Investigating potential of wind energy i

WIND ENGINEERING Volume 39, No 4, 2015 PP 369–384 369 Investigating Potential of Wind Energy in Mahshahr, Iran Abbas Asakereh+, Mahmoud Omid*, Reza Alimardani and Fereydoon Sarmadian Faculty of Agricultural Engineering and Technology, School of Agricultural and Natural Resources, University of Tehran, P.O Box 4111, Karaj 31587-77871, Iran Received 04/15/2014; Revised 04/08/2015; Accepted 04/08/2015 ABSTRACT In this paper, measured time-series wind speed data in two sites in Mahshahr (Iran) were analyzed to find wind energy potential of this region The objective was to evaluate the most important characteristics of wind energy in the studied sites using means of meteorological and Weibull distribution functions The statistical approach was used to estimate mean wind speed, wind speed distribution function, mean wind power density and mean wind energy density of the sites at heights of 10 and 40m Annual mean wind speeds at heights of 10 and 40 m above the ground were 4.11 and 6.08 m/s, respectively The results revealed wide variation in the values of monthly power and energy densities in the region The lowest and highest values of mean wind energy densities at height of 10 m were 30.8 and 111.8 kWh/m2/month in July and January, respectively The winds had mean energy density of 1210 kWh/m2/year at 40 m height It can be concluded that Mahshahr region has a relatively good potential for harnessing winds Keywords: wind energy, wind power density, weibull distribution, Mahshahr, Iran INTRODUCTION Wind presents an attractive source of renewable energy for many countries Fossil fuels have limited resources and, at current rates of exploitation, they are expected to deplete within the next century [1, 2] Furthermore, their over-utilization can cause environmental degradation due to incomplete combustion when used as an energy source In addition to these issues, as the world population increases, demand for energy resources increases as well These cases are the main reasons why clean, sustainable and environmentally friendly alternative energy resources are currently sought Renewable energy has the capacity to provide cost-effective energy for remote communities without added investment of providing fossil generation By 2050, demand for energy could be doubled or even tripled as global population grows and developing countries expand their economies Accordingly, all aspects of energy production and consumption, including energy efficiency, clean energy, global carbon cycle, carbon sources, sinks and biomass and their relationships with climatic and natural resource issues should be explored [3] Among various renewable energy sources, wind power provides the lowest production cost and has the smallest environmental impact Wind power seems to be a very promising investment for the next two decades Investment in wind power is an opportunity, not just for power producers, but for consumers and manufacturing facilities as well A do-it-yourself wind turbine with capacity of about one kW or less is available to the residential; so they can install their own wind turbines Similarly, large scale wind turbines are available for manufacturing facilities [4] The number of installed wind power plants is increasing every year and many nations have made plans to make large investments in wind power in near future As wind energy has proven to be an effective renewable and emission-free power generation technology, it should be at the forefront of any transition to renewable energies [5] Wind energy does not have a transportation problem and does *Corresponding Author: E-mail address: Omid@ut.ac.ir +Abbas Asakereh currently works at Faculty of Biosystems Engineering, Shahid Chamran University, Ahvaz, Khuzestan Province, I.R.Iran 370 Investigating Potential of Wind Energy in Mahshahr, Iran not require a high technology for utilization It has many advantages like cleanliness, low cost and abundance everywhere on the world In order to get benefits from wind energy sources, it must be first converted into a different energy type The kinetic energy in wind is converted into mechanical energy, which is then converted into electrical energy Wind electricity generation systems convert wind energy into electricity by means of wind turbines [6, 7] The technology of converting wind energy to other energy types is more economical compared to other conversion systems Wind is a plentiful source available in the nature which could be utilized by mechanically converting it to electrical energy using wind turbines In the last two decades, the potential of wind power has been studied in many countries worldwide [6] Prediction of wind speed is essential for the characterization of wind energy resources Because power output of a wind turbine is proportional to the third power of the wind speed, wind energy content may vary significantly from one region to another However, potential of wind energy is not