## 1 Introduction
Parked EVs of the PV-based charging station are assigned to charging/discharging operation based on the maximum amount of discharging power of EVs determined by the future trends of local load demand and PV power production that are predicted by the GRU network. Compared with the power management scheme with conventional EV charging/discharging scheduling algorithm utilizing only the currently measured local load demand and PV power production, the power management scheme with the proposed EV charging/discharging scheduling algorithm considering the variation trends of local load demand and PV power production can further reduce the operating cost of the PV-based charging station. The proposed power management scheme is also compared with the conventional scheme using the EV charging scheduling algorithm not considering discharging of EVs. As a result of comparing the performance of the power management schemes presented in this paper, a substantial difference is observed in the aspect of operational efficiency and economic feasibility of the PV-based charging station
3. In the PV-based charging station, several EVs are connected to the charging system via individual EV charging connectors. Since the parked EVs are interconnected to the MG through the electricity network, some EVs can be charged by the utility grid, PV system, and discharging of other EVs, and some EVs can be discharged to supplement the local load demand and charging demand of EVs among EV charging/discharging candidates. In this study, driving pattern of EVs is considered according to distribution of arrival at home and workplace. The EV charging/discharging candidates are established based on each EV's information such as predetermined parking time including margin time, current state of charge (SOC) value, and initiation of charging/discharging process. Using the proposed EV charging/discharging scheduling strategy, parked EVs of the PV-based charging station are utilized as ESSs in the MG.
4. In the proposed power management scheme, multi-objective optimization is applied for the power management of interdependent MG and EV fleets, which determines the coordinated power distribution of power sources and the charging/discharging operation of parked EVs. Multi-objective optimization for optimal power management of nanogrid is presented in [52]. Through the multi-objective optimization, the operating cost of the PV-based charging station and grid dependency of the MG is minimized, and the utilization of EV charging/discharging is maximized for economical and energy-efficient operation of the PV-based charging station. In addition, the collected information such as remaining parking time and the current SOC of EVs are considered to satisfy the charging requirements of parked EVs, and the operating SOC range and the initiation of charging/discharging process are reflected to prevent over-charging and over-discharging and improve battery health. This paper is organized as follows. In Section 2, the system architecture including the PV-based charging station is described. Section 3 explains the proposed power management scheme for obtaining the charging/discharging scheduling of EVs in the PV-based charging station. Section 4 shows the simulation results of the comparison of power management schemes, and Section 5 presents the conclusion of this study.