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IEA-GHG project and findings to date

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IEAGHG -Carbon Capture and Storage (CCS) in Integrated Assessment Models Climate Scenarios.

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IEA-GHG project and findings to date

  1. 1. Environmental Research Institute University College Cork IEAGHG - Carbon Capture and Storage (CCS) in Integrated Assessment Models Climate Scenarios. Work in Progress: Please do not cite James Glynn, Paul Deane, Richard Millar, Niall MacDowell, Myles Allen, Brian Ó Gallachóir Joint Global Change Research Institute of Pacific Northwest Laboratory (PNNL) & University of Maryland (UMD), USA 71st ETSAP-MEETING | 11th July 2017
  2. 2. Project Consortium Contracting Party: IEA GHG Ltd Keith Burnard Tim Dixon (TBC) Environmental Change Institute, University of Oxford, UK Prof Myles Allen Dr Richard Millar MaREI Centre Environmental Research Institute, University College Cork Dr James Glynn James.glynn@ucc.ie Project contact point Dr Paul Deane Prof Brian Ó Gallachóir Imperial College London, UK Dr Niall Mac Dowell
  3. 3. High level aims of the study • Provide transparency on the data inputs and calibration of CCS in IAMS • Document the range of outcomes for CCS in the most influential IAMS • Provide insights into the important drivers of the range of outcomes in these IAMs • Provide an assessment of best practice and review up-to-date data for configuring CCS by technology type and region in IAMs.
  4. 4. IPCC AR5 - Energy System Scenarios
  5. 5. Are these scenarios achievable? Colours show total policy cost in US$2005 Total emissions in scenarios in IPCC WGIII “430-480ppm” (lowest) scenario category Figures courtesy of Richard Millar based on IPCC AR5 WBIII database hosted by IIASA From Millar et al, 2016?
  6. 6. Task 1 – List influential IAMS and Scenarios • Influential IAMS in the Past • What is influence? • Amount of participation in Global Model Inter-comparison Projects • Inclusion in IPCC-AR5, Number of scenarios in AR5 database? • Utilised by G7 country governments? Or Regional Assemblies (EU • Used by IEA, • Used by Oil Majors • Used by Global Technology companies? • Used by Global financial institutions (WorldBank) • Influence in the future? • Leading SR1.5 scenario analysis? • Providing Marker models for the Shared Socioeconomic Pathways for AR6. GCAM IMAGE MESSAGE REMIND WITCH AIM POLES DNE21 GEM-E3 Phoenix IMACLIM MERGE TIAM 9 6 6 6 6 5 5 4 4 4 3 3 3
  7. 7. Models with >10 scenarios in AR5 DB Model # of AR5 Scenarios Scenario Publications REMIND (1.1, 1.2, 1.3, 1.4, 1.5) 158 Leimbach et al., 2010; Luderer et al., 2012a; b; Arroyo-Currás et al., 2013;Bauer et al., 2013;Aboumahboub et al., 2014; Tavoni et al., 2014;Klein et al., 2014; Kriegler et al., 2014a; b;Riahi et al., 2014 MESSAGE (V.1, V.2, V.3, V.4) 140 Krey and Riahi, 2009; Riahi et al., 2011, 2012, 2014; van Vliet et al., 2012; Kriegler et al., 2014a; b; McCollum et al., 2014; Tavoni et al., 2014 GCAM (2.0, 3.0, 3.1, MiniCAM) 139 Calvin, Edmonds, et al., 2009;Calvin et al., 2012, 2013, 2014; Iyer et al., 2013; Kriegler, Tavoni, et al., 2014 ; Tavoni et al., 2014 WITCH (AME, AMPERE, EMF22, EMF27, LIMITS, RECIPE, ROSE) 132 Bosetti et al., 2009; de Cian et al., 2012; Massetti and Tavoni, 2012; De Cian et al., 2013a; Kriegler et al., 2014a; b;Marangoni and Tavoni, 2013; Riahi et al., 2013; Tavoni et al., 2013 IMAGE (2.4) 79 van Vliet et al., 2009, 2014; van Ruijven et al., 2012; Lucas et al., 2013; Kriegler et al., 2014a; b;Riahi et al., 2014; Tavoni et al., 2014 POLES (AMPERE, EMF27, AME) 79 Dowling and Russ, 2012;Griffin et al., 2014; Kriegler et al., 2014a; Riahi et al., 2014 IMACLIM (v1.1) 53 Bibas and Méjean, 2013;Kriegler et