Wind Turbine Selection Research Articles

A fuzzy decision-making network model for offshore wind turbine selection based on simulated annealing algorithm

As global demand for renewable energy surges, offshore wind power has become an indispensable component in the transition towards a more sustainable energy landscape. Selecting appropriate wind turbines for offshore wind power facilities is a critical process influenced by various factors, including turbine parameters, environmental conditions, and economic indicators. Traditional decision-making methods often fall short in addressing the complexity and dynamic nature of real-world scenarios, relying heavily on expert experience. This study proposes a novel and scalable method for offshore wind turbine selection by leveraging a fuzzy decision-making network and a conditional probability table optimized through a simulated annealing algorithm. The method is structured into three key modules. It allows for learning from existing similar cases and transferring knowledge to the current selection task in the absence of expert opinion. It has proven its worth in a case study at the Rudong Wind Farm in Jiangsu Province, China. The results show that this method can account for the complexities inherent in turbine selection and serve as a more comprehensive and adaptable tool for decision-makers. This study contributes to the renewable energy sector by providing a novel, data-driven, and practical method for offshore wind turbine selection and other real-world related applications.

Strategic selection of wind turbines for low wind speed regions: Impact on cost and environmental benefits

ABSTRACT Integrating renewable energy sources into the power system is essential for rapid and cost-effective decarbonization. This study evaluates the potential of a wind farm by analyzing three turbine models – VESTAS, GAMESA, and W2E – with power capacities from 2.5 MW to 8 MW. The analysis focuses on Region III, characterized by wind speeds below 7.5 m/s. Using the RETScreen model, the study identifies an optimized wind-rated power (OWRP) that balances financial viability, technical performance, and capacity factors. Monte Carlo simulations and sensitivity analyses assess financial indicators such as the levelized cost of energy, net present value, and equity payback period. The framework also considers greenhouse gas (GHG) credits. Results show that turbines with 4.5 MW power, rotor diameters of 105–128 m, and hub heights of 150 m are the most feasible. The project would reduce annual CO2 emissions by 187,097 tons and avoid 82,031 tons of fossil fuel consumption, equivalent to 17,559 hectares of forest CO2 absorption or 190,915 people cutting energy use by 20%. Without GHG credits, the equity payback period is 6.2 years; with a €50/tCO2 credit, it drops to 2.7 years. These findings offer policymakers in Albania and similar low-wind regions a guide to meeting 2050 energy and climate targets.

Strategic analysis of wind energy potential and optimal turbine selection in Al-Jouf, Saudi Arabia

Wind power is considered one of the most environmentally friendly and rapidly growing form of renewable energy. This study aims at assessment of wind power potential for Al-Jouf region in Saudi Arabia. A long term historical wind speed data of 21 years (2000–2021) was analytically modeled using Weibull distribution function to determine the wind characteristics. The Weibull parameters and average wind speed for monthly and yearly were evaluated using MATLAB program. An online wind power calculator developed by Meteotest was used to evaluate the wind energy by selecting various turbines. Six types of commercial wind turbines of 3 MW capacity were selected for wind power assessment to optimize the turbine selection. Our analysis showed that average wind speed varies between 3.88 m/s and 4.99 m/s and an overall average wind speed is 4.38 m/s. The most frequent wind speed observed was 3.9 m/s with a probability of 20 % approximately. The Weibull distribution parameters, shape parameter “k” values ranged between 1.89 and 2.21 with an average of 1.98 and scale factor “c” values varied between 4.41 and 5.66 m/s with a mean value of 4.86 m/s. Performance evaluations of selected wind turbine models reveal that the Vestas V126 turbine outperforms others at the Al-Jouf site, generating an annual energy yield of 3779400 kWh at a capacity factor of 14.4 %. These results suggest that by constructing a wind farm consisting of 100 V126 turbines may compensate for the energy needs of 46000 individuals. These findings establish Al-Jouf as a viable location for utility-scale wind power projects, offering valuable insights for turbine manufacturers, developers, operators, and policymakers interested in deploying large-capacity turbines in the region. The method used in this study for wind power analysis and turbine selection is simple and helpful to provide an initial assessment about the site suitability and turbine selection without undergoing exhaustive efforts.

