Colin Foote PhD Thesis March 2007

New Methods for New Challenges in Electricity

Distribution Network Planning

Colin E. T. Foote

MEng

Submitted for the Degree

of

Doctor of Philosophy

Institute for Energy and Environment

Department of Electronic and Electrical Engineering

University of Strathclyde

Glasgow G1 1XW

United Kingdom

March 2007

i

The copyright of this thesis belongs to the author under the terms of the United

Kingdom Copyright Acts as qualified by University of Strathclyde Regulation 3.50.

Due acknowledgement must always be made of the use of any material contained in,

or derived from, this thesis.

ii

Acknowledgements

I would like to thank Dr Graeme Burt and Professor Jim McDonald, my academic

supervisors, for providing guidance, support, encouragement and resources for this

research together with all manner of other opportunities.

I would also like to thank the rest of my colleagues within the Institute for Energy

and Environment at the University of Strathclyde who provided such a stimulating

and vibrant working environment. In particular, I extend thanks to Dr Graham Ault

for his guidance and encouragement.

I extend my gratitude to the organisations that have provided funding and technical

support for this research, namely East Midlands Electricity, ScottishPower, Rolls-

Royce, the European Commission and the Department for Trade and Industry.

Involvement in various projects has allowed me to meet people from a variety of

countries and organisations and many of them have contributed to this research

through discussion and insight. I thank them all for their contribution.

I would also like to thank my family, my friends and Lesley for all their support and

encouragement.

iii

Abstract

The planning and design of electricity distribution networks is facing new challenges

as new technologies are introduced and new demands are placed on the ageing assets

in developed countries around the world. In particular, the expansion of distributed

generation presents a range of challenges that distribution network planners must

resolve. This research offers a new perspective on the conventional approach to

distribution network planning and presents a comprehensive assessment of the new

challenges facing planners in the 21st century. A number of specific shortcomings in

the conventional approach are identified.

It is argued that the application of methods from other domains and the development

of new tools can help explore the shortcomings and update and improve the

conventional approach. Engineering design theory, decision support methods,

information management, scenario analysis, and dynamic modelling for power

system simulation have all been investigated and novel contributions have been made

in a number of areas to offer a valuable contribution to the development of electricity

distribution network planning.

Case studies and examples are used to examine distributed generation in detail,

compare its use as an alternative to conventional options, and assess its likely level of

penetration in future networks. Detailed dynamic studies of networks with wind

farms explore some of the most important issues of concern to grid operators.

Together, the application of methods from various domains show how the

conventional approach to electricity distribution network planning can be enhanced

so that distribution network operators are able to meet the challenges of the 21st

century.

iv

Table of Contents

Acknowledgements ...................................................................................................... ii

Abstract .......................................................................................................................iii

Table of Contents ........................................................................................................ iv

List of Figures ............................................................................................................. ix

List of Tables............................................................................................................. xvi

Abbreviations ..........................................................................................................xviii

1. Introduction .............................................................................................................. 1

1.1. Aim.................................................................................................................... 1

1.2. Principal Contributions ..................................................................................... 1

1.3. Publications ....................................................................................................... 4

2. Conventional Perspective on Distribution Network Planning ................................. 7

2.1. The Distribution Network Planning “Problem”................................................ 7

2.2. Model of the Conventional Approach to Distribution Network Planning ........ 8

2.2.1. Knowledge Modelling Methodology ......................................................... 8

2.2.2. Knowledge Models .................................................................................... 9

2.3. The Complete Model ...................................................................................... 26

2.4. Review of Chapter........................................................................................... 30

2.5. Chapter References ......................................................................................... 30

3. Drivers and New Directions in Distribution Networks.......................................... 33

3.1. Technology Drivers in Distribution Networks................................................ 34

3.1.1. Distributed Generation............................................................................. 34

3.1.2. Energy Storage ......................................................................................... 38

3.1.3. Demand Side Technology Developments................................................ 39

3.1.4. Power Electronics..................................................................................... 39

3.1.5. Communications and Control Technologies............................................ 39

3.2. Commercial and Regulatory Drivers in Distribution Networks ..................... 40

3.3. Future Directions in Distribution Networks.................................................... 42

3.3.1. Network Architectures and Operation ..................................................... 42

3.3.2. Institutional and Organisational Changes ................................................ 46


v


4. Shortcomings in the Conventional Approach and the Need for New Methods..... 53

4.1. The Use of Conventional Technologies and Methods .................................... 54

4.2. The Need for More Analysis........................................................................... 55

4.3. Organisational Separation and Loss of Control .............................................. 57

4.4. Difficulties in Formulating Strategies and Making Decisions........................ 58

4.5. The Need for Knowledge Management .......................................................... 59



5. Engineering Design Theory ................................................................................... 63

5.1. Solution-Neutral Problem Definition.............................................................. 64

5.2. Level of Risk and Innovation.......................................................................... 65

5.3. Decision Classifications .................................................................................. 65

5.4. Design Concurrency........................................................................................ 66

5.5. Alternative Designs......................................................................................... 66

5.6. Design Rationale ............................................................................................. 67

5.6.1. Structured Decision Making Methods ..................................................... 70

5.6.2. Generic Justifications ............................................................................... 71

5.6.3. Knowledge Modelling Methods............................................................... 72



6. Decision Support in Distribution Network Planning ............................................. 76

6.1. Multiple Criteria Decision Making ................................................................. 76

6.1.1. General Structure for MCDM .................................................................. 78

6.1.2. Calculation of Alternative Decision Ratios ............................................. 82

6.2. MCDM Case Study......................................................................................... 83

6.2.1. Development Issues ................................................................................. 84

6.2.2. Development Options .............................................................................. 85

6.2.3. Quantification........................................................................................... 86

6.2.4. Analysis.................................................................................................... 89

6.2.5. Decision Making ...................................................................................... 91

6.2.6. Sensitivity Analysis.................................................................................. 97

vi

6.3. Review of Chapter......................................................................................... 107

6.4. Chapter References ....................................................................................... 108

7. Information Management in Distribution Network Planning .............................. 111

7.1. Novel Combination of Methods to Assist in Collecting Information ........... 112

7.1.1. Structured Approach .............................................................................. 113

7.1.2. Standard Formats ................................................................................... 113

7.1.3. Generic Data........................................................................................... 114

7.2. Representation and Exchange of Power System Models and Data............... 115

7.2.1. Models and Data in Power Systems....................................................... 116

7.2.2. Horizontal and Vertical Exchange of Power System Models and Data. 117

7.2.3. Representation and Exchange of Power System Data ........................... 118

7.2.4. Representation and Exchange of Power System Models....................... 119

7.3. Information Management for Distributed Generation .................................. 120

7.3.1. The Need for Information on DG........................................................... 121

7.3.2. A Structured Approach for Collecting Information on DG ................... 123

7.3.3. Examples of Distributed Generation Analysis....................................... 135



8. Scenario Analysis................................................................................................. 140

8.1. Methods for Managing Uncertainty and Risk............................................... 141

8.1.1. Scenario Analysis................................................................................... 141

8.1.2. Decision Trees........................................................................................ 142

8.1.3. Sensitivity Analysis................................................................................ 143

8.1.4. Probabilistic Choice Versus Risk Analysis............................................ 143

8.1.5. Flexibility as a Means of Dealing with Uncertainty .............................. 144

8.2. Distributed Generation Penetration Scenarios .............................................. 144

8.2.1. Methodology for Penetration Assessment ............................................. 145

8.2.2. Top-Down Approach ............................................................................. 146

8.2.3. Bottom-Up Approach............................................................................. 151

8.2.4. Combining Top-down and Bottom-up Results ...................................... 155



vii

9. Dynamic Modelling of Wind Farms .................................................................... 162

9.1. List of Symbols ............................................................................................. 164

9.2. PSS/E Requirements for Generator Modelling ............................................. 165

9.3. A Dynamic Model of a Wind Farm .............................................................. 165

9.3.1. Wind Speed Model................................................................................. 166

9.3.2. Aerodynamic Model............................................................................... 167

9.3.3. Mechanical Systems............................................................................... 168

9.3.4. Generator and Grid Interface ................................................................. 172

9.3.5. Control Systems ..................................................................................... 178

Power Quality ...................................................................................................... 182

9.3.6. Ancillary Systems .................................................................................. 191

9.3.7. Protection ............................................................................................... 192

9.3.8. Wind Farm Electrical Network .............................................................. 193

9.3.9. Electricity Network ................................................................................ 193

9.4. Model Validation or Verification.................................................................. 194

9.5. Case Studies .................................................................................................. 194

9.5.1. Aggregate Models of Wind Farms............................................................. 194

9.5.2. Study of the Effect of a High Penetration of Wind Farms......................... 197



10. Conclusions ........................................................................................................ 205

Appendix A. DG Penetration Survey Results .......................................................... 212

A.1. Scenario 1. Residential Ring, Germany ....................................................... 213

A.2. Scenario 2. Commercial Mesh, Germany .................................................... 214

A.3. Scenario 3. Mixed Radial, Germany ............................................................ 215

A.4. Scenario 4. Urban Meshed, UK ................................................................... 216

A.5. Scenario 5. Rural, Poland............................................................................. 217

A.6. Scenario 6. Urban Link, Poland ................................................................... 218

A.7. Scenario 7. Urban Radial, France................................................................. 219

A.8. Scenario 8. Rural, Italy................................................................................. 220

A.9. Scenario 9. Urban Radial, Italy .................................................................... 221

A.10. Scenario 10. Urban Link, Greece ............................................................... 222

viii

A.11. Scenario 11. Rural South Coast, Spain ...................................................... 223

A.12. Scenario 12. Rural Link, Netherlands ........................................................ 224

A.13. Scenario 13. Rural Ring, Belgium ............................................................. 225

A.14. Scenario 14. Urban Ring, Denmark ........................................................... 226

A.15. Scenario 15. Rural Ring, Austria ............................................................... 227

Appendix B. Aggregate Models of a Wind Farm .................................................... 228

B.1. Methodology for Study of Aggregate Models of a Wind Farm ................... 228

B.1.1. Test Networks........................................................................................ 228

B.1.2. Test Models ........................................................................................... 230

B.1.3. Test Procedure ....................................................................................... 231

B.2. Results for Study of Aggregate Models of a Wind Farm............................. 232

B.2.1. Study Set One........................................................................................ 232

B.2.2. Study Set Two ....................................................................................... 235

B.2.3. Study Set Three ..................................................................................... 237

B.2.4. Study Set Four ....................................................................................... 240

Appendix C. The Effects of a High Penetration of Wind Farms ............................. 244

C.1. Methodology for Study of the Effects of a High Penetration of Wind Farms

.............................................................................................................................. 244

C.1.1. Test Network ......................................................................................... 244

C.1.2. Test Models ........................................................................................... 245

C.1.3. Test Scenarios........................................................................................ 249

C.1.4. Test Procedures ..................................................................................... 250

C.2. Results for Study of the Effects of a High Penetration of Wind Farms ....... 251

ix

List of Figures

Figure 2.1 – Symbols used in knowledge models........................................................ 9

Figure 2.2 – Task Model: Plan and design the distribution network ......................... 11

Figure 2.3 – Task Model: Identify requirements ....................................................... 12

Figure 2.4 – Task Model: Consider goals and responsibilities .................................. 13

Figure 2.5 – Task Model: Consider development drivers.......................................... 14

Figure 2.6 – Task Model: Consider new connections................................................ 15

Figure 2.7 – Task Model: Consider general load growth........................................... 16

Figure 2.8 – Task Model: Forecast load..................................................................... 16

Figure 2.9 – Task Model: Identify insecurities and exceeded ratings........................ 17

Figure 2.10 – Task Model: Consider asset replacement or refurbishment ................ 18

Figure 2.11 – Task Model: Assess condition of plant................................................ 19

Figure 2.12 – Task Model: Identify redundant network ............................................ 19

Figure 2.13 – Task Model: Consider network performance ...................................... 20

Figure 2.14 – Task Model: Combine requirements from different considerations.... 21

Figure 2.15 – Task Model: Design network additions and alterations ...................... 22

Figure 2.16 – Task Model: Determine substation, feeder and ancillary equipment

details ................................................................................................................. 23

Figure 2.17 – Task Model: Determine programmes of implementation.................... 24

Figure 2.18 – Task Model: Assess network access constraints ................................. 25

Figure 2.19 – Task Model: Revise policies, procedures and standards ..................... 25

Figure 2.20 – The complete model of the conventional approach, part 1 of 2 .......... 27

Figure 2.21 – The complete model of the conventional approach, part 2 of 2 .......... 28

Figure 2.22 – Text-only version of the complete model of the conventional approach

............................................................................................................................ 29

Figure 6.1 – General structure for multiple criteria decision making ........................ 79

Figure 6.2 – Impact of Varying Equipment Cost on Benefit / Cost Ratio ................. 98

Figure 6.3 – Impact of Assessment Lifetime on Benefit / Cost Ratio ....................... 99

Figure 6.4 – Impact of Varying Gas Cost on Benefit / Cost Ratio .......................... 100

Figure 6.5 – Impact of Varying Diesel Cost on Benefit / Cost Ratio ...................... 101

x

Figure 6.6 – Impact of Varying Interruptions / Year Criterion Weight on Benefit /

Cost Ratio......................................................................................................... 103

Figure 6.7 – Impact of Varying Power Quality Criterion Weight on Benefit / Cost

Ratio ................................................................................................................. 104

Figure 6.8 – Impact of Varying Standalone Capability Criterion Weight on Benefit /

Cost Ratio......................................................................................................... 105

Figure 6.9 – Impact of Varying Visual Environmental Impact Criterion Weight on

Benefit / Cost Ratio.......................................................................................... 106

Figure 7.1 – Varying content and representation in power system models and data118

Figure 7.2 – Simple example of a possible operating chart for a distributed generator

.......................................................................................................................... 127

Figure 7.3 – Simple example of a possible response rate chart for a distributed

generator........................................................................................................... 128

Figure 7.4 – Simple example of a possible daily cost curve for a distributed generator

.......................................................................................................................... 129

Figure 9.1 – Components of a complete wind farm dynamic model ....................... 166

Figure 9.2 – Typical power versus wind speed characteristics for an 800kW wind

turbine .............................................................................................................. 168

Figure 9.3 - Two-mass representation of a wind turbine shaft ................................ 169

Figure 9.4 – Performance coefficient as a function of tip speed ratio with pitch angle

as a parameter................................................................................................... 180

Figure 9.5 – Response of electromagnetic torque to changes in rotor voltages....... 186

Figure 9.6 – Cascaded PI controller for rotor quadrature current ............................ 187

Figure 9.7 – Response of power and reactive power to changes in direct axis rotor

voltage .............................................................................................................. 189

Figure 9.8 – Simple PI control system for reactive power....................................... 190

Figure 9.9 – Real power in branch 102-103 for the three categories of study set one

.......................................................................................................................... 196

Figure 9.10 – Bus angle at bus 103 for the three categories of study set three........ 197

Figure 9.11 – Bus 1 voltage for medium load condition and different wind farm

conditions ......................................................................................................... 198

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Figure 9.12 – Bus 30 voltage for low load condition and different wind farm

scenarios (different MW base levels)............................................................... 199

Figure 9.13 – Bus 30 wind farm output for low load condition and different wind

farm scenarios (different MW base levels) and the adjusted pattern of input

power................................................................................................................ 200

Figure B.1 – Test network for category A studies ................................................... 229

Figure B.2 – Test network for category B studies ................................................... 229

Figure B.3 – Test network for category C studies ................................................... 230

Figure B.4 – Real power in branch 102-103 for the three categories of study set one

.......................................................................................................................... 232

Figure B.5 – Reactive power in branch 102-103 for the three categories of study set

one .................................................................................................................... 233

Figure B.6 – Bus voltage at bus 103 for the three categories of study set one ........ 234

Figure B.7 – Bus angle at bus 103 for the three categories of study set one ........... 234

Figure B.8 – Real power in branch 102-103 for the three categories of study set two

.......................................................................................................................... 235


two.................................................................................................................... 236

Figure B.10 – Bus voltage at bus 103 for the three categories of study set two ...... 236

Figure B.11 – Bus angle at bus 103 for the three categories of study set two ......... 237

Figure B.12 – Real power in branch 102-103 for the three categories of study set

three.................................................................................................................. 238


three.................................................................................................................. 238

Figure B.14 – Bus voltage at bus 103 for the three categories of study set three .... 239

Figure B.15 – Bus angle at bus 103 for the three categories of study set three ....... 240

Figure B.16 – Real power in branch 102-103 for the three categories of study set four

.......................................................................................................................... 241


four ................................................................................................................... 241

Figure B.18 – Bus voltage at bus 103 for the three categories of study set four ..... 242

Figure B.19 – Bus angle at bus 103 for the three categories of study set four ........ 243

xii

Figure C.1 – Diagram of 30-bus network used in wind farm studies ...................... 244

Figure C.2 – Bus 1 voltage for no wind farms and different load conditions.......... 252

Figure C.3 – Bus 30 voltage for no wind farms and different load conditions........ 253

Figure C.4 – Bus 1 voltage for 0MW wind farm outputs and different load conditions

.......................................................................................................................... 254

Figure C.5 – Bus 30 voltage for 0MW wind farm outputs and different load

conditions ......................................................................................................... 255


.......................................................................................................................... 256


conditions ......................................................................................................... 257


conditions ......................................................................................................... 258


conditions ......................................................................................................... 259


conditions ......................................................................................................... 260


conditions ......................................................................................................... 261


conditions ......................................................................................................... 262


conditions ......................................................................................................... 263


conditions with high protection setting ............................................................ 264


conditions with high protection setting ............................................................ 265

Figure C.16 – Bus 1 voltage for low load condition and different wind farm

conditions ......................................................................................................... 266


conditions ......................................................................................................... 267

xiii

Figure C.18 – Bus 1 voltage for medium load condition and different wind farm

conditions ......................................................................................................... 268


conditions ......................................................................................................... 269

Figure C.20 – Bus 1 voltage for high load condition and different wind farm

conditions ......................................................................................................... 270


conditions ......................................................................................................... 271

Figure C.22 – Bus 30 wind farm output for wind farm scenario 0 (0MW base level)

and different load conditions............................................................................ 272

Figure C.23 – Bus 2 generator output for wind farm scenario 0 (0MW base level) and

different load conditions .................................................................................. 273

Figure C.24 – Bus 30 voltage for wind farm scenario 0 (0MW base level) and


Figure C.25 – Bus 2 voltage for wind farm scenario 0 (0MW base level) and


Figure C.26 – Bus 30 wind farm output for wind farm scenario A (5MW base level)


Figure C.27 – Bus 2 generator output for wind farm scenario A (5MW base level)


Figure C.28 – Bus 30 voltage for wind farm scenario A (5MW base level) and


Figure C.29 – Bus 2 voltage for wind farm scenario A (5MW base level) and


Figure C.30 – Bus 30 wind farm output for wind farm scenario B (10MW base level)


Figure C.31 – Bus 2 generator output for wind farm scenario B (10MW base level)


Figure C.32 – Bus 30 voltage for wind farm scenario B (10MW base level) and


Figure C.33 – Bus 2 voltage for wind farm scenario B (10MW base level) and


xiv

Figure C.34 – Bus 30 wind farm output for wind farm scenario C (15MW base level)


Figure C.35 – Bus 2 generator output for wind farm scenario C (15MW base level)


Figure C.36 – Bus 30 voltage for wind farm scenario C (15MW base level) and


Figure C.37 – Bus 2 voltage for wind farm scenario C (15MW base level) and


Figure C.38 – Bus 30 wind farm output for wind farm scenario D (20MW base level)


Figure C.39 – Bus 2 generator output for wind farm scenario D (20MW base level)


Figure C.40 – Bus 30 voltage for wind farm scenario D (20MW base level) and


Figure C.41 – Bus 2 voltage for wind farm scenario D (20MW base level) and


Figure C.42 – Bus 30 wind farm output for low load condition and different wind

farm scenarios (different MW base levels) ...................................................... 292

Figure C.43 – Bus 2 generator output for low load condition and different wind farm




Figure C.45 – Bus 2 voltage for low load condition and different wind farm scenarios

(different MW base levels)............................................................................... 295

Figure C.46 – Bus 30 wind farm output for low load condition and different wind

farm scenarios (different MW base levels) and the adjusted pattern of input

power................................................................................................................ 296

Figure C.47 – Bus 2 generator output for low load condition and different wind farm

scenarios (different MW base levels) and the adjusted pattern of input power297


scenarios (different MW base levels) and the adjusted pattern of input power298

xv

Figure C.49 – Bus 2 voltage for low load condition and different wind farm scenarios

(different MW base levels) and the adjusted pattern of input power............... 299

Figure C.50 – Bus 30 wind farm output for medium load condition and different

wind farm scenarios (different MW base levels) ............................................. 300

Figure C.51 – Bus 2 generator output for medium load condition and different wind






Figure C.54 – Bus 30 wind farm output for high load condition and different wind


Figure C.55 – Bus 2 generator output for high load condition and different wind farm






xvi

List of Tables

Table 6.1 – Initial Construction Cost Data................................................................. 87

Table 6.2 – Ongoing Cost Data.................................................................................. 87

Table 6.3 – Reliability Data ....................................................................................... 88

Table 6.4 – Analysis for the Single Gas Turbine Option........................................... 90

Table 6.5 – Summary of Performance Results for all Criteria and all Options ......... 91

Table 6.6 – Normalised Results for all Criteria and all Options................................ 92

Table 6.7 – Evaluation Criteria Weight Values ......................................................... 93

Table 6.8 – Results of Decision Analysis .................................................................. 94

Table 6.9 – Results of Environmental Cost Analysis ................................................ 96

Table 7.1 – Information Requirements for Characterisation of Distributed Generation

and Examples of Specific Device Information and Generic Category Data.... 124

Table 8.1 Electricity from renewable sources in 1997 and targets for 2010 for EU

countries ........................................................................................................... 146

Table 8.2 – DG Penetration Forecast for Italy ......................................................... 147

Table 8.3 – DG Penetration Forecast for Poland ..................................................... 148

Table 8.4 – DG Penetration Forecast for the UK..................................................... 149

Table 8.5 – Summary of DG penetration forecasts.................................................. 150

Table 8.6 – Penetration forecasts for LV connected DG ......................................... 151

Table 8.7 – Summary of LV Grid Scenarios............................................................ 152

Table 8.8 – Forecast DG Penetration Rankings in the LV Grid Scenarios for 2010154


Table 8.10 – Possible strategy for defining DG penetrations in Scenario 1 ............ 157

Table 9.1 – The four main types of wind turbine generator .................................... 172

Table A.1 – Questionnaire results for scenario 1..................................................... 213







xvii



Table A.10 – Questionnaire results for scenario 10................................................. 222






Table B.1 – Doubly fed induction generator parameters ......................................... 230

Table B.2 – Rotor voltage controller parameters ..................................................... 231

Table B.3 – Division of power between machines in test categories B and C ........ 240

Table C.1 – Models used in the study of high penetration of wind farms ............... 245

Table C.2 – Doubly fed induction generator parameters ......................................... 245

Table C.3 – Rotor voltage controller parameters ..................................................... 246

Table C.4 – Pattern of power input variations ......................................................... 246

Table C.5 – Salient pole generator parameters ........................................................ 247

Table C.6 – Round rotor generator parameters ........................................................ 248

Table C.7 – Turbine governor parameters ............................................................... 248

Table C.8 – Simple excitation system parameters ................................................... 248

Table C.9 – Load values for the three scenarios ...................................................... 249

Table C.10 – Wind farm scenarios........................................................................... 250

Table C.11 – New pattern of input power variations ............................................... 296

xviii

Abbreviations

AC Alternating Current

BTU British Thermal Unit

CHP Combined Heat and Power

CIM Common Information Model

DC Direct Current

DFIG Doubly Fed Induction Generator

DG Distributed Generation

DNO Distribution Network Operator

E2I Electricity Innovation Institute

EPRI Electric Power Research Institute

EU European Union

GIS Geographic Information System

GSP Grid Supply Point

GWh Gigawatt Hours

kVA Kilo Volt Amperes

kW Kilowatt

kWh Kilowatt Hours

LV Low Voltage (below 1kV)

MCDM Multiple Criteria Decision Making

MW Megawatts

MWe Megawatts Electrical

NMS Network Management System

O&M Operation and Maintenance

PV Photovoltaic

SMART Simple Multiple Attribute Rating Technique

TWh Terawatt Hours

XML Extensible Markup Language

1

1. Introduction

Electricity distribution networks are among the most important elements of large-

scale infrastructure that underpin modern society. The planning and design of these

networks is becoming increasingly complex as new technologies are introduced and

new demands are placed on the ageing assets in developed countries around the

world. The time is ripe to offer new methods that can support distribution planners

as they attempt to meet the new challenges in their domain.

1.1. Aim

The thesis of this work is that there are new challenges facing electricity distribution

networks in the 21st century and that an assessment of the conventional approach to

planning and design will identify a number of shortcomings. Furthermore, in

exploring the application of a range of methods from other domains and the

development of new tools, it will be possible to specify ways in which the

conventional approach can be updated to meet the new challenges. In particular, the

use of engineering design theory, decision support methods, information

management, scenario analysis, and the development of new models for simulation

and analysis are proposed as valuable. This research and its conclusions are timely

given the radical changes in electricity distribution networks being discussed across

the developed world. In particular, the expansion of distributed generation (DG)

presents a range of challenges that distribution network planners must resolve.

1.2. Principal Contributions

In the research reported here, the conventional approach to the planning and design

of electricity distribution networks is analysed and discussed using a modified

knowledge modelling methodology. This novel assessment of the domain is

followed by an up-to-date review of the drivers and new directions in distribution

network planning, with a particular emphasis on DG and its impact. Given the

review of the conventional approach and the assessment of drivers and new

2

directions, specific shortcomings in distribution network planning and design are

identified.

Following this review of conventional practice, new challenges and shortcomings, a

number of methods from other domains are examined to determine how they might

support future planning and design of electricity distribution networks.

Engineering design theory offers a range of concepts and methods that can be applied

to electricity distribution network planning to address the shortcomings in the

conventional approach and help meet the new challenges of the 21st century. A

selection of these concepts and methods is considered, with an assessment of how

each of them relates to and influences distribution network planning. This

assessment has not been performed before. The result is particular emphasis being

put on the concept of design rationale, which is identified as being of primary

importance in improving distribution network planning.

Planning and design is ultimately all about making decisions. Decisions require

information to be gathered and may rely on various forms of analysis but tools are

also available to support the actual decision making process. The use of multiple

criteria decision making (MCDM) techniques is discussed, highlighting their value in

making explicit the identification, quantification and analysis of decision criteria. A

new general structure for MCDM-based planning is defined and its use is

demonstrated in a case study where MCDM techniques are used to assess the

financial viability and technical desirability of a number of DG options. This

includes the novel specification and use of alternative ratios within the MCDM

framework to provide a new perspective and enhance the information available to

decision makers. The case study demonstrates the assessment of novel solutions

based on DG alongside conventional grid reinforcement solutions.

Planning and design, whether for electricity distribution networks or for other

systems, involve the management and processing of information. However, the

management of information can impose a huge burden on analysts and decision

3

makers, and this burden is growing with the new challenges of the 21st century. A

novel combination of methods is suggested to reduce the burden of information

processing and thereby enhance the productivity of analysts and decision makers.

The modelling and analysis of new technologies and the greater exploitation of

resources through improved representation and exchange of power system models

and data is discussed. By way of demonstrating the ideas presented and further

examining the principal challenge in network planning, a comprehensive set of

information requirements for DG is defined.

Trends like industry restructuring and DG are increasing the level of uncertainty and

risk faced by distribution network planners. A number of methods for managing

uncertainty and risk are discussed, including scenario analysis. A novel scenario

development methodology is demonstrated with an assessment of the expected

growth in DG in low voltage grids. This spotlights the primary challenge currently

facing planners and shows how gathered information can be used to generate

scenarios to support further analysis.

Power system operators are concerned with the expansion of wind power and the

effect it will have on their networks. This requires appropriate modelling and

analysis of wind farms within power system simulation software. A comprehensive

analysis of the problem is presented, supporting the development of new wind farm

models for the PSS/E simulation environment. Studies are performed to assess the

equivalence of single and aggregate models of wind farms and also to assess the

effects of a high penetration of wind farms on distribution networks.

In summary, the principle contributions of this work are: the detailed examination of

electricity distribution network planning and the new challenges facing planners in

the 21st century; the identification of shortcomings in the conventional approach to

planning and design; and the novel application of a range of methods from other

domains and the development of new tools leading to conclusions on ways in which

the conventional approach can be updated to meet the new challenges.

4

1.3. Publications

A number of publications have arisen from the research and related work. These are

listed below.

(i) Currie,R.A.F., Ault,G.W., Foote,C. E. T., McDonald,J.R.; “Active Power Flow

Management Utilising Operating Margins for the Increased Connection of

Distributed Generation”; Accepted for publication by IEE Proceedings

Generation, Transmission and Distribution, Reference GTD-2006-0035.R1

(ii) Foote,C.E.T., Burt,G.M., Elders,I.M., Ault,G.W.; “Developing Distributed

Generation Penetration Scenarios”; International Conference on Future Power

Systems, FPS 2005, 16-18 November 2005, Amsterdam, The Netherlands

(iii) Foote,C.E.T., Roscoe,A.J., Currie,R.A.F., Ault,G.W., McDonald,J.R.;

“Ubiquitous Energy Storage”; International Conference on Future Power


(iv) Ault,G.W., Foote,C.E.T., McDonald, J.R.; “UK research activities on advanced

distribution automation”; IEEE Power Engineering Society General Meeting

2005, June 12-16, 2005, p.2365-2368

(v) Foote,C.E.T., Ault,G.W., Burt,G.M., McDonald,J.R., Silvestro,F.,

“Information Requirements and Methods for Characterising Distributed

Generation”, 18th International Conference and Exhibition on Electricity

Distribution, CIRED 2005, June 2005, Turin, Italy

(vi) Foote,C.E.T., Ault,G.W., McDonald,J.R., Beddoes,A.J., “The Impact of

Network Splitting on Fault Levels and Other Performance Measures”, 18th

International Conference and Exhibition on Electricity Distribution, CIRED

2005, June 2005, Turin, Italy

(vii) Currie,R.A.F., Ault,G.W., Foote,C.E.T., Burt,G.M., McDonald,J.R.,

“Fundamental Research Challenges for the Active Management of Distribution

Networks with High Levels of Renewable Generation”, Universities’ Power

Engineering Conference (UPEC) 2004, Bristol, U.K., Conference Proceedings

Volume 3, p.1024-1028

(viii) Mienski,R., Pawelek,R., Wasiak,I., Gburczyk,P., Foote,C., Burt,G., Espie,P.,

“Power Quality Improvement in LV Networks Using Distributed Generation”,

5

2004 International Conference on Harmonics and Quality of Power, Lake

Placid, New York, USA, September 2004

(ix) Mienski,R., Pawelek,R., Wasiak,I., Gburczyk,P., Foote,C., Burt,G., Espie,P.,

“Voltage Dip Compensation in LV Networks Using Distributed Energy

Resources”, 2004 International Conference on Harmonics and Quality of

Power, Lake Placid, New York, USA, September 2004

(x) McMorran,A.W., Ault,G.W., Elders,I.M., Foote,C.E.T., Burt,G.M.,

McDonald,J.R., “Translating CIM XML Power System Data to a Proprietary

Format for System Simulation”, IEEE Transactions on Power Systems, vol.19,

issue 1, February 2004, p.229-235, February 2004

(xi) Espie,P., Foote,C. E. T., Burt,G. M., McDonald,J. R., Wasiak,I., “Improving

Electrical Power Quality Using Distributed Generation: Part 1 - Assessing DG

Impact and Capability”, 7th International Conference on Electrical Power

Quality and Utilisation, September 2003

(xii) McMorran,A.W., Ault,G.W., Foote,C.E.T., Burt,G.M., McDonald,J.R., “Web

Services Platform For Power System Development Planning”, Aristotle

University of Thessaloniki, 38th International Universities Power Engineering

Conference, September 2003

(xiii) Ault,G.W., Foote,C.E.T., McDonald,J.R., “Distribution System Planning in

Focus”, IEEE Power Engineering Review, Volume 22, Number 1, January

2002, pp 60-62., January 2002

(xiv) Foote,C.E.T., Watson,A.S., Espie,P., Ault,G.W., Burt,G.M., McDonald,J.R.,

“An evaluation strategy for electricity distribution network planning and

design”, 3rd Mediterranean Conference and Exhibition on Power Generation,

Transmission, Distribution and Energy Conversion, MED POWER 2002,

Athens, Greece, November 2002

(xv) Espie,P., Foote,C.E.T., Ault,G.W., McDonald,J.R., “A Multiple Criteria Model

for Evaluating Distributed Generation Development Options”, Second

International Symposium on Distributed Generation: Power System and

Market Aspects, Stockholm, Sweden, October 2002

6

(xvi) Foote,C.E.T., Ault,G.W., Burt,G.M., McDonald,J.R, “Enhancing flexibility

and transparency in the connection of dispersed generation”, CIRED 2001,

Amsterdam, June 2001

(xvii) Foote,C.E.T., Ault,G.W., Burt,G.M., McDonald,J.R., Green,J.P, “Towards a

structured methodology for distribution network design applications”, 35th

Universities Power Conference, Belfast, September 2000

7

2. Conventional Perspective on Distribution Network Planning

In this Chapter, the conventional perspective of electricity distribution network

planning is presented. The first section briefly introduces the distribution network

planning “problem”, describing how planners must abide by regulations, standards

and guidelines. The rest of the Chapter presents a model of the conventional

approach to distribution network planning that has been developed through the novel

application of a modified version of a formal knowledge modelling methodology. In

the following Chapters, drivers and future directions in distribution networks are

discussed along with the resultant shortcomings that are exposed in this conventional

approach to distribution planning.

2.1. The Distribution Network Planning “Problem”

The basic purpose of electricity distribution networks is to deliver electrical power

from the energy source, which means the the transmission network, to consumers.

This must be achieved within acceptable standards of safety and quality and at an

acceptable cost. Normally, the requirements that must be met by distribution

network operators (DNOs) are defined in legislation, regulations, standards and other

formal documents. These objectives may be augmented further by the goals and

ambitions of the DNO itself. Electricity distribution networks are constrained by

what is technically possible, what society and communities permit, and the cost of

implementation. Thus, as with other complex engineering tasks, the basic electricity

distribution network planning “problem” is to meet, or get as close as possible to, all

the objectives while meeting all the constraints.

In electricity distribution network planning, objectives and constraints are concerned

mainly with safety, quality, reliability and cost. Most issues may be viewed as both

objectives and constraints. For example, most DNOs will claim to strive for ever-

improving safety while also having to meet statutory safety standards. As with most

complex problems, many of the objectives will be conflicting, e.g. minimise costs

versus technical improvements.

8

The detailed requirements vary from one area to another but in the simplest terms,

distribution networks are very similar all over the world. There are transformers,

switches and ancillary equipment in substations, which are connected to sources,

consumers and other substations by overhead lines and underground cables. In the

developed world, electricity supply systems have been in operation for more than a

century and now reach almost the entire population. Thus, there is a substantial

installed base of assets to build upon. Furthermore, many items of equipment have a

service life running into decades so may have to operate through widely varying

circumstances. Network planners must tackle the problem of extending and

renewing the system to meet all the demands put upon it, now and in the future.

2.2. Model of the Conventional Approach to Distribution Network Planning

In this section, the conventional approach to the planning and design of electricity

distribution networks is described using a modified knowledge modelling

methodology.

2.2.1. Knowledge Modelling Methodology

A formal modelling approach was adopted to provide some structure in the analysis

of the conventional approach, to facilitate its representation in diagrammatic form,

and to present a widely known subject in a novel way. The methodology is a

simplified version of the KADS methodology [2.1] and was used because of the

clarity that it can provide in modelling engineering activities. This type of modelling

approach has been found to be very effective in capturing and representing

knowledge in a diverse range of topics. It also facilitates the codification of

knowledge, which is necessary in the development of knowledge based or automated

systems. The full KADS methodology was not used because the purpose of the

analysis was only to understand the structure of the planning activity. Application of

the full KADS methodology was unnecessary and out of the scope of this work.

The KADS methodology was simplified by neglecting the inference and domain

layers to leave only the task layer. In the full methodology, the inference and domain

9

layers provide further detail on the knowledge resources and processes necessary to

fulfil tasks. The use of the task layer only allows a useful description of the activity

to be constructed, as demonstrated with the model below. Other researchers may

find a similar approach useful in assessing other engineering topics.

Distribution planning and design is represented below in terms of tasks that are

broken down into sub-tasks or methods. A task is a specific activity that must be

undertaken to satisfy its parent task. A method is a tool or technique that may be

used to satisfy its parent task. The symbols used are shown in Figure 2.1. The

rectangular boxes with double lines and rounded corners represent tasks and sub-

tasks. The ellipses represent methods. The arrow indicates that a task is broken

down further into sub-tasks or methods.

Figure 2.1 – Symbols used in knowledge models

MethodTask MethodTaskTask

2.2.2. Knowledge Models

The models are presented in levels, where level one represents the highest level of

task. It was found that five levels were sufficient to represent the breakdown of

tasks. In each section an introductory explanation is provided together with

summary information identifying the parent task one level up and child tasks where

the model goes to another level of detail.

The models were compiled using a number of sources including literature [2.2-2.15]

and discussion with utility personnel [2.16, 2.17].

Level One: Plan and design the distribution network

The conventional approach to the task of planning and designing the distribution

network can be split into six broad sub-tasks (Figure 2.2). There is a need to identify

10

requirements and then design the necessary additions and alterations to the network.

These designs are then implemented in programmes of work. Through all planning

and design activities there is a need to maintain communication and exchange

information with others, be it within the organisation, with neighbouring networks,

with customers or with the regulator. As a check that the planning and design

process is satisfying requirements, there should be some review of plans actually

implemented, which will then inform future activities. Finally, the policies,

procedures and standards upon which planning and design is based may be revised

when appropriate. These tasks are continuous and ongoing as the electricity

distribution network is updated and extended to meet changing requirements.

Parent: None

Children: Level Two: Identify requirements

Level Two: Design network additions and alterations

Level Two: Determine programmes of implementation

Level Two: Revise policies, procedures and standards

11

Figure 2.2 – Task Model: Plan and design the distribution network

Plan and design the distribution network

Design network additions and

alterations

Maintain communication and

exchange information with others

Identify requirements

Review implementation to see that

requirements are met

Determine programmes of implementation

Revise policies, procedures and

standards

Plan and design the distribution networkPlan and design the distribution network


alterations


alterations





Identify requirementsIdentify requirements








standards


standards

Level Two: Identify requirements

In identifying the requirements of the distribution network (Figure 2.3), planners

must consider their basic goals and responsibilities. Further to that, there is a set of

development drivers that influence plans for the network and must be considered.

Finally, the range or requirements that emerge from considering goals and drivers

must be combined.

12

Parent: Level One: Plan and design the distribution network

Children: Level Three: Consider goals and responsibilities

Level Three: Consider development drivers

Level Three: Combine requirements from different considerations

Figure 2.3 – Task Model: Identify requirements


Consider goals and responsibilities

Combine requirements from different considerations

Consider development driversIdentify requirementsIdentify requirements





Consider development drivers


Level Three: Consider goals and responsibilities

Distribution network companies are normally subject to strong government

regulation and their basic goals and responsibilities will be defined in standards and

regulations (Figure 2.4). However, it is useful to also highlight specific goals and

responsibilities as being: maintain safety; maintain supply; optimise economic

efficiency; and minimise environmental impact. Regulatory regimes vary from place

to place, but a common factor is that regulators sometimes alter the incentives and

targets for distribution companies. Part of the planning task is to respond to these

changing requirements. Over time, standards and regulations are likely to become

more stringent, requiring better safety, improvements in the quality and reliability of

the electricity supply, and reduced environmental impact – or even active

environmental improvement. In many cases, regulators will demand all these

improvements while reducing the money available.

Parent: Level Two: Identify requirements

Children: None

13

Figure 2.4 – Task Model: Consider goals and responsibilities


Maintain safety

Optimise economic efficiency

Satisfy all relevant standards and

regulations

Maintain supply

Minimise environmental impact



Maintain safetyMaintain safety




regulations


regulations

Maintain supplyMaintain supply



Level Three: Consider development drivers

In the conventional approach to distribution network planning and design, six

principal development drivers can be identified (Figure 2.5): new connections; load

growth; asset replacement or refurbishment; network performance; interfacing with

the transmission system; and new products and concepts. The first four sub-tasks are

dealt with in more detail below. Transmission network developments are an

important driver on distribution networks because transmission-level effects cascade

down through the voltage levels. There must be open communication between

transmission and distribution companies. Finally, the development of distribution

networks have always been driven to some degree by the introduction of new

products or new concepts, e.g. covered conductors, or containerised substations.

New products and concepts must undergo testing and evaluation and they will be

introduced to the network gradually.

14


Children: Level Four: Consider new connections

Level Four: Consider general load growth

Level Four: Consider asset replacement or refurbishment

Level Four: Consider network performance

Figure 2.5 – Task Model: Consider development drivers


Consider general load growth

Consider network performance

Consider new connections

Consider asset replacement or refurbishment

Consider transmission network developments

Consider new products and concepts











Consider transmission network developmentsConsider transmission network developments



Level Four: Consider new connections

In considering new connections to the network (Figure 2.6), planners must be

proactive in predicting where and when new connections will be made. But it is also

necessary to respond to applications for connection that may have been predicted or

15

not. In the conventional approach, as practiced by distribution companies over the

last few decades, new connections are mostly for new loads, like a new housing

estate or commercial development. Distribution network planners have always had

to consider the connection of generators but for most companies, over the last four or

five decades, this has been rare enough to be managed separately and on a purely

reactive basis.

Parent: Level Three: Consider development drivers

Children: None

Figure 2.6 – Task Model: Consider new connections


Predict new connections

Assess applications for new connections



Predict new connectionsPredict new connections



Level Four: Consider general load growth

In conventional distribution network planning, an important element is forecasting

load growth (Figure 2.7). Greater demands on the network result in insecurities and

ratings being exceeded, which must be identified if action is to be taken.


Children: Level Five: Forecast load

Level Five: Identify insecurities and exceeded ratings

16

Figure 2.7 – Task Model: Consider general load growth


Forecast load

Identify insecurities and exceeded ratings



Forecast loadForecast load



Level Five: Forecast load

In forecasting load a variety of approaches have been adopted but two of the most

widely used methods are forecasts based on economic growth estimates and forecasts

based on local knowledge and trending (Figure 2.8). Combining the results of

different methods can produce more confidence in the overall forecasts made.

Parent: Level Four: Consider general load growth

Children: None

Figure 2.8 – Task Model: Forecast load

Engineering forecast based on local knowledge

and trending

Commercial forecast based on economic growth

estimatesForecast load

Engineering forecast based on local knowledge

and trending

Commercial forecast based on economic growth

estimatesForecast loadForecast load

Level Five: Identify insecurities and exceeded ratings

The identification of insecurities and exceeded ratings relies on feedback from

experience of operating the network and on network modelling and analysis (Figure

2.9). These two methods broadly describe the ongoing analysis of the network’s

performance and are used in a number of tasks.

17

Feedback from operational experience might be concerned with plant where ratings

have been, or have been close to being, exceeded. Or operators might identify

portions of network that could be reconfigured to improve performance.

Network modelling and analysis covers the use of a wide range of simulation and

analysis software tools. Depending on the circumstances, this may cover the full

range of power system dynamics, from generation expansion planning to

electromagnetic transients, and also steady state analysis such as load flow.

However, in conventional distribution planning and design the range of tools used

would be limited; analysis would focus on static studies of voltage levels, power

flows and fault levels.

