Project Grant 2515977
- 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...
- Georgia Tech Research Corporation was awarded a $399,495 project grant by the National Science Foundation Division of Information and Intelligent Systems under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The grant will fund collaborative research between U.S. and Australian researchers to develop responsible artificial intelligence frameworks, techniques, and algorithms for enabling an equitable Internet of Energy. Specifically, the researchers...
- The National Science Foundation (NSF) awarded a $200,000 project grant under the Computer and Information Science and Engineering (CISE) program to North Carolina State University (NC State). The award, titled "Collaborative Research: OAC CORE: AI-Surrogate Enhanced Heterogeneous Acceleration for Large-Scale Power Grid Simulation", aims to develop a novel AI-surrogate enhanced cyberinfrastructure to accelerate power grid simulations. The key innovations include program-behavior...
- The National Science Foundation (NSF) awarded a $200,000 Project Grant under the Computer and Information Science and Engineering (CISE) Federal Grant Program to Oregon State University (OSU). The grant, titled "COLLABORATIVE RESEARCH: OAC CORE: AI-SURROGATE ENHANCED HETEROGENEOUS ACCELERATION FOR LARGE-SCALE POWER GRID SIMULATION", aims to develop a novel AI-surrogate enhanced cyberinfrastructure to accelerate power grid simulations. Key innovations include program-behavior analysis...
- This $500,000 National Science Foundation (NSF) CAREER award, under the Engineering program (CFDA 47.041), aims to improve the computational efficiency of economics-driven transmission planning for electric power systems by up to three orders of magnitude. The project, awarded to the University of Missouri System's Missouri University of Science & Technology, will develop a multi-faceted framework that integrates innovations in modeling, simulation, computing, and design to transform lengthy...
- This Project Grant award from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), provides $120,000.00 to the University of Georgia Research Foundation (UGA Research Foundation) to develop deep-learning-enabled distributed optimization algorithms to enhance power system operations with renewable energy integration. The key objectives of the project are: (i) designing a holistic, three-stage, deep neural...
- This $600,000 federal Project Grant award, issued by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program, aims to develop a data center control framework that can regulate power consumption in artificial intelligence (AI) data centers while providing performance guarantees to users. The key objectives are to: Design AI data center planning and runtime optimization policies that balance power grid and carbon constraints with user...
- This National Science Foundation (NSF) CAREER project award under CFDA 47.041 (Engineering) aims to advance the autonomy of power grids by developing fundamental theory and strategies to enhance decision speed, resilience, and societal/sustainability awareness of distributed grid management models and algorithms. The $500,000 award, effective from March 1, 2025 to February 28, 2030, supports the University of Texas at Austin in addressing three critical research questions: leveraging agent...
- This Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) provides $299,999 in funding to the Texas A&M Engineering Experiment Station (Tees) to develop novel dynamic grid optimization algorithms and modeling tools to effectively accommodate high penetration of renewable energy and ensure reliable grid operation. The research aims to address the critical challenges of uncertainty and stability arising from the...
- 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...
The National Science Foundation (NSF) awarded a $150,000 Early-Concept Grants for Exploratory Research (EAGER) grant under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) to Georgia Tech Research Corporation. The funding aims to translate the AI4OPT Institute's foundational advances in AI-enabled optimization methods for power grid operations into a commercially-viable AI-assisted platform. In collaboration with Southern Company, the project seeks to integrate innovations in trustworthy optimization learning, temporal fusion transformers, scenario generation, and stochastic optimization for high-dimensional time series forecasting. The platform is expected to produce tools for improved unit commitment, near-optimal market clearing algorithms, and real-time risk management simulators to evaluate system-level, asset-level, and financial risk for power system operators, generators, and energy traders. The award period runs from August 1, 2025 to July 31, 2026.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $150.0k | 8/5/25 |