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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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    8. 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.
    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. 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.
    11. 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.
    12. 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).
    13. 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).

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