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Wind speed variability and wind power potential over Turkey: Case studies for Çanakkale and İstanbul
Renewable Energy 145 (2020) 1020e1032
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Wind speed variability and wind power potential over Turkey: Case _ studies for Çanakkale and Istanbul Hilal Arslan a, Hakki Baltaci b, *, Bulent Oktay Akkoyunlu c, Salih Karanfil d, Mete Tayanc a _ €ztepe, Istanbul, Department of Environmental Engineering, Marmara University, 34722, Go Turkey Regional Weather Forecast and Early Warning Center, Turkish State Meteorological Service, Istanbul, Turkey c _ €ztepe, Istanbul, Department of Physics, Marmara University, 34722, Go Turkey d European University of Lefke, Lefke, Cyprus a
b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 20 July 2018 Received in revised form 10 May 2019 Accepted 21 June 2019 Available online 23 June 2019
In this study, variability of wind speed and its effects on power generation for the 1980e2013 period over Turkey was studied. Hourly wind speed data of 335 stations was obtained from Turkish State Meteorological Service and subjected to quality control. 77 station data was found reliable and used in this work. For the 1980e2013 period, highest hourly average wind speed values equal or larger than 3.80 m/s € kçeada, Çanakkale and Mardin stations located at Aegean, Marmara and Southeastern were found in Go regions of Turkey. Monthly average wind speed is observed to be the highest in July with a value of 2.22 m/s. As an Automated Weather Observation System (AWOS), highest average wind speed for the _ 2007e2013 period was found in Çatalca-Radar, Istanbul with a value of 7.08 m/s. Wind power was analyzed by Weibull distribution and seasonal power density analysis of Çanakkale reveals spring, summer and autumn seasonal average power densities as 49.11 W/m2, 51.12 W/m2 and 50.16 W/m2, together with a winter maximum of 81.68 W/m2. According to results, Çatalca was found as the largest wind energy potential in Turkey, not just having the largest wind speed but also having large rural districts for possible wind farm installment. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Renewable energy Wind speed and potential Weibull distribution
1. Introduction Nowadays, energy demand has been increasing with the industrial production and population growth in Turkey. Most of Turkey's energy production facilities are fundamentally depend on different fossil fuel types. Every year, Turkey has importing large amounts of fossil fuel and consumption of fossil fuel causes air quality and associated health and environmental problems. For this reason, air pollution is one of the biggest environmental problems Turkey has been confronting. Rapid growth of primary energy consumption and uncontrolled use of low quality domestic lignite has been leading to increases in SO2 and PM emissions during recent years in Turkey [1,2]. Coal has disadvantages compared to renewable energy as it produces much larger amounts of pollutants du [3] expressed that the per unit of energy produced. Erdog emission amounts of CO2, NOx and SO2 per kWh energy produced are 838, 0.696 and 0.351 g/kWh, respectively.
* Corresponding author. E-mail address: [email protected] (H. Baltaci). https://doi.org/10.1016/j.renene.2019.06.128 0960-1481/© 2019 Elsevier Ltd. All rights reserved.
In this respect, renewable energy has a significant role to play in reducing the pollutant emissions and mitigating the effects on health, environment and climate. Renewable technologies are already enabling countries like Germany, Spain, Sweden, United States, and several other countries to reduce the consumption of fossil fuel [4]. Decision makers in Turkey could benefit from renewable energy sources such as solar, wind, biomass, hydro and geothermal, and among those, wind energy comes in front with its great power potential for Turkey. For recent 10 years or so, wind has become very attractive among the other energy sources, not only having high power potential but also of being renewable, clean and sustainable. Wind turbines can economically generate electricity with minimal pollutant production. Wind power density can be an important indicator for determining the potential of wind resources, for describing the variability of wind energy at a particular location and for evaluating the performance of wind turbines. For different applications of wind energy, several probability distribution functions are used in the literature, and among them, Weibull distribution function is one of the most popular and widely used statistical distributions. The distribution function was first described in detail
H. Arslan et al. / Renewable Energy 145 (2020) 1020e1032
and applied to a variety of scientific problems by Weibull [5] and it was named after him. Later, Weibull distribution function has become popular and has been applied to a wide field of problems of engineering, hydrology, ecology, environment and energy [5e9]. The method has been used worldwide in assessing wind power density for its simplicity, flexibility, adaptability and favorable capability to fit with the wind data [10e15]. Therefore, Weibull distribution function is used in this study for the wind power _ density calculations of Çanakkale and Çatalca-Radar, Istanbul. Main purpose of this work is to study wind speed variability and wind power potential over Turkey by carrying out an overall _ analysis and case studies for Çanakkale and Çatalca-Radar, Istanbul. To accomplish this aim, wind data belonging to the 34-year period of 1980e2013 was chosen that is enough to represent the climatic variability in wind speed. Initially, quality control of the wind data was done. Then temporal and spatial wind speed analyses were conducted and wind speed frequencies were found with respect to height. In this study, we hypothesize that wind speed decreases during warm and dry periods. Tayanç et al. [16] mentioned about a cold and a warm period experienced in Turkey in the chosen 34year period; it was cold during 1980e1993 period and warm during 1994e2013. Thus, the effect of cold and warm periods on the wind speed can be investigated. Power density analysis of Çan_ akkale and Çatalca-Radar, Istanbul was conducted for the 2007e2013 period, and seasonal shape parameters (k) and scale parameters c (m/s) of the wind speed Weibull distribution function were calculated.
2. Data, study area and methodology To investigate wind speed variability with respect to climate change, hourly wind speed data for the 1980e2013 period was used. Hourly wind speed and direction data of 335 meteorological stations were taken from the Turkish State Meteorological Service (TSMS). Fig. 1 presents the distribution of these station over Turkey. Data was subjected to intensive quality control in terms of missing data, repetitive forms and jumps in the series. Stations having inhomogeneous data, the months having more than 15 days missing data and the years having more than 6 months
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missing data were eliminated from the data set in order to develop a reliable wind speed data base for the analysis. As examples, the time series of Izmir and Goztepe stations can be shown (Fig. 2). It is intuitively clear that there are several jumps in the wind series that categorize them as inhomogeneous. It is believed that this problem has occurred because of the location and equipment changes in the stations. One known information about those stations is the replacement of the old instruments with the automatic ones after 2006. Therefore, stations having inhomogeneous data and jumps were excluded from the study. Focusing on the 1980e2013 period and carrying out quality control and correction analysis led to a data set involving 77 stations that have reliable data and more or less homogeneously distributed over Turkey. Distribution of 77 meteorological stations having high quality wind speed data for the 1980e2013 period is illustrated in Fig. 3. € kçeada Çanakkale is one of the three stations together with Go and Mardin having average wind speeds equal or larger than 3.8 m/ s in the 1980e2013 period. Monthly, seasonal and annual wind speed variability and wind speed power density analysis based on wind speeds of Çanakkale was conducted for the most recent period of 2007e2013, that is the Automated Weather Observation System (AWOS) observation period. Çanakkale station has an altitude of 6 m (WMO No: 17,112) and located at 40.14 N latitude and 26.40 E longitude as can be seen in Fig. 3. Çanakkale is a seaport in Turkey, located on the western Anatolia and south of Thrace. According to the 2014 estimations, population of the Çanakkale city is 186,116. Çanakkale Province, like Istanbul Province, has territory in both Europe and Asia. Çanakkale Strait, 68 km long and 1.2 km wide, connects the Sea of Marmara with the Mediterranean in the southwest. _ Çatalca-Radar, Istanbul is investigated in detail in this study owing to the fact that it has the highest average wind speed in the 2007e2013 period with a value of 7.08 m/s. The station is an _ automated one, AWOS station, operating in Istanbul since 2007. In order to have comparable results with Çanakkale, similar analysis _ were conducted for Çatalca-Radar, Istanbul; monthly, seasonal and annual wind speed and their associated power densities were estimated for the AWOS data period of 2007e2013. Çatalca-Radar
Fig. 1. Distribution of 335 meteorological stations over Turkey. The borders indicate seven different geographical regions of the country.
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Fig. 2. Several inhomogeneous jumps in the average wind speed series of a) Izmir and b) Goztepe in the 1980e2013 period. Red lines indicate the trends of the stations. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3. a) Distribution of 77 meteorological stations over Turkey having high quality wind data, and Google Earth view of b) Çanakkale and c) Çatalca-Radar stations.
station has an altitude of 381 m (WMO No: 17,047, 41.341,138 N, 28.356,870 E) and can be seen in Fig. 3. Çatalca is the largest rural _ district of Istanbul according to the surface area. It is located in East Thrace, on the ridge between the Marmara and the Black Sea. Çatalca has an area of 1715 km2 and 135 km of coastline. Its Province to the neighbors include Silivri to the south and Tekirdag €y lie to the southeast and east. west. Büyükçekmece and Arnavutko
2.1. Variation of wind speed with height Wind speed generally increases with height and in the boundary layer this increase is owing to less friction at higher elevations. For wind turbine engineering, the variation of wind speed with height can be defined relative to wind measured at a reference height of 10 m by a power law profile such as
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a h vðhÞ ¼ vð10Þ 10 where: v(h) is the wind speed at height h, v(10) is the wind speed at the anemometer measurement height of 10 m and the exponent a varying between 0.11 and 0.40 depending on the surface roughness and atmospheric stability. For neutral stability condition and smooth open country a is provided as 0.14 by Rao [17]. The result of using the power law profile was illustrated by Vallero [18] with 1/7 as the value of a which is approximately 0.14. This value is widely applicable to a low surface and well exposed site and thus has been used in many scientific studies to obtain the vertical profile of wind speed in the boundary layer [19]. So, in this study we used 0.14 for a and calculated wind speeds at 50 m, 80 m, 100 m and 120 m altitudes by using the measured 10 m anemometer level wind speeds.
