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Quantifying the impacts of climate change and extreme climate events on energy systems

Nature Energy volume 5, pages 150–159 (2020)Cite this article

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Abstract

Climate induced extreme weather events and weather variations will affect both the demand of energy and the resilience of energy supply systems. The specific potential impact of extreme events on energy systems has been difficult to quantify due to the unpredictability of future weather events. Here we develop a stochastic-robust optimization method to consider both low impact variations and extreme events. Applications of the method to 30 cities in Sweden, by considering 13 climate change scenarios, reveal that uncertainties in renewable energy potential and demand can lead to a significant performance gap (up to 34% for grid integration) brought by future climate variations and a drop in power supply reliability (up to 16%) due to extreme weather events. Appropriate quantification of the climate change impacts will ensure robust operation of the energy systems and enable renewable energy penetration above 30% for a majority of the cities.

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Fig. 1: Understanding the influences of future climate changes during energy system design.
Fig. 2: Considering the climate-induced uncertainties in the design phase.
Fig. 3: Wind, temperature and energy demand variation under 13 climate scenarios.
Fig. 4: Impacts of future climate variations on energy demand and renewable energy generation.
Fig. 5: Renewable energy generation potential in four cities.
Fig. 6: Potential to integrate renewable energy technologies.

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Data availability

The data relevant to the energy and climate models not found in the in Supplementary Notes 1–3 are available from the corresponding author upon reasonable request. The raw climate data are available through Coordinated Regional Climate Downscaling Experiment (http://www.cordex.org/). Source data for Figs. 3–6 are provided with the paper.

Code availability

The computational code is available from the corresponding author for academic purposes upon reasonable request.

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Acknowledgements

We thank E. Kjellström, G. Strandberg and G. Nikulin from the Rossby Centre at the Swedish Meteorological and Hydrological Institute (SMHI) for their help with the climate data and assessment. We thank E. Davin of ETH Zurich for his interesting insights in relation to this study. We also thank K. Javanroodi for creating the map of Sweden for this publication. This work was supported by the Swiss Competence Center for Energy Research SCCER FEEB&D of the Swiss Innovation Agency Innosuisse (CTI.2014.0119), the Swedish Research Council for Sustainable Development (Formas 2016-20123), the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technologies of the United States Department of Energy (Contract no. DE-AC02-05CH11231) and the Swedish national strategic research program BECC and 398 MERGE. Some of the computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at the National Supercomputer Centre in Sweden (NSC).

Author information

Authors and Affiliations

  1. Solar Energy and Building Physics Laboratory (LESO-PB), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    A. T. D. Perera & Jean-Louis Scartezzini

  2. Urban Energy Systems Laboratory, EMPA, Dübendorf, Switzerland

    A. T. D. Perera

  3. Division of Building Physics, Department of Building and Environmental Technology, Lund University, Lund, Sweden

    Vahid M. Nik

  4. Division of Building Technology, Department of Civil and Environmental Engineering, Chalmers University of Technology, Gothenburg, Sweden

    Vahid M. Nik

  5. Institute for Future Environments, Queensland University of Technology, Garden Point Campus, Brisbane, QLD, Australia

    Vahid M. Nik

  6. Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden

    Deliang Chen

  7. Building Technology and Urban Systems Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Tianzhen Hong

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Contributions

A.T.D.P., V.M.N., D.C., J.L.S. and T.H. designed the research. V.M.N. synthesized the climate data and conducted the uncertainty analysis and impact assessment of climate change on energy demand and renewable generation. A.T.D.P. developed the energy system optimization algorithm and conducted the energy system analysis. A.T.D.P., V.M.N., D.C., J.L.S. and T.H. wrote the manuscript.

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Correspondence to A. T. D. Perera.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Notes 1–10, Figs. 1–7, Table 1 and refs. 1–41.

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Source Data Fig. 3

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Source Data Fig. 4

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Source Data Fig. 6

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Perera, A.T.D., Nik, V.M., Chen, D. et al. Quantifying the impacts of climate change and extreme climate events on energy systems. Nat Energy 5, 150–159 (2020). https://doi.org/10.1038/s41560-020-0558-0

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