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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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    1. Pookpunt, Sittichoke & Ongsakul, Weerakorn, 2013. "Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients," Renewable Energy, Elsevier, vol. 55(C), pages 266-276.
    2. González, Javier Serrano & Gonzalez Rodriguez, Angel G. & Mora, José Castro & Santos, Jesús Riquelme & Payan, Manuel Burgos, 2010. "Optimization of wind farm turbines layout using an evolutive algorithm," Renewable Energy, Elsevier, vol. 35(8), pages 1671-1681.
    3. Shin, Joohyun & Lee, Jay H. & Realff, Matthew J., 2017. "Operational planning and optimal sizing of microgrid considering multi-scale wind uncertainty," Applied Energy, Elsevier, vol. 195(C), pages 616-633.
    4. Marmidis, Grigorios & Lazarou, Stavros & Pyrgioti, Eleftheria, 2008. "Optimal placement of wind turbines in a wind park using Monte Carlo simulation," Renewable Energy, Elsevier, vol. 33(7), pages 1455-1460.
    5. Changshui, Zhang & Guangdong, Hou & Jun, Wang, 2011. "A fast algorithm based on the submodular property for optimization of wind turbine positioning," Renewable Energy, Elsevier, vol. 36(11), pages 2951-2958.
    6. AfDB AfDB, . "Annual Report 2012," Annual Report, African Development Bank, number 461.
    7. Malen, Joel & Marcus, Alfred A., 2017. "Promoting clean energy technology entrepreneurship: The role of external context," Energy Policy, Elsevier, vol. 102(C), pages 7-15.
    8. Grady, S.A. & Hussaini, M.Y. & Abdullah, M.M., 2005. "Placement of wind turbines using genetic algorithms," Renewable Energy, Elsevier, vol. 30(2), pages 259-270.
    9. Omer, Abdeen Mustafa, 2008. "Energy, environment and sustainable development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(9), pages 2265-2300, December.
    10. Shakoor, Rabia & Hassan, Mohammad Yusri & Raheem, Abdur & Rasheed, Nadia, 2016. "Wind farm layout optimization using area dimensions and definite point selection techniques," Renewable Energy, Elsevier, vol. 88(C), pages 154-163.
    11. Parada, Leandro & Herrera, Carlos & Flores, Paulo & Parada, Victor, 2017. "Wind farm layout optimization using a Gaussian-based wake model," Renewable Energy, Elsevier, vol. 107(C), pages 531-541.
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    2. Houssem R. E. H. Bouchekara & Yusuf A. Sha’aban & Mohammad S. Shahriar & Makbul A. M. Ramli & Abdullahi A. Mas’ud, 2023. "Wind Farm Layout Optimization/Expansion with Real Wind Turbines Using a Multi-Objective EA Based on an Enhanced Inverted Generational Distance Metric Combined with the Two-Archive Algorithm 2," Sustainability, MDPI, vol. 15(3), pages 1-32, January.
    3. 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.
    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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