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Investigation of wind energy application possibilities for a specific island (Bozcaada) in Turkey
WREC 1996
INVESTIGATION OF WIND ENERGY APPLICATION POSSIBILITIES FOR A SPECIFIC ISLAND (BOZCAADA) IN TURKEY C. DDNDAR State MeteorologicalService, Research Department, Ankara /Turkey
D. INAN AukaraITurkey
Ihu&bqeumversny,Physics~~
ABSTRACT 1975-1984 wind data and other pammemmsuchasobstacleinkmationweretakenforaspeci6cmhabimd island ( Bozzaada ) on the west coast of Turkey and used for calculation of wind energy usage pomibihties on this island. Calculations were made by using appropriate computer programs which were prepared by the authors and also a computer program WASP ( Wind Atlas Analysis and Application Program, Mortenseq 1993 ) was used. Conchtsions were classified according to the height above ground level and were discus& with the performancxe of some available commercial wind turbine systems in these conditions.
INTRODUCTION The aim of this study was to calculate the wind energy potential for the Bozcaada Bland and to assess the efficiency of electricity production by using the wind data recorded at the Bozcaada Meteorological Station. For the vertical extrapolation of measured wind speeds, a numeric model which is known as WASP (Wind Atlas Analysis and Application Program, Mortensen et al., 1993) was used. The main principle of this model is that the wind data should be distributed according to the Weibuh distribution with two parameters. WASP also includes some sub-models for correction of the impacts CBusedby topography and obstaclea on the wind speed and direction
DATA Wind data used in this study were measored hourly at Bozcaada State Meteorological Station at 10 m above ground level between the years 1975 and 1984. Bozcaada Station is located at 390 50’N and 26’ 04’E and its elevation is 28 m above the sea level. Missing data represent 7.2 % of the total, and 20 % of them are dispersed randomly over the measurement period. Rest of missing data cover the January, February, August., September, October, November, and Decemberin 1980. Allofmissingdatawerenottakeninto accountinthestudy. 27 obstacles around the m easurement point was determined (Uyar et al., 1988). The e&cts of these obstacles are corrected by the WASP. The topographical impacts on wind stream were ignored.
822
WREC 1996
METHOD OF ANALYSIS The two-parameter Weibull distribution is expressed mathematically as (Troen et al., 1989), f(v) =
$(y
expp,q
where gv) is the frequency of wind speed v. Thus two Weibull parameters are usually referred to as the scale parameter A and the dimensionless shape factor k. The cumulative Weibull distribution F(v) gives the probability of the wind speed exceeding the tie v and is given by the following simple expression, F(v) = ex
-(jr 1 P[
The available wind power density is proportional to the mean cube of the wind speed and given as (Troen et al., 1989)
where E is power density (w/m?, r is air density (-1,2 kg/m3 at a temperature of 1Y C and a standard pressure of 1013 hPa) and G is the gamma distribution. For any wind stream with speed v (m/s), of cross-sectional area S (m’), and air density r (kg/m3), the power in the wind, P (W), is given by (Adekoya et al, 1992) P=
$Sv’.
However, the theoretical maximum fraction of extracted power is (16/27)P. This maximum is called the Betz maximum (Adekoya et al., 1992).
RESULTS The sectoral frequencies of wind direction are given in Table-I and wind rose is shown in Figurel. shows that the prevalent winds are from the NE and NW directions.
Analysis
Frequency diagram of wind speed was produced to show the distribution pattern at 10 m ( Table-II and Figure-2 ). Wind speed data distribution is consistent with the two parameter WeibuU distribution for k-2 in literature (Troen et al., 1989).
Vertical change of wind velocity (v), wind power density (E) and Weibull parameters (A,k) were calculated by using WASP with obstacle information from I&UTet al., 1988 and are given in Table4II. Monthly mean diurnal distribution of wind speeds is also given in TabLIV. In the winter months, wind velocities are greater than those in summer months. In addition, day time velocities are greater than night time velocities.
823
WRJX 1996 Approximately 68 % of total wind speed data was found to be greater than 4.5 m/s as calculated from the observed meteorological wind data. This value is suitable for most Wind Energy Conversion Systems (WECS) that are used for electricity production.
