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Wind resource assessment of Northern Cyprus
Renewable and Sustainable Energy Reviews 55 (2016) 180–187
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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser
Wind resource assessment of Northern Cyprus Davut Solyali a,n, Mustafa Altunç b, Süleyman Tolun c, Zafer Aslan d a
Eastern Mediterranean University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Famagusta, Mersin 10, Turkey University of Kyrenia, Faculty of Maritime Studies, Nicosia, Turkey University of Kyrenia, Faculty of Aeronautics and Astronautics, Nicosia, Turkey d Aydın University, Faculty of Engineering, Department of Computer Engineering, Florya, 34295 Istanbul, Turkey b c
art ic l e i nf o
a b s t r a c t
Article history: Received 22 March 2015 Received in revised form 11 August 2015 Accepted 26 October 2015 Available online 21 November 2015
This paper presents a technical assessment of wind power potential for Selvilitepe site in Northern Cyprus. The wind speed data was collected for 10 min intervals between years 2007 and 2014 at this site. Weibull distribution method using 3 different algorithms called maximum likelihood, least squares and WAsP was used for the statistical analysis of the measured data. Power law exponent method was used to create diurnal and monthly averaged wind speed variations at the heights of 50 m, 80 m and 90 m. Based on the determined standard deviation of the wind speed, turbulence category of this site is calculated and categorized. Shear profile and surface roughness of this site has also been analyzed and determined. & 2015 Elsevier Ltd. All rights reserved.
Keywords: Cyprus Selvilitepe Weibull distribution Wind assessment
Contents 1. 2. 3.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Region and data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Wind speed profile assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1. Maximum likelihood method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2. Least squares method (graphical method) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3. WAsP method (equivalent energy method) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Turbulence intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Shear profile and surface roughness. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1. Log law. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2. Power law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Results and discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Site location and data collection at Selvilitepe in Northern Cyprus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Wind speed frequency distribution of Selvilitepe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Turbulence intensity of Selvilitepe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4. Shear profile and surface roughness of Selvilitepe. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5. Wind speed variations of Selvilitepe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6. Summary for Selvilitepe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
180 181 181 181 182 182 183 184 184 184 184 184 184 184 185 185 185 186 186 186
1. Introduction
n
Corresponding author. E-mail addresses: [email protected] (D. Solyali), [email protected] (M. Altunç), [email protected] (S. Tolun), [email protected] (Z. Aslan). http://dx.doi.org/10.1016/j.rser.2015.10.123 1364-0321/& 2015 Elsevier Ltd. All rights reserved.
In Northern Cyprus, increasing population, rising life standards, and rapidly growing tourism and industry sectors have led to increased energy demands. Being an isolated energy system, this increase in energy demand is causing a high degree of dependence
D. Solyali et al. / Renewable and Sustainable Energy Reviews 55 (2016) 180–187
on imported fuel. Owing to the increasing cost of energy supply, limited oil storage capacity, and the need for environmental preservation (i.e. reduction of green house gas emissions, conservation of the natural and visual beauty of the island), Northern Cyprus is looking into effective exploitation of renewable sources besides its installed conventional power stations. The objective of this study is to assess the wind resource and determine the wind characteristics at Selvilitepe site in Northern Cyprus. Actual measured data for seven years in 10 min intervals was used. The study determined the Weibull parameters at 30 m and 90 m heights, turbulence parameters, power density, wind power class, power law exponent, surface roughness, and roughness class of the site. Carrasco-Díaz et al. [1] demonstrated a spatial and temporal wind potential estimation using existing reanalysis data sets using the Wind Atlas Analysis and Application (WAsP) software for the cost calculation at Tamaulipas, Northeastern Mexico. According to the study the reanalysis wind product and observed wind assessment had a good agreement between the two at several meteorological stations located throughout the state. However, the need for high quality wind data measurement at different locations and altitudes for better estimation of wind resource potential is also emphasized. In order to improve the wind potential estimation, Medimorec and Tomšić [2] used portfolio theory on locations with multiple wind measurements. Their study showed that the method reduced both the uncertainty and error, even when using least accurate and most uncertain measurements. Without wind monitoring equipment at each potential installation site, local authorities or project developers can only rely on low resolution wind speed databases to identify the best sites. In the UK, the most frequently used databases, the DECC wind speed database (NOABL) wind estimator tend to provide an inaccurate estimates with a mean error of 23% over the 73 sites out of 91 [3]. Therefore, long term data, at least of two years is required for more accurate assessments. Study conducted by Hernandez-Escobedo et al. [4] performed wind resource assessment, both spatially and temporally throughout the day and months of at least two years for the Northern Mexican states, except at Baja California Norte. Most common practice when assessing the wind energy potential of a particular site is to use Weibull distribution methods [5]. Study conducted by Irwanto, Gomesh and Yusoff [6] analyzed the wind speed characteristics and calculated the wind power generation potential using Weibull distribution function at Chuping and Kangar in Perlis, Malaysia. The wind power and energy potential were presented as functions of tower height. There are several Weibull distribution methods. Chandel, Ramasamy and Murthy [7] provided a summary of the comparison highlights of some of the statistical methods to assess wind speed and power potential of a location. The discussed methods were maximum likelihood method, modified maximum likelihood method, equivalent energy method, graphical method, moment method, empirical method and wind energy pattern factor method (WEPF). These methods are used to estimate wind power density (WPD) by evaluating Weibull parameters k and c. Also, Rayleigh distribution method, which is a special case of the Weibull distribution, can be used [3]. For example, Bataineh and Dalalah [8] presented a technical assessment of wind potential for several locations in Jordon using Rayleigh distribution method using the monthly averaged data. Their study also demonstrated use of seventh power law to estimate the power output according to the selection for wind turbines and their corresponding hub height. Furthermore, Weibull distribution analysis can be expanded upon using wavelet analysis methods. Janajreh, Su and Alan [9] subjected annual records of different heights at different temporal resolutions at Masdar city to linear regression, spectrum and
181
wavelet analysis. Then the data was modeled using the maximum likelihood method of Weibull distribution method. The data was subjected to FFT spectrum analysis. As the intermittency was identified for the collected data, wavelet analysis was further explored to remedy the short coming of the FFT spectrum.
2. Region and data Study analyzing the wind potential of Northern Cyprus was conducted at various other sites such as Sınırüstü, Kalecik, Yenierenköy, Sadrazamköy and Taşkent by Altunc et al. [10] to extend the cover of measured data to wider regions of the Northern Cyprus. The study has demonstrated seasonal variations of wind speed and energy potentials at the listed stations in Northern Cyprus for the period between 1998 and 2003. Table 1 is presenting the summary of findings of the study at the height of 30 m for each site. At the moment, there are no wind farms either under construction or operational in Northern Cyprus. However, following the initiatives of the European Directives, following the considerations and approval process under The Clean Development Mechanism (CDM) protocols, various wind generation projects have been privately funded in the Southern Cyprus. The CDM is under the authority and guidance of the parties to the Kyoto Protocol. The information regarding these projects is currently publicly available via United Nations Framework Convention on Climate Change (UNFCCC) website for CDM projects. The project design documents of several registered wind farm projects in Southern Cyprus, namely Orites, Kambi, Stivo, Klavdia, Agiaanna, Alexigros and Mari, are used and condensed into Table 3 [12–18]. It can be observed that short term measured mean wind speeds at high altitudes range from 4.7 m/s to 6.7 m/s yielding capacity factors of wind farms from 16% to 25%. Also, it should be noted that the biggest wind farm is the Orites wind farm with 144 MW of capacity. Currently, this is the only wind farm that has been operating for at least 1 year [11]. The over layered picture of Cyprus is given in Fig. 1. The figure shows the locations of the measurement sites presented in Table 1 and the measurement site of this study, Selvilitepe. In addition, the figure shows the locations and sizes of the CDM registered wind farms given in Table 3.
