REPOWERING OF A WIND FARM AT EDAYARPALAYAM

REPOWERING OF A WIND FARM AT

EDAYARPALAYAM

A PROJECT REPORT

Submitted by

M.Arthanareswaran

K.Ashokkumar

R.Hariprasanth

R.Sriram

In partial fulfilment for the award of the degree

of

POST GRADUATE DIPLOMA

In

WIND RESOURCE ANALYSIS

DEPARTMENT OF ENERGY

PSG COLLEGE OF TECHNOLOGY (Autonomous Institution)

COIMBATORE – 641 004

PSG COLLEGE OF TECHNOLOGY (Autonomous Institution)

COIMBATORE – 641 004

BONAFIDE CERTIFICATE

Certified that this project report “REPOWERING OF A WIND FARM

AT EDAYARPALAYAM” is the bonafide work of “M.Arthanareswaran,

K.Ashokkumar, R.Hariprasanth and R.Sriram” who carried out the project work

under my supervision.

Dr R. VELAVAN Dr S. BALACHANDRAN Associate Professor and Project Supervisor Head of The Department

Energy Engineering Energy Engineering

PSG College Of Technology PSG College Of Technology

Coimbatore – 641 004 Coimbatore – 641 004

Submitted for the final Viva-voce Examination held on 21.08.2012

INTERNAL EXAMINER EXTERNALEXAMINER

ABSTRACT

The main objective of the project is to assess the repowering potential of a wind farm

using the wind atlas analysis and application program (WAsP). With repowering, the first-

generation wind turbines can be replaced with modern multi-megawatt wind turbines. To

carry-out the study an old wind farm located at Edayarpalayam near Pappampatti is selected.

The wind farm was commissioned in 1990’s with a capacity of 11.58MW, which consists of

39 Wind Turbines.

The intent of this project is to calculate the generation of the existing wind farm using

WAsP and to compare with the actual generation. To carry out the micro-siting for the same

wind farm with different wind turbines and to predict the annual energy output of the wind

farm after the repowering. Further, the energy yield ratio and repowering ratio of this

repowering project also to be calculated. This will facilitate to develop a method to assess the

repowering potential, since the best locations for wind in India are occupied by old wind

turbines with lower energy output compared with new wind turbines.

TABLE OF CONTENTS

CHAPTER NO TITLE PAGE NO

ABSTRACT i

LIST OF TABLES ii

LIST OF FIGURES iii

LIST OF ABBREVATIONS iv

1 INTRODUCTION 1

1.1 GENERAL 1

1.2 OBJECTIVES OF THE PROJECT 2

1.3 ORGANISATION OF THE PROJECT 4

2 RE-POWERING OF WIND FARMS 5

2.1 INTRODUCTION 5

2.2 NEED FOR REPOWERING 5

2.3 ADVANTAGES OF WIND REPOWERING 6

2.4 METHODOLOGY TO ASSESS REPOWERING

POTENTIAL 8

2.5 SUMMARY 10

3. WIND ATLAS, ANALYSIS AND APPLICATION PROGRAM

(WAsP) 10

3.1 INTRODUCTION 10

3.2 WIND POWER PRODUCTION CALCULATION 12

3.3 SUMMARY 15

4. MICROSITING OF WIND TURBINES 16

4.1 INTRODUCTION 16

4.2 WIND RESOURCE ASSESSMENT METHODOLOGY 16

4.3 MICROSURVEY & MICROSITING 17

4.7 SUMMARY 17

5. CALCULATION OF EXISTING GENERATION USING

WAsP 18

5.1 INTRODUCTION 18

5.2 EXISTING INSTALLED CAPACITY AND RATING OF

TURBINES 18

5.3 WAsP OUTPUT - EXISTING WIND FARM GENERATION 18

5.4 SUMMARY 23

6. ESTIMATION OF NEW INSTALLED CAPACITY AND

GENERATION AFTER REPOWERING 23

6.1 INTRODUCTION 23

6.2 INPUTS REQUIRED FOR WAsP 23

6.3 EXISTING WIND TURBINES 33

6.4 NEW TECHNOLOGY SELECTION FOR REPOWERING 39

6.5 AEP CALCULATION OF REPOWERED WIND FARM 42

6.6 CALCULATION OF AEP FROM WAsP FOR

CONFIGURATION I 45

6.7 CALCULATION OF AEP FROM WAsP FOR

CONFIGURATION II 50

6.8 SUMMARY 51

7. CO2 REDUCTION FOR THE REPOWERED WIND FARM 55

7.1 INTRODUCTION 55

7.2 CALCULATION OF CO2 REDUCTION FOR

CONFIGURATION I 55

7.3 CALCULATION OF CO2 REDUCTION FOR

CONFIGURATION II 58

7.4 SUMMARY 59

8. CONCLUSION 61

8.1 INTRODUCTION 61

8.2 PROJECT SUMMARY 61

ANNEXURES 63

REFERENCES 67

LIST OF TABLES

TABLE NO. TITLE PAGENO.

5.1 Existing Installed Capacity and Rating of Turbines 18

6.1 Summary of the verification for wind speed for each modelling 25

8.1 Summary of the Work done 62

LIST OF FIGURES

Fig No. TITLE Page No.

5.1 Existing Wind Farm Layout. 19

5.2 Google Synchronised 3D Image. 20

5.3 AEP Calculation. 21

5.4 Energy Losses Due to Wake 21

6.1 Numerical Wind Atlas 24

6.2 Wind atlas for Edayarpalayam. 26

6.3 Vector Map 27

6.4 Creating Vector Map in Surfer 29

6.5 Change the Coordinate System to UTM 30

6.6 Making Contour Map in DXF format 31

6.7 Making WAsP Contour Map by Map Editor 32

6.8 WAsP ASCII Map 32

6.9 Power Curve for VESTAS ‘V39’ 500kW 33

6.10 Power Curve for VESTAS ‘V27’ 225kW 34

6.11 Power Curve for SUZLON ‘S33’ 350kW 35

6.12 Power Curve for Pioneer Wincon 250kW 36

6.13 Power Curve for Enercon ‘E30’ 200kW 37

6.14 Power Curve for BONUS 300kW. 38

6.15 Power Curve for SUZLON ‘S64’ 1250Kw 39

6.16 Power Curve for GAMESA ‘G90’ 2.0MW. 40

6.17 Power Curve for SUZLON ‘S88’ 2.1MW. 41

6.18 Power Curve for GAMESA ‘G114’ 2.0 MW. 42

6.19 Layout for Configuration I 43

6.20 Vector Map 44

6.21 Vector Map 45

6.22 AEP for GAMESA G90 45

6.23 Wake Losses for GAMESA G90 46

6.24 AEP for SUZLON S88. 47

6.25 Wake Losses for SUZLON S88. 47

6.26 AEP for GAMESA G114. 48

6.27 Wake Losses for GAMESA G114. 49

6.28 AEP for GAMESA G90 50

6.29 Wake Losses for GAMESA G90 50

6.30 AEP for SUZLON S88. 51

6.31 Wake Losses for SUZLON S88. 52

6.32 AEP for GAMESA G114. 53

6.33 Wake Losses for GAMESA G114. 53

LIST OF ABBREVIATIONS

WAsP : WIND ATLAS ANALYSIS APPLICATION PROGRAM

AEP : ANNUAL ENERGY PRODUCTION

PLF : PLANT LOAD FACTOR

CUF : CAPACITY UTILIZATION FACTOR

SRTM : SHUTTLE RADAR TOPOGRAPHY MISSION

WTG : WIND TURBINE GENERATOR

PEI : PRIMARY ENERGY INPUT

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CHAPTER 1

INTRODUCTION

1.1 GENERAL

India started with a unit size of 55 kW in 1986, when the first demonstration wind farms

were built. Installation of 90 kW, 110 kW, and 150 kW unit sizes quickly followed.

