Market Interfaces for Electric Vehicle Charging

被引:4
|
作者
Stein, Sebastian [1 ]
Gerding, Enrico H. [1 ]
Nedea, Adrian [1 ]
Rosenfeld, Avi [2 ]
Jennings, Nicholas R. [3 ,4 ]
机构
[1] Univ Southampton, Southampton, Hants, England
[2] Jerusalem Coll Technol, Jerusalem, Israel
[3] Imperial Coll, London, England
[4] King Abdulaziz Univ, Jeddah, Saudi Arabia
基金
英国工程与自然科学研究理事会;
关键词
SITUATION AWARENESS; MECHANISM; DEMAND;
D O I
10.1613/jair.5387
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:175 / 227
页数:53
相关论文
共 50 条
  • [41] Wireless Charging for Electric Vehicle with Microwaves
    Shinohara, Naoki
    Kubo, Yuta
    Tonomura, Hiroshi
    [J]. 2013 3RD INTERNATIONAL ELECTRIC DRIVES PRODUCTION CONFERENCE (EDPC), 2013, : 398 - 401
  • [42] Determination of Electric Vehicle Charging Demand
    Pekarek, Jan
    [J]. INNOVATION MANAGEMENT AND SUSTAINABLE ECONOMIC COMPETITIVE ADVANTAGE: FROM REGIONAL DEVELOPMENT TO GLOBAL GROWTH, VOLS I - VI, 2015, 2015, : 1211 - 1220
  • [43] Planning of Electric Vehicle Charging Infrastructure
    Dharmakeerthi, C. H.
    Mithulananthan, N.
    Saha, T. K.
    [J]. 2013 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PES), 2013,
  • [44] Autonomous Electric Vehicle Charging System
    Behl, Madhur
    DuBro, Jackson
    Flynt, Taylor
    Hameed, Imaan
    Lang, Grace
    Park, Felix
    [J]. 2019 SYSTEMS AND INFORMATION ENGINEERING DESIGN SYMPOSIUM (SIEDS), 2019, : 317 - 322
  • [45] Electric Vehicle Charging Station Placement
    Lam, Albert Y. S.
    Leung, Yiu-Wing
    Chu, Xiaowen
    [J]. 2013 IEEE INTERNATIONAL CONFERENCE ON SMART GRID COMMUNICATIONS (SMARTGRIDCOMM), 2013, : 510 - 515
  • [46] Scalable Electric Vehicle Charging Protocols
    Zhang, Liang
    Kekatos, Vassilis
    Giannakis, Georgios B.
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2017, 32 (02) : 1451 - 1462
  • [47] Analysis of Electric Vehicle Charging Process
    Medved, Dusan
    Kiraly, Jozef
    Hyseni, Ardian
    Kolcun, Michal
    Margita, Frantisek
    Mazur, Damian
    [J]. 2024 24TH INTERNATIONAL SCIENTIFIC CONFERENCE ON ELECTRIC POWER ENGINEERING, EPE 2024, 2024, : 242 - 247
  • [48] Vehicle-to-Grid Charging Optimization of Electric Vehicle
    Kim, Hyunsup
    Myeong, Hanseung
    Park, Inseok
    Choi, Jae Hyuk
    Kim, Kyoungjoo
    [J]. 2020 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA), 2020, : 1048 - 1053
  • [49] The future of electric vehicle charging infrastructure
    Khurram Afridi
    [J]. Nature Electronics, 2022, 5 : 62 - 64
  • [50] Contactless Charging Technology of Electric Vehicle
    Huang, Dexu
    Lv, Xiao
    Lu, Shouyin
    Tan, Lin
    Zhao, Jinlong
    Qi, Hun
    Wang, Tongbin
    [J]. ADVANCES IN ENERGY SCIENCE AND TECHNOLOGY, PTS 1-4, 2013, 291-294 : 907 - +