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Edge computing-based distributed power restoration for active distribution networks considering switching sequence

Author

Listed:
  • Yu, Hao
  • Zhang, Zhicheng
  • Li, Peng
  • Ji, Haoran
  • Yu, Jiancheng
  • Song, Guanyu
  • Sun, Bing
  • Zhao, Jinli

Abstract

With the integration of edge computing technology in distribution networks, distributed power restoration has emerged as a promising alternative to enhance efficiency in responding to power failures. However, the practical implementation of distributed power restoration remains challenging, primarily due to the complex sequential coordination between switches and distributed generators (DGs). This paper proposes a distributed power restoration method considering the switching sequence. It addresses the sequential power restoration problem by solving the target network state and the transition strategy from the initial to the target network separately, thereby reducing overall computational complexity. A modified alternating direction method of multipliers (ADMM) algorithm is developed to enhance the solving efficiency. The proposed method can be employed online to generate the power restoration strategy concurrently with its execution, thereby minimizing the waiting time for strategy generation. Case studies conducted on the modified IEEE 33-node test system and the modified Taiwan 94-node test system demonstrate the advantages of the proposed method in terms of both power restoration paths and decision-making time.

Suggested Citation

  • Yu, Hao & Zhang, Zhicheng & Li, Peng & Ji, Haoran & Yu, Jiancheng & Song, Guanyu & Sun, Bing & Zhao, Jinli, 2025. "Edge computing-based distributed power restoration for active distribution networks considering switching sequence," Applied Energy, Elsevier, vol. 401(PC).
    • Handle: RePEc:eee:appene:v:401:y:2025:i:pc:s0306261925015181
    DOI: 10.1016/j.apenergy.2025.126788
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    References listed on IDEAS

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    1. Ren, Yi & Fan, Dongming & Feng, Qiang & Wang, Zili & Sun, Bo & Yang, Dezhen, 2019. "Agent-based restoration approach for reliability with load balancing on smart grids," Applied Energy, Elsevier, vol. 249(C), pages 46-57.
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    3. Kim, Min Jae & Kim, Tong Seop, 2019. "Integration of compressed air energy storage and gas turbine to improve the ramp rate," Applied Energy, Elsevier, vol. 247(C), pages 363-373.
    4. Sadeghi, M. & Kalantar, M., 2023. "Fully decentralized multi-agent coordination scheme in smart distribution restoration: Multilevel consensus," Applied Energy, Elsevier, vol. 350(C).
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