3. Methodology 3.1. Wind speed profile assessment Wind speed profile assessment studies typically use the Weibull distribution to characterize the breadth of the distribution of wind speeds. The following equations give the probability distribution function and the cumulative distribution function of the Table 1 Mean wind speeds at various measurement sites. Site name
Site location
Kalecik Sınırüstü Yenierenköy Sadrazamköy Taşkent
N N N N N
34°330 35°290 35°540 35°380 35°250
E E E E E
33° 33° 34° 32° 33°
Average wind speed (m/s) 980 850 240 950 390
2.6 3.1 4.3 4.6 3.8
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D. Solyali et al. / Renewable and Sustainable Energy Reviews 55 (2016) 180–187
two parameter Weibull distribution [1–9,19,20]. " # k U k1 U k f ðU Þ ¼ :exp c c c " F ðU Þ ¼ 1 exp
k # U c
ð1Þ
ð2Þ
The following equation gives the relationship between the scale factor c and the long-term average wind speed: 1 ð3Þ U ¼ cГ þ 1 k The Weibull k value reflects the breadth of the distribution; the broader the distribution, the lower the value of the Weibull k. Fig. 2 shows several Weibull distributions, all with an average wind speed of 7 m/s, but with the Weibull k value varying from 1.5 to 3.5.
The Weibull distribution often fits measured wind speed distributions well. Fig. 3 illustrates a measured wind speed distribution along with the best-fit Weibull distribution. According to the figure, the best-fit curve is when k ¼1.5 and c ¼6 m/s. Therefore, the long term measured data can be characterized by using only two parameters c and k. Three different algorithms or methods used in this study are called maximum likelihood, least squares and WAsP. 3.1.1. Maximum likelihood method The maximum likelihood method fits a Weibull distribution to a set of measured wind speeds [5,,21]. This method employs the following equation to calculate, in an iterative fashion, the Weibull k parameter: 0
N P
B B k ¼ Bi ¼ 1 N @ P
i¼1
Table 2 Nomenclature. Nomenclature U U U(z) U* Ui c k Г N X
Wind speed (m/s) Average wind speed (m/s)
The wind speed (m/s) at some height above ground z (m) The friction velocity (m/s) Wind speed in time step i Weibull c factor (m/s) Weibull k factor (dimensionless) Gamma function Number of time steps Proportion of the observed wind speeds that exceed the mean observed wind speed WPD Mean power density (W/m2) v1 Average wind speed at some height h1 above ground (m) v2 Average wind speed at some height h2 above ground (m) h1 Height above ground (m) h2 Height above ground (m) α Power law exponent ρ Air density (kg/m3) σi Standard deviation of the wind speed within time step i I Turbulence intensity (dimensionless) κ The Von Karman's constant (0.4) z0 The surface roughness (m)
U ki lnðU i Þ U ki
11 lnðU i ÞC C i ¼ 1 C A N N P
ð4Þ
Once the shape parameter k has been found, the following equation gives the value of the scale parameter c: 0
11k k U iC B B C c ¼ Bi ¼ 1 C @ N A N P
ð5Þ
After establishing the parameters c and k that describe the Weibull function and therefore the behavior in terms of wind speed, the wind potential was determined from the studies area by calculating the wind power available multiplied by the number of hours included in the study. 3.1.2. Least squares method (graphical method) Some algebraic manipulations are required in order to be able to use a linear least squares algorithm to fit a Weibull distribution to measure wind speed data [5,,7,,21]. Eq. (2) can be re-arranged as follows: ln½1 F ðU Þ ¼
k U c
Fig. 1. Map of Cyprus over layered with the measurement sites from Table 1, CDM registered wind farms from Table 2 and measurement site of Selvilitepe.
ð6Þ
D. Solyali et al. / Renewable and Sustainable Energy Reviews 55 (2016) 180–187
183
Table 3 List of CDM registered projects. Wind farm
KAMBI
STIVO
KLAVDIA
AGIA-ANNA
ALEXI-GROS
ORITES
MARI
Province Total capacity (MW) Measured site wind speed
Nicosia 9.6 5.7 m/s at 50 m 18.70 1638 15,726
Larnaca 27 NA
Larnaca 42 5.82 m/s at 8 m 16.19 1419 59,580
Larnaca 20 5.85 m/s at 78 m 17.61 1540 30,860
Larnaca 34.5 6.7 m/s at 85 m 25.10 2202 76,002
Paphos 144 5.85 m/s at 85 m 16.90 1485 213,920
Larnaca 12 4.9 m/s at 50 m 20.18 1768 21,219
Capacity factor (%) Annual production per each MW installed capacity (MWh) Wind farm annual production (MWh)
16.70 1459 39,385
Weibull Probability Density Function
Best-fit Weibull Distribution
0.2
8
0.18
7
0.14
Frequency (%)
Probability
0.16 0.12 0.1 0.08 0.06 0.04
6 5 4 3 2
0.02
1
0 1
4
7
10
13
16
19
22
25
0
Wind Speed (m/s) k=1.5
k=2.0
k=2.5
0 k=3.0
The equation can be re-written as follows: k 1 U ln ¼ 1 F ðU Þ c
Using the rule: A ¼ ln A ln B ln B Eq. (9) can be re-written as: 1 ¼ k lnU k lnc ln ln 1 F ðU Þ
6
k=3.5
8
10
12
14
16
18
20
22
24
Actual Data
Best Fit Weibull distribution (k=1.5, c=6 m/s)
Fig. 3. Demonstration of best-fit Weibull distribution to actual data by using two parameters.
ð7Þ
Taking the natural logarithm of both sides: 1 U ¼ kln ln ln 1 F ðU Þ c
4
Wind speed (m/s)
Fig. 2. The breath of the distribution for varying k at an average wind speed of 7 m/s.
