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Wind resource assessment and wind power potential of Mil-E Nader region in Sistan and Baluchestan Province, Iran – Part 1: Annual energy estimation
Energy Conversion and Management 79 (2014) 273–280
Contents lists available at ScienceDirect
Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman
Wind resource assessment and wind power potential of Mil-E Nader region in Sistan and Baluchestan Province, Iran – Part 1: Annual energy estimation A. Tizpar, M. Satkin ⇑, M.B. Roshan, Y. Armoudli Wind and Wave Energy Department, Renewable Energy Organization of Iran, Iran
a r t i c l e
i n f o
Article history: Received 19 March 2013 Accepted 1 October 2013 Available online 8 January 2014 Keywords: Wind power potential Iran Capacity factor Mil-E Nader Sistan and Baluchestan
a b s t r a c t In this paper, wind power potential of Mil-E Nader region is statistically analyzed based on 10 min measured short term wind data. Weibull parameters at 40 m height have been estimated and used to describe the distribution of wind data and its frequencies. Additionally, diurnal and monthly wind speed variations have been calculated. Based on power law model and average wind speed at three heights (10, 30 and 40 m), wind speeds at higher elevations have been extrapolated. Energy analysis has been carried out to find best hub height by comparing energy production of several wind turbines with different classes and hub heights. The energy production analysis showed that the wind turbines with 80 m height have high production in comparison to the others. Ó 2014 Published by Elsevier Ltd.
1. Introduction Nowadays, scientists and the public are more concerned and sensitive to the need for environmental friendly energy sources. The energy demand is increasing continuously and the only way to fulfill this demand is to start using renewable energy sources. Wind is one of those major free, clean and inexhaustible renewable energy sources [1]. Wind is an abundant resource available in nature that could be utilized by mechanically converting wind power to electricity using wind turbines [2]. Technology of the extraction of power from wind with modern turbines is a well established industry, at present. Parameters such as improvement of wind farm efficiency as well as reducing wind turbine component costs, make wind power generation competitive to the conventional sources. Furthermore, the wind power has an additional advantage of being a non-polluting source of energy [3]. Preparing technical and economical feasibility study is a vital step before investing in a wind farm project. This study gives an outlook to investors about costs and economical aspects of a wind farm project. Accurate and proper information on a project conditions (such as site and wind characteristics) play a key role in wind farm feasibility studies. So, inappropriate information may lead to disinvestment and waste of money. In contrast with, select a suitable site for a wind turbine regarding to the parameters such as: turbine size, blade shape, capacity, etc. resulting in high ⇑ Corresponding author. Tel.: +98 21 88090209; fax: +98 21 88578807. E-mail address: [email protected] (M. Satkin). 0196-8904/$ - see front matter Ó 2014 Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.enconman.2013.10.004
efficiency of a wind farm. It is quite important to know several fundamental properties of the site such as wind behavior, availability, continuity, and probability in the proposed region. To make decisions with those properties, statistical and dynamic wind characteristics of the site should be found out using wind observations and statistical wind data [1]. This paper presents the detailed wind data analysis and wind availability at Mil-E Nader, an area in Sistan and Baluchestan Province of Iran.
2. Site description Sistan and Baluchestan Province is one of the 31 provinces of Iran. It is in the southeast of the country, bordering Pakistan and Afghanistan and its capital is Zahedan (Fig. 1a). The province is subject to seasonal winds from different directions, the most important of which are the 120-day wind of Sistan known as Levar, the Qousse wind, the seventh (Gav-kosh) wind, the Nambi or south wind, the Hooshak wind, the humid and seasonal winds of the Indian Ocean, the North or (Gurich) wind and the western (Gard) wind [4]. Mil-E Nader is an area near Zabol city in north eastern part of the province. The area is exposed to the 120-day wind of Sistan. The 120 day winds of Sistan blow from middle of May to Middle of September which affect large areas of Sistan and Baluchestan Province in eastern part of Iran. Continuous blowing as well as suitable wind speeds are the main characteristics of this kind of winds. Prevailing wind direction of these winds varies in different parts of
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(a)
(b)
Fig. 1. (a) Sistan va Baluchestan Province in Iran map and (b) location of Mil-E Nader wind monitoring station near Zabol city in Sistan va Baluchestan Province.
province. For targeted region (Mile-Nader), the prevailing wind direction is north eastern [5]. Based on Zabol local synoptic data, it can be found that the maximum monthly mean wind speed from middle may to middle September is 6.5–9 m/s [6]. So Mil-E Nader region is supposed to have great wind energy potential. Therefore a wind monitoring station was installed in this area in 2010 to collect wind regime data. The wind monitoring mast is located at 31° 050 1100 N, 61° 090 2300 E and its elevation is 481 m above the sea level (Fig. 1b). It is located in a flat area with a few obstacles around it. The mast was installed by renewable energy organization of Iran (SUNA) in order to measure the meteorological properties of the region. Lattice type mast with height of 40 m was used for this purpose and different types of sensors were mounted on it at heights of 10, 30 and 40 m. Data acquisition has done by wind anemometer, wind vane, temperature, radiation and humidity sensors.
2
1 4 3
3. Wind speed data The wind data used in this study were captured using a cup generator anemometer. The wind data were measured by 10 min sampling period at heights of 10, 30 and 40 m above ground level between years 2010–2012 (Fig. 2). The measurements include wind speed, wind direction, temperature, humidity and global radiation at the site. Technical specification of the mentioned sensors (using Theodor Friedrichs Company’s catalogues) is shown in Table 1. Data logger device records minimum, average, maximum, standard deviation values of wind speed and average, standard deviation values of wind direction. Additionally average values of temperature, humidity and global radiation is recorded. Maximum 144 data correspondent to the records mentioned above can be collected. These data should be validated and then processed to produce the summary reports of analysis. Afterwards, the collected data were analyzed and processed by using Windographer software. During the analysis process, the errors arising from sensors stopping, turbulent air flow, data logger malfunction, icing and etc. were flagged and removed.
Fig. 2. Met mast of Mil-E Nader’s wind monitoring station. 1: Anemometers, 2: Wind vane booms, 3: Humidity and temperature sensor, 4: Radiation sensor.
