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A wind farm control strategy for power reserve maximization

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  • Siniscalchi-Minna, Sara
  • Bianchi, Fernando D.
  • De-Prada-Gil, Mikel
  • Ocampo-Martinez, Carlos

Abstract

Nowadays, in many countries wind energy is responsible for a significant part of the electricity generation. For this reason, Transmission System Operators (TSOs) are now demanding the wind power plants (WPPs) to contribute with ancillary services such as frequency support. To this end, WPPs must be able to temporally increase the active power delivered into the grid to compensate consume and demand imbalances. This implies that WPPs now work below their maximum capacity to keep some power reserve to be able to inject extra power into the grid when needed. This reserve depends on the available wind power, which is directly connected with the wind speed faced by each turbine within the WPP. However, wind speed is negative affected by the wakes caused by the upstream turbines. This paper proposes a control algorithm to distribute the power contribution of each turbine seeking to minimize the wake effects and thus maximize the power reserve. The proposed algorithm is evaluated by simulations for the case of a WPP of 12 wind turbines.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:131:y:2019:i:c:p:37-44
    DOI: 10.1016/j.renene.2018.06.112
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    References listed on IDEAS

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    4. 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.
    5. 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).
    6. Del Pozo Gonzalez, Hector & Bianchi, Fernando D. & Dominguez-Garcia, Jose Luis & Gomis-Bellmunt, Oriol, 2023. "Co-located wind-wave farms: Optimal control and grid integration," Energy, Elsevier, vol. 272(C).
    7. Gaigalis, Vygandas & Katinas, Vladislovas, 2020. "Analysis of the renewable energy implementation and prediction prospects in compliance with the EU policy: A case of Lithuania," Renewable Energy, Elsevier, vol. 151(C), pages 1016-1027.
    8. Wang, Ni & Li, Jian & Yu, Xiang & Zhou, Dao & Hu, Weihao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2020. "Optimal active and reactive power cooperative dispatch strategy of wind farm considering levelised production cost minimisation," Renewable Energy, Elsevier, vol. 148(C), pages 113-123.
    9. Edmunds, Calum & Martín-Martínez, Sergio & Browell, Jethro & Gómez-Lázaro, Emilio & Galloway, Stuart, 2019. "On the participation of wind energy in response and reserve markets in Great Britain and Spain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    10. Pombo, Daniel Vázquez & Martinez-Rico, Jon & Spataru, Sergiu V. & Bindner, Henrik W. & Sørensen, Poul E., 2023. "Decarbonizing energy islands with flexibility-enabling planning: The case of Santiago, Cape Verde," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).
    11. Iván Pazmiño & Sergio Martinez & Danny Ochoa, 2021. "Analysis of Control Strategies Based on Virtual Inertia for the Improvement of Frequency Stability in an Islanded Grid with Wind Generators and Battery Energy Storage Systems," Energies, MDPI, vol. 14(3), pages 1-18, January.
    12. 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.
    13. Davide Astolfi & Fabrizio De Caro & Alfredo Vaccaro, 2023. "Characterizing the Wake Effects on Wind Power Generator Operation by Data-Driven Techniques," Energies, MDPI, vol. 16(15), pages 1-19, August.

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