This Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) provides $393,890 to the University of Texas at Austin to advance the autonomy of power grids. The project aims to develop novel machine learning-assisted optimization techniques to speed up decision-making by individual power grid agents, use AI and generative modeling to enhance the resilience of multi-agent power grid management, and integrate societal benefits and sustainability metrics into...
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...
The National Science Foundation (NSF) awarded a $1,200,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program to the University of Texas at Austin. The grant, running from June 1, 2024 to May 31, 2027, aims to develop theoretical frameworks and practical algorithms for learning data-driven models and control strategies in networked cyber-physical systems, with a focus on power distribution systems. Key areas of work include designing...
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 $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...
The National Science Foundation (NSF) awarded a $145,871 Project Grant under the NSF Engineering program (CFDA 47.041) to the Regents of the University of California at Riverside (UC Riverside) to develop novel data-driven control methods for the safe and secure operation of grid-edge resources (GERs) in modern power systems. The research aims to address the challenges and opportunities presented by the rapid proliferation of distributed energy resources, such as renewable generators, smart...
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 awarded a $154,573 project grant to the University of Houston System to support research titled "COLLABORATIVE RESEARCH: POWER SYSTEM FLEXIBILITY: METRIC, ASSESSMENT, AND ALGORITHM" from April 15, 2021 through March 31, 2024. The grant is part of NSF's Engineering program (CFDA 47.041), which seeks to improve quality of life and economic strength through engineering research and education. Under this award, the University of Houston will develop...
This Project Grant award of $409,927.00 from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) aims to develop novel algorithmic solutions to advance the optimal scheduling of distributed energy resources (DERs) in power distribution grids. The key objectives are to: 1) reduce data communication requirements for grid operators to orchestrate DERs by strategically identifying influential data sources, 2) leverage machine learning tools to transform distilled data for...
This $199,964 federal Project Grant awarded by the National Science Foundation (NSF) Engineering program (CFDA 47.041) will fund research at the University of California, Santa Barbara (UCSB) to address challenges and opportunities presented by the rapid proliferation of grid-edge resources (GERs) in modern power systems. The project aims to develop novel data-driven control strategies and advance the understanding of GER behavior to ensure the safe and secure operation of these distributed...