INVESTIGATION OF THE WIND POWER POTENTIAL OF THE HERNÁD VALLEY

Transcription

1 AGD Landscape & Environent 5 () INVESTIGATION OF THE WIND POWER POTENTIAL OF THE HERNÁD VALLEY KÁROLY TAR 1, ANDREA KIRCSI SÁNDOR SZEGEDI TAMÁS TÓTH RÓBERT VASS LÁSZLÓ KAPOCSKA 1 College of Nyíregyháza, Institute of Touris and Geography; University of Debrecen, Departent of Meteorology; e-ail: tar.karoly@nyf.hu Received 06 April 011; accepted in revised for 7 Deceber 011 Abstract The University of Debrecen, Departent of Meteorology has carried out research into the cliatic and social-econoic conditions of the Hernád valley in the scope of a scientific project (OTKA K 75794) between 009 and 01. The ai is to find out the optial area for wind and solar energy, as well as bioass utilization. Our purpose is to work out a odel wherein the coplex evaluation of natural and social-econoic conditions and effects can eventually result in a sustainable and conflictfree land use. The results of the research will be useful in working out a regional iproveent based on the use of renewable energy sources to help the local decision-aking process. Keywords: statistical cliatology, Hernád valley, wind profile, wind potential 1. Introduction Exaining the cliate of saller regions always serves the solution of a practical proble. The character of use deterines the iportance and soundness of a cliatologic description but these descriptions are often used for background inforation in other research. It is not an easy task since for saller areas observational data covering the longer and shorter ter are very rare. The lower course of the Hernád River valley in Hungary is a lesser known region fro a cliatologic perspective, especially regarding wind conditions. Our area under exaination belongs to the Northern Mountains cliatic zone (Bartholy and Weidinger, 1997) which includes various icrocliatic regions, with plentiful precipitation-cool suer ountainous, and warer-drier inner basins and highlands. Deterining the cliatic zones of Hungary Péczely (1979) used a classification that characterizes the water- and heat supply based on the average teperature of the aridity index and vegetation period. He categorized the northern areas of the Hernád valley as a teperate cool-dry cliate, while the southern regions as a teperate hot-dry cliate. 93

2 The Hungarian Meteorological Service (OMSZ) operates a regional centre for eteorological observations in Miskolc, where the calculated ean annual teperature is 9.6 C, the annual average precipitation is 533 and the annual hours of sunshine are no ore than 1,797 based on a 30 year long period running fro 1971 to 000. The estiated annual average wind speed is between and.5/s. The Hernád valley runs north-east and south-west between Cserehát and the Tokaj Hill creating a natural contact between the North-western Carpathians and the Great Plain. Based on its geographical position and relief we suppose that it creates a natural direction for the air oveent fro the Eastern Beskid and participates in local air exchange between ountain and plain areas (Dobosy and Feléry, 1994; Péczely, 1976). The Hernád valley certainly affects the direction of air oveents - the wind fro Kassa is often called the Kassai wind by the people living along the border. In this article we present our preliinary statistical wind cliatological results in this region, based on our wind easureent progra which was carried out in Methods With the ai of researching wind cliatology a 0 high wind ast was set up in the north part of the Hernád valley to easure wind direction and wind speed profile. The station is situated to the west of Hidasnéeti, 500 eters fro the edge of the settleent (Fig.1); the geographic latitude and longitude are N and 1 13 E, and the station is 179 above sea level. The datalogger (which was ade by Aonit Gbh) collects 10 inute average wind speeds at heights of 10 and 0 above surface, and wind direction at a height of 10 etres. We used optically scanned cup aneoeters and a wind wane (without heating). The anufacturer of the instruents was Thies and the aneoeters coply with all the requireents of IEC (005-1). In this article we present the results of the dataset between 4 April and 5 Deceber

3 AGD Landscape & Environent 5 () Fig. 1 Geographical position of the wind ast used in the research. 3. Results 1.1 Basic statistics The average wind speed for the coplete period of our easureent is 3.0 /s at 10 and 3.4 /s at 0. Thus it is also higher at a lower level than the /s long-ter cliate-average entioned above. Standard deviations equal to 1.5 and 1.4 /s, naely the variation coefficients (standard deviations/ean) are 0.50 and 0.41 respectively. In other words the wind speed at a higher level is less variable than at 10. Monthly averages are shown in Fig.. 95

