## 5 Results
### Electric Vehicle Charging Schedule  
As previously mentioned, in the baseline scenario, EV charging is not optimized. It is assumed that vehicles are connected to chargers immediately upon arrival at the depot and charged to 100% or the maximum SOC while EVs are waiting for the next trip. Consequently, this section will primarily focus on the EV charging optimization scenario, where the EVO is employed to optimize the charging schedules of the electric trucks. The case study focuses exclusively on night-shift operations, which last for 8 h and begin at 22:00. However, it is important to note that some vehicles return to the depot intermittently during the night, resting short periods and covering more than one trip. This can be observed in Table 2, where the EV schedule considered is presented. The simulation results obtained from the EV charging scheduler are presented in Figures 10 and 11. Where the baseline scenario and EV charging optimization scenario (referred to as EVO) are compared. Figure 10 presents the power demanded by the charging infrastructure during the day. The results show that the EVO solution charges the vehicles when there is more PV production, different from the baseline case, in which the vehicles are charged as they arrive at the depot. This results in cost savings and a higher rate of use of the renewable resource. Furthermore, Figure 11 shows the available energy for each vehicle. The discharging profile is approximated considering the average energy requested per trip. The charging profiles show how EVO delays the charging events, ensuring that not only is the photovoltaic production shared with the rest of the industrial loads, but also the idle time of the EVs is at a lower SOC, reducing the calendar degradation cost. \begin{table}
\begin{tabular}{c c c c} \hline
**Trip** & **Departure** & **Arrival** & **Vehicle** \\ \hline
1 & 22:00 & 03:00 & V7 \\
2 & 22:00 & 03:00 & V2 \\
3 & 22:00 & 04:00 & V1 \\
4 & 22:15 & 04:00 & V4 \\
5 & 22:15 & 04:30 & V10 \\
6 & 22:15 & 04:00 & V5 \\
7 & 22:15 & 05:00 & V6 \\
8 & 22:30 & 03:30 & V3 \\
9 & 22:45 & 04:00 & V9 \\
10 & 23:00 & 05:15 & V8 \\
11 & 03:15 & 06:00 & V2 \\
12 & 03:45 & 06:00 & V3 \\
13 & 04:30 & 05:45 & V1 \\ \hline \end{tabular}
\end{table} 
Table 2: Trip schedule and EV assignment considered for the case study. Additionally, a cost analysis is conducted for three different forecasting horizons: one, three, and five days. The results are summarized in Table 3. It is observed that the highest savings, 18.3% compared to the baseline, are achieved with a 1-day horizon. As the forecasting horizon increases, the savings decrease, with the lowest savings observed at the 5-day horizon. This reduction in savings is attributed to the increased uncertainty associated with longer forecasting horizons, which may lead to greater deviations in the optimal charging times. Finally, the EVO scalability is assessed by increasing the EV fleet. Table 4 presents the execution time of EVO for different fleet sizes. This shows that the approach proposed can reach execution times that are practically feasible for large EV fleets of up to 100 vehicles. However, it is essential to note the exponential increase in execution time, which may necessitate a longer execution time (>15 min). However, for EV fleets with hundreds of vehicles, the step size could be increased. Further investigation in this regard is out of the scope of this study. Figure 11: Energy track of all EVs analyzed in this study, comparing the baseline scenario (dashed lines) with the optimized EVO solution (solid lines). Figure 10: Aggregated power demand from the EV fleet. Comparison of baseline scenario and EV charging optimization (EVO). Green and blue lines correspond to power-related variables (**left axis**), and the electricity price is shown on the (**right axis**), in orange.