4.1. Probability distribution function Due to the variability of the wind speed it is found useful to plot the wind speed probability distribution function (the percentage of time that the wind spends at each speed) to understand the character of the variation. One of the commonly used functions for this purpose is the Weibull distribution given by:
f ðv Þ ¼ 4. Methodology Wind energy resource is highly variable both in space and time [7]. Therefore, to understand the characteristics of the resource, various parameters were considered.
k1 k v v k eð c Þ c c
ð1Þ
where f(v) is the probability of observing wind speed (v), c(m/s) is the Weibull scale parameter and k is the dimensionless shape factor. The cumulative probability function of the Weibull distribution is given by:
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Table 1 Technical specification of the sensors. No.
Sensor type
Manufacturer
Specifications
1
Wind anemometer
Theodor Friedrichs & CO
2 3
Wind vane Combined temperature/humidity sensor
Theodor Friedrichs & CO Theodor Friedrichs & CO
4
Radiation sensor
Theodor Friedrichs & CO
Measuring range: 0–70 m/s, Starting threshold: <0.3 m/s, Accuracy: ±0.2 m/s Measuring range: 360°, Initial deflection: <0.2 m/s Measuring range: 0–100% rH, Calibrating accuracy: ±1.5%rH, Operating temperature: (40) – (+45 °C), Max. error ±0.25% Viewing angel: 2p sr, Sensitivity: 9–15 lV/Wm2, Response time: 99% 55 s–63% 5 s
v
Fðv Þ ¼ 1 eð c Þ
k
ð2Þ
Determining k and c requires a good fit of Eq. (2) to the recorded discrete cumulative frequency function. Taking the natural logarithm of both sides of Eq. (2) twice [1,7,8] gives:
lnð lnð1 Fðv ÞÞ ¼ kðlnðv Þ lnðcÞÞ
ð3Þ
Plotting ln(ln(1F(v)) against ln(v) presents a straight line whose gradient is k and the y-intercept is –k ln(c) from which c can be calculated [7].
A Wind rose is a polar plot that represents the percentage of the time that the wind direction falls within the sector of the compass. This is important for determining the type of turbine to be used in a particular location and also its orientation to the wind stream [7]. In this paper wind direction is shown for two heights of 30 m and 37.5 m which are used as correspondent wind direction for near wind speed sensor height. 4.3. Wind speed extrapolation As stated before, the anemometer heights are 10 m, 30 m and 40 m. However, modern wind turbines (especially turbines in MW range) have hub heights longer than 60 m and usually have 80 m hub height. So in order to estimate harvested wind energy, wind speeds magnitude at greater heights are required. Therefore, equations that predict the wind speeds at one height in terms of the measured speed at another height are required. The power law model has received extensive application in the field of wind energy basically to estimate the hub height wind speeds at various potential sites [1,7,8]. The formula takes the form:
a z2 z1
Ii ¼
ri
ð5Þ
vi
The calculated mean turbulence value is used to determine turbulence classes according to IEC 61400-1 ed.3 [10].
4.2. Wind rose
v2 ¼ v1
longitudinal, lateral, and vertical components of the wind turbulence. For the selection and energy production of wind turbine, of special interest is the longitudinal component of the turbulence. As a measure of wind turbulence, intensity of turbulence I, defined as ratio of the standard deviation of wind speed and average wind speed m, is used. Intensity of turbulence can be calculated for each 10 min interval i according to the following relation [9]:
ð4Þ
where v2 is the extrapolated wind speed at height z2 and v1 is the measured speed at z1. The exponent a depends on such factors as nature of terrain (surface roughness), wind speeds and temperature for neutral stability the exponent value of 1/7 has widely been chosen as a good representative of the prevailing conditions [7]. The power law mathematical relation was used to extrapolate the wind speeds at the higher heights. 4.4. Wind speed turbulence Each deviation of the instantaneous value of wind speed from its average value within the corresponding 10 min interval represents turbulence. The causes of turbulence are obstacles on the soil surface, roughness of the terrain, and local dynamic variations of the air pressure and temperature. For this reason, vector of the wind speed should be regarded as a spatial vector of the time varying direction and intensity. Therefore, it is possible to define: the
4.5. Wind power density The maximum power P, available from the wind [7,8,11]:
P¼
1 qAv 3 2
ð6Þ
where q is the density of air and A is the swept area of the rotor. q is a function of the air pressure B and temperature T [11], thus:
q ¼ q0
288B 760T
ð7Þ
where q0 is the density of dry air at standard temperature and pressure (1.226 (kg/m3) at 288 K, 760 mm Hg). The actual amount would be less since all available energy is not extractable. Monthly or annual wind power density per unit area, PW of a site based on Weibull probability density function can be expressed as [7,9]:
PW ¼
1 3 3 qc 1 þ 2 k
ð8Þ
The two significant parameters k and c have been shown to be related to the mean value of the wind speed [7,8,12]:
v ¼ cC
1 1þ k
ð9Þ
where the mean wind speed value is used to determine wind turbine class according to IEC 61400-1 ed.3 [10]. The mean wind speed can be calculated by [12]:
v ¼
n 1 X vi n i¼1
! ð10Þ
And C is the upper incomplete gamma function defined as [12]:
CðxÞ ¼
Z
1
t x1 et dt
ð11Þ
0
Weibull parameter k can be calculated using the following approximation [12]:
k¼
r1:086
v
ð1 6 k 6 10Þ
ð12Þ
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Table 2 Classes of wind power density at 10 m and 50 m. Wind power class
10 m (33 ft)
1 2 3 4 5 6 7
50 m (164 ft)
Wind power density (W/m)
Speed m/s (mph)
6100 6150 6200 6250 6300 6400 61000
64.4 65.1 65.6 66.0 66.4 67.0 69.4
(9.8) (11.5) (12.5) (13.4) (14.3) (15.7) (21.1)
Rating
Wind power density (W/m)
Speed m/s (mph)
6200 6300 6400 6500 6600 6800 62000
65.6 (12.5) 66.4 (14.3) 67.0 (15.7) 67.5 (16.8) 68.0 (17.9) 68.8 (19.7) 611.9 (26.6)
Poor Marginal Fair Good Excellent Outstanding Very outstanding
Table 3 Recovery ratio of data at different heights. Height (m)
Data columns
Valid data points
Possible data points
Data recovery rate (%)
40 30 10
1 1 1
73,401 73,401 66,230
80,109 80,109 80,109
91.62 91.62 82.67
where [8,12]:
"
r¼
n 1 X ðv i v Þ2 n 1 i¼1
#0:5 ð13Þ
The maximum extractable power PE (W/m2), by a system working at its optimum efficiency, is limited by the power coefficient called the Betz limit whose value is 16/27 or 0.593 and is therefore given by [7]:
PE ¼ 0:593P
ð14Þ
This power coefficient makes the maximum extractable power approximately 59.3% of the theoretical power density. 4.6. Wind power classes The wind power density incorporates in a single number the combined effect of the frequency distribution of wind speeds and the dependence of the wind power on air density and on the cube of the wind speed [13]. Classification of wind power density is a method of characterizing the power potential of the site and determining the viability of harnessing wind energy. There are seven wind classes which rates from poor to very outstanding [14]. Table 2 defines the wind power classes in terms of mean wind power density and mean wind speed at 10 m (98 ft) and 50 m (164 ft) above ground level [13]. 4.7. Plant load factor and capacity factor One of the most important wind energy parameters is the plant load factor (PLF). This factor is used in determining the annual energy output of wind turbines. PLF of a wind energy generator is the ratio of the actual output of it over one year (the power can be generated by wind turbine according to wind characteristics of the region) to its potential output which can be generated at rated power for the same year [15]: PLF ¼
Actual output of wind turbine for one year Potential output which can be generated at rated power for one year ð15Þ
However, the capacity factor Cf, is a very significant index of productivity of a wind turbine. It represents the fraction of the total energy delivered over a period Eout, divided by the maximum
Fig. 3. Probability distribution function at 40 height vs. the measured wind speed data.
