compilation and evaluation of solar and wind energy resources in sudan

~ ) Pergamon Renewable Energy, Vol. 12, No. 1, pp. 3 9 4 9 , 1997

© 1997 Elsevier Science Ltd All rights reserved. Printed in Great Britain

PII: S0960-1481(97)00009-8 0960-1481/97 $17.00+0.00

COMPILATION A N D EVALUATION OF SOLAR A N D WIND ENERGY RESOURCES IN

S U D A N

A B D E E N M U S T A F A O M E R ERI, P.O. Box 4032, Khartoum, Sudan

(Received 10 December 1995;accepted 21 January 1997)

Abstraet--A number of years worth of data concerning the solar radiation on a horizontal surface, sunshine duration and wind speed in Sudan have been compiled, evaluated and presented in this article.

Measurements of global solar radiation on a horizontal surface at 16 stations for several years are compared with predictions made by several independent methods. In the first method the Angstrom formula was used to correlate relative global solar irradiance to the corresponding relative duration of bright sunshine.

Regression coefficients are obtained and used for prediction of global solar irradiance. The predicted values were consistent with measured values (__+ 8.01% variation).

In the second method, by Barbaro et al. [Solar Energy, 1978, 20, 431] sunshine duration and minimum air mass were used to drive an empirical correlation for the global radiation. The predicted values compared well with measured values (+ 12% variation).

The diffuse solar irradiance is estimated. The results of two formulas have close agreement. A radiation map of Sudan was prepared from the estimated radiation values. The annual daily mean global radiation ranges from 3.05 to 7.62 kWh m -2 per day.

Routine wind data from 70 stations were analyzed. Monthly averaged wind speed and average powers were determined for each station. The derived annual average speeds range from 1.53 to 5.07 m s-1. Maximum extractable average wind powers were found to vary between 1.35 and 49.5 W m -2. A wind map of Sudan was also prepared.

Sudan possessed a relatively high abundance of sunshine and moderate wind speed. It is concluded that Sudan is blessed with abundant solar and wind energy. © 1997 Elsevier Science Ltd.

INTRODUCTION During the past few years the economic development of Sudan has been slowed, partly by the rapidly increasing price of petroleum. As Sudan is a tropical country with high solar radiation and moderate wind, solar and wind energies seem to be attractive sources of energy. As assessment of solar and wind energies is considered to be the most important

39

40 A.M. OMER

step to be taken before systematic harvesting of both energies, the assessment result will provide the foundation for sound technical, economic and social decisions. Fortunately, a network of weather stations had been established in Sudan recently and recording stations under the Sudan Meterological Department (SMD), can supply a limited amount of radiation and wind data. It is thus possible to carry out a preliminary assessment of solar and wind potential in Sudan.

The objectives of this research are :

(1) To compile and store solar and wind data collected from various sources in a computer system.

(2) To generate a useful set of numerical values for solar and wind energy which will serve as a frame of reference for calculation of potential energy for the engineering specifications of various conversion systems.

(3) To prepare solar and wind energy profiles of Sudan.

This article includes the analysis of data on global solar radiation and sunshine hour; these data are obtained from the Sudan Meteorological Department Office in Khartoum. It also discusses the estimation of diffuse solar radiation from the global values. In addition to solar energy, wind energy is also considered. Data from 70 stations over the country are analyzed.

The data presented herein are in good agreement, but still needed extensive data collec- tion. The data presented at this stage can serve as a good indicator for researchers and policy makers in planning the utilization of solar and wind energy in Sudan. The need for solar and wind information is essential in the design and study of solar and wind energy conversion devices. Other uses of such information include agricultural studies, meteorological forecasting, environment and energy conservation.

SUDAN CLIMATE

Sudan is the largest country of the African nations with an area of ca. 2.5 million square kilometers, extending between longitudes 21 ° 45'E and 38 ° 'E, and latitudes 3 ° 'N and 23 ° 'N; and has a population of approximately 25 million. The growth rate is 2.7% and population density is 10 persons per square kilometer [1].

Sudan has a predominately continental climate which roughly divides into three climatological regions :

Region 1 is situated north of latitude 19 ° 'N. The summers are invariably hot (mean max. 41°C and mean rain. 25°C with large decimal variation; low relative humidity averages 25 %). Winters can be quite cool. Sunshine is very prevalent. Dust storms occur in summer. The climate is a typical desert climate where rain is infrequent and diurnal (annual rainfall of 75-300 nm). The annual variation in temperatures is large (maximum and minimum pattern corresponding to winter and summer). The fluctuations are due to the dry and rainy seasons. Region 2 is situated south of latitude 19 ° 'N. The climate is a typical tropical continental climate. Region 3 comprises the areas along the Red Sea coast and eastern slopes of the Red Sea hills. The climate is basically as in region 1, but it is affected by the maritime influence of the Red Sea.

Energy resources in Sudan 41

The two main air movements determine the general nature of the climate. Firstly, a very dry air movement from the north that prevails throughout the year, but lacks uniformity ; and secondly, a major flow of marit ime origin that enters Sudan f rom the south carrying moisture and bringing rain.