2.2. Weibull parameters and probability assessment of wind power density Generally, wind speed data well matches the Weibull shape and many studies [19e28] used Weibull distribution and the following equations to assess the properties of wind speed and the corresponding wind power densities at the various places of the world. Weibull wind speed probability density function can be represented as
f ðvÞ ¼
k1 k k v v exp c c c
where f(v) is the probability of observing wind speed v, k is the dimensionless Weibull shape parameter and c is the Weibull scale parameter in m/s. Weibull shape and scale parameters k and c are related with the mean wind speed vm by the equation
1 vm ¼ ct 1 þ k where G is the gamma function. After estimating the mean and the variance of the wind speed data, Weibull parameters, k and c can be calculated by the following approximated equations
k¼
s
1:086
vm
c¼
vm
t 1 þ 1k
‘Most probable wind speed’ vmp (m/s) defined as the most frequent wind speed for a given wind speed probability distribution function can be computed by
1 k1 k ¼c k
vmp
=
‘Wind speed corresponding to the maximum energy’ vmax (m/s) can be calculated by using the Weibull parameters k and c as follows:
1 kþ2 k vmax ¼ c k
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2.3. Wind power density Pd (W/m2) can be determined by
1 3 Pd ¼ rc3 G 1 þ 2 k where r is the density of air in kg/m3. 3. Historical perspective According to Global Wind Energy Council [29], by the year of 2013 German and Spain were leaders in terms of installed wind power capacity in Europe with values of 34,250 and 22,959 MW. Turkey had the tenth largest installed wind power capacity with a value of 2959 MW and expected to increase its wind power capacity and probably rise to upper positions in the near future. According to the European Wind Energy Association [30] 2011 statistics, when wind power capacity compared to country size and population, Denmark is the leader. Wind power in Denmark covers 26% of its total electricity consumption, by far the largest share of any country in the world. Distribution of installed capacity for wind power plants in Turkey were investigated by Turkish Wind Energy Association [31]. Although there have been various forms of activities to benefit from wind energy such as drawing water from well or to rotate mill wheel, it was not until 1998 that any professional investment in wind energy production taken place. In that year Turkey's first _ wind power plant was installed in Çes¸me, Izmir with a capacity of 8.7 MW (Fig. 4). In terms of historical perspectives, it can be expressed that Bozcaada, Çanakkale installation by the year of 2000 has been started to produce energy at Bozcaada, Çanakkale with a _ €y, Istanbul capacity of 10.2 MW. Hadımko plant has been producing wind energy since 2013 with a capacity of 1.2 MW. These plants are located at Aegean and Marmara regions of Turkey where the wind potential is high. After 2006, a sudden increase can be seen in the yearly installed wind power capacities topping to 538 MW in 2010. Fig. 5 presents regions of Turkey according to their 2013 percentages of the installed wind power capacity with respect to the total. It can be expressed that by the year of 2013, highest percentages of installed wind power in Turkey can be found in Aegean and Marmara regions with the values of 40.90 and 35.66%. Provinces of Turkey are presented in Fig. 6 according to their _ installed wind power capacities. First three places, Balıkesir, Izmir and Manisa are located at the Aegean region. Çanakkale and _ Istanbul, located at the Marmara region, occupies 6th and 7th places with 133.7 MW and 121.05 MW installed capacities. Although Marmara region has a high potential for wind energy, it can be expressed that the first installments belong to the provinces of Aegean region. 4. Results and discussion Monthly average wind speeds belonging to 77 stations were analyzed for the 1980e2013 period and the temporal variation is presented in Fig. 7. Highest average wind speed of 2.2 m/s was observed in July. This month is characterized by a strong PersianGulf low pressure system extending towards Turkey and producing large pressure gradient with a relatively higher pressure system over Europe causing strong winds over the region. Second highest wind speed belongs to March with a value of 2.16 m/s. Since the main climate of Turkey is Mediterranean type, during this month low pressure systems affecting the area start to move northwards and subtropical high pressure penetrates into the region from south decreasing the precipitation amounts and creating high pressure
=
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Fig. 4. Temporal variation of installed wind power capacity in Turkey. Values on blue bars illustrate cumulative power. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5. Regions of Turkey according to their 2013 percentages of the installed wind power capacity.
gradient associated with subtropical jetstream in turn generating strong winds at the surface. October was found to have the lowest wind speed with a value of 1.74 m/s. Similarly, September and
November also have low average wind speed levels, leading to an autumn seasonal average value of 1.80 m/s. These months are mainly characterized by dominant high pressure systems over Turkey associated with low pressure gradient and low wind speeds. Generally, wind speed values are higher in summer season for most of the stations. The total average wind speed level of all Turkish stations is found as 2.00 m/s. Seasonal analysis showed that the average wind speeds of winter, spring, summer and autumn were obtained as 1.98 m/s, 2.08 m/s, 2.14 m/s, 1.80 m/s, respectively. It is obvious that the largest wind speed exists in summer owing to the same reason as explained above for July. Station based monthly average wind speed analysis for the 1980e2013 period reveal that the highest wind speed was detected in July with a value of 5.09 m/s and belongs to Antakya station which is the southernmost station of Anatolia. One should note that _ AWOS station Çatalca-Radar, Istanbul station having 7-year data period (2007e2013) is not included in the 34-year periodic time
Fig. 6. Provinces according to their installed capacity of operational wind power plants (MW).
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Fig. 7. Monthly averages of wind speed belonging to all stations in the 1980e2013 period.
series analysis (1980e2013). On the other hand, the lowest monthly average wind speed with a value of 0.68 m/s belonging to December was observed at Kahramanmaras¸ located at a more inland place on Anatolia compared to Antakya. Bilgili et al. [32] mention about the highest wind speeds obtained at Antakya belongs to summer months that is in parallel with our findings for the whole Turkey. 4.1. Regional analysis and comparison of 1980e93 mean with the mean of 1994e2013 Average wind speed values of the 1980e2013 period and regional analysis belonging to the Marmara, Aegean, Mediterranean, Black Sea, Central Anatolia, Eastern Anatolia and Southeastern Anatolia regions were studied. Climate change study conducted by Tayanç et al. [16] showed a cooling period extending from early 1960s up to 1993 and then a significant warming trend was observed. Thus, it can be said that the period of the wind data extending from 1980 to 1993 is colder than the 1994e2013 period. To check the hypothesis that the wind speed can decrease during warm and dry periods, we compared the station based mean wind speeds of 1980e1993 period with those of 1994e2013 period. Station based mean wind speed values belonging to the 1980e2013 period (right part of Fig. 8) and the differences between the mean values of 1994e2013 and 1980e1993 periods are shown in Fig. 8 (left part). The subtraction of the 1980e1993 initial period mean value from the 1994e2013 final period mean value provide the magnitude of the wind change and its sign. From the average wind speeds of a) Marmara Region having 10 stations, Çanakkale, S¸ile and Kireçburnu stations were found to have average wind speeds larger than 3 m/s, with the largest value belonging to Çanakkale as 3.8 m/s. On the other hand, lowest stations average wind speeds belong to Sakarya, Edirne and Uludag with a value of 1.7 m/s. On the right-hand side of Fig. 8a, differences in the average wind speeds between the periods of 1994e2013 and 1980e1993 can be seen. It is intuitively clear that the average wind speeds of Marmara stations decreased during the last period except Zirve. In Edirne and Çanakkale, from Edirne, Çanakkale and Uludag average wind speed increased by a value of 0.1 m/s after 1993. Zirve didn't show any change. In Average wind speed of Uludag
terms of percentages, it can be said that % 70 of the stations have decreasing trends in their wind data. Highest wind speed decrease _ from 4.00 m/s to 2.7 m/s was found at S¸ile (Istanbul) with a value of 1.3 m/s. 17 stations were found to have high quality wind data for the period of 1980e2013 in the Aegean Region. Left side of Fig. 8b shows us the average 10 m height wind speed values in the Aegean €kçeada are larger Region. Average wind speeds of Bergama and Go € kçeada than 3 m/s. Highest average wind speed was found at Go with a value of 3.8 m/s. Lowest average wind speed was found at Denizli with 1.2 m/s. From the right-hand side of the figure it can be said that average wind speeds of the stations in Aegean region generally show decreases after 1993 except for Us¸ak, Denizli and € kçeada. Go € kçeada has an average wind speed increase of 0.3 m/s. Go Average wind speed of Us¸ak didn't change after 1993. Highest increase was found at Denizli with a value of 0.4 m/s. % 82 of the Aegean stations show decrease in their wind speeds. Highest _ decrease was found for Seferihisar (Izmir) with a value of 0.9 m/s. Seferihisar average wind speed decreased from 3.3 to 2.4 m/s. 10 stations were found to have high quality data for the period of 1980e2013 in the Mediterranean Region. Average 10 m height wind speeds on the left side of Fig. 8c show that Ulukıs¸la has the largest average wind speed in the region equal to 3 m/s. Lowest average wind speed was found at Antakya with the value of 1.4 m/s. From the right-hand side of the figure it can be said that average wind speeds of the stations in the Mediterranean Region all show decreases after 1993. Highest decrease s was found at Antakya from 1.9 to 1.1 m/with a drop value of 0.8 m/s. 13 stations having been subjected to the analysis in the Black Sea Region. Fig. 8d shows us both the average wind speed values belonging to the 1980e2013 period in the Black Sea region and the difference in the mean wind speeds of the two periods of 1994e2013 and 1980e1993. No station exist having average wind speed larger than 3 m/s at 10 m altitude. Highest average wind speed was found at Zonguldak and Hopa with a value of 2.4 m/s. Lowest average wind speed belongs to Rize, Bolu and Kastamonu with a value of 1.3 m/s. The right hand side of the figure reveal that the average wind speeds of % 85 of the stations in the Black Sea Region decreased between the two periods, except for Zonguldak and Kastamonu that do not show any change. Highest decrease was
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Fig. 8. Station based mean wind speed values belonging to the 1980e2013 period and the differences between the mean values of 1994e2013 and 1980e1993 periods.
found at Amasya and Bayburt. In Amasya and Bayburt, average wind speed decreased with a value of 0.5 m/s. 16 stations were subjected to the analysis in the Central Anatolia Region. It can be seen from the left part of Fig. 8e that there are no stations having 34-year average wind speed larger than 3 m/s. Highest average wind speed was found at Cihanbeyli with a value of 2.8 m/s. Lowest average wind speed was found at Sivas with a value of 1.2 m/s. The right side of the figure that show the difference in the average wind speeds between the two periods provide that most of the stations (81%) in the Central Anatolia Region have
li Konya. negative values except from Sivas, Aksaray and Ereg Average wind speed of Sivas didn't change after 1993. Average wind li increase by a magnitude of 0.1 m/s after speed of Aksaray and Ereg 1993. Highest decrease was found at Gemerek from 2.0 to 1.4 m/s leading to a drop of 0.6 m/s. Less number of stations in the Eastern Anatolia Region had reliable wind data in the 1980e2013 period. Altogether 6 stations were studied from this high altitude region of Turkey and it was found that no station had average wind speed larger than 3 m/s (Fig. 8f). Highest and lowest average wind speeds was found at Kars
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Fig. 8. (continued).
and Tunceli with the values of 2.6 m/s and 1.2 m/s, respectively. Similar to the other regions, majority of the stations (83%) have negative differences between the two periods 1994e2013 and 1980e1993. Among the negative values there is a zero value belonging to Hakkari. Highest negative value was found for Kars and Sarıkamıs¸ with a value of 0.6 m/s. At Kars and Sarıkamıs¸, average wind speeds decreased from 2.9 to 2.3 m/s and 2.0 to 1.4 m/ s, respectively. Fig. 8g show the results of the analysis of Southeastern Anatolia. Average wind speed of Mardin with a value of 3.8 m/s is considerably high compared to those of the other stations. On the other hand, Siirt has 1.1 m/s average wind speed that is the lowest value among the values of all of the stations of Turkey. All of the stations have negative values ranging from 0.3 to 0.6 m/s. Highest negative value belongs to Gaziantep and Adıyaman with a magnitude of 0.6 m/s. At Gaziantep average wind speeds decreased from 1.5 m/s in the 1980e1993 period to 0.9 m/s in the 1994e2013 period and at Adıyaman from 2.2 to 1.6 m/s. It is obvious in Fig. 8 that Turkey has important wind energy potentials especially located in Marmara and Aegean regions and in some other disjunctive locations. The results show us that Kir€kçeada, Bergama, Ulukıs¸la and Mardin eçburnu, Çanakkale, S¸ile, Go
have high wind energy potential. Average wind speeds of stations in Turkey generally showed decreases after 1993, especially in the Mediterranean and Southeastern Anatolia regions. Highest _ decrease was obtained at S¸ile (Istanbul) located in the Marmara Region. At S¸ile, average wind speed decreased from 4.00 to 2.7 m/s. On the other hand, Denizli located in the Aegean region of Turkey has the highest increase, from 0.9 to 1.3 m/s. Urbanization is an important factor playing a role in the decrease of wind speeds. Construction of tall buildings prevents ventilation and this can lead to decrease in the wind speeds. But, stations like Kireçburnu, S¸ile, etc. that are far away from cities also show decrease in their wind speeds. This can be explained with the number of low pressure systems. During cool and wet periods the cyclones affecting the area can be in higher number and strength compared to the warm and dry periods. In turn, the pressure gradient force that determines the magnitude of wind speed is larger during cool and wet years leading to stronger winds.