Thereafter, 200-kW wind-energy generators were used in the 20 MW demonstration wind-

farms built with assistance from the Danish International Development Agency (DANIDA).

When the private sector entered the wind market in the early 1990s, turbines of 225 kW to

300 kW unit sizes were the preferred choices. Today, 600 kW, 750 kW, 800 kW, 1250 kW,

2000 kW and 2500kW are popular unit sizes in India. The hub height of wind-turbines, which

was 26 m to start with, has increased to about 90 m today. Also, the energy generation per

kW rating of these WTGs or capacity factor was around 15-20%. In current scenario, much

larger capacity WTGs are available with taller tower, higher rotor diameter and advanced

design features. Consequently the CUF now available is almost double. Similarly, the rotor

diameter has increased from 16 m to 100 m in the larger unit sizes now in operation. The

pace has quickened now. The standard commercially available wind turbine size, which

was150 kW, 15 Years ago and 500 kW, 10 Years ago, has now moved up to 2500 kW. In

India, old wind turbines were placed at locations where the wind is often very good. Since the

best locations for wind in India are occupied by old wind turbines with lower energy output

compared with new wind turbines. Programs are started to replace the old turbines with new

ones. With repowering, the first-generation wind turbines can be replaced with modern multi-

megawatt wind turbines. This study is essential for devising a method for assessing the

repowering potential and to improve the energy output from the wind farms. Repowering

seeks to efficiently harness the wind energy potential and subsequently increase energy

generation per hectare of land area used. As a thumb rule re-powering is a process which,

with half the infrastructure, will double the capacity and triple the energy. In addition re-

powering offers several technical, operational, financial and environmental advantages also.

India has significant re-powering potential in some of its most wind rich states including

Tamilnadu, Gujarat, Andhra Pradesh and Karnataka. Wind repowering in India is still at the

demonstration stage and is expected to take off only by 2012. So an opportunity exists to

repower the wind mill turbines, which are operational for more than 15 Years with the

presently available high efficient high capacity turbines. This project evaluates the

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repowering opportunities for wind farms using the Wind Atlas Analysis and Application

Program (WAsP).

1.2 OBJECTIVE OF THE PROJECT

The objective of the project work is

To calculate the gross and net generation of the existing wind farm using WAsP.

To estimate the new installed capacity and gross and net generation after repowering

using WAsP with different micro-siting and turbine spacing criteria and turbine rating

selection and calculating energy yield ratio and re-powering ratio.

1.3 LITERATURE REVIEW

A. Filgueira et al (2009) described the technical and economic aspects of the

repowering process for the wind farms in Bustelo and S. Xoan, situated in the municipalities

of Muras (Lugo) and As Pontes de Garcia Rodriguez (A Coruna), Galicia, North-Western

Spain. This process was the result of a growing demand for renewable energies, facilitated by

the great potential of wind energy for Galicia. Both farms were set up in 1998. The other

factors they have in common - the same type of machinery, their location and a shared

substation- mean they can be studied together and independently. L. M. Neto et al (2009)

described the useful life of winding insulation. When retrofitting is undertaken an increase

to a higher insulation class is recommendable. So the generator‘s capacity should be

increased, and this will not just more than fully compensate for the investment made it will

also result in a more efficient use of the raw materials used and thus contribute to sustainable

development. Brazilian experience shows that retrofitting with repowering is successful. The

objective of this study is to present two cases of repowering, in which the old insulating

materials were replaced by other, modern ones. So, eight SG of a Power Plant in Cubatao,

S.P and two SG of CEMIG, MG had its power increased up to 40%. Niels G. et al (2008)

described the Wind Atlas Analysis and Application Program (WAsP). It is a software

program for horizontal and vertical extrapolation of wind data. The program contains a

complete set of models to calculate the effects on the wind of sheltering obstacles, surface

roughness changes and terrain height variations. The analysis part consists of a

transformation of an observed wind climate (speed and direction distributions) to a wind atlas

data set. The wind atlas data set can subsequently be applied for estimation of the wind

climate and wind power potential, as well as forsiting of specific wind turbines. Facility

includes a Quick Start Tutorial, a User's Guide and a Technical Reference.

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Rajendra Kharul Sr. Fellow and Head, Centre for Wind Power, World Institute of

Sustainable Energy, outlines the most important highlights of the wind-power market,

including installations, geographical spread, and wind-turbine technology up gradation in

India. Also explains the variations in capacities of wind-turbines installed in Tamil Nadu and

in India. It further introduces the concept of re-powering and deliberates on various ways of

re-powering, its need, its benefits, barriers and associated concerns in India. Also covered the

criteria for selection of project; selection of Tamil Nadu for study and describes the different

projects sites selected as samples in the state. It also provides details of each project chosen

for the study, and the type of data collected from selected sites. It includes a detailed

methodology to calculate repowering potential considering different technology and

micrositing alternatives. Jacques Roeth (2010) presented industry-accepted guidelines for

planning and conducting a wind resource assessment program. A comprehensive overview of

the wind monitoring process, which involves the siting, installation, and operation of a

meteorological towers, as well as advanced remote sensing technologies are discussed.

Recommended best practices for the subsequent data collection and validation are provided.

These analyses include extrapolating observed wind measurements to hub height, adjusting

the measured data to the long-term historical norm, wind flow modelling and the assessing

the uncertainty associated with resulting energy production estimates. Jacques Roeth (2009)

investigated the influence of rugged terrain on the predictions by the wind analysis and

application program (WAsP) using a case study of field measurements taken over 3 and.

Years in rugged terrain. The parameters that could cause substantial errors in a prediction are

identified and discussed. In particular, the effects from extreme orography are investigated. A

suitable performance indicator is, developed which predicts the sign and approximate

magnitude of such prediction error. This procedure allows the user to assess the consequences

of using WAsP outside its operating envelope and could provide a means of correcting for

rugged terrain effects. Infraline energy in its report (2011) ―Repowering of old wind farms:

Opportunities and challenges‖ identifies the potential and opportunities available for the

concerned stakeholders to take up the wind re-powering projects at different windy sites in

the country. The report also discusses several advantages along with the cost estimates of

repowering projects and different policy initiatives that are required to accelerate the

repowering activities in India. MICRO-SITING Guidelines of C-WET explains Micro-siting

techniques and procedures, level and complex terrain sites and micro-siting rules. It is the art

of developing wind machines in a most optimal manner for achieving best wind farm

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capacity. A number of wind turbines are installed in arrays and spacing between these arrays

is generally 5Dx7D. (D is the rotor diameter). The factor by which the output of a wind farm

would be less than what we should ideally get is known as ―array efficiency‖. Array

efficiency is not affected in case of strong wind conditions, but is strongly affected in the case

of low wind conditions.

1.4 ORGANIZATION OF THE PROJECT

The project is organized as follows:

Chapter 2 Provides the concept of re-powering, need for repowering, methodology to assess

repowering potential and advantages of wind repowering.

Chapter 3 Describes about Wind Atlas, Analysis and Application Program (WAsP).

Chapter 4 Deals with the wind resource assessment methodology micro survey & micro

siting.

Chapter 5 Deals with calculation of existing generation using WAsP and comparison between

actual generation and WAsP output.

Chapter 6 Deals with the estimation of new installed capacity and generation after

repowering.

Chapter 7 Provides the details of CO2 reduction for the repowered wind farm.

