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Offshore wind farm repowering optimization

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  • Hou, Peng
  • Enevoldsen, Peter
  • Hu, Weihao
  • Chen, Cong
  • Chen, Zhe

Abstract

Decommissioning is usually the last stage of the offshore wind farm life cycle. Due to the challenges of the decommissioning process, such as the impact on the marine environment, severe weather conditions, vessel limitations and lack of operational experience, the decommissioning strategy should be planned to avoid complications, which ultimately can cause radical changes to the levelized cost of energy (LCoE) and the wind farm owner’s business case. Instead of dismantling, repowering may be a sustainable alternative solution to extend the lifetime of a wind farm. In this paper, the research is focused on optimization of offshore wind farm repowering, which is one option for the wind farm owner at end of life for the offshore wind farm. The LCoE is used as the evaluation index to identify whether it is economical to invest in such a way. In an optimized repowering strategy, different types of wind turbines are selected to replace the original wind turbines to reconstruct the wind farm, which is demonstrated to be better than the refurbishment approach which replaces the old wind turbines with the same type. The simulations performed in this research reveal that the reconstructed wind farm, which consists of multiple types of wind turbine, has a smaller LCoE (10.43%) than the refurbishment approach, which shows the superiority of the proposed method. This research contributes an optimization tool to the wind industry, which consequently drives down the cost of energy produced by offshore wind turbines.

Suggested Citation

  • Hou, Peng & Enevoldsen, Peter & Hu, Weihao & Chen, Cong & Chen, Zhe, 2017. "Offshore wind farm repowering optimization," Applied Energy, Elsevier, vol. 208(C), pages 834-844.
    • Handle: RePEc:eee:appene:v:208:y:2017:i:c:p:834-844
    DOI: 10.1016/j.apenergy.2017.09.064
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    Citations

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    Cited by:

    1. Leite, Gustavo de Novaes Pires & Weschenfelder, Franciele & Farias, João Gabriel de & Kamal Ahmad, Muhammad, 2022. "Economic and sensitivity analysis on wind farm end-of-life strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    2. Wang, Xuefei & Zeng, Xiangwu & Yang, Xu & Li, Jiale, 2019. "Seismic response of offshore wind turbine with hybrid monopile foundation based on centrifuge modelling," Applied Energy, Elsevier, vol. 235(C), pages 1335-1350.
    3. Sun, Haiying & Yang, Hongxing & Gao, Xiaoxia, 2019. "Investigation into spacing restriction and layout optimization of wind farm with multiple types of wind turbines," Energy, Elsevier, vol. 168(C), pages 637-650.
    4. 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.
    5. Nguyen, Thi Anh Tuyet & Chou, Shuo-Yan, 2018. "Impact of government subsidies on economic feasibility of offshore wind system: Implications for Taiwan energy policies," Applied Energy, Elsevier, vol. 217(C), pages 336-345.
    6. Isabel C. Gil-García & Ana Fernández-Guillamón & M. Socorro García-Cascales & Angel Molina-García, 2021. "A Multi-Factorial Review of Repowering Wind Generation Strategies," Energies, MDPI, vol. 14(19), pages 1-25, October.
    7. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Levelized income loss as a metric of the adaptation of wind and energy storage to variable prices," Applied Energy, Elsevier, vol. 238(C), pages 1179-1191.
    8. Serrano González, Javier & Burgos Payán, Manuel & Riquelme Santos, Jesús Manuel, 2018. "Optimal design of neighbouring offshore wind farms: A co-evolutionary approach," Applied Energy, Elsevier, vol. 209(C), pages 140-152.
    9. Dong, Hongyang & Zhang, Jincheng & Zhao, Xiaowei, 2021. "Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations," Applied Energy, Elsevier, vol. 292(C).
    10. Ma, Hongliang & Ge, Mingwei & Wu, Guangxing & Du, Bowen & Liu, Yongqian, 2021. "Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production," Applied Energy, Elsevier, vol. 303(C).
    11. Cao, Jiu Fa & Zhu, Wei Jun & Shen, Wen Zhong & Sørensen, Jens Nørkær & Sun, Zhen Ye, 2020. "Optimizing wind energy conversion efficiency with respect to noise: A study on multi-criteria wind farm layout design," Renewable Energy, Elsevier, vol. 159(C), pages 468-485.
    12. Artigao, Estefania & Martín-Martínez, Sergio & Honrubia-Escribano, Andrés & Gómez-Lázaro, Emilio, 2018. "Wind turbine reliability: A comprehensive review towards effective condition monitoring development," Applied Energy, Elsevier, vol. 228(C), pages 1569-1583.
    13. Martínez, E. & Latorre-Biel, J.I. & Jiménez, E. & Sanz, F. & Blanco, J., 2018. "Life cycle assessment of a wind farm repowering process," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 260-271.
    14. Andrzej Jezierski & Cezary Mańkowski & Rafał Śpiewak, 2021. "Energy Savings Analysis in Logistics of a Wind Farm Repowering Process: A Case Study," Energies, MDPI, vol. 14(17), pages 1-23, September.
    15. Nguyen, Thi Anh Tuyet & Chou, Shuo-Yan, 2019. "Improved maintenance optimization of offshore wind systems considering effects of government subsidies, lost production and discounted cost model," Energy, Elsevier, vol. 187(C).
    16. Anne P. M. Velenturf, 2021. "A Framework and Baseline for the Integration of a Sustainable Circular Economy in Offshore Wind," Energies, MDPI, vol. 14(17), pages 1-41, September.
    17. Mamdouh Abdulrahman & David Wood, 2019. "Wind Farm Layout Upgrade Optimization," Energies, MDPI, vol. 12(13), pages 1-25, June.

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