Energy Sources, 22:845 –850, 2000 Copyright © 2000 Taylor & Francis 0272-6343 / 00 $12.00 1
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Applicability of Wind–Solar Thermal Hybrid Power Systems in the Northeastern Part of the Arabian Peninsula AHMET Z. SAHIN Department of Mechanical Engineering King Fahd University of Petroleum and Minerals Dhahran, Saudi Arabia The statistical correlation of wind and solar thermal power availability in the northeastern part of the Arabian Peninsula is investigated. Simple models were developed to determine wind, solar, and hybrid power resources per unit area. Correlations between solar and wind power data were carried out on hourly, daily, and monthly basis. The results of correlations indicate that possible applications of hybrid systems could be considered for efficient utilization of these resources. Keywords: solar energy, solar–wind hybrid thermal power systems, wind energy
Extensive research is being carried out on solar thermal power systems and wind energy conversion systems in recent years. Solar and wind energy resources are often complementary in nature. Therefore, the efficiency and power output can be improved by designing a solar–wind hybrid thermal power system. The wind is caused by the temperature differences in the atmosphere due to solar radiation heating. The directional, diurnal, and nocturnal characteristics of wind depend also on the effects of local topographic conditions, which complicate the relationship between wind and solar radiation. Therefore, the peak power potentials due to solar and wind do not occur at the same time. Wind power availability in many areas becomes maximum in the winter, when solar power availability is minimum. Day and night variations of these availabilities should also be considered. Thus, it is important to study the relationship between wind speed and solar radiation in order to determine the practicability of a hybrid wind–solar thermal power system. For this purpose, a detail statistical analysis of both wind and solar energy potential is needed. Since both resources are intermittent in nature, their possible complementing characteristics may indicate that utilization of wind–solar thermal hybrid power system is fruitful in a short-term and/or long-term basis.
Received 6 July 1999; accepted 14 September 1999. The author acknowledges the support of King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia, for this work. Address correspondence to Ahmet Z. Sahin, Department of Mechanical Engineering , King Fahd University of Petroleum and Minerals, P.O. Box 1461, Dhahran 31261, Saudi Arabia. E-mail:
[email protected] a
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Photovoltaic solar energy conversion systems and wind energy conversion systems are being used extensively for electricity supply in isolated locations that are far from the electrical distribution network. These systems can be designed to operate in a reliable and unattended manner for extended periods of time. However, these systems suffer from the fluctuating characteristics of available solar and wind power sources, and may not provide continuous and smooth power generation. Solar and wind generators in combination as part of a hybrid system can provide less fluctuating power generation. However, the cost of such a system may be a determining factor, since the complications of both systems must be considered for the design of a hybrid system. The cost of individual systems is closely related to their size. Therefore, optimum size distribution in a hybrid system is an important parameter for providing a satisfactory power supply at a minimum cost. Individual sizing and cost analyses of solar power and wind power generators are available in the literature (Duffie & Beckman, 1991; Barley & Winn, 1978; Brandemuehl & Beckman, 1979; Gordon, 1987; Keoppl, 1982). Sizing and cost analysis of hybrid power generators, however, have not been studied in detail yet (Borowy & Salameh, 1994). In a recent study, Markvart (1996) studied the sizing of hybrid photovoltaics–wind energy systems considering the seasonal variation in the south of England. In this study, the statistical correlation of wind and solar thermal power availability in the northeastern part of the Arabian Peninsula is investigated. The solar and wind energy potentials of the northeastern part of Saudi Arabia is investigated based on realtime, high-resolution (minute-by-minute ) solar radiation, temperature, and wind velocity measurements of a complete year. High-resolution data collection is especially important for determination of wind power availability, since wind power is a nonlinear function of both wind velocity and ambient temperature. For long-time operation, daily and even monthly averages are better representative figures.
Power Availabi lity The experimental data were collected at a location on the shoreline of the Arabian Gulf in northeastern Saudi Arabia, near Dhahran (26°N, 50°E). This area is considered to be semidesert, due to its extremely low precipitation rate, high temperatures, and frequent windstorms. The region is under the influence of an anticyclonic flow in January, by cyclonic flow in July (Riehl, 1979 ). This study was carried out in the year 1995 for one complete year. The surface air temperature and the global solar radiation were measured at the 2-m level. The wind velocity measurements were made at the 10-m level. The meteorogical data were collected every minute using two identical computers. The meteorogical sensors were checked on a periodic basis, and quality control strategies were implemented to ascertain the quality of the collected data. The mean, maximum, and minimum hourly values were processed from the raw data stored for every minute. From the hourly data set, daily and monthly statistics were made in order to obtain observed temperature, solar radiation, and wind velocity data. In order to compare and correlate the solar and wind power potentials, these power potentials are calculated per unit surface area (W/m 2 ). The total solar radiation (W/m 2 ) on a horizontal surface is considered. The wind energy potential is calculated from
W · W5
1 q V3 2
(1)
where W · w is the wind power available to W/m2, r is the density of ambient air in kg/m3, and V is the velocity of wind in m/s. The density of air is calculated as
Wind–Solar Hybrid Systems in Arabian Peninsula q 5
847
P RT
(2)
where P is the ambient pressure in kPa, R is the gas constant in kJ/kg K, and T is the ambient temperature in K.
