This paper proposes a collaborative planning model for active distribution network (ADN) and electric vehicle (EV) charging stations that fully considers vehicle-to-grid (V2G) function and reactive power support of EVs in different regions. This paper employs a sequential decomposition method based on physical characteristics of the problem

breaking down the holistic problem into two sub-problems for solution. Subproblem I optimizes the charging and discharging behavior of autopilot electric vehicles (AEVs) using a mixed-integer linear programming (MILP) model. Subproblem II uses a mixed-integer second-order cone programming (MISOCP) model to plan ADN and retrofit or construct V2G charging stations (V2GCS)

as well as multiple distributed generation resources (DGRs). The paper also analyzes the impact of bi-directional active-reactive power interaction of V2GCS on ADN planning. The presented model is tested in the 47-node ADN in Longgang District

Shenzhen

China

and the IEEE 33-node ADN

demonstrating that decomposition can significantly improve the speed of solving large-scale problems while maintaining accuracy with low AEV penetration.