atmosphere

Article
The Significance of W ind T urbines Layout
Optimization on the Predicted Farm Energy Y ield
Mohammad Al-Addous 1 , * , Mustafa Jaradat 1 , Aiman Albatayneh 1 , Johannes W ellmann 2
and Sahil Al Hmidan 3
1 Department of Energy Engineering, German Jor danian University , Amman Madaba Str eet, P .O. Box 35247,
Amman 11180, Jordan; [email protected] (M.J.); [email protected] (A.A.)
2 Environmental Pr ocess Engineering, Department of Environmental T echnology , T echnische Universität
Berlin, O ffi ce KF 2, Strasse des 17. Juni 135, 10623 Berlin, Germany; [email protected]
3 Royal Scientific Society (RSS), National Energy Resear ch Centre, PO Box 1438, Amman 11941, Jor dan;
[email protected]
* Correspondence: [email protected]
Received: 31 December 2019; Accepted: 17 January 2020; Published: 20 January 2020
     
  

Abstract:
Securing ener gy supply and diversifying the energy sour ces is one of the main goals of
ener gy strategy for most countries. Due to climate change, wind energy is becoming incr easingly
important as a method of CO
2
-fr ee energy generation. In this paper , a wind farm with five turbines
located in Jerash, a city in northern Jor dan, has been designed and analyzed. Optimization of
wind farms is an important factor in the design stage to minimize the cost of wind energy to
become mor e competitive and economically attractive. The analyses have been carried out using
the W indFarm software to examine the significance of wind turbines’ layouts (M, straight and ar ch
shapes) and spacing on the final ener gy yield. In this r esearch, arranging the turbines facing the main
wind dir ection with five times rotor diameter distance between each turbine has been simulated,
and has r esulted in 22.75, 22.87 and 21.997 GWh / year for the M shape, Straight line and Arch shape,
r espectively . Whereas, r educing the distance between turbines to 2.5 times of the rotor diameter (D)
r esulted in a reduction of the wind farm ener gy yield to 22.68, 21.498 and 21.5463 GWh / year for the
M shape, Straight line and Ar ch shape, respectively . The ener getic e ffi ciency gain for the optimized
wind turbines compar ed to the modeled layouts regar ding the distances between the wind turbines.
The ener getic e ffi ciency gain has been in the range between 8.9% for 5D (r otor diameter) straight
layout to 15.9% for 2.5D straight layout.
Keywords: wind ener gy; optimization; W indFarm 3D; wake e ff ects; topography e ff ects
1. Introduction
Nowadays, wind turbines are usually installed in so-called wind farms. For space and cost
r easons, the wind turbines should be as close as possible to each other , but not mutually influence each
other . T o avoid mutual interference, the wind turbines need to be arranged in a layout accor ding to the
pr evailing wind direction, in or der to harvest the maximum energy [ 1 ].
Ther e are several criteria which have been consider ed for the selection of a site for a largescale
wind ener gy farm, such as annual average wind speed, main direction and ener gy generation.
Also, other factors are consider ed that depend upon the site selection, such as local infrastructure,
soil composition and availability of electrical network.
W ind turbines can be adapted to meteorological conditions due to their aer odynamic shape.
In or der to take the site conditions into account, the potential location of a wind turbine must be
examined in advance. For the selection of suitable locations, numerical simulations ar e mostly used
Atmosphere 2020 , 11 , 117; doi:10.3390 / atmos11010117 www .mdpi.com / journal / atmosphere

Atmosphere 2020 , 11 , 117 2 of 14
which examine the meteor ological and orographic conditions in advance. The wake flow has e ff ects on
the envir onment, and in particular on the wind field of other wind turbines.
These e ff ects of turbulent wake flow on meteor ological sizes have been investigated with numerical
simulations by Pr
ó
sper et al. [
2
], Astolfi et al. [
3
] and Han et al. [
4
]. However , computer models can
only be an appr oximation of nature, since the simulations ar e always subject to uncertainties. For each
application an estimation of the r epresentation of the r elevant processes must be made. For pr ocesses
that cannot be mapped, parameterizations need to be found. The di ff erent ways to model wind
turbines depend upon the physics of the models, the used parameters and the objective of the study .
The model and the parameterization must be selected accor ding to the examination objective. Thus,
very high-r esolution models can calculate the aerodynamics of the r otor blade, but due to the numerical
e ff ort, they are not able to simulate the flow and wake-up flow over a lar ger area than in the immediate
vicinity of the turbine. However , if the turbulent wake flows of a wind turbine or even an entir e wind
farm should be consider ed, a much coarser area must be used to captur e the area of inter est.
One of the main issues in designing an optimized wind farm layout is the definition of the optimal
location of each single turbine, while simultaneously considering the avoidance of wake losses under
certain r estrictions like the topographic, land availability and the prominent wind dir ection. This issue
has been examined in many r ecent studies using di ff erent optimization algorithms [
5
]. W ind farm
turbines’ layout optimization and wake steering should corr espond, since substantial annual energy
generation enhancements can be obtained by wake steering in a wind farm layout that is optimized to
minimize wake losses [ 6 ].
Several studies have developed mathematical models to optimize the wind turbine arrangement
in the wind plant. Genetic algorithms have been applied to optimize the placement of wind turbines
such as in Mosetti et al. [
7
], Grady , et al. [
8
], Ju et al. [
9
] and Br ogna et al. [
10
], also by applying
Monte Carlo simulation like in W ang et al. [
11
] and Marmidis et al. [
12
]. In a previous study , a new
mathematical model has been developed to optimize the wind turbine arrangement in the wind farm.
The new optimization method is based on numerical simulations focusing upon maximizing the total
electricity generation of the wind farm. The results have shown significant impr ovements in wind
farm power generation r eaching up to 6% compared to other methods like genetic algorithms [ 13 ].
A Gaussian wake model has been applied in other studies to find the optimal layout for a wind
farm [
14
]. The results have shown that the use of genetic algorithms can r educe annual maintenance
costs and incr ease the e ffi ciency of a wind farm in di ff erent scenarios [ 14 ].
Other studies have been carried out to solve the problem of wind farm layout optimization by
developing new mathematical pr ogramming. By applying the new model to several previous layouts,
it has been shown that these optimized layouts could generate extra energy with a higher symmetric
layout [ 15 ].
Another study has analyzed the e ff ect of wind turbine layout on the energy generation fr om the
wind farm [
16
]. This r esearch compar ed regular layouts to micr o-siting models based on the energy
output. The results have shown that the micr o-siting model array shows higher conversion rate than
the aligned and stagger ed layouts by 4.09% and 2.18%, respectively [ 16 ].
For the purpose of r eaching the maximum wind energy generation, other studies have been
developed to optimize the wind farm layout. In the resear ch of [
17
], ther e has been designed a model
based on parameters like wind distribution, wake loss and wind dir ection. The model results show
significant impr ovements in energy generation compar ed to a regular layout without optimization.
Furthermor e, some mor e related studies have optimized the wind farm layout using a
thr ee-dimensional model depending on the so-called “greedy” algorithm. The numerical method
applied is based on multiple hub heights to maximize the energy output and minimize the cost.
Compar ed to layouts created by the genetic algorithm and identical hub height, the pr oposed greedy
algorithm shows lower computational r equirements and better r esults [ 18 ].
The 2D Jensen–Gaussian wake model or Jensen’s wake model have been used as genetic algorithm
wind farm turbines layout optimization programs, wher e r ecent studies indicate that the significance

