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Compilation and evaluation of solar and wind energy resources in Sudan
Renewable Energy, Vol. 12, No. 1, pp. 3 9 4 9 , 1997
~ ) Pergamon PII: S0960-1481(97)00009-8
© 1997 Elsevier Science Ltd All rights reserved. Printed in Great Britain
0960-1481/97$17.00+0.00
COMPILATION A N D EVALUATION OF SOLAR A N D W I N D E N E R G Y RESOURCES IN SUDAN ABDEEN MUSTAFA OMER 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 k W h 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
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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 collection. 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
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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 maritime origin that enters Sudan from 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. P R O C E D U R E FOR EXTRACTING THE DATA F R O M LITERATURE A N D PRESENTATION
Consideration has been given to the consistent and effective presentation of data. Original data were extracted from published reports by S M D 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. S O L A R 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. M a n y 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 k W 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
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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
Item
Name of station
Latitude (°)
Longitude (°)
Altitude (m)
Testing period
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Halaib Wadi Halfa Station 6 Port Sudan Abu Hamed Dongola Gebeit Karima Toker Aqiq Atbara Derudeb Hudeiba Shendi Aroma Wadi Seidna Shambat Khartoum Kassla Jebel Aulia H a l f a el G e d i d a Abu Quta El S h o w a k Wad Madani Medina Block Kutum E1 G a d a r i f Ed Dueim Wad E1Huri E1 F a s h e r Sennar Doka El G e n e i n a Kosti E10beid Dankog Umm Benein Nierteti Zalingei Murundu Abu Na'ama El N a h u d Dereisa Kas Garsila Nyala Mukgur Rashed Ed Damazin Er Renk
22 ° 1 3 ' N 21 ° 5 5 ' N 20 ° 4 5 ' N 19 ° 3 5 ' N 19 ° 3 2 ' N 19 ° 1 0 ' N 18 ° 5 7 ' N 18 ° 3 3 ' N 18 ° 2 6 ' N 18 ° 1 4 ' N 17 ° 4 0 ' N 17 ° 3 5 ' N 17 ° 3 4 ' N 16 ° 4 2 ' N 15 ° 5 0 ' N 15 ° 4 0 ' N 15 ° 4 0 ' N 15 ° 3 6 ' N 15 ° 2 8 ' N 15 ° 2 4 ' N 15 ° 1 9 ' N 14 ° 5 5 ' N 14 ° 2 4 ' N 14 ° 2 3 ' N 14 ° 2 2 ' N 14 ° 1 2 ' N 14 ° 0 2 ' N 13 ° 5 9 ' N 13 ° 5 6 ' N 13 ° 3 8 ' N 13 ° 3 3 ' N 13 ° 3 1 ' N 13 ° 2 9 ' N 13 ° 1 0 ' N 13 ° 1 0 ' N 13 ° 0 5 ' N 13 ° 0 4 ' N 12 ° 5 8 ' N 12 ° 5 4 ' N 12 ° 4 9 ' N 12 ° 4 4 ' N 12 ° 4 2 ' N 12 ° 4 1 ' N 12 ° 3 1 ' N 12 ° 2 2 ' N 12 ° 0 4 ' N 11 ° 5 7 ' N 11 ° 5 2 ' N 11 ° 4 9 ' N 11 ° 4 5 ' N
36 ° 3 9 ' E 31 ° 2 1 ' E 32 ° 3 3 ' E 37 ° 1 3 ' E 33 ° 2 0 ' E 30 ° 2 9 ' E 36 ° 5 1 ' E 31 ° 5 1 ' E 37 ° 4 4 ' E 38 ° 11'E 33 ° 5 8 ' E 36 ° 0 6 ' E 33 ° 5 6 ' E 33 ° 2 6 ' E 36 ° 0 9 ' E 32 ° 3 2 ' E 32 ° 3 2 ' E 32 ° 3 3 ' E 36 ° 2 4 ' E 32 ° 3 0 ' E 35 ° 3 6 ' E 32 ° 4 4 ' E 35 ° 5 1 ' E 33 ° 2 9 ' E 33 ° 1 9 ' E 24 ° 4 0 ' E 35 ° 2 4 ' E 32 ° 2 0 ' E 35 ° 1 4 ' E 25 ° 2 0 ' E 33 ° 3 7 ' E 35 ° 4 6 ' E 22 ° 2 7 ' E 32 ° 4 0 ' E 33 ° 1 4 ' E 23 ° 5 9 ' E 33 ° 5 7 ' E 24 ° 0 4 ' E 23 ° 2 9 ' E 23 ° 0 9 ' E 34 ° 0 7 ' E 28 ° 2 6 ' E 22 ° 4 6 ' E 24 ° 16'E 23 ° 0 8 ' E 42 ° 5 3 ' E 23 ° 17'E 31 ° 0 3 ' E 34 ° 2 4 ' E 32 ° 4 7 ' E
52.00 190.00 470.00 5.00 315.00 225.00 795.00 250.00 20.00 N.A. 345.00 510.00 350.00 360.00 430.00 385.00 380.00 380.00 500.00 380.00 450.00 390.00 510.00 405.00 405.00 1160.00 600.00 380.00 N.A. 733.00 420.00 N.A. 805.00 380.00 570.00 965.00 435.00 N.A. 900.00 N.A. 445.00 565.00 N.A. N.A. N.A. 655.00 N.A. 885.00 470.00 380.00
