Massive Simplification of the Wind Farm Layout Optimization Problem

Stanley, Andrew P. J.; Ning, Andrew

doi:10.5194/wes-2019-37

Cited by 3 publications

(4 citation statements)

References 20 publications

(22 reference statements)

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“…Engineering models, in this context, are computationally efficient models that include enough physics to predict wake steering behavior while running fast enough to be optimized in real-time. These models can then be used in the design of wind farm control strategies (Simley et al (2019); Fleming et al (2019a)), layout optimizations (Gebraad et al (2017); Stanley and Ning (2019)), or real-time control (Annoni et al (2019)).…”

Section: Introductionmentioning

confidence: 99%

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Controls-Oriented Model for Secondary Effects of Wake Steering

King¹,

Fleming²,

King³

et al. 2020

Preprint

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Abstract. This paper presents a model to incorporate the secondary effects of wake steering in large arrays of turbines. Previous models have focused on the aerodynamic interaction of wake steering between two turbines. The model proposed in this paper builds on these models to include yaw-induced wake recovery and secondary steering seen in large arrays of turbines when wake steering is performed. Turbines operating in yaw misaligned conditions generate counter-rotating vortices that entrain momentum and contribute to the deformation and deflection of the wake at downstream turbines. Rows of turbines can compound the effects of wake steering that benefit turbines far downstream. This model quantifies these effects and demonstrates that wake steering has greater potential to increase the performance of a wind farm due to these counter-rotating vortices especially for large rows of turbines. This is validated using numerous large eddy simulations for two-turbine, three-turbine, five-turbine, and wind farm scenarios.

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Section: Introductionmentioning

confidence: 99%

“…It has been used to design a controller for a field campaign in Fleming et al (2019a), study wake steering robustness (Simley et al (2019)), and has been validated with lidar measurements (Annoni et al (2018)). The Gaussian model is also used in wind farm design optimization in Stanley and Ning (2019).…”

Section: Introductionmentioning

confidence: 99%

Controls-Oriented Model for Secondary Effects of Wake Steering

King¹,

Fleming²,

King³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Specifically, this upfront cost requires 944 core-seconds, but then each 5 Case study: wind power plant layout optimization Wind power plant layout optimization is the practice of placing wind turbines within a plant to minimize the power production losses caused by wakes from upstream turbines. This is a well-studied and challenging optimization problem due to the inherent multimodality of the design space (Samorani, 2013;Baker et al, 2019;Khan and Rehman, 2013;Stanley and Ning, 2019).…”

Section: Optimization Resultsmentioning

confidence: 99%

Effectively using multifidelity optimization for wind turbine design

Jasa

Bortolotti

Zalkind

et al. 2021

Preprint

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Abstract. Wind turbines are complex multidisciplinary systems that are challenging to design because of the tightly coupled interactions between different subsystems. Computational modeling attempts to resolve these couplings so we can efficiently explore new wind turbine systems early in the design process. Low-fidelity models are computationally efficient but make assumptions and simplifications that limit the accuracy of design studies, whereas high-fidelity models capture more of the actual physics but with increased computational cost. This paper details the use of multifidelity methods for optimizing wind turbine designs by using information from both low- and high-fidelity models to find an optimal solution at reduced cost. Specifically, a trust-region approach is used with a novel corrective function built from a nonlinear surrogate model. We find that for a diverse set of design problems—with examples given in rotor blade geometry design, wind turbine controller design, and wind power plant layout optimization—the multifidelity method finds the optimal design using 38 %–58 % the computational cost of the high-fidelity-only optimization. The success of the multifidelity method in disparate applications suggests that it could be more broadly applied to other wind energy or otherwise generic applications.

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“…A square wind power plant that has been discretized with a square grid for wind turbine number and layout optimization. (Stanley and Ning, 2019b). The boundary-grid parameterization has the huge benefit of keeping the same number of design variables regardless of the number of turbines being optimized.…”

Section: Boundary-grid Design Variablesmentioning

confidence: 99%

Objective and Algorithm Considerations When Optimizing the Number and Placement of Turbines in a Wind Power Plant

Stanley¹,

Roberts²,

King³

et al. 2021

Preprint

Self Cite

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Abstract. Optimizing turbine layout is a challenging problem that has been extensively researched in literature. However, optimizing the number of turbines within a given boundary has not been studied as extensively and is a difficult problem because it introduces discrete design variables and a discontinuous design space. An essential step in performing wind power plant layout optimization is to define the objective function, or value, that is used to express what is valuable to a wind power plant developer, such as annual energy production, cost of energy, or profit. In this paper, we demonstrate the importance of selecting the appropriate objective function when optimizing a wind power plant. We optimize several different wind power plants with different wind resources and boundary sizes. Results show that the optimal number of turbines varies drastically depending on the objective function. For a simple, one-dimensional, land-based scenario, we found that a wind power plant optimized for minimal cost of energy produced just 72 % of the profit as the wind power plant optimized for maximum profit, which corresponded to a loss of about $2 million each year. This paper also compares the performance of several different optimization algorithms, including a novel repeated-sweep algorithm that we developed. We found that the performance of each algorithm depended on the number of design variables in the problem as well as the objective function.

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Massive Simplification of the Wind Farm Layout Optimization Problem

Cited by 3 publications

References 20 publications

Controls-Oriented Model for Secondary Effects of Wake Steering

Controls-Oriented Model for Secondary Effects of Wake Steering

Effectively using multifidelity optimization for wind turbine design

Objective and Algorithm Considerations When Optimizing the Number and Placement of Turbines in a Wind Power Plant

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