Investigation of Two Analytical Wake Models Using Data From Wind Farms

2011 ◽  
Author(s):  
Anshul Mittal ◽  
Lafayette K. Taylor ◽  
Kidambi Sreenivas ◽  
Abdollah Arabshahi
Keyword(s):  
Genetic Algorithm ◽  
Wind Farm ◽  
Wind Farms ◽  
Maximum Error ◽  
Wind Regime ◽  
Wind Farm Optimization ◽  
Wake Model ◽  
Using Data ◽  
The Cost ◽  
Large Wind

A code ‘Wind Farm Optimization using a Genetic Algorithm’ (referred as WFOG) was developed for optimizing the placement of wind turbines in large wind farms. It utilizes an analytical wake model (by Jensen et al.) to minimize the cost per unit power for the wind farm. In this study, a new wake model by Ishihara et al. is tested in WFOG. The wake model takes into account the effect of atmospheric turbulence and rotor generated turbulence on the wake recovery. Results of the two wake models are compared with data from Horns Rev and Nysted wind farm. The maximum error (Horns Rev wind farm) for Ishihara’s wake model was 7% as compared to 15% for Jensen’s wake model. The optimal results obtained in earlier studies (using Jensen’s wake model) are compared to wind farm configurations obtained for Ishihara’s wake model. The optimization is carried out for the simplest wind regime: Constant wind speed and fixed wind direction.

Optimization of Large Wind Farms Using a Genetic Algorithm

2012 ◽  
Author(s):  
Anshul Mittal ◽  
Lafayette K. Taylor
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Wind Farms ◽  
Unit Power ◽  
Wake Model ◽  
Variable Wind ◽  
Large Wind

Optimizing the placement of the wind turbines in a wind farm to achieve optimal performance is an active area of research, with numerous research studies being published every year. Typically, the area available for the wind farm is divided into cells (a cell may/may not contain a wind turbine) and an optimization algorithm is used. In this study, the effect of the cell size on the optimal layout is being investigated by reducing it from five rotor diameter (previous studies) to 1/40 rotor diameter (present study). A code is developed for optimizing the placement of wind turbines in large wind farms. The objective is to minimize the cost per unit power produced from the wind farm. A genetic algorithm is employed for the optimization. The velocity deficits in the wake of the wind turbines are estimated using a simple wake model. The code is verified using the results from the previous studies. Results are obtained for three different wind regimes: (1) Constant wind speed and fixed wind direction, (2) constant wind speed and variable wind direction, and (3) variable wind speed and variable wind direction. Cost per unit power is reduced by 11.7% for Case 1, 11.8% for Case 2, and 15.9% for Case 3 for results obtained in this study. The advantages/benefits of a refined grid spacing of 1/40 rotor diameter (1 m) are evident and are discussed. To get an understanding of the sensitivity of the power produced to the wake model, optimized layout is obtained for the Case 1 using a different wake model.

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 Optimizing the layout of wind turbines by upgrading a wind farm

2021 ◽  
Vol 297 ◽  
pp. 01038
Author(s):  
Abdelouahad Bellat ◽  
Khalifa Mansouri ◽  
Abdelhadi Raihani
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Calculation ◽  
Cost Of Energy ◽  
Reasonable Cost ◽  
Wake Model ◽  
Different Characteristics

The optimization of the size of wind farms is little studied in the literature. The objective of this study is to renew the existing wind farms by inserting new wind turbines with different characteristics. To evaluate our approach, a genetic algorithm was chosen to optimize our objective function, which aims to maximize the power of the wind farm studied at a reasonable cost, the Jensen wake model was chosen for the power calculation of the park. The results obtained from the simulation on the Horns-rev wind farm showed a significant increase in energy and a relatively reasonable cost of energy.

Layout optimization for a large offshore wind farm using Genetic Algorithm

2020 ◽  
Author(s):  
K Narender Reddy ◽  
S Baidya Roy
Keyword(s):  
Genetic Algorithm ◽  
Wind Energy ◽  
Wind Farm ◽  
Selection Process ◽  
Wind Farms ◽  
Cost Effective ◽  
Layout Optimization ◽  
Offshore Wind ◽  
Previous Generation ◽  
Large Wind

