Assessment of Wind Characteristics for Energy Generation

Energy Sources, 26:1227–1237, 2004 Copyright © Taylor & Francis Inc. ISSN: 0090-8312 print/1521-0510 online DOI: 10.1080/00908310390268083

Assessment of Wind Characteristics for Energy Generation KORAY ULGEN Solar Energy Institute Ege University Bornova, Izmir, Turkey

ASIR GENC Department of Statistics Selcuk University Konya, Turkey

ARIF HEPBASLI Department of Mechanical Engineering Faculty of Engineering Ege University Bornova, Izmir, Turkey

GALIP OTURANC Department of Mathematics Selcuk University Konya, Turkey Wind technology in Turkey has gained considerable maturity over the last five years, and wind energy projects are becoming commercially attractive in the country. In practice, it is essential to describe the variation of wind speeds for optimizing the design of the systems resulting in less energy generating costs. The wind variation for a typical site is usually described using the so-called Weibull distribution. In this study, the two Weibull parameters of the wind speed distribution function, the shape parameter k (dimensionless) and the scale parameter c (m/s), were computed from the wind speed data for Aksehir in Konya, located in Central Anatolia in Turkey (latitude: 38.35◦ and longitude: 31.42◦ ). Wind data, consisting of hourly wind speed records over a 6 year period, 1997–2002, were obtained from the Aksehir State Meteorological Station. Based on the experimental data, it was found that the numerical values of both Weibull parameters (k and c) for Aksehir vary over a wide range. The yearly values of k range from 1.756 to 2.076, while those of c are in the range of 2.956 to 3.444. Average seasonal Weibull distributions for Aksehir are given. The wind speed Received 21 April 2003; accepted 23 May 2003. Address correspondence to Dr. Koray Ulgen, Solar Energy Institute, Ege University 35100, Bornova, Izmir, Turkey. E-mail: [email protected]

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K. Ulgen et al. distributions are represented by Weibull distribution and also by Rayleigh distribution with a special case of the Weibull distribution for k = 2. The Rayleigh distribution is found to be suitable to represent the actual probability of wind speed data for the site studied. Keywords Rayleigh distribution, Turkey, Weibull distribution, Weibull parameters, wind characteristics, wind data, wind energy, wind energy systems

The power of the wind has been utilized for at least three thousand years. Until the early twentieth century, wind power was used to provide mechanical power to pump water or to grind grain. At the beginning of modern industrialization, the use of the fluctuating wind energy resource was substituted by fossil fuel fired engines or the electrical grid, which provided a more consistent power source. In the early 1970s, with the first oil price shock, the interest in the power of the wind re-emerged. This time, however, the main focus was on wind power providing electrical energy instead of mechanical energy. The first wind turbines for electricity generation had already been developed at the beginning of the twentieth century. The technology was improved step-by-step since the early 1970s. By the end of the 1990s, wind energy has re-emerged as one of the most important sustainable energy resources. During the last decade of the twentieth century, world-wide wind capacity has doubled approximately every three years (Ackermann and Söder, 2002). Wind energy plays a significant role among Turkey’s renewable energy sources, with a total theoretically available power potential of about 88,000 MW/yr. As for studies conducted in Turkey on the basis of wind energy applications, electricity generation through wind energy for general use was first realized at Cesme Altinyunus Resort Hotel (The Golden Dolphin Hotel) in Izmir, Turkey in 1986 with a 55 kW nominal wind energy capacity. Up to date, three wind power plants were installed with a total capacity of about 18.9 MW, while some installations are still in progress, as given in detail elsewhere (Ozgener and Hepbasli, 2002; Hepbasli and Ozgener, 2003; Ozgener et al., 2003). In practice, it is very important to describe the variation of wind speeds for optimizing the design of the systems resulting in less energy generating costs. The wind variation for a typical site is usually described using the so-called Weibull distribution (DWTMA, 2003). In this regard, over the last decade, a number of studies have been carried out by various investigators in order to assess wind power around the world (Rehman et al., 1994; Sopian, et al., 1995; Shabbaneh and Hasan, 1997; Mayhoub and Azzam, 1997; Deaves and Lines, 1997; Algifri, 1998; Sahin and Aksakal, 1998, 1999; Persaud et al., 1999; Seguro and Lambert, 2000; Lun and Lam, 2000; Sulaiman et al., 2002). As for the studies conducted in Turkey, some investigators have performed the assessments of wind power in western Turkey on the basis of individual locations (Incecik and Erdogmus, 1994; Sahin and Sen, 1995; Sen and Sahin, 1997; Borhan, 1998; Sen, 2001; Ulgen and Hepbasli, 2002; Celik, 2003a). In these studies, much consideration has been given to the Weibull two-parameter (k, shape parameter and c, scale parameter) function because it has been found to fit a wide collection of wind data (Lun and Lam, 2000). The main objectives of this study are to determine the two parameters, k and c, of a Weibull density distribution function for Aksehir in Konya, Turkey to be able to predict the energy output of wind energy systems.

