Bi-Level Optimization of Electric Vehicle Charging Scheduling Using Hybrid Genetic Algorithm and Reinforcement Learning

The efficient management of electric vehicle (EV) charging infrastructure is critical to meeting the growing demand for sustainable transportation. This study addresses the Electric Vehicle Charging Scheduling Problem (EVCSP), focusing on maximizing the number of satisfied charging demands. A bi-level optimization framework is developed, with the upper-level problem solved using two approaches: a classical Genetic Algorithm (GA) and a Hybrid Genetic Algorithm (HGA) enhanced with reinforcement learning via Q-learning. The HGA incorporates a Q-table to dynamically guide mutation decisions, balancing exploration and exploitation for improved performance. At the lower level, energy allocation is optimized using a mathematical programming model, ensuring feasibility and compliance with grid and charger constraints. Computational results demonstrate the HGA’s superior ability to handle large-scale instances due to its integration of adaptive learning and optimal energy allocation. The proposed framework advances EV charging management by combining evolutionary algorithms and reinforcement learning to address the complexities of real-world charging scenarios.