easily estimated because, contrary to solar energy, it depends on site characteristics and topography to a large degree since wind speed is strongly influenced by local topographical features [8] The classification and characterization of an area in terms of being high or low in wind potential requires significant efforts because wind speed and direction present extreme transitions in most sites and demand detailed study of spatial and temporal variations of wind speed values The objective of this study was two-fold: First, to evaluate the most important characteristics of wind energy in Mahshahr, located in southwestern part of Iran Second, to find out the potential of wind energy in the region Two methods of meteorological and Weibull distribution function were used to estimate average wind speed, wind speed distribution function, mean wind power and energy densities in two sites at heights of 10 and 40 m MATERIAL AND METHODS 2.1 Data collection and site description Mahshahr, located in Khuzestan province of Iran, has average annual rainfall of only 240 mm and also is the hottest region in northern coast of the Persian Gulf Its altitude and area are m above the sea level and over 7,300 km2, respectively The data on wind speed for this study were taken from two sites: (1) Meteorological center at Bandar-e Mahshahra airport (Longitude: 49° 09’ E, Latitude: 30° 33’ N), and (2) Site of Iran’s Power Ministry in the northeast of Mahshahr port (Longitude: 49°13’ E, Latitude: 30° 36’ N) The meteorological masts with 40 m height were installed in suitable coordinates by Power Ministry The applied data logger had three sensors for measuring wind velocities at 10, 30 and 40 m heights and also two sensors for measuring wind directions at 30 and 37.5 m heights without any extrapolation [9] Measured wind speed and direction data (on the bases of three-hour periods) were recorded from 1988 to 2009 at 10 m for Mahshahr airport site and wind speed and direction were collected over the one year period (2008) in the time interval of 10 at 10 and 40 m heights in the site of Iran’s Power Ministry and were recorded in SI unit (m/s) As the requirement of statistical processing, all the units were converted to SI (m/s) 2.2 Analysis of wind data 2.2.1 Wind speed distribution Statistical analysis can be used to determine the wind energy potential and the wind energy output in these sites Their use requires a wide range of applications from the techniques used for identifying parameters of distribution functions [10–12] to the utilization of these functions for analyzing wind speed data and wind energy economics [13, 14] To describe the statistical distribution of wind speed, various probability functions have been suggested as appropriate models for wind speed [15–23] In order to calculate the mean power from a wind turbine over a range of mean wind speeds, a generalized expression is needed for the probability density distribution an expression which gives a good fit to wind data is known as the Weibull distribution Currently, this statistical method is widely accepted for evaluating local wind load probabilities and can be almost considered as a standard approach [3, 13, 24] WIND ENGINEERING Volume 39, No 4, 2015 371 The Weibull probability density function can be written as [25, 26]: ⎛ k⎞ ⎛U⎞ P(U ) = ⎜ ⎟ ⎜ ⎟ ⎝ c⎠ ⎝ c ⎠ k −1 ⎡ ⎛U⎞k ⎤ exp ⎢ − ⎜ ⎟ ⎥ ⎣ ⎝ c⎠ ⎦ (1) where P(U) is probability of observing wind speed U In order to fit wind speed data to this equation, we need a value for the shape factor k (dimensionless) and c, scale factor k is often obtained by some form of fitting procedure to the measured probability distribution but this is unnecessarily complicated Higher values of k indicate sharper peaked curves while lower k means flatter or more evenly distributed speeds Often, wind turbine manufacturers provide standard performance figures for their turbines using eqn (1) [27] To fit eqn (1) to the measured data, following form are used to find k and c [3,25]: N σu = ∑ (U i =1 i −U) (2) N −1 ⎛σ ⎞ k=⎜ u⎟ ⎝ U⎠ −1.086 (3) c k 2.6674 = U 0.184 + 0.816 k 2.73855 (4) − where σu and U represent the standard deviation and average of wind speed, respectively The average of wind speed is obtained by: U= N ∑Ui N i =1 (5) And, the σu is defined [25]: σu = U Γ (1 + / k ) −1 Γ (1 + / k ) (6) One way to define probability density function (PDF) is that the probability of wind speed occurring between Ua and Ub is given obtained by [25, 28]: Ub P(U a < U < U b ) = ∫ P (U )dU (7) Ua Also, the total area under probability distribution curve is given by: ∞ ∫P(U )dU = (8) Cumulative distribution function represents the time fraction or probability that the wind speed is smaller than or equal to a given wind speed, U It is obtained by [3, 25]: ⎡ ⎛U⎞k ⎤ F (U ) = − exp ⎢ − ⎜ ⎟ ⎥ ⎣ ⎝ c⎠ ⎦ (9) 2.2.2 Turbulence intensity Wind turbulence is caused by dissipation of its kinetic energy into thermal energy via the creation and destruction of