## 3 Results and Discussion
### Discussion  
The analytical model described in this article was designed to capture the main complexities of the selected scenarios, while maintaining a simple and lean structure. Therefore, some of the simplifying assumptions implemented in the model could affect the outputs and the accuracy in some specific areas. As already stated in Section 2.1, the model did not update the utilization ratio of the already deployed charging infrastructure to take into account incremental improvement during the time period. This could potentially lead to a sub-optimal use of the charging infrastructure. Overall, infrastructure oversizing can be estimated in just a few percentage points from optimum, since it is directly proportional to utilization ratio. On the other hand, the model evaluates the average daily values, thus simulating an average use of the charging infrastructure; this could lead to an underestimation of peak power demand from the grid, as well as to a possible underestimation of the number of CS needed by the circulating xEV fleet. Finally, the model has to deal with the lack of real \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Charging Energy Request Classes—\(E_{j}\) (kWh)**} \\ \cline{2-11}
**CS Power Levels** & **6** & **12** & **18** & **24** & **30** & **36** & **42** & **48** & **54** & **60** \\ \hline
2 kW & \(r_{r,j}\) & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
7 kW & \(r_{s,j}\) & 3 & 3 & 3 & 2 & 2 & 2 & 2 & 2 & 0 \\
22 kW & \(r_{m,j}\) & 8 & 8 & 8 & 4 & 4 & 4 & 3 & 3 & 3 \\
50 kW & \(r_{f,j}\) & 12 & 12 & 12 & 12 & 8 & 8 & 8 & 8 & 4 & 4 \\
100 kW & \(r_{f,j}\) & 17 & 17 & 17 & 12 & 12 & 12 & 8 & 8 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Maximum number of charging events daily manageable by each level of energy requests, for each Charging energy request class. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **2020** & **2025** & **2030** \\ \hline Share of BEV using the publicly accessible charging infrastructure \(\left(\circ\partial BEV_{p}\right)_{y}\) & 56.5\% & 66.9\% & 75.8\% \\ \hline Share of PHEV using the publicly accessible charging infrastructure \(\left(\circ\partial HEV_{p}\right)_{y}\) & 45.6\% & 58.6\% & 69.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Share of PHEV and BEV expected to be using the publicly accessible charging infrastructure. data related to charging behaviors of users, and with the uncertainty of the forecasts used as inputs; this situation clearly leads to a corresponding range of variability of the outputs. All the following outputs will be thus presented in a min-max format, given also the fact that they will summarize nine different analyzed scenarios for each geographical area. Moreover, it must be highlighted that the maximum values refer to a threshold situation related to the turnover trends of passenger vehicles circulating fleet and thus have to be considered as an \"upper limit\". That said, the main results related to public charging infrastructure are reported in Table 13, while the main results related to private charging infrastructure are highlighted in Table 14, both for Florence and Bruxelles area. The average amount of daily energy required by xEV fleet charged on public infrastructure is assumed to reach more than 80 MWh for Florence and almost 245 MWh for Bruxelles by 2030, for the most demanding scenario. These values correspond to an average daily electricity consumption of about 13,500 families in Florence, and about 35,000 families in Bruxelles [53]. Considering an average delivery window of 20 h, compatible with the assumptions made in the study, this energy demand theoretically corresponds to an average continuous power requirement of 4 MW for Florence and 12.3 MW for Bruxelles. The same analysis, when applied to the private residential charging infrastructure, give as results for the 2030 values of almost 50 MWh for the Florence and almost 155 MWh for Bruxelles for the most demanding scenario.