Joint Planning Method of Shared Energy Storage and Multi-Energy Microgrids Based on Dynamic Game with Perfect Information

Qibo He, Changming Chen (), Xin Fu, Shunjiang Yu, Long Wang and Zhenzhi Lin
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Qibo He: State Grid Wuxi Power Supply Company of Jiangsu Electric Power Co., Ltd., Wuxi 214061, China
Changming Chen: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Xin Fu: State Grid Wuxi Power Supply Company of Jiangsu Electric Power Co., Ltd., Wuxi 214061, China
Shunjiang Yu: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Long Wang: State Grid Wuxi Power Supply Company of Jiangsu Electric Power Co., Ltd., Wuxi 214061, China
Zhenzhi Lin: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Energies, 2024, vol. 17, issue 19, 1-20

Abstract: Under the background of the Energy Internet and the shared economy, it is of great significance to explore the collaborative planning strategies of multi-energy microgrids (MEMGs) and a shared energy storage operator (SESO) supported by shared energy storage resources. In this context, a joint planning method of SESO and MEMG alliances based on a dynamic game with perfect information is proposed in this paper. First, an upper-level model for energy storage capacity configuration and pricing strategy planning of SESO is proposed to maximize the total planning and operational income of SESO. Then, a lower-level model for the optimal configuration of MEMGs’ alliance considering SES is proposed to minimize the total planning and operational costs of the MEMG alliance. On this basis, a solving algorithm based on the dynamic game theory with perfect information and the backward induction method is proposed to obtain the Nash equilibrium solution of the proposed bi-level optimization models. Finally, a case study with one SESO and an alliance consisting of five MEMGs is conducted, and the simulation results show that the proposed bi-level optimization method can increase SESO’s net income by 1.47%, reduce the average planning costs for each MEMG at least by 1.7%, and reduce model solving time by 62.9% compared with other counterpart planning methods.

Keywords: shared energy storage; multi-energy microgrid; dynamic game with perfect information; optimal configuration; backward induction method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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