Wind farms providing secondary frequency regulation: evaluating the performance of model-based receding horizon control
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 implemen...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-01-01
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| Series: | Wind Energy Science |
| Online Access: | https://www.wind-energ-sci.net/3/11/2018/wes-3-11-2018.pdf |
| Summary: | 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, <q>RegA</q> and <q>RegD</q>, 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. |
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| ISSN: | 2366-7443 2366-7451 |