New Framework Addresses Urgent Need for Grid Planning for Extreme Weather Events

Results Can Help Build Resilient Power Systems

The increasing frequency and intensity of extreme weather events are putting unprecedented stress on electric power systems, revealing the limits of relying only on reactive responses and underscoring the need for proactive resilience planning. Most current approaches either concentrate on responding during or after outages, or use oversimplified models that separate proactive and reactive strategies, missing their crucial connection.

In a new study, researchers highlight that the best way to safeguard communities is not to wait until disaster strikes but to plan ahead—making smart investments that strengthen the grid before hazards occur. To support this approach, they developed a data-driven planning tool that can both look ahead to forecast the risks posed by extreme weather and look within to consider how utilities might respond when outages occur. By integrating these two perspectives, the framework enables utilities to make more effective long-term investment decisions while still accounting for the immediate actions needed to keep the lights on during emergencies.

The study, by researchers at Carnegie Mellon University, appears as a paper on arXiv and recently received 2025 INFORMS ENRE Best Student Paper Award.

“Events such as the northeastern United States being hit by three winter storms in two weeks in March 2018, resulting in $4 billion in economic losses, underscore the growing vulnerability of modern power grids and highlight the urgent need for resilient grid planning that can both withstand and recover from large-scale disruptions,” says Shixiang (Woody) Zhu, assistant professor of data analytics at Carnegie Mellon’s Heinz College, who coauthored the study.

Researchers used a decision framework designed to enhance the resilience of power grids in the face of extreme weather events by explicitly modeling the sequential nature of decision making under uncertainty. Specifically, they proposed a tri-level formulation that captures the key stages of utility planning and response, integrating long-term investment decisions, probabilistic modeling of disruptions, and real-time operational adjustments.

To demonstrate the practical utility of their framework, researchers then applied it to a semi-synthetic case study on power line undergrounding using data from Massachusetts provided by local utilities. Undergrounding overhead distribution lines is widely recognized as a long-term strategy for enhancing the resilience of grids by mitigating weather-induced outages, but its high cost requires strategic investment decisions under strict budget constraints.

In experiments on both real and synthetic data, the framework consistently out-performed conventional robust and two-stage methods, achieving lower worst-case losses and allocating resources more efficiently, especially under tight operational constraints and significant uncertainty.

“There is growing recognition of the need for proactive resilience strategies—interventions taken before extreme events occur—to reduce risk and mitigate long-term consequences,” notes Shuyi Chen, a second year Ph.D. student at Heinz, who led the study.

“Our results underscore the importance of anticipatory planning, robust uncertainty modeling, and scalable solvers in building resilient power systems,” adds Ramteen Sioshansi, professor of engineering and public policy at Carnegie Mellon University, who coauthored the study.

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Summarized from a paper on arXiv, Adaptive Robust Optimization with Data-Driven Uncertainty for Enhancing Distribution System Resilience, by Chen, S (Carnegie Mellon University), Zhu, S (Carnegie Mellon University), and Sioshansi, R (Carnegie Mellon University). Copyright 2025. All rights reserved.

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