Ahmet Duran Şahin

On the regional wind energy potential of Turkey

Continuous uses of fossil fuels are bound to pollute the atmosphere and consequently unwanted greenhouse and climate change effects will come to dominate every part of the earth. It is, therefore, advised to exploit clean energy resources, and for many nations in the world to try to assess their environmentally friendly, clean energy resources such as wind energy. Hence, it is an urgent situation to determine the wind energy potential in every country. This paper gives the wind velocity, topography and wind energy variation maps obtained for Turkey with local and regional interpretations.

Regional assessment of wind power in western Turkey by the cumulative semivariogram method

Regional patterns of wind energy potential along the western Aegean Sea coastal part of Turkey are evaluated by considering its regional variability through the use of cumulative semivariogram (CSV) models. This innovative technique provides clues about the regional variations along any direction. Since in the western Anatolian coast winds are predominantly northerly or east northerly or west northerly, the regional variability is investigated along the north-south direction. The CSV techniques yielded the radius of influence for wind velocity and Weibull distribution parameters. Dimensionless Standard Regional Dependence (SRD) functions are obtained from the sample CSV. These SRD functions help to make simple regional predictions for the wind energy or wind velocity distribution parameters. The methodology has been applied for predicting the wind velocity in Turkey along the Aegean sea coast. Similar predictions can be achieved for wind energy and Weibull distribution parameters.

Spatial interpolation and estimation of solar irradiation by cumulative semivariograms

The main purpose of this paper is to find a regional procedure for estimating the solar irradiation value of any point from sites where measurements of solar global irradiation already exist. The spatial weights are deduced through the regionalized variables theory and the cumulative semivariogram (CSV) approach. The CSV helps to find the change of spatial variability with distance from a set of given solar irradiation data. It is then employed in the estimation of solar irradiation value at any desired point through a weighted average procedure. The number of adjacent sites considered in this weighting scheme is based on the least squares technique which is applied spatially by incrementing nearest site numbers successively from one up to the total site number. The validity of the methodology is first checked with the cross validation technique prior to its application to sites with no solar irradiation records. Hence, after the cross-validation each site will have different number of nearest adjacent sites for spatial interpolation. The application is achieved for monthly solar irradiation records over Turkey by considering 29 measurements stations. It has been shown that the procedure presented in this paper is better than the classical techniques such as the inverse distance or inverse distance square approaches.

Regional wind energy evaluation in some parts of Turkey

Abstract The main purpose of this paper is to propose a standard regional dependence function (SRDF) based on the concepts of semivariogram and especially cumulative semivariogram. In fact, SRDF is a function of regional dependence which decreases with distance. These functions present quantitatively the regional dependence of the wind phenomenon recorded at irregularly scattered sites over an area. The SRDF provides a unique opportunity for the establishment of a regional objective prediction method whereby the wind velocity and/or energy can be predicted by use of the weighted averages. The weightings are obtained through the SRDF provided that the distance between two sites is known. The implementation of the proposed methodology is presented for some wind velocity measurement stations in Turkey. For the application the experimental SRDF forms are first obtained from the available data and then these are employed directly in the regional estimation procedure. The reliability of the methodology is measured through the cross validation procedure and it is observed that the procedure is valid with less than 5% error. The same procedure can be used in any part of the world.

Hourly wind velocity exceedence maps of Turkey

Abstract Risk analysis is one of the very important topics in engineering applications and scientific researches. In wind engineering, the possible extreme wind velocities constitute the basic targets in risk evaluations. In practice, lower extremes are significant for low energy generation possibilities and for power-cut risks, depending on the turbine type. However, upper extremes endanger the stability of the wind turbines. Additionally, the maxima of wind velocity variations have fundamental importance in engineering structural designs. Besides, from the meteorological point of view, maximum wind velocities directly relate to storms and thunderstorms. In this paper, wind velocity exceedence maps over 10, 12, 15 and 20 m/s are produced for Turkey, and the necessary interpretations are given. These maps show that especially the western part of Turkey and, particularly, coastal areas are risky locations for structural stability and wind erosion.

