Wind farms providing secondary frequency regulation: evaluating the performance of model-based receding horizon control

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.

[1]  Johan Meyers,et al.  Wind farms providing secondary frequency regulation: Evaluating the performance of model-based receding horizon control , 2016 .

[2]  Kathryn E. Johnson,et al.  Comparison and testing of power reserve control strategies for grid‐connected wind turbines , 2014 .

[3]  J. Meyers,et al.  Optimal control of energy extraction in wind-farm boundary layers , 2015, Journal of Fluid Mechanics.

[4]  Johan Meyers,et al.  Dynamic wake modeling and state estimation for improved model-based receding horizon control of wind farms , 2017, 2017 American Control Conference (ACC).

[5]  L.Y. Pao,et al.  Control of variable-speed wind turbines: standard and adaptive techniques for maximizing energy capture , 2006, IEEE Control Systems.

[6]  Jan-Willem van Wingerden,et al.  Wind turbine wake estimation and control using FLORIDyn, a control-oriented dynamic wind plant model , 2015, 2015 American Control Conference (ACC).

[7]  C. Meneveau,et al.  Altering Kinetic Energy Entrainment in Large Eddy Simulations of Large Wind Farms Using Unconventional Wind Turbine Actuator Forcing , 2015 .

[8]  Paul Fleming,et al.  An Active Power Control System for Wind Turbines Capable of Primary and Secondary Frequency Control for Supporting Grid Reliability , 2013 .

[9]  C. Meneveau,et al.  Large eddy simulation study of fully developed wind-turbine array boundary layers , 2010 .

[10]  Kathryn E. Johnson,et al.  Tutorial of Wind Turbine Control for Supporting Grid Frequency through Active Power Control: Preprint , 2012 .

[11]  Kathryn E. Johnson,et al.  A tutorial of wind turbine control for supporting grid frequency through active power control , 2012, 2012 American Control Conference (ACC).

[12]  Alfio Borzì,et al.  Computational Optimization of Systems Governed by Partial Differential Equations , 2012, Computational science and engineering.

[13]  Yingchen Zhang,et al.  Computational fluid dynamics simulation study of active power control in wind plants , 2016, 2016 American Control Conference (ACC).

[14]  D. Kirschen,et al.  A Survey of Frequency and Voltage Control Ancillary Services—Part I: Technical Features , 2007, IEEE Transactions on Power Systems.

[15]  Andreas Sumper,et al.  Participation of wind power plants in system frequency control: Review of grid code requirements and control methods , 2014 .

[16]  C. Meneveau,et al.  Large Eddy Simulations of large wind-turbine arrays in the atmospheric boundary layer , 2010 .

[17]  Peter J Seiler,et al.  An experimental investigation on the effect of individual turbine control on wind farm dynamics , 2016 .

[18]  Charles Meneveau,et al.  Temporal structure of aggregate power fluctuations in large-eddy simulations of extended wind-farms , 2014, 1412.7238.

[19]  Roger Temam,et al.  DNS-based predictive control of turbulence: an optimal benchmark for feedback algorithms , 2001, Journal of Fluid Mechanics.

[20]  Charles Meneveau,et al.  Large eddy simulation studies of the effects of alignment and wind farm length , 2014, 1405.0983.

[21]  David J. Thuente,et al.  Line search algorithms with guaranteed sufficient decrease , 1994, TOMS.

[22]  William H. Press,et al.  Numerical Recipes 3rd Edition: The Art of Scientific Computing , 2007 .

[23]  Charles Meneveau,et al.  A concurrent precursor inflow method for Large Eddy Simulations and applications to finite length wind farms , 2014, 1405.0980.

[24]  Johan Meyers,et al.  Model-based receding horizon control of wind farms for secondary frequency regulation , 2017 .

[25]  Charles Meneveau,et al.  A scale-dependent Lagrangian dynamic model for large eddy simulation of complex turbulent flows , 2005 .

[26]  Jay Apt,et al.  The cost of curtailing wind turbines for secondary frequency regulation capacity , 2014 .

[27]  Johan Meyers,et al.  Power smoothing in large wind farms using optimal control of rotating kinetic energy reserves , 2015 .