Streamlining decisions for green hydrogen import pathways

The European Union plans to import green hydrogen from renewable-rich regions to support its energy transition. Numerous studies have optimized the techno-economic aspects of hydrogen production and importation, considering different locations, energy carriers, and transportation methods. These studies typically describe the optimal solution provided by the model. However, such an approach is fragile, as it overlooks non-quantifiable factors such as stakeholder preferences, evolving regulations, site-specific technical constraints, and social acceptance. These factors can render the mathematically optimal solution unfeasible or undesirable. To address this limitation, we explored diverse near-optimal alternatives for system design (i.e., PV, wind, electrolyzer, battery and hydrogen storage capacity) within an acceptable levelized cost of hydrogen (LCOH) margin using Modeling to Generate Alternatives (MGA). The hydrogen production and transportation system optimization model considered hydrogen, methane, methanol, and ammonia as energy carriers, with shipping and pipelines as transportation methods. To derive actionable insights from the overwhelming number of near-optimal alternatives, we applied interpretable machine learning. The results reveal a vast near-optimal solution space, where eliminating PV, wind, hydrogen storage, or battery storage technologies still keeps cost increase within 10% of the optimal LCOH. From this space, three main design directions emerge: (1) a PV-dominated direction with high electrolyzer and hydrogen storage usage but low reliance on wind and batteries; (2) a wind-dominated direction with minimal reliance on other technologies, leading to a storage-free designs; and (3) a mixed electricity generation direction, where lower battery capacity requires a larger electrolyzer and vice versa. In conclusion, significant flexibility exists in designing systems for hydrogen import pathways while maintaining near-optimal economic performance. Unexpected constraints or preferences, such as those related to renewable production or storage, allow clear alternatives to remain within a cost-effective range. Future work will focus on decision making regarding energy carriers and assessing cost uncertainties across different design directions.