Learning-by-doing and the Photovoltaics case
Resources for the Future workshop on
“Learning-by-Doing in Energy Technologies”
June 17-18, 2003 in Washington, D.C.
Richard D. Duke
• PV technology and markets
• The PV experience curve
• Benefit-cost analysis of buydowns
• Buydown implementation strategies
• Prioritizing scarce buydown funds
• APPENDIX: Buydown economics
CRYSTALLINE STILL DOMINATES THE 450 MWp PV MARKET IN 2002
multicrystalline
46%
thin-film
7.9%
ribbon crystalline
3.7%
monocrystalline
43%
Source: Strategies Unlimited (2003). Note that Heterojunction with Intrinsic Thin layer (HIT) modules, produced by
Sanyo, are composed of a monocrystalline cell surrounded by thin amorphous-silicon layers. Total shipments for
these modules are split evenly between thin-film and monocrystalline categories. HIT modules offer the highest
solar conversion efficiency available for any terrestrial PV technology (19.5% cell efficiency).
SUBSIDIZED GRID-CONNECTED MARKETS ARE BECOMING DOMINANT
500
450
Other subsidized grid plus off-grid commercial sales
Subsidized residential PV in Germany
Subsidized residential grid PV in Japan
400
350
MWp
300
250
200
150
100
50
0
1993
1994
1995
1996
1997
1998
1999
2000
Source: Berger (2001); Weiss and Sprau (2001); Hirschman and Takano (2003); Krampitz and Schmela (2003)
2001
2002
JAPANESE RESIDENTIAL PROGRAM HAS DRIVEN SYSTEM COSTS DOWN
$35
2002 US$ per Wp
$30
Installation
Balance of systems
Modules
$25
$20
$15
$10
$0.69
$1.48
$5
$3.97
$0
1993
1994
1995
1996
1997
1998
1999
Source: Kurokawa and Ikki (2001); NEF (2001); Hirshman and Takano (2003)
2000
2001
2002
GERMAN RESIDENTIAL PV PROGRAM HAS HAD MIXED PRICE RESULTS
Modules
Balance of systems
Installation
$8
2002 US$ per Wp
$7
$0.69
$6
$5
$1.43
$0.55
$0.54
$1.26
$1.30
$0.40
$1.08
$4
$3
$2
$4.75
$5.10
1999
2000
$5.63
$4.81
$1
$0
Source: Krampitz and Schmela (2003)
2001
2002
• PV technology and markets
• The PV experience curve
• Benefit-cost analysis of buydowns
• Buydown implementation strategies
• Prioritizing scarce buydown funds
• APPENDIX: Buydown economics
REMARKABLY CONSISTENT ALL-PV EXPERIENCE CURVE
$100
Wholesale price (2002$/Wp)
1976
PR=0.80
R2 = 0.99
$10
2002
$1
0
1
10
100
cumulative PV production (GWp)
Source: Johnson (2002) and Dunay (2003)
1,000
10,000
SPURIOUS MICROSTRUCTURE IN PV EXPERIENCE CURVE
100
Price (USD(2000)/Wp)
Nitsch 1976-84
PR=.84
Nitsch 1984-87
PR=.53
10
Nitsch 1976-96
PR=.80
Nitsch 1987-96
PR=.79
Harmon (PR=.80)
Strategies Unlimited (PR=.80)
1
0.1
1
10
100
Cumulative Sales (MW)
1000
10000
LEARNING IS THE PRIMARY DRIVER FOR THE PV EXPERIENCE CURVE
• Input prices may vary, but:
– Random price shocks do not impact long-term buydown economics
– Buydowns will generally drive long-term input prices lower (e.g. new dedicated PV-grade
silicon factories)
• Scale economies are crucial, but:
– Learning-by-doing is integral to the process of scaling up manufacturing facilities and
markets
– Available econometric evidence suggests learning effects dominate scale impacts for PV
• Supply-push research and development efforts matter, but:
– Learning-by-doing becomes increasingly important as products move from the lab to fullscale deployment
– Private research and development is often funded in proportion to sales levels
– Econometric efforts to distinguish supply-push from learning effects remain unsatisfactory
(e.g. adding time as an additional independent variable does not substantially improve the
model fit)
• PV technology and markets
• The PV experience curve
• Benefit-cost analysis of buydowns
• Buydown implementation strategies
• Prioritizing scarce buydown funds
• APPENDIX: Buydown economics
THE CONVENTIONAL BREAK-EVEN METHOD HAS SERIOUS FLAWS
• Does not account for sales in niche markets under the no-subsidy scenario
