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) 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 $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 $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 $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 $300,000 National Science Foundation project grant supports research into distributed optimization-based control of large-scale nonlinear systems with uncertainties from 2022-2025. Funded under the NSF Engineering program (CFDA 47.041), the award to the University of California, San Diego will advance mathematical foundations for distributed optimization algorithms robust to uncertainties. Researchers will design tracking controllers for local systems to follow optimization-derived...
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 five-year $500,000 Project Grant from the National Science Foundation's Engineering program (CFDA 47.041) funds research at the University of California, San Diego to develop next-generation integrated hybrid DC-DC converters. The university will conduct engineering research from February 2021 through January 2026 to foster innovation in converter design for future power systems relying more heavily on direct current transmission. As the NSF Engineering program seeks to improve quality of...
This three-year $240,000 Project Grant from the National Science Foundation's Division of Electrical, Communications and Cyber Systems, under the Engineering program (CFDA 47.041), will support research to advance the practical application of distributed learning-enabled control systems. The University of Pennsylvania and Columbia University will collaborate on developing foundational theory and integrated approaches for scalable and communication-efficient distributed control. This includes...
This five-year $550,000 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) supports research at the University of California, San Diego (UCSD) to develop performance-guaranteed learning and control algorithms for real-world energy systems. The research aims to address three key challenges: (1) incorporating control-theoretic tools into reinforcement learning for stable and optimal distribution grid voltage regulation; (2) designing operator learning to accelerate computationally expensive building control applications; and (3) bridging the gap in deploying learning-based control algorithms in real-world settings with time-varying network topologies and perturbed sensor inputs. The award will fund the development of novel algorithms that will be validated on UCSD's NSF-funded DERCONNECT testbed. Additionally, the project includes a comprehensive curriculum to train the next generation of engineers and researchers in the interdisciplinary skills of energy, control, and artificial intelligence.