Carbon capture and storage network optimization under uncertainty
Carbon capture and storage is a method for emissions reductions that can be applied to both the electric sector and industrial sources. Significant uncertainties surround the technologies, policy and extent to which CCS will be deployed in the future. For widespread deployment, future CCS demand should be considered during infrastructure planning. This study presents a novel model that considers spatial information and uncertainty in generating an optimal CCS network. The two-stage stochastic model, utilizes both geographic information systems (GIS) and mixed integer programming (MIP), to generate an optimal near-term hedging strategy. The strategy considers one discrete uncertainty distribution: the future demand for CO₂ storage. A case study in the Texas Gulf Coast demonstrates the value of considering uncertainty of future demand. The optimal solution is selected from a candidate network consisting of twelve sources and five reservoirs that can be linked via a network of pipelines and ship routes. The results demonstrate that optimal hedging strategies lead to transportation cost savings of up to 14% compared to a ‘naive approach’ in which only the expected value is considered. The transportation selection also highlights the benefit of utilizing ship transport in uncertain scenarios due to their ability to be reassigned to a different route or sold.