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GCREEDER2009,AmmanJordan,March31stApril2nd2009

Estimation and Assessment of Wind Energy in Some Areas inLibya

Estimation of wind characteristics is considered as the first essential step to evaluate a wind energy project

based on information about all aspects of the implementation and operation of the project. It's therefore

necessary to have detailed knowledge of the wind to select the suitable wind turbine for a certain zone and alsoto estimate its performance accurately.

This project studies the wind energy and wind assessment in some selected sites such as Misurata,

Beniwalid, , Ghariat, Nalut, Esspeea, Tripoli air port, Elzawia, Hon, Obary, Ghat. This project first provides

background information about wind power including a review of available data, which are obtained from therepresentative meteorological stations.

The mean wind speed, the Weibull distribution, annual energy and annual capacity factor are calculated foreach site. The annual energy and annual capacity factor calculation are based on specification of two types of

wind turbines. This study indicates that wind energy is available in some sites in Libya, and Misurata has the

maximum annual energy and capacity factor.1- INTRODUCTION

Wind energy is an indirect form of solar

energy. Between 1-2% of the solar radiation that

reaches the Earth is converted into energy in the

wind. Winds result from an unequal heating of

different parts of the Earth's surface, causing

cooler dense air to circulate to replace warmer,

lighter air. While some of the sun's energy is

absorbed directly into the air, most of the energy

in the wind is first absorbed by the surface of the

Earth and then transferred to the air by

convection.

The wind speed increases with the height abovethe ground, due to the frictional drag of the

ground, vegetation and buildings. It is clear that

any plans to harness the wind must take into

account these variables.

This paper outlines physical phenomena that are

related to the characteristics of the wind for the

selected areas (Misurata, Beniwalid, , Ghariat, Nalut, Esspeea, Tripoli air port, Elzawia, Hon,

Obary, Ghat)

Because the cost of wind energy development

depends sensitively on the nature of the wind

resource, any detailed evaluation of wind energyeconomics requires a series of wind assessmentstudies. A wind energy assessment is an

integrated analysis of the potential wind energy

resources of a particular area. Such anassessment begins with an understanding of the

general wind patterns of the area, and progresses

to the collection and analysis of wind data. Wind

assessment may also involve a monitoring

program and, at the most advanced stages,

computer simulations of wind flow to determine

wind turbine micro-sitting

2-WIND ASSESSMENT

Once an area has been chosen forassessment, it is necessary to collect wind speed

and direction data. A complete wind resource

assessment involves a dense network ofanemometers (wind monitoring stations)

recording continuous wind data for at least oneyear. Since such wind monitoring efforts are

time consuming and costly, wind researchers

often obtain data sets that have been previouslyrecorded.

Several sources may be helpful in obtaining

existing meteorological databases. For example,Climatological stations, and airports are likely to

maintain reliable records.

If possible, existing data sets should be

supplemented with spot measurements. Whenchoosing sites to examine for potential wind

development, the researcher should focus onareas likely to have enhanced wind speeds.

In this paper data are obtained from the localmeteorological station of each area,

Samples of this data are shown in tables 1 and 2.

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Table 1. Monthly average wind speed for Misurata station (m/s)

Months 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Jan 5.218 4.437 4.479 4.927 4.514 4.855 4.875 5.357 5.64 5.885

