Long-term wind speed data for thirteen meteorological stations, measured over a five-year period, were statistically analyzed using the two-parameter Weibull distribution function. The purpose of this study is to reveal for the first time the wind power potentials in Chad and to provide a comprehensive wind map of the country. The results show that the values of the shape and scale parameters varied over a wide range. Analysis of the seasonal variations showed that higher wind speed values occur when the weather condition is generally dry and they drop considerably when the weather condition is wet. It was also observed that the wind speed increases as one moves from the southern zone to the Saharan zone. Although the wind power at each site varies significantly, however, the potentials of most of the sites were encouraging. Nevertheless, according to the PNNL classification system, they are favorable for small-scale applications only. A few stations in the middle of Sudanian and Sahel regions are found to be not feasible for wind energy generation due to their poor mean wind speed. The prevailing wind direction for both Saharan and Sahel regions is dominated by northeastern wind, while it diverged to different directions in the Sudanian zone.
The fast growing population in developing countries and their lack of access to electricity supply particularly in rural or remote areas make some of these nations face the challenge of generating more energy sources and establishing a new form of energy supply structure in an effort to meet current and future increasing electricity demands. Permanent electricity supply is considered as one of the major factors responsible for sustainable economic and social development of a nation [
As such, the majority of people in Chad tend to rely on traditional biomass for cooking and heating purposes particularly in rural areas as it is the case for many African nations [
Currently, a great deal of extensive research on wind energy is taking place almost all over the world due to the exceptional benefits that wind energy could offer. Many regional countries like Morocco, Egypt, Tunisia, Algeria, and so forth are already harvesting wind energy by establishing wind farms in the open Sahara desert and coastal areas [
Chad experiences wind and sunlight throughout the year. Thus, it is interesting to explore the abundant untapped potentials of the wind and the possibilities of using the Wind Energy Conversion System (WECS) as a source of electricity in the effort to fulfill the energy requirements in the country and to reduce this exclusive dependency on biomass and fossil fuels. However, although the region is found to be situated in the midst of enormous wind power potentials, to date, there is no extensive study undertaken to assess the prospects of wind energy as an alternative source in the country, except in [
Chad is a landlocked country with a 1,284,000 km2 area, situated in Central Northern Africa. It is the 5th largest in the African continent and 21st in the world in terms of land mass. It is bordered by Libya form the north, Sudan from the east, the Central African Republic from the south, Niger from the west, and Cameroon and Nigeria from the southwest. It lies at 7–23° north of the equator and its larger part is Sahara desert. The vast majority of the area is flat with some plateaus around the country.
Unlike many countries in the world, the climate in Chad is highly varying throughout the country due to nonuniform geographical regions. These regions could be categorized into three in terms of geographical aspects as in Figure
Geographical map of Chad [
The region in the middle has an arid subtropical climate and it forms a belt of about 500 kilometers wide in the center of the country between the Sahara and Sudanian savanna. The raining season in this region begins a bit late with almost three months of rainfall duration. The first water drops are seen in June and then raining ends in September, while winter is between October and February. The Sahara desert occupies the northern part of the country with less population though it is the largest region as shown in Figure
The wind data for this study are collected from the National Meteorological General Administration (Direction Générale de la Météorologie Nationale) at the Hassan Djamous International Airport in N’Djamena. The geographical coordinates of the thirteen major meteorological stations across the country are furnished in Table
Physical features of the meteorological stations.
