Wind energy potential in Tunisia

ARTICLE IN PRESS

Renewable Energy 33 (2008) 758–768 www.elsevier.com/locate/renene

Wind energy potential in Tunisia M. Elamouria,, F. Ben Amara,b a

Preparatory Institute for Engineering Studies of Sfax, Tunisia Network and Machines Electric Research Unit, INSAT, Tunisia

b

Available online 4 June 2007

Abstract The purpose of this paper is to constitute a database for the users of the wind power. It presents the study of 17 synoptic sites distributed on all the territory of Tunisia. From the meteorological data provided by the Meteorology National Institute (INM), two statistical methods (meteorological and Weibull) are presented to evaluate the wind speed characteristics and the wind power potential at a height of 10 m above ground level and in an open area. An extrapolation of these characteristics with the height is also carried out. The results obtained give a global vision of the distribution of the wind potential in Tunisia and define the most windy zones. r 2007 Elsevier Ltd. All rights reserved. Keywords: Tunisia; Wind speed characteristics; Wind power potential; Meteorological method; Weibull method; Wind rose

1. Introduction In any installation of a wind system, two natural elements must to be considered: the site and wind. Indeed, the choice of the site very favorably conditions the wind turbine performances taking into account the wind strong local variations which can be recorded. The best sites are in general where the wind blows most regularly. In addition, the knowledge of the wind characteristics is essential to estimate the annual wind power of the site. For that, complete meterological data on site must be taken and analyzed at least during a year to justify the credibility of the wind as a source of energy. Our study is mainly related to the analysis of the 17 synoptic sites indicated on the Tunisian territory geographical chart (Fig. 1).

2. Relief Tunisia, with a surface of 164  150 km2, is the smallest Maghreb country. It occupies a geographical zone between 30 and 371N latitude and between 8 and 121E longitude (Fig. 2). It opens largely on the Mediterranean Sea with 1298 km of coasts, delimited in the West by Algeria and in Corresponding author. Tel.: +216 74 241 403; fax: +216 74 246 347.

E-mail addresses: [email protected] (M. Elamouri), [email protected] (F. Ben Amar). 0960-1481/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.renene.2007.04.005

the south by Libya. Tunisia is a flat country as a whole, except for the North–West and the West which are mountainous areas. Maximum altitude is 1544 m to the mountain Chaambi, close to Kasserine. The mountain is replaced gradually by low steppes, then by eastern coasts which develop from CapBon to the Libyan border. In the south, it is the desert of stony Hamadas which are spread out, starting from the mounts of Ksour, in the direction of the dunes of large Eastern Erg. The coasts are cut out by deep gulfs (Bizerte, Tunis, Hammamet, Gabes) and many islands (Kerkenna, Djerba, etc.). Tunisia undergoes the Mediterranean influence as well as the continentalism which appears as soon as one moves away from the coast. In summer, the aridity appears by heat and the dryness related to the Saharan air flow, with the presence of the Chehili (hot wind) which blows from the desert in spring. 3. Study plan Our contribution in this paper is the study of 17 synoptic sites indicated on the Tunisian territory geographical chart (Fig. 1). The meteorological data of the wind are provided by the Meteorology National Institute (INM). These data are gathered for different periods (see Table 1). It is, however, very delicate to directly compare these data of the wind taking into account, in particular, the sites, variety and sensor height. With an aim of a comparative study

ARTICLE IN PRESS M. Elamouri, F. Ben Amar / Renewable Energy 33 (2008) 758–768

between the sites, it is necessary to correct the measurements of the wind of the local constraints (topography and roughness of the site, sensor height above the ground) [3]. The sites geographical analysis, by the physical cards 1/500,000th, has made it possible to establish the corrections necessary to bring back the initial data of the wind to a standard site in an open area and to 10 m height (Table 1).

759

In this study, our effort is mainly devoted to the evaluation of the wind characteristics of each site by the statistical methods: Meteorological and Weibull. Our objective is to estimate the wind energy available and characteristic speeds (the mean speed, the most energetic speed and the most frequent speed) at 10 m height in open area and to undertake a comparative study of these characteristics in relation to the height. 4. Methods of meteorological data analysis The evaluation of the wind potential energy in a site passes by the analysis of the distribution of the mean wind speed in intensity and direction. In this study, we use two computation methods of wind characteristics:

 

meteorological method using the tables of cumulated frequencies of mean speeds; Weibull distribution analytical method.

