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.

A Comparative Study of the Influence of Different Wake Models on Wind Farm Layout Optimization

2021 ◽  
Author(s):  
Puyi Yang ◽  
Hamidreza Najafi
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Vital Role ◽  
Layout Optimization ◽  
Zone Model ◽  
Wind Farm Layout ◽  
Yaw Control ◽  
Wind Farm Layout Optimization ◽  
Wake Model ◽  
Large Wind

Abstract The accuracy of analytical wake models applied in wind farm layout optimization (WFLO) problems plays a vital role in the present era that the high-fidelity methods such as LES and RANS are still not able to handle an optimization problem for large wind farms. Based on a verity of analytical wake models developed in the past decades, FLOw Redirection and Induction in Steady State (FLORIS) has been published as a tool integrated several widely used wake models and the expansions for them. This paper compares four wake models selected from FLORIS by applying three classical WFLO scenarios. The results illustrate that the Jensen wake model is the fastest one but the defect of underestimation of velocity deficit is obvious. The Multi Zone model needs to be applied additional tunning on the parameters inside the model to fit specific wind turbines. The Gaussian-Curl wake model as an advanced expansion of the Gaussian wake model does not perform an observable improvement in the current study that the yaw control is not included. The default Gaussian wake model is recommended to be used in the WFLO projects which implemented under the FLROIS framework and has similar wind conditions with the present work.

scholarly journals Wind farm layout optimization using pseudo-gradients

2021 ◽  
Vol 6 (3) ◽  
pp. 815-839
Author(s):  
Erik Quaeghebeur ◽  
René Bos ◽  
Michiel B. Zaaijer
Keyword(s):  
Large Scale ◽  
Wind Farm ◽  
Optimization Algorithms ◽  
Wind Farms ◽  
Layout Optimization ◽  
Offshore Wind ◽  
Proof Of Concept ◽  
Wind Farm Layout ◽  
Wind Farm Layout Optimization ◽  
Gradient Based

Abstract. This paper presents a heuristic building block for wind farm layout optimization algorithms. For each pair of wake-interacting turbines, a vector is defined. Its magnitude is proportional to the wind speed deficit of the waked turbine due to the waking turbine. Its direction is chosen from the inter-turbine, downwind, or crosswind directions. These vectors can be combined for all waking or waked turbines and averaged over the wind resource to obtain a vector, a “pseudo-gradient”, that can take the role of gradient in classical gradient-following optimization algorithms. A proof-of-concept optimization algorithm demonstrates how such vectors can be used for computationally efficient wind farm layout optimization. Results for various sites, both idealized and realistic, illustrate the types of layout generated by the proof-of-concept algorithm. These results provide a basis for a discussion of the heuristic's strong points – speed, competitive reduction in wake losses, and flexibility – and weak points – partial blindness to the objective and dependence on the starting layout. The computational speed of pseudo-gradient-based optimization is an enabler for analyses that would otherwise be computationally impractical. Pseudo-gradient-based optimization has already been used by industry in the design of large-scale (offshore) wind farms.

scholarly journals Wind farm layout optimization using pseudo-gradients

2020 ◽  
Author(s):  
Erik Quaeghebeur ◽  
René Bos ◽  
Michiel B. Zaaijer
Keyword(s):  
Large Scale ◽  
Wind Farm ◽  
Optimization Algorithms ◽  
Wind Farms ◽  
Layout Optimization ◽  
Offshore Wind ◽  
Proof Of Concept ◽  
Wind Farm Layout ◽  
Wind Farm Layout Optimization ◽  
Gradient Based

Abstract. This paper presents a heuristic building block for wind farm layout optimization algorithms. For each pair of wake-interacting turbines, a vector is defined. Its magnitude is proportional to the wind speed deficit of the waked turbine due to the waking turbine. Its direction is chosen from the inter-turbine, downwind, or crosswind directions. These vectors can be combined for all waking or waked turbines and averaged over the wind resource to obtain a vector, a pseudo-gradient, that can take the role of gradient in classical gradient-following optimization algorithms. A proof-of-concept optimization algorithm demonstrates how such vectors can be used for computationally efficient wind farm layout optimization. Results for various sites, both idealized and realistic, illustrate the types of layout generated by the proof-of-concept algorithm. These results provide a basis for a discussion of the heuristic's strong points–speed, competitive reduction in wake losses, flexibility – and weak points – partial blindness to the objective and dependence on the starting layout. The computational speed of pseudo-gradient-based optimization is an enabler for analyses that would otherwise be computationally impractical. Pseudo-gradient-based optimization has already been used by industry in the design of large-scale (offshore) wind farms.

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.

The Effect of Using Different Wake Models on Wind Farm Layout Optimization: A Comparative Study

2021 ◽  
pp. 1-28
Author(s):  
Puyi Yang ◽  
Hamidreza Najafi
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Layout Optimization ◽  
Navier Stokes ◽  
Zone Model ◽  
Wind Farm Layout ◽  
Yaw Control ◽  
Eddy Simulation ◽  
Wind Farm Layout Optimization ◽  
Wake Model

Abstract The accuracy of analytical wake models applied in wind farm layout optimization (WFLO) problems is of great significance as the high-fidelity methods such as large eddy simulation (LES) and Reynolds-averaged Navier-Stokes (RANS) are still not able to handle an optimization problem for large wind farms. Based on a variety of analytical wake models developed in the past decades, Flow Redirection and Induction in Steady State (FLORIS) have been published as a tool that integrated several widely used wake models and their expansions. This paper compares four wake models selected from FLORIS by applying three classical WFLO scenarios. The results illustrate that the Jensen wake model is the fastest, but the issue of underestimating the velocity deficit is obvious. The Multi-zone model needs additional tuning on the parameters inside the model to fit specific wind turbines. The Gaussian-curl hybrid (GCH) wake model, as an advanced expansion of the Gaussian wake model, does not perform an observable improvement in the current study, where the yaw control is not included. The Gaussian wake model is recommended for the WFLO projects implemented under the FLROIS framework and has similar wind conditions with the present work.

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