Wind Characteristics and Energy

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Wind Characteristics and Energy

Potential in Belen-Hatay, TurkeyBesir Sahin

a& Mehmet Bilgili

a

aFaculty of Engineering and Architecture, Mechanical Engineering

Department, Cukurova University, Adana, Turkey

Version of record first published: 07 Apr 2009.

To cite this article: Besir Sahin & Mehmet Bilgili (2009): Wind Characteristics and Energy Potential inBelen-Hatay, Turkey, International Journal of Green Energy, 6:2, 157-172

To link to this article: http://dx.doi.org/10.1080/15435070902784947

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WIND CHARACTERISTICS AND ENERGY POTENTIALIN BELEN-HATAY, TURKEY

Besir Sahin and Mehmet Bilgili

Faculty of Engineering and Architecture, Mechanical Engineering Department,

Cukurova University, Adana, Turkey

In this study, wind characteristics in the Belen-Hatay province situated in southern Turkey were

investigated by using the Wind Atlas Analysis and Application Program (WAsP) for future windpower generation projects. Hourly wind speeds and directions between the years 2004 and 2005

were collected by the General Directorate of Electrical Power Resources Survey Administration

(EIEI). Before the construction of the wind turbine generator in Belen-Hatay province, several

fundamental properties of the site such as wind behavior, availability, continuity, and probability

were carried out in order to provide the necessary information to the potential investors about

cost and economical aspects of the planning wind energy project. The dominant wind directions,

probability distributions, Weibull parameters, mean wind speeds, and power potentials were

determined according to the wind directions, years, seasons, months, and hours of day,

separately. Finally, at a 10 m height above ground level, mean wind speed and power

potential of the site were found to be 7.0 m/s and 378 W/m2, respectively.

Keywords: Wind speed; Wind power potential; Weibull parameters; Wind characteristics

INTRODUCTION

The demand for energy in the world grows rapidly and is expected to continue to

grow in the near future as a result of social, economic, and industrial developments and high

population levels. Parallel to this development, renewable energy sources have received

increasing attention from the world due to limited reserves of fossil fuels and their negative

impacts on the environment. In this regard, the utilization of renewable energy resources,

such as solar, geothermal, and wind energy, appears to be one of the most efficient andeffective solutions (Hepbasli and Ozgener 2004).

Turkey does not have large fossil fuel reserves. Almost all types of oil and natural gas

are imported from neighboring countries. Excluding lignite, reserves of coal, oil, and

natural gas in country are limited and far from being able to meet the projected domestic

demand. Coal is a major fuel source for Turkey. Domestically produced coal accounted for

about 24% of the countrys total energy consumption, used primarily for power generation,

steel manufacturing, and cement production. Turkey is a large producer of lignite; proven

reserves of lignite are in order of 8,075 million tons, of which 7,339 million tons is

economically feasible to use (Kaya 2006).

International Journal of Green Energy, 6: 157172, 2009

Copyright Taylor & Francis Group, LLC

ISSN: 1543-5075 print / 1543-5083 online

DOI: 10.1080/15435070902784947

Address correspondence to Besir Sahin, Professor of Energy Division, Faculty of Engineering and Architecture,

Mechanical Engineering Department, Cukurova University, 01330 Adana, Turkey. E-mail: [email protected]

157


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Turkey has substantial reserves of renewable energy resources. Renewable energy

production represented about 14.4% of total primary energy supply. The main renewable

energy resources are hydro, biomass, wind, biogas, geothermal, and solar (Kaya 2006).

As reported by the Turkish Statistical Institute (TSI) and the Turkish Electricity

Transmission Company (TETC) in 2006, the installed capacity of electric power plantsin 2005 in Turkey was 38,843.5 MW, with annual electricity production of 161,983.3 GWh,

as seen in Table 1. In this year, 122,174.0 GWh of energy was produced in operating

thermal power plants. On the other hand, annual electricity productions of hydropower

plants and wind power plants were 39,658.1 GWh and 56.6 GWh, respectively. During next

20 years, the electric power plants installed capacity is expected to reach 109,227 MW and

annual electricity production by 623,835 GWh. Statistical evaluations on hydropower

performed by the General Directorate of State Hydraulic Works (GDSHW) in 2006 is

presented in Table 2. At present Turkey has 135 hydroelectric power plants in operation

with total installed capacity of 12,631 MW, generating an average of 45,325 GWh/year,

which is 36% of the economically viable hydroelectric potential. Forty-one hydroelectricpower plants are currently under construction with 3,187 MW of installed capacity to

generate an average 10,645 GWh energy annually, representing 8% of the economically

viable potential. Furthermore, 502 more hydroelectric power plants will be constructed in the

future to be able to utilize maximum use of the remaining 71,411 GWh/year of economically

viable hydropower energy potential. Consequently, a total of 678 hydroelectric power plants

with the installed capacity of 36,260 MW will be in use in coming years.

