Introduction to Wind Power Generation

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Introduction to Wind Energy

GenerationJulio Lemaitre

Infrastructure - PowerFebruary 5, 2009


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Renewable Generation

Example of Renewable Sources:

Hydro (large small)

Wind

Geothermal Heat

Bio-Fuels

Water Sea Waves, Sea Tides, Rivers


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Introduction

Wind Power:

Wind Power, derived through the kinetic energy of

the wind, utilizes wind blowing over the blades of a

turbine to create a lift which makes the blades

rotate.

The turning blades of the wind turbine rotate the

connected shaft which drives the generator,

converting the mechanical energy into electricity.

The power generated in the individual wind turbinegenerators (WTGs) is collected at a substation

which steps up the voltage to the grid level.


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Wind Turbines

Rotor bladesNacelle

Tower

Rotor HubGenerator

Gear-Box

Hub height

One Blade WTG Two Blade WTG Three Blade WTG

Yaw drive

System

Pitch

System

The three blade WTG is the predominant technology used


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Wind Power

Wind Poweris determined by:

oWind speed at hubheight

oWind direction

oWind distribution: Weibull k-factor

oAir density

oTurbulence

oPower curve of Wind Turbine GeneratorsoLosses

oUncertainties


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Wind Power (cont.) Wind Power is calculated from:

P=* air *A*V3*Cp (Watt)

P = Power (Watt)

air =air density (kg/m3)A= rotor area (m2)

V= wind speed (m/s)

Cp= power coefficient (dimensionless)

E = Energy (kWh)

Emax-annual=P*8.76 (kWh)


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air = air density (kg/m3) depends mainly on:

Air temperature (average on site)

Altitude (topography + hubheight)

Cp = coefficient depending on WTG design and speed.

Cp aver= 0.3 to 0.4 (Cp max=0.59 theoretical max)

V = annual averaged 10minute-average windspeed

Wind speed is measured using special measurement masts that

are installed at the wind farm location (usually more than one) to

register wind speed, wind direction and air temperature.

Wind mast have anemometers (of a certain accuracy class for

wind speed measurment) at 2 or 3 altitudes on the mast, a wind

wane (for wind direction measurement) and thermometers.

Wind speed is measured at least 1 time each 2 seconds (IEC).

Wind Power (cont.)


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Wind power is directly proportional to: Air density (lower density at higher altitudes)

Rotor area (harvested wind area in m2)

(latest average rotor diameters are 80 100m)

Wind Power (cont.)

The

harvested

power

increases

with therotor

swept

area

Source: Danish Wind Industry Association


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Wind Power (cont.)

And increases with the cube of: Wind speed (v3)

(Wind speed is the key evaluation factor for wind projects, as it

is water for hydroprojects)

Definition of Plant Factor (or Capacity Factor):

The plant factor (pf) is the annual energy output divided by thetheoretical maximum output, if the machine were running at its

rated (maximum) power during all of the 8766 hours of the year.

It measures the effective use of the investment (installed power)in the production of energy over a period of time (usually a year).

pf =Ereal (kWh)/ P (kW)*8766(h)


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General Layout of a WTG

WTG with gearbox.

The gearbox is one

of most stressed

components of the

WTG. It increases the

Blade/Rotor speed in

about 40-60 times tooperate the generator.

The pitch regulation

allows for optimal

adjustments of the

blades to the wind

conditions (speed).

The yaw control

positions the nacelle

(rotor) to the wind

direction.

Source: Dirk Kooman


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Power Curve

0

500

1000

1500

2000

2500

1 3 5 7 911

13

15

17

19

21

23

25

PinkW

Va in m/s

Cut in Wind speed

Cut out Wind speed

Nominal Wind speed

The power curve

gives the relation of

wind speed vs. power

for a given WTG.

The WTG requires a

minimum speed to

start generating (cut in

wind speed).

The power produced

will depend on the

wind speed until

reaching the nominal

value.

