A Hierarchical Optimisation Framework for Integrated Electric-Hydrogen-Transport Systems
The system models integrated electric-hydrogen transport by coupling vehicle scheduling and energy dispatch through dynamic demand. A solver-free greedy heuristic handles vehicle assignments to minimize delays, while a Deep Reinforcement Learning agent optimizes battery, hydrogen tank, and PV allocation to minimize operational costs. This hierarchical structure ensures low service delays and minimum energy expenses.