1 jorge blazquez universidad complutense, madrid november 9, 2015 linking renewables adoption to...
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Jorge BlazquezUniversidad Complutense, MadridNovember 9, 2015
Linking Renewables Adoption to Market Mechanics
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Jorge Blazquez
Tamim ZamrikNora Nezamuddin
Shahad Albardi Amro Elshurafa
Lawrence Haar
Transitions Team
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Research Question
How do different policy instruments affect the speed of renewables adoption under different market
conditions?
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Step 1:Policy Targets
Step 2:Policy Instruments
Step3: Implementation
Results
MW
Feed in Tariff• C.F.D.• “Classical”• “Spanish”
Feed in PremiumProduction Tax Credit
Investment Credit
DeterministicModel
- Speed of adoption
- Yield for project
Description of the problem: the policymaking process
Decision under uncertainty
Policymaking Process
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Description of the problem: Defining Policy Instruments in a Stochastic Environment
LCOE
LCOE after IC
Price
IC
FIT Price
Price
FIT: CFD
FIT Floor
Price
FIT: Classical
FIT Cap
FIT Floor
Price
FIT: Spanish
LCOE
Price
FIP
FIP
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Policy Instrument “Fine tuning” policy instrument Description
Feed- in tariff (FIT)
Contract for difference FIT is constant price per MWh generated
“Classical”FIT is a minimum price. When market price is above FIT, project receives market price
“Spanish” FIT is like a “classical” one, but there is a cap.
Feed- in premium (FIP)
Fixed sumA constant amount of money added on top of market price
Investment Credit Percentage discount A percentage discount on initial investment
Description of the problem: Defining Policy Instruments in a Stochastic Environment
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Methodology: Assumption and description of the problem
Wholesale traded electricity prices evolve as a Geometric Browning motion
LCOE of technology decays as a power function over time
1000 projects with different LCOE and WACC sampled from a normal distribution
Investors are rational, profit-maximizers, and risk-neutral
Entry point is the month at which the expected NPV (EPV) of each project is maximized
For each project, the support policy is locked upon investment with a PPA
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Methodology: The representative project and the stochastic environment
Description of Environment
No. of Investors (equal in capacity, based on Spanish wind projects) 1,000
Wholesale Daily Electricity Prices (Geometric Brownian Motion)
Germany, France, Italy,
Spain, & Nordpool
Drift= 1.5% Volatility=39%
Initial Price (EUR/MWh) 49.0
LCOE (normal distribution, EUR/MWh)
Avg= 72.0 Std= 8.6
for Tech (LCOE decay factor) 0.97
WACC (normal distribution, %) Avg= 7.4Std= 0.4
Description of Representative Spanish Onshore Wind Project
Capacity Factor (%) 24
Size (MW) 33.7
Maturity (years) 20
CAPEX (EUR/MW) 1,491,000
OPEX (EUR/MWh) 17.6
WACC (%) 7.4
LCOE (EUR/MWh) 72
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Methodology: Spanish onshore wind data: 2006-2014
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Methodology: Onshore wind LCOE evolution
Sep-09
Nov-09
Jan-1
0
Mar-1
0
May-
10
Jul-1
0
Sep-10
Nov-10
Jan-1
1
Mar-1
1
May-
11
Jul-1
1
Sep-11
Nov-11
Jan-1
2
Mar-1
2
May-
12
Jul-1
2
Sep-12
Nov-12
Jan-1
3
Mar-1
3
May-
13
Jul-1
3
Sep-13
Nov-13
Jan-1
4
Mar-1
4
May-
14
Jul-1
4
Sep-14
Nov-14
Jan-1
5
Mar-1
5
May-
1570
75
80
85
90
95
100
Onshore wind LCOE evolutionUS$/MWh
Source: Bloomberg New Energy Finance
Cost evolution for onshore wind technology
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Methodology: Determine policy instruments that lead to the same result in terms of Net Present Value in a deterministic environment
FIT79 EUR/MWh
FIP24.1
EUR/MWh
IC (%)33
NPV = 154,000 EUR/MW
Yield = 10% for FIT & FIP-PTCYield = 17% for IC
Starting point for the new study under uncertainty
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LCOE for technology
Price
Entry month
LCO
E &
Pric
e of
ele
ctric
ity
Policy ends (10 years)
LCOE for this project
Project Decommissioned
20 years
Methodology: Assumption and description of the problem
Time
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Methodology: Our Approach
Month
0 50 100 150 200 250 300 350
Price
s
0
50
100
150
200
250
300
350
LCOE = 0.97
Guaranteed Wholesale Electricity Prices Euro/MWh Against LCOE and FIT
Traded Electricity PriceLCOE
Floor FIT
Cap FITGuaranteed Price
1. For each project we compute the EPV term structure over 60 months (policy life)
2. We select the month that maximizes EPV for each project
3. We repeat this procedure over 1000 projects under each of the 5 policy instruments
4. The adoption curve is constructed
Simulation approach?240 months for project maturity x 120 months of policy spam x 5 policy x 1000 projects x 1000 random price paths = 14 Billion points
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Mathematical resolution: derivation of the Expected Value of Guaranteed Prices under FIT
Let the traded price be and the guaranteed price be .
