IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v150y2020icp656-669.html
   My bibliography  Save this article

A non-centralized predictive control strategy for wind farm active power control: A wake-based partitioning approach

Author

Listed:
  • Siniscalchi-Minna, Sara
  • Bianchi, Fernando D.
  • Ocampo-Martinez, Carlos
  • Domínguez-García, Jose Luis
  • De Schutter, Bart

Abstract

Owing to wake effects, the power production of each turbine in a wind farm is highly coupled to the operating conditions of the other turbines. Wind farm control strategies must take into account these couplings and produce individual power commands for each turbine. In this case, centralized control approaches might be prone to failures due to the high computational burden and communication dependency. To overcome this problem, this paper proposes a non-centralized scheme based on splitting the wind farm into almost uncoupled sets of turbines by solving a mixed-integer partitioning problem. In each set of turbines, a model predictive control strategy seeks to optimize the distribution of the power set-points among turbines such that the impact of the power losses due to the wake effect is reduced. Then, a supervisory controller coordinates the generation of each group to satisfy the power demanded by the grid operator. The effectiveness of the proposed control scheme in terms of reduction of computational costs and power regulation is confirmed by simulations for a wind farm of 42 turbines.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:150:y:2020:i:c:p:656-669
    DOI: 10.1016/j.renene.2019.12.139
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148119320129
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2019.12.139?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 search for a different version of it.

    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. Siniscalchi-Minna, Sara & Bianchi, Fernando D. & De-Prada-Gil, Mikel & Ocampo-Martinez, Carlos, 2019. "A wind farm control strategy for power reserve maximization," Renewable Energy, Elsevier, vol. 131(C), pages 37-44.
    3. Ciri, Umberto & Rotea, Mario A. & Leonardi, Stefano, 2017. "Model-free control of wind farms: A comparative study between individual and coordinated extremum seeking," Renewable Energy, Elsevier, vol. 113(C), pages 1033-1045.
    4. Hansen, Anca D. & Sørensen, Poul & Iov, Florin & Blaabjerg, Frede, 2006. "Centralised power control of wind farm with doubly fed induction generators," Renewable Energy, Elsevier, vol. 31(7), pages 935-951.
    5. Juan M. Grosso & Carlos Ocampo-Martinez & Vicenç Puig, 2017. "A distributed predictive control approach for periodic flow-based networks: application to drinking water systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(14), pages 3106-3117, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    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. Del Pozo González, Héctor & Domínguez-García, José Luis, 2022. "Non-centralized hierarchical model predictive control strategy of floating offshore wind farms for fatigue load reduction," Renewable Energy, Elsevier, vol. 187(C), pages 248-256.
    4. 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.

    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. Siniscalchi-Minna, Sara & Bianchi, Fernando D. & De-Prada-Gil, Mikel & Ocampo-Martinez, Carlos, 2019. "A wind farm control strategy for power reserve maximization," Renewable Energy, Elsevier, vol. 131(C), pages 37-44.
    2. Sales-Setién, Ester & Peñarrocha-Alós, Ignacio, 2020. "Robust estimation and diagnosis of wind turbine pitch misalignments at a wind farm level," Renewable Energy, Elsevier, vol. 146(C), pages 1746-1765.
    3. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Senjyu, Tomonobu & Kaneko, Toshiaki & Uehara, Akie & Yona, Atsushi & Sekine, Hideomi & Kim, Chul-Hwan, 2009. "Output power control for large wind power penetration in small power system," Renewable Energy, Elsevier, vol. 34(11), pages 2334-2343.
    5. Fernández, R.D. & Mantz, R.J. & Battaiotto, P.E., 2007. "Impact of wind farms on a power system. An eigenvalue analysis approach," Renewable Energy, Elsevier, vol. 32(10), pages 1676-1688.
    6. Li, Pengfei & Hu, Weihao & Hu, Rui & Huang, Qi & Yao, Jun & Chen, Zhe, 2019. "Strategy for wind power plant contribution to frequency control under variable wind speed," Renewable Energy, Elsevier, vol. 130(C), pages 1226-1236.
    7. Pasta, Edoardo & Faedo, Nicolás & Mattiazzo, Giuliana & Ringwood, John V., 2023. "Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    8. Shi, Jie & Wang, Luhao & Lee, Wei-Jen & Cheng, Xingong & Zong, Xiju, 2019. "Hybrid Energy Storage System (HESS) optimization enabling very short-term wind power generation scheduling based on output feature extraction," Applied Energy, Elsevier, vol. 256(C).
    9. Eissa (SIEEE), M.M., 2015. "Protection techniques with renewable resources and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1645-1667.
    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. Dou, Bingzheng & Qu, Timing & Lei, Liping & Zeng, Pan, 2020. "Optimization of wind turbine yaw angles in a wind farm using a three-dimensional yawed wake model," Energy, Elsevier, vol. 209(C).
    12. Aju, Emmanuvel Joseph & Kumar, Devesh & Leffingwell, Melissa & Rotea, Mario A. & Jin, Yaqing, 2023. "The influence of yaw misalignment on turbine power output fluctuations and unsteady aerodynamic loads within wind farms," Renewable Energy, Elsevier, vol. 215(C).
    13. Yang, Shanghui & Deng, Xiaowei & Ti, Zilong & Yan, Bowen & Yang, Qingshan, 2022. "Cooperative yaw control of wind farm using a double-layer machine learning framework," Renewable Energy, Elsevier, vol. 193(C), pages 519-537.
    14. Dong, Xinghui & Li, Jia & Gao, Di & Zheng, Kai, 2021. "Wind speed modeling for cascade clusters of wind turbines Part 2: Wind speed reduction and aggregation superposition," Energy, Elsevier, vol. 215(PB).
    15. Mohd Ashraf Ahmad & Shun-ichi Azuma & Toshiharu Sugie, 2014. "A Model-Free Approach for Maximizing Power Production of Wind Farm Using Multi-Resolution Simultaneous Perturbation Stochastic Approximation," Energies, MDPI, vol. 7(9), pages 1-23, August.
    16. Li, Qing'an & Wang, Ye & Kamada, Yasunari & Maeda, Takao & Xu, Jianzhong & Zhou, Shuni & Zhang, Fanghong & Cai, Chang, 2022. "Diagonal inflow effect on the wake characteristics of a horizontal axis wind turbine with Gaussian model and field measurements," Energy, Elsevier, vol. 238(PB).
    17. Fernandez, L.M. & Garcia, C.A. & Jurado, F., 2008. "Comparative study on the performance of control systems for doubly fed induction generator (DFIG) wind turbines operating with power regulation," Energy, Elsevier, vol. 33(9), pages 1438-1452.
    18. 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.
    19. Karabacak, Murat, 2019. "A new perturb and observe based higher order sliding mode MPPT control of wind turbines eliminating the rotor inertial effect," Renewable Energy, Elsevier, vol. 133(C), pages 807-827.
    20. Amer Saeed, M. & Mehroz Khan, Hafiz & Ashraf, Arslan & Aftab Qureshi, Suhail, 2018. "Analyzing effectiveness of LVRT techniques for DFIG wind turbine system and implementation of hybrid combination with control schemes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2487-2501.

    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:renene:v:150:y:2020:i:c:p:656-669. 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/renewable-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.