Wind farm layout optimization using a Gaussian-based wake model

Parada, Leandro; Herrera, Carlos; Flores, Paulo; Parada, Víctor

doi:10.1016/j.renene.2017.02.017

Cited by 145 publications

(56 citation statements)

References 30 publications

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“…The most common current wind farm layout definitions include defining the location of each turbine directly (Feng and Shen, 2015;Guirguis et al, 2016;Gebraad et al, 2017), preassigning some locations in a wind farm as suitable turbine locations to limit the size of the design space (Emami and Noghreh, 2010;Parada et al, 2017;Ju and Liu, 2019) and parameterizing the turbines as a grid (Neubert et al, 2010;González et al, 2017;Perez-Moreno et al, 2018). Defining the location of every wind turbine directly allows the most freedom but also requires two variables for each turbine.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Massive simplification of the wind farm layout optimization problem

Stanley

Ning

2019

Wind Energ. Sci.

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“…These algorithms are based on different optimization techniques in which heuristic methods widely used. These methods includes genetic algorithm [5,6], evolutionary algorithm [7], particle swarm optimization algorithm [8] and among others methods that can found in detail in these references [2,9,10].…”

Section: Introductionmentioning

confidence: 99%

Realistic wind farm design layout optimization with different wind turbines types

Charhouni

Sallaou

Mansouri

2019

Int J Energy Environ Eng

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Seeking for an appropriate design of wind farm (WF) layout constitutes a complex task in a wind energy project. An optimization approach is seriously needed to deal with this complexity, especially with current trend of large WFs area with important number of wind turbines (WTs). The present paper investigates optimization study of realistic offshore WF design layout (horns-rev1). The main objective of the current study is to design WF area that maximizes the extraction of wind power with low cost. In the first step, an optimization model using genetic algorithm with continuous layout representation is developed to look for the optimal design as a function of WTs placement. The effectiveness of such a methodology is validated and compared with the reference and irregular layout of hors-rev1 offshore WF. With the aim to analyze the impact of WTs types on WF objectives, four commercial WTs are considered in the second step. The results showed that designing WF with big WTs gives best design layout. In addition, it demonstrated that selecting WTs based uniquely on rotor diameter size is not always a good idea. It should includes as well the number of WTs that influence significantly the power production and WF cost.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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“…The most commonly used grid placement model in the literature is 10×10. However, there has been a study conducted with GA using a 20×20 grid [34]. This study was added to Tab.…”

Section: Resultsmentioning

confidence: 99%

“…The wake radius is smaller than the size of the 10×10 grid cells. Thus, turbines are positioned in the radial direction and closer together in the 20×20 grid cells [34]. Although the potential positions of turbines in a 20×20 grid are closer to each other, the minimum distance between turbines was protected.…”

Section: Resultsmentioning

confidence: 99%

Optimal Placement of Wind Turbines Using Novel Binary Invasive Weed Optimization

2019

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Wind turbines -which are significant in terms of clean energy production globally -are environmentally friendly, consistent and economical systems. Wind turbines, due to developing technology, have become one of the most widely used renewable energy resources, and every country has worked to satisfy its electricity demands with the help of wind energy. As the importance of wind energy increases all around the world, the importance of wind turbine placement also rises. In this study, the aim was to position wind turbines over a certain area of a wind farm to obtain maximum turbine power with minimum investment cost, thereby achieving the highest power efficiency. The experimental studies were conducted over a 2×2 km area; this area was divided into a 10×10 grid, and a 20×20 grid for more efficient placement. Because these operations occurred in a binary search space, Invasive Weed Optimization (IWO) -normally used to solve unceasing optimization problems -was used in this study by obtaining fourteen different binary Invasive Weed Optimization (BIWO1 to BIWO14) algorithms with the help of ten different transfer functions (four from the sshaped family, four from the v-shaped family, two based on modulo 2, ceil, ceil-round, ceil-floor and round-floor). The proposed method was compared with other studies carried out in the binary search space found in published literature. As a result, it was seen that the proposed algorithm was an efficient algorithm for solving the problem of wind turbine placement to achieve an optimal placement.

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Wind farm layout optimization using a Gaussian-based wake model

Cited by 145 publications

References 30 publications

Massive simplification of the wind farm layout optimization problem

Massive simplification of the wind farm layout optimization problem

Realistic wind farm design layout optimization with different wind turbines types

Optimal Placement of Wind Turbines Using Novel Binary Invasive Weed Optimization

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