Multi-Objective Optimization of Grid Mix Scenarios for Green Hydrogen Production in Germany: Balancing Environmental Impact and Energy Costs

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As global decarbonization accelerates, the environmental and economic viability of hydrogen production largely depends on the evolving electricity supply mix. This study focused on alkaline water electrolysis (AWE) to identify the key factors affecting the competitiveness of green hydrogen. In this study, the temporal dynamics of grid transformation in Germany and the EU over a 20-year period (2025–2045) were addressed by developing a multi-objective optimization framework that integrates environmental impact analysis with machine-learning surrogate models to evaluate trade-offs between the carbon footprint and energy cost per kilogram of hydrogen. Grid-mix scenarios were generated via constrained Latin Hypercube Sampling under policy constraints, including coal phase-out and ≥80% renewables, screened for Pareto optimality, and clustered into distinct archetypes. The results indicated that cost-effective, low-carbon hydrogen production can be achieved through balanced portfolios that emphasize hydropower, biomass, and solar energy. Scenarios that minimize energy costs alone tend to breach environmental targets, whereas ultra-low-emission paths incur steep energy cost penalties. A representative scenario for 2034 (GWP = 24.57 kg CO2-eq/kg H2; Energy Cost = 9.47 €/kg H2) demonstrated a realistic synergy between policy constraints, cost, and environmental impact.