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Technology Learning Curves and the Future Cost of Electric Power Generation Technology
Ines Azevedo,a Paulina Jaramillio,a Ed Rubina and Sonia Yehb
a Dep’t of Engineering and Public Policy, Carnegie Mellon University b Institute of Transportation Studies, University of California, Davis
Presentation to the EPRI
18th Annual Energy and Climate Change Research Seminar
Washington, DC
May 22, 2013
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Background
• EPRI has developed the REGEN model to assess the technical, economic, and environmental impacts of U.S. energy supply options and policies
• Assumptions about the future cost of energy supply technologies are critical to model projections; at the present time EPRI uses exogenous specifications of technology-specific capital and O&M costs over time.
• Various types of “learning curves” (experience curves) also have been proposed to relate future technology costs to key parameters such as installed power plant capacity and other factors
• However, there has been little systematic study of how alternative cost projection methods and models affect the outcomes of large-scale energy-economic models
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Study Objectives
• Conduct a literature review to characterize the current state of technology learning models for different types of electric power plants
• Review selected large-scale computer models that incorporate endogenous technology learning to draw insights about effects on model results
• Suggest preliminary computer experiments in REGEN to study the impacts of alternative cost projections (based on learning models)
• Provide recommendations for future testing and representation of technological change in REGEN
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Technologies of Interest
• PC plants
• PC with CCS
• IGCC plants
• IGCC with CCS
• NGCC plants
• NGCC with CCS
• NG turbines
• Biomass plants
• Nuclear
• Hydroelectric
• Geothermal
• On-shore wind
• Off-shore wind
• Solar PV
• Conc. solar thermal
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Draft Report Under Review
TABLE OF CONTENTS
1 Executive Summary
2 Introduction
3 Theory of Technological Change
4 Review of Learning Curves for Power Generation Technologies
5 Endogenous Technological Learning in Energy Models
6 Summary and Conclusions
7 Acknowledgements
8 References
9 Appendices A – D
I will present a few highlights from our report
Theory of technological change
and learning rate results
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Theory of Technological Change
Key drivers of cost reduction include:
• Diffusion/adoption of technology
• Research and development (R&D)
• “Cluster” learning
• “Spillover” effects
• Policies that promote the above
Various types of quantitative models have been proposed to account for these effects
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One-Factor Learning Curves are the Most Prevalent
where, Ci = cost to produce the i th unit
xi = cumulative production or capacity thru period i
b = learning rate exponent
a = coefficient (constant)
General equation:
- Fractional cost reduction for a doubling of cumulative production is defined as the learning rate: LR = 1 – 2b
Ci = a xi –b
- Some studies report the progress ratio: PR = 1 – LR
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Examples of One-Factor Learning (Experience) Curves—Wind Farms
Source: Junginger 2005
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0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
22%
24%
26%
1980 1985 1990 1995 2000 2005
Lear
nin
g R
ate
Period Covered
Region: Europe. Dependent Variable: $/kW
McDonald 2001 Junginger 2005 Neij 2003 IEA 2000
Spain
UK
Spain
UK
Spain
Spain
Denmark
Spain & Denmark
Denmark Germany
Germany
Denmark Sweden
Examples of reported learning rates for
wind turbines based on
$/kW
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-5%
-3%
0%
3%
5%
8%
10%
13%
15%
18%
20%
23%
25%
28%
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
Lear
nin
g R
ate
Period Covered
Region: Europe. Dependent Variable: $/kWh
Ibenholt 2002 IEA 2000 Neij 2003
EU
Germany
UK
Denmark
Germany
0%
5%
10%
15%
20%
25%
30%
35%
1975 1980 1985 1990 1995 2000 2005 2010
Lear
nig