CONCLUSION The average of observed wind speeds at 10 m was cakulated to be 6.2 m/s. After the corrections were made to eliminate the obstacles e%cts, 10 m average wind speed is found to be 6.4 m/s from WASP. The topographical effects could be included in calculation.and a further correction could be done and the accumcy could be increased. However, a problem occurred to fit the topographical data to WASP and authors applied to the makers of WASP to solve the problem. As seen from Table-III, Weibull parameters, wind speeds and energy densities are increasing with height. WA@ comains some WJXS to calculate the yearly electricity production. One of them that de&ted as V39500 has 40.5 m hubheight is 500 kW power. This WECS can produce approximately 2000 MWhIyear electricity at this conditions. In the 1985 and 1990 censuses, the population of this island was about 2000 and there was not any considerable change in the population a&x the last census. According to the latest energy statistics, energy consumption per capita in 1994 is 1324 kWh in Turkey. This value is estimated to be 1936 kWh for the year of 2000. So, the electricity consumption in 2000 will be approximately 4000 MWh. Only two WECS mentioned above can supply sufficient electricity for this island.
Table-I. Sectoral Frequencies Sector (in degrees)
Frequ-~ %
0 30 60 90 120 150 180 210 240 270
5.3 33.5 8.2 2.4 6.8 7.9 3 9.5 1.8 1
300
4.1
330
16.4
0
180
Figure-l. Wind Rose
824
WREC 1996
Table-I... Frequency of wind qxed clbta Frequency %
WindSpeed Frequency Range (m/s) (hours) 0.0-2.9 17890
3.0-5.9 6.0-8.9 9.0-l 1.9 12.0-14.9 15.0-17.9 18.0-20.9 21.0-30.0 Total
23907 22035
21.9 29.3 27.0
12292 3990 1226 267 34 81641
15.1 4.9 1.5 0.3 0.04 100
Figure-2. Frequency diagram
* heightabovegroundlevel
Table-N. Mean montMy distribution of the c&urnalwind qxed ckl&l. S
LMT”iJIFIMIAIMIJIJIAI-,_,-, 3 7.4 7.8 6.1 6 7.5 7.8 6.3 9 7.7 7.8 6.5
4.7 4.7 5.1
3.9 4 4.7
3.5 3.7 4.8
4.1 4.4 5.5
4.6 4.8 5.9 1.9 111
18 21
7.7 I
II I
714 1 7 i
7
I
5.8 I
55
1 6.1
i
ii
-i
i
14 ,1 7.2 1, 7.7 .._ ,1 5.8 ___ ,1 4.5 .__ ,1 3.8 DAY
1 7.6
1 7.9
1 6.7
1 5.4
1 4.9
I,
3:,7
6.5 46 1 319 ,I 3:b l, 3.9 .._ 1 4.8 1 5.5
* Local MeridianTime
825
7 47 ,1 4.2 . 1 5.9
1
4.7 4.9 5.6 7.4 8
1 1 ,1 1
6.4 4.6 4.4 5.8
0
i
5.3 5.6 5.8 7.2 ..6.4 53 5.1 -.6.1
i
6.6 7 7.4
79
1 1 1, 1
N
7.4 7.3 7.8
79
[ 1 1, 1
... 6.9 6.3 6.4 -.6.9
5.6 6.1 7.4
8
1 I ,1 1
7.3 7.5 711 7.6
77
1 I 1 1
... 6.7 5.6 5.3 6.2
WREC 1996
REFERENCES 1. 2. 3. 4. 5. 6. 7. 8.
I. Troen, E. L. Petersen, European Wind Atlas, Riso National Laboratory, Roskilde, Denmark (1989). N. G. Mortensen, L. Landberg, 1. Troen, E.L. Petersen, Wind Atlas Analysis and Application Program (WASP) Users Guide, Rise National Laboratory, Roskilde, Denmark (1993). H. G. Beyer, K. Nottebaum, Synthesis of Long-term Hourly Wind Speed Time Series on the Basis of European Wind Atlas Data, Solar Energy, 54,351-355 (1995). L. 0. Adekoya, A. A. AdewaIe, Wind Energy Potential of Nigeria, Renewable Energy, 2, 35,39 (1992). J. D. Preumatikoq Wind Energy Potential in NW Pelopomwse-Greece, Renewable Energy, 1, 137139 (1991). S. Pashardes, C. Christofides, Statistical Analysis of Wind Speed an Direction in Cypn~~, Solar Energy, 55,405-414 (1995). R. K. Panda, T. K. Sarkar, A. K. Bhattacharya, Stochastic Study of the Wind-Energy Potential of India, Energy, 15,921-930 (1990). T. S. Uyar, A. Yazar, M. N..Alpay, Bozcaada, Qqme, Fethiye ve Sukanhiw &n Hesaplanan Riizgar istatistikleri, Tubitak-Mam, Kocaeli, Turkey (1988).
