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Charging powers of the electric vehicle fleet: Evolution and implications at commercial charging sites

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  • Simolin, Toni
  • Rauma, Kalle
  • Viri, Riku
  • Mäkinen, Johanna
  • Rautiainen, Antti
  • Järventausta, Pertti

Abstract

Electric vehicle (EV) charging is widely studied in the scientific literature. However, there seems to be a notable research gap regarding the charging power limitations of the on-board chargers of the EVs. In this paper, the present state of the maximum charging powers of the on-board chargers is thoroughly analysed using data from two commercial charging sites. Furthermore, the results of the analysis are used along with an EV fleet development model to form realistic future scenarios, which are then used for a simulation model that couples the charging sessions with measured charging profiles. The results of the simulations show that, due to the evolution of the EV fleet, the average energy consumption in commercial locations will increase by 134% on average from 5.6 to 8.7 kWh/EV to 13.0–19.6 kWh/EV during 2020–2040. Similarly, the peak of the normalized power increases by 77% on average from 1.1 to 1.4 kW/EV to 1.6–2.9 kW/EV. These values are essential to guide long-term decisions such as optimal sizing of charging infrastructure and parking policies.

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  • Simolin, Toni & Rauma, Kalle & Viri, Riku & Mäkinen, Johanna & Rautiainen, Antti & Järventausta, Pertti, 2021. "Charging powers of the electric vehicle fleet: Evolution and implications at commercial charging sites," Applied Energy, Elsevier, vol. 303(C).
    • Handle: RePEc:eee:appene:v:303:y:2021:i:c:s0306261921010187
    DOI: 10.1016/j.apenergy.2021.117651
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    References listed on IDEAS

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    1. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    2. Shepero, Mahmoud & Munkhammar, Joakim, 2018. "Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data," Applied Energy, Elsevier, vol. 231(C), pages 1089-1099.
    3. Zhao, Yang & Wang, Zhenpo & Shen, Zuo-Jun Max & Sun, Fengchun, 2021. "Data-driven framework for large-scale prediction of charging energy in electric vehicles," Applied Energy, Elsevier, vol. 282(PB).
    4. Pareschi, Giacomo & Küng, Lukas & Georges, Gil & Boulouchos, Konstantinos, 2020. "Are travel surveys a good basis for EV models? Validation of simulated charging profiles against empirical data," Applied Energy, Elsevier, vol. 275(C).
    5. Scorrano, Mariangela & Danielis, Romeo & Giansoldati, Marco, 2020. "Dissecting the total cost of ownership of fully electric cars in Italy: The impact of annual distance travelled, home charging and urban driving," Research in Transportation Economics, Elsevier, vol. 80(C).
    6. Wolbertus, Rick & Kroesen, Maarten & van den Hoed, Robert & Chorus, Caspar, 2018. "Fully charged: An empirical study into the factors that influence connection times at EV-charging stations," Energy Policy, Elsevier, vol. 123(C), pages 1-7.
    7. Haupt, Leon & Schöpf, Michael & Wederhake, Lars & Weibelzahl, Martin, 2020. "The influence of electric vehicle charging strategies on the sizing of electrical energy storage systems in charging hub microgrids," Applied Energy, Elsevier, vol. 273(C).
    8. Deshmukh, Swaraj Sanjay & Pearce, Joshua M., 2021. "Electric vehicle charging potential from retail parking lot solar photovoltaic awnings," Renewable Energy, Elsevier, vol. 169(C), pages 608-617.
    9. Gunkel, Philipp Andreas & Bergaentzlé, Claire & Græsted Jensen, Ida & Scheller, Fabian, 2020. "From passive to active: Flexibility from electric vehicles in the context of transmission system development," Applied Energy, Elsevier, vol. 277(C).
    10. 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).
    11. Xiang, Yue & Jiang, Zhuozhen & Gu, Chenghong & Teng, Fei & Wei, Xiangyu & Wang, Yang, 2019. "Electric vehicle charging in smart grid: A spatial-temporal simulation method," Energy, Elsevier, vol. 189(C).
    12. Philipp Andreas Gunkel & Claire Bergaentzl'e & Ida Gr{ae}sted Jensen & Fabian Scheller, 2020. "From passive to active: Flexibility from electric vehicles in the context of transmission system development," Papers 2011.05830, arXiv.org.
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    1. Zeynali, Saeed & Nasiri, Nima & Ravadanegh, Sajad Najafi & Marzband, Mousa, 2022. "A three-level framework for strategic participation of aggregated electric vehicle-owning households in local electricity and thermal energy markets," Applied Energy, Elsevier, vol. 324(C).
    2. Sridharan, S. & Sivakumar, S. & Shanmugasundaram, N. & Swapna, S. & Vasan Prabhu, V., 2023. "A hybrid approach based energy management for building resilience against power outage by shared parking station for EVs," Renewable Energy, Elsevier, vol. 216(C).
    3. Simolin, Toni & Rauma, Kalle & Rautiainen, Antti & Järventausta, Pertti, 2022. "Increasing charging energy at highly congested commercial charging sites through charging control with load balancing functionality," Applied Energy, Elsevier, vol. 326(C).

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