Project Grant 2331776
- This $400,000 Project Grant awarded by the National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems aims to revolutionize the design of learning-enabled, safety-critical systems, with a focus on power systems. The project, titled "COLLABORATIVE RESEARCH: SLES: SAFETY UNDER DISTRIBUTIONAL SHIFT IN LEARNING-ENABLED POWER SYSTEMS", will develop proactive, antifragile systems that can anticipate and adapt to changes, utilize multi-agent systems for...
- This $199,964 federal Project Grant awarded by the National Science Foundation (NSF) Engineering program (CFDA 47.041) will fund research at the University of California, Santa Barbara (UCSB) to address challenges and opportunities presented by the rapid proliferation of grid-edge resources (GERs) in modern power systems. The project aims to develop novel data-driven control strategies and advance the understanding of GER behavior to ensure the safe and secure operation of these distributed...
- This three-year Project Grant award of $470,000 from the National Science Foundation's (NSF) Division of Electrical, Communications and Cyber Systems, administered under the Engineering program (CFDA 47.041), supports fundamental research on network vulnerability and resilience in large-scale interconnected dynamical systems. The research, conducted by the University of California, Santa Barbara beginning October 1, 2025 through September 30, 2028, aims to produce mathematical frameworks and...
- The National Science Foundation (NSF) awarded a $484,965 project grant under the Engineering (CFDA 47.041) program to New York University (NYU) to develop transformative concepts and methodologies to enhance situational awareness of electric power distribution systems. The project aims to address challenges in integrating distributed renewable energy generation by enabling real-time tracking of distribution system operating states. Key objectives include learning-based continuous-time system...
- The National Science Foundation (NSF) awarded a $145,871 Project Grant under the NSF Engineering program (CFDA 47.041) to the Regents of the University of California at Riverside (UC Riverside) to develop novel data-driven control methods for the safe and secure operation of grid-edge resources (GERs) in modern power systems. The research aims to address the challenges and opportunities presented by the rapid proliferation of distributed energy resources, such as renewable generators, smart...
- 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...
- 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...
- The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded a $500,000 Project Grant to the University of California, Berkeley to support research titled "CAREER: EFFICIENT COMPUTATIONAL METHODS FOR NONLINEAR OPTIMIZATION AND MACHINE LEARNING PROBLEMS WITH APPLICATIONS TO POWER SYSTEMS" from January 15, 2021 through December 31, 2025. The grant funding will support the development of efficient computational methods for solving nonlinear optimization...
- This $200,000 National Science Foundation project grant supports the development of data-driven power systems control with stability guarantees. Funded through the NSF Engineering program (CFDA 47.041), the award to Carnegie Mellon University will support three thrusts of collaborative research over a 30-month period ending February 2025. The research aims to design a new framework integrating reinforcement learning algorithms with Lyapunov stability theory to provide stability guarantees for...
This $400,000 Project Grant awarded by the National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems under CFDA No. 47.041 Engineering aims to revolutionize the design of learning-enabled, safety-critical systems with a focus on power systems. The project at the University of California, Berkeley will introduce the concept of "antifragility" to promote system enhancement through change and uncertainty, rather than perceiving them as detriments. Key research thrusts include developing proactive, antifragile systems that adapt to distributional shifts, leveraging multi-agent systems for cooperative decision-making, and applying advanced techniques for system validation and stress testing. This research seeks to enhance the resilience and reliability of critical power system infrastructure, while also fostering cross-disciplinary dialogue on safe decision-making and STEM outreach. No sub-awards are planned for this award, which runs from September 1, 2023 to August 31, 2026.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $400.0k | 8/17/23 |