Atmosphere 2020 , 11 , 117 3 of 14
of the 2D Jensen–Gaussian wake model means more theor etical importance and r eal data in wind
ener gy operation [ 19 ].
For the wind speed fluctuations, various statistical methods have been applied [
20
], wher e the
W eibull distribution is most commonly used. The wind dir ection variation is typically pr esented
in a wind r ose diagram. Mer ging these two, the r ecorded wind data can be built into sector -wise
W eibull distributions, which can be used for wind resour ce assessment and annual energy yield
calculations. The wind modeling techniques have been implemented in di ff erent studies on wind
farm layout optimization. In or der to perform the analysis, the compass is divided into 12 sectors,
each one r epresenting 30 degr ees of the horizon. A wind rose may also be drawn for 8 or 16 sectors,
but a 12 sector layout tends to be the standar d set by the European W ind Atlas, where some studies
used 12 sectors for wind dir ection such as in Dobri ´ c et al. [
21
] and Rivas et al. [
22
], while others used
24 sectors [ 23 ].
However , there is a need of mor e optimization techniques to optimize wind farm layout for
harvesting the maximum possible ener gy , and in recent years mor e developments have been done for
a better arrangement of wind turbines in lar ger wind farms [ 24 ].
In this r esearch, a wind farm with five turbines located in Jerash in Jor dan, has been designed and
analyzed. The analyses ar e carried out using the W indFarm softwar e to examine the significance of
wind turbines’ layouts (M, straight and ar ch shapes) and spacing on the final energy yield.
2. Description of the Site
In this r esearch, the examined wind farm is located in the Umm Al Rumman hill, Jerash, Jor dan.
The used wind data fr om the site has been collected by a meteorological station, which is located 2 km
in linear distance fr om the study location. The hill has low , sparse vegetation, and is accessible by a
small service r oad. However , it can be consider ed as a suitable location for a wind farm, as there ar e no
obstacles or r esidential areas ar ound. Fr om an environmental point of view , the site is not consider ed
as a pr otected nor a military area. The topography data for the wind farm location in meters is shown
in Figur e 1 . The site is characterized by a valley in orientated directions. Furthermor e, there ar e strong
height di ff er ences of more than 800 m, cr eating an influence of the wind emergence on the downhill
sides. The valley acts here like a funnel, concentrating the wind and incr easing the velocity to allow an
economical use of the wind.
Atmosphere 202 0 , 11 , x FOR PEER REVIEW 3 of 14