Table 4 Weibul parameters of measured data at different heights. Wind speed sensor’s height (m)
Weibull (k)
Weibull c (m/s)
40 30 10
1.6 1.55 1.32
8.07 7.62 5.85
energy that could have been delivered if the turbine was used at maximum capacity over the entire period, Er = 8760Pr. The capacity factor Cf of a wind turbine can be calculated as [2]:
Cf ¼
Eout Er
ð16Þ
5. Results and discussion For the analysis of wind characteristics and wind energy potential of the Mil-E Nader region, the measurement data for the 18 months period from 01/09/2010 at 1:30 to 10/03/2012 at 9:00 have been used. Verifying measurement indicate that about 0.01% of the observation data (5 raw of observation data) were missing. These missing data were due to machine calibration, servicing and malfunction machine. Data for other periods were checked but unfortunately big gap for the sensor in 10 m height
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Fig. 4 (continued)
Fig. 4. (a) Mean diurnal profile and (b) monthly average wind speeds profile.
was encountered. This big gap originated by sensor malfunction. During data collection period, the wind anemometer at 10 m height was defected and eventually replaced with new one. Unfortunately the second one had the same problem and was replaced again. So, some data in the specified period of time has lost. Before beginning statistical analysis, data should be verified and validated. The validated data will be used in statistical analysis. The validation process is done by data screening and data verification methods [16]. In this step all of the collected data are inspected for completeness and reasonableness. Then the erroneous values were eliminated. So the collected data were verified and the invalid data were removed. Finally, the validated data which had data recovery rate of 91.62% at 40 m height was produced. Comprehensive description of possible and valid data points together with data recovery rate at different heights is shown in Table 3. Statistical processing of the data, comprising: the frequency of occurrence of wind speed and direction and the corresponding probability distribution function, wind rose, monthly and diurnal variations in wind speed, has been carried out.
Table 5 Average of measured wind speed data at heights of 10, 30 and 40 m as well as extrapolated wind speeds up to 100 m heights. Height (m)
Mean wind speed (m/s)
100 90 85 83 80 70 40 30 10
8.77 8.58 8.47 8.43 8.37 8.13 7.21 6.84 5.38
the related region. The Weibull scale parameter (c), varies from 5.85 m/s at 10 m height to 8.07 m/s at 40 m height for the frequency distribution of wind speed. The Weibull shape parameter (k), varies from 1.32 at 10 m height to 1.60 at 40 m height for frequency distributions of wind speed as well. Table 4, shows the Weibull parameters of data at different heights. 5.2. Monthly and diurnal average value of the wind speed
5.1. Weibull parameters Fig. 3 shows the probability distribution function at 40 m measurement height based in Eq. (1), compared to the measured wind speed data where the wind speed is within the interval given by the width of the columns (here 0.5 m/s is selected). Parameters of the Weibull distribution (k and c) have been calculated according to Eqs. (9) and (12). A wind speed frequency histogram is obtained by dividing the number of observations in each interval by the total number of observations in the data set. These actual wind speed observations were fitted in the widely wind energy by Weibull distribution [9]. Weibull distributions were applied in order to wind potential evaluation and estimation of wind turbine annual production in
Fig. 4, shows average wind speeds, hour by hour and month by month, for all three heights during 18 months measurement period. It can be seen, spring and summer months are windier than other seasons. Regarding to higher electrical energy consumption in spring and summer compared to autumn and winter seasons, it is certainly deducted that this condition has required compatibility with energy consumption of the region. Mean diurnal profile (Fig. 4a) shows that at 40 m and 30 m heights, the maximum and minimum wind speeds are nearly occurred at 8:00–12:00 am and 16:00–20:00 pm respectively. As it mentioned in the previous section, there is a notable gap in monthly average values at height of 10 m as shown in Fig. 4b. which caused by anemometer sensor malfunction.
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Fig. 5. (a) Wind shear profile at the location measurement mast and (b) wind rose at the location of measurement mast.
Fig. 6. (a) Turbulence intensity of wind at height of 40 m vs. wind speed data and (b) distribution of turbulence intensity of wind speed data in different direction.
5.3. Wind shear profile Fig. 5a shows the wind speed variations vs. different heights at the location of the measurement mast. The diagram was drawn by averaging wind speeds at three heights. Then the power law exponent (equals 0.212) was calculated by entering these values in Eq. (4). The average of measured wind speed data at heights of 10, 30 and 40 m as well as extrapolated wind speeds up to 100 m height are shown in Table 5. 5.4. Wind rose Distribution of wind speeds in different directions at height of 40 m is shown in Fig. 5b. It can be seen that the prevailing wind direction with frequency of 30% is in 60 corresponding to geographical direction ENE. 5.5. Turbulence
Fig. 5 (continued)
Calculation of wind turbulence intensity was carried out by using Eq. (5). Fig. 6a shows the diagram of wind turbulence
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Fig. 6b, shows the distribution of turbulence intensity in different directions by a polar diagram. This diagram was drawn by data of direction sensor at height of 37.5 m. It indicates that according to the wind rose diagram, there is minimum turbulence in prevailing wind direction. 5.6. Energy production and wind power density
Fig. 6 (continued)
CF (%)
Capacity factor of wind turbines
Fig. 7. Capacity factors vs. the different wind turbines.
intensity at height of 40 m vs. wind speed data. According to Fig. 6a, the turbulence intensity of wind is lower than C category in IEC 61400-1 standard. It can be concluded that the targeted region is characterized by a low level of wind turbulence class.
