IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i19p4792-d1485407.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/19/4792/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/19/4792/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rodrigues, Daniel L. & Ye, Xianming & Xia, Xiaohua & Zhu, Bing, 2020. "Battery energy storage sizing optimisation for different ownership structures in a peer-to-peer energy sharing community," Applied Energy, Elsevier, vol. 262(C).
    2. Liu, Jingkun & Zhang, Ning & Kang, Chongqing & Kirschen, Daniel & Xia, Qing, 2017. "Cloud energy storage for residential and small commercial consumers: A business case study," Applied Energy, Elsevier, vol. 188(C), pages 226-236.
    3. Ma, Mingtao & Huang, Huijun & Song, Xiaoling & Peña-Mora, Feniosky & Zhang, Zhe & Chen, Jie, 2022. "Optimal sizing and operations of shared energy storage systems in distribution networks: A bi-level programming approach," Applied Energy, Elsevier, vol. 307(C).
    4. Yang, Xiaohui & Chen, Zaixing & Huang, Xin & Li, Ruixin & Xu, Shaoping & Yang, Chunsheng, 2021. "Robust capacity optimization methods for integrated energy systems considering demand response and thermal comfort," Energy, Elsevier, vol. 221(C).
    5. Aliprantis, Charalambos D., 1999. "On the backward induction method," Economics Letters, Elsevier, vol. 64(2), pages 125-131, August.
    6. Mu, Yunfei & Chen, Wanqing & Yu, Xiaodan & Jia, Hongjie & Hou, Kai & Wang, Congshan & Meng, Xianjun, 2020. "A double-layer planning method for integrated community energy systems with varying energy conversion efficiencies," Applied Energy, Elsevier, vol. 279(C).
    7. Shao, Zhentong & Cao, Xiaoyu & Zhai, Qiaozhu & Guan, Xiaohong, 2023. "Risk-constrained planning of rural-area hydrogen-based microgrid considering multiscale and multi-energy storage systems," Applied Energy, Elsevier, vol. 334(C).
    8. Chen, Changming & Wu, Xueyan & Li, Yan & Zhu, Xiaojun & Li, Zesen & Ma, Jien & Qiu, Weiqiang & Liu, Chang & Lin, Zhenzhi & Yang, Li & Wang, Qin & Ding, Yi, 2021. "Distributionally robust day-ahead scheduling of park-level integrated energy system considering generalized energy storages," Applied Energy, Elsevier, vol. 302(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bian, Yifan & Xie, Lirong & Ye, Jiahao & Ma, Lan, 2024. "A new shared energy storage business model for data center clusters considering energy storage degradation," Renewable Energy, Elsevier, vol. 225(C).
    2. Wang, Yongli & Guo, Lu & Wang, Yanan & Zhang, Yunfei & Zhang, Siwen & Liu, Zeqiang & Xing, Juntai & Liu, Ximei, 2024. "Bi-level programming optimization method of rural integrated energy system based on coupling coordination degree of energy equipment," Energy, Elsevier, vol. 298(C).
    3. Zhu, Yilin & Xu, Yujie & Chen, Haisheng & Guo, Huan & Zhang, Hualiang & Zhou, Xuezhi & Shen, Haotian, 2023. "Optimal dispatch of a novel integrated energy system combined with multi-output organic Rankine cycle and hybrid energy storage," Applied Energy, Elsevier, vol. 343(C).
    4. Han, Ouzhu & Ding, Tao & Zhang, Xiaosheng & Mu, Chenggang & He, Xinran & Zhang, Hongji & Jia, Wenhao & Ma, Zhoujun, 2023. "A shared energy storage business model for data center clusters considering renewable energy uncertainties," Renewable Energy, Elsevier, vol. 202(C), pages 1273-1290.
    5. Yan, Rujing & Wang, Jiangjiang & Wang, Jiahao & Tian, Lei & Tang, Saiqiu & Wang, Yuwei & Zhang, Jing & Cheng, Youliang & Li, Yuan, 2022. "A two-stage stochastic-robust optimization for a hybrid renewable energy CCHP system considering multiple scenario-interval uncertainties," Energy, Elsevier, vol. 247(C).
