With the popularization of electric private cars and the increase of charging facilities in residential areas, disorderly charging will affect the power supply efficiency of their distribution transformers and the quality of electricity used by users in residential areas. In severe cases, it may even cause vibration of the power grid, causing serious three-phase imbalance problems such as single-phase burnout of transformers or insulation breakdown of household appliances. This paper analyzes the influencing factors of the unbalanced operation of each phase of the distribution transformer and the electrical load characteristics of typical residential areas. Based on the photovoltaic output of the station area, the charging and discharging capacity of the energy storage system, and the orderly charging plan of residential electric vehicles, a local orderly charging strategy for electric vehicles based on energy routers under the three-phase balance of the residential area is proposed. This strategy can realize the three-phase balance control of the distribution transformer. The effectiveness of the method is verified by a typical scenario example. The control method is changed to minimize the three-phase imbalance in residential areas and improve the low utilization rate of the distribution network and the comprehensive utilization efficiency of adjustable resources in residential areas.

In order to play the important role of electric vehicles to promote the realization of the 3060 double carbon target, electric vehicles have seen explosive growth. However, due to the tight symmetry between the number and distribution of electric vehicles and their corresponding charging facilities, the layout of charging facilities has higher requirements. This paper collects travel data in the form of a traffic travel questionnaire for electric vehicle users. Based on the vehicle parking demand model of the queuing theory and Monte Carlo simulation, the paper gives the number of stopping vehicles and the time of vehicles stopping in different places such as residential areas, workplaces, supermarket parking and roadside. In addition, based on the Bass prediction model, the main parameters are modeled in the model, and the price correction coefficient is introduced. The improved Bass model is used to predict the growth trend of electric vehicles in different regions in different years and in different incentive sites. By predicting the ownership of urban electric vehicles and accurately grasping the distribution and operation of electric vehicles, this paper can provide guidance and suggestions for the planning and construction of charging facilities in different regions, effectively reduce the investment cost of charging facilities and guide local governments to formulate reasonable planning schemes.