Pergamon 0960-1481(95)00089-5

Reneuable Energy, Voh 6, No. 7, pp. 679 685, 1995 Copyright ~ 1995 Elsevier Science Ltd

Printed in Great Britain. All rights reserved 0960 14Sl/95 $9.50+0.00

THE W I N D E N E R G Y POTENTIAL OF GOK(~EADA IN THE N O R T H E R N A E G E A N SEA

S. T O L U N , * S. M E N T E ~ , ' ~ Z. A S L A N : ~ a n d M. A. Y U K S E L E N *

* Faculty of Aeronautics and Astronautics, Aeronautical Engineering Department, Istanbul Technical University, Maslak 80626, Istanbul, Turkey

t Faculty of Aeronautics and Astronautics, Meteorological Engineering Department, Istanbul Technical University, Maslak 80626, Istanbul, Turkey

:~ Kafkas University, Faculty of Forestry, Artvin, Turkey

(Received 19 December 1994; accepted 3 May 1995)

Abstract--Wind data collected over a period of three years at four different locations in G6kgeada were evaluated to estimate the potential of wind energy in the north-western part of Turkey. The data from the selected stations were used to estimate the regional mean wind speed distribution of G6k~eada, which revealed that four distinct regions exist with values from 1.0 to 4.0; 4.1 to 6.0, 6.1 to 8.0 and greater than 8.1 m/s. For each station, an extensive analysis was carried out to find the monthly average wind speed and its distribution. Several types of probabilistic distribution were matched to these distributions including the Weibull distribution. It was observed that one third of the island enjoys annual wind speeds greater than 6.0 m/s. This potential can be converted to electrical energy by means of wind farms, and the energy extracted from this source may supply electricity to the island and a small part of the mainland.

1. INTRODUCTION

Theoretical and experimental methods for calculating the potential for wind energy which are used in this study have been presented in a publication by the World Meteorological Organization [1]. Several stud- ies have been performed to estimate the wind potential in different parts of the world by Darwish and Sayigh [2], Yamaza and Kondo [3], Aspuden [4], Barros [5], Solari [6] and Harper [7]. However, further inves- tigations are generally necessary on the assessment of wind energy and the role of local effects in greater detail.

Analysis of some routine wind data in Turkey, par- ticularly in the western part of Anatolia and the assess- ment of wind energy resources have already been dis- cussed by (3ney et al. [8], Tolun [9], and Asian et al. [10l.

In this paper, a case study is realized for the island G6kgeada to estimate the wind energy potential in the north-western part of Turkey. The results of statistical analysis and of the model employed, including local effects, are presented. For this study, wind data were collected over a period of three years at four different locations in G6k~eada. For each station, an extensive analysis was carried out to find the monthly average wind speed and its distribution. Several types of pro- babilistic distribution were matched to these dis- tributions including the Weibull distribution.

2. MATERIAL AND METHOD

The study area covers both the urban and rural areas of G6k~eada. The four stations, namely, ~m- aralti, Doruktepe, U~urlu and the Nat ional Weather Service (NWS) are located in different parts of the island as shown in Fig. 1. Some of the characteristics of these stations are listed in Table 1.

Anemograph print-outs have been evaluated at the NWS station. The hourly averages of wind velocity for each month were manually loaded onto a computer. Mobile wind tower velocity measuring systems were used in all of these stations, except the NWS station. The mobile stations with automated wind-velocity measuring and recording systems had thermocouple units, 256 K memory modules and RS-232 transfer units for a P.C.

The presentation of wind data makes use of the Weibull distribution as a tool to represent the fre- quency distribution of wind speed in a compact form [11]. The two-parameter cumulative Weibull dis- tribution is expressed mathematically as :

F(u) = exp [-- (u/A)~], (1)

where F(u) gives the probability of the wind speed exceeding the value u. The two Weibull parameters thus defined are usually referred to as the scale par- ameter A (m/s) and the shape parameter k.

679

680 S. TOLUN et al.

AEGEAN SEA

Onaralf~ <~ iJ.J

z <{ iJJ t ~ MJ

UOurlu

A E GEAN SEA

0 0.5 1 1.5 2 H H ~ , U H / H ~ km

Fig. I. Map of G6kqeada indicating locations of the stations.

