LAUSR

With the rapid integration of renewables, optimal economic dispatch in active distribution systems faces great challenges because of the randomness and volatility of renewable energy. This paper presents a robust energy management model considering the temporal and spatial correlation of solar energy in active distribution network. The Pearson autocorrelation and cross-correlation coefficients are calculated to verify the necessity of temporal and spatial correlation, respectively, based on historical data in Jiangsu Province, China. Next, correlation constraints are proposed based on the confidence level, which is nonlinear, and can be linearized due to the discrete feature of polyhedral single-interval uncertainty sets. Then, a two-stage min-max-min robust energy management model considering the correlation constraints and the uncertainty of solar energy is proposed. The first stage aims to determine the operating state of capacity banks and on-load tap changers. The second stage optimizes power dispatch in the worst-case scenario. The column-and-constraints algorithm is implemented to obtain an optimal dispatch strategy that minimizes the operating cost under the worst-case scenario. A case study demonstrates the accuracy and efficiency of the proposed model and presents the influence of temporal and spatial correlation.