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Integrating demand forecasting and deep reinforcement learning for real-time electric vehicle charging price optimization

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Listed:
  • Mahmud, Monowar
  • Abedin, Tarek
  • Mahfuzur Rahman, Md
  • Ashraf Shoishob, Shamiul
  • Sieh Kiong, Tiong
  • Nur-E-Alam, Mohammad

Abstract

The rapid growth of electric vehicles (EVs) demands efficient, grid-friendly charging systems. This study introduces a dynamic pricing framework combining short-term demand forecasting and deep reinforcement learning. Using Adaptive Charging Network (ACN) data, XGBoost predicts charging demand accurately (R2 = 0.84, MAE = 0.45 kW). Compared to a uniform rate applied to all charging usage, set at 0.15 USD/kWh across all hours, with no adjustment for system demand conditions or time-of-day, the optimized strategy enhanced total daily revenue by 133 % and diminished load variance by 72.37 %. The PPO agent also surpassed traditional Time-of-Use and demand-based pricing models by 67–94 %, while ensuring pricing stability with a price standard deviation of 0.132 USD/kWh. The simulation results illustrate the framework's efficacy in facilitating off-peak charging and improving grid reliability.

Suggested Citation

  • Mahmud, Monowar & Abedin, Tarek & Mahfuzur Rahman, Md & Ashraf Shoishob, Shamiul & Sieh Kiong, Tiong & Nur-E-Alam, Mohammad, 2025. "Integrating demand forecasting and deep reinforcement learning for real-time electric vehicle charging price optimization," Utilities Policy, Elsevier, vol. 96(C).
    • Handle: RePEc:eee:juipol:v:96:y:2025:i:c:s0957178725001535
    DOI: 10.1016/j.jup.2025.102038
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    References listed on IDEAS

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    1. Md. Rayid Hasan Mojumder & Fahmida Ahmed Antara & Md. Hasanuzzaman & Basem Alamri & Mohammad Alsharef, 2022. "Electric Vehicle-to-Grid (V2G) Technologies: Impact on the Power Grid and Battery," Sustainability, MDPI, vol. 14(21), pages 1-53, October.
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    1. Yon-Hon Tsai & Ming-Tang Tsai, 2025. "Electricity Demand Forecasting and Risk Assessment for Campus Energy Management," Energies, MDPI, vol. 18(20), pages 1-16, October.

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