Wind Thesis2

A Cost Benefit Analysis

of Wind Power

by

Eleanor Denny B.A., M.B.S.

Thesis submitted for the degree of

Philosophiae Doctorfrom the

School of Electrical, Electronic and Mechanical Engineering

National University of Ireland

University College Dublin, Ireland

Supervisor of Research: Prof. M. O’MalleyCo-Supervisor of Research: Prof. J. Fitz Gerald

Nominating Professor: Prof. M. O’Malley

January 19, 2007

Abstract

Concern over global warming has led policy makers worldwide to accept the im-

portance of reducing greenhouse gas emissions. As wind generation does not itself

create any harmful emissions, policy makers often promote it as a means of reduc-

ing national emission levels while also meeting renewable obligations. In fact, wind

generation has been the fastest growing form of renewable energy in Europe and the

United States in the past decade. Like other forms of generation reliant on underly-

ing weather conditions, wind generation output is variable, i.e. the output of these

units depends upon weather conditions that cannot be controlled by the operator

of the generator. As well as being variable, wind generation also faces a challenge

of being relatively unpredictable. Since the underlying energy source cannot be di-

rectly controlled, the renewable generation is high when conditions are favourable

and low when unfavourable.

The function of power system operators is to supply electricity to customers

in a reliable manner at a sustainable cost. This involves ensuring that the gener-

ation meets the demand at all times and that any short term gaps between load

and generation are bridged quickly and precisely to maintain the integrity of the

power system. Since the output of some renewable generators, in particular wind

generation, cannot be actively controlled and is difficult to predict accurately, this

balancing of generation and load can become more challenging as renewable pene-

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trations increase. Thus, the cost of operating the power system can increase. The

work presented here discusses in detail the costs imposed on the power system by

wind generation and also the benefits than can accrue from added wind generation.

The costs included are the capital and operating costs of wind generation, the deep

network reinforcement costs, the additional reserve costs and the costs associated

with additional cycling of conventional units. The benefits are the capacity benefit

of wind, the emissions savings and the savings in fuel costs.

In order to investigate the costs and benefits of wind generation, it is necessary

to analyse wind in the context of the power system within which it is installed.

Thus, a case study of a real electricity system is conducted and a thorough model

of the power system is developed. This model determines the operating levels of

conventional generators as penetrations of wind generation increase. The resulting

operating schedules are then analysed to determine wind’s impact on the operation

of conventional generators and their resulting cycling costs, emissions and fuel costs.

The costs and benefits are combined to generate net benefit curves which show

the wind penetrations where the costs exceed the benefits, beyond which no further

investment should be made in wind generation. Three test years are examined and

the sensitivity of the net benefits to a large range of assumptions is tested. It is

found that increased interconnection, high CO2 prices and a flexible plant mix are

particularly beneficial for wind generation, and that there are positive net benefits

for wind energy penetrations of 17% and higher under the chosen set of assumptions

in the case study.

A number of applications relating to the case study were conducted in this anal-

ysis and these are also discussed in detail. The impact of carbon prices on the merit

order of generators and their resulting cycling costs is examined. The results show

that by introducing a carbon price, the cycling costs can actually exceed the value

of the saved CO2, and the effect on cycling costs is exacerbated by the addition of

wind generation. An alternative method of operating a power system with wind

generation known as the fuelsaver approach is investigated as are a range of connec-

tion policy options. The impacts of tidal generation on power system operation are

also investigated.

Acknowledgements

I would like to thank everyone who helped and supported me throughout my PhD,

in particular, special thanks goes to the following people.

Professor Mark O’ Malley, whose guidance, support and encouragement has been

invaluable throughout this project. Mark’s commitment to my PhD has been an

inspiration from the very outset and despite having the most demanding of work

schedules, he always makes time for his PhD students. I am extremely grateful for

all he has done for me, without him I would not have completed this PhD project.

I am greatly indebted to him. Thank you.

Professor John Fitz Gerald of the Economic and Social Research Institute whose

advice and guidance on all things economic was invaluable in completing this thesis.

John devoted a lot of time and effort to my project and for this I am truly grateful.

Dr. Laura Malaguzzi Valeri of the Economic and Social Research Institute for her

many useful discussions, interactions and suggestions.

Colleagues in industry, for the provision of vital data, advice and observations,

particularly Michael O’Mahony from ESB Power Generation, Donal Phelan in ESB

Networks, Michael Kelly in EirGrid, Morgan Bazillian in SEI and Dermot O’Kane

from Airtricity.

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Ronan Doherty and Garth Bryans with whom I collaborated on some of the work

presented in this thesis.

All my new postgrad friends especially to Andrew Keane for his help, advice, en-

couragement, and friendship. Also particular thanks goes to Aidan Tuohy for proof

reading this thesis. Thanks all round to the occupants of room 157 over the last

four years, Ciara, Gill, Shane, Rebecca, Ronan D, Alan, Hugh, Garth, Tim, Ronan,

Emma and Daniel for all the moral support, the tea breaks, the stories and for

wholeheartedly welcoming an economist into the world of engineering!

Rose Mary Logue who works tirelessly to look after us postgraduates. Thanks for

all the encouragement over the last four years.

All my friends for putting up with me and supporting my social life for the last

four years! You’ve been great, and at last I might have an answer to the dreaded

question ‘so when will you be finished?’ !

My fantastic family, Michael, Frances, Conor and Clıodhna. Thank you for your

constant support and encouragement, not just over the last four years but in ev-

erything I do. Thanks also to my extended family, in particular Granddad Paddy

who is an inspiration to everyone he meets and Sarah with whom I lived for a most

enjoyable year at the start of my PhD and who can throw a cracking party! I love

you all. Thank you.

And finally Eoin, for the endless encouragement, support, humour, patience and love

which have guided me through the last number of years. Thank you for everything.

Publications Arising from Thesis

Journal Papers:

1. E. Denny, L. Malaguzzi Valeri, J. Fitz Gerald & M. O’Malley, “Carbon Prices

and Asset Degradation - A Costly Combination for Electric Power Systems”,

The Energy Journal (in review), January 2007. Also to be presented at IAEE

International Energy Conference, Wellington, New Zealand, Feb. 2007.

2. E. Denny & M. O’Malley, “Quantifying the Total Net Benefits of Grid Inte-

grated Wind”, IEEE Transactions on Power Systems (in press), Nov 2006.

3. A. Keane, E. Denny, and M. OMalley, Quantifying the Impact of Connection

Policy on Distributed Generation, IEEE Transactions on Energy Conversion,

vol. 22, no. 1, Mar. 2007. Also to be presented at IEEE PES General Meeting,

Tampa, Florida, June 2007.

4. E. Denny & M. O’Malley, “Wind Generation, Power System Operation and

Emissions Reduction”, IEEE Transactions on Power Systems, vol. 21, no. 1,

pp. 341-347, 2006.

5. M. Bazilian, E. Denny, & M. O’Malley, “Challenges of Increased Wind Energy

Penetration in Ireland”, Wind Engineering Journal, vol. 28, no. 1, pp. 43-56,

2003.

6. A. Keane, Q. Zhou, E. Denny, J. Bialek, and M. O’Malley, ”A Novel Curtail-

ment Method for Distributed Generation Voltage Management”, IEEE Trans-

actions on Power Systems, in preparation, 2007.

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Conference Papers:

7. E. Denny, & M. O’Malley, “A Cost Benefit Analysis of Tidal Generation”, in

9th IAEE European Energy Conference (in review), Florence, Italy, June 2007.

8. E. Denny, G. Bryans, J. Fitz Gerald & M. O’Malley, “A Quantitative Analysis

of the Net Benefits of Grid Integrated Wind”, in IEEE Power Engineering

Society General Meeting (Panel Session), Montreal, Quebec, June 2006.

9. E. Denny, & M. O’Malley, “Impact of Wind Generation on Emissions under

Alternative Power System Operation Approaches”, in 40th Universities Power

Engineering Conference, Cork, Ireland, 2005.

10. E. Denny & M. O’Malley, “Impact of Increasing levels of Wind Generation in

Electricity Markets on Emissions Reduction”, in 7th IAEE European Energy

Conference, Bergen, Norway, Aug, 2005.

11. G. Bryans, E. Denny, B. Fox, P. Crossley, & M. O’Malley, “Study of the Effect

of Tidal Generation on the Irish Grid System: Resource and Emissions”, in

CIGRE Symposium on Power Systems with Dispersed Generation, Athens,

Greece, April 2005.

12. R. Doherty, E. Denny, & M. O’Malley, “System Operation with a Significant

Wind Power Penetration”, in IEEE Power Engineering Society General Meet-

ing, Denver, Colorado, USA, June 2004.

Contents

Abstract i

Acknowledgements iii

Publications Arising from Thesis v

List of Figures xii

List of Tables xv

1 Introduction 1

1.1 Wind Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2 The Impacts of Wind Generation . . . . . . . . . . . . . . . . . . . . 6

1.3 Quantifying the Costs & Benefits of Wind . . . . . . . . . . . . . . . 12

1.4 The Scope of this Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 18

1.5 An Outline of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 21

2 The Case Study 23

2.1 Wind Generation in Ireland . . . . . . . . . . . . . . . . . . . . . . . 25

2.2 Ireland’s Electricity Demand . . . . . . . . . . . . . . . . . . . . . . 28

2.3 Ireland’s Electricity Market . . . . . . . . . . . . . . . . . . . . . . . 30

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CONTENTS viii

2.3.1 Dominance in the Irish Electricity Market . . . . . . . . . . . 31

2.4 Ireland’s Current Generation Plant Mix . . . . . . . . . . . . . . . . 33

2.5 The Future Plant Mix of the Irish System . . . . . . . . . . . . . . . 37

2.6 Summary of the Key Features of the Case Study . . . . . . . . . . . 38

3 The Dispatch Model 41

3.1 Optimisation in the Dispatch Model . . . . . . . . . . . . . . . . . . 42

3.1.1 Validating the Dispatch Model . . . . . . . . . . . . . . . . . 44

3.2 Wind in the Dispatch Model . . . . . . . . . . . . . . . . . . . . . . 45

3.2.1 Wind Curtailment . . . . . . . . . . . . . . . . . . . . . . . . 47

3.2.2 Wind Shortfall . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.2.3 Load Factors for the Wind Generation . . . . . . . . . . . . . 48

3.3 The Dispatch Model Representing an Electricity Market . . . . . . . 49

3.4 Specifics of the Dispatch Model for Case Study . . . . . . . . . . . . 51

3.4.1 Reserve Capacity . . . . . . . . . . . . . . . . . . . . . . . . . 51

3.4.2 Hydro and Pumped Storage . . . . . . . . . . . . . . . . . . . 52

3.4.3 Outage Schedules . . . . . . . . . . . . . . . . . . . . . . . . . 52

3.4.4 Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

3.4.5 Fuel Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

3.4.6 Peat Generation . . . . . . . . . . . . . . . . . . . . . . . . . 53

3.4.7 Wind Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.4.8 Network Constraints . . . . . . . . . . . . . . . . . . . . . . . 54

3.5 Summary of Key Features of the Dispatch Model . . . . . . . . . . . 55

4 The Cost of Wind Generation 56

4.1 Wind Development Costs . . . . . . . . . . . . . . . . . . . . . . . . 57

4.2 Network Upgrade Costs . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.3 Additional Reserve Requirement . . . . . . . . . . . . . . . . . . . . 60

4.4 Cycling Conventional Units . . . . . . . . . . . . . . . . . . . . . . . 62

4.4.1 The Cost of Cycling . . . . . . . . . . . . . . . . . . . . . . . 66

4.5 Summary of Costs Associated with Wind Generation . . . . . . . . . 70

CONTENTS ix

5 The Benefits of Wind Generation 72

5.1 Capacity Benefit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

5.2 Emissions in Conventional Generators . . . . . . . . . . . . . . . . . 75

5.2.1 Carbon Dioxide Formation . . . . . . . . . . . . . . . . . . . 75

5.2.2 Sulphur Dioxide Formation . . . . . . . . . . . . . . . . . . . 76

5.2.3 Nitrogen Oxides Formation . . . . . . . . . . . . . . . . . . . 76

5.2.4 Coal, Peat and Heavy Fuel Oil Generators . . . . . . . . . . . 77

5.2.5 Gas Fired Generators . . . . . . . . . . . . . . . . . . . . . . 78

5.2.6 Emissions During Cycling . . . . . . . . . . . . . . . . . . . . 81

5.3 Emissions Benefits of Wind Generation . . . . . . . . . . . . . . . . . 81

5.4 Fuel Savings Benefit . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

5.5 Summary of Benefits of Wind Generation . . . . . . . . . . . . . . . 86

6 The Net Benefits of Wind Generation 88

6.1 The Net Benefits of Wind Generation in the Base Case . . . . . . . . 88

6.2 Sensitivity Analysis of Net Benefits . . . . . . . . . . . . . . . . . . . 91

6.2.1 Wind Load Factor Scenario . . . . . . . . . . . . . . . . . . . 94

6.2.2 Wind Power Forecast Accuracy Scenario . . . . . . . . . . . . 95

6.2.3 Demand Growth Scenario . . . . . . . . . . . . . . . . . . . . 96

6.2.4 Fuel Price Scenario . . . . . . . . . . . . . . . . . . . . . . . . 97

6.2.5 Capital Cost Scenario . . . . . . . . . . . . . . . . . . . . . . 97

6.2.6 Cycling Cost Scenario . . . . . . . . . . . . . . . . . . . . . . 98

6.2.7 Emissions Price Scenario . . . . . . . . . . . . . . . . . . . . . 99

6.2.8 Discount Rate Scenario . . . . . . . . . . . . . . . . . . . . . 99

6.2.9 Worst and Best Case Scenarios . . . . . . . . . . . . . . . . . 100

6.3 Conclusions of Net Benefits . . . . . . . . . . . . . . . . . . . . . . . 101

7 Applications of the Net Benefits Methodology 103

7.1 Carbon Prices and Cycling Costs . . . . . . . . . . . . . . . . . . . . 104

7.1.1 Emissions Prices in the Cycling Analysis . . . . . . . . . . . . 107

7.1.2 Results & Discussion . . . . . . . . . . . . . . . . . . . . . . . 108

CONTENTS x

7.1.3 Summary of Carbon Prices and Cycling Costs . . . . . . . . . 114

7.2 The Fuelsaver Approach to Wind Generation . . . . . . . . . . . . . 115

7.2.1 Analysing the Fuelsaver Approach . . . . . . . . . . . . . . . 116

7.2.2 Fuelsaver Cycling Costs . . . . . . . . . . . . . . . . . . . . . 120

7.2.3 Fuelsaver Emissions Benefits . . . . . . . . . . . . . . . . . . 120

7.2.4 Fuelsaver Fuel Benefits . . . . . . . . . . . . . . . . . . . . . . 122

7.2.5 Net Benefits of Wind under the Fuelsaver Approach . . . . . 123

7.2.6 Conclusions of Fuelsaver Approach . . . . . . . . . . . . . . . 124

7.3 The Impact of Connection Policy on Distributed Generation . . . . . 125

7.3.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

7.3.2 Ireland’s Distribution Network . . . . . . . . . . . . . . . . . 128

7.3.3 The Energy Resources . . . . . . . . . . . . . . . . . . . . . . 130

7.3.4 Optimal Allocations of Distributed Generation . . . . . . . . 131

7.3.5 Load Flow Simulation . . . . . . . . . . . . . . . . . . . . . . 132

7.3.6 Scaling Outputs . . . . . . . . . . . . . . . . . . . . . . . . . 133

7.3.7 Distributed Generation in the Dispatch Model . . . . . . . . 134

7.3.8 The Costs and Benefits of Distributed Generation . . . . . . 135

7.3.9 Net Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

7.3.10 Conclusion of Connection Policy . . . . . . . . . . . . . . . . 138

7.4 Tidal Generation on the Irish System . . . . . . . . . . . . . . . . . . 139

7.4.1 Modelling the Tidal Generation . . . . . . . . . . . . . . . . . 141

7.4.2 Tidal Cycling Costs . . . . . . . . . . . . . . . . . . . . . . . 142

7.4.3 Tidal Emissions Benefits . . . . . . . . . . . . . . . . . . . . . 143

7.4.4 Fuel Savings with Tidal Generation . . . . . . . . . . . . . . 144

7.4.5 The Net Benefits of Tidal Generation . . . . . . . . . . . . . 144

7.4.6 Conclusions of Tidal Generation in Ireland . . . . . . . . . . 147

8 Discussion and Conclusions 149

8.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

8.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

8.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

CONTENTS xi

References 160

A The Characteristics of Generators on the Irish System 181

B The Optimisation of the Distribution Network 187

List of Figures

1.1 Net benefits of wind generation . . . . . . . . . . . . . . . . . . . . . 19

2.1 Existing wind farms in RoI in January 2007 . . . . . . . . . . . . . . 26

2.2 Comparison of electricity consumption in the EU15 . . . . . . . . . . 29

2.3 Typical daily demand patterns in RoI in 2006 . . . . . . . . . . . . . 29

2.4 Installed generation capacity by fuel type in Ireland . . . . . . . . . 33

2.5 The age of the generating units on the Irish system . . . . . . . . . . 34

2.6 Load duration curve with merit order of conventional units . . . . . 34

2.7 Conventional generation availability in Ireland . . . . . . . . . . . . 36

3.1 Generator reserve characteristics . . . . . . . . . . . . . . . . . . . . 44

3.2 Wind power data series . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.3 Assumed load factors with increasing installed wind generation . . . 49

3.4 Illustration of market model . . . . . . . . . . . . . . . . . . . . . . . 50

4.1 Assumed capital cost per MW of installed wind generation . . . . . 58

4.2 Network reinforcement costs with increased wind capacity . . . . . . 60

4.3 System reserve level with different wind power forecast errors . . . . 61

4.4 Damage associated with different cycling activities . . . . . . . . . . 64

4.5 Creep fatigue interaction resulting in premature component failure . 65

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LIST OF FIGURES xiii

4.6 Operating profiles of two Irish units with different wind penetrations 66

4.7 Cycling costs with increasing penetrations of wind generation . . . . 70

5.1 The capacity credit of wind generation in Ireland . . . . . . . . . . . 74

5.2 Typical NOX emissions from a CCGT and an OCGT . . . . . . . . . 80

5.3 Emissions savings with increasing wind generation . . . . . . . . . . 82

5.4 Value of emissions savings with increasing wind generation . . . . . . 84

5.5 Fuel consumption with increasing wind generation . . . . . . . . . . 85

5.6 Value of fuel savings with increasing wind generation . . . . . . . . . 86

6.1 The base case net benefits of wind generation . . . . . . . . . . . . . 90

6.2 The Critical Values of Wind Generation Part I . . . . . . . . . . . . 92

6.3 The Critical Values of Wind Generation Part II . . . . . . . . . . . . 93

6.4 The net benefits of wind with changes in load factor . . . . . . . . . 94

7.1 Merit order with carbon price of e30/ton . . . . . . . . . . . . . . . 108

7.2 Cycling costs and CO2 savings at different carbon prices . . . . . . . 109

7.3 Cycling costs and CO2 savings with wind and e30/ton CO2 . . . . . 111

7.4 Cycling costs with wind generation and carbon prices . . . . . . . . 112

7.5 Carbon savings with wind generation and carbon prices . . . . . . . 113

7.6 Net savings with wind generation and carbon prices . . . . . . . . . 113

7.7 Wind curtailment in 2010 under fuelsaver . . . . . . . . . . . . . . . 118

7.8 Average number of committed units under fuelsaver . . . . . . . . . 118

7.9 Average operating levels of committed units under fuelsaver . . . . . 119

7.10 Cycling costs under the fuelsaver approach . . . . . . . . . . . . . . . 121

7.11 Emissions savings under the fuelsaver approach . . . . . . . . . . . . 121

7.12 Fuel savings under the fuelsaver approach . . . . . . . . . . . . . . . 123

7.13 The net benefits of wind under the fuelsaver approach . . . . . . . . 124

7.14 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

7.15 Future distribution of installed wind power capacity in percent . . . 130

7.16 Output of DG generators on a sample day . . . . . . . . . . . . . . . 133

7.17 The marine current turbines design . . . . . . . . . . . . . . . . . . . 141

LIST OF FIGURES xiv

7.18 The power output during a spring and neap tide . . . . . . . . . . . 142

7.19 Cycling costs with increases in tidal generation . . . . . . . . . . . . 143

7.20 Tidal generation emissions savings . . . . . . . . . . . . . . . . . . . 144

7.21 Tidal generation fuel savings . . . . . . . . . . . . . . . . . . . . . . 145

7.22 The total benefits and cycling costs of tidal generation . . . . . . . . 146

A.1 Generator Information for 2007 . . . . . . . . . . . . . . . . . . . . . 182




A.5 Generator Operating Characteristics . . . . . . . . . . . . . . . . . . 186

List of Tables

2.1 The Irish electricity system (at year end 2005) . . . . . . . . . . . . 24

2.2 Price support caps for wind generation under REFIT . . . . . . . . . 28

3.1 Fuel costs in e 2006/GJ . . . . . . . . . . . . . . . . . . . . . . . . . 53

4.1 Costs associated with increased cycling of units . . . . . . . . . . . . 67

4.2 Cost per single on-off cycle . . . . . . . . . . . . . . . . . . . . . . . 67

4.3 Cycling activity expressed as an equivalent hot start . . . . . . . . . 68

5.1 Typical components of various fuel types . . . . . . . . . . . . . . . . 77

5.2 Typical components of natural gas . . . . . . . . . . . . . . . . . . . 78

5.3 Emissions prices in e per ton . . . . . . . . . . . . . . . . . . . . . . 83

6.1 Base case assumptions summarised . . . . . . . . . . . . . . . . . . . 90

6.2 Worst case and best case assumptions summarised . . . . . . . . . . 100

7.1 Assumed Energy Resource for the Five Network Sections (MW) . . . 131

7.2 Allocations for the five network sections . . . . . . . . . . . . . . . . 131

7.3 Scaled allocations for the whole system . . . . . . . . . . . . . . . . . 134

7.4 Connection costs for wind generation . . . . . . . . . . . . . . . . . . 135

7.5 Additional reserve costs for distributed wind generation . . . . . . . 136

xv

LIST OF TABLES xvi

7.6 Additional cycling costs with wind generation . . . . . . . . . . . . . 136

7.7 Emissions savings (in tons) with wind generation . . . . . . . . . . . 137

7.8 Fuel savings in petajoules with wind generation . . . . . . . . . . . . 137

7.9 Net benefits of wind generation (in em) . . . . . . . . . . . . . . . . 138

7.10 Boundary annual costs . . . . . . . . . . . . . . . . . . . . . . . . . . 147

CHAPTER 1

Introduction

HUMAN activities are changing the composition of the Earth’s atmosphere

through the buildup of greenhouse gases. This has resulted in an increase in

the temperature of the earth’s surface by about 1◦celsius in the past century, with

the 10 warmest years of the 20th century occurring in the 15 years between 1985 and

2000 (US EPA, 2006a). The snow cover in the Northern Hemisphere and floating

ice in the Arctic Ocean have decreased, worldwide rainfall has increased by about

one percent and global sea levels have risen by 4-8 inches over the past century.

The consequences of global warming range from manageable to catastrophic

(Goodstein, 2002). According to the World Health Organisation (2002) approxi-

mately 150,000 people currently die needlessly each year as a direct result of global

warming. Agricultural output will suffer significantly as the land becomes drier and

outputs fall. This situation will be particularly severe in many Third World coun-

tries which lack resources for irrigation. The Hadley Centre (2006) predicts that

by 2010, 300 million people in sub-Saharan Africa will be suffering from chronic

malnutrition and a further 30 million people worldwide will be at risk of hunger

due to climate change by 2050. Massive disruption of natural ecosystems is likely

1

Chapter 1. Introduction 2

to occur as forests die and up to 50 million species could be driven to extinction by

2050 (Roach, 2004). Diseases and pests are also likely to thrive in a warmer climate

(Goodstein, 2002). An increase in sea-level would flood many areas of the world and

as many as 1 billion people could be directly impacted. Low lying countries such as

Bangladesh and the Netherlands and many coastal areas along the eastern coast of

the United States and the Gulf Coast would become uninhabitable (Lovgren, 2004).

From an economic perspective, it has been shown that ignoring climate change will

eventually lead to major disruption to economic and social activity later this cen-

tury, on a scale similar to those associated with World War II and the economic

depression of the first half of the 20th century, and it will be difficult or impossible

to reverse these effects (Stern, 2006).

From being a research topic, global warming has now moved to the political

stage and policy makers worldwide have accepted the importance of reducing green-

house gas emissions (Helm, 2005). As a result, there has been an international

movement in the promotion of policy mechanisms for the reduction of greenhouse

gas emissions. The Kyoto Protocol is an agreement made under the United Nations

Framework Convention on Climate Change (Kyoto, 1992) and countries that ratify

this protocol commit to reducing their emissions of carbon dioxide. Emissions trad-

ing regimes which came into force EU-wide in 2005 (EU ETS, 2003), and globally

in 2008, will aid countries struggling to meet their greenhouse gas obligations un-

der the Kyoto Protocol. In order to reduce European emissions of sulphur dioxide,

nitrogen oxides (NOX), volatile organic compounds and ammonia, the Gothenburg

Protocol indicates national emissions ceilings for each of these compounds for all EU

Member States for 2010. Once the Protocol is fully implemented, it is projected that

Europe’s NOX emissions should be cut by at least 41% and its sulphur emissions by

63% compared to 1990 levels (The Gothenburg Protocol, 1999).

In addition to national emission limits, there are also directives limiting emissions

from individual installations. One such directive is the Large Combustion Plant

Directive (LCPD) which limits NOX, SO2, and dust emissions from combustion

plants over 50MW in size. Under the LCPD, any combustion plant which will not


meet its emissions targets must cease operation in 2010 or run for a limited number

of hours between 2010 and 2015 (EU Directive 2001/80/EC, 2001).

As well as being a major contributor of greenhouse gas and other emissions, the

electricity industry is also highly fossil fuel dependent. This reliance on fossil fuels

leaves the industry highly exposed to price increases as global fossil fuel resources

become more depleted (EIA, 2004). The increasing concentration of the remaining

resources in certain geographical areas may also make fossil fuel prices more volatile.

In a bid to reduce their reliance on fossil fuels, while simultaneously reducing power

system emissions, there has also been a move towards the promotion of clean renew-

able technologies for electricity generation. In an aim to achieve a European wide

target of 22.1% of gross electricity consumption produced from renewable energy

sources by 2010, the EU Directive 2001/77/EC obliges all member states to achieve

a given percentage of their electricity consumption from renewable energy sources.

These targets are based on the current installed capacity in each country, the po-

tential for future renewable generation and their Kyoto Protocol emission targets:-

the renewable energy targets range from between 5.7% for Luxembourg to 78.1% for

Austria (EU Directive 2001/77/EC, 2001).

As wind generation does not itself create any harmful emissions, policy makers

often promote it as a means of reducing a country’s national emission levels while

meeting its renewable obligations. This has ensured that wind energy has become

the new renewable energy of choice for most northern European countries. This is

reflected in figures showing wind energy as the fastest growing form of renewable

energy in Europe (EWEA, 2005). In addition, the United States is currently ex-

periencing the strongest wind capacity growth period ever witnessed (Smith et al.,

2004). As the market for wind energy has grown, the costs per kilowatt hour have

reduced dramatically. Ambitious renewable energy targets together with successes

to date and reducing costs, should ensure that the increased penetration of wind

energy continues in European electricity networks.


1.1 Wind Generation

The majority of renewable energy ultimately comes from the sun. The sun radiates

174,423,000,000,000 kilowatts to the Earth per hour (DWEA, 2006). According to

the BWEA (2006) this equates to more energy per second than all the electricity

used in the UK in a month! About 1 to 2 per cent of the energy coming from the sun

is converted into wind energy (DWEA, 2006). Wind is formed by the movement of

air over the surface of the Earth, from areas of high pressure to low pressure. These

areas of high and low pressure are caused by the heating of the Earth’s surface by

the sun and the rotation of the earth (BWEA, 2006).

Globally air rises from the equator and moves north and south in the upper

atmosphere until it reaches an area of low pressure where it will fall. As the air rises

from the equator there will be a low pressure area close to ground level attracting air

back from the north and south. This determines the prevailing wind in a country,

however, local climatic conditions also have an influence on the most common wind

directions. Land masses are heated by the sun more quickly than the sea in the

daytime. The air rises, flows out to the sea, and creates a low pressure at ground

level which attracts the cool air from the sea. At night, the temperature of the

land drops much faster than the sea and the wind blows in the opposite direction.

In addition, close to the Earth’s surface, wind can be affected by friction from

geographical features such as mountains, hills, forests, cities etc. (DWEA, 2006;

BWEA, 2006).

A wind turbine converts the energy of the wind into a torque acting on the

rotor blades which then drives a generator. The amount of energy which the wind

transfers to the rotor depends on the density of the air, the rotor area, and the wind

speed (Miller et al., 1997). Thus, the kinetic energy in the wind is converted to

mechanical energy which is then converted to electrical energy (Ahmed and Cohen,

1994).

Wind generation, like solar, tidal and wave generation, exhibits ‘variable’ output.

The output of these units depend upon weather conditions that cannot be controlled

by the operator of the generator. This is known as being ‘non-dispatchable’. For


example, the amount of electricity generated by a wind turbine fluctuates as wind

speed changes and that of a photovoltaic array with the intensity of sunlight. Thus,

the control of their output is limited as operators can only reduce the potential

output of such generators (Gross et al., 2006). When significant penetrations of

these forms of generation are connected to an electricity network, it can result in a

requirement to alter the operation of the system to accommodate the variability of

these generators.

As well as being variable, wind generation also faces a challenge of being rela-

tively unpredictable. Since the underlying resource cannot be directly controlled,

the renewable generation is high when conditions are favourable and low when un-

favourable. Thus, forecasts of weather conditions are crucial when examining renew-

able generation sources. Tidal generation is governed by the gravitational forces of

the moon and the sun on the Earth’s oceans and can be predicted almost perfectly

over long time horizons (Bryans, 2006). Wind generation on the other hand, requires

complex forecasting techniques which account for wind speed, wind direction, hub

height, geographical surroundings, wind farm size, turbine dispersion, etc. Given the

large number of factors which must be taken into account when forecasting wind

generation, the margin for error can be significant and increases as the time horizon

lengthens (Giebel et al., 2003).

The function of power system operators is to supply electricity to customers in

a reliable manner at a sustainable cost. Reliability of electricity supply is defined

as “the ability to supply adequate electric service on a nearly continuous basis with

few interruptions over an extended period of time” (IEEE/CIGRE, 2004). This

involves ensuring that the generation meets the load at all times and that any

short term gaps between load and generation are bridged quickly and precisely to

maintain the integrity of the power system (Kirschen and Strbac, 2004). Generators

are scheduled to meet the forecasted load and must alter their operating levels

to follow the load as it fluctuates throughout the day. Since the output of some

renewable generators, in particular wind generation, cannot be actively controlled

and is difficult to predict accurately, this balancing of the generation and the load


can become more challenging (Gross et al., 2006).

Given the optimistic targets for renewable energy in Europe and the specific

challenges associated with increased variable wind generation, there has been much

debate on the advantages and disadvantages associated with wind generation. Some

argue that wind generation imposes high costs on the power system operation and

increased wind power threatens the stability of the system and could result in

greatly increased prices (Etherington, 2006). Others argue that wind generation

is the panacea for the challenge of depleting fossil fuel resources and that it should

be widely supported and if necessary subsidised to ensure its further development

(Brown, 2006).

Policy makers are continually bombarded with groups on both sides of the wind

debate and as a result, it is often the case that vague objectives with regard to wind

generation are made. It was thus recognised that research was required to identify

and quantify the costs and benefits associated with increased wind penetration. The

work presented here discusses in detail these costs and benefits, quantifies the im-

pacts that wind generation has on an electricity system and determines the optimal

penetrations of wind generation beyond which no further investment should be made

in wind generation.

1.2 The Impacts of Wind Generation

The interaction of wind energy and the electricity system is complex and there

are significant challenges posed to system operators when large amounts of vari-

able generation are introduced (Bazilian et al., 2004). Under the European Union

EU Directive 96/92/EC (1996) the Transmission System Operator (TSO) is respon-

sible for “ensuring a secure, reliable and efficient electricity system and, in that con-

text, for ensuring the availability of all necessary ancillary services”. These ancillary

services include the provision of sufficient reserve capacity to meet the load under

unexpected system operating conditions (Wang and Billington, 2004). As shown in

Zhu et al. (2000) these unexpected system conditions can include the loss of a unit,

a transmission line trip and unexpected load fluctuations. Soder (1993) showed that


an increase in the capacity of wind generation on an electricity system increases the

uncertainty in the system, as wind generation is relatively unpredictable and non-

dispatchable, which results in a requirement to carry additional reserve capacity in

order to maintain system security.

Depending on the electricity system, there are a number of reserve categories

which a system operator must carry - from fast acting reserve, with a reaction time

in the order of seconds, to reserve which responds more slowly (Rashidi-Nejad et al.,

2002; Kundur, 2004). It has been shown in Watson and Landberg (2003), Marti et al.

(2006) and Kariniotakis and Pinson (2003), that the standard deviation of wind

power forecast errors over short time frames are small. As such Doherty and O’Malley

(2005), Dany (2001) and Gouveia and Matos (2004), found that increasing wind

power capacity has little effect on the reserve categories that operate over a short

time frame (seconds to minutes), however, wind capacity causes a greater increase

in the need for categories of reserve that act over longer periods of time.

In addition to the added reserve requirement with increases in wind generation,

an increase in variable generation on an electricity system may require the system

operator to alter how conventional generation is dispatched (Gardner et al., 2003;

Hatziargyriou et al., 2000). Conventional generation may be obliged to operate at

lower operating levels in order to be available to ramp up to accommodate the

inherent variability of the wind generation (Gross et al., 2006). There may also be an

increase in the number of start-ups and shut-downs of conventional units as system

operators attempt to coordinate the following of the fluctuating load throughout

the day and the variable output of the wind generation (Holttinen, 2004; EirGrid,

2004).

