An Optimal Wind Farm Operation Strategy for the Provision of Frequency Containment Reserve Incorporating Active Wake Control

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, conside...

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Bibliographic Details
Published in:	IEEE transactions on sustainable energy 2024-01, Vol.15 (1), p.1-14
Main Authors:	Kayedpour, Nezmin, De Kooning, Jeroen D. M., Samani, Arash E., Kayedpour, Farjam, Vandevelde, Lieven, Crevecoeur, Guillaume
Format:	Article
Language:	eng
Subjects:	Active control Aerodynamics Algorithms Axial induction factor Complexity Containment Day-ahead market Decision making Deep learning Flexible operation Frequency control Kinetic energy Machine learning Multiple objective analysis Neural networks Optimization Particle swarm optimization Renewable energy Stochastic processes Stochastic programming Stochasticity Supervisory control Uncertainty Wind energy Wind farms Wind power Wind power generation Yaw
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Summary:	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.
ISSN:	1949-3029 1949-3037