提出新方法发现绿氢进口路径的多样可行解，突破单一最优方案局限。
Revealing design archetypes and flexibility in e-molecule import pathways using Modeling to Generate Alternatives and interpretable machine learning
- 用生成替代方案模型构建近优解集，考虑未建模不确定性
- 发现成本仅超最优10%内就有多种路径可选，灵活性高
- 适合能源规划者评估政策与实际约束下的系统鲁棒性
鉴于绿色电子分子进口在欧洲能源转型中的核心作用，众多研究优化进口路径并识别单一成本最优方案。然而，成本最优性脆弱，因现实实施受法规、空间及利益相关方约束影响，这些因素难以在优化模型中体现，可能导致最优设计不可行。为此，我们利用生成替代方案模型，在可接受成本范围内生成大量近似最优替代解，涵盖未建模不确定性。随后采用可解释机器学习分析解空间。该方法应用于以氢气、氨、甲烷和甲醇为载体的氢进口路径。结果揭示出广泛的近优解空间，具有高度灵活性：在成本超出最优值10%以内时，无需严格依赖太阳能、风能或储能。风能受限时更倾向太阳能-储能甲醇路径；储能受限则偏好基于风能的氨或甲烷路径。
原文摘要 · Abstract (English)
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.
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