PULLMAN, Wash. — As hurricanes intensify, wildfires spread, floods overwhelm infrastructure and heat waves strain electricity demand, power utilities face a difficult question: which investments will most effectively prevent the next major outage? Researchers at Washington State University have developed a planning framework designed to help answer that question before extreme weather strikes.
The new model connects weather hazards to the damage they can cause across transmission and distribution networks, then evaluates which long-term investments could reduce the consequences. Rather than treating resilience as a single upgrade or emergency response, the framework considers a portfolio of options, including hardening power lines, installing protective devices and deploying distributed generation such as local solar, storage or backup generators.
The research, conducted by scientists in WSU’s School of Electrical Engineering and Computer Science, addresses a growing challenge for utilities. Extreme weather events are becoming more frequent and costly, while electric grids are aging and becoming more interconnected. In the United States, the average number of weather-related disasters causing at least $1 billion in damage has risen to 23 during the past five years, compared with an average of nine in earlier decades. Every major event can expose weaknesses in a system that communities depend on for hospitals, communications, water treatment and basic safety.
“The main goal of this work is to understand how a weather event impacts the power grid, and how we can better plan the grid for similar events in the future,” said Anamika Dubey, Huie-Rogers Endowed Chair and associate professor at WSU. Dubey noted that the central problem is not simply responding to an outage after it happens. Utilities must make interconnected decisions years or even decades ahead, while facing uncertainty about future weather, equipment failures, electricity demand and available funding.
To tackle that problem, the researchers created a two-stage, risk-based optimization framework. The first stage uses historical weather and outage data to build a probabilistic relationship between an extreme event and the components it may damage. For example, wind speed, direction and duration can be linked to the probability that specific transmission or distribution lines will fail. The model can also account for the location and characteristics of grid equipment, allowing it to estimate how a weather event could cascade through the network.
The second stage converts those potential failures into consequences for consumers and utilities. If several lines are damaged, the model can estimate the resulting loss of service, the number of customers affected, the duration of outages and the economic costs associated with interrupted electricity. It then compares those risks with the cost and expected performance of potential resilience measures. This cost-benefit structure allows utilities to examine whether a particular line should be reinforced, whether local generation should be added or whether multiple smaller upgrades would provide better protection than one large project.
“We were trying to assess how weather events, specifically wind events, would impact the transmission and distribution grids, and what kind of investments would make more sense if we were to reduce the associated impact,” said Abodh Poudyal, the study’s lead author and a recent WSU doctoral graduate in electrical engineering. The framework is designed to reflect different attitudes toward risk. A utility that places a high priority on avoiding even rare, catastrophic outages may choose a different investment strategy from one focused on minimizing average costs.
That flexibility is important because no single resilience solution will work everywhere. A coastal utility may face hurricanes and flooding, while a western utility may be more concerned about wildfire, drought and wind-driven damage. Mountainous regions may encounter ice storms, and densely populated areas may face severe consequences from even short outages. The model can be adapted to specific systems by using multi-year records of local weather events, equipment failures and customer outages.
Utility companies have already begun investing in stronger infrastructure, grid upgrades and distributed energy resources. However, these measures are often evaluated separately, which can make it difficult to understand how they interact. Reinforcing one line may reduce the likelihood of failure, while adding local generation may allow critical customers to continue operating when the wider network is disrupted. By examining these choices together, the WSU framework is intended to reveal trade-offs that may be missed when projects are planned in isolation.
The researchers tested and validated the approach on simulated power grids and are beginning to apply it to real-world utility data in the United States. The framework does not prescribe a universal answer or identify one upgrade as the best solution for every system. Instead, it gives planners a way to compare strategies under different weather scenarios, risk levels and budget constraints. “It’s not telling you that this is the solution that you should implement,” Dubey said. “It’s actually helping you evaluate the cost-benefit trade-off of the solution, so that you can come up with a portfolio that makes sense for your system.” Supported by the U.S. Department of Energy and the National Science Foundation CAREER Program, the work offers utilities a computational tool for turning increasingly volatile weather risks into practical, long-term grid decisions.
Subject of Research: Resilience planning for electric power systems facing extreme weather events
Article Title: Resilience-Driven Planning of Electric Power Systems Against Extreme Weather Events
Web References: https://doi.org/10.1049/gtd2.70330
References: IET Generation, Transmission & Distribution; DOI: 10.1049/gtd2.70330
Keywords: extreme weather, power grid resilience, electric utilities, transmission systems, distribution networks, risk-based optimization, grid hardening, distributed generation, climate change, computational modeling
