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A Cellular Automata Agent-Based Hybrid Simulation Tool to Analyze the Deployment of Electric Vehicle Charging Stations

Author

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  • Amaro García-Suárez

    (Department of Computer Architecture and Technology, Universidad de Sevilla, Avenida Reina Mercedes s/n, 41012 Sevilla, Spain)

  • José-Luis Guisado-Lizar

    (Department of Computer Architecture and Technology, Universidad de Sevilla, Avenida Reina Mercedes s/n, 41012 Sevilla, Spain
    Research Institute of Computer Engineering (I3US), Universidad de Sevilla, Avenida Reina Mercedes s/n, 41012 Sevilla, Spain)

  • Fernando Diaz-del-Rio

    (Department of Computer Architecture and Technology, Universidad de Sevilla, Avenida Reina Mercedes s/n, 41012 Sevilla, Spain
    Research Institute of Computer Engineering (I3US), Universidad de Sevilla, Avenida Reina Mercedes s/n, 41012 Sevilla, Spain)

  • Francisco Jiménez-Morales

    (Department of Condensed Matter Physics, Universidad de Sevilla, Avenida Reina Mercedes s/n, 41012 Sevilla, Spain)

Abstract

We present a hybrid model combining cellular automata (CA) and agent-based modeling (ABM) to analyze the deployment of electric vehicle charging stations through microscopic traffic simulations. This model is implemented in a simulation tool called SIMTRAVEL, which allows combining electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) that navigate in a city composed of streets, avenues, intersections, roundabouts, and including charging stations (CSs). Each EV is modeled as an agent that incorporates complex behaviors, such as decisions about the route to destination or CS, when to drive to a CS, or which CS to choose. We studied three different CS arrangements for a synthetic city: a single large central CS, four medium sized distributed CSs or multiple small distributed CSs, with diverse amounts of traffic and proportions of EVs. The simulator output is found to be robust and meaningful and allows one to extract a first useful conclusion: traffic conditions that create bottlenecks around the CSs play a crucial role, leading to a deadlock in the city when the traffic density is above a certain critical level. Our results show that the best disposition is a distributed network, but it is fundamental to introduce smart routing measures to balance the distribution of EVs among CSs.

Suggested Citation

  • Amaro García-Suárez & José-Luis Guisado-Lizar & Fernando Diaz-del-Rio & Francisco Jiménez-Morales, 2021. "A Cellular Automata Agent-Based Hybrid Simulation Tool to Analyze the Deployment of Electric Vehicle Charging Stations," Sustainability, MDPI, vol. 13(10), pages 1-14, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5421-:d:553300
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    References listed on IDEAS

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    1. Li, Xin-Gang & Jia, Bin & Gao, Zi-You & Jiang, Rui, 2006. "A realistic two-lane cellular automata traffic model considering aggressive lane-changing behavior of fast vehicle," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 367(C), pages 479-486.
    2. Hong Gao & Kai Liu & Xinchao Peng & Cheng Li, 2020. "Optimal Location of Fast Charging Stations for Mixed Traffic of Electric Vehicles and Gasoline Vehicles Subject to Elastic Demands," Energies, MDPI, vol. 13(8), pages 1-16, April.
    3. Md Mainul Islam & Hussain Shareef & Azah Mohamed, 2017. "Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-20, December.
    4. Kong, Weiwei & Luo, Yugong & Feng, Guixuan & Li, Keqiang & Peng, Huei, 2019. "Optimal location planning method of fast charging station for electric vehicles considering operators, drivers, vehicles, traffic flow and power grid," Energy, Elsevier, vol. 186(C).
    5. Zhang, Jin & Wang, Zhenpo & Liu, Peng & Zhang, Zhaosheng, 2020. "Energy consumption analysis and prediction of electric vehicles based on real-world driving data," Applied Energy, Elsevier, vol. 275(C).
    6. Deb, Sanchari & Gao, Xiao-Zhi & Tammi, Kari & Kalita, Karuna & Mahanta, Pinakeswar, 2021. "A novel chicken swarm and teaching learning based algorithm for electric vehicle charging station placement problem," Energy, Elsevier, vol. 220(C).
    7. Chunlin Guo & Jingjing Yang & Lin Yang, 2018. "Planning of Electric Vehicle Charging Infrastructure for Urban Areas with Tight Land Supply," Energies, MDPI, vol. 11(9), pages 1-17, September.
    8. Wojciech Cieslik & Filip Szwajca & Wojciech Golimowski & Andrew Berger, 2021. "Experimental Analysis of Residential Photovoltaic (PV) and Electric Vehicle (EV) Systems in Terms of Annual Energy Utilization," Energies, MDPI, vol. 14(4), pages 1-21, February.
    9. Xu, Min & Meng, Qiang, 2020. "Optimal deployment of charging stations considering path deviation and nonlinear elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 135(C), pages 120-142.
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    Cited by:

    1. Sanchari Deb, 2021. "Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review," Energies, MDPI, vol. 14(23), pages 1-19, November.

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