4.1. Meteorological method The national meteorology tables indicate the cumulative frequencies of the wind speed superior or equal at the classified speeds. The occurrence frequencies are defined by the following relation: f ðV¯ Þ ¼ F ðV¯ Þ  F ðV¯ þ 1Þ,

(1)

where V¯ is the classified speed, f is the occurrence frequency and F is the cumulated frequency. The recoverable power of the wind P, per unit area, is estimated by the Betz formula: P¼

n X 1 16 3 rð1 þ 3I 2 Þ V¯ i f ðV¯ i Þ ðW=m2 Þ, 2 27 i¼1

(2)

where r is the air density (1.225 kg/m3) and I is the turbulence intensity (I ¼ 0.2 at 10 m in open areas). Therefore, the available annual energy E, per unit area, is Fig. 1. Distribution of meteorological stations over Tunisian territory. (in on line version the filled circle is in red) The location of stations under study.

E¼

24  365 P ðkWh=m2 =yearÞ. 1000

Fig. 2. Orography of the Tunisian Region (obtained from Ref. [1]).

(3)

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Table 1 Descriptions of the different wind station sites and the correction factors relating to the height, roughness and topograhy [2] Sites and measurement period

Sensor height (m)

Bizerte, 1974–2004

12

Klibia, 1974–2004 Tabarka, 1974–2004

Tunis-Carthage, 1974–2004 Jendouba, 1974–2004

Monastir, 1974–2004

Kairouan, 1970–1979

6 12

12 12.2

12

13

Altitude (m)

Azimuth sector

Height correction

All directions E–S

1.029

28.5

All directions NNE–SSW

0.695

21

NNW–E ESE–S SSW–NW

6

4 142.5

3

68

1.29 1.029 1.184 1.029

All directions WNW–N ESE–SW

1.032

NNW–NNE NNE–SSW SW–W NE–NW

1.292 1.029

1.103 0.831

0.987 1.121 0.788 1.045 0.824 0.762

Thala, 1976–2004

12

1090

All directions

1.029

Sidi-Bouzid, 1978–2004

10

354

All directions NNW–NNE

1.029

Sfax, 1974–2004

12

23

All directions

1.29

Gafsa, 1970–1979

11

314

All directions S–NW E–SSE NW–N

1.011

All directions SW–WNW

1.029

Tozeur, 1970–1979 Gabes, 1970–1979

Djerba, 1970–1979

12 12

12

92 5

4

N–ENE E ESE SE–SW WSW–W WNW–NNW All directions SW–NE

0.74

1.029 0.703 0.713 1.029 1.09

117

W–N NNE–WSW

Remada, 1974–2004

12.5

301

NW–E ESE–SSW SW–WNW

1.037

All directions

1.029

258

The mean speed Vm, the most energetic speed VE and the most frequent speed VF are defined by the following expressions: Vm ¼

n X

V¯ i f ðV¯ i Þ,

1.014

0.868 1.012 0.961

10

12

1.244

0.81 1.05 1.05

Medenine, 1977–1979

Elborma, 1979–2004

Topographic correction

1.262

W–E ESE–WEW

NNW–NE ENE–SSE S–SSW SW–W WNW–NW

Roughness correction

0.85 0.85 0.94 0.968

V F ¼ V¯ ðf ðV¯ Þ max iÞ.

(6)

(4)

4.2. Weibull method

(5)

The Weibull law [4,5] is the most frequently used model to describe the distribution of the wind speed. The probability

i¼0

V E ¼ V¯ ðEðV¯ Þ max iÞ,

ARTICLE IN PRESS M. Elamouri, F. Ben Amar / Renewable Energy 33 (2008) 758–768

density function of the wind speed is given by "  K #  K1 ¯ K V V¯ , exp  f ðV¯ Þ ¼ A A A

(7)

where f ðV¯ Þ is the distribution probability of wind speed V¯ , A is Weibull scale parameter and K is the dimensionless Weibull shape parameter. In addition, these two Weibull parameters and the characteristic speeds of the studied site (Vm, VE and VF) are connected by the following relations:   1 V m ¼ AG 1 þ , (8) K

761

equivalent factor of correction Ce for all the directions. This factor is calculated at the base of the relation of energy (3) and has as an expression 11=3 0pn P P 3 ¯ ¯ Bj¼1 i¼1 f j ðV i Þ½V i =C j  C B C Ce ¼ B (14) C , n P @ A f ðV¯ i Þ½V¯ i 3 i¼1

where fj and Cj are, respectively, the distribution probability and the corrector coefficient, for direction j, with 1  j  p; p 2 N. 5.1. Wind speed characteristic estimation and analysis

