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Wind Farm Layout Optimization/Expansion of Real Wind Turbines with a Parallel Collaborative Multi-Objective Optimization Algorithm

Author

Listed:
  • Houssem R. E. H. Bouchekara

    (Department of Electrical Engineering, University of Hafr Al Batin, Hafr Al Batin 31991, Saudi Arabia)

  • Makbul A. M. Ramli

    (Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Mohammad S. Javaid

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

Abstract

The objective of this paper is to study the Wind Farm Layout Optimization/expansion problem. This problem is formulated here as a Multi-Objective Optimization Problem considering the total power output and net efficiency of Wind Farms as objectives along with specific constraints. Once formulated, this problem needs to be solved efficiently. For that, a new approach based on a combination of five Multi-Objective Optimization algorithms, which is named the Parallel Collaborative Multi-Objective Optimization Algorithm, is developed and implemented. This technique is checked on seven test cases; for each case, the goal is to find a set of optimal solutions called the Pareto Front, which can be exploited later. The acquired solutions were compared with other approaches and the proposed approach was found to be the better one. Finally, this work concludes that the proposed approach gives, in a single run, a set of optimal solutions from which a designer/planner can select the best layout of a designed Wind Farm using expertise and applying technical and economic constraints.

Suggested Citation

  • Houssem R. E. H. Bouchekara & Makbul A. M. Ramli & Mohammad S. Javaid, 2024. "Wind Farm Layout Optimization/Expansion of Real Wind Turbines with a Parallel Collaborative Multi-Objective Optimization Algorithm," Energies, MDPI, vol. 17(22), pages 1-32, November.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5632-:d:1518324
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    References listed on IDEAS

    as
    1. 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.
    2. Yang, Shanghui & Deng, Xiaowei & Yang, Kun, 2024. "Machine-learning-based wind farm optimization through layout design and yaw control," Renewable Energy, Elsevier, vol. 224(C).
    3. Ulku, I. & Alabas-Uslu, C., 2019. "A new mathematical programming approach to wind farm layout problem under multiple wake effects," Renewable Energy, Elsevier, vol. 136(C), pages 1190-1201.
    4. Wang, Quan & Xu, Tangjie & von Terzi, Dominic & Xia, Wei & Wang, Zhenhai & Zhang, Haoran, 2024. "Synchronized optimization of wind farm start-stop and yaw control based on 3D wake model," Renewable Energy, Elsevier, vol. 223(C).
    5. Ling, Ziyan & Zhao, Zhenzhou & Liu, Yige & Liu, Huiwen & Ali, Kashif & Liu, Yan & Wen, Yifan & Wang, Dingding & Li, Shijun & Su, Chunhao, 2024. "Multi-objective layout optimization for wind farms based on non-uniformly distributed turbulence and a new three-dimensional multiple wake model," Renewable Energy, Elsevier, vol. 227(C).
    6. Yu, Xiaobing & Lu, Yangchen, 2023. "Reinforcement learning-based multi-objective differential evolution for wind farm layout optimization," Energy, Elsevier, vol. 284(C).
    7. Hu, Weicheng & Yang, Qingshan & Yuan, Ziting & Yang, Fucheng, 2024. "Wind farm layout optimization in complex terrain based on CFD and IGA-PSO," Energy, Elsevier, vol. 288(C).
    8. Cazzaro, Davide & Koza, David Franz & Pisinger, David, 2023. "Combined layout and cable optimization of offshore wind farms," European Journal of Operational Research, Elsevier, vol. 311(1), pages 301-315.
    9. Yamani Douzi Sorkhabi, Sami & Romero, David A. & Beck, J. Christopher & Amon, Cristina H., 2018. "Constrained multi-objective wind farm layout optimization: Novel constraint handling approach based on constraint programming," Renewable Energy, Elsevier, vol. 126(C), pages 341-353.
    10. Zheng, Yue & Wang, Jie-Sheng & Zhu, Jun-Hua & Zhang, Xin-Yue & Xing, Yu-Xuan & Zhang, Yun-Hao, 2024. "MORSA: Multi-objective reptile search algorithm based on elite non-dominated sorting and grid indexing mechanism for wind farm layout optimization problem," Energy, Elsevier, vol. 293(C).
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