estimating the cost of novel (pre-commercial) systems for co2 capture - ed rubin, carnegie-mellon...

Estimating the Cost of Novel

(Pre-Commercial) Systems for CO2 Capture

Edward S. Rubin Department of Engineering and Public Policy

Department of Mechanical Engineering Carnegie Mellon University

Pittsburgh, Pennsylvania

Presentation to the

CCS Cost Workshop International Energy Agency (IEA)

Paris, France

November 6, 2013

E.S. Rubin, Carnegie Mellon

Characteristics of Novel

(Pre-Commercial) Capture Systems

• Includes any technology not yet deployed or available for purchase at a commercial scale

– Current stage of development may range from

concept to large pilot or demonstration project

• Process design details still preliminary or incomplete

• Process performance not yet validated at scale, or under a broad range of conditions

• May require new components and/or materials that are not yet manufactured or used commercially


Examples of Pre-Commercial Systems: Everything beyond Present

Source: USDOE, 2010

Time to Commercialization

Advanced physical solvents

Advanced chemical solvents

Ammonia

CO2 com-pression

Amine solvents

Physical solvents

Cryogenic oxygen

Chemical looping

OTM boiler

Biological processes

CAR process

Ionic liquids

Metal organic frameworks

Enzymatic membranes

Present

Co

st

Re

du

cti

on

Be

ne

fit

5+ years 10+ years 15+ years 20+ years

PBI membranes

Solid sorbents

Membrane systems

ITMs

Biomass co-firing

Post-combustion (existing, new PC)

Pre-combustion (IGCC)

Oxycombustion (new PC)

CO2 compression (all)

Time to Commercialization



Ammonia

CO2 com-pression

Amine solvents

Physical solvents

Cryogenic oxygen

Chemical looping

OTM boiler


CAR process

Ionic liquids


Enzymatic membranes

Present

Co

st

Re

du

cti

on

Be

ne

fit


PBI membranes

Solid sorbents

Membrane systems

ITMs

Biomass co-firing







Ammonia

CO2 com-pression

Amine solvents

Physical solvents

Cryogenic oxygen

Chemical looping

OTM boiler


CAR process

Ionic liquids


Enzymatic membranes

Present

Co

st

Re

du

cti

on

Be

ne

fit


PBI membranes

Solid sorbents

Membrane systems

ITMs

Biomass co-firing










Typical Cost Trend of a New Technology

Research Development Demonstration Deployment Mature TechnologyResearchResearch Development Development DemonstrationDemonstration DeploymentDeployment Mature TechnologyMature Technology

Time or Cumulative Capacity

Cap

ita

l C

ost

pe

r U

nit

of

Cap

ac

ity

Research Development Demonstration Deployment Mature TechnologyResearchResearch Development Development DemonstrationDemonstration DeploymentDeployment Mature TechnologyMature Technology


Cap

ita

l C

ost

pe

r U

nit

of

Cap

ac

ity

Cap

ital C

ost

per

Un

it o

f C

ap

acit

y


Research Development Demonstration Deployment Mature Technology

Adspted from EPRI TAG


Historical Trends of FGD and SCR (DeSOx and DeNOx) Costs . . .

1968

1972

1974

1975

19761980

1982

1990

1995

0

50

100

150

200

250

300

0 10 20 30 40 50 60 70 80 90 100

Cumulative World Wet FGD Installed

Capacity (GW)

Cap

ital

Cos

ts ($

/kW

) in

1997

$

(1000 MW, eff =80-90%)

(200 MW, eff =87%) 1972

1975

1976

19801990

1995

1968

1974

1982

0

1

2

3

4

5

6

7

8

0 10 20 30 40 50 60 70 80 90 100

Cumulative World Wet FGD Installed

Capacity (GW)

O&

M C

osts

(199

7$/M

Wh)

(1000 MW, eff =80-90%)

(200 MW, eff =87%)

Source: Rubin et al. 2007

1983

1989

1993

19952000

-20 -10 0 10 20 30 40 50 60 70 80

Cumulative World SCR Installed Capacity (GW)

