IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i17p4404-d404440.html
   My bibliography  Save this article

Stochastic Modeling Method of Plug-in Electric Vehicle Charging Demand for Korean Transmission System Planning

Author

Listed:
  • Jong Hui Moon

    (Department of Electrical Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do 54896, Korea)

  • Han Na Gwon

    (Department of Electrical Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do 54896, Korea)

  • Gi Ryong Jo

    (Department of Electrical Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do 54896, Korea)

  • Woo Yeong Choi

    (Department of Electrical Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do 54896, Korea)

  • Kyung Soo Kook

    (Department of Electrical Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do 54896, Korea)

Abstract

The number of plug-in electric vehicles (PEVs) has rapidly increased owing to the government’s active promotion policy worldwide. Consequently, in the near future, their charging demand is expected to grow enough for consideration in the planning process of the transmission system. This study proposes a stochastic method for modeling the PEV charging demand, of which the time and amount are uncertain. In the proposed method, the distribution of PEVs is estimated by the substations based on the number of electricity customers, PEV expansion target, and statistics of existing vehicles. An individual PEV charging profile is modeled using the statistics of internal combustion engine (ICE) vehicles driving and by aggregating the PEV charging profiles per 154 kV substation, the charging demand of PEVs is determined for consideration as part of the total electricity demand in the planning process of transmission systems. The effectiveness of the proposed method is verified through case studies in the Korean power system. It was found that the PEV charging demand has considerable potential as the additional peak demand in the transmission system planning because the charging time could be concentrated in the evening peak time.

Suggested Citation

  • Jong Hui Moon & Han Na Gwon & Gi Ryong Jo & Woo Yeong Choi & Kyung Soo Kook, 2020. "Stochastic Modeling Method of Plug-in Electric Vehicle Charging Demand for Korean Transmission System Planning," Energies, MDPI, vol. 13(17), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4404-:d:404440
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/17/4404/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/17/4404/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sylvester Johansson & Jonas Persson & Stavros Lazarou & Andreas Theocharis, 2019. "Investigation of the Impact of Large-Scale Integration of Electric Vehicles for a Swedish Distribution Network," Energies, MDPI, vol. 12(24), pages 1-22, December.
    2. Crozier, Constance & Morstyn, Thomas & McCulloch, Malcolm, 2020. "The opportunity for smart charging to mitigate the impact of electric vehicles on transmission and distribution systems," Applied Energy, Elsevier, vol. 268(C).
    3. Jian, Linni & Zheng, Yanchong & Xiao, Xinping & Chan, C.C., 2015. "Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid," Applied Energy, Elsevier, vol. 146(C), pages 150-161.
    4. Tabatabaee, Sajad & Mortazavi, Seyed Saeedallah & Niknam, Taher, 2017. "Stochastic scheduling of local distribution systems considering high penetration of plug-in electric vehicles and renewable energy sources," Energy, Elsevier, vol. 121(C), pages 480-490.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Narongkorn Uthathip & Pornrapeepat Bhasaputra & Woraratana Pattaraprakorn, 2021. "Stochastic Modelling to Analyze the Impact of Electric Vehicle Penetration in Thailand," Energies, MDPI, vol. 14(16), pages 1-23, August.
    2. Ri Piao & Deok-Joo Lee & Taegu Kim, 2020. "Real-Time Pricing Scheme in Smart Grid Considering Time Preference: Game Theoretic Approach," Energies, MDPI, vol. 13(22), pages 1-19, November.

