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

  • Authors:

  • Richard S. Middleton;Michael J. Kuby;Ran Wei;Gordon N. Keating;Rajesh J. Pawar

  • Affiliations:

  • Los Alamos National Laboratory, Earth and Environmental Sciences, Los Alamos, NM 87545, USA;School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, USA;School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, USA;Los Alamos National Laboratory, Earth and Environmental Sciences, Los Alamos, NM 87545, USA;Los Alamos National Laboratory, Earth and Environmental Sciences, Los Alamos, NM 87545, USA

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2012

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Abstract

CO"2 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 CO"2 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, SimCCS^T^I^M^E, that is capable of spatially and temporally optimizing CO"2 management-capture, transport, and storage of large quantities of CO"2. The model minimizes CCS infrastructure costs while simultaneously deciding where, how much, and when to capture, transport, and store CO"2. We demonstrate the SimCCS^T^I^M^E model using real data from the Texas panhandle. Results show that the model minimizes CCS costs, while meeting rising demand to capture and store CO"2, 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 SimCCS^T^I^M^E offers significant advantages over myopic models that cannot integrate infrastructure through time.