Coordinated planning of fast charging station and distribution network based on an improved flow capture location model

The increasing penetration of plug-in electric vehicles (PEVs) should lead to a significant reduction in greenhouse gas emissions. Nevertheless, the development of PEVs is limited by the lack of charging facilities, which is constrained by the coupled transportation-distribution network. This paper presents a stochastic bi-level model for the optimal allocation of fast charging stations (FCSs) and distribution network expansion planning (DNEP). First, a sequential capacitated flow-capturing location-allocation model (SCFCLM) is proposed at the lower level to optimize the allocation of FCSs on the transportation network. Monte-Carlo simulation (MCS) is utilized to estimate daily charging load requirements. Then, we propose an economic model for DNEP at the upper level, and the chance constrained method is employed to relax power flow constraints to address the uncertainties of loads. Numerical experiments are conducted to illustrate the proposed planning method. The influences of the flow capturing sequence and relaxed confidence level on the PEV charging load, FCS planning strategies and DNEP schemes are analyzed.