A Distribution Network Flexible Resource Capacity Configuration Method with Large Renewable Energy Sources Access

Extensive research have been done to improve the rate of renewable energy consumption. However, how to select an appropriate scheme among these methods has become a key issue for distribution network investors. In this paper, a comprehensive evaluation index system is put forward and a flexible resources allocation model is established. Firstly, a RES output scenario reduction method based on machine learning is utilized to reduce the impact of the uncertainty output of renewable energy sources (RES). Further, a comprehensive evaluation index system for the distribution network is established to guide the configuration result of flexible resources. Moreover, a flexible resource configuration model considering both system flexibility and economy is established, and the second-order cone relaxation method is introduced to deal with the nonlinear terms. Finally, an IEEE-33 system is selected as a case study to verify the presented models. The result shows that the flexible resource capacity configuration model can effectively improve the DG consumption rate and alleviate the transmission pressure of distribution network lines. The economic benefits index characterizes the rationality and validity of the configuration model proposed in this paper, which provides decision-making assistance for the main investors of distribution network.