Multi‐Agent and Forecast‐Conscious DRL ‐ MPC Framework of Safe and Efficient EV Fast‐Charging Wit

ABSTRACT

The growing adoption of electric vehicles (EVs) puts a lot of pressure on fast‐charging and distribution grids. The current methods of charging control, which are rule based, stand‐alone Model Predictive Control (MPC), and deep reinforcement learning (DRL), have several important limitations: poor scalability, absence of coordination across chargers, the safety of exploration and reactive behavior because of the no‐forecasting of demand. Such deficiencies lead to grid constraint breach, ineffective charging as well as poor system reliability in peak demand states. To solve these dilemmas, this paper suggests a Digital Twin‐based, forecast‐aware Multi‐ Agent Deep Reinforcement Learning (MARL) architecture with MPC‐based safety oversight in coordinated EV fast charging. The electro‐thermal battery dynamics, charger constraints and grid limits of a high‐fidelity digital twin is electronegative and allows safe learning and realistic evaluation in closed‐loop. The agent observations are equipped with short‐term load forecasting, which facilitates grid‐aware decision‐making, that is, proactive charging. MARL will regulate individual chargers, and an MPC supervisor will implement strict operational and safety limitations. Extensive experimental analyses reveal that the proposed framework has a significant reduction of charging completion time, charging efficiency, smooths aggregate grid load, and an overall result that eliminates grid and thermal constraint violations using MPC only, DRL only, and single‐agent DRL MPC baselines. Scalability test also proves its strong performance when scaling the number of charging agents, which proves the appropriateness of the framework to large‐scale rollout of EV fast‐charging infrastructure.