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Research on day-ahead transactions between multi-microgrid based on cooperative game model

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  • Chen, Weidong
  • Wang, Junnan
  • Yu, Guanyi
  • Chen, Jiajia
  • Hu, Yumeng

Abstract

Microgrids are one of the most common forms of distributed energy participation in the electricity market. This paper discusses the lack of market competition among independent microgrids as a factor in setting up a cooperative alliance among microgrids. Independent microgrids aim to minimize the system's overall operating costs. The first principle is to maximize scenery output and consumption. We develop and solve an optimization model to obtain the interactive power with the distribution network and the charging and discharging power arrangement for the energy storage module. We then construct a cooperative game model among multiple microgrids on this basis. Nash bargaining is used to coordinate the distribution of benefits among microgrids, as well as to analyze the optimal trading power and tariffs among microgrids. The research proves that the cooperative game among microgrids can realize the flexible consumption of renewable energy in the region. Microgrids also have lower operating costs. The Nash bargaining helps the members in the coalition to get satisfactory trading power and tariff. Additionally, it effectively improves the overall operational efficiency and market competitiveness of microgrid systems.

Suggested Citation

  • Chen, Weidong & Wang, Junnan & Yu, Guanyi & Chen, Jiajia & Hu, Yumeng, 2022. "Research on day-ahead transactions between multi-microgrid based on cooperative game model," Applied Energy, Elsevier, vol. 316(C).
    • Handle: RePEc:eee:appene:v:316:y:2022:i:c:s0306261922004913
    DOI: 10.1016/j.apenergy.2022.119106
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    References listed on IDEAS

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    Cited by:

    1. Jiankai Gao & Yang Li & Bin Wang & Haibo Wu, 2023. "Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm," Energies, MDPI, vol. 16(7), pages 1-21, April.
    2. Mohseni, Shayan & Pishvaee, Mir Saman, 2023. "Energy trading and scheduling in networked microgrids using fuzzy bargaining game theory and distributionally robust optimization," Applied Energy, Elsevier, vol. 350(C).
    3. Han, Fengwu & Zeng, Jianfeng & Lin, Junjie & Zhao, Yunlong & Gao, Chong, 2023. "A stochastic hierarchical optimization and revenue allocation approach for multi-regional integrated energy systems based on cooperative games," Applied Energy, Elsevier, vol. 350(C).
    4. Rajib Mahamud & Chanwoo Park, 2022. "Theory and Practices of Li-Ion Battery Thermal Management for Electric and Hybrid Electric Vehicles," Energies, MDPI, vol. 15(11), pages 1-45, May.
    5. Yuzhe Xie & Yan Yao & Yawu Wang & Weiqiang Cha & Sheng Zhou & Yue Wu & Chunyi Huang, 2022. "A Cooperative Game-Based Sizing and Configuration of Community-Shared Energy Storage," Energies, MDPI, vol. 15(22), pages 1-17, November.
    6. Zhou, Kaile & Fei, Zhineng & Hu, Rong, 2023. "Hybrid robust decentralized optimization of emission-aware multi-energy microgrids considering multiple uncertainties," Energy, Elsevier, vol. 265(C).
    7. Han, Zhixin & Fang, Debin & Yang, Peiwen & Lei, Leyao, 2023. "Cooperative mechanisms for multi-energy complementarity in the electricity spot market," Energy Economics, Elsevier, vol. 127(PB).
    8. Jani, Ali & Jadid, Shahram, 2023. "Two-stage energy scheduling framework for multi-microgrid system in market environment," Applied Energy, Elsevier, vol. 336(C).

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