4 Fig. Average wind speed values (*- whole onth data not displayed because of instruent error in April and in July) According to Fig. wind speed at both heights is highest in June. The difference between the averages of the two heights is the greatest (0.6 /s) in Noveber. Fig. 3 Diurnal course of wind speed averages per hour Diurnal variations of hourly averages and the differences between the can be seen in Fig.3. The axiu average wind speed at both heights occurs between 1 and 3 p, while the iniu is between 1 and 3 a, which corresponds to the Hungarian wind cliate. The diurnal fluctuation at 10 is 1.7 /s, and at 0 is 1.5 /s. This indicates that the wind speed at the higher level has saller diurnal aplitude. The difference between the average values of wind speeds at the two heights is greater by /s at night-tie; the iniu (average) speed occurs within 1- hours after sunrise. There are different results in suer and in autun: average suer wind speed is 3.3 /s at 10, with a fluctuation of

5 AGD Landscape & Environent 5 () /s, and 3.6 /s at 0, with a fluctuation of 1.7 /s. Values are lower in autun: the averages are.8 and 3. /s and the fluctuations are 1.6 and 1.3 /s respectively. The diurnal variations in hourly averages and their differences in two seasons (suer and autun) can be seen in Fig.4. The diagra shows that at both heights the axiu wind speed sets in at around p in suertie and at around 1p in autun. At nights, the average speed at 0 in autun exceeds that easured in suer at 10. The ranking during the day is the following: suer at 0, suer at 10, autun at 0, autun at 10. There is no difference between the variance aplitudes: they are 0.4 /s in both seasons. However, diurnal courses of variance differ substantially fro each other; for instance they can be about 0 in the early and orning hours in suer. Fig. 4 Diurnal variation of average wind speeds per hour in suer and autun 1. Wind speed-change with height/wind profile The exaination of the differences between the two levels is iportant in ters of extrapolating the results to higher altitude. The so-called Hellann-equation describes the change of wind speed with height in wind energetic: v v 1 h h 1, (1) 97

6 where v 1 is a wind speed at h 1 elevation and v at h elevation, the exponent depends on the balance of ground roughness and the stability of the air. Because of the latter it is teperature-dependent and so has a diurnal and seasonal course. Generally in the location of the planned wind power station the average value of can be deterined fro long ter wind speed easureents at two heights. One of the heights is expediently 10 thus the survey data can be coparable with data fro the nearest eteorological station where this height is required. A height above 10 is theoretically optional but a height of 0-30 is practical to iniize expenses. After deterining the average value of the exponent the ean wind speed of any other height can be estiated by the forula above. Based on the calculations fro our data the ean exponent concerning the whole period is This corresponds to the 0. value that is ordinarily used for the country areas, independently of seasons. Different authors suggest different values depending on ground roughness. According to Aujeszky (1949) a good approach can be achieved by a 0. value up to 50. Ledács-Kiss (1977, 1983), Tóth et al. (001) and Patay (001a, 001b, 003) used this for. However, based on the data fro eteorological towers and energetic wind easures the value of can be specified according to surface friction. According to Kajor (00) the value changes fro 0.14 (over sooth sea surface) to 0.34 (over rough land surfaces). According to Radics (004) the exponent value is 0.14 over flat terrain and bodies of water, 0. over rough, hilly areas, and reaches 0.8 over settleents. The ost detailed data for the exponent can be found in the work of Sebery and Tóth (004) who gave values of 0.1 over flat country, 0.16 over open country, 0.5 over wooded plains, 0.35 over settleents with low buildings and 0.50 over settleents with high buildings. At the sae tie, in accordance with Péczely (1979) the exponent depends on wind speed and theral stratification of the atosphere in addition to surface roughness. For exaple it is 0.3 over grasslands in the case of daily, onthly, yearly average wind speeds. According to the latest research concerning Hungarian wind and solar energy, the use of a 0.5 exponent is suggested for national calculations (Dobi and Mika, 007). As entioned above is teperature-dependent in accordance with atospheric stability, which eans that in the case of low surface teperature (stable conditions) the value is higher, while during high surface teperature (unstable conditions) the value is lower (Radics, 004). This is illustrated in Fig.5 where onthly average values of the exponent are shown. The lowest values can be seen in the first two suer onths (even though the values for May and October are close to it) and the highest are in Noveber, probably in the coldest onth of the period. Seasonal values are ore convincing: the exponent average is 0.17 in suer and 0. in autun. 98

