Abstract
At present, wind farms control their power production by using a closed-loop feedback control approach, which distributes the total power to the wind turbines. However, the total power is distributed according to the turbines’ available power only. The use of model-predictive control allows considering multiple objectives, nonetheless, since it is open-loop, it can result in poor tracking of the total power reference. This work is the first to combine the standard, closed-loop feedback controller with model-predictive optimization (MPO) in order to yield the benefits of both approaches. As such, we developed an optimization-based dispatch function employed in a closed-loop feedback controller. The dispatch function uses model-predictive, multi-objective optimization to determine the distribution of the total power to the wind turbines. The model employed in the developed dispatch function is the Dynamic Flow Predictor, which uses Kalman-filter-driven feedback to correct the wind farm flow model dynamically. The developed optimization-based dispatch function is compared to dispatch functions commonly employed in present wind farms in a secondary regulation scenario in dynamic simulation. The comparison is carried out on an 80-turbine, large-scale wind farm. The newly developed, optimization-based dispatch function yields a reduction of the mean error and the normalized root-mean-square (NRMS) error by 43% and 36% with respect to the best-performing, commonly used dispatch function. Furthermore, for the large-scale wind farm, the duration of the MPO is only 0.21 s, which is two orders of magnitude faster than comparable approaches in the literature.
Highlights
T HE wind energy market has been growing rapidly at a rate of 16% throughout the past decade reaching 539 123 MW of global, installed capacity in 2017 [1]
To provide grid balancing services and as such to follow a reference for the total power of the wind farm, power controllers of wind farms typically use closed-loop feedback control
The closed-loop feedback controller is introduced; second, the static and proportional dispatch functions are described for reference; and the model-predictive optimization (MPO)-based dispatch function is presented
Summary
T HE wind energy market has been growing rapidly at a rate of 16% throughout the past decade reaching 539 123 MW of global, installed capacity in 2017 [1]. To provide grid balancing services and as such to follow a reference for the total power of the wind farm, power controllers of wind farms typically use closed-loop feedback control. Remarkably fast, such duration still results in a delay in introducing the optimized control actions to the wind turbines To address these challenges, this work introduces a modelpredictive, closed-loop feedback controller including a computationally fast model. The outer control loop is the closed-loop feedback controller that is commonly employed in present wind farms as introduced above It ensures the accurate tracking of the reference for the total power of the wind farm, allowing it to operate in a mode that ensures available active power reserve to be used in ancillary services.
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