Name of station | Coordinates | ||||
---|---|---|---|---|---|
Latitude (N) | Longitude (E) | Elevation (m) | |||
Deg. | Min. | Deg. | Min. | ||
Abéché | 14 | 40 | 20 | 50 | 545 |
Am Timan | 11 | 02 | 20 | 16 | 433 |
Ati | 13 | 14 | 18 | 18 | 332 |
Bokoro | 12 | 23 | 17 | 04 | 300 |
Bousso | 10 | 29 | 16 | 43 | 335 |
Doba | 08 | 42 | 16 | 50 | 387 |
Faya-Largeau | 17 | 55 | 19 | 06 | 235 |
Mao | 14 | 08 | 15 | 18 | 327 |
Mongo | 12 | 10 | 18 | 40 | 431 |
Moundou | 08 | 37 | 16 | 04 | 429 |
N'Djaména | 12 | 08 | 15 | 02 | 295 |
Pala | 09 | 22 | 14 | 55 | 467 |
Sarh | 09 | 09 | 18 | 22 | 365 |
Furthermore, although most of the data involved in this study are recent, the last observation made by some stations was in late 1978. This might be attributed to the fact that some of these stations are no longer recording such data in a consistent manner. However, due to the need to uncover the potential of these regions, they are being considered in this study by selecting five consecutive years, as it was learned that old or new data have a minor effect on the wind data assessment process as long as the study period is long enough [
It is commonly agreed that assessment of wind potential of a site using solely the conventional meteorological wind speed data is not sufficient. This is because depending on average wind speed alone to estimate the potential of a site might be misleading [
Many statistical methods are available to closely estimate the wind power potentials and wind speed characteristics of a given terrain. Weibull distribution is among the widely accepted approaches to statistically assess the wind behavior at a particular site clearly [
Thus, in the current study, the meteorological data are statistically analyzed using the effective two-parameter Weibull distribution function. This approach is particularly useful in studying wind speed characteristics and wind energy density. It is typically adopted due to its simplicity, flexibility, and ability to show good agreement with the observed data [
The two parameters are the dimensionless shape function,
The cumulative distribution, given as in the equation below, is the integral of the probability density function:
Thus, such estimation is quite important for the sake of revealing the wind speed that may carry the highest energy which could probably assist in knowing the maximum amount that can be expected from a particular site. Moreover, it is also used in selecting a suitable wind turbine or an appropriate rated wind speed [
Generating electricity from kinetic energy via wind that is flowing through a blade swept area,
The wind power density based on Weibull distribution analysis is calculated using the following equation [
After knowing the wind power density of a given site, one of the important wind characteristics could be estimated which is the wind energy density. It is the product of wind power density for a certain duration or period of time which can reveal the amount of energy density that could be expected at the site under study. The wind energy density for the desired time can easily be determined using the following equation [
The potential of wind energy in Chad was investigated at thirteen stations for a period of five years. Knowledge of monthly wind speed variations is essential to draw a clear picture of the seasonal wind speed behavior at the potential site. These fluctuations are significant in designing and selecting an appropriate WCES, energy storage, and load scheduling. These variations were determined using (
Monthly variation of wind speeds for all the selected stations.
The trends of monthly and yearly mean wind speed for all the stations under investigation exhibit almost a similar pattern. The monthly mean wind speed varies in the range of 2 to 4 m/s for the Saharan region and in the range of 1 to 4 m/s for the Sahel and tropical Savanna regime. It was also observed that the wind speed tends to increase significantly when the weather condition is generally dry (November to May) and decreases considerably when the weather conditions are wet (June to October). It was learned that the highest demand for electricity is also at the dry period [
In general, it was established that stronger wind speed is available as one moves from south to north, towards the Sahara as depicted in Figure
Wind speed pattern in Chad.
Moreover, the seasonal wind speed variations among the three regions become more noticeable particularly during winter, though this variation is marginal between the Sahel and Saharan regions. The month March showed the highest mean wind speed with the value of 4.5 m/s, and August showed the lowest mean wind speed value of 0.8 m/s.
The results presented also showed that, within five years under study, the highest corresponding annual average wind speed at 10 m height from the ground is approximately 3.5 m/s, and it was found in the eastern part of the country at Abéché specifically, while the minimum is found at Bousso with a wind speed of 1.0 m/s. Thus, according to the PNNL (Pacific Northwest National Laboratory) classification system, wind energy potentials in Chad could only be used for small-scale applications due to the current wind conversion technologies and cost factor [
However, it is worth noting that although the presented station from the Saharan region (Faya-Largeau) showed a bit lower wind speed compared to some stations in the Sahel region, data for wind speed at 30 m height proves otherwise. Thus, the Sahara zone could be considered more promising as depicted in Figure
Although wind speed characteristics are essential in drawing a clear picture of the wind potentials of a site, wind power density is believed to be a better indicator than wind speed. In this study, the wind power and energy density are evaluated using the Weibull equations (
Comparison of yearly mean wind speeds and mean wind power density.
