This National Science Foundation Project Grant of $993,643 will fund the acquisition of a large-scale real-time digital simulator for cyber-physical energy systems at the University of Nevada, Reno from September 1, 2022 to August 31, 2025. The simulator will allow researchers to model power grids incorporating events like natural disasters, communication failures and cyberattacks. This equipment, called a real-time digital simulator, will enable multi-disciplinary research, education and...
This National Science Foundation (NSF) Engineering Directorate (CFDA 47.041) Project Grant award of $397,000.00 to North Carolina State University (NC State) aims to develop an Artificial Intelligence Engineering System Analysis Assistant (AIESAA) to automate the creation of integrated transmission-distribution grid models. Key objectives include: Leveraging advanced machine learning techniques to streamline three crucial modeling tasks: scenario classification, reduced-order model selection and...
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 National Science Foundation (NSF) Engineering (CFDA 47.041) project grant award to Iowa State University of Science and Technology totaling $411,904 will develop new methods to accelerate long-term electromagnetic transient (EMT) simulations of electric grids powered by renewable energy resources. The key research objectives are to: Model transmission lines using improved lumped-parameter approaches to minimize simulation errors, Engineer a specialized numerical integration algorithm to...
The National Science Foundation (NSF) awarded a $250,000 Project Grant under the Engineering program (CFDA 47.041) to the University of Vermont & State Agricultural College (UVM) to develop a generalized distributed framework for solving large-scale power grid problems. The project aims to advance the state-of-the-art in nonlinear programming, physics-inspired graph-partitioning, and combinatorial optimization to enable fast and robust simulations and optimizations of the future power...
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 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 from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), provides $276,203 to Southern Methodist University to develop new computational techniques for solving core mathematical equations modeling large-scale power systems. Key products include fast and accurate screening techniques for high-degree contingency analysis using state-of-the-art algebraic multigrid on weighted graph Laplacians....
This National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems Federal Project Grant, awarded under CFDA 47.041 Engineering program, provides $350,000 to Arizona State University to develop advanced dynamic modeling and stability assessment tools for distribution power systems with high penetration of distributed energy resources (DERs). The key objectives are to: 1) identify and model critical nonlinear dynamics missing from existing load models, 2) create...
This National Science Foundation (NSF) Project Grant award of $200,000 to Kansas State University, under the Mathematical and Physical Sciences program (CFDA 47.049), aims to develop and validate deep-learning-enabled distributed stochastic algorithms to solve large-scale, stochastic security-constrained unit commitment problems within power systems. The project will focus on designing a holistic, three-stage, deep neural network-based machine learning approach, developing solution strategies...