Revealing design archetypes and flexibility in e-molecule import pathways using Modeling to Generate Alternatives and interpretable machine learning

TL;DR

This paper introduces a method combining Modeling to Generate Alternatives and interpretable machine learning to explore diverse, flexible hydrogen import pathways, revealing multiple near-optimal solutions resilient to real-world uncertainties.

Contribution

It presents a novel approach to identify a broad set of feasible import pathways, addressing limitations of cost-optimal solutions by considering uncertainties and stakeholder constraints.

Findings

Broad near-optimal solution space with >10% cost margin.

Flexibility in energy source and storage options for hydrogen import.

Pathway preferences influenced by wind and storage constraints.

Abstract

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.