Quantifying distribution system resilience from utility data: large event risk and benefits of investments

Paper: arXiv 2407.10773

Authors: Arslan Ahmad, Ian Dobson

Abstract

We focus on large blackouts in electric distribution systems caused by extreme winds. Such events have a large cost and impact on customers. To quantify resilience to these events, we formulate large event risk and show how to calculate it from the historical outage data routinely collected by utilities’ outage management systems. Risk is defined using an event cost exceedance curve. The tail of this curve and the large event risk is described by the probability of a large cost event and the slope magnitude of the tail on a log-log plot. 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 12% and 22% reduction in the probability of a large cost event due to 10% wind hardening and 10% faster restoration respectively. 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.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper introduces a practical, data-driven ‘rerunning history’ framework to evaluate power distribution system resilience using utility outage logs and power-law tail fitting. While the underlying mathematics is relatively straightforward, the empirical methodology is robust, realistic, and highly relevant for risk management and regulatory justification.

Research Flowchart

  flowchart TD
    A[Research Goal: Quantify Distribution System Resilience & Investment Benefits] --> B[Input: Historical Utility OMS Outage Data & Extreme Wind Events]
    B --> C[Method: Formulate Event Cost Exceedance Curve & Tail Risk Parameters]
    C --> D[Simulation: 'Rerun History' with Investments Hardening/Restoration]
    D --> E[Recalculate Large Event Risk & Evaluate Slope/Probability Changes]
    E --> F[Findings: Resilience Improved<br/>10% Hardening = 12% Risk Reduction<br/>10% Faster Restoration = 22% Risk Reduction]

Cited References (42)

1. Logarithmic Resilience Risk Metrics That Address the Huge Variations in Blackout Cost — → 2505.12016
2. Quantifying the economic costs of power outages owing to extreme events: A systematic review (2025) — link
3. Hazard Resistance-Based Spatiotemporal Risk Analysis for Distribution Network Outages During Hurricanes (2024) — link
4. Resilience assessment and planning in power distribution systems: Past and future considerations (2023) — link
5. Towards Using Utility Data to Quantify How Investments Would Have Increased the Wind Resilience of Distribution Systems (2023) — link
6. Shedding light on the economic costs of long-duration power outages: A review of resilience assessment methods and strategies (2023) — link
7. Risk-Based Active Distribution System Planning for Resilience Against Extreme Weather Events (2023) — link
8. Models, Metrics, and Their Formulas for Typical Electric Power System Resilience Events (2023) — link

1 of 42 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.