are provided by many active safety systems. Long-term cooling of core is an ultimate goal of all mitigation actions for plant safety and feed and bleed (F and B) operation strategy is one of long-term cooling strategies in conventional pressurized water reactor (PWR). The important point of F and B operation is that, in conventional mitigation strategy, injection for feed operation is performed by only high pressure injection (HPSI) pump. Low pressure injection (LPSI) pump such as shut down cooling pump (SCP) cannot be used for F and B operation. Thus, when F and B operation is needed, if high-pressure injection pump fails, core should be damaged. In this study, F and B operation strategy with LPSI and H-SIT is developed. This is a new concept for the long-term cooling operation. If this strategy is applied, low pressure injection pump can be successfully used for F and B operation thus operator has the additional mitigation way. As this strategy make plant safe even though HPSI and PAFS are both failed, it can effectively enhance the plant safety. For this strategy two RCGVSs and two POSRVs are needed as a depressurization system for bleed operation and only one LPSI is enough for feed operation. H-SIT operation is also needed to make up core inventory during bleed operation. For this operation, four H-SITs have to be used to make up core safely. Based on the risk analysis using PSA method, if this strategy is applied, core damage frequency is 1.868e-6 which declined 7 percent from original model.

Optimal Sizing and Control Strategy Design for Heavy Hybrid Electric Truck

Directory of Open Access Journals (Sweden)

Yuan Zou

2012-01-01

Full Text Available Due to the complexity of the hybrid powertrain, the control is highly involved to improve the collaborations of the different components. For the specific powertrain, the components' sizing just gives the possibility to propel the vehicle and the control will realize the function of the propulsion. Definitely the components' sizing also gives the constraints to the control design, which cause a close coupling between the sizing and control strategy design. This paper presents a parametric study focused on sizing of the powertrain components and optimization of the power split between the engine and electric motor for minimizing the fuel consumption. A framework is put forward to accomplish the optimal sizing and control design for a heavy parallel pre-AMT hybrid truck under the natural driving schedule. The iterative plant-controller combined optimization methodology is adopted to optimize the key parameters of the plant and control strategy simultaneously. A scalable powertrain model based on a bilevel optimization framework is built. Dynamic programming is applied to find the optimal control in the inner loop with a prescribed cycle. The parameters are optimized in the outer loop. The results are analysed and the optimal sizing and control strategy are achieved simultaneously.

Optimized control strategy for crowbarless solid state modular power supply

International Nuclear Information System (INIS)

Upadhyay, R.; Badapanda, M.K.; Tripathi, A.; Hannurkar, P.R.; Pithawa, C.K.

2009-01-01

Solid state modular power supply with series connected IGBT based power modules have been employed as high voltage bias power supply of klystron amplifier. Auxiliary compensation of full wave inverter bridge with ZVS/ZCS operations of all IGBTs over entire operating range is incorporated. An optimized control strategy has been adopted for this power supply needing no output filter, making this scheme crowbarless and is presented in this paper. DSP based fully digital control with same duty cycle for all power modules, have been incorporated for regulating this power supply along with adequate protection features. Input to this power supply is taken directly from 11 kV line and the input system is intentionally made 24 pulsed to reduce the input harmonics, improve the input power factor significantly, there by requiring no line filters. Various steps have been taken to increase the efficiency of major subsystems, so as to improve the overall efficiency of this power supply significantly. (author)

Optimal Dynamic Advertising Strategy Under Age-Specific Market Segmentation

Science.gov (United States)

Krastev, Vladimir

2011-12-01

We consider the model proposed by Faggian and Grosset for determining the advertising efforts and goodwill in the long run of a company under age segmentation of consumers. Reducing this model to optimal control sub problems we find the optimal advertising strategy and goodwill.

Optimal risky bidding strategy for a generating company by self-organising hierarchical particle swarm optimisation

International Nuclear Information System (INIS)

Boonchuay, Chanwit; Ongsakul, Weerakorn

2011-01-01

In this paper, an optimal risky bidding strategy for a generating company (GenCo) by self-organising hierarchical particle swarm optimisation with time-varying acceleration coefficients (SPSO-TVAC) is proposed. A significant risk index based on mean-standard deviation ratio (MSR) is maximised to provide the optimal bid prices and quantities. The Monte Carlo (MC) method is employed to simulate rivals' behaviour in competitive environment. Non-convex operating cost functions of thermal generating units and minimum up/down time constraints are taken into account. The proposed bidding strategy is implemented in a multi-hourly trading in a uniform price spot market and compared to other particle swarm optimisation (PSO). Test results indicate that the proposed SPSO-TVAC approach can provide a higher MSR than the other PSO methods. It is potentially applicable to risk management of profit variation of GenCo in spot market.

Optimizing Reservoir Operation to Adapt to the Climate Change

Science.gov (United States)

Madadgar, S.; Jung, I.; Moradkhani, H.

2010-12-01

Climate change and upcoming variation in flood timing necessitates the adaptation of current rule curves developed for operation of water reservoirs as to reduce the potential damage from either flood or draught events. This study attempts to optimize the current rule curves of Cougar Dam on McKenzie River in Oregon addressing some possible climate conditions in 21th century. The objective is to minimize the failure of operation to meet either designated demands or flood limit at a downstream checkpoint. A simulation/optimization model including the standard operation policy and a global optimization method, tunes the current rule curve upon 8 GCMs and 2 greenhouse gases emission scenarios. The Precipitation Runoff Modeling System (PRMS) is used as the hydrology model to project the streamflow for the period of 2000-2100 using downscaled precipitation and temperature forcing from 8 GCMs and two emission scenarios. An ensemble of rule curves, each associated with an individual scenario, is obtained by optimizing the reservoir operation. The simulation of reservoir operation, for all the scenarios and the expected value of the ensemble, is conducted and performance assessment using statistical indices including reliability, resilience, vulnerability and sustainability is made.

A hybrid multi-level optimization approach for the dynamic synthesis/design and operation/control under uncertainty of a fuel cell system

International Nuclear Information System (INIS)

Kim, Kihyung; Spakovsky, Michael R. von; Wang, M.; Nelson, Douglas J.

2011-01-01

During system development, large-scale, complex energy systems require multi-disciplinary efforts to achieve system quality, cost, and performance goals. As systems become larger and more complex, the number of possible system configurations and technologies, which meet the designer's objectives optimally, increases greatly. In addition, both transient and environmental effects may need to be taken into account. Thus, the difficulty of developing the system via the formulation of a single optimization problem in which the optimal synthesis/design and operation/control of the system are achieved simultaneously is great and rather problematic. This difficulty is further heightened with the introduction of uncertainty analysis, which transforms the problem from a purely deterministic one into a probabilistic one. Uncertainties, system complexity and nonlinearity, and large numbers of decision variables quickly render the single optimization problem unsolvable by conventional, single-level, optimization strategies. To address these difficulties, the strategy adopted here combines a dynamic physical decomposition technique for large-scale optimization with a response sensitivity analysis method for quantifying system response uncertainties to given uncertainty sources. The feasibility of such a hybrid approach is established by applying it to the synthesis/design and operation/control of a 5 kW proton exchange membrane (PEM) fuel cell system.

Optimal combined purchasing strategies for a risk-averse manufacturer under price uncertainty

Directory of Open Access Journals (Sweden)

Qiao Wu

2015-09-01

Full Text Available Purpose: The purpose of our paper is to analyze optimal purchasing strategies when a manufacturer can buy raw materials from a long-term contract supplier and a spot market under spot price uncertainty. Design/methodology/approach: This procurement model can be solved by using dynamic programming. First, we maximize the DM’s utility of the second period, obtaining the optimal contract quantity and spot quantity for the second period. Then, maximize the DM’s utility of both periods, obtaining the optimal purchasing strategy for the first period. We use a numerical method to compare the performance level of a pure spot sourcing strategy with that of a mixed strategy. Findings: Our results show that optimal purchasing strategies vary with the trend of contract prices. If the contract price falls, the total quantity purchased in period 1 will decrease in the degree of risk aversion. If the contract price increases, the total quantity purchased in period 1 will increase in the degree of risk aversion. The relationship between the optimal contract quantity and the degree of risk aversion depends on whether the expected spot price or the contract price is larger in period 2. Finally, we compare the performance levels between a combined strategy and a spot sourcing strategy. It shows that a combined strategy is optimal for a risk-averse buyer. Originality/value: It’s challenging to deal with a two-period procurement problem with risk consideration. We have obtained results of a two-period procurement problem with two sourcing options, namely contract procurement and spot purchases. Our model incorporates the buyer’s risk aversion factor and the change of contract prices, which are not addressed in early studies.

Risk based economic optimization of investment decisions of regulated power distribution system operators; Risikobasierte wirtschaftliche Optimierung von Investitionsentscheidungen regulierter Stromnetzbetreiber

Energy Technology Data Exchange (ETDEWEB)

John, Oliver

2012-07-01

The author of the contribution under consideration reports on risk-based economic optimization of investment decisions of regulated power distribution system operators. The focus is the economically rational decision behavior of operators under certain regulatory requirements. Investments in power distribution systems form the items subject to decisions. Starting from a description of theoretical and practical regulatory approaches, their financial implications are quantified at first. On this basis, optimization strategies are derived with respect to the investment behavior. For this purpose, an optimization algorithm is developed and applied to exemplary companies. Finally, effects of uncertainties in regulatory systems are investigated. In this context, Monte Carlo simulations are used in conjunction with real options analysis.

Automatic CT simulation optimization for radiation therapy: A general strategy

Energy Technology Data Exchange (ETDEWEB)

Li, Hua, E-mail: huli@radonc.wustl.edu; Chen, Hsin-Chen; Tan, Jun; Gay, Hiram; Michalski, Jeff M.; Mutic, Sasa [Department of Radiation Oncology, Washington University, St. Louis, Missouri 63110 (United States); Yu, Lifeng [Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905 (United States); Anastasio, Mark A. [Department of Biomedical Engineering, Washington University, St. Louis, Missouri 63110 (United States); Low, Daniel A. [Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095 (United States)

2014-03-15

Purpose: In radiation therapy, x-ray computed tomography (CT) simulation protocol specifications should be driven by the treatment planning requirements in lieu of duplicating diagnostic CT screening protocols. The purpose of this study was to develop a general strategy that allows for automatically, prospectively, and objectively determining the optimal patient-specific CT simulation protocols based on radiation-therapy goals, namely, maintenance of contouring quality and integrity while minimizing patient CT simulation dose. Methods: The authors proposed a general prediction strategy that provides automatic optimal CT simulation protocol selection as a function of patient size and treatment planning task. The optimal protocol is the one that delivers the minimum dose required to provide a CT simulation scan that yields accurate contours. Accurate treatment plans depend on accurate contours in order to conform the dose to actual tumor and normal organ positions. An image quality index, defined to characterize how simulation scan quality affects contour delineation, was developed and used to benchmark the contouring accuracy and treatment plan quality within the predication strategy. A clinical workflow was developed to select the optimal CT simulation protocols incorporating patient size, target delineation, and radiation dose efficiency. An experimental study using an anthropomorphic pelvis phantom with added-bolus layers was used to demonstrate how the proposed prediction strategy could be implemented and how the optimal CT simulation protocols could be selected for prostate cancer patients based on patient size and treatment planning task. Clinical IMRT prostate treatment plans for seven CT scans with varied image quality indices were separately optimized and compared to verify the trace of target and organ dosimetry coverage. Results: Based on the phantom study, the optimal image quality index for accurate manual prostate contouring was 4.4. The optimal tube

Automatic CT simulation optimization for radiation therapy: A general strategy.

Science.gov (United States)

Li, Hua; Yu, Lifeng; Anastasio, Mark A; Chen, Hsin-Chen; Tan, Jun; Gay, Hiram; Michalski, Jeff M; Low, Daniel A; Mutic, Sasa

2014-03-01

In radiation therapy, x-ray computed tomography (CT) simulation protocol specifications should be driven by the treatment planning requirements in lieu of duplicating diagnostic CT screening protocols. The purpose of this study was to develop a general strategy that allows for automatically, prospectively, and objectively determining the optimal patient-specific CT simulation protocols based on radiation-therapy goals, namely, maintenance of contouring quality and integrity while minimizing patient CT simulation dose. The authors proposed a general prediction strategy that provides automatic optimal CT simulation protocol selection as a function of patient size and treatment planning task. The optimal protocol is the one that delivers the minimum dose required to provide a CT simulation scan that yields accurate contours. Accurate treatment plans depend on accurate contours in order to conform the dose to actual tumor and normal organ positions. An image quality index, defined to characterize how simulation scan quality affects contour delineation, was developed and used to benchmark the contouring accuracy and treatment plan quality within the predication strategy. A clinical workflow was developed to select the optimal CT simulation protocols incorporating patient size, target delineation, and radiation dose efficiency. An experimental study using an anthropomorphic pelvis phantom with added-bolus layers was used to demonstrate how the proposed prediction strategy could be implemented and how the optimal CT simulation protocols could be selected for prostate cancer patients based on patient size and treatment planning task. Clinical IMRT prostate treatment plans for seven CT scans with varied image quality indices were separately optimized and compared to verify the trace of target and organ dosimetry coverage. Based on the phantom study, the optimal image quality index for accurate manual prostate contouring was 4.4. The optimal tube potentials for patient sizes

Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Yuying Wang

2017-11-01

Full Text Available This paper presents an energy management strategy for plug-in hybrid electric vehicles (PHEVs that not only tries to minimize the energy consumption, but also considers the battery health. First, a battery model that can be applied to energy management optimization is given. In this model, battery health damage can be estimated in the different states of charge (SOC and temperature of the battery pack. Then, because of the inevitability that limiting the battery health degradation will increase energy consumption, a Pareto energy management optimization problem is formed. This multi-objective optimal control problem is solved numerically by using stochastic dynamic programming (SDP and particle swarm optimization (PSO for satisfying the vehicle power demand and considering the tradeoff between energy consumption and battery health at the same time. The optimization solution is obtained offline by utilizing real historical traffic data and formed as mappings on the system operating states so as to implement online in the actual driving conditions. Finally, the simulation results carried out on the GT-SUITE-based PHEV test platform are illustrated to demonstrate that the proposed multi-objective optimal control strategy would effectively yield benefits.

Optimal electricity dispatch on isolated mini-grids using a demand response strategy for thermal storage backup with genetic algorithms

International Nuclear Information System (INIS)

Neves, Diana; Silva, Carlos A.

2015-01-01

The present study uses the DHW (domestic hot water) electric backup from solar thermal systems to optimize the total electricity dispatch of an isolated mini-grid. The proposed approach estimates the hourly DHW load, and proposes and simulates different DR (demand response) strategies, from the supply side, to minimize the dispatch costs of an energy system. The case study consists on optimizing the electricity load, in a representative day with low solar radiation, in Corvo Island, Azores. The DHW backup is induced by three different demand patterns. The study compares different DR strategies: backup at demand (no strategy), pre-scheduled backup using two different imposed schedules, a strategy based on linear programming, and finally two strategies using genetic algorithms, with different formulations for DHW backup – one that assigns number of systems and another that assigns energy demand. It is concluded that pre-determined DR strategies may increase the generation costs, but DR strategies based on optimization algorithms are able to decrease generation costs. In particular, linear programming is the strategy that presents the lowest increase on dispatch costs, but the strategy based on genetic algorithms is the one that best minimizes both daily operation costs and total energy demand, of the system. - Highlights: • Integrated hourly model of DHW electric impact and electricity dispatch of isolated grid. • Proposal and comparison of different DR (demand response) strategies for DHW backup. • LP strategy presents 12% increase on total electric load, plus 5% on dispatch costs. • GA strategy presents 7% increase on total electric load, plus 8% on dispatch costs

What SCADA systems can offer to optimize field operations

International Nuclear Information System (INIS)

McLean, D.J.

1997-01-01

A new technology developed by Kenomic Controls Ltd. of Calgary was designed to solve some of the problems associated with producing gas wells with high gas to liquids ratios. The rationale and the system architecture of the SCADA (Supervisory Control and Data Acquisition) system were described. The most common application of SCADA is the Electronic Flow Measurement (EFM) installation using a solar or thermo-electric generator as a power source for the local electronics. Benefits that the SCADA system can provide to producing fields such as increased revenue, decreased operating costs, decreased fixed capital and working capital requirements, the planning and implementation strategies for SCADA were outlined. A case history of a gas well production optimization system in the Pierceland area of northwest Saskatchewan was provided as an illustrative example. 9 figs

Design and development of bio-inspired framework for reservoir operation optimization

Science.gov (United States)

Asvini, M. Sakthi; Amudha, T.

2017-12-01

Frameworks for optimal reservoir operation play an important role in the management of water resources and delivery of economic benefits. Effective utilization and conservation of water from reservoirs helps to manage water deficit periods. The main challenge in reservoir optimization is to design operating rules that can be used to inform real-time decisions on reservoir release. We develop a bio-inspired framework for the optimization of reservoir release to satisfy the diverse needs of various stakeholders. In this work, single-objective optimization and multiobjective optimization problems are formulated using an algorithm known as "strawberry optimization" and tested with actual reservoir data. Results indicate that well planned reservoir operations lead to efficient deployment of the reservoir water with the help of optimal release patterns.

Optimal mode of operation for biomass production

NARCIS (Netherlands)

Betlem, Ben H.L.; Roffel, Brian; Mulder, P.

2002-01-01

The rate of biomass production is optimised for a predefined feed exhaustion using the residue ratio as a degree of freedom. Three modes of operation are considered: continuous, repeated batch, and repeated fed-batch operation. By means of the Production Curve, the transition points of the optimal

Parallel strategy for optimal learning in perceptrons

International Nuclear Information System (INIS)

Neirotti, J P

2010-01-01

We developed a parallel strategy for learning optimally specific realizable rules by perceptrons, in an online learning scenario. Our result is a generalization of the Caticha-Kinouchi (CK) algorithm developed for learning a perceptron with a synaptic vector drawn from a uniform distribution over the N-dimensional sphere, so called the typical case. Our method outperforms the CK algorithm in almost all possible situations, failing only in a denumerable set of cases. The algorithm is optimal in the sense that it saturates Bayesian bounds when it succeeds.