progressively smaller eddies (or gusts) In the wind energy industry, turbulence 372 Investigating Potential of Wind Energy in Mahshahr, Iran is quantified with a metric called turbulence intensity (TI) – the standard deviation of the horizontal wind speed divided by the average wind speed over some time period If the wind fluctuates rapidly, then the turbulence intensity will be high Conversely, steady winds have a lower turbulence intensity TI is obtained by [3, 28]: TI = σU U (10) Turbulence intensity for the site of Iran’s Ministry of Power was surveyed for a one year period (2008) in the time interval of 10 2.2.3 Power and energy density The best way to evaluate the wind resource available at a potential site is by calculating the wind power density The power of the wind that flows at speed v through a blade sweep area, A, is proportional to the third power of the wind speed Wind turbines for grid electricity therefore need to be especially efficient at greater wind speeds The wind power density (P̅ /A) is: ∞ P 1 3⎞ ⎛ = ρ ∫U P (U ) dU = ρ c Γ ⎜ + ⎟ ≈ ρ U ⎝ A c k⎠ 2 (11) where ρ is standard air density For standard conditions (sea level, ISOC), the density of air is 1.225 kg/m3 and P̅ /A is in Watt per square meter (W/m2) The wind energy density is given by: E ⎛ P⎞ = (NΔ t) A ⎜⎝ A ⎟⎠ (12) where N is the number of measurement periods, ∆t The Betz limit calculates the maximum power that can be extracted from the wind, independent of the design of a wind turbine in open flow According to it, no turbine can capture more than 59.3 percent of the kinetic energy in wind [29] Another significant wind speeds characteristic for wind energy estimation is the wind speed carrying maximum energy that can be used to estimate the wind turbine design or rated wind speed It can be expressed respectively as [3]: ⎛ k + 2⎞ U me = c ⎜ ⎝ k ⎟⎠ 1/ k (13) The most probable wind speed is important wind characteristic which corresponds to the peak of the probability density function It can be also found from: ⎛ 1⎞ U mp = c ⎜ − ⎟ ⎝ k⎠ 1/ k (14) RESULTS 3.1 Average wind speed The wind speeds in different years and months from Mahshahr airport site at the height of 10 m and wind speeds in different months from site of Iran’s Ministry of Power at the heights of 10 and 40 m − are shown in Table The monthly mean wind speed values U and standard deviations σ for Mahshahr port during 1988–2009 are also presented in Table The trends of the monthly means were similar for different years Most of the average wind speeds were in the range of and m/s with a frequency of approximately 33.0% Remaining frequencies in the data were as follows: less than m/s (2.1%); to m/s (18.6%); to m/s (25.0%); to m/s (12.5%) and greater than m/s (8.7%) In the studied period, the mean maximum wind speed was 7.5 m/s during January 1993 and the mean minimum wind speed was 1.73 m/s in December 1999 The maximum wind speed belonged to years 1988 and 1993 with 5.16 while the minimum wind speed belonged to year 2005 with 3.17 Having analyzed the 264 months of wind speed data, it could be concluded that the ParaMonth meter 1988 − Jan U 3.01 σ 2.92 − Feb U 4.89 σ 3.75 − Mar U 5.73 σ 3.59 − Apr U 5.01 σ 3.11 − May U 6.73 σ 3.88 − Jun U 6.96 σ 4.09 − Jul U 5.66 σ 3.28 − Aug U 5.70 σ 3.18 − Sep U 5.21 σ 3.50 − Oct U 4.49 σ 2.85 − Nov U 4.35 σ 2.79 − Dec U 4.06 σ 2.87 year υ 5.16 σ 3.50 1990 3.22 2.81 3.98 3.16 4.71 3.23 5.48 3.46 5.82 3.13 6.21 3.66 6.28 3.76 5.75 3.58 4.42 3.12 3.88 3.23 3.28 3.39 2.68 2.79 4.71 3.48 1989 3.92 3.05 3.69 2.64 4.60 3.78 4.24 3.08 6.62 3.69 6.10 3.64 4.47 3.11 4.81 3.63 5.15 3.44 3.75 3.39 3.67 2.69 3.56 3.01 4.71 3.50 2.88 3.06 3.72 3.12 3.99 3.32 6.07 3.32 7.03 3.60 6.49 4.02 6.26 3.72 6.12 3.77 5.23 3.30 4.61 3.42 3.37 2.94 4.92 3.29 5.07 3.66 1991 3.39 3.32 4.19 3.70 3.65 2.95 5.17 3.65 5.70 3.63 6.94 4.01 7.01 3.78 5.22 3.55 4.49 3.44 3.65 3.16 4.50 3.12 4.23 3.26 4.84 3.66 1992 3.25 2.82 4.15 3.68 5.12 3.43 5.62 3.43 6.77 3.59 7.50 3.77 5.84 3.48 5.46 3.57 4.74 3.21 2.83 2.56 5.07 3.53 2.93 2.61 5.16 3.78 1993 3.42 3.26 4.70 3.72 4.47 3.36 4.62 3.09 5.36 3.19 6.43 3.94 6.73 3.58 6.05 3.99 3.64 3.04 4.91 3.51 4.73 3.87 3.37 2.88 4.90 3.63 1994 3.73 3.35 2.80 2.99 3.58 3.53 4.37 3.32 6.07 3.41 4.90 3.47 6.99 3.65 5.85 3.71 5.06 3.37 3.85 2.84 3.82 2.95 2.53 2.49 4.38 3.51 1995 2.95 3.04 3.32 2.98 4.04 3.51 5.06 3.28 4.40 3.17 5.79 3.38 4.21 2.90 4.11 2.80 4.35 2.87 3.00 2.96 2.90 2.51 2.63 2.51 3.89 3.14 1996 2.75 2.76 3.84 2.88 3.27 3.10 3.89 3.46 3.53 3.10 5.20 3.28 6.15 3.93 6.16 3.36 3.70 3.29 3.09 3.29 2.38 2.99 1.85 2.63 3.82 3.46 1997 2.58 3.04 2.76 3.22 3.40 3.36 3.66 3.21 4.21 3.08 4.72 3.26 4.85 3.65 3.61 3.00 3.70 3.06 2.08 2.46 1.85 2.43 2.00 2.54 3.29 3.20 1998 2.53 2.81 3.12 3.06 3.39 3.33 3.87 3.39 4.36 3.44 4.75 3.33 5.06 3.16 3.65 2.84 4.45 3.55 2.68 2.74 3.15 3.00 1.73 2.54 3.97 3.99 1999 3.34 3.35 3.55 2.88 4.20 3.61 4.34 3.42 5.24 3.40 4.76 3.55 3.88 2.83 3.85 3.01 3.53 3.20 3.70 3.18 2.09 2.48 2.95 2.86 3.78 