Project Grant 2242930
Award Date
7/1/23
Completion Date
6/30/26
Dollars Obligated
$225K
Federal Agency
Division of Electrical, Communications and Cyber Systems
Federal Grant Program
47.041
Assistance Type
Project Grant
Place of Performance
Boulder, CO 80309, USA
Similar Awards
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 $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...
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 $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 $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 Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) aims to enhance electric power grid operators' situational awareness, improve dynamic model quality, and enable online controls to ensure secure power system operation with high penetration of inverter-based resources (IBRs) such as solar, wind, and battery energy storage. The $397,111 award, effective February 1, 2025 through January 31, 2030, will fund research to develop a generalized,...
This National Science Foundation (NSF) project grant, funded under CFDA 47.041 - Engineering, aims to develop novel use-inspired approaches for creating linear power flow models tailored for specific applications and operating conditions. The project's key objectives are to create certifiably optimal power flow linearizations for deterministic optimization, stochastic optimization, and dynamic modeling of power systems. The $425,288 award, effective June 1, 2025 through May 31, 2028, will enable...
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...
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 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...