Two-stage optimal scheduling strategy for community integrated energy system based on uncertainty and integrated demand response model

Abstract

This study presents an optimization management framework for community integrated energy systems. First, the output scenarios of renewable energy are generated by the Monte Carlo method, and the K-means method is used to reduce the scenarios. According to the spatio-temporal characteristics of electric vehicles, this study proposes a charging/discharging decision-making method based on the fuzzy theory. Moreover, an integrated demand response model based on the real-time price mechanism and a two-stage optimization scheduling strategy is formulated. The results show that, compared with two traditional scenarios, the proposed strategy reduces comprehensive operating costs on typical days by 8.81% and 3.22% in summer and 6.55% and 3.33% in winter, respectively, while ensuring overall satisfaction. This study also analyzes the impact of the number of electric vehicles and the weight of the objective function on the optimization scheduling results.