Wind resource assessment for turbine class identification in Bayanzhaganxiang, China

Abstract The wind resource assessment has been used effectively to identify the classification of wind turbines at a particular wind farm site. The current study used WAsP software and various statistical methods such as graphical, energy pattern factor, standard deviation, and Rayleigh distribution methods to find the Weibull parameters by evaluating the raw data collected from August 2005 until July 2006 at four (4) different heights of the meteorological mast station in Bayanzhaganxiang, China. The Weibull parameters were utilized to find the annual mean wind speed, probability density, and cumulative distribution functions of wind conditions at the reference heights of 70 m, 50 m, 30 m, and 10 m. The wind shear coefficient was 0.130 with an overall roughness factor of 0.0385 m, suggesting the site vicinity is an open country with no significant structures and vegetation. The results also showed that the post-processed output from WAsP and standard deviation method at the sensor’s height of 70 m have a correlation coefficient and confidence level of 0.99977 and above 95%, respectively. Based on the turbine classification from GL Wind 2003 and IEC 61400-1 Ed.2, it was found that the turbine class ideal for the site is class III wind turbines with an annual mean wind speed of 7.439 m/s at a hub height of 99 m. The measured wind power density at hub height was calculated according to IEC 61400-12-1, which yields 464.36 W/m2. The characteristic wind turbulence at 70 m high is IEC subclass B. Among the selected wind turbines, the net annual energy production with efficiency is 8,059.57 MWh/year using Avantis AV1010, with the highest capacity factor of 40.05%. It has been found that the lowest energy generation cost is US$ 0.0292/kWh for a period of 20 years.

Wind energy cost evaluation based on a techno-economic assessment in the Algerian highlands

Wind energy is an important and growing source of renewable energy. It is becoming increasingly integrated into electricity markets around the world. In Algeria in particular, the integration of wind power into the electricity grid is being developed based on a government project to achieve a high penetration rate. In this context, this study is elaborated to analyse the techno-economic assessment of the electrical energy production from four selected wind turbines: Vestas V-80; Suzlon S-82; Enercon E-58 and Gamesa G-114, installed at three sites in the Algerian highlands, namely El Beidh, Sétif and Djelfa. The three highland sites are chosen due to their good wind potential. On the other hand, the Algerian transmission electrical network DZ-114 bus presents some technical problems in the highlands related to low voltage profiles. Therefore, wind farm integration in these three sites can be beneficial as voltage magnitude support. Besides, it can be an important tool to reduce fossil fuel consumption and pollutant gases from conventional plants. Wind potential assessment and economic analysis were carried out for the three selected sites using wind speed data measured 10 m above ground level over 10 years. The results obtained confirm that the Gamesa G114 wind turbine is the most suitable for all the sites and in particular for El Bayadh, as it offers both the highest annual energy production (AEP) and capacity factor (CF) values in all the selected sites and guarantees the lowest unit energy cost (UEC) compared to other types of wind turbines.

Maximizing value through optimized annual selection of Pareto-optimal wind turbine operating strategies

Wind turbines as an investment aim at turning an initial expense into a profit. Naturally, the aim of developers and operators alike is to create an economic benefit that is as high as possible. Little changes to a wind turbine structure are possible once it is built, and wind as the ultimate power source cannot be influenced. The only available means to achieve a high economic benefit is through improved operations and maintenance. We developed a method for optimizing the operation of wind turbines that builds on condition-based adaptation of the power setpoint. It aims at balancing loads and power generation such that lifetime is increased, and the total economic value is improved over simpler operating schemes. In this contribution, we extend the method with an annually varying selection of different optimal operating strategies from an initial multiobjective optimization. We show three case studies that highlight the potential of annual variation and the margin with which it improves on operating at nominal power and over a fixed optimized operating strategy. In each case study, the net present value can be improved slightly over a lifetime-constant operating point that is selected once from the Pareto-front.