  2 1=K VE ¼ A 1 þ , K

(9)

  1 1=K VF ¼ A 1  . K

(10)

where G is the defined gamma function, for any reality x positive not null, by Z þ1 GðxÞ ¼ tx1 et dt. (11) 0

The cumulated value of the Weibull distribution so that the speed of the wind is superior or equal at the classified speed V is given by "  K # Z þ1 V¯ F ðV¯ Þ ¼ f ðV¯ Þ dV¯ ¼ exp  . (12) A V¯ The theoretical maximum power that can be extracted from an optimum wind turbine is limited by the Betz equation:   1 16 3 3 2 rð1 þ 3I ÞA G 1 þ (13) P¼ ðW=m2 Þ. 2 27 K The annual energy production E is then calculated by expression (3). 5. Wind resource evaluation The evaluation of the wind resource in each site makes it possible to highlight the zones most favorable to the exploitation of the wind. The comparative study of these resources requires the transposition of measurements of wind to the relative values to a site in an open area, with height 10 m above ground level. Indeed, the wind measurements provided by the INM are strongly influenced by the characteristics of the site (effects of roughness, of wake and topographic) surrounding the measuring station and the sensor height. Thus, we have to make corrections necessary to these wind measurements. Let us note that these corrections can affect only certain directions. To adapt the law of Weibull to the sites whose directions are affected by various weightings, we have defined an

The energy quantity which could be actually extracted from the wind resource in Tunisia is determined by the knowledge of the wind characteristics. The two parameters of Weibull A and K are determined by the least-squares method. Table 2 gives the annual values of these geographical parameters and the equivalent correction Ce corresponding to the various sites. These parameters make it possible to plot the curves of the classified frequencies and the energies of wind speeds. Fig. 3 illustrates the good recovery of meteorological data by the Weibull distribution. The distribution of the wind speed vector of each site is represented by wind rose of 16 directions (Fig. 4). This presentation makes it possible to reveal the direction and the intensity of the wind dominant. It shows that the West and the North–East are the most prevalent directions of the wind in Tunisia. The obtained numerical results by the two methods (meteorological and Weibull) are presented in Tables 3 and 4. We note that these results are comparable, which seems

Table 2 The weibull parameters and the equivalent factor of correction for different sites Sites

A (m/s)

K

Ce

Thela Klebia Bizerte Monastir Tunis-Chartage Elborma Remada Gabes Jandouba Djerba Kairaouan Sfax Medenine Tabarka Gafsa Sidi-Bouzid Tozeur

5.423 4.773 4.389 4.465 4.334 4.589 3.727 4.246 3.630 3.955 3.678 3.488 2.879 2.908 3.378 2.915 2.472

1.747 1.581 1.580 1.621 1.558 1.752 1.379 1.881 1.424 1.708 1.576 1.527 1.223 1.329 1.746 1.517 1.373

1.280 0.744 1.050 1.117 1.036 1.029 0.971 0.832 0.908 1.072 0.861 1.029 0.938 1.186 0.949 1.029 0.909

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normal, since the model of Weibull is established from the initial data of the meteorological method. However, a light difference on the values of frequent speed and the most energetic speed is noticed. Wind mean speed in Tunisia is modest. It varies between 2 and 4.8 m/s. The examination of Table 2 shows that the annual mean speed exceeds 4 m/s in two areas: Thala in the West (the mountainous area) and Klibia on the Northeastern coast. Moreover, these two areas are most favorable for wind power exploitation because they have the highest wind energy. On the other hand, the most frequent speed in Tunisia does not exceed 4 m/s.

The computation results of the power and recoverable annual energy for the 17 synoptic sites, per unit of area, are carried out in Table 4. In order to prove the most frequent directions of the wind in Tunisia, we have traced a wind rose chart with 16 directions for each site. The examination of these wind roses (Fig. 4) shows that the dominant directions of the wind are the West (effect of the Atlantic Ocean) and the North–East (effect of the Mediterranean). In the same way, the histograms and their adjustments by the Weibull law of the wind frequency distribution (all confused directions) and of the wind energy available for

Fig. 3. Comparison of the distributions of the mean wind speed and the available wind energy for diffrent sites (10 m in open area).