1975-1985 1981-1985 1975-1985 1975-1985 1979-1985 1975-1985 1975-1985 1975-1985 1976-1985 1975-1985 1975-1985 1975-1985 1975-1985 1976-1985 N.A. 1975-1985 N.A. 1975-1985 1975-1985 1975-1985 1975-1985 1976-1985 1975-1985 1975-1985 1978-1985 1975-1985 1975-1985 1975-1985 N.A. 1975-1985 1975-1985 1975-1985 1975-1985 1975-1985 1975-1985 1979-1985 1975-1985 1975-1985 1975-1985 1975-1985 N.A. 1975-1985 1979-1985 1975-1985 1975-1985 1975-1985 1975-1985 1975-1985 1975-1985 1975-1985
Energy resources in Sudan
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Table 1--Continued Item
Name of station
Latitude (°)
Longitude (°)
51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70
Ghazala Gawazat Babanusa Kadugli Kurmuk Malakal Bentiu Aweil Nasir Raga Gambeila Akobo Wau Tonj Rumbek Bor Maridi Juba Yambio Torit Yei
11° 28'N 11° 20'N 11 ° 00'N 10° 33'N 09 ° 33'N 09 ° 14'N 80 ° 46'N 08 ° 37'N 08 ° 28'N 08 ° 15'N 07 ° 47'N 07 ° 42'N 07 ° 17'N 06 ° 48'N 06 ° 12'N 04 ° 55'N 04 ° 52'N 04 ° 34'N 04 ° 25'N 04 ° 05'N
26 ° 27'E 27 ° 40'E 29 ° 43'E 24 ° 17'E 31 ° 39'E 29 ° 50'E 27 ~ 24'E 33 ° 04'E 25 ° 41'E 34° 35'E 33 ° 01'E 28 ° 01'E 28 ° 45'E 29 ° 42'E 31 ° 33'E 29 ° 28'E 31 ° 35'E 28 ° 24'E 32 ° 33'E 30 ° 40'E
Altitude (m)
Testing period
480.00 543.00 501.00 690.00 387.00 390.00 415.00 400.00 545.00 450.00 400.00 435.00 430.00 420.00 420.00 750.00 460.00 650.00 625.00 830.00
1975--1985 1979-1985 1975-1985 1977-1985 1975-1985 1975-1985 1975-1985 1975-1985 1975-1985 1968-1985 N.A. 1975-1985 1975-1985 1975-1985 1975-1985 1975-1985 1975-1985 1977-1985 1978-1985 1975-1985
solar r a d i a t i o n penetrates into the atmosphere, it is a b s o r b e d a n d scattered. O n l y the longer wavelengths (visible, infra-red a n d radio) reach the lower layers of the atmosphere. A p p r o x i m a t e l y 9 8 % o f the total solar r a d i a t i o n flux consists o f r a d i a t i o n , the wavelength o f which ranges f r o m 0.3 to 4 pm. All the ultra-violet rays with wavelengths o f less t h a n 0.3 p m are a b s o r b e d by water v a p o u r [4].
PREDICTION OF GLOBAL SOLAR RADIATION H M a n y m e t h o d s have been developed for the prediction o f the a m o u n t o f solar energy i n c i d e n t o n a h o r i z o n t a l p l a n e at the earth's surface. The simplest models are the empirical f o r m u l a s presented by G o l d b e r g et al. [5]. The first m e t h o d to have been used is the A n g s t r o m correlation relation [6, 7] ;
H/Ho = a + b n / N
(1)
where H is the m o n t h l y m e a n daily global i r r a d i a n c e o n a h o r i z o n t a l surface. Ho is the extraterrestrial solar i r r a d i a n c e o n the 15th o f the m o n t h , a a n d b are regression c o n s t a n t s empirical constants, n is the m o n t h l y m e a n daily hours o f bright sunshine. N i s the m a x i m u m daily hours o f bright s u n s h i n e (i.e. the length o f the average day o f the m o n t h ) , n / N is the fraction o f the m a x i m u m possible n u m b e r o f bright sunshine h o u r s ; a n d H / H is the a t m o s p h e r i c t r a n s m i s s i o n coefficient. The values o f N are c o m p u t e d from C o o p e r ' s f o r m u l a [8] ;
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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 ~bCos 0 Sin w + (27tw/360) Sin ~hSin ~]
(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)
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 : where
n m = g(nm) 1"24 ( a n ) -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.,=
1.00-1.13K~
(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)
Energy resources in Sudan
45
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.
