Energy-Efficient and Perturbation-Aware Dwell-Recharge Integrated Strategy with Deep Reinforcement Learning for Catenary-Free Tramway

Mounting global energy challenges necessitate a transition toward green and lean operational strategies across contemporary industries to bring eco-economic benefits about. This imperative extends to transportation systems, where emerging catenary-free tramways with novel onboard-offboard power supply architectures exhibit sustainable mobility while enhancing urban aesthetics. However, such systems still face critical challenges: simultaneous power demands during recharging cycles at stations risk destabilizing the tram traction power network, while mixed-traffic urban environments introduce operational vulnerabilities, manifesting as delays and congestion due to shared rights-of-way. To address these challenges, this study presents an integrated dwell time regulation and recharging scheduling method for catenary-free tramway, leveraging deep reinforcement learning (DRL) to balance dynamic energy demands with operational efficiency. The system dynamics are formalized through discrete event simulation (DES), and the decision-making process is formulated as an event-driven Markov decision process (MDP) to optimize real-time actions. The case study on a real-world catenary-free tramline in China demonstrates that our method can effectively diminish the peak power superimposition on local power network and the energy costs. Compared with representative heuristic and online optimization methods, DRL approach delivers a captivating solution for agile decision-making of intelligent tramway in the dynamic urban environments.