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A design methodology for wind farm layout considering cable routing and economic benefit based on genetic algorithm and GeoSteiner

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  • Wu, Yan
  • Zhang, Shuai
  • Wang, Ruiqi
  • Wang, Yufei
  • Feng, Xiao

Abstract

Wind farm designing is a crucial stage to realize the application of wind energy. This work studies the problem of wind farm layout optimization (WFLO). A new method based on power production, wind distribution, wake loss is proposed to optimize the layout of wind farm. Genetic algorithm (GA) is utilized to optimize the locations of wind turbine in the wind farm. GeoSteiner algorithm is used to optimize the layouts of cable which has important influence on power transmission. The objective function is annual economic benefit (AEB) including annual production benefit (APB) and the costs of energy, cable and land. In the case study, the wind farm size is 3850 m × 3850 m. The number of wind turbines (WTs) of the cases changes from 2 to 58. The capacity achieves 87 MW when the number of WTs is 58. The result shows that the case considering all factors mentioned above has the highest AEB with 1.87 × 109 ¥ per year. There is a 27.01% increase compared with the original case with APB as objective function. Specifically, the investment of cable is 3.68 × 106 ¥ comparing with 4.06 × 106 ¥ of the case only considering APB.

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  • 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.
    • Handle: RePEc:eee:renene:v:146:y:2020:i:c:p:687-698
    DOI: 10.1016/j.renene.2019.07.002
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    Cited by:

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    2. 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.
    3. Qing Yu & Weifeng Li & Haoran Zhang & Dongyuan Yang, 2020. "Mobile Phone Data in Urban Customized Bus: A Network-based Hierarchical Location Selection Method with an Application to System Layout Design in the Urban Agglomeration," Sustainability, MDPI, vol. 12(15), pages 1-20, July.
    4. Magnus Daniel Kallinger & José Ignacio Rapha & Pau Trubat Casal & José Luis Domínguez-García, 2023. "Offshore Electrical Grid Layout Optimization for Floating Wind—A Review," Clean Technol., MDPI, vol. 5(3), pages 1-37, June.
    5. Croonenbroeck, Carsten & Hennecke, David, 2021. "A comparison of optimizers in a unified standard for optimization on wind farm layout optimization," Energy, Elsevier, vol. 216(C).
    6. 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.
    7. Pedersen, Jaap & Weinand, Jann Michael & Syranidou, Chloi & Rehfeldt, Daniel, 2024. "An efficient solver for large-scale onshore wind farm siting including cable routing," European Journal of Operational Research, Elsevier, vol. 317(2), pages 616-630.

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