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1. 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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2. Power Grid Cascading Failure Prediction Based on Transformer

   https://doi.org/10.1007/978-3-030-91434-9_15  

   Zhou, Tianxin; Li, Xiang; Lu, Haibing (December 2021, Lecture notes in computer science)

   Smart grids can be vulnerable to attacks and accidents, and any initial failures in smart grids can grow to a large blackout because of cascading failure. Because of the importance of smart grids in modern society, it is crucial to protect them against cascading failures. Simulation of cascading failures can help identify the most vulnerable transmission lines and guide prioritization in protection planning, hence, it is an effective approach to protect smart grids from cascading failures. However, due to the enormous number of ways that the smart grids may fail initially, it is infeasible to simulate cascading failures at a large scale nor identify the most vulnerable lines efficiently. In this paper, we aim at 1) developing a method to run cascading failure simulations at scale and 2) building simplified, diffusion based cascading failure models to support efficient and theoretically bounded identification of most vulnerable lines. The goals are achieved by first constructing a novel connection between cascading failures and natural languages, and then adapting the powerful transformer model in NLP to learn from cascading failure data. Our trained transformer models have good accuracy in predicting the total number of failed lines in a cascade and identifying the most vulnerable lines. We also constructed independent cascade (IC) diffusion models based on the attention matrices of the transformer models, to support efficient vulnerability analysis with performance bounds. 

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3. Risk Assessment and Mitigation of Cascading Failures Using Critical Line Sensitivities

   https://doi.org/10.1109/TPWRS.2023.3305093  

   Dai, Yitian; Noebels, Matthias; Preece, Robin; Panteli, Mathaios; Dobson, Ian (March 2024, IEEE Transactions on Power Systems)

   Security concerns have been raised about cascading failure risks in evolving power grids. This paper reveals, for the first time, that the risk of cascading failures can be increased at low network demand levels when considering security-constrained generation dispatch. This occurs because critical transmission cor- ridors become very highly loaded due to the presence of central- ized generation dispatch, e.g., large thermal plants far from de- mand centers. This increased cascading risk is revealed in this work by incorporating security-constrained generation dispatch into the risk assessment and mitigation of cascading failures. A se- curity-constrained AC optimal power flow, which considers eco- nomic functions and security constraints (e.g., network con- straints, 𝑵 − 𝟏 security, and generation margin), is used to pro- vide a representative day-ahead operational plan. Cascading fail- ures are simulated using two simulators, a quasi-steady state DC power flow model, and a dynamic model incorporating all fre- quency-related dynamics, to allow for result comparison and ver- ification. The risk assessment procedure is illustrated using syn- thetic networks of 200 and 2,000 buses. Further, a novel preventive mitigation measure is proposed to first identify critical lines, whose failures are likely to trigger cascading failures, and then to limit power flow through these critical lines during dispatch. Results show that shifting power equivalent to 1% of total demand from critical lines to other lines can reduce cascading risk by up to 80%. 

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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. An Efficient Multifidelity Model for Assessing Risk Probabilities in Power Systems under Rare Events

   Xu, Yijun; Korkali, Mert; Mili, Lamine; Chen, Xiao (January 2020, 53rd Hawaii International Conference on System Sciences 2020)

   Risk assessment of power system failures induced by low-frequency, high-impact rare events is of paramount importance to power system planners and operators. In this paper, we develop a cost-effective multi-surrogate method based on multifidelity model for assessing risks in probabilistic power-flow analysis under rare events. Specifically, multiple polynomial-chaos-expansion-based surrogate models are constructed to reproduce power system responses to the stochastic changes of the load and the random occurrence of component outages. These surrogates then propagate a large number of samples at negligible computation cost and thus efficiently screen out the samples associated with high-risk rare events. The results generated by the surrogates, however, may be biased for the samples located in the low-probability tail regions that are critical to power system risk assessment. To resolve this issue, the original high-fidelity power system model is adopted to fine-tune the estimation results of low-fidelity surrogates by reevaluating only a small portion of the samples. This multifidelity model approach greatly improves the computational efficiency of the traditional Monte Carlo method used in computing the risk-event probabilities under rare events without sacrificing computational accuracy. 

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