To solve the overload problem caused by the high proportion of renewable energy into the power system, it is particularly important to find a suitable distribution network planning scheme. Existing studies have effectively reduced the planning cost by incorporating virtual power plants into the distribution planning process, but there is no quantitative analysis of the flexible resources inside the virtual power plant. At the same time, the traditional planning process does not pay much attention to the acquisition of photovoltaic and load data. Therefore, in this paper, we propose a distribution network planning method considering the flexibility of distributed energy resources in virtual power plants. Firstly, taking the distribution network planning including the virtual power plant as the research object, the flexibility of the distributed energy resource of the virtual power plant was quantified. Then, in order to achieve the goal of minimizing the operating cost of system planning, a distribution network planning model considering the flexibility of distributed energy resources in the virtual power plant is established. In this model, the impact of virtual power plants flexibility on the distribution network planning process is mainly considered. Secondly, this paper uses the improved k-means clustering algorithm to obtain the typical data of PV and load. The algorithm effectively overcomes the impact of PV and load output fluctuations on the planning process. Finally, the simulation results show that the proposed planning model can effectively reduce the operation cost of system planning by using distributed energy storage system and distributed energy resource flexibility. At the same time, the PV absorption rate of the PV power station inside the distribution network is improved.

The selection of the optimal 35 kV network structure is crucial for modern distribution networks. To address the problem of balancing investment costs and reliability benefits, as well as to establish the target network structure, firstly, the investment cost of the distribution network is calculated based on the determined number of network structure units. Secondly, reliability benefits are measured by combining the comprehensive function of user outage losses with the System Average Interruption Duration Index (SAIDI). Then, a multi-objective planning model of the network structure is established, and the weighted coefficient transformation method is used to convert reliability benefits and investment costs into the total cost of power supply per unit load. Finally, by using the influencing factors of the network structure as the initial population and setting the minimum total cost of the unit load as the fitness function, the DE algorithm is employed to obtain the optimal grid structure under continuous load density intervals. Case studies demonstrate that different load densities correspond to different optimal network structures. For load densities ranging from 0 to 30, the selected optimal network structures from low to high are as follows: overhead single radial, overhead three-section with two ties, cable single ring network, and cable dual ring network.