Dynamic Energy Management for EV-Integrated Grids Using Reinforcement Learning

The rapid expansion of electric vehicles (EVs) in modern power grids introduces opportunities and challenges for grid operators. Efficiently managing the bidirectional energy flow between EVs and the grid is essential to mitigate voltage deviations, minimize energy losses, and enhance overall grid stability. Traditional optimization methods often fail to adapt to dynamic grid conditions, limiting their effectiveness in real-time applications. To address these challenges, this paper proposes a hybrid scheduling framework that combines classical optimization techniques with a novel reinforcement learning (RL)-based control system for EV energy management. The proposed RL model optimizes charging and discharging schedules by leveraging real-time grid data such as voltage profiles, load variations, and state-of-charge (SOC) of connected EVs. Unlike conventional methods, the RL approach continuously learns optimal control strategies through interaction with the grid environment, offering adaptive solutions under varying load scenarios. Simulation results demonstrate that the proposed method significantly reduces energy losses, improves voltage stability, and balances grid demand, even during peak load conditions. Integrating advanced ML techniques with grid optimization represents a transformative step toward sustainable energy systems that efficiently integrate EVs.