CYPRUS UNIVERSITY OF TECHNOLOGY
FACULTY OF ENGINEERING AND TECHNOLOGY
Master Thesis
“AN INTEGRATED METHOD FOR WIND POWER
ESTIMATION: APPLICATION FOR WEST CYPRUS
AREAS”
Ioannis Kastanas
Limassol 2013
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“We cannot direct the wind, but we can adjust the sails!”
English proverb
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CYPRUS UNIVERSITY OF TECHNOLOGY
FACULTY OF ENGINEERING AND TECHNOLOGY
DEPARTEMENT OF CIVIL ENGINEERS AND GEOMATICS
“AN INTEGRATED METHOD FOR WIND POWER
ESTIMATION: APPLICATION FOR WEST CYPRUS
AREAS”
Ioannis Kastanas
A dissertation submitted in partial fulfillment of the
requirements for the degree of
MSc Civil Engineering and Sustainable Design
Limassol 2013
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APPROVAL FORM
Master Thesis
“An Integrated Method for Wind Power Estimation:
Application for West Cyprus Areas”
Presented by
Ioannis Kastanas
Dissertation Supervisor: Assistant Professor Dr. Evangelos Akylas
Committee Member: Associate Professor Dr. Diofantos Hatzimitsis
Committee Member: Lecturer Dr. Lisandros Pantelidis
Cyprus University of Technology
September, 2013
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Declaration
Copyright © Ioannis Kastanas, 2013
All rights reserved.
The approval of the thesis from the Civil Engineers and Geomatics Department does not
necessarily imply acceptance the opinions of the author, on behalf of the Department.
I Giannis Kastanas, confirm that this is my work submitted for assessment is my own and is
expressed in my own words. Any uses made within it of the works of other authors in any
form (e.g. ideas, equations, figures, text, tables, programs) are properly acknowledged at the
point of their use. A full list of the references employed has been included.
Signed …………………………………………………..
Date ……………………………………………………..
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PREFACE
Carrying out my Master Thesis is a lengthy and extensive task, which cannot be
completed without the involvement and help of a variety of people and institutions. Firstly, I
would like to thank my Supervisor Dr. Evangelos Akylas for his continuous support and
guidance. His contribution was continuous throughout the course of processing the thesis. I
would also like to thank him for his confidence both in the selection of the dissertation topic,
and in the award of the subject.
Special thanks for valuable suggestions and instructions and for providing data,
deserve to the Director of the Cyprus Meteorology Services Mr. Stelios Passiardis. Without
his help, my dissertation couldn’t have been complete. I am grateful to Mr. Andreas Georgiou
for the unwavering and generous scientific support. Undoubtedly he was always near in my
side whenever I asked him.
Finally, I would like to thank my family for unparalleled support throughout the
duration of my studies. Without their support this thesis would not have been possible. Also, I
would like to express gratitude to Katerina Loukaidou, Floros Papagewrgiou, Nicolas Loizou,
Eleftheria Tsoukka as well as to all of my friends.
“I am indebted to my father for living, but to my teacher for living well”
Alexander the Great
Ioannis Kastanas
Limassol, September 2013
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ABSRACT
The motivation of this study was to explore the possibility of estimating the wind energy potential at
several areas in order to cover the western part of the island with the respective information. The
methodology of the analysis was based on a standard application program, WAsP. Monthly wind
speed and direction statistics (on a bi – daily basis, every 12 hours) for six stations from 2001 – 2008,
show a strong influence of sea – breeze, which is very intense, especially in the southern coast of the
island. In the case of the northern coast (Polis station and Kato Pyrgos), the wind speed remains
relatively high also during the night, exhibiting much lower daily variation, although still changing its
variation. The wind statistics obtained here, served as the basis in order to estimate corrected
statistical distributions over the extended areas of application through Wind Atlas Analysis and
Application Program (WAsP) which modifies the wind flow due to local topographic effects.
Aggregation of the data with statistical weighting methods, allowed the extrapolation of the results
and the visualization over the western part of the island. It appears that coastal areas are affected by
local flows of sea breeze that is dependent by the succession of land and sea. Mean wind speed values
at stations of Limassol and Mallia range to 2 – 5m/s, at Pafos about 5m/s, at Prodromos at 3 – 4m/s,
at Kato Pyrgos at 2.5m/s – 3m/s, and at Polis at 3m/s. The application indicates that interesting
points with higher wind energy potential, suitable for wind resource exploitable exist. The wind
potential analysis through WAsP showed that all areas are influenced significantly by the complex
orography model and the wind speed easily reaches the value of 10m/s. However, further application
and inclusion of all the data available from nearby stations is needed in order to improve the
accuracy and complete the coverage of the island. The particular methodological framework applied
and the results obtained can be utilized by potential investors and wind energy developers.
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ΠΕΡΙΛΗΨΗ
Η παρούσα Μεταπτυχιακή Διατριβή αποτελεί την διερεύνηση και την εκτίμηση του αιολικού δυναμικού
σε έξι περιοχές, καλύπτοντας έτσι το δυτικό κομμάτι του νησιού. Η μεθοδολογία για την ανάλυση του
αιολικού δυναμικού βασίστηκε στο πρόγραμμα WAsP. Εξετάστηκαν, στατιστικά στοιχεία του ανέμου
που προέκυψαν από την επεξεργασία μετρήσεων της μετεωρολογικής υπηρεσίας από το 2001 – 2008. Η
στατιστική περιγραφή έγινε σε μηνιαία ανά δωδεκάωρο βάση (08:00 – 19:00 και 20:00 – 07:00) για
τους έξι σταθμούς, όπου παρατηρήθηκε ισχυρή επιρροή προερχόμενη από την θάλασσα προς την ξηρά,
η οποία είναι πολύ έντονη στα Νότια παράλια του νησιού. Στην περίπτωση των Βόρειων παραλιακών
σταθμών (Πόλις Χρυσοχού και Κάτω Πύργος), παρατηρήθηκε ότι η ταχύτητα του ανέμου παραμένει
σχετικά υψηλή κατά την διάρκεια της νύχτας, με μικρότερη διακύμανση και σχετικά χαμηλότερες
ταχύτητες κατά την διάρκεια της μέρας. Τα στατιστικά στοιχεία για τους σταθμούς μελέτης
χρησιμοποιήθηκαν για διόρθωση των στατιστικών κατανομών πάνω από τις εκτεταμένες περιοχές
διαμέσου του προγράμματος WAsP που τροποποιεί την ροή του ανέμου εν σχέση με την τοπογραφία των
περιοχών μελέτης. Διορθώσεις και ομαδοποιήσεις των μετεωρολογικών δεδομένων προήλθαν μέσω
στατιστικών των μεθόδων της παλινδρόμησης και βαρυτικών μεθόδων, επιτρέποντας έτσι την επέκταση
των μετρήσεων που υπολείπονταν για την σωστή απεικόνιση του αιολικού. Από την ανάλυση φαίνεται
ότι οι παραλιακές περιοχές είναι επηρεασμένες από θαλάσσιες αύρες λόγο της εναλλαγής θερμοκρασίας
θάλασσας και εδάφους. Οι μέσες τιμές των ταχυτήτων που προέκυψαν από την στατιστική περιγραφή
δεικνύουν ότι στην Λεμεσό και στα Μαλλιά οι ταχύτητες κυμαίνονται από 2 – 5m/s, στην Πάφο 5m/s,
Πρόδρομος 3 – 4m/s, Κάτω Πύργος 2.5 – 3m/s και Πόλις Χρυσοχούς 3m/s. Από την εφαρμογή
προέκυψαν σημαντικές περιοχές με υψηλό και αξιοποιήσιμο αιολικό δυναμικό. Από την ανάλυση του
αιολικό δυναμικού διαμέσου το μοντέλου του WAsP προέκυψε ότι εύκολα το αιολικό δυναμικό μπορεί
να φτάσει τα 10m/s λόγο της επίδρασης της έντονης τοπογραφίας του εδάφους. Ωστόσο, απαιτούνται
περαιτέρω εφαρμογές και ενσωμάτωση περισσότερων σταθμών προκειμένου να βελτιωθεί η ακρίβεια
της παρούσας μεταπτυχιακής μελέτης αλλά και για να εκτιμηθεί το αιολικό δυναμικό σε όλο το νησί. Τα
αποτελέσματα που προέκυψαν από αυτή την μελέτη μπορούν να χρησιμοποιηθούν από επενδυτές και
κατασκευαστές αιολικών πάρκων.
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CONTENTS
ABSRACT ................................................................................................................................ xi
ΠΕΡΙΛΗΨΗ ............................................................................................................................. xii
LIST OF TABLES ................................................................................................................ xvii
LIST OF FIGURES ................................................................................................................ xix
PERFORMANCE OF TERMS ........................................................................................... xxvii
INTRODUCTION ................................................................................................................ xxix
Topic xxix
Aims and objectives ........................................................................................................... xxx
Research Methodology .................................................................................................... xxxii
Dissertation Structure ...................................................................................................... xxxii
Expected Results ............................................................................................................. xxxiii
1 REVIEW OF EXISTING LITERATURE ......................................................................... 1
1.1 Introduction ............................................................................................................... 1
1.2 Studying of Wind Energy Potential ........................................................................... 1
1.3 Wind Energy Development ....................................................................................... 7
1.3.1 Global Wind Energy Development ..................................................................... 7
1.3.2 Wind Energy Development in Cyprus .............................................................. 12
1.4 Models for Wind Energy Analysis .......................................................................... 14
1.4.1 Wind Atlas Model ............................................................................................. 17
1.4.2 Models for Wind Energy Comparison .............................................................. 25
1.4.3 Comparison between Models ............................................................................ 29
1.5 Conclusion ............................................................................................................... 34
2 WIND ENERGY ASSESSMENT AND ANALYSIS ..................................................... 37
2.1 The Physical Basis of Wind Atlas Analysis Model ................................................. 37
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2.1.1 Wind Atlas Application ..................................................................................... 43
2.2 Meteorology of Wind .............................................................................................. 45
2.3 Atmospheric Stability .............................................................................................. 49
2.4 Geostrophic Wind .................................................................................................... 53
2.5 The Roughness Change the Model .......................................................................... 54
2.6 The Shelter Model ................................................................................................... 56
2.7 The Orography Model ............................................................................................. 58
2.8 Climatology ............................................................................................................. 61
2.9 Wind Speed Statistics .............................................................................................. 63
2.10 The Statistical Review of Model ............................................................................. 65
2.10.1 Weibull Distribution .......................................................................................... 68
2.10.2 Determining the Weibull Parameters ................................................................ 70
2.11 Errors of Model and Data ........................................................................................ 72
3 METHODOLOGY ........................................................................................................... 77
3.1 Introduction ............................................................................................................. 77
3.2 Study Areas ............................................................................................................. 78
3.3 Physical Environment .............................................................................................. 80
3.4 Climatology ............................................................................................................. 81
3.5 Land Uses and Features of Areas ............................................................................ 82
3.6 Maps Preparation for admission to WAsP .............................................................. 84
3.6.1 Simple Extraction Method ................................................................................. 84
3.6.2 Reliable Method ................................................................................................ 85
3.7 Measuring Variables and Data Processing .............................................................. 90
3.8 Pre – Statistical Data Processing ............................................................................. 92
3.9 Export of Final Results Using the Wind Atlas Analysis and Application Program 94
3.9.1 Problems and Limitations .................................................................................. 97
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3.10 Visualization for Maps of Wind Potential at Studying Area – Wind Atlas ............ 98
4 RESULTS AND ANALYSIS ........................................................................................ 101
4.1 Introduction ........................................................................................................... 101
4.2 Statistical Results for Study Stations ..................................................................... 101
4.3 Monthly Distribution of Average Wind Speeds .................................................... 111
4.4 Hourly Distribution of Average Wind Speeds ...................................................... 115
4.5 Wind Energy Potential Final Maps and Analysis .................................................. 121
CONCLUSIONS ................................................................................................................... 139
5.1. Introduction ........................................................................................................... 139
5.2. Achieving the Aims and Objectives ...................................................................... 139
5.3. Overview of the Findings ...................................................................................... 140
5.4. Evaluation of Results and Recommendations ....................................................... 145
BIBLIOGRAPHY ................................................................................................................. 147
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LIST OF TABLES
Table 1 Regional distribution of global wind energy potential has been used for onshore
development. The table below represents a comparison between different studies. It follows
that the four studies have improved similar results. Source: (Hoogwijk et al., 2004) and
(Hoogwijk & Graus, 2008). ....................................................................................................... 5
Table 2 Global installed wind power capacity in MW – Regional Distribution ..................... 11
Table 3 Parameters for vertical wind speed profiles calculation ............................................. 40
Table 4 Meteorological stations specifications ....................................................................... 78
Table 5 Soil Roughness Values ............................................................................................... 83
Table 6 Monthly averaged speeds for the stations of Limassol, Pafos, Polis, Pyrgos,
Prodromos and Malia ............................................................................................................ 112
Table 7 Hourly averaged speeds for the stations of Limassol, Pafos, Polis, Pyrgos, Prodromos
and Malia ............................................................................................................................... 117
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LIST OF FIGURES
Figure 1 The global onshore wind power potential. Map shows ‘feasible’ power potential that
could be extracted as electricity (wooded/ permafrost/ urban was excluded). Suitable mid-
west states have power density of approximately 3 – 4 W/m2. Moreover, round the
Mediterranean the wind potential is about 0.0 – 1.2 W/m2 with the maximum amount of
energy occurring at the Turkey coastal area. Source: (X. Lu et al., 2009). ............................... 2
Figure 2 Mean Wind Speed in Europe at 80m height. Source: (Jacobson, 2007) ..................... 3
Figure 3 Mean Wind Speed in North America at 80m height. Source: (Jacobson, 2007) ........ 3
Figure 4 A global Wind Energy Power map 5km x 5km as was estimated by 3TIER. Source:
(3TIER, 2009) ............................................................................................................................ 6
Figure 5 The Global annual installed wind capacity 1996 – 2012. Source: (GWEC, 2012) .... 7
Figure 6 Trends in the global market. World total installed capacity in MW. Source:
(Schilling, 2010) ........................................................................................................................ 8
Figure 7 World wind energy development growth rate. As is defined from the graph the wind
capacity doubles every 3 years. Source: (Schilling, 2010) ........................................................ 9
Figure 8 The top 10 countries in wind energy development. More countries were invested in
wind energy. Specifically Marocco, New Zealand, and Turkey were turned to wind energy
when at the same time the market became bigger than 100MW by the 2009. Source:
(Schilling, 2010) ........................................................................................................................ 9
Figure 9 The total wind capacity of 10 top countries by 2009. Source: (Schilling, 2010) ...... 10
Figure 10 The total installed wind capacity from 1997 – 2020 versus development and the
prognosis. The prognosis predicts 10 times higher capacity during the next 7 years. Source:
(Schilling, 2010) ...................................................................................................................... 11
Figure 11 The inter annual average wind speeds in various areas of Cyprus according to Dr.
John Gleka. Source (CIE, 2000) .............................................................................................. 13
Figure 12 An indicative Map for the wind farm installation in Cyprus. The map shows the
planning zones, urban areas, archaeological sites, hill - mountains tops, and green protected
areas from Natura 2000. Source: (MCIT, 2005) ..................................................................... 13
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Figure 13 In this picture emerge routines that are used in the program WAsP for the
calculation of wind potential in Limassol ............................................................................... 18
Figure 14 Wind Atlas Analysis and Application Model for wind potential assessment.
Source: (Mortemsen et al., 2004) ............................................................................................ 19
Figure 15 The Figure shows the wind flow over an ideal – imaginary hill. The wind profile
passes through the upstream side of hill. The two distances characterizing the wind flow. The
L is the characteristic mountain length, which is the half at the middle of the hill. l is the
height where the maximum wind speed occurs as the wind profile penetrates along the hill.
Source: (Troen & Petersen, 1989) ........................................................................................... 20
Figure 16 The contours map of the hill Blasheval at Scotland. The heights above the sea level
are shown by contour lines per 10m. Source: (Troen & Petersen, 1989) ................................ 21
Figure 17 The orographic model of hill Blasheval, Scotland. The hill is seen from the South.
The vertical scale is presented with a factor 5. Source: (Troen & Petersen, 1989) ................. 21
Figure 18 Modification of the wind speed along the horizontal line at the top of the hill
Blasheval. The horizontal axis shows the distance in meters from the hill top. The vertical
axis presents the factor of the relative wind speed increasing and measured at 8m above the
ground surface. The shaded graph below shows the section height of the hill ....................... 21
Figure 19 The Karshruhe Atmospheric Mesoscale Model (KAMM). Source: (Badger, 2006)
................................................................................................................................................. 26
Figure 20 The above map presents the energy flux density E in w/m2 at 45m above the
ground level simulated by the KAMM model on a grid with resolution of 2.5km. Source
(Meso-scale Models, 2011) ..................................................................................................... 26
Figure 21 The combination of KAMM/WAsP to estimate and resolve the local wind climate
................................................................................................................................................. 30
Figure 22 The WAsP methodology of wind resource estimation. From the station statistics
the geostrophic wind can be extrapolated and then using reversely preceding the wind power
of each grid point at studying area is estimated ...................................................................... 31
Figure 23 The WAsP Resource grid. The calculation area and the resolution analysis of the
wind power estimation at area of interest. ............................................................................... 33
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Figure 24 The figure shows the WAsP minus CFD result compare layer draped on elevation
data. Here it is easy to understand that it is in the valleys where the wind speeds are estimated
a bit higher with the WAsP model compared to the CFD model. Source: (WindPro.v.2.9,
2013) ........................................................................................................................................ 33
Figure 25 The Wind Atlas Methodology. The database of wind measurements with the
characteristics of station, the terrain classification around the meteorological station and the
mountain terrain topography heights are used for the calculation of regional climatology.
Then an antistrophe similar procedure is used to estimate the wind flow at each resource grid
point. Source: (Riso Laboratory, 2013) ................................................................................... 38
Figure 26 A schematic representation of the Wind Atlas analysis model. Source: (Troen &
Petersen, 1989) ........................................................................................................................ 39
Figure 27 The vertical profile of wind speed distribution above the terrain surface. Source:
(Chiras, 2010) .......................................................................................................................... 39
Figure 28 A schematic representation of the Wind Atlas application model. Source: (Troen &
Petersen, 1989) ........................................................................................................................ 44
Figure 29 Anemometer height should be at the position to represent absolutely the region
climatology of the studying area. For the purpose of representative measurements the height
should be 2times higher if the anemometer is nearby building areas or 10times far away. Also
planting areas and hills areas are plained extremely important role to the wind speeds
information. Source: (INFORSE, 2013) .................................................................................. 48
Figure 30 In the left one is shown the topography map and at the right is the roughness map.
Source: Željko Ɖurišić Jovan Mikulović ................................................................................. 48
Figure 31 Wind profile characteristics: graphs to the left show a range of wind speed profiles
(shaded area) corresponding to a constant geostrophic wind speed of 10 m/s and a typical
range of surface heat flux. The graphs to the right correspond to G = 20m/s and the same
range of surface heat flux. Source: (Troen & Petersen, 1989) ................................................ 52
Figure 32 Schematic of geostrophic drag law and the geostrophic wind representation.
Source: (WW2010: University of Illinois, 2010) .................................................................... 54
Figure 33 The geostrophic draw law. Source: (Eastern Illinois University, 2013) ................. 54
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Figure 34 The polar zooming grid employed by the model for calculation of flow in complex
terrain. Part of the Great Valley Scotland is seen from a point above Loch Ness. The grid is
superimposed on the terrain and centered on the meteorological station Augustus. The side
length of the upper figure is 12km and the figure shows a smaller part with a side length of
2km. The vertical scale is exaggerated by a factor of 5 .......................................................... 61
Figure 35 Wind speed measured 30 m above flat homogenous terrain in Denmark. Each
graph shows the measured wind speed over the time period indicated. The number of data
points in each graph is 1200, each data point corresponding to the speed averaged over
1/1200 of the period. Vertical axis is wind speed, 0-20 ms-1
(Courtney, 1988). Source: (Troen
& Petersen, 1989) .................................................................................................................... 66
Figure 36 The power spectrum of wind speeds measured continuously over a flat
homogenous terrain in Denmark (Courtney, 1988). The data were collected over one year
with a sampling frequency of 8 Hz. The spectrum is shown in a log-linear, area-true
representation. Source: (Troen & Petersen, 1989) .................................................................. 67
Figure 37 Meteorological Stations Network of Cyprus. Source: (Meteorology Department of
Cyprus, 2003) .......................................................................................................................... 79
Figure 38 Locations of the meteorological stations and area of application ........................... 79
Figure 39 Transition from polygonal suffix – cover to linear ................................................. 82
Figure 40 Buffer zones – maps arrounding each meteorological station. It has to be noted that
the buffer zones – cycles have 20km radius from the meteorological station and 5km
overlapping from the near station map .................................................................................... 86
Figure 41 Limassol buffer zone map. Map 14 ........................................................................ 86
Figure 42 Polis Chrisochous first half buffer zone map. Map 0A ........................................... 87
Figure 43 Polis Chrisochous second half buffer zone map. Map 0B ...................................... 87
Figure 44 Pafos (Airport) first half buffer zone map. Map 2A ............................................... 87
Figure 45 Pafos (Airport) first half buffer zone map. Map 2B ............................................... 88
Figure 46 Mallia first half buffer zone map. Map 3A ............................................................. 88
Figure 47 Mallia second half buffer zone map. Map 3B ......................................................... 88
Figure 48 Mallia third half buffer zone map. Map 3B ............................................................ 89
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Figure 49 Mallia fourth half buffer zone map. Map 3D .......................................................... 89
Figure 50 Prodromos buffer zone map. Map 4 ........................................................................ 89
Figure 51 Kato Pirgos buffer zone map. Map 5A ................................................................... 90
Figure 52 Kato Pirgos buffer zone map. Map 5B ................................................................... 90
Figure 53 In figure (a) and (c) are wind roses showing the percentage variation in wind
direction during the month of January over Lyneham and Heathrow respectively. Diagrams
(b) and (d) show the percentage frequency of wind speed distribution with a Weibull fit, for a
month of January over Lyneham and Heathrow respectively. Source: (Maphosa, 2000) ...... 94
Figure 54 This figure is defined the routines that are used from the application WAsP for the
wind potential analysis in Limassol area buffer zone ............................................................. 96
Figure 55 The figure shows the flow of the wind over an imaginary hill. The wind profile is
passing upstream the hill top to the other side. The two dimensions – distances symbols are
characterizing the wind flow along the hill, where: L is the characteristic length of the hill,
which is the half hill length from middle of the hill, and l is the height where the maximum
wind speed that pass upstream the hill, when the wind flow profile is across the hill. Source:
(Troen & Petersen, 1989) ........................................................................................................ 96
Figure 56 An indicative final visualized map of Wind Energy Potential in the 6 studying
areas ....................................................................................................................................... 100
Figure 57 Monthly wind speed and direction statistics (on a bi – daily basis, every 12 hours)
for the six stations .................................................................................................................. 107
Figure 58 Inter annual (monthly) variation of the wind speed at the stations of Limassol,
Pafos, Polis, Pyrgos, Prodromos and Malia .......................................................................... 112
Figure 59 Daily variation of the wind speed at the stations of Limassol, Pafos, Polis, Pyrgos,
Prodromos and Malia ............................................................................................................ 118
Figure 60 Wind speed distribution based on the WAsP predictions for studied area – Wind
Atlas ....................................................................................................................................... 128
Figure 61 Pafos urban area. Quickbird satellite image .......................................................... 131
Figure 62 Pafos urban area. Quickbird satellite image .......................................................... 131
Figure 63 Oreites Wind Farm location. June 08:00 – 19:00 hours ....................................... 132
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ABBREVIATIONS
EIA: Energy Information Agency
GHG: Green Household Gasses
IEA: International Energy Agency
OECD: Organisation for Economic Co-operation and Development
RWC: Regional Wind Climate
C.F.D: Computational Fluid Dynamics
LINCOM: Linearized Computational
EAC: Electricity Authority of Cyprus
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PERFORMANCE OF TERMS
EJ/yr: Exajoule per year. 1 EJ = 1018
Joule.
1kWh: Kilowatt per hour.
1TWh: Terawatt per hour.
W: Watt. Watt is equal to
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INTRODUCTION
Topic
Growth, sustainability and the use of technology in the evolution of human and his
society is inextricably connected with energy consumption. Human as being for survival,
always has sought arrest of his food, formation of the house and care of himself. “Gatherer”
as we can characterize him, he invented tools and methods for finding food. Stone, wood,
bones and at the later years the use of metal, are used as materials for tools and they made
him life easily. Animals used for plowing, to transport water, food and fruit harvest. Later, by
the passage of time, human has been using advanced forms of energy such as Wind Energy
(Miranda & Infield, 2002), for water pumping at the beginning, reaching so far to use it for
the production of useable electricity, until today (Purohit, 2007).
Moreover, people have been making an effort for years now to harness wind energy.
Firstly windmills were used for water pumping in ancient Babylonia and Iran (Golding,
1976). Towards the time, wind turbine has established for small energy production and giving
a great opportunity of an alternative clean energy (Johnson, 2001). During its transformation
from these crude and heavy devices to today’s efficient and sophisticated machines, the
technology went through various phases of development (Sorensen, 1995).
Nowadays, Cyprus is during its early stages for wind resource developing. Previous
studies and assessments have not indicated a particularly abundant wind potential (Pashardes
& Christofides, 1995). I any case, the recording and studying of spatial and temporal
distributions of this natural resource of Cyprus Republic, are essential (Jacovides et al.,
2002). Also, the last 5 years considerable effort has been done in encouraging investment on
wind energy plans in Cyprus (Georgiou et al., 2012). In addition, the two wind farms which
have worked are shown significant electric power production for the island exists. This paper
is attempting a step forward towards an integrated method for the estimation and analysis of
potential wind energy resources in Cyprus, and is presented – applied, at six selected sites to
cover all the western part of the island (Kastanas et al., 2013). The wind statistics achieved
serve as the basis in order to predict corrected statistical distributions over the areas of
interest through Wind Atlas Analysis and Application Program (WAsP) developed at RisØ
National Laboratory, Rosklilde, Denmark (Mortensen, 2004), which converts the wind speed
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appropriate to local topographic and roughness conditions. Aggregation of the data with
statistical weighting methods, allows the extrapolation of the results and the visualization
over the western part of the island, focusing into the inter–annual and especially the daily
variation of the wind resources which proved to be strong (Akylas et al., 1999). This clear
daily pattern is of great importance, both for the proper site selection as for the correct
planning and short term estimation of the wind potential (Akylas et al., 1997). The results of
the work are indicative, but also they give an interesting perspective on the continuation and
completion of the study at more extended areas. Concluding, the results of the present
analysis will serve, in a future step, as the basis for testing and extending the application over
the whole island.
“Of all the forces of nature, I should think the wind contains the greatest amount of
power”
Abraham Lincoln
Aims and objectives
This work aims to investigate and analyze the impact of wind power estimation through
an integrated method using Wind Atlas Analysis and Application Program. In order to
proceed to the research, hourly wind speed and direction measurements, analysis and finally
conclusions, specific aims and objectives have to be set keeping the research on a certain path
while trying to investigate and answer certain questions. The dissertation is based on the
following aims and corresponding objectives which are listed below:
1. Aim: Review and analyze existing literature and knowledge which concentrate
on the impact of wind energy potential – analysis and assessment.
Objectives: (i) Identify what research on the topic should be carried out.
(ii) Identify the best appropriate methodology to estimate the wind energy in
the western part of Cyprus. (iii) Identify the data needed to estimate the wind
power at studying areas. (iv) Establish benchmarks for later validation of the
results. (v) Search for the specific needs for wind power development in the
island.
2. Aim: Understand Wind Energy Analysis and the WAsP model.
Objective: (i) Gain the ability to use the Wasp model for the wind analysis.
xxxi
(ii) Understanding the physical basis and characteristics of the model.
(iii) Identify the benefits and limitations of the application and understand the
potential of the expected results. Good programming and understanding to
assemble the model with its needs.
3. Aim: Collect and analyze the necessary wind speed and direction
measurements for each study area.
Objective: (i) Gain in depth knowledge of how this information could be
useful. (ii) Understanding of each area’s behavior in terms of the wind and
wind energy pattern. (iii) Explanation and first guess of the wind potential at
the studying stations. Check every factor that could be playing a significant
role for the prevailing wind speed. (iv) Daily, monthly and annual variations
of wind speed should be studied. (v) Statistical analysis of wind speed and
direction data for selected meteorological stations. The Weibull distribution
and Power density curves will be determined.
4. Aim: Maps creation with areas’ topographic and roughness characteristics.
Objective: (i) Maps could be cut and design to cover all Cyprus areas. (ii)
Topographic and Roughness effect might keep in mind. (iii) Find out the
characteristics of stations (Height, Longitude, and Latitude).
5. Aim: Setting up of a model and methodology that could ensue for wind
potential extrapolation.
