Given the central role of green e-molecule imports in the European energy transition, many studies optimize import pathways and identify a single cost-optimal solution. However, cost optimality is fragile, as real-world implementation depends on regulatory, spatial, and stakeholder constraints that are difficult to represent in optimization models and can render cost-optimal designs infeasible. To address this limitation, we generate a diverse set of near-cost-optimal alternatives within an acceptable cost margin using Modeling to Generate Alternatives, accounting for unmodeled uncertainties. Interpretable machine learning is then applied to extract insights from the resulting solution space. The approach is applied to hydrogen import pathways considering hydrogen, ammonia, methane, and methanol as carriers. Results reveal a broad near-optimal space with great flexibility: solar, wind, and storage are not strictly required to remain within 10% of the cost optimum. Wind constraints favor solar-storage methanol pathways, while limited storage favors wind-based ammonia or methane pathways.

翻译：鉴于绿色电子分子进口在欧洲能源转型中的核心作用，许多研究致力于优化进口路径并确定单一成本最优解。然而，成本最优性具有脆弱性，因为实际实施取决于监管、空间和利益相关方约束，这些约束难以在优化模型中体现，并可能导致成本最优设计不可行。为克服这一局限，我们采用建模生成替代方案方法，在可接受的成本范围内生成一组多样化的近成本最优替代方案，以应对未建模的不确定性。随后应用可解释机器学习从所得解空间中提取洞见。该方法应用于考虑以氢气、氨气、甲烷和甲醇作为载体的氢能进口路径。结果表明存在广阔且具有高度灵活性的近最优空间：为维持在成本最优解的10%范围内，太阳能、风能和储能并非严格必需。风能约束倾向于选择太阳能-储能甲醇路径，而有限储能则倾向于基于风能的氨气或甲烷路径。