scholarly journals Reducing wind farm power variance from wind direction using wind farm layout optimization

2021 ◽  
pp. 0309524X2098828
Author(s):  
Bertelsen Gagakuma ◽  
Andrew P J Stanley ◽  
Andrew Ning
Keyword(s):  
Wind Direction ◽  
Wind Farm ◽  
Reducing Power ◽  
Wind Farms ◽  
Layout Optimization ◽  
Wind Farm Layout ◽  
Wind Farm Layout Optimization ◽  
The Mean ◽  
Power Variance ◽  
Plant Power

This paper investigates reducing power variance caused by different wind directions by using wind farm layout optimization. The problem was formulated as a multi-objective optimization. The [Formula: see text] constraint method was used to solve the bi-objective problem in a two-step optimization framework where two sequential optimizations were performed. The first was maximizing the mean wind farm power alone and the second was minimizing the power variance with a constraint on the mean power. The results show that the variance in power estimates can be greatly reduced, by as much as [Formula: see text], without sacrificing mean plant power for the different farm sizes and wind conditions studied. This reduction is attributed to the multi-modality of the design space which allows for unique solutions of high mean plant power with different power variances due to varying wind direction. Thus, wind farms can be designed to maximize power capture with greater confidence.

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.

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.

Multi-Objective Wind Farm Layout Optimization Considering Energy Generation and Noise Propagation With NSGA-II

2014 ◽  
Vol 136 (9) ◽  
Author(s):  
Wing Yin Kwong ◽  
Peter Yun Zhang ◽  
David Romero ◽  
Joaquin Moran ◽  
Michael Morgenroth ◽  
...  
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Energy Generation ◽  
Health Impact ◽  
Layout Optimization ◽  
Nsga Ii ◽  
Design Constraint ◽  
Wind Farm Layout ◽  
Multi Objective ◽  
Wind Farm Layout Optimization

Recently, the environmental impact of wind farms has been receiving increasing attention. As land is more extensively exploited for onshore wind farms, they are more likely to be in proximity with human dwellings, increasing the likelihood of a negative health impact. Noise generation and propagation remain an important concern for wind farm's stakeholders, as compliance with mandatory noise limits is an integral part of the permitting process. In contrast to previous work that included noise only as a design constraint, this work presents continuous-location models for layout optimization that take noise and energy as objective functions, in order to fully characterize the design and performance spaces of the wind farm layout optimization (WFLOP) problem. Based on Jensen's wake model and ISO-9613-2 noise calculations, single- and multi-objective genetic algorithms (GAs) are used to solve the optimization problem. Results from this bi-objective optimization model illustrate the trade-off between energy generation and noise production by identifying several key parts of Pareto frontiers. In particular, it was observed that different regions of a Pareto front correspond to markedly different turbine layouts. The implications of noise regulation policy—in terms of the actual noise limit—on the design of wind farms are discussed, particularly in relation to the entire spectrum of design options.

scholarly journals A Model to Calculate Fatigue Damage Caused by Partial Waking during Wind Farm Optimization

2020 ◽  
Author(s):  
Andrew P. J. Stanley ◽  
Jennifer King ◽  
Christopher Bay ◽  
Andrew Ning
Keyword(s):  
Fatigue Damage ◽  
Wind Farm ◽  
Wind Farms ◽  
Fatigue Loading ◽  
Layout Optimization ◽  
Computationally Efficient ◽  
Wind Farm Layout ◽  
Wind Farm Layout Optimization ◽  
Velocity Turbulence ◽  
Research And Design

Abstract. Wind turbines in wind farms often operate in waked or partially waked conditions, which can greatly increase the fatigue damage. Some fatigue considerations may be included, but currently a full fidelity analysis of the increased damage a turbine experiences in a wind farm is not considered in wind farm layout optimization because existing models are too computationally expensive. In this paper, we present a model to calculate fatigue damage caused by partial waking on a wind turbine that is computationally efficient and can be included in wind farm layout optimization. The model relies on analytic velocity, turbulence, and loads models commonly used in farm research and design, and captures some of the effects of turbulence on the fatigue loading. Compared to high-fidelity simulation data, our model accurately predicts the damage trends of various waking conditions. We also perform example wind farm layout optimizations with our presented model in which we maximize the annual energy production (AEP) of a wind farm while constraining the damage of the turbines in the farm. The results of our optimization show that the turbine damage can be significantly reduced, more than 10 %, with only a small sacrifice of around 0.07 % to the AEP, or the damage can be reduced by 20 % with an AEP sacrifice of 0.6 %.

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 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 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.

scholarly journals Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3268 ◽  
Author(s):  
Nicolas Kirchner-Bossi ◽  
Fernando Porté-Agel
Keyword(s):  
Genetic Algorithms ◽  
Wind Farm ◽  
Gaussian Model ◽  
Wind Farms ◽  
Optimization Methods ◽  
Layout Optimization ◽  
Power Losses ◽  
Wind Farm Layout ◽  
Wind Farm Layout Optimization ◽  
Wake Model

Wind Farm Layout Optimization (WFLO) can be useful to minimize power losses associated with turbine wakes in wind farms. This work presents a new evolutionary WFLO methodology integrated with a recently developed and successfully validated Gaussian wake model (Bastankhah and Porté-Agel model). Two different parametrizations of the evolutionary methodology are implemented, depending on if a baseline layout is considered or not. The proposed scheme is applied to two real wind farms, Horns Rev I (Denmark) and Princess Amalia (the Netherlands), and two different turbine models, V80-2MW and NREL-5MW. For comparison purposes, these four study cases are also optimized under the traditionally used top-hat wake model (Jensen model). A systematic overestimation of the wake losses by the Jensen model is confirmed herein. This allows it to attain bigger power output increases with respect to the baseline layouts (between 0.72% and 1.91%) compared to the solutions attained through the more realistic Gaussian model (0.24–0.95%). The proposed methodology is shown to outperform other recently developed layout optimization methods. Moreover, the electricity cable length needed to interconnect the turbines decreases up to 28.6% compared to the baseline layouts.

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