The extent of penetration into the country by air flow from the south determines the annual volume of a rainfall and its monthly distribution. The average monthly rainfall for Sudan indicates the decreasing trend in the volume of rainfall, as well as in the duration as one moves generally from the south towards the north and from east towards west.

PROCEDURE FOR EXTRACTING THE DATA FROM LITERATURE AND PRESENTATION

Consideration has been given to the consistent and effective presentation of data. Original data were extracted f rom published reports by SMD and converted into more useful working units, i.e. solar radiation in calories per square centimeter per day was converted to megajoules per square meter per day ; and wind speed in miles per hour to m s- i.

The relative data available on wind speed and ambient temperature were recorded by 70 stations, while sunshine duration and solar radiation measurements on horizontal surface were made at 16 stations. Station names are listed in Table 1.

SOLAR ENERGY

Equipment has been developed to utilize solar energy in their operation. Some of the equipment, e.g. solar water heaters, solar stills and solar dryers, use solar energy in the form of heat. Some have solar cells to transform the solar energy into electricity. Design of these for high efficiency and sustainability, with respect to a particular working area needs reliable information on the potentiality of solar energy in that area. As an illustration, in designing a solar water heater, the place where the heater is to be instituted needs to be known, the amount of solar radiation and the number of consecutive days that given intensity of solar radiation can be obtained are also needed. This information is absolutely necessary in estimating the capacity of an auxiliary water heater and size storage tank to fit the existing system. However, due to the high cost of devices for measuring the amount of solar radiation, the measurement cannot be done by every meteorological station in Sudan. Many investigators have tried to relate the solar radiation to other meteorological factors. The relationships, when found, can be used without direct measurement, to give an approximate solar radiation.

SOLAR RADIATION

The sun is a sphere of intensely hot gaseous matter with a diameter of 1.39 × 1 0 6 km and, is on average, a distance of 1.5 × 108 km from earth [2] Energy occurring in the sun comes from the thermonuclear reaction; the reaction causes the reduction in solar mass by approximately 4 × 1 0 9 kg s - l ; and simultaneously releases energy at a rate of 3.85 × 1 0 23

kW. However, only 1.79 × 1014 kW of solar energy is received by the earth [3]. Solar radiation is an electromagnetic wave directly emitted from the sun's disc, it reaches

the earth about 8 min after the emission process. Solar radiation covers an extremely wide range of wave-lengths from 10 -4/~m up to wavelengths of the order of 104 m. When direct

42 A. M. O M E R

T a b l e 1. G e o g r a p h i c a l l o c a t i o n o f s t a t i o n s

L a t i t u d e L o n g i t u d e A l t i t u d e I t e m N a m e o f s t a t i o n (°) (°) (m) T e s t i n g pe r i od

1 H a l a i b 22 ° 13 ' N 36 ° 39 'E 52.00 1975-1985 2 W a d i H a l f a 21 ° 5 5 ' N 31 ° 2 1 ' E 190.00 1981-1985 3 S t a t i o n 6 20 ° 4 5 ' N 32 ° 3 3 ' E 470 .00 1975-1985 4 P o r t S u d a n 19 ° 3 5 ' N 37 ° 13 'E 5.00 1975-1985 5 A b u H a m e d 19 ° 3 2 ' N 33 ° 2 0 ' E 315.00 1979-1985 6 D o n g o l a 19 ° 10 ' N 30 ° 2 9 ' E 225.00 1975-1985 7 G e b e i t 18 ° 5 7 ' N 36 ° 51 'E 795.00 1975-1985 8 K a r i m a 18 ° 3 3 ' N 31 ° 51 'E 250.00 1975-1985 9 T o k e r 18 ° 2 6 ' N 37 ° 4 4 ' E 20.00 1976-1985

