Wind resource potentials at Quetta

Solar & Wind Technolo#y Vol. 6, No. 5, pp. 60~609, 1989 Printed in Great Britain.

0741-983X/89 $3.00+.00 Pergamon Press plc

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Wind resource potentials at Quetta YASMIN ZAHRA JAFRI, N. FAROOQUI, A. U. DURRANI and S. M. RAZA Department of Statistics, Department of Physics, University of Bahichistan, Quetta, Pakistan

(Received 13 July 1988; accepted 16 November 1988) Abstract--Hourly wind data of Quetta airport (Samungii) for 1984 and 1985 are analysed on the basis of speed frequency curves, diurnal variations and of the speed duration curves. In this paper, we conclude that the speed frequency curves give more reliable information as compared to speed duration curves, from the view-point of the wind energy utility, i.e. the estimation of the average power output. However, windtunnel testing is required to observe wind flow over the topography of Quetta and to estimate the longterm wind power. We conclude, in addition that the location of data collection as well as the accuracy of input wind data becomes critical to wind resource assessment; which required computer simulation of wind data over at least a period of I0 years by numerical analysis and mathematical models.

INTRODUCTION

RESULTS AND DISCUSSIONS

Wind represents a large and non-depletable energy resource that can be used with no significant impact on the environment and is distributed over all the world. Wind resource potential employs significant features for energy development which includes: (i) resources evaluation, (ii) technology evaluation and (iii) life cycle cost analysis. The random nature of wind necessitates a probabilistic treatment of long-term data for generating confidence levels about resource availability. Under these circumstances various frequency distribution functions have to be examined by computer simulations, numerical analysis and mathematical models. Some of the functions that have been used to model long-term wind speed variations [I] include Rayleigh, Weibull and Inverse Gaussian distributions. The strength of the surface winds over any location is governed by the magnitude of the pressure gradient just above the planetary boundary level, the atmospheric stability in the boundary layer as well as the modifying influences of local topography. Near the coasts, winds tend to be stronger due to the additional pressure gradient provided by the thermal contrast between the land and the sea. Rahmatullah and Waliulla [2] while studying the wind potential along the coastal belt of Pakistan concluded windmills to become more viable because the wind along the coast is more persistent and stronger than elsewhere in Pakistan. Since Quetta is in a valley, at an altitude of 1799 m above sea level, extreme winds can be tapped by using wind generators on cliffs. A valley which is covered with dry mountains from all sides, receives only part oftbe high wind speed which usually blows from the north and travels towards the southeast. During winter, people often say that the Siberian wind in Quetta is blowing from the north. Wind speed data are affected by the anemometer height, and the exposure of the anemometer as regards the surrounding obstructions. Mountains in Quetta may appear as obstructions to wind speeds. Therefore, careful studies must be undertaken with respect to anemometer height.

Figure 1 describes speed duration curves for the period 1984-1985. The horizontal axis is in h per year, with a maximum value of 8760 h with 365 days. The vertical axis gives the wind speed that is exceeded for the number o f h per year on the horizontal axis. Mean wind speeds for 1984 and 1985 are recorded as 4.01 m/s and 4.82 m/s, respectively. Using 10 years data for speed duration curves and their corresponding yearly mean wind speeds, one can obtain a rough estimate of the occurrence of mean wind speeds as 3250 and 3375 h (nearly equal) for 1984 and 1985, of a total 8760 h. Speed duration curves can be used to determine the number of hours of operation of a specific wind turbine. Speed duration curves do not lend themselves to many features of wind turbine design or selection. It is difficult to determine the optimum rated wind speed or the average power output from a speed duration curve. Considering Fig. 2, i.e. the speed frequency curves for 1984 and 1985, we observe the most frequent speed as 2 m/s. These curves show the number of h per year that the wind is in a given interval of, say 1 m/s interval. The summation of the number of h at each wind speed over all the wind speed intervals becomes the total number of hours in the years. One important characteristic which we note from Fig. 2 is a non-zero intercept on the vertical axis which shows the existence of calm spells at any site. Another feature is that the most frequent speed, i.e. 2 m/s, is lower than the mean speeds for 1984 and 1985. We also observe that the duration of the most frequent speed is decreasing with increasing mean speed (for 1984, duration is 1800 h/yr with a mean speed of 4.01 m/s; and for 1985, duration is 1320 h/yr with a mean speed of 4.82 m/s). An anomaly in speed frequency curves is observed as shown by data points in Fig. 2 which do not fit into the curves. This appears at a wind speed of 5.1 m/s. Perhaps, it is due to site effect. Extreme winds passing over the mountains are reflected back into the valley causing a continuous whirlpool phenomenon. This may be attributed to an anomaly but its occurrence at the same speed refers to some

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Fig. I. Speed-duration curves for 1984-1985, Quetta.

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Fig. 2. Speed frequency curves for 1984-1985, Quetta.

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Data Bank kind of site-wind-intercept characteristics which need to be exploited. Now, we consider Fig. 3 which shows diurnal variations at Quetta for a 2-year period. The average wind speed for this period was 4.41 m/s at an anemometer height of 10 m above the ground. The summer (June, July, August) and the autumn (September, October, November) seasons are seen to have the highest winds, i.e. 9.6 and 8 m/s respectively at 1500 h of day; while the spring and winter seasons have relatively less wind speeds, i.e. 7.6 and 4.9 m/s respectively but their occurrence are at 1200 h of day. Speed frequency

curves and diurnal variations are used in studies to develop an estimate of the seasonal and annual available wind power density. Tables 1 and 2 describe the conservative estimate of the power output for monthly averaged wind velocities. From data in Tables I and 2, we can obtain power duration curves ; the area defined within these curves will give us the total energy captured by the wind. There are several methods for estimating average power output, such as described by Justus et al. [3, 4] and Golding [5]. An estimate of average power output is already presented by using Weibull parameters and

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Fig. 3. Diurnal wind-speed pattern at Quetta, 1984-1985.

