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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Veröffentlicht in:	IEEE transactions on intelligent transportation systems Jg. 23; H. 4; S. 3654 - 3669
Hauptverfasser:	Bae, Sangjun, Jang, Inmo, Gros, Sebastien, Kulcsar, Balazs, Hellgren, Jonas
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
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Abstract	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.
AbstractList	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 k-means clustering algorithm. The proposed algorithm, referred to as k-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. 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. 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 [Formula Omitted]-means clustering algorithm. The proposed algorithm, referred to as [Formula Omitted]-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.
Author	Kulcsar, Balazs Bae, Sangjun Hellgren, Jonas Jang, Inmo Gros, Sebastien
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SubjectTerms	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
Title	A Game Approach for Charging Station Placement Based on User Preferences and Crowdedness
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