IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i12p3208-d374155.html
   My bibliography  Save this article

Optimization Strategy of Electric Vehicles Charging Path Based on “Traffic-Price-Distribution” Mode

Author

Listed:
  • Wanhao Yang

    (College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China)

  • Hong Wang

    (School of Economics & Management, Tongji University, Shanghai 200092, China)

  • Zhijie Wang

    (College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China)

  • Xiaolin Fu

    (College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China)

  • Pengchi Ma

    (College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China)

  • Zhengchen Deng

    (College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

  • Zihao Yang

    (College of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

According to the current optimization problem of electric vehicle charging path planning, a charging path optimization strategy for electric vehicles is proposed, which is under the “traffic-price-distribution” mode. Moreover, this strategy builds an electric vehicle charging and navigation system on the basis of the road traffic network model, real-time electricity price model and distribution network model. Based on the Dijkstra shortest path algorithm and Monte Carlo time-space prediction method, it gets the optimal charging path navigation with the goal of minimizing the charging cost of electric vehicles. The simulation results in MATLAB and MATPOWER (MATLAB R2018a, MATPOWER3.1b2, PSERC, Cannell University) show that the electric vehicle charging path optimization strategy can solve the local traffic congestion problem better and improve the safety and stability of the distribution network because of the fully considering the convenience of electric vehicle charging.

Suggested Citation

  • Wanhao Yang & Hong Wang & Zhijie Wang & Xiaolin Fu & Pengchi Ma & Zhengchen Deng & Zihao Yang, 2020. "Optimization Strategy of Electric Vehicles Charging Path Based on “Traffic-Price-Distribution” Mode," Energies, MDPI, vol. 13(12), pages 1-26, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3208-:d:374155
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/12/3208/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/12/3208/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Matteo Muratori, 2018. "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," Nature Energy, Nature, vol. 3(3), pages 193-201, March.
    2. Cesar Diaz-Londono & Luigi Colangelo & Fredy Ruiz & Diego Patino & Carlo Novara & Gianfranco Chicco, 2019. "Optimal Strategy to Exploit the Flexibility of an Electric Vehicle Charging Station," Energies, MDPI, vol. 12(20), pages 1-29, October.
    3. Mu, Yunfei & Wu, Jianzhong & Jenkins, Nick & Jia, Hongjie & Wang, Chengshan, 2014. "A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles," Applied Energy, Elsevier, vol. 114(C), pages 456-465.
    4. Martin Spitzer & Jonas Schlund & Elpiniki Apostolaki-Iosifidou & Marco Pruckner, 2019. "Optimized Integration of Electric Vehicles in Low Voltage Distribution Grids," Energies, MDPI, vol. 12(21), pages 1-19, October.
    5. Nasim Jabalameli & Xianging Su & Sara Deilami, 2019. "An Online Coordinated Charging/Discharging Strategy of Plug-in Electric Vehicles in Unbalanced Active Distribution Networks with Ancillary Reactive Service in the Energy Market," Energies, MDPI, vol. 12(7), pages 1-17, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fretzen, Ulrich & Ansarin, Mohammad & Brandt, Tobias, 2021. "Temporal city-scale matching of solar photovoltaic generation and electric vehicle charging," Applied Energy, Elsevier, vol. 282(PA).
    2. Liu, Ke & Liu, Yanli, 2023. "Stochastic user equilibrium based spatial-temporal distribution prediction of electric vehicle charging load," Applied Energy, Elsevier, vol. 339(C).
    3. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    4. Qiang Xing & Zhong Chen & Ziqi Zhang & Xiao Xu & Tian Zhang & Xueliang Huang & Haiwei Wang, 2020. "Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior," Energies, MDPI, vol. 13(6), pages 1-32, March.
    5. Hartvigsson, Elias & Taljegard, Maria & Odenberger, Mikael & Chen, Peiyuan, 2022. "A large-scale high-resolution geographic analysis of impacts of electric vehicle charging on low-voltage grids," Energy, Elsevier, vol. 261(PA).
    6. Natascia Andrenacci & Roberto Ragona & Antonino Genovese, 2020. "Evaluation of the Instantaneous Power Demand of an Electric Charging Station in an Urban Scenario," Energies, MDPI, vol. 13(11), pages 1-19, May.
    7. Reza Fachrizal & Joakim Munkhammar, 2020. "Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles," Energies, MDPI, vol. 13(5), pages 1-19, March.
    8. Andrade, Carlos & Selosse, Sandrine & Maïzi, Nadia, 2022. "The role of power-to-gas in the integration of variable renewables," Applied Energy, Elsevier, vol. 313(C).
    9. Ahmadian, Amirhossein & Ghodrati, Vahid & Gadh, Rajit, 2023. "Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework," Applied Energy, Elsevier, vol. 352(C).
    10. Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
    11. Julia Vopava & Christian Koczwara & Anna Traupmann & Thomas Kienberger, 2019. "Investigating the Impact of E-Mobility on the Electrical Power Grid Using a Simplified Grid Modelling Approach," Energies, MDPI, vol. 13(1), pages 1-23, December.
    12. Zhao, Yang & Jiang, Ziyue & Chen, Xinyu & Liu, Peng & Peng, Tianduo & Shu, Zhan, 2023. "Toward environmental sustainability: data-driven analysis of energy use patterns and load profiles for urban electric vehicle fleets," Energy, Elsevier, vol. 285(C).
    13. Kuang, Yanqing & Chen, Yang & Hu, Mengqi & Yang, Dong, 2017. "Influence analysis of driver behavior and building category on economic performance of electric vehicle to grid and building integration," Applied Energy, Elsevier, vol. 207(C), pages 427-437.
    14. Viktor Slednev & Patrick Jochem & Wolf Fichtner, 2022. "Impacts of electric vehicles on the European high and extra high voltage power grid," Journal of Industrial Ecology, Yale University, vol. 26(3), pages 824-837, June.
    15. Dingyi Lu & Yunqian Lu & Kexin Zhang & Chuyuan Zhang & Shao-Chao Ma, 2023. "An Application Designed for Guiding the Coordinated Charging of Electric Vehicles," Sustainability, MDPI, vol. 15(14), pages 1-16, July.
    16. Zhang, Cong & Greenblatt, Jeffery B. & MacDougall, Pamela & Saxena, Samveg & Jayam Prabhakar, Aditya, 2020. "Quantifying the benefits of electric vehicles on the future electricity grid in the midwestern United States," Applied Energy, Elsevier, vol. 270(C).
    17. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    18. Kalghatgi, Gautam, 2018. "Is it really the end of internal combustion engines and petroleum in transport?," Applied Energy, Elsevier, vol. 225(C), pages 965-974.
    19. Bai, Linquan & Li, Fangxing & Cui, Hantao & Jiang, Tao & Sun, Hongbin & Zhu, Jinxiang, 2016. "Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty," Applied Energy, Elsevier, vol. 167(C), pages 270-279.
    20. Hasan Huseyin Coban & Wojciech Lewicki & Ewelina Sendek-Matysiak & Zbigniew Łosiewicz & Wojciech Drożdż & Radosław Miśkiewicz, 2022. "Electric Vehicles and Vehicle–Grid Interaction in the Turkish Electricity System," Energies, MDPI, vol. 15(21), pages 1-19, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3208-:d:374155. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.