r. m. margolis, hdgc seminar, oct. 16, 2002, page 1 experience curves and photovoltaic technology...

R. M. Margolis, HDGC Seminar, Oct. 16, 2002, page 1

Experience Curves and Photovoltaic Technology Policy

Robert M. Margolis

Human Dimensions of Global Change Seminar

Carnegie Mellon University

October 16, 2002


Outline• History/Origins of Experience Curves• Background on PV• Application to Solar PV• Thinking Prospectively Using Experience Curves• Concluding Thoughts


Origins of the Learning Curve• The “learning curve” describes how marginal

labor cost declines with cumulative production (for a given manufactured good and firm).– Wright’s 1936 study of airplane manufacturing found

that the number of hours required to produce an airframe (an airplane body with out engines) was a decreasing function of cumulative airframes, of a particular type, produced.

– Learning curves reflect a process of learning-by-doing or learning-by-producing within a factory setting.


Origins of the Experience Curve• The “experience curve” generalizes the labor

productivity learning curve to include all the cost necessary to research, develop, produce and market a given product.– Boston Consulting Group’s 1968 study across a range

of industries.


Boston Consulting Group’s 1968 Study – Empirically BCG found that, “costs appear to go down

on value added at about 20 to 30% every time total product experience doubles for the industry as a whole, as well as for individual producers.”

– In BCG analysis cost included, “all costs of every kind required to deliver the product to the ultimate user, including the cost of intangibles which affect perceived value. There is no question that R&D, sales expense, advertising, overhead, and everything else are included ” (p. 12).


The General Form of the Experience Curve is the Power Curve

• P(t) = P(0) x [q(t)/q(0)]^-bWhere:

P(t) = average price of a product at time t

q(t) = cumulative production at time t

b = learning coefficient

• PR = 2^-bWhere:

PR = progress ratio. For each doubling of cumulative production the MC decreases by (1-PR) percent.


Illustrative Learning for Three Progress Ratios

$0

$10

$20

$30

$40

$50

$60

$70

$80

$90

$100

0 50 100 150 200 250 300 350

Cumulative Units Produced

Ave

rage

Pri

ce (

per

unit

)

PR = 90%

PR = 80%

PR = 70%

$1

$10

$100

1 10 100 1000

Cumulative Units ProducedA

vera

ge P

rice

(pe

r un

it)

PR = 90%

PR = 80%

PR = 70%

Where: P(0) = 100

q(0) = 1


Example from BCG Study: Silicon Transistors, 1954-1969

Key Milestones:1947: 1st Demonstration (Bell Labs)1954: 1st Commercial Production (TI)1960s: Expansion into Televisions (Sony)

0.01

0.1

1.

10.

100.

0.01 0.1 1. 10. 100. 1,000. 10,000.

Cumulative Units Produced (million)

Ave

rage

Rev

enue

per

Uni

t (c

onst

ant

$)

PR = 90%

PR = 80%

PR = 70%


Why Might Marginal Cost of Production Decline?

• Changes in production– process innovations, learning effects and economies of

scale.

• Changes in the product itself– product innovations, product redesign, and product

standardization.

• Changes in input prices Experience curves typically aggregate all of these

factors.


Distribution of Progress Ratios 22 Field Studies (Dutton and Thomas 1984)

0

2

4

6

8

10

12

14

50 60 70 80 90 100

Progress Ratio

Fre

quen

cy

Note: These progressratios are firm level (notindustry wide) studies.


Distribution of Energy Progress Ratios(McDonald and Schrattenholzer 2001)

0

1

2

3

4

5

6

7

60-65 70-75 80-85 90-95 100-105 110-115

Progress Ratio

Fre

quen

cy


Background on PV• PV technology overview

– Types of cells

• PV market overview– Historical Production

– Shifting Market Segmentation

– PV Module Historical Experience

– An Industry Projection


Two Main Classes of PV Cells• Mono/Polycrystalline Crystalline Silicon Cells

– Industry standard

– Multiple cells assembled into a module

– Accounted for > 80% production during the 1990s

• Thin-film Solar Cells– Main contenders include: Amorphous Silicon,Cadmium

Telluride (CdTe), and Copper Indium Diselenide (CIS)

– Monolithic design

– Emerging technologies

* A Single Learning Curve?


