giannoskastanas_msc civilsustainable dissertation - an integrated method for wind power estimation...

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

“We cannot direct the wind, but we can adjust the sails!”

English proverb

iii

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

v

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 ……………………………………………………..

ix

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

xxiii

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

xxv

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

xxvii

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

xxviii

xxix

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

xxx

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.

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.

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.

75

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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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).

85

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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Figure 57 Monthly wind speed and direction statistics (on a bi – daily basis, every 12 hours) for the six

stations

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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



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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

122

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

123

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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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.

131

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

132

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




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.

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



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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