4.2. Frequency analysis Wind speed data originally measured at 10 m altitude was used to calculate the wind speed values at 50 m, 80 m, 100 m and 120 m
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Fig. 9. Frequency analysis of calculated wind speed at a) 50 m and b) 120 m altitudes.
altitudes. Average wind speeds of 77 stations for the 1980e2013 period were subjected to frequency analysis with respect to their magnitudes and the results for 50 m and 120 m are illustrated in Fig. 9. Frequency analysis of wind speed at 50 m altitude is shown in Fig. 9a. The number of stations having average wind speeds in the ranges of 1e2 m/s, 2e3 m/s, 3e4 m/s and 4e5 m/s is calculated to be 20, 35, 18 and 4, respectively. No station exist having average wind speed larger than 5 m/s at 50 m altitude. Fig. 9b shows the frequency analysis of wind speed at 120 m altitude The number of stations having average wind speeds in the ranges of 1e2 m/s, 2e3 m/s and 3e4 m/s, 4e5 m/s and 5e6 m/s is calculated to be 14, 34, 21, 5 and 3, respectively. No station exist having average wind speed larger than 6 m/s at 120 m altitude.
values in winter are found to be higher than the other seasons, and the lowest wind speed average belonging to the summer. For Çanakkale station, wind speeds are calculated for 50 m, 80 m, 100 m and 120 m altitudes for the 1980e2013 period and shown in Fig. 11. Wind speeds of Çanakkale at 10 m, 50 m, 80 m, 100 m and 120 m altitudes are estimated to increase in this
4.3. Analysis for Çanakkale Monthly average wind speeds at Çanakkale for the period of 1980e2013 is presented in Fig. 10. It is found that the average wind speed at Çanakkale is lowest in June and highest in December, ranging from 3.3 m/s to 4.3 m/s with an annual average of 3.8 m/s. Seasonal analysis reveal average wind speeds in winter, autumn, summer and autumn as 4.30 m/s, 3.76 m/s, 3.65 m/s, 3.68 m/s, respectively. Contrary to the countrywide averages, wind speed
Fig. 11. Average wind speeds of Çanakkale at 10 m, 50 m, 80 m, 100 m and 120 m heights.
Fig. 10. Monthly variability of wind speed at Çanakkale.
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Fig. 12. Mean and standard deviation of annual wind speeds of Çanakkale (1980e2013).
manner: 3.8 m/s, 4.8 m/s, 5.1 m/s, 5.3 m/s, 5.4 m/s. Annual wind speed values and their standard deviations belonging to the Çanakkale station is shown in Fig. 12 That figure indicate that the wind speed values were higher during 2002 and 2004e2006 than the other years. Annual wind speed values belonging to 2002, 2004, 2005 and 2006 were found to be 4.6 m/s, 4.6 m/s, 4.8 m/s and 4.7 m/s, respectively. To check the hypothesis that the wind speed decreases during warm and dry periods, we compared the average wind speed of Çanakkale in 1980e1993 cool period with the average wind speed in 2007 and 2013 years characterized with severe drought and high temperatures. Average wind speeds were 3.3 m/s and 3.5 m/s in
2007 and 2013, while 1980e2013 average was considerably larger than the individual yearly averages with a value of 3.8 m/s, thus we can conclude that station basis annual average wind speed can decrease up to 0.5 m/s during warm and dry periods. 4.4. Wind speed Weibull probability distribution function and associated parameters Before analyzing the wind speed variations by Weibull probality distribution, we extracted the frequencies of wind speeds and prevailing wind directions for Çanakkale and Çatalca-Radar stations (Fig. 13). According to results, northeasterly winds are shown
Fig. 13. Wind rose diagram of a) Çanakkale and b) Çatalca-Radar stations.
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Table 1 Monthly shape parameter k and scale parameter c in the units of m/s of Çanakkale in the 2007e2013 period. Month
Parameters
2007
2008
2009
2010
2011
2012
2013
January
k c k c k c k c k c k c k c k c k c k c k c k c
1.05 3.47 1.39 3.36 1.25 3.65 1.39 2.09 1.45 2.82 1.80 2.84 1.54 2.95 1.67 2.97 1.47 2.53 1.43 2.37 1.25 3.46 1.70 3.67
1.42 2.83 1.28 3.73 1.38 4.59 1.53 3.83 1.36 2.52 1.64 3.53 1.92 3.86 2.32 5.19 1.90 3.42 1.67 3.58 1.23 3.46 1.44 3.29
1.77 4.07 1.54 4.26 1.27 3.34 1.54 3.36 1.41 2.76 1.36 2.87 1.74 3.44 2.92 5.06 1.54 2.80 1.28 3.13 1.25 2.33 1.27 4.37
1.30 4.30 1.29 4.00 1.41 3.15 1.38 2.99 1.25 2.55 1.34 2.33 1.68 2.97 2.26 4.31 1.70 3.75 1.43 3.18 0.96 4.33 1.68 5.80
1.53 2.68 1.72 3.52 1.54 4.06 2.19 4.49 1.76 3.27 1.53 2.90 1.50 2.91 2.43 4.80 2.00 3.73 2.06 4.09 1.40 3.32 1.16 3.28
1.27 3.66 1.49 3.27 1.49 3.25 1.50 4.23 1.44 2.76 1.64 3.73 2.39 4.38 1.70 3.01 1.73 3.39 1.19 3.02 1.49 3.88 1.43 3.56
1.22 3.97 0.99 3.01 1.35 2.11 1.50 2.82 1.33 3.33 1.37 2.65 2.35 4.54 2.47 4.76 1.40 2.26 1.36 2.57 1.29 3.02 1.35 3.08
February March April May June July August September October November December
as dominant in Çanakkale and Çatalca-Radar regions. While, 10% of the wind speeds exceed 6 m/s in Çanakkale, almost 30% of the total wind speed exceed 6 m/s threshold level in Çatalca-Radar station. This implies that sustainable stronger northeasterly winds passes over Black Sea and reach Çatalca-Radar area without encounter any barrier such as mountains, big towers etc. Wind power potentials of the two places having considerably _ high wind speeds in Turkey, Çanakkale and Çatalca-Radar, Istanbul is studied by statistical methods for the aim of obtaining the maximum energy potential of the regions in question. In this respect, monthly, seasonal and annual wind speed averages of _ Çanakkale and Çatalca-Radar, Istanbul for the 2007e2013 period were used, that correspond to the AWOS data period. By applying the Weibull probability distribution function to the wind data, Weibull shape and the scale parameters were calculated statistically for these stations. In 2007 Çanakkale renewed its station with an automated weather observation system (AWOS). Thus, data obtained after this date has the highest reliability and 2007e2013 period was chosen for the power density analysis. Monthly Weibull shape (k) and scale (c) parameters of Çanakkale were obtained statistically and are presented in Table 1. Those two significant parameters, k and c closely related with the mean value of the wind speed, vm and it can be said that, when the scale parameters increase, wind speed also increase. It can be seen from the table that the shape (k) and the
Table 3 Annual statistical values of wind speed and power densities of Çanakkale. Year
vm (m/s)
k (m/s)
c (m/s)
vmp (m/s)
vmax (m/s)
Pd (W/m2)
2007 2008 2009 2010 2011 2012 2013 2007e2013
3.29 3.88 3.72 3.85 3.75 3.77 3.50 3.68
1.43 1.56 1.49 1.35 1.67 1.57 1.54 1.52
3.20 3.77 3.59 3.68 3.65 3.65 3.58 3.59
1.37 1.96 1.71 1.36 2.11 1.91 1.81 1.75
5.92 6.38 6.33 7.19 5.85 6.18 6.14 6.28
37.62 60.17 52.95 59.11 53.52 54.70 49.11 52.45
scale parameter (c) show a wide monthly variability. Monthly shape parameter k was at its minimum during November 2010 with a value of 0.96 m/s and topped to its maximum during August 2009 with 2.92 m/s. Highest monthly scale parameter c is found to be 5.80 m/s in December 2010. The lowest scale parameter is obtained as 2.09 m/s in April 2007. It's clear that the parameter k has a much smaller variability than the parameter c. From the above information, it can be said that the monthly scale parameters are ranging from 2.09 m/s to 5.80 m/s and shape parameters from 0.96 m/s to 2.92 m/s. Seasonal variation of the shape and scale parameters is presented in Table 2. The highest scale parameter is found as 4.21 m/s for the summer season of 2012. The lowest scale parameter belongs to the fall of 2013 with a value of 2.61 m/s. The seasonal shape and scale parameters range from 2.61 m/s to 4.21 m/s and from 1.21 to 2.30 m/s, respectively. In general, values of the scale parameters are low during autumn and high during winter. Statistical values of annual wind speed and power densities of Çanakkale is estimated and presented in Table 3. Table 3 show ‘Most probable wind speed’ vmp (m/s), ‘Wind speed corresponding to the maximum energy’ vmax (m/s) and wind power density Pd (W/m2) besides the shape, scale parameters and mean wind speed. The highest scale parameter was obtained as 3.77 m/s for the year 2008. The lowest scale parameter of 3.20 m/s belongs to 2007. Lower wind speeds were detected during 2007 and 2013 that both years were characterized with severe drought and high temperatures. The lowest wind speed with a value of 3.29 m/s corresponds to the year 2007. The highest wind speed was found as 3.88 m/s for the year 2008. Thus, it can be expressed that low average wind power potential exist for 2007 and 2013, and high average wind power potential exist for 2008 and 2010. The highest most probable wind speed, vmp (m/s) belongs to the year 2011 with a value of 2.11 m/s. Highest wind speed carrying the maximum energy, vmax (m/s) was obtained as 7.19 m/s in the year of 2010. Seasonal wind speed characteristics of Çanakkale for the 2007e2013 period is shown in Table 4. Highest wind speeds were experienced in winter with a mean value of 3.97 m/s and lowest in spring with 3.50 m/s. As expected, corresponding wind power densities is 81.68 W/m2 and 49.11 W/m2, in order. Therefore, it can
Table 2 Seasonal shape parameters k (m/s) and scale parameters c (m/s) of Çanakkale in the 2007e2013 period. Season
Parameters
2007
2008
2009
2010
2011
2012
2013
2007e2013
Winter
k c k c k c k c
1.41 3.73 1.39 3.10 1.68 2.95 1.40 2.95
1.46 3.49 1.43 3.80 1.83 4.00 1.53 3.55
1.63 4.02 1.43 3.28 1.83 3.74 1.38 2.90
1.31 3.97 1.38 2.97 1.59 3.07 1.21 3.83
1.48 3.85 1.66 3.79 1.61 3.28 1.84 3.80
1.32 3.50 1.47 3.48 2.30 4.21 1.49 3.58
1.25 3.69 1.45 3.07 1.71 3.54 1.32 2.61
1.43 3.93 1.56 3.63 1.79 3.66 1.47 3.53
Spring Summer Autumn
