Distributionally Robust Optimisation of Multi-source Microgrids Accounting for Renewable Energy Uncertainty

Based on the shortcomings of both traditional stochastic programming and robust optimization algorithms in dealing with wind power output uncertainty, the distributionally robust optimization method is applied to the optimal scheduling problem of electro-hydrogen hybrid microgrid, and a two-stage scheduling model with gas turbine and electric-to- hydrogen device as coupling elements is established. A data-driven two-stage distributionally robust optimization model is used to inscribe the intra-day source-load bilateral uncertainty, and the integrated 1- and ∞-parametric constrained uncertainty probability distribution confidence sets are used to avoid the conservativeness of the operation scheme while guaranteeing the robustness of the multi-energy microgrid operation. Meanwhile, the stage 2 problem of the day-ahead model can be decomposed into multiple small-scale subproblems and can be processed in parallel by the column constraint generation algorithm without complex pairwise computations. Finally, the effectiveness of the distributionally robust optimization algorithm is verified by arithmetic examples.