## 4 Discussion  
In this work, actual data obtained by measuring the utilization of the charging infrastructure in the region of Trentino-South Tyrol were compared with data obtained by means of the _embopy_ simulation tool, with which it was possible to derive hourly charging profiles and the average duration and demand of energy to the grid. The extensive detail and variety of the results provided by the measurements allows an initial comparison with the results so far found in the literature. Compared to the  
Figure 17: Distribution of electricity obtained from the grid and charging duration from simulated data for AC ((**top**), orange) and DC ((**bottom**), green) charging stations, following the same color scheme as in Figure 11.  
study of [PERSON] et al. [15], the wider time span considered allows analysis of the strong increase in the number of recharging points installed in the area, with the number of charging points increasing by 44%, and an even-higher rise in charging sessions, increasing by over 150% annually. These values, higher than those present in a similar study by [PERSON] et al. [18], indicate a clear sign of the strong diffusion of electric mobility (and its necessary infrastructure) within this Italian region. Compared to the study of [PERSON] et al. [20], this study also provided a differentiation considering the diversity in the charging infrastructure, showing that while AC charging points were more numerous, DC chargers delivered nearly equal the total energy. This aspect highlights the importance of a diverse charging infrastructure to meet various user needs. The advantage of having also considered a case study, as broad as the regional one, made it possible to show the influence of geographical location on the amount of charging points present and their utilisation. In their study, [PERSON] et al. [31] calculated the consumption of a single charging station and then redistributed the load, depending mainly on parameters such as the population present. This paper shows instead the influence of other parameters that were not considered until now, such as the presence of areas with a lower population, but which represent important tourist areas, able to attract many people and, therefore, potentially also electric cars. Those tourist areas show a high demand that requires a tailored infrastructure deployment. The influence of tourism and other annual phenomena is also crucial in the analysis of the variation of energy required for charging during the year. The presence of an increase in the energy required for charging in August and December is closely related to the characteristics of the territory considered. Compared to the study of [PERSON] et al. [30], this demonstrates the need to consider a long period for the collection of monitoring data, so that this variance can be understood. In addition, any data obtained by simulation must be correctly redistributed over the year if they start from a shorter simulation period. From the point of view of charging technology, the presented study focuses on different charging behaviours depending on whether charging is fast or slow, hence on the nominal capacity available, less evaluated by a similar study of [PERSON] et al. [29]. AC charging sessions tended to have lower energy consumption but longer durations, compared to DC sessions. This difference is likely due to the technical capabilities of the chargers and their typical use cases (e.g., AC chargers being used for longer-term parking). Another aspect presented in this study is the differentiation between the type of plug used for charging, distinguishing between CHAdeMO and CCS, showing how most of the recharges occur with the second type, especially regarding the recharges in DC. This aspect is an important analytical tool for considering the type of vehicles that use these charging systems. An important novelty of the article is the comparison between measured and simulated data, allowing a more complete analysis of the modelling pattern of the tool.