Cooperative Agreement DEEE0011377

This three-year, $283,995 Project Grant from the National Science Foundation's Division of Mathematical Sciences will support the development of robust methods for grid operational planning that account for non-Gaussian uncertainties in renewable energy forecasts. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the grantee—Northwestern University—will work with its sub-awardee to create new mathematical models, theory, algorithms, and computer implementations to...

This three-year, $110,155 project grant from the National Science Foundation's Division of Mathematical Sciences aims to develop new mathematical methods, computer models, and algorithms for electric grid operational planning under the Mathematical and Physical Sciences program (CFDA 47.049). Specifically, the University of Chicago researchers will contribute a general methodology, including novel mathematical models, theory, and algorithms, to systematically account for non-Gaussian error...

This three-year, $290,000 National Science Foundation project grant supports research at The Pennsylvania State University to develop new mathematical methods, computer models, and algorithms for electric grid operational planning under non-Gaussian uncertainties in renewable energy forecasts. Funded through NSF's Mathematical and Physical Sciences program (CFDA 47.049), the research directly addresses challenges in integrating intermittent renewable resources like wind and solar power into...

This $500,000 National Science Foundation project grant funds the development of algorithms and computational tools to optimize electric power system planning and operations during extreme events such as wildfires and hurricanes. Awarded under the Engineering program (CFDA 47.041), the five-year award to the Georgia Tech Research Corporation from February 2022 to January 2027 aims to address computational challenges associated with power grid nonlinearities, uncertainties from renewable energy...

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 Project Grant award of $1,233,079.00 from the National Science Foundation (NSF) Engineering Directorate (CFDA 47.041) aims to develop a comprehensive theoretical framework for modeling, designing, sensing, and controlling the post-fault stability of future power systems with varying levels of inverter-based resources and synchronous generators. The key products and services to be delivered under this grant include: Establishing the theoretical foundations of energy functions for...

The U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy awarded a $2,200,000 Cooperative Agreement (CFDA 81.087 Renewable Energy Research and Development) to Midcontinent Independent System Operator Inc. (MISO) on October 1, 2024. The objective of this project is to build an "Operations Uncertainty Platform" that addresses the operational needs for managing variability and uncertainty in large electric grids. This platform is intended to provide capability,...

This Cooperative Agreement award from the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy under the Renewable Energy Research and Development program (CFDA 81.087) provides $3,430,784 to the University of South Florida to develop and demonstrate advanced tools for grid operators to monitor and predict the dynamics of inverter-based resources (IBRs) in real-time. The project aims to enable accurate estimation of grid strength and inertia, dynamic state estimation,...

This five-year, $500,000 National Science Foundation project grant will support the development of new modeling and quantification techniques for interdependent power grid uncertainties at Syracuse University. Funded through NSF's Engineering Directorate under the CFDA 47.041 program, the grant aims to address challenges posed by increasing renewable energy resources and weather-related outages through hybrid stochastic models informed by machine learning, cascading failure analysis, and...

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...