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    A Game Approach for Charging Station Placement Based on User Preferences and Crowdedness  
    Bae, sangjun(Chalmers University of Technology)
    United States | IEEE Transactions on Intelligent Transportation Sy
    2020-12-09 | 바로가기
    Charging_stations, Games, Planning
    Cited by

    ■  View full text

    IEEE Transactions on Intelligent Transportation Systems 

    Date of Publication: 09 December 2020

    https://doi.org/10.1109/TITS.2020.3038938

     

     

    ■  Researchers

    Sangjun Bae1, Inmo Jang2, Sébastien Gros1,3, Balázs Kulcsár1, Jonas Hellgren3

     

    1  Automatic Control Group, Department of Electrical Engineering, Chalmers University of Technology

    2  Robotics for Extreme Environment Group, The University of Manchester

    3  Department of Engineering Cybernetics, Norwegian University of Science and Technology

    4  Automation, Volvo Groups Truck Technology

     

     

    ■  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 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.

     

     

    ■  Conclusion

    Reasonable placement of EVCSs is an important premise for the rapid development of the EV industry. The proposed algorithm, k-GRAPE, aims to attract investments to the electric car industry by determining the position of the fast-charging stations effectively based on the individual preferences of each EVU. Our contributions are as follows: 1) k-GRAPE allows strategic and competitive decision-making of the EVUs depending on crowdedness and their preferences with privacy preserved data from the EVCSs; 2) it is proven that the proposed algorithm converges to a local optimum in a finite number of iterations and the solution at least guarantees 50% of suboptimality; 3) k-GRAPE proposes geographically unconstrained individual EVCS locations that satisfy the preferences of EVUs; 4) numerical experiments using the real-world data from Gothenburg in Sweden show that k-GRAPE outperforms the baseline approach in terms of balanced energy distribution, satisfaction of EVUs and closeness centrality. Here, some weaknesses of the proposed approach are pointed out for further studies. First, we only have shown that the suboptimality of the proposed algorithm is 50%, even if the numerical results provide near-optimal solutions. Second, although this framework can consider individual preferences and crowdedness from the customer perspective, it cannot include the service providers’ viewpoint. One natural progression of this study is to consider both the EVU’s perspective and the fast-EVCSs’ perspective. Finally, due to the limitation of the data access, we assume the individual preferences of EVUs. One interesting study would be to consider real-world data for modeling the EVUs. This will improve the fidelity of the EVU model in this field and practicalize this framework. Further research might explore a cooperative way to solve the EVCSPP, i.e., it is assumed that customers cooperate with each other to maximize their utilities. This could be expected in public transportation cases such as car-sharing services

     

     

     

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