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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. 

While tropical cyclone (TC) and heatwave (HW) compound hazard extremes are rare in the historical record, they have been recently emerging and are expected to become more frequent under future climate projections. Joint TC-HW hazards can exacerbate heat stress felt by residents, particularly in densely populated urban communities or areas suffering from storm-related power outages. The Princeton Urban Canopy Model (PUCM) has been used to evaluate heatwave conditions in urban environments, but has yet to be used to model joint TC-HW conditions. In this study, we model joint TC-HW hazards by adjusting the surface energy and water budgets of the PUCM to account for TC flood and extreme wind hazards. We investigate joint hazard interactions during Hurricane Laura (2020) using the Weather Research and Forecasting model (WRF) to simulate both Laura's wind field to drive subsequent hydrodynamic modeling of inundation and post-storm atmospheric conditions. The WRF and hydrodynamic modeling results are then used to drive the PUCM to assess the interaction of joint flooding, wind, and heat and their impacts on the city of Lake Charles in Louisiana. Results show that accounting for TC inundation up to a week after landfall can cause over 3°C reductions in daytime heat stress and 1.5°C increases in nighttime heat stress compared to simulations that ignore the presence of flooding. Accounting for defoliation from extreme TC winds can increase maximum nighttime heat stress by more than 4°C. 

Rapid global electrification is deepening cross-sector interdependence, fundamentally reshaping the resilience of energy systems in the face of intensifying climate extremes. While increased integration across energy generation, transmission, and consumption sectors can significantly enhance operational flexibility, it can also amplify the risk of cross-sector cascading failures under extreme weather events, giving rise to an emerging resilience paradox that remains insufficiently understood. This study examines evolving cross-sector interactions and their implications for climate resilience by analyzing global electrification trends and regional cases in Texas, integrated with global and downscaled projections of climate extremes. By identifying critical vulnerabilities and flexibility associated with increasing sectoral interdependence, this study highlights the necessity of adopting resilience-oriented, system-level strategies for system operators and policymakers to mitigate cross-sector cascading risks and maximize the benefits of electrification in a changing climate. 

Distribution networks, with large-scale integration of distributed renewable resources, particularly rooftop solar photovoltaic systems, represent the most extensive yet vulnerable components of modern electric power systems during climate extremes such as hurricanes. However, existing day-ahead electricity dispatch approaches primarily focus on the transmission network and lack the capability to manage the spatiotemporal risks associated with the vast distribution networks, which can potentially lead to significant power imbalances due to the mismatches between scheduled generation and actual demand. To address this increasingly critical gap under intensifying climate extremes and growing distributed renewable integration, we introduce Risk-aware Electricity Dispatch under Climate Extremes with Renewable integration (REDUCER), a risk-aware day-ahead electricity dispatch model that incorporates high-resolution spatiotemporal risk analysis for distribution networks with large-scale distributed renewable integration into an Entropic Value-at-Risk-constrained mixed-integer convex optimization framework. Applied to the 2022 Puerto Rico power grid under Hurricane Fiona, the proposed REDUCER model is seen to effectively manage these risks with substantially less reliance on additional flexibility resources to cope with power imbalances, reducing overall operational costs by about 30% under extreme cases compared to standard unit commitment strategies already informed by average demand loss. Also, the proposed REDUCER model consistently demonstrates its effectiveness in managing the increasing temporal net demand variability introduced by growing large-scale distributed solar integration while maintaining minimal operational costs. This model offers a practical solution for cost-effective and resilient electricity dispatch of modern power systems with large-scale renewable integration facing intensifying climate risks. 