Using the rule: 1 ¼ ln A ln A
2
ð8Þ
ð9Þ
ð10Þ
ð11Þ
Eq. (11) is now in the general slope intercept form y ¼ mx þ b. Therefore, plotting lnðUÞ on the x-axis and ln n h io ln 1 1F ðU Þ on the y-axis will produce a straight line with slope equal to k and intercept equal to k lnc. Therefore, c is equal to intercept ð12Þ c ¼ exp slope 3.1.3. WAsP method (equivalent energy method) The WAsP method has defined a requirement for fitting the Weibull distribution to measured wind speed data [7,,20,,21,,24]. The WAsP algorithm, also known as equivalent energy method, does not attempt to directly fit the measured frequency histogram, but rather requires that:
1. The mean power density of the fitted Weibull distribution is equal to that of the observed distribution. 2. The proportion of values above the mean observed wind speed is the same for the fitted Weibull distribution as for the observed distribution. The following equation gives the mean power density (WPD) of the Weibull distribution, assuming constant air density: 1 3 WPD ¼ ρc3 Г þ 1 ð13Þ 2 k Also, an equation for the mean power density of the observed wind speeds, again assuming constant air density can be written as: WPD ¼
1 X 3 ρ U 2N N i
ð14Þ
Considering the first requirement, Eqs. (13) and (14) shall be equal: 3 1X 3 U ð15Þ c3 Г þ 1 ¼ k N N i Solving the above expression for c gives vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u N u P 3 u 3 Ui u
c ¼ t i ¼ 13 NГ k þ 1
ð16Þ
The cumulative distribution function F ðU Þ gives the proportion of values that are less than U, so 1 F ðU Þ is the proportion of values that exceed U. Considering the second requirement, a symbol X can be defined to represent the proportion of the observed wind
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D. Solyali et al. / Renewable and Sustainable Energy Reviews 55 (2016) 180–187
speeds that exceed the mean observed wind speed as follows:
1F U ¼ X ð17Þ Eq. (1), the cumulative distribution function of the Weibull distribution, holds for any wind speed U, therefore it holds for the mean wind speed. Rearranging Eq. (1) as 2 !k 3 U 5 ð18Þ 1 F U ¼ exp4 c By substituting for X, Eq. (18) can be rewritten as: 2 !k 3 U 5 4 X ¼ exp c Taking the logarithm on both sides gives: !k U ¼ lnðX Þ c
ð19Þ
ð20Þ
By substituting Eq. (16) into Eq. (20), the following expression, whose only unknown is k, is given: 0
1k
B C B C B C U BvffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiC ¼ lnðX Þ Bu P C N Bu C 3 U 3i A @t
In order to solve Eq. (21) for the parameter k, first X is calculated using Eq. (17). Then, Eq. (21) is calculated iteratively, using the Brent method. 3.2. Turbulence intensity The turbulence intensity (I), or TI, is a dimensionless number defined as the standard deviation of the wind speed within a time step divided by the mean wind speed over that time step. The turbulence intensity in each time step can be calculated using Eq. (22) [9,19], [22:40].
σi
The value of exponent α depends on surface roughness and atmospheric stability. The value of α is between the range of 0.05 and 0.5. For data sets containing wind speeds at a known height above ground, using Eq. (24), the vertical wind speed profile or the wind speed at a different height can be calculated.
4. Results and discussions
i ¼ 1
Ui
3.3.2. Power law The power law exponent (sometimes called the power law coefficient or simply ‘alpha’) is a number that characterizes the rate at which wind speed changes with height above ground. It is a parameter in the power law, which states that the wind speed varies with the height above ground according to the following equation: α v2 h2 ¼ ð24Þ v1 h1
ð21Þ
N Гð3k þ 1Þ
I¼
parameter in the logarithmic law, which states that the wind speed varies logarithmically with the height above ground according to the following equation: 8 < U ln z ; z 4 z 0 z0 κ U ðz Þ ¼ ð23Þ : 0; z 4 z0
ð22Þ
The 3rd edition of IEC 61400-1 provides a tabular reference for four turbulence categories based on mean turbulence intensity at a wind speed of 15 m/s [23]. In order to analyze turbulence, the actual data set for all the time steps is used. The data is then filtered or subdivided into bins. In this case, the data is filtered for mean speed of 15 m/s. For each bin, the set of turbulence intensity values is taken and their mean, standard deviation, and peak value are calculated. Then, the representative turbulence intensity for 3rd edition IEC standard is compared against these values. 3.3. Shear profile and surface roughness The variation in the wind speed with height above ground is called the wind shear profile. In the field of wind resource assessment, analysts typically use one of the two mathematical relations to characterize the measured wind shear profile: the logarithmic law (or log law) with its parameter called the surface roughness, and the power law, with its parameter called the power law exponent [22:43-45]. 3.3.1. Log law The surface roughness (sometimes called surface roughness length or just roughness length) is a measure of the rate at which the wind speed changes with height above ground. It is a
4.1. Site location and data collection at Selvilitepe in Northern Cyprus Selvilitepe is located at N 35°190 and E 33°90 at the height of 1018 m. The data was collected for 10 min intervals at 30 of height between years 2007 and 2014. The location of the site is also shown in Fig. 1. 4.2. Wind speed frequency distribution of Selvilitepe Wind speed frequency distribution at 30 m of collected (actual) data at Selvilitepe is presented in Fig. 4. The figure also represents the data using three other algorithms; maximum likelihood, least squares and WAsP. These algorithms employ their methods to fit the curve of the Weibull distribution of the actual data. The maximum likelihood method uses an iterative method introduced by Stevens and Smulders, 1979 in order to fit a Weibull distribution to a set of measured wind speeds [5]. Least squares algorithm, sometimes called the ‘graphical method’, works by transforming the axes of the cumulative distribution function so that a Weibull distribution would appear as a straight line, then finding the straight line that best fits the actual data [21]. WAsP algorithm, used by the WAsP wind flow model, finds the Weibull distribution that exactly matches that actual distribution in terms of two parameters: the mean wind power density and the proportion of values that exceed the measured mean [24]. Table 4 lists the resultant parameters for each algorithm. It can be seen that with R2 values from 0.993 to 0.998, the match to the actual data is very high. For example, among the algorithms, WAsP has the highest match with R2 at 0.998. The Weibull k value is calculated to be 1.581 and c value is 5.698. This corresponds to 206.7 W/m2 power density at Selvilitepe. Using the power law method, the actual data collected at the height of 30 m is synthesized to the height of 90 m at which most of the 1 MW or above capacity wind turbine height is. The 90 m synthesized data is presented in Fig. 5. As per the actual data at 30 m, the synthesized data is also matched with using maximum likelihood, least squares and WAsP methods.
D. Solyali et al. / Renewable and Sustainable Energy Reviews 55 (2016) 180–187
Wind Speed Frequency Distribution at 30m
Table 5 Weibull parameters of Selvilitepe at 90 m.
7 6
Frequency (%)
185
5
Algorithm
Weibull k Weibull c (m/s)
Mean (m/s)
Power density (W/m2)
R2
Maximum likelihood Least squares WAsP
1.563
6.628
5.956
331.6
0.978
1.504 1.581
6.714 6.646
6.059 5.965
368.8 327.9
0.973 0.979
4 3 2 1 0 0
2
4
6
8
10
12
14
16
18
20
22
24
Turbulence Intensity at 30 m
Wind speed (m/s) Maximum likelihood
Least squares
WAsP
Fig. 4. Wind speed frequency distribution at 30 m of Selvilitepe.
