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Collaborative Optimization of Direct Current Distribution Network Based on Scaled Electric Vehicles Charging and Discharging and Soft Open Points Topology Reconfiguration

Author

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  • Yongqiang Kang

    (School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Gang Lu

    (School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Meng Chen

    (School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Xinglong Li

    (School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Shuaibing Li

    (School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

Abstract

In order to reduce the impact of the performance degradation of a direct current (DC) distribution network system caused by the access of scaled electric vehicles (EVs), this paper proposes a collaborative optimization method for a DC distribution network based on scaled EVs charging and discharging and soft open points (SOPs) topology reconfiguration. Firstly, based on the normal charging of scaled EVs, the EV discharge power model and the discharge response model were established based on the V2G (vehicle-to-grid) characteristic. Based on the characteristics of SOPs regulating voltage and power distribution, the SOP model and its equivalent model of DC system are established to identify the collaborative optimization of scaled EVs charging and discharging and SOPs topology reconstruction. Secondly, the bi-level model that optimizes multi-objects, including distribution network system loss, total voltage deviation and customer benefits, is established. The upper and lower models use the multi-objective particle swarm optimization (MOPSO) algorithm and simulated annealing algorithm to jointly optimize the optimal EV discharge power and the optimal SOP access configuration simultaneously. Finally, the effectiveness of the proposed collaborative optimization method is verified by a modified IEEE 33-node DC system example.

Suggested Citation

  • Yongqiang Kang & Gang Lu & Meng Chen & Xinglong Li & Shuaibing Li, 2025. "Collaborative Optimization of Direct Current Distribution Network Based on Scaled Electric Vehicles Charging and Discharging and Soft Open Points Topology Reconfiguration," Energies, MDPI, vol. 18(2), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:373-:d:1568817
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    References listed on IDEAS

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    1. Xia, Weiyi & Ren, Zhouyang & Li, Hui & Pan, Zhen, 2024. "A data-driven probabilistic evaluation method of hydrogen fuel cell vehicles hosting capacity for integrated hydrogen-electricity network," Applied Energy, Elsevier, vol. 376(PB).
    2. Ji, Haoran & Wang, Chengshan & Li, Peng & Zhao, Jinli & Song, Guanyu & Wu, Jianzhong, 2018. "Quantified flexibility evaluation of soft open points to improve distributed generator penetration in active distribution networks based on difference-of-convex programming," Applied Energy, Elsevier, vol. 218(C), pages 338-348.
    3. Li, Peng & Ji, Haoran & Yu, Hao & Zhao, Jinli & Wang, Chengshan & Song, Guanyu & Wu, Jianzhong, 2019. "Combined decentralized and local voltage control strategy of soft open points in active distribution networks," Applied Energy, Elsevier, vol. 241(C), pages 613-624.
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    Cited by:

    1. Jie Fang & Wenwu Li & Dunchu Chen, 2025. "Electric Vehicle and Soft Open Points Co-Planning for Active Distribution Grid Flexibility Enhancement," Energies, MDPI, vol. 18(3), pages 1-15, February.

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