On the basis of presented statistical analysis of the measured data (Table 5) and analysis of the wind turbulence, it is concluded that according to IEC 61400-1-2005 standard, Mil-E Nader region is defined as medium wind class (IEC class II-C). However, for the heights upper than 100 m, It is reasonable, all of the analysis be taken according to wind class I. There are several wind turbines manufacturers producing wind turbines for the selected wind classes. For the purpose of verifying the wind potential at the targeted location, calculation of capacity factors (CF) for different wind turbine models, made by different manufacturers, have been carried out. (Fig. 7). The turbines were located at the site of the measurement mast. The calculations were performed on different hub heights of the wind turbines to find a proper hub height for harnessing maximum wind energy. Two turbines (class I) with 100 m hub height were used to study conditions upper than 100 m hub heights in the region. The turbines are given in Table 6. By applying the methodology described in Sections 4.5 and 4.6, capacity factor of each turbine is calculated. For the purpose of real CF calculation, it is required that losses due to the wind turbine downtime, wake, icing, power curve hysteresis and connection grid downtime, should be taken into account. These losses are calculated for each wind power plant separately. For a modern wind farm the total losses do not exceed 20% in the majority of cases [9]. The aim of this study is to calculate of wind energy potential of Mil-E Nader region. Therefore assuming 10% losses seems to be a good estimation. Fig. 7, shows the capacity factors for different wind turbines. The x-axis indicates the turbine’s names which are arrayed sequentially from minimum hub height (61.5 m) up to maximum hub height (100 m). It should be noted that the last two turbines are in IEC class I. It can be deduced that DF100-2500 wind turbine with 80 m hub height manufactured by Dongfang, is the best choice for this case. This turbine has the highest CF (43.71%) among all of the turbines. The net mean power output of this turbine is shown in Fig. 8. It is clear that the direction of maximum wind power is similar to the wind prevailing direction so that an ideal condition for production of wind power can be predicted. Wind power density of the region was calculated as 692 W/m2 at 50 m height which was sorted in the wind power class 6 according to Table 2. This value confirms the primary assumption of this document about high wind power potential of Mil-E Nader region.
Table 6 Specification of the used wind turbines in energy calculation. Manufacturer
Turbine
Wind turbine class
Hub height (m)
Rotor diameter (m)
Rated power (MW)
Alstom Dongfang Dongfang Goldwind Hanjin Hanjin Hyosung Hyundai Hyundai IMPSA Gamesa Repower
ECO 110/3000 DF77-1500 DF100-2500 GW 77 HJWT 1500-77 HJWT 2000-87 HS90 AV928 HQ1650 IWP-83 G80-2.0 3.4M104
IIa IIa IIa IIa IIa IIa IIa II II IIa Ia Up to Ib/IIa
7590 70 80 61.5, 85, 100 70, 80 85 80 80 70, 80 85 100 100
100 77 100 77 77 87 90.6 93.2 77, 82 83 80 104
3 1.5 2.5 1.5 1.5 2 2 2.5 1.65 2.1 2 3.4
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5. The important result derived from this study, encourages construction of wind farms at Mil-E Nader for electricity generation using large wind turbines (with capacity upper than 1000 kW) and use of wind turbine model ‘‘DF100-250000 with 2.5 MW capacity is recommended. 6. Mil-E Nader region has high wind power potential with value of 692 W/m2 at 50 m height which was sorted in the wind power class 6.
Acknowledgments The authors would like to acknowledge the renewable energy organization of Iran (SUNA) for their kindly help in providing the data. We are very grateful to Mr. Mohammad Ali Ramazani (deputy managing director in SUNA) for supporting research and investigation. References
Fig. 8. Wind power rose of DF100-2500 wind turbine.
6. Conclusions The investigations show that in the targeted Mil-E Nader region there is technically usable wind energy potential at 80 m hub height. The basic properties of the wind energy in the targeted region are: 1. 18 months average wind speeds at heights 10, 30 and 40 m are: 5.38 m/s, 6.84 m/s and m/s, 7.21 m/s respectively. 2. The wind rose is characterized by two dominant wind directions at 30 and 60. These directions are similar to minimum turbulence directions and maximum wind power production directions so that an ideal condition for production of wind power can be predicted. 3. During the summer season, high value of mean wind speed 11–13 m/s occurs (at height of 40 m), so the wind potential is sufficient at this season for high expected power generation. 4. The turbulence intensity of wind is lower than C category in IEC 61400-1 standard.
[1] Karsli VM, Geçit C. An investigation on wind power potential of Nurdag˘ıGaziantep. Turkey, Renew Energy 2003;280:823–30. [2] Ahmed S, Hanitsch AS. Electricity generation and wind potential assessment at Hurghada. Egypt, Renew Energy 2008;33:141–8. [3] Ozerdem B, Ozer S, Tosun M. Feasibility study of wind farms: a case study for Izmir. Turkey, J Wind Eng Ind Aerod 2006;94:725–43. [4] Sistan, Baluchestan Province. [accessed 03.06.13]. [5] Sistan, Baluchestan meteorological report. Iran Meteorological Organization. Official, report; 2001. [6] Hossenzadeh Sa’id R. One hundred and twenty – days winds of Sistan. J Geogr Res 1997:103–27. [7] Kamau JN, Kinyua R, Gathua JK. 6 years of wind data for Marsabit, Kenya average over 14 m/s at 100 m hub height. Anal Wind Energy Potential 2010;35:1298–302. [8] Kavak Akpinar E, Akpinar S. Determination of the wind energy potential for Maden-Elazig. Turkey, Energy Convers Manage 2004;45:2901–14. [9] Ðurišic´ Zˇ, Mikulovic´ J. Assessment of the wind energy resource in the South Banat region, Serbia. Renew Sustain Energy Rev 2012;16:3014–23. [10] IEC: Wind turbines – Part 1: Design requirements, IEC61400-1; 2005. [11] Kitaneh R, Alsamamra H, Aljunaidi A. Modeling of wind energy in some areas of Palestine. Energy Convers Manage 2012;62:64–9. [12] Zhou W, Yang H, Fang Z. Wind power potential and characteristic analysis of the Pearl River Delta region. China, Renew Energy 2006;31:739–53. [13] Elliott DL, Holladay CG, Barchet WR, Foote HP, Sandusky WF. Wind Energy Resource Atlas of the United States. DOE/CH 10093-4. Golden, Colorado: Solar Energy Research Institute; 1987. [14] Introduction to Wind Resource Data. Alaska Energy Authority. [accessed date: 05.06.13]. [15] Green Clean Guide. [accessed date: 02.06.13. [16] Wind Resource Assessment Handbook, Fundamentals for Conducting a Successful Monitoring Program, AWS Scientific, Inc. 1997.