Feb 6.711 5.317 5.207 4.887 5.132 5.793 5.568 6.173 4.95 5.393

Mar 4.898 5.839 6.898 5.287 4.929 5.291 6.726 5.492 5.862 4.597

Apr 5.922 5.688 5.544 4.875 5.832 5.785 5.139 5.287 5.680 5.398

May 5.029 5.255 5.332 4.319 4.971 5.802 5.266 5.711 5.870 4.458

Jun 5.167 5.192 4.688 4.476 4.744 4.017 4.229 4.117 4.510 3.562

Jul 4.774 4.601 4.441 4.25 4.994 4.965 4.288 4.066 4.121 3.739

Aug 4.722 4.198 4.161 3.43 4.142 4.313 4.701 3.874 4.402 3.871

Sep 5.115 4.667 4.802 4.218 4.624 4.971 4.622 4.405 4.628 4.607

Oct 5.361 4.036 5.453 4.132 4.265 4.003 4.489 4.341 3.258 3.575

Nov 5.049 5.158 4.635 4.845 4.358 5.752 5.257 4.388 4.695 3.072

Dec 5.206 5.145 4.867 4.898 4.821 5.451 4.556 5.918 5.077 4.207

Average 5.264 4.961 5.042 4.545 4.777 5.083 4.976 4.927 4.891 4.363

Table 2. Monthly average of wind speed for Beniwaleid station (m/s)

Months 1998 1999 2000 2001 2002 2003 2004 2005

January 2.697 4.198 4.446 5.05 4.315 5.075 4.971 5.176

February 2.287 4.221 4.899 5.414 4.991 5.069 4.701 5.240

March 3.397 4.350 4.622 5.048 5.982 4.753 4.691 4.682

April 3.145 4.029 5.617 5.798 5.802 5.482 5.145 5.467

May 1.920 3.285 5.461 6.022 5.801 5.561 5.359 4.715

June 3.328 4.171 5.081 4.913 4.547 4.203 4.808 4.154

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July 2.755 4.587 5.230 4.628 4.938 4.039 4.128 4.003

August 2.990 3.943 4.215 4.574 5.150 3.673 3.958 4.315

September 2.978 4.356 4.643 5.139 4.622 4.242 4.261 4.815

October 3.056 4.026 4.236 3.723 4.446 3.922 3.623 3.785

November 3.371 4.287 4.021 5.383 5.031 3.866 4.757 6.356

December 4.036 5.390 4.495 5.453 4.558 5.112 4.63 4.786

Average 2.996 4.236 4.747 5.095 5.01 4.583 4.586 4.787

3 WIND DATA ANALYSISThe analysis of wind data include a knowledge

of wind direction and wind speed data in order to

estimate wind power production in particular

site. Long term wind data from the

meteorological stations near the candidate site

can be used for making the estimation. These

data which may be available for long periods

should be extrapolated to represent the wind

profile at the potential site.

3.1 MEAN WIND SPEED

The mean wind speed is the most commonly

used indicator of wind production potential

where defined as

.(1

Where N is the sample size and Vi is the

observation value

3.2 WIND SPEED VARIATION WITHHEIGHT

Wind speed near the ground changes with height,

at height about 2km above the ground the

change in the wind speed becomes zero. The

most common expressions for the variation of

wind speed with hub height are based onexperiments are given below.

POWER LAW FUNCTION

The power law represent a simple model for

vertical wind speed profile having the following

form.

=

rr z

zzVzV *)()( .(2

)

Where )(zV is the wind speed at height Z,

)(r

zV is the reference wind speed at height rz ,

and is the power law exponent which depend

on the roughness of the terrain. Atypical value of

might be 0.1.

LOGARITHMIC FUNCTION (LOGLAW)

The log law function used to estimate windspeed from a reference height to anther level. Its

basic form is

=

00

lnln)()(z

zz

zzVzV rr

.(3

=

=N

iim

VN

V1

1

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Where )(r

zV is the wind at height rz above

the ground level, and 0z is the roughness length.

The parameters and 0z for different types of

terrain are shown in table(3)

Table3. Wind speed parameters for calculating a

vertical profile

Type of terrainRoughness

class

roughness length, 0z (m)Exponent,

Water areas 0 0.001 0.01

Open country, few surface

features1 0.12 0.12

Farmland with buildings

and hedges2 0.05 0.16

Farmland with many trees,

forests, villages3 0.3 0.28

4 WIND STATISTICS

Wind speed distribution can typically be

described in terms of the Weibull distribution.

The equation of non-cumulative weibull

distribution is:

(4

While the cumulative Weibull distribution is:

.. (5)

Where k is the shape parameter and C is the

scale parameter. Finding a best fit Weibull

distribution is a convenient way to approximate a

continuous wind speed distribution from the

discrete observed values. In addition, thismethod is also useful in that the wind regime of

an area can then be described using only the two

Weibull parameters, k and C.

