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Planning minimum regret $CO_2$ pipeline networks

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  • Stephan Bogs
  • Ali Abdelshafy
  • Grit Walther

Abstract

The transition to a low-carbon economy necessitates effective carbon capture and storage (CCS) solutions, particularly for hard-to-abate sectors. Herein, pipeline networks are indispensable for cost-efficient $CO_2$ transportation over long distances. However, there is deep uncertainty regarding which industrial sectors will participate in such systems. This poses a significant challenge due to substantial investments as well as the lengthy planning and development timelines required for $CO_2$ pipeline projects, which are further constrained by limited upgrade options for already built infrastructure. The economies of scale inherent in pipeline construction exacerbate these challenges, leading to potential regret over earlier decisions. While numerous models were developed to optimize the initial layout of pipeline infrastructure based on known demand, a gap exists in addressing the incremental development of infrastructure in conjunction with deep uncertainty. Hence, this paper introduces a novel optimization model for $CO_2$ pipeline infrastructure development, minimizing regret as its objective function and incorporating various upgrade options, such as looping and pressure increases. The model's effectiveness is also demonstrated by presenting a comprehensive case study of Germany's cement and lime industries. The developed approach quantitatively illustrates the trade-off between different options, which can help in deriving effective strategies for $CO_2$ infrastructure development.

Suggested Citation

  • Stephan Bogs & Ali Abdelshafy & Grit Walther, 2025. "Planning minimum regret $CO_2$ pipeline networks," Papers 2502.12035, arXiv.org.
    • Handle: RePEc:arx:papers:2502.12035
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    References listed on IDEAS

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    1. Sun, Liang & Chen, Wenying, 2017. "Development and application of a multi-stage CCUS source–sink matching model," Applied Energy, Elsevier, vol. 185(P2), pages 1424-1432.
    2. Tapia, John Frederick D. & Lee, Jui-Yuan & Ooi, Raymond E.H. & Foo, Dominic C.Y. & Tan, Raymond R., 2016. "Optimal CO2 allocation and scheduling in enhanced oil recovery (EOR) operations," Applied Energy, Elsevier, vol. 184(C), pages 337-345.
    3. Nicolle, Adrien & Massol, Olivier, 2023. "Build more and regret less: Oversizing H2 and CCS pipeline systems under uncertainty," Energy Policy, Elsevier, vol. 179(C).
    4. Abdoli, B. & Hooshmand, F. & MirHassani, S.A., 2023. "A novel stochastic programming model under endogenous uncertainty for the CCS-EOR planning problem," Applied Energy, Elsevier, vol. 338(C).
    5. Middleton, Richard S. & Bielicki, Jeffrey M., 2009. "A scalable infrastructure model for carbon capture and storage: SimCCS," Energy Policy, Elsevier, vol. 37(3), pages 1052-1060, March.
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