Parent: Level Four: Consider general load growth

Children: None

Figure 2.9 – Task Model: Identify insecurities and exceeded ratings

Network modelling and analysis

Feedback from operational experience






Level Four: Consider asset replacement or refurbishment

In most developed countries, where electricity distribution networks are mature and

assets are reaching the end of their predicted lives, asset management has become

more important as a development driver, often becoming the primary focus for

distribution companies. Determining whether assets need replacing or refurbishing

requires assessing the condition of the plant and identifying redundant network assets

(Figure 2.10). Many planners will also use statistical modelling and analysis of

distribution assets, which are numerous and suited to statistical studies.

18


Children: Level Five: Assess condition of plant

Level Five: Identify redundant network

Figure 2.10 – Task Model: Consider asset replacement or refurbishment

Statistical modelling and

analysis of asset replacement


Assess condition of plant

Identify redundant network

Statistical modelling and

analysis of asset replacement







Level Five: Assess condition of plant

Plant is most commonly assessed through visual inspection but there are also

diagnostic tools available to provide information to planners and designers (Figure

2.11). However, the tools available are not used comprehensively and in some cases

plant will only be identified as being in poor condition when it fails or its

performance deteriorates. Thus, planners must utilise feedback from operational

experience as well as inspections and diagnostic tools.

Parent: Level Four: Consider asset replacement or refurbishment

Children: None

19

Figure 2.11 – Task Model: Assess condition of plant

Diagnostic tools to assess plant

condition

Visual inspections of plant



Diagnostic tools to assess plant

condition

Visual inspections of plant




Level Five: Identify redundant network

Identifying redundant network requires the same combination of feedback from

operational experience and network modelling and analysis as is required to identify

insecurities and exceeded ratings (Figure 2.12).

Parent: Level Four: Consider asset replacement or refurbishment

Children: None

Figure 2.12 – Task Model: Identify redundant network








Level Four: Consider network performance

The network performance measures that must be met will be defined in standards and

regulations. Determining whether these performance targets are being met rely once

again on feedback from operational experience and network modelling and analysis

20

(Figure 2.13). Any failure to meet performance targets will drive network

developments. The greatest pressure from customers and other stakeholders,

including the government, will come when there are significant network disruptions.

For example, severe storms can cause widespread disruption and prompt strong

criticism. Thus, an important objective is to try and minimise disruption when

storms occur and learn lessons from any past experience. The need to maintain

safety has already been identified as one of the specific goals and responsibilities of

distribution companies. However, it is useful to highlight how safety related issues

are an explicit development driver.


Children: None

Figure 2.13 – Task Model: Consider network performance




Experience of significant network

disruptions

Safety related issues





Experience of significant network

disruptions

Safety related issues

Level Three: Combine requirements from different considerations

The multiple goals and responsibilities of distribution companies and the multiple

development drivers for the network result in a complex set of interrelated

requirements for planning and design. An important task is the combining of these

21

requirements into manageable blocks (Figure 2.14). This is most commonly done on

the basis of geography, network impact and time horizon.


Children: None

Figure 2.14 – Task Model: Combine requirements from different considerations

Split requirements into manageable

blocks

Combine requirements by geography (Geographic

Information System)

Combine requirements by network impact

(Network Management System)

Combine requirements by

time horizon


Split requirements into manageable

blocks

Combine requirements by geography (Geographic

Information System)

Combine requirements by network impact

(Network Management System)

Combine requirements by

time horizon



Level Two: Design network additions and alterations

The conventional approach to distribution network design focuses on the three

elements that make up the network: substations, feeders and ancillary equipment

(Figure 2.15). These high-level descriptions disguise the considerable detail

involved. In addition, network design also takes into account how the network

components will be configured or how they might be reconfigured. Network

operation also influences designs; e.g. designers will sometimes specify switching

methodologies to accompany network designs.

22


Children: Level Three: Determine substation, feeder and ancillary equipment

details

Figure 2.15 – Task Model: Design network additions and alterations


alterations

Determine substation site, capacity and

configuration

Determine ancillary equipment type and

location

Determine feeder route, capacity and connection

Determine configuration and operational methodologies


alterations


alterations


configuration


configuration


location


location





Level Three: Determine substation, feeder and ancillary equipment details

For all three elements of the conventional approach to physical network design, the

methods used are similar (Figure 2.16). Academic researchers have proposed vary

many complex optimisation methods based on esoteric mathematics or the latest,

fashionable technique [2.15]. Despite this, distribution company engineers are more

likely to turn to simpler and more robust approaches. These include referring to

written procedures or accepted “rules of thumb”. Conventionally, lists of standard

designs and components have been used, although there has always been scope to

gradually incorporate new products and concepts. To reduce the burden of analysis,

prepared tables and charts have emerged that specify design details such as

conductor diameters to meet certain conditions. However, designers will perform

some network modelling and analysis where it is required. The application of

personal knowledge, experience and engineering judgement has always been

23

important. In particular, engineers will apply experience of similar projects that they

have encountered in the past.

Parent: Level Two: Design network additions and alterations

Children: None

Figure 2.16 – Task Model: Determine substation, feeder and ancillary equipment details


configuration



location

Follow company policy and procedure or accepted “rules of

thumb”

Utilise standard designs and

components (gradually incorporate new

products)

Refer to prepared tables and charts (with liberal tolerances and

safety factors)


Apply knowledge, experience and

engineering judgement


configuration


configuration




location


location

Follow company policy and procedure or accepted “rules of

thumb”

Utilise standard designs and

components (gradually incorporate new

products)

Refer to prepared tables and charts (with liberal tolerances and

safety factors)


Apply knowledge, experience and

engineering judgement

Level Two: Determine programmes of implementation

Having identified requirements on the network and designed the additions and

alterations necessary, these changes must be implemented, and revised, as

programmes of work (Figure 2.17). Performing construction or significant

maintenance work in the network will require outages so the constraints on network

access must be assessed. In addition, the availability of the resources required to do

the work may influence the work programmes. This will include people like

contractors and authorised personnel, machinery and materials, and, perhaps most

24

important of all, finance. Finally, distribution networks exist in a dynamic

environment and all plans must be revised as conditions change.


Children: Level Three: Assess network access constraints

Figure 2.17 – Task Model: Determine programmes of implementation


Assess network access constraints

Revise plans as conditions change

Assess implementation resource constraints





Revise plans as conditions changeRevise plans as

conditions change



Level Three: Assess network access constraints

Assessing network access constraints requires feedback from operational experience

and appropriate network modelling and analysis (Figure 2.18). In particular,

planners must ensure contingency plans are in place to meet security and reliability

requirements while work is being undertaken on the network.

Parent: Level Two: Determine programmes of implementation

Children: None

25

Figure 2.18 – Task Model: Assess network access constraints




Contingency analysis





Contingency analysis

Level Two: Revise policies, procedures and standards

Planning and design of the distribution network relies very heavily on policies,

procedures and standards. These can be internal to the distribution company and

external, perhaps as industry-wide agreements or as part of statutory regulations.

These policies, procedures and standards are revised as issues emerge in the planning

and design of networks (Figure 2.19). These tasks must look to the future to identify

emerging issues and possible externally imposed changes to ensure that policies,

procedures and standards are in place for when they are needed.


Children: None

Figure 2.19 – Task Model: Revise policies, procedures and standards


standards

Revise internal policies, procedures and

standards

Propose or support changes to industry and statutory policies, procedures and

standards


standards


standards


standards


standards


standards


standards

26

2.3. The Complete Model

The complete model is shown in the fewest separate parts as is practical for this

document in Figure 2.20 and Figure 2.21. The model can also be described in text-

only form as shown in Figure 2.22.

The knowledge modelling methodology as applied here results in a complete model

that is concise and clear, providing a new description of the conventional approach to

distribution network planning and design. Although based on a review of literature,

supplemented through discussions with distribution network operators and

experienced practitioners, this is a novel representation that provides an opportunity

to assess the conventional approach in new ways.

While the model is simply a description of the conventional approach, not being

codified in any way other than as in the diagrams, this new description allows

specific parts of the process to be highlighted and seen in context. When considered

alongside the drivers and new directions in the next Chapter the model exposes some

shortcomings in the conventional approach. These shortcomings and the methods

proposed for addressing them are discussed in more detail in later Chapters.

However, even before the new challenges facing DNOs are considered, the model

points to some important aspects of and weaknesses in the conventional approach.

“Network modelling and analysis” and “Feedback from operational experience” both

appear in the model a number of times, highlighting the importance of these

activities. While there are tasks to “Predict new connections” and “Forecast load”,

the process is largely a reactive one based on external drivers. This is appropriate

since the network should reflect the needs of its users but it can place a burden on the

DNO to perform very complex planning and design tasks in a short time. While

“Consider new products and concepts” is one of the development drivers, the model

shows how network additions and alterations are designed using conventional

methods and solutions. Overall, the model shows that distribution network planning

involves a complex set of tasks and methods that mean highly prescriptive and

restrictive methods are inappropriate.

27

Figure 2.20 – The complete model of the conventional approach, part 1 of 2

Engineeringforecast based

on localknowledge and

trending

Commercialforecast basedon economic

growthestimates

Forecast load

Networkmodelling and

analysis

Feedback fromoperationalexperience

Identify insecuritiesand exceeded ratings

Consider generalload growth

Diagnostic toolsto assess plant

condition

Visualinspections of

plant

Assess condition ofplant



analysis


Identify redundantnetwork

Statisticalmodelling and

analysis of assetreplacement

Consider assetreplacement orrefurbishment

Consider newconnections

Predict newconnections

Assess applicationsfor new connections

Considerdevelopment drivers

Consider goals andresponsibilities

Maintain safety

Optimise economicefficiency

Satisfy all relevantstandards and

regulations

Maintain supply

Minimiseenvironmental impact

Identify requirementsPlan and design thedistribution network


analysis


Consider networkperformance

Experience ofsignificantnetwork

disruptions

Safety relatedissues

Considertransmission network

developments

Consider newproducts and

concepts

28

Figure 2.21 – The complete model of the conventional approach, part 2 of 2

Splitrequirements

into manageableblocks

Combinerequirements by

geography(GeographicInformation

System)

Combinerequirements bynetwork impact

(NetworkManagement

System)

Combinerequirements by

time horizon

Combinerequirements from

differentconsiderations


Plan and design thedistribution network

Determine substationsite, capacity and

configuration

Determine feederroute, capacity and

connection

Determine ancillaryequipment type and

location

Follow companypolicy and

procedure oraccepted �rules

of thumb Ó

Utilise standarddesigns andcomponents(gradually

incorporate newproducts)

Refer toprepared tablesand charts (withliberal tolerances

and safetyfactors)


analysis

Applyknowledge,

experience andengineeringjudgement

Design networkadditions and

alterations

Determineconfiguration and

operationalmethodologies


analysis


Assess networkaccess constraints

Contingencyanalysis

Determineprogrammes ofimplementation

Revise plans asconditions change

Assessimplementation

resource constraints

Revise policies,procedures and

standards

Revise internalpolicies, procedures

and standards

Propose or supportchanges to industry

and statutorypolicies, procedures

and standards

Maintaincommunication and

exchangeinformation with

others

Reviewimplementation to

see thatrequirements are met

29

Figure 2.22 – Text-only version of the complete model of the conventional approach

Plan and design the distribution network o Identify requirements

� Consider goals and responsibilities • Satisfy all relevant standards and regulations • Maintain safety • Maintain supply • Optimise economic efficiency • Minimise environmental impact

� Consider development drivers • Consider new connections

o Predict new connections o Assess applications for new connections

• Consider general load growth o Forecast load

� Commercial forecast based on economic growth estimates � Engineering forecast based on local knowledge and trending

o Identify insecurities and exceeded ratings � Feedback from operational experience � Network modelling and analysis

• Consider asset replacement or refurbishment o Assess condition of plant

� Visual inspections of plant � Diagnostic tools to assess plant condition � Feedback from operational experience

o Identify redundant network � Feedback from operational experience � Network modelling and analysis

o Statistical modelling and analysis of asset replacement • Consider network performance

o Feedback from operational experience o Network modelling and analysis o Experience of significant network disruptions o Safety related issues

• Consider transmission network developments • Consider new products and concepts

� Combine requirements from different considerations • Combine requirements by geography (Geographic Information System) • Combine requirements by network impact (Network Management System) • Combine requirements by time horizon • Split requirements into manageable blocks

o Design network additions and alterations � Determine substation site, capacity and configuration

Determine feeder route, capacity and connection Determine ancillary equipment type and location

• Follow company policy and procedure or accepted “rules of thumb” • Utilise standard designs and components (gradually incorporate new products) • Refer to prepared tables and charts (with liberal tolerances and safety factors) • Network modelling and analysis • Apply knowledge, experience and engineering judgement

� Determine configuration and operational methodologies o Determine programmes of implementation

� Assess network access constraints • Feedback from operational experience • Network modelling and analysis • Contingency analysis

� Assess implementation resource constraints � Revise plans as conditions change

o Maintain communication and exchange information with others o Review implementation to see that requirements are met o Revise policies, procedures and standards

� Revise internal policies, procedures and standards � Propose or support changes to industry and statutory policies, procedures and standards

30

2.4. Review of Chapter

In this Chapter, electricity distribution network planning was introduced with a novel

analysis of the conventional approach using a modified knowledge modelling

methodology. The methodology is a simplified version of the KADS methodology

and was used because of the clarity that it can provide in modelling engineering

activities. The full KADS methodology was not used because the purpose of the

analysis was only to examine the structure of the planning activity. The models

identified and examined the main tasks in network planning, with reference to a wide

range of sources. This analysis reveals the structure of the distribution network

planning activity and supports its further assessment and proposed modifications in

later chapters, particularly the identification of shortcomings in Chapter 4.

The complete set of knowledge models provide a novel perspective on the

conventional approach to electricity distribution network planning, highlighting some

of the important features and weaknesses. It serves as a useful tool for learning and

facilitates reassessment in the light of new challenges, which are discussed in the

next Chapter. In particular, the models highlight the role of diverse factors in

network planning and illustrate how the optimisation of substation site and size,

feeder route and capacity and ancillary devices – so often the focus of academic

work on distribution network planning – play a relatively small role in the overall

task facing planners. The models also indicate the role of network modelling and

analysis, which is identified in Chapter 4 as one of the shortcomings that requires

particular attention.

2.5. Chapter References

2.1. Schreiber,G., Akkermans,H., Anjewierden,A., de Hoog,R., Shadbolt,N., Van

de Velde,W., Weilinga,B.; “Knowledge Engineering and Management: The

CommonKADS Methodology”; 2000, Massachusetts Institute of Technology;

ISBN 0-262-19300-0

2.2. C.R.Bayliss; “Transmission and Distribution Electrical Engineering”;

Butterworth-Heinemann; 1996; ISBN 0750622873; D621.319BAY

31

2.3. Lakervi,E., Holmes,E.J.; “Electricity distribution network design”; 2nd edition;

1996; Peter Peregrinus Ltd.; ISBN 0863413099

2.4. CIRED Experts Group, Session No.1 – Group Report; Survey of CIRED

members; May 1997

2.5. Stowell,P.; “Manweb Distribution”; Journal IEE, Jan 1958, p.15-21

2.6. UNIPEDE Distribution Study Committee 50.04.DISNET; “Distribution

network configuration and design – Applied practices, likely trends and

evaluation of technical solutions”; September 1995; Ref.: 05004Ren9540


network configuration and design – Network design - Applied practices in

European countries”; September 1995; Ref.: 05004Ren9539


network configuration and design – Evaluation of technical solutions”;

September 1995; Ref.: 05004Ren9538


network configuration and design – Likely trends in distribution systems”;

September 1995; Ref.: 05004Ren9537

2.10. Burke,J.J.; “Power Distribution Engineering: Fundamentals and Applications”;

1994; Marcel Dekker, Inc.; ISBN 0-8247-9237-8; D621.319BUR

2.11. Pansini,A.J.; “Electrical distribution engineering”; 2nd ed; 1992; The Fairmont

Press, Inc.; ISBN 0-88173-121-8; D621.319PAN

2.12. Raytheon Engineers & Constructors, EBASCO Division, Electric Power

Systems; “Electric Distribution Systems Engineering Handbook”; 3rd ed.;

1992; McGraw Hill, Inc.; ISBN 0-07-607079-4

2.13. Eggleton,M.N., Mazzoni,M., Van Geert,E., Van Der Meijden,M.A.M.M.,

Kling,W.L.; “Network structure in sub-transmission systems. Features and

practices in different countries.”; CIRED 12th International Conference on

Electricity Distribution, 1993, Subject Area 6: Design and Planning of Public

Supply Systems; IEE Conference Publication No.373, p.6.9.1-6.9.8

2.14. Carr, J., McCall, L.V.; “Divergent Evolution and Resulting Characteristics

Among the World’s Distribution Systems”; IEEE Trans. on Power Delivery,

vol.7, no.3, July 1992, p.1601-1609

32

2.15. Willis, H.L., Northcote-Green, J.E.D.; “Comparison of Several Computerized

Distribution Planning Methods”; IEEE Trans. on Power Apparatus and

Systems, vol.PAS-104, no.1, January 1985, p.233-240

2.16. Foote, C.; “Planning and Design of the Distribution Network”; February 2000;

CEPE Report EME/EP/KT/1999-002A; University of Strathclyde

2.17. Foote, C.; “Review of Distribution Planning and Design Techniques”; March

2000; CEPE Report EME/EP/REV/2000-002; University of Strathclyde

33

3. Drivers and New Directions in Distribution Networks

In this Chapter, the drivers and new directions in electricity distribution network

planning, operation and management are examined. This review of current trends

helps expose the shortcomings in the conventional approach to distribution networks

described in the previous Chapter. These shortcomings are discussed explicitly in

the next Chapter and provide the justification for the application of new tools and

techniques, which is presented in later Chapters. The choice of which tools and

techniques to test and develop was driven by this assessment of new challenges in

distribution network planning and by the new directions in the electricity industry

and technology in general.

Electricity distribution networks connect sources of electrical energy, typically

transmission grid substations, to consumers of electrical energy, e.g. industrial,

commercial and domestic consumers. Distribution networks have traditionally been

designed for uni-directional power flow, from higher to lower voltages. Distribution

networks represent a huge installed asset base, with thousands of kilometres of

individual circuits at low voltage – distribution networks in the UK have a total

length of 767,376 kilometres [3.1]. These networks are actively monitored only at

the higher voltage levels. Due to the large scale, a “fit-and-forget” approach has

often been adopted, with assets installed to accommodate an expected future growth

in demand. In general, distribution networks have proved very effective, delivering

electricity at reasonable cost, quality and safety.

Established distribution networks in developed countries are now undergoing

considerable change. A range of drivers is influencing their management and

development. Broadly, drivers for change in electricity distribution networks can be

split in two: technology drivers and commercial/regulatory drivers. These drivers

present a range of new challenges to distribution companies and will result in

distribution networks being taken in new directions.

34

3.1. Technology Drivers in Distribution Networks

Technology drivers can be separated into power technologies and communications

and control technologies [3.21, 3.22]. Power technology drivers include distributed

generation, energy storage, the demand side and power electronics. All of these

technology areas have seen advances in recent years and are affecting electricity

distribution networks. Communications and control technologies that are driving

change in distribution networks include the Internet, mobile communications,

automation and the ever-increasing processing power of computer systems. The

sections below discuss these technology drivers in more detail.

3.1.1. Distributed Generation

Distributed generation (DG) refers to generators connected directly to distribution

networks. Typically, DG installations are smaller than conventional power stations,

which are connected at transmission level, and may utilise unconventional energy

sources or conversion methods, such as renewable sources or power electronics. DG

is also sometimes called embedded generation because it is embedded in distribution

networks, or dispersed generation because small generators can be seen as dispersed

around the system rather than being centralised like large, conventional power plants.

In this document the terms distributed generation or DG will be used.

Distributed generation is probably the single, most significant issue driving change in

modern electricity distribution networks at the start of the 21st century.

Consequently, there is a large volume of published work covering all aspects of DG

and its impact on distribution networks and concise summaries of most of the issues

are available [3.2, 3.3, 3.4, 3.5, 3.6]. Industry rules and standards have been

established for many aspects of DG although many are being reviewed in light of the

expansion of DG [3.7, 3.8, 3.9]. This highlights the importance of the level one task

in the knowledge model of the conventional approach: “Revise policies, procedures

and standards”.

The amount of small-scale generation connected to distribution networks is

increasing in many countries [3.13]. This is a result of a combination of factors

35

including changes to market and regulatory structures, advances in generation

technology and changing attitudes with respect to the environment. In an

increasingly competitive energy market, the modularity and short lead times of DG

increase its attractiveness in comparison to larger investments by reducing the risk

associated with individual projects. The vertically integrated utilities of some

countries also see DG as an opportunity to offset investments in transmission and

distribution infrastructure. This will require novel approaches to network design and

management and enhancements to the “Network Modelling and Analysis” method

used at various points in the knowledge model of the conventional approach. With

appropriate operation and control, DG has the potential to improve voltage profiles,

reduce losses, and improve operational flexibility.

3.1.1.1. Technical Issues

Distributed generation can affect the distribution network in many ways. Generator

performance and the impact on network performance depend very much on network

and generator characteristics. All aspects of a network’s performance, from network

power flows to reliability, may be influenced although the most important issues vary

from case to case. The technical issues most frequently highlighted for close

examination are network capacity, electrical losses, fault levels and stability.

Generally, technical solutions are available to problems faced in connecting DG but

they add costs, which raise important questions about who pays, as discussed in the

next section. Various tools and techniques are required to support the analysis of

different scenarios [3.2], and some solutions are new or have not been previously

used by DNOs.

3.1.1.2. Economic Issues

Economic conditions influence the installation and operation of DG. Most simply,

growth in the economy increases the overall demand for electricity. For the

economic reasons mentioned above of risk reduction and better matching supply to

demand, new capacity to replace retired generators or meet future increases in

demand is likely, in the near-term, to come from smaller-scale installations. The

comparative economic benefits of economies of scale in power plants have been

36

reduced by improvements in small-scale generator technologies. The argument for

smaller-scale, and therefore distributed, generation is enhanced by government

incentives for environmentally friendly sources like renewable energy and combined

heat and power [3.10].

The viability of DG schemes is dependent on a number of other economic factors,

including the energy trading mechanism and the costs imposed by the DNO. The

cost of connecting to a network can be a controversial subject because all parties

must ensure that they can recover any costs incurred through their revenue streams.

For DG operators this means the sale of energy, the value of which will depend on

the market rate, unless some form of subsidy or guaranteed price is on offer.

DG can influence both the capital and operational expenditure of distribution

companies. They must be confident that any investment in their network to

accommodate DG will be fully recovered, either from the DG operator directly or

through future payments from users of the system. The opportunity exists for

distribution companies to make investment in the connection of DG to reduce

operational expenditure and reduce the need for investment in areas such as load

related reinforcement, asset replacement and performance improvement. As

monopolies, distribution companies are usually subject to price control regulation of

some sort. If the price control mechanism does not make allowance for funding

contracts with DG as an alternative to investment in network assets then DG may not

be financially attractive to distribution companies.

Where the use of DG proves more expensive than conventional solutions, this

additional cost must be covered. Whether through higher energy prices charged by

the DG operator, higher network use of system charges by the DNO, or taxes to pay

for government subsidies, the higher costs will ultimately be passed on to the

consumers of electrical energy.

Economic issues, and others affecting DG, are subject to regulation and may change.

Thus, planners must be aware of existing regulations and possible future changes.

37

3.1.1.3. Environmental Issues

Environmental issues are amongst the principal drivers for DG [3.6]. The incentive

to reduce emissions is provided by pollution taxes and other government-imposed

incentives. The use of combined heat and power (CHP), and technologies such as

wind turbines and photovoltaics help reduce emissions; and these technologies are

more suited to small-scale generation.

The potential for renewable and CHP generation in a particular area is a function of

the local environment. Some areas may provide large amounts of wind energy while

other areas may provide more solar energy. The potential for combined heat and

power plants depends on the requirements of suitable nearby heat loads. Another

environmental issue is planning permission. One of the perceived advantages of

conventional large-scale power stations was their distance from population centres.

DG is different and may face opposition due to the impact of generators on their

immediate environment.

3.1.1.4. Government Policies

Many governments of developed countries have energy policies similar to that of the

UK, namely to ensure secure, diverse and sustainable supplies of energy at

competitive prices [3.10,3.14]. The response to climate change is a central element

to policy in many countries and it is widely recognised that renewables and CHP

have a significant role to play in reducing emissions. In the case of the UK, the

government also sees renewables and CHP as contributing to employment and rural

development.

3.1.1.5. Distributed Generation Technologies

Distributed generation is a very broad term and can cover a wide range of generation

technologies [3.11, 3.12, 3.13, 3.14]. Renewables are often the focus of discussion

but there is also much research and development of small-scale, non-renewable

technologies, particularly micro turbines and fuel cells. These technologies are made

38

particularly attractive when used to provide combined heat and power. All of these

DG technologies are under continuous development – a problem in itself for

distribution companies trying to keep up with the latest technologies.

3.1.2. Energy Storage

One of the primary assumptions in the design and operation of electricity supply

systems is that electrical energy cannot be stored. In reality, energy storage has been

utilised for a long time, mainly in the form of pumped-storage hydro schemes,

although such schemes represent a tiny fraction of total system load. Various forms

of battery technology have long been used as backup supplies for the short-term

support of critical loads. This includes emergency lighting and uninterruptible power

supplies.

The range of effective energy storage technologies is expanding [3.15, 3.16, 3.17].

There have been improvements in battery and flywheel technology. Perhaps the

most exciting advances in storage technologies are associated with regenerative fuel

cells and flow batteries. These technologies generate electricity directly from

chemical reactions, and through management of the chemical products used in the

reaction can provide a flexible means of energy storage. Flow batteries are finding

applications from critical loads to system support at distribution and transmission

level.

The advances in energy storage technologies offer distribution companies new

options and new challenges in network design and operation. By undermining the

primary assumption upon which modern electricity systems have been designed,

energy storage is potentially the most disruptive of technology drivers. For example,

in the future it may be possible to perform some degree of load levelling at all levels

on the power system, thereby utilising assets more efficiently [3.18]. But this will

require big changes in system design and operation.

39

3.1.3. Demand Side Technology Developments

Demand side technology developments present new challenges to distribution

companies in a number of ways [3.17,3.21]. Increasingly, some loads require higher

standards of power quality and reliability. Distribution companies sometimes have

to design their system differently or install additional equipment to meet the

requirements of special customers. While electricity consumption is still rising in the

developed world, environmental and financial concerns mean there is an increasing

focus on energy efficiency. The implementation of energy-saving schemes can

affect patterns of electricity usage, perhaps shifting energy use between times of the

day or reducing the overall demand. But perhaps the greatest potential impact on the

way distribution systems are designed and operated is the prospect of more active

management of loads. As with distributed generation, integration of distributed load

management resources with network management offers potential benefits in asset

utilisation and avoided network expansion.

3.1.4. Power Electronics

Advances in materials, designs and control methods have resulted in great

improvements in power electronics [3.21]. This has made more options available in

various applications, including AC/DC conversion, variable-speed drives and

generators, switching, and ancillary devices like dynamic voltage restorers. These

new and improved technologies are being used more widely, by electricity

consumers and generators, and within transmission and distribution.

3.1.5. Communications and Control Technologies

In the last two decades there has been a massive expansion of new technologies and

market changes in the telecommunications industry. The Internet and related

systems make possible the concept of ubiquitous communications, all the time

reducing costs while expanding the services available.

Electricity distribution companies can now utilise a wide range of communications

media. These include the traditional land-based telephone networks, the new mobile

40

telephone networks, satellite communications, microwave and radio links, and power

line carrier. In various combinations, these communications technologies offer

unprecedented scope for monitoring and control of electricity distribution networks,

loads and generators.

The growing power and falling costs of computer technology have matched the

changes in communications. This has brought ever more complex computations

within reach of distribution network engineers, both in real time for system operation

purposes and off-line for simulation, analysis and network planning and design.

Allied to the new communications technologies, this computational power makes

new approaches to network planning and system operation possible, even where

there are hundreds or even thousands of small, distributed generators.

Control of distribution networks has also been enhanced by new technologies for

network automation. Improvements in automated sectionalising and on-line tap-

changers provide new options. The developments can be characterised as bringing

transmission technologies to the distribution level.

Finally, advances in control system design and other algorithms and methods make

possible more active and more precise control of numerous and distributed resources.

3.2. Commercial and Regulatory Drivers in Distribution Networks

In the final decades of the 20th century, commercial and regulatory drivers have

probably had a greater effect on the way distribution companies are run than any

advances in technology [3.1,3.6]. Distribution companies have been influenced by

changes in the electricity industry specifically, the energy industry more broadly, and

ultimately in the way large industries and essential services are perceived by

governments and markets across the world.

The privatisation of state-owned industries and associated restructuring, re-regulation

and introduction of competition has had a profound effect on the energy sector and

the electricity industry in particular. As natural monopolies, as providers of an

41

essential service, and as the part of the industry closest to customers, distribution

companies have probably been shielded more than any other part of the industry

from these changes; but their effect has still been felt. Where industry restructuring

has been taken furthest, the monopoly role of distribution network operator has been

clearly separated from other roles that can be made open to competition. However,

the approach to network planning has remained essentially the same. The knowledge

model of the conventional approach is still valid although some of the details, such as

the requirements of standards and regulations, have changed.

For example, the regulatory regime imposed upon a distribution company determines

the incentives and goals for company managers. This will influence the approaches

taken to asset management and network performance. The push for greater returns

on investments and a new focus on shareholder value has undoubtedly resulted in

some improvements in efficiency and value for money in distribution companies, but

it has also resulted in investment in network assets being reduced to an unsustainable

level [3.1]. With distribution network operators cut back to the bare minimum just to

maintain their existing system, resources are not currently available for the changes

that are required to accommodate distributed generation.

New regulations and government policies on the environment and energy, allied to

technical advances and market opportunities, have resulted in the growth of

renewable energy and combined heat and power. These distributed generation

technologies present a range of new challenges to distribution companies, as

discussed above. If distribution companies are going to play their role in meeting

government objectives, then appropriate commercial and regulatory incentives must

be put in place [3.30].

Governments still exert considerable control and influence over their domestic

energy sectors, including the electricity industry. However, more supranational

agreements on energy and other areas of commerce, such as those within the

European Union (e.g., the directive on the promotion of electricity from renewable

sources [3.19]), mean changes can be imposed on distribution companies from

42

outside the national sphere. Furthermore, the globalisation of markets in equipment

and services means that new technologies or methods in one country can quickly

have an impact in other countries. Effects can also be felt due to changes in policy

and regulation in other countries. For example, the UK government chose not to

invest in wind power but the governments of other countries, like Denmark and

Germany, did. This resulted in cost effective wind turbines becoming available and

having an impact on the UK, despite no significant push for this from the UK

government, who now have to react to this external technology driver.

3.3. Future Directions in Distribution Networks

The technology, commercial and regulatory drivers discussed above are producing

changes in the way electricity distribution networks are planned, managed and

operated. These changes require the application of new tools and methods, such as

those presented in later Chapters. It is possible to identify some specific concepts

and ideas now coming to the fore in distribution network development. These can be

split into those concerned with network architectures and operation, and those

concerned with institutional and organisational changes.

3.3.1. Network Architectures and Operation

The advances in technology and the changing demands of customers and other

stakeholders are being reflected in changes in distribution network architecture and

operation [3.20, 3.21,3.31]. Most simply, there are ongoing changes in design

practice. For example, previously distribution feeders were tapered to reflect the

lower power flow further down the feeder. But this approach places a constraint on

the possibilities for connecting DG at the end of the feeder. If power flow on

distribution networks can no longer to be assumed to be uni-directional then issues

like circuit utilisation and feeder tapering must be re-assessed. Many changes have

been handled in the past and the knowledge model of the conventional approach

makes explicit the consideration of new products and concepts. However, the

combination of changes in different areas, and in particular the integration of DG, is

43

having an impact on network architectures and operation that is much more

significant than the gradual change of the last half century.

3.3.1.1. Protection and Control

Safety is of primary importance in electricity distribution, and protection systems

must be proven and reliable. Protection is technically complex and can be

commercially controversial when it is concerned with the interface between a

network and a generator or customer. This can place constraints on new

developments and changes in the way things are done. However, there is plenty of

innovation being reported and there is scope for developments in many areas of DG

protection [3.23]. The major issues are concerned with detecting loss of mains and

island conditions. The possibility of exploiting improved communications is one of

the main avenues being explored. To make a significant impact, there must be a

standardisation of approaches and solutions in DG protection.

The connection of dynamic devices, like generators, to distribution networks present

a number of challenges but also offer opportunities to enhance network performance.

Modern control design methodologies mean the numerous and distributed resources

in electricity networks can be effectively controlled to meet local or global

objectives, as discussed in the following section. In addition, there are opportunities

with smaller DG installations for protection and control systems to be integrated in

combined interface units that provide cost-effective control without sacrificing the

safety provided by protection [3.24].

Larger DG installations have a larger impact on the network and, apart from their

own protection and control, will necessitate changes to the DNO’s protection and

control systems. This presents a number of challenges to network operators,

including wider variations in power flows and conditions on the network, which may

require the introduction of more adaptive protection and control [3.25]. These new

technologies can only be implemented if the DNO has a clear understanding of them

and is confident that they will operate as intended. This results in the need for more

analysis and new modelling tools, as discussed in later Chapters.

44

3.3.1.2. Active Distribution Networks

Conventional distribution networks are described as passive because there is very

limited on-line control of the network and almost all connections are for consumers.

The expansion of distributed generation introduces energy sources into distribution

networks, which in turn will require more active control of those networks. Thus,

there will be a transition to active distribution networks [3.27]. Active distribution

networks require monitoring and control akin to that currently used in transmission

networks. Distribution network operators will have to manage generator-network

interaction and a range of system issues like constraints, outage co-ordination,

stability and security, and system recovery and restoration. This will require a new

set of capabilities, in direct network control issues like voltage and power flow, and

in network simulation and analysis like state estimation and real-time studies. All of

these new requirements highlight the need for DNOs to adapt to the new challenges.

In most cases, the transition to active distribution networks is likely to be gradual.

Over time, more capabilities will be introduced as and when they are needed. This is

necessary because of the massive installed asset base and need to maintain service at

a level expected by existing users of the network. Network technologies like fault

current limiters, cancellation current transformers and Statcoms will gradually be

introduced along with greater monitoring of the network [3.26,3.27]. Implementing

changes like this will also require an expansion in the number of people working in

DNOs as well as improvements in their productivity through the use of new methods

for planning and management of information as discussed in later Chapters.

With the prospect of hundreds or even thousands of distributed generators added to a

distribution network, to help keep additional costs to an acceptable level it will be

necessary to utilise automated controllers that can maintain satisfactory network

conditions without the need for human intervention. One such concept under

development is the Power Quality Management System (PoMS) being developed in

the DISPOWER project [3.28]. These devices will monitor the conditions on low

voltage networks and instruct local generators to act in certain ways. For example,

45

some generators might be instructed to mitigate harmonic effects on the network, or

generator outputs may have to be adjusted to operate within network constraints.

3.3.1.3. Semi-Autonomous Networks

As mentioned above, active distribution networks will develop through the gradual

upgrading of existing networks. Where no network exists, as in a new building

development, there is an opportunity to design the energy supply systems from the

ground up. This provides an opportunity to exploit the latest technologies to their

fullest. For example, new residential areas are being built that include community-

run renewable energy sources, with the accompanying distribution network designed

to accommodate the generators.

Some new industrial facilities require a higher standard of power supply than

normally provided. The concept of “custom power parks” has been proposed to

supply these customers with the energy services they need [3.29]. Custom power

parks would utilise a range of technologies like DG, energy storage and power

electronics to provide the high power quality and reliability demanded by some

sensitive industrial processes.

Community-run renewable schemes and custom power parks exhibit some degree of

independence from the public electricity network, taking management and perhaps

ownership of the network away from the local distribution company. Such schemes

may be grouped under the broad heading of semi-autonomous networks. They will

still be connected to the main distribution system but will be operated with some

degree of autonomy. Where generation sources make it possible, such networks may

be operated completely in isolation from the main system.

3.3.1.4. DC Distribution

The improvements in power electronics make possible new ways of using direct

current rather than conventional alternating current [3.21,3.29]. This includes the

conversion of energy from direct-power sources like photovoltaics and fuel cells.

Such conversion is necessary to connect these sources to the main AC system. But if

46

DC sources become widespread then there is the possibility of connecting them

together by DC and even supplying some loads through a DC network. AC-DC-AC

conversion makes it possible to decouple multi-frequency systems, increasing the

independence of semi-autonomous networks.

3.3.2. Institutional and Organisational Changes

There have been many institutional and organisational changes forced on the

electricity industry in the course of restructuring and re-regulation. But given the

pressures on distribution companies now, there are likely to be further changes.

These can be considered in three categories: ownership and responsibility; energy

markets; and regulation and legislation.

3.3.2.1. Ownership and Responsibility

Across modern, industrial economies there are numerous models for the division of

ownership and responsibility within the electricity supply industry [3.11, 3.31].

Electricity distribution is a natural monopoly but distribution companies sometimes

also own generating assets and sometimes also perform the marketing role of selling

electricity to customers. To ensure fair competition in distributed generation,

distribution companies might have the responsibility of providing open access to

their whole network. This might conflict with their generation interests. If

distribution companies are not permitted to own generation and must act as an

independent system operator then some opportunities for integrating the network and

distributed resources might be lost. Governments and regulators must consider these

issues and specify ownership and responsibility as they see fit.

In some countries, distribution companies have used outsourcing and sub-contracting

to shift many aspects of distribution network operation to other companies.

Regulators have encouraged this to free up as many areas as possible to competition.

Distribution companies can then focus on the core, monopoly activities of asset

management, network operation and planning. In the future, it is likely that even

more activities will be outsourced. For example, a separate company under contract

to the distribution company may perform network maintenance. Likewise, external

47

groups could perform network analysis. Network operation need not be tied to asset

management and these activities could be performed by two separate entities within

one geographic area. However, experience in other industries, particularly the

railways in the UK, may make regulators reluctant to divide responsibilities between

too many different bodies.

The possibility of semi-autonomous networks suggests that outsourcing and sub-

contracting might be taken to its fullest degree, i.e. parts of the distribution network

might be divested and run entirely by a separate commercial enterprise or community

group.

In many countries, formerly state-owned electricity industries have been privatised

but great diversity remains in the ownership and responsibilities of distribution

companies. For example, many are owned and operated by a municipal authority.

However, further changes are possible. The pressures of new technologies and

stakeholder requirements might result in distribution companies being taken back

into public-sector ownership.

3.3.2.2. Energy Markets

The drivers impacting on distribution companies, like distributed generation, will

also have an impact on energy markets [3.10]. The development of open market

environments will have to take account of network constraints, especially where

generation is distributed around the network. Energy markets will also need to

consider the participation of renewable sources and energy storage. Some

intermittent renewable sources may want to participate in the market in combination

with others, forming hybrid systems, e.g. wind power combined with gas turbines.

In facilitating a market for distributed resources, even the smallest units should be

given the opportunity to trade.

The semi-autonomous network concept opens the way for new ways of operating and

managing parts of the network. It also offers new ways for managing the trading of

energy within a semi-autonomous network and with the rest of the market. It may be

48

possible to operate a conventional optimisation strategy within an area, which then

trades with other areas, generators and customers in a market structure.

It should be remembered that electricity is just one component of the energy mix that

consumers use to satisfy their needs. Wider use of gas-fired combined heat and

power will result in greater links between the electricity and gas markets. Renewable

sources are already being encouraged in some areas with the development of markets

in renewable credits, which can be traded separately from energy. Thus, the

relationships between different markets are likely to become more complex as a

result of the changes taking place [3.31].

3.3.2.3. Regulation and Legislation

To keep up with the changes taking place in the electricity industry, governments

will have to update regulations and legislation. The accommodation of distributed

and renewable resources will require changes to market rules and to engineering

standards and recommendations. For example, in the UK the government and the

regulator have established industry working groups to address a broad range of issues

[3.30, 3.31].

Legislation and regulations must take account of the different organisation structures

that might emerge, including the possibility of semi-autonomous networks run by

parties separate from the local distribution company.

Change can only happen if there is a political will. Furthermore, the decisions of

politicians to a large extent determine the directions taken in electricity supply.

There are opportunities to move toward a more environmentally friendly electricity

supply, but if the market mechanism discriminates against renewables then this

opportunity will be lost. Thus, political decisions are required on the priorities for

the industry.

It should be noted that changes to regulation and legislation take considerable time.

Technology and commercial drivers may push the industry quickly in directions not

49

favoured by the government and then the introduction of regulation and legislation

would become much more difficult.


This Chapter has explored the drivers and new directions in distribution networks,

both from a technical perspective and a commercial and regulatory perspective.

Distributed generation was identified as the single biggest challenge facing DNOs at

the start of the 21st century. The impact of DG will be wide-ranging, encompassing

technical, economic and environmental issues. Other technologies that will impact

on networks in a variety of ways are energy storage, demand side management,

power electronics and communications and control. A shift in the commercial and

regulatory environment to something more liberal and market-oriented has already

had a profound impact on DNOs.

DNOs will move in new directions both in network architecture and operation, and in

institutional and organisational structures. Integrated protection and control, active

distribution networks, semi-autonomous networks and DC distribution may all

feature in the distribution networks of the future. And there will be ongoing changes

in ownership and responsibility, energy markets and regulation and legislation.

These changes will go some way towards meeting the new challenges. However, as

explained in the next Chapter, there are a number of shortcomings in the

conventional approach to distribution network planning that will have to be

addressed to enable DNOs to successfully meet the new challenges and move in new

directions. Later Chapters present a number of methods that could help DNOs meet

the challenges facing them and will enhance the conventional approach described in

the knowledge model of Chapter 2.

