Project Grant 2301938
- 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 $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 $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) 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...
- 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...
- Lehigh University was awarded a $431,508 project grant from the National Science Foundation to develop data-driven algorithms for dynamic state-estimation of modern power systems. The three-year award, issued under the NSF's Engineering program (CFDA #47.041), aims to transform dynamic state-estimators through the use of machine learning and signal processing techniques applied to available measurements. This will generate new knowledge on monitoring modern power systems and improve smart grid...
- Arizona State University was awarded a three-year $360,000 project grant from the National Science Foundation Division of Electrical, Communications and Cyber Systems under the Engineering (47.041) federal grant program. The grant aims to improve situational awareness of distributed energy resources throughout the electric power system by leveraging advanced sensors and data science methods. Specifically, the university will develop new algorithms to extract useful information from high-fidelity...
- 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...
- Arizona State University was awarded a $500,000 Project Grant from the National Science Foundation Division of Electrical, Communications and Cyber Systems. The award is part of NSF's Engineering program (CFDA 47.041) to support CAREER: FAITHFUL, REDUCIBLE, AND INVERTIBLE LEARNING IN DISTRIBUTION SYSTEM FOR POWER FLOW. Under this five-year project grant awarded February 1, 2021 with a completion date of January 31, 2026, Arizona State University will conduct research at its Tempe, Arizona campus...
- The National Science Foundation awarded a $280,000 Project Grant to the University of Texas at Austin under the Engineering (47.041) federal grant program. The award will support research and development of grid-forming inverter technologies to advance the energy transition and increase renewable energy grid integration. Key deliverables include optimization of grid-forming voltage control loops; development of advanced functionalities for three-port microinverters integrating solar, storage and...
New Mexico State University (NMSU), through its Office of Sponsored Programs, has been awarded a $200,000 project grant from the National Science Foundation to develop machine learning solutions for operating power distribution grids with high levels of inverter-based energy resources. The two-year award, made under the Engineering (47.041) federal grant program, will support research objectives including developing sample-efficient hybrid learning techniques combining deep reinforcement learning with simplified grid models; graph reinforcement learning for real-time network reconfiguration; and distributed learning and control at the grid edge. The proposed framework aims to provide scalable, learning-based control for distribution systems integrating massive inverter-based resources like solar and wind power. This work should contribute to a more resilient and sustainable power grid through innovative operational approaches addressing challenges from high renewable penetration.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $200.0k | 6/1/23 |