IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i7p1764-d1371547.html
   My bibliography  Save this article

A Stochastic Methodology for EV Fast-Charging Load Curve Estimation Considering the Highway Traffic and User Behavior

Author

Listed:
  • Leonardo Nogueira Fontoura da Silva

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

  • Marcelo Bruno Capeletti

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

  • Alzenira da Rosa Abaide

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

  • Luciano Lopes Pfitscher

    (Sustainability and Energy Departament, Federal University of Santa Catarina, Araranguá 88906-072, Santa Catarina, Brazil)

Abstract

The theoretical impact of the electric vehicle (EV) market share growth has been widely discussed with regards to technical and socioeconomic aspects in recent years. However, the prospection of EV scenarios is a challenge, and the difficulty increases with the granularity of the study and the set of variables affected by user behavior and regional aspects. Moreover, the lack of a robust database to estimate fast-charging stations’ load curves, for example, affects the quality of planning, allocation, or grid impact studies. When this problem is evaluated on highways, the challenge increases due to the reduced number of trips related to the reduced number of charger units installed and the limited EVs range, which influence user anxiety. This paper presents a methodology to estimate the highway fast-charging station operation condition, considering regional and EV user aspects. The process is based in a block of traffic simulation, considering the traffic information and highway patterns composing the matrix solution model. Also, the output block estimates charging stations’ operational conditions, considering infrastructure scenarios and simulated traffic. A Monte Carlo simulation is presented to model entrance rates and charging times, considering the PDF of stochastic inputs. The results are shown for the aspects of load curve and queue length for one case study, and a sensibility study was conducted to evaluate the impact of model inputs.