Global PV Cell/Module Production

0

40

80

120

160

200

240

280

320

360

400

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

Year

PV

Cel

l/M

odu

le P

rod

ucti

on (

MW

)

0%

10%

20%

30%

40%

50%

60%

70%

80%

ROW

Europe

Japan

U.S.

U.S. Share

5 Year Average Annual

Growht Rate(1996-2001)

= 35%

Note: ROW = Rest of World


Shifting PV Market Segments

• Industrial (off-grid)– Telcom, Instrumentation,

Warning Signals, etc.

• Rural (off-grid)– Rural Solar Home Systems,

Water Pumping, etc.

• Consumer– Watches, Calculators, etc.

• Grid-connected– Rooftop, Building

Integrated, Central Generation.

Market Segment

1994 1997

Industrial 38% 29% Rural 28% 27% Consumer 14% 8% Grid-connected 20% 36% Source: Strategies Unlimited 1998, p. 14.

Notes: Production grew between 1994 and 1997 from 58 to 110 MW. In 2000 grid-connected systems accounted for approximately 50% of the 300MW market.


Example Rural PV Applications

Solar Home System (China)

Solar Home System

(Honduras)

PV Water Pumping(Tanzania)


Example Grid-Connected Applications

BP Service Station (Spain)

Sanyo Building with PV

Façade (Japan)

House withPV IntegratedRoof (Japan)


Historical Experience for PV Modules, 1976-1998(Data from Various Sources)

1

10

100

0.1 1 10 100 1000

Cummulative Production (MWp)

PV

Mod

ule

Pri

ce (

1996

$/W

p) EIA/DOE(US)

Maycock(PV News)

Harmon(Global)

StrategiesUnlimited

Tsuchiya(Japan)


An Industry Projection, 2000-2005

0

100

200

300

400

500

600

700

800

900

1999 2000 2001 2002 2003 2004 2005

PV

In

du

str

y M

W

Grid-tied

Large Scale Power

Off-Grid-Rural

Off-Grid Industrial

Source: Strategies Unlimited, April 2000

1 GW

Rooftops (Japan, Germany)Remote HabitationTelecommunicationsCorporate Image (BP)

Non-subsidized Residential RooftopsCommercial Building Facades (BIPV)PV Integrate Products


Application to Solar PV

• Why should government have a role in encouraging learning?

• Summary tables from other studies• A typical learning based projection for PV


A Government Role?• Benefits of learning have a tendency to spillover to

other firms. – A firm can hire employees from a competitor, use

reverse engineering, rely on informal contacts with employees at rival firms, or pursue industrial espionage.

– As a result firms tend to under-supply a technology that exhibits strong learning effects.

• For a technology that exhibits strong learning effects and provides public benefits, the case for government sponsored demand-pull efforts is particularly strong.


PV Progress RatiosStudy Progress

Ratio # of obs

Years Scope Cost/Price Measure

Maycock and Wakefield (1975) 78% 16 1959-1974 US PV Module Sale Price Williams and Terzian (1993) 81.6% 17 1976-1992 Global Factory Module Price, based

on Strategies Unlimited Data (from 1993)

Cody & Tiedje (1997) 78.0% 13 1976-1988 Global Factory Module Price, based on Strategies Unlimited Data (from 1989)

Williams (1998) 82.0% 19 1976-1994 Global PV Module Price Maycock (1998) 68.0% 18 1979-1996 Global PV Module Price, from text

Tsuchiya (2000) 83.8% 20 1979-1998 Japan PV Module Government Purchasing Price, vs. Japanese cum. Production (sign. fluctuation over time)

Harmon (2000) 79.8% 21 1968-1998 Global PV Module Price, based on mix of sources (including Maycock, Ayres, NREL, Thomas, and Watanabe)

IEA (2000) 65% 11 1985-1995 EU PV Electricity Costs (ECU/kWh), vs. cum. kWh produced using a PV system (includes BOS and shift from SHS to BIPV)

IEA (2000) 84% 53% 79%

9 4

10

1976-1984 1984-1987 1987-1996

EU PV Module Price, based on EU-Atlas project data, vs global production.


