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Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey
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Renewable Energy 32 (2007) 1739–1752 www.elsevier.com/locate/renene
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Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey Murat Go¨kc- eka,, Ahmet Bayu¨lkenb, S- u¨kru¨ Bekdemira a
Faculty of Mechanical Engineering, Yildiz Technical University, Besiktas, 34349 Istanbul, Turkey b Institute of Energy, Istanbul Technical University, Maslak, 34469, Istanbul, Turkey Received 6 July 2006; accepted 27 November 2006 Available online 24 January 2007
Abstract Utilization of wind energy as an energy source has been growing rapidly in the whole world due to environmental pollution, consumption of the limited fossil fuels and global warming. Although Turkey has fairly high wind energy potential, exploitation of the wind energy is still in the crawling level. In the current study, wind characteristics and wind energy potential of Kırklareli province in the Marmara Region, Turkey were analyzed taking into account the wind data measured as hourly time series. The wind data used in the study were taken from Electrical Power Resources Survey and Development Administration (EIEI) for the year 2004. The measured wind data were processed as annual, seasonal and monthly. Weibull and Rayleigh probability density functions of the location are calculated in the light of observed data and Weibull shape parameter k and scale parameter c are found as 1.75 and 5.25 m/s for the year 2004. According to the power calculations done for the site, annual mean power density based on Weibull function is 138.85 W/m2. The results indicate that investigated site has fairly wind energy potential for the utilization. r 2007 Elsevier Ltd. All rights reserved. Keywords: Wind energy; Weibull distribution; Wind power; Kırklareli; Turkey
1. Introduction Utilization of energy is an important indicator of social development and economic growth. A portion of the world’s electricity energy requirement has been supplied by thermal power plants that use hydrocarbon fuels. As known, anthropogenic emissions Corresponding author. Tel.: +90 212 259 70 70/2490; fax: +90 212 261 66 59.
E-mail address: [email protected] (M. Go¨kc- ek). 0960-1481/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.renene.2006.11.017
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released from various sources such as CO2, NOx and SOx have been causing negative effects (global warming, ozone layer depletion, acid rains, etc.) on the atmosphere. Renewable energy sources are inexhaustible, clean and free. These sources offer many environmental and economical benefits in contrast to conventional energy sources (hard coal, lignite, natural gas, etc.). Thus, recently, the role of renewable resources such as wind, solar, and geothermal energy has been growing by leaps and bounds within other resources as their generating costs decrease. Although mankind has been using the wind energy since ancient times, its utilization to produce electricity effectively is over the last two decade by means of modern wind turbine [1]. Today, wind energy is widely used to produce electricity in many countries such as Germany, Spain, United States, India, and Denmark. Total wind power installed in Europe that is equivalent to 69% of the worldwide wind energy generating capacity, is 40,504 MW at the end of 2005. It is expected that the installed capacity in Europe will have reached 70,000 MW by the end of 2010. Wind power installed capacity is 59,322 MW for the whole world at the end of 2005 [2]. Turkey as a bridge between Europe and Asia Continent has been developing economically and technologically day-by-day. Electrical energy in Turkey is mainly produced by thermal and hydroelectric power plants. Because of limited energy sources, Turkey is heavily dependent on imported oil and gas. The primary energy consumption of Turkey is about 87.7 million tones of equivalent oil (Mtep) according to 2004 records of the Turkish Ministry of Energy and Natural Resources [3]. Utilization of renewable energy as indigenous source in the electricity generation is an important fact for Turkey in terms of both security of energy supply and environmental concerns. When it comes to Turkey’s situation pertaining to wind energy exploitation, it can be seen that Turkey is rather unsuccessful in using its potential. Turkey’s wind energy installed capacity was 20.1 MW at the end of 2005 [4]. At present, there are five wind power plants having total 50.1 MW installed capacity and 54 wind turbines. The biggest present wind energy power plant (BARES wind energy plant) with 20 wind turbines and 30 MW installed power, in Turkey was built at Bandırma-Balıkesir in 2006 [5]. Determining of wind energy potential for the selected site is made by investigating detailed knowledge of the wind characteristics, such as speed, direction, continuity, and availability. Thus, proper wind turbine selection and micrositting process for the wind power plants are obtained. In the last decade, a lot of studies related to the wind characteristics and wind power potential have been made in many countries worldwide [6–19]. Sahin et al. [6] investigated the wind power potential for selected seven different sites in the southern Anatolia. Their results show that at 25 m height above the ground level, the mean wind energy potentials reach 500 W/m2 in many investigated areas. The most suitable location among investigated areas is Samandag˘. Karsli and Gecit [7] evaluated the wind power potential of Nurdag˘ı-Gaziantep in the southern Turkey and determined mean wind speed of 7.3 m/s at 10 m height above the ground level. They concluded that highest speed value for Nurdag˘ı is 23.3 m/s and mean power density is 222 W/m2. Akpinar et al. [8] carried out a study using Weibull density function to demonstrate wind energy potential of Maden-Elazıg˘ in eastern Turkey. Results reveal that the mean speed for investigated site varies between 5 and 6 m/s and yearly average power density is 244.65 W/m2. A study of wind energy potential estimation and micrositting process for campus area of Izmir Technology Institute is made by Ozerdem and Turkeli [9]. The wind data used in the study is collected at 10 and 30 m heights for a period of 16 months. While the mean wind speeds at 10 and 30 m heights are 7.03 and 8.14 m/s,
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respectively, prevailing wind direction is determined as north. In addition to wind characteristics, they created wind energy map for campus area. Li and Li [10] analyzed wind potential and characteristics of Waterloo region, Canada as annual, seasonal, monthly, and diurnal for the five years period (1999–2003). The results of the study show that the windy months in Waterloo are from November to May, with February being the windiest month. The day and night time wind power densities in the cold season of Waterloo are calculated as 180 and 111 W/m2, respectively. Turkey has important wind energy potential especially in the Marmara region, coasts of western and southern Anatolia. The main purpose of this study is to investigate the wind energy potential of Kırklareli province in the northwestern Marmara region, Turkey. Wind data at 10 m height above the ground level related to the selected site were taken from EIE (Electrical Power Resources Survey and Development Administration) for the year 2004. 2. Wind data collection In this study, wind speed data measured as hourly time-series for the year 2004 in Kırklareli, located in Thrace part of Marmara region, were statistically analyzed. Fig. 1. shows the location of chosen site (411490 N; 271140 E) in the Marmara region. Kırklareli province is located in the northwest Marmara region. The Marmara region is affected by mild Mediterranean climate during the summer and experiences a dry and hot period about 4–5 months in a year. Besides, the region is exposed to high pressure of Siberia and the Balkan Peninsula during the winter season. Consequently, this region is influenced by the winds blowing from the directions of north and west [20]. All measurements in the wind observation station are recorded using the cup anemometer at a height of 10 m above the ground level.