One of the main renewable energy resources all over the world is wind, which has

played a long and important role in the history of human civilization. Wind power has been

harnessed by mankind for thousands of years. Since earliest recorded history, wind power

has been used to move ships, grind grain, and pump water (Hepbasli and Ozgener 2004).

The last decade was characterized by rough development of wind power engineering all

over the world. Leading positions are taken by Germany, Spain, and the United States. The

Table 1 The installed capacity and annual electricity production of electric power plants in the year 2005 in Turkey

(TSI 2006; TETC 2006).

Power plants Installed capacity Annual production

MW % GWh %

Thermal 25902.3 66.68 122174.0 75.42

Hydro 12906.1 33.23 39658.1 24.48

Geothermal 15 0.04 94.6 0.06Wind 20.1 0.05 56.6 0.04

Total 38843.5 100 161983.3 100

Table 2 Potential of hydro power plants in Turkey (GDSHW 2006).

Status of economically

viable potential

Number of

hydro-electric plants

Total installed

capacity (MW)

Average annual

generation (GWh/year)

Rate (%)

In operation 135 12631 45325 36

Under construction 41 3187 10645 8

To be constructed 502 20442 71411 56

Total potential 678 36260 127381 100

158 SAHIN AND BILGILI


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rates of growth of this branch of power engineering exceed 39% annually. No other branch

of power engineering developed with such higher rates (Kenisarin et al. 2006).

Previous meteorological stations located in _Iskenderun and Antakya is now surrounded

by obstacles and buildings. The heights of the measuring devices are 10 m above ground level.

For example, Bilgili and others (2004) and Sahin and others (2005) have predicted the level ofwind speed and energy using WAsP. Presently evaluated results are approximately doubled

comparing to the results of old stations. Around the present wind speed measuring stations, there

are no obstacles at all. A velocity measuring device is connected directly to the data logger. The

time interval between readings is 1 hour. There is no manual interference in recording data.

Consequently, the present wind speed measuring station constructed for the purpose of defining

the wind energy potential of Turkey by meteorological Belen-Hatay is very reliable.

In this study, wind power characteristics in the Belen-Hatay province situated in the

eastern Mediterranean region of Turkey were investigated using a computer package program

called WAsP. Before the construction of the wind turbine generator in Belen-Hatay, several

fundamental properties of the site, such as wind behavior, availability, continuity, andprobability, were investigated in order to provide the necessary information to the potential

investors about cost and economical aspects of the planning wind energy project. Wind speed

probability distribution, wind direction frequency distribution, Weibull parameters, mean

wind speed, and power potential variations were determined for the years 2004 and 2005.

All of these wind characteristics were studied according to the wind directions, years, seasons,

months, and hours of day, separately.

DISTRIBUTION OF WIND POWER PLANTS IN TURKEY

Turkey has a land surface area of 774,815 km2. It is surrounded by the Black Sea in the

north, the Marmara and Aegean Seas in the west, and the Mediterranean Sea in the south,providing very long seashores. Especially, the regions of Aegean, Marmara and East-

Mediterranean have high wind energy potential. But not all the land area of Turkey is suitable

for the installation of wind turbines, due to topographic structure (Hepbasli and Ozgener

2004). Although Turkey has sufficient wind energy potentials, the practical utilization of

wind energy as known is limited by installed capacity of 131.35 MW as of May 2007 (EMRA

2007). On the other hand, the cumulative installed capacity of wind energy worldwide is

59,206 MW by the end of 2005, an increase of 24.45% compared to 2004. The countries with

the highest total installed capacity are Germany (18,427 MW), Spain (10,028 MW), the

United States (9,142 MW), India (4,434 MW), and Denmark (3,127 MW) (WPHPBA 2006).

