Evaluation of wind energy potential and electricity generation at six locations in Turkey

Uçar, Aynur; Balo, Figen

doi:10.1016/j.apenergy.2008.12.016

Cited by 216 publications

(83 citation statements)

References 22 publications

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“…The key step of DPI method relies on finding an estimate of the density functional R(f 00 (v)) in Eq. (6). The detailed information of this method can refer to Ref.…”

Section: Selection Of Bandwidthmentioning

confidence: 99%

“…To handle with these features, wind speed is assumed to be a random variable which follows various types of distributions, such as Weibull, Rayleigh, Gauss and etc. [1][2][3][4][5][6][7][8]. Therefore, wind farms and generating capacity assessment of power system incorporating wind energy [9][10][11][12][13][14][15][16] should be conducted with previously published wind speed distribution analysis.…”

Section: Introductionmentioning

confidence: 99%

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Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems

Li²,

Yang

et al. 2016

J. Mod. Power Syst. Clean Energy

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An accurate probability distribution model of wind speed is critical to the assessment of reliability contribution of wind energy to power systems. Most of current models are built using the parametric density estimation (PDE) methods, which usually assume that the wind speed are subordinate to a certain known distribution (e.g. Weibull distribution and Normal distribution) and estimate the parameters of models with the historical data. This paper presents a kernel density estimation (KDE) method which is a nonparametric way to estimate the probability density function (PDF) of wind speed. The method is a kind of data-driven approach without making any assumption on the form of the underlying wind speed distribution, and capable of uncovering the statistical information hidden in the historical data. The proposed method is compared with three parametric models using wind data from six sites.The results indicate that the KDE outperforms the PDE in terms of accuracy and flexibility in describing the longterm wind speed distributions for all sites. A sensitivity analysis with respect to kernel functions is presented and Gauss kernel function is proved to be the best one. Case studies on a standard IEEE reliability test system (IEEE-RTS) have verified the applicability and effectiveness of the proposed model in evaluating the reliability performance of wind farms.

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“…The key step of DPI method relies on finding an estimate of the density functional R(f 00 (v)) in Eq. (6). The detailed information of this method can refer to Ref.…”

Section: Selection Of Bandwidthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems

Li²,

Yang

et al. 2016

J. Mod. Power Syst. Clean Energy

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show abstract

“…Distributions for measured probability of wind speed are generally fitted using Lognormal, Rayleigh, and Weibull functions. Weibull in wind data description is frequently preferred as a statistical distribution describing data [15]. Besides, Akpinar and Akpinar [24] showed that over a period of 12 months, Weibull distribution gave improved power density estimates in all months compared to Rayleigh based on a study conducted in Turkey.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Their main point of focus is the characteristic of wind speed together with its capability to produce enough power for various uses in a lot of countries [1][2][3][4][5][6][7], [11][12][13][14][15][16][17][18][19]. A study done by Mirhosseini et al [18] is a good example in which they carried out an evaluation and determination of good locations to establish wind farms.…”

Section: Introductionmentioning

confidence: 99%

Güney Batı Karadeniz Kıyıları Boyunca Rüzgar Güç Potansiyelinin Değerlendirilmesi

