Data-driven Robust Optimization Method for Multi Energy Complementary Microgrids

In order to accurately describe the impact of the fluctuation and randomness of new energy output power on the operation of integrated energy complementary microgrid systems, a data-driven robust optimization method for multi energy microgrids 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 multi energy complementary microgrids is further established, and the column and constraint generation algorithm(C&CG) is used to solve the model. Finally, simulation comparisons are conducted through examples, and the results show that the data-driven robust optimization method for multi energy complementary microgrids can reduce conservatism and improve the robustness of optimization results, demonstrating the effectiveness of the proposed method.