Surveying Game Theoretic Approaches for Wind Farm Optimization

Marden, Jason R.; Ruben, Shalom; Pao, Lucy Y.

doi:10.2514/6.2012-1154

Cited by 46 publications

(48 citation statements)

References 7 publications

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“…To obtain an alternative solution, a dynamic model of the wind farm has been proposed in [14] by approximating a delay structure when a wake travels from one turbine to another, where a model-free approach has been proposed based on the Fixed-Step Maximum Power Point Tracking (FS-MPPT), in which local knowledge is assumed to be available to each turbine. The results show much faster convergence with acceptable degradation in the maximum power production compared with the GT-based approach in [11].…”

Section: Introductionmentioning

confidence: 93%

“…So far, several model-free approaches for wind farm optimization problems have been investigated. In [11] and its extended version [12], Game Theory (GT) based approaches have been proposed for optimizing power production in a wind farm. There, distributed learning algorithms have been applied with both local and global knowledge, which are available to each turbine in a wind farm configuration.…”

Section: Introductionmentioning

confidence: 99%

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A Model-Free Approach for Maximizing Power Production of Wind Farm Using Multi-Resolution Simultaneous Perturbation Stochastic Approximation

2014

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This paper provides a model-free approach based on the Multi-Resolution Simultaneous Perturbation Stochastic Approximation (MR-SPSA) for maximizing power production of wind farms. The main advantage is that the method based on MR-SPSA can achieve fast controller tuning without any plant model by exploiting the information of the wind farm configuration such as turbines location and wind direction. In order to simulate the performance of the model-free scheme, a wind farm model with dynamic characterization of wake interaction between turbines is used and then the proposed method is applied to the Horns Rev wind farm. Simulation results illustrate that the method based on MR-SPSA achieves the maximum total power production with faster convergence compared with other existing model-free methods.

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

A Model-Free Approach for Maximizing Power Production of Wind Farm Using Multi-Resolution Simultaneous Perturbation Stochastic Approximation

2014

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“…greedy yaw control: This reference control is derived from the baseline, it refers to the situation, that every individual turbines tries to locally maximize its power output by yawing directly into the wind direction without any intentional yaw misalignment. The term greedy control was introduced by Marden et al in the scope of induction-based wind farm control (Marden et al, 2012). In the next step we are extending our evaluation on the above-mentioned four control strategies based on the actual time series 5 from the wind direction measurements.…”

Section: Evaluation Of Control Algorithmsmentioning

confidence: 99%

“…A number of algorithms can be used for the computation of these kinds of optimization problems, including the intuitive game theoretic approach presented in (Marden et al, 2012). This algorithm has the benefit that it works on complex, non-linear 20 problems and does not depend on any gradients, but unfortunately it converges relatively slow.…”

mentioning

confidence: 99%

Robust active wake control in consideration of wind direction variability and uncertainty

Rott¹,

Doekemeijer²,

Seifert³

et al. 2018

Preprint

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Abstract. The prospects of active wake deflection control to mitigate wake-induced power losses in wind farms have been demonstrated by large eddy simulations, wind tunnel experiments and recent field tests. However, it has not yet been fully understood how the yaw control of wind farms should take into account the variability of current environmental conditions in the field and the uncertainty of their measurements. This research investigated the influence of dynamic wind direction changes on active wake deflection by intended yaw misalignment. For this purpose the wake model FLORIS was used together with 5 wind direction measurements recorded at an onshore met mast in flat terrain. The analysis showed that active wake deflection has a high sensitivity towards short-term wind directional changes. This can lead to an increased yaw activity of the turbines.Fluctuations and uncertainties can cause the attempt to increase the power output to fail. Therefore a methodology to optimise the yaw control algorithm for active wake deflection was introduced, which considers dynamic wind direction changes and inaccuracies in the determination of the wind direction. The evaluation based on real wind direction time series confirmed that 10 the robust control algorithm can be tailored to specific meteorological and wind farm conditions and that it can indeed achieve an overall power increase in realistic inflow conditions. Furthermore recommendations for the implementation are given which could combine the robust behaviour with reduced yaw activity.

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“…Therefore, expectations for wind energy are at important levels as the goal is for wind energy to become a prevailing source for global electricity needs. One of the keys to realizing this goal in a cost-efficient manner is to utilize existing wind farms in a more efficient manner through improved control algorithms [1].…”

Section: Introductionmentioning

confidence: 99%

Fast Numerical Modelling Method for Wind Flow Investigation Based on Depth-Averaged Equations

Avramenko¹,

Agafonova²,

Sorvari³

et al. 2017

Computational Methods and Experimental Measurements XVIII

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An important phase of wind farm design is solving the wind farm layout optimization problem, which consists in optimally positioning the turbines within the wind farm so that the wake effects are minimized and therefore the expected power production maximized. Simulating a wind farm with more than one fully detailed wind turbine and possibly complex terrain geometry requires significant computational power and time. This paper concerns the study of the depth-averaged flow of the wind turbine with a CFD-RANS approach. The complex three-dimensional (3D) geometry need not be modelled or discretized in the pre-processing state: instead, the geometry of the terrain is only described with source terms in the depth-averaged equations, which are then solved in a very simple and fixed two-dimensional (2D) domain. This approach reduces the equations from 3D to 2D and decreases the elapsed time of CFD simulations from hours to minutes. Thus, it is a very practicable modelling method in real time optimization work. 2D CFD simulations are compared with full 3D models and experiments. This paper gives an overview of the existing work and discusses the challenges that may be overcome by future research.

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Surveying Game Theoretic Approaches for Wind Farm Optimization

Cited by 46 publications

References 7 publications

A Model-Free Approach for Maximizing Power Production of Wind Farm Using Multi-Resolution Simultaneous Perturbation Stochastic Approximation

A Model-Free Approach for Maximizing Power Production of Wind Farm Using Multi-Resolution Simultaneous Perturbation Stochastic Approximation

Robust active wake control in consideration of wind direction variability and uncertainty

Fast Numerical Modelling Method for Wind Flow Investigation Based on Depth-Averaged Equations

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