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Mechanism Design for Efficient Offline and Online Allocation of Electric Vehicles to Charging Stations

Author

Listed:
  • Emmanouil S. Rigas

    (School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Enrico H. Gerding

    (Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK)

  • Sebastian Stein

    (Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK)

  • Sarvapali D. Ramchurn

    (Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK)

  • Nick Bassiliades

    (School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

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 CO 2 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.

Suggested Citation

  • Emmanouil S. Rigas & Enrico H. Gerding & Sebastian Stein & Sarvapali D. Ramchurn & Nick Bassiliades, 2022. "Mechanism Design for Efficient Offline and Online Allocation of Electric Vehicles to Charging Stations," Energies, MDPI, vol. 15(5), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1660-:d:756668
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    References listed on IDEAS

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    1. Soares, João & Ghazvini, Mohammad Ali Fotouhi & Borges, Nuno & Vale, Zita, 2017. "Dynamic electricity pricing for electric vehicles using stochastic programming," Energy, Elsevier, vol. 122(C), pages 111-127.
    2. Perez-Diaz, Alvaro & Gerding, Enrico & McGroarty, Frank, 2018. "Coordination and payment mechanisms for electric vehicle aggregators," Applied Energy, Elsevier, vol. 212(C), pages 185-195.
    3. Edward Clarke, 1971. "Multipart pricing of public goods," Public Choice, Springer, vol. 11(1), pages 17-33, September.
    4. Kovačič, Miha & Šarler, Božidar, 2014. "Genetic programming prediction of the natural gas consumption in a steel plant," Energy, Elsevier, vol. 66(C), pages 273-284.
    5. William Vickrey, 1961. "Counterspeculation, Auctions, And Competitive Sealed Tenders," Journal of Finance, American Finance Association, vol. 16(1), pages 8-37, March.
    6. Groves, Theodore, 1973. "Incentives in Teams," Econometrica, Econometric Society, vol. 41(4), pages 617-631, July.
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