As wind speeds during the dry season are higher as compared to other periods of the year [
The monthly mean scale parameter
Monthly Weibull parameters (
Station | Parameter | Month | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | ||
Abéché |
|
1.59 | 1.59 | 1.72 | 1.65 | 1.56 | 1.50 | 1.54 | 1.43 | 1.39 | 1.52 | 1.61 | 1.52 |
|
4.10 | 4.10 | 4.85 | 4.40 | 3.94 | 3.63 | 3.82 | 3.28 | 3.09 | 3.70 | 4.22 | 3.77 | |
Am Timan |
|
0.91 | 0.94 | 0.99 | 1.02 | 0.96 | 0.87 | 0.86 | 0.79 | 0.82 | 0.83 | 0.85 | 0.89 |
|
1.17 | 1.27 | 1.44 | 1.56 | 1.32 | 1.05 | 1.00 | 0.81 | 0.88 | 0.91 | 0.98 | 1.10 | |
Ati |
|
0.97 | 0.99 | 1.08 | 0.99 | 0.97 | 1.01 | 1.02 | 0.86 | 0.82 | 0.89 | 0.96 | 0.97 |
|
1.36 | 1.44 | 1.75 | 1.44 | 1.34 | 1.49 | 1.53 | 1.00 | 0.88 | 1.10 | 1.32 | 1.36 | |
Bokoro |
|
0.99 | 1.00 | 1.08 | 1.03 | 1.06 | 1.07 | 1.08 | 0.96 | 0.89 | 0.93 | 1.01 | 1.08 |
|
1.41 | 1.46 | 1.75 | 1.58 | 1.68 | 1.73 | 1.75 | 1.34 | 1.10 | 1.22 | 1.49 | 1.75 | |
Bousso |
|
0.80 | 0.88 | 0.86 | 0.93 | 0.93 | 0.94 | 0.77 | 0.74 | 0.81 | 0.80 | 0.79 | 0.84 |
|
0.81 | 1.05 | 1.00 | 1.22 | 1.24 | 1.29 | 0.74 | 0.67 | 0.86 | 0.83 | 0.81 | 0.95 | |
Doba |
|
1.28 | 1.37 | 1.36 | 1.46 | 1.35 | 1.24 | 1.17 | 1.16 | 1.16 | 1.12 | 1.04 | 1.10 |
|
2.59 | 2.97 | 2.93 | 3.44 | 2.92 | 2.45 | 2.09 | 2.07 | 2.07 | 1.90 | 1.61 | 1.82 | |
Faya-Largeau |
|
1.62 | 1.55 | 1.50 | 1.37 | 1.27 | 1.31 | 1.16 | 1.16 | 1.33 | 1.42 | 1.53 | 1.60 |
|
4.29 | 3.87 | 3.61 | 3.00 | 2.54 | 2.71 | 2.09 | 2.11 | 2.83 | 3.25 | 3.77 | 4.15 | |
Mao |
|
1.31 | 1.31 | 1.34 | 1.22 | 1.18 | 1.22 | 1.21 | 1.11 | 1.08 | 1.15 | 1.24 | 1.28 |
|
2.71 | 2.71 | 2.83 | 2.31 | 2.14 | 2.31 | 2.26 | 1.85 | 1.73 | 2.02 | 2.38 | 2.59 | |
Mongo |
|
1.28 | 1.50 | 1.55 | 1.52 | 1.57 | 1.58 | 1.44 | 1.36 | 1.28 | 1.38 | 1.45 | 1.37 |
|
2.61 | 3.61 | 3.87 | 3.70 | 4.01 | 4.08 | 3.35 | 2.97 | 2.59 | 3.06 | 3.40 | 3.02 | |
Moundou |
|
1.48 | 1.42 | 1.31 | 1.40 | 1.28 | 1.25 | 1.27 | 1.26 | 1.08 | 1.15 | 1.23 | 1.27 |
|
3.52 | 3.23 | 2.76 | 3.16 | 2.61 | 2.45 | 2.54 | 2.52 | 1.77 | 2.04 | 2.35 | 2.54 | |
N'Djaména |
|
1.60 | 1.69 | 1.75 | 1.58 | 1.47 | 1.60 | 1.57 | 1.36 | 1.31 | 1.31 | 1.49 | 1.56 |
|
4.17 | 4.64 | 5.03 | 4.03 | 3.49 | 4.13 | 3.99 | 2.93 | 2.71 | 2.71 | 3.59 | 3.96 | |
Pala |
|
1.59 | 1.66 | 1.68 | 1.63 | 1.54 | 1.45 | 1.31 | 1.22 | 1.30 | 1.27 | 1.41 | 1.54 |
|
4.08 | 4.50 | 4.59 | 4.29 | 3.84 | 3.40 | 2.73 | 2.33 | 2.66 | 2.52 | 3.21 | 3.86 | |
Sarh |
|
1.10 | 1.06 | 1.19 | 1.35 | 1.31 | 1.20 | 1.00 | 1.03 | 0.99 | 1.00 | 0.88 | 1.06 |
|
1.82 | 1.68 | 2.21 | 2.88 | 2.71 | 2.26 | 1.46 | 1.58 | 1.44 | 1.46 | 1.08 | 1.70 |
Yearly Weibull parameters
Station | Parameters | |||||
---|---|---|---|---|---|---|
|
|
|
|
|
| |
Abéché | 1.56 | 3.91 | 3.52 | 6.65 | 68.80 | 602.72 |
Am Timan | 0.90 | 1.13 | 1.18 | 4.11 | 8.04 | 70.39 |
Ati | 1.00 | 1.34 | 1.36 | 4.26 | 10.06 | 88.09 |
Bokoro | 1.02 | 1.52 | 1.51 | 4.42 | 12.21 | 106.97 |
Bousso | 0.85 | 0.96 | 1.04 | 4.02 | 6.73 | 58.95 |
Doba | 1.24 | 2.40 | 2.25 | 5.20 | 26.03 | 228.0 |
Faya-Largeau | 1.41 | 3.19 | 2.90 | 5.95 | 45.29 | 396.72 |
Mao | 1.22 | 2.32 | 2.17 | 5.11 | 24.07 | 210.85 |
Mongo | 1.45 | 3.36 | 3.05 | 6.11 | 50.54 | 442.77 |
Moundou | 1.29 | 2.62 | 2.43 | 5.41 | 31.41 | 275.17 |
N’Djaména | 1.53 | 3.79 | 3.42 | 6.54 | 64.63 | 566.18 |
Pala | 1.48 | 3.51 | 3.17 | 6.26 | 54.80 | 480.01 |
Sarh | 1.11 | 1.86 | 1.79 | 4.70 | 16.58 | 145.24 |
The frequency and cumulative distributions of monthly average wind speed for all the stations are presented, respectively, in Figures
Frequency distributions of monthly mean wind speed for all stations.
