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Probabilistic feasibility space of scaling up green hydrogen supply

Nature Energy volume 7, pages 854–865 (2022)Cite this article

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Abstract

Green hydrogen and derived electrofuels are attractive replacements for fossil fuels in applications where direct electrification is infeasible. While this makes them crucial for climate neutrality, rapidly scaling up supply is critical and challenging. Here we show that even if electrolysis capacity grows as fast as wind and solar power have done, green hydrogen supply will remain scarce in the short term and uncertain in the long term. Despite initial exponential growth, green hydrogen likely (≥75%) supplies <1% of final energy until 2030 in the European Union and 2035 globally. By 2040, a breakthrough to higher shares is more likely, but large uncertainties prevail with an interquartile range of 3.2–11.2% (EU) and 0.7–3.3% (globally). Both short-term scarcity and long-term uncertainty impede investment in hydrogen end uses and infrastructure, reducing green hydrogen’s potential and jeopardizing climate targets. However, historic analogues suggest that emergency-like policy measures could foster substantially higher growth rates, expediting the breakthrough and increasing the likelihood of future hydrogen availability.

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Fig. 1: Historical development and future announcements of electrolysis projects.
Fig. 2: Illustration of the three uncertain parameters of the market ramp up.
Fig. 3: Truncated normal probability distributions of initial capacity in 2023 and emergence growth rates in the conventional growth case.
Fig. 4: Probabilistic feasibility space of electrolysis growth in the conventional growth case.
Fig. 5: Historical examples of technologies with unconventional growth and probabilistic feasibility space of electrolysis under such growth rates.
Fig. 6: Uncertainty of breakthrough year and electrolysis capacity over time.

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Data availability

All data are publicly available. We use data from the IEA Hydrogen Projects Database32 for electrolysis project capacity, complemented by our own market research and provided in the GitHub repository. Data on wind and solar power capacity is taken from the BP Statistical Review of World Energy 202160. Data sources for the unconventional growth case are listed in the GitHub repository and include, among others, refs. 71,72,73,74,75. Final energy scenario data is taken from the IEA NZE9 on the global level and from the INNOPATHS project63 for the European Union. Scenario data from the IPCC SR1.5 and IPCC AR6 are available from the respective scenario explorers64,66.

Code availability

The R model code, including all input data apart from the IPCC scenarios (for Extended Data Fig. 8), is available on GitHub: https://github.com/aodenweller/green-h2-upscaling/. Pre-run simulation results to reproduce all figures are available on Zenodo: https://zenodo.org/record/6567669#.Yuf_5-zMJ3k.

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Acknowledgements

We gratefully acknowledge funding from the Kopernikus-Ariadne project (FKZ 03SFK5A; A.O., F.U., M.J., G.L.) and the INTEGRATE project (FKZ: 01LP1928A; F.U., G.L.) by the German Federal Ministry of Education and Research and the German Federal Environmental Foundation (Deutsche Bundesstiftung Umwelt; A.O.). We thank J.M. Bermudez for comments and R. Rodrigues for providing final energy scenario data.

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Authors and Affiliations

  1. Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany

    Adrian Odenweller, Falko Ueckerdt & Gunnar Luderer

  2. Global Energy Systems Analysis, Technische Universität Berlin, Berlin, Germany

    Adrian Odenweller & Gunnar Luderer

  3. La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA

    Gregory F. Nemet

  4. adelphi research gGmbH, Berlin, Germany

    Miha Jensterle

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Contributions

F.U. and G.L. suggested the research question. A.O. and F.U. jointly conceived and designed the study in consultation with G.F.N. and G.L. A.O. implemented the model and created the visualizations. A.O. and F.U. interpreted the results. A.O. wrote the manuscript with contributions from G.F.N., M.J. and G.L. A.O. and M.J. verified and updated relevant electrolysis project announcements. A.O. collected data for the unconventional growth case.

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Correspondence to Falko Ueckerdt.

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Extended data

Extended Data Fig. 1 Historical capacities and exponential growth rates of solar and wind power.

a, Solar power in the EU, b, Solar power globally, c, Wind power in the EU, d, Wind power globally. The zoom panel displays the period of fastest growth until 2010, from which we estimate exponential growth rates in 7-year slices. The percentage values indicate the corresponding growth rate for the subsequent 7-year period. These growth rates define the emergence growth rate distributions in Fig. 3c-d. To illustrate the idea of our adapted logistic technology diffusion model, we also calculate the implicit demand-pull from 2011-2020 (dotted line, 5-year rolling mean) that replicates the historically observed capacities based on the average emergence growth rate taken from the 1995-2010 interval (see Eq. 5). The approximately linear shape of the implicit demand pull demonstrates that this is a well-founded assumption, which we therefore also apply to the analysis of electrolysis capacity upscaling.

Extended Data Fig. 2 Emergence growth rate distributions in the conventional growth case under varying interval lengths.

a, in the EU, b, globally. The distributions are robust to the length of the time slice (of wind and solar power) that underlies the estimate of the exponential growth rate. We therefore use the intermediate 7-year slice length, which also corresponds to the 2023-2030 time window that is of particular concern for 2030 policy targets as the model starts in 2023.