Table 4 Weibull parameters of Selvilitepe at 30 m. Algorithm
Weibull k Weibull c (m/s)
Mean (m/s)
Power density (W/m2)
R2
Maximum likelihood Least squares WAsP Actual data
1.563
5.107
209.0
0.997
5.195 5.115 5.118
232.5 206.7 206.7
0.993 0.998
Turbulence Intensity
Actual Data
1 0.8 0.6 0.4 0.2 0
5.683
1.504 5.756 1.581 5.698 (62,689 time steps)
0
3
7
11
15
19
23
Wind Speed (m/s) Representative TI IEC Category A
Mean TI IEC Category B
Fig. 6. Turbulence intensity at 30 m of Selvilitepe.
Wind Speed Frequency Distribution at 90 m Frequency (%)
7 6 5 4 3
Table 6 Turbulence parameters. Quantity
Value
Data points in 15 m/s bin Mean TI at 15 m/s Representative TI at 15 m/s IEC3 turbulence category
292 0.10 0.17 C
2 1 0 0
2
4
6
8 10 12 14 16 18 20 22 24 26 28
Wind speed (m/s) Actual Data
Maximum likelihood
Least squares
WAsP
Fig. 5. Wind speed frequency distribution at 90 m of Selvilitepe.
Table 5 tabulates the resultant parameters for each method. R2 values vary from 0.973 to 0.979. WAsP method being the highest match with 0.989 has Weibull k parameter value of 1.581 and c parameter value of 6.646 for mean speed of 5.965 m/s. Using WAsP method, it is calculated that the power density at synthesized height of 90 m at Selvilitepe is 327.9 W/m2. This value was observed to be 206.7 W/m2 at 30 m height using actual data.
4.3. Turbulence intensity of Selvilitepe Fig. 6 presents the turbulence intensity data at 30 m with respect to 3rd edition IEC categories A, B and C. The data set contained 292 time steps that contained valid data in which the wind speed fell within the 14.5–15.5 m/s range. The resulting values of turbulence intensity exhibited a mean value of 0.10 and a representative turbulence intensity of 0.17. Therefore, the IEC3
turbulence category falls into category C. Table 6 summarizes the turbulence parameters. 4.4. Shear profile and surface roughness of Selvilitepe Fig. 7 presents the vertical wind shear profile at Selvilitepe for various heights. The actual measured data at 30 m and power law fitted and log law fitted for further heights is graphed. It is calculated that power law exponent value is 0.14 and surface roughness is 0.04 m. This corresponds to the roughness class of 1.26 [25,26]. 4.5. Wind speed variations of Selvilitepe In Fig. 8, the diurnal cycle of wind speed averaged to a single day is presented. The actual data at 30 m and synthesized data by using power law at heights of 50 m, 80 m and 90 m is shown. During the day, the mean wind speeds forms a reversed bell shape. The day can be divided several time slots. Between 20:00 and 02:00 h, the mean wind speeds are the highest and vary between 6 and 6.2 m/s at a height of 30 m and 7–7.24 m/s at a height of 90 m. Between 09:00 and 15:00 h, the mean speeds are the lowest and vary between 3.8 and 3.95 m/s at a height of 30 m and 4.4– 4.9 m/s at a height of 90 m. Between 02:00 and 09:00 h the mean wind speeds are ramping down. Between 15:00 and 20:00 h, the wind mean speeds are ramping up. During ramping hrs, the mean wind speeds vary between 3.8 and 7 m/s at a height of 30 m and 4.9–7.24 m/s at a height of 90 m.
Height Above Ground (m)
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Vertical Wind Shear Profile
Table 7 Summary for Selvilitepe.
120 100 80 60 40 20 2
3
4
Power law fit
5
6
7
Log law fit
Measured data
Fig. 7. Vertical wind shear profile at Selvilitepe.
Mean Wind Speed (m/s)
Value
Power density at 50 m Wind power class Power law exponent Surface roughness Roughness class Mean at 90 m
221 W/m² 2 (Marginal) 0.14 0.0408 m 1.26 5.96 m/s
0
Mean Wind Speed (m/s)
crated in the area of India and expanded until the area of Cyprus during the summer; sea breezes generated in coastal areas as a result of the different heat capacities of sea and land, which give rise to different rates of heating and cooling; and mountain valley winds created when cool mountain air warms up in the morning and begins to rise while cool air from the valley moves to replace it. During the night the flow reverses.
Diurnal Wind Speed Variations 4.6. Summary for Selvilitepe
8 7
See Table 7.
6 5 4
5. Conclusions
3 2 1 0 0
4
8
12
16
20
24
Hour of Day Synthesized 90 m Synthesized 50 m
Synthesized 80 m Actual Data at 30m
Fig. 8. Diurnal wind speed profile at various heights of Selvilitepe.
Mean Wind Speed (m/s)
Variable
Monthly Wind Speed Variations 8 7 6
One way to reduce dependency on imported fuels is to implement renewable energy solutions such as wind energy in the northern part of the island. Therefore, wind energy analysis is essential for wind energy assessment studies. The data used in this study was collected for 10 min intervals between years 2007 and 2014. The mean speed varies diurnally as well as monthly. From the collected data and analysis, it is calculated that at 30 m power density is at 207 W/m2 and mean wind speed is 5.11 m/s, at 50 m power density is at 221 W/m2 and at 90 m power density is 329 W/m2 with mean speed of 5.96 m/s. The resulting values of turbulence intensity exhibited a mean value of 0.10 and a representative turbulence intensity of 0.17. Therefore, the IEC3 turbulence category falls into category C. Also, it is calculated that power law exponent value is 0.14 and surface roughness is 0.04 m. This corresponds to a value of 1.26 which is between roughness classes 1 and 1.5.
5 4
References
3 1
2
3
4
5
6
7
8
9
10
11
12
Months Synthesized 90 m
Synthesized 80 m
Synthesized 50 m
Actual Data at 30m
Fig. 9. Monthly averaged wind speed profile at various heights.
Fig. 9 presents the monthly averaged wind speed profile at various heights at Selvilitepe. The actual data was measured at a height of 30 m and the remaining data at heights of 50 m, 80 m and 90 m are synthesized by using the power law. Lowest monthly mean speed is recorded in August as 4 m/s at 30 m height whereas the highest monthly mean speed is calculated to be in December at 7.9 m/s at a height of 90 m. Costas [27] presented in his study that wind energy in Cyprus is affected by anticyclones moving from west to east, from the Siberian anticyclone during the winter and from the low pressure
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[18] Christos, Konomis. Kambi Wind Farm Project, project design document version 8, Aerotricity Ltd., Nicosia, Cyprus. Available: 〈https://cdm.unfccc.int/Pro jects/DB/TUEV-RHEIN1250391030.42〉; 10.12.09 [Online]. [19] Urišić Ž, Mikulović J. Assessment of the wind energy resource in the South Banat region, Serbia. Renew Sustain Energy Rev 2012;16(5):3014–23. http: //dx.doi.org/10.1016/j.rser.2012.02.026. [20] Fazelpour F, Soltani N, Soltani S, Rosen MA. Assessment of wind energy potential and economics in the North-western Iranian cities of Tabriz and Ardabil. Renew Sustain Energy Rev 2015;45:87–99. http://dx.doi.org/10.1016/j. rser.2015.01.045. [21] Costa Rocha PA, de Sousa RC, de Andrade CF, da Silva MEV. Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil. Appl Energy 2012;89 (1):395–400. [22] Manwell James F, McGowan Jon G, Rogers Anthony L. Wind energy explained: theory, design and application. The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ. United Kingdom: John Wiley & Sons; 2010. p. 40 ISBN: 9780470686287. [23] IEC. Wind turbines – Part 1: design requirements. Geneva: International Electrotechnical Commission; 2005 IEC 61400-1, third edition. [24] Frank Helmut P, RathMann Ole, Mortensen Niels G, Landberg Lars. The numerical wind atlas. Roskilde, Denmark: Risø National Laboratory; 2001 June. [25] Wind Energy Reference Manual. Available: 〈www.windpower.org〉; 2015 [Online]. [26] Kaoshan D, Anthony B, Chao L, Wei-Ning X, Zhenhua H. Environmental issues associated with wind energy – a review. Renew Energy 2015;75(2015):911– 21. [27] Costas PAPASTAVROS, Ministry of Agriculture, Mediterranean and National Strategies for Sustainable Development. Priority Field of Action 2: Energy and Climate Change, Energy Efficiency and Renewable Energy. Cyprus-National study's summary; March 2007.