Objective: (i) Apply the framework of an integrated method for the estimation
and analysis of potential wind energy resources at the western coast – line of
the island. (ii) Estimation of correct statistical distributions over the extended
areas of model through the WAsP, using the statistical analysis of each area’s
results. (iii) Modification of the wind flow due to local topographic effects and
roughness using the prepared maps. (iv) Aggregation of the data with
statistical weighting methods, allows for the extrapolation of the results and
visualization over the western part of the island.
6. Aim: Evaluation of the results, form conclusions and suggest future research.
Objective: (i) Identify the reliability and valuableness of the results and try to
improve further similar research. (ii) Comparison between similar studies in
Cyprus. (iii) Suggestion about the wind energy developing and investing on
the pick points at extended areas.
xxxii
Research Methodology
This study is an integrated method of potential wind energy recourses in Cyprus
western coast–line. The application/model uses geo–data and the wind resource base in order
to cover the area around each station. Using the flow model, a modification of the wind flow
may occur. Local effects, topographic at each area and changes at the surface roughness
could modify strongly the wind speed and the power too. Finally, using statistical weighting
methods, the visualization of the results for all the area of interest is possible. It should be
pointed out that, the main intention of this thesis is to identify positions with significant wind
energy potential for further studying and future investigation.
Dissertation Structure
Chapter 1 of this dissertation reviews the existing literature and tries to gain the
required information required to carry out the research. It consists of four main sections: (i)
Studying of Wind Energy Potential, (ii) Wind Energy Development, (iii) Wind Atlas Model,
(iv) Models for Wind Energy Analysis.
Chapter 2 uses the information deduced from the previous chapter to understand the
wind energy and how a model could be established. Moreover, the chapter covers a variety of
parameters that play a crucial role in wind energy estimation, like topography, orography
roughness, meteorology, review of statistical model etc. Possible errors and inaccuracies of
the model’s application and of the data used are also discussed.
Chapter 3 of this dissertation presents the methodology that followed for the application
of the Wind Atlas Model for each area of interest. Step by step, all the application’s
requirements are fulfilled, up to the final visualization of the wind potential for the western
coast – line Cyprus area.
Chapter 4 “Results and Analysis”, reviews the results of the analysis and attempts a
fruitful discussion, comparing the new findings with previous studies. Comments on the
results are given and the limitations of the analysis are pointed out.
xxxiii
Chapter 5 contains an overview of the findings and evaluates the results in terms of
matching the original aims and objectives. Moreover, comments on the interpreted results and
suggestions for the wind energy potential resources are drawn. Finally, the chapter concludes
with recommendations for research in the future.
Expected Results
The expected outcome of this study is the exploitation of the wind energy at five
measuring stations and the full coverage of the western part of the island with information
regarding the wind potential. The methodology of the analysis is based on the standard
application program, WAsP. Effects and parameters like climatology, orography and
topography, and roughness are very important for the formation of wind energy resources at
different areas. It is known that in the rainy season Cyprus is influenced by depressions
crossing the Mediterranean eastwards. Also, in dry season the island is subjected the Indian
trough (Meteorology Department of Cyprus, 2003). That is why a sea – breeze circulation is
usually very strong due to the large differential heating between sea and land (Jacovides et
al., 2002). Due to that phenomenon the presence of interesting peak points with higher wind
power during the day, suitable for exploitation is very possible. In this direction, hourly wind
measurements were divided in this study into twelve hour periods, from 08:00 – 19:00 and
20:00 – 7:00. The Cyprus topographic map, the position of the stations and roughness maps
are also constructed and used as input data. Furthermore, a geo–data and wind resource base
is prepared in the region of each station. Aggregation of information concerning the wind
statistics from all stations and from the surroundings, as estimated by WAsP, allows the
extrapolation of the results and their visualization over the whole western part of the island.
The results offer a better overview of the wind potential in Cyprus opening opportunities for
further investigation and developing.
xxxiv
1
1 REVIEW OF EXISTING LITERATURE
1.1 Introduction
Wind energy investigation is growing rapidly worldwide and will continue to do so for
the foreseeable future. It offers significant power to reduce the use of conventional fuels and
GHG emissions too (IPCC, 2007). The wind energy power placed by the end of 2009 was
competent of meeting about 1.8% of global electricity require, and that subscription could
raise to a proportion greater than 20% by 2050 (IEA Wind, 2010).
Onshore wind energy technology is already being deployed at a rapid pace, therefore
offering an immediate option for reducing GHG emissions in the electricity sector (Archer &
Jacobson, 2005) and (Johnson et al., 2004). According to IEA (IEA Wind, 2010a), “New
Policies” scenario and the EIA 2010 (EIA, 2010) “Reference case” scenario has estimated
increasing to 358GW of forecasted electricity generation and 277GW by 2015,
correspondingly (IEA, 2010c). Wind energy industry estimates more rapid deployment rates,
pointing that the past IEA and EIA forecasts have underestimated actual growth by sizable
margin (BTM, 2010), (GWEC, 2010a). As a result of these, a reasonable estimation is that
wind energy will contribute to about 5% of global electricity supply by 2015. Asia, North
America and Europe are intended to lead in the wind energy potential over this period. Also,
Cyprus has made its first step on wind development and is expected to pass the 6% by the end
of 2020 (Kastanas et al., 2013).
This chapter begins with the overview of various studies regarding Wind Energy
Potential. Studying of Wind Energy Potential, Wind Energy Development, the Wind Atlas
Model and Models for Wind Analysis are the main subjects of the section. In addition, a
critical comparison between different models for wind energy analysis is undertaken and
presented. Finally, the conclusions and summary of the chapter will clarify the choice of the
specific model for the present analysis which will be analyzed through the next chapters.
1.2 Studying of Wind Energy Potential
The wind energy potential has been estimated by the global annual flux at 6000 EJ/yr,
theoretically. It cannot be denied that 1% of the total solar power absorbed by the earth is
2
converted to kinetic energy of the atmosphere (American Institude of Physics, 2013).
Specifically, the total sun power which reaches to earth is 1.740*1017
W. If this energy was
distributed equivalently this would correspond to a total wind power of about 3.4*1014
W on
the land mass. At the same time the total power that was consumed in the world was
14.3*1012
W at 2002 and in USA was 3.3*1012
W by the 2008. Comparing these numbers, it
easily comes in mind that the wind energy can cover all the energy needs. Especially, given
that theoretically a maximum of 59.3% from wind energy can be transformed to electric
energy from a wind turbine. However, wind energy potential is not homogeneously
distributed in the earth surface, but is different from place to place. Land areas receive less
amounts of wind power than the ocean due to the topographic effects and the ground cover
which increases the friction force.
Figure 1 The global onshore wind power potential. Map shows ‘feasible’ power potential that could be
extracted as electricity (wooded/ permafrost/ urban was excluded). Suitable mid-west states have power
density of approximately 3 – 4 W/m2. Moreover, round the Mediterranean the wind potential is about 0.0
– 1.2 W/m2 with the maximum amount of energy occurring at the Turkey coastal area. Source: (X. Lu et
al., 2009).
A number of studies have evaluated the energy potential of the world. Some of these
studies have been contacted both in Europe and United States (see Figures 1-3). The use of
wind measurements is essential component of any resource assessment or wind analysis and
estimation. In contrast for wind information assessing, it is meaningful to realize their
limitations. Generally, two methods can be used: (i) Wind speed measurements can construct
a surface of wind distribution and statistical description at area of interest, (ii) Secondly, wind
3
energy analysis and model applying for the sites can extract the wind potential inclusive of
topographic and roughness effects.
Figure 2 Mean Wind Speed in Europe at 80m height. Source: (Jacobson, 2007)
Figure 3 Mean Wind Speed in North America at 80m height. Source: (Jacobson, 2007)
4
For instance, information has been gathered for the study of wind energy potential, and
many wind speed and direction measuring stations are found near or in cities, in relatively flat
terrain or areas with low elevation. This type of measurements can obtain an overview of the
wind potential within a large area, but naturally does not provide enough data for the detailed
classification of nominee sites for wind investment.
Studying the existing literature, the IPCC’s Fourth Assessment Report acknowledged
600 EJ/yr of onshore wind energy potential (IPCC, 2007). Using the direct equivalent method
of deriving primary energy equivalence, the IPCC estimates that the onshore wind energy
potential is 180 TWh/yr, which is more than two times greater from the gross global
electricity production of 2008 (IEA Wind, 2010a).
Moreover, there is not a standard approach to estimate the wind energy potential with a
global (Johanson et al, 2004) sense. In particular, the differences between data, the methods’
assumptions and even the assessments for technical potential complicate any comparison.
Therefore, the studies show a spacious and broad range of results. Particularly, the global
technical wind potential has been estimated from 70EJ/yr to 450EJ/yr according the above
studies, so that have consisted more development. This wide different apart from one to six
times the global electricity production in 2008. Also, if the projects for the wind investigation
have fewer restrictions, then without doubt the wind technical potential will be more than
3000EJ/yr. In keeping with IPCC the range is estimated to become from 19400TWh/yr to
840000TWh/yr, where is approximately 7 times greater than the current one. Of course, the
result from project to project divagates. Consist of parameters like the wind speed data,
assumptions for wind analysis model application, the sites assumed available for wind
investment, the output energy from wind turbines at land area and the power curve from
assumed turbine, lead to different power productivity (Elliott, 2002) and (Elliot et al, 2004).
Likewise, hub height and turbine technology are indissolubly connected (Hoogwijk et al,
2004).
Differences and comparison in terms of region due to the different wind measurements
and wind flow are showing in the Table 1. Regions shown in the table are defined by each
selected study and sometimes are combined to improve comparativeness. Four studies have
been compared each other where (Hoogwijk & Graus, 2008) and (Hoogwijk et al, 2004) has
presented identical results.
5
Table 1 Regional distribution of global wind energy potential has been used for onshore development.
The table below represents a comparison between different studies. It follows that the four studies have
improved similar results. Source: (Hoogwijk et al, 2004) and (Hoogwijk & Graus, 2008).
Grubb and Meyer (1993) WEC (1994) Krewitt et al. (2009) Lu et al. (2009)
Region % Region % Region % Region %
Western Europe 9 Western
Europe 7
OECD
Europe 5
OECD
Europe 4
North America 26 North
America 26
OECD North
America 42
North
America 22
Latin America 10
Latin
America
and
Caribbean
11 Latin
America 10
Latin
America 9
Eastern Europe
and Former
Soviet Union
20
Eastern
Europe and
CIS
22 Transition
Economies 17
Non –
OECD
Europe and
Former
Soviet
Union
26
Africa 20
Sub –
Saharan
Africa
7 Africa and
Middle East 9
Africa and
Middle
East
17
Australia 6
Middle East
and North
Africa
8 OECD
Pacific 14 Oceania 13
Rest of Asia 9 Pacific 14 Rest of Asia 4 Rest of
Asia 9
Rest of
Asia 4
Nevertheless, the installed wind turbine and there energy capacity in OECD North
America and Eastern Europe are located to be particularly sizeable, while some regions of
non – OECD Asia and OECD Europe come into view to have less onshore energy potential.
Furthermore, (Hoogwijk et al, 2004) compared onshore wind power toward location
electricity using in 1996. From the above study, is defined that the result of more than 17
assessed regions passed electricity consumption in 1996.
Also, a wind resource map with 5km by 5km resolution is showed (see Figure 4) the
wind power around the Latin America and Africa where are found very important amount of
wind energy (3TIER, 2009). However, the map has different wind speed distributions than
other global technical wind potential estimations which are found for East Asia and other
regions to offer more wind power for investigation (Fellows, 2000).
6
Figure 4 A global Wind Energy Power map 5km x 5km as was estimated by 3TIER. Source: (3TIER,
2009)
A technique, such as the United Nations Environment Program’s Solar and Wind
Energy Resource Assessment, provides wind resource information for a wide range of
countries in over the world. In addition, the European Bank for Reconstruction and
Development has built up Renewable Energy Assessment in its countries of relevance (Black
& Veatch, 2003); the world Bank’s Asia Sustainable and Alternative Energy Program has
provided wind atlases for the Pacific Islands and Southeast Asia (ASTAE, 2008); and wind
resource assessments for sites of the Mediterranean region are available through
‘Observatoire Mediterraneen de l’Energie’. A number of other publications and assessments
have been produced by the US National Renewable Energy Laboratory (NREL, 2012),
Denmark’s RisØ Laboratory and others (RisØ, 2013). These above reports and studies are
important instruments for assessment of wind energy potential on the world. Besides, these
studies and models have straightly set up and they find wind analysis for investment and
development. According these studies the wind global potential has been estimated greater
than the others as we referred before. This is due to improved data, spatial resolution and
analytic techniques and also the result of wind turbine technology developments, for
example, higher hub heights and improved machine behavior (Elliott, 2002) and Elliot et al,
2004). Nonetheless, it is a fact that larger spatial and temporal resolution and a better
7
confirmation of model results with wind measurements are required, as it is reported in
extended studies (Schreck et al, 2008) and (IEA, 2009). Finally, these developments will
allow the more fine-tuning of estimation of the wind energy potential, bringing to light
regions with high – quality technical potential that would not have been recognized erstwhile.
1.3 Wind Energy Development
1.3.1 Global Wind Energy Development
In 1970s the field of wind energy was significantly blossomed. In order to find
alternative sources of energy, the United States, Denmark and Germany invested in research
money. Although that alternative sources of energy had been vanished considerably in the
United States, in Europe a notable amount of wind energy installations was exacted due to the
successive investments during the years (EERE, 2013) (see Figure 5).
Particularly, from 1980 to 1990, the wind industry was characterized by four basic
features, which was equal with the wind farms in California. First of all, there was rapid
growth in the produce of wind energy. This was followed by the development of
intermediate-sized wind turbines (100 – 600 kW), specifically designed with no government
funding (NREL, 2013). Instead of the fast development, US had to face the strong foreign
competition, mainly from Europe and principally Danish manufacture, which became an
important factor in wind industry. In addition to the above, the support for wind energy in
1988 increased by far in Europe, whereas in U.S. fell to a low of $8 million.
Figure 5 The Global annual installed wind capacity 1996 – 2012. Source: (GWEC, 2012)
8
From 1990 to 2000 the rapid growth continued incredibly. From now on, the
installation of wind farms took place in countries outside the United States and Europe. Wind
turbines with 1,220 MW installed in India and their size increased from 200 kW to megawatt
size by the end of 2000 (EERE, 2013). Moreover, European manufactures continued leading
the market as they first set offshore wind farms in the European fields. At last, one of the
basic features of that period was the fact that there was a development of large wind turbines
without gearboxes (Wind Power Monthly, 2013).
Figure 6 Trends in the global market. World total installed capacity in MW. Source: (Schilling, 2010)
During the years 2000 – 2010, wind turbines were becoming required elements of the
planning process for the design of electric plants in many countries. In the same time, the
development of multimegawatt turbines and the installation of more and more capacity were
continued strongly in European fields (see Figure 6). Cranes, also, which first shown in the
market, were wind turbines with less installation of megawatt. They were much more
economical than other turbines because of the economical size and they mainly installed in
specific fields, especially in islands and some remote locations in the world. Another
important outcome of the extensive installation of wind farms was the village electrification,
particularly in many developing countries as it was far cheaper to have village power than to
extend transmission lines (GWEC, 2010a).
9
Figure 7 World wind energy development growth rate. As is defined from the graph the wind capacity
doubles every 3 years. Source: (Schilling, 2010)
By the end of 2012, the global wind market increased by more than 10% compared to
the last yea (see Figure 7). In fact, 24 – countries: including 1 in South America (Brazil); 3 in
North America (Canada, Mexico, U.S.); 4 in Asia-Pacific Ocean (China, India, Japan &
Australia) & 16 in Europe, presented more than 1,000 MW installed capacity (see Figure 8).
Figure 8 The top 10 countries in wind energy development. More countries were invested in wind energy.
Specifically Marocco, New Zealand, and Turkey were turned to wind energy when at the same time the
market became bigger than 100MW by the 2009. Source: (Schilling, 2010)
The prospects, however, for wind power market development were varying. Due to the
obvious policy uncertainty linked with the underway vigorous crises, the expectations for the
2013 wind market, both in Europe and U.S., are unsecure. Nonetheless, 2012 became a
record year for European and North America market as they shown a significant number of
wind power installations. Relating Asia market, the consolidation and rationalization in the
10
China market as far the lapse in India policy were the high reasons for the obvious slowdown
in Asia in 2012, but these facts are estimated to eliminate and Asian wind power is illustrated
to continue in global wind market. At last Canada, Brazil and Mexico are estimated to have
strong years in 2013 since new projects in Mongolia, Pakistan, the Philippines and Thailand
will expand unbelievably the global installation map (GWEC, 2012).
Figure 9 The total wind capacity of 10 top countries by 2009. Source: (Schilling, 2010)
The prospects, however, for wind power market development were modified. Due to
the obvious policy uncertainty linked with the underway vigorous crises, the expectations for
the 2013 wind market, both in Europe and U.S., are unsecure. Nonetheless, 2012 became a
record year for European and North America market as they shown a significant number of
wind power installations (see Figure 9). Relating Asia market, the consolidation and
rationalization in the China market as far the lapse in India policy were the high reasons for
the obvious slowdown in Asia in 2012, but these facts are estimated to eliminate and Asian
wind power is illustrated to continue in global wind market (see Figure 10). At last Canada,
Brazil and Mexico are estimated to have strong years in 2013 since new projects in Mongolia,
Pakistan, the Philippines and Thailand will expand unbelievably the global installation map
(GWEC(c), 2013).
11
Figure 10 The total installed wind capacity from 1997 – 2020 versus development and the prognosis. The
prognosis predicts 10 times higher capacity during the next 7 years. Source: (Schilling, 2010)
Table 2 Global installed wind power capacity in MW – Regional Distribution
Region Country End
2011
New
2012
Total (End
2012)
AFRICA &
MIDDLE EAST
Tunisia 54 50 104
Ethiopia - 52 52
Egypt 550 - 550
Morocco 291 - 291
Iran 91 - 91
Cape Verde 24 - 24
Israel, Jordan, Kenya, Libya, Nigeria, South
Africa 23 - 23
Total 1033 102 1135
ASIA
China 62364 12960 75324
India 16084 2336 18421
Japan 2536 88 2614
Taiwan 564 - 564
South Korea 407 76 483
Pakistan 6 50 56
Bangladesh, Indonesia, Philippines, Sri Lanka,
Thailand, Viednam 109 - 108
Total 82070 15510 97570
EUROPE
Germany 29071 2415 31308
Spain 21674 1222 22796
UK 6556 1897 8445
Italy 6878 1273 8144
France 6807 757 7564
Portugal 4379 145 4525
Denmark 3956 217 4162
Sweden 2899 846 3745
Poland 1616 880 2497
Netherlands 2272 119 2391
Turkey 1806 506 2312
Romania 982 923 1905
Greece 1634 117 1749
Ireland 1614 125 1738
12
Austria 1084 296 1378
Bulgaria, Croatia, Cyprus, Czech Republic,
Estonia, Finland, Faroe Islands, FYROM,
Hungary, Iceland, Latvia, Liechtenstein,
Lithuania, Luxembourg, Malta, Norway,
Romania, Russia, Switzerland, Slovakia,
Slovenia, Ukraine
3815 1106 4922
Total 97043 12744 109581
LATIN
AMERICA &
CARIBBEAN
Brazil 1431 1077 2508
Argentina 113 54 167
Costa Rica 132 15 147
Nicaragua 62 40 102
Venezuela - 30 30
Uruguay 43 9 52
Caribbean 271 - 271
Colombia, Chile, Ecuador, Peru 229 - 229
Total 2280 1225 3505
NORTH
AMERICA
USA 46929 13214 60070
Canada 5265 935 6200
Mexico 596 801 1370
Total 52763 14860 67576
PACIFIC
REGION Australia 2226 358 2584
New Zealand 623 - 623
Pacific Islands 12 - 12
Total 2861 358 3219
Word Total 238050 44799 282587
Source: (GWEC, 2012)
1.3.2 Wind Energy Development in Cyprus
Even if the wind power effectiveness in Cyprus is not particularly high, however there
are some areas that they have offered and will offer even more in the future operation of wind
energy. It is estimated that a wind farm can be viable with an average wind speed 5.4 – 5.8
m/s (see Figure 11-12). In our country, the average speed in some areas is around 5.0 – 6.0
m/s, whereas in others it is estimated to reach 7.0 m/s, based on hourly measurements at
meteorological stations. In fact, Cyprus has a wind potential of about 150 MW and it is
anticipated to reach 250 MW.
13
Figure 11 The inter annual average wind speeds in various areas of Cyprus according to Dr. John Gleka.
Source (CIE, 2000)
Regarding the operation of the available wind power in Cyprus, it is a fact that the local
wind farms will contribute to the most significant share of electricity generation compared
with the rest renewable energy technologies. Particularly, it has been already achieved the
installation of a wind farm in Orites area in Paphos and also in Alethriko area in Larnaka
district with a wind energy capacity of 31, 5MW, since it is expected to turn out of 60,0 MW
due to the installation of new wind farms (Κασίνης, 2008).
Moreover, the scope of an installation with a total capacity 165 MW by 2015 is
expected to increase the contribution of renewable technologies in electricity generation by
about 4.5 %. In today’s Cyprus there is a contribution of almost 6, 0% and it has set itself the
target of 9.0% by 2014. In conclusion, it is evident that Cyprus has a potential role in wind
energy development and the expectations are becoming high (CIE, 2000).
Figure 12 An indicative Map for the wind farm installation in Cyprus. The map shows the planning
zones, urban areas, archaeological sites, hill - mountains tops, and green protected areas from Natura
2000. Source: (MCIT, 2005)
14
1.4 Models for Wind Energy Analysis
Wind resource estimation is the most important task for the sitting process of wind
farms. The task allows determination of technical economic feasibility of wind farm
deployment. Initially, windy areas will need to be identified. For micro-sitting and final
evaluation of the project’s economic status, it is required that as much detail as possible from
the spatial variability and temporal variation is gathered. These details must be derived from
wind resources across the entire site as well as over long time scales.
Moreover, the wind resources are analyzed from the large scale at national or regional
levels down to the micro-scale where the background wind climate is modified by the local
topography. As a scope of this work, an overview of methodologies for wind energy
estimation will be provided.
Firstly, Putman (Putman, 1948) and (Golding, 1977) as well as the books of Hiester and
Pennell (Hiester & Pennell, 1981), Johnson (Johnson, 1985), Freris (Freris) and Rohatgi and
Nelson (Rohatgi & Nelson, 1994) are referred about the wind energy application for
collecting wind energy data. After a few years, later, Bailey et al (Baley et al, 1996), referred
about the wind resource appreciation. Far ahead, a range of wind estimation methodologies is
provided by Landberg et al (Landberg et al, 2003).
More information can be found in the journal article of Landberg et al (Landberg et al,
2003), where he describes eight different methods of wind resource estimation. According to
his journal, the eight different methods depended on the amount of information available and
the utilized models. The author, also reports that the ‘Foklore’ method is based on the
interviews of local people and a combination of mesoscale and microscale models. With the
exception of the ‘Folklore’ method, all the other methodologies have been further developed
over the last years even though they remain basically the same in terms of usage principles.
Since 2003, significant progresses have been made to wind energy estimation methods,
particularly: mesoscale models are being used much more for regional wind mapping,
nonlinear microscale models based on Computational Fluid Dynamics are being developed
and commercialized, and remote sensing instruments, especially LIDAR systems, have been
introduced as a complement to mast – based instrumentation. These advanced techniques
require significant efforts from the wind industry in terms of training and upgrading of their
traditional methodologies.
15
Furthermore, wind resource analysis can be achieved from hundreds of kilometers to
the most extensive analysis of decades of meters. More specifically, wind energy is analyzed
from a large scale, at national or regional levels with the use of a wind atlas. The microscale
where the background wind climate is located is then modified by the local topography.
Consistent with Larsen et al (X.G & J, 2009), Wind Atlases are used for regional
planning by users and for site estimating by wind energy developers. With the growing use of
mesoscale models, provincial wind maps are becoming increasingly popular at resolutions in
the order of 1 to 10km. The resolution is not satisfactorily high to resolve the speed-up effects
generated by the local topography, a task which is taken care of by microscale models.
When the expectancy phases are over and positions with remarkable wind energy
potential are collected, the micrositing phase starts in order to determine if the identified
position is economically attainable and technically livable for the installation of a wind
turbine or a wind farm. To this end, onsite measurements are conducted in order to monitor
the wind conditions for a sufficiently extensive time. Examining the inter-annual variability
of the wind it shall be necessary to use 5 – 10 years of measurements to cover up the term
scales. As this is not practical, it is typically the case to use quantities of one to three year
periods. Using estimation methods to extrapolate wind resources to a time span of at least
twenty years, estimates of long-term average energy yield can be made. These statistical
methods build the relationship between wind data evaluated in a target position with
concurrent data at a nearby reference site where long – term measurements are accessible
(Rogers et al, 2005).Where collected metrics are unavailable, virtual met masts can be
reserved by downscaling from global circulation models. Another option is re-analyses
produced by meteorological centers like NCEP/NCAR or ECMWF.
The evolution of multi – megawatt wind turbines has resulted in increasing wind
turbine hub heights and rotor diameters, affecting the costs regarding mast – based
measurement campaigns that are also on a continuous and considerable rise. Remote sensing
techniques based on the emission and detection of light and sound (LIDAR and SODAR
respectively) offer the possibility of using ground – based equipment to measure wind speed
up to heights of typically 150 – 200m. Such devices are being ever more introduced in the
wind energy estimation, for different types of applications. In the case of wind energy
prediction, they are used for the characterization of the wind profile at greater heights. The
performance of these systems is proven in flat terrain (Antoniou et al, 2007). However,
16
failure is possible when the terrain gets rugged due to the lack of homogeneity of the flow (a
fundamental assumption of the measurement technique). To overcome such a limitation, the
error in complex terrain can be estimated making use of numerical models that can simulate
the mean 3D flow field that the remote sensor is actually seeing (Bingol et al, 2008).
Some mesoscale models like RAMS (Regional Atmospheric Modeling System) build
up at the Colorado State University, trough nesting, are able to downscale further to
microscale level but at a considerable computational cost. On the other hand, the standard
practice in wind energy is to make use of built – to – purpose microscale models that can
integrate not only a numerical model for the simulation of the wind flow but also wind energy
specific tools for wind farm design and energy yield estimation (Pielke et al, 1992).
During the 80’ the European Wind Atlas [Ib. Troen and E.L, Petersen. (1989).
European Wind Atlas. Department of Meteorology and Wind Energy. RisØ National
Laboratory, Roskidle, Denmark] appeared. The standard model for wind resource assessment
has been WAsP (Wind Atlas Analysis and Application Program) with its Wind Atlas
Methodology. The model, based on linearization of the Navier Stokes equations originally
introduced by Jakson and Hunt (S. & R, 1975), is meant to be used reliably in near – neutral
atmospheric conditions over gently undulating terrain, with sufficiently gentle slopes in order
to ensure fully attached flows. Nevertheless, due to its simple usage and the increasing
experience of the users with the model, WAsP has been also used out of its range of
applicability, making use of the ruggedness index (RIX), which accounts for the extent of
steep slopes around a site. This index helps judging whether WAsP is working within or
outside its performance envelope (A. & Mortensen, 1996) and has also been used to correct
energy estimations in complex terrain.
The alternative to linear models is to retain the non-linearity of the Navier Stokes
equations and simulate both momentum and turbulence with CFD models adapted to
atmospheric flows. Although, the computational cost is extensively larger compared to linear
models, it is nowadays affordable for conventional PCs. The application of CFD in wind
resource assessment is still largely based on RANS (Reynolds-Averaged Navier Stokes)
turbulence models since LES (Large-Eddy Simulation) that currently remains far more
expensive and only few academic simulations have been made in fairly small sites (Silva et
al, 2007). Based on RANS simulations, CFD models are being developed for wind resource
assessment with the aim of complementing linear models in complex terrain.
17
Due to the result of the wind resource assessment process, the amount of wind power that can
be installed and the amount of energy that can be harvested from the wind farm, is known by
the wind energy developer. Furthermore, a prediction of how profitable the wind power plant
will be over the lifetime of twenty years can be calculated through the above information that
constitute the basic input for a feasibility analysis. In addition to the net energy yield
assessment, the assessment of wind conditions at each wind turbine position is of great
significance and must be taken care of by both the developer and wind turbine manufacturer,
so that they can be within the IEC design limits.
The subsections below are shown the WAsP methodology to estimate the wind
potential. Also, a comparison between methods and methodologies to calculate the wind
power are going to enclose this chapter.
1.4.1 Wind Atlas Model
Wind Atlas Analysis and Application Program is a microscale wind analysis model
developed by RisØ National Laboratory, Rosklilde, Denmark (Mortensen et al, 2004), which
modifies the wind flow as is measured by meteorological stations, due to local topographic
and roughness conditions at studying areas.
Unfortunately, WAsP model is used wind speed measurements and it creates wind
power estimation for the selected areas and for the desired heights. Actually, model is
extracted wind data from sites where it is available to sites with no wind data. Moreover to
the extraction along the horizontal plane, wind speed information is extracted along the
vertical direction for different height. Following the concept above a spatial analysis of wind
speed along the three axes is extrapolated. As a result of this, areas – meteorological stations
with wind data are used for wind analysis to calculate the wind potential to other areas.