Fig. 1. Location of Kırklareli wind observation station (K).
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3. Calculation methodology Knowledge of the wind speed distribution is a very important factor to evaluate the wind potential in the windy areas. In addition to speed distribution, meteorological data and topographical information for considered site have same importance. If ever the wind speed distribution in any windy site is known, the power potential and the economic feasibility belonging to the site can be easily obtained. Wind data obtained with various observation methods has the wide ranges. Therefore, in the wind energy analysis, it is necessary to have only a few key parameters that can explain the behavior of a wide range of wind speed data. The simplest and most practical method for the procedure is to use a distribution function. There are several density functions, which can be used to describe the wind speed frequency curve. The most common two are the Weibull and Rayleigh functions. The studies done using these functions can be found in literature [6–19]. 3.1. Weibull and Rayleigh distribution function The Weibull distribution function that is a special case of generalized gamma distribution for wind speed is expressed with Eq. (1). k vk1 v k f w ðvÞ ¼ exp , (1) c c c where v is the wind speed, c is a Weibull scale parameter in m/s and k is a dimensionless Weibull shape parameter. Besides, the cumulative probability function of the Weibull distribution is calculated as below v k F w ðvÞ ¼ 1 exp . (2) c There are several methods of determining Weibull k and c parameters, such as leastsquare fit to observed distribution method, mean wind speed-standard deviation method, etc. In this study, the two parameters, k and c, are obtained using Eqs. (3) and (4), namely using mean wind speed-standard deviation method [21]. s1:086 k¼ ð1pkp10Þ, (3) v¯ c¼
v¯ , Gð1 þ 1=kÞ
(4)
where v¯ is the mean wind speed and is calculated using Eq. (5), s is the standard deviation and is calculated using Eq. (6) [11]. ! n 1 X v¯ ¼ vi , (5) n i¼1 "
n 1 X s¼ ðvi v¯ Þ2 n 1 i¼1
#0:5 ,
(6)
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where n is the number of hours in the period of the considered time such as month, season or year. The mean speed value for the density function f(v) can be expressed by equation given below. Z 1 v¯ ¼ vf ðvÞ dv. (7) 0
If Eq. (1) is used in the Eq. (7), the mean speed is obtained as Eq. (8). Z 1 k1 vk v v k v¯ ¼ exp dv. c c c 0
(8)
If a variable change is done using x ¼ (v/c)k in the above equation, the mean speed is obtained as Z 1 v¯ ¼ c x1=k expðxÞ dx. (9) 0
The gamma function, G(y) for any y value, is usually written in the below form Z 1 expðxÞxy1 dx. GðyÞ ¼
(10)
0
If the y value in the Eq. (10) is taken as y ¼ 1+1/k, consequently, Eq. (4) is obtained. Another distribution function used in determination of the wind speed potential is Rayleigh distribution. This distribution is a special case of Weibull distribution and validate situation where the dimensionless shape parameter k of the Weibull distribution is assumed to be equal to 2. Probability density and cumulative function of the Rayleigh distribution are given by Eqs. (11) and (12), respectively, pv p v 2 f R ðvÞ ¼ 2 exp , (11) 4 v¯ 2¯v p v 2 F R ðvÞ ¼ 1 exp . (12) 4 v¯ 3.2. Calculations of wind power The wind power per unit area in any windy site is of importance in assessing of the wind power projection for the power plants. The wind power density of the considered site per unit area based on any probability density function can be expressed as [11] Z 1 1 Pm ¼ r v3 f ðvÞ dv, (13) 2 0 where r is the standard air density, 1.225 kg/m3, v is the wind speed, m/s. In the current study, the power of the wind was calculated using Weibull function and observed data. When the Weibull function is chosen as distribution function f(v), the average wind power density is calculated as below 1 Gð1 þ 3=kÞ Pmw ¼ r¯v3 . 2 ½Gð1 þ 1=kÞ3
(14)
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3.3. Variation of wind speed with height The wind speed measurements to determine the wind potential in the site are generally made at the standard elevations such as 10 m. The wind data also used in this study is measured at 10 m height above ground level. The wind speed is proportional to elevation. Therefore, it is necessary to know the wind speeds at the various turbine hub heights for the wind farm projects. The power law [22], Eq. (15), is used to obtain the extrapolated wind speeds for Kırklareli observation station. a h v ¼ v0 , (15) h0 where v0 is the original wind speed recorded at anemometer height (h0), v is the wind speed to be determined for the desired heights (h), a is the power law exponent and depends on the surface roughness. In this study, power law was calculated using the 0.14 value as a exponent. 4. Results and discussion 4.1. Mean wind speed Fig. 2 shows the monthly mean wind speeds in the site for the year 2004. As can be seen in the Fig. 2, the mean wind speed varies between 3.74 and 6.17 m/s. The maximum value of the mean wind speed is in the month of January while the minimum value is in the month of November. Table 1 shows the seasonal and annual mean wind speeds for the investigated site. In general, higher mean wind speeds were observed in the course of the winter season and lower values in the course of autumn season. The winter season has 8 7
Mean wind speed [m/s]
6 5
6.17 5.22 5.32 4.89
4.88
4.75 4.39
4
3.88
4.88
4.20 3.79 3.74
3 2 1 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months
Fig. 2. Monthly mean wind speeds in the site for the year 2004.
