Environmental Research InstituteUniversity 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Work in Progress: Please do not cite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

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 CentreEnvironmental Research Institute,University College Cork

Dr James GlynnJames.glynn@ucc.ieProject contact point

Dr Paul Deane

Prof Brian Ó Gallachóir

Imperial College London, UK

Dr Niall Mac Dowell

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.

IPCC AR5 - Energy System Scenarios

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?

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

Models with >10 scenarios in AR5 DB

Model # of AR5 Scenarios Scenario Publications

REMIND (1.1, 1.2, 1.3, 1.4, 1.5) 158Leimbach 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) 140Krey 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) 139Calvin, 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) 79van 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

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.

IMAGEUtrecht University (UU), Netherlands, http://www.uu.nl.

PBL Netherlands Environmental Assessment Agency (PBL), Netherlands, http://www.pbl.nl.

IPETSNational 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

REMINDPotsdam Institut für Klimafolgenforschung (PIK), Germany, https://www.pik-

potsdam.de/research/sustainable-solutions/models/remind.

TIAM-UCLUniversity College London (UCL), UK, https://www.bartlett.ucl.ac.uk/energy.

main users: Energy modellers

WITCHFondazione Eni Enrico Mattei (FEEM), Italy, http://www.feem.it.

Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Italy, http://www.cmcc.it.

• 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

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)

Initial Database Setup

• LIMITS

• AMPERE

• AR5

• MILES

• Still no access to ADVANCE. Probably too late.

• Need to Add recently oublishedSSP+RCP database (Riahai et al 2016)

• >10Million Variables

• SQL database.

• Identify key outlier scenarios and models and projections • (In Progress)

EMF27 – Low Carbon Pathways w/wo CCS

SSP marker models Primary Energy - RCP2.6Total, Fossil, Fossil-wCCS, Fossil-woCCS

Task 3 – Identify data input and assumptionsStill 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 GasRetro 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 CoalPartial 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 CoalRetro 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

Task 3 – Identify data input and assumptions

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.

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.

Thank youQUESTIONS?