Specifically, for the calculation of the wind data in desired areas is used a grid. In particular,
the studying area is covered from at least 20km x 20km area with a 100 x 100 m grid and
wind potential is calculated at all grids points. Moreover, using weights that are based on
inverse distance between studying areas and the referred areas, allows the model of Wasp to
estimate the wind potential at areas of interest.
In Figure 14, is shown all the steps and data that are needed to estimate the wind flow
from wind measurements for wind at meteorological stations to studying areas. Wasp’s
18
methodology is to seriatim eliminate the obstacles’ affects, topographic effects, and
roughness in order to generate a RWC. The arrow that shows up indicates the above option.
Namely, the generalized regional wind climatology is defined in an equable wind flow layer
‘Geostrophic Wind’ above the earth surface using the logarithmic equation. Then the RWC is
modified to studying area following the height of anemometer, the surface effects –
roughness and the topography at each grids point using the *map and then the wind potential
is extrapolated (see Figure 13).
More extensively, the wind is more potent at the hill top than the surrounding area.
Therefore, the top of a hill may be useful for the wind turbines installation. For a simple case
of hillcrest located perpendicularly to the wind, the speed increasing ΔS and the height in the
site where the maximum speed is displayed can be easily calculated as follows:
Figure 13 In this picture emerge routines that are used in the program WAsP for the calculation of wind
potential in Limassol
(
)
Also, if the pick point with height H, is equal to the height l, then the increasing of
wind speed is defined below:
{
⁄
⁄
19
Where, l is the width of the mountain (see Figure 15). Thereby, the wind turbine
installation on the hill top is accomplished with the determining of Weibull statistic
parameters with increasing parameter c ‘Or else as sometimes is denoted as a, the Scale
parameter’ for the studying areas where the wind is accelerated to a hillside:
On the other hand, the Schematic dimensionless parameter Weibull k remains the same.
It should be pointed that this procedure only applies to sites on top of an isolated ridge and
that the slopes should not exceed ~ 0.3.
Figure 14 Wind Atlas Analysis and Application Model for wind potential assessment. Source:
(Mortemsen et al, 2004)
20
Furthermore, the increase of speed on a smooth and single hillcrest is listed in Figure
15, 16 and 17, which show the results obtained from the application of orography model on
the hill at Blashesheval Scotland. These disturbances caused are caused by the hill effect was
the subject of a study as described from Mason and King in 1985 (Troen & Petersen, 1989).
The defining of the contour of the hill is shown in Figure 7 and a model of the relief of the
hill is shown in Figure 8.
In Figure 18, the relative wind speed of 8m above ground for winds from directions
210o, is shown for positions – points along the hill ridge top. The hill line on the top of
mountain range is presented in Figure 9. Excessive wind speed is provided at the top where is
closed to 70% which also is the observed value. Similarly, it is possible to estimate the
increase in speed using the equation:
(
)
Figure 15 The Figure shows the wind flow over an ideal – imaginary hill. The wind profile passes through
the upstream side of hill. The two distances characterizing the wind flow. The L is the characteristic
mountain length, which is the half at the middle of the hill. l is the height where the maximum wind speed
occurs as the wind profile penetrates along the hill. Source: (Troen & Petersen, 1989)
Where, the surface roughness is 0.01m and according to Equation 5, the height l can be
simply calculated. It is worth noting that the wind speed is increased in its maximum at 2.5m
height. When the values are adding, Equation 5 provides an acceleration of 68%.
Nevertheless, the Equation 5 can be implemented in the case of a single hill, to estimate the
increasing of wind speed at the top of the hill.
21
Figure 16 The contours map of the hill Blasheval at Scotland. The heights above the sea level are shown
by contour lines per 10m. Source: (Troen & Petersen, 1989)
Figure 17 The orographic model of hill Blasheval, Scotland. The hill is seen from the South. The vertical
scale is presented with a factor 5. Source: (Troen & Petersen, 1989)
Figure 18 Modification of the wind speed along the horizontal line at the top of the hill Blasheval. The
horizontal axis shows the distance in meters from the hill top. The vertical axis presents the factor of the
relative wind speed increasing and measured at 8m above the ground surface. The shaded graph below
shows the section height of the hill
However, the WAsP uses routines of correcting the wind data measured at the certain
point and turn them into a set to describe the wind climate of an area “Wind Potential”, the so
– called Wind Atlas. In addition, the model – application is used these datasets to assess the
wind conditions at any particular point and height in the region, mainly using the same
routines and models (Τριανταφυλλίδης, 2009). As it mentioned before, when the model is
taking in it account the station statistics, it calculates logarithmic wind profiles to a single
level “Geostrophic Wind”. Then, using the same models and routines, the application takes
the wind from the single reference layer and then in inclusive of the roughness at the terrain
22
and the site topography, the wind potential can be calculated at each location of studying
areas.
It should be noted that the reliability of wind analysis export results, is proportional to
the reliability of the data used. Furthermore, if we have strong orography or unaudited
measurements, the validity of the calculated wind potential results are reduced at areas of
interest. Thus, the results would not be representative.
According to experts’ predictions and independent assessments of WAsP for more
complex terrain conditions, which outstretch overly within its operating envelope, usually
affirm the trustworthiness of the estimations under these situations. To begin with, Holttinen
and Peltoda showed suitable WAsP estimations stack up against the measured wind data for
various areas on the relatively surface conditions at the western coastline in Finland
(Holttinen & Peltola, 1993). Also, Sandström studied and compared the field measurements
taken at Vårdkasen, a 175m wooded mountain and a reference coastline area about 5km
away. He summarised that WAsP simualation fits very well to the wind field conditions and
the wind potential estimation is accepted (Sandström, 1994).
Though the above that were analysed, more representative wind potential estimations
may be obtained provided, as shown below: (i) The meteorological station and the expected
estimated region are subject to the same overall weather system conditions, (ii) Stable
ascendant weather conditions, (iii) Reliable wind measurements, (iv) The surrounding area
surface of sites should be smooth and ample to provide mostly attached flows, and (v) The
topography input maps are effectual and reliable (Bowen & Mortese, 1996; Mortesen &
Petersen, 1998).
In fact the sharpness of steep slopes of the ground surface around an area, is defined as
the percentage fraction of the ground surface within a certain distance from a specific area
which is steeper than some critical slope, say 0.3 (Bowen & Mortesen, 1996; Wood, 1995).
This indicator – factor of ruggedness was intended as a measure parameter for extent of flow
separation which the surface is changed the conditions on linearly flow like WAsP.
Consequently, the roughness index so-called RIX, has also been used to develop an
orographic performance indicator for WAsP estimations in complex terrain, where the
indicator is defined as the difference in the percentage fractions between the estimated and
the reference area (Bowen & Mortesen, 1996). This indicator may provide the sign and
23
approximate magnitude of the estimation error for situations where one or both of the sites
are situated in terrain well outside the recommended operational envelope. WAsP is small-
scale model and the domain is on the order of 10km x 10km (Mortesen & Petersen, 1998).
The last decade more studies were published to cover the micro scaling wind flow
potential estimation with WAsP both in Europe and Mediterranean. Firstly, Turksoy suggests
that there is exploitable wind resource in Bozcaada Island, an average speed of 6.4m/s and
wind potential of 324W/m2 in the meteorological station Boscaada (Turksoy, 1995).
More extensively, the Karsli and Gecik estimated the Wind Potential in South Turkey
at Nurdagi Gaziantep. They used Weibull parameters for the distribution of speeds. The
results showed that there is considerable wind potential of 222 W/m2 for average wind speeds
of 7.3m/s at 10m height (Karsli & Gecit, 2003).
Afterwards Sahin et al, is argued that there is a significant wind potential for investment
in the coastal regions of eastern Turkey. The research remarked noticeable wind potential to
exploit medium capacity of 500 W/m2 at 25 m above the ground elevation, using the software
package WAsP. For the assessment of Wind Energy Potential were used measures of the
Turkish Meteorological Service of seven stations from 1992 to 2001, but also took into
account the roughness of the soil in each area for proper representation of the calculated wind
speed in the study area (Sahin et al, 2005). This fact reinforces that there exploitable wind
resource not only on the coastal areas of Turkey but also throughout the Mediterranean.
Cyprus has started slowly to estimate the wind potential for investment (Jacovides et
al., 2002). Oreites in Pafos area and Alexirgo in Larnaka are the two biggest wind farms in
Cyprus. However previous studies and assessments have not indicated a particular abundant
wind potential (Pashardes & Christofides, 1995). Even that, the two wind farms has shown a
significant alternative clear energy for development and electric production.
Furthermore, considerable effort has been done in encouraging investment on the wind
energy plans in Cyprus the five last years (Georgiou et al, 2012). The study showed
significant wind potential for wind farm installed in Larnaka area. Moreover, Kastanas has
found areas with good wind energy potential both in Limassol and Pafos. He showed that an
area near by the Germasogeia dam has significant wind energy for wind development. Also
Zygi, Mari has proved small but essential wind energy for small turbines installations. On the
other site, Polis Chrysohous showed different behaviour in wind flow than Limassol
24
‘Limassol is characterised by sea breeze at day hours’. Both in day and night are remarked
approximate the same wind power which is due to the fact that Polis is located at the
northwest part of the Island between mountains, so keenly phenomena hills and valleys
phenomena are observed with wind speed strengthening. As a result of this has observed
wind power from 500 – 1000W/m2 in Giolou, Stavros tis Psokas, Gialias’ area and Pomos
area. However, Kastanas pointed to the need of field measurements in pick points of
significant wind potential at studying areas to determine the capacity of the wind flow
(Καστάνας, 2012).
At 2013 Kastanas et al, found interesting wind energy and potential in Cyprus. The
application of the above showed that the western Cyprus areas indicate strong influence of
sea-breeze on the wind potential recovering interesting points with higher wind energy
potential, suitable for wind resource exploitation. The results of the work served the basis for
testing and extending the application over the whole island. Also the results obtained can be
utilized by potential investors and wind energy developers (Kastanas et al, 2013).
To specify, this Thesis is attempting a step forward towards an integrated method for
the estimation and analysis of potential wind energy resources in Cyprus, and is presented –
applied, at five selected sites to cover all the western part of the island. The wind statistics
achieved serve as the basis in order to predict corrected statistical distributions over the areas
of interest through Wind Atlas Analysis and Application Program (WAsP) developed at RisØ
National Laboratory, Rosklilde, Denmark, which converts the wind speed appropriate to local
topographic and roughness conditions. Aggregation of the data with statistical weighting
methods, allows the extrapolation of the results and the visualization over the western part of
the island, focusing into the inter–annual and especially the daily variation of the wind
resources which proved to be strong. This clear daily pattern is of great importance, both for
the proper site selection as for the correct planning and short term estimation of the wind
potential. The results of the work are indicative, but also they give an interesting perspective
on the continuation and completion of the study at extended areas.
In conclusion, a subsection of comparison between wind energy estimation
methodologies are coming to enclosed the chapter. Final, an overview conclusion is going to
summarize the above literature.
25
1.4.2 Models for Wind Energy Comparison
There are three different methods and models to estimate the wind flow and also the
wind potential. The analysis of wind resource at areas of studying could be simply
extrapolated using Meso – scale Models, C.F.D Models, and Microscale Models. The above
models are used wind data to calculate the wind flow at various heights. In this subchapter is
described the each model and its difference. In addition, a comparison of estimating models is
given below with an emphasis of limitations and the prospect of each model.
Meso – scale Models:
Mesoscaling Models are used to calculate the wind flow and the weather phenomena
using spatial resolution of 20 – 2000 km and a temporal resolution from hours to days. These
models utilise reworked data, elevation and roughness data. The treated measures retain the
extrapolated wind flow of the model though boundary conditions. The most popular meso –
scale models are KAMM (Wetter et al, 2004), MM5 (Pennsylvania State University, 2008),
and MC2 (Enviroment Canadian Meteorological Centre, 2002). The resolution of these
models is a few kilometres and the coverage is a few hundred kilometres. The basis of
models’ equation is correlated to the retention of mass, momentum, and energy is solved in a
finite element grid with a temporal resolution. Calculations with meso – scale models give a
statistical option of wind speed and its direction (Landberg et al, 2003).
The statistical dynamical purpose of regionalization of extensive scale climatology is
used to find out the site wind climate with KAMM. An assumption is used for the area
surface layer climate that is extrapolated from the parameters of larger, synoptic scale, and
surface parameter. Also the parameter of space is disintegrated into the representative
conditions. Numerical representation of these conditions is accomplished with meso – scale
model. As a result of this, the meso – scale climatology is simply extrapolated from the
results of representative runs in concert with the frequency of the conventional conditions.
Furthermore, significant and major parameters for the surface wind climatology of mid
– latitudes are the strength and direction of the large – scale wind pressure concentration, or
Geostrophic wind, the atmosphere interleave, also insistent antistrophes, changes in terrain
height – orography, and surface roughness. At the coastline areas the temperature defences
from sea and land. The large temperature defences due to the phenomenon of sea breeze.
26
Also, for the high wind speeds energy potential interest for investment in mid – latitudes are
the parameters of orography height, the terrain roughness and the geostrophic wind.
The simulation model KAMM is composed of a hydrostatic, geostrophic basic state. In
time the wind speed throw the topography is completed the model and the wind energy
potential is extrapolated. Sometimes the model is composed by vertical profile of geostrophic
wind and potential temperature (Brower et al).
Generally, the Karsruche Atmosheric Mesoscale Model ‘KAMM’ is a three
dimensional, non – hydrostatic atmospheric meso – scale model which hypothecate non –
divergent wind field with reference to do not simulate the sound waves (Adrian & Fielder,
1991). The sub grid variability is parameterized with the use of a synthetic model of length
and stability where stand on turbulent perfusion coefficients in stable layer foliated flow, and
non – local closure for synthetic convection layer (Landberg et al, 2003).
Figure 19 The Karshruhe Atmospheric Mesoscale Model (KAMM). Source: (Badger, 2006)
Figure 20 The above map presents the energy flux density E in w/m2 at 45m above the ground level
simulated by the KAMM model on a grid with resolution of 2.5km. Source (Meso-scale Models, 2011)
27
Spatial derivatives are extrapolated in the model by centered differences using a non –
staggered grid. The KAMM model content a tensely vertical coordinate which is terrain
following at the surface (see Figure 19, 20). The model top is based on stable height.
Eventually, the height from the sea surface of the grid levels is less than the mountain. As a
result of these, the wind flow and is computed using a flux corrected transport algorithm
(Hugelmann, 1988).
Lateral boundaries conditions assume zero gradients normally at the inflow boundary
conditions. At the same time the radiative circumstance leave signals to throw out of the
model without any effect of reflection (Orlanski, 1976). However, gravity waves can pass
throw in the upper boundary outwardly and pass the limits with use of boundary condition as
it noticed by Klemp and Durran (Klemp & Durran, 1983). Moreover, the atmospheric layer
can be associated to a planting – vegetation terrain model. Although in the most cases the
model was only solve – calculate in a semi – stable state. In that case the soil model does not
take any place at solve and calculations, when at the same time the soil surface temperature is
kept stable.
Combine of Meso scale and micro scale models is usually used to find out and explain
the wind flow – resource (Frank et al, 2001). Moreover, one specifically overused
combination is KAMM and WAsP which is analysed in the next subsection (Andrian et al,
1996).
C.F.D Models:
For the most of cases CFD models include a higher order turbulence model, are flexible
regarding the calculation grid used, work very efficacious and are affirmed for the most
simulations studies. The analysis for wind energy estimation is, due to the size of the area of
interest model, limited to a finest analysis resolution of 20m, and therefore is able of
recalculating small scale analysis models. Referred to the use of k – ε turbulence model, the
formation of turbulence by the topography and its transport can be solved.
In order to explain the use of CFD models, the computational fluid dynamics models
are used for the model analysis for the reason below:
Airflow in complex terrain, such as airflow at mountains and hills areas
Thermal effects.
28
The CFD models can prognosticate a better turbulence representation, when very high
spatial resolution is used (Undheim, 2003). Generally, the CFD models are based on
calculating Reynolds average Navier Stokes equations and at the same time they took in their
account the turbulence models. There are two popular 3D CFD software programs for wind
resource estimation, the WindSim and 3dWind. These kinds of programs are used for meso
and microscopic wind energy modelling. Outcomes of 3D CFD models are a stable state time
– independent solution of wind speed and direction. The input data that is used to CFD
models are: a) Digital terrain model, b) Map roughness, and c) Multiple wind speed and
direction measurements for more than one year. The processes of the model analysis are: a)
The spatial founding is gridded into cells; b) Cells can be further nested in model features of
interest, c) Simulation begin and the Navier Stokes are calculated continuously until steady
state is achieved. The production outcome of models is the wind speed time series at each
grid point at studying area (Undheim, 2005).
Many of CFD models assume a neutral atmospheric stratification. For strong winds the
above wind estimation method gives good approximation. However, if the wind speeds are
higher than 10 – 15 m/s stable stratification has been perceived, for example at coastal areas
(Aasen & Svein, 1995). Even though, a limitation in the CFD modelling is defined according
to the above literature. Moreover, limiting factor is the finite number of directions applied in
the production of the annual average wind speed. For an average annual representation in
whole, for example 12 sectors for wind flow direction composing may be too small to cover
and present the long – term wind speed (Delaunay, 2004).
A well known commercial wind resource analysis CFD application is, today, the
Meteodyn WTTM
. The application uses a turbulence flow method, specifically Reynolds
averaged Navier-Stokes, and calculates the three dimensional momentum and mass
conservation equations to predict the 3D wind speed vector (Meteodyn WT, 2013).
The turbulence environment is provided by materialization of a transport equation for
turbulence kinetic energy, which contemplates topography and thermal influences and the
presence of forests. For the simulate initialization, the application uses a logarithmic profile
with a changeable height to the ground for each simulated wind direction, with or without
thermal stability.
29
Input wind data from expressions is also applied to truthful outcomes. Particularly, data
time series can be used into Meteoryn WTTM
. The turbulence outcomes can be emended with
the measured turbulence context, when the turbulence outcomes are predicted.
Finally, CFD models are also utilized in micro – scale wind resource assessment. The
subchapter below present a comparison of CFD and WAsP models. Also the limitations of
both wind estimation methods are remarked and discussed.
1.4.3 Comparison between Models
Different models and methodologies are found to use for wind energy estimation
according to the above (Andrian, 1996). For the combination of meso – scale simulations
with WAsP the direction is spitted to 12 – 16 sectors of the geostrophic wind. Every sector is
disunited to various wind speeds classification of equal frequency. Specifically, if the
frequent of each sector for the direction simulation is repeated, then it is needed more speed
classes for the representation and statistical analysis in the next step (Mengelkamp et al,
1997). The atmospheric stratification does not modify as much as the geostrophic wind. In
addition the atmospheric stratification is more significant and interesting at low wind flows.
According to the Froyde number is defined that the lowest speed classes in a sector are more
allocated (Helmutt & Landberg, 1997). In addition, different conditions with geostrophic
winds from one direction sector have about the similar frequency as is shown from the above
Froude Number Equation (Fr-1
).
Where, N is the Brunt – Vaisala – frequency, L is the typical length sale of the terrain,
and V a velocity scale.
Moreover, the categories of frequencies are about the equivalent, as they are allocated
at every topic point of the large – scale analysis and interpolation at grid points of meso –
scale simulations. This shows a similar inhomogeneity on larger scale.
Thus, the geostrophic wind is the most important external parameter for the surface
wind, the representative categories must be chosen when the first three moments of
geostrophic wind speed is lost, and it cannot be awaited that the full force of wind density
30
near the surface wind will be simulated (Badger, 2012). This could be the major source of
errors in some ineffectual efforts to represent wind climate (Watson & Landberg, 1999).
Generally, the meso –scale modelling cannot simulate and calculate the soil surface
characteristics below the grid size. This can be simply done with the use of small – scale
models such WAsP. The combination of KAMM/WAsP (see Figure 21) is presented below
(Rathman, F et al, 2001). A wind atlas file is solved from the simulations very much as the
real measurements (Mortensen et al, 2003). The simulated wind is changed and is represented
with the roughness and orographic effects on the KAMM grid as in WAsP (Yamaguchi &
Ishihara, 2003). The representation of the morphological – orography is calculated for
neutrally stratified, non – rotating flow. Stratification transition effects are not accounted for
in WAsP (Mortensen et al, 2006). Thereupon, they should remain in the cleaned data such as
these remain in cleaned observations. As Sempreviva said, the model is changed by the
roughness effect, as it is used to calculate the disturbance relative to an upstream roughness
which is assigned as in WAsP (Sempreviva et al, 1990). The upstream roughness is used to
convert the cleaned wind to the roughness categories of a wind atlas file using the
geostrophic drag law (Blackadar & Tennekes, 1968) (see Figure 22).
Figure 21 The combination of KAMM/WAsP to estimate and resolve the local wind climate
31
Figure 22 The WAsP methodology of wind resource estimation. From the station statistics the geostrophic
wind can be extrapolated and then using reversely preceding the wind power of each grid point at
studying area is estimated
A particular care should be taken to simulate the wind flow in direction sectors
categorization which is essential for wind atlas files. The geostrophic wind classes have a
width of several tens of degrees. Therefore, on average, a simulated surface wind
representing a sector of the same width. If it falls near the boundary of the domain categories
of direction for the wind atlas, should be taken into account in both fields of wind atlas.
Consequently, each simulated wind is divided into a number of wind vectors. The split – up
winds are taken from the interpolated with the surface wind from the neighboring geostrophic
wind category that is most identical to the geostrophic wind category which is divided. The
most identical wind category is the one in which the converse Froude number, defined from
the geostrophic wind and the average stratification, is nearby to the converse number of
Froude split – up geostrophic wind category. The neighboring surface wind is also staggered
to the same geostrophic speed by means of the geostrophic drag law before the interpolation
becomes. After the simulated winds have been separate, there are several values to
extrapolate frequencies and fit Weibull distributions for dissimilar sectors. Slightly different
methods of calculating wind atlas tested. The method depicted above yielded the best
outcomes.
The models used for the purification of wind simulation used various parameters, such
as WAsP. Typically, the standard values of WAsP. Some tests were constructed with different
parameter values. Specifically, the roughness change the Wasp model does not eliminate
completely simulated wind speed dissimilarities at significant roughness changes, such along
coastlines. Apparently, KAMM / WAsP model these conditions differently. Different
32
conditions – parameters could reduce the differences a little. But also the changes are not
much and there is no general amelioration of the outcomes.
More studies in the future will be improving further correction models applied to the
meso – scale modelling outcomes. The roughness change the model of WAsP attended to the
grid data of KAMM cannot completely remove the effects of roughness changing at coastline
areas. Perhaps, the roughness sub – model LINCOM will agree better with KAMM. LINCOM
is linear flow pattern similar to the flow model of WAsP, but run on regular Cartesian grids.
Though, LINCOM does not use upstream roughness, which depends on the direction of the
wind. LINCOM only has an average roughness, which is the same for all wind directions
(Dunkerley et al, 2001).
The LINCOM model infrastructure basis is on an analytical solution in Fourier space to
a set of linear equations obtained from the normal nonlinear mass – and momentum equations
for incompressible fluid flows. The linear equations present the derangements in velocity and
pressure which the real terrain affects in an equilibrium flow corresponding to a flat terrain
with equable surface roughness (Jakob et al, 2000). Admittedly, this must be checked and
compared with the present roughness shift correction for wind atlas files extrapolation
(Astrup et al, 1996).
According to Mortensen et al, the flow model of LINCOM is different from the WAsP
in diverse expressions (Mortensen et al, 1993). WAsP treats as Fourier – Bessel extension on
a polar zooming grid and estimates the wind speed at the central point only. The zooming
grid recalculates the roughness and topography closer to the centre, which is evidently
condign. LINCOM predicts the wind vector by Fourier techniques in every covered point of a
rectangular grid (see Figure 23). This is applicable for WAsP engineering for the reason of:
a) A wind speed at the model dependent roughness at sea it needs to know the
wind speed all over the body the water body.
b) The turbulence model uses the flow upwind from the point of interest as input.
It is essential to calculate the wind resource in more than one grid point (Santabarbara et al,
1994).
Furthermore, the model analysis with daily cycles of radiation and the flow field were
not successful. The meso – scale information – measurements are required more checks and
33
tests for the model corrections of its orography. It would be possibly better to utilize the soil –
vegetation model of KAMM in lieu of its older force – retrieve soil model.
Figure 23 The WAsP Resource grid. The calculation area and the resolution analysis of the wind power
estimation at area of interest.
In addition CFD models are compared with WAsP model (see Figure 24). Some of
them is WindSim (WindSim, 2013) and 3DWind were used to Norwegian wind resource
assessment (Undheim, 2005). The great benefit of CFD models is that, in theory, they can
cope with some of the non – linear effects, present in surface wind flow. This is more distinct
in complex terrain or where obstacle or forestation appears. Several CFD models can be used
to resolve thermal and stratification influences (Castro et al, 2010).
Figure 24 The figure shows the WAsP minus CFD result compare layer draped on elevation data. Here it
is easy to understand that it is in the valleys where the wind speeds are estimated a bit higher with the
WAsP model compared to the CFD model. Source: (WindPro.v.2.9, 2013)
Rectangular
Resource Grid
34
1.5 Conclusion
The recent technical revolution of the wind energy industry and development in over
the global has led to the development of new technologies for the systematic identification
and evaluation of candidate wind project sites. In addition, higher with larger produce
electricity energy turbines are installed both in onshore and offshore sites. To summing up
wind energy is a clear alternative energy that can be simply use to cover all needs of world
electricity energy.
In Cyprus the wind energy is not particularly high, however there are some areas where
have offered and will offer even more in the future operation of wind energy. It is estimated
that a wind farm can be viable with an average wind speed 5.4 – 5.8 m/s. In our country, the
average speed in some areas is around 5.0 – 6.0 m/s, whereas in others estimates to reach 7,0
m/s, based on hourly measurements at meteorological stations. In fact, Cyprus has a wind
potential of about 150 MW and it is anticipated to reach 250 MW (CWEA, 2013).
Moreover, the scope of an installation with a total capacity 165 MW by 2015 is
illustrated to increase the contribution of renewable technologies in electricity generation by
about 4.5 % (The WindPower, 2013). In today’s Cyprus there is a contribution of almost 6,
0% and it has set itself the target of 9.0% by 2014. Also, according to Ellinas Renewable
Energy plans, “A total of 247MW will be possible with connection to the existing grid lines
with some planned modifications for Larnaka sites. Licenses were applied for as follows:
143.5 MW for the Archimandritha site at Paphos area, 84MW in Plataniskia at Limassol area,
41MW for Amalas / Chirokitia at Larnaka area and 40 MW for Akrotiri site at Limassol
area”. Consequently, it is evident that Cyprus has a potential role in wind energy
development and the expectations are becoming high (CIE, 2000).
Several methods are available to model the wind flow at both microscale, meso – scale
and CFD analysis levels. Firstly, the model and analysis Wind Atlas Application Programm
has a great reproducibility, with the chance of simply obtain the same outcomes, provided
they use the same database for the model construction – analysis. In WAsP model scope is to
manufacture the correctly construe outcomes and appraise in feasibility (Mortensen et al,
1993).
Also, CFD model analysis can be very well tuned to fit results to data from expressions
– observations. However, the CFD model require one more than one locations and with wind
measured observations database at different heights, several model setup parameters can be
35
adapted by an experienced user to achieve better outcomes in a particular spot. Moreover
could be not possibly available for the analysis and that is the drawback of CFD models. In
addition the model cannot use in any location but also new parameters and solving of NAvier
Stokes equation is needed, when at the same time the model set up can be much complex and
outcomes are quite dependent on user option. On other hand as we noticed the accuracy of
these models are better (Stull, 1988). However, WAsP as linear application has a good
accuracy and representation of wind resource at any region.
Besides, meso – scale analysis models can estimate the wind resource for larger regions
of more than ten thousand square kilometers (Holton, 2004). To analyze an identical area
with wind data would necessitate many stations. This is a disadvantage, since it takes a long
time to acquire the climatology of meteorological stations. Thereby, they are good models for
wind energy overview for a large region. On the contrary, meso – scale models cannot be
utilized for wind turbines sitting because the grid resolution of these models is too big. In that
case of wind farms sitting the high resolution analysis of WAsP microscale model is essential
(Mortensen et al, 1993; Mortensen & Petersen, 1998).
Finally, WAsP is very well tested tool for wind resource estimation from a high quality
wind database. It is based on the physical theory of atmospheric boundary layer flow and
consults the roughness effects, sheltering effects due to buildings – and other obstacles, and
the orography of the studying areas, where then it modifies the wind inflicted around the
meteorological station.