10 A q i q 18 ° 1 4 ' N 38 ° 11 'E N . A . 1975-1985 11 A t b a r a 17 ° 4 0 ' N 33 ° 58 'E 345.00 1975-1985 12 D e r u d e b 17 ° 3 5 ' N 36 ° 0 6 ' E 510.00 1975-1985 13 H u d e i b a 17 ° 3 4 ' N 33 ° 56 'E 350.00 1975-1985 14 Shend i 16 ° 4 2 ' N 33 ° 2 6 ' E 360.00 1976-1985 15 A r o m a 15 ° 5 0 ' N 36 ° 0 9 ' E 430.00 N . A . 16 W a d i Se idna 15 ° 4 0 ' N 32 ° 32 'E 385.00 1975-1985 17 S h a m b a t 15 ° 4 0 ' N 32 ° 32 'E 380.00 N . A . 18 K h a r t o u m 15 ° 3 6 ' N 32 ° 33 'E 380.00 1975-1985 19 K a s s l a 15 ° 2 8 ' N 36 ° 2 4 ' E 500.00 1975-1985 20 Jebe l A u l i a 15 ° 2 4 ' N 32 ° 30 'E 380.00 1975-1985 21 H a l f a el G e d i d a 15 ° 1 9 ' N 35 ° 36 'E 450.00 1975-1985 22 A b u Q u t a 14 ° 5 5 ' N 32 ° 4 4 ' E 390.00 1976-1985 23 El S h o w a k 14 ° 2 4 ' N 35 ° 51 'E 510.00 1975-1985 24 W a d M a d a n i 14 ° 2 3 ' N 33 ° 2 9 ' E 405 .00 1975-1985 25 M e d i n a B lock 14 ° 2 2 ' N 33 ° 19 'E 405 .00 1978-1985 26 K u t u m 14 ° 1 2 ' N 24 ° 4 0 ' E 1160.00 1975-1985 27 E1 G a d a r i f 14 ° 0 2 ' N 35 ° 2 4 ' E 600.00 1975-1985 28 Ed D u e i m 13 ° 5 9 ' N 32 ° 2 0 ' E 380.00 1975-1985 29 W a d E 1 H u r i 13 ° 5 6 ' N 35 ° 14 'E N . A . N . A . 30 E1 F a s h e r 13 ° 3 8 ' N 25 ° 20 'E 733.00 1975-1985 31 S e n n a r 13 ° 3 3 ' N 33 ° 3 7 ' E 420.00 1975-1985 32 D o k a 13 ° 3 1 ' N 35 ° 4 6 ' E N . A . 1975-1985 33 El G e n e i n a 13 ° 2 9 ' N 22 ° 2 7 ' E 805.00 1975-1985 34 K o s t i 13 ° 1 0 ' N 32 ° 4 0 ' E 380.00 1975-1985 35 E 1 0 b e i d 13 ° 10 ' N 33 ° 14 'E 570.00 1975-1985 36 D a n k o g 13 ° 0 5 ' N 23 ° 59 'E 965.00 1979-1985 37 U m m B e n e i n 13 ° 0 4 ' N 33 ° 57 'E 435 .00 1975-1985 38 Nie r t e t i 12 ° 5 8 ' N 24 ° 0 4 ' E N . A . 1975-1985 39 Za l i nge i 12 ° 5 4 ' N 23 ° 29 'E 900.00 1975-1985 40 M u r u n d u 12 ° 4 9 ' N 23 ° 0 9 ' E N . A . 1975-1985 41 A b u N a ' a m a 12 ° 4 4 ' N 34 ° 0 7 ' E 445 .00 N . A . 42 El N a h u d 12 ° 4 2 ' N 28 ° 2 6 ' E 565.00 1975-1985 43 D e r e i s a 12 ° 4 1 ' N 22 ° 4 6 ' E N . A . 1979-1985 44 K a s 12 ° 3 1 ' N 24 ° 16 'E N . A . 1975-1985 45 G a r s i l a 12 ° 2 2 ' N 23 ° 0 8 ' E N . A . 1975-1985 46 N y a l a 12 ° 0 4 ' N 42 ° 53 'E 655.00 1975-1985 47 M u k g u r 11 ° 5 7 ' N 23 ° 17 'E N . A . 1975-1985 48 R a s h e d 11 ° 5 2 ' N 31 ° 0 3 ' E 885.00 1975-1985 49 Ed D a m a z i n 11 ° 4 9 ' N 34 ° 2 4 ' E 470 .00 1975-1985 50 Er R e n k 11 ° 4 5 ' N 32 ° 4 7 ' E 380.00 1975-1985

Energy resources in Sudan

Table 1--Continued

43

Latitude Longitude Altitude Item Name of station (°) (°) (m) Testing period

51 Ghazala Gawazat 11 ° 28'N 26 ° 27'E 480.00 1975--1985 52 Babanusa 11 ° 20'N 27 ° 40'E 543.00 1979-1985 53 Kadugli 11 ° 00'N 29 ° 43'E 501.00 1975-1985 54 Kurmuk 10 ° 33'N 24 ° 17'E 690.00 1977-1985 55 Malakal 09 ° 33'N 31 ° 39'E 387.00 1975-1985 56 Bentiu 09 ° 14'N 29 ° 50'E 390.00 1975-1985 57 Aweil 80 ° 46'N 27 ~ 24'E 415.00 1975-1985 58 Nasir 08 ° 37'N 33 ° 04'E 400.00 1975-1985 59 Raga 08 ° 28'N 25 ° 41'E 545.00 1975-1985 60 Gambeila 08 ° 15'N 34 ° 35'E 450.00 1968-1985 61 Akobo 07 ° 47'N 33 ° 01'E 400.00 N.A. 62 Wau 07 ° 42'N 28 ° 01'E 435.00 1975-1985 63 Tonj 07 ° 17'N 28 ° 45'E 430.00 1975-1985 64 Rumbek 06 ° 48'N 29 ° 42'E 420.00 1975-1985 65 Bor 06 ° 12'N 31 ° 33'E 420.00 1975-1985 66 Maridi 04 ° 55'N 29 ° 28'E 750.00 1975-1985 67 Juba 04 ° 52'N 31 ° 35'E 460.00 1975-1985 68 Yambio 04 ° 34'N 28 ° 24'E 650.00 1977-1985 69 Torit 04 ° 25'N 32 ° 33'E 625.00 1978-1985 70 Yei 04 ° 05'N 30 ° 40'E 830.00 1975-1985

solar rad ia t ion penetrates into the atmosphere, it is absorbed and scattered. Only the longer wavelengths (visible, infra-red and radio) reach the lower layers of the atmosphere. Approx imate ly 98% of the total solar rad ia t ion flux consists of radiat ion, the wavelength of which ranges f rom 0.3 to 4 pm. All the ultra-violet rays with wavelengths of less than 0.3 pm are absorbed by water vapour [4].