Contents lists available at ScienceDirect
Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser
Wind resource assessment of Northern Cyprus Davut Solyali a,n, Mustafa Altunç b, Süleyman Tolun c, Zafer Aslan d a
Eastern Mediterranean University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Famagusta, Mersin 10, Turkey University of Kyrenia, Faculty of Maritime Studies, Nicosia, Turkey University of Kyrenia, Faculty of Aeronautics and Astronautics, Nicosia, Turkey d Aydın University, Faculty of Engineering, Department of Computer Engineering, Florya, 34295 Istanbul, Turkey b c
art ic l e i nf o
a b s t r a c t
Article history: Received 22 March 2015 Received in revised form 11 August 2015 Accepted 26 October 2015 Available online 21 November 2015
This paper presents a technical assessment of wind power potential for Selvilitepe site in Northern Cyprus. The wind speed data was collected for 10 min intervals between years 2007 and 2014 at this site. Weibull distribution method using 3 different algorithms called maximum likelihood, least squares and WAsP was used for the statistical analysis of the measured data. Power law exponent method was used to create diurnal and monthly averaged wind speed variations at the heights of 50 m, 80 m and 90 m. Based on the determined standard deviation of the wind speed, turbulence category of this site is calculated and categorized. Shear profile and surface roughness of this site has also been analyzed and determined. & 2015 Elsevier Ltd. All rights reserved.
Keywords: Cyprus Selvilitepe Weibull distribution Wind assessment
Contents 1. 2. 3.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Region and data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Wind speed profile assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1. Maximum likelihood method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2. Least squares method (graphical method) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3. WAsP method (equivalent energy method) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Turbulence intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Shear profile and surface roughness. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1. Log law. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2. Power law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Results and discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Site location and data collection at Selvilitepe in Northern Cyprus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Wind speed frequency distribution of Selvilitepe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Turbulence intensity of Selvilitepe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4. Shear profile and surface roughness of Selvilitepe. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5. Wind speed variations of Selvilitepe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6. Summary for Selvilitepe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
180 181 181 181 182 182 183 184 184 184 184 184 184 184 185 185 185 186 186 186
1. Introduction
n
Corresponding author. E-mail addresses: [email protected] (D. Solyali), [email protected] (M. Altunç), [email protected] (S. Tolun), [email protected] (Z. Aslan). http://dx.doi.org/10.1016/j.rser.2015.10.123 1364-0321/& 2015 Elsevier Ltd. All rights reserved.
In Northern Cyprus, increasing population, rising life standards, and rapidly growing tourism and industry sectors have led to increased energy demands. Being an isolated energy system, this increase in energy demand is causing a high degree of dependence
D. Solyali et al. / Renewable and Sustainable Energy Reviews 55 (2016) 180–187
on imported fuel. Owing to the increasing cost of energy supply, limited oil storage capacity, and the need for environmental preservation (i.e. reduction of green house gas emissions, conservation of the natural and visual beauty of the island), Northern Cyprus is looking into effective exploitation of renewable sources besides its installed conventional power stations. The objective of this study is to assess the wind resource and determine the wind characteristics at Selvilitepe site in Northern Cyprus. Actual measured data for seven years in 10 min intervals was used. The study determined the Weibull parameters at 30 m and 90 m heights, turbulence parameters, power density, wind power class, power law exponent, surface roughness, and roughness class of the site. Carrasco-Díaz et al. [1] demonstrated a spatial and temporal wind potential estimation using existing reanalysis data sets using the Wind Atlas Analysis and Application (WAsP) software for the cost calculation at Tamaulipas, Northeastern Mexico. According to the study the reanalysis wind product and observed wind assessment had a good agreement between the two at several meteorological stations located throughout the state. However, the need for high quality wind data measurement at different locations and altitudes for better estimation of wind resource potential is also emphasized. In order to improve the wind potential estimation, Medimorec and Tomšić [2] used portfolio theory on locations with multiple wind measurements. Their study showed that the method reduced both the uncertainty and error, even when using least accurate and most uncertain measurements. Without wind monitoring equipment at each potential installation site, local authorities or project developers can only rely on low resolution wind speed databases to identify the best sites. In the UK, the most frequently used databases, the DECC wind speed database (NOABL) wind estimator tend to provide an inaccurate estimates with a mean error of 23% over the 73 sites out of 91 [3]. Therefore, long term data, at least of two years is required for more accurate assessments. Study conducted by Hernandez-Escobedo et al. [4] performed wind resource assessment, both spatially and temporally throughout the day and months of at least two years for the Northern Mexican states, except at Baja California Norte. Most common practice when assessing the wind energy potential of a particular site is to use Weibull distribution methods [5]. Study conducted by Irwanto, Gomesh and Yusoff [6] analyzed the wind speed characteristics and calculated the wind power generation potential using Weibull distribution function at Chuping and Kangar in Perlis, Malaysia. The wind power and energy potential were presented as functions of tower height. There are several Weibull distribution methods. Chandel, Ramasamy and Murthy [7] provided a summary of the comparison highlights of some of the statistical methods to assess wind speed and power potential of a location. The discussed methods were maximum likelihood method, modified maximum likelihood method, equivalent energy method, graphical method, moment method, empirical method and wind energy pattern factor method (WEPF). These methods are used to estimate wind power density (WPD) by evaluating Weibull parameters k and c. Also, Rayleigh distribution method, which is a special case of the Weibull distribution, can be used [3]. For example, Bataineh and Dalalah [8] presented a technical assessment of wind potential for several locations in Jordon using Rayleigh distribution method using the monthly averaged data. Their study also demonstrated use of seventh power law to estimate the power output according to the selection for wind turbines and their corresponding hub height. Furthermore, Weibull distribution analysis can be expanded upon using wavelet analysis methods. Janajreh, Su and Alan [9] subjected annual records of different heights at different temporal resolutions at Masdar city to linear regression, spectrum and
181
wavelet analysis. Then the data was modeled using the maximum likelihood method of Weibull distribution method. The data was subjected to FFT spectrum analysis. As the intermittency was identified for the collected data, wavelet analysis was further explored to remedy the short coming of the FFT spectrum.
