Project Grant 2505120

Award Date 5/1/25
Completion Date 4/30/28
Dollars Obligated $200K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Raleigh, NC 27695, USA
Similar Awards
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $199,996 to the College of William & Mary to develop a novel AI-surrogate enhanced cyberinfrastructure for accelerating power grid simulations. The project aims to address the urgent need for more efficient computational models to meet real-time decision-making demands as power grid simulations grow in complexity. The key innovations...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $200,000 to Oregon State University (OSU) to develop a novel AI-surrogate enhanced cyberinfrastructure for accelerating power grid simulations. The project aims to create three key innovations: (1) program-behavior analysis to identify optimal code regions for AI surrogate replacement, (2) semi-automatic AI surrogate construction...
This National Science Foundation (NSF) Engineering Directorate (CFDA 47.041) Project Grant award of $397,000.00 to North Carolina State University (NC State) aims to develop an Artificial Intelligence Engineering System Analysis Assistant (AIESAA) to automate the creation of integrated transmission-distribution grid models. Key objectives include: Leveraging advanced machine learning techniques to streamline three crucial modeling tasks: scenario classification, reduced-order model selection and...
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 National Science Foundation (NSF) CAREER grant, awarded under the Engineering program (CFDA 47.041), aims to advance the autonomy of power grids by developing fundamental theory and techniques to enhance decision speed, resilience, and societal/sustainability awareness in distributed grid management models and algorithms. The $393,890 award to the University of Texas at Austin will fund research to create novel machine learning-assisted optimizers for faster agent-level decision making, use...
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....
The University of Tennessee received a three-year, $350,000 Project Grant from the National Science Foundation's Engineering program (CFDA 47.041) to develop artificial intelligence-assisted algorithms for automatic electric power grid modeling. The university will create models of realistic U.S. electric grids under various operating scenarios involving conventional and renewable energy sources. This will help identify and mitigate risks to electric grids by significantly reducing model...
This $199,339 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program to the California State University San Marcos Corp (CSUSM) will develop novel AI and machine learning models for supervisory control of wind farm connections to the electric grid for stability monitoring. The project aims to create innovative AI/ML models that can directly analyze raw power data to enable accurate fault prediction and detection, which...
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 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 Project Grant award from the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) provides $200,000 to North Carolina State University to develop a novel AI-surrogate enhanced cyberinfrastructure for accelerating power grid simulations. The key products and services to be delivered under this 3-year award include:

  1. Program-behavior analysis to identify optimal code regions for AI surrogate replacement
  2. Semi-automatic AI surrogate construction that incorporates domain-specific physical knowledge
  3. Heterogeneous computing with multi-fidelity modeling that dynamically balances AI surrogates and traditional models across CPU/GPU resources

This research aims to transform AI surrogates from auxiliary tools into essential elements for power grid planning, in order to address the urgent need for more efficient computational models to meet real-time decision-making demands for large-scale power grid simulations. The project will also enable interdisciplinary research and education between power systems and computer science domains.

Generated 5/13/25, 5:09 AM