Title:  Large Scale Data Analytics for Resilience of Energy Networks

Committee:

Dr. Chuanyi Ji, ECE, Chair, Advisor

Dr. Scott Ganz, School of Business, George Town University

Dr. Mark Davenport, ECE

Dr. Deepak Divan, ECE

Dr. Valerie Thomas, ISyE

Abstract: Massive power failures are induced frequently by natural disasters in a changing climate. Two fundamental challenges arise in face of such failures: First, how recovery can be resilient to the increasing severity of disruptions and their impact on service users in a changing climate. Second, how can we measure the impact of failures and recovery and its heterogeneity on customers with different characteristics. 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 (90%) of customers recovers in a small fraction (10%) of total downtime. However, recovery degrades with the severity of disruptions: large failures that cannot recover rapidly increase by 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. Next, a dynamic resilience metric is developed using spatiotemporal failure and recovery processes incorporating the cost imposed on customers. The resilience metric is then combined with inference to design a framework on how to study the dynamic cost and its heterogeneity on customers with different characteristics. Our framework is validated on large scale data from multiple states in the US.