Abstract
We investigate the use of wind farms to provide secondary frequency regulation for a power grid. Our approach uses model-based receding horizon control of a wind farm that is tested using a large eddy simulation (LES) framework. In order to enable real-time implementation, the control actions are computed based on a time-varying one-dimensional wake model. This model describes wake advection and interactions, both of which play an important role in wind farm power production. This controller is implemented in an LES model of an 84-turbine wind farm represented by actuator disk turbine models. Differences between the velocities at each turbine predicted by the wake model and measured in LES are used for closed-loop feedback. The controller is tested on two types of regulation signals, “RegA” and “RegD”, obtained from PJM, an independent system operator in the eastern United States. Composite performance scores, which are used by PJM to qualify plants for regulation, are used to evaluate the performance of the controlled wind farm. Our results demonstrate that the controlled wind farm consistently performs well, passing the qualification threshold for all fastacting RegD signals. For the RegA signal, which changes over slower time scales, the controlled wind farm's average performance surpasses the threshold, but further work is needed to enable the controlled system to achieve qualifying performance all of the time.
Highlights
Recent market trends are rapidly changing the composition of power grid energy sources, replacing conventional dispatchable power sources with non-dispatchable, variable resources, such as wind energy
In this study we further characterize the performance of wind farms, providing secondary frequency regulation using the dynamic-model control framework proposed in Shapiro et al (2017a)
The control approach is tested using a “virtual wind farm” represented by large-eddy simulation (LES) of an 84-turbine wind farm with turbines modeled as actuator disks
Summary
Recent market trends are rapidly changing the composition of power grid energy sources, replacing conventional dispatchable power sources with non-dispatchable, variable resources, such as wind energy. Recent work (Aho et al, 2013; Jeong et al, 2014) has shown that stand-alone wind turbines can effectively provide secondary frequency regulation, but recent fluid dynamics simulations (Fleming et al, 2016) have shown that interactions between wakes can lead to poor tracking performance when these single-turbine control strategies are applied to an array of turbines (Aho et al, 2013; Jeong et al, 2014). Our recent work (Shapiro et al, 2017a) sought to overcome these challenges by developing a time-varying extension of the classic Jensen wake model (Katicet al., 1986) that accounts for the dynamics of wake advection through the farm This new wake model was incorporated into a predictive model-based receding horizon control framework to coordinate an array of wind turbines to provide secondary frequency regulation by modulating the thrust coefficients of individual turbines.
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