## 4 Conclusions and Perspectives  
The experiments conducted on CEA Cadarache site benefits from the city-size of the research center and from its CPO, eMSP and DSO roles. They involve nearly 100 out of the 376 EV users and more than 40 different car models. The objectives of these experiments were to fully recharge the battery of the users before their forecast departure time and to maximize the self-production rate of the system, which is constituted by 24 charging points and of part of the production of a PV plant. The first goal was reached, except in very few cases. We consider that the second goal was partially reached. Indeed, we compared the self-production rates obtained with or without control. Our analysis showed that the charging control always increases this ratio. Some days, especially during grey ones, the ratio drastically increased (we gain sometimes more than 60 points of percentage). However, there are several days, especially sunny ones, when the increase was rather low. We also showed that, sometimes, the ratio was rather low, even with control. From our point of view, this is mostly due to the users that do not give us control over the charge of their EVs. One way to improve the ratio is to convince these users to change their mind. This could be done, for example, by organizing \"solar charging\" contests, by communicating intensively in the Cadarache center on obtained experimental results or by offering a financial incentive. Another way would be to enforce smart charging for all EVs but the risk is to reduce the users' satisfaction and their involvement in the experiments. We believe that such an option could be counterproductive because thisinvolvement is mandatory to get access to key data such as the initial SOC and the forecast time of departure. As perspectives, we plan to improve our planning algorithm in three main ways. First, we plan to improve the models that relate the value of the set points and the power withdrawn by the car, as suggested in [39]. These models are used during the allocation of the available power. In the current implementation of our algorithm, any error in the model translates into in a loss of power. In other words, this loss of power is not allocated to other cars. To improve such models, we expect to increase the number of charging points, of users, and thus, of car's models. We also forecast to monitor other physical variables useful for the modeling, such as the SOC and the battery temperature. Second, we consider turning the planning algorithm into an optimization problem and then to apply, for example, Mixed-Integer Linear Programming (MILP) methods. At last, the retrieved data could serve as a basis for defining other Key Performance Indicators, such as the state of health of car battery, the occupancy rate of each charging station or the amount of power not extracted from the grid thanks to the charging control. This latter could be used to quantify the economic benefit of EV solar charging. The satisfaction of users is also a key issue that could be estimated. This is especially true when users are fully involved and are customized to giving their charging features at the beginning of the charging sessions. In a more long-term horizon, we plan to assess the EV capability to provide flexibility services to the electricity network and to estimate the benefit for the grid. Conceptualization, B.R. and S.G.; methodology, B.R. ; software, S.G.; validation, B.R., L.M., G.V., S.G. and A.M.; formal analysis, B.R. and L.M. ; investigation, B.R. and L.M. ; resources, S.G., B.R. and L.M. ; data curation, S.G. and B.R. ; writing--original draft preparation, B.R. and L.M. ; writing--review and editing, G.V., A.M., S.G., L.M. and B.R. ; visualization, S.G.; supervision, B.R. ; project administration, B.R. and G.V. ; funding acquisition, G.V., A.M. and B.R. All authors have read and agreed to the published version of the manuscript. This research was partially funded by RTE and CEA.