Analysis of Near-Surface Wind Shear Characteristics over Land in China

Wind shear is one of the crucial parameters in wind resource assessment and also serves as a vital parameter and basis for determining wind turbines’ selection and hub height. Existing studies have only focused on typical underlying surface areas, but a relatively limited comprehensive analysis of wind shear characteristics in different complex environments remains. This study analyzes the daily and monthly variations in wind shear index (α) at the station scale based on the observations from 754 wind measurement towers across land surfaces in China. The distribution and empirical values of wind shear in different wind regions and underlying surface types are also investigated. The research findings indicate that the wind shear index derived from fitting the complete annual average wind speeds at multiple height levels of meteorological towers can accurately characterize the stratification state of the atmospheric boundary layer. The variation pattern of solar radiation influences the daily α value in typical regions. In mountainous and desert areas, the monthly variation tends to be higher in autumn and winter and lower in spring and summer. However, its monthly variation shows relatively smaller fluctuations in plain regions. The comprehensive α value over land regions in China is 0.135. The α values for I, II, III, and IV wind fields are 0.111, 0.163, 0.1, and 0.153, respectively. Its values for mountainous, plains, grassland, and desert regions are 0.12, 0.273, 0.123, and 0.104, respectively. By conducting statistical analysis on α values across different wind regions, guidance is provided for extrapolating surface wind speeds to hub-height wind speeds. This serves as a reference for wind energy resource assessment, wind turbine selection, and hub height determination in the atmospheric boundary layer of China.

Wind energy resource assessment and wind turbine selection analysis for sustainable energy production

The objective of this study is to perform an analysis to determine the most suitable type of wind turbine that can be installed at a specific location for electricity generation, using annual measurements of wind characteristics and meteorological parameters. Wind potential analysis has shown that the analyzed location is suitable for the development of a wind farm. The analysis was carried out for six different types of wind turbines, with a power ranging from 1.5 to 3.0 MW and a hub height set at 80 m. Wind power potential was assessed using the Weibull analysis. The values of the scale coefficient c were determined, and a large monthly variation was observed, with values ranging from 1.92 to 8.36 m/s and an annual value of 4.95 m/s. Monthly values for the shape coefficient k varied between 0.86 and 1.53, with an annual value of 1.07. Additionally, the capacity factor of the turbines was determined, ranging from 17.75 to 22.22%. The Vestas turbine, with a nominal power of 2 MW and a capacity factor of 22.22%, proved to be the most efficient wind turbine for the specific conditions of the location. The quantity of greenhouse gas emissions that will be reduced if this type of turbine is implemented was also calculated, considering the average CO2 emission intensity factor (kg CO2/kWh) of the national electricity system.

Wind energy feasibility and wind turbine selection studies for the city Surat, India

Abstract Wind energy represents a clean, abundant and cost-effective power source, fostering job growth and environmental mitigation. Although wind energy harnesses several gigawatts today, its availability hinges on diverse factors, with geographical location standing out. Commercial turbines, with varying capacity ranges, saturate the market. Locating site-specific suitability and matching the appropriate turbine to meet specific requirements are of paramount importance. This study aims to assess the feasibility of wind energy in Surat, Gujarat, India and select an optimal small commercial turbine for residential use. The research involves Rayleigh and Weibull probability distribution functions based on yearlong velocity data. These distributions are fitted with actual data, revealing the most probable velocity (vmf = 3 m/s) and velocity at maximum power (vpmax = 5 m/s). The power availability of the site has been assessed as 42.6 W/m2 using both graphical and analytical methods. Several commercial turbines have been shortlisted based on on-site power criteria and their specifications are evaluated against site power availability. A comparative analysis culminates in identifying the most suitable turbine for the location. The best suitable turbine for the site with an annual energy yield of 8 MW has been suggested amongst selected turbines for small-scale residential applications.

The Effect of Varying Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Parameters on Wind Energy Prediction: A Comparative Study

Owing to the development of technology, the majority of nations throughout the world now rely on fossil fuels and nuclear power plants to meet their energy needs. However, as academic research on this subject has shown, it has become clear that alternative energy uses are necessary due to the gradual depletion of these fuels and their significant negative effects on the environment. In order to ensure energy diversity and end the energy shortage, the development of renewable energy sources is crucial. The prediction of wind power is crucial for effectively utilizing the potential of wind energy. In this study, an adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network (ANN) have been developed for the prediction of wind power. In this study, data sets were created by taking the daily average wind speeds of the selected wind turbine, the daily average power values it produces, and the daily average wind speed values in the Velimese region. By creating single-hidden layer and multi-hidden layer ANN models, the network was trained multiple times with different activation functions and different numbers of neurons, and wind power prediction was performed. In the ANFIS model, the number of membership functions is kept constant, and wind power prediction is performed using different membership functions. With these ANFIS and ANN models developed with different parameter combinations, it is aimed to determine the most efficient model by performing daily average wind power prediction. Parameter combinations were tested to determine the appropriate models, and as a result, the ANN and ANFIS models were compared with each other.