H. Arslan et al. / Renewable Energy 145 (2020) 1020e1032 Table 4 Seasonal statistical values of wind speed and power densities of Çanakkale. Season
vm (m/s)
k (m/s)
c (m/s)
vmp (m/s)
vmax (m/s)
Pd (W/m2)
Winter Spring Summer Autumn
3.97 3.50 3.64 3.59
1.58 1.54 1.81 1.50
4.35 3.58 3.66 3.56
2.31 1.81 2.35 1.72
7.28 6.14 5.51 6.25
81.68 49.11 51.12 50.16
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regions. As an example, Çanakkale station has an altitude of 6 m, on the other hand, Çatalca-Radar has an altitude of 381 m and it is located on top of a hill, suggesting that the neighboring areas have lower altitudes. It is clear that, there are no any obstacles or surface roughness elements around to decrease the ventilation at ÇatalcaRadar. In this respect, very high values of wind speeds, Weibull parameters and power densities become meaningful at this station. 5. Conclusions
be expressed that the highest seasonal wind power potential belongs to the winter and lowest to the spring. Summer and autumn have power potentials slightly above the spring season as can be seen from the table; 51.12 W/m2 and 50.16 W/m2. _ 4.5. Analysis for Çatalca-Radar, Istanbul Annual and the seasonal wind speed characteristics of Çatalca_ Radar, Istanbul for the 2007e2013 period are presented in Tables 5 and 6. The highest scale parameter was obtained as 7.96 m/s for the year of 2011. The lowest scale parameter is 7.19 m/s belonging to 2010. Table 5 shows also that the lowest wind speed with a value of 6.51 m/s corresponds to year 2013, a year characterized with severe drought and high temperatures. Highest wind speed was found as 7.37 m/s for 2008. Highest mean wind speeds were experienced in winter with a value of 7.71 m/s and the lowest in summer as 6.58 m/s (Table 6). Therefore, higher average wind power potential can be expected in winter compared to the other seasons. Contrary to the hypothesis _ of the article, power density of Çatalca-Radar, Istanbul was found to be higher in 2007 than compared to the other years; at 10 m height, the average power density in 2007 is estimated as 430.57 W/m2. By the 7-year periodic analysis (2007e2013) of AWOS stations, the highest average seasonal power density was found in winter at _ Çatalca-Radar, Istanbul with a value of 591.97 W/m2. Spring, summer and autumn has mean power densities as 419.35 W/m2, 290.03 W/m2 and 377.26 W/m2, respectively. These values are significantly larger than those of Çanakkale and than those of the other stations used in this study. Since we mainly focused on stations having data in the 1980e2013 period, we had to exclude recently located and operated rural stations from the analysis in this study, except for Çatalca-Radar. Thus, the stations used in this study generally resided in the residential areas that have lower wind speeds compared to the surrounding rural
Table 5 _ Annual wind speed characteristics at Çatalca-Radar, Istanbul. Year
vm (m/s)
k (m/s)
c (m/s)
vmp (m/s)
vmax (m/s)
Pd (W/m2)
2007 2008 2009 2010 2011 2012 2013 2007e2013
7.36 7.37 7.05 7.07 7.10 7.22 6.51 7.10
1.98 2.12 2.41 1.81 2.51 2.10 2.20 2.16
7.56 7.67 7.89 7.19 7.96 7.36 7.37 7.57
5.30 5.69 6.32 4.61 6.50 5.41 5.59 5.63
10.76 10.49 10.14 10.86 10.06 10.12 9.90 10.33
430.57 435.79 426.47 383.20 432.55 394.10 351.95 407.80
Table 6 _ Seasonal wind speed characteristics at Çatalca-Radar, Istanbul. Season
vm (m/s)
k (m/s)
c (m/s)
vmp (m/s)
vmax (m/s)
Pd (W/m2)
Winter Spring Summer Autumn
7.71 6.90 6.58 7.08
2.12 2.17 2.29 1.99
8.74 7.80 6.69 7.22
6.45 5.88 5.21 5.09
11.96 10.53 8.80 10.23
591.97 419.35 290.03 377.26
Hourly wind speed data of 335 stations was taken from the Turkish State Meteorological Service. These stations were subjected to quality control and the period of 1980e2013 was determined as the optimum period in terms of high number of stations having data and considerably long time period, leaving 77 stations that are homogeneously distributed over Turkey. Results show a large variability in the wind speed data corresponding to spatial and temporal scales (daily, monthly, seasonal and annual). General average wind speed, including the mean of all of the station based wind speeds, is found to be highest in the year of 1981 and lowest in 2002 with values of 2.37 m/s and 1.74 m/s, in order. Total general average of all station based hourly wind speeds is obtained as 1.98 m/s suggesting that on a country basis the long eterm average wind speed is mediocre. Monthly average wind speed is observed to be the highest in July with a value of 2.22 m/s, suggesting that july can have the maximum wind power potential. 1980e2013 period studies reveal that the station based highest 34-year average wind speed is 3.80 m/s belonging to three stations, € kçeada, Çanakkale and Mardin stations, located in Aegean, Go Marmara and Southeastern regions, in order. On the other hand, lowest average wind speed is found at Siirt station located in Southeastern Anatolia region with a value of 1.0 m/s. Thus, we can conclude by expressing that Turkey has important wind energy potentials at certain locations, especially, many places in the Marmara and Aegean regions have the lion's share. For most stations in Turkey, 1980e93 period wind speed averages are found to be higher than those of the last period, 1994e2013. After the comparison of the average wind speeds belonging to the 1980e1993 cool period with those of the 1994e2013 warm period, it is found that many stations in Turkey have lower averages in the warmer period of 1994e2013. Main reason of this result can be related with the number of low pressure systems. During cool and wet periods, the cyclones affecting the area can be greater in number and strength compared to the warm and dry periods. In turn, the pressure gradient force that determines the magnitude of wind speed is smaller during warm and dry years leading to weaker winds. Simply, we can conclude that the measured wind speeds are in a decrease with changing climatic conditions, especially with the warming-up of the region. 2007e2013, 7-year, periodic wind energy studies were conducted for specific AWOS stations having significant wind energy _ potentials like Çanakkale and Çatalca-Radar, Istanbul. Wind speed characteristics of those stations are studied by using Weibull probability distribution function. Power density of Çanakkale is found to be highest for the year 2008 with a value of 60.17 W/m2. Contrary, it was 37.62 W/m2 in 2007, when there was a severe drought and frequent heat waves affecting the region. The highest average seasonal power density at 10 m height is observed in winter, with a value of 81.68 W/m2. Spring, summer and autumn power densities are much lower as 49.11 W/m2, 51.12 W/m2 and 50.16 W/m2, respectively. On the other hand, as an AWOS station, highest average wind speed is found at Çatalca-Radar with a value of 7.10 m/s. As it is expected, the largest power densities are found for this place, especially during winter the density topped to 591.97 W/m2. Spring, summer and autumn power densities are
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H. Arslan et al. / Renewable Energy 145 (2020) 1020e1032
found as 419.35 W/m2, 290.03 W/m2 and 377.26 W/m2, in order. Although the Turkish 34-year averages put forward July as having the maximum monthly wind speed, both, Çanakkale and ÇatalcaRadar analyses show that the highest wind power potential correspond to winter season. Çatalca has the largest wind energy potential in Turkey, not just having the largest wind speed but also having large rural districts for possible wind farm installment. Its _ close proximity to the energy market of Istanbul, largest city of Turkey and industrial regions, make it a center of attraction for wind energy. References [1] M. Tayanç, An assessment of spatial and temporal variation of sulfur dioxide levels over Istanbul Turkey, Environ. Pollut. 107 (1) (2000) 61e69. [2] K. Kaygusuz, Energy use and air pollution issues in Turkey, Clean. - Soil, Air, Water 35 (2007) 539e547. du, On the wind energy in Turkey, Renew. Sustain. Energy Rev. 13 [3] E. Erdog (2009) 1361e1371. [4] K. Kaygusuz, Environmental impacts of energy utilization and renewable energy policies in Turkey, Energy Policy 30 (2002) 689e698. [5] W. Weibull, A statistical distribution function of wide applicability, J. Appl. Mech.-Trans. ASME 18 (3) (1951) 293e297. [6] Djeddi Ahmed Zohair, Hafaifa Ahmed, Salam Abudura, Operational reliability analysis applied to a gas turbine based on three parameter Weibull distribution, Mechanics 21 (3) (2015), 187 e 92. [7] Pierleoni, A., Di Francesco, S., Biscarini, C., et al. Numerical approach for the evaluation of Weibull distribution parameters for hydrologic purposes. Proceedings of the International Conference on Numerical Analysis and Applied Mathematics 2015 (ICNAAM-2015), AIP Conference Proceedings, Volume: vol. 1738 Article Number: 270013 Published: 2016. [8] E.Z. Martinez, J.A. Achcar, A.A. J acome, J.S. Santos, Mixture and non-mixture cure fraction models based on the generalized modified Weibull distribution with an application to gastric cancer data, Comput. Methods Progr. Biomed. 112 (3) (2013) 343e355. [9] H. Wang, Z. Wang, X. Li, B. Gong, L. Feng, Y. Zhou, A robust approach based on Weibull distribution for clustering gene expression data, Algorithm Mol. Biol. 6 (1) (2011) 14. [10] J.P. Coelingh, A.J.M. van Wijk, A.A.M. Holtslag, Analysis of wind speed observations over the North Sea, J. Wind Eng. Ind. Aerodyn. 61 (1996) 51e69. [11] D. Weisser, A wind energy analysis of Grenada: an estimation using the ‘Weibull’ density function, Renew. Energy 28 (2003) 1803e1812. [12] T.P. Chang, F.J. Liu, H.H. Ko, S.P. Cheng, L.C. Sun, S.C. Kuo, Comparative analysis on power curve models of wind turbine generator in estimating capacity factor, Energy 73 (2014) 88e95.
