Post on 25-Jan-2017
Photovoltaic and Wind Cost Decrease:Implications for Investment Analysis
by
Ignacio Maulen.
Dept. of Economics and Business Management.Universidad Rey Juan Carlos, Madrid, Spain.
ignacio.mauleon@urjc.es
Index.Index.
1. Introduction.
2. Methodology.
2.1. Learning Rate & simulation.
2.2. Published PV & Wind LR.
2.3. Photovoltaic LR.
2.4. Wind LR.
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3. Results.
3.1. Framework.
3.2. Total investment.
3.3. Risk analysis.
4. Summary & Implications.
5. Pending research.
1. Introduction.Introduction.
Total Investment and paths implied by Renewable Energy targets.
COP 21IEA, estimates 2.5 tr. US $
Analysis:
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Price decreases with deployment.
Uncertain estimates: Simulations. Risk analysis
2.1. Learning Rate & simulation.2.1. Learning Rate & simulation.
Pt = k Ctb
Pt , Price (module, turbines)Ct , Capacity installed.b , Learning coefficient.
Learning Rate (LR):
Doubling capacity % price decrease
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Doubling capacity % price decrease
but:
b unknown estimated statistically uncertain
LR uncertain price decreases uncertain simulated
2.2. Published PV & Wind Learning Rates.2.2. Published PV & Wind Learning Rates.
REPORTED LEARNING RATES (%) (Rubin e.a, 2015)
N of studies Mean Range
Wind - onshore 18 12 -11 ; 23
Solar PV 16 22 10 ; 47
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Solar PV 16 22 10 ; 47
Too much variability.
Insufficient stat. detail.
2.3. The photovoltaic Learning Rate.2.3. The photovoltaic Learning Rate.
PV Cost Model Estimation.
Learning by doing, PV costs & Installed Capacity.
2.5
3
3.5
4
4.5
MODULE PRICES w.r.t. CAPACITY O.L.S. regression (logs.) & Learning Rate (LR)
Log(P)=3.98-0.33*Log(Cap) (45.) (26.)
R2=0.95, D.W.=0.44 LR=20% (23.1)
1978
1979
1980
1981
19821983
19841985
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-0.5
0
0.5
1
1.5
2
2.5
0 2 4 6 8 10 12
Log(
mod
ule
pric
es)
Log(Installed capacity)
19851986
1987
19881989
199019911992
199319941995
1996199719981999
20002001 20022003
20042005
2006 2007 2008
2009
2010
2011
20122013
2.3. The photovoltaic Learning Rate.2.3. The photovoltaic Learning Rate.
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2.4. The wind Learning Rate.2.4. The wind Learning Rate.
Insuficient reliable data world level IRENA working on data base.
Data
- Turbine prices Denmark- Turbine, LCOE, US
Overall results:
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Overall results:
Learning Rate ~ 13% Confidence bands estimated.
3.1. Framework.3.1. Framework.
CAPACITY FORECASTS
(world Gw.)
PV Wind Total
2020 390 640 1030
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2030 1720 1600 3320
2050 4670 2700 7370
IEA, International Energy Agency; Technology Roadmaps.
3.1. Framework.3.1. Framework.
Simulating Total Investment.
Total amount of funds vs. unitary price.
Price depends on investment.
( ) TIPI nn tt =1
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( ) TIPI ntt =1It , increase in capacity.
Pt , module / turbine price.
TIn , total accumulated investment; years 1 to n.
3.2. Total Investment.3.2. Total Investment.
TOTAL INVESTMENT (b. us $)
Random Not random
2030 mean 1580 1444
(50%) 1473
(90%) 2063
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2050 mean 2779 2513
Randomness => Median (50%)
Risk: 1.4 , 40% hi.
3.2. Total Investment.3.2. Total Investment.
TOTAL INVESTMENT (b. us $)
Slow Path Fast Path
2030 676 2492
2050 2765 2831
Discounting (3%)
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2050 1486 2066
2050, similar 2031, sharp drop (fast) Discounting
3.2. Total Investment.3.2. Total Investment.
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3.2. Total Investment.3.2. Total Investment.
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3.3. Risk analysis.3.3. Risk analysis.
Expected Investment at Risk (EIaR):
Expected Investment, if prices rise above a given high value.
[ ][ ]
TITIob =Pr
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[ ][ ] EIRTITITIE =|
e.g., valuexupperx ,,,%,90=TI, Total Investment
3.3. Risk analysis.3.3. Risk analysis.
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4. Summary & implications.4. Summary & implications.
Cost models efficiently estimated.
Learning Rates: PV > 23%
Wind ~ 13%
Parameter uncertainty SimulaDons Risk
Accelerated Investment paths: = Invest. 2050
2031 !.
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Caveat: Price decreases for ever ?.
4. Summary & implications.4. Summary & implications.
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5. Pending research.5. Pending research.
Costs.
- Balance of system costs (BoS).- Discounting. - Investment paths (speed, smoothness, )
Benefits.
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- GHG, - Value electricity.-
Thank you for your attention !
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