Let be the maximum decision-making time (assumed 10 years), be the life time of the project (assumed 20 years), and be the total range.
For each month within we need to find the value of the expected guaranteed electricity price, or .
Given that is a GBM and using the Feynman-Kac theorem, then solves the following PDE .
The solution of the partial differential equation is given by
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The functions are given by
is the Gaussian error function given by
The classical (no-cap) FIT is the case where .
Mathematical resolution: derivation of the Expected Value of Guaranteed Prices under FIT
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The CFD FIT scheme is a special (degenerate) case of the general (Spanish) FIT, where . As such and .
Under a FIP scheme, , where is the FIP level and is the initial traded price.
Under an IC scheme, there is no guaranteed price . The policy discounts the LCOE, and as such the only expression we have is the .
We can see from the cases above how these policies are independent of volatility.
In other words, the investor’s maximum expected present value of each MWh or is given by
It is also useful to compute the maximum investment yield defined by
, .
Mathematical resolution: derivation of the Expected Value of Guaranteed Prices under FIT
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Results: Types of solution for individual projectsE
PV
te
rm S
truc
ture
timeEntry
1Entry
2
1
2
Figure 1
T=60 T=60 Entry point =60
The EPV term structure of the vast majority of projects under FIP and IC Result S curves
All of the projects under a CFD Do not result in S curves
EPV term structure for laggards Do not result in S curves
time
Figure 2
Entry Point, t=1E
PV
te
rm S
truc
ture
time
Figure 3
EP
V t
erm
Str
uctu
re
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Results for FIT: Contract for difference
20 40 60 80 100 1200
100
200
300
400
500
600
700
800
900
Entry Month
Pro
ject
s
20 40 60 80 100 1200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Entry Month
Ma
x Y
ield
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Results: Classical FIT
20 40 60 80 100 1200
100
200
300
400
500
600
700
800
900
1000
Entry Month
Pro
ject
s
20 40 60 80 100 1200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Entry Month
Ma
x Y
ield
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Results: Spanish FIT
20 40 60 80 100 1200
100
200
300
400
500
600
700
800
900
1000
Entry Month
Pro
ject
s
20 40 60 80 100 1200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Entry Month
Ma
x Y
ield
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Results: FIP
20 40 60 80 100 1200
100
200
300
400
500
600
700
800
900
1000
Entry Month
Pro
ject
s
20 40 60 80 100 1200
0.2
0.4
0.6
0.8
1
1.2
1.4
Entry Month
Ma
x Y
ield
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Results: Investment Credit
20 40 60 80 100 1200
100
200
300
400
500
600
700
800
900
1000
Entry Month
Pro
ject
s
20 40 60 80 100 1200
0.2
0.4
0.6
0.8
1
1.2
1.4
Entry Month
Ma
x Y
ield
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Summary of Results for the 5 policy instruments
Policy Early adopters (%) Success ratio (%)Laggards
(month 60) (%)
Feed-in Tariff - Contract for difference 80.3 80.3 0
Feed-in Tariff – Classical 3.6 99.7 91.9
Feed-in Tariff – Spanish (+50%) 20.2 94.1 7.3
Feed in Premium 2.3 100.0 12.1
Investment Credit 2.4 100.0 8.8
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Early Adopters Ratio under FIT
Electricity Price Drift
-0.01 0 0.01 0.02 0.03 0.04 0.05
Ann
ual V
ola
tility
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8Early Adopters Ratio under FIT
Electricity Price Drift -0.01 0 0.01 0.02 0.03 0.04 0.05
Ann
ual V
olat
ility
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Results: An example for the Spanish FIT. Early adoption sensitivity to WACC
Mean WACC = 7.2% Mean WACC = 2.0%
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Historical Spanish Wind Adoption Curve
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 20140
5000
10000
15000
20000
25000
Years
Inst
alle
d C
apac
ity M
W
1990 1994 1999 2004 2009 2014
Actual Data 0 0.2 6.1 36.8 83.4 100
Experiment 0 0.6 28.3 85.3 98.9 100
Percentage of cumulative wind capacity
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Results Insights
The adoption S-curve is an output of the model, whereas adoption literature assumes that an S-curve is an input.
The traded price volatility is irrelevant under a CFD, FIP, and IC policies.
Subsidized loans – which reduce projects’ WACC- invariably lead to a reduction of the speed of adoption.
Under a FIT, the lower the cap the higher the rate of early adopters. Under a the classical and Spanish FIT, price drift has a higher impact on success ratios and early adoption than price volatility.
We can create equally attractive FIP and IC policies that generate similar success ratios compared to FIT, without causing the tax-payer to bear the risk.
To select a policy we need to know:- Which policy yields the highest success within a time frame (this work)
- The associated policy cost on tax-payers (next work)
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Thank You