SC

R C

ap

ita

l C

ost

s ($

/kW

), 1

99

7$ First Japan commercial installation on a coal-fired power plant

← First German commercial installation

↓ First US commercial installation

←

1980

1983

1989

1993

19952000

40

50

60

70

80

90

100

110

120

1977

1978

1979

1980

All costs based on a standardized

500 MW coal-fired power plant

(except where noted)

. . . display the characteristics

seen in the previous slide


Five Approaches to Cost Estimation for Novel Systems

• Conventional engineering-economic estimates

• Conventional with probabilistic inputs

• Application of learning (experience) curves

• Expert judgment /elicitations

• Don’t ask, don’t tell

Conventional engineering-

economic costing



Most novel processes use conventional costing methods

Many cost elements depend on the technical maturity of the process


Cost estimates for novel processes often ignore “process contingency cost” guidelines

• Process Contingency Cost “factor applied to new technology … to quantify the uncertainty in the technical performance and cost of the commercial-scale equipment.” - EPRI TAG

Technology Status

Process

Contingency

Cost

(% of associated

process capital)

New concept with limited data 40+

Concept with bench-scale data 30-70

Small pilot plant data 20-35

Full-sized modules have been

operated 5-20

Process is used commercially 0-10

Source: EPRI, 1993; AACE, 2011

• Most studies of advanced capture systems for full-scale power plants (~500 MW) assume much smaller values than these guidelines call for, e.g.:

• 7% (IEAGHG, 2011)

• <20% (EPRI, 2011)

• 18% (USDOE, 2010)

• 10% (IECR, 2008)


What system are we trying to cost?

• Early commercial deployment? (first of a kind), or

• Mature widely-deployed system? (Nth of a kind)

The major difference is usually in the system design (though some cost factors also may change)

Whichever case is chosen, process contingency cost

depends on the current state of technical


Cost studies also commonly ignore guidelines for “project contingency cost”

• Project Contingency Cost “factor covering the cost of additional equipment or other costs that would result from a more detailed design of a definitive project at an actual site.” - EPRI TAG

EPRI Cost

Classification Design Effort

Project

Contingency (% of total process

capital, eng’g &home

office fees, and process

contingency)

Class I (~AACE Class 5/4)

Simplified 30–50

Class II (~AACE Class 3)

Preliminary 15–30

Class III (~ AACE Class 3/2)

Detailed 10–20

Class IV (~AACE Class 1)

Finalized 5–10

Source: EPRI, 1993

• Many Class I-III studies assume

≤10%

Conclusion:

• The total contingency cost for novel capture processes is grossly under-estimated in most cost estimates

• (e.g., totals of ~20-30% rather than ~40-100%)


Example of Contingency Cost Impact on Total Cost

Process + Project Contingency Cost

for the CO2 capture process alone

Parameter 20% 50% 100%

Capture System Capital Cost

($/kW) 17% 42% 85%

Capture System COE

($/MWh) 8% 20% 40%

Total Plant Capital Cost

($/kW) 5% 13% 26%

Total Plant COE

($/MWh) 4% 11% 18%

Increase in Capital Cost and COE

(based on a 2-stage membrane capture system)

Conventional costing with

probabilistic variables



Case Study of PC Plant with a 2-Stage Membrane Capture System

Membrane System Parameter Unit Nominal Value Distribution Function

Ideal CO2 Permeance (S.T.P.) gpu 1000 triangular (500, 1000,5000)

Ideal CO2/N2 Selectivity (S.T.P.) ratio 50 triangular (40, 50, 75)

Feed-side Compressor Efficiency % 85 uniform (70, 85)

Vacuum Pump Efficiency % 85 uniform (70, 85)

Expander Efficiency % 85 uniform (70, 85)

CO2 Compressor Efficiency % 80 uniform (70, 85)

Cost Parameters

Membrane Module Cost $/sq ft 4.645 triangular (2.322, 4.645,18.58)

Total Indirect Capital Cost % PFC 37 uniform (20, 60)

CO2 T&S Costs $/ton 5 uniform (1,10)

Source: IECM v.8.0.2 (2013)

