Modeling the temporal and economic feasibility of electric vehicles providing vehicle-to-grid services in the electricity market under different charging scenarios

Electric vehicles (EVs) could potentially act as the distributed energy storage devices to provide vehicle-to-grid (V2G) services to benefit the electric power system. Correspondingly, EV users can earn revenue based on the provision of grid services in a market environment. However, EVs would suffer the extra battery degradation incurred by the V2G operation. As such, it is essential to investigate the feasibility of EVs to conduct the V2G operation under different charging scenarios. Compared to existing studies focusing solely on the economic evaluation of V2G operation, this paper investigates the potential of EVs providing V2G services from two perspectives: time availability and economic viability. To this end, two critical indicators, viz., time indicator and economic indicator, are introduced in this paper to model the temporal and economic feasibility of EVs providing grid services to explore the applicable V2G scenarios. In addition, three charging scenarios at home, in work -place, and in fast charging stations (FCSs) are evaluated regarding the feasibility for V2G services. The simulation results reveal that, there is sufficient idle time for those EVs in the home-based and workplace-based charging scenarios and it is profitable to provide V2G services in the electricity market according to the cost-benefit analysis. However, for those EVs with FCS-based charging, it is unlikely for EV users to spend the extra time to conduct the V2G operation due to the fact that the V2G operation will prolong the grid-connection time of EVs.

Automated summary

Electric vehicles can act as distributed energy storage devices to provide vehicle-to-grid services, allowing EV users to earn revenue. However, V2G operation can cause extra battery degradation, so it is important to investigate the feasibility of EVs conducting V2G operation under different charging scenarios.