Interval-Partitioned and Correlated Uncertainty Set Based Robust Optimization of Microgrid

The dramatic increase in renewable energy sources has created significant uncertainties in the operation of power systems. This article investigates a day-ahead economic dispatch problem for a typical microgrid, considering the uncertainties of renewable energy sources and load demand. An interval-partitioned and temporal-correlated uncertainty set based robust optimization model is proposed, which allows a more accurate characterization of the distribution of uncertainties. The proposed robust optimization model can reduce the conservativeness of the optimal solution by avoiding scenarios that are low-probability or even impossible in reality. The model is then decomposed into a master problem and a nonlinear bi-level subproblem and solved by the $C \& CG$ method and Big-M method. However, this method requires the introduction of a large number of auxiliary variables and related constraints, significantly increasing the computation burden. To tackle this problem, an efficient solution method, Improved- $C \& CG$ , is developed by integrating an outer approximation method into the $C \& CG$ method. Finally, case studies verify the effectiveness of the proposed model, uncertainty set, and solution methods.