al., 2014a; Riahi et al., 2014 MERGE-ETL (2011) 48 Marcucci and Turton, 2013; Kriegler et al., 2014a;Riahi et al., 2014 MERGE (AME, EMF22, EMF27) 44 Blanford et al., 2009, 2013; Calvin et al., 2012 DNE21+ (v.11, v.12) 43 Akimoto et al., 2012; Wada et al., 2012; Kriegler et al., 2014a; Riahi et al., 2014;Sano et al., 2014 AIM-Enduse (12.1; backcast 1.0) 41 Akashi et al., 2014;Kriegler et al., 2014b; Tavoni et al., 2014 TIAM-World (2007, 2012.02, Mar2012) 41 Loulou et al., 2009;Labriet et al., 2012;Kanudia et al., 2013 Phoenix (2012.4) 31 Fisher-Vanden et al., 2012;Kriegler et al., 2014c BET (1.5) 23 Yamamoto et al., 2014 EC-IAM 2012 21 Kriegler et al., 2014c ENV-Linkages (WEO2012) 17 Kriegler et al., 2014c GRAPE (ver1998, ver2011) 14 Calvin et al., 2012; Kriegler et al., 2014c FARM (3.0) 12 Sands et al., 2014 TIAM-ECN 12 Kober et al., 2014;Kriegler et al., 2014b; Tavoni et al., 2014 GEM-E3-ICCS 11 Kriegler et al., 2014a
  8. 8. ADVANCE – Data not public yet AIM-CGE National Institute for Environmental Studies (NIES), Japan, . DNE21+ Research Institute of Innovative Technology for the Earth (RITE), Japan, . GEM-E3 Institute of Communication And Computer Systems (ICCS), Greece, . IMACLIM Centre international de recherche sur l'environnement et le développement (CIRED), France, http://www.centre-cired.fr. Societe de Mathematiques Appliquees et de Sciences Humaines (SMASH), France, http://www.smash.fr. IMAGE Utrecht University (UU), Netherlands, http://www.uu.nl. PBL Netherlands Environmental Assessment Agency (PBL), Netherlands, http://www.pbl.nl. IPETS National Center for Atmospheric Research (NCAR), USA, https://www2.cgd.ucar.edu/sections/tss/iam/iam- modeling. MESSAGE-GLOBIOM International Institute for Applied Systems Analysis (IIASA), Austria, http://data.ene.iiasa.ac.at/message- globiom/. main users: IIASA, the MESSAGE model is distributed via the International Atomic Energy Agency (IAEA) to member countries POLES JRC - Joint Research Centre - European Commission (EC-JRC), Belgium, http://ec.europa.eu/jrc/en/poles. main users: - European Commission, JRC- Universite de Grenoble UPMF, France - Enerdata REMIND Potsdam Institut für Klimafolgenforschung (PIK), Germany, https://www.pik- potsdam.de/research/sustainable-solutions/models/remind. TIAM-UCL University College London (UCL), UK, https://www.bartlett.ucl.ac.uk/energy. main users: Energy modellers WITCH Fondazione Eni Enrico Mattei (FEEM), Italy, http://www.feem.it. Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Italy, http://www.cmcc.it.
  9. 9. • Most influential models currently and into the future for IPCC 6th Assessment Report (AR6) are likely to be the SSP marker models. • SSP1 - Sustainability- IMAGE (PBL) – Hybrid systems dynamics and GE • SSP2 - Middle of the Road - MESSAGE-GLOBIOM (IIASA) – Hybrid • SSP3 – Regional Rivalry - AIM/CGE (NIES) – General Equilibrium (GE) • SSP4 – Inequality - GCAM4 (PNNL) – Partial Equilibrium (PE) • SSP5 - Fossil fuelled Development - REMIND-MAGPIE (PIK) – GE • Others • WITCH-GLOBIOM (FEEM) – General Equilibrium Task 1 – Share Socioeconomic Pathways
  10. 10. Task 2 – Comparative Overview of CCS projections – Identify Outliers • Compile database of scenario projected sequestration rates, volumes, by technology, scenario, and IAM type (Near complete) • LIMITS • AMPERE • AR5 • MILES • Add SSP • Identify key outlier scenarios and models and projections • (In Progress)
  11. 11. Initial Database Setup • LIMITS • AMPERE • AR5 • MILES • Still no access to ADVANCE. Probably too late. • Need to Add recently oublished SSP+RCP database (Riahai et al 2016) • >10Million Variables • SQL database. • Identify key outlier scenarios and models and projections • (In Progress)
  12. 12. EMF27 – Low Carbon Pathways w/wo CCS
