# PV Sizing for EV Workplace Charging Stations--An Empirical Study in France
## 2 State of the Art
### Sizing PVCS at the Concept Development Step
Vehicle mobility data (arrival time, parking time) are statistically simulated using probability distributions derived from the Ohio State University's parking garage empirical data. Various incentives and tax deductions from multiple levels of authority are taken into consideration for the financial model. MatlabTM is utilized for EV charging demand simulation and grid emission, while PV production and cash flow computation are performed in the System Advisor Model (SAM) [20,21]. A constant 6.6 kW charging power is simulated in this case. To follow up with this, the authors also conducted a similar study with a more sophisticated charging algorithm using dynamic programming and optimization technics [22]. This algorithm maximizes the use of PV energy and reduces the impact on the grid while satisfying the energy demand of EVs. Parametric analyses are conducted with multiple varying factors (PV size, PV installation costs, incentives, electricity rates, parking rates) to study their financial impacts (mainly in terms of payback time). The work in [23] presents a methodology for sizing different energetic components of a PVCS in the microgrid context, including the size of the PV system, the stationary battery systems and the transformer. Theses sizes and other operational constraints are part of the objective function of an optimization algorithm that aims to minimize the entire system's economic costs. The EVs' initial SOCs are assumed to follow Gaussian distribution within the range of 0.2 and 0.5. The EVs' battery capacities, number of daily EVs and arrival time are also synthesized using Gaussian distributions. The paper concluded that with this methodology, the cost of the optimally sized configuration without PV would be higher than that of an optimal configuration with PV installation. In one of their numerical examples, they consider a charging station made of four chargers of 40 kW each, a 300 kWp plant, a 600 kWh battery and a 200 kW transformer. The authors in [24] introduced a rather simple multi-phased methodology to size a microgrid made of a PV plant, a BES and a charging station. This method is formalized with an Excel-based tool customized for various stakeholders and authorities. The customization of EV charging profiles and of physical as well as financial parameters allows users from different backgrounds to efficiently analyze the performance of the charging infrastructure from multiple aspects. The energy management strategy in this work is rather straightforward and representative of the real deployment strategy: energy from the grid would be required if PV production (and sequentially a stationary battery system production) is insufficient. However, if PV production surpasses the EV demand, then the surplus would be stored in the stationary battery or injected into the grid if there is still excess energy. The aim of this control is to avoid overloading the power grid and guarantee a high percentage of PV energy. In [25], the authors study the integration of EV and PV in an office building located in southern Italy. In this study, one EV is charged during working hours. Four different charging needs are considered (from 0 km per day to 120 km per day). They also compare two charging modes: the first one is an AC charge without control, and the second one is a DC charge with control such that the EV consumes the surplus of the PV production. The authors compute the self-consumption and self-production rates obtained for four different values of PV peak powers (from 4.5 kWp to 9 kWp) for the two charging modes and for different sizes of charging points. In [26], the authors study the integration of EV and PV in a household. Several scenarios are simulated over a long-term period (ten years) in order to take into account the aging of the battery and the PV panels.