sign if icanc e o f t h e 2D Jense n –G a u ss i a n wake m o de l m e ans m o re t h eoret i ca l im p o rt ance and rea l dat a
in win d ener gy oper ation [19].
For the win d speed fluctuations, v a rio u s statist i cal me thods have been applied [20], where th e
Weibu ll di st ri but i on i s mos t commonly used . The wi nd di rect ion vari at ion is t y pica ll y pre s ent e d i n
a wind rose d i agr a m. M e rg ing these two, the record ed wind d a t a c a n be built int o sect or-wi s e Weibu ll
d i s t r i bu ti o n s, w h i c h c a n be u s e d f o r wi nd r e s o u r c e as sessment and ann u al en e r gy yi e l d ca l c u l a t i o ns.
The wind m o delin g tech niques h a ve been imp l em ented in d i fferent studie s on w i nd far m layo ut
opti mi za ti on. In order to perf orm the a n al ysi s , th e compass is d i vided into 12 sectors , e a c h one
r e pr es enting 30 d e g r e e s of the horiz o n. A wind ro se m a y also b e drawn fo r 8 o r 1 6 se ct or s , b u t a 1 2 se ct o r
l a you t t e n d s to b e t h e s t a n da r d s e t b y th e E u r o pea n Wind A t las, wher e s o me s t udie s us ed 12 s e ctors for
wind direct io n such as in D o bri ć et al . [21] a n d Ri vas et al. [22] , whil e others used 24 sect ors [23].
However, there i s a need of more optimi za ti on techni ques to opti miz e wi nd fa rm la yout for
harvestin g th e maxim u m possible ener gy, and in re cent years m o re develop m ents have b een done
for a better ar rangement o f wind t u rbine s in large r wind farms [24].
In t h is re se ar ch, a wind fa rm wit h f i ve t u rbines lo cat e d in Jer a sh i n Jo rdan , ha s been de signe d
and an aly z ed . The ana l y s es are ca rrie d o u t us ing t h e WindF a rm so ftware to examine the sign ificanc e
of wi nd tu rbines’ la you t s (M, stra ight and a r ch sh ap es) and spac ing on the fin a l ener gy yie l d .
2. D e scrip ti o n of th e S i t e
In t h is rese arc h , t h e examin ed wind f a rm is loc a t e d in t h e Umm Al R u mman hi ll , J e rash , Jord an.
The used win d d a ta from t h e site h a s be en collecte d b y a met e oro l o g ic al st at ion, which is loc a t e d 2
km in lin ear dist anc e f r om t h e st ud y loc a t i on. The hill h a s low, sp arse veget a tion, an d is acc e ssib l e
by a sm al l ser v ice ro ad . Ho wever, it c a n be cons idered a s a s u it ab le l o cat i on for a wind f a rm, a s t h ere
are no ob st ac les or res i den t ial are a s aro u nd. F r om an envi ronmenta l poi n t of vi ew, the si te i s not
consider ed as a protected nor a militar y ar ea. The t o pogra p hy da ta f o r the wi nd fa rm l o cati on i n
m e t e rs i s sh own in F i gu re 1. The sit e i s ch ar act e riz e d b y a v a l l ey in ori e nt at ed d i rec t ions.
Furthermore, there are strong height differences o f m o r e t h a n 8 0 0 m , c r e a t i n g a n i n f l u e n c e o f t h e
wind emer ge nce on the do wnhill sides. The valley ac t s here like a funnel, concen trating the w i nd and
incre a sin g t h e veloc i t y t o a llow an econo m ica l u s e o f t h e wind .

Figure 1. T o p o g r a p h y o f t h e w i n d f a r m i n t h e s t u d y s i t e ( U m A l R u m m a n , J o r d a n ) a s s e e n f r o m s o u t h -
west 3D ( left ) a n d 2D ( right ).
C o nsider ing al l g i v e n con d it ions and t h e t o p o grap h y of t h e s i t e , a des i red win d fa rm si ze o f 10
M W s h a l l b e o p t i m i z e d . T h e p r o p o s e d w i n d f a r m c o n s i s t s o f f i v e w i n d t u r b i n e s w i t h a d e s i g n
ca pa ci ty of 2 MW ea ch. In order to evalua te the loc a l wind potential (m ain l y wind speed and wind
di recti o n) f o r l o ng term wind condi t i o ns of the si te, raw da ta f r om the nea r est m e teorol ogi c al ma st
ha s been used. The f i rst step i s the determi n a t i o n of the exa c t l o ca ti on of the wi nd pa rk and the
specific topo graphy o f th e open are a of the wind farm , an d the influen c e o n wind ve loc i ty and
direct ion s . Th is st ep is per f ormed by a si t e an al ys is in t h e Win d F a r m So ft ware d e scribed in Se ct ion

Figure 1.
T opography of the wind farm in the study site (Um Al Rumman, Jordan) as seen fr om
south-west 3D ( left ) and 2D ( right ).
Considering all given conditions and the topography of the site, a desired wind farm size of
10 MW shall be optimized. The proposed wind farm consists of five wind turbines with a design
capacity of 2 MW each. In or der to evaluate the local wind potential (mainly wind speed and wind
dir ection) for long term wind conditions of the site, raw data from the near est meteorol ogical mast has
been used. The first step is the determination of the exact location of the wind park and the specific