  13. 13. SSP marker models Primary Energy - RCP2.6 Total, Fossil, Fossil-wCCS, Fossil-woCCS
  14. 14. Task 3 – Identify data input and assumptions Still Compiling database Source Fuel Capture Type Plant type HHV Efficiency (%) Overnight Full CAPEX $2015/kW Range Min Range Max FOM ($/kW/year) Range FOM MAX ($/kW/year) Range FOM MIN ($/kW/year ) VOM $/MWh Range MIN VOM $/MWh Range MAX VOM $/MWh Build date of Plant Capture rate CO2 Transport Cost ($/Tonne CO2) Range CO2 Transport Cost ($/Tonne CO2) CO2 Storage Costs ($/Tonne CO2) Range MIN CO2 Storage Costs ($/Tonne CO2) NETL Coal Post Combustion Subcritical 31.2 4,523 90% 2.3744 - 9.2114 9.42 NETL Coal Post Combustion Supercritical 32.5 4,593 1,802 2,862 90% 2.3744 - 9.2114 9.42 NETL Gas Post Combustion Combined Cycle 45.7 1,912 1,166 1,908 90% 2.3744 - 9.2114 9.42 DECC Gas Post Combustion Combined Cycle 44 1,540 1,246 1,980 23 18 26 2.46 2.13 2.93 2025 90% - 5.89 DECC Gas Retro Post Combustion Combined Cycle 44 1,027 1,246 1,980 23 18 26 2.46 2.13 3.01 2025 90% - 5.89 DECC Gas Pre Combustion Combined Cycle 38 1,466 2,126 3,153 22 18 26 2.79 2.42 3.30 2025 93% - 5.89 DECC Gas Oxy Combined Cycle 42 1,540 2,420 3,666 61 52 71 2.65 2.27 3.15 2025 100% - 5.89 DECC Coal Oxy Supercritical 32 2,493 2,493 4,033 50 43 57 4.18 3.59 4.77 2025 89% - 5.89 DECC Coal Pre Combustion IGCC 30 2,860 1,540 2,493 48 40 55 0.00 3.67 - 2025 90% - 5.89 DECC Coal Post Combustion Supercritical 32 3,061 2,493 4,033 58 49 66 2.23 1.91 2.57 2025 89% - 5.89 DECC Coal Partial post combustion Supercritical 38 1,906 1,393 2,200 41 35 48 2.22 1.91 2.57 2025 33% - 5.89 DECC Coal With ammonia Supercritical 32 3,080 2,126 3,226 58 50 67 2.23 1.91 2.57 2025 89% - 5.89 DECC Coal Retro Post Combustion Supercritical 32 1,760 1,833 2,640 59 51 68 2.24 1.91 2.57 2025 89% - 5.89 DECC Coal Oxy Supercritical 32 2,493 2,640 3,959 50 43 57 4.18 3.59 4.77 2025 91% - 5.89 DECC Coal Partial Pre combustion IGCC 35 2,053 5,279 8,359 38 32 44 3.67 3.15 4.18 2025 30% - 5.89 DECC Coal Retro Pre Combustion IGCC 27 3,080 1,540 1,980 60 51 70 4.71 4.03 5.50 2025 89% - 5.89 DECC Biomass Post Combustion Conventional Boiler 15 6,379 1,246 1,613 102 87 117 5.76 4.91 6.60 2025 89% - 5.89
  15. 15. Task 3 – Identify data input and assumptions
  16. 16. Task 6 – Workshop & Report Outcomes • Many IAMs employed a simplistic representation of CCS transport and storage costs, with a variation in capture costs depending on the CCS technologies represented. Where data is available, IAMs should aim to have cost curves (and, potentially, learning rates) for capture, transport and storage. • NETL have gathered and estimated baseline CCS datasets critical to developing detailed state-of-the-art cost curves for capture, storage and transport that could be used for CCS calibration in IAMs. The data has not yet been widely distributed among IAM teams. • Communication between CCS technology experts and IAM modellers needs to be enhanced. Such communication should include a regular meeting, with accessible, open and transparent data-sharing essential.
  17. 17. Invite to share CCS calibration data. • Identify the underlying assumptions, data, sensitivities and calculations behind the results that would have an impact on the CCS projections in Global and National Models. • Explore similarities and differences, for example, in the range of technologies included, the timing of entry of various more advanced technologies and the performance data and costs applied to those technologies. • Required technical parameters such as; • CCS costs curves, learning curves, deployment rates, Capture Rates, start years, lifespan, CAPEX, OPEX, resources by region, sink volumes by region, and sink geological types.
  18. 18. Thank you QUESTIONS?

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