Extended Data Fig. 3 Comparison between the adapted logistic model and the Gompertz model.

a,b, Comparison of the logistic model with increasing demand pull and the Gompertz model with full demand pull for the mean parameter specification of the initial capacity in 2023 and the emergence growth rate, for the EU (a) and globally (b). The Gompertz model is parameterised so that the growth within the initial 7-year period from 2023-2030 corresponds to the emergence growth rate, which is in line with the parameterisation of the conventional growth case (see Methods). c,d, Probabilistic feasibility space of the adapted logistic model for the EU (c) and globally (d). These panels are a copy of Fig. 4 and included here only for comparison. e,f, Probabilistic feasibility space of the Gompertz model, leading to a much earlier damping of the market ramp up than in c-d. This implies a substantially reduced long-term availability and illustrates the broad methodological difficulties of long-term projections. Nevertheless, our two main conclusions are robust as the Gompertz model also reveals short-term scarcity and long-term uncertainty of green hydrogen supply. While both models lead to an asymmetric adoption curve that approaches the asymptote more gradually than the default logistic function, our adapted logistic technology diffusion model allows for a more precise control of the increasing market volume that can be informed by additional information about policy targets and scenario results.

Extended Data Fig. 4 Sensitivity analysis of probabilistic feasibility spaces for the conventional growth case under varying demand pull anticipation levels.

a,c,e, EU with no anticipation, 10 years anticipation, and a hypothetical case of full anticipation of the long-term market size, respectively. b,d,f, globally. Both short-term scarcity and long-term uncertainty are robust to the level of demand pull anticipation. However, in the long-term capacity deployment is higher under full anticipation of the demand pull (that is the default logistic function), especially in the EU, which has higher growth rates than globally.

Extended Data Fig. 5 Comparison of conventional growth percentiles with project announcements until 2030 for validation.

a, in the EU. b, globally. Both panels show the conventional growth case (like wind and solar) with five years demand pull anticipation, similar to Fig. 4. In both regions, cumulative project announcements surpass the median of the diffusion model results at all times. This is in line with the observation that the vast majority of announcements are fundamentally uncertain as they are not backed by a final investment decision yet. The comparison between the results of our probabilistic technology diffusion model and the cumulative project announcements demonstrates the plausibility of our modelling approach.

Extended Data Fig. 6 Sensitivity analysis of probabilistic feasibility spaces for the unconventional growth case under varying demand pull anticipation levels.

a,c,e, EU with no anticipation, 10 years anticipation, and a hypothetical case of full anticipation of the long-term market size, respectively. b,d,f, globally. In both regions, short-term scarcity can be overcome to an even greater extent under full anticipation of the demand-pull. Notably, under unconventional growth rates and full demand pull anticipation, short-term uncertainty results as indicated by the marginal distributions in 2030. As the probabilistic feasibility space is primarily determined by the demand pull after 2030 in both regions, under full demand pull anticipation the saturation market volume is reached already around 2035 with a high probability in both regions.

Extended Data Fig. 7 Probability distribution of electrolysis capacity in 5-year time steps between 2025-2050.

a,b,e,f,i,j, in the EU, c,d,g,h,k,l, globally. This diagram is an extension of Fig. 6c-d with additional years (2025, 2035, 2045, 2050). The left axis shows electrolysis capacity in GW, the right axis the corresponding final energy share that can be supplied with this capacity. In 2025, green hydrogen supply is minimal in terms of the final energy share in both regions and largely irrespective of the growth rate. In 2035, unconventional growth rates enable a final energy share that is almost 4 times larger than under conventional growth rates in the EU, and more than 10 times larger globally. In 2045, and even more so in 2050, the demand pull acts as the main constraint such that the differences between conventional and unconventional growth rates become smaller again.

Extended Data Fig. 8 Comparison of our probabilistic diffusion model results with IAM climate mitigation scenarios, targets, and further studies.

In both the EU and globally, IAM scenarios tend to use (far) less green hydrogen than envisaged by policy targets or other studies such as by Hydrogen Europe, the Hydrogen Council, the IEA, or IRENA. This is especially true for the global IPCC SR1.5 scenarios, which contain hardly any hydrogen produced via electrolysis. Most of all, this comparison demonstrates that the awareness of hydrogen’s critical importance for climate change mitigation and potential for technological learning is just emerging and already acknowledged by the IEA and IRENA. However, this awareness has not yet penetrated into the IAM community, even though electrolysis capacity deployment levels in climate mitigation scenarios have increased from the IPCC SR1.5 to the IPCC AR6 globally, as well as from the IPCC AR6 to the more recent EU-focused INNOPATHS scenarios. We believe that our analysis can help to parameterise plausible expansion pathways of green hydrogen for climate change mitigation scenarios in IAMs. Note that in the long run the technology diffusion pathways are asymptotically constrained by the exogenously assumed final market volume (see Table 1). The long-run capacity levels achieved in reality may exceed this level. The box plots show the median (50% quantile) as the centre, the 25% and 75% quantile as the bounds of box, and the whiskers as the minima and maxima of the full data set. Data sources: refs. 9,10,63,64,65,66,67.

Extended Data Table 1 List of aspects when comparing potential electrolysis growth to historical wind and solar growth
Full size table

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Odenweller, A., Ueckerdt, F., Nemet, G.F. et al. Probabilistic feasibility space of scaling up green hydrogen supply. Nat Energy 7, 854–865 (2022). https://doi.org/10.1038/s41560-022-01097-4

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