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1. Models, Metrics, and Their Formulas for Typical Electric Power System Resilience Events

   https://doi.org/10.1109/TPWRS.2023.3300125  

   Dobson, Ian (November 2023, IEEE Transactions on Power Systems)

   Poisson process models are defined in terms of their rates for outage and restore processes in power system resilience events. These outage and restore processes easily yield the perfor- mance curves that track the evolution of resilience events, and the area, nadir, and duration of the performance curves are standard resilience metrics. This letter analyzes typical resilience events by analyzing the area, nadir, and duration of mean performance curves. Explicit and intuitive formulas for these metrics are de- rived in terms of the Poisson process model parameters, and these parameters can be estimated from utility data. This clarifies the calculation of metrics of typical resilience events, and shows what they depend on. The metric formulas are derived with lognormal, exponential, or constant rates of restoration. The method is illus- trated with a typical North American transmission event. Similarly nice formulas are obtained for the area metric for empirical power system data. 

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2. The improvement in transmission resilience metrics from reduced outages or faster restoration can be calculated by rerunning historical outage data

   https://doi.org/10.1109/PESGM52009.2025.11225704  

   Ahmad, Arslan; Dobson, Ian; Ekisheva, Svetlana; Claypool, Christopher; Lauby, Mark (July 2025, IEEE)

   Transmission utilities routinely collect detailed outage data, including resilience events in which outages bunch due to weather. The resilience events and associated metrics can readily be extracted from this historical outage data. Improvements such as asset hardening or investments in restoration lead to reduced outages or faster restoration. In this paper, we show how to rerun the historical events including the effects of the reduced outages or faster restorations to measure the resulting improvement in resilience metrics, thus quantifying the benefits of these investments. This is demonstrated with case studies for specific events (a derecho and a hurricane), and all large events or large thunderstorms in the Midwest USA. Instead of predicting future extreme events with models, which is very challenging, rerunning historical events readily quantifies the benefits of resilience investments if these investments had been made in the past. Rerunning historical events is particularly vivid in making the case for resilience investments as it quantifies the benefits for events actually experienced, rather than for uncertain future events. 

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3. Assessing Transmission Resilience during Extreme Weather with Outage and Restore Processes

   https://doi.org/10.1109/PMAPS53380.2022.9810645  

   Ekisheva, Svetlana; Dobson, Ian; Rieder, Rachel; Norris, Jack (June 2022, Probability Methods Applied to Power Systems)

   We automatically extract resilience events and novel outage and restore processes from standard transmission utility detailed outage data. This new processing is applied to the outage data collected in NERC’s Transmission Availability Data System to introduce and analyze statistics that quantify resilience of the transmission system against extreme weather events. These metrics (such as outage rate and duration, number of elements outaged, rated capacity outaged, restore duration, maximum simultaneous outages, and element-days lost) are calculated for all large weather-related events on the North American transmission system from 2015 to 2020 and then by extreme weather type that caused them such as hurricanes, tornadoes, and winter storms. Finally, we study how performance of the system changed with respect to the resilience metrics by season and year. 

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4. Impact of Extreme Weather on North American Transmission System Outages

   https://doi.org/978-1-6654-0507-2/21/$31.00  

   Ekisheva, Svetlana; Rieder, Rachel; Norris, Jack; Lauby, Mark; Dobson, Ian (January 2021, IEEE Power Energy Society General Meeting)

   The impact of weather on the power grid has been a focus of multiple studies, and its importance has grown with the number and magnitude of extreme weather events. This paper uses transmission outage and inventory data collected in Transmission Availability Data System (TADS) to identify and analyze weather related transmission events and quantify their impact on the North American Bulk Electric System. The impact of a transmission event is measured by several factors: the number of outages, affected miles and MVA, event duration, and number of groups of simultaneous outages (known as generations of outages). We analyze the largest events from 2015 to 2019, and use an event propagation metric to estimate the probability of small, medium, and large events, and track how these probabilities change from year-to-year. 

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5. 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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