Chapter 8 Review the entire works done in the course of the project.

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CHAPTER 2

RE-POWERING OF WIND FARMS

2.1 INTRODUCTION

This chapter throws light on the concept of Re-powering. Repowering refers to the

refurbishment of older wind turbines, or to their removal and replacement with newer, more

efficient turbines. Where older turbines have been removed and replaced with newer turbines,

these have generally been accomplished by installing fewer, larger turbines.

2.2 NEED FOR REPOWERING

In countries that started early with wind energy, old wind turbines were placed at

locations where the wind is often very good. Since the best locations for wind in these

countries are occupied by old wind turbines with lower energy output compared with new

wind turbines, programs are started to replace the old turbines with new ones. With

repowering, the first-generation wind turbines can be replaced with modern multi megawatt

wind turbines. In general, many factors speak in favour of repowering programs:

More wind power from the same area of land: wind power generation is multiplied

without the need for utilizing additional land.

Fewer wind turbines: the number of turbines can be reduced while enhancing the

natural landscape. The construction height can be raised.

Higher efficiency, lower costs: modern turbines make better use of available wind

energy. The cost of production is significantly lowered.

Better appearance: modern turbines rotate at much lower speeds and are thus more

visually pleasing than older, faster-rotating turbines.

Better power grid integration: modern turbines offer much better grid integration,

since they use a connection method similar to conventional power plants and also

achieve a higher utilization degree.

Wind speed and direction are known: at an existing wind turbine location wind speed

and direction are already known, so it is easy to calculate the expected annual energy

production for an existing location.

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Additionally, it is often easier to get licenses to build a wind turbine (farm) on an existing

location than on a new location. But also for government and local authorities, the results of

repowering can be positive:

Additional wind energy power will create a larger basis for wind energy;

Although the wind turbines are higher after repowering, the quality of the landscape is

often perceived as being improved, since the number of wind turbines is reduced;

Replacement can be used to achieve national (or local) targets for renewable energy

or for CO2 reduction.

But there are also practical reasons for repowering; for example, in situations in which

the manufacturer of the wind turbine no longer exists, and no other company can carry out

the refurbishment of the wind turbine.

2.3 ADVANTAGES OF WIND REPOWERING

Wind energy plants typically have a life span of approximately 20 Years. However,

the rapid development of technology in the last two Years has made it economically

justifiable to replace the older low capacity turbine by more efficient and larger turbine even

before expiration of the technical life span.

2.3.1 TECHNICAL ADVANTAGE

Repowering is the replacement of first –generation small capacity turbines of less than

500kW rating usually operating for more than 15 Years with the modern high capacity and

more sophisticated wind-turbines .This results in the efficient utilisation of potential wind

sites and producing high quantum of energy. In addition, the modern WTGs come with much

higher efficiency, which improves the total Capacity Utilization Factor (CUF) significantly

for the wind farm. The CUF for the old turbine was around 15-20 percentage, which would

get doubled post repowering mainly because of improved design, taller tower and higher

rotor diameter.

2.3.2 OPERATIONAL ADVANTAGE

The re-powering of wind turbine results in the reduction of operation and maintenance

(O&M) cost of the farm as the number of turbines operating in the farm reduces by more than

half. Presently older turbines are fitted with critical and outdated component which causes

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high failure rate and increased Mean Time between Failures (MTBF), lapses in O&M and

increased machine down time and which in-turn reduces the total energy production.

Additionally, for aging WTGs, wear and tear due to longer operating hours also increases

O&M costs. In comparison, repowering would deploy more advanced and state-of-the-art

technology wind turbines, which requires less maintenance and incur very low O&M cost as

compared to the previously installed low rating small turbines. Modern wind turbines are

fitted with modern power electronics converters which use similar connection method as used

in conventional power plant. This offers much better integration of the wind farm with the

grid, which results achieving a higher degree of utilization.

2.3.3 FINANCIAL ADVANTAGES

Repowering results in more wind turbine capacity addition per unit of land area,

which also increases total kWh of electricity produced because of the improved CUF.

Further, wind speed and direction known for longer duration and a particular site makes it

easy to estimate the expected annual energy production from the modern high capacity wind

turbines. This helps in maximizing the revenue from the project, thus achieving better wind

power economics. A prominent barrier faced by the wind power developers today is the

availability of sites with sufficient wind velocity and its acquisition thereafter. Repowering of

old turbines with larger turbines would result in significant reduction in land area/MW of

wind farm. Further, increased electricity output post re-powering presents an opportunity for

the states to achieve the Renewable Purchase Obligations (RPO) targets and national targets

as set in the National Action Plan on Climate Change (NAPCC).It also offers prospects to the

developers to generate and sell Renewable Energy Certificates (RECs), thus improving the

return on investment and reducing the payback period. In addition to the clean development

mechanism (CDM) benefits can be maximised by reducing more greenhouse gas (GHG)

emissions from the project. An additional foreign exchange can be generated from the project

through the sale of certified emission reductions (CERs).

2.3.4 SOCIAL AND ENVIRONMENT ADVANTAGE

Repowering offers many social and environmental advantages over the old turbines.

The modern turbines rotate at much lower speed and have much quite operation than the

typical first and second generation design. A reduced density of wind turbines and their

reduced speed would not only increase the visual appeal of the farm but would also ring

8

down the number of collision of birds and addresses the avian mortality issue to a great

extent. The quality of the landscape also improves as the number of turbines are much less

per unit area, which results in maximizing the benefits from ancillary land uses, such as

access roads ,intercropping and transmission lines, right-of-ways etc., Presently, majority of

the onshore wind power projects are located far from the public view and away from the

residential locations. Repowering would enable the wind industry to rehabilitate to sites with

modern, more aesthetically pleasing designs and less dense arrays causing less noise

pollution .This would increase the visibility of wind plants and improve the public acceptance

for the same.

2.4 METHODOLOGY TO ASSESS REPOWERING POTENTIAL

To assess financial impacts and implications of re-powering wind power project, wind

energy generation estimates are required. Establishing a methodology for calculation or

repowering serves a two-fold purpose. It gives new wind power potential capacity and

estimates of energy generation. To calculate the re-powering potential of any site or wind

power project, the following important technical aspects need to be considered.

1. Wind resource at the site.

2. Existing installed capacity (MW), rating of turbines.

3. New technology selection (higher capacity turbine specifications).

4. Available land area and necessary set-off.

5. Estimation of new installed capacity after re-powering (with different micrositing or

turbine-spacing criteria and turbine-rating selection, the estimation of new capacity

will vary).

6. Estimation of gross and net energy generation (with different micrositing criteria).

7. Energy-yield ratio (ratio of new generation to old generation from same land area or

same project location). Re-powering ratio (ratio of new wind power project capacity

to old project capacity).

2.5 SUMMARY

The concept of re-powering of wind farms, its methodology and its advantages are discussed

briefly.

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CHAPTER 3

WIND ATLAS, ANALYSIS AND APPLICATION PROGRAM (WAsP)

3.1 INTRODUCTION

WAsP was developed and distributed by the Wind Energy Department at Risǿ DTU,

Denmark. It is a PC program for predicting wind climate, wind resources and power

production from wind turbine and wind farm- includes complex terrain flow model,

roughness change model, and model for sheltering obstacles. Its predictions are based on

wind data measured at 10 minute/hour intervals at stations in the same region for a Year, and

site details such as contour map, turbine location, turbine characteristics, etc.