Progress in wind energy technology in recent years has drawn the attention of theprivate sector to these wind energy resources. The distribution of wind energy plants installed

as of May 2007 is illustrated in Table 3 and Figure 1 (EMRA 2007). Although the first

Turkish wind turbine was constructed in Cesme at the Golden Dolphin Hotel by Vestas in

1985 (55 kW), the development of modern Turkish wind power engineering began in

November 1998 when the first 3 Enercon E40 wind turbines of 500 kW began to operate

at Alacat, _Izmir. Then, the wind farm consisting of 12 Vestas V44/600 turbines was

constructed at the same location in November 1998. The third wind farm with total installed

capacity of 10.2 MW started to operate in June 2000 at Bozcaada Island (Hepbasli and

Ozgener 2004; Kenisarin et al. 2006). A wind farm consisting of 20 General Electric GE/1.5

MW turbines was constructed at Bandrma, Balkesir, in September 2006 (WPHPBA, 2006).

Total installed wind power capacity of Turkey is 131.35 MW as of May 2007. As seen inTable 3, according to the projection of the Energy Market Regulatory Authority (EMRA), the

WIND CHARACTERISTICS AND ENERGY POTENTIAL IN BELEN-HATAY 159


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Table 3 Distribution of Turkeys wind energy installations by regional as of May 2007 (EMRA 2007) (*: In

operation, others: Under construction).

Place Company Date of

commissioning

Installed capacity

(MW)

Cumulative installed

capacity (MW)

Izmir-Cesme* Demirer A.S. 1998 1.5 1.5

Izmir-Cesme* Gucbirligi A.S. 1998 7.2 8.7

Canakkale-

Bozcaada*

Demirer-Enercon 2000 10.2 18.9

_Istanbul-

Hadmkoy*

Sunjut A.S. 2003 1.2 20.1

Balkesir-

Bandrma*

Bares A.S. I/2006 30.0 50.1

_Istanbul-Silivri* Erturk A.S. II/2006 0.85 50.95_Izmir-Cesme* Mare A.S. I/2007 39.2 90.15

Manisa-Akhisar* Deniz A.S. I/2007 10.8 100.95

Canakkale-_Intepe* Anemon A.S. I/2007 30.4 131.35

Canakkale-

Gelibolu

Dogal A.S. II/2007 15.2 146.55

Manisa-Sayalar Dogal A.S. II/2007 30.4 176.95

Hatay-Samanda g Deniz A.S. II/2007 30.0 206.95_Istanbul-

Gaziosmanpasa

Lodos A.S. I/2008 24.0 230.95

_Izmir-Aliaga _Innores A.S. I/2008 42.5 273.45

Aydn-Cine Sabas A.S. I/2008 19.5 292.95_Istanbul-Catalca Erturk A.S. I/2008 60.0 352.95

Canakkale As Makinsan

Temiz A.S.

II/2008 30.0 382.95

_Izmir-Kemalpasa Ak-El A.S. II/2008 66.6 449.61

Hatay-Samanda g Ezse Ltd. Sti. II/2008 35.1 484.61Hatay-Samanda g Ezse Ltd. Sti. II/2008 22.5 507.11

Balkesir-Saml Baki A.S. II/2008 90.0 597.11

Balkesir-

Bandrma

Banguc A.S. II/2008 15.0 612.11

Osmaniye-Bahce Rotor A.S. I/2009 130.0 742.11

*stanbul-Hadmky/1.2MWstanbul-atalca/60MW

stanbul-Gaziosmanpaa/24MW* stanbul-Silivri/0.85MW

*Balkesir-Bandrma/30MW

Osmaniye-Bahe/130MW

Hatay-Samanda/22.5MW

Hatay-Samanda/30MWHatay-Trbe/35.1MW

anakkale-Gelibolu/15.2

MW*anakkale-ntepe/30.4MW

*anakkale-Bozcaada/10.2MW

anakkale/30MW

*zmir-eme/1.5MW*zmir-eme/7.2MW*zmir-eme/39.2MW

*Manisa-Akhisar/10.8MW

zmir-Kemalpaa/66.66MW

zmir-Aliaa/42.5MW

Aydn-ine/19.5MW

Manisa-Akhisar/30.4MW

Balkesir-Bandrma/15MW0 5

0100

150

200

250

Km

Balkesir-aml/90MW

TURKEY

Figure 1 Wind power plants in Turkey (EMRA 2007) (*: In operation, others: Under construction).



