A dynamic model for optimally phasing in CO2 capture and storage infrastructure

CO2 capture and storage (CCS) is a climate-change mitigation strategy that requires an investment of many billions of dollars and tens of thousands of miles of dedicated CO2 pipelines. To be effective, scientists, stakeholders, and policy makers will have to understand how as well as when to deploy large-scale CCS infrastructure. This will require comprehensive modeling that takes into account detailed costs, engineering, and environmental concerns. We introduce a new and comprehensive model, SimCCSTIME, that is capable of spatially and temporally optimizing CO2 management—capture, transport, and storage of large quantities of CO2. The model minimizes CCS infrastructure costs while simultaneously deciding where, how much, and when to capture, transport, and store CO2. We demonstrate the SimCCSTIME model using real data from the Texas panhandle. Results show that the model minimizes CCS costs, while meeting rising demand to capture and store CO2, by gradually expanding the CCS network. The model identifies non-intuitive cost savings by overbuilding infrastructure in early time periods, and then fully utilizing this infrastructure in later years. Further, results show that there is significant benefit for planning a cooperative and integrated CCS system. Finally, we show how SimCCSTIME offers significant advantages over myopic models that cannot integrate infrastructure through time.

► CCS is a climate-change strategy requiring investment of many billions of dollars.
► This requires detailed modeling of economics, engineering, and the environment.
► SimCCSTIME designs an integrated CCS network, temporally optimizes CCS investments.
► We identify non-intuitive savings by overbuilding infrastructure in early periods.
► SimCCSTIME offer significant advantages over other models, helps make CCS a reality.