When the wind

reaches the cut out

wind speed, the WTG

stops generating to

avoid mechanical

stresses.


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Wind speed is very variable and changes permanently with the

time of day, the seasons and the years, as well as with the altitudeabove the ground. To evaluate the wind potential at a site, at least

one year of wind data (speed and direcction) is required (two or

more years are optimal at hub height of the WTG).

The measured wind data (1-2 sec sampling) is filtered andaveraged (10-min averages) per wind direction sector.

The measured wind data is than correlated to a long term data

source of 5-10 years (e.g. close by metheorological station).

Once the wind speed data is available, a wind speed distribution is

calculated at hub height of the specified turbine. From the wind distribution curve, the WTG power curve, the wind

farm layout + topography, the gross energy production over a

period of time (usually a year) can be calculated.

Wind Data and Energy Yield


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Using industry software tools (e.g. WASP1), that account for

topografy, surface roughness, etc., the gross energy yield of thewind farm is calculated.

The net energy production is calculated after considering all

elements affecting the energy production (losses, unavailability,

etc.)

Wind Data and Energy Yield (cont.)

The wind speed distribution showsthe frequency (in hours or per unit-pu) of occurrence of the measured

wind speeds. Combined with the

power curve of the WTG, the gross

energy production over a period oftime can be calculated.

Note 1: WASP is a PC program for predicting wind climates, wind resources and power productions from wind turbines and wind farms.

Frequency of occurrence


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The P50 value corresponds to the annual expected (mean)

net value calculated from the gross output of the measured

data minus the losses.

Main Losses: Range Typical

- Wake effect (wind shade effect from other WTGs) 3% - 10% (7%)

- Electrical losses (cables, grid, etc.) 2% - 4% (3%)

- Non Availability of WTG1 2% - 4% (3%)

- Non Availability of Grid & SS 1% - 4% (1%)

- Icing and blade contamination 0.5% - 1% (1%)

- Others (bird migration, local issues)

- TOTAL 12% - 18% (14%)Note 1: For Offshore ~ 10%

Note 2: In some calculations the topographic/roughness effect is included in the total losses increasing them up to ~25%-30%

Energy Yield


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Uncertainties for the calculation of higher probability of

exceedance values (e.g. P75, P90) are assumedindependent and normal distributed around P50, for 1 year

and 10 year energy yield periods.

Main Uncertainties: Typical- Wind speed statistics 5% - 8%

- Power curve performance 3% - 5%

- Model (topography, wake effect) 2% - 3%

- Metering 0.5% - 1%- Others (array losses, special issues)

- TOTAL Standard Deviation (SD) 11% - 17%

The main uncertainties result in a SD valueUsually the uncertainty (SD) is calculated for a 1year and a 10year period (SD1year>SD10year)

Energy Yield (cont.)


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Energy Yield (cont.) P50 best estimate 50% probability of exceedance

Based on a normal distribution and a Standard Deviation (SD),the 75%, 90% or 95% probability of exceedance values can becalculated.

The SD is calculated from the uncertainties of the data andP50 energy yield calculation.

P75 = P50 - 0,675*SD P90 = P50 - 1,28*SD

P95 = P50 - 1,64* SD

Example: output calculated gross: 100 GWh

Losses: 13% so P50 = 87 GWh Uncertainty SD calculated as 7.3% =6.35 GWh

P75 = 87-4.3= 82.7 GWh

P90 = 87- 8.1=78.9 GWh

=>In the financial evaluation, the P9010year is often used as the base case with

sensitivities ensuring that P901year (or P951year) still enables debt service.