Abstract Tropical cyclone (TC) hazards coupled with dense urban development along the coastline have resulted in trillions in US damages over the past several decades, with an increasing trend in losses in recent years. So far, this trend has been driven by increasing coastal development. However, as the climate continues to warm, changing TC climatology may also cause large changes in coastal damages in the future. Approaches to quantifying regional TC risk typically focus on total storm damage. However, it is crucial to understand the spatial footprint of TC damage and ultimately the spatial distribution of TC risk. Here, we quantify the magnitude and spatial pattern of TC risk (in expected annual damage) across the US from wind, storm surge, and rainfall using synthetic TCs, physics-based hazard models, and a county-level statistical damage model trained on historical TC data. We then combine end-of-century TC hazard simulations with US population growth and wealth increase scenarios (under the SSP2 4.5 emission scenario) to investigate the sensitivity of changes in TC risk across the US Atlantic and Gulf coasts. We find that not directly accounting for the effects of rainfall and storm surge results in much lower risk estimates and smaller future increases in risk. TC climatology change and socioeconomic change drive similar magnitude increases in total expected annual damage across the US (roughly 160%), and that their combined effect (633% increase) is much higher. 

Conventional computational models of climate adaptation frameworks inadequately consider decision-makers’ capacity to learn, update, and improve decisions. Here, we investigate the potential of reinforcement learning (RL), a machine learning technique that efficaciously acquires knowledge from the environment and systematically optimizes dynamic decisions, in modeling and informing adaptive climate decision-making. We consider coastal flood risk mitigations for Manhattan, New York City, USA (NYC), illustrating the benefit of continuously incorporating observations of sea-level rise into systematic designs of adaptive strategies. We find that when designing adaptive seawalls to protect NYC, the RL-derived strategy significantly reduces the expected net cost by 6 to 36% under the moderate emissions scenario SSP2-4.5 (9 to 77% under the high emissions scenario SSP5-8.5), compared to conventional methods. When considering multiple adaptive policies, including accomodation and retreat as well as protection, the RL approach leads to a further 5% (15%) cost reduction, showing RL’s flexibility in coordinatively addressing complex policy design problems. RL also outperforms conventional methods in controlling tail risk (i.e., low probability, high impact outcomes) and in avoiding losses induced by misinformation about the climate state (e.g., deep uncertainty), demonstrating the importance of systematic learning and updating in addressing extremes and uncertainties related to climate adaptation. 

Coastal flooding from tropical cyclone (TC)‐induced storm surges is among the most devastating natural hazards in the US. Accurately quantifying storm surge hazards is crucial for risk mitigation and climate adaptation. In this study, we conduct climatology‐hydrodynamic modeling to estimate TC surge hazards along the US northeast coastline under future climate scenarios. In this methodology, we generate synthetic TCs for the northeastern US to drive a hydrodynamic model (ADCIRC) to simulate storm surges. Observing their significant effect on storm surge, for the first time, we bias‐correct landfall angles of synthetic TCs, in addition to bias‐correcting their frequency and intensity. Our findings show that under the combined effects of sea level rise (SLR) and TC climatology change, historical 100‐year extreme water levels (EWLs) along the US northeast coastline would occur annually at the end of the century in both SSP2‐4.5 and SSP5‐8.5 emissions scenarios. 500‐year EWLs are also projected to occur every 1–60 (1–20) years under SSP2‐4.5 (SSP5‐8.5). SLR is the dominant factor in the dramatic changes in the EWLs. However, while in higher latitudes () TC climatology change modestly affect EWLs ( contribution for 100‐year and for 500‐year EWL changes), in lower latitudes the impact is more significant (up to 40% contribution to 100‐year and 55% for 500‐year EWL changes). Extending previous methods, the physics‐based probabilistic framework presented here can be applied to project future coastal flood hazards under the effects of SLR and storm climatology change for any TC‐prone region. 

Measuring and managing the risk of extensive distribution network outages during extreme events is critical for ensuring system-level energy balance in transmission network operations. However, existing risk measures used in stochastic optimization of power systems are computationally intractable for this problem involving large numbers of discrete random variables. Using a new coherent risk measure, Entropic Value-at-Risk (EVaR), that requires significantly less computational complexity, we propose an EVaR-constrained optimal power flow model that can quantify and manage the outage risk of extensive distribution feeders. The optimization problem with EVaR constraints on discrete random variables is equivalently reformulated as a conic programming model, which allows the problem to leverage the computational efficiency of conic solvers. The superiority of the proposed model is validated on the real-world Puerto Rico transmission grid combined with its large-scale distribution networks.