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Energy resources in Sudan
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Table 6. Correlation of solar radiation with other weather parameters in Sudan (yearly average) Mean temp. Station
(°C)
Sunshine duration (h)
Port Sudan Shambat Wad Medani E1 Fasher Abu N a ' a m a Ghazala Gawazat Malakal Juba Dongola Toker Hudeiba Aroma E1 Showak Zalingei Babanusa Kadugli
28.40 29.70 28.40 25.80 28.20 27.20 27.90 27.60 27.20 28.80 29.30 29.10 26.30 24.50 28.20 27.50
9.00 9.90 9.80 9.60 8.80 9.30 7.80 7.80 10.50 7.30 10.00 9.60 9.70 8.80 8.90 8.50
Solar radiation Wind velocity
Relative
(MJm -2 day-1)
(MPH)
humidity (%)
20.87 22.82 22.84 22.80 21.90 21.72 19.90 19.59 24.06 17.60 22.37 21.40 22.90 22.98 21.73 21.30
7.70 8.90 7.30 5.30 6.70 6.80 6.10 3.40 10.50 6.70 6.30 6.20 6.00 3.80 6.20 5.60
65 31 40 33 46 43 54 66 27 53 25 37 39 39 40 48
Table 7. Solar radiation over Sudan-regression coefficients Latitude Station 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.
Port Sudan Dongola Toker Hudeiba Aroma Shambat Wad Medani E1 Showak E1Fasher Zalingei Abu N a ' a m a Ghazala Gawazat Babanusa Kadugli Malakal Juba
Annual average
Regression coefficients
(°)
1t/1-1o
n/N
a
b
a+b
19.58 19.17 18.43 17.57 15.83 15.67 14.38 14.24 13.63 12.90 12.73 11.47 11.33 11.00 9.55 4.87
0.62 0.72 0.62 0.67 0.63 0.67 0.66 0.67 0.66 0.66 0.64 0.63 0.62 0.61 0.57 0.55
0.75 0.88 0.80 0.84 0.80 0.84 0.82 0.81 0.80 0.74 0.75 0.78 0.70 0.71 0.65 0.64
0.32 0.21 0.40 0.21 0.46 0.28 0.36 0.33 0.36 0.33 0.43 0.35 0.35 0.29 0.34 0.40
0.40 0,57 0.20 0,54 0.21 0.47 0.37 0.42 0.37 0.46 0.27 0.35 0.35 0.46 0.36 0.36
0.72 0.78 0.60 0.75 0.67 0.75 0.73 0.75 0.73 0.78 0.70 0.70 0.70 0.75 0.70 0.70
E S T I M A T I O N O F DIFFUSE SOLAR RADIATION V a l u e s o f the m o n t h l y a v e r a g e d a i l y diffuse s o l a r r a d i a t i o n at 16 stations have been c o m p u t e d b y using the t w o c o r r e l a t i o n r e l a t i o n s (7) a n d (9). T h e results are p r e s e n t e d in T a b l e 9. A linear r e l a t i o n s h i p b e t w e e n g l o b a l r a d i a t i o n levels a n d n u m b e r o f sunshine h o u r s was
E n e r g y r e s o u r c e s in S u d a n
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Energy resources in Sudan
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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 D A T A 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.
Energy resources in Sudan
55
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Energy resources in Sudan
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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) (2) (3) (4) (5)
Accuracy of the recording equipment and technique. Type of data collected. Exposure of the recording equipment. Recording period (yr). Recording rate/interval. AVAILABLEWIND 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
where V~ is the unknown wind speed at height h~ = 10 m; II2 is the known wind speed measured at height hz (anemometer's height) ; h~ is the height at which the wind speed is to be estimated; and n is an exponent related to the surface roughness and determined from measurements at the different heights. Values of (1/7) to (1/6) are commonly used for the exponent n.
