IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v189y2024ipas1364032123008110.html
   My bibliography  Save this article

Driving change: Electric vehicle charging behavior and peak loading

Author

Listed:
  • Williams, B.
  • Bishop, D.
  • Hooper, G.
  • Chase, J.G.

Abstract

Electric vehicles (EVs) are projected to comprise 40 % of Aotearoa New Zealand's light vehicle fleet by 2040. However, charging decisions made by EV drivers, such as whether to charge immediately or delay charging, will affect peak electricity demand and lifetime of distribution network components. This study uses an agent-based model (ABM) of EV charging to investigate the effect of different EV penetration levels and owner charging decisions on components in New Zealand's residential electricity networks, although the methodology is wholly generalizable to other countries or regions. Monte Carlo simulation is performed for EV charging in a neighborhood of 71 houses, based on a representative residential distribution network, and simulated for 20 days. The key outcome measure is the rate of ‘Exceedance’ of the 300 kVA baseline transformer limit, where greater Exceedance entails shorter lifecycle and increased maintenance or capital costs to the provider. Results show delayed-charging algorithms (‘Altruistic charging’) decrease peak electricity demand and Exceedance, while drivers charging immediately (‘Selfish charging’) increases Exceedance. New Zealand's residential electricity networks are expected to accommodate a 40 % EV transition with 100 % Altruistic charging, as Exceedance is expected to increase less than 20 % from Exceedance without EVs. However, Selfish charging increases the rate of Exceedance by more than 250 %. Longer-term, increasing EV penetration and household electricity demand will require increased workplace charging infrastructure, electricity network upgrades, and/or automated and Internet of Things (IoT)-enabled Demand Side Management (DSM) of EV charging to avoid high rates of Exceedance and increased maintenance and replacement costs.