n R
ate
Period Covered
Region: Multiple. Dependent Variable: $/kWh
IEA 2000 McDonald 2001 Qui 2012
US
California
China
Examples of reported learning rates for
wind turbines based on $/kWh
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Histogram of Reported Learning Rates for On-Shore Wind Turbines
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% Larger than 30%
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%
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Learning Rates for Solar PV
10%
15%
20%
25%
30%
35%
40%
1950 1960 1970 1980 1990 2000 2010
Le
arn
ing
Ra
te
Year
Harmon, 2000
Maycock, 1975
IEA, 2000
IEA, 2000
IEA, 2000
IEA, 2000
IEA, 2000
Harmon, 2000
Miketa and Schrattenholzer, 2004
Tsuchiya (1992)
Cody and Tiedje (1997)
Williams and Terzian (1993)
Parente et al. (2002)
OECD/IEA (2000)
Schaeffer et al. (2004)
Schaeffer et al. (2004)
Schaeffer et al. (2004)
Schaeffer et al. (2004)
Schaeffer et al. (2004)
Maycock (2002), referred to in Nemet (2006)
Strategies Unlimited (2003), referred to in Schaeffer et al. (2004)
Strategies Unlimited (2003), referred to in Schaeffer et al. (2004)
Watanabe, 1999
EU
unknown
unknownUS Germanyglobal
EUJapan
JapanEU
unknownglobal
unknown globalglobal
EU
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Histogram for Solar PV
0
2
4
6
8
10
12
5 10 15 20 25 30 35 40
Nu
mb
er
of
stu
die
s
Learning Rate Intervals (%)
Mean = 22%
Median = 21%
Std Dev = 10%
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Examples of One-Factor Learning Curves for Power Plant Components
y = 515.00x-0.08
PR = 0.95
100
1000
0 1 10 100 1000 10000
Cumulative World Pulverized-Coal Plant Installed Capacity (GW)
Su
bcrit
ica
l P
C U
nit
Co
st (
19
94
$/k
W)
1942, EF=29.9%
1965
1999, US DOE
EF=37.6%Pulverized
Coal-Fired
Boilers
y = 94254x-0.157
R2 = 0.43
10000
100000
1000 10000 100000
Cumulative Oxygen Production since 1980
(Billion cubic feet)
Real cap
ital co
st
(US
$2003/t
pd
)
Oxygen Production
1968
1972
1974
1975
19761980
1982
1990
1995
0
50
100
150
200
250
300
0 10 20 30 40 50 60 70 80 90 100
Cumulative World Wet FGD Installed
Capacity (GW)
Cap
ital
Cos
ts ($
/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%)
Flue Gas
Desulfurization
Systems
NGCC Systems
Gas Turbine
Combined Cycle
Sources: Rubin et al. 2007; Colpier 2002
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0%
5%
10%
15%
20%
25%
1955 1960 1965 1970 1975 1980 1985 1990 1995
Lear
nin
g R
ate
Period Covered
Natural Gas Turbine. Dependent Variable: $/kW
MacGregor 1991
World
World
World
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
35%
40%
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998
Lear
nin
g R
ate
Period Covered
NGCC/GTCC. Dependent Variable: $/kW
Colpier 2002 Kouvaritakis 2000 IEA 2000
World
OECD
EU & NAFTA
EU & US
Examples of reported capital
cost learning rates for natural gas-fired plants
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Reported Cost Trends for U.S. and French Nuclear Plants
Source: Grubler, 2010
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Two-Factor Learning Curves
Model form: Ci = ai (xi –bLBD) (RDi
–bLBR)
where:
Ci = unit cost of technology
xi = cumulative adoption of technology i
RDi = cumulative R&D investment or knowledge stock for i
bLBD = learning-by-doing parameter
bLBR = learning-by-researching parameter
ai = unit cost at unit cumulative capacity and knowledge stock for I
These models suggest that R&D expenditures
contribute significantly to cost reductions; but …
Data limitations have limited the practical
applications of this two-factor model
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Range of Technology Learning Rates from the Literature Review
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 0.65% to 5.3% 2.4% to 17.7% 1980-1998
Nuclear 4 4 0 0 <0% to 6% 1975-1993
Wind (on-shore) 35 29 6 -3% to 32% 10% to 23.8% 1980-2010
Solar PV 23 22 1 10% to 53% 10% 1959-2001
BioPower
Biomass production 4 4 0 12% to 45% 1971-2006
BioPower generation 7 7 0 0% to 24% 1976-2005
Geothermal power 3 0 0 1980-2005
Hydropower 3 0 2 0.5% to 11.4% 2.6% to 20.6% 1980-2001
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Conclusions about Learning Rates
• Historical experience indicates that the real cost of most power generation technologies has declined over time.
