With the current urge around the globe for switching to “green energy” and Renewable Energy Sources (RES) utilization, the electrification of the transportation sector has become a necessity. While the emerging Electric Vehicles (EVs) can provide several benefits to the power grid, such as voltage and frequency regulation and power quality improvement, the large and immense EV fleets penetration can have the exact opposite results, if no suitable charging method is utilized. Therefore, the uncontrollable EV charging, that is being utilized today, will not be capable of offering sustainable energy to the future EVs with respect to the power grid limits and hence, today’s research field has turned to optimized smart-charging techniques. However, most of these algorithms treat the problem of smart-charging deterministically, assuming 100% accurate prediction of the input data, while it has been shown that the optimality of results can be seriously deteriorated even under small prediction error.This thesis purpose is to address the impact and potential management of several uncertainties related with EV smart charging: PV Generation, Load Demand, arrival SOC, Arrival and Departure time of the EVs & (Frequency Containment Regulation) FCR Reserves provision uncertainties. The main handling technique, utilized in this thesis, is the Robust Optimization (RO) approach, taking advantage of its very lower computational expense and protection against the worst-case scenario of the uncertainty, with the combination of the Receding Horizon Optimization (RHO), that is already implemented in the Benchmark algorithm. After improving the Benchmark algorithm by providing it with prediction feature, this thesis proves the capability of improved robustly EV charging with the combination of RO-RHO approach with prediction, which reduces the economical (in terms of charging costs/income) and “customer satisfaction” (in terms of unfinished charging gap impacts) of all the uncertainties considered. A more robust and “realistic” FCR reserves provision model has been developed, as well. Last but not least, the well-known RO drawback of potential over-conservativeness, in other words high deterioration of optimality at expense of robustness, is address for every uncertainty.The results of the investigation provide valuable results about which uncertainty has the greater impact, is more robustly manageable or inflicts the highest “Price of Robustness”, regarding overconservativeness. For example, it has been found that the FCR reserves uncertainty inflicts the highest economical and charging gap impacts, however it is the most robustly manageable uncertainty as well, if RO and prediction feature are utilized. Moreover, while arrival SOC and Parking Time uncertainties affect highly the unfinished charging gap, the PV Generation and Load Demand have practically only economic impact, which is also lower than the other uncertainties, hence they are defined as the “uncertainties with the least impact”. Finally, there are 3 types of nodes studied: the “Home” Node, the “Semi-Public” Node & the “Public” Nodes. Observing their behavior during the different uncertainties’ study cases, interesting results have been found about their robust management and affection by the various uncertainties.