Project Grant 2317079
- 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 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...
- 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 $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...
- 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 $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 $220,000 National Science Foundation project grant under the Engineering program (CFDA 47.041) will support research at Kansas State University from July 2022 to June 2024 to leverage smart meter data for enhanced situational awareness of power distribution systems. The university will investigate fundamental approaches to effectively integrate advanced metering infrastructure data to increase operational awareness of distribution grids. Researchers will focus on integrating machine...
- This NSF CAREER award of $500,000 funds fundamental research aimed at advancing the autonomy and operational efficiency of modern power grids through innovative distributed management strategies. Awarded by the National Science Foundation's Division of Electrical, Communications and Cyber Systems under the Engineering program (CFDA 47.041), the project is being conducted by the University of Texas at Austin from March 1, 2025, through February 28, 2030. The research will develop machine learning...
- This $223,406 National Science Foundation project grant, awarded under the Engineering program (CFDA 47.041), will fund the development of novel optimization models and algorithms for the operation of future electric power systems at the Massachusetts Institute of Technology from January 2023 through February 2024. Specifically, the principal investigator will create efficient and robust algorithms for optimizing power flow and network topology to support the integration of renewable, demand...
- This Project Grant award from the National Science Foundation (NSF) Integrative Activities program (CFDA 47.083) aims to develop a comprehensive federated learning framework for the condition monitoring, data sharing, and cybersecurity of distributed wind energy systems (DWSS) in rural areas. The $300,000 award to Mississippi State University will establish an interdisciplinary collaboration with the University of Miami to research innovative technologies, including collaborative federated...
This $600,000 federal Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) aims to integrate federated learning with power systems to better predict electricity consumption and lower the cost of electricity generation. The project will develop machine learning methods, specifically recurrent neural networks, to forecast day-ahead electricity consumption using distributed data from smart meters while preserving consumer privacy. Key scientific advances required include creating effective federated learning methods to handle both global and local models, as well as neural architectures capable of capturing long-term dependencies in time series data on electricity usage. The funding will support research at Trustees of Boston University over a 3-year period from May 2024 to April 2027. If successful, this project could reduce electricity costs throughout the United States and provide momentum for initiatives to install smart meters with robust privacy protections.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $600.0k | 4/8/24 |