I have a joint research project with Prof. Lincoln Pratson (Nicholas School, Duke University) to develop a spatial-engineering economic model called OptimaCCS. This is my work at the Nicholas Institute for Environmental Policy Solutions and OptimaCCS is a propietary software package owned by Duke University.
OptimaCCS maps out cost-efficient options for overall Carbon Capture and Storage (CCS) network design, including pipeline routes, necessary pipe diameters and lengths, efficiencies from using shared pipelines, and the impact of sequestration costs.
CCS will allow coal-fired power generation to remain a major component of the nation’s energy mix while also reducing US carbon emissions. The cost of capturing CO2 will affect the deployment of CCS, as will the costs for CO2 pipeline transport and underground injection. The latter can increase CCS costs by $2-$100 per ton of CO2 depending on the locations of coal plants relative to storage sites, the quantity of captured CO2, and the rate that it can be pumped underground. Transportation and storage costs can be minimized, however, by optimizing the design of the transport system.
The figures below illustrate the types of results produced by OptimaCCS. Both figures show cost-efficient designs for CCS networks that connect existing coal plants to potential storage sites in the state of Texas. In the illustrations below, the cost of injection is lowest at the Frio storage site. The top illustration shows the resultant pipeline networks when injection costs are ignored, while the bottom illustration shows the single pipeline network when injection costs are accounted for.
OptimaCCS is a multi‑platform software package and is implemented as an add-on module of ArcMap 9.3.1 which means it can be installed to any ArcGIS software package. Because of its multi-platform characteristics, OptimaCCS is an integrated software package that includes different components: ArcGIS 9.3.1, ArcObject (C++ Com Objects), GAMS 23.2.1, Microsoft Access VBA Script, and Microsoft Excel VBA Script. The cost minimization is implemented as mixed-integer linear programming (MILP) under a GAMS environment with a solver that is based on a leading mixed-integer optimizer (IBMs ILOG CPLEX). Because of OptimaCCS’s requirement of intensive computation and large memory usage, the system platform specifies 64-bit servers to achieve full-scale performance gains from multi-core technologies and enhanced memory management techniques for 48 gigabyte RAM configuration.
Darmawan Prasodjo, PhD