Mean Robust Energy and Reserve Dispatch Method for Wind-Storage Integrated Power Grids

This paper proposes a mean robust energy and reserve dispatch method for wind-storage integrated power grids, which is based on a data compression algorithm within a distri-butionally robust optimization framework. The method aims to effectively address the uncertainty in wind farm forecast errors while enhancing computational efficiency. First, an dispatch model for wind-storage integrated power grids is developed. Then, a mean robust optimization (MRO) approach with chance constraints, based on wasserstein distance, is introduced to man-age the uncertainty in wind farm output. Reserve adjustments are allocated to each generator and energy storage to accommodate forecast errors. Next, the data-compressed expression of the MRO with chance constraints is derived, and the MRO dispatch model is formulated as a mixed-integer optimization problem. In the final evaluation, the proposed approach is assessed for its effectiveness through simulation experiments conducted on the IEEE 24-bus test system. The results demonstrate that, compared to traditional distributionally robust dispatch method, the proposed method significantly reduces computational time while effectively limiting the violation probability of the chance constraints.