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...
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 University of North Carolina at Charlotte will deliver collaborative research project services under a $249,929 Project Grant award from the National Science Foundation. The grant is part of NSF's Computer and Information Science and Engineering program (CFDA 47.070), which supports investigator-initiated research and education in computing, communications, and information science and engineering. Specifically, the University will conduct research on "Timely Computing and Learning...
The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded a $217,276 project grant to The University of North Carolina at Charlotte to support research titled "COLLABORATIVE RESEARCH: LEARNING-BASED SCALABLE PREDICTIVE CONTROL STRATEGIES FOR HETEROGENEOUS TRAFFIC NETWORKS." The two-year project beginning January 1, 2022 falls under the NSF Engineering Program (CFDA #47.041), which aims to improve quality of life and economic strength through...
The National Science Foundation (NSF) awarded a $250,000 Project Grant under the Engineering program (CFDA 47.041) to the University of Vermont & State Agricultural College (UVM) to develop a generalized distributed framework for solving large-scale power grid problems. The project aims to advance the state-of-the-art in nonlinear programming, physics-inspired graph-partitioning, and combinatorial optimization to enable fast and robust simulations and optimizations of the future power...
This $504,913 Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) supports research by the University of North Carolina at Charlotte (UNC Charlotte) on networked multi-agent systems (NMS). The project aims to develop an optimal control-communication theory to guide decision-making for both agents and network managers in NMS, such as fleets of drones, connected autonomous vehicles, or smart power grids. Key objectives include designing...
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...
The National Science Foundation (NSF) awarded a $250,000 Project Grant to Carnegie Mellon University (CMU) under the Engineering program (CFDA 47.041). The grant supports the development of a "Collaborative Research: Scalable Circuit Theoretic Framework for Large Grid Simulations and Optimizations" project. The project aims to create a generalized distributed framework for solving large-scale power grid problems that are both fast and robust, enabling transformative changes in future...
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 two-year Project Grant from the National Science Foundation's Division of Computing and Communication Foundations, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $134,320 to North Carolina A&T State University for development of scalable and reliable deep learning-driven embedded control applied in renewable energy integration. The grant supports investigator-initiated research and education in areas of computing, communications, and...