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Co-firing plants with retrofitted carbon capture and storage for power-sector emissions mitigation

Nature Climate Change volume 13, pages 807–815 (2023)Cite this article

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Abstract

Given that the global fleet of coal-fired power plants is mostly new, coal–biomass co-firing power plants with retrofitted carbon capture and storage (CBECCS) are regarded as a promising option for CO2 emissions reduction. However, the effectiveness of CBECCS remains largely unexplored. Here we develop a comprehensive assessment framework featuring a macro power system combined with spatially explicit biomass sources, coal-fired units and geological storage sites. We apply this framework to investigate the spatiotemporal deployment of CBECCS in China. The results indicate that a transition to CBECCS in 2025 could supply 0.97 GtCO2 yr–1 sequestration potential, with 90% at a levelized cost between $30 and $50 tCO2–1. A higher CO2 mitigation of 1.6 Gtyr–1 could be achieved in 2040 by increasing the unit utilization hours, corresponding to a cumulative contribution of 41.2 GtCO2 over the period 2025–2060. This study provides a useful reference for transforming coal-dominated power systems.

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Fig. 1: Spatial distribution of biomass, storage basins, coal-fired power plants and selection procedures of CBECCS plants in China.
Fig. 2: Optimal source‒sink links via the pipeline network between CBECCS plants and storage sites in China under different scenarios.
Fig. 3: The supply potential and levelized costs under various CBECCS scenarios.
Fig. 4: Power system model simulation results over the 2020–2060 period for scenarios including CBECCS technology with geographical constraints.
Fig. 5: The dynamic and optimal layout for China’s CBECCS deployment under various phases.
Fig. 6: Aggregated emission reduction potential from the CBECCS system and its breakdown in the 2025–2040 scenarios.

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Data availability

The datasets used in this study are available online at https://doi.org/10.17632/xrf3h89t73.2 and can also be obtained from fan@cumtb.edu.cn upon reasonable request. Source data are provided with this paper.

Code availability

The codes used in this study are available online at https://doi.org/10.17632/tt7pc5s72k.2 and can also be obtained from fan@cumtb.edu.cn upon reasonable request.

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Acknowledgements

We gratefully acknowledge the financial support of the National Natural Science Foundation of China (72174196 and 71874193 to J.-L.F., 72025401 to X.L. and 41971250 to J.F.), Huo Yingdong Education Foundation (171072 to J.-L.F.), Open Fund of the State Key Laboratory of Coal Resources and Safe Mining (SKLCRSM21KFA05 to J.-L.F.), the Fundamental Research Funds for the Central Universities (2022JCCXNY02 to J.-L.F.) and Tsinghua University-Inditex Sustainable Development Fund to X.L. We also acknowledge the contributions of G. Leng, Y. Xian, Z. Ding, G. Lin, Z. Li, X. Li, J. Li, Y. Mao, S. Liu, X. Huang, W. Fan and Y. Wang to the paper revision.

Author information

Author notes

  1. These authors contributed equally: Jing-Li Fan, Jingying Fu, Xian Zhang.

Authors and Affiliations

  1. Centre for Sustainable Development and Energy Policy Research, School of Energy and Mining Engineering, China University of Mining and Technology, Beijing, China

    Jing-Li Fan, Kai Li, Wenlong Zhou & Shuo Shen

  2. State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Beijing, China

    Jing-Li Fan

  3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Jingying Fu

  4. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China

    Jingying Fu

  5. The Administrative Centre for China’s Agenda 21, Ministry of Science and Technology, Beijing, China

    Xian Zhang

  6. Integrated Research on Energy, Environment and Society (IREES), Energy and Sustainability Research Institute Groningen (ESRIG), University of Groningen, Groningen, the Netherlands

    Klaus Hubacek

  7. School of Advanced International Studies, Johns Hopkins University, NW Washington, DC, USA

    Johannes Urpelainen

  8. BOE Technology Group Co., Ltd, Beijing, China

    Shuo Shen

  9. Institute of Energy, Environment and Economy, Tsinghua University, Beijing, China

    Shiyan Chang & Siyue Guo

  10. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China

    Xi Lu

  11. Institute for Carbon Neutrality, Tsinghua University, Beijing, China

    Xi Lu

  12. Beijing Laboratory of Environmental Frontier Technologies, Tsinghua University, Beijing, China

    Xi Lu

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Contributions

X.Z., S.C., J.-L.F. and X.L. designed the research. J.F. modelled the biomass resources. K.L., W.Z., J.-L.F. and S.S. performed the comprehensive model simulation and data compiling processes. J.-L.F. wrote the article, with major contributions provided by X.L., X.Z., K.L., W.Z., J.F., K.H., J.U., S.C. and S.G. All the authors contributed to the discussions and paper revision.

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Correspondence to Xian Zhang, Shiyan Chang or Xi Lu.

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Nature Climate Change thanks Isabela Butnar, Jay Fuhrman, Ning Wei 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 The comprehensive assessment model framework of full-chain CBECCS technology that consists of supply potential and mitigation contribution modules.

A comprehensive framework of full-chain CBECCS technology was developed to explore optimal CBECCS deployment from supply potential and mitigation contribution perspectives in China’s power sector. A, The CBECCS supply potential assessment model, consisting of four interlinked modules, that is, a biomass resource potential projection module, a screening module for the retrofitting of CCS-qualified coal-fired power generation units, an optimal matching model between biomass sources and coal-fired power plants, and a static cost-minimized CBECCS source‒sink matching model between co-firing power plants and storage sites. B, The CBECCS mitigation contribution assessment model, consisting of three interlinked modules, that is, a macro top-down China dynamic computable general equilibrium (CGE) model, a core power system optimization model (PSOM), and a CBECCS dynamic cost-minimized source‒sink matching model.

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Fan, JL., Fu, J., Zhang, X. et al. Co-firing plants with retrofitted carbon capture and storage for power-sector emissions mitigation. Nat. Clim. Chang. 13, 807–815 (2023). https://doi.org/10.1038/s41558-023-01736-y

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