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Data Bank Table 1. 1984 wind data

Months Jan. Feb. Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec.

Average wind velocity (m/s)

Using eq. (4) in eq. (3) we have

Average Average power energy output, output, P (W/m 2) ( k W h / m 2)

3.5 4.4 4.9 4.9 4.8 3.4 4.0 4.5 3.8 3.4 3.0 3.4

4.3 8.5 11.8 l 1.8 11.0 3.9 6.4 9.1 5.5 3.9 2.7 3.9

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Pw=~pAv-

3

=

1.742p , 3 T ,'iv W.

For air, at standard pressure and temperature, p = 101.3 kPa, T = 273K. Equation (5) reduces to : Pw = 0.647Av 3 = 0.647v 3 W t m 2.

2.3 4.6 6.8 6.8 6.8 2.2 4.0 6.0 3.4 2.4 1.6 2.2

(5)

(6)

Ideal power from the turbine (using the principle of aerodynamics) is defined as 11-8/'2',]1(16)

where (16t27) = 0.593 is the Betz coefficient. Considering eq. (4), for standard pressure and temperature, i.e. P = 3.485 p = 0.647 kg/m 3.

(8)

Using eq. (8) in eq. (7), we have

,(,6)

Table 2. 1985 wind data

Months Jan. Feb. Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec.

Average wind velocity (m/s)

P~=2P

Average Average power energy output, output, P ~P (W/m 2) (kWh/m")

3.3 4.1 5.2 5.5 5.6 5.7 6.8 4.8 3.8 4.2 3.8 4.6

3.5 7.0 14.0 16.5 17.6 18.5 31.4 11.0 5.5 7.5 5.5 9.7

2.0 3.9 8.7 9.9 10.5 11.2 21.4 7.0 3.2 4.2 2.7 5.2

cumulative probabilities. Golding [5] refers to P = 0.593kAy 3

(1)

where P is power in kW, A is the area swept by the machine blade in m 2, v is the wind speed in k m / h and k = 0.0000137. However, we m a d e our own estimate of power output from each square m of area swept by a wind rotor, as given by P = 0.19v 3 W / m 2

(2)

where v is the wind speed in m/s. We take the average power output as mentioned in Tables 1 and 2, due to average wind velocities for each m o n t h of the year. Total energy is obtained by multiplying the average power with the n u m b e r of hours, i.e. in k W h / m 2. Our estimate as explained in eq. (2) is based on the following theory. Ideal power in the wind, Pw = ~Pav3 W

(3)

p = 3.485 P k g / m 3.

(4)

where A is in m 2 and 1

27 Av3 = 0 " 1 9 A v 3 W '

(9)

Using estimated value of T = 300K, p = 60.1 kPa we get p = 0.349 kg/m 3. Putting this value in eq. (7) we obtain power output from an ideal turbine, as expressed by eq. (2). Comparison of eqs (6) and (9), shows that 71% of the power is extracted from the wind by the ideal turbine. An actual wind turbine cannot extract more than 59.3% of the power in the wind which shows that an ideal turbine is not worth mentioning. The remaining 11.7% of the ideal wind power is not extracted due to existing turbines. Perhaps, it is either inappropriate efficiency of the turbine which cannot utilize the remaining 11.7% of the wind power, or the effect o f pressure and temperature. We have chosen the second possibility, which shows that even in the case of an ideal turbine, power output is influenced due to change in temperature and pressure. On the other hand, comparison o f eqs (2) and (9) yields 83% power output from the ideal turbine, in our estimated conditions o f pressure and temperature, which is much larger as compared to 71% power output. We conclude from the above discussion that the density of air is an important parameter which changes the efficiency of the machine by about + 10%. Keeping in view the efficiency of the machine at standard temperature and pressure, turbine efficiency can be increased by 10% in our estimated value of temperature and pressure.

CONCLUSIONS We conclude from above discussions the following : (1) Change in the density of air m a y cause an improvement in the power output of wind turbines, due to relatively less load offered by the air. (2) Power output from the turbine is affected by the working efficiency o f the wind machines. (3) Coefficient o f performance is not constant, it varies with the wind speed, the rotation speed of the turbine and blade parameters such as angle of attack and pitch angle. (4) Power output is also influenced due to coupling of the load through a transmission or gear box. Load m a y be a pump, compressor, grinder, electrical generator, etc. In addition, turbine shaft power and torque are influenced by varied designs.

Data Bank

Acknowledgements--We are thankful to Mr Mansoor Jafri, an architect, who made drawings of the figures. Special thanks are due to Mr Abdul Rauf for typing this manuscript. REFERENCES 1. S. M. Raza and Yasmin Zahra Jafri, Wind energy estimation at Quetta, Proc. VIll Int. Symp. Alternate Energy Sources (Edited by T. N. Veziroglu), University of Miami, 14-16 December 1987.

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2. M. Rahmatullah and Q. Waliulla, Meteorological evaluation of wind energy potential in the coastal belt of Pakistan. Report Pakistan Meteorological Department (1986). 3. C. G. Justus, Methods for estimating wind speed frequency distributions. J. Appl. Meteorol. 17, 350 (1978). 4. C. G. Justus, Wind~ and System Performance. Franklin Institute Press, Philadelphia (1978). 5. E. W. Golding, The Generation of Electricity by Wind Power. Spon, London (1976).