Thermal units are designed to be at their most efficient when online and running

at a stable load (Flynn, 2003). It has been shown by the IEEE Subcommittee (1990),

that in general, units are optimised for continuous rather than cyclical operation and

when operating in their normal range can operate for relatively long periods with

relatively low risk of failure and loss of equipment life. An increase in the ramping

and starting of conventional units, as a result of increased wind penetration, can


result in increased wear and tear on the machine and result in a shortening of the life

span of the unit (Lefton et al., 1997). It is estimated that one on-off cycle for a single

unit can cost as much as e500,000 (Grimsrud and Lefton, 1995), thus an increase

in cycling associated with increased wind generation could result in significant cost

increases.

Currently many wind turbines have protection equipment installed which discon-

nects the turbine following a fault (Mullane et al., 2005). This sympathetic tripping

can cause serious problems for system security, increasingly so with larger numbers

of wind generators connecting to the system (Xiang et al., 2006). Essentially, this

sympathetic tripping aggravates a fault event resulting in increasing pressure on

reserves and reliability standards. In periods of low demand and high wind speeds

(e.g. during windy nights) a significant contribution of wind power can be reached

even when the overall share of wind power in the electricity supply is still modest

(Hurley and Watson, 2002). When the wind power penetration level is considerable

it will no longer be feasible for wind turbines to disconnect during voltage or fre-

quency disturbances, as this would lead to a large generation deficit and could lead

to a reduction in the stability of the system (McArdle, 2004; Lalor et al., 2005). This

issue has led to a review of many grid codes across Europe to include a necessity

for wind turbines to have fault ride through capabilities (Causebrook and Fox, 2004;

Johnson and Urdal, 2004).

The most ideal sites for wind generation often coincide with remote locations

which are frequently in weak areas of the electricity grid. This can result in con-

nection challenges for wind generation with issues such as voltage control, harmonic

emissions, short circuit levels and losses playing a significant role (Keane and O’Malley,

2005b). This has led to the possibility of distribution networks switching to ‘active’

rather than ‘passive’ systems through the installation of complex control systems

and by equipping transformers with tap changers that can control voltage locally

(IEE, 2003). Rural locations for wind farms also cause problems for developers. For

cost minimisation the location of plant is very important and there is often a trade-

off between the windiest sites and easy access to the distribution network. There are


large economies of scale associated with wind farm development which often cannot

be realised in remote areas due to limitations on access (DWEA, 2006). In addition,

wind generation developers often come up against significant planning opposition

from local residents in remote areas which in conjunction with lengthy connection

application procedures can considerably delay the development process (SEI, 2003;

IWEA, 2004).

Wind generation is variable and its output at any particular time in the future

is significantly less-assured than conventional fuel-based technologies. However, in

some respects all power plants are variable in that their reliability is never perfectly

guaranteed, and system operators have simply learned how to deal with unplanned

outages (Ahmed and Cohen, 1994). It is a common myth in the media that since

the wind generation cannot be relied upon 100% of the time, that every MW of

installed wind generation must be matched by a MW of additional conventional

generation. For example, as stated by the well known environmental commenta-

tor David Bellamy “wind turbines are completely effete because they need backup

all the time and help to produce CO2, not reduce it” (Gross et al., 2006). This is

entirely incorrect as wind generation does in fact add to the capacity of a system,

and the extent to which it does is generally given by its capacity credit. According

to Castro and Ferreira (2001), the capacity credit of a unit can be defined as “the

amount of conventional resources (mainly thermal) that could be ‘replaced’ by the

renewable production without making the system any less reliable”. It is generally

considered that wind generation has a capacity factor of between 20 and 40% de-

pending on location, which is a function of the existing installed wind, meaning

that 100 MW of installed wind generation can avoid the building of between 20 and

40MW of conventional generation.

For system security reasons, it is highly beneficial for a country to generate its

electricity from a diverse range of sources so as not to be reliant on the supply

of an individual fuel source. Wind generation adds to the plant portfolio and the

underlying energy source is free and will never be depleted. Since it reduces a

country’s reliance on fossil fuel it reduces its exposure to fluctuations in supply as a


result of international politics (Ahmed and Cohen, 1994).

In addition to the capacity benefit of installing wind generation, the opera-

tion of wind generation can provide significant savings to the economy. Under

EU Directive 2001/77/EC (2001), wind generation must receive priority dispatch.

This means that if the wind is blowing, the system operator must accept the elec-

tricity produced by the wind generator. Since generation must always equal de-

mand, this can result in the displacement of the output of a conventional unit

(Malik and Awsanjli, 2004). A reduction in output generally leads to a reduction in

fuel consumption and accordingly the fuel bill. Given the rapid economic growth in

Eastern countries such as China and India and conflicts in fossil fuel rich countries,

fossil fuel prices have been growing worldwide. This rise in prices is likely to continue

as fossil fuel resources become more depleted (EIA, 2004). Thus, the fuel savings

from wind generation are likely to grow in value into the future as fossil fuel prices

rise.

In addition to saving on the fuel costs by displacing conventional generation,

wind can also reduce the harmful emissions from the burning of these fuels. Harmful

emissions are created in combustion plants through the burning of fuels at elevated

temperatures. Carbon Dioxide (CO2) is a greenhouse gas and is one of the main

emissions responsible for global warming (US EPA, 2006a). The amount of carbon

dioxide released is in direct proportion with the amount of carbon in the fuel and

the quantity of fuel burnt. Thus, if wind generation results in a reduction in the

operation of a generation plant which burns a carbon intensive fuel, it will result

in a reduction in carbon dioxide emissions. Sulphur Dioxide (SO2) is a colourless

gas which damages trees and crops and can lead to chronic breathing difficulties in

humans. It is also a contributor to acid rain (Alberta Environment, 2003). Like

carbon dioxide, sulphur dioxide emissions are directly related to the sulphur content

of the fuel and the calorific value of the fuel, and reduced operation will result in

reduced SO2 emissions. Nitrogen Oxide (NOx) is an atmospheric pollutant which

contributes to ozone-depletion and reacts with hydrocarbons, ozone and light to

produce smog. NOx also reacts with water to produce acid, which causes corrosion


problems and acid rain. Electric power plants are among the highest contributors of

NOx emissions along with motor vehicles and industrial boilers (CEPAARB, 2004).

Nitrogen Oxides formation is more complex than CO2 and SO2 formation. NOx

does not depend solely on the nitrogen content of the fuel but is also affected by the

flame temperature, the oxygen concentration and the residence time (Kesgin, 2003).

It is not always the case that a reduction in output will lead to a reduction in NOX

emissions (Mansour et al., 2001).

It has been shown by Sims et al. (2003) that wind generation is among the

cheapest ways of reducing carbon dioxide emissions and is far cheaper than solar

and carbon sequestration. With current political pressure to reduce CO2 emissions

under the Kyoto Protocol and the cost to an economy of purchasing carbon permits

(Conniffe et al., 1997), emissions reduction from wind generation is highly signifi-

cant.

Wind generation development can also greatly aid local communities. The con-

struction and operation of wind turbines creates local jobs, and Mazza (2005) esti-

mates that the construction of a 75MW wind farm could create employment for up

to 200 labourers. Additionally, a study for the United States Department of Energy

(US DOE, 2005) found that wind energy produces 27% more jobs per kilowatt hour

than coal plants and 66% more jobs than gas fired plants. Private landowners can

earn significant annual rents from developers for the citing of wind turbines on their

land (Ritsema et al., 2003). The land is not made redundant by the turbines and

can continue to be used as agricultural land for the duration of the turbine life. The

combination of these benefits multiplies within communities as the wages earned by

employees of the wind projects may be spent in local businesses, the supplies for

construction and maintenance may be purchased locally and an increase in tourism

can result (Galluzzo, 2005). In order to access wind generation sites, developers

often need to enhance the quality of the local road network. The cost of these im-

provements to the local infrastructure are covered by the developers and improve

the standard of living for all those living in the vicinity.

Wind generation is a publicly visible example of an environmentally friendly


technology and a country’s commitment to renewable generation. Wind generation

can act as a reminder to those who see it that global warming is a serious issue, and

this can have a knock-on effect of encouraging people to be more energy efficient in

their daily lives. Wind generation is accessible engineering for young children and

is often used by environmental groups to promote environmental awareness from a

young age (DWEA Kids, 2006).

This is not an exhaustive summary of the impacts of wind generation but rather

an illustration of some of the more direct impacts. Depending on the scope of

an analysis, the costs and benefits of wind generation investigated could be much

further reaching, such as the reduction in future health costs as a result of a reduction

in carbon dioxide emissions, or the environmental impact costs of construction in

elevated areas etc. However, the costs and benefits investigated in this thesis are

limited to the most direct costs and benefits, and are summarised in Section 1.4.

1.3 Quantifying the Costs & Benefits of Wind

A number of studies in the past have attempted to estimate the value of these costs

and benefits of wind generation and a selection of these are reviewed here.

Kennedy (2005) give a generic approach to calculating the long term costs and

benefits of wind generation. Here the average variable, fixed and environmental costs

for conventional generators over a fixed period of time are calculated without wind

in the plant portfolio. A fixed capacity of offshore wind generation is then added

and the generator’s costs are recalculated. The difference in the costs is taken to be

the social benefit of wind generation. However, this analysis is limited to looking at

the impact on two distinct units rather than the impact on the electricity system

as a whole. These units are highly efficient advanced gas fired units and relatively

low emitters of CO2 and NOX with no SO2 emissions, which underestimates the

potential emissions benefits of wind generation. In addition, load duration curves

are used to evaluate the operation of the units rather than a time sequential model

of the system as a whole. A perfect wind forecast is assumed and no account is

made of additional costs associated with increased cycling of the conventional units


and any additional reserve that may be required.

Munksgaard and Larsen (1998) compare the socio-economic impacts of wind gen-

eration in Denmark to central gas and coal fired plants. This paper presents an

interesting study on both the internal and external costs and benefits of wind gener-

ation when compared to conventional coal and gas units. However, the wind turbine

sizes analysed are small in comparison to today’s standards, in addition, the authors

tend to underestimate the capacity credit of wind generation at between just 10 -

20%. However, the main drawback of this work is admitted by the authors when

they state that to accurately calculate the costs and benefits of wind generation “re-

quires a simulation model which can simulate the operation of the electricity system.

A model like this has not been used in this study”.

Bergmann et al. (2006) claim to “estimate the magnitude of these external costs

and benefits for the case of renewable technologies in Scotland”. The approach

adopted uses a ‘choice experiment’ technique to determine the social welfare gain

of increased wind generation. This involved members of the public ranking the

advantages and disadvantages of wind generation in terms of importance as they

see them and their willingness to pay a subsidy on their electricity bill for these

benefits. This approach is highly limited in that no attempt was made to quantify

the external cost of wind generation on the electricity system and the use of eco-

nomic utility curves to determine the value of wind generation is highly subjective.

Alvarez-Farizo and Hanley (2002) use a similar methodology to estimate the value

of the environmental benefits of wind generation through public preferences.

Pepermans et al. (2005) provide a comprehensive summary of the costs and ben-

efits that can accrue from small scale wind generation installed on the distribution

network. Although this paper provides insightful analysis of the costs and benefits

of wind generation, it does not attempt to place a monetary value on these costs

and benefits.

A number of more comprehensive studies have been conducted on different elec-

tricity systems in order to quantify the real cost of integrating wind generation onto

a network. The results from these different papers are difficult to compare due to


different methodologies, data, tools, as well as terminology and metrics used in the

results (Holttinen et al., 2006). In addition, many of the studies concentrate on just

a single cost such as the added reserve cost. Also, many of the studies provide only

a very limited estimate of the benefits associated with wind generation, if quanti-

fied at all. Comparisons across work done in different countries must be done with

caution as the costs and benefits are relevant only in the context of the particular

system in which they are embedded (Gross et al., 2006). However, a brief summary

of the methodologies used in a selection of these studies for different countries and

the salient results are presented here.

The European Union currently contributes approximately 75% of the installed

wind generation worldwide (Montes et al., 2007). According to EWEA (2005), an

increase in the existing contribution of wind power for electricity production in Eu-

rope is technically and economically feasible. Large penetrations of wind generation

require changes in approaches to operating power systems to maintain a stable and

reliable supply. EWEA (2005) found that the impact of wind power depends on the

wind power penetration level, the power system size, generation capacity mix, the

degree of interconnection and load variations. Across Europe, it is estimated that

the cost of providing additional reserve for wind generation ranges from e1-4/MWh

(EWEA, 2005).

For the UK, Dale et al. (2004) estimate the costs that underlie the price paid by

electricity end users, i.e. the generation costs, the conventional units’ fuel costs and

network costs. This analysis is conducted for two scenarios, a ‘conventional’ scenario

which sees all electricity produced by mainly thermal units in 2020 and a ‘wind’ sce-

nario which has 20% of electricity produced by wind generation in 2020. The results

are based on the work presented in the comprehensive report by ILEX and Strbac

(2002) which uses a system model to determine the likely operating levels of conven-

tional units with increased wind penetration. The results show that the predominant

cost associated with increased wind generation is related to the non-dispatchability

of the wind generation resulting in additional reserve requirement and lower con-

ventional unit efficiency. Gross et al. (2006) provide a systematic review of a large


number of studies on the costs and impacts of intermittent generation with an em-

phasis on the UK electricity system. In this study, intermittency refers to generation

which is both variable and relatively unpredictable. They found that of over 200

studies analysed, the major issue for wind generation integration was its variability

and its unpredictability. This variability and unpredictability results in a need for

additional balancing of system load and generation and a need to maintain capac-

ity to ensure reliability of supplies. For the UK, they found that the cost of this

intermittency ranged from £2-£3/MWh (£1 = e1.48 in Jan 2007). If the costs

associated with capacity developments required to ensure future reliability of sup-

ply are included, then the cost of additional intermittent generation ranges from

£5-£8/MWh.

In Germany, renewable energy sources currently account for 10% of electricity

generation consumption. The goal of the DENA (2005) study undertaken by the

German Energy Agency was to enable fundamental and long term energy-economy

planning with increased wind generation. The main focus of this work was on the

extension of the electricity network, however, the additional reserve requirement was

also calculated. It was found that on average wind generation required an increase

in reserve capacity by an amount equal to 9% of the installed wind generation.

The study also found that the development of wind power requires a shift in the

conventional plant mix towards more flexible units.

The Norwegian electricity system is almost entirely made up of hydro genera-

tion (99% electricity consumption) and currently has a shortage of capacity which

has led to a reliance on imports to help meet its electricity demand. As a result,

the Norwegian government has committed to installing 1000MW of wind generation

by 2010 (Hagstrom et al., 2005). A comprehensive analysis of the impacts of wind

generation on the entire Nordic system is detailed in Holttinen (2004). The costs

analysed include reserves and balancing costs and the benefits covered include re-

placed energy, reduced fossil fuel consumption and CO2 emissions savings. It was

found that wind generation resulted in a requirement for an increase in the provision

of reserve in the 15min - 1hour time frame. It was estimated that wind generation


would result in a saving of 620-700gCO2/kWh by displacing mainly coal units in

Finland and Denmark. Although the results shown by Holttinen (2004) are very

interesting, the availability of a large amount of hydro generation which can be used

to smooth the variability of the wind generation may tend to underestimate the

costs of wind generation which would be incurred in a system which was less flexi-

ble. A large amount of interconnection is also apparent in the Nordic system which

is beneficial to wind generation. Emissions of SO2 and NOx are not considered in

this study.

Ireland presents a very interesting case study for wind generation as it is a

relatively small island system with limited interconnection and a large and growing

wind energy penetration. A report by Gardner et al. (2003) analysed the impact of

wind generation on the island of Ireland and estimated that 1000MW of installed

wind could result in fuel savings of e75m per annum. No estimate was made in

this study on the extent of the system costs of wind generation such as additional

reserve requirement. In addition, this study is rather simplistic and assumes that

“the conventional generation that would run if there was no wind generation on the

system will also run when wind generation is connected. As the output of wind

generation is increased, the output of the conventional generators will be reduced

accordingly, but no conventional generation will be shut down”. This method of

power system operation is known as the ‘fuelsaver’ approach. This study assumes

that wind generation has no capacity benefit and results in an underestimation of the

potential benefits of wind generation. A study undertaken by EirGrid (2004), the

system operator in Ireland, attempted to estimate the future benefits and limitations

of wind generation in the Republic of Ireland. This study utilised the Siemens

PROMOD software to dispatch the system with wind generation. This study utilised

conservative estimates of the plant mix and the findings of the study are critical of

wind generation. EirGrid (2004) conclude that the cost of CO2 abatement with wind

generation would exceed e120/tonne and 1500MW of installed wind generation

would add e196m to the cost of electricity generation per annum. This study

omitted the potential of SO2 and NOx emissions reductions.


The Production Tax Credit in the United States has been extended through the

end of 2007 and is resulting in the strongest wind capacity growth period ever wit-

nessed in the country (Smith, 2006). A large number of studies have been conducted

on wind generation in the United States. Dragoon and Milligan (2003) estimate the

relative value of wind generation compared to other conventional resources using

dispatch models for the American utility PacifiCorp in Wyoming. The costs of

wind generation analysed were the increased reserve requirements and imbalance

costs associated with increased wind penetration. The system analysed has a large

penetration of hydro generation (14%) which could be used to balance some of the

variation in the wind generation. However, an ‘off the shelf’ dispatch model was

used in this analysis which was not detailed enough to deal with the operation of

the hydro units so optimising their operation with respect to the wind generation

was not considered in this study. Thus, the variability costs of wind to this particular

system may have been overestimated. By using a commercially available dispatch

model, the authors were limited in the assumptions that could be altered and since

the model was not designed to incorporate wind generation, perfect wind forecasts

were assumed. No attempt is made to quantify the benefits accruing to additional

wind generation.

A study on the XCEL-NORTH system by Brooks et al. (2003) used traditional

utility simulation scheduling to estimate the system costs of increased wind gen-

eration. This study concentrated solely on the additional reserve cost of 280MW

installed wind generation in Minnesota and found that the integration costs were

$1.85/MWh ($1 = e0.77 in Jan 2007). However, the models used in this analysis

are rather crude by today’s standards and the assumptions regarding forecasts were

conservative (Smith et al., 2004). Increased wind penetrations of 15% installed ca-

pacity on this system were investigated in Zavadil et al. (2004) and it was found

that the integration costs rose to $4.60/MWh.

GE Energy (2005) investigated the ability of the New York electricity grid to

accommodate 10% wind by capacity by 2008. This study used locational marginal

pricing to conduct a market based approach to determine the market prices with


and without wind generation and it was found that net revenue for wind generation

amounted to $35/MWh, the same price as the current power purchase agreement for

wind in New York. This study highlighted the benefits accruing to improvements in

wind power forecasting with savings of up to $10/MWh.

Work by Hirst (2001, 2002) for the Bonneville Power Administration concen-

trated on additional ancillary service provision for wind generation as did other

studies by We Energies (2003) for Wisconsin, GRE Study (2003) for Minnesota and

Kirby et al. (2003) for California (Smith et al., 2004). The general results from these

studies found that the costs associated with wind integration ranged from $1.47 -

4.53/MWh depending on the wind penetration and the underlying plant mix.

While many of the wind integration studies discussed here are interesting, the

results are difficult to compare across different systems and many have concentrated

solely on quantifying the costs and not the benefits. In addition, none of the cost

benefit studies have attempted to quantify the optimal level of wind generation for

a particular system. In much of the previous work conducted, the system modelling

techniques are questionable and some studies omit a system model altogether. The

work presented in this thesis aims to overcome some of these limitations.

1.4 The Scope of this Thesis

This thesis quantifies the costs and benefits of wind generation. The approach

adopted attempts to maximise social welfare and thus incorporates both direct and

indirect costs and benefits into the analysis. Thus, it is assumed that society pays

the capital cost for every MW installed of wind generation and reaps the benefits of

every MWh of electricity produced from it. Any savings in the fuel bill are societal

savings and any premature depreciation in the lifespan of a conventional generator

from increased stresses is considered a societal cost. A comprehensive model of the

system is developed and is used to analyse the impact of wind generation on the

operating profiles of the conventional units.

The costs considered are the development costs of wind generation, such as the

capital, operation and maintenance costs. The system costs analysed are the cost


of deep reinforcement of the network, the additional reserve cost and the cost of

any additional cycling of the conventional units. Wind generation is systematically

added to the plant mix in the case study and the generating units are dispatched

accordingly. The resulting dispatches are then used to calculate any changes in

costs. The benefits investigated include the capacity benefit of wind generation, the

emissions savings benefits and the fuel savings as increasing penetrations of wind

generation are added.

The costs and benefits are then brought together for each penetration level of

wind generation, and the net benefits are determined. It is hypothesised that the

shape of the net benefits curve for wind generation would be similar to the curve

in Figure 1.1. This hypothesis is tested with regard to the case study. The optimal

level of wind generation penetration is illustrated by the marker and is the point

beyond which no further investment should be made in wind generation.

Installed Wind

Ne

t B

en

efits

0

Optimal

Figure 1.1: Net benefits of wind generation

A comprehensive model of the case study system is designed and simulations are

carried out for a year on a variety of plant mixes for a range of wind generation

penetrations. This thesis provides a wide ranging sensitivity analysis to determine

the impact of key assumptions on the costs and benefits of wind generation. However,

in order to limit the scope of this study, a number of assumptions were required:

Markets: This study represents a near perfectly competitive gross pool electricity


market (Katz and Rosen, 1998). Thus, the generators are assumed to be profit

maximisers and price takers and gaming of the electricity market by individual

generators is not taken into account (Guan et al., 2001). However, it is likely that if

a generating company had a large degree of market power (and was not being actively

controlled by the electricity system regulator) that strategic bidding behaviour could

alter the market outcome with generators withholding output in order to achieve

higher prices (Guan et al., 2001). This results in suboptimal solutions and since this

study aims to maximise social welfare, a perfectly competitive market is assumed. As

discussed in Correia (2006) this approach to electricity market modelling is entirely

valid.

Incentives: This thesis excludes areas such as direct investments and renewable

generation support incentives that may be needed for large scale wind power, how-

ever, carbon prices are included in the analysis and in reality these are likely to aid

in the promotion of wind generation.

Network: Issues relating to voltage control, short circuit levels etc. are not explic-

itly covered in this work. However, the dispatch model developed is used as part of

a cost-benefit study on distribution connection policies (Keane et al., 2006), and is

discussed in Chapter 7. The cost-benefit analysis of connection policies forms part of

a larger study on distribution connected generation by Keane and O’Malley (2005b,

2006) which incorporates detailed network analysis. Initial estimates of network

reinforcement costs with wind generation are however included in the analysis.

Network Congestion and System Dynamics: These are important issues for

wind integration, (Palsson et al., 2003; Akhmatov et al., 2000; CER, 2004a), but are

highly system specific and require a large scale engineering system model beyond

the scope of this study and as such have been omitted in this analysis. However, the

likely impacts of these issues on the net benefits of wind generation are discussed

briefly in Chapter 8.

Other: In addition, in an attempt to limit the number of assumptions required, it

was necessary to omit ‘softer’ factors such as the visual impact of wind generation,

the creation of jobs, improvements in infrastructure, etc. The likely impact of these


issues on the net benefits of wind generation are discussed briefly in Chapter 8.

1.5 An Outline of the Thesis

This thesis consists of eight chapters covering different aspects of the cost benefit

analysis and detailed results, discussions and conclusions are given in each chapter.

Two appendices are also included. The first appendix details the characteristics of

all the generators on the Irish electricity system. The second appendix presents the

network study conduced by Keane and O’Malley (2005b, 2006) which is used as the

basis for some of the results shown in Chapter 7.

Chapter 2 gives a detailed description of the underlying system in the case study.

Since many of the results shown throughout this thesis are dependent on the un-

derlying plant mix, this chapter details the unit types and characteristics as well as

the proposed market arrangements and operational details of the power system in

the case study. Appendix A is provided to detail further the characteristics of the

generators on this system. The feasibility of wind generation and the likely resources

are shown. This study analyses three test years: 2010; 2015 and 2020. The assumed

plant mixes for each of these years is also described in detail.

Chapter 3 details the economic dispatch model used in this analysis and how it

is validated against a full unit commitment model of the case study system. The

intricacies of both models are described in this chapter along with details of the bid

prices for the generators. The wind power data and the inclusion of wind forecasts

in the dispatch model is discussed in detail in this chapter.

Chapter 4 discusses the costs of wind generation. These costs include the de-

velopment and operation costs of the wind turbines, the cost of deep network rein-

forcement, the additional reserve requirement with increasing penetrations of wind

generation and the cost of cycling of generators on the system. The dispatches from

the model are used to calculate the cycling costs and the costs are shown for each

of the three years for the case study.

Chapter 5 describes the benefits of wind generation. The value of the added

capacity of wind generation is quantified in addition to the emissions and fuel saving


benefits.

Chapter 6 combines the results from chapters 4 and 5. The net benefits of in-

creasing penetrations of wind generation are calculated by subtracting the costs from

the benefits. The penetration level where the costs equal the benefits is the critical

point beyond which the costs will exceed the benefits and no further investment

should be made in wind generation. This critical level is tested under a wide range

of scenarios and is discussed in detail in this chapter.

Chapter 7 details four applications of the dispatch model and the cost benefit

methodology employed in previous chapters. The first application investigates the

impact of carbon prices on the cycling costs and carbon dioxide emissions of gen-

erators. The second application investigates an alternative method for including

wind generation in system operation, and is known as the fuelsaver approach. The

third application analyses the costs and benefits of different distribution connection

policies to determine the access policy which will yield the maximum net benefits.

The final application investigates the costs and benefits of tidal generation for the

case study.

Chapter 8 presents a discussion of the salient issues raised in this thesis and

considers the impact of including additional costs and benefits. The main conclu-

sions of the thesis are presented and some directions for possible future research are

provided.

CHAPTER 2

The Case Study

IRELAND is the case study chosen for this analysis. It is a small, open and

trade dependent economy with a population of approximately 4.24 million in the

Republic and 1.7 million in Northern Ireland (CSO, 2006). Over the last decade the

level of GDP in the Republic has almost doubled and the economy outperformed all

other European economies in the 1990s recording a growth rate three times the EU

average, with an average growth of 8.1%, coining the term The Celtic Tiger. In 2004,

the IMD World Competitiveness Year Book (IMD, 2004) ranked the Republic of

Ireland (RoI) third for GDP per capita in the world, ahead of the United States (5th)

and Switzerland (11th). The Republic is currently operating at full employment,

aided by a favourable corporate tax rate and a knowledge based workforce, which

has led to widespread foreign direct investment, particularly in the pharmaceutical

and information technology industries (IDA, 2006). Economic growth in Northern

Ireland (NI) has also been strong in the last decade, however, growth rates did not

reach the same heights as those in the Republic and have averaged at approximately

3% per annum over the last four years (NICS, 2005). Economic growth is the single

most important driver of electricity demand and as such both the Republic of Ireland

23

Chapter 2. The Case Study 24

and Northern Ireland have seen a consistent growth in electricity demand over the

last 15 years.

The Irish electricity system is made up of two separately operated but AC inter-

connected systems, one in the Republic and one in Northern Ireland. In August 2004,

the Commission for Energy Regulation in the Republic of Ireland and the Northern

Ireland Authority for Energy Regulation made a commitment to develop a single

wholesale electricity market on the island of Ireland. As such, this thesis studies an

‘all island’ electricity system, covering the Republic of Ireland and Northern Ireland

(referred to jointly as ‘Ireland’). Some characteristics of the electricity systems in

RoI and NI are summarised in Table 2.1 below (AIP, 2005; EirGrid, 2005b; SONI,

2005). Given the size of the electricity system in the Republic compared to Northern

Ireland, the information on the case system provided in this chapter is driven by the

characteristics of the electricity system in the Republic1.

Table 2.1: The Irish electricity system (at year end 2005)

RoI NI All Island

Peak Demand (MW) 4,566 1,663 6,229

Total Generation (MW) 6,403 2,013 8,416

Ireland has a large and growing installed wind capacity which currently repre-

sents 8% of total installed capacity. In addition, Ireland has limited interconnection

making the effects of wind generation on system operation easier to identify as they

are not influenced by interconnection to other systems. There is a relative generation

capacity shortage in the Republic and temporary diesel generators are currently em-

ployed during periods of peak demand during the winter months (EirGrid, 2005b).

This illustrates the need for additional capacity on the system but, in conjunction

with limited interconnection, also highlights the limitations of the system in re-

sponding to large fluctuations in wind generation production. Ireland is thus an

ideal case study to investigate the system impacts of wind generation as the effects

1The total peak demand in Table 2.1 has been calculated as the sum of the ROI and NI systempeaks. In reality, the times of peak demand may not actually occur at the same time.


are relatively easier to identify.

Section 2.1 discusses the current and future wind situation in Ireland. Section

2.2 details the load growth and daily load profile for the Irish system. The proposed

electricity market design is discussed in Section 2.3. The current generation plant

portfolio is described in Section 2.4 and Section 2.5 describes the assumptions made

about the plant mix for the different years of the undertaken study. A summary of

the key characteristics of Ireland as a case study are given in Section 2.6.

2.1 Wind Generation in Ireland

Wind generation currently represents approximately 8% of the total installed gen-

erating capacity in Ireland and is expected to grow to 12% by 2010 and 20% by

2020 (EirGrid, 2005b). A study undertaken by SEI (2004a) estimates that the ac-

cessible, feasible wind generation penetration in Ireland for 2020 could be as high

as 10,000MW installed.

Wind generation has grown rapidly in Ireland from 70MW installed at the be-

ginning of 2000 to 740 MW at the end of 2006. There are currently an additional

675MW of wind generation with signed connection agreements and 3,286MW in

the connection process awaiting decisions (EirGrid, 2006c). Of the 740MW of wind

generation currently installed, approximately 44% is connected to the transmission

system with the remainder embedded on the distribution network. The rate of

growth has been so rapid that in March 2004 the system operator was forced to

impose a wind moratorium which prohibited any new offers for connections to the

transmission or distribution systems being issued to wind farms. This was largely

due to the lack of a comprehensive grid code for wind generation leading to concerns

regarding security of supply. This cessation of connection was lifted in July 2004

following the publication of a new grid code for wind. Figure 2.1 illustrates the

locations around the Republic of Ireland where wind generation is connected and

committed, as of December 2006 (EirGrid, 2006d). It can be seen that the majority

of wind generation is connected in the west of the country and there is currently

just one offshore development, located off the east coast.


Figure 2.1: Existing wind farms in RoI in January 2007

In order to deal with the large number of interacting applications for wind gener-

ation in Ireland, the Transmission and Distribution System Operators (TSO/DSO)

in Ireland, have introduced a new method through which wind generators will be

granted connection offers. The previous method, on a first come first served basis,

while effective for large conventional generating plant and for a small number of in-

teracting applications, is not effective for large scale management of interacting wind

generation applications. Under the new scheme, applications are grouped according

to their location on the network and their interaction. The TSO/DSO then issues

a connection offer to the individual applications within the group. This connection

offer will remain valid irrespective of whether other applicants in the same group


accept their connection offers. In the case of a major change in the shared connec-

tion design, the connection offer may vary from the initial offer. This is to avoid

over-development of the network and the creation of stranded network assets (ESB,

2004). This new grouping process should greatly reduce the processing time with

which applications are dealt. Wind generation connection on the distribution net-

work is discussed further in Chapter 7 and proposals are made to optimally utilise

the existing distribution network to allow the connection of increased renewable

generation.

To date, wind generation in Ireland has been supported by the Irish Government

through the Alternative Energy Requirement (AER) scheme. The AER scheme has

been funded by a levy imposed on all electricity customers, known as the Public

Service Obligation (DCMNR, 2002). Under the AER programme, developers of

wind generation were invited to tender for the development and operation of wind

projects with a 15 year power purchase agreement with the Electricity Supply Board

(ESB). The tender prices bid by developers were intended to reflect the prices at

which they would be willing to sell their electricity to the ESB. Those developments

with the lowest bid prices were then offered an AER contract. Other technologies

covered by the AER scheme included small-scale hydro, combined heat and power

and biomass generation. There were six rounds of auctions which resulted in the

supporting of 500MW of renewable generation (DCMNR, 2006).

In 2006, a new support mechanism for renewable generation was announced,

called the Renewable Energy Feed In Tariff (REFIT) programme. One of the main

problems with the AER scheme was the awarding of support to renewable projects

which were never actually developed. In order to alleviate this problem, under

the REFIT support scheme, developers of renewable generation must have a signed

network connection agreement prior to applying for support, this should ensure that

projects are developed within a reasonable time frame. Under REFIT, renewable

generators can negotiate their purchase price with any supplier (not just ESB) in

the liberalised market. The REFIT programme will then reimburse the supplier

up to the REFIT price cap. Like the AER scheme, the funding for this support


mechanism will come from the public service obligation levy (DCMNR, 2002). The

REFIT scheme is similar to other fixed feed in tariffs which have proven successful

in many EU countries (REFIT, 2006). The fixed price caps were announced in

September 2006 and are shown for wind generation in Table 2.2. These are the

prices up to which the Government will reimburse suppliers for contracting with

wind generators.

Table 2.2: Price support caps for wind generation under REFIT

Technology Size Tariff (per MWh)

Large Scale Wind > 5MW e 57

Small Scale Wind < 5MW e 59

Putting these figures into perspective, in Germany the fixed feed in tariff ranges

from e60 - 89/MWh, in France the feed in tariff is e30.50 - 83.80/MWh and in

Spain it is just e27/MWh (RISØ and EWEA, 2005).

