Project Grant 2229012
- 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 $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...
- The National Science Foundation (NSF) awarded a $500,000 Project Grant under the Engineering program (CFDA 47.041) to the University of Texas at Austin. The 5-year award, effective March 1, 2025, aims to advance the autonomy of power grids by developing fundamental theory to enhance decision speed, resilience, and societal/sustainability awareness of distributed grid management models and algorithms. The project will leverage machine learning and artificial intelligence to rapidly solve...
- 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....
- The National Science Foundation (NSF) awarded a $240,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the Regents of the University of California at Riverside. The grant, with a performance period from July 1, 2025 to June 30, 2028, will fund collaborative research to develop innovative topological data analysis techniques and higher-order network models. These models aim to improve the integration and control of ultra-high-dimensional distributed energy...
- 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 $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...
- The National Science Foundation (NSF) awarded a $111,469 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to Portland State University. The grant, titled "COLLABORATIVE RESEARCH: AMPS: SIMPLICIAL-TOPOLOGICAL MODELING FOR FLEXIBLE INTEGRATION OF ULTRA-HIGH-DIMENSIONAL DISTRIBUTED ENERGY RESOURCES," aims to develop advanced graph generation, topological data analysis, and deep learning techniques to model and control wide-area power transmission networks...
- 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 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...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $150,000 Project Grant to the University of Arizona, doing business as the Arizona Board of Regents, for a 2-year collaborative research project titled "AMPS: Rare Events in Power Systems: Novel Mathematics, Statistics and Algorithms." The project aims to build a comprehensive theoretical and algorithmic framework for applying artificial intelligence (AI) and machine learning (ML) techniques to detect, track, forecast, and mitigate extreme and rare events in power systems. Key technical objectives include developing physics-informed statistical models, computational inference methods for extreme events, and learning algorithms to quantify model uncertainties. This work will contribute to enhancing the resilience of critical power infrastructure by enabling early detection and mitigation of rare but consequential cascading failures.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $150.0k | 7/26/23 |