    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. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    2. Francesco Lo Franco & Mattia Ricco & Riccardo Mandrioli & Gabriele Grandi, 2020. "Electric Vehicle Aggregate Power Flow Prediction and Smart Charging System for Distributed Renewable Energy Self-Consumption Optimization," Energies, MDPI, vol. 13(19), pages 1-25, September.
    3. 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.
    4. Youssef Amry & Elhoussin Elbouchikhi & Franck Le Gall & Mounir Ghogho & Soumia El Hani, 2022. "Electric Vehicle Traction Drives and Charging Station Power Electronics: Current Status and Challenges," Energies, MDPI, vol. 15(16), pages 1-30, August.
    5. Fachrizal, Reza & Shepero, Mahmoud & Åberg, Magnus & Munkhammar, Joakim, 2022. "Optimal PV-EV sizing at solar powered workplace charging stations with smart charging schemes considering self-consumption and self-sufficiency balance," Applied Energy, Elsevier, vol. 307(C).
    6. Graabak, Ingeborg & Wu, Qiuwei & Warland, Leif & Liu, Zhaoxi, 2016. "Optimal planning of the Nordic transmission system with 100% electric vehicle penetration of passenger cars by 2050," Energy, Elsevier, vol. 107(C), pages 648-660.
    7. 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.
    8. 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).
    9. Wu, Di & Radhakrishnan, Nikitha & Huang, Sen, 2019. "A hierarchical charging control of plug-in electric vehicles with simple flexibility model," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Mehrdad Tarafdar-Hagh & Kamran Taghizad-Tavana & Mohsen Ghanbari-Ghalehjoughi & Sayyad Nojavan & Parisa Jafari & Amin Mohammadpour Shotorbani, 2023. "Optimizing Electric Vehicle Operations for a Smart Environment: A Comprehensive Review," Energies, MDPI, vol. 16(11), pages 1-21, May.
    11. Jurasz, Jakub & Dąbek, Paweł B. & Kaźmierczak, Bartosz & Kies, Alexander & Wdowikowski, Marcin, 2018. "Large scale complementary solar and wind energy sources coupled with pumped-storage hydroelectricity for Lower Silesia (Poland)," Energy, Elsevier, vol. 161(C), pages 183-192.
    12. Peng, Ruoqing & Tang, Justin Hayse Chiwing G. & Yang, Xiong & Meng, Meng & Zhang, Jie & Zhuge, Chengxiang, 2024. "Investigating the factors influencing the electric vehicle market share: A comparative study of the European Union and United States," Applied Energy, Elsevier, vol. 355(C).
    13. Heydarian-Forushani, E. & Golshan, M.E.H. & Shafie-khah, M., 2016. "Flexible interaction of plug-in electric vehicle parking lots for efficient wind integration," Applied Energy, Elsevier, vol. 179(C), pages 338-349.
    14. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Akif Zia Khan & Dong Ryeol Shin, 2017. "Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective," Energies, MDPI, vol. 10(4), pages 1-47, April.
    15. García-Villalobos, J. & Zamora, I. & Knezović, K. & Marinelli, M., 2016. "Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks," Applied Energy, Elsevier, vol. 180(C), pages 155-168.
    16. Seddig, Katrin & Jochem, Patrick & Fichtner, Wolf, 2017. "Integrating renewable energy sources by electric vehicle fleets under uncertainty," Energy, Elsevier, vol. 141(C), pages 2145-2153.
    17. Pavić, Ivan & Capuder, Tomislav & Kuzle, Igor, 2016. "Low carbon technologies as providers of operational flexibility in future power systems," Applied Energy, Elsevier, vol. 168(C), pages 724-738.
    18. Alicia Triviño & José M. González-González & José A. Aguado, 2021. "Wireless Power Transfer Technologies Applied to Electric Vehicles: A Review," Energies, MDPI, vol. 14(6), pages 1-21, March.
    19. Xie, Xiangmin & Peng, Fei & Zhang, Yan, 2022. "A data-driven probabilistic harmonic power flow approach in power distribution systems with PV generations," Applied Energy, Elsevier, vol. 321(C).
    20. Mortaz, Ebrahim & Vinel, Alexander & Dvorkin, Yury, 2019. "An optimization model for siting and sizing of vehicle-to-grid facilities in a microgrid," Applied Energy, Elsevier, vol. 242(C), pages 1649-1660.

    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:17:p:4404-:d:404440. 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.