IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v347y2026ics0360544226003671.html

Wake model-free active power control for wind-storage coordination using a state-decoupled MATD3 algorithm

Author

Listed:
  • Yao, Qi
  • Liang, Zemin
  • Guan, Yuanpeng
  • Ke, Yiming
  • Hu, Yang
  • Fang, Fang
  • Liu, Jizhen

Abstract

Active wake control (AWC) is a key research direction for enhancing the energy efficiency of existing wind farms. However, existing research has significant limitations, particularly its reliance on predefined wind turbine layouts and wake models, as well as the potential for substantial fluctuations in active power. This study proposes a wind-energy storage-coordinated AWC strategy based on a novel control strategy of wind turbines. This approach, informed by mechanistic characteristics, constructs a wind turbine-specific AWC optimization framework that does not require the turbine layout and wake modeling. The solution is based on the multi-agent double-delayed deep deterministic policy gradient (MATD3) algorithm, which treats wind turbines and energy storage batteries as separate agents. Taking into account their physical properties, a state-decoupled offline training strategy and online deployment strategy are designed. Experimental results show that the proposed strategy is effective at low to medium wind speeds, improving and smoothing active power while also reducing the charging and discharging of the energy storage system, and exhibiting good robustness against wind farm wake mismatch and noise.

Suggested Citation

  • Yao, Qi & Liang, Zemin & Guan, Yuanpeng & Ke, Yiming & Hu, Yang & Fang, Fang & Liu, Jizhen, 2026. "Wake model-free active power control for wind-storage coordination using a state-decoupled MATD3 algorithm," Energy, Elsevier, vol. 347(C).
    • Handle: RePEc:eee:energy:v:347:y:2026:i:c:s0360544226003671
    DOI: 10.1016/j.energy.2026.140265
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544226003671
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2026.140265?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Li, Mengjia & Li, Fangfang & Qiu, Jun & Zhou, Han & Wang, Hongru & Lu, Houliang & Zhang, Nan & Song, Zhengyu, 2024. "Multi-objective optimization of non-fossil energy structure in China towards the carbon peaking and carbon neutrality goals," Energy, Elsevier, vol. 312(C).
    2. Göçmen, Tuhfe & Liew, Jaime & Kadoche, Elie & Dimitrov, Nikolay & Riva, Riccardo & Andersen, Søren Juhl & Lio, Alan W.H. & Quick, Julian & Réthoré, Pierre-Elouan & Dykes, Katherine, 2025. "Data-driven wind farm flow control and challenges towards field implementation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 216(C).
    3. Shen, Wen Zhong & Lin, Jian Wei & Jiang, Yu Hang & Feng, Ju & Cheng, Li & Zhu, Wei Jun, 2023. "A novel yaw wake model for wind farm control applications," Renewable Energy, Elsevier, vol. 218(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. 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).
    6. Kumar, Dipesh & Chatterjee, Kalyan, 2016. "A review of conventional and advanced MPPT algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 957-970.
    7. 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).
    8. 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.
    9. He, Ruiyang & Yang, Hongxing & Lu, Lin, 2023. "Optimal yaw strategy and fatigue analysis of wind turbines under the combined effects of wake and yaw control," Applied Energy, Elsevier, vol. 337(C).
    10. Huang, Chaoneng & Wang, Li & Huang, Qian & Song, Dongran & Yang, Jian & Dong, Mi & Joo, Young Hoo & Duić, Neven, 2025. "Bi-level multi-objective optimization framework for wake escape in floating offshore wind farm," Applied Energy, Elsevier, vol. 377(PD).
    11. Liu, Yige & Zhao, Zhenzhou & Liu, Yan & Liu, Huiwen & Wei, Shangshang & Ma, Yuanzhuo & Ling, Ziyan & Luo, Qiao, 2024. "Combined wake control of aligned wind turbines for power optimization based on a 3D wake model considering secondary wake steering," Energy, Elsevier, vol. 308(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. Yao, Qi & Hu, Yang & Deng, Hui & Luo, Zhiling & Liu, Jizhen, 2020. "Two-degree-of-freedom active power control of megawatt wind turbine considering fatigue load optimization," Renewable Energy, Elsevier, vol. 162(C), pages 2096-2112.
    14. 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.
    15. Zhang, Zihang & Li, Jiayi & Lei, Zhenyu & Zhu, Qianyu & Cheng, Jiujun & Gao, Shangce, 2024. "Reinforcement learning-based particle swarm optimization for wind farm layout problems," Energy, Elsevier, vol. 313(C).
    16. Wang, Jinjun & Wang, Xinyu & Chen, Heng & Gao, Yue & Chen, Honggang & Liu, Wenyi & Sun, Ying & Zhang, Lei, 2025. "Capacity configuration of a hybrid energy storage system for the fluctuation mitigation and frequency regulation of wind power based on Aquila Optimizer and Variational Mode Decomposition," Energy, Elsevier, vol. 330(C).
