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 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 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) Integrative Activities (CFDA 47.083) Project Grant award of $762,500 aims to enhance the stability, resilience, and cybersecurity of the U.S. power grid. The project will acquire a real-time simulator that will allow the research team and other researchers to study power grid dynamics and performance, and develop, test, and validate new solutions to harden the grid against extreme weather events and cyber-attacks. The key products and services to be...
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,...
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...
The National Science Foundation (NSF) awarded a $159,860 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to the Trustees of the Stevens Institute of Technology in Hoboken, New Jersey. This 3-year grant supports the development of efficient algorithms to rigorously quantify uncertainties in state estimation and network topology identification for smart electricity distribution systems. The project aims to enable more accurate modeling and secure, cost-effective...
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 $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 beginning September 1, 2022 will produce the following key deliverables:
A novel stochastic hybrid system model incorporating continuous dynamics and discrete events for cyber-physical contingencies in power grids.
New computational methods for estimation and prediction, building on Wonham filtering for hidden Markov chains to enable more feasible detection of discrete changes. Analysis will include rates of convergence.
Fundamental concepts like joint observability and associated new estimation algorithms for coordinated prediction of contingencies.
A computable scheme based on Markov chain approximation for optimal stopping rules to facilitate early and rapid detection, quantitatively assessing risks of potential cascading events.
Evaluation and validation through utility data, large-scale simulations, and hardware-in-loop emulation on a microgrid to test theoretical advances.