Optimal Spatial Deployment of CO2 Capture and Storage Given a Price on Carbon

Abstract

Carbon dioxide capture and storage (CCS) links together technologies that separate carbon dioxide (CO2) from fixed point source emissions and transport it by pipeline to geologic reservoirs into which it is injected underground for long-term containment. Previously, models have been developed to minimize the cost of a CCS infrastructure network that captures a given amount of CO2. The CCS process can be costly, however, and large-scale implementation by industry will require government regulations and economic incentives. The incentives can price CO2 emissions through a tax or a cap-and-trade system. This paper extends the earlier mixed-integer linear programming model to endogenously determine the optimal quantity of CO2 to capture and optimize the various components of a CCS infrastructure network, given the price per tonne to emit CO2 into the atmosphere. The spatial decision support system first generates a candidate pipeline network and then minimizes the total cost of capturing, transporting, storing, or emitting CO2. To illustrate how the new model based on CO2 prices works, it is applied to a case study of CO2 sources, reservoirs, and candidate pipeline links and diameters in California.