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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