Power Output and Cost of a Solar–Wind Hybrid Generator The power output from a solar–wind hybrid generator, W · H , is given by
W · H5
As W · s1
A wW ·w
(3)
where coefficients As and Aw are used to account for the size and overall efficiency of the individual solar and wind power generators, respectively. The main goal in designing the hybrid power generator is to select the optimum values of As and Aw for minimum cost and to produce a total power output W · H to meet the demand of power throughout the year. Assuming the cost to be a linear function of the size, the total cost of a hybrid generator, CH, can be written as C H=CsA s1 CwA w
(4)
where Cs and Cw represent the cost per unit power potential of individual solar and wind power generators, respectively. This total cost is minimized, with the constraint
W · H$
W ·D
(5)
where W · D is the power demand. Although the demand of power varies throughout the year, the analysis in this study is based on the assumption of constant power demand of 100 W. The correlation between the solar power availability and the wind power availability can best be explained by calculating the correlation coefficient that is given by (Montgomery & Runger, 1994)
q
s, w
where the covariance is
1 n
Cov W · s, W ·w 5
å
Cov W · s, W ·w s ss w
5
å
n
i5 1
W · s i2
(6)
·s W
· wi2 W
standard deviations are
s
s
5
1 n
n
i5 1
· si2 W
·s W
mean powers are
W · s5
1 n
2
1 2
å ·W
and
n
i5 1
s i
and
s
w
5
W · w5
1 n
1 n
å
n
i5 1
·w W · w i2 W
· å W
(7)
W ·w
2
1 2
(8)
n
i5 1
w i
(9)
and n is the number of data to be correlated. The correlation can take values from 2 1 to 1 1. The best correlation for complementary data such as solar and wind power would be 2 1. As the correlation approaches 1 1, the complementary nature of the data disappears.
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Results and Discussion Daily mean solar radiation and wind power data shown in Figure 1 indicate that the solar energy values are higher in the summer months and lower in the winter months. Although the wind power availability shows a considerable amount of scatter throughout the year, it is clear that high values of wind power are found mostly on winter days. The highest daily mean global solar radiation of 351 W/m 2 was recorded on June 20. On the other hand, the highest daily mean wind power potential of 1,341 W/m 2 was recorded on November 26. The daily average of solar power potential shows a consistent variation, but the wind power potential variation has a large scatter as a result of wind velocity and ambient temperature variations. The monthly averages of solar, wind, and total power potentials are shown in Figure 2. The complementary characteristics of solar and wind power availabilities are clear, and the total power potential shows less variation throughout the year. This suggests that using a solar–wind hybrid generator, a continuous and nearly constant power output could be obtained which can utilize the solar and wind power potentials in an optimum way. It is interesting to note that maximum average solar power potential (328 W/m 2 ) occurred in the month of June, when the wind power potential is the minimum (80.82 W/m 2 ). The minimum solar power potential (98 W/m 2 ) occurred in December, and the maximum wind power potential (233.5 W/m 2 ) occurred in November. The correlation between the solar and wind power potential shown in Figure 2 is calculated to be 2 0.75. The size variation of solar–wind hybrid power generators that could produce a constant total power of 100 W throughout the year continuously is shown in Figure 3. As the size of solar power generator increases, the required size of wind generator decreases. However, this decrease is not linear for the whole range of hybrid power generators. There exists a minimum total size of hybrid power generator at about As = 0.2. Aw for a stand-alone wind power generator, ( Aw)max = 1.237, is higher than As for a stand-alone solar power generator, ( As )max = 1.01. It should be noted that As and Aw include not only the area but also the efficiency of the individual solar and wind power systems.
Figure 1. Daily average solar and wind power observations throughout the year.
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Figure 2. Monthly averages of available solar, wind, and total power potentials .
Figure 3. Size variation of solar–wind hybrid power generators to meet a total power demand of 100 W for the site considered .
Figure 4 shows the variation of the total cost of the hybrid generators given in Figure 3 using the cost fraction, Cs/Cw, as a parameter. For the cost fraction Cs/Cw . 0.565, the minimum total cost appears to be for the hybrid generator with As = 0.2. For the case of Cs/Cw , 0.565, stand-alone solar generators are the optimum systems. For most practical applications, wind power generation systems are more costly than solar power generators. For this reason, cost fractions of Cs/Cw , 1.0 are shown in Figure 4.
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Figure 4. Total cost variation of the solar–wind hybrid power generators given in Figure 3.
Conclusions The solar and wind power potentials at a coastal location on the northeastern part of the Arabian Peninsula were investigated based on high-precision data obtained on a minute-byminute basis. The complementary nature of solar and wind power potentials was demonstrated. The optimum size of a solar–wind hybrid power generator was studied. It was found that an optimum overall size of hybrid power generator exists which provides a constant continuous demand of power and has a minimum overall cost.
References Barley, C. D., & C. B. Winn. 1978. Optimal sizing of solar collectors by the method of relative areas. Solar Energy 21:279. Borowy, B. C., & Z. S. Salameh. 1994. Optimum photovoltaic array size for a hybrid wind/PV system. IEEE Trans. Energy Conversion 9:482–485. Brandemuehl, M. J., & W. A. Beckman. 1979. Economic evaluation and optimization of solar heating systems. Solar Energy 23:1. Duffie, J., & W. Beckman. 1991. Solar engineering and thermal processes, 2d ed. New York: Wiley. Gordon, J. M. 1987. Optimal sizing of stand-alone photovoltaic solar power systems. Solar Cells 20:295. Keoppl, G. W. 1982. Putnam’s power from the wind. New York: Van Nostrand Reinhold. Markvart, T. 1996. Sizing of hybrid photovoltaic-wind energy systems. Solar Energy 57(4):277–281. Montgomery, D. C., & G. C. Runger. 1994. Applied statistics and probability for engineers. New York: Wiley. Riehl, H. 1979. Climate and weather in the tropics. New York: Academic Press.