PV “Buy-down” Cost EstimatesStudy Time

PeriodTarget Level

Estimated Buy-Down Cost

Notes

Neij (1997) 1995-various

5 cents/kWh

$100 billion(0.8 PR) $20 billion(0.7 PR)

Subsidize all future purchases above target cost. Total system cost.

Wene (1999) 1997-not specified

$0.5/Wp $60 billion Subsidize all future purchases above target cost. PV module only.

Wene (1999) 1998-2007

$3/Wp $1.2 billion Japanese government learning investment. Total system cost. (Global subsidies are estimated to be $18 billion.)

Williams (1998) 1997-2006

$1000/kW $50 billion (x-Si)$120 million (a-Si targeted program)

Subsidize all future purchases above target cost. PV module only.

Williams and Terzian (1993)

1995-2020

$1100/kW $5.4 billion (Net benefits accounting for env. ext.)

Subsidize only additional purchases relative to baseline. Total system cost.


A Typical Learning Based Projection for PV

0.1

1.

10.

100.

0 1 10 100 1,000 10,000 100,000 1,000,000


PV

Mod

ule

Pri

ce (

1996

$/W

p)

Maycock(PV News)

80% PR

70% PR

90% PR

Note: Total US Electricity Capacity in 1998 was 775,000 MW

13,000 MW

51,000 MW


Thinking Prospectively

• What is the right target level?• Module vs. System Costs• Need to calculate impacts relative to a baseline • Single progress ratio is an over simplification• What are realistic cost reductions over time?


What’s the Right Target Level?

• Depends on targeted applicationRooftop/BIPV: Retail Electricity Rate

– Large-Scale Power: Wholesale Rate

– Telecom: Currently competitive in many remote locations

– Solar Home Systems: Economically viable when remote from the grid


Average Residential Electricity Prices for Selected Countries, 1997

Country

Average Price (cent per kWh)

USA 8.5 Japan 20.7

Germany 16.1 Switzerland 13.6 Netherlands 13.0


PV System vs. Electricity Costs

0

4

8

12

16

20

24

28

32

36

40

44

$0.00$1.00$2.00$3.00$4.00$5.00$6.00$7.00$8.00$9.00

Installed PV System Cost ($/Wp)

Cos

t of

Gen

erat

ed E

lect

rici

ty (

cen

ts/k

Wh

)

Pennsylvania Retail Rate

Japanese Retail Rate

German Retail Rate

Additional Assumptions:System Lifetime = 20 yearsReal Interest Rate = 6%O&M = 0.1 cent per kWh

Capactiy Factor = 0.25(South West U.S)

Capacity Factor = 0.2(U.S. Average)

California Retail Rate


Module vs Systems Costs

• Really a compound learning curve– PV module– Balance of System components

• Rooftop/BIPV offers many opportunities for cost reduction– Elimination of Storage– Substitute structurally– Elimination of frame

– Installation

• Different components may have different learning rates.


Impacts Relative to a Baseline• PV has niche markets that are likely to grow • Can target subsidies (as in Japan and Germany)• A simple illustration:

– Baseline annual growth: 10% or 20% (?)– Subsidy increases annual growth to 30%– PR = 0.8, System Cost in 1998 = $7/WpAchieve $3/Wp: 2020 (10% growth); 2012 (20%

growth); 2009 (30% growth).Subsidy required:

All Production = $11 billion;Extra Prod. = $6 billion (increase growth from 10% to 30%);Extra Prod. = $4 billion (increase growth from 20% to 30%).


Projected PV Annual Production Under Three Growth Scenarios

0

500

1,000

1,500

2,000

2,500

3,000

1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

Year

An

nu

al G

lob

al P

V M

odu

le P

rod

uct

ion

(M

W)

30% Growth

20% Growth

10% Growth


Projected PV System Price Three Growth Scenarios (PR = 0.8)

0

1

2

3

4

5

6

7

8

1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

Year

Ave

rage

PV

Sys

tem

Pri

ce (

$/M

W)

30% Growth

20% Growth

10% Growth


Using a Single Progress Ratio?

• There is considerable uncertainty in historical progress ratios– What is the relationship between R&D and progress

ratios?

