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Economic dispatch of microgrid generation-load-storage based on dynamic bi-level game of multiple stakeholders

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  • Yang, Mao
  • Wang, Jinxin
  • Cao, Xudong
  • Gu, Dake

Abstract

During the participation of microgrid operators(MGO) and shared energy storage investors(SEI) in electricity market operations, unclear positioning of shared energy storage(SES) in the trading mechanism leads to decision-making limitations and mutual pricing constraints among stakeholders. Therefore, a dynamic bi-level game model based on multiple stakeholders is established in the paper. Considering the full life cycle investment costs, leasing costs, and load peak-shaving ancillary service revenue, establish a net revenue model for SEI. In the upper level, with SEI as leaders and MGO as followers, establishing a master-slave game model for optimizing SES leasing prices, and formulate leasing strategies. In the lower level, with MGO as leaders and cluster users as followers, establishing a master-slave game model for optimizing electricity prices and formulate scheduling strategies. A hybrid multi-strategy improved whale optimization algorithm based on simulated annealing(HSWMO) is proposed, combined with the theory of fixed points to solve the optimization problem of the upper and lower levels until obtaining the optimal solution that converges to a fixed point. Through case analysis, it is verified that the operational mode of SES as a market entity can bring higher economic benefits to multiple stakeholders such as MGO and cluster users. Meanwhile, the analysis from the perspective of SEI examines the optimal capacity investment and payback period for SES, providing effective reference for the comprehensive decision-making of SEI and MGO.

Suggested Citation

  • Yang, Mao & Wang, Jinxin & Cao, Xudong & Gu, Dake, 2024. "Economic dispatch of microgrid generation-load-storage based on dynamic bi-level game of multiple stakeholders," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224037095
    DOI: 10.1016/j.energy.2024.133931
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    References listed on IDEAS

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    1. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
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    4. Yang, Mao & Zhao, Meng & Huang, Dawei & Su, Xin, 2022. "A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time warping distance and deep autoencoder," Renewable Energy, Elsevier, vol. 194(C), pages 659-673.
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    Cited by:

    1. Lei Zhang & Yuxing Yuan & Su Yan & Hang Cao & Tao Du, 2025. "Advances in Modeling and Optimization of Intelligent Power Systems Integrating Renewable Energy in the Industrial Sector: A Multi-Perspective Review," Energies, MDPI, vol. 18(10), pages 1-50, May.

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