Optimal charging scheduling of plug-in electric vehicles for maximizing penetration within a workplace car park

This paper proposes an optimal charging scheduling strategy, which is based on an integrated grid-to-vehicle (G2V) and vehicle-to-grid (V2G) charging approach, for maximizing the penetration of plug-in electric vehicles (PEVs) within a workplace car park. The driving pattern of PEVs is modeled with statistical methods using probability density functions. Based on the developed driving pattern, a fuzzy inference system is designed to model the PEVs' energy requirement. A genetic algorithm (GA) with heuristic initialization is then utilized for performing the optimal charging scheduling of PEVs. The proposed strategy is implemented for charging of PEVs in a workplace car park and based on the evaluation of maximum possible PEV penetration, optimal location(s) are determined for the car park in the industrial and commercial laterals of a 38-node distribution system. The simulation results demonstrate that the optimal charging strategy can prove beneficial in: 1) minimizing the daily total cost incurred by the parking operator; 2) reducing the network peak load; 3) providing frequency regulation service; and 4) preventing the overloading of distribution transformer and distribution lines.