## 3 Results and Discussion
### Discussion
Considering an average delivery window of 8 h, compatible with the assumptions made in the study of only night charges, this energy requests corresponds to an average continuous power requirement of 6.3 MW for Florence and 19.4 MW for Bruxelles. These outputs show that the share of total energy demands supplied by private residential charging infrastructure ranged between slightly more than 50% on year 2020 to less than 40% at the end of the time period, on year 2030. These results were strictly related with the assumption on the share of xEV using private infrastructure defined in Table 12. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Publicly Accessible Charging Infrastructure**} \\ \cline{3-8}  & & & **Florence** & & & **Bruxelles** & \\ \hline
**Output (Cumulative** & & & & & & \\
**Values)** & & **2020** & **2025** & **2030** & **2020** & **2025** & **2030** \\ \hline EV fleet/Tot fleet (\%) & Min & 0.1\% & 0.5\% & 2.3\% & 0.3\% & 1.4\% & 4.6\% \\  & Max & 0.9\% & 5.0\% & 17.8\% & 1.5\% & 5.4\% & 16.5\% \\ \hline Total CS & Min & 12 & 66 & 194 & 201 & 580 & 1210 \\  & Max & 199 & 795 & 2053 & 1014 & 2538 & 5537 \\ \hline Total costs (k\(\ell\)) & Min & 24.4 & 169.6 & 743.7 & 369.2 & 1366.2 & 4496.5 \\  & Max & 385.1 & 2309.5 & 8323.9 & 1959.1 & 6993.4 & 21,381.9 \\ \hline Average daily charged & Min & 0.3 & 2.2 & 11.0 & 4.3 & 20.4 & 68.7 \\ energy (kWh) & Max & 3538 & 21,317 & 81,363 & 19,514 & 74,215 & 244,712 \\ \hline Global utilization rate & Min & 10.61\% & 9.59\% & 8.88\% & 11.70\% & 10.77\% & 10.27\% \\  & Max & 16.60\% & 14.77\% & 14.34\% & 14.69\% & 16.19\% & 14.44\% \\ \hline EV/CS ratio & Min & 10 & 12 & 18 & 8 & 11 & 15 \\  & Max & 18 & 19 & 27 & 9 & 13 & 19 \\ \hline “Slow A” CS/“Fast” CS ratio & Min & n.a. B & 59 & 188 & n.a. & 166 & 31 \\  & Max & n.a. & n.a. & n.a. & n.a. & n.a. & 72 \\ \hline \hline \end{tabular}  
* Here 7 kW and 22 kW power levels are assumed as “Slow”; B “n.a.”, whenever present, means that the corresponding charging infrastructure does not have 50 kW and 100 kW CS.  
\end{table} 
Table 13: Model results for xEV fleet forecast and publicly accessible charging infrastructure for Florence and Bruxelles area. Another interesting perspective for the comparison of both private and publicly accessible infrastructure was the total cost of installation: private infrastructure results were always more expensive, up to twice the cost for some scenarios. This poor performance was related to the lowest EV/CS ratio of residential CS that was assumed to be equal to 1, so that basically for every xEV that uses the private infrastructure a CS is needed. Finally, with the aim of verifying the effort to be made to achieve the results described by the model, the average number of charging stations to be installed each year and the related annual cost were then calculated. The results were averaged over the two periods 2020-2025 and 2025-2030 for the areas of Florence and Bruxelles and are shown in Table 15.  
After this brief overall presentation of the results, in the following sections more in-depth analyses would be presented on specific arguments. Publicly accessible infrastructure composition:  
The most important result obtained from the analysis of model outputs was that the higher the power CS was the smaller share of the total, as Figure 9 shows. This was related to their worst charging events/cost ratio \(r_{i,j}/(C_{i})_{y}\) comparing to slow and medium ones. In other words, the high-power CS was able to manage less daily charging events for the cost. Moreover, an inversion trend between slow and medium ones was highlighted during the timeframe of the analysis. This behavior was related to the shift to higher \(E_{j}\) classes of the energy requests moving through the time period. The energy distribution pattern between the various power level also confirmed these findings, showing an even stronger predominance of the medium, 22 kW, CS power level.