E n e r g y r e s o u r c e s in S u d a n
59
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Energy resources in Sudan
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Energy resources in Sudan
65
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. D a t a 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 m a y block the flow of wind, differences in surface roughness and obstacles between anemometer and p u m p site must be taken into account when estimating wind speeds for the site. In Sudan, unequal measuring heights at different stations, in towns like K h a r t o u m , 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 P O W E R CALCULATION
Formulae [23, 24] derived from this equation : P=(1/2)×Cp(Betz)xaaxAxV
3 W m -~
(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 from monthly mean speeds which were calculated according to the following procedure : given a monthly mean wind speed, V, the m a x i m u m 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 W m -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 D a t a is given by the Meteorological Department Office, Sudan. Measurements were with a cup anemometer coupled to a chart recorder for selected stations. M e a n 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 m a p was developed showing the distribution of wind speeds all over the country (Fig. 2). The isovent m a p reflects the very good potential for wind energy in Sudan. Due to local conditions, there may be m a n y 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, Karima) has relatively high wind speeds exceeding 4.5 m s-1. The K h a r t o u m 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 observations for the 70 stations in Sudan, at 10 m AGL
Item 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
Name of station
Altitude (m)
Halaib Wadi Halfa Station 6 Port Sudan Abu Hamed Dongola Gebeit Karima Toker Aqiq Atbara Derudeb Hudeiba Shendi Aroma Wadi Seidna Shambat Khartoum Kassla Jebei Aulia Halfa El Gedida Abu Quta E1 Showak Wad Madani Medina Block Kutum E1 Gadarif Ed Dueim Wad El Huri E1 Fasher Sennar Doka , El Geneina Kosti El Obeid Dankog U m m Benein Nierteti Zalingei Murundu Abu Na'ama El Nahud Dereisa Kas Garsila Nyala Mukgur Rashed Ed Damazin
52.00 190.00 470.00 5.00 315.00 225.00 795.00 250.00 20.00 N.A. 345.00 510.00 350.00 360.00 430.00 385.00 380.00 380.00 500.00 380.00 450.00 390.00 510.00 405.00 405.00 1160.00 600.00 380.00 N.A. 733.00 420.00 N.A. 805.00 380.00 570.00 965.00 435.00 N.A. 900.00 N.A. 445.00 565.00 N.A. N.A. N.A. 655.00 N.A. 885.00 470.00
Annual Annual mean mean wind speeds wind speeds (mph) (ms-1) 11.33 10.33 10.17 11.25 10.67 10.50 9.00 10.42 9.08 9.25 9.42 9.00 9.00 9.00 N.A. 9.90 N.A. 10.00 9.00 10.08 9.17 9.83 9.17 10.00 10.25 7.83 8.92 9.00 N.A. 7.67 7.00 6,83 6,83 9,00 7.58 7.00 7.00 7.00 6.00 6.00 N.A. 8.83 6.00 6.00 6.00 5.75 6.08 6.42 10.00
5.07 4.622 4.548 5.032 4.771 4.697 4.026 4.659 4.063 4.138 4.212 4.026 4.026 4.026 N.A. 4.436 N.A. 4.473 4.026 4.510 4.100 4.399 4.100 4.473 4.585 3.504 3.988 4,026 N.A. 3.429 3.131 3.057 3.057 4.026 3,392 3.131 3.131 3.131 2,684 2.684 N.A. 3.951 2.684 2.684 2.684 2.572 2.721 2.870 4.473
Annual Annual mean mean wind power wind power (Wm-2) (yr) 49.43 37.48 35.69 48.36 41.22 39.32 24.76 38.39 25.45 26.88 28.36 24.76 24.76 24.76 N.A. 33.12 N.A. 33.96 24.76 34.82 26.16 32.29 26.16 33.96 36.58 16.33 24.08 24.76 N.A. 15.31 11.65 10.84 10.84 24.76 14.81 11.65 11,65 11,65 7.34 7.34 N.A. 23.41 7.34 7.34 7.34 6.46 7.65 8.97 33.96
10.00 4.00 10.00 10.00 6.00 10.00 10.00 10.00 9.00 10,00 10.00 10,00 10,00 9,00 N.A. 10.00 N,A. 10.00 10.00 10.00 10.00 9.00 10.00 10.00 7.00 10.00 10.00 10.00 N.A. 10.00 10.00 10.00 10.00 10.00 10.00 6.00 10.00 10.00 10.00 10.00 N.A. 10.00 6.00 10.00 10.00 10.00 10.00 10.00 10.00
Energy resources in Sudan
67
Table 12--Continued
Item
Name of station
50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70
Er Renk Ghazala Gawazat Babanusa Kadugli Kurmuk Malakal Bentiu Aweil Nasir Raga Gambeila Akobo Wau Tonj Rumbek Bor Maridi Juba Yambio Torit Yei
Altitude (m)
Annual Annual Annual Annual mean mean mean mean wind speeds wind speeds wind power wind power (mph) (ms -1) (Win -2) (yr)
380.00 480.00 543.00 501.00 690.00 387.00 390.00 415.00 400.00 545.00 450.00 400.00 435.00 430.00 420.00 420.00 750.00 460.00 650.00 625.00 830.00
6.25 6.75 6.17 5.92 6.25 6.25 6.08 6.00 8.00 6.00 6.00 N.A. 3.83 5.67 6.00 6.00 6.00 3.42 6.00 6.42 6.25
,--
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Fig. 2. Annual average wind speeds of Sudan.
8.29 10.45 7.96 7.03 8.29 8.29 7.65 7.34 17.39 7.34 7.34 N.A. 1.91 6.18 7.34 7.34 7.34 1.35 7.34 8.97 8.29
10.00 10.00 6.00 10.00 8.00 10.00 I0.00 10.00 10.00 10.00 17.00 N.A. 10.00 10.00 10.00 10.00 10.00 10.00 8.00 7.00 10.00
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 Applications, 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.
Energy resources in Sudan
69
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 Netherlands, December 1988. 19. Eisa, E. I., Weibull distribution in wind energy statistics. Proceedings of the 4th International 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. 261279.
