This National Science Foundation (NSF) project grant under CFDA program 47.041 "Engineering" aims to develop new distributionally robust quadratic optimization models and methods to effectively integrate highly uncertain renewable energy into power systems. The $300,000 award to the University of Illinois will fund research to: (1) develop conic reformulations for distributionally robust quadratic programming problems, (2) design efficient algorithms to solve these problems, and (3)...
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...
This three-year, $110,155 project grant from the National Science Foundation's Division of Mathematical Sciences aims to develop new mathematical methods, computer models, and algorithms for electric grid operational planning under the Mathematical and Physical Sciences program (CFDA 47.049). Specifically, the University of Chicago researchers will contribute a general methodology, including novel mathematical models, theory, and algorithms, to systematically account for non-Gaussian error...
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 Project Grant award of $240,000.00 from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) supports collaborative research to develop novel topological and graph-based modeling techniques for integrating large-scale, distributed energy resources into power grid systems. The key objectives are to create a data-adaptive graph generation module, apply topological data analysis and higher-order network models, and design deep neural...
This Project Grant award, valued at $397,111 and provided by the National Science Foundation's Engineering program (CFDA 47.041), aims to enhance electric power grid operators' situational awareness, improve dynamic model quality, and enable online controls to ensure secure power system operation with high penetration of inverter-based resources (IBRs) such as solar, wind, and battery energy storage. The key research objectives include developing a generalized observability theory for...
This three-year, $283,995 Project Grant from the National Science Foundation's Division of Mathematical Sciences will support the development of robust methods for grid operational planning that account for non-Gaussian uncertainties in renewable energy forecasts. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the grantee—Northwestern University—will work with its sub-awardee to create new mathematical models, theory, algorithms, and computer implementations to...
This three-year, $290,000 National Science Foundation project grant supports research at The Pennsylvania State University to develop new mathematical methods, computer models, and algorithms for electric grid operational planning under non-Gaussian uncertainties in renewable energy forecasts. Funded through NSF's Mathematical and Physical Sciences program (CFDA 47.049), the research directly addresses challenges in integrating intermittent renewable resources like wind and solar power into...
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...