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Hydrogen Production and Utilization in a Membrane Reactor
Published on: March 10, 2023
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Endogenous learning for green hydrogen in a sector-coupled energy model for Europe
Elisabeth Zeyen1,2, Marta Victoria3,4, Tom Brown5,6
1Department of Digital Transformation in Energy Systems, Faculty of Process Engineering, TU Berlin, Einsteinufer 25 (TA 8), Berlin, 10587, Berlin, Germany. e.zeyen@tu-berlin.de.
Nature Communications
|June 23, 2023
Summary
Accelerating renewable and electrolysis capacity is crucial for cost-optimal green hydrogen production, enabling the 1.5°C climate target. Ignoring learning effects in modeling significantly delays scale-up and overestimates costs.
Area of Science:
- Energy Systems Analysis
- Climate Change Mitigation
- Green Hydrogen Economy
Background:
- Hydrogen is vital for decarbonizing hard-to-electrify sectors.
- Previous energy models lacked crucial features like demand sectors, temporal variability, and learning effects for hydrogen.
- Accurate modeling is needed to assess hydrogen's role in the energy transition.
Purpose of the Study:
- To address limitations in previous hydrogen modeling by incorporating learning-by-doing for the green hydrogen production chain.
- To assess the role of green hydrogen in achieving stringent climate targets (+1.5°C) within a European sector-coupled model.
- To analyze the impact of learning effects on electrolysis scale-up and overall system costs.
Main Methods:
- Development of a detailed European sector-coupled model.
- Inclusion of learning-by-doing dynamics for the entire green hydrogen production chain.
- Simulation of different climate targets, including the strictest +1.5°C scenario.
Main Results:
- A faster scale-up of electrolysis and renewable capacities than currently planned by the EU is cost-optimal for the +1.5°C target.
- Green hydrogen production becomes dominant, replacing grey hydrogen and excluding blue hydrogen.
- Learning-by-doing significantly reduces hydrogen production costs to €1.26/kg by 2050.
Conclusions:
- Dynamic learning effects are critical for accurate hydrogen modeling and achieving climate goals.
- Failure to account for learning-by-doing leads to delayed electrolysis scale-up and overestimated system and hydrogen costs.
- Policy and investment should prioritize rapid scaling of green hydrogen infrastructure to meet ambitious climate targets cost-effectively.
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