Contents lists available at ScienceDirect
Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman
Wind resource assessment and wind power potential of Mil-E Nader region in Sistan and Baluchestan Province, Iran – Part 1: Annual energy estimation A. Tizpar, M. Satkin ⇑, M.B. Roshan, Y. Armoudli Wind and Wave Energy Department, Renewable Energy Organization of Iran, Iran
a r t i c l e
i n f o
Article history: Received 19 March 2013 Accepted 1 October 2013 Available online 8 January 2014 Keywords: Wind power potential Iran Capacity factor Mil-E Nader Sistan and Baluchestan
a b s t r a c t In this paper, wind power potential of Mil-E Nader region is statistically analyzed based on 10 min measured short term wind data. Weibull parameters at 40 m height have been estimated and used to describe the distribution of wind data and its frequencies. Additionally, diurnal and monthly wind speed variations have been calculated. Based on power law model and average wind speed at three heights (10, 30 and 40 m), wind speeds at higher elevations have been extrapolated. Energy analysis has been carried out to find best hub height by comparing energy production of several wind turbines with different classes and hub heights. The energy production analysis showed that the wind turbines with 80 m height have high production in comparison to the others. Ó 2014 Published by Elsevier Ltd.
1. Introduction Nowadays, scientists and the public are more concerned and sensitive to the need for environmental friendly energy sources. The energy demand is increasing continuously and the only way to fulfill this demand is to start using renewable energy sources. Wind is one of those major free, clean and inexhaustible renewable energy sources [1]. Wind is an abundant resource available in nature that could be utilized by mechanically converting wind power to electricity using wind turbines [2]. Technology of the extraction of power from wind with modern turbines is a well established industry, at present. Parameters such as improvement of wind farm efficiency as well as reducing wind turbine component costs, make wind power generation competitive to the conventional sources. Furthermore, the wind power has an additional advantage of being a non-polluting source of energy [3]. Preparing technical and economical feasibility study is a vital step before investing in a wind farm project. This study gives an outlook to investors about costs and economical aspects of a wind farm project. Accurate and proper information on a project conditions (such as site and wind characteristics) play a key role in wind farm feasibility studies. So, inappropriate information may lead to disinvestment and waste of money. In contrast with, select a suitable site for a wind turbine regarding to the parameters such as: turbine size, blade shape, capacity, etc. resulting in high ⇑ Corresponding author. Tel.: +98 21 88090209; fax: +98 21 88578807. E-mail address: [email protected] (M. Satkin). 0196-8904/$ - see front matter Ó 2014 Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.enconman.2013.10.004
efficiency of a wind farm. It is quite important to know several fundamental properties of the site such as wind behavior, availability, continuity, and probability in the proposed region. To make decisions with those properties, statistical and dynamic wind characteristics of the site should be found out using wind observations and statistical wind data [1]. This paper presents the detailed wind data analysis and wind availability at Mil-E Nader, an area in Sistan and Baluchestan Province of Iran.
2. Site description Sistan and Baluchestan Province is one of the 31 provinces of Iran. It is in the southeast of the country, bordering Pakistan and Afghanistan and its capital is Zahedan (Fig. 1a). The province is subject to seasonal winds from different directions, the most important of which are the 120-day wind of Sistan known as Levar, the Qousse wind, the seventh (Gav-kosh) wind, the Nambi or south wind, the Hooshak wind, the humid and seasonal winds of the Indian Ocean, the North or (Gurich) wind and the western (Gard) wind [4]. Mil-E Nader is an area near Zabol city in north eastern part of the province. The area is exposed to the 120-day wind of Sistan. The 120 day winds of Sistan blow from middle of May to Middle of September which affect large areas of Sistan and Baluchestan Province in eastern part of Iran. Continuous blowing as well as suitable wind speeds are the main characteristics of this kind of winds. Prevailing wind direction of these winds varies in different parts of
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(a)
(b)
Fig. 1. (a) Sistan va Baluchestan Province in Iran map and (b) location of Mil-E Nader wind monitoring station near Zabol city in Sistan va Baluchestan Province.
province. For targeted region (Mile-Nader), the prevailing wind direction is north eastern [5]. Based on Zabol local synoptic data, it can be found that the maximum monthly mean wind speed from middle may to middle September is 6.5–9 m/s [6]. So Mil-E Nader region is supposed to have great wind energy potential. Therefore a wind monitoring station was installed in this area in 2010 to collect wind regime data. The wind monitoring mast is located at 31° 050 1100 N, 61° 090 2300 E and its elevation is 481 m above the sea level (Fig. 1b). It is located in a flat area with a few obstacles around it. The mast was installed by renewable energy organization of Iran (SUNA) in order to measure the meteorological properties of the region. Lattice type mast with height of 40 m was used for this purpose and different types of sensors were mounted on it at heights of 10, 30 and 40 m. Data acquisition has done by wind anemometer, wind vane, temperature, radiation and humidity sensors.
2
1 4 3
3. Wind speed data The wind data used in this study were captured using a cup generator anemometer. The wind data were measured by 10 min sampling period at heights of 10, 30 and 40 m above ground level between years 2010–2012 (Fig. 2). The measurements include wind speed, wind direction, temperature, humidity and global radiation at the site. Technical specification of the mentioned sensors (using Theodor Friedrichs Company’s catalogues) is shown in Table 1. Data logger device records minimum, average, maximum, standard deviation values of wind speed and average, standard deviation values of wind direction. Additionally average values of temperature, humidity and global radiation is recorded. Maximum 144 data correspondent to the records mentioned above can be collected. These data should be validated and then processed to produce the summary reports of analysis. Afterwards, the collected data were analyzed and processed by using Windographer software. During the analysis process, the errors arising from sensors stopping, turbulent air flow, data logger malfunction, icing and etc. were flagged and removed.