Suggested Citation

  • Leonardo Nogueira Fontoura da Silva & Marcelo Bruno Capeletti & Alzenira da Rosa Abaide & Luciano Lopes Pfitscher, 2024. "A Stochastic Methodology for EV Fast-Charging Load Curve Estimation Considering the Highway Traffic and User Behavior," Energies, MDPI, vol. 17(7), pages 1-27, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1764-:d:1371547
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/7/1764/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/7/1764/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Motoaki, Yutaka & Shirk, Matthew G., 2017. "Consumer behavioral adaption in EV fast charging through pricing," Energy Policy, Elsevier, vol. 108(C), pages 178-183.
    2. Chao Luo & Yih-Fang Huang & Vijay Gupta, 2018. "Stochastic Dynamic Pricing for EV Charging Stations with Renewables Integration and Energy Storage," Papers 1801.02128, arXiv.org.
    3. Daneshzand, Farzaneh & Coker, Phil J & Potter, Ben & Smith, Stefan T, 2023. "EV smart charging: How tariff selection influences grid stress and carbon reduction," Applied Energy, Elsevier, vol. 348(C).
    4. Langbroek, Joram H.M. & Franklin, Joel P. & Susilo, Yusak O., 2017. "When do you charge your electric vehicle? A stated adaptation approach," Energy Policy, Elsevier, vol. 108(C), pages 565-573.
    5. Zarazua de Rubens, Gerardo & Noel, Lance & Kester, Johannes & Sovacool, Benjamin K., 2020. "The market case for electric mobility: Investigating electric vehicle business models for mass adoption," Energy, Elsevier, vol. 194(C).
    6. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    7. Antonio Colmenar-Santos & Carlos De Palacio & David Borge-Diez & Oscar Monzón-Alejandro, 2014. "Planning Minimum Interurban Fast Charging Infrastructure for Electric Vehicles: Methodology and Application to Spain," Energies, MDPI, vol. 7(3), pages 1-23, February.
    8. Das, H.S. & Rahman, M.M. & Li, S. & Tan, C.W., 2020. "Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    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. Quintero Fuentes, Abel & Hickman, Mark & Whitehead, Jake, 2025. "Zone substations' readiness to embrace electric vehicle adoption: Brisbane case study," Energy, Elsevier, vol. 322(C).
    2. Gönül, Ömer & Duman, A. Can & Güler, Önder, 2021. "Electric vehicles and charging infrastructure in Turkey: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    3. Song, Yanqiu & Shangguan, Lingzhi & Li, Guijun, 2021. "Simulation analysis of flexible concession period contracts in electric vehicle charging infrastructure public-private-partnership (EVCI-PPP) projects based on time-of-use (TOU) charging price strateg," Energy, Elsevier, vol. 228(C).
    4. Motoaki, Yutaka & Yi, Wenqi & Salisbury, Shawn, 2018. "Empirical analysis of electric vehicle fast charging under cold temperatures," Energy Policy, Elsevier, vol. 122(C), pages 162-168.
    5. Anamarija Falkoni & Antun Pfeifer & Goran Krajačić, 2020. "Vehicle-to-Grid in Standard and Fast Electric Vehicle Charging: Comparison of Renewable Energy Source Utilization and Charging Costs," Energies, MDPI, vol. 13(6), pages 1-22, March.
    6. 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).
    7. Ruisheng Wang & Zhong Chen & Qiang Xing & Ziqi Zhang & Tian Zhang, 2022. "A Modified Rainbow-Based Deep Reinforcement Learning Method for Optimal Scheduling of Charging Station," Sustainability, MDPI, vol. 14(3), pages 1-14, February.
    8. Steinbach, Sarah A. & Blaschke, Maximilian J., 2024. "Enabling electric mobility: Can photovoltaic and home battery systems significantly reduce grid reinforcement costs?," Applied Energy, Elsevier, vol. 375(C).
    9. Lagomarsino, Maria & van der Kam, Mart & Parra, David & Hahnel, Ulf J.J., 2022. "Do I need to charge right now? Tailored choice architecture design can increase preferences for electric vehicle smart charging," Energy Policy, Elsevier, vol. 162(C).
    10. Jian Chen & Fangyi Li & Ranran Yang & Dawei Ma, 2020. "Impacts of Increasing Private Charging Piles on Electric Vehicles’ Charging Profiles: A Case Study in Hefei City, China," Energies, MDPI, vol. 13(17), pages 1-17, August.
    11. Manríquez, Francisco & Sauma, Enzo & Aguado, José & de la Torre, Sebastián & Contreras, Javier, 2020. "The impact of electric vehicle charging schemes in power system expansion planning," Applied Energy, Elsevier, vol. 262(C).
    12. Teevrat Garg & Ryan Hanna & Jeffrey Myers & Sebastian Tebbe & David G. Victor, 2024. "Electric Vehicle Charging at the Workplace: Experimental Evidence on Incentives and Environmental Nudges," CESifo Working Paper Series 11445, CESifo.
    13. Wu, Wei & Lin, Boqiang, 2021. "Benefits of electric vehicles integrating into power grid," Energy, Elsevier, vol. 224(C).
    14. Nandan Gopinathan & Prabhakar Karthikeyan Shanmugam, 2022. "Energy Anxiety in Decentralized Electricity Markets: A Critical Review on EV Models," Energies, MDPI, vol. 15(14), pages 1-40, July.
    15. Steffen Limmer, 2019. "Dynamic Pricing for Electric Vehicle Charging—A Literature Review," Energies, MDPI, vol. 12(18), pages 1-24, September.
    16. Golsefidi, Atefeh Hemmati & Hipolito, F. & Pereira, Francisco Câmara & Samaranayake, Samitha, 2025. "Incremental expansion of large scale fixed and mobile charging infrastructure in stochastic environments: A novel graph-based Benders decomposition approach," Applied Energy, Elsevier, vol. 380(C).
    17. Kandpal, Bakul & Pareek, Parikshit & Verma, Ashu, 2022. "A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid," Energy, Elsevier, vol. 249(C).
    18. Boud Verbrugge & Mohammed Mahedi Hasan & Haaris Rasool & Thomas Geury & Mohamed El Baghdadi & Omar Hegazy, 2021. "Smart Integration of Electric Buses in Cities: A Technological Review," Sustainability, MDPI, vol. 13(21), pages 1-23, November.
    19. Duggal, Angel Swastik & Singh, Rajesh & Gehlot, Anita & Gupta, Lovi Raj & Akram, Sheik Vaseem & Prakash, Chander & Singh, Sunpreet & Kumar, Raman, 2021. "Infrastructure, mobility and safety 4.0: Modernization in road transportation," Technology in Society, Elsevier, vol. 67(C).
    20. Jiaan Zhang & Chenyu Liu & Leijiao Ge, 2022. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN," Energies, MDPI, vol. 15(7), pages 1-25, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:17:y:2024:i:7:p:1764-:d:1371547. 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.