[13] F.A.L. Jowder, Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain, Appl. Energy 86 (2009) 538e545. [14] T. Arslan, Y.M. Bulut, A. Yavuz, Comparative study of numerical methods for determining Weibull parameters for wind energy potential, Renew. Sustain. Energy Rev. 40 (2014) 820e825. [15] K. Mohammadi, O. Alavi, A. Mostafaeipour, N. Goudarzi, M. Jalilvand, Assessing different parameters estimation methods of Weibull distribution to compute wind power density, Energy Convers. Manag. 108 (2016) 322e335. _ M. Dog ruel, M. Karaca, Climate change in Turkey for the last [16] M. Tayanç, U. Im, half century, Clim. Change 94 (2009) 483e502. [17] C.S. Rao, Environmental Pollution Control Engineering, second ed., New Age International (P) Limited, Publishers, 2006. [18] D.A. Vallero, Fundamentals of Air Pollution, fourth ed., Academic Press, 2008. [19] E.K. Akpinar, S. Akpinar, A statistical analysis of wind speed data used in installation of wind energy conversion systems, Energy Convers. Manag. 46 (2005) 515e532. [20] B.D. Katsoulis, A survey on the assessment of wind energy potential in Greece, Theor. Appl. Climatol. 47 (1993) 51e63. [21] A.B. Mayhoub, A. Azzam, A survey on the assessment of wind energy potential in Egypt, Renew. Energy 11 (1997) 235e247. [22] K. Ulgen, A. Hepbasli, Determination of Weibull parameters for wind energy analysis of Izmir, Turkey, Int. J. Energy Res. 26 (2002) 494e506. [23] L. Lu, H. Yang, J. Burnett, Investigation on wind power potential on Hong Kong islands-an analysis of wind power and wind turbine characteristic, Renew. Energy 27 (2002) 1e12. [24] A.S.A. Shata, R. Hanitsch, Evaluation of wind energy potential and electricity generation on the coast of Mediterranean Sea in Egypt, Renew. Energy 31 (2006) 1183e1202. [25] H.S. Bagiorgas, M.N. Assimakopoulos, D. Theoharopoulos, D. Matthopoulos, G.K. Mihalakakou, Electricity generation using wind energy conversion systems in the area of Western Greece, Energy Convers. Manag. 48 (2007) 1640e1655. [26] M. Gӧkcek, A. Bayülken, S. Bekdemir, Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey, Renew. Energy 32 (2007) 1739e1752. [27] R. Gupta, A. Biswas, Wind data analysis of silchar (Assam, India) by Rayleigh and Weibull methods, J. Mech. Eng. Res. 2 (2010) 10e24. [28] F.G. Akgül, B. Senoglu, T. Arslan, An alternative distribution to Weibull for modeling the wind speed data: inverse Weibull distribution, Energy Convers. Manag. 114 (2016) 234e240. [29] Global Wind Energy Council (GWEC), Global Wind Statistics 2013, 2014, pp. 1e4. [30] European Wind Energy Association (EWEA), Wind in Power: 2011 European Statistics, 2012, pp. 1e11. [31] Turkish Wind Energy Association (TWEA), Turkish Wind Statistics Report, Turkish Wind Energy Congress, 2014, pp. 1e36. [32] M. Bilgili, B. Sahin, A. Kahraman, Wind energy potential in Antakya and Iskenderun regions, Turkey, Renew. Energy 29 (2004) 1733e1745.
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Renewable Energy journal homepage: www.elsevier.com/locate/renene
Wind speed variability and wind power potential over Turkey: Case _ studies for Çanakkale and Istanbul Hilal Arslan a, Hakki Baltaci b, *, Bulent Oktay Akkoyunlu c, Salih Karanfil d, Mete Tayanc a _ €ztepe, Istanbul, Department of Environmental Engineering, Marmara University, 34722, Go Turkey Regional Weather Forecast and Early Warning Center, Turkish State Meteorological Service, Istanbul, Turkey c _ €ztepe, Istanbul, Department of Physics, Marmara University, 34722, Go Turkey d European University of Lefke, Lefke, Cyprus a
b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 20 July 2018 Received in revised form 10 May 2019 Accepted 21 June 2019 Available online 23 June 2019
In this study, variability of wind speed and its effects on power generation for the 1980e2013 period over Turkey was studied. Hourly wind speed data of 335 stations was obtained from Turkish State Meteorological Service and subjected to quality control. 77 station data was found reliable and used in this work. For the 1980e2013 period, highest hourly average wind speed values equal or larger than 3.80 m/s € kçeada, Çanakkale and Mardin stations located at Aegean, Marmara and Southeastern were found in Go regions of Turkey. Monthly average wind speed is observed to be the highest in July with a value of 2.22 m/s. As an Automated Weather Observation System (AWOS), highest average wind speed for the _ 2007e2013 period was found in Çatalca-Radar, Istanbul with a value of 7.08 m/s. Wind power was analyzed by Weibull distribution and seasonal power density analysis of Çanakkale reveals spring, summer and autumn seasonal average power densities as 49.11 W/m2, 51.12 W/m2 and 50.16 W/m2, together with a winter maximum of 81.68 W/m2. According to results, Çatalca was found as the largest wind energy potential in Turkey, not just having the largest wind speed but also having large rural districts for possible wind farm installment. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Renewable energy Wind speed and potential Weibull distribution
1. Introduction Nowadays, energy demand has been increasing with the industrial production and population growth in Turkey. Most of Turkey's energy production facilities are fundamentally depend on different fossil fuel types. Every year, Turkey has importing large amounts of fossil fuel and consumption of fossil fuel causes air quality and associated health and environmental problems. For this reason, air pollution is one of the biggest environmental problems Turkey has been confronting. Rapid growth of primary energy consumption and uncontrolled use of low quality domestic lignite has been leading to increases in SO2 and PM emissions during recent years in Turkey [1,2]. Coal has disadvantages compared to renewable energy as it produces much larger amounts of pollutants du [3] expressed that the per unit of energy produced. Erdog emission amounts of CO2, NOx and SO2 per kWh energy produced are 838, 0.696 and 0.351 g/kWh, respectively.
* Corresponding author. E-mail address: [email protected] (H. Baltaci). https://doi.org/10.1016/j.renene.2019.06.128 0960-1481/© 2019 Elsevier Ltd. All rights reserved.
In this respect, renewable energy has a significant role to play in reducing the pollutant emissions and mitigating the effects on health, environment and climate. Renewable technologies are already enabling countries like Germany, Spain, Sweden, United States, and several other countries to reduce the consumption of fossil fuel [4]. Decision makers in Turkey could benefit from renewable energy sources such as solar, wind, biomass, hydro and geothermal, and among those, wind energy comes in front with its great power potential for Turkey. For recent 10 years or so, wind has become very attractive among the other energy sources, not only having high power potential but also of being renewable, clean and sustainable. Wind turbines can economically generate electricity with minimal pollutant production. Wind power density can be an important indicator for determining the potential of wind resources, for describing the variability of wind energy at a particular location and for evaluating the performance of wind turbines. For different applications of wind energy, several probability distribution functions are used in the literature, and among them, Weibull distribution function is one of the most popular and widely used statistical distributions. The distribution function was first described in detail
H. Arslan et al. / Renewable Energy 145 (2020) 1020e1032
and applied to a variety of scientific problems by Weibull [5] and it was named after him. Later, Weibull distribution function has become popular and has been applied to a wide field of problems of engineering, hydrology, ecology, environment and energy [5e9]. The method has been used worldwide in assessing wind power density for its simplicity, flexibility, adaptability and favorable capability to fit with the wind data [10e15]. Therefore, Weibull distribution function is used in this study for the wind power _ density calculations of Çanakkale and Çatalca-Radar, Istanbul. Main purpose of this work is to study wind speed variability and wind power potential over Turkey by carrying out an overall _ analysis and case studies for Çanakkale and Çatalca-Radar, Istanbul. To accomplish this aim, wind data belonging to the 34-year period of 1980e2013 was chosen that is enough to represent the climatic variability in wind speed. Initially, quality control of the wind data was done. Then temporal and spatial wind speed analyses were conducted and wind speed frequencies were found with respect to height. In this study, we hypothesize that wind speed decreases during warm and dry periods. Tayanç et al. [16] mentioned about a cold and a warm period experienced in Turkey in the chosen 34year period; it was cold during 1980e1993 period and warm during 1994e2013. Thus, the effect of cold and warm periods on the wind speed can be investigated. Power density analysis of Çan_ akkale and Çatalca-Radar, Istanbul was conducted for the 2007e2013 period, and seasonal shape parameters (k) and scale parameters c (m/s) of the wind speed Weibull distribution function were calculated.
2. Data, study area and methodology To investigate wind speed variability with respect to climate change, hourly wind speed data for the 1980e2013 period was used. Hourly wind speed and direction data of 335 meteorological stations were taken from the Turkish State Meteorological Service (TSMS). Fig. 1 presents the distribution of these station over Turkey. Data was subjected to intensive quality control in terms of missing data, repetitive forms and jumps in the series. Stations having inhomogeneous data, the months having more than 15 days missing data and the years having more than 6 months
1021
missing data were eliminated from the data set in order to develop a reliable wind speed data base for the analysis. As examples, the time series of Izmir and Goztepe stations can be shown (Fig. 2). It is intuitively clear that there are several jumps in the wind series that categorize them as inhomogeneous. It is believed that this problem has occurred because of the location and equipment changes in the stations. One known information about those stations is the replacement of the old instruments with the automatic ones after 2006. Therefore, stations having inhomogeneous data and jumps were excluded from the study. Focusing on the 1980e2013 period and carrying out quality control and correction analysis led to a data set involving 77 stations that have reliable data and more or less homogeneously distributed over Turkey. Distribution of 77 meteorological stations having high quality wind speed data for the 1980e2013 period is illustrated in Fig. 3. € kçeada Çanakkale is one of the three stations together with Go and Mardin having average wind speeds equal or larger than 3.8 m/ s in the 1980e2013 period. Monthly, seasonal and annual wind speed variability and wind speed power density analysis based on wind speeds of Çanakkale was conducted for the most recent period of 2007e2013, that is the Automated Weather Observation System (AWOS) observation period. Çanakkale station has an altitude of 6 m (WMO No: 17,112) and located at 40.14 N latitude and 26.40 E longitude as can be seen in Fig. 3. Çanakkale is a seaport in Turkey, located on the western Anatolia and south of Thrace. According to the 2014 estimations, population of the Çanakkale city is 186,116. Çanakkale Province, like Istanbul Province, has territory in both Europe and Asia. Çanakkale Strait, 68 km long and 1.2 km wide, connects the Sea of Marmara with the Mediterranean in the southwest. _ Çatalca-Radar, Istanbul is investigated in detail in this study owing to the fact that it has the highest average wind speed in the 2007e2013 period with a value of 7.08 m/s. The station is an _ automated one, AWOS station, operating in Istanbul since 2007. In order to have comparable results with Çanakkale, similar analysis _ were conducted for Çatalca-Radar, Istanbul; monthly, seasonal and annual wind speed and their associated power densities were estimated for the AWOS data period of 2007e2013. Çatalca-Radar
Fig. 1. Distribution of 335 meteorological stations over Turkey. The borders indicate seven different geographical regions of the country.
