Robust Optimization Method of Industrial Park Microgrid Based on Data-driven

In order to accurately describe the impact of the fluctuation and randomness of new energy output power on the operation of industrial park microgrid systems, a data-driven robust optimization method for industrial park microgrid is proposed. Firstly, based on the traditional interval set, the uncertain parameters of new energy output are modeled using a polyhedral set. Then, an ellipsoidal uncertainty set is established using historical data of new energy output with spatiotemporal correlation. 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 C&CG was used to solve the model. Finally, simulation comparisons were conducted through examples, and the results showed that the data-driven robust optimization method for industrial park microgrid can reduce conservatism and improve the robustness of optimization results, demonstrating the effectiveness of the proposed method.