7 AGD Landscape & Environent 5 () Fig. 5 Monthly and whole ter average values of the Hellann exponent (*- whole onth data not displayed because of instruent error in April and in July) According to Péczely et al. (1979) the exponent also depends on wind speed. On the basis of Figs. and 5 the linear correlation coefficient between the onthly averages of and the 10 wind speed is , which eans that the stochastic correlation can be considered as real at the 0.1 significance level. Monthly averages were deterined at 0, 50 and 100 fro 10 onthly average wind speeds in Fig.6 using the data fro Figs. and 5. The error of the Hellann-odel can be deterined fro the data easured and counted at 0. Its axiu - 4% - is in June, the iniu - 0.4% - is in Noveber and the average is %. The average diurnal course of has to be known to produce the average diurnal course of the wind speed at a certain height. The diurnal course of the wind speed usually shows the characteristics observed in Figs.3 and 4 only at the lower height of ; above this height it turns: night axius and 1- p inius are experienced. Calculating with a constant exponent this diurnal course does not reoccur. The exponent and the hourly averages of 10 wind speeds are illustrated together in Fig.7. According to the opposite diurnal course, the stochastic correlation of wind speed and can be best described with a decreasing power function. In this case the correlation coefficient value is the highest at (Fig.8). 99

8 Fig. 6 Monthly average wind speeds easured at 10 and 0, odelled (M) at 0, 50, 100 The diurnal course of follows the diurnal course of 0 and 10 hourly wind speed differences (Figs. 3 and 4), i.e. the value is extree between 3 a and 6 a. According to other exainations night values are higher but less changeable than those in the orning. A regular diurnal course can be seen in Fig.9 which was ade up fro whole year easures (Tar, 004, 007a). The difference fro this is caused by our incoplete data base. Fig. 7 Diurnal course of the average Hellann exponent () and average wind speed at

9 AGD Landscape & Environent 5 () Fig. 8 The relationship between the Hellann exponent () and the hourly average wind speed at 10 Fig. 9 An exaple of the regular diurnal course of the Hellann exponent (Paks, 000 and 001, calculated fro 0 and 50 wind speeds) According to the diurnal course of, the average diurnal course of wind speed at heights higher than the easured levels can be produced. This is decisive in ters of potential wind energy quantity. Our experient referred to heights of 50, 100, 150 and 00 as is shown in Fig.10. It can be seen that the previously entioned inverse diurnal course (axiu at night-tie) does not occur, not even at 00 ; however wind speeds at dawn definitely increased. Besides the incoplete data base, the reason for this irregular diurnal course can be caused by orographic factors that can accelerate the winds at 0 height in these hours. Our own data base of easureents of wind directions over a uch longer period and a detailed analysis of wind data fro an adjacent eteorological station can help us to ake a decision. 101

10 Fig. 10 Diurnal course of wind speed easured at 10 and odelled in different heights according to the diurnal variation of the Hellann exponent 1.3 Definition of diurnal average specific wind potential Specific wind potential is the kinetic energy of an air ass that passes through a 1 vertical surface in 1 second. It can be calculated at a definite oent with the following forula: 3 Pf v () where ρ is air density and v is wind speed. The total specific wind potential of a definite period, e.g. a day, can be deterined fro wind speeds easured at certain ties of the ter. There are two possibilities: either we change v in equation () for the average wind speed of the ter, or we add up the values obtained in each (discreet) tie of the ter. Logically, the second option is closer to reality. However, the dependence of the su on the nuber of the ter oents causes the proble. This dependence can be reduced by averaging, but cannot be copletely eliinated. The value of the average diurnal specific wind potential, which actually eans an average potential for an hour of the day, is also not independent of the nubers of the ties considered (hours). Moreover, it depends on what ties we have worked with. However, there exists a principled solution to avoid this dependence: we have to deterine the under-curve-area of the function that gives the diurnal variation of wind speed-cubes and ultiply by /. Thus, we can obtain the exact value of the total specific wind potential. Of course we can only do this with nuerical integration since the function usually cannot be specified analytically. However, we can attept to deterine the average specific wind potential (P fd ) regarding a 10

