Investigating potential of wind energy i

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 40

Investigating Potential of Wind Energy

in Mahshahr, Iran

Faculty of Agricultural Engineering and Technology, School of Agricultural and Natural Resources, University of Tehran, P.O Box 4111, Karaj 31587-77871, Iran

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 40 m 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

Received 04/15/2014; Revised 04/08/2015; Accepted 04/08/2015

1 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.

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

2 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 3 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 min 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]

The Weibull probability density function can be written as [25, 26]:

(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]:

(2)

(3)

(4)

where σu and U − represent the standard deviation and average of wind speed, respectively The average of wind speed is obtained by:

(5)

And, the σuis defined [25]:

(6)

One way to define probability density function (PDF) is that the probability of wind speed

occurring between U a and U bis given obtained by [25, 28]:

(7)

Also, the total area under probability distribution curve is given by:

(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]:

(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

c

U

U c

( )

⎝⎜

⎞

⎠⎟

⎛

⎝⎜

⎞

⎛

⎝⎜

⎞

⎠⎟

⎡

⎣

⎦

⎥

−

∑

−

= U U

u

i N i

1

k

U u 1.086 σ

⎝⎜

⎞

⎠⎟

−

c U

k k

2.6674 2.73855

=

+

∑

=

=

U

1

i

N i

1

k

1 2 /

u

( )

U U

a

b

∫

0

∞

⎝⎜

⎞

⎠⎟

⎡

⎣

⎦

⎥

c

k

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]:

(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 min

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:

(11)

where ρ is standard air density For standard conditions (sea level, ISOC), the density of air is 1.225 kg/m3and P̅ /A is in Watt per square meter (W/m2)

The wind energy density is given by:

(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]:

(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:

(14)

3 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 1 The monthly mean wind speed values U − and standard deviations σ for Mahshahr port during 1988–2009 are also presented in Table 1 The trends of the monthly means were similar for different years Most of the average wind speeds were in the range of 3 and 4 m/s with a frequency of approximately 33.0% Remaining frequencies in the data were as follows: less than 2 m/s (2.1%); 2 to 3 m/s (18.6%); 4 to 5 m/s (25.0%); 5 to 6 m/s (12.5%) and greater than 6 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

σ

=

TI U U

∫

⎝⎜

⎞

∞

P

1 2

1

2

c

E A

P

⎝⎜

⎞

k

2

me

k

1/

⎝⎜

⎞

⎠⎟

k

mp

k

1/

⎝⎜

⎞

⎠⎟

Ta

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 2 shows monthly mean wind speed in the 10 min 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 3 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 0 and 6 m/s; but, some were over 9 m/s The average frequency values for the whole year shows that only 6.92% wind speed was more than 9 m/s while, in 1993, it was 13.31% Also, frequency values of 6 to 7.5 and 7.5 to 9 m/s were 16.48% and 7.87%, respectively

The annual mean wind speed per half hour is demonstrated in Fig 4 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 4 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

8 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 8 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

0 1 2 3 4 5 6 7 8 Jan Feb

Mar

Apr

May

Jun

Jul Aug

Sep

Oct

Nov

Dec

year

0 1 2 3 4 5 6 7 8 Jan Feb

Mar

Apr

May

Jun

Jul Aug

Sep Oct Nov Dec year

0 1 2 3 4 5 6 Jan Feb

Mar

Apr

May

Jun Jul Aug Sep

Oct

Nov

Dec

year

0 1 2 3 4 5 6 7 Jan Feb

Mar

Apr

May

Jun

Jul Aug

Sep Oct Nov Dec year

Figure 1 Monthly mean wind speed in Mahshahr 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

Figure 3 Frequency of wind speed in airport site (1988–2009)

Figure 2 Monthly mean wind speed in 2008 for 10 and 40 m in ministry of power site

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

height 40 m height 10 m

0-2

2-4

4-6

6-7.5

7.5-9

>9

Frequency(%)

1993 1992 1991 1990 1989 1988

0-2 2-4 4-6 6-7.5 7.5-9

>9

Frequency(%)

1999 1998 1997 1996 1995 1994

0-2

2-4

4-6

6-7.5

7.5-9

>9

Frequency(%)

2004 2003 2002 2001 2000

0-2 2-4 4-6 6-7.5 7.5-9

>9

Frequency(%)

2009 2008 2007 2006 2005

Figure 5 (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 Figure 5 Wind direction in airport site, 1988–2009

Figure 4 Mean wind speed at different hours of the year

0

1

2

3

4

5

6

7

0

50

100

150

200

250

300

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec year

1988 1989 1990 1991 1992 1993

0 50 100 150 200 250 300

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec year

1994 1995 1996 1997 1998 1999

0

50

100

150

200

250

300

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec year

2000 2001 2002 2003 2004

0 50 100 150 200 250 300

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec year

2005 2006 2007 2008 2009

Power are illustrated in Figure 6 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 min for site of Ministry of Power is shown in Figure 7 Maximum turbulence intensity reached 8.3 and 8 at 10 and 40 m heights, respectively, while their minimum was 0 Also, the average of turbulence intensity was 0.141 and 0.101 for 10 m and 40 m heights, respectively

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 2 It can be concluded that values

of k were all less than 2 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

Figure 7 Turbulence intensity of Mahshahr, height of 10 m (a) and 40 m (b)

Figure 6 Frequency of Wind directions for height 37.5 m in ministry of power site

0-22.5

22.5-45

45-67.5

67.5-90

90-112.5

112.5-135

135-157.5

157.5-180

180-202.5

202.5-225

225-247.5

247.5-270

270-292.5

292.5-315

315-337.5

337.5-360

Freguency (%)

Table 2 Yearly Weibull parameters and characteristic speeds and wind power and energy

density in Mahshahr airport site

Wind Wind Wind Wind

years

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 6 (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 8 (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 9 (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 2 m/s for 79.05% and 90.99% of the time in the year, respectively