Hu, ZhixiongXu, YijunKorkali, MertChen, XiaoMili, Lamine M.Valinejad, Jaber2024-01-232024-01-232020-08-101949-3029https://hdl.handle.net/10919/117616The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To estimate the uncertainty in the stochastic economic dispatch (SED) problem for the purpose of forecasting, the conventional Monte-Carlo (MC) method is prohibitively time-consuming for practical applications. To overcome this problem, we propose a novel Gaussian-process-emulator (GPE)-based approach to quantify the uncertainty in SED considering wind power penetration. Facing high-dimensional real-world data representing the correlated uncertainties from wind generation, a manifold-learning-based Isomap algorithm is proposed to efficiently represent the low-dimensional hidden probabilistic structure of the data. In this low-dimensional latent space, with Latin hypercube sampling (LHS) as the computer experimental design, a GPE is used, for the first time, to serve as a nonparametric, surrogate model for the original complicated SED model. This reduced-order representative allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus test system reveal the impressive performance of the proposed method.Pages 671-68111 page(s)application/pdfenPublic Domain (U.S.)UncertaintyComputational modelingEconomicsBayes methodsStochastic processesRenewable energy sourcesPower systemsStochastic economic dispatchreduced-order modelingmanifold learninguncertainty estimationrenewable energyArticle - Refereedhttps://doi.org/10.1109/TSTE.2020.3015353121Mili, Lamine [0000-0001-6134-3945]1949-3037