Preprints
https://doi.org/10.5194/wes-2025-254
https://doi.org/10.5194/wes-2025-254
08 Dec 2025
 | 08 Dec 2025
Status: this preprint is currently under review for the journal WES.

Scaled testing of maximum-reserve active power control

Simone Tamaro, Davide Bortolin, Filippo Campagnolo, Franz V. Muehle, and Carlo L. Bottasso

Abstract. We present the scaled experimental validation of an active power control (APC) algorithm designed to optimize the tracking accuracy of a wind farm in the presence of turbulent wind lulls. This is obtained by maximizing the minimum local power availability (called reserve) across the farm. This method combines an offline-computed open-loop setpoint scheduler aimed at the power reserves, with a fast closed-loop corrector tasked with enhancing tracking accuracy. The open-loop component is synthesized using an augmented version of FLORIS, which models the combined effects of yaw misalignment and power curtailment, wind tunnel-specific inflow characteristics, and the influence of the chord-based Reynolds number (caused by the small size of the models) on the performance of the rotors.

A preliminary simulation-based steady-state analysis indicates that the approach effectively enhances local power availability across the wind farm by leveraging both induction and wake steering control. The methodology is then experimentally demonstrated in a large boundary-layer wind tunnel with minimal blockage, in different scenarios, including dynamically varying wind direction conditions. Its performance is benchmarked against three alternative APC strategies sourced from the literature. During the experiments, the APC algorithms run in real time on a dedicated  cabinet and communicate with the turbine controllers via a dedicated network. Dynamic wind direction changes are generated using a large turntable driven by a field-recorded wind direction time series, scaled to match the temporal dynamics of the wind tunnel.

Results show that in both static and dynamic scenarios, the proposed control strategy outperforms the reference methods in power tracking accuracy, particularly under high power demand conditions, while maintaining a limited impact on structural fatigue. Notably, the algorithm effectively manages wake interactions and redistributes local power demands to increase local power  reserves, thereby mitigating saturation effects and improving overall tracking performance.

Competing interests: Carlo L. Bottasso is the Editor in Chief of WES.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Simone Tamaro, Davide Bortolin, Filippo Campagnolo, Franz V. Muehle, and Carlo L. Bottasso

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Simone Tamaro, Davide Bortolin, Filippo Campagnolo, Franz V. Muehle, and Carlo L. Bottasso

Data sets

Scaled testing of maximum-reserve active power control S. Tamaro et al. https://doi.org/10.5281/zenodo.17551525

Model code and software

Scaled testing of maximum-reserve active power control S. Tamaro et al. https://doi.org/10.5281/zenodo.17551525

Simone Tamaro, Davide Bortolin, Filippo Campagnolo, Franz V. Muehle, and Carlo L. Bottasso
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Short summary
This work presents the scaled experimental validation of an active power control (APC) algorithm that improves wind-farm power-tracking accuracy during turbulent wind lulls. Tests in a large low-blockage wind tunnel, including dynamic wind-direction changes, show that the method outperforms three reference APC strategies, especially under high power demand, while keeping structural fatigue low.
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