~ ) Pergamon PII: S0960-1481(97)00009-8
© 1997 Elsevier Science Ltd All rights reserved. Printed in Great Britain
0960-1481/97$17.00+0.00
COMPILATION A N D EVALUATION OF SOLAR A N D W I N D E N E R G Y RESOURCES IN SUDAN ABDEEN MUSTAFA OMER 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 k W h 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 collection. 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 maritime origin that enters Sudan from 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. P R O C E D U R E FOR EXTRACTING THE DATA F R O M LITERATURE A N D PRESENTATION
Consideration has been given to the consistent and effective presentation of data. Original data were extracted from published reports by S M D 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. S O L A R 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. M a n y 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 k W 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
A. M. O M E R
42
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
Item
Name of station
Latitude (°)
Longitude (°)
Altitude (m)
Testing period
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Halaib Wadi Halfa Station 6 Port Sudan Abu Hamed Dongola Gebeit Karima Toker Aqiq Atbara Derudeb Hudeiba Shendi Aroma Wadi Seidna Shambat Khartoum Kassla Jebel Aulia H a l f a el G e d i d a Abu Quta El S h o w a k Wad Madani Medina Block Kutum E1 G a d a r i f Ed Dueim Wad E1Huri E1 F a s h e r Sennar Doka El G e n e i n a Kosti E10beid Dankog Umm Benein Nierteti Zalingei Murundu Abu Na'ama El N a h u d Dereisa Kas Garsila Nyala Mukgur Rashed Ed Damazin Er Renk
22 ° 1 3 ' N 21 ° 5 5 ' N 20 ° 4 5 ' N 19 ° 3 5 ' N 19 ° 3 2 ' N 19 ° 1 0 ' N 18 ° 5 7 ' N 18 ° 3 3 ' N 18 ° 2 6 ' N 18 ° 1 4 ' N 17 ° 4 0 ' N 17 ° 3 5 ' N 17 ° 3 4 ' N 16 ° 4 2 ' N 15 ° 5 0 ' N 15 ° 4 0 ' N 15 ° 4 0 ' N 15 ° 3 6 ' N 15 ° 2 8 ' N 15 ° 2 4 ' N 15 ° 1 9 ' N 14 ° 5 5 ' N 14 ° 2 4 ' N 14 ° 2 3 ' N 14 ° 2 2 ' N 14 ° 1 2 ' N 14 ° 0 2 ' N 13 ° 5 9 ' N 13 ° 5 6 ' N 13 ° 3 8 ' N 13 ° 3 3 ' N 13 ° 3 1 ' N 13 ° 2 9 ' N 13 ° 1 0 ' N 13 ° 1 0 ' N 13 ° 0 5 ' N 13 ° 0 4 ' N 12 ° 5 8 ' N 12 ° 5 4 ' N 12 ° 4 9 ' N 12 ° 4 4 ' N 12 ° 4 2 ' N 12 ° 4 1 ' N 12 ° 3 1 ' N 12 ° 2 2 ' N 12 ° 0 4 ' N 11 ° 5 7 ' N 11 ° 5 2 ' N 11 ° 4 9 ' N 11 ° 4 5 ' N
36 ° 3 9 ' E 31 ° 2 1 ' E 32 ° 3 3 ' E 37 ° 1 3 ' E 33 ° 2 0 ' E 30 ° 2 9 ' E 36 ° 5 1 ' E 31 ° 5 1 ' E 37 ° 4 4 ' E 38 ° 11'E 33 ° 5 8 ' E 36 ° 0 6 ' E 33 ° 5 6 ' E 33 ° 2 6 ' E 36 ° 0 9 ' E 32 ° 3 2 ' E 32 ° 3 2 ' E 32 ° 3 3 ' E 36 ° 2 4 ' E 32 ° 3 0 ' E 35 ° 3 6 ' E 32 ° 4 4 ' E 35 ° 5 1 ' E 33 ° 2 9 ' E 33 ° 1 9 ' E 24 ° 4 0 ' E 35 ° 2 4 ' E 32 ° 2 0 ' E 35 ° 1 4 ' E 25 ° 2 0 ' E 33 ° 3 7 ' E 35 ° 4 6 ' E 22 ° 2 7 ' E 32 ° 4 0 ' E 33 ° 1 4 ' E 23 ° 5 9 ' E 33 ° 5 7 ' E 24 ° 0 4 ' E 23 ° 2 9 ' E 23 ° 0 9 ' E 34 ° 0 7 ' E 28 ° 2 6 ' E 22 ° 4 6 ' E 24 ° 16'E 23 ° 0 8 ' E 42 ° 5 3 ' E 23 ° 17'E 31 ° 0 3 ' E 34 ° 2 4 ' E 32 ° 4 7 ' E
52.00 190.00 470.00 5.00 315.00 225.00 795.00 250.00 20.00 N.A. 345.00 510.00 350.00 360.00 430.00 385.00 380.00 380.00 500.00 380.00 450.00 390.00 510.00 405.00 405.00 1160.00 600.00 380.00 N.A. 733.00 420.00 N.A. 805.00 380.00 570.00 965.00 435.00 N.A. 900.00 N.A. 445.00 565.00 N.A. N.A. N.A. 655.00 N.A. 885.00 470.00 380.00
