## 3 Results and Discussion
### Input Values Definition for the Implemented Scenarios
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Area**} & \multirow{2}{*}{**Scenario**} & \multicolumn{3}{c}{**BEV**} & \multicolumn{3}{c}{**PHEV**} \\ \cline{3-7}  & & **2020** & **2025** & **2030** & **2020** & **2025** & **2030** \\ \hline \multirow{3}{*}{Florence} & Low & 0.0\% & 0.2\% & 1.0\% & 0.0\% & 0.3\% & 1.3\% \\  & Medium & 0.1\% & 0.7\% & 3.2\% & 0.3\% & 1.5\% & 5.0\% \\  & High & 0.3\% & 2.0\% & 7.0\% & 0.6\% & 3.0\% & 10.8\% \\ \hline \multirow{3}{*}{Bruxelles} & Low & 0.1\% & 0.7\% & 2.1\% & 0.2\% & 0.7\% & 2.5\% \\  & Medium & 0.3\% & 1.2\% & 3.7\% & 0.5\% & 2.0\% & 5.2\% \\ \cline{1-1}  & High & 0.5\% & 2.0\% & 7.3\% & 1.0\% & 3.4\% & 9.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: BEV and PHEV fleet forecasts for the area of Florence and Bruxelles municipalities—shares of total circulating fleet. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**2020**} & \multicolumn{2}{c}{**2030**} \\ \cline{2-5}  & **BEV** & **PHEV** & **BEV** & **PHEV** \\ \hline Average specific consumption (kWh km\({}^{-1}\)) \((av_{V})_{y}\) & 0.192 & 0.108 & 0.154 & 0.086 \\ Average battery useful capacity (kWh) \((but_{V})_{y}\) & 31.6 & 7.14 & 59.5 & 9.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: xEV average consumption and capacity assumed forecast values. the power of the recharging system [53]. In conclusion, a literature research was carried out in order to evaluate the possible future developments of the infrastructure in terms of power levels and usage [34]. At the end of the research phase, the following power values were identified for the various charging systems used in the model, and finally applied both to Florence and Bruxelles area:  
* Private residential charging points P\({}_{r}\): 2 kW
* Publicly accessible charging points: Three power levels and corresponding charging systems, called slow, medium and fast, have been implemented. The selected power values are listed in the following Table 8:  
An installation cost has been defined for each CS power level; this value includes the cost of the charging station, including installation and grid connection costs. Operating costs were not considered, as they were not part of the scope of the study, nor were maintenance costs; the latter were not implemented in order maintain the model simple enough. Learning curves were applied to capital, installation and connection costs, to take into account the reduction of costs over time related to the effects of industrialization and rationalization of processes. All the information derives from an extensive literature research (cfr. [10; 31; 41; 46; 54; 55]) and brought to the definition of \(\left(C_{s}\right)_{y^{\prime}}\left(C_{m}\right)_{y^{\prime}}\left(C_{f} \right)_{y}\) and \(\left(C_{r}\right)_{y^{\prime}}\) related to the y-th year and described in Table 9.  
A literature search was then carried out to verify the actual use ratios of existing charging infrastructures [20; 56]. The driving and parking behaviors and daily timeframes of an average user were mainly derived from the study [23], which specifically analyses driving behavior in various European countries, including Italy. The results of these evaluations, together with other usage constraints and the energy request levels \(E_{j}\), brought to the maximum number of charging events for level of energy requests reported in Table 10 for each power level of the CS:  
Driving and charging behavior of users:  
For what it concerns the average daily travelled distance, two different values have been calculated, for Florence and Bruxelles, as shown in Table 11 the most recent data available (2015) have been used, from:  
* Odyssee-Mure Database [57]: Italy and EU-28. * ACEA [58]: Belgium. * ACI--CENSIS [59]: Italy.