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installed capacity of wind energy in Turkey will reach 742.11 MW by the year 2009 (EMRA

2007). It is also clear from the table that there are three wind power plants under construction

with total installed capacity of 87.6 MW in Hatay province.

One of the most suitable areas of Turkey for wind power generation is some locations

of the eastern Mediterranean region. Along the Mediterranean coast, valleys and mountainsare not perpendicularly formed, and the Taurus Mountains are situated away from the

seacoast in many regions. Therefore, these regions are exposed to southerly winds that are

not as strong as the northerly winds that occur over the Black Sea. In the vicinity of_Iskenderun Bay especially, there are suitable locations for wind power generation (Durak

and Sen 2002). In addition to deciding the most suitable site for a wind turbine and defining

the necessary parameters about turbines, such as size, blade shape, total capacity, and

direction, it also requires a feasibility report on the fundamental properties of the site, such

as wind behavior, availability, continuity, and probability in the proposed region. In order

to use those properties, statistical and dynamic characteristics of wind of the site should be

obtained using wind observations and statistical wind data (Karsli and Gecit 2003).

MATERIALS AND METHODS

Location of the Site

The data used in this study were collected from Belen-Hatay station, located in the

eastern Mediterranean region of Turkey. This station has been set up by the General

Directorate of Electrical Power Resources Survey Administration (EIEI). The map of the

region and the location of the station are presented in Figure 2. Adana and Hatay are two of

the industrialized provinces in the eastern Mediterranean region of Turkey, with a population

of around 3.2 million. With the neighboring provinces the population goes up to

Black Sea

Aegean

Sea

Mediterranean Sea

40

28 32 36 40

0 100

200

Km

44

28 32 36 40 4436

40

36

TURKEY

MEDITERRANEAN

SEA

Belen

skenderunBay

Amik

Lowland

AmanosM

ountain

AdanaMersin

Hatay

(Antakya)

Figure 2 The map of the region and the location of the station.



7/17

approximately 6 million according to the population census done in 2004. Estimated popula-

tion of Adana and Antakya combined is presently 4.2 million and with neighboring provinces

the population goes up to 7.8 million. Electricity production from wind power should be

locally preferred over thermal power plants (Sahin et al. 2005).

Two important mountain passes, Gulek mountain pass and Belen mountain pass,provide a highway route between Europe and the Middle East. More specifically, Belen

mountain pass is situated along the wide spread valley between east of Mediterranean

region and Amik lowland, on the Amanos mountain in the city of Belen. In this region, there

is a wide range of land that is suitable for wind turbine farming. Belen has fairly high wind

speeds that vary proportional to the altitude. A Mediterranean climate is dominant in this

region, usually hot and dry in summer and lukewarm and rainy in winter. But climate

properties vary depending on the height above sea level. On the slope of a mountain looking

at the sea, an increase of terrestrial effects on climate is observed. However, the weather in

this region does not show intense terrestrial climate due to the Mediterranean Sea effect.

Wind Data

The long-term wind data, containing hourly wind speeds and directions, cover the period

from 2004 to 2005. The mean monthly, annual, and diurnal wind speeds were calculated from

these hourly wind speed data. The anemometer of the station is 10 m above ground level.

Geographical coordinates and measurement period of this station are given in Table 4. The

wind observation station is situated at the coordinates of 361200N latitude and 382801E

longitude. The height of the station is 474 m above sea level. Around the measurement area,

there were no obstacles that would cause an impact on the wind speeds and directions.

WAsP Program

In this study, Wind Atlas Analysis and Application Program (WAsP) was used to

investigate wind characteristics in Belen-Hatay city. WAsP program and associated soft-

ware has been developed by Riso National Laboratory, Denmark. WAsP is a PC program

for the vertical and horizontal extrapolation of wind climate statistics. It contains several

models to describe the wind flow over different terrains and close to sheltering obstacles.

WAsP consists of five main calculation blocks: analysis of raw data, generation of wind

atlas data, wind climate estimation, estimation of wind power potential, and calculation of

wind farm production (Mortensen et al. 2001).

