Project Grant 2523934
- This $150,000 project grant awarded by the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) to Iowa State University aims to develop novel dynamic grid optimization algorithms and modeling tools to effectively accommodate high penetration of renewable energy and ensure reliable power grid operation. The project will focus on addressing key challenges posed by the uncertainty of renewable energy resources and the stability concerns of power grids with high...
- 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...
- 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 $299,900 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research by Virginia Polytechnic Institute & State University (Virginia Tech) on renewable energy integration and grid resiliency. The project focuses on developing new mathematical frameworks and computational strategies to enhance the modeling, monitoring, and analysis of power networks. This work aims to improve the management of...
- 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...
- The National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems awarded the University of Texas at Austin a $250,000 Project Grant under the NSF Engineering program (CFDA 47.041) to develop novel learning-based approaches for estimating the flexibility amount of grid edge resources (GERs) and designing equitable resource coordination and management methods. The project aims to transform the management of flexible energy resources in distribution electricity...
- This $260,000 National Science Foundation project grant will fund the development of data-enabled modeling, monitoring, and optimization algorithms targeting power system dynamics from 2022-2025. The University of Texas at Austin, through its parent organization the University of Texas System, will receive funding under the NSF Engineering program (CFDA 47.041) to correlate synchrophasor data and develop Gaussian process and stability-aware optimal power flow tools. Key outcomes will include...
- This $326,900 project grant, awarded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) Federal Grant Program, supports the development of novel combinatorial optimization techniques for smart grids and power networks at William Marsh Rice University. The key objectives are to advance the knowledge base in microgrids and their utility within the electrical grid structure, create computationally efficient algorithms to address challenges related to...
- 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...
- 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 Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) provides $299,999 in funding to the Texas A&M Engineering Experiment Station (Tees) to develop novel dynamic grid optimization algorithms and modeling tools to effectively accommodate high penetration of renewable energy and ensure reliable grid operation. The research aims to address the critical challenges of uncertainty and stability arising from the increasing growth of renewable energy sources on the electric power grid. Key deliverables include open-source implementations of the developed algorithms, which can provide a computational infrastructure and benchmark for assessing long-term energy integration plans or evaluating the daily operational efficiency and reliability of power grids. The award period runs from September 15, 2025 to August 31, 2027.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $300.0k | 8/6/25 |