In recent years, while the rapid development of global electric vehicles (EVs) has been driving energy transition, their large-scale integration into power grids has posed significant challenges to the stability of power systems and the reliability of electricity supply. Vehicle-to-Grid (V2G) technology demonstrates potential in balancing grid peak-valley differences and facilitating renewable energy integration, thereby effectively mitigating issues caused by disorderly grid integration of massive EVs. This paper proposes a “Vehicle-to-Station-to-Grid” (VSG) aggregated system based on V2G technology. In this system, the multi-state stochastic power output and demand of EVs are considere... More >

This study introduces a novel approach for enhancing production decision-making by applying Reinforcement Learning to optimize the Economic Manufacturing Quantity (EMQ) model within discrete-time production-inventory systems. By incorporating machine status, inventory levels, and production choices, a Markov Decision Process (MDP) is constructed and combined with the Q-learning algorithm to derive an adaptive control method. This method enables the dynamic adaptation of production decisions, by effectively balancing the normal operation and shutdown for rest states. Numerical simulations show that the suggested Reinforcement Learning model surpasses conventional EMQ models and steady-state p... More >