Observa t ion or measurement of wind at any locat ion reveals tha t bo th speed and direct ion vary rapidly in time. In addi t ion to the rapid change at a single point , the speed and direct ion of the wind change from poin t to poin t at a given instant . The reason for the var ia t ion of wind is the turbulence in the a tmospher ic bounda ry layer. In order to define a meaningful way of measur ing wind speed, it mus t be referred to an averaging per iod T:

a = ~ u(t) dt, (2)

where ~ indicates the mean value of u. The wind power density available over a t ime interval T is given by :

/~ J 3 l l I ~ = ~pu = ~ TO0 pu 3(t) dr. (3)

Table 1. The salient features and topography of the different stations

Altitude Latitude/ Observation Remarks Station (m) longitude period

National weather 71 4 0 . 7 4 ' ~ N 1979-1988 service 25.92°E 1991-1993

Doruktepe 670 40.17°N November (mobile wind tower 25.82°E December 1991 station)

U~urlu 35 40.11°N 1991-1993 (mobile wind tower 25.73°E station)

(~lnaralti 250 40.20°N April 1992 1993 (mobile wind tower 25.83°E station)

Urban, interior. North and northeasterly winds are representative, but southerly winds are perturbed by the topography.

Rural, interior; the highest hill of the island. Data have been partly perturbed during the northly flows. Data recorded at this station can be considered representative for all wind directions.

Rural, coastal, sparcely vegetated. Representative for southerly and easterly winds.

Rural, coastal, vegetated. The topographic conditions are affected by southerly and westerly flows.

The wind energy potential of G6kqeada

In this equat ion, air density may be taken as cons tan t 35. (p = 1.225 kg/m 3) with an error of less than a few percent, except for m o u n t a i n o u s areas and inter ior s ta t ions [12]. Hence eq. (3) becomes:

~ 1 -3 =~pu . (4)

At h igh wind speeds, the wind profile over flat and reasonably homogeneous terra in is well modelled using the logar i thmic law :

H, Z u(z) = ~ - l n T 0 , (5)

where u(z) is the wind speed at height z above g round level ; z0 is the surface roughness length ; k is the von K a r m a n cons tant , here t aken as 0.4 ; and u , is the so- called friction velocity. Wind speed da ta are extra- pola ted by using the following power law :

V r = ( Z r y (6) Va \Za/ "

Here Vr and Va are the ext rapola ted and observed wind speeds, za is the anemomete r height, z r is the ext rapola ted height and p is the power law exponent . Both Zo a n d p are funct ions of a tmospher ic stability. In order to define roughness class and average roughness length for each stat ion, the terraine s tructure at every 30 ° angle of view was studied [1].

By using the W A S P program, obstacle, o rographic and roughness effects on the wind were el iminated, as was shown by Troen and Peterson [13]. For the o rographic effects, G6kqeada ' s 1/100,000 scale topo- graphic map was scanned and the height at each pixel was loaded onto a compute r using A u t o C A D . Con- tour map files were evaluated for every 50 m height increment at each station.

681

3 0

2 5 -

20- v

15-

10- Best Fit :

0 = EXP (-0.0004587511) ~ 20.4874

4'o io 1;0 time

Fig. 2. Time variation of monthly wind speed in G6kqeada NWS station.

3. ANALYSIS

The mon th ly average wind speed var ia t ion based on long-term da ta dur ing the per iod 1979-1989 at the G6k~eada N W S sta t ion is shown in Fig. 2. The negative slope of the l inear t rend emphasizes the u rban iza t ion effect of the inter ior par t of G6kqeada.

Table 2 demonst ra tes the ou tpu t parameters of the W A S P model based on wind velocity observat ions dur ing the per iod J a n u a r y - A u g u s t 1992 at the N W S station, disregarding roughness and shelter effects. The same parameters are presented for 1993, including roughness and shelter effects, in Table 3. The highest month ly average of the scale pa rame te r (6.9 m/s) and wind speed (5.8 m/s) were observed in N o v e m b e r 1993, but the highest wind energy was present in January.

