## 1 Introduction
Other studies have therefore placed the focus on quantifying the factors that have an influence in determining user connection periods at public charging stations. [PERSON] et al. [18] analyzed more than 17,000 charging events to evaluate the impact of the charging location (i.e., school, workplace, etc.). In a similar study, they considered instead the variation of the charging starting time and duration, based on the period of the day [19]. Although these studies provide a detailed analysis and a high degree of disaggregation of results, they refer to a small number of charging points and have a spatial frame at the urban level, not including spatial variability from a broader point of view. Following instead the technological development of vehicle charging technology, other studies have analyzed the differences between fast and slow charging points. [PERSON] et al. [20] proposed a techno-economic analysis of the impact of the charging strategy on the Total Cost of Ownership (TCO) of a fleet of electric buses in Shanghai. The results included the hourly electricity demand for charging, which is also analyzed in this paper, but for vehicles with strict daily schedules, which did not allow consideration of the charging behavior due to the geographical position of the charging stations. [PERSON] et al. [21] instead shifted the focus to private cars, monitoring the data of public charging points, but only at the urban level, only partially providing an analysis of geographical and spatial influences on the use of charging points. [PERSON] et al. [22], expanded the case study, monitoring more than 700 charging points across Ireland to study the different use of slow and fast charging points. Although the data provided represents a strong step forward in defining the charging habits of electric vehicles, the analysis falls short in providing data on daily charging profiles, which are crucial in defining the increase in power demand due to the electrification of the transport sector. A different approach is proposed by [PERSON] et al. [23], in which real data of daily taxi movements in the Beijing area were considered to identify the optimal locations in which to build charging points. Although this study presents an innovative approach in the use of real data, it refers only to taxis and does not consider private cars, whose users may follow different rules for the choice of charging points. Furthermore, although economic considerations are present, analyses of potential charging profiles and energy considerations are missing. Due to the difficult availability of open data, another possible approach is to use simulations to estimate future charging infrastructure requirements and potential usage. [PERSON] et al. [24] provide a comprehensive review of the state of EV scheduling, identifying key challenges such as energy management, battery degradation, and grid stability. They emphasize the need for adaptive algorithms that account for real-time fluctuations in energy demand and the importance of considering the impact of EVs on grid infrastructure. The paper also highlights future research opportunities, including the development of predictive models and decentralized scheduling algorithms to enhance EV network resilience. Among the studies that assessed electric vehicle scheduling, including power grid considerations, [PERSON] et al. [25] proposed a system that included both charging demand and renewable energy sources to evaluate the differences between controlled and uncontrolled charging, but this was a micro-grid case study, thus lacking the consideration of the influence of different geographical position on the production and demand profiles. [PERSON] et al. [26] proposed a comparable charging station model but employed a Markov Chain process to represent EV arrivals. However, their approach does not account for user behavior variables, such as charging point selection or charging duration, which can significantly impact the charging process.