• Does not allow estimation of the optimal sales path under buydown
THE OPTIMAL PATH METHOD
Faster
P↓ w/
buydown
• Computationally determine the optimal buydown subsidy/output path to maximize NPV
Pt derived from experience curve given cumulative production at time t
– Demand schedule derived from empirical estimation
• Advantages
– estimation of transfer subsidies
– realistic modeling of sales growth under the NSS
–
OUTWARD SHIFTS IN PV DEMAND UNDER THE OPTIMAL PATH METHOD
$5.00
break-even value of modules ($/Wp)
$4.50
$4.00
$3.50
$3.00
$2.50
$2.00
X(50)=(25/(1+exp(-0.09*50+2))*P^-2
$1.50
X(10)=(25/(1+exp(-0.09*10+2))*P^-2
$1.00
$0.50
X(1)=(25/(1+exp(-0.09*2+2))*P^-2
$0.00
0
2
4
6
8
10
12
GWp per year
14
16
18
20
This figure compares the logistically shifting all-market PV demand schedule used in the optimal path method analysis with the
annual OECD residential PV demand schedule (i.e. the breakeven schedule for PV demand in new U.S. homes increased by roughly
a factor of 4 to account for retrofits as well as markets in Europe and Japan). In the first year of the analysis (2003) annual demand
falls short of the breakeven schedule, but the two schedules overlap by year 10 after markets have matured. After 50 years, demand
shifts out by another factor of four due to growth in commercial buildings and developing country markets. At the price floor of
$0.50/Wp, this yields a mature sales rate of 100 GWp/y. Even in the first year, the all-market demand schedule exceeds the OECD
residential PV schedule for sales levels below 1 GWp/y because it includes off-grid PV markets for which the willingness to pay is
much greater than for distributed grid-connected PV. Similarly, the all-market demand schedule extends beyond 10 GWp/y (at
which point the OECD residential PV demand schedule tapers off) because other markets open up at these low prices—including
commercial buildings and a full range of grid-connected applications in developing countries.
SNAPSHOT OF THE FIRST YEAR OF AN OPTIMAL PV BUYDOWN
Year One
$10
quantity demanded
w/o
year-one subsidy
$9
quantity
demanded w/
year-one subsidy
$8
$7
$/Wp
$6
$5
consumer
surplus
current price
$4
$3
free riders
$2
minimum possible subsidy
cost
failure to price
discriminate
TMC
$1
$0
0
0.2
0.4
0.6
0.8
1
1.2
GWp/y
• True marginal cost (TMC) exceeds the $0.50/Wp price floor since r > 0
• Transfer subsidies are modest
1.4
SNAPSHOT OF THE TWENTIETH YEAR OF AN OPTIMAL PV BUYDOWN
Year 20
$2.0
quantity
demanded w/o
year-20 subsidy
$1.5
quantity
demanded w/
year-20
subsidy
consumer
surplus
$/Wp
current price
$1.0
minimum possible subsidy
cost
failure to price
discriminate
free riders
TMC
$0.5
$0.0
0
5
10
15
GWp/y
• TMC approaching the $0.50/Wp price floor
• Transfer subsidies are potentially more severe
20
25
30
APPLYING THE OPTIMAL
PATH METHOD TO
GLOBAL PV MARKETS
• Optimal path requires tripling
$ billions per year (r=0.05)
$4
$3
$2
$1
$0
0
10
20
30
current PV subsidies and
sustaining support for 43 years
50
60
70
80
90
100
70
80
90
100
70
80
buydown year
minimum subsidies + transfer subsidies
minimum subsidies
100
90
80
GWp per year
• $235 buydown benefits
- $144 NSS benefits
- $37 minimum possible cost
$54 billion NPV (r = 0.5)
40
70
60
50
40
30
20
10
0
• PV provides 5% of OECD
electricity by 2030 vs. <1% under
NSS
0
10
20
30
40
50
60
buydown year
buydow n
• Excludes environmental benefits
NSS
$5
• Annual cost < 0.5% of OECD
electricity expenditures
$/Wp
$4
$3
$2
$1
$0
0
10
20
30
40
50
60
buydown year
buydown price
buydown net price
NSS price
90
100
SENSITIVITY OF THE NPV ESTIMATES FOR AN OPTIMAL PV BUYDOWN
• Transfer subsidies valuation is controversial
– NPV is $54 billion with marginal excess burden estimate of zero