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H. Arslan et al. / Renewable Energy 145 (2020) 1020e1032
Fig. 2. Several inhomogeneous jumps in the average wind speed series of a) Izmir and b) Goztepe in the 1980e2013 period. Red lines indicate the trends of the stations. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3. a) Distribution of 77 meteorological stations over Turkey having high quality wind data, and Google Earth view of b) Çanakkale and c) Çatalca-Radar stations.
station has an altitude of 381 m (WMO No: 17,047, 41.341,138 N, 28.356,870 E) and can be seen in Fig. 3. Çatalca is the largest rural _ district of Istanbul according to the surface area. It is located in East Thrace, on the ridge between the Marmara and the Black Sea. Çatalca has an area of 1715 km2 and 135 km of coastline. Its Province to the neighbors include Silivri to the south and Tekirdag €y lie to the southeast and east. west. Büyükçekmece and Arnavutko
2.1. Variation of wind speed with height Wind speed generally increases with height and in the boundary layer this increase is owing to less friction at higher elevations. For wind turbine engineering, the variation of wind speed with height can be defined relative to wind measured at a reference height of 10 m by a power law profile such as
H. Arslan et al. / Renewable Energy 145 (2020) 1020e1032
a h vðhÞ ¼ vð10Þ 10 where: v(h) is the wind speed at height h, v(10) is the wind speed at the anemometer measurement height of 10 m and the exponent a varying between 0.11 and 0.40 depending on the surface roughness and atmospheric stability. For neutral stability condition and smooth open country a is provided as 0.14 by Rao [17]. The result of using the power law profile was illustrated by Vallero [18] with 1/7 as the value of a which is approximately 0.14. This value is widely applicable to a low surface and well exposed site and thus has been used in many scientific studies to obtain the vertical profile of wind speed in the boundary layer [19]. So, in this study we used 0.14 for a and calculated wind speeds at 50 m, 80 m, 100 m and 120 m altitudes by using the measured 10 m anemometer level wind speeds.
2.2. Weibull parameters and probability assessment of wind power density Generally, wind speed data well matches the Weibull shape and many studies [19e28] used Weibull distribution and the following equations to assess the properties of wind speed and the corresponding wind power densities at the various places of the world. Weibull wind speed probability density function can be represented as
f ðvÞ ¼
k1 k k v v exp c c c
where f(v) is the probability of observing wind speed v, k is the dimensionless Weibull shape parameter and c is the Weibull scale parameter in m/s. Weibull shape and scale parameters k and c are related with the mean wind speed vm by the equation
1 vm ¼ ct 1 þ k where G is the gamma function. After estimating the mean and the variance of the wind speed data, Weibull parameters, k and c can be calculated by the following approximated equations
k¼
s
1:086
vm
c¼
vm
t 1 þ 1k
‘Most probable wind speed’ vmp (m/s) defined as the most frequent wind speed for a given wind speed probability distribution function can be computed by
1 k1 k ¼c k
vmp
=
‘Wind speed corresponding to the maximum energy’ vmax (m/s) can be calculated by using the Weibull parameters k and c as follows:
1 kþ2 k vmax ¼ c k
1023
2.3. Wind power density Pd (W/m2) can be determined by
1 3 Pd ¼ rc3 G 1 þ 2 k where r is the density of air in kg/m3. 3. Historical perspective According to Global Wind Energy Council [29], by the year of 2013 German and Spain were leaders in terms of installed wind power capacity in Europe with values of 34,250 and 22,959 MW. Turkey had the tenth largest installed wind power capacity with a value of 2959 MW and expected to increase its wind power capacity and probably rise to upper positions in the near future. According to the European Wind Energy Association [30] 2011 statistics, when wind power capacity compared to country size and population, Denmark is the leader. Wind power in Denmark covers 26% of its total electricity consumption, by far the largest share of any country in the world. Distribution of installed capacity for wind power plants in Turkey were investigated by Turkish Wind Energy Association [31]. Although there have been various forms of activities to benefit from wind energy such as drawing water from well or to rotate mill wheel, it was not until 1998 that any professional investment in wind energy production taken place. In that year Turkey's first _ wind power plant was installed in Çes¸me, Izmir with a capacity of 8.7 MW (Fig. 4). In terms of historical perspectives, it can be expressed that Bozcaada, Çanakkale installation by the year of 2000 has been started to produce energy at Bozcaada, Çanakkale with a _ €y, Istanbul capacity of 10.2 MW. Hadımko plant has been producing wind energy since 2013 with a capacity of 1.2 MW. These plants are located at Aegean and Marmara regions of Turkey where the wind potential is high. After 2006, a sudden increase can be seen in the yearly installed wind power capacities topping to 538 MW in 2010. Fig. 5 presents regions of Turkey according to their 2013 percentages of the installed wind power capacity with respect to the total. It can be expressed that by the year of 2013, highest percentages of installed wind power in Turkey can be found in Aegean and Marmara regions with the values of 40.90 and 35.66%. Provinces of Turkey are presented in Fig. 6 according to their _ installed wind power capacities. First three places, Balıkesir, Izmir and Manisa are located at the Aegean region. Çanakkale and _ Istanbul, located at the Marmara region, occupies 6th and 7th places with 133.7 MW and 121.05 MW installed capacities. Although Marmara region has a high potential for wind energy, it can be expressed that the first installments belong to the provinces of Aegean region. 4. Results and discussion Monthly average wind speeds belonging to 77 stations were analyzed for the 1980e2013 period and the temporal variation is presented in Fig. 7. Highest average wind speed of 2.2 m/s was observed in July. This month is characterized by a strong PersianGulf low pressure system extending towards Turkey and producing large pressure gradient with a relatively higher pressure system over Europe causing strong winds over the region. Second highest wind speed belongs to March with a value of 2.16 m/s. Since the main climate of Turkey is Mediterranean type, during this month low pressure systems affecting the area start to move northwards and subtropical high pressure penetrates into the region from south decreasing the precipitation amounts and creating high pressure
=
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H. Arslan et al. / Renewable Energy 145 (2020) 1020e1032
Fig. 4. Temporal variation of installed wind power capacity in Turkey. Values on blue bars illustrate cumulative power. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5. Regions of Turkey according to their 2013 percentages of the installed wind power capacity.
gradient associated with subtropical jetstream in turn generating strong winds at the surface. October was found to have the lowest wind speed with a value of 1.74 m/s. Similarly, September and
November also have low average wind speed levels, leading to an autumn seasonal average value of 1.80 m/s. These months are mainly characterized by dominant high pressure systems over Turkey associated with low pressure gradient and low wind speeds. Generally, wind speed values are higher in summer season for most of the stations. The total average wind speed level of all Turkish stations is found as 2.00 m/s. Seasonal analysis showed that the average wind speeds of winter, spring, summer and autumn were obtained as 1.98 m/s, 2.08 m/s, 2.14 m/s, 1.80 m/s, respectively. It is obvious that the largest wind speed exists in summer owing to the same reason as explained above for July. Station based monthly average wind speed analysis for the 1980e2013 period reveal that the highest wind speed was detected in July with a value of 5.09 m/s and belongs to Antakya station which is the southernmost station of Anatolia. One should note that _ AWOS station Çatalca-Radar, Istanbul station having 7-year data period (2007e2013) is not included in the 34-year periodic time
Fig. 6. Provinces according to their installed capacity of operational wind power plants (MW).
H. Arslan et al. / Renewable Energy 145 (2020) 1020e1032
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Fig. 7. Monthly averages of wind speed belonging to all stations in the 1980e2013 period.
series analysis (1980e2013). On the other hand, the lowest monthly average wind speed with a value of 0.68 m/s belonging to December was observed at Kahramanmaras¸ located at a more inland place on Anatolia compared to Antakya. Bilgili et al. [32] mention about the highest wind speeds obtained at Antakya belongs to summer months that is in parallel with our findings for the whole Turkey. 4.1. Regional analysis and comparison of 1980e93 mean with the mean of 1994e2013 Average wind speed values of the 1980e2013 period and regional analysis belonging to the Marmara, Aegean, Mediterranean, Black Sea, Central Anatolia, Eastern Anatolia and Southeastern Anatolia regions were studied. Climate change study conducted by Tayanç et al. [16] showed a cooling period extending from early 1960s up to 1993 and then a significant warming trend was observed. Thus, it can be said that the period of the wind data extending from 1980 to 1993 is colder than the 1994e2013 period. To check the hypothesis that the wind speed can decrease during warm and dry periods, we compared the station based mean wind speeds of 1980e1993 period with those of 1994e2013 period. Station based mean wind speed values belonging to the 1980e2013 period (right part of Fig. 8) and the differences between the mean values of 1994e2013 and 1980e1993 periods are shown in Fig. 8 (left part). The subtraction of the 1980e1993 initial period mean value from the 1994e2013 final period mean value provide the magnitude of the wind change and its sign. From the average wind speeds of a) Marmara Region having 10 stations, Çanakkale, S¸ile and Kireçburnu stations were found to have average wind speeds larger than 3 m/s, with the largest value belonging to Çanakkale as 3.8 m/s. On the other hand, lowest stations average wind speeds belong to Sakarya, Edirne and Uludag with a value of 1.7 m/s. On the right-hand side of Fig. 8a, differences in the average wind speeds between the periods of 1994e2013 and 1980e1993 can be seen. It is intuitively clear that the average wind speeds of Marmara stations decreased during the last period except Zirve. In Edirne and Çanakkale, from Edirne, Çanakkale and Uludag average wind speed increased by a value of 0.1 m/s after 1993. Zirve didn't show any change. In Average wind speed of Uludag
terms of percentages, it can be said that % 70 of the stations have decreasing trends in their wind data. Highest wind speed decrease _ from 4.00 m/s to 2.7 m/s was found at S¸ile (Istanbul) with a value of 1.3 m/s. 17 stations were found to have high quality wind data for the period of 1980e2013 in the Aegean Region. Left side of Fig. 8b shows us the average 10 m height wind speed values in the Aegean €kçeada are larger Region. Average wind speeds of Bergama and Go € kçeada than 3 m/s. Highest average wind speed was found at Go with a value of 3.8 m/s. Lowest average wind speed was found at Denizli with 1.2 m/s. From the right-hand side of the figure it can be said that average wind speeds of the stations in Aegean region generally show decreases after 1993 except for Us¸ak, Denizli and € kçeada. Go € kçeada has an average wind speed increase of 0.3 m/s. Go Average wind speed of Us¸ak didn't change after 1993. Highest increase was found at Denizli with a value of 0.4 m/s. % 82 of the Aegean stations show decrease in their wind speeds. Highest _ decrease was found for Seferihisar (Izmir) with a value of 0.9 m/s. Seferihisar average wind speed decreased from 3.3 to 2.4 m/s. 10 stations were found to have high quality data for the period of 1980e2013 in the Mediterranean Region. Average 10 m height wind speeds on the left side of Fig. 8c show that Ulukıs¸la has the largest average wind speed in the region equal to 3 m/s. Lowest average wind speed was found at Antakya with the value of 1.4 m/s. From the right-hand side of the figure it can be said that average wind speeds of the stations in the Mediterranean Region all show decreases after 1993. Highest decrease s was found at Antakya from 1.9 to 1.1 m/with a drop value of 0.8 m/s. 13 stations having been subjected to the analysis in the Black Sea Region. Fig. 8d shows us both the average wind speed values belonging to the 1980e2013 period in the Black Sea region and the difference in the mean wind speeds of the two periods of 1994e2013 and 1980e1993. No station exist having average wind speed larger than 3 m/s at 10 m altitude. Highest average wind speed was found at Zonguldak and Hopa with a value of 2.4 m/s. Lowest average wind speed belongs to Rize, Bolu and Kastamonu with a value of 1.3 m/s. The right hand side of the figure reveal that the average wind speeds of % 85 of the stations in the Black Sea Region decreased between the two periods, except for Zonguldak and Kastamonu that do not show any change. Highest decrease was
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Fig. 8. Station based mean wind speed values belonging to the 1980e2013 period and the differences between the mean values of 1994e2013 and 1980e1993 periods.