3.26 2000 2.81 2.67 3.00 2.96 3.14 2.95 3.98 3.03 5.71 3.35 5.61 3.46 4.92 3.19 3.71 2.44 2.96 2.60 2.49 2.31 3.11 2.63 2.89 2.55 3.70 3.06 2001 2.92 2.56 2.97 3.02 2.73 2.76 3.53 3.03 4.62 2.87 4.73 2.94 4.47 2.97 4.67 2.84 3.57 2.60 1.86 1.96 2.17 2.24 3.00 2.68 3.44 2.89 2002 2.33 2.51 3.49 3.10 3.80 3.03 4.56 3.16 3.28 2.88 4.47 2.84 5.30 3.03 3.29 2.49 2.98 2.78 3.16 2.68 2.42 2.53 2.71 2.38 3.54 3.01 2003 2.60 2.53 3.52 2.88 3.09 2.41 3.34 2.61 3.66 2.59 4.56 2.86 3.47 2.18 4.17 2.57 2.80 2.30 2.16 2.04 2.82 2.49 2.94 2.15 3.26 2.56 2004 2.23 2.41 2.77 2.48 3.21 2.34 3.69 2.49 3.42 2.56 4.63 2.81 4.22 2.63 3.44 2.62 3.34 2.42 2.67 2.14 2.30 2.05 2.15 2.38 3.17 2.57 2005 Table Monthly mean wind speeds and standard deviations in Mahshahr airport site, Iran 2.74 2.54 2.83 2.18 3.48 2.38 2.89 2.21 4.25 2.63 3.93 2.61 4.86 2.95 3.17 1.87 2.88 2.27 4.04 2.61 3.84 2.11 3.56 2.95 3.55 2.54 2006 3.92 2.29 3.87 2.81 4.34 3.07 4.80 2.98 4.14 2.16 5.68 2.69 5.73 3.17 4.63 2.41 3.65 2.41 2.78 2.08 4.21 2.63 4.35 2.44 4.35 2.73 2007 3.60 2.49 4.46 3.15 4.88 2.81 4.54 2.56 5.39 3.13 6.34 2.99 5.05 3.04 4.65 2.39 4.32 2.38 3.79 2.62 3.66 2.24 2.92 2.35 4.46 2.84 2008 2.44 2.76 3.73 3.42 3.17 2.87 3.23 2.96 3.90 3.01 3.93 2.98 4.95 3.46 4.07 3.26 3.51 3.21 2.62 2.58 2.08 2.54 1.78 2.48 3.28 3.10 2009 WIND ENGINEERING Volume 39, No 4, 2015 373 374 Investigating Potential of Wind Energy in Mahshahr, Iran 1988 1989 1990 1991 1992 Jan year Dec Nov 1993 1994 1996 Jan 1997 Feb Mar Dec Apr Oct Nov Jun Aug Jul a) b) 2000 Jan 2003 year 2001 2004 2005 2006 Feb 2008 2009 Feb Dec Mar Mar 3 2 Apr Nov Apr 0 Oct May Sep Jun Aug 2007 Nov Jan year Dec Apr May Jul 1999 2002 Mar Sep Jun Aug 1998 Feb Oct May Sep year 1995 Jul c) Oct May Sep Jun Aug Jul d) Figure Monthly mean wind speed in Mahshahr airport site, 1988–2009 monthly wind speeds were significantly different The monthly and yearly standard deviation values were between 1.87 and 4.09 m/s for August 2006 and June 1988, respectively The monthly mean wind speeds are illustrated in Fig.1 (a-d) It is also clear from these figures that the whole year’s wind speed had the lowest value in month of January and the highest one in months of July and June The average value for overall 22 years was 4.11 m/s Figure shows monthly mean wind speed in the 10 intervals for different months in 2008 at 10 and 40 m heights The monthly mean wind speed in 10 and 40 m heights was between 3.54– 5.66 m/s and 5.9–7.66 m/s, respectively The maximum and minimum wind speeds at 10 m height occurred in June and December while, at 40 m height, it was in June and January, respectively This result was the same as the findings from Mahshahr airport site Mean wind speeds at 10 and 40 m heights were, respectively, 4.27 and 6.08 m/s at site of Ministry of Power in 2008 Figure shows the frequency of wind speed during each year from 1988 to 2009 The trends of the frequency of wind speed for different years were similar Most of the frequency values of wind speed were between and m/s; but, some were over m/s The average frequency values for the whole year shows that only 6.92% wind speed was more than m/s while, in 1993, it was 13.31% Also, frequency values of to 7.5 and 7.5 to m/s were 16.48% and 7.87%, respectively The annual mean wind speed per half hour is demonstrated in Fig This figure shows hours of day with suitable wind speed during 2008 The best wind speed of 5.59 m/s at 10 m height occurred mostly at p.m while wind speed distribution of 40 m height was not significantly different (except around 10 a.m.) From the data, it can be found that, at height 10 m in the daytime, from 10 a.m to p.m., it was windy throughout the year while the night time was relatively calm Afternoons were characterized by decreasing wind speeds which tended to settle down after p.m 3.2 Wind direction The wind has a variable speed and its direction varies all the time Due to the electronic and mechanical control systems, wind turbines are always oriented in the direction of winds and make the highest use of wind energy Therefore, wind direction does not play an important role in WIND ENGINEERING Volume 39, No 4, 2015 375 height 40 m 9.00 height 10 m 8.00 Average wind speed (m/s) 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec >9 1993 >9 1999 7.5-9 1992 7.5-9 1998 1991 6-7.5 1990 4-6 1989 2-4 1988 Wind spees (m/s) Wind spees (m/s) Figure Monthly mean wind speed in 2008 for 10 and 40 m in ministry of power site 0-2 1997 6-7.5 1996 4-6 1995 2-4 1994 0-2 10 20 30 10 Frequency(%) a) 30 40 50 b) >9 2004 7.5-9 2003 6-7.5 2002 4-6 2001 Wind spees (m/s) Wind spees (m/s) 20 Frequency(%) 2000 2-4 0-2 >9 2009 7.5-9 2008 2007 6-7.5 2006 4-6 2005 2-4 0-2 10 20 Frequency(%) 30 40 c) 10 20 30 40 50 Frequency(%) d) Figure Frequency of wind speed in airport site (1988–2009) selecting location and design of wind turbines [1] However, wind direction is taken into consideration when turbines are installed in the wind farm where wind direction