2. Region and data Study analyzing the wind potential of Northern Cyprus was conducted at various other sites such as Sınırüstü, Kalecik, Yenierenköy, Sadrazamköy and Taşkent by Altunc et al. [10] to extend the cover of measured data to wider regions of the Northern Cyprus. The study has demonstrated seasonal variations of wind speed and energy potentials at the listed stations in Northern Cyprus for the period between 1998 and 2003. Table 1 is presenting the summary of findings of the study at the height of 30 m for each site. At the moment, there are no wind farms either under construction or operational in Northern Cyprus. However, following the initiatives of the European Directives, following the considerations and approval process under The Clean Development Mechanism (CDM) protocols, various wind generation projects have been privately funded in the Southern Cyprus. The CDM is under the authority and guidance of the parties to the Kyoto Protocol. The information regarding these projects is currently publicly available via United Nations Framework Convention on Climate Change (UNFCCC) website for CDM projects. The project design documents of several registered wind farm projects in Southern Cyprus, namely Orites, Kambi, Stivo, Klavdia, Agiaanna, Alexigros and Mari, are used and condensed into Table 3 [12–18]. It can be observed that short term measured mean wind speeds at high altitudes range from 4.7 m/s to 6.7 m/s yielding capacity factors of wind farms from 16% to 25%. Also, it should be noted that the biggest wind farm is the Orites wind farm with 144 MW of capacity. Currently, this is the only wind farm that has been operating for at least 1 year [11]. The over layered picture of Cyprus is given in Fig. 1. The figure shows the locations of the measurement sites presented in Table 1 and the measurement site of this study, Selvilitepe. In addition, the figure shows the locations and sizes of the CDM registered wind farms given in Table 3.
3. Methodology 3.1. Wind speed profile assessment Wind speed profile assessment studies typically use the Weibull distribution to characterize the breadth of the distribution of wind speeds. The following equations give the probability distribution function and the cumulative distribution function of the Table 1 Mean wind speeds at various measurement sites. Site name
Site location
Kalecik Sınırüstü Yenierenköy Sadrazamköy Taşkent
N N N N N
34°330 35°290 35°540 35°380 35°250
E E E E E
33° 33° 34° 32° 33°
Average wind speed (m/s) 980 850 240 950 390
2.6 3.1 4.3 4.6 3.8
182
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two parameter Weibull distribution [1–9,19,20]. " # k U k1 U k f ðU Þ ¼ :exp c c c " F ðU Þ ¼ 1 exp
k # U c
ð1Þ
ð2Þ
The following equation gives the relationship between the scale factor c and the long-term average wind speed: 1 ð3Þ U ¼ cГ þ 1 k The Weibull k value reflects the breadth of the distribution; the broader the distribution, the lower the value of the Weibull k. Fig. 2 shows several Weibull distributions, all with an average wind speed of 7 m/s, but with the Weibull k value varying from 1.5 to 3.5.
The Weibull distribution often fits measured wind speed distributions well. Fig. 3 illustrates a measured wind speed distribution along with the best-fit Weibull distribution. According to the figure, the best-fit curve is when k ¼1.5 and c ¼6 m/s. Therefore, the long term measured data can be characterized by using only two parameters c and k. Three different algorithms or methods used in this study are called maximum likelihood, least squares and WAsP. 3.1.1. Maximum likelihood method The maximum likelihood method fits a Weibull distribution to a set of measured wind speeds [5,,21]. This method employs the following equation to calculate, in an iterative fashion, the Weibull k parameter: 0
N P
B B k ¼ Bi ¼ 1 N @ P
i¼1
Table 2 Nomenclature. Nomenclature U U U(z) U* Ui c k Г N X
Wind speed (m/s) Average wind speed (m/s)
The wind speed (m/s) at some height above ground z (m) The friction velocity (m/s) Wind speed in time step i Weibull c factor (m/s) Weibull k factor (dimensionless) Gamma function Number of time steps Proportion of the observed wind speeds that exceed the mean observed wind speed WPD Mean power density (W/m2) v1 Average wind speed at some height h1 above ground (m) v2 Average wind speed at some height h2 above ground (m) h1 Height above ground (m) h2 Height above ground (m) α Power law exponent ρ Air density (kg/m3) σi Standard deviation of the wind speed within time step i I Turbulence intensity (dimensionless) κ The Von Karman's constant (0.4) z0 The surface roughness (m)
U ki lnðU i Þ U ki
11 lnðU i ÞC C i ¼ 1 C A N N P
ð4Þ
Once the shape parameter k has been found, the following equation gives the value of the scale parameter c: 0
11k k U iC B B C c ¼ Bi ¼ 1 C @ N A N P
ð5Þ
After establishing the parameters c and k that describe the Weibull function and therefore the behavior in terms of wind speed, the wind potential was determined from the studies area by calculating the wind power available multiplied by the number of hours included in the study. 3.1.2. Least squares method (graphical method) Some algebraic manipulations are required in order to be able to use a linear least squares algorithm to fit a Weibull distribution to measure wind speed data [5,,7,,21]. Eq. (2) can be re-arranged as follows: ln½1 F ðU Þ ¼
k U c
Fig. 1. Map of Cyprus over layered with the measurement sites from Table 1, CDM registered wind farms from Table 2 and measurement site of Selvilitepe.
ð6Þ
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Table 3 List of CDM registered projects. Wind farm
KAMBI
STIVO
KLAVDIA
AGIA-ANNA
ALEXI-GROS
ORITES
MARI
Province Total capacity (MW) Measured site wind speed
Nicosia 9.6 5.7 m/s at 50 m 18.70 1638 15,726
Larnaca 27 NA
Larnaca 42 5.82 m/s at 8 m 16.19 1419 59,580
Larnaca 20 5.85 m/s at 78 m 17.61 1540 30,860
Larnaca 34.5 6.7 m/s at 85 m 25.10 2202 76,002
Paphos 144 5.85 m/s at 85 m 16.90 1485 213,920
Larnaca 12 4.9 m/s at 50 m 20.18 1768 21,219
Capacity factor (%) Annual production per each MW installed capacity (MWh) Wind farm annual production (MWh)
16.70 1459 39,385
Weibull Probability Density Function
Best-fit Weibull Distribution
0.2
8
0.18
7
0.14
Frequency (%)
Probability
0.16 0.12 0.1 0.08 0.06 0.04
6 5 4 3 2
0.02
1
0 1
4
7
10
13
16
19
22
25
0
Wind Speed (m/s) k=1.5
k=2.0
k=2.5
0 k=3.0
The equation can be re-written as follows: k 1 U ln ¼ 1 F ðU Þ c
Using the rule: A ¼ ln A ln B ln B Eq. (9) can be re-written as: 1 ¼ k lnU k lnc ln ln 1 F ðU Þ
6
k=3.5
8
10
12
14
16
18
20
22
24
Actual Data
Best Fit Weibull distribution (k=1.5, c=6 m/s)
Fig. 3. Demonstration of best-fit Weibull distribution to actual data by using two parameters.