Table 2. Monthly averages of wind speed (u), Weibull scale parameter (A), shape parameter (k) and wind energy potential (E) in the NWS station

A (m/s) k u (m/s) E (W/m 2)

Month 10 m 30 m 50 m 10 m 30 m 50 m 10 m 30 m 50 m 10 m 30 m 50 m

January 5.2 6.4 7.1 1.89 1.86 1.91 4.5 5.6 6.3 146 228 304 February 4.3 5.4 6.0 1.34 1.41 1.46 4.0 4.9 5.4 126 217 274 March 5.4 6.7 7.4 1.84 1.96 2.05 4.8 6.0 6.6 143 252 326 April 3.9 4.9 5.5 1.69 1.83 1.94 3.5 4.4 4.9 62 108 139 May 4.3 5.4 6.0 1.79 1.90 1.99 3.9 4.8 5.3 75 136 179 June 3.2 4.0 4.4 1.63 1.72 1.79 2.8 3.5 3.9 34 61 80 July 3.7 4.6 5.1 1.63 1.72 1.79 3.3 4.1 4.6 53 96 125 August 4.8 6.0 6.6 2.47 2.65 2.78 4.3 5.3 5.9 76 140 185

682 S. T O L U N et al.

Table 3. Monthly averages of u, and output parameter of the WASP model for the NWS station, 1993

Raw data Processed data (WASP)

u (m/s) E (W/m 2) A (m/s) k u A

Month (m/s) (m/s) k 10m 3 0 m 5 0 m 1 0 m 3 0 m 5 0 m 10m 3 0 m 5 0 m 10m 3 0 m 5 0 m

January 4.9 5.5 1.37 5.2 7.0 7.9 260 620 895 5.7 7.7 8.7 1.40 1.41 1.42 February 5.3 6.5 2.13 5.6 7.6 8.7 226 572 843 6.3 8.6 9.8 1.80 1.81 1.82 March 4.5 5.3 1.87 4.8 6.5 7.4 140 337 494 5.4 7.3 8.3 1.84 1.87 1.90 April 4.1 4.9 2.43 4.3 5.8 6.6 86 195 287 4.9 6.5 7.4 2.23 2.33 2.4 May 2.8 3.3 1.72 2.9 3.8 4.4 37 78 112 3.2 4.3 5.0 1.62 1.73 1.81 June 3.1 3.7 1.86 3.2 4.1 4.6 47 92 123 3.6 4.6 5.2 1.65 1.76 1.84 July 3.7 4.4 2.14 4.0 5.3 6.1 70 169 250 4.5 6.0 6.9 2.09 2.12 2.16 August 4.6 5.5 2.62 4.8 6.5 7.4 116 279 409 5.5 7.3 8.4 2.33 2.35 2.39 September 3.4 3.9 1.46 3.6 4.8 5.5 78 187 272 3.9 5.3 6.1 1.46 1.47 1.50 October 3.3 3.6 1.21 3.5 4.7 5.4 93 217 313 3.8 5.1 5.8 1.30 1.31 1.33 November 5.8 6.9 3.02 6.0 8.1 9.2 207 496 723 6.8 9.1 10.3 2.51 2.58 2.62 December 3.8 4.5 1.85 4.0 5.5 6.3 87 212 309 4.5 6.2 7.0 1.79 1.83 1.86

Annual 4.1 4.8 1.67 4.3 5.8 6.6 120 285 415 4.8 6.5 7.4 1.63 1.65 1.68

T h e w i n d ve loc i ty o b s e r v a t i o n s a n d W A S P m o d e l

o u t p u t s for D o r u k t e p e a re s h o w n in T a b l e 4. T h e

w i n d speed d i s t r i b u t i o n a p p r o a c h e s a R a y l e i g h dis-

t r i b u t i o n as the a l t i t ude i nc rease s f r o m 10 to 50 m.

T h e w i n d speed a v e r a g e is lower t h a n t he co r r ec t ed

speed va lue o b t a i n e d f r o m the W A S P mode l .

