IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i20p7131-d1262173.html
   My bibliography  Save this article

Non-Centralised Balance Dispatch Strategy in Waked Wind Farms through a Graph Sparsification Partitioning Approach

Author

Listed:
  • Tong Shu

    (School of Electronic and Information Engineering, Jiujiang University, Jiujiang 332005, China
    School of IT Information and Control Engineering, Kunsan National University, Kunsan 54150, Republic of Korea)

  • Young Hoon Joo

    (School of IT Information and Control Engineering, Kunsan National University, Kunsan 54150, Republic of Korea)

Abstract

A novel non-centralised dispatch strategy is presented for wake redirection to optimise large-scale offshore wind farms operation, creating a balanced control between power production and fatigue thrust loads evenly among the wind turbines. This approach is founded on a graph sparsification partitioning strategy that takes into account the impact of wake propagation. More specifically, the breadth-first search algorithm is employed to identify the subgraph based on the connectivity of the wake direction graph, while the PageRank centrality computation algorithm is utilised to determine and rank scores for the shared turbines’ affiliation with the subgraphs. By doing so, the wind farm is divided into smaller subsets of partitioned turbines, resulting in decoupling. The objective function is then formulated by incorporating penalty terms, specifically the standard deviation of fatigue thrust loads, into the maximum power equation. Meanwhile, the non-centralisation sequential quadratic programming optimisation algorithm is subsequently employed within each partition to determine the control actions while considering the objectives of the respective controllers. Finally, the simulation results of case studies prove to reduce computational costs and improve wind farm power production by balancing accumulated fatigue thrust loads over the operational lifetime as much as possible.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7131-:d:1262173
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/20/7131/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/20/7131/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Park, Junyoung & Park, Jinkyoo, 2019. "Physics-induced graph neural network: An application to wind-farm power estimation," Energy, Elsevier, vol. 187(C).
    2. Doekemeijer, Bart M. & van der Hoek, Daan & van Wingerden, Jan-Willem, 2020. "Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions," Renewable Energy, Elsevier, vol. 156(C), pages 719-730.
    3. Gu, Bo & Meng, Hang & Ge, Mingwei & Zhang, Hongtao & Liu, Xinyu, 2021. "Cooperative multiagent optimization method for wind farm power delivery maximization," Energy, Elsevier, vol. 233(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. 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.
    6. Song, Dongran & Tu, Yanping & Wang, Lei & Jin, Fangjun & Li, Ziqun & Huang, Chaoneng & Xia, E & Rizk-Allah, Rizk M. & Yang, Jian & Su, Mei & Hoon Joo, Young, 2022. "Coordinated optimization on energy capture and torque fluctuation of wind turbines via variable weight NMPC with fuzzy regulator," Applied Energy, Elsevier, vol. 312(C).
    7. Taimoor Asim & Sheikh Zahidul Islam & Arman Hemmati & Muhammad Saif Ullah Khalid, 2022. "A Review of Recent Advancements in Offshore Wind Turbine Technology," Energies, MDPI, vol. 15(2), pages 1-30, January.
    8. 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).
    9. 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.
    10. Shabnam Hosseinzadeh & Amir Etemad-Shahidi & Rodney A. Stewart, 2023. "Site Selection of Combined Offshore Wind and Wave Energy Farms: A Systematic Review," Energies, MDPI, vol. 16(4), pages 1-33, February.
    11. Hur, Sung-ho, 2018. "Modelling and control of a wind turbine and farm," Energy, Elsevier, vol. 156(C), pages 360-370.
    12. 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).
    13. Song, Dongran & Liu, Junbo & Yang, Yinggang & Yang, Jian & Su, Mei & Wang, Yun & Gui, Ning & Yang, Xuebing & Huang, Lingxiang & Hoon Joo, Young, 2021. "Maximum wind energy extraction of large-scale wind turbines using nonlinear model predictive control via Yin-Yang grey wolf optimization algorithm," Energy, Elsevier, vol. 221(C).
