## 4 Real-Time Experimental Tests
### Experimental Test 2  
The case of 27 October 2021, in Compiegne, France, is considered. Figure 23 shows \(P_{PV}\)\(MPPT\)\(PR\)\(PPV\)\(MPPT\), where the irradiations are intermediate and the weather is a bit cloudy, so there are low fluctuations. The IIREV demand power is based on the data in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case Operation** & **Grid Cost (cC)** & **Storage Cost (cC)** & **Total Cost (cC)** \\ \hline Real-time exp w/o opt & 13.90 & 8.52 & 22.73 \\ Real-time exp with opt & 59.18 & 5.68 & 64.86 \\ Opt for real conditions & 5.51 & 5.61 & 11.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Energy system cost—experimental test 1. Figure 22: EV energy distribution for “real-time exp” (**a**) without opt and (**b**) with opt—experimental test 1. Figure 24 shows the power flow and storage state of charge for \"real-time exp\" without optimization and the DC bus voltage--experimental test 2. Figure 23: PV MPPT real and predicted powers and IIREV demand power—experimental test 2. Figure 24: Power flow and storage state of charge for “real-time exp” without optimization (**a**,**b**) storage state of charge and DC bus voltage—experimental test 2. In Figure 24a, the storage is always prioritized to be either charged or discharged. However, after EV5 arrives and around 14:45, the storage is empty. The grid supplies power, but it is insufficient to fully charge the EVs, and therefore, EV shedding is applied from 14:45 until the departure of EV5 from the IIREVs at 14:55. After EV5's departure, the grid continues supplying power to the IIREVs as the storage is empty. Figure 24b shows the evolution of the storage SOC, where it is always discharging almost all the time until it is empty around 14:45, and the stability of the DC bus voltage is present even with small fluctuations. Spikes of a few voltages happen when each EV starts charging and when it finishes charging. Figure 25 shows the power flow and storage state of charge for \"real-time exp\" with optimization and the DC bus voltage--experimental test 2b. In Figure 25a, the power flow of the storage and the grid is based on the coefficient \(k_{D}\). From 12:00 until 14:20, the PV injects little energy to the grid during the peak hour, yet some fluctuations still happen where the grid supplies power. However, when EV5 arrives, the IIREV demand power is greater than the PV and storage power that they can supply. Therefore, the grid supplies power to charge the EVs with maximum power, and the storage is preserved. From 15:15 to 16:15, the storage discharges energy until it is empty to avoid the high cost of grid supply power, as it is considered a peak period. After 16:15, the grid supplies power, regardless of the \(k_{D}\) value. Figure 25b shows the evolution of the storage SOC, where the storage discharges energy from 09:10 to 10:50, 14:25 to 14:50 and around 15:15 to 16:15. Figure 25b also shows the stability of the DC bus voltage even with  
Figure 25: Power flow and storage state of charge for “real-time exp” with optimization (**a**,**b**) the storage state of charge and DC bus voltage—experimental test 2b. small fluctuations, which are due to the switching of DC converters, and the spikes of a few voltages happen when each EV starts charging and when it finishes charging. Table 5 shows the energy system cost for \"real-time exp\" without optimization, where the energy costs are higher than in optimization due to the cost of EV shedding. The real-time experiment with optimization is closer to the optimization for real conditions, as it avoids EV shedding and gives better energy costs of 60.91 c. In \"Opt for real conditions\", where the optimization is performed without uncertainties, it gives the optimal energy cost without error, which is 53.37 c. It avoids EV shedding and grid supply energy, and when EV5 arrives, the storage is discharged to the maximum power, then becomes empty around 14:45, provoking EV shedding.