Atmosphere 2020 , 11 , 117 4 of 14
topography of the open ar ea of the wind farm, and the influence on wind velocity and directions.
This step is performed by a site analysis in the W indFarm Software described in Section 3 . Then,
the topography data for the wind farm location and the transformation factors of the coor dinates in
Jor dan (Central Meridian, origin latitude, scale factor , false easting and northing) have been applied in
the Mensuration Services Pr ogram (MSP) Geographic T ranslator (GEOTRANS, V ersion 3.4) [ 25 ].
3. Methodology
3.1. Wind Data
For this r esearch, ther e are 52,704 data sets available which have been r ecorded during one full
year . They ar e repr esented in 10-min readings, taking into account that the year 2008 was a leap year .
However , the total valid data during the measur ement period is lowered to 49,098 data sets during
340.95 days of the year , while the calm wind speed, measuring less than 2 m / s, is 0%. The missing data
in the examined time frame is 3605 samples r epresenting 6.8% fr om all samples.
In or der to identify the suitable site of the wind farm, and to analyze the wind potential of the
wind farm location, the measured wind speeds and their dir ections for at least one calendar year
should be analyzed.
In or der to evaluate the local wind potential for long term wind conditions area, the raw data of
wind speed and dir ection from the meteor ological weather station which has been installed in Um Al
Rumman have been used. The station is located within the latitude and longitude of 32
◦
10
0
56.0
0 0
N
and 35
◦
49
0
36.1
0 0
E. The data set collected has been r ecorded fr om 1 July 2007 until 30 June 2008, and
its sampling rate is one sample per second. The data acquisition system calculates 10 min averaged
values, along with the minimum, maximum and the standar d deviation for each measured sensor .
However , the data analysis is conducted using the W ind Rose software “version V4.15 A6.0”.
W ind Rose is a tool used to analyze wind characteristics, such as direction, speed, temperatur e and
turbulences. The data analysis complies with all of the mandatory requir ements in the International
Electr otechnical Commission (IEC) in Geneva-Switzerland and the Measuring Network of W ind Ener gy
Institutes (MEASNET) standar ds that developed the guideline for the evaluation of site-specific wind
conditions [ 26 ].
3.2. Wind T urbine Characteristics
Each of the turbines in the site is a 2 MW pitch-regulated wind turbine, wher e the V estas, V90 Class
II is selected. The V estas V90-2.0 MW wind turbine is a pitch-regulated upwind turbine with active yaw
and a thr ee-blade rotor . The V estas V90-2.0 MW turbine has a rotor diameter of 90 m with a generator
rated at 2.0 MW , depending upon wind conditions. The turbine utilizes a micropr ocessor pitch control
system called OptiT ip
®
and the OptiSpeedTM (variable speed) featur e. W ith these features, the wind
turbine is able to operate the rotor at variable speed (rpm), helping to maintain the output at or near the
rated power . The power curve of the turbine is used for the ener gy production calculations, wher eas
the thrust curve is used for the wake e ff ects estimations. The cut in speed is 4 m / s, the rated speed is
13.5 m / s, and its cut o ff speed is 25 m / s, while the power is 89 kW , 2000 kW and 2000 kW , respectively .
3.3. Simulation Software
In this study , the W indFarm Simulation softwar e is used, which is able to calculate the energy
yield of a wind farm, including topographic and wake e ff ects, optimizing the turbine layout
for maximum ener gy yield. W e then perform noise calculations (showing the noise contours),
analyze wind turbine data, perform measure-corr elate-predict analysis of wind speed data, create
zone-of-visual-influence maps, display wir e frame views of wind farms, calculate shadow flicker ,
and cr eate 3D visualizations [ 27 ].
W indFarm by ReSoft Ltd., London-United Kingdom, is a computational fluid dynamics (CFD)
model softwar e package. It also employs the Reynolds-averaged Navier –Stokes method (RANS

Atmosphere 2020 , 11 , 117 5 of 14
equations ar e time-averaged equations of motion for fluid flow) to solve the non-linear Navier–Stokes
equations with an MS-Micr o solver . It also has options to provide various closur es to the turbulence
equations. W indFarm takes into account geostr ophic balance, roughness variations, height variations,
turbulence, a logarithmic wind pr ofile and a specific and uniform stability .
The model is based on a division of an assumed neutrally-stratified flow field into inner and
outer layers. The outer layer is characterized by inviscid, potential flow , while in the inner layer ,
a balance between advective, pressur e-gradient and turbulent-viscous forces is assumed, and turbulent
transfers ar e modeled with a simple mixing length closure scheme. Fourier transforms ar e used to
pr ovide the solution. The wind turbulence information and wind profile ar e modeled during the wind
distribution calculation in the W indFarm Software, which is consider ed as a vital model in the wind
flow simulation.
A unique featur e of W indFarm that has not been found on other CFD modeling softwar e, is that it
can be used alongside “W asP”, “W indSim” and “Meteodyn WT”, wher e wind field files fr om those
pr ograms can be imported and used in W indFarm. This is advantageous, because wind data from
all of the models previously listed can be compar ed in W indFarm, and a user is able to find the most
accurate and satisfying r esults for their project by using only one pr ogram (27).
The number of grid points to each dir ection is 161
×
161
×
51 points in the main-flow-dir ection (x),
span wise dir ection (y) and vertical direction (z), r espectively . The grid widths of directions x and y
wer e approximately uniform intervals, with a horizontal r esolution of approximately 100 m.
The W indFarm simulation tool has been used for the prediction of the wind flow over the
topography . The background terrain height information extends 20 km
×
20 km the simulation
domain size extends 12.8 km
×
12.8 km, and the inner r egion with boundary condition independency
appr oximately 6 km × 6 km.
4. Results and Discussion
4.1. Wind Data Analysis
The wind r ose and wind data analysis of the mast location Um Al Rumman ar e presented in the
following. The wind data and the wind potential of the studied location ar e correlated based on the
wind data of Um Al Rumman.
4.1.1. W ind Speed Brief Statistics
The mean wind speed at 10 m height is 5 m / s, where the maximum average wind speed was
r ecorded in 29 January 2008, 20:00 is 19.8 m / s. The maximum gust happened in 29 January 2008, 8:30
and it r eached 28.9 m / s. Moreover , the uncertainty of the measurement is 0.2 m / s and mean turbulence
intensity is 13.3% at a height of 10 m.
4.1.2. W eibull Distribution Parameters
The identification of the W eibull distribution relies mainly on two parameters, being the shape
factor (k) that describes the form of distribution and the scale factor (C) that r epresents the wind
speed, wher e the values of these parameters are 1.88 and 5.8, r espectively . Figur e 2 shows the W eibull
distribution curve of the selected location.