11 AGD Landscape & Environent 5 () given ter (e.g. onth, season, year) which consists of days, with the help of an approxiating function, since it can be seen that P fd is equal to the su of wind speed cube averages per hour, ultiplied by /. Thus hourly wind speeds (0, 1,,..., 3) for every single day of a period are given. We can define the average specific wind potential for a single day of the ter in the following equation: ultiply half of the air density and the under-curve-area of the function approxiating the diurnal course of wind speed cube averages per hour. Let the approxiating function be the following: f (x) a 0 x x (a cos b sin ) 1 N N (3) naely, the first two eleents of a Fourier series that consist of trigonoetric polynoials, where N=4, x=0,,, N-1. The correctness of the approxiation can be given by the so-called residual variance: s s 1 0.5A (4) where s0 s n, that is variance, A is the -wave aplitude. It is evident that s depends on the nuber of the eleents, which is why it is not suitable for coparison. Because of this we defined the relative index of approxiation correctness. s0 s s 0 (5) s0 that is between 0 and 1, the axiu occurs in the case of a perfect fit. The priitive function of the function (3) is the following: a b F (x) a 0x ( sin x - cos x) (6) 1 π where α =. If we use the tieline of hourly wind speed cube averages for N deterining the a and b factors, the average specific wind potential for a single day of the ter is: 103

12 A egújuló energiaforrások isertségének és alkalazásának jelenlegi helyzete a Hernád-völgy hátrányos helyzetű településein. Pfd [F (3) - F (0)] (7) where F (3)-F (0)= T ga is the under-curve-area (Tar, 004, 007a, 007b). The onthly averages of wind speed and wind speed cubes, the deviation of the latter, the onthly index of the approxiation correctness and the under-curve-area can be seen in Table 1. According to the table the best approxiation occurs in April and Septeber, but it is quite slight in Noveber. There is probably a linear correlation between the average wind speed, as well as the wind speed cube average and the under-curve-area (which is proportional to the average specific wind potential for a single day of the ter). This stateent, which sees trivial, does not result fro a coplicated, analytically inexpressible relation to this quantity. Table 1. Monthly average and standard deviation of wind speed and wind speed cubes, the index of the approxiation correctness and the onthly values of under-curve-area at a height of ean wind speed cube of wind speed cube of standard deviation fitting April* May June July* August Septeber October Noveber T ga Raising both sides of the equation (1) to the third power we obtain the dependence of the wind speed cubes on height. Thus, their proportion is given by raising the proportion of the heights to the 3-power. Wind speed cubes for higher levels can be calculated fro hourly wind speed cubes deterined fro the data easured at 10. As a first step we disregard the diurnal course of, considering only the onthly changes in it. A height of 60 was chosen as an exaple, where the diurnal course trend of wind speed probably corresponds to the diurnal course of 10. In Table we can see the onthly average and the standard deviation of hourly wind speed cubes at 60, as well as the index of the approxiation correctness and the under-curve-area. Coparing with Table 1 it can be observed 104

13 AGD Landscape & Environent 5 () that the onthly speed averages, and the increase of under-curve-area which is proportional to the onthly average specific wind potential, are changing by the onth. This results fro the onthly difference in the exponent. The axiu increase of under-curve-area can be observed in Noveber, its value is 4.5 ties higher than the value experienced at 10. However, this proportion is only 1.8 in June. Table. Monthly average and standard deviation of wind speed and wind speed cubes, the index of the approxiation correctness and the onthly values of under-curve-area at 60 height. ean wind speed cube of wind speed 60 cube of standard deviation fittin g April* May June July* August Septeber October Noveber The average diurnal course of wind speed cubes relating to the whole period can be seen in Fig.11 at the two heights entioned above. According to the figure the difference in potential energy can be regarded as constant during night hours; in the orning it increases until 1- p and starts decreasing later. The under-curve-area fitted to these diurnal courses - naely the diurnal average specific wind potential - is 3.7 ties higher at 60 than at 10. T ga Fig. 11 Average diurnal course of wind speed cubes at 10 and 60 for the whole period 105

