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Mechanism design for efficient offline and online allocation of electric vehicles to charging stations

Mechanism design for efficient offline and online allocation of electric vehicles to charging stations
Mechanism design for efficient offline and online allocation of electric vehicles to charging stations

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.

Charging, Electric vehicles, Fixed price, Mechanism design, Scheduling, VCG
1996-1073
Rigas, Emmanouil S.
e4093177-4f6b-40b4-a35a-075cd559150e
Gerding, Enrico H.
d9e92ee5-1a8c-4467-a689-8363e7743362
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Ramchurn, Sarvapali D.
1d62ae2a-a498-444e-912d-a6082d3aaea3
Bassiliades, Nick
46e70a8f-015c-4888-890a-de879c9bff61
Rigas, Emmanouil S.
e4093177-4f6b-40b4-a35a-075cd559150e
Gerding, Enrico H.
d9e92ee5-1a8c-4467-a689-8363e7743362
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Ramchurn, Sarvapali D.
1d62ae2a-a498-444e-912d-a6082d3aaea3
Bassiliades, Nick
46e70a8f-015c-4888-890a-de879c9bff61

Rigas, Emmanouil S., Gerding, Enrico H., Stein, Sebastian, Ramchurn, Sarvapali D. and Bassiliades, Nick (2022) Mechanism design for efficient offline and online allocation of electric vehicles to charging stations. Energies, 15 (5), [1660]. (doi:10.3390/en15051660).

Record type: Article

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.

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Accepted/In Press date: 20 February 2022
e-pub ahead of print date: 23 February 2022
Published date: 1 March 2022
Additional Information: Funding Information: Funding: This research study was co-financed by Greece and the European Union (European Social Fund—ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning” in the context of the project “Reinforcement of Postdoctoral Researchers-2nd Cycle” (MIS-5033021), implemented by State Scholarships Foundation (IKY). Copyright 2022 Elsevier B.V., All rights reserved.
Keywords: Charging, Electric vehicles, Fixed price, Mechanism design, Scheduling, VCG

Identifiers

Local EPrints ID: 455806
URI: http://eprints.soton.ac.uk/id/eprint/455806
ISSN: 1996-1073
PURE UUID: 55c55e3f-1681-4969-a6f7-def79e198f16
ORCID for Enrico H. Gerding: ORCID iD orcid.org/0000-0001-7200-552X
ORCID for Sebastian Stein: ORCID iD orcid.org/0000-0003-2858-8857
ORCID for Sarvapali D. Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

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Date deposited: 05 Apr 2022 17:13
Last modified: 28 Apr 2022 01:59

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Contributors

Author: Emmanouil S. Rigas
Author: Enrico H. Gerding ORCID iD
Author: Sebastian Stein ORCID iD
Author: Sarvapali D. Ramchurn ORCID iD
Author: Nick Bassiliades

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