Probability

distributions

assigned to 6

performance

variables and 3

cost variables


Case Study: SCPC-CCS (550 MWnet)

w/ 2-Stage Membrane Capture System

Conclusion:

• Explicit characterization of uncertainties can improve cost estimates by revealing risks as well as opportunities

95% CONFIDENCE INTERVALS=

COE:

DV +31/ -6 MWh (+51%/ -10%)

CO2 Avoidance Cost:

DV +49/ -8 $/ton= (+62%/ -10%)

0%

20%

40%

60%

80%

100%

0 30 60 90 120 150

Cum

ula

tive P

robabili

ty

Added Cost for CCS (Constant 2011 $/MWh)

Mean = $72 /MWh2.5% = $55 /MWh97.5% = $97 /MWh

Deterministic

Value (DV)

0%

20%

40%

60%

80%

100%

0 30 60 90 120 150 180

Cum

ula

tive P

robabili

ty

Cost of CO2 Avoided (Constant 2011 $/ton)

Mean = $94 /ton2.5% = $71 /ton97.5% = $128 /ton

Deterministic

Value (DV)

Cost estimates based on

learning curves



One-Factor Learning (Experience) Curves are the Most Prevalent

where, Ci = cost to produce the i th unit

xi = cumulative capacity thru period i

b = learning rate exponent

a = coefficient (constant)

Model equation:

Fractional cost reduction for a doubling of cumulative capacity (or production) is defined as

the learning rate: LR = 1 – 2b

Ci = a xi –b

20000

10000

5000

1000

10010 100 1000 10000 100000

1982

1987

1963

1980

Windmills (USA)

RD&D

Commercialization

USAJapan

Cumulative MW installed

19811983

500

Photovoltaics

Gas turbines (USA)

1995

1992

200

2000

Source: IIASA, 1996

• Most appropriate for projecting future cost of a current technology already commercially deployed

• Application to novel (pre-commercial) processes requires careful consideration of the “starting point” (cost and experience base) for future cost reductions


Reported Learning Rates for Power Plant Technologies Vary a Lot

Table ES-1: Summary of studies characterizing historical learning rates for electric power

generation technologies.

Technology

Number

of studies

reviewed

Number

of studies

with one

factor

Number

of studies

with two

factors

Range of

learning rates

for “learning by

doing” (LBD)

Range of rates

for “learning by

researching”

(LBR)

Years

covered

across all

studies

Coal* PC 2 2 0 5.6% to 12%

1902-2006

IGCC 1 1 0 2.5% to 7.6%

Projections

Natural Gas* 8 6 2 -11% to 34% 2.38% to 17.7% 1980-1998

Nuclear 4 4 0 <0 to 6%

1975-1993

Wind (on-shore) 35 29 6 -3% to 32% 10% to 26.8% 1980-2010

Solar PV 24 22 2 10% to 53% 10% to 18% 1959-2001

BioPower Biomass production 4 4 0 12% to 45%

1971-2006

Power generation** 7 7 0 0% to 24%

1976-2005

Geothermal power 3 0 0

1980-2005

Hydropower 3 0 2 0.48% to 11.4% 2.63% to 20.6% 1980-2001

*Does not include plants with CCS. **Includes combined heat and power (CHP) and biodigesters.

0

1

2

3

-15% to -9%

-10% to -4%

-5% to -1%

0% to 4%

5% to 9%

10% to 14%

15% to 19%

20% to 24%

25% to 29%

30% to 34%

Fre

qu

en

cy

Learning RateDependent Variable = $/kW (n=9) Dependent Variable = $/kWh (n=2)

Mean = 14%Median = 13%Std Dev = 13%

Natural Gas-Fired Plants

0

2

4

6

8

10

12

-5% to -1%

0% to 4% 5% to 9% 10% to 14%

15% to 19%

20% to 24%

24% to 29%

30% to 34%

Co

un

t

Learning Rate

Histogram of Learning Rates in the Literature

Dependent Variable = $/kWh (n=12) Dependent Variable = $/kW (n=23)

