## 3 Results and Discussion
### Discussion
Number of daily charging xEV as a percentage of the total EV fleet:  
A consequence of vehicle range and user behavior assumptions, only a share of xEV ranging from 34% to 89% of theoretical total value \(\left[\left(BEV_{P}\right)_{y}+\left(PHEV_{P}\right)_{y}\right]\) used the charging infrastructure on an average, daily basis, with the expected trend shown in Figure 10. This finding directly reflected into less charging events with average higher energy requests. This could lead to a smaller than \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Private Residential Charging Infrastructure**} \\ \cline{3-7}  & & \multicolumn{2}{c}{**Florence**} & \multicolumn{2}{c}{**Bruxelles**} \\ \hline
**Output (Cumulative Values)** & & **2020** & **2025** & **2030** & **2020** & **2025** & **2030** \\ \hline Total CS & Min & 85 & 442 & 1676 & 877 & 3285 & 8559 \\  & Max & 967 & 4694 & 13,401 & 4191 & 13,253 & 31,386 \\ \hline Total costs (\(\mathbf{k\ell}\)) & Min & 76.5 & 367.8 & 1267.4 & 1078.2 & 3736.4 & 8917.4 \\  & Max & 870.3 & 3910.2 & 10,257.6 & 3771.9 & 11,163.1 & 24,382.1 \\ \hline Average daily charged energy & Min & 365 & 1828 & 6318 & 4778 & 18,033 & 42,722 \\ (\(\mathbf{kWh}\)) & Max & 3962 & 18,739 & 49,524 & 22,047 & 67,663 & 154,527 \\ \hline Share of total charging energy & Min & 52.1\% & 45.6\% & 36.5\% & 52.9\% & 46.9\% & 38.3\% \\  & Max & 52.8\% & 46.8\% & 37.8\% & 53.0\% & 47.7\% & 38.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Model results for xEV fleet forecast and private residential charging infrastructure for Florence and Bruxelles area. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Florence**} & \multicolumn{2}{c}{**Bruxelles**} \\ \hline
**Output (Annual Mean Values)** & & **2020–2025** & **2025–2030** & **2020–2025** & **2025–2030** \\ \hline CS to be installed in a year & Min & 11 & 23 & 108 & 112 \\  & Max & 143 & 223 & 464 & 536 \\ \hline Yearly deployment costs & Min & 25,387 \(\mathbf{\ell}\) & 102,789 \(\mathbf{\ell}\) & 238,265 \(\mathbf{\ell}\) & 550,855 \(\mathbf{\ell}\) \\  & Max & 373,816 \(\mathbf{\ell}\) & 1,075,794 \(\mathbf{\ell}\) & 1,192,045 \(\mathbf{\ell}\) & 2,570,283 \(\mathbf{\ell}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Yearly steps to deploy the publicly accessible charging infrastructure as sized by the model. expected infrastructure but with the same average energy request thus, consequentially, possible higher power request. The higher values calculated for Bruxelles area could be related to the higher circulating fleet, thus to the higher number of PHEV; in fact, PHEV were modeled to a much smaller battery capacity, so they usually charge more often. EV/CS ratio:  
This parameter describes the number of xEV virtually assigned to each existing CS of the infrastructure. Higher EV/CS led to costs reduction for the infrastructure and higher revenues for each  
Figure 10: Publicly accessible charging infrastructure daily charging events for Florence (light blue) and Bruxelles (light green). Figure 9: CS distribution (left) and energy requests distribution within power level (right) for the publicly accessible charging infrastructure. CS; anyway, also negative side effects could occur, such as a higher risk of finding the CS occupied by other charging xEV. A balance between these aspects were thus found; AFI Directive suggests a 10:1 ratio [4], while Italian PNIRE suggest an average figure of 7:1 [5]. The following equation was used within the model to define this parameter:  
\[\left(EV/CS\right)_{y}=\sum_{i=1}^{y}\left(\frac{\left(BEV_{P}\right)_{i}+ \left(PHEV_{P}\right)_{i}}{\left[\sum_{j}(s_{MIN}+m_{MIN}+f_{MIN})\right]_{i}} \right). \tag{20}\]  
A rising trend is shown in Figure 11, going through the timespan of the analysis; this is due to a growing path in the installation of higher power CS--such as medium and fast ones--capable of higher numbers of daily charging event.