Wu Hao, Meng Xiangyi, Danziger Michael M, Cornelius Sean P, Tian Hui, Barabási Albert-László
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
Center for Complex Networks Research, Department of Physics, Northeastern University, Boston, 02115, USA.
Nat Commun. 2022 Nov 30;13(1):7372. doi: 10.1038/s41467-022-35104-9.
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.
由于缺乏实际停电数据，尤其是大规模停电数据集，人们对配电网恢复过程的理解受到限制。通过分析美国三家电力公司的数据，我们发现停电的恢复持续时间与其附近停电的停机时间和停电强度（定义为停电期间的停电峰值数量）有关，但与受影响的客户数量无关。我们提出了一个基于聚类的恢复框架，以分析性地描述停电之间的依赖性，并解释停电强度在恢复过程中所起的主导作用。停电的恢复并非随机的，而是具有一种普遍模式，该模式与中断原因、灾后网络结构和详细的修复策略无关。我们的研究表明，抑制停电强度是加快恢复速度的一种有前景的方法。
