A Game Approach for Charging Station Placement Based on User Preferences and Crowdedness

The placement of electric vehicle charging stations (EVCSs), which encourages the rapid development of electric vehicles (EVs), should be considered from not only operational perspective such as minimizing installation costs, but also user perspective so that their strategic and competitive charging...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 23; no. 4; pp. 3654 - 3669
Main Authors: Bae, Sangjun, Jang, Inmo, Gros, Sebastien, Kulcsar, Balazs, Hellgren, Jonas
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Business competition
Charging stations
Clustering
Clustering algorithm
Clustering algorithms
Decision making
Decision theory
Electric vehicle charging
Electric vehicle charging station placement
Electric vehicles
game theory
Games
Installation costs
Optimization
Partitioning algorithms
Placement
Planning
ISSN:1524-9050, 1558-0016, 1558-0016
Online Access:Get full text
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Summary:The placement of electric vehicle charging stations (EVCSs), which encourages the rapid development of electric vehicles (EVs), should be considered from not only operational perspective such as minimizing installation costs, but also user perspective so that their strategic and competitive charging behaviors can be reflected. This paper proposes a methodological framework to consider crowdedness and individual preferences of electric vehicle users (EVUs) in the selection of locations for fast-charging stations. The electric vehicle charging station placement problem (EVCSPP) is solved via a decentralized game theoretical decision-making algorithm and <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-means clustering algorithm. The proposed algorithm, referred to as <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-GRAPE, determines the locations of charging stations to maximize the sum of utilities of EVUs. In particular, we analytically present that 50% of suboptimality of the solution can be at least guaranteed, which is about 17% better than the existing game theoretical based framework. We show a few variants to describe the utility functions that may capture the difference in preferences of EVUs. Finally, we demonstrate the viability of the decision framework via three real-world data-based experiments. The results of the experiments, including a comparison with a baseline method are then discussed.
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ISSN:1524-9050
1558-0016
1558-0016
DOI:10.1109/TITS.2020.3038938