Data-driven industrial park microgrids robust optimization method - Ru, Chuanhong; Li, Lei; Lu, Ji; Jiang, Beini (Frontiers in Energy Research, 2025)
In order to accurately describe the impact of the volatility and randomness of renewable energy output power on the operation of industrial park microgrids, a data-driven robust optimization method for industrial park microgrids is proposed. Firstly, based on the traditional interval set, the uncertain parameters of renewable energy output are modeled using a polyhedral set. Then, an ellipsoidal uncertainty set is established using historical data of renewable energy output. By connecting high-dimensional ellipsoidal vertices, a data-driven convex hull polyhedron set is established. Then, the uncertain parameters are better enveloped by scaling the convex hull set. A data-driven robust optimization model for industrial park microgrid was further established, and the column and constraint (C&CG) generation algorithm was used to solve the model. Finally, simulation comparisons were conducted through examples, and the results showed that the data-driven industrial park microgrids robust optimization method can reduce conservatism and improve the robustness of optimization results, demonstrating the effectiveness of the proposed method.