Power Restoration Prediction Following Extreme Events and Disasters

Abstract

This article examines electric power restoration following catastrophic damage in modern cities and regions due to extreme events and disasters. Recovery time and non-restoration probability are derived using new data from a comprehensive range of recent massive hurricanes, extensive wildfires, severe snowstorms, and damaging cyclones. Despite their totally disparate origins, over three orders of magnitude severe wildfires and hurricanes have the same non-restoration probability trends, which are of simple exponential form. The results fall into categories that are dependent on and grouped by the degree of damage and social disruption. The implications are discussed for emergency response planning. These new results demonstrate that the scientific laws of probability and human learning, which dominate risk in modern technologies and societies are also applicable to a wide range of disasters and extreme events.

Keywords

Damage categories / Hurricanes / Restoration probability / Storms / Wildfires

References

[1] Adibi MM, Milanicz DP. Estimating restoration duration. IEEE Transactions Power Systems, 1999, 14(4): 1493-1497

[2] Billinton R, Fotuhi-Firuzabad M, Bertiing L. Bibliography on the application of probability methods in power system reliability evaluation 1996–2001. IEEE Transactions Power Systems, 2001, 16(4): 596-602.

[3] Bluefire Studios. 2018. PowerOutage. US: An on-going project created to track, record, and aggregate power outages across the United States. http://www.poweroutage.us/. Accessed 5 Apr 2018.

[4] Bulmer MG. Principles of statistics, 1969, New York: Dover Publications

[5] Chow M-Y, Taylor LS, Chow M-S. Time of outage restoration analysis in distribution systems. IEEE Transactions Power Delivery, 1996, 11(3): 1652-1658

[6] Crowell M, Coulton K, Johnson C, Westcott J, Bellamo D, Edeleman S, Hirsch E. An estimate of the US population living in 100-year coastal flood zone hazard areas. Journal of Coastal Research, 2010, 26(2): 201-211

[7] DHS (Department of Homeland Security, United States). 2008. National response framework. Washington, DC: Department of Homeland Security. https://www.fema.gov/pdf/emergency/nrf/nrf-core.pdf. Accessed 23 Oct 2017.

[8] Duffey, R.B. 2014. The Fukushima scenario: Estimating the probability of core melt or damage following loss of total power and cooling. In 2014 Proceedings of the 22nd International Conference on Nuclear Engineering, 7–11 July 2014, Prague, Czech Republic. Vol. 6. https://doi.org/10.1115/icone22-30326.

[9] Duffey, R.B. 2015. Extreme events: Causes and prediction. Proceedings of 2015 International Topical Meeting on Probabilistic Safety Assessment and Analysis, 26–30 April 2015, Sun Valley, Idaho, USA.

[10] Duffey RB. Dynamic theory of losses in wars and conflicts. European Journal of Operational Research, 2017, 261(3): 1013-1027

[11] Duffey RB. Submarine warfare and intelligence in the Atlantic and Pacific in WW2: Comparisons and lessons learned for two opponents. Journal of Maritime Research, 2017, 19(2): 143-167

[12] Duffey RB. Search theory: The probabilistic physics and psychology of memory and learning. Journal of Scientific Research and Studies, 2017, 4(5): 107-120.

[13] Duffey RB, Ha T-S. The probability and timing of power system restoration. IEEE Transactions Power Systems, 2013, 28(1): 3-9

[14] Duffey RB, Saull JW. Managing risk: The human element, 2008, London: John Wiley and Sons

[15] EEI (Edison Electric Institute). 2016. Understanding the electric power industry’s response and restoration process. Washington, DC: Edison Electrical Institute. http://www.eei.org/issuesandpolicy/electricreliability/mutualassistance/Documents/MA_101FINAL.pdf. Accessed 27 Aug 2018.

[16] EIA (U.S. Energy Information Administration). 2016. EIA data show average frequency and duration of electric power outages. https://www.eia.gov/todayinenergy/detail.php?id=27892. Accessed 30 Sept 2017.