3.1.2 WHAT IS WASP?

WAsP is a PC-program for the vertical and horizontal extrapolation of wind climate

statistics. It contains several models to describe the wind flow over different terrains and

close to sheltering obstacles. WAsP consists of five main calculation blocks:

Analysis of raw data:

This option enables an analysis of any time-series of wind measurements to provide a

statistical summary of the observed, site-specific wind climate. This part is implemented in a

separate tool, the Observed Wind Climate (OWC) Wizard.

Generation of wind atlas data:

Analysed wind data can be converted into a regional wind climate or wind atlas data set.

In a wind atlas data set the wind observations have been 'cleaned' with respect to site-specific

conditions. The wind atlas data sets are site independent and the wind distributions have been

reduced to some standard conditions.

Wind climate estimation:

Using a wind atlas data set calculated by WAsP or one obtained from another source –

e.g. the European Wind Atlas – the program can estimate the wind climate at any specific

point by performing the inverse calculation as is used to generate a wind atlas. By introducing

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descriptions of the terrain around the predicted site, the models can predict the actual,

expected wind climate at this site.

Estimation of wind power potential:

The total energy content of the mean wind is calculated by WAsP. Furthermore, an

estimate of the actual, annual mean energy production of a wind turbine can be obtained by

providing WAsP with the power curve of the wind turbine in question.

Calculation of Wind Farm Production:

Given the thrust coefficient curve of the wind turbine and the wind farm layout,

WAsP can finally estimate the wake losses for each turbine in a farm and thereby the net

annual energy production of each wind turbine and of the entire farm, i.e. the gross

production minus the wake losses. The program thus contains analysis and application parts,

which may be summarised as follows:

Analysis

1. Time-series of wind speed and direction —> observed wind climate (OWC).

2. Observed wind climate + met. station site description —> regional wind climate

(RWC, wind atlas data sets)

Application

1. Regional wind climate + turbine site description —> predicted wind climate (PWC).

2. Predicted wind climate + power curve —> annual energy production (AEP) of wind

turbine

Wind farm production

1. Predicted wind climates + WTG characteristics —> gross AEP of wind farm

2. Predicted wind climates + WTG characteristics + wind farm layout —> wind farm

wake losses

3. Gross annual energy productions + wake losses —> net AEP of wind farm.

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3.2 WIND POWER PRODUCTION CALCULATION

We need to equip with the following to predict the wind power production of a wind

farm:

• A contour map of the area

• The wind data from the airport

• A simple description of the land use in the area

• An annotated sketch of the buildings near the met. Station

• A description of the power-generating characteristics of the turbine

These data have been converted into digital files, as follows:

• A digital map of elevations and roughness

• A data file containing wind data

• A data file describing the buildings at the site

• A data file containing a power production curve for the turbine

3.2.1 METHODOLOGY

From engineering data, we know how much power will be generated by the turbine at

a given wind speed. If the plan was to erect the turbine at exactly the same place where the

meteorological data had been collected, then it would be a really simple task to work out how

much power to expect.

However, just from looking at the map if the proposed turbine site is completely

different from the meteorological station: the properties of the meteorological station itself

will affect the wind data recorded there. In addition, the properties of the turbine site will

have an effect on the way that the wind behaves near the turbine. It is also unlikely that the

hub height of the turbine would be the same as the height of the anemometer. What we need

is a way to take the wind climate recorded at the meteorological station, and use it to predict

the wind climate at the turbine site. That is what WAsP does. Using WAsP, we can analyse

the recorded wind data, correcting for the recording site effects to produce a site-independent

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characterization of the local wind climate. This site independent characterization of the local

wind climate is called a wind atlas data set or regional wind climate. We can also use WAsP

to apply site effects to wind atlas data to produce a site-specific interpretation of the local

wind climate.

3.2.1.1 Calculating the wind atlas

Setting up a met. Station

To Setting up a met. Station WAsP requires the following

A description of the data-recording site

A summary of the wind data recorded at the site

Adding Wind Observations

Now we need to insert some wind data to the hierarchy. Select the met. Station and insert

an Observed wind climate describing the site. Now WAsP needs to know about the site where

the data were collected at the met. Station site, if buildings and shelterbelts of trees were

found in the vicinity of the anemometer mast WAsP needs to know about these.

The Atlas Calculation

WAsP is now ready to calculate the wind atlas for WAsPdale. Now get WAsP to generate

the wind atlas. In a wind atlas data set the wind observations have been 'cleaned' with respect

to the site specific conditions. The wind atlas data sets are site-independent and the wind

distributions have been reduced to some standard conditions; i.e. four standard roughness

classes and five standard heights above ground level.

3.2.1.2 Estimating Wind Power

Setting Up a Turbine Site

Now the project contains a wind atlas with site-independent wind climate data, we can

apply those data to the proposed turbine site. WAsP will adjust the data for the situation

found at the turbine site, and will produce a prediction of the wind climate for the site itself.

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WAsP now requires:

The location of the site in the map

A description of the type of wind turbine that you propose to use.

If there are no obstacles near the hilltop, so there is no need to add an obstacle list to this site.

Locating the Turbine Site

First, locate the turbine site in the map. Because the map and the turbine site are in the

same project, WAsP automatically knows that the site lies in the area covered by the map. All

that we need to do is provide the co-ordinates.

Assigning the Power Curve

In order to predict how much power will be produced by the turbine, WAsP needs to

know the power production characteristics of the turbine. We provide this information to

WAsP by associating a wind turbine generator hierarchy member with the turbine site.

Predicting Wind Climate and AEP

WAsP is now ready to predict the wind climate at the turbine site. We can now open

the turbine site window to view the results. WAsP will estimate that about GWh per Year

would be generated by erecting a turbine on the hilltop. This number is referred to as the

Annual Energy Production (AEP).

3.2.1.3 Estimating wind farm production

Setting up a wind farm

WAsP now requires

The locations of wind farm turbine sites in the map

A description of the type of wind turbine that you propose to use

There are still no obstacles near the hilltop, so there is no need to add an obstacle list to this

wind farm.

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Locating the turbine sites

First, locate the turbine site in the map. Because the map and the turbine site are in the same

project, WAsP automatically knows that the site lies in the area covered by the map. All that

we need to do is provide the co-ordinates.

Assigning wind turbine generators

In order to predict how much power will be produced by the wind farm, WAsP needs to

know the power production and thrust curve characteristics of each turbine. If the turbines in

your farm are all of the same type, you provide this information to WAsP by associating a

wind turbine generator hierarchy member with the wind farm. If one or more turbines in a

farm are different from the rest, we must provide a separate wind turbine generator hierarchy

member for this or these turbines.

Predicting wind farm production

WAsP is now ready to predict the power production of the wind farm.

3.3 SUMMARY

The concept and the working of WAsP are discussed in detail along with the procedure for

the calculation of wind power production.

15

CHAPTER 4

MICROSITING OF WIND FARMS

4.1 INTRODUCTION

This section describes the wind resource assessment methodology, micro-survey &

micro-siting and location of the site selected to carry out the study on repowering and the

wind resource at the site, the terrain description, orographic variations and orographic

elements and the turbine characteristics of the existing wind turbines and the turbines chosen

for repowering.

