AI 中文总结
针对集成式电-氢-运输系统中车辆调度与多能源调度的强耦合问题，提出分层优化框架。车辆调度层用SFGH算法，能源调度层用DRL方法，经案例研究等验证了框架有效性、鲁棒性及策略泛化能力。
AI 中文摘要
随着电动汽车（EV）和氢汽车（HV）在运输系统中的部署增加，集成式电-氢基础设施变得越发重要。然而，车辆调度与多能源调度之间的强耦合带来了重大运营挑战。本文对集成式电-氢-运输系统（EHTS）进行建模，并提出一种分层优化框架，该框架通过顺序需求驱动的两层结构将车辆调度与下游能源调度耦合。在车辆调度层，开发了无求解器贪婪启发式（SFGH）算法以避免重复优化求解，实现非抢占服务和区间内顺序分配下的实时EV充电和HV加氢。所得的充电和加氢需求随后传递到能源调度层，在此设计了基于深度强化学习（DRL）的方法来优化电池运行、氢罐运行和光伏发电分配，以在满足预定运输需求的同时最小化EHTS的总体运营成本。代表性案例研究以及比较、消融和泛化分析证明了所提框架的有效性和鲁棒性。此外，学习到的调度策略在无需重新训练的情况下，在各种运输需求场景中均保持强大性能，展示了其在实际部署中的强大泛化能力。
英文摘要
Integrated electric-hydrogen infrastructures are becoming increasingly important with the growing deployment of electric vehicles (EVs) and hydrogen vehicles (HVs) in transport systems. However, the strong coupling between vehicle scheduling and multi-energy dispatch introduces significant operational challenges. This paper models an integrated electric-hydrogen-transport system (EHTS) and proposes a hierarchical optimisation framework that couples vehicle scheduling and downstream energy dispatch through a sequential, demand-driven two-layer structure. In the vehicle scheduling layer, a solver-free greedy heuristic (SFGH) algorithm is developed to avoid repeated optimisation solving, enabling real-time EV charging and HV refuelling under non-preemptive service and within-interval sequential assignment. The resulting charging and refuelling demands are subsequently passed to the energy dispatch layer, where a deep reinforcement learning (DRL)-based approach is designed to optimise battery operation, hydrogen-tank operation, and PV generation allocation to minimise the overall operational cost of the EHTS while satisfying the scheduled transport demand. Representative case studies, together with comparative, ablation, and generalisation analyses, demonstrate the effectiveness and robustness of the proposed framework. Furthermore, the learned dispatch policy maintains strong performance across diverse transport-demand scenarios without retraining, demonstrating robust generalisation capability for practical deployment.
