the$ENERGY$lab$ N"

Thomas$A.$Sarkus$

Reducing$CCS$Costs:$Learning$Curves,$Learning$through$Research$&$Development,$and$Learning$by$Doing$

Division'Director,'Major'Projects'Division'

May'11,'2015'

Cost$Share$Ensures$Commercial$Relevance$DO

E$Re

search$Program

s$

Basic$Research$ Applied$Research$Bridges$basic$research$&$technology$

development$programs$

Process$&$Engineering$Development$

Pilot$plants,$ProofIof$concept$(POC)$units,$MiniIdemonstraMons$

DemonstraMon$&$CommercializaMon$

Research$Phases$Industry$ParMcipaMon$&$Cost$Sharing$Increases,$over$Mme$

DemonstraMons$

Fossil$Energy$Core$Programs$

Office$of$$Science$Research$

Fossil$Energy$$Advanced$Research$

$

Total$Project$Cost$

0$%$Cost$Share$ 0$to$20$%$$Cost$Share$

20$to$50$%$$Cost$Share$

>$=$50$%$$Cost$Share$

$Carbon$Storage$Program$Technology"Readiness"Levels"(TRLs)"

The'clean'room'for'the'manufacturing'of'the'300ºC'Fiber'OpDc'Seismic'

Sensors'(FOSS)™'

Field'deployment'and'tesDng'of'the'5'level'3C'array'prototype'at'an'industrial'well'in'California'

Advanced$Coal$Power$Technologies!

State&of&the&Art !!!!!2nd&Genera0on!!!!!!!!!Transforma0onal!

Today’s!IGCC!!

Today’s!Supercri0cal!

PC!

Advanced!H2!Turbines!!

Syngas!Cleanup!!!

!Advanced!Pre&combus0on!Capture!!

Advanced!Ultra&!Supercri0cal!(AUSC)!PC!

!Advanced!Post&combus0on!

Capture!!

AUSC!Oxycombus0on!

Integrated!Gasifica0on!Fuel!Cells!(IGFC)!

3100°F!H2!Turbine!

Direct!Power!Extrac0on!

Chemical!Looping!Combus0on!

Supercri0cal!CO2!Cycles!

Pulse!!Combus0on!

Transforma0onal!CO2!Separa0on!

Transforma0onal!H2!Produc0on!

Pressurized!!Oxycombus0on!

Aspects!Applicable!to!Natural!Gas!

Chemical!Looping!Gasifica0on!

2010 2020 2030

Science,+ARPA/E,+and+Fossil+Energy+Advanced+Concepts

Available(for Deployment

1st Generation+Technologies

2nd Generation+Technologies

Available(for Deployment

Available(for Deployment

ITM,+WGC,+H2 Membrane+Separation,+2,650+F+TIT+HT+Turbine++

IGCC+and+Combustion+with+CCS,CCUS,+Co/production

Technology+R&D/Large/Scale+Tests

Technology+R&D Demonstrations

Demonstrations

Transformational+Technologies

Demonstrations

Pressure+Gain+Combustion,+Direct+Energy+Conversion,+Supercritical+CO2 ,+IGFC,+Chemical+Looping+

Technology+R&D/Large/Scale+Tests

2040 2050Historic+

Background$–$Learning$Curves$

•  Developed'by'T.P.'Wright'in'1936'aOer'observing'labor'Dme'reducDons'to'assemble'airplanes.'

'

•  In'1998'Mackay'&'Probert'showed'that'a'similar'rule'could'be'applied'to'capital'cost'reducDons'in'renewable'energy.'

• Models'including'NEMS'rely'on'this'curve'to'predict'future'capital'costs.'

Source:''“Learning'and'Cost'ReducDons'for'GeneraDng'Technologies'in'the'NaDonal'Energy'Modeling'System'(NEMS)”'E.Gumerman'&'C.Marnay.'Lawrence'Berkeley'NaDonal'Laboratory,'University'of'California'

Berkeley.'January'2004,'h`p://eetd.lbl.gov/ea/emp/reports/52559.pdf'

Sample'Learning'Curve'FuncDon'

a = Cost of first unit x = Number of units produced b = Learning rate exponent 1 - 2-b = Learning Rate, reduction in capital

cost for doubling of capacity

Y = axb

Background$I$Large$VariaMon$in$Learning$Curves$for$Energy$Technology$

Technology Region

of Study Time Period

of Study Estimated

Learning Rate Reference

Coal Power Plants USA 1960 – 1980 1.0% – 6.4% Joskow & Rose (1985)

Coal for Electric Utilities USA 1948 – 1969 25% Fisher (1974)

Crude Oil at the Well USA 1869 – 1971 5% Fisher (1974)

Solar PV Modules World 1976 – 1992 18% IEA (2000)

Wind Power USA 1985 - 1994 32% IEA (2000)

Wind Power EU 1980 – 1995 18% IEA (2000)

Data Source: McDonald & Schrattenholzer, 2001.

