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NuRER 2012 – III. International Conference on Nuclear & Renewable Energy Resources İstanbul, TURKEY, 20-23 May 2012

EVALUATION OF WIND POWER POTENTIAL AND ECONOMIC ANALYSIS FOR ÇATALCA IN MARMARA REGION Tufan Giroğlu1, Ali Durusu1 , Ali Erduman1, Recep Yumurtacı1, Muğdeşem Tanrıöven1 Yıldız Technical University

1

Abstract In this study, wind energy potential of Çatalca in Marmara region is analyzed by using hourly average wind speed data which has measured 10 m height from ground level in 2010. The wind speed distribution is modeled by using Weibull distribution. Weibull parameters, shape and scale parameters, are calculated by using two different methods which are graphical method and approximated method. After the calculation of Weibull parameters three wind turbines are selected from different manufacturers. Capacity factor of turbines are determined and also economic analysis is added to this study for choosing the most economical and efficient wind turbine.

Keywords: Wind Energy Potential, Weibull Parameters, Çatalca, Economic Analysis 1. Introduction As a result of an emerging technology and population growth, world electricity consumption is increasing rapidly. World energy production for 2010 was 21248 TWh and %68.1 of this production has been made by conventional thermal (Gas, Oil, Coal) sources. Renewable energy sources (incl. Hydro) had %19.5 of this production. In Turkey energy production for 2010 was 211 TWh and %73 of this production has been made by conventional thermal (Gas, Oil, Coal) sources. Renewable energy sources (incl. Hydro) had %26.2 of this energy production [1]. What is more, fossil fuels contain carbon and hydrogen that has been fixated by ancient plants. However, near the end of the seventies it seemed that natural resources would soon be exhausted. At that time countries all over the world started to work on clean energy as producing electricity by renewable energy sources [2]. Sun, wind and water are available at almost all places and they are almost inexhaustible. Today, clean, domestic and renewable energy is commonly accepted as the key for future life, not only for Turkey but also for the world. As wind energy is an alternative clean energy source compared to the fossil fuels that pollute the atmosphere, renewable energy resources appear to be one of the most efficient and effective solutions for sustainable energy development and environmental pollution prevention in Turkey. On the other hand, investments in wind energy sector are increasing [3]. During 2010, 528 MW of wind power was installed across Turkey and total wind energy production increased to 1329.15 MW. Also in 2011, 476.7 MW of wind power was installed and total wind energy production increased to 1805.85 MW [4]. Till the end of 2012 517.55 MW of wind power will be installed and total wind energy production will be increased to 2323.4 MW. Above all, licensed wind power plants energy production capacity is 5444 MW [4] and thus, it is clear that total energy production from wind energy for Turkey will reach higher numbers soon. So as a result of increase at investments in wind energy sector, energy production from wind energy will have an important percentage of total energy production in Turkey. In this study, wind energy potential of Çatalca in Marmara Region is analyzed by Weibull distribution with making use of hourly average wind speed data [5]. Also economic analysis of three selected wind turbine from different manufacturers is added to this study. 2. Materials and Methods The wind data were taken from Turkish State Meteorological Service, Catalca Synoptic Station. Çatalca is located at east of İstanbul in Marmara Province, Turkey. Figure 1 shows the location of Çatalca Synoptic Station. The sampling period was 60 min and the altitude of the measurement point was 10 meters above the ground in 2010. A program was written in Matlab to determine the frequency of each speed in every month. Table 1 shows the frequency of wind speeds for height of 50 m for 2010 determined from the program. In order to process the data, mathematical model Weibull probability distribution function has been used.

E-mail of Corresponding Author: [email protected]

NuRER 2012 – III. International Conference on Nuclear & Renewable Energy Resources İstanbul, TURKEY, 20-23 May 2012

Figure 1. Location of Çatalca Synoptic Station 3. Weibull Distribution The Weibull distribution is a mathematical expression, which provides a good approximation to many measured wind speed distributions. The Weibull distribution is therefore frequently used to characterize wind speed distribution [6]. Such a distribution has two parameters to analyze the wind power; in the first place scale parameter, which is closely related to the mean wind speed, and secondly the shape parameter, which is a measurement of the width of the distribution [7]. There are several methods used to determine the shape parameter and scale parameter for Weibull distribution. In this paper two approaches are used; the graphical method and the approximated method [8]. The probability density function of a Weibull distribution is given by; f (v) =

exp

(1)

k is the shape parameter, c is the scale parameter and v is the wind speed [m/s]. The cumulative distribution function is given by; F(v) = 1- exp (2) 3.1 Approximated method In this method by calculating standard deviation coefficient σ and average wind speed value parameters of Weibull distribution can be determined and given as; (1 ≤ k ≤ 10) c= is the average wind speed, N is the number of records for each month, Table 1. σ is the standard deviation coefficient, Γ is the gamma function [9].