50


3.1. Energy Networks Association; www.energynetworks.org; “Key facts &

figures”

3.2. Jenkins,N., Allan,R., Crossley,P., Kirschen,D., Strbac,G.; “Embedded

Generation”; 2000; The Institution of Electrical Engineers; ISBN 0852967748

3.3. Willis,H.L., Scott,W.G.; “Distributed Power Generation. Planning and

Evaluation”; 2000; Marcel Dekker,Inc.; ISBN 0 8247 0336 7

3.4. Rogers,W.J.S.; “Impact of Embedded Generation on Design, Operation and

Protection of Distribution Networks”; IEE Colloquium on The impact of

embedded generation on distribution networks, London, 15 October 1996;

Digest No:1996/191

3.5. N.Hadjsaid, J.F.Canard, F.Dumas; “Dispersed generation impact on

distribution networks”; IEEE Computer Applications in Power, Vol.12, No.2,

April 1999, p22-28; ISSN 08950156

3.6. Ault,G.,W.; “A Planning and Analysis Framework for Evaluating Distributed

Generation and Utility Strategies;” PhD Thesis, September 2000, Centre for

Electrical Power Engineering, Department of Electronic and Electrical

Engineering, University of Strathclyde

3.7. Electricity Association; “Engineering Recommendation G.59/1 -

Recommendations for the connection of embedded generating plant to the

regional electricity companies’ distribution systems”; 1990

3.8. DTI, Ofgem; “First Annual Report of the Distributed Generation Co-ordinating

Group (2001/2002)”; March 2003; www.distributed-generation.gov.uk

3.9. Alderfer,R.B., Eldridge,M.M., Starrs,T.J.; “Making Connections. Case Studies

of Interconnection Barriers and their Impact on Distributed Power Projects”;

National Renewable Energy Laboratory, NREL/SR-200-28053, May 2000;

www.eren.doe.gov/distributedpower/barriersreport/

3.10. Department of Trade and Industry (United Kingdom); “Energy White Paper:

Our energy future – creating a low carbon economy”; February 2003

3.11. The European Commissions AGORES project (A Global Overview of

Renewable Energy Sources), www.agores.org

51

3.12. The United States Department of Energy, Energy Efficiency and Renewable

Energy site, www.eere.energy.gov

3.13. The Distributed Generation Information Centre run by Resource Dynamics

Corporation in the US, www.distributed-generation.com

3.14. The United Kingdom Department of Trade and Industry Energy Group site,

www.dti.gov.uk/energy

3.15. Ribeiro,P.F., Johnson,B.K., Crow,M.L., Arsoy,A., Liu,Y.; “Energy Storage

Systems for Advanced Power Applications”; Proceedings of the IEEE, vol.89,

no.12, December 2001

3.16. Price, A., Bartley, S., Male, S., Cooley, G.; “A novel approach to utility scale

energy storage”; IEE Power Engineering Journal, June 1999, p.122-9

3.17. Roscoe, A.; “Demand response and embedded storage to facilitate diverse and

renewable power generation portfolios in the UK”; University of Strathclyde,

MSc dissertation, 2004;

http://ftp.strath.ac.uk/Esru_public/documents/MSc_2004/roscoe.pdf

3.18. Foote,C.E.T., Roscoe,A.J., Currie,R.A.F., Ault,G.W., McDonald,J.R.;

“Ubiquitous Energy Storage”; International Conference on Future Power


3.19. Directive 2001/77/EC of the European Parliament and of the Council of 27

September 2001 on the promotion of electricity produced from renewable

energy sources in the internal electricity market; Official Journal L283 ,

27/10/2001 P.0033-0040

3.20. Taylor,T.M., Willis,H.L., Engel,M.V.; “New considerations for distribution

network planning and design”; CIRED 1997; London, UK; IEE Conference

Publication no.438; p.6.1.1-6.1.5; 1997

3.21. Bergman,S.; “Visions of future Distribution Systems”; CIRED 1997; London,

UK; IEE Conference Publication no.438; p.6.4.1-6.4.4; 1997

3.22. Mott MacDonald and BPI for Ofgem; “Innovation in Electricity Distribution

Networks”; Final Report, March 2004

3.23. Ault,G., Booth,C., Dysko,A., McDonald,J., Banks,R., Cooke,R., Sasse,C.,

Stockton,M.; “Opportunities for a New Generation of Protection Devices for

Distributed Generation”; Proceedings of the Second International Symposium

52

on Distributed Generation: Power System and Market Aspects, 2-4 October

2002, Stockholm, Sweden

3.24. Sheaffer,P.; “Overview of Currently Available UIT Systems”; U.S. Department

of Energy Universal Interconnection Technology Workshop, July 25-26, 2002,

Chicago, IL

3.25. Brahma,S.M., Girgis,A.A.; “Development of adaptive protection scheme for

distribution systems with high penetration of distributed generation”; IEEE

Transactions on Power Delivery, January 2004, vol.19, issue.1, p.56-63, ISSN

0885-8977

3.26. Hart,D.G., Uy,D., Northcote-Green,J., LaPlace,C., Novosel,D.; “Automated

Solutions for Distribution Feeders”; IEEE Computer Applications in Power,

October 2000, vol.13, no.4, p.25-30; ISSN 0895-0156

3.27. Collinson,A., Dai,F., Beddoes,A., Crabtree,J.; “Solutions for the Connection

and Operation of Distributed Generation”; July 2003, DTI/Ofgem Technical

Steering Group Workstream 3 – Short-Term Solutions, Distributed Generation

Co-ordinating Group (www.distributed-generation.gov.uk)

3.28. Bertani,A., Bossi,C., Delfino,B., Lewald,N., Massucco,S., Metten,E.,

Meyer,T., Silvestro,F., Wasiak,I.; “Electrical Energy Distribution Networks:

Actual Situation and Perspectives for Distributed Generation”; 17th

International Conference on Electricity Distribution, CIRED 2003, 12-15 May

2003, Barcelona, Spain

3.29. Hingorani,N.G.; “Introducing custom power”; Spectrum, IEEE , Volume 32,

Issue 6, June 1995, p41-48

3.30. Ofgem; “Distributed generation: A review of progress”; January 2003; Ofgem

02/03; (and Open Letter from Callum McCarthy to DNOs, January 2003)

3.31. Botting,D.; “Technical Architecture – A First Report, The Way Ahead”; IEE

Power Systems and Equipment Professional Network, sponsored by the

DTI/Ofgem Distributed Generation Coordination Group, 23 December 2004,

DGCG 3/05

53

4. Shortcomings in the Conventional Approach and the Need for

New Methods

A large number of shortcomings and areas for improvement have been identified in

distribution network planning, by others [4.1, 4.2] and in the previous Chapters.

Some of these are long-standing problems that still pose problems and result in

inefficiencies; for example, inaccuracies in load forecasting and difficulties in

minimising total lifetime costs. Other deficiencies in planning methodologies have

arisen from changes in the electricity industry, as discussed in the previous Chapter,

and planning methods not keeping pace with those changes.

Just as shortcomings can be identified, it is also possible to identify desirable features

of methods for distribution network planning. It has been argued that it is of crucial

importance to evaluate planning methods from diverse fields and adopt “best

practice” for distribution network planning [4.3]. In summary, distribution network

planning methods should satisfy the following [4.2]:

• Handle multiple criteria and be decision focused

• Use appropriate time scales and planning horizons

• Enable consideration of multiple and diverse solutions while being able to

provide whole system solutions

• Enhance planner productivity and provide robustness to limited planning

resources

• Be modular in terms of access to analytical components and provide means of

integrating analytical modules and interfacing with other applications

• Make appropriate use of computer-based tools including simulation,

optimisation, graphical interfaces, bulk data handling facilities and mathematical

decision techniques

• Be automated or interactive as appropriate

• Manage uncertainty and deal explicitly in terms of risk

• Provide insight to the planning problem and solutions

• Produce tractable planning records to facilitate reuse of data, models, solutions

and planning rationale and provide leverage to future activities

54

Following the assessment of the conventional approach to distribution network

planning in Chapter 2, and new drivers and directions in Chapter 3, specific

shortcomings in distribution network planning were identified in five broad areas:

• The use of conventional technologies and methods

• The need for more analysis

• Organisational separation and loss of control

• Difficulties in formulating strategies and making decisions

• The need for knowledge management

These shortcomings and their implications are discussed below.

4.1. The Use of Conventional Technologies and Methods

As a mature industry, electricity distribution is characterised by a relatively slow rate

of change of technology. While great improvements have been made over the years

in overhead lines, underground cables, transformers, switches and circuit breakers,

the fundamental design principles of electricity networks have remained the same.

The need for low costs, high reliability and unquestionable safety has limited the

scope for experimentation. Thus, the conventional approach to planning and design

assumes that conventional technology will be used. However, DG, energy storage

technologies, solid-state devices and expanding capabilities in communications and

control are exerting a growing influence. A special effort is required to consider, let

alone incorporate, these novel technologies. Typically, DNOs will select from a

limited number of technically acceptable designs based on what has been done

before. New technologies present more risk and may be more expensive initially.

DNOs may lack the incentives necessary for a realistic business case to adopt new

technologies to be made. Also, new planning and design methods are required to

examine the impact of new technologies and exploit their use.

This issue will be of particular importance in the near future because in the

developed world, many distribution companies have an ageing asset base that will

largely be replaced over the next decade or two. Cutbacks in investment have

55

exacerbated the problem of ageing assets that will require wholesale replacement.

New technologies will offer some opportunities to extend asset life but in replacing

assets full account must be taken of the new demands that will be made of

distribution networks, such as the integration of DG.

The use of conventional technology is noted in the knowledge model of the

conventional approach where the “Design Network Additions and Alterations” task

assumes the use of conventional solutions. This shortcoming justifies the use of new

approaches to encourage solution-neutral thinking and the incorporation of new

technologies. In particular, the consideration of engineering design theory and the

use of a formal MCDM framework encourages the identification and consideration

of novel solutions, as will be explained further in Chapters 5 and 6.

However, there are considerable barriers to be overcome. DNOs are slow to adopt

new technologies for a range of reasons. Principal among these is cost effectiveness.

Many new technologies offer slight improvements in performance or flexibility but,

until they are widely adopted, remain too expensive. The use of new technologies

will only occur where DNOs feel that they fully understand the technology. This

provides justification for new modelling and analysis, such as that discussed in

Chapter 9, and enhancement of DNOs’ abilities to absorb the outcomes of research

and development. The use of radically different approaches also has to overcome the

inertia of familiarity and corporate procedures as well as a culture built around the

conventional approach to network planning.

4.2. The Need for More Analysis

One of the primary challenges facing distribution companies is the modelling,

simulation and analysis of network performance and behaviour. New technologies,

such as DG and power electronics, require the development and validation of new

models. And with different incentives for stakeholders in the network and new ways

of operating, new types of analysis will have to be performed. DNOs need not be the

ones who develop new models and analysis methods – this task can be undertaken by

academia or other external service providers – but they must have the capacity to

56

absorb the outcomes and understand the results of new analyses. In line with

changes in the industry and regulation, distribution companies must also conduct

more comprehensive economic and financial evaluation. This includes the proper

consideration of externalities like environmental factors. Thus, the analysis that must

be conducted by distribution companies is both more extensive and more complex.

This poses a problem because conventional analysis methods are too expensive, in

terms of time, talent and other resources. High-level planning decisions are

sometimes made without sufficient technical analysis. For example, lower voltage

assets are treated as numerous enough and cheap enough to be replaced based on

statistical analysis. New connections are sometimes agreed without detailed analysis

of the implications. Generally, there is a different approach to major and minor

projects due to the high expense of investigation. It is assumed that the system can

accommodate small changes but limits are eventually reached. If the burden

imposed by technical analysis was smaller then decision-makers could be better

informed about the implications of particular courses of action.

In the past, distribution networks have been over-designed and their components

over-sized. This was due to a number of reasons. The additional capacity and

security provided cover for the uncertainties of demand predictions. The acceptance

of generous margins also made it possible to apply rules of thumb and use prepared

tables and charts in design. However, the excess capacity built into the networks of

the past has been taken up by load growth, and reduced expenditure in recent years

means the excess capacity has not been replaced. Pressures on DNOs now mean that

networks must be designed more precisely with less, potentially useless, spare

capacity. This requires new rigour in the analysis of requirements and design of

solutions.

Power system simulation and analysis is an important activity in the design and

management of modern electricity supply systems. Simulation is used to ensure that

the required standards of security and stability are met and that system design is

optimised. Power system simulation can be a difficult and expensive task, requiring

57

considerable knowledge and experience to perform detailed analyses. There are a

wide variety of simulation tools and huge libraries of models and data available; and

there are many approaches that may be taken to achieve a specific goal. As

discussed, modern electricity systems are undergoing considerable change with the

introduction of new technologies like distributed and renewable generation, and this

is imposing an ever-greater simulation burden on the industry.

“Network modelling and analysis” appears as a method at a number of points in the

knowledge model of the conventional approach. This highlights its importance and

the fundamental role it plays in network planning. The new challenges demand even

more analysis, highlighting a significant shortcoming in the conventional approach.

One way of tackling this shortcoming is through improved information management;

this is discussed below in Chapter 7.

DNOs must be able to accommodate and analyse the new technologies being

connected to their networks, or at least be able to understand and apply the results of

analysis by others. Chief among these is doubly fed induction generators (DFIGs),

the most common generator technology used in new wind farms. Assessing the

impact of DFIGs on the network requires simulation and analysis and this requires

the development of new models that are acceptable to DNOs and others in the

industry. Chapter 9 describes the dynamic modelling of wind farms, in particular the

development of new models of DFIGs and associated systems. This illustrates the

difficulties faced by DNOs and the industry as a whole when trying to introduce

analysis tools for new technologies.

4.3. Organisational Separation and Loss of Control

Across the world there is great diversity in the structure, ownership and management

of electricity supply industries. But where industry restructuring and re-regulation

has taken place there has been a general shift towards organisational separation and a

consequent break down of old lines of control and influence. This is perhaps most

keenly felt at the distribution level with distribution companies expected to maintain

58

the same standards of quality and service while facilitating open access to new

market participants.

With responsibilities changing and private investors keen to exploit new

opportunities, the role of the central planner has diminished, or even disappeared.

DG and private distribution networks interconnected with franchise utility networks

are starting to influence distribution system planning effectiveness. This is one of

the key areas of concern among DNOs in the UK [4.2].

Furthermore, when distribution companies were part of large, vertically integrated

entities or state-owned, they perhaps benefited from some subsidy in research and

development. With the core DNO role isolated from other parts of the industry, and

a constant pressure on cost reduction, distribution companies may sacrifice

investment in research and development – to the long-term detriment of society.

The knowledge model of the conventional approach does include as a level one task:

“Maintain Communication and Exchange Information with Others”. In the new

environment this is becoming increasingly challenging. One way to address this

challenge is through improved information management, with the representation of

models and data being an area where particular enhancements are possible, as

discussed in Chapter in 7.

4.4. Difficulties in Formulating Strategies and Making Decisions

With all the changes in technology, regulation and the commercial environment, one

of the primary challenges faced by distribution companies is the formulation of

strategies. With so many new issues to contend with, within an ever-changing

incentive structure, managers and engineers need new methods to support decision

making.

Conventional approaches to electricity distribution network planning are often

deficient in their accommodation of multiple objectives or multiple criteria. The

multitude of optimisation algorithms that have been proposed [4.4] are typically

59

based on a three-pronged approach addressing load forecasting, substation site and

size, and feeder route and capacity. These algorithms rely on a limited expression of

requirements. They might minimise costs – calculated as a function of substation

and feeder type and capacity – with predicted customer demand profiles as the goal

to be satisfied; statutory and company design standards might be included as

constraints. An optimisation algorithm focusing on a limited number of factors

might be used to produce the first version of a plan then constraints imposed to take

account of externalities like environmental issues. Optimisation methods may also

be used to define design standards, which are then applied in a wide variety of

circumstances.

Planning and design would be improved by explicitly including more factors

amongst the objectives considered in the first place. Attempts have been made to

extend optimisation algorithms to take account of additional issues, reliability being

one example, but it can be difficult to translate continually changing regulatory,

environmental and financial drivers into tangible engineering objectives. In

electricity distribution, there are many stakeholders with different and ill-defined

objectives so the emphasis must be on finding an acceptable solution rather than an

optimum. For this reason, conventional optimisation algorithms are inappropriate

[4.5]. Greater network complexity will necessitate the explicit consideration of even

more issues, reducing further the value of limited optimisation algorithms.

In general, in the conventional approach to distribution network planning and design,

there is a lack of support at the higher decision making level. There have been some

recent advances in the integration of computer-based tools but integration and

interpretation of results is still mainly a manual task. This shortcoming justifies the

application of new methods, including MCDM, as discussed in Chapter 6, and

scenario analysis, as discussed in Chapter 8.

4.5. The Need for Knowledge Management

As with other engineering enterprises, DNOs rely on the knowledge of their staff.

Information systems, including databases and documentation, should be maintained

60

and kept up to date to be of use. But some DNOs have, due to pressures of time and

money, not maintained these systems, some of which date from the days of the

nationalised industry structure. Instead, the knowledge and experience of staff is

relied upon to compensate for missing data or inaccuracies. The loss of knowledge

as people move can be damaging. In particular, centralisation of DNO functions has

resulted in the loss of people with in-depth knowledge of a specific geographical

area. This local knowledge used to compensate for missing or poor quality

information.

Knowledge management is an important issue for DNOs and exposes a shortcoming

in the conventional approach. The methods used in planning and design must take

account of this by facilitating the capture of the reasoning behind decisions as well as

the decisions themselves. The knowledge modelling methodology used in Chapter 2

to describe the conventional approach to network planning demonstrates how

knowledge can be represented in a cohesive and navigable form. The application of

the knowledge management techniques would facilitate the capture of knowledge

from important staff, helping to spread that knowledge and provide insurance against

their departure. This shortcoming is addressed in Chapter 5 below, where methods

for the management of rationale in planning decisions are proposed. In particular,

the use of structured decision making is highlighted, justifying further the application

of MCDM, discussed in Chapter 6.


Following discussion of the conventional approach to distribution network planning

and the identification of drivers and new directions, this Chapter highlighted some

specific shortcomings in the approaches to planning and decision making.

The use of conventional technologies and methods is a shortcoming because it limits

the scope for innovation and adoption of new solutions. New technologies and

methods will be required to meet the new challenges facing distribution planners.

And efforts will be necessary to ensure that the new technologies that are available

are fully exploited by DNOs.

61

The new challenges in network planning result in a need for more analysis that

DNOs are not equipped to perform. This shortcoming in analysis resources must be

addressed through improved methods and greater efficiency. For example, new

models are required for new technologies like DFIGs used in wind farms.

Changes in regulation and business structure have led to organisational separation

and loss of control. This has become a shortcoming in planning because it disrupts

the flow of information and the capability to reach decisions that satisfy the diverse

requirements of different stakeholders.

A combination of new challenges results in difficulties in formulating strategies and

making decisions. This is at the core of electricity network planning so is a

considerable shortcoming for DNOs.

There is a need for knowledge management because it offers a way of improving

performance and efficiency in distribution planning, thereby supporting other

activities that will help to address the other shortcomings.

A number of specific areas have been identified in which the planning process could

be improved to meet the new challenges being faced by DNOs. Addressing these

shortcomings requires methods from other domains to be applied. This provides the

justification for the work presented below, which demonstrates a number of methods

that can help DNOs update their processes and face the new challenges in network

planning. Specifically, the methods proposed are engineering design theory, decision

support, information management, and scenario analysis. The following Chapters

explain how these methods might be applied but it should be noted that the

complexity and diversity of distribution network planning and the range of new

challenges to be faced mean that highly prescriptive and detailed methodologies are

inappropriate because they are inflexible.

62


4.1. Berrie,T.W.; “Electricity Economics and Planning”; 1st edition, London:

Peregrinus, 1992

4.2. Ault,G.W., Foote,C.E.T., McDonald,J.R.; “Distribution system planning in

focus”; IEEE Power Engineering Review; January 2002, Volume 22, Issue 1,

p.60-62; ISSN: 0272-1724

4.3. Ault,G.W., Cruden,A., McDonald,J.R.; “Specification and testing of a

comprehensive strategic analysis framework for distributed generation”

Proceedings IEEE Power Engineering Society Summer Meeting 2000, 2000

4.4. Khator,S.K., Leung,L.C.; “Power Distribution Planning: A Review of Models

and Issues”; IEEE Transactions on Power Systems; vol.12, no.3; p.1151-1159;

August 1997

4.5. Miranda,V., Proença,L.M.; “Why risk analysis outperforms probabilistic

choice as the effective decision support paradigm for power system planning”;

IEEE Transactions on Power Systems; vol.13, no.2; p.643-648; May 1998

63

5. Engineering Design Theory

The application of methods from other domains can help improve the conventional

approach to distribution network planning and address some of the shortcomings

identified above. One source of ideas and methods that can contribute to the

adoption of best practice in distribution network planning is engineering design

theory [5.1, 5.2, 5.3].

Engineering design theory includes descriptive theories, such as the classification of

models and design level, and various prescriptive theories. However, design

theorists recognise that there is often a mismatch between theory and practice.

Prescriptive theories are normally problem focused, while the practical approach

adopted by designers is often solution focused. A purely systematic approach is of

limited value and is unrepresentative of how designers actually go about their work.

Design in practice is an interactive and recursive process that relies on conjecture as

much as problem specification.

Furthermore, design theory tends to be focused on product engineering in the

mechanical engineering domain. An electricity distribution network is a unique and

evolving entity that lasts for decades and undergoes continuous update. All

alterations must take into account the existing network, although each project that

alters the network could be viewed as a single product, albeit a one-off. The

continuous development of the network means decisions must also be made on the

best time to implement plans. In some instances, a problem will arise and plans will

be prepared to deal with it only to be abandoned when conditions on the network

change again and the problem ceases to exist and changes into something quite

different. Nevertheless, engineering design theory still offers some useful ideas on

how to deal with the challenges faced by distribution companies [5.4].

This Chapter examines a number of concepts and methods from engineering design

theory and applies them to distribution network planning in the context of the new

challenges and shortcomings identified above. There is a particular emphasis on the

64

concept of design rationale, which is identified as being of primary importance in

improving distribution network planning.

5.1. Solution-Neutral Problem Definition

The systematic engineering design approach involves clarification of a problem

followed by the development of conceptual designs before forming more detailed

designs. An important stage is expressing problems in an abstract, solution-neutral

way. The use of some modelling methodology is suggested to establish the overall

structure of the problem. This is supposed to lead to the development of novel

solutions, unconstrained by inherent preferences in the problem definition.

Distribution network planners lack the ability to express complex problem structures

in an abstract way, i.e. focus first of all on what customers really need and want

rather than making assumptions about how a problem will be solved. In addition,

there is often no incentive for DNOs to perform such an analysis. The conventional

approach to distribution network planning bypasses the solution-neutral expression

of problems and moves straight on to solution-oriented specifications. This is

illustrated in the knowledge model of the conventional approach where the design of

network additions and alterations is directly linked to the specification of

conventional solutions. From expected load profiles, characterising the requirements

of consumers in conventional terms, planners will immediately specify problems in

terms of solutions, e.g. overhead lines, cables and transformers. Furthermore, with

developments in DG, power electronics and differentiated quality and reliability,

load profiles no longer provide sufficient information to characterise requirements.

A more solution-neutral perspective would consider the overall service requirements

of customers then assess all the different options available for satisfying those

requirements.

Design theory advocates that the conceptual design stage of the systematic approach

should include some abstraction of the specification to promote innovation [5.2].

Distribution design, like many other fields of design, moves into solution terms

quickly, e.g. discussing particular transformer and cable sizes. Although this may

65

restrict innovation, there are good reasons for this approach. Standardisation and

consistency offer economies of scale and confidence in proven performance. These

are of particular value to distribution companies who must manage very large and

expensive networks. However, incorporating novel technologies may require some

abstraction to facilitate innovation. It will also require suitable incentives for DNOs

from a business perspective.

5.2. Level of Risk and Innovation

Engineering design theory uses the concept of design level to reflect the level of risk

and innovation in a design [5.1, 5.2]. Design level can be classified as REPEAT,

VARIANT, ADAPTIVE or ORIGINAL. Moving from one level to the next

represents an increase in product or process innovation and therefore an associated

increase in risk. The review of the conventional approach in Chapter 2 revealed that

electricity distribution network design is largely REPEAT or VARIANT with

gradual changes to the system and its components and low levels of risk and

innovation. However, incorporating new technologies in distribution networks may

require the greater innovation and risk of ADAPTIVE and ORIGINAL design.

Recognising this helps manage the risk in network development. By ensuring that

regulators recognise the level of risk, it is more likely that they will allow the

additional revenues necessary to innovate.

5.3. Decision Classifications

One of the many changes in the electricity industry is that the detailed design of new

additions or alterations to the network is now being done outside of the DNO. This

makes the definition of functional specifications even more important. Engineering

design has been described as a series of decisions classified as FUNDAMENTAL,

INTERMEDIATE and MINOR [5.3]. Distribution design has long been a case of

defining a strategy with fundamental decisions then making intermediate and minor

decisions for each project. This allows the benefits of standardisation and

consistency to be enjoyed. Nowadays, distribution asset managers may make the

fundamental decisions then leave the rest to external contractors. However,

66

increasing complexity may require more fundamental decisions at the project stage.

Distribution companies will either have to accept this devolution of design freedom

and decision making power, or revise their planning and design policies to deal with

the issues being faced. In any case, a rethink of the approaches to organising the

planning and decision making process is required.

5.4. Design Concurrency

The range of issues considered in distribution network planning must be broadened

to include externalities like damage to the environment. But it is also important to

consider the whole life cycle of a project. A powerful theme in modern design

theory is concurrency [5.1, 5.3]. This involves taking into account all aspects of a

design including manufacture, installation, maintenance, operation, failure and

retirement. This can be implemented with only slight modifications to the

conventional approach as detailed in the knowledge model of Chapter 2. The

“Identify Requirements” task already highlights the importance of taking different

factors into account. A more robust assessment of requirements, taking the full life

cycle into account, would provide the concurrency advocated by design theory. The

planning and design of distribution networks can benefit greatly from such changes

to the conventional approach. However, organisational separation, the downsizing of

distribution companies and associated subcontracting of functions, and the near-term

horizon of price control reviews may disrupt attempts to introduce concurrency to

planning and design.

5.5. Alternative Designs

Design theory supports the development of a number of designs at every stage of

development. The distribution network design literature supports this. Alternative

designs may be based on different solution concepts or may address the possibility of

different scenarios emerging. This helps decision-makers manage the risk associated

with predicting the future and offers them real choices in network design. Designers

are advised to develop a number of alternative designs at every stage, either based on

different solution concepts or to address uncertainty and the possibility of different

67

scenarios emerging [5.1]. As the range of distribution network technologies

expands, the spectrum of realistic solution concepts will grow. This will make the

selection task for planners and designers more difficult.

Growing uncertainty in the distribution business requires planning for different

scenarios. It is obvious that the most acceptable approach for large, long-life entities

like distribution networks is to design them to be robust to a range of scenarios,

rather than just to suit the scenario deemed most likely to occur. Modern thought

supports risk analysis approaches for dealing with uncertainty in distribution

planning rather than previously used probabilistic approaches [5.5]. This is

discussed briefly in Chapter 8.

5.6. Design Rationale

Documentation is of crucial importance in all types of engineering design.

Traditionally, this might have represented only the final designs. One aspect of

modern design theory is the capture of the rationale lying behind the final designs.

This should be of particular value in distribution networks where assets remain in use

for decades, long after the original designers have left. Design rationale offers a way

to combat the identified shortcoming of a need for knowledge management.

With a strict regulatory regime pushing revenues down and increasing focus on

performance and the environment, distribution planners and designers must provide

full justification for strategies and expenditure, both internally and externally.

Distribution companies have also been restructuring and reducing their number of

employees. A feature of this downsizing has been the subcontracting of distribution

utility functions, particularly construction, to focus on asset management.

Distribution companies must be prepared for all possible changes, in technology,

government policy, and regulation.

The concept of design rationale [5.6, 5.7, 5.8] is linked to information management

but has the goal of creating a “reasoning trail” not just a “data trail”. Design

rationales can include the argumentation and justification behind a design decision,

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the alternatives considered, and the trade-offs evaluated. A design rationale system

is a tool for capturing design rationales and making them easily accessible. The

recording of rationale is most important for fundamental decisions that occur early

on and have the greatest leverage, or impact, on a project.

By helping to evaluate the issues and alternatives being examined, design rationales

can provide better design support and improve the formation and comparison of

strategies. This includes tracking dependencies among requirements and the

components that satisfy them. Rationales can help identify past designs that may be

relevant to current problems, or the rationales themselves may be of value.

Collaboration between groups can be improved by providing a common vocabulary

and project memory.

Almost anything, from formal specification documents to informal telephone

conversations, may contribute to rationale. Apart from problems with the volume of

material, anything represented and stored must be accessible so must have some

structure. Informal representations are easy to create but unstructured and difficult to

archive and retrieve. Formalising knowledge is costly and requires all objects and

relations to be defined as formal objects.

The benefits of design rationale will be increasingly valuable as distribution planning

and design becomes more complex. Distribution network assets typically have a life

longer than the careers of the people who plan and design the network. Combined

with modifications to the network made over time and restructuring of distribution

companies, planners and designers can lack valuable insight into earlier decisions.

By explaining design decisions, rationales can support modifications, which are

inevitable in the long life of distribution networks. The recording of rationales can

capture expertise and help tackle problems in the restructuring of DNOs.

Apart from supporting other planners and designers, design rationales can contribute

to documentation for managers and external groups, including the industry regulator.

Rationales may also be useful in supporting patent applications, were distribution

69

companies to seek intellectual property protection for network designs. It is

important that the recording of rationale and other forms of knowledge management

are tailored to the particular domain in which they are applied. One way of formally

recording rationale is to associate it with structured decision making methods, as

proposed below.

Design rationale theory [5.1, 5.3, 5.7] suggests that effective capture requires the

recording of four things:

1. The justification for decisions

2. The argumentation behind decisions

3. The alternatives considered in a decision

4. The trade-offs made in reaching a decision

Unfortunately, capturing rationale effectively and facilitating its reuse is not easy.

There is a conflict between capture and subsequent access. For example, an

engineer’s logbook might contain details of all the thought processes and information

used in reaching design decisions, ideally fully capturing the rationale. But

accessing this knowledge is difficult because of the unstructured and often untidy

nature of a logbook written by one person for his or her own use. The logbook may

be of great value to the engineer who wrote it but of limited value to others.

Different approaches can be adopted to address this conflict. In the theory of design

rationale, three perspectives are identified:

1. The communication perspective, where all naturally occurring discourse in

reaching the decision is captured

2. The documentation perspective, where rationale must be identified and recorded

in documentation

3. The argumentation perspective, where some sort of procedure or system is

implemented that captures rationale in a structured form throughout the decision

process

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It is important that the time required by the user to document rationale is kept to a

minimum. Forcing the user to spend a great deal of time documenting rationale can

be counter-productive. The user must understand the benefits of capturing rationale.

This is to ensure that the user fully comprehends the type of knowledge that it is

important to capture, permitting them to be as concise as possible when inputting

knowledge. Given the requirements and potential problems in capturing rationale,

different approaches may be necessary for different circumstances and different

decisions.

In this section, three methods are proposed to support the capture and reuse of

rationale in distribution network planning. Firstly, structured decision making

methods encourage the formal identification of alternatives, objectives and decision

criteria. Attaching rationale to the assignment of weightings and scores, which can

be a source of opacity, can enhance these methods further. This combines the

communication and argumentation perspectives by introducing structured support for

the decision process that will change the ways decisions are made while forcing the

recording of decision discourse. Secondly, through an analysis of the planning task,

a number of generic or repeated justifications have been identified, which can form a

basis for the capture of rationale. This might fall under all three perspectives

depending on when and how the generic justification is identified and recorded.

Finally, knowledge modelling methods can be applied to the capture and reuse of

rationale. This is likely to involve posterior analysis of a decision and falls squarely

under the documentation perspective.

5.6.1. Structured Decision Making Methods

Structured decision making methods, such as multiple criteria and multiple objective

decision making, can help capture some of the elements that make up the rationale.

A structured decision making process will normally require the formal identification

of alternatives. It will also require the identification and quantification of decision

criteria or objectives. Even in the most limited methods, there should be

identification of plus-points and minus-points. The weightings and scores used in

structured decision making methods quantify the trade-offs made between

71

alternatives. However, the weightings and scores can appear arbitrary and opaque.

Assigning some rationale directly to the weightings and scores can ameliorate this.

The MCDM example in Chapter 6 illustrates how a decision can be analysed and the

reasons for selecting a particular option made explicit. This approach could be

further enhanced with the capture of rationale associated with each of the weightings

and scores in the decision analysis.

5.6.2. Generic Justifications

Instead of a free-text description of rationale, within structured decision making or

just in rationale capture in general, it should be possible to offer users a pre-set list of

generic justifications. These would be formed from the most frequently used

justifications behind decisions. These could form a basis for the capture of rationale

but would normally require additional information, either as additional free-text

description, or as reference to something providing background knowledge or

guidance.

Examples of generic or repeated justifications might include the following:

• Meeting a particular law or regulatory rule. Reference could be made to the law

or regulation in question.

• Following company strategy or policy, in business or engineering. Reference

could be made to the appropriate policy or strategy document.

• Selection based on cost. The cheapest option will often be chosen but the

rationale should make clear how the overall cost was calculated. Ideally, full

lifetime cost will have been determined.

• Due to the results of analysis and simulation. Studies might determine which

option is the best. Reference could be made to the results, ideally with

information on the tools and methods used.

• Exploitation of a concurrent opportunity. In some instances the best option will

depend on other activities on the system. Often, this will depend on and

influence the timing of actions. Reference could be made to the other

opportunities.

72

• Due to local reasons. This type of justification might be entirely correct but does

not provide much information. This would have to be expanded upon with

explanation of why local circumstances demand a particular solution.

• Due to past experience. Engineers learn to know which solutions are best in

different circumstances and past experience might be the primary reason for

selecting a particular option. Reference could be made to the earlier decision in

question. Ideally, this would have been based on an objective analysis that was

properly recorded.

Each of these generic justifications can provide a starting point for further

explanation of why a decision was made. They could also be used in combination.

As decision makers use the list, it would be tuned to the particular application and

organisation.

5.6.3. Knowledge Modelling Methods

Knowledge modelling methods provide a formal approach to capturing, structuring

and presenting knowledge. A knowledge modelling methodology was used in

Chapter 2 described the conventional approach to distribution network planning.

Such methods could be applied in the capture of rationale and the identification of

knowledge resources associated with particular planning decisions. This would be

conducted after a decision was made and would involve interviewing the decision

maker to identify:

• What decision was taken?

• What factors drove the decision?

• What were the outcomes of the decision?

• What knowledge sources were used in reaching the decision?

• What tools and techniques were applied and what additional knowledge did they

produce?

• Why was the final decision as it was?

The knowledge model of the conventional approach already includes a level one task

where such knowledge modelling methods might be applied: “Review

73

implementation to see that requirements are met”. The application of knowledge

modelling methods would provide scope for very detailed capture of knowledge

associated with a decision. This detail comes at considerable cost in time and effort

required to fully review a planning decision and process it according to the

knowledge management methodology. However, this investment would provide a

return in the form of faster or better decisions in future through exploiting the

captured knowledge.


Engineering design theory offers a range of concepts and methods that can be applied

to electricity distribution network planning to address the shortcomings in the

conventional approach and help meet the new challenges of the 21st century.

A selection of these concepts and methods was considered, with an assessment of

how each of them relates to and influences distribution network planning. The value

of solution-neutral problem definition was highlighted and contrasted with the

conventional approach to network planning, which moves into traditional solution

terms all too quickly. The concept of a level of risk and innovation was explored,

exposing conventional network planning as being mostly low risk whereas the

incorporation of new technologies will require higher-risk planning and design, a

departure from the norm in a risk-adverse industry. The concept of decision

classifications was used to highlight the benefits of standardisation and the potential

dangers in outsourcing. The concept of concurrency was identified as being

extremely useful but is already applied to some degree in conventional network

planning. Likewise, the method of producing alternative designs is already used in

the conventional approach but will have to be expanded to incorporate the possible

use of new technologies. All of these methods are useful only if DNOs are given the

appropriate incentives to change the way they do things.

The capture and effective representation of the rationale behind decisions offers great

scope for adding value to decisions and contributing to the collation of corporate

knowledge. The use of some structure or process for capturing rationale forces

74

decision-makers to make explicit the reasons for their decisions. This should lead to

better decisions but also provides a valuable resource for those facing similar

problems in the future. A novel combination of methods was suggested to assist in

the capture of rationale in distribution network planning: the use of formal MCDM

techniques; making reference to a defined list of generic rationales; and post-decision

analysis with knowledge engineering methods.

Among the concepts and methods considered, the descriptive theories provide a new

perspective on the conventional approach to network planning and the prescriptive

theories offer some ideas on new approaches. The development of the planning

function will benefit from the consideration of engineering design concepts like level

of risk while new procedures will have to be implemented at various stages of the

planning process to effectively capture decision rationale.


5.1. Birmingham,R., Cleland,G., Driver,R., Maffin,D.; “Understanding Engineering

Design: Context, Theory and Practice”; 1997; Prentice Hall Europe; ISBN 0-

13-525650-X; D620.0042UND

5.2. Pahl,G., Beitz,W.; “Engineering Design: a systematic approach”; The Design

Council; 1988

5.3. Starkey,C.V.; “Engineering Design Decisions”; 1992; Edward Arnold; ISBN

0-340-54378-7; D620-0042STA

5.4. Foote,C.E.T., Ault,G.W., Burt,G.M., McDonald,J.R., Green,J.P, “Towards a

structured methodology for distribution network design applications”, 35th

Universities Power Conference, Belfast, September 2000



IEEE Transactions on Power Systems; vol.13, no.2; p.643-648; May 1998

5.6. O’Shaughnessy,K., Sturges,R.H.; “A Systematic Approach to Conceptual

Engineering Design”; Proceedings of Design Theory and Methodology

DTM’92; American Society of Mechanical Engineers, Design Engineering

Division; p.283-291; 1992

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5.7. Lee,J.; “Design Rationale Systems: Understanding the Issues”; IEEE Expert;

vol.12, no.3; p.78-85; May/June 1997

5.8. Shipman,F.M., McCall,R.J.; “Integrating Different Perspectives on Design

Rationale: Supporting the Emergence of Design Rationale from Design

Communication”; Artificial Intelligence in Engineering Design, Analysis, and

Manufacturing (AIEDAM); vol.11, no.2; p.141-154; April 1997

76

6. Decision Support in Distribution Network Planning

Perhaps the most important element of network planning is the reaching of decisions.

Decisions require information to be gathered and may rely on various forms of

analysis but tools are also available to support the actual decision making process. In

this Chapter, the use of multiple criteria decision making (MCDM) techniques is

discussed, highlighting their value in making explicit the identification,

quantification and analysis of decision criteria. Decision support is crucial to

addressing the difficulties in formulating strategies and making decisions, which was

identified in Chapter 4 as one of the main shortcomings in distribution network

planning.

The second half of this Chapter describes a case study that demonstrates the use of

MCDM techniques to assess the financial viability and technical desirability of a

number of DG options for a large industrial consumer. This includes the

specification and use of alternative ratios within the MCDM framework to provide a

new perspective and enhance the information available to decision makers. The

study demonstrates the assessment of novel solutions based on DG alongside

conventional grid reinforcement solutions. This is the kind of study that planners

will have to undertake to exploit the advantages of new technologies, as noted in

Chapter 4.

6.1. Multiple Criteria Decision Making

The purpose of MCDM techniques is to help a decision-maker to think

systematically about complex decision problems, and improve the quality of the

resulting decisions. Decision problems can involve a number of different criteria and

have several different courses of action or options available to the decision-maker.

When the number of options and criteria becomes large, the need for formal,

structured decision making techniques becomes apparent. It is not unusual to find

some of the criteria to be conflicting in nature, thereby complicating the problem

further. The use of MCDM techniques in such circumstances allows this process to

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be tackled consistently, in a manner such that the most suitable option – if one exists

– is identified.

MCDM is proposed here as a means of addressing a number of the shortcomings

identified in the conventional approach to distribution network planning. It will be

shown that MCDM can help address the difficulties in formulating strategies and

making decisions, support the fair consideration of new technologies and facilitate

the effective capture of decision rationale and management of knowledge.

The conventional approach to network planning can be enhanced with MCDM by

applying it to the principal decision points in the knowledge model. On level one

these are “Design Network Additions and Alterations” and “Determine Programmes

of Implementation”. The design task can be enhanced by modifying the

conventional three-element, solution-oriented approach to produce something more

like the general structure for MCDM outlined below.

MCDM problems can be classified into two groups [6.1]:

• Multiple objective programming problems – where there is a very large or

infinite number of alternatives, which are described through the use of decision

variables.

• Multiple attribute problems – which have a relatively small number of

alternatives (typically 4-10), represented in terms of attributes.

There are many different multiple objective and multiple attribute techniques

available to a decision-maker. This presents the decision-maker with the problem of

selecting an appropriate MCDM technique. The ultimate decision on which

technique should be selected depends on a number of factors [6.2]:

• Number and type of objectives, criteria, alternatives and constraints

• Type of information the decision-maker wishes to input – some techniques

require more complex and sophisticated information from the user

• Decision making style and problem structure

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• Time and effort the decision-maker wishes to spend on the problem – some

techniques reach a solution quicker than others and have a lower cognitive

burden

• Accuracy – most techniques cannot handle uncertain outcomes, and some do not

guarantee to produce a non-dominated solution or a complete ranking of all the

alternatives

• Restrictiveness of underlying assumptions

Even after the above factors have been considered the decision-maker may still be

left with several techniques that will produce the required outputs. In this situation,

personal preference or supporting software availability would become the deciding

factors.

6.1.1. General Structure for MCDM

As noted above, a wide range of MCDM techniques is available, each involving a

slightly different methodology. However, a broad assessment of the techniques and

consideration of their application in the distribution network planning domain

suggests a general structure for MCDM as shown in Figure 6.1 and described further

in the sections below. As with design theory, considering distribution network

planning from the perspective of this MCDM structure highlights ways in which the

conventional approach can be improved, as discussed in the sections below.

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Figure 6.1 – General structure for multiple criteria decision making

DEVELOPMENT ISSUES

DEVELOPMENT OPTIONS

QUANTIFICATION

ANALYSIS

DECISION MAKING

DEVELOPMENT ISSUES

DEVELOPMENT OPTIONS

QUANTIFICATION

ANALYSIS

DECISION MAKING

6.1.1.1. Identify “DEVELOPMENT ISSUES”

The first step in reaching a decision is to properly consider all the relevant issues.

For a technical study, this might be concerned with design specifications and

technical requirements. For more strategic analyses, this might include higher-level

economic, business and social issues. It is at this stage that planners must ensure that

they use appropriate time scales and planning horizons. This will be determined by

the task being undertaken; for example, asset replacement strategies must be assessed

in the long term while the response to an application for connection from a new

generator may demand changes to the network in the near-term. In defining the

development issues, planners can endeavour to incorporate the engineering design

theories of solution-neutral problem definition and concurrency. This should

encourage an objective assessment of the complete life cycle of the development

under consideration.

Having identified high-level issues, these can be translated into specific evaluation

criteria to facilitate the decision analysis. For example, an issue like power quality

can be translated into specific objectives and requirements for particular power

quality measures. This is completed more fully in the quantification stage.

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6.1.1.2. Identify “DEVELOPMENT OPTIONS”

Options are proposed as a means of addressing the issues faced. In identifying

development options, planners should be cognisant of design theory and consider

alternative designs. Planners should try to introduce new technologies in to the

assessment to help keep abreast of the latest developments and exploit them when

they offer improvements over conventional approaches. In this way, this stage can

enable the consideration of multiple and diverse solutions. This is desirable from the

perspective of ensuring the best solutions are found but also supports decision

making and the recording of rationale by providing alternatives against which

options can be assessed and measured.

The separate identification of development issues and options contrasts with the

solution-oriented conventional approach to the design of network additions and

alterations. The adoption of this general structure for MCDM delivers immediate

benefits through this explicit consideration of issues and options and also supports

the further enhancement of the conventional approach with other methods like the

capture of decision rationale.

6.1.1.3. “QUANTIFICATION” of the Issues and Options

The quantification step involves the acquisition of relevant information, data and

models and selection of appropriate analytical tools to perform the necessary

analysis. The collection of information supports all subsequent stages and is thus

very important. Increasing complexity in network planning and design means a

significant increase in the burden of information processing. This is further

increased by the need to properly manage uncertainty in the information collected.

Various tools and methods can be applied to support planners. These include

methods for the collection, representation and exchange of information either

manually or through computer-based systems, as discussed in Chapter 7. The level

of automation and interactivity will depend on the particular application.

Furthermore, the properly managed exchange of information is crucial in meeting the

challenges of re-organisation and changing responsibilities that have resulted from

changes in the industry’s structure.

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6.1.1.4. “ANALYSIS”

Analysis is performed with the results of the quantification stage, taking into account

the issues and options identified. This stage will employ the appropriate use of

computers, normally with considerable interaction from the user as they direct the

analysis and make an intellectual contribution based on their knowledge and

experience. Greater complexity in distribution network planning means more

analysis will be required. This might include power systems studies with models of

new technologies or financial studies that evaluate the cost benefits of DG.

Analytical tools to identify patterns and trends in the results might include statistical

analysis or comparison with standards or performance targets. With a greater need

for analysis, but increasing pressure on resources, it is crucial that productivity is

enhanced. This requires the provision of an effective toolkit of analytical

components that are available as modules that interface with one another without

imposing too great a burden of translation and conversion upon the planner. Also,

planners must be able to properly exploit the knowledge available within their

organisation. This requires appropriate knowledge management, which includes the

recording of the rationale behind the choices made by planners to provide a tractable

record of their work that provides some insight into the problems and solutions they

have considered. Drawing on the past experience of others in this way will enhance

the productivity of less-experienced planners.

6.1.1.5. “DECISION MAKING”

This stage takes relevant outputs from the analysis stage and processes the results to

help decision-makers reach a decision. It is important that through the identification

of issues and options, the quantification and analysis, planners remain focused on the

need to ultimately reach a decision. Decision making techniques should handle

multiple criteria, which will be drawn from the identification of development issues.