Four basic data are considered necessary for the WAsP packet program that is used inthe determination of wind power potential. These are hourly wind speed and direction,

sheltering obstacles, surface roughness changes, and orographic data (terrain height).

Obstacles have an impact on the wind speed and alter the wind direction, particularly for

lower level of height. If there are obstacles around the measurement station, wind speeds

must be reconsidered again by taking the effect of these parameters into account (Bilgili

Table 4 Geographical coordinate and measurement period of Belen station.

Station Latitude Longitude Altitude (m) Period Anemometer height (m)

Belen 36 12 00 N 38 28 01 E 474 20042005 10



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et al. 2004). In deciding about the effectiveness of the wind speed measurement around a site,

the topographic and climatic conditions must be taken into consideration. Any method of

wind speed predictions should consider the topographic and climatologic features. The wind

speed measured at a site is determined mainly by two factors: the overall weather systems

(which usually have an extent of several hundred kilometers) and the nearby topographywithin few kilometers of the station. The collective effect of the terrain surface and obstacles,

leading to an overall retarding of the wind near the ground, is referred to as the roughness of

the terrain. Orographic elements, such as hills, cliffs, ridges, and escarpments, exert an

additional influence on the wind. Roughness and orography are among the main factors

that affect the wind speed (Durak and Sen 2002).

RESULTS AND DISCUSSION OF WIND CHARACTERISTICS

Wind Speed Probability Distribution

Generally, previously measured wind data are used for the estimation of wind power

potential of any area. First of all, at any location, hourly wind speeds and wind directions

are first observed and monitored. These results are used for frequency and probability

modeling. Wind speed data in time-series format is usually arranged in the frequency

distribution format since it is more convenient for statistical analysis. Therefore, the available

time-series data were translated into frequency distribution format. The wind speed

probability distributions and the functions representing them mathematically are the main

tools used in the wind-related literature. Their use includes a wide range of applications, from

the techniques used to identify the parameters of the distribution functions to the use of such

functions for analyzing the wind speed data and wind energy economics (Celik 2003). The

frequency distribution and probability density of the wind speeds help toward answering

questions of how long a wind power plant is out of action in the case of lack of wind, which is

the range of the most frequent wind speeds, and how often the wind power plant achieves its

rated output (Pashardes and Christofides 1995). Probability density for each wind class

according to the relation is stated as

pvi fiPN

i1

fi

Here,fi is frequency of occurrence of each speed class andNis number of hours in theperiod of time considered. The cumulative probability density is determined as

Pvi XNi1

pvi

Yearly cumulative distributions derived from the long-term wind speed data of

Belen-Hatay are presented in Figure 3. It is also clear from the figure that the two curves

show the same variation according to each other. This figure indicates that the hourly wind

speeds in Belen-Hatay are higher than 5 m/s in about 70% of occasions. Seasonal cumulative

distributions for the years 2004 and 2005 are presented in Figure 4. It is interesting to note thatthe wind speeds of summer months in Belen-Hatay are higher than the others.



9/17

Dominant Wind Direction

There are two important aspects in the selection of the type of orientation of the wind

turbines and their location. These are the wind speed distribution in each direction and the most

frequent wind directions (Torres et al. 1999). Besides the level and structure of wind speeds, the

direction of the wind is of decisive significance for the evaluation of the possibilities of utilizing

wind power. The direction statistics play an important role in optimal positioning of a wind

turbine farm in a given area (Pashardes and Christofides 1995). Wind directions and speeds are

also affected by topography (Matsui et al. 2002). Yearly dominant wind directions of

Belen-Hatay station are presented in Figure 5. It is also clearly seen from the figure that the

two curves show the same variation. The data of the wind direction show that the maximum

frequency occurs at the west-north (WN) direction. In this region, northwestern winds are

effective and the winds from WN direction continuously blow during 42.5% of the time for the

year 2004 with a speed of 9.23 m/s. The effect of wind that is observed at the lowest degree is in

the direction of south (S), south-west (SW), and west-south (WS), with a speed of 2.21 m/s,

2.06 m/s, and 2.69 m/s, respectively. Monthly dominant wind directions of Belen-Hatay station

for the year 2004 are presented in Figure 6. It is also clear from the figure that during spring,

summer, and autumn months, wind speeds at the west-north (WN) direction are effective. Onthe other hand, during winter months, wind speeds at the south-east (SE) direction are effective.