2.2 Ireland’s Electricity Demand

Ireland’s electricity demand is relatively small with a peak demand of just 6,229MW

in 2006. Figure 2.2 illustrates Ireland’s electricity consumption relative to the other

countries within the EU15 (Eurostat, 2006; IAEA, 2006). Although Ireland’s elec-

tricity demand is small in relative terms (with Luxembourg being the only EU15

country with less), recent annual growth rates in electricity demand in Ireland and

projected into the future are uniquely positive for a developed country (Deloitte,

2005). Economic growth is the predominant driver for electricity demand and in the

period between 1990 and 2000, total annual demand in Ireland increased by 69%,

corresponding to a 5.4% increase per year, compared to an IEA average of 2.8% per

annum (Deloitte, 2005).

In Ireland, forecasts of electricity demand are calculated by the System Opera-

tor and are given in the Generation Adequacy Report (EirGrid, 2005b). Demand

forecasts are calculated based on predictions of growth in GDP and in the Personal


0

100

200

300

400

500

600

Austr

ia

Belg

ium

Denm

ark

Fin

land

Fra

nce

Germ

any

Gre

ece

Irela

nd

Italy

Luxem

bourg

Neth

erlands

Port

ugal

Spain

Sw

eden

UK

TW

h

1990

2004

Figure 2.2: Comparison of electricity consumption in the EU15

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

0 6 12 18 24

Hour

De

ma

nd

MW

Winter Peak

Typical Winter

Typical Summer

Summer Minimum

Figure 2.3: Typical daily demand patterns in RoI in 2006


Consumption of Goods and Services Index (PCGS). It is envisaged that although

demand will continue to grow, the rate of growth is likely to be less than the record

growth rates experienced in the 1990s. Three scenarios are presented as represen-

tative of the likely levels of electricity demand growth into the future. The ‘high’

demand scenario envisages growth rates of 4.3%, the ‘medium’ scenario with growth

rates of 3.7% and the ‘low’ scenario with 2.5% growth. These growth rates are used

as inputs in the model of the Irish system, discussed further in Chapter 3.

In Ireland, peak demand occurs during winter weekday evenings and minimum

demand occurs during summer weekend night-time hours. Figure 2.3 shows the

typical daily demand profile for the Republic of Ireland in 2006 (EirGrid, 2006a).

It can be seen that the magnitude of the demand is much lower in summer than in

winter, and in addition, the peak demand in the summer occurs much earlier in the

day than in the winter (EirGrid, 2005b). Electricity demand in Northern Ireland

follows the same general pattern (SONI, 2005).

2.3 Ireland’s Electricity Market

Ireland’s electricity market is currently based on highly regulated bilateral trading

with a top-up and spill balancing mechanism. The current arrangements are ex-

pected to be phased out in 2007 to make way for a Single Electricity Market (SEM)

which will commence trading on 1st November 2007. The SEM is considered to be

the first stage in creating an all island electricity system and is expected to result in

reduced electricity costs to consumers and greater security of supply (AIP, 2006b).

The SEM will be a mandatory gross pool market, all trading must take place

through the pool and no physical bilateral trading will be permitted outside the

pool. The market will have a single market operator (MO) and will commence

with two system operators with graduation to a single system operator (SO) over

time. All generators will submit energy bids to the MO/SO in price quantity pairs.

The market will have centralised commitment with a single market clearing price.

All dispatched generators will receive the single marginal price regardless of their

initial bid. Financial contracts, such as contracts for differences (CfDs), can be


made between participants outside of the pool as a hedging instrument to minimise

exposure to variable prices (SEM, 2005).

The SEM will also contain a capacity payments mechanism which will reward

generators for having available plant. This capacity mechanism is intended to aid

generators in recouping their capital costs and should act as an incentive to new

entrants in the market (SEM, 2005). In addition, the presence of a capacity mech-

anism should ensure the energy bids from generators do not incorporate a capital

costs element. The model described in detail in Chapter 3, aims to reflect the salient

features of the proposed SEM market design.

2.3.1 Dominance in the Irish Electricity Market

Given its small size, Ireland’s electricity supply North and South was traditionally

governed by two separate vertically integrated utilities. In the Republic there was

the Electricity Supply Board (ESB) and in the North, Northern Ireland Electricity

(NIE). These utilities were responsible for the generation, supply and transmission

of electricity.

The ESB was founded in 1927 with the development of a hydro power station

at Ardnacrusha. ESB is 95% owned by the Irish Government with the remaining

shares held by employees. ESB has a number of divisions which are ringfenced

and operate separately in the electricity market. These entities are ESB Power

Generation, ESB Customer Supply and ESB Networks. System operations were

traditionally controlled by ESB National Grid, however, this responsibility has now

been passed to an independent system operator, EirGrid (ESB, 2006).

NIE traditionally controlled all the generation, supply, transmission and distri-

bution in Northern Ireland. In 1992, NIE sold four generating facilities in private

transactions and in 1993 the remainder of NIE generation was sold on the London

Stock Exchange. Following this privatisation, the generation, supply and distri-

bution functions of NIE were divided up and are now fully owned subsidiaries of

Viridian Group PLC. These subsidiaries operate independently and are made up of:

Viridian Power and Energy responsible for generation; Huntstown power generation


which is a power station owned by Viridian in the Republic of Ireland; NIE customer

supply; Energia customer supply; and NIE infrastructure division which controls the

transmission and distribution network in Northern Ireland. System operations are

controlled by the independent System Operator Northern Ireland (SONI) (Viridian,

2006).

Liberalisation of the electricity market is required under EU Directive 96/92/EC

(1996). Generation and supply are generally considered as two segments of the elec-

tricity industry where competition is possible (transmission and distribution net-

works are considered natural monopolies). Even with an all island electricity market,

it is likely that the ESB will maintain a highly dominant position in the electricity

market with ownership of over 60% of the generating capacity (AIP, 2005). With a

pool type market using a single marginal price, there exists scope for gaming of the

market by a dominant participant. The marginal unit will be the price setter, and

all other dispatched units will be price takers. Currently ESB owns the majority

of units which are likely to be operating on the margin. This places ESB in a po-

sition which would allow it to bid high prices on the margin, ensuring a high price

for its portfolio of generating assets. In addition, strategic manipulation of outage

schedules could ensure higher prices by withholding the output of baseloaded units

(Deloitte, 2005).

The regulator has decided that it will heavily regulate the operation of ESB in

the SEM through firm market control, transparency and bidding rules rather than

attempt to disaggregate its generating and supply assets to reduce it’s market share

(CER, 2004, 2006a; AIP, 2006a). Given the complex nature of gaming behaviour,

it has been assumed in this thesis that gaming of the Irish electricity market will

be comprehensively controlled by the regulator and will not occur. Thus the model

described in Chapter 3 represents a near perfectly competitive market, which would

provide the most optimal solutions for society.


2.4 Ireland’s Current Generation Plant Mix

Ireland’s generation plant mix was traditionally based on large coal and oil fired

generation plant with a small number old thermal gas generators and peat plants.

Peat is made up of partially decomposed plant debris and is considered an early

stage in the development of coal (EIA, 2004). These peat units use fluidised bed

technology and their operation is similar to that of coal fired units. Peat fired

generation accounts for approximately 350MW of Ireland’s installed capacity. Since

1990, the share of high carbon content fuels such as coal has fallen in Ireland with

a large increase in the use of natural gas CCGT plants. Gas fired generation now

accounts for over 45% of the installed capacity in Ireland (AIP, 2005). Figure 2.4

illustrates the current installed generating plant mix on the island of Ireland.

Gas: 45%

Oil: 23%

Hydro: 6%

Coal: 14%

Wind & Other RE: 9%

Peat: 4%

Figure 2.4: Installed generation capacity by fuel type in Ireland

A profile of the age, size and unit types of generators on the Irish system is

shown in Figure 2.5. In 2004, a number of peat fired units were decommissioned

and were replaced with more efficient fluidised bed units. Thus, as can be seen from

Figure 2.5, with the exception of the replacement of the peat units, all investment

in new generation in Ireland has been in gas fired units, predominantly CCGTs. It

is anticipated that this trend is likely to continue into the future (EirGrid, 2005b;

Doherty et al., 2006; CER, 2006b).


0

10

20

30

40

50

60

70

80

0 100 200 300 400 500 600

Size of Plant (MW)

Ag

ein

Ye

ars

HydroPeatOilGasCoal

Figure 2.5: The age of the generating units on the Irish system

Gas

Distillate

2000 4000 6000 8000

Hours

1500

3000

4500

6000

7500

Dem

and M

W

2450

0

Hydro & Peat

Coal

Gas

Oil

Figure 2.6: Load duration curve with merit order of conventional units


Figure 2.6 illustrates a load duration curve for the island of Ireland. This shows

the number of hours in the year when the demand exceeds a given MW level. Also

illustrated is the typical dispatch order for the Irish generating units in 2006, based

on fuel costs (AIP, 2005). The hydro generation is dependent on the available

resource and therefore does not operate at all hours during the year. However, for

ease of illustration, in Figure 2.6 the hydro units are shown at the base as they are

the cheapest units on the system.

Figure 2.6 shows that the load on the island of Ireland is above 2450MW for

all 8760 hours of the year. Thus, the units that operate below this demand level,

operate all 8760 hours of the year and are said to operate at the base. From Figure

2.6 this is seen to be the hydro, peat, coal and some gas fired units. This dispatch

order is based on efficiency and fuel costs with the exception of the peat fired units.

Despite being more expensive and inefficient the Irish Government made a policy

decision to adopt a ‘must run’ approach for all peat fired generators for security of

supply reasons, thus making them baseloaded. The peat is then subsidised through

the public service obligation levy on all electricity bills (DCMNR, 2002). For ease

of illustration, the wind generation has been omitted from Figure 2.6 as it is not

considered dispatchable and the output varies throughout the day and depends on

the time of year.

As the demand increases above 2450MW more gas fired units are dispatched,

followed by oil fired units, then peaking units fired by gas and distillate. Thus, the

marginal unit will be gas, oil or distillate depending on the level of demand. For

example, gas fired units will operate at the margin for approximately 3,510 hours

of the year (8760-5250), oil fired units for 4,450 hours (5250-800), followed by gas

and distillate units during the small number of hours of peak demand in the year.

These marginal units will be required to alter their output in order to follow the

load variations throughout the day.

Ireland has one interconnector, known as the Moyle Interconnector, which con-

nects Northern Ireland to Scotland through submarine cables. The capacity of this

interconnector is 500MW with an operating capacity of 400MW and it entered into


full commercial operation in April 2002. The 400MW capacity of this interconnector

is not always available and it is estimated that only 155MW of its capacity will be

available in the market due to existing contracts (NIAER, 2003).

Apart from Ireland, only one other country in the EU15, the Netherlands, has a

similar reliance on imported gas and oil, and only three countries in the EU15 have

levels above 40% (Deloitte, 2005). In addition, those countries that have material

penetrations of oil and gas capacity, almost always have compensating capacity of

nuclear and hydro generation which have lower operating costs which reduces their

exposure to fluctuations in imported fuel prices and supplies. Ireland has no nuclear

generation and a small penetration of hydro generation and all feasible sites for hydro

generation are considered to be exhausted. The issue of fuel import dependence is

important and leaves Ireland highly exposed to international fuel shortages, which

could have a dramatic effect on the reliability of the Irish system.

Ireland’s generating units already suffer from availability levels well below the

EU average. Figure 2.7 shows the average availability of the conventional units on

the Irish system from 1994 to 2005 (EirGrid, 2005b). It can be seen that there is no

consistent improvement in generator availability apparent in recent years and that

the availability of units in 2003 was particularly bad.

1995 1997 1999 2001 2003 200576

78

80

82

84

86

88

Syst

emA

vailab

ility

(%)

Figure 2.7: Conventional generation availability in Ireland


The poor availability of the generating units on the Irish system adds to the

inflexibility of the system in responding to large variations in wind generation. In

addition, the availability of certain units could temporarily reduce further in the

coming years as they undergo extensive overhauls in line with EU emissions reduc-

tion directives (e.g. EU Directive 2001/80/EC (2001)). It is expected that the coal

units will install fuel gas desulphurisation (FGD) technology to reduce sulphur diox-

ide emissions, and Selective Catalytic Reduction (SCR) technology, also known as

scrubbers, to reduce nitrogen oxides (NOX) emissions. Two of the oil units will also

install SCRs to reduce NOX emissions (ESB, 2003). Emissions from individual units

will be described in more detail in Chapter 5.

Further details of the operating characteristics, including fuel type, maximum

capacity and year of commission of the units on the Irish system are shown in

Appendix A.

2.5 The Future Plant Mix of the Irish System

In this thesis, the costs and benefits are calculated for Ireland for the years 2010,

2015 and 2020. The assumed plant mixes for these years were based on the pro-

jected generation plant mix considering increased wind penetration for Ireland from

EirGrid (2005b) and the optimal future generation portfolio for Ireland to accommo-

date large penetrations of wind generation from Doherty et al. (2006). Preliminary

results from the Government sponsored ‘All Island Renewable Grid Study’ (AIRGS,

2006b) specifically aimed at determining the optimal plant mix for 2020 were also

used to inform the assumptions on the generation portfolios. Increases in hydro

generation and pumped storage were considered in AIRGS (2006b) and were found

not to be viable for the Irish system, and are thus not considered in this thesis.

The plant portfolio for 2010 was based on generation projections given in EirGrid

(2005b). Three new gas fired units are expected to be in operation by 2010:

Huntstown II, Aughinish Aluminum and Tynagh (see Appendix A). In addition,

the FGD and SCR technologies in the coal and oil units are assumed to be installed

by 2010.


For 2015 it is envisaged that new additions to the conventional plant mix will be

gas fired (EirGrid, 2005b; CER, 2006b; Doherty et al., 2006). As such, three new

gas fired units are expected to come online by 2015 (EirGrid, 2005b). In addition

it is assumed that two of the oil fired units that were in operation in 2010 will be

retired (Doherty et al., 2006; CER, 2006a).

In 2004, the Irish Government approved the development of an electrical inter-

connector between Ireland and Wales for security of supply and competition pur-

poses. According to EirGrid (2005b), this interconnector is likely to be completed

in 2012. Its installed capacity will be 500MW but its maximum operating capacity

is expected to be 400MW. This interconnector is included in the plant mix of 2015

and 2020 but not 2010. The Moyle interconnector with a capacity of 400MW is

included in the plant mix for each year.

By 2020, another two oil fired units are retired due to age and are replaced with

an additional four gas fired units (Doherty et al., 2006). The details of the plant

portfolios in each of the three test years are given in Appendix A.

In two of the additional applications investigated in Chapter 7 the assumed

test year is 2007. The reasons for the change in test year in these applications are

discussed in Chapter 7 and a summary of the plant mix in 2007 is given in Appendix

A.

2.6 Summary of the Key Features of the Case Study

Ireland is an ideal case study to investigate the costs and benefits of wind generation

as it has limited interconnection allowing for a controlled study on the impacts of

its large and growing wind penetration. The costs and benefits presented in this

work are, in the main, dependent on the underlying electricity system. Thus, the

most significant characteristics of the Irish system with regard to wind generation

are summarised here:

• Ireland has a large wind resource and there is currently 740MW installed,

675MW under development and 3286MW in the planning process, leading to


a potential penetration of over 4000MW. If all 4000MW were developed, it is

projected it could represent over 40% of the installed generating capacity in

2010.

• Ireland has a high electricity demand growth requiring the development

of new capacity on the system. It is envisaged that any new conventional

development will be gas fired. In addition, a 500MW interconnector to Wales

is expected to be completed by 2012 (EirGrid, 2005b).

• Ireland’s underlying plant mix is currently made up of predominantly gas, oil

and coal units. The plant mix is relatively inflexible with only a small number

of fast acting open cycle gas turbines.

• Ireland has a small penetration of hydro units and it is assumed that all

feasible sites for hydro generation have been exhausted. Thus increased pen-

etrations of hydro generation are considered not viable for Ireland (AIRGS,

2006b).

• Ireland has one pumped storage unit. In other countries, pumped storage

can be used to help reduce the variability in the wind generation, however, this

is not possible in Ireland as this unit is already being used for system security

purposes. Increases in pumped storage were considered in AIRGS (2006b) but

were found to be not viable for the Irish system and as such are not considered

in this thesis.

• Ireland’s electricity market will be an all island market, covering the Repub-

lic and Northern Ireland. This will improve the security of the system with

generation capacity increased by a third over the Republic alone.

• The proposed electricity market is a centralised gross pool electricity market

with a separate capacity mechanism. This should ensure that bids in the

energy market are marginal cost based. It is assumed that the dominance of

the ESB will be actively controlled by the electricity regulator


Further details on particular aspects of the Irish system will be described where

relevant in Chapters 3, 4 and 5, and detailed descriptions of the units on the Irish

system are given in Appendix A. In Chapter 6 the costs and benefits are brought

together to determine the optimal penetrations of wind generation. This is in con-

trast to work conducted by Doherty et al. (2006) where an optimal conventional

plant portfolio was determined for given installations of wind generation. Here, the

optimal penetrations of wind generation are determined for a given conventional

plant portfolio.

CHAPTER 3

The Dispatch Model

IN order to quantify the costs and benefits of wind generation it is necessary to

analyse it in the context of the power system in which it is installed and to

investigate wind’s impacts on the operation of the underlying plant mix. An electric

power system based predominantly on hydro power will result in very different costs

and benefits for wind generation than a power system made up of coal units. This

chapter details how the operation of the case study power system was modelled to

determine how wind generation impacted on the operation of the conventional units.

The model produces operating schedules for all the conventional generators on the

system with increasing penetrations of wind generation. These dispatches are then

used to quantify the costs and benefits of wind generation in subsequent chapters.

The plant mix in the case system is described in Chapter 2 and details on the

characteristics of the individual units are shown in Appendix A. As discussed in

Chapter 2, the proposed all Ireland single electricity market (SEM) is a gross pool

market with centralised commitment (SEM, 2005). The model used in this paper

aims to reflect this market and is validated against a full unit commitment model

of the system. Although many electricity markets are moving away from centrally

41

Chapter 3. The Dispatch Model 42

controlled dispatch, it has been shown in Xu and Christie (1999) that a model which

allows the generators to bid incrementally and self commit produces solutions al-

most identical to those from a centralised unit commitment (Xu and Christie, 1999;

Ede et al., 2000). In fact, centralised unit commitment can be used for the predic-

tion of operating decisions in decentralised markets (Xu and Christie, 1999). Thus,

although the model used here reflects a centralised gross pool electricity market, it

could also reflect the operating decisions in alternative electricity market designs.

Section 3.1 describes the optimisation algorithm used to dispatch the generators.

Section 3.2 details how wind generation was included in the model. A practical

description of how the model represents an electricity market is given in Section 3.3.

The assumptions in the model to tune it to the Irish system are given in Section

3.4. A summary of the key features of the model are given in Section 3.5.

3.1 Optimisation in the Dispatch Model

The dispatch model aims to represent the salient features of the proposed market

design in Ireland. Thus, it is a centralised gross pool electricity market and it is

assumed that generators bid their marginal energy costs. Generators are assumed

to have linear marginal cost reflective bids for energy and since there will be a

separate capacity payment mechanism, these bids should reflect their fuel costs and

not their capital costs. In addition, the dispatch model also dispatches for operating

reserves. Thus, each generator also bids into the pool to provide reserve capacity.

The dispatch model uses a linear programming market clearing formulation to co-

optimise unit operating points and reserve levels to find a least cost solution on an

hourly basis. The aim is given by the following objective function:

min

(

N∑

i=1

cpiPi +N∑

i=1

criRi

)

(3.1)

where Pi is the power from unit i and Ri is the operating reserve from unit i. The

energy and reserve bids of generator i are given by cpi and cri respectively and N

is the number of generators. If losses are neglected then the minimisation is subject


to a load balancing constraint and an operating reserve target.

N∑

i=1

Pi = Load (3.2)

N∑

i=1

Ri ≥ Reserve (3.3)

The characteristics for each unit i are given by equations (3.4) to (3.7).

0 ≤ Pi ≤ Pmaxi (3.4)

0 ≤ Ri ≤ Rmaxi (3.5)

Pi −1

Rslopei

Ri ≤ Pmaxi (3.6)

−R maxi

P miniPi + Ri ≤ 0 (3.7)

Equation (3.4) ensures that no generator can operate above its maximum rated

capacity and Equation (3.5) ensures that no generator can be dispatched for more

than its maximum reserve capacity. Equations (3.6) and (3.7) describe the reserve

characteristics of the generators. Equation (3.6) ensures that a generator cannot be

dispatched in such a way as its power output plus its reserve exceed its maximum

capacity. Equation (3.7) ensures that a generator cannot be dispatched to provide

reserve unless it is dispatched for power and is operating above its minimum operat-

ing point. The nature of these reserve constraints is illustrated graphically in Figure

3.1.

Discrete decisions must be made about the on/off status of a generator to ensure

it is not dispatched below its minimum rated capacity. Since the dispatch model

does not explicitly contain discrete decision variables, the following approach was

adopted to mimic these on/off decisions. The model is first run with each generator’s


Pmin i Pmax i

Power

Reserve

Rmax i

slope = -1

Figure 3.1: Generator reserve characteristics

power output permitted to range between 0 and maximum generation. This results

in optimality, however some units may be dispatched below their minimum operating

point. Since it is assumed that generators have linear bids for energy and reserve it

is assumed that those units which have been dispatched below their minimum must

be utilised to ensure load balance. These units are thus required to be turned on,

and as such, this first run of the dispatch model essentially determines which units

should be on and which off. The algorithm is then run a second time, this time only

including those units which have been turned on in the first stage and forcing them

to operate between their minimum and maximum rated capacities. The algorithm

now returns a feasible dispatch. The dispatch model was run for each hour for an

entire month for each of the test years. The results were then scaled up by a weighted

factor for each month to represent the entire year.

3.1.1 Validating the Dispatch Model

A full unit commitment model of the case system was designed in the PLEXOS

(2006) environment by Bryans (2006). This model used mixed integer programming

and accounted for temporal constraints including minimum stable generation, max-

imum generation, maximum ramp-up rates, maximum ramp-down rates, minimum

down-times, start times (from cold, warm and hot) and scheduled outages. Although

this model was extremely rigourous, the length of time for each monthly model run

(over 72 hours) made it too cumbersome to use for sensitivity analysis and economic

evaluation. In addition, the PLEXOS model was unable to handle updated wind


forecasts and as such a perfect wind forecast was required.

The dispatch model was benchmarked against the PLEXOS model at a number

of different installed wind capacities to ensure that the dispatches from the dispatch

model were reasonable. It was found that although the dispatch model does not

explicitly include temporal constraints, the dispatch model returned realistic dis-

patches when compared with the PLEXOS dispatches. In addition, the resulting

operating schedules for each unit from the dispatch model were examined to deter-

mine their compliance with the temporal constraints. It was found that the dispatch

model produced operating schedules which were over 95% compliant with the ramp

rate constraints and had compliance levels in excess of 90% for the minimum up and

down time and start time constraints.

3.2 Wind in the Dispatch Model

It is the two stage approach of the dispatch model which is one of its key advantages

when looking at wind generation. The first stage of the model essentially returns

a ‘commitment’ decision, by deciding which units should be turned on and which

should be off. The second stage of the model does not turn on or off any units but

determines their operating levels (economic dispatch). This approach can account

for the unpredictability of wind generation forecasts by using a long range wind

forecast in the first stage and an updated wind generation forecast in the second

stage, representing actual system operation. As such, the commitment decisions

are based on a longer range wind forecast and the operating levels are based on an

updated forecast as would be expected in reality. In order to reflect wind forecasting

characteristics, the time between the two model runs was assumed to be 6 hours

(Marti et al., 2006; Meibom et al., 2006).

This two stage approach, with commitment and dispatch taking account of

wind power forecasts, has also been adopted in the Siemens PROMOD software,

used in EirGrid (2004), and the two stage methodology is also employed in the

MORE CARE control software, which is described in detail for an island system in

Hatziargyriou et al. (2002) and Hatziargyriou et al. (2000).


In the process of establishing a transparent market for the island of Ireland, the

Irish system regulators gathered a comprehensive data set of operating characteris-

tics of all generators on the system, including a detailed wind generation data series

(AIP, 2005). This real wind power data is assumed to represent the updated wind

power forecast in the second run of the dispatch model.

The wind power forecast used in the first run of the model is generated based

on this real wind power output and the standard deviations of wind power forecast

errors (Doherty and O’Malley, 2005). An ‘average’ wind power forecast error with

a standard deviation of 9% of the installed capacity was used in the base case

(Doherty and O’Malley, 2005). This implies that 68% of the time, the forecast of

the wind power output is within 1 standard deviation (9% installed capacity) of the

actual wind power output. A ‘best’ case and a ‘worst’ case wind power forecast with

a 7% and 14% standard deviation respectively are also tested, representing the most

accurate and the least accurate the total wind power forecast error is likely to be, for

a forecast horizon of 6 hours ahead (Doherty and O’Malley, 2005)1. The sensitivity

of the net benefits of wind generation to changes in the wind power forecast error is

investigated in Chapter 6.

Figure 3.2 represents a two day sample of the wind series data used in the

dispatch model for an assumed wind penetration of 1000MW. The long range forecast

is based on a standard deviation of wind power forecasts equal to 9% of the installed

capacity of the wind generation. This forecast is used in the first run of the dispatch

model. The updated wind power forecast, used in the second run of the model, is

given by the real wind power output from AIP (2005).

Under EU Directive 2001/77/EC (2001) of the European Union, when it is avail-

able, wind generation must receive priority dispatch. In order to comply with this

directive, the wind generation is assumed to bid into the dispatch model with a

marginal cost of zero. This ensured it was always dispatched when available.

1Assuming the ‘worst’ case wind power forecast scenario could also represent a situation wherecommitment and dispatch were made further than 6 hours apart i.e. if commitment decisions weremade more than 6 hours ahead, the wind power forecasts would be less accurate and the errorslarger.


0

100

200

300

400

500

600

700

800

900

0 6 12 18 24 30 36 42 48

Hour

Win

dP

ow

er

Ou

tpu

tM

W

Long Range Wind Forecast(commitment)

Real Wind Output (dispatch)

Figure 3.2: Wind power data series

3.2.1 Wind Curtailment

If the long range wind forecast for a given hour grossly underestimated the actual

wind output, surplus conventional generation could be committed in the first stage

of the dispatch model, i.e. generation could exceed the load even when all committed

conventional generators are running at their minimum. If this situation occurs then

no conventional unit is switched off and the wind generation is curtailed instead.

Wind generation is curtailed if Equation (3.8) holds.

N∑

i=1

(δi,jPmin,i) + Windj > Loadj ∀j (3.8)

where N is the number of generators on the system, Pmin,i is the minimum

generation for unit i, δ is either 1 or 0 depending on whether unit i has been

committed in hour j. Windj is the updated wind power forecast for hour j and

Loadj is the load in hour j.

This situation results in the curtailment of wind generation by an amount equal


to the load minus the sum of the minimum generation of the committed generators.

This new wind output is then used as the updated wind forecast for that hour in

the second run of the dispatch model. This situation occurred rarely but increased

as the installed wind capacity increased.

3.2.2 Wind Shortfall

If the long range wind power forecast overestimates the actual wind output too few

plant may be committed to meet the load in the first run of the dispatch model.

This would happen if Equation 3.9 were to hold.

N∑

i=1

(δi,j(Pmax,i − Ri)) + Windj < Loadj ∀j (3.9)

where Pmax,i is the maximum capacity of generator i and Ri is the dispatched

reserve for unit i. When this happens, the dispatch model forces on fast acting plant,

such as Open Cycle Gas Turbines (OCGTs), to meet the shortfall in generation in the

second stage of the model. The breach of constraint (3.9) occurred more frequently

than (3.8) and increased as the installed wind capacity increased.

3.2.3 Load Factors for the Wind Generation

The electricity output of a wind generator is based on its load factor. The load

factors for wind generation in Ireland typically range from 25% - 40% depending on

a large range of factors including location, turbine size, season etc. (BWEA, 2005).

A range of different load factors are tested in this thesis and are summarised in

Figure 3.3.

It is assumed that the most favourable sites for wind turbines will be developed

first and these are likely to have the highest load factor. For this reason, it is

assumed that the load factor per MW installed decreases with increasing installed

capacity in the ‘low’ and ‘mid’ load factor cases as the number of feasible favourable

sites reduces. For comparative purposes, a ‘constant’ load factor has been included

and the ‘high’ load factor case represents the situation where new development in


0 500 1000 1500 2000 2500 3000 3500 40000

5

10

15

20

25

30

35

40

45

50

High Load FactorConstant Load FactorMid Load FactorLow Load Factor

Wind Generation MW

Loa

dFac

tor

%

Figure 3.3: Assumed load factors with increasing installed wind generation

wind penetration is in offshore sites with higher load factors and in onshore sites

with advances in wind turbine efficiency. These load factors are used when the wind

generation output from AIP (2005) is being scaled up for increasing penetrations of

wind generation. The sensitivity of the net benefits of wind generation to the chosen

load factor is investigated in Chapter 6.

3.3 The Dispatch Model Representing an Electricity

Market

The dispatch model aims to represent a realistic electricity market. The first stage

of the dispatch model can be considered to represent the forward market where

generators bid for energy and reserve and the MO/SO determines commitment. The

second stage of the dispatch model mimics gate closure where the final operating

and reserve levels are determined. Running the model in this manner is a realistic

way of capturing the operation of the market and also the error in the wind forecast

between ahead markets and real time. Figure 3.4 illustrates this methodology. Here,


the system is being dispatched to generate electricity and reserve for hour j.

aaaaaaaaaaaaaaaaaa

aaaaaaaaaaaaaaaaaa

aaaaaaaaaaaaaaaaaa

aaaaaaaaaaaaaaaaaa

aaaaaaaaaaaaaaaaaa

aaaaaaaaaaaaaaaaaa

aaaaaaaaaaaaaaaaaa

Load

Time

Reserve

Conventional Generation

Wind

Wind or Conventional Generationaaaaa

aaaaa

aaaaa

aaaaa

aaaaa

ForwardMarket

GateClosure

a

b

c

RealTime

MWHour j

Figure 3.4: Illustration of market model

In Figure 3.4, the x-axis represents the time-line approaching real time. The

long range wind power forecast is bid into the forward market (point a in Figure

3.4) and the conventional generation is dispatched up to the forecasted load plus the

reserve target (b). Thus, in this forward market those generators to be turned on

to be available for generation and reserve at real time are determined, based on the

long range wind forecast. It is assumed that due to the large levels of installed wind

generation being investigated, the wind generation forecast errors will dominate the

load forecast errors (Doherty and O’Malley, 2005) thus a perfect load forecast is

assumed. This assumption could be relaxed by altering the load in the second run

of the dispatch model in a similar manner to how the updated wind forecasts are


included.

Between the forward market and gate closure, the wind forecasts are readjusted

as forecasting becomes more accurate as the forecast horizon decreases. Thus, at

gate closure the wind profile has been adjusted to an ‘updated’ profile (c) and the

operating levels of those generators switched on in the forward market are set. It

may be necessary at gate closure to curtail the wind generation or turn on fast acting

units depending on the magnitude of the error in the wind forecasts (Sections 3.2.1

and 3.2.2). In this thesis, all of the costs and benefits are calculated on the dispatched

levels of the generators at gate closure. In reality, between gate closure and real time

the wind power output may change further. This balancing is represented by the

white shaded area in Figure 3.4. This real time error is not considered in this thesis

but will be discussed briefly in Chapter 8.

3.4 Specifics of the Dispatch Model for Case Study

The specific input assumptions for the dispatch model are described here in order

to tune the model for the Irish system.

3.4.1 Reserve Capacity

In many systems, the amount of operating reserve carried is equal to the size of the

largest infeed, which for Ireland is 480MW (EirGrid, 2006b). An increase in the

capacity of wind generation on an electricity system increases the uncertainty in the

system which results in a requirement to carry additional reserve capacity in order to

maintain system security (Soder, 1993). Thus, in this analysis, the assumed reserve

requirement is equal to the size of the largest infeed plus the additional reserve based

on the installed wind generation. The levels and cost of this additional reserve

requirement with wind generation is discussed in detail in Chapter 4. Generator

reserve capacities and bid prices (based on Doherty and O’Malley (2005)) are shown

in Appendix A.


3.4.2 Hydro and Pumped Storage

Ireland has one pumped storage station and a small concentration of hydro power

plants. Given the complex operating nature of these units, the hydro and pumped

storage plant were optimised in the full Unit Commitment model described in Section

3.1.1, and the operating levels were then used as an input in the dispatch model.

3.4.3 Outage Schedules

No unit is available for all hours of the year due to maintenance and outages. In

order to account for this, the calendar for generators’ scheduled outages from the

data set in AIP (2005) was used to incorporate the outage rates. A generator’s

maximum capacity was then set to zero for all 24 hours of the days it was scheduled

to be offline.

3.4.4 Demand

Hourly demand data for the Republic of Ireland is taken from the Irish transmission

system operator (EirGrid, 2006a). The Northern Ireland electricity system is about

one third of the size of the system in the Republic of Ireland so the load values for

the Republic were scaled up by a factor of 1.33 to represent the two systems. The

demand levels for 2010, 2015 and 2020 were based on the projected load growth

levels in EirGrid (2005b). Three demand growths are investigated: 2.5%, 3.7% and

4.3%. In order to calculate the demand for each year the following formula was used.

Lj,y = Lj,2005 × (1 + g)y−2005 ∀j (3.10)

where Lj,y is the load in hour j for year y and y is either 2010, 2015 or 2020.

The demand in each of these years is grown from the base year of 2005. Thus, the

load in hour j in 2005 is given by Lj,2005. The assumed growth rate is g. Thus a

flat growth rate for each hour of each day is assumed. This may not necessarily be

the case in reality, with the demand in some hours growing more than in others,

however, based on EirGrid (2005b), it is assumed that the average growth over the


year will be one of the three growth rates given.

