This project proposes offline and online scheduling algorithms for the charging of electric vehicles (EVs) in a single charging station (CS). The station has available cheaper, but limited, energy from renewable energy sources (RES). The EVs are capable of and willing to participate in vehicle to vehicle (V2V) energy transfers that are used to reduce the charging cost and increase the RES utilization. The algorithms are centralized and aim to minimize the total charging cost for the EVs. We formulate the problem as a mixed integer programming (MIP) one and we solve it optimally assuming full knowledge of the EV demand and energy generation. Later, we propose an online algorithm that iteratively calls the offline one and copes with unknown future interruptions by arriving the EVs and with the inability to predict accurately RES production. In addition, a novel technique called virtual demand is developed that increases the demand of already existing EVs, in order to store renewable energy and later transfer it via V2V to EVs that will arrive at the CS in the future. This technique is used for mitigating the inefficiency due to the uncertainty about future actions that real-time scheduling entails. In a setting with up to 150 EVs and using real data regarding the

RES production, our algorithms are shown to have low execution times, while the use of virtual demand increases RES utilization by 12% and reduces cost by 3.3%.

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