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

Authors/Creators

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

Author Identifier (ORCID)

Mohammad Nur-E-Alam: https://orcid.org/0000-0003-1969-3348

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.

Document Type

Journal Article

Date of Publication

10-1-2025

Volume

96

Publication Title

Utilities Policy

Publisher

Elsevier

School

School of Science

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Comments

Mahmud, M., Abedin, T., Rahman, M. M., Shoishob, S. A., Kiong, T. S., & Nur-E-Alam, M. (2025). Integrating demand forecasting and deep reinforcement learning for real-time electric vehicle charging price optimization. Utilities Policy, 96, 102038. https://doi.org/10.1016/j.jup.2025.102038