Project Grant 2229408

This three-year, $290,000 National Science Foundation project grant supports research at The Pennsylvania State University to develop new mathematical methods, computer models, and algorithms for electric grid operational planning under non-Gaussian uncertainties in renewable energy forecasts. Funded through NSF's Mathematical and Physical Sciences program (CFDA 47.049), the research directly addresses challenges in integrating intermittent renewable resources like wind and solar power into the nation's energy mix while maintaining grid reliability. Key outcomes will include generalized methodologies and chance-constrained optimization techniques to systematically account for non-normal and multi-modal error distributions in load and renewable forecasts during planning. The researchers will also design novel non-Gaussian ambiguity sets to rigorously model parameter misspecification. Real utility data and practical recommendations will help validate models and facilitate adoption. The project aims to advance algorithms for this problem while training students in energy systems optimization.