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Example of Energy YieldTurbine V90 3MW

Hub Height (m) 105

Rated Capacity (MW) 3Number of Turbines 52

Site Capacity (MW) 156

Hub Height Wind speed (m/s) 7.1

Gross Wind Farm Energy Production (GWh/annum) 387.3

Gross Plant Factor 28.3%

Corrections and Losses

Topographic/Roughness Effect 0.997

Wake Losses 0.933

Electrical Transmission Efficiency 0.980

Substation Maintenance 0.998Grid downtime 0.990

Turbine Availability 0.970

Power Curve Performance 0.980

Icing and blade degradation 0.995

Wake from existing wind farms 0.998

Bird Migration Effect 0.990

Overall Conversion Efficiency 0.842

P - Values

Net P50 Wind Farm Yield (GWh/annum) 325.99

Net P50 Plant Factor 23.9%

Uncertainty over 1 year (incl . annual w ind speed variation)

Standard error in result 17.60%

P90 Energy Yield over 1 year (GWh/annum) 252.55

P90 Plant Factor over 1 year 18.5%

Uncertainty over 10 years (incl. annual wind speed variation)

Standard error in result 12.00%

P75 Energy Yield over 10 years (GWh/annum) 299.58

P75 Plant Factor over 10 years 21.9%

P90 Energy Yield over 10 years (GWh/annum) 275.92

P90 Plant Factor over 10 years 20.2%


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The final wind farm energy yield calculation can only be done

once the micrositting has been completed.

Micrositing is the process of optimally locating the individual WTGs

in the available land spaces to maximize the wind farm energy

production and minimize the wake and array losses1.

Appropiate spacing between WTGs is essential to achieve balance

between wake losses and construction costs (roads, electrical

network)

The adequate ditance will depend on the prevailing wind speed

and direction distribution at the site (wind rose). A spacing of 5 9 rotor diameters is recommended in the

prevailing wind direction.

A spacing of 3 5 rotor diameters is recommended in the

directions perpendicular to the prevailing wind direction.Note 1: Array losses originate from wind distortions created by close by located WTGs.

Wind Farm Layout


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Wind Farm Layout (cont.)

Prevailing wind direction

5-9 rotor diameters apart

in the wind direction

3-5 rotor diameters apart

Source: Danish Wind Industry Association


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In the construction and financing of a Wind Farm, several

contractual arrangements and legal aspects play an important

role:

Land Agrements (Lease or Purchase)

Turbine Supply Agreement (TSA) Engineering, Procurement and Construction (EPC) &

Installation or Balance of Plant (BOP)

Operation and Maintenance Agreement (O&M)

Power Purchase Agreement (PPA) Grid Interconnection Agreement (GIA)

Warranties and Guaranties, Insurance, etc.

Contractual Arrangements


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Vestas (DENMARK)

GE Energy (USA)

Gamesa (SPAIN)Enercon (GERMANY)

Suzlon (INDIA)

Siemens (GERMANY)

Goldw ind (CHINA)

Sinovel (CHINA)

Nordex (GERMANY)

Others

2007 Market Share of Top 10 Global WTG

Manufacturers

Top 10 WTG Manufactures

21%

15%

14%

13%

7%

10%

21%

4%

3%

3%

Source: BTM Consult Aps


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Based on wind farms developed so far we can comment on

some reference values for the main topics:

Investment costs

O&M costs

Performance

Schedule

Benchmarking Values


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Investment Costs

Benchmarking Values

Project WTG-Type WTG Total WTG

MW MW # Total Inv $/kW /kW Hard Cost Soft Cost Non EPC

$ million % CAPEXKavarna V90 - 3MW 3.0 156.0 52 378.0 2,423 1,731 84% 16% 4.6%(Bulgaria)Totoral V90 - 2MW 2.0 46.0 23 136.0 2,957 2,112 91% 9% 12.0%(Chile)

Zorlu GE - 2.5MW 2.5 135.0 54 294.0 2,178 1,556 85% 15% 10.0%(Turkey)

Samana E57 - 0.8MW 0.8 100.8 126 126 1,250 893 N/A N/A 10.0%Saundatti E57 - 0.8MW 0.8 82.4 103 106.0 1,286 919 N/A N/A 10.0%(India)

