A method for reducing multi-modality in the wind farm layout optimization problem

Thomas, Jared J.; Ning, Andrew

doi:10.1088/1742-6596/1037/4/042012

Cited by 37 publications

(47 citation statements)

References 15 publications

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“…The original formulation of the model does not define the wake deficit in the near-wake region, creating undefined regions which make optimization difficult. To mitigate this issue, Thomas and Ning added a linear interpolation of the wake loss from the turbine up to where it is defined by the wake model, which is the version used in this paper (Thomas and Ning, 2018). The most important equation for this Gaussian wake model is shown in Eq.…”

Section: Wake Modelmentioning

confidence: 99%

“…Because γ = 0 throughout this paper, cos(γ ) = 1 meaning that σ y = σ z . Wakes were combined with a linear combination method, about which more details can be found in the cited literature (Bastankhah and Porté-Agel, 2016;Thomas and Ning, 2018). To find the effective wind speed across the entire wind turbine to be used in turbine power calculation, we averaged the velocities sampled at several points across the rotor.…”

Section: Wake Modelmentioning

confidence: 99%

“…Compared to gradient-free optimization, the exact analytic gradients are vital. The direct optimization with a gradient-free technique would be near impossible because of the massive required computational expense (Ning and Petch, 2016;Thomas and Ning, 2018).…”

Section: Computational Expensementioning

confidence: 99%

“…Because of the multimodality of the space and the often discontinuous models used in wind farm modeling, the wind industry is heavily dependent on gradient-free techniques for wind farm layout optimization (Herbert-Acero et al, 2014). Although these methods can be highly effective for small numbers of design variables, the computational expense required to converge scales poorly, approximately quadratically, with increasing numbers of variables (Zingg et al, 2008;Rios and Sahinidis, 2013;Lyu et al, 2014;Ning and Petch, 2016;Thomas and Ning, 2018). Because of this poor computational scaling, many companies and researchers have been limited in the size of wind farms they can optimize, as the number of variables typically increases with the number of turbines.…”

Section: Introductionmentioning

confidence: 99%

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Massive simplification of the wind farm layout optimization problem

Stanley

Ning

2019

Wind Energ. Sci.

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Abstract. The wind farm layout optimization problem is notoriously difficult to solve because of the large number of design variables and extreme multimodality of the design space. Because of the multimodality of the space and the often discontinuous models used in wind farm modeling, the wind industry is heavily dependent on gradient-free techniques for wind farm layout optimization. Unfortunately, the computational expense required with these methods scales poorly with increasing numbers of variables. Thus, many companies and researchers have been limited in the size of wind farms they can optimize. To solve these issues, we present the boundary-grid parameterization. This parameterization uses only five variables to define the layout of a wind farm with any number of turbines. For a 100-turbine wind farm, we show that optimizing the five variables of the boundary-grid method produces wind farms that perform just as well as farms where the location of each turbine is optimized individually, which requires 200 design variables. Our presented method facilitates the study and both gradient-free and gradient-based optimization of large wind farms, something that has traditionally been less scalable with increasing numbers of design variables.

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Section: Wake Modelmentioning

confidence: 99%

Section: Wake Modelmentioning

confidence: 99%

Section: Computational Expensementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Massive simplification of the wind farm layout optimization problem

Stanley

Ning

2019

Wind Energ. Sci.

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show abstract

“…Compared to gradientfree optimization, the exact analytic gradients are vital. The direct optimization with a gradient-free technique would be near impossible because of the massive required computational expense (Ning and Petch, 2016;Thomas and Ning, 2018).…”

mentioning

confidence: 99%

Massive Simplification of the Wind Farm Layout Optimization Problem

Stanley¹,

Ning²

2019

Preprint

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The wind farm layout optimization problem is notoriously difficult to solve because of the large number of design variables and extreme multimodality of the design space. Because of the multimodality of the space and often discontinuous models used in wind farm modeling, the wind industry is heavily dependent on gradient-free techniques for wind farm layout optimization. Unfortunately, the computational expense required with these methods scales poorly with increasing numbers of variables. Thus, many companies and researchers have been limited in the size of wind farms they can optimize. To solve 5 these issues, we present the boundary-grid parameterization. This parameterization uses only five variables to define the layout of a wind farm with any number of turbines. For a 100 turbine wind farm, we show that optimizing the five variables of the boundary-grid method produces wind farms that perform within 0.5% of farms where the location of each turbine is optimized individually, which requires 200 design variables. Our presented method unlocks the ability to optimize and study large wind farms, something that has been mostly infeasible in the past.

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Reliability‐based layout optimization in offshore wind energy systems

et al. 2021

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Existing methods for optimizing wind array layouts typically use power or cost objectives and rarely consider reliability-based objectives. Component and system failure rates, however, are dependent on location-specific wind conditions, are influenced by array layout and wake interactions, and have a direct and significant impact on capital costs, operational costs, and power production. Although wind power plant models exist that calculate wind loads with sufficient resolution to capture component loading dynamics from wind conditions, they are computationally expensive and thus not suitable for research applications requiring many evaluations, particularly optimization. This study describes the development of computationally efficient, reliability-based layout optimization methods, enabling us to explore the relationship between component reliability and layout optimization. These methods include the surrogate modeling of the planet bearing life based on varying wind conditions simulated in FAST.Farm and the formulation of reliability-based objectives based on failure cost and power production models. Through demonstration of this method, we explore how wind conditions, objective functions, and capacity density influence reliability-based layout optimization. Results indicate that considering reliability alongside power production can reduce failure costs associated with replacement costs and downtime whilemaintaining or improving power production. Our conclusions highlight the opportunity for wind power plant developers to integrate reliability and operational expenditures alongside performance and capital expenditure objectives in plant design and development to improve plant performance and costs.

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A method for reducing multi-modality in the wind farm layout optimization problem

Cited by 37 publications

References 15 publications

Massive simplification of the wind farm layout optimization problem

Massive simplification of the wind farm layout optimization problem

Massive Simplification of the Wind Farm Layout Optimization Problem

Reliability‐based layout optimization in offshore wind energy systems

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