– NPV drops to $44 billion assuming MEB = $0.33 but no transfer subsidies
– NPV drops to $27 billion assuming MEB = $0.33 and government has no ability to reduce
transfer subsidies by 1) excluding free riders or using price discrimination when doling out
subsidies; 2) re-optimizing the buydown to account for the MEB of transfer subsidies
• Minimizing transfer subsidies is therefore crucial if MEB > 0 and/or buydown funding is
constrained by politics rather than benefit-cost criteria
• Progress ratio and discount rate assumptions are also crucial
PV BUYDOWNS MITIGATE MULTIPLE MARKET FAILURES
• Current prices do not reflect the full value of PV electricity
– A $120/tC tax would increase the cost of natural gas electricity by $0.01/kWh and coal
electricity by $0.03/kWh
– Local air pollution externalities (primarily health impacts from particulate emissions) range
from about $0.02/kWh in California (natural gas and hydro) to $0.13/kWh for coal-fired
Midwestern electricity
– PV electricity reduces peak demand, alleviates strain on the transmission and distribution
system, and reduces price volatility
• Demand-side market failures from cognitive biases/limitations
– Consumers tend to under-invest in highly cost-effective energy efficiency technologies
possibly due to the high cost of processing information about the available alternatives
– Risk-averse homeowners/businesses may worry about individual system performance
variability
• Supply-side market failures from spillover of learning-by-doing
– Manufacturing spillovers
– System spillovers
• PV technology and markets
• The PV experience curve
• Benefit-cost analysis of buydowns
• Buydown implementation strategies
• Prioritizing scarce buydown funds
• APPENDIX: Buydown economics
PROMISING BUYDOWN IMPLEMENTATION STRATEGIES
• Loosely coordinated regional buydowns
– Reduce transfer subsidies by allowing better market segmentation to reduce free riders
and allow “price discrimination” when distributing subsidies
– Bypass the collective action problem
– Reduce the risk to manufacturers of a global market slow down
– Foster learning-by-buying down
• Quantity mandates (e.g. renewable portfolio standard with a PV tranche)
– Reduce information burden
• Fair-share of global PV buydown
• Subsidy cap contains risk to obligated parties
– Well-suited to long-term optimal buydown
• PV technology and markets
• The PV experience curve
• Benefit-cost analysis of buydowns
• Buydown implementation strategies
• Prioritizing scarce buydown funds
• APPENDIX: Buydown economics
RATIONALE FOR LIMITING SUPPORT TO THE CLEAN ENERGY SECTOR
• Non-learning public benefits
• Slow diffusion rates
• Severe system spillovers
• Strong experience curve effects in some important cases
• Benefits are easy to estimate based on the market price of
incumbent substitutes
• Energy is a commodity with thin profit margins making forwardpricing particularly difficult
CRITERIA FOR SELECTING SPECIFIC CLEAN ENERGY TECHNOLOGIES
• Technology picking is essential for buydowns
– Buydowns are too expensive to permit a “shotgun” approach
– There is relatively good information available at the deployment stage when buydowns are
relevant
• Governments should restrict buydowns to clean energy technologies
characterized by:
1) Strong non-learning public benefits
2) A competitive (high spillover) industry
3) Strong experience curves and low expected price floors
4) Slow current sales relative to a large long-term market potential
5) The technology is at least as promising as foreseeable substitutes
PV IS AT LEAST AS PROMISING AS FORESEEABLE SUBSTITUTES
• Low risk from incumbent or emerging substitutes for PV
– EIA predicts nearly flat U.S. residential electricity rates through 2020
– Fuel cells not a major threat