Application of genetic algorithm for determination of Angström equation coefficients

Abstract Genetic algorithm analysis of the monthly average daily irradiation and sunshine duration data of four Turkish locations has been performed for determining the Angstrom equation coefficients. This analysis provides the parameter estimations as new data are recorded and consequently yields the time variability of the parameters. Application of the genetic algorithm procedure is presented in this paper for determining the coefficients and their statistical distributions as well as the relationships between them. Good correlation is obtained ( r ≥0.93) in all the cases, showing the validity of the Angstrom equation for Turkish locations. Besides, the analysis presents a new way of estimating the Angstrom equation parameters.

Monthly clearness index values of Turkey by harmonic analysis approach

The clearness index is among the most significant concepts in solar engineering. It gives more information about the atmospheric characteristics of solar stations in addition to the degree of solar energy potential of stations and their surrounding areas. Most often, the clearness index is related to sunshine duration measurements at a single station without spatial feature searches. In this study, harmonic analysis is used to model the monthly clearness index values recorded at nine stations of Turkey with distinct meteorological conditions. Consequently, the coefficients of harmonics, amplitude variances and phase angles of the harmonics are calculated, and then, maps of the total variances are evaluated for spatial interpolations. It is seen that up to the seventh harmonic, more than 90% of the total variance can be presented. It is shown that the western and eastern parts of Turkey have nearly similar characteristics. The contribution of each harmonic to total variance is calculated, and then, regional variance maps are evaluated.

Solar irradiation polygon concept and application in Turkey

Classical approaches based on the Angstrom equation for expressing the solar global irradiation in terms of sunshine durations are abundant in the literature. However, all of them include mostly linear and to a lesser extent nonlinear relationships between these variables. The parameters in these relationships are determined invariably by the least squares technique leading to regression lines or curves as models. None of these models provides within year variations in the parameters and they are all very rigid in the application yielding to a single solar global irradiation estimate for a given sunshine duration value. This paper presents a solar irradiance polygon (SIP) concept for evaluating both qualitatively and quantitatively the within year variations in the solar energy variables. On the basis of SIPs, monthly, seasonal and annual parameters of the classical Angstrom equation are calculated.

Regional wind energy assessment technique with applications

The purpose of this paper is to estimate the average wind speed in some parts of Jordan using a standard regional dependence function (SRDF) based on the concept of the point cumulative semivariogram (PCSV). The SRDF method provides weights for different regional sites depending on the distance from the pivot site. The closer the site the larger are the weights of the site, and these weights are used as predicting tools. This method gives reliable results in estimating wind speed in Jordan during most months in the year, except in summer. The method can be used also in determining the borders between different wind regimes. The PCSV and SRDF graphs explain the transition zones between the climatic regions of Jordan.

Determination of the Representative Time Horizons for Short-term Wind Power Prediction by Using Artificial Neural Networks

Wind power is one of the major renewable energy sources, and this source has reached to compete with conventional energies. Wind speed has very complex variations during different time horizons. Prediction of wind speed shows some uncertainties depending on atmospheric parameters, such as temperature, pressure, solar irradiation, and relative humidity. Additionally, wind turbines could not generate electricity at all wind speed values that are less than cut-in and greater than cut-out. These conditions add new uncertainties to the prediction of this meteorological parameter. In this case, wind power prediction from generated electricity data will be better than direct wind speed or other meteorological parameters. Generally, in engineering applications wind power prediction is based on hourly mean, in other words, one hour time horizon data. This time horizon is accepted as a reference and representative but physically this horizon does not represent data homogeneity and should be changed depending on location and time of year. The main aim of this article is to determine the highest representative time horizon to predict wind power in a considered system. First, artificial neural networks methodology is applied to generated power with a 1.5-kW wind turbine by using 1 and 60 minutes average data. It is seen that 35 minutes time horizon gives the best representative scale for wind power prediction in April and 15 minutes in August, respectively. During these months, errors between measured and testing values are decreased to 9.72 and 3.62%, respectively, for the mentioned time horizons. Especially, it is seen that mean values that are evaluated from less than 8–10 minutes and greater than 40 minutes time horizon data give high errors. In other words, using very short time horizons of power data cause high prediction variations and, as a result of these deviations, wind power prediction shows high errors.