Managing Uncertainties in the Development of CO2 Capture, Transport, & Storage Infrastructure: A Scenario Optimization Approach

Abstract

In designing pathways to U.S. economy-wide net-zero greenhouse gas emissions by 2050, Princeton’s Net Zero America study illuminated the important role carbon capture and sequestration will play. To meet the future large-scale need for sequestration in the face of first-mover risk and high early-entrant costs, carbon capture, transport and sequestration (CCS) hubs have been proposed as an approach for cost and risk sharing. A major challenge facing the development of hubs are uncertainties of various types, e.g., cost or geophysical. Previous CCS hub modeling efforts have primarily embodied methods for cost-optimized CCS infrastructure design, with incidental (if any) consideration of uncertainties. Here, I develop a novel, scenario-optimization approach for designing CCS infrastructure robust to varying degrees of uncertainties associated with CO2 storage reservoir characteristics. I build on the cost-minimizing modeling framework designed for the commercial software, SimCCS, while drawing on robust optimization techniques from the literature. I develop a hypothetical case study in the Louisiana Gulf-Coast region to demonstrate the application and effectiveness of my approach. I construct 30 scenarios, each with a unique set of reservoir parameter values (injectivity, storage capacity, and cost) reflecting realistic data. Robustness of a CCS network in my formulation equates to the probability that a user-specified regional CO2 storage target is met with that network design. By using different values of my model’s robustness tuning parameter, I generate several pipeline network designs, each cost-optimized for a different degree of robustness. For each network, for each scenario that does not meet the CO2 target, I evaluate the cost to adapt the network to meet the target. I compare the average adaptation cost across scenarios with the average cost to adapt traditional deterministically cost-optimized pipeline networks. I show that a robust CCS infrastructure, i.e., one more likely to be able to meet the storage target across scenarios, is often more expensive initially than a deterministically cost-optimized design but can end up being less costly if realized uncertainties require network adaptation. Comparing adaptation costs for the worst-case and best-case scenarios system costs offers additional insights. My thesis demonstrates the utility of a scenario-optimization approach for designing CCS hubs in the face of uncertainties. I also discuss in the thesis some limitations of my approach and suggest a number of directions for future work to overcome these.