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An optimization‐based CCUS source‐sink matching model for dynamic planning of CCUS clusters

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  • Qian Wu
  • Qianguo Lin
  • Qiang Yang
  • Yang Li

Abstract

Carbon capture, utilization and storage (CCUS) is a critical technology option in achieving large‐scale CO2 mitigation for power and industrial sectors. A full‐chain CCUS cluster could be formed based on numerous scattered capture sources and one or more storage sites connected by a pipeline network. Reasonable source‐sink matching planning for a full‐chain CCUS cluster could substantially reduce the system overhead. However, most of the previous studies could hardly address the dynamic source‐sink matching planning problem of a full‐chain CCUS cluster with multiple types of emission sources and sinks during multiple periods. Therefore, the objective of this study is to investigate an optimized source‐sink matching scheme within a CCUS cluster through developing an optimization‐based CCUS source‐sink matching model. The proposed model is based on multistage mixed integer linear programming techniques with the objective of least‐cost strategy; thus, it can deal with dynamics of capacity expansion associated with CCUS activities. The developed method is then applied to a CCUS cluster facing long‐term dynamic planning issues. The modeling results suggest that the optimization‐based CCUS source‐sink matching model is applicable in reflecting dynamics of time, scale and location of CO2 capture, transportation and storage within a CCUS cluster. The obtained solutions can provide decision bases for formulating an optimal planning scheme of a full‐chain CCUS cluster under evolving reduction targets or constraints. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd.

Suggested Citation

  • Qian Wu & Qianguo Lin & Qiang Yang & Yang Li, 2022. "An optimization‐based CCUS source‐sink matching model for dynamic planning of CCUS clusters," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 12(4), pages 433-453, August.
    • Handle: RePEc:wly:greenh:v:12:y:2022:i:4:p:433-453
    DOI: 10.1002/ghg.2159
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