Project Grant 2131175
- 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...
- The National Science Foundation awarded a $224,089 project grant to The University of North Carolina at Charlotte under the Engineering federal grant program (CFDA 47.041) for research from June 1, 2023 to May 31, 2026. The grant supports collaborative research between UNC Charlotte and Clemson University to develop graph-optimized cellular connectionism for data-driven modeling and optimization of complex systems. The universities will create mathematical tools to automatically infer graph...
- This Project Grant from the National Science Foundation's $200,000 Engineering program (CFDA 47.041) will fund research at the University of California, San Diego to develop a new data-driven power systems control framework with stability guarantees. The three-year award beginning March 2022 aims to design reinforcement learning algorithms for inverter-based frequency and voltage control of power grids that provide formal stability through a novel approach bridging Lyapunov control theory and...
- This $100,000 National Science Foundation Project Grant supports research at the University of Washington to develop a new data-driven power systems control framework with stability guarantees. Funded under the NSF Engineering program (CFDA 47.041), the research aims to design reinforcement learning algorithms for inverter-based frequency and voltage control of power grids that provide formal stability assurances. Over the two-year period from March 2022 to February 2025, university...
- The National Science Foundation (NSF) Directorate for Engineering awarded a $229,135 Project Grant to the Regents of the University of Minnesota to develop new computing methods aimed at improving the reliability, efficiency, and sustainability of the electric grid. The project will explore the use of electronic analog and hybrid computing approaches to address challenges posed by the rapid adoption of distributed energy resources, such as solar and wind power, and electric vehicle charging. Key...
- This National Science Foundation (NSF) Project Grant award of $200,000 to Kansas State University, under the Mathematical and Physical Sciences program (CFDA 47.049), aims to develop and validate deep-learning-enabled distributed stochastic algorithms to solve large-scale, stochastic security-constrained unit commitment problems within power systems. The project will focus on designing a holistic, three-stage, deep neural network-based machine learning approach, developing solution strategies...
- The National Science Foundation (NSF) awarded a $350,000 Project Grant to Northeastern University under the Engineering program (CFDA 47.041) to develop a robust and efficient state estimator that can trace the fast dynamics of inverter-based renewable energy sources. The project aims to enable effective control feedback signals and facilitate the integration of these renewable sources into power grids, resulting in cleaner, less costly, and more reliable energy delivery. Key aspects of the...
- This NSF Directorate for Engineering (CFDA 47.041) project grant award to the New Jersey Institute of Technology (NJIT) for $1,499,991 from September 1, 2023 to August 31, 2027 aims to develop a unified framework for modernizing power systems and integrating multiple renewable energy resources. The project, titled "ASCENT: From Sensors to Multiscale Digital Twin to Autonomous Operation of Resilient Electric Power Grids," will leverage advancements in control, power electronics, and...
- 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 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 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 information science and engineering to advance the use of cyberinfrastructure and accelerate innovation. Specifically, the university will develop deep learning techniques for embedded control systems to more efficiently integrate renewable energy sources like solar and wind power into the electrical grid, helping advance reliable and sustainable energy technologies.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $134.3k | 8/13/21 |