ARTICLE IN PRESS M. Elamouri, F. Ben Amar / Renewable Energy 33 (2008) 758–768

763

V≥7.5 m/s 4.5≤V<7.5 m/s 2.5≤V<4.5 m/s 0.5≤V<2.5 m/s V<0.5 m/s

Fig. 4. Wind roses charts for the different sites.

various speed classes are represented in Fig. 3. We note that the most energetic speed varies between 8.1 m/s with Ke´libia (correspondent at a frequency of 11.5%) and 4.9 m/s with Sidi-Bouzid (correspondent at a frequency of 3.6%). 5.2. Wind energy estimation and analysis The wind power and energy are evaluated, respectively, by relations (3) and (4). Table 4 shows the obtained results, by the two methods, for the 17 synoptic sites to 10 m in open area.

Analysis of this table has led to the classification of these sites in three following groups:



A first group of good wind potential, noted (A), in which energy varies from 500 to 900 kWh/m2/year. It is formed of Thela, Klebia, Bizerte, Monastir, Tunis-Carthage and Elborma. It covers 54.4% of the recoverable total energy of the 17 sites. These sites extend from the Cape Serat to the Sahel, along the North-eastern coast then the mountainous area of Thala in the West and the South of the country

ARTICLE IN PRESS M. Elamouri, F. Ben Amar / Renewable Energy 33 (2008) 758–768

764 Table 3 Characteristic speeds for different sites Sites

Thela Klebia Bizerte Monastir Tunis-Chartage Elborma Remada Gabes Jandouba Djerba Kairaouan Sfax Medenine Tabarka Gafsa Sidi-Bouzid Tozeur

Meteorological

Weibull

VF (m/s)

f(VF) (%)

VE (m/s)

f(Ve) (%)

Vm (m/s)

VF (m/s)

VE (m/s)

Vm (m/s)

2.3 2.7 1.0 2.7 2.9 2.9 2.1 3.6 1.1 1.9 2.3 1.9 2.1 1.7 2.1 1.9 2.2

11.8 19.1 13.6 12.9 16.6 14.2 18.5 18.0 19.0 19.2 23.6 20.2 24.3 20.0 26.8 16.6 20.7

7.8 8.1 6.7 9.0 6.8 5.8 6.2 6.0 7.7 5.6 7.0 5.8 6.4 5.9 7.4 4.9 5.5

8.4 11.5 7.2 7.2 7.0 7.8 5.5 10.0 6.1 6.0 6.5 5.4 3.6 2.5 4.3 3.6 2.7

4.8 4.2 3.6 3.9 3.9 3.9 3.3 3.6 3.0 3.5 3.1 3.2 2.7 2.6 2.9 2.4 2.0

3.3 2.5 2.3 2.5 2.2 2.8 1.5 2.8 1.5 2.4 1.9 1.7 0.7 1.0 2.1 1.4 1.0

8.4 8.0 7.4 7.3 7.4 7.1 7.1 6.2 6.7 6.2 6.2 6.0 6.4 5.8 5.2 5.1 4.8

4.8 4.3 3.9 4.0 3.9 4.1 3.4 3.8 3.3 3.5 3.1 3.1 2.7 2.7 3.0 2.6 2.3

Table 4 Maximum wind power and annual energy production for different sites Sites

Thela Klebia Bizerte Monastir Tunis-Chartage Elborma Remada Gabes Jandouba Djerba Kairaouan Sfax Medenine Tabarka Gafsa Sidi-Bouzid Tozeur





Groups

Meteorological

Weibull

P (W/m2)

E (kWh/m2/year)

P (W/m2)

E (kWh/m2/year)

A

101.94 81.00 64.52 63.64 62.13 61.29

893.00 709.55 565.23 557.47 544.23 536.96

101.85 80.60 62.75 63.35 61.90 61.32

892.21 706.05 549.71 554.98 542.21 537.18

B

49.46 44.96 43.19 39.87 36.96 32.85 30.61

433.30 393.86 378.39 349.28 323.79 287.79 268.15

49.81 44.30 43.02 40.64 37.08 33.40 30.64

436.29 388.14 376.88 356.08 324.83 292.55 268.45

C

25.96 25.215 20.11 14.63

227.42 220.89 176.15 128.15

25.66 24.60 19.72 14.64

224.75 215.55 172.73 128.30

with El Borma. This group presents the energy zone and most promising for the exploitation of the wind power. A second group of average wind potential, noted (B), whose energy varies between 250 and 500 kWh/m2/year. It comprises seven sites and accounts for 34.8% of total energy. A third group of low wind potential, noted (C), concerns four sites and covers only 10.8% of total energy. Its wind energy is lower than 250 kWh/m2/year.