3.4.5 Fuel Prices

There are a range of fuel prices investigated in the analysis, based on CER (2006b),

UK DTI (2004) and Doherty et al. (2006), and shown in Table 3.1. The same growth

rate is not used for each fuel price as some fuel prices are expected to grow by more

than others. For example, it is likely that gas prices will grow more than coal due

to an increase in the development of cleaner more efficient gas fired plant (CER,

2006b).

Table 3.1: Fuel costs in e 2006/GJ

Year Scenario Gas Coal Oil Peat Distillate

2010

Low 4.16 2.16 4.13 3.14 7.83

Mid 4.37 2.17 4.27 3.23 8.08

High 4.58 2.18 4.40 3.33 8.34

2015

Low 4.41 2.17 4.30 3.25 8.14

Mid 5.04 2.22 4.71 3.53 8.92

High 5.66 2.26 5.12 3.82 9.70

2020

Low 4.91 2.21 4.63 3.48 8.76

Mid 6.41 2.31 5.62 4.15 10.65

High 7.91 2.41 6.61 4.83 12.52

The bid price for each generator is obtained by multiplying the appropriate fuel

price by the generator’s consumption of energy per MWh (given for each generator

in Appendix A). The sensitivity of the net benefits of wind generation to the chosen

fuel price is investigated in Chapter 6.

3.4.6 Peat Generation

Ireland has a large indigenous natural peat resource and peat fired generation ac-

counts for approximately 350MW of Ireland’s installed capacity. The Irish Gov-

ernment have adopted a ‘must-run’ attitude towards peat generation and in 2001

the European Commission agreed to allow the Irish Government to continue this


policy. Under this scheme, electricity customers subsidise the use of indigenous

peat as a fuel for power through a Public Service Obligation on all electricity bills

(DCMNR, 2002). This is part of the national policy of fuel diversity, which is part

of Ireland’s strategy to ensure security of electricity supply. This ‘must-run’ policy

however, distorts the optimal scheduling of the generators. Since this is a social wel-

fare maximising study, it is assumed for the purposes of this analysis, that the peat

generators must bid their marginal cost into the gross pool like all other generators.

The assumed prices are shown in Table 3.1 previously.

3.4.7 Wind Capacity

In this thesis installed wind capacities up to 4000MW are investigated. While this

thesis does not explicitly conduct a resource evaluation of wind generation on the

case study system, other studies suggest that the feasible resource could be larger

than 4000MW (SEI, 2004a). However, it is deemed that the dispatch model may

become less reliable at very high levels of installed wind generation. Thus, it is

suggested that a more complex engineering model which can include the temporal

characteristics of the generators and issues of system dynamics would be required

for higher assumed installed wind capacities.

3.4.8 Network Constraints

Network constraints are not explicitly included in the dispatch model. However

initial estimates from the Irish transmission system operator of the network rein-

forcement necessary with increases in wind generation are discussed in Chapter 4.

These network reinforcements will help ameliorate potential congestion. Network

congestion is discussed briefly in Chapter 8. In addition, an application of the dis-

patch model has been conducted in Chapter 7 which utilises network analysis for

the distribution system.


3.5 Summary of Key Features of the Dispatch Model

This chapter detailed the model used to simulate the operating schedules of gener-

ators on the Irish system with increasing penetrations of wind generation. The key

features of this model are summarised here.

• The model used is called the dispatch model and it is a linear programming

formulation which cooptimises generators operating and reserve levels on an

hourly basis.

• The model represents a gross pool electricity market. The generators en-

ergy bids consist of their marginal fuel cost (prices given in Table 3.1 and

energy consumption per MWh in Appendix A). Their reserve bids are given

in Appendix A.

• The model is run in two stages with the commitment decisions made in the

first stage and the operating decisions made in the second. The model is

validated against a full unit commitment model of the system.

• The wind generation bids into the pool with a marginal cost of zero. A long

range forecast is used in the commitment stage of the model and an updated

forecast is used to determine the generators’ operating levels.

• The operation of the hydro and pumped storage units is not optimised

and is based on the schedules from the unit commitment model.

• A perfect demand forecast is assumed. A range of demand growths are in-

vestigated for the years 2010, 2015 and 2020.

• The network is simplified to a single bus-bar system and the optimisation

does not account for losses.

The sensitivity of the costs and benefits of wind generation to many of the

assumptions discussed in this Chapter are investigated in Chapter 6.

CHAPTER 4

The Cost of Wind Generation

THIS chapter presents the costs associated with wind generation. As this study

represents a social welfare maximising analysis, all the costs associated with

wind generation are assumed to accrue to society in general rather than any par-

ticipant in particular. For example, it has been debated whether wind generation

should be charged for the additional reserve costs it may impose on a system. This

work does not answer this question but rather attempts to estimate the magnitude

of the costs.

The costs of developing and operating wind generation is a cost of increased wind

generation and is included here. A number of scenarios are investigated with respect

to wind capital costs and are discussed in Section 4.1 (CER, 2001). Work done by

AIRGS (2006b) included initial estimates of the additional network upgrade costs

associated with increasing penetrations of wind generation in Ireland. These initial

estimates of deep network reinforcement costs are discussed in Section 4.2.

An increase in variable and relatively unpredictable wind generation on the sys-

tem can increase the uncertainty for system operators. As such they may be required

to carry additional reserve capacity to maintain standards of security. The level, cost

56

Chapter 4. The Cost of Wind Generation 57

and implications of this additional reserve capacity is discussed in Section 4.3 of this

Chapter.

An increase in variable wind generation may result in an increase in the cycling

of conventional units on the system as operators attempt to coordinate the following

of the fluctuating load throughout the day and the variations in the wind generation

(EirGrid, 2004). The dispatch model described in Chapter 3 is used to investigate

the changes in the operating patterns of conventional generators as the installed

wind penetration increases. These costs are discussed in Section 4.4. A summary of

the costs of wind generation is provided in Section 4.5.

4.1 Wind Development Costs

Wind generation has zero fuel costs, thus, its costs are based mainly on its capital

cost, which represents about 75-80% of its total cost. Average capital costs for

wind generation currently range from e900 to e1,100 per kW installed including

the cost of shallow connection to the grid (EWEA, 2003). Shallow connection costs

are the costs of the local assets required to connect a generator to the electricity

network. Deep connection costs are the cost of the transmission system assets that

may be required with an increase in generation. These deep costs are discussed in

the following Section 4.2. Four different capital cost curves for wind generation in

Ireland are investigated in this thesis and are shown in Figure 4.1.

In each of the four scenarios, the capital costs are initially assumed to be e1

million per MW installed. In ‘High Capital’, the wind development costs grow as

the installed wind generation increases. This is due to increases in the costs of

production of wind turbines associated with a growth in fossil fuel prices worldwide,

an increase in the development of more expensive offshore wind farms and a reduction

in the economies of scale for smaller onshore wind farms. As a control scenario,

‘Constant Capital’ assumes that the cost per MW remains constant as the installed

wind generation increases. In ‘Mid Capital’ and ‘Low Capital’ the cost reduces

incrementally, most likely as a result of advances in technology and turbine size.

Under ‘Mid Capital’ it has been assumed that the most accessible and efficient sites


0 500 1000 1500 2000 2500 3000 3500 40000.8

0.85

0.9

0.95

1

1.05

1.1

High CapitalConstant CapitalMid CapitalLow Capital

Installed Wind Capacity MW

Cap

ital

Cos

tper

MW

ine

m

Figure 4.1: Assumed capital cost per MW of installed wind generation

are developed first. As more and more wind generation is connected, the number of

feasible wind farm sites reduces and the size of wind farms is likely to decline. It is

typically the case that there is a reduction in the economies of scale for smaller wind

farms resulting in higher costs per MW installed. Thus, these reduced economies

of scale combined with a likely increase in off-shore wind development result in the

assumption that the cost per MW will begin to increase beyond a certain point in

‘Mid Capital’. This point, where the capital cost begins to increase, was based on

a forecast of the current feasible installed wind capacity on the island of Ireland,

given in SEI (2004b). Capital costs continue to decrease under ‘Low Capital’ under

the assumption of technology advances.

The total capital cost is given by the total installed wind capacity multiplied

by the cost per MW from Figure 4.1. However, throughout this thesis the costs

are analysed on an annual basis, thus, in order to annualise the capital costs, they

are assumed to be equivalent to an annuity (CER, 2006b; RAE, 2004). The annual

capital cost is therefore given by multiplying the total capital cost by A in Equation

4.1.


A =i

(

1 − (1 + i)−T) (4.1)

where i is the assumed interest rate and T is the term over which the capital

costs are discounted. The interest rates of 6%, 8% and 10% are tested in Chapter

6. The term is assumed to be 20 years (CER, 2006b; RAE, 2004).

Operating and maintenance costs for wind energy include repair and insurance

and represent about 20-25% of the total production costs per kWh for wind energy

(EWEA, 2003). Operation and maintenance costs are assumed to be e35,000 per

MW installed per annum (Doherty et al., 2006).

4.2 Network Upgrade Costs

As the installed penetration of wind generation increases, it is likely that the trans-

mission and distribution networks will require upgrading. Workstream 3 of the

Government Sponsored All-Ireland Renewable Grid Study (AIRGS, 2006a) will in-

vestigate the network development options and costs associated with increases in

wind generation penetration in Ireland. As yet, no results are available from this

study, however, Workstream 2A includes initial estimates from the transmission

system operator of the likely deep network reinforcement costs with increased wind

generation. These estimates are illustrated in Figure 4.2.

As with the capital costs of wind generation, these network costs must be ex-

pressed as an annual cost for the purposes of this study. This is done by expressing

them as an annuity using Equation (4.1) with an interest rate of 8% and a term of

20 years.

Workstream 3 will include a load flow analysis of the Irish system to determine

potential constraints and bottlenecks (AIRGS, 2006a). This requires a large scale

engineering model of the system which is considered to be beyond the scope of this

study. Thus, the model described in Chapter 3 is based on a single bus bar system

and transmission system losses are ignored.


0 500 1000 1500 2000 2500 3000 3500 40000

100

200

300

400

500

600

700

800

Deep Network Costs


Net

wor

kC

ostse

m

Figure 4.2: Network reinforcement costs with increased wind capacity

4.3 Additional Reserve Requirement

In many systems, the amount of operating reserve carried is equal to the size of

the largest infeed (EirGrid, 2006b). An increase in the capacity of wind generation

on an electricity system increases the uncertainty in the system as wind generation

is relatively unpredictable and non-dispatchable. This results in a requirement to

carry additional reserve capacity in order to maintain system security Soder (1993).

The level of this additional reserve requirement for Ireland was based on the results

shown in Doherty and O’Malley (2005).

Doherty and O’Malley (2005) relate the reserve level on the system in each hour

to the reliability of the system over the year. The reserve is thus allocated in such

a way as to keep the average risk of a load shedding incident in each hour the

same for all hours. Doherty and O’Malley (2005) incorporate both load and wind

power forecast errors when calculating the reserve requirement for each hour. Load

forecast errors are not particularly sensitive to the forecast horizon and are usually


proportional to the load at any given hour. The wind power forecast errors generally

increase as the forecast horizon increases. In Doherty and O’Malley (2005), the load

and wind forecast errors for hour h are modelled as Gaussian stochastic variables

with a mean of zero and a standard deviation of σload,h and σwind,h respectively and

the total system forecast error is given by Equation (4.2).

σtotal,h =√

σ2

load,h + σ2

wind,h (4.2)

Based on results given in Pinson and Kariniotakis (2003), Doherty and O’Malley

(2005) determined the most accurate and the least accurate wind power forecasts

errors are likely to be. It was found that the average wind power forecast error

had a standard deviation of approximately 9% of the installed wind capacity. The

best and the worst case forecast errors had standard deviations of 7 and 14% respec-

tively (See Section 3.2 in Chapter 3). Figure 4.3 below indicates the reserve capacity

required to maintain reliability on the Irish system associated with these three stan-

dard deviations of wind power forecast errors. These graphs are extrapolated from

Doherty and O’Malley (2005).

0 500 1000 1500 2000 2500 3000 3500 40000

200

400

600

800

1000

1200

1400

1600

Worst Wind ForecastsAverage Wind ForecastsBest Wind Forecasts

Installed Wind Capacity (MW)

Res

erve

(MW

)

Figure 4.3: System reserve level with different wind power forecast errors


The sensitivity of the net benefits to changes in the forecast error of the wind

power output is tested in Chapter 6. Thus, when a wind power forecast error

with a standard deviation of 7% is tested in Chapter 6, the ‘best case’ reserve

requirement shown in Figure 4.3 is also used. Thus, in this analysis, the assumed

reserve requirement is equal to the size of the largest infeed (480MW) plus the

additional reserve based on the installed wind generation.

Total wind power variations over short time frames are small, thus increasing

wind power capacity has little effect on the reserve categories that operate over a

short time frame (seconds to minutes) (Doherty and O’Malley, 2005). Increasing

amounts of wind capacity causes a greater increase in the need for categories of

reserve that act over longer periods of time (20min - 1 hour), known as replace-

ment reserve. This is the reserve category illustrated in Figure 4.3. The Irish TSO

pays generators e1.15 per MW per hour for the replacement reserve they provide

(EirGrid, 2006b). Thus, the additional reserve cost associated with increases in wind

generation is assumed to be equal to e1.15 multiplied by the additional reserve ca-

pacity from Figure 4.3.

Since the dispatch model described in Chapter 3 cooptimises energy and reserve,

there are occasions when units are switched on in order to be dispatched to provide

reserve. As the reserve requirement increases, the committment of units to provide

reserve increases. In addition, the burden of having to provide an increased level

of reserve results in the operation of more plants at lower levels of operation and

thus lower efficiencies. These indirect costs of additional reserve are captured by a

greater cycling cost which is discussed in Section 4.4 plus lower emissions and fuel

benefits, discussed in Chapter 5.

4.4 Cycling Conventional Units

In order to meet the fluctuating electricity demand throughout the day, generating

units are often required to start up and shut down and vary their output in line

with the demand changes. Certain units, generally the cheapest ones, are on all the

time and are referred to as baseloaded units. A mid merit unit would turn on in


the morning and turn off again at night when the electricity demand is low and a

shoulder unit turns on once a day when the electricity is at its highest. A peaking

unit is a flexible unit which is required less than once a day when the demand reaches

unusually high levels or when other units are unavailable (EirGrid, 2006a).

In general, most conventional units are designed for continuous rather than

variable operation and when operating in their normal range can operate for rel-

atively long periods with relatively low risk of failure and loss of equipment life

(Lefton et al., 1997). When a generating unit is required to vary its output to meet

the demand, the components in the unit are subject to stresses and strains. This

is known as cycling and includes ramping up and down during load following and

switching on and off. When a unit is turned off and on, the boiler, steam lines,

turbine and auxiliary components undergo large temperature and pressure stresses

which result in damage. This damage accumulates over time and eventually leads

to accelerated component failures and forced outages (Lefton et al., 1997).

As illustrated in Figure 4.4, once a unit is on, changing the output is the least

damaging of the cycling activities. Starting a unit is much more damaging. There

are three types of starts, a hot start, a warm start and a cold start. A hot start

refers to a unit starting up within a few hours of shutting down, while the metals in

the unit are still hot. A warm start would be when the unit has been off for slightly

longer, and the metals have begun to cool but are not yet cold. A cold start would

occur if the unit had been off for a long period and the metals were completely

cooled. This is the most damaging of all cycling activities (Lefton and Besuner,

2001).

This wear and tear on the components of the generating units is exacerbated by

a phenomenon known as creep-fatigue interaction. Creep is the change in the size or

shape of a material due to constant stress on the material over time. This is likely

in units which are operated at continuous output over long periods of time, such as

baseloaded units. Creep stems from continuous exposure to elevated temperatures

and high pressures. (Grimsrud and Lefton, 1995). Fatigue occurs when a material

is exposed to fluctuating stresses resulting in fractures and failures. Fatigue is likely


LoadFollowing

HotStart

WarmStart

ColdStart

Pmax

Pmin

Op

erat

ing

Lev

el

Damage

Figure 4.4: Damage associated with different cycling activities

during cyclical operation when the materials experience large transients in both

pressures and temperatures (Lefton et al., 1998).

Older units which were designed and used for baseloaded operation over a number

of years and are then forced to cycle on a regular basis are very susceptible to com-

ponent failure through creep and fatigue damage interaction (Grimsrud and Lefton,

1995). A brand new component can withstand a lot of fatigue damage before it

fails. However, a component that has already undergone 50% of its life creep damage

can fail with only 10% fatigue damage (American Society of Mechanical Engineers,

1990). This phenomenon is illustrated in Figure 4.5. The unit illustrated has

been operating at a relatively stable output over a long period of time (such as

a baseloaded unit) and is 50% through its expected component lifespan at point A

on the curve. If it were to continue to operate in this manner, it would achieve its

design life expectancy and would fail at point B with 100% of its creep damage.

However, if at point A, the unit is required to ramp up and down and turn on

and off more frequently, the components will experience fatigue damage. The unit

will then fail prematurely with only 10% of allowable fatigue damage at point C

(American Society of Mechanical Engineers, 1990).


Co

mp

on

ent

Lif

e R

emai

nin

g

Damage

50% Creep Damage

100%

50%

0

10%

A

BC

Creep

Fatigue

Figure 4.5: Creep fatigue interaction resulting in premature component failure

This depletion of the life expectancy of conventional units is a serious issue when

analysing wind generation penetration as it is likely that some units will be moved

down the merit order (as wind generation is assumed to have priority dispatch

EU Directive 2001/77/EC (2001)) and will switch from being baseloaded to being

required to be more flexible. In order to illustrate this point, Figure 4.6 shows the

operating profiles of two units on the Irish system in 2010 with 0MW and 3000MW

of installed wind.

Figure 4.6 illustrates the operating levels for a CCGT unit and a coal unit.

Both of these units operate at baseload almost all the time at low levels of installed

wind generation. It can be seen however, that when the installed wind generation

increases to 3000MW, these units become much more cyclical in operation. These

units are likely to have already undergone significant creep damage and thus the

issue of creep-fatigue interaction will be paramount if these units are required to

cycle more frequently with increased wind generation. In addition, switching from

baseloaded to variable operation will have an impact on the revenues earned by

generators in the market and may even result in the stranding of assets. This issue

is discussed in Chapter 8.


0

50

100

150

200

250

300

350

400

0 12 24 36 48 60 72 84 96

Hour

Ou

tpu

tM

W

CCGT 0

CCGT 3000

Coal 0

Coal 3000

Figure 4.6: Operating profiles of two Irish units with different wind penetrations

The actual cost of cycling is very difficult to estimate and must be conducted

on a plant by plant basis (Grimsrud and Lefton, 1995). The following approach has

been adopted to estimate the cost of cycling for conventional units.

4.4.1 The Cost of Cycling

Cycling results in an increased fuel cost for the unit as well as other more complex

costs. The costs associated with cycling are given in Table 4.1 (Lefton et al., 1997).

It is estimated that these costs can range from e200 to e500,000 per single on-off

cycle depending on the type of unit (Lefton and Besuner, 2001). Grimsrud and Lefton

(1995) calculated each of the other cycling costs as shown in Table 4.1 above and

these are shown in the ‘True Cost’ column in Table 4.2. From Table 4.2 it is clear

that the true cost of a single on-off cycle greatly exceeds the fuel cost. The cycling

costs depend mainly on the type of boiler in the unit rather than the fuel burnt,

however, for ease of reference, Table 4.2 illustrates the typical fuel types used in


Table 4.1: Costs associated with increased cycling of units

1. Replacement energy & capacity due to changes in forced outage rates

2. Additional O&M spending associated with overhaul

3. Higher heat rates due to low load and variable load operation

4. Efficiency changes due to component degradation

5. Auxiliary power, fuel and chemicals during start up

6. Unit life shortening

7. Increased operator error as a result of greater hands-on operation

Table 4.2: Cost per single on-off cycle

Boiler Type Common Fuel Used Typical Fuel Cost e True Cost e

HRSG Gas 500 1,500 - 25,000

Subcritical Drum Coal or Oil 5,000 15,000 - 100,000

Large Supercritical Coal 10,000 30,000 - 500,000

each boiler as well as their typical fuel costs for an on-off cycle.

From Table 4.2, combined and open cycle gas turbines often use a Heat Recovery

Steam Generator (HRSG) boiler, also known as a waste heat boiler. The majority

of the gas plants on the Irish system use HRSG boilers. The terms subcritical and

supercritical come from the definition of the temperature and pressure at which

water vapour and liquid water are indistinguishable, known as the critical point.

This occurs at a temperature of 374◦C and a pressure of 22MPa. Thus, subcritical

boilers operate below this critical point and are often found in coal and oil plants. All

of the Irish coal and oil plants use this boiler design (EirGrid, 2005b). Supercritical

units operate at pressures and temperatures above the critical point to improve

efficiency and can be found in some large coal units (Cregan and Flynn, 2003). There

are currently none of these units installed on the Irish system, however they can be

found in some European countries, Asia and the United States (Dalton, 2004). It is

apparent from Table 4.2 that the true cost of cycling is many times greater than the


fuel cost. The fuel cost typically represents only a fraction, between 2% and 30%,

of the real cost of cycling a generating unit.

In order to estimate the cycling costs of the generators on the Irish system with

increases in wind generation, the following approach was adopted (Lefton et al.,

1997). Once the dispatch of the generators had been determined, as described in

Chapter 3, the number of cold, warm and hot starts for each generator were counted.

In addition, when a generator’s output varied, the change in output was expressed

as a percentage of the maximum rated capacity of the generator. In order to compile

the total cycling cost, these cycling activities were expressed in terms of the damage

from a single hot start (Combined Cycle Journal, 2004). The rate of temperature

and pressure change under each of the cycling activities was determined and this was

considered to be equivalent to the damage incurred for each activity (Lefton et al.,

1997). Each of the cycling activities could then be expressed in terms of the damage

incurred during a hot start, referred to from here as an ‘equivalent hot start’ (EHS)

as shown in Table 4.3 (Lefton et al., 1997).

Table 4.3: Cycling activity expressed as an equivalent hot start

Activity EHS

Cold Start 2.28

Warm Start 1.77

Hot Start 1.00

10% Load Swing 0.05

Thus, from Table 4.3, the damage from a cold start is equivalent to 2.28 times the

damage from a hot start. A load swing equal to 10% of the rated capacity of the unit

is the equivalent of 0.05 times a hot start. Thus, each category of cycling activity

was multiplied by the figures in Table 4.3 to give the total number of equivalent hot

starts for each generator over the course of the year.

Once the number of EHS for each generator had been determined, the costs

could be calculated. As shown in Table 4.2, the fuel cost for a cycling activity

represents between 2 and 30% of the true cost of cycling depending on the unit. Thus,


using specific generator information (Appendix A), the age and previous operating

patterns of the generators (in an attempt to estimate creep damage and flexibility)

and unit cycling descriptions from Lefton et al. (1998), the fuel cost for each unit

was assumed to represent a percentage of its total cycling cost.

For example, Ireland has a number of large coal fired units that have been in

operation for over 20 years. These units are baseloaded throughout the year, in

fact, Lefton et al. (1998) found that these units are more baseloaded than any in

the United States. The energy consumed in one hot start for one of these units is

4,360GJ (Appendix A). Using the ’mid’ fuel price for coal of e2.17/GJ (see Table 3.1

in Chapter 3) this implies that the fuel cost of a single hot start is e9,461. These

units use large subcritical drum boilers and have been heavily baseloaded over a

long period of time, and are likely to have incurred a large amount of creep damage

(Lefton et al., 1998). The fuel cost is therefore estimated to represent 10% of the

total cycling cost for these units. Thus, the true cost of a single hot start for one of

these coal units is e9,461 ÷ 0.10 = e94,610.

The dispatch model described in Chapter 3 was used to determine the operating

schedules of conventional generators with increasing penetrations of wind generation.

In order to calculate the cycling costs associated with increased wind generation, the

total cycling cost with zero installed wind generation was determined as described

above. This was then subtracted from the cycling costs at each level of installed

wind generation. This was conducted for each of the test years and the resulting

additional cycling costs are shown in Figure 4.7.

As Figure 4.7 shows, wind generation causes an increase in the cost of cycling

across each of the three test years. By analysing the operating schedules of the

generators, it is seen that wind generation causes an increase in the cycling of the

marginal units. In 2010 and 2015 there is a reduction in the added cycling costs

between 500 and 2000MW of installed wind generation. This is due to the wind

generation pushing the oil units down the merit order so they are used less frequently

and are replaced on the margin by more flexible gas fired generation. As the installed

wind generation increases beyond 2000MW, more units are displaced and plants


0 500 1000 1500 2000 2500 3000 3500 40000

20

40

60

80

100

120

140

160

180

200

201020152020


Cycl

ing

Cos

t(e

m)

Figure 4.7: Cycling costs with increasing penetrations of wind generation

which had been previously baseloaded are moved down to the margin. These are

units with very high cycling costs and thus, at high penetrations of wind generation,

the cycling costs are large.

4.5 Summary of Costs Associated with Wind Genera-

tion

This chapter outlines the costs associated with increasing penetrations of wind gen-

eration. The costs included were the capital costs of wind generation, the operation

and maintenance costs, the deep network reinforcement costs and the cost of the

additional cycling of the conventional units. The salient results associated with each

of these costs is summarised here:

• Four different capital cost profiles will be examined in this thesis. These

capital costs include the assumed shallow connection cost to the network and


are represented as an annuity with a term of 20 years.

• The operation and maintenance costs are assumed to be equal to e35,000

per annum per MW installed.

• The deep network reinforcement costs are taken from AIRGS (2006b)

and come into force above 1500MW installed. These costs are included as an

annuity with a term of 20 years.

• The additional reserve costs are based on the reserve capacities with in-

creased wind generation in Doherty and O’Malley (2005). Three different re-

serve scenarios will be examined in Chapter 6 based on the accuracy of the

assumed wind power forecasts. The reserve cost is assumed to be e1.15/MWh

(EirGrid, 2006b).

• The additional cycling costs are the only costs calculated based on the dis-

patches from the model described in Chapter 3. The cycling activities are

expressed in terms of equivalent hot starts and the cost is calculated based

on their fuel cost during a hot start. Increasing penetrations of wind genera-

tion cause an increase in cycling of marginal units across all three test years

resulting in increased costs.

The sensitivity of the net benefits to many of the assumptions discussed here will

be investigated in Chapter 6. In addition, the costs of wind generation described

in this chapter are not an exhaustive list of the costs of wind generation, thus the

implications of including other costs in the analysis is discussed in Chapter 8.

CHAPTER 5

The Benefits of Wind Generation

THIS chapter discusses the benefits associated with increased wind generation.

As with the costs, the benefits are not enjoyed by any entity in particular but

are assumed to be attributable to society in general.

The capacity credit of wind generation is a measure of the amount of conventional

generation that could be displaced by the renewable production without making the

system any less reliable (Castro and Ferreira, 2001). For low levels of installed wind

generation, the capacity credit tends to approximate the average wind output. How-

ever, as wind penetrations increase, the capacity credit tends to reduce as the corre-

lation of the wind generation output on the system increases (Castro and Ferreira,

2001). The value of the capacity benefit of wind generation is discussed in Section

5.1.

As the installed capacity of wind generation increases it displaces conventional

generation which has an impact on the emissions from the conventional units. Emis-

sions of carbon dioxide (CO2) and sulphur dioxide (SO2) depend on the quantity of

carbon and sulphur in the fuel respectively and the quantity of fuel burnt. Thus,

a reduction in the operation of a thermal unit will result in a reduction in CO2

72

Chapter 5. The Benefits of Wind Generation 73

and SO2 emissions. Nitrogen Oxides (NOX) formation is more complex and does

not depend solely on the nitrogen content of the fuel. The formation of emissions

in combustion plants is discussed in Section 5.2. The dispatch model described in

Chapter 3 is used to quantify the emissions savings from wind generation and these

benefits are shown in Section 5.3.

When wind generation displaces electricity produced from thermal units, the

quantity of fuel burnt by the thermal units reduces and wind generation provides

a fuel saving (Malik and Awsanjli, 2004). The value of this saving depends on the

price of the fuel saved and is impacted by a number of factors. The load factor of

the wind generation gives the MWh produced per MW installed and as this reduces

the incremental fuel saving reduces. An increase in the cycling of conventional units

will reduce the fuel savings from wind generation as start ups are fuel expensive.

The fuel savings associated with increases in wind generation are determined using

the dispatch model and are discussed here in Section 5.4.

A summary of the benefits of wind generation and the main assumptions dis-

cussed in this Chapter are given in Section 5.5.

5.1 Capacity Benefit

One of the key benefits associated with increased wind generation is the additional

capacity it adds to the system. The extent to which wind generation can substitute

for conventional generation without reducing the reliability of the system is given

by the capacity credit of wind (Castro and Ferreira, 2001). Variable sources of

generation, such as wind, make a different contribution to the capacity on the system

than dispatchable generation. Although wind generation can serve a large proportion

of the load, it may not necessarily be the case that the times of high wind generation

coincide with times of high demand (Doherty et al., 2006).

Doherty et al. (2006) use a monte-carlo approach to determine the capacity

credit of wind generation in Ireland. The reliability criteria to be maintained with

increasing wind generation was a loss of load expectation (LOLE) of 8 hours per

year. Wind generation was added to the system, and the wind power profiles were


subtracted from the load profile. This resulted in a reduction in the LOLE as the

installed generation on the system was relatively larger compared to the load. The

load was then systematically increased until the LOLE returned to 8 hours per year.

The capacity credit of wind generation was calculated by dividing the installed wind

generation by the increase in load. Figure 5.1 illustrates the capacity credit of wind

generation from Doherty et al. (2006) which is used in this analysis.

0 500 1000 1500 2000 2500 3000 3500 40000

0.1

0.2

0.3

0.4

0.5

0 500 1000 1500 2000 2500 3000 3500 4000

20

40

60

80

100

Capacity Credit

Value of Capacity Benefit


Cap

acity

Cre

dit

Ben

efit

ine

m

Figure 5.1: The capacity credit of wind generation in Ireland

As illustrated by Figure 5.1, as wind penetrations increase, the capacity credit

reduces. This is due to an increase in the correlation of the wind generation output

on the system (Castro and Ferreira, 2001). As the capacity credit is defined as

the extent to which wind generation can substitute for conventional generation, the

capacity benefit of wind generation can therefore be deemed as the saved cost of

having to build and maintain additional conventional generation in its place. In

other words, from Figure 5.1, 1000MW of installed wind generation has a capacity

credit of 34%. Thus, the development of 1000MW of wind generation would save


building and maintaining a 378MW conventional plant with an availability of 90%

(378MW × 0.9 = 340MW).

For the purposes of this thesis, it is assumed that new conventional generation

built in Ireland will be gas fired, with a capital cost of e600,000 per MW installed

and operation and maintenance costs of e50,000 per MW per year (CER, 2006b;

Doherty et al., 2006). The capital cost is converted to an annuity in order to be

expressed as an annual cost. The broken line in Figure 5.1, represents the annual

capacity benefit of wind generation with the monetary values on the secondary y-

axis.

5.2 Emissions in Conventional Generators

Harmful emissions are created in combustion plants through the burning of fuels

at elevated temperatures. Wind generation can result in a reduction in the output

of conventional units on the system, however, it is not necessarily the case that a

reduction in output will result in a reduction in emissions. This thesis investigates

the impact of wind generation on emissions of carbon dioxide (CO2), sulphur dioxide

(SO2) and nitrogen oxide (NOX). This Section describes how these emissions are

created in combustion units.

5.2.1 Carbon Dioxide Formation

Carbon dioxide is generated by the combustion of fuels containing carbon. The

amount of carbon dioxide released is in direct relationship with the amount of carbon

in the fuel and the quantity of fuel burnt (EIA, 2004). Thus a generation plant which

burns a carbon intensive fuel will generate more carbon dioxide at increased levels

of operation. The following expression was used to calculate the CO2 emissions of

a combustion plant.

CO2/MWh =

(

E

CV

)

(CO2/kgfuel) (5.1)

where E represents the generator’s energy consumed in megajoules (MJ) per


MWh and CV is the calorific value of the fuel in MJ per kilogram. CO2/kg fuel

represents the amount of CO2 emissions released per kilogram of fuel burnt.

5.2.2 Sulphur Dioxide Formation

Sulphur (S) is found in hydrocarbon fuels and is mostly in pure form. During com-

bustion sulphur combines with carbon, hydrogen and oxygen to form SO2, SO3, SO,

CS, CH, COS, H2S, S and S2 (Hunter, 1982). Given the high temperatures and

oxygen concentrations during combustion, sulphur dioxide (SO2) is the principal

sulphur compound formed in combustion. Even with 20% air deficiency, 90% of the

sulphur is in the form of SO2 and as little as 0.1% is as SO3; SO accounts for the

remainder of the sulphur (Harris, 1990). As a result, the analysis that follows will

concentrate on emissions of SO2 rather than the alternative sulphur compounds.

Also the NECD and the Large Combustion Plant Directive indicate limits on allow-

able emissions of SO2 (EU Directive 2001/80/EC, 2001). The expression used to

calculate sulphur dioxide emissions is the same as that given in (5.1) substituting

SO2 for CO2.

5.2.3 Nitrogen Oxides Formation

Oxides of Nitrogen (NOX) are formed by the combination of nitric oxide (NO) and

nitrogen dioxide (NO2). NO and NO2 are formed during combustion by the reaction

of nitrogen present in the combustion system, either in the fuel or in the combustion

environment. Normally NO is formed in much larger amounts than NO2, and NO2

is formed by further reaction of NO (Cunningham, 1978). Thus, NO formation

determines the total amount of NOX emitted.

