Research on Active Distribution Network User-side Resource Mining and Scheduling Optimization Methods

The installed capacity and electricity production from renewable energy sources, including wind power and photovoltaics, have been consistently rising, facilitating the ecological transformation of the energy framework. Nonetheless, the unpredictability and variability of renewable energy sources provide problems to the power system, necessitating the evolution of the contemporary power system into a smart and efficient framework to harness the potential of demand-side resources. This study focuses on the load of electric vehicles (EVs) and analyzes their usage and charging habits through surveys. A demand response model that takes into account users' travel and battery degradation is established. This model aims to reduce the grid peak-to-valley difference, improve system stability, and reduce operating costs. Meanwhile, this paper also pays attention to electric vehicle aggregators and establishes a demand response model to refine the charging power curve, maximizing their interests. The results show that electric vehicles, as a high-quality demand response resource, can effectively promote the stability of the power system and the consumption of renewable energy. Electric vehicle aggregators can also obtain greater benefits by optimizing their scheduling, providing users with higher-quality demand response services.