Data-Driven Climate Outage Risk Characterization and Resilience Analysis in Joint Power-Communication Networks
Yoneke Graham, Gelila Webster, Tina Tran, Sohini Roy

TL;DR
This study develops a data-driven framework combining empirical outage data and cascade failure simulation to assess climate-related power outage risks and resilience in joint power-communication networks, highlighting coastal vulnerabilities.
Contribution
It introduces an integrated approach that combines empirical analysis with cascade simulation to better understand climate outage risks in interconnected power and communication systems.
Findings
Climate-related outages increase by about 9,100 annually.
Severe Weather is the main predictor of outage severity.
Coastal scenarios show significantly larger resilience gaps.
Abstract
Climate-driven power outages pose a growing threat to U.S. grid reliability, yet empirical outage studies and interdependency-based resilience analyses are rarely integrated. This paper presents a data-driven framework that integrates empirical outage characterization with cascade failure simulation in joint power-communication networks. Using the EAGLE-I national outage dataset (2015-2023, above 525,000 records), we characterize the climate-outage landscape through descriptive analysis and hypothesis testing, finding that climate-related outages increase by roughly 9,100 events per year and impose a significantly greater severity burden on coastal states. An interpretable logistic regression model then identifies the main predictors of severe outage risk, with Severe Weather emerging as the dominant factor. Guided by these findings, we construct four geographically representative…
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