The National Science Foundation (NSF) Engineering Directorate awarded Brigham Young University a $397,875 Project Grant under the Engineering program (CFDA 47.041). The three-year grant, from June 1, 2023 to May 31, 2026, supports the development of new techniques for modeling cyber-physical systems to address challenges with scale and complexity in modern engineering. The project aims to transform human interaction with critical infrastructure such as interconnected energy networks through a...
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 awarded a $400,000 Project Grant to The Johns Hopkins University under the Engineering federal grant program (CFDA 47.041) for the period of June 1, 2023 through May 31, 2026. This NSF grant aims to develop new data-driven modeling and analysis techniques for cyber-physical systems through a novel combination of machine learning and physics-based approaches. Specifically, Johns Hopkins University will work to transform human interaction with complex engineered...
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 $225,000 National Science Foundation (NSF) Project Grant award under the CFDA 47.041 Engineering program aims to develop a physics-informed, real-time optimal power flow model using machine learning techniques. The project seeks to address gaps in providing close to optimal solutions for power plant outputs while considering practical dynamical constraints to avoid frequency fluctuations and grid instabilities. The key scientific and engineering contributions include: (1) advancements in...
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 award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) provides $181,004 to Purdue University to develop data-enabled and physics-informed modeling, monitoring, and optimization solutions targeting power system dynamics. The project aims to leverage synchrophasor data and machine learning to improve the understanding and stability of interconnected power grids, supporting the rapid decarbonization and deployment of flexible, distributed energy...
The National Science Foundation (NSF) Directorate for Engineering (CFDA Program 47.041) awarded a $344,126 Project Grant to the University of Wisconsin System for a 3-year research project focused on developing new mathematical methods and computational tools to enable the use of complex data formats, such as visual and thermal images, for advanced model predictive control (MPC) systems. The key objectives are to: Integrate concepts from control theory, topology, machine learning, and Bayesian...
The National Science Foundation awarded a $225,000 Project Grant under the Engineering (47.041) federal grant program to the University of Colorado from July 1, 2023 to June 30, 2026. The University will develop a physics-informed real-time optimal power flow model using machine learning techniques to provide close to optimal solutions for power plant outputs while considering dynamic constraints to avoid grid instabilities. Key activities include advancing techniques combining...
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...