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Wind farm control using distributed economic MPC scheme under the influence of wake effect

Author

Listed:
  • Wang, Wenwen
  • Kong, Xiaobing
  • Li, Gangqiang
  • Liu, Xiangjie
  • Ma, Lele
  • Liu, Wenting
  • Lee, Kwang Y.

Abstract

As wind farms (WFs) expand in scale, there is a growing need for active power control to track the reference power benchmark issued by the grid dispatch center and also the imperative to reduce the fatigue load on key components of each wind turbine (WT). The presence of the wake effect causes a decrease in power generation for downstream WTs and an increase in the fatigue load. Consequently, the suppression of the wake effect has emerged as a critical control objective for WFs. For tackling the challenge, this article designs a hierarchical WF control framework. The upper-level controller employs a sequential convex programming (SCP) approach to maximize the WF's captured wind energy function and determine the optimal induction factors for the WTs. The lower-layer controller uses a distributed economic model predictive control (DEMPC) scheme to control the WT locally to achieve reference power tracking while reducing the fatigue load on each WT. Finally, the effectiveness of the designed algorithm was verified by conducting the simulation on a wind farm containing nine WTs.

Suggested Citation

  • Wang, Wenwen & Kong, Xiaobing & Li, Gangqiang & Liu, Xiangjie & Ma, Lele & Liu, Wenting & Lee, Kwang Y., 2024. "Wind farm control using distributed economic MPC scheme under the influence of wake effect," Energy, Elsevier, vol. 309(C).
    • Handle: RePEc:eee:energy:v:309:y:2024:i:c:s0360544224026768
    DOI: 10.1016/j.energy.2024.132902
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    References listed on IDEAS

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    1. Zhang, Ming & Cong, Nan & Song, Yan & Xia, Qing, 2024. "Cost analysis of onshore wind power in China based on learning curve," Energy, Elsevier, vol. 291(C).
    2. Park, Jinkyoo & Law, Kincho H., 2015. "Layout optimization for maximizing wind farm power production using sequential convex programming," Applied Energy, Elsevier, vol. 151(C), pages 320-334.
    3. Dong, Mi & Sun, Mingren & Song, Dongran & Huang, Liansheng & Yang, Jian & Joo, Young Hoon, 2022. "Real-time detection of wind power abnormal data based on semi-supervised learning Robust Random Cut Forest," Energy, Elsevier, vol. 257(C).
    4. Wang, Qiang & Luo, Kun & Yuan, Renyu & Wang, Shuai & Fan, Jianren & Cen, Kefa, 2020. "A multiscale numerical framework coupled with control strategies for simulating a wind farm in complex terrain," Energy, Elsevier, vol. 203(C).
    5. Fleming, Paul A. & Gebraad, Pieter M.O. & Lee, Sang & van Wingerden, Jan-Willem & Johnson, Kathryn & Churchfield, Matt & Michalakes, John & Spalart, Philippe & Moriarty, Patrick, 2014. "Evaluating techniques for redirecting turbine wakes using SOWFA," Renewable Energy, Elsevier, vol. 70(C), pages 211-218.
    6. Saleem, Arslan & Kim, Man-Hoe, 2020. "Aerodynamic performance optimization of an airfoil-based airborne wind turbine using genetic algorithm," Energy, Elsevier, vol. 203(C).
    7. He, Ruiyang & Yang, Hongxing & Sun, Shilin & Lu, Lin & Sun, Haiying & Gao, Xiaoxia, 2022. "A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control," Applied Energy, Elsevier, vol. 326(C).
    8. Yang, Kyoungboo & Kwak, Gyeongil & Cho, Kyungho & Huh, Jongchul, 2019. "Wind farm layout optimization for wake effect uniformity," Energy, Elsevier, vol. 183(C), pages 983-995.
    9. Khorramdel, Benyamin & Raoofat, Mahdi, 2012. "Optimal stochastic reactive power scheduling in a microgrid considering voltage droop scheme of DGs and uncertainty of wind farms," Energy, Elsevier, vol. 45(1), pages 994-1006.
    10. 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).
    11. Yao, Qi & Hu, Yang & Zhao, Tianyang & Guan, Yuanpeng & Luo, Zhiling & Liu, Jizhen, 2022. "Fatigue load suppression during active power control process in wind farm using dynamic-local-reference DMPC," Renewable Energy, Elsevier, vol. 183(C), pages 423-434.
    12. Junejo, Allah Rakhio & Gilal, Nauman Ullah & Doh, Jaehyeok, 2023. "Physics-informed optimization of robust control system to enhance power efficiency of renewable energy: Application to wind turbine," Energy, Elsevier, vol. 263(PB).
    13. Al kez, Dlzar & Foley, Aoife M. & McIlwaine, Neil & Morrow, D. John & Hayes, Barry P. & Zehir, M. Alparslan & Mehigan, Laura & Papari, Behnaz & Edrington, Chris S. & Baran, Mesut, 2020. "A critical evaluation of grid stability and codes, energy storage and smart loads in power systems with wind generation," Energy, Elsevier, vol. 205(C).
    14. Gouvalas, Nikolaos K. & Gonos, Ioannis F. & Stathopulos, Ioannis A., 2014. "Impact study of short-circuit calculation methods on the design of a wind farm's grounding system," Renewable Energy, Elsevier, vol. 66(C), pages 25-32.
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