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Deep learning framework for day-ahead optimal charging scheduling of electric vehicles in parking lot

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  • Gharibi, Mohamad Amin
  • Nafisi, Hamed
  • Askarian-abyaneh, Hossein
  • Hajizadeh, Amin

Abstract

The increase of electric vehicles (EVs) in the power system, in addition to environmental benefits, also has economic advantages. Charging parking lots can perform better economically through aggregators the benefits of participating in energy markets and developing different charging programs. The main challenge for participating in the electricity market for parking lots is the uncertainties in the behavior of EV owners and the day-ahead market (DAM) price. Therefore, this paper proposes a deep learning-based framework for day-ahead optimal charging scheduling, which is in three stages. In the first stage, the parameters of charging sessions are modeled using the proposed Copula generative adversarial network (CopulaGAN). In the second stage, the DAM price is forecasted using the time series multi-input nonlinear autoregressive neural network for 24 h ahead. In addition, in the proposed framework, the balancing energy market (BEM) is used to keep the charging power balance on the implementation day of the optimal charging program. In the third stage, optimal day-ahead EV scheduling is performed. The simulation is carried out using Grey Wolf Optimization (GWO) based on the Caltech charging sessions dataset and the Germany energy market dataset. The simulation results show that using the proposed deep learning framework reduces the charging cost for the parking lots compared to the fixed price approaches, the use of the BEM, the time of use (TOU) program, and using normal distribution modeling about 19.94 €, 159.97 €, 14.27 €, and 114.03 € respectively.

Suggested Citation

  • Gharibi, Mohamad Amin & Nafisi, Hamed & Askarian-abyaneh, Hossein & Hajizadeh, Amin, 2023. "Deep learning framework for day-ahead optimal charging scheduling of electric vehicles in parking lot," Applied Energy, Elsevier, vol. 349(C).
    • Handle: RePEc:eee:appene:v:349:y:2023:i:c:s0306261923009789
    DOI: 10.1016/j.apenergy.2023.121614
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    References listed on IDEAS

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