The parameters C and k for the Weibull

frequency distribution can be found by plotting

ln(V) against ln(-ln(P(V)), where ln is the

logarithm to base e, and fitting a straight line to

the points. The slop of the line is equal to k and

C is equal to exp(ln V), or V, where ln(-ln(P(V))

is zero. This technique is based on taking

logarithms of cumulative Weibull distribution

twice.

5. ANNUAL ENERGY AND CAPACITYFACTOR

Calculation of annual energy out put requires a

knowledge of wind speed frequency distribution

and the system power out put of each turbine as a

function of wind speed. The long-term wind

speed distribution is combined with the power

curve of the turbine to give the energy generated

kk

C

V

C

V

C

kVp

=

exp)(

1

=

C

VVP exp)(

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at each wind speed and hence the total energy

generated throughout the year. Bin width of wind

speed is usually 1 m/s. The general equation for

calculating annual energy out put is.

Energy=

==

ni

i

UiPUiH1

)().( ..(6)

Where

H(Ui) is the number of hours in wind speed bin

Ui, and P(Ui) is the power output at that wind

speed.

Another measure is the capacity factor (CF) is

defined as the ratio of the actual annual energy

output to the theoretical maximum output, if the

machine were running at its rated power during

all the 8760 h of the year. The capacity factor, iscalculated as

1008760

(%)

=apacityrated

outputenergyactualCf

There are several similar measures of power

plant performance. To avoid confusion when

comparing the performance of wind plant, the

precise definitions of availability or load factor

should be clearly understood.

RESULTS AND DISCUSSION

To determine Weibull frequency distribution and

Weibull cumulative distribution, it is necessary

to determine first the scle parameter (C) and the

shape parameter (k). figures 1 and 2 show the

technique that used to determine these

parameters for Misurata and Benwalied cities (as

a sample), the values of scale parameter was C=

6.13 m/s to Misurata city and C= 5.8 m/s to

Beniwalied city. While the slop of straight line is

the value of the shape parameter which was k=

1.98 for Misurata and k= 1.93 for Beniwalied,

and the values of these parameters for other areas

are indicated in figure 7.

Figures 3 and 4 show the histogram for the

probability of wind speed which drawn by using

the values of scale and shape parameters with

equation 4, from this histogram its clear that the

wind speed that has maximum frequency was 4

m/s in Misurata (profitability= 13.8 %) and 4 m/s

also in Beniwalied (Profitability = 14.4 %), and

the annual mean wind speed can be estimated

from the histogram of the probability of wind

speed by take a summation of multiply each

wind speed in its profitability, the mean wind

speed was 4.88 m/s in Misurata and 4.47 m/s in

Beniwalied, while figure 8 shows the values of

mean wind speed and the wind speed of

maximum frequency for the other areas. Figures

5 and 6 show the Weibull cumulative

distribution which gives the probability of wind

speed exceeding the value of any given wind

speed.

The calculations of annual energy and capacity

factor for each site are based on the data of

Vestas V52 wind turbine, which has the rotor

diameter of 52 meters and rated power of 850

kW. Figure 9 shows the annual energy for each

area, the maximum energy was 1327.6 MWhin Misurata, while the minimum one was 173.2

MWh in Obary, from these values it seems that

this type of wind turbine is proper in some areas

like Misurata and it is not adequate for another

locations such as Obary and Esspeea.

