This Project Grant from the National Science Foundation Division of Mathematical Sciences provides $429,158 to develop computational tools for modeling, prediction and control of distributed and reconfigurable renewable energy systems. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), key outcomes include noise-resilient identification methods for transient dynamics, stochastic models integrating statistical closure with topology-aware data, and optimal control...
This $249,980 National Science Foundation (NSF) EAGER project grant awarded to North Dakota State University (NDSU) aims to develop a novel network-based framework for analyzing and mitigating sub-synchronous oscillations in inverter-dominated power grids. The project will provide new insights into the mechanisms driving these oscillations, which can jeopardize grid stability and reliability as renewable energy sources with inverters are increasingly integrated. The framework is expected to...
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 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 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...
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...
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 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 $295,149 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) aims to address protection challenges arising from the increasing penetration of renewable energy in modern electric grids. The University of Denver is the prime recipient, and the project will run from August 1, 2024 to July 31, 2027. The project will explore novel model-driven and data-driven solutions to ensure dependable fault detection and secure relay operation for power grids...
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...