This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $199,996 to the College of William & Mary to develop a novel AI-surrogate enhanced cyberinfrastructure for accelerating power grid simulations. The project aims to address the urgent need for more efficient computational models to meet real-time decision-making demands as power grid simulations grow in complexity. The key innovations...
This Project Grant award from the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) provides $200,000 to North Carolina State University to develop a novel AI-surrogate enhanced cyberinfrastructure for accelerating power grid simulations. The key products and services to be delivered under this 3-year award include: Program-behavior analysis to identify optimal code regions for AI surrogate replacement Semi-automatic AI surrogate construction that...
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 National Science Foundation (NSF) Engineering Directorate (CFDA 47.041) Project Grant award of $397,000.00 to North Carolina State University (NC State) aims to develop an Artificial Intelligence Engineering System Analysis Assistant (AIESAA) to automate the creation of integrated transmission-distribution grid models. Key objectives include: Leveraging advanced machine learning techniques to streamline three crucial modeling tasks: scenario classification, reduced-order model selection and...
This National Science Foundation (NSF) CAREER grant, awarded under the Engineering program (CFDA 47.041), aims to advance the autonomy of power grids by developing fundamental theory and techniques to enhance decision speed, resilience, and societal/sustainability awareness in distributed grid management models and algorithms. The $393,890 award to the University of Texas at Austin will fund research to create novel machine learning-assisted optimizers for faster agent-level decision making, use...
This National Science Foundation (NSF) Engineering program (CFDA 47.041) Career award of $500,000 provides funding to the University of Missouri System's Missouri University of Science & Technology (Missouri S&T) from September 1, 2024 to August 31, 2029. The project aims to improve the computational efficiency of economics-driven transmission planning for electric power systems by up to three orders of magnitude. This will enable more agile power grid planning to adapt to the rapidly...
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 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...
The National Science Foundation (NSF) awarded a $250,000 Project Grant to Carnegie Mellon University (CMU) under the Engineering program (CFDA 47.041). The grant supports the development of a "Collaborative Research: Scalable Circuit Theoretic Framework for Large Grid Simulations and Optimizations" project. The project aims to create a generalized distributed framework for solving large-scale power grid problems that are both fast and robust, enabling transformative changes in future...
This $420,000 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will develop a novel AI surrogate cyberinfrastructure for large-scale spatiotemporal simulations of coastal circulation. The project aims to create AI-driven tools to generate coastal ocean current simulations with faster speed and lower energy consumption compared to traditional numerical models. Key research deliverables include...