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Market interfaces for electric vehicle charging

Market interfaces for electric vehicle charging
Market interfaces for electric vehicle charging
We consider settings where owners of electric vehicles (EVs) participate in a market mechanism to charge their vehicles. Existing work on such mechanisms has typically assumed that participants are fully rational and can report their preferences accurately via some interface to the mechanism or to a software agent participating on their behalf. However, this may not be reasonable in settings with non-expert human end-users. Thus, our overarching aim in this paper is to determine experimentally if a fully expressive market interface that enables accurate preference reports is suitable for the EV charging domain, or, alternatively, if a simpler, restricted interface that reduces the space of possible options is preferable. In doing this, we measure the performance of an interface both in terms of how it helps participants maximise their utility and how it affects deliberation time. Our secondary objective is to contrast two different types of restricted interfaces that vary in how they restrict the space of preferences that can be reported. To enable this analysis, we develop a novel game that replicates key features of an abstract EV charging scenario. In two experiments with over 300 users, we show that restricting the users' preferences significantly reduces the time they spend deliberating (by up to half in some cases). An extensive usability survey confirms that this restriction is furthermore associated with a lower perceived cognitive burden on the users. More surprisingly, at the same time, using restricted interfaces leads to an increase in the users' performance compared to the fully expressive interface (by up to 70%). We also show that some restricted interfaces have the desirable effect of reducing the energy consumption of their users by up to 20% while achieving the same utility as other interfaces. Finally, we find that a reinforcement learning agent displays similar performance trends to human users, enabling a novel methodology for evaluating market interfaces.
1076-9757
175-227
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Nedea, Adrian
f636a77e-28ab-4733-99c4-43d09c6ef950
Rosenfeld, Avi
4a81409b-7d71-4a4a-bc86-9869a0305b01
Jennings, Nicholas
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Nedea, Adrian
f636a77e-28ab-4733-99c4-43d09c6ef950
Rosenfeld, Avi
4a81409b-7d71-4a4a-bc86-9869a0305b01
Jennings, Nicholas
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Stein, Sebastian, Gerding, Enrico, Nedea, Adrian, Rosenfeld, Avi and Jennings, Nicholas (2017) Market interfaces for electric vehicle charging. Journal of Artificial Intelligence Research, 59, 175-227. (doi:10.1613/jair.5387).

Record type: Article

Abstract

We consider settings where owners of electric vehicles (EVs) participate in a market mechanism to charge their vehicles. Existing work on such mechanisms has typically assumed that participants are fully rational and can report their preferences accurately via some interface to the mechanism or to a software agent participating on their behalf. However, this may not be reasonable in settings with non-expert human end-users. Thus, our overarching aim in this paper is to determine experimentally if a fully expressive market interface that enables accurate preference reports is suitable for the EV charging domain, or, alternatively, if a simpler, restricted interface that reduces the space of possible options is preferable. In doing this, we measure the performance of an interface both in terms of how it helps participants maximise their utility and how it affects deliberation time. Our secondary objective is to contrast two different types of restricted interfaces that vary in how they restrict the space of preferences that can be reported. To enable this analysis, we develop a novel game that replicates key features of an abstract EV charging scenario. In two experiments with over 300 users, we show that restricting the users' preferences significantly reduces the time they spend deliberating (by up to half in some cases). An extensive usability survey confirms that this restriction is furthermore associated with a lower perceived cognitive burden on the users. More surprisingly, at the same time, using restricted interfaces leads to an increase in the users' performance compared to the fully expressive interface (by up to 70%). We also show that some restricted interfaces have the desirable effect of reducing the energy consumption of their users by up to 20% while achieving the same utility as other interfaces. Finally, we find that a reinforcement learning agent displays similar performance trends to human users, enabling a novel methodology for evaluating market interfaces.

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Accepted/In Press date: 14 May 2017
e-pub ahead of print date: 22 June 2017
Organisations: Electronics & Computer Science, Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 411767
URI: https://eprints.soton.ac.uk/id/eprint/411767
ISSN: 1076-9757
PURE UUID: cfa489fc-1d8c-404a-8c35-f75285bc5558

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Date deposited: 23 Jun 2017 16:31
Last modified: 19 Oct 2017 04:03

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