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

Nature Energy volume 1, Article number: 16052 (2016) Cite this article

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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.

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Figure 1: System and customer disruption rates of the four DSOs.
Figure 2: Scaling law for customer disruptions.
Figure 3: Empirical probability of disruptions.
Figure 4: Downtime durations in the four DSO service regions.
Figure 5: Probability density function of recovery.
Figure 6: Geographical distribution of the cost in Upstate New York during Super Storm Sandy.

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Acknowledgements

The authors thank J. Love, D. Mitra, M. Rodriguez, T. Spatz and M. Worden for their help and insightful discussions. In addition, the authors thank D. Mitra as an academic adviser for the project, M. Rodriguez for sharing observations relating to the non-local impact to customers from other hurricanes, and A. Afsharinejad for critiquing the manuscript. Support from the New York State Energy Research and Development Authority (NYSERDA) to Georgia Tech is gratefully acknowledged. The opinions in this paper are of the authors and do not represent those of the New York State Department of Public Service.

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332–0250, USA

    Chuanyi Ji, Yun Wei & Henry Mei

  2. National Grid, Waltham, Massachusetts 02451, USA

    Jorge Calzada & Robert Wilcox

  3. New York State Public Service Commission, Albany, New York 12223-1350, USA

    Matthew Carey, Gregory Stella & Matthew Wallace

  4. New York State Electric and Gas Corporation, Binghamton, New York 13904, USA

    Steve Church

  5. Central Hudson Gas & Electric Corp., Poughkeepsie, New York 12601, USA

    Timothy Hayes

  6. Orange & Rockland Utilities, Pearl River, New York 10965, USA

    Brian Nugent & Joe White

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Contributions

C.J. formulated the problem; Y.W. and H.M. conducted the experiments and wrote codes with contributions from M.C.; J.C., S.C., T.H., B.N., J.W. and R.W. gathered granular data; G.S. and M.W. provided their related Data Envelopment Analysis (DEA); C.J. derived the analytical model with Y.W.; C.J., Y.W. and H.M. analysed the results with insightful suggestions from all the other authors; C.J., Y.W. and H.M. wrote the paper with contributions from M.W.

Corresponding author

Correspondence to Chuanyi Ji.

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Competing interests

C.J. and Y.W. are co-authors of a pending patent application; C.J., Y.W. and H.M. are co-authors of a pending provisional patent application.

Supplementary information

Supplementary Information

Supplementary Notes 1–6, Supplementary Tables 1–6, Supplementary Figures 1–7, Supplementary References. (PDF 1819 kb)

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Ji, C., Wei, Y., Mei, H. et al. Large-scale data analysis of power grid resilience across multiple US service regions. Nat Energy 1, 16052 (2016). https://doi.org/10.1038/nenergy.2016.52

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