826
INVESTIGATION OF WIND ENERGY APPLICATION POSSIBILITIES FOR A SPECIFIC ISLAND (BOZCAADA) IN TURKEY C. DDNDAR State MeteorologicalService, Research Department, Ankara /Turkey
D. INAN AukaraITurkey
Ihu&bqeumversny,Physics~~
ABSTRACT 1975-1984 wind data and other pammemmsuchasobstacleinkmationweretakenforaspeci6cmhabimd island ( Bozzaada ) on the west coast of Turkey and used for calculation of wind energy usage pomibihties on this island. Calculations were made by using appropriate computer programs which were prepared by the authors and also a computer program WASP ( Wind Atlas Analysis and Application Program, Mortenseq 1993 ) was used. Conchtsions were classified according to the height above ground level and were discus& with the performancxe of some available commercial wind turbine systems in these conditions.
INTRODUCTION The aim of this study was to calculate the wind energy potential for the Bozcaada Bland and to assess the efficiency of electricity production by using the wind data recorded at the Bozcaada Meteorological Station. For the vertical extrapolation of measured wind speeds, a numeric model which is known as WASP (Wind Atlas Analysis and Application Program, Mortensen et al., 1993) was used. The main principle of this model is that the wind data should be distributed according to the Weibuh distribution with two parameters. WASP also includes some sub-models for correction of the impacts CBusedby topography and obstaclea on the wind speed and direction
DATA Wind data used in this study were measored hourly at Bozcaada State Meteorological Station at 10 m above ground level between the years 1975 and 1984. Bozcaada Station is located at 390 50’N and 26’ 04’E and its elevation is 28 m above the sea level. Missing data represent 7.2 % of the total, and 20 % of them are dispersed randomly over the measurement period. Rest of missing data cover the January, February, August., September, October, November, and Decemberin 1980. Allofmissingdatawerenottakeninto accountinthestudy. 27 obstacles around the m easurement point was determined (Uyar et al., 1988). The e&cts of these obstacles are corrected by the WASP. The topographical impacts on wind stream were ignored.
822
WREC 1996
METHOD OF ANALYSIS The two-parameter Weibull distribution is expressed mathematically as (Troen et al., 1989), f(v) =
$(y
expp,q
where gv) is the frequency of wind speed v. Thus two Weibull parameters are usually referred to as the scale parameter A and the dimensionless shape factor k. The cumulative Weibull distribution F(v) gives the probability of the wind speed exceeding the tie v and is given by the following simple expression, F(v) = ex
-(jr 1 P[
The available wind power density is proportional to the mean cube of the wind speed and given as (Troen et al., 1989)
where E is power density (w/m?, r is air density (-1,2 kg/m3 at a temperature of 1Y C and a standard pressure of 1013 hPa) and G is the gamma distribution. For any wind stream with speed v (m/s), of cross-sectional area S (m’), and air density r (kg/m3), the power in the wind, P (W), is given by (Adekoya et al, 1992) P=
$Sv’.
However, the theoretical maximum fraction of extracted power is (16/27)P. This maximum is called the Betz maximum (Adekoya et al., 1992).
RESULTS The sectoral frequencies of wind direction are given in Table-I and wind rose is shown in Figurel. shows that the prevalent winds are from the NE and NW directions.
Analysis
Frequency diagram of wind speed was produced to show the distribution pattern at 10 m ( Table-II and Figure-2 ). Wind speed data distribution is consistent with the two parameter WeibuU distribution for k-2 in literature (Troen et al., 1989).
Vertical change of wind velocity (v), wind power density (E) and Weibull parameters (A,k) were calculated by using WASP with obstacle information from I&UTet al., 1988 and are given in Table4II. Monthly mean diurnal distribution of wind speeds is also given in TabLIV. In the winter months, wind velocities are greater than those in summer months. In addition, day time velocities are greater than night time velocities.
823
WRJX 1996 Approximately 68 % of total wind speed data was found to be greater than 4.5 m/s as calculated from the observed meteorological wind data. This value is suitable for most Wind Energy Conversion Systems (WECS) that are used for electricity production.
