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

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

Utilities Policy, 2025, vol. 96, issue C

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.

Keywords: EV charging; Dynamic pricing; XGBoost; PPO; Grid stability; Sustainable infrastructure (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:juipol:v:96:y:2025:i:c:s0957178725001535

DOI: 10.1016/j.jup.2025.102038

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