Unlike CO2 and SO2, NOX formation does not depend solely on the nitrogen con-

tent of the fuel. It is also significantly affected by the flame temperature, the oxygen

concentration and the residence time. The formation of NOX can be attributed

to four distinct chemical kinetic processes: thermal NOX formation, prompt NOX

formation, fuel NOX formation and reburning. Thermal NOX is formed by the

oxidation of atmospheric nitrogen present in the combustion air. Prompt NOX is


produced by high-speed reactions at the flame front, and fuel NOX is produced by

oxidation of nitrogen contained in the fuel. The reburning mechanism reduces the to-

tal NOX formation by accounting for the reaction of NO with hydrocarbons (Kesgin,

2003). Although these processes are different they may operate concurrently.

5.2.4 Coal, Peat and Heavy Fuel Oil Generators

As described above, the carbon dioxide and sulphur dioxide emissions from a gen-

eration plant depend on the chemical content and the calorific value of the fuel. A

summary of the characteristics of coal, peat and heavy fuel oil are given in Table

5.1 (EIA, 2004; Molero, 1997). Gas fired units are dealt with separately in Section

5.2.5.

Table 5.1: Typical components of various fuel types

Carbon Sulphur Nitrogen Calorific Value CO2/kg fuel

Coal 65% 0.8% 1.3% 28 MJ/kg 2.49

Peat 25% 0.15% 0.65% 7.75 MJ/kg 1.25

Heavy Fuel Oil 87% 2% 0.2% 40 MJ/kg 0.79

The composition of a fuel depends on the composition of the source fuel and is

manufacturer dependent. Since this is unique and varies from source to source and

geographical origin, Table 5.1 give typical values only. It is clear from Table 5.1 that

although coal may contain more carbon than peat and as a result will produce more

CO2 per kilogram of fuel used, the quantity of peat required to produce one MWh is

significantly more than that required of coal. Heavy fuel oils usually contain higher

amounts of sulphur than other petroleum products as sulphur tends to concentrate in

the residue during the refining processes (Lightman and Street, 1981). Low sulphur

heavy fuel oil would have a lower sulphur content, about 0.5 - 1.0%

Fuel NOX is the major source of NOX emissions from the combustion of nitrogen

bearing fuels such as heavy oils, coal and peat (Li et al., 2003). During combustion

the fuel bound nitrogen is converted to fixed nitrogen species such as HCN (a combi-


nation of hydrogen, carbon and nitrogen) and ammonia (NH3). These, in turn, are

readily oxidised to form NO when they reach the lean zone of the flame. Between

20 and 80 percent of the bound nitrogen is typically converted to NOX, depending

on the design of the combustion equipment (Li et al., 2003).

5.2.5 Gas Fired Generators

Gas fired generation, differs from coal, oil and peat generation and is dealt with

separately here. The typical characteristics of natural gas are shown in Table 5.2.

Table 5.2: Typical components of natural gas

Carbon Sulphur Nitrogen Calorific Value CO2/kg

Gas 70% 0% 1.3% 48 MJ/Nm3 2.68

Carbon dioxide formation in a gas fired generator is formed as described in

Equation (5.1). As shown in Table 5.2, natural gas contains a negligible amount

of sulphur, thus emissions of SO2 are not significant for gas turbines (EIA, 2000).

Thermal NOX is the predominant source of NOX emissions from a gas turbine. Due

to the increasing number of gas fired generation plants on electricity systems, and

their unique NOX characteristics, the formation of NOX in a gas turbine will be

outlined in more detail below.

Thermal NOX is formed by the following reactions, known as the Zeldovich

mechanism (Delabroy et al., 1998):

O + N2 ⇔ NO + N (5.2)

N + O2 ⇔ NO + O (5.3)

N + OH ⇔ NO + H (5.4)

The reaction (5.2) determines the rate of thermal NOX production, and shows


that NO can be minimised by reducing the concentration of [O] or [N2]. This rate

of thermal NOX production becomes significantly faster at high temperatures, thus,

reducing the temperature results in a reduction in NO formation (Delabroy et al.,

1998). However, reducing the flame temperature reduces the efficiency of the plant.

Combined cycle gas turbine developers are constantly striving to improve the effi-

ciency of the turbines and this is often done through increased flame temperature.

Combined cycle gas turbine (CCGT) systems generate around 40% more elec-

tricity than conventional generating systems from the same amount of gas. In order

to achieve reduced emissions, gas turbine manufacturers have adopted lean premixed

combustion as a standard technique. This premix (of fuel and air) achieves low levels

of pollutant emissions without the need for additional hardware for steam injection

or selective catalytic reduction. By premixing the fuel and air prior to firing, lo-

calised regions of near stoichiometric fuel-air mixtures are avoided and a subsequent

reduction in thermal NOX can be realised (Mansour et al., 2001). Lean premixed

combustion is limited by the presence of combustion instabilities, which induce high

pressure fluctuations, which can produce turbine damage, flame instability and even

flame extinction (Cabot et al., 2004). For this reason the fuel and air premix is not

possible during startup and at reduced load levels (below about 65-70% of maxi-

mum capacity). As a result, NOX emissions in a CCGT increase significantly at

lower loads. Figure 5.2 shows the NOX characteristics of a CCGT and an open cycle

gas turbine (OCGT), also included for illustrative purposes are the NOX emissions

from a coal unit which has installed a Selective Catalytic Reducer (SCR or scrubber)

to reduce its NOX emissions (O’Mahony, 2004).

From Figure 5.2 it is clear that if a CCGT is forced to operate below approxi-

mately 70% of its maximum rated capacity, its NOX emissions will increase threefold.

For an OCGT, operation below 60%, will result in NOX emissions increasing by up

to eight times. The NOX characteristics of these units are critical when examining

wind generation. As discussed in Chapter 4, wind generation may result in the

system operator requiring more units to operate at lower efficiencies due to the in-

creased reserve requirement. In addition, wind generation causes an increase in the


0 10 20 30 40 50 60 70 80 90 1000

0.5

1

1.5

2

2.5

3

3.5

4

4.5

CCGTOCGTCoal SCR

Percentage of Maximum Capacity

NO

XE

mis

sion

s(k

g/M

Wh)

Figure 5.2: Typical NOX emissions from a CCGT and an OCGT

cycling of marginal units. Thus, an increase in wind generation may result in more

CCGT units operating at lower loads, increasing their NOX emissions. In other

words, although wind generation itself does not produce any harmful emissions, it

may in fact result in an increase in NOX emissions through the reduced operation

of gas fired units.

Figure 5.2 also shows the NOX emissions from a coal fired unit with a scrubber in-

stalled. Under the large combustion plant directive, all the coal units on the Irish sys-

tem are expected to have this technology installed by 2010 (EU Directive 2001/80/EC,

2001; ESB, 2003). As such, a coal fired unit could actually produce less NOX emis-

sions than a CCGT, if the gas unit was operating at less than 70% of maximum

capacity.


5.2.6 Emissions During Cycling

According to the IPPC (1996), CO2 emissions during start up must be included in

the total CO2 emissions of a unit. These can be very large. For example, a 285MW

coal fired unit on the Irish system uses 14,620GJ of energy when started from cold

(see Appendix A). From Table 5.1, the calorific value of coal is 28MJ/kg and the

CO2 emissions per kg of fuel burnt are 2.49kg. Thus, using Equation (5.2.1), the

CO2 emissions for a single cold start are given by:

CO2 = (14, 620)

(

1

0.028

)

(2.49) = 1, 300tons (5.5)

From Table 3.1, if the price for coal was assumed to be e 2.17/GJ, then the fuel

cost of a cold start would be e 31,725. If the price of carbon was e 30/ton, then

the carbon cost of a cold start would be e 39,004. In other words, with a carbon

price of e 30/ton, the carbon cost of starting this unit would exceed its fuel cost.

CO2 and SO2 emissions do not change significantly during ramping. However,

due to the increased oxygen levels present during ramping, NOx emissions increase

by about 10% over steady state conditions during periods of significant ramping

(O’Mahony, 2004).

5.3 Emissions Benefits of Wind Generation

Once the operating levels of the conventional units had been attained using the

model described in Chapter 3, the resulting CO2, SO2 and NOx emissions from

the conventional units were calculated for each hour by using specific emissions

information for each individual generator (EirGrid, 2006a; O’Mahony, 2004; AIP,

2005). Figure 5.3 illustrates the emissions benefits from increasing levels of wind

generation for CO2, SO2 and NOx for the 2010 plant mix and load.

The magnitude of CO2 emissions is much larger than for the other two emissions,

however, for ease of illustration all three emissions have been plotted on the same axis

in Figure 5.3. It can be seen that as wind generation increases, the system emissions

of CO2, SO2 and NOx are reduced. However, the relationship is non-linear. It


0 500 1000 1500 2000 2500 3000 3500 40000

5

10

15

20

25

30

20000

25000

30000

CO2SO2NOx


Kilot

ons

Figure 5.3: Emissions savings with increasing wind generation

can be seen that across all three emissions the incremental savings reduce as the

wind increases and that for high levels of installed wind generation, the emissions

actually begin to increase. This is due to a number of reasons. Firstly, as the wind

generation increases, its load factor reduces and thus the conventional generation it

displaces reduces. Secondly, increasing penetrations of wind generation require an

increased reserve capacity to be carried on the system, this results in more units

operating at reduced levels. Operating at a reduced output, the generating units

are less efficient and thus the emissions savings are reduced. Thirdly, as shown in

Chapter 4, increasing wind generation causes an increase in the cycling of units.

Thus, the potential CO2 and NOX emissions savings are reduced due to an increase

in startups and ramping respectively.

At low levels of wind capacity, the marginal units are mainly oil and peat fired,

thus displacing these units results in reduced CO2, SO2 and NOX emissions. How-

ever, as the installed wind goes beyond 2500MW, the marginal units tend to be

made up of gas fired units which produce less CO2 emissions per MWh and no SO2


emissions, hence the negligible SO2 reductions. Above about 1000MW of installed

wind, the reduction in NOX emissions from the displacement of oil units is coun-

teracted by an increase in emissions from CCGTs running at lower loads. Beyond

3000MW there is an increased use of peaking units which results in an increase in

SO2 emissions from the distillate generators and in NOX emissions from the OCGTs.

For the purposes of this analysis it is necessary to express these emissions savings

in monetary terms. Under the EU ETS (2003), there is currently an EU wide CO2

emissions market where generators buy and sell allowances for CO2. Thus, the

CO2 emissions will be valued at representative market prices of CO2. There is not

currently an emissions market for SO2 and NOx in Europe, however, there is a

market for these emission in the United States. Thus, the assumed value of these

emissions are based on the prices in these emissions markets in the United States

(US EPA, 2006b,c). Four different emissions prices are considered in this thesis and

are summarised in Table 5.3.

Table 5.3: Emissions prices in e per ton

Emission Low Mid High Highest

CO2 10 30 50 70

SO2 50 150 250 350

NOx 1000 3000 5000 7000

In the dispatch model, an emissions factor is included in the bid price of each

generator to represent the price of CO2, SO2 and NOx. This is done by calculating

the average emissions level per MWh of production for each generator on the Irish

system. These levels were then multiplied by the assumed emissions prices in Table

5.3 and added to the fuel costs to give the bid prices of generators. At the margin,

all emissions must be included, thus generators are assumed to pay the emissions

price for the full extent of their emissions1.

1The inclusion of emissions prices in the bids of generators can alter the merit order, with heavypolluters becoming more expensive relative to light polluters. This can have a significant impact onthe cycling costs. The interaction between emissions prices and cycling costs is developed in detailin Chapter 7.


Figure 5.4 illustrates the monetary value of the emissions savings from wind in

2010. For each curve, the emissions savings are valued at the emissions prices, and

these prices are also used in the dispatch of the generators.

0 500 1000 1500 2000 2500 3000 3500 40000

50

100

150

200

250

300

350

400

450

500Highest Emission PricesHigh Emission PricesMid Emission PricesLow Emission Prices


Em

issi

ons

Ben

efit

(em

)

Figure 5.4: Value of emissions savings with increasing wind generation

It can be seen that the emissions savings peak at 3000MW installed and then

reduce in line with the actual emissions savings. The sensitivity of the net benefits

to changes in the emissions prices are discussed in detail in Chapter 6.

5.4 Fuel Savings Benefit

As wind generation displaces electricity produced from thermal units the quantity

of fuel burnt by the thermal units changes. However, like the emissions savings, a

reduction in output does not mean a linear reduction in fuel burnt. When operating

at low loads, generators are less efficient and thus their fuel consumption per MWh

is higher. In addition, an increase in the cycling of generators causes increased fuel

consumption.


The model described in Chapter 3 determined the operating schedules for the

conventional generators with increasing penetrations of wind generation. Once these

dispatches had been determined, the consumption of fuel was calculated by analysing

the GJ of energy consumed per MWh for each generator (see Appendix A). Figure

5.5 illustrates the fuel consumption by conventional generators on the Irish system

for the year 2010 as the installed wind capacity increases.

0 500 1000 1500 2000 2500 3000 3500 40000

20

40

60

80

100

120

140

160

180

200

GasCoalPeatOil


Ener

gyC

onsu

med

inPet

ajo

ule

s

Figure 5.5: Fuel consumption with increasing wind generation

Figure 5.5 illustrates the annual fuel consumption in petajoules for each fuel type

on the Irish system for 2010, and shows how the consumption changes with increasing

wind generation. The majority of generation on the Irish system is gas fired so wind

generation has a significant impact on the consumption of gas. Although much

less generation is provided by oil and peat units, their consumption is significantly

reduced by the wind generation. The coal units tend to be baseloaded and thus

their fuel consumption is really only affected at high wind penetrations.

For the purposes of this cost benefit analysis, these fuel savings are valued at the

assumed fuel prices for each fuel type. The assumed fuel prices are shown in Table


3.1 in Chapter 3. Figure 5.6 shows the value of the fuel savings in 2010 for each of

the three fuel price scenarios.

0 500 1000 1500 2000 2500 3000 3500 40000

50

100

150

200

250

High Fuel PricesMid Fuel PricesLow Fuel Prices


Fuel

Sav

ings

ine

m

Figure 5.6: Value of fuel savings with increasing wind generation

It can be seen from Figure 5.6, that although changes in the assumed fuel prices

do alter the value of the fuel savings, the impact is not hugely significant. As was

seen with the emissions savings, the value of the fuel savings increase up to 3000MW

installed and beyond this point they reduce again.

5.5 Summary of Benefits of Wind Generation

This chapter outlined the benefits associated with increased wind capacity on the

Irish system. The capacity benefit was determined based on the capacity credit of

wind generation while the emissions and fuel savings were based on the operating

schedules from the dispatch model. A summary of the key points from this chapter

are given here.

• Although it is variable in nature, wind generation adds to the capacity on the


system and the level of this benefit is given by the capacity credit. The capacity

credit decreases as the installed wind generation increases and is valued as the

saved cost of having to build and maintain an equivalent conventional unit.

• The creation of harmful emissions in conventional units was discussed and

it was shown that reduced operation reduces CO2 and SO2 emissions but not

necessarily NOX emissions. In addition, reduced operating levels and increased

cycling may ameliorate the potential emissions savings from wind generation.

• It was shown that wind generation provides emissions benefits at all penetra-

tions, however the incremental savings reduce as the installed wind generation

increases. The emissions savings were based on the operating schedules in the

dispatch model.

• Wind generation provides fuel savings for conventional units, however, the

incremental savings reduce as the installed wind generation increases due to

low load and cyclical operation by conventional units.

The sensitivity of the net benefits to changes in the assumptions surrounding

the benefits of wind generation will be investigated in Chapter 6. In addition, this

chapter touched on the impact of emissions prices on the dispatch of the generators,

this will be discussed in detail in Chapter 7. The softer benefits of wind generation,

such as the creation of jobs and improvements in local infrastructure are not explic-

itly dealt with in this thesis, however, the implications of including these benefits is

discussed briefly in Chapter 8.

CHAPTER 6

The Net Benefits of Wind Generation

CHAPTERS 4 and 5 set out the costs and benefits associated with wind gen-

eration for the case study. In this chapter, the quantified costs and benefits

are brought together to determine the optimal levels of installed wind generation for

the case study.

Section 6.1 shows the net benefits curve and discusses the optimal penetration

levels of wind generation in the base case for each of the test years. The assumptions

underlying the base case are also summarised. Section 6.2 presents a sensitivity

analysis and discusses the implications of changing the assumptions from the base

case and determines the optimal penetrations of wind generation for each sensitivity.

6.1 The Net Benefits of Wind Generation in the Base

Case

The annual costs of wind generation were described in detail in Chapter 4 and in-

cluded the capital cost of wind generation, the operation and maintenance costs, the

deep network reinforcement costs, the reserve costs and the cycling costs. In order

88

Chapter 6. The Net Benefits of Wind Generation 89

to calculate the net benefits of wind generation, these annual costs are combined to

give a single total cost (TC) for each level of installed wind generation w, shown by

Equation (6.1):

TCw =T∑

t

1

(1 + r)t(Cw + OMw + NWw + Rw + CYw) (6.1)

where r represents the assumed discount rate and T the term over which the costs

and benefits are discounted. The capital costs for wind penetration w are expressed

as an annuity and are denoted by Cw. The annual operation and maintenance costs

are given by OM w, NW w is the network reinforcement costs expressed as an annuity,

Rw is the additional annual reserve cost and CY w is the added annual cycling costs.

The investigated benefits of wind generation were discussed in Chapter 5 and

are summed to give the total benefits TBw, shown in Equation (6.2).

TBw =T∑

t

1

(1 + r)t(CPw + Ew + FSw) (6.2)

where CPw is the capacity benefit of wind generation, derived from the saved

cost of building and maintaining equivalent conventional generation, and is expressed

as an annuity. The annual emissions savings are given by Ew and the annual fuel

savings by FSw.

The net benefits for each level of installed wind generation are then determined

by subtracting the total costs from the total benefits for each level of installed

wind capacity. This Section discusses the net benefits of wind generation under the

assumptions chosen in the base case. These assumptions were discussed in Chapters

3, 4 and 5 and are summarised in Table 6.1. Figure 6.1 illustrates the net benefits

curve for wind generation for each of the test years 2010, 2015 and 2020 under these

base case assumptions.

The net benefit curves illustrate where the incremental net benefits begin to

decline and more importantly, the points where the costs equal the benefits (where

net benefits equal 0), representing the critical points beyond which no further in-

vestment should be made in wind generation. Beyond the critical point, the costs


Table 6.1: Base case assumptions summarised

Assumption Scenario

Load Factor ‘Mid’ scenario in Figure 3.3

Wind Power Forecast Error Standard Deviation of 9% (Section 3.2)

Demand Growth 3.7% per year

Fuel Price Growth ‘Mid’ prices in Table 3.1

Wind Capital Costs ‘Mid Capital’ in Figure 4.1

Wind O&M Costs e35,000 p.a. per MW installed

Network Upgrade Costs See Figure 4.2

Reserve Costs ‘Average’ curve in Figure 4.3

Cycling Cost See Figure 4.7

Capacity Benefit See Figure 5.1

Emissions Costs ‘Mid’ emissions prices in Table 5.3

Discount rate 8%

Term 20 years

0 500 1000 1500 2000 2500 3000 3500 4000−3000

−2000

−1000

0

1000

2000

3000

4000

202020152010

Optimal


Net

Ben

efite

m

Figure 6.1: The base case net benefits of wind generation


will exceed the benefits and the net benefits will be negative. The critical point

for 2010 occurs at approximately 2,790MW of installed wind capacity, representing

approximately 21% of total electricity generation. The net benefits for 2010 are

less than in 2015 and 2020 due to the installed plant mix being more inflexible and

having less interconnection. In addition, the presence of an east west interconnector

with a capacity of 400MW makes a highly significant impact on the net benefits of

wind generation in 2015 and 2020. The replacement of the oil fired units with gas

fired units is also highly beneficial to wind in 2015 and 2020. From 2010 to 2020 the

underlying plant mix evolves to represent more closely the optimal plant portfolio

for the test system with large penetrations of wind generation (Doherty et al., 2006).

The optimal plant portfolio for the case system should have less baseloaded units

and more open cycle gas turbines, as is the case for the 2020 plant mix. The critical

point for wind generation in 2015 is 2,994MW and in 2020 is 3,705MW, representing

approximately 21.5% and 22.2% of electricity generated respectively.

Given the large number of assumptions required in this study, perhaps more

interesting than the absolute values of the critical points of wind generation are the

sensitivity of these critical points to changes in the underlying assumptions. Thus,

Section 6.2 investigates the impact on the net benefits of wind generation of altering

the assumptions in base case.

6.2 Sensitivity Analysis of Net Benefits

A range of scenarios are examined in this Section and for ease of comparison across

the three test years, the critical values will be expressed in terms of percentage of

electricity generated from wind rather than the installed MW. For each scenario, the

net benefits curve is recalculated and the critical value of installed wind generation,

where the net benefits equal zero, is determined. Unless otherwise stated, the only

value altered in each scenario is the one under investigation, and all other values

are as they are in the base case. Figures 6.2 and 6.3 represent a graphical summary

of the critical values for wind generation across each of the investigated scenarios.

Each scenario is then explained and discussed in turn in the following subsections.

Chapter

6.

The

Net

Ben

efits

ofW

ind

Gen

eratio

n92

BC = Base Case

LLF = Low Load Factor

CLF = Constant Load Factor

HLF = High Load Factor

WFC =Worst Forecasts

BFC = Best Forecasts

LD = Low Demand

HD = High Demand

LFP = Low Fuel Prices

HFP = High Fuel Prices

LCp = Low Capital

CCp = Constant Capital

HCp = High Capital

Figure 6.2: The Critical Values of Wind Generation Part I

Chapter

6.

The

Net

Ben

efits

ofW

ind

Gen

eratio

n93

BC = Base Case

50Cy = Low Cycling Costs

80Cy = Very Low Cycling Costs

LEp = Low Emissions Prices

HEp = High Emissions Prices

HHEP = Highest Emissions Prices

6% = Discount Rate of 6%

10% = Discount Rate of 10%

Bad = Worst Case Scenario

Good = Best Case Scenario

Figure 6.3: The Critical Values of Wind Generation Part II


Although this thesis analysed only installed wind capacities up to 4000MW (rep-

resenting 27.6% of electricity generated in 2010) Figures 6.2 and 6.3 show percentages

up to 30%. This is due to the fact that in some scenarios, the critical values were

above 4000MW and in these cases, the critical values are found by linear extrapola-

tion from the net benefits curve at 4000MW.

6.2.1 Wind Load Factor Scenario

In Chapter 3 the assumptions regarding the load factors of wind generation were

discussed. Four scenarios were illustrated with low, mid, constant and high load

factors respectively. Represented in Figure 6.2 by the columns ‘LLF’ meaning low

load factor, ‘CLF’ meaning constant load factor and ‘HLF’ for high load factor in

Figure. The mid load factor was assumed in the base case scenario (BC). Figure 6.4

illustrates the impact assuming the other load factors would have on the net benefits

of wind generation in 2010.

0 500 1000 1500 2000 2500 3000 3500 4000−3000

−2000

−1000

0

1000

2000

3000

High Load Factor (HLF)Constant Load Factor (CLF)Base Case (BC)Low Load Factor (LLF)

Installed Wind Generation

Net

Ben

efitse

m

Figure 6.4: The net benefits of wind with changes in load factor


It can be seen from Figure 6.4 that assuming a high load factor shifts the net

benefits curve upwards from the base case. This is because for the same installed

capacity, more energy is reaped from the wind generation resulting in higher fuel

and emissions savings but for the same capital cost. However, the upward shift is

not the same across all penetrations of wind generation and it can be seen that the

critical value for the ‘constant’ and ‘high’ cases are similar. This is because although

there are higher benefits to a high load factor, at increased penetrations of wind,

there will also be an impact on the cycling costs. A low load factor shifts the net

benefits curve significantly downwards resulting in a reduced critical value. It is also

interesting to note that at a low load factor, the net benefits of wind generation at

low levels of penetration actually dip below zero. This is due to the lower emissions

and fuel benefits but a relatively high capital cost.

The net benefits curves for the load factor sensitivities for 2015 and 2020 were

also calculated and the curves followed the same general patterns. These net benefits

curves were calculated for each test year for all the sensitivities to determine the

optimal penetrations in each, however, for ease of illustration, only the net benefits

curves for the sensitivity to changes in the load factor are shown. A summary of the

actual critical points for all the sensitivities are given in Figures 6.2 and 6.3.

6.2.2 Wind Power Forecast Accuracy Scenario

Wind power forecasts were included in the dispatch model by assuming a long range

forecast in the commitment stage of the model and an updated forecast in the

dispatch stage. The base case assumption for these wind forecasts was that the

standard deviation of the wind power forecast errors was 9% of the installed wind

capacity. The impact on the net benefits of wind generation if a worst case wind

forecast, with 14% standard deviation is used and if a best case wind power forecast

with 7% standard deviation was examined for each of the test years. These scenarios

are represented in Figure 6.2 by ‘WFC’ and ‘BFC’ respectively.

It was found that assuming the worst case wind power forecasts has a dramatic

impact on the critical values of wind generation, dropping to 16% in 2010. Wind


power forecasts impact on the reserve capacity being carried on the system, as

discussed in Section 4.3. Thus, assuming the worst case wind power forecasts results

in more reserve capacity being carried on the system. This increases the reserve costs.

Also, more units are operating at lower efficiencies resulting in reduced emissions

and fuel saving benefits. In addition, there is an increase in the switching on of

peaking units to deal with the unexpected shortfalls in generation when wind power

forecasts overestimate the actual production. This has a knock-on effect on cycling

costs. Each of these factors combine to increase the costs of wind generation and

reduce the benefits, thereby reducing the overall net benefits of wind generation,

and shifting the net benefits curve downwards. As the generation plant mix in

2020 is more flexible, the cost of poor wind forecast accuracy is lower. On the

other side, assuming the best case wind power forecasts, significantly improves the

critical values of wind generation by requiring less reserve capacity, more efficient

conventional unit operation and lower cycling costs.

6.2.3 Demand Growth Scenario

Chapter 3 discussed the assumptions regarding the assumed demand growth for the

years 2010, 2015 and 2020. A ‘mid’ level demand growth of 3.7% from EirGrid

(2005b) was assumed in the base case. Here a ‘low’ demand growth of 2.5% and a

‘high’ demand growth of 4.3% are investigated and are given in Figure 6.2 as ‘LD’

and ‘HD’ respectively.

This scenario shows an interesting result whereby high demand growth is pre-

ferred to low demand growth. With low demand growth, wind generation represents

a higher proportion of the dispatched plant. This results in the marginal plants be-

ing higher up the merit order, and in Ireland, this means older, more inefficient

coal fired units. These units are then required to cycle more frequently resulting in

large cycling costs. On the other hand, if the demand growth was high, the wind

generation represents a smaller proportion of the dispatched plant and the marginal

units are more likely to be gas fired units which are more flexible and have lower

cycling costs.


6.2.4 Fuel Price Scenario

Chapter 3 set out the fuel price assumptions used in this analysis. A low, mid and

high range of fuel prices was provided for each of the test years and the mid prices

were assumed in the base case. The impact on the critical values of wind generation

of assuming the other fuel prices are shown in Figure 6.2 and are given by ‘LFP’,

‘MFP’ and ‘HFP’ representing low, mid and high fuel prices respectively.

It is seen that high fuel prices are beneficial for wind generation since the value

of the savings in the fuel bill from wind generation are increased. This is partic-

ularly evident under the high fuel prices option in 2020 where the gas prices are

particularly high and the critical point is 24.5%. The impact of fuel prices on the

net benefits of wind generation is relatively intuitive. Not illustrated in Figure 6.2

is an alternative scenario which assumes high fuel prices as well as high capital costs

for wind generation. This could be due to an increase in manufacturing costs for

wind turbines associated with the high fuel prices. It is found assuming the high

capital cost scenario outweighed the benefits of the increased fuel savings and the

critical values were slightly lower than in the base case.

6.2.5 Capital Cost Scenario

Chapter 4 discussed four scenarios relating to the capital costs of wind generation.

The ‘High Capital’ scenario showed capital costs starting at e1m/MW and increas-

ing to e1.075m/MW at 4000MW installed. The ‘Constant Capital’ scenario held

the capital cost constant at e1m/MW. The ‘Mid Capital’ case showed costs initially

decreasing and then increasing again beyond 2750MW installed. The ‘Low Capi-

tal’ scenario showed capital costs decreasing to e0.855m/MW at 4000MW. These

scenarios are represented in Figure 6.2 by ‘LCP’, ‘CCP’ and ‘HCP’ indicating low,

constant and high capital costs respectively. The ‘mid’ scenario was used in the base

case assumptions, and is thus incorporated in the ‘BC’ results.

From Figure 6.2, it is clear that higher capital costs lead to a lower critical

value. In addition, it is interesting to note that although wind generation is highly

capital intensive, assuming a constant capital cost still maintains the shape of the


net benefits curve and the critical value is less than the base case. A number of

fairly intuitive additional scenarios with respect to the cost of wind generation were

also run but are not illustrated in Figure 6.2. The first is an alteration in the

operating and maintenance costs of wind generation. The base case assumed O&M

costs of e35,000/MW per annum. Increasing these costs to e50,000/MW, which is

the average cost for an offshore wind turbine reduces the critical values to 20% in

2010 and 21% and 21.2% in 2015 and 2020 respectively. The second investigates the

impact of changing the interest rate faced by the developer of the wind turbine from

8% to 6%. This increases the critical value of wind by approximately 1% across each

of the years. Decreasing the term of the loan faced by the developer from 20 to 15

years has a slightly larger impact on the critical values which decrease by between

1.5% and 2%.

6.2.6 Cycling Cost Scenario

When the cycling costs were considered in Chapter 4, they were calculated based on

the fuel cost of each unit, their age, previous operating profiles etc. The fuel cost was

assumed to represent between 2 and 30% of the true cost of cycling depending on

the unit. Here the sensitivity of the net benefits of wind to a reduction in these costs

is investigated. A more flexible plant mix would have a lower cost of cycling, thus,

two scenarios relating to lower cycling costs were investigated. The first assumed

that the fuel costs represented 50% of the true cycling costs for all units, and the

second that the fuel costs represented 80% of the true costs illustrated by ‘50Cy’

and ‘80Cy’ respectively in Figure 6.3.

As would be expected, it is seen that as the cost of cycling the generating units

reduces, the net benefits of wind generation increase. Wind generation causes an

increase in the cycling of units on the margin, thus with more flexible units on the

margin, with lower cycling costs, the cost imposed by wind generation is reduced.

This is also seen by comparing the critical values across the three test years, with

2020 having the most flexible plant mix.


6.2.7 Emissions Price Scenario

Chapter 5 discussed in detail the creation of emissions in combustion units and

calculated the emissions savings with increases in wind capacity. The value of these

saved emissions is based on the assumed emissions prices and four emissions price

scenarios were set out in Chapter 5: a ‘low’ emissions price; a ‘mid’ price (which was

used in the base case); a ‘high’ price; and a ‘highest’ price. Figure 6.3 represents

these scenarios as ‘LEp’, ‘HEp’ and ‘HHEp’ for the low, high and highest cases.

It was found when calculating the results that given the magnitude of the CO2

emissions, the emissions benefits were mainly dependent on the price of CO2 rather

than the other emissions. At the ‘low’ emissions price, the merit order of the con-

ventional units is virtually unchanged by the inclusion of the emissions price in the

marginal costs of generators and the wind generation displaces some oil units and

many gas units on the margin. The gas units have the lowest CO2 emissions per

MWh of all the conventional units on the Irish system. This explains the dramatic

reduction in emissions savings at the low emissions price. As the emissions price

increases to the ‘mid’ scenario (the base case), the coal units are shifted down the

merit order where they operate on the margin more often, resulting in much greater

emissions savings. However, this shift of coal units to the margin also increases

the cycling costs, thus as the emissions price increase further, the critical value is

restrained at a level not much greater than the base case. This interaction between

emissions prices, wind generation and cycling costs is an interesting issue which is

developed further in Chapter 7.

6.2.8 Discount Rate Scenario

When the net benefits of wind generation are being calculated, each of the costs and

benefits are assumed to accrue annually for a certain number of years and these are

then discounted by the assumed interest rate. In the base case, this discount rate

was 8%. Figure 6.3 illustrates the impact of reducing the discount rate for all costs

and benefits to 6% and increasing them to 10%. As would be expected, a lower

discount rate increases the net benefits of wind generation and a higher discount


rate reduces them.

6.2.9 Worst and Best Case Scenarios

The previous sections illustrated the sensitivity of the net benefits of wind generation

to changes in the underlying assumptions. These results are now brought together

to determine the two extremes for wind generation. The ‘worst case scenario’ for

wind generation embodies all of the assumptions that brought about the lowest net

benefits for wind generation, and the ‘best case scenario’ captures all the assumptions

which resulted in the highest critical values. Table 6.2 summarises the assumptions

which have been altered to generate the two extreme scenarios. The critical values

of the worst case and best case scenarios for wind generation are shown in Figure

6.3 as ‘bad’ and ‘good’ respectively.

Table 6.2: Worst case and best case assumptions summarised

Assumption Worst Case Best Case

Load Factor ‘Low’ ‘High’

Wind Power Forecast Error Worst (std dev 14%) Best (std dev 7%)

Demand Growth ‘Low’ ‘High’

Fuel Price Growth ‘Low’ ‘High’

Wind Capital Costs ‘High Capital’ ‘Low Capital’

Cycling Cost As in Base Case 80%

Emissions Costs ‘Low’ ‘Highest’

Discount rate 10% 6%

Term 15 years 20 years

By assuming the worst case scenario for wind generation, the critical values of

wind generation are very low, representing 549MW, 629MW and 1275MW installed

in 2010, 2015 and 2020 respectively. These penetrations represent 5%, 6% and 9%

of electricity generated by wind in 2010, 2015 and 2020. If the best case is assumed,

the critical values are above 30% of electricity generated in each of the three test

years.


6.3 Conclusions of Net Benefits

This Chapter presented the net benefits associated with wind generation and the

optimal penetrations of wind generation were determined under a wide range of

scenarios. The key results are summarised here.

