In this study, wind characteristics and wind power potential are analyzed for three meteorological stations in the Sudanese zone of Chad for the period of 35 years (from 1975 to 2010). Assessment of the wind power potential was carried out using the two parameters of Weibull distribution. Results of the study shows that the average annual wind speeds at 10 m above ground for Moundou, Pala and Sarh are 2.69, 2.33 and 1.91 m/s, respectively. The mean annual value of the Weibull shape parameter k and scale parameter c range from 2.376 to 3.255 and 2.099 to 3.007, respectively. The maximum annual power density of 204.85 W/m2 was obtained at Moundou. Results of this study further shows that the selected locations are not suitable for large-scale wind energy production at 10 m altitude. However, by extrapolation, assessment of wind speed at 67 m altitude combines with wind turbine Vestas 2 MW/80 that adapts to the Sudanese local conditions, and the wind power potential can be exploited for water pumping, heating and production of electricity.
Energy plays a significant role in human and economic development. Demand for energy is growing exponentially. In addition, conventional energy resources are limited and their use contribute major proportion to environmental pollution [
The knowledge of wind characteristics based on the wind velocity is essential, not only for the evaluation of the wind power potential but also for the dimensioning of the wind power stations and the selection of suitable choice of the aerogenerators [
Chad is a country with 80% of her population living in rural area, without a particular grid system of electricity. Only 2% to 4% of the population has access to electricity. Therefore, renewable energy is fitted to its development and the well-being of its population [
Vast of 1,284,000 km2, Chad extends between 7˚ and 24˚ of north latitude and between 14˚ and 24˚ of longitude east. A country in central Africa at an average altitude of 200 meters above the sea, Chad is totally enclave. It is surrounded by six countries: in the north Libya, in the east Sudan, in the south the Central African Republic and in the West Cameroon, Nigeria and Niger. There are three major climatic zones in Chad: the Saharan Zone in the North, the Sahelian zone in the Center and the Sudanese zone in the South. In these three climatic zones of Chad, the speed of the wind was measuredin the weather observation stations. The data for this study were obtained from the division of Climatology of Chad National Meteorological General Direction (
The Weibull distribution is usually used, accepted and recommended in the literature. It proved not only adapted for the description of the statistical properties
Station | Longitude | Latitude | Elevation | Period of | Height of the |
---|---|---|---|---|---|
˚E | ˚N | (m) | measurement (year) | Mast (m) | |
Moundou | 16.4 | 8.37 | 420 | 20 | 10 |
Pala | 14.55 | 9.22 | 420 | 30 | 10 |
Sarh | 18.23 | 9.9 | 364 | 20 | 10 |
of the wind but gives a good agreement with the experimental data [
where
The determination of k and c called parameters of Weibull makes it possible to know the distribution of the winds for a given site. Once these parameters are determined, the mean velocity of the wind is calculated according to the following expression [
where
where,
The most probable speed of wind can be determined from the shape parameter and scale parameter of Weibull distribution function. The speed of the most probable wind is obtained as [
Determination of the maximum speed of wind energy can be calculated from the shape parameter and scale parameter of Weibull distribution function. The wind speed carrying the maximum wind power can be to calculate as [
The power density of the wind power is the most significant characteristic of the wind. It represents the quantity of energy produced by the wind. Assuming that A is the cross sectional area through which the wind spins out perpendicularly. The power of the wind is given by the following relation [
where,
The precise evaluation of the wind potential for a given site requires the knowledge the speed of the wind to various heights. The standard height of measurement is generally of 10 m, but during a prospection of a site, in order to draw up a wind project, it is preferable to take measures with two or three levels for one period at least of a year in order to know the evolution the speed of the wind at altitudes representing an energy interest. To model the vertical profile is written by the law of power by the expression [
where,
The Weibull parameters at measurement height are related to the parameters at the wind turbine height by the following expressions:
where
In addition, for a height less than 130 m, the power density of the wind above the ground level is given by:
where
In order to determine which wind turbine is the most efficient and best suited to the area studied, three turbines characterized by a starting speed lower than the average annual speed of the site were selected. Two of the machines are high powers and one of low power. Wind turbine performance is estimated with the capacity factor (
The capacity factor (
In this study, wind speed data analysis was carried out using MATLAB and Excel®. The dimensionless Weibull scale parameter c and shape parameter k were estimated using the
In addition, Weibull mean speed Vm, shape parameter (k), and scale parameter (c) for the three selected locations Moundou, Pala and Sarh are summarized in
Sites | Vm (m/s) | k (−) | c (m/s) |
---|---|---|---|
Moundou | 2.69 | 3.101 | 3.007 |
Pala | 2.33 | 3.255 | 2.602 |
Sarh | 1.91 | 2.376 | 2.099 |
Chad. It is noticed that Moundou and Pala are the two sites for which the speeds are respectively 2.69 and 2.33 m/s. Sarh is the site of the Sudanese zone for which the speed of the wind is lowest, 1.91 m/s. Pala, the site which possesses the highest parameter of form that is 3.225. Whereas, the site with the lowest parameter of form is Sarh with k = 2.376.