• Most analytical models of such “learning” relate changes in the unit capital cost of a technology to cumulative installed capacity in a region (accounting for assumed “spillover” effects). Some models relate the unit cost of generation to cumulative electricity production.
• Our literature review reveals a wide range in the learning rates from these “one-factor” models. In general we found:
Largest rates are for renewable energy sources (esp. wind and PV)
Smaller learning rates for fossil fuel plant types
Mostly negative rates for existing nuclear plants
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Learning Rate Conclusions (con’t.)
• More complex models also have been proposed to explain the “learning” phenomenon in terms of additional factors, such as expenditures on R&D
• Alternative formulations of one-factor models also have been proposed to more realistically model the “shape” of historical experience for some power plant technologies (e.g., an S-shaped learning curve).
• In general, data limitations severely limit the ability to test and validate alternative models except in limited situations
• Given the large uncertainties, energy-economic models used for planning and policy analysis should explore a wide range of cost projection models to seek robust conclusions
Endogenous learning in
large-scale energy models
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We Prepared Brief Reviews of Seven Energy-Economic Models
Model MESSAGE
Modeling Type Optimization
Geographic Scope Global
Data Sources Rao, Keppo, and Riahi (Rao et al. 2006)
Type of learning Default is exogenous (AEEI). Endogenous learning - single factor and constant learning rate is applied in some
studies
Technology
representation/details
A total of 18 technologies are assumed to have ETL. Learning rates range from 0-15%. Exogenous learning rates
of 3-5% are assumed according to the B2 scenario for the other technologies.
Cluster learning
Spillover across tech. ‘technology clusters’ has been applied in several modeling approaches (Seebregts et al.
(Seebregts et al. 2000); Riahi et al. (Riahi et al. 2005)). Technological spillovers can occur within a cluster (for
example: carbon capture technologies, centralized and decentralized solar PV) but not from outside the cluster
(for example: improvements in the semi-conductor industry).
Spillover
Spillover across regions. The learning process for technology improvements is assumed to take place on a global
scale. Although this might not necessarily be consistent with the existence of trade barriers, regional economic
blocks or the importance of localized learning
MACRO
MESSAGE and MACRO are linked iteratively to include the impact of policies on energy costs, GDP and on
energy demand. MACRO, a top-down macroeconomic equilibrium model captures capital stock, available labor,
and energy inputs determine the total output of an economy according to a nested constant elasticity of
substitution (CES) production function. The linking of a bottom-up technology-rich model and a top-down
macroeconomic model results in a fully consistent evolution of energy demand quantities, prices, and
macroeconomic indicators (such as GDP, investments and savings).
Key insights
1. The existence of technological learning while reducing overall energy system costs becomes particularly
important in the context of a long-term climate policy. 2. Spillovers across technologies and regions due to
learning results in increased upfront investments and hence lower costs of carbon free technologies, thus resulting
in technology deployment and emissions reductions, especially in developing countries.3. Learning and spillover
effects can lead to technologically advanced cost-effective global energy transition pathways. 4. Earlier studies
using the MESSAGE model (Roehrl and Riahi (Roehrl & Riahi 2000); Nakicenovic and Riahi (Nakicenovic &
Riahi 2001)) have shown that alternative parameterizations of technological change have significant implications
for the technology portfolio as well as associated costs.