Stochastic multi-objective model for optimal energy exchange optimization of networked microgrids with presence of renewable generation under risk-based strategies.

Science.gov (United States)

Gazijahani, Farhad Samadi; Ravadanegh, Sajad Najafi; Salehi, Javad

2018-02-01

The inherent volatility and unpredictable nature of renewable generations and load demand pose considerable challenges for energy exchange optimization of microgrids (MG). To address these challenges, this paper proposes a new risk-based multi-objective energy exchange optimization for networked MGs from economic and reliability standpoints under load consumption and renewable power generation uncertainties. In so doing, three various risk-based strategies are distinguished by using conditional value at risk (CVaR) approach. The proposed model is specified as a two-distinct objective function. The first function minimizes the operation and maintenance costs, cost of power transaction between upstream network and MGs as well as power loss cost, whereas the second function minimizes the energy not supplied (ENS) value. Furthermore, the stochastic scenario-based approach is incorporated into the approach in order to handle the uncertainty. Also, Kantorovich distance scenario reduction method has been implemented to reduce the computational burden. Finally, non-dominated sorting genetic algorithm (NSGAII) is applied to minimize the objective functions simultaneously and the best solution is extracted by fuzzy satisfying method with respect to risk-based strategies. To indicate the performance of the proposed model, it is performed on the modified IEEE 33-bus distribution system and the obtained results show that the presented approach can be considered as an efficient tool for optimal energy exchange optimization of MGs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Blackjack in Holland Casino's : Basic, optimal and winning strategies

NARCIS (Netherlands)

van der Genugten, B.B.

1995-01-01

This paper considers the cardgame Blackjack according to the rules of Holland Casino's in the Netherlands. Expected gains of strategies are derived with simulation and also with analytic tools. New effiency concepts based on the gains of the basic and the optimal strategy are introduced. A general

Fault-Tolerant Control of ANPC Three-Level Inverter Based on Order-Reduction Optimal Control Strategy under Multi-Device Open-Circuit Fault.

Science.gov (United States)

Xu, Shi-Zhou; Wang, Chun-Jie; Lin, Fang-Li; Li, Shi-Xiang

2017-10-31

The multi-device open-circuit fault is a common fault of ANPC (Active Neutral-Point Clamped) three-level inverter and effect the operation stability of the whole system. To improve the operation stability, this paper summarized the main solutions currently firstly and analyzed all the possible states of multi-device open-circuit fault. Secondly, an order-reduction optimal control strategy was proposed under multi-device open-circuit fault to realize fault-tolerant control based on the topology and control requirement of ANPC three-level inverter and operation stability. This control strategy can solve the faults with different operation states, and can works in order-reduction state under specific open-circuit faults with specific combined devices, which sacrifices the control quality to obtain the stability priority control. Finally, the simulation and experiment proved the effectiveness of the proposed strategy.

Optimal processor for malfunction detection in operating nuclear reactor

International Nuclear Information System (INIS)

Ciftcioglu, O.

1990-01-01

An optimal processor for diagnosing operational transients in a nuclear reactor is described. Basic design of the processor involves real-time processing of noise signal obtained from a particular in core sensor and the optimality is based on minimum alarm failure in contrast to minimum false alarm criterion from the safe and reliable plant operation viewpoint

Application of optimal interation strategies to diffusion theory calculations

International Nuclear Information System (INIS)

Jones, R.B.

1978-01-01

The geometric interpretation of optimal (minimum computational time) iteration strategies is applied to one- and two-group, two-dimensional diffusion-theory calculations. The method is a ''spectral/time balance'' technique which weighs the convergence enhancement of the inner iteration procedure with that of the outer iteration loop and the time required to reconstruct the source. The diffusion-theory option of the discrete-ordinates transport code DOT3.5 was altered to incorporate the theoretical inner/outer decision logic. For the two-dimensional configuration considered, the optimal strategies reduced the total number of iterations performed for a given error criterion

Operating multireservoir hydropower systems for downstream water quality

International Nuclear Information System (INIS)

Hayes, D.F.

1990-01-01

Hydropower reservoir operations often impact tailwater quality and water quality in the stream or river below the impoundment for many miles. Determining optimal operating strategies for a system of hydropower reservoirs involves solving a highly dimensional nonlinear, nonconvex optimization problem. This research adds the additional complexities of downstream water quality considerations within the optimization formulation to determine operating strategies for a system of hydropower reservoirs operating in series (tandem) or parallel. The formulation was used to determine operating strategies for six reservoirs of the upper Cumberland river basin in Tennessee and Kentucky. Significant dissolved oxygen (DO) violations occur just upstream of Nashville, Tennessee below Old Hickory dam during the months of August and September. Daily reservoir releases were determined for the period of June through September which would produce the maximum hydropower revenue while meeting downstream water quality objectives. Optimal releases for three operational strategies were compared to historical operations for the years 1985, 1986, and 1988. These strategies included: spilling as necessary to meet water quality criteria, near normal operation (minimal spills), and drawdown of reservoirs as necessary to meet criteria without spills. Optimization results showed an 8% to 15% hydropower loss may be necessary to meet water quality criteria through spills and a 2% to 9% improvement in DO below Old Hickory may be possible without significant spills. Results also showed that substantial increases in initial headwater elevations would be necessary to meet daily DO criteria and avoid spills. The optimal control theory algorithm used to solve the problem proved to be an efficient and robust solver of this large optimization problem

Optimizing refiner operation with statistical modelling

Energy Technology Data Exchange (ETDEWEB)

Broderick, G [Noranda Research Centre, Pointe Claire, PQ (Canada)

1997-02-01

The impact of refining conditions on the energy efficiency of the process and on the handsheet quality of a chemi-mechanical pulp was studied as part of a series of pilot scale refining trials. Statistical models of refiner performance were constructed from these results and non-linear optimization of process conditions were conducted. Optimization results indicated that increasing the ratio of specific energy applied in the first stage led to a reduction of some 15 per cent in the total energy requirement. The strategy can also be used to obtain significant increases in pulp quality for a given energy input. 20 refs., 6 tabs.

Optimal Design and Operation of Permanent Irrigation Systems

Science.gov (United States)

Oron, Gideon; Walker, Wynn R.

1981-01-01

Solid-set pressurized irrigation system design and operation are studied with optimization techniques to determine the minimum cost distribution system. The principle of the analysis is to divide the irrigation system into subunits in such a manner that the trade-offs among energy, piping, and equipment costs are selected at the minimum cost point. The optimization procedure involves a nonlinear, mixed integer approach capable of achieving a variety of optimal solutions leading to significant conclusions with regard to the design and operation of the system. Factors investigated include field geometry, the effect of the pressure head, consumptive use rates, a smaller flow rate in the pipe system, and outlet (sprinkler or emitter) discharge.

Deterministic operations research models and methods in linear optimization

CERN Document Server

Rader, David J

2013-01-01

Uniquely blends mathematical theory and algorithm design for understanding and modeling real-world problems Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering. Addressing the importance of the algorithm design process. Deterministic Operations Research focuses on the design of solution methods for both continuous and discrete linear optimization problems. The result is a clear-cut resource for understanding three cornerstones of deterministic operations resear

Optimizing Biorefinery Design and Operations via Linear Programming Models

Energy Technology Data Exchange (ETDEWEB)

Talmadge, Michael; Batan, Liaw; Lamers, Patrick; Hartley, Damon; Biddy, Mary; Tao, Ling; Tan, Eric

2017-03-28

The ability to assess and optimize economics of biomass resource utilization for the production of fuels, chemicals and power is essential for the ultimate success of a bioenergy industry. The team of authors, consisting of members from the National Renewable Energy Laboratory (NREL) and the Idaho National Laboratory (INL), has developed simple biorefinery linear programming (LP) models to enable the optimization of theoretical or existing biorefineries. The goal of this analysis is to demonstrate how such models can benefit the developing biorefining industry. It focuses on a theoretical multi-pathway, thermochemical biorefinery configuration and demonstrates how the biorefinery can use LP models for operations planning and optimization in comparable ways to the petroleum refining industry. Using LP modeling tools developed under U.S. Department of Energy's Bioenergy Technologies Office (DOE-BETO) funded efforts, the authors investigate optimization challenges for the theoretical biorefineries such as (1) optimal feedstock slate based on available biomass and prices, (2) breakeven price analysis for available feedstocks, (3) impact analysis for changes in feedstock costs and product prices, (4) optimal biorefinery operations during unit shutdowns / turnarounds, and (5) incentives for increased processing capacity. These biorefinery examples are comparable to crude oil purchasing and operational optimization studies that petroleum refiners perform routinely using LPs and other optimization models. It is important to note that the analyses presented in this article are strictly theoretical and they are not based on current energy market prices. The pricing structure assigned for this demonstrative analysis is consistent with $4 per gallon gasoline, which clearly assumes an economic environment that would favor the construction and operation of biorefineries. The analysis approach and examples provide valuable insights into the usefulness of analysis tools for

Evolution strategy based optimal chiller loading for saving energy

International Nuclear Information System (INIS)

Chang, Y.-C.; Lee, C.-Y.; Chen, C.-R.; Chou, C.-J.; Chen, W.-H.; Chen, W.-H.

2009-01-01

This study employs evolution strategy (ES) to solve optimal chiller loading (OCL) problem. ES overcomes the flaw that Lagrangian method is not adaptable for solving OCL as the power consumption models or the kW-PLR (partial load ratio) curves include convex functions and concave functions simultaneously. The complicated process of evolution by the genetic algorithm (GA) method for solving OCL can also be simplified by the ES method. This study uses the PLR of chiller as the variable to be solved for the decoupled air conditioning system. After analysis and comparison of the case study, it has been concluded that this method not only solves the problems of Lagrangian method and GA method, but also produces results with high accuracy within a rapid timeframe. It can be perfectly applied to the operation of air conditioning systems

Examples of radiation protection optimization in design and operation

International Nuclear Information System (INIS)

Gonzalez, A.J.; Palacios, E.; Curti, A.; Agatiello, O.; Majchrzak, J.

1982-01-01

The practical use of the requirement of optimization of radiological protection is presented. Application examples for designing ventilation systems and for maintenance operations of nuclear plants are given. A method is developed for the application of the optimization requirement to the design of ventilation systems in contaminated environments. Representative values of the main parameters are presented and their relevant features are discussed. A practical example shows actual results for a radioisotope production plant. Causes influencing collective doses incurred by the workers during maintenance operations are analyzed. A method is presented for the optimization of both the level of training of personnel and the apportionment of individual doses. As an example, this methodology is applied to the maintenance operations in a nuclear power plant. (author)

Multilevel Optimization Framework for Hierarchical Stiffened Shells Accelerated by Adaptive Equivalent Strategy

Science.gov (United States)

Wang, Bo; Tian, Kuo; Zhao, Haixin; Hao, Peng; Zhu, Tianyu; Zhang, Ke; Ma, Yunlong

2017-06-01

In order to improve the post-buckling optimization efficiency of hierarchical stiffened shells, a multilevel optimization framework accelerated by adaptive equivalent strategy is presented in this paper. Firstly, the Numerical-based Smeared Stiffener Method (NSSM) for hierarchical stiffened shells is derived by means of the numerical implementation of asymptotic homogenization (NIAH) method. Based on the NSSM, a reasonable adaptive equivalent strategy for hierarchical stiffened shells is developed from the concept of hierarchy reduction. Its core idea is to self-adaptively decide which hierarchy of the structure should be equivalent according to the critical buckling mode rapidly predicted by NSSM. Compared with the detailed model, the high prediction accuracy and efficiency of the proposed model is highlighted. On the basis of this adaptive equivalent model, a multilevel optimization framework is then established by decomposing the complex entire optimization process into major-stiffener-level and minor-stiffener-level sub-optimizations, during which Fixed Point Iteration (FPI) is employed to accelerate convergence. Finally, the illustrative examples of the multilevel framework is carried out to demonstrate its efficiency and effectiveness to search for the global optimum result by contrast with the single-level optimization method. Remarkably, the high efficiency and flexibility of the adaptive equivalent strategy is indicated by compared with the single equivalent strategy.

Optimal sampling strategies for detecting zoonotic disease epidemics.

Directory of Open Access Journals (Sweden)

Jake M Ferguson

2014-06-01

Full Text Available The early detection of disease epidemics reduces the chance of successful introductions into new locales, minimizes the number of infections, and reduces the financial impact. We develop a framework to determine the optimal sampling strategy for disease detection in zoonotic host-vector epidemiological systems when a disease goes from below detectable levels to an epidemic. We find that if the time of disease introduction is known then the optimal sampling strategy can switch abruptly between sampling only from the vector population to sampling only from the host population. We also construct time-independent optimal sampling strategies when conducting periodic sampling that can involve sampling both the host and the vector populations simultaneously. Both time-dependent and -independent solutions can be useful for sampling design, depending on whether the time of introduction of the disease is known or not. We illustrate the approach with West Nile virus, a globally-spreading zoonotic arbovirus. Though our analytical results are based on a linearization of the dynamical systems, the sampling rules appear robust over a wide range of parameter space when compared to nonlinear simulation models. Our results suggest some simple rules that can be used by practitioners when developing surveillance programs. These rules require knowledge of transition rates between epidemiological compartments, which population was initially infected, and of the cost per sample for serological tests.

Optimal sampling strategies for detecting zoonotic disease epidemics.

Science.gov (United States)

Ferguson, Jake M; Langebrake, Jessica B; Cannataro, Vincent L; Garcia, Andres J; Hamman, Elizabeth A; Martcheva, Maia; Osenberg, Craig W

2014-06-01

The early detection of disease epidemics reduces the chance of successful introductions into new locales, minimizes the number of infections, and reduces the financial impact. We develop a framework to determine the optimal sampling strategy for disease detection in zoonotic host-vector epidemiological systems when a disease goes from below detectable levels to an epidemic. We find that if the time of disease introduction is known then the optimal sampling strategy can switch abruptly between sampling only from the vector population to sampling only from the host population. We also construct time-independent optimal sampling strategies when conducting periodic sampling that can involve sampling both the host and the vector populations simultaneously. Both time-dependent and -independent solutions can be useful for sampling design, depending on whether the time of introduction of the disease is known or not. We illustrate the approach with West Nile virus, a globally-spreading zoonotic arbovirus. Though our analytical results are based on a linearization of the dynamical systems, the sampling rules appear robust over a wide range of parameter space when compared to nonlinear simulation models. Our results suggest some simple rules that can be used by practitioners when developing surveillance programs. These rules require knowledge of transition rates between epidemiological compartments, which population was initially infected, and of the cost per sample for serological tests.

Optimal Energy Control Strategy Design for a Hybrid Electric Vehicle

Directory of Open Access Journals (Sweden)

Yuan Zou

2013-01-01

Full Text Available A heavy-duty parallel hybrid electric truck is modeled, and its optimal energy control is studied in this paper. The fundamental architecture of the parallel hybrid electric truck is modeled feed-forwardly, together with necessary dynamic features of subsystem or components. Dynamic programming (DP technique is adopted to find the optimal control strategy including the gear-shifting sequence and the power split between the engine and the motor subject to a battery SOC-sustaining constraint. Improved control rules are extracted from the DP-based control solution, forming near-optimal control strategies. Simulation results demonstrate that a significant improvement on the fuel economy can be achieved in the heavy-duty vehicle cycle from the natural driving statistics.

Optimal scope of supply chain network & operations design

NARCIS (Netherlands)

Ma, N.

2014-01-01

The increasingly complex supply chain networks and operations call for the development of decision support systems and optimization techniques that take a holistic view of supply chain issues and provide support for integrated decision-making. The economic impacts of optimized supply chain are

Perfect commuting-operator strategies for linear system games

Science.gov (United States)

Cleve, Richard; Liu, Li; Slofstra, William

2017-01-01

Linear system games are a generalization of Mermin's magic square game introduced by Cleve and Mittal. They show that perfect strategies for linear system games in the tensor-product model of entanglement correspond to finite-dimensional operator solutions of a certain set of non-commutative equations. We investigate linear system games in the commuting-operator model of entanglement, where Alice and Bob's measurement operators act on a joint Hilbert space, and Alice's operators must commute with Bob's operators. We show that perfect strategies in this model correspond to possibly infinite-dimensional operator solutions of the non-commutative equations. The proof is based around a finitely presented group associated with the linear system which arises from the non-commutative equations.

Balanced Sourcing As An Important Attribute Of Operations Strategy ...

African Journals Online (AJOL)

Balanced Sourcing As An Important Attribute Of Operations Strategy: Reality Or ... the questions “who should perform an activity or process in the value chain? ... Operations Strategy, Strategic Sourcing, Sustainable Competitive Advantage, ...

Identifying optimal agricultural countermeasure strategies for a hypothetical contamination scenario using the strategy model

International Nuclear Information System (INIS)

Cox, G.; Beresford, N.A.; Alvarez-Farizo, B.; Oughton, D.; Kis, Z.; Eged, K.; Thorring, H.; Hunt, J.; Wright, S.; Barnett, C.L.; Gil, J.M.; Howard, B.J.; Crout, N.M.J.

