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

Abstract

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.