<p>Wind Farm Layout Optimization Problem (WFLOP) is an important issue to be addressed when installing a large wind farm. Many studies have focused on the WFLOP but only for a limited number of turbines (10 – 100 turbines) and idealized wind speed distributions. In this study, we apply the Genetic Algorithm (GA) to solve the WFLOP for large wind farms using real wind data.</p><p>The study site is the Palk Strait located between India and Sri Lanka. This site is considered to be one of the two potential hotspots of offshore wind in India. An interesting feature of the site is that the winds here are dominated by two major monsoons: southwesterly summer monsoon (June-September) and northeasterly winter monsoon (November to January). As a consequence, the wind directions do not drastically change, unlike other sites which can have winds distributed over 360<sup>o</sup>. This allowed us to design a wind farm with a 5D X 3D spacing, where 5D is in the dominant wind direction and 3D is in the transverse direction (D- rotor diameter of the turbine - 150 m in this study).</p><p>Jensen wake model is used to calculate the wake losses. The optimization of the layout using GA involves building a population of layouts at each generation. This population consists of, the best layouts of the previous generation, crossovers or offspring from the best layouts of the previous generation and few mutated layouts. The best layout at each generation is assessed using the fitness or objective functions that consist of annual power production by the layout, cost incurred by layout per unit power produced, and the efficiency of the layout. GA mimics the natural selection process observed in nature, which can be summarised as survival of the fittest. At each generation, the layouts performing the best would enter the next generation where a new population is created from the best performing layouts.</p><p>GA is used to produce 3 different optimal layouts as described below. Results show that:</p><p>A ~5GW layout – has 738 turbines, producing 2.37 GW of power at an efficiency of 0.79</p><p>Layout along the coast – has 1091 turbines, producing 3.665 GW of power at an efficiency of 0.82.</p><p>Layout for the total area – has 2612 turbines, producing 7.82 GW of power at an efficiency of 0.74.</p><p>Thus, placing the turbines along the coast is more efficient as it makes the maximum use of the available wind energy and it would be cost-effective as well by placing the turbines closer to the shores.</p><p>Wind energy is growing at an unprecedented rate in India. Easily accessible terrestrial resources are almost saturated and offshore is the new frontier. This study can play an important role in the offshore expansion of renewables in India.</p>

Wind Farm Layout Optimization Using a Viral Systems Algorithm

2013 ◽  
Vol 4 (4) ◽  
pp. 27-40 ◽  
Author(s):  
Jose F. Espiritu ◽  
Carlos M. Ituarte-Villarreal
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Optimal Number ◽  
Wind Park ◽  
Variable Direction ◽  
The Cost ◽  
Variable Wind ◽  
Large Wind

In the present research a new viral systems optimization algorithm is developed to find the optimal number and position of wind turbines in large wind farms with the main objective of minimizing the cost per unit power produced from the wind park. The developed algorithm is applied to three well known problems in literature which are: 1) constant wind speed and unidirectional uniform wind, 2) constant wind speed with variable direction, and 3) non-uniform variable wind speed with variable direction. In the final results, the distance between two wind turbines is reduced to one meter, compared to 5 rotor diameters in previous studies.

scholarly journals Layout optimization for offshore wind farms in India using the genetic algorithm technique

2020 ◽  
Vol 54 ◽  
pp. 79-87
Author(s):  
Narender Kangari Reddy ◽  
Somnath Baidya Roy
Keyword(s):  
Genetic Algorithm ◽  
Power Generation ◽  
Wind Farm ◽  
Selection Process ◽  
Wind Farms ◽  
Critical Issue ◽  
Layout Optimization ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Wake Model

Abstract. Wind Farm Layout Optimization Problem (WFLOP) is a critical issue when installing a large wind farm. Many studies have focused on the WFLOP but only for a limited number of turbines and idealized wind speed distributions. In this study, we apply the Genetic Algorithm (GA) to solve the WFLOP for large hypothetical offshore wind farms using real wind data. GA mimics the natural selection process observed in nature, which is the survival of the fittest. The study site is the Palk Strait, located between India and Sri Lanka. This site is a potential hotspot of offshore wind in India. A modified Jensen wake model is used to calculate the wake losses. GA is used to produce optimal layouts for four different wind farms at the specified site. We use two different optimization approaches: one where the number of turbines is kept the same as the thumb rule layout and another where the number of turbines is allowed to vary. The results show that layout optimization leads to large improvements in power generation (up to 28 %), efficiency (up to 34 %), and cost (up to 25 %) compared to the thumb rule due to the reduction in wake losses. Optimized layouts where both the number and locations of turbines are allowed to vary produce better results in terms of efficiency and cost but also leads to lower installed capacity and power generation. Wind energy is growing at an unprecedented rate in India. Easily accessible terrestrial wind resources are almost saturated, and offshore wind is the new frontier. This study can play an important role while taking the first steps towards the expansion of offshore wind in India.

