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Nature volume 643, pages 1263–1270 (2025)
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Climate change may amplify the frequency and severity of supply–demand mismatches in future power systems with high shares of wind and solar energy1,2. Here we use a dispatch optimization model to assess potential increases in hourly costs associated with the climate-intensified gaps under fixed, high penetrations of wind and solar energy generation. We further explore various strategies to enhance system resilience in the face of future climate change. We find that extreme periods—defined as hours in the upper decile of hourly costs (that is, the most costly 10% of hours)—are likely to become more costly in the future in most countries, mainly because of the increased need for investments in flexible energy capacity. For example, under the Shared Socioeconomic Pathway SSP1–2.6 scenario, 47 countries that together account for approximately 43.5% of global future electricity generation are projected to experience more than a 5% increase in average hourly costs during extreme periods, with the largest reaching up to 23.7%. The risk of rising costs could be substantially mitigated through tailored, country-specific strategies involving the coordinated implementation of multiple measures to address supply–demand imbalances and enhance system flexibility. Our findings provide important insights for building future climate-resilient power systems while reducing system costs.
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All the raw CMIP6 future climatic data in the study are publicly available from the Earth System Grid Federation repository (https://esgf-node.llnl.gov/projects/cmip6/). Future shares of different electricity generation technologies for different countries under various climate scenarios are collected from the International Institute for Applied Systems Analysis (https://data.ece.iiasa.ac.at/ar6/). The derived country-level wind and solar capacity factors are available at Zenodo81 (https://doi.org/10.5281/zenodo.15499583). All maps were created based on freely available shapefiles from the Database of Global Administrative Boundaries (https://gadm.org/), using Python v.3.11 with the GeoPandas v.1.1 (https://geopandas.org/en/stable/) and Matplotlib v.3.7.1 (https://matplotlib.org/) libraries. The illustrations representing the four measures are available for free download in PNG format and were designed by Freepik (www.freepik.com). Source data are provided with this paper.
The code for the dispatch optimization model developed in this study is available at Zenodo81 (https://doi.org/10.5281/zenodo.15499583).
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We thank the Carbon Neutrality and Energy System Transformation (CNEST) Program. This work was funded by the National Natural Science Foundation of China (grant nos. W2412154 and 72274106) and the China Meteorological Administration ‘Research on Value Realization of Climate Ecological Products’ Youth Innovation Team Project (no. CMA2024QN15). Q.Z. acknowledges the support by the New Cornerstone Science Foundation through the Xplorer Prize. We acknowledge the support from the High Performance Computing Center, Tsinghua University. We acknowledge J. Ren and X. Xin for their valuable assistance in developing the dispatch model.
These authors contributed equally: Dongsheng Zheng, Xizhe Yan
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
Dongsheng Zheng, Xizhe Yan, Dan Tong, Yuanyuan Lin, Yaqin Guo, Jingyun Li, Peng Wang, Liying Ping, Shijie Feng, Deliang Chen & Qiang Zhang
Department of Earth System Science, Stanford University, Stanford, CA, USA
Steven J. Davis, Ken Caldeira & Jing Cheng
Gates Ventures, Kirkland, WA, USA
Ken Caldeira
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA
Ken Caldeira
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
Yang Liu & Kebin He
Institute for Carbon Neutrality, Tsinghua University, Beijing, China
Kebin He & Qiang Zhang
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Q.Z. and D.T. conceived and designed the study. D.Z. led the model development and conducted the simulations. D.T. and Q.Z. performed the analyses with support from D.Z., X.Y., Y. Lin, J.L., P.W., Y.G., L.P., S.F., Y. Liu and J.C. on data compilation, and from S.J.D., K.C., D.C. and K.H. on analytical approaches. D.T., D.Z. and Q.Z. interpreted the results. X.Y. and D.Z. prepared the figures. D.T., D.Z., Q.Z. and S.J.D. wrote the paper with input from all co-authors.
Correspondence to Dan Tong or Qiang Zhang.
The authors declare no competing interests.
Nature thanks Konstantinos Chalvatzis, Gunther Glenk, Xi Liang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The framework comprises five key components: input, model optimization, output, post-process results, and strategy design.
In each case, the bars show the average contributions across dispatch model simulations with climate model ensembles; the vertical lines show the 33rd to 67th percentile ranges; and the horizontal lines show the median values.
a, Average changes in system costs across dispatch model simulations with climate model ensembles under the SSP126 and SSP245 scenarios. b, Average annual additional climate-related costs under the SSP126 and SSP245 scenarios.
a,b, Changes in hourly costs and their specific contributions from different technologies during extreme (a) and normal (b) periods in 26 major countries. In each case, the bars show the average changes or technology-based contributions across dispatch model simulations with climate model ensembles; the vertical lines show the 33rd to 67th percentile ranges; the horizontal lines show the median values.
a,b, Median contributions to hourly costs (a) and median supply (b) across dispatch model simulations with climate model ensembles during extreme period. c,d, Median contributions to annual costs (c) and median supply (d) during normal period. Striped and solid bars in (a) and (c) represent variable and fixed costs, respectively. It is noted that optimized wind/solar generation ratio varies across different years and climate models.
The digits represent the 50th percentile of relative cost reductions (%) across dispatch model simulations with climate model ensembles in 26 major countries, calculated as the difference between average hourly costs in the baseline and after implementation of one or more strategies under future climate (2056–2060).
In each case, the bars show the average reductions in hourly costs across various combinations of measures; the vertical lines show the 33rd to 67th percentile ranges of dispatch model simulations with climate model ensembles; and the horizontal lines show the median values.
Points represent average increases in hourly costs due to climate change, while the vertical lines show the median values, and the horizontal lines show the 33rd to 67th percentile ranges of dispatch model simulations with climate model ensembles. Bars depict the maximal cost reduction under a certain number of mitigation measures at a 50% likelihood.
In each case, the bars show the average changes in hourly costs across dispatch model simulations with climate model ensembles; the vertical lines show the 33rd to 67th percentile ranges; the horizontal lines show the median values; the circle and cross markers on the scatterplot represent the 95th percentiles and the maximum values.
The red lines represent the relationship between the number of measures (ranging from 1 to 4) and the likelihood of mitigating hourly cost increases in the top 5% of events with the largest cost increases (that is, those above the 95th percentile of dispatch model simulations with climate model ensembles) during extreme and normal periods.
This file contains Supplementary Texts 1–3, Supplementary Figs. 1–19, Supplementary Tables 1–28 and Supplementary References.
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Zheng, D., Yan, X., Tong, D. et al. Strategies for climate-resilient global wind and solar power systems. Nature 643, 1263–1270 (2025). https://doi.org/10.1038/s41586-025-09266-7
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