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 based on hybrid distributed parameter system control theory, and extensively validating the proposed algorithms using large-scale real-world power system datasets. The successful completion of this 3-year project, from September 1, 2023 to August 31, 2026, will enable power system operators to adopt cutting-edge algorithms that significantly enhance their operational practices with renewable generation.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $200.0k | 8/14/23 |