A Stochastic Optimization Method for Energy Storage Sizing Based on an Expected Value Model

Energy storage technologies have been rapidly evolving in recent years. Energy storage plays different roles in various scenarios. For electricity consumers, they are concerned with how to use the energy storage system (ESS) to reduce their costs of electricity or increase their profits. In this paper, a stochastic optimization method for energy storage sizing based on an expected value model for consumers with Photovoltaic Generation (PV) is proposed. Firstly, the Gaussian mixture model clustering method is used to cluster the historical load and PV data and calculate the probability of each cluster. Secondly, the optimal model of total system profit is established. Finally, according to the expected value model, the optimal ESS power and capacity are determined. Two case studies are used to demonstrate the calculation of optimal ESS capacity. The results obtained by the method proposed in this paper are compared with the results produced by the deterministic method. Through the analysis and comparison, the validity and superiority of the method proposed in this paper are verified. The profits obtained by the method proposed in this paper are 0.87% to 127.16% more than the deterministic method.

[1]  G. Coppez,et al.  Impacts of energy storage in distributed Power Generation: A review , 2010, 2010 International Conference on Power System Technology.

[2]  H. Bludszuweit,et al.  A Probabilistic Method for Energy Storage Sizing Based on Wind Power Forecast Uncertainty , 2011, IEEE Transactions on Power Systems.

[3]  Ahmad Mohamed Abd el Motaleb,et al.  Optimal sizing for a hybrid power system with wind/energy storage based in stochastic environment , 2016 .

[4]  Tanveer Ahmad,et al.  RETRACTED: A hybrid grid connected PV battery energy storage system with power quality improvement , 2016 .

[5]  Hamid Lesani,et al.  A stochastic approach to optimal sizing of energy storage systems in a microgrid , 2016, 2016 Smart Grids Conference (SGC).

[6]  Nanming Chen,et al.  Determination of optimal contract capacities and optimal sizes of battery energy storage systems for time-of-use rates industrial customers , 1995 .

[7]  Richard M. Dinsdale,et al.  Energy Storage for Active Network Management on Electricity Distribution Networks with Wind Power , 2012 .

[8]  Xiang Yupen.  Life Cycle Cost Based Optimal Configuration of Battery Energy Storage System in Distribution Network , 2015 .

[9]  Gabriela Hug,et al.  Optimal sizing of energy storage systems: a combination of hourly and intra-hour time perspectives , 2016 .

[10]  Gabriela Hug,et al.  Energy Storage Sizing Taking Into Account Forecast Uncertainties and Receding Horizon Operation , 2017, IEEE Transactions on Sustainable Energy.

[11]  Gongguo Tang,et al.  A game-theoretic approach for optimal time-of-use electricity pricing , 2013, IEEE Transactions on Power Systems.

[12]  D. J. Vowles,et al.  A review of energy storage and its application in power systems , 2015, 2015 Australasian Universities Power Engineering Conference (AUPEC).

[13]  F. Pilo,et al.  Optimal integration of energy storage in distribution networks , 2009, 2009 IEEE Bucharest PowerTech.

[14]  Ujjwal Maulik,et al.  Performance Evaluation of Some Clustering Algorithms and Validity Indices , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Morris Goldberg,et al.  A Clustering Scheme for Multispectral Images , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[16]  Yasser Abdel-Rady I. Mohamed,et al.  Mobile Energy Storage Scheduling and Operation in Active Distribution Systems , 2017, IEEE Transactions on Industrial Electronics.

[17]  Ehab F. El-Saadany,et al.  Adequacy Evaluation of Distribution System Including Wind/Solar DG During Different Modes of Operation , 2011, IEEE Transactions on Power Systems.

[18]  A. Costa,et al.  Demand Side Management Using Time of Use and Elasticity Price , 2016, IEEE Latin America Transactions.

[19]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[20]  Raymond A. de Callafon,et al.  Optimal energy storage sizing and residential load scheduling to improve reliability in islanded operation of distribution grids , 2017, 2017 American Control Conference (ACC).

[21]  Li Wang,et al.  Generalized Expected Value Model for Stochastic Programming and Its Application in Transportation Problems , 2009, 2009 First International Conference on Information Science and Engineering.

[22]  Vassilios G. Agelidis,et al.  Distributed Control for State-of-Charge Balancing Between the Modules of a Reconfigurable Battery Energy Storage System , 2016, IEEE Transactions on Power Electronics.

[23]  Hans-Arno Jacobsen,et al.  Bringing Distributed Energy Storage to Market , 2016, IEEE Transactions on Power Systems.

[24]  A. Arabali,et al.  A Framework for Optimal Placement of Energy Storage Units Within a Power System With High Wind Penetration , 2013, IEEE Transactions on Sustainable Energy.