ð7Þ
Taking the natural logarithm of both sides: 1 U ¼ kln ln ln 1 F ðU Þ c
4
Wind speed (m/s)
Fig. 2. The breath of the distribution for varying k at an average wind speed of 7 m/s.
Using the rule: 1 ¼ ln A ln A
2
ð8Þ
ð9Þ
ð10Þ
ð11Þ
Eq. (11) is now in the general slope intercept form y ¼ mx þ b. Therefore, plotting lnðUÞ on the x-axis and ln n h io ln 1 1F ðU Þ on the y-axis will produce a straight line with slope equal to k and intercept equal to k lnc. Therefore, c is equal to intercept ð12Þ c ¼ exp slope 3.1.3. WAsP method (equivalent energy method) The WAsP method has defined a requirement for fitting the Weibull distribution to measured wind speed data [7,,20,,21,,24]. The WAsP algorithm, also known as equivalent energy method, does not attempt to directly fit the measured frequency histogram, but rather requires that:
1. The mean power density of the fitted Weibull distribution is equal to that of the observed distribution. 2. The proportion of values above the mean observed wind speed is the same for the fitted Weibull distribution as for the observed distribution. The following equation gives the mean power density (WPD) of the Weibull distribution, assuming constant air density: 1 3 WPD ¼ ρc3 Г þ 1 ð13Þ 2 k Also, an equation for the mean power density of the observed wind speeds, again assuming constant air density can be written as: WPD ¼
1 X 3 ρ U 2N N i
ð14Þ
Considering the first requirement, Eqs. (13) and (14) shall be equal: 3 1X 3 U ð15Þ c3 Г þ 1 ¼ k N N i Solving the above expression for c gives vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u N u P 3 u 3 Ui u
c ¼ t i ¼ 13 NГ k þ 1
ð16Þ
The cumulative distribution function F ðU Þ gives the proportion of values that are less than U, so 1 F ðU Þ is the proportion of values that exceed U. Considering the second requirement, a symbol X can be defined to represent the proportion of the observed wind
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D. Solyali et al. / Renewable and Sustainable Energy Reviews 55 (2016) 180–187
speeds that exceed the mean observed wind speed as follows:
1F U ¼ X ð17Þ Eq. (1), the cumulative distribution function of the Weibull distribution, holds for any wind speed U, therefore it holds for the mean wind speed. Rearranging Eq. (1) as 2 !k 3 U 5 ð18Þ 1 F U ¼ exp4 c By substituting for X, Eq. (18) can be rewritten as: 2 !k 3 U 5 4 X ¼ exp c Taking the logarithm on both sides gives: !k U ¼ lnðX Þ c
ð19Þ
ð20Þ
By substituting Eq. (16) into Eq. (20), the following expression, whose only unknown is k, is given: 0
1k
B C B C B C U BvffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiC ¼ lnðX Þ Bu P C N Bu C 3 U 3i A @t
In order to solve Eq. (21) for the parameter k, first X is calculated using Eq. (17). Then, Eq. (21) is calculated iteratively, using the Brent method. 3.2. Turbulence intensity The turbulence intensity (I), or TI, is a dimensionless number defined as the standard deviation of the wind speed within a time step divided by the mean wind speed over that time step. The turbulence intensity in each time step can be calculated using Eq. (22) [9,19], [22:40].
σi
The value of exponent α depends on surface roughness and atmospheric stability. The value of α is between the range of 0.05 and 0.5. For data sets containing wind speeds at a known height above ground, using Eq. (24), the vertical wind speed profile or the wind speed at a different height can be calculated.
4. Results and discussions
i ¼ 1
Ui
3.3.2. Power law The power law exponent (sometimes called the power law coefficient or simply ‘alpha’) is a number that characterizes the rate at which wind speed changes with height above ground. It is a parameter in the power law, which states that the wind speed varies with the height above ground according to the following equation: α v2 h2 ¼ ð24Þ v1 h1
ð21Þ
N Гð3k þ 1Þ
I¼
parameter in the logarithmic law, which states that the wind speed varies logarithmically with the height above ground according to the following equation: 8 < U ln z ; z 4 z 0 z0 κ U ðz Þ ¼ ð23Þ : 0; z 4 z0
ð22Þ
The 3rd edition of IEC 61400-1 provides a tabular reference for four turbulence categories based on mean turbulence intensity at a wind speed of 15 m/s [23]. In order to analyze turbulence, the actual data set for all the time steps is used. The data is then filtered or subdivided into bins. In this case, the data is filtered for mean speed of 15 m/s. For each bin, the set of turbulence intensity values is taken and their mean, standard deviation, and peak value are calculated. Then, the representative turbulence intensity for 3rd edition IEC standard is compared against these values. 3.3. Shear profile and surface roughness The variation in the wind speed with height above ground is called the wind shear profile. In the field of wind resource assessment, analysts typically use one of the two mathematical relations to characterize the measured wind shear profile: the logarithmic law (or log law) with its parameter called the surface roughness, and the power law, with its parameter called the power law exponent [22:43-45]. 3.3.1. Log law The surface roughness (sometimes called surface roughness length or just roughness length) is a measure of the rate at which the wind speed changes with height above ground. It is a
4.1. Site location and data collection at Selvilitepe in Northern Cyprus Selvilitepe is located at N 35°190 and E 33°90 at the height of 1018 m. The data was collected for 10 min intervals at 30 of height between years 2007 and 2014. The location of the site is also shown in Fig. 1. 4.2. Wind speed frequency distribution of Selvilitepe Wind speed frequency distribution at 30 m of collected (actual) data at Selvilitepe is presented in Fig. 4. The figure also represents the data using three other algorithms; maximum likelihood, least squares and WAsP. These algorithms employ their methods to fit the curve of the Weibull distribution of the actual data. The maximum likelihood method uses an iterative method introduced by Stevens and Smulders, 1979 in order to fit a Weibull distribution to a set of measured wind speeds [5]. Least squares algorithm, sometimes called the ‘graphical method’, works by transforming the axes of the cumulative distribution function so that a Weibull distribution would appear as a straight line, then finding the straight line that best fits the actual data [21]. WAsP algorithm, used by the WAsP wind flow model, finds the Weibull distribution that exactly matches that actual distribution in terms of two parameters: the mean wind power density and the proportion of values that exceed the measured mean [24]. Table 4 lists the resultant parameters for each algorithm. It can be seen that with R2 values from 0.993 to 0.998, the match to the actual data is very high. For example, among the algorithms, WAsP has the highest match with R2 at 0.998. The Weibull k value is calculated to be 1.581 and c value is 5.698. This corresponds to 206.7 W/m2 power density at Selvilitepe. Using the power law method, the actual data collected at the height of 30 m is synthesized to the height of 90 m at which most of the 1 MW or above capacity wind turbine height is. The 90 m synthesized data is presented in Fig. 5. As per the actual data at 30 m, the synthesized data is also matched with using maximum likelihood, least squares and WAsP methods.
D. Solyali et al. / Renewable and Sustainable Energy Reviews 55 (2016) 180–187
Wind Speed Frequency Distribution at 30m
Table 5 Weibull parameters of Selvilitepe at 90 m.
