## 1 Introduction  
With the deepening of research, there have been studies on wind and photovoltaic capacity selection in distribution networks, mainly taking classical particle swarm optimization and genetic algorithm as examples [1]. These two algorithms have some problems, such as slow training speed, falling easily into local minima, complex parameter setting and so on, which gradually become bottlenecks restricting their further development [2; 3]. In the study of distributed generation capacity selection, many studies have been carried out as follows. Reference [4] proposed a distributed energy resource aggregator technique to quantify the benefits of large-scale distributed generation access to the distribution network. Reference [5] proposed a planning model for multi-energy systems that considered gas pipelines and distribution networks, and used the minimum total investment cost as the optimization goal to conduct multi-stage planning and multi-scenario analysis of the model. However, this research mainly focused on economic factors [4; 5] and did not consider the impact of the distributed generation capacity and location changes on the network. Reference [6] proposed a model considering the amplitude of the node voltage and the variation of wind speed, to solve the layout problem of wind power generation in the distribution network. However, the model did not account for the impact of different types of distributed generation capacity changes on the distribution network. Reference [7] studied a distributed power grid-connected problem with the minimum active loss as the objective function. However, the characteristics of the distributed power output were only analyzed from a technical point of view, and the effects of different types and position changes on the network stability were not considered. In recent years, research on electric vehicles has continued to expand. Reference [8] established a two-layer optimization model for electric vehicle charging based on node blocking electricity prices, which maximized the economic benefits for the power grid and users. Reference [9] maximized and modeled the agent and owner's respective interests as the objective function, and a master-slave game model was established to achieve an electric vehicle charging management method based on the electricity price. However, this only studied electric vehicle charging models and did not consider the interaction with distributed generation [8,9]. Reference [10] established an accurate model of electric vehicles based on their actual behavior to study the influence of electric vehicle charging characteristics on the power grid. Reference [11] studied the influence of the electric vehicles on the network loss at different penetration rates. References [10,11] only analyzed the impact of electric vehicles on the network stability and loss from a technical perspective and did not consider their interaction with distributed generation. The large-scale access of wind photovoltaic power and electric vehicles has caused a fundamental change in the topology of traditional distribution networks, changing it from a single radiating network to a complex source network. The above research on distributed generation or electric vehicles did not fully consider the changes in the topology of the distribution network, and the impact of the large amount of data obtained by various measurement methods on the distributed generation capacity configuration problem. Therefore, how to realize the coordinated control of the active distribution network under the premise of considering the large-scale access of distributed generation and electric vehicle, so that the planning result is more suitable with the actual needs of the project, is one of the important issues in the research of distributed generation capacity configuration problems.