## 5 Conclusions  
In this study, the power management scheme of interdependent MG and EV fleets aided by a novel EV charging/discharging scheduling strategy is described. To prevent the peak load of the MG that can occur by simultaneous charging demand of EVs and decrease the operating cost of the PV-based charging station, the power management scheme is applied to the MG. The operating cost of PV-based charging station was reduced by the proposed EV charging/discharging scheduling algorithm. For economical  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Power Management Scheme** & **Operating Cost of** \\  & **PV-Based Charging Station** \\ \hline With proposed EV Ch/Dch scheduling & \$–35.45 \\ With (conventional) EV Ch/Dch scheduling & \$–27.51 \\ With (conventional) EV Ch scheduling & \$–13.24 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of operating cost of PV-based charging station according to power scheduling scheme.  
Figure 13: (**a**) The sum of remaining parking time of EVs to be charged; (**b**) The sum of remaining parking time of EVs to be discharged.  
and energy-efficient operation of the PV-based charging station, the multi-objective optimization was implemented to minimize the operating cost of the PV-based charging station and grid dependency, and maximize the charging/discharging utilization of EVs, simultaneously. The electricity loads considered in this study were mainly composed of local load demand, which is general electricity load, and the charging demand of parked EVs dispensed from the PV-based charging station. To meet these electricity loads, the electricity is supplied from the utility grid, PV system, and discharging of EVs. For more economical and energy-efficient operation of the PV-based charging station, the future trends of local load demand and PV power production were considered in the proposed EV charging/discharging scheduling algorithm. In the EV charging/discharging scheduling algorithm, the maximum amount of discharging power of EVs is determined based on the difference between local load demand and PV power production, and parked EVs were assigned to charging/discharging operation through multi-objective optimization procedure. The future trends of local load demand and PV power production were predicted by the GRU network. To verify the performance of the proposed EV charging/discharging scheduling strategy, the simulations were performed indicating operating cost reduction, grid dependency reduction, and utilization of EV charging/discharging while satisfying the charging requirements of EVs. With the proposed EV charging/discharging scheduling algorithm, the operating cost of the PV-based charging station is decreased by 167.71% compared with the EV charging scheduling algorithm. The operating cost is decreased by 28.85% relative to the results of the conventional EV charging/discharging scheduling algorithm. From the simulation results, it is evident that the economical and energy-efficient operation of the PV-based charging station can be achieved by actively utilizing parked EVs as ESSs depending on the proposed EV charging/discharging strategy.  
Conceptualization, H.J., S.L. and D.H.; methodology, H.J. and S.L.; software, H.J. and S.L.; validation, H.J., S.L. and S.H.N.; formal analysis, H.J.; investigation, H.J.; resources, S.L. and S.H.N.; data curation, S.L. and S.H.N.; writing--original draft preparation, H.J.; writing--review and editing, D.H.; visualization, H.J.; supervision, D.H.; project administration, D.H.; funding acquisition, D.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Energy Cloud Research and Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT, under Grant 2019M3F2A1073314.  
The authors declare no conflict of interest.