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Decentralised optimisation for large offshore wind farms using a sparsified wake directed graph

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  • Shu, Tong
  • Song, Dongran
  • Hoon Joo, Young

Abstract

Considering the wake effects, developing an efficient wake-based graph clustering algorithm is essential for the decentralised coordinated online control of large offshore wind farms. In this study, the idea of graph sparse is introduced into the algorithm, in which the sparsified wake-directed graph is generated while preserving the original wake-directed graph critical wake coupling relationship between turbines. The key is to construct the original wake-directed graph by quantifying wake intensity and applying the graph sparseness constraints algorithm to achieve an ultra-sparse wake subgraph. Based on the sparsified wake-directed graph and wake intensity weighting matrix, the wind farm is split into almost uncoupled cluster subsets to establish decentralised sparse communication architectures. By doing so, a decentralised sequential quadratic programming optimisation strategy is proposed to solve a nonconvex optimisation problem, in which the thrust load problem is converted into a power controlling optimisation problem, and the thrust load distribution is balanced using the power margin. Simulation results reveal that the proposed scheme can maximise wind farm power production while minimising thrust loads in various turbulence intensities, making it functional for real-time operations on a large-scale wind farm.

Suggested Citation

  • Shu, Tong & Song, Dongran & Hoon Joo, Young, 2022. "Decentralised optimisation for large offshore wind farms using a sparsified wake directed graph," Applied Energy, Elsevier, vol. 306(PA).
  • Handle: RePEc:eee:appene:v:306:y:2022:i:pa:s0306261921012897
    DOI: 10.1016/j.apenergy.2021.117986
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    References listed on IDEAS

    as
    1. Gionfra, Nicolò & Sandou, Guillaume & Siguerdidjane, Houria & Faille, Damien & Loevenbruck, Philippe, 2019. "Wind farm distributed PSO-based control for constrained power generation maximization," Renewable Energy, Elsevier, vol. 133(C), pages 103-117.
    2. Lin, Zhongwei & Chen, Zhenyu & Liu, Jizhen & Wu, Qiuwei, 2019. "Coordinated mechanical loads and power optimization of wind energy conversion systems with variable-weight model predictive control strategy," Applied Energy, Elsevier, vol. 236(C), pages 307-317.
    3. Famoso, Fabio & Brusca, Sebastian & D'Urso, Diego & Galvagno, Antonio & Chiacchio, Ferdinando, 2020. "A novel hybrid model for the estimation of energy conversion in a wind farm combining wake effects and stochastic dependability," Applied Energy, Elsevier, vol. 280(C).
    4. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2016. "Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model," Applied Energy, Elsevier, vol. 174(C), pages 192-200.
    5. Zhao, Ning & You, Fengqi, 2020. "Can renewable generation, energy storage and energy efficient technologies enable carbon neutral energy transition?," Applied Energy, Elsevier, vol. 279(C).
    6. Syed, Abdul Haseeb & Javed, Adeel & Asim Feroz, Raja M. & Calhoun, Ronald, 2020. "Partial repowering analysis of a wind farm by turbine hub height variation to mitigate neighboring wind farm wake interference using mesoscale simulations," Applied Energy, Elsevier, vol. 268(C).
    7. Liao, Hao & Hu, Weihao & Wu, Xiawei & Wang, Ni & Liu, Zhou & Huang, Qi & Chen, Cong & Chen, Zhe, 2020. "Active power dispatch optimization for offshore wind farms considering fatigue distribution," Renewable Energy, Elsevier, vol. 151(C), pages 1173-1185.
    8. Park, Jinkyoo & Law, Kincho H., 2016. "A data-driven, cooperative wind farm control to maximize the total power production," Applied Energy, Elsevier, vol. 165(C), pages 151-165.
    9. Kühnbach, Matthias & Pisula, Stefan & Bekk, Anke & Weidlich, Anke, 2020. "How much energy autonomy can decentralised photovoltaic generation provide? A case study for Southern Germany," Applied Energy, Elsevier, vol. 280(C).
    10. Morabito, Alessandro & Hendrick, Patrick, 2019. "Pump as turbine applied to micro energy storage and smart water grids: A case study," Applied Energy, Elsevier, vol. 241(C), pages 567-579.
    11. Zhang, Yuquan & Zang, Wei & Zheng, Jinhai & Cappietti, Lorenzo & Zhang, Jisheng & Zheng, Yuan & Fernandez-Rodriguez, E., 2021. "The influence of waves propagating with the current on the wake of a tidal stream turbine," Applied Energy, Elsevier, vol. 290(C).
    12. Vakilifard, Negar & A. Bahri, Parisa & Anda, Martin & Ho, Goen, 2019. "An interactive planning model for sustainable urban water and energy supply," Applied Energy, Elsevier, vol. 235(C), pages 332-345.
    13. Siniscalchi-Minna, Sara & Bianchi, Fernando D. & Ocampo-Martinez, Carlos & Domínguez-García, Jose Luis & De Schutter, Bart, 2020. "A non-centralized predictive control strategy for wind farm active power control: A wake-based partitioning approach," Renewable Energy, Elsevier, vol. 150(C), pages 656-669.
    14. Qian, Guo-Wei & Ishihara, Takeshi, 2021. "Wind farm power maximization through wake steering with a new multiple wake model for prediction of turbulence intensity," Energy, Elsevier, vol. 220(C).
    15. Sun, Haiying & Qiu, Changyu & Lu, Lin & Gao, Xiaoxia & Chen, Jian & Yang, Hongxing, 2020. "Wind turbine power modelling and optimization using artificial neural network with wind field experimental data," Applied Energy, Elsevier, vol. 280(C).
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    Cited by:

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    2. Wang, Yu & Wei, Shanbi & Yang, Wei & Chai, Yi, 2023. "Adaptive economic predictive control for offshore wind farm active yaw considering generation uncertainty," Applied Energy, Elsevier, vol. 351(C).
    3. 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).
    4. Shu, Tong & Song, Dongran & Joo, Young Hoon, 2022. "Non-centralised coordinated optimisation for maximising offshore wind farm power via a sparse communication architecture," Applied Energy, Elsevier, vol. 324(C).
    5. Tong Shu & Young Hoon Joo, 2023. "Non-Centralised Balance Dispatch Strategy in Waked Wind Farms through a Graph Sparsification Partitioning Approach," Energies, MDPI, vol. 16(20), pages 1-21, October.
    6. Yanfang Chen & Young-Hoon Joo & Dongran Song, 2021. "Modified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach," Energies, MDPI, vol. 14(21), pages 1-24, November.

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