4.2 WIND RESOURCE ASSESSMENT METHODOLOGY

Understanding the characteristics of the wind resource is critical to all aspects of wind

energy utilization right from identification of suitable sites to economic viability of projects,

design of turbines, etc. The presence or absence of certain essential factors will decide

whether or not a particular site can become a potential wind farm. Thus, wind resource

assessment is the first step in designing any wind power project. This activity includes the

estimation and review of the existing wind resource data, nature of terrain, vegetation cover,

accessibility and other features in the region of interest. The quality of a wind resource

assessment program depends on sound siting, measurement techniques, quality equipment

and data analysis techniques. The various steps involved in wind resource assessment process

are,

Large area screening & Field visit

Validation (Data collection & Screening)

Micro siting

Estimation of the wind resource ranges from overall estimates of the mean energy

content of the wind over a large area called Regional assessment to the prediction of the

average Yearly energy production of a specific wind turbine or wind farm at a specific

location called Siting. If there is no on-site data available, modeling is commonly used to

translate long-term reference station data to the site. Statistical dynamical downscaling

method is one of the methods used to model the potential of a remote location from a bigger

picture. Modeling can be accurate in many cases, but should not replace on-site

measurements for more formal wind farm energy assessment. It is also possible to make

16

predictions of wind speeds at a site using numerical wind atlas methodology. Measure-

Correlate-Predict – is the method that involves comparing the wind speeds on the site with

the wind speeds at the reference station and using the comparison to estimate the long-term

wind speed on the site.

4.3 MICROSURVEY & MICROSITING (OPTIMIZATION)

In a wind farm, turbines will typically be placed in rows perpendicular to the

prevailing wind direction. Due to wake losses, wind shear, turbulence in wind and turbulence

added by the turbines power generation from the turbines will reduce. Because If the wind

striking a second row turbine before the wind speed has been restored from striking the first

row turbine, then the energy production from the second row turbine will be reduced compare

to the normal production. So proper distance should be maintained between turbines. If the

space between the turbines is more, then each turbine will produce maximum power, but less

number of turbines can only be installed and this could make the project activity

uneconomical. So an optimum layout is required with optimum number of turbines and

optimum amount of generation. This can be done with the help of micrositing.

Micrositing can provide high quality estimate over the wind farm area so that each

turbine can be placed for optimal energy yield. Energy estimates must be adjusted to reflect

long term yield of the wind farm, generally for 20 Years. The micro siting process involves

conducting surveys, monitoring and flow modeling at individual sites to quantify small scale

variations in the wind resource over the area. Modeling requires three types of inputs

essentially and they are,

Topographical inputs (site characteristics)

Climatological inputs ( wind characteristics)

Wind turbine generator characteristics

4.4 SUMMARY

The wind resource assessment methodology and micro-survey & micro-siting were discussed

in this section.

17

CHAPTER 5

CALCULATION OF EXISTING GENERATION USING WAsP

5.1 INTRODUCTION

This section outlines the input files calculated for WAsP and the calculation of

Existing generation. Also the calculated the existing generation and actual generation were

compared and conclusions were arrived.

5.2 EXISTING INSTALLED CAPACITY AND RATING OF TURBINES

Existing farm consist of 39 turbines of total capacity 11.58MW of which 36 turbines

ranging from 200kw to 500kw commissioned during the Year 1990-95 and three turbines of

1250kw was commissioned recently.

MAKE CAPACITY NOs

VESTAS RRB

225kw 9

500kw 9

SUZLON

350kw 5

1250kw 3

BONUS 300kw 4

PIONEER WINCON 250kw 6

ENERCON 200kw 3

Table 5.1 Existing Installed Capacity and Rating of Turbines

5.3 WAsP OUTPUT - EXISTING WIND FARM GENERATION

The created WAsP inputs are given to WAsP and the annual energy output of the

wind farm are estimated. WAsP will give average wind speed of each machine and the Gross

output and Net output of each machine. Then the Final output also calculated by considering

the following parameters

18

5.3.1 Existing Wind Farm Layout

Fig.5.1 Existing Wind Farm Layout.

5.3.2 Google Synchronised 3D Image

Fig. 5.2 Google Synchronised 3D Image.

19

5.3.4 AEP Calculation

Fig. 5.3 AEP Calculation.

AEP from WAsP⇒28.679GWh

5.3.5 Energy Losses Due To Wake

Fig. 5.4 Energy Losses Due To Wake

20

Then the Actual output is calculated by assuming the following parameters

Machine availability 95 %

Grid availability 90%

Transmission Efficiency 95%

WAsP prediction error 5%

Then the total generation of the wind farm and the plant load factor is calculated.

Existing Capacity ⇒11.58MW

Theoretical AEP ⇒ Farm Capacity×8760hrs

⇒101.4298GWh

AEP from WAsP ⇒28.679GWh

Actual Generation ⇒AEP from WAsP ×0.95×0.95×0.95×0.90

⇒22.130GWh

Plant Load Factor ⇒0.218

5.3.6 Observation from the results

The actual generation of the wind farm is very low when compare to the WAsP

predicted output

One of the reason behind is, the efficiency of the machines is reducing due to the

ageing of the machines

The above can be eliminated by repowering with high capacity (megawatt) machines

by accurate micrositing

5.4 SUMMARY

Using WAsP the generation of the existing wind farm was calculated. For the calculation

input files were created using the data collected during the field visit. Finally the existing

generation was calculated and compared with the actual generation.

21

CHAPTER 6

ESTIMATION OF NEW INSTALLED CAPACITY AND GENERATION

AFTER REPOWERING

6.1 INTRODUCTION

Using WAsP the annual energy output of the wind farm after the repowering was

predicted. Further, the energy yield ratio and repowering ratio of this repowering project also

calculated.

6.2 INPUTS REQUIRED FOR WAsP

6.2.1 NUMERICAL WIND ATLAS

CWET in association with Riso DTU, Denmark has developed Numerical wind atlas

of India. Numerical wind atlas methodologies have been devised to solve the issue of

insufficient wind measurements. One such methodology is the so-called KAMM/WAsP

method developed at Risø National Laboratory, Denmark.

In this methodology an approach called statistical-dynamical downscaling is used

(Frey-Buness et al, 1995). The basis for the method is that there is a robust relationship

between meteorological situations at the large scale and meteorological situations at the small

scale.

Karlsruhe Atmospheric Mesoscale Model (KAMM) is used to model the mesoscale

effects on the wind flow over India using modeling domains. KAMM calculates the

mesoscale wind field using as input a description of the synoptic-scale climatology, as well as

suitable orography and roughness maps. The climatology of the post-processed simulated

wind fields and the local orography and roughnesses are subsequently used by WAsP (Wind

Atlas Analysis and Application Programme) to predict the local wind climate.

Creating a numerical wind atlas demands a large computational effort, and this

computation effort increases with the size of the region to be mapped. India‘s very large size

means that it is not possible to perform the numerical wind atlas calculations at sufficient

resolution for the whole country using a single modeling domain. Therefore it was decided to

split the numerical wind atlas effort into twelve calculation domains (See Fig.6.1).

22

Fig. 6.1 Numerical Wind Atlas

Figure.6.1 Map of India showing the 12 modeling domains used. A complete

numerical wind atlas calculation is made for each domain.

The .lib files with 5 km resolution generated by KAMM have been verified with the

.lib file generated by WAsP with reference to the actual measurements at very limited

location. Summary of the verification for wind speed for each modelling domain at 10/20m

agl. Is given in table 6.1. (For more details please refer Indian Wind Atlas book published by

CWET, Chennai)

The output of KAMM wind atlas file (.lib file) can be used as an input file of WAsP

for the further analysis after the validation of results with nearby sites. Figure 6.2 gives an

example of wind atlas file (.lib). This can also be referred to get an idea of wind

characteristics over the given area at different height levels with reference to the roughness.

23

Table 6.1 Summary of the Verification for Wind Speed for Each Modeling Domain

.

Domain Nos. of stations

used for verification

Mean abs. error of wind

speed at 10/20m (%)

ISA 5 10.77

ISB 5 13.32

ICA 5 12.45

ICB 4 7.68

ITE 4 10.17

ITF 1 6.64

ITB 3 17.05

ITC 4 18.7

ITD 4 33.69

INU 5 51.30

IIW 1 6.92

IIE 2 30.95

24

Fig. 6.2 Wind atlas for edayarpalayam (long. 77.150E lat. 10.950N) obtained from

CWET.