7 6
Frequency (%)
185
5
Algorithm
Weibull k Weibull c (m/s)
Mean (m/s)
Power density (W/m2)
R2
Maximum likelihood Least squares WAsP
1.563
6.628
5.956
331.6
0.978
1.504 1.581
6.714 6.646
6.059 5.965
368.8 327.9
0.973 0.979
4 3 2 1 0 0
2
4
6
8
10
12
14
16
18
20
22
24
Turbulence Intensity at 30 m
Wind speed (m/s) Maximum likelihood
Least squares
WAsP
Fig. 4. Wind speed frequency distribution at 30 m of Selvilitepe.
Table 4 Weibull parameters of Selvilitepe at 30 m. Algorithm
Weibull k Weibull c (m/s)
Mean (m/s)
Power density (W/m2)
R2
Maximum likelihood Least squares WAsP Actual data
1.563
5.107
209.0
0.997
5.195 5.115 5.118
232.5 206.7 206.7
0.993 0.998
Turbulence Intensity
Actual Data
1 0.8 0.6 0.4 0.2 0
5.683
1.504 5.756 1.581 5.698 (62,689 time steps)
0
3
7
11
15
19
23
Wind Speed (m/s) Representative TI IEC Category A
Mean TI IEC Category B
Fig. 6. Turbulence intensity at 30 m of Selvilitepe.
Wind Speed Frequency Distribution at 90 m Frequency (%)
7 6 5 4 3
Table 6 Turbulence parameters. Quantity
Value
Data points in 15 m/s bin Mean TI at 15 m/s Representative TI at 15 m/s IEC3 turbulence category
292 0.10 0.17 C
2 1 0 0
2
4
6
8 10 12 14 16 18 20 22 24 26 28
Wind speed (m/s) Actual Data
Maximum likelihood
Least squares
WAsP
Fig. 5. Wind speed frequency distribution at 90 m of Selvilitepe.
Table 5 tabulates the resultant parameters for each method. R2 values vary from 0.973 to 0.979. WAsP method being the highest match with 0.989 has Weibull k parameter value of 1.581 and c parameter value of 6.646 for mean speed of 5.965 m/s. Using WAsP method, it is calculated that the power density at synthesized height of 90 m at Selvilitepe is 327.9 W/m2. This value was observed to be 206.7 W/m2 at 30 m height using actual data.
4.3. Turbulence intensity of Selvilitepe Fig. 6 presents the turbulence intensity data at 30 m with respect to 3rd edition IEC categories A, B and C. The data set contained 292 time steps that contained valid data in which the wind speed fell within the 14.5–15.5 m/s range. The resulting values of turbulence intensity exhibited a mean value of 0.10 and a representative turbulence intensity of 0.17. Therefore, the IEC3
turbulence category falls into category C. Table 6 summarizes the turbulence parameters. 4.4. Shear profile and surface roughness of Selvilitepe Fig. 7 presents the vertical wind shear profile at Selvilitepe for various heights. The actual measured data at 30 m and power law fitted and log law fitted for further heights is graphed. It is calculated that power law exponent value is 0.14 and surface roughness is 0.04 m. This corresponds to the roughness class of 1.26 [25,26]. 4.5. Wind speed variations of Selvilitepe In Fig. 8, the diurnal cycle of wind speed averaged to a single day is presented. The actual data at 30 m and synthesized data by using power law at heights of 50 m, 80 m and 90 m is shown. During the day, the mean wind speeds forms a reversed bell shape. The day can be divided several time slots. Between 20:00 and 02:00 h, the mean wind speeds are the highest and vary between 6 and 6.2 m/s at a height of 30 m and 7–7.24 m/s at a height of 90 m. Between 09:00 and 15:00 h, the mean speeds are the lowest and vary between 3.8 and 3.95 m/s at a height of 30 m and 4.4– 4.9 m/s at a height of 90 m. Between 02:00 and 09:00 h the mean wind speeds are ramping down. Between 15:00 and 20:00 h, the wind mean speeds are ramping up. During ramping hrs, the mean wind speeds vary between 3.8 and 7 m/s at a height of 30 m and 4.9–7.24 m/s at a height of 90 m.
Height Above Ground (m)
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D. Solyali et al. / Renewable and Sustainable Energy Reviews 55 (2016) 180–187
Vertical Wind Shear Profile
Table 7 Summary for Selvilitepe.
120 100 80 60 40 20 2
3
4
Power law fit
5
6
7
Log law fit
Measured data
Fig. 7. Vertical wind shear profile at Selvilitepe.
Mean Wind Speed (m/s)
Value
Power density at 50 m Wind power class Power law exponent Surface roughness Roughness class Mean at 90 m
221 W/m² 2 (Marginal) 0.14 0.0408 m 1.26 5.96 m/s
0
Mean Wind Speed (m/s)
crated in the area of India and expanded until the area of Cyprus during the summer; sea breezes generated in coastal areas as a result of the different heat capacities of sea and land, which give rise to different rates of heating and cooling; and mountain valley winds created when cool mountain air warms up in the morning and begins to rise while cool air from the valley moves to replace it. During the night the flow reverses.
Diurnal Wind Speed Variations 4.6. Summary for Selvilitepe
8 7
See Table 7.
6 5 4
5. Conclusions
3 2 1 0 0
4
8
12
16
20
24
Hour of Day Synthesized 90 m Synthesized 50 m
Synthesized 80 m Actual Data at 30m
Fig. 8. Diurnal wind speed profile at various heights of Selvilitepe.
Mean Wind Speed (m/s)
Variable
Monthly Wind Speed Variations 8 7 6
One way to reduce dependency on imported fuels is to implement renewable energy solutions such as wind energy in the northern part of the island. Therefore, wind energy analysis is essential for wind energy assessment studies. The data used in this study was collected for 10 min intervals between years 2007 and 2014. The mean speed varies diurnally as well as monthly. From the collected data and analysis, it is calculated that at 30 m power density is at 207 W/m2 and mean wind speed is 5.11 m/s, at 50 m power density is at 221 W/m2 and at 90 m power density is 329 W/m2 with mean speed of 5.96 m/s. The resulting values of turbulence intensity exhibited a mean value of 0.10 and a representative turbulence intensity of 0.17. Therefore, the IEC3 turbulence category falls into category C. Also, it is calculated that power law exponent value is 0.14 and surface roughness is 0.04 m. This corresponds to a value of 1.26 which is between roughness classes 1 and 1.5.
5 4
References
3 1
2
3
4
5
6
7
8
9
10
11
12
Months Synthesized 90 m
Synthesized 80 m
Synthesized 50 m
Actual Data at 30m
Fig. 9. Monthly averaged wind speed profile at various heights.
Fig. 9 presents the monthly averaged wind speed profile at various heights at Selvilitepe. The actual data was measured at a height of 30 m and the remaining data at heights of 50 m, 80 m and 90 m are synthesized by using the power law. Lowest monthly mean speed is recorded in August as 4 m/s at 30 m height whereas the highest monthly mean speed is calculated to be in December at 7.9 m/s at a height of 90 m. Costas [27] presented in his study that wind energy in Cyprus is affected by anticyclones moving from west to east, from the Siberian anticyclone during the winter and from the low pressure
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