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Co-optimization of repairs and dynamic network reconfiguration for improved distribution system resilience

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Listed:
  • Shi, Qingxin
  • Li, Fangxing
  • Dong, Jin
  • Olama, Mohammed
  • Wang, Xiaofei
  • Winstead, Chris
  • Kuruganti, Teja

Abstract

In this paper, a post-disaster distribution system repair and restoration (DSRR) strategy is proposed to improve distribution system resilience. The DSRR strategy is formulated as a two-stage optimization. The first stage is a comprehensive co-optimization of repair crew scheduling, dynamic network reconfiguration, and distributed energy resource (DER) dispatch based on the forecast load profile. The goal is to minimize the accumulative operating cost caused by the load reduction payment as well as DER operating cost. In particular, since the number of available repair crews is usually smaller than the number of faulted lines after a disaster event, the DSRR strategy determines the optimal scheduling for repairing faulted lines. The second stage is a re-dispatch of the DER power output and load shedding based on the real-time load demand of each bus. The proposed algorithm is validated by case studies of the IEEE 33-bus and 123-bus test systems. We consider those scenarios in which faults occur in multiple heavy-loaded feeders. The simulation results demonstrate that the DSRR strategy effectively coordinate the repair scheduling, network reconfiguration and load shedding to minimize the operating cost.

Suggested Citation

  • Shi, Qingxin & Li, Fangxing & Dong, Jin & Olama, Mohammed & Wang, Xiaofei & Winstead, Chris & Kuruganti, Teja, 2022. "Co-optimization of repairs and dynamic network reconfiguration for improved distribution system resilience," Applied Energy, Elsevier, vol. 318(C).
    • Handle: RePEc:eee:appene:v:318:y:2022:i:c:s0306261922006043
    DOI: 10.1016/j.apenergy.2022.119245
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    References listed on IDEAS

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    1. Dehghani, Nariman L. & Jeddi, Ashkan B. & Shafieezadeh, Abdollah, 2021. "Intelligent hurricane resilience enhancement of power distribution systems via deep reinforcement learning," Applied Energy, Elsevier, vol. 285(C).
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    3. Ding, Tao & Lin, Yanling & Bie, Zhaohong & Chen, Chen, 2017. "A resilient microgrid formation strategy for load restoration considering master-slave distributed generators and topology reconfiguration," Applied Energy, Elsevier, vol. 199(C), pages 205-216.
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    Cited by:

    1. Li, Tingjun & Han, Xiaoqing & Wu, Wenchuan & Sun, Hongbin, 2023. "Robust expansion planning and hardening strategy of meshed multi-energy distribution networks for resilience enhancement," Applied Energy, Elsevier, vol. 341(C).
    2. Wu, Chuantao & Wang, Tao & Zhou, Dezhi & Cao, Shankang & Sui, Quan & Lin, Xiangning & Li, Zhengtian & Wei, Fanrong, 2023. "A distributed restoration framework for distribution systems incorporating electric buses," Applied Energy, Elsevier, vol. 331(C).
    3. Yang, Yesen & Li, Zhengmao & Mandapaka, Pradeep V. & Lo, Edmond Y.M., 2023. "Risk-averse restoration of coupled power and water systems with small pumped-hydro storage and stochastic rooftop renewables," Applied Energy, Elsevier, vol. 339(C).

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