PREDICTION OF GLOBAL SOLAR RADIATION H

M a n y methods have been developed for the predict ion of the a m o u n t of solar energy incident on a hor izonta l p lane at the earth 's surface. The simplest models are the empirical formulas presented by Goldberg et al. [5].

The first method to have been used is the Angs t rom correlat ion relat ion [6, 7] ;

H/Ho = a + b n / N (1)

where H is the mon th ly mean daily global i r radiance on a hor izonta l surface. Ho is the extraterrestrial solar i r radiance on the 15th of the month , a and b are regression constants empirical constants , n is the mon th ly mean daily hours of bright sunshine. N i s the m a x i m u m daily hours of bright sunshine (i.e. the length of the average day of the month) , n/N is the fract ion of the m a x i m u m possible n u m b e r of bright sunshine hours ; and H / H is the a tmospher ic t ransmiss ion coefficient.

The values of N are computed from Cooper ' s fo rmula [8] ;

44 A . M . OMER

N = 2/15Cos -1 ( - T a n q S t a n 3 ) (2)

where ~b is the latitude and 0 is the solar declination. The value of declination can be found from the equation of Cooper [8] ;

3 = 23.45 Sin [360(284 + m)/365] (3)

where m is the number of the day in the year (1-365). Extraterrestrial radiation on a horizontal surface at any time between sunrise and sunset

is given by [9] ;

H0 = [(24 x 3600/d~) x (1 +0.33 Cos (360 x m)/365] x

[Cos ~b Cos 0 Sin w + (27tw/360) Sin ~h Sin ~] (4)

where w is the sunset, sunrise hour angle, in degrees. Is is the solar constant (1350 Win-2). Another method which requires only the sunshine hours and the minimum air mass as

input parameters was proposed by Sivkov [10, 11] for the latitudes 35-65 ° North :

n m = 4.9("m) TM + 10,500(Sin S,) 2~ (5)

w h e r e H m is the monthly global irradiance, Cal cm -2, nm is the monthly sunshine hours, and Sn is the noon altitude of the sun at the 15th of the month [ 9 0 - (~b - 3)]. Barbaro et al. [12] modified the formula to make it fit 31 Italian stations which they divided into three zones according to their climatological characteristics.

The modified formula used is :

n m = g(nm) 1"24 ( an ) -0"19 + 10,550(Sin Sn) 2' (6)

where K is the zone parameter (8, 9.5, 11) for three different regions in Italy. Relation (6), which was proposed for high latitudes (35-65°N), was tested by Khogali

[13] for low latitudes (4-19°N). It was found applicable with a good degree of accuracy provided that the parameter K is appropriately adjusted.

PREDICTION OF DIFFUSE SOLAR IRRADIANCE

As no information is available on the diffuse solar irradiance in Sudan, two theoretical methods were used for its estimation. A well-known relation for this purpose is the Page correlation [14]:

/-/.//4., = 1 . 0 0 - 1 . 1 3 K ~ (7)

where Hd is the monthly mean daily diffuse solar irradiance, Hm is the monthly mean daily total global irradiance and KT is the ratio of cloudiness index or transmission coefficient.

KT = H,~/Hoavg (8)

where Hoavg is the average value of Ho over the whole month under consideration. The other commonly used correlation [15] was developed by Klein [16], to take the form,

Hd/H,~ = 1.390 -- 4.027Kr + 5.531K 2 -- 3.108K~-. (9)


The numerical coefficients in eqs (7) and (9) are empirical. These two correlations are used for the prediction of the diffuse solar irradiance.

The direct beam component Ib can be deduced from the relation [17] :

Hm = Hd +Ib Sin (Sn) (10)

where Ib Sin (Sn) is the average horizontal beam component. This is useful for various types of solar concentrating systems.

ANALYSIS AND RESULTS

The experimental data used in this article were supplied by the Sudan Meteorological Department in Khartoum. At the 16 stations, bright sunshine periods were recorded by the Campbell-Stokes heliograph and the daily global solar irradiance was recorded by a Robitsch pyranometer over seven yr periods (Tables 2-6). The accuracy of these instruments was estimated to 5%.

PREDICTION OF H

The monthly mean daily global solar radiation was calculated from eq. (1). The coefficients a and b were calculated from the values of (H/Ho) and (n/N) for each station for each month of the year. The measured values of the monthly mean daily global radiation H were obtained from the measured data provided by the Meteorological Department. The values of the monthly mean daily extraterrestrial radiation 1to were calculated from eq. (4), as outlined previously. The measured monthly mean daily number of bright sunshine hours n were also obtained from the data provided by the Meteorological Department. The monthly mean daily theoretical values of sunshine hours N were calculated from eq. (2). Values of (H/Ho) and (n/N) for each month were determined. Accordingly, 12 equations (one for each month) may be written. The least square method was then used to calculate the regression coefficients a and b ofeq. (1) for each station (Table 7).