Mean = 16%Median = 12%Std Dev = 14%

On-Shore Wind Plants

Source: Azevedo et al,2013


Effect of Learning Curve Uncertainties

for Power Plants w/ CCS

0

5

10

15

20

25

30

Perc

en

t R

ed

ucti

on

in

CO

E

NGCC PC IGCC Oxyfuel

% COE REDUCTION AFTER

100 GW EXPERIENCE

• Projected reduction in cost of

electricity (COE) for each

plant type varies by a factor

of ~2 to 4

• Nonetheless, the judicious

use of learning curves can

suggest a pathway from

FOAK to NOAK costs for

novel technologies

Source: Rubin et al., 2007

Cost estimates based on

expert elicitations



Common Applications of Expert Judgment

• Estimating the future value of a parameter used in a model or cost calculation

• Direct estimates of the future value of a cost or other characteristic of interest


One of four amine

system parameters

estimated by experts

and used to calculate

expected cost reductions

from advanced solvents

(below)

Example of Expert Elicitations (1)


Absorption

Precombustion

Oxyfuel

Chemical Looping

$/tCO2

density

Scen.3: Minimum cost of CO2 avoided ($/tCO2) across 7 technologies

30 40 50 60 70 80 90 100 110 1200

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Abs=0.20

Ads=0.03

Mem=0.02

Oth=0.04

Pre=0.30

Oxy=0.24

CLC=0.17

2025

Source: Jenni, K.E., Baker, E.D., Nemet, G.F. (2013).

IJGGC, 12, 136-145.

Source: Nemet, G.F., Baker, E., Jenni, K.E. (2013).

Energy, 56, 218–228.

Example of Expert Elicitations (2)

Elicitations by Nemet et al. of future energy penalty and avoidance cost

for seven capture technologies as a function of three policy scenarios


How does expert judgment compare to costs projected using learning rates ?

950

1,000

1,050

1,100

1,150

1,200

2015 2020 2025 2030 2035 2040 2045 2050

Co

st ($

/kW

)

NGCC w/o CCS

Original EPRI

Base Case

ALT 1 - CES

ALT 2 - CTAX20

ALT Base Case

4,600

4,700

4,800

4,900

5,000

5,100

5,200

5,300

5,400

2015 2020 2025 2030 2035 2040 2045 2050

Co

st ($

/kW

)

Nuclear

Original EPRI

Base Case

ALT 1 - CES

ALT 2 - CTAX20

ALT Base Case

0

500

1,000

1,500

2,000

2,500

2015 2020 2025 2030 2035 2040 2045 2050

Co

st ($

/kW

)

On Shore Wind

Original EPRI

Base Case

ALT 1 - CES

ALT 2 - CTAX20

ALT Base Case

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

2015 2020 2025 2030 2035 2040 2045 2050

Co

st ($

/kW

)

Solar PV (Rooftop)

Original EPRI

Base Case

ALT 1 - CES

ALT 2 - CTAX20

ALT Base Case

EXPERTS

EXPERTS

EXPERTS

EXPERTS

Conclusion:

• Expert judgment is often inconsistent with learning curve projections

Source: Azevedo et al,2013

Don’t ask, don’t tell



Avoid Cost Estimates for Processes at Early Stages of Development

• This approach favors the use of performance metrics, rather that cost estimates, to evaluate and screen novel components or process designs in the early stages of development (e.g., TRL levels 1-3 or higher)

• For novel CO2 capture processes, projected reductions in the power plant energy penalty is a favored metric

• No guarantee, however, that improvements in key performance metrics will necessarily result in lower overall cost (e.g., COE) for the novel system

So where does this leave us ?



Conclusions and Recommendations

Conclusions

• A variety of methods are available to estimate the future

cost of new, pre-commercial capture systems; but …

• Some methods are not commonly used; others are often

used inappropriately; no clear consistency across method

Recommendation

• Establish a task force to develop improved guidelines,

emphasizing needs for transparency and incorporation

of uncertainties in cost estimates for novel processes


A Final Word of Wisdom

“It’s tough to make predictions,

especially about the future”

- Yogi Berra

Future

CCS

Costs


Thank You

[email protected]

estimating the cost of novel (pre-commercial) systems for co2 capture - ed rubin, carnegie-mellon...

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