Multi-Timescale Active Distribution Network Optimal Dispatching Based on SMPC

Abstract

Smoothing power fluctuations and arranging reserves play a more crucial role in the secure and economic operation of the active distribution network (ADN), as the high penetration rate of uncertain renewable energy sources (RES) increases. In this article, a multitimescale ADN optimal dispatching model based on stochastic model predictive control (SMPC) is proposed to track the random fluctuation of RES and address the operation risk. First, load shedding and RES curtailment risk are quantified by a conditional value at risk based on the probability distribution and a reserve arrangement strategy considering the tradeoff between reserve cost and operation risk is further proposed. Second, the copula theory is introduced to establish a high-dimensional RES prediction error model for the purpose of capturing its spatial–temporal characteristics. Furthermore, according to prediction error distribution, based on the scenario method, typical RES output scenarios for the day-ahead, intraday, and real-time stages are generated. Then, a multitimescale optimal dispatching framework based on SMPC is established in which the outputs of units are optimized and coordinated under different time scales. Finally, simulation results on a modified IEEE 33-bus power system demonstrated the superiority of the proposed method in tracking the fluctuation of RES and also verify the effectiveness of the method to improve the economy of system operation.