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Table 1 Seasonal and annual mean wind speed for the 2004 data Period (season and year)
Mean wind speed (m/s)
Winter Spring Summer Autumn Annual
5.32 5.09 4.15 4.13 4.68
Table 2 Monthly and annual mean wind speed (m/s) at different heights above ground level Kırklareli wind observation station
Months
Annual mean
Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. Wind speed (m/s)
a b c d
7.20 7.49 7.93 8.26
5.20 5.94 6.28 6.54
6.09 6.34 6.71 6.98
6.20 6.46 6.84 7.12
5.54 5.77 6.10 6.36
4.53 4.71 4.99 5.19
5.12 5.33 5.64 5.87
4.90 5.10 5.40 5.62
5.69 5.93 6.27 6.53
4.42 4.60 4.87 5.07
4.36 4.54 4.81 5.00
5.69 5.93 6.27 6.53
5.45 5.67 6.00 6.25
a, b, c and d rows in the table show the mean wind speeds for extrapolation heights, 30, 40, 60 and 80 m, respectively.
the biggest mean value with 5.32 m/s compared to other seasons. The maximal value of the wind speed in the investigated site is measured as 20.15 m/s in the month of February. In addition, in order to evaluate the variation of the wind speed with elevation, the mean speed values were calculated using the power law both monthly and annually for the various heights (30, 40, 60 and 80 m) in the wind observation station. Obtained results are presented in Table 2. Annual mean wind speed at the 80 m height is determined as 6.25 m/s while the mean wind speed for the month of January is 8.25 m/s. 4.2. Probability density functions Probability density functions such as Weibull or Rayleigh function are usually used to determine the wind speed distribution of a windy site in a period of time. In the current study, determination of wind speed distributions for the investigated site was made using Weibull and Rayleigh probability density functions. Fig. 3 reveals Weibull with the two parameters and Rayleigh distributions derived from observed data for the year 2004. As seen in this figure, the top point of the curve is the most frequent wind speed. The peak probability values vary between 0.15 and 0.165 depending on the wind speeds for the considered distribution functions (Weibull, Rayleigh and actual probability distribution). Shape (k) and scale (c) parameters of the Weibull function were calculated using the method mentioned in the earlier section. The results of the calculation show that dimensionless shape parameter k is 1.75 while scale parameter c is 5.25 m/s for the site analyzed in the year 2004. In order to evaluate the probability density function based on seasons, Weibull and Rayleigh distributions for each season are obtained for the year 2004. The seasonal probability density distributions derived from the observed distribution for the wind
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0.20
Probability density distribution
Observed data Weibull distribution Rayleigh distribution
0.15
0.10
0.05
0.00 0
5
10 15 Wind speed [m/s]
20
25
Fig. 3. Wind speed frequency distributions in the site for the year 2004.
speeds are shown in the Fig. 4. The peak probability values in the winter season vary about 0.125 and 0.145 depending on the wind speed for the all calculated distribution functions. These values for the autumn season become between about 0.15 and 0.185. In the Table 3, seasonal Weibull parameters (k and c) and the standard deviations are summarized. As seen from this table, Weibull shape parameter k varies between 1.57 and 2.21 while scale parameter c varies between 4.56 and 5.93 m/s. In addition, Weibull parameters and the standard deviations for each month of the year 2004 are presented in Table 4. The range of Weibull shape parameter k is between 1.38 and 2.41, while the scale parameter c values vary from 4.06 to 6.90 m/s. The lowest c value is in the month of November and the highest value is in the month of January. The lowest standard deviation is calculated in the month of June as 1.81 m/s. The cumulative probability distributions for considered functions are shown in Fig. 5 for the year 2004. To evaluate the performance of the considered distributions, mean root-square error (RMSE) parameter and Chi-square (w2) test are used in the study. Detailed information related to the evaluation parameters can be found in literature [8,10,17]. For the Weibull distribution function related to yearly data, RMSE value is 0.0031, while the w2 value is 0.01. On the other hand, these values for Rayleigh distribution function are 0.0073 and 0.1959, respectively. The smaller the value of the RMSE and w2 is, the better the calculated distribution function approximates to the observed data. According to RMSE parameter and w2 test calculations, Weibull distribution function obtained for the investigated site is the more suitable than Rayleigh distribution function. Some advantages of the Weibull distribution are listed in Ref. [21]. The comparison of the Weibull and Rayleigh distributions with the observed probability distribution of the wind speeds is given in Table 5 for the winter season. As it can be read in the Table 5, RMSE value regarding Weibull distribution is 0.0034 for the winter season. Result of the w2 test is also 0.000269.
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0.20 Observed data Weibull distribution Rayleigh distribution
0.15
Probability density distribution
Probability density distribution
0.20
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0.10
0.05
0.15
0.10
0.05 Spring
Winter
0.00
0.00 0
5
10 15 Wind speed [m/s]
20
25
0
10 15 Wind speed [m/s]
20
25
0.20 Probability density distribution
Probability density distribution
0.20
5
0.15
0.10
0.05
0.15
0.10
0.05
Summer
Autumn
0.00
0.00 0
5
10 15 Wind speed [m/s]
20
25
0
5
10 15 Wind speed [m/s]
20
25
Fig. 4. Seasonal wind speed frequency distributions in the site for the year 2004.