1975-1985 1981-1985 1975-1985 1975-1985 1979-1985 1975-1985 1975-1985 1975-1985 1976-1985 1975-1985 1975-1985 1975-1985 1975-1985 1976-1985 N.A. 1975-1985 N.A. 1975-1985 1975-1985 1975-1985 1975-1985 1976-1985 1975-1985 1975-1985 1978-1985 1975-1985 1975-1985 1975-1985 N.A. 1975-1985 1975-1985 1975-1985 1975-1985 1975-1985 1975-1985 1979-1985 1975-1985 1975-1985 1975-1985 1975-1985 N.A. 1975-1985 1979-1985 1975-1985 1975-1985 1975-1985 1975-1985 1975-1985 1975-1985 1975-1985
Energy resources in Sudan
43
Table 1--Continued Item
Name of station
Latitude (°)
Longitude (°)
51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70
Ghazala Gawazat Babanusa Kadugli Kurmuk Malakal Bentiu Aweil Nasir Raga Gambeila Akobo Wau Tonj Rumbek Bor Maridi Juba Yambio Torit Yei
11° 28'N 11° 20'N 11 ° 00'N 10° 33'N 09 ° 33'N 09 ° 14'N 80 ° 46'N 08 ° 37'N 08 ° 28'N 08 ° 15'N 07 ° 47'N 07 ° 42'N 07 ° 17'N 06 ° 48'N 06 ° 12'N 04 ° 55'N 04 ° 52'N 04 ° 34'N 04 ° 25'N 04 ° 05'N
26 ° 27'E 27 ° 40'E 29 ° 43'E 24 ° 17'E 31 ° 39'E 29 ° 50'E 27 ~ 24'E 33 ° 04'E 25 ° 41'E 34° 35'E 33 ° 01'E 28 ° 01'E 28 ° 45'E 29 ° 42'E 31 ° 33'E 29 ° 28'E 31 ° 35'E 28 ° 24'E 32 ° 33'E 30 ° 40'E
Altitude (m)
Testing period
480.00 543.00 501.00 690.00 387.00 390.00 415.00 400.00 545.00 450.00 400.00 435.00 430.00 420.00 420.00 750.00 460.00 650.00 625.00 830.00
1975--1985 1979-1985 1975-1985 1977-1985 1975-1985 1975-1985 1975-1985 1975-1985 1975-1985 1968-1985 N.A. 1975-1985 1975-1985 1975-1985 1975-1985 1975-1985 1975-1985 1977-1985 1978-1985 1975-1985
solar r a d i a t i o n penetrates into the atmosphere, it is a b s o r b e d a n d scattered. O n l y the longer wavelengths (visible, infra-red a n d radio) reach the lower layers of the atmosphere. A p p r o x i m a t e l y 9 8 % o f the total solar r a d i a t i o n flux consists o f r a d i a t i o n , the wavelength o f which ranges f r o m 0.3 to 4 pm. All the ultra-violet rays with wavelengths o f less t h a n 0.3 p m are a b s o r b e d by water v a p o u r [4].
PREDICTION OF GLOBAL SOLAR RADIATION H M a n y m e t h o d s have been developed for the prediction o f the a m o u n t o f solar energy i n c i d e n t o n a h o r i z o n t a l p l a n e at the earth's surface. The simplest models are the empirical f o r m u l a s presented by G o l d b e r g et al. [5]. The first m e t h o d to have been used is the A n g s t r o m correlation relation [6, 7] ;
H/Ho = a + b n / N
(1)
where H is the m o n t h l y m e a n daily global i r r a d i a n c e o n a h o r i z o n t a l surface. Ho is the extraterrestrial solar i r r a d i a n c e o n the 15th o f the m o n t h , a a n d b are regression c o n s t a n t s empirical constants, n is the m o n t h l y m e a n daily hours o f bright sunshine. N i s the m a x i m u m daily hours o f bright s u n s h i n e (i.e. the length o f the average day o f the m o n t h ) , n / N is the fraction o f the m a x i m u m possible n u m b e r o f bright sunshine h o u r s ; a n d H / H is the a t m o s p h e r i c t r a n s m i s s i o n coefficient. The values o f N are c o m p u t e d from C o o p e r ' s f o r m u l a [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 ~bCos 0 Sin w + (27tw/360) Sin ~hSin ~]
(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)
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 : where
n m = g(nm) 1"24 ( a n ) -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.,=
1.00-1.13K~
(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)
Energy resources in Sudan
45
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.
