Project Grant 2243204
- The University of Tennessee received a $500,000 Project Grant award from the National Science Foundation Division of Electrical, Communications and Cyber Systems on March 1, 2021 to support research towards enhanced grid robustness through discrete controls on emerging power technologies. The award is part of the NSF Directorate for Engineering's Engineering program (CFDA 47.041), which aims to foster innovation in engineering research and education to improve quality of life and economic...
- This National Science Foundation (NSF) Engineering program project grant to the University of Tennessee aims to develop a unified multi-timescale modeling and simulation framework for analyzing the complex dynamics of inverter-dense power grids integrating renewable energy resources. The $350,257 award, with a project period from Mar 1, 2024 to Feb 28, 2027, will establish heterogeneous multiscale methods and semi-analytical solution techniques to enable accurate and efficient power system...
- The University of Texas at Austin received a $350,000 Project Grant award from the National Science Foundation Division of Electrical, Communications and Cyber Systems on September 1, 2021 to support work on "LEARNING-ENABLED MODELING, MONITORING, AND DECISION MAKING FOR DISTRIBUTION GRIDS." This award is part of NSF's Engineering (CFDA 47.041) program, which aims to improve quality of life and economic strength through engineering research and education. Specifically, the University...
- This NSF CAREER award of $500,000 funds fundamental research aimed at advancing the autonomy and operational efficiency of modern power grids through innovative distributed management strategies. Awarded by the National Science Foundation's Division of Electrical, Communications and Cyber Systems under the Engineering program (CFDA 47.041), the project is being conducted by the University of Texas at Austin from March 1, 2025, through February 28, 2030. The research will develop machine learning...
- This Project Grant from the National Science Foundation's $191,580 Computer and Information Science and Engineering program will support the development of a cyber resilient 5G-enabled virtual power system to address growing power demands. Tennessee State University will implement a pole-mounted solar and battery system with smart controllers to remotely control and optimize performance as a virtual power plant. A secure 5G communication protocol will enable data sharing between photovoltaic,...
- 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 $260,000 National Science Foundation project grant will fund the development of data-enabled modeling, monitoring, and optimization algorithms targeting power system dynamics from 2022-2025. The University of Texas at Austin, through its parent organization the University of Texas System, will receive funding under the NSF Engineering program (CFDA 47.041) to correlate synchrophasor data and develop Gaussian process and stability-aware optimal power flow tools. Key outcomes will include...
- 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 $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 federal Project Grant award, totaling $199,996 and provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, supports the development of a novel AI-surrogate enhanced cyberinfrastructure to accelerate power grid simulations. The key innovations being delivered under this 3-year project (5/1/2025 - 4/30/2028) include: (1) program-behavior analysis to identify optimal code regions for AI surrogate replacement, (2) semi-automatic AI...
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 development time and considering more operating conditions. Key activities include deploying a multi-stage approach combining physics-based and AI methods to convert generator dispatch scenarios to full power system cases. The university will also develop a data-driven problem discovery algorithm with human intervention assistance. Undergraduate and graduate students from underrepresented groups will be involved. Outreach to K-12 students will provide opportunities to learn about potential AI applications in real power grids.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $350.0k | 6/1/23 |