2005-01-01

A spatially implemented model designed to assist the identification of optimal countermeasure strategies for radioactively contaminated regions is described. Collective and individual ingestion doses for people within the affected area are estimated together with collective exported ingestion dose. A range of countermeasures are incorporated within the model, and environmental restrictions have been included as appropriate. The model evaluates the effectiveness of a given combination of countermeasures through a cost function which balances the benefit obtained through the reduction in dose with the cost of implementation. The optimal countermeasure strategy is the combination of individual countermeasures (and when and where they are implemented) which gives the lowest value of the cost function. The model outputs should not be considered as definitive solutions, rather as interactive inputs to the decision making process. As a demonstration the model has been applied to a hypothetical scenario in Cumbria (UK). This scenario considered a published nuclear power plant accident scenario with a total deposition of 1.7 x 10 14 , 1.2 x 10 13 , 2.8 x 10 10 and 5.3 x 10 9 Bq for Cs-137, Sr-90, Pu-239/240 and Am-241, respectively. The model predicts that if no remediation measures were implemented the resulting collective dose would be approximately 36 000 person-Sv (predominantly from 137 Cs) over a 10-year period post-deposition. The optimal countermeasure strategy is predicted to avert approximately 33 000 person-Sv at a cost of approximately pound 160 million. The optimal strategy comprises a mixture of ploughing, AFCF (ammonium-ferric hexacyano-ferrate) administration, potassium fertiliser application, clean feeding of livestock and food restrictions. The model recommends specific areas within the contaminated area and time periods where these measures should be implemented

Parametric Optimization of Some Critical Operating System Functions--An Alternative Approach to the Study of Operating Systems Design

Science.gov (United States)

Sobh, Tarek M.; Tibrewal, Abhilasha

2006-01-01

Operating systems theory primarily concentrates on the optimal use of computing resources. This paper presents an alternative approach to teaching and studying operating systems design and concepts by way of parametrically optimizing critical operating system functions. Detailed examples of two critical operating systems functions using the…

The Optimal Nash Equilibrium Strategies Under Competition

Institute of Scientific and Technical Information of China (English)

孟力; 王崇喜; 汪定伟; 张爱玲

2004-01-01

This paper presented a game theoretic model to study the competition for a single investment oppertunity under uncertainty. It models the hazard rate of investment as a function of competitors' trigger level. Under uncertainty and different information structure, the option and game theory was applied to researching the optimal Nash equilibrium strategies of one or more firm. By means of Matlab software, the paper simulates a real estate developing project example and illustrates how parameter affects investment strategies. The paper's work will contribute to the present investment practice in China.

Fuzzy multiobjective models for optimal operation of a hydropower system

Science.gov (United States)

Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.

2013-06-01

Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.

Design Optimization of Mechanical Components Using an Enhanced Teaching-Learning Based Optimization Algorithm with Differential Operator

Directory of Open Access Journals (Sweden)

B. Thamaraikannan

2014-01-01

Full Text Available This paper studies in detail the background and implementation of a teaching-learning based optimization (TLBO algorithm with differential operator for optimization task of a few mechanical components, which are essential for most of the mechanical engineering applications. Like most of the other heuristic techniques, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. A differential operator is incorporated into the TLBO for effective search of better solutions. To validate the effectiveness of the proposed method, three typical optimization problems are considered in this research: firstly, to optimize the weight in a belt-pulley drive, secondly, to optimize the volume in a closed coil helical spring, and finally to optimize the weight in a hollow shaft. have been demonstrated. Simulation result on the optimization (mechanical components problems reveals the ability of the proposed methodology to find better optimal solutions compared to other optimization algorithms.

Waterflooding optimization in uncertain geological scenarios

DEFF Research Database (Denmark)

Capolei, Andrea; Suwartadi, Eka; Foss, Bjarne

2013-01-01

, robust optimization has been suggested to improve and robustify optimal control strategies. In robust optimization of an oil reservoir, the water injection and production borehole pressures (bhp) are computed such that the predicted net present value (NPV) of an ensemble of permeability field...... inherits the features of both the reactive and the RO strategy. Simulations reveal that the modified RO strategy results in operations with larger returns and less risk than the reactive strategy, the RO strategy, and the certainty equivalent strategy. The returns are measured by the expected NPV...... of most reservoir engineers. Feedback reduces the uncertainty and this is the reason for the similar performance of the two strategies....

Operational scale entomological intervention for malaria control: strategies, achievements and challenges in Zambia

Directory of Open Access Journals (Sweden)

Chanda Emmanuel

2013-01-01

Full Text Available Abstract Background While consensus on malaria vector control policy and strategy has stimulated unprecedented political-will, backed by international funding organizations and donors, vector control interventions are expansively being implemented based on assumptions with unequaled successes. This manuscript reports on the strategies, achievements and challenges of the past and contemporary malaria vector control efforts in Zambia. Case description All available information and accessible archived documentary records on malaria vector control in Zambia were reviewed. Retrospective analysis of routine surveillance data from the Health Management Information System (HMIS, data from population-based household surveys and various operations research reports was conducted to assess the status in implementing policies and strategies. Discussion and evaluation Empirical evidence is critical for informing policy decisions and tailoring interventions to local settings. Thus, the World Health Organization (WHO encourages the adoption of the integrated vector management (IVM strategy which is a rational decision making process for optimal use of available resources. One of the key features of IVM is capacity building at the operational level to plan, implement, monitor and evaluate vector control and its epidemiological and entomological impact. In Zambia, great progress has been made in implementing WHO-recommended vector control policies and strategies within the context of the IVM Global Strategic framework with strong adherence to its five key attributes. Conclusions The country has solid, consistent and coordinated policies, strategies and guidelines for malaria vector control. The Zambian experience demonstrates the significance of a coordinated multi-pronged IVM approach effectively operationalized within the context of a national health system.

Operation strategy analysis of a geothermal step utilization heating system

International Nuclear Information System (INIS)

Zheng, Guozhong; Li, Feng; Tian, Zhe; Zhu, Neng; Li, Qianru; Zhu, Han

2012-01-01

Geothermal energy has been successfully applied in many district heating systems. In order to promote better use of geothermal energy, it is important to analyze the operation strategy of geothermal heating system. This study proposes a comprehensive and systematic operation strategy for a geothermal step utilization heating system (GSUHS). Calculation models of radiator heating system (RHS), radiant floor heating system (RFHS), heat pump (HP), gas boiler (GB), plate heat exchanger (PHE) and pump are first established. Then the operation strategy of the GSUHS is analyzed with the aim to substantially reduce the conventional energy consumption of the whole system. Finally, the energy efficiency and geothermal tail water temperature are analyzed. With the operation strategy in this study, the geothermal energy provides the main heating amount for the system. The heating seasonal performance factor is 15.93. Compared with coal-fired heating, 75.1% of the standard coal equivalent can be saved. The results provide scientific guidance for the application of an operation strategy for a geothermal step utilization heating system. -- Highlights: ► We establish calculation models for the geothermal step utilization heating system. ► We adopt minimal conventional energy consumption to determine the operation strategy. ► The geothermal energy dominates the heating quantity of the whole system. ► The utilization efficiency of the geothermal energy is high. ► The results provide guidance to conduct operation strategy for scientific operation.

A strategy for the economic optimization of combined cycle gas turbine power plants by taking advantage of useful thermodynamic relationships

International Nuclear Information System (INIS)

Godoy, E.; Benz, S.J.; Scenna, N.J.

2011-01-01

Optimal combined cycle gas turbine power plants characterized by minimum specific annual cost values are here determined for wide ranges of market conditions as given by the relative weights of capital investment and operative costs, by means of a non-linear mathematical programming model. On the other hand, as the technical optimization allows identifying trends in the system behavior and unveiling optimization opportunities, selected functional relationships are obtained as the thermodynamic optimal values of the decision variables are systematically linked to the ratio between the total heat transfer area and the net power production (here named as specific transfer area). A strategy for simplifying the resolution of the rigorous economic optimization problem of power plants is proposed based on the economic optima distinctive characteristics which describe the behavior of the decision variables of the power plant on its optima. Such approach results in a novel mathematical formulation shaped as a system of non-linear equations and additional constraints that is able to easily provide accurate estimations of the optimal values of the power plant design and operative variables. Research highlights: → We achieve relationships between power plants' economic and thermodynamic optima. → We achieve functionalities among thermodynamic optimal values of decision variables. → The rigorous optimization problem is reduced to a non-linear equations system. → Accurate estimations of power plants' design and operative variables are obtained.

Optimal strategy analysis based on robust predictive control for inventory system with random demand

Science.gov (United States)

Saputra, Aditya; Widowati, Sutrisno

2017-12-01

In this paper, the optimal strategy for a single product single supplier inventory system with random demand is analyzed by using robust predictive control with additive random parameter. We formulate the dynamical system of this system as a linear state space with additive random parameter. To determine and analyze the optimal strategy for the given inventory system, we use robust predictive control approach which gives the optimal strategy i.e. the optimal product volume that should be purchased from the supplier for each time period so that the expected cost is minimal. A numerical simulation is performed with some generated random inventory data. We simulate in MATLAB software where the inventory level must be controlled as close as possible to a set point decided by us. From the results, robust predictive control model provides the optimal strategy i.e. the optimal product volume that should be purchased and the inventory level was followed the given set point.

SAGD operating strategies

Energy Technology Data Exchange (ETDEWEB)

Nasr, T. N.; Golbeck, H.; Korpany, G.; Pierce, G. [Alberta Research Council, Edmonton, AB (Canada)

1998-12-31

Steam-assisted gravity drainage (SAGD) has become one of the most effective means of recovering oil from oil sands deposits that are too deeply buried for surface mining. In general terms, the process involves drilling paired horizontal wells, one well above the other, and separated by a distance, near the bottom of the oil-bearing formation. The top well is used to inject the steam, heating up the oil and allowing it to drain under the action of gravity, into the bottom well. This paper reviews advances in SAGD operating strategies, based on work at the Underground Test Facility (UTF) in Alberta. Problems that continue to challenge researchers and operators, and concepts to overcome these challenges are discussed in terms of laboratory studies at UTF, designed to improve SAGD oil-to-steam ratios and to reduce water requirements for the process. 8 refs., 20 figs.

Greenhouse gases emission assessment in residential sector through buildings simulations and operation optimization

International Nuclear Information System (INIS)

Stojiljković, Mirko M.; Ignjatović, Marko G.; Vučković, Goran D.

2015-01-01

Buildings use a significant amount of primary energy and largely contribute to greenhouse gases emission. Cost optimality and cost effectiveness, including cost-optimal operation, are important for the adoption of energy efficient and environmentally friendly technologies. The long-term assessment of buildings-related greenhouse gases emission might take into account cost-optimal operation of their energy systems. This is often not the case in the literature. Long-term operation optimization problems are often of large scale and computationally intensive and time consuming. This paper formulates a bottom-up methodology relying on an efficient, but precise operation optimization approach, applicable to long-term problems and use with buildings simulations. We suggest moving-horizon short-term optimization to determine near-optimal operation modes and show that this approach, applied to flexible energy systems without seasonal storage, have satisfactory efficiency and accuracy compared with solving problem for an entire year. We also confirm it as a valuable pre-solve technique. Approach applicability and the importance of energy systems optimization are illustrated with a case study considering buildings envelope improvements and cogeneration and heat storage implementation in an urban residential settlement. EnergyPlus is used for buildings simulations while mixed integer linear programming optimization problems are constructed and solved using the custom-built software and the branch-and-cut solver Gurobi Optimizer. - Highlights: • Bottom-up approach for greenhouse gases emission assessment is presented. • Short-term moving-horizon optimization is used to define operation regimes. • Operation optimization and buildings simulations are connected with modeling tool. • Illustrated optimization method performed efficiently and gave accurate results.

Research on a Microgrid Subsidy Strategy Based on Operational Efficiency of the Industry Chain

Directory of Open Access Journals (Sweden)

Yong Long

2018-05-01

Full Text Available Government subsidy is a powerful tool to motivate the development of a new energy industry. At the early stage of microgrid development, for the sake of the cost and benefit issue, it is necessary for the government to subsidize so as to support and promote the development of microgrids. However, a big challenge in practice is how to optimize the operational efficiency of the microgrid industry chain with varying targets and methods of subsidy. In order to explore this problem, we construct a subsidy model based on the microgrid industry chain, involving government, investor, operator, equipment supplier, and user. Through calculation and solution of this model, we obtain price and return indicators of each microgrid industry chain participant when the subsidy target differs. Based on that, we contrast and compare the optimal subsidy strategy and influencing factors when operational efficiency indicators vary. Finally, we validate and analyze this model with numerical analysis and discuss the impact of development stage, technological level, and change in subsidy amount on the operational efficiency of the microgrid industry chain and on the returns of each participant. This result is of great significance to subsidy practice for microgrids and the development of microgrids.

Optimal Design and Operation Management of Battery-Based Energy Storage Systems (BESS) in Microgrids

DEFF Research Database (Denmark)

Anvari-Moghaddam, Amjad; Dulout, Jeremy; Alonso, Corinne

2017-01-01

of energy storage units requires certain performance measures and constraints, which has to be well considered in design phase and embedded in control and management strategies. This chapter mainly focuses on these aspects and provides a general framework for optimal design and operation management......-scale integration of renewables into the grid environment. Energy storage options can also be used for economic operation of energy systems to cut down system’s operating cost. By utilizing ESSs, it is very possible to store energy in off-peak hours with lower cost and energize the grid during peak load intervals...... at supply/demand side which is helpful for load levelling or peak shaving purposes. Last but not least, ESSs can provide frequency regulation services in offgrid locations where there is a strong need to meet the power balance in different operating conditions. Each of the abovementioned applications...

Development of MCP transient operation strategy for the SMART-P

International Nuclear Information System (INIS)

Yoo, S. E.; Choi, B. S.; Kang, H. O.; Yoon, J. H.; Ji, S. K.

2003-01-01

SMART-P MCP(Main Coolant Pump) transient operation strategies are developed. A Modular Modeling System (MMS) computer code is used for the evaluation of the developed operation strategies. In the SMART-P, normal operating modes are classified into MCP high speed(3600 rpm) mode and MCP low speed mode. Also, natural circulation mode is defined as a performance test case. MCP operation transients occur when changing modes from one to another, and system parameters(core power, system pressure, temperature) are having transients. These transients affect on system performance and, in some cases, limit system operation. In this study, MCP operation strategies are developed and obtained acceptable results

On the robust optimization to the uncertain vaccination strategy problem

International Nuclear Information System (INIS)

Chaerani, D.; Anggriani, N.; Firdaniza

2014-01-01

In order to prevent an epidemic of infectious diseases, the vaccination coverage needs to be minimized and also the basic reproduction number needs to be maintained below 1. This means that as we get the vaccination coverage as minimum as possible, thus we need to prevent the epidemic to a small number of people who already get infected. In this paper, we discuss the case of vaccination strategy in term of minimizing vaccination coverage, when the basic reproduction number is assumed as an uncertain parameter that lies between 0 and 1. We refer to the linear optimization model for vaccination strategy that propose by Becker and Starrzak (see [2]). Assuming that there is parameter uncertainty involved, we can see Tanner et al (see [9]) who propose the optimal solution of the problem using stochastic programming. In this paper we discuss an alternative way of optimizing the uncertain vaccination strategy using Robust Optimization (see [3]). In this approach we assume that the parameter uncertainty lies within an ellipsoidal uncertainty set such that we can claim that the obtained result will be achieved in a polynomial time algorithm (as it is guaranteed by the RO methodology). The robust counterpart model is presented

On the robust optimization to the uncertain vaccination strategy problem

Energy Technology Data Exchange (ETDEWEB)

Chaerani, D., E-mail: d.chaerani@unpad.ac.id; Anggriani, N., E-mail: d.chaerani@unpad.ac.id; Firdaniza, E-mail: d.chaerani@unpad.ac.id [Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Padjadjaran Indonesia, Jalan Raya Bandung Sumedang KM 21 Jatinangor Sumedang 45363 (Indonesia)

2014-02-21

In order to prevent an epidemic of infectious diseases, the vaccination coverage needs to be minimized and also the basic reproduction number needs to be maintained below 1. This means that as we get the vaccination coverage as minimum as possible, thus we need to prevent the epidemic to a small number of people who already get infected. In this paper, we discuss the case of vaccination strategy in term of minimizing vaccination coverage, when the basic reproduction number is assumed as an uncertain parameter that lies between 0 and 1. We refer to the linear optimization model for vaccination strategy that propose by Becker and Starrzak (see [2]). Assuming that there is parameter uncertainty involved, we can see Tanner et al (see [9]) who propose the optimal solution of the problem using stochastic programming. In this paper we discuss an alternative way of optimizing the uncertain vaccination strategy using Robust Optimization (see [3]). In this approach we assume that the parameter uncertainty lies within an ellipsoidal uncertainty set such that we can claim that the obtained result will be achieved in a polynomial time algorithm (as it is guaranteed by the RO methodology). The robust counterpart model is presented.

Optimal Power Flow Using Gbest-Guided Cuckoo Search Algorithm with Feedback Control Strategy and Constraint Domination Rule

Directory of Open Access Journals (Sweden)

Gonggui Chen

2017-01-01

Full Text Available The optimal power flow (OPF is well-known as a significant optimization tool for the security and economic operation of power system, and OPF problem is a complex nonlinear, nondifferentiable programming problem. Thus this paper proposes a Gbest-guided cuckoo search algorithm with the feedback control strategy and constraint domination rule which is named as FCGCS algorithm for solving OPF problem and getting optimal solution. This FCGCS algorithm is guided by the global best solution for strengthening exploitation ability. Feedback control strategy is devised to dynamically regulate the control parameters according to actual and specific feedback value in the simulation process. And the constraint domination rule can efficiently handle inequality constraints on state variables, which is superior to traditional penalty function method. The performance of FCGCS algorithm is tested and validated on the IEEE 30-bus and IEEE 57-bus example systems, and simulation results are compared with different methods obtained from other literatures recently. The comparison results indicate that FCGCS algorithm can provide high-quality feasible solutions for different OPF problems.

Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

Science.gov (United States)

Janahiraman, Tiagrajah V.; Ahmad, Nooraziah; Hani Nordin, Farah

2018-04-01

The CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of input-output and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique.

Development of Abnormal Operating Strategies for Station Blackout in Shutdown Operating Mode in Pressurized Water Reactor

Energy Technology Data Exchange (ETDEWEB)

Yoon, Duk-Joo; Lee, Seung-Chan; Sung, Je-Joong; Ha, Sang-Jun [KHNP CRI, Daejeon (Korea, Republic of); Hwang, Su-Hyun [FNC Tech. Co., Yongin (Korea, Republic of)

2016-10-15

Loss of all AC power is classified as one of multiple failure accident by regulatory guide of Korean accident management program. Therefore we need develop strategies for the abnormal operating procedure both of power operating and shutdown mode. This paper developed abnormal operating guideline for loss of all AC power by analysis of accident scenario in pressurized water reactor. This paper analyzed the loss of ultimate heat sink (LOUHS) in shutdown operating mode and developed the operating strategy of the abnormal procedure. Also we performed the analysis of limiting scenarios that operator actions are not taken in shutdown LOUHS. Therefore, we verified the plant behavior and decided operator action to taken in time in order to protect the fuel of core with safety. From the analysis results of LOUHS, the fuel of core maintained without core uncovery for 73 minutes respectively for opened RCS states after the SBO occurred. Therefore, operator action for the emergency are required to take in 73 minutes for opened RCS state. Strategy is to cooldown by using spent fuel pool cooling system. This method required to change the plant design in some plant. In RCS boundary closed state, first abnormal operating strategy in shutdown LOUHS is first abnormal operating strategy in shutdown LOUHS is to remove the residual heat of core by steam dump flow and auxiliary feedwater of SG.

