Photovoltaic and wind cost decreases implications for investment
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Transcript of Photovoltaic and wind cost decreases implications for investment
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Photovoltaic and Wind Cost Decrease:Implications for Investment Analysis
by
Ignacio Maulen.
Dept. of Economics and Business Management.Universidad Rey Juan Carlos, Madrid, Spain.
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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.
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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
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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
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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.
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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
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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.
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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.
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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.
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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.
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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
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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
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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 ?.
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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.-
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Thank you for your attention !
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