project

usaee.org

project

Sustainable Energy for Panama within the

Framework of the Kyoto Protocol:

A Stochastic Analysis of Different Options

Irina Falconett and Ken Nagasaka

Tokyo University of Agriculture and Technology


Outline

Clean Development Mechanism Projects in Panama

Baseline Methodology

CO 2 Avoided Emissions

Model & Data

Monte Carlo Simulation

Probability Distributions

The Simulation Results and Conclusions


The CDM Projects in Panama

Energy Efficiency

2%

Methane Capture

6%

Transportation

2%

Reforestation

3%

Renewable Energies

87%

The CDM portfolio of Panama contains 108 projects

Wind Projects

12%

Biomass and Biofuel

3%

Hydro Projects

85%


The CDM Projects Pipeline

Status Title Type Sub-type ktCO 2 /yr

Crediting

period

years

Credit

start

Credit buyer

Registered

Los Algarrobo Hydro Run of river 37 7 1-Jan-08

Macho de Monte Hydro Existing dam 11 7 1-Dec-01

Dolega Hydro Existing dam 12 7 1-Jul-01

Spain (Union

Fenosa)

Spain (Union

Fenosa)

Spain (Union

Fenosa)

Concepcion Hydro Run of river 36 7 15-Jan-07 n.a.

Paso Ancho Hydro Run of river 22 7 1-Jan-08

UK

(EcoSecurities)

At

Validation

Esti Hydro Run of river 316 7 15-Mar-04 n.a.

Bayano Hydro Existing dam 32 7 15-Mar-04 n.a.

Source: UNEP/RISOE project pipeline database


The Emission Baseline

• The quantity of the Certified Emission

Reductions (CERs) is determined by comparing

the project emissions to the emission baseline.

• The emission baseline is the best estimate of

what would have happened in the absence of

the project activities.

• The level at which the emission baseline is set

as well as the value of CERs critically affect the

revenue of the CDM projects.


The CO 2 Avoided Emissions

‣ The Grid-connected Projects

In this research, the combined margin was employed for the calculation of

the CO 2 avoided emissions of the grid connected projects

E

combined −margin

E

=

The operating margin emission factor

GENCO m

E

operating −margin

z n

∑ =

z

=

= 1

( E * X )

tech z

X

t m

+ E

2

u z

Build −margin

Emission factor of the

generating company

X

co2

=

m

∑ = y

m=

1

( E * X )

GENCO m

GENCO m

Annual CO 2 emissions

from electric generation

operating −margin

=

X

X

CO

E

2

thermal

0.78 CO 2

ton/MWh


The CO 2 Avoided Emissions

‣ The Grid-connected Projects

• The build margin is based on the emissions rate of the fossil-fuelled

power plants that the construction of the renewable project cancelled

or postponed.

E

combined −margin

=

E

operating −margin

+ E

2

Build −margin

The build margin was estimated using the

recent capacity additions to the electric

grid from 2002 to 2007

The build margin = 0.49 CO 2

ton/MWh

The combined margin = 0.64 CO 2

ton/MWh

CO *

2 Avoided Emissions = Ecombined

−margin

GenerationRE-

project


The CO 2 Emissions from Electric Generation

Stated-owned Electricity Industry

Wholesale Market

Competition


The model

Benefits

Value of the

Energy Output

The stochastic NPV Model

Environmental

Benefits

m

NPV

t m

X

= ∑ =

=1 1+

t

t

t

( I )

− X

i

Governmental

Subsidies

Forecast

Costs

Initial Investment

Transaction Costs

O&M Costs

The Monte Carlo Simulation

Stopping Criteria

Number of trial = 10,000

Confidence level = 95%


Simulation Results

Independent Power Producer

The NPV Forecast of the Hydroelectric Projects

(a) Installed Capacity 10 MW (b) Installed Capacity 500 kW

The Contribution to the Variance of the

NPV Forecast for 10 MW Hydro Project

(Certified Emission Reductions)

The CERs Profits as Percentage of the Project NPV

Installed Capacity Hydro Wind

10 MW 15 % 18 %

5 MW 15 % 18 %

1 MW 13 % 15 %


Simulation Results

Transaction Costs – CDM projects

The Certainty Level of the CERs NPV Forecast (Minimum US$ 0)

100

%

Certainty Level

80

60

40

20

0

0 400 800 1200 1600

CERs per Year

The Crediting Period: 21 Years

The transaction costs of the

Panamanian CDM projects are

from US$ 75,000 to US$ 125,000

CERs price in the primary market (2006)

