10.1007/s40095-019-00320-5

A new approximate capacity factor method for matching wind turbines to a site: case study of Humber region, UK

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  • Chidiebere Diyoke*1,
  1. Department of Mechanical Engineering, Enugu State University of Science and Technology, Enugu, NG School of Engineering, Faculty of Science and Engineering, University of Hull, Hull, HU6 7RX, GB
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Published in Issue 2019-09-06

How to Cite

Diyoke, C. (2019). A new approximate capacity factor method for matching wind turbines to a site: case study of Humber region, UK. International Journal of Energy and Environmental Engineering, 10(4 (December 2019). https://doi.org/10.1007/s40095-019-00320-5
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Abstract

Abstract Wind-based power is one of the renewable base power sources that are tipped to play a great role in decarbonising the globe. To achieve this potential, more wind farms are likely to be built. The capacity factor of a wind farm and hence its profitability is dependent on whether it is properly sized and sited. In fact, some wind power plants have failed wholly or underperformed, because the wind turbine plant installed did not match the wind site. In this paper, a new approximate capacity factor equation has been derived for matching wind turbines to potential site for optimum yield and profitability. The indexes of capacity factor and cost of electricity were used as metrics in the model. The proposed model was applied to the climatic conditions and wind turbine characteristics of Kappadagudda and Mailiao wind farms in India and Taiwan, respectively. The result obtained showed good agreement with measured data for the two wind farms. With respect to the Kappadagudda wind farm, the model computed CF of 38% is close to the Kappadagudda real wind farm annual CF of 36% representing an absolute error of 2% and a mean square error of 0.96%. In addition, it was found that the proposed model followed the same general trend with other six existing models compared.

Keywords

  • Renewable energy,
  • Capacity factor,
  • Wind power,
  • Cost of electricity,
  • Wind power analysis
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