Project Grant 2420974
Award Date
5/1/25
Completion Date
4/30/28
Dollars Obligated
$304K
Federal Agency
Division of Mathematical Sciences
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Buffalo, NY 14228, USA
Similar Awards
This $350,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research at the University of California, Santa Barbara (UCSB) to develop new tools for designing efficient market policies that maximize the benefits of battery energy storage systems (BESS) and incentivize BESS operators to provide valuable grid services. The project will create stochastic modeling frameworks to address the short-term operation of...
This $181,004 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) will develop computational tools to model, monitor, and optimize power system dynamics. The project aims to transform grid dynamic modeling, inference, and stability-enforcing solutions by leveraging synchrophasor data and advanced machine learning techniques. Key research activities include: 1) Correlating synchrophasor data to efficiently unveil power grid impulse response, 2)...
This NSF Engineering program Project Grant, awarded to Rochester Institute of Technology (RIT), aims to develop novel modeling and optimization approaches to enable more efficient utilization of renewable energy and energy storage resources (ESRs) for low-carbon power grid operation. The $395,783 award, with a funding period from July 1, 2024 to June 30, 2029, will support research to create ESR market participation models, formulation tightening techniques, and optimization methods to address...
The National Science Foundation (NSF) awarded a $300,000 Project Grant to Rensselaer Polytechnic Institute (RPI) under the Mathematical and Physical Sciences (CFDA 47.049) program. The grant will fund a 3-year project titled "AMPS: Mathematical Foundations of Market Operations with Renewable Bidders." The project aims to develop the mathematical foundations for electricity markets that allow renewable energy generators to bid risk-adjusted cost curves, instead of acting as price...
This five-year, $500,000 project grant from the National Science Foundation's Engineering program (CFDA 47.041) will support research and education activities aimed at improving electricity market transparency and efficiency. The grantee, North Carolina State University, will advance understanding of pricing challenges arising from non-convex generating unit characteristics. Analytical frameworks will be developed to evaluate and adapt evolving market mechanisms. Game-theoretic and data-driven...
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 $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 National Science Foundation (NSF) Engineering program (CFDA 47.041) Career award of $500,000 provides funding to the University of Missouri System's Missouri University of Science & Technology (Missouri S&T) from September 1, 2024 to August 31, 2029. The project aims to improve the computational efficiency of economics-driven transmission planning for electric power systems by up to three orders of magnitude. This will enable more agile power grid planning to adapt to the rapidly...
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 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...