DC Voltage Control and Power-Sharing of Multi-Terminal DC Grids Based on Optimal DC Power Flow and Flexible Voltage Droop Strategy

Directory of Open Access Journals (Sweden)

F. Azma

2015-06-01

Full Text Available This paper develops an effective control framework for DC voltage control and power-sharing of multi-terminal DC (MTDC grids based on an optimal power flow (OPF procedure and the voltage-droop control. In the proposed approach, an OPF algorithm is executed at the secondary level to find optimal reference of DC voltages and active powers of all voltage-regulating converters. Then, the voltage droop characteristics of voltage-regulating converters, at the primary level, are tuned based on the OPF results such that the operating point of the MTDC grid lies on the voltage droop characteristics. Consequently, the optimally-tuned voltage droop controller leads to the optimal operation of the MTDC grid. In case of variation in load or generation of the grid, a new stable operating point is achieved based on the voltage droop characteristics. By execution of a new OPF, the voltage droop characteristics are re-tuned for optimal operation of the MTDC grid after the occurrence of the load or generation variations. The results of simulation on a grid inspired by CIGRE B4 DC grid test system demonstrate efficient grid performance under the proposed control strategy.

Industrial waste recycling strategies optimization problem: mixed integer programming model and heuristics

Science.gov (United States)

Tang, Jiafu; Liu, Yang; Fung, Richard; Luo, Xinggang

2008-12-01

Manufacturers have a legal accountability to deal with industrial waste generated from their production processes in order to avoid pollution. Along with advances in waste recovery techniques, manufacturers may adopt various recycling strategies in dealing with industrial waste. With reuse strategies and technologies, byproducts or wastes will be returned to production processes in the iron and steel industry, and some waste can be recycled back to base material for reuse in other industries. This article focuses on a recovery strategies optimization problem for a typical class of industrial waste recycling process in order to maximize profit. There are multiple strategies for waste recycling available to generate multiple byproducts; these byproducts are then further transformed into several types of chemical products via different production patterns. A mixed integer programming model is developed to determine which recycling strategy and which production pattern should be selected with what quantity of chemical products corresponding to this strategy and pattern in order to yield maximum marginal profits. The sales profits of chemical products and the set-up costs of these strategies, patterns and operation costs of production are considered. A simulated annealing (SA) based heuristic algorithm is developed to solve the problem. Finally, an experiment is designed to verify the effectiveness and feasibility of the proposed method. By comparing a single strategy to multiple strategies in an example, it is shown that the total sales profit of chemical products can be increased by around 25% through the simultaneous use of multiple strategies. This illustrates the superiority of combinatorial multiple strategies. Furthermore, the effects of the model parameters on profit are discussed to help manufacturers organize their waste recycling network.

Strategy for control and integrated optimization of chemical processes; Estrategia para o controle e otimizacao integrada de processos quimicos

Energy Technology Data Exchange (ETDEWEB)

Lacerda, Antonio Ignacio de [Universidade Federal Fluminense, Niteroi, RJ (Brazil). Dept. de Engenharia Quimica]. E-mail: ailac@vm.uff.br; Araujo, Ofelia de Queiroz Fernandes; Medeiros, Jose Luiz de [Universidade Federal, Rio de Janeiro, RJ (Brazil). Escola de Quimica]. E-mail: ofelia@eq.ufrj.br; jlm@eq.ufrj.br

2004-12-01

The increasingly market competitiveness, the frequent changes in costs of raw materials and imposition of environmental restrictions require quick responses from the industries and better control of their production. The growing increase of the computational systems processing capacity and advances in analysis and instrumentation systems favor the formulation of new strategies geared to the operational optimization of industrial processes. The optimization of a process, within a more rigid context, assumes that it is made through the optimal control theory. In this aspect, comparative studies are carried out between some formulations of the problem in terms of optimal control and a new methodology of economic optimization. The study process was a pyrolysis oven for which an economic function was developed. Such function considers the effects of the oven operation on the other subsequent parts of the Ethylene Plant, taking into account their energy consumptions and their operational restrictions. A rigorous thermal-dynamic analysis was made in the development thereof involving major parts of the product separation system upstream the oven. The results obtained met the expectations and the new optimization criterion tested can be implemented in a relatively simple computational system using personal computers currently available. Although the work is oriented towards the pyrolysis of hydrocarbons the proposed structure may be applied to other types of chemical and petrochemical processes with the same topology: a reaction system and a separation system. (author)

Cost-effectiveness analysis of optimal strategy for tumor treatment

International Nuclear Information System (INIS)

Pang, Liuyong; Zhao, Zhong; Song, Xinyu

2016-01-01

We propose and analyze an antitumor model with combined immunotherapy and chemotherapy. Firstly, we explore the treatment effects of single immunotherapy and single chemotherapy, respectively. Results indicate that neither immunotherapy nor chemotherapy alone are adequate to cure a tumor. Hence, we apply optimal theory to investigate how the combination of immunotherapy and chemotherapy should be implemented, for a certain time period, in order to reduce the number of tumor cells, while minimizing the implementation cost of the treatment strategy. Secondly, we establish the existence of the optimality system and use Pontryagin’s Maximum Principle to characterize the optimal levels of the two treatment measures. Furthermore, we calculate the incremental cost-effectiveness ratios to analyze the cost-effectiveness of all possible combinations of the two treatment measures. Finally, numerical results show that the combination of immunotherapy and chemotherapy is the most cost-effective strategy for tumor treatment, and able to eliminate the entire tumor with size 4.470 × 10"8 in a year.

Comparison of operation optimization methods in energy system modelling

DEFF Research Database (Denmark)

Ommen, Torben Schmidt; Markussen, Wiebke Brix; Elmegaard, Brian

2013-01-01

In areas with large shares of Combined Heat and Power (CHP) production, significant introduction of intermittent renewable power production may lead to an increased number of operational constraints. As the operation pattern of each utility plant is determined by optimization of economics......, possibilities for decoupling production constraints may be valuable. Introduction of heat pumps in the district heating network may pose this ability. In order to evaluate if the introduction of heat pumps is economically viable, we develop calculation methods for the operation patterns of each of the used...... energy technologies. In the paper, three frequently used operation optimization methods are examined with respect to their impact on operation management of the combined technologies. One of the investigated approaches utilises linear programming for optimisation, one uses linear programming with binary...

Integrated testing strategies can be optimal for chemical risk classification.

Science.gov (United States)

Raseta, Marko; Pitchford, Jon; Cussens, James; Doe, John

2017-08-01

There is an urgent need to refine strategies for testing the safety of chemical compounds. This need arises both from the financial and ethical costs of animal tests, but also from the opportunities presented by new in-vitro and in-silico alternatives. Here we explore the mathematical theory underpinning the formulation of optimal testing strategies in toxicology. We show how the costs and imprecisions of the various tests, and the variability in exposures and responses of individuals, can be assembled rationally to form a Markov Decision Problem. We compute the corresponding optimal policies using well developed theory based on Dynamic Programming, thereby identifying and overcoming some methodological and logical inconsistencies which may exist in the current toxicological testing. By illustrating our methods for two simple but readily generalisable examples we show how so-called integrated testing strategies, where information of different precisions from different sources is combined and where different initial test outcomes lead to different sets of future tests, can arise naturally as optimal policies. Copyright © 2017 Elsevier Inc. All rights reserved.

A proposal of optimal sampling design using a modularity strategy

Science.gov (United States)

Simone, A.; Giustolisi, O.; Laucelli, D. B.

2016-08-01

In real water distribution networks (WDNs) are present thousands nodes and optimal placement of pressure and flow observations is a relevant issue for different management tasks. The planning of pressure observations in terms of spatial distribution and number is named sampling design and it was faced considering model calibration. Nowadays, the design of system monitoring is a relevant issue for water utilities e.g., in order to manage background leakages, to detect anomalies and bursts, to guarantee service quality, etc. In recent years, the optimal location of flow observations related to design of optimal district metering areas (DMAs) and leakage management purposes has been faced considering optimal network segmentation and the modularity index using a multiobjective strategy. Optimal network segmentation is the basis to identify network modules by means of optimal conceptual cuts, which are the candidate locations of closed gates or flow meters creating the DMAs. Starting from the WDN-oriented modularity index, as a metric for WDN segmentation, this paper proposes a new way to perform the sampling design, i.e., the optimal location of pressure meters, using newly developed sampling-oriented modularity index. The strategy optimizes the pressure monitoring system mainly based on network topology and weights assigned to pipes according to the specific technical tasks. A multiobjective optimization minimizes the cost of pressure meters while maximizing the sampling-oriented modularity index. The methodology is presented and discussed using the Apulian and Exnet networks.

Optimization of operation conditions for the startup of aerobic granular sludge reactors biologically removing carbon, nitrogen, and phosphorous.

Science.gov (United States)

Lochmatter, Samuel; Holliger, Christof

2014-08-01

The transformation of conventional flocculent sludge to aerobic granular sludge (AGS) biologically removing carbon, nitrogen and phosphorus (COD, N, P) is still a main challenge in startup of AGS sequencing batch reactors (AGS-SBRs). On the one hand a rapid granulation is desired, on the other hand good biological nutrient removal capacities have to be maintained. So far, several operation parameters have been studied separately, which makes it difficult to compare their impacts. We investigated seven operation parameters in parallel by applying a Plackett-Burman experimental design approach with the aim to propose an optimized startup strategy. Five out of the seven tested parameters had a significant impact on the startup duration. The conditions identified to allow a rapid startup of AGS-SBRs with good nutrient removal performances were (i) alternation of high and low dissolved oxygen phases during aeration, (ii) a settling strategy avoiding too high biomass washout during the first weeks of reactor operation, (iii) adaptation of the contaminant load in the early stage of the startup in order to ensure that all soluble COD was consumed before the beginning of the aeration phase, (iv) a temperature of 20 °C, and (v) a neutral pH. Under such conditions, it took less than 30 days to produce granular sludge with high removal performances for COD, N, and P. A control run using this optimized startup strategy produced again AGS with good nutrient removal performances within four weeks and the system was stable during the additional operation period of more than 50 days. Copyright © 2014 Elsevier Ltd. All rights reserved.

Optimal strategies for pricing general insurance

OpenAIRE

Emms, P.; Haberman, S.; Savoulli, I.

2006-01-01

Optimal premium pricing policies in a competitive insurance environment are investigated using approximation methods and simulation of sample paths. The market average premium is modelled as a diffusion process, with the premium as the control function and the maximization of the expected total utility of wealth, over a finite time horizon, as the objective. In order to simplify the optimisation problem, a linear utility function is considered and two particular premium strategies are adopted...

Optimal Strategy Analysis of a Competing Portfolio Market with a Polyvariant Profit Function

International Nuclear Information System (INIS)

Bogolubov, Nikolai N. Jr.; Kyshakevych, Bohdan Yu.; Blackmore, Denis; Prykarpatsky, Anatoliy K.

2010-12-01

A competing market model with a polyvariant profit function that assumes 'zeitnot' stock behavior of clients is formulated within the banking portfolio medium and then analyzed from the perspective of devising optimal strategies. An associated Markov process method for finding an optimal choice strategy for monovariant and bivariant profit functions is developed. Under certain conditions on the bank 'promotional' parameter with respect to the 'fee' for a missed share package transaction and at an asymptotically large enough portfolio volume, universal transcendental equations - determining the optimal share package choice among competing strategies with monovariant and bivariant profit functions - are obtained. (author)

Transitions in optimal adaptive strategies for populations in fluctuating environments

Science.gov (United States)

Mayer, Andreas; Mora, Thierry; Rivoire, Olivier; Walczak, Aleksandra M.

2017-09-01

Biological populations are subject to fluctuating environmental conditions. Different adaptive strategies can allow them to cope with these fluctuations: specialization to one particular environmental condition, adoption of a generalist phenotype that compromises between conditions, or population-wise diversification (bet hedging). Which strategy provides the largest selective advantage in the long run depends on the range of accessible phenotypes and the statistics of the environmental fluctuations. Here, we analyze this problem in a simple mathematical model of population growth. First, we review and extend a graphical method to identify the nature of the optimal strategy when the environmental fluctuations are uncorrelated. Temporal correlations in environmental fluctuations open up new strategies that rely on memory but are mathematically challenging to study: We present analytical results to address this challenge. We illustrate our general approach by analyzing optimal adaptive strategies in the presence of trade-offs that constrain the range of accessible phenotypes. Our results extend several previous studies and have applications to a variety of biological phenomena, from antibiotic resistance in bacteria to immune responses in vertebrates.

Optimal design and operation of a thermal storage system for a chilled water plant serving pharmaceutical buildings

Energy Technology Data Exchange (ETDEWEB)

Henze, Gregor P. [University of Nebraska, Architectural Engineering, Omaha, NE 68182 (United States); Biffar, Bernd; Kohn, Dietmar [Boehringer Ingelheim Pharma GmbH and Co. KG, Biberach D-88400 (Germany); Becker, Martin P. [University of Applied Sciences Biberach, Architectural Engineering, Biberach D-88400 (Germany)

2008-07-01

A group of buildings in the pharmaceutical industry located in Southern Germany is experiencing a trend of growing cooling loads to be met by the chilled water plant composed of 10 chillers of greatly varying cost effectiveness. With a capacity shortfall inevitable, the question arises whether to install an additional chiller or improve the utilization of the existing chillers, in particular those with low operating costs per unit cooling, through the addition of a chilled water thermal energy storage (TES) system. To provide decision support in this matter, an optimization environment was developed and validated that adopts mixed integer programming as the approach to optimizing the chiller dispatch for any load condition, while an overarching dynamic programming based optimization approach optimizes the charge/discharge strategy of the TES system. In this fashion, the chilled water plant optimization is decoupled but embedded in the TES control optimization. The approach was selected to allow for arbitrary constraints and optimization horizons, while ensuring a global optimum to the problem. Optimization scenarios have been defined that include current load conditions as well cooling loads that are elevated by 25% from current conditions in order to reflect the expected growth in cooling demand in the near future; both scenarios analyzed the impact of storage capacity by investigating several TES tank capacities. The annual optimization runs revealed that - based on the elevated cooling load scenario - the smallest TES system pays back the incremental investment necessary for the TES system in about three years; based on today's cooling loads the static payback is approximately six years. As the efficiency and cost of operating the existing chillers vary over a wide range, the TES system allows for a reduction in operating costs for the chilled water plant by avoiding the operation of inefficient chillers (such as the single-stage absorption type) and

Dynamic modeling and evaluation of solid oxide fuel cell - combined heat and power system operating strategies

Science.gov (United States)

Nanaeda, Kimihiro; Mueller, Fabian; Brouwer, Jacob; Samuelsen, Scott

Operating strategies of solid oxide fuel cell (SOFC) combined heat and power (CHP) systems are developed and evaluated from a utility, and end-user perspective using a fully integrated SOFC-CHP system dynamic model that resolves the physical states, thermal integration and overall efficiency of the system. The model can be modified for any SOFC-CHP system, but the present analysis is applied to a hotel in southern California based on measured electric and heating loads. Analysis indicates that combined heat and power systems can be operated to benefit both the end-users and the utility, providing more efficient electric generation as well as grid ancillary services, namely dispatchable urban power. Design and operating strategies considered in the paper include optimal sizing of the fuel cell, thermal energy storage to dispatch heat, and operating the fuel cell to provide flexible grid power. Analysis results indicate that with a 13.1% average increase in price-of-electricity (POE), the system can provide the grid with a 50% operating range of dispatchable urban power at an overall thermal efficiency of 80%. This grid-support operating mode increases the operational flexibility of the SOFC-CHP system, which may make the technology an important utility asset for accommodating the increased penetration of intermittent renewable power.

Operation Characteristics Optimization of Low Power Three-Phase Asynchronous Motors

Directory of Open Access Journals (Sweden)

VLAD, I.

2014-02-01

Full Text Available Most published papers on low power asynchronous motors were aimed to achieve better operational performances in different operating conditions. The optimal design of the general-purpose motors requires searching and selecting an electric machine to meet minimum operating costs criterion and certain customer imposed restrictive conditions. In this paper, there are many significant simulations providing qualitative and quantitative information on reducing active and reactive energy losses in motors, and on parameters and constructive solution. The optimization study applied the minimal operating costs criterion, and it took into account the starting restrictive conditions. Thirteen variables regarding electromagnetic stresses and main constructive dimensions were considered. The operating costs of the optimized motor decreased with 25.6%, as compared to the existing solution. This paper can be a practical and theoretical support for the development and implementation of modern design methods, based on theoretical and experimental study of stationary and transient processes in low power motors, to increase efficiency and power factor.

Optimal Investment-Consumption Strategy under Inflation in a Markovian Regime-Switching Market

Directory of Open Access Journals (Sweden)

Huiling Wu

2016-01-01

Full Text Available This paper studies an investment-consumption problem under inflation. The consumption price level, the prices of the available assets, and the coefficient of the power utility are assumed to be sensitive to the states of underlying economy modulated by a continuous-time Markovian chain. The definition of admissible strategies and the verification theory corresponding to this stochastic control problem are presented. The analytical expression of the optimal investment strategy is derived. The existence, boundedness, and feasibility of the optimal consumption are proven. Finally, we analyze in detail by mathematical and numerical analysis how the risk aversion, the correlation coefficient between the inflation and the stock price, the inflation parameters, and the coefficient of utility affect the optimal investment and consumption strategy.

Evolution strategies and multi-objective optimization of permanent magnet motor

DEFF Research Database (Denmark)

Andersen, Søren Bøgh; Santos, Ilmar

2012-01-01

When designing a permanent magnet motor, several geometry and material parameters are to be defined. This is not an easy task, as material properties and magnetic fields are highly non-linear and the design of a motor is therefore often an iterative process. From an engineering point of view, we...... of evolution strategies, ES to effectively design and optimize parameters of permanent magnet motors. Single as well as multi-objective optimization procedures are carried out. A modified way of creating the strategy parameters for the ES algorithm is also proposed and has together with the standard ES...

