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A new optimization algorithm for solving wind turbine placement problem: Binary artificial algae algorithm

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  • Beşkirli, Mehmet
  • Koç, İsmail
  • Haklı, Hüseyin
  • Kodaz, Halife

Abstract

The wind turbine has grown out to be one of the most common renewable energy sources around the world in recent years. As wind energy becomes more important, the significance of wind turbine placement also increases. This study was intended to position the wind turbines on a wind farm to achieve the highest performance possible. The turbine placement operation was designed for a 2 km × 2 km area. The surface of the area was calculated by dividing it into a 10 × 10 grid and a 20 × 20 grid with the use of binary coding. The calculation revealed ten different new binary algorithms using ten different transfer functions of the Artificial Algae Algorithm (AAA) that has been successfully applied to solve continuous optimization problems. These algorithms were applied to the turbine placement problem, and the algorithm that obtained the best result was called the Binary Artificial Algorithm (BinAAA). The results of the proposed algorithm for the binary turbine placement optimization problem were compared with those of other well-known algorithms in the relevant literature. The algorithm that was proposed in the study is an efficient algorithm for the placement problem of wind turbines since it optimized the binary search space and achieved the most successful result.

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  • Beşkirli, Mehmet & Koç, İsmail & Haklı, Hüseyin & Kodaz, Halife, 2018. "A new optimization algorithm for solving wind turbine placement problem: Binary artificial algae algorithm," Renewable Energy, Elsevier, vol. 121(C), pages 301-308.
    • Handle: RePEc:eee:renene:v:121:y:2018:i:c:p:301-308
    DOI: 10.1016/j.renene.2017.12.087
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    2. Hsien-Hsin Cheng & Yi-Ya Hsu, 2022. "Integrating spatial multi-criteria evaluation into the potential analysis of culture-led urban development – A case study of Tainan," Environment and Planning B, , vol. 49(1), pages 335-351, January.
    3. Wu, Chutian & Yang, Xiaolei & Zhu, Yaxin, 2021. "On the design of potential turbine positions for physics-informed optimization of wind farm layout," Renewable Energy, Elsevier, vol. 164(C), pages 1108-1120.
    4. Dhiman, Harsh S. & Deb, Dipankar, 2020. "Fuzzy TOPSIS and fuzzy COPRAS based multi-criteria decision making for hybrid wind farms," Energy, Elsevier, vol. 202(C).

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