In this study, we raise the concern that current understandings of user perceptions and
decision-making processes may jeopardize the sustainable development of charging
infrastructure and wider EV adoption. This study addresses three main concerns: (1)
most research focuses solely on battery electric vehicle users, neglecting plug-in
hybrid (PHEV) and non-EV owners, thus failing to identify common preferences or
transitional perceptions that could guide an inclusive development plan; (2) potential
factors influencing charging station selection, such as the availability of nearby
amenities and the role of information from social circles and user reviews, are often
overlooked; and (3) used methods cannot reveal individual items' importance or
uncover patterns between them as they often combine or transform the original items.
To address these gaps, we conducted a survey experiment among 402 non-EV, PHEV
and EV users and applied network analysis to capture their charging station selection
decision-making processes. Our findings reveal that non-EV and PHEV users prioritize
accessibility, whereas EV owners focus on the number of chargers. Furthermore,
certain technical features, such as vehicle-to-grid capabilities, are commonly
disregarded, while EV users place significant importance on engaging in amenities
while charging. We also report an evolution of preferences, with users shifting their
priorities on different types of information as they transition from non-EV and PHEV to
EV ownership. Our results highlight the necessity for adaptive infrastructure strategies
that consider the evolving preferences of different user groups to foster sustainable
and equitable charging infrastructure development and broader adoption of EVs.
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A Reliability-aware Distributed Framework to Schedule Residential Charging of Electric Vehicles
Residential consumers have become active participants in the power distribution network after being equipped with residential EV charging provisions. This creates a challenge for the network operator tasked with dispatching electric power to the residential consumers through the existing distribution network infrastructure in a reliable manner. In this paper, we address the problem of scheduling residential EV charging for multiple consumers while maintaining network reliability. An additional challenge is the restricted exchange of information: where the consumers do not have access to network information and the network operator does not have access to consumer load parameters. We propose a distributed framework which generates an optimal EV charging schedule for individual residential consumers based on their preferences and iteratively updates it until the network reliability constraints set by the operator are satisfied. We validate the proposed approach for different EV adoption levels in a synthetically created digital twin of an actual power distribution network. The results demonstrate that the new approach can achieve a higher level of network reliability compared to the case where residential consumers charge EVs based solely on their individual preferences, thus providing a solution for the existing grid to keep up with increased adoption rates without significant investments in increasing grid capacity.
more » « less- PAR ID:
- 10385314
- Date Published:
- Journal Name:
- Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
- Page Range / eLocation ID:
- 5115 to 5121
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.24963/ijcai.2022/710