, shape and scale

(3) (4) is the wind speed value that given at

3.2 Graphical method In this method eq. (5) is derived by taking decimal on both sides of eq. (2) and plot of y versus x presents a straight line with a slope of m. The values of the shape and scale parameters k and c can be determined by using least square fitting of the data [10] and given as; (5)

NuRER 2012 – III. International Conference on Nuclear & Renewable Energy Resources İstanbul, TURKEY, 20-23 May 2012

y = b + mx y= x = ln(v)

(6) (7) (8)

k=m

(9)

c=

(10)

4. Power density The average power density using weibull probability distribution is calculated as follows [8]; (11) is the air density in kg/

and given as 1.225 kg/

.

4. Capacity factor of wind turbines Capacity factor for wind turbines is the ratio of the actual output power and rated output power of the turbine. Capacity factor related to shape and scale parameters and can be given as [8]; Cf=

(12)

is the cut-in wind speed of the wind turbine, is the rated wind speed of the wind turbine, speed of the wind turbine, p = / , γ is incomplete gamma function.

is the cut-out wind

5. Extrapolation of wind speed The wind data at a specific height can be calculated to any height [11] by using; (13) Where is the actual wind speed measured at a height of , the Hellman coefficient and it depends on the surface roughness.

is the wind speed at the required height

.

is

6. Economic Analysis Present Value of Costs (PVC) method has been used to analyze the total cost of installation. Then cost per kilowatthour of electricity production has been calculated and payback period of wind turbine investment has been determined [12]. (14) I is the investment cost (Turbine Price + Installation Costs (30% of turbine price)) [13][14], B is yearly maintenance and repair cost, i is annual inflation, r is annual bank interest, S is scrap value, t is life span of turbine.

Cost per kilowatt-hour (€cent/kWh) = is the turbine rated power,

(15)

is the capacity factor of turbine.

Payback Period (years) = w is the reference price per kilowatt.

(16)

NuRER 2012 – III. International Conference on Nuclear & Renewable Energy Resources İstanbul, TURKEY, 20-23 May 2012

7. Analysis of Wind Data and Discussion The wind speed record of each hour for Çatalca Synoptic Station was 8106 datum for 2010. Station was under maintenance in July so there were some missing datum, also the wind speeds higher than 25 m/s has not included to analyze because of selected wind turbines cut out speed value. The wind data which has measured at 10m height above the ground has been extrapolated to 50m height (Table 1) by using eq. (13). The Hellman coefficient depends on surface roughness and because of the location of Çatalca it has been determined as 0.2 [15]. Table 1. The frequency of the wind speeds for height of 50 m for 2010 0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

