# PV Sizing for EV Workplace Charging Stations--An Empirical Study in France
## 2 State of the Art
### Sizing PVCS at the Concept Development Step  
The study in [9] details the sizing of the main components of a system constituted of a household, a battery, a PV plant and bidirectional EV chargers. Three EVs with identical parking time schedules during weekdays (8 a.m.-6 p.m.) are considered. The authors integrate the sizing problem into the optimal power management problem by considering, in addition to the EV and battery power profiles, the components ratings as decision variables. These variables are the BES capacity (kWh) and their charger power ratings (kW), the PV system power rating (kW), the inverter power rating (kW) and the EV charger power rating (kW). Power management minimizes the costs (including those due to battery degradation) by taking into account network and component constraints. Such constraints allow for elaborate business models based on self-consumption, energy arbitrage and FCR market participation. The optimization algorithm is formulated and implemented in GAMS (Generic Algebraic Modeling System) [13]. The authors in [14] propose an optimal sizing-control methodology for residential microgrids where EVs are viewed as controllable loads. The microgrid contains PV, Wind Turbines (WT), a bidirectional inverter and a local Battery Energy storage System (BES). The optimization algorithm is based on Mixed Integer Linear Programming (MILP) and solved using CPLEX [15]. It is designed to minimize the annual cost of electricity. The size of the components are the decision variables of this algorithm, in addition to the dispatched power profile. In this paper, the number of EVs is fixed and, so, is not considered as part of the sizing decision variables. EV mobility data include three EVs with a deterministic nature. The authors in [16] identified the optimal sizes of PV, WT and BES in a smart home microgrid configuration and vehicle-to-home context. The energy management system aims at minimizing the annual cost of electricity, and it follows rule-based logic, whereas the sizing problem utilizes the particle swarm optimization technique [17]. The meteorological and EV mobility data (for one single vehicle) are randomly generated (based on probabilistic distribution functions obtained from various datasets). The authors in [18] study a microgrid made of an EV charging station, a BES and a PV plant. This microgrid is located at a workplace. The operation of the charging stations is controlled using customized expert rules that aim to minimize the energy fed/drawn from the grid. The authors propose a methodology for determining the optimal PV panel tilt and the BES size for two scenarios and for eight different charging profiles. The optimal values are found by testing a set of values in a given range and selecting the value that gives the best results. In this paper, for example, the author tests the capacity of the BES from 5 kWh to 75 kWh by steps of 5 kWh. Such a technique that tests a given set of parameters will further be called \"parametric analysis\". In [12], the authors consider a system constituted of a PV plant and EVs (that embed bidirectional chargers). In one of their numerical examples, they consider a 2.64 MWp plant and 184 EVs. The algorithm that controls the charge/discharge of the EV is designed to minimize the difference between the day-ahead production commitment of the PV plant and its real production. The optimization problem is formulated as a linear optimization problem. The authors consider a deterministic charging demand, i.e., all the vehicles are supposed to be available from 9 a.m. to 6 p.m. Lastly, they compared the results of their control algorithm for different ratios between the number of EVs and the peak power of the PV plant. An economic study of EV parking lots at workplaces equipped with PV in the states of Ohio and California in the US [19] was conducted to demonstrate the economic attractiveness of such an installation with different PV installed capacities and financial hypothesis.