Estimation of the spatial distribution pattern of wind speed for assessment of wind energy potential in Iran

Document Type : Full length article


1 Associate Professor, Water Engineering Department, Faculty of Water and Soil, University of Zabol, Iran

2 Instructor of Water Engineering, Faculty of Water and Soil, University of Zabol, Iran

3 PhD Candidate in Irrigation and Drainage, Water Engineering Department, Faculty of Water and Soil, University of Zabol, Iran


Nowadays, the exploitation of the renewable energy sources such as wind plays a key role in human life. Although, Iran has a high potential for wind power generation, there is not an efficient energy planning yet. Environmental variables such as wind speed have variations according to spatial points. It seems reasonable to consider that there exists a spatial correlation between wind speed data at different locations. In geostatistics the spatial autocorrelation of data could be investigated by calculating the experimental semivariogram. The parameters of the fitted semivariogram model may be used to estimate the wind speed at unknown locations through kriging algorithms.
In order to describe the behaviour of wind speed at a particular location, the data distribution should be first fitted by a suitable distribution function. There are different wind speed distribution models used to fit the wind speed distributions over a period of time. Among them, Weibull distribution function has been found to be the best all over the world because of its great flexibility and simplicity.
The aim of this study is to simulate the daily mean and maximum wind speed probability distribution using Weibull distribution function and to investigate spatial variability of the wind speed data. This study was also aimed to interpolate the means of Weibull distribution functions of daily mean wind speed data observed at stations over Iran.
Materials and Methods
Study area and data set
The study is based on a long term (20 years) wind data recorded in 104 synoptic stations spread over Iran. The wind data are recorded at 10m above the ground level (a.g.l.) and contain daily mean and maximum wind speed (m/s).
The Weibull distribution function
For each site, the daily mean and maximum wind speed data were fitted by a two-parameter Weibull distribution, whose parameters (shape and scale) were determined through the maximum likelihood (ML) technique. The Weibull probability density function is defined as follows:
where V is wind speed (m/s), 𝑐 is the scale parameter (m/s) and 𝑘 is shape parameter (dimensionless). The high and low 𝑘 values indicate the sharpness and the broadening of Weibull peak, respectively. The Weibull probability density function curve could be displayed if the 𝑘 and 𝑐 values are obtained. This could be conducted through different ways, such as maximum likelihood method as:
where Vj is the wind speed for jth sample and n is the number of sample data. Equation (3) is an implicit equation and could be solved through an iteration method.
Two interpolation methods including inverse distance weighing and ordinary kriging were used to estimate the theoretical mean values of the previously determined Weibull distributions of the wind speed data at unsampled locations.
Inverse Distance Weighing (IDW)
In absence of data spatial autocorrelation, IDW is usually used as an alternative method for spatial estimation of random field. IDW is a weighted averaging interpolator in which data is weighted according to their distance to the estimation point such that more distant points get less weight than closer points.
Ordinary Kriging (OK)
The OK is the most popular kriging approach used in the spatial interpolation of the regionalized variables. It needs the parameters of the best fitted semivariogram model to incorporate spatial dependence of data on the estimation process. The semivariogram quantifies the dissimilarity between observations as the separation distance between them increases.
Results and Discussion
According to the obtained results, Semnan and Bandar-Abbass had the lowest and highest shape (k) factor of the fitted weibull distribution functions to the daily maximum wind speed data, respectively. For daily mean wind speed data, Nehbandan and Bandar-Abbass had the lowest and highest shape (k) factor of the fitted theoretical Weibull distributions, respectively. A high k value means less variation of the wind speed.
The annual duration of daily wind velocity exceed 4 m/s. It is also calculated for each site in order to obtain the first diagnostic sign of most promising areas in terms of wind energy potential. According to the results, the cities of Rafsanjan, Zabol, Torbate Jam, Khodabandeh, Ardebil, Bijar and Kahnouj have the highest potential in high wind speed.
The auto-correlation analysis showed that wind speed is moderately correlated in space with spatial structure model of spherical and a correlation distance of about 500 km (Figure 1 (a)). There was no apparent drift within the range of 500 km. The best semivariogram model was selected according to the cross validation results as well as the highest correlation coefficient (r) and the lowest residual sum of squares (RSS) functionally of GS+ software.
To predict the spatial distribution pattern of wind speed over Iran, Weibull mean wind speed data were interpolated over a point grid superimposed to the map of Iran using IDW and OK. The cross validation results indicated that both methods performed similarly. However, the maps generated were visually different. Besides, unlike IDW, OK represented the map of estimation error which is useful in decision-making as it provides a measure of uncertainty.
According to wind speed map generated by OK (Figure 1 (b)), eastern Iran (e.g. the cities of Zabol, Rafsanjan and Torbate Jam) and northwestern provinces (e.g. Ardebil) are the most promising areas for wind energy planning. 
The spatial variability of wind speed and duration across Iran has been investigated. First, the frequency distribution of daily mean and maximum wind speed data during recent 20 years was simulated by Weibull function. Then, the mean values of the theoretical Weibull probability distribution functions are used to investigate the spatial variability and predict the spatial distribution pattern of wind speed across the country. According to the results, wind speed is moderately correlated in space with an influence range of about 500 km. The maps of wind speed at 10 m a.g.l. generated using IDW and OK encourage the utilization of wind energy on the eastern (e.g. Rafsanjan, Zabol, Torbate Jam) and northwestern (e.g. Ardebil) regions. Besides, additional measurements may be considered in the areas of highest estimation of uncertainty (e.g. center and eastern parts).
(a)                                                                                (b)



