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

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 (R 2 = 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. • XGBoost accurately forecasts EV demand, achieving an R 2 of 0.84 and a low MAE. • Reinforcement learning enabled adaptive, demand-responsive pricing. • Integrated forecasting improved revenue and reduced load volatility. • Trained RL agent delivered stable, low-variance pricing strategies.