As for
The monthly and annual values of the most probable wind speed variations (Vmp) and the highest energy velocity (VmaxE) at a height of 10 m are shown in
m/s and 3.539 m/s, respectively, while Sarh and Pala have the lowest annual values of Vmp and VmaxE which are 1.296 m/s and 3.042 m/s, respectively. The most likely wind speed (Vmp) varies from 0.441 m/s at Sarhin in January to 3.286 m/s at Moundou in February, while the highest wind speed (VmaxE) varies from 1.829
Locations | Annual mean wind speed (m/s) | Annual mean power density (W/m2) | Annual energy (KWh/m2/year) |
---|---|---|---|
Moundou | 2.692 | 17.071 | 148.85 |
Pala | 2.333 | 11.212 | 97.816 |
Sarh | 1.908 | 12.762 | 110.799 |
Month | Moundou | Pala | Sarh | |||
---|---|---|---|---|---|---|
k | c | k | c | k | c | |
January | 3.613 | 3.439 | 3.193 | 2.903 | 1.194 | 2.017 |
February | 3.242 | 3.682 | 3.539 | 3.221 | 1.232 | 2.353 |
March | 3.256 | 3.458 | 3.143 | 3.24 | 1.307 | 2.601 |
April | 2.876 | 3.702 | 3.205 | 3.126 | 1.621 | 3.573 |
May | 3.028 | 3.246 | 3.482 | 3.001 | 3.396 | 2.56 |
June | 3.144 | 3.128 | 3.277 | 2.676 | 3.73 | 2.215 |
July | 3.306 | 3.009 | 3.983 | 2.317 | 3.741 | 1.882 |
August | 2.705 | 2.586 | 3.559 | 1.888 | 3.144 | 1.564 |
September | 3.058 | 2.349 | 3.108 | 1.9 | 3.144 | 1.564 |
October | 2.397 | 2.256 | 3.646 | 2.107 | 2.924 | 1.569 |
November | 3.235 | 2.343 | 2.64 | 2.25 | 1.678 | 1.567 |
December | 3.355 | 2.896 | 2.263 | 2.596 | 1.294 | 1.731 |
Annual | 3.101 | 3.007 | 3.255 | 2.602 | 2.376 | 2.099 |
Moundou | Pala | Sarh | ||||
---|---|---|---|---|---|---|
Month | Vmp | VmaxE | Vmp | VmaxE | Vmp | VmaxE |
January | 3.144 | 3.885 | 2.581 | 3.381 | 0.441 | 4.597 |
February | 3.286 | 4.27 | 2.932 | 3.655 | 0.606 | 5.151 |
March | 3.089 | 4.006 | 2.869 | 3.79 | 0.859 | 5.29 |
April | 3.191 | 4.447 | 2.781 | 3.636 | 1.976 | 5.867 |
May | 2.843 | 3.838 | 2.723 | 3.419 | 2.31 | 2.934 |
June | 2.77 | 3.659 | 2.395 | 3.095 | 2.037 | 2.485 |
July | 2.699 | 3.472 | 2.155 | 2.566 | 1.732 | 2.111 |
August | 2.18 | 3.173 | 1.72 | 2.14 | 1.385 | 1.829 |
September | 2.064 | 2.77 | 1.677 | 2.23 | 1.385 | 1.829 |
October | 1.801 | 2.905 | 1.929 | 2.375 | 1.36 | 1.875 |
November | 2.09 | 2.719 | 1.879 | 2.786 | 0.914 | 2.502 |
December | 2.606 | 3.329 | 2.006 | 3.435 | 0.551 | 3.561 |
Annual | 2.647 | 3.539 | 2.304 | 3.042 | 1.296 | 3.336 |
m/s at Sarh in August and September to 5.867 m/s in April.