Background$I$ExplanaMons$for$Variability$

•  Experience$depreciaMon$•  ShortIterm$pricing$behavior$•  Differences$in$performance$measures$•  DefiniMonal$differences$•  Varying$intensiMes$of$R&D$•  Economies$of$scale$•  Cost$variability$for$factors$such$as$land$costs,$wages,$

and$interest$payments$

$Source:'Alan'McDonald'&'Leo'Schra`enholzer'(2000).''Learning'Rates'for'Energy'Technologies.''Energy'Policy'29,'255d261.'''

SO2$&$NOx$Control$Learning$Curves$

Yeh, S., Rubin, E.S., Hounshell, D.A., and Taylor, M.R. (2009) Uncertainties in Technology Experience Curves, for Integrated Assessment Models, Environmental Science & Technology, 43 (18), 6907-14.'

Non-linear learning curves are prevalent in power plant emission control technologies.'

10'

Source:'“CCS'Theory'of'Change”'by'John'Thompson,'Clean'Air'Task'Force,'Nov.'21,'2013;'adapted'from'“Anthropogenic'Sulfur'Dioxide'Emissions:'1850d2005'Supplementary'Material”'S.J.'Smith'et.'Al.'

'

CO2$Analogous$to$US$Power$SO2$Emissions$'

Background$I$Key$Findings$from$Literature$

•  Both$Research$and$Development$$(R&D)$and$learningIbyIdoing$play$an$important$role$in$innovaMon$and$the$cost$of$energy$technologies$in$the$marketplace1$

$

•  CauMon$should$be$taken$when$using$this$approach$due$to$the$following$issues:$–  Wide'variaDon'in'learning'curve'rates'and'behavior'

–  Cannot'separate'effects'of'R&D'from'learningdbyddoing'

1For'more'details'on'the'models,'see'Alan'McDonald'&'Leo'Schra`enholzer,'“Learning'Rates'for'Energy'Technologies.”''Energy'Policy,'29'(2001),'255d261;'and'Sonia'Yeh'and'Edward'Rubin,'“A'Review'of'UncertainDes'in'Technology'Experience'Curves.”''Energy'Economics'34'(3)'(2012),'762d771.'

Choice$of$Learning$Rate$

•  Rubin'et'al'(2007),'idenDfied'historic'learning'rates'from'similar'power'plant'technologies:'

–  11%''FGD'–  12%''SCR'–  10%''GTCC'–  '5%''PC'Boilers'

'

•  Riahi'et'al'(2004),'esDmated'a'13%'learning'rate'for'CCUS'

technologies.'

•  Assume'a'10%'learning'rate'represenDng'the'average'of'the'above'learning'rates'and'a'3%'error'band'to'reflect'inherent'uncertainty.'

''

0%

20%

40%

60%

80%

100%

0 50 100 150 200 250

CCUS

,Capita

l,Cost,,As

,a,Pe

rcen

tage,of,

Initial,Installatio

n,Co

st,for,C

urrent,Te

chno

logy,

Installed,Capacity,(Number,of,Plants)

Reductions,in,Cost,Based,on,Learning,by,Doing,Current,Technology,(10%,LR,,±3%)

How$Many$LearningIByIDoing$Plants$$=$R&D$Goal?$

R&D$Goal$50%$Capital$

Cost$ReducMon$

10%$L.R.,$50%$capital$cost$reducMon$=$90$plants,$capital$cost$$45B$

13%$L.R.,$50%$capital$cost$reducMon$=$30$plants,$capital$cost$$15B$

Y$=$axb$$

a$=$100%$b$=$I0.152$L.R.$=$10%$

0%

20%

40%

60%

80%

100%

0 50 100 150 200 250

CCUS

,Capita

l,Cost,,As

,a,Pe

rcen

tage,of,

Initial,Installatio

n,Co

st,for,C

urrent,Te

chno

logy,

Installed,Capacity,(Number,of,Plants)

Reductions,in,Cost,Based,on,Learning,by,Doing,

Current,Technology,(10%LR,,,±3%) NETL,R&D,Goal,(10%LR,,,±3%)

Learning$Occurs$With$R&D,$Too$

R&D$Goal$50%$Capital$

Cost$ReducMon$

Y$=$axb$$

a$=$100%$b$=$I$0.152$L.R.$=$10%$

a$=$50%$b$=$I$0.152$L.R.$=$10%$

Aier$installaMon$on$90$plants,$esMmated$total$capital$costs$are$$23$billion*$

Aier$installaMon$on$90$plants,$esMmated$total$capital$costs$are$$45$billion$

*R&D'case'includes'capital'costs'but'not'R&D'costs.'