Tools should support the management of uncertainty and deal explicitly with risk. In

reaching decisions, it can be beneficial to draw on the collective knowledge and

experience of the organisation. Thus, knowledge management has a strong role to

play. The effective capture of rationale associated with decisions can support the

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development of knowledge management systems. Design theory classifies decisions

and the risk or innovation associated with them into different levels. Consideration

of decisions from these perspectives might help planners reach appropriate decisions.

Or, if it is decided that additional quantification or analysis is required then further

iterations through the process can be undertaken.

6.1.2. Calculation of Alternative Decision Ratios

In terms of applications, MCDM techniques have been applied to the following

electricity industry decision problems [6.3 – 6.8]:

• Electricity generation expansion planning

• Electricity distribution system planning

• Electric utility resource allocation

• Assessing renewable generation technology options

The above applications of MCDM techniques to electricity industry decision

problems have improved the modelling and representation of the various factors

influencing the decision process. However, these applications of MCDM techniques

are not without criticism. Of particular concern is the inclusion of financial and

technical criteria together within a multiple criteria environment. Research suggests

[6.9] that while there is no doubt that such widely varying criteria are required to

accurately model and represent the decision making process, in many decision

problems the inclusion of financial criteria within a multiple criteria environment

may lead to substandard decision outcomes. This is because options that have a low

desirability and provide little benefit may be rewarded because of their low cost

values, while an option that provides significant benefits and is very desirable will be

penalised because of its high cost values. The inclusion of financial criteria in such

circumstances effectively masks the technical benefits that each option can provide,

with concomitant loss of value to the decision-maker because the most desirable

solution from both a technical and financial point of view cannot be readily

identified.

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Extending this idea, it may be desirable to separate out criteria other than just

financial cost. In particular, environmental criteria could be assessed separately to

produce an environmental score or cost for each option. Ratios could then be

calculated between technical benefit, environmental cost and financial cost. These

different ratios would reveal the relationships between technical, environmental and

financial performance for each option and provide decision makers with additional

insight into the trade-offs to be made. This enhancement of the information and

understanding available to decision makers should lead to better decisions and

therefore helps to address one of the principal shortcomings and represents an

improvement to the conventional approach.

6.2. MCDM Case Study

This section describes the use of the MCDM decision support methodology

described above to an electricity supply planning task.

Given that financial criteria normally conflict with technical criteria (i.e. the need to

minimise overall costs while maximising technical desirability), a conventional

MCDM technique was modified to separate financial and technical desirability

values determined for each option. This ensures that the most economically efficient

option, providing the greatest technical benefit per unit cost, can be identified as well

as the option with the lowest overall cost. Even in situations where the difference in

cost between options is fairly small, the ability to determine the most financially

efficient investment option is a significant benefit that cannot be achieved by

simultaneously assessing all criteria (whether financial or technical), or by

assessment using only cost minimisation procedures. This is in line with the

conventional approach to distribution network planning, which has a number of

development drivers that may not always be consistent and complementary.

However, the use of decision support techniques like this goes some way to

addressing the identified shortcoming in formulating strategies and making

decisions.

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The decision problem considered here concerns a large industrial company that is

upgrading its facilities and requires additional electrical capacity. The maximum

energy demand of the company will be 6MW while the existing utility connection

has a capacity of 4MW. Given the fairly remote location of the company’s

manufacturing site in relation to the nearest grid supply point (GSP), the company

has decided to consider investing in a DG scheme as a possible alternative to

upgrading their existing grid connection. Whichever investment option is selected

must be capable of delivering high quality power and have a high reliability.

Minimising environmental impacts is also an important objective.

6.2.1. Development Issues

The first stage is to identify all relevant development issues that will influence the

decision problem. From there, an appropriate set of evaluation criteria can be

produced. Given that this decision problem is concerned with the evaluation of

options to provide additional electrical capacity at a high power quality and

reliability, the following development issues were identified:

1. Financial cost – a key criterion in any investment decision

2. Reliability – compare the reliability of the various DG schemes with that

achieved by the distribution utility connection

3. Electrical performance – determine how the options affect the power quality or

dynamic performance of the manufacturing facilities

4. Environmental impact – consider the visual, noise or emissions effects likely to

be produced by each option

5. Space available at the industrial site

Using these development issues as a foundation, ten specific evaluation criteria were

identified.

1. Net present cost per kWh (c/kWh) – includes initial capital and installation,

annual operation and maintenance, energy costs, and pollution taxes (calculations

are based on a project economic lifetime of 15 years, although the effect of

varying this is tested with sensitivity analysis)

2. Reliability – number of interruptions per year

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3. Reliability – annual interruption time (hours)

4. Power quality

5. Dynamic performance – impact on voltage and frequency

6. Standalone operation capability (if grid supply is lost)

7. Local environmental impact – emissions

8. Local environmental impact – noise

9. Local environmental impact – visual impact

10. On-site space requirement

6.2.2. Development Options

The next stage is to identify the set of development options to be considered. In this

case study, seven DG schemes will be considered alongside the upgrading of the

existing distribution network connection. It should be noted that while this case

study considers a relatively small number of options for the sake of simplicity, a

larger number of options could be assessed using the same approach. The eight

options considered are:

• Single Gas Turbine

• Multiple Gas Turbines

• Diesel Engine

• Fuel Cell

• Energy Storage

• Wind Power and Storage

• Photovoltaics and Storage

• Grid Reinforcement

The single gas turbine and diesel engine are conventional choices. They provide a

useful benchmark to gauge the performance of the fuel cell, energy storage, and two

renewable technologies. A multiple gas turbine combination is considered to assess

whether there is any benefit in adopting multiple, dispersed generator installations.

The two renewable schemes include an energy storage system to compensate for the

stochastic output of these technologies. The storage system included with the two

schemes is essentially the same as the energy storage option itself. The storage

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system is not defined in any detail and assumes some technology able to deliver the

required amount of power and energy. With current technology, this is likely to

require standby generation coupled with some use of batteries in uninterruptible

power supplies.

6.2.3. Quantification

Once the development options and issues have been identified, it is necessary to

provide some quantification of their impact on one another. It is necessary to

identify the appropriate analytical tools, models and data to quantify and analyse the

issues and options. The primary sources of data for this case study were the

California Energy Commission [6.10] for cost data and DG literature [6.11] for cost

and reliability data.

Cost data was collected to enable the calculation of net present cost for each of the

investment options. To determine the net present cost in cents per kilowatt-hour

(c/kWh) for each option, four cost components were considered:

1. Initial construction costs

2. Annual operation and maintenance (O&M) costs

3. Annual energy costs

4. Annual pollution tax costs

Initial construction costs were specified as a cost per kW installed capacity for

generators and the grid reinforcement, and a cost per MW-hour for energy storage.

The required energy storage capacity was calculated to be 24 MWh. This assumes a

peak power capacity of 2MW – the new load on the industrial site – and a daily load

factor of 0.5. For most of the options it was assumed that they could supply the full

2MW at all times if necessary. For the wind and photovoltaic options, whose output

is not always at full capacity, capacity factors of 0.3 and 0.2 respectively were

assumed. This requires the installation of more capacity to ensure that sufficient

energy is produced to meet the expected demand. Thus, the installed capacity of

wind and photovoltaics was a function of peak load, load factor and capacity factor.

The initial construction cost data is summarised in Table 6.1.

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Table 6.1 – Initial Construction Cost Data

Technology Cost / kW power

capacity ($/kW)

Power capacity

(kW)

Cost / MWh energy

capacity ($/MWh)

Energy capacity (MWh)

Total ($)

Single GT 510 2000 N/a N/a 1020000 Multiple GT 510 2000 N/a N/a 1020000 Diesel Engine 400 2000 N/a N/a 800000 Fuel Cell 3500 2000 N/a N/a 7000000 Energy Storage N/a N/a 100000 24 2400000 Wind & Storage 800 3333 100000 24 5066667 PV & Storage 4500 5000 100000 24 24900000 Grid Reinforcement 750 2000 N/a N/a 1500000

Annual operation and maintenance costs were specified as a cost per kW installed

capacity. Energy costs in c/kWh for the fossil fuel options were dependent on a heat

rate in BTU/kWh and the cost of the fuel. For the renewable options, energy costs

were set at zero. The energy storage option would use grid electricity but its energy

cost increased because its efficiency was assumed to be 89%. Pollution tax costs

were set as a percentage of the energy costs, benefiting renewable technologies. The

ongoing cost data is summarised in Table 6.2.

Table 6.2 – Ongoing Cost Data

Technology Annual O&M Costs

($/kW)

Heat Rate (BTU/kWh)

Fuel Cost ($/MBTU)

Energy Costs

(c/kWh)

Pollution Tax Costs (c/kWh)

Single GT 35 12400 2 2.48 0.248 Multiple GT 35 13400 2 2.68 0.268 Diesel Engine 66 14000 4 5.6 0.56 Fuel Cell 66 9000 2 1.8 0.18 Energy Storage 175 N/a N/a 3.37 0.337 Wind & Storage 202 N/a N/a 0 0 PV & Storage 400 N/a N/a 0 0 Grid Reinforcement 20 N/a N/a 3 0.3

Reliability data was collected to estimate the number of interruptions each year and

the expected annual interruption time. These values were calculated for each of the

options by using values obtained from literature for interruptions per year and

average length of interruptions for different components. For example, the single gas

88

turbine option is calculated from the figures for natural gas supply and the single gas

turbine itself. The reliability data is summarised in Table 6.3.

Table 6.3 – Reliability Data

Reliability Components Interruptions per year Average length of interruption (hours)

Electric grid supply 1.8 1.6 Natural gas supply 0.3 1.6 Diesel supply 0.01 12 Single Gas Turbine 1.7 3 Multiple Gas Turbines 1.4 1.7 Diesel Engine 1.7 3 Fuel Cell 1.7 3 Energy Storage 1.7 3 Wind Turbines 1.5 3 Photovoltaic Arrays 0.5 2

For the purpose of this case study, subjective assessments based on engineering

knowledge and judgement have been used to provide data for the power quality,

dynamic performance, standalone operation, environmental impact, and space

requirement criteria.

• Power quality for each option was assessed in terms of whether it was “Better”,

“Worse” or the “Same” as the existing grid connection.

• The dynamic performance of each option was assigned an ordinal ranking, with

“1” representing the best and higher numbers representing poorer performance.

Some of the options were ranked equally.

• Standalone operation capability of each option was specified as either “Yes” or

“No”.

• The local environmental impact on emissions for each option was quantified

using a symbolic scale from “Zero” (representing the most desirable), through

“Low” and “Medium” to “High”.

• The local noise and visual environmental impacts were assessed using an ordinal

ranking, with “1” representing the most desirable. Some of the options were

ranked equally.

89

• The space requirement criterion was evaluated using four symbolic values, from

“None” at the top of the scale, through “Negligible” and “Some” to

“Significant”.

In a real-life study of DG options, it is likely that a detailed analysis would be

performed for these criteria, rather than adopting subjective measures. For example,

simulation studies might be run to determine the power quality impact of each

option. This would require models of the different technologies and simulations to

determine their performance. Results from these simulations could then be used

within the decision framework.

The use of subjective data and simplification of the quantification and analysis is

considered acceptable in this case study because the aim is to demonstrate the

MCDM method. This reduced the analysis burden for this case study but also

demonstrates the flexibility of the MCDM framework in accepting different types of

criteria.

6.2.4. Analysis

Having collated the data, analysis was performed to produce a net present cost in

c/kWh and the required reliability figures for each of the options. The calculation of

net present cost used a discount rate of 10% and assessed costs over 15 years. For

the purposes of this study, it was assumed that all the options had the same lifetime.

In reality, different options are likely to have different lifetimes requiring a slightly

more complicated analysis. An assessment period of 15 years is typical for

investments of this type. To determine a net present cost in c/kWh, the net present

cost in purely monetary terms must be divided by an energy value. This energy

value is a discounted total equivalent value, which takes account of the discount rate

over the assessment lifetime. This is necessary to accurately reflect the present costs

of future energy use. An alternative approach would have been to calculate the costs

in terms of an equivalent annuity and divide that value by the annual energy

production.

90

As mentioned above, the study assumes a new load of 2MW peak with a daily load

factor of 0.5, resulting in an annual energy requirement of 8760MWh. The other

non-financial criteria were assigned their values through subjective assessment. The

analysis performed for each of the eight options is illustrated in Table 6.4, which

shows the analysis for the Single Gas Turbine. A summary of the results for all eight

options is shown in Table 6.5. The analysis was performed using a standard

spreadsheet tool.

Table 6.4 – Analysis for the Single Gas Turbine Option

Initial Construction Costs Cost per kW installed capacity ($/kW) 510 Installed Capacity (MW) 2 $1,020,000 Annual Operation & Maintenance Costs Cost per MW installed capacity ($/kW) 35.04 Installed Capacity (MW) 2 $70,080 Annual Energy Costs Heat rate (BTU/kWh) 12.4 Natural Gas cost ($/MBTU) 2 Energy cost (c/kWh) 2.48 Annual MWh 8760 $217,248 Annual Pollution Tax Costs Tax (c/kWh) 0.248 Annual MWh 8760 $21,725 Net Present Cost ($) $3,370,680 Discounted total equivalent MWh 66629 Net Present Cost per kWh (c/kWh) 5.06 Non-Financial Criteria Interruptions per year 2 Annual Interruption Time (hrs) 5.58 Power Quality Better Dynamic Performance 3 Standalone Operation Capability Yes Local Environmental Impact - Emissions Medium Local Environmental Impact - Noise 6 Local Environmental Impact - Visual 2 New space required on site Significant

91

Table 6.5 – Summary of Performance Results for all Criteria and all Options

Local Environmental

Impact

Option

Net

Pre

sent

Cos

t (c/

kWh)

Inte

rrup

tions

per

yea

r

Ann

ual I

nter

rupt

ion

Tim

e (h

ours

)

Pow

er Q

ualit

y

Dyn

amic

Per

form

ance

Stan

dalo

ne C

apab

ility

Em

issi

ons

Noi

se

Vis

ual

New

spac

e re

quir

ed o

n si

te

Single GT 5.06 2 5.58 Better 3 Yes Medium 6 2 Significant Multiple GT 5.28 1.7 2.86 Better 3 Yes Medium 4 2 Some Diesel Engine 8.86 1.71 5.22 Better 3 Yes High 5 2 Significant Fuel Cell 13.99 2 5.58 Worse 4 Yes Low 3 2 Significant Energy Storage 11.31 3.5 7.98 Worse 2 No Zero 1 2 Significant Wind Power and Storage 15.29 3.2 9.6 Worse 2 Yes Zero 5 4 Significant Photovoltaics and Storage 60.21 2.2 6.1 Worse 2 Yes Zero 1 3 Negligible Grid Reinforcement 6.01 1.8 2.88 Same 1 No Zero 2 1 None

6.2.5. Decision Making

Once the results describing the performance of each option for each of the evaluation

criteria have been compiled, a MCDM technique can be applied to assess the

desirability of each investment option. For this case study, the MCDM technique

that will be applied is a modified version of the Simple Multi-Attribute Rating

Technique (SMART) [6.1]. Other MCDM techniques are applicable and may be

used depending on the complexity and functionality required. With SMART, the

values for each criterion are converted to an equivalent, normalised value between

zero and one. The value one represents the most desirable value and zero represents

the least desirable value in each criterion. A linear relationship was adopted to scale

values between the most and least desirable values in each criterion. The results of

this normalisation are shown in Table 6.6.

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Table 6.6 – Normalised Results for all Criteria and all Options

Local Environmental

Impact

Option

Net

Pre

sent

Cos

t (c/

kWh)

Inte

rrup

tions

per

yea

r

Ann

ual I

nter

rupt

ion

Tim

e (h

ours

)

Pow

er Q

ualit

y

Dyn

amic

Per

form

ance

Stan

dalo

ne C

apab

ility

Em

issi

ons

Noi

se

Vis

ual

New

spac

e re

quir

ed o

n si

te

Single GT 1.000 0.833 0.596 0.900 0.333 1.000 0.300 0.000 0.667 0.000 Multiple GT 0.996 1.000 1.000 0.900 0.333 1.000 0.300 0.400 0.667 0.300 Diesel Engine 0.931 0.994 0.650 0.900 0.333 1.000 0.000 0.200 0.667 0.000 Fuel Cell 0.838 0.833 0.596 0.100 0.000 1.000 0.600 0.600 0.667 0.000 Energy Storage 0.887 0.000 0.240 0.100 0.667 0.000 1.000 1.000 0.667 0.000 Wind Power and Storage 0.815 0.167 0.000 0.100 0.667 1.000 1.000 0.200 0.000 0.000 Photovoltaics and Storage 0.000 0.722 0.519 0.100 0.667 1.000 1.000 1.000 0.333 0.600 Grid Reinforcement 0.983 0.944 0.997 0.500 1.000 0.000 1.000 0.800 1.000 1.000

6.2.5.1. Criteria Weightings

Almost all MCDM techniques, including SMART, require the weighting of each

evaluation criterion to indicate its importance in the decision analysis. The weight

values are then used with the normalised evaluation data to obtain an overall value

between zero and one indicating the desirability of each of the eight investment

options. The elicitation of criteria weight values is one of the most contentious

issues related to MCDM techniques as the chosen weight values have a significant

impact on the overall results obtained.

As discussed in Chapter 5, the structured decision making process can be used to

capture the rationale behind the various decisions and assumptions. This includes the

rationale behind the selection of criterion weight values. The capture of rationale

behind criteria weight values is an area of ongoing research but can be supported by

the use of specific methods and techniques in the elicitation of those values [6.12].

Further insight into the effect of different weightings can be gained through

sensitivity analysis. This reveals how the different criteria affect the decision

outcome. In this case study, sensitivity analysis was used to examine criteria

weightings, as discussed later.

93

In the modified MCDM approach used here, the net present cost criterion is excluded

from the evaluation of the overall score. Thus, it is not assigned a weighting. The

weight values selected for the nine normalised evaluation criteria are shown in Table

6.7. The sum of these weight values equals one. The weight values reflect the

importance attached to different criteria in the decision analysis. The weights for all

nine criteria are relatively close together, reflecting the view that all are considered

important. However, some distinction was drawn between the criteria and different

weightings assigned, and some rationale behind the values is provided in the

following paragraph.

Table 6.7 – Evaluation Criteria Weight Values

Criterion Weight Interruptions per year 0.120 Annual Interruption Time 0.120 Power Quality 0.140 Dynamic Performance 0.085 Standalone Operation Capability 0.120

Emissions 0.110 Noise 0.110

Local Environmental Impact

Visual 0.110 New space required on site 0.085

Power quality is of primary importance in this case study because the industrial load

being considered was deemed very sensitive to fluctuations in power quality. The

reliability and standalone capability criteria were assigned the next highest ratings

because of the direct effect of these criteria on output from the industrial process.

The local environmental impact criteria were considered the next most important as

they affect the conditions on site. Space requirements on site and dynamic

performance were considered to be of the lowest importance because of their

relatively lower impact on the industrial process.

6.2.5.2. Results of Benefit/Cost Evaluation

Using the performance results outlined in Table 6.5 and the weight values shown in

Table 6.7, an overall desirability score can be calculated for each investment option

94

using the SMART approach. The net present cost has not been included in Table 6.7

because in the modified version of SMART being used it is not combined with the

other criteria at this stage. Espie [6.9] advocated this approach in his use of MCDM

in distribution network planning. The separation of cost from other aspects of the

assessment reinforces the point that it is not necessary for all criteria to be converted

to an equivalent financial value. It also makes explicit the relationship between

alternatives in terms of their costs and benefits, which could be obscured by the use

of a single overall score for each alternative. The net present cost is combined with

the outcome of the MCDM analysis to calculate a benefit / cost ratio of each of the

eight options. This ratio is simply the total benefit score, from the weighted

combination of individual criteria scores, divided by the calculated cost, which in

this case is expressed as c/kWh. The overall desirability scores for each of the eight

options as well as the net present costs and the benefit / cost ratios are shown in

Table 6.8.

Table 6.8 – Results of Decision Analysis

Option Overall Score Net Present Cost (c/kWh)

Benefit / Cost Ratio (Score /

c/kWh) Single GT 0.552 5.06 0.109 Multiple GT 0.690 5.28 0.131 Diesel Engine 0.567 8.86 0.064 Fuel Cell 0.511 13.99 0.037 Energy Storage 0.393 11.31 0.035 Wind Power and Storage 0.343 15.29 0.022 Photovoltaics and Storage 0.647 60.21 0.011 Grid Reinforcement 0.781 6.01 0.130

From the results shown in Table 6.8, it is evident that with the chosen criteria weight

values, the Grid Reinforcement option obtains the highest desirability score and the

Wind Power and Storage option obtains the lowest. The option with the lowest net

present cost is the Single Gas Turbine option while the Photovoltaic and Storage

option yields the highest net present cost. In terms of the benefit obtained for a given

investment, the option with the best benefit / cost ratio is the Multiple Gas Turbine

option while the Photovoltaic and Storage option yields the poorest.

95

The results demonstrate how neither the option with the highest score or the option

with the lowest cost is actually the best in terms of the benefit / cost ratio. This is the

reason why the MCDM approach was modified to use a separate cost and desirability

score for each option. This supports the decision maker in the assessment of the

various options as three investment choices are now available:

• the option with the lowest cost value

• the option with the highest desirability score

• the option with the best benefit / cost ratio

By considering the three investment choices a decision maker has more useful

information than is normally available from the outcome of a MCDM technique

where all criteria, including costs, are directly considered within the decision making

process.

Note that this holistic evaluation of all the options, including grid reinforcement and

distributed resources, assumes a single decision maker seeking the best overall

solution. In reality, regulatory frameworks in the electricity industry often break up

the supply chain to foster competition where possible and regulate the monopolies

that remain. The separation of networks from generation can present problems in

performing integrated assessment of alternatives. This was highlighted in the chapter

on new challenges.

6.2.5.3. Evaluation of Alternative Ratios

An MCDM framework provides flexibility for decision-makers to view the problem

and possible solutions from different perspectives. The calculation of three different

scores or performance measures, as described above, is one example of that

flexibility. An MCDM analysis like that described here might also be used to

examine more closely the environmental costs and benefits of different options. The

environmental impact of planning decisions has risen in profile in recent years such

that in some cases it becomes the primary consideration.

96

Table 6.9 shows overall scores for the eight options excluding both financial costs

and environmental costs and assuming equal weightings for the remaining criteria.

The table also shows a value of overall “environmental cost” for each option. This is

calculated by dividing one by the average of the normalised scores for the three

Local Environmental Impact criteria. The values show that the Grid Reinforcement

option has the lowest environmental cost, as assessed here, and the Diesel Engine

option has the highest environmental cost. The final column in the table shows the

ratio of the score to the environmental cost, i.e the new total benefit score (excluding

environmental criteria) divided by the new environmental “cost”, which is specified

as an inverse score rather than in financial terms. This analysis shows that the Grid

Reinforcement option offers the highest ratio of benefit to local environmental

impact.

Table 6.9 – Results of Environmental Cost Analysis

Option

Score excluding financial cost and

environmental impact

Overall Environmental

Cost

Benefit / Environmental

Cost

Single GT 0.611 3.103 0.197

Multiple GT 0.756 2.195 0.344

Diesel Engine 0.646 3.462 0.187

Fuel Cell 0.422 1.607 0.262

Energy Storage 0.168 1.125 0.149

Wind Power and Storage 0.322 2.500 0.129

Photovoltaics and Storage 0.601 1.286 0.468

Grid Reinforcement 0.740 1.071 0.691

The use of an MCDM technique such as that demonstrated here provides flexibility

to calculate alternative scores and ratios, which can provide an alternative

perspective on decisions. This is useful to decision-makers, and those they report to,

as they seek to explore problems and provide justification for the decisions that they

reach.

97

6.2.6. Sensitivity Analysis

The results obtained from the MCDM analysis are highly dependent on the input data

describing the performance of each investment option in each criterion as well as the

chosen criteria weight values. The sensitivity of the calculated benefit / cost ratio of

the eight options to variations in these data values was assessed. This contributes to

an understanding of how the input data affects the results and, among other benefits,

can help decision-makers arrive at suitable criteria weight values.

The figures below highlight how the benefit / cost ratio of each option varies

depending on changes to some of the input data. In each figure, the variable on the

X-axis was changed in small steps and the MCDM results re-calculated for each step.

98

Figure 6.2 shows the effect of varying equipment costs. This uses an “Equipment

Cost Multiplier”, which is used to scale the cost of initial construction by a factor

from 0.1 to 2. Thus, when the multiplier is equal to 1, the values on the plot match

the results as calculated above.

Figure 6.2 – Impact of Varying Equipment Cost on Benefit / Cost Ratio

99

Figure 6.3 shows the effect of varying the assessment lifetime used in the net present

cost calculations. The value used in the calculations above was 15 years. The results

were re-calculated for values between 1 and 30 years. This has the effect of

changing the relative impact of initial and ongoing costs although it can be seen that

the position of the different options relative to each other does not change very much.

Figure 6.3 – Impact of Assessment Lifetime on Benefit / Cost Ratio

100

Figure 6.4 shows the effect of scaling the cost of gas. This used a multiplier with

values between 0.1 and 2. With a multiplier value of 1 the results are as calculated in

the original results described above. As the multiplier changes, only the cost of gas

is changed in the calculations. Thus, only those options where the cost of gas has an

influence – particularly the GT options – show changes in their overall benefit score /

cost ratio.

Figure 6.4 – Impact of Varying Gas Cost on Benefit / Cost Ratio

101

Figure 6.5 shows the effect of scaling the cost of diesel. As with the gas cost

multiplier, the results for a multiplier value of 1 match those in the original

calculation while changes in the cost of diesel does not affect most of the options. It

is clear that as diesel becomes more expensive, the total cost of that option rises and

the benefit / cost ratio falls. Conversely, if diesel were very cheap then this option

would be very attractive.

Figure 6.5 – Impact of Varying Diesel Cost on Benefit / Cost Ratio

102

It is clear from the plots that different options are influenced differently by changes

to input parameters. Of greatest interest are the instances where the lines for

different options cross, particularly if they are the options with the highest benefit /

cost ratios. For example, Figure 6.4 shows how the gas turbine options drop below

grid reinforcement as the gas cost increases.

The criteria weight values are important factors in determining the outcomes

obtained from the decision making process. To help decision-makers arrive at

suitable weightings and to ensure that the outcomes are robust and justifiable, the

sensitivity of the benefit / cost ratio of each option across the nine criteria was

evaluated. This involved re-calculating all the results for changes in the criteria

weight values. The weight values used in the original calculation were put aside.

For the sensitivity analysis a more mechanistic approach was used. For each

criterion, its weight was varied from 0.1 to 1. The weights of the other eight criteria

were set equal to one another such that the sum of all nine criteria weights was one.

For example, if a weight value of 0.4 is assigned to the criterion in question, then

weight values of (1-0.4)/8 = 0.075 were assigned to all other criteria.

The sensitivities of the benefit / cost ratio to changes in the criteria weights for four

of the criteria are shown in the figures below.

103

Figure 6.6 shows how the results change if the weight assigned to the interruptions

per year criterion is varied between 0.1 and 1. As described above, the weights for

the other criteria also change so that the total of all weights is 1. The cost of each

option remains the same. As the weight assigned to interruptions per year increases,

the options that score highly in this criterion achieve higher overall benefit scores

and higher benefit / cost ratios.

Figure 6.6 – Impact of Varying Interruptions / Year Criterion Weight on Benefit / Cost Ratio

Figure 6.7 shows the effect of changing the weight assigned to the power quality

criterion. As the weight assigned to this criterion is increased, the weights assigned

to all other criteria are reduced and the overall benefit score for each option changes.

The cost of each option remains the same. With a weight value for this criterion of

104

0.1, the weights assigned to all nine criteria are approximately equal and the results

are very close to those of the original decision analysis in Table 6.8, with the grid

reinforcement option coming out top. In the assessment of performance, summarised

in Table 6.5 then quantified in Table 6.6, the grid reinforcement option was scored

less highly for this criterion than the gas turbine and diesel engine options. Thus, as

the weight assigned to this criterion increases, the overall score of these other options

increases. As the weights assigned to the other criteria must fall as a consequence,

the overall score for the grid reinforcement option drops.

Figure 6.7 – Impact of Varying Power Quality Criterion Weight on Benefit / Cost Ratio

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Weight Value for Power Quality Criterion

Single GTMultiple GTDiesel EngineFuel CellEnergy StorageWind Power and StoragePhotovoltaics and StorageGrid Reinforcement

105

Figure 6.8 shows the effect of increasing the weight assigned to the standalone

capability criterion. As in Figure 6.7, with the weights all approximately equal (on

the left hand side of the plot), the grid reinforcement option has the highest benefit /

cost ratio. As the weight for this criterion increases and the weights for all the other

criteria fall, the total benefit of the grid reinforcement option decreases because it

scored poorly in this criterion.

Figure 6.8 – Impact of Varying Standalone Capability Criterion Weight on Benefit / Cost Ratio

106

Figure 6.9 shows the impact of increasing the weight assigned to the visual

environmental impact criterion. The changes in the relative positions of the different

options are not as great as when other criteria weights are increased because for this

criterion the scores assigned to each option were approximately reflective of the

overall total scores calculated with all criteria. The financial cost of each option

remains the same and so when the benefit / cost ratio is calculated the outcomes are

roughly the same whether all criteria are weighted evenly (on the left of the plot) or

only the visual environmental impact criterion is taken into account (on the right of

the plot).

Figure 6.9 – Impact of Varying Visual Environmental Impact Criterion Weight on Benefit /

Cost Ratio

107

It is evident from examination of the figures above that the benefit / cost ratio of an

option can change significantly if the weight value for a criterion is increased. For

example, Figure 6.8 shows how increases in the Standalone Capability criterion

weight causes the benefit / cost ratio for the Grid Reinforcement Option to fall

dramatically, cutting across the lines for other options. This indicates that this

criterion could have a significant influence on the decision outcome. In contrast,

Figure 6.9 shows that the order of options from best to worst changes little as the

Visual Environmental Impact criterion weight changes. This suggests that the

weighting for the Visual Environmental Impact criterion could be reduced without

having a great effect on the decision outcome. This kind of analysis can help

decision-makers arrive at appropriate criteria weight values and provide some

rationale for why some weights are increased and others reduced.


Difficulties in formulating strategies and making decisions was identified as one of

the main shortcomings in electricity distribution network planning. Ultimately,

distribution network planning is all about making decisions. Decision makers rely on

the results of various forms of analysis, such as power system simulation and cost-

benefit studies, but tools and approaches like MCDM can support the decision

making process itself and help address this shortcoming.

Formal MCDM methods support objective analysis and the reaching of justifiable

decisions by making explicit the identification, quantification and analysis of

decision criteria. Thus, the use of MCDM also contributes to the capture and

management of knowledge, another shortcoming identified in the conventional

approach and the primary lesson drawn from the assessment of engineering design

theory. It also assists with the incorporation of new technologies by requiring the

solution-neutral identification of all issues and options.

There are a wide variety of MCDM methods available and planners must chose those

that are most appropriate for particular tasks. However, a general structure for

MCDM-based planning was presented and a case study used to demonstrate its

108

application. A suggested novel variation on established methods is to calculate ratios

of benefit to environmental or other costs, where the benefit or “cost” need not be

expressed in purely financial terms but as a score or some measure that still allows

objective comparison of different options. Such an approach helps decision makers

to assess the options from different perspectives. This type of calculation might also

be of interest to regulators, where they want to make sure that distribution companies

are properly considering the environmental impact of their planning decisions.

The case study demonstrated how MCDM might be used in a power system planning

problem. The analysis of alternative options for expanding the supply to a remote

industrial facility found that grid reinforcement had the highest overall desirability

score. This suggests that despite the excitement over DG and its growth,

conventional grid reinforcement is still an attractive option. Grid reinforcement also

scored highest in the assessment of benefit against environmental costs. However,

the option with the lowest net present cost was found to be the single gas turbine

option and the option with the highest benefit / cost ratio was the multiple gas turbine

option. This shows that DG options are valid alternatives that may be selected

depending on the specification of decision criteria. The fair evaluation of DG is

subject to the regulatory framework, which may forbid network operators from

owning DG and require complex commercial agreements to be reached between

parties, thereby obscuring the opportunities to find and implement the least cost or

best value solution. Such complex commercial agreements are another new

challenge for network operators but will facilitate the exploitation of new resources

like DG and should therefore be fully explored.


6.1. Mollaghasemi, M., and Pet-Edwards, J.: “Making Multiple Objective

Decisions (IEEE Computer Society Technical Briefing)”, 1st Ed., IEEE

Computer Society Press, Los Alamitos, 1997.

6.2. Espie, P., Ault, G.W., and McDonald, J.R.: “Multiple criteria decision making

in distribution utility investment planning”, Proceedings of the International

109

Conference on Electric Utility Deregulation and Restructuring and Power

Technologies 2000, DRPT2000, London, UK, April 4 – 7, 2000, pp. 576 – 581.

6.3. Koritarov, V.S., Conzelmann, G., Veselka, T.D., Buehring, W.A., Cirillo, R.R.,

and Podinovski, V.V.: “Incorporating environmental concerns into electric

system expansion planning using a multi-criteria decision support system”, Int.

J. of Global Energy Issues, Vol. 12, No. 1 – 6, pp. 60 – 67, 1999.

6.4. Xiangjun, S., Xuefeng, M., Qiang, F., and Jianwen, L.: “MOPCA: a multi-

criteria decision model for power system expansion planning – methodology

and case study”, Int. J. of Global Energy Issues, Vol. 12, No. 1 – 6, pp. 68 – 80,

1999.

6.5. Pan, J., and Rahman, S.: “Multiattribute utility analysis with imprecise

information: an enhanced decision support technique for the evaluation of

electric generation expansion strategies”, Electric Power Systems Research,

Vol. 46, No. 2, pp. 101 – 109, 1998.

6.6. El-Hami, M.: “Application of Decision Support Systems to Power Distribution

System Planning”, IEEE PowerTech International: Symposium on Electrical

Engineering, pp. 756 – 760, 1995.

6.7. Wenstop, F.E., and Carlsen, A.J.: “Ranking Hydroelectric Power Projects with

Multicriteria Decision Analysis”, INTERFACES, Vol. 18, No. 4, pp. 36 – 48,

1988.

6.8. Georgopoulou, E., Lasa, D., and Papagiannakis, L.: “A Multicriteria Decision

Aid approach for energy planning problems: The case of renewable energy

option”, European J. of Operational Research, Vol. 103, No. 1, pp. 38 – 54,

1997.

6.9. Espie,P., Ault,G.W., Burt,G.M., McDonald,J.R.; “Multiple criteria decision

making techniques applied to electricity distribution system planning”; IEE

Proceedings Generation Transmission and Distribution, vol.150, no.5,

September 2003, p.527-535

6.10. California Energy Commission, “Distributed Energy Resource Guide:

Economics of Owning and Operating DER technologies”,

www.energy.ca.gov/distgen/index.html, 2002.

110

6.11. Willis, H.L., and Scott., W.G.: “Distributed Power Generation: Planning and

Evaluation”, Marcel Dekker Inc., New York, 2000.

6.12. Watson, S.R., and Buede, D.M.: “Decision synthesis”, Cambridge University

Press, UK, 1987.

111

7. Information Management in Distribution Network Planning

Planning and design, whether for electricity distribution networks or for other

systems, involve the management and processing of information. The manual

conversion of one form of information to another is where the principal intellectual

contributions are made and value is added. This is clear in the model of the

conventional approach to distribution network planning, where all tasks are

concerned with the processing of information.

However, the management of information can impose a huge burden on DNO

analysts and decision makers. Apart from the difficulties inherent in processing

information to create knowledge and make decisions about network development,

this burden includes less intellectually intensive tasks like the collection,

representation and exchange of information. The methods proposed in this Chapter

aim to reduce the burden of information processing and thereby enhance the

productivity of analysts and decision makers, which is necessary if distribution

companies are going to handle the new challenges that they face. Support can be

provided in a number of ways by introducing new approaches or methods and

utilising appropriate technologies.

The collection of information – to identify and quantify development issues and

options – can consume considerable resources. As distribution network planning

becomes more complex, this burden will grow. In particular, the expansion of DG

will require the collection and processing of significantly more information. In the

first part of this Chapter, a novel combination of three methods is presented that

could assist planners in the collection of information. Adoption of these methods

would improve productivity and enhance the ability of DNOs to deal with the

increasing burden of information management.

To keep up with the demands of managing distribution networks that are growing in

complexity, distribution companies will have to better exploit modelling and

simulation, as discussed in previous chapters and demonstrated in Chapter 9. This

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includes the modelling and analysis of new technologies and also the greater

exploitation of resources through improved representation and exchange of power

system models and data. This can be addressed through effective use of information

and communication technology and is discussed in the second part of this Chapter.

In the final part of this Chapter, by way of demonstrating the ideas presented and

further examining the principal challenge in network planning, there is discussion of

information management for DG. A comprehensive set of information requirements

for DG has been defined to produce a structured approach for collecting information.

The methods proposed in this Chapter are illustrated with reference to DG, showing

how their use by DNOs would improve their capabilities and make possible new

approaches to network planning and operation.

7.1. Novel Combination of Methods to Assist in Collecting Information

In the past, the collection of information to support network planning and design has

been ad-hoc and specific to the task in hand. This was acceptable because expert

engineers were involved directly with the processing of information and because the

number of new connections to an existing network was manageable in this way. It

has been argued in previous chapters that the expansion of DG and other challenges

will make the collection of information more burdensome for distribution companies.

To help provide assistance in this area, a novel combination of methods is proposed.

Each of the individual methods is well known and has been applied in different ways

within electricity network planning but the combined approach provides a new

perspective. Each of the three methods (a structured approach, standard formats,

generic device types), and their role in the combined approach, is explained further in

the sections below.

DNOs have not adopted all three methods in combination before because the need

was not pressing and the incentive not clear. As DG has emerged as a bigger and

bigger challenge, DNOs have sought new ways of dealing with it through

information collection [7.1,7.8,7.9]. However, these have been hindered by inertia in

planning methods, rapid advances in DG technologies and difficulties in forming

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consistent approaches across DNOs and across technology types. The time has come

for DNOs to implement new approaches in information management, particularly

with respect to DG. Doing so would speed up the planning process, benefiting both

DNOs and those that seek to connect to their networks.

7.1.1. Structured Approach

A structured approach with standard categories of information reduces the costs of

collecting information by providing consistency and formalism. Outlining the

categories and sub-categories of information allows information collectors to clearly

communicate their requirements to information providers. This is recognised in

many countries as an element of industry rules and regulations. For example, the UK

Distribution Code [7.1] defines the information that must be provided for new

connections to the DNO.

A structured approach for collecting information is necessary for and supports the

application of MCDM and the recording of rationale. These concepts are presented

elsewhere in this document. It is clear that a structured approach to information

management is central to distribution network planning, underpinning many other

methods and techniques.

The final part of this Chapter outlines a structured approach for the collection of

information on DG. This comprehensive set of information requirements provides

further insight into the potential impact of DG on distribution networks and

illustrates how a structured approach can assist in collecting information – see the

structure of information requirements for DG in Table 7.1 in section 7.3.2.

7.1.2. Standard Formats

A structured set of information requirements specifies the types of information

required but does not specify the exact format of that information. This type of

approach, where content is specified but format is not, is common in the electricity

supply sector. For example, the Distribution Code [7.1] takes this approach.

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However, when information is expressed in non-standard formats, there is a need for

translation into the formats required for particular applications.

Without standard formats, the burden of information translation lies with the

information collector, in this case the DNO. If the provision of information had to

follow a pre-determined, standard format then the burden of translation would lie

with the information providers. This information could then be transferred directly

into the analysis and management systems used by DNOs. Standards have already

been established for the representation and exchange of data associated with energy

management systems. The Common Information Model (CIM) defines the format of

data and uses the eXtensible Markup Language (XML) as a vehicle for data

exchange [7.2]. These principles are being extended to data associated with network

planning and operation [7.3].

It seems inevitable that software vendors – if not the utilities and manufacturers –

will gradually converge on standard formats. An important aspect of most modern

data formats is their extensibility and interoperability so the traditional problems

associated with lock-in to standard formats is mostly overcome.

7.1.3. Generic Data

Where information is being collected on large numbers of small installations – e.g.

future domestic CHP systems – the concept of generic device types or generic

category data may be useful.

For DG or other network development drivers, generic types could be established,

with particular properties and fully specified information in all the required

categories of information. The description of the generic type might cover a range of

acceptable values, which are certified by DNOs as being acceptable. If specific

devices or projects fell within range for all information categories then they could

immediately be labelled as being a certified generic type. In many cases, that would

provide the DNO with all the information they need to conduct analysis, specify

connection conditions and manage their network. Only if there were something

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particularly unusual about a specific device or network development would

additional information need to be collected.

Applying the same principal in a more flexible way, generic data could be specified

separately in each of the information requirements categories or sub-categories. In

providing information for a specific device, generic data could be selected in some

categories and specific data supplied for other categories. For example, for a new

DG installation there may be generic power and energy capabilities or generic types

of electrical connection. It may be possible to fully specify a project in terms of

generic data in each of the categories. Only if the project were particularly unusual

would non-generic information need to be collected.

The use of generic data and a structured approach to information requirements is

demonstrated in Table 7.1 in section 7.3.2. The fourth column of the table gives

examples of the data categories that might be used to describe a DG installation in

terms that are specific enough to be useful to planners but general enough to not be

too intrusive to the DG owners.

7.2. Representation and Exchange of Power System Models and Data

As discussed in previous chapters, the new challenges faced by DNOs will

necessitate an expansion of modelling, simulation and analysis of networks and their

components. To successfully meet this increased burden, the capabilities of DNOs

must be improved and models and data must be exploited more effectively. This will

require more frequent transfers of models and data between software tools and

between organisations. In addition, the making available of data publicly is being

encouraged, or required, by regulators and this will require the use of flexible and

translatable data formats. Thus, a significant aspect of power system simulation and

planning that must be addressed is the representation and exchange of models and

data.

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7.2.1. Models and Data in Power Systems

Discussion of models and data can engender considerable confusion as the terms can

have multiple meanings. This is especially true in the field of power systems

engineering. Thus, to help establish meanings and to facilitate further discussion, it

is worthwhile considering the dictionary definitions.

• A model is: “a schematic description of a system, theory, or phenomenon that

accounts for its known or inferred properties and may be used for further study

of its characteristics” [7.4]; or “a description of observed behaviour, simplified

by ignoring certain details. Models allow complex systems to be understood and

their behaviour predicted within the scope of the model, but may give incorrect

descriptions and predictions for situations outside the realm of their intended use.

A model may be used as the basis for simulation.” [7.5]

• Data is: “1. Factual information, especially information organized for analysis or

used to reason or make decisions. 2. Numerical or other information represented

in a form suitable for processing by computer. 3. Values derived from scientific

experiments.” [7.4].

Within the power systems domain, a multitude of models are used, from models of

the electromechanical behaviour of generating sets to models of the degradation of

cable insulation. However, some of these models have become so widely used that

they are accepted as standard so that assumptions may be made or the model may be

implied by the data provided. For example, the pi-equivalent circuit of a

transmission line with the three-phase system represented by symmetrical

components translated to per-unit values, and with the negative and zero sequences

neglected, is a standard model used in power system simulation. When presented

with values of resistance, reactance and admittance, a power engineer may

confidently assume that the standard model of a transmission line is being used. The

same power engineer might consider the term “model” to refer to a model of a

complete distribution network (made up of multiple models of buses, branches,

generators, loads, etc.) or a model of a specific item of plant, such the generator,

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exciter and governor models associated with a generating set. Thus, care must be

taken when discussing models and data in the power systems domain.

The nature and complexity of a power system model depends on the simulation and

analysis being performed. Models may have to be expanded to meet the demands of

a particular study; for example, assumptions implicit in the model may be invalid or

additional dynamic terms may have to be introduced. Alternatively, it may be

sensible to simplify a given model because the analysis being performed does not

warrant the additional complexity. The software environment in which models are to

be used will also influence the complexity required. This in turn determines the data

associated with a model.