Minimum US$ 6.80

Average US$ 10.90

Maximum US$ 24.75


Simulation Results

CERs generated from solar, wind and hydro projects

The Volume of CERs per year. Installed Capacity 500 kW

Grid-connected

Off-grid

Grid-connected

Emission Baseline

Off-grid = 0.88 tCO 2

/MWh

Grid-connected = 0.64 tCO 2

/MWh


Simulation Results – Residential Projects

Production kWh KWh

Wind Power System 850 W 1 kW 2 kW 2.5 kW 6 kW

1,504 1,598 4,041 5,189 12,800

Contribution 25.1% 26.6% 67.4% 86.5%

Extra Generation

Production KWh kWh

PV System 1 kWp 2 kWp 2.5 kWp 3 kWp 4 kWp 6 kWp

1,588 3,176 3,970 4,764 6,352 9,552

Contribution 26.5% 52.9% 66.2% 79.4% Extra Generation Extra Generation

The NPV Forecast of the 2 kW Wind System

Applying Different Support Mechanism

Annual household

electricity consumption

6000 kWh

The NPV Forecast of the 3 kW PV System

Applying Different Support Mechanism

0.8

0.6

0.4

0.2

0.0

- 4,000 - 2,000 0 2,000 4,000 US$

0.8

0.6

0.4

0.2

0.0

- 10,000 - 5,000 0 5,000 US$


Simulation Results – Residential Projects

0.4

0.3

0.2

0.1

0.0

The NPV Forecasts of Net Metering

-15,000 -10,000 -5,000 0 US$

Wind turbine

capacity

Contribution to the

household energy

consumption

850 W 25.1 %

1 kW 26.6 %

2 kW 67.4 %

2.5 kW 86.5 %

6 kW Extra Generation

The NPV Forecasts of Feed in Tariffs (FiT)

0.48

0.36

0.24

0.12

0

0 11,000 22,000 33,000

US$


Conclusion

The small scale RE-CDM projects can assist to meet the growing electricity

demand in a sustainable manner while directly mitigating the emissions of the

greenhouse gases. Nevertheless, these projects face several barriers that make

them less attractive for the investors such as less volume of CERs, high initial

investment as well as the transaction costs.

In order to encourage the implementation of RE-CDM projects, several

alternatives should be considered such as payment of a premium for CERs from

renewable energy projects, additional support mechanism like Feed in Tariffs

(FiT), choosing an effective baseline methodology as well as less transaction

costs. Otherwise, the clean development mechanism is likely to produce a limited

effect on the support of renewable energy technologies

In the case of homeowner projects using net metering, the simulation results

confirmed the importance of choosing the appropriated PV system or wind

turbine capacity that matches the household energy consumption. Feed in tariffs

as support mechanism can provide the best financial returns of the investment for

the PV system and wind system homeowner projects.


Thank you for your kind attention


The Value of the Energy Output

The Independent Power Producer

Projects Evaluated

The Contract Market Price

The System Marginal Costs

Hydro Wind Solar

Bundled 500 kW

From 500 kW up to 10 MW

The Residential Projects

Projects Evaluated

Wind Solar

The Retail Prices

The value of the

energy supplied to

the electric grid

Feed in Tariffs

US$0.5/kWh

Net Metering

From 850 W to 6 kW


Small-scale projects fall under the

following three categories:

• Renewable energy project activities with a maximum

output capacity equivalent of up to 15 MW.

• Energy efficiency improvement project activities

which reduce energy consumption, on the supply

and/or demand side, by up to the equivalent of 15

GWh per year.

• Other project activities that both reduce

anthropogenic emissions by sources and directly

emit less than 15 kilotons of carbon dioxide

equivalent annually.


Bundled Solar System

610 off-grid solar PV projects:

• 30 projects of 500 W

• 350 projects of 600 W

• 106 project of 800 W

• 24 projects of 1.5 kW

• 100 projects of 1.7 kW

The total installed capacity of the bundled projects

is 515.8 kW.


Feed in Tariffs

• this term refers to the regulatory, minimum

guaranteed price per kWh that an electricity utility

has to pay to a private or independent producer of

renewable power fed into the grid.

• In the present research, the feed in tariffs level was

fixed to

US $ 0.5/kWh.


Net Metering

• It means that when the renewable system is supplying all of the

power that the homeowner needs, the electricity meter stops

spinning. However, if there is more generation than current

needs, the meter will turn backwards and the kWh read

decreases. When the renewable system isn't operating, the

electric utility provides power to the household and the meter

spins forward. Thus, the read increases again. In effect, the

utility becomes a kWh “bank” for the excess generation.

Household

Excess of

Generation

Electric Grid

Cloudy days,

nights


Monte Carlo Simulation

• Randomly generates values for

uncertain variables over and

over to simulate a model.

• For each uncertain variable a

probability distribution was

defined. The type of distribution

was selected based upon the

conditions surrounding that

variable as well as the available

data.

• The distribution and the

parameter values critically affect

the results of the simulation.


BETA DISTRIBUTION

f

( x)

=

z

( α −1)

( β −1)

β

(1 −

z)

( α,

β )

z

if 00

Otherwise 0

=

x − Min

Max − Min

Parameters

Minimum value, Maximum value, Alpha

(α ), Beta ( β)

β ( α,

β ) =

Γ(

α)

Γ(

β )

Γ(

α + β )

Variable represented: Household Energy Consumption, Sunshine Hour


GAMMA DISTRIBUTION

f

( x)

=




x


s

β −1

L ⎞

⎟ e


Γ(

β ) s

x−L


s

if x>L, 0


NORMAL DISTRIBUTION

f

( x)

=

1

e

2πσ

−(

x−μ)

2

/ 2σ

2

For -∞


TRIANGULAR DISTRIBUTION

Variable represented: Cost of the PV system, O&M costs, contract market,

Discount rate, CERs price


UNIFORM DISTRIBUTION

Variable represented: Cost of Wind turbine, percentage of energy trade in

the spot market and contract market, transaction cost


WEIBULL DISTRIBUTION

Variable represented: Inverter price, wind regime

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