Large-scale data analytics for resilient recovery services from power failures

Abstract

<h2>Summary</h2> Massive power failures are induced frequently by natural disasters. A fundamental challenge is how recovery can be resilient to the increasing severity of disruptions in a changing climate. We conduct a large-scale study on recovery from 169 failure events at two operational distribution grids in the states of New York and Massachusetts. Guided by unsupervised learning from non-stationary data, our analysis finds that under the widely adopted prioritization policy favoring large failures, recovery exhibits a scaling property where a majority (<mml:math><mml:mrow><mml:mo>∼</mml:mo></mml:mrow></mml:math>90%) of customers recovers in a small fraction (<mml:math><mml:mrow><mml:mo>∼</mml:mo></mml:mrow></mml:math>10%) of total downtime. However, recovery degrades with the severity of disruptions: large failures that cannot recover rapidly increase by <mml:math><mml:mrow><mml:mo>∼</mml:mo></mml:mrow></mml:math>30% from the moderate to extreme events. Prolonged small failures dominate entire recovery processes. Further, our analysis demonstrates the promise of mitigating the degradation by enhancing recovery of a small fraction of large failures through distributed generation and storage.