PEMFC Optimization Strategy with Auxiliary Power Source in Fuel Cell Hybrid Vehicle

Directory of Open Access Journals (Sweden)

Tinton Dwi Atmaja

2012-02-01

Full Text Available Page HeaderOpen Journal SystemsJournal HelpUser You are logged in as...aulia My Journals My Profile Log Out Log Out as UserNotifications View (27 new ManageJournal Content SearchBrowse By Issue By Author By Title Other JournalsFont SizeMake font size smaller Make font size default Make font size largerInformation For Readers For Authors For LibrariansKeywords CBPNN Displacement FLC LQG/LTR Mixed PMA Ventilation bottom shear stress direct multiple shooting effective fuzzy logic geoelectrical method hourly irregular wave missile trajectory panoramic image predator-prey systems seawater intrusion segmentation structure development pattern terminal bunt manoeuvre Home About User Home Search Current Archives ##Editorial Board##Home > Vol 23, No 1 (2012 > AtmajaPEMFC Optimization Strategy with Auxiliary Power Source in Fuel Cell Hybrid VehicleTinton Dwi Atmaja, Amin AminAbstractone of the present-day implementation of fuel cell is acting as main power source in Fuel Cell Hybrid Vehicle (FCHV. This paper proposes some strategies to optimize the performance of Polymer Electrolyte Membrane Fuel Cell (PEMFC implanted with auxiliary power source to construct a proper FCHV hybridization. The strategies consist of the most updated optimization method determined from three point of view i.e. Energy Storage System (ESS, hybridization topology and control system analysis. The goal of these strategies is to achieve an optimum hybridization with long lifetime, low cost, high efficiency, and hydrogen consumption rate improvement. The energy storage system strategy considers battery, supercapacitor, and high-speed flywheel as the most promising alternative auxiliary power source. The hybridization topology strategy analyzes the using of multiple storage devices injected with electronic components to bear a higher fuel economy and cost saving. The control system strategy employs nonlinear control system to optimize the ripple factor of the voltage and the current

Modelling and Optimal Control of Typhoid Fever Disease with Cost-Effective Strategies.

Science.gov (United States)

Tilahun, Getachew Teshome; Makinde, Oluwole Daniel; Malonza, David

2017-01-01

We propose and analyze a compartmental nonlinear deterministic mathematical model for the typhoid fever outbreak and optimal control strategies in a community with varying population. The model is studied qualitatively using stability theory of differential equations and the basic reproductive number that represents the epidemic indicator is obtained from the largest eigenvalue of the next-generation matrix. Both local and global asymptotic stability conditions for disease-free and endemic equilibria are determined. The model exhibits a forward transcritical bifurcation and the sensitivity analysis is performed. The optimal control problem is designed by applying Pontryagin maximum principle with three control strategies, namely, the prevention strategy through sanitation, proper hygiene, and vaccination; the treatment strategy through application of appropriate medicine; and the screening of the carriers. The cost functional accounts for the cost involved in prevention, screening, and treatment together with the total number of the infected persons averted. Numerical results for the typhoid outbreak dynamics and its optimal control revealed that a combination of prevention and treatment is the best cost-effective strategy to eradicate the disease.

Development of Operation Strategy for Hybrid-SIT in SBO

International Nuclear Information System (INIS)

Jeon, In Seop; Kang, Hyun Gook

2015-01-01

The Fukushima accident was not managed properly due to a lack of effective mitigation systems against Station Black Out (SBO) accident. For this reason, development of passive system is suggested as an alternative way for active system because passive system doesn't need external energy source and passive system can also increase the diversity of mitigation technique of Nuclear Power Plant (NPP). H-SIT is a passive injection system that is newly planned to adjust into the Advanced Power Reactor plus (APR+). This system is specialized for mitigation of SBO scenarios because it is passive system and it can inject coolant even in high pressure condition. Main function of H-SIT is injection of coolant to the Reactor Coolant System (RCS) in a passive way. The H-SIT system can inject water using the pressure from nitrogen gas as a normal SIT in low pressure accidents such as large and medium break loss-of-coolant accidents. This operation strategy is divided according to numbers of PAFS which can be used. When one H-SIT is used, H-SIT is recommended to use operation strategy which is explained as follow. In case of operation number, 1+1+1+1 strategy is the best and first operation timing, the time when upper plenum level is 5% is the best and next operation timing, the time when water level of H-SIT which is operated in previous round is 5% and operation order, 4-3-2-1 is the best. Even if one PAFS can be used, the minimum flow of H-SIT can maintain core in normal condition before H-SIT dried out thus if two PAFS can be used, the strategy which is used in the condition one PAFS can be operated is also used as a best operation strategy

Optimal football strategies: AC Milan versus FC Barcelona

OpenAIRE

Papahristodoulou, Christos

2012-01-01

In a recent UEFA Champions League game between AC Milan and FC Barcelona, played in Italy (final score 2-3), the collected match statistics, classified into four offensive and two defensive strategies, were in favour of FC Barcelona (by 13 versus 8 points). The aim of this paper is to examine to what extent the optimal game strategies derived from some deterministic, possibilistic, stochastic and fuzzy LP models would improve the payoff of AC Milan at the cost of FC Barcelona.

Stability Analysis and Optimal Control Strategy for Prevention of Pine Wilt Disease

Directory of Open Access Journals (Sweden)

Kwang Sung Lee

2014-01-01

Full Text Available We propose a mathematical model of pine wilt disease (PWD which is caused by pine sawyer beetles carrying the pinewood nematode (PWN. We calculate the basic reproduction number R0 and investigate the stability of a disease-free and endemic equilibrium in a given mathematical model. We show that the stability of the equilibrium in the proposed model can be controlled through the basic reproduction number R0. We then discuss effective optimal control strategies for the proposed PWD mathematical model. We demonstrate the existence of a control problem, and then we apply both analytical and numerical techniques to demonstrate effective control methods to prevent the transmission of the PWD. In order to do this, we apply two control strategies: tree-injection of nematicide and the eradication of adult beetles through aerial pesticide spraying. Optimal prevention strategies can be determined by solving the corresponding optimality system. Numerical simulations of the optimal control problem using a set of reasonable parameter values suggest that reducing the number of pine sawyer beetles is more effective than the tree-injection strategy for controlling the spread of PWD.

The influence of operational constraints in the production strategy definition; Influencia de restricoes operacionais na definicao da estrategia de producao

Energy Technology Data Exchange (ETDEWEB)

Magalhaes, Tasso C.B. de; Schiozer, Denis J. [Universidade Estadual de Campinas, SP (Brazil)

2004-07-01

Production strategies definition, applied to petroleum fields, must consider physical, operational and economic constraints. It is common to consider only the reservoir conditions on the optimization processes, simplifying, many times, the process by not taking into account the operational constraints due to production facilities. There are two main reasons: considering the operational constraints makes the process much complex and it is assumed that this simplification can affect the economic indicators but dos not affect significantly the optimization process (number e location of wells, for example). The capacity of a production unit can be limited by many constrains such as: maximum liquid rate, capacity of water and gas treatment, gas compression, water or gas injection, number of wells, etc. In this work, we show that these limitations have a direct influence in the oil production and consequently in the economic indicators and they can cause significant impact at production strategy definition, influencing the number of production and injection wells, their locations and their operational conditions. We presented an example of an offshore field with a limitation on the liquid rate. Production strategies were selected with and without constraints in order to observe the differences in the technical and economic indicators, such as NPV (Net Present Value), production and injection of fluids and the number and location of the production and injection wells. It was possible to observe yet that the amount and location of the wells were significantly affected by the restriction. (author)

Total output operation chart optimization of cascade reservoirs and its application

International Nuclear Information System (INIS)

Jiang, Zhiqiang; Ji, Changming; Sun, Ping; Wang, Liping; Zhang, Yanke

2014-01-01

Highlights: • We propose a new double nested model for cascade reservoirs operation optimization. • We use two methods to extract the output distribution ratio. • The adopted two methods perform better than the widely used methods at present. • Stepwise regression method performs better than mean value method on the whole. - Abstract: With the rapid development of cascade hydropower stations in recent decades, the cascade system composed of multiple reservoirs needs unified operation and management. However, the output distribution problem has not yet been solved reasonably when the total output of cascade system obtained, which makes the full utilization of hydropower resources in cascade reservoirs very difficult. Discriminant criterion method is a traditional and common method to solve the output distribution problem at present, but some shortcomings cannot be ignored in the practical application. In response to the above concern, this paper proposes a new total output operation chart optimization model and a new optimal output distribution model, the two models constitute to a double nested model with the goal of maximizing power generation. This paper takes the cascade reservoirs of Li Xianjiang River in China as an instance to obtain the optimal total output operation chart by the proposed double nested model and the 43 years historical runoff data, progressive searching method and progressive optimality algorithm are used in solving the model. In order to take the obtained total output operation chart into practical operation, mean value method and stepwise regression method are adopted to extract the output distribution ratios on the basis of the optimal simulation intermediate data. By comparing with discriminant criterion method and conventional method, the combined utilization of total output operation chart and output distribution ratios presents better performance in terms of power generation and assurance rate, which proves it is an effective

Optimization strategies for cask design and container loading in long term spent fuel storage

International Nuclear Information System (INIS)

2006-12-01

As delays are incurred in implementing reprocessing and in planning for geologic repositories, storage of increasing quantities of spent fuel for extended durations is becoming a growing reality. Accordingly, effective management of spent fuel continues to be a priority topic. In response, the IAEA has organized a series of meetings to identify cask loading optimisation issues in preparation for a technical publication on Optimization Strategies for Cask/Container Loading in Long Term Spent Fuel Storage. This publication outlines the optimisation process for cask design, licensing and utilization, describing three principal groups of optimization activities in terms of relevant technical considerations such as criticality, shielding, structural design, operations, maintenance and retrievability. The optimization process for cask design, licensing, and utilization is outlined. The general objectives for the design of storage casks, including storage casks that are intended to be transportable, are summarized. The nature of optimization within the design process is described. The typical regulatory and licensing process is outlined, focusing on the roles of safety regulations, the regulator, and the designer/applicant in the optimization process. Based on the foregoing, a description of the three principal groups of optimization activities is provided. The subsequent chapters of this document then describe the specific optimization activities within these three activity groups, in each of the several design disciplines

Optimization Under Uncertainty for Wake Steering Strategies

Energy Technology Data Exchange (ETDEWEB)

Quick, Julian [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Annoni, Jennifer [National Renewable Energy Laboratory (NREL), Golden, CO (United States); King, Ryan N [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dykes, Katherine L [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Fleming, Paul A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ning, Andrew [Brigham Young University

2017-08-03

Offsetting turbines' yaw orientations from incoming wind is a powerful tool that may be leveraged to reduce undesirable wake effects on downstream turbines. First, we examine a simple two-turbine case to gain intuition as to how inflow direction uncertainty affects the optimal solution. The turbines are modeled with unidirectional inflow such that one turbine directly wakes the other, using ten rotor diameter spacing. We perform optimization under uncertainty (OUU) via a parameter sweep of the front turbine. The OUU solution generally prefers less steering. We then do this optimization for a 60-turbine wind farm with unidirectional inflow, varying the degree of inflow uncertainty and approaching this OUU problem by nesting a polynomial chaos expansion uncertainty quantification routine within an outer optimization. We examined how different levels of uncertainty in the inflow direction effect the ratio of the expected values of deterministic and OUU solutions for steering strategies in the large wind farm, assuming the directional uncertainty used to reach said OUU solution (this ratio is defined as the value of the stochastic solution or VSS).

Optimal Operations and Resilient Investments in Steam Networks

Energy Technology Data Exchange (ETDEWEB)

Bungener, Stéphane L., E-mail: stephane.bungener@a3.epfl.ch [Industrial Process and Energy Systems Engineering, École Polytechnique Fédérale de Lausanne, Lausanne (Switzerland); Van Eetvelde, Greet [Environmental and Spatial Management, Faculty of Engineering and Architecture, Ghent University, Ghent (Belgium); Maréchal, François [Industrial Process and Energy Systems Engineering, École Polytechnique Fédérale de Lausanne, Lausanne (Switzerland)

2016-01-20

Steam is a key energy vector for industrial sites, most commonly used for process heating and cooling, cogeneration of heat and mechanical power as a motive fluid or for stripping. Steam networks are used to carry steam from producers to consumers and between pressure levels through letdowns and steam turbines. The steam producers (boilers, heat and power cogeneration units, heat exchangers, chemical reactors) should be sized to supply the consumers at nominal operating conditions as well as peak demand. First, this paper proposes an Mixed Integer Linear Programing formulation to optimize the operations of steam networks in normal operating conditions and exceptional demand (when operating reserves fall to zero), through the introduction of load shedding. Optimization of investments based on operational and investment costs are included in the formulation. Though rare, boiler failures can have a heavy impact on steam network operations and costs, leading to undercapacity and unit shutdowns. A method is therefore proposed to simulate steam network operations when facing boiler failures. Key performance indicators are introduced to quantify the network’s resilience. The proposed methods are applied and demonstrated in an industrial case study using industrial data. The results indicate the importance of oversizing key steam producing equipments and the value of industrial symbiosis to increase industrial site resilience.

Optimal Operations and Resilient Investments in Steam Networks

International Nuclear Information System (INIS)

Bungener, Stéphane L.; Van Eetvelde, Greet; Maréchal, François

2016-01-01

Steam is a key energy vector for industrial sites, most commonly used for process heating and cooling, cogeneration of heat and mechanical power as a motive fluid or for stripping. Steam networks are used to carry steam from producers to consumers and between pressure levels through letdowns and steam turbines. The steam producers (boilers, heat and power cogeneration units, heat exchangers, chemical reactors) should be sized to supply the consumers at nominal operating conditions as well as peak demand. First, this paper proposes an Mixed Integer Linear Programing formulation to optimize the operations of steam networks in normal operating conditions and exceptional demand (when operating reserves fall to zero), through the introduction of load shedding. Optimization of investments based on operational and investment costs are included in the formulation. Though rare, boiler failures can have a heavy impact on steam network operations and costs, leading to undercapacity and unit shutdowns. A method is therefore proposed to simulate steam network operations when facing boiler failures. Key performance indicators are introduced to quantify the network’s resilience. The proposed methods are applied and demonstrated in an industrial case study using industrial data. The results indicate the importance of oversizing key steam producing equipments and the value of industrial symbiosis to increase industrial site resilience.

Desiccant wheel thermal performance modeling for indoor humidity optimal control

International Nuclear Information System (INIS)

Wang, Nan; Zhang, Jiangfeng; Xia, Xiaohua

2013-01-01

Highlights: • An optimal humidity control model is formulated to control the indoor humidity. • MPC strategy is used to implement the optimal operation solution. • Practical applications of the MPC strategy is illustrated by the case study. - Abstract: Thermal comfort is an important concern in the energy efficiency improvement of commercial buildings. Thermal comfort research focuses mostly on temperature control, but humidity control is an important aspect to maintain indoor comfort too. In this paper, an optimal humidity control model (OHCM) is presented. Model predictive control (MPC) strategy is applied to implement the optimal operation of the desiccant wheel during working hours of a commercial building. The OHCM is revised to apply the MPC strategy. A case is studied to illustrate the practical applications of the MPC strategy

Towards Sustainability: Effective Operations Strategies, Quality Management and Operational Excellence in Banking

OpenAIRE

Vesna Tornjanski; Sanja Marinković; Željka Jančić

2017-01-01

This paper sets out to extend and deepen the understanding the ways toward economic sustainability through efficient and effective growth operations strategies, quality management and operational excellence in banking. In this study we define new quality management practices based on developed conceptual architecture of digital platform for operations function in banking. Additionally, we employ decision making framework consisted of two parts: introduction of new operations services using To...

Implementation of an optimal control energy management strategy in a hybrid truck

NARCIS (Netherlands)

Mullem, D. van; Keulen, T. van; Kessels, J.T.B.A.; Jager, B. de; Steinbuch, M.

2010-01-01

Energy Management Strategies for hybrid powertrains control the power split, between the engine and electric motor, of a hybrid vehicle, with fuel consumption or emission minimization as objective. Optimal control theory can be applied to rewrite the optimization problem to an optimization

The Research on Operation Strategy of Nuclear Power Plant with Multi-reactors

Energy Technology Data Exchange (ETDEWEB)

Fang, Maoyao; Peng, Minjun; Cheng Shouyu [Harbin Engineering University, Harbin (China)

2014-08-15

In this paper, the operation characteristics and control strategy of nuclear power plant (NPP) with multi-modular pressurized water reactors (PWR) were researched through simulation. The main objective of this research was to ensure the coordinated operation and satisfy the convenience of turbine-generator and reactor's load adjustment in NPP with multi-reactors (MR). According to the operation characteristics of MR-NPP, the operation and control strategy was proposed, which was 'he average allocation of load for each reactor and maintaining average temperature of coolant at a constant? The control system was designed based the operation and control strategy. In order to research the operation characteristics and control strategy of MR-NPP, the paper established the transient analysis model which included the reactors and thermal hydraulic models, turbine model, could simulate and analyze on different operating conditions such as load reducing, load rising. Based on the proposed operation and control strategy and simulation models, the paper verified and validated the operation strategy and control system through load reducing, load rising. The results of research simulation showed that the operation strategy was feasible and can make the MR-NPP running safely as well as steadily on different operating conditions.

