Collective wind farm operation based on a predictive model increases utility-scale energy production

Abstract

Wind turbines located in wind farms are operated to maximize only their own power production. Individual operation results in wake losses that reduce farm energy. In this study, we operate a wind turbine array collectively to maximize total array production through wake steering. The selection of the farm control strategy relies on the optimization of computationally efficient flow models. We develop a physics-based, data-assisted flow control model to predict the optimal control strategy. In contrast to previous studies, we first design and implement a multi-month field experiment at a utility-scale wind farm to validate the model over a range of control strategies, most of which are suboptimal. The flow control model is able to predict the optimal yaw misalignment angles for the array within ±5 • for most wind directions (11-32% power gains). Using the validated model, we design a control protocol which increases the energy production of the farm in a second multi-month experiment by 2.7% and 1.0%, for the wind directions of interest and for wind speeds between 6 and 8 m/s and all wind speeds, respectively. The developed and validated predictive model can enable a wider adoption of collective wind farm operation.

Cross-shore changes in upwelling from offshore wind farm development in California

“…Also worth considering are advances in turbine technology that allow for 15 MW or higher-capacity turbines relative to the 10 MW turbines modeled here. The use of higher capacity turbines can result in a smaller or lower density build-out, or a more strategic placement of turbines within an area of interest, all of which can alter the magnitude and structure of the wind wake [37] and therefore influence changes in upwelling. As with all modeling efforts, uncertainties are undoubtedly present.…”

Reinforcement Learning-Based Wind Farm Control: Toward Large Farm Applications via Automatic Grouping and Transfer Learning

“…Site test results in Refs. [9], [10] proved this fact, showing that advanced wind farm control methods are needed to optimize farm-level power generation.…”

Enabling control co-design of the next generation of wind power plants

“…Because of these promising simulations and experiments, wake steering is being adopted more frequently at existing sites. For example, there have now been several demonstrations of wake steering implemented at commercial wind power plants (Fleming et al, 2017(Fleming et al, , 2019(Fleming et al, , 2020Simley et al, 2021;Howland et al, 2022). There have also been several announcements of commercial implementations of wind plant control, provided by either the wind turbine original equipment manufacturers or consultants.…”