Resilience Planning for Coupled Power–Transportation Systems Based on Scenario-Feature Identification and Adversarial Reinforcement Learning

New loads, represented by electric vehicles, are emerging rapidly. Distribution networks therefore face increasingly complex spatiotemporal load fluctuations. Conventional planning methods inadequately consider traffic factors. They also overlook traffic-induced dynamic load variations. Accordingly, this study proposes a traffic load-coupled planning method for distribution networks. The primary novelty lies in identifying load features. It also uses classified scenarios as model inputs. This design reveals nonlinear impacts of dynamic traffic flow transfers on grid resilience across functional scenarios. First, load-feature clustering identifies scenario attributes for nodes, power lines, and traffic roads. Second, classified scenarios and historical data are used as model inputs. An improved model then generates high-confidence prediction intervals for power load and traffic flow. A dynamic mapping model is established between traffic flow and nodal power load. Then, adversarial reinforcement learning is used to obtain the planning scheme. An attack–defense game further enhances robustness under extreme disturbances. Finally, comparative simulations verify the superiority of the proposed scheme.