A novel approach for wind farm micro-siting in complex terrain based on an improved genetic algorithm

Hu, Weicheng; Yang, Qingshan; Chen, Hua-Peng; Guo, Kunpeng; Zhou, Tong; Liu, Min; Zhang, Jian; Yuan, Ziting

doi:10.1016/j.energy.2022.123970

Cited by 23 publications

(2 citation statements)

References 45 publications

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“…Subsequent research successively relaxed these assumptions. For example, [3] discuss a continuous problem domain, while [4] propose methods for complex terrain situations, and [5] optimize with respect to hub heights.…”

Section: Introductionmentioning

confidence: 99%

Wind Farm Layout Optimization Subject to Cable Cost, Hub Height, and a Feasible 3D Gaussian Wake Model Implementation

Croonenbroeck,

Hennecke

2024

J Energ Power Technol

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We address the Wind Farm Layout Optimization (WFLO) problem and tackle the optimal placement of several turbines within a specific (wind farm) area by incorporating additional aspects of an economically driven target function. With this, we contribute three refinements for WFLO research: First, while many research contributions optimize the turbines’ locations subject to maximum energy production or energy efficiency, we instead pursue a strategy of maximizing a profit objective. This enables us to incorporate inner-farm wiring costs (underground cable installation). For this, we explore the impact of using MSTs (Minimum Spanning Trees) and adding junction (so-called “Steiner”) points to the terrain plane. Second, while most research focuses on finding optimal x and y coordinates (i.e., address two-dimensional turbine placement), we also optimize the turbines’ hub heights z. Third, we also provide a software implementation of the Gaussian wake model. The latter finds entrance to the open-source WFLO research framework that comes as package <strong>wflo</strong> for statistical software R. We find that taking wiring cost into account may lead to very different turbine placements, however, increasing overall profit significantly. Allowing the optimizer to vary the hub heights may have an ambiguous impact on the wind farm profit.

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Section: Introductionmentioning

confidence: 99%

Wind Farm Layout Optimization Subject to Cable Cost, Hub Height, and a Feasible 3D Gaussian Wake Model Implementation

Croonenbroeck,

Hennecke

2024

J Energ Power Technol

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“…They also based their wake model on the Gaussian model [4], while keeping the reference base flow velocity spatially variable. Hu et al [15] presented a genetic algorithm based approach for siting wind turbines in complex terrain. To account for wake effects, they used an adapted Jensen model and Gaussian model based on the Brogna et al [13] formulation.…”

Section: Introductionmentioning

confidence: 99%

An Analytical Model for Wind Turbine Wakes under Pressure Gradient

Dar

Porté‐Agel

2022

Energies

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In this study, we present an analytical modeling framework for wind turbine wakes under an arbitrary pressure gradient imposed by the base flow. The model is based on the conservation of the streamwise momentum and self-similarity of the wake velocity deficit. It builds on the model proposed by Shamsoddin and Porté-Agel, which only accounted for the imposed pressure gradient in the far wake. The effect of the imposed pressure gradient on the near wake velocity is estimated by using Bernoulli’s equation. Using the estimated near wake velocity as the starting point, the model then solves an ordinary differential equation to compute the streamwise evolution of the maximum velocity deficit in the turbine far wake. The model is validated against experimental data of wind turbine wakes on escarpments of varying geometries. In addition, a comparison is performed with a pressure gradient model which only accounts for the imposed pressure gradient in the far wake, and with a model that does not account for any imposed pressure gradient. The new model is observed to agree well with the experimental data, and it outperforms the other two models tested in the study for all escarpment cases.

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An integrated method with adaptive decomposition and machine learning for renewable energy power generation forecasting

Zhang

et al. 2023

Environ Sci Pollut Res

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A novel approach for wind farm micro-siting in complex terrain based on an improved genetic algorithm

Cited by 23 publications

References 45 publications

Wind Farm Layout Optimization Subject to Cable Cost, Hub Height, and a Feasible 3D Gaussian Wake Model Implementation

Wind Farm Layout Optimization Subject to Cable Cost, Hub Height, and a Feasible 3D Gaussian Wake Model Implementation

An Analytical Model for Wind Turbine Wakes under Pressure Gradient

An integrated method with adaptive decomposition and machine learning for renewable energy power generation forecasting

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