Fig. 1. Experimental semivariogram along with the best fitted model (a) and the interpolation map of mean wind speed at 10 m a.g.l. generated by OK (b)


Main Subjects

امیدوار، ک. و دهقان طزرجانی، م. (1391). پتانسیل سنجی و برآورد مشخصه‌های نیروی باد برای تولید انرژی در ایستگاه‌های همدیدی استان یزد، فصلنامةتحقیقاتجغرافیایی، 27(2): 149-168.
ثقفی، م. (1382). انرژی‌های تجدیدپذیر نوین. مؤسسة انتشارات و چاپ دانشگاه تهران، چاپ دوم.
جعفری، ح.؛ عزیزی، ع.؛ نصیری، ح. و عابدی، س. (1392). تحلیل تناسب اراضی جهت استقرار نیروگاه‌های بادی در استان اردبیل با استفاده از مدل AHP و SAW در محیط سیستم اطلاعات جغرافیایی (GIS)، علوموتکنولوژیمحیطزیست، 15(2): 23-41.
سایت تابناک. (1392).
صلاحی، ب. (1382). پتانسیل‌سنجی انرژی باد و برازش احتمالات واقعی وقوع باد با استفاده از تابع توزیع چگالی احتمال ویبول در ایستگاه‌های سینوپتیک استان اردبیل، فصلنامة تحقیقات جغرافیایی، 72: 78-104.
گندم‌کار، ا. (1388). ارزیابی انرژی پتانسیل باد در کشور ایران، مجلةجغرافیاوبرنامه‌ریزیمحیطی، 36(4): 85-100.
مجرد، ف. و همتی، ش. (1392). ارزیابی قابلیت‌های انرژی باد در استان‌های کرمانشاه و کردستان، نشریةتحقیقاتکاربردیعلومجغرافیایی، 29: 137-157.
Bagiorgas, H.S.; Giouli, M.; Rehman, S. and Al-Hadhrami, L.M. (2011). Weibull Parameters Estimation Using Four Different Methods and Most Energy Carrying Wind Speed Analysis, International Journal of Green Energy, 8: 529–554.
Bayem, H.; Petit, M.; Dessante, Ph.; Dufourd F. and Belhomme, R. (2007). Probabilistic Characterization of Wind Farms for Grid Connection Studies, EWEC "European Wind Energy Conference & Exhibition", 7-10, Milan.
Celluraa, M.; Cirrincioneb, G.; Marvugliaa, A. and Miraouic, A. (2008). Wind speed spatial estimation for energy planning in Sicily: Introduction and statistical analysis, Renewable Energy, 33: 1237–1250.
Daniel, A.R.; Chen, A.A. (1991). Stochastic simulation and forecasting of hourly average wind speed sequences in Jamaica. Sol Energy, 46: 1–11.
Delbari, M.; Afrasiab, P. and Jahani, S. (2013). Spatial interpolation of monthly and annual rainfall in northeast of Iran, Meteorology and Atmospheric Physics, 122(1-2): 103-113.
ESRI (Environmental Systems Research Institute Inc) (2004). ArcGIS 9. Getting Started with ArcGIS. ESRI, Redlands.
Goovaerts, P. (1997). Geostatistics for natural resources evaluation. Oxford University Press, New York.
Isaaks, E.H. and Srivastava, R.M. (1989). An Introduction to Applied Geostatistics, New York: Oxford University Press.
Keyhani, A.; Ghasemi-Varnamkhasti, M.; Khanali, M. and Abbaszadeh, R. (2010). An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran. J. Energy, 35: 188–201.
Luo, W.; Taylor, M.C. and Parker, S.R. (2008). A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales, Int. J. Climatol, 28: 947–959.
Mojarrad, F. and Hemmati, Sh. (2013). Evaluation of wind energy potentials in Kermanshah and Kurdistan, Applied research in geographical science, 13(29): 137-157.
Mostafaeipour, A.; Sedaghat, A.; Dehghan-Niri, A.A. and Kalantar, V. (2011). Wind energy feasibility study for city of Shahrbabak in Iran, Renewable and Sustainable Energy Reviews, 15: 2545– 2556.
Phillips, D.L. and Marks, D.G. (1996). Spatial uncertainty analysis: propagation of interpolation errors in spatially distributed models, Ecological Modelling, 91: 213-229.
Robertson, G.P. (2000). GS+: Geostatistics for the environment sciences. GS+ User´s Guide Version 5, Plainwell, Gamma design software, 200 p.
Saghafi, M. (2003). The new renewable energies, 2nd ed., Tehran university press.
Salahi, B. (2004). Evaluation of Wind Energy and Fitting of Actual Probabilities of Wind Occurrence with Using Weibull Probability distribution Function at Synoptic Station of Ardebil Province, Journal of Geographical Research, 72: 87-104.
Stevens, M.J.M. and Smulders, P.T. (1979). The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes, Wind Eng., 3(2):132–45.
Tackle, E.S. and Brown, J.M. (1978). Note on the use of Weibull statistics to characterize wind speed data, J Appl Meteorol, 17: 556–9.