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Distribution-level electricity reliability: Temporal trends using statistical analysis

Author

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  • Eto, Joseph H.
  • LaCommare, Kristina H.
  • Larsen, Peter
  • Todd, Annika
  • Fisher, Emily

Abstract

This paper helps to address the lack of comprehensive, national-scale information on the reliability of the U.S. electric power system by assessing trends in U.S. electricity reliability based on the information reported by the electric utilities on power interruptions experienced by their customers. The research analyzes up to 10 years of electricity reliability information collected from 155 U.S. electric utilities, which together account for roughly 50% of total U.S. electricity sales. We find that reported annual average duration and annual average frequency of power interruptions have been increasing over time at a rate of approximately 2% annually. We find that, independent of this trend, installation or upgrade of an automated outage management system is correlated with an increase in the reported annual average duration of power interruptions. We also find that reliance on IEEE Standard 1366-2003 is correlated with higher reported reliability compared to reported reliability not using the IEEE standard. However, we caution that we cannot attribute reliance on the IEEE standard as having caused or led to higher reported reliability because we could not separate the effect of reliance on the IEEE standard from other utility-specific factors that may be correlated with reliance on the IEEE standard.

Suggested Citation

  • Eto, Joseph H. & LaCommare, Kristina H. & Larsen, Peter & Todd, Annika & Fisher, Emily, 2012. "Distribution-level electricity reliability: Temporal trends using statistical analysis," Energy Policy, Elsevier, vol. 49(C), pages 243-252.
    • Handle: RePEc:eee:enepol:v:49:y:2012:i:c:p:243-252
    DOI: 10.1016/j.enpol.2012.06.001
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    References listed on IDEAS

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    1. Hines, Paul & Apt, Jay & Talukdar, Sarosh, 2009. "Large blackouts in North America: Historical trends and policy implications," Energy Policy, Elsevier, vol. 37(12), pages 5249-5259, December.
    2. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    3. Wansbeek, Tom & Kapteyn, Arie, 1989. "Estimation of the error-components model with incomplete panels," Journal of Econometrics, Elsevier, vol. 41(3), pages 341-361, July.
    4. Baltagi, Badi H. & Chang, Young-Jae, 1994. "Incomplete panels : A comparative study of alternative estimators for the unbalanced one-way error component regression model," Journal of Econometrics, Elsevier, vol. 62(2), pages 67-89, June.
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    Cited by:

    1. Larsen, Peter H. & LaCommare, Kristina H. & Eto, Joseph H. & Sweeney, James L., 2016. "Recent trends in power system reliability and implications for evaluating future investments in resiliency," Energy, Elsevier, vol. 117(P1), pages 29-46.
    2. Chen, Haoling & Zhao, Tongtiegang, 2020. "Modeling power loss during blackouts in China using non-stationary generalized extreme value distribution," Energy, Elsevier, vol. 195(C).
    3. Harker Steele, Amanda J. & Burnett, J. Wesley & Bergstrom, John C., 2021. "The impact of variable renewable energy resources on power system reliability," Energy Policy, Elsevier, vol. 151(C).

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