scholarly journals Wake expansion continuation: Multi‐modality reduction in the wind farm layout optimization problem

Wind Energy ◽  
2022 ◽  
Author(s):  
Jared J. Thomas ◽  
Spencer McOmber ◽  
Andrew Ning
Keyword(s):  
Wind Farm ◽  
Optimization Problem ◽  
Layout Optimization ◽  
Wind Farm Layout ◽  
Wind Farm Layout Optimization

Two Stage Mini-Max Algorithm for Grid-Based Wind Farm Layout Optimization

2017 ◽  
Author(s):  
Ning Quan ◽  
Harrison Kim
Keyword(s):  
Power Output ◽  
Wind Farm ◽  
Optimization Problem ◽  
Layout Optimization ◽  
Discrete Optimization Problem ◽  
Real World Data ◽  
Two Stage ◽  
Wind Farm Layout ◽  
Wind Farm Layout Optimization ◽  
Grid Based

The power maximizing grid-based wind farm layout optimization problem seeks to determine the layout of a given number of turbines from a grid of possible locations such that wind farm power output is maximized. The problem in general is a nonlinear discrete optimization problem which cannot be solved to optimality, so heuristics must be used. This article proposes a new two stage heuristic that first finds a layout that minimizes the maximum pairwise power loss between any pair of turbines. The initial layout is then changed one turbine at a time to decrease sum of pairwise power losses. The proposed heuristic is compared to the greedy algorithm using real world data collected from a site in Iowa. The results suggest that the proposed heuristic produces layouts with slightly higher power output, but are less robust to changes in the dominant wind direction.

scholarly journals Massive simplification of the wind farm layout optimization problem

2019 ◽  
Vol 4 (4) ◽  
pp. 663-676 ◽  
Author(s):  
Andrew P. J. Stanley ◽  
Andrew Ning
Keyword(s):  
Wind Farm ◽  
Optimization Problem ◽  
Grid Method ◽  
Wind Farms ◽  
Layout Optimization ◽  
Wind Farm Layout ◽  
Wind Farm Layout Optimization ◽  
Design Variables ◽  
Gradient Based ◽  
Large Wind

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.

scholarly journals Massive Simplification of the Wind Farm Layout Optimization Problem

2019 ◽  
Author(s):  
Andrew P. J. Stanley ◽  
Andrew Ning
Keyword(s):  
Wind Farm ◽  
Optimization Problem ◽  
Grid Method ◽  
Wind Farms ◽  
Layout Optimization ◽  
Wind Farm Layout ◽  
The Past ◽  
Wind Farm Layout Optimization ◽  
Design Variables ◽  
Large Wind

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 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 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.

scholarly journals Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4403 ◽  
Author(s):  
Yang ◽  
Cho
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Wind Turbines ◽  
Wind Farm ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Layout Optimization ◽  
Wind Farm Layout ◽  
Wind Farm Layout Optimization ◽  
Annealing Algorithm

The optimal layout of wind turbines is an important factor in the wind farm design process, and various attempts have been made to derive optimal deployment results. For this purpose, many approaches to optimize the layout of turbines using various optimization algorithms have been developed and applied across various studies. Among these methods, the most widely used optimization approach is the genetic algorithm, but the genetic algorithm handles many independent variables and requires a large amount of computation time. A simulated annealing algorithm is also a representative optimization algorithm, and the simulation process is similar to the wind turbine layout process. However, despite its usefulness, it has not been widely applied to the wind farm layout optimization problem. In this study, a wind farm layout optimization method was developed based on simulated annealing, and the performance of the algorithm was evaluated by comparing it to those of previous studies under three wind scenarios; likewise, the applicability was examined. A regular layout and optimal number of wind turbines, never before observed in previous studies, were obtained and they demonstrated the best fitness values for all the three considered scenarios. The results indicate that the simulated annealing (SA) algorithm can be successfully applied to the wind farm layout optimization problem.

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