0

20

40

60

80

100

0 2 4 6 8 10 12 14 16 18 20 22

Wind speed (m/s)

Cumulativedensity(%)

2004

2005

Figure 3 Yearly cumulative probability distributions of wind speeds.

10

10

30

50

70

90

110

0 2 4 6 8 10 12 14 16 18 20 22

Wind speed (m/s)

Cumulativedensity(%)

Spring (2004)

Spring (2005)

Summer (2004)

Summer (2005)

Autumn (2004)

Autumn (2005)

Winter (2004)

Winter (2005)

Figure 4 Seasonal cumulative probability distributions of wind speeds for the year 2004 and 2005.



10/17

Variation of Weibull Parameters

It is known that there are several efforts to construct an adequate statistical model for

describing the wind speed frequency distribution, which may be used for predicting the

energy output. The Weibull distribution has been accepted to give a good fit to wind data

for wind energy applications (Incecik and Erdogmus 1995). The Weibull distribution is atwo-parameter distribution. This distribution is expressed as

pwv k

c

v

c

k1exp

v

c

k

where pw

(v) is the probability of observing wind speed v, kis the Weibull shape parameter

(dimensionless), and c is the Weibull scale parameter, which has a reference value in the

units of wind speed (Akpinar and Akpinar 2004).

For the years 2004 and 2005, the Weibull parameters according to the wind directions

in Belen-Hatay station are given in Table 5. The Weibull shape parameters (k) are withinthe range 1.353.81 for the whole year. The Weibull scale parameters (c) also vary between

0

10

20

30

40

Frequency(%)

N NE EN E ES SE S SW WS W WN NW

Wind direction

2004

2005

Figure 5 Yearly dominant wind directions.

10

10

30

50

70

90

110


Wind direction

Frequency(%)

January FebruaryMarch AprilMay JuneJuly AugustSeptember October November December

Figure 6 Monthly dominant wind directions for the year 2004.



11/17

2.2 m/s and 10.3 m/s. It can be seen that the maximum c parameter appears in the direction of

WN with 10.3 m/s. On the other hand, the minimum c parameter is in the direction of SW

with 2.2 m/s. The highest kparameter for the whole year is in the direction of WN with 3.81,

while the lowest appears in the direction of EN with 1.35. The monthly Weibull parameters of

Belen-Hatay station are given in Table 6. It is seen from the table that the monthly Weibull

shape parameters (k) for the whole year range from a low of 1.82 in December to a high of

5.84 in July. While the highest monthly Weibull scale parameter (c) value is determined as

11.9 m/s in August, the lowest monthly c value is found as 5.3 m/s in December.

Variation of Wind Speeds

The production of wind energy is essentially dependent on the magnitude and

regularity of wind speeds (Pashardes and Christofides 1995). Wind speed is the most

important parameter in the design and study of wind energy conversion systems

Table 5 The Weibull parameters according to the wind directions.

Wind direction 2004 2005 Whole

c (m/s) k c (m/s) k c (m/s) k

0 (N) 2.7 1.29 2.8 1.52 2.8 1.44

30 (NE) 1.6 1.75 3.5 1.54 3.1 1.4

60 (EN) 6.3 1.43 4.7 1.47 5.1 1.35

90 (E) 8.9 3.17 7.6 2.73 8.3 2.85

120 (ES) 5.7 1.81 5.3 1.69 5.5 1.74

150 (SE) 4.9 1.8 5.2 2.2 5.1 1.95

180 (S) 2.5 1.54 2.1 1.54 2.3 1.54

210 (SW) 2.3 1.84 2.0 2.03 2.2 1.86

240 (WS) 3.0 2.58 3.3 1.39 3.2 1.74

270 (W) 6.7 1.81 10.7 4.32 9.6 2.97

300 (WN) 10.2 3.92 10.4 3.72 10.3 3.81

330 (NW) 7.8 2.85 7.0 2.32 7.4 2.61

Table 6 The monthly Weibull parameters.