JAN

0

FEB

28

0

7

14

37

52

44

35

31

35

38

39

25

29

30

29

18

13

11

2

13

34

38

45

65

38

48

52

57

41

28

34

29

21

21

10

9

MAR APR

0

1

23

19

32

48

80

54

66

60

53

51

31

29

27

17

29

25

31

0

5

16

31

45

54

67

65

72

61

52

38

35

22

35

37

18

24

13

MAY JUNE

0

3

26

41

63

85

105

86

90

81

62

24

15

13

8

8

5

8

1

12

29

31

40

82

114

92

79

81

56

31

21

13

8

9

12

2

JULY AUG

0

3

15

23

39

50

42

35

45

43

49

37

30

28

10

2

6

1

12

24

29

37

70

105

89

83

85

57

35

23

27

11

9

15

SEPT

0

1

1

7

28

40

70

62

58

51

50

37

22

30

29

15

OCT

0

7

24

29

33

44

61

60

65

76

57

57

49

31

28

NOV

0

11

27

60

64

56

69

48

38

45

37

31

31

14

17

DEC

0

5

6

6

29

23

49

28

41

50

57

56

42

70

47

42

19

20

21

22

23

24

25

1

2

5

0

1

0

0

10

14

9

9

7

3

1

13

10

12

13

8

4

4

12

7

5

4

1

0

0

5

1

2

1

2

1

3

1

3

4

0

0

0

0

0

0

4

0

0

0

0

1

0

0

0

2

3

3

0

0

0

0

0

0

19

30

29

21

7

7

6

1

2

1

26

16

12

19

6

10

5

9

0

5

3

16

19

15

18

20

12

13

14

9

4

3

32

31

35

21

20

13

9

4

7

1

With making use of frequency of the wind speeds for height of 50 m, standard deviation constant σ and Weibull distribution parameters k and c has been determined by graphical and approximated methods. (Table 2) Table 2. Comparison between Weibull parameters estimated from graphical and approximated methods

JANUARY FEBRUARY MARCH APRIL MAY JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER Average

Standard Deviation σ

Graphical k

Graphical c

Approximated k

Approximated c

4,4

2,47

10,3

2,33

10,8

5,41

1,67

9,22

1,81

10,5

5,34

2

11,2

2,05

11,7

4,6

2,13

10,1

2,17

10,6

3,65

2,04

8,6

2,18

8,45

3,3

2,07

7,89

2,41

8,38

3,5

2,29

8,5

2,48

9,1

3,38

2,1

8,16

2,46

8,74

4,81

2,69

11,8

2,33

11,9

4,83

1,79

9,87

2,07

10,7

5,97

1,41

9,08

1,54

9,87

5,21

1,86

12,2

2,4

13,1

4,54

2,04

9,74

2,19

10,32

After determination of Weibull parameters by graphical and approximated methods, average wind speed and power density has been calculated from actual data and graphical and approximated methods results (Table 3). Comparison of Table 3 results shows that approximated method gives the closest values to actual data. Graphical method results has no big difference from actual data but approximated method gives the best results. Also probability density function of wind speeds at 50 m height has given at Figure 2.

NuRER 2012 – III. International Conference on Nuclear & Renewable Energy Resources İstanbul, TURKEY, 20-23 May 2012

Table 3. Comparison between average monthly speed and power density calculated using Weibull parameters estimated from graphical and approximated methods Actual Data Months

JANUARY FEBRUARY MARCH APRIL MAY JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER Avarage

Graphical Method

Vm (m/s) 9,6

P (W/ ) 900,5

Vm (m/s) 9,18

9,36

1066,3

8,23

10,3

1257,9

9,92

9,41

900,7

7,48

Approximated Method Vm (m/s) 9,7

P (W/ ) 901,49

802,29

9,36

1066,3

1140,5

10,4

1259,9

8,92

782,52

9,41

900,7

453,9

7,62

506,92

7,46

451,97

7,43

406,7

6,99

386,07

7,43

406,7

8,07

511,47

7,53

440,71

8,07

511,47

7,75

455,46

7,23

421,89

7,75

455,46

10,5

1183,6

10,5

1049,9

10,3

1180,6

9,45

953,2

8,78

890,19

9,45

953,26

8,88

1127,5

8,27

1039,4

8,89

1124,5

11,7

1576,4

10,8

1586,6

11,9

1578,4

9,16

899,4

8,66

816,78

9,12

898,5

P (W/ ) 754,37

Figure 2. Probability density function of wind speeds at 50m height.

As second step of this study, three wind turbines from different manufacturers has been selected and capacity factor of turbines has been determined by using Eq. (12). Also economic analysis has been added for choosing the most economic and efficient wind turbine. Firstly, selected wind turbines data has been given at Table 4 [16-17-18]. Also power curve of three selected turbines has been given at Figure 3. With making use of this data in Eq. (12), capacity factor of wind turbines has been calculated and given in Table 5 . Economic analysis for selected wind turbines has been made by using Eq. (14-15-16). Turbine price as €/kW has been taken as 1500€ [10-11]. Annual inflation and annual bank interest has been accepted as %4 and %4.58. Also reference price per kilowatt has been taken as 6.5 €cent from the web site of Ministry of Energy and Natural Resources [13]. Economic analyze results has been given in Table 5. Table 4. Wind Turbine Data Cut-in Wind Speed

