Game theory-based multi-agent capacity optimization for integrated energy systems with compressed air energy storage

Abstract The capacity optimization of integrated energy systems (IESs) is directly related to economy and stability, while centralized optimization methods are difficult to solve for scenarios in which energy units belong to different operators. This study proposes a game theory-based multi-agent capacity optimization method for an IES to analyze the benefit interactions among independent operators in decision-making processes. The IES is composed of four plants: solar photovoltaic, wind turbine, combined heating and power system, and compressed air energy storage (CAES), wherein plant operators act as players, and the net present value (NPV) is selected as the utility function. The Nash equilibrium is proven to exist and is solved by the best response algorithm for analyzing self-interested optimization. The Shapley value method is adopted to deal with benefit allocation in cooperative coalition, considering both stability and fairness. Several case studies are conducted to analyze all fifteen possible game models among four players. The results show that the access of CAES could improve the environmental and economic performance of the IES. The completely cooperative game model yields better economic performance for the whole and for individuals; its total NPV is 20.15% higher than when individuals act alone.

[1]  Chenghui Zhang,et al.  An integrated design for hybrid combined cooling, heating and power system with compressed air energy storage , 2018 .

[2]  T. Başar,et al.  Coalitional Game Theory for Communication Networks: A Tutorial , 2009, ArXiv.

[3]  Christos Verikoukis,et al.  Optimal Power Equipment Sizing and Management for Cooperative Buildings in Microgrids , 2019, IEEE Transactions on Industrial Informatics.

[4]  Jian-xing Ren,et al.  Profit allocation analysis among the distributed energy network participants based on Game-theory , 2017 .

[5]  H. R. E. H. Bouchekara,et al.  Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm , 2018, Renewable Energy.

[6]  Xiuli Qu,et al.  Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market , 2011 .

[7]  George Mavrotas,et al.  Multi-objective optimization and comparison framework for the design of Distributed Energy Systems , 2019, Energy Conversion and Management.

[8]  Yiping Dai,et al.  A preliminary dynamic behaviors analysis of a hybrid energy storage system based on adiabatic compressed air energy storage and flywheel energy storage system for wind power application , 2015 .

[9]  Zhu Han,et al.  Coalitional game theory for communication networks , 2009, IEEE Signal Processing Magazine.

[10]  Shengwei Mei,et al.  Game Approaches for Hybrid Power System Planning , 2012, IEEE Transactions on Sustainable Energy.

[11]  Hoseyn Sayyaadi,et al.  A methodology to obtain the foremost type and optimal size of the prime mover of a CCHP system for a large-scale residential application , 2018 .

[12]  Li Li,et al.  Virtual generation tribe based robust collaborative consensus algorithm for dynamic generation command dispatch optimization of smart grid , 2016 .

[13]  Zheng Li,et al.  An engineering approach to the optimal design of distributed energy systems in China , 2013 .

[14]  Marc A. Rosen,et al.  Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storage , 2018, Energy.

[15]  Songli Fan,et al.  Bargaining-based cooperative energy trading for distribution company and demand response , 2018, Applied Energy.

[16]  Alireza Lorestani,et al.  Optimal integration of renewable energy sources for autonomous tri-generation combined cooling, heating and power system based on evolutionary particle swarm optimization algorithm , 2018 .

[17]  Ali Mohammad Ranjbar,et al.  An autonomous demand response program for electricity and natural gas networks in smart energy hubs , 2015 .

[18]  Walid Saad,et al.  Game-Theoretic Methods for the Smart Grid: An Overview of Microgrid Systems, Demand-Side Management, and Smart Grid Communications , 2012, IEEE Signal Processing Magazine.

[19]  Jian-xing Ren,et al.  Benefit allocation for distributed energy network participants applying game theory based solutions , 2017 .

[20]  Fan Li,et al.  A hybrid optimization-based scheduling strategy for combined cooling, heating, and power system with thermal energy storage , 2019 .

[21]  I. Dincer,et al.  Exergy analysis and performance evaluation of a newly developed integrated energy system for quenchable generation , 2019, Energy.

[22]  A Q Huang,et al.  The Future Renewable Electric Energy Delivery and Management (FREEDM) System: The Energy Internet , 2011, Proceedings of the IEEE.

[23]  Yue Xiang,et al.  Cost-benefit analysis of integrated energy system planning considering demand response , 2020, Energy.

[24]  Zhi Zhou,et al.  An evolutionary game approach to analyzing bidding strategies in electricity markets with elastic de , 2011 .

[25]  You-Yin Jing,et al.  Optimization of capacity and operation for CCHP system by genetic algorithm , 2010 .

[26]  Peng Li,et al.  Modeling and optimal operation of community integrated energy systems: A case study from China , 2018, Applied Energy.

[27]  Shengwei Mei,et al.  Robust Optimization of Static Reserve Planning With Large-Scale Integration of Wind Power: A Game Theoretic Approach , 2014, IEEE Transactions on Sustainable Energy.

[28]  Rahman Saidur,et al.  Application of Artificial Intelligence Methods for Hybrid Energy System Optimization , 2016 .

[29]  Shantha Gamini Jayasinghe,et al.  A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system , 2017 .

[30]  Wencong Su,et al.  A game-theoretic economic operation of residential distribution system with high participation of distributed electricity prosumers , 2015 .

[31]  Chenghui Zhang,et al.  Multi-objective optimal operation and energy coupling analysis of combined cooling and heating system , 2016 .

[32]  John S. Vardakas,et al.  Cooperation in microgrids through power exchange: An optimal sizing and operation approach , 2017 .

[33]  Mohammad Shahidehpour,et al.  Robust Co-Optimization Scheduling of Electricity and Natural Gas Systems via ADMM , 2017, IEEE Transactions on Sustainable Energy.

[34]  Wayes Tushar,et al.  Energy Management for Joint Operation of CHP and PV Prosumers Inside a Grid-Connected Microgrid: A Game Theoretic Approach , 2016, IEEE Transactions on Industrial Informatics.

[35]  Anuradha M. Annaswamy,et al.  A real-time demand response market through a repeated incomplete-information game , 2018 .

[36]  Joshua M. Pearce,et al.  Performance of U.S. hybrid distributed energy systems: Solar photovoltaic, battery and combined heat and power , 2015 .

[37]  Meng Wang,et al.  Multi-objective optimization of a neighborhood-level urban energy network: Considering Game-theory inspired multi-benefit allocation constraints , 2018, Applied Energy.

[38]  Sen Guo,et al.  Investigation of discharge characteristics of a tri-generative system based on advanced adiabatic compressed air energy storage , 2018, Energy Conversion and Management.