Month 2004 2005 Whole

c (m/s) k c (m/s) k c (m/s) k

January 6.4 1.65 6.9 2.02 6.7 1.84

February 7.1 2.00 6.4 1.98 6.8 1.99

March 7.1 2.70 6.0 2.4 6.6 2.55

April 7.2 2.04 7.4 2.27 7.3 2.16

May 8.9 3.64 8.8 2.97 8.9 3.31

June 10.3 5.09 10.4 3.81 10.4 4.45

July 10.8 4.90 12.0 6.78 11.4 5.84

August 11.5 4.59 12.2 5.36 11.9 4.98

September 8.6 3.78 8.8 5.36 8.7 4.57

October 6.0 2.36 5.8 2.24 5.9 2.3

November 6.1 1.96 5.6 2.26 5.9 2.11

December 6.1 1.65 4.5 1.98 5.3 1.82



12/17

(Akpinar and Akpinar 2005). A detailed knowledge of the wind speed distribution and

the most frequent wind directions are substantially important when choosing wind

turbines and locations (Torres et al. 1999). For this purpose, yearly mean wind speeds

according to the wind directions, monthly mean wind speeds, and daily variation of

wind speeds of Belen-Hatay station were determined by using the WAsP program. The

variation of yearly mean wind speeds according to the wind directions of Belen-Hatay

station is presented in Figure 7. It is also clear from the figure that the two curves show

the same variation according to each other. The highest yearly mean wind speed for the

year 2004 is in the direction of WN with 9.23 m/s, while the lowest appears in the

direction of SW with 2.06 m/s. In this region, wind speeds observed in the direction of

WN are effective and strong.

The monthly variation of mean wind speeds of Belen-Hatay station for the year 2004

and 2005 is presented in Figure 8. The highest monthly mean wind speeds for the year 2005

occur mainly in July and August with 11.1 m/s and 11.3 m/s respectively, and the lowest

wind speeds occur in November and December with 4.7 m/s and 3.9 m/s respectively. It isapparent from the figure that monthly mean wind speed data of 2004 and 2005 agree well

with each other. The variation of diurnal wind speeds of Belen-Hatay station for the year

0

2

4

6

8

10

Wind

speed(m/s)


Wind direction

2004

2005

Figure 7 The variation of yearly mean wind speeds according to the wind directions.

0

2

4

6

8

10

12

Windspeed(m/s)

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

Month

2004

2005

Figure 8 The monthly variation of mean wind speeds for the year 2004 and 2005.



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2004 and 2005 is presented in Figure 9. It is also clear from the figure that the three curves

show the same variation. Diurnal wind speed for the whole year varies between 6.05 m/s

and 7.76 m/s. The diurnal wind speed has its minimum value during the morning hours and

its maximum value during the afternoon hours. In other words, in this region, the wind

speed is higher during the day and lower during the night. This is due to the high level ofsolar intensity during the day. Figure 10 shows the variation of diurnal mean wind speeds

for the seasons of winter, spring, summer, and autumn in the year 2005. The graph reveals

that diurnal wind speeds during the summer are higher than diurnal wind speeds during the

winter, spring, and autumn.

Variation of Wind Power Potential

The variation of yearly mean wind power potential according to the wind

directions of Belen-Hatay station is presented in Figure 11. It is also clear from the

figure that the two curves show the same variation according to each other. Thehighest yearly mean wind power potential for the year 2004 is in the direction of WN

5

5,5

6

6,5

7

7,5

8

2 0 2 4 6 8 10 12 14 16 18 20 22 24

Hour of day

Windspee

d(m/s)

2004 2005 Whole

Figure 9 The variation of diurnal wind speeds for the year 2004 and 2005.

2

4

6

8

10

12

2 0 2 4 6 8 10 12 14 16 18 20 22 24

Hour of day

Windspeed(m/s)

Winter Spring

Summer Autumn

Figure 10 The variation of diurnal mean wind speeds for the seasons in the year 2005.



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with 599 W/m2, while the lowest appears in the direction of SW with 11 W/m2. In

this region, wind power potential determined in the direction of WN is effective and

fairly strong.

