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...
This Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) aims to enhance electric power grid operators' situational awareness, improve dynamic model quality, and enable online controls to ensure secure power system operation with high penetration of inverter-based resources (IBRs) such as solar, wind, and battery energy storage. The $397,111 award, effective February 1, 2025 through January 31, 2030, will fund research to develop a generalized,...
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 National Science Foundation (NSF) CAREER award, under the Engineering (CFDA 47.041) program, provides $394,768 to North Carolina State University (NC State) from October 1, 2024 to August 31, 2029. The project aims to develop a unified framework for modeling, simulating, and analyzing power system dynamics with large-scale integration of inverter-based resources (IBRs), which challenge existing approaches. Key objectives include: 1) establishing a symbolic framework for formulating...
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 $260,000 National Science Foundation project grant will fund the development of data-enabled modeling, monitoring, and optimization algorithms targeting power system dynamics from 2022-2025. The University of Texas at Austin, through its parent organization the University of Texas System, will receive funding under the NSF Engineering program (CFDA 47.041) to correlate synchrophasor data and develop Gaussian process and stability-aware optimal power flow tools. Key outcomes will include...
This Project Grant award from the National Science Foundation (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) program provides $365,897 to the Regents of the University of Michigan - University of Michigan-Dearborn to enhance grid reliability and stability through the integration of distributed energy resources. Key goals include developing fundamental knowledge to assess real-world power grid reliability, novel control algorithms to improve grid stability, and a shared platform for...
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...
This $270,000 Project Grant from the National Science Foundation's Engineering program (CFDA 47.041) supports the development of grid-forming inverter technologies to advance the energy transition and increase renewable energy grid integration. A joint team from the University of Texas at Austin and the University of Central Florida will optimize grid-forming voltage control, develop advanced three-port microinverters with black start and islanded operation capabilities, and design innovative...