Abstract
Abstract. This paper is an extended version of our paper presented at the 2016 TORQUE conference (Shapiro et al., 2016). We investigate the use of wind farms to provide secondary frequency regulation for a power grid using a model-based receding horizon control 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 wake interactions, both of which play an important role in wind farm power production. In order to test the control strategy, it is implemented in a large-eddy simulation (LES) model of an 84-turbine wind farm using the actuator disk turbine representation. Rotor-averaged velocity measurements at each turbine are used to provide feedback for error correction. The importance of including the dynamics of wake advection in the underlying wake model is tested by comparing the performance of this dynamic-model control approach to a comparable static-model control approach that relies on a modified Jensen model. We compare the performance of both control approaches using two types of regulation signals, “RegA” and “RegD”, which are used by PJM, an independent system operator in the eastern United States. The poor performance of the static-model control relative to the dynamic-model control demonstrates that modeling the dynamics of wake advection is key to providing the proposed type of model-based coordinated control of large wind farms. We further explore the performance of the dynamic-model control via composite performance scores used by PJM to qualify plants for regulation services or markets. Our results demonstrate that the dynamic-model-controlled wind farm consistently performs well, passing the qualification threshold for all fast-acting RegD signals. For the RegA signal, which changes over slower timescales, the dynamic-model control leads to average performance that surpasses the qualification threshold, but further work is needed to enable this controlled wind farm to achieve qualifying performance for all regulation signals.
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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