Fig. 5 highlights these three groups and particularly shows the importance of the first two sites of group A:

Thala and Ke´libia. In addition, the Thala site occupies the first position in Tunisia where the annual wind potential, evaluated to 10 m height in open area, is about 900 kWh/ m2/year. 6. Extrapolation of wind characteristics with height The Weibull distribution has the advantage of giving an excellent recovery of the experimental data and of envisaging the wind power for various heights. Indeed, according to Refs. [4–9], if one knows the Weibull parameters to a Za height different of Z height wished for the wind turbine, the parameters A(Z) and K(Z) are connected

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765

Wind energy(kWh/m2/year)

1000 Meteorological Weibull

900 800 700 600 500 400 300 200 100

Group A

Group B

Tozeur

Sidi-Bouzid

Gafsa

Tabarka

Medenine

Sfax

Kairaouan

Djerba

Jandouba

Gabes

Remada

Elborma

Tunis-Carthage

Monastir

Bizerte

Klebia

Thela

0

Group C

Fig. 5. Annual wind energy for the various groups calculated by two methods.

to A(Za) and K(Za) by the following relations:  n Z AðZÞ ¼ AðZ a Þ , Za 

KðZÞ ¼

(15)

Sites

n



KðZ a Þ 1  0:088 ln½Z a =10   , 1  0:088 ln½Z=10

(16)

where Za is the reference height and n is a scalar which can be obtained by the relation n¼

Table 5 Maximum wind power and annual energy production at heights of 30, 50 and 70 m

0:37  0:088 lnðAref Þ . 1  0:088 lnðZ ref =10Þ

(17)

6.1. Effect on characteristic speeds For three heights (Z ¼ 30, 50 and 70 m) in open area, Table 5 gives an estimate of parameter n, mean speed and the most energetic speed for various sites. The examination of this table shows the effect of the height on the wind speed characteristics. Indeed, for the mean speed (the most energetic speed), the passage of the height 10–30 m allows a profit approximately of 30% (21%), of 10–50 m the profit becomes approximately 48% (32%) and of 10–70 m the profit reaches approximately 62% (41%). However, to confirm the preceding results and to carry out a complete and rigorous study on the evolution of speed characteristics in relation to the height, we have represented in Figs. 6 and 7 the variation of the ratio between the mean speed (the most energetic speed) calculated with a height Z and that to 10 m in open area. The plotted curves are comparable and they present, for a given height, almost the same profits of speed (mean and most energetic).

Z ¼ 30 m

Z ¼ 50 m

Z ¼ 70 m

Vm

VE

Vm

VE

Vm

VE

Thela Klebia Bizerte Monastir Tunis-Chartage Elborma

0.221 0.223 0.240 0.238 0.240 0.236

6.1 5.5 5.0 5.0 5.0 5.3

10.0 9.5 8.8 8.8 8.9 8.6

6.9 6.2 5.7 5.7 5.7 5.9

10.8 10.3 9.6 9.6 9.7 9.4

7.4 6.7 6.2 6.3 6.2 6.4

11.5 11.7 10.2 9.9 10.2 10.0

Remada Gabes Jandouba Djerba Kairaouan Sfax Medenine

0.254 0.243 0.257 0.249 0.255 0.260 0.277

4.4 4.9 4.3 4.6 4.3 4.1 3.6

8.5 7.7 8.1 7.6 7.5 7.4 7.6

5.0 5.6 4.9 5.2 4.9 4.7 4.1

9.3 8.4 8.8 8.4 8.3 8.1 8.3

5.5 6.0 5.3 5.7 5.4 5.1 4.5

9.8 9.0 9.4 8.9 8.8 8.6 8.8

Tabarka Gafsa Sidi-Bouzid Tozeur

0.276 0.263 0.276 0.290

3.6 4.0 3.5 3.0

7.1 6.5 6.3 5.9

4.1 4.6 4.0 3.5

7.7 7.2 7.0 6.5

4.5 5.0 4.4 3.9

8.2 7.7 7.5 7.0

6.2. Effect on wind energy available Fig. 8 represents the variation of the relationship between the available annual wind energy calculated to a height Z and energy at 10 m in open area. We note that it is possible to estimate this ratio by a power relation in relation to height Z, of the form EðZÞ ¼ aZb , Eð10Þ

(18)

where a and b are real parameters which depend on the site characteristics.

