Wind Energy Projection for the Philippines Based on ... Projection.pdf · Wind Energy Projection for the Philippines Based on Climate Change Modeling A. Silang1,2,5, ... Solar WIND

Wind Energy Projection for the Philippines

Based on Climate Change Modeling

A. Silang1,2,5, S. Uy3, J. Dado3, F. Cruz3, G. Narisma3,4, N. Libatique1,2 and G. Tangonan1

1Ateneo Innovation Center, Ateneo de Manila University2Electronics, Computer and Communications Engineering, Ateneo de Manila University

3Manila Observatory4Atmospheric Science Program, Physics Department, Ateneo de Manila University

5Alternergy Philippine Holdings Corporation

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Pullman Bangkok King Power Hotel

31 May 2013

Effects on AgricultureNatural

Calamities

Increase in RainfallTemperature

Increase

Reforestation

Water and Energy

Conservation

Government policies

Renewable Energy: BiomassHydroSolar

WIND

Source: www.southchinasea.org

How would climate change

affect the production of a wind farm during its

25-year lifetime?

Projections show that there will be an increase of in wind

power density, an increase of in energy production and a

decrease of in levelized cost of electricity (LCOE) for the

proposed wind farm in Pililla, Rizal, Philippines.

6%

6%

7%

Western US High Plains (Greene, 2012)2

1970-2000 2040-2070

7%-17%

China (Ren, 2010)1

1971-2000 2071-2100

14%

NARCCAP

8 climate models in CMIP3 archive

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1 Ren D.; Effects of Global Warming on Wind Energy Availability. Journal of Renewable and Sustainable Energy 2, September 2010.2 Greene J. S., Chatelain M., Morrissey M., Stadler S.; Projected Future Wind Speed and Wind Power Density Trends Over the Western US High Plains. Atmospheric and

Climate Sciences, vol. 2, pp. 32-40, January 2012.

2008-2012+

-

2013-2017

+

-

2018-2022

+

-

2023-2027

+

-

2028-2032

+

-

2033-2037

• A baseline period of five years is chosen to make the projection results of

this research comparable to the on-site measurement of wind developers.

• A period ≥ 5 years is more acceptable for an analysis using a regional climate

model.

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WIND ENERGY POTENTIAL OF THE

PHILIPPINES

According to the study by National Renewable

Energy Laboratory (NREL) in 2001, the Philippines

has a wind potential of 76 GW.3

RENEWABLE ENERGY POLICY

In the Philippines, the Feed-in-Tariff for renewable

energy was determined on October 2012.

Three wind farm projects were declared commercial

by the Philippine Department of Energy (DOE)

recently.

AVAILABLE WIND MEASUREMENTS

Most local wind measurements are not appropriate

for long-term wind correlation for wind assessment.

Wind developers measure at the site location.

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3 Elliot D.L.; Philippines Wind Energy Resource Atlas Development. National Renewable Energy Laboratory. November 2000.

• Located in Pililla, Rizal, Philippines

with coordinates 14.4773N, 121.366E

(WGS84)

• Has sloping terrain with elevation of

250 m to 400 m above sea level

• Vegetation in the area is mostly

composed of cogon grass, shrubs and

agricultural crops

• The nearest connection point to the

power grid is located 5-10 km from

the wind farm

• The proposed Pililla Wind Farm is

being developed by Alternergy, which

was awarded with the Certificate of

Commerciality by DOE on May 16,

2013.

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Source: Alternergy

• The work uses the International Centre for Theoretical Physics (ICTP)

Regional Climate Model version 3 (RegCM3) to make projections for the

wind fields at Pililla, Rizal.

• Projection simulations of the model assumes A1B scenario of the

European Centre/Hamburg 5 (ECHAM5) global climate model. The

A1B scenario pertains to a world where economic development

continues at its current pace but mitigation measures are being

implemented.

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• A relatively coarse spatial resolution of 40 km x 40 km over the

Philippine domain is simulated. Results from this run are used to

provide boundary conditions for another run at a finer resolution of 10

km x 10 km over the region of interest.

• RegCM3 results for Pililla, Rizal at heights 10 m and 110.8 m from 2008-

2037 are used in this research.

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Source: www.wmo.int

A. Interpolation of wind speed to the hub height of 80 meters

where v is the wind speed at height z and vref is

the wind speed at the reference height, zref while

a is the power law exponent

Power Law:

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Source: www.bats.org.uk

B. Sorting wind data by wind direction

Weibull distribution

where f(v|A,k) is the probability density

function of wind speed v, A is the scale

parameter and k is the shape parameter

where p is the probability of the wind

coming from the predominant wind

direction

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C. Computing for Wind Power Density

where P/A is the wind power density in W/m2, ρ is the air density in

kg/m3 and v is the wind speed in m/s

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D. Computing for Energy Production

Energy Production:

where N is the number of samples, t is the

time period, Pv is the power produced for a

given wind speed v in the power curve of

Nordex N80-2.5MW and fv is the frequency

of v based on Weibull distribution

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Wind Direction

No change in the

prevailing NE

wind direction.

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2008-2012 2013-2017 2018-2022

2023-2027 2028-2032 2033-2037

Wind Speed

0

1

2

3

4

5

6

7

8

9

10

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Win

d S

peed

(m

/s)

Month

2008-2012

2013-2017

2018-2022

2023-2027

2028-2032

2033-2037

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Minimal changes

in wind speed

seasonality.

The wind speed during the baseline years has a unimodal distribution. The same is

observed among all the 5-year periods of RegCM3 projection from 2013-2037.

Wind Speed

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Wind Speed

Period

scale parameter,

A

shape parameter,

k

2008-2012 6.191 2.199

2013-2017 6.456 2.38

2018-2022 6.401 2.47

2023-2027 6.358 2.176

2028-2032 6.473 2.251

2033-2037 6.099 2.078

Comparison of Weibull distribution functions of RegCM3 5-year interval

results from 2008-2037

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2013-2017 2018-2022 2023-2027 2028-2032 2033-2037

2008-2012 6% 1% 9%

12%

1%

6%

average

2008-2012

2013-2017 2018-2022 2023-2027 2028-2032 2033-2037

8% 3% 9%

12%

0%

6%

average

Wind Power Density

Energy Production

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LCOE= the average levelized cost of electricity generation

It = investment expenditures in the year t

Mt = operations and maintenance expenditures in the year t

Ft = is the fuel expenditures in the year t

Et = electricity generation in the year t

r = discount rate

n = life of the system

n

tt

t

n

tt

ttt

r

E

r

FMI

1

1

)1(

)1(LCOE

LCOE decreases as the

electricity generation

increases.

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LCOE Computation4

4 Renewable Power Generation Costs in 2012: An Overview. International Renewable Energy Agency. Retrieved from http://www.irena.org/Publications

• Use measured wind data from developers

• Apply the model to other wind farm locations, onshore and

offshore

• Use other climate models to compare the results of this research

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Contact e-mail: [email protected]