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Review on optimisation methods of wind farm array under three classical wind condition problems

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  • Azlan, F.
  • Kurnia, J.C.
  • Tan, B.T.
  • Ismadi, M.-Z.

Abstract

The arrangement of wind turbines in clusters presents two noteworthy issues: (1) diminished power generation brought about by wake wind speed deficits and (2) expanded unique loads on the blades caused by higher turbulence levels. The drop in power generation of downstream wind turbines can reach up to 46% of the upstream wind turbines in entirely created wake conditions. Thus, wind farm layout optimisation is an essential design criterion of a wind farm. Focusing on optimising the micro-position of the turbines within the wind farm to minimise the wake effects can maximise the expected power output. There are several crucial aspects in designing a wind farm layout, and most of the previous studies have been focusing on maximising the overall energy yield and minimise the initial investment. Several optimisation methods based on heuristic techniques have successfully found optimal solution to these challenges. However, far too little attention has been paid on comparing these methodologies to each other. Therefore, this work aims to survey the existing approaches used to-date in solving the wind turbine positioning within the wind farm. This work firstly briefs the historical background of the conventional development of a wind farm. Then, key features and basic components of a wind farm modelling are discussed and categorised into the following: wind farm siting, layout design, wake models and objective functions. Several research gaps and suggestion of future research areas in this field are highlighted which shall enable to guide researchers to explore further in the upcoming studies.

Suggested Citation

  • Azlan, F. & Kurnia, J.C. & Tan, B.T. & Ismadi, M.-Z., 2021. "Review on optimisation methods of wind farm array under three classical wind condition problems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    • Handle: RePEc:eee:rensus:v:135:y:2021:i:c:s1364032120303385
    DOI: 10.1016/j.rser.2020.110047
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