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… plus a summary of
an IPCC review of
global top-down models
with endogenous
learning (17 studies)
Study Model ETC
channel
Number of
production
sectors
Number
of
regions
Major results
(impact of ETC) Comments
Focus of
analysis
Bosetti et al., 2006
FEEM-RICE
LBD
1 8 An index of energy
technological change
increases elasticity of
substitution. Learning-by-
doing in abatement and
R&D investments raise the
index. Energy technological
change explicitly decreases
carbon intensity.
Experimental model
exploring high inertia.
Crassous et al., 2006
IMACLIM-R
GCE
R&D and LBD
1 5 Cumulative investments
drive energy efficiency.
Fuel prices drive energy
efficiency in transportation
and residential sector.
Learning curves for energy
technologies (electricity
generation).
Endogenous labour
productivity, capital deepening.
Edenhofer et al., 2006
MIND Optimal growth
LBD
1 1 R&D investments improve
energy efficiency. Factor
substitution in a constant-
elasticity-of-substitution
(CES) production function.
Carbon-free energy from
backstop technologies
(renewables) and CCS.
Learning-by-doing for
renewable energy. R&D
investments in labour
productivity. Learning-by-
doing in resource extraction
Gerlagh, 2006
DEMETER-1 CCS
LBD
1 1 Factor substitution in CES
production. Carbon-free
energy from renewables and
CCS. Learning-by-doing for
both and for fossil fuels.
Masui et al.,
2006
AIM/Dy
namic - Global
R&D
9 6 Factor substitution in CES
production. Investments in
energy conservation capital
increase energy efficiency
for coal, oil, gas and
electricity. Carbon-free
energy from backstop
technology
(nuclear/renewables).
Focus on
energy efficiency with limited supply-side substitution.
Popp, 2006
ENTICE-BR
R&D
1 1 Factor substitution in Cobb-
Douglas production. R&D
investments in energy
efficiency knowledge stock.
Carbon-free energy from
generic backstop
technology
R&D investments lower price of energy from backstop
technology.
Rao et al., 2006
MESSAGE/MACRO CGE
LBD
1 11 Carbon-free energy from
backstop technologies
(renewables, carbon
scrubbing & sequestration).
Learning curves for
electricity generation and
renewable hydrogen
production
Factor substitution in CES production in MACRO.
Barker et
al., 2006
E3MG,
econometric
LBD and
R&D
41 20 Cumulative investments and
R&D spending determine
energy demand via a
technology index. Learning
Econometri
c model. Investments beyond
Long-term
costs of stabilization
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Endogenous Learning Rates (%) in Several Bottom-Up Energy Models
Technology
(a) One-factor learning curves (b) Two-factor learning curves
ERIS MARKAL
MERGE
-ETL MESSAGE ERIS MERGE-ETL
Learning Mode: LBD LBR LBD LBR
Advanced coal 5 6 6 7 11 5 6 4
NG combined cycle 10 11 11 15 24 2 11 1
New nuclear 5 4 4 7 4 2 4 2
Fuel cell 18 13 19 - 19 11 19 11
Wind power 8 11 12 15 16 7 12 6
Solar PV 18 19 19 28 25 10 19 10
Source: IPCC (2007) LBD= learning by doing; LBR= learning by researching
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Learning Rates for New Generation Components in NEMS
Source: EIA (2012)
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Conclusions from Energy Models with Endogenous Learning
• Endogenous technological learning tends to reduce overall energy system costs and becomes particularly important in the context of a long-term climate policy
• Endogenous learning results in increased upfront investment costs, but lowers the overall costs of carbon-free technologies, resulting in greater technology deployment and emissions reductions, especially in developing countries.
• Spillovers across technologies and regions can lead to more cost-effective global energy transition pathways.
• Alternative parameterizations of technological change can have significant implications for the technology portfolio as well as associated costs.
Remaining tasks
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Work in Progress
• Finalize the literature review (Phase I) report
• Analyze sample cost trajectories and overall results from REGEN
• Suggest alternative cost trajectories based on learning models, and assess their impact on key results from REGEN
• Prepare a brief Phase II report including recommendations for future work