36
37
2 WIND ENERGY ASSESSMENT AND ANALYSIS
The analysis of the wind flow is affected by many parameters. Several of them are
shown below and are analysed, such as the ‘Meteorology of Wind’, ‘Distribution of Wind’,
‘Atmospheric Stability’, Geostrophic Wind’, ‘The roughness Change Model’, ‘The shelter
Model’, ‘The Orography Model’, ‘Climatology’, ‘Wind Speed Statistics’, The Statistical
Review of Model’, ‘ Wind Atlas Analysis and Application Program’, ‘Wind Speed
Measurements and Stations Characteristics’, and ‘Errors of Model and Data’.
2.1 The Physical Basis of Wind Atlas Analysis Model
To begin with, the basis of infrastructure of WAsP model is to provide with a suitable
database for estimating the wind flow. Generally, it is needed an interannual hourly database
of wind speed and direction for each meteorological station. In addition, the physical model
basis is the flow in the atmospheric boundary layer taking into account the complex terrain
surfaces, the sheltering effects due to buildings and other obstacles, the local roughness effect
of the areas at the studying region, and the modification of the wind imposed by the specific
variations of the height of ground around the meteorological station. The WAsP model
concept and methodology is shown below at Figure 25 (Riso Laboratory, 2013).
WAsP ensue a procedure in which the regional wind climatology of a station is used as
input to be resolved and by following the respective reverse procedure to find out the wind
climatology around the site (Mortensen et al, 1993). Moreover, the regional climatology, the
station site’s description and the model are used in order to transform the measured data set
of wind speed and direction from any station to what would have been measured at the
station’s location if the surroundings were: a) Flat and homogenous terrain, b) No effects of
obstacles nearby, and c) Measurements had been taken at heights of 10, 25, 50, 100 and
200m. The application uses the extrapolated statistical review data for stations as a regional
climatology. Then, the model of logarithmic profiles for the wind flow is used to take into
account the topographic and terrain roughness effects (see Equation 7). From the
meteorological input data, a boundary unified wind for the studying region is extrapolated as
a representative Geostrophic Wind. After this, the application re – allocates the wind flow at
38
each resource grid point along the area of interest, while at the same time emends due to local
topographic and roughness effects (see Figure 26).
Figure 25 The Wind Atlas Methodology. The database of wind measurements with the characteristics of
station, the terrain classification around the meteorological station and the mountain terrain topography
heights are used for the calculation of regional climatology. Then an antistrophe similar procedure is used
to estimate the wind flow at each resource grid point. Source: (Riso Laboratory, 2013)
WAsP uses the logarithmic law and provides great accuracy in the case of wind speed
measurements at a known station altitude calculating the wind speed at high altitudes, more
than 30 – 50m above the station’s height (see Figure 27). The logarithmic profile function is
given as:
(
)
(
)
where, V(10) is the wind speed at 10m height above terrain surface and z0 is the length of
terrain roughness (see Table 3) (Λειβαδά, 2000).
39
Figure 26 A schematic representation of the Wind Atlas analysis model. Source: (Troen & Petersen, 1989)
Figure 27 The vertical profile of wind speed distribution above the terrain surface. Source: (Chiras, 2010)
40
Table 3 Parameters for vertical wind speed profiles calculation
Surface Type Classification of soil rub Length of surface
roughness, z0 (m) Exponent α
Water Zones 0 0.001 0.01
Open Areas, few
Obstacles 1 0.12 0.12
Agriculture Areas 2 0.05 0.16
Villages, Forests 3 0.3 0.28
Source: (Walker & Jenkins, Αιολική Ενέργεια και Ανεμογεννήτριες, 2007)
The implementation of Wind Atlas model process can be summarized as follows:
i. Input data are in the form of histograms for each 12 azimuth sectors, giving the
frequency of existence of wind speeds in bins of 1m/s width.
ii. Wind speed – sovereign correction factors are calculated for each azimuth sector.
Three categories of parameters are contemplated:
The correction parameters for obstacle types, calculated using the shelter model, here
marked as
for the jth azimuth sector (Mortensen et al, 2004).
The roughness effect parameters
. The roughness modified model correlates the
velocity at the station to the velocity upstream of the specified roughness transforms.
Additionally, the area weighting of surface roughness afford an efficacious upstream
surface roughness
.
The correction parameters for orography, calculated by WAsP of the orographic map
model. The model is applied using as input a wind profile with direction in the centre
of each sector. The effective surface roughness is taken into account as parameters in
the orographic model. Following this, the
and
are acquired, where
are
degrees of turning of the wind vector calculated by the orographic model (Troen &
Petersen, 1989).
Besides, each combined azimuth and wind speed bin is converted with the use of these
parameters. Contemplating the jth sector and the wind speed bin from uk to u
k+1, modelling of
the obstacle amendment parameter
provides the tallying values which would concern if
the obstacles were eliminated. Likewise, the orographic emendations and the roughness
change emendations are used to change the bin boundaries to values for upstream fettles
41
(Mortensen et al, 1993). Moreover, the turning of the azimuthal boundaries, the orographic
rotating angles are attended using the average of the two values nearby the boundary
contemplated (Holttinen & Peltola, 1993).
Also, the efficacious upstream surface roughness
is applied with each of new bin
boundaries in the geostrophic drag law to find the analogous boundaries and with
conjoined directions
and
from the low and high area of the original azimuth bin.
The geostrophic drag law Equation is given as:
√( (
) )
Where: G is the geostrophic wind, α is the angle between the winds nears the terrain
surface and the geostrophic wind, f is the Coriolis factor and A is assumed as 1.8 and B is
assumed as 4.5 empirically. The geostrophic wind can be extrapolated from the surface
pressure gradient and is frequently estimated by the wind speed measurements by
radiosondes over the boundary layer. The geostrophic drag law equation can be expanded to
non – neutral stability cases, where A and B turn to parameters of the stability function μ.
As we mentioned before, this alteration process the frequency of the fact in the bin is
confected. The geostrophic wind is used as a reference wind layer of the regional station
climatology, and then the application is taking in its account the roughness for the wind
distribution around the site. Specifically, from the use of geostrophic drag law Equation, V*
variables for the standard surface roughness are calculated using , and wind
directions from the D values upper. The respective variables of wind speeds at the reference
level of 10m are calculated using the Logarithmic Profile Equation as given below:
Where V(z) is the wind speed at z altitude above the terrain surface, z0 is the surface
roughness length, k is the von Kármán equal to 0.40, and V* is the friction velocity.
42
Afterwards, the correspondence to each of the standard azimuth of 30o and speed of
1m/s are calculated. This routine is continued for each azimuth and speed bin in the input
database and the outcomes are four sets of histograms with similar form like the input
histograms, but relating to the reference altitude of 10m and to each of the four roughness
classes (see Table 3). From the above procedure the Weibull statistics parameters are
calculated using the fitting process of the respective frequency of occurrence that is extracted
at each azimuth sector (see Sub – Chapter: “The Statistical Review of the Model”). The
parameters of Weibull respective to the expected real altitude of zn are following calculated
using the transformation of the logarithmic profile which takes into account the variability
influences of warm through (Mortensen et al, 2004). The average and root mean square warm
through are determined singly for over - terrain and over – sea conditions (A. & Mortensen,
1996).
Also, the stability effect factors of mean values and standard deviation are assumed as:
(
( )
)
(
| |)
(
)
(
)
Where V is the vertical variation of the relative mean speed mean deviation, σV is the
standard deviation, and f(z) is the profile function derived from the first order expansion.
These expressions are applied in the analysis to find the degree of contamination by stability
effects in the database and to insert again applicable values of contamination when
calculating conditions at different height and surface conditions (Troen & Petersen, 1989).
In particular, these expressions are estimated for contamination in the input data using
anemometer height, distance to the coastline, and up with equipoise surface roughness in
each azimuth sector. Likewise, the contamination is extrapolated for the different standard
heights, and the proportion of these values to those on input are applied to atone the Weibull
parameters extrapolated using a logarithmic profile. The respective means and standard
deviations are calculated using the expressions of Weibull parameters as is referred:
43
{
(
)
(
)
(
)
(
)
[ (
) (
)]
(
)
Where A is the scale parameter, k is the shape parameter, and Γ is the gamma function
is referred in bibliography (Mortensen et al, 1991-2005). Concluding, from the above
calculation, roughness class 0 pertains to conditions over water and the three other roughness
classes are atoned to conditions well in up – country to the vantage of any coast effect, as
shown in Table 3 (Bowen & Mortesen, 1996).
2.1.1 Wind Atlas Application
The theoretical – basis of WAsP modelling was described in the previous sub – section.
To summarise, the Wind Analysis and Application Program WAsP enclosed models for the
horizontal and vertical extrapolation of wind data which take into account sheltering
obstacles, surface roughness modifications and terrain height variability (Mortensen et al,
1993) (see Figure 28). These models are applied doubly in the procedure of estimating the
given wind resource from a region of measurements to a different area. Initially, regional
wind climatology is computed from the wind flow measured database. For example, wind
distributions for 12 directional sectors for the geostrophic wind are computed. It is then
assumed that the climatology of geostrophic wind is unified for the studying areas (Troen &
Petersen, 1989). Afterwards, the WAsP models are utilized to estimate the wind power –
energy for the studying sites from the wind climatology computed in the first step (Petersen,
1993). The result leads to the estimation of Weibull wind speed distributions in 12
dimensional sectors.
44
In addition, the amendment parameters for topical – local shelter, orography, and
roughness effects are computed precisely such as in the model analysis, now of course with
use of the obstacle list, roughness explanation, and orographic data referring to the site where
the Atlas data are to be used.
For the height contemplated, the Wind Atlas is calculated and the Weibull parameters
are extrapolated for each azimuth sector in as well as to the sector frequency fj. For
differences in heights (compared to the standard heights) and for terrain roughness differing
from the standard values, a logarithmic intercession is applied. The roughness values of
terrain applied for each sector are the values computed in the roughness modified model z0e.
The rectification factors are utilized to the first Weibull parameter at each sector, while
keeping the k parameter values to the table values.
Figure 28 A schematic representation of the Wind Atlas application model. Source: (Troen & Petersen,
1989)
After that the model computes values for the sector – wise parameters and sector
frequencies for a chosen regional climatology, according to: a) the height above the terrain
surface, b) terrain roughness, c) sheltering obstacles, and d) orographic model. The internal
consistency is verified by calculating the station climatology with the utilization of regional
climatology originated from the same station through the model analysis. Finally, it is
45
possible to utilize the reference meteorological station to estimate the topical – local
climatology of another station nearby.
2.2 Meteorology of Wind
The Wind Meteorology is discussed by different authors (Halliday, 1988). It has been
blossomed as an implemental science, equably founded on boundary – layer meteorology, but
with influences – connections from climatology and geography (Petersen, 1998).
Meteorology is a basic science in its widest sense. It corresponds with the atmospheric
thermodynamics and chemistry, the qualitative and quantitative explanation of atmospheric
movement, and of the interplay between the atmosphere and the Earth’s surface and
biosphere in general. Its role is the all – embracing apprehension and precision at the
estimating of atmospheric phenomena (Haupt, 2012).
In addition, when it deals with the wind flow, it sets one’s hand on three main options:
a) Wind turbines sitting and installation, b) Regional wind resource assessment, and c) short –
term estimation of the wind power. Following a global view of the wind power is retraced: a)
Wind profiles and Shear, b) Turbulence and gust, and c) Extreme Winds.
The Wind Meteorology utilizes information from three sources: a) Wind measurements
from meteorological station for the studying area, b) The synoptic networks, and c) The re –
analysis projects. For each of the countries the participant selected the stations from which
data subsequently acquired. In the collection a number of aims were intended are defined
below:
Adequate covertures for each country: each climatic area should if possible
supply data. Each area which is far away from mountains this intermediary data
from stations dissociated less than about hundred kilometres. On the other hand
areas which contains mountain require only the spot wise outcomes (Troen &
Petersen, 1989).
Adequate time period. Climatic means are traditionally related to a 30 year
period, but in this case it is requisite to confine the period covered to 10 years.
The main explanation for this is the importance attached to the credential
description of anemometric conditions and appliance exactness.
46
Accurate installed exhibit anemometer far away from obstacles and building
covered areas. This necessity cannot be possibly satisfied always.
Accuracy of anemometric conditions and data of 10 min or hourly averages
collected for each 3 hour period throughout the 24 hour a day (Troen &
Petersen, 1989).
Eventually, these necessities are mostly not fulfilled (Hardesty & Brewer, 2012). The
data are assumed to be representative and at good quality. In assessment of data, it is possible
to find imperfections like:
Unnaturally high wind speeds
An unnatural number of remarks in certain wind speed classes and / or wind
direction sectors (Troen & Petersen, 1989).
Certain patterns affected by the modification of database originally referred in
Beaufort to metres per second.
Also, the remediation for these data imperfections is very easy (Hardesty & Brewer,
2012). The unnaturally high wind speeds are manually removed. Only very few
measurements are eliminated by this process. Unnaturally appearances of wind speeds and
directions are aligned with climatology (Emeis, 2013). The patterns induced by
measurements modification are expunged by the above process: if the discretization of wind
speed V and direction D is afford by ΔV and ΔD, then a new value is allocated for each espial:
Where α and b are equably haphazardly allotted over the interval [–0.5, +0.5].
According to Troen & Petersen, there is another data problem which can be caused
during night measurements (Troen & Petersen, 1989). The authors referred that they used
observations for every three hours as required in the selection criteria for wind – observation
sited mentioned above. However, in predefined sites it was ineluctable to embody
meteorological stations that miss many night observations. Afterwards, the filling of missing
data is an imperative procedure that should be followed before the analysis of the other
stations measurements-data. The reason for that is that the minimum of the average diurnal
cycle of wind speed happens during night – time espials. Thus, the straightforward
47
application of frequency tables generated from these measurements would have resulted in
bias against higher mean wind speeds (Emeis, 2013).
The process in general replaces the missing measurements by linearly interpolating
over the time interval between the last night and the first morning espial. This process is
applied by Troen & Petersen at the table for each of eight 3hours sets of time periods (Troen
& Petersen, 1989). Then the process is applied at each time period in order to generate the
missing data.
Furthermore, the stations with the problem of night – time espials can be identified
from the wind climatological fingerprint and the table of means in the station is explained
because the average is missing for some hours.
Afterwards the topography map and its data at the studying area, is coupled with the
wind speed measurements, and later it is transformed into numbers which could be applied as
a participation to the roughness, shelter, and orography model. Specifically, the roughness is
verified using maps on scales of 1:25000 to 1:50000.
Next, for each station, the horizon is divided into 30o direction sectors, and the duty of
surface roughness lengths is accomplished sector by sector. The classification expanded to at
least 5km from station. If an expansive water surface or other influential alteration in terrain
supervened and passed far away, the categorization is expanded to 10km or more. The
outcomes of the roughness cession are given for each station in the station statistics.
Information on obstacles nearby the anemometer area that might have affected the wind
flow data is either obtained by the partakers as an absolute obstacle explanation form for each
station, or it is extrapolated from maps, photos and other depictions (Shreck et al, 2008) (see
Figure 29). Since the wind speed measurements are affected by buildings and obstacles a
strongly influenced anemometer is not representative in order to explore the regional wind
climatology. Consequently, for the station’s selection only stations that are in open areas
should be taken into account.
48
Figure 29 Anemometer height should be at the position to represent absolutely the region climatology of
the studying area. For the purpose of representative measurements the height should be 2times higher if
the anemometer is nearby building areas or 10times far away. Also planting areas and hills areas are
plained extremely important role to the wind speeds information. Source: (INFORSE, 2013)
Finally, the induction for the model is provided by digitalizing the height contours
from geographical maps. The topographical maps (see Figure 30) should be on scales of
1:25000 or 1:100000. Nearby stations the contours should be digitalized as thoroughly as
possible, using a standard digitizer ‘At our study, we used ArgGIS to modified to one map
with the roughness effects using the Corine Land 2000 leveling and the topography with
contour lines’ (Georgiou et al, 2012).
Figure 30 In the left one is shown the topography map and at the right is the roughness map. Source:
Željko Ɖurišić Jovan Mikulović
49
2.3 Atmospheric Stability
The Atmospheric stability is an essential parameter that is taking into account for local
and meso – scale atmospheric circulation yet relatively little is known about the frequency of
different stability conditions at coastal areas (Coppin et al, 1996). When the air flows over a
coastal discontinuity, then two types of change can affect the flow: a discontinuous change in
roughness, which affects the momentum fluctuation, and a change in the availability of heat
and moisture (Kaimal & Finnigan, 1994). As is known the atmospheric stability is defined as
the vertical with the variation of air temperature. Especially, the atmospheric stability is
measured by tendency of the mass of air that is moving vertically, or not to return to it is
originally position (Λειβαδά, 2000). If the temperature gradient of the atmosphere is less than
the adiabatic temperature gradient, then the mass of air will be moved to a higher temperature
than the surrounding air where it will not move down “Unstable Atmosphere”. Otherwise, it
will be moved back down to its original position “Stable Atmosphere”. In keeping with
Leivada, I. L (Λειβαδά, 2000) the atmospheric stability can be expressed in relation to the
formal parameter stability of Richardson Ri, as follows:
Where g is the gravity acceleration, T is the absolute air temperature, and cp is the
specific temperature at constant air pressure.
The amendment of atmospheric stability occurs on and off – shore for various time
scales and coastal regions exhibit larger seasonal temperature differences than areas far off –
shore (Joffre, 1985). The seasonal change in sea surface temperature dawdle changes in land
temperature (Korevaar, 1990). Moreover, the coastal areas are subject to thermal driven
impacts such as the sea – breeze (Coelingh et al, 1998) and low flows (Smedman et al, 1996).
Parameters influencing wind speeds in coastal studying areas are shown below:
Air – sea temperature variations
The locality of the coastline area
50
Ascendant wind speed and direction
Water depth
Latitude
Distance from the coastal discontinuity
For the reason of these approximations deportment the effects, also, of the surface heat
flux changing with no the necessity of the model particularity for every entity wind profile, a
streamline process is affiliated which only requires insertion of data are the modification of
climatological average and root mean square (Sempreviva et al, 1994). Following that, the
model is originated from the geostrophic drag law and the wind speed profile by the first
stretch in surface heat flux from neutral state (Anthony et al, 2004). The differential of
Equation 8 that is given before is shown below, where the G, f and z0 are kept stable:
[(
) (
)
]
Then with the use of Equation 8 and entering the neutral values of miscellaneous
coefficients and disregarding the small terms (see Equations 18), the various sizes are
calculated as shown below:
[
]
Where the numerical constant Unfortunately, the above Equation is applied to
appraise the offset from neutral value of V*, taking the climatological mean value of the
surface heat flux as dH, and to assess the root mean square of fluctuations of V* using the
RMS heat fux for dH. In this case the geostrophic wind speed G is taken equivalent to the
value where speed frequency distribution has a maximum in power density (Troen &
Petersen, 1989).
The severalty of the wind profile is given in Equation 19 as:
[ (
) (
)]
51
Inserting neutral values of the coefficients as above and using Equation 18, an
observation is procured for the height above the ground zm where the first order effects of
surface heat flux configuration disappear, and as a result of this, there is a minimum in
variation of wind speed (setting dV(zm) = 0) outcomes, videlicet (see Equation 20) (Troen &
Petersen, 1989).
⁄
(
)
Where the new numerical constant α is the slope at neutral of the ψ function with a
value between 4 and 5 conditional on whether observations of stable or unstable conditions
are utilized (Troen & Petersen, 1989). Using the simplified neutral drag law (Jensen et al,
1984):
Equation 20 can be fancier typified as:
(
)
Where the constant ≈ 0.1 and the surface Rossby number is equal to:
Ultimately this observation can be approached with the power law:
⁄
Where the constants utilized are and . It is noticeable that the
height zm is pithily constant over large areas because of the weak dependency of
. An exemption is faced on coast lines, where zm over sea is located to be
precipitately half of the over land value.
The impressions of non – neutral stabilities are modelled through their impressions on
the vertical profile of the climatological mean value and standard deviation of wind speed
using the above observations.
52
The height of minimum divergence zm is allocated from Equation 22. At this height the
relative deviation from neutral value of the mean speed is assumed as a sum of the deviation
affected by an average heat flux offset import as ΔHoff and a suscription from the varying heat
flux ΔHrms:
(
) (
)
(
)
Where Loff is the Monin – Obukhov length corresponding to ΔHoff and Lrms reciprocates
to FrmsΔHrms. The factor Frms is a pattern factor which calculates for the fact that due to the
differentiation in the form of the ψ function from stable to unstable conditions there will be
on the average a bias against higher values of wind speed at the height zm (Holtslag & Bruin,
1988). This can be shown from the unambiguous forms which are assumed below as (Jensen
et al, 1984; Bousinger et al, 1971; Dyer, 1974; Arya, 1995; Troen & Petersen, 1989):
⁄
{
(
)
Depending of the above, the much smaller variation with z of the unstable affects the
wind speed at zm to be dislocated to the unstable side on the average even in the case where
there is a zero average surface heat flux (see Figure 31). The resultful acceptable heat flux is
assumed to be related to the rms value by sector Frms.
Figure 31 Wind profile characteristics: graphs to the left show a range of wind speed profiles (shaded
area) corresponding to a constant geostrophic wind speed of 10 m/s and a typical range of surface heat
flux. The graphs to the right correspond to G = 20m/s and the same range of surface heat flux. Source:
(Troen & Petersen, 1989)
53
The variability on the vertical of relative mean deviation of mean speed V and standard
deviation σV are finally entitled in the form, as we mentioned before (see Equations 10-12).
Consists of that, is that in coastal regions are comported as the midway among the land
cover’ regions and sea cover’ regions. This is executed by contemplating the distance to the
coast in the upwind direction (x) and implementing the stability amendment characterizing to
land cover’ regions and sea cover’ regions aggravated with a factor w:
Where c is the width of the coastal region, taken here to be 10km. (Particularly in this
thesis, for the overlapping and to cover all the Cyprus wind potential using the WAsP
application, are used 20km maps width for every region. See Chapter 3: Methodology). More
detailed about the stability of the model and the application model was described before in
the Sub – Section of “The Physical Basis of Wind Atlas Analysis Model”.
2.4 Geostrophic Wind
As mentioned previously, since an air mass is in the initial phase of stillness then will
start moving from high pressure to low pressure region, because of the growing power
(Γεωργίου, 2008). Nevertheless, through with the start of this motion, however, it starts the
effect of Coriolis force and the consequent deviation to the right or left depending to the
hemisphere. When the wind moves parallel to the isobars, it is called Geostrophic Wind
(Meteorology Department of Cyprus, 2003). The Geostrophic Wind (see Equation 8) is
present at 1000m height above the Earth’s surface, in the free troposphere above the
atmospheric boundary layer, because frictional forces are negligible there, as an easiest and
most fundamental balance of forces (Μπαλτάς, 2006; Emeis, 2013) (see Figure 32-33).
54
Figure 32 Schematic of geostrophic drag law and the geostrophic wind representation. Source:
(WW2010: University of Illinois, 2010)
Figure 33 The geostrophic draw law. Source: (Eastern Illinois University, 2013)
2.5 The Roughness Change the Model
The application of the logarithmic wind profile is acceptable only if the upwind terrain
is homogenous. Otherwise, deviations will be observed in the measurements and might it is
not feasible to define an exclusive roughness length to the terrain. Although the effective
roughness length can be applied by several methods, the application will rely on the height of
observation. The geostrophic drag law, also, could be an exception to the above which
implicitly gives the effective roughness length.
The median surface stress and surface wind speed must depend on surface features only
up to a constant upstream distance. More specifically, distant obstacles are generated by the
55
tendency of the boundary layer to create equilibrium between the pressure gradient force and
friction. The distance scale concerned is relative to the Rossby radius G/f and is of the order
of 10 – 100km. Regarding the wind frequency distribution it is adequate to consider surface
features out to distances of the order of 10km. According to some considerations about the
surface layer, it is likely, in the case of small scale terrain inhomogeneity, to form the change
of surface stress which takes place when wind flows from a surface of roughness length z01 to
another surface with a roughness z02. Under these circumstance an internal boundary layer
(IBL) is developed downwind to the roughness change and at a distance x downwind from
the change, the IBL increases to a height h given by (Panofsky, 1973):
(
)
( )
In addition, it is found that the change of surface friction velocity is well described by
using the following equation:
( ⁄ )
( ⁄ )
Where: V*2 is the surface friction velocity at the point considered and V*1 is the surface
stress upwind from the change.
The wind outline is disturbed in the IBL and the surface friction velocity cannot be
estimated from wind speeds using the logarithmic profile. Otherwise, trial evidence
(Sempreviva, 1989), as well as outcomes from numerical models (Rao et al, 1974), indicate
that the perturbed outline can be modeled with three logarithmic parts:
{
⁄
⁄
⁄
⁄
⁄
⁄
Where: (
⁄ ) (
⁄ ), (
⁄ ) (
⁄ ), c1= 0.3, and c2=
0.09.
56
From the above relations and with the support of Equation 31, the surface friction
velocity V*2 can be linked to the friction velocity upstream of a change in surface roughness.
For more roughness alterations Equation 31 can be used in a sequence, and therefore a
modified wind speed can be treated for measuring the surface friction velocity far upstream.
Nonetheless, successive roughness changes have to not happen too close to each other and
thus the next distance rule is applied. If xn is the distance to the nth change in surface
roughness, the upstream roughness have to be anticipated as an average covering the area
between the distance xn and 2 xn in the azimuth section measured. The factor 2 is fairly
arbitrary, and the rule may be deflected from in cases where clear roughness limitations are
set up, for instance at a coastline (Troen & Petersen, 1989).
Moreover, moving more upstream, the roughness change model will show outcomes
deviating from realness as it does not include the above mentioned boundary layer approach
to balance. In fact, the incongruities are given to be small clutters and a single model is
produced by taking into consideration the asymptotic behavior. The far-upstream surface
situations have to mislay significance as x/D becomes large, where D is the chosen
equilibrium distance and additionally the above surface layer relations have to be set for x
much slighter than D. That behavior is succeeded by weighting the roughness changes by a
factor Wn :
(
)
The value (
⁄ ) replaces instead of considering a change from
z0n + z0n+1 at distance xn (Troen & Petersen, 1989). By use of this weighting in order, a value
of the surface friction velocity far upstream is kept jointly with a value of the relevant
equilibrium surface roughness to which the geostrophic drag law applies (Mortesen &
Petersen, 1998).
2.6 The Shelter Model
The effect of friction action in a land surface is caused by strain on surface-mounted
obstacles fluctuating from grass, sand grains, leaves to large trees and structures. Their effect
is shown over the surface roughness length. Near to an individual obstacle, the wind profile is
unsettled, mainly in the downstream wake, and the object have to be used discretely. In the
57
wake immediately back of a blunt object, for example a house or a row of trees, the details of
the object introduce a critical impact on the effects. In addition, the wake back to a building
depends on the detailed geometry of the roof and the incidence angle of the wind. The wakes,
also, from other close objects may interlope, producing the problem to become very difficult.
Moreover, the shelter model created for applying in the analysis must be used as a tool
for correcting data influenced by sole obstacles that are adequately distant to make the
disorders small and to avoid the involutions of the close wakes. The expressions given by
(Perera, 1981) are used:
(
)
Where:
(
( ⁄ )
)
And:
P: porosity= open area/total area
h: height of obstacle
za: height considered (anemometer)
x: downstream distance
In theory, the distances to and heights of objects crossed by the ray are marked. If a sole
ray crosses some obstacles, each of these crossings is primarily used as a single semi-infinite
obstacle. Regarding most distant one, the shelter on all downstream obstacles is estimated in
sequence. If objects are so near to each other that their zones of separation merge, the
downstream sheltering is lessened by the comparative area of the downstream obstacle which
is fixed in the separation zone of the upstream obstacle.
In this part the divided zone upwind of a two-dimensional obstacle is found to be
restricted by a straight line from the top of the obstacle down to the surface at a distance
twice the height of the obstacle.
Following to this calculation of the shelter at the point marked from the sequence of
objects, the sheltering for each ray is varied with neighboring rates. This is applied to shape
58
the actual mixing of momentum deficit at the edge of the wake. In conclusion, the average
shelter is calculated over an azimuth sector by summarizing the sheltering calculated on each
ray. Specifically, eight rays are used per 30o azimuth sector and an efficient lateral spreading
over an angle of 12o.
2.7 The Orography Model
The Orography, like roughness and shelter effects, is utilized to amend the wind flow
measurements where affect the local and surrounding area of the studying meteorological
station. Unfortunately, the Orography is depicted in most topographical maps by the height
contour lines of the terrain surface. Also the height contours can be designate in digital form
as a vector map, which comprises the “x,y” coordinates and elevation of the contour lines
(Troen & Petersen, 1989). At the position of WAsP model is using digital maps directly
(Jakson & Hunt, 1975; Troen & de Baas, 1986; Walmsley et al, 1982).
Refined raster maps can easily be obtained from point – device vector maps, whereas
the transformation of raster maps to vector maps outcomes in some loss in information, stand
on the actual grid size cell size of the digital terrain model (Petersen et al, 2006).
The Orographic model is identical with the MS3DJH family of models (Walmsley et al,
1982), which is also grounded on the original analytical solution by (Jackson & Hunt, 1975).
