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Perspectives of CCS power plants in Europe under different climate policy regimes

This study analyzes the impact of carbon capture and storage (CCS) power plants on greenhouse gas reduction in Europe under different climate policy regimes. The study uses the TIMES PanEU model to assess the costs and potential of CCS technology in achieving long-term GHG reduction targets. The results show that the success of CCS heavily depends on capture costs and GHG reduction targets, with CCS becoming essential in achieving a 60% reduction target by 2040. However, CCS costs may have more influence on market penetration than GHG reduction targets. In 2050, the climate regime determines the use of CCS, with reductions in capture costs leading to increased electricity consumption and nearly complete decarbonization of the public power generation sector.

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Perspectives of CCS power plants in Europe under different climate policy regimes

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  1. Perspectives of CCS power plants in Europe under different climate policy regimes Tom Kober, Markus Blesl Institute of Energy Economics and the Rational Use of Energy, University of Stuttgart International Energy Workshop 17. June 2009 in Venice

  2. Agenda • Motivation and target • Scope • Model characteristics and methodology • TIMES PanEU characteristics • Parametric Programming • Perspectives of CCS power plants - results of the analysis • Conclusion

  3. Motivation and target • EU- 20-20 and 2°C targets of European Commission stated but post Kyoto long term GHG reduction obligation of European countries? • Hugh investments in European power generation sector to compen-sate decommissioning capacities and to enlarge generation system! • Carbon Capture and Storage (CCS) promising alternative for low carbon fossil fueled power generation?! Analysis of capture costs of CCS power plants and their impact on contribution of CCS power plants as a GHG reduction technology.

  4. Scope

  5. TIMES PanEU model characteristics • Technology oriented bottom-up model with prefect foresight • Minimization of total system costs • 30 region model (EU 27 + IS, NO, CH) [named EU-27+3] • Modelling horizon 2000 – 2050, 12 time slices (4 seasonal, 3 day level) • Energy system model with detailed representation of the demand sectors (industry, residential, commercial, transport and agriculture) as well as public and industrial electricity and heat production and refineries and other fuel conversion • Detailed power generation sector based on a IER power plant database with 25,000 units included • Renewable potential (onshore wind, offshore wind, geothermal, biomass, biogas, hydro) • District heat expansion potentials • GHG: CO2, CH4, N2O and pollutants: CO, NOX, SOX, particles • Detailed electricity exchange capacities based on ETSO and EC • Country specific CO2 storage options

  6. 950 MW 800 MW 250 MW 600 MW 600 MW 110 MW 1320 MW 500 MW 500 MW 1000 MW 700 MW 1400 MW 900 MW 600 MW 1400 MW 1800 MW 1200 MW 600 MW 700 MW 1800 MW 2330 MW 200 MW Regions in TIMES PanEU and plannedelectricity interconnection extensions

  7. Regional CO2 storage potenials in TIMES PanEU Norway Potential in Gt CO2 UK Denmark Netherlands Germany Slovakia France Spain

  8. Parametric Programming in TIMES PanEU – a tool for advanced result analysis Analysis of impact of marginal changes of model input data A,b,c on the model solution (solving intervall problem)  base of optimal solution remains Standard Sensitivity Analysis Variation of model input data A,b,c over broarder range of value (Additionally solving of the boundary point problem for the calculation of the optimal partition in the next intervall)  base of optimal solution changes Parametric Programming

  9. Possible parameter variations

  10. Modeling of the generic capture process  Variable carbon capture costs to reflect uncertainty

  11. Introducing climate policy regimes and CCS cost variations • 5 different climate policy regimes • GHG-60, GHG-65, GHG-71, GHG-75, GHG-80 2 1 Variation of CCS costs CCS20, CCS30, CCS40, CCS50, CCS60 3

  12. Carbon emissions in EU-27+3 under 71% GHG reduction comp. Kyoto base and high CCS capture costs (60€/tCO2 in 2040) 1

  13. Carbon emissions of EU-27+3 under different climate policy regimes at high CCS costs 2 1 Δ120 MtCO2

  14. Impact of CCS cost variations on sector carbon emissions in EU-27+3 in 2030 3

  15. Impact of CCS cost variations on carbon emissions of public power sector in EU-27+3

  16. Effects on electricitity generation in EU-27+3

  17. Impacts on electricity generation in EU-27+3 in 2030

  18. Impacts on electricity generation in EU-27+3 in 2050

  19. Carbon capture depending on CCS costs and climate target in EU-27+3

  20. Conclusions • In 2030 CCS market share heavyly depends on capture costs and GHG reduction targets. CCS technologies face intersectoral competition. CCS costs may have more impact on CCS market penetration than GHG reduction targets. • In 2040 CCS becomes essential under climate regimes aiming minimum GHG reduction of 60% compared to Kyoto base. GHG reduction target determines primary level of carbon capture. Little influence of CCS costs on use of CCS technologies. • In 2050 climate regime determines use of CCS. Reductions of capture costs can lead to increase of electricity consumption. Nearly complete decarbonisation of public power generation.

  21. Tom Kober, Dipl.-Wi.-Ing. Institute for Energy Economics and the Rational Use of Energy University of Stuttgart Hessbruehlstr. 49a D - 70565 Stuttgart Phone: +49 711 685 878 26 Mail: tom.kober@ier.uni-stuttgart.de Thank you for your attention!

  22. Changes of final energy consumption of EU-27+3 in 2030

  23. Parametric Programming TIMES Model generator Model data TIMES input file MPS-Matrix Preprozessor Creation of Matrix in C and external reduktion algorithm in GAMS 3 Definition of the result parameters via user constraints Internal reduktion algorithm User constraints 2 Equation matrix in GAMS 1 Model equations Solver Result procedure Input file: Info about variation of parameters Solver Routine of Parametric Programming in GAMS Result balance 4 Flexible result analysis in Veda BackEnd Parametric Programming in TIMES

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