    14. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    15. 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.
    16. 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).
    17. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huang, Zishuo & Wu, Wenchuan, 2024. "An efficient solution for large offshore wind farm power optimization with the Porté-Agel wake model: Optimality and efficiency," Energy, Elsevier, vol. 306(C).
    2. Cai, Wei & Hu, Yang & Fang, Fang & Yao, Lujin & Liu, Jizhen, 2023. "Wind farm power production and fatigue load optimization based on dynamic partitioning and wake redirection of wind turbines," Applied Energy, Elsevier, vol. 339(C).
    3. 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).
    4. 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).
    5. 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).
    6. Yanfang Chen & Young Hoon Joo & Dongran Song, 2022. "Multi-Objective Optimisation for Large-Scale Offshore Wind Farm Based on Decoupled Groups Operation," Energies, MDPI, vol. 15(7), pages 1-24, March.
    7. Kim, Taewan & Kim, Changwook & Song, Jeonghwan & You, Donghyun, 2024. "Optimal control of a wind farm in time-varying wind using deep reinforcement learning," Energy, Elsevier, vol. 303(C).
    8. Zhiwen Deng & Chang Xu & Zhihong Huo & Xingxing Han & Feifei Xue, 2023. "Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model," Energies, MDPI, vol. 16(9), pages 1-20, May.
    9. van den Broek, Maarten J. & De Tavernier, Delphine & Sanderse, Benjamin & van Wingerden, Jan-Willem, 2022. "Adjoint optimisation for wind farm flow control with a free-vortex wake model," Renewable Energy, Elsevier, vol. 201(P1), pages 752-765.
    10. Michael F. Howland & John O. Dabiri, 2020. "Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation," Energies, MDPI, vol. 14(1), pages 1-20, December.
    11. 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).
    12. Song, Dongran & Shen, Xutao & Gao, Yang & Wang, Lei & Du, Xin & Xu, Zhiliang & Zhang, Zhihong & Huang, Chaoneng & Yang, Jian & Dong, Mi & Joo, Young Hoo, 2023. "Application of surrogate-assisted global optimization algorithm with dimension-reduction in power optimization of floating offshore wind farm," Applied Energy, Elsevier, vol. 351(C).
    13. Gao, Xiaoxia & Zhang, Shaohai & Li, Luqing & Xu, Shinai & Chen, Yao & Zhu, Xiaoxun & Sun, Haiying & Wang, Yu & Lu, Hao, 2022. "Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test," Energy, Elsevier, vol. 254(PA).
    14. Kumarasamy Palanimuthu & Ganesh Mayilsamy & Ameerkhan Abdul Basheer & Seong-Ryong Lee & Dongran Song & Young Hoon Joo, 2022. "A Review of Recent Aerodynamic Power Extraction Challenges in Coordinated Pitch, Yaw, and Torque Control of Large-Scale Wind Turbine Systems," Energies, MDPI, vol. 15(21), pages 1-27, November.
    15. Zhu, Xiaoxun & Chen, Yao & Xu, Shinai & Zhang, Shaohai & Gao, Xiaoxia & Sun, Haiying & Wang, Yu & Zhao, Fei & Lv, Tiancheng, 2023. "Three-dimensional non-uniform full wake characteristics for yawed wind turbine with LiDAR-based experimental verification," Energy, Elsevier, vol. 270(C).
    16. Fathy, Ahmed & Rezk, Hegazy & Yousri, Dalia & Kandil, Tarek & Abo-Khalil, Ahmed G., 2022. "Real-time bald eagle search approach for tracking the maximum generated power of wind energy conversion system," Energy, Elsevier, vol. 249(C).
    17. Sun, Jili & Chen, Zheng & Yu, Hao & Gao, Shan & Wang, Bin & Ying, You & Sun, Yong & Qian, Peng & Zhang, Dahai & Si, Yulin, 2022. "Quantitative evaluation of yaw-misalignment and aerodynamic wake induced fatigue loads of offshore Wind turbines," Renewable Energy, Elsevier, vol. 199(C), pages 71-86.
    18. Li, Siyi & Robert, Arnaud & Faisal, A. Aldo & Piggott, Matthew D., 2024. "Learning to optimise wind farms with graph transformers," Applied Energy, Elsevier, vol. 359(C).
    19. Bart Matthijs Doekemeijer & Eric Simley & Paul Fleming, 2022. "Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms," Energies, MDPI, vol. 15(6), pages 1-23, March.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7131-:d:1262173. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.