A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain

Brogna, Roberto; Feng, Jian; Shen, Wen Zhong; Porté‐Agel, Fernando

doi:10.1016/j.apenergy.2019.114189

Cited by 83 publications

(40 citation statements)

References 48 publications

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“…The relationship between C T and C P can also be defined through the concept of actuator disk theory. This theory relates the turbine power output and thrust through the axial induction factor, a, which can be calculated using the definitions from Burton et al (2002) and Bastankhah and Porté-Agel (2016):…”

Section: Turbine Power Output Modelmentioning

confidence: 99%

“…From these values, the power can then be calculated for turbines under steady-state and yaw-misalignment conditions, using the following relationship provided by Burton et al (2002):…”

Section: Turbine Power Output Modelmentioning

confidence: 99%

“…where ρ is the air density, A is the rotor-swept area, u is the rotor-averaged wind speed, and p is a tuneable parameter that accounts for the power losses due to yaw misalignment seen in simulations (Burton et al, 2002;Fleming et al, 2017a). Thus far, the turbine model discussed in this section does not consider the effects that turbulence may have on the relationship between power output and wind speed.…”

Section: Turbine Power Output Modelmentioning

confidence: 99%

“…where D j denotes the diameter of turbine j , and A overlap refers to the fraction of the rotor-swept area of the downstream turbine that intersects with the cross-sectional area of the wake from the upstream turbine. The axial induction factor, a j , is evaluated based on the value of C T , as defined in Burton et al (2002) and Bastankhah and Porté-Agel (2016).…”

Section: Atmospheric Stabilitymentioning

confidence: 99%

See 3 more Smart Citations

Design and analysis of a wake model for spatially heterogeneous flow

et al. 2021

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Abstract. Methods of turbine wake modeling are being developed to more accurately account for spatially variant atmospheric conditions within wind farms. Most current wake modeling utilities are designed to apply a uniform flow field to the entire domain of a wind farm. When this method is used, the accuracy of power prediction and wind farm controls can be compromised depending on the flow-field characteristics of a particular area. In an effort to improve strategies of wind farm wake modeling and power prediction, FLOw Redirection and Induction in Steady State (FLORIS) was developed to implement sophisticated methods of atmospheric characterization and power output calculation. In this paper, we describe an adapted FLORIS model that features spatial heterogeneity in flow-field characterization. This model approximates an observed flow field by interpolating from a set of atmospheric measurements that represent local weather conditions. The objective of this method is to capture heterogeneous atmospheric effects caused by site-specific terrain features, without explicitly modeling the geometry of the wind farm terrain. The implemented adaptations were validated by comparing the simulated power predictions generated from FLORIS to the actual recorded wind farm output from the supervisory control and data acquisition (SCADA) recordings and large eddy simulations (LESs). When comparing the performance of the proposed heterogeneous model to homogeneous FLORIS simulations, the results show a 14.6 % decrease for mean absolute error (MAE) in wind farm power output predictions for cases using wind farm SCADA data and a 18.9 % decrease in LES case studies. The results of these studies also indicate that the efficacy of the proposed modeling techniques may vary with differing site-specific operational conditions. This work quantifies the accuracy of wind plant power predictions under heterogeneous flow conditions and establishes best practices for atmospheric surveying for wake modeling.

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Section: Turbine Power Output Modelmentioning

confidence: 99%

“…From these values, the power can then be calculated for turbines under steady-state and yaw-misalignment conditions, using the following relationship provided by Burton et al (2002):…”

Section: Turbine Power Output Modelmentioning

confidence: 99%

Section: Turbine Power Output Modelmentioning

confidence: 99%

Section: Atmospheric Stabilitymentioning

confidence: 99%

See 2 more Smart Citations

Design and analysis of a wake model for spatially heterogeneous flow

et al. 2021

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“…A review of available methods for the wind farm layout optimization problem has been performed by Herbert‐Acero et al 8 Most of these approaches, including more recent ones such as Shakoor et al, 9 used the classic Park wake model and consider the optimization problem in flat terrains. In Brogna et al, 10 a systematic comparison of eight algorithms is presented, and a new engineering model to simulate the wake of complex terrains is proposed.…”

Section: Introductionmentioning

confidence: 99%

Wind farm power output optimization using cooperative control methods

2020

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We study the application of cooperative control and game theoretic approaches to wind farm optimization. The conventional (greedy) wind farm control strategy seeks to individually maximize each turbine power. However, this strategy does not maximize the overall power production of wind farms due to the aerodynamic interactions (wake effect) between the turbines. We formulate the wind farm power optimization problem as an identical interest game which can also be used to solve other cooperative control problems. Two model‐free learning algorithms are developed to obtain the optimal axial induction factors of the turbines and maximize power production. The algorithms are simulated for a four‐turbine wind farm and the Princess Amalia wind farm and are compared to a learning algorithm that uses a game‐theoretic approach. It is shown that the proposed algorithms improve upon benchmark algorithm in terms of both performance and actuation effort.

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Research of a combined wind speed model based on multi‐objective ant lion optimization algorithm

Wang

et al. 2021

Int Trans Electr Energ Syst

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In the past few decades, wind power generation has gradually become an important source in the global energy market, which effectively meets the energy demand of human production activities. However, the instability and diffusion of wind speed bring difficulties to the wind energy development and promotion. For improving the predictive accuracy of original sequence, scholars have proposed a variety of prediction models, but many current forecasting models often neglect the importance of data processing and can be easily limited by a single model, which causes poor performances. Therefore, a combined model is built that mainly includes the complete ensemble empirical mode decomposition with adaptive noise, several single models, and multiobjective ant lion optimization algorithm. This combined model not only reduces the impact of high-frequency noise, but also extracts original sequences features as much as possible, combines the advantages of multiple single models, and greatly improves forecasting accuracy and stability.

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A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain

Cited by 83 publications

References 48 publications

Design and analysis of a wake model for spatially heterogeneous flow

Design and analysis of a wake model for spatially heterogeneous flow

Wind farm power output optimization using cooperative control methods

Research of a combined wind speed model based on multi‐objective ant lion optimization algorithm

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