6.2.2 VECTOR MAP

Vector maps are used to describe the elevation (orography) and land cover (surface

roughness) of the area surrounding calculation sites such as meteorological stations, reference

sites, turbine sites or the sites in a resource grid.

WAsP uses vector maps, in which terrain surface elevation is represented by height

contours and roughness lengths by roughness change lines. The map coordinate system must

be Cartesian and the coordinates must be given in meters.

It is not possible to create and edit maps from within the WAsP program itself; this

must be done with the WAsP Map Editor which can be invoked from the Tools menu.

25

Furthermore, there is no direct link between WAsP and the Map Editor – they only

communicate through the map file itself. When a map file has been changed in the Map

Editor, it must be reloaded into WAsP in order to take effect.

Fig. 6.3 Vector Map

6.2.3 TRANSFORMING SRTM DATA TO WAsP MAPS

SRTM coordinates are non-projected (latitude, longitude). Horizontal reference

system (datum) is WGS84 and vertical reference is the EGM96 geoid. Transforming SRTM

data to WAsP elevation maps therefore require the following:

Transformation of geo. Coordinates to a metric system

Transformation of grid point elevations to height contours

26

Transformation of WGS84 to another datum – if need be

The tools required for transforming SRTM data to vector map are:

Surfer

WAsP Map Editor

WAsP Geo projection utility

Step 1: Download data from the SRTM data for the required site

SRTM HGT format is supported by Surfer.

Step 2: Convert the HGT file to GRD format using Surfer

Unzip the downloaded ZIP file

Rename the HGT file to DEM

Create HDR and STX files (with the same file name)

Insert upper left corner coordinates (signed) in the HDR file

Start Surfer and choose Grid | Convert…

Open the *.HDR file

Save grid as *.GRD file (with the same file name)

27

Fig.6.4 Creating Vector Map in Surfer

The result is a Surfer GRD file in geographical coordinates (WGS84). Inspect the grid

for voids (undefined values) and spikes and wells using Surfer. Remove spikes and wells by

inserting a sensible elevation value using the Surfer grid editor.

Step 3: Change the coordinate system to UTM

1. First, convert the grid file to a list-of-points file in Surfer:

2. Choose Grid | Convert…

3. Open GRD file and save as ASCII XYZ (*.dat)

Now, you can use the Geo-Projection Transformer utility program to make this

transformation, using File | Transform XYZ-file.

28

Fig. 6.5 Change the Coordinate System to UTM

The result in both cases should be an ASCII XYZ file in metric map coordinates

(WGS84).

Step 4: Make a metric GRD file

In Surfer, choose Grid | Data…

Open the XYZ file as ‗Golden Software Data‘

1. Choose ‗Skip leading spaces‘ and ‗Treat consecutive delimiters as one‘

2. Choose a name for ‗Output Grid File‘

3. Invoke Filter data... if you want exclude e.g. certain high z-values in the data

4. Set values for ‗Grid Line Geometry‘, i.e. grid size and extents of modelling domain

5. The result is a Surfer GRD file in metric map coordinates (WGS84) covering the

modelling domain. Surfer has made a complete grid without voids by interpolation

(e.g. Kriging).

Step 5: Make a contour map in DXF format

1. Create a new contour map in Surfer, using the GRD file as input

2. Choose the appropriate contour levels in the Properties | Levels window

29

3. Export the height contours to a 3-D AutoCAD DXF file from the Map | Contour

map... menu

Fig. 6.6 Making Contour Map in DXF format

Step 6: Make a WAsP contour map

1. Open the DXF file in the WAsP Map Editor

2. Add and Replace... to merge several maps

3. Check the map contours for spikes and wells

4. Transform to any other datum, if need be

5. Compare to a scanned background map

6. Check vertical datum‘s and compare elevations

7. Add spot heights and other details close to the site(s)

30

8. Add roughness change lines – including the coastline, if any

9. Save the map as WAsP ASCII map file (*.map)

Fig. 6.7 Making WAsP Contour Map by Map Editor

The result is a WAsP ASCII map that can used for WAsP analysis and/or application

Fig. 6.8 WAsP ASCII Map

31

6.2.4 WIND TURBINE GENERATOR FILE

Wind turbine generators are used to describe wind turbines. They can be associated

with (be a child of) turbine sites, turbine site groups, wind farms and resource grids. If a wind

turbine generator is inserted at the project level, it will be used for all sites and grids in the

project. WAsP can also read wind turbine data in the standard WAsP *.pow format.

Wind turbine generators contain information about how turbines transform wind

energy into electrical power, and the hub height usual for the turbine when deployed. The

wind turbine generator file also contains the rotor diameter, values of the thrust coefficient,

Ct, and some general information relating to the wind turbine generator. Wind turbine

generator files can contain several performance tables, each relating to a specific air density

or noise level.

32

The power curve and other turbine characteristics of the turbine used are given below:

6.3 EXISTING WIND TURBINES

6.3.1 VESTAS ‘V39’ 500kW

Fig. 6.9 Power Curve for VESTAS ‗V39‘ 500kW

33

6.3.2 VESTAS ‘V27’ 225kW

Fig. 6.10 Power Curve for VESTAS ‗V27‘ 225kW

34

6.3.3 SUZLON ‘S33’ 350kW

Fig. 6.11 Power Curve for SUZLON ‗S33‘ 350kW

35

6.3.4 Pioneer Wincon 250kW

Fig. 6.12 Power Curve for Pioneer Wincon 250kW

36

6.3.5 Enercon ‘E30’ 200kW

Fig. 6.13 Power Curve for Enercon ‗E30‘ 200kW

37

6.3.6 Bonus 300kW

Fig. 6.14 Power Curve for BONUS 300kW.

38

6.3.7 SUZLON ‘S64’ 1250kW

Fig. 6.15 Power Curve for SUZLON ‗S64‘ 1250kW

6.4 NEW TECHNOLOGY SELECTION FOR REPOWERING

For Repowering three different types of wind turbines were selected namely

‗GAMESA G90‘, ‗GAMESA G114 and ‗SUZLON S88. GAMESA G90 is doubly fed

induction generator with rotor diameter of 90m and GAMESA G114 is also a doubly fed

induction generator with rotor diameter of 114m.Both have a rated capacity of 2000kW.And

SUZLON S88 is an asynchronous generator with rotor diameter of 88m rated capacity of

2100kW The wind turbine specifications and the power curve and thrust curves are shown

below

39

6.4.1 GAMESA ‘G90’ 2.0MW

Fig. 6.16 Power Curve for GAMESA ‗G90‘ 2.0MW

40

6.4.2 SUZLON ‘S88’ 2.1MW

Fig. 6.17 Power Curve for SUZLON ‗S88‘ 2.1MW

41

6.4.3 GAMESA ‘G114’ 2.0 MW

Fig. 6.18 Power Curve for GAMESA ‗G114‘ 2.0 MW

6.5 AEP CALCULATION OF REPOWERED WIND FARM

Repowering is carried out for 36 turbines on existing wind farm with capacity from

200kw to 500kw. Annual energy production is calculated for different turbine models and

different spacing options. Then the energy yield ratio and repowering ratio are estimated. The

various turbines model are selected for repowering are listed below

SUZLON S88-2.1MW

GAMESA G90-2MW

GAMESA G114-2MW

42

6.5.1 MICROSITING FOR REPOWERING

6.5.1.1 Configuration I:

This is the art of developing wind machines in a most optimal manner for achieving best

wind farm capacity

In this set up 15 turbines are used.

