Multitime Scale Active and Reactive Power Coordinated Optimal Dispatch in Active Distribution Network Considering Multiple Correlation of Renewable Energy Sources

Abstract

To improve the adaptability for renewable energy sources (RES), a multitime scale optimal dispatch method, which considers the multiple correlations of RES, is proposed in the active distribution network (ADN). The proposed method includes a multiple correlation model of RES and a scheduling framework. The multiple correlation model of RES is formulated by t Copula function, Sklar's theorem, and conditional distribution of multivariate t distribution. The multiple correlation model can estimate the forecast error of the future period according to the historical data and the latest prediction. The scheduling framework further employs the revised forecast to coordinate the fast/slow-response regulation resources in the ADN under three timescales. In the day-ahead stage, the operation state of slow-response resources is optimized and determined. In the intraday stage, the output of the fast-response resource, which is transferred to the real-time stage as reference trajectory, is optimized based on the short-term prediction. In the real-time stage, the stochastic model predictive control is adopted to adjust the output of fast-response resources based on ultra-short-term prediction and reference trajectory. Case analyses indicate that the proposed method can effectively reduce the voltage fluctuation, promote the utilization of demand-side resources, and improve the system economic performance by introducing the multiple correlation model.