Optimizing hydrogen transportation network in wind-to-hydrogen systems

The wind-to-hydrogen systems are innovative energy conversion systems that enable the conversion of wind-generated electricity to hydrogen energy on offshore electrolysis hydrogen production platforms (OEHPPs). This paper studies a two-stage stochastic programming model to optimize both strategical and tactical levels of the hydrogen transportation network in wind-to-hydrogen systems. In the strategical level, we determine the deployment of OEHPPs, submarine cable connections between existing wind turbines and OEHPPs, and hydrogen transportation routes. In the tactical level, we optimize the hydrogen transportation volumes on the OEHPPs under uncertainty in wind-generated electricity in the system. The objective is to minimize the total costs of the hydrogen transportation network, which include strategic-level deployment cost and tactical-level operational cost. To address the computational complexity of the model, we develop a tailored heuristic algorithm that combines variable neighborhood search and simulated annealing, enhanced with problem-specific acceleration strategies. Computational experiments are conducted to validate the effectiveness of the proposed model and algorithm based on real data and synthetic data. Sensitivity analyses are performed and managerial insights are derived that are useful for the wind-to-hydrogen system managers. For example, OEHPP’s electricity consumption capacity should be matched to the total wind-generated electricity rather than maximized unconditionally. Our results also confirm the practical benefit of integrating routing and volume optimization: compared to greedy heuristic or rule-based strategies, our model reduces total cost by up to 3% and 2.5%, respectively. Finally, robustness tests demonstrate that the model remains effective resilience to variations in wind-generated electricity. These findings provide actionable guidance for deploying hydrogen transportation network in wind-to-hydrogen systems efficiently under real-world conditions.