This National Science Foundation (NSF) Project Grant award of $200,000 to Kansas State University, under the Mathematical and Physical Sciences program (CFDA 47.049), aims to develop and validate deep-learning-enabled distributed stochastic algorithms to solve large-scale, stochastic security-constrained unit commitment problems within power systems. The project will focus on designing a holistic, three-stage, deep neural network-based machine learning approach, developing solution strategies...
This $500,000 National Science Foundation project grant funds the development of algorithms and computational tools to optimize electric power system planning and operations during extreme events such as wildfires and hurricanes. Awarded under the Engineering program (CFDA 47.041), the five-year award to the Georgia Tech Research Corporation from February 2022 to January 2027 aims to address computational challenges associated with power grid nonlinearities, uncertainties from renewable energy...
This $100,000 National Science Foundation Project Grant supports research at the University of Washington to develop a new data-driven power systems control framework with stability guarantees. Funded under the NSF Engineering program (CFDA 47.041), the research aims to design reinforcement learning algorithms for inverter-based frequency and voltage control of power grids that provide formal stability assurances. Over the two-year period from March 2022 to February 2025, university...
This $225,000 National Science Foundation (NSF) Project Grant award under the CFDA 47.041 Engineering program aims to develop a physics-informed, real-time optimal power flow model using machine learning techniques. The project seeks to address gaps in providing close to optimal solutions for power plant outputs while considering practical dynamical constraints to avoid frequency fluctuations and grid instabilities. The key scientific and engineering contributions include: (1) advancements in...
This $350,000 federal Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) aims to develop a new reduced-order dynamic modeling paradigm for accurately representing the impacts of massive distributed energy resource (DER) integration in carbon-neutral power systems. The project, awarded to Arizona State University, will leverage tools in dynamic systems, nonlinear system identification, and machine learning to create physics-based and machine...
This Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), provides $276,203 to Southern Methodist University to develop new computational techniques for solving core mathematical equations modeling large-scale power systems. Key products include fast and accurate screening techniques for high-degree contingency analysis using state-of-the-art algebraic multigrid on weighted graph Laplacians....
This Project Grant from the National Science Foundation's $200,000 Engineering program (CFDA 47.041) will fund research at the University of California, San Diego to develop a new data-driven power systems control framework with stability guarantees. The three-year award beginning March 2022 aims to design reinforcement learning algorithms for inverter-based frequency and voltage control of power grids that provide formal stability through a novel approach bridging Lyapunov control theory and...
This National Science Foundation (NSF) project grant, awarded under the Engineering program (CFDA 47.041), aims to develop novel "use-inspired" approaches for linearizing complex physical laws in order to enable accurate and computationally efficient power flow modeling and optimization. The $425,288 award will span 3 years from June 2025 to May 2028 and focus on three key thrusts: 1) deterministic optimization, 2) stochastic optimization, and 3) dynamic modeling of power systems....
This Project Grant award of $300,000 from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) supports a collaborative research project between the University of Arizona and the University of Illinois Urbana-Champaign. The project aims to develop new distributionally robust quadratic optimization models and methodologies to better integrate renewable energy sources into power systems, providing cleaner, more reliable, and cost-effective energy solutions. Key objectives...
This $393,890 National Science Foundation (NSF) Engineering program (CFDA 47.041) project grant, awarded to the University of Texas at Austin (UT Austin) on March 1, 2025, aims to advance the autonomy of power grids by developing novel machine learning and artificial intelligence-enabled strategies. The key objectives are to: Enhance decision-making speed for individual power grid agents through neural approximators that map optimization inputs to feasible outputs, eliminating the need for...