The 15 turbines are installed in arrays.

Spacing in these arrays are generally 5Dx7D. (D is the rotor diameter)

The factor by which the output of a wind farm would be less than what we should

ideally get is known as ―array efficiency‖

Array efficiency is not affected in case of strong wind conditions, but is strongly

affected in the case of low wind conditions.

In the same wind farm site and in the same land area new selected wind turbine were

placed with 5D×7D spacing and the images are shown below

Layout for configuration I:

Fig. 6.19 Layout for Configuration I

43

Vector Map for Configuration I

Fig. 6.20 Vector Map

6.5.1.2 Configuration II

In this set up 9 turbines are used.

In the wind rose shown in figure 6.2, 9th

sector is having more wind speed for most of

the times in a Year. The 9 turbines are placed in a manner according to the wind

direction in order to reduce wake losses as shown in the figure 6.21.

44

Vector Map with Turbine Spacing for Configuration II

Fig. 6.21 Vector Map

6.6 CALCULATION OF AEP FROM WAsP FOR CONFIGURATION I

6.6.1 FOR GAMESA G90

Fig. 6.22 AEP for GAMESA G90

45

Wake Losses

Fig. 6.23 Wake Losses for GAMESA G90

Farm Capacity ⇒ 33.75MW

Theoretical AEP ⇒ 295.65GWh

AEP Using WAsP ⇒ 102.556GWh

Actual Generation ⇒ 79.136GWh

Plant Load Factor ⇒ 0.268

Repowering ratio ⇒ Capacity of the existing farm : Capacity of the repowered wind farm

⇒ 1:2.915

Energy yield ratio ⇒ Energy from the existing farm : Energy from the repowered farm

⇒ 1:3.576

46

6.6.2 FOR SUZLON S88:

Fig. 6.24 AEP for SUZLON S88.

Wake Losses

Fig. 6.25 Wake Losses for SUZLON S88.

47






Repowering ratio ⇒ Capacity of the existing farm: Capacity of the repowered wind farm

⇒ 1:3.044


⇒ 1:3.141

6.6.3 FOR GAMESA G114:

Fig. 6.26 AEP for GAMESA G114.

48

Wake Losses







Repowering ratio ⇒ Capacity of the existing farm: Capacity of the repowered wind farm

⇒ 1:2.915


⇒ 1:5.655

49

6.7 CALCULATION OF AEP FROM WAsP FOR CONFIGURATION II

6.7.1 FOR GAMESA G90:


Wake Losses


50







⇒ 1:1.878


⇒ 1:2.344

6.7.2 FOR SUZLON S88:

Fig. 6.30 AEP for SUZLON S88

51

Wake Losses

Fig. 6.31 Wake Losses for SUZLON S88







⇒ 1:1.956


⇒ 1:2.219

52

6.7.3 FOR GAMESA G114


Wake Losses

Fig. 6.33 Wake Losses for GAMESA G114.

53







⇒ 1:1.878


⇒ 1:3.525

54

CHAPTER 7

CO2 REDUCTION FOR THE REPOWERED WIND FARM

7.1 INTRODUCTION

The power sector accounts for around 40% of global CO2 emissions, and it is clear

that we cannot win the fight against climate change without a dramatic shift in the way we

produce and consume electricity. With dramatic increases in global power demand,

renewable energy technologies must be rolled out quickly to provide emissions-free

renewable electricity for industrialised and developing countries alike.

7.2 HOW MUCH CO2 CAN WIND ENERGY SAVE?

Wind energy does not emit any greenhouse gases, and has an extremely good energy

balance. The calculations on just how much CO2 could be saved by wind energy is based on

an assumption for the carbon intensity of the global electricity sector, i.e. the typical amount

of CO2 emitted by producing one kWh of power. Individual countries emissions differ

substantially, but here we use the IEA‘s estimate of 0.950/MWh as an average value for the

carbon dioxide reduction to be obtained from wind generation.

In India, wind energy is expected to generate up to 338 TWh of electricity in 2020,

which would reduce CO2 emissions by 203 tons. Again based on a reduction of 15% from

the business-as-usual scenario by 2020, India could achieve 46-74% of the emissions

reductions required in the energy sector by wind energy only (depending on IEA model).

7.2 CALCULATION OF CO2 REDUCTION FOR CONFIGURATION I

7.2.1 FOR GAMESA G90 FARM

1. Determination of the Fossil Primary Energy Input (PEI) of the Project

⇒0 (only power from the wind turbine is used)

2. Determination of the Direct CO2 Emissions Produced by the Project

⇒0 (the wind turbine produces no CO2 emissions for

electricity production)

3. The PEI Baseline

The efficiency factor in the Farm is assumed with 26.8%.

Thus, the PEI ⇒ 79.136 GWh / Year/26.8%

55

⇒ (79.136 x 100) / 26.8

⇒ 295.28 GWh / Year

4. Calculation of the CO2 Emissions Baseline

Take the electricity production and multiply it with the country´s emission factor in

electricity production (Table A.1):

⇒ 79.136 GWh / Year x 0.000950 tCO2 / kWh

⇒ 79136000 kWh / Year x 0.000950 tCO2 / kWh

⇒ 75179 tCO2 / Year

5. Calculation of the Reduction in PEI and CO2 Emissions

PEI: Baseline (Step 4) – PEI (Step 1)

⇒ 295280000 kWh / Year – 0

⇒ 295280000 kWh

CO2: Baseline (Step 5) – CO2project (Step2)

⇒ 75179 tCO2 – 0

⇒ 75179 tCO2 / Year

7.2.2 FOR SUZLON S88 FARM






3. The PEI Baseline


Thus, the PEI ⇒ 69.504 GWh / Year/ 22.5%

⇒ (69.504 x100) / 22.5

⇒ 308.906 GWh / Year




⇒ 69.504 GWh / Year x 0.000950 tCO2 / kWh

⇒ 69504000 kWh / Year x 0.000950 tCO2 / kWh

⇒ 66028.8 tCO2 / Year

56



⇒ 308906000 kWh / Year – 0

⇒ 308906000 kWh


⇒ 66028.8 tCO2 – 0

⇒ 66028.8 tCO2 / Year







3. The PEI Baseline


Thus, the PEI ⇒ 125.156 GWh / Year / 42.3%

⇒ (125.156 x100) / 42.3

⇒ 295.877 GWh / Year




⇒ 125.156 GWh / Year x 0.000950 tCO2 / kWh

⇒ 125156000 kWh / Year x 0.000950 tCO2 / kWh

⇒ 118898.2 tCO2 / Year



⇒ 295877000 kWh / Year – 0

⇒ 295877000 kWh


⇒ 118898.2 tCO2 – 0

⇒ 118898.2 tCO2 / Year

57

7.3 CALCULATION OF CO2 REDUCTION FOR CONFIGURATION II







3. The PEI Baseline


Thus, the PEI ⇒ 51.875GWh / Year / 27.2%

⇒ (51.875x100) / 27.2

⇒ 190.716 GWh / Year




⇒ 51.875 GWh / Year x 0.000950 tCO2 / kWh

⇒ 51875000kWh / Year x 0.000950 tCO2 / kWh

⇒ 49281.25 tCO2 / Year



⇒ 190716000 kWh / Year – 0

⇒ 190716000 kWh


⇒ 49281.25 tCO2 – 0

⇒ 49281.25 tCO2 / Year

7.3.2 FOR SUZLON S88 FARM






58

3. The PEI Baseline



⇒ (49.098x100) / 24.7

⇒ 198.777 GWh / Year




⇒ 49.098 GWh / Year x 0.000950 tCO2 / kWh

⇒ 49098000 kWh / Year x 0.000950 tCO2 / kWh

⇒ 46643.1 tCO2 / Year



⇒ 198777000 kWh / Year – 0

⇒ 198777000 kWh

CO2: Baseline (Step 5) – CO2 project (Step2)