Depending on the wind speed classes, the frequency distribution of the measured wind speed has been established and presented in
It is observed in
Characteristics | Bonus 300 kW/33 | BONUS 1 MW/54 | Vestas 2 MW/80 |
---|---|---|---|
hub height (m) | 30 | 50 | 67 |
Rated power Pr (kW) | 300 | 1000 | 2000 |
Diameter | 33.4 | 54.2 | 80 |
Cut-in wind speed Vc (m/s) | 3 | 3 | 4 |
Rated wind speed Vr (m/s) | 14 | 15 | 16 |
Cut-off wind speed Vf (m/s) | 25 | 25 | 25 |
which implies that the site is windy. The lowest average speed is 1.4 m/s at Sarh in the autumn, while the highest at 4.1 m/s is observed at Moundou in the spring. Thus, it can be concluded that Moundou remains a site where the characteristics of the wind are important and favorable throughout the four seasons.
In this study, the monthly and annual distributions of wind distribution and
Moundou | Pala | Sarh | |||||||
---|---|---|---|---|---|---|---|---|---|
Month | POUT | Cf (%) | EWT (kWh/ mon) | POUT | Cf (%) | EWT (kWh/ mon) | POUT | Cf (%) | EWT (kWh/ mon) |
January | 4.14 | 1.38% | 2977.98 | 3.32 | 1.11% | 2387.04 | 17.64 | 5.88% | 12,697.74 |
February | 7.78 | 2.59% | 5603.73 | 3.43 | 1.14% | 2472.13 | 23.05 | 7.68% | 16,593.8 |
March | 6.11 | 2.04% | 4400.9 | 5.4 | 1.8% | 3889.52 | 25.39 | 8.46% | 18,283.7 |
April | 11.3 | 3.77% | 8133.07 | 4.4 | 1.47% | 3168.83 | 34.44 | 11.48% | 24,795.38 |
May | 6.16 | 2.05% | 4432.26 | 2.72 | 0.91% | 1959.69 | 1.46 | 0.49% | 1054 |
June | 4.72 | 1.57% | 3399.19 | 2.1 | 0.7% | 1514.54 | 0.41 | 0.14% | 292.07 |
July | 3.37 | 1.12% | 2426.52 | 0.37 | 0.12% | 268.97 | 0.12 | 0.04% | 84.01 |
August | 3.78 | 1.26% | 2721.72 | 0.17 | 0.06% | 121.32 | 0.08 | 0.03% | 54.02 |
September | 1.52 | 0.51% | 1093.36 | 0.41 | 0.14% | 293.37 | 0.08 | 0.03% | 54.02 |
October | 3.33 | 1.11% | 2394.39 | 0.33 | 0.11% | 238.28 | 0.14 | 0.05% | 97.68 |
November | 1.16 | 0.39% | 838.35 | 2.28 | 0.76% | 1640.81 | 2.89 | 0.96% | 2078.49 |
December | 2.71 | 0.9% | 1951.21 | 6.84 | 2.28% | 4924.41 | 9.82 | 3.27% | 7073.74 |
Moundou | Pala | Sarh | |||||||
---|---|---|---|---|---|---|---|---|---|
Month | POUT | Cf (%) | EWT (kWh/ mon) | POUT | Cf (%) | EWT (kWh/ mon) | POUT | Cf (%) | EWT (kWh/ mon) |
January | 18.61 | 1.86% | 13,402.66 | 15.96 | 1.6% | 11,493.18 | 76.73 | 7.67% | 55,243.14 |
February | 33.54 | 3.35% | 24,151.84 | 15.89 | 1.59% | 11,441.89 | 96.76 | 9.68% | 69,665.62 |
March | 27.01 | 2.7% | 19,449.59 | 24.44 | 2.44% | 17,597.86 | 105.27 | 10.53% | 75,792.19 |
April | 47.84 | 4.78% | 34,442.1 | 20.33 | 2.03% | 14,641.1 | 136.84 | 13.68% | 98,527.15 |
May | 27.66 | 2.77% | 19,914.79 | 13.07 | 1.31% | 9407.57 | 7.87 | 0.79% | 5664 |
June | 21.73 | 2.17% | 15,647.8 | 10.78 | 1.08% | 7761.49 | 2.7 | 0.27% | 1943 |
July | 15.99 | 1.6% | 11,512.7 | 2.4 | 0.24% | 1725.11 | 1.1 | 0.11% | 790 |