The Research on Operation Strategy of Nuclear Power Plant with Multi-reactors

International Nuclear Information System (INIS)

Fang, Maoyao; Peng, Minjun; Cheng Shouyu

2014-01-01

In this paper, the operation characteristics and control strategy of nuclear power plant (NPP) with multi-modular pressurized water reactors (PWR) were researched through simulation. The main objective of this research was to ensure the coordinated operation and satisfy the convenience of turbine-generator and reactor's load adjustment in NPP with multi-reactors (MR). According to the operation characteristics of MR-NPP, the operation and control strategy was proposed, which was 'he average allocation of load for each reactor and maintaining average temperature of coolant at a constant? The control system was designed based the operation and control strategy. In order to research the operation characteristics and control strategy of MR-NPP, the paper established the transient analysis model which included the reactors and thermal hydraulic models, turbine model, could simulate and analyze on different operating conditions such as load reducing, load rising. Based on the proposed operation and control strategy and simulation models, the paper verified and validated the operation strategy and control system through load reducing, load rising. The results of research simulation showed that the operation strategy was feasible and can make the MR-NPP running safely as well as steadily on different operating conditions

Operational optimization in the downstream; Otimizacao operacional no downstream

Energy Technology Data Exchange (ETDEWEB)

Silberman, Luis; Cunha, Filipe Silveira Ramos da [Petroleo Ipiranga, Porto Alegre, RS (Brazil)

2004-07-01

On the present competitive down stream's market, there is a great necessity of optimization aiming to guarantee the best price and quality of our clients. Our goal is to attend these expectations while we guarantee an efficient operation. The greatest question is how far we are from the ideal model. This way, a lot of projects have been executed during the last years aiming the operational optimization of all our activities. We divide the projects in 4 areas: Logistic (new modals distribution), Transport (transport optimization - quality and more deliveries with less trucks), Client Support (Internet Ipiranga and Support Center), Distribution Terminals Productivity (automation and environment). This work intend to present our ideal, perfect and complete Downstream Operation model. We will talk about how close we are of this ideal model and we will present the projects that we had already developed and implanted on the automation of the terminals and the logistics area. (author)

Optimal coal import strategy

International Nuclear Information System (INIS)

Chen, C.Y.; Shih, L.H.

1992-01-01

Recently, the main power company in Taiwan has shifted the primary energy resource from oil to coal and tried to diversify the coal supply from various sources. The company wants to have the imported coal meet the environmental standards and operation requirements as well as to have high heating value. In order to achieve these objectives, establishment of a coal blending system for Taiwan is necessary. A mathematical model using mixed integer programming technique is used to model the import strategy and the blending system. 6 refs., 1 tab

Numerical simulation of energy efficiency measures: control and operational strategies

International Nuclear Information System (INIS)

Ardehali, M. M.

2006-01-01

The inherent limitation in performance of building envelop components and heating ventilating and air conditioning (HVAC) equipment necessitates the examination of operational strategies for improvement in energy-efficient operation of buildings. Due to the ease of installation and increasing availability of electronic controllers, operational strategies that could be programmed are of particular interest. The Iowa Energy Center in the US has taken the initiative to conduct the necessary assessment of current HVAC technology and the commonly-used operational strategies for commercial and industrial buildings, as applied to the midwestern part of the country, with weather and energy cost data for Des Moines, Iowa. The first part of this study focused on the energy consumption and cost effectiveness of HVAC systems. The objectives of the second part is concerned with examination of various operational strategies, namely, night purge (NP), fan optimum start and stop (OSS), condenser water reset (CWR), and chilled water reset (CHWR) applied to order and newer-type commercial office buildings. The indoor air quality requirement are met and the latest applicable energy rates from local utility companies are used. The results show that, in general, NP is not an effective strategy in buildings with low thermal mass storage, OSS reduced fan energy, and CWR and CHWR could be effective and require chillers with multi-stage unloading characteristics. The most operationally efficient strategies are the combination of OSS, CWR, and CHWR for the older-type building, and OSS for the newer-type building. Economically, the most effective is the OSS strategy for the older-type building and the CHWR strategy for the newer-type building.(Author)

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

Science.gov (United States)

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

Distributed Strategy for Optimal Dispatch of Unbalanced Three-Phase Islanded Microgrids

DEFF Research Database (Denmark)

Vergara Barrios, Pedro Pablo; Rey-López, Juan Manuel; Shaker, Hamid Reza

2018-01-01

This paper presents a distributed strategy for the optimal dispatch of islanded microgrids, modeled as unbalanced three-phase electrical distribution systems (EDS). To set the dispatch of the distributed generation (DG) units, an optimal generation problem is stated and solved distributively based......-phase microgrid. According to the obtained results, the proposed strategy achieves a lower cost solution when compared with a centralized approach based on a static droop framework, with a considerable reduction on the communication system complexity. Additionally, it corrects the mismatch between generation...

Optimal Claiming Strategies in Bonus Malus Systems and Implied Markov Chains

Directory of Open Access Journals (Sweden)

Arthur Charpentier

2017-11-01

Full Text Available In this paper, we investigate the impact of the accident reporting strategy of drivers, within a Bonus-Malus system. We exhibit the induced modification of the corresponding class level transition matrix and derive the optimal reporting strategy for rational drivers. The hunger for bonuses induces optimal thresholds under which, drivers do not claim their losses. Mathematical properties of the induced level class process are studied. A convergent numerical algorithm is provided for computing such thresholds and realistic numerical applications are discussed.

Optimization of the bank's operating portfolio

Science.gov (United States)

Borodachev, S. M.; Medvedev, M. A.

2016-06-01

The theory of efficient portfolios developed by Markowitz is used to optimize the structure of the types of financial operations of a bank (bank portfolio) in order to increase the profit and reduce the risk. The focus of this paper is to check the stability of the model to errors in the original data.

Site utility system optimization with operation adjustment under uncertainty

International Nuclear Information System (INIS)

Sun, Li; Gai, Limei; Smith, Robin

2017-01-01

Highlights: • Uncertainties are classified into time-based and probability-based uncertain factors. • Multi-period operation and recourses deal with uncertainty implementation. • Operation scheduling are specified at the design stage to deal with uncertainties. • Steam mains superheating affects steam distribution and power generation in the system. - Abstract: Utility systems must satisfy process energy and power demands under varying conditions. The system performance is decided by the system configuration and individual equipment operating load for boilers, gas turbines, steam turbines, condensers, and let down valves. Steam mains conditions in terms of steam pressures and steam superheating also play important roles on steam distribution in the system and power generation by steam expansion in steam turbines, and should be included in the system optimization. Uncertainties such as process steam power demand changes and electricity price fluctuations should be included in the system optimization to eliminate as much as possible the production loss caused by steam power deficits due to uncertainties. In this paper, uncertain factors are classified into time-based and probability-based uncertain factors, and operation scheduling containing multi-period equipment load sharing, redundant equipment start up, and electricity import to compensate for power deficits, have been presented to deal with the happens of uncertainties, and are formulated as a multi-period item and a recourse item in the optimization model. There are two case studies in this paper. One case illustrates the system design to determine system configuration, equipment selection, and system operation scheduling at the design stage to deal with uncertainties. The other case provides operational optimization scenarios for an existing system, especially when the steam superheating varies. The proposed method can provide practical guidance to system energy efficiency improvement.

Optimal reservoir operation policies using novel nested algorithms

Science.gov (United States)

Delipetrev, Blagoj; Jonoski, Andreja; Solomatine, Dimitri

2015-04-01

Historically, the two most widely practiced methods for optimal reservoir operation have been dynamic programming (DP) and stochastic dynamic programming (SDP). These two methods suffer from the so called "dual curse" which prevents them to be used in reasonably complex water systems. The first one is the "curse of dimensionality" that denotes an exponential growth of the computational complexity with the state - decision space dimension. The second one is the "curse of modelling" that requires an explicit model of each component of the water system to anticipate the effect of each system's transition. We address the problem of optimal reservoir operation concerning multiple objectives that are related to 1) reservoir releases to satisfy several downstream users competing for water with dynamically varying demands, 2) deviations from the target minimum and maximum reservoir water levels and 3) hydropower production that is a combination of the reservoir water level and the reservoir releases. Addressing such a problem with classical methods (DP and SDP) requires a reasonably high level of discretization of the reservoir storage volume, which in combination with the required releases discretization for meeting the demands of downstream users leads to computationally expensive formulations and causes the curse of dimensionality. We present a novel approach, named "nested" that is implemented in DP, SDP and reinforcement learning (RL) and correspondingly three new algorithms are developed named nested DP (nDP), nested SDP (nSDP) and nested RL (nRL). The nested algorithms are composed from two algorithms: 1) DP, SDP or RL and 2) nested optimization algorithm. Depending on the way we formulate the objective function related to deficits in the allocation problem in the nested optimization, two methods are implemented: 1) Simplex for linear allocation problems, and 2) quadratic Knapsack method in the case of nonlinear problems. The novel idea is to include the nested

Spatial optimization of operationally relevant large fire confine and point protection strategies: Model development and test cases

Science.gov (United States)

Yu Wei; Matthew P. Thompson; Jessica R. Haas; Gregory K. Dillon; Christopher D. O’Connor

2018-01-01

This study introduces a large fire containment strategy that builds upon recent advances in spatial fire planning, notably the concept of potential wildland fire operation delineations (PODs). Multiple PODs can be clustered together to form a “box” that is referred as the “response POD” (or rPOD). Fire lines would be built along the boundary of an rPOD to contain a...

Control strategy optimization of HVAC plants

Energy Technology Data Exchange (ETDEWEB)

Facci, Andrea Luigi; Zanfardino, Antonella [Department of Engineering, University of Napoli “Parthenope” (Italy); Martini, Fabrizio [Green Energy Plus srl (Italy); Pirozzi, Salvatore [SIAT Installazioni spa (Italy); Ubertini, Stefano [School of Engineering (DEIM) University of Tuscia (Italy)

2015-03-10

In this paper we present a methodology to optimize the operating conditions of heating, ventilation and air conditioning (HVAC) plants to achieve a higher energy efficiency in use. Semi-empiric numerical models of the plant components are used to predict their performances as a function of their set-point and the environmental and occupied space conditions. The optimization is performed through a graph-based algorithm that finds the set-points of the system components that minimize energy consumption and/or energy costs, while matching the user energy demands. The resulting model can be used with systems of almost any complexity, featuring both HVAC components and energy systems, and is sufficiently fast to make it applicable to real-time setting.

Control strategy optimization of HVAC plants

International Nuclear Information System (INIS)

Facci, Andrea Luigi; Zanfardino, Antonella; Martini, Fabrizio; Pirozzi, Salvatore; Ubertini, Stefano

2015-01-01

In this paper we present a methodology to optimize the operating conditions of heating, ventilation and air conditioning (HVAC) plants to achieve a higher energy efficiency in use. Semi-empiric numerical models of the plant components are used to predict their performances as a function of their set-point and the environmental and occupied space conditions. The optimization is performed through a graph-based algorithm that finds the set-points of the system components that minimize energy consumption and/or energy costs, while matching the user energy demands. The resulting model can be used with systems of almost any complexity, featuring both HVAC components and energy systems, and is sufficiently fast to make it applicable to real-time setting

Optimization of ejector design and operation

Directory of Open Access Journals (Sweden)

Kuzmenko Konstantin

2016-01-01

Full Text Available The investigation aims at optimization of gas ejector operation. The goal consists in the improvement of the inflator design so that to enable 50 liters of gas inflation within ~30 milliseconds. For that, an experimental facility was developed and fabricated together with the measurement system to study pressure patterns in the inflator path.

A Particle Swarm Optimization Algorithm for Optimal Operating Parameters of VMI Systems in a Two-Echelon Supply Chain

Science.gov (United States)

Sue-Ann, Goh; Ponnambalam, S. G.

This paper focuses on the operational issues of a Two-echelon Single-Vendor-Multiple-Buyers Supply chain (TSVMBSC) under vendor managed inventory (VMI) mode of operation. To determine the optimal sales quantity for each buyer in TSVMBC, a mathematical model is formulated. Based on the optimal sales quantity can be obtained and the optimal sales price that will determine the optimal channel profit and contract price between the vendor and buyer. All this parameters depends upon the understanding of the revenue sharing between the vendor and buyers. A Particle Swarm Optimization (PSO) is proposed for this problem. Solutions obtained from PSO is compared with the best known results reported in literature.

Genetic optimization of steam multi-turbines system

International Nuclear Information System (INIS)

Olszewski, Pawel

2014-01-01

Optimization analysis of partially loaded cogeneration, multiple-stages steam turbines system was numerically investigated by using own-developed code (C++). The system can be controlled by following variables: fresh steam temperature, pressure, and flow rates through all stages in steam turbines. Five various strategies, four thermodynamics and one economical, which quantify system operation, were defined and discussed as an optimization functions. Mathematical model of steam turbines calculates steam properties according to the formulation proposed by the International Association for the Properties of Water and Steam. Genetic algorithm GENOCOP was implemented as a solving engine for non–linear problem with handling constrains. Using formulated methodology, example solution for partially loaded system, composed of five steam turbines (30 input variables) with different characteristics, was obtained for five strategies. The genetic algorithm found multiple solutions (various input parameters sets) giving similar overall results. In real application it allows for appropriate scheduling of machine operation that would affect equable time load of every system compounds. Also based on these results three strategies where chosen as the most complex: the first thermodynamic law energy and exergy efficiency maximization and total equivalent energy minimization. These strategies can be successfully used in optimization of real cogeneration applications. - Highlights: • Genetic optimization model for a set of five various steam turbines was presented. • Four various thermodynamic optimization strategies were proposed and discussed. • Operational parameters (steam pressure, temperature, flow) influence was examined. • Genetic algorithm generated optimal solutions giving the best estimators values. • It has been found that similar energy effect can be obtained for various inputs

A Single-Degree-of-Freedom Energy Optimization Strategy for Power-Split Hybrid Electric Vehicles

Directory of Open Access Journals (Sweden)

Chaoying Xia

2017-07-01

Full Text Available This paper presents a single-degree-of-freedom energy optimization strategy to solve the energy management problem existing in power-split hybrid electric vehicles (HEVs. The proposed strategy is based on a quadratic performance index, which is innovatively designed to simultaneously restrict the fluctuation of battery state of charge (SOC and reduce fuel consumption. An extended quadratic optimal control problem is formulated by approximating the fuel consumption rate as a quadratic polynomial of engine power. The approximated optimal control law is obtained by utilizing the solution properties of the Riccati equation and adjoint equation. It is easy to implement in real-time and the engineering significance is explained in details. In order to validate the effectiveness of the proposed strategy, the forward-facing vehicle simulation model is established based on the ADVISOR software (Version 2002, National Renewable Energy Laboratory, Golden, CO, USA. The simulation results show that there is only a little fuel consumption difference between the proposed strategy and the Pontryagin’s minimum principle (PMP-based global optimal strategy, and the proposed strategy also exhibits good adaptability under different initial battery SOC, cargo mass and road slope conditions.

Battery sizing and rule-based operation of grid-connected photovoltaic-battery system: A case study in Sweden

International Nuclear Information System (INIS)

Zhang, Yang; Lundblad, Anders; Campana, Pietro Elia; Benavente, F.; Yan, Jinyue

2017-01-01

Highlights: • Battery sizing and rule-based operation are achieved concurrently. • Hybrid operation strategy that combines different strategies is proposed. • Three operation strategies are compared through multi-objective optimization. • High Net Present Value and Self Sufficiency Ratio are achieved at the same time. - Abstract: The optimal components design for grid-connected photovoltaic-battery systems should be determined with consideration of system operation. This study proposes a method to simultaneously optimize the battery capacity and rule-based operation strategy. The investigated photovoltaic-battery system is modeled using single diode photovoltaic model and Improved Shepherd battery model. Three rule-based operation strategies—including the conventional operation strategy, the dynamic price load shifting strategy, and the hybrid operation strategy—are designed and evaluated. The rule-based operation strategies introduce different operation parameters to run the system operation. multi-objective Genetic Algorithm is employed to optimize the decisional variables, including battery capacity and operation parameters, towards maximizing the system’s Self Sufficiency Ratio and Net Present Value. The results indicate that employing battery with the conventional operation strategy is not profitable, although it increases Self Sufficiency Ratio. The dynamic price load shifting strategy has similar performance with the conventional operation strategy because the electricity price variation is not large enough. The proposed hybrid operation strategy outperforms other investigated strategies. When the battery capacity is lower than 72 kW h, Self Sufficiency Ratio and Net Present Value increase simultaneously with the battery capacity.

Optimal Operation of Interdependent Power Systems and Electrified Transportation Networks

Directory of Open Access Journals (Sweden)

M. Hadi Amini

2018-01-01

Full Text Available Electrified transportation and power systems are mutually coupled networks. In this paper, a novel framework is developed for interdependent power and transportation networks. Our approach constitutes solving an iterative least cost vehicle routing process, which utilizes the communication of electrified vehicles (EVs with competing charging stations, to exchange data such as electricity price, energy demand, and time of arrival. The EV routing problem is solved to minimize the total cost of travel using the Dijkstra algorithm with the input from EVs battery management system, electricity price from charging stations, powertrain component efficiencies and transportation network traffic conditions. Through the bidirectional communication of EVs with competing charging stations, EVs’ charging demand estimation is done much more accurately. Then the optimal power flow problem is solved for the power system, to find the locational marginal price at load buses where charging stations are connected. Finally, the electricity prices were communicated from the charging stations to the EVs, and the loop is closed. Locational electricity price acts as the shared parameter between the two optimization problems, i.e., optimal power flow and optimal routing problem. Electricity price depends on the power demand, which is affected by the charging of EVs. On the other hand, location of EV charging stations and their different pricing strategies might affect the routing decisions of the EVs. Our novel approach that combines the electrified transportation with power system operation, holds tremendous potential for solving electrified transportation issues and reducing energy costs. The effectiveness of the proposed approach is demonstrated using Shanghai transportation network and IEEE 9-bus test system. The results verify the cost-savings for both power system and transportation networks.

Modeling and operation optimization of a proton exchange membrane fuel cell system for maximum efficiency

International Nuclear Information System (INIS)

Han, In-Su; Park, Sang-Kyun; Chung, Chang-Bock

2016-01-01

Highlights: • A proton exchange membrane fuel cell system is operationally optimized. • A constrained optimization problem is formulated to maximize fuel cell efficiency. • Empirical and semi-empirical models for most system components are developed. • Sensitivity analysis is performed to elucidate the effects of major operating variables. • The optimization results are verified by comparison with actual operation data. - Abstract: This paper presents an operation optimization method and demonstrates its application to a proton exchange membrane fuel cell system. A constrained optimization problem was formulated to maximize the efficiency of a fuel cell system by incorporating practical models derived from actual operations of the system. Empirical and semi-empirical models for most of the system components were developed based on artificial neural networks and semi-empirical equations. Prior to system optimizations, the developed models were validated by comparing simulation results with the measured ones. Moreover, sensitivity analyses were performed to elucidate the effects of major operating variables on the system efficiency under practical operating constraints. Then, the optimal operating conditions were sought at various system power loads. The optimization results revealed that the efficiency gaps between the worst and best operation conditions of the system could reach 1.2–5.5% depending on the power output range. To verify the optimization results, the optimal operating conditions were applied to the fuel cell system, and the measured results were compared with the expected optimal values. The discrepancies between the measured and expected values were found to be trivial, indicating that the proposed operation optimization method was quite successful for a substantial increase in the efficiency of the fuel cell system.