A. M. OMER
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Energy resources in Sudan
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Energy resources in Sudan
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Table 6. Correlation of solar radiation with other weather parameters in Sudan (yearly average) Mean temp. Station
(°C)
Sunshine duration (h)
Port Sudan Shambat Wad Medani E1 Fasher Abu N a ' a m a Ghazala Gawazat Malakal Juba Dongola Toker Hudeiba Aroma E1 Showak Zalingei Babanusa Kadugli
28.40 29.70 28.40 25.80 28.20 27.20 27.90 27.60 27.20 28.80 29.30 29.10 26.30 24.50 28.20 27.50
9.00 9.90 9.80 9.60 8.80 9.30 7.80 7.80 10.50 7.30 10.00 9.60 9.70 8.80 8.90 8.50
Solar radiation Wind velocity
Relative
(MJm -2 day-1)
(MPH)
humidity (%)
20.87 22.82 22.84 22.80 21.90 21.72 19.90 19.59 24.06 17.60 22.37 21.40 22.90 22.98 21.73 21.30
7.70 8.90 7.30 5.30 6.70 6.80 6.10 3.40 10.50 6.70 6.30 6.20 6.00 3.80 6.20 5.60
65 31 40 33 46 43 54 66 27 53 25 37 39 39 40 48
Table 7. Solar radiation over Sudan-regression coefficients Latitude Station 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.
Port Sudan Dongola Toker Hudeiba Aroma Shambat Wad Medani E1 Showak E1Fasher Zalingei Abu N a ' a m a Ghazala Gawazat Babanusa Kadugli Malakal Juba
Annual average
Regression coefficients
(°)
1t/1-1o
n/N
a
b
a+b
19.58 19.17 18.43 17.57 15.83 15.67 14.38 14.24 13.63 12.90 12.73 11.47 11.33 11.00 9.55 4.87
0.62 0.72 0.62 0.67 0.63 0.67 0.66 0.67 0.66 0.66 0.64 0.63 0.62 0.61 0.57 0.55
0.75 0.88 0.80 0.84 0.80 0.84 0.82 0.81 0.80 0.74 0.75 0.78 0.70 0.71 0.65 0.64
0.32 0.21 0.40 0.21 0.46 0.28 0.36 0.33 0.36 0.33 0.43 0.35 0.35 0.29 0.34 0.40
0.40 0,57 0.20 0,54 0.21 0.47 0.37 0.42 0.37 0.46 0.27 0.35 0.35 0.46 0.36 0.36
0.72 0.78 0.60 0.75 0.67 0.75 0.73 0.75 0.73 0.78 0.70 0.70 0.70 0.75 0.70 0.70
E S T I M A T I O N O F DIFFUSE SOLAR RADIATION V a l u e s o f the m o n t h l y a v e r a g e d a i l y diffuse s o l a r r a d i a t i o n at 16 stations have been c o m p u t e d b y using the t w o c o r r e l a t i o n r e l a t i o n s (7) a n d (9). T h e results are p r e s e n t e d in T a b l e 9. A linear r e l a t i o n s h i p b e t w e e n g l o b a l r a d i a t i o n levels a n d n u m b e r o f sunshine h o u r s was
E n e r g y r e s o u r c e s in S u d a n
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Energy resources in Sudan
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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 D A T A 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.
Energy resources in Sudan
55
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Energy resources in Sudan
57
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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) (2) (3) (4) (5)
Accuracy of the recording equipment and technique. Type of data collected. Exposure of the recording equipment. Recording period (yr). Recording rate/interval. AVAILABLEWIND 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
where V~ is the unknown wind speed at height h~ = 10 m; II2 is the known wind speed measured at height hz (anemometer's height) ; h~ is the height at which the wind speed is to be estimated; and n is an exponent related to the surface roughness and determined from measurements at the different heights. Values of (1/7) to (1/6) are commonly used for the exponent n.