Table 8 shows the comparison between measured and estimated values of global radiation by using eq. (1). The percentage errors between the measured and estimated values of monthly mean daily global solar radiation have values less than + 8.01%. These percentage errors lie within the standard value of _+ 5.5 % which makes the model acceptable. Moreover, the difference between estimated and measured annual mean daily solar radiation is better than +4.82%.

Another method to predict H was employed. In this method an empirical relation due to Barbaro et al. [12], which used sunshine duration and minimum air mass as inputs, was used. The daily solar irradiance data were computed using eq. (6). The computation of H is based on knowledge of appropriate zone parameters, long-term average sunshine hours and altitude of the sun (Table 8). It is shown that values obtained using this model are in good agreement with those measured with a maximum percentage error of _+ 12.00%.

The total solar irradiance lies as shown in Fig. 1, in the desert of north-western Sudan. The radiation decreases southwards with increase in cloudiness. It also decreases towards the Red Sea.

46

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Table 6. Correlation of solar radiation with other weather parameters in Sudan (yearly average)

Station

Mean temp. Sunshine Solar radiation Wind velocity Relative duration

(°C) (h) (MJm -2 day-1) (MPH) humidity (%)

Port Sudan 28.40 9.00 20.87 7.70 65 Shambat 29.70 9.90 22.82 8.90 31 Wad Medani 28.40 9.80 22.84 7.30 40 E1 Fasher 25.80 9.60 22.80 5.30 33 Abu Na 'ama 28.20 8.80 21.90 6.70 46 Ghazala Gawazat 27.20 9.30 21.72 6.80 43 Malakal 27.90 7.80 19.90 6.10 54 Juba 27.60 7.80 19.59 3.40 66 Dongola 27.20 10.50 24.06 10.50 27 Toker 28.80 7.30 17.60 6.70 53 Hudeiba 29.30 10.00 22.37 6.30 25 Aroma 29.10 9.60 21.40 6.20 37 E1 Showak 26.30 9.70 22.90 6.00 39 Zalingei 24.50 8.80 22.98 3.80 39 Babanusa 28.20 8.90 21.73 6.20 40 Kadugli 27.50 8.50 21.30 5.60 48

Table 7. Solar radiation over Sudan-regression coefficients

Station

Latitude

(°)

Annual average Regression coefficients

1t/1-1o n /N a b a + b

1. Port Sudan 19.58 0.62 0.75 0.32 0.40 0.72 2. Dongola 19.17 0.72 0.88 0.21 0,57 0.78 3. Toker 18.43 0.62 0.80 0.40 0.20 0.60 4. Hudeiba 17.57 0.67 0.84 0.21 0,54 0.75 5. Aroma 15.83 0.63 0.80 0.46 0.21 0.67 6. Shambat 15.67 0.67 0.84 0.28 0.47 0.75 7. Wad Medani 14.38 0.66 0.82 0.36 0.37 0.73 8. E1 Showak 14.24 0.67 0.81 0.33 0.42 0.75 9. E1Fasher 13.63 0.66 0.80 0.36 0.37 0.73

10. Zalingei 12.90 0.66 0.74 0.33 0.46 0.78 11. Abu Na 'ama 12.73 0.64 0.75 0.43 0.27 0.70 12. Ghazala Gawazat 11.47 0.63 0.78 0.35 0.35 0.70 13. Babanusa 11.33 0.62 0.70 0.35 0.35 0.70 14. Kadugli 11.00 0.61 0.71 0.29 0.46 0.75 15. Malakal 9.55 0.57 0.65 0.34 0.36 0.70 16. Juba 4.87 0.55 0.64 0.40 0.36 0.70

ESTIMATION OF DIFFUSE SOLAR RADIATION

Values o f the m o n t h l y average da i ly diffuse so lar r ad i a t i on at 16 s ta t ions have been c o m p u t e d by using the two cor re la t ion re la t ions (7) and (9). The results are presented in Tab le 9.

A l inear re la t ionsh ip be tween g loba l r ad i a t i on levels and n u m b e r o f sunshine hours was

E ne rgy resources in S u d a n 51

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tried for the stations that record global radiation, but failed to give meaningful results. A linear regression between daily values of H/Ho vs n/N suggested by Duffle and Beckman [2] failed to meet any goodness of fit, where H and Ho are the ground and extraterrestrial radiation, respectively, and n and N are the sunshine duration and the maximum possible sunshine duration, respectively. The reasons may be attributed to :

(1) Augmentation of global radiation by multiple reflectors between ground and clouds ; (2) Measurement error related to the Campbell-Stokes sunshine records ; (3) Measurement error associated with bimetallic global radiation records.

W I N D ENERGY

Wind power has been ignored so far despite the fact that the use of wind as a source of power has a long history. Man has been familiar with the use of wind energy for thousands of years. Alongside windmills and pumps, sailing ships were, in the past, the most significant example of its technical utilization. However, during the last decade interest has been refocused on natural renewable energy sources due to the increasing prices and foreseeable exhaustion of fossil fuel sources. Particular priority in the use of renewable energies is in remote areas of low population density where the implementation of a central power system would be uneconomical, the decentralized utilization of wind energy can provide a substantial contribution to the development in such locations.