• Unlikely to compete well in individual residences
• Struggling to get past demonstration phase
• Fuel cell vehicles plugged in at work could reduce value of commercial PV but concept
unproven and residential markets are the focus of this analysis
– Advanced energy efficiency would still leave most homes needing 4 kWp PV systems
– Other renewables not a serious threat for the distributed residential market
• The optimal path method would need to be modified to consider buydown
candidates with overlapping markets (e.g. advanced biomass and wind electricity)
– Must analyze both/all simultaneously
– Demand for each defined as total market potential minus sales of the other
– Conduct joint optimization to determine simultaneously the buydown subsidy/output path for
each that maximizes total NPV
• Increasing returns to adoption likely to suggest buying down one or the other
• If markets only partially overlap or government wants to diversify buydown performance
risk then simultaneous buydowns could be optimal
• PV technology and markets
• The PV experience curve
• Benefit-cost analysis of buydowns
• Buydown implementation strategies
• Prioritizing scarce buydown funds
• APPENDIX: Buydown economics
THE TECHNOLOGY POLICY TRIAD
MYOPIC MONOPOLIST CASE
[firm’s r = infinity, social r = 0, zero spillover]
Learning/experience
Experience curvecurve
True
Marginal
Cost
• True marginal cost (TMC) = cost floor (since must produce initial high cost units sooner or later)
• Current profit shown by the lightly-shaded box
• Welfare loss equals the darkly-shaded triangle (TMC would be higher and the welfare loss
would be lower if the social discount rate exceeded zero)
FORWARD-PRICING MONOPOLIST CASE
[firm’s r = 0, social r = 0, zero spillover]
Learning/experience
Experience curvecurve
True
Marginal
Cost
• Forward-pricing monopolist suffers current loss (light shading) but maximizes profit over
technology lifecycle by reducing costs more quickly along its experience curve
• Output closer to optimum than with myopic monopolist, but monopoly welfare losses remain
ECONOMIC RATIONALE FOR BUYDOWNS
[Perfect spillover/competition, r = 0]
Learning/experience
curve
Experience curve
True
Marginal
Cost
• Perfect spillover implies:
– Perfect competition is possible
– Price = unit cost (including a fixed competitive profit margin)
– There is no welfare loss from market power but there is a welfare loss from the failure of
firm’s to forward-price
• High spillover is common and buydowns can eliminate the associated welfare losses
MARGINAL EXCESS BURDEN OF TAXATION (MEB)
• Traditional literature [Pigou (1928); Harberger (1964); Browning (1976)]
– Social costs of public spending include welfare loss from distortionary taxes
– MEB estimates range as high as $1.65, i.e. projects need BCR > 2.65 (Feldstein, 1997)
– Parry (1999) surveys literature and provides central estimate of MEB = $0.33
• Recent revolution
– Kaplow (1996) shows MEB = 0 a better default estimate
• MEB exactly zero if finance a public good with income tax adjustments that offset the
benefits of the public good (e.g. progressive taxes used to finance environmental
improvements that the rich value more than the poor)
• MEB > 0 implies taxes and associated spending reduce regressivity and vice-versa
• Must consider the combined social value of the efficiency and distributional impacts
– Kaplow (1998) offers withering response to critique by Browning and Liu (1998)
– Ng (2000) argues MEB < 0 in some cases, e.g. due to benefits of pollution taxes
– Slemrod and Yitzhaki (2001) agrees that both distributional and distortionary impacts are
important and “there is a tendency for more progressive tax schemes…to be more
distorting” but points out that there are exceptions