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 $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 Project Grant award from the National Science Foundation's (CFDA 47.049 - Mathematical and Physical Sciences) will fund research at Portland State University to develop new data-adaptive and topological modeling approaches for integrating ultra-high-dimensional distributed energy resources into power grid systems. The $111,469 award, running from July 1, 2025 to June 30, 2028, aims to transcend current power grid modeling limitations by designing novel graph generation, topological data...
The National Science Foundation awarded a $225,000 Project Grant under the Engineering (47.041) federal grant program to the University of Colorado from July 1, 2023 to June 30, 2026. The University will develop a physics-informed real-time optimal power flow model using machine learning techniques to provide close to optimal solutions for power plant outputs while considering dynamic constraints to avoid grid instabilities. Key activities include advancing techniques combining...
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 $225,000 National Science Foundation (NSF) Project Grant award under the CFDA 47.041 Engineering program aims to develop a physics-informed, real-time optimal power flow model using machine learning techniques. The project seeks to address gaps in providing close to optimal solutions for power plant outputs while considering practical dynamical constraints to avoid frequency fluctuations and grid instabilities. The key scientific and engineering contributions include: (1) advancements in...
This NSF Project Grant award of $450,000 from the Directorate for Engineering (CFDA #47.041) aims to address oscillation issues in power grids with high levels of renewable energy generation. The key efforts include: Developing scalable, computationally manageable, and linearized models to simulate power grid dynamics and the associated cyber layer with realistic impacts like data packet drops and delays. Designing a centralized damping control scheme that uses phasor measurement unit (PMU)...
This National Science Foundation (NSF) project grant, funded under CFDA 47.041 - Engineering, aims to develop novel use-inspired approaches for creating linear power flow models tailored for specific applications and operating conditions. The project's key objectives are to create certifiably optimal power flow linearizations for deterministic optimization, stochastic optimization, and dynamic modeling of power systems. The $425,288 award, effective June 1, 2025 through May 31, 2028, will enable...
This Project Grant award, titled "HYPERTRAN: HIGH-PERFORMANCE TRANSIENT STABILITY SIMULATION OF POWER SYSTEMS ON MODERN PARALLEL COMPUTING HARDWARE", was provided by the National Science Foundation (NSF) under CFDA program 47.041 - Engineering. The $239,384 award to North Carolina State University aims to develop a high-performance software framework that enables parallelization of power system transient stability simulations to leverage modern computing hardware. The key products...
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...