W I N D ENERGY DATA BASE

The objectives of creating a wind resource data base for Sudan are to :

(1) Analyze the wind energy potential in Sudan using available wind data for the country.

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(2) Refine recorded data and develop an accurate estimate of global wind energy available in Sudan.

(3) Identify wind characteristics required for the design of wind energy conversion systems.

METHODOLOGY

Available wind data from the Meteorological Department (Khartoum) were used. The data were subsequently stratified according to quality, based on the following factors :

(1) Accuracy of the recording equipment and technique. (2) Type of data collected. (3) Exposure of the recording equipment. (4) Recording period (yr). (5) Recording rate/interval.

AVAILABLE WIND DATA

Wind energy data from the Meteorological Department consist of mean monthly wind speeds and wind directions measured at a height of 10 m above ground from stations throughout Sudan (Tables 10 and 11).

Data were collected by relatively accurate and properly maintained anemometers. Vanes and Dine's pressure-tube anemographs were used to record hourly mean wind speeds at 22 stations [18], other stations used beamfort estimates [18]. For most of the stations, the recording period was greater than 10 yr and average recording intervals of an hour were satisfactory. Monthly wind speed frequency distribution was also tabulated. The major parameter affecting the accuracy of the data was the exposure of the recording equipment to climate conditions, accordingly ca. 6% of the stations throughout the country were ignored in the analysis on grounds of inaccuracy. These data were utilized to determine annual wind speed frequency distribution, a major parameter in computing wind power density at a given site.

Anemometers were mounted on poles at a fixed height above the ground, usually 5, 10 or 15 m. Under normal conditions, wind speeds were greater at higher distance above ground. This is largely because the effects of surface features and turbulence diminish as the height increases. The variability depends on distance from the ground and roughness of the terrain [19].

The speed data indicated the height at which the data were collected (i.e. the height of the anemometer) [18]. The most commonly accepted measure of the difference that can be expected in wind speeds between anemometer's reference height and proposed height of 10 m is given by the "one-seventh to one-sixth power law :

( V ~ / V 2 ) = (h~/h2) n

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Energy resources in S u d a n 59

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It is much more difficult to predict average monthly wind speeds if the reference height at which the data were recorded is less than 6 m. Data collected at heights of less than 6 m should not be used to select a windmill or predict performance [20, 21]. In relatively fiat areas with no trees or buildings in the immediate vicinity, site selection is not critical [21]. However, in mountainous areas or places where obstacles may block the flow of wind, differences in surface roughness and obstacles between anemometer and pump site must be taken into account when estimating wind speeds for the site.

In Sudan, unequal measuring heights at different stations, in towns like Khar toum, Atbara and El Obeid were measured at 15 m, in semi-towns at 10 m, and in the remaining at 5 m [22]. The accuracy of the instruments was estimated to 5%.

WIND POWER CALCULATION

Formulae [23, 24] derived from this equation :

P = ( 1 / 2 ) × C p ( B e t z ) x a a x A x V 3 Wm -~ (11)

where P is the available wind in W m - 2 ; CP (Betz) = 16/27 = 0.593; ~a is the average density of air in Sudan at the height of 10 m, taken as 1.15 kg m -3 [25] ; A is the area swept by rotor, projected in a plane perpendicular to the direction of wind m 3 and Vis the average wind speed ms-%

Annual mean wind speeds were derived from original monthly mean wind speeds. Annual mean wind powers were derived f rom monthly mean speeds which were calculated according to the following procedure : given a monthly mean wind speed, V, the maximum extractable monthly mean wind power per unit cross-sectional area, P is given by :

P = 0.3409 V 3 (12)

where V is in ms -1 and P is in Wm -2. The constant 0.3409 takes Betz limit into account and is derived from the factors given by Golding [23, 24]. This analysis procedure is similar to that reported by Lysen [26].

RESULTS AND DISCUSSION

Data is given by the Meteorological Depar tment Office, Sudan. Measurements were with a cup anemometer coupled to a chart recorder for selected stations. Mean monthly wind speeds were tabulated for the 70 meteorological stations and mean annual wind power was derived as shown in Table 12. Based on these data an isovent map was developed showing the distribution of wind speeds all over the country (Fig. 2).

The isovent map reflects the very good potential for wind energy in Sudan. Due to local conditions, there may be many high-wind sites in low wind areas and conversely at a given site can be several times less than that calculated on the basis of mean annual wind speeds. This is due to the cubic power in the relationship between wind power and wind speed. Referring to Fig. 2, the eastern region of Sudan (Halaib, Port Sudan) has very high wind speeds (greater than 5 m s-~). The northern region (Dongola, Kar ima) has relatively high wind speeds exceeding 4.5 m s-1. The Khar toum and Gezira regions also enjoy good wind power potentials. The western regions have comparatively low wind speeds, while the southern regions have the poorest potential because of the prevailing low wind speeds.