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Figure 2. Hi sto g ram of the wi nd spee d and t h e approximated We ibu ll dis t ribu tion.
The main wind dir e ctions are WSW, W , ENE with 51 .2 8 % , 2 8 .35% a n d 26 .4 5%, respect i vel y , a s
shown in Fig u re 2.
4.1.3. Expected Win d Ener gy Pro d uctio n
Wi nd R o se Sof t wa re a s sum e s the i n st a l l a ti on of the wind turbi n e ra ther tha n the meteorol ogi c al
mast in or der to calculate the expected en ergy prod uction of the w i n d turbin e, based on the me asured
data an d its p o wer curve. Accordin gly , the expected energy production produce d by the so ftware is
4, 23 5, 2 0 2 k W h, and t h e c a p a cit y f a ct or i s 25 .9% . The r e su lt s al so sh ow t h e annu a l ene r gy p r od uct i on,
wi th the ca p a ci ty fa ctors based on the time di stri bution a n d Wei b ul l di stri but i on.
4. 1. 4. The Bes t Sect ors
The best sect or re gar d ing t h e ener gy co ntent and the ti me di stri b u ti on cha r t ha v e been f o und t o
b e WSW w i t h 51 .2 8%, an d W wit h 2 8 . 3 5 %, re sp ec t i vely . Wh ile t h e second-b est sect or in energ y
content is W , and the secon d -best sector in time d i st rib u t i on i s E N E, as shown in F i gur e s 3 and 4.

Figure 3. Wind Rose based on energ y .

Figure 2. Histogram of the wind speed and the approximated W eibull distribution.
The main wind dir ections are WSW , W , ENE with 51.28%, 28.35% and 26.45%, r espectively ,
as shown in Figur e 2 .
4.1.3. Expected W ind Energy Pr oduction
W ind Rose Software assumes the installation of the wind turbine rather than the meteor ological
mast in or der to calculate the expected energy pr oduction of the wind turbine, based on the measured
data and its power curve. Accordingly , the expected ener gy production pr oduced by the software is
4,235,202 kWh, and the capacity factor is 25.9%. The results also show the annual ener gy production,
with the capacity factors based on the time distribution and W eibull distribution.
4.1.4. The Best Sectors
The best sector r egarding the ener gy content and the time distribution chart have been found to
be WSW with 51.28%, and W with 28.35%, respectively . While the second-best sector in ener gy content
is W , and the second-best sector in time distribution is ENE, as shown in Figures 3 and 4 .
Atmosphere 202 0 , 11 , x FOR PEER REVIEW 6 of 14

Figure 2. Hi sto g ram of the wi nd spee d and t h e approximated We ibu ll dis t ribu tion.
The main wind dir e ctions are WSW, W , ENE with 51 .2 8 % , 2 8 .35% a n d 26 .4 5%, respect i vel y , a s
shown in Fig u re 2.
4.1.3. Expected Win d Ener gy Pro d uctio n
Wi nd R o se Sof t wa re a s sum e s the i n st a l l a ti on of the wind turbi n e ra ther tha n the meteorol ogi c al
mast in or der to calculate the expected en ergy prod uction of the w i n d turbin e, based on the me asured
data an d its p o wer curve. Accordin gly , the expected energy production produce d by the so ftware is
4, 23 5, 2 0 2 k W h, and t h e c a p a cit y f a ct or i s 25 .9% . The r e su lt s al so sh ow t h e annu a l ene r gy p r od uct i on,
wi th the ca p a ci ty fa ctors based on the time di stri bution a n d Wei b ul l di stri but i on.
4. 1. 4. The Bes t Sect ors
The best sect or re gar d ing t h e ener gy co ntent and the ti me di stri b u ti on cha r t ha v e been f o und t o
b e WSW w i t h 51 .2 8%, an d W wit h 2 8 . 3 5 %, re sp ec t i vely . Wh ile t h e second-b est sect or in energ y
content is W , and the secon d -best sector in time d i st rib u t i on i s E N E, as shown in F i gur e s 3 and 4.

Figure 3. Wind Rose based on energ y .

Figure 3. W ind Rose based on energy .

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Figure 4. Wind Rose based on tim e .
The main win d speed, d i re ction and d a ta distri buti on f o r the Umm Al Rumman meteorologic al
mast are sho w n in F i g u res 5–7.

Figure 5. Wi n d Ros e mai n res u l t s regard ing the mean wi nd s p eed for the Um Al Rumma n
meteorological mast .

Figure 6. Win d Rose m a in r e su lts reg a rdin g the m a in wind direct ions f o r the Um Al Ru m m a n
meteorological mast .

Figure 4. W ind Rose based on time.
The main wind speed, dir ection and data distribution for the Umm Al Rumman meteor ological
mast ar e shown in Figures 5 – 7 .
Atmosphere 202 0 , 11 , x FOR PEER REVIEW 7 of 14

Figure 4. Wind Rose based on tim e .
The main win d speed, d i re ction and d a ta distri buti on f o r the Umm Al Rumman meteorologic al
mast are sho w n in F i g u res 5–7.

Figure 5. Wi n d Ros e mai n res u l t s regard ing the mean wi nd s p eed for the Um Al Rumma n
meteorological mast .

Figure 6. Win d Rose m a in r e su lts reg a rdin g the m a in wind direct ions f o r the Um Al Ru m m a n
meteorological mast .

Figure 5.
W ind Rose main results r egarding the mean wind speed for the Um Al Rumman
meteorological mast.
Atmosphere 202 0 , 11 , x FOR PEER REVIEW 7 of 14

Figure 4. Wind Rose based on tim e .
The main win d speed, d i re ction and d a ta distri buti on f o r the Umm Al Rumman meteorologic al
mast are sho w n in F i g u res 5–7.

Figure 5. Wi n d Ros e mai n res u l t s regard ing the mean wi nd s p eed for the Um Al Rumma n
meteorological mast .

Figure 6. Win d Rose m a in r e su lts reg a rdin g the m a in wind direct ions f o r the Um Al Ru m m a n
meteorological mast .

Figure 6.
W ind Rose main results r egarding the main wind directions for the Um Al Rumman
meteorological mast.