The final results of calculations are summarized

in table 4

Table 4 Performance of the Areas under study

Annual

capacity

factor(%)

Annual

energy

(MWh)

Wind speed of

max frequency

(m/s)

Annual mean

wind

speed(m/s)

Shape

parameter

k

Scale

parameter

C(m/s)

City

17.8 1327.6 4 4.88 1.98 6.13 Misurata

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15.6 1166.4 4 4.47 1.93 5.80 Beniwaleid

14.0 1043.7 3 4.18 1.72 5.07 Elghariat

11.3 846.9 3 3.81 1.79 5.04 Tripoli Air Port

08.3 621.7 2 2.99 1.41 4.16 Ghat

13.5 1006.6 3 4.01 1.73 4.76 Hon

06.2 467.3 2 2.72 1.62 3.94 Al-zawia

10.9 814.4 3 3.81 1.73 5.03 Nalut

03.6 173.2 1 1.92 1.41 3.10 Obary

04.7 356.4 2 2.09 1.51 3.51 Esspeea

Figure 1. Graphical determination of Weibull parameters for Musrata city

y = 1.9847x - 3.6

-4

-3

-2

-1

0

1

2

3

0 0.5 1 1.5 2 2.5 3 3.5

ln(v)

ln(-ln(P(v)))

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Figure 2.Graphical determination of Weibull parameters for Beniwaleid city

y = 1.9349x - 3.4

-4

-3

-2

-1

0

1

2

3

0 0.5 1 1.5 2 2.5 3 3.5

in(v)

in(-in(P(v)))

'

Figure3. Histogram and weibull function for the probability of Misurata city

0

2

4

6

8

10

12

14

16

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

wind speed(m/s)

Profitability(%

)

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Figure 4 Histogram and weibull function for the probability of Beniwaleid city

0

2

4

6

8

10

12

14

16

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

wind speed(m/s)

Profitability(%)

Figure 5. Cumulative Weibull distribuation for Misurata city

0

10

20

30

40

50

60

70

80

90

100

110

0 1 2 3 4 5 6 7 8 9 10 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8

Wind speed(m/s)

Percentageoftimewindsp

eedexeddsv

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Figure 6. Cumulative Weibull distribuation fo Beniwaleid city

0

10

20

30

40

50

60

70

80

90

100

110

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Wind speed(m/s)

Percentageoftimewindspeedexedds(v)

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Figure7. Scale and Shape parameter

0

1

2

3

4

5

6

7

Beniwaleid

MisurataHon

ElghriatNalut Ghat

AlZawia

Essbeea

TripoliA/PObary

Scalleparameter(m/s)andshapeparameter

scale parameter

shape parameter

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Figure 8. Mean wind speed and wind speed of maximum frequency

0

1

2

3

4

5

6

Nalut Hon Elghriat Tripoli A/P Al Zawia Ghat Beniwaleid Misurata Obary Esspeea

Windspeed(m/s)

Annual mean wind speed

Wind speed of maximum frequncy

Figure9. Annual energy by using Vestas v-52 wind turbines

0

200

400

600

800

1000

1200

1400

Beniwaleid

Misurata

ElghriatHon

TripoliA/PNalutGhat

AlZawia

Obary

Essbeea

Energy(MWh)

ConclusionandRecommendations 1. This study indicates that the wind power

varies from location to another, hence we

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should install the proper wind turbine in

the right suitable zone

2. Misurata has the maximum annual

energy and capacity factor while Obary

has the minimum annual energy and

capacity factor

3. Existing data resources indicates that the

mean annual wind speed of over 4.88 m/s

at Misurata with theoretical capacity

factor exceeding 17.8 %. These values

indicate that Misurata could generate

1327.625 MWh

4. This work should be extend to study the

wind energy at different locations, this

will help the resources in this field

5. Making campaigns to measure wind

speed data in order to cover the majority

in our country, paving the way for

making a wind Atlas

6. Studying the effect of the geographic

distribution of the wind power stations on

the actual power of the wind energy

7. Making studies about the effect of

entering the wind energy systems to the

general electric grid

8. The whole area of the country should be

examined to detect the fields proper for

the establishment of wind turbine farms,

and public initiatives should start

establishing wind energy farms in the

selected areas.

9. One or more pilot project should be

implemented to demonstrate feasibility

and to develop skills. A pilot project

requires careful preparation and planning

in order to be successful. Essentialcomponents in pilot project include the

following.

Cost and performance data from wind turbine

manufacturers

Information about current electricity

generation

Preliminary and final project desires

The final decision on pilot project

implementation is dependent on site data

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