Cumulative distributions of monthly mean wind speed for all stations.
Evaluation of wind direction helps to expose the impact of the geographical features on the wind and to obtain the prevailing direction and magnitude of the most frequent wind. In Figures
Polar diagram: wind direction for Abéché.
Polar diagram: wind direction for Faya-Largeau.
Polar diagram: wind direction for Moundou.
Polar diagram: wind direction for Ndjamena.
Polar diagram: wind direction for Pala.
Polar diagram: wind direction for Sarh.
In this study, the monthly and yearly wind speed distribution and wind power density for thirteen meteorological stations in Chad were evaluated. The novel two-parameter Weibull distribution function was employed to analyze the five-year period data for each site. While the data used in this study are being published for the first time, they are collected for the purpose of studying the wind energy potential and to have a comprehensive wind database or a wind map in Chad.
The seasonal variations of the mean wind speed data show that higher wind speeds are available when the weather condition is generally dry, that is, November to May, and lower wind speed is found when the weather condition is wet, that is, June to October. This trend is applicable for all the thirteen sites under investigation. Interestingly, it was learned that higher electrical energy demand in the country is also in this period (dry season).
Moreover, the data presented also revealed that the monthly mean wind speed varies in a wide range in each geographical zone. In the Sahel zone, for example, it ranged between 1 and 4 m/s, though most of the stations in this region have shown reasonably strong wind speed of more than 2 m/s. The same behavior was also noticed at the Sudanian zone.
It was observed that the sites in the middle of Sahel and Sudanian zones tend to show lower mean wind speed compared to the others. However, in general, higher wind potentials are witnessed as one moves towards the Saharan region. It is also worth noting that the peak monthly mean wind speed was 4.5 m/s and it was found in Ndjamena in the month of March. Meanwhile, the lowest monthly average wind speed was less than a unit and was found in Bousso.
Based on yearly averaged data, the most recommended site for wind energy generation in Chad would be Abéché, followed by Ndjamena, Pala, Mongo, and Faya-Largeau, which are the stations that possess mean wind speed of more than 3 m/s at 10 m height above ground level. These sites exhibited wind power density in the range of 45–69 W/m2 and corresponding wind energy density values ranged between 400 and 600 kWh/m2/year. However, although the wind energy potential in Chad is promising for most of the sites under study for small-scale applications, it is concluded that few stations in the middle of Sudanian and Sahel regions (Am Timan, Bousso, Bokoro, and Ati) are not feasible for wind energy generation due to their weak mean wind speed of around 1 m/s.
In terms of wind direction, the prevailing wind direction for both Saharan and Sahel region was dominated by northeastern wind. However, the wind direction in every station at the Sudanian region prevailed at different directions. The most probable wind speed direction was southwest in Moundou, dominated by northern wind in Pala and southern wind in Sarh.
It is recommended in the future to develop a wind map at the other parts of the Saharan region and some locations close to Lake Chad. The data should be recorded at various heights above ground level and it will then be used for developing wind atlas for Chad which will encourage the development of wind energy projects in the country.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
The authors would like thank Universiti Tun Hussein Onn Malaysia for providing financial support of this research under Grant U416. The authors also wish to express their sincere gratitude to the National Meteorological General Administration (ASECNA) in Chad for the provision of the meteorological data.