Atmosphere 2020 , 11 , 117 8 of 14
Atmosphere 202 0 , 11 , x FOR PEER REVIEW 8 of 14

Figure 7. Mean and Max Wind Speed for selective months.
Fig u re 7 sho w s t h e m e an and m a xim u m wind sp ee d for sample months durin g the test perio d
over 12 months. The best month was January, in which the mea n a n d ma ximum wind speed comes
from three di rections (W S W , SW and S S W) . The win d di rect ion c o mes f r om t h e specif ic sect o r W S W
in Ma rch and Apr il. Reg a r d ing t h e sum m er s e ss ion, most wind co mes from t h e west , wh ile i n t h e
autumn session the wind c o mes from the WSW and E N E sectors.

Figure 7. Mean and Max W ind Speed for selective months.
Figur e 7 shows the mean and maximum wind speed for sample months during the test period
over 12 months. The best month was January , in which the mean and maximum wind speed comes
fr om three dir ections (WSW , SW and SSW). The wind dir ection comes from the specific sector WSW
in Mar ch and April. Regarding the summer session, most wind comes fr om the west, while in the
autumn session the wind comes fr om the WSW and ENE sectors.

Atmosphere 2020 , 11 , 117 9 of 14
4.2. Layout Arrangement
4.2.1. Conventional Arrangement
The distance between the wind turbines varies between 5 and 2.5 times of the r espective rotor
diameters of the selected wind turbine. The simulation results of the wind shear at the met mast
location have an influence on the power generation of each turbine. The e ff ect of the topography to the
average wind speed r esults in an acceleration and concentration, especially wher e the slopes of the
terrain ar e steep.
Arranging the turbines in di ff er ent layouts (M shape, Straight line and Arch shape) facing main
wind dir ections with 5 (5D) and 2.5 (2.5D) times the distance of the rotor diameter (D) between each
turbine as shown in Figur e 8 .
Atmosphere 202 0 , 11 , x FOR PEER REVIEW 9 of 14

4. 2. Layo ut Ar range m en t
4. 2. 1. C o nven t i onal Arr a ng em ent
The dist ance between the wind turbin e s var i es betw een 5 and 2.5 times of the respective rot o r
diameter s o f the se lected wind t u rbine . The simula t i on r e sults of the w i nd sh ear at the me t mast
locat i on h a ve an in fl uence on t h e power generat i on o f each turbine . The effect of the topography to
the aver ag e wind speed r e sults in an acceler a tion and concentr ation, espec i ally wher e the slopes o f
the terrain ar e steep.
Arran g ing the turbines in different layo uts (M sh ap e, Straight line and Arch sh ape) facin g main
wind d i rect io ns wit h 5 ( 5 D ) and 2. 5 (2 .5 D) t i m e s t h e dist anc e of t h e rot o r d i am e t er (D) b e t w e e n each
t u rbine as sh own in Fig u r e 8 .

( a ) 5D M sh ape ( b ) 2.5D M shape

( c ) 5D Straigh t line ( d ) 2.5D Stra ight lin e

( e ) 5D Arch s hape ( f ) 2.5D Arch shape
Figure 8. Wind tu rbines layou t s and spac ing.
It has been c a lc ulated that the maximum predict e d energy gen e ration from each turbin e is
aroun d 6. 8 9 G W h p e r ye a r ( i n t o t a l for t h e five t u rb i n es: 3 4 . 4 7 G W h/ year ). A l so, applying different
lay o uts h a s r e sulted in different ener gy yie l ds bec a u s e of di ffer e n t losse s, s u ch as w a ke ef fec t s and
topographic influences. Th e wake effect can be des cr ibed as accumulated losses on the w i n d farm
energy y i eld, caused by turbulences and reduced wi nd veloc i t i es by neighb oring win d t u rbines .
Whi l e the topogra p hy of the si te i n cl udes hi ll s, edg es, cl if fs an d mount a inou s, t h e t e rr ain ha s a
subst a nt i a l in flu ence on t h e wind speed and d i rect ion at t h is spec if i c sit e .

Figure 8. W ind turbines layouts and spacing.
It has been calculated that the maximum predicted ener gy generation from each turbine is
ar ound 6.89 GWh per year (in total for the five turbines: 34.47 GWh / year). Also, applying di ff erent
layouts has r esulted in di ff erent ener gy yields because of di ff erent losses, such as wake e ff ects and
topographic influences. The wake e ff ect can be described as accumulated losses on the wind farm
ener gy yield, caused by turbulences and r educed wind velocities by neighboring wind turbines. While
the topography of the site includes hills, edges, cli ff s and mountainous, the terrain has a substantial
influence on the wind speed and dir ection at this specific site.

