Techno-Economy Study of wind energy in Khvaf in Razavi Khorasan Province in Iran

Document Type : Research Paper

Authors

1 Center of Excellence in Design and Optimization of Energy Systems, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 School of Mechanical Engineering, École polytechnique fédérale de Lausanne (Epfl)

3 Fuel Cell Research Lab (FCReL), Mechatronic System Engineering, Simon Fraser University, Vancouver, Canada

Abstract

In this paper, the 10-minutes period measured wind speed data at different heights (10 m, 30 m, and 40 m) are presented for Khvaf, which is one of the major counties with high wind potential in Khorasan provinces in Iran. To the author’s knowledge, there hasn’t been any assessment works in the mentioned site. From the primary evaluation and by determining mean wind speed and also the Weibull function, the results show that the measurement site falls under class 7 of the International System Wind Classification, which means that the station has very suitable conditions for installing and operating wind farms. On the other hand, a new approach is utilized for evaluating potential power of a region based on comparison between maximum power generation pattern and daily and monthly energy consumption patterns. Furthermore, by using wind roses of speed and turbulence simultaneously, the best direction for installing wind turbines is determined. On the other hand, the situation of topography and surface conditions of Khvaf station has been analyzed; because of its smooth surface, it is appropriate for installing wind turbines. Besides, several types of Vestas Company turbines have been compared by their capacity factors and three of them with highest capacity factor are selected for economic evaluations. One of the important issues in the assessment of wind energy potential is the economic evaluation. This is a major gap that many researches do not have sufficient attention to it. Hence, an economic analysis was performed based on NPV, IRR, and Normal Payback methods in order to select the best wind turbine for this site.

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Volume 47, Issue 1
June 2016
Pages 53-66
  • Receive Date: 28 February 2016
  • Revise Date: 06 April 2016
  • Accept Date: 01 May 2016