Johannes Herold

CO2 Highways for Europe: Modeling a Carbon Capture, Transport and Storage Infrastructure for Europe

This paper presents a mixed integer, multi-period, cost-minimising model for a carbon capture, transport and storage (CCTS) network in Europe. The model incorporates endogenous decisions about carbon capture, pipeline and storage investments. The capture, flow and injection quantities are based on given costs, certificate prices, storage capacities and point source emissions. The results indicate that CCTS can theoretically contribute to the decarbonisation of Europe’s energy and industrial sectors. This requires a CO2 certificate price rising to €55 per tCO2 in 2050, and sufficient CO2 storage capacity available for both on- and offshore sites. Yet CCTS deployment is highest in CO2-intensive industries where emissions cannot be avoided by fuel switching or alternative production processes. In all scenarios, the importance of the industrial sector as a first-mover to induce the deployment of CCTS is highlighted. By contrast, a decrease in available storage capacity or a more moderate increase in CO2 prices will significantly reduce the role of CCTS as a CO2 mitigation technology, especially in the energy sector. Furthermore, continued public resistance to onshore CO2 storage can only be overcome by constructing expensive offshore storage. Under this restriction, reaching the same levels of CCTS penetration would require a doubling of CO2 certificate prices.

Modeling a Carbon Capture, Transport, and Storage Infrastructure for Europe

In this paper, we develop a model to analyze the economics of carbon capture, transport, and storage (CCTS) in the wake of expected rising CO2 prices. We present a scalable mixed integer, multiperiod, welfare-optimizing network model for Europe, called CCTS-Mod. The model incorporates endogenous decisions on carbon capture, pipeline and storage investments, as well as capture, flow and injection quantities based on given costs, CO2 prices, storage capacities, and point source emissions. Given full information about future costs of CCTS-technology, and CO2 prices, the model determines a cost minimizing strategy on whether to purchase CO2 certificates, or to abate the CO2 through investments into a CCTS-chain on a site by site basis. We apply the model to analyze different scenarios for the deployment of CCTS in Europe, e.g., under high and low CO2 prices, respectively. We find that beyond CO2 prices of €50 per t, CCTS can contribute to the decarbonization of Europe’s industry sectors, as long as one assumes sufficient storage capacities (onshore and/or offshore). We find that CCTS is only viable for the power sector if the CO2 certificate price exceeds €75 per t.

Carbon Capture and Storage Investment and Management in an Environment of Technological and Price Uncertainties

We have conducted a dynamic stochastic investment analysis of carbon capture, transport and storage (CCTS) retro tting in an environment of CO2 price and technology uncertainty. It includes the option to invest in, and to use or shut down, the CCTS unit. While modeling certificate price uncertainties is standard, stochastic processes including diminishing increments are implemented for thermal efficiency and investment cost improvements. Following a careful quantification of parameters, the optimal investment strategies are derived and used in Monte Carlo simulations to produce results on aggregate investment decision and resulting profits. All simulation data were presented graphically from a low optimal strategy level to an aggregated expected value level. Our results show, that the main determinate for the application of CCTS is the certificate price. However, realized technology learning results in an earlier application of the technology by electricity producers and also acts as an insurance against the low carbon prices which prohibit profitable CCTS operation.

Modeling the Diffusion of Carbon Capture and Storage Under Carbon Emission Control and Learning Effects

This paper examines the interrelationship of learning effects and emission control on the diffusion of carbon capture and storage (CCS). We introduce a dynamic model in which an electricity producer maximizes profits subject to emissions control. All technologies are characterized by specific linear marginal costs and CO2 emissions which need to be covered by limited emission permits. The fossil fuel-based plants can be replaced by the CCS technology which is associated with higher capital costs and a lower system efficiency. Both parameters improve due to learning effects if the technology is applied. The model is formulated as a non-linear optimization problem, and solved using GAMS. Given the assumed technological data, the results for a data set of Germany show that CCS is essential to reach carbon emission goals. However, particularly in the case of learning, CCS can help renewable technologies to become competitive.

Carbon Capture and Storage, Wind and Nuclear Power, and Technology Learning

Limiting the carbon emissions of electricity generation will require significant investments in low-carbon technologies. Moreover, the phaseout of nuclear power generation induces the need for replacement investments. One of the replacement options is the carbon capture, transport and storage technology (CCTS). We analyze the impact of carbon restrictions on the diffusion of low-carbon technologies. All technologies are characterized by specific costs and CO2 emissions, which need to be covered by constraint emission permits. The fossil fuel-based capacity can be replaced by low-carbon energy technologies which are associated with initially higher costs and, in the case of CCTS, with a lower thermal efficiency. Both parameters improve due to endogenous learning effects if the technology is applied. Using a numerical model for the European Union we show that CCTS plays an important role for the replacement of nuclear power capacities.