66 A . M . OMER

Table 12. Annual average wind speeds, annual average wind powers and number of years of obser- vations for the 70 stations in Sudan, at 10 m AGL

Annual Annual Annual Annual mean mean mean mean

Altitude wind speeds wind speeds wind power wind power Item Name of station (m) (mph) (ms-1) (Wm-2) (yr)

1 Halaib 52.00 11.33 5.07 49.43 10.00 2 Wadi Halfa 190.00 10.33 4.622 37.48 4.00 3 Station 6 470.00 10.17 4.548 35.69 10.00 4 Port Sudan 5.00 11.25 5.032 48.36 10.00 5 Abu Hamed 315.00 10.67 4.771 41.22 6.00 6 Dongola 225.00 10.50 4.697 39.32 10.00 7 Gebeit 795.00 9.00 4.026 24.76 10.00 8 Karima 250.00 10.42 4.659 38.39 10.00 9 Toker 20.00 9.08 4.063 25.45 9.00

10 Aqiq N.A. 9.25 4.138 26.88 10,00 11 Atbara 345.00 9.42 4.212 28.36 10.00 12 Derudeb 510.00 9.00 4.026 24.76 10,00 13 Hudeiba 350.00 9.00 4.026 24.76 10,00 14 Shendi 360.00 9.00 4.026 24.76 9,00 15 Aroma 430.00 N.A. N.A. N.A. N.A. 16 Wadi Seidna 385.00 9.90 4.436 33.12 10.00 17 Shambat 380.00 N.A. N.A. N.A. N,A. 18 Khartoum 380.00 10.00 4.473 33.96 10.00 19 Kassla 500.00 9.00 4.026 24.76 10.00 20 Jebei Aulia 380.00 10.08 4.510 34.82 10.00 21 Halfa El Gedida 450.00 9.17 4.100 26.16 10.00 22 Abu Quta 390.00 9.83 4.399 32.29 9.00 23 E1 Showak 510.00 9.17 4.100 26.16 10.00 24 Wad Madani 405.00 10.00 4.473 33.96 10.00 25 Medina Block 405.00 10.25 4.585 36.58 7.00 26 Kutum 1160.00 7.83 3.504 16.33 10.00 27 E1 Gadarif 600.00 8.92 3.988 24.08 10.00 28 Ed Dueim 380.00 9.00 4,026 24.76 10.00 29 Wad El Huri N.A. N.A. N.A. N.A. N.A. 30 E1 Fasher 733.00 7.67 3.429 15.31 10.00 31 Sennar 420.00 7.00 3.131 11.65 10.00 32 Doka N.A. 6,83 3.057 10.84 10.00 33 , El Geneina 805.00 6,83 3.057 10.84 10.00 34 Kosti 380.00 9,00 4.026 24.76 10.00 35 El Obeid 570.00 7.58 3,392 14.81 10.00 36 Dankog 965.00 7.00 3.131 11.65 6.00 37 Umm Benein 435.00 7.00 3.131 11,65 10.00 38 Nierteti N.A. 7.00 3.131 11,65 10.00 39 Zalingei 900.00 6.00 2,684 7.34 10.00 40 Murundu N.A. 6.00 2.684 7.34 10.00 41 Abu Na'ama 445.00 N.A. N.A. N.A. N.A. 42 El Nahud 565.00 8.83 3.951 23.41 10.00 43 Dereisa N.A. 6.00 2.684 7.34 6.00 44 Kas N.A. 6.00 2.684 7.34 10.00 45 Garsila N.A. 6.00 2.684 7.34 10.00 46 Nyala 655.00 5.75 2.572 6.46 10.00 47 Mukgur N.A. 6.08 2.721 7.65 10.00 48 Rashed 885.00 6.42 2.870 8.97 10.00 49 Ed Damazin 470.00 10.00 4.473 33.96 10.00


Table 12--Cont inued

67

Item Altitude

Name of station (m)

Annual Annual Annual Annual mean mean mean mean

wind speeds wind speeds wind power wind power (mph) (ms -1) (Win -2) (yr)

50 Er Renk 380.00 6.25 2.796 8.29 10.00 51 Ghazala Gawazat 480.00 6.75 3.019 10.45 10.00 52 Babanusa 543.00 6.17 2.758 7.96 6.00 53 Kadugli 501.00 5.92 2.647 7.03 10.00 54 Kurmuk 690.00 6.25 2.796 8.29 8.00 55 Malakal 387.00 6.25 2.796 8.29 10.00 56 Bentiu 390.00 6.08 2.721 7.65 I0.00 57 Aweil 415.00 6.00 2.684 7.34 10.00 58 Nasir 400.00 8.00 3.578 17.39 10.00 59 Raga 545.00 6.00 2.684 7.34 10.00 60 Gambeila 450.00 6.00 2.684 7.34 17.00 61 Akobo 400.00 N.A. N.A. N.A. N.A. 62 Wau 435.00 3.83 1.715 1.91 10.00 63 Tonj 430.00 5.67 2.535 6.18 10.00 64 Rumbek 420.00 6.00 2.684 7.34 10.00 65 Bor 420.00 6.00 2.684 7.34 10.00 66 Maridi 750.00 6.00 2.684 7.34 10.00 67 Juba 460.00 3.42 1.528 1.35 10.00 68 Yambio 650.00 6.00 2.684 7.34 8.00 69 Torit 625.00 6.42 2.870 8.97 7.00 70 Yei 830.00 6.25 2.796 8.29 10.00

, - - 4 . 5 . . . . . . . ~ . . . .