T h e W A S P m o d e l p a r a m e t e r s for the U ~ u r l u

s t a t i o n are s h o w n in T a b l e s 5 a n d 6. T h e m a x i m u m

a v e r a g e w i n d speed was o b s e r v e d in D e c e m b e r 1991

a n d Ju ly 1993. T h e h i g h e s t m o n t h l y a v e r a g e w i n d

speeds were 7.4 a n d 12.4 m / s respect ively . T h e a v e r a g e

w i n d speed va lues b a s e d on the W A S P m o d e l were

s l ight ly h i g h e r t h a n the r aw d a t a excep t for the m o n t h s

o f M a r c h , M a y a n d S e p t e m b e r 1993, b u t the a n n u a l

a v e r a g e va lue o f w i n d speed w a s n o t a f fec ted by t opo -

g r a p h i c a n d r o u g h n e s s p a r a m e t e r s .

Table 4. Wind Speed and WASP model output parameters for Doruktepe (November-December 1991)

WASP model output Raw data

u (m/s) u (m/s) E (W/m 2) A (m/s) k

10m 10m 3 0 m 5 0 m 10m 3 0 m 5 0 m 10m 3 0 m 5 0 m 10m 3 0 m 5 0 m 7.8 8.2 10.1 10.8 757 1370 1609 9.2 11.4 12.2 1.7 1.8 1.86

Table 5. WASP model parameters in U~urlu between September 1991 and August 1992

A k u (m/s) E (W/m 2)

Month 10m 3 0 m 5 0 m 10m 3 0 m 5 0 m 10m 3 0 m 5 0 m 10m 3 0 m 5 0 m

September 1991 1.8 2.3 2.5 1.52 1.67 1.79 1.6 2.0 2.3 7 12 15 October 1991 4.6 5.7 6.3 1.13 1.18 1.22 4.4 5.4 5.9 236 391 481 November 1991 5.8 7.2 7.9 1.67 1.78 1.87 5.2 6.4 7.0 203 347 435 December 1991 8.3 9.9 10.8 1.60 1.64 1.67 7.4 8.9 9.6 616 1038 1287 January 1992 5.8 7.3 8.0 2.09 2.23 2.35 5.2 6.4 7.1 156 281 367 February 1992 3.6 4.5 5.0 1.08 1.15 1.20 3.5 4.3 4.7 127 206 252 March 1992 2.7 3.4 3.8 0.89 0.93 0.96 2.9 3.5 3.9 122 196 236 April 1992 1.6 2.0 2.3 0.76 0.81 0.85 1.9 2.3 2.6 56 84 95 May 1992 . . . . . June 1992 . . . . . July 1992 . . . . . . August 1992 8.1 9.9 10.9 2.58 2.74 2.88 7.2 8.8 9.7 356 624 799

The wind energy potential of G6kCeada

Table 6. WASP model parameters in Ugurlu in 1993

683

Raw data

u A Month (m/s) (m/s) k

Processed data (WASP)

u (m/s) E (W/m 2) A (m/s) k

10m 30m 50m 10m 30m 50m 10m 30m 50m 10m 30m 50m

January 5.2 5.4 1.19 February 5.5 6.5 1.68 March 5.0 5.4 1.36 April 5.7 6.0 1.18 May 6.6 7.2 1.41 June 8.5 9.2 1.42 July 12.4 14.5 2.16 August 8.3 9.6 4.05 September 4.0 4.2 1.31 October 3.9 4.0 1.19 November 6.4 7.8 2.38 December 5.5 6.0 1.38

Annual 6.1 6.8 1.34

5.4 6.4 7.2 323 508 713 5.8 6.9 7.8 1.30 1.35 1.36 5.8 6.9 7.8 344 556 742 6.4 7.7 8.6 1.45 1.50 1.55 5.0 6.0 6.7 248 397 529 5.4 6.6 7.4 1.34 1.41 1.45 5.8 7.0 7.8 450 775 1042 6.1 7.5 8.4 1.22 1.25 1.27 6.5 7.9 8.8 506 856 1168 7.1 8.7 9.7 1.41 1.45 1.46 8.6 10.4 11.6 1039 1778 2458 9.6 11.6 12.9 1.53 1.56 [.56

12.9 15.6 17.3 2280 3894 5404 14.6 17.6 19.6 2.23 2.29 2.28 8.6 10.8 12.0 500 977 1330 9.6 12.0 13.3 3.67 3.85 3.93 4.0 5.1 5.7 120 232 314 4.4 5.6 6.3 1.42 1.46 1.49 4.0 5.1 5.7 127 241 325 4.3 5.5 6.2 1.32 1.40 1.44 6.5 8.2 9.2 323 636 864 7.3 9.3 10.4 1.99 2.06 2.10 5.9 7.4 8.2 366 692 913 6.5 8.2 9.1 1.44 1.48 1.51

6.1 7.5 8.3 494 850 1155 6.6 8.1 9.1 1.29 1.33 1.34

Table 7 shows the results of the (~maralti wind speed analysis for 1993 based on raw data and the WASP model. The observed monthly wind speed averages were lower than the WASP model results. The highest monthly average speed of the raw data observed in April was 10.8m/s. Between June and October, it exhi- bited an exponential distribution, but in August the shape parameter was k = 3.4.

