Distributionally Robust Chance-Constrained Energy Management for Islanded Microgrids

With the development of smart grid, energy management becomes critical for reliable and efficient operation of power systems. In this paper, we develop a chance-constrained energy management model for an islanded microgrid, which includes distributed generators, energy storage system (ESS), and renewable generation, such as wind power. The objective function of this model consists of generation cost, emission cost, and ESS degradation cost. To capture the uncertainty of renewable generation, a novel ambiguity set is introduced without knowing its probability distribution or exact moment information. Based on the ambiguity set, the chance constraint can be processed with distributionally robust optimization method and the energy management problem is reformulated as a tractable second-order conic programming problem. The proposed approach is tested with a case study and simulation results indicate that it is effective and reliable. Moreover, the comparison with the method based on known moment information and some other methods is also conducted to show the performance of the proposed method.