Wind Energy Resource Assessment in South Western of Algeria

Wind Energy Resource Assessment in South Western of Algeria

Asma AbderrahimNassera Ghellai Zakaria Bouzid Younes Menni

Unit of Research on Materials and Renewable Energies, Department of Physics, Faculty of Sciences, Abou Bekr Belkaid University, P.O. Box 119-13000-Tlemcen, Algeria

Corresponding Author Email: 
abder.asmaa@gmail.com
Page: 
157-162
|
DOI: 
https://doi.org/10.18280/mmep.060201
Received: 
29 April 2019
|
Accepted: 
4 June 2019
|
Published: 
20 June 2019
| Citation

OPEN ACCESS

Abstract: 

The aim of this paper is to evaluate the wind potential in southwestern Algeria, according to months, seasons and entire years and contribute to the updating of the wind map in Algeria at 10 m from the ground, using hourly data for wind collected over a period of more than 30 years. The wind data analysis was done using the Weibull function at 10 m from the ground. Then we did the statistical analysis, which includes several fundamental properties, such as Weibull parameters, mean wind speed and average power density. The results give the city of Tindouf as the one with the highest annual average speed with 5.39 m/s at 10 m from the ground. As for the temporal study, it gives that spring is the best windy period.

Keywords: 

wind energy, wind resource, assessment wind potential, Weibull parameters, the southwest of Algeria

1. Introduction

An unprecedented boom, especially after the 1973 oil crisis that alerted states producing fossil energy [1]. In fact, after the year 2000, the fluctuating context of fossil fuels, the explosion of worldwide demand for electricity and environmental awareness have drawn attention to the need for clean and sustainable energy.

Electricity generation in North Africa using renewable energy resources has recently gained momentum through several projects such as the Desertec Industrial Initiative, where bulk wind, CSP and PV large scale export is the most feasible option [2].

North African countries with high levels of direct solar radiation, the goal of these plans is to create a new renewable energy generation capacity, using solar and wind energy on the basin Mediterranean [3].

In Algeria, the objectives fixed by the company to capital action NEAL (Novel Energy Algeria), centered on the increase of the renewable power production in 1400 MW in 2030 and 7500 MW at the beginning of 2050. The electrical energy will result from solar power plants, exclusively solar, or from hybrid solar power plants, which also use other renewable or conventional forms of energy, rather natural gas [4]. Recently, Boudghene Stambouli concluded that there is a considerable potential in Algeria for the renewable use of a source of energy [5], in particular as regards solar and wind energy producing fewer greenhouse gas emissions [6].

In the field of the evaluation of the wind potential, the scientific literature proposes recent studies on different areas of the globe; among them: the study of wind resource assessment offshore the Atlantic Iberian coast with the WRF model in Spain [7]; Use of spatio-temporal calibrated wind shear model to improve accuracy of wind resource assessment in the USA [8]; 3D statistical mapping of Germany 's wind resource using WSWS [9]; Wind resource assessment and economics of electric generation at four locations in Sinai Peninsula, Egypt [10]; Multi criteria decision analysis for offshore wind energy potential in Egypt [11]; Wind energy characteristics and wind park installation in Shark El-Ouinat, Egypt [12]; Offshore wind resource assessment and wind power plant optimization in the Gulf of Thailand [13]; Offshore wind resource assessment of Persian Gulf using uncertainty analysis and GIS [14]; Wind resource assessment using SODAR and meteorological mast – A case study of Pakistan [15]; Wind resource potential assessment using a long term tower measurement approach: A case study of Beijing in China [16]; Validation of wind resource in 14 locations of Nepal [17]; Statistical learning approach for wind resource assessment in United Kingdom [18]; Wind resource assessment of Northern Cyprus in Turkey [19]; A new methodology for urban wind resource assessment in Portugal [20] et. The wind resource assessment around coastal areas of the Korean peninsula [21-22].

Numerous works indicated that Algeria was characterized by the most important wind potential; in particular, the first approach is the establishment of Atlas and wind map in Algeria. In this context, we can quote the contribution to the updating of the card of the wind of Algeria, adding the study of Hassi R’mel South of the country to the Atlas of the wind by Challali and al [23], few studies were led to estimate the wind resources, although the publication of the Atlas of wind by Kasbadji [24-25] gave rise to a growth of the domain of the evaluation of the wind. The second approach is the assessment of wind potential and the design of wind energy conversion systems. In this context, we can mention the studies by Himri et al. [26-31] who were among the first to give a statistical analysis of the wind speed in different regions of Algeria and the work on the evaluation of wind resources at different sites in Algeria (Boudia et al. [32-37], Benmedjahed et al. [38-39], Bouzid et al. [40], Diaf et al. [41], Aksas et al. [42]).

