This three-year, $283,995 Project Grant from the National Science Foundation's Division of Mathematical Sciences will support the development of robust methods for grid operational planning that account for non-Gaussian uncertainties in renewable energy forecasts. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the grantee—Northwestern University—will work with its sub-awardee to create new mathematical models, theory, algorithms, and computer implementations to systematically model the non-normal and multi-modal nature of renewable forecast errors. This will benefit a variety of planning tools in the electric power sector and broader energy industry. The researchers will also train students in energy systems optimization and electric power grid operations. Key innovations include the first general treatment of non-Gaussian errors in load and renewable forecasts for grid planning and novel methodologies for optimization under non-Gaussian probabilistic constraints using Gaussian mixture models. Outcomes aim to improve the reliability and environmental performance of future energy systems.
Generated 1/6/24, 10:14 PM