Quantifying Distribution System Resilience From Utility Data: Large Event Risk and Benefits of Investments

Ahmad, Arslan; Dobson, Ian

doi:10.1049/gtd2.70179

Quantifying Distribution System Resilience From Utility Data: Large Event Risk and Benefits of Investments

Journal Article · Sun Nov 02 00:00:00 EDT 2025 · IET Generation, Transmission, & Distribution

DOI:https://doi.org/10.1049/gtd2.70179· OSTI ID:3013642

^[1]; Dobson, Ian ^[1]

Iowa State University, Ames, IA (United States)

We focus on blackouts in electric distribution systems that have a large cost to customers. To quantify resilience to these events, we show how to calculate risk metrics from the historical outage data routinely collected by utilities' outage management systems. Risk is defined using a customer cost exceedance curve. The exceedance curve has a heavy tail that implies large fluctuations in large blackout costs, and this makes estimating the mean large cost in the usual way impractical. To avoid this problem, we use new resilience metrics describing the large event risk; these metrics are the probability of a large cost event, the annual log cost resilience index, and the average of the logarithm of the cost of large-cost events or the slope magnitude of the tail on a log–log exceedance curve. Resilience can be improved by planned investments to upgrade system components or speed up restoration. The benefits that these investments would have had if they had been made in the past can be quantified by “rerunning history” with the effects of the investment included, and then recalculating the large event risk to find the improvement in resilience. An example using utility data shows a 2% reduction in the probability of a large cost event due to 10% wind hardening and 6%–7% reduction due to 10% faster restoration in two different areas of a distribution utility. This new data-driven approach to quantify resilience and resilience investments is realistic and much easier to apply than complicated approaches based on modeling all the phases of resilience. Moreover, an appeal to improvements to past lived experience may well be persuasive to customers and regulators in making the case for resilience investments.

View Accepted Manuscript (DOE)

Research Organization:: Leland Stanford Junior University, Redwood City, CA (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Hydrogen Fuel Cell Technologies Office (HFTO)

Grant/Contract Number:: EE0010724

OSTI ID:: 3013642

Journal Information:: IET Generation, Transmission, & Distribution, Journal Name: IET Generation, Transmission, & Distribution Journal Issue: 1 Vol. 19; ISSN 1751-8687; ISSN 1751-8695

Publisher:: Institution of Engineering and Technology (IET)Copyright Statement

Country of Publication:: United States

Language:: English

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24 POWER TRANSMISSION AND DISTRIBUTION
Use of data
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electric distribution systems
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larger effect
metrics
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risk

Quantifying Distribution System Resilience From Utility Data: Large Event Risk and Benefits of Investments

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