Table 3 Seasonal Weibull parameters and standard deviations for Kırklareli station Parameters
k c (m/s) s
Seasons Winter
Spring
Summer
Autumn
1.59 5.93 3.47
2.20 5.74 2.55
2.21 4.83 2.06
1.57 4.56 2.72
4.3. Wind power density Fig. 6 shows monthly variations for the mean power density that is calculated using both observed data and Weibull function for the year 2004. As seen from Fig. 6, the mean wind power densities decrease from January to June. In the second half of the year 2004, the
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Table 4 Monthly and annually Weibull parameters and standard deviations for Kırklareli station Parameters
k c (m/s) s
Months
Annual
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
1.70 6.90 3.78
1.46 5.39 3.44
2.16 5.88 2.56
1.99 6.00 2.82
2.27 5.35 2.22
2.28 4.36 1.81
2.41 4.36 1.95
1.87 4.73 2.36
2.07 5.50 2.49
1.42 4.17 2.73
1.38 4.06 2.76
1.70 5.45 2.98
1.75 5.25 2.79
1
Cumulative distribution
0.8 Observed data Weibull distribution Rayleigh distribution
0.6
0.4
0.2
0 0
5
10 Wind speed [m/s]
15
20
Fig. 5. Wind speed cumulative probability distributions in the site for the year 2004.
changes of the mean power density show almost similar characteristics. The highest mean power density of 332.88 W/m2 regarding actual data is calculated in the month of January while the lowest is in the month of June with the value of 60.83 W/m2. In addition, in the Fig. 7 seasonal variation of the mean power density is presented. As can be seen in Fig. 7, the value of the mean power density for the winter season is 247.63 W/m2 while this value is 80 W/m2 in summer for the actual data. Mean power density of the year 2004 regarding actual data and Weibull function are calculated as 142.75 and 138.85 W/m2. The wind power density distributions that are calculated using Weibull and Rayleigh functions and actual data are shown in Fig. 8. As is apparent from this figure, the wind power density for the wind speeds between 7.5 and 8.5 m/s is calculated as about 16.5 W/m2 regarding the actual data. On the other hand, the wind power density according to Weibull distribution is calculated as about 17.5 W/m2. At the lower wind speeds, all wind power density distributions according to considered functions exhibit similar characteristics. Whereas, at the higher wind speeds, the wind power density distributions
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Table 5 Wind speeds, probability densities, w2 and RMSE values for the winter season in the site i
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Speed class ui (m/s)
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 18–19 19–20 20–21
Probability distribution Observed data
Weibull
Rayleigh
0.0567 0.1140 0.1250 0.1227 0.1172 0.1021 0.0906 0.0677 0.0517 0.0444 0.0338 0.0251 0.0233 0.0059 0.0050 0.0054 0.0018 0.0032 0.0032 0.0000 0.0004
0.0610 0.1064 0.1250 0.1275 0.1196 0.1057 0.0890 0.0721 0.0564 0.0427 0.0314 0.0225 0.0158 0.0108 0.0072 0.0047 0.0030 0.0019 0.0012 0.0007 0.0004
0.0275 0.0782 0.1166 0.1382 0.1424 0.1318 0.1117 0.0874 0.0636 0.0431 0.0274 0.0163 0.0091 0.0048 0.0024 0.0011 0.0005 0.0002 0.0001 0.0000 0.0000
3.4 103 2.694 104
1.55 102 5.309 103
Root mean-square error (RMSE) Chi-square (w2)
400 Weibull distribution
Mean power density [W/m2]
350
Observed data
300 250 200 150 100 50 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months
Fig. 6. Monthly variation of the mean power densities depending on Weibull function and observed data.
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1750
250 Weibull distribution Observed data
Mean power density [W/m2]
200
150
100
50
0 Winter
Spring
Summer
Autumn
Seasons Fig. 7. Seasonal variation of the mean power densities depending on Weibull function and observed data.
Power density distribution [W/m2]
20 Observed data Weibull distribution Rayleigh distribution
15
10
5
0 0
5
10 15 Wind speed [m/s]
20
25
Fig. 8. Wind power density distributions according to observed data, Weibull and Rayleigh functions for the year 2004.
have different characteristics. In the higher wind speed conditions, a small variation of the wind speed can caused larger wind power predictions due to fact that the wind power is proportional to the cube of wind speed. Therefore, it should be preferred to choose a suitable model in order to make a correct estimation at the higher wind speeds.
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5. Conclusions In this study, assessments of the wind characteristics and wind power potential in Kırklareli located northwest Marmara region using Weibull and Rayleigh functions for the year 2004 were made. The following conclusions can be drawn from the results of the present study. 1. The mean wind speed for the Kırklareli wind observation station at a height of 10 m above the ground level was calculated as 4.68 m/s while the maximal value of the wind speed value in the investigated site was recorded as 20.15 m/s for the year 2004. Observed data shows that the maximum monthly wind speed takes place in the month of January. 2. Weibull parameters, k and c in the site year were calculated as 1.75 and 5.25 m/s, respectively for the year 2004. The monthly value of Weibull parameters k and c in the site varies between 1.38 and 2.41, 4.06 and 6.90, respectively. 3. Weibull distribution function calculated for the investigated location was found to be the more suitable than Rayleigh distribution function according to w2 and root meansquare error (RMSE) controls. 4. The mean power densities calculated using observed data and Weibull function were calculated as 142.75 and 138.85 W/m2, respectively for the year 2004. The highest mean power density regarding observed data was calculated as 332.88 W/m2 in the month of January for the related year. 5. The current study is an investigation study in order to estimate wind energy potential of Kırklareli province located northwest Marmara region, Turkey. Consequently, the location investigated in this study can be evaluated as marginal area for economical electrical energy generation.