is of paramount importance for the possibility assessment of using wind energy Similarly, it plays a significance role in optimal positioning of a wind farm in a given area [28] Changes in wind direction are due to the general circulation of atmosphere on an annual (seasonal) basis to the mesoscale (4–5 days) The seasonal changes of prevailing wind direction could be as little as 30ᵒ in trade wind regions to as high as 180ᵒ in temperate regions 376 Investigating Potential of Wind Energy in Mahshahr, Iran Average wind speed (m/s) Figure Mean wind speed at different hours of the year 1988 1989 1990 1991 1992 300 1993 250 1994 Mean wind direction (degree) Mean wind direction (degree) 300 200 150 100 50 1995 1996 1999 200 150 100 50 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec year a) 2000 2001 b) 2002 2003 300 2004 250 Mean wind direction (degree) Mean wind direction (degree) 1998 0 300 1997 250 200 150 100 50 2005 2006 2007 2008 2009 250 200 150 100 50 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec year c) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec year d) Figure Wind direction in airport site, 1988–2009 Figure (a–d) illustrates monthly prevailing wind direction in Mahshahr airport during 1988– 2009 (at 10 m height) The trends of the monthly means were almost similar in different years It could be observed that yearly mean wind direction at heights of 10 m varied between 150.9 and 214.5 degrees The maximum and minimum mean wind directions happened in July 2006 and December 2009, respectively Wind directions for height of 37.5 m at site of Iran’s Ministry of WIND ENGINEERING Volume 39, No 4, 2015 377 Power are illustrated in Figure Most of the mean wind directions were between 292.5 and 337.5 degrees (about 47%) The remaining frequency data were as follows: between 315 and 337.5 degrees (10.1%); between 135 and 157.5 degrees (7.1%) and others (35.9%) 3.3 Turbulence intensity Turbulence intensity at heights of 10 and 40 m for a one year period from 1/1/2008 to 12/31/2008 in the time interval of 10 for site of Ministry of Power is shown in Figure Maximum turbulence intensity reached 8.3 and at 10 and 40 m heights, respectively, while their minimum was Also, the average of turbulence intensity was 0.141 and 0.101 for 10 m and 40 m heights, respectively Wind direction (degree) 3.4 Weibull distribution The yearly values of the two Weibull parameters, scale parameter c (m/s), shape parameter k (dimensionless) and the overall 22-year average are listed in Table It can be concluded that values of k were all less than Annual values of k changed from 0.99 to 1.66 with the average value of 1.3 The minimum value of k was found in year 1999; but, the maximum was belonged to 2007 Based on the obtained results, the value of c was minimal in 1998 with value of 3.33, while, in 1988, the value of c was the highest with 5.73 The average value of c was 4.43 in this study period 337.5-360 315-337.5 292.5-315 270-292.5 247.5-270 225-247.5 202.5-225 180-202.5 157.5-180 135-157.5 112.5-135 90-112.5 67.5-90 45-67.5 22.5-45 0-22.5 10 15 20 Freguency (%) Figure Frequency of Wind directions for height 37.5 m in ministry of power site a) Figure Turbulence intensity of Mahshahr, height of 10 m (a) and 40 m (b) b) 25 378 Investigating Potential of Wind Energy in Mahshahr, Iran Table Yearly Weibull parameters and characteristic speeds and wind power and energy density in Mahshahr airport site Observe year K (–) c (m/s) Ump 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Whole years 1.52 1.38 1.39 1.42 1.35 1.40 1.39 1.27 1.26 1.11 1.03 0.99 1.17 1.23 1.21 1.19 1.30 1.26 1.44 1.66 1.64 1.06 1.25 5.73 5.16 5.17 5.58 5.29 5.67 5.37 4.73 4.19 3.98 3.33 3.95 4.00 3.96 3.67 3.77 3.54 3.42 3.92 4.87 4.99 3.35 4.30 2.84 2.03 2.06 2.38 1.96 2.32 2.14 1.41 1.20 0.51 0.11 0.78 1.00 0.87 0.82 1.15 0.98 1.71 2.79 2.80 0.23 1.17 Ume 9.94 9.87 9.83 10.34 10.34 10.68 10.23 9.92 8.91 10.00 9.49 12.01 9.34 8.70 8.21 8.58 7.23 7.27 7.18 7.84 8.13 9.11 9.26 Weibull Wind Wind Wind Wind power energy power energy density density density density (W/m2) (kWh/m2/year) (W/m2) (kWh/m2/year) 124.20 108.86 107.05 126.11 118.75 136.98 116.42 95.54 68.42 78.57 56.26 116.84 68.79 62.06 50.63 57.47 39.06 37.60 45.36 68.78 75.68 55.16 77.53 1087.99 953.59 937.76 1104.71 1040.22 1199.93 1019.87 836.98 599.38 688.28 492.82 1023.54 602.65 543.60 443.51 503.45 342.20 329.34 397.33 602.53 663.00 483.19 679.22 132.84 117.60 116.82 139.69 132.36 151.29 131.69 109.15 78.00 94.42 72.29 134.72 82.20 70.28 58.19 65.35 43.39 42.32 47.37 70.69 77.81 66.59 86.61 1163.72 1030.21 1023.30 1223.66 1159.50 1325.32 1153.58 956.18 683.31 827.10 633.28 1180.13 720.03 615.68 509.75 572.45 380.10 370.69 415.01 619.20 681.59 583.31 758.74 The monthly values of the scale and shape parameters are presented in Table 3, which were calculated from 22-year period (1988–2009) of wind data The average values of k and c for these years were 1.29 and 4.31, respectively