Analysis of wind energy potential and Weibull parameter estimation methods: a case study from Turkey

A detailed investigation of the wind characteristics is an indispensable process for the wind energy potential for installing of wind power plants. Choosing the wind speed distribution that provides the better fit to the data is of great advantages. The present study investigates the appropriateness of four different numerical methods for forecasting the Weibull distribution parameters using wind speed information from İzmir‒Turkey. To determine the robustness of the methods, the root-mean-square error, the coefficient of determination, and the Chi-square goodness-of-fit tests have been used. In addition, an economic analysis to represent probability of installing wind turbines ranging between 800 and 4200 kW in site has been also done. The results demonstrate that the standard deviation–mean wind speed method is the most appropriate. Moreover, the estimated cost of electricity from wind was calculated as US$0.0111/kWh.

[1]  T. W. von Backström,et al.  Assessment of the wind power potential at SANAE IV base, Antarctica: a technical and economic feasibility study , 2003 .

[2]  R. Poore,et al.  Development of an Operations and Maintenance Cost Model to Identify Cost of Energy Savings for Low Wind Speed Turbines: July 2, 2004 -- June 30, 2008 , 2008 .

[3]  Mustafa Serdar Genç,et al.  Evaluation of electricity generation and energy cost of wind energy conversion systems (WECSs) in Central Turkey , 2009 .

[4]  Carla Freitas de Andrade,et al.  Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil , 2012 .

[5]  Jamel Belhadj,et al.  Wind resource estimation and wind park design in El-Kef region, Tunisia , 2012 .

[6]  H. S. Bagiorgas,et al.  Wind power potential assessment for seven buoys data collection stations in Aegean Sea using Weibull distribution function , 2012 .

[7]  H. Saleh,et al.  Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt , 2012 .

[8]  Esmail M. A. Mokheimer,et al.  Modeling and optimization of hybrid wind–solar-powered reverse osmosis water desalination system in Saudi Arabia , 2013 .

[9]  Xiaozhong Liao,et al.  Evaluation of wind power production prospective and Weibull parameter estimation methods for Babaurband, Sindh Pakistan , 2014 .

[10]  A. Gungor.  Effects of global warming on wind energy potential , 2015 .

[11]  Önder Güler,et al.  A novel energy pattern factor method for wind speed distribution parameter estimation , 2015 .

[12]  O. Guerri,et al.  Evaluation of wind energy potential and estimation of cost using wind energy turbines for electricity generation in north of Algeria , 2015 .

[13]  Kasra Mohammadi,et al.  Assessing different parameters estimation methods of Weibull distribution to compute wind power density , 2016 .

[14]  Murat Gökçek,et al.  Technical and economic evaluation of freshwater production from a wind-powered small-scale seawater reverse osmosis system (WP-SWRO) , 2016 .

[15]  Melih Soner Çeliktaş,et al.  Statistical analysis of wind speed using two-parameter Weibull distribution in Alaçatı region , 2016 .

[16]  Frede Blaabjerg,et al.  Distributed Power-Generation Systems and Protection , 2017, Proceedings of the IEEE.

[17]  Ke Ma,et al.  Wind Energy Systems , 2017, Proceedings of the IEEE.

[18]  R. Saidur,et al.  Economic viability and production capacity of wind generated renewable hydrogen , 2018 .

[19]  M. Abolarin,et al.  A Review of Onshore and Offshore Wind Energy Potential in Nigeria , 2018, IOP Conference Series: Materials Science and Engineering.

[20]  Sajid Ali,et al.  Statistical analysis of wind characteristics using Weibull and Rayleigh distributions in Deokjeok-do Island – Incheon, South Korea , 2018, Renewable Energy.

[21]  Kyungnam Ko,et al.  Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea , 2018 .

[22]  Omar Charrouf,et al.  Techno-economic Analysis of Wind Turbines in Algeria , 2018 .

[23]  P. Chaurasiya,et al.  Comparative analysis of Weibull parameters for wind data measured from met-mast and remote sensing techniques , 2018 .