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Renewable Energy 32 (2007) 1739–1752 www.elsevier.com/locate/renene
Data Bank
Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey Murat Go¨kc- eka,, Ahmet Bayu¨lkenb, S- u¨kru¨ Bekdemira a
Faculty of Mechanical Engineering, Yildiz Technical University, Besiktas, 34349 Istanbul, Turkey b Institute of Energy, Istanbul Technical University, Maslak, 34469, Istanbul, Turkey Received 6 July 2006; accepted 27 November 2006 Available online 24 January 2007
Abstract Utilization of wind energy as an energy source has been growing rapidly in the whole world due to environmental pollution, consumption of the limited fossil fuels and global warming. Although Turkey has fairly high wind energy potential, exploitation of the wind energy is still in the crawling level. In the current study, wind characteristics and wind energy potential of Kırklareli province in the Marmara Region, Turkey were analyzed taking into account the wind data measured as hourly time series. The wind data used in the study were taken from Electrical Power Resources Survey and Development Administration (EIEI) for the year 2004. The measured wind data were processed as annual, seasonal and monthly. Weibull and Rayleigh probability density functions of the location are calculated in the light of observed data and Weibull shape parameter k and scale parameter c are found as 1.75 and 5.25 m/s for the year 2004. According to the power calculations done for the site, annual mean power density based on Weibull function is 138.85 W/m2. The results indicate that investigated site has fairly wind energy potential for the utilization. r 2007 Elsevier Ltd. All rights reserved. Keywords: Wind energy; Weibull distribution; Wind power; Kırklareli; Turkey
1. Introduction Utilization of energy is an important indicator of social development and economic growth. A portion of the world’s electricity energy requirement has been supplied by thermal power plants that use hydrocarbon fuels. As known, anthropogenic emissions Corresponding author. Tel.: +90 212 259 70 70/2490; fax: +90 212 261 66 59.
E-mail address: [email protected] (M. Go¨kc- ek). 0960-1481/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.renene.2006.11.017
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released from various sources such as CO2, NOx and SOx have been causing negative effects (global warming, ozone layer depletion, acid rains, etc.) on the atmosphere. Renewable energy sources are inexhaustible, clean and free. These sources offer many environmental and economical benefits in contrast to conventional energy sources (hard coal, lignite, natural gas, etc.). Thus, recently, the role of renewable resources such as wind, solar, and geothermal energy has been growing by leaps and bounds within other resources as their generating costs decrease. Although mankind has been using the wind energy since ancient times, its utilization to produce electricity effectively is over the last two decade by means of modern wind turbine [1]. Today, wind energy is widely used to produce electricity in many countries such as Germany, Spain, United States, India, and Denmark. Total wind power installed in Europe that is equivalent to 69% of the worldwide wind energy generating capacity, is 40,504 MW at the end of 2005. It is expected that the installed capacity in Europe will have reached 70,000 MW by the end of 2010. Wind power installed capacity is 59,322 MW for the whole world at the end of 2005 [2]. Turkey as a bridge between Europe and Asia Continent has been developing economically and technologically day-by-day. Electrical energy in Turkey is mainly produced by thermal and hydroelectric power plants. Because of limited energy sources, Turkey is heavily dependent on imported oil and gas. The primary energy consumption of Turkey is about 87.7 million tones of equivalent oil (Mtep) according to 2004 records of the Turkish Ministry of Energy and Natural Resources [3]. Utilization of renewable energy as indigenous source in the electricity generation is an important fact for Turkey in terms of both security of energy supply and environmental concerns. When it comes to Turkey’s situation pertaining to wind energy exploitation, it can be seen that Turkey is rather unsuccessful in using its potential. Turkey’s wind energy installed capacity was 20.1 MW at the end of 2005 [4]. At present, there are five wind power plants having total 50.1 MW installed capacity and 54 wind turbines. The biggest present wind energy power plant (BARES wind energy plant) with 20 wind turbines and 30 MW installed power, in Turkey was built at Bandırma-Balıkesir in 2006 [5]. Determining of wind energy potential for the selected site is made by investigating detailed knowledge of the wind characteristics, such as speed, direction, continuity, and availability. Thus, proper wind turbine selection and micrositting process for the wind power plants are obtained. In the last decade, a lot of studies related to the wind characteristics and wind power potential have been made in many countries worldwide [6–19]. Sahin et al. [6] investigated the wind power potential for selected seven different sites in the southern Anatolia. Their results show that at 25 m height above the ground level, the mean wind energy potentials reach 500 W/m2 in many investigated areas. The most suitable location among investigated areas is Samandag˘. Karsli and Gecit [7] evaluated the wind power potential of Nurdag˘ı-Gaziantep in the southern Turkey and determined mean wind speed of 7.3 m/s at 10 m height above the ground level. They concluded that highest speed value for Nurdag˘ı is 23.3 m/s and mean power density is 222 W/m2. Akpinar et al. [8] carried out a study using Weibull density function to demonstrate wind energy potential of Maden-Elazıg˘ in eastern Turkey. Results reveal that the mean speed for investigated site varies between 5 and 6 m/s and yearly average power density is 244.65 W/m2. A study of wind energy potential estimation and micrositting process for campus area of Izmir Technology Institute is made by Ozerdem and Turkeli [9]. The wind data used in the study is collected at 10 and 30 m heights for a period of 16 months. While the mean wind speeds at 10 and 30 m heights are 7.03 and 8.14 m/s,
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respectively, prevailing wind direction is determined as north. In addition to wind characteristics, they created wind energy map for campus area. Li and Li [10] analyzed wind potential and characteristics of Waterloo region, Canada as annual, seasonal, monthly, and diurnal for the five years period (1999–2003). The results of the study show that the windy months in Waterloo are from November to May, with February being the windiest month. The day and night time wind power densities in the cold season of Waterloo are calculated as 180 and 111 W/m2, respectively. Turkey has important wind energy potential especially in the Marmara region, coasts of western and southern Anatolia. The main purpose of this study is to investigate the wind energy potential of Kırklareli province in the northwestern Marmara region, Turkey. Wind data at 10 m height above the ground level related to the selected site were taken from EIE (Electrical Power Resources Survey and Development Administration) for the year 2004. 2. Wind data collection In this study, wind speed data measured as hourly time-series for the year 2004 in Kırklareli, located in Thrace part of Marmara region, were statistically analyzed. Fig. 1. shows the location of chosen site (411490 N; 271140 E) in the Marmara region. Kırklareli province is located in the northwest Marmara region. The Marmara region is affected by mild Mediterranean climate during the summer and experiences a dry and hot period about 4–5 months in a year. Besides, the region is exposed to high pressure of Siberia and the Balkan Peninsula during the winter season. Consequently, this region is influenced by the winds blowing from the directions of north and west [20]. All measurements in the wind observation station are recorded using the cup anemometer at a height of 10 m above the ground level.
