## 4 Discussion
Hourly electricity demand shows similar profiles, with peaks in the late afternoon hours. However, the simulated recharges via _enmopy_ are longer and have lower consumption, due to the structure of the code, whereby vehicles are often recharged with already high SOCs and use all the time available while parked. The findings of this study have significant theoretical and practical implications. Theoretically, this research contributes to the body of knowledge on EV infrastructure deployment and usage patterns, providing a comprehensive dataset that can be used to refine and validate predictive models of energy consumption and charging behaviors. This data is essential for upcoming studies related to sustainable transportation systems and grid integration of electric vehicles. Practically, the results offer valuable insights for policymakers, urban planners, and utility companies. Policymakers can use the data to inform decisions on where to allocate resources for new charging infrastructure, ensuring it meets the actual demand and supports widespread EV adoption. Urban planners can benefit from understanding the spatial and temporal distribution of charging sessions to design more efficient and user-friendly recharging networks. Utility companies can use the findings to optimize grid management and plan for future demand, ensuring reliable energy supply and minimizing the risk of overloading the grid. Additionally, businesses involved in the EV market, such as charging station operators and automotive manufacturers, can leverage these insights to enhance their services and products, ultimately contributing to the development of a more robust and sustainable EV ecosystem. The results regarding the presence of charging points installed in the territory and their actual use can provide crucial insights for the operator in the charging infrastructure market. If the data can identify areas that may require an investment for the construction of new charging points and the analysis of total electricity consumption, together with the time of use, they also provide important information on the use of charging points currently available, thus providing an important tool on the effectiveness of installations that have already been completed in recent years. For manufacturers and fleet operators, incorporating advanced simulation tools can lead to more accurate predictions of energy consumption, enhancing range accuracy and extending battery lifespan. This results in better vehicle design, more reliable performance, and greater customer satisfaction. Additionally, the analysis of the share of charging connectors used during the monitored timeframe provides crucial information that can guide manufacturers in designing vehicles with charging systems that align with actual usage patterns. Understanding these preferences allows for more targeted development of new EV models, ensuring they meet specific user needs and driving conditions. By leveraging these insights, the EV industry can refine its products and contribute to the creation of more efficient and sustainable electric vehicles. The present study collects detailed and granular data of public charging stations. Nevertheless, to have a full picture of the impact of e-mobility on the electricity grid, the same data would be needed for charging stations in private homes, offices, hotels, and restaurants. Usually, most charging events occur at these places, leading to a higher electricity demand than the one of public chargers. However, these private charging stations are usually just a few of the many electricity consumption points connected to a consumer's electricity meter, making data collection and analysis more challenging. Another limitation of the study presented is related to _embpy_, the tool used to simulate daily recharging events, evaluating their duration and finally deriving the hourly recharging profile. Since the _embpy_ tool provides the type of place where the vehicles are located during the day (e.g., school, work, etc.)