Physics-inspired and data-driven two-stage deep learning approach for wind field reconstruction with experimental validation

Accurate and reliable wind forecasts for urban blocks play a pivotal role in the construction of zero-energy communities by guiding the selection and placement of wind turbines and the aerodynamic design optimization of ducted openings. While relatively accurate wind fields are available based on numerical methods, their heavy computational cost and discontinuity make it necessary to explore an interactive and end-to-end method. In this study, we develop a physics-inspired and data-driven two-stage deep learning approach that can reconstruct complex wind fields precisely. The proposed method integrates a physical feature extraction model of the flow field with a sparse measurement data-driven error correction approach. In particular, a well-designed and well-trained flow field feature extraction model (original model) can preserve salient features of CFD modelling, while data-driven error correction techniques may harvest the uncertainty features and fill the remaining gaps between the original model predictions and the measured data. The proposed method is verified by a measured dataset from a community in Beijing. Experimental validation illustrates that the proposed algorithm successfully accomplishes wind field reconstruction in complex terrains using sparse datasets. We show that the proposed two-stage strategy exhibits significantly improved prediction results over the purely original method, with an average accuracy improvement of 47.17% and a maximum accuracy improvement of 72.59%. Overall, the proposed method delivers the potential in accurate wind field construction and urban wind energy forecasting.

Optimal Planning for Wind Turbines in Mega Seaports Considering Practical Application Constraints: A Case Study of Ningbo-Zhoushan Port

In the context of global carbon neutrality, ports face significant electricity demand for cargo handling and pressure to reduce carbon emissions. The abundant wind energy resources in port areas make wind power highly promising for port applications. The optimal selection of site and turbine types for wind power systems can effectively reduce emissions in ports, achieving sustainability and improving economic benefits. The practical implementation of wind energy systems considering practical constraints holds significant research significance. Taking Ningbo-Zhoushan Port as an example, this paper analyzes the wind energy resources in the port area and provides an overview of wind power system construction sites. Based on the actual conditions of the port area, this paper comprehensively reviews the site selection of wind turbines from the perspectives of wind resources, specific climates, and noise impacts. With the consideration of engineering preferences, this paper selects performance indicators based on the four mainstream turbine models and proposes a comprehensive weight determination method using the entropy weight method and analytic hierarchy process (AHP) to determine the weights of the indicators. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is then employed to score and compare four turbine plans, enabling the turbine selection process to consider both engineering preferences and objectivity, thereby enhancing the accuracy and reliability of wind turbine planning and achieving significant ecological and economic benefits through benefit analysis.

HyDesign: a tool for sizing optimization of grid-connected hybrid power plants including wind, solar photovoltaic, and lithium-ion batteries

Abstract. Hybrid renewable power plants consisting of collocated wind, solar photovoltaic (PV), and lithium-ion battery storage connected behind a single grid connection can provide additional value to the owners and society in comparison to individual technology plants, such as those that are only wind or only PV. The hybrid power plants considered in this article are connected to the grid and share electrical infrastructure costs across different generation and storing technologies. In this article, we propose a methodology for sizing hybrid power plants as a nested-optimization problem: with an outer sizing optimization and an internal operation optimization. The outer sizing optimization maximizes the net present values over capital expenditures and compares it with standard designs that minimize the levelized cost of energy. The sizing problem formulation includes turbine selection (in terms of rated power, specific power, and hub height), a wind plant wake loss surrogate, simplified wind and PV degradation models, battery degradation, and operation optimization of an internal energy management system. The problem of outer sizing optimization is solved using a new parallel “efficient global optimization” algorithm. This new algorithm is a surrogate-based optimization method that ensures a minimal number of model evaluations but ensures a global scope in the optimization. The methodology presented in this article is available in an open-source tool called HyDesign. The hybrid sizing algorithm is applied for a peak power plant use case at different locations in India where renewable energy auctions impose a monetary penalty when energy is not supplied at peak hours. We compare the hybrid power plant sizing results when using two different objective functions: the levelized cost of energy (LCoE) or the relative net present value with respect to the total capital expenditure costs (NPV/CH). Battery storage is installed only on NPV/CH-based designs, while the hybrid design, including wind, solar, and battery, only occurs on the site with good wind resources. Wind turbine selection on this site prioritizes cheaper turbines with a lower hub height and lower rated power. The number of batteries replaced changes at the different sites, ranging between two or three units over the lifetime. A significant oversizing of the generation in comparison to the grid connection occurs on all NPV/CH-based designs. As expected LCoE-based designs are a single technology with no batteries.

