# PV Sizing for EV Workplace Charging Stations--An Empirical Study in France
## 2 State of the Art
### Our Contribution  
We consider that the EV charging demand data are one of the most important input data in prefeasibility/feasability studies. More precisely, we think that, in order to obtain precise results, this EV charging demand has to be as \"representative\" as possible of the use-case under study. In particular, this data has to encounter the variability of the EV users' behavior over a long period but also has to take into account the characteristics of the EVs and of the charging points. We also think that it could be possible to define \"standard\" EV charging demands corresponding to:  
* Workplace parking lots (where the EVs remain plugged in during work hours);
* Shopping centers (where the users arrive all day long and stay only a couple of hours);
* Residential (where the users arrive at the end of the day and leave in the morning);
* Delivery fleet (where the EV arrives at a fixed time in the day and also leaves at another fixed time).  
The workplace parking lots use-case illustrates this article; we have collected empirical data over a horizon of more than six years at an industrial and research complex in southern France. As detailed in Section 4.2, the dataset includes more than 32,000 transactions, 350 EV users, more than 40 EV models and 80 charging points. According to the literature review and to the best of our knowledge, this paper is the first that describes a sizing procedure based on a large and real EV charging dataset.  
Furthermore, the computational logic of the proposed methodology can be split into four main relationships between input data and internal variables (defined in Section 3.3). Obtaining relationship 4, which relates the self-production ratio and the Production-to-Consumption ratio, is a rather complex task; the designer has to (1) collect and process input data (that are mainly the EV demand and the PV production and forecast), (2) perform simulations and (3) aggregate all the simulations results. Relationship 1 relates the price of the energy and the self-production rate. This relationship may be quite easily adapted to all the business models that aim at increasing this self-production rate. At last, relationships 2 and 3 are quite easy to compute. Thus:  
* When the PVCS designer assumes that the EV charging demand is a standard one, the pre-computations of relationship 4 can be re-used. The designer may also modify relationship 1 if the considered business model is not the same as the one described in this article. In the two cases, minimum effort, in terms of time and resources (i.e., a few hours for a non-specialist), is required;
* When the designer encounters a specific use-case (i.e., non-standard), pre-processed data proposed in this paper is not usable. The four relationships of the methodology have to be computed. This level of involvement allows us to obtain an estimation of the project's performance with the highest precision, but the effort is also the highest. We estimate that 3 or 4 weeks with decent programming competences are required.  
In other words, the modular structure of our method enables us to modify the business models independently of the other parameters. It also enables to pre-compute intermediate results. Once these pre-computations are performed, the sizing procedure may be quick and simple. To our knowledge, this is the only method that has such properties. It is of particular interest, for example, for a CPO that will have to size many charging stations with approximately the same charging demand. In that case, the CPO will first have to compute all the relationships from the charging demand of a representative charging station. Next, he will re-use the relationships (in particular, the 4 th) to drastically speed up the sizing of the other charging stations.