Atmosphere 2020 , 11 , 117 10 of 14
Arranging the turbines facing the main wind dir ection with five times the rotor diameter distance
between each turbine r esults in 22.75, 22.87 and 21.997 GWh / a for the M shape (Figur e 8 a), Straight line
(Figur e 8 c), and Arch shape (Figur e 8 e), respectively . While reducing the distance between turbines to
2.5 times the r otor diameter results in a r eduction in the wind farm energy yield to 22.68 (Figur e 8 b),
21.498 (Figur e 8 d) and 21.546 (Figure 8 f) GWh / a for M shape, Straight line and Arch shape, r espectively .
T able 1 summarizes the distances between the turbines, the topographic e ff ects, the wake losses, and the
total ener gy of the arrangement of the turbines in di ff erent layouts (a–f in Figur e 8 ).
T able 1. Summary of the wind turbines arrangement.
Layout Distance between
the T urbines (m)
T opographic
E ff ects (%)
W ake
Losses (%)
T otal Energy
(GWh)
5D M shape 450 30.10 5.57 22.75
2.5D M shape 225 27.83 14.03 22.68
5D Straight 450 29.29 6.17 22.87
2.5D Straight 225 26.80 14.8 21.498
5D Arch Shape 450 30.87 7.68 21.997
2.5D Arch Shape 225 25.61 15.97 21.546
4.2.2. Optimized Arrangement
Designing the optimum layout of the wind turbines on this hypothetical site, including a few
practical r estrictions, is a fairly straightforward pr oblem. However , applying optimization algorithms
to r eal sites necessitates combining many boundary conditions that exist for a real site. These constraints
include land ownership, property boundaries, wake e ff ects of one turbine to another , the resulting noise
level at nearby dwellings, as well as natural r estrictions like steep slopes, rivers, bogs and planning
constraints. The main points of the optimization are the following constraints:
• Maximization of the ener gy yield;
• Containment of the wind farm within the for eseen area for the development;
• Distance between the wind turbines gr eater than three times the r otor diameter .
Figur e 9 shows the position of the wind turbines after optimization, presented on the wind
speed map.
Atmosphere 202 0 , 11 , x FOR PEER REVIEW 11 of 14

Figure 9. Po sition of the w i nd turbines after o p timization on wind spee d ma p.
The t o pograp hy from a sp ecif ic point of int e rest f a cin g t h e wind fa rm are a i s sh own in Fi gur e
10 . Thi s f a ci lit at es t h e asse s s m e nt of t h e v i su al im p a ct of the wind farm within the close v i cin i t y from
the perspective of h u man s .

Figure 10. Lo ca tion of viewpoints of interest in th e surrounding wind farms (WFs) from th e SSW .
The optimization procedur e aims to the arr a ngemen t of the wi nd t u rbi n es under the constra i nts
of the exp l oi t a ti on of the regi on wi th the hi gh wi nd po t e nt ial, whi l e simu lt aneo us ly keeping t h e wa ke
losse s at the lowest poss ible leve l. For th e optimized la yout, i t is ev i d ent tha t the wi nd f a rm uni t s a r e
opti ma ll y distri buted over the hi gh wind speed a t the top of the hil l . Figure 11 shows tha t the
optimized layout signific antly red u ces topography losses wh ile simult aneously keepin g the wak e
effect s at a lo w leve l.

Figure 9. Position of the wind turbines after optimization on wind speed map.

Atmosphere 2020 , 11 , 117 11 of 14
The topography fr om a specific point of interest facing the wind farm ar ea is shown in Figure 10 .
This facilitates the assessment of the visual impact of the wind farm within the close vicinity fr om the
perspective of humans.
Atmosphere 202 0 , 11 , x FOR PEER REVIEW 11 of 14

Figure 9. Po sition of the w i nd turbines after o p timization on wind spee d ma p.
The t o pograp hy from a sp ecif ic point of int e rest f a cin g t h e wind fa rm are a i s sh own in Fi gur e
10 . Thi s f a ci lit at es t h e asse s s m e nt of t h e v i su al im p a ct of the wind farm within the close v i cin i t y from
the perspective of h u man s .

Figure 10. Lo ca tion of viewpoints of interest in th e surrounding wind farms (WFs) from th e SSW .
The optimization procedur e aims to the arr a ngemen t of the wi nd t u rbi n es under the constra i nts
of the exp l oi t a ti on of the regi on wi th the hi gh wi nd po t e nt ial, whi l e simu lt aneo us ly keeping t h e wa ke
losse s at the lowest poss ible leve l. For th e optimized la yout, i t is ev i d ent tha t the wi nd f a rm uni t s a r e
opti ma ll y distri buted over the hi gh wind speed a t the top of the hil l . Figure 11 shows tha t the
optimized layout signific antly red u ces topography losses wh ile simult aneously keepin g the wak e
effect s at a lo w leve l.

Figure 10. Location of viewpoints of interest in the surr ounding wind farms (WFs) from the SSW .
The optimization pr ocedure aims to the arrangement of the wind turbines under the constraints
of the exploitation of the r egion with the high wind potential, while simultaneously keeping the wake
losses at the lowest possible level. For the optimized layout, it is evident that the wind farm units
ar e optimally distributed over the high wind speed at the top of the hill. Figur e 11 shows that the
optimized layout significantly r educes topography losses while simultaneously keeping the wake
e ff ects at a low level.
Atmosphere 202 0 , 11 , x FOR PEER REVIEW 12 of 14

Figure 11. Top o g r aphic effect s and wak e l o ss es of s i x lay o u t s and optim i ze d arrang em ent of the w i nd
turbines.
The overall increase in en ergy y i eld fr om the op ti mi za ti on process ra nges f r om 9 % to a l most
16% , com p ar ed t o t h e f i n a l Jer a sh wind farm ener gy yiel d o f 2 4 . 9 2 G W h/ a, as s h own in F i gu re 12 . It
coul d be shown tha t the p e rcenta ge change i n the ov erall energ y g e neration f o r the opti mi zed wi nd
turbines com p ared to the modeled layo uts h a s been in the ran g e between 8.9% (for the 5D straigh t
l a yout) to 15 .9 % (f or the 2.5 D strai g ht layout) . Fi gure 1 2 shows the compa r ison of the tota l energy
generat i on of the six ex am ined layo uts and th e optimized arr a ng ement of the w i n d turbine s .

