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 National Science Foundation (NSF) Project Grant award, funded under the Engineering program (CFDA 47.041), aims to advance the autonomy of power grids by developing fundamental theory and innovative strategies to enhance decision-making 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 support research focused on three critical areas: leveraging agent-level autonomy to...
This Project Grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering federal grant program (CFDA 47.041), provides $480,172 to develop modeling and experimentation frameworks for studying energy dynamics in modern power systems and power electronics. The University of Texas at Austin, through its parent organization the University of Texas System, will create equivalent circuit models for multidisciplinary systems incorporating...
The National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems awarded the University of Texas at Austin a $250,000 Project Grant under the NSF Engineering program (CFDA 47.041) to develop novel learning-based approaches for estimating the flexibility amount of grid edge resources (GERs) and designing equitable resource coordination and management methods. The project aims to transform the management of flexible energy resources in distribution electricity...
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...
The National Science Foundation awarded a $225,000 Project Grant under the Engineering (47.041) federal grant program to the University of Colorado from July 1, 2023 to June 30, 2026. The University will develop a physics-informed real-time optimal power flow model using machine learning techniques to provide close to optimal solutions for power plant outputs while considering dynamic constraints to avoid grid instabilities. Key activities include advancing techniques combining...
This National Science Foundation (NSF) Project Grant award in the Engineering program (CFDA 47.041) provides $425,288 to the University of Wisconsin - Madison to develop novel use-inspired approaches for power flow modeling and optimization. The research aims to bridge the gap between modeling and real-world applications by creating tailored linear power flow models for specific tasks such as bulk system dispatch, distributed energy resource management, and dynamic modeling. By incorporating...
This Project Grant award of $1,233,079.00 from the National Science Foundation (NSF) Engineering Directorate (CFDA 47.041) aims to develop a comprehensive theoretical framework for modeling, designing, sensing, and controlling the post-fault stability of future power systems with varying levels of inverter-based resources and synchronous generators. The key products and services to be delivered under this grant include: Establishing the theoretical foundations of energy functions for...
This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) provides $181,004 to Purdue University to develop data-enabled and physics-informed modeling, monitoring, and optimization solutions targeting power system dynamics. The project aims to leverage synchrophasor data and machine learning to improve the understanding and stability of interconnected power grids, supporting the rapid decarbonization and deployment of flexible, distributed energy...