Electric Vehicles (EV) are gaining large popularity in the transportation sector, and that raises numerous concerns for the power sector. The repercussions of such increased demand are notable at the distribution side with different aspects of EV usage. To accommodate this increased number, multiple Charging Stations (CS) are being deployed to assist users in enhancing their charging experience. However, public stations are underutilized due to lack of useful performance information, such as waiting time, and outlet availability. Therefore, users will favor home charging over public charging. This behavior will come with a drastic increase in power demand on the residential network. In addition, various behaviors of EV users, such as mass charging and preconditioning, can deteriorate the network's Quality-of-Service (QoS). Therefore, the impact of an elevated number of EVs and increased number of level 2 chargers on the residential distribution network is analyzed. Subsequently, the competency of dynamic pricing in handling this such elevated load in investigated. Afterwards, the repercussions from users preconditioning their vehicles during winter is inspected. In addition, we assess the competency of network reconfiguration in holding the network performance within operational range during preconditioning window. The performance of network reconfiguration will degrade when presented with high number of EVs; therefore this elevated number is leveraged through Vehicle-to-Grid (V2G) technology to assist network reconfiguration in balancing the preconditioning load.
Finally, a data-driven performance model for public charging stations is derived to gain more knowledge on its operation. Metrics such as waiting time, reneging probability and blocking probability are derived and analyzed to assist users in their charging processes and operators enhancing the station deployment.