Cost analysis of individual EV charging in different community networks

The integration of electric vehicles (EVs) in the community network is gaining increasing attention nowadays. This paper proposes a new analytical methodology to calculate the energy costs of an individual EV in different community networks. Firstly, four representative EV charging demand (short journey vehicles, commuting vehicles, taxis, long journey vehicles) are classified statistically, according to their charging time, charging power, charging location and charging duration. It is a cost-effective methodology to provide feasible solutions through rational deductions, without the requirement for excessive data of charging activities. Thereafter, six scenarios in terms of different charging/discharging strategies, types of renewables and charging locations are proposed, to assess the different energy costs of EV charging behaviors. Further, the genetic algorithm (GA) is utilized in the optimal charging scenarios to calculate the most cost saving charging/discharging sequence and maximize the capture of renewables. The demonstration shows that the proposed analytical method reflects the characteristics of individual EV storage profiles and energy costs in dissimilar residential and commercial community networks. It also illustrated that the optimal charging strategy can reduce up to 96% energy cost for the electricity user.