Fig. 1. Location of Kırklareli wind observation station (K).
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3. Calculation methodology Knowledge of the wind speed distribution is a very important factor to evaluate the wind potential in the windy areas. In addition to speed distribution, meteorological data and topographical information for considered site have same importance. If ever the wind speed distribution in any windy site is known, the power potential and the economic feasibility belonging to the site can be easily obtained. Wind data obtained with various observation methods has the wide ranges. Therefore, in the wind energy analysis, it is necessary to have only a few key parameters that can explain the behavior of a wide range of wind speed data. The simplest and most practical method for the procedure is to use a distribution function. There are several density functions, which can be used to describe the wind speed frequency curve. The most common two are the Weibull and Rayleigh functions. The studies done using these functions can be found in literature [6–19]. 3.1. Weibull and Rayleigh distribution function The Weibull distribution function that is a special case of generalized gamma distribution for wind speed is expressed with Eq. (1). k vk1 v k f w ðvÞ ¼ exp , (1) c c c where v is the wind speed, c is a Weibull scale parameter in m/s and k is a dimensionless Weibull shape parameter. Besides, the cumulative probability function of the Weibull distribution is calculated as below v k F w ðvÞ ¼ 1 exp . (2) c There are several methods of determining Weibull k and c parameters, such as leastsquare fit to observed distribution method, mean wind speed-standard deviation method, etc. In this study, the two parameters, k and c, are obtained using Eqs. (3) and (4), namely using mean wind speed-standard deviation method [21]. s1:086 k¼ ð1pkp10Þ, (3) v¯ c¼
v¯ , Gð1 þ 1=kÞ
(4)
where v¯ is the mean wind speed and is calculated using Eq. (5), s is the standard deviation and is calculated using Eq. (6) [11]. ! n 1 X v¯ ¼ vi , (5) n i¼1 "
n 1 X s¼ ðvi v¯ Þ2 n 1 i¼1
#0:5 ,
(6)
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where n is the number of hours in the period of the considered time such as month, season or year. The mean speed value for the density function f(v) can be expressed by equation given below. Z 1 v¯ ¼ vf ðvÞ dv. (7) 0
If Eq. (1) is used in the Eq. (7), the mean speed is obtained as Eq. (8). Z 1 k1 vk v v k v¯ ¼ exp dv. c c c 0
(8)
If a variable change is done using x ¼ (v/c)k in the above equation, the mean speed is obtained as Z 1 v¯ ¼ c x1=k expðxÞ dx. (9) 0
The gamma function, G(y) for any y value, is usually written in the below form Z 1 expðxÞxy1 dx. GðyÞ ¼
(10)
0
If the y value in the Eq. (10) is taken as y ¼ 1+1/k, consequently, Eq. (4) is obtained. Another distribution function used in determination of the wind speed potential is Rayleigh distribution. This distribution is a special case of Weibull distribution and validate situation where the dimensionless shape parameter k of the Weibull distribution is assumed to be equal to 2. Probability density and cumulative function of the Rayleigh distribution are given by Eqs. (11) and (12), respectively, pv p v 2 f R ðvÞ ¼ 2 exp , (11) 4 v¯ 2¯v p v 2 F R ðvÞ ¼ 1 exp . (12) 4 v¯ 3.2. Calculations of wind power The wind power per unit area in any windy site is of importance in assessing of the wind power projection for the power plants. The wind power density of the considered site per unit area based on any probability density function can be expressed as [11] Z 1 1 Pm ¼ r v3 f ðvÞ dv, (13) 2 0 where r is the standard air density, 1.225 kg/m3, v is the wind speed, m/s. In the current study, the power of the wind was calculated using Weibull function and observed data. When the Weibull function is chosen as distribution function f(v), the average wind power density is calculated as below 1 Gð1 þ 3=kÞ Pmw ¼ r¯v3 . 2 ½Gð1 þ 1=kÞ3
(14)
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3.3. Variation of wind speed with height The wind speed measurements to determine the wind potential in the site are generally made at the standard elevations such as 10 m. The wind data also used in this study is measured at 10 m height above ground level. The wind speed is proportional to elevation. Therefore, it is necessary to know the wind speeds at the various turbine hub heights for the wind farm projects. The power law [22], Eq. (15), is used to obtain the extrapolated wind speeds for Kırklareli observation station. a h v ¼ v0 , (15) h0 where v0 is the original wind speed recorded at anemometer height (h0), v is the wind speed to be determined for the desired heights (h), a is the power law exponent and depends on the surface roughness. In this study, power law was calculated using the 0.14 value as a exponent. 4. Results and discussion 4.1. Mean wind speed Fig. 2 shows the monthly mean wind speeds in the site for the year 2004. As can be seen in the Fig. 2, the mean wind speed varies between 3.74 and 6.17 m/s. The maximum value of the mean wind speed is in the month of January while the minimum value is in the month of November. Table 1 shows the seasonal and annual mean wind speeds for the investigated site. In general, higher mean wind speeds were observed in the course of the winter season and lower values in the course of autumn season. The winter season has 8 7
Mean wind speed [m/s]
6 5
6.17 5.22 5.32 4.89
4.88
4.75 4.39
4
3.88
4.88
4.20 3.79 3.74
3 2 1 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months
Fig. 2. Monthly mean wind speeds in the site for the year 2004.