    17. Jiang, Yuzheng & Dong, Jun & Huang, Hexiang, 2024. "Optimal bidding strategy for the price-maker virtual power plant in the day-ahead market based on multi-agent twin delayed deep deterministic policy gradient algorithm," Energy, Elsevier, vol. 306(C).
    18. Herp, Jürgen & Poulsen, Uffe V. & Greiner, Martin, 2015. "Wind farm power optimization including flow variability," Renewable Energy, Elsevier, vol. 81(C), pages 173-181.
    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. Wang, Yize & Liu, Zhenqing & Yu, Zhongze, 2025. "An efficient optimization algorithm for active yaw control to increase wind farm power while considering load reduction," Energy, Elsevier, vol. 340(C).
    2. 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).
    3. Zhang, Teng & Xu, Xiaosen & Wang, Shuaishuai & Xing, Yihan & Dou, Peng & Ji, Renwei & Yang, Puyi, 2025. "Investigation of wake steering control effects on the dynamic responses of 15 MW semi-submersible floating wind farms," Renewable Energy, Elsevier, vol. 254(C).
    4. Tao, Siyu & Yang, Jisheng & Jiang, Fuqing & Yang, Hongxing & Zheng, Gang & Feijóo-Lorenzo, Andrés E. & He, Ruiyang, 2026. "Active yaw control strategy for a hybrid offshore wind farm under typical wind conditions," Renewable Energy, Elsevier, vol. 259(C).
    5. 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).
    6. 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).
    7. Shen, Lian & Zhou, Pinhan & Han, Yan & Mi, Lihua & Xu, Wenxiang & Xu, Jialu, 2025. "Wind farm layout optimization based on dynamic Levy sparrow search algorithm: A multi-parameter analysis with active yaw control," Energy, Elsevier, vol. 324(C).
    8. Balakrishnan, Raj Kiran & Son, Eunkuk & Hur, Sung-ho, 2024. "Optimization of wind farm power output using wake redirection control," Renewable Energy, Elsevier, vol. 235(C).
    9. Dong, Guodan & Qin, Jianhua & Wu, Chutian & Xu, Chang & Yang, Xiaolei, 2026. "Reinforcement learning-enhanced genetic algorithm for wind farm layout optimization," Renewable Energy, Elsevier, vol. 259(C).
    10. 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).
    11. Yang, Kun & Zhang, Mingming & Yang, Shanghui & Song, Yuwei & Dong, Xinhui & Deng, Yanfei & Deng, Xiaowei, 2025. "Pareto frontier for multi-objective wind farm layout optimization balancing power production and turbine fatigue life," Renewable Energy, Elsevier, vol. 252(C).
    12. Tu, Yu & Chen, Yaoran & Zhang, Kai & He, Ruiyang & Han, Zhaolong & Zhou, Dai, 2025. "A multi-fidelity framework for power prediction of wind farm under yaw misalignment," Applied Energy, Elsevier, vol. 377(PC).
    13. Mahmud, Sakib & Sayed, Aya Nabil & Himeur, Yassine & Nhlabatsi, Armstrong & Bensaali, Faycal, 2026. "A comprehensive review of deep reinforcement learning applications from centralized power generation to modern energy internet frameworks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PE).
    14. Wang, Yize & Liu, Zhenqing & Hu, Yilu & Bai, Guangpu, 2026. "A coherent power-load optimization algorithm for wind farm-level yaw control considering wake effects via deep neural network," Renewable Energy, Elsevier, vol. 257(C).
    15. Wang, Bingchen & Ding, Lifu & Xiao, Tannan & Chen, Ying, 2026. "An integrated multi-timescale MPC framework for coordinated wind farm power maximization, load management, and grid support," Renewable Energy, Elsevier, vol. 256(PB).
    16. Wang, Pengda & Xiao, Jinxin & Huang, Sheng & Wu, Qiuwei & Zhang, Menglin & Wu, Xuan & Shen, Feifan & Ma, Kuichao, 2025. "An accelerated asynchronous distributed control for DFIG wind turbines and collection system loss minimization in waked wind farm," Applied Energy, Elsevier, vol. 377(PD).
    17. Zhang, Yujun & Zhang, Zihang & Zhong, Rui & Yu, Jun & Houssein, Essam H. & Zhao, Juan & Gao, Zhengming, 2025. "Under complex wind scenarios: Considering large-scale wind turbines in wind farm layout optimization via self-adaptive optimal fractional-order guided differential evolution," Energy, Elsevier, vol. 323(C).
    18. Tu, Yu & Dong, Zhikun & Chen, Yaoran & Zhang, Kai & Zhou, Dai & Yang, Hongxing, 2026. "Global optimization of wake steering for large-scale wind farms using generalized serial refinement method," Applied Energy, Elsevier, vol. 406(C).
    19. Yang, Xiaolei & Sotiropoulos, Fotis & Sørensen, Jens Nørkær, 2026. "Wind farm fluid mechanics for high-penetration wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PC).
    20. 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).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    Statistics

    Access and download statistics

    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:eee:energy:v:347:y:2026:i:c:s0360544226003671. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.