Reinforcement learning-based multi-objective smart energy management for electric vehicle charging stations with priority scheduling

Abstract

Reinforcement learning (RL)-based control structures represent a transformative approach to optimizing energy management in electric vehicle (EV) charging stations, offering unparalleled adaptability and efficiency. This paper introduces a novel RL-based intelligent control strategy, designed to address multi-objective challenges in EV charging, such as energy efficiency, cost-effectiveness, and user prioritization. Central to this study is the development of a unique environment model, which incorporates dynamic variables including vehicle priority, arrival times, and real-time pricing data, ensuring realistic and practical applications. Additionally, a custom reward strategy is proposed, enabling the RL agents to effectively learn and adapt to complex operational demands. The study evaluates the performance of three RL algorithms-Q-Learning, SARSA, and Expected SARSA-within the proposed environment model, demonstrating their capabilities in reducing charging costs and improving profitability. Experimental results indicate that the Q-Learning agent achieved an average cost reduction of up to 66.4% compared to conventional charging strategies, with energy costs dropping from 11.78 to 3.96 per unit in high-priority cases. Expected SARSA exhibited a competitive performance, yielding up to 44.3% cost savings, whereas SARSA consistently resulted in the lowest cost efficiency, with reductions of only 46.6% in comparable scenarios. Furthermore, the RL-based scheduling framework successfully shortened peak-hour waiting times by 40%, while ensuring equitable prioritization of charging requests. Through a comprehensive set of realistic case studies and scenarios, the effectiveness of these algorithms is analyzed, focusing on their capacity to manage energy costs, enhance profitability, and adapt to fluctuating pricing conditions.