Fig. 2. Met mast of Mil-E Nader’s wind monitoring station. 1: Anemometers, 2: Wind vane booms, 3: Humidity and temperature sensor, 4: Radiation sensor.
4.1. Probability distribution function Due to the variability of the wind speed it is found useful to plot the wind speed probability distribution function (the percentage of time that the wind spends at each speed) to understand the character of the variation. One of the commonly used functions for this purpose is the Weibull distribution given by:
f ðv Þ ¼ 4. Methodology Wind energy resource is highly variable both in space and time [7]. Therefore, to understand the characteristics of the resource, various parameters were considered.
k1 k v v k eð c Þ c c
ð1Þ
where f(v) is the probability of observing wind speed (v), c(m/s) is the Weibull scale parameter and k is the dimensionless shape factor. The cumulative probability function of the Weibull distribution is given by:
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Table 1 Technical specification of the sensors. No.
Sensor type
Manufacturer
Specifications
1
Wind anemometer
Theodor Friedrichs & CO
2 3
Wind vane Combined temperature/humidity sensor
Theodor Friedrichs & CO Theodor Friedrichs & CO
4
Radiation sensor
Theodor Friedrichs & CO
Measuring range: 0–70 m/s, Starting threshold: <0.3 m/s, Accuracy: ±0.2 m/s Measuring range: 360°, Initial deflection: <0.2 m/s Measuring range: 0–100% rH, Calibrating accuracy: ±1.5%rH, Operating temperature: (40) – (+45 °C), Max. error ±0.25% Viewing angel: 2p sr, Sensitivity: 9–15 lV/Wm2, Response time: 99% 55 s–63% 5 s
v
Fðv Þ ¼ 1 eð c Þ
k
ð2Þ
Determining k and c requires a good fit of Eq. (2) to the recorded discrete cumulative frequency function. Taking the natural logarithm of both sides of Eq. (2) twice [1,7,8] gives:
lnð lnð1 Fðv ÞÞ ¼ kðlnðv Þ lnðcÞÞ
ð3Þ
Plotting ln(ln(1F(v)) against ln(v) presents a straight line whose gradient is k and the y-intercept is –k ln(c) from which c can be calculated [7].
A Wind rose is a polar plot that represents the percentage of the time that the wind direction falls within the sector of the compass. This is important for determining the type of turbine to be used in a particular location and also its orientation to the wind stream [7]. In this paper wind direction is shown for two heights of 30 m and 37.5 m which are used as correspondent wind direction for near wind speed sensor height. 4.3. Wind speed extrapolation As stated before, the anemometer heights are 10 m, 30 m and 40 m. However, modern wind turbines (especially turbines in MW range) have hub heights longer than 60 m and usually have 80 m hub height. So in order to estimate harvested wind energy, wind speeds magnitude at greater heights are required. Therefore, equations that predict the wind speeds at one height in terms of the measured speed at another height are required. The power law model has received extensive application in the field of wind energy basically to estimate the hub height wind speeds at various potential sites [1,7,8]. The formula takes the form:
a z2 z1
Ii ¼
ri
ð5Þ
vi
The calculated mean turbulence value is used to determine turbulence classes according to IEC 61400-1 ed.3 [10].
4.2. Wind rose
v2 ¼ v1
longitudinal, lateral, and vertical components of the wind turbulence. For the selection and energy production of wind turbine, of special interest is the longitudinal component of the turbulence. As a measure of wind turbulence, intensity of turbulence I, defined as ratio of the standard deviation of wind speed and average wind speed m, is used. Intensity of turbulence can be calculated for each 10 min interval i according to the following relation [9]:
ð4Þ
where v2 is the extrapolated wind speed at height z2 and v1 is the measured speed at z1. The exponent a depends on such factors as nature of terrain (surface roughness), wind speeds and temperature for neutral stability the exponent value of 1/7 has widely been chosen as a good representative of the prevailing conditions [7]. The power law mathematical relation was used to extrapolate the wind speeds at the higher heights. 4.4. Wind speed turbulence Each deviation of the instantaneous value of wind speed from its average value within the corresponding 10 min interval represents turbulence. The causes of turbulence are obstacles on the soil surface, roughness of the terrain, and local dynamic variations of the air pressure and temperature. For this reason, vector of the wind speed should be regarded as a spatial vector of the time varying direction and intensity. Therefore, it is possible to define: the
4.5. Wind power density The maximum power P, available from the wind [7,8,11]:
P¼
1 qAv 3 2
ð6Þ
where q is the density of air and A is the swept area of the rotor. q is a function of the air pressure B and temperature T [11], thus:
q ¼ q0
288B 760T
ð7Þ
where q0 is the density of dry air at standard temperature and pressure (1.226 (kg/m3) at 288 K, 760 mm Hg). The actual amount would be less since all available energy is not extractable. Monthly or annual wind power density per unit area, PW of a site based on Weibull probability density function can be expressed as [7,9]:
PW ¼
1 3 3 qc 1 þ 2 k
ð8Þ
The two significant parameters k and c have been shown to be related to the mean value of the wind speed [7,8,12]:
v ¼ cC
1 1þ k
ð9Þ
where the mean wind speed value is used to determine wind turbine class according to IEC 61400-1 ed.3 [10]. The mean wind speed can be calculated by [12]:
v ¼
n 1 X vi n i¼1
! ð10Þ
And C is the upper incomplete gamma function defined as [12]:
CðxÞ ¼
Z
1
t x1 et dt
ð11Þ
0
Weibull parameter k can be calculated using the following approximation [12]:
k¼
r1:086
v
ð1 6 k 6 10Þ
ð12Þ
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Table 2 Classes of wind power density at 10 m and 50 m. Wind power class
10 m (33 ft)
1 2 3 4 5 6 7
50 m (164 ft)
Wind power density (W/m)
Speed m/s (mph)
6100 6150 6200 6250 6300 6400 61000
64.4 65.1 65.6 66.0 66.4 67.0 69.4
(9.8) (11.5) (12.5) (13.4) (14.3) (15.7) (21.1)
Rating
Wind power density (W/m)
Speed m/s (mph)
6200 6300 6400 6500 6600 6800 62000
65.6 (12.5) 66.4 (14.3) 67.0 (15.7) 67.5 (16.8) 68.0 (17.9) 68.8 (19.7) 611.9 (26.6)
Poor Marginal Fair Good Excellent Outstanding Very outstanding
Table 3 Recovery ratio of data at different heights. Height (m)
Data columns
Valid data points
Possible data points
Data recovery rate (%)
40 30 10
1 1 1
73,401 73,401 66,230
80,109 80,109 80,109
91.62 91.62 82.67
where [8,12]:
"
r¼
n 1 X ðv i v Þ2 n 1 i¼1
#0:5 ð13Þ