Optimal Placement of Horizontal- and Vertical-Axis Wind Turbines in a Wind Farm for Maximum Power Generation Using a Genetic Algorithm

2011 ◽  
Author(s):  
Xiaomin Chen ◽  
Ramesh Agarwal
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbines ◽  
Wind Farm ◽  
Optimization Problem ◽  
Vertical Axis ◽  
Optimal Placement ◽  
Vertical Axis Wind Turbines ◽  
Wake Effects ◽  
Horizontal Axis Wind Turbines ◽  
Wake Model

In this paper, we consider the Wind Farm layout optimization problem using a genetic algorithm. Both the Horizontal–Axis Wind Turbines (HAWT) and Vertical-Axis Wind Turbines (VAWT) are considered. The goal of the optimization problem is to optimally place the turbines within the wind farm such that the wake effects are minimized and the power production is maximized. The reasonably accurate modeling of the turbine wake is critical in determination of the optimal layout of the turbines and the power generated. For HAWT, two wake models are considered; both are found to give similar answers. For VAWT, a very simple wake model is employed.

Wind farm cooperative control for optimal power generation

2018 ◽  
Vol 42 (6) ◽  
pp. 547-560 ◽  
Author(s):  
Fa Wang ◽  
Mario Garcia-Sanz
Keyword(s):  
Power Generation ◽  
Wind Turbines ◽  
Cooperative Control ◽  
Wind Farm ◽  
Wind Farms ◽  
Practical Method ◽  
Individual Case ◽  
Power Coefficient ◽  
The Individual ◽  
Large Wind

The power generation of a wind farm depends on the efficiency of the individual wind turbines of the farm. In large wind farms, wind turbines usually affect each other aerodynamically at some specific wind directions. Previous studies suggest that a way to maximize the power generation of these wind farms is to reduce the generation of the first rows wind turbines to allow the next rows to generate more power (coordinated case). Yet, other studies indicate that the maximum generation of the wind farm is reached when every wind turbine works at its individual maximum power coefficient CPmax (individual case). This article studies this paradigm and proposes a practical method to evaluate when the wind farm needs to be controlled according to the individual or the coordinated case. The discussion is based on basic principles, numerical computations, and wind tunnel experiments.

scholarly journals Tuning turbine rotor design for very large wind farms

2018 ◽  
Vol 474 (2220) ◽  
pp. 20180237 ◽  
Author(s):  
Takafumi Nishino ◽  
William Hunter
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Theoretical Method ◽  
Turbine Rotor ◽  
Aerodynamic Optimization ◽  
Rotor Design ◽  
Coupling Procedure ◽  
Future Extension ◽  
Large Wind ◽  
Bem Theory

A new theoretical method is presented for future multi-scale aerodynamic optimization of very large wind farms. The new method combines a recent two-scale coupled momentum analysis of ideal wind turbine arrays with the classical blade-element-momentum (BEM) theory for turbine rotor design, making it possible to explore some potentially important relationships between the design of rotors and their performance in a very large wind farm. The details of the original two-scale momentum model are described first, followed by the new coupling procedure with the classical BEM theory and some example solutions. The example solutions, obtained using a simplified but still realistic NREL S809 aerofoil performance curve, illustrate how the aerodynamically optimal rotor design may change depending on the farm density. It is also shown that the peak power of the rotors designed optimally for a given farm (i.e. ‘tuned' rotors) could be noticeably higher than that of the rotors designed for a different farm (i.e. ‘untuned' rotors) even if the blade pitch angle is allowed to be adjusted optimally during the operation. The results presented are for ideal very large wind farms and a possible future extension of the present work for real large wind farms is also discussed briefly.

scholarly journals Flow visualization using momentum and energy transport tubes and applications to turbulent flow in wind farms

2013 ◽  
Vol 715 ◽  
pp. 335-358 ◽  
Author(s):  
Johan Meyers ◽  
Charles Meneveau
Keyword(s):  
Turbulent Flow ◽  
Flow Visualization ◽  
Energy Transport ◽  
Three Dimensional ◽  
Wind Farms ◽  
Mean Flow ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Using Data ◽  
Large Wind

AbstractAs a generalization of the mass–flux based classical stream tube, the concept of momentum and energy transport tubes is discussed as a flow visualization tool. These transport tubes have the property that no fluxes of momentum or energy exist over their respective tube mantles. As an example application using data from large eddy simulation, such tubes are visualized for the mean-flow structure of turbulent flow in large wind farms, in fully developed wind-turbine-array boundary layers. The three-dimensional organization of energy transport tubes changes considerably when turbine spacings are varied, enabling the visualization of the path taken by the kinetic energy flux that is ultimately available at any given turbine within the array.

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