The result shows, the value of k was minimal in December as 1.03; in June, it was at the highest value of 1.62 Annual values of c ranged from 3.03 (December) to (June) The values of the shape parameter in 2008 at 10 and 40 m heights at site of Ministry of Power were 1.697 and 1.999, respectively (Table 4) Also, values of c at 10 and 40 m heights were 4.695 and 6.515, respectively, during the same period This was in close agreement with the findings of Bandar-e Mahshahr airport site at 10 m (k = 1.64 and c = 4.99 m/s) The corresponding wind data and best fits to a two-parameters Weibull distribution at heights of 10 and 40 m in 2008 are shown in Fig (a–b) It must be noted that the Weibull distribution had a good fit to the experimental data at heights 40 It can be concluded from Weibull distribution that the expansion of curves would lead to higher speeds and it could be illustrated that produced energy in this site was desirable Figure (a–c) shows the cumulative distribution and the best fits to cumulative distribution based on the measurement data and Weibull distribution in a one year period (2008) at site of Ministry of Power at 10 and 40 m heights It can be noted that, for example, the wind speeds at 10 and 40 m heights were higher than m/s for 79.05% and 90.99% of the time in the year, respectively WIND ENGINEERING Volume 39, No 4, 2015 379 Table Monthly Weibull parameters and characteristic speeds and wind power and energy density in Mahshahr airport site (1988–2009) Observe year K (–) c (m/s) Ump Ume Wind power density (W/m2) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1.05 1.15 1.22 1.37 1.51 1.62 1.50 1.47 1.3 1.12 1.12 1.03 3.04 3.74 4.11 4.68 5.40 6.00 5.97 4.97 4.19 3.30 3.28 3.03 0.16 0.65 1.02 1.81 2.62 3.33 2.86 2.30 1.36 45 0.43 0.10 8.42 8.96 9.08 9.01 9.45 9.85 10.51 8.89 8.58 8.25 8.23 8.65 41.46 61.52 69.74 80.35 105.34 130.25 150.21 85.40 65.46 45.45 44.00 44.96 Weibull Wind energy density (kWh/m2/ month) Wind power density (W/m2) Wind energy density (kWh/m2/ month) 30.84 41.34 51.89 57.85 78.38 93.78 111.76 63.53 47.13 33.81 31.68 33.45 51.58 71.01 79.57 88.76 112.98 137.36 154.67 92.03 72.38 53.31 52.68 54.64 38.37 47.72 59.21 63.91 84.06 98.89 115.07 68.47 52.11 39.67 37.93 40.66 Table Weibull parameters and characteristic speeds and wind power density and energy in ministry of power site (2008) Observe Weibull Height K (–) c (m/s) Ump Ume Wind Wind Wind Wind power energy power energy density density density density (W/m2) (kWh/m2/year) (W/m2) (kWh/m2/year) 10 m 40 m 7.43 9.22 66.91 138.14 1.697 1.999 4.695 6.515 2.78 4.60 a) Figure Wind speed distribution at 10 m (a) and b at 40 m (b) heights 586.13 1210.13 61.35 133.63 b) 537.44 1170.57 380 Investigating Potential of Wind Energy in Mahshahr, Iran 100 Cumulative distribution (%) Cumulative distribution (%) 100 80 Weibull distribution Measurement data (40 m) 60 40 20 80 Weibull distribution 60 Measurement data (10 m) 40 20 10 Wind speed (m/s) a) Cumulative distribution (%) 20 30 15 Wind10speed (m/s) b) 20 25 100 80 Height 40 m 60 Height 10 m 40 20 0.05 1.25 2.45 3.65 4.85 6.05 7.25 8.45 9.65 10.85 12.05 13.25 14.45 15.65 16.85 18.25 19.95 21.3 Wind speed (m/s) C) Figure Cumulative distribution of wind speed based upon the measurement data and Weibull distribution at 40 m (a) and 10 m (b), cumulative distribution of wind speed, Weibull distribution at 10 and 40 m (c) 3.5 Power and energy density Since none of the existing wind machines can completely convert all of the output power to usable form, the Betz relation [29] assigns a power coefficient of 0.593 for the maximum extractable power from an optimum wind energy conversion system Based on Betz relation, monthly energy density for each year from 1988 to 2009 related to meteorological and Weibull prediction is shown in Table It is clear that the values of wind energy density changed in different months Also, power density on the probability distribution function was almost a little more than that of the measured data The discrepancies may be due to low wind speed values in this site The discrepancies between the Weibull function predictions and wind speed data got much wider as the wind speed approached its lower limit [13, 30] The results show that the highest values of wind energy densities belonged to May, July, June and August while, in January, October, November and December, they were at the lowest level The range between 8.84 and 194.53 kWh/m2 per month were related to meteorological prediction and range of 9.63 to 199.39 kWh/m2 per month was related to Weibull prediction Considering the energy density values of different months in the studied period, it is clear that October 2002 and June 1993 had the lowest and greatest values, respectively Based on Betz relation and with respect to the observed data and Weibull prediction, annual power and energy densities were evaluated from 1988 to 2009 at 10 m height, as shown in Table As is indicated, few