Project Grant 2338559
- 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 $100,000 National Science Foundation Project Grant supports research at the University of Washington to develop a new data-driven power systems control framework with stability guarantees. Funded under the NSF Engineering program (CFDA 47.041), the research aims to design reinforcement learning algorithms for inverter-based frequency and voltage control of power grids that provide formal stability assurances. Over the two-year period from March 2022 to February 2025, university...
- 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...
- This $550,000 five-year Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) aims to develop performance-guaranteed learning and control algorithms for real-world energy systems. The research plan focuses on two key applications: distribution grid voltage regulation and building system control. The project objectives are to provide stability, computational tractability, and robustness guarantees for these energy system control challenges through...
- This $599,972 Faculty Early Career Development (CAREER) award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports research to develop a new approach to multifidelity scientific machine learning. The research aims to combine data from both high-fidelity and low-fidelity computational simulations in a mathematically rigorous way, enabling the creation of machine-learned models that provide high-accuracy predictions at low computational cost. This work has the...
- The National Science Foundation (NSF) Engineering program awarded a 5-year, $589,527 Faculty Early Career Development (CAREER) grant to the University of Texas at Dallas (UTD) on June 1, 2024. The grant supports research to develop set-based dynamic modeling and control frameworks for improving the safety and reliability of thermal management systems in complex energy systems. Key objectives include: Create set-based modeling and control techniques to account for the uncertain behaviors of...
- This $200,000 National Science Foundation project grant supports the development of data-driven power systems control with stability guarantees. Funded through the NSF Engineering program (CFDA 47.041), the award to Carnegie Mellon University will support three thrusts of collaborative research over a 30-month period ending February 2025. The research aims to design a new framework integrating reinforcement learning algorithms with Lyapunov stability theory to provide stability guarantees for...
- This National Science Foundation (NSF) CAREER grant (CFDA 47.041 - Engineering) awarded to Trustees of Dartmouth College provides $397,111 in funding from September 1, 2025 through January 31, 2030. The project aims to enhance electric power grid operators' situational awareness, improve dynamic model quality, and enable online controls to ensure secure power system operation with high penetration of inverter-based resources (IBRs) such as solar, wind, and battery energy storage. The research...
- 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 National Science Foundation (NSF) Faculty Early Career Development (CAREER) Program award provides $581,320 in funding to the University of Vermont (UVM) over a 5-year period from June 1, 2024 to May 31, 2029. The research project, titled "A Universal Framework for Safety-Aware Data-Driven Control and Estimation", aims to develop a framework for the simultaneous design of control policies and safety measures for complex systems like robotics and power systems using data-driven...
This National Science Foundation (NSF) Faculty Early Career Development (CAREER) Program grant award provides $508,455 to the University of Washington to support research that will leverage artificial intelligence technologies to enhance the resilience and efficiency of automated control systems in energy infrastructure. The project aims to develop an expert-guided, distributionally robust optimization framework that integrates reinforcement learning with mathematical optimization to improve online decision-making for managing complex energy systems. The research will address key challenges around efficiently leveraging expert feedback, identifying states requiring expert intervention, and achieving optimal and stable control policies. Results from this 5-year project are expected to yield advancements in the efficiency, stability, and security of energy infrastructure systems, as well as provide educational and outreach opportunities to train students in reinforcement learning.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $508.5k | 1/4/24 |