Dynamic Tariff Optimization for EV Charging Stations Using Reinforcement Learning

The growing adoption of electric vehicles (EV s) presents both opportunities and challenges for power grids and EV charging stations. This research explores the application of reinforcement learning (RL) for intelligent tariff optimization at charging stations, demonstrating its adaptability to real-world problems. By analyzing real-time data encompassing grid load, hourly electricity prices, and renewable energy availability, our study reveals the RL algorithm's ability to adapt to dynamic conditions and generate tariff schedules that balance grid stability with the seamless integration of renewable energy sources. This work highlights the potential of RL in solving complex energy management challenges and offers valuable insights for energy stakeholders seeking innovative pricing models that align with sustainable energy practices.