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

As global energy crises and climate change intensify, offshore wind energy, as a renewable energy source, is given more attention globally. The wind power generation system is fundamental in harnessing offshore wind energy, where the control and design significantly influence the power production performance and the production cost. As the scale of the wind power generation system expands, traditional methods are time-consuming and struggle to keep pace with the rapid development in wind power generation systems. In recent years, artificial intelligence technology has significantly increased in the research field of control and design of offshore wind power systems. In this paper, 135 highly relevant publications from mainstream databases are reviewed and systematically analyzed. On this basis, control problems for offshore wind power systems focus on wind turbine control and wind farm wake control, and design problems focus on wind turbine selection, layout optimization, and collection system design. For each field, the application of artificial intelligence technologies such as fuzzy logic, heuristic algorithms, deep learning, and reinforcement learning is comprehensively analyzed from the perspective of performing optimization. Finally, this report summarizes the status of current development in artificial intelligence technology concerning the control and design research of offshore wind power systems, and proposes potential future research trends and opportunities.

Assessment of Offshore Wind Power Potential and Wind Energy Prediction Using Recurrent Neural Networks

In recent years, Taiwan has actively pursued the development of renewable energy, with offshore wind power assessments indicating that 80% of the world’s best wind fields are located in the western seas of Taiwan. The aim of this study is to maximize offshore wind power generation and develop a method for predicting offshore wind power, thereby exploring the potential of offshore wind power in Taiwan. The research employs machine learning techniques to establish a wind speed prediction model and formulates a real-time wind power potential assessment method. The study utilizes long short-term memory networks (LSTM), gated recurrent units, and stacked recurrent neural networks with LSTM units as the architecture for the wind speed prediction model. Furthermore, the prediction models are categorized into annual and seasonal patterns based on the seasonal characteristics of the wind. The research evaluates the optimal model by analyzing the results of the two patterns to predict the power generation conditions for 1 to 12 h. The study region includes offshore areas near Hsinchu and Kaohsiung in Taiwan. The novelty of the study lies in the systematic analysis using multiple sets of wind turbines, covering aspects such as wind power potential assessment, wind speed prediction, and fixed and floating wind turbine considerations. The research comprehensively considers the impact of different offshore locations, turbine hub heights, rotor-swept areas, and wind field energy on power generation. Ultimately, based on the research findings, it is recommended to choose the SG 8.0-167 DD wind turbine system for the Hsinchu offshore area and the SG 6.0-154 wind turbine system for the Kaohsiung offshore area, serving as reference cases for wind turbine selection.

Research on Wind Turbine Location and Wind Energy Resource Evaluation Methodology in Port Scenarios

Wind energy is widely distributed in China as a renewable energy source. Aiming to alleviate the issues resulting from fossil fuel consumption faced by developing and developed countries (e.g., climate change) and to meet development needs, this study innovatively proposed methods for the location selection of wind farms and wind turbines in port areas based on the fuzzy comprehensive evaluation method. Considering that the wind turbine location is crucial to wind power generation, this paper focuses on locating wind turbines within a specific set of sea ports. The primary objectives of this paper are to evaluate the potential of wind power generation under different port scenarios and develop a method for assessing the potential of wind energy resources in wind farm areas. Firstly, a method is proposed for identifying the boundaries of wind farms in the port areas and locating wind turbines at sea ports. Furthermore, this study used the National Aeronautics and Space Administration (NASA) wind speed database to test the proposed method with the real-world wind power projects of the Ports of Tianjin, Shanghai, Xiamen, Shenzhen, and Hainan, which are top ports within five major coastal port clusters in China. It is found that the potential power generation capacity of the wind power farms at the above ports is 30.71 GWh, 19.82 GWh, 16.72 GWh, 29.45 GWh, and 24.42 GWh, respectively. Additionally, sensitive results for different types of wind turbines are conducted in the following experiment. The results of this study are fundamental for enriching the research of evaluating wind energy resources of sea ports and promoting the development and use of clean energy in practical environments. Further, the method proposed in this study is essential for optimizing the location and construction of wind turbines, which may help ports in adopting a low-carbon and green development path, thereby mitigating air pollution, and promoting sustainable development.

