Project Grant 2339956
- The National Science Foundation (NSF) awarded a $500,000 CAREER grant to the Texas A&M Engineering Experiment Station (Tees) to develop a novel framework for transmission expansion planning (TEP) of large-scale electric grids with high penetration of renewable energy resources. This 5-year project, under the NSF Engineering program (CFDA 47.041), aims to address computational and modeling challenges in designing transmission networks to enable greater integration of wind, solar, and...
- This National Science Foundation (NSF) CAREER project award under CFDA 47.041 (Engineering) aims to advance the autonomy of power grids by developing fundamental theory and strategies to enhance decision speed, resilience, and societal/sustainability awareness of distributed grid management models and algorithms. The $500,000 award, effective from March 1, 2025 to February 28, 2030, supports the University of Texas at Austin in addressing three critical research questions: leveraging agent...
- This $500,000 National Science Foundation project grant funds the development of algorithms and computational tools to optimize electric power system planning and operations during extreme events such as wildfires and hurricanes. Awarded under the Engineering program (CFDA 47.041), the five-year award to the Georgia Tech Research Corporation from February 2022 to January 2027 aims to address computational challenges associated with power grid nonlinearities, uncertainties from renewable energy...
- This National Science Foundation (NSF) Faculty Early Career Development (CAREER) Program grant, under CFDA 47.041 Engineering, aims to develop energy engineering solutions that incorporate the preferences and needs of diverse residential electricity consumers. The $500,000 award to Arizona State University, received on Aug 1, 2024, will fund research to enable the co-management of utility-owned and customer-owned distributed energy assets using artificial intelligence algorithms. The project...
- The National Science Foundation (NSF) awarded a $150,000 Early-Concept Grants for Exploratory Research (EAGER) grant under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) to Georgia Tech Research Corporation. The funding aims to translate the AI4OPT Institute's foundational advances in AI-enabled optimization methods for power grid operations into a commercially-viable AI-assisted platform. In collaboration with Southern Company, the project...
- This $349,969 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of advanced algorithms and data analysis methods to enhance the monitoring, control, and overall performance of modern power distribution systems. Specifically, the project aims to integrate and analyze heterogeneous data from power grid infrastructure, such as advanced metering, supervisory control, and micro-phasor measurement systems,...
- 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) 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 award of $111,469, provided by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049), aims to develop advanced modeling and analysis techniques for integrating ultra-high-dimensional distributed energy resources into wide-area power transmission networks. The project at Portland State University will create a data-adaptive graph generation module, apply topological data analysis with multiple filtrations, and develop higher-order...
- This five-year, $500,000 National Science Foundation project grant will support research at Arizona State University to develop innovative solutions for time-synchronized estimation in power systems. Funded through NSF's Engineering program (CFDA 47.041), this CAREER award reflects the agency's mission to advance fundamental engineering research and education. The grantee will create new mathematical techniques in convex programming, interval-theoretic learning, and distributed optimization to...
This $500,000 National Science Foundation (NSF) CAREER award, under the Engineering program (CFDA 47.041), aims to improve the computational efficiency of economics-driven transmission planning for electric power systems by up to three orders of magnitude. The project, awarded to the University of Missouri System's Missouri University of Science & Technology, will develop a multi-faceted framework that integrates innovations in modeling, simulation, computing, and design to transform lengthy transmission planning processes into an agile, responsive system. Key research focuses include network reduction, decomposition, GPU computing, AI-based transmission option design, and parametric analysis-based refinement. The project seeks to enable more effective transmission expansion strategies, facilitate cross-sector infrastructure integration, and raise awareness of electric transmission systems' critical role in clean energy integration and climate change mitigation. The award period runs from September 1, 2024 to August 31, 2029.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $106.0k | 9/5/24 | ||
| Not listed | $394.0k | 1/18/24 |