In this study, we suggest making an analysis of the wind resource in the Southwest region of Algeria by choosing two sites. Furthermore, the objective of this work is the contribution to the updating of the wind Atlas of Algeria in 10 m of the ground by using meteorological data according to the wind speed adjusted by the distribution of Weibull. So, the estimation of the average speed and the average density of power, according to the months, seasons and of the year.

2. Sites Selections and Weather Data

2.1 Sites presentation

Bechar is the largest city in southwestern Algeria, located 852 km northeast of Tindouf which is located at the extreme southwestern tip of Algeria. The geographic location of the two weather stations is shown in Figure 1.

 

Figure 1. The geographic location of the wind measurement station in Algerian map

2.2 Data analysis of wind speeds

In this article, the geographic coordinates of weather stations and years of measurements are presented in Table 1.

Table 1. Geographical coordinates of the data collection station used in the study

Stations

Station code (AWS)

Longitude

(°)

Latitude

(°)

Altitude

(m)

Measurement

Period

Bechar

605710

-2,27

31.646

811.1

01/01/1988

31/12/2017

Tindouf

606560

-8,167

27.7

442.9

01/01/1987

31/12/2017

 

We used wind speeds at 10 meters above ground level recorded every hour. These data were obtained via "NCDC Climate Data Online" [43].

3. Wind Analysis Model

3.1 Weibull statistics of wind speed

To estimate the potential of wind energy of any site, it is important to characterize the probability distribution of the wind speed of the site. The distribution of Weibull is the most wide-spread to adjust the distributions of the wind speed for a period of time [44-45]. He is defined by the following equation:

$f(v)=\left(\frac{K}{A}\right)\left(\frac{v}{A}\right)^{K-1} e^{-\left(\frac{v}{A}\right)^{K}}$  (1)

where f (v) is the probability to observe the wind speed, v is the wind speed (m/s), K is the parameter of shape of Weibull (dimensionless), and A is the parameter of scale of Weibull (m/s).

There are different methods for estimating parameters of the Weibull distribution [46-47]. In this work, for the analysis of the wind data, the maximum likelihood method was used, taking into account its good performance [48]. The parameters of Weibull K and A can be estimated by the following equations:

$K=\left[\frac{\sum_{i=1}^{n} v_{i}^{k} \ln v_{i}}{\sum_{i=1}^{n} v_{i}^{K}}-\frac{1}{N} \sum_{i=1}^{n} \ln v_{i}\right]$  (2)

$A=\left(\frac{1}{N} \sum_{i=1}^{n} v_{i}^{K}\right)^{\frac{1}{K}}$  (3)

where vi is the non-zero speed of the wind at a time i and N the number of nonzero wind speed data. The average wind speed Vm can be calculated according to Weibull parameters as follows [49-51]:

$V_{m}=A \Gamma\left(1+\frac{1}{K}\right)$  (4)

where Γ is the gamma function.

3.2 Wind power density

The power of the wind that flows at speed v through a blade sweep area S (m2) is the cubic of its velocity and is given by the following equation [52]:

$P(v)=\frac{1}{2} S \cdot \rho \cdot V_{m}^{3}$  (5)

where ρ is the density of the air (Kg/m3). The wind power density of a site can be expressed using the Weibull parameters by the following relation:

$\frac{P}{S}=\frac{1}{2} \cdot \rho \cdot A^{3} \Gamma\left(1+\frac{3}{K}\right)$  (6)

4. Results and Discussion

Figure 2 represents the annual Weibull distributions for both Bechar and Tindouf sites; according to the results obtained, we find that the wind speed covers an interval of up to 8 m / s for the Bechar site and 10 m/s for the Tindouf site. The scale factor values for Bechar and Tindouf are respectively 4.2533 m/s and 5.7846 m/s. The shape of the two curves is determined by a shape parameter equal to 2.8529 for Bechar and 3.4964 for Tindouf.

The seasonal Weibull distribution at 10 m for the two sites studied is shown in Figure 3. It is shown that all curves have a similar trend of wind speeds for the Weibull distribution. There is no season in the two studied sites where the wind speed does not exceed 10 m/s except for the spring of the Tindouf site where the speed interval extends up to 12 m/s.

Figure 2. Annual Weibull wind distribution at 10m

Table 2. Annual average speed and power density at 10m

 

Bechar

Tindouf

V (m/s)

3.8116

5.3980

P (W/m²)

33.9173

96.3376

 

From Table 2, we note that the average annual speed does not exceed 5.4 m/s.

