Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage Uncertainty
Carbon Capture and Storage (CCS) is a critical technology for reducing anthropogenic CO<sub>2</sub> emissions, but its large-scale deployment is complicated by uncertainties in geological storage performance. These uncertainties pose significant financial and operational risks, as underp...
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2025-02-01
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| author | Daniel Olson Sean Yaw |
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| description | Carbon Capture and Storage (CCS) is a critical technology for reducing anthropogenic CO<sub>2</sub> emissions, but its large-scale deployment is complicated by uncertainties in geological storage performance. These uncertainties pose significant financial and operational risks, as underperforming storage sites can lead to costly infrastructure modifications, inefficient pipeline routing, and economic shortfalls. To address this challenge, we propose a novel optimization workflow that is based on mixed-integer linear programming and explicitly integrates probabilistic modeling of storage uncertainty into CCS infrastructure design. This workflow generates multiple infrastructure scenarios by sampling storage capacity distributions, optimally solving each scenario using a mixed-integer linear programming model, and aggregating results into a heatmap to identify core infrastructure components that have a low likelihood of underperforming. A risk index parameter is introduced to balance trade-offs between cost, CO<sub>2</sub> processing capacity, and risk of underperformance, allowing stakeholders to quantify and mitigate uncertainty in CCS planning. Applying this workflow to a CCS dataset from the US Department of Energy’s Carbon Utilization and Storage Partnership project reveals key insights into infrastructure resilience. Reducing the risk index from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>15</mn><mo>%</mo></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0</mn><mo>%</mo></mrow></semantics></math></inline-formula> is observed to lead to an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>83.7</mn><mo>%</mo></mrow></semantics></math></inline-formula> reduction in CO<sub>2</sub> processing capacity and a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>77.1</mn><mo>%</mo></mrow></semantics></math></inline-formula> decrease in project profit, quantifying the trade-off between risk tolerance and project performance. Furthermore, our results highlight critical breakpoints, where small adjustments in the risk index produce disproportionate shifts in infrastructure performance, providing actionable guidance for decision-makers. Unlike prior approaches that aimed to cheaply repair underperforming infrastructure, our workflow constructs robust CCS networks from the ground up, ensuring cost-effective infrastructure under storage uncertainty. These findings demonstrate the practical relevance of incorporating uncertainty-aware optimization into CCS planning, equipping decision-makers with a tool to make informed project planning decisions. |
| format | Article |
| id | doaj-art-25b379286ae44c8fa1b59662a40dd85d |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Energies |
| spelling | doaj-art-25b379286ae44c8fa1b59662a40dd85d2025-08-20T02:44:35ZengMDPI AGEnergies1996-10732025-02-0118492610.3390/en18040926Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage UncertaintyDaniel Olson0Sean Yaw1School of Computing, Montana State University, Bozeman, MT 59717, USASchool of Computing, Montana State University, Bozeman, MT 59717, USACarbon Capture and Storage (CCS) is a critical technology for reducing anthropogenic CO<sub>2</sub> emissions, but its large-scale deployment is complicated by uncertainties in geological storage performance. These uncertainties pose significant financial and operational risks, as underperforming storage sites can lead to costly infrastructure modifications, inefficient pipeline routing, and economic shortfalls. To address this challenge, we propose a novel optimization workflow that is based on mixed-integer linear programming and explicitly integrates probabilistic modeling of storage uncertainty into CCS infrastructure design. This workflow generates multiple infrastructure scenarios by sampling storage capacity distributions, optimally solving each scenario using a mixed-integer linear programming model, and aggregating results into a heatmap to identify core infrastructure components that have a low likelihood of underperforming. A risk index parameter is introduced to balance trade-offs between cost, CO<sub>2</sub> processing capacity, and risk of underperformance, allowing stakeholders to quantify and mitigate uncertainty in CCS planning. Applying this workflow to a CCS dataset from the US Department of Energy’s Carbon Utilization and Storage Partnership project reveals key insights into infrastructure resilience. Reducing the risk index from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>15</mn><mo>%</mo></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0</mn><mo>%</mo></mrow></semantics></math></inline-formula> is observed to lead to an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>83.7</mn><mo>%</mo></mrow></semantics></math></inline-formula> reduction in CO<sub>2</sub> processing capacity and a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>77.1</mn><mo>%</mo></mrow></semantics></math></inline-formula> decrease in project profit, quantifying the trade-off between risk tolerance and project performance. Furthermore, our results highlight critical breakpoints, where small adjustments in the risk index produce disproportionate shifts in infrastructure performance, providing actionable guidance for decision-makers. Unlike prior approaches that aimed to cheaply repair underperforming infrastructure, our workflow constructs robust CCS networks from the ground up, ensuring cost-effective infrastructure under storage uncertainty. These findings demonstrate the practical relevance of incorporating uncertainty-aware optimization into CCS planning, equipping decision-makers with a tool to make informed project planning decisions.https://www.mdpi.com/1996-1073/18/4/926carbon capture and storageinfrastructure designoptimizationinteger linear programmingMonte Carlo simulationheat map |
| spellingShingle | Daniel Olson Sean Yaw Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage Uncertainty Energies carbon capture and storage infrastructure design optimization integer linear programming Monte Carlo simulation heat map |
| title | Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage Uncertainty |
| title_full | Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage Uncertainty |
| title_fullStr | Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage Uncertainty |
| title_full_unstemmed | Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage Uncertainty |
| title_short | Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage Uncertainty |
| title_sort | planning amidst uncertainty identifying core ccs infrastructure robust to storage uncertainty |
| topic | carbon capture and storage infrastructure design optimization integer linear programming Monte Carlo simulation heat map |
| url | https://www.mdpi.com/1996-1073/18/4/926 |
| work_keys_str_mv | AT danielolson planningamidstuncertaintyidentifyingcoreccsinfrastructurerobusttostorageuncertainty AT seanyaw planningamidstuncertaintyidentifyingcoreccsinfrastructurerobusttostorageuncertainty |