Surmising linear equations of motion, the model utilizes polar representation and polar
zooming grid to generate higher resolution of the terrain closest to the studying area. It
computes at first, the wind flow stramash affected by the terrain (Troen, I, 1990). Afterwards,
the wind flow disengagement is changed to accommodate, in an approach notion, the
impressions of surface friction in the internal layer nearby the terrain surface (Anthony et al,
2004).
Moreover, the necessity of orographic model are the approximations of neutrally stable
wind flows over low, smooth hills with attached flows, in a replicate for to the original
analytical model as it mentioned first by Jackson and Hunt (Jackson & Hunt, 1975). This
analytical model appears to provide reasonably good results on the hill top and upstream for
situations with h/L ≤ 0.4, depending on the value of L/z0. Here, it is assumed the h is the hill
height, the L as the hill half length, and z0 the surface roughness length as it is referred
59
previously in the sub-section of “The Physical Basis of Wind Atlas Analysis Model” (Taylor
et al, 1987). The corresponding hill slope limit, θc, would be rather greater than 0.2,
depending on the sure-enough shape of the upper half of the hill profile (Hunt et al, 1988;
Hunt et al, (b) 1988).
Firstly, in the model is computation of the potential flow derangement affected by the
terrain and responding to a unit wind vector in the undistracted wind direction. This
procedure is defined below, as the velocity derangement is mess around to the potential by:
Where x is the potential and is the three dimensional vector of velocity
derangements.
If vanishing potential is supposed at a given outer model radius R, an overall
disengagement to the potential flow problem in polar coordinates can be typified as a sum in
terms of the form:
(
) (
)
Where Knj are arbitrary coefficients, Jn the nth order Bessel function, r radius, Φ
azimuth, z height, and are the ith zero of Jn. For a respective situation, the coefficients are
specified by the boundary conditions, which are the surface kinematic boundary condition:
|
Where: wo is the terrain induced vertical velocity, the basic state velocity vector and
h the height of terrain (Troen & Petersen, 1989). The functions (
) form an orthogonal
set of radial Furier – Bessel series functions for each n, and the azimuth remonstrance
similarly forms an orthogonal set. The coefficients can consequently be
computed singly from the Equation 29 (Oberhettinger, 1973).
However, the polar remonstrance has some main advantages over the more common
Cartesian systems utilized in referred models, while predicating the benefits of the spectral
fission. By interpreting the model centre to concur with the interest point, it is presumable to
centralize the model resolution there and therefore to confine the computations to the
stramash at this point. For the centre point r = 0, the following solution is given as:
60
(
)
The outcomes of the first computation of the model is thus a series of coefficients K1j
from which the solution of the potential flow derangement is assumed as a sum of the terms
signified in Equation 31. Each of terms has an affiliated horizontal scale
, which is
as well the representative depth to which the derangement percolates.
Secondly, the model is contained of the amendment of potential flow solution to adapt
in an approach sense the surface friction influences (Troen, I, 1990). Potential flow indicates
equipoise among the pressure gradient force and advection of momentum in the equations of
momentum and vanishing turbulent momentum transmission (Troen & Petersen, 1989). Close
to the surface the turbulent cannot be disregarded. The deviation from the potential flow
comportment is confined to a layer whose depth is of the order lj with . According
to Jensen et al the value of lj is computed (Jensen et al, 1984) as:
(
)
Where: z0j is the surface roughness length of the scale contemplated. For homogenous
conditions z0j = z0. For inhomogeneous areas the surface roughness length is gotten as an
exponentially weighted average from r = 0 to r = 5Lj in the upwind direction.
However, for the smaller heights than lj, turbulent transfer forces a balance among
stress and wind shear, leading to a logarithmic profile of the velocity derangement. For
heights comparable with lj maximum flow derangement occurs, and this derangement
surpassed the value estimated from potential flow (Troen & Petersen, 1989). Moreover, the
derangement profile is configured for each term in the expanding by allocating a
derangement to the height z of the proportion of ΔVj:
| |
| ( )|
| ( )|
Where V0(z) is the basic state velocity at height z and is compeer to max (z, lj).
The computation of the coefficients K1j via the projection method includes numerical
integrations over the azimuth and radius (Troen & Petersen, 1989). This is exhibited on a grid
61
explicated in Figure 34. The radial grid size is smallest at the centre and is rise by a stable
factor equal to 1.06 externally to each grid cell. At the beginning, the requisite input is the
height of the terrain in every grid point, but a much more facile procuration method for the
terrain height is the contour lines as defined on the typical topographical maps. The model
was created, consequently, to imminently concede peremptorily concluded contour lines as
input and unifies the appreciation of grid point values and the numerical unification in one
procedure. The grid comprises of 100 radial stations and proceeding resolution nearby the
centre is about 2m for a model with R = 10km, an about 10m for R = 50km. Finally,
resolution is restricted in practice only by the veracity and denseness between the contour
lines from the topographical maps.
Figure 34 The polar zooming grid employed by the model for calculation of flow in complex terrain. Part
of the Great Valley Scotland is seen from a point above Loch Ness. The grid is superimposed on the
terrain and centered on the meteorological station Augustus. The side length of the upper figure is 12km
and the figure shows a smaller part with a side length of 2km. The vertical scale is exaggerated by a factor
of 5
2.8 Climatology
For the several weather phenomena in Europe, there is not a good database that could
be used as basis for further studying. There is only few studies that have been generated for
some regions, like (Barnolas, 2001-2004); (Barnolas & Llasat, 2005) for Catalonia; (Gayá,
2005) for Spain; (Giaiotti et al, 2003) for northern Italy; (Leitão, 2003) for Portugal;
(Marcinoniene, 2003) for Lithuania, (Setvák et al, 2003) for Czech Rebublic; (Sioutas, 2003)
for Greece; and (Tyrell, 2003) for Ireland. However, in USA a database of this form is
62
dispensable for years now, as is referred from National Weather Service of USA, where these
phenomena are recorded in a data base. Some phenomena should be tornados, hail and strong
winds of convective origins (Tous & Romero, 2006).
Moreover, the climate variability is a virtual feature particularity, because of the matter
of the changes in weather from year to year and decades too. In particular, the data which
constitute the fundamental for the calculation of any wind potential study cover a minimum
period of time, which in most of case is approximately 10 years (Petersen, 1998). According
to the study of Troen and Petersen presents the variability in wind energy of up to 30% can be
tended from decade to decade (Troen & Petersen, 1989). In addition, another one study was
showed the expected power attribution for a 45m high wind turbine over the period of 22
years, where the interannual variability in power consists to a mean relative standard
deviation of about 13% (Petersen, 1998).
Nevertheless, the possible suasion of the increment of CO2 emissions in the atmosphere
might be a continuing modification in the global climate. If this phenomenon happens, then
reversible changes will be effect and modifies the magnitude of climate mean levels and
climate variability of the wind potential power can be tended. No firm establishments have
been still provided until today.
Generally, the sifting of applicable regional wind climatology for area of interest is an
issue of selecting the statistics from one of the analysed stations (Troen & Petersen, 1989).
This consideration is very important for the wind potential assessing in mountains and coastal
sites (Kiss, 2009). The draftee meteorological station should rather no more than 100km from
the studying area.
Power estimations for region with terrain type 5 are likely to be inconstant, and it is
suggested that the area of studying and the draftee station should be situated in terrains which
bear a resemblance to each other closely. As we notify before, nevertheless, the turbine
installation in a mountain top and mountain terrain can altogether be extrapolated by mean of
a numerical orographic model (Troen & Petersen, 1989).
The accuracy of the statistics from a meteorological station can be estimated from the
information afforded in the station statistics:
The description of local conditions afforded for each station
The raw data statistics
63
The wind climatological fingerprints.
The descriptions might intimate problems with information – data quality, that the
measurements were obtained at the top of a building or with significant sheltering obstacles
nearby (Troen & Petersen, 1989). The unalloyed data statistics can reveal many irregularities
of data like channelling of wind speed which extrapolates very high frequencies of
occurrence in predefined wind direction sectors and frequently in two diametrically different
sectors. Focus on the UK station, Fort August, Troen and Petersen presents that the statics for
these stations has impressions from Great Glen Valley on the wind speed (Troen & Petersen,
1989). In addition, the authors suggest that the high sheltering conditions leading to high
frequencies of wind speed below 1m/s may be revealed by these statistics.
Also, the wind climatological finger prints can be applied to the estimation where the
characteristics like daily and yearly mutability are in agreement with general experiment
(Troen, 1990). If possible, the utility of sheltered stations should be abstained in sitting. To
conclude, an area with complex orography modelling leads to channelling of the wind speed
flow is demonstrable, a possibility to use a nearby radiosondes station is essential.
2.9 Wind Speed Statistics
The speed of the wind is fluctuating, making it desirable to define the wind by
statistical methods. There are some basic theories of probability and statistics which are
considered significant. To start with, one statistical measure is the average or arithmetic
mean. Having a set of numbers ui, for example a set of measured wind speeds, the mean of
the set is assigned as:
∑
Where: n is the sample size or the number of measured.
Furthermore, another measure is the median. If n is uneven, the median is the middle
number after all the numbers have been adjusted in order of size. Equally, if n is even the
median is halfway among the two middle numbers when we range the numbers (Manwel et
al, 2009).
64
Besides the mean, there is an interest in the variability of the set of number. The
incongruity or variation of each number is found desirable as well as some sort of average of
these deviations. The mean of the deviations Vi -V is zero. Therefore, each deviation squared
to get all positive quantities. The variance σ2 of the data is then found as:
∑
The standard deviation σ is then assigned as the square root of the variance:
√
Wind speeds are generally calculated in integer values, thus each integer value is
marked several times during a year of observations (Troen & Petersen, 1989). The numbers
of observations of a specific wind speed ui will be found as mi. The mean is then:
∑
Where w is the number of different values of wind speed observed and n is the total
number of observations.
It can be given that the variance is defined by:
[∑
(∑
)
]
The two terms within the brackets are almost equal. Moreover, full accuracy requires to
be maintained during the computation, which is not uneasy since the calculators are in
majority hand in use. Many hand calculators have a built-in routine for computing mean and
standard deviation.
Together the mean and the standard deviation will differ from one location to another
or from one period to another. It might be of interest to certain people to organize these
values in rank order from smallest to largest. Then the classification to the smallest, the
median, and the largest value is attained. The use of the terms smallest and largest is unusual
in statistics since the option that one value might be widely separated from the rest. The
regular practice of estimating peak wind speed is percentiles; the 90 percentile mean wind
would state to that mean wind speed which is surpassed by just 10% of the calculated means.
65
Similarly, if there are 100 recording stations, the 90 percentile standards deviation would be
the standard deviation of station number 90 when totaled in increasing rank order from the
station with the smallest standard deviation. Consequently, this method of using percentiles
lets us to study cases away from the median with not being too concerned about a particular
radical value (Manwel et al, 2009).
The probability p of the selected wind speed Vi being observed as:
Regarding this definition, the amount of all probabilities will be unity.
∑
It is also worth to define a summative distribution function F(Vi) such as the probability
that a calculated wind speed will be less than or equal to Vi.
∑ ( )
Where, the summative distribution function has the properties:
2.10 The Statistical Review of Model
Measurement or observation of wind at any site tells that equally speed and direction
are fluctuating in time, as shown in Figure 35. Wind speed calculated over 100 days is
illustrated on the first graph, followed by graphs which in sequence zoom in on smaller and
smaller parts of the series (Courtney, 1988). It is notable the far larger relative difference in
the longer time series, since contrasted with the time series covering hours or less. This
divining of the variance on different time scales is shown by the power spectrum in Figure
36.
66
Figure 35 Wind speed measured 30 m above flat homogenous terrain in Denmark. Each graph shows the
measured wind speed over the time period indicated. The number of data points in each graph is 1200,
each data point corresponding to the speed averaged over 1/1200 of the period. Vertical axis is wind
speed, 0-20 ms-1
(Courtney, 1988). Source: (Troen & Petersen, 1989)
67
Figure 36 The power spectrum of wind speeds measured continuously over a flat homogenous terrain in
Denmark (Courtney, 1988). The data were collected over one year with a sampling frequency of 8 Hz.
The spectrum is shown in a log-linear, area-true representation. Source: (Troen & Petersen, 1989)
The mechanisms that push the wind to blow are in comparison changing only gradually
with time, just like the weather changes. Furthermore, direction and speed modify from point
to point at any given time. The reason for the alterations of the wind is the spin in the
atmospheric boundary layer. Also, speed it has to be given to an averaging period T. Ideally,
calculations must be obtained with a fast-responding instrument and the average shaped by
unity:
∫
Particularly because of changes in instrumental setup, reporting and data reduction,
averaging periods range from a few minutes to hours. These data sets under the scope of
some basic observations which eventually give one value of . The data, also, include no
evidence about wind fluctuations over periods far shorter than the averaging time T.
However, these boisterous fluctuations contribute to the theoretical wind power density and
thus they have to be considered when the data are used to the appreciation of wind power
potential (Troen & Petersen, 1989). The wind power density available over a time interval T
is shown by:
∫
The air density in this equation could be given as a constant with an error of less than a
few per cent. Therefore Equation 51 turns to be:
68
The momentary wind speed can be written as the average value plus a declination from
the average:
Direct operations give:
Representing the bulk of the rms-value of the turbulent fluctuations σV and the
turbulence intensity i one can write:
The frequency allocation of defines apart from the correction term 3i2. Turbulence
intension bases on surface conditions and height. For homogeneous surface roughness and
neutral situations an easy relation is found (Troen & Petersen, 1989).
( ⁄ )
2.10.1 Weibull Distribution
The release of wind data let use of the Weibull distribution (Weibull, W, 1951) as a tool
to reflect the frequency distribution of wind speed in a solid form. The two- parameters
Weibull distribution is stated mathematically given as:
(
)
( (
)
)
69
Where: f(V) is the frequency of apparition of wind speed V (Shata & Hanitsh, 2006).
The two Weibull parameters consequently assigned are generally stated to as the scale
parameter A and the shape parameter k (Vogiatzis et al, 2004). For k > 1 the maximum occurs
at values V > 0, while the function declines monotonically for 0<k<1.
The Weibull distribution can break into two different distributions, namely k=1 the
exponential distribution and for k=2 the Rayleigh distribution. Since remarked wind data
exhibit frequency distributions which are frequently well defined by the Rayleigh
distribution, this one-parameter distribution is occasionally used to declare wind data (Jamil
et al, 1995). However, the general two – parameter Weibull distribution is applied from stem
to stern (Christofferson & Gillette, 1987).
The summative Weibull distribution F(V) gives the prospect of the wind speed
overtopping the value u and is shown by the expression:
( (
)
)
The Weibull distribution makes Weibull-distributed advanced powers; if V is Weibull-
distributed with parameters A and k, then straightly Vm is Weibull-distributed with the
parameters Am and k/m.
Moreover, the offered wind power density is analogic to the mean cube of the wind
speed:
(
)
Where: E is the power density and ρ is the air density. Γ is the known gamma function
as it found in the bibliography.
The wind speeds at which the highest power density is available is given by:
(
)
⁄
Thus, for a Rayleigh distribution, the wind speed which encloses the highest energy on
the average is approximately twice the most frequent speed.
Many several methods can be applied for the fitting of the two Weibull parameters to a
histogram showing the frequency of apparition of wind speed in a number of recesses. If the
70
experimental data are well characterized by the Weibull distribution over the total range of
speeds, then the fitting method can be chosen at will. Generally speaking, nevertheless,
noticed histograms will give deviations through a number of reasons, and a fitting procedure
have to be select which emphases on the wind speed range related to the application.
Eventually, the emphasis is on the higher wind speeds and an instant fitting method is used
which focuses on the higher but not the rabid wind speeds (Akyla et al, 2011).
2.10.2 Determining the Weibull Parameters
There are various methods offered for determining the Weibull parameters c and k.
Justus has designated that a suitable approximation for k could be the below equation:
(
)
This is a practically model approximation over the range . Once that k has
been determined, c is given by:
( ⁄ )
Justus explored the wind speed distributions at 140 locations through the continental
United States measured at heights near 10 m, and found out that k seems to be analogic to the
square root of the mean wind speed:
√
The constant d1 is a site specific constant with an average value of 0.94 when the mean
wind speed is found in meters per second. Moreover, the constant d1 is between 0.73 and
1.05 for 80% of the locations. The average value of d1 is usually effectual for wind power
measures, but if more precision is desired, some months of wind data can be gathered and
evaluated in more detail to compute c and k. These values of k can be plotted versus √ on
log-log paper, a line drawn over the points, and d1 designated from the slope of the line.
71
Furthermore, encloses an exponential, so generally exponentials are linearized by
using the logarithm. Under this circumstance, because the exponent is itself raised to a power,
we have to take logarithms twice:
[ ( )]
This is in the pattern of an equation of a straight line:
Where: x and y are variables, a is the slope of the straight line equation, and b is the
intercept of the line on the y axis.
In particular:
[ ( )]
Data will be cleared in the type of pairs of values of Vi and F(Vi). For each wind speed
Vi there is a relating value of the summative distribution function F(Vi). Once specified values
for and , we can find values for and . In fact, these pairs of
values do not lead exactly on a straight line. It can be presented that the main values for a and
b are:
∑
∑ ∑
∑
∑
∑
∑
∑
∑
From the above equations, and are the mean values of and , and w is the total
number of pairs of values offered. The final outcomes for the Weibull parameters are:
72
(
)
2.11 Errors of Model and Data
In the following we discuss the briefly disadvantages – errors, limitations and
assumptions of the WAsP model application. It has been discussed from many authors that
the size of any error by WAsP in the wind potential estimation and specifically for mean wind
speeds is mostly depend on the degree of the input data, if that are topographic / roughness
effects of the terrain surface or inaccurate measurements or atmospheric conditions (Bowen
et al, 2004).
There are various articles and authors that focus on WAsP analysis and its accuracy.
Firstly, Walmsley and Troen focus on estimation of wind flow from WAsP application over
isolated hills and they compared well the measured data from the two benchmark field
measurements of Blasheval and Askervein (Walmsley et al, 1990; Troen, 1990). Moreover,
they compared the WAsP model with others models, for instance, the WAsP is less accurate
for low flow wind speeds, which is defined clearly in the above study of Askervein.
Furthermore, WAsP is more accurate, except for the weathering rate of the derangement
downstream of the precipice ridgeline (Bowen & Mortesen, 1996).
In addition, there are plethoras of assessment regarding the WAsP analysis in complex
terrain conditions, which outstretch largely within its operating envelope, and particularly
affirm the validity of the estimations under these conditions (A. & Mortensen, 1996).
Holttinen and Peltola mentioned that the WAsP estimations compared to site measured data
for many sites in the point of flat at western coast of Finland shows satisfactory results for the
wind potential estimation (Holttinen & Peltola, 1993). Also, Sandström referred about the
comparison of the measurements received at Vårdkasen region and the WAsP wind flow.
Vårdkasen is a 175m sylvatic mountain which is approximately 5km away from the coastline.
He notified that WAsP simulation for the wind flow provides very well fitting in regions with
complex terrain (Bowen & Mortesen, 1996).
Specifically, hilly and sharp terrain leads to flow break off, precisely on the lee location
of a ridge lying an obtuse angle to the wind flow. The expansion of the steep terrain within
73
the studying area surrounding the region is an imminent measure of the ruggedness of the
region. When the wind flow is elicited from the terrain surface, the effective orography is
changed to something that is less complex and gruff than the pragmatic terrain. The terrain
shear stresses are changed too. If the splitting – break of sites are important in expansion over
the surrounding terrain, then the wind speed above more high altitudes’ terrain such as a
hillcrest could be expected to be surely less than if the flow remains to be attached.
Many of studies work at the wind potential estimation over gruff and complex terrain;
for example, Sempreviva et al are focus on over – estimations for the Mt. Arci area, 700m
away from the coast of Sardinia. Even though there is a strong thermal vividness
characteristic of this area, high frequencies of 46% of neutral stability affected from strong
winds that were measured at Mt. Arci (Sempreviva et al, 1986). Sandström studied on a
successful WAsP analysis comparison at Vårdkasen, and noticed that even the whole mean
wind speed was overestimated by 4%, over – estimations by WAsP of up to 80% in the
northern sectors could be attached to the steep western and northern slopes of Vårdkasen with
slopes up to 0.48 (Sandström, 1994). Grussel et al as well studied the wind potential
estimation by WAsP over 2 coastal hills in Sweden using measurements from 2 nearby airport
meteorological stations. Nevertheless, the hill morphs are not on hand (Grussel, 1994). Also,
Watson used WAsP to estimate the conditions at 2 hill tops in the Republic of Ireland, 15.7km
each, using each other in turn as the reference area (Watson, 1994). One hill is within terrain
ruggedness limits for WAsP, while the other lies outside the limits due to the high ridge 1km
far from coastline. WAsP over – estimated at the 30m height from the hillcrest surface when
utilizing the smoother hill as the reference area. No more errors were founded at 10m height.
In comparison, WAsP particularly under – estimated when utilized in the unfavourable
direction for the less gruff hill (Anthony et al, 2004). Moreover, about the over – estimation
is established by Bowen and Saba threw many flat to rugged hill areas nearby the coast in
New Zealand (Bowen et al, 2004; Bowen & Saba, 1995).
The gradient for over – estimation of hillcrest areas should influence the same well for
the Analysis and Application process as the Atlas file can be contemplated to characterize the
sure – enough reference area, which is flat and without specific – interest characteristics
(Anthony & Mortensen, 1996). So, the application process is shown at the Equation 75
below:
74
Where, VPe is the estimated mean wind speed.
On the other hand, when analysing the dereference area wind speed data ‘real station
measurements’, VRm, at the reference area to generate the representative speed in the Atlas
file, VA, a further accurate speed – up emendation, ΔV1, with its conjoin error, E1, is included
(Anthony & Mortensen, 1996). This Analysis process includes the orographic model in the
contrary sense like as:
On the whole, the estimation procedure uses both the Analysis and Application
processes in continuity. Therefore, combining the two above functions to discard VA, the
Equation defines as:
The extrapolated wind speed at the estimated area of studying, VPe, is composed of the
proper speed, VPm, and the total estimation error, which has centralized from the two stages of
the estimation procedure. The representative – accuracy estimation at the estimated areas is
assumed to include no errors and is comprised from the Equations, as they follow:
The estimation error on the whole in the WAsP estimation procedure is finally E1 or/and
E2. Errors due to orography at the reference area – region are illustrated in E1 and errors due
to the estimated – expected analysis area in E2. The degrees of the individual proceeding
errors are based on the degree of that each area challenges the performance limits of the
WAsP estimation model. Both errors as shown, share the same sign because both the
reference and estimated areas are immutable more rugged than the without special features’
area impersonated by the Wind Atlas general data file. The sign of the totally estimation error
may be positive or negative hinge on the relative degrees of the two individual proceeding
errors. A certain magnitude of annulment among the two proceeding errors is finally likely to
occur.
The relevant sizes of the two proceeding errors, E1 and E2, which are commensurate to
the individual are ruggedness, and therefore, the determination of accuracy and bias of the
totally estimation from the WAsP application.
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From the other perspective – view of the WAsP analysis model, errors can be found
consist of non – standard atmospheric conditions, which are affected the flow with influences
usually from: a) Atmospheric stability, b) Stratification, c) Diurnal sea breezes, d) Down
slope winds, and blocking or channelling in valleys. The cross – correlation coefficient for
mean wind speeds among the two areas is defined and shows by WAsP to be unity, signifying
that both areas are refer to the same weather conditions. The necessity of high correlation
between both of the studying area and reference area is expected than ever for the accuracy
potential estimation by WAsP.
An average period of 1 hour may be more applicable than the 10minutes averages
utilized in order to allow a circumstantial wind event to envelope naturally the two areas.
Although that, only small ameliorations in the cross correlation coefficients was succeeded
with 1 hour mean wind speeds. Site observations also include those monthly, seasonal and
even yearly variations importantly induce the correlation values if the measuring length is
relevantly short. The measurements are intended to create a standard Weibull frequency
distribution. The importance of any estimation error is induced by the degree of modification
applied by the Analysis process in order to extrapolate the Atlas file.
The direction rose is frequently spited – sliced to 12 equal direction sectors. Hilly,
oblique mountainsides induces the direction on the wind flow and may affect and change the
wind direction at the studying area to fall into a vicinal direction sector compared to that
occurring at the petition area.
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77
3 METHODOLOGY
3.1 Introduction
The overall methodology aims are the production of a complete wind power map of
Cyprus on a monthly and bi – daily ‘day – night’ basis. Wind measurements were analyzed in
order to produce statistics in the form of the Weibull distribution (Seguro & Lambert, 2000).
It has to be noted that the power density method for the estimation of the Weibull parameters
was chosen, since it is more suitable for wind power statistics (Akdağ & Dinler, 2009). The
WAsP program was then used in order to produce the corrected wind speed and power
statistics over the extended area around each station and the resulting data are aggregated and
visualized through ArC View Package.
The methodology begins with a description of the studying areas ‘Limassol (Old Port),
Polis Chrisochou, Pafos (Airport), Mallia, Prodromos, and Kato Pirgos’ and their
characteristics. Further, Physical Environment will be complete the areas specifications.
Climatology of studying areas is an essential thing for the help and understanding the
boundary conditions and atmospheric effects. Land Uses and the Areas Features are indicated
the roughness effect to the areas. In addition, an overview of the maps export methodology
will be explain the maps extrapolation to take into account the obstacles – roughness and
topography effects in the model of WAsP, and finally estimate the wind potential. The
recording wind speed and wind direction measurements of average hourly 7 years data base
are fixed to excel files for every month spitted to day – night data sets ‘24 day – night data
sets’. The missing measurements are reformed as we mentioned before. Then, Pre –
Statistical Data Processing follows to extrapolate the statistical characteristics of each station
and its regional climatology. Finally, assessment of the spatial and temporal distribution of
wind potential is extrapolated with WAsP application using the maps of topographic
conditions and the local climate conditions of the surrounding stations area, to elevate them
to a wider scope in areas of interest.
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3.2 Study Areas
The island of Cyprus is located in southeastern Mediterranean between 34.6o to 35.6
o N
latitude and 32o to 34.5
o E longitude. During the rainy season (November to March) Cyprus
is fairly frequently influenced by depressions crossing the Mediterranean Sea eastwards, but
during the dry season the island is subjected the trough which extends from the continental
depression centered over Asia.
Climatological, Cyprus consists of 156 meteorological stations, as regards for rainfall
recordings, climate and summary data (see Figure 37). Nevertheless, for the records about the
wind speed and direction, there are only 14 meteorological stations. In the context of this
master thesis, we worked specifically for six meteorological stations to cover the western part
of the island ‘Limassol (Old Port), Polis Chrisochou, Pafos (Airport), Mallia, Prodromos, and
Kato Pirgos’ (see Figure 38). The Table 4 summarizes the studying areas’ meteorological
stations characteristics.
Table 4 Meteorological stations specifications
METEOROLOGICAL STATIONS CHARACTERISTICS
A/A STATION
NAME
NUMBER
OF MET.
STATION
ALTITUDE LATITUDE LONGITUDE TYPE OF MET.
STATION
1 LIMASSOL
(OLD PORT) 391 5 33
O40΄19΄΄ 33
Ο03΄24΄΄
CLIMATOLOGAL/
AUTOMATIC
2 POLIS
CHRISOCHOU 041 15 35
O02΄ 32
Ο26΄
CLIMATOLOGAL/
AUTOMATIC
3 PAFOS
(AIRPORT) 082 8 34
O43΄ 32
Ο29΄
SYNOPTIC/
AUTOMATIC
4 MALLIA 203 645 34O49΄ 32
Ο47΄
CLIMATOLOGAL/
AUTOMATIC
5 PRODROMOS 225 1380 34O57΄ 32
Ο57΄
CLIMATOLOGAL/
AUTOMATIC
6 KATO
RIRGOS 160 5 35
Ο11΄ 32
Ο41΄ AUTOMATIC
Source: (Meteorology Department of Cyprus, 2003)
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Figure 37 Meteorological Stations Network of Cyprus. Source: (Meteorology Department of Cyprus,
2003)
Figure 38 Locations of the meteorological stations and area of application
Nevertheless, for the study of wind potential of the western part of Cyprus, as we will
see below, it has been used the software WAsP, taking into account various parameters
‘including the roughness of terrain and the altitude’ and with the use of measurements from
stations, in order to estimate final wind speeds maps. More specifically, the application /
model uses geo – data and a wind resource base to formulate the area around each station.
Using the model, a modification of the wind flow may observe. Local effects, topographic at
each areas and the surface roughness could be change the wind speed and the power too.
Concluding, using statistical weighting methods, the visualization for all the area of interest
can extrapolate.
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3.3 Physical Environment
As we mentioned before Cyprus is on average at 35o latitude and at 33
o longitude and is
surrounded by the eastern Mediterranean Sea. In addition, Cyprus has an area of 9254km2
and is divided into four natural regions:
The Troodos Mountains, located in the central – western part of the island and the
highest peaks of Olympus, has a height o 1951m above the mean sea level.
The Pentadactylos mountain range, which has a relatively small and extends along
the northern coasts of the island with peaks up to 1000m in height.