Optimal control of load-following operations in a pressurized water reactor

International Nuclear Information System (INIS)

Zhao Fuyu; Zhou Dawei

2000-01-01

According to the optimal control theory, the problem of load-following operation in a pressurized water reactor is formulated as a nonlinear-quadratic optimal control problem. One-dimensional core model is adopted. A successful optimization algorithm DDPSR is proposed to solving the obtained problem. The research results show that the DDPSR can converge with a long time interval and needs very small iteration number and computing time, and the practical reactor can be fairly operated in an optimal load-following manner and axial offset satisfies the required value from beginning to end. Control characters of boron concentration are discussed specially

Online gaming for learning optimal team strategies in real time

Science.gov (United States)

Hudas, Gregory; Lewis, F. L.; Vamvoudakis, K. G.

2010-04-01

This paper first presents an overall view for dynamical decision-making in teams, both cooperative and competitive. Strategies for team decision problems, including optimal control, zero-sum 2-player games (H-infinity control) and so on are normally solved for off-line by solving associated matrix equations such as the Riccati equation. However, using that approach, players cannot change their objectives online in real time without calling for a completely new off-line solution for the new strategies. Therefore, in this paper we give a method for learning optimal team strategies online in real time as team dynamical play unfolds. In the linear quadratic regulator case, for instance, the method learns the Riccati equation solution online without ever solving the Riccati equation. This allows for truly dynamical team decisions where objective functions can change in real time and the system dynamics can be time-varying.

The effect of pre-operative optimization on post-operative outcome in Crohn's disease resections

DEFF Research Database (Denmark)

El-Hussuna, Alaa; Iesalnieks, Igors; Horesh, Nir

2017-01-01

BACKGROUND: The timing of surgical intervention in Crohn's disease (CD) may depend on pre-operative optimization (PO) which includes different interventions to decrease the risk for unfavourable post-operative outcome. The objective of this study was to investigate the effect of multi-model PO on...

Optimal portfolio strategies under a shortfall constraint | Akume ...

African Journals Online (AJOL)

We impose dynamically, a shortfall constraint in terms of Tail Conditional Expectation on the portfolio selection problem in continuous time, in order to obtain optimal strategies. The nancial market is assumed to comprise n risky assets driven by geometric Brownian motion and one risk-free asset. The method of Lagrange ...

An adaptive immune optimization algorithm with dynamic lattice searching operation for fast optimization of atomic clusters

International Nuclear Information System (INIS)

Wu, Xia; Wu, Genhua

2014-01-01

Highlights: • A high efficient method for optimization of atomic clusters is developed. • Its performance is studied by optimizing Lennard-Jones clusters and Ag clusters. • The method is proved to be quite efficient. • A new Ag 61 cluster with stacking-fault face-centered cubic motif is found. - Abstract: Geometrical optimization of atomic clusters is performed by a development of adaptive immune optimization algorithm (AIOA) with dynamic lattice searching (DLS) operation (AIOA-DLS method). By a cycle of construction and searching of the dynamic lattice (DL), DLS algorithm rapidly makes the clusters more regular and greatly reduces the potential energy. DLS can thus be used as an operation acting on the new individuals after mutation operation in AIOA to improve the performance of the AIOA. The AIOA-DLS method combines the merit of evolutionary algorithm and idea of dynamic lattice. The performance of the proposed method is investigated in the optimization of Lennard-Jones clusters within 250 atoms and silver clusters described by many-body Gupta potential within 150 atoms. Results reported in the literature are reproduced, and the motif of Ag 61 cluster is found to be stacking-fault face-centered cubic, whose energy is lower than that of previously obtained icosahedron

Multi-infill strategy for kriging models used in variable fidelity optimization

Directory of Open Access Journals (Sweden)

Chao SONG

2018-03-01

Full Text Available In this paper, a computationally efficient optimization method for aerodynamic design has been developed. The low-fidelity model and the multi-infill strategy are utilized in this approach. Low-fidelity data is employed to provide a good global trend for model prediction, and multiple sample points chosen by different infill criteria in each updating cycle are used to enhance the exploitation and exploration ability of the optimization approach. Take the advantages of low-fidelity model and the multi-infill strategy, and no initial sample for the high-fidelity model is needed. This approach is applied to an airfoil design case and a high-dimensional wing design case. It saves a large number of high-fidelity function evaluations for initial model construction. What’s more, faster reduction of an aerodynamic function is achieved, when compared to ordinary kriging using the multi-infill strategy and variable-fidelity model using single infill criterion. The results indicate that the developed approach has a promising application to efficient aerodynamic design when high-fidelity analyses are involved. Keywords: Aerodynamics, Infill criteria, Kriging models, Multi-infill, Optimization

Optimal Coordinated Strategy Analysis for the Procurement Logistics of a Steel Group

Directory of Open Access Journals (Sweden)

Lianbo Deng

2014-01-01

Full Text Available This paper focuses on the optimization of an internal coordinated procurement logistics system in a steel group and the decision on the coordinated procurement strategy by minimizing the logistics costs. Considering the coordinated procurement strategy and the procurement logistics costs, the aim of the optimization model was to maximize the degree of quality satisfaction and to minimize the procurement logistics costs. The model was transformed into a single-objective model and solved using a simulated annealing algorithm. In the algorithm, the supplier of each subsidiary was selected according to the evaluation result for independent procurement. Finally, the effect of different parameters on the coordinated procurement strategy was analysed. The results showed that the coordinated strategy can clearly save procurement costs; that the strategy appears to be more cooperative when the quality requirement is not stricter; and that the coordinated costs have a strong effect on the coordinated procurement strategy.

OPF-Based Optimal Location of Two Systems Two Terminal HVDC to Power System Optimal Operation

Directory of Open Access Journals (Sweden)

Mehdi Abolfazli

2013-04-01

Full Text Available In this paper a suitable mathematical model of the two terminal HVDC system is provided for optimal power flow (OPF and optimal location based on OPF such power injection model. The ability of voltage source converter (VSC-based HVDC to independently control active and reactive power is well represented by the model. The model is used to develop an OPF-based optimal location algorithm of two systems two terminal HVDC to minimize the total fuel cost and active power losses as objective function. The optimization framework is modeled as non-linear programming (NLP and solved by Matlab and GAMS softwares. The proposed algorithm is implemented on the IEEE 14- and 30-bus test systems. The simulation results show ability of two systems two terminal HVDC in improving the power system operation. Furthermore, two systems two terminal HVDC is compared by PST and OUPFC in the power system operation from economical and technical aspects.

Research on Operation and Control Strategy of 600MW PWR in Load Follow

Energy Technology Data Exchange (ETDEWEB)

Qu, Bing Yang; Cao, Xin Rong [Harbin Engineering University, Harbin (China); Li, Han Chen [China Nuclear Power Engineering Co., Beijing (China)

2014-08-15

600MW Pressurized Water Reactor (PWR) is designed to operate in Constant Axial Offset Control (CAOC) strategy with base load originally. By calculations over a typical load follow scenario '12-3-6-3 {sup (}100-50-100%FP) via the CASMO-4E and SIMULATE-3 package, values of core operating parameter have been examined. With the progress of the nuclear power industry, advanced reactors are considered to have a good performance in load follow, economy and flexibility. Under the premise of fuel loading and structural dimensions unchanged, two independent control rod groups M and AO are used in 600MW pressurized water reactor to provide fine control of both the core reactivity and axial power distribution, which is named ' Improved G strategy .' The influences of different control rod distributions, composition materials, and overlap steps had in power changes have been examined in a comparative study to choose the optimal one.Then we simulate a range of load follow scenarios of the redesigned 600MW core without adjusting soluble boron concentration in the begin, middle and end of first cycle. This paper additionally demonstrated the moderator temperature coefficient and shutdown margin values of the reactor in Improved G strategy to compare with the thermal safety design criteria. It's demonstrated that adequate adjustment of control rod groups enable the core to perform load follow through Improved G strategy in 80% of cycle and save a large volume of liquid effluent particularly toward the end of cycle.

Tank waste remediation system optimized processing strategy with an altered treatment scheme

International Nuclear Information System (INIS)

Slaathaug, E.J.

1996-03-01

This report provides an alternative strategy evolved from the current Hanford Site Tank Waste Remediation System (TWRS) programmatic baseline for accomplishing the treatment and disposal of the Hanford Site tank wastes. This optimized processing strategy with an altered treatment scheme performs the major elements of the TWRS Program, but modifies the deployment of selected treatment technologies to reduce the program cost. The present program for development of waste retrieval, pretreatment, and vitrification technologies continues, but the optimized processing strategy reuses a single facility to accomplish the separations/low-activity waste (LAW) vitrification and the high-level waste (HLW) vitrification processes sequentially, thereby eliminating the need for a separate HLW vitrification facility

Optimal Portfolio Strategy under Rolling Economic Maximum Drawdown Constraints

Directory of Open Access Journals (Sweden)

Xiaojian Yu

2014-01-01

Full Text Available This paper deals with the problem of optimal portfolio strategy under the constraints of rolling economic maximum drawdown. A more practical strategy is developed by using rolling Sharpe ratio in computing the allocation proportion in contrast to existing models. Besides, another novel strategy named “REDP strategy” is further proposed, which replaces the rolling economic drawdown of the portfolio with the rolling economic drawdown of the risky asset. The simulation tests prove that REDP strategy can ensure the portfolio to satisfy the drawdown constraint and outperforms other strategies significantly. An empirical comparison research on the performances of different strategies is carried out by using the 23-year monthly data of SPTR, DJUBS, and 3-month T-bill. The investment cases of single risky asset and two risky assets are both studied in this paper. Empirical results indicate that the REDP strategy successfully controls the maximum drawdown within the given limit and performs best in both return and risk.

A new Methodology for Operations Strategy

DEFF Research Database (Denmark)

Koch, Christian; Rytter, Niels Gorm; Boer, Harry

2005-01-01

This paper proposes a new methodology for developing and implementing Operations Strategy (OS). It encompasses both content and process aspects of OS and differs thereby from many of the present OS methodologies. The paper outlines its paradigmatic foundation and presents aim, process, dimensions...

A new inertia weight control strategy for particle swarm optimization

Science.gov (United States)

Zhu, Xianming; Wang, Hongbo

2018-04-01

Particle Swarm Optimization is a member of swarm intelligence algorithms, which is inspired by the behavior of bird flocks. The inertia weight, one of the most important parameters of PSO, is crucial for PSO, for it balances the performance of exploration and exploitation of the algorithm. This paper proposes a new inertia weight control strategy and PSO with this new strategy is tested by four benchmark functions. The results shows that the new strategy provides the PSO with better performance.

Optimal Load-Tracking Operation of Grid-Connected Solid Oxide Fuel Cells through Set Point Scheduling and Combined L1-MPC Control

Directory of Open Access Journals (Sweden)

Siwei Han

2018-03-01

Full Text Available An optimal load-tracking operation strategy for a grid-connected tubular solid oxide fuel cell (SOFC is studied based on the steady-state analysis of the system thermodynamics and electrochemistry. Control of the SOFC is achieved by a two-level hierarchical control system. In the upper level, optimal setpoints of output voltage and the current corresponding to unit load demand is obtained through a nonlinear optimization by minimizing the SOFC’s internal power waste. In the lower level, a combined L1-MPC control strategy is designed to achieve fast set point tracking under system nonlinearities, while maintaining a constant fuel utilization factor. To prevent fuel starvation during the transient state resulting from the output power surging, a fuel flow constraint is imposed on the MPC with direct electron balance calculation. The proposed control schemes are testified on the grid-connected SOFC model.

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

Directory of Open Access Journals (Sweden)

Xiaolin Ayón

2017-04-01

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

Establishment of an immortalized mouse dermal papilla cell strain with optimized culture strategy

Directory of Open Access Journals (Sweden)

Haiying Guo

2018-01-01

Full Text Available Dermal papilla (DP plays important roles in hair follicle regeneration. Long-term culture of mouse DP cells can provide enough cells for research and application of DP cells. We optimized the culture strategy for DP cells from three dimensions: stepwise dissection, collagen I coating, and optimized culture medium. Based on the optimized culture strategy, we immortalized primary DP cells with SV40 large T antigen, and established several immortalized DP cell strains. By comparing molecular expression and morphologic characteristics with primary DP cells, we found one cell strain named iDP6 was similar with primary DP cells. Further identifications illustrate that iDP6 expresses FGF7 and α-SMA, and has activity of alkaline phosphatase. During the process of characterization of immortalized DP cell strains, we also found that cells in DP were heterogeneous. We successfully optimized culture strategy for DP cells, and established an immortalized DP cell strain suitable for research and application of DP cells.

Optimal bidding strategies in oligopoly markets considering bilateral contracts and transmission constraints

Energy Technology Data Exchange (ETDEWEB)

A.Badri; Jadid, S. [Department of Electrical Engineering, Iran University of Science and Technology (Iran); Rashidinejad, M. [Shahid Bahonar University, Kerman (Iran); Moghaddam, M.P. [Tarbiat Modarres University, Tehran (Iran)

2008-06-15

In electricity industry with transmission constraints and limited number of producers, Generation Companies (GenCos) are facing an oligopoly market rather than a perfect competition one. Under oligopoly market environment, each GenCo may increase its own profit through a favorable bidding strategy. This paper investigates the problem of developing optimal bidding strategies of GenCos, considering bilateral contracts and transmission constraints. The problem is modeled with a bi-level optimization algorithm, where in the first level each GenCo maximizes its payoff and in the second level a system dispatch will be accomplished through an OPF problem in which transmission constraints are taken into account. It is assumed that each GenCo has information about initial bidding strategies of other competitors. Impacts of exercising market power due to transmission constraints as well as irrational biddings of the some generators are studied and the interactions of different bidding strategies on participants' corresponding payoffs are presented. Furthermore, a risk management-based method to obtain GenCos' optimal bilateral contracts is proposed and the impacts of these contracts on GenCos' optimal biddings and obtained payoffs are investigated. At the end, IEEE 30-bus test system is used for the case study in order to demonstrate the simulation results and support the effectiveness of the proposed model. (author)

Optimal bidding strategies in oligopoly markets considering bilateral contracts and transmission constraints

International Nuclear Information System (INIS)

Badri, A.; Jadid, S.; Rashidinejad, M.; Moghaddam, M.P.

2008-01-01

In electricity industry with transmission constraints and limited number of producers, Generation Companies (GenCos) are facing an oligopoly market rather than a perfect competition one. Under oligopoly market environment, each GenCo may increase its own profit through a favorable bidding strategy. This paper investigates the problem of developing optimal bidding strategies of GenCos, considering bilateral contracts and transmission constraints. The problem is modeled with a bi-level optimization algorithm, where in the first level each GenCo maximizes its payoff and in the second level a system dispatch will be accomplished through an OPF problem in which transmission constraints are taken into account. It is assumed that each GenCo has information about initial bidding strategies of other competitors. Impacts of exercising market power due to transmission constraints as well as irrational biddings of the some generators are studied and the interactions of different bidding strategies on participants' corresponding payoffs are presented. Furthermore, a risk management-based method to obtain GenCos' optimal bilateral contracts is proposed and the impacts of these contracts on GenCos' optimal biddings and obtained payoffs are investigated. At the end, IEEE 30-bus test system is used for the case study in order to demonstrate the simulation results and support the effectiveness of the proposed model. (author)

Application of evolution strategy algorithm for optimization of a single-layer sound absorber

Directory of Open Access Journals (Sweden)

Morteza Gholamipoor

2014-12-01

Full Text Available Depending on different design parameters and limitations, optimization of sound absorbers has always been a challenge in the field of acoustic engineering. Various methods of optimization have evolved in the past decades with innovative method of evolution strategy gaining more attention in the recent years. Based on their simplicity and straightforward mathematical representations, single-layer absorbers have been widely used in both engineering and industrial applications and an optimized design for these absorbers has become vital. In the present study, the method of evolution strategy algorithm is used for optimization of a single-layer absorber at both a particular frequency and an arbitrary frequency band. Results of the optimization have been compared against different methods of genetic algorithm and penalty functions which are proved to be favorable in both effectiveness and accuracy. Finally, a single-layer absorber is optimized in a desired range of frequencies that is the main goal of an industrial and engineering optimization process.

Footprints of Optimal Protein Assembly Strategies in the Operonic Structure of Prokaryotes

Directory of Open Access Journals (Sweden)

Jan Ewald

2015-04-01

Full Text Available In this work, we investigate optimality principles behind synthesis strategies for protein complexes using a dynamic optimization approach. We show that the cellular capacity of protein synthesis has a strong influence on optimal synthesis strategies reaching from a simultaneous to a sequential synthesis of the subunits of a protein complex. Sequential synthesis is preferred if protein synthesis is strongly limited, whereas a simultaneous synthesis is optimal in situations with a high protein synthesis capacity. We confirm the predictions of our optimization approach through the analysis of the operonic organization of protein complexes in several hundred prokaryotes. Thereby, we are able to show that cellular protein synthesis capacity is a driving force in the dissolution of operons comprising the subunits of a protein complex. Thus, we also provide a tested hypothesis explaining why the subunits of many prokaryotic protein complexes are distributed across several operons despite the presumably less precise co-regulation.