Data by itself is meaningless, typically being a set of numbers or symbolic

parameters. As discussed above, when presented with data a certain model may

sometimes be assumed. But generally, data must be associated with a model of the

system or phenomenon being represented. In most cases, a model will have data

associated with it where the data influences the behaviour of the model and any

results produced from simulation.

7.2.2. Horizontal and Vertical Exchange of Power System Models and Data

The exchange of models and data can be considered as being “horizontal” or

“vertical”. The exchange of models and data between organisations is likely to be

horizontal in the sense that the models and data will be used for the same purpose but

perhaps in different software environments. For example, different organisations

may use different network analysis packages to perform load flow calculations. This

type of exchange may also be necessary within a single organisation. Models and

data may also be passed vertically between applications. This is most likely to be

within a single organisation. An example would be the exchange of topology data

between a load flow program and a reliability assessment tool.

Horizontal exchange of models and data will require some translation in the

representation or format but all the data should be available. In contrast, vertical

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exchange of models and data will normally require some additions or reductions to

the actual content. For example, the exchange from steady state analysis to dynamic

simulation will require the addition of dynamics data. This variation in content and

representation is illustrated in Figure 7.1.

Figure 7.1 – Varying content and representation in power system models and data

Load flow program 1

Load flow program 2

Dynamic simulation program

Electromagnetic transients program

Asset management

database

Reliability assessment tool

Varying Representation

Var

ying

Con

tent

Load flow program 1

Load flow program 2

Dynamic simulation program

Electromagnetic transients program

Asset management

database

Reliability assessment tool

Varying Representation

Var

ying

Con

tent

7.2.3. Representation and Exchange of Power System Data

For data to be put to any use, methods of representation and exchange must be used.

Most simply, this might be a list of numbers written on a piece of paper. However,

interpretation of data normally requires it to be represented in some sort of structure

or format, often associated with a particular model of the system or component under

consideration. Computers provide a range of options for improving the

representation of data and its exchange between users and between formats.

Over the years, a number of formats for power systems data in simulation software

have been proposed and adopted to varying degrees. These include common formats

agreed on by industry groups or standards bodies, and vendor formats developed by

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the makers of simulation software. These formats are based on models of power

systems that are bus/branch oriented, which is suitable for basic network planning

functions. Software vendors normally provide conversion tools to convert from the

most popular formats to their own proprietary formats, thereby facilitating data

exchange. The scope of these formats is necessarily limited and they are not

designed for extension.

A more recent, and ongoing, development is the Common Information Model (CIM),

which has been developed by the Electric Power Research Institute (EPRI) in

collaboration with others [7.2]. CIM has been developed to represent power system

modelling information for control centres and energy management systems, and

through data exchange facilitates the interoperation of electric utility software in

these application areas. CIM is node/breaker oriented, as is required for its target

applications, rather than bus/branch oriented, as in traditional planning and analysis

models.

XML is now being used as a means of holding and transferring data in the CIM

format [7.2]. XML provides a flexible and well-supported means of data exchange.

It offers the advantage of being able to hold meta-knowledge and semantics about the

data within a document. It can serve as a generic form of data representation, which

can then be translated to whatever format is required by particular software packages.

7.2.4. Representation and Exchange of Power System Models

The representation and exchange of models is much more complex than for data.

Models can be composed of various entities or modules, each described by their

behaviour and the relationships with one another and the outside world. The means

of representation and exchange will depend very much on the application.

For example, in presenting the common pi-equivalent transmission line model to a

human user, it would normally be represented by a textual description, diagrams and

various equations. Within power system simulation software, the same model is

represented in computer code that is difficult for most people to understand.

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Alternatively, a system block diagram with transfer functions expressed in the S-

domain might be used to represent a generator excitation system. Humans might

readily understand this same representation, albeit with some background

knowledge, and it might serve as an input to software through a graphical model-

description interface.

In some cases it is desirable to use the same model in multiple simulation

environments. However, the generic representation of models is difficult and

previous efforts in model translation between formats have not been very successful.

This has prompted the development of a framework that retains models in their

native formats but allows the linking of multiple simulation tools, thereby gaining the

benefits of integration without having to transfer models between formats and tools

[7.6]. XML and intelligent agents are used to facilitate the inter-operation of the

multiple simulation environments. XML documents define the system structure in

terms of the models, the links between them, and the software in which each of them

run. The information held on each model includes commentary on modelling

assumptions. Information on initialisation and set-up of models is also required.

7.3. Information Management for Distributed Generation

Technical developments in distribution systems, particularly the connection of DG,

and changes in the commercial and regulatory environment pose a number of new

challenges for DNOs [7.7]. DNOs will need to perform new types of analysis to plan

and design their network, develop management and control strategies, and implement

appropriate commercial arrangements to ensure a reliable and high-quality electricity

supply is maintained. But these new studies will require appropriate information to

be collected. This might be information on the electrical network itself, new DG

installations or new technologies associated with network developments.

The task of collecting and processing information on DG could represent a

significant new burden for DNOs. There are now a wide variety of DG technologies

available, and at first sight, it seems that each requires slightly different information

for characterisation. For example, the UK Engineering Recommendation G83 for the

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connection of small generators to low voltage networks has separate annexes for

each generation technology [7.8]. Furthermore, in low voltage networks, where DG

installations may be small but numerous, the cost of collecting information on every

installation may outweigh the benefits.

Earlier in this Chapter, it was proposed that the burden of collecting information

could be reduced through the use of a number of methods. These include a

structured approach, the use of standard formats, and the identification of generic

device types or categories. These methods will help DNOs to collect all the

information they need to evaluate DG in their networks. Most DNOs have not yet

started collecting in-depth information on DG like this because the numbers have

been small enough for conventional approaches to be maintained. As the new

challenges become greater, DNOs will benefit from work such as that described here,

which will assist them in managing the large amounts of information resulting from

DG and other changes to the network.

Section 7.3.1 discusses the need for information on DG. A structured approach for

collecting information on DG is outlined in section 7.3.2. Section 7.3.3 presents

some examples of the types of analysis relating to DG that DNOs must perform, and

how information on DG would be used to improve planning and operation.

7.3.1. The Need for Information on DG

There have always been some generators connected at distribution level but the

expansion of DG poses new challenges for DNOs. When a new generator applies for

connection, analysis must be conducted to determine the impact on network planning

and design, management and operation, and commercial arrangements. This is

existing practice, but the expansion of DG will mean DNOs having to deal with

many more connection requests and adapting their methods appropriately. This will

be true especially when connecting small DG units at low voltage becomes

commonplace. So while DNOs have not needed to collect so much information on

DG before, they can expect to have to collect much more in future.

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7.3.1.1. Information for Planning and Design

Previously in most DNOs, DG would be considered in the planning and design

process only in response to a new DG connection application. With DG becoming

widespread, the ongoing planning and design of networks must consider it explicitly

in a more forward-looking manner. The treatment of DG within network

development must become proactive rather than reactive. Thus, information must be

collected on DG to gain an understanding of the different types being connected.

To ensure effective simulation and analysis of DG, both prior to connection and on

an ongoing basis, there is a need to characterise DG. Information must be collected

on all aspects relevant to DNO activities. The expansion of DG is due in part to the

emergence of new generation technologies. Devices like micro gas turbines and

interfaces utilising power electronics are not part of the standard libraries used by

DNOs for modelling their network. Thus, there is a need to characterise DG to

facilitate modelling and simulation.

7.3.1.2. Information for Management and Operation

In the past, the lower voltage levels of distribution networks have often been

installed on the basis of “fit-and-forget”. Network management has been passive,

with the network delivering power in one direction from substations fed by high-

voltage transmission systems to consumer loads. The introduction of distributed

energy resources, perhaps even at low voltage, will require more active management

of networks [7.9].

The degree of active management necessary must be determined. In some cases,

probably with very small generators, DG may be installed and left to operate

autonomously, being treated just like most loads have in the past. While in other

cases, probably with larger generators, extensive communication and control systems

may have to be implemented to provide integrated management of the network and

generator. Operating constraints for DG may have to be specified and then

implemented. There may be some trade-off to be evaluated between operating

constraints and reinforcing the network [7.10].

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Ongoing management of the network and its assets requires knowledge of what is

connected and the influence it might have. DG could be particularly influential,

affecting maintenance programmes and asset replacement decisions. The

incorporation of DG within network management and operation will require analysis

methods to be revised; and this will require new information to be collected.

7.3.1.3. Information for Commercial Arrangements

DNOs must also consider commercial issues. A charging structure for DG must be

determined and applied to each new installation. There has been much discussion of

“deep” and “shallow” charging for the connection of DG to a network [7.11]. It may

also be necessary to establish pricing structures for the provision of network support

services from DG installations; e.g. voltage and frequency support. The charging

systems adopted by a DNO will have to be agreed with the appropriate regulator.

Within the bounds of any agreed system, there will probably be scope for DNOs to

negotiate with DG to tailor charging structures to suit particular circumstances.

These negotiations will have to be supported by analysis of a type that DNOs may

not have performed in the past. This analysis will require DNOs to gain an

understanding of the commercial issues associated with DG and be able to collect

and process appropriate information.

7.3.2. A Structured Approach for Collecting Information on DG

Although different DG technologies have different operating principles and different

associated data, it is possible to identify a basic set of information requirements that

facilitate a structured approach to information collection. A comprehensive set of

structured information requirements is proposed and discussed below. The

information requirements are summarised in the two left-most columns of Table 7.1.

Each component is included because it will support analysis that DNOs may have to

perform. Column 3 of Table 7.1 contains an example of specific device information

for a micro gas turbine.

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Table 7.1 illustrates two of the three methods proposed at the start of this Chapter as

being of use in information management. The first two columns define the

structured approach, specifying all the information to be collected. The fourth

column gives examples of generic data, with examples of the categories that might

be used for each element of the information structure. The third method, standard

formats, requires more formality in the way information is expressed. This could be

achieved in a number of ways including on-line or electronic forms or prescribed

data formats.

Other parties have undertaken the task of outlining basic information requirements

for DG for different purposes. For example, in the UK, the Distribution Code [7.1]

defines the information that must be provided for new connections to the DNO

including new DG. In the USA, EPRI and the Electricity Innovation Institute (E2I)

are developing object models that define the information exchange necessary for

monitoring and controlling DG [7.12].

Table 7.1 – Information Requirements for Characterisation of Distributed Generation and

Examples of Specific Device Information and Generic Category Data

Category Sub-Category Example of Specific Device Information

Example of Generic Category Data

1.1. Technology Type Micro Gas Turbine. Generic Micro Gas Turbine 1.2. Network Location Connected to 11kV bus

serving Customer X. N/A

1. General Description

1.3. Ownership and Responsibility

Owned and operated by Customer X facilities management.

N/A

2.1. Normal Operating Range

0 – 28 kW (maximum reduces with higher ambient temperatures – refer to data sheets)

Maximum between 20 and 40 kW

2.2. Overload Capabilities None Assume none 2.3. Response Rates 10 kW/second up and down Between 5 and 20 kW/sec. 2.4. Start-up and Shutdown Times

1 minute for start-up. 1 minute for shutdown.

Less than 3 minutes for start-up or shutdown

2. Power and Energy Capabilities

2.5. Power and Energy Over Time

Continuous operation possible subject to gas supply but operated in heat-led load-following mode (typical profiles available).

Flexible depending on application

3.1. Cost of Fuel Variable but currently 3c/kWh

Less than 5c/kWh for input fuel

3.2. Fuel Efficiency or Losses

25% ± 2 electrical efficiency 70% ± 5 total efficiency

Between 20% and 40% electrical efficiency

3.3. Operational Degradation Costs

Unknown Less than 2c/kWh

3. Variable Costs

3.4. Start-up and Shutdown Costs

Negligible Less than $5 to start-up or shutdown

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3.5. Environmental Costs Emissions certificates costs of 1.2c/kWh

Less than 3c/kWh

4.1. Installation Costs $25,000 $20,000 - $40,000 4.2. Ongoing Fixed Costs $500 p.a. Less than $2000 p.a.

4. Fixed Costs

4.3. Technology Maturity Commercially available Commercially available 5.1. Generator or Interface Parameters

Standard PWM switched, three-phase inverter and standard three-phase 400/llkV transformer

Standard inverter and transformer

5.2. Transformer Parameters Rating = 30kVA Impedance = 10%

Rating 20 to 50 kVA Impedance less than 20%

5.3. Operating Limits 360 – 528 VAC, 10 – 60 Hz and THD<20% at inverter terminals

390 – 410 VAC, 45 – 55 Hz and THD<15%

5.4. Start-up and Shutdown Behaviour

Soft-start capability produces smooth current increase on starting and decrease on stopping

No significant effect on network

5.5. Power Quality Effects Negligible THD<15%, PSD<1, PLT<1

5. Electrical Connection

5.6. Short Circuit Current Up to 65A Between 50A and 100A 6.1. Protection Types Built-in protection:

under/over voltage and frequency, loss of mains detection

According to standard engineering recommendation.

6. Protection

6.2. Means of Isolation Inverter blocking plus isolator switch on grid side of transformer

Full isolation must be possible

7.1. Pre-programmed Control Strategies

Fully programmable through computer interface. Set to follow supermarket heat load.

Must have constant power factor and voltage control options

7.2. Control Changes Implemented immediately through computer control interface.

Manually within 24 hours of notice being received

7. Control

7.3. Island Operation Not possible in current configuration.

Not required

8.1. Interface and Protocol USB connection to PC running proprietary software

Direct link to PC

8.2. Inputs Heat and power schedules, voltage control options

Heat, power and reactive power settings at least

8. Communications

8.3. Outputs All system parameters for monitoring and control

Heat, power and reactive power values at least

In some cases of information collection, it may be appropriate to just make reference

to existing sources of information, such as manufacturer data sheets. Some of the

information may be provided from monitoring or simulation so reference could be

made to the appropriate results. However, for some generators, the information

simply will not be available. In addition, generators may not wish to reveal

information relating to cost or to innovative designs or operating methods that they

regard as valuable commercial or intellectual property. The sharing of information

between generators and the DNO must be acceptable to all parties and governed by

appropriate confidentiality agreements.

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7.3.2.1. General Description

A general description of the technology should be provided, covering technology

type, network location, and ownership and responsibility. This provides DNOs with

the basic information to identify a DG installation.

Technology Type

A brief description of the technology should be given in terms of its energy source

and means of energy conversion, e.g. “a natural gas fired micro turbine with high-

speed asynchronous generator and AC-DC-AC grid interface”.

Network Location

The location of the unit within the DNO network should be noted. This will identify

the unit uniquely and determine the electrical environment in which it operates.

Ownership and Responsibility

Information about the owners and those responsible for operating and maintaining it

should be noted alongside the more technical information.

7.3.2.2. Power and Energy Capabilities

The power and energy capabilities should describe the basic operational performance

of the unit under consideration. This information is required by DNOs to conduct

power flow and other studies of network performance.

The performance of a generation or storage system may be limited by the energy

source, the means of energy conversion, or the grid interface. For example, the ramp

rate of a CHP unit might be limited by its boiler dynamics but the limit on total

power output might depend on its generator or on the power electronics in the grid

interface. The exact source of any limits should be noted.

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Normal Operating Range

The range of operation in normal, continuous operation should be described in terms

of limits on real and reactive power. This may be described by limits, equations or

with an operating chart, e.g. as shown in Figure 7.2.

Figure 7.2 – Simple example of a possible operating chart for a distributed generator

Power (kW)

Reactive power(kVAr)

0 5-5

10

5

Power (kW)

Reactive power(kVAr)

0 5-5

10

5

Overload Capabilities

The ability to operate outside of the normal range for short periods, i.e. overload

capabilities, should be noted. This may be described by extensions to the limits, new

equations or an extended operating chart and associated time limits.

Response Rates

The rate of change possible in increasing and decreasing both real and reactive power

should be noted. This may be described as rates, W/s or VA/s, or in whatever form

is necessary to describe the capabilities. Response rates may also vary depending on

operating conditions, e.g. as shown in Figure 7.3.

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Figure 7.3 – Simple example of a possible response rate chart for a distributed generator

0 10 12

1

P(kW)

dP/dt(kW/s)

-1

2 6.50 10 12

1

P(kW)

dP/dt(kW/s)

-1

2 6.5

Start-up and Shutdown Times

There may be minimum, and perhaps maximum, limits on the time required for start-

up and shutdown of a unit. There may be links to the response rates category.

Information should be provided on the specific time limitations of start-up or

shutdown in whatever form is appropriate to the technology.

Power and Energy Resource Availability

The amount of power and energy available over time should be described. This will

depend very much on the technology so should be described in whatever form is

most suitable. However, the description should include information on the source of

energy and its availability over time, or the probability of its availability. In simplest

terms, this could be represented by a single capacity factor. Preferably, the despatch-

ability or stochastic nature of the unit should be noted. For example, power output

from a CHP unit might depend on heat loads. The power from PV panels will

depend on expected patterns of solar irradiation. Energy storage units will have

finite capacity, further complicated by constraints on charge and discharge.

7.3.2.3. Variable Costs

Variable costs are those costs that depend on how a unit is operated and the amount

of electrical energy delivered. In the fullest detail, they could be specified in terms

of fuel costs, fuel efficiency, equipment degradation, and start-up and shutdown

costs. It may also be useful to specify environmental costs separately so they can be

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evaluated explicitly. Information on variable costs would be useful to DNOs in

optimising operational strategies.

However, owners and operators of DG may wish to keep their operating costs secret.

The information disclosed to the DNO will depend on the market and network

management structure. For example, the distributed generator may present the DNO

with a simple cost curve like that shown in Figure 7.4. A simple single-cost

approach is most straightforward but it may be necessary to specify separate “bid”

and “offer” prices within a market dispatch structure.

Figure 7.4 – Simple example of a possible daily cost curve for a distributed generator

00:00 06:00 10:00 16:00 20:00 00:00

Cost per energy unit($/kWh)

0.100

0.075

0.050

Time of day (hh:mm)00:0000:00 06:0006:00 10:0010:00 16:0016:00 20:0020:00 00:0000:00

Cost per energy unit($/kWh)

0.100

0.075

0.050

Time of day (hh:mm)

Cost of Fuel

The cost of fuel may vary over time depending on the contract for supply. The cost

of fuel may internalise external costs such as pollution taxes and this should be

noted. The exact format of the description will depend on the technology. Some

technologies, such as photovoltaics, may not have a cost of fuel.

Fuel Efficiency or Losses

To determine operating cost, the cost of fuel must be combined with fuel efficiency

or losses. Fuel efficiency and losses may depend on the operating point and dynamic

operation of the unit. For example, efficiency may be different for operation at

different capacities, and efficiency may vary if the unit is ramping up or down at

different rates. This may be described by values, equations or additional information

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on the operating chart. For some technologies, this characteristic will not be

applicable.

Operational Degradation Costs

The degradation of equipment, and thus the operating cost, may depend on its

operation. For example, equipment may degrade more quickly when operated close

to its capacity. Operating for short periods outside of the normal limits may be

possible but may degrade the equipment, imposing additional costs. These

operational degradation costs should be described in whatever form is most suited to

the technology under consideration, but preferably in a manner similar to the cost of

fuel.

Start-up and Shutdown Costs

Start-up and shutdown of a unit may incur specific costs. It may be possible to

describe these costs under fuel efficiency or operational degradation costs. Where

this is not possible, information should be provided on the specific costs incurred as a

result of start-up or shutdown in whatever form is appropriate to the technology.

Environmental Costs

It may be possible to internalise environmental costs within the cost of fuel.

However, it may be valuable to keep environmental costs separate. Sensitivity to

environmental effects may depend on conditions and the time of day. For example,

the “cost” of emissions from fossil-fuel-fired generation may be greater at times

when emissions from transport are already high.

7.3.2.4. Fixed Costs

Fixed costs are those costs that do not depend on how a unit is operated and are of

less relevance to the DNO. In fact, owners and operators of DG will probably wish

to keep this cost information secret. However, if available, this information is

valuable for analysis such as long-term planning.

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Installation Costs

Ideally, installation costs should be described in terms of cost/kW rating and

cost/kWh capacity. In addition, the costs of additional components and systems

should be noted, e.g. communication and control systems. Different technologies

will be composed of different components so the description will depend on the

technology.

Ongoing Fixed Costs

Ongoing fixed costs not related to operation should be noted. For example, some

maintenance and replacement of parts may be necessary whether the unit produces

power or not. Some fixed costs, such as property taxes and staff costs, are

independent of the technology but vary with location.

Technology Maturity

Related to the cost of installation is the maturity of the technology. In simplest

terms, it should be noted whether the technology is widely available commercially or

whether it is a test or demonstration project. This information helps DNOs stay

abreast of technology trends.

7.3.2.5. Electrical Connection

Information should be provided on the electrical connection. This will strongly

influence the operational capabilities of the generator or storage device, and thus is

of great interest to DNOs. In particular, information on the electrical connection is

required for accurate modelling and simulation.

Generator or Interface Parameters

The type of electrical generator and/or grid interface should be noted. If this is of a

standard type that can be represented by a standard model then parameter values

should be provided. If it is not of a standard type then some description should be

provided to inform work on the creation of models. This should include details of

the voltage level and whether it is a single or three-phase connection.

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Transformer Parameters

If a transformer is used to connect to the grid then details necessary for modelling

should be provided. This should include information on in-rush currents if these will

affect the performance of the unit.

Operating Limits

The operating limits for the electrical connection should be described. This should

include information on maximum and minimum grid voltages, maximum and

minimum grid frequency, and total harmonic distortion that the technology can

tolerate. If any of these conditions vary with operating point, e.g. different on start-

up or shutdown, then details should be provided.

Power Quality Effects

When running normally, the technology may have an effect on power quality on the

grid. For example, it may inject harmonic currents. Alternatively, it may be able to

mitigate against some undesirable power quality phenomenon. Any behaviour that

may affect grid power quality, for better or worse, should be noted.

Start-up and Shutdown Behaviour

The requirements and behaviour of the technology on start-up and shutdown should

be described. The primary concern is the effect on the grid and power quality, e.g.

waveform distortion. In particular, any behaviour that may cause further damage to

the grid when it is already in a vulnerable state should be noted.

Short Circuit Current

The ability of the device to supply short circuit fault current should be described.

This may be in the form of sub-transient and transient reactances, and time constants

for decay of any DC-offset components of fault current. Fault levels have been

identified as the primary network issue of concern for DG in urban areas in the UK

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and Engineering Recommendation G74 [7.13] describes the procedure to be used to

calculate short-circuit currents and the data required from generators.

7.3.2.6. Protection

Information should be provided on the types of protection associated with the unit

and what makes them operate. Protection may be intrinsically linked to the

technology type or it may be standard protection that is used with all DG resources.

Often, the protection required will be specified by the DNO, subject to the

appropriate standards and regulations. In addition, the detailed design of protection

must be coordinated with the DNO network protection.

Protection Types

For each type of protection installed, some information should be provided on the

means of measurement, relay operation and the limits of operation.

Means of Isolation

The means of isolating the unit from the grid should be described. In particular, the

use of conventional circuit breakers or solid-state switches should be noted.

Reference should be made to the conditions that trigger isolation, the time it takes for

isolation to be achieved, and any grid-visible effects that can arise.

7.3.2.7. Control

Information should be provided on the control of the unit. This information will be

required if a DNO implements active management of DG resources.

Pre-programmed Control Strategies

An outline of the pre-programmed control strategies available should be provided.

These might include “maximise energy output”, “maintain bus voltage”, “fixed

power factor”, or some combination. If no pre-programmed control strategies are

available then this should be noted.

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Control Changes

Any restrictions on the changing of control strategies should be noted. For example,

must the unit be shutdown before a new control strategy can be implemented? Can

new control strategies be implemented remotely or must changes be made at the

unit? Response times should also be noted. Some reference to Communications, see

below, may be necessary.

Island Operation

Where possible, the conditions necessary for the unit to continue operating as part of

a power island, independently from the grid supply, should be described. This may

refer to the operating limits described above, to some other aspects of the grid

conditions, or to some aspects of the unit itself. In the future, and subject to legal

and regulatory approval, DNOs may be interested in operating power islands because

of the potential improvements in overall supply reliability and availability.

7.3.2.8. Communications

Details of the communications interfaces should be provided. This may include

reference to the control information. If DNOs are going to exert any active control

over DG, then communication links will be required.

Interface and Protocol

Details of the communications interface and protocol should be provided, e.g. DNP3,

RS232, Ethernet, modem. This should include details of existing capabilities for

remote control and monitoring.

Inputs

Details should be provided of the input parameters to the generator or storage unit.

This will determine what information must be provided to the unit to control

operation.

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Outputs

Details should be provided of the parameters that can be monitored or extracted from

the unit, including the sampling rates. This will determine whether additional

measurements must be taken to satisfy all monitoring requirements.

7.3.3. Examples of Distributed Generation Analysis

DNOs require information on DG to perform the analysis necessary to manage their

networks. The following examples illustrate how the information provided might be

used by DNOs. These examples show how the collection of comprehensive

information on DG in an effective way, using methods such as those outlined above,

will facilitate the analysis and network planning and operation that will be necessary

to accommodate DG on existing and future distribution networks. This includes

scenario analysis to evaluate different possible outcomes, as discussed in the next

Chapter. Without collecting suitable information – as is current practice in many

DNOs – the necessary analysis will not be possible and network planning and

operation will be held back.

7.3.3.1. Modelling of New Technologies

The expansion of DG is driven in part by the development of new generation

technologies. A DNO may have to develop new models to perform computer

simulation and determine the effect of new technologies on their network. For basic

power flow studies, new models may be developed using information provided on

power and energy capabilities for a DG installation.

For example, the expected production profile of a CHP unit might be used to study

the variation of network voltage profiles. If provided in the correct format, this

information might be inserted directly into generic models that the DNO has

available. However, if information is provided in another format then the DNO will

have to translate it before it can be used.

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Models may also be required to simulate the DG response to network disturbances.

Information provided on the electrical connection and protection systems would help

the DNO establish suitable models.

7.3.3.2. Designing Active Management Systems

Over time, it is expected that DNOs will have to shift from passive to more active

management of their networks to accommodate DG. This will require an

understanding of the DG resources, which the information requirements described

above should provide. In particular, the design of active management systems will

require information on the communications and control systems associated with a

DG installation.

For example, a new generator may have a number of control set points that determine

its operation. These set points may be controllable remotely using a modem

connection. If the DNO is to exert control over this unit, they will have to establish a

modem connection and understand the effects of the different control set points.

Software might be developed to control the DG unit and integrate it with the DNO

network management systems. In addition, the nature of the underlying operating

costs of DG units might be required to assess the most economic way to manage the

DG resources.

7.3.3.3. Contracting for Voltage Support Services

Some DG has the potential to provide ancillary services to the network, such as

voltage support. However, the provision of such services may be subject to

commercial arrangements. A DNO might use the information provided on operating

capabilities and variable costs to design a suitable contract. Given the importance of

maintaining network voltages, the engineering solution using DG will have to be

closely examined. Equally, the contract agreed between the DNO and DG will have

to cover all eventualities and provide the correct incentives to maintain network

performance.

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The growth of DG and other developments in distribution network planning impose

new challenges on DNOs, including new burdens in analysis and the collection of

information. Three methods were suggested to support information collection: a

structured approach to information requirements; the use of standard formats for

representing that information; and the use of generic device types or device

information categories. DNOs do not currently use these methods in combination

but doing so would improve their information management capabilities and put them

in a better position to tackle the new challenges they face, particularly the expansion

of DG.

The representation and exchange of power system models and data was discussed.

The potential confusion associated with the terms “model” and “data” was noted and

the concept of horizontal and vertical exchange was presented. Further discussion

highlighted how the representation and exchange of models and data are being

improved through the development of new formats and the exploitation of

information technology. DNOs will have to utilise new technologies in their

information processing and analysis as well as on the networks themselves.

A comprehensive set of information requirements was presented for characterising

DG. This covered a general description, power and energy capabilities, variable and

fixed costs, the electrical connection, protection, control and communications.

Collecting this information would allow DNOs to perform the analysis necessary to

manage the growth of DG in their low voltage networks, including scenario analysis

or other ways of dealing with risk and uncertainty as discussed in the next Chapter.

However, it is accepted that some information may not be available or generators

may not wish to provide it, particularly when it comes to costs or innovative designs.

It was noted that in the requirements described above, the exact format of the

information is left to the provider. This leaves the collector of the information with

the task of translating information to the format required. The approach would be

even more structured, and the burden of translation would be shifted from DNO to

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DG, if all information had to conform to an agreed pre-defined format and structure,

as outlined in this Chapter. Reaching agreement on this issue may require the

intervention of government or industry regulators.

The concepts and approaches described in this section have been applied in the

development of management systems for DG in LV networks. The concept of pre-

defined data formats and structures was explored further in the development of

markup languages for power system planning and associated platforms for web

services, including on-line submission and automated checking of DG-related data

[7.3]. These applications demonstrate the importance and value of formal methods

for the characterisation of DG.


7.1. Distribution Code Review Panel, “The Distribution Code of Licensed

Distribution Network Operators of Great Britain”, Issue 2, March 2003

7.2. McMorran,A.W., Ault,G.W., Elders,I.M., Foote,C.E.T., Burt,G.M.,

McDonald,J.R., “Translating CIM XML Power System Data to a Proprietary

Format for System Simulation”, IEEE Transactions on Power Systems, vol.19,

issue 1, February 2004, p.229-235

7.3. McMorran,A.W., Ault,G.W., Foote,C.E.T., Burt,G.M., McDonald,J.R., “Web

Services Platform For Power System Development Planning”, Proceedings of

the 38th International Universities Power Engineering Conference,

Thessaloniki, Greece, September 2003

7.4. The American Heritage® Dictionary of the English Language, Fourth Edition,

Copyright © 2000 by Houghton Mifflin Company, through dictionary.com

7.5. The Free On-line Dictionary of Computing, through dict.org

7.6. McArthur,S.D.J., Davidson,E.M., Dudgeon,G.J.W., McDonald,J.R.; “Toward a

model integration methodology for advanced applications in power

engineering” IEEE Transactions on Power Systems, Volume 18, Issue 3,

August 2003, pages 1205-1206

7.7. Jenkins,N., Allan,R., Crossley,P., Kirschen,D., Strbac,G., Embedded

Generation, 2000, The Institution of Electrical Engineers, ISBN 0 85296 774 8

139

7.8. Energy Networks Association, “Engineering Recommendation G83: The

connection of small-scale embedded generators (up to 16 a per phase) in

parallel with public low voltage distribution networks”, UK, 2002.

7.9. First Annual Report of the Distributed Generation Coordinating Group

(2001/2002), March 2003, Department of trade and Industry, Ofgem

7.10. Foote,C.E.T., Ault,G.W., Burt,G.M., McDonald,J.R., “Enhancing flexibility

and transparency in the connection of dispersed generation”, Proceedings of

CIRED 2001, Amsterdam, June 2001

7.11. Watson,J., “The Regulation of UK Distribution Networks: Pathways to

Reform”, Proceedings of the Second International Symposium on Distributed

Generation, Stockholm, Sweden, 2-4 October 2002

7.12. Electricity Innovation Institute, “Open Communication Architecture for

Distributed Energy Resources in Advanced Distribution Automation

(DER/ADA)”, November 2003, Project description available from

http://www.e2i.org/e2i/ceids/technical/DER.html

7.13. Energy Networks Association (originally produced by the Electricity

Association), “Engineering Recommendation G74: Procedure to Meet the

Requirements on IEC 909 for the Calculation of Short-Circuit Currents in

Three-Phase AC Power Systems”, UK, 1992

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8. Scenario Analysis

Distribution network planners have always faced uncertainty, for example in load

forecasting. However, trends like industry restructuring and DG, as outlined in the

Chapter on drivers and new directions, are increasing the factors over which DNOs

cannot exert control and increasing the level of uncertainty and risk.

A number of approaches are widely used by power system planning engineers to deal

with uncertainty and risk. The most common approaches used are scenario analysis,

decision trees and sensitivity analysis. These provide planners with a means to

quantitatively assess an uncertain situation and explore the boundaries of what might

happen. This is essential to fully understand the full range of issues and options in a

decision problem. In addition, the use of more sophisticated risk analysis techniques

have been proposed for power network planning [8.1, 8.2]. Where uncertainty

proves difficult to characterise and there is the prospect of radical change at some

unknown point in the future, it is important to ensure that systems and approaches are

flexible and adaptable to change.

This Chapter discusses scenario analysis and some of the other methods available to

manage uncertainty and risk in distribution network planning. It is proposed that

flexibility is the only way to effectively deal with uncertainty. A novel scenario

development approach is demonstrated with an assessment of the expected growth in

DG in low voltage grids. This spotlights the primary challenge currently facing

planners and shows how gathered information can be used to generate scenarios to

support further analysis. The scenarios developed here are useful in providing base-

line forecasts of DG in low voltage grids. This provides DNO planners and other

researchers with something they can work with to explore the impact of DG and start

to devise the flexible solutions that will be robust to a range of uncertain futures.

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8.1. Methods for Managing Uncertainty and Risk

This section reviews some of the methods available for managing uncertainty and

risk in electricity distribution network planning, and proposes that flexibility is the

most important feature of planning tools and processes.

8.1.1. Scenario Analysis

Scenario analysis allows planners to forecast a range of possible outcomes and then

quantitatively assess the boundaries of their decision problems. With reference to the

decision analysis structure described in Chapter 6, it can be presented as involving

four main steps [8.3]. The first step is to select the network development options or

strategies to be evaluated. Following this step a number of scenarios must be

constructed by considering each of the development issues and assigning plausible

values to uncertain parameters. For each combination of scenario and option,

technical analysis must be performed to evaluate the outcome in terms of a score or

other overall value. The most suitable option will then be selected according to a

given decision criterion. Commonly used decision criteria include the following

[8.3, 8.4]:

• With the Expected-Score (or Expected-Cost) criterion, a probability value is

assigned to each scenario and the weighted average of the total scores of a

particular option under the different scenarios yields an Expected Score for each

option. The option with the maximum Expected Score is then selected for

implementation.

• The Laplace criterion is similar to the Expected Score criterion except that all

scenarios are assumed to have an equal probability of occurrence.

• The Minimise-Maximum-Regret criterion represents a risk analysis approach in

contrast to the probabilistic approach of the Expected-Score criterion. An ideal

option is identified in each scenario and the regret for each of the other options

calculated – regret is the difference between the option score and the score of the

ideal option. The option with the lowest regret value over all scenarios is

selected as the most desirable choice for implementation.

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• In the Von-Neumann-Morgenstern criterion, it is assumed that one of the

extreme scenarios will occur, either the most pessimist or optimistic viewpoint.

The option that performs best under this scenario is selected for implementation.

• The Hurwicz criterion is a variation of this that allows the explicit consideration

of the decision-maker’s attitude to risk using a parameter �, which is given a

value between 0 and 1. If � = 0 or 1 the results correspond with the pessimistic

and optimistic scenarios respectively considered under the Von-Neumann-

Morgenstern criterion.

Although each of these decision criteria has its own merits and collectively can allow

uncertainty to be dealt within in a number of innovative ways the key disadvantage

with such approaches is related to the number of scenarios. In order to readily apply

scenario analysis techniques an appropriate number of scenarios must be considered.

However, if the number of scenarios being considered becomes too large, which can

very easily be the case when multiple sources of uncertainty are included and all

combinations of events considered, the application of such approaches becomes

highly impractical because of the large analysis burden and difficulty in interpreting

results.

8.1.2. Decision Trees

Decision trees are also commonly used to deal with planning uncertainty [8.3, 8.4].

With this technique, an event tree describing each possible course of action is

developed with each future represented by a separate branch of the tree. By

developing a path through the tree to an end-level future or course of action for each

uncertainty, the effects of time-related decisions can be assessed. The structure of

the decision tree provides a useful graphical display of each event and the path of a

particular decision is useful in providing insight into the problem itself. While the

main advantage of the decision tree technique is the graphical display and associated

visual representation, as with scenario approaches, including a large number of

events and all possible courses of action for these events may quickly lead to a

decision tree with an impractical number of end-level nodes.

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8.1.3. Sensitivity Analysis

Sensitivity analysis is also commonly used to assess the sensitivity of outcomes to

changes in parameters. In power system planning for example, it is unlikely that the

exact value of a particular variable (e.g. expected load at a specific time of day) will

be known with absolute certainty. Statistical distributions can however be used to

quantify the expected bounds in simulation results [8.5] and when incorporated

within a sensitivity analysis will bring increased mathematical rigour to what can

otherwise be a fairly ad hoc assessment. The key issue with sensitivity analysis is

ensuring that a sufficient number of parameter changes are performed and also that

they adequately represent the various types of uncertainties and possible events. As

with scenario analysis and decision trees, assessing the impact of uncertainty

associated with multiple sources each with a wide range of disparate values or events

may lead to an excessive number of parameter changes being considered within the

sensitivity analysis. Sensitivity analysis was used in the MCDM example in Chapter

6.

8.1.4. Probabilistic Choice Versus Risk Analysis

In managing uncertainty and risk, there is a choice to be made between the

probabilistic choice and risk analysis paradigms [8.1]. For power system planning,

the risk analysis approach is preferable because it focuses on decisions rather than

the possible solutions, as is the case with probabilistic choice. Probabilistic choice

relies on the law of large numbers and the specification of probabilities. A drawback

associated with the three approaches described above is that events or outcomes that

are considered to have a low probability of occurrence are often disregarded or not

included in the assessment, regardless of the consequences of the event actually

occurring. Many power system planning decisions are not frequent enough to rely

on estimated probabilities, and the possibility of catastrophic situations disrupts the

stochastic process. Nevertheless, power system planning still relies on forecasts of

the future and an assessment of probabilities in areas like load forecasting or failure

rates for low-value but numerous assets, such as those at low voltage.

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8.1.5. Flexibility as a Means of Dealing with Uncertainty

Considering the new challenges facing planners, as outlined in Chapter 3, and the

review of methods for managing uncertainty described above, it is clear that in the

management of uncertainty and risk, the only certainty is that there will be

uncertainty. When faced with an unpredictable future, the best way to reduce risk is

to maintain flexibility. Thus, the electricity sector in general must be made more

flexible, adaptable and responsive so that they are able to handle any unforeseen or

sudden changes. This applies both to the network extensions that are designed and

built, and to the tools and methodologies used by planners. It was already noted in

Chapter 4 that the complexity and diversity of distribution network planning and the

range of new challenges to be faced mean that highly prescriptive and detailed

methodologies are inappropriate because they are inflexible

Flexibility in tools and methodologies could be achieved in a number of ways. For

example, processes and methods should not be based on very narrow assumptions

because they will ultimately have to be modified to accommodate changed

circumstances. Software selected for activities such as power system simulation

should have scope for extending the type of analysis performed and incorporating

models of new technologies. Perhaps most importantly, people must be trained to be

able to assess changing circumstances and adapt their own behaviour as unforeseen

events unfold. However, care must be taken to ensure that flexibility is not used as

an excuse to avoid investing in tools and systems that improve efficiency.

These initial proposals are limited in their application but the provision of flexibility

in planning tools and methods is probably an area deserving of further research.

8.2. Distributed Generation Penetration Scenarios

The first level one task in the knowledge model of the conventional approach is

“Identify Requirements”. Managing the expansion of DG requires an understanding

of the expected levels of DG and relative capacities of different DG technologies. To

this end, and as part of the DISPOWER project [8.6], an assessment of the likely

145

penetration of DG in different low voltage (LV) grids was conducted. This allows

the specification of realistic scenarios to enable further analysis and help planners to

assess the problems to be addressed in their networks.

8.2.1. Methodology for Penetration Assessment

This assessment employed an original methodology in which the future penetration

of DG was analysed from two perspectives, termed “top-down” and “bottom-up”.

• The top-down approach involved the collation of forecasts made at European,

national or regional levels by various governments and other institutions. The

top-down approach provides a high-level perspective of the total expected levels

of DG.

• The bottom-up approach involved a survey with a questionnaire in which

respondents were asked to rank different DG technologies in terms of their

suitability to different LV grid scenarios. The bottom-up approach identifies

which DG technologies are most likely to be installed in different LV grid

circumstances.

The assignment of DG to LV grid scenarios involves combining the results of the

top-down and bottom-up approaches, while also taking into account the objectives of

any analysis being performed. Three different strategies for performing this

assignment are proposed.

This methodology contrasts with earlier approaches [8.7] firstly by interpreting high-

level national forecasts for DG or renewable power in terms of the likely impact on

LV grids, secondly by producing quantitative technology rankings based on a new

survey of expert opinion, and thirdly by offering three strategies for combining the

two perspectives to produce scenarios of interest. This combined approach provides

results that are robust from the perspective of high-level forecasts of DG penetration

and from the perspective of how suitable each technology is to a particular LV grid.

The new methodology is described in more detail and demonstrated in the sections

below.

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8.2.2. Top-Down Approach

A number of governments and other organisations have produced forecasts of the

uptake of DG and related technologies. Many forecasts have been produced for

renewable energy technologies but these include large-scale technologies that will

not be installed at LV and often exclude small-scale non-renewable technologies that

will form a part of the LV DG mix. However, no specialised forecasts were

identified covering the specific topic of DG penetration in LV networks. The data

from forecasts had to be processed to take account of these issues and a number of

assumptions had to be made.

EU directive 2001/77/EC commits countries in the EU to setting targets for

renewable energy in 2010 [8.8]. Meeting these targets will require an expansion of

both large-scale installations and renewable DG, including DG in LV grids. Thus,

the targets – shown in Table 8.1 – provide some indication of how DG might grow in

EU member countries (the EU 15 pre 2004).

Table 8.1 Electricity from renewable sources in 1997 and targets for 2010 for EU countries

Electricity from renewable sources in

1997 (TWh)

Electricity from renewable sources in

1997 (% of total)

Target for electricity from renewable

sources for 2010 (% of total)

Belgium 0.86 1.1 6 Denmark 3.21 8.7 29 Germany 24.91 4.5 12.5 Greece 3.94 8.6 20.1 Spain 37.15 19.9 29.4 France 66 15 21 Ireland 0.84 3.6 13.2 Italy 46.46 16 25 Luxembourg 0.14 2.1 5.7 Netherlands 3.45 3.5 9 Austria 39.05 70 78.1 Portugal 14.3 38.5 39 Finland 19.03 24.7 31.5 Sweden 72.03 49.1 60 United Kingdom 7.04 1.7 10 Total 338.41 13.9 22

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More detailed forecast data was obtained for Italy, Poland, Germany and the UK.

The processed data is presented below and the results drawn together in a summary.

8.2.2.1. Italy

It is estimated that by 2010 Italy [8.9] will have a total installed capacity of

generation of between 95 and 110GW. Table 8.2 shows the estimated installed

capacities of various DG technologies in 2010 in MW and as a percentage of

102.5GW – the middle of the range of estimated total capacity.

Table 8.2 – DG Penetration Forecast for Italy

DG Technology Estimated capacities in 2010 (MW)

Percentage of estimated total installed capacity

Thermal plants (<25MW) 5500 – 9000 5.37 – 8.78 Small Hydro (<10MW) 2500 – 2700 2.44 – 2.63 Wind 2500 – 3500 2.44 – 3.41 Photovoltaic 100 – 200 0.10 – 0.20 Biomass and Waste 1500 – 1800 1.46 – 1.76 Total DG 12100 – 17200 11.80 – 16.78

8.2.2.2. Poland

In Poland [8.10], the contribution of distributed sources (unconventional and

renewable) to total electric energy production is to be increased to 7.5% by 2010.

The total from distributed sources is expected to be 14082GWh, suggesting a total

electrical energy production from all sources of 187760GWh. If an average

production factor of 45% is assumed, then the total system capacity in Poland in

2010 can be estimated to be approximately 48GW.

Table 8.3 shows the expected energy contributions from different DG technologies.

By assuming production factors for each technology, the expected installed capacity

can be estimated and given as a percentage of total system installed capacity of

48GW.