The Mesaoria champaign located between Troodos and Pentadaktylos and has a
low altitude, which in the Nicosia area does not exceed 180 meters.
The coastal champaigns and valleys along the coast.
Hot dry summers from mid – May to mid – September and rainy, rather changeable,
winters from November to mid – March are separated by short autumn and spring seasons of
rapid change in weather conditions (Meteorology Department of Cyprus, 2003).
During summer the island and generally throughout the eastern Mediterranean is under
the influence of a shallow trough of low pressure extending from the great continental
depression centered over southwest Asia. It is a season of high temperatures with almost
cloudless skies.
On the other hand, in winter, it is near the track of fairly frequent small depressions
which cross the Mediterranean Sea from west between the continental anticyclone of Eurasia
and the generally low pressure belt of North Africa.
Finally, the Troodos mountains and to a lesser extend Pentadactylos mountain range
play a significant role in forming of the meteorological conditions in the various regions of
Cyprus and to the creation of the local phenomena. The presence also of the sea that
surrounds the island is a cause of local phenomena in coastal areas (Meteorology Department
of Cyprus, 2003).
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3.4 Climatology
In the eastern Mediterranean general winds are mainly light to moderate and they come
from west or northwest at winter period and from north or northwest at the summer period.
Very strong winds are rare in Mediterranean.
In various areas in Cyprus, the general winds are modified by local winds. These local
winds are sea breezes and land breezes in coastal areas and anabatic and katabatic in
mountains areas. In any case, in coastal areas the local sea breeze circulation is usually very
strong due to the large differential heating between sea and land (Jacovides et al., 2002).
Sea and land breezes effect observable at coastal areas can be felt until 35km distance
from the beach. This air circulation system is basically due to the temperature difference
between the land and sea, which are creating differences in air pressure above the land and
seawater.
The corresponding phenomena in mountains areas are the anabatic winds ‘Valley
breeze’ per day and katabatic winds ‘mountain breezes’ at night. And in this case the causes
of the creation of these local winds are the different degree of heating or cooling nearby
adjacent areas.
The sea breezes at the coastal areas and the anabatic winds at mountains areas have
strongest intension during the summer period, while land breezes in the coastal areas and
katabatic winds in mountain areas have stronger intension during winter period.
Regarding, the wind speeds in Cyprus are mostly low to medium. Finally, strong wind
flows of upper 24knots speed are short, when they rarely appeared unless in cases of bad
weather conditions. The very strong winds ‘wind speeds upper of 34knots’ are rare and occur
mainly in windward areas when systems of low pressure affect the Cyprus area. Similarly,
tornadoes with diameter of 100m rarely appear above the seawater surface or above the
ground surface.
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3.5 Land Uses and Features of Areas
An important factor to accurately calculate the wind potential is the roughness of the
terrain and therefore the creation of input maps for the studying areas ‘Limassol (Old Port),
Polis Chrisochou, Pafos (Airport), Mallia, Prodromos, and Kato Pirgos’.
In this case, are calculated the superficially areas’ surface obstacles according to the
type of vegetation, the adjacent settlements or if there are water mass ‘seas, lakes’, which are
obtained as information by Corine Land Cover suffix (Καστάνας, 2012).
Initially, the most important part is the conversion of the polygonal Corine Land Cover
suffix – cover to linear suffix – cover through ArcGISTM. The purpose of that transformation
is to provide the right and left values of the linear data concerning the type of vegetation in
each case (see Figure 39). It is an essential process for the definition of roughness values
based on the type of vegetation (Kastanas et al, 2013).
Figure 39 Transition from polygonal suffix – cover to linear
Then, the values that given from the vegetation file of Corine Land Cover, must be
converted according the land use in the analogous – correspond roughness code as defined in
manual of WAsP (Mortensen et al, 2004).
Thus, the Table 5, that is following, presents the grouped soil roughness values and
their corresponding land use codes. Afterwards the codes have become grouping should be
assigned the roughness values with the corresponding code. Thereupon, in the linear land use
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file that has been created before, are created two new fields with the exact name of:
ROUGH_L and ROUGH_R. This designation is necessary for the subsequent analysis that is
following.
Finally, in order to make the correspondence of the codes with the roughness values,
we have created an excel file which has been used through the software ArcView ‘command
join’.
Table 5 Soil Roughness Values
Ζo (m) Terrain Surface
Characteristics
Corine Land Cover
Codes
1.00 City 111-112/ 121/ 141
0.80 Forest 311-313
0.50 Suburbs 122-123/ 131-133/ 142
0.30 Shelter Belts
0.20 Many trees and/ or bushes 323-324
0.10 Farmland with closed
appearance 221-223
0.05 Farmland with open appearance 211-213/ 333
0.03 Farmland with very few
buildings/ trees 242-244
0.02 Airport areas with buildings and
trees 124
0.01 Airport runway areas
0.008 Mown grass 231, 241, 321
0.005 Bare soil (smooth) 322, 332, 334
0.001 Snow surfaces (smooth) 335
0.0003 Sand surfaces (smooth) 331
0.0001 Water areas (lakes, fjords, open
sea)
411-412/ 421-423/ 511-
512/ 521-523
Source: (Corine land cover 2000 (CLC2000) seamless vector database, 2012)
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3.6 Maps Preparation for admission to WAsP
After we have identified and taken into account the land use and areas characteristics,
then the files should be converted to appropriate format to be recognized by the software,
most notably by the WAsP Map Editor and WAsP 8. This procedure is accomplished by two
methods, which will be discussed below, however, the second method is more representative,
and also reflects the whole procedure that followed in this study. Nevertheless, for the
purposes of this master thesis, where our goals is to study the wind potential in the western
part of Cyprus, we extrapolate maps in form of *map ready to use in WAsP, to study the wind
potential after we have taken into account not only the statistical wind characteristics for each
station but also the roughness and orography effects.
3.6.1 Simple Extraction Method
At first, with the use of ArcView as a supporting application, the following
requirements data are added: a) The digital elevation model (DEM) of Cyprus, b) The
contours lines of the island, and c) The linear Corine Land Cover file as is created before.
For the extrapolation the final map *map from the above data, it is needed the
additional mandate WAsP Exporter in the toolbar. To do this, from the installation folder of
the software is copied the waspmaexp.avx file and pasted to the installation folder of
ArcView, with the given process:
c:\esri\arcview\av_gis30\ext32. Next, from the ArcView toolbar
(ProgramExtensions) is activated the additional command WAsP
Exporter.
With the use of this command, are contained the file that contains the
elevations information and the file that contains the vegetation values right and
left of linear element and the output file is generated in the form of *map.
Basically, the data that will be used are the following, with the particular order
below:
1. Digital Elevation model of terrain (DEM).
2. Contour lines (Selected from the field which contains the elevation
values).
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3. The linear file of roughness (Selected fields which includes the
roughness values right and left).
3.6.2 Reliable Method
The second method is the most reliable and quickest way to export files at the WAsP
form. Firstly, with the use of software Global Mapper V1.2, the necessary input data of
contours lines and linear suffix – cover are used to extract the *map file throw one flexible
and fast procedure:
The process is the following:
Loading of files (Contours & Roughness).
File Export Vector map.
In the appeared window is selected the WAsP Map.
Considerable, is the fact that the WAsP application lacks in two significant points that
we took in to account:
For the wind potential assessing, the WAsP model does not allow the insertion
and use of more than one meteorological station.
Also, it has the limitation of a maximum 1000000 grid points for each map.
Following that consideration, for the handling of these modelling inabilities, we have
created buffer zones – maps to cover all areas around of each station ‘19 buffer zones maps –
cycles with 20km radius around of each meteorological station’. These buffer zones are
overlapping and occupy the whole of the study area of Cyprus. With the overlapping at buffer
zones the errors of wind potential ‘variety of each pixel – grid point at each WAsP
extrapolated map’ are minimizing and also the gaps between the map points. Moreover, the
inaccuracy of the points’ values due to the different distances from each station are covered
and minimized to some extent. In addition, it was necessary in some areas – maps to be
created individual buffer zones – maps ‘for better extrapolation and of course due to large
volume of data some maps are cut in smaller pieces of maps with overlapping’ due to the
large volume data (see Figure 40 - 52).
Furthermore, in order to face effectively the excessive grid points the final map was
treated accordingly, resulting to a reduction of the points without missing information. As a
86
result, the final maps are covered with contours discretisized every 20 m up to the height of
520m, every 40m from 540 – 1000m elevation, and finally every 50m up to maximum
elevation of 1900m. Therefore, the volume of data has been reduced without any serious
alteration of data, since the final map of wind energy potential extrapolated from WAsP will
has highest resolution of 100m and the lowest per 1km in the purpose of country study ‘in our
study the resolution of each surrounding map for each meteorological station region is 200m’.
Figure 40 Buffer zones – maps arrounding each meteorological station. It has to be noted that the buffer
zones – cycles have 20km radius from the meteorological station and 5km overlapping from the near
station map
Figure 41 Limassol buffer zone map. Map 14
87
Figure 42 Polis Chrisochous first half buffer zone map. Map 0A
Figure 43 Polis Chrisochous second half buffer zone map. Map 0B
Figure 44 Pafos (Airport) first half buffer zone map. Map 2A
88
Figure 45 Pafos (Airport) first half buffer zone map. Map 2B
Figure 46 Mallia first half buffer zone map. Map 3A
Figure 47 Mallia second half buffer zone map. Map 3B
89
Figure 48 Mallia third half buffer zone map. Map 3B
Figure 49 Mallia fourth half buffer zone map. Map 3D
Figure 50 Prodromos buffer zone map. Map 4
90
Figure 51 Kato Pirgos buffer zone map. Map 5A
Figure 52 Kato Pirgos buffer zone map. Map 5B
3.7 Measuring Variables and Data Processing
In this study, we procured from the Meteorological Department hourly time series
measurements of wind speed and direction for the 6 stations, for the period of 2001 – 2008, in
Excel files. The Excel files contain minimum and maximum speed, average wind speed,
standard deviation, and direction of wind speed, both at 2m and 10m anemometer height
above the ground level.
For the purpose of this study we utilized only the data average wind speeds and wind
direction at 10m, where we will transport the wind energy potential at every study region.
91
However, lack of measurements – missing and problematic data appeared, mainly due
to wrong anemometer measuring. The wrong wind measurements are tended to be
underestimated the wind energy potential capacity at studying areas, so it has to removed and
corrected where are required.
In addition, it follows the cleaning and consolidation of the average wind speed time
series at each station separately. More specifically, we created an algorithm, in which we
fixed the wind measurements using an appropriate methodology and from the comparison
between maximum and minimum speeds the missing wind flow data are generated. From the
comparison of wind flow measurements at 2m anemometer height above the ground level, we
identified mistakenly estimates at 10m average data, which are corrected with the use of
linear regression statistical analysis method (Οικονομικό Πανεπιστήμιο Αθηνών, 2012).
Although, the problematic anemometer wind flow measurements are detected at smaller
than 1m/s measurements, due to the anemometer measuring failure to record the slower wind
speeds. In that case, the correction is carried out from the average wind speed at 2m
anemometer height, which is found to correspond nicely at small wind speed. Particularly, the
smaller than 1m/s correction process, based on an acceptable method of correcting the
measured speeds at 2m anemometer height.
Furthermore, in individual cases, where the given wind flow anemometer
measurements at 10m and 2m, are problematic and wrong, then these values are considered
as unacceptable and do not included for the region statistical wind flow processing. Finally,
after the wind flow were consolidated and rid from the wrong – missing data, we proceeded
to elaboration of data, which is the first step to be able to extrapolate the statistical
characteristics processing of the wind (for further information about the Measuring Variables
and Data Processing is annotated before in the sub – section of Meteorology of Wind).
Thereinafter, we smelt – sort the hourly average data to yearly – time base, sorting
throw in Excel from the oldest year (2001) to the newest (2008). Then, we sort the yearly
wind data to daily wind data base (year to daily). Afterwards, we transport and sort the daily
wind data to 12 monthly base. At last, we separate – split the monthly wind data to day –
night base, from 08:00 – 19:00 and 00:00 – 07:00 / 20:00 – 23:00, thus at twelve hour
monthly wind data base for each studying station. Consequently, we have 24 new wind data
Excel files for each of the 6 studying areas.
92
The separation of the original hourly time series measurements and wind directions at
10m height from the station ground level, is divided into day and night monthly base, so that
we dissociate the day and night hours for better understanding of the wind potential
behaviour not only for night and day, but also for monthly and seasonal base. According to
Kastanas, I , at the morning hours the wind direction change to different direction until the
noon hours which the wind flow comes from south at Limassol and from the North at Polis
Chrysochou. Moreover, at night hours the wind direction changes to the opposite direction
(Καστάνας, 2012). For that reason we grouped to day and night monthly time series from
08:00 – 19:00 and 00:00 – 07:00 / 20:00 – 23:00, because of the changing of wind direction
in hourly base wind flow, and expunge the chance of wind potential underestimation.
Finally, all day and night monthly wind flow series (24 Excel files) are saved to the *prn
format (Format Text: Space Decimal), to reduce the chance of mistakes and wrongs at the
next step of the Pre – Statistical Data Processing. In every *prn file we change the decimal
from (,) to (.) inasmuch as the Observed Wind Climate Wizard of WAsP (OWC) recognize
only the last average wind speed and direction values at 10m height above the ground level
(AGM) to extrapolate the statistical characteristics (the histogram of theoretical statistical
analysis Weibull distribution) for each region – meteorological station.
3.8 Pre – Statistical Data Processing
The pre – processing of statistical data is obtained by the insertion of *prn files to
Observed Wind Climate Wizard of WAsP (OWC). OWC uses the new method of Power
Density, when it has an assessment scale and schematic parameters to provide a simple mode
and easier implementation, while are required less computational procedures (Risø DTU,
2010; Risø Laboratory, 2013).
The Power Density Method is a modification of Weibull distribution method and is
expressed below (Zaccheus et al, 2012):
∫
Where: ρ is the air density of the area.
Using the Equations 80 and 81, the Equation 82 comes up:
93
⁄
⁄
⁄
Where: is the mean wind speed powered in cube and ⁄ is the known Energy
Patern Factor (Epf) and according to the bibliography and also from empirical research,
which were carried out is range from 1.45 to 4.4 for the most wind distribution in the world
(Seguro & Lambert, 2000; Akdağ & Dinler, 2009). The Weibull schematic parameter (k) is
calculated by solving the Energy Pattern Factor with the use of the empirical appoximation of
Equation 83:
( )
Also, the scale parameter (c) is calculated by solving the Equation 84:
(
)
Specifically, the Power Density Method requires the average wind speeds in cube
and also the average wind speeds. In our thesis, the average wind speeds time series are
disposed, and the power too (derived from the wind speed in cube and multiplied 0.5,
multiplied by the air density, multiplied the perpendicular surface over the wind direction).
Then the Equation 85 of Power density Method sequence as follows:
Subsequently, the Energy Pattern Factor can be simply extrapolated from the Equation 86
and 87. In addition, the Weibull parameters (k, c) can be obtained without the presence of all
wind speed time series values.
∑
∑
94
Where: n is the number of wind speed values.
Concluding, as it is known the Weibull schematic parameter k takes values between k =
1.2 – 2.75 for the most cases. Moreover, the Power density method estimates the Weibull
parameters very well at this range (Jamil, 1994). Thereby the calculation of Power density
Equation is done with the use of *prn files and insert them in OWC one by one for every
period from 20:00 – 07:00 and 08:00 – 19:00 for each station. Weibull Power density
histogram is extrapolated and presents the wind speed statistics for the station (see Figure
53). Also, the direction rode with 12 sectors is calculated and shows the major direction of
the wind (see Figure 53). Finally, a *tap file is concluded the statistical characteristic for
each time period at the station to define the observe region climate. Following the tap
extrapolation, *tap file and *map for each time period at every studying area are inserted to
WAsP application to calculate the wind potential (Wind Atlas) throw roughness and
orography / topography effects and obstacles.
Figure 53 In figure (a) and (c) are wind roses showing the percentage variation in wind direction during
the month of January over Lyneham and Heathrow respectively. Diagrams (b) and (d) show the
percentage frequency of wind speed distribution with a Weibull fit, for a month of January over
Lyneham and Heathrow respectively. Source: (Maphosa, 2000)
3.9 Export of Final Results Using the Wind Atlas Analysis and
Application Program
As we referred before, WAsP is a widely used program for predicting wind climate,
wind resources and power production from wind turbines and wind farms. The predictions
are based on wind data measured at stations in the same region. The program includes a
complex terrain flow model, a roughness change model and a model for sheltering obstacles
(Mortensen et al, 2004). WAsP requires a fraction of the computational cost compared to the
95
advanced and more universal Computational Fluid Dynamics (CFD) models such as the
Reynolds average Navier Stokes (RANS) model and have been proven to be as capable to
reproduce the average neutral ABL velocity fields over gentle terrains. It makes use of input
records that typically include roughness maps, wind measurements, and topography maps and
contains sub-models for horizontal and vertical extrapolation of wind data taking into account
sheltering obstacles, downloaded by surface roughness changes, and terrain height variations.
For the wind potential estimation, we worked with the creation of the orography map
*map, as mentioned before. Then, we created – extrapolated the statistical data and the
observed region climate at the station area (*tap). In addition, the climate at each studying
area can be produced with the use of statistical data (*tap) for meteorological regions, as
were recorded from the meteorological wind data, and the digital orography model map
(*map).
Then, WAsP is using logarithmic profile for the wind, which is taking in it account the
topographic effects and the roughness of the terrain to modify and export the wind potential
in the resolution of 200m. Unfortunately, the model is computed a uniform single wind form
the use of the meteorological data, that is called Geostrophic wind (Maphosa, 2000). After
that, the application model redefines the blowing at each grid point in studying area ‘from the
Geostrophic wind to the wind flow using the topography and roughness effects in studying
areas’, and re – corrects the wind flow (see Figure 54).
Particularly, at the hill top, the wind flow is stronger than the surrounding area. Thus,
the hillcrest might be useful for wind turbines installation.
For the simple hillcrest case located perpendicular to the wind flow, the speed
increasing ΔS (see Equation 88) and the maximum wind speed height l (see Equation 89), can
be simply calculated, as follows:
96
Figure 54 This figure is defined the routines that are used from the application WAsP for the wind
potential analysis in Limassol area buffer zone
(
)
If the central top point with height H is not equal to the height l, then the speed
increasing ΔS for the height H is calculated, as follows:
{
⁄
⁄
Where: l is the width of the hill (see Figure 55). The wind turbine installation on hill
top is in respecting with the Weibull scale parameter c ‘Sometimes is denoted as a’, for areas
where the wind is accelerated on a hill top, as given:
On the other hand, the schematic dimensionless Weibull parameter k is not corrected.
Figure 55 The figure shows the flow of the wind over an imaginary hill. The wind profile is passing
upstream the hill top to the other side. The two dimensions – distances symbols are characterizing the
wind flow along the hill, where: L is the characteristic length of the hill, which is the half hill length from
97
middle of the hill, and l is the height where the maximum wind speed that pass upstream the hill, when
the wind flow profile is across the hill. Source: (Troen & Petersen, 1989)
However, the WAsP uses routines to correct the wind data which were measured at a
certain grid point and modify them in complete climate set at areas of interest ‘Wind
Potential’, the so – called Wind Atlas. Moreover, WAsP is using datasets to assess the wind
conditions in every particular grid point and height in studying areas, mainly with the use of
the same routines and models (Τριανταφυλλίδης, 2009). Generally, after the application
model took the statistical characteristics for the meteorological station, with the use of
logarithmic profile is calculating one uniform single wind profile ‘Geostrophic Wind’. Then,
with the same models and routines modifies the uniform wind profile according the area
terrain roughness and topography – orography effects to the wind for each grid point location
in studying area (Maphosa, 2000).
However, it should be noted that the reliability of the export results of the wind
potential analysis threw the WAsP application, is proportionate to the reliability of the used
data. That is eventually caused, if the orography is complex or the wind measurements are
not correct / representative, where that is reducing the model analysis accuracy and therefore
the wind power analysis results (Troen & Petersen, 1989).
3.9.1 Problems and Limitations
Despite that the WAsP is key tool for this thesis for the studying of the wind resource;
there are some limitations that should be take into account, both for the preparation of input
orography maps and input wind data, before they were inserted to the application (Βελγάκη
& Βασιλειάδης, 2005).
The restrictions that should be taken into account are:
1. The meteorological wind data must be correct. The anemometer wind
measurements should be checked about their representative. In case that the
measurements are required corrections, then they must be conducted with the
regression analysis or reduction from lower wind speeds to high wind speeds
and vice versa, as discussed before in the sub – section of ‘Meteorology of
Wind’.
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2. The wind time series measurements duration – availability. Hourly wind
measurements more than 5years, may be considered sufficient to extrapolate the
representative statistical characteristic for the studying area.
3. The distance between station and each grid point of studying area. The WAsP
application supports maps in the form of *map with buffer zone cycle radius 5 –
10km. In this master thesis, we used maps with 20km buffer zone cycle radius
for the purpose to cover the Cyprus wind potential in the future. As we
mentioned before this dissertation is an integral part of the whole studying of
Cyprus wind energy potential analysis.
4. Befitting and proper anemometer placement in the digital map before the
calculation of the application. The coordinates of the anemometer must be
placed correctly, as obtained from the meteorological department. Any incorrect
placement – installation or omission with the use of wrong anemometer –
station coordinates deposit outcomes miscalculations.
5. It should be used the correct terrain roughness code.
6. The input orography and topography map. If the studying area morphology of
the terrain is more intense, then the error rate is being higher.
For more information about WAsP limitations and errors of the model are described in
detail in the sub – section of ‘Errors of Model and Data’.
3.10 Visualization for Maps of Wind Potential at Studying Area – Wind
Atlas
After the WAsP application and its export wind power analysis maps in the form of
*surfer grid and also the saving of WAsP analysis export in the form of *Workspace, then the
analysis maps must be consolidated in a uniform map for each time period analysis (24 Wind
Atlas Maps) and visualized at final.
Initially, the WAsP export analysis of mean wind speeds, must be saved throw WAsP to
the format of *surfer grid. The *surfer grid format is the only form of raster file that
identifies the program WAsP. Using the surfer grid, may export in the next step, a mosaic file
called raster with all the required information.
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Then, the surfer grid files are entered to the ERDAS Imagine application to be able to
extrapolate export files in the format of ESRI Grid using the command ‘Export’, which that
form is acceptable for the ArcMap application. In other words in is saved as raster file.
Afterwards, the raster file ‘ESRI Grid’ is imported into the ArcMap application, where
we applying masking around the area of interest. Particularly, throw ArcMap application, we
can remove all the unnecessary information and keep the values – information of interest in
each under studying area. Specifically, we remove all the sea mean wind speed values and
keep the values inside of the coastline, in the field of our study.
Finally, we visualize the maps that we applied the masking in step below, with focus on
area of interest. Thus, the maps take their final form with all the necessary information about
the wind potential capacity in the cover area of 6 stations (see Figure 56).
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Figure 56 An indicative final visualized map of Wind Energy Potential in the 6 studying areas
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4 RESULTS AND ANALYSIS
4.1 Introduction
In this section, it will be described the way of how the methodology that we presented
previously is implemented. The results of the wind potential analysis in the western part of
the island are presented and analyzed. Moreover, comparisons between previous relevant
studies, not only in Cyprus but also abroad are performed. Explanations about the wind
energy distribution in the studying area will give an option of better and more detailed focus
in the future, revealing more accurately specific points for wind farm development and
investment. The chapter includes Statistical Results from the examined Stations, seasonal and
daily Distribution of Wind Speeds, Wind Energy Potential visualized Maps and Analysis.
4.2 Statistical Results for Study Stations
In Figure 57, we present statistics for the wind direction and wind speed measured at
each meteorological station for the studying areas. The results refer to monthly averaged data
measured at 10m above the ground level of each station anemometer for two periods of the
day, 20:00 – 7:00 and 8:00 – 19:00. This separation aims to recover basic features of the daily
variation of the wind pattern over the island connected with the sea – breeze occurrence.
From the graphs it becomes profound that in all stations strong modification of the wind
direction exists between the daytime and nighttime hours, expect Prodromos station. At Coast
lines of Polis and Kato Pirgos, Limassol, the wind direction almost reverses, ‘from N to S for
Polis and Kato Pirgos, and from S to NE for Limassol’, reflecting the sea – breeze influence.
In Pafos (Airport) the wind direction reverses, ‘from SW to NE’, reflecting the sea – breeze,
with the same pattern such as Limassol. On the other hand, Mallia is affected from the sea
and also the complex topography of the entire area which turns the wind direction from E to
W.
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103
104
105
106
107
Figure 57 Monthly wind speed and direction statistics (on a bi – daily basis, every 12 hours) for the six
stations
108
As concerns, the statistical analysis for Limassol station, the wind is distributed in all
months which show significant wind speeds with about the same behavior. Generally, at
evening to night period in Limassol, the wind flows from Northern directions, when at the
day to evening period the wind comes from South, and especially from Southwestern
directions; those are from sea–breeze wind circulation caused by the different temperature of
land and sea.
Limassol indicative wind speed range for the night time regime with wind speeds below
2m/s, while during the day period, the wind speed rise to 5m/s or little more. The relatively
high wind speeds in Limassol are consistent with the wind flow from sea to the land and the
development of a local occurrence, especially sea occurrence at the day time and land
depressions at night, with the weak phenomenon of lower density at night time. However,
during the day period, the wind speed increases in a maximum, since the flow of the wind
comes from the sea ‘South’ to the land ‘North’. The occurrence in this case, is a clear ‘Sea
breeze’ (see Figure 57) effect. This information is useful, because on one hand shows that the
wind potential at night period is potentially small. There is, however, a significant wind
resource during the day, which could be used for wind energy development or energy
extrapolation, for the greater area. It is evident that in Limassol when the wind changes its
direction at morning hours, enhances intensity and starting coming from South, with strong
flow from the sea. Specifically in Limassol the wind speeds are higher at the day period.
Namely, in Limassol the wind comes from sea to land and is about 5m/s at day period and at
night the wind change its direction, with the flow comes from inside area to the sea with
mean average wind speed about 2m/s.
Furthermore, the power density is higher during the day period, which is about
80W/m2. Of course, there is the possibility that in some hours of the day the power becomes
higher and maybe could easily reach 100 or 200W/m2. Especially, not in the centre of
Limassol, but in neighbouring mountains and hills, which we will identify at the analysis of
the final maps, where the orography effects enhance the wind through tunnelling effects or
valley effects, resulting to high wind potential. Generally, the topographic effects of Limassol
can easily double the measured wind speed. On the other hand at the night period when the
wind turns its direction towards the land, the power is estimated about 15W/m2 which is very
low.
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The direction pattern in Polis is much clearer than in Limassol. Of course somehow
follows the similar situation. At Polis, the wind speed remains relatively high during the
hours of 08:00 – 19:00, exhibiting much lower variation at 20:00 – 07:00, although still
changing its direction. This feature could be possibly attributed to specific topographic
characteristics of the site that accelerate the natural wind flow from the land to the sea during
the night. However, besides the relatively high speeds observed, the overall variation remains
small indicating that the wind speed maxima are not so high. This is also concluded by the
estimated mean wind power, which remains almost steady, close to 30 W/m2 for both day and
night time periods. At the same time the monthly modification is small without altering the
mean picture. In contrast, for the other stations of Malia and Limassol, the mean wind power
which is almost negligible during the night time hours increases markedly during the daytime
and in the cases of Limassol reaches values from 60 to 100 W/m (see Figure 57).
In night periods, the winds are from southern directions ‘from the land to sea’, even
more profound compared to Limassol. Though, during the day hours, it is observed that the
wind turns from north direction of the sea (sea breeze) to land, in opposite direction of
Limassol. This phenomenon in Polis is due to the different topography than Limassol.
Especially, the sea is on the north site of Polis, and is also rounded of mountains. Also, the
transitional times, show that the wind direction’s pattern is clearer. This pattern is the same at
all months. Nonetheless, it is acknowledgeable that in Polis the wind direction is very clear.
The wind comes from the land ‘South’ to the sea ‘North’ at night. On the other hand, at the
day-time the wind flow is from the sea ‘North’ to ‘South’. Although, in Limassol the average
wind speed is approximately from 2m/s at night and almost 5m/s at day, in Polis the wind
blow a little uniformly with wind speed about 3m/s throughout all the 24 hours. This
happens, due to the topography of Polis Chrisochous, which is surrounded from mountains
and hills on one side and the sea at the front. Most probably, in this area mountain breeze is
moving over the mountain area, to couple the land breeze with valley effects or tunneling,
increasing the wind speed at night. Due to that, it is possible to find exploitable sides near
mountains and hills with high wind energy potential. It has to be noted that doubling of wind
speed due to the orography model will enhance the power multiply up to eight times.
Even at the continental station of Malia one may observe this modification ‘from E to
W’ although the station is far from the sea and the topography complex. In terms of the wind
speed statistics, for the stations of Limassol and Malia there is serious acceleration during the
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daytime hours with mean speeds reaching 5m/s. In contrast, during the night the measured
wind speed does not exceed 2 m/s, showing much less variation. In terms of the Weibull
distributions, the fitting of the measured wind speed distributions to the theoretical curves is
much more accurate during the daytime, mainly due to the occurrence of higher speeds and
the lack of calms (see Figure 57).
