# Presentation: 687235

## Photovoltaic Technology Experience Curves and Markets

- Robert M. Margolis
- Department of Engineering and Public Policy
- Carnegie Mellon University
- Presentation at NCPV and
- Solar Program Review Meeting
- Denver, Colorado
- March 24, 2003

## Outline

- History/Origins of Experience Curves
- Application to Solar PV Technology
- 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).
- Empirically the Boston Consulting Group’s study 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.”

## The General Form of the Experience Curve is the Power Curve

- P(t) = P(0)•[q(t)/q(0)]-b
- Where:
- P(t) is the average price of a product at time t
- q(t) is the cumulative production at time t
- b the learning coefficient
- PR = 2-b
- Where:
- PR = progress ratio.  For each doubling of cumulative production the MC decreases by (1-PR) percent.

## Illustrative Learning for Three Progress Ratios

- Where:  P(0) = 100
- 		       q(0) = 1

## 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)

- Note: These progress ratios are firm level (not industry wide) studies.

- Lockheed’s
- L1011 Tristar

## Distribution of Energy Progress Ratios(McDonald and Schrattenholzer 2001)

- Note:  Includes a
- wide range of
- Energy technologies:
- oil extraction, power
- conversion, model T
- Ford, CF lights, etc.

- GTCC

- CFL

## PV Progress Ratios from Selected Studies

| Study | PR | # of obs | Years | Scope | Cost/Price Measure |
| --- | --- | --- | --- | --- | --- |
| Maycock & Wakefield (1975) | 78% | 16 | 1959-1974 | US | PV Module Sale Price |
| Williams & Terzian (1993) | 82% | 17 | 1976-1992 | Global | Factory Module Price |
| Cody & Tiedje (1997) | 78% | 13 | 1976-1988 | Global | Factory Module Price |
| Williams (1998) | 82% | 19 | 1976-1994 | Global | PV Module Price |
| Maycock (1998) | 68% | 18 | 1979-1996 | Global | PV Module Price |
| Tsuchiya (1999) | 84% | 20 | 1979-1998 | Japan | PV Module 
Gov’t Purchase Price. |
| Harmon (2000) | 80% | 21 | 1968-1998 | Global | PV Module  |
| IEA (2000) | 65% | 11 | 1985-1995 | EU | PV System Electricity Cost (ECU/kWh) |
| IEA (2000) | 84%
53%
79% | 9
4
10 | 1976-1984
1984-1987
1987-1996 | EU | PV Module Price. |

## A Typical Learning Based Projection for PV

- 13 GW

- 51 GW

- Note:  IEA World
- Outlook (2000)
- expects 3,000 GW
- capacity to be added
- between 1997-2020

- ~6,500 GW

- 1999

- 1976

## PV “Buy-down” Cost from Selected Studies

| Study | Time Period | Target Level | Estimated Buy-Down Cost | Notes |
| --- | --- | --- | --- | --- |
| Neij (1997) | 1995-various | $0.05 /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 investment. Total system cost. |
| Williams (1998) | 1997-2006 | $1000 /kW | $50 billion 
(x-Si targeted)
$120 million 
(a-Si targeted) | 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  environmental externalities) | Subsidize only additional purchases relative to baseline. Total system cost.  |

## Thinking Prospectively

- Five key factors help to explain the wide variation in PV buy-down costs estimates:
- Choosing an appropriate target level
- Focusing on the module vs. system costs
- Calculating program costs relative to a baseline
- Using a single vs. a range of progress ratios
- The availability of breakthrough technologies

## What’s the Right Target Level?

- Depends on targeted application
- Rooftop/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
- Recalculating Neij’s estimate with alternative targets:

| Target Level 
( ~ retail rate in) | Subsidy Required to Meet Target |  |
| --- | --- | --- |
|  | PR = 0.8 | PR = 0.7 |
| $0.05/kWh (Neij) | $100 billion | $20 billion |
| $0.085/kWh (U.S.)  | $32 billion | $9 billion |
| $0.16/kWh (Germany) | $6 billion | $2 billion |
| $0.21/kWh (Japan) | $2 billion | $1 billion |

## PV System vs. Electricity Costs

- Note:  In Japan with subsidy set at
- 33-48% and system cost of $7/Wp,
- COE is 18-23 cents/kWh.  In 2000
- Germany had 40% subsidy and 51
- cents/kWh buy-back 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.

## Japanese Rooftop Program Experience, 1994-2000

- 1994-1997 Rapid decline in
- BOS and Installation Costs.

## 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:
- PR = 0.8, System Cost in 1998 = $7/Wp
- To achieve $3/Wp target by 2009 (i.e., 30% growth)

| Baseline Growth Rate | Subsidy Required to Achieve $3/Wp Target by 2009 |
| --- | --- |
| 0% | $11 billion |
| 10% | $6 billion |
| 20% | $4 billion |

**Notes:**
What is the appropriate scale for analysis?  Firm, National, Global?
Distortions in prices vs. costs? R&D funding, aid subsidies, firm losses (big oil in PV in the 1980s)?
Cumulative production of what?  kW, kWh, cells, modules, systems?  Grid-tied rooftop, BIPV, utility large-scale, SHSs?
Grouping technologies? x-Si, a-Si, CIS, CdTe?  Potential for discontinuities?

## Using a Single Progress Ratio?

- There is considerable uncertainty in historical progress ratios
- What is the relationship between R&D and progress ratios?
- Results are highly sensitive to progress ratio
- Need to include sensitivity analysis.

## Sensitivity of Global PV System Subsidy Cost to PR

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

- $5 billion

- $12 billion

- $130 billion

## Assumptions about Breakthroughs

- The potential for breakthroughs is difficult to quantify
- Little basis for drawing experience curves for emerging PV technologies
- Yet, breakthroughs could have dramatic impact on PV technology development path.

## Illustrating a Breakthrough in PV Technology

- 1999

- 2010
- w/20%
- growth

## Concluding Thoughts

- Process of innovation is inherently uncertain
- prospects for future learning with existing technologies
- breakthroughs (i.e., through R&D investments)
- market developments (i.e., how rapidly the grid-connected and rural home markets will 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 encourage the development and diffusion of a diverse set of PV technologies.

- * 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.