Large-scale data analysis of power grid resilience across multiple US service regions

Chuanyi Ji (), Yun Wei, Henry Mei, Jorge Calzada, Matthew Carey, Steve Church, Timothy Hayes, Brian Nugent, Gregory Stella, Matthew Wallace, Joe White and Robert Wilcox
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Chuanyi Ji: School of Electrical and Computer Engineering, Georgia Institute of Technology
Yun Wei: School of Electrical and Computer Engineering, Georgia Institute of Technology
Henry Mei: School of Electrical and Computer Engineering, Georgia Institute of Technology
Jorge Calzada: National Grid
Matthew Carey: New York State Public Service Commission
Steve Church: New York State Electric and Gas Corporation
Timothy Hayes: Central Hudson Gas & Electric Corp.
Brian Nugent: Orange & Rockland Utilities
Gregory Stella: New York State Public Service Commission
Matthew Wallace: New York State Public Service Commission
Joe White: Orange & Rockland Utilities
Robert Wilcox: National Grid

Nature Energy, 2016, vol. 1, issue 5, 1-8

Abstract: Abstract Severe weather events frequently result in large-scale power failures, affecting millions of people for extended durations. However, the lack of comprehensive, detailed failure and recovery data has impeded large-scale resilience studies. Here, we analyse data from four major service regions representing Upstate New York during Super Storm Sandy and daily operations. Using non-stationary spatiotemporal random processes that relate infrastructural failures to recoveries and cost, our data analysis shows that local power failures have a disproportionally large non-local impact on people (that is, the top 20% of failures interrupted 84% of services to customers). A large number (89%) of small failures, represented by the bottom 34% of customers and commonplace devices, resulted in 56% of the total cost of 28 million customer interruption hours. Our study shows that extreme weather does not cause, but rather exacerbates, existing vulnerabilities, which are obscured in daily operations.

Date: 2016
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DOI: 10.1038/nenergy.2016.52

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