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- Published: 23 December 2025
Nature Communications , Article number: (2025) Cite this article
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Abstract
Energy scenario analysis with optimization approaches rarely goes beyond a small number of scenarios. Disadvantages include limited coverage of uncertainties and assumptions, and a limited ability to provide robust policy advice. We present an …
- Article
- Open access
- Published: 23 December 2025
Nature Communications , Article number: (2025) Cite this article
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.
Abstract
Energy scenario analysis with optimization approaches rarely goes beyond a small number of scenarios. Disadvantages include limited coverage of uncertainties and assumptions, and a limited ability to provide robust policy advice. We present an approach that enables the multi-criterial evaluation of more than 11,000 scenarios and demonstrate it for the German power system. We vary both a wide range of input parameters and method choices. The resulting scenarios are assessed through a number of indicators on affordability, supply-security and sustainability. The most significant impacts on the results stem from considering multiple weather years. Furthermore, we estimate the number of runs required for robust energy systems analyses – well over 100 scenarios are needed. Nevertheless, fewer scenarios may be sufficient for limited scopes. Our analysis also underlines a challenge for future energy system design: cost-efficient decarbonization while conserving natural resources.
Data availability
The scenario data generated in this study have been deposited in b2share under accession code 7dfe93339c3e4e34bf4c47f880186466. The model instances generated in this study have been deposited in b2share under accession code 3717dab82cbb4de0a02726ab3ff7702e. The techno-economic data used in this study are available in b2share under accession code (4e5e2d11b8224fb8809cdc2d07eeff04). The used modeling frameworks REMix and AMIRIS are published in JOSS at (https://doi.org/10.21105/joss.06330) and (https://doi.org/10.21105/joss.05041), respectively. The codes to obtain the REMix basic model are published in Gitlab (https://gitlab.com/dlr-ve/esy/remix/projects/powger).The codes for determining the indicators, the scenario generator tool and the configuration for controlling the HPC workflow with jube are published in Gitlab (https://gitlab.com/dlr-ve/esy/remix/projects/unseen). Data that supports the figures and other findings of the study are provided in the Supplementary information (SI). Further data, such as sampled model inputs and intermediate results are maintained at the Jülich Supercomputing Centre and can be made accessible after registration in the JuDoor portal given the size (40 TB) of the data sets generated.
Code availability
All individual components of the HPC workflow are published open source30,32,34. PIPS-IPM++ is available on (https://github.com/PIPS-IPMpp/). The JUBE-software is openly available40. The work flow manager ioproc is available at (https://gitlab.com/dlr-ve/esy/ioproc). The code for running the HPC workflow, and parts of the indicator processing is available on (https://gitlab.com/dlr-ve/esy/remix/projects/unseen). The code for the statistical evaluation will be made available upon request.
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Acknowledgements
The research was funded by the German Federal Ministry for Economic Affairs and Energy under grant number 03EI1004A-E. The authors highly appreciate the support of Andreas Meurer for the visualization of data. We would like to thank Aileen Böhme for developing and supporting the scenario driver, Manuel Wetzel for his support with REMix and PIPS-IPM++, and Kai von Krbek, Sonja Simon and Mengzhu Xiao for their contributions to the indicator processing. The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for funding this project by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS at Jülich Supercomputing Centre (JSC).
Funding
Open Access funding enabled and organized by Projekt DEAL.
Author information
Authors and Affiliations
German Aerospace Center (DLR), Institute of Networked Energy Systems, Curiestr. 4, Stuttgart, Germany
Ulrich Joachim Frey, Karl-Kiên Cao & Shima Sasanpour 1.
University of Graz, Department of Environmental Systems Sciences, Merangasse 18, Graz, Austria
Ulrich Joachim Frey 1.
German Aerospace Center (DLR), Institute of Networked Energy Systems, Carl-von-Ossietzky-Str. 15, Oldenburg, Germany
Jan Buschmann 1.
Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich Supercomputing Centre, Jülich, Germany
Thomas Breuer
Authors
- Ulrich Joachim Frey
- Karl-Kiên Cao
- Shima Sasanpour
- Jan Buschmann
- Thomas Breuer
Contributions
U.J.F, K.-K.C., S.S., J.B. and T.B. drafted the manuscript. U.J.F, S.S., J.B. and T.B. did data curation and implemented required model modifications. U.J.F, K.-K.C. and S.S. conducted the investigation process. U.J.F and K.-K.C. conceptualized the study, acquired funding and finalized the manuscript. K.-K.C. and T.B. acquired the computing resources. U.J.F prepared the visualization of data and conducted the formal analyses. K.-K.C. designed the methodology and coordinated the research activities, T.B. implemented the software for HPC.
Corresponding authors
Correspondence to Ulrich Joachim Frey or Karl-Kiên Cao.
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The authors declare no competing interests.
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Cite this article
Frey, U.J., Cao, KK., Sasanpour, S. et al. The benefits of exploring a large scenario space for future energy systems. Nat Commun (2025). https://doi.org/10.1038/s41467-025-67593-9
Received: 02 October 2024
Accepted: 03 December 2025
Published: 23 December 2025
DOI: https://doi.org/10.1038/s41467-025-67593-9