differences were noticeable between the observed data and Weibull distribution The values of power and energy densities predicted by Weibull model were slightly greater than those values related to measured data The discrepancies might be due to low wind speed values in this site However, for the purpose of estimating wind power potentials, these differences could be ignored for the wind turbines, in which little or even no energy output is provided under such low wind speeds For higher wind speeds, the Weibull distribution function coincide with the wind data very well [13] The results show that year 1993 had the highest potential with the mean power density of 136.98 W/m2 and 151.29 W/m2 based on the observed and Weibull prediction, respectively, and followed by 1991 and 1988 The lowest one was found in 2005 Observe 34.49 47.31 32.11 35.89 50.59 31.87 44.64 50.73 34.91 25.93 30.03 24.55 47.07 24.53 23.93 18.90 21.32 16.19 22.85 34.29 32.54 23.60 Year 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 May Jun Observe Weibull Observe Weibull Observe Weibull Observe Weibull Observe Weibull Observe Weibull Observe Weibull Observe Weibull 93.95 85.94 60.65 83.81 67.91 65.69 42.22 79.34 51.19 49.23 44.36 71.04 44.70 24.30 32.75 29.69 20.41 25.11 20.03 31.05 41.70 45.78 96.58 92.80 64.03 89.34 77.46 73.90 49.12 87.32 54.78 59.42 50.65 81.87 53.78 29.42 35.48 34.82 21.57 28.84 21.66 32.45 42.14 53.96 Weibull 101.52 95.78 120.46 142.88 102.43 110.69 152.22 129.59 50.32 124.97 49.21 44.36 52.71 35.03 62.41 30.77 45.12 35.70 18.70 50.35 50.30 65.32 Sep 58.68 66.50 61.29 82.02 55.58 28.57 92.67 47.09 42.41 43.47 16.54 30.36 56.68 20.73 9.63 34.51 14.15 18.53 43.89 18.29 40.40 27.99 Oct 54.02 63.50 54.27 77.55 46.82 24.41 81.31 42.18 33.40 41.80 16.12 24.31 46.59 16.27 8.84 30.07 11.43 15.60 42.20 15.73 38.38 22.63 Observe 94.58 83.85 111.37 132.02 94.61 102.30 139.44 119.90 46.57 116.73 39.64 37.50 46.09 31.52 60.73 28.28 41.91 30.11 17.72 50.52 48.58 59.07 Aug Weibull 104.21 66.75 148.78 145.72 175.97 118.34 158.81 174.28 55.08 152.72 97.93 82.10 47.47 79.69 62.10 84.32 26.73 47.43 69.74 102.30 77.99 91.65 Jul Nov Dec 48.80 52.69 46.21 51.61 35.20 39.15 42.14 48.85 47.58 52.82 26.92 29.27 36.46 43.07 78.95 83.76 59.03 65.65 57.88 67.95 87.16 94.44 25.33 30.64 88.90 103.48 34.51 42.39 42.06 48.74 19.84 25.13 22.48 26.86 20.51 22.96 26.52 27.25 17.50 18.95 14.53 13.95 16.80 19.62 33.45 43.10 15.80 13.73 15.30 15.61 29.43 38.21 27.82 31.60 23.35 28.72 13.17 15.31 28.07 32.99 18.56 20.84 18.91 23.32 22.62 25.95 19.15 20.65 10.52 14.39 14.96 13.52 27.91 29.53 44.28 46.86 43.37 45.93 43.40 45.40 27.10 29.17 20.97 24.33 16.72 18.50 15.04 17.68 Observe 114.00 119.56 161.70 173.65 161.70 173.65 179.06 190.77 97.67 86.42 99.08 52.58 59.37 155.20 154.00 124.57 131.01 61.71 69.88 76.32 99.82 102.83 103.63 103.67 130.21 136.09 142.66 56.89 66.62 110.32 116.11 163.33 173.55 151.89 166.22 134.40 42.01 47.94 94.50 102.80 110.23 120.93 172.05 185.57 167.42 85.53 94.49 97.15 105.39 151.22 160.56 194.53 199.39 113.08 66.86 76.42 59.56 67.09 83.57 91.74 155.10 158.94 147.83 57.24 71.94 64.19 70.69 114.99 124.01 78.81 87.56 170.51 65.79 75.57 76.28 84.19 61.56 67.77 104.65 96.17 49.44 40.35 49.50 58.85 68.84 42.58 51.70 81.99 87.47 147.36 46.37 61.71 44.87 55.56 51.91 60.66 68.54 75.24 87.18 46.16 60.19 56.17 65.79 67.01 77.88 69.05 78.57 74.21 71.81 83.33 62.38 74.24 86.58 96.43 77.78 88.18 42.31 34.00 42.61 47.86 53.46 105.53 108.57 102.93 106.78 75.48 25.71 31.33 39.91 47.61 58.24 62.10 61.98 64.73 58.14 46.86 52.46 63.79 68.09 37.55 41.56 53.80 56.42 82.35 24.03 27.13 29.77 33.19 34.61 37.79 55.47 58.63 24.61 24.53 26.76 32.61 34.63 30.34 34.07 50.68 58.70 41.31 29.32 30.76 19.04 20.70 47.38 48.06 40.22 40.91 70.06 60.03 62.93 66.17 67.41 34.52 36.10 80.11 82.10 103.29 64.83 66.37 49.28 50.31 88.74 90.30 116.16 113.89 76.40 33.30 40.14 33.80 42.32 47.42 53.39 47.69 51.26 84.44 Apr Weibull 96.99 35.61 54.06 49.51 81.77 77.43 88.50 28.97 42.26 43.57 40.24 42.27 41.44 38.03 37.78 46.43 41.14 23.64 18.31 42.09 63.50 61.03 Mar Observe 94.92 32.11 46.94 43.19 74.42 63.12 75.95 25.25 34.65 38.97 35.95 32.02 35.56 30.01 32.04 39.78 37.10 20.86 16.92 40.05 60.13 54.82 Feb Weibull 40.59 55.19 38.78 35.38 59.63 39.19 57.15 64.41 39.70 33.63 32.40 26.41 60.04 31.60 29.27 18.37 26.44 15.82 27.52 35.06 34.59 23.48 Jan Table Monthly energy density (kWh/m2/month) WIND ENGINEERING Volume 39, No 4, 2015 381 382 Investigating Potential of Wind Energy in Mahshahr, Iran Observe Wind power (w/m2) Weibull Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 10 Monthly variation of the wind power density in airport site (1988–2009) Moreover, this level of power density might be adequate for non-grid connected electrical and mechanical applications, such as wind generators, battery charging and water pumping as well as agricultural applications It was noticed that energy density values were between 329.3–1199.9 and 370.7–1325.3 kWh/m2/year related