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An optimal wind farm operation strategy for the provision of frequency containment reserve incorporating active wake control

Nezmin Kayedpour (UGent) , Jeroen De Kooning (UGent) , Arash Ebneali Samani (UGent) , Farjam Kayedpour, Lieven Vandevelde (UGent) and Guillaume Crevecoeur (UGent)
(2024) IEEE TRANSACTIONS ON SUSTAINABLE ENERGY. 15(1). p.276-289
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Abstract
This study proposes a novel operation strategy for wind farms' optimal Frequency Containment Reserve (FCR) provision that simultaneously distributes FCR and optimally controls wake formation. The power reserve allocation is dynamically decided at the wind farm supervisory control level, considering the intermittent wind power and direction, grid frequency stochasticity, and the aerodynamic complexity of the wake. A two-stage stochastic programming approach supports decision-making for an optimal contribution to day-ahead energy/FCR markets considering sub-hourly wind power and grid frequency uncertainty. Moreover, a novel method is used to reduce the computational complexity by employing a data-driven surrogate model of wake formation in the optimizer. This surrogate model consists of a neural network trained on the Gauss-Curl-Hybrid wake model in FLORIS. This deep learning approach allows fast estimation of the wake control parameters, i.e., the optimal yaw angles and axial induction factors. Then, a coevolutionary-based multi-objective particle swarm optimization searches for the optimal deloading of the WTs and maximizes the total power production and kinetic energy while minimizing wake. The performance of the proposed algorithm is evaluated on the C-Power wind farm layout in the North Sea. Simulation results demonstrate its effectiveness in improving the wind farm's overall performance for different operational conditions.
Keywords
Renewable Energy, Sustainability and the Environment, Wind energy, Deep learning, Axial induction factor, Optimization, Flexible operation, Day-ahead market

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Citation

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MLA
Kayedpour, Nezmin, et al. “An Optimal Wind Farm Operation Strategy for the Provision of Frequency Containment Reserve Incorporating Active Wake Control.” IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, vol. 15, no. 1, 2024, pp. 276–89, doi:10.1109/tste.2023.3288130.
APA
Kayedpour, N., De Kooning, J., Ebneali Samani, A., Kayedpour, F., Vandevelde, L., & Crevecoeur, G. (2024). An optimal wind farm operation strategy for the provision of frequency containment reserve incorporating active wake control. IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 15(1), 276–289. https://doi.org/10.1109/tste.2023.3288130
Chicago author-date
Kayedpour, Nezmin, Jeroen De Kooning, Arash Ebneali Samani, Farjam Kayedpour, Lieven Vandevelde, and Guillaume Crevecoeur. 2024. “An Optimal Wind Farm Operation Strategy for the Provision of Frequency Containment Reserve Incorporating Active Wake Control.” IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 15 (1): 276–89. https://doi.org/10.1109/tste.2023.3288130.
Chicago author-date (all authors)
Kayedpour, Nezmin, Jeroen De Kooning, Arash Ebneali Samani, Farjam Kayedpour, Lieven Vandevelde, and Guillaume Crevecoeur. 2024. “An Optimal Wind Farm Operation Strategy for the Provision of Frequency Containment Reserve Incorporating Active Wake Control.” IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 15 (1): 276–289. doi:10.1109/tste.2023.3288130.
Vancouver
1.
Kayedpour N, De Kooning J, Ebneali Samani A, Kayedpour F, Vandevelde L, Crevecoeur G. An optimal wind farm operation strategy for the provision of frequency containment reserve incorporating active wake control. IEEE TRANSACTIONS ON SUSTAINABLE ENERGY. 2024;15(1):276–89.
IEEE
[1]
N. Kayedpour, J. De Kooning, A. Ebneali Samani, F. Kayedpour, L. Vandevelde, and G. Crevecoeur, “An optimal wind farm operation strategy for the provision of frequency containment reserve incorporating active wake control,” IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, vol. 15, no. 1, pp. 276–289, 2024.
@article{01H3VEKX4NK0YV1P9THXB9ZCTP,
  abstract     = {{This study proposes a novel operation strategy for wind farms' optimal Frequency Containment Reserve (FCR) provision that simultaneously distributes FCR and optimally controls wake formation. The power reserve allocation is dynamically decided at the wind farm supervisory control level, considering the intermittent wind power and direction, grid frequency stochasticity, and the aerodynamic complexity of the wake. A two-stage stochastic programming approach supports decision-making for an optimal contribution to day-ahead energy/FCR markets considering sub-hourly wind power and grid frequency uncertainty. Moreover, a novel method is used to reduce the computational complexity by employing a data-driven surrogate model of wake formation in the optimizer. This surrogate model consists of a neural network trained on the Gauss-Curl-Hybrid wake model in FLORIS. This deep learning approach allows fast estimation of the wake control parameters, i.e., the optimal yaw angles and axial induction factors. Then, a coevolutionary-based multi-objective particle swarm optimization searches for the optimal deloading of the WTs and maximizes the total power production and kinetic energy while minimizing wake. The performance of the proposed algorithm is evaluated on the C-Power wind farm layout in the North Sea. Simulation results demonstrate its effectiveness in improving the wind farm's overall performance for different operational conditions.}},
  author       = {{Kayedpour, Nezmin and De Kooning, Jeroen and Ebneali Samani, Arash and Kayedpour, Farjam and Vandevelde, Lieven and Crevecoeur, Guillaume}},
  issn         = {{1949-3029}},
  journal      = {{IEEE TRANSACTIONS ON SUSTAINABLE ENERGY}},
  keywords     = {{Renewable Energy, Sustainability and the Environment,Wind energy,Deep learning,Axial induction factor,Optimization,Flexible operation,Day-ahead market}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{276--289}},
  title        = {{An optimal wind farm operation strategy for the provision of frequency containment reserve incorporating active wake control}},
  url          = {{http://doi.org/10.1109/tste.2023.3288130}},
  volume       = {{15}},
  year         = {{2024}},
}
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