Suggested Citation

  • Williams, B. & Bishop, D. & Hooper, G. & Chase, J.G., 2024. "Driving change: Electric vehicle charging behavior and peak loading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
  • Handle: RePEc:eee:rensus:v:189:y:2024:i:pa:s1364032123008110
    DOI: 10.1016/j.rser.2023.113953
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032123008110
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2023.113953?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Baxter Williams & Daniel Bishop & Patricio Gallardo & J. Geoffrey Chase, 2023. "Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations," Energies, MDPI, vol. 16(13), pages 1-28, July.
    2. Yimin Zhou & Zhifei Li & Xinyu Wu, 2018. "The Multiobjective Based Large-Scale Electric Vehicle Charging Behaviours Analysis," Complexity, Hindawi, vol. 2018, pages 1-16, October.
    3. Zhou, Kaile & Cheng, Lexin & Lu, Xinhui & Wen, Lulu, 2020. "Scheduling model of electric vehicles charging considering inconvenience and dynamic electricity prices," Applied Energy, Elsevier, vol. 276(C).
    4. Morton, Craig & Anable, Jillian & Yeboah, Godwin & Cottrill, Caitlin, 2018. "The spatial pattern of demand in the early market for electric vehicles: Evidence from the United Kingdom," Journal of Transport Geography, Elsevier, vol. 72(C), pages 119-130.
    5. Bruno Canizes & João Soares & Angelo Costa & Tiago Pinto & Fernando Lezama & Paulo Novais & Zita Vale, 2019. "Electric Vehicles’ User Charging Behaviour Simulator for a Smart City," Energies, MDPI, vol. 12(8), pages 1-20, April.
    6. Abotalebi, Elnaz & Scott, Darren M. & Ferguson, Mark R., 2019. "Why is electric vehicle uptake low in Atlantic Canada? A comparison to leading adoption provinces," Journal of Transport Geography, Elsevier, vol. 74(C), pages 289-298.
    7. Mart van der Kam & Annemijn Peters & Wilfried van Sark & Floor Alkemade, 2019. "Agent-Based Modelling of Charging Behaviour of Electric Vehicle Drivers," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(4), pages 1-7.
    8. Pagani, M. & Korosec, W. & Chokani, N. & Abhari, R.S., 2019. "User behaviour and electric vehicle charging infrastructure: An agent-based model assessment," Applied Energy, Elsevier, vol. 254(C).
    9. Hsu, Chih-Wei & Fingerman, Kevin, 2021. "Public electric vehicle charger access disparities across race and income in California," Transport Policy, Elsevier, vol. 100(C), pages 59-67.
    10. Morrissey, Patrick & Weldon, Peter & O’Mahony, Margaret, 2016. "Future standard and fast charging infrastructure planning: An analysis of electric vehicle charging behaviour," Energy Policy, Elsevier, vol. 89(C), pages 257-270.
    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. Graham Town & Seyedfoad Taghizadeh & Sara Deilami, 2022. "Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning," Energies, MDPI, vol. 15(4), pages 1-30, February.
    2. Nazari-Heris, Morteza & Loni, Abdolah & Asadi, Somayeh & Mohammadi-ivatloo, Behnam, 2022. "Toward social equity access and mobile charging stations for electric vehicles: A case study in Los Angeles," Applied Energy, Elsevier, vol. 311(C).
    3. Hu, Dingding & Zhou, Kaile & Li, Fangyi & Ma, Dawei, 2022. "Electric vehicle user classification and value discovery based on charging big data," Energy, Elsevier, vol. 249(C).
    4. 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).
    5. Sikder, Sujit Kumar & Nagarajan, Magesh & Mustafee, Navonil, 2023. "Augmenting EV charging infrastructure towards transformative sustainable cities: An equity-based approach," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    6. Mehdizadeh, Milad & Nordfjaern, Trond & Klöckner, Christian A., 2022. "A systematic review of the agent-based modelling/simulation paradigm in mobility transition," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    7. Chen, Yu & Lin, Boqiang, 2022. "Are consumers in China’s major cities happy with charging infrastructure for electric vehicles?," Applied Energy, Elsevier, vol. 327(C).
    8. Ken’ichi Matsumoto & Yui Nakamine & Sunyong Eom & Hideki Kato, 2021. "Demographic, Social, Economic, and Regional Factors Affecting the Diffusion of Hybrid Electric Vehicles in Japan," Energies, MDPI, vol. 14(8), pages 1-14, April.
    9. Kacperski, Celina & Ulloa, Roberto & Klingert, Sonja & Kirpes, Benedikt & Kutzner, Florian, 2022. "Impact of incentives for greener battery electric vehicle charging – A field experiment," Energy Policy, Elsevier, vol. 161(C).
    10. Helmus, Jurjen R. & Lees, Michael H. & van den Hoed, Robert, 2022. "A validated agent-based model for stress testing charging infrastructure utilization," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 237-262.
    11. Tim Jonas & Noah Daniels & Gretchen Macht, 2023. "Electric Vehicle User Behavior: An Analysis of Charging Station Utilization in Canada," Energies, MDPI, vol. 16(4), pages 1-19, February.
    12. Yuan-Yuan Wang & Yuan-Ying Chi & Jin-Hua Xu & Jia-Lin Li, 2021. "Consumer Preferences for Electric Vehicle Charging Infrastructure Based on the Text Mining Method," Energies, MDPI, vol. 14(15), pages 1-20, July.
    13. Yunhan Zheng & David R. Keith & Shenhao Wang & Mi Diao & Jinhua Zhao, 2024. "Effects of electric vehicle charging stations on the economic vitality of local businesses," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    14. Jonas, Tim & Macht, Gretchen A., 2024. "Analyzing the urban-rural divide: Understanding geographic variations in charging behavior for a user-centered EVSE infrastructure," Journal of Transport Geography, Elsevier, vol. 116(C).
    15. Shi, Lefeng & Hao, Ying & Lv, Shengnan & Cipcigan, Liana & Liang, Jun, 2021. "A comprehensive charging network planning scheme for promoting EV charging infrastructure considering the Chicken-Eggs dilemma," Research in Transportation Economics, Elsevier, vol. 88(C).
    16. Nnaemeka Vincent Emodi & Scott Dwyer & Kriti Nagrath & John Alabi, 2022. "Electromobility in Australia: Tariff Design Structure and Consumer Preferences for Mobile Distributed Energy Storage," Sustainability, MDPI, vol. 14(11), pages 1-18, May.
    17. Mandev, Ahmet & Plötz, Patrick & Sprei, Frances & Tal, Gil, 2022. "Empirical charging behavior of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 321(C).
    18. Baresch, Martin & Moser, Simon, 2019. "Allocation of e-car charging: Assessing the utilization of charging infrastructures by location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 388-395.
    19. 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.
    20. Lucio Ciabattoni & Stefano Cardarelli & Marialaura Di Somma & Giorgio Graditi & Gabriele Comodi, 2021. "A Novel Open-Source Simulator Of Electric Vehicles in a Demand-Side Management Scenario," Energies, MDPI, vol. 14(6), pages 1-16, March.

    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:eee:rensus:v:189:y:2024:i:pa:s1364032123008110. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.