Figure 3. Seasonal Weibull wind distribution at 10m

Table 3. Seasonal average power speed and density at 10m

 

Bechar

 

Tindouf

 

Season

V (m/s)

P (W/m²)

V (m/s)

P (W/m²)

Spring

4.8153

68.3868

6.6295

178.4226

Summer

4.1230

42.9282

5.4584

99.6075

Autumn

3.0512

17.3995

4.60

59.6172

Winter

3.2166

20.3838

4.8575

70.2008

 

According to Table 3, we note that the minimum value of the average wind speed is in the autumn season for the two sites Bechar and Tindouf with 3.0512 m/s and 4.60 m/s respectively and the maximum value is in season spring with 4.8153 m/s and 6.6295 m/s respectively.

Figure 4. Seasonal Weibull distribution parameters at 10m

In addition, the value of the average power density varies between 17.39 W/m² and 68.38 W/m² for Bechar and between 59.61 W/m² and 178.42 W/m² for Tindouf.

Figure 4 represents the seasonal distribution of the two Weibull parameters during the study period. We note that the scale factor A reaches its maximum value in spring for Bechar and Tindouf with 5.0985 m/s and 6.8494 m/s respectively. In addition, its minimum value is in autumn for both Bechar and Tindouf sites with 3.3896 m/s and 4.9263 m/s respectively. We also note that the form factor K takes its maximum in spring with 3.8478 for Tindouf and 3.2507 for Bechar.

The monthly Weibull wind distribution study presented in Figure 5 and the monthly distribution of the two Weibull parameters presented in Figure 6 give an increase of the K shape parameter in the warm months from 2.8423 to 3.3410 for Bechar and between 3.4089 and 3.8941 for Tindouf. The minimum values are for cold months from 1.9109 to 2.6504 for Bechar and between 3.0484 and 3.3436 for Tindouf. The analysis also gives a maximum value of the A scale parameter in May for the two Bechar and Tindouf sites with respectively 5.3271 m/s and 7.0083 m/s.

Figure 5. Monthly Weibull wind distribution at a height of 10 m

The monthly variation of mean wind speed and the mean power density at 10 m above ground level is mentioned in the table 4. For the Bechar site, we note that the minimum value of the mean speed is in December with 2.4538 m/s and the maximum value is in May with 5.0555 m/s. In addition, the mean power density varies between 9.0492 W/m² in December and 79.1420 W/m² in May. For the Tindouf site, we observe that the monthly mean wind speed varies between 4.2501 m/s in December and a maximum value of 6.8501 m/s in May. Furthermore, the mean power density varies between 47.0230 W/m² and 196.8801 W/m².

Figure 6. Monthly Weibull distribution parameters at 10

Table 4. Monthly variations of mean wind speed and power density at 10m

 

Bechar

Tindouf

Months

V (m/s)

P (W/m²)

V (m/s)

P (W/m²)

January

2.7395

12.5931

4.5445

57.4867

February

3.4735

25.6688

5.0155

77.2790

March

4.1080

42.4622

5.5397

104.1263

April

4.8835

71.3363

6.5815

174.6145

May

5.0555

79.1420

6.8501

196.8801

June

4.4927

55.5446

6.5198

169.7526

July

4.2641

47.4897

5.3744

95.0790

August

4.0206

39.8080

5.2882

90.5774

September

3.8973

36.2571

5.7682

117.5516

October

3.2891

21.7945

4.5867

59.1039

November

3.0735

17.7829

4.4910

55.4797

December

2.4538

9.0492

4.2501

47.0230

 
5. Conclusion

Through this study, the monthly, seasonal and annual Weibull parameters, mean wind speed and wind power densities are determined at a height of 10 m at two sites in the southwestern of Algeria, to provide information of wind resources. The results of this study can be concluded as follows:

The analysis gives a potential correlation between the mean temperature of the air and the factor of shape K in both sites where the biggest values are determined in the hot months. His minimal monthly value estimated at Becher equal to 1.9109 in December while the maximum is estimated at 3.8941 in May at Tindouf. The minimal seasonal value estimated at Becher equal to 2.4239 in autumn while the maximum equals to 3.8478 in Spring at Tindouf.

The seasonal analysis of the mean wind speed allowed us to show clearly that the spring remains the best beloved season. The maximal monthly value of the mean speed for both sites Bechar and Tindouf is in May with 5.0555 m/s and 6.8501 m/s respectively.

The spring supplies the highest mean wind power density on both sites Becher and Tindouf with 68.3868 w/m² and 178.4226 w/m² respectively.

The present study leads to estimate completely the wind potential in the region Sahara of Algeria and to request afterward a possible feasibility of a project of the wind farm.

Nomenclature

A

The parameter of scale of Weibull, m/s

K

P

V

The parameter of shape of Weibull

The power of the wind, W/m2

The wind speed, m/s.

Greek symbols

 

Γ

The gamma function

ρ

The density of the air, Kg/m3

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