Net$Present$Value$Tool$

•  QuesMon:$How$many$plants$would$install$CCUS$if$there$were$a$price$for$CO2?$

$

•  Compared:$$Current'technology'costs'

EPEC'R&D'Goals'='50%'reducDon'in'CCUS'retrofit'costs'

EPEC'R&D'Goals'Lite'='25%'reducDon'in'CCUS'retrofit'costs''

•  Examined$how$many$plants$retrofit$with$CCUS,$how$many$rebuild$with$CCUS,$and$how$many$conMnue$running$business$as$usual$(BAU).$

0

50

100

150

200

250

300

350

400

450

20'30 30'40 40'50 50'60 60'70 70'80

Num

ber'o

f'Plants

CO2 Price,'$/tonne'CO2

EPEC'R&D'Goals

BAU

Rebuild5with5CCUS

Retrofit5with5CCUS

0

50

100

150

200

250

300

350

400

450

20'30 30'40 40'50 50'60 60'70 70'80

Num

ber'o

f'Plants

CO2 Price,'$/tonne'CO2

Current'Technology

BAU

Rebuild5with5CCUS

Retrofit5with5CCUS

There$Can$be$No$LearningIbyIDoing$if$There$is$No$Doing$

If$R&D$is$not$performed,$the$cost$is$too$high$for$most$plants$to$install$technology$or$replace$with$new$CCUS$faciliMes;$learningIbyIdoing$never$gets$off$the$ground.$

High$CO2$market$price$is$required$to$get$retrofits$and$rebuilds$to$begin.$

High$CO2$market$price,$substanMally$more$retrofits$$

Low$CO2$market$price,$no$plants$retrofit,$thus$no$

learning$occurs.$

Low$CO2$market$price,$some$plants$retrofit;$learning$begins.$

Current'CCUS'Technology' 50%'ReducDon'in'CCUS'System'Costs'

0

50

100

150

200

250

300

350

400

450

20'30 30'40 40'50 50'60 60'70 70'80

Num

ber'o

f'Plants

CO2 Price,'$/tonne'CO2

50%'of'EPEC'R&D'Goals

BAU

Rebuild5with5CCUS

Retrofit5with5CCUS

0

50

100

150

200

250

300

350

400

450

20'30 30'40 40'50 50'60 60'70 70'80

Num

ber'o

f'Plants

CO2 Price,'$/tonne'CO2

Current'Technology

BAU

Rebuild5with5CCUS

Retrofit5with5CCUS

What$if$R&D$Only$Achieves$a$25%$CCUS$System$Cost$ReducMon?$

Even$if$the$NETL$R&D$program$falls$short$of$its$goals,$there$is$sMll$value$in$the$form$of$reduced$CCUS$system$costs,$increased$deployment,$&$earlier$learningIbyIdoing.$

High$CO2$market$price$is$required$to$get$retrofits$and$rebuilds$to$begin.$

A$25%$cost$reducMon$allows$for$significant$

learning.$

Low$CO2$market$price,$no$plants$retrofit,$thus$no$

learning$occurs.$

Low$CO2$market$price,$very$few$plants$retrofit;$but$

learning$begins.$

Current'CCUS'Technology' 25%'ReducDon'in'CCUS'System'Costs'

“CCS$Theory$of$Change”$by$John$Thompson,$Nov.$21,$2013.$$“Climate$Change,$Technology$InnovaMon,$and$the$Future$of$Coal”$by$Edward$Rubin,$in$Cornerstone,$Vol.$1,$Issue$1,$Spring$2013.$hqp://cornerstonemag.net/climateIchangeItechnologyIinnovaMonIandItheIfutureIofIcoal/$$$“EvaluaMng$the$Impact$of$R&D$and$Learning$by$Doing$on$Fossil$Energy$Cost$ReducMons:$There$Can$be$No$Learning$if$There$is$No$Doing”$by$Katrina$Krulla,$DOE/NETLI342/020613,$Feb.$2013.$hqp://netl.doe.gov/File%20Library/Research/Energy%20Analysis/PublicaMons/LearningICurveIAnalysisIReport.pdf$$$“Moore’s$Law$vs.$Wright’s$Law”,$Forbes,$March$25,$2013.$hqp://www.forbes.com/sites/jimhandy/2013/03/25/mooresIlawIvsIwrightsIlaw/$$$StaMsMcal$Basis$for$PredicMng$Technological$Progress”$by$Bela$Nagy,$et$al.,$Feb.$28,$2013.$hqp://www.plosone.org/arMcle/fetchObject.acMon?uri=info%3Adoi%2F10.1371%2Fjournal.pone.0052669&representaMon=PDF$$$

References$

NARUC"2014"Annual"Mee>ng"

For$AddiMonal$InformaMon$thomas.sarkus@netl.doe.gov$

NARUC"2014"Annual"Mee>ng"

Office!of!Fossil!Energy!www.fe.doe.gov!!

NETL!www.netl.doe.gov!