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An Insurance Perspective on U.S. Electric Grid Disruption Costs

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Abstract

Large yet infrequent disruptions of electrical power can impact tens of millions of people in a single event, triggering significant economic damages, portions of which are insured. Small and frequent events are also significant in the aggregate. This article explores the role that insurance claims data can play in better defining the broader economic impacts of grid disruptions in the U.S. context. We developed four case studies, using previously unpublished data for specific actual grid disruptions. The cases include the 1977 New York City blackout, the 2003 Northeast blackout, multi-year national annual lightning-related electrical damage and multi-year national line-disturbance events. Insured losses represent between 3 and 64 per cent of total loss costs across the case studies. The household sector emerges as a larger locus of costs than indicated in previous studies, and short-lived events emerge as important sources of loss costs.

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Notes

  1. Executive Office of the President (2013).

  2. Hines et al. (2009).

  3. LaCommare and Eto (2006).

  4. Lecomte et al. (1998); Eto et al. (2001); Lineweber and McNulty (2001); RMS (2004).

  5. Bruch et al. (2011).

  6. USDOE (2013); Larsen et al. (2014).

  7. The Geneva Association (2009); Executive Office of the President (2013); van Vliet et al. (2016).

  8. Campbell (2012).

  9. USGCRP (2009).

  10. Lecomte et al. (1998).

  11. Zola and Bourne (2012); Claverol (2013).

  12. Healey (2014).

  13. Slavin (2010); USDOE (2013).

  14. Blume and Holmer (2013).

  15. NIST (2015).

  16. Klinger et al. (2014); McElroy (2015).

  17. Verisk Climate and HSB (2014).

  18. Zola and Bourne (2012).

  19. Kenealy (2015).

  20. Rodentis (1999).

  21. Zinkewicz (2005).

  22. Bartley and Rhode (2013).

  23. NAIC (2007).

  24. Bloomberg News (2003).

  25. Sullivan et al. (2015).

  26. USDOE (2013).

  27. The Insurance Services Office (www.verisk.com/iso.html) is an insurance data-collection service specialising in loss data, market data and related topics such as building code effectiveness. Their focus is on property-casualty insurance as distinct from life-health.

  28. Massman (2012).

  29. Marsh (2012).

  30. Johnson and Churan (2004); Standler (2011a, 2011b); Claverol (2013); Fickenscher (2013).

  31. Greenwald (2014).

  32. Johnson (2001).

  33. Widin (2009).

  34. The National Flood Insurance Program provides coverage for power outages (including food in freezers and damages due to failed pumps) if the damage causing the outage occurs on the insured property (NFIP, 2014).

  35. Smith and Katz (2013).

  36. Pendleton et al. (2013).

  37. Findings of research by librarians at the Insurance Library Association of Boston, Massachusetts, and Davis Library at St. John’s University, Manhattan Campus, New York.

  38. www.verisk.com/verisk/property-claim-services/pcs-catastrophe-serial-numbers-verisk-insurance-solutions.html, The cut-off point was $5 million prior to 1997 and $1 million prior to 1982.

  39. New York Times (2007).

  40. U.S.-Canada Power System Outage Task Force (2004).

  41. Information on the duration of the outage, particularly by and within states, is remarkably scarce.

  42. Anderson and Geckil (2003).

  43. Burch et al. (2011).

  44. According to III, 95 per cent of homeowners had insurance vs 29 per cent for renters (www.iii.org/fact-statistic/renters-insurance). As of 2003, 32 per cent of households were renters (U.S. Census). The net effect is 74 per cent of all households (owned and rented) being insured.

  45. III (2015a).

  46. III (2007).

  47. Bendre et al. (2004).

  48. Notably, the 2003 blackout represented the largest all-time number of daily claims for HSB, with the rank-ordering by state differing from that of the entire industry (PCS data for all types of insured losses). Hurricane Irene, the Southwest blackout of 8 September, and the Northwest storm on 29 October resulted in record line-disturbance claims. However, these events are rare, and the aggregate cost of small, frequent events is greater.

  49. Kolodziej (1998).

  50. Insureds include over 5 million business and industry customers; 350,000 farm customers and 300,000 residential customers.

  51. Sullivan et al. (2015).

  52. Levick (2003).

  53. LaCommare and Eto (2004).

  54. Spencer (2013).

  55. Johnson and Churan (2004).

  56. Campbell (2012); Executive Office of the President (2013).

  57. Galovich (2015).

  58. Smith (2014).

  59. Holbrook (2010); Business Insurance (2014).

  60. Lineweber and McNulty (2001).

  61. NFIP (2014).

  62. USGCRP (2009); Campbell (2012).

  63. van Vliet et al. (2016).

  64. Klinger et al. (2014).

  65. Mills (2009).

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Acknowledgements

This work was supported by the U.S. Department of Energy, Office of Electricity Delivery and Energy Reliability under Contract No. DE-AC02-05CH11231. Useful comments were provided by Joe Eto (LBNL), Robert Muir-Wood (RMS), Anthony Wagar (Willis), Howard Kunnreuther (Wharton), Tom Phillips (CARB, retired), Eric Rollison and Sharon Hernandez (USDOE) and two anonymous reviewers.

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Authors and Affiliations

  1. Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 90-2000, Berkeley, 94720, CA, U.S.A.

    Evan Mills

  2. Engineering and Research, Hartford Steam Boiler Insurance and Inspection Company, One State Street, PO Box 5024, Hartford, 06102, CT, U.S.A.

    Richard B Jones

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Correspondence to Evan Mills.

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Disclaimer: This document was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favouring by the United States Government or any agency thereof, or the Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California.

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Mills, E., Jones, R. An Insurance Perspective on U.S. Electric Grid Disruption Costs. Geneva Pap Risk Insur Issues Pract 41, 555–586 (2016). https://doi.org/10.1057/gpp.2016.9

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