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Table 8.3 – DG Penetration Forecast for Poland

DG Technology Expected energy

production in 2010 (GWh)

Assumed production

factor

Estimated capacity in 2010 (MW)

Percentage of estimated total

installed capacity

Small hydroelectric power plants

800 0.42 217 0.45

Wind power plants

1200 0.18 761 1.59

Photovoltaic systems

2 0.11 2 0.004

Biogas power 2480 0.32 885 1.84 Biomass fired heat and power

9600 0.32 3425 7.14

Total DG 14082 - 5290 11.02

8.2.2.3. Germany

In Germany [8.11], renewable sources are to provide 12.5% of electrical energy by

2010. The expected energy production from renewable sources in 2010 is 77TWh.

This includes energy from onshore wind, offshore wind, hydro, biomass,

photovoltaic, geothermal and other renewable sources. Many of these installations

can be expected to be DG (connected to either medium voltage or LV networks) but

some will be connected to the high voltage transmission network and should be

removed from the DG calculation. The total for renewable sources can be adjusted

by removing all the offshore wind energy and 50% of the onshore wind energy. This

leaves 55TWh as a rough approximation of the energy from renewable DG in

Germany in 2010. If a production factor of 0.26 is assumed for renewable energy

technologies, this corresponds to an installed capacity of renewable DG of

approximately 24GW. Lacking a forecast for non-renewable DG, this is assumed to

make a negligible contribution to the total amount of DG.

In Germany, the total energy production in 2001 was 477.5TWh and total

consumption was around 500TWh, the difference being made up with imports. In

2001, the total installed capacity of conventional generation greater than 1MW was

approximately 112GW. The 2001 capacity of DG less than 1MW was negligible.

The capacity of conventional generation is expected to decrease as DG becomes

more widespread but overall energy demand is expected to increase at around 0.6%

149

per annum [8.12, 8.13]. If it is assumed that this increase in demand is translated

into an increase in total capacity, whether it is conventional or DG, then the total

installed capacity of all generation sources in Germany in 2010 can be estimated to

be approximately 118GW.

Thus, DG capacity in Germany in 2010 is expected to be approximately 20% of total

installed capacity.

8.2.2.4. United Kingdom

The total installed capacity of electrical generation in the UK [8.14] in 2010 is

expected to be around 74GW. Table 8.4 shows estimates of the installed capacities

of different DG technologies in MW and as a percentage of total installed capacity.

The figure for wind power represents 50% of the total expected onshore wind power;

offshore wind was ignored for this DG penetration study, as was CHP above

50MWe.

Table 8.4 – DG Penetration Forecast for the UK

DG Technology Estimated capacities in 2010 (MW)

Percentage of estimated total installed capacity

Wind (adjusted) 1500 – 4000 2.03 – 5.41 Biomass including energy crops 300 – 1000 0.41 – 1.35 Landfill gas 1100 – 1100 1.49 – 1.49 Waste incineration 500 – 500 0.68 – 0.68 Domestic (Micro CHP + PV) 0 – 2400 0.00 – 3.24 Mini CHP (5-500kWe) 200 – 300 0.27 – 0.41 Small CHP (500kWe-5MWe) 500 – 800 0.68 – 1.08 Medium CHP (5-50MWe) 2000 – 2700 2.70 – 3.65 Total DG 6100 – 12800 8.24 – 17.30

8.2.2.5. Summary of Results

The DG penetration forecasts for Italy, Poland, Germany and the UK are summarised

in Table 8.5. The results were calculated as percentages of the total installed

capacity of electrical generation. By assuming a plant margin (peak load / installed

capacity) of 85% and an average load factor (average demand / peak demand) of

65%, the DG penetration levels can be expressed as percentages of peak load and

average load.

150

Table 8.5 – Summary of DG penetration forecasts

Forecast 2010 DG capacity as percentage of total

capacity

2010 DG capacity as percentage of peak

load

2010 DG capacity as percentage of average

load Italy 11.80 – 16.78 13.88 – 19.74 21.36 – 30.37 Poland 11.02 12.96 19.95 Germany 20 23.5 36.2 UK 8.24 – 17.30 9.69 – 20.35 14.91 – 31.31 Minimum 8.24 9.69 14.91 Average 14.14 16.63 25.58 Maximum 20.00 23.50 36.20

These forecasts for 2010 represent a significant increase in DG but they should be

compared with existing DG penetration levels in Denmark and the Netherlands. In

Denmark, DG already represents more then 30% of the total installed capacity of

generation. In the Netherlands, about 25% of the total installed generation capacity

can be considered as DG [8.15].

Thus, it is predicted that by 2010, DG (defined in general terms as being generation

connected to the sub-transmission or distribution networks) capacity will represent

between 8% and 20% of total installed generating capacity in Europe. This capacity

will represent between 10% and 23% of maximum demand and between 15% and

36% of average demand.

It is likely that much of this DG will consist of medium-sized generators and will be

installed at voltage levels above LV. It is difficult to predict what fraction of DG

will be connected at LV – defined in EN50160 as being voltages below 1kV [8.16].

However, rough figures for Germany and Denmark suggest that LV-connected DG

might represent between 3% and 30% of total DG [8.15].

Table 8.6 summarises the results of the analysis of penetration forecasts. Taking the

full range of values into account, it is forecast that LV connected DG could amount

to between 0.45% and 10.86% of average load. The mid-range prediction is for the

capacity of LV Connected DG to be approximately 4.22% of average load.

151

Table 8.6 – Penetration forecasts for LV connected DG

2010 LV connected DG capacity as

percentage of total capacity

2010 LV connected DG capacity as

percentage of peak load

2010 LV connected DG capacity as percentage of average load

3% of DG connected at LV

Min.: 0.25 Ave.: 0.42 Max.: 0.60

Min.: 0.29 Ave.: 0.50 Max.: 0.71

Min.: 0.45 Ave.: 0.77 Max.: 1.09

16.5% of DG connected at LV

Min.: 1.36 Ave.: 2.33 Max.: 3.30

Min.: 1.60 Ave.: 2.74 Max.: 3.88

Min.: 2.46 Ave.: 4.22 Max.: 5.97

30% of DG connected at LV

Min.: 2.47 Ave.: 4.24 Max.: 6.00

Min.: 2.91 Ave.: 4.99 Max.: 7.05

Min.: 4.47 Ave.: 7.67

Max.: 10.86

8.2.3. Bottom-Up Approach

The bottom-up approach to assessing DG penetration in LV grids was based on a

survey of the opinions of experts working in the DG domain. A questionnaire was

distributed with a description of 15 LV grid scenarios – as described in Table 8.7.

For each scenario, respondents were asked to rank the DG technologies from a list

provided. Rankings were requested for both 2010 and 2020.

The list of DG technologies was as follows:

• Reciprocating engines

• Micro gas turbines

• Fuel cells

• Photovoltaics

• Wind turbines

• Micro hydro

• Biogas engines

• Other DG (Please specify)

• No distributed generation

8.2.3.1. LV Grid Scenarios

The fifteen LV grid scenarios were specified firstly in terms of structure, operation,

area type and network type. Three additional criteria were identified as being

152

particularly relevant to the assessment of DG penetration: types of loads,

meteorological climate, and the social and political environment. However, it was

assumed in all scenarios that the social and political environment was positive and

supportive towards DG. A less supportive social and political environment would

simply reduce the overall level of DG and not differentiate between different

technologies.

The scenarios were specified to provide a range of types of loads and the

meteorological climates found in Europe. The 15 LV grid scenarios are described in

Table 8.7.

Table 8.7 – Summary of LV Grid Scenarios

Scenario 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Label

Res

iden

tial R

ing,

Ger

man

y

Com

mer

cial

Mes

h, G

erm

any

Mix

ed R

adia

l, G

erm

any

Urb

an M

eshe

d, U

K

Rur

al, P

olan

d

Urb

an L

ink,

Pol

and

Urb

an R

adia

l, Fr

ance

Rur

al, I

taly

Urb

an R

adia

l, Ita

ly

Urb

an L

ink,

Gre

ece

Rur

al S

outh

Coa

st, S

pain

Rur

al L

ink,

Net

herl

ands

Rur

al R

ing,

Bel

gium

Urb

an R

ing,

Den

mar

k

Rur

al R

ing,

Aus

tria

Structure Radial X X X X X X Link X X X Ring X X X X Meshable X X

Radial X X X X X X X X X X X X X Operation

Meshed X X Rural X X X X X X

Area Type Urban X X X X X X X X X Overhead X X X X X X

Network Type Underground X X X X X X X X X Residential X X X X X X X X X X X Retail X X X X X X X Farms X X X X X Offices X X X X X X X

Load Type

Industrial (Small) X X X

Solar irradiation

1100

1100

1000

950

1050

1050

1200

1500

1500

1500

1750

950

950

950

1100

Meteorological Climate

Average wind speed 5 5 5 6 6 5 3 4 3 3 6 7 6 6 4

153

8.2.3.2. DG Penetration Survey Results

For each of the LV grid scenarios, respondents to the questionnaire ranked the DG

technologies in terms of which are most likely to be installed in the scenario

conditions by 2010 and by 2020. A ranking of “1” indicated the technology is most

likely to be installed in the associated grid conditions. Rankings of “2”, “3”, “4”, etc.

indicated decreasing likelihood of technologies being installed. The rankings from

different respondents were combined to calculate average rankings or scores for 2010

and 2020 for each scenario. These scores, which give an indication of which

technologies are considered most suited to each grid scenario and are most likely to

be installed, are shown in the results in Appendix A.

8.2.3.3. Summary of Questionnaire Results

Table 8.8 and Table 8.9 summarise the top three ranked technologies in each of the

LV grid scenarios for 2010 and 2020. It is apparent that photovoltaics are the

dominant technology in 2010 with reciprocating engines and wind turbines also

appearing frequently. In 2020, photovoltaics retain their dominance and

reciprocating engines and wind turbines feature strongly. However, new

technologies like micro gas turbines, biogas engines and fuel cells start to feature

more prominently.

154


Scenario Highest ranking Second highest ranking

Third highest ranking

Residential Ring, Germany Photovoltaics Reciprocating engines

Stirling engines

Commercial Mesh, Germany Photovoltaics Reciprocating engines

Fuel cells

Mixed Radial, Germany Photovoltaics Reciprocating engines

No distributed generation

Urban Meshed, UK Reciprocating engines

Photovoltaics No distributed generation

Rural, Poland Wind turbines Photovoltaics Micro hydro Urban Link, Poland Reciprocating

engines Photovoltaics Micro gas turbines

Urban Radial, France Photovoltaics Reciprocating engines


Rural, Italy Photovoltaics No distributed generation

Reciprocating engines

Urban Radial, Italy Photovoltaics Reciprocating engines


Urban Link, Greece Photovoltaics Reciprocating engines


Rural South Coast, Spain Photovoltaics Wind turbines Biogas engines Rural Link, Netherlands Wind turbines Photovoltaics Biogas engines

Rural Ring, Belgium Wind turbines Photovoltaics Biogas engines Urban Ring, Denmark Reciprocating

engines Micro gas turbines Photovoltaics

Rural Ring, Austria Photovoltaics Micro hydro Biogas engines

155


Scenario Highest ranking Second highest ranking

Third highest ranking

Residential Ring, Germany Photovoltaics Reciprocating engines

Fuel cells

Commercial Mesh, Germany Photovoltaics Reciprocating engines

Fuel cells

Mixed Radial, Germany Photovoltaics Micro gas turbines Fuel cells Urban Meshed, UK Photovoltaics Micro gas turbines Reciprocating

engines Rural, Poland Wind turbines Photovoltaics Micro hydro

Urban Link, Poland Photovoltaics Micro gas turbines CHP / Stirling engines

Urban Radial, France Photovoltaics Micro gas turbines Fuel cells Rural, Italy Photovoltaics No distributed

generation Reciprocating

engines Urban Radial, Italy Photovoltaics Micro gas turbines Fuel cells Urban Link, Greece Photovoltaics Micro gas turbines Fuel cells

Rural South Coast, Spain Photovoltaics Wind turbines Biogas engines Rural Link, Netherlands Wind turbines Photovoltaics Biogas engines

Rural Ring, Belgium Wind turbines Photovoltaics Biogas engines Urban Ring, Denmark Micro gas turbines Photovoltaics Reciprocating

engines Rural Ring, Austria Photovoltaics Micro hydro Biogas engines

8.2.4. Combining Top-down and Bottom-up Results

The top-down and bottom-up approaches provided two different perspectives on the

penetration of DG in LV grids. To propose specific DG penetrations in each of the

LV grid scenarios, the two approaches must be combined.

The top-down forecasts were calculated from system-wide, national perspectives and

suggest overall levels of DG that are quite low: between 0.45% and 10.86% of

average load. In reality, some LV grids will contain much more DG than others. In

fact it is recognised that in some instances, the amount of DG in an LV grid may

even exceed 100% of average load. Research and development in this area is

primarily concerned with grids where there is a high penetration of DG. Therefore,

while the top-down analysis provides a guide to expected overall DG levels, the

definition of scenarios for further analysis requires much larger values of DG

penetration.

156

The results of the questionnaire indicate which DG technologies are most likely to be

installed in each of the LV grid scenarios. The translation of these rankings into

actual DG penetration levels requires some decisions to be made about the overall

level of DG and the sharing of that total between technologies.

The overall level of DG in scenarios used for analysis will be influenced by the

results of the top-down approach but will be modified to produce scenarios with

higher penetrations of DG. The sharing of total DG between technologies might

follow one of three strategies, as outlined below. Any of the strategies outlined

below could be used to produce useful scenarios. The choice of strategy for

combining the top-down and bottom-up results will depend on the precise

requirements of the analysis being performed.

8.2.4.1. Strategy One – Dominant Technology

It might be assumed that in a given LV grid, one DG technology will be dominant.

This may be because the local conditions favour one type of DG significantly or DG

installation may be a result of specific initiatives. For example, the developer of a

new housing scheme may decide to install roof-integrated PV panels. This would

result in a high penetration of PV and probably exclude other DG. Thus, scenarios

may be specified with only the DG technology ranked highest by the questionnaire

results.

For example, for scenario 15 (Rural Ring, Austria) in 2010 scenarios could be

defined with only photovoltaic DG connected at LV. If it were assumed that this LV

grid contained levels of DG equivalent to the upper range of average expected levels

then DG capacity would be 11% of average load. If average load were 150kW, then

this LV grid scenario would have 16.5kW of photovoltaic DG.

8.2.4.2. Strategy Two – Prioritised but Arbitrary Assignment

The total DG level in an LV grid could be divided between technologies by assigning

larger shares to the technologies ranked more highly. The lower ranked technologies

could be neglected. The total amount of DG could be split between the top three

157

ranked technologies only. The split could be based on an arbitrary division, with the

top-ranked technology taking half, the second ranked technology taking a third, and

the third ranked technology taking the remaining sixth. This arbitrary division

between technologies would produce useable scenarios.

For example, applying this approach to scenario 8 (Rural, Poland) in 2010 would

produce LV-connected DG in the following proportion: 50% Wind turbines; 33%

Photovoltaics; and 17% Micro hydro. If this grid hosted above average DG levels

equivalent to 50% of average load, and if the average load was 100kW, then the

assignment of DG to this scenario would be 25kW Wind turbines; 16.5kW

Photovoltaics; and 8.5kW Micro hydro.

8.2.4.3. Strategy Three – Function of Score Assignment

The total level of DG in an LV grid could be divided between the top three ranked

technologies based on a mathematical function of the scores produced by the

questionnaire results. The scores are the result of combining rankings from different

respondents and provide some measure of how likely each DG technology is to be

installed. The scores could be used in a mathematical formula to weight the share

taken by each DG technology.

For example, the process described in Table 8.10 for scenario 1 (Residential Ring,

Germany) in 2010 might be followed.

Table 8.10 – Possible strategy for defining DG penetrations in Scenario 1

DG Technology

Score Reciprocal Fraction of DG Total Percentage of DG Total

Photovoltaics 1.3 1 / 1.3 = 0.77 0.77 / (0.77+0.43+0.20) 55% Reciprocating engines

2.3 1 / 2.3 = 0.43 0.43 / (0.77+0.43+0.20) 31%

Stirling engines

5.0 1 / 5.0 = 0.20 0.20 / (0.77+0.43+0.20) 14%

If a very high penetration of DG were assumed with capacity equal to 100% of an

assumed 200kW average load in this LV grid, then the analysis scenario would

158

contain 110kW of photovoltaics, 62kW of reciprocating engines and 28kW of

Stirling engines.


One of the greatest challenges for distribution network planners is dealing with

uncertainty and risk. This Chapter discussed scenario analysis and some of the other

methods available to manage uncertainty and risk in distribution network planning.

It was proposed that flexibility is the only way to effectively deal with uncertainty

and some initial proposals were made as to how flexibility might be achieved in tools

and methodologies: processes and methods should not be based on very narrow

assumptions; software should have scope for extending the type of analysis

performed and incorporating models of new technologies; and people must be trained

to be able to assess changing circumstances and adapt their own behaviour. These

initial proposals are limited in their application but the provision of flexibility in

planning tools and methods is probably an area deserving of further research.

An original methodology was employed to evaluate expected penetrations of DG,

both in terms of overall capacities and in terms of the relative share of different

technologies, and therefore provide support in understanding its impact. The

methodology for analysis of penetration scenarios combined two perspectives. The

top-down approach collated forecasts at a national level to produce expected overall

levels of DG in European networks in 2010. The bottom-up approach collated

opinions on the suitability of different DG technologies to different LV grid

conditions.

The top-down forecasts suggest overall levels of DG that are quite low. Taking the

full range of values into account, it is forecast that LV connected DG could amount

to between 0.45% and 10.86% of average load. The mid-range prediction is for the

capacity of LV Connected DG to be approximately 4.22% of average load. In

reality, some LV grids will contain much more DG than others; in some instances,

the amount of DG in an LV grid may even exceed 100% of average load. Therefore,

while the top-down analysis provides a guide to expected overall DG levels, the

159

definition of interesting scenarios for further analysis may require much larger values

of DG penetration.

A questionnaire was designed to collect opinions on the suitability of different DG

technologies to different LV grid conditions. The results of the questionnaire

identified the top three ranked technologies in each of the LV grid scenarios for 2010

and 2020. Photovoltaics emerged as the dominant technology in 2010 with

reciprocating engines and wind turbines also appearing frequently. In 2020,

photovoltaics retain their dominance and reciprocating engines and wind turbines

again feature strongly. However, new technologies like micro gas turbines, biogas

engines and fuel cells also start to feature more prominently in 2020.

The DG penetration studies presented here provide useful guidance on the expected

overall levels of DG and the DG technologies expected to feature most strongly in

different LV grid conditions. To support the translation of overall DG levels and

technology rankings into actual technology penetration values, three different

strategies were proposed. The application of these strategies and the detailed

definition of scenarios for further analysis will depend on the precise objectives of

those analyses but the results and methodologies produced from this work provide a

valuable resource for scenario development.




IEEE Trans on Power Systems, vol.13, no.2, May 1998, p.643-8

8.2. Miranda,V.; “Distribution System Planning Based on a Risk Analysis

Approach”; CIRED 12th International Conference on Electricity Distribution,

1993, Subject Area 6: Design and Planning of Public Supply Systems; IEE

Conference Publication No.373, p.6.11.1-6.11.5

8.3. Van Geert,E.; “Increased Uncertainty a New Challenge for Power System

Planners”; IEE Colloquium, 1998

160

8.4. Methods for Planning under Uncertainty: “Towards Flexibility in Power

System Development”; Paper prepared by Working Group 37.10 of CIGRE

(Convenor: E. Van Geert)

8.5. Hiskens,I.A., Pai,M.A., Nguyen,T.B.; “Bounding Uncertainty in Power System

Dynamic Simulations”; IEEE Power Engineering Society Winter Meeting, 23-

27 January 2000, Singapore

8.6. Bertani,A., Bossi,C., Delfino,B., Lewald,N., Massucco,S., Metten,E.,

Meyer,T., Silvestro,F., Wasiak,I.; “Electrical Energy Distribution Networks:

Actual Situation and Perspectives for Distributed Generation”; 17th

International Conference on Electricity Distribution, CIRED 2003, 12-15 May

2003, Barcelona, Spain

8.7. Watson,A.S.; “Modelling the performance of distribution networks with

distributed generation”; Submitted for the Degree of Master of Philosophy,

University of Strathclyde; August 2003

8.8. Directive 2001/77/EC of the European Parliament and of the Council of 27

September 2001 on the promotion of electricity produced from renewable

energy sources in the internal electricity market; Official Journal L283 ,

27/10/2001 P.0033-0040

8.9. Centro Elettrotecnico Sperimentale Italiano; 2003

8.10. Polish Ministry of Environment; “Strategy for Development of Renewable

Electric Power”; 2003

8.11. German Department of Environment, Conservation and Nuclear Safety;

Forecasts under the “Erneuerbare Energien Gesetz (EEG)” (Renewable

Energies Law); 2003

8.12. Verband der Elektrizitätswirtschaft – VDEW – e.V. (German association of

power supply companies), May 2002

8.13. RWE Power AG, Germany, 29th of April 2003

8.14. Distributed Generation Co-ordinating Group, DTI and Ofgem; “Scenarios of

Distributed Generation Development”; WS1 P06-D01 V1.2; www.distributed-

generation.org.uk

8.15. DTI New and Renewable Energy Programme; “Survey Study of Status and

Penetration Levels of Distributed Generation (DG) in Europe and the US

161

(Stage One)”; Report Number: K/EL/00306/02/REP one – URN 03/896;

KEMA Limited – Tuncay Tuerkucar, David Gailey

8.16. European Committee for Electrotechnical Standardisation (CENELEC);

European Standard EN50160; “Voltage Characteristics of Electricity Supplied

by Public Distribution Systems”; November 1994

162

9. Dynamic Modelling of Wind Farms

Previous chapters have considered the application of various methods and techniques

to address the shortcomings identified in the conventional approach to distribution

network planning. Some of these methods are new to this domain while others have

been applied to varying degrees by some DNOs. All DNOs currently perform power

system analysis in some form. This is necessary to understand how the network

operates and make plans for future developments. In Chapter 4, the need for more

analysis was identified as one of the principal shortcomings in the conventional

approach. DNOs will have to extend their analysis capabilities to deal with new

technologies and take full advantage of them. In particular, at this time power

system operators are concerned with the expansion of wind power and the effect it

will have on their networks. This requires appropriate modelling and analysis of

wind farms within power system simulation software.

Although the number of wind farms has been growing strongly for a decade or more,

there continues to be a lack of models and data to facilitate the power system analysis

that DNOs and others wish to perform. The challenge of modelling wind turbines

and wind farms has been undertaken by a number of different groups, including

researchers, network operators, manufacturers and software providers. While there

have been some efforts at collaboration there has been a resistance on the part of

manufacturers to provide the information required to model the technology fully.

Many of the models that have been developed remain proprietary, specific to

particular wind turbines, and not widely available. This Chapter examines the

dynamic modelling of wind farms as a demonstration of the type of analysis DNOs

may have to do in future and an illustration of one way in which the new challenges

in network planning can be met. The findings of this research as presented here

provide a useful – and publicly available – resource for those wishing to develop

models and will hopefully advance the general understanding of wind farms and their

dynamic performance from a power system’s perspective.

163

Wind power differs from conventional generation sources in a number of ways. This

highlights how the assumption that conventional technology will be used is a

shortcoming of the conventional approach. Firstly, the power output from wind

farms is dependent on the wind conditions so is not controllable or predictable in the

same way as conventional generation. Secondly, wind turbines have, until recently,

been specified such that they disconnect from the network if there is a disturbance.

When there was very little wind power, this approach was acceptable. With a

significant proportion of system load supplied from wind farms, it is unacceptable for

wind farms to disconnect when there is a disturbance. The ability of wind farms to

“ride-through” disturbances was an important subject for investigation a few years

ago and prompted some of the early modelling efforts. Finally, modern wind farms

do not use conventional synchronous and induction generators. To facilitate

variable-speed operation, most modern wind turbines use power electronics to some

degree. The most commonly-used type of machine in new wind turbines is the

doubly-fed induction generator, which uses power electronics in its rotor circuit.

Thus, modelling new wind farms requires the development of new models and the

adaptation of simulation tools to accommodate these new generation technologies.

The precise modelling requirements are dependent on what issues are being

considered. To assess power system stability in response to network disturbances, a

simulation tool such as PSS/E [9.1] is appropriate and models must be developed that

represent the behaviour of wind farms and their interaction with the electricity

network in the frequency range of about 1 to 10 Hz, which captures the

electromechanical response of power systems.

Wind farm models for the PSS/E simulation environment were developed. This

Chapter discusses a dynamic model of a wind farm for PSS/E, including the

assumptions and simplifications that were made. The model is demonstrated with

studies that assess the equivalence of single and aggregate models of wind farms and

also the effects of a high penetration of wind farms on distribution networks.

164

9.1. List of Symbols

The following symbols are used in this Chapter. All are introduced more fully at the

appropriate point in the discussion but are listed here for reference.

• ρ is air density and can normally be assumed to be 1.225 kg/m3

• A is the area swept by the rotor in m2

• v is velocity in m/s

• Cp is the power coefficient

• θ is either the blade pitch angle or shaft twist

• λ is the tip-speed ratio

• H is an inertia constant (Ws/VA)

• T is torque, either mechanical or electromagnetic (pu)

• ω is rotational velocity of the windmill or generator (equivalent electrical rad/s)

• baseω is base frequency of the system ( fπ2= where f is grid frequency)

• K is shaft stiffness (pu torque / electrical rad)

• E is energy in joules or watt-seconds

• J is the moment of inertia (kg.m2)

• M is mass (kg)

• L is length (m)

• D is the diameter of the rotor (m)

In the generator equations, the subscripts d and q indicate direct and quadrature axis

components respectively and the subscripts s and r indicate stator and rotor

components respectively. All variables are in per unit.

• v is voltage (et is the terminal voltage)

• i is current

• R is resistance

• L is inductance (Lm is the mutual inductance)

• Ψ is flux

• iqr or IQR is the quadrature component of the rotor current

• idr or IDR is the direct component of the rotor current

165

• vqr or VQR is the quadrature component of rotor voltage

• vdr or VDR is the direct component of the rotor voltage

• P is real or active power

• Q is reactive power

• s is slip

• p is the derivative operator

9.2. PSS/E Requirements for Generator Modelling

The models were developed and tested using PSS/E version 26.2 and Digital Visual

Fortran version 5.0D on a Windows 2000 system.

PSS/E was designed for analysis of large power systems, and makes simplifications

in the representation of system components to reduce the computational burden.

Models designed for use with PSS/E need only be as complicated as the PSS/E

environment itself. This allows some simplifying assumptions to be made. PSS/E

was designed to have a bandwidth of approximately 1 to 10 Hz. The default

simulation time step is 0.01 seconds. This can be reduced but there are limits on the

effective bandwidth. For example, the transmission network representation assumes

that all transients in lines and transformers die away within each time step, so the

time step can only be reduced to a level at which this assumption remains valid. All

these requirements of the simulation environment influence the design of the models.

9.3. A Dynamic Model of a Wind Farm

To produce a complete dynamic model of a wind farm, it is sensible to split it into a

number of components (see Figure 9.1), each of which makes a different contribution

to the overall model. For some of the model components, different wind turbine

technologies require different models. For all of the components, there are choices to

be made in the approach to modelling. Each of the components is discussed below.

166

Figure 9.1 – Components of a complete wind farm dynamic model

Wind Speed Model

Mechanical Systems

Control Systems

Protection

Public Electricity Network

Aerodynamic Model

Wind Farm Site Network

Generator and Grid Interface

Ancillary Systems

9.3.1. Wind Speed Model

Wind speed is a random variable. The Rayleigh or Weibull distributions can be used

to produce a typical pattern of ten-minute average wind speeds. However, a wind

speed signal for use in dynamic simulations with sub-second time steps requires

other methods, such as those based on power spectral densities. Analysis of wind

speed spectra reveals little energy at frequencies above 3Hz [9.2]. Furthermore, in

applying a wind speed sequence to a wind turbine, it is necessary to take into account

the variation in the wind across the area of the blades. This has the effect of filtering

out higher frequencies. Filter approaches have been used to produce single-point

wind speed sequences [9.2].

Models were produced that control the mechanical power input to the standard

PSS/E induction generator models, CIMTR1 and CIMTR3 [9.1]. These can be used

to assess the impact on the network of varying wind speed.

However, the stochastic variation of wind speed is not considered a primary issue for

power system stability analysis because it is thought that in the time scales involved,

constant wind speed may be assumed. Models must be valid over the range of

possible wind speeds and the dynamic response of a turbine may vary with its power

167

output. Wind speed distributions will help determine the probability of the most

serious conditions.

9.3.2. Aerodynamic Model

The energy in the wind is collected by the wind turbine blades, which experience

aerodynamic effects. These effects can influence machine degradation and expected

lifetime, and can raise power quality issues on the electrical network. A full analysis

of the aerodynamics would involve the use of blade element theory but the various

effects can be more simply represented by introducing variations at different

frequencies in the mechanical power transmitted through the turbine. However, in

terms of the immediate dynamic response to disturbances on the electrical network, it

is thought that the aerodynamic effects can largely be neglected.

For the purposes of dynamic analysis in PSS/E, it is considered sufficient to

represent the turbine aerodynamics just by the power collected from the wind, using

Equation 9.1 [9.2].

3

21

AvCP p ρ=

Equation 9.1

ρ is air density and can normally be assumed to be 1.225 kg/m3

A is the area swept by the rotor

v is the wind speed in m/s

Cp is the power coefficient, a measure of how much of the energy in the wind is

actually extracted, and is a function of the relative speed of the rotor and the wind.

Even if constant wind speed is assumed, changes in rotor speed in response to

network disturbances will affect the power coefficient and hence the power extracted

from the wind. The power coefficient is also affected by blade pitch angle so the

pitch control system may have to be modelled. Thus, it may be necessary to include

a power coefficient and input power calculation in a dynamic model. Heier [9.2]

gives a function for approximating the power coefficient (Equation 9.2 and Equation

9.3). The coefficients should be adjusted to reflect the characteristics of particular

turbines.

168

λθλ

′−

��

��

� −−′

=21

54.0116

5.0 eC p

Equation 9.2

where 1035.0

08.011

3 +−

+=

′ θθλλ

Equation 9.3

θ is the blade pitch angle. λ is the tip-speed ratio (wind

rotortip

v

v=λ )

The power curve produced by the equations above must be amended to take account

of cut-in speed and cut-out speed. Imposing these limits produces a realistic

representation of power versus wind speed, such as that shown in Figure 9.2.

Figure 9.2 – Typical power versus wind speed characteristics for an 800kW wind turbine

Power (kW)

0

100

200

300

400

500

600

700

800

900

0 5 10 15 20 25 30

Wind speed, U (m/s)

9.3.3. Mechanical Systems

Very complex drive train models have been produced in the past for the purposes of

wind turbine design, but for power systems analysis simpler models suffice. Many

studies have used a single lumped mass for the rotating system but a number of

authors [9.3, 9.4, 9.5] have argued that a multi-mass model of the drive train is

required to produce accurate simulations with conventional wind turbines. It has

been shown that fluctuations in variables like voltage and power are greater with

multi-mass models than with a single lumped mass.

169

However, in modelling a wind turbine shaft it is necessary to distinguish between

constant speed and variable speed wind turbines. In constant speed wind turbines,

the shaft should be taken into account in the following situations:

• Analysis of faults, because the potential energy stored in the shaft influences the

rotor acceleration and after clearing the fault the shaft causes an oscillation

• Power quality studies, because the dynamics associated with the shaft cause

oscillations which can be observed in the power spectral density of the output

power

In variable speed wind turbines, it has been argued that variable speed achieved with

power electronics means that the mechanical and electrical systems are de-coupled,

making a multi-mass drive train model unnecessary.

A two-mass representation, neglecting damping torques, should be sufficient for

dynamic analysis with PSS/E, as shown in Figure 9.3. The diagram and associated

equations are shown below.

Figure 9.3 - Two-mass representation of a wind turbine shaft

θ

K

H EH M

ωEωM

TETM

HM - inertia constant of the windmill (Ws/VA)

HE - inertia constant of the generator rotor (Ws/VA)

TM - mechanical torque of the windmill (pu)

TE - electromagnetic torque of the generator (pu)

Mω - windmill speed (equivalent electrical rad/s)

Eω - generator rotor speed (electrical rad/s)

baseω - base frequency of the system (electrical rad/s) ( fπ2= where f is grid

frequency)

170

K - shaft stiffness (pu torque / electrical rad)

θ - shaft twist (electrical rad)

The system is described by three differential equations:

θωω

KTdt

dHM

M

base

M −=2 Equation 9.4

EE

base

E TKdt

dH −= θωω2

Equation 9.5

EMdtd ωωθ −= Equation 9.6

If a single-mass model, with a single inertia constant and single rotational speed, is

deemed sufficient then only one differential equation is required:

EMbase

TTdtdH −=ω

ω2

Equation 9.7

9.3.3.1. Determining the Windmill Inertia Constant

The inertia constant (H) is defined as the stored energy at synchronous speed per

volt-ampere of machine rating. The units of H are watt-seconds per volt-ampere (or

just seconds).

base

stored

VAE

H = Equation

9.8

For a rotating system, stored energy (joules or watt-seconds) is given by:

2

21 ωJEstored =

Equation

9.9

where J is the moment of inertia (kg.m2) and ω is the rotational velocity (rad/s).

The mass, and hence the moment of inertia, of a windmill hub is split between the

blades and the shaft. To determine the moment of inertia of a single blade, it can be

171

considered as a slender rod with the axis of rotation at one end. For this type of

structure the moment of inertia is given by:

2

31

MLJ = Equation 9.10

where M is the mass of the blade (kg) and L is its length (m).

If the total blade mass of a three-bladed wind turbine rotor, Mblades (kg), is divided

equally between three blades, and if the diameter of the rotor is Dblades (m), then the

total moment of inertia of the blades is given by:

��

�

�

��

�

��

��

�=2

2331

3 bladesbladesblades

DMJ Equation 9.11

The shaft can be considered a cylinder, for which the moment of inertia is given by: 2

221

��

��

�= shaft

shaftshaft

DMJ Equation 9.12

where Mshaft is the mass of the shaft (kg) and Dshaft is its diameter (m).

Substituting Equation 9.11 and Equation 9.12 into Equation 9.9 gives:

222

81221 ω

��

�

�

��

�

�+= shaftshaftbladesblades

stored

DMDME Equation 9.13

Converting the rotational velocity from rad/s to RPM gives: 222

602

81221

��

��

��

�

�

��

�

�+= RPM

shaftshaftbladesbladesstored

DMDME ωπ

Equation 9.14

Substituting Equation 9.14 into Equation 9.8 and tidying up gives:

��

�

�

��

�

�+=

8121800

2222shaftshaftbladesblades

base

RPMDMDM

VAH

ωπ Equation 9.15

172

9.3.3.2. The Link Between Eigen-frequency and Shaft Stiffness

The system of differential equations describing the two-mass shaft model has an

eigen-value that is the square of the natural or modal frequency of the shaft. It can

be shown that the eigen-frequency is linked to the shaft stiffness and inertias by the

following:

��

��

�+=

ME

baseeigen HH

K 112

2 ωω or ��

��

�

+=

ME

ME

base

eigen

HHHH

Kωω 22

Equation 9.16

eigenω - natural/modal/eigen-frequency of the shaft (rad/s)

baseω - base frequency of the system (electrical rad/s)

( fπ2= where f is grid frequency)

K - shaft stiffness (pu torque / electrical rad)

HM - inertia constant of the mill (Ws/VA)

HE - inertia constant of the generator rotor (Ws/VA)

9.3.4. Generator and Grid Interface

It is in the type of generator, and associated grid interface and converter systems,

used that the most important differences lie from the perspective of dynamic stability

analysis of wind farms in power systems. The four main types of generator are

summarised in Table 9.1 and their modelling requirements described in the following

sections.

Table 9.1 – The four main types of wind turbine generator

Type Turbine Speed Generator Converter 1 Fixed Asynchronous None 2 Variable Asynchronous Partial (rotor circuit) 3 Variable Asynchronous Full 4 Variable Synchronous Full

A fully comprehensive analysis capability would include full models of all turbine

types connected to a power system. However, with some types being used only in

small numbers it may be considered unnecessary to model them fully. In the

immediate future, it is expected that the largest wind farms, and the ones that must be

suitably represented in power systems studies, will utilise machines of type 2 –

173

doubly fed induction generators with partial power converters. The modelling

options for this type of generator and converter are covered in detail below.

All the models presented here assume that the power electronic components are ideal

and act fast enough to be considered instantaneous. This assumption is fair given the

purpose of the models and the frequency bandwidth of interest (1 to 10 Hz).

9.3.4.1. Fixed Speed Turbine - Asynchronous Generator - No Converter

Most wind turbines installed up until the end of the 1990s operate at a fixed speed

and have an asynchronous, induction generator. Some turbines operate at two fixed

speeds and drive asynchronous wound-rotor generators that can switch between 4

and 6 poles. Many designs also include thyristor controlled soft-start features. As

standard induction generators, dynamic models of these machines should be readily

developed although, as mentioned above, it will be necessary to include multi-mass

drive train models.

There is also some debate about the representation of wind-powered induction

generators in dynamic stability programs. Knudsen and Akhmatov [9.6] argue that

an EMTP-type transient model of the generator including representation of stator

transients must be used. However, this is inconsistent with the neglect of stator

transients in dynamic stability programs like PSS/E, as explained by Kundur [9.7].

This issue is discussed in greater detail in the next section.

9.3.4.2. Variable Speed Turbine - Asynchronous Generator - Partial Power

Converter

The type of turbine currently being installed in the majority of large wind farms is

variable speed with a doubly fed wound rotor induction generator. The variable

speed capability, provided by a power electronic converter controlling the rotor

voltage, is limited and only a small proportion of the total output power is fed

through the converter. This allows the power electronics rating to be approximately

25% of the overall machine rating. This makes the machine cheaper than equivalent

machines that require power electronics with a 100% rating.

174

The models described below use the d-q transformation, which was introduced in the

past to simplify analysis and compensate for the lack of computational power. With

computing power much greater than it was, some modern approaches to machine

modelling dispense with transformation methods. In this case, the models need to

interface with the power system representation used in PSS/E so the d-q

transformation was used.

The use of the d-q transformation illustrates the need for new models like this to

remain consistent with existing modelling platforms even where advances in

computing and simulation technology may have superseded them. Existing system

models and their various components have been established over a number of years,

decades in some cases. It would be expensive to replace these legacy models and so

it is preferred if new models are compatible with the existing resources. With some

new technologies this will not be possible because the assumptions underlying the

existing models may not be applicable.

Standard Induction Machine Representation

The standard set of equations for induction machines is given below [9.7]. The

subscripts d and q indicate direct and quadrature axis components respectively. The

subscripts s and r indicate stator and rotor components respectively. The values are

per unit and currents are positive when the machine is generating. The variable s

represents slip and p is the derivative operator.

dsqssdssds piRv ψψω +−−= Equation 9.17

qsdssqssqs piRv ψψω ++−= Equation 9.18

drqrsdrrdr psiRv ψψω +−−= Equation 9.19

qrdrsqrrqr psiRv ψψω ++−= Equation 9.20

The per unit flux linkage equations are as follows.

175

( ) drmdsmsds iLiLL −+−=ψ Equation 9.21

( ) qrmqsmsqs iLiLL −+−=ψ Equation 9.22

( ) dsmdrmrdr iLiLL −+−=ψ Equation 9.23

( ) qsmqrmrqr iLiLL −+−=ψ Equation 9.24

The electromagnetic torque is given by the following.

qrdrdrqre iiT ψψ −= Equation 9.25

There are three different approaches to the modelling of induction machines for

stability analysis:

• Neglect of both stator and rotor transients

• Neglect of stator transients and retention of rotor transients

• Retention of both stator and rotor transients

Each of these approaches is discussed below along with the modifications required to

describe the doubly fed wound rotor induction machine.

Neglect of Both Stator and Rotor Transients

This approach was espoused by Slootweg et al [9.8]; and was implemented in PSS/E

in a slightly modified form. Stator transients are neglected to make the model

compatible with the models of other system components in stability studies,

particularly the transmission network. Rotor transients are neglected on the basis of

the argument that their dynamics are fast enough to be outside the standard PSS/E

bandwidth.

These assumptions reduce the generator model to a set of four algebraic equations.

The stator voltages are taken from the network solution at each time step and rotor

voltages are determined by the power electronics. This gives a set of four equations

with four unknowns (Equation 9.26 to Equation 9.29) that can be solved to give the

176

machine currents, and from there, the values of torque and power required for the

simulation.

( )qrmqssssdssds iLiLiRv ++−= ω Equation 9.26

( )drmdssssqssqs iLiLiRv +−−= ω Equation 9.27

( )qsmqrrrsdrrdr iLiLsiRv ++−= ω Equation 9.28

( )dsmdrrrsqrrqr iLiLsiRv +−−= ω Equation 9.29

Neglect of Stator Transients and Retention of Rotor Transients

The standard approach to induction machine modelling in stability studies is to

neglect stator transients to ensure compatibility with the models of other system

components [9.7]. A further assumption normally made is that the rotor is short-

circuited and hence that the rotor voltages are zero. This second assumption is not

valid for the doubly fed machine so the standard second-order model must be

modified to retain the rotor voltages. This approach has been suggested by Usaola

and Ledesma [9.9,9.10] and by CIGRE [9.11]. The resultant equations are as

follows.

iqssdssdds iXiRvv ′+−=′− Equation 9.30

idssqssqqs iXiRvv ′−−=′− Equation 9.31

( ) ( )[ ] qrmr

msqsssdqsd v

LLL

iXXvT

vsvp+

−′−−′′

−′=′ ωω0

1 Equation 9.32

( ) ( )[ ] drmr

msdsssqdsq v

LLL

iXXvT

vsvp+

−′−−′′

−′−=′ ωω0

1 Equation 9.33

Where:

qrmr

msd LL

Lv ψω

+=′ Equation 9.34

drmr

msq LL

Lv ψω

+−=′ Equation 9.35

177

r

mr

RLL

T+=′0 Equation 9.36

( )msss LLX += ω Equation 9.37

��

��

�

+−+=′

mr

mmsss LL

LLLX

2

ω Equation 9.38

Electromagnetic torque is given by:

qsqdsde ivivT ′+′= Equation 9.39

In a submission to the British Isles Wind Technical Panel, Efthymiadis [9.12] has

proposed a slightly different approach where instead of retaining vdr and vqr from the

standard induction machine equations, vd' and vq' are replaced by terms incorporating

rotor voltage components.

Retention of Both Stator and Rotor Transients

Akhmatov et al [9.3,9.4,9.6] have suggested that the neglect of stator transients is not

applicable to induction machines because the effect of the DC offset on speed must

be taken into account. It is argued that when interfacing with the network, stator

transients must be ignored but they can be retained internally within the machine

model. Saenz et al [9.13] propose a similar fourth-order model. If this approach is

adopted then the equations describing the machine are as follows.

qssdssdsds iRvp ψωψ ++= Equation 9.40

dssqssqsqs iRvp ψωψ −+= Equation 9.41

qrsdrrdrdr siRvp ψωψ ++= Equation 9.42

drsqrrqrqr siRvp ψωψ −+= Equation 9.43

The flux linkages are states in the dynamic model, updated by the differential

equations shown above. The stator voltages are determined by the network solution.

The rotor voltages are determined by the power electronic converter, in line with the

178

control strategies. The currents can be found by solving the system of linear

equations, Equation 9.17 to Equation 9.20, shown above.

9.3.4.3. Variable Speed Turbine - Asynchronous Generator - Full Power

Converter

The third type of wind turbine is variable-speed with a cage-rotor induction generator

connected to the grid fully through a power electronic converter. With all power

flowing through the converter, the overall dynamic response will be determined by

the power electronics. The response will depend on how the converters are

programmed. The power electronics themselves act almost instantaneously in

comparison to conventional power system components. It may be possible to

represent the converters simply by the limits of current and voltage that cause the

converters to switch off and isolate the wind turbine and generator. However, it may

be necessary to model the generator to determine when the limits of the power

electronics are reached. The standard PSS/E induction generator models may suffice

in this instance.

