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Outage management of hybrid AC/DC distribution systems: Co-optimize service restoration with repair crew and mobile energy storage system dispatch

Author

Listed:
  • Zhang, Lu
  • Yu, Shunjiang
  • Zhang, Bo
  • Li, Gen
  • Cai, Yongxiang
  • Tang, Wei

Abstract

To achieve the most efficient restoration of hybrid AC/DC distribution system, this paper proposes an outage management through co-optimizing service restoration with repair crew (RC) and mobile energy storage system (MESS) dispatch. Firstly, this paper proposes a hybrid AC/DC distribution system restoration (DSR) model considering network reconfiguration, distributed generation (DG) islanded operation and voltage source converter (VSC) based DSR, which means adjusting VSC control mode to realize uninterrupted power supply of loads. In the DSR model, a restoration coordination model that can coordinate network reconfiguration and the control modes of VSCs, DGs and MESS discharge stations is developed to ensure the radial configuration of the system. Moreover, to accurately calculate the power flow of the system during a failure of VSCs, a new power flow model considering VSC’s operation status is proposed in the DSR model. Then, a bi-level co-optimization method is developed based on the DSR model and improved dispatch models of RC and MESS. The upper level co-optimizes hybrid AC/DC distribution system restoration with RC and MESS dispatch to maximize the restored loads. The lower level further optimizes the power outputs of VSCs and DGs to minimize the voltage deviation of the restored loads. Finally, simulations studies are performed to verify the effectiveness and superiority of the proposed method.

Suggested Citation

  • Zhang, Lu & Yu, Shunjiang & Zhang, Bo & Li, Gen & Cai, Yongxiang & Tang, Wei, 2023. "Outage management of hybrid AC/DC distribution systems: Co-optimize service restoration with repair crew and mobile energy storage system dispatch," Applied Energy, Elsevier, vol. 335(C).
    • Handle: RePEc:eee:appene:v:335:y:2023:i:c:s0306261922016798
    DOI: 10.1016/j.apenergy.2022.120422
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    References listed on IDEAS

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    1. Wang, Yi & Qiu, Dawei & Strbac, Goran, 2022. "Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems," Applied Energy, Elsevier, vol. 310(C).
    2. Li, Peng & Ji, Jie & Ji, Haoran & Song, Guanyu & Wang, Chengshan & Wu, Jianzhong, 2020. "Self-healing oriented supply restoration method based on the coordination of multiple SOPs in active distribution networks," Energy, Elsevier, vol. 195(C).
    3. Ghasemi, Sasan & Moshtagh, Jamal, 2022. "Distribution system restoration after extreme events considering distributed generators and static energy storage systems with mobile energy storage systems dispatch in transportation systems," Applied Energy, Elsevier, vol. 310(C).
    4. Wu, Tao & Wang, Jianhui & Lu, Xiaonan & Du, Yuhua, 2022. "AC/DC hybrid distribution network reconfiguration with microgrid formation using multi-agent soft actor-critic," Applied Energy, Elsevier, vol. 307(C).
    5. Wang, Y. & Rousis, A. Oulis & Strbac, G., 2022. "Resilience-driven optimal sizing and pre-positioning of mobile energy storage systems in decentralized networked microgrids," Applied Energy, Elsevier, vol. 305(C).
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