The maximum extractable power PE (W/m2), by a system working at its optimum efficiency, is limited by the power coefficient called the Betz limit whose value is 16/27 or 0.593 and is therefore given by [7]:
PE ¼ 0:593P
ð14Þ
This power coefficient makes the maximum extractable power approximately 59.3% of the theoretical power density. 4.6. Wind power classes The wind power density incorporates in a single number the combined effect of the frequency distribution of wind speeds and the dependence of the wind power on air density and on the cube of the wind speed [13]. Classification of wind power density is a method of characterizing the power potential of the site and determining the viability of harnessing wind energy. There are seven wind classes which rates from poor to very outstanding [14]. Table 2 defines the wind power classes in terms of mean wind power density and mean wind speed at 10 m (98 ft) and 50 m (164 ft) above ground level [13]. 4.7. Plant load factor and capacity factor One of the most important wind energy parameters is the plant load factor (PLF). This factor is used in determining the annual energy output of wind turbines. PLF of a wind energy generator is the ratio of the actual output of it over one year (the power can be generated by wind turbine according to wind characteristics of the region) to its potential output which can be generated at rated power for the same year [15]: PLF ¼
Actual output of wind turbine for one year Potential output which can be generated at rated power for one year ð15Þ
However, the capacity factor Cf, is a very significant index of productivity of a wind turbine. It represents the fraction of the total energy delivered over a period Eout, divided by the maximum
Fig. 3. Probability distribution function at 40 height vs. the measured wind speed data.
Table 4 Weibul parameters of measured data at different heights. Wind speed sensor’s height (m)
Weibull (k)
Weibull c (m/s)
40 30 10
1.6 1.55 1.32
8.07 7.62 5.85
energy that could have been delivered if the turbine was used at maximum capacity over the entire period, Er = 8760Pr. The capacity factor Cf of a wind turbine can be calculated as [2]:
Cf ¼
Eout Er
ð16Þ
5. Results and discussion For the analysis of wind characteristics and wind energy potential of the Mil-E Nader region, the measurement data for the 18 months period from 01/09/2010 at 1:30 to 10/03/2012 at 9:00 have been used. Verifying measurement indicate that about 0.01% of the observation data (5 raw of observation data) were missing. These missing data were due to machine calibration, servicing and malfunction machine. Data for other periods were checked but unfortunately big gap for the sensor in 10 m height
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Fig. 4 (continued)
Fig. 4. (a) Mean diurnal profile and (b) monthly average wind speeds profile.
was encountered. This big gap originated by sensor malfunction. During data collection period, the wind anemometer at 10 m height was defected and eventually replaced with new one. Unfortunately the second one had the same problem and was replaced again. So, some data in the specified period of time has lost. Before beginning statistical analysis, data should be verified and validated. The validated data will be used in statistical analysis. The validation process is done by data screening and data verification methods [16]. In this step all of the collected data are inspected for completeness and reasonableness. Then the erroneous values were eliminated. So the collected data were verified and the invalid data were removed. Finally, the validated data which had data recovery rate of 91.62% at 40 m height was produced. Comprehensive description of possible and valid data points together with data recovery rate at different heights is shown in Table 3. Statistical processing of the data, comprising: the frequency of occurrence of wind speed and direction and the corresponding probability distribution function, wind rose, monthly and diurnal variations in wind speed, has been carried out.
Table 5 Average of measured wind speed data at heights of 10, 30 and 40 m as well as extrapolated wind speeds up to 100 m heights. Height (m)
Mean wind speed (m/s)
100 90 85 83 80 70 40 30 10
8.77 8.58 8.47 8.43 8.37 8.13 7.21 6.84 5.38
the related region. The Weibull scale parameter (c), varies from 5.85 m/s at 10 m height to 8.07 m/s at 40 m height for the frequency distribution of wind speed. The Weibull shape parameter (k), varies from 1.32 at 10 m height to 1.60 at 40 m height for frequency distributions of wind speed as well. Table 4, shows the Weibull parameters of data at different heights. 5.2. Monthly and diurnal average value of the wind speed
5.1. Weibull parameters Fig. 3 shows the probability distribution function at 40 m measurement height based in Eq. (1), compared to the measured wind speed data where the wind speed is within the interval given by the width of the columns (here 0.5 m/s is selected). Parameters of the Weibull distribution (k and c) have been calculated according to Eqs. (9) and (12). A wind speed frequency histogram is obtained by dividing the number of observations in each interval by the total number of observations in the data set. These actual wind speed observations were fitted in the widely wind energy by Weibull distribution [9]. Weibull distributions were applied in order to wind potential evaluation and estimation of wind turbine annual production in
Fig. 4, shows average wind speeds, hour by hour and month by month, for all three heights during 18 months measurement period. It can be seen, spring and summer months are windier than other seasons. Regarding to higher electrical energy consumption in spring and summer compared to autumn and winter seasons, it is certainly deducted that this condition has required compatibility with energy consumption of the region. Mean diurnal profile (Fig. 4a) shows that at 40 m and 30 m heights, the maximum and minimum wind speeds are nearly occurred at 8:00–12:00 am and 16:00–20:00 pm respectively. As it mentioned in the previous section, there is a notable gap in monthly average values at height of 10 m as shown in Fig. 4b. which caused by anemometer sensor malfunction.
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Fig. 5. (a) Wind shear profile at the location measurement mast and (b) wind rose at the location of measurement mast.
Fig. 6. (a) Turbulence intensity of wind at height of 40 m vs. wind speed data and (b) distribution of turbulence intensity of wind speed data in different direction.
