Abstract
The industry related to electric vehicles (EVs) has seen a substantial increase in recent years, as such vehicles have the ability to significantly reduce total CO2 emissions and the related global warming effect. In this paper, we focus on the problem of allocating EVs to charging stations, scheduling and pricing their charging. Specifically, we developed a Mixed Integer Program (MIP) which executes offline and optimally allocates EVs to charging stations. On top, we propose two alternative mechanisms to price the electricity the EVs charge. The first mechanism is a typical fixed-price one, while the second is a variation of the Vickrey–Clark–Groves (VCG) mechanism. We also developed online solutions that incrementally call the MIP-based algorithm and solve it for branches of EVs. In all cases, the EVs’ aim is to minimize the price to pay and the impact on their driving schedule, acting as self-interested agents. We conducted a thorough empirical evaluation of our mechanisms and we observed that they had satisfactory scalability. Additionally, the VCG mechanism achieved an up to 2.2% improvement in terms of the number of vehicles that were charged compared to the fixed-price one and, in cases where the stations were congested, it calculated higher prices for the EVs and provided a higher profit for the stations, but lower utility to the EVs. However, in a theoretical evaluation, we proved that the variant of the VCG mechanism being proposed in this paper still guaranteed truthful reporting of the EVs’ preferences. In contrast, the fixed-price one was found to be vulnerable to agents’ strategic behavior as non-truthful EVs can charge instead of truthful ones. Finally, we observed the online algorithms to be, on average, at 95.6% of the offline ones in terms of the average number of serviced EVs.
Highlights
The unprecedented change in the earth’s climate has made evident the need to shift to technologies that produce low or zero carbon emissions
We present the evaluation of the proposed algorithms in terms of their execution time and scalability (EXP1), their ability to service many electric vehicles (EVs) (EXP2), the charging cost for the EVs and the profit that the charging stations make (EXP3) and the impact the non-truthful reporting of agents’ preferences has on the charging of the EVs (EXP4)
We assumed 50 points in time to exist, each point being equal to 15 min, 8 charging stations and up to 200 EVs
Summary
The unprecedented change in the earth’s climate has made evident the need to shift to technologies that produce low or zero carbon emissions. The successful transition to the new type of vehicle depends heavily on customers’ acceptance of the EVs. To date, there are three main barriers to the wide adoption of EVs, i.e., (1) their relatively short driving range; (2) the long charging times and the unavailability of charging stations, especially in rural locations; and (3) the higher cost of buying an electric compared to a conventional vehicle. There are three main barriers to the wide adoption of EVs, i.e., (1) their relatively short driving range; (2) the long charging times and the unavailability of charging stations, especially in rural locations; and (3) the higher cost of buying an electric compared to a conventional vehicle Such problems are more evident in under-developed and poor countries which, account for a significant percentage of the produced pollutants. The use of efficient scheduling algorithms that can coordinate the simultaneous charging of large numbers of EVs considering the available stations and the power grid constraints, in parallel with the fair pricing of the electricity, can partially raise limitations 2 and 3
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