A Hierarchical Optimisation Framework for Integrated Electric-Hydrogen-Transport Systems
arXiv:2607.25776
The paper presents a two-layer hierarchical optimisation framework that coordinates vehicle scheduling for electric and hydrogen vehicles with downstream multi-energy dispatch using a greedy heuristic and deep reinforcement learning to minimize operational costs.
Abstract
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.