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Optimization of wind turbine micrositing: A comparative study

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  • Rajper, Samina
  • Amin, Imran J.

Abstract

The need for energy is an attention required issue for the developing countries. Developing countries are in the grip of the deficit of fossils or hydrocarbons sources of energy. Many countries are looking for the optimal solutions of energy production which are more reliable, pollution free and presume less cost. Pakistan is also in the list of those countries who want to get rid of expensive and polluted means of power production. Power production to fulfill the demand of the country is the biggest challenge for Pakistan. Therefore, many sites are under consideration for greener solutions of the problem. The proposed study is undertaken for the under consideration site, Gharo, Sindh, Pakistan. The present research is undertaken to find out the optimal solution for the wind turbine micrositings. A comparison of present study with different past studies (using different optimization techniques, i.e., genetic algorithm, Monte Carlo simulation method etc.) have been undertaken to prove the results of the present study as better results. The basic objective of the study is to find out the most optimal solution for cost per unit power; therefore, the number of wind turbines is not an issue in the undertaken study however, cost is the function of number of wind turbines and to optimize the solution, MS-Excel is used first to prove that power is a function of Wind speed. Second, genetic algorithm is also used for minimal value of fitness function.

Suggested Citation

  • Rajper, Samina & Amin, Imran J., 2012. "Optimization of wind turbine micrositing: A comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 5485-5492.
    • Handle: RePEc:eee:rensus:v:16:y:2012:i:8:p:5485-5492
    DOI: 10.1016/j.rser.2012.06.014
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    References listed on IDEAS

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    1. Yang, Kun & Deng, Xiaowei & Ti, Zilong & Yang, Shanghui & Huang, Senbin & Wang, Yuhang, 2023. "A data-driven layout optimization framework of large-scale wind farms based on machine learning," Renewable Energy, Elsevier, vol. 218(C).
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    3. Iqbal, M. & Azam, M. & Naeem, M. & Khwaja, A.S. & Anpalagan, A., 2014. "Optimization classification, algorithms and tools for renewable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 640-654.
    4. Emin Sertaç Ari & Cevriye Gencer, 2020. "Proposal of a novel mixed integer linear programming model for site selection of a wind power plant based on power maximization with use of mixed type wind turbines," Energy & Environment, , vol. 31(5), pages 825-841, August.
    5. Serrano González, Javier & Burgos Payán, Manuel & Santos, Jesús Manuel Riquelme & González-Longatt, Francisco, 2014. "A review and recent developments in the optimal wind-turbine micro-siting problem," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 133-144.
    6. Moreno, Sinvaldo Rodrigues & Pierezan, Juliano & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2021. "Multi-objective lightning search algorithm applied to wind farm layout optimization," Energy, Elsevier, vol. 216(C).
    7. Liu, Weiqi & Liu, Weixing & Zhang, Liang & Sheng, Qihu & Zhou, Binzhen, 2018. "A numerical model for wind turbine wakes based on the vortex filament method," Energy, Elsevier, vol. 157(C), pages 561-570.
    8. Wu, Yan & Xia, Tianqi & Wang, Yufei & Zhang, Haoran & Feng, Xiao & Song, Xuan & Shibasaki, Ryosuke, 2022. "A synchronization methodology for 3D offshore wind farm layout optimization with multi-type wind turbines and obstacle-avoiding cable network," Renewable Energy, Elsevier, vol. 185(C), pages 302-320.
    9. Shakoor, Rabia & Hassan, Mohammad Yusri & Raheem, Abdur & Wu, Yuan-Kang, 2016. "Wake effect modeling: A review of wind farm layout optimization using Jensen׳s model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1048-1059.
    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. 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.
    12. Parada, Leandro & Herrera, Carlos & Flores, Paulo & Parada, Victor, 2018. "Assessing the energy benefit of using a wind turbine micro-siting model," Renewable Energy, Elsevier, vol. 118(C), pages 591-601.
    13. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2016. "Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model," Applied Energy, Elsevier, vol. 174(C), pages 192-200.
    14. Neha Gupta & Mohini Agarwal & Pratibha Garg & Manoj Bansal, 2021. "Revenue optimization modeling for renewable energy resource mix for sustainable development," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(2), pages 108-115, April.
    15. Katarzyna Wolniewicz & Adam Zagubień & Mirosław Wesołowski, 2021. "Energy and Acoustic Environmental Effective Approach for a Wind Farm Location," Energies, MDPI, vol. 14(21), pages 1-17, November.

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