A cost-effective chance-constrained programming approach for designing resilient <i>CCUS</i> networks under uncertainties and disruption risks

Purpose The Carbon Capture, Utilization and Storage (CCUS) network integrates pipelines, offshore shipping and trucks to transport captured CO2 to utilization facilities or storage sites like depleted oil fields and saline aquifers. However, it faces risks such as natural disasters and storage site and/or utilization facility disruptions, leading to uncertainty. Enhancing resilience in CCUS network design is essential to address these risks. This paper aims to develop a resilient CCUS supply chain network (SCN) that minimizes expected total cost under disruption risks while ensuring the required CO2 emission reduction target is met. Design/methodology/approach This paper proposes a novel chance-constrained programming approach with multiple-resilience strategies for optimal designing the resilient CCUS SCN problem under storage site and/or utilization facility disruptions, where the storage site and/or utilization facility capacity loss and the storage site and/or utilization facility fortification cost are assumed as uncertain parameters. The proposed uncertain model is transformed into a tractable deterministic equivalent model. Findings A case study from Guangdong Province, China, verifies the effectiveness of the proposed model. The sensitivity analysis examines the effects of the expected values of uncertain parameters on the total SCN cost. The results show that for capacity losses under 50%, the reactive transshipment strategy is more economical, particularly as losses decrease. Over 50%, proactive fortifications are more cost-effective, especially with greater losses. Originality/value This paper is the first to introduce two resilience strategies (fortifying storage sites and/or utilization infrastructure and lateral transshipment via offshore vessels) to bolster the CCUS network’s capacity to endure unforeseen disruptions. The two strategies are integrated into the proposed chance-constrained model. Our novel approach could help companies develop effective, sustainable and reliable CCUS networks capable of withstanding unforeseen risks.