Project Grant 2229378

Award Date 11/1/22
Completion Date 10/31/25
Dollars Obligated $276K
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Dallas, TX 75205, USA
Similar Awards
This $181,004 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) will develop computational tools to model, monitor, and optimize power system dynamics. The project aims to transform grid dynamic modeling, inference, and stability-enforcing solutions by leveraging synchrophasor data and advanced machine learning techniques. Key research activities include: 1) Correlating synchrophasor data to efficiently unveil power grid impulse response, 2)...
This $149,940 federal Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences will support research at Auburn University Montgomery (AUM) to develop a deeper understanding of the impact of "topological disturbances" on power grid networks. The research aims to rigorously analyze how changes to a power network's connectivity structure affect the full set of power flow solutions, leveraging the machinery of toric deformations and convex...
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...
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 three-year, $110,155 project grant from the National Science Foundation's Division of Mathematical Sciences aims to develop new mathematical methods, computer models, and algorithms for electric grid operational planning under the Mathematical and Physical Sciences program (CFDA 47.049). Specifically, the University of Chicago researchers will contribute a general methodology, including novel mathematical models, theory, and algorithms, to systematically account for non-Gaussian error...
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 National Science Foundation (NSF) Division of Mathematical Sciences Project Grant, titled "AMPS: Scalable Methods for Real-Time Estimation of Power Systems Under Uncertainty", will provide $280,000 in funding from Sep 1, 2023 to Aug 31, 2026. The project aims to develop computational methods that are scalable, exploit problem structures, and are robust to uncertainties in power system models. Key objectives include identifying influential parameters, efficiently estimating model...
This $326,900 project grant, awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) Federal Grant Program, supports the development of novel combinatorial optimization techniques for smart grids and power networks at William Marsh Rice University. The key objectives are to advance the knowledge base in microgrids and their utility within the electrical grid structure, create computationally efficient algorithms to address challenges related to...
This Project Grant award of $199,940.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program is supporting research by Rensselaer Polytechnic Institute (RPI) to develop algorithms that can quickly predict and rectify large-scale disruptions in power systems. The key objectives are: 1) Quickly and reliably detect ambient-level anomalies in power systems and distinguish them from random noise; 2) Localize any detected anomalies; and 3) Determine the...
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 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. Additionally, multi-scale graph neural networks will be developed for regression in power grid analysis. An optimal parallel-in-time integrator will also be created for dynamical power systems. Robust models incorporating uncertainties will combine surrogates from the graph neural networks with traditional power system models. The award will support the stated goals of the Mathematical and Physical Sciences program to strengthen the scientific enterprise through increasing mathematical and physical sciences knowledge and understanding major national problems.

Generated 1/6/24, 1:37 PM