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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Qibo He & Changming Chen & Xin Fu & Shunjiang Yu & Long Wang & Zhenzhi Lin, 2024. "Joint Planning Method of Shared Energy Storage and Multi-Energy Microgrids Based on Dynamic Game with Perfect Information," Energies, MDPI, vol. 17(19), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4792-:d:1485407
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    References listed on IDEAS

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