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Global assessment of the carbon–water tradeoff of dry cooling for thermal power generation

Nature Water volume 1, pages 682–693 (2023)Cite this article

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Abstract

Water scarcity and climate change are dual challenges that could potentially threaten energy security. Yet, integrated water–carbon management frameworks coupling diverse water- and carbon-mitigation technologies at high spatial heterogeneity are largely underdeveloped. Here we build a global unit-level framework to investigate the CO2 emission and energy penalty due to the deployment of dry cooling—a critical water mitigation strategy—together with alternative water sourcing and carbon capture and storage under climate scenarios. We find that CO2 emission and energy penalty for dry cooling units are location and climate specific (for example, 1–15% of power output), often demonstrating notably faster efficiency losses than rising temperature, especially under the high climate change scenario. Despite energy and CO2 penalties associated with alternative water treatment and carbon capture and storage utilization, increasing wastewater and brine water accessibility provide potential alternatives to dry cooling for water scarcity alleviation, whereas CO2 storage can help to mitigate dry cooling-associated CO2 emission tradeoffs when alternative water supply is insufficient. By demonstrating an integrative planning framework, our study highlights the importance of integrated power sector planning under interconnected dual water–carbon challenges.

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Fig. 1: Age structure of global dry cooling generation capacity and associated water–carbon interactions.
Fig. 2: Spatial pattern of global dry cooling generation units associated water–carbon interactions.
Fig. 3: Share of generation capacity and water–carbon impacts by efficiency loss.
Fig. 4: Increasing dry cooling units’ efficiency loss under a warming climate.
Fig. 5: Historical integrated dry cooling with alternative water sourcing and carbon mitigation.

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Data availability

Data used to perform this work can be found in Supplementary Information. Numerical results for Figs. 1–5 and Extended Data Fig. 1 will be provided with this paper as source data, any further data that support the findings of this study are available from the corresponding authors upon request. Source data are provided with this paper.

Code availability

Computer code or algorithm used to generate results that are reported in the paper and central to the main claims are available from the corresponding authors upon request.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant 72140003 to C.H., grant 42277482 to Y.Q., and grant 42130708 and grant 42277087 to C.H.). C.H. acknowledges support from the Scientific Research Start-up Funds (QD2021030C) from Tsinghua Shenzhen International Graduate School. J.M.B. was funded by the United States National Science Foundation Innovations at the Nexus of Food, Energy, and Water Systems (INFEWS grant 1739909) and National Research Traineeship (NRT grant 1922666) and the Alfred P. Sloan Foundation’s Net-Zero and Negative Emissions Technologies programme (grant 2020–12,466). G.H. acknowledges support from the Global Energy Initiative at ClimateWorks Foundation (no. 23-2515). J.B. at RU was funded by The Netherlands Organization for Scientific Research (NWO), grant number 016.Vici.170.190.

Author information

Authors and Affiliations

  1. The Key Laboratory of Water and Sediment Sciences, Ministry of Education, Peking University, Beijing, China

    Yue Qin

  2. College of Environmental Science and Engineering, Peking University, Beijing, China

    Yue Qin, Shiyu Li & Liangdian Huang

  3. Institute of Carbon Neutrality, Peking University, Beijing, China

    Yue Qin & Hang Deng

  4. Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Yaoping Wang

  5. College of Engineering, Peking University, Beijing, China

    Hang Deng

  6. Department of Physical Geography, Utrecht University, Utrecht, The Netherlands

    Niko Wanders

  7. Department of Environmental Science, Radboud University, Nijmegen, The Netherlands

    Joyce Bosmans

  8. Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration, Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

    Chaopeng Hong

  9. State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China

    Chaopeng Hong

  10. International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria

    Edward Byers

  11. Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, Columbus, OH, USA

    Daniel Gingerich & Jeffrey M. Bielicki

  12. Department of Integrated Systems Engineering, The Ohio State University, Columbus, OH, USA

    Daniel Gingerich

  13. Sustainability Institute, The Ohio State University, Columbus, OH, USA

    Daniel Gingerich & Jeffrey M. Bielicki

  14. John Glenn College of Public Affairs, The Ohio State University, Columbus, OH, USA

    Jeffrey M. Bielicki

  15. Department of Technology and Society, Stony Brook University, Stony Brook, NY, USA

    Gang He

  16. Marxe School of Public and International Affairs, Baruch College, City University of New York, New York, NY, USA

    Gang He

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Contributions

Y.Q. and C.H. designed this study, Y.Q., Y.W., S.L., H.D., N.W., J.B., L.H. and C.H. analysed the data, Y.Q., E.B., D.G., J.M.B. and G.H. wrote the paper with input from all co-authors.

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Correspondence to Yue Qin or Chaopeng Hong.

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Nature Water thanks Vincent Tidwell, Kelly Sanders and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Unit-level and aggregated dry cooling units’ efficiency loss against ambient temperature.

(a) Exposure of unit-level dry cooling fleets with different engine types to monthly ambient temperature, and their corresponding turbine efficiency loss-temperature responses. n represents sample sizes. The mean (white dot), 25th and 75th percentiles (box), and 10th and 90th percentiles (botom and upper short black horizontal lines) are displayed, and minima/maxima are indicated by the violin plot range. The majority thermal units are exposed to ambient temperature either above its stationary point (for example, combustible steam) or between the minimum and maximum stationary points (as defined in Supplementary Table 1), thus are mostly demonstrating non-linear turbine efficiency loss increases with increasing temperature. (b) Relative share of different dry cooling engine types, which is dominated by combustible steam. (c) Relative increasing rates between unit-level turbine efficiency losses and ambient temperature (Tas), illustrating faster turbine efficiency loss increases than ambient temperature for different dry cooling engine types. (d) Slopes and corresponding linear regression for aggregated dry cooling fleets under different RCP scenarios in main text Fig. 4c, upper and lower 95% confidence interval indicate the 97.5th and 2.5th percentile, respectively. (e) Relative increasing rates between aggregated efficiency losses and corresponding ambient temperature (Tas).

Source data

Supplementary information

Supplementary Information

Supplementary notes, Figs. 1–23 and Tables 1–3.

Reporting Summary

Source data

Source Data Fig. 1

Age-specific dry cooling generation capacity.

Source Data Fig. 2

Unit-level geo-coordinates, avoided water withdrawal, increased CO2 emissions, increased efficiency losses and increased CO2 emissions per avoided water withdrawal for dry cooling units compared with once-through freshwater cooling.

Source Data Fig. 3

Generation capacity share for dry cooling units with different levels of efficiency losses by fuel, region and seasons.

Source Data Fig. 4

Model-specific ambient temperature, efficiency loss and Δefficiency loss/ΔTas under different RCP scenarios, together with unit-level scaling factors for three RCPs.

Source Data Fig. 5

Unit-level efficiency loss, RWA and CCS.

Source Data Extended Data Fig./Table 1

Fuel-specific ambient temperature exposure; statistic regression between temperature and efficiency loss.

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Qin, Y., Wang, Y., Li, S. et al. Global assessment of the carbon–water tradeoff of dry cooling for thermal power generation. Nat Water 1, 682–693 (2023). https://doi.org/10.1038/s44221-023-00120-6

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