The monthly variation of mean wind power potential of Belen-Hatay station for

the year 2004 and 2005 is presented in Figure 12. The highest monthly mean wind

power potential for the year 2005 occurs mainly in July and August with 911 W/m2

and 993 W/m2 respectively, and the lowest in November and December with 122 W/m2

and 75 W/m2 respectively. It is apparent that monthly mean wind power potential

data of 2004 and 2005 agree well with each other. The variation of diurnal wind

power potential of Belen-Hatay station for the year 2004 and 2005 is presented inFigure 13. It is also clear that the three curves show the same variation with a good

agreement. Diurnal wind power potential for the whole year varies between 291.5 W/m2

and 448 W/m2. The diurnal wind power potential has its minimum value during the

morning hours and its maximum value during the afternoon hours. In other words, in

this region, the wind power potential is higher during the day and lower during the

night.

0

100

200

300

400

500

600

700

Windpowerp

otential

(W/m2

)


Wind direction

2004

2005

Figure 11 The variation of yearly mean wind power potential according to the wind directions.

0

200

400

600

800

1000

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

Month

2004

2005

Windpowerpotential

(W/m2)

Figure 12 The monthly variation of mean wind power potential for the year 2004 and 2005.



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OVERALL DISCUSSIONS

Turkeys energy demand is growing rapidly and expected to continue to grow in the

near future. The annual increase in energy consumption is 68%, except for recession years.

The installed capacity of electric power plants in the year 2005 was 38,843.5 MW with

annual electricity production 161,983.3 GWh. Presently, 66.68% of this energy is

produced in operating thermal power plants. The combustion of coal, lignite, petroleum,

wood, agricultural and animal waste, causes environmental pollution and will be a

serious problem in the future. In this regard, renewable energy resources appear to be

one of the most efficient and effective solutions for sustainable energy development and

environmental pollution prevention in Turkey (Ocak et al. 2004).

The Belen-Hatay region has a reasonably good wind power potential. In this region,

the hourly wind speeds are higher than 5 m/s in about 70% of occasions at 10 m height

above ground level. The monthly mean wind speeds are higher than 5 m/s during nine

months of the year; on the other hand the monthly mean wind power potentials are higher

than 200 W/m2 during eight months of the year. While a wind generator is being installed

in this region, the yearly wind characteristics should be provided basic information about

the wind strength and consequently about the supply of wind power. The variations of the

yearly mean wind characteristics measured in 2004 and 2005 are given in Table 7. It can

be seen from this table that in 2004, the mean wind speed is 7.1 m/s and the mean wind

power potential is 374 W/m2. On the other hand in 2005, the mean wind speed is 7.0 m/s

and the mean wind power potential is 382 W/m2. According to the critical values of meanwind speed, having wind speed of 7.1 m/s is good for the utilization of the wind energy

potential.

0

100

200

300

400

500

2 0 2 4 6 8 10 12 14 16 18 20 22 24

Hour of day

Windpowerpote

ntial(W/m2)

2004 2005 Whole

Figure 13 The variation of diurnal wind power potential for the year 2004 and 2005.

Table 7 The yearly mean wind characteristics measured in the year of 2004 and 2005.

Wind characteristics 2004 2005 Whole

Weibull parameter (c) (m/s) 8.30 8.20 8.30

Weibull parameter (k) 2.63 2.31 2.46

Wind speed (m/s) 7.10 7.00 7.00

Standard deviation of wind speeds (m/s) 3.50 3.66 3.58Wind power potential (W/m2) 374 382 378



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CONCLUSIONS

It can be concluded that present regions are suitable for the plantation of wind energy

turbines. Several locations can quite reasonably be considered favorable for the production

of wind energy. It is known that there is no wind turbine placed in the region of Belen-Hatayyet for electricity production. The present results suggest that it can be profitable to

establish a wind farm in this region. At 10 m height above the ground level, mean wind

speed and power potential of the site are 7.0 m/s and 378 W/m2, respectively. The hourly

wind speeds are higher than 5 m/s in about 70% of occasions. The monthly mean wind

speeds are higher than 5 m/s during nine months of the year; on the other hand the monthly

mean wind power potentials are higher than 200 W/m2 during eight months of the year. In

this provision, there is very large land area available for constructing wind energy farms.

However, their suitability and availability for these applications are acceptable in terms of

other aspects such as being in close proximity to the electrical grid lines, land ownership,

road network infrastructure, and so forth.

ACKNOWLEDGMENT

The authors wish to thank the office of Scientific Research Projects of Cukurova University for

funding this project under contract no. MMF2006D18.

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