Recourse-Cost Constrained Robust Optimization for Microgrid Dispatch With Correlated Uncertainties

To accomplish more practical scheduling of microgrids under source-load uncertainties, this article first proposes a novel recourse-cost constrained adaptive robust optimization (RC-ARO) model with binary recourse variables. The dispatch plan in the nominal scenario is optimized in the first-stage to get the minimal operation cost, then the adjustment plan in the worst scenario is determined in the second-stage that minimizes the recourse-cost. This model has overcome the defect of conventional adaptive robust optimization (ARO), which can only get the scheduling plans in the worst scenario. Second, a spatiotemporal correlation model of wind power uncertainty is further developed based on the similarities of power time sequences, aimed at avoiding impossible scenarios in reality and reducing the conservativeness of independent uncertainty sets. Third, a new column-and-constraint generation (C&CG) algorithm with alternating optimization procedure (AOP) is developed to directly obtain the binary solution, which helps accelerating the solution of RC-ARO model using traditional nested-C&CG. Finally, case studies demonstrate the effectiveness and superiority of the proposed RC-ARO model, the developed uncertainty sets, and the novel solving algorithm. The solving time of C&CG-AOP reduces by half compared with nested-C&CG, and a larger scale of decision variables under uncertainties brings more significant speedup by the proposed algorithm.