found at Amasya and Bayburt. In Amasya and Bayburt, average wind speed decreased with a value of 0.5 m/s. 16 stations were subjected to the analysis in the Central Anatolia Region. It can be seen from the left part of Fig. 8e that there are no stations having 34-year average wind speed larger than 3 m/s. Highest average wind speed was found at Cihanbeyli with a value of 2.8 m/s. Lowest average wind speed was found at Sivas with a value of 1.2 m/s. The right side of the figure that show the difference in the average wind speeds between the two periods provide that most of the stations (81%) in the Central Anatolia Region have
li Konya. negative values except from Sivas, Aksaray and Ereg Average wind speed of Sivas didn't change after 1993. Average wind li increase by a magnitude of 0.1 m/s after speed of Aksaray and Ereg 1993. Highest decrease was found at Gemerek from 2.0 to 1.4 m/s leading to a drop of 0.6 m/s. Less number of stations in the Eastern Anatolia Region had reliable wind data in the 1980e2013 period. Altogether 6 stations were studied from this high altitude region of Turkey and it was found that no station had average wind speed larger than 3 m/s (Fig. 8f). Highest and lowest average wind speeds was found at Kars
H. Arslan et al. / Renewable Energy 145 (2020) 1020e1032
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Fig. 8. (continued).
and Tunceli with the values of 2.6 m/s and 1.2 m/s, respectively. Similar to the other regions, majority of the stations (83%) have negative differences between the two periods 1994e2013 and 1980e1993. Among the negative values there is a zero value belonging to Hakkari. Highest negative value was found for Kars and Sarıkamıs¸ with a value of 0.6 m/s. At Kars and Sarıkamıs¸, average wind speeds decreased from 2.9 to 2.3 m/s and 2.0 to 1.4 m/ s, respectively. Fig. 8g show the results of the analysis of Southeastern Anatolia. Average wind speed of Mardin with a value of 3.8 m/s is considerably high compared to those of the other stations. On the other hand, Siirt has 1.1 m/s average wind speed that is the lowest value among the values of all of the stations of Turkey. All of the stations have negative values ranging from 0.3 to 0.6 m/s. Highest negative value belongs to Gaziantep and Adıyaman with a magnitude of 0.6 m/s. At Gaziantep average wind speeds decreased from 1.5 m/s in the 1980e1993 period to 0.9 m/s in the 1994e2013 period and at Adıyaman from 2.2 to 1.6 m/s. It is obvious in Fig. 8 that Turkey has important wind energy potentials especially located in Marmara and Aegean regions and in some other disjunctive locations. The results show us that Kir€kçeada, Bergama, Ulukıs¸la and Mardin eçburnu, Çanakkale, S¸ile, Go
have high wind energy potential. Average wind speeds of stations in Turkey generally showed decreases after 1993, especially in the Mediterranean and Southeastern Anatolia regions. Highest _ decrease was obtained at S¸ile (Istanbul) located in the Marmara Region. At S¸ile, average wind speed decreased from 4.00 to 2.7 m/s. On the other hand, Denizli located in the Aegean region of Turkey has the highest increase, from 0.9 to 1.3 m/s. Urbanization is an important factor playing a role in the decrease of wind speeds. Construction of tall buildings prevents ventilation and this can lead to decrease in the wind speeds. But, stations like Kireçburnu, S¸ile, etc. that are far away from cities also show decrease in their wind speeds. This can be explained with the number of low pressure systems. During cool and wet periods the cyclones affecting the area can be in higher number and strength compared to the warm and dry periods. In turn, the pressure gradient force that determines the magnitude of wind speed is larger during cool and wet years leading to stronger winds.
4.2. Frequency analysis Wind speed data originally measured at 10 m altitude was used to calculate the wind speed values at 50 m, 80 m, 100 m and 120 m
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Fig. 9. Frequency analysis of calculated wind speed at a) 50 m and b) 120 m altitudes.
altitudes. Average wind speeds of 77 stations for the 1980e2013 period were subjected to frequency analysis with respect to their magnitudes and the results for 50 m and 120 m are illustrated in Fig. 9. Frequency analysis of wind speed at 50 m altitude is shown in Fig. 9a. The number of stations having average wind speeds in the ranges of 1e2 m/s, 2e3 m/s, 3e4 m/s and 4e5 m/s is calculated to be 20, 35, 18 and 4, respectively. No station exist having average wind speed larger than 5 m/s at 50 m altitude. Fig. 9b shows the frequency analysis of wind speed at 120 m altitude The number of stations having average wind speeds in the ranges of 1e2 m/s, 2e3 m/s and 3e4 m/s, 4e5 m/s and 5e6 m/s is calculated to be 14, 34, 21, 5 and 3, respectively. No station exist having average wind speed larger than 6 m/s at 120 m altitude.
values in winter are found to be higher than the other seasons, and the lowest wind speed average belonging to the summer. For Çanakkale station, wind speeds are calculated for 50 m, 80 m, 100 m and 120 m altitudes for the 1980e2013 period and shown in Fig. 11. Wind speeds of Çanakkale at 10 m, 50 m, 80 m, 100 m and 120 m altitudes are estimated to increase in this
4.3. Analysis for Çanakkale Monthly average wind speeds at Çanakkale for the period of 1980e2013 is presented in Fig. 10. It is found that the average wind speed at Çanakkale is lowest in June and highest in December, ranging from 3.3 m/s to 4.3 m/s with an annual average of 3.8 m/s. Seasonal analysis reveal average wind speeds in winter, autumn, summer and autumn as 4.30 m/s, 3.76 m/s, 3.65 m/s, 3.68 m/s, respectively. Contrary to the countrywide averages, wind speed
Fig. 11. Average wind speeds of Çanakkale at 10 m, 50 m, 80 m, 100 m and 120 m heights.
Fig. 10. Monthly variability of wind speed at Çanakkale.
H. Arslan et al. / Renewable Energy 145 (2020) 1020e1032
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Fig. 12. Mean and standard deviation of annual wind speeds of Çanakkale (1980e2013).
manner: 3.8 m/s, 4.8 m/s, 5.1 m/s, 5.3 m/s, 5.4 m/s. Annual wind speed values and their standard deviations belonging to the Çanakkale station is shown in Fig. 12 That figure indicate that the wind speed values were higher during 2002 and 2004e2006 than the other years. Annual wind speed values belonging to 2002, 2004, 2005 and 2006 were found to be 4.6 m/s, 4.6 m/s, 4.8 m/s and 4.7 m/s, respectively. To check the hypothesis that the wind speed decreases during warm and dry periods, we compared the average wind speed of Çanakkale in 1980e1993 cool period with the average wind speed in 2007 and 2013 years characterized with severe drought and high temperatures. Average wind speeds were 3.3 m/s and 3.5 m/s in
2007 and 2013, while 1980e2013 average was considerably larger than the individual yearly averages with a value of 3.8 m/s, thus we can conclude that station basis annual average wind speed can decrease up to 0.5 m/s during warm and dry periods. 4.4. Wind speed Weibull probability distribution function and associated parameters Before analyzing the wind speed variations by Weibull probality distribution, we extracted the frequencies of wind speeds and prevailing wind directions for Çanakkale and Çatalca-Radar stations (Fig. 13). According to results, northeasterly winds are shown
Fig. 13. Wind rose diagram of a) Çanakkale and b) Çatalca-Radar stations.
