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Charging management of electric vehicles with consumption of renewable energy

Author

Listed:
  • Ni, Fangyuan
  • Xiang, Yue
  • Wang, Shiqian
  • Hu, Zechun
  • Liu, Fang
  • Xu, Xiao
  • Jiang, Yi
  • Wang, Yang

Abstract

The consumption of renewable energy (RE) faces significant challenges, including supply-demand imbalances and grid access constraints. With the rapid expansion of electric vehicles (EVs), managing EV charging to align with RE availability presents a novel solution that enhances RE utilization and generates additional revenue for electric vehicle aggregators (EVAs). This study introduces a framework for EV charging management focused on optimizing RE consumption. Firstly, the Pearson correlation coefficient with a sliding time window (STW)is employed to match the RE output curves with the electric vehicle charging station (EVCS) load curves, identifying optimal time slots for different types of EVCSs to engage in RE consumption under EVAs. Secondly, a multi-objective optimization model is developed, incorporating price-demand elasticity to adjust charging fees hourly during consumption periods, thereby maximizing both RE utilization and EVA's revenue. The results show that the Pearson correlation coefficient is more effective in smoothing the RE curve, resulting in a reduction of the variance of the RE curve by about 3 %–8 %. Compared with the existing time-of-use (TOU) tariff mechanism, the proposed hourly charging management increases RE consumption by about 15 % and EVA's revenue by around 16 %. Moreover, in comparison to EVAs that only consume hydropower, the integration of RE from water, wind, and solar sources can extend the consumption periods, thereby further enhancing the consumption efficiency and economic benefits.

Suggested Citation

  • Ni, Fangyuan & Xiang, Yue & Wang, Shiqian & Hu, Zechun & Liu, Fang & Xu, Xiao & Jiang, Yi & Wang, Yang, 2025. "Charging management of electric vehicles with consumption of renewable energy," Energy, Elsevier, vol. 321(C).
    • Handle: RePEc:eee:energy:v:321:y:2025:i:c:s0360544225009065
    DOI: 10.1016/j.energy.2025.135264
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    References listed on IDEAS

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    1. Došljak, Velibor & Ćorović, Velimir & Mihailovic, Andrej, 2025. "A two-layer optimization model for electric vehicle charging station distribution using a custom genetic algorithm: Application to Montenegro," Energy, Elsevier, vol. 330(C).
    2. Silva, Carlos A.M. & Andrade, José R. & Ferreira, Amândio & Gomes, André & Bessa, Ricardo J., 2025. "Dynamic incentives for electric vehicles charging at supermarket stations: Causal insights on demand flexibility," Energy, Elsevier, vol. 341(C).
    3. Asiri Tayri & Xiandong Ma, 2025. "Grid Impacts of Electric Vehicle Charging: A Review of Challenges and Mitigation Strategies," Energies, MDPI, vol. 18(14), pages 1-38, July.

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