to the observed and Weibull prediction, respectively Figure 10 shows monthly variation of the wind power density and compares the results from the measurement data and Weibull distribution The results from Fig 10 and Table revealed great variations in the monthly wind power and energy density so that, for example, energy density changed from 30.8 to 111.8 kWh/m2/month and 38.4 to 115.1 kWh/m2/month related to the measurement data and Weibull distribution, respectively It was obvious that the highest value of wind energy density was in July followed by June and May while the lowest one was found in January Such considerable differences in the wind power density might be accounted for by the fact that the power was proportional to the cube of wind speed Significant monthly changes underscore importance of distinguishing different months of a year when a wind power project is assessed or designed [13] Table shows power and energy densities, based on Betz limitation, during 2008 for the site of Ministry of Power Power densities at 10 and 40 m heights were 66.9 and 138.1 W/m2 for the measured data while they were 61.4 and 133.6 W/m2, respectively, for the Weibull distribution function These power and energy densities were less than the power and energy densities obtained from the data of airport site However, the differences were small Based on the observed data, annual wind energies at 10 and 40 m heights in 2008 were 586 and 1210 kWh/m2/year, respectively The most probable wind speed (Ump) and the wind speed producing maximum energy (Ume) are shown in Tables 3, and CONCLUSION In this study, wind characteristics, wind power and energy potentials of two stations in Mahshahr (Iran) were assessed Probability density functions were derived and distribution parameters were identified The aim was to develop a Weibull-representative of wind data instead of using measured time-series data for estimating wind energy output The Weibull function parameters were calculated analytically according to the measured time-series data The most important outcomes of this study are summarized as follows: The Weibull distribution indicated good agreement with the data obtained from actual measurements WIND ENGINEERING Volume 39, No 4, 2015 383 The results and parameters of Weibull distribution show that Mahshahr has a relatively good potential for harnessing wind power [31] specially for non-grid connected electrical and mechanical applications [13] May, June and July were the three months in which average wind speed and power and energy densities were at the highest level all over the year The winds had energy densities of 679 and 1210 kWh/m2/year at 10 m and 40 m heights, respectively The best wind speed of 5.59 m/s at height of 10 m occurred at p.m and its mean was higher in the daytime throughout the year However, wind speed distribution at height of 40 m was not significantly different at all hours These results could be useful for studying the effectiveness and implementation of small wind turbines for water pumping and also they could provide a strong basis for harnessing winds in Mahshahr region ACKNOWLEDGEMENTS This research was supported by University of Tehran The authors also would like to thank Iran Meteorological Organization and Renewable Energies Organization of Iran (SUNA) for providing the data for this research study REFERENCES [1] Mostafaeipour, A., Feasibility study of offshore wind turbine installation in Iran compared with the world, Renewable and Sustainable Energy Reviews, 2010, 14, 1722–43 [2] Mostafaeipour, A., Sedaghatb, A., Dehghan-Niric, A and Kalantarc, V., Wind energy feasibility study for city of Shahrbabak in Iran, Renewable and Sustainable Energy Reviews, 2011, 15, 2545–56 [3] Mirhosseini, M., Sharifi, F., Sedaghat, A., Assessing the wind energy potential locations in province of Semnan in Iran, Renewable and 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3558–69 [29] Betz, A., Windenergie und ihre Amcendung dureh Windmuhlen Gottingen, Vanderhoeck & Ruprecht, 1942 [30] Zhou, W., Yang, H and Fang, Z., Wind power potential and characteristic analysis of the Pearl River Delta region, China, Renewable Energy, 2006, 31, 739–53 [31] Aydin, N.Y., GIS-based site selection approch for wind and solare energy systems: a case study from western turky, M.Sc thesis Middle East Technical University, 2009  ... where wind direction is of paramount importance for the possibility assessment of using wind energy Similarly, it plays a significance role in optimal positioning of a wind farm in a given area... turbulence 372 Investigating Potential of Wind Energy in Mahshahr, Iran is quantified with a metric called turbulence intensity (TI) – the standard deviation of the horizontal wind speed divided by... classification and characterization of an area in terms of being high or low in wind potential requires significant efforts because wind speed and direction present extreme transitions in most sites