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...
The National Science Foundation (NSF) awarded a $160,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to the Old Dominion University Research Foundation (ODURF). The grant, titled "Collaborative Research: AMPS - Uncertainty Quantification of State Estimation and Topology Identification in Smart Electricity Distribution Systems", aims to develop efficient numerical algorithms that rigorously quantify uncertainties and leverage prior information to...
The National Science Foundation (NSF) awarded a $350,000 Project Grant to Northeastern University under the Engineering program (CFDA 47.041) to develop a robust and efficient state estimator that can trace the fast dynamics of inverter-based renewable energy sources. The project aims to enable effective control feedback signals and facilitate the integration of these renewable sources into power grids, resulting in cleaner, less costly, and more reliable energy delivery. Key aspects of the...
The National Science Foundation (NSF) awarded a $418,561 federal Project Grant to The Trustees of the Stevens Institute of Technology under the Engineering program (CFDA 47.041). The 5-year project, titled "CAREER: Evaluating Cooperative Intelligence in Connected Communities", is exploring how groups of buildings can autonomously share and manage distributed energy resources such as solar panels, batteries, and generators to enhance energy efficiency and sustainability. Through a...
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) provided a $400,000 Project Grant from its Engineering program (CFDA 47.041) to Tufts University for a 3-year collaborative research effort to develop new techniques for modeling complex cyber-physical systems. The project aims to combine data-driven machine learning approaches with physics-based modeling to create abstract yet quantitative models that can improve human interaction with engineered systems, including critical infrastructure like energy...
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...
The National Science Foundation (NSF) awarded a $1,200,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program to the University of Texas at Austin. The grant, running from June 1, 2024 to May 31, 2027, aims to develop theoretical frameworks and practical algorithms for learning data-driven models and control strategies in networked cyber-physical systems, with a focus on power distribution systems. Key areas of work include designing...
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...