Figure 12. Total energy generati on of the six l a youts and the optimized arrangeme n t of the wind
turbines.
5. Con c lus i o n s
D u r i n g r e c e n t y e a r s , m o r e d e v e l o p m e n t s h a v e b een d o ne for an opt i mized arran g ement o f w i nd
t u r b i n e s i n l a r g e r w i n d f a r m s . I n t h i s r e s e a r c h i t c o u l d b e s h o w n t h a t o p t i m i z a t i o n t e c h n i q u e s h a v e
been used to opti mi z e the wi nd f a rm l a yout to harvest the maximum possible energ y fr om the
prevailing w i nd. It h a s b een fo und t h at the max i mum predict e d energ y g e neration fro m the
opti mi z e d wind turbi n es is 34 .47 GWh/ a i n tota l. A l so, it has bee n shown that apply i ng different
l a youts has a si gnif i c a n t ef f e ct to the energy yiel ds d u e to mai n ly ef f e c t s l i k e the wa ke e f f e c t a n d

Figure 11.
T opographic e ff ects and wake losses of six layouts and optimized arrangement of the
wind turbines.
The overall incr ease in energy yield fr om the optimization process ranges fr om 9% to almost 16%,
compar ed to the final Jerash wind farm energy yield of 24.92 GWh / a, as shown in Figur e 12 . It could be
shown that the per centage change in the overall energy generation for the optimized wind turbines
compar ed to the modeled layouts has been in the range between 8.9% (for the 5D straight layout) to
15.9% (for the 2.5D straight layout). Figure 12 shows the comparison of the total ener gy generation of
the six examined layouts and the optimized arrangement of the wind turbines.

Atmosphere 2020 , 11 , 117 12 of 14
Atmosphere 202 0 , 11 , x FOR PEER REVIEW 12 of 14

Figure 11. Top o g r aphic effect s and wak e l o ss es of s i x lay o u t s and optim i ze d arrang em ent of the w i nd
turbines.
The overall increase in en ergy y i eld fr om the op ti mi za ti on process ra nges f r om 9 % to a l most
16% , com p ar ed t o t h e f i n a l Jer a sh wind farm ener gy yiel d o f 2 4 . 9 2 G W h/ a, as s h own in F i gu re 12 . It
coul d be shown tha t the p e rcenta ge change i n the ov erall energ y g e neration f o r the opti mi zed wi nd
turbines com p ared to the modeled layo uts h a s been in the ran g e between 8.9% (for the 5D straigh t
l a yout) to 15 .9 % (f or the 2.5 D strai g ht layout) . Fi gure 1 2 shows the compa r ison of the tota l energy
generat i on of the six ex am ined layo uts and th e optimized arr a ng ement of the w i n d turbine s .

Figure 12. Total energy generati on of the six l a youts and the optimized arrangeme n t of the wind
turbines.
5. Con c lus i o n s
D u r i n g r e c e n t y e a r s , m o r e d e v e l o p m e n t s h a v e b een d o ne for an opt i mized arran g ement o f w i nd
t u r b i n e s i n l a r g e r w i n d f a r m s . I n t h i s r e s e a r c h i t c o u l d b e s h o w n t h a t o p t i m i z a t i o n t e c h n i q u e s h a v e
been used to opti mi z e the wi nd f a rm l a yout to harvest the maximum possible energ y fr om the
prevailing w i nd. It h a s b een fo und t h at the max i mum predict e d energ y g e neration fro m the
opti mi z e d wind turbi n es is 34 .47 GWh/ a i n tota l. A l so, it has bee n shown that apply i ng different
l a youts has a si gnif i c a n t ef f e ct to the energy yiel ds d u e to mai n ly ef f e c t s l i k e the wa ke e f f e c t a n d

Figure 12.
T otal energy generation of the six layouts and the optimized arrangement of the wind turbines.
5. Conclusions
During r ecent years, more developments have been done for an optimized arrangement of wind
turbines in lar ger wind farms. In this resear ch it could be shown that optimization techniques have been
used to optimize the wind farm layout to harvest the maximum possible ener gy from the pr evailing
wind. It has been found that the maximum pr edicted energy generation fr om the optimized wind
turbines is 34.47 GWh / a in total. Also, it has been shown that applying di ff er ent layouts has a significant
e ff ect to the ener gy yields due to mainly e ff ects like the wake e ff ect and topographic losses. The wake
can be described as the accumulated e ff ect on the wind farm ener gy yield, resulting fr om fluctuations
in wind speed caused by the neighboring wind turbines. While the topography of the site includes
hills, edges, cli ff s and mountainous terrain, the specific conditions have a substantial influence upon
the wind speed and dir ection.
For the five turbines that have been optimized in the site, the arrangement of the turbines facing
the main wind dir ection, with five times the rotor diameter distance between each turbine, r esulted in
22.75, 22.87 and 21.997 GWh / a for the M shape, Straight line and Ar ch shape, respectively . Reducing
the distance between THE turbines to 2.5 times the rotor diameter decr eased the wind farm ener gy
yield to 22.68, 21.498 and 21.5463 GWh / a for the M shape, Straight line and Arch shape, r espectively .
It can be concluded that an optimized arrangement allows a significant increase in power generation
while consequently r educing the costs of the wind farm.
Author Contributions:
Conceptualization, M.A.-A. and A.A.; methodology , M.J. and S.A.H.; softwar e, S.A.H.;
formal analysis, M.A.-A. and A.A.; investigation, M.J. and S.A.H.; resour ces, J.W .; data curation, M.A.-A.;
writing—original draft, M.J.; writing—review and editing, J.W .; supervision, M.A.-A. All authors have read and
agreed to the published version of the manuscript.
Funding: This resear ch received no external funding.
Acknowledgments:
The authors ar e grateful for the support of the Deanship of Graduate Studies and Research at
the German Jordanian University .
Conflicts of Interest: The authors declare no conflict of inter est.

Atmosphere 2020 , 11 , 117 13 of 14
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