Data and Methodology Used The Turkish State Meteorological Service (TSMS), founded in 1937, is the only legal organization in Turkey to provide all meteorological data and information. A map of

Assessment of Wind Characteristics

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Figure 1. Wind atlas of Turkey (TSMS, 2003).

Turkey’s various wind regimes drawn by TSMS, ranging up to 5 m/s with an interval of 1 m/s (TSMS, 2003), is illustrated in Figure 1, where the site studied is also designated. In this study, experimental data obtained from the Aksehir State Meteorological Station in Konya, Turkey over a 6 year period, 1997–2002, were used to determine the two parameters, k and c (m/s). All measurements in this station (latitude: 38.35◦ and longitude: 31.42◦ ) are recorded at a height of 10 m above the ground level. In practice, three basic methods are used in wind energy assessments: (1) statistical analysis of the existing wind energy potential and other meteorological data, and topographical information; (2) qualitative indicators of long-term wind speed levels; and (3) application of boundary layer similarity theory and the use of surface pressure observations (Spera, 1995). Measurements of wind speed distribution or frequency distribution are used for calculating the output of the wind energy in a particular site if available. If not, the wind speed distribution can be represented by other analytical distribution functions for the occurrence of wind speeds. One of these functions is the Weibull distribution function (named after the Swedish physicist Weibull, who applied it when studying material strength in tension and fatigue in the 1930s), which has recently been proposed by some investigators (Spera, 1995; Sen and Sahin, 1997; Sahin and Aksakal, 1998; Persaud et al., 1999; Lun and Lam, 2000; Seguro and Lambert, 2000; Ulgen and Hepbasli, 2002; Celik, 2003a,b). This is due to its greater flexibility and simplicity, as well as good agreement with experimental data (Lun and Lam, 2000). In other words, this analytical distribution for fitting wind speed data is generally accepted for energy assessment analyses and wind load studies (Spera, 1995; Mayhoub and Azzam, 1997). The general form of the Weibull distribution function, which is a two-parameter function, for wind speed is given by (Spera, 1995; Persaud et al., 1999) k v k−1 v k f (v) = (1) exp − c c c

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where f (v) is the probability of observing wind speed v, k the dimensionless Weibull shape parameter (or factor), and c reference value in the units of wind speed (so called Weibull scale parameter). The k values range from 1.5 to 3.0 for most wind conditions. The Rayleigh distribution is a special case of the Weibull distribution, in which the shape parameter is 2.0 (Spera, 1995; Persaud et al., 1999). The cumulative distribution function is given as (Persaud et al., 1999; Lun and Lam, 2000) v k F (v) = 1 − exp − (2) c Determination of the parameters of the Weibull distribution requires a good fit of Equation (2) to the recorded discrete cumulative frequency distribution. Taking the natural logarithm of both sides of Equation (2) twice gives: ln{− ln[1 − F (v)]} = k ln(v) − k ln c

(3)

So, a plot of ln{− ln[1 − F (v)]} versus ln v presents a straight line. The gradient of the line is k and the intercept with the y-axis is −k ln c. The two significant parameters k and c are closely related to the mean value of the wind speed vm as (Mayhoub and Azzam, 1997): 1 (4) vm = c 1 + k where ( ) is the gamma function of ( ).

