Generating candidate networks for optimization: The CO2 capture and storage optimization problem

We develop a new framework for spatially optimizing infrastructure for CO2 capture and storage (CCS). CCS is a complex and challenging problem: domestically deploying CCS at a meaningful scale will require linking hundreds of coal-fired power plants with CO2 sequestration reservoirs through a dedicated and extensive (many tens-of-thousands of miles) CO2 pipeline network. We introduce a unique method for generating a candidate network from scratch, from which the optimization model selects the optimal set of arcs to form the pipeline network. This new generation method can be applied to any network optimization problem including transmission line, roads, and telecommunication applications. We demonstrate the model and candidate network methodology using a real example of capturing CO2 from coal-fired power plants in the US Midwest and storing the CO2 in depleted oil and gas fields. Results illustrate the critical need to balance CCS investments with generating a candidate network of arcs.

► We optimize infrastructure for capturing, transporting, and injecting/storing CO2.
► We introduce a unique methodology for developing a candidate network.
► The candidate network is applied to CO2 pipelines, but can be used for any network optimization.
► We show that realistic geospatial networks are critical for minimizing CO2 emissions through CCS.
► SimCCS is an ideal model for managing CO2 in order to mitigate climate change.