Rated Wind Speed

Cut-out Wind Speed

Vc (m/s)

Vr (m/s)

Vf (m/s)

ENERCON E48-800kW

3

13

28

SUZLON S52-600kW

4

13

25

GAMESA G58-850kW

3

12

23

Turbine Model

NuRER 2012 – III. International Conference on Nuclear & Renewable Energy Resources İstanbul, TURKEY, 20-23 May 2012

Figure 3. Power Curve of Wind Turbines Table 5. Comparison between capacity factor, electricity generation cost per kWh and payback period of investment calculated by economic analysis estimated from graphical and approximated method parameters. Graphical Method Turbine Model

Approximated Method

ENERCON E48-800kW

Capacity Factor 0,4

Cost per kWh (€cent/kWh) 3,04

Payback Period (Years) 9,3

Capacity Factor 0,44

Cost per kWh (€cent/kWh) 2,8

Payback Period (Years) 8,6

SUZLON S52-600kW

0,39

3,06

9,4

0,43

2,83

8,7

GAMESA G58-850kW

0,45

2,7

8,3

0,48

2,5

7,7

8. Conclusion In this study wind power potential and economic analysis for Çatalca in Marmara Region is presented. In Table 3 it shows that for actual data, graphical and approximated method results. Highest wind speed estimated in December for graphical and approximated methods, as 10,8 m/s and 11,9 m/s respectively. Lowest wind speed estimated in June for graphical and approximated methods, as 6,99 m/s and 7,43 m/s respectively at a height of 50m . Highest wind power density is determined in December for actual datum, graphical and approximated method, as 1576.4 W/ , 1586.6 W/ , 1578. 4 W/ respectively and lowest wind power density is determined in June as 406.7 W/ , 386.07 W/ , 406.7 W/ respectively. Determination of capacity factors of selected turbines, electricity generation cost per kWh and investment payback period results has been given in Table 5. For all methods Gamesa G-58 has the highest capacity factor, lowest cost per kWh for electricity generation and payback period of investment. Most important element for capacity factor and economic analysis is wind turbine rated wind speed. Gamesa has lowest rated wind speed as 12 m/s and turbine starts to produce more energy in lower speeds. Therefore Gamesa is the most efficient wind turbine for our analysis. In addition to this other two turbine results are close to each other also economic and efficient. Because other selected turbines rated wind speeds are also close to Gamesa as 13 m/s. The results showed that approximated method of Weibull distribution is the best fitted method for Çatalca and gives better results than graphical method. Also Çatalca has high wind speed potential and suitable location for the establishment of wind turbines leading to cost free natural energy source. 9. References [1] ‘Global Energy Year Book 2011’, ENERDATA, ( 2011) [2] Hemerik L , Huisman T ,’Wind Energy Computed, The relation between wind speed and efficiency’, p.3 (2007) [3] ‘Wind in Power European Statics 2011’, EWEA, (2011) [4] ‘Turkish Wind Energy Statics Report’, TWEA, (2012) [5] www.tumas.dmi.gov.tr/wps/portal/ (2012) [6] Akdağ S, Güler Ö, ‘Weibull Dağılım Parametrelerini Belirleme Metodlarının Karşılaştırılması’, (2008) [7] www.wind-energy-the-facts.org (2012) [8] Jowder F, ‘Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain’, (2008) [9] Sebah P, Gourdon X, ‘Introduction to the Gamma Function’, p.2-3 (2002) [10] Miller S, ‘The Method of Least Square’ [11] Özgonenel O, Thomas D, ‘Short-term wind speed estimation based on weather data’, p.337 (2010) [12] Ay S ‚‘ Elektrik Enerjisi Ekonomisi’, (2008) [13] Yen-Nakafuji D, ‘Strategic value analysis – economics of wind energy in California’, p.22 (2005) [14] ‘Wind Energy - The Facts’, EWEA, p.201-203 (2009) [15] Kaltschmitt M, Streicher W, Wiese A, ‘Renewable energy: technology, economics, and environment’, p.55 (2007) [16] www.enercon.de (2012) [17] www.gamesacorp.com (2012) [18] www.www.suzlon.com (2012) [19] www.enerji.gov.tr (2012)