## 3 Simulation Results and Analyses
### Case 1--High Irradiation Profile without Fluctuations  
The case of 29 June 2019, in Compiegne, France, is considered. Figure 5 shows \begin{table}
\begin{tabular}{c c c c c c} \hline \(SOC_{S,min}\) & 20\% & \(P_{G\_1\_max}\) & 50 kW & \(c_{S}\) & 0.01 \(\upxi\)/kWh \\ \(SOC_{S\_max}\) & 80\% & \(P_{G\_5\_max}\) & 50 kW & \(c_{PVS}\) & 1.2 \(\upxi\)/kWh \\ \(SOC_{EV\_min}\) & 20\% & \(P_{S\_max}\) & 34.5 kW & \(V_{ref}\) & 400 V \\ \(SOC_{EV\_max}\) & 100\% & \(Np_{V}\) & 84 PV & \(E_{Bat}\) & 90 kWh \\ \(SOC_{S_{0}}\) & 50\% & \(p_{PV\_MPPT}\) & 28.98 kWp & \(E\) & 50 kWh \\ \(P_{EV\_fast\_max}\) & 50 kW & \(c_{G\_NH}\) & 0.1 \(\upxi\)/kWh & & \\ \(P_{EV\_aver\_max}\) & 22 kW & \(c_{G\_PH}\) & 0.7 \(\upxi\)/kWh & & \\ \(P_{EV\_slow\_max}\) & 7 kW & \(c_{EV\_penalty}\) & 2.5 \(\upxi\)/kWh & & \\ \hline \end{tabular}
\end{table} 
Table 1: Optimization and simulation parameter values. \begin{table}
\begin{tabular}{c c c c c c} \hline
**EVs** & \(SOC_{EV\_arr}\) & \(SOC_{EV\_des}\) & \(t_{arr}\) & \(t_{est\_ch}\) & \(M\) \\ \hline EV1 & 29\% & 74\% & 09:10 & 03:13 & Slow \\ EV2 & 23\% & 78\% & 09:40 & 01:15 & Average \\ EV3 & 22\% & 88\% & 12:20 & 04:43 & Slow \\ EV4 & 32\% & 78\% & 14:20 & 03:18 & Slow \\ EV5 & 29\% & 70\% & 14:30 & 00:25 & Fast \\ \hline \end{tabular}
\end{table} 
Table 2: Assumed options by the EV users. In this case, the PV power production is considered significant since the weather is sunny and clear, so the irradiation is high, and there are no fluctuations. The IIREVs demand power is based on the data given in Table 2. Figure 6 shows the power flow and storage state of charge for \"Sim w/o opt\" and simulation with optimization \"Sim with opt\" for case 1, which is based on introducing the \(k_{D}\), which is calculated in the optimization layer, into the real-time operation algorithm in Simulink. In Figure 6a, the storage has priority over the grid either to be discharged or to be charged. However, when EV5 arrives, the IIREVs demand power greater than the PV and storage powers that they can supply, where the black dotted lines represent the maximum  
Figure 5: PV MPPT real and predicted powers—case 1. Figure 6: Power flow and storage state of charge in (**a**) “Sim w/o opt” and (**b**) “Sim with opt”—case 1.  
storage power and the red dotted lines represent the maximum grid power that can be reached. Therefore, the grid supplies power to charge the EVs. On the other hand, in Figure 6b, the power flow of the storage and the grid is based on the coefficient \(k_{D}\). Since between 12:00 and 13:00 is considered a peak period, by selling energy to the grid operator, it is possible to make profits and, thus, reduce the total cost of energy. However, after 13:00, the storage can be recharged to be able to charge the future EVs with sufficient storage energy. Therefore, when EV5 arrives, the PV, storage and grid can together supply the EVs. Figure 7 shows the EV energy distribution for \"Sim w/o opt\" and \"Sim with opt\". The calculation of EV energy distribution is detailed in [50]. EV1, EV3, and EV4 depend mainly on PV energy since they charge in slow mode. EV2 depends on PV and storage with a slightly equal percentage. EV5 depends on the PV, storage and grid energy. The percentage of grid energy is significantly greater than the other EVs, since it is charging in fast mode. Figure 8a shows the energy system distribution for \"Sim w/o opt\", \"Sim with opt\" and \"Opt for real conditions\". There is no grid injection in the \"Sim w/o opt\", while for the \"Sim with opt\" and \"Opt for real conditions\", there is grid injection, which indicates that selling energy to the grid and the charging energy of the storage was sufficient to get the best energy distribution for the EVs. The percentage of accuracy is the ratio of the total cost over the total cost of the \"Opt for real conditions\". The closer the percentage to 100%, the more accurate it is.