⇒ 46643.1 tCO2 – 0

⇒ 46643.1 tCO2 / Year







3. The PEI Baseline



⇒ (78.015 x100) / 40.9

⇒ 190.745 GWh / Year




59

⇒ 78.015 GWh / Year x 0.000950 tCO2 / kWh

⇒ 78015000 kWh / Year x 0.000950 tCO2 / kWh

⇒ 74114.25 tCO2 / Year



⇒ 190745000 kWh / Year – 0

⇒ 190745000 kWh

CO2: Baseline (Step 5) – CO2project (Step 2)

⇒ 74114.25 tCO2 – 0

⇒ 74114.25 tCO2 / Year

7.4 SUMMARY

The importance of CO2 reduction and methodology of calculating CO2 reduction for the

wind farm are discussed briefly.

60

CHAPTER 8

CONCLUSION

8.1 INTRODUCTION

The purpose of this chapter is to review the significant results obtained during present

work. In the larger interest of the nation, the repowering activities should be taken up on a

priority basis which would significantly increase the share of renewable energy in the total

energy mix.

8.2 SUMMARY OF THE WORKDONE

This thesis aims at assessing the repowering potential of a wind farm. To carry out the

study an old wind farm located at Edayarpalayam near Pappampatti, Tamilnadu was selected.

The methodology to assess the repowering potential and the repowering potential of India

and the various states are discussed. The Wind Atlas, Analysis and application program

(WAsP) and its features and the methodology to calculate the annual energy productions are

described. The input data for the WAsP were collected and converted into WAsP input files.

Annual energy production of the Edayarpalayam wind farm was calculated and the annual

energy output of the wind farm after repowering also predicted.

The results of the WAsP for Existing generation and the output of the wind farm after

repowering are analysed to understand the significance of repowering to overcome the energy

crisis of the nation. The following are the observations and conclusions from the above study.

REPOWERING

EXISTING Configuration I Configuration II

G90 S88 G114 G90 S88 G114

AEP in GWh

/ Year 79.136 69.504 125.156 51.875 49.098 78.015 23.130

PLF in % 26.8 22.5 42.3 27.2 24.7 40.9 21.8

Repowering

Ratio 1:2.915 1:3.044 1:2.915 1:1.878 1:1.956 1:1.878 -

Energy Yield

Ratio 1:3.576 1:3.141 1:5.655 1:2.344 1:2.219 1:3.525 -

Wake Losses

in % 7.41 9.06 6.22 3.67 4.63 2.99 6.5

61

Table 8.1 Summary of the Work done

1. Configuration I is the most efficient in which G114 and G90 are the two dominant

machines giving more energy yield, however G114 is not entered into the Indian wind

industry, it‘s just preferred to show how the generation varies when the diameter and hub

height increases. Hence G90 is opted in our project.

2. Plant load factor (PLF) is increased from 21.8 % to 26.8 % for GAMESA G90

3. Energy yield ratio is 1:3.576 for GAMESA G90. i.e. Generation of the wind farm is

increased more than 3 times.

4. Repowering ratio for GAMESA G90 is 1:2.915 i.e. Capacity of the wind farm became

triple.

62

ANNEXURE-I

Region/C

ountry

tCO2/

MWh

Region/Co

untry

tCO2/

MWh

Region/

Country

tCO2/

MWh

Region/Cou

ntry

tCO2/M

Wh

OECD

Americas

0.485 Armenia 0.145 Singapor

e

0.523 Marocco 0.690

USA

(average)

0.531 Azerbaijan 0.462 Sri

Lanka

0.425 Mozambiqu

e

0.000

Canada 0.184 Belarus 0.300 Thailand 0.530 Namibiae 0.253

Mexico 0.455 Bosnia-

Herzegovi

na

0.908 Vietnam 0.409 Nigeria 0.396

Chile 0.398 Bulgaria 0.492 Other

Asia

0.274 Senegal 0.594

OECD

Europe

0.341 Croatie 0.337 Middle

East

0.687 South

Africa

0.900

Austria 0.183 Estonia 0.735 Bahrain 0.718 Sudan 0.470

Belgium 0.239 FYR of

Macedoni

a

0.753 Cyprus 0.755 Togo 0.271

Czech

Republic

0.534 Georgia 0.127 Iraq 0.731 Tunisia 0.547

Denmark 0.311 Gibraltar 0.756 Islamic

Rep. Of

Iran

0.609 United Rep.

OfTanzani

0.257

Finland 0.207 Kazakhsta

n

0.485 Israel 0.721 Zambia 0.003

France 0.089 Kyrgyzsta

n

0.087 Jordan 0.586 Zimbabwe 0.619

Germany 0.447 L.atvia 0.160 Kuwait 0.810 Other

Africa

0.489

Greece 0.739 Lithuania 0.116 Lebanon 0.698 America 0.178

Hungary 0.326 Malta 0.904 Oman 0.859 Argentina 0.358

Iceland 0.001 Republico

fMoldova

0.513 Qatar 0.496 Bolivia 0.368

Ireland 0.482 Romania 0.436 Saudi

Arabia

0.740 Brazil 0.075

Italy 0.416 Russia 0.322 Syria 0.649 Colombia 0.136

Luxembo

urg

0.382 Serbia 0.662 United

Arab

Emirates

0.694 Costa Rica 0.058

Netherlan

ds

0.389 Slovenia 0.337 Yemen 0.649 Cuba 0.735

Norway 0.010 Tajikistan 0.031 Africa 0.641 Dominican

Republic

0.633

Poland 0.652 Turkmenis

tan

0.810 Algeria 0.590 Ecuador 0.301

Portugal 0.379 Ukraine 0.373 Angola 0.220 El Salvador 0.304

Slovak

Republic

0.223 Uzbekista

n

0.462 Benine 0.695 Guatemala 0.354

Spain 0.337 Banglades

h

0.575 Botswan

ae

1.916 Haiti 0.513

Sweden 0.041 Brunei

Darussala

m

0.738 Cameroo

n

0.228 Honduras 0.391

Switzerla

nd

0.040 China

(mci. Hong

Kong)

0.765 Congoe 0.139 Jamaica 0.478

Turkey 0.484 Chinese

Taipei

0.647 Côte

dIvoire

0.428 Netherlands

Antilles

0.707

United

Kingdom

0.480 DPR of

Korea

0.483 DR of

Congo

0.003 Nicaragua 0.506

OECD

Asia

0.503 India 0.950 Egypt 0.459 Panama 0.297

Australia 0.862 Indonesia 0.757 Eritrea 0.665 Paraguay 0.000

Japan 0.435 Malaysia 0.638 Ethiopia 0.094 Peru 0.225

Korea 0.471 Myanmar 0.249 Gabon 0.366 Trinidad and

Tobago

0.725

New

Zealand

0.191 Nepal 0.004 Ghana 0.254 Uruguay 0.221

Non-

OECD

0.503 Pakistan 0.447 Kenya 0.321 Venezuela 0.203

Albania 0.023 Philippine

s

0.471 Libya 0.868 Other Latin

America

0.242

63

Table A.1 Electricity Emission Factors (EFel) For Different Countries (tCO2/MWh)2

REPOWERING OF A WIND FARM AT EDAYARPALAYAM

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