Learning Curves for Wind:
Handle With Care!
Henry D. Jacoby
Joint Program on the Science and Policy of Global Change
Massachusetts Institute of Technology
Workshop on Learning-by-Doing in
Energy Technologies
Resources for the Future
17-18 June 2003
What Is Known About Wind?
• The question
– Costs of wind have fallen . . . but why?
– What future path and influence?
• Why important?
– Forecasting (when competitive?)
– Justification of subsidy programs
• Current status: closer to a research
agenda than to an answer
My Path
• The method and its uses
• The difficulties in analyzing wind
power (ÄÄ research agenda)
• Incorporating wind power (and
perhaps LBD) into CGE models
The Learning Specification
ln CCUM = ln C0 + b ln CUM
(IEA, 2000)
Valuation of Subsidies
(IEA, 2000)
The Research Agenda
The data
Specification
Which
variable
What
comparison
(IEA, 2000)
The Specification
• Zero intercept
• Heterogeneous product
• Black box model, covering
– Learning [Cost = f(cum experience)]
– Technology change
– Scale of industry
– Scale of units
Scale of Units
160 m
120 m
80 m
40 m
Rated Capacity:
Rotor Diameter:
Tower Height:
50 kW
15 m
25 m
300 kW
34 m
40 m
750 kW
48 m
60 m
1000 kW
60 m
70 m
2000 kW
72 m
80 m
5000 kW
112 m
100 m
Washington
Monument
170 m
The Cost Data
• Not cost per kWh but per kW
• In general not cost data
– Use price of units
– Often, list price
Which Variable?
• The “experience” data
– Number of units?
– Cumulative kWh?
• What scope
– National?
– Global?
What Standard for
Competitiveness?
• A substitute for fossil generation?
– Delivered cost to consumer?
– Busbar cost of fossil energy?
– Variable (fuel) cost of fossil?
• Negative stochastic load?
Hourly windspeeds from the
Buzzards Bay tower show higher,
but noisy windspeeds in winter.
Actual hourly electricity
demand for 2001 shows
weekdays, weekends and
heat waves.
Buzzards Bay Hourly Windspeeds (m/s, 2001)
36
66
96
126
156
Day of Year
New England Hourly Electricity Demand (GWs, 2001)
6
186
216
246
276
306
336
2
4
0
6
3
8
6
10
9
12
12
14
15
S.R. Connors: MIT L.F.E.E.
16
18
18
20
21
24
22
27
24
2
4
0
6
2
8
4
10
6
8
12
10
14
12
16
14
18
16
20
18
22
20
24
Two Approaches to the
Capacity Credit for Wind
• In regulated, planned system:
– What peaking capacity doesn’t have to be
built because of a kW of wind?
• In a de-regulated system: how will the
ISO credit the wind resource for
– Day ahead market (or other forward mkts)
– Spot generation
– Ancillary services
The MIT EPPA Model
Regions
USA
Canada
Mexico
Europe
Japan
Aus. & N.Z.
Russia
E. Europe
Production sectors
China
India
Persian Gulf
Indonesia
Africa
Latin America
East Asia
Rest of World
Household Sector
Own & purchased transport
Other goods & services
Non-Energy
Agriculture
Energy Intensive
Other Industry
Transport
Energy
Crude & refined oil
Coal
Natural gas
Electricity
Fossil
Nuclear, hydro
Wind
Solar
Biomass
Production Function Notation
X
X
K
σ
output
L K
K
L
CES: X t = at (bK K ρ
σ=0
KL
+ bLLρ
KL
1
) ρ
KL
L
σ KL =
Leontief: (fixed coefficients) Min( aK , bL )
1
1 − ρ KL
Inclusion in a CGE Model
(Method 1)
Electric Sector
Gross Output
σ<∞
Wind
Labor
Capital
σ=∞
FF
Fossil
Nuclear
Etc.
Inclusion in a CGE Model
(Method 2)
Electric Sector
Gross Output
σ=∞
Wind
σ=∞
Extra
capacity
Fossil
Labor
Capital
FF
Nuclear
Etc.