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...
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...
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 (NSF) awarded a $159,860 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to the Trustees of the Stevens Institute of Technology in Hoboken, New Jersey. This 3-year grant supports the development of efficient algorithms to rigorously quantify uncertainties in state estimation and network topology identification for smart electricity distribution systems. The project aims to enable more accurate modeling and secure, cost-effective...
Lehigh University received a $398,806 Project Grant award from the National Science Foundation on September 1, 2021 to support research titled "CAREER: COMPUTATIONAL DESIGN OF SUSTAINABLE HYDROGENATION SYSTEMS VIA A NOVEL COMBINATION OF DATA SCIENCE, OPTIMIZATION, AND AB INITIO METHODS." The award is part of the NSF's Engineering program (CFDA 47.041), which seeks to improve quality of life and economic strength through engineering research and education. Under the award, Lehigh...
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...
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 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) provides $181,004 to Purdue University to develop data-enabled and physics-informed modeling, monitoring, and optimization solutions targeting power system dynamics. The project aims to leverage synchrophasor data and machine learning to improve the understanding and stability of interconnected power grids, supporting the rapid decarbonization and deployment of flexible, distributed energy...
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...