Proportional intuitionistic fuzzy AHP method: Wind turbine selection application

The direct assignment of decimal numbers for membership and non-membership degrees of an element in intuitionistic fuzzy sets is not practical. The problem is that the expert cannot assign the same values to the degrees of membership, non-membership and hesitancy in decimal numbers for the same proposition in every attempt. Rather than the former, the assignment of proportional relationships between membership and non-membership degrees is more appropriate. We propose proportion-based models for intuitionistic fuzzy sets that include arithmetic and aggregation operators. Proportional intuitionistic fuzzy (PIF) sets require only the proportion relations between an intuitionistic fuzzy set’s parameters. These models will make it easier to define intuitionistic fuzzy sets with more accurate data that better represents expert judgments. We transform AHP method, one of the traditional multi-criteria decision making methods, to PIF AHP using PIF sets. We compare the proposed PIF AHP method by interval-valued intuitionistic fuzzy AHP method existing in the literature. A wind turbine selection problem is handled to show the validity of the proposed PIF AHP method.

Prediction of wind fields in mountains at multiple elevations using deep learning models

To optimize the site selection of wind turbines, it is essential to understand the variability of wind speeds at different elevations on mountains and their wind characteristics. The erection of anemometer at a large altitude in mountainous regions poses significant financial and safety challenges. Therefore, it is of utmost importance to design a method that facilitates the estimation of multiple high-elevation wind speeds utilizing low-elevation measurements. This study built a graph data structure by assimilating prior knowledge, including the mountain topography and relative position of the anemometer. A custom-designed graph neural network model was designed to predict wind speed data at high-elevation regions based on wind speed data at a low-elevation region. Moreover, an enhanced UNet model was employed to predict wind field data at higher elevations utilizing predicted wind speed data. To improve the stability and performance of the model, the flux conservation equation was integrated into the loss function of the graph neural network. The results suggest that integrating mountain information and physical loss in the model can significantly improve the accuracy of wind speed data predictions. The graph neural network has remarkably enhanced the performance of the UNet model.

Stochastic modelling of wind speed over northern states in Nigeria

This paper presents a stochastic modeling of wind speed over sixteen (16) Northern states in Nigeria using thirty-seven years (1984-2020) wind speed real time data. A Markov Chain Model was developed for the monthly wind speed state across study locations. In order to obtain the Markov chain transitional probabilities, the wind speed data was categories into various states using the Beaufort wind scale. It was observed that only the first four description of wind speed state A, B, C and D exist in the study locations. Uniform random states were also formed by generating uniform random number. The comparison of monthly simulated and actual wind speed state clearly shows that the model simulated over six months correctly across study locations except Niger. Given a current wind speed state conditions, the stochastic models available in this paper can be adapted to generate future wind speed state condition. The understanding of wind speed state helps in wind turbine design and selection of wind farm sites for wind energy generation.

Assessment of economic, energy, and exergy efficiencies using wind measurement mast data for different wind turbines.

Today, increasing concerns about greenhouse gas emissions, climate change, and resource depletion from fossil fuels have drawn attention to wind energy. In this context, wind turbine technologies are constantly evolving to eliminate such concerns by using wind energy. The wind speed from the measurement mast at a height of 80m was used in wind turbines of different capacities and was investigated. To assess the potential of the system that produces electricity from wind energy, it has been analyzed in terms of energy, exergy, and economic. The energy and exergy efficiencies of each wind turbine were analyzed with the wind speed and meteorological data. When the average monthly power calculated for each turbine is proportioned to the turbine capacity, the energy efficiency varies between 10 and 70%. Enercon_1500 and Enercon_3050 values are high, while Enercon_3500 and Enercon_2350 have low efficiency compared to other turbines. The annual total energy production is 12.19 GWh for the highest Enercon_4200 and 4.48 GWh for the lowest Enercon_1500. The exergy efficiencies range from 20 to 79% for selected wind turbines. In the last part of the study, monthly average electricity production costs were determined by using the turbines selected for the determined region. When compared in terms of unit electricity cost, the Enercon_1500 turbine is higher, while the Enercon_4200 is lower.