[17] ESB (Eire Supply Board). 2017. Distribution system operator licence, doc.# DOC-291111-BJK. https://www.esbnetworks.ie/who-we-are/about-esb. Accessed 29 Oct 2017.

[18] FEMA (Federal Emergency Management Agency). 2016. The Federal Emergency Management Agency publication 1. https://www.fema.gov/media-library-data/1462196227387-c10c40e585223d22e2595001e50f1e5c/Pub1_04-07.pdf. Accessed 23 Oct 2017.

[19] FPL (Florida Power & Light). 2017. FPL newsroom: news release, 22 September 2017. http://newsroom.fpl.com/2017-09-22-FPL-completes-service-restoration-to-more-than-4-4-million-customers-impacted-by-historic-Hurricane-Irma. Accessed 22 Sept 2017.

[20] Guikema SD, Nateghi R, Quiring SM, Staid A, Reilly AC, Gao M. Predicting hurricane power outages to support storm response planning. IEEE Access, 2014, 2: 1364-1373

[21] Han, S.R. 2008. Estimating hurricane outage and damage risk in power distribution systems. Ph.D. thesis, Civil Engineering, Texas A&M University. http://oaktrust.library.tamu.edu/bitstream/handle/1969.1/ETD-TAMU-2923/HAN-DISSERTATION.pdf?sequence=1. Accessed 24 Aug 2018.

[22] IEEE (Institute of Electrical and Electronic Engineers). 2013. Task force report: Power system restoration dynamics. Special publication: TPC2. Prepared by the Task Force on Power System Restoration Dynamics, of the Power System Stability Controls Subcommittee, of the Power System Dynamic Performance Committee, of the Power Engineering Society.

[23] Jaynes ET. Probability theory: The logic of science, 2003, Cambridge, UK: Cambridge University Press

[24] Klinger C, Landeg O, Murray V. Power outages, extreme events and health: A systematic review of the literature from 2011–2012, 2014, PLOS Currents: Disasters

[25] Lewis EE. Introduction to reliability engineering, 1994 2 New York: John Wiley and Sons

[26] Masozera M, Bailey M, Kerchner C. Distribution of impacts of natural disasters across income groups: A case study of New Orleans. Journal of Ecological Economics, 2007, 63(2–3): 299-306

[27] Nateghi R, Guikema SD, Quiring SM. Forecasting hurricane-induced power outage durations. Natural Hazards, 2014, 74(3): 1795-1811

[28] Ohlsson S. Learning from performance errors. Psychological Review, 1996, 103(2): 241-262

[29] Stephens, J.C. 2017. Utilities must go beyond ‘hardening’ the current grid. The Wall Street Journal, 14 November 2017.

[30] Thompson DDP. Leveraging learning to improve disaster management outcomes. International Journal of Disaster Risk Science, 2012, 3(4): 195-206

[31] U.S. Office of the President. 2013. Economic benefits of increasing electric grid resilience to weather outages. Washington DC: President’s Council of Economic Advisers, U.S. Department of Energy’s Office of Electricity Delivery and Energy Reliability, and White House Office of Science and Technology. https://energy.gov/sites/prod/files/2013/08/GridResiliencyReportFINAL.pdf. Accessed 22 Oct 2017.

[32] U.S. DOE. 2017. Electric emergency and disturbance report – calendar year 2017. Report OE-417. https://www.oe.netl.doe.gov/OE417_annual_summary.aspx. Accessed 30 Dec 2017.

[33] U.S.-Canada Power System Outage Task Force. 2004. Final report on the 14 August 2003. Blackout in the U.S. and Canada: Causes and Recommendations. Washington, DC: U.S. Department of Energy and Ottawa, Canada: Ministry of Natural Resources. https://www.energy.gov/sites/prod/files/oeprod/DocumentsandMedia/BlackoutFinal-Web.pdf. Accessed 6 April 2004.

[34] Wannier GH. Statistical Physics, 1987, New York: Dover Publications

[35] Wirfs-Brock, J. 2014. Data: Explore 15 years of power outages. Inside Energy, 18 August 2014. http://insideenergy.org/2014/08/18/data-explore-15-years-of-power-outages/. Accessed 23 Oct 2017.