Akpınar

2016

SAUFenBilDer

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Information about wind characteristic together with the analysis of its potential are a key step when it comes to planning and performance estimation of wind energy projects. In the current study, hence, a statistical analysis on wind energy potential and wind characteristics over the coasts of the South Western Black sea is carried out. The aim is to determine fields where wind force is strong and consistent for energy production along south western coasts of the Black Sea. For this purpose, seven meteorological stations (Kumköy, Şile, Akçakoca, Zonguldak, Amasra, Cide, İnebolu) from west to east are selected, and 10 year wind data acquired from TSMS (Turkish Meteorological Service) measured hourly at a height of 10 m were used. Annual, seasonal, monthly and diurnal average wind speed and power density variations are evaluated at all stations, after which conclusions on dominant wind directions and frequency distributions at all sites are made. Based on overall results, Amasra and İnebolu indicated the highest potential of wind power among the other sites with Amasra having about 159 W/m 2 maximum yearly average wind power density and about 1392 kWh/m 2 /year maximum yearly average energy density.Keywords: wind speeds, wind power potential, the south western Black Sea coasts Güney Batı Karadeniz kıyıları boyunca rüzgar güç potansiyelinin değerlendirilmesi ÖZ Bir bölgenin rüzgar karakteristiğinin belirlenmesi ve onun potansiyelinin farklı açılardan analizi, rüzgar enerji projelerinin planlanması ve performans tahminlerinin yapılabilmesi için gerekli olan en önemli adımdır. Bu çalışmada, Karadeniz'in güney batı kıyılarının rüzgar karakteristiğinin ve rüzgar enerji potansiyelinin detaylı istatistiksel analizi gerçekleştirilmiştir. Çalışmadaki hedef, Karadeniz'in Türkiye kıyıları boyunca enerji üretimi için rüzgar gücü yüksek olan uygun alanları belirlemektir. Bu hedef için, bahsedilen kıyı şeridinde rüzgar ölçümlerinin gerçekleştirildiği 7 meteorolojik istasyon seçilmiş ve istatistiksel analiz için Türkiye Meteoroloji Genel Müdürlüğü tarafından arşivlenmiş 10 metre yükseklikteki saatlik rüzgar verisi (hızı ve yönü) 10 yıllık süre boyunca temin edilmiştir. Bütün istasyonlarda hakim rüzgar yönleri ve frekans dağılımları belirlendikten sonra yıllık, mevsimlik, aylık ve günlük ortalama rüzgar hız ve güç yoğunluk değişimleri de irdelenmiştir. Bütün sonuçlara dayanarak, Amasra ve İnebolu lokasyonlarının diğer alanlara kıyasla en yüksek rüzgar güç potansiyeline sahip oldukları, Amasra'nın yaklaşık 159 W/m 2 maksimum yıllık ortalama rüzgar güç yoğunluğuna ve yaklaşık 1392 kWh/m 2

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“…A series of research from Europe [1][2][3][4][5][6], around the Middle East , Africa [30][31][32][33][34][35][36][37][38][39] and the Far East [40][41][42][43][44][45][46][47][48] has looked at wind resources to identify energy potential. These have both site-specific [4][5][6][7][8][9][10][11][12][14][15][16][17][18][19]21,25,27,29,[31][32][33][34][35][37][38]…”

Section: Introductionmentioning

confidence: 99%

Levelized Cost of Energy Analysis of a Wind Power Generation System at Búrfell in Iceland

et al. 2015

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Wind energy harnessing is a new energy production alternative in Iceland. Current installed wind power in Iceland sums to 1.8 MW, which in contrast is 0.1% of the country's total electricity production. This article is dedicated to the exploration of the potential cost of wind energy production at Búrfell in the south of Iceland. A levelized cost of energy (LCOE) approach was applied to the estimation of the potential cost. Weibull simulation is used to simulate wind data for calculations. A confirmation of the power law is done by comparing real data to calculated values. A modified Weibull simulation is verified by comparing results with actual on-site test wind turbines. A wind farm of 99 MW is suggested for the site. Key results were the capacity factor (CF) at Búrfell being 38.15% on average and that the LCOE for wind energy was estimated as 0.087-0.088 USD/kWh (assuming 10% weighted average cost of capital (WACC)), which classifies Búrfell among the lowest LCOE sites for wind energy in Europe.

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Evaluation of wind energy potential and electricity generation at six locations in Turkey

Cited by 216 publications

References 22 publications

Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems

Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems

Güney Batı Karadeniz Kıyıları Boyunca Rüzgar Güç Potansiyelinin Değerlendirilmesi

Levelized Cost of Energy Analysis of a Wind Power Generation System at Búrfell in Iceland

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