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Insights into residential EV charging behavior using energy meter data

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  • Kim, Jae D.

Abstract

Mass adoption of the plug-in electric vehicle (EV) technology is imperative for the rapid electrification of the transportation sector to mitigate catastrophic effects from climate change. Rapid integration of a large number of EVs will inevitably cause uncertainty and variability on the operation of the existing electric power system. There is high uncertainty on not only the speed and scale of EV adoption but also the EV energy and power requirements that depends on EV charging patterns. This study uses energy meter-level data from the San Diego region to analyze the energy load profiles of residential customers under the time-of-use (TOU) rate with and without EV charging requirements. Unlike previous forecasts on the effects of EV charging loads, the energy load profile of TOU customers with EVs reveal a “twin demand peak” where there is a peak demand during the evening hours and another at midnight. Results reveal potential issues for grid operations with greater EV adoption and the importance of careful TOU rate design.

Suggested Citation

  • Kim, Jae D., 2019. "Insights into residential EV charging behavior using energy meter data," Energy Policy, Elsevier, vol. 129(C), pages 610-618.
    • Handle: RePEc:eee:enepol:v:129:y:2019:i:c:p:610-618
    DOI: 10.1016/j.enpol.2019.02.049
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    References listed on IDEAS

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    1. Kelly, Jarod C. & MacDonald, Jason S. & Keoleian, Gregory A., 2012. "Time-dependent plug-in hybrid electric vehicle charging based on national driving patterns and demographics," Applied Energy, Elsevier, vol. 94(C), pages 395-405.
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    3. Aree Wangsupphaphol & Surachai Chaitusaney, 2022. "Subsidizing Residential Low Priority Smart Charging: A Power Management Strategy for Electric Vehicle in Thailand," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    4. 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).
    5. Tikka, Ville & Haapaniemi, Jouni & Räisänen, Otto & Honkapuro, Samuli, 2022. "Convolutional neural networks in estimating the spatial distribution of electric vehicles to support electricity grid planning," Applied Energy, Elsevier, vol. 328(C).
    6. Meintjes, Tiago & Castro, Rui & Pires, A.J., 2021. "Impact of vehicle charging on Portugal's national electricity load profile in 2030," Utilities Policy, Elsevier, vol. 73(C).
    7. Mauro Zucca & Vincenzo Cirimele & Jorge Bruna & Davide Signorino & Erika Laporta & Jacopo Colussi & Miguel Angel Alonso Tejedor & Federico Fissore & Umberto Pogliano, 2021. "Assessment of the Overall Efficiency in WPT Stations for Electric Vehicles," Sustainability, MDPI, vol. 13(5), pages 1-19, February.
    8. Aritra Ghosh, 2020. "Possibilities and Challenges for the Inclusion of the Electric Vehicle (EV) to Reduce the Carbon Footprint in the Transport Sector: A Review," Energies, MDPI, vol. 13(10), pages 1-22, May.
    9. Zhang, Tianren & Huang, Yuping & Liao, Hui & Liang, Yu, 2023. "A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network," Applied Energy, Elsevier, vol. 351(C).
    10. 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.
    11. Brinkel, N.B.G. & Schram, W.L. & AlSkaif, T.A. & Lampropoulos, I. & van Sark, W.G.J.H.M., 2020. "Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits," Applied Energy, Elsevier, vol. 276(C).
    12. Ma, Shao-Chao & Yi, Bo-Wen & Fan, Ying, 2022. "Research on the valley-filling pricing for EV charging considering renewable power generation," Energy Economics, Elsevier, vol. 106(C).

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