Hurricanes often lead to large and long-lasting blackouts, with serious social and economic consequences. This project aims at developing models and software tools to provide guidance for the smart and preventive operation of power systems, using available weather forecast data. It is anticipated that the developed tools will better estimate the impacts of hurricanes on the electric power grid and identify operation strategies that are resilient to the damages induced by the hurricane. Ultimately, the outcomes of this project will help reduce the size and duration of power blackouts during hurricanes. The avoided blackout costs can save the U.S. economy hundreds of millions to several billion dollars each year. This is an interdisciplinary research project, where electrical engineering, civil engineering, and atmospheric science students will closely collaborate and broaden their skill sets, thus advancing their education and empowering the nation's trained workforce.

This project exploits the availability of weather forecast data to guide preventive power system operation during hurricanes. Currently, weather forecast data is not systematically integrated into the power system operation and, thus, preventive operation is not possible. The current research approaches employ an integrated framework, mainly based on physical and engineering models, which uses the hurricane forecast information to predict the component damage scenarios for the power system. The preventive operation decisions are, then, determined with stochastic optimization, through explicit modeling of the damage scenarios. The existing models have two main shortcomings: 1) they ignore the weather forecast uncertainties; and 2) they are extremely computationally demanding. There are substantial levels of inherent uncertainties in weather forecast data and component damage models, which affect both the effectiveness of the final preventive operation strategies as well as the model?s computational efficiency. Moreover, the computational time in real-time power system operation is extremely limited. The hypothesis governing this project is that a hybrid approach, which exploits the availability of data, while also relying on physical and engineering principles, can overcome the two challenges, mentioned above. First, using historical and real-time data, this project reduces uncertainties in order to improve both the solution quality and the computational needs. Second, by applying machine learning techniques on synthetic, historical, and real-time data, the project replaces computationally-demanding stochastic optimization with effective proxy deterministic models, to achieve computational tractability for real-time operation. Thus, this project will enable, for the first time, appropriate integration of uncertain weather forecast data within power system operation during hurricanes, to quickly identify effective preventive operation strategies.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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University of Utah
Salt Lake City
United States
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