• The net benefits curves were presented for the base case and the optimal

penetrations were found where the net benefits equal zero. This was found

to be 2790MW in 2010, 2994MW in 2015 and 3705MW in 2020, representing

21%, 21.5% and 22.2% of electricity generated respectively.

• The underlying plant mix is also very important and assuming a flexible

plant mix, as in 2020, significantly increases the net benefits of wind generation.

• The optimal points were tested under a large range of scenarios and it was

found that they were highly sensitive to the assumed load factor. A high

load factor results in much increased net benefits due to greater emissions and

fuel savings with the same capital costs as under low load factor scenarios.

• The capital cost of wind generation has an impact on the net benefits and

low capital costs increase the net benefits of wind generation.

• Accurate wind power forecasting increases the net benefits of wind gener-

ation by reducing the reserve and cycling costs.

• A low demand growth reduces the net benefits of wind generation signifi-

cantly.

• High fuel prices are beneficial for wind generation, increasing the value of

the saved fuel consumption with increasing penetrations of wind generation.

• A more flexible plant mix would have lower cycling costs thus two scenarios

with lower cycling costs were investigated. It was found that the net benefits

of wind generation were increased when the cycling costs were reduced.

• A reduced discount rate increased the net benefits of wind generation.


• Low emissions prices have a significant detrimental impact on the net ben-

efits of wind generation for two reasons. Firstly the saved emissions are worth

less at lower emissions prices. And secondly, with a low emissions price, the

wind generation displaces mainly gas fired units which have relatively lower

emissions per MWh. As the emissions price increases, the merit order shifts

such that heavier polluters are displaced by the wind generation. Thus a higher

emissions price results in more emissions saved and these emissions are valued

at a higher price.

• A worst case scenario was developed which incorporated all of the assump-

tions which would be detrimental to wind generation. Under this assumption,

it was found that just 5%, 6%, and 9% of wind generation should be developed

in 2010, 2015 and 2020 respectively.

• A best case scenario was also developed embodying all of the assumptions

which increased the net benefits of wind generation. It was found that in each

of the three test years, the optimal penetration of wind generation exceeded

30% of electricity generated in this scenario. These worst case and best case

scenarios can be thought of as the extremes for the potential wind generation

deployment in Ireland.

This chapter illustrated the net benefits results for wind generation for the case

study. The costs and benefits were discussed in Chapters 4 and 5 and were based on

the output of the dispatch model. A number of further applications of the dispatch

model and the methodology employed in calculating the net benefits are developed

in the following Chapter 7.

CHAPTER 7

Applications of the Net Benefits Methodology

THIS chapter discusses the results of some interesting applications of the dis-

patch model presented in Chapter 3 and related to the issues of renewable

generation and system impacts. As discussed in Chapter 6 emissions prices alter

the merit order in which generators are dispatched, with heavy polluters becoming

more expensive relative to light polluters. This can result in units which were pre-

viously baseloaded, being required to operate on the margin more often which can

have a knock on impact on cycling costs. The impact of emissions prices on cycling

costs is investigated in detail in Section 7.1. While the previous chapters investi-

gated many of the costs and benefits of wind generation, this application on carbon

prices isolates just the cycling costs and the emissions benefits and investigates the

relationship between the two.

In the work presented so far, the power system was operated by incorporat-

ing wind generation forecasts into the dispatch model. However, in a study by

Gardner et al. (2003) for the same case system, an alternative approach to power

system operation is assumed. Here, the units are committed regardless of the wind

power forecasts and then at real time, if the wind generation is available, these con-

103

Chapter 7. Applications of the Net Benefits Methodology 104

ventional units are deloaded to accommodate the wind generation. This method is

known as the fuelsaver approach and is discussed in detail in Section 7.2.

Section 7.3 discusses the impact that different connection policies have on the

development of renewable generation on the distribution network. The resulting

renewable energy penetration are then analysed in terms of their net benefits to

determine the connection policy which yields the maximum net benefits. This work

was conducted in collaboration with Keane and O’Malley (2005b).

Wind generation is variable and relatively unpredictable, whereas, tidal genera-

tion on the other hand is variable and almost perfectly predictable with in the time

frames of interest to power system operators. Thus, Section 7.4 discusses the fea-

sible tidal generation penetrations on the Irish system. The capital costs necessary

to ensure positive net benefits for tidal generation are also shown. Bryans (2006)

investigated the tidal generation resources in Ireland and his findings feed into the

results shown in this Section.

7.1 Carbon Prices and Cycling Costs

Due to international concern over climate change, policy makers worldwide are in-

troducing numerous instruments to help curb global emissions, such as carbon taxes,

emissions trading, allocation credits, emissions caps etc. These instruments cover

a wide range of industries including electricity generation, and are intended to in-

ternalise the cost of emissions for polluters with the aim of altering their behaviour

patterns to reduce their greenhouse gas emissions. In electricity markets, genera-

tors generally consider their operating costs to be made up of their internal costs.

However, these policy instruments have made the additional emission costs appli-

cable to generators based on the amount of environmental pollution they cause

(Baumol and Oates, 1975).

In January 2005, the European Union Greenhouse Gas Emission Trading Scheme

(EU ETS) commenced operation as the largest multi-country, multi-sector green-

house gas emission trading scheme worldwide. Phase I of the EU ETS covers the

period 2005-2007 and Phase II will run from 2008-2012 to coincide with the first


Kyoto Protocol commitment period. Under the EU ETS, each generator must sub-

mit a number of allowances equal to their total emissions for the preceding calender

year. These allowances for the year are subsequently cancelled. In Phase I any

installation with annual emissions exceeding their submitted number of allowances,

will be penalised e40 for each additional ton of carbon dioxide1. In addition to the

payment of this excess emissions penalty, the installation is also required to submit

allowances for those excess emissions when submitting allowances in relation to the

following calendar year (EU ETS, 2003).

An emissions trading scheme requires electricity generators to internalise their

emissions costs. This in turn alters the marginal costs of generators such that

heavy polluters become relatively more expensive compared to lighter polluters

(Borchiellini et al., 2000). As shown by Doherty et al. (2005), placing a cost on

carbon emissions results in a shift towards fuels with a lower carbon content, and

in particular a shift from coal fired production to gas. This result is supported

by Klaassen and Riahi (2006) who show that the use of coal for electricity pro-

duction declines steadily into the future with the introduction of a carbon tax.

Voorspools and D’haeseleer (2006) studied the impact of a carbon tax on a num-

ber of EU countries and their results illustrate the significant impact that a carbon

tax has on the operation of the coal plants, which shift from being baseloaded or

mid merit to operating on the margin much more frequently with a carbon tax

of just e10 per ton. Putting this value in context, the price of carbon in Phase

I of the EU ETS has averaged at e18.52/ton with a peak price of e32.25/ton

(European Climate Exchange, 2006; Point Carbon, 2006).

Palmer et al. (2006) conducted a cost benefit analysis of emissions reduction in

the electricity sector, encompassing the electricity generation costs and the social

benefits. This study found that the benefits of emissions reduction far outweigh the

costs. However, this analysis did not account for the cost associated with the change

in operating patterns of units which had been previously baseloaded and were now

operating on the margin.

1This will rise to e100 from January 2008.


As discussed in Chapter 4, generating units are usually designed to be at their

most efficient when turned on and running at a stable output (Le et al., 1990). If

a unit is operating on the margin, it will have to cycle more frequently to meet the

varying load. The cost of cycling significantly increases if a unit is operated at a

continuous output over a long period of time and is then required to cycle. Thus, if

a carbon price is introduced which causes a coal fired unit to become relatively more

expensive and shift from operating continuously as a baseload plant to operating on

the margin, the resulting cycling costs could increase significantly and could dampen

the benefits associated with its reduced emissions.

The introduction of a carbon price can act as an incentive for wind generation

which becomes relatively cheaper compared to the conventional carbon emitting

units. Under the EU Directive 2001/77/EC (2001), the output from a wind gen-

erator must be accepted when it is available. As a result, wind generation can

displace the output of a conventional unit and thereby reduce carbon dioxide (CO2)

emissions, as shown in Chapter 5. However, as discussed in Chapter 4, wind gen-

eration also increases the cycling costs of the marginal units. Given that a carbon

price could change the type of unit operating at the margin, the effect of increasing

penetrations of wind generation could further exacerbate the cycling costs resulting

from a carbon price. Thus, in this Section, three aspects of the issues surrounding

cycling costs and carbon emissions are investigated. Firstly the impact of carbon

prices on cycling costs and carbon emissions is determined. Secondly the impact

of wind generation on cycling costs and carbon emissions is shown, and thirdly the

combined impact of a carbon price plus increased wind generation is investigated. In

an attempt to reduce the number of assumptions which could impact on the results,

a number of changes were made to the assumptions used in net benefits study.

• The assumed plant mix and load for this analysis are for 2007. The charac-

teristics of the system in 2007 are known with much more certainty than for

2010 and beyond and are discussed in detail in EirGrid (2005b). A summary

of the assumed plant mix for 2007 is given in Appendix A.

• The dispatch model described in Chapter 3 was used to determine the dis-


patches with increasing emissions prices and wind generation. However, in an

attempt to reduce the number of factors which may influence the results, the

model was simplified to dispatch for energy only i.e. the reserve constraint was

omitted. Including the reserve constraints introduces technical complications

without changing the fundamental insights of the results of this analysis. In

fact, adding reserves increases the cycling costs, thus the magnitude of the

cycling costs may even be underestimated in this Section.

• In the net benefits analysis, the peat fired generation was assumed to bid into

the dispatch model like all other units. However, for 2007, it is expected that

the Government will continue to impose its must-run policy towards peat fired

generation, thus, in this Section, the peat fired generation bids a low price into

the dispatch model to ensure baseloaded operation.

Section 7.1.1 summarises how the emissions prices were included in the bid prices

of generators and the impact that this has on the merit order. Section 7.1.2 illus-

trates the results showing the cycling costs and emissions savings firstly with the

introduction of a carbon price, secondly with an increase in wind generation and

thirdly with a combination of a carbon price and wind generation.

7.1.1 Emissions Prices in the Cycling Analysis

As discussed in Chapter 5, to include the carbon price in the bid price of generators,

their CO2 emissions per MWh were multiplied by the carbon price and this was

added to the fuel price per MWh to give their bid price. When the generating units

internalise their carbon costs it significantly altered the merit order of the dispatch.

The merit order in the absence of carbon costs was illustrated graphically in Figure

2.6 and is shown in Appendix A. Figure 7.1 here, illustrates what would happen to

the merit order if the price of carbon was e30 per ton of CO2, and the units were

dispatched based on their marginal cost plus their CO2 emissions.

The baseloaded plant is now almost entirely made up of gas fired units and the

coal units have been pushed up to the margin. As discussed in Section 4.4, units


Gas

Distillate

2000 4000 6000 8000

Hours

1500

3000

4500

6000

7500D

eman

d M

W

2450

0

Hydro & Peat

Coal

Gas

Oil

Gas

Figure 7.1: Merit order with carbon price of e30/ton

that were previously operated at baseload and are then required to cycle are most

prone to creep-fatigue interaction and will have very high cycling costs. If the coal

units are operating on the margin their operating profiles will change dramatically

which is likely to result in a significant increase in cycling costs.

7.1.2 Results & Discussion

The dispatch model was run for each hour for an entire year for a large range of

scenarios and the associated cycling and carbon dioxide emissions were calculated.

Figure 7.2 illustrates the annual additional cycling cost in e millions on the primary

y-axis, and the saved CO2 emissions in millions of tons on the secondary axis (shown

by the dotted line). The value of the CO2 emissions savings are calculated by

multiplying the emissions savings (in millions of tons) by the carbon price at each

point (illustrated by the dashed line).


0 10 20 30 40 50 60 700

50

100

150

200

250

0 10 20 30 40 50 60 700

2

4

6

8

10

Saved CO2

Added Cycling CostValue of Saved CO2

CO2 price in e

em

illion

Million

Ton

sC

O2

saved

Figure 7.2: Cycling costs and CO2 savings at different carbon prices

Figure 7.2 illustrates an interesting result, that at a carbon price of less than

e50 per ton, the cycling costs would far exceed the value of the saved CO2. As can

be seen at a carbon price of e20/ton, the added cycling costs amount to e140m

while the amount of saved carbon is just 1.8 million tons, which at a value of e20

per ton amounts to a benefit of e36m. It is also clear that between a carbon price

of e10 and e20 per ton, the cycling costs escalate rapidly. This is because above

e10/ton, the marginal cost of coal exceeds that of gas. This results is supported

by Voorspools and D’haeseleer (2006). Increasing the price of carbon above e20

per ton does not have such a dramatic impact on the merit order and coal and oil

continue to operate on the margin. However, coal does get relatively more expensive

compared to the oil units and as a result gets utilised less and less as the carbon

price increases. This explains the stabilising of the cycling costs beyond e20 per

ton, and the slight reduction beyond e40 per ton.

In reality it is likely that given their large cycling costs, these coal units may bid


into the market in such a way as to minimise their cycling activities. They may alter

their bidding strategies in order to ensure they are baseloaded or to ensure they are

adequately compensated if they are required to operate on the margin. Alterations

in bidding strategies such as these may lead to the stranding of assets in the long

run which can cause significant security of supply issues. In addition, without a

more complex model it is unclear if the electricity system would in fact be able to

physically operate with such a significant alteration in the merit order. These issues

of strategic bidding behaviour and feasible operation are beyond the scope of this

study but raise interesting issues which could be covered in future research.

The introduction of a carbon price may result in an increase in wind generation

penetrations. As shown in Chapter 5, wind generation can aid in the reduction of

CO2 emissions by displacing generation on the margin. However, wind generation

can also causes an increase in the cycling of generating units on the margin (EirGrid,

2004). Figure 7.3 illustrates CO2 savings with increasing penetrations of wind as

well as the cycling costs in 2007. In order to clearly see the impact of wind gener-

ation alone on the cycling costs and emissions savings, Figure 7.3 assumes that the

conventional generators on the system have not internalised any of their CO2 costs.

For ease of comparison with Figure 7.2, Figure 7.3 values the carbon dioxide savings

at a price of e30/ton.

Figure 7.3 shows that although wind generation does cause an increase in cycling

costs, at a carbon price of e30/ton the carbon emissions savings exceed the cycling

costs at all penetrations. Thus, even in the absence of generators internalising their

carbon costs, wind generation results in carbon dioxide emissions savings by dis-

placing generation on the margin. By comparing Figures 7.2 and 7.3, it is apparent

that both a carbon price and wind generation can reduce CO2 emissions, however,

cycling costs are much higher when a carbon price is used to reduce emissions than

when wind generation is employed2.

As discussed in Section 7.1.1, the introduction of a price on carbon results in a

2As stated previously, this Section isolates the issues of cycling costs and emissions prices. Theseresults were embedded in the cost benefit analysis conducted in previous Chapters but are investi-gated in isolation in this Chapter.


0 500 1000 1500 2000 2500 30000

20

40

60

80

100

0 500 1000 1500 2000 2500 30000

2

4

6

8

10

Saved CO2

Added Cycling CostValue of Saved CO2


em

illion

Million

Ton

sC

O2

saved

Figure 7.3: Cycling costs and CO2 savings with wind and e30/ton CO2

shift in the merit order, with the baseloaded units operating on the margin more

often. Since wind generation causes an increase in the cycling of the marginal units,

a carbon price combined with increased wind generation could further exacerbate

the cycling costs. Figure 7.4 illustrates the cycling costs when generators internalise

their carbon costs combined with an increase in wind generation. It can be seen

that both wind generation and a price on carbon increase cycling costs and the

combination of both result in even higher cycling costs. This is due to the carbon

price resulting in previously baseloaded coal units with high cycling costs operating

on the margin, combined with the wind generation increasing the cycling of these

units. As shown in Voorspools and D’haeseleer (2006), carbon prices pushed coal to

the margin and Holttinen (2004) showed that wind generation increases the cycling

of marginal units. It is clear from Figures 7.4 that the main driver of the cycling

costs is the switching of the coal units from baseloaded to variable operation.

Figure 7.5 shows how wind generation and carbon prices interact to result in


0 10 20 30 40 50 60 700

20

40

60

80

100

120

140

160

180

200

3000MW Wind2000MW Wind1000MW Wind0MW Wind

Carbon Price (e)

Cycl

ing

Cos

t(e

m)

Figure 7.4: Cycling costs with wind generation and carbon prices

CO2 savings. With a carbon price of zero, 3000MW of wind generation results in

the saving of almost 2.7 million tons of CO2. With zero installed wind generation,

a carbon price of e70/ton generates a saving of 3 million tons. However, when

wind generation and a carbon price are combined, the savings amount to more than

simply the sum of the two parts. A carbon price of e70/ton combined with 3000MW

of wind generation results in a CO2 emissions saving of almost 7.5 million tons. This

is due to the wind generation displacing the marginal units which, because of the

carbon price, are the heavy polluting coal units.

In order to illustrate the combined results of the cycling costs and the carbon

dioxide emissions savings, the cycling costs were subtracted from the value of the

saved emissions to give a ‘net saving’. These net savings are illustrated in Figure 7.6.

The CO2 emissions savings are valued at the price of carbon. Thus with 1000MW

of wind generation and a carbon price of zero, the net savings are made up of just

the cycling costs from wind generation as the emissions savings equal zero.


0 10 20 30 40 50 60 700

1

2

3

4

5

6

7

8


Carbon Price (e)

CO

2Sav

ings

(Mto

n)

Figure 7.5: Carbon savings with wind generation and carbon prices

0 10 20 30 40 50 60 70−150

−100

−50

0

50

100

150

200

250

300

350


Carbon Price (e)

Net

Sav

ing

(em

)

Figure 7.6: Net savings with wind generation and carbon prices


The point where the net savings are at a minimum occurs at a carbon price

of e20/ton and installed wind generation of zero. At this point, the cycling costs

exceed the CO2 emissions savings by over e100m. As wind generation is added to

the system, the CO2 emissions savings it produces are greater than the cycling costs

it adds so the deficit in net savings reduces. However, even with 3000MW of wind

installed, net savings are negative at e20/ton. At the peak, with a carbon price of

e70/ton and wind generation of 3000MW, the value of the savings in carbon dioxide

exceed the cycling costs by almost e340m for the year. This represents CO2 savings

of over 2.8 times the cycling costs. At this point, the coal and oil units are on the

margin, and although this results in high cycling costs (Figure 7.4) it also means

that the wind is displacing the highest emitting units on the system resulting in

much greater CO2 savings.

7.1.3 Summary of Carbon Prices and Cycling Costs

It was shown here that carbon dioxide reduction mechanisms can have a dramatic

impact on system cycling costs and may end up costing more to the system than

the value of the saved CO2 emissions. This situation is particularly severe in Ire-

land where there is a large proportion of the plant mix made up of large relatively

inflexible units with very large cycling costs. By increasing the flexibility of the

plant mix, the cycling costs could reduce significantly. For example, an increase in

open cycle gas turbines, responsive demand and hydro generation, could reduce the

system cycling costs by carrying some of the variability on the margin. It is likely

that an increase in interconnection could help reduce the need to cycle the marginal

units by helping to smooth out the variations on the margin.

Carbon mechanisms result in a shift in the merit order with high carbon emitters

becoming relatively more expensive compared to low emitters. This can result in

units which were previously baseloaded, operating on the margin much more fre-

quently. This can have a significantly large impact on cycling costs. In this Section

the impact that carbon prices can have on the cycling costs of generators and on

their carbon dioxide emissions savings was investigated. It was found that the car-


bon dioxide reduction benefits can be completely outweighed by the added cycling

costs. For the case study it was found that the cycling costs exceeded the carbon

savings when carbon prices were less than e50/ton. In addition, it was shown that

the most significant contributor to the cycling costs is the switching of coal units to

the margin. The coal units in the case system become more expensive than gas at

a carbon price of between e10 and e20/ton.

A carbon mechanism can promote increased penetrations of wind generation and

thus the impacts of wind generation on the system were also investigated. Wind

generation is variable and relatively unpredictable which causes an increases in the

cycling of generating units on the margin. However, the wind generation also dis-

places production by the marginal units and thus results in carbon dioxide emissions

savings. It was found that the carbon dioxide emissions savings produced by wind

generation outweigh the cycling costs it imposes.

The impact on cycling costs and carbon dioxide emissions when a carbon price is

combined with increased wind generation was then analysed. A carbon price shifts

high emitting baseloaded units to the margin and the wind generation increases the

cycling of the marginal units. Thus when a carbon price and wind generation are

combined, it was found that the cycling costs are increased further as the marginal

units have higher cycling costs. However, the combination of a carbon price and

wind generation have a much greater impact on carbon dioxide reductions. With a

carbon price the marginal units are the high carbon emitting units, thus when wind

generation is introduced it displaces some of the output of the heaviest polluting

units on the system. This results in carbon dioxide savings much greater than

the sum of the savings from the carbon price and the wind generation separately.

However, it was found that even with 3000MW of installed wind generation, the

cycling costs can still exceed the value of the saved carbon dioxide emissions.

7.2 The Fuelsaver Approach to Wind Generation

For the cost benefit analysis discussed in previous chapters, the dispatch model was

run with long range wind power forecasts in the first model run and updated wind


power forecasts in the second model run (Chapter 3). In other words, commitment

decisions of conventional units were made based on long range wind forecasts and

operating levels were decided based on an updated wind power forecast. Operating

a power system in this manner is highly efficient with regard to wind generation,

however, it may require an alteration in the mentality of the power system operator

who traditionally dealt with units which were almost entirely dispatchable.

A simpler approach to operating a power system with wind generation, known

as the fuelsaver approach, has been presented in Gardiner et al. (2003) and DETI

(2003). Under this approach, wind generation is not considered in the scheduling

of the plants and the unit commitment decisions are made ignoring any installed

wind capacity. Once the commitment decision has been made, the wind generation

is considered. If wind generation is available it is used and marginal conventional

plants which were dispatched are deloaded to accommodate the wind generation.

A conventional plant could be deloaded as far as its minimum but no plants are

switched off. If wind production reaches a level such that no more conventional

generation can be deloaded, then any further wind production is curtailed. This

operational strategy considers that the only benefit of wind generation is a fuel-

saving one and it assumes that wind generation has a capacity value of zero. This

is a simplistic approach and it allows issues of forecasting and reliability of wind

production to be ignored. However, this method of power system operation with

wind generation results in an over commitment of conventional units, and these

units will be running at much lower efficiencies than under the approach adopting

wind power forecasts. The impact of operating the power system using a fuelsaver

approach is investigated here and it is shown that it is a highly inefficient way to

operate the system with significant wind power penetration.

7.2.1 Analysing the Fuelsaver Approach

The impact of operating the power system under a fuelsaver approach is investigated

here and the net benefits of wind generation are determined for the year 2010. In

order to simulate the fuelsaver approach, the dispatch model described in Chapter


3 was altered slightly. In the commitment stage of the model, where conventional

generators’ output can range between 0 and maximum generation, wind power out-

put is assumed to equal zero. This implies that the commitment decisions are made

regardless of wind generation. In the second stage of the dispatch model, the wind

power is introduced and the operating levels of the committed conventional units

are determined. If the sum of the minimum stable generation of the committed

units plus the wind power output exceeds the load for a given hour, then the wind

generation is curtailed.

By analysing the dispatches resulting from fuelsaver model, it is seen that with

very low installed capacities of wind generation, the dispatches are quite similar to

those from the model using wind power forecasts. However, as the wind capacity

increases, the fuelsaver method becomes less and less efficient with much greater

wind power curtailment, more units turned on, and more units running at lower

efficiencies. Figure 7.7 illustrates the amount of wind curtailment for the year 2010

under the fuelsaver approach when compared to the original approach, referred to

from here as the ‘forecasted’ approach. The wind curtailment is shown in GWh

curtailed over the year.

As can be see from Figure 7.7, under the fuelsaver approach, wind generation is

curtailed from a lower installed capacity and at a much higher magnitude. Under

the fuelsaver approach, with 4000MW of installed wind generation, there would

be 3450GWh of curtailed wind, representing over 30% of the annual wind power

production (assuming a ‘mid’ load factor). In the forecasted approach curtailment

results when the long range wind power forecast grossly underestimates the actual

wind output. At 4000MW installed, the likely annual curtailment is approximately

11% of annual production. Thus, with the forecasted approach, significantly more

energy is reaped from the same installed capacity of wind generation operated under

a fuelsaver approach (2.2TWh over the year at 4000MW installed).

Figure 7.8 illustrates the average number of units dispatched to meet the load

under both the fuelsaver and forecasted approaches. Under the forecasted approach,

less and less units are dispatched to meet the load since wind generation is considered


0 500 1000 1500 2000 2500 3000 3500 40000

500

1000

1500

2000

2500

3000

3500FuelsaverForecasted


GW

hof

Win

dC

urt

ailm

ent

Figure 7.7: Wind curtailment in 2010 under fuelsaver

0 500 1000 1500 2000 2500 3000 3500 400012

13

14

15

16

17

18

19

20

21



Aver

age

Num

ber

ofC

omm

itte

dU

nit

s

Figure 7.8: Average number of committed units under fuelsaver


in the scheduling of the units. The reduction saturates at approximately 2000MW

installed as the reserve requirement with wind ensures that a certain number of units

must be committed. With the fuelsaver approach however, the units are scheduled

independently of the wind generation. Since the load is unchanged across each of the

wind penetrations in the fuelsaver scenario, the number of units committed across

each of the wind penetrations remains unchanged.

In addition to more units being on under the fuelsaver approach, those units are

also running at lower operating levels than under the forecasted approach. Figure

7.9 shows the average operating level, as a percentage of maximum capacity, under

the two approaches.

0 500 1000 1500 2000 2500 3000 3500 400040

45

50

55

60



Aver

age

Oper

atio

nas

%of

Max

imum

Cap

acity

Figure 7.9: Average operating levels of committed units under fuelsaver

It is clear from Figure 7.9 that the operating levels of the conventional units are

dramatically reduced under the fuelsaver approach when compared to the forecasted

approach, this has a knock on effect on the efficiency of these units. Operating at

reduced levels results in increased fuel consumption and emissions per MWh.


The costs and benefits which depend on the schedule of generators are the cycling

costs and the emissions and fuel savings. Under the fuelsaver approach, the wind

power forecasts are disregarded and the system is dispatch as though there were

no wind generation. Thus, it is assumed in the fuelsaver approach that there is no

increased reserve requirement with wind generation as the wind does not add to the

uncertainty of the system. The fuelsaver approach alters the dispatch of generators

from the forecasted approach, thus, the costs and benefits which are discussed here

are the cycling costs, the emissions benefits and the fuel savings.

7.2.2 Fuelsaver Cycling Costs

Under the forecasted approach, the main cause of cycling costs was the ramping

of units on the margin to deal with changes in the hourly wind power output, in

addition to the switching on of some peaking units if the forecasted wind output

was greater than actual. Under the fuelsaver approach however, there are no cycling

costs attributable to on/off cycling as it is assumed under fuelsaver that no units

are switched on or off. As shown in Figure 7.7, the fuelsaver approach results in the

curtailment of a large proportion of the wind generation. This actually smooths out

the wind power production and thus, under the fuelsaver approach, cycling costs are

actually less than under the forecasted approach as illustrated in Figure 7.10.

However, although the fuelsaver approach results in lower cycling costs, the fact

that the units are operating at much lower efficiencies has a dramatic impact on the

emissions and fuel savings as shown here in Section 7.2.3 and 7.2.4

7.2.3 Fuelsaver Emissions Benefits

The dispatches from the fuelsaver approach were analysed with regard to the emis-

sions savings with increases in wind capacity. Since the conventional units operate

at much lower outputs under the fuelsaver approach, they are less efficient. In addi-

tion, as discussed in Chapter 5, NOX emissions increase significantly in CCGTs and

OCGTs if they are operated at reduced outputs. Figure 7.11 illustrates the absolute

emissions reduction with increases in wind generation under the fuelsaver approach.


0 500 1000 1500 2000 2500 3000 3500 40000

20

40

60

80

100



Cycl

ing

Cos

te

m

Figure 7.10: Cycling costs under the fuelsaver approach

0 500 1000 1500 2000 2500 3000 3500 40000

5

10

15

20

25

30

20000

25000

30000

0 500 1000 1500 2000 2500 3000 3500 40000

100

200

’Forecast’ Savings’Fuelsaver’ Savings

CO2SO2NOx


Em

issi

ons

inkilot

ons

Val

ue

ofE

mis

sion

sSav

ingse

m

Figure 7.11: Emissions savings under the fuelsaver approach


The emissions in kilotons are illustrated on the primary y-axis and it is seen that the

CO2 emissions greatly exceed the SO2 and NOX emissions. The secondary y-axis

illustrates the value of these saved emissions at the ‘mid’ emissions price scenario

(Table 5.3) in Chapter 5 and also shows the value of the saved emissions under the

forecasted approach for ease of comparison

It is clear from Figure 7.11 that the forecasted approach results in much improved

emissions savings than the fuelsaver approach. In addition, it is very interesting to

note that if a fuelsaver approach is adopted, wind generation can actually cause an

increase in NOX emissions over a case with no wind. This is due to the CCGT units

operating at low loads. Thus, although wind generation does not itself produce

harmful emissions, it can actually cause an increase in system NOX emissions if the

power system is operated under a fuelsaver approach.

7.2.4 Fuelsaver Fuel Benefits

As with the emissions savings, the fuelsaver approach was also analysed with respect

to the reductions it produced in fuel consumption. Figure 7.12 illustrates the re-

duction for each of the fuel types in petajoules over the year on the primary y-axis,

and the monetary savings assuming the ‘mid’ fuel prices on the secondary y-axis.

Again, the fuel savings under the forecasted approach are included for illustrative

purposes.

Since no units are switched off under the fuelsaver approach, no units are entirely

displaced by the wind generation. Thus the fuel savings stem from a reduction in

output of the committed generators. As the wind generation increases, more and

more units are required to operate at lower levels of operation. Thus, a saving in

each of the fuel types is seen, however this is not a linear reduction as the units are

less efficient at lower loads. It is seen from the secondary y-axis that the value of

the savings are significantly less than in the forecasted approach.


0 500 1000 1500 2000 2500 3000 3500 40000

50

100

150

200

0 500 1000 1500 2000 2500 3000 3500 40000

100

200

300

Forecasted SavingsFuelsaver Savings

GasCoalPeatOil


Fuel

Con

sum

ed(P

J)

Val

ue

ofFuel

Sav

ingse

m

Figure 7.12: Fuel savings under the fuelsaver approach

7.2.5 Net Benefits of Wind under the Fuelsaver Approach

The cycling costs and the emissions and fuel saving benefits of wind generation under

a fuelsaver approach have been calculated in the previous Sections. It is assumed

that the development, operation and maintenance costs of wind generation, remain

unchanged regardless of the method of system operation with wind generation as do

the network costs since these costs depend on the level of installed wind generation.

It is considered that operating the system under a fuelsaver approach requires no

additional reserve capacity. The fuelsaver approach assumes that wind generation

has a capacity value of zero thus the only benefits of wind generation under the fuel-

saver methodology are the emissions and the fuel savings. Figure 7.13 illustrates the

total cost function, the total benefit function and the net benefits of wind generation

operated under a fuelsaver approach.

Figure 7.13 shows that under the fuelsaver approach the costs exceed the benefits

at all levels of installed wind generation. Thus, at all levels of wind generation, wind


0 500 1000 1500 2000 2500 3000 3500 4000−6000

−4000

−2000

0

2000

4000

6000CostsBenefitsNet Benefits


em

Figure 7.13: The net benefits of wind under the fuelsaver approach

power forecasts should be included in the dispatch decisions of generators as if they

are omitted, the costs will exceed the benefits at all levels.

7.2.6 Conclusions of Fuelsaver Approach

This Section analysed an alternative power system operation approach with wind

generation, known as the fuelsaver approach, which was employed in Gardner et al.

(2003) and DETI (2003). The fuelsaver approach ignores wind power forecasts in the

scheduling of generation and wind power is only considered if it is available at real

time. The fuelsaver approach results in large amounts of wind energy curtailment,

of up to 30% of the annual output at high levels of installed wind power. In addition,

it results in the over commitment of conventional generation and those committed

generators are required to operate at low operating levels. This has a knock on

impact on the emissions and fuel savings of wind generation. In fact, under the

fuelsaver approach, NOx emissions can actually be greater than in the absence of any


wind generation. By adopting the fuelsaver approach the costs of wind generation

exceed the benefits at all levels of installed wind generation, which implies that to

employ this method of power system operation would be entirely futile.

7.3 The Impact of Connection Policy on Distributed

Generation

Much of the wind generation development to date has been in low capital, small

scale developments connected to local distribution networks (Collinson et al., 1999).

These local distribution networks are low voltage networks which were traditionally

designed for the delivery of electricity to end use customers rather than the gener-

ation of electricity. However, with the connection of small scale generators, such as

wind turbines, the characteristics of the distribution network are changing leading

to changes in the operation of these networks (OFGEM, 2003).

This small-scale generation, which is not centrally dispatched and is connected

to local distribution networks is referred to as distributed generation (DG). Wind

generation is the fastest growing form of DG, with significant penetrations connected

already in many countries (Keane et al., 2006). Traditionally distribution system

operators have only allowed DG to connect to the distribution network on a ‘firm’

basis. This means that the generator can connect up to a capacity which will

ensure that the generator will never violate the technical constraints of the network,

even when operating at its limits. Thus the output from a firm generator will be

accepted onto the network at all times. The impact of a firm connection policy

on the potential for DG has been demonstrated in Keane and O’Malley (2005a) and

Wallace and Harrison (2003). Adopting a firm connection policy can lead to network

sterilisation, which limits the potential for further generation capacity connecting to

that section of network3 (Keane and O’Malley, 2005b; Vovos et al., 2005).