CONCLUSION The average of observed wind speeds at 10 m was cakulated to be 6.2 m/s. After the corrections were made to eliminate the obstacles e%cts, 10 m average wind speed is found to be 6.4 m/s from WASP. The topographical effects could be included in calculation.and a further correction could be done and the accumcy could be increased. However, a problem occurred to fit the topographical data to WASP and authors applied to the makers of WASP to solve the problem. As seen from Table-III, Weibull parameters, wind speeds and energy densities are increasing with height. WA@ comains some WJXS to calculate the yearly electricity production. One of them that de&ted as V39500 has 40.5 m hubheight is 500 kW power. This WECS can produce approximately 2000 MWhIyear electricity at this conditions. In the 1985 and 1990 censuses, the population of this island was about 2000 and there was not any considerable change in the population a&x the last census. According to the latest energy statistics, energy consumption per capita in 1994 is 1324 kWh in Turkey. This value is estimated to be 1936 kWh for the year of 2000. So, the electricity consumption in 2000 will be approximately 4000 MWh. Only two WECS mentioned above can supply sufficient electricity for this island.
Table-I. Sectoral Frequencies Sector (in degrees)
Frequ-~ %
0 30 60 90 120 150 180 210 240 270
5.3 33.5 8.2 2.4 6.8 7.9 3 9.5 1.8 1
300
4.1
330
16.4
0
180
Figure-l. Wind Rose
824
WREC 1996
Table-I... Frequency of wind qxed clbta Frequency %
WindSpeed Frequency Range (m/s) (hours) 0.0-2.9 17890
3.0-5.9 6.0-8.9 9.0-l 1.9 12.0-14.9 15.0-17.9 18.0-20.9 21.0-30.0 Total
23907 22035
21.9 29.3 27.0
12292 3990 1226 267 34 81641
15.1 4.9 1.5 0.3 0.04 100
Figure-2. Frequency diagram
* heightabovegroundlevel
Table-N. Mean montMy distribution of the c&urnalwind qxed ckl&l. S
LMT”iJIFIMIAIMIJIJIAI-,_,-, 3 7.4 7.8 6.1 6 7.5 7.8 6.3 9 7.7 7.8 6.5
4.7 4.7 5.1
3.9 4 4.7
3.5 3.7 4.8
4.1 4.4 5.5
4.6 4.8 5.9 1.9 111
18 21
7.7 I
II I
714 1 7 i
7
I
5.8 I
55
1 6.1
i
ii
-i
i
14 ,1 7.2 1, 7.7 .._ ,1 5.8 ___ ,1 4.5 .__ ,1 3.8 DAY
1 7.6
1 7.9
1 6.7
1 5.4
1 4.9
I,
3:,7
6.5 46 1 319 ,I 3:b l, 3.9 .._ 1 4.8 1 5.5
* Local MeridianTime
825
7 47 ,1 4.2 . 1 5.9
1
4.7 4.9 5.6 7.4 8
1 1 ,1 1
6.4 4.6 4.4 5.8
0
i
5.3 5.6 5.8 7.2 ..6.4 53 5.1 -.6.1
i
6.6 7 7.4
79
1 1 1, 1
N
7.4 7.3 7.8
79
[ 1 1, 1
... 6.9 6.3 6.4 -.6.9
5.6 6.1 7.4
8
1 I ,1 1
7.3 7.5 711 7.6
77
1 I 1 1
... 6.7 5.6 5.3 6.2
WREC 1996
REFERENCES 1. 2. 3. 4. 5. 6. 7. 8.
I. Troen, E. L. Petersen, European Wind Atlas, Riso National Laboratory, Roskilde, Denmark (1989). N. G. Mortensen, L. Landberg, 1. Troen, E.L. Petersen, Wind Atlas Analysis and Application Program (WASP) Users Guide, Rise National Laboratory, Roskilde, Denmark (1993). H. G. Beyer, K. Nottebaum, Synthesis of Long-term Hourly Wind Speed Time Series on the Basis of European Wind Atlas Data, Solar Energy, 54,351-355 (1995). L. 0. Adekoya, A. A. AdewaIe, Wind Energy Potential of Nigeria, Renewable Energy, 2, 35,39 (1992). J. D. Preumatikoq Wind Energy Potential in NW Pelopomwse-Greece, Renewable Energy, 1, 137139 (1991). S. Pashardes, C. Christofides, Statistical Analysis of Wind Speed an Direction in Cypn~~, Solar Energy, 55,405-414 (1995). R. K. Panda, T. K. Sarkar, A. K. Bhattacharya, Stochastic Study of the Wind-Energy Potential of India, Energy, 15,921-930 (1990). T. S. Uyar, A. Yazar, M. N..Alpay, Bozcaada, Qqme, Fethiye ve Sukanhiw &n Hesaplanan Riizgar istatistikleri, Tubitak-Mam, Kocaeli, Turkey (1988).
826