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Table 1 Monthly shape parameter k and scale parameter c in the units of m/s of Çanakkale in the 2007e2013 period. Month
Parameters
2007
2008
2009
2010
2011
2012
2013
January
k c k c k c k c k c k c k c k c k c k c k c k c
1.05 3.47 1.39 3.36 1.25 3.65 1.39 2.09 1.45 2.82 1.80 2.84 1.54 2.95 1.67 2.97 1.47 2.53 1.43 2.37 1.25 3.46 1.70 3.67
1.42 2.83 1.28 3.73 1.38 4.59 1.53 3.83 1.36 2.52 1.64 3.53 1.92 3.86 2.32 5.19 1.90 3.42 1.67 3.58 1.23 3.46 1.44 3.29
1.77 4.07 1.54 4.26 1.27 3.34 1.54 3.36 1.41 2.76 1.36 2.87 1.74 3.44 2.92 5.06 1.54 2.80 1.28 3.13 1.25 2.33 1.27 4.37
1.30 4.30 1.29 4.00 1.41 3.15 1.38 2.99 1.25 2.55 1.34 2.33 1.68 2.97 2.26 4.31 1.70 3.75 1.43 3.18 0.96 4.33 1.68 5.80
1.53 2.68 1.72 3.52 1.54 4.06 2.19 4.49 1.76 3.27 1.53 2.90 1.50 2.91 2.43 4.80 2.00 3.73 2.06 4.09 1.40 3.32 1.16 3.28
1.27 3.66 1.49 3.27 1.49 3.25 1.50 4.23 1.44 2.76 1.64 3.73 2.39 4.38 1.70 3.01 1.73 3.39 1.19 3.02 1.49 3.88 1.43 3.56
1.22 3.97 0.99 3.01 1.35 2.11 1.50 2.82 1.33 3.33 1.37 2.65 2.35 4.54 2.47 4.76 1.40 2.26 1.36 2.57 1.29 3.02 1.35 3.08
February March April May June July August September October November December
as dominant in Çanakkale and Çatalca-Radar regions. While, 10% of the wind speeds exceed 6 m/s in Çanakkale, almost 30% of the total wind speed exceed 6 m/s threshold level in Çatalca-Radar station. This implies that sustainable stronger northeasterly winds passes over Black Sea and reach Çatalca-Radar area without encounter any barrier such as mountains, big towers etc. Wind power potentials of the two places having considerably _ high wind speeds in Turkey, Çanakkale and Çatalca-Radar, Istanbul is studied by statistical methods for the aim of obtaining the maximum energy potential of the regions in question. In this respect, monthly, seasonal and annual wind speed averages of _ Çanakkale and Çatalca-Radar, Istanbul for the 2007e2013 period were used, that correspond to the AWOS data period. By applying the Weibull probability distribution function to the wind data, Weibull shape and the scale parameters were calculated statistically for these stations. In 2007 Çanakkale renewed its station with an automated weather observation system (AWOS). Thus, data obtained after this date has the highest reliability and 2007e2013 period was chosen for the power density analysis. Monthly Weibull shape (k) and scale (c) parameters of Çanakkale were obtained statistically and are presented in Table 1. Those two significant parameters, k and c closely related with the mean value of the wind speed, vm and it can be said that, when the scale parameters increase, wind speed also increase. It can be seen from the table that the shape (k) and the
Table 3 Annual statistical values of wind speed and power densities of Çanakkale. Year
vm (m/s)
k (m/s)
c (m/s)
vmp (m/s)
vmax (m/s)
Pd (W/m2)
2007 2008 2009 2010 2011 2012 2013 2007e2013
3.29 3.88 3.72 3.85 3.75 3.77 3.50 3.68
1.43 1.56 1.49 1.35 1.67 1.57 1.54 1.52
3.20 3.77 3.59 3.68 3.65 3.65 3.58 3.59
1.37 1.96 1.71 1.36 2.11 1.91 1.81 1.75
5.92 6.38 6.33 7.19 5.85 6.18 6.14 6.28
37.62 60.17 52.95 59.11 53.52 54.70 49.11 52.45
scale parameter (c) show a wide monthly variability. Monthly shape parameter k was at its minimum during November 2010 with a value of 0.96 m/s and topped to its maximum during August 2009 with 2.92 m/s. Highest monthly scale parameter c is found to be 5.80 m/s in December 2010. The lowest scale parameter is obtained as 2.09 m/s in April 2007. It's clear that the parameter k has a much smaller variability than the parameter c. From the above information, it can be said that the monthly scale parameters are ranging from 2.09 m/s to 5.80 m/s and shape parameters from 0.96 m/s to 2.92 m/s. Seasonal variation of the shape and scale parameters is presented in Table 2. The highest scale parameter is found as 4.21 m/s for the summer season of 2012. The lowest scale parameter belongs to the fall of 2013 with a value of 2.61 m/s. The seasonal shape and scale parameters range from 2.61 m/s to 4.21 m/s and from 1.21 to 2.30 m/s, respectively. In general, values of the scale parameters are low during autumn and high during winter. Statistical values of annual wind speed and power densities of Çanakkale is estimated and presented in Table 3. Table 3 show ‘Most probable wind speed’ vmp (m/s), ‘Wind speed corresponding to the maximum energy’ vmax (m/s) and wind power density Pd (W/m2) besides the shape, scale parameters and mean wind speed. The highest scale parameter was obtained as 3.77 m/s for the year 2008. The lowest scale parameter of 3.20 m/s belongs to 2007. Lower wind speeds were detected during 2007 and 2013 that both years were characterized with severe drought and high temperatures. The lowest wind speed with a value of 3.29 m/s corresponds to the year 2007. The highest wind speed was found as 3.88 m/s for the year 2008. Thus, it can be expressed that low average wind power potential exist for 2007 and 2013, and high average wind power potential exist for 2008 and 2010. The highest most probable wind speed, vmp (m/s) belongs to the year 2011 with a value of 2.11 m/s. Highest wind speed carrying the maximum energy, vmax (m/s) was obtained as 7.19 m/s in the year of 2010. Seasonal wind speed characteristics of Çanakkale for the 2007e2013 period is shown in Table 4. Highest wind speeds were experienced in winter with a mean value of 3.97 m/s and lowest in spring with 3.50 m/s. As expected, corresponding wind power densities is 81.68 W/m2 and 49.11 W/m2, in order. Therefore, it can
Table 2 Seasonal shape parameters k (m/s) and scale parameters c (m/s) of Çanakkale in the 2007e2013 period. Season
Parameters
2007
2008
2009
2010
2011
2012
2013
2007e2013
Winter
k c k c k c k c
1.41 3.73 1.39 3.10 1.68 2.95 1.40 2.95
1.46 3.49 1.43 3.80 1.83 4.00 1.53 3.55
1.63 4.02 1.43 3.28 1.83 3.74 1.38 2.90
1.31 3.97 1.38 2.97 1.59 3.07 1.21 3.83
1.48 3.85 1.66 3.79 1.61 3.28 1.84 3.80
1.32 3.50 1.47 3.48 2.30 4.21 1.49 3.58
1.25 3.69 1.45 3.07 1.71 3.54 1.32 2.61
1.43 3.93 1.56 3.63 1.79 3.66 1.47 3.53
Spring Summer Autumn
H. Arslan et al. / Renewable Energy 145 (2020) 1020e1032 Table 4 Seasonal statistical values of wind speed and power densities of Çanakkale. Season
vm (m/s)
k (m/s)
c (m/s)
vmp (m/s)
vmax (m/s)
Pd (W/m2)
Winter Spring Summer Autumn
3.97 3.50 3.64 3.59
1.58 1.54 1.81 1.50
4.35 3.58 3.66 3.56
2.31 1.81 2.35 1.72
7.28 6.14 5.51 6.25
81.68 49.11 51.12 50.16
1031
regions. As an example, Çanakkale station has an altitude of 6 m, on the other hand, Çatalca-Radar has an altitude of 381 m and it is located on top of a hill, suggesting that the neighboring areas have lower altitudes. It is clear that, there are no any obstacles or surface roughness elements around to decrease the ventilation at ÇatalcaRadar. In this respect, very high values of wind speeds, Weibull parameters and power densities become meaningful at this station. 5. Conclusions
be expressed that the highest seasonal wind power potential belongs to the winter and lowest to the spring. Summer and autumn have power potentials slightly above the spring season as can be seen from the table; 51.12 W/m2 and 50.16 W/m2. _ 4.5. Analysis for Çatalca-Radar, Istanbul Annual and the seasonal wind speed characteristics of Çatalca_ Radar, Istanbul for the 2007e2013 period are presented in Tables 5 and 6. The highest scale parameter was obtained as 7.96 m/s for the year of 2011. The lowest scale parameter is 7.19 m/s belonging to 2010. Table 5 shows also that the lowest wind speed with a value of 6.51 m/s corresponds to year 2013, a year characterized with severe drought and high temperatures. Highest wind speed was found as 7.37 m/s for 2008. Highest mean wind speeds were experienced in winter with a value of 7.71 m/s and the lowest in summer as 6.58 m/s (Table 6). Therefore, higher average wind power potential can be expected in winter compared to the other seasons. Contrary to the hypothesis _ of the article, power density of Çatalca-Radar, Istanbul was found to be higher in 2007 than compared to the other years; at 10 m height, the average power density in 2007 is estimated as 430.57 W/m2. By the 7-year periodic analysis (2007e2013) of AWOS stations, the highest average seasonal power density was found in winter at _ Çatalca-Radar, Istanbul with a value of 591.97 W/m2. Spring, summer and autumn has mean power densities as 419.35 W/m2, 290.03 W/m2 and 377.26 W/m2, respectively. These values are significantly larger than those of Çanakkale and than those of the other stations used in this study. Since we mainly focused on stations having data in the 1980e2013 period, we had to exclude recently located and operated rural stations from the analysis in this study, except for Çatalca-Radar. Thus, the stations used in this study generally resided in the residential areas that have lower wind speeds compared to the surrounding rural
Table 5 _ Annual wind speed characteristics at Çatalca-Radar, Istanbul. Year
vm (m/s)
k (m/s)
c (m/s)
vmp (m/s)
vmax (m/s)
Pd (W/m2)
2007 2008 2009 2010 2011 2012 2013 2007e2013
7.36 7.37 7.05 7.07 7.10 7.22 6.51 7.10
1.98 2.12 2.41 1.81 2.51 2.10 2.20 2.16
7.56 7.67 7.89 7.19 7.96 7.36 7.37 7.57
5.30 5.69 6.32 4.61 6.50 5.41 5.59 5.63
10.76 10.49 10.14 10.86 10.06 10.12 9.90 10.33
430.57 435.79 426.47 383.20 432.55 394.10 351.95 407.80
Table 6 _ Seasonal wind speed characteristics at Çatalca-Radar, Istanbul. Season
vm (m/s)
k (m/s)
c (m/s)
vmp (m/s)
vmax (m/s)
Pd (W/m2)
Winter Spring Summer Autumn
7.71 6.90 6.58 7.08
2.12 2.17 2.29 1.99
8.74 7.80 6.69 7.22
6.45 5.88 5.21 5.09
11.96 10.53 8.80 10.23
591.97 419.35 290.03 377.26
Hourly wind speed data of 335 stations was taken from the Turkish State Meteorological Service. These stations were subjected to quality control and the period of 1980e2013 was determined as the optimum period in terms of high number of stations having data and considerably long time period, leaving 77 stations that are homogeneously distributed over Turkey. Results show a large variability in the wind speed data corresponding to spatial and temporal scales (daily, monthly, seasonal and annual). General average wind speed, including the mean of all of the station based wind speeds, is found to be highest in the year of 1981 and lowest in 2002 with values of 2.37 m/s and 1.74 m/s, in order. Total general average of all station based hourly wind speeds is obtained as 1.98 m/s suggesting that on a country basis the long eterm average wind speed is mediocre. Monthly average wind speed is observed to be the highest in July with a value of 2.22 m/s, suggesting that july can have the maximum wind power potential. 1980e2013 period studies reveal that the station based highest 34-year average wind speed is 3.80 m/s belonging to three stations, € kçeada, Çanakkale and Mardin stations, located in Aegean, Go Marmara and Southeastern regions, in order. On the other hand, lowest average wind speed is found at Siirt station located in Southeastern Anatolia region with a value of 1.0 m/s. Thus, we can conclude by expressing that Turkey has important wind energy potentials at certain locations, especially, many places in the Marmara and Aegean regions have the lion's share. For most stations in Turkey, 1980e93 period wind speed averages are found to be higher than those of the last period, 1994e2013. After the comparison of the average wind speeds belonging to the 1980e1993 cool period with those of the 1994e2013 warm period, it is found that many stations in Turkey have lower averages in the warmer period of 1994e2013. Main reason of this result can be related with the number of low pressure systems. During cool and wet periods, the cyclones affecting the area can be greater in number and strength compared to the warm and dry periods. In turn, the pressure gradient force that determines the magnitude of wind speed is smaller during warm and dry years leading to weaker winds. Simply, we can conclude that the measured wind speeds are in a decrease with changing climatic conditions, especially with the warming-up of the region. 2007e2013, 7-year, periodic wind energy studies were conducted for specific AWOS stations having significant wind energy _ potentials like Çanakkale and Çatalca-Radar, Istanbul. Wind speed characteristics of those stations are studied by using Weibull probability distribution function. Power density of Çanakkale is found to be highest for the year 2008 with a value of 60.17 W/m2. Contrary, it was 37.62 W/m2 in 2007, when there was a severe drought and frequent heat waves affecting the region. The highest average seasonal power density at 10 m height is observed in winter, with a value of 81.68 W/m2. Spring, summer and autumn power densities are much lower as 49.11 W/m2, 51.12 W/m2 and 50.16 W/m2, respectively. On the other hand, as an AWOS station, highest average wind speed is found at Çatalca-Radar with a value of 7.10 m/s. As it is expected, the largest power densities are found for this place, especially during winter the density topped to 591.97 W/m2. Spring, summer and autumn power densities are
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