Project Grant 2319996
Award Date
5/1/24
Completion Date
4/30/27
Dollars Obligated
$200K
Federal Agency
Division of Mathematical Sciences
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Troy, NY, USA
Similar Awards
This $200,000 Project Grant from the National Science Foundation Division of Mathematical Sciences will support research at Wayne State University to develop stochastic algorithms for early detection and risk prediction of hidden contingencies in modern power systems. Funded under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), which aims to strengthen the scientific enterprise through increasing knowledge and understanding of major national problems, this three-year award...
This $223,406 National Science Foundation project grant, awarded under the Engineering program (CFDA 47.041), will fund the development of novel optimization models and algorithms for the operation of future electric power systems at the Massachusetts Institute of Technology from January 2023 through February 2024. Specifically, the principal investigator will create efficient and robust algorithms for optimizing power flow and network topology to support the integration of renewable, demand...
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...
The National Science Foundation (NSF) awarded a $199,940 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to the University of Georgia Research Foundation, Inc. (UGA Research Foundation) for the project titled "AMPS: Scalable Graph Models for Anomaly Detection in Large-Scale Smart Grids." The project aims to develop advanced, computationally efficient data-driven algorithms for real-time anomaly detection and diagnosis in smart power grids, which are...
This NSF Engineering program Project Grant, awarded to Rochester Institute of Technology (RIT), aims to develop novel modeling and optimization approaches to enable more efficient utilization of renewable energy and energy storage resources (ESRs) for low-carbon power grid operation. The $395,783 award, with a funding period from July 1, 2024 to June 30, 2029, will support research to create ESR market participation models, formulation tightening techniques, and optimization methods to address...
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) Division of Mathematical Sciences Project Grant, with CFDA number 47.049, aims to build a comprehensive theoretical and algorithmic framework using artificial intelligence and machine learning (AI/ML) for detecting, tracking, forecasting, and mitigating extreme and rare but consequential events in power systems. The 2-year, $150,000 award to The Leland Stanford Junior University (Stanford University) will fund research in three areas: (A) physics-informed...
This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) provides $199,998 to Michigan Technological University to develop advanced real-time signal processing capabilities for fault diagnosis in next-generation power grids. The project aims to create a novel wavelet transform theory and machine learning-based framework to rapidly detect, classify, and predict faults in complex AC and DC power systems, including those with high penetration of...
This $149,178 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to improve power system surveillance through convex relaxation techniques. Funded under the American Rescue Plan Act, the grant supports research at the University Corporation to strengthen the dependability and robustness of the electric power grid. The project will develop more efficient convex relaxation-based approaches to power system state estimation to provide...
This National Science Foundation project grant of $300,000 supports research at the University of Tulsa to develop a decentralized artificial intelligence framework for distribution system fault detection, identification, and power restoration. Under the NSF Engineering program (CFDA 47.041), the university will create a graph capsule network to recognize spatial and temporal patterns in distribution systems and identify fault types and locations. Researchers will also devise a novel...