The annual averages of wind speed, wind potential and Weibull parameters at 10 m at the four stations are listed in Table 8. The same parameters for the data in the interval between cut-in and cut-off speeds are shown in Table 9.

The maximum wind speeds recorded in the

Doruktepe, (~maralti, Ugurlu and NWS stations were 42, 19.2, 32 and 14.1 m/s, respectively. The prevailing wind directions for these stations were NNE, SSW, N N E and ENE, respectively [12].

The seasonal wind speed analysis indicated that higher values were observed in winter at the NWS and Doruktepe stations and the interior part of the island. In contrast , at the coastal stations U~urlu, and (~ln- aralti, greater wind speeds were recorded in summer periods because of the Etesien winds and a i ~ s e ~ l a n d interactions [14].

Figure 3 shows the probabili ty distributions of wind speed and power at 10 m in U~urlu. Cut-in and cut- off speeds for this station were 3.7 and 17.7 m/s,

Table 7. WASP model parameters for ~lnaralti

Month

Raw data

u A (m/s) (m/s) k

Processed data (WASP)

u (m/s) E (W/m 2) A (m/s) k

10m 30m 50m 10m 30m 50m 10m 30m 50m 10m 30m 50m

January 10.6 12.0 4.36 11.5 16.1 - - 1318 3460 12.9 17.9 2.96 3.17 February March April 10.8 12.0 2.69 May 7.6 8.7 1.58 8.1 12.3 13.7 876 3524 5150 9.0 13.4 14.9 1.51 1.38 1.33 June 6.7 7.8 1.76 7.1 10.4 11.5 529 1653 2300 8.0 11.6 12.8 1.65 1.63 1.60 July 8.3 9.5 1.89 8.7 12.3 13.5 906 2647 3518 9.8 13.8 15.1 1.74 1.68 1.67 August 5.7 6.7 3.95 6.4 8.8 9.8 211 570 773 7.1 9.9 10.9 3.36 3.26 3.28 September 3.4 3.7 1.29 3.7 4.9 5.4 102 235 303 4.0 5.3 5.9 1.32 1.33 1.38 October 3.4 3.4 1.02 4.2 5.7 6.3 183 449 572 4.4 6.0 6.7 1.19 1.20 1.23 November 6.5 7.7 2.23 7.3 9.7 10.6 428 1015 1360 8.2 11.0 12.0 2.12 2.12 2.16 December 5.1 5.8 1.48 5.3 6.4 7.0 251 352 428 5.9 7.2 7.8 1.49 1.78 1.86

Annual 5.9 6.8 1.50 6.4 3.8 9.6 459 1379 1877 7.0 9.5 10.4 1.44 1.35 1.30

684 S. TOLUN et al.

Table 8. Annual averages of parameters u, E, A and k in G6kqeada

Station

u (m/s) E (W/m s) A (m/s) k

1991 1992 1 9 9 3 1991-1992 1 9 9 3 1991-1992 1993 1991 1992 1993

NWS Doruktepe U~urlu (~maralti

4.0 raw/4.2 4.1 raw/4.3 102 120 4.7 4.8 1.7 1.6 8.2 - - 767 - - 9.2 1.7 - -

5.5 raw/5.8 6.1 raw/6.1 464 494 6.2 6.6 1.2 1.3 5.9 raw/6.4 459 - - 6.8 - - 1.4

Table 9. Annual averages of parameters u, E, A and k in 1993

Cut-in/off Station speeds (m/s) u (m/s) E (W/m 2) A (m/s) k

NWS 3.5-17 3.8 118 4.2 1.3 U~urlu 3.7-17.7 3.9 238 3.8 0.96 (~lnaralti 3.6-17.2 5.9 342 6.5 1.5

0.15

0.10 ~ .~ i=n (4.Sin/s) ::3 Anual Average(6.1 m/s)

p . ~ P i E , U ) I'1 0 - 0 5 :i I-I P(/U) ~ J L,[ / WI II

n

o . o ( .