9.3.4.4. Variable Speed Turbine - Synchronous Generator - Full Power

Converter

The more common type of wind turbine that passes all power through a power

electronic converter uses a directly driven synchronous generator. The overall

dynamic response will be determined by the power electronics but it may also be

necessary to model the generator. The range of PSS/E standard synchronous

machine models may include something suitable to represent these machines but

these was not examined in detail.

9.3.5. Control Systems

9.3.5.1. Control Objectives

Older wind turbine designs, using conventional induction generators and installed in

relatively small numbers, had limited control objectives. Modern variable speed

wind turbines with power electronics are able to pursue additional objectives.

179

Protect Equipment

Wind turbine control is closely linked with protection. Older wind farms were

designed to disconnect from the power system at the first sign of a disturbance. This

ensured that the wind farm equipment was protected from potentially damaging

transient effects and reduced the possible negative effects on the power system.

With more wind power connected to the system, fault ride-through capability

becomes important. There is the danger that large amounts of wind power will

disconnect when there is a system disturbance. Thus, the objective of protecting

equipment must be traded against supporting the power system.

The majority of large wind turbines now being installed are variable speed turbines

that use power electronics in their interface with the grid. An important factor in

these machines is that the power electronics must be protected from excessive

voltages and currents. The limits of the power electronics can be quite low, with

higher limits incurring a higher cost in power electronic hardware. This protection is

an important objective of the control/protection system and is implemented with so-

called “crowbar” protection. This applies a short-circuit across the terminals of the

power electronics in the rotor circuit, disconnecting them and effectively turning the

generator into a conventional induction machine. Crowbar protection has not been

modelled explicitly in the models developed in this work, although it could be added.

Rotor current is accessible as a variable and its value can be used to trigger the

disconnection of the machine or some other action. When models of a doubly fed

induction generator were tested with disturbances, the current in the rotor was

identified as the first variable to trigger protection and disconnection of the machine.

In normal operation, wind turbines must be protected from mechanical stresses that

can arise from variations in wind speed and aerodynamic effects such as blade-tower

interaction. In variable speed machines, torque pulsations in the shaft can be reduced

by adjusting the electromagnetic torque of the generator. However, this results in

pulsations in the output power. Thus, there is a trade-off to be made between torque

pulsations in the shaft and power pulsations at the output.

180

Maximise Energy Capture

An important control objective of wind turbines is to maximise the energy captured

from the wind. All wind turbines turn to face the wind as its direction changes, but

energy capture is also dependent on the speed of rotation of the blades. Older wind

turbines operate at a speed that is approximately fixed. Variable speed wind turbines

can maximise the energy captured from the wind by rotating at the optimum speed.

This variation in speed can be achieved through pitch control and through control of

the generator’s electromagnetic torque using power electronics.

The power coefficient is a measure of the efficiency of energy capture. It is normally

presented as a function of tip speed ratio and pitch angle. The tip speed ratio is

found by dividing the speed of the tip of the turbine blades (proportional to rotational

speed) by the wind speed. Figure 9.4 shows how at any given tip speed ratio, the

power coefficient can be maximised by adjusting the pitch angle.

Figure 9.4 – Performance coefficient as a function of tip speed ratio with pitch angle as a

parameter

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 2 4 6 8 10 12 14 16

Tip speed ratio, lambda

Perf

orm

ance

coe

ffic

ient

, Cp

0 deg2 deg5 deg10 deg15 deg25 deg

181

System Support

Given the prospect of wind power representing a significant share of total generation,

system operators would like wind farms to comply with additional control

requirements [9.14]. These would be implemented at different times depending on

system conditions.

Power Control

Wind farms should be able to limit the power they are producing when commanded

to do so by system operators. This may be to alleviate constraints on the network or

to respond to a high frequency. Wind farms may also be asked to participate in more

general frequency control by operating at 90% of their potential output, thereby

providing scope for increasing and decreasing output power as required. In addition,

the rate of change of power from a wind farm should not exceed a specified rate.

This may require switching off turbines in anticipation of high wind speeds that will

force turbines to switch off anyway.

Voltage Control

The power electronic converters used on variable speed turbines have the capability

to control reactive power supplied to the grid. Large wind farms will be expected to

contribute to network voltage control. This will require being able to operate across

a range of power factors.

In comparison with conventional generators, wind farms present additional problems

because they consist of a number of small machines rather than a few large

machines. Each wind turbine has its own transformer to step the voltage up to

connect to the wind farm site network. The site will then be connected to the wider

power network through more transformers. The additional transformer on each wind

turbine will absorb reactive power, reducing the effectiveness of any reactive power

contribution from the generator.

182

Power Quality

The effect of wind farms on power quality must be considered and the mitigation of

power quality problems can be a control objective.

The fluctuating wind speed combined with blade transients and blade-tower

interaction mean that the torque from a wind turbine is not steady. This can be

translated as flicker on to the power system. Absorbing torque fluctuations in the

mechanical systems of the wind turbine, by allowing speed to vary, may reduce

flicker but this imposes stress on the turbine shaft.

The power electronics used in variable speed machines may introduce harmonics on

to the network. Care must be taken to ensure this does not affect other grid users.

Farm-Wide Control

Large wind farms being installed today consist of dozens of turbines, each with their

own control systems. Power system operators are concerned primarily with the wind

farm as a whole. Control must be exerted over the whole wind farm, either to share

the burden of control requirements, or to shutdown and start-up individual turbines as

required.

9.3.5.2. Control Options

Yaw Control

The yaw control systems of wind turbines turn the nacelle and rotor to face into the

wind. In large turbines, this involves moving a very large object so the dynamics of

yaw control are too long-term to have an impact on normal power system stability

studies. It will be necessary to model longer-term dynamic effects such as yaw

control if simulations are to cover a period of minutes, and if changes in wind

direction are considered.

183

Blade Pitch Angle Control

In older and smaller turbines, the aerodynamic stalling effect is used to limit the

speed of rotation and the power from the generator. Most manufacturers now use

blade pitch angle control for large turbines, sometimes in combination with stall

control.

As shown in Figure 9.4, the pitch of the blades influences the energy captured from

the wind. Pitch angle control can be used to maximise the energy captured up to the

limit of the generator, and to limit the rotor speed in strong winds. It may be used to

smooth the flow of power from the machine, or to reduce output in response to a

system disturbance. Pitch control may also be used to operate the turbine below its

full potential then provide a rapid power increase on demand. This kind of response

may be a requirement of system operators.

Although modern wind turbines are very large with blades that might be 40m or

more, pitch control can act very quickly. It is possible to move the blade pitch angle

through its whole range in a matter of seconds. In normal operation, the blade pitch

will change at around 5-10 degrees/second. In emergencies, the blade pitch can be

made to change at 10-20 degrees/second. Thus, blade pitch angle control should be

considered in power system stability studies. In addition, the blade positioning

system consumes approximately 1% of a turbine’s rated power in normal use and

more in extreme situations [9.2].

The wind turbine aerodynamics form a complex, non-linear system. Publications

suggest that from a measured wind speed, a non-linear function or look-up table is

used to determine the optimum rotor speed and blade pitch angle. Pitch angle control

is used in combination with electromagnetic torque control, described below, to bring

the turbine to the desired state.

Publications suggest that once the desired pitch angle has been determined, a

Proportional-Integral (PI) controller is used in a feedback control loop, taking into

account the dynamics of the actuator and the blades themselves.

184

Power Electronic Converters

The doubly fed induction generator uses power electronic converters to control

currents and voltages in the generator and to control the interface with the grid. The

main task of the back-to-back converters in the rotor circuit is to regulate the active

and reactive power of the induction machine. The converters use pulse width

modulation (PWM) to inject the desired voltages into the rotor circuit.

The generator stator is directly coupled to the grid and this circuit carries most of the

power, typically around 90%. The converter closest to the rotor can inject voltages

to the rotor circuit to control the speed (or slip) and reactive power of the machine.

The converter closest to the grid can act like a STATCOM, controlling reactive

power exchange with the grid. The converters are bi-directional so power can flow

both into and out of the rotor circuit, depending on the speed of the turbine.

The rotor-side converter of a doubly fed induction generator acts as a voltage source

in the rotor circuit. Most publications use the rotor currents as intermediate

variables, which are converted to voltages for injection into the rotor circuit. In some

models, the desired rotor currents may be used directly in the electrical model of the

machine. It has been found that reactive power is directly dependent on the direct

axis component of rotor current and that electromagnetic torque is directly dependent

on the quadrature axis component of rotor current.

The use of controllers in rotor current control varies across publications. In

particular, the choice of algebraic functions or PI controllers at different points in the

control loop varies. This is discussed in more detail in the sections below on

electromagnetic torque / speed control and reactive power / voltage control.

Electromagnetic Torque and Speed Control

In the doubly fed induction generator, power electronics in the rotor circuit allow the

electromagnetic torque of the generator to be controlled. This can be used to control

the torque exerted on the wind turbine shaft and therefore the speed of rotation.

185

Fluctuations in the torque from the wind turbine can exert significant mechanical

stresses on the turbine shaft. Adjusting the electromagnetic torque of the generator

to follow the changes in torque from the turbine can reduce these. The twisting

torque on the shaft is then reduced. However, varying the electromagnetic torque

results in variations in the output power from the generator. Thus, the converters can

reduce the mechanical stresses on the wind turbine or reduce the fluctuations in the

power delivered but there is some conflict between these objectives.

As described in the section on pitch angle control, for a given wind speed, there is an

optimum rotating speed for the wind turbine. The generator’s electromagnetic torque

can be adjusted to bring the turbine to that optimum speed. Thus, turbine speed can

be controlled through two separate means – pitch angle and electromagnetic torque –

and these must be co-ordinated within the control system.

Most publications on the doubly fed induction generator note that electromagnetic

torque is directly dependent on the quadrature axis component of rotor current.

Slootweg et al [9.8] present the following equation, Equation 9.44, as the relationship

between rotor quadrature current and electromagnetic torque.

qrms

tme i

LLeL

T+

−=

Equation 9.44

Te is the electromagnetic torque from the generator

Lm is the mutual inductance

Ls is the stator leakage inductance

et is the terminal voltage

iqr is the quadrature component of the rotor current

The relationship between electromagnetic torque and the quadrature component of

rotor voltage, VQR, can be investigated using the standard equations for induction

machines – see section 9.3.4.2.

Figure 9.5 shows the resultant electromagnetic torque for changes in VQR and VDR,

representing control action by the rotor-side converter. VDR was held constant at

zero while VQR was varied, and vice-versa. Other parameters were held constant

186

throughout: VDS=0, VQS=0.99, RS=RR=0.01, LS=LR=0.1, LM=3.0, SLIP=-0.005.

A linear approximation is shown to TELECR as a function of VQR.

This analysis suggests that it should be possible to use VQR directly as the control

variable for electromagnetic torque, rather than using IQR as an intermediate

variable.

Figure 9.5 – Response of electromagnetic torque to changes in rotor voltages

y = 96.252x + 0.7771

-10

-5

0

5

10

15

-0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1

VQR or VDR (pu)

TELE

CR

(pu)

TELECR for VQR TELECR for VDR Linear (TELECR for VQR)

The use of controllers in determining IQR and VQR varies across publications. In

some [9.8, 9.15], IQR is determined using an algebraic or numerical function of rotor

speed, reflecting the non-linear turbine characteristic. A dynamic component, in the

form of a PI controller, may be introduced in the conversion of rotor current to rotor

voltage. Alternatively [9.9, 9.16], the rotor currents can be the outputs of PI

controllers with rotor speed as inputs. These may then be transformed algebraically

to rotor voltages or passed through another PI dynamic block to produce the rotor

voltages for injection to the machine. Another alternative [9.10] is to use a PI

controller with rotor speed error as the input and electromagnetic torque as the output

then determine rotor quadrature current as a proportional function of electromagnetic

187

torque. Yet another alternative [9.17] is to use Proportional-Integral-Derivative

controllers with power and reactive power as inputs and the rotor currents as outputs.

Depending on its design, the control system may require inputs of actual and desired

rotor speed, actual and desired electromagnetic torque, and actual and desired rotor

currents and voltages. Speed, voltages and currents can be measured but in real

machines, electromechanical torque must be calculated from other parameters, as it

cannot be measured directly.

Rostoen et al [9.16] present a simple cascaded PI control system for control of

electromagnetic torque and rotor speed; see Figure 9.6. It can be seen that two PI

controllers are used in determining the rotor quadrature current, IQR, which is then

transformed, either algebraically or with another PI controller, to VQR for

application to the rotor side converter.

Figure 9.6 – Cascaded PI controller for rotor quadrature current

PIController+

-PI

Controller

actualrotor

speed

desiredrotor

speed

electromagnetictorque

rotorquadrature

current+

-

Rostoen et al [9.16] note that fast control of electric torque (with a small time

constant in the torque control loop) results in oscillations in the torque transmitted

through the shaft when there is a step change in the input torque from the turbine. If

there are mechanical reasons to avoid oscillations in shaft torque then a larger time

constant will help reduce those oscillations. To avoid unnecessary oscillations, the

gain at the resonance frequency of the shaft system should be small.

Torque pulsations from the turbine, perhaps from tower vortex interaction, can be

damped with a large proportional gain constant in the speed control loop. This

makes the electromagnetic torque vary more, thereby tracking the pulsations from

the turbine and reducing the pulsations transmitted in the shaft. However, the

pulsations in electromagnetic torque will also appear in the output power. Thus, in

188

setting the gain constant in the speed control loop, there is a trade off between

pulsations in the shaft torque and pulsations in the output power.

Reactive Power and Voltage Control

In the doubly fed induction generator, power electronics in the rotor circuit allow the

power factor of the generator and reactive power exchanged with the grid to be

controlled. The rotor side can control the reactive power of the generator, and the

grid side converter can act like a STATCOM, providing an alternative control option

for reactive power exchange with the grid.

Most publications on the doubly fed induction generator note that reactive power is

directly dependent on the direct axis component of rotor current. Slootweg et al [9.8]

present the following equation, Equation 9.45, as the relationship between rotor

direct current and reactive power.

ms

drmtotal LL

iLQ

+−=

Equation 9.45

Qtotal is the total reactive power produced by the machine

Lm is the mutual inductance

Ls is the stator leakage inductance

idr is the direct component of the rotor current

Thus, in Slootweg’s model, the rotor direct current is used to control reactive power

in the machine. The direct component of the rotor current is split into two parts:

ms

tmagdr L

ei

ω

2

, −= ,

which is required to magnetise the generator itself; and

Equation

9.46

gendri , ,

which determines reactive power exchanged with the grid.

The relationship between reactive power and the direct component of rotor voltage,

VDR, can be investigated using the standard equations for induction machines – see

section 9.3.4.2.

189

Figure 9.7 shows the resultant real and reactive power for changes in VDR,

representing control action by the rotor-side converter. Other parameters were held

constant: VDS=0, VQS=0.99, VQR=0, RS=RR=0.01, LS=LR=0.1, LM=3.0, SLIP=-

0.005. It can be seen that reactive power varies linearly with VDR.

This analysis suggests that it should be possible to use VDR directly as the control

variable for reactive power, rather than using IDR as an intermediate variable.

Figure 9.7 – Response of power and reactive power to changes in direct axis rotor voltage

y = 95.771x - 0.3308

-10

-8

-6

-4

-2

0

2

4

6

8

10

-0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1

VDR

pu P

and

Q

P Q Linear (Q)

As with the IQR/VQR controllers for electromagnetic torque and speed, there are a

variety of controllers for IDR/VDR presented in publications. This covers different

combinations of algebraic and dynamic functions for determining IDR and VDR.

Rostoen et al [9.16] present a very simple PI control system for control of reactive

power; see Figure 9.8. A single PI controller is used to determine IDR, which could

then be transformed, either algebraically or with another PI controller, to VDR for

application to the rotor side converter.

190

Figure 9.8 – Simple PI control system for reactive power

PIController

desiredreactivepower

actual reactivepower

rotorquadrature

current+

-

An alternative approach to reactive power and voltage control is to use the grid side

converter like a STATCOM device [9.18]. The rotor side converter can be used to

provide reactive power for magnetisation of the generator. The grid side converter

can then have sole control over reactive power exchange with the grid. In

publications on doubly fed induction generators, this approach is less common than

using only the rotor side converter.

The power electronics have limitations and their reactive power capabilities vary

depending on conditions. Meeting the reactive power requirements may affect other

aspects of the machine’s operation. For example, it has been claimed that operating

at low power factors results in an increase in flicker. This may be because the

voltages and currents required in the converters to produce the desired reactive

power reduce the scope to mitigate fluctuations in torque from the turbine.

9.3.5.3. Modelling Control Systems in PSS/E

Despite efforts by network operators and others over a number of years to have

information made available, wind turbine manufacturers are reluctant to release

details of their controllers, seeing them as valuable intellectual property. This

missing information presented one of the main challenges in modelling wind farms.

The only controller models available were those proposed by university researchers

in publications. For a DNO wishing to develop models and perform studies, the

work done and presented here should prove useful.

As described in the sections above, most publications suggest PI controllers for

control of reactive power and speed. Cascaded control loops are frequently used;

191

particularly to control speed where there can be cascaded control loops for blade

pitch angle, rotor speed and electromagnetic torque.

For example, Tapia et al [9.19] describe a control structure with two cascaded control

loops. Numerous transformations are required between the three-phase

measurements from the machine, the two-phase representations used in the

controllers, and the three-phase voltages injected into the rotor. The details of how

the desired rotor currents are specified for desired values of P and Q are not given.

PI controllers are used to convert the rotor current errors to desired rotor voltages.

Heier [9.2] identifies a number of advanced control methods that may be applied to

wind turbines including fuzzy controllers, self-tuning control systems, system-

oriented controller design, and neural networks. The non-linear nature of a wind

turbine means that parameters can cover a very broad range of values depending on

the status of the turbine [9.20]. It is argued that non-linear control systems are

required to improve control behaviour.

The models developed allow the direct and quadrature components of the rotor

voltage to be varied independently. The models have been set up such that external

controller models can control the rotor voltages. This is possible with the PSS/E

modelling framework, which allows the separate specification of generator,

governor/prime mover, and exciter/automatic voltage regulator (AVR) models. It is

also possible to specify power system stabilisers and other components. These

separate models facilitate the testing of different control strategies with the same,

generic generator model.

9.3.6. Ancillary Systems

The ancillary systems associated with wind farms will affect the dynamic response to

varying degrees. Ancillary systems may influence when machines are tripped off

during disturbances. For example, uninterruptible power supplies may be required to

keep control systems operating. Features such as compensating capacitors will also

affect the dynamic response. Many wind turbines use thyristor soft-start systems to

192

reduce the impact of induction machine start-up. In general, the modelling

requirements for ancillary systems are likely to vary on a case-by-case basis and are

not dealt with further here.

9.3.7. Protection

At the interface between a wind farm and the wider power network, protection

systems should be designed to comply with the relevant standards. However, the

introduction of very large wind farms may necessitate changes in the approach to

protection. Dynamic simulations with accurate wind turbine models will be required

to determine the most appropriate protection settings. There is scope to incorporate

protection models in dynamic simulations but the action of protection devices may

also be simulated through manually enacting switching events. Network operators

and wind farm operators will work together so that settings grade with network

protection.

New requirements for the connection of large wind farms to electricity networks

have been developed. These build on existing, long-standing recommendations for

connecting DG in the UK; Engineering Recommendation G59/1 requires that the

protection systems must be able to detect the following conditions:

• Over and under voltage

• Over and under frequency

• Loss of mains

• Neutral voltage displacement

• Over current

• Earth current

• Reverse power

9.3.7.1. Protection Modelling in PSS/E

PSS/E includes a library of models covering generators, excitation systems, turbine-

governors and some relays. User-developed models, written in a Fortran derivative

language called Flex, can also be added. The PSS/E library models were examined

193

to determine whether any were appropriate to wind farm protection modelling.

However, it was found necessary to write additional code to provide the functionality

required. A user-developed model of a reverse power relay was produced. It uses

the PSS/E function LINTRP to trip a line when the appropriate reverse power

conditions are met.

In conclusion, modelling protection was not a primary consideration in the design of

PSS/E version 26. In particular, the program is not capable of dynamic simulation of

unbalanced conditions so some aspects of wind farm protection could not be

modelled. Nevertheless, as the program is used for other purposes, it is useful to

develop protection modelling capabilities. User-developed models offer flexibility in

modelling different types of protection. It is also possible to incorporate protection

effects within models of a wind farm or wind turbine.

9.3.8. Wind Farm Electrical Network

Large wind farms cover large areas of land resulting in a significant electrical

network within the wind farm site itself. This may necessitate modelling of the wind

farm electrical network in power systems analysis performed by the operator of the

public system. If the wind farm electrical network is a conventional design then this

should not pose any great challenges, except obtaining data from wind farm

operators. However, large wind farms of the future may utilise unconventional

electrical networks, such as direct current networks. Such designs would have to be

carefully assessed.

9.3.9. Electricity Network

To accurately determine the effect of wind farms on the network, and vice-versa, the

electricity network must be represented accurately. Network operators would use

their own data to model portions of their network as necessary.

The network representation used in PSS/E is greatly simplified because the program

is designed to simulate very large networks. This restricts the interaction between

the network and machines and influences the approach to machine modelling.

194

9.4. Model Validation or Verification

Operational data is required to validate dynamic models. Test results from

manufacturers and wind farm developers would support the development and

validation of models. However, efforts to obtain useful data from manufacturers

failed to produce the specific data required for dynamic modelling. This continues to

be a problem for those wishing to develop and validate dynamic models of wind

farms. Commercial interests and the competition between manufacturers and

between software providers mean that collaboration is restricted and information is

kept secret.

An alternative to full validation is verification by comparison of results with those

produced using another model. The results from the models described here were

sufficiently similar to those reported in literature [9.3, 9.8, 9.9, 9.13, 9.16, 9.17] to be

deemed acceptable for the purposes they were put to in this research. Likewise, if

other models match the results produced in this work then this will provide some

degree of verification.

9.5. Case Studies

The dynamic models developed in this work were tested and used in a number of

different studies. The sections below present highlights from two case studies.

Fuller results from the studies are presented in Appendix B and Appendix C. The

first examined the equivalence of multiple wind turbine models and a single model to

represent a complete wind farm. The second examined the impact of a high

penetration of wind farms on a distribution network.

9.5.1. Aggregate Models of Wind Farms

The modular approach to modelling a single wind turbine provides flexibility but

from the system operator’s perspective, the response of each individual turbine

matters less than the response of complete wind farms. Thus, aggregate models of

195

complete wind farms would be preferable. However, there are a number of issues to

be considered, including:

• The presence of multiple, independently-controlled machines

• A non-uniform wind speed pattern across a wind farm

• A substantial electrical network within a large wind farm

The scope for aggregate wind farm models was explored with tests conducted to

study whether a single machine could be used in power system simulation to

represent the multiple machines in a wind farm. The results suggest that from a

wider network perspective, a wind farm with multiple machines may be equally well

represented by a single machine as with multiple machines as long as the wind farm

electrical network is not too large and so places a much higher impedance between

the network and the generators.

This is illustrated in Figure 9.9, which shows an example of the very similar

responses obtained for three different studies using three different representations of

a wind farm:

• Category A studies used a single generator of rating 20MVA

• Category B studies used ten 2MVA generators connected at a single bus

• Category C studies used ten 2MVA generators connected to separate buses in an

electrical network with low impedance lines

196

Figure 9.9 – Real power in branch 102-103 for the three categories of study set one

-40

-30

-20

-10

0

10

20

30

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

a1 b1 c1

Differences in response emerged when the impedance of the lines in the wind farm

network itself were increased, i.e. if the wind farm network is bigger and spread over

a larger area. Figure 9.10 shows that bus angle responses A and B behave quite

differently to response C. In A and B, bus angle drops and settles at a new value. In

C, bus angle starts rising but is restored to its initial value when the fault is cleared.

This is due to greater line impedances in the wind farm network. This higher

impedance alters the dynamic relationship between the network and the wind turbine

models. The result is that the turbines are more stable and the bus angle at bus 103

rises then returns to its original value in response C but changes rapidly and shifts to

a new steady state value in responses A and B.

197

Figure 9.10 – Bus angle at bus 103 for the three categories of study set three

-400

-300

-200

-100

0

100

200

0 0.5 1 1.5 2 2.5

a3 b3 c3

Full details of the study and all the results can be found in Appendix B.

9.5.2. Study of the Effect of a High Penetration of Wind Farms

In the second case study, tests were conducted to examine the effect of a high

penetration of wind farms on a distribution network. A modified version of a

standard IEEE test network was used and a single model of a doubly fed induction

generator was used to represent each wind farm. Studies were conducted under three

different load conditions and six different wind farm conditions. Two main types of

study were performed: the dynamic response to a line fault; and the dynamic

response to changes in wind power.

The results in Appendix C illustrate some of the possible effects of a high penetration

of wind farms on a distribution network. These include variations in system stability

depending on wind farm output, and voltage fluctuations as a result of changes in

wind power. Some areas for possible further investigation were identified.

198

The results for the line fault disturbance tests suggest that a high penetration of wind

farms may cause network instability. The example in Figure 9.11 shows how the

smoothness of post-fault recovery worsens as the wind farm outputs are increased.

Ultimately, wind farm outputs of 20MW result in post-fault instability.

Figure 9.11 – Bus 1 voltage for medium load condition and different wind farm conditions

The system only became unstable following a line fault when the wind farms were

operating at full output and all tripped off due to the fault disturbance. The results

illustrate why fault ride-through capabilities were of great interest to network

operators at the beginning of the decade as large numbers of wind farms were

installed. In the simulations performed in this study, the wind farms do little to

support the network and have inadequate fault ride-through performance. In the last

few years the fault ride-through performance of modern turbines has been improved

with changes to the control systems used. The models developed here could be

revised to implement these new control systems as information on their behaviour

becomes available.

199

The results for the varying input power tests suggest that changes in input power will

result in changes and oscillations in the power output from wind farms, which will

cause shifts and oscillations in network voltages. The grid in-feed to a network with

a high penetration of wind farms must be able to compensate for the variations in

power that result from changes in wind speed.

For example, Figure 9.12 shows how the voltage at bus 30 – far from the swing bus

and with a wind farm connected – varies for the different wind farm scenarios in the

low load condition. The variations in wind farm power are reflected in the bus

voltage. The base level of the voltage at this bus is different for the different wind

farm scenarios; higher wind farm outputs result in higher bus voltages.

Figure 9.12 – Bus 30 voltage for low load condition and different wind farm scenarios (different

MW base levels)

Furthermore, it was shown that the oscillations caused by changes in input power

interfere with one another. The timing of such changes lead to constructive or

200

destructive interference. A brief supplementary study revealed that unfortunate

timing of changes in input power could lead to significant constructive interference

of waveforms and the exceeding of protection limits. For example, Figure 9.13

shows that with the final ramp in input power delayed by three seconds, there is

constructive rather than destructive interference of the power output waveforms

resulting in oscillations of much greater amplitude. In fact, the additive effects are

enough in scenario d to drive the machine power and rotor current magnitude high

enough that the protection is operated and the machine trips. This issue could be

investigated further. In particular, the damping of the oscillations must be improved,

perhaps through improvements in the control systems.

Figure 9.13 – Bus 30 wind farm output for low load condition and different wind farm scenarios

(different MW base levels) and the adjusted pattern of input power

The results of these simulations, which are presented in full in Appendix C, illustrate

effects that may be experienced on distribution networks with a high penetration of

wind farms. However, as noted above, the models used have not been validated

against real-life systems.

201


This chapter explained the need for dynamic models of modern wind farms and

described the development of a dynamic model for power system simulation in

PSS/E. The various options available and the associated assumptions and

simplifications were explored to provide a comprehensive statement of the problem.

Models were developed and shown to work effectively in PSS/E. It was

acknowledged that the models require validation to be applied more fully and this

was identified as one of the areas for further work. The principal barrier to model

development was, and remains, the lack of publicly available detailed information on

the behaviour of modern wind turbines, particularly their control systems.

The development of dynamic models of wind farms provides an example of the

extensions required in power system simulation to manage the changes in the

electricity system, as discussed in previous Chapters. The expansion of renewables

and DG requires new approaches and new tools to support planning and design of the

network.

From the experience described in this Chapter, it can be concluded that the

development of models of new technologies is an onerous task. It requires an

understanding of the simulation software being used as well as the physical

characteristics of the device being modelled. Information is likely to be lacking

because new technologies are subject to intellectual property protection. For

example, the control systems on the model were designed rather than representing an

actual piece of plant. There are very many design decisions that can influence the

model in different ways and each decision normally reduces the scope or

applicability of the model. A formal design process was not used in the work

described here. The application of more formal and rigorous methods may lead to

more robust models but is also likely to incur additional costs in time and effort.

Thus, it can be concluded that while new models must be developed to support

analysis of new technologies in future distribution networks, this presents a very

202

considerable challenge to DNOs. In providing a comprehensive review of the task

and offering a non-proprietary model for others to use in their work, this research

contributes to the general understanding of the dynamic modelling of wind farms.

While the models described here are limited in their scope, being designed for

assessment of power system stability at electromechanical frequencies, and have not

been validated with experimental data, they can still be of value to system planners

as they endeavour to meet the challenges facing them.

The study of aggregate wind farm models suggested that for normal power system

simulation, a single machine could be used to represent a wind farm with multiple

turbines as long as the wind farm electrical network did not place high impedances

between turbines and the grid. This would greatly simplify the modelling

requirements and is in line with the perspective of system operators, who will wish to

view wind farms as single entities.

The study of the effect of a high penetration of wind farms on distribution networks

revealed that the response depended on the wind farm operating conditions. The

wind farms rode through faults when at medium and low power output levels but

became unstable when at full power output. Thus, the results indicated that it was

possible, although unlikely, for large amounts of wind power to trip off under certain

fault conditions and so highlighted the importance of fault ride-through capabilities.

The studies of varying input power highlighted the resultant variations in network

power flows and voltages and suggested that unfortunate timing of input changes

might cause constructive interference of oscillations and lead to instability.

It is understood that modern wind turbines have more advanced control systems and

have addressed some of the potential problems highlighted in this report. However,

while the control systems and detailed information on performance remain a closely

guarded secret, it is difficult for researchers and network operators without access to

this information to perform realistic studies. When information on the actual control

systems is made available, they can be modelled within the PSS/E model framework

established in this research.

203


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9.2. Heier,S.; “Grid Integration of Wind Energy Conversion Systems”; 1998; John

Wiley & Sons; ISBN 0 471 97143 X

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in the electric power supply”; Electrical Power and Energy Systems, 22 (2000),

p.421-434; Elsevier; ISSN 01420615

9.4. Akhmatov,V., Nielsen,A.H., Knudsen,H.; “Electromechanical interaction and

stability of power grids with windmills”; Proceedings of the IASTED

International Conference, Power and Energy Systems, September 19-22, 2000,

Marbella, Spain

9.5. Salman,S.K., Teo,A.L.J., Rida,I.M.; “The Effect of Shaft Modelling on the

Assessment of fault CCT and Power Quality of a Wind Farm”; Proceedings of

2000 International Conference on Harmonics and Quality of Power, vol.3, 1-4

October 2000, Orlando, FL, USA

9.6. Knudsen,H., Akhmatov,V.; “Induction Generator Models in Dynamic

Simulation Tools”; IPST’99, International Conference on Power Systems

Transients, June 20-24, 1999, Budapest, Hungary

9.7. Kundur,P.; “Power System Stability and Control”; 1993; McGraw-Hill Inc.;

ISBN 0 07 035958 X

9.8. Slootweg,J.G., Polinder,H., Kling,W.L.; “Dynamic Modelling of a Wind

Turbine with Doubly Fed Induction Generator”; IEEE Power Engineering

Society Summer Meeting, 15-19 July 2001, Vancouver, Canada

9.9. Usaola,J., Ledesma,P.; “Dynamic incidence of wind turbines in networks with

high wind penetration”; IEEE Power Engineering Society Summer Meeting,

15-19 July 2001, Vancouver, Canada

9.10. Ledesma,P., Usaola,J.; “Minimum Voltage Protections in Variable Speed Wind

Farms”; 2001 IEEE Porto Power Tech (PPT 2001) Conference, 10-13

September 2001, Porto, Portugal; 0-7803-7139-9

9.11. CIGRE; “CIGRE Technical Brochure on Modeling New Forms of Generation

and Storage”; TF 38.01.10; November 2000; contributions by N.Hatziargyriou,

204

M.Donnelly, S.Papathanassiou, J.A.Pecas Lopes, M.Takasaki, H.Chao,

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Modelling of Double Fed Induction Generators (DFIG)”; British Isles Wind

Technical Panel; November 2001

9.13. Saenz,J.R., Tapia,G., Ostolaza,X., Tapia,A., Criado,R., Berasategui,J.L.;

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2001, Amsterdam, The Netherlands; 18 - 21 June 2001

9.14. SP Power Systems Limited, (Dallachy,J.L.); “Transmission Connection

Requirements for Wind Farms – DRAFT”; Issue No.1.8, 04/02/02

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2002, Manchester, UK

9.16. Rostoen,H.O., Undeland,T.M., Gjengedal,T.; “Doubly Fed Induction

Generator in a Wind Turbine”; IEEE Norway Section Workshop on Wind

Power, Oslo, Norway, 17-18 June 2002

9.17. Atkinson,D.J., Lakin,R.A., Jones,R.; “A vector-controlled doubly-fed

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Unbalance in weak Grids Connected to Wind Plants”; IEEE Norway Section

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205

10. Conclusions

In the research reported here, it has been shown that there are new challenges in

electricity distribution network planning that mean the conventional approach must

be reviewed and updated to address a number of shortcomings. The expansion of

distributed generation (DG) presents the greatest challenge and must be addressed by

enhancing planners’ capabilities in dealing with it. It has been demonstrated that the

application of methods from other domains can help identify the shortcomings in the

conventional approach and help improve it. This research produced examples using

methods drawn from the domains of engineering design theory, multiple criteria

decision making (MCDM), information management and scenario analysis. In

conjunction with these new methods, the development of dynamic models of wind

farms demonstrated how modelling resources must be expanded to incorporate new

technologies. This is necessary if network planners are to properly understand the

impact on their networks and so design them to accommodate new technologies.

Thus, the thesis of the work has been proven.

A new model of the conventional approach to electricity distribution network

planning provides a means of analysing the conventional approach and identifying

shortcomings in it. The model was developed using a modified knowledge

modelling methodology, a stripped-down version of the KADS methodology, which

itself might be deployed in the analysis of other engineering activities. The

knowledge model provides a new perspective on the distribution planning activity by

identifying the separate tasks that must be fulfilled and the methods used to perform

these tasks.

An up-to-date review of the drivers and new directions in distribution network

planning revealed that DG is the primary issue presenting new challenges. DNOs

will move in new directions both in network architecture and operation, and in

institutional and organisational structures.

206

In constructing the new model of the conventional approach, engineering design

theory was applied and this helped to identify particular shortcomings in the growing

burden of power system simulation and analysis, limited effectiveness in information

and knowledge management, and a failure to incorporate new technologies as

options in network planning and design.

The conventional approach must change to improve the recording of the rationale

behind decisions and provide more effective management of knowledge. This is

necessary to enhance the productivity of planners and may be accomplished by the

introduction of structured decision making techniques, pre-defined justifications that

planners can re-use, and ex-post use of knowledge modelling methods.

The conventional approach must change to improve the management of information

because planners face new challenges in handling new technologies and new

commercial and operational systems. An assessment of the problem identified three

methods that could improve the collection of network planning information as a

structured approach, standard formats and generic devices or data categories. For the

purposes of distribution network planning, DG can be effectively characterised by

collecting information using the new structured approach developed here and

including eight categories: general description; power and energy capabilities;

variable costs; fixed costs; electrical connection; protection; control; and

communications. However, the problem with structured approaches for information

collection, standard formats and generic data is that they restrict the expression of

diversity and nuance. In certain circumstances this is certainly a bad thing but when

presented with having to collect and process vast amounts of information in a short

time, the value of such methods becomes clear. From study in this area it is

concluded that it is inevitable that software vendors – if not the utilities and

manufacturers – will gradually converge on standard formats. An important aspect

of most modern data formats is their extensibility and interoperability so the

traditional problems associated with lock-in to standard formats are mostly overcome

and the risk of adopting any given standard is thereby reduced.

207

The adoption of structured decision making through the use of a framework such as

that outlined in Chapter 6 would offer benefits to electricity distribution network

planning in the incorporation of new technologies and the recording of rationale.

The formalised collection of information facilitates solution-neutral identification of

issues and options, as is recommended in engineering design theory for the

incorporation of new technologies and development of novel ideas. This would mark

a significant change from the conventional approach, which assumes the use of

conventional solutions. The explicit identification of issues, options and the analysis

used to make decisions supports the recording of rationale, which is important in

enhancing planner productivity and in meeting the need for tractable and transparent

decisions.

The calculation of alternative cost-benefit ratios, where the benefit or “cost” need not

be expressed in purely financial terms but as a score or some measure that still allows

objective comparison of different options, offers a new perspective on planning

decisions, including a means of evaluating the relative environmental cost of

different options. This is of value to decision makers in a regulatory and social

environment that places ever more emphasis on protection of the environment.

Regulators may wish to enforce the calculation and presentation of such ratios to

ensure that issues that are sometimes peripheral to planning decisions are properly

taken account of.

The analysis of alternative options for expanding the supply to a remote industrial

facility found that grid reinforcement had the highest overall desirability score. It

also presented the best value in terms of the environmental cost / benefit ratio

(defined using non-financial measures). This confirms that despite the excitement

over DG and its growth, conventional grid reinforcement is still an attractive option.

However, the option with the lowest net present cost was found to be the single gas

turbine and the option with the highest benefit / cost ratio was the multiple gas

turbine option. This shows that DG options are valid alternatives that may be the

best option depending on the specific decision criteria being applied in a given

circumstance.

208

The fair evaluation of DG is subject to the regulatory framework, which may forbid

network operators from owning DG and require complex commercial agreements to

be reached between parties, thereby obscuring the opportunities to find and

implement the least cost or best value solution. This complexity in commercial

agreements presents another new challenge for network operators but it is one that

they should face up to because it will facilitate the exploitation of new resources like

DG.

Scenario analysis was identified as the premier method for managing uncertainty and

risk in network planning and handling DG and its growing impact. While driven by

parties separate from the network operator, DG presents particular challenges

because its expansion, in terms of size, location and timing, is very different from

load growth, which is what the conventional approach is focused upon. Of the

methods described for managing uncertainty and risk, scenario analysis was

highlighted as fitting within an MCDM framework and facilitating the flexible

assessment of a range of issues and options. It also supports risk-based analysis

rather than relying on the law of large numbers necessary to validate probabilistic

results.

An original methodology was devised for the creation of scenarios with DG

connected to LV grids. This methodology is original in its combination of a number

of features: firstly, interpreting high-level national forecasts for DG or renewable

power in terms of the likely impact on LV grids; secondly, by producing quantitative

technology rankings based on a survey of expert opinion; and thirdly, by offering

three strategies for combining the two perspectives to produce scenarios of interest.

This combined approach provides results that are robust from the perspective of

high-level forecasts of DG penetration and from the perspective of how likely each

technology is to be installed in a particular LV grid.

The top-down forecasts used in the study suggest overall levels of DG that are quite

low but in reality some LV grids will contain much more DG than others. The

209

penetration level will depend on the suitability of local conditions to DG deployment,

particularly in terms of the weather, the capability of the existing grid, and the nature

of the load customers. It will also depend on the regulatory framework and the

sophistication of commercial agreements between network operators and owners of

DG. The more complexity required in commercial agreements, the greater the

barrier to DG expansion, so vertically integrated utilities, who may have more

flexibility in defining the commercial framework, are in a better position to exploit

the benefits of DG in their networks more quickly.

The survey of expert opinion identified photovoltaics as the dominant DG

technology in LV grids in both 2010 and 2020. Reciprocating engines and wind

turbines also appeared frequently in the top three ranked technologies in the fifteen

scenarios, with wind turbines coming first in a number of scenarios based on rural

areas in northern Europe. In 2020, the pattern remains much the same but with new

technologies like micro gas turbines, biogas engines and fuel cells featuring more

prominently.

The expansion of renewables and DG requires new models to support planning and

design of the network. The development of dynamic models of wind farms is one

example of the extensions required in power system simulation to manage the

changes in the electricity system. Models such as these are essential to system

planners as they endeavour to meet the challenges facing them.

A comprehensive analysis was performed of the modelling challenges posed by

modern wind farms and the need for dynamic models. Models were developed and

shown to work effectively in PSS/E, a widely used power system simulation

package. It was acknowledged that the models require validation to be applied more

fully and this was identified as one of the areas for further work. However, the lack

of publicly available information on modern wind turbines, particularly their control

systems, was a significant barrier to this research and remains so now, hindering the

development of models by others including network operators.

210

The study of aggregate wind farm models suggested that for normal power system

simulation, a single machine could be used to represent a wind farm with multiple

turbines. This would greatly simplify the modelling requirements and is in line with

the perspective of system operators, who will wish to view wind farms as single

entities. The results start to diverge as the wind farm electrical network becomes

large and its high impedance reduces the coupling between the network and

individual machines. This indicates that wind farms spread over very large areas

may not be accurately represented with an aggregate model, although the studies do

not support firm conclusions on the exact size of networks affected. This could be an

area for further work.

The study of the effect of a high penetration of wind farms on distribution networks

revealed that the wind farms rode through faults when at medium and low power

output levels but became unstable when at full power output. Thus, the results

indicated that it was possible for large amounts of wind power to trip off under fault

conditions. This illustrates why fault ride-through capabilities on wind turbines are

so important to system operators wishing to maintain system stability. However, the

probability of instability problems caused by wind power dropping off during a

disturbance is low due to the low probability that the wind farms will actually be

producing close to their maximum output. The studies of varying input power

highlighted the resultant variations in network power flows and voltages and

suggested that unfortunate timing of input changes might cause constructive

interference of oscillations and lead to instability.

It is understood that modern wind turbines have more advanced control systems and

have addressed some of the potential problems highlighted in this report. However,

while the control systems and detailed information on performance remain a closely

guarded secret, it is difficult for researchers and network operators without access to

this information to perform realistic studies. When information on the actual control

systems is made available, they can be modelled within the PSS/E model framework

established in this research.

211

It was concluded that while new models must be developed to support analysis of

new technologies in future distribution networks, this presents a very considerable

challenge to DNOs. It requires an understanding of the physical characteristics of

the device being modelled, which is obviously difficult for new technologies.

Information is likely to be lacking and there are very many design decisions that can

influence the model in different ways. It also requires a deep understanding of the

simulation software being used. The expertise in modelling and simulation will vary

from DNO to DNO but some of them are likely to require external assistance. This

provides another example of the organisational shortcomings highlighted in Chapter

4.

In conclusion, the changes taking place in electricity distribution network planning

are dramatic and the challenges are great. The conventional approach must be

updated and this can be supported by the application of methods from other domains

and the development of new tools such as those described here. Engineering design

theory, decision support and scoring methods for quantitative assessment,

information management, particularly for DG, a methodology for robust scenario

preparation, and new dynamic models of wind farms have all been investigated and

offer a valuable contribution to the development of electricity distribution network

planning and the changes necessary to meet the new challenges of the 21st century.

212

Appendix A. DG Penetration Survey Results

This appendix details the results of the survey used in the bottom-up assessment of

DG penetration described in section 8.2.3. For each of the LV grid scenarios,

respondents to the questionnaire ranked the DG technologies in terms of which are

most likely to be installed in the scenario conditions by 2010 and by 2020. A

ranking of “1” indicates the technology is most likely to be installed in the associated

grid conditions. Rankings of “2”, “3”, “4”, etc. indicate decreasing likelihood of

technologies being installed. The rankings from different respondents were

combined to calculate average rankings or scores for 2010 and 2020 for each

scenario. These scores, which give an indication of which technologies are

considered most suited to each grid scenario and are most likely to be installed, are

shown in the tables below. Respondents also provided comments to support their

rankings. The comments are summarised in the results shown below.

