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Optimization of wind turbines siting in a wind farm using genetic algorithm based local search

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  • Abdelsalam, Ali M.
  • El-Shorbagy, M.A.

Abstract

The present work is devoted to search for the optimum wind farm layout using binary real coded genetic algorithm (BRCGA) based local search (LS); gathering robust single wake model with suitable wake interaction modeling. The binary part of genetic algorithm (GA) is used to represent the location of turbines; while the real part is used to give the power generated by each turbine at its location. In addition, the solution quality is improved by implementing LS technique; where it intends to find the optimal solution near the approximated solution obtained by BRCGA. The Jensen wake model along with the sum of squares model are used to obtain the available power for each turbine; where it is considered one of the most common analytical models used for wind farm optimization. Siting improvement is achieved, as compared with earlier studies.

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  • Abdelsalam, Ali M. & El-Shorbagy, M.A., 2018. "Optimization of wind turbines siting in a wind farm using genetic algorithm based local search," Renewable Energy, Elsevier, vol. 123(C), pages 748-755.
    • Handle: RePEc:eee:renene:v:123:y:2018:i:c:p:748-755
    DOI: 10.1016/j.renene.2018.02.083
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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. M. A. El-Shorbagy & A. Y. Ayoub & A. A. Mousa & I. M. El-Desoky, 2019. "An enhanced genetic algorithm with new mutation for cluster analysis," Computational Statistics, Springer, vol. 34(3), pages 1355-1392, September.
    4. Yeo, Eng Jet & Kennedy, David M. & O'Rourke, Fergal, 2022. "Tidal current turbine blade optimisation with improved blade element momentum theory and a non-dominated sorting genetic algorithm," Energy, Elsevier, vol. 250(C).
    5. Wu, Yan & Zhang, Shuai & Wang, Ruiqi & Wang, Yufei & Feng, Xiao, 2020. "A design methodology for wind farm layout considering cable routing and economic benefit based on genetic algorithm and GeoSteiner," Renewable Energy, Elsevier, vol. 146(C), pages 687-698.
    6. Mohammed A. El-Shorbagy & Islam M. Eldesoky & Mohamady M. Basyouni & Islam Nassar & Adel M. El-Refaey, 2022. "Chaotic Search-Based Salp Swarm Algorithm for Dealing with System of Nonlinear Equations and Power System Applications," Mathematics, MDPI, vol. 10(9), pages 1-30, April.
    7. Petrović, A. & Đurišić, Ž., 2021. "Genetic algorithm based optimized model for the selection of wind turbine for any site-specific wind conditions," Energy, Elsevier, vol. 236(C).
    8. Kaldellis, John K. & Triantafyllou, Panagiotis & Stinis, Panagiotis, 2021. "Critical evaluation of Wind Turbines’ analytical wake models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    9. Wang, Longyan & Zuo, Ming J. & Xu, Jian & Zhou, Yunkai & Tan, Andy C., 2019. "Optimizing wind farm layout by addressing energy-variance trade-off: A single-objective optimization approach," Energy, Elsevier, vol. 189(C).
    10. M. A. El-Shorbagy & A. A. Mousa & M. A. Farag, 2019. "An intelligent computing technique based on a dynamic-size subpopulations for unit commitment problem," OPSEARCH, Springer;Operational Research Society of India, vol. 56(3), pages 911-944, September.
    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. Yuanhang Qi & Peng Hou & Guisong Liu & Rongsen Jin & Zhile Yang & Guangya Yang & Zhaoyang Dong, 2021. "Cable Connection Optimization for Heterogeneous Offshore Wind Farms via a Voronoi Diagram Based Adaptive Particle Swarm Optimization with Local Search," Energies, MDPI, vol. 14(3), pages 1-21, January.
    13. 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).
    14. Dhunny, A.Z. & Timmons, D.S. & Allam, Z. & Lollchund, M.R. & Cunden, T.S.M., 2020. "An economic assessment of near-shore wind farm development using a weather research forecast-based genetic algorithm model," Energy, Elsevier, vol. 201(C).
    15. Yunqi Xiao & Yi Wang & Yanping Sun, 2018. "Reactive Power Optimal Control of a Wind Farm for Minimizing Collector System Losses," Energies, MDPI, vol. 11(11), pages 1-15, November.

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