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Online mechanisms for charging electric vehicles in settings with varying marginal electricity costs

Online mechanisms for charging electric vehicles in settings with varying marginal electricity costs
Online mechanisms for charging electric vehicles in settings with varying marginal electricity costs
We propose new mechanisms that can be used by a demand response aggregator to flexibly shift the charging of electric vehicles (EVs) to times where cheap but intermittent renewable energy is in high supply. Here, it is important to consider the constraints and preferences of EV owners, while eliminating the scope for strategic behaviour. To achieve this, we propose, for the first time, a generic class of incentive mechanisms for settings with both varying marginal electricity costs and multi-dimensional preferences. We show these are dominant strategy incentive compatible, i.e., EV owners are incentivised to report their constraints and preferences truthfully. We also detail a specific instance of this class, show that it achieves ≈ 98% of the optimal in realistic scenarios and demonstrate how it can be adapted to trade off efficiency with profit.
2610-2616
ACM
Hayakawa, Keiichiro
29e1e6b7-c964-44c2-85be-e4495188032b
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Shiga, Takahiro
bf654efd-51e9-4b6e-8f06-8158d27135a4
Hayakawa, Keiichiro
29e1e6b7-c964-44c2-85be-e4495188032b
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Shiga, Takahiro
bf654efd-51e9-4b6e-8f06-8158d27135a4

Hayakawa, Keiichiro, Gerding, Enrico, Stein, Sebastian and Shiga, Takahiro (2015) Online mechanisms for charging electric vehicles in settings with varying marginal electricity costs. In IJCAI'15 Proceedings of the 24th International Conference on Artificial Intelligence. ACM. pp. 2610-2616 .

Record type: Conference or Workshop Item (Paper)

Abstract

We propose new mechanisms that can be used by a demand response aggregator to flexibly shift the charging of electric vehicles (EVs) to times where cheap but intermittent renewable energy is in high supply. Here, it is important to consider the constraints and preferences of EV owners, while eliminating the scope for strategic behaviour. To achieve this, we propose, for the first time, a generic class of incentive mechanisms for settings with both varying marginal electricity costs and multi-dimensional preferences. We show these are dominant strategy incentive compatible, i.e., EV owners are incentivised to report their constraints and preferences truthfully. We also detail a specific instance of this class, show that it achieves ≈ 98% of the optimal in realistic scenarios and demonstrate how it can be adapted to trade off efficiency with profit.

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Accepted/In Press date: 16 April 2015
Published date: 25 July 2015
Venue - Dates: 24th International Joint Conference on Artificial Intelligence (IJCAI), , Buenos Aires, Argentina, 2015-07-25 - 2015-07-31
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 377236
URI: http://eprints.soton.ac.uk/id/eprint/377236
PURE UUID: a4bc2c29-16ce-4c3c-9ba4-f699b8c798f9
ORCID for Enrico Gerding: ORCID iD orcid.org/0000-0001-7200-552X

Catalogue record

Date deposited: 19 May 2015 10:58
Last modified: 24 Nov 2020 17:31

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