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Table 1 Seasonal and annual mean wind speed for the 2004 data Period (season and year)
Mean wind speed (m/s)
Winter Spring Summer Autumn Annual
5.32 5.09 4.15 4.13 4.68
Table 2 Monthly and annual mean wind speed (m/s) at different heights above ground level Kırklareli wind observation station
Months
Annual mean
Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. Wind speed (m/s)
a b c d
7.20 7.49 7.93 8.26
5.20 5.94 6.28 6.54
6.09 6.34 6.71 6.98
6.20 6.46 6.84 7.12
5.54 5.77 6.10 6.36
4.53 4.71 4.99 5.19
5.12 5.33 5.64 5.87
4.90 5.10 5.40 5.62
5.69 5.93 6.27 6.53
4.42 4.60 4.87 5.07
4.36 4.54 4.81 5.00
5.69 5.93 6.27 6.53
5.45 5.67 6.00 6.25
a, b, c and d rows in the table show the mean wind speeds for extrapolation heights, 30, 40, 60 and 80 m, respectively.
the biggest mean value with 5.32 m/s compared to other seasons. The maximal value of the wind speed in the investigated site is measured as 20.15 m/s in the month of February. In addition, in order to evaluate the variation of the wind speed with elevation, the mean speed values were calculated using the power law both monthly and annually for the various heights (30, 40, 60 and 80 m) in the wind observation station. Obtained results are presented in Table 2. Annual mean wind speed at the 80 m height is determined as 6.25 m/s while the mean wind speed for the month of January is 8.25 m/s. 4.2. Probability density functions Probability density functions such as Weibull or Rayleigh function are usually used to determine the wind speed distribution of a windy site in a period of time. In the current study, determination of wind speed distributions for the investigated site was made using Weibull and Rayleigh probability density functions. Fig. 3 reveals Weibull with the two parameters and Rayleigh distributions derived from observed data for the year 2004. As seen in this figure, the top point of the curve is the most frequent wind speed. The peak probability values vary between 0.15 and 0.165 depending on the wind speeds for the considered distribution functions (Weibull, Rayleigh and actual probability distribution). Shape (k) and scale (c) parameters of the Weibull function were calculated using the method mentioned in the earlier section. The results of the calculation show that dimensionless shape parameter k is 1.75 while scale parameter c is 5.25 m/s for the site analyzed in the year 2004. In order to evaluate the probability density function based on seasons, Weibull and Rayleigh distributions for each season are obtained for the year 2004. The seasonal probability density distributions derived from the observed distribution for the wind
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0.20
Probability density distribution
Observed data Weibull distribution Rayleigh distribution
0.15
0.10
0.05
0.00 0
5
10 15 Wind speed [m/s]
20
25
Fig. 3. Wind speed frequency distributions in the site for the year 2004.
speeds are shown in the Fig. 4. The peak probability values in the winter season vary about 0.125 and 0.145 depending on the wind speed for the all calculated distribution functions. These values for the autumn season become between about 0.15 and 0.185. In the Table 3, seasonal Weibull parameters (k and c) and the standard deviations are summarized. As seen from this table, Weibull shape parameter k varies between 1.57 and 2.21 while scale parameter c varies between 4.56 and 5.93 m/s. In addition, Weibull parameters and the standard deviations for each month of the year 2004 are presented in Table 4. The range of Weibull shape parameter k is between 1.38 and 2.41, while the scale parameter c values vary from 4.06 to 6.90 m/s. The lowest c value is in the month of November and the highest value is in the month of January. The lowest standard deviation is calculated in the month of June as 1.81 m/s. The cumulative probability distributions for considered functions are shown in Fig. 5 for the year 2004. To evaluate the performance of the considered distributions, mean root-square error (RMSE) parameter and Chi-square (w2) test are used in the study. Detailed information related to the evaluation parameters can be found in literature [8,10,17]. For the Weibull distribution function related to yearly data, RMSE value is 0.0031, while the w2 value is 0.01. On the other hand, these values for Rayleigh distribution function are 0.0073 and 0.1959, respectively. The smaller the value of the RMSE and w2 is, the better the calculated distribution function approximates to the observed data. According to RMSE parameter and w2 test calculations, Weibull distribution function obtained for the investigated site is the more suitable than Rayleigh distribution function. Some advantages of the Weibull distribution are listed in Ref. [21]. The comparison of the Weibull and Rayleigh distributions with the observed probability distribution of the wind speeds is given in Table 5 for the winter season. As it can be read in the Table 5, RMSE value regarding Weibull distribution is 0.0034 for the winter season. Result of the w2 test is also 0.000269.
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0.20 Observed data Weibull distribution Rayleigh distribution
0.15
Probability density distribution
Probability density distribution
0.20
1747
0.10
0.05
0.15
0.10
0.05 Spring
Winter
0.00
0.00 0
5
10 15 Wind speed [m/s]
20
25
0
10 15 Wind speed [m/s]
20
25
0.20 Probability density distribution
Probability density distribution
0.20
5
0.15
0.10
0.05
0.15
0.10
0.05
Summer
Autumn
0.00
0.00 0
5
10 15 Wind speed [m/s]
20
25
0
5
10 15 Wind speed [m/s]
20
25
Fig. 4. Seasonal wind speed frequency distributions in the site for the year 2004.