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Table 6 gives the parameter values a and b. These parameters are identified by the ‘‘Origin 5.0’’ software with an accuracy of nearly 1/1000. They make it possible to directly calculate the available minimal energy of each sites group by relation (18). It is also noticed that the sites of less energy present a better gain in energy. Indeed, the passage of a height 10 m to the height 100 m makes it possible to multiply the available energy 3.6 for the site of most energy, Thala, and 5 for the site of less energy, Tozeur. In addition, it is not interesting to exploit the wind energy with heights smaller than 10 m considering that this energy is very weak. 6.3. Effect on days number for V¯ X4 m=s The recoverable wind energy by a wind machine depends at the same time on the aerogenerator characteristics and the wind statistical distribution to the implatation site. The work duration of this machine depends directly on the value of the cut in wind speed V¯ d . For a speed V¯ XV¯ d ,

the working probable days number Nj can be estimated by the following relation: "  KðZÞ # V¯ N j ðZÞ ¼ exp   365. (19) AðZÞ The histogram of Fig. 9 represents the annual percentage of the operating time for V¯ d ¼ 4 m=s at 10 m height in open area. Fig. 10 shows the variation of the ratio Nj(Z)/Nj(10) for the wind speed V¯ d ¼ 4 m=s, in relation to height Z. The curves of this figure reveal that the sites of less energy present a better evolution of the working time.

Table 6 Parameters a and b of the empirical model of energy Group

a

b

A B C

0.27552 0.25229 0.22266

0.55639 0.59312 0.64713

Fig. 6. Variation of the annual mean speed with the height Z in open area.

Fig. 7. Variation of the annual energy speed with the height Z in open area.

Fig. 8. Variation of the annual wind energy available with the height Z in open area.

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60 50 40 30 20 10

Tozeur

Sidi-Bouzid

Gafsa

Tabarka

Medenine

Sfax

Kairaouan

Djerba

Jandouba

Gabes

Remada

Elborma

Tunis-Carthage

Monastir

Bizerte

Klebia

0 Thela

Annual percentage of the day number (x)

M. Elamouri, F. Ben Amar / Renewable Energy 33 (2008) 758–768

Fig. 9. Percentage of days per year for a wind speed X4 m/s at 10 m height in open area.

Fig. 10. Number of days per year as a function of height Z for a wind speed X4 m/s in open area.

Indeed, the passage of a height 10 m to the height 100 m makes it possible to multiply the working days number by 1.5 for the site of most energy (for example, Thala), and 3.3 for the site of least energy (for example, Tozeur).



7. Conclusion In this study, we have evaluated the wind resource in Tunisia. This reveals that the Tunisian territory can be divided into three groups of sites:





Group A (Thela, Klebia, Bizerte, Monastir, TunisCarthage and Elborma) of good wind potential, of which the annual mean speed lies between 3.6 and 4.8 m/s and the available energy varies from 537 to 893 kWh/m2/year. The essential and promising areas for the exploitation of the wind power are along the North-eastern coast (of the Cape Serat to the Sahel), in the mountainous area of Thala and Kasserine and in the South of the country with El Borma. Group B (Ramada, Gabes, Jendouba, Djerba, Kairouan, Sfax and Medenine) of average wind potential, of which the annual mean speed lies between 2.7 and 3.6 m/s

and the available energy varies from 268 to 433 kWh/ m2/year. Group C (Tabarka, Gafsa, Sidi-Bouzid and Tozeur) of low wind potential, of which the annual mean speed lies between 2 and 2.9 m/s and the available energy varies from 128 to 227 kWh/m2/year.

The aforementioned values are given with a height 10 m above the ground and in open area. These values increase according to a power function with the height. A study of the influence from the height makes it possible to improve considerably the wind characteristics. Indeed, when one passes from the height 10 to 100 m, one notes that

  

the annual mean speed (the most energetic speed) increases approximately by 80% (50%), the available annual energy grows in a gigantic way and follows a power function (18) with the height. It will be multiplied approximately by a factor of 4, the average number of days during 1 year for a wind speed increases for all the sites and this all the more for the little windy stations. It is then necessary to carry out an optimal compromise between a maximum availability of energy and a high operating time.

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M. Elamouri, F. Ben Amar / Renewable Energy 33 (2008) 758–768

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