August | 18.85 | 1.89% | 13,574.65 | 1.48 | 0.15% | 1069.07 | 0.94 | 0.09% | 676.66 |
September | 8.54 | 0.85% | 6151.73 | 3.1 | 0.31% | 2230.59 | 0.94 | 0.09% | 676.66 |
October | 17.59 | 1.76% | 12,663.71 | 2.36 | 0.24% | 1701.16 | 1.47 | 0.15% | 1057.55 |
November | 6.72 | 0.67% | 4838.38 | 12.57 | 1.26% | 9052 | 16.67 | 1.67% | 12,000 |
December | 13.2 | 1.32% | 9506.73 | 32.42 | 3.24% | 23,342.94 | 46.45 | 4.64% | 33,441.39 |
Moundou | Pala | Sarh | |||||||
---|---|---|---|---|---|---|---|---|---|
Month | POUT | Cf (%) | EWT (kWh/ mon) | POUT | Cf (%) | EWT (kWh/ mon) | POUT | Cf (%) | EWT (kWh/ mon) |
January | 33.23 | 1.66% | 23,927.4 | 26.08 | 1.3% | 18,778.4 | 130.21 | 6.51% | 93,751.06 |
February | 60.54 | 3.03% | 43,588.64 | 27.56 | 1.38% | 19,844.78 | 167.33 | 8.37% | 120,475.48 |
March | 47.84 | 2.39% | 34,442.22 | 42.13 | 2.11% | 30,330.98 | 183.12 | 9.16% | 131,845.55 |
April | 85.71 | 4.29% | 61,709.82 | 34.54 | 1.73% | 24,867.3 | 244.71 | 12.24% | 176,188.53 |
May | 47.62 | 2.38% | 34,287.9 | 21.83 | 1.09% | 15,718.07 | 11.7 | 0.58% | 8422.51 |
June | 36.89 | 1.84% | 26,560.62 | 16.67 | 0.83% | 12,004.92 | 3.28 | 0.16% | 2365.2 |
July | 26.69 | 1.33% | 19,213.97 | 3.08 | 0.15% | 2217.82 | 0.93 | 0.05% | 669.58 |
August | 28.97 | 1.45% | 20,857.96 | 1.34 | 0.07% | 964.22 | 0.6 | 0.03% | 433.48 |
September | 11.9 | 0.6% | 8568.4 | 3.21 | 0.16% | 2311.22 | 0.6 | 0.03% | 433.48 |
October | 25.3 | 1.27% | 18,217.75 | 2.66 | 0.13% | 1914.21 | 1.09 | 0.05% | 783.95 |
November | 9.21 | 0.46% | 6631.96 | 17.53 | 0.88% | 12,618.15 | 22.51 | 1.13% | 16,208.21 |
December | 21.56 | 1.08% | 15,526.74 | 51.23 | 2.56% | 36,887.12 | 74.34 | 3.72% | 53,523.47 |
Location | BONUS 300 kW/33 | BONUS 1MW/54 | VESTAS 2MW/V80 | |||
---|---|---|---|---|---|---|
Pe,av (kW/Year) | Cf (%) | Pe,av (kW/Year) | Cf (%) | Pe,av (kW/Year) | Cf (%) | |
Moundou | 56.07 | 18.69 | 257.30 | 25.73 | 435.46 | 21.77 |
Pala | 31.78 | 10.59 | 154.81 | 15.48 | 196.62 | 9.83 |
Sarh | 115.50 | 38.50 | 493.72 | 49.37 | 840.42 | 42.02 |
Month | Jan. | Feb. | Mar. | Apr. | May | June | July | Aug. | Sept. | Oct. | Nov. | Dec. | Annual |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Moundou | 16.709 | 19.124 | 17.516 | 21.862 | 14.89 | 12.713 | 11.488 | 7.944 | 5.442 | 5.689 | 5.286 | 10.187 | 148.85 |
Pala | 10.435 | 12.464 | 14.596 | 12.589 | 11.217 | 7.842 | 5.002 | 2.773 | 2.862 | 3.833 | 5.137 | 9.066 | 97.816 |
Sarh | 12.149 | 16.035 | 20.644 | 33.966 | 7.052 | 4.31 | 2.732 | 1.651 | 1.598 | 1.718 | 2.717 | 6.226 | 110.799 |
Month | Jan. | Feb. | Mar. | Apr. | May | June | July | Aug. | Sept. | Oct. | Nov. | Dec. | Annual |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Moundou | 1.03 | 1.12 | 1.05 | 1.15 | 1.00 | 0.96 | 0.91 | 0.81 | 0.72 | 0.72 | 0.71 | 0.88 | 0.92 |
