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Collaborative Optimal Configuration of a Mobile Energy Storage System and a Stationary Energy Storage System to Cope with Regional Grid Blackouts in Extreme Scenarios

Author

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  • Weicheng Zhou

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

  • Ping Zhao

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

  • Yifei Lu

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

Abstract

To address regional blackouts in distribution networks caused by extreme accidents, a collaborative optimization configuration method with both a Mobile Energy Storage System (MESS) and a Stationary Energy Storage System (SESS), which can provide emergency power support in areas of power loss, is proposed. First, a time–space model of MESS with a coupled transportation network and power grids is constructed, as a MESS is more flexible than a SESS. Considering resilience and recovery, a minimization objective function for total cost, encompassing the hybrid energy storage investment cost, the power grid operation cost, and the load shedding penalty cost, is established. Moreover, considering SESS constraints and operational constraints, a hybrid configuration model is established. Then, considering the probability of extreme accidents, the scenario analysis method is used to address randomness, ensuring that the configuration results can be adapted to various scenarios. The proposed method can fully combine the time–space flexibility of MESS and the economic advantages of SESS, which can reduce the total cost and ensure the power system’s reliability. Finally, the effectiveness of the proposed method is verified by the improved IEEE33 system.

Suggested Citation

  • Weicheng Zhou & Ping Zhao & Yifei Lu, 2023. "Collaborative Optimal Configuration of a Mobile Energy Storage System and a Stationary Energy Storage System to Cope with Regional Grid Blackouts in Extreme Scenarios," Energies, MDPI, vol. 16(23), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7903-:d:1293818
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    References listed on IDEAS

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    1. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    2. Lin, Yanling & Bie, Zhaohong, 2018. "Tri-level optimal hardening plan for a resilient distribution system considering reconfiguration and DG islanding," Applied Energy, Elsevier, vol. 210(C), pages 1266-1279.
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