– The potential for breakthroughs is difficult to quantify

• Results are highly sensitive to progress ratio Need to include sensitivity analysis.


Sensitiveity of Global PV System Subsidy Cost to PR

$0

$20

$40

$60

$80

$100

$120

$140

70% 75% 80% 85% 90%

Progress Ratio

Subs

idy

Cos

t (b

illi

on $

)

Assumes:System Cost in 1998 = $7/WpBuy down all systems to $3/Wp


Japanese Rooftop Program Experience, 1994-2000

0

2

4

6

8

10

12

14

16

18

1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

PV

Sys

tem

Pri

ce (

1998

$/W

p)

0

20

40

60

80

100

120

PV

Roo

ftop

Sys

tem

s In

stal

led

(M

W)


PV Budget Comparison (Million US$)

Program Area U.S (FY2002)

Japan (FY2002)

Germany (FY2001)

R&D 72 56 27

Demonstration -- 41 6

Market Stimulation

* 473 30

Total 72 570 62

Source: Maycock (2002); EIA (2001)* There is considerable activity at the state level in the U.S.


Illustrating a Breakthrough in PV Technology

0.1

1.

10.

100 1,000 10,000 100,000 1,000,000


PV

Mod

ule

Pri

ce (

1996

$/W

p)

x-Si Curve

Alternative PV Curve

Tra

nsiti

onP

erio

d


What are Realistic Cost Reductions?

• Material Costs– Crystalline Silicon: $0.43-0.68/Wp (silicon, glass,

EVA, other), for 13% efficient cell (Maycock 1998)

– Amorphous Silicon: ~ $0.31/Wp (Carlson 1993)

– CdTe: ~ 0.48/Wp (Zweibel 1999)

• Average Module Production Cost in 2000 for U.S. PVMaT participating companies was $2.94/Wp

• Average Module Price in 1998 was $3.82/Wp


PVMaT Cost-Capacity Historical Data (1992-2000) and Projection (2001-2006)

0

1

2

3

4

5

0 100 200 300 400 500 600 700 800 900

Cummulative Production CAPACITY (MWp)

Ave

rage

PV

Mod

ule

Pro

du

ctio

n C

OS

T

(199

6$/W

p)

Historical

Projected

1992

2000

2006


Concluding Thoughts• Process of innovation is inherently uncertain

– prospects for learning with existing technologies,

– breakthroughs (i.e., through R&D investments),

– market developments (i.e., how rapidly will the grid-connected and rural home markets grow).

Need to be cautious!– Simplistic use of industry-wide experience curves can

easily mask the underlying dynamics of the process of innovation.


Concluding Thoughts (cont.)• With respect to PV technology we are in what

Cowan (2000) calls the “narrow windows” and “blind giants” stage of technology development.*– There is a wide range of emerging PV technologies. – It is currently unclear which PV technology will

dominate the market in the long-run. Government should strive to encourage the

development and diffusion of a diverse set of PV technologies (to avoid lock-in to an inferior PV technology).

* That is, effective policy-making is only possible during the early stages of competition between technologies, yet that is when analysts and policy-makers know the least about what to do.


Policy Recommendations

• Pursue (demand-pull and supply-push) policies that will encourage the range of emerging PV technologies to continue to advance from the laboratory into production: – Demand-pull: net metering, setting interconnection standards,

instituting a federal subsidy (tax credit or loan subsidy) for rooftop and building-integrated PV systems, and instituting a renewable portfolio standard (RPS) that would include distributed PV systems.

– Supply-push: Expand R&D programs like the Thin-Film PV Partnership project and the PVMaT project. These projects have fostered innovation in a wide array of PV technologies and should continue to pursue a multi-technology strategy.


Directions for Research• Need to improve our understanding of the

mechanisms underlying the process of technological change – How do demand-pull and supply-push policies interact?

– How much can we speed up the process of learning?

• Can we quantify the value of R&D?

r. m. margolis, hdgc seminar, oct. 16, 2002, page 1 experience curves and photovoltaic technology...

Documents

learning curves

illustrative learning

learning coefficientpr

process of learning

given product

progress ratioswhere

progress ratiofreqency60

cost of intangibles