E n e r g y r e s o u r c e s in S u d a n
59
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Energy resources in Sudan
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Energy resources in Sudan
65
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. D a t a 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 m a y block the flow of wind, differences in surface roughness and obstacles between anemometer and p u m p site must be taken into account when estimating wind speeds for the site. In Sudan, unequal measuring heights at different stations, in towns like K h a r t o u m , 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 P O W E R CALCULATION
Formulae [23, 24] derived from this equation : P=(1/2)×Cp(Betz)xaaxAxV
3 W m -~
(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 from monthly mean speeds which were calculated according to the following procedure : given a monthly mean wind speed, V, the m a x i m u m 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 W m -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 D a t a is given by the Meteorological Department Office, Sudan. Measurements were with a cup anemometer coupled to a chart recorder for selected stations. M e a n 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 m a p was developed showing the distribution of wind speeds all over the country (Fig. 2). The isovent m a p reflects the very good potential for wind energy in Sudan. Due to local conditions, there may be m a n y 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, Karima) has relatively high wind speeds exceeding 4.5 m s-1. The K h a r t o u m 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 observations for the 70 stations in Sudan, at 10 m AGL
Item 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
Name of station
Altitude (m)
Halaib Wadi Halfa Station 6 Port Sudan Abu Hamed Dongola Gebeit Karima Toker Aqiq Atbara Derudeb Hudeiba Shendi Aroma Wadi Seidna Shambat Khartoum Kassla Jebei Aulia Halfa El Gedida Abu Quta E1 Showak Wad Madani Medina Block Kutum E1 Gadarif Ed Dueim Wad El Huri E1 Fasher Sennar Doka , El Geneina Kosti El Obeid Dankog U m m Benein Nierteti Zalingei Murundu Abu Na'ama El Nahud Dereisa Kas Garsila Nyala Mukgur Rashed Ed Damazin
52.00 190.00 470.00 5.00 315.00 225.00 795.00 250.00 20.00 N.A. 345.00 510.00 350.00 360.00 430.00 385.00 380.00 380.00 500.00 380.00 450.00 390.00 510.00 405.00 405.00 1160.00 600.00 380.00 N.A. 733.00 420.00 N.A. 805.00 380.00 570.00 965.00 435.00 N.A. 900.00 N.A. 445.00 565.00 N.A. N.A. N.A. 655.00 N.A. 885.00 470.00
Annual Annual mean mean wind speeds wind speeds (mph) (ms-1) 11.33 10.33 10.17 11.25 10.67 10.50 9.00 10.42 9.08 9.25 9.42 9.00 9.00 9.00 N.A. 9.90 N.A. 10.00 9.00 10.08 9.17 9.83 9.17 10.00 10.25 7.83 8.92 9.00 N.A. 7.67 7.00 6,83 6,83 9,00 7.58 7.00 7.00 7.00 6.00 6.00 N.A. 8.83 6.00 6.00 6.00 5.75 6.08 6.42 10.00
5.07 4.622 4.548 5.032 4.771 4.697 4.026 4.659 4.063 4.138 4.212 4.026 4.026 4.026 N.A. 4.436 N.A. 4.473 4.026 4.510 4.100 4.399 4.100 4.473 4.585 3.504 3.988 4,026 N.A. 3.429 3.131 3.057 3.057 4.026 3,392 3.131 3.131 3.131 2,684 2.684 N.A. 3.951 2.684 2.684 2.684 2.572 2.721 2.870 4.473
Annual Annual mean mean wind power wind power (Wm-2) (yr) 49.43 37.48 35.69 48.36 41.22 39.32 24.76 38.39 25.45 26.88 28.36 24.76 24.76 24.76 N.A. 33.12 N.A. 33.96 24.76 34.82 26.16 32.29 26.16 33.96 36.58 16.33 24.08 24.76 N.A. 15.31 11.65 10.84 10.84 24.76 14.81 11.65 11,65 11,65 7.34 7.34 N.A. 23.41 7.34 7.34 7.34 6.46 7.65 8.97 33.96
10.00 4.00 10.00 10.00 6.00 10.00 10.00 10.00 9.00 10,00 10.00 10,00 10,00 9,00 N.A. 10.00 N,A. 10.00 10.00 10.00 10.00 9.00 10.00 10.00 7.00 10.00 10.00 10.00 N.A. 10.00 10.00 10.00 10.00 10.00 10.00 6.00 10.00 10.00 10.00 10.00 N.A. 10.00 6.00 10.00 10.00 10.00 10.00 10.00 10.00
Energy resources in Sudan
67
Table 12--Continued
Item
Name of station
50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70
Er Renk Ghazala Gawazat Babanusa Kadugli Kurmuk Malakal Bentiu Aweil Nasir Raga Gambeila Akobo Wau Tonj Rumbek Bor Maridi Juba Yambio Torit Yei
Altitude (m)
Annual Annual Annual Annual mean mean mean mean wind speeds wind speeds wind power wind power (mph) (ms -1) (Win -2) (yr)
380.00 480.00 543.00 501.00 690.00 387.00 390.00 415.00 400.00 545.00 450.00 400.00 435.00 430.00 420.00 420.00 750.00 460.00 650.00 625.00 830.00
6.25 6.75 6.17 5.92 6.25 6.25 6.08 6.00 8.00 6.00 6.00 N.A. 3.83 5.67 6.00 6.00 6.00 3.42 6.00 6.42 6.25
,--
4.2.i
4.5 .......
"--'t
1.5.N~.._.... ~
2.796 3.019 2.758 2.647 2.796 2.796 2.721 2.684 3.578 2.684 2.684 N.A. 1.715 2.535 2.684 2.684 2.684 1.528 2.684 2.870 2.796
~. . . .
~I/\
~t
"~.
Fig. 2. Annual average wind speeds of Sudan.
8.29 10.45 7.96 7.03 8.29 8.29 7.65 7.34 17.39 7.34 7.34 N.A. 1.91 6.18 7.34 7.34 7.34 1.35 7.34 8.97 8.29
10.00 10.00 6.00 10.00 8.00 10.00 I0.00 10.00 10.00 10.00 17.00 N.A. 10.00 10.00 10.00 10.00 10.00 10.00 8.00 7.00 10.00
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
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Energy resources in Sudan
69
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 Netherlands, December 1988. 19. Eisa, E. I., Weibull distribution in wind energy statistics. Proceedings of the 4th International 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. 261279.