5.3. Wind shear profile Fig. 5a shows the wind speed variations vs. different heights at the location of the measurement mast. The diagram was drawn by averaging wind speeds at three heights. Then the power law exponent (equals 0.212) was calculated by entering these values in Eq. (4). The average of measured wind speed data at heights of 10, 30 and 40 m as well as extrapolated wind speeds up to 100 m height are shown in Table 5. 5.4. Wind rose Distribution of wind speeds in different directions at height of 40 m is shown in Fig. 5b. It can be seen that the prevailing wind direction with frequency of 30% is in 60 corresponding to geographical direction ENE. 5.5. Turbulence
Fig. 5 (continued)
Calculation of wind turbulence intensity was carried out by using Eq. (5). Fig. 6a shows the diagram of wind turbulence
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Fig. 6b, shows the distribution of turbulence intensity in different directions by a polar diagram. This diagram was drawn by data of direction sensor at height of 37.5 m. It indicates that according to the wind rose diagram, there is minimum turbulence in prevailing wind direction. 5.6. Energy production and wind power density
Fig. 6 (continued)
CF (%)
Capacity factor of wind turbines
Fig. 7. Capacity factors vs. the different wind turbines.
intensity at height of 40 m vs. wind speed data. According to Fig. 6a, the turbulence intensity of wind is lower than C category in IEC 61400-1 standard. It can be concluded that the targeted region is characterized by a low level of wind turbulence class.
On the basis of presented statistical analysis of the measured data (Table 5) and analysis of the wind turbulence, it is concluded that according to IEC 61400-1-2005 standard, Mil-E Nader region is defined as medium wind class (IEC class II-C). However, for the heights upper than 100 m, It is reasonable, all of the analysis be taken according to wind class I. There are several wind turbines manufacturers producing wind turbines for the selected wind classes. For the purpose of verifying the wind potential at the targeted location, calculation of capacity factors (CF) for different wind turbine models, made by different manufacturers, have been carried out. (Fig. 7). The turbines were located at the site of the measurement mast. The calculations were performed on different hub heights of the wind turbines to find a proper hub height for harnessing maximum wind energy. Two turbines (class I) with 100 m hub height were used to study conditions upper than 100 m hub heights in the region. The turbines are given in Table 6. By applying the methodology described in Sections 4.5 and 4.6, capacity factor of each turbine is calculated. For the purpose of real CF calculation, it is required that losses due to the wind turbine downtime, wake, icing, power curve hysteresis and connection grid downtime, should be taken into account. These losses are calculated for each wind power plant separately. For a modern wind farm the total losses do not exceed 20% in the majority of cases [9]. The aim of this study is to calculate of wind energy potential of Mil-E Nader region. Therefore assuming 10% losses seems to be a good estimation. Fig. 7, shows the capacity factors for different wind turbines. The x-axis indicates the turbine’s names which are arrayed sequentially from minimum hub height (61.5 m) up to maximum hub height (100 m). It should be noted that the last two turbines are in IEC class I. It can be deduced that DF100-2500 wind turbine with 80 m hub height manufactured by Dongfang, is the best choice for this case. This turbine has the highest CF (43.71%) among all of the turbines. The net mean power output of this turbine is shown in Fig. 8. It is clear that the direction of maximum wind power is similar to the wind prevailing direction so that an ideal condition for production of wind power can be predicted. Wind power density of the region was calculated as 692 W/m2 at 50 m height which was sorted in the wind power class 6 according to Table 2. This value confirms the primary assumption of this document about high wind power potential of Mil-E Nader region.
Table 6 Specification of the used wind turbines in energy calculation. Manufacturer
Turbine
Wind turbine class
Hub height (m)
Rotor diameter (m)
Rated power (MW)
Alstom Dongfang Dongfang Goldwind Hanjin Hanjin Hyosung Hyundai Hyundai IMPSA Gamesa Repower
ECO 110/3000 DF77-1500 DF100-2500 GW 77 HJWT 1500-77 HJWT 2000-87 HS90 AV928 HQ1650 IWP-83 G80-2.0 3.4M104
IIa IIa IIa IIa IIa IIa IIa II II IIa Ia Up to Ib/IIa
7590 70 80 61.5, 85, 100 70, 80 85 80 80 70, 80 85 100 100
100 77 100 77 77 87 90.6 93.2 77, 82 83 80 104
3 1.5 2.5 1.5 1.5 2 2 2.5 1.65 2.1 2 3.4
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5. The important result derived from this study, encourages construction of wind farms at Mil-E Nader for electricity generation using large wind turbines (with capacity upper than 1000 kW) and use of wind turbine model ‘‘DF100-250000 with 2.5 MW capacity is recommended. 6. Mil-E Nader region has high wind power potential with value of 692 W/m2 at 50 m height which was sorted in the wind power class 6.
Acknowledgments The authors would like to acknowledge the renewable energy organization of Iran (SUNA) for their kindly help in providing the data. We are very grateful to Mr. Mohammad Ali Ramazani (deputy managing director in SUNA) for supporting research and investigation. References
Fig. 8. Wind power rose of DF100-2500 wind turbine.
6. Conclusions The investigations show that in the targeted Mil-E Nader region there is technically usable wind energy potential at 80 m hub height. The basic properties of the wind energy in the targeted region are: 1. 18 months average wind speeds at heights 10, 30 and 40 m are: 5.38 m/s, 6.84 m/s and m/s, 7.21 m/s respectively. 2. The wind rose is characterized by two dominant wind directions at 30 and 60. These directions are similar to minimum turbulence directions and maximum wind power production directions so that an ideal condition for production of wind power can be predicted. 3. During the summer season, high value of mean wind speed 11–13 m/s occurs (at height of 40 m), so the wind potential is sufficient at this season for high expected power generation. 4. The turbulence intensity of wind is lower than C category in IEC 61400-1 standard.
[1] Karsli VM, Geçit C. An investigation on wind power potential of Nurdag˘ıGaziantep. Turkey, Renew Energy 2003;280:823–30. [2] Ahmed S, Hanitsch AS. Electricity generation and wind potential assessment at Hurghada. Egypt, Renew Energy 2008;33:141–8. [3] Ozerdem B, Ozer S, Tosun M. Feasibility study of wind farms: a case study for Izmir. Turkey, J Wind Eng Ind Aerod 2006;94:725–43. [4] Sistan, Baluchestan Province.