EV charging scheduling under limited charging constraints by an improve proximal policy optimization algorithm

The rapid growth in the number of electric vehicles (EVs) has revealed critical limitations in existing charging infrastructure: 40 % of public charging stations experience power overload during peak hours, while 35 % remain underutilized during off-peak periods. Current optimization approaches, including genetic algorithms and standard reinforcement learning techniques, struggle to effectively coordinate user demand and grid stability due to static constraint handling and delayed responses to demand fluctuations. To tackle these issues, this paper proposes an improved Proximal Policy Optimization (PPO) algorithm to optimize EV charging scheduling. The improved PPO model dynamically adjusts the charging schedule while considering both the capacity limitations of charging stations and the time-of-use electricity pricing. Using Monte Carlo simulations to model user charging behavior, the proposed method efficiently allocates charging stations and power resources, thus alleviating the strain on the grid during peak demand and lowering total charging expenses. Compared to traditional methods, includes genetic algorithms, mixed integer linear programming, and standard PPO, our approach achieves a 6.46 % reduction in charging costs, a 7.64 % decrease in peak load variance, and a 24.5 % improvement in convergence speed, demonstrating significant advantages in cost-effectiveness, system stability, and computational efficiency.