A Historical-Correlation-Driven Robust Optimization Approach for Microgrid Dispatch

Abstract

Robust optimization (RO) has been a proficient approach for uncertainty dispatch in microgrids. In view of the strong conservativeness of traditional RO models, this article proposes a historical-correlation-driven (HCD) RO method for the MG dispatch problem. Firstly, the distinct spatiotemporal correlations in renewable energy (RE) generation are confirmed based on the historical data series of RE power. To effectively extract the correlation characteristics, the new intervals that envelop all correlation data points of similar days are formulated via the line-fitting approach, and the interval boundaries are added as liner constraints into the polyhedral uncertainty set that actively integrates the spatiotemporal correlations and avoids unreasonable scenarios. Secondly, a gradient-approximating decomposition algorithm with equilibrium constraints is put forward to address the nonlinear RO problem caused by non-independent uncertainties. The alternative optimization procedure ensures the gradient ascent of the objective, thus achieving the fast solution convergence of the Max-Min model with binary recourse variables. Besides, the equilibrium constraints realize the precise linearization of bilinear terms, as well as avoid the introduction of numerous binaries. Case tests verify the effectiveness and superiority of the proposed HCD-RO method comparing with existing models and algorithms.