    6. Zhang, Wen-Yi & Zheng, Boshen & Wei, Wei & Chen, Laijun & Mei, Shengwei, 2022. "Peer-to-peer transactive mechanism for residential shared energy storage," Energy, Elsevier, vol. 246(C).
    7. Wang, Qi & Miao, Cairan & Tang, Yi, 2022. "Power shortage support strategies considering unified gas-thermal inertia in an integrated energy system," Applied Energy, Elsevier, vol. 328(C).
    8. Zhang, Wen-Yi & Chen, Yue & Wang, Yi & Xu, Yunjian, 2023. "Equilibrium analysis of a peer-to-peer energy trading market with shared energy storage in a power transmission grid," Energy, Elsevier, vol. 274(C).
    9. Wang, Yubin & Zheng, Yanchong & Yang, Qiang, 2023. "Day-ahead bidding strategy of regional integrated energy systems considering multiple uncertainties in electricity markets," Applied Energy, Elsevier, vol. 348(C).
    10. Bian, Yifan & Xie, Lirong & Ye, Jiahao & Ma, Lan & Cui, Chuanshi, 2024. "Peer-to-peer energy sharing model considering multi-objective optimal allocation of shared energy storage in a multi-microgrid system," Energy, Elsevier, vol. 288(C).
    11. He, Ye & Wu, Hongbin & Wu, Andrew Y. & Li, Peng & Ding, Ming, 2024. "Optimized shared energy storage in a peer-to-peer energy trading market: Two-stage strategic model regards bargaining and evolutionary game theory," Renewable Energy, Elsevier, vol. 224(C).
    12. Yang, Xinyi & Li, Yaowang & Liu, Ziwen & Zhang, Shixu & Liu, Yuliang & Zhang, Ning, 2023. "Optimal planning of energy storage system under the business model of cloud energy storage considering system inertia support and the electricity-heat coordination," Applied Energy, Elsevier, vol. 349(C).
    13. Wang, Yuwei & Song, Minghao & Jia, Mengyao & Li, Bingkang & Fei, Haoran & Zhang, Yiyue & Wang, Xuejie, 2023. "Multi-objective distributionally robust optimization for hydrogen-involved total renewable energy CCHP planning under source-load uncertainties," Applied Energy, Elsevier, vol. 342(C).
    14. Zhou, Yuan & Wang, Jiangjiang & Li, Yuxin & Wei, Changqi, 2023. "A collaborative management strategy for multi-objective optimization of sustainable distributed energy system considering cloud energy storage," Energy, Elsevier, vol. 280(C).
    15. Song, Xiaoling & Zhang, Huqing & Fan, Lurong & Zhang, Zhe & Peña-Mora, Feniosky, 2023. "Planning shared energy storage systems for the spatio-temporal coordination of multi-site renewable energy sources on the power generation side," Energy, Elsevier, vol. 282(C).
    16. Ma, Mingtao & Huang, Huijun & Song, Xiaoling & Peña-Mora, Feniosky & Zhang, Zhe & Chen, Jie, 2022. "Optimal sizing and operations of shared energy storage systems in distribution networks: A bi-level programming approach," Applied Energy, Elsevier, vol. 307(C).
    17. Fan, Wei & Tan, Zhongfu & Li, Fanqi & Zhang, Amin & Ju, Liwei & Wang, Yuwei & De, Gejirifu, 2023. "A two-stage optimal scheduling model of integrated energy system based on CVaR theory implementing integrated demand response," Energy, Elsevier, vol. 263(PC).
    18. Jungsub Sim & Minsoo Kim & Dongjoo Kim & Hongseok Kim, 2021. "Cloud Energy Storage System Operation with Capacity P2P Transaction," Energies, MDPI, vol. 14(2), pages 1-13, January.
    19. Ziad Ragab & Ehsan Pashajavid & Sumedha Rajakaruna, 2024. "Optimal Sizing and Economic Analysis of Community Battery Systems Considering Sensitivity and Uncertainty Factors," Energies, MDPI, vol. 17(18), pages 1-20, September.
    20. Zhou, Yuekuan & Lund, Peter D., 2023. "Peer-to-peer energy sharing and trading of renewable energy in smart communities ─ trading pricing models, decision-making and agent-based collaboration," Renewable Energy, Elsevier, vol. 207(C), pages 177-193.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4792-:d:1485407. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.