Power tracking is an emerging application for wind farm control designs that allows farms to participate in a wider range of grid services, such as secondary frequency regulation. Control designs that enable large wind farms to follow a time-varying power trajectory are complicated by aerodynamic interactions that make it impossible to decouple upstream wind turbine control actions from downstream power production. This coupling is particularly important in applications where the reference trajectory is changing faster than, or at a similar rate as, the propagation of turbine wakes through the farm. In this work we overcome these difficulties by using a dynamic wake model that accounts for wake expansion, advection, and multi-wake interactions within a model-based receding horizon controller for coordinated control of a large multi-turbine wind farm. An ensemble Kalman filter is employed for state estimation and error correction at the turbine level. We implement the controller in high-fidelity numerical simulations of a wind farm with 84 turbines and then test the controlled farm's ability to track a power reference signal. The results demonstrate the ability of the control algorithm to track two types of power reference signals used by a US independent system operator.
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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 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,
RegAand
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. more » « less
- Award ID(s):
- 1635430
- NSF-PAR ID:
- 10060826
- Date Published:
- Journal Name:
- Wind Energy Science
- Volume:
- 3
- Issue:
- 1
- ISSN:
- 2366-7451
- Page Range / eLocation ID:
- 11 to 24
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Wake models play an integral role in wind farm layout optimization and operations where associated design and control decisions are only as good as the underlying wake model upon which they are based. However, the desired model fidelity must be counterbalanced by the need for simplicity and computational efficiency. As a result, efficient engineering models that accurately capture the relevant physics—such as wake expansion and wake interactions for design problems and wake advection and turbulent fluctuations for control problems—are needed to advance the field of wind farm optimization. In this paper, we discuss a computationally-efficient continuous-time one-dimensional dynamic wake model that includes several features derived from fundamental physics, making it less ad-hoc than prevailing approaches. We first apply the steady-state solution of the model to predict the wake expansion coefficients commonly used in design problems. We demonstrate that more realistic results can be attained by linking the wake expansion rate to a top-down model of the atmospheric boundary layer, using a super-Gaussian wake profile that smoothly transitions between a top-hat and Gaussian distribution as well as linearly-superposing wake interactions. We then apply the dynamic model to predict trajectories of wind farm power output during start-up and highlight the improved accuracy of non-linear advection over linear advection. Finally, we apply the dynamic model to the control-oriented application of predicting power output of an irregularly-arranged farm during continuous operation. In this application, model fidelity is improved through state and parameter estimation accounting for spanwise inflow inhomogeneities and turbulent fluctuations. The proposed approach thus provides a modeling paradigm with the flexibility to enable designers to trade-off between accuracy and computational speed for a wide range of wind farm design and control applications.more » « less
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Improved integration of wind farms into frequency regulation services is vital for increasing renewable energy production while ensuring power system stability. This work generalizes a recently proposed model-based receding horizon wind farm controller for secondary frequency regulation to arbitrary wind farm layouts and augments the controller to enable power modulation through storage of kinetic energy in the rotor. The new design explicitly includes actuation of blade pitch and generator torque, which facilitates implementation in existing farms as it takes advantage of current wind turbine control loops. This generalized control design improves control authority by individually controlling each turbine and using kinetic energy stored in the rotor in a coordinated manner to achieve farm level control goals. Numerical results demonstrate the effectiveness of this approach; in particular, the controller achieves accurate power tracking and reduces loss of revenue in the bulk power market by requiring less setpoint reduction (derate) than the power level control range.more » « less
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null (Ed.)This work focuses on the optimization of performance and profitability of a wind farm carried out by means of an economic model and Reynolds-Averaged Navier-Stokes (RANS) simulations of wind turbine wakes. Axisymmetric RANS simulations of isolated wind turbine wakes are leveraged with a quadratic super-positioning model to estimate wake interactions within wind farms. The resulting velocity field is used with an actuator disk model to predict power production from each turbine in the wind farm. Design optimization is performed by considering a site in North Texas, whose wind resource statistics are obtained from a meteorological tower. The RANS solver provides capabilities to simulate different incoming wind turbulence intensities and, hence, the wind farm optimization is performed by taking the daily cycle of the atmospheric stability into account. The objective functional of the optimization problem is the levelized cost of energy (LCoE) encompassing capital cost, operation and maintenance costs, land cost and annual power production. At the first level of the optimization problem, the wind farm gross capacity is determined by considering three potential turbine types with different rated power. Subsequently, the optimal wind farm layout is estimated by varying the uniform spacing between consecutive turbine rows. It is found that increasing turbine rated power, the wind farm profitability is enhanced. Substituting a wind farm of 24 turbines of 2.3-MW rated power with 18, 3-MW turbines could reduce the LCoE of about 1.56 $/MWh, while maintaining a similar gross capacity factor. The optimization of the spacing between turbine rows was found to be sensitive to the land cost. For a land cost of 0.05 $/m2, the layout could be designed with a spacing between 6 to 15 rotor diameters without any significant effect on the LCoE, while an increased land cost of 0.1 $/m2 leads to an optimal spacing of about 6 rotor diameters.more » « less
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Abstract Wind turbine farms suffer from wake losses, where the downstream turbines generate less power, and/or the leading turbines are throttled to reduce the downstream power losses. In this paper, we focus on possible external modifications that can enhance the wind turbines' performance when they are operating in a farm environment. In particular, this study is interested in enhancing the performance of the downstream turbines in wind farms. The idea is to move each turbine's wake down and away from subsequent turbines. This goal is achieved by using stationary external airfoils that are placed in proximity to the rotating blades. A number of different designs are tested and the design concepts are tested using Reynolds‐Averaged Navier‐Stokes simulations of an aligned array of 2 wind turbines. The turbines are modeled as actuator disks with axial induction and are placed in a velocity field that is modeled as a turbulent atmospheric boundary layer. It is found that fixed external airfoils can enable partial or full power recovery at turbine separations of as small as 3 rotor diameters downstream. We will also demonstrate that some devices can also improve the performance of the upstream turbine. The physical reasons for these power recovery phenomena are discussed.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/wes-3-11-2018
