## 3 Results
### Comparison with Data from Simulations  
This section compares the values obtained by measurement with the values obtained by simulation using the _embpy_ tool, the aim being to analyze the similarities and differences obtained about different reference values, to then effectively study the behavior of the simulation tool. Figure 15 shows the total energy consumption required by users in the analyzed infrastructure. The total consumption is then allocated in the hour at the start of the session and finally normalized for each of the maximum output powers available. Figure 16 displays the simulation results obtained using the _embpy_ tool. The upper section, which illustrates the profile of public charging points with a maximum output power of 22 kW, shows an average daily pattern consistent with actual measured values (Figure 15--Top), thereby validating the methodology used. Notably, there is a distinct peak in charging activity in the late afternoon, when many users leave workplaces and head to other destinations, recharging when charging stations are available. Conversely, when examining the electricity demand from high-power charging points on the grid, a discrepancy is observed compared to the measured data (Figure 15--Bottom). The profile does not exhibit regular and consistent values throughout the day, with lower values during the night; instead, it shows a pronounced peak in the mid-afternoon. This behavior can be attributed to the assumptions used within the tool, which considers that all possible destinations have both slow and fast charging points, with the latter being less frequently used due to their limited availability. Since no destinations exclusively feature fast charging points, the profile more closely resembles that of slow charging points, maintaining the afternoon peak. Figure 15: Normalized consumption in recharging points divided by type of maximum output power, \(P\), and by hour of session start. In the next comparison, two critical parameters are analyzed to evaluate the use of charging points: the average duration of charging sessions and the average energy drawn from the grid. The analysis involves a detailed comparison between real-world data obtained through monitoring and the data generated by simulations. This comparison specifically focuses on the different behaviours observed when using slow charging points and fast charging points. The top part of Figure 17 illustrates the comparison between the simulated data for slow charging points, which have a nominal capacity of less than 45 kW. Compared to the measured data in the top of Figure 11, the simulation data indicate a longer average duration of charging sessions, accompanied by a simultaneous reduction in the average electricity drawn from the grid for each recharge. A possible explanation for this discrepancy could be related to the structure of the simulation code. The code includes a high frequency of home charging and does not impose a maximum state of charge (SOC) limit before vehicles are recharged. Consequently, vehicles often arrive at public charging points with a relatively high battery level, utilizing the available time to recharge but drawing only a limited amount of electricity from the grid. The same comparison between measured data and simulated data through _embryy_ can be done by considering instead the fast DC charging points. When comparing the measured data (Figure 11--Bottom) from the fast DC charging points with the simulation results (Figure 17--Bottom), a similar pattern emerges as observed with the AC charging points. There is an increase in the average charging time coupled with a reduction in the energy required for charging, for the reasons previously mentioned. However, an aspect that validates the correct functioning of the simulation tool is that, in this case as well, the comparison shows that the fast DC charging sessions are shorter but demand more energy than the AC charging sessions.