A stochastic model for quantifying the impact of PHEVs on a residential distribution grid

This paper presents a methodology for modeling and controlling the load demand in a residential distribution grid due to plug-in hybrid electric vehicle (PHEV) battery charging and discharging. To take the stochastic nature of start charging time, charging during and initial state of charge (SOC) into consideration, this paper built a stochastic model for PHEV in a residential distribution grid close to real-world scenarios. The authors proposed a smart charging and vehicle-to-grid (V2G) strategy based on particle swarm optimization algorithm. The objective of this control strategy is to improve the power quality and flatten the load demand in the studied system. Then simulations are carried out at different PHEV penetration levels for three different charging scenarios: the uncoordinated charging, the proposed smart charging without V2G and the proposed smart charging with V2G. The results show that uncoordinated charging will seriously increase the peak load and cause large voltage deviation, while the proposed smart charging method can effectively reduce the voltage deviation and flatten the load demand curve. It is found that when V2G is considered in the proposed smart charging method, the peak load will decrease and the voltage deviation will be smaller too at a low PHEV penetration level, but with the increase of PHEV penetration level, the advantages of V2G will decrease.