Reinforcement Learning for Real-Time Pricing and Scheduling Control in EV Charging Stations

This article proposes a reinforcement-learning (RL) approach for optimizing charging scheduling and pricing strategies that maximize the system objective of a public electric vehicle (EV) charging station. The proposed algorithm is x201C;onlinex201D; in the sense that the charging and pricing decisions made at each time depend only on the observation of past events, and is x201C;model-freex201D; in the sense that the algorithm does not rely on any assumed stochastic models of uncertain events. To cope with the challenge arising from the time-varying continuous state and action spaces in the RL problem, we first show that it suffices to optimize the total charging rates to fulfill the charging requests before departure times. Then, we propose a feature-based linear function approximator for the statex2013;value function to further enhance the efficiency and generalization ability of the proposed algorithm. Through numerical simulations with real-world data, we show that the proposed RL algorithm achieves on average 138.5x0025; higher charging-station profit than representative benchmark algorithms.

Automated summary

This article proposes a reinforcement-learning approach for optimizing charging scheduling and pricing strategies, achieving 138.5% higher charging-station profit compared to representative benchmark algorithms. The algorithm is "online" and "model-free", and a feature-based linear function approximator is proposed to enhance efficiency and generalization ability.