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Fragmentation of outage clusters during the recovery of power distribution grids

Author

Listed:
  • Hao Wu

    (State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications
    Northeastern University)

  • Xiangyi Meng

    (Northeastern University)

  • Michael M. Danziger

    (Northeastern University)

  • Sean P. Cornelius

    (Ryerson University)

  • Hui Tian

    (State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications)

  • Albert-László Barabási

    (Northeastern University)

Abstract

The understanding of recovery processes in power distribution grids is limited by the lack of realistic outage data, especially large-scale blackout datasets. By analyzing data from three electrical companies across the United States, we find that the recovery duration of an outage is connected with the downtime of its nearby outages and blackout intensity (defined as the peak number of outages during a blackout), but is independent of the number of customers affected. We present a cluster-based recovery framework to analytically characterize the dependence between outages, and interpret the dominant role blackout intensity plays in recovery. The recovery of blackouts is not random and has a universal pattern that is independent of the disruption cause, the post-disaster network structure, and the detailed repair strategy. Our study reveals that suppressing blackout intensity is a promising way to speed up restoration.

Suggested Citation

  • Hao Wu & Xiangyi Meng & Michael M. Danziger & Sean P. Cornelius & Hui Tian & Albert-László Barabási, 2022. "Fragmentation of outage clusters during the recovery of power distribution grids," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35104-9
    DOI: 10.1038/s41467-022-35104-9
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    References listed on IDEAS

    as
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