Web malware spread modelling and optimal control strategies

Science.gov (United States)

Liu, Wanping; Zhong, Shouming

2017-02-01

The popularity of the Web improves the growth of web threats. Formulating mathematical models for accurate prediction of malicious propagation over networks is of great importance. The aim of this paper is to understand the propagation mechanisms of web malware and the impact of human intervention on the spread of malicious hyperlinks. Considering the characteristics of web malware, a new differential epidemic model which extends the traditional SIR model by adding another delitescent compartment is proposed to address the spreading behavior of malicious links over networks. The spreading threshold of the model system is calculated, and the dynamics of the model is theoretically analyzed. Moreover, the optimal control theory is employed to study malware immunization strategies, aiming to keep the total economic loss of security investment and infection loss as low as possible. The existence and uniqueness of the results concerning the optimality system are confirmed. Finally, numerical simulations show that the spread of malware links can be controlled effectively with proper control strategy of specific parameter choice.

Local Optimization Strategies in Urban Vehicular Mobility.

Directory of Open Access Journals (Sweden)

Pierpaolo Mastroianni

Full Text Available The comprehension of vehicular traffic in urban environments is crucial to achieve a good management of the complex processes arising from people collective motion. Even allowing for the great complexity of human beings, human behavior turns out to be subject to strong constraints--physical, environmental, social, economic--that induce the emergence of common patterns. The observation and understanding of those patterns is key to setup effective strategies to optimize the quality of life in cities while not frustrating the natural need for mobility. In this paper we focus on vehicular mobility with the aim to reveal the underlying patterns and uncover the human strategies determining them. To this end we analyze a large dataset of GPS vehicles tracks collected in the Rome (Italy district during a month. We demonstrate the existence of a local optimization of travel times that vehicle drivers perform while choosing their journey. This finding is mirrored by two additional important facts, i.e., the observation that the average vehicle velocity increases by increasing the travel length and the emergence of a universal scaling law for the distribution of travel times at fixed traveled length. A simple modeling scheme confirms this scenario opening the way to further predictions.

Strategies of operation cycles in BWR type reactors

International Nuclear Information System (INIS)

Molina, D.; Sendino, F.

1996-01-01

The article analyzes the operation cycles in BWR type reactors. The cycle size of operation is the consequence on the optimization process of the costs with the technical characteristics of nuclear fuel and the characteristics of demand and production. The authors analyze the cases of Garona NP and Cofrentes NP, both with BWR reactors. (Author)

Optimal Operation of Energy Storage in Power Transmission and Distribution

Science.gov (United States)

Akhavan Hejazi, Seyed Hossein

In this thesis, we investigate optimal operation of energy storage units in power transmission and distribution grids. At transmission level, we investigate the problem where an investor-owned independently-operated energy storage system seeks to offer energy and ancillary services in the day-ahead and real-time markets. We specifically consider the case where a significant portion of the power generated in the grid is from renewable energy resources and there exists significant uncertainty in system operation. In this regard, we formulate a stochastic programming framework to choose optimal energy and reserve bids for the storage units that takes into account the fluctuating nature of the market prices due to the randomness in the renewable power generation availability. At distribution level, we develop a comprehensive data set to model various stochastic factors on power distribution networks, with focus on networks that have high penetration of electric vehicle charging load and distributed renewable generation. Furthermore, we develop a data-driven stochastic model for energy storage operation at distribution level, where the distribution of nodal voltage and line power flow are modelled as stochastic functions of the energy storage unit's charge and discharge schedules. In particular, we develop new closed-form stochastic models for such key operational parameters in the system. Our approach is analytical and allows formulating tractable optimization problems. Yet, it does not involve any restricting assumption on the distribution of random parameters, hence, it results in accurate modeling of uncertainties. By considering the specific characteristics of random variables, such as their statistical dependencies and often irregularly-shaped probability distributions, we propose a non-parametric chance-constrained optimization approach to operate and plan energy storage units in power distribution girds. In the proposed stochastic optimization, we consider

An enhancement of selection and crossover operations in real-coded genetic algorithm for large-dimensionality optimization

Energy Technology Data Exchange (ETDEWEB)

Kwak, Noh Sung; Lee, Jongsoo [Yonsei University, Seoul (Korea, Republic of)

2016-01-15

The present study aims to implement a new selection method and a novel crossover operation in a real-coded genetic algorithm. The proposed selection method facilitates the establishment of a successively evolved population by combining several subpopulations: an elitist subpopulation, an off-spring subpopulation and a mutated subpopulation. A probabilistic crossover is performed based on the measure of probabilistic distance between the individuals. The concept of ‘allowance’ is suggested to describe the level of variance in the crossover operation. A number of nonlinear/non-convex functions and engineering optimization problems are explored to verify the capacities of the proposed strategies. The results are compared with those obtained from other genetic and nature-inspired algorithms.

Optimization strategies based on sequential quadratic programming applied for a fermentation process for butanol production.

Science.gov (United States)

Pinto Mariano, Adriano; Bastos Borba Costa, Caliane; de Franceschi de Angelis, Dejanira; Maugeri Filho, Francisco; Pires Atala, Daniel Ibraim; Wolf Maciel, Maria Regina; Maciel Filho, Rubens

2009-11-01

In this work, the mathematical optimization of a continuous flash fermentation process for the production of biobutanol was studied. The process consists of three interconnected units, as follows: fermentor, cell-retention system (tangential microfiltration), and vacuum flash vessel (responsible for the continuous recovery of butanol from the broth). The objective of the optimization was to maximize butanol productivity for a desired substrate conversion. Two strategies were compared for the optimization of the process. In one of them, the process was represented by a deterministic model with kinetic parameters determined experimentally and, in the other, by a statistical model obtained using the factorial design technique combined with simulation. For both strategies, the problem was written as a nonlinear programming problem and was solved with the sequential quadratic programming technique. The results showed that despite the very similar solutions obtained with both strategies, the problems found with the strategy using the deterministic model, such as lack of convergence and high computational time, make the use of the optimization strategy with the statistical model, which showed to be robust and fast, more suitable for the flash fermentation process, being recommended for real-time applications coupling optimization and control.

The optimization of nuclear power plants operation modes in emergency situations

Science.gov (United States)

Zagrebayev, A. M.; Trifonenkov, A. V.; Ramazanov, R. N.

2018-01-01

An emergency situations resulting in the necessity for temporary reactor trip may occur at the nuclear power plant while normal operating mode. The paper deals with some of the operation c aspects of nuclear power plant operation in emergency situations and during threatened period. The xenon poisoning causes limitations on the variety of statements of the problem of calculating characteristics of a set of optimal reactor power off controls. The article show a possibility and feasibility of new sets of optimization tasks for the operation of nuclear power plants under conditions of xenon poisoning in emergency circumstances.

Exploring optimal fertigation strategies for orange production, using soil-crop modelling

NARCIS (Netherlands)

Qin, Wei; Heinen, Marius; Assinck, Falentijn B.T.; Oenema, Oene

2016-01-01

Water and nitrogen (N) are two key limiting factors in orange (Citrus sinensis) production. The amount and the timing of water and N application are critical, but optimal strategies have not yet been well established. This study presents an analysis of 47 fertigation strategies examined by a

An optimization strategy for a biokinetic model of inhaled radionuclides

International Nuclear Information System (INIS)

Shyr, L.J.; Griffith, W.C.; Boecker, B.B.

1991-01-01

Models for material disposition and dosimetry involve predictions of the biokinetics of the material among compartments representing organs and tissues in the body. Because of a lack of human data for most toxicants, many of the basic data are derived by modeling the results obtained from studies using laboratory animals. Such a biomathematical model is usually developed by adjusting the model parameters to make the model predictions match the measured retention and excretion data visually. The fitting process can be very time-consuming for a complicated model, and visual model selections may be subjective and easily biased by the scale or the data used. Due to the development of computerized optimization methods, manual fitting could benefit from an automated process. However, for a complicated model, an automated process without an optimization strategy will not be efficient, and may not produce fruitful results. In this paper, procedures for, and implementation of, an optimization strategy for a complicated mathematical model is demonstrated by optimizing a biokinetic model for 144Ce in fused aluminosilicate particles inhaled by beagle dogs. The optimized results using SimuSolv were compared to manual fitting results obtained previously using the model simulation software GASP. Also, statistical criteria provided by SimuSolv, such as likelihood function values, were used to help or verify visual model selections

Conceptualizing operations strategy processes

DEFF Research Database (Denmark)

Rytter, Niels Gorm; Boer, Harry; Koch, Christian

2007-01-01

Purpose - The purpose of this paper is to present insights into operations strategy (OS) in practice. It outlines a conceptualization and model of OS processes and, based on findings from an in-depth and longitudinal case study, contributes to further development of extant OS models and methods......; taking place in five dimensions of change - technical-rational, cultural, political, project management, and facilitation; and typically unfolding as a sequential and parallel, ordered and disordered, planned and emergent as well as top-down and bottom-up process. The proposed OS conceptualization...

Optimal intervention strategies for cholera outbreak by education and chlorination

Science.gov (United States)

Bakhtiar, Toni

2016-01-01

This paper discusses the control of infectious diseases in the framework of optimal control approach. A case study on cholera control was studied by considering two control strategies, namely education and chlorination. We distinct the former control into one regarding person-to-person behaviour and another one concerning person-to-environment conduct. Model are divided into two interacted populations: human population which follows an SIR model and pathogen population. Pontryagin maximum principle was applied in deriving a set of differential equations which consists of dynamical and adjoin systems as optimality conditions. Then, the fourth order Runge-Kutta method was exploited to numerically solve the equation system. An illustrative example was provided to assess the effectiveness of the control strategies toward a set of control scenarios.

Design of Underwater Robot Lines Based on a Hybrid Automatic Optimization Strategy

Institute of Scientific and Technical Information of China (English)

Wenjing Lyu; Weilin Luo

2014-01-01

In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal;the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body’s minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.

Closed-loop optimization of chromatography column sizing strategies in biopharmaceutical manufacture.

Science.gov (United States)

Allmendinger, Richard; Simaria, Ana S; Turner, Richard; Farid, Suzanne S

2014-10-01

This paper considers a real-world optimization problem involving the identification of cost-effective equipment sizing strategies for the sequence of chromatography steps employed to purify biopharmaceuticals. Tackling this problem requires solving a combinatorial optimization problem subject to multiple constraints, uncertain parameters, and time-consuming fitness evaluations. An industrially-relevant case study is used to illustrate that evolutionary algorithms can identify chromatography sizing strategies with significant improvements in performance criteria related to process cost, time and product waste over the base case. The results demonstrate also that evolutionary algorithms perform best when infeasible solutions are repaired intelligently, the population size is set appropriately, and elitism is combined with a low number of Monte Carlo trials (needed to account for uncertainty). Adopting this setup turns out to be more important for scenarios where less time is available for the purification process. Finally, a data-visualization tool is employed to illustrate how user preferences can be accounted for when it comes to selecting a sizing strategy to be implemented in a real industrial setting. This work demonstrates that closed-loop evolutionary optimization, when tuned properly and combined with a detailed manufacturing cost model, acts as a powerful decisional tool for the identification of cost-effective purification strategies. © 2013 The Authors. Journal of Chemical Technology & Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Synergy optimization and operation management on syndicate complementary knowledge cooperation

Science.gov (United States)

Tu, Kai-Jan

2014-10-01

The number of multi enterprises knowledge cooperation has grown steadily, as a result of global innovation competitions. I have conducted research based on optimization and operation studies in this article, and gained the conclusion that synergy management is effective means to break through various management barriers and solve cooperation's chaotic systems. Enterprises must communicate system vision and access complementary knowledge. These are crucial considerations for enterprises to exert their optimization and operation knowledge cooperation synergy to meet global marketing challenges.

Strategy for optimal operation of a biomass-fired cogeneration power plant

International Nuclear Information System (INIS)

Prasertsan, S.; Krukanont, P.; Nigamsritragul, P.; Kirirat, P.

2001-01-01

Biomass-fired cogeneration not only is an environmentally friendly energy production, but also possesses high energy conversion efficiency. Generally, the wood product industry requires both heat and electricity. Combined heat and power generation (cogeneration) using wood residue has a three-fold benefit: waste minimization, reduction of an energy-related production cost and additional income from selling the excess electricity to the utility. In reality, the process heat demand fluctuates according to the production activities in the factory. The fluctuation of process heat demand affects the cogeneration efficiency and the electricity output and, consequently, the financial return, since the prices of heat and electricity are different. A study by computer simulation to establish a guideline for optimum operation of a process heat fluctuating cogeneration power plant is presented. The power plant was designed for a sawmill and an adjacent plywood factory using wood wastes from these two processes. The maximum boiler thermal load is 81.9 MW while the electricity output is in the range 19-24 MW and the process heat 10-30 MW. Two modes of operation were studied, namely the full (boiler) load and the partial (boiler) load. In the full load operation, the power plant is operated at a maximum boiler thermal load, while the extracted steam is varied to meet the steam demand of the wood-drying kilns and the plywood production. The partial load operation was designed for the partially fuelled boiler to provide sufficient steam for the process and to generate electricity at a desired capacity ranging from the firmed contract of 19 MW to the turbine maximum capacity of 24 MW. It was found that the steam for process heat has an allowable extracting range, which is limited by the low pressure feed water heater. The optimum operation for both full and partial load occurs at the lower limit of the extracting steam. A guideline for optimum operation at various combinations of

The CEV Model and Its Application in a Study of Optimal Investment Strategy

Directory of Open Access Journals (Sweden)

Aiyin Wang

2014-01-01

Full Text Available The constant elasticity of variance (CEV model is used to describe the price of the risky asset. Maximizing the expected utility relating to the Hamilton-Jacobi-Bellman (HJB equation which describes the optimal investment strategies, we obtain a partial differential equation. Applying the Legendre transform, we transform the equation into a dual problem and obtain an approximation solution and an optimal investment strategies for the exponential utility function.

Data reconciliation and optimal operation of a Catalytic naphtha reformer

Directory of Open Access Journals (Sweden)

Tore Lid

2008-10-01

Full Text Available The naphtha reforming process converts low-octane gasoline blending components to high-octane components for use in high-performance gasoline fuels. The reformer also has an important function as the producer of hydrogen to the refinery hydrotreaters. A process model based on a unit model structure, is used for estimation of the process condition using data reconciliation. Measurements are classified as redundant or non redundant and the model variables are classified as observable, barely observable or unobservable. The computed uncertainty of the measured and unmeasured variables shows that even if a variable is observable it may have a very large uncertainty and may thereby be practically unobservable. The process condition at 21 data points, sampled from two years of operation, was reconciled and used to optimize the process operation. There are large seasonal variations in the reformer product price and two operational cases are studied. In case 1, the product price is high and throughput is maximized with respect to process and product quality constraints. In case 2, the product price is low and the throughput is minimized with respect to a low constraint on the hydrogen production. Based on the characteristics of the optimal operation, a "self optimizing" control structure is suggested for each of the two operational cases.

Optimized Skip-Stop Metro Line Operation Using Smart Card Data

Directory of Open Access Journals (Sweden)

Peitong Zhang

2017-01-01

Full Text Available Skip-stop operation is a low cost approach to improving the efficiency of metro operation and passenger travel experience. This paper proposes a novel method to optimize the skip-stop scheme for bidirectional metro lines so that the average passenger travel time can be minimized. Different from the conventional “A/B” scheme, the proposed Flexible Skip-Stop Scheme (FSSS can better accommodate spatially and temporally varied passenger demand. A genetic algorithm (GA based approach is then developed to efficiently search for the optimal solution. A case study is conducted based on a real world bidirectional metro line in Shenzhen, China, using the time-dependent passenger demand extracted from smart card data. It is found that the optimized skip-stop operation is able to reduce the average passenger travel time and transit agencies may benefit from this scheme due to energy and operational cost savings. Analyses are made to evaluate the effects of that fact that certain number of passengers fail to board the right train (due to skip operation. Results show that FSSS always outperforms the all-stop scheme even when most passengers of the skipped OD pairs are confused and cannot get on the right train.

Optimal sizing of energy storage system for microgrids

Indian Academy of Sciences (India)

strategies and optimal allocation methods of the ESS devices are required for the MG. ... for the optimal design of systems managed optimally according to different .... Energy storage hourly operating and maintenance cost is defined as a ...

The Bio-Inspired Optimization of Trading Strategies and Its Impact on the Efficient Market Hypothesis and Sustainable Development Strategies

Directory of Open Access Journals (Sweden)

Rafał Dreżewski

2018-05-01

Full Text Available In this paper, the evolutionary algorithm for the optimization of Forex market trading strategies is proposed. The introduction to issues related to the financial markets and the evolutionary algorithms precedes the main part of the paper, in which the proposed trading system is presented. The system uses the evolutionary algorithm for optimization of a parameterized greedy strategy, which is then used as an investment strategy on the Forex market. In the proposed system, a model of the Forex market was developed, including all elements that are necessary for simulating realistic trading processes. The proposed evolutionary algorithm contains several novel mechanisms that were introduced to optimize the greedy strategy. The most important of the proposed techniques are the mechanisms for maintaining the population diversity, a mechanism for protecting the best individuals in the population, the mechanisms preventing the excessive growth of the population, the mechanisms of the initialization of the population after moving the time window and a mechanism of choosing the best strategies used for trading. The experiments, conducted with the use of real-world Forex market data, were aimed at testing the quality of the results obtained using the proposed algorithm and comparing them with the results obtained by the buy-and-hold strategy. By comparing our results with the results of the buy-and-hold strategy, we attempted to verify the validity of the efficient market hypothesis. The credibility of the hypothesis would have more general implications for many different areas of our lives, including future sustainable development policies.

Operations Strategy under Chaos –Lessons to be learned from a new Paradigm

DEFF Research Database (Denmark)

Koch, Christian

2004-01-01

The purpose of this paper is to introduce a new paradigm being able to conceptualize content and process aspects of Operations Strategy. Based on a critical reading of literature; two opposing paradigms of Operations Strategy are identified and described. The first focuses on content issues...... of Operations Strategy and relies on a normative orientation and the second focuses on process issues of Operations Strategy and relies on a descriptive orientation. To compare and evaluate the two paradigms; the results of a longitudinal case-study of Operations Strategy formulation and implementation...... in practice are shown. These results promote the need for a new or third paradigm to integrate and balance the two former paradigms. The new paradigm is labeled as a moderate constructivist paradigm using the metaphor of chaos and seems suitable for conceptualizing Operations Strategy as it is in practice...