SMART-EV: a stochastic macroscopic DRL-based method for enhancing distribution network resilience via EV coordination

The increasing penetration of electric vehicles (EVs) provides unprecedented flexibility for enhancing the resilience of distribution networks under extreme events. However, the inherent stochasticity in EV mobility and the need for real-time coordination with power system operations present significant challenges for disaster restoration. To address these challenges, this paper proposes a novel stochastic macroscopic adaptive resilience-trained EV (SMART-EV) method. Firstly, a stochastic macroscopic mobility model is developed to describe the random movement of EVs in a two-dimensional plane, enabling more realistic yet tractable modeling of large-scale fleet dynamics. Subsequently, the proposed SMART-EV achieves adaptive learning-driven physical systems by integrating deep reinforcement learning (DRL)-based decision-making of EV mobility and physical-layer grid operation. Finally, an improved deep deterministic policy gradient (DDPG) algorithm is introduced, which explicitly optimizes grid resilience objectives, accelerates convergence, and enhances training stability through auxiliary Nash-style (AuxiNash) multi-objective regularization. Case studies in IEEE 33-bus system demonstrate that the proposed SMART-EV method achieves in a 17.9 % increase in the resilience index and substantial reductions in computational effort.