Non-firm access has been proposed as a method to increase the penetration

3Network sterilisation results when capacity is allocated to the bus/buses whose voltage and/orshort circuit levels are most sensitive to power injections. Thus no more generation can be connectedas the buses are constrained.


of distributed generation (Collinson et al., 1999). Non firm access refers to output

greater than the firm amount, at which generators may be allowed operate depending

on the load and generation levels throughout the year. Active controllers are then

used to manage any constraint breaches, and if necessary to curtail some of the

output of the distributed generators. Keane and O’Malley (2006) have shown the

scope for DG if non firm access is employed.

Collaboration work with Keane and O’Malley (2006) form the basis for the re-

sults shown in this Section. Keane and O’Malley (2005b, 2006) determined the

optimal allocation of energy resources on the distribution network under three con-

nection policy scenarios. It is found that by employing non-firm connection policies,

significantly more renewable generation can connect to the distribution network

without the need for additional investment in the network, thus ensuring efficient

use of the existing infrastructure.

Using the methodologies set out in Chapters 3 to 6 a cost benefit analysis is

conducted on the optimal penetrations to determine the overall monetary benefit

of using one connection policy over another. It is shown that a significant increase

in the net benefits of distributed generation is gained if the appropriate connection

policy is utilised from the outset and conversely that significant costs are incurred

if ad hoc policies are employed. Furthermore, it is shown that non firm access has

the scope to facilitate a significant extra amount of distributed generation capacity.

The methodology employed to determine the optimal penetrations of generation

on the distribution network is outlined in Section 7.3.1. The distribution network

for the case study is discussed in Section 7.3.2 and the potential energy resources

are shown in 7.3.3. Section 7.3.4 illustrates the results of the optimisation of the

distribution network. The load flow analysis, system scaling and the dispatch model

are discussed in Sections 7.3.5 to 7.3.7. The costs and benefits of the optimal pene-

trations are discussed in Section 7.3.8 and the net benefits are given in Section 7.3.9.

The conclusions are given in Section 7.3.10.


7.3.1 Methodology

The optimisation of generation on the distribution network is discussed in detail in

Keane and O’Malley (2005b, 2006) and Keane et al. (2006). The objective of the

methodology is to maximise the amount of energy output per euro of investment

in distributed generation by making best use of the existing network assets and

available energy resources. This is done subject to the technical constraints on

the network. Appendix B summarises the methodology which was employed by

Keane and O’Malley (2005b) to optimise the allocation of distributed generation on

the distribution network. The resulting allocations are then used as the basis for

the cost benefit analysis which is the focus of the work presented here.

A number of representative samples of the distribution network were analysed.

These sections were chosen based on their geographical location and also on their

characteristics (i.e. rural or semi urban). Likely energy resource portfolios were asso-

ciated with each section. Using the methodology shown in Appendix B, each section

was analysed and the optimal allocation of the energy resources was determined for

3 cases:

• Firm

• Non Firm

• Firm + Non Firm

The first case is the base case of the maximum firm allocation optimised on a

MWh/e basis, i.e. the amount of energy per euro of connection costs is maximised.

The objective function and constraints utilised by Keane and O’Malley (2005b) for

the ‘firm’ allocation are shown in Appendix B. The second case refers to the max-

imum non firm access permitted. Here voltage sensitivities are utilised in the ob-

jective function to reduce the instances of overvoltage (see Appendix B). The third

case examined is the case where firm access is initially only permitted and then non

firm access is permitted at a later stage. These two non firm cases are optimised on

a MWh/ekV basis.


A summary of the implementation and stages of the methodology are shown in

Figure 7.14. Portion ‘A’ of the Figure highlights the ‘network’ part of the analysis.

Based on the technical constraints, the allocations under the firm and non firm

objectives are calculated. The firm allocation (MWh/e) is fed as a constraint into

the non firm allocation, which yields the ‘firm + non firm’ allocation. This process

of determining the optimal penetrations of distributed generation was conducted by

Keane and O’Malley (2005b) and is summarised in Appendix B.

Portion ‘B’ of Figure 7.14 translates the installed DG capacities into annual

energy outputs. Each of the three allocations are simulated on the distribution

network over a year using load flow analysis (Keane et al., 2006). This load flow

analysis determines the instances of DG output curtailment in the non firm cases.

The analysis for the network section is then scaled up to represent the system as a

whole.

Portion ‘C’ of the analysis inputs the annual energy profiles of the DG allocations

into the dispatch model to determine their impact on the operating levels of the other

conventional units on the system. The resulting dispatches are then used to calculate

the costs and benefits of each of the allocations.

7.3.2 Ireland’s Distribution Network

In Ireland, distributed generation is typically connected at 38kV or by direct feed

to the 38kV/MV station on the MV network (10kV or 20kV). Voltage rise tends to

be the dominant constraint due to the network in Ireland, which outside the main

cities is typically a weak rural network with a large amount of conductor. A weak

network means a network with a low short circuit level or fault level.

Five network sections are chosen as a representative sample of the types of net-

work encountered on the Irish distribution network where generation will seek to

connect. Figure 7.15 shows the likely future distribution of installed wind power per

county, reproduced from Doherty and O’Malley (2005). The five network sections

were selected from along the western coastline and also in the southeast of the coun-

try. It is evident from Figure 7.15 that it is in these locations that connections of


FIRM ALLOCATION

(MWh/Euro)

NON FIRM

ALLOCATION

(MWh/Euro kV)

COST BENEFIT ANALYSIS(Chapters 4 – 6)

CAPITAL, O&M & CONNECTION COSTS,

RESERVE & CYCLING COSTS,

CAPACITY BENEFIT,

EMISSIONS BENEFIT,

FUEL SAVING.

ANNUAL LOAD FLOW SIMULATION & CURTAILMENT

SCALING

ANNUAL ECONOMIC DISPATCH(Chapter 3)

TECHNICAL

CONSTRAINTS

SAMPLE

NETWORK

SECTIONS

A

B

C

Figure 7.14: Methodology


DG, and in particular wind generation, will and are being sought.

Figure 7.15: Future distribution of installed wind power capacity in percent

The network sections were also selected based on their characteristics and struc-

ture from network data available from ESB Networks (2005). They represent the

types of network encountered on the Irish distribution network, from voltage con-

strained rural networks to more urban networks where voltage is less of a problem

and short circuit level can become significant.

7.3.3 The Energy Resources

A representative energy resource portfolio was drawn up for each network section

based on its geographical location and with reference to ESBI and ETSU (1997)

and Figure 7.15. Using typical values for the load factor of each type of gener-

ation (BWEA, 2006), these resources are incorporated into the objective function

(Equation (B.1) in Appendix B). Each network section has its own distinct assumed

energy resource. Table 7.1 shows the total assumed energy resource and load factors

across the five representative network sections.


Table 7.1: Assumed Energy Resource for the Five Network Sections (MW)

Section Capacity (MW)

Energy Source 1 2 3 4 5 Total Load Factor

Wind 20 31.5 39 23.5 24 138 0.35

Biomass 0 0 10 10 15 35 0.85

LFG 12 0 0 0 0 12 0.76

Hydro 2 2 1 0.8 0 5.8 0.32

Total 34 33.5 50 34.3 39 190.8 -

7.3.4 Optimal Allocations of Distributed Generation

The allocations for the five network sections were determined under the three con-

nection policies using the methodology described in Appendix B and the total allo-

cations are shown in Table 7.2.

Table 7.2: Allocations for the five network sections

Firm Non FirmFirm +

Non Firm

Installed MW

Wind 48.6 94 94

Biomass 34.4 35 35

LFG 12 12 12

Hydro 5.0 5.8 5.8

Energy GWh

Wind 153 291 289

Biomass 259 264 264

LFG 80 80 80

Hydro 14 16.5 16.5

Given their significantly higher load factor, any available biomass and landfill

gas (LFG) are allocated the available firm capacity first. As a result, there is very

little difference, between their firm and non firm capacities, leading to the conclusion

that when a maximisation of energy strategy is followed, forms of generation with

higher load factors are largely independent of the connection policy as they will


always be connected first. The small scale hydro generation also has very little

difference between its firm and non firm allocation, but this is due to the limited

resource available. When these factors are combined with the high wind resource,

it is evident that it is the costs and benefits of wind generation that are affected

by the connection policy and it is wind generation therefore that is the focus of the

results in this Section (Keane et al., 2006).

7.3.5 Load Flow Simulation

Once the allocations of DG capacity on the network sections are determined, the an-

nual energy output for each DG resource is ascertained. For the firm case, the annual

energy output is determined using actual historical data series from ESB Networks

(2005). For the non firm cases, over the year, constraint breaches will occur at times

of high generation and low load. Annual simulations are carried out for the network

sections to determine the time and magnitude of energy curtailment. These simu-

lations consist of load flow calculations carried out for half hourly data. The data

included in the simulations includes active and reactive load and generation profiles

for each of the energy resources, along with data on the frequency of N-1 outages

and the sending voltage at the transmission station (Keane et al., 2006).

If there is a constraint breach in the non firm case, due to over voltage, the output

of the distributed generation is curtailed. The level of curtailment is calculated using

Equation (7.1).

PCurtail i =Vi − VMax

2νii ∀ N. (7.1)

Where PCurtail i (MWh) is the amount of energy that is curtailed over a half

hour period at the ith bus. Vi and VMax (kV) are the voltage at the ith bus and the

maximum permissable voltage respectively. νi is the voltage sensitivity (kV/MW)

of the ith bus and N is the number of distribution buses. νi is calculated for each

bus drawing on the analysis used in Keane and O’Malley (2005b). This curtailment

method results in the least amount of energy being curtailed, by curtailing the

generator who has the highest voltage sensitivity at that bus. The annual load flow


simulation yields the amount of energy curtailed over the year, which in turn gives

modified generation profiles, which can then be used in the dispatch model. Figure

7.16 shows the output profile for each of the four allocated DG types over a single

day in March on a single section of network. This day is illustrated as it is an

example of a day of particularly high wind generation when the output had to be

curtailed in the non-firm case.

0:00 4:00 8:00 12:00 16:00 20:00 0:00

10

20

30

40

50

60

70

80

90

100

% G

ener

atio

n

Time

BiomassLandFill GasWindNon Firm WindHydro

Figure 7.16: Output of DG generators on a sample day

7.3.6 Scaling Outputs

From Table 7.2 it is evident that given a large wind resource, non firm access has

the potential to facilitate much higher penetrations than firm access (94MW com-

pared to 48.6MW). The allocations shown in Table 7.2 are only for five network

sections, however, to understand fully the effect of the connection policy of DG,

it was necessary to scale the allocations up to represent the whole system. This

scaling incorporates the characteristics of the distribution network, and the likely


penetrations and locations of renewable energy in Ireland (ESBI and ETSU, 1997;

AIRGS, 2006b; Doherty et al., 2006; ESB Networks, 2005). The installed capacities

and the annual energy outputs for the whole system, based on actual output data

series (AIP, 2005) and the total curtailed energy, are given in Table 7.3.

Table 7.3: Scaled allocations for the whole system

Firm Non FirmFirm +

Non Firm

Installed MW

Wind 655 1392 1392

Biomass 401 410 410

LFG 96 96 96

Hydro 64 77 77

Energy GWh

Wind 2065 4333 4302

Biomass 3018 3089 3089

LFG 639 639 639

Hydro 184 221 221

Table 7.3 illustrates the scaled installed capacities and also the annual energy

outputs for each technology. It is seen that for the same installed capacity, the non

firm scenario produces more energy output than the firm + non firm case. This is

because there is more freedom in the allocating of capacity on the network in the

non firm case resulting in reduced capacity allocation at the most voltage sensitive

buses. This results in less curtailment under the non firm case when compared to

the ‘firm + non firm’ case.

It should be noted that the capacities given here are representative of the dis-

tribution connected generation and not transmissions connected. Thus they are less

than the capacities investigated in previous Chapters.

7.3.7 Distributed Generation in the Dispatch Model

The annual output from the distributed generation is determined as described in

Section 7.3.6. In order to quantify the net benefits of distributed generation, it is


necessary to quantify the impact that it has on the operation of the power system

as a whole. Thus, the dispatch model described in Chapter 3 is used. In order

to include the output profiles of the DG, the dispatch model was altered slightly.

The assumed plant mix and load were for the year 2007 and the model was run at

half hour intervals for the entire year (rather than hourly intervals as in previous

Chapters). The test year 2007 was used in the dispatch model for consistency as

the network analysis was based on local load profiles which were provided by the

Irish distribution network operator for 2007 (ESB Networks, 2005). The DG profiles

were bid into the commitment stage of the dispatch model with a bid prices of zero,

ensuring they were always on when available. The resulting dispatches were then

used to calculate the cycling costs and emissions and fuel savings.

7.3.8 The Costs and Benefits of Distributed Generation

The total connection costs for wind under the three connection policies across the

whole system were calculated by Keane et al. (2006) and were assumed to be variable

per km of line and are taken to be 50,000e/km. The connection costs are shown

in Table 7.4. A considerable saving in connection costs is made when a non firm

connection policy is pursued from the outset. In some cases, the extra non firm

capacity can share the existing connection line, but it is seen from the results that

on other occasions new connection lines must be built.

Table 7.4: Connection costs for wind generation

Connection Policy Installed MW Connection Cost

Firm 655 e61,300,000

Non Firm 1392 e128,000,000

Firm + Non Firm 1392 e156,800,000

As discussed in Section 7.3.7, the DG output levels for each technology under the

three connection policy scenarios are bid into the dispatch model with a bid price

of zero, ensuring priority dispatch. The resulting dispatches are then analysed to


determine the costs and benefits of each of the three connection policy approaches.

The additional reserve requirement for the system with increased wind penetra-

tion was discussed in Chapter 4 and is valued at e1.15/MWh. The reserve costs

for the distributed wind generation allocations are given in Table 7.5. The installed

wind capacity is the same in both of the non firm cases thus the additional annual

reserve costs are the same.

Table 7.5: Additional reserve costs for distributed wind generation

Connection Policy Installed MW Additional Reserve Cost

Firm 655 e101,090

Non Firm 1392 e290,990

Firm + Non Firm 1392 e290,990

The additional annual cycling costs over an installed wind penetration of zero

were calculated as described in Chapter 4 and are given in Table 7.6.

Table 7.6: Additional cycling costs with wind generation

Connection Policy Additional Cycling Cost

Firm e52,396,400

Non Firm e66,437,200

Firm + Non Firm e64,858,700

The reason for the slight difference in the cycling costs for the two non firm cases

is the energy output. The non firm case has less curtailment than the ‘firm + non

firm’ case for the same installed capacity resulting in higher annual output. This

means that over the year the non firm capacity displaces more generation. However,

at times of low load this displaced generation can be units which have higher cycling

costs. This non firm case causes a slight increase in the cycling of these units over

the ‘firm + non firm’ case.

The dispatch model was also used to calculate the CO2, SO2 and NOx emissions


for each of the conventional generators. Table 7.7 shows the emission savings across

the year compared to a no wind case. The emissions savings are valued at the ‘mid’

emissions prices.

Table 7.7: Emissions savings (in tons) with wind generation

Connection CO2 SO2 NOx Benefit e

Firm 1,086,116 2,685 1,147 36,830,000

Non Firm 2,189,700 3,659 1,965 72,136,000

Firm + Non Firm 2,181,300 3,659 1,959 71,463,000

The additional wind generation on the system also provides a saving in fuel

consumption as thermal units are displaced. Table 7.8 shows the fuel savings with

increased wind generation. The fuel prices given in Table 7.8 were also used in the

dispatch of the generators.

Table 7.8: Fuel savings in petajoules with wind generation

Fuel Type Gas Coal Peat OilBenefit e

Price e/GJ 4.16 2.15 3.14 4.13

Firm 9.97 0.05 0.98 4.75 64,273,000

Non Firm 19.99 0.22 2.25 7.63 122,240,000

Firm + Non Firm 19.87 0.22 2.25 7.63 121,740,000

7.3.9 Net Benefits

The costs and benefits above were combined and the net benefits of each connec-

tion policy were determined. The capital cost of wind generation is assumed to be

e900,000 per MW here. This is less than that assumed in Chapter 5 as the shallow

connection costs were included in the capital cost in Chapter 5. However, in this

analysis they are dealt with separately and are illustrated in Table 7.4. The capacity

benefit is taken to be 37% at 655MW and 31% at 1392MW, as discussed in Chapter


5. It was found that although the benefits outweighed the costs under all three

connection policies there was a significant difference in the net benefits. Table 7.9

shows the net benefits of each of the connection policies.

Table 7.9: Net benefits of wind generation (in em)

Cap. (MW) Total Cost Total Benefit Net Benefit

Firm 655 1,346 1,379 33

Non Firm 1392 2,461 2,684 223

Firm+NonFirm 1392 2,476 2,673 197

As is evident from Table 7.9, while the two non firm policies both result in the

same installed capacity of generation, the costs and benefits of wind generation show

significant differences. There is a significant increase of 12% in net benefits under

non firm connection than under a firm and then non firm scenario for the same level

of installed wind. The increases in net benefits results from the different pattern on

connected generation across the buses, which leads to altered connection costs and

curtailed energy, which in turn impacts upon a number of the costs and benefits as

detailed above.

It is clear that the connection policy affects the energy output and connection

costs and furthermore that this has a considerable knock on effect on system costs

and benefits such as cycling costs, emissions and fuel use. It has also been shown

that as some costs, such as the additional reserve cost and the capital cost, are

dependent on the capacity of the plant installed, they are unaffected by the non

firm connection policy employed.

7.3.10 Conclusion of Connection Policy

Much of the recent development in renewable generation has been in low capital,

small scale developments connected to local distribution networks. Distribution

networks were traditionally designed for the delivery of electricity to end users rather

than the generation of electricity, leading to changes in the characteristics of the


distribution network and changes in the operation of these networks. To ensure

a sustainable future electricity supply, renewable generation should be facilitated

in such a way as to maximise connection while making best use of the existing

distribution network infrastructure.

This Section compared the optimal allocation of energy resources on the distribu-

tion network under three connection policy scenarios. It is found that by employing

non-firm connection policies, significantly more renewable generation can connect to

the distribution network without the need for additional investment in the network,

thus ensuring efficient use of the existing infrastructure.

The optimal penetrations of distributed generation under a number of planning

policies are determined and these are then used in the dispatch model to determine

their impact on the system as a whole. A complete cost benefit analysis is then

conducted on these penetrations to determine the overall monetary benefit of using

one connection policy over another.

It is shown that a significant increase in the net benefits of distributed gener-

ation is gained if the appropriate connection policy is utilised from the outset and

conversely that significant costs are incurred if ad hoc policies are employed. Fur-

thermore, it is shown that non firm access has the scope to facilitate a significant

extra amount of distributed generation capacity.

7.4 Tidal Generation on the Irish System

Wind generation is variable and relatively unpredictable. As discussed in Chapter 3,

wind power forecasts can be incorporated into the scheduling of generators, however,

there still remains a degree of uncertainty and a need for real time balancing with

wind curtailment and the switching on of peaking units. Here tidal generation is

investigated as an alternative to wind generation as although it is variable, it is

almost perfectly forecastable within the time frames of interest to power system

operators.

The majority of the energy contained within the tides is generated from the

gravitational forces of the sun and moon on the deep oceans. The rotation of the


earth relative to both the sun and the moon produces a 12.4 hour cycle resulting in

two high waters and two low waters per day. The size of the high water is dependent

on the position of the moon relative to the sun. When they are in line the forces

are constructive and there is a spring tide. When they are at 90◦ the forces are

destructive and there is a smaller neap tide (Bryans et al., 2005).

Traditionally tidal energy has been harnessed using a barrage system to establish

a head of water, which can in turn power a turbine, much as in a hydroelectric

dam. An example of such a scheme can be seen at the La Rance tidal barrage,

Brittany, France. Recent developments in tidal energy devices (TEDs) have focused

on harnessing the kinetic energy present whilst the tide is flowing into the shelf

sea, rather than to harness the potential energy established by the rise in sea level

(Bryans et al., 2005; Bryans, 2006).

The progress of TED development has been slow, with only 15 projects in de-

velopment around the world. One tidal device is almost market ready, developed by

Marine Current Turbines, and two other devices are still in the development stage:-

the Engineering Business’s ‘Stingray’ project and the Hammerfest Strøm project.

The design developed by Marine Current Turbines (MCT) is illustrated in Figure

7.17. It utilises two turbines on a wing either side of a support beam driven into the

seabed. The wing holding the turbines can be jacked up the beam enabling the tur-

bines to be raised out of the water, removed and serviced on land. The MCT device

has been designed to take advantage of the best tidal resources and is commercially

viable in areas of 20 to 40m water where the peak spring tidal current velocity is

greater than 2.25 m/s (Bryans et al., 2005; Whittaker et al., 2003). MCT installed a

prototype with a single 750 kW turbine off Lynmouth in the Bristol Channel during

2003.

Bryans et al. (2004) determined the resource for tidal energy around Ireland us-

ing a 2 dimensional tidal model to simulate the tidal flows for the waters surrounding

the entire island with a 405m by 405m grid. They found that the resource currently

accessible to the tidal device shown in Figure 7.17 is 374 MW around Ireland. How-

ever, Bryans et al. (2004) predict that into the future, TED development will lead


Figure 7.17: The marine current turbines design

to larger turbines which will be financially viable at greater depths and lower spring

current velocities. Based on the predictions by Bryans (2006), a tidal resource of up

to 560MW is investigated here.

7.4.1 Modelling the Tidal Generation

The power output from the potential tidal generation in Ireland was determined by

Bryans et al. (2005) and is shown for a spring tide and a neap tide in Figure 7.18

with 565MW installed.

The power output from a turbine or group of turbines will only reach its max-

imum output during a spring tide, which occurs for a short time twice a month.

Therefore it is not envisaged that developers would consider it economically viable

to rate the electrical equipment to harness all of the energy available at a spring

tide. Instead, the maximum power from the turbine would be down rated by alter-

ing the pitch of the blades. This has been termed Electrical Down Rating (EDR).

It is anticipated that in Ireland, the installed TEDs will undergo 40% downrating

of the maximum rated capacity of the turbines. Thus, the maximum power output

realised is 336MW although there are 560MW installed, as seen in Figure 7.18. It is


00:00 06:00 12:00 18:00 00:00

100

200

300

400

500SpringNeap

Time of Day

Pow

erO

utp

ut

(MW

)

Figure 7.18: The power output during a spring and neap tide

envisaged that developers will balance the cost of the grid connection and the rating

of electrical components against the capacity saving from spilling energy at higher

tidal flow rates (Bryans, 2006; Bryans et al., 2006).

In order to model the tidal generation, a net load curve was developed by sub-

tracting the tidal generation from the load. In addition, as the tidal generation is

considered to be perfectly forecastable, no additional reserve was required with in-

creasing capacities of tidal generation. The model was run as described in Chapter

3 with the installed wind generation assumed to equal zero. The following figures

illustrate the cycling costs, and emissions and fuelsaving benefits resulting from tidal

generation.

7.4.2 Tidal Cycling Costs

As illustrated in Figure 7.18, the tidal generation has four peaks and troughs per

day representing the tidal current coming in and out twice a day. This fluctuation is


particularly apparent during a spring tide when the variations are at their maximum.

The conventional generation on the system will be required to ramp up and down

inline with these variations in the tidal generation which results in cycling costs. The

magnitude of these variations increase as the installed tidal generation increases,

thus, the cycling costs increase as illustrated in Figure 7.19.

0 80 160 240 320 400 480 5600

5

10

15

20

25

30

35

40

Tidal Cycling Costs

Installed Tidal Capacity (MW)

Cycl

ing

Cos

tse

m

Figure 7.19: Cycling costs with increases in tidal generation

7.4.3 Tidal Emissions Benefits

Figure 7.20 illustrates the emissions benefits from increasing installed penetrations

of tidal generation on the Irish system for the 2010 plant mix and load. It is seen

that the emission savings are quite modest as the installed levels of tidal generation

increase. This is because the installed penetrations are relatively small compared to

the size of the system (representing less than 2% of electricity generation (Bryans,

2006)). The tidal generation output peaks four times a day with a maximum output

of 60% of the installed capacity. This results in the tidal generation having a rela-

tively low load factor when compared to the wind generation - approximately 22%


for tidal generation compared to over 40% for low levels of installed wind generation

(Bryans, 2006).

0 80 160 240 320 400 480 5600

5

10

15

20

25

30

20000

25000

30000

0 80 160 240 320 400 480 5600

10

20

30

40

50

CO2SO2NOx

Value of Emissions Savings

Installed Capacity (MW)

Kilot

ons

Val

ue

ofE

mis

sion

sSav

ings

ine

m

Figure 7.20: Tidal generation emissions savings

7.4.4 Fuel Savings with Tidal Generation

The fuel savings with increases in tidal generation are shown in Figure 7.21.

As with the emissions savings, because of the relative size of the installed tidal

with respect to the size of the system, the fuel savings are modest. The value of the

saved fuel is shown on the secondary y-axis.

7.4.5 The Net Benefits of Tidal Generation

The previous sections discussed the cycling costs associated with increases in tidal

generation and the emissions and fuel savings. Because tidal generation is per-

fectly forecastable it does not add to the reserve requirement of the system. Bryans

(2006) calculated the capacity benefit of tidal generation to be 22% using the MCT


0 80 160 240 320 400 480 5600

50

100

150

200

0 80 160 240 320 400 480 5600

20

40

60

GasCoalOilPeat

Value of Fuel Savings

Installed Capacity (MW)

Ener

gyC

onsu

med

(PJ)

Val

ue

ofFuel

Sav

ings

ine

m

Figure 7.21: Tidal generation fuel savings

technology with 40% EDR. Without any EDR, this drops to 18%. Thus, the capac-

ity benefit of tidal generation can be thought of as the saved cost of building and

maintaining a conventional generator with a capacity of 22% of the installed tidal

generation.

Since tidal generation is still in its infancy clearly defined capital costs have

not yet been established and forecasting the likely capital costs for 2010 could be

erroneous. In addition, there have been no comprehensive network reinforcement

studies completed for Ireland with respect to tidal generation. Thus, rather than

illustrating the total net benefits of tidal generation, Figure 7.22 illustrates the net

present value of the cycling costs, and the net present value of the capacity, emissions

and fuel saving benefits with an assumed discount rate of 8% and a term of 20 years.

From Figure 7.22 the total benefits of tidal generation are seen to exceed the

cycling costs at all penetrations of tidal generation, however, the capital, operation

and network costs have still to be included. Table 7.10 illustrates the maximum that


0 80 160 240 320 400 480 5600

100

200

300

400

500

600

700

800

NPV of Total BenefitsNPV of Cycling Costs

Installed Tidal Capacity (MW)

em

Figure 7.22: The total benefits and cycling costs of tidal generation

these other costs could be each year to ensure that the benefits of tidal generation

were greater than the total costs.

The amounts in Table 7.10 represents the maximum that the combined capital,

O&M and network costs can be each year to ensure positive net benefits for tidal

generation. In other words, if the annual capital, O&M and network reinforcement

costs exceeded the amounts shown in Table 7.10 then the costs of tidal generation

will exceed the benefits and the resource should not be developed.

Putting these figures into perspective, if it is assumed that the operation and

maintenance costs of tidal generation were equal to e50,000 per MW per annum

(less than that of an offshore wind farm (Doherty et al., 2006)), annual O&M cost

for 160MW of tidal generation would be e8m. If it were assumed that no network

reinforcement was required with 160MW of tidal, then the capital costs would have

to be less than e2,257,807 per annum to ensure positive net benefits (e 10,257,807

- e8m). If this is the annual cost of capital, then the total capital cost of 160MW


Table 7.10: Boundary annual costs

Installed Tidal Generation Annual Cost em

0 0

80 4,059,204

160 10,257,807

240 14,602,503

320 17,973,800

400 22,023,430

480 28,582,690

560 33,693,617

of tidal would be e22.2m (assuming an interest rate of 8% and a term of 20 years).

This represents an unrealistic capital cost of approximately e140k per MW installed

of tidal generation. In other words, to ensure tidal generation produced positive net

benefits the capital cost would have to be equal to or less than e140,000 per MW

installed. Considering that the best new entrant on the Irish system has a capital cost

of e600,000/MW installed, it is not unreasonable to conclude that tidal generation

will produce negative net benefits at all penetrations (CER, 2006b).

7.4.6 Conclusions of Tidal Generation in Ireland

This section has discussed the potential for tidal generation in Ireland. The feasible

tidal resource and output profiles were based on Bryans et al. (2005) and Bryans

(2006) and these were then subtracted from the load to give a net load curve. The

dispatch model was then run with this net load curve to determine the costs and

benefits of tidal generation. It was found that tidal generation resulted in increased

cycling costs as tidal penetrations increased. The nature of the tidal generation,

with four daily peaks and troughs in output, results in a low load factor for tidal

generation. This leads to relatively small emissions and fuel saving benefits for tidal

generation. To calculate the net benefits of tidal generation it was assumed that

there were no deep network reinforcements necessary with increased tidal generation


and the operation and maintenance costs were assumed to be slightly less than those

for an offshore wind turbine. However, even with these assumptions, in order to

produce positive net benefits for tidal generation, the capital costs would have to

be less than approximately e140,000 per MW installed. This is considered to be an

unrealistic level of capital cost, thus, it is concluded that tidal generation is currently

not a feasible option for Ireland.

CHAPTER 8

Discussion and Conclusions

This thesis presented a cost benefit analysis of wind generation with specific reference

to Ireland as a case study. A range of costs and benefits were included and a large

number of assumptions were tested. A number of additional applications of the

methodology were presented analysing cycling costs, fuelsaver operation, connection

policy and tidal generation. In order to limit the scope of this thesis, it was necessary

to concentrate on the most direct costs and benefits.

Section 8.1 presents a discussion on a number of costs and benefits which could

be included in a wider cost benefit study and some additional issues which were

raised in this thesis are discussed. Section 8.2 reviews the main conclusions of the

work presented throughout this thesis. Section 8.3 outlines some areas for future

research arising from the work presented in this thesis.

8.1 Discussion

In Chapter 1 some costs and benefits of wind generation were introduced which

were not examined in detail in the body of this thesis. The possible implications

of including these costs and benefits are investigated here. In addition, some wider

149

Chapter 8. Discussion and Conclusions 150

issues regarding electricity prices, wind power forecasting, gaming and conventional

plant portfolios are also discussed briefly here.

The dispatch model described in Chapter 3 is a single bus bar system which

neglects any load flow analysis. Thus, issues of transmission congestion are not

included. Congestion limits supplier’s ability to make the most economic decisions

in planning, sourcing and supplying electricity to customers (ESBIE, 2005) resulting

in increased costs. However, as stated by Milborrow (2006), ‘no general rules apply

to transmission congestion economics, which can only be studied on a case-by-case

basis’. This is because congestion is highly system specific and depends on the

location of the generators and loads on the network. Thus, without modelling the

exact location of generators and the network characteristics, it is not possible to

quantify congestion costs. An increase in wind generation, like any conventional

generation, will increase the flows on the lines which may have a knock on effect

on congestion (SEI, 2004c). Workstream 3 of the All Island Renewable Grid study

(AIRGS, 2006a) will conduct load flow analysis for the case study and will identify

potential bottlenecks with increased penetrations of renewable generation. However,

although the cost benefit analysis presented in this thesis did not explicitly include

congestion costs, it did include a cost for network reinforcement. Increased network

capacity ameliorates potential bottlenecks and congestion, thus, the costs of any

additional congestion with increases in wind generation, which were not included

here, may not in fact be significant.

Another cost of wind generation which was not included in this analysis, is the

potential visual impact turbines have on the landscape. Wind turbines tend to

be situated in elevated areas, and as such are considered by some to be a blight

on the landscape (Etherington, 2006). Hurtado et al. (2004) and Moller (2006)

present methodologies to determine a ‘visibility index’ criteria which establishes the

visibility of a wind farm from neighbouring dwellings, however, neither of these

studies attempt to place a value on the cost of this visibility . The cost of the

visual impact of wind generation is very difficult to determine and previous studies

tend to rely on highly subjective public preference models which use questionnaires


to determine the public’s perception of wind turbines (Gamboa and Munda, 2007;

Bishop and Miller, 2007). A more concrete approach to estimating the cost of the

visual impact of wind generation may be to investigate its impact on house prices in

the local area. A report by the Royal Institute of Chartered Surveyors (RICS, 2004)

found that a planning application for a wind farm affects local house prices, however,

the negative impact diminishes as time goes by. In other words, the biggest impact

on the value of local property is at the time of the application for the wind farm,

however, house prices tend to recover within two years of wind farm operation. This

survey also included an assessment for agricultural land, and in some cases, it was

found that the presence of wind turbines on the land actually increased the value of

the agricultural land. This leads to the conclusion that the perception of the visual

impact of wind farms is in fact much more than the actual impact.

This thesis did not investigate issues of dynamic security with increasing pen-

etrations of wind generation. As discussed in Lalor et al. (2005), rates of change

of system frequency will increase as wind generation displaces conventional units.

If doubly fed induction generator (DFIG) wind turbines are employed, additional

static reserve may have to be available to maintain system frequency above a given

threshold. This thesis included additional reserves for increasing penetrations of

wind generation, however, it did not break these down into different types of re-

serves i.e. it did not distinguish between static and dynamic reserve. Thus, a future

cost benefit analysis could include the costs and implications of providing additional

static reserve.

In this thesis, additional operating reserve capacity was dispatched with increas-

ing penetrations of wind generation. This assumes that the existing conventional

generation portfolio has the capability of providing this additional reserve capacity.

It may however, be the case that high penetrations of installed wind generation re-

quire investment in conventional generation to continue to maintain the necessary

levels of reserve capacity for system security. If this were to be the case, it could be

considered a cost attributable to wind generation (although it would be improving

the security of the system as a whole). This long term reserve capacity was not


considered in this thesis. In addition, capacity issues relating to the operating of the

system were not included. It may be the case that by including large penetrations

of wind generation in the plant mix some conventional units will be pushed out

of the merit order entirely. The cost of these stranded assets and any premature

retirement of conventional units was not considered here. Also not considered here

was the physical ability of the system to operate at the extremes e.g. the feasibil-

ity of running the system with a carbon price of e70 per ton resulting in minimal

electricity generation from coal. This may diminish the quality of the results from

the dispatch model. These issues could be included in a wider analysis of wind

generation on a power system.