Typical 2,000-3,000 1,400-2,200 80% 20% 10%-15%

1U$ = 1.4 India 1,200-1,600 860 - 1,150

3.6% Funded +

Sponsor Support

7% under PFA

Funds

2.8% Development Fee

CAPEX

5% Funded +

Contingency

Sponsor SupportSponsor Support


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O&M Costs (per year)

Benchmarking Values

Project WTG-Type WTG Total WTG Total Inv

MW MW # $ million Type of $/kW $/kWh $/WTG % CAPEX TotalO&M $/year

Kavarna V90 - 3MW 3.0 156.0 52 378.0 WTG Suppl ier 44.4 2.11 133,135 1.83% 6,923,000(Bulgaria) Own (after 4y) 21.8 1.04 65,520 0.90% 3,407,040

Totoral V90 - 2MW 2.0 46.0 23 136.0 WTG Suppl ier 43.0 1.80 86,000 1.45% 1,978,000(Chile) Own (after 3y) 33.5 1.40 66,990 1.13% 1,540,770

Zorlu GE - 2.5MW 2.5 135.0 54 294.0 Own 37.4 1.25 93,411 1.72% 5,044,200(Turkey)

Samana E57 - 0.8MW 0.8 100.8 126 126 WTG Suppl ier 14.1 0.57 11,288 1.13% 1,422,225Saundatti E57 - 0.8MW 0.8 82.4 103 106.0 WTG Suppl ier 14.1 0.62 11,288 1.10% 1,162,613(India)

Typical WTG Suppl ier 42 - 46 1.7 - 2.4 80k - 130k 1.5% - 2.5%1U$ = 1.4 Own 20 - 37 1.0 - 1.4 40k - 90k 1.0% - 1.5%

India 15 - 20 0.5 - 1.0 10k - 30k 1.0% - 1.5%

O&M


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Performance

Benchmarking Values

Project WTG-Type WTG Total WTG Total Inv Total Losses Availability Average P50 Energy

MW MW # $ million % % P50 P90 Wind Speed per MW inst.

m/s P50 P90 MWh/year

Kavarna V90 - 3MW 3.0 156.0 52 378.0 15.8% 96.0% 23.9% 20.2% 7.10 326.6 276.0 2094(Bulgaria)

Totoral V90 - 2MW 2.0 46.0 23 136.0 26.3% 95.0% 27.3% 21.0% 6.65 110.0 84.6 2391(Chile)Zorlu GE - 2.5MW 2.5 135.0 54 294.0 12.6% 95.0% 34.2% 28.1% 7.20 404.2 332.2 2994

(Turkey)Samana E57 - 0.8MW 0.8 100.8 126 126 20.7% 96.0% 28.5% 22.5% 6.90 251.4 198.4 2492Saundatti E57 - 0.8MW 0.8 82.4 103 106.0 19.2% 96.0% 25.8% 18.7% 6.80 186.5 135.4 2262(India)

1U$ = 1.4 Typical 12%-18% 95% - 97% 25%-35% 20%-25% 6.0 - 8.0

* High value due to Topographic/Roughness effect (*) 15%-25%

Plant Factor - pf Energy

GWh/year


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Project Schedule (*)

Benchmarking Values

(*) From Permitting to Commercial Op.

(Resource Assessment & Site Selection

could add 6 12 months to the process)

Project WTG-Type WTG Total WTG Total InvMW MW # $ million

Kavarna V90 - 3MW 3.0 156.0 52 378.0(Bulgaria)Totoral V90 - 2MW 2.0 46.0 23 136.0(Chile)Zorlu GE - 2.5MW 2.5 135.0 54 294.0(Turkey)

Samana E57 - 0.8MW 0.8 100.8 126 126

Saundatti E57 - 0.8MW 0.8 82.4 103 106.0(India)

Typical1U$ = 1.4

19 - 22

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23 - 24

(Months)

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Total Project Schedule

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Documents

Introduction to Wind Power Generation