Results and Discussion In this study, wind speed data for Aksehir in Konya, Turkey over a 6 year period, 1997– 2002, were analyzed. Based on these data, first wind speeds measured were statistically processed. Calculations were then made to obtain the Weibull distribution parameters with the aid of common statistics software, as reported by Lun and Lam (2000). The main results obtained from the present study can be summarized as follows. Mean Wind Speed Table 1 shows yearly cumulative times the wind blows according to wind speeds, while a histogram of the monthly average wind speeds is illustrated in Figure 2. As can be seen in Table 1, for 36.9% of the time the wind blows, the wind speed ranges between 1 and 2 m/s, while for 16.5% of that, it ranges from 2 to 3 m/s. It is also clear from Figure 2 that the average wind speed has the lowest value in the month of October and the maximum in the month of April, ranging from 1.38 to 2.20 m/s with an annual average of 1.73 m/s. Weibull Distribution Table 2 shows the yearly and seasonal values of the two Weibull parameters, the scale parameter c (m/s) and the shape parameter k (dimensionless) for the site studied. For


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Table 1 Yearly cumulative times the wind blows according to wind speeds in an hour Wind velocity (m/s)

1997

1998

1999

2000

2001

2002

Whole year

0–1 1–2 2–3 3–4 4–5 5–6 6–7 7–8 8–9 9–10 10–11 11–12 12–13 13–14 14–15 15–16 16–17 17–18

3,025 3,029 1,458 693 296 138 54 39 20 3 2 2 1 0 0 0 0 0

2,987 3,247 1,444 641 286 84 38 22 3 2 1 0 0 1 2 1 1 0

3,076 3,175 1,458 529 236 129 62 29 23 16 12 9 2 1 3 0 0 0

3,052 3,410 1,387 499 238 108 45 16 8 8 2 8 0 3 0 0 0 0

2,678 3,182 1,556 695 293 148 87 46 37 22 7 4 2 3 0 0 0 0

3,209 3,354 1,391 520 171 68 19 8 7 6 0 3 2 0 1 1 0 0

18,027 19,397 8,694 3,577 1,520 675 305 160 98 57 24 26 7 8 6 2 1 0

Total

8,760

8,760

8,760

8,784

8,760

8,760

52,584

Figure 2. Monthly average wind speeds in Aksehir, Turkey.

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K. Ulgen et al. Table 2 Yearly and seasonal shape parameters, k and scale parameters, c in Aksehir, Turkey Year

v (m/s)

k

c (m/s)

1997 1998 1999 2000 2001 2002 Winter Spring Summer Autumn Whole year

1.775 1.699 1.741 1.666 1.897 1.584 1.731 2.018 1.722 1.443 1.727

1.363 1.421 1.266 1.375 1.311 1.450 3.858 3.462 2.927 3.580 2.593

1.954 1.879 1.894 1.836 2.076 1.756 1.916 2.246 1.931 1.603 1.943

Table 3 Monthly shape parameters, k and scale parameters, c in Aksehir, Turkey Month Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Yearly

Parameters

1997

1998

1999

2000

2001

2002

Whole year

k c k c k c k c k c k c k c k c k c k c k c k c

1.308 1.588 1.358 1.918 1.356 2.281 1.468 3.380 1.696 2.078 1.678 1.796 1.947 2.062 1.719 1.831 1.979 1.710 1.466 1.779 1.532 1.156 1.428 2.135

1.112 1.585 1.498 1.927 1.247 2.406 1.528 2.258 1.625 1.929 1.866 1.672 1.698 2.053 1.767 1.856 1.726 1.856 1.799 1.740 1.304 1.620 1.402 1.707

0.897 1.849 1.345 2.841 1.538 1.992 1.563 1.932 1.583 2.036 1.751 2.062 1.833 1.892 1.910 1.833 1.856 1.518 1.596 1.406 1.063 1.758 0.375 0.560

1.295 1.816 1.071 1.871 1.351 2.359 1.412 2.610 1.762 1.638 1.911 1.875 2.036 1.927 1.930 1.939 1.744 1.728 1.722 1.469 1.411 1.290 1.326 1.632

1.328 1.433 1.144 2.324 1.286 2.630 1.420 2.537 1.540 2.214 1.899 2.364 1.804 1.993 2.079 1.912 1.929 1.805 1.920 1.458 1.190 2.164 1.137 2.302

1.211 1.197 1.267 1.538 1.179 2.148 1.319 1.953 1.785 1.902 1.742 1.989 1.846 2.048 1.839 1.813 1.611 1.604 1.635 1.433 1.468 1.456 2.876 1.986

1.211 1.197 1.267 1.538 1.179 2.148 1.785 1.902 1.785 1.902 1.742 1.989 1.846 2.048 1.839 1.813 1.611 1.604 1.635 1.433 1.468 1.456 2.876 1.986

k c

1.363 1.954

1.421 1.879

1.741 1.894

1.375 1.836

1.311 2.076

1.450 1.756

2.593 1.943


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Figure 3. Yearly Weibull probability density distributions for the period of 1997–2002 in Aksehir, Turkey.

