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

   https://doi.org/10.1049/gtd2.70179  

   Ahmad, Arslan; Dobson, Ian (January 2025, IET Generation, Transmission & Distribution)

   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. 

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2. North American Blackout Time Series Statistics and Implications for Blackout Risk

   https://doi.org/10.1109/TPWRS.2015.2510627  

   Carreras, Benjamin A; Newman, David E; Dobson, Ian (November 2016, IEEE Transactions on Power Systems)

   We use North American Electric Reliability Corporation historical data to give improved estimates of distributions of blackout size, time correlations, and waiting times for the Eastern and Western interconnections of the North American grid. We then explain and estimate the implications of the power law region (heavy tails) in the empirical distribution of blackout size in the historical data for the Western interconnection. Annual mean blackout size has high variability and the risk of large blackouts exceeds the risk of medium size blackouts. Ways to communicate blackout risk are discussed. 

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3. Quantifying cascading power outages during climate extremes considering renewable energy integration

   https://doi.org/10.1038/s41467-025-57565-4  

   Xu, Luo; Lin, Ning; Poor, H Vincent; Xi, Dazhi; Perera, A_T D (March 2026, Nature Communications)

   Climate extremes, such as hurricanes, combined with large-scale integration of environment-sensitive renewables, could exacerbate the risk of widespread power outages. We introduce a coupled climate-energy model for cascading power outages, which comprehensively captures the impacts of climate extremes on renewable generation, and transmission and distribution networks. The model is validated with the 2022 Puerto Rico catastrophic blackout during Hurricane Fiona – a unique system-wide blackout event with complete records of weather-induced outages. The model reveals a resilience pattern that was not captured by the previous models: early failure of certain critical components enhances overall system resilience. Sensitivity analysis on various scenarios of behind-the-meter solar integration demonstrates that lower integration levels (below 45%, including the current level) exhibit minimal impact on system resilience in this event. However, surpassing this critical level without pairing it with energy storage can exacerbate the probability of catastrophic blackouts. 

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4. Tropical cyclone-blackout-heatwave compound hazard resilience in a changing climate

   https://doi.org/10.1038/s41467-022-32018-4  

   Feng, Kairui; Ouyang, Min; Lin, Ning (December 2022, Nature Communications)

   Abstract Tropical cyclones (TCs) have caused extensive power outages. The impacts of TC-caused blackouts may worsen in the future as TCs and heatwaves intensify. Here we couple TC and heatwave projections and power outage and recovery process analysis to investigate how TC-blackout-heatwave compound hazard risk may vary in a changing climate, with Harris County, Texas as an example. We find that, under the high-emissions scenario RCP8.5, long-duration heatwaves following strong TCs may increase sharply. The expected percentage of Harris residents experiencing at least one longer-than-5-day TC-blackout-heatwave compound hazard in a 20-year period could increase dramatically by a factor of 23 (from 0.8% to 18.2%) over the 21 st century. We also reveal that a moderate enhancement of the power distribution network can significantly mitigate the compound hazard risk. Thus, climate adaptation actions, such as strategically undergrounding distribution network and developing distributed energy sources, are urgently needed to improve coastal power system resilience. 

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5. IEEE Transactions on Power Systems

   Kancherla, Sameera; Dobson, Ian (January 2018, IEEE transactions on power systems)

   null (Ed.)

   The empirical probability distribution of transmission line restoration times is obtained from 14 years of field data from a large utility. The distribution of restoration times has a heavy tail that indicates that long restoration times, although less frequent, routinely occur. The heavy tail differs from the convenient assumption of exponentially distributed restoration times, impacts power system resilience, and makes estimates of the mean restoration time highly variable. 

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