At Prodromos the daily modification of wind direction is not clear. From January to
April the wind direction comes from several directions due to the complex terrain at the
mountain area of Troodos, and at the other months the wind direction is from south due to the
decaying sea breeze that is coming from Limassol area. As we mentioned before, the sea and
land breezes observable at coastal areas can be felt up to 35km distance from the coast, so the
effects of sea breeze are fair at the Prodromos. The corresponding phenomena in Troodos
mountains areas are the valley breeze winds ‘Valley breeze’ per day and mountain breeze
winds ‘land breeze’ at night. So, the causes of that local wind creation and therefore
differences at wind direction are due to the different degree of heating or cooling nearby the
adjacent areas. However, the Valley breeze and also the sea breeze appears stronger during
the summer period, while mountain breeze winds in mountains areas and also the land
breezes in the coastal areas have stronger intension during winter period.
The wind speed in Prodromos is about 3.5m/s with not very large fluctuations. Also the
power is approximately 70W/m2 at windy periods. Maybe an hourly distribution about
average wind speeds variability could show more clearly the behavior of each station area.
Kato Pyrgos is found to have a more interesting behavior similar to Polis. Specifically, in
Kato Pyrgos the wind blows from North at the day hours and from South at night hours. Also
at the day the wind speed is higher due to the sea breeze that comes from the North. The
location of Kato Pyrgos is expected have significant points with good energy potential, much
higher than the one estimated directly at the measuring site. As it is noted in Figure 57, the
station shows power which reaches the 40W/m2. In this point of our study, this amount of
energy is not important but with the doubling of the 3m/s that the station has, the power
would be multiplied by eight. Of course the complex orography at the south side of station is
expected to show regions with significant amount of energy (see Figure 57).
Pafos (Airport) station is located at the Southwest part of the island. Also, in Pafos
(Airport) the wind direction reverses, ‘from SW to NE’, reflecting the sea – breeze, with the
same pattern such as in Limassol. The strong influence of sea – breeze occurrence increases
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wind speed at the day period while at the night period the wind is low. The complex
orography at the southeast area near the station may create significant points for possible
wind farm investment. At that area, the wind farm at Oreites is established and functions.
According to the graphs, Pafos (Airport) is found to have higher wind speeds than Limassol
station. This is maybe due to the lower roughness at this area. The calculated amount of
110W/m2 during the daylight period promises significant wind power. Namely, with the
doubling of the average wind speed from 5m/s to 10m/s due to orography, the power will
increase to the exploitable 1000W/m2 and maybe more.
However, for a more representative indication of the behaviour of the wind, in the study
stations, it would be better to check the monthly variations of wind speeds on an inter-annual
basis. Also, the hourly distribution of average wind speeds may show more clearly the wind
flow behavior at each time in the stations. This could be more helpful in order to understand
each station’s pattern, before the extrapolation of the final wind potential maps.
4.3 Monthly Distribution of Average Wind Speeds
From the study of the monthly fluctuations of wind speed at the meteorological
stations, it is notable that Limassol has monthly averaged wind speeds from 2.6 to 2.8m/s (see
Table 6), while at the station of Polis it is notable a variation from 3.2 to 3.4m/s (see Figure
58). In any case, the monthly variation is not that intense as the daily and cannot alone
identify significant periods with excessive wind potential. However, based on the graph, the
meteorological station of Polis indicates slightly higher average speeds than the Limassol
station. This is due to the fact that as shown earlier, Limassol exhibit very low winds during
the night. Thus, the observations of monthly average wind speed distribution show that in
Limassol the average monthly speeds are smaller than in the case of Polis. However, as seen
at the Figure 57 the power calculated from the statistics of the meteorological station alone
shows that Limassol has higher power at day time than the Polis that shows some wind
potential at night time also.
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Table 6 Monthly averaged speeds for the stations of Limassol, Pafos, Polis, Pyrgos, Prodromos and Malia
Figure 58 Inter annual (monthly) variation of the wind speed at the stations of Limassol, Pafos, Polis,
Pyrgos, Prodromos and Malia
Month/
Station
Limassol
Pafos
Polis
Pyrgos
Prodromos
Malia
1 2.82 4.01 3.36 2.93 3.49 2.62
2 2.99 4.21 3.46 2.94 3.18 2.82
3 2.89 4.12 3.37 2.89 3.18 2.77
4 2.94 4.03 3.22 2.79 3.14 2.73
5 2.79 3.94 3.19 2.73 2.73 2.48
6 2.69 3.92 3.23 2.71 2.86 2.53
7 2.70 3.97 3.31 2.61 2.75 2.52
8 2.66 3.95 3.32 2.81 2.56 2.39
9 2.59 3.93 3.38 2.84 2.75 2.44
10 2.27 3.70 3.29 2.43 2.66 2.05
11 2.58 3.98 3.48 2.50 3.12 2.12
12 2.92 4.00 3.15 2.84 3.05 2.26
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The explanation of the paradox is that the average montly variation of wind speed at
station of Limassol and Polis, cannot give a complete overview as regards the distribution of
speeds and behaviour of speeds per hour. Considering the average of wind speed values, it
appears that Polis has a much more uniform distribution of speed and that was noticed clearly
from the sub – section before (see Figure 57). On the other hand, according to the statistics of
wind speed for the station of Limassol, it is observed that the maximum values are taken only
in day time. At monthly averaging though, we cannot distinguish the hourly fluctuations of
wind speeds. For example, at the hours of day the wind speed reaches to 5m/s, while during
the morning hours the speed at the Limassol station is very low at 2m/s. Then, considering
the average to calculate the monthly average speeds, observe that the monthly average speed
is at 2.5m/s. This is due to the fact that during the hours of the day at the station of Limassol,
the speed reaches at 5m/s, as the statistical data reported previously, while other times the
wind shows a weaker form. For this reason, taking into account only the monthly fluctuations
cannot describe properly the mainstream. Also, they do not show modifications in wind flow
and speed.
The Mallia meteorological station presents average monthly wind speeds about 2.8 –
2.5 from January to September. Namely, it seems that in winter the wind speeds are higher
but not lower compared to Prodromos’ station. As we have seen before, the station of Malia
also shows the typical daily modification in the direction from east to west. Although the
station is far from the sea and the topography is complex. In addition, as we mentioned
before, Mallia has a serious acceleration during the daytime hours with mean speeds reaching
the 5m/s. On the other hand during night hours the station exhibits lower wind speeds around
2m/s. Also, the monthly fluctuation is lower (see Figure 58). Moreover, the high speeds in
Malia stations are present during the day. However, the station does not show high speeds
compared to Polis, although has the same pattern during all day hours.
Prodromos presents strong modifications with sharp fluctuations in monthly wind
speeds. In general the pattern of station’s behaviour is not so clear. Namely, the station
presents monthly average wind speeds from 2.7 – 3.5m/s, diminishing to approximately
2.1m/s at the winter period. According to that, the highest speed at the station is found during
the period from March to April. This, monthly fluctuation is shown clearly in Figure 57.
Namely, from January to April the station reveals power from 50W/m2 to 80W/m
2. Later at
the summer months the power and also the wind speed decline rapidly to approximate 2.5m/s.
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Also, it is possible regions nearby the stations to reflect a good potential, but as we show the
station of Prodromos has not stable wind flow behaviour.
Kato Pirgos is shown to have the same behaviour at monthly average wind speed
fluctuations such as Polis. Specifically, the monthly wind speed variation is negligible
ranging from 2.8 to 2.9. Increasing wind speeds are founded during the summer season and
diminishing of wind speeds during the winter season. This fluctuation is due to the fact that in
summer season the wind is strongly influenced by sea breeze at the day time that comes from
North (see Figure 58). In Kato Pyrgos the wind blows from North at the day hours and from
South at night. Also, the location of Kato Pyrgos is expected to show significant sites with
good energy potential, much higher than the one shown at the histograms (see Figure 57).
It is notable that Pafos (Airpot) has monthly average wind speeds around to 4m/s (see
Table 6), while at the station of Polis one could observe a variation from 3.2 – 3.35m/s (see
Figure 58), with both of them having a similar pattern according to the graph. Particularly,
Pafos (Airport) has the highest monthly averaged wind speeds during the studying period.
This fact is affected strongly from the high wind speeds that appear at Pafos as we discussed
before (see Figure 57). The wind direction changes in Pafos where during the daylight hours
the wind blows from southwest to northeast, reflecting the sea – breeze, with the same pattern
such as in Limassol. The strong influences of sea – breeze occurrence result to increasing of
wind speed at the day period, when at the night period the wind is fairly low. The orography
at the southwest area near the station affects strongly the wind speed by forming acceleration
paths of the wind flow. This homogeneous monthly wind speed pattern promises strongly the
existence of sites with good wind power at Pafos area. As we mentioned before (see Figure
57) Pafos is found to have higher wind speeds than Limassol station. This is maybe due to the
relatively lower roughness at this area. Still, Limassol has found to have strong and high wind
speeds only at the day time period. It is not clear to characterize the wind flow only for
monthly wind speed variation, but it is necessary to see through the hourly distribution of
wind speed to understand better each station.
Nevertheless, the existence of the wind farm at Oreites area shows that this area is very
interesting for more study and development. The complex orography at the southeast area
near the station may show significant points for more wind farm investment, studying and
development. It has to note again, that doubling of wind speed will multiply the power by
eight, because the power is a cube of the function of speed. Finally, for a more representative
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indication of the behaviour of the wind, in the study stations, the hourly variation will also be
checked.
4.4 Hourly Distribution of Average Wind Speeds
The table (see Table 7) and graph (see Figure 59) below, present the distribution and
the process of hourly average wind speeds at each station during the day. It is evident that the
speed variation on an hourly basis is considerably higher in Pafos station, which shows low
speeds of about 3.5m/s during the morning and night hours and crowns to around to the
remarkable of 5m/s and more from 08:00 – 20:00.
This behaviour of Pafos station obviously leads to approximately the same average or
even slightly lowers average values of overall speed, but it presents a much greater variation
in speeds between small and large wind speed values which justifies the largest power values.
Do not forget that the power depends on the cube of speed, so for time period of 08:00
– 20:00, the station presents higher wind speeds which firing the calculated available power
of wind to larger overall values (see Figure 59). This strong diurnal variation in Pafos station
justified mainly because of the influence of sea breeze. It is clear that follows the pattern of
the fall of the wind during the night and the increase of wind speed during the day where this
is corroborated by the statistics that clearly presents before (see Figure 57).
It is undoubted that in all months during the day, the wind blow ‘from SW to NE’,
reflecting the sea – breeze. The strong influences of sea – breeze occurrence show increasing
of wind speed at the day period when at the night period the wind is low. The complex
orography at the southeast area near the station may show significant points for more wind
farm investment. Also the wind farm at Oreites area is shown that this area is very interesting
for more study and development. According to the graphs, however, Pafos (Airport) is found
to have less high wind speeds than Limassol station. This may reach because of roughness at
this area. The amount of 110W/m2 during the day bi – daily period is promised significant
wind power (see Figure 57). Also the hourly variations of average wind speed promise
exploitable wind energy potential. Namely, with the double at average wind speed from 5m/s
to 10m/s causes of orography, the power will increase multiply to the amount of 1000W/m2
and maybe more. In addition Limassol station shows very low speeds of about 1.5m/s during
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the evening to night and also at early morning hours, when on the other peaks up around to
5m/s during the midday hours (see Figure 59 and Table 7).
In contrast, at Polis station, the speed presents more uniformly distributed wind speeds.
Moreover, the station presents smaller diurnal variation ranging between to 3.5 – 4m/s. This
behaviour is almost constant during the morning and night hours with a decline of wind speed
between the hours of 06:00 – 08:00 and 18:00 – 20:00. This behaviour is caused of the
changing of wind direction at transition hours. Also, this fact leads to approximately the same
average, or even slightly higher average values in overall speed compared to the station of
Limassol. However, in the second case, it is present a much greater variation in speed
between small and large values justifying the larger power values (see Figure 59). Do not
forget that the power depends on the cube of speed, so even in short time, we observe higher
average wind speeds displayed in Limassol station, firing the calculated available power of
wind, to larger overall wind speed values than at Polis station. The strong diurnal variation in
Limassol station is justified mainly because of the influence of sea breeze. It is clear that
follows the pattern of the fall of wind speed during the night and the increase of wind speed
during the day and this is corroborated by the shown statistics of the station (see Figure 57). It
is observed that in all months during the night the wind blows from northern directions in
Limassol, from land to sea, and during the early morning hours turns and comes from
southern, southwest directions, namely from the sea side.
Polis also shows the pattern of reversal of the direction, which indicates the effect of
sea breeze too. However, it appears weaker maybe due to the local topography of the region
or as a result of smaller temperature difference between land and sea. Specifically, in the
northern parts in Cyprus, generally, temperatures are a bit lower during the day. Also, there
are other factors that might weaken the sea breeze, so it does not reach to as high levels as
Pafos and Limassol. At the same time, the observed enhanced wind speeds during the night
hours are due to the phenomenon of land breeze occurrence, which is enhanced by the
topography and orography features with possibly tunnels effects and so it reveals a relative
high wind speed regime (see Figure 59).
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Table 7 Hourly averaged speeds for the stations of Limassol, Pafos, Polis, Pyrgos, Prodromos and Malia
Hour/
Station
Limassol
Pafos
Polis
Pyrgos
Prodromos
Malia
0 1.63 3.36 3.32 2.89 3.04 1.62
1 1.58 3.41 3.42 2.95 3.09 1.61
2 1.57 3.47 3.50 2.97 3.13 1.57
3 1.57 3.47 3.51 2.99 3.16 1.55
4 1.55 3.46 3.52 2.99 3.18 1.56
5 1.58 3.48 3.52 3.04 3.17 1.54
6 1.61 3.42 3.43 3.05 3.12 1.49
7 1.71 3.20 3.08 3.10 3.04 1.43
8 1.92 3.04 2.71 3.08 2.91 1.81
9 2.37 3.33 2.75 2.93 2.87 2.43
10 3.10 3.99 3.07 2.72 2.88 3.05
11 3.80 4.77 3.56 3.13 2.92 3.54
12 4.32 5.40 4.00 3.34 2.94 3.96
13 4.70 5.80 4.25 3.37 2.93 4.26
14 4.91 5.95 4.29 3.33 2.93 4.38
15 4.92 5.87 4.18 3.22 2.92 4.35
16 4.71 5.50 3.84 2.86 2.87 4.15
17 4.20 4.86 3.34 2.49 2.73 3.64
18 3.43 4.01 2.79 2.60 2.59 2.84
19 2.66 3.29 2.39 2.76 2.67 2.13
20 2.18 2.99 2.37 2.86 2.83 1.74
21 1.93 2.99 2.59 2.84 2.93 1.60
22 1.77 3.13 2.91 2.90 2.97 1.59
23 1.67 3.25 3.15 2.94 3.00 1.61
24 1.63 3.36 3.32 2.89 3.04 1.62
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Figure 59 Daily variation of the wind speed at the stations of Limassol, Pafos, Polis, Pyrgos, Prodromos
and Malia
Furthermore, the sea breeze seems to be faster and consequently has high power values.
Possibly, the roughness of the ground is such that to leave the wind flow to pass extensively
at the region of Limassol. In addition, the seaside is more extended in Limassol, which
appears to have wide – flat bays, large land cover without mountains or narrow bay front of
the coastline without seriously complex topography like Pafos, Kato Pyrgos and Polis.
Additionally, Limassol is urbanized close to the coast. That phenomenon leads to high
temperature difference between land and sea and may increase higher the sea breeze. Over
and above, usually the dominant winds in Cyprus are coming from southern directions. This
fact help to increase the phenomenon of high speeds in Limassol, in contrast to the Polis,
where the wind speeds during the day are not so high.
On the other hand, Polis has something very important. There is land breeze which
reaches 3.5m/s at the night period, while in Limassol at night the wind speeds are very low
because of the weak land breeze of about 1.5m/s at night. This is because the Polis has a
valley relief – terrain and it is surrounding of mountains which descends the wind flow
towards to the sea and possibly due to mountains breeze channelling from the surrounding
mountains. All the above factors strongly affect the phenomenon of higher wind during the
night at Polis than in Limassol where the wind speeds are very low. However, the maximum
wind speed values are not so high during midday such as Limassol, but they are higher than
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Limassol during the evening and night time where are reached to 3.5m/s, and therefore they
raise the power even at the night.
Therefore, according to daily variation of the wind speed at the stations (see Figure 59)
and their statistical characteristics (see Figure 57) in Limassol, Pafos, Polis and Malia at the
time of 24 hours, we expect that the energy will expressed during the midday hours. Also, it
could be possible to find sites with very significant wind resource for wind turbines
installation in the future at Limassol and Pafos areas to modify the income wind energy
coming at midday or even to produce exploitable energy on 24 hours using small wind
turbines.
In Polis, however, the power showed to be smaller and the pattern of wind flow is
different than other stations. We would not expect to appear great power in the area around
the station. But, it may probably be able to find wind generators which will work steadily at 2
– 4m/s, mainly small wind turbines for home using. Nonetheless, maybe there are areas for
wind farming sitting in the future at high altitudes surrounding mountains and hills. In
addition in Polis, there is more constant power for development, certainly with less density
and lower power values, namely for small wind turbines installation.
Also, at the continental station of Malia one may observe this modification ‘from E to
W’ although the station is far from the sea and the topography complex. In terms of the
hourly wind speed variations, Mallia station presents wind speeds about 1.5 – 4.2 during the
midday hours. In contrast, at the evening and early morning hours the station shows very low
wind speeds at 1.5m/s. This modification on the station means that in general in Malia station
the power is high during the day hours and specifically at midday hours. In that fact the
station does not show high speeds than Polis which has the similar modification during
midday hours. However, the complex terrain at this area may change a lot the wind speed that
it does not show at this phase, but is possibly show some areas with a good potential.
In terms of the wind speed statistics, for the stations of Limassol and Malia there is
serious acceleration during the daytime hours with mean speeds reaching the 5m/s and
4.2m/s, respectively. In contrast, during the night the measured wind speed does not exceed 2
m/s, showing much less variation (see Figure 59). In terms of the statistical characteristics at
the station, the fitting of the measured wind speed distributions to the theoretical curves is
much more accurate during the daytime, mainly due to the occurrence of higher speeds and
the lack of calms (see Figure 57).
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In a sequence, the Pyrgos station is found to have a more uniform picture during all the
day time with the wind speed ranges 2.9 – 3.2m/s (see Figure 59). At Pyrgos the wind blows
from North at the day hours and from South at night. Also, the location of Pyrgos is expected
to show sites with good energy potential at night. However, the hourly average wind speed
variability does not show high wind speeds during the midday hours. On the other hand,
maybe there some areas with good potential. Do not forget that double in wind speed is
multiply to eight the power. Also the orography south of the station maybe reveals regions for
more studying in the future. As it is presented in Figure 57, the station shows power which
reaches even the 40W/m2. In this point of our study, this amount of energy is not important
but with the double of the 3m/s, the power will increase the multiply to eight. Of course the
complex orography at the south side of station is expected to show regions with significant
amount of energy (see Figure 57).
Finally, Prodromos shows also a uniform daily pattern. However, the pattern of station
behaviour is not so clear. Namely, the station presents hourly average wind speeds from 2.7 –
3.2m/s. According that, we do not expect so much about the wind potential in this station (see
Figure 59). In particular, at Prodromos station the wind direction comes from several during
January to April affected by the complex terrain at the mountain area of Troodos, and to the
other months the wind direction is from North due to the strong sea breeze that is coming
from Limassol area (see Figure 57). In general, the sea and land breezes observable at coastal
areas can be intrude until 35km distance from the coast, so the effects of sea breeze are fair at
the Prodromos. Also the corresponding phenomena in Troodos mountains areas are the valley
breeze winds ‘Valley breeze’ per day and mountain breeze winds ‘land breeze’ at night. So,
the causes of that local wind creation and therefore differences at wind direction are due to
the different degree of heating or cooling nearby the adjacent areas. However, the Valley
breeze and also the sea breeze is founded stronger during the summer period, while mountain
breeze winds in mountains areas and also the land breezes in the coastal areas have stronger
intension during winter period. The wind speed in Prodromos is about 3.5m/s with not very
large fluctuations (see Figure 57), which that explain the almost constant hourly wind speed
variability (see Figure 59). Also the power is approximately 70W/m2 in windy period due to
the land breeze at this period (see Figure 57). Also, it is possible regions nearby the stations
to reflect a good potential, but as we show the station of Prodromos has not stable wind flow
behaviour and the wind speed is low. However, the same behaviour is found to be same at
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night time, which signalizes possible wind potential at night periods, something that the other
station does not have, expect from Polis and Kato Pyrgos.
Finally, the wind energy potential final maps are going to present extensively the
behaviour not only at the stations regions but in the areas around the stations (see Figure 38).
Also, the roughness / topography map was entered to the model analysis of WAsP to
extrapolate the wind power of areas. In the next sub – section the Wind Atlas for every
season is going to be presented on a bi – daily basis for every month of the year. Final,
discussion for the results and their extrapolation will be closed this study.
4.5 Wind Energy Potential Final Maps and Analysis
In Figure 60, the results of the WAsP predictions in a 200m×200m grid resolution are
given seasonally. The original data have been aggregated using binomial statistical weighting
in ArcView. The increase of the wind speed during the day time hours is evident for the whole
area of application, except Polis, Kato Pirgos and Pafos ‘northern from Pafos Airport station’
that show significant wind speeds and during the night. The general pattern shows small
monthly modification, with the occurrence of maximum speeds during the summer period,
when the sea breeze is more intense at coastal areas and the mountain breeze is probably
stronger in mountains areas.
Significant points with mean speeds exceeding 11 m/s are recovered especially at the
southern coast and mainly in Limassol close to “Akrotiri coastal area (‘34o37
΄05.93΄΄N,
32ο58
΄45.34΄΄Ε’ and ‘34
ο37΄05.29΄΄N, 32
ο55΄43.81΄΄S’), Pisouri coastal area
(‘34o38΄57.56΄΄N, 32
ο42΄21.55΄΄Ε), nearby Germasogeia dam area hills (‘34
ο44΄50.29΄΄N,
33ο03΄15.83΄΄Ε’, ‘34
ο44΄24.85΄΄N, 33
ο03΄08.38΄΄Ε’, ‘34
o45΄45.19΄΄N, 33
ο03΄12.87΄΄Ε’), at
Akrounta mountains area (‘34o46΄24.22΄΄N, 33
ο05΄23.33΄΄Ε’), Palodia mountains
(‘34o44΄13.46΄΄N, 33
ο01΄07.50΄΄Ε’), Alassa area (‘34
ο46΄26.26΄΄N, 32
ο55΄00.83΄΄Ε’), Zigi
coastal area (‘34o44΄13.49΄΄N, 33
ο20΄01.00΄΄Ε’), Mari coastal area (‘34
ο44΄06.41΄΄N,
33ο17΄55.91΄΄Ε)”.
Also Pafos shows significant points with less lower mean wind speeds exceeding 9m/s
than Limassol area, which are shown important wind energy “Agia Marinouda mountains
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area (‘34o46΄30.14΄΄N, 32
ο29΄14.57΄΄Ε’), and Agia Varvara mountains area
(‘34o46΄18.22΄΄N, 32
ο32΄07.38΄΄Ε’).
In addition Mallia produce significant wind energy power with wind speed which reach
7m/s “Holou at Ezousas river area (‘34o5252΄34.22΄΄N, 32
o33΄08.07΄΄Ε’), Holou
(‘34o52΄38.42΄΄N, 32
ο33΄43.21΄΄Ε’)”.
Prodromos mountains complex topography shows that changes the model and leads to
interesting points of energy exploitation such as shown with yellow – red color that reach
8m/s “Prodromos mountains (‘34ο57΄52.96΄΄N, 32
ο53΄08.49΄΄Ε) and Lemithou mountains
(‘34o55΄06.74΄΄N, 32
ο40΄38.15΄΄Ε’ and ‘34
o57΄35.56΄΄N, 32
o48΄09.82΄΄Ε’)”.
It is clear that in Limassol area are present locations that show strong accelerations of
the wind, especially at midday. According to Figure 57 – 59, the wind turns during the
midday hours with mainly southwestern directions, increases its intension and then doubles
the speed as shown in Figure 60. In addition, Limassol amplifies in maximum from 15:00 –
17:00 and subsequently weakens in the evening. It is worth mentioning that while initially
based on statistical characteristics (see Figure 57) with an average speed of 5m/s at the station
received power 112W/m2, of the following maps of the wind potential of the wind speed, we
observe that the mean wind speed is doubled and therefore the power is expected to be eight
times higher, that is much more efficient for the wind potential of island.
The mean wind speed decelerates northwards and close to Polis area remains around 4
– 6 m/s. However, in the last case it is profound that the wind is considerably active also
during the night period. Especially during the winter (December –February) the general
pattern recovers the existence of higher wind speeds during the night-time hours. This picture
is possibly related with the formation of local katabatic flows from the surrounding mountain
area of Troodos and the enhancement of a general southern circulation over the island. This
feature could prove important for local wind energy plans, especially since it seems that, in
general, the wind potential during the night is weak at the southern part of the island.
Specifically, the wind comes from the land to the sea at the evening when at the day the
wind comes from the sea to land with strongly acceleration of the wind flow affected by sea
breeze when is passing the local topography. Also, during the midday the wind comes
strongly from S to N, the wind speed increase its acceleration. Namely, the land breeze passes
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upstream of hills and increases its intensity. Then, the land breeze passes through the valleys
and enforce through channeling.
Interesting points are recovered especially at the northern coast which found to have
wind potential also during the night at Polis area such as “Pomos (35o09΄13.84΄΄N,
32ο33΄30.90΄΄Ε), Faslee mountains area (‘34
o59΄08.50΄΄N, 32
ο21΄27.90΄΄Ε’), Drouseia near
the village (‘34o57΄41.75΄΄N, 32
ο23΄34.94΄΄Ε)”, Stavros tis Psokas (‘35
o02΄00.76΄΄N,
32ο37΄43.71΄΄Ε’), mountains near Gialia village (‘35
o05΄54.02΄΄N, 32
ο31΄΄48.34΄΄Ε’), Giolou
(‘34o55΄20.15΄΄N, 32
ο28΄33.49΄΄Ε’). It is worth mentioning that while initially based on
statistical characteristics of station with a mean wind speed of 3.5m/s at Polis station (see
Figure 57), the station reached the power of 37W/m2, while from the following maps the
mean wind speed is tripled (see Figure 60).
In any case, we have to emphasize the significant results diversification in Limassol
and Polis. Firstly, Limassol is founded to have strong diurnal variation, with the maximum of
wind speed during the day hours and specifically to the midday due to the wind flow
influence from the sea. Also, there is much more wind speed divergence. Namely, the mean
wind speeds in Limassol station and the surrounding area compared with those of in Polis
area; give higher wind speeds because of large wind speed dispersion which leads to higher
exploitable power (see Figure 60). The explanation of this phenomenon is that in Limassol
there can be found much more calm periods where the wind speed is practically unusable,
because the wind flow is very low. Nevertheless, there are times when wind blow is very
high, and then at this time period the wind turbines would be produce significant energy,
specifically in midday hours.
However, in Polis site the wind blows more steadily without a very high variability.
There are not many differences between explicit intervals – periods with many calms and also
periods with high wind flow (see Figure 59 and 60).
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125
126
127
128
Figure 60 Wind speed distribution based on the WAsP predictions for studied area – Wind Atlas
129
It is evident that the overall power is lower in Polis. Nevertheless, even in Polis is
emerging points on Wind Atlas study maps, which are possibly have increase of wind speed
and maybe are useable for wind turbines sitting, as we mentioned before. Also more wind
speed measurements from near station are necessitated.
Moreover, Pyrgos is shown to have the same behaviour such as Polis, but also with
higher wind speed that range 6 – 8m/s and some specific points are reaching to the mean
wind speed of 10m/s. High fluctuations with increasing in wind speed are founded during the
summer season but also higher wind speeds are founded from autumn with the maximum in
winter, and specifically from January to February during the day period with the maximum
values of 10m/s during the night. This phenomenon is remarkable in Pyrgos because as we
present before in Figure 59, the station showed to be constantly during all the day time with
the wind speed ranges 2.0m/s – 3.2m/s. The explanation of that high wind speed in Pyrgos
area is because of the wind flow that comes from North ‘sea breeze’ at the day hours and
from South at night ‘mountain breeze’ stronger due to the topography southern of station.
However, the wind Atlas shows that during the day and seasonal Pyrgos shows higher mean
wind speeds.
The corresponding phenomenon of higher wind speeds during the night hours is due to
the katabatic winds at night ‘mountain breezes’ that come strongly affected the whole area.
Stavros tis Psokas hils and so the mountains around are playing catalytic role to change the
model. Local conditions of caused of the different degree of heating over the sea and cooling
over the mountains is increasing the acceleration of wind speed when the wind flow is
passing downstream the hills to create high wind speeds at night hours.