Table 3 Seasonal Weibull parameters and standard deviations for Kırklareli station Parameters
k c (m/s) s
Seasons Winter
Spring
Summer
Autumn
1.59 5.93 3.47
2.20 5.74 2.55
2.21 4.83 2.06
1.57 4.56 2.72
4.3. Wind power density Fig. 6 shows monthly variations for the mean power density that is calculated using both observed data and Weibull function for the year 2004. As seen from Fig. 6, the mean wind power densities decrease from January to June. In the second half of the year 2004, the
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Table 4 Monthly and annually Weibull parameters and standard deviations for Kırklareli station Parameters
k c (m/s) s
Months
Annual
Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
1.70 6.90 3.78
1.46 5.39 3.44
2.16 5.88 2.56
1.99 6.00 2.82
2.27 5.35 2.22
2.28 4.36 1.81
2.41 4.36 1.95
1.87 4.73 2.36
2.07 5.50 2.49
1.42 4.17 2.73
1.38 4.06 2.76
1.70 5.45 2.98
1.75 5.25 2.79
1
Cumulative distribution
0.8 Observed data Weibull distribution Rayleigh distribution
0.6
0.4
0.2
0 0
5
10 Wind speed [m/s]
15
20
Fig. 5. Wind speed cumulative probability distributions in the site for the year 2004.
changes of the mean power density show almost similar characteristics. The highest mean power density of 332.88 W/m2 regarding actual data is calculated in the month of January while the lowest is in the month of June with the value of 60.83 W/m2. In addition, in the Fig. 7 seasonal variation of the mean power density is presented. As can be seen in Fig. 7, the value of the mean power density for the winter season is 247.63 W/m2 while this value is 80 W/m2 in summer for the actual data. Mean power density of the year 2004 regarding actual data and Weibull function are calculated as 142.75 and 138.85 W/m2. The wind power density distributions that are calculated using Weibull and Rayleigh functions and actual data are shown in Fig. 8. As is apparent from this figure, the wind power density for the wind speeds between 7.5 and 8.5 m/s is calculated as about 16.5 W/m2 regarding the actual data. On the other hand, the wind power density according to Weibull distribution is calculated as about 17.5 W/m2. At the lower wind speeds, all wind power density distributions according to considered functions exhibit similar characteristics. Whereas, at the higher wind speeds, the wind power density distributions
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Table 5 Wind speeds, probability densities, w2 and RMSE values for the winter season in the site i
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Speed class ui (m/s)
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 18–19 19–20 20–21
Probability distribution Observed data
Weibull
Rayleigh
0.0567 0.1140 0.1250 0.1227 0.1172 0.1021 0.0906 0.0677 0.0517 0.0444 0.0338 0.0251 0.0233 0.0059 0.0050 0.0054 0.0018 0.0032 0.0032 0.0000 0.0004
0.0610 0.1064 0.1250 0.1275 0.1196 0.1057 0.0890 0.0721 0.0564 0.0427 0.0314 0.0225 0.0158 0.0108 0.0072 0.0047 0.0030 0.0019 0.0012 0.0007 0.0004
0.0275 0.0782 0.1166 0.1382 0.1424 0.1318 0.1117 0.0874 0.0636 0.0431 0.0274 0.0163 0.0091 0.0048 0.0024 0.0011 0.0005 0.0002 0.0001 0.0000 0.0000
3.4 103 2.694 104
1.55 102 5.309 103
Root mean-square error (RMSE) Chi-square (w2)
400 Weibull distribution
Mean power density [W/m2]
350
Observed data
300 250 200 150 100 50 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months
Fig. 6. Monthly variation of the mean power densities depending on Weibull function and observed data.
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250 Weibull distribution Observed data
Mean power density [W/m2]
200
150
100
50
0 Winter
Spring
Summer
Autumn
Seasons Fig. 7. Seasonal variation of the mean power densities depending on Weibull function and observed data.
Power density distribution [W/m2]
20 Observed data Weibull distribution Rayleigh distribution
15
10
5
0 0
5
10 15 Wind speed [m/s]
20
25
Fig. 8. Wind power density distributions according to observed data, Weibull and Rayleigh functions for the year 2004.
have different characteristics. In the higher wind speed conditions, a small variation of the wind speed can caused larger wind power predictions due to fact that the wind power is proportional to the cube of wind speed. Therefore, it should be preferred to choose a suitable model in order to make a correct estimation at the higher wind speeds.
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5. Conclusions In this study, assessments of the wind characteristics and wind power potential in Kırklareli located northwest Marmara region using Weibull and Rayleigh functions for the year 2004 were made. The following conclusions can be drawn from the results of the present study. 1. The mean wind speed for the Kırklareli wind observation station at a height of 10 m above the ground level was calculated as 4.68 m/s while the maximal value of the wind speed value in the investigated site was recorded as 20.15 m/s for the year 2004. Observed data shows that the maximum monthly wind speed takes place in the month of January. 2. Weibull parameters, k and c in the site year were calculated as 1.75 and 5.25 m/s, respectively for the year 2004. The monthly value of Weibull parameters k and c in the site varies between 1.38 and 2.41, 4.06 and 6.90, respectively. 3. Weibull distribution function calculated for the investigated location was found to be the more suitable than Rayleigh distribution function according to w2 and root meansquare error (RMSE) controls. 4. The mean power densities calculated using observed data and Weibull function were calculated as 142.75 and 138.85 W/m2, respectively for the year 2004. The highest mean power density regarding observed data was calculated as 332.88 W/m2 in the month of January for the related year. 5. The current study is an investigation study in order to estimate wind energy potential of Kırklareli province located northwest Marmara region, Turkey. Consequently, the location investigated in this study can be evaluated as marginal area for economical electrical energy generation.
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