the purposes of calculating seasonal mean wind speeds, the months in each of the four seasons in the northern hemisphere are generally divided as follows: (a) winter: December, January, and February; (b) spring: March, April, and May; (c) summer: June, July, and August; (d) autumn: September, October, and November (Spera, 1995). Values of c and k were determined using the methodology described in the earlier section and are shown in Table 2. It is clear that the parameter k has a much smaller spatial variation than the parameter c. The yearly values of k range between 1.266 and 1.450. The lowest value of c is 1.756 m/s and is found in the year of 2002, whilst the highest value is 2.076 m/s, occurred in the year of 2001. The monthly values of k and c are also in Table 3. The range of k is between 1.179 and 2.876, while the c values vary from 1.197 to 2.148 m/s. In order to observe the Weibull distribution by region, the Weibull probability density distributions for each of the 6 years were analyzed. The distributions obtained are illustrated in Figure 3, while a comparison of seasonal Weibull probability density function distributions is shown in Figure 4. It can be seen from Figure 3 that the distribution is similar for a 6 year period. Comparison of Weibull and Rayleigh Distributions The root mean square error (RMSE) was used in statistically evaluating the performance of two Weibull and Rayleigh distributions and was computed from the following equation (Ulgen and Hepbasli, 2002) n

RMSE = (yi − xi )2 /n (5) i=1

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Figure 4. A comparison of seasonal Weibull probability density function distributions in Aksehir, Turkey.

Figure 5. Weibull and Rayleigh approximations of the actual probability distribution of wind speeds.

which provides information on the short-term performance. The value of RMSE is always positive, representing ‘zero’ in the ideal case. The Weibull and Rayleigh approximations of the actual probability distribution of wind speeds are shown in Figure 5, while a comparison of two approximations with the actual probability distribution is given in Table 4. Cliff (1977) suggests that sites with


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Table 4 Comparison of the actual probability distribution of wind speeds with Weibull and Rayleigh approximations f (v)

Wind speed, v (m/s)

Actual

Weibull

Rayleigh

1 2 3 4 5 6 7 8 9 10

0.3986 0.2843 0.1360 0.0505 0.0156 0.0041 0.0010 0.0002 0.0000 0.0000

0.3874 0.4756 0.1220 0.0063 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000

0.4045 0.3674 0.1478 0.0312 0.0037 0.0002 0.0000 0.0000 0.0000 0.0000

0.0596

0.0263

RMSE

annual average wind speeds greater than 4.5 m/s tend to have a near-Rayleigh cumulative wind distribution. As can be seen in Table 4, it can be concluded that the Rayleigh distribution is suitable to represent the actual probability of wind speed data for the site at annual average wind speeds up to 5 m/s. By taking into account the overall evaluation of the two distributions, we computed RMSE is equal 0.0596 and 0.0263 for the Weibull and Rayleigh approximations, respectively. Accordingly, the Rayleigh approximation is found to be the most accurate distribution for the test method of RMSE.

Conclusions The following main conclusions can be drawn from the present study: 1) Using wind data, consisting of hourly wind speed records over a 6 year period, 1997–2002, wind characteristics in Aksehir- Konya, Turkey were investigated. 2) The average wind speeds for the site studied were found to range between 1–2 m/s for most times the wind blows, while the maximum wind speed recorded is 17 m/s. 3) The yearly values of k and c at this site were found to vary between 1.756–2.076, and 2.596–3.444, respectively. 4) Rayleigh distribution was found to give a better agreement with the actual data than that of Weibull distribution. 5) The current work is only a preliminary study in order to estimate wind energy analysis of Aksehir, Konya. In order to have a comprehensive wind database and obtain good predictions prior to construction and installing wind energy conversion systems, more detailed studies should be carried out at each site separately. It is anticipated that the present study will give useful insights to engineers and scientists dealing with wind energy.

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Nomenclature c Weibull scale parameter or factor (m/s) f (v) probability of observing wind speed F (v) cumulative distribution function k Weibull shape parameter or factor in Equation (1), dimensionless RMSE Root mean square error (−) v wind speed (m/s) vm mean wind speed (m/s) Greek Letters

( ) gamma function of ( ).

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