In addition, as was mentioned in Chapter 3, the costs and benefits of wind gener-

ation calculated in this thesis were based on the scheduled outputs of the dispatched

generators at gate closure. In reality, there will be some real time balancing required

to accurately follow the load and wind variations. This may involve the utilisation

of some of the dispatched reserve, the deloading of conventional units or the curtail-

ment of wind generation. This real time balancing of the generation and load with

wind is an interesting issue which could be included in future research.

This thesis omitted some of the ‘softer’ benefits of wind generation development

such as the creation of local jobs, improvements in local infrastructure leading to

improvements in the standard of living in rural areas. These benefits are very difficult

to estimate and will vary depending on the location of the wind farm. Recent

research by Murphy and Walsh (2002) and Forfas et al. (2003) suggest that very

little benefit should be attributed to creating additional jobs in Ireland because there

is effectively full employment. In fact they suggest that at most you should include a

value of between just 10 - 20% of a job. In addition, wind generation also reduces the

reliance on imported fuels and as such can act as a hedge against international fuel

price and supply variations. This benefit was not included in this thesis. The Irish

Government currently supports the operation of peat fired generation in Ireland by

a levy on all electricity bills, known as the public service obligation (DCMNR, 2002).

The reasoning behind the support of peat is for fuel diversity purposes for security


of supply (ESB, 2001). For the years 2004, 2005 and 2006, the average income

from the PSO levy for the 350MW of installed peat generation has been e57.88m

per annum (CER, 2004b; EirGrid, 2005a). Thus, the Irish public pay on average

e57.88m per year for the security of supply benefits and the local economy benefits

of the peat fired generation. If wind generation was assumed to create these same

benefits, given it’s capacity factor, the value of 350MW of installed wind generation

could be assumed to equal 42% of e57.88m (e24.3m). This equates to a benefit of

e69,450 per MW installed which is approximately e19/MWh. Alternatively, the

feed-in tariff price proposed by the Irish Government could be seen to represent the

value of the fuel diversity benefits of wind generation. As discussed in Chapter 2,

the feed-in tariff for wind generation is approximately e58/MWh.

While this thesis presented a cost benefit analysis of wind generation, it did not

investigate the impact of wind generation on wholesale consumer electricity prices.

Holttinen (2004) found that in the Nordic market, the inclusion of wind generation

in the plant mix results in a reduction in the average spot price and this result

is supported by GE Energy (2005) who showed that spot prices could reduce by

up to 10% in the New York area with the addition of wind generation. Reduced

electricity prices are beneficial for the economy with knock on effects on the standard

of living, industry, trade, employment etc. These potential indirect benefits of wind

generation have not been included here but if they were they would shift the net

benefits curve shown in Chapter 6 to the right resulting in higher critical values for

wind generation. As a caveat, low electricity prices are not a goal in themselves.

Economies should strive for ‘sustainable’ electricity prices as if prices are too low,

there will be issues associated with investment in the electricity industry which could

lead to serious security of supply issues.

Wind forecasting is based on weather forecasting and historical behaviour. The

historical aspect refers to the specific behaviour of the site in response to meteoro-

logical conditions in the past. The accuracy of the wind power forecast is a func-

tion of the method used and the comprehensiveness of the historical data employed

(GE Energy, 2005). This thesis examined three scenarios for wind power forecast


accuracy and it was found that high wind power forecast accuracy increased the

net benefits for wind generation by requiring less reserve capacity, more efficient

conventional unit operation and lower cycling costs. However, it has been shown

by GE Energy (2005) that the benefits to improvements in wind power forecasting

are limited. This study analysed the economic benefits of including 3,300MW of

wind on the New York electricity system under a range of operating approaches. It

found that including wind power forecasts in the operating decisions of conventional

generators resulted in much greater savings than simply assuming a ‘fuelsaver’ type

approach (this result has also been supported in this thesis in Chapter 7). Wind fore-

casts based on the current ‘best practice’ were used in this analysis and the benefits

were compared to a perfect wind forecast. Using the wind forecasts in the commit-

ment decisions resulted in a reduction in electricity system variable costs by $430m

per annum compared to $335 when a ‘fuelsaver’ type approach was used. However,

assuming perfect wind power forecasts provided only a further $25m saving. This

shows that additional investment in improving wind power forecast accuracy above

current levels may in fact result in only relatively small benefits in electricity sys-

tem operating costs. In fact, if the cost of research, development and equipment to

improve wind power forecasts were included, the cost of improved accuracy could

be greater than the benefits it provides.

This thesis assumed a perfectly competitive electricity market, however, in reality

it is possible that generators may behave strategically and distort the optimal results.

There has been a large body of work examining gaming behaviour in electricity

markets, such as Guan et al. (2001), Gountis and Bakirtzis (2004) and Hobbs et al.

(2000). An interesting study conducted by Wen and David (2001) compares the

social welfare benefits of a perfectly competitive electricity market to one where

generators strategically bid into the market to maximise their own revenue based

on rival bids. This study simulated a numerical example and showed that strategic

bidding resulted in higher market clearing prices and thus revenues to the majority

of generators. With reference to the Irish system, a report on potential gaming in the

Irish electricity market prepared by ILEX energy consulting for the Irish regulator


found that strategic behaviour would result in increased prices throughout the day,

but in particular, during periods of peak demand (ILEX, 2003). With reference to

the net benefits of wind generation discussed in Chapter 6, strategic bidding could

result in alterations to the schedule of conventional generators on the system. This

could have a knock-on impact on the cycling costs and the emissions and fuel saving

benefits of wind generation. Analysing the impact of electricity market gaming on

the benefits of renewable generation would provide an interesting study and it is

proposed in Section 8.3 that this is an area of potential future work.

The results shown in this thesis were given for a case study on the Irish system.

In general, the costs and benefits of wind generation will be highly dependent on

the underlying plant mix, however, the methodology described in this thesis could

easily be applied to other systems and other forms of renewable generation. This

thesis examined the optimal penetrations of wind generation given a number of

specified conventional plant mixes on the Irish system. However, looking into the

future, if it is envisaged that wind generation will play a major role in the plant

portfolio, then the conventional plant mix should be optimised to accommodate this

wind generation. A study conducted by Doherty et al. (2006) examines the optimal

future conventional plant portfolios with high levels of installed wind generation.

Their analysis shows that with increasing penetrations of wind generation, there is

a reduction in the necessity for baseloaded generation and an increase in peaking

capacity. In particular, the results point towards a reduction in coal fired generation

and an increase in OCGTs with increasing wind generation penetrations.

8.2 Conclusions

This thesis presented a methodology for determining the optimal penetrations of

wind generation on a power system given specified conventional plant mixes. The

methodology was applied to a case study and a cost benefit analysis of wind gener-

ation on the case system was conducted. In addition, a number of applications of

the net benefits methodology were also presented. The main conclusions arising out

of the cost benefits analysis and the applications are presented here.


• It was found that the additional reserve requirement for wind generation causes

a knock on effect on the cycling costs, and the emissions and fuel saving ben-

efits.

• The cycling cost of conventional units on the system were shown to increase as

the installed wind generation increased, particularly at high levels of installed

wind generation when previously baseloaded units began to be displaced by

the wind output.

• It was found that wind generation results in a reduction in carbon dioxide,

sulphur dioxide and nitrogen oxide emissions. The carbon dioxide emissions

are of a much greater magnitude than the other two emissions, thus the price

of carbon was the main determinant in the value of the emissions savings. The

emissions reductions are dependent on the particular units being displaced and

are affected by low load operation and cyclical operation.

• The most significant fuel savings with wind generation for the case study were

in gas. The fuel savings are also affected by low load and cyclical operation. In

addition, the load factors of wind generation have an impact on the potential

emissions and fuel savings.

• It was shown that wind generation has positive net benefits up to 21% of

electricity generated in 2010, and 21.5% in 2015 and 22.2% for the base case

assumptions for the case study.

• A sensitivity analysis of the net benefits of wind showed that the net benefits of

wind were significantly affected by the underlying plant mix. It was also shown

that high load factors, low cycling costs, low capital costs and accurate wind

power forecasts significantly increased the net benefits of wind generation. Low

emissions prices significantly reduced the net benefits as did increased discount

rates and low demand growth.

• A worst and best case scenario were developed which illustrate the extreme

critical values for wind generation. In the worst case, wind generation ex-


pressed negative net benefits with penetrations of just 5% of electricity gener-

ation. Positive net benefits were experienced up to 30% of electricity generated

through wind in the best case scenario.

• The carbon dioxide reduction benefits of a carbon price can be completely

outweighed by the added cycling costs. For the case study it was found that

the cycling costs exceeded the carbon savings when carbon prices were less

than e50/ton. In addition, it was shown that the most significant contributor

to the cycling costs is the switching of coal units to the margin.

• When a carbon price and wind generation are combined, the cycling costs

are increased further, however, the combination of a carbon price and wind

generation have a much greater impact on carbon dioxide reductions. It was

found that even with 3000MW of installed wind generation, the cycling costs

can still exceed the value of the saved carbon dioxide emissions.

• Operating a power system under a fuelsaver approach is entirely futile as it

produces negative net benefits at all levels of installed wind generation.

• Employing non-firm connection policies significantly increases the renewable

generation capacity that can connect to the distribution network without the

need for additional investment. Also a significant increase in the net benefits of

distributed generation is gained if the appropriate connection policy is utilised

from the outset and conversely that significant costs are incurred if ad hoc

policies are employed.

• The net benefits of tidal generation were investigated and it was shown that

the capital costs of tidal generation would have to be less than e140k per MW

installed for tidal generation to have positive net benefits for the case study

in 2010. This is an infeasibly low capital cost resulting in the conclusion that

tidal generation is not a feasible option for the Irish system at the given time.


8.3 Future Work

The costs and benefits calculated in this thesis were based on the output of the

dispatch model. In this model, generators bids were based on their fuel costs. Given

the significant nature of the cycling costs as illustrated in Chapters 4 and 6, it is

likely that in reality generators bids would be more complex to ensure they were

compensated for any starts, shutdowns or excessive cycling. As the dispatch model

did not account for temporal constraints the impact of complex bids could not be

accounted for. A full unit commitment type model would be required for this type of

analysis and a cost benefit analysis of wind generation based on a unit commitment

model would provide an interesting area for future research.

This thesis also assumed a perfectly competitive electricity market, which in

reality may not always be the case. Strategic bidding by generators could alter the

merit order in which they are dispatched which would have a knock on effect on

the costs and benefits of wind generation. The impact of gaming in the electricity

market on the net benefits of wind generation is a complex issue and could provide

insightful results on the realistic operation of electricity markets.

This thesis concentrated on the costs and benefits of wind and tidal generation in

isolation, however the methodology could easily be adapted to incorporate the costs

and benefits of other forms of renewable generation. A broader study comprising

other forms of renewable generation would provide very useful results indicating

the forms of renewable generation which are most suited to the underlying plant

mix and also, the forms of renewable generation which should be promoted and if

necessary supported by Government. Also, other forms of generation such as nuclear

or advanced coal units could be included.

In addition, this thesis did not investigate support mechanisms for renewables.

An interesting area for future research would be on the analysing different support

mechanisms for wind generation and determining which one is most appropriate to

achieve the optimal levels of penetration at least cost. In addition, if the net benefits

of other forms of renewable energy were included, those support mechanisms could

be tested to determine if they supported the appropriate technologies, to the optimal


penetrations and at the correct time.

Issues relating to network congestion and system dynamics were not included

in this study. It is likely that wind generation will have an impact on these issues

and future work to incorporate these factors in a cost benefit analysis would be very

interesting.

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APPENDIX A

The Characteristics of Generators on the Irish System

This Appendix sets out the characteristics of the generators on the Irish system and

is based on the data set available at AIP (2005). Figure A.1 gives the generators

and their characteristics for the year 2007. The order of the generators in column

1 is the merit order in which they were dispatched. The energy prices in e/MWh

are based on the ‘mid’ fuel prices shown in Table 3.1. Figures A.2,A.3 and A.4 set

out the assumed plant mixes and generator characteristics for 2010, 2015 and 2020

respectively. Figure A.5 gives the no load and start up energy for each generator.

The following abbreviations are used to specify boiler type:

• SC - D refers to a ‘subcritical drum’ boiler

• HRSG refers to a ‘heat recovery steam generator’ boiler, also known as a

waste heat boiler

• FB - D refers to a ‘fluidised bed drum’ boiler

• OT - SC refers to a ‘once through subcritical’ boiler

181

Appendix A. Generator Information 182

UnitFuel

TypeMin Max GJ/MWh Euro/MWh

Mid Fuel PricesReserve

ReserveEuro/MWh

Year of

Comm-

ission

Boiler

Type

Ardnacrusha 1 hydro 12 21 0 0.00 4 0.5 1929 NA




Erne 1 hydro 4 10 0 0.00 3 0.5 1950 NA

Erne 2 hydro 4 10 0 0.00 3 0.5 1950 NA

Erne 3 hydro 5 23 0 0.00 5 0.5 1950 NA

Erne 4 hydro 5 23 0 0.00 5 0.5 1950 NA

Lee 1 hydro 3 15 0 0.00 0 0.5 1957 NA

Lee 2 hydro 1 4 0 0.00 0 0.5 1957 NA

Lee 3 hydro 3 8 0 0.00 0 0.5 1957 NA

Liffey 1 hydro 3 15 0 0.00 1 0.5 1944 NA

Liffey 2 hydro 3 15 0 0.00 1 0.5 1944 NA

Liffey 3 hydro 0 4 0 0.00 0 0.5 1944 NA

Liffey 4 hydro 0 4 0 0.00 0 0.5 1944 NA

Moneypoint 3 coal 115 285 11.03 23.78 45 1.6 1985 SC-D



Kilroot coal 64 201 11.10 23.92 30 1.6 1981 SC-D

Kilroot coal 64 201 11.10 23.92 30 1.6 1981 SC-D

Dublin Bay Power gas 200 396 6.55 27.26 42 2.6 2002 HRSG

Coolkeeragh CCGT gas 260 404 6.60 27.47 40 2.6 2005 HRSG

Huntstown gas 223 343 6.65 27.69 25 2.6 2002 HRSG

Ballylumford CCGT gas 116 240 6.83 28.41 37 2.6 2002 HRSG


Poolbeg CCGT gas 280 480 6.99 29.10 150 2.6 2000 HRSG

West Offaly Power peat 46 137 9.86 30.94 9 2 2004 FB-D

Lough Ree Power peat 40 91 10.07 31.61 12 2 2004 FB-D

North Wall 4 gas 112 163 9.60 39.96 32 2.6 1982 HRSG

Marina gas 77 112 9.71 40.41 35 2.6 1979 HRSG

Edenderry peat 40 118 13.07 41.01 9 2 2000 FB

Aghada gas 35 258 10.00 41.62 20 1.8 1981 OT-SC

Tarbert 4 oil 35 241 10.70 44.19 25 1.8 1977 OT-SC

Tarbert 3 oil 35 241 10.90 45.02 25 1.8 1977 OT-SC

Poolbeg 3 gas 56 242 11.00 45.78 20 1.7 1981 OT-SC

Poolbeg 1 gas 55 110 11.15 46.41 8 1.7 1971 OT-SC

Ballylumford 5 gas 56 170 11.20 46.61 35 1.7 1968 OT-SC

Poolbeg 2 gas 55 110 11.30 47.03 8 1.7 1971 OT-SC


Gt. Island 3 oil 30 108 11.70 48.32 20 1.7 1971 SC-D


Aghada OCGT gas 10 88 12.15 50.57 20 1 1982 NA

Tarbert 1 oil 25 54 12.84 53.02 3 1.7 1969 SC-D

Tarbert 2 oil 25 54 12.86 53.11 3 1.7 1969 SC-D

Gt. Island 1 oil 25 54 12.87 53.15 3 1.7 1967 SC-D

Gt. Island 2 oil 25 54 12.91 53.32 3 1.7 1967 SC-D

Aghada distillate 5 52 14.12 59.63 14 1 1981 NA

Rhode 1 distillate 5 52 10.63 83.92 20 1 unknown NA


Asahi distillate 5 52 10.63 84.90 0 1 unknown NA

Aghada CT distillate 10 90 12.14 97.16 20 1 1982 NA


North Wall 5 distillate 5 108 12.90 101.41 72 1 1983 NA

Coolkeeragh distillate 8 53 14.20 112.72 16 1 1959 NA

Kilroot distillate 5 29 14.35 113.96 7 1 1982 NA


Ballylumford 2 distillate 8 53 14.45 115.73 15 1 1968 NA


Figure A.1: Generator Information for 2007


UnitFuel



ReserveEuro/MWh

Year of

Comm-

ission

Boiler

Type





Erne 1 hydro 4 10 0 0.00 3 0.5 1950 NA

Erne 2 hydro 4 10 0 0.00 3 0.5 1950 NA

Erne 3 hydro 5 23 0 0.00 5 0.5 1950 NA

Erne 4 hydro 5 23 0 0.00 5 0.5 1950 NA

Lee 1 hydro 3 15 0 0.00 0 0.5 1957 NA

Lee 2 hydro 1 4 0 0.00 0 0.5 1957 NA

Lee 3 hydro 3 8 0 0.00 0 0.5 1957 NA

Liffey 1 hydro 3 15 0 0.00 1 0.5 1944 NA

Liffey 2 hydro 3 15 0 0.00 1 0.5 1944 NA

Liffey 3 hydro 0 4 0 0.00 0 0.5 1944 NA

Liffey 4 hydro 0 4 0 0.00 0 0.5 1944 NA




Kilroot coal 64 201 11.10 24.09 30 1.6 1981 SC-D

Kilroot coal 64 201 11.10 24.09 30 1.6 1981 SC-D


Tynagh CCGT gas 202 404 6.57 28.71 32 2.6 2006 HRSG






Huntstown 2 gas 200 401 7.00 30.59 50 2.6 2007 HRSG



Aughinish CCGT gas 40 150 8.72 38.12 10 2.6 2006 HRSG


Edenderry peat 40 118 13.07 42.22 9 2 2000 FB

Marina gas 77 112 9.71 42.43 35 2.6 1979 HRSG

Aghada gas 35 258 10.00 43.70 20 1.8 1981 OT-SC

Tarbert 4 oil 35 241 10.70 45.69 25 1.8 1977 OT-SC

Tarbert 3 oil 35 241 10.90 46.54 25 1.8 1977 OT-SC

Poolbeg 3 gas 56 242 11.00 46.97 20 1.7 1981 OT-SC

Poolbeg 1 gas 55 110 11.15 47.61 8 1.7 1971 OT-SC

Poolbeg 2 gas 55 110 11.30 48.25 8 1.7 1971 OT-SC



Gt. Island 3 oil 30 108 11.70 49.96 20 1.7 1971 SC-D


Aghada OCGT gas 10 88 12.15 53.10 20 1 1982 NA

Tarbert 1 oil 25 54 12.84 54.82 3 1.7 1969 SC-D

Tarbert 2 oil 25 54 12.86 54.91 3 1.7 1969 SC-D

Gt. Island 1 oil 25 54 12.87 54.95 3 1.7 1967 SC-D

Gt. Island 2 oil 25 54 12.91 55.12 3 1.7 1967 SC-D

Aghada distillate 5 52 14.12 61.70 14 1 1981 NA



Asahi distillate 5 52 10.63 85.93 0 1 unknown NA




Coolkeeragh distillate 8 53 14.20 114.72 16 1 1959 NA







UnitFuel



ReserveEuro/MWh

Year of

Comm-

ission

Boiler

Type





Erne 1 hydro 4 10 0 0.00 3 0.5 1950 NA

Erne 2 hydro 4 10 0 0.00 3 0.5 1950 NA

Erne 3 hydro 5 23 0 0.00 5 0.5 1950 NA

Erne 4 hydro 5 23 0 0.00 5 0.5 1950 NA

Lee 1 hydro 3 15 0 0.00 0 0.5 1957 NA

Lee 2 hydro 1 4 0 0.00 0 0.5 1957 NA

Lee 3 hydro 3 8 0 0.00 0 0.5 1957 NA

Liffey 1 hydro 3 15 0 0.00 1 0.5 1944 NA

Liffey 2 hydro 3 15 0 0.00 1 0.5 1944 NA

Liffey 3 hydro 0 4 0 0.00 0 0.5 1944 NA

Liffey 4 hydro 0 4 0 0.00 0 0.5 1944 NA




Kilroot coal 64 201 11.10 24.64 30 1.6 1981 SC-D

Kilroot coal 64 201 11.10 24.64 30 1.6 1981 SC-D





New CCGT (1) gas 200 450 6.71 33.82 20 1 2012 HRSG

New CCGT (2) gas 200 420 6.72 33.87 20 1 2012 HRSG

Ballylumford CCGT gas 116 240 6.83 34.40 37.1 2.6 2002 HRSG







Edenderry peat 40 118 13.07 46.14 9.4 2 2000 FB


Marina gas 77 112 9.71 48.94 35 2.6 1979 HRSG

Tarbert 4 oil 35 241 10.70 50.40 25 1.8 1977 OT-SC

Aghada gas 35 258 10.00 50.40 20 1.8 1981 OT-SC

Tarbert 3 oil 35 241 10.90 51.34 25 1.8 1977 OT-SC

Gt. Island 3 oil 30 108 11.70 55.11 20 1.7 1971 SC-D

Poolbeg 3 gas 56 242 11.00 55.44 20 1.7 1981 OT-SC

Poolbeg 1 gas 55 110 11.15 56.20 8 1.7 1971 OT-SC


Poolbeg 2 gas 55 110 11.30 56.95 8 1.7 1971 OT-SC


Tarbert 1 oil 25 54 12.84 60.47 3 1.7 1969 SC-D

Tarbert 2 oil 25 54 12.86 60.57 3 1.7 1969 SC-D

Aghada gas 5 52 14.12 71.16 14 1 1982 NA

New OCGT gas 10 160 10.63 94.86 15.5 1 2012 NA





Kilroot distillate 5 29 14.35 128.01 7.25 1 1982 NA


Ballylumford 2 distillate 8 53 14.45 128.86 14.5 1 1968 NA




UnitFuel



ReserveEuro/MWh

Year of

Comm-

ission

Boiler

Type

Ardnacrusha 1 hydro 11.9 21 0 0.00 4 0.5 1929 NA




Erne 1 hydro 4 10 0 0.00 3 0.5 1950 NA

Erne 2 hydro 4 10 0 0.00 3 0.5 1950 NA

Erne 3 hydro 5 22.5 0 0.00 5 0.5 1950 NA

Erne 4 hydro 5 22.5 0 0.00 5 0.5 1950 NA

Lee 1 hydro 3 15 0 0.00 0 0.5 1957 NA

Lee 2 hydro 1 4 0 0.00 0 0.5 1957 NA

Lee 3 hydro 3 8 0 0.00 0 0.5 1957 NA

Liffey 1 hydro 3 15 0 0.00 1 0.5 1944 NA

Liffey 2 hydro 3 15 0 0.00 1 0.5 1944 NA

Liffey 3 hydro 0.4 4 0 0.00 0 0.5 1944 NA

Liffey 4 hydro 0.2 4 0 0.00 0 0.5 1944 NA




Kilroot coal 64 201 11.10 25.64 30 1.6 1981 SC-D

Kilroot coal 64 201 11.10 25.64 30 1.6 1981 SC-D






Huntstown gas 222.8 342.7 6.65 42.64 25 2.6 2002 HRSG

CCGT (1) gas 200 450 6.71 43.01 20 1 2012 HRSG

CCGT (2) gas 200 420 6.72 43.08 20 1 2012 HRSG

New CCGT or similar gas 220 400 6.79 43.52 3 1.7 2018 HRSG

New CCGT or similar gas 230 400 6.79 43.52 3 1.7 2018 HRSG





Edenderry peat 40 117.6 13.07 54.24 9.4 2 2000 FB


Tarbert 4 oil 35 241 10.70 60.13 25 1.8 1977 OT-SC

Tarbert 3 oil 35 241 10.90 61.26 25 1.8 1981 OT-SC


New CCGT gas 63 103 9.70 62.18 8 2.6 2013 HRSG

Marina gas 77 112 9.71 62.24 35 2.6 1979 HRSG

Aghada gas 35 258 10.00 64.10 20 1.8 1981 OT-SC

Gt. Island 3 oil 30 108 11.70 65.75 20 1.7 1971 SC-D

OCGT gas 10 160 10.63 94.86 15.5 1 2012 NA

New OCGT gas 10 80 10.63 68.17 15.5 1 2018 NA

Poolbeg 3 gas 56 242 11.00 70.51 20 1.7 1981 OT-SC

Poolbeg 1 gas 55 110 11.15 71.47 8 1.7 1971 OT-SC


Poolbeg 2 gas 55 110 11.30 72.43 8 1.7 1971 OT-SC


New OCGT gas 15 52 14.12 90.51 0 0 2018 NA

Aghada gas 5 52 14.12 90.51 14 1 1982 NA










UnitNo Load

(GJ/hour)

Cold Start

GJ

Warm Start

GJ

Cold Start

GJ

Moneypoint 3 148 14620 6920 4360

Moneypoint 1 148 14620 6920 4360

Moneypoint 2 148 14620 6920 4360

Kilroot 213 2247 1645 973

Kilroot 213 2247 1645 973

Dublin Bay Power 533 7700 2600 2600

Tynagh CCGT 467 2811 1633 1144

Coolkeeragh CCGT 496 5000 2000 1000

Huntstown 350 1000 650 500

Ballylumford CCGT 496 50 50 50

Ballylumford CCGT 496 50 50 50

Poolbeg CCGT 716 100 100 100

West Offaly Power 124 500 500 500

Lough Ree Power 90 320 320 320

Aughinish CCGT 506 2900 1700 1150

North Wall 4 347 80 80 80

Marina 251 50 50 50

Edenderry 498 2010 1084 436

Aghada 187 4302 2185 1273

Tarbert 4 258 3180 1934 1072

Tarbert 3 258 3180 1934 1072

Poolbeg 3 274 4302 2185 1273

Poolbeg 1 84 1025 625 353

Ballylumford 5 179 2124 1527 847

Poolbeg 2 88 1025 625 353

Ballylumford 6 179 2124 1527 847

Gt. Island 3 105 743 600 293

Ballylumford 1 98 50 50 50

Aghada OCGT 87 50 50 50

Tarbert 1 50 562 449 218

Tarbert 2 50 562 449 218

Gt. Island 1 51 562 449 218

Gt. Island 2 51 562 449 218

Aghada 187 4302 2185 1273

Rhode 1 85 50 50 50

Rhode 2 85 50 50 50

Asahi 85 50 50 50

Aghada CT 268 63 63 63

Aghada CT 268 63 63 63

North Wall 5 317 50 50 50

Coolkeeragh 177 16 16 16

Kilroot 101 8 8 8

Kilroot 101 8 8 8



Figure A.5: Generator Operating Characteristics

APPENDIX B

The Optimisation of the Distribution Network

This Appendix sets out the methodology employed in Keane and O’Malley (2005b,

2006) and Keane et al. (2006) to optimise the generation capacity on the distribution

network.

Objective Function

The optimisation problem is formulated as a linear program, with the amount of

constraint breaches that arise taken into account. Load factors express the energy

output of a generator as a fraction of the maximum possible energy output that is

produced by a generator in a year and these are included in the objective function.

This ensures that the available capacity is allocated based on the amount of energy

that is delivered. The objective function is given in Equation (B.1).

J =

M∑

j=1

N∑

i=1

PAvail jPlantijLFj

ConnCostijνi(B.1)

Where PAvail j is the jth available energy resource. Plantij are the control variables

representing the fraction of PAvail j allocated to the ith bus. LFj is the load factor

187

Appendix B. Optimisation of the Distribution Network 188

of the jth energy resource. νi gives the total voltage sensitivity of the ith bus to

power injections at all other buses. ConnCostij is the connection costs of the jth

energy resource at the ith bus. M & N are the number of energy resources and

buses respectively. The geographical dispersion of DG impacts the connection costs.

The connection costs of each resource to each bus are determined and are minimised

in the objective function. The objective function is maximised with respect to

the technical constraints described below. The thermal constraint, the transformer

rating, the short circuit level and the short circuit ratio constraints are the same in

both the firm and non firm cases and are shown in Equations (B.2), (B.3), (B.5)

and (B.6) (Keane et al., 2006). The only difference between the firm and non firm

case is the voltage constraint (B.9), which is not included in the non firm scenario.

Thermal Constraint

This is a stand alone constraint, which ensures that the rated current of the lines

must not be exceeded. It is given by Equation (B.2).

Ii < IRatedi i ∀ N. (B.2)

Where Ii is the current flowing from generator i to bus i and IRatedi is the

maximum rated current for the line between each generator and its corresponding

bus. Under standard voltage and power factor conditions the rated current of the

line can be translated directly into a rated active power for that line.

Transformer Capacity

The amount of generation connected minus the summer valley load must not ex-

ceed the rating of the transformer at the higher voltage. If there is some existing

generation then this must be subtracted from the total. The result is the remaining

capacity available below that station. In the case of two parallel transformers, the

capacity is taken as the rating of the smaller transformer plus the summer valley

load. The constraint is expressed formally as in Equation (B.3).


PTx ≤ PTrafoCap. (B.3)

Where PTx refers to power flow through the transmission substation transformer

and PTrafoCap refers to the rating of that transformer.

Short Circuit Level

A maximum short circuit rating for all equipment is laid down in the distribution

code (ESB Networks, 2002). A short circuit calculation is carried out to ensure that

this constraint is not exceeded as the level of installed capacity increases. The short

circuit level (SCL) is highest at the transmission system bus. Buses close to this

bus may find their capacity limited as a result. The constraint is given by Equation

(B.4).

SCLTx < SCLRated. (B.4)

Where SCLTx is the short circuit level at the transmission substation busbar and

SCLRated is the highest current that switchgear can safely break under fault condi-

tions. The contribution of increasing levels of generation at each bus to SCLTx is

determined by short circuit analysis. The SCL contributions of generation at each

bus are combined and formalised into an algebraic equation as shown in Equation

(B.5).

N∑

j=1

δjTxPDG j + αTx ≤ SCLRated. (B.5)

Where δjTx is the dependency of the SCL at the transmission station to power

injections at bus j, i.e. the slope of the SCL vs. power injection characteristic of

the jth bus. PDG j is the power injection at the jth bus, αTx is the initial SCL at

the transmission bus with no generation present.


Short Circuit Ratio

The short circuit ratio (SCR) is the ratio of generator power PDG(MW) at each bus

to the short circuit level at each bus SCLBus(MVA). It gives an indication of the

voltage dip experienced near the generation in the event of a feeder outage and is

shown in Equation (B.6).

PDG i − 0.1 cos(φ)N∑

j=1

δjiPDG j ≤ 0.1 cos(φ)αi. (B.6)

Where cos(φ) is the power factor at the generator. For this constraint the short

circuit level used at the transmission station is the summer night valley level.

Voltage Rise Effect

The voltage at the generator is given by Equation (B.7).

⇒ VG = VL +RPL + XQL

VL

+ jXPL − RQL

VL

(B.7)

Where PDG and QDG are the active and reactive power of the generator respec-

tively, Z=R+jX is the impedance of the line, PL and QL are active and reactive

power at the bus and VG and VL are the voltages at the generator and bus respec-

tively. Thus it can be seen that the generator voltage will be the load/bus voltage

plus some value related to the impedance of the line and the power flows along that

line. It is evident that the larger the impedance and power flow the larger the volt-

age rise. The increased active power flows on the distribution network have a large

impact on the voltage level because the resistive element of the lines on distribution

networks are higher than other lines. This leads to an X/R ratio of approximately

1 rather than a more typical value of 5 on transmission networks. The voltage must

be kept within standard limits at each bus as given by Equation (B.8).

Vmin i < Vi < Vmax i i ∀ N. (B.8)

Where Vmin i & Vmax i refer to the minimum and maximum voltage limits at the


ith bus. The relationship between voltage and power injections at each bus is de-

termined. As MWs are added at each bus the voltage rises. Increasing levels of

generation are added incrementally at each bus in turn and load flow analysis is

carried out to determine a voltage vs. active power characteristic for each bus. Next

the interdependence of the bus voltage levels is examined. Once again increasing

levels of generation are added incrementally at each bus, but now the voltage level

at every other bus is examined. Thus characteristics are determined for voltage

levels at each bus due to generation at all other buses. By combination of these

characteristics the voltage constraint may be formalised into algebraic equations for

each bus as shown in Equation (B.9).

µiPDG i + βi +N∑

j=1

µjiPDG j ≤ Vmax i i ∀ N, i 6= j. (B.9)

Where µi is the dependency of the voltage level at bus i on power injections at bus

i, i.e. the slope of the voltage vs. power injection characteristic of the ith bus. βi

refers to the initial voltage level at the ith bus with no generation, µji refers to the

dependency of the voltage level at bus i on power injections at bus j. This analysis

is carried out under minimum load conditions as this is the worst case scenario for

voltage rise. Both the standby and normal forward feed conditions are considered.

There is usually more than one possible standby feeding arrangement, but the most

severe feeding condition is usually readily identifiable.

Non Firm Constraint Management

Voltage rise is one of the dominant technical constraints on DG. The assessment of

the voltage constraint at a N-1 peaking condition can present a significant barrier

to further penetration of DG. While infrequent, it is important for the operation

of the system that the voltage stays within its limits. A number of voltage control

techniques have been proposed to mitigate the voltage rise effect (Hird et al., 2004;

Hill et al., 2005). These techniques along with others facilitate non firm management

of the voltage constraint. The short circuit level may be dominant in more urban


areas. However, active management of fault levels is some way off and is likely to

be very expensive (KEMA, 2005). Hence, it is non firm management of the voltage

constraint which is considered here.

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