The National Science Foundation (NSF) awarded a $250,000 Project Grant under the Engineering program (CFDA 47.041) to the University of Vermont & State Agricultural College (UVM) to develop a generalized distributed framework for solving large-scale power grid problems. The project aims to advance the state-of-the-art in nonlinear programming, physics-inspired graph-partitioning, and combinatorial optimization to enable fast and robust simulations and optimizations of the future power...
This $240,000 Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) Federal Grant Program supports collaborative research on developing advanced topological modeling and machine learning techniques for integrating ultra-high-dimensional distributed energy resources into wide-area power transmission networks. The project aims to transcend the limitations of conventional grid topology models by creating a data-adaptive graph...
This $326,900 project grant, awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) Federal Grant Program, supports the development of novel combinatorial optimization techniques for smart grids and power networks at William Marsh Rice University. The key objectives are to advance the knowledge base in microgrids and their utility within the electrical grid structure, create computationally efficient algorithms to address challenges related to...
This $223,406 National Science Foundation project grant, awarded under the Engineering program (CFDA 47.041), will fund the development of novel optimization models and algorithms for the operation of future electric power systems at the Massachusetts Institute of Technology from January 2023 through February 2024. Specifically, the principal investigator will create efficient and robust algorithms for optimizing power flow and network topology to support the integration of renewable, demand...
The National Science Foundation awarded a $225,000 Project Grant under the Engineering (47.041) federal grant program to the University of Colorado from July 1, 2023 to June 30, 2026. The University will develop a physics-informed real-time optimal power flow model using machine learning techniques to provide close to optimal solutions for power plant outputs while considering dynamic constraints to avoid grid instabilities. Key activities include advancing techniques combining...
This Project Grant award from the National Science Foundation's (CFDA 47.049 - Mathematical and Physical Sciences) will fund research at Portland State University to develop new data-adaptive and topological modeling approaches for integrating ultra-high-dimensional distributed energy resources into power grid systems. The $111,469 award, running from July 1, 2025 to June 30, 2028, aims to transcend current power grid modeling limitations by designing novel graph generation, topological data...
This $200,000 National Science Foundation project grant supports the development of data-driven power systems control with stability guarantees. Funded through the NSF Engineering program (CFDA 47.041), the award to Carnegie Mellon University will support three thrusts of collaborative research over a 30-month period ending February 2025. The research aims to design a new framework integrating reinforcement learning algorithms with Lyapunov stability theory to provide stability guarantees for...
The National Science Foundation (NSF) awarded a $267,323 Project Grant to the CAL Poly Corporation at California Polytechnic State University, San Luis Obispo. The grant, funded through the NSF Directorate for Engineering (CFDA 47.041), supports the development of new computing methods to improve the reliability, efficiency, and sustainability of the electric power grid. Specifically, the project team will research electronic analog and hybrid computing approaches to address challenges...
This $360,000 National Science Foundation project grant supports research to advance graph signal processing techniques for electric power distribution system monitoring and control from July 2022 through June 2025. Funded under the NSF Engineering program (CFDA 47.041), the awardee Cornell University will develop a novel mathematical approach incorporating physical grid modeling into machine learning algorithms. The approach interprets system states as graph signals to extract features...
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...