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68 A.M. OMER

CONCLUSION

(1) The meteorological parameters reported in this paper are mainly intended to verify the climatic conditions likely to affect the operation of solar and wind systems that may be set up at a later date. (2) Most solar and wind energy technologies do require the averages and variances of solar radiation and wind speed for design purposes. (3) It can be concluded that Sudan has an excellent annual mean solar insolation of 5.44 kWh m -2 d -1 compared to that over Europe or U.S.A. (3.5 kWh m -2 d-~). (4) Mean wind speeds of 4.5 m s -~ are available over 50% of Sudan, which is well suited for water lifting and intermittent power requirements, while there is one region in the eastern part of Sudan that has a wind speed of 6 m s - l which is suitable for power production. (5) The data presented in this paper can be considered as nucleus information for executing research and development of solar and wind energy projects ; at the same time, they could determine sites that are likely to have a better prospect. (6) Finally, several automatic weather stations that record data on a temporal and spatial basis will be needed. These stations will be considered as complementary to the existing stations and will serve as a good source of information for statistical analyses and correlation among various stations.

REFERENCES

1. Omer, A. M., Solar energy technology applications in the Sudan. Jordanian First Engineering Conference, Amman, Jordan, 1995.

2. Duffle, J. A. and Beckman, W. A., Solar Engineering of Thermal Process. New York, U.S.A., 1980.

3. Kirtikara, K., Solar radiation and measurement. Seminar on Solar Energy and Appli- cations, Bangkok, Thailand, 1983.

4. World Meteorological Organization, Meteorological aspects of the utilization of solar energy as an energy source, Technical Note 172. WMO, Geneva, Switzerland, 1981.

5. Goldberg, B., Klein, W. H. and McCartney, R. D., A comparison of some simple models to predict irradiance on a horizontal surface. Solar Energy, 1979, 23, 81.

6. Angstrom, A., Solar and terrestrial and radiation. Q. J. R. Meteor. Soc., 1924, 50, 121. 7. Angstrom, A., On computation of global radiation from records of sunshine. Arkiv.

Geophisk, 1956, 3, 551. 8. Cooper, P. I., The absorption of solar radiation in solar stills. Solar Energy, 1969, 12,

3. 9. Duffle, J. A. and Beckman, W. A., Solar Energy, Thermal Processes. Wiley Interscience,

New York, 1974. 10. Sivkov, S. I., To the methods of computing possible radiation in Italy. Trans. Main

Geophys. Obs., 1964, 160. 11. Sivkov, S. I., On the computation of the possible and relative duration of sunshine.

Trans. Main Geophs. Obs., 1964, 160. 12. Barbaro, S., Coppolino, S., Leone, C. and Sinagra, E., Global solar radiation in Italy.

Solar Energy, 1978, 20, 431. 13. Khogali, A., Global and diffuse solar irradiance in Yemen. Solar Energy, 1982, 31, 55. 14. Page, J. K., The estimation of monthly mean values of daily total short wave radiation

on vertical and inclined surfaces from sunshine records for latitudes 40°N to 40°S. Proc. UN New Sources of Energy, 1964, Vol. 4, p. 378.


15. Liu, B. Y. and Jordan, R. C., The inter-relationship and characteristics distribution of direct, diffuse and total solar radiation. Solar Energy, 1960, 4, 1.

16. Klein, S. A., Calculation of monthly average insolation on tilted surfaces. Solar Energy, 1977, 19, 325.

17. Black, J. N., Bonython, C. W. and Prescott, J. M., Solar radiation and duration of sunshine. Q. J. R. Meteor. Soc., 1954, 90, 231.

18. Abu Bakr, E. H., The boundary layer wind regime at a representative tropical Africa region, central Sudan. Ph.D. thesis, Eindhoven University of Technology, The Nether- lands, December 1988.

19. Eisa, E. I., Weibull distribution in wind energy statistics. Proceedings of the 4th Inter- national Conference on Wind Energy and Mini Hydro, Rome, Italy, June 1984, pp. 16-- 20.

20. Omer, A. M., Wind speeds and wind power potential in Sudan. 4th Arab International Solar Energy Conference, Amman, Jordan, November 1993.

21. Hamid, Y. H. and Jansen, W. A. M., Wind energy in Sudan. CWD report 81-2, The Netherlands, 1981.

22. Omer, A. M., Solar atlas for Sudan. P.G. thesis, University of Khartoum, Khartoum, Sudan, April 1990.

23. Golding, E. W., The Generation of Electricity by WindPower. Sport, London, 1976, pp. 22-24.

24. Golding, E. W., The Generation of Electricity by Wind Power. Spon, London, 1976, pp. 153-154.

25. Eisa, E. I., A design study for a wind pump system for use in the Sudan. Ph.D. thesis, University of Reading, Reading, 1981.

26. Lysen, E. H., Introduction to Wind Energy. CWD, The Netherlands, 1983, pp. 261- 279.

compilation and evaluation of solar and wind energy resources in sudan

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