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A GRASP-VNS algorithm for optimal wind-turbine placement in wind farms

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  • Yin, Peng-Yeng
  • Wang, Tai-Yuan

Abstract

The wake effect is the key factor affecting the low efficiency of wind power production. It is very important to predict the relationship between the cost and the produced power for various wind-turbine placements under various wind speeds and directions. This paper proposes a GRASP-VNS algorithm for the optimal placement of wind turbines. Four different wind-farm conditions were considered: (a) uniform wind with single direction, (b) uniform wind with variable directions, (c) non-uniform wind with variable directions, and (d) non-uniform and variable-direction wind with land constraint. The proposed GRASP-VNS algorithm combines two well-known metaheuristics, GRASP and VNS, to create additional advantages in yielding the search trajectory. Intensive experiments assuming the four wind-farm conditions were performed. Statistical analyses show that the proposed GRASP-VNS algorithm significantly outperforms three existing GA-based methods.

Suggested Citation

  • Yin, Peng-Yeng & Wang, Tai-Yuan, 2012. "A GRASP-VNS algorithm for optimal wind-turbine placement in wind farms," Renewable Energy, Elsevier, vol. 48(C), pages 489-498.
    • Handle: RePEc:eee:renene:v:48:y:2012:i:c:p:489-498
    DOI: 10.1016/j.renene.2012.05.020
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    2. Al-Shammari, Eiman Tamah & Shamshirband, Shahaboddin & Petković, Dalibor & Zalnezhad, Erfan & Yee, Por Lip & Taher, Ros Suraya & Ćojbašić, Žarko, 2016. "Comparative study of clustering methods for wake effect analysis in wind farm," Energy, Elsevier, vol. 95(C), pages 573-579.
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    4. Hu, Jiaxiang & Hu, Weihao & Cao, Di & Huang, Yuehui & Chen, Jianjun & Li, Yahe & Chen, Zhe & Blaabjerg, Frede, 2024. "Bayesian averaging-enabled transfer learning method for probabilistic wind power forecasting of newly built wind farms," Applied Energy, Elsevier, vol. 355(C).
    5. Dalibor Petković & Siti Hafizah Ab Hamid & Žarko Ćojbašić & Nenad T. Pavlović, 2014. "RETRACTED ARTICLE: Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 463-475, November.
    6. Yin, Peng-Yeng & Wu, Tsai-Hung & Hsu, Ping-Yi, 2017. "Risk management of wind farm micro-siting using an enhanced genetic algorithm with simulation optimization," Renewable Energy, Elsevier, vol. 107(C), pages 508-521.
    7. Bansal, Jagdish Chand & Farswan, Pushpa, 2017. "Wind farm layout using biogeography based optimization," Renewable Energy, Elsevier, vol. 107(C), pages 386-402.
    8. Alrobaian, Abdulrahman A. & Alsagri, Ali Sulaiman, 2023. "Multi-agent-based energy management for a fully electrified residential consumption," Energy, Elsevier, vol. 282(C).
    9. Yin, Peng-Yeng & Wu, Tsai-Hung & Hsu, Ping-Yi, 2017. "Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D," Energy, Elsevier, vol. 141(C), pages 579-597.
    10. Dalibor Petković & Siti Ab Hamid & Žarko Ćojbašić & Nenad Pavlović, 2014. "Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 463-475, November.
    11. Yin, Peng-Yeng & Cheng, Chun-Ying & Chen, Hsin-Min & Wu, Tsai-Hung, 2020. "Risk-aware optimal planning for a hybrid wind-solar farm," Renewable Energy, Elsevier, vol. 157(C), pages 290-302.

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