Pala | 0.89 | 0.96 | 0.99 | 0.95 | 0.90 | 0.81 | 0.68 | 0.56 | 0.58 | 0.63 | 0.71 | 0.83 | 0.79 |
Sarh | 0.47 | 0.58 | 0.69 | 1.09 | 0.77 | 0.66 | 0.56 | 0.48 | 0.48 | 0.49 | 0.48 | 0.46 | 0.60 |
Moundou | Pala | Sarh | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Seasons | V (m/s) | K | C (m/s) | Vmp | VmaxE | V (m/s) | K | C (m/s) | Vmp | VmaxE | V (m/s) | K | C (m/s) | Vmp | VmaxE |
Winter | 3 | 3.4 | 3.34 | 3.01 | 3.83 | 2.6 | 3 | 2.91 | 2.51 | 3.49 | 1.9 | 1.24 | 2.03 | 0.53 | 4.44 |
Spring | 3.1 | 3.05 | 3.47 | 3.04 | 4.1 | 2.8 | 3.28 | 3.12 | 2.79 | 3.62 | 2.6 | 2.11 | 2.91 | 1.72 | 4.7 |
Summer | 2.6 | 3.05 | 2.91 | 2.55 | 3.43 | 2.1 | 3.61 | 2.29 | 2.09 | 2.6 | 1.7 | 3.54 | 1.89 | 1.72 | 2.14 |
Autumn | 2.1 | 2.9 | 2.32 | 1.99 | 2.8 | 1.9 | 3.13 | 2.09 | 1.83 | 2.46 | 1.4 | 2.58 | 1.57 | 1.22 | 2.07 |
Annual | 2.7 | 3.1 | 3.01 | 2.65 | 3.54 | 2.35 | 3.26 | 2.6 | 2.31 | 3.04 | 1.9 | 2.37 | 2.1 | 1.3 | 3.34 |
wind energy density during the period 1975-2010 of selected three stations in the Sudanese zone of Chad were evaluated. The analysis was done on the basis of Weibull distribution function with two parameters. Based on the results of this study, it can be concluded that:
1) The minimum monthly average wind speed of 1.4 m/s in August, September, October and November in Sarh and a maximum value of 3.3 m/s in Moundou in February and April were recorded. A maximum value of the average annual wind speed of 2.692 m/s is obtained at Moundou.
2) The annual mean value of the Weibull c scale parameter ranges from 2.099 m/s to 3.007 m/s whereas the annual value of the form parameter k varies from 2.376 to 3.255. The highest of the values c and k are found in the Moundou and Pala stations, respectively. The average annual wind densities for Moundou, Pala and Sarh are 17.071 W/m2, 11.212 W/m2 and 12.762 W/m2, respectively.
3) The wind turbine in turn offers a more profitable possibility for the Sudanese zone of Chad with the wind turbine Vestas 2 MW/80.
The wind power potential can be exploited to ensure sustainable development in the rural areas for pumping of water, heating of water, and production of electricity. The study recommends a long-term (at least ten years) wind speed data analysis for better understanding of energy potential and the design of suitable wind turbine for the selected cities.
We make a point of thanking the persons in charge of the National Meteorology for Chad, as the personnel who deal with the collection and the processing weather data on these sites, to have placed at our disposal the data which were used in our work.
Soulouknga, M.H., Oyedepo, S.O., Doka, S.Y. and Kofane, T.C. (2017) Assessment of Wind Energy Potential in the Sudanese Zone in Chad. Energy and Power Engineering, 9, 386-402. https://doi.org/10.4236/epe.2017.97026