On the other hand Kato Pyrgos showed to have monthly wind speeds which are in
range 2.81 – 2.93m/s and also high fluctuations with increasing of wind speeds during the
summer and declining of wind speed during the winter (see Figure 58). However, as we
noticed from Figure 60, the local conditions of mountains area that covered behind of Kato
Pyrgos are playing significant role of the climatology at the area. In addition the complex
terrain and high orography of 1100m behind the station are change the local wind speed
conditions with increasing of acceleration.
As we mentioned before in statistical approach of station (see Figure 57) showed power
which reaches even the 40W/m2 and mean wind speed at 3m/s. Now, when the complex
orography at the south side of station showed to be important for the climatology of Kato
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Pyrgos area with approximately triple in mean wind speed at 10m/s (see Figure 60). Do not
forget, that a double in wind speed, it multiplies the power eight times.
Furthermore some sites in Kato Pyrgos area with significant amount of power are
“Agios Theodoros (‘35o10΄52.65΄΄S, 32
ο38΄51.72΄΄Ε), Pomos (35
o09΄13.84΄΄S,
32ο33΄30.90΄΄Ε), and Stavros tis Psokas (‘35
o02΄00.76΄΄S, 32
ο37΄43.71΄΄Ε’)” shown with red
colour in Figure 60.
Following, at Pafos (Airport) the wind direction reverses, ‘from SW to NE’, reflecting
the sea – breeze, with the same pattern such as Limassol. The strong influences of sea –
breeze occurrence show increasing of wind speed at the day period when at the night period
the wind is low except some areas northern the station that influences from the katabatic
winds of mountain around to Polis. The complex orography at the southeast area near the
station presents interesting points for more wind farm investment. According to Wind Atlas
maps, however, Pafos (Airport) area is found to have lower wind speeds than Limassol area.
This behaviour of Pafos area (see Figure 61) obviously leads to the fact that Limassol is
urbanized close to the coast (see Figure 62). That phenomenon leads to high temperature
difference between land and sea and may increase higher the sea breeze. Over and above,
usually the dominant winds in Cyprus are coming from southern directions. This
phenomenon helps to increase significantly the mean wind speed in Limassol than Pafos.
Moreover, the roughness of the ground is such that to leave the wind flow to passing
extensively at the region of Limassol. In addition, the seaside is more extensive in Limassol,
which appears to have wide – flat bays, large land cover without mountains or narrow bay
front of the coastline or any complex topography like Pafos, Kato Pyrgos and Polis. In
addition, the sea breeze shows to be faster and consequently has high power values.
The amount of 110W/m2 during the daytime period showed significant wind power in
Pafos (see Figure 57). Also the hourly variations of average wind speed presented exploitable
wind energy potential. Specifically, according to the Wind Atlas maps, the wind potential in
Pafos area is founded to be around to 8m/s with increasing of wind speed at 10m/s during the
summer season (see Figure 60). However, as the Wind Atlas visualized maps are founded
areas that reveal more power than the Oreites. Generally, as the Figure 63 shows, Oreites has
only 3 – 6m/s only at the summer season when at the other seasons the mean wind speed is
smaller.
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Figure 61 Pafos urban area. Quickbird satellite image
Figure 62 Pafos urban area. Quickbird satellite image
The results shows that with mean wind speeds of 3 – 6m/s could be install smaller wind
turbines. Also, more extensive measurements at this area should identify the wind energy
capacity (see Figure 63). Still, this possibility needs to be further justified experimentally and
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with measurements from Pafos station and from Oreites production data. Namely, areas such
as Agia Marinouda mountains area and Agia Varvara mountains area, shows significant wind
energy power points with mean wind speeds exceeding 9m/s.
It has to be noted that Pafos is located at the southern part of the island. Also, the area
has closed bays and more gruff roughness than Limassol, explaining the fact that Limassol
shows higher wind speeds. In addition, the identification of points with important wind power
should be checked with measurements recorded at these locations for at least one year.
At Malia continental area one may observe this modification ‘from E to W’ although
the station is far from the sea and the topography complex. In terms of the wind speed Malia
shows mean wind speeds of 6.5m/s which at some points such as Holou at Ezousas river area
and Holou reach to 10m/s during the spring and summer period. Also Malia is founded to
have very low speeds that reach 3m/s at night such as Pafos and Limassol, except Akrotiri
area that shows wind potential at night during spring and winter season, that is not very high,
but is interesting if will use wind turbines that work at wind speeds of 3 – 4m/s (see Figure
60).
Figure 63 Oreites Wind Farm location. June 08:00 – 19:00 hours
As we have referred, the station of Malia shows serious acceleration during the daytime
hours with mean speeds reaching the 5m/s. In contrast, during the night the measured wind
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speed does not exceed 2m/s, showing much less variation. In terms of the Weibull
distributions, the fitting of the measured wind speed distributions to the theoretical curves is
much more accurate during the daytime, mainly due to the occurrence of higher speeds and
the lack of calms (see Figure 57).
More evidently, Malia shows double of wind speed during the day bi – daily period. It
is obviously that in Mallia area is effect from the sea breeze that comes from south.
Especially, the wind flow is passing through the mountain and valleys with channeling of
wind flow and acceleration of wind speed. Although, the station is far away from the coastal
which that explain the lower wind speeds than Limassol coastal areas. Also, mountain breeze
is showed that affected strongly the whole area to show interesting points for wind energy
harvesting.
At Prodromos area the wind direction comes from several directions during January to
April affected by the complex terrain at the mountain area of Troodos, and to the other
months the wind direction is from North due to the strong sea breeze that is coming from
Limassol area. In general, the sea and land breezes observable at coastal areas can be felt
until 35km distance from the coast, so the effects of sea breeze is fair at the Prodromos. Also
the corresponding phenomena in Troodos mountains areas are the valley breeze winds
‘Valley breeze’ during bi - daily day period and mountain breeze winds ‘land breeze’ at night
bi – daily period. So, the causes of that local wind creation and therefore differences at wind
direction are due to the different degree of heating or cooling nearby the adjacent areas.
However, the Valley breeze and also the sea breeze is founded stronger during the summer
period, while mountain breeze winds in mountains areas and also the land breezes in the
coastal areas have stronger intension during winter period.
The wind speed in Prodromos is about 6m/s with not very large fluctuations during the
winter months. Also, from spring to autumn the wind speed increases dramatically with some
spots such as Prodromos mountains and Lemithou mountains that are reaching even the 8m /s
(see Figure 60). However, Prodromos is founded to be more stable than Malia station during
the seasons unless winter that the station of Pyrgos is founded to have wind potential at day
and night period.
It is evident that the results are promising, as also seem logical according to the
topography of the regions. It should be emphasized that they are not the final results. In
contrary, the results must be assessed with more wind speed measurements at these points to
134
identify how the results are correct. In general, they are encouraging because they show that
there is easily increasing of wind speed because of complex topography in study areas (see
Figure 60).
It would be useful probable points that have been identified by this study with
significant exploitable wind potential to be verified with local measurement. Also, for the
Limassol area, it is necessary to check with more station observation. In addition, if there is
any wind farm close to the identified points or any proposed wind farm planed location, then
it would be good to check and validate further our study areas. If, finally, there is a proposal
for wind farm sitting in the region, then such a fact implies that our extrapolated results are
comprised sites of wind energy harvesting.
Of course, to be comprehensive, this study should be getting data from other stations
around. Namely, the station of Menogeia could give a more accurate picture about the wind
potential closed to Limassol. However, the wind farm in Alethriko shows that the North
stations have significant amount of harvestable energy. Surely, when the mean wind speeds
values exceed the 7m/s, specifically in Limassol, Pafos and Pirgos areas are encouraging and
strongly support the fact of significant points for wind energy development. However, in
isolated regions – locations in Polis with lower wind resources could be used from inhabitants
in these areas to cover their energy needs or even to sell electricity to EAC. For example,
Akrotiri, Pisouri coastal area, Palodia, Germasogeia, Alassa, Akrounta mountains area, Agia
Marinouda Pafos mountains area, Agia Varvara Pafos mountain area, and Agios Theodoros
are comprised places – regions with high wind potential and which could be locations for
wind farming, after they were become in minimum one year measurements in these spots to
identified the corresponding of our results. Also, areas such as Zygi, Mari and residential area
of Palodia, and Pomos are positions with lower wind resource; however they could be used
for domestic use by households for purposes of saving energy but also for selling electricity
to EAC.
Moreover, in certain positions of Polis, we observe a continuing high wind speeds that
promising interesting power capacity. Also, Kato Pyrgos shows more significant points with
higher wind speeds than Polis. However, it is worth noting that there are isolated areas in
Polis, although their low wind energy potential could be used by locals. For example, places
with high wind potential are Stavros tis Psokas, Gialia area and Giolou mountain area which
could be exploitable by wind farms installation. Nonetheless, it should be noted again that
135
this Master Thesis comprises a first approach for identifying possible locations which
displayed increasing in wind potential. More studying is needed to points that showed large
amount of wind energy to find the suitable area for wind turbines sitting. Also, Pomos
presents a lower wind speed than other areas, but is also significant and could be used for
tourist complex area to their autonomy in energy or even the generated energy could be sold
to EAC.
Also, mountains area of Prodromos should be good enough for wind energy
extrapolation. Specifically, Lemithou mountains and Prodromos mountains could be use
finely for energy production and autonomy of village or selling energy to EAC. Citizens
could be installed small wind turbines that work at 3 – 4m/s for domestic use to produce
energy during all the period of day and selling energy to EAC. Moreover, Malia shows
important spots for wind turbines installation. The river of Ezousa is a good region for wind
turbines installation and Holou village. Still, this possibility needs to be further justified
experimentally.
A number of studies have evaluated the energy potential of the world. Some of these
studies have been contacted both in Europe and United States. Specifically, X. Lu et al
presented the global onshore wind power potential map. The map presented ‘feasile’ power
potential that could be extracted as electricity (wooded/ permafrost/ urban was excluded).
Suitable mid – west states have power density of approximately 3 – 4 W/m2. Moreover,
round the Mediterranean the wind potential is about 0.0 – 1.2 W/m2 with the maximum
amount of energy occurring at the Turkey coastal area (X. Lu et al., 2009).
Another, study founded to analyze the mean wind speed in Europe at 80m height
(Jacobson, 2007). The map shows that Cyprus has smaller wind speed than 5.9m/s at 80m
height. In that work, Cyprus was represented by a grid point only. In addition, a wind
resource map with 5km by 5km resolution showed the wind power around the Latin America,
Europe and Africa where they found very important amounts of wind energy (3TIER, 2009).
However, the map uses different wind speed distributions than other technical wind potential
estimations (Fellows, 2000). Generally, the 5km x 5km wind energy power map presents that
in Cyprus the wind speeds is of the order of 6m/s (3TIER, 2009). A number of other
publications and assessments have been produced by the US National Renewable Energy
Laboratory (NREL, 2012), Denmark’s RisØ Laboratory and others (RisØ, 2013). Especially,
Dr. Glekas showed the map of inter – annual average wind speeds in various areas of Cyprus
136
(CIE, 2000). From the map defines that Limassol has inter – annual average wind speed
around to 4.5 – 6.5m/s, Pafos 4.5m/s, Polis 4.5 – 5m/s, Prodromos and Malia around to 4 –
4.5m/s, and Kato Pirgos around to 4m/s.
In our work, as we mentioned before, we utilized the study areas separately in a
monthly and bi – daily basis (every 12 hours) in order to cover the western part of the island,
and not as a single mean wind speed value for the whole island. Moreover, the analysis was
performed in resolution 200m x 200m resource grid for more accuracy and better
representation for each area.
The Limassol area should be check about the agreement of results and of how the
results are representative. Still, they show agreement with the study of (Pashardes &
Christofides, 1995), where they studied the annual distribution of wind over all Cyprus area.
The study results showed that Cyprus is not characterized by high intensity winds, with great
prospects. However, many areas have mean wind speed at 5m/s for 10m altitude above the
ground level. These sites are very promising for wind turbines installation. This fact
heartened our research that there are points with high wind potential in the area of Limassol.
As they mentioned, these areas are in the southern coastal areas and on hilltops. On the other
hand, the suitability and availability of these areas for wind turbines sitting depends on other
factors such as techno – economics, distance from generating electricity station, land
ownership and road network. This article was found in good agreement with our finding, that
in summer months in coastal areas, the high wind speeds were measured during the evening
and the lower at 05:00 – 06:00 in the morning. At mountains area closed to Kouris dam, the
highest intensities recorded around to 14:00, while the lower records measured in two
separate periods. The first one measured at 07:00 – 08:00, and the second one at the afternoon
and 05:00 – 06:00 at the morning. In mountains area the wind speed intensity is high during
the midday and low at 07:00 – 08:00 in the morning and also at the afternoon. Also, the
authors referred that in winter, the maximum wind speed measurements observed early in the
afternoon between 14:00 – 15:00, while the minimum wind speed measurements shown at
late night and early morning hours. The reason that high wind speeds defined is the complex
terrain and topography of land. Apart from the terrain morphology, as indicated, the range of
wind speed in mountains areas is much lower than in coastal areas, as also this study
revealed.
137
In conclusion, this work gives for a first time, a detailed description of the geographical
and the seasonal distribution on the eastern part of Cyprus. Further studying at points with
significant wind potential is needed in order to provide more detailed site measurements to
verify the positions of high wind energy at study areas. Such a study will comprise the next
step of this work and complete the picture about the accuracy of wind energy potential in
each study area. The wind turbines installation can be a significant proportion of clean
alternative energy for the entire island of Cyprus and will provide a solution to the energy
problem that exists in this phase on the island.
138
139
CONCLUSIONS
5.1. Introduction
In this chapter we summarize the main steps of this study and focus on conclusions and
discussion regarding the characteristics of the wind and wind potential at the study areas. We
should emphasize that this study does not stand alone. But, in fact, it is a first attempt for the
analysis of the wind potential in western island areas. We hope that in the future will be able
to collect more experimental data and measurements, necessary in order to check the validity
of the extrapolated results. Moreover, the study should be generalised for the whole area of
Cyrus, with the use of measurements from other stations in order to be able to have a more
representative picture about the wind potential in Cyprus. The chapter closes with
suggestions for the exploitation of wind resources at the studied areas.
5.2. Achieving the Aims and Objectives
Referring back to the aims posed in the introduction of this study it can be stated that all
of them have been achieved but at varying degrees of success. The first aim was to ‘Review
and analyze existing literature and knowledge which concentrate on the impact of wind
energy potential – analysis and assessment’. This aim and its respective objectives are met in
chapter 1.
Also chapter 1 and chapter 2 fulfilled the requirements of the second aim; ‘Understand
Wind Energy Analysis and the WAsP model’. Finally, aims 3, 4 and 5 have been partially
covered in chapter 3 (Methodology) but mostly through chapter 4 where the results are
presented, analyzed and evaluated. Conclusions and suggestions for future research are part
of this chapter.
140
5.3. Overview of the Findings
In this study, the framework of an integrated method for the estimation and analysis of
the potential wind energy resources in Cyprus was presented and a first test – case was
applied, at six selected sites to cover the western part of the island. The advantage of this
study is that it does not face the wind speed overall in one value of mean wind speed and
direction throughout the year, but we have focused on monthly wind speed and direction on a
bi–daily basis, capturing seasonal and daily variations of mean wind speeds and direction.
Statistical analysis of wind speed and direction data shows strong influence of sea –
breeze which is very intense, especially in the southern coast. Also, the northern stations of
the analysed data show that the wind potential during the night is affected by the mountain
breeze. In the case of the southern coast of Limassol station, the wind speed remains relative
high during the day. Generally, at evening to night period in Limassol, the wind flows from
North directions, when at the day to evening period the wind comes from South, and
especially from Southwestern directions those are from sea with uplift of wind caused by the
different temperature of land and sea.
Limassol representative wind speeds rate from the night hours with wind speeds below
2m/s, up to the daylight period, when the mean wind speed reaches to 5m/s or more. The high
wind speeds in Limassol during the day are consistent with the formation of wind flow from
sea to the land and the development of local occurrences, especially sea breeze occurrence at
the day time and land depressions at night, with the weak phenomenon of low density at night
time. However, during the day period, the wind speed increases in its maximum, since the
flow of the wind comes from the sea ‘South’ to the land ‘North’. The occurrence in this case,
is known as ‘Sea breeze’.
This information is very useful, because on one hand shows that the wind potential at
the night period is potentially small. There is, however, a significant wind resource during the
day, which could be used for wind energy development or energy extrapolation, for the
extended area.
At Polis, the wind speed remains relatively high during the hours of 08:00 – 19:00,
exhibiting much lower variation at 20:00 – 07:00, although still changing its direction. This
feature could be possibly attributed to specific topographic characteristics of the site that
accelerate the natural wind flow from the land to the sea during the night. However, besides
the relatively high speeds observed, the overall variation remains small indicating that the
141
wind speed maxima are not so high. This is also concluded by the estimated mean wind
power, which remains almost steady, close to 30 W/m2 for both day and night time periods.
At the same time the monthly modification is small without altering the mean picture. In
contrast, for the other stations of Malia and Limassol, the mean wind power which is almost
negligible during the night time hours increases markedly during the daytime and in the cases
of Limassol reaches values from 60 to 100 W/m.
During night-time periods, the winds are clearly from southern directions ‘from the
land to sea’. Though, during the day hours, it is observed that the winds turns towards
northerly directions from sea ‘sea breezes’ to land, opposite from Limassol. This
phenomenon in Polis is due to the different topography than Limassol. Especially, the sea is
on the north site of Polis, and is also surrounded of mountains.
At midday in Limassol the Weibull distribution in more representative, when the wind
flow is increasing its intensity at these hours. The wind speed distribution poses clearly the
explanation, as the range of variation increases with larger mean values.
On the other hand, at Polis the Weibull distribution is almost always accurate and
representative because of the lack of calms. Moreover, the wind speed distribution shows less
intense variability than in the case of Limassol and lower mean wind speeds. This fact
consolidates the outcomes that in Polis appear less mean wind speeds and less energy in total.
The mean wind speed decelerates northwards and close to Polis area remains around 4
– 6 m/s. However, in the last case it is profound that the wind is considerably active also
during the night period. Especially during the winter (December – February) the general
pattern recovers the existence of higher wind speeds during the night-time hours. This picture
is possibly related with the formation of local katabatic flows from the surrounding mountain
area of Troodos and the enhancement of a general southern circulation over the island. This
feature could prove important for local wind energy plans, especially since it seems that, in
general, the wind potential during the night is weak at the southern part of the island.
Interesting points are recovered especially at the northern coast which found to have
wind potential also during the night at Polis area such as Pomos, Faslee mountains area,
Drouseia near the village, Stavros tis Psokas mountains, mountains near Gialia village, and
Giolou. It is worth mentioning while the statistical characteristics of station with a mean wind
speed of 3.5m/s at Polis station result to the power of 37W/m2, the wind speed is tripled in
these areas. Indeed, when the mean wind speeds values exceed the 7m/s, specifically in
Limassol, Pafos and Pirgos areas it is encouraged and strongly supported the existence of
142
significant points for wind energy development. However, even some isolated regions –
locations in Polis with lower wind resources could be used from inhabitants in order to cover
their energy needs or even to sell electricity to EAC.
More specifically, Akrotiri, Pisouri coastal area, Palodia, Germasogeia, Alassa,
Akrounta mountains area, Agia Marinouda Pafos mountains area, Agia Varvara Pafos
mountain area, and Agios Theodoros are comprised places – regions with high wind potential
and could be locations for wind farming. In contrary, areas such as Zygi, Mari and the
residential area of Palodia, and Pomos are positions with lower wind resource; however they
could be used for domestic use by households for purposes of saving energy but also for
selling electricity to EAC.
Moreover, in certain positions of Polis, we observe prevailing high wind speeds that
promising interesting power capacity. Also, Kato Pyrgos shows many significant points with
higher wind speeds than Polis. However, it is worth noting that there are isolated areas in
Polis, where their low wind energy potential could still be used by locals. For example, places
with high wind potential are Stavros tis Psokas, Gialia area and Giolou mountain area which
could be exploitable by wind farms installation. Nonetheless, it should be noted again that
this Master Thesis comprises a first approach for identifying possible locations which
displayed increasing in wind potential. More extended and specialized study is needed at
points that showed large amounts of wind energy to find the exact suitable area for wind
turbines sitting. Also, Pomos presents a lower wind speed compared to other areas, but is also
significant and could be used for tourist complex area to their autonomy in energy or even the
generated energy could be sold to EAC.
The mountain area of Prodromos looks good enough for wind energy extrapolation.
Specifically, Lemithou mountains and Prodromos mountains could be used for energy
production and autonomy of village or for selling energy to EAC. Citizens could install small
wind turbines that work at 3 – 4m/s for domestic use to produce energy during all the period
of the day and for selling energy to EAC, however the isolated and complex topography
makes it difficult.
In the case of Pafos (Airport) the wind direction reverses, ‘from SW to NE’, reflecting
the sea – breeze, with the same pattern such as Limassol. The strong influences of sea –
breeze occurrence increases the wind speed at the day period while at the night period the
wind is lower except from some areas northern the station that are influenced by katabatic
winds of the mountain around. The complex orography at the southeast area near the station
143
presents interesting points for more wind farm investment. According to Wind Atlas maps,
however, Pafos (Airport) area is found to exhibit lower wind speeds than Limassol area.
Over and above, usually the dominant winds in Cyprus are coming from southern
directions. This phenomenon helps to increase significantly the mean wind speed in Limassol
than Pafos. Moreover, the roughness of the ground and the topography is such that leave the
wind flow to pass extensively at the region of Limassol. In addition, the seaside is more
extended in Limassol, which appears to have wide – flat bays, large land cover without
mountains or narrow bay front of the coastline or any complex topography like Pafos, Kato
Pyrgos and Polis. In addition, the sea breeze shows to conclude faster and consequently has
higher power values.
The estimated amount of 110W/m2 wind power during the daylight period is considered
as significant in Pafos. The hourly variations of average wind speed presented exploitable
wind energy potential. Specifically, according to the Wind Atlas maps, the wind potential in
Pafos area is founded to be around to 8m/s with increasing to 10m/s during the summer
season. However, as the Wind Atlas maps shown areas can be are found that have more
power even than the area of Oreites wind farm in Pafos. Generally, Oreites show only 3 –
6m/s during the summer (especially at June 08:00 – 19:00 hours) when at the other seasons
the mean wind speed is smaller. However, this result needs further justification and testing
taking into account measurements from the wind farm.
At Malia continental area one may still observe the general pattern of a modification
‘from E to W’ although the station is far from the sea and the topography complex. In terms
of the wind speed Malia shows mean wind speeds of 6.5m/s which at some points such as
Holou at Ezousas river area and Holou reach even 10m/s during the spring and summer
period. However, in general, Also Malia area is founded to have very low speeds not
exceeding 3m/s especially during the night like, almost like in Pafos and Limassol, except
from Akrotiri area that shows wind potential at night during spring and winter season, that is
not very high, but is interesting if will use wind turbines that work at wind speeds of 3 –
4m/s.
At the statistical point of view Malia station shows serious acceleration during the
daytime hours with mean speeds reaching the 5m/s. In contrast, during the night the measured
wind speed does not exceed 2m/s, showing much less variation. In terms of the Weibull
distributions, the fitting of the measured wind speed distributions to the theoretical curves is
much more accurate during the daytime, mainly due to the occurrence of higher speeds and
144
the lack of calms. It is obvious that Mallia area is affected from the sea breeze that comes
from south. Especially, the wind flow is passing through the mountain and valleys with
channeling of wind flow and acceleration of wind speed. Although, the station is relatively
far from the coast, fact that explains the lower wind speeds compared to the Limassol coastal
areas. Also, mountain breeze is present, affecting strongly the whole area and recovering
interesting points for wind energy exploitation. Moreover, around Malia important spots for
wind turbines installation exist. The river of Ezousa is a good region for wind turbines
installation and Holou village. Still, this possibility needs to be further justified
experimentally.
In the case of Pyrgos, a similar behaviour with Polis can be drown, but with higher
wind speed that range 6 – 8m/s and some specific points reaching the mean wind speed of
10m/s. Higher fluctuations with increased wind speed are founded during the summer season
but also higher wind speeds are present from autumn with the maximum during winter, and
specifically from January to February during the daylight period with the maximum values of
10m/s even during the night. This phenomenon is remarkable in Pyrgos because, the stations’
statistics showed to be almost constant during all the day with the wind speed to range 2.0m/s
– 3.2m/s. The explanation of that high wind speed in Pyrgos area is due to wind flow that
comes from North ‘sea breeze’ at the day hours and from South at night ‘mountain breeze’
which becomes stronger due to the topography around the station.
The corresponding phenomenon of higher wind speeds during the night hours due to
the katabatic winds at night ‘mountain breezes’ that strongly affect the whole area. Stavros tis
Psokas hills and the mountains around are playing catalytic role to change the wind pattern.
Local conditions causing the different degree of heating over the sea and cooling over the
mountains result in the creation and the acceleration of local wind systems when the wind
flow passes downstream the hills to create high wind speeds at night hours.
As we mentioned before in the statistical description of the station, it showed power
which reaches even the 40W/m2 and mean wind speed at 3m/s. Now, the complex orography
at the south side of station seems to be important for the climatology of Kato Pyrgos area,
forming in some areas approximately triple mean wind speed up to 10m/s. Do not forget, that
doubling the wind speed, multiplies the power at eight times. Of course, it would be helpful if
this study could have get and utilized data from other available stations around. Namely, the
inclusion of the station of Menogeia could give a more accurate picture about the wind
145
potential close to Limassol. The presence of a wind farm in Alethriko shows that the northern
stations may have significant amount of harvestable energy.
It is evident that the results look consistent, showing a clear pattern according the
topography of the regions. However, it should be emphasized that the present outcomes are
not the final results. The results must be assessed with more wind speed measurements at the
points of interest to identify a more realistic picture. In general, they are encouraging because
they show that there is present increasing of wind speed due to topography in the study areas.
To recapitulate, a further analysis at points with significant wind potential will be able
to provide comprehensive survey on – site measurements, verifying the positions of high
wind energy at study areas. Such study will be the next step and complete the results
increasing the accuracy of wind energy potential description in the study area. The wind
turbines installation could result to a significant proportion of clean alternative energy for the
entire island of Cyprus providing with a solution to the energy problem that exists in the
Democracy.
5.4. Evaluation of Results and Recommendations
‘Is it possible to apply the framework of an integrated method for the estimation and
analysis of potential wind energy resources at the western coast – line of the island using
WAsP application in high resolution of 200m x 200m grid in a bi – daily, monthly basis?’
This was the primary and major research question that this study aimed to answer. The results
show clearly that there are many positions with significant amount of wind power. However,
the accuracy of the extrapolated results should be checked and enforced with site
measurements in the identified points. Although, the limitations of model analysis were
reduced with the use of regression methods to correct the measurements and through the
overlapping of the topography maps (at least 1 – 5km), although the bi – daily wind speed
variation every 12 hours and a high resolution spatial analysis of 200m x 200m were utilized,
the “point to point truth” of the estimated wind pattern is not a fact. The results should be
faced as recovering of main trends of the wind pattern over the island and as a first
reasonable approximation of the reality.
Specifically, from the whole study outcomes and the wind energy analysis at the
western part of Cyprus, it comes out that Limassol, Pafos and Pirgos areas are encouraging
146
and strongly supporting the existence of significant points for wind energy development. For
example, Akrotiri, Pisouri coastal area, Palodia, Germasogeia, Alassa, Akrounta mountains
area, Agia Marinouda Pafos mountains area, Agia Varvara Pafos mountain area, and Agios
Theodoros are comprised places – regions with high wind potential and which could be
locations for wind farming, after they were become in minimum one year measurements in
these spots to identified the corresponding of our results. Areas such as Zygi, Mari and the
residential area of Palodia, and Pomos are positions with lower wind resources; however they
could be used for domestic use by households for purposes of saving. Even, in isolated
regions – locations in Polis with lower wind resources in general several places could be used
from inhabitants to cover their energy needs or even to sell electricity to EAC. Moreover, in
certain positions of Polis, we observe continuously high wind speeds that promises
interesting power capacity. Also, Kato Pyrgos shows significant points with higher wind
speeds than Polis. For example, places with high wind potential are Stavros tis Psokas, Gialia
area and Giolou mountain area which could be exploitable by wind farms installation. Pomos
shows a lower wind speed regime than other areas, but it could be used for tourist complex
area to their autonomy in energy or even the generated energy could be sold to EAC.
The mountain area of Prodromos should be good enough for wind energy extrapolation.
Specifically, Lemithou mountains and Prodromos mountains could be use finely for energy
production and autonomy of village or selling energy to EAC. Citizens could be installed
small wind turbines that work at 3 – 4m/s for domestic use to product energy during all the
period of day (for 24 hours energy production) and selling energy to EAC. At Malia
important spots for wind turbines installation can be found. The river of Ezousa is a good
region for wind turbines installation and Holou village. Still, this possibility needs to be
further justified experimentally.
147
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