## 4 Simulation Results  
### Simulation Setup  
To verify the effect of the power management scheme with the proposed EV charging/discharging scheduling algorithm, the simulation is executed based on the system architecture described in Figure 1. For the local load and charging demand of EVs, the utility grid, PV system and discharging power of EVs are used as power sources. The proposed EV charging/discharging scheduling algorithm is applied to the power management of the MG to achieve the cost-effective and energy-efficient operation of the PV-based charging station considering the local load demand of the MG. In the simulation, the DR program determines the electricity rate of grid power, and the sum of SMP and weighted REC is used as the selling price of PV power and discharging power of EVs for electric company. It is assumed that the PV-based charging station is located around workplace and the maximum number of EVs that can be parked at the PV-based charging station is 50. Each EV has a battery capacity of 64 kWh, and the SOC values of parked EVs at the moment connected to the charging port are provided depending on the normal distribution with mean \(\mu=15\) and standard deviation \(\sigma=5\). The expected parking time is determined based on the distribution of arrival time of vehicles at home and workplace presented in Figure 2. The charging rate is assigned to each EV, depending on its expected parking time and the current SOC, between \(M_{1}\)(7 kW) and \(M_{2}\)(19.2 kW). To charge EVs up to target SOC, \(PW^{max}\) is set to about 157 kW to meet the local load demand and charging demand of EVs, simultaneously. By comparing the difference between local load demand and PV power production with \(PW^{flag}\), the maximum amount of discharging power of EVs at each time interval is determined. In the proposed EV charging/discharging scheduling algorithm, \(PW^{dcb,max(1)}\) is set to 12 kW, indicating that discharging of EVs is relatively more activated, and \(PW^{dcb,max(2)}\) is set to 2 kW, pointing out that discharging of EVs is relatively less activated. \(PW^{flag}\) is set to 86 kW to determine the appropriate EV discharging period according to the amount of local load demand and PV power production. The local load demand and PV power production data used in this study are presented as in Figure 7.  
In this study, the GRU network is utilized to predict the local load demand of the MG and PV power production of the PV-based charging station. The parameters, adopted from [68] which studied a GRU network with similar structure, are applied to train the GRU network, as presented in Table 1. The total number of training epoch and batch size are set to 1000 and 200 in our simulations. Additionally, the ADAM optimization algorithm is applied with the learning rate of 0.005, gradient moving average of 0.9, dropout rate as 0.2, and gradient threshold as 1 to update weighting coefficients to minimize the error during training the GRU network. Three different EV charging/discharging scheduling algorithms are applied to the power management scheme of MG in this simulation. The \"EV Ch Scheduling\" scheme represents the power management scheme of the MG with EV charging scheduling algorithm. The \"EV Ch/Dch Scheduling\" scheme refers to the power management scheme of MG with conventional EV charging/discharging scheduling algorithm, following the EV discharging condition in (9). The \"Proposed EV Ch/Dch Scheduling\" scheme represents the power management scheme of MG with the proposed EV charging/discharging scheduling algorithm, following the proposed EV discharging condition in (10). In the Figures 8-13, the green dash-dotted line with square marker, the red dashed line with triangle marker, and the blue solid line with circle marker represent the \"EV Ch Scheduling\" scheme, the \"EV Ch/Dch Scheduling\" scheme, and the \"Proposed EV Ch/Dch Scheduling\" scheme, respectively.