AUTHOR=Ru Chuanhong , Li Lei , Lu Ji , Jiang Beini 

TITLE=Data-driven industrial park microgrids robust optimization method

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2025.1535211

DOI=10.3389/fenrg.2025.1535211

ISSN=2296-598X

ABSTRACT=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.