14 4. Conclusion The easured paraeters show that despite the teporal fluctuation the use of wind energy in the area can be appropriate and efficient, principally in the case of wind power plants with low speed start-up or wind engines. We could not entirely solve all the probles during the analysis of our short data base. Besides continuous onitoring and processing it is necessary to control our data with the data of one or ore eteorological stations, close to Hidasnéeti, if possible. 5. Acknowledgeents The research was supported by the National Scientific Research Foundation (K 75794) and TÁMOP-4../B-10/ copetition. References Aujeszky, L. (1949): Preparatory eteorological work for wind load calculations in large scale construction. (In Hungarian: Meteorológiai előunkálatok a agasépítésben végzendő szélterhelés száításokhoz.) Időjárás 53 : Bartholy, J. Weidinger, T. (1997): Cliatic conditions of Hungary. (In Hungarian: Magyarország éghajlati képe.) In: The land of Hungary. (In Hungarian: Magyarország földje.). A view of the Carpathian Basin. (In Hungarian: Kitekintéssel a Kárpát-edencére.) ed: Karátson, D., Magyar Könyvklub, Budapest pp Dobosi, Z. Feléry, L. (1994): Cliatology (In Hungarian: Kliatológia). Nezeti Tankönyvkiadó, Budapest p Dobi I. Mika J. (007): Contributions to the use of solar and wind energy connected to the 007 IPCC Fourth Assessent Report. (In Hungarian: Adalékok a nap- és szélenergia felhasználásához az IPCC 007. évi negyedik jelentéséhez kapcsolódva.) Kedvező széllel Kunhegyestől Debrecenig. Tiszteletkötet Dr. Tar Károly 60. születésnapjára, pp Kajor, B. (00): Is it possible to establish a wind energy park in Hungary? (In Hungarian: Létesíthető-e szélenergia park Magyarországon?) Magyar Energetika 5: Ledács Kiss, A. (1977): The scale of Hungarian wind energy resources. (In Hungarian: Magyarország szélenergiakincsének nagyságrendje). Energia és Atotechnika 30 (10): Ledács Kiss, A. (1983): Possibilities of wind energy utilization in Hungary. (In Hungarian: A szélenergia hasznosításának lehetőségei Magyarországon.) Energia és Atotechnika 36 (4): Patay, I. (001a): Analysing the operation of wind power plants. (In Hungarian: Szélerőűvek üzeviszonyainak elezése.) Szélenergia konferencia előadásai. Magyar Szélenergia Tudoányos Egyesület, pp Patay, I. (001b): Modelling the operation of wind power plants. (In Hungarian: Szélerőűvek üzeviszonyainak odellezése.) TSF Tudoányos Közleények 1 (1) Patay, I. (003): Wind energy utilization. (In Hungarian: A szélenergia hasznosítása.) Szaktudás Kiadó Ház, Budapest Péczely, Gy. (1976): Local winds. (In Hungarian: Helyi szelek.) Légkör 1 (4): Péczely, Gy. (1979): Cliatology. (In Hungarian: Éghajlattan.) Tankönyvkiadó, Budapest. 106

15 AGD Landscape & Environent 5 () Radics, K. (004): Possibilities of wind energy utilization in Hungary: our wind cliate, estiating and odelling the available wind energy. (In Hungarian: A szélenergia hasznosításának lehetőségei Magyarországon: hazánk szélklíája, a rendelkezésre álló szélenergia becslése és odellezése.) Doktori (PhD) értekezés, ELTE, Budapest. Sebery, P. Tóth, L. eds. (004): Conventional and renewable energies. (In Hungarian: Hagyoányos és egújuló energiák) Szaktudás Kiadó Ház. Tar, K. (004): Estiating techniques to deterine the Hungarian wind energy potential. (In Hungarian: Becslési ódszerek a agyarországi szélenergia potenciál eghatározására.) Magyar Energetika 1 (4): Tar, K. (007a): Techniques to estiate the Hungarian wind energy potential. (In Hungarian: Módszerek a agyarországi szélenergia potenciál becslésére.) Dissertationes Savarienses 44, Societas Scentiariu Savariensis, Savaria University Press, Szobathely. Tar, K. (007b): Diurnal course of potential wind power with respect to the synoptic situation. Időjárás 111 (4): Tóth, T. Kapocska L. (011): The actual state of the awareness and use of renewable energy sources in disadvantageous settleents of Hernád valley. (In Hungarian: A egújuló energiaforrások isertségének és alkalazásának jelenlegi helyzete a Hernád-völgy hátrányos helyzetű településein.). II. Környezet és Energia Konferencia, pp Tóth, G. Horváth, G. Tóth, L. (001): Energetic wind easureents and wind aps. (In Hungarian: Energetikai célú szélérés és széltérkép készítése) Szélenergia konferencia előadásai. Magyar Szélenergia Tudoányos Egyesület, pp