Study of Potential, Characteristics and Parameters of the Wind Energy Case study: Dehloran County

Abstract

Introduction Geographical location and climatic conditions are the important factors affecting the wind energy potential of each region. Iran is a vast country with different climates and the exploitation of its wind energy needs to study and research on the meteorological data. In the study area during the warm season and the hottest hours of the day, coinciding with peak electricity consumption in the region and the country, wind blowing continuously carried out. The surpassed consumption over production of electricity in summer and vice versa in winter is considered as one of the country's problems. The aim of this study was to investigate the parameters of the wind energy and the feasibility of wind potential (in study area) in the warm season in particular and other seasons to supply the needed electrical power of area, avoid of unwanted blackouts, development of wind energy as an important renewable energy, attraction of investors, and policymakers to build wind farms in the study area. Materials and Methods This study was conducted in the Dehloran city, located in the southern part of Ilam province. The region has a temperate winter and very hot and dry summer. The important criteria for construction of wind power plants and using of its energy are wind power density and the annual wind speed average. For this reason and analysis, and statistical analyzes, wind data includes three-hour direction and speed were obtained from the meteorological organization and during 2004 to 2013. The average of annual, monthly and daily wind speed and their standard deviation were calculated. Based on the commercial turbines in the country, and the rotor blades are at altitudes up to about 80 meters, the wind speed at altitudes of 40, 60 and 80 meters was calculated. To evaluate the potential of wind speed the Rayleigh and Weibull distribution functions were used and their parameters were calculated. The wind energy potential using the available data and the Weibull and Rayleigh functions were calculated. Results and Discussion Based on the results of the ten-year data, average of wind speed had relatively slight variation, with the highest and the lowest value of 3.6 and 3.25 m/s in 2007 and 2010, respectively. The annual average was about 6 m/s in height of 50 meters that seems appropriate. The highest and the lowest monthly average values were 4.62 m/s and 2.24 m s-1 in June 2005 and November 2006, respectively. Generally, the warm months had significantly higher wind speed than that of cold months. The Weibull distribution function parameters, k and c were calculated. Minimum and maximum amount of k were 1 and 1.828, in December 2006 and May 2011, respectively. The minimum and maximum amount of c was 2.37 and 5.69 in November 2004 and June 2013, respectively. The highest value of wind power density was 312 w m-2 in June. The lowest power density was observed in November. Therefore, we can say that the wind energy potential of the region has coincident with peak electricity consumption in the warm months. The most frequent and the least frequent wind direction were the southeast and northeast, respectively. Conclusions Daily evaluation of wind speed during different months, seasons and years showed a significant change during the day that represented the high value of the wind speed in noon and afternoon. The highest value of monthly wind energy density was for the warm season. The lowest and highest power density was in November and June, respectively. Therefore, we can say that the peak of wind energy potential of the region has a coincident with the country's peak power consumption in warm months. With considering that the study area has a warm climate and high consumption of energy in the hot days of a year and the probability of unwanted blackout of electricity in warm months, and the long hours of the wind blowing in the mentioned times, construction of wind farm in these areas can be reasonable.

Keywords

• wind energy
• weibull distribution function
• wind direction
• wind speed