Economic Dispatch of Integrated Energy Systems Based on Improved Data-driven Adjustable robust model

To promote wind power consumption and enhance energy efficiency in integrated energy systems, this paper proposes an Imprecise Dirichlet model data-driven two-stage adjustable robust optimization method based on an improved uncertainty set using Wasserstein generative adversarial networks. First, Wasserstein generative adversarial networks is used to expand the historical statistical data set, and the data-driven uncertainty set is constructed to describe the uncertainty of wind power output. Second, to enhance the operational economy of the integrated energy systems, a two-stage adjustable robust economic dispatch model based on improved uncertainty sets is developed. Third, the column and constraint algorithm, duality theory, and Big-M method are employed to solve the two-stage model. Finally, the effectiveness of the proposed model and method is validated through the example analysis.