OmO SO 10"0 15.0 ~ ~ '0 25. 0 30.0 350 40"0

Cut-in Cut-off u (m/s) Fig. 3. Probability distributions of wind speed and power in

U~urlu (1993).

I - 4 m / s 4 .1 - 6 rnls 6.1 - 8 .0rnls 8.1 rn/s

i:~.:~,;:;:::.1 unknown

Fig. 4. Regional distribution of mean wind speed in G6k~eada.

respectively. Figure 4 shows the regional dis t r ibut ion of mean wind speed in G6kgeada .

4. CONCLUSION

The result of this study encourages the uti l izat ion of the wind energy potential . This study has explicitly demons t ra t ed the presence of high wind speeds and power in G6kgeada, as well as in the nor th-western par t of Turkey. Using a great deal of data , the related wind energy parameters have been evaluated, which should place G/Sk~eada well on the European wind atlas. When the parameters are compared to those in the coastal regions of the Adria t ic and Trenien seas, 4 0 - 5 0 % greater values were observed in G6kgeada. The wind energy potent ia l of this island is similar in pa t t e rn to tha t in D e n m a r k and Grea t Britain.

Acknowledyment~This study was performed in the frame of DPT project No. 30/90 K 121100. The authors wish to thank the State Planning Organization (DPT), the State Meteorological Organization (DMI), G6k~eada National Weather Service and the Research Fund of Istanbul Tech- nical University (ITU) for their support.

R E F E R E N C E S

1. World Meteorological Organization, Meteorological aspects of the utilization of wind as an energy source, Report No. 575, p 181. WMO, Geneva (1981).

2. A. S. K. Darwish and A. A. M. Sayigh, Wind energy potential in Iraq. Solar Wind Technol. 5, 215 222 (1988).

3. H. Yamaza and J. Kondo, Empirical statistical method to estimate the surface wind speed over complex terrain. J. app. Met. 28, 999 1001 (1989).

4. C. I. Aspuden, Wind turbine siting, SMR/462-7, ICTP, Trieste, Italy (1990).

The wind energy potential of G6kqeada

5. V. Barros, Coupling a mass-consistent model with a non- stationary one-dimensional model, SMR/462, 15, ICTP, Trieste Italy (1990). 11.

6. G. Solari, Statistical analysis of extreme wind speeds, ICTP, SMR/462-11, Trieste, Italy (1990). 12.

7. M. Harper, Planning to make the future renewable. The role of local authorities in renewable energy resource assessments. Renewable Energy 3, 159-170, (1993).

8. S. Oney, T. Yarman and B. Tekes, Wind energy utiliz- ation possibilities in Turkey. Symp. on Alternative Energy Sources, Vol. 4, pp. 1635-1656. Hemisphere 13. (1980).

9. S. Tolun, Wind energy converting systems and wind 14. energy in Turkey, 5th Energy Congress of Turkey, pp. 539 556 (1990) (in Turkish).

10. Z. Aslan, S. Mente~, M. A. Y~kselen, and S. Tolun, Global wind energy assessment of Turkey and a case

685

study in the northwest, Int. Energy Symp. in 21st Century, Istanbul, 28-30 April (1994). D. T. Swift-Hook, Describing wind data, Wind Engng 3, 167 (1979). S. Tolun, M. Z. Erim, Z. Asian, M. A. Y~kselen, S. Mente~ and G. Be~erik, Determination of the wind potential around ~anakkale and design of a wind turbine of low power convenient for this region. State Planning Organization (DPT) Project No. 30/90 K 121100, Istan- bal Technical University (1994) (in Turkish). I. Troen and E. L. Peterson, European WindAtlas. Com- mission of the European Communities (1989). Z. Aslan, S. Mente~ and S. Tolun, Determination of the wind potential at G6kqeada. Int. Energy Syrup. on Clean Energy, Istanbul Technical University (1993) (in Turkish).