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.
Subscribe to Journal
Get full journal access for 1 year
only $5.17 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Bryan, W. N. Hurricane Sandy Situation Report 20 (Office of Electricity Delivery and Energy Reliability of U. S. Department of Energy, 2012).
Executive Office of the President Economic Benefits of Increasing Electric Grid Resilience to Weather Outages Technical Report (President’s Council of Economic Advisers and the U. S. Department of Energy’s Office of Electricity Delivery and Energy Reliability, 2013).
Brown, R. E. Electric Power Distribution Reliability 2nd edn (CRC Press, 2008).
Bloomberg, M. R. A Stronger, More Resilient New York PlaNYC Report (The City of New York, 2013).
Hoffman, P. et al. Hardening and Resiliency: U. S. Energy Industry Response to Recent Hurricane Seasons OE/ISER Final Report (Office of Electricity Delivery and Energy Reliability of U. S. Department of Energy, 2010).
Committee on Enhancing the Robustness and Resilience of Future Electrical Transmission and Distribution in the United States to Terrorist Attack, Board on Energy and Environmental Systems, Division on Engineering and Physical Sciences, National Research Council Terrorism and the Electric Power Delivery System (National Academies, 2012).
Wei, Y., Ji, C., Galvan, F., Couvillon, S. & Orellana, G. Dynamic modelling and resilience for power distribution. In Proc. IEEE SmartGridComm 2013 Symposium 85–90 (IEEE, 2013).
Committee on Increasing National Resilience to Hazards and Disasters, Committee on Science, Engineering, and Public Policy Disaster Resilience: A National Imperative (National Academies, 2012).
Erjongmanee, S. & Ji, C. Large-scale network-service disruption: dependencies and external factors. IEEE Trans. Netw. Serv. Manage. 8, 375–386 (2011).
Liu, H., Davidson, R. A., David, R. V. & Stedinger, J. R. Negative binomial regression of electric power outages in hurricanes. J. Infrastruct. Syst. 11, 258–267 (2005).
Nateghi, R., Guikema, S. D. & Quiring, S. M. Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes. Risk Anal. 31, 1897–1906 (2011).
Angalakudati, M. et al. Improving emergency storm planning using machine learning. In 2014 IEEE PES T&D Conf. Exposition 1–6 (IEEE, 2014).
Rudin, C. et al. Machine learning for the New York City power grid. IEEE Trans. Pattern Anal. Mach. Intell. 34, 328–345 (2012).
Wei, Y. et al. Learning geotemporal nonstationary failure and recovery of power distribution. IEEE Trans. Neural Netw. Learn. Syst. 25, 229–240 (2014).
Larsen, P. H., LaCommare, K. H., Eto, J. H. & Sweeney, J. L. Assessing Changes in the Reliability of the U. S. Electric Power System Report LBNL188741 (Lawrence Livermore National Laboratory, 2015).
Rose, A., Oladosu, G. & Liao, S. Y. Business interruption impacts of a terrorist attack on the electric power system of Los Angeles: customer resilience to a total blackout. Risk Anal. 27, 513–531 (2007).
Gallager, R. G. Stochastic Processes: Theory for Applications (Cambridge Univ. Press, 2014).
Hajek, B. Random Processes for Engineers (Cambridge Univ. Press, 2015).
Dobson, I., Carreras, B. A., Lynch, V. E. & Newman, D. E. Complex systems analysis of series of blackouts: cascading failure, critical points, and self-organization. Chaos 17, 026103 (2007).
Liu, C. C., Jung, J., Heydt, G. T., Vittal, V. & Phadke, A. G. The strategic power infrastructure defense (SPID) system. A conceptual design. IEEE Control Syst. Mag. 20, 40–52 (2000).
Tollefson, J. U. S. electrical grid on the edge of failure. Nature http://dx.doi.org/10.1038/nature.2013.13598 (2013).
Kersting, W. H. Distribution System Modelling and Analysis 3rd edn (CRC Press, 2012).
Clauset, A., Shalizi, C. R. & Newman, M. E. Power-law distributions in empirical data. SIAM Rev. 51, 661–703 (2009).
Consolidated Edison Co. of New York and Orange and Rockland Utilities Post Sandy Enhancement Plan Technical Report (2013).
Phadke, A. G. & Thorp, J. S. Synchronized Phasor Measurements and their Applications 1st edn (Springer, 2008).
Lin, N., Emanuel, K., Oppenheimer, M. & Vanmarcke, E. Physically based assessment of hurricane surge threat under climate change. Nature Clim. Change 2, 462–467 (2012).
Emre, Y., Bent, R. & Backhaus, S. Resilient upgrade of electrical distribution grids. In Proc. 29th AAAI Conference on Artificial Intelligence 1233–1240 (AAAI Publications, 2015).
Dorland, C., Tol, R. S. & Palutikof, J. P. Vulnerability of the Netherlands and Northwest Europe to storm damage under climate change. Climatic Change 43, 513–535 (1999).
Sathaye, J. A. et al. Estimating impacts of warming temperatures on California’s electricity system. Glob. Environ. Change 23, 499–511 (2013).
Francisco, E., Botzen, W. J. W. & Tol, R. S. J. Economic losses from US hurricanes consistent with an influence from climate change. Nature Geosci. 8, 880–884 (2015).
Mahnovski, S., Delaney, M. & Tsay, C. Utilization of Underground and Overhead Power Lines in the City of New York Technical Report (Office of Long-Term Planning and Sustainability, Office of the Mayor and City of New York Technical Report, 2013).
Office of Electric, Gas, and Water 2012 Electric Reliability Performance Report Technical Report (Department of Public Service of the State of New York, 2013).
Blake, E. S., Kimberlain, T. B., Berg, R. J., Cangialosi, J. P. & Beven II, J. L. Tropical Cyclone Report: Hurricane Sandy (AL182012) 22–29 October 2012 Technical Report (National Hurricane Center, National Oceanic and Atmospheric Administration, 2013).
National Hurricane Center & National Oceanic and Atmospheric Administration Data File of Storm Number 18 of 2012 (Automated tropical cyclone forecast (ATCF) archive best track database, 2012); ftp://ftp.nhc.noaa.gov/atcf/archive/2012
National Hurricane Center, National Oceanic and Atmospheric Administration Hurricane Sandy Advisory Number 29 (2012); http://www.nhc.noaa.gov/archive/2012/al18/al182012.public.029.shtml
Gong, J. Mobile LiDAR data collection and analysis for post-Sandy disaster recovery. Int. Workshop Comput. Civ. Eng. 677–684 (2013).
IEEE Power & Energy Society IEEE Draft Guide for Electric Power Distribution Reliability Indices. IEEE P1366/D6 1–40 (2011).
Zhao, Y., Chen, J., Goldsmith, A. & Poor, H. V. Identification of outages in power systems with uncertain states and optimal sensor locations. IEEE J. Sel. Top. Signal Process. 8, 1140–1153 (2014).
Bertsimas, D. & Mourtzinou, G. Transient laws of non-stationary queueing systems and their applications. Queueing Syst. Theory Appl. 25, 115–155 (1997).
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.
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.
About this article
Cite this article
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) doi:10.1038/nenergy.2016.52
The sensitivity of electric power infrastructure resilience to the spatial distribution of disaster impacts
Reliability Engineering & System Safety (2020)
Computer-Aided Civil and Infrastructure Engineering (2019)
Exploratory analysis of high-resolution power interruption data reveals spatial and temporal heterogeneity in electric grid reliability
Energy Policy (2019)
EPL (Europhysics Letters) (2019)
Chaos: An Interdisciplinary Journal of Nonlinear Science (2019)