213

A.1. Scenario 1. Residential Ring, Germany

Table A.1 – Questionnaire results for scenario 1

DG Technology Score 2010

Score 2020

Comments

Reciprocating engines 2.3 3.7 Governments support CHP engines because of potential CO2 reductions. Diesel engines may be used to provide backup power but other technologies are expected to take over by 2020.

Micro gas turbines 6.7 5.3 Micro gas turbines are more likely in industrial applications but may be used in domestic CHP by 2020

Fuel cells 5.3 3.7 Governments support fuel cells because of the potential CO2 reductions. Fuel cells are still in the early stages of development but may find applications in domestic combined heat and power, especially by 2020.

Photovoltaics 1.3 1.0 PV is the most advanced DG technology at the moment and in Germany benefits from the highest feed-in tariff.

Wind turbines 7.3 7.0 Wind turbines are more likely to be connected at MV or HV and are unlikely in an urban area.

Micro hydro 7.7 7.7 Micro hydro is unlikely in an urban area. Biogas engines 7.3 7.3 Biogas is unlikely in an urban area with residential

loads. Other DG 5.0 4.7 Domestic CHP systems with Stirling Engines are now

available and may be suited to this scenario. If available, geothermal might be used.

No distributed generation 6.3 6.7

Photovoltaics are clearly considered the most likely technology in this scenario.

Reciprocating engines, in CHP or backup power applications, are in second place in

2010 but fuel cells are predicted to catch up by 2020. Domestic CHP systems with

Stirling Engines are considered the next most likely technology to be installed in this

scenario.

214

A.2. Scenario 2. Commercial Mesh, Germany



Score 2020

Comments

Reciprocating engines 2.0 3.7 Governments support CHP engines because of potential CO2 reductions. In these circumstances, especially with commercial loads, backup power may be provided with diesel engines most likely in 2010 but other technologies expected to take over by 2020.

Micro gas turbines 6.0 4.7 Micro gas turbines are more likely in industrial applications although they may also be used in retail or office applications, especially by 2020.

Fuel cells 5.3 4.3 Governments support fuel cells because of the potential CO2 reductions. Fuel cells are still in the early stages of development but may find applications in small-scale combined heat and power, especially by 2020.

Photovoltaics 1.3 1.0 PV is the most advanced DG technology at the moment and in Germany benefits from the highest feed-in tariff.

Wind turbines 7.0 6.7 Wind turbines are more likely to be connected at MV or HV and are unlikely in an urban area.

Micro hydro 7.7 7.7 Micro hydro is unlikely in an urban area. Biogas engines 7.3 7.3 Biogas is unlikely in an urban area. Other DG 5.3 4.3 CHP systems with Stirling Engines are now available

and may be suited to this scenario. If available, geothermal might be used.



Reciprocating engines, in CHP or backup power applications, are in second place.

Small-scale CHP systems with Stirling Engines or fuel cells are considered the next

most likely technology to be installed in this scenario.

215

A.3. Scenario 3. Mixed Radial, Germany



Score 2020

Comments

Reciprocating engines 3.0 5.0 In these circumstances, especially with commercial loads, backup power may be provided, with diesel engines most likely in 2010 but other technologies expected to take over by 2020.

Micro gas turbines 5.5 3.0 Micro gas turbines may find applications in small-scale combined heat and power and in providing backup supplies to commercial loads, especially by 2020.

Fuel cells 5.5 4.0 Fuel cells may find applications in small-scale combined heat and power and in providing backup supplies to commercial loads, especially by 2020.

Photovoltaics 1.5 1.0 This scenario does not have the best conditions for PV but it is still likely to be the most common form of DG.

Wind turbines 6.5 6.0 Wind turbines are unlikely in an urban area. Micro hydro 7.0 7.0 Micro hydro is unlikely in an urban area. Biogas engines 6.5 6.5 Biogas is unlikely in an urban area. Other DG 6.5 5.0 Micro CHP systems with Stirling Engines are now

available and may be suited to this scenario. If available, geothermal might be used.



Reciprocating engines, in CHP or backup power applications, are in second place in

2010 but fall behind micro gas turbines and fuel cells in 2020.

216

A.4. Scenario 4. Urban Meshed, UK



Score 2020

Comments

Reciprocating engines 3.0 4.5 In these circumstances, with commercial loads, backup power may be provided, with diesel engines most likely in 2010 but other technologies expected to take over by 2020.



Photovoltaics 3.5 2.5 The lower solar radiation and lower temperatures in the UK makes small-scale CHP more attractive than PV but PV is still likely to be installed in significant numbers.

Wind turbines 5.5 5.0 Wind turbines are unlikely in an urban area unless there are significant developments in small-scale, unobtrusive designs.

Micro hydro 7.0 7.0 Micro hydro is unlikely in an urban area. Biogas engines 7.0 7.0 Biogas is unlikely in an urban area. Other DG 5.5 4.5 Micro CHP systems with Stirling Engines are now

available and may be suited to this scenario where central heating is likely to be widely installed. If available, geothermal might be used.


No technologies emerge with very high scores in this scenario. Reciprocating

engines, probably to provide backup power, and photovoltaics, despite the less than

perfect conditions, come out highest in 2010. By 2020 it is predicted that micro gas

turbines will have overtaken reciprocating engines.

217

A.5. Scenario 5. Rural, Poland



Score 2020

Comments

Reciprocating engines 5.7 5.7 In a rural area with mostly residential loads, it is unlikely that there will be many applications for reciprocating engines although they may be used as backup supplies.

Micro gas turbines 6.0 5.0 In a rural area with mostly residential loads, it is unlikely that there will be many applications for micro gas turbines.

Fuel cells 7.0 6.3 In a rural area with mostly residential loads, it is unlikely that there will be many applications for fuel cells.

Photovoltaics 3.0 2.7 With relatively high solar irradiation, PV could be attractive in this scenario if the necessary financial incentives are there. Solar energy may also be used for heating.

Wind turbines 3.0 2.0 Wind speed is sufficient to make wind turbines viable and they may be accepted in the rural area.

Micro hydro 3.7 4.0 If the resources are available, micro hydro may feature although the potential is limited.

Biogas engines 4.3 4.0 Biogas fuel may be available from farms or sewage refineries but the residential loads in this scenario make biogas fuelled DG less likely.

Other DG 5.0 4.7 Small CHP fed by agricultural waste, wood crops and forest residues. Geothermal, if available


The renewable power sources – photovoltaics, wind turbines and micro hydro – score

most highly in this scenario although none of them are favoured particularly strongly.

Biogas engines are the next most likely technology to be installed.

218

A.6. Scenario 6. Urban Link, Poland



Score 2020

Comments

Reciprocating engines 2.3 4.0 In these circumstances, especially with commercial loads, backup power may be provided with diesel engines most likely in 2010 but other technologies expected to take over by 2020.



Photovoltaics 2.7 2.0 Solar irradiation is not as high as other areas but PV is still appropriate, although the expense means some financial support must be provided.

Wind turbines 6.0 5.7 Wind turbines are unlikely in an urban area. Micro hydro 6.7 6.7 Micro hydro is unlikely in an urban area. Biogas engines 5.3 5.3 Biogas might be available from refuse dumps and

sewage refineries but is unlikely in an urban area. Other DG 5.0 3.7 Small CHP may be fed by municipal waste

Micro CHP systems with Stirling Engines are now available and may be suited to this scenario. Geothermal may be used, if available


Reciprocating engines and photovoltaics emerge as the highest scorers in this

scenario for 2010 and PV moves further ahead in 2020. Micro gas turbines move

into second place by 2020, followed in third place by other DG – probably small

CHP fed by municipal waste or with a Stirling engine.

219

A.7. Scenario 7. Urban Radial, France



Score 2020

Comments

Reciprocating engines 3.0 5.0 In these circumstances, backup power may be provided with diesel engines most likely in 2010 but other technologies expected to take over by 2020.

Micro gas turbines 5.5 3.5 Micro gas turbines may find applications in backup power, especially by 2020.

Fuel cells 5.5 4.0 Fuel cells may find applications in backup power, especially by 2020.

Photovoltaics 1.5 1.0 This scenario is most suited to PV. Wind turbines 7.0 7.0 Wind turbines are unlikely in an urban area with a low

wind speed. Micro hydro 7.0 7.0 Micro hydro is unlikely in an urban area. Biogas engines 6.5 6.5 Biogas is unlikely in an urban area. Other DG 6.5 5.5 Micro CHP systems with Stirling Engines are now

available and may be suited to this scenario. Geothermal may be used, if available.


Photovoltaics are the clear winner in this scenario because of the high solar

irradiation, far ahead of other technologies in both 2010 and 2020. Reciprocating

engines are in second place in 2010, most likely in backup power applications, but

are expected to fall behind micro gas turbines and fuel cells by 2020.

220

A.8. Scenario 8. Rural, Italy



Score 2020

Comments

Reciprocating engines 3.7 4.0 In these circumstances, backup power may be provided to farms. Diesel engines are most likely because they are good at partial load and are reliable.

Micro gas turbines 6.7 6.0 Micro gas turbines may find applications in backup power if there is a gas network available but they are better used to supply a continuous CHP load. Also, their reliability has still to be demonstrated and costs are too high so new technology development is required.

Fuel cells 7.7 6.3 Currently, only phosphoric acid fuel cells are commercial. Other types are too expensive. Fuel cells may find applications in backup power if there is a fuel delivery network.

Photovoltaics 1.7 1.3 PV is the technology most suited to this scenario but its widespread adoption will depend on government finance programmes.

Wind turbines 5.0 4.3 Although farms probably provide the best location for small wind turbines, the wind speed is too low and small turbines are too expensive.

Micro hydro 5.0 5.0 If the resources are available, micro hydro may be installed but there are limited sites where the geography makes it economic.

Biogas engines 4.0 4.0 If biogas is available it may be possible but a sparsely populated area may not have the electrical network infrastructure to support it.

Other DG 6.7 6.0 Biomass-fired CHP with external combustion engines (Stirling cycle or organic Rankine cycle) but these need new technology development If resources are available, geothermal may be possible but it would depend on the strength of the electrical network.


Photovoltaics again emerge as the clear winner in this scenario. The second highest

ranking in 2010 goes to No DG with reciprocating engines for backup power in third

place. By 2020, it is thought that reciprocating engines and biogas engines will be as

likely as No DG.

221

A.9. Scenario 9. Urban Radial, Italy



Score 2020

Comments

Reciprocating engines 2.3 4.3 Some of the commercial or industrial loads may use diesel or gas engines as backup power supplies because they are good at partial load and are reliable.

Micro gas turbines 4.7 2.7 Micro gas turbines require a gas supply network, their reliability has still to be demonstrated and their cost is currently too great. New technology development is required. They are better used to supply a continuous CHP load but may surpass diesels as the primary choice for backup supplies.

Fuel cells 6.0 4.0 Currently, only phosphoric acid fuel cells are commercial. Other types are too expensive. Fuel cells may find applications in backup power if there is a fuel delivery network.

Photovoltaics 1.7 1.3 With high solar irradiation and suitable loads, PV should be the most common form of DG but their high ranking here assumes government finance programmes.

Wind turbines 8.0 8.0 Low average wind speed in an urban area suggests no wind turbines. Small turbines are too expensive.

Micro hydro 6.3 6.3 There are limited sites where the geography makes micro hydro economic but it is unlikely in an urban area.

Biogas engines 7.0 7.0 Biogas is limited availability of the fuel but is unlikely in an urban area where other options are available.

Other DG 6.7 6.0 Micro CHP systems with Stirling Engines are now available and may be suited to this scenario. Biomass-fired CHP with external combustion engines (Stirling cycle or organic Rankine cycle) might be used but require new technology development. Geothermal might be used, if available


Photovoltaics are ranked most highly in this scenario for both 2010 and 2020.

Reciprocating engines come second in 2010 but are replaced by micro gas turbines in

2020. In both 2010 and 2020, the third ranked option is No DG.

222

A.10. Scenario 10. Urban Link, Greece



Score 2020

Comments

Reciprocating engines 3.5 5.0 Some of the commercial or industrial loads may use diesel engines as backup power supplies.

Micro gas turbines 5.5 3.5 Micro gas turbines may surpass diesels as the primary choice for backup power and may find additional applications in CHP.

Fuel cells 5.5 4.0 Fuel cells may be used as backup and for CHP but are likely to remain more expensive than the alternatives

Photovoltaics 1.5 1.0 With high solar irradiation and suitable loads, PV should be the most common form of DG.

Wind turbines 7.5 7.5 Low average wind speed in an urban area suggests no wind turbines.

Micro hydro 7.0 7.0 Micro hydro is unlikely in an urban area. Biogas engines 6.0 6.0 Biogas is unlikely in an urban area where other options

are available. Other DG 8.0 7.5 If resources are available, geothermal may be possible. No distributed generation 5.0 5.5

Photovoltaics are ranked highest in 2010 and 2020. In 2010, reciprocating engines

are thought next most likely to be installed. Other technologies all come behind No

DG, which is ranked third. However, by 2020 micro gas turbines are ranked second

and fuel cells third.

223

A.11. Scenario 11. Rural South Coast, Spain



Score 2020

Comments

Reciprocating engines 5.0 5.7 Some farms may use diesel engines as backup supplies. Micro gas turbines 6.0 5.7 It is unlikely that micro gas turbines will find an

application in this scenario. Fuel cells 7.7 6.0 It is unlikely that fuel cells will find an application in

this scenario. Photovoltaics 1.0 1.0 High solar irradiation makes PV the most promising

DG technology in this scenario. Wind turbines 2.0 2.0 A relatively high wind speed in a rural area with mostly

farm loads makes wind turbines an attractive DG option.

Micro hydro 5.3 5.7 If the resources are available, micro hydro might be exploited but PV and wind seem like the more likely options.

Biogas engines 4.0 4.0 If the biogas fuel is available from the farms then this might be an option but PV and wind seem more promising.

Other DG 8.0 7.7 If geothermal resources are available they might be exploited but PV and wind are likely to be used instead. Storage technologies like flywheels may be used.


Photovoltaics are ranked highest in this scenario but are closely followed by wind

turbines. The next highest ranked technology is biogas engines. These rankings

apply in both 2010 and 2020.

224

A.12. Scenario 12. Rural Link, Netherlands



Score 2020

Comments

Reciprocating engines 4.5 4.5 Some farms may use reciprocating engines for backup power.

Micro gas turbines 7.0 6.5 Micro gas turbines are unlikely unless there is a specific application for them.

Fuel cells 7.0 6.0 Fuel cells are unlikely unless there is a specific application but even then other options will probably be favoured.

Photovoltaics 3.5 2.5 Solar irradiation is not particularly high but PV may still find a place in the DG mix.

Wind turbines 1.5 1.5 The high average wind speed makes wind turbines attractive and the farms should provide a place to host them.

Micro hydro 4.5 4.5 If the resource is available, micro hydro could be exploited.

Biogas engines 4.0 4.0 Biogas engines may be utilised if the fuel was readily available.

Other DG 8.0 7.5 If the resource is available, geothermal could be exploited.


Wind turbines are the highest ranked technology in this scenario, in both 2010 and

2020. Photovoltaics come second and are closer to wind in 2020 than in 2010.

Biogas engines are ranked third most likely to be installed in both time horizons.

225

A.13. Scenario 13. Rural Ring, Belgium



Score 2020

Comments




Photovoltaics 3.5 2.5 Solar irradiation is not particularly high but PV may still find a place in the DG mix.

Wind turbines 1.5 1.5 The relatively high average wind speed makes wind turbines attractive and the farms should provide a place to host them.




No distributed generation 5.0 6.5 The good wind resource in this scenario means there should be some DG by 2010 and more by 2020.

This scenario produces exactly the same predictions as scenario 12. Wind turbines

are the highest ranked technology, in both 2010 and 2020. Photovoltaics come

second and are closer to wind in 2020 than in 2010. Biogas engines are ranked third

most likely to be installed in both time horizons.

226

A.14. Scenario 14. Urban Ring, Denmark



Score 2020

Comments

Reciprocating engines 3.0 4.5 Reciprocating engines may be installed to provide backup power but are likely to be superseded.

Micro gas turbines 4.0 2.0 Micro gas turbines could be used as providers of backup power and CHP to the commercial and industrial loads.

Fuel cells 5.5 4.5 Fuel cells could be used as providers of backup power and CHP to the commercial and industrial loads but are less likely than micro gas turbines.

Photovoltaics 4.0 2.5 The relatively low solar irradiation and availability of other options reduce the attractiveness of PV but it is still likely to be installed in small numbers.

Wind turbines 5.5 5.0 The wind resource is there but it is unlikely that the commercial and industrial customers will favour wind power.

Micro hydro 7.0 7.0 A micro hydro resource is unlikely to be available in this urban area.

Biogas engines 7.0 7.0 Biogas engines running on waste or other fuels available in the urban area may be installed in this area as long as the commercial and industrial customers are willing.

Other DG 6.0 5.0 Micro CHP with Stirling Engine is available now in small sizes and may provide a CHP solution for some of these commercial and small industrial loads. Geothermal might be used, if available


These rankings are based on the general scenario description but it is accepted that

Denmark already has large amounts of what may be considered DG, with many wind

farms and district CHP schemes. In 2010, reciprocating engines are ranked highest

followed by micro gas turbines and photovoltaics. In 2020, micro gas turbines move

into first place, photovoltaics into second, and reciprocating engines drop to be level

with fuel cells.

227

A.15. Scenario 15. Rural Ring, Austria



Score 2020

Comments




Photovoltaics 2.0 1.5 Solar irradiation is relatively high and PV is likely to find a place in the DG mix.

Wind turbines 5.5 5.0 The low average wind speed makes wind turbines unattractive.





Photovoltaics are ranked most highly in this scenario, micro hydro comes second and

biogas engines third. These rankings apply to both 2010 and 2020.

228

Appendix B. Aggregate Models of a Wind Farm

B.1. Methodology for Study of Aggregate Models of a Wind Farm

B.1.1. Test Networks

The models were tested on a simple four-bus network, which was extended to

represent a wind farm network in some studies. The studies were split into three

categories:

• Category A studies used a single 20 MVA generator connected at bus 103 as

shown in Figure B.1.

• Category B studies used ten 2 MVA generators connected at bus 103 as shown in

Figure B.2.

• Category C studies included ten additional buses and lines to represent the

electrical network of a wind farm with one 2 MVA generator at each of the extra

buses, as shown in Figure B.3.

As indicated in the figures, the base voltage at all buses was 33kV and all of the main

branches had resistance 0.01pu and reactance 0.05pu. In most category C studies,

the extra lines representing the wind farm network had resistance 0.001pu and

reactance 0.005pu but this was varied in one study, as explained below. System base

was 100 MVA. The load at bus 104 was 50 MW, 20 MVAr in all studies but the

generator outputs were varied in the different studies as explained below.

229

Figure B.1 – Test network for category A studies

Figure B.2 – Test network for category B studies

230

Figure B.3 – Test network for category C studies

B.1.2. Test Models

A second-order electrical machine model with a first-order turbine shaft model

represented the doubly fed induction generators. The machine rotor voltages were

controlled by simple PI controllers with target values of voltage and rotor speed set

equal to their initial values. Power input to the generators was held constant

throughout the simulations. The doubly fed induction generators and rotor voltage

controllers were modelled with the parameters as shown in Table B.1 and Table B.2

respectively.

Table B.1 – Doubly fed induction generator parameters

Parameter Value LM 4.0 pu LS 0.1 pu LR 0.1 pu RS 0.005 pu RR 0.005 pu

HROTOR 3.0 sec

231

HGEN 0.5 sec DAMP 0.01

Table B.2 – Rotor voltage controller parameters

Parameter Value KDP 1 KDI 0.1 KQP 1 KQI 1

VMRLIM 0.01 pu MSPEED 0 MVOLTS 0 IMRLIM 100 pu

In the studies, the machines were given different base MVA values depending on

whether there were multiple machines or a single machine. In studies with ten

machines, each machine was assigned a base of 2MVA. In studies with a single

machine, the machine was assigned a base of 20MVA. However, the same model

parameter values were used in all studies.

In all studies, the generator at bus 101 was modelled using the GENCLS model from

the PSS/E library with zero inertia and zero damping to emulate the response of an

infinite bus.

B.1.3. Test Procedure

The simulations were run with a time step of 10ms. All studies involved the same

basic procedure:

• The simulation was run up to 1 second.

• A fault was applied on circuit 2 between buses 102 and 104.

• The simulation was run up to 1.2 seconds.

• Circuit 2 between buses 102 and 104 was tripped.

• The simulation was run up to 10 seconds.

A wide variety of simulation variables were recorded during simulation but the post-

simulation analysis focused on the real and reactive power flowing from bus 103 to

232

102 and the bus voltage and bus angle at bus 103. These variables were thought to

give the most effective means of comparing the different wind farm representations

from the perspective of the network.

Four sets of three studies were performed; each of the three categories (A, B and C)

being studied under four different conditions.

B.2. Results for Study of Aggregate Models of a Wind Farm

B.2.1. Study Set One

In study set one, the total output from the wind farm representation at bus 103 was

set to 15 MW and 0 MVAr. In categories B and C the real and reactive power was

divided evenly between the ten machines.

Figure B.4 shows that the three real power responses are very similar. A and B are

exactly the same as one another. Response C shows slight differences in the time

steps immediately following fault clearance.

Figure B.4 – Real power in branch 102-103 for the three categories of study set one

-40

-30

-20

-10

0

10

20

30

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

a1 b1 c1

233

Figure B.5 shows that the three reactive power responses are very similar. There are

slight differences in the short-term transient response when the fault is cleared. All

three categories show an oscillation as the controllers try to restore reactive power to

its initial value.

Figure B.5 – Reactive power in branch 102-103 for the three categories of study set one

-4

-2

0

2

4

6

8

10

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

a1 b1 c1

Figure B.6 shows that the three bus voltage responses are very similar. Response C

is slightly different to responses A and B at the point where the fault is cleared and

voltage is restored.

234

Figure B.6 – Bus voltage at bus 103 for the three categories of study set one

0

0.2

0.4

0.6

0.8

1

1.2

0 0.5 1 1.5 2 2.5

a1 b1 c1

Figure B.7 shows that the three bus angle responses are very similar. However, all

three categories display an undesirable shift in bus voltage angle.

Figure B.7 – Bus angle at bus 103 for the three categories of study set one

-50

0

50

100

150

200

250

300

350

400

0 0.5 1 1.5 2 2.5

a1 b1 c1

235

B.2.2. Study Set Two

In study set two, the total output from the wind farm representation at bus 103 was

set to 10 MW and -5 MVAr. In categories B and C the real and reactive power was

divided evenly between the ten machines.

Figure B.8 shows that the three real power responses are very similar.

Figure B.8 – Real power in branch 102-103 for the three categories of study set two

-50

-40

-30

-20

-10

0

10

20

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

a2 b2 c2

Figure B.9 shows that the three reactive power responses are very similar. There are

slight differences in the short-term transient response when the fault is cleared.

236

Figure B.9 – Reactive power in branch 102-103 for the three categories of study set two

-8

-6

-4

-2

0

2

4

6

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

a2 b2 c2




Figure B.10 – Bus voltage at bus 103 for the three categories of study set two

0

0.2

0.4

0.6

0.8

1

1.2

0 0.5 1 1.5 2 2.5

a2 b2 c2

237


three categories display an undesirable shift in bus voltage angle.

Figure B.11 – Bus angle at bus 103 for the three categories of study set two

-50

0

50

100

150

200

250

300

350

400

0 0.5 1 1.5 2 2.5

a2 b2 c2

B.2.3. Study Set Three

In study set three, the total output from the wind farm representation at bus 103 was

set to 15 MW and 5 MVAr. In categories B and C the real and reactive power was

divided evenly between the ten machines. In this study, the line impedances within

the wind farm network in category C were made the same as in the main network, i.e.

R=0.01pu and X=0.05pu.

Figure B.12 shows that the three real power responses are very similar. A and B are

exactly the same as one another. Response C is slightly different in the period

immediately following fault clearance.

238

Figure B.12 – Real power in branch 102-103 for the three categories of study set three

-10

0

10

20

30

40

50

60

70

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

a3 b3 c3

Figure B.13 shows that the three reactive power responses are very similar. There

are slight differences in the short-term transient response when the fault is cleared.

Figure B.13 – Reactive power in branch 102-103 for the three categories of study set three

-60

-50

-40

-30

-20

-10

0

10

20

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

a3 b3 c3

239




Figure B.14 – Bus voltage at bus 103 for the three categories of study set three

0

0.2

0.4

0.6

0.8

1

1.2

0 0.5 1 1.5 2 2.5

a3 b3 c3

Figure B.15 shows that bus angle responses A and B behave quite differently to

response C. In A and B, bus angle drops and settles at a new value. In C, bus angle

starts rising but is restored to its initial value when the fault is cleared. This is due to

the greater line impedances in the wind farm network: R=0.01pu and X=0.05pu

rather than R=0.001pu and X=0.005pu as in the other studies. This higher

impedance alters the dynamic relationship between the network and the wind turbine

models. The result is that the turbines are more stable and the bus angle at bus 103

rises then returns to its original value in response C but changes rapidly and shifts to

a new steady state value in responses A and B.

240

Figure B.15 – Bus angle at bus 103 for the three categories of study set three

-400

-300

-200

-100

0

100

200

0 0.5 1 1.5 2 2.5

a3 b3 c3

B.2.4. Study Set Four

In study set four, the total output from the wind farm representation at bus 103 was

set to 10 MW and -5 MVAr as in study set two. However, in categories B and C the

real and reactive power was divided between the ten machines according to the

pattern shown in Table B.3.

Table B.3 – Division of power between machines in test categories B and C

Category B Machine

Category C Machine

Real Power (MW)

Reactive Power (MVAr)

Bus 103 Machine 1 Bus 110 Machine 1 0.5 0.0 Bus 103 Machine 2 Bus 111 Machine 1 0.6 -0.1 Bus 103 Machine 3 Bus 112 Machine 1 0.7 -0.2 Bus 103 Machine 4 Bus 113 Machine 1 0.8 -0.3 Bus 103 Machine 5 Bus 114 Machine 1 0.9 -0.4 Bus 103 Machine 6 Bus 115 Machine 1 1.1 -0.6 Bus 103 Machine 7 Bus 116 Machine 1 1.2 -0.7 Bus 103 Machine 8 Bus 117 Machine 1 1.3 -0.8 Bus 103 Machine 9 Bus 118 Machine 1 1.4 -0.9

Bus 103 Machine 10 Bus 119 Machine 1 1.5 -1.0 Total 10.0 -5.0

241

Figure B.16 shows that the three real power responses are very similar.

Figure B.16 – Real power in branch 102-103 for the three categories of study set four

-50

-40

-30

-20

-10

0

10

20

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

a4 b4 c4

Figure B.17 shows that the three reactive power responses are very similar. There

are slight differences in the short-term transient response when the fault is cleared.

Figure B.17 – Reactive power in branch 102-103 for the three categories of study set four

-8

-6

-4

-2

0

2

4

6

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

a4 b4 c4

242




Figure B.18 – Bus voltage at bus 103 for the three categories of study set four

0

0.2

0.4

0.6

0.8

1

1.2

0 0.5 1 1.5 2 2.5

a4 b4 c4

243


three categories display an undesirable shift in bus voltage.

Figure B.19 – Bus angle at bus 103 for the three categories of study set four

-50

0

50

100

150

200

250

300

350

400

0 0.5 1 1.5 2 2.5

a4 b4 c4

244

Appendix C. The Effects of a High Penetration of Wind Farms

C.1. Methodology for Study of the Effects of a High Penetration of Wind Farms

C.1.1. Test Network

The tests were conducted using a modified version of the IEEE 30-bus test network.

The network has buses at 132kV and 33kV; there are 21 loads and four conventional

generators. Fifteen wind farms were added, spread across the network. Some

capacitors were also added to provide power factor correction. A diagram of the

network is shown in Figure C.1. Information on network loads is given below.

Figure C.1 – Diagram of 30-bus network used in wind farm studies

245

C.1.2. Test Models

C.1.2.1. Wind Farms

The wind farms were each represented by a model of a doubly fed induction

generator: a second-order electrical machine model with a first-order turbine shaft

model. The equivalence of single and multiple turbine models was demonstrated in

other studies. The machine rotor voltages were controlled by simple PI controllers

with target values of voltage and rotor speed set equal to their initial values.

The tests were conducted using the versions of the different model components

described in Table C.1. Four separate model components allow the different

components to be updated on their own. The four models all sit within the PSS/E

modelling environment and communicate with each other using PSS/E variables.

Table C.1 – Models used in the study of high penetration of wind farms

Model Description DFIGBB-16 Second-order model of the doubly fed induction generator and

wind turbine shaft DFIGVC-15 Rotor voltage controllers for the DFIG DFIGIN-03 Power input controller for the DFIG DFIGPR-01 Protection system for the DFIG

The doubly fed induction generators and rotor voltage controllers were modelled

with parameters shown in Table C.2 and Table C.3 respectively.

Table C.2 – Doubly fed induction generator parameters

Parameter Value LM 4.0 pu LS 0.1 pu LR 0.1 pu RS 0.005 pu RR 0.005 pu

HROTOR 3.0 sec HGEN 0.5 sec DAMP 0.01

246

Table C.3 – Rotor voltage controller parameters

Parameter Value KDP 1 KDI 0.1 KQP 1 KQI 1

VMRLIM 0.01 pu MSPEED 0 MVOLTS 0 IMRLIM 100 pu

For the fault disturbance tests, no power input model was used so power input to the

wind farms was held constant through each simulation. For the power variation tests,

a version of the DFIGIN power input model was used with a pattern of power input

variations as described in Table C.4. This pattern of input power variations was

adjusted for a supplementary set of studies, as explained below.

Table C.4 – Pattern of power input variations

Time Period Input Power Variations 0 to 5 seconds Power held at its initial value 5 to 10 seconds Power ramped up to 100% of MVA base 10 to 20 seconds Power held at 100% of MVA base 20 to 30 seconds Power ramped down to zero 30 to 40 seconds Power held at zero 40 to 45 seconds Power ramped back up to its initial value After 45 seconds Power held at its value

A protection system model was introduced to model the effect of wind farms tripping

under disturbance conditions. This first version of the protection model only

monitors one condition. If the rotor current magnitude rises too high then the

machine is tripped. After some preliminary studies, the rotor current protection was

set to trip if the magnitude reached 1.5pu. This level was chosen because it results in

some of the wind farms tripping and some of them riding through the line fault

disturbance for most of the wind farm scenarios.

247

The rotor current protection setting was adjusted to 50pu for a supplementary set of

studies to examine the effect of all wind farms staying connected, as explained

below.

C.1.2.2. Conventional Generators

The conventional generator at bus 2, the swing bus, was modelled using the classic

generator model from the PSS/E library (GENCLS). Inertia and damping values

were set to 10 to simulate large grid inertia with considerable damping. The

parameters for the other conventional generators were extracted from typical and

recommended values provided in the PSS/E user manuals. The conventional

generator at bus 1 was modelled using the salient pole generator model from the

PSS/E library (GENSAL), with parameters as shown in Table C.5.

Table C.5 – Salient pole generator parameters

GENSAL Parameter Value T'do 8 T"do 0.05 T"qo 0.12

H 1.1 D 0

Xd 1.8 Xq 1.35 X'd 0.6 X"d 0.2 Xl 0.02

S(1.0) 0.03 S(1.2) 0.25

The conventional generators at buses 5 and 8 were modelled using the round rotor

generator model from the PSS/E library (GENROU), with parameters as shown in

Table C.6.

248

Table C.6 – Round rotor generator parameters

GENROU Parameter Value T'do 2 T"do 0.05 T'qo 1 T"qo 0.05

H 5 D 0

Xd 1.4 Xq 1.35 X'd 0.3 X'q 0.6 X"d 0.2 Xl 0.1

S(1.0) 0.03 S(1.2) 0.4

The conventional generators at buses 1, 5 and 8 were modelled with the same

governor and excitation systems. The steam turbine governor, TGOV1, model and

simple excitation system, SEXS, model were used at all three machines with

parameters as shown in Table C.7 in Table C.8.

Table C.7 – Turbine governor parameters

TGOV1 Parameter Value R 0.05 T1 0.05

VMAX 1.1 VMIN 0.1

T2 2 T3 6 Dt 0

Table C.8 – Simple excitation system parameters

SEXS Parameter Value TA/TB 0.1

TB 10 K 100

TE 0.01 EMIN 0 EMAX 4

249

C.1.3. Test Scenarios

Tests were conducted under three different load conditions and six different wind

farm conditions. The load and wind farm conditions were combined to produce

eighteen test scenarios for each of the test procedures.

C.1.3.1. Load Conditions

Load conditions were varied on the network to produce three scenarios: low (“lo”),

medium (“md”) and high (“hi”). In the low load scenario, all loads were assigned

values 50% of the values on the medium load scenario, for both real and reactive

power. In the high load scenario, all loads were assigned values 150% of the

medium load scenario. The load values for the three load scenarios are shown in

Table C.9.

Table C.9 – Load values for the three scenarios

Low Load Scenario Medium Load Scenario High Load Scenario Bus P (MW) Q (MVAr) P (MW) Q (MVAr) P (MW) Q (MVAr)

2 10.85 6.35 21.7 12.7 32.55 19.05 3 1.2 0.6 2.4 1.2 3.6 1.8 4 3.8 0.8 7.6 1.6 11.4 2.4 5 47.1 9.5 94.2 19 141.3 28.5 7 11.4 5.45 22.8 10.9 34.2 16.35 8 15 15 30 30 45 45 9 2.9 1 5.8 2 8.7 3

10 5.6 3.75 11.2 7.5 16.8 11.25 11 3.1 0.8 6.2 1.6 9.3 2.4 12 4.1 1.25 8.2 2.5 12.3 3.75 13 1.75 0.9 3.5 1.8 5.25 2.7 14 4.5 2.9 9 5.8 13.5 8.7 15 1.6 0.45 3.2 0.9 4.8 1.35 16 4.75 1.7 9.5 3.4 14.25 5.1 17 1.1 0.35 2.2 0.7 3.3 1.05 18 8.75 5.6 17.5 11.2 26.25 16.8 20 1.6 0.8 3.2 1.6 4.8 2.4 21 4.35 3.35 8.7 6.7 13.05 10.05 23 1.75 1.15 3.5 2.3 5.25 3.45 26 1.2 0.45 2.4 0.9 3.6 1.35 27 5.3 0.95 10.6 1.9 15.9 2.85

Totals 141.7 63.1 283.4 126.2 425.1 189.3

250

C.1.3.2. Wind Farm Conditions

The wind farm conditions were varied by changing the status and power output of

each wind farm. All fifteen wind farms were set to the same condition. In all

scenarios, the wind farms were specified to operate at unity power factor with

reactive power of zero. The wind farm scenarios were as described in Table C.10.

Table C.10 – Wind farm scenarios

Wind Farm Scenario

Wind Farm Status

Output of each Wind Farm (MW)

Total Output from 17 Wind Farms (MW)

Scenario X Off 0 0 Scenario 0 On 0 0 Scenario A On 5 85 Scenario B On 10 170 Scenario C On 15 255 Scenario D On 20 340

C.1.3.3. Output of Conventional Generators

The output of the conventional generators at buses 1,5 and 8 were set to 100MW in

each simulation. The swing bus made up the balance depending on the loads, wind

farm outputs and network losses. Reactive power varied at each generator depending

on voltage control requirements in each scenario. Reactive power limits for the four

conventional generators were set to ±240MVAr.

C.1.4. Test Procedures

For each of the eighteen scenarios (combining three load conditions with six

generator conditions), two separate tests were performed: a line fault disturbance test

and a varying power input test.

C.1.4.1. Line Fault Disturbance Test

The line fault disturbance test was designed to study the effect on network voltages

of the response of a high penetration of wind farms to a significant fault disturbance.

The line fault disturbance test involved:

251

• running the simulation to one second;

• applying a line fault on line 1-2;

• running the simulation for 200ms with the fault applied;

• tripping the faulted line; and

• running the simulation on to 5 seconds

The simulation was run with a time step of 0.01seconds. Bus voltages were

monitored at every time step.

C.1.4.2. Varying Power Input Test

The varying power input test was designed to assess the effect on the network of a

varying power output from wind farms. Variation in wind farm power was achieved

using the DFIGIN-03 model, as described above. The test involved running a

simulation for 60 seconds.

The simulation was run with a time step of 0.01 seconds. Given the nature of the

test, bus voltages and machine powers were monitored at every ten time steps.

C.2. Results for Study of the Effects of a High Penetration of Wind Farms

For each of the two test procedures, results are presented using plots with some

comments attached. Each plot compares the results for a single load condition and

multiple wind farm outputs or a single wind farm output and multiple load

conditions.

252

C.2.1.1. Line Fault Disturbance Test

C.2.1.1.1. No Wind Farms and Different Load Conditions

Figure C.2 – Bus 1 voltage for no wind farms and different load conditions

This plot demonstrates how the network recovers after the line fault under all three

load conditions when there are no wind farms connected.

253

Figure C.3 – Bus 30 voltage for no wind farms and different load conditions

This plot shows how the voltage at bus 30, far from the fault on line 1-2, does not dip

as low as the voltage at buses closer to the fault and has a satisfactory post-fault

recovery.

254

C.2.1.1.2. 0MW Wind Farm Outputs and Different Load Conditions



load conditions when the wind farms are connected. Comparison with Figure C.2

shows that the presence of wind farms does not prevent the voltage at bus 1 dropping

to zero during the fault. Although not evident in the plot, the wind farm at bus 3 trips

as a result of the fault for all three load conditions.

255


This plot shows how the voltage at bus 30 is maintained close to its target level

throughout the line-fault simulation. Comparison with Figure C.3 shows that the

presence of the wind farms has helped to maintain the voltage at bus 30.

256




load conditions when the wind farms are connected and are producing 5MW each.

Comparison with Figure C.2 and Figure C.4 shows that the presence of wind farms

does not prevent the voltage at bus 1 dropping to zero during the fault, even if they

are producing power. Although not evident in the plot, the wind farm at bus 3 trips

as a result of the fault for all three load conditions.

257



throughout the line-fault simulation. Comparison with Figure C.5 shows greater

variation in the bus 30 voltage under the different load conditions when the wind

farms are producing power.

258



This plot demonstrates how the network recovers after the line fault with the wind

farms producing 10MW. Greater power output from the wind farms does not

prevent the voltage at bus 1 dropping to zero during the fault. Comparisons with

previous plots of the bus voltage show more obvious differences between the traces

for the three load conditions. Under the low and medium load conditions, wind

farms at buses 3, 7 and 28 trip as a result of the fault. Under the high load condition,

only the wind farm at bus 3 trips.

259



through the line-fault but then settles slightly below its original value.

260



This plot demonstrates how the network recovers after the line fault with the wind

farms producing 15MW. The post-fault recovery is less smooth than in previous

simulations. The voltage at bus 1 still drops to zero during the fault. Under all three

load conditions, wind farms at buses 3, 7 and 28 trip as a result on the fault.

261



through the line-fault but then settles slightly below its original value.

262



This plot shows how the line fault leads to instability when the wind farms are each

producing 20MW at the time of the fault. With the wind farm machine models

operating at a higher power output level, the line fault causes the rotor current

magnitudes to exceed the limit specified for protection. All 15 wind farms trip for all

three load conditions.

263


This plot shows how the voltage collapses at bus 30, with small oscillations

established due to the unstable swings of the conventional generators.

264

C.2.1.1.7. 20MW Wind Farm Outputs and Different Load Conditions and High

Protection Setting

A further study was performed for wind farm scenario d (20MW) with the protection

setting raised to a level (50pu) that avoids all wind farm trips. This produced a

different set of results, as shown below.

Figure C.14 – Bus 1 voltage for 20MW wind farm outputs and different load conditions with

high protection setting

This plot shows the voltage at bus 1 recovering after the line fault under the high

load condition. Through comparison with Figure C.12 it is clear that it is the tripping

of the wind farms and the loss of their power output that causes instability.

265

Figure C.15 – Bus 30 voltage for 20MW wind farm outputs and different load conditions with

high protection setting

This plot shows that if the wind farms remain connected through the fault

disturbance then the voltage at buses deep in the network, such as bus 30 shown here,

can be maintained through a fault disturbance.

This issue of fault ride-through depending on different protection settings is worthy

of further investigation but was not examined any further in these studies.

266

C.2.1.1.8. Low Load Condition and Different Wind Farm Conditions

Figure C.16 – Bus 1 voltage for low load condition and different wind farm conditions

This plot shows how the smoothness of post-fault recovery worsens as the wind farm

outputs are increased. Ultimately, wind farm outputs of 20MW result in post-fault

instability.

267

Figure C.17 – Bus 30 voltage for low load condition and different wind farm conditions

This plot shows firstly how the pre-fault voltage at bus 30 is different for each of the

wind farm conditions. The voltage drop during the fault depends on whether wind

farms are connected (no wind farms in scenario x) and whether they stay connected

(all wind farms trip in scenario d). The voltage recovers post-fault for all scenarios

except for scenario d.

268

C.2.1.1.9. Medium Load Condition and Different Wind Farm Conditions

Figure C.18 – Bus 1 voltage for medium load condition and different wind farm conditions



instability.

269

Figure C.19 – Bus 30 voltage for medium load condition and different wind farm conditions






270

C.2.1.1.10. High Load Condition and Different Wind Farm Conditions

Figure C.20 – Bus 1 voltage for high load condition and different wind farm conditions



instability. Comparison with Figure C.16 and Figure C.18 show how in this case, the

response for scenario b is grouped with those of scenarios 0 and a, whereas in the

low and medium load conditions, scenario b is distinct. For all three load conditions,

only the wind farm at bus 3 trips for scenarios 0 and a; scenario c results in trips at

buses 3, 7 and 28; and scenario d results in all the wind farms tripping. For scenario

b, under low and medium load conditions, the wind farms at buses 3, 7 and 28 trip;

but under high load conditions, only the wind farm at bus 3 trips.

271

Figure C.21 – Bus 30 voltage for high load condition and different wind farm conditions






272

C.2.1.2. Varying Power Input Test

C.2.1.2.1. Wind Farm Scenario 0 (0MW Base Level) and Different Load

Conditions

Figure C.22 – Bus 30 wind farm output for wind farm scenario 0 (0MW base level) and different

load conditions

This plot shows how the output from a wind farm varies over the 60-second

simulation. The abrupt changes in input power defined in the model code become

overshoots and oscillations in the output power from the wind farm.

273

Figure C.23 – Bus 2 generator output for wind farm scenario 0 (0MW base level) and different

load conditions

This plot shows the output from the generator at the swing bus. Its base level is

different for the different load conditions and the output changes in response to the

changing outputs from the wind farms.

274

Figure C.24 – Bus 30 voltage for wind farm scenario 0 (0MW base level) and different load

conditions

This plot shows how the voltage at bus 30 – far from the swing bus and with a wind

farm connected – varies as the power from the wind farms vary. The variations in

wind farm power are reflected in the bus voltage. The base level of the voltage at

this bus is the same for the low and medium load conditions and different for the

high load condition.

275

Figure C.25 – Bus 2 voltage for wind farm scenario 0 (0MW base level) and different load

conditions

This plot shows how the voltage at bus 2 – the swing bus – varies as the power from

the wind farms vary. The base level of the voltage at this bus is the same for the

different load conditions.

276

C.2.1.2.2. Wind Farm Scenario A (5MW Base Level) and Different Load

Conditions

Figure C.26 – Bus 30 wind farm output for wind farm scenario A (5MW base level) and

different load conditions

This plot shows how the output from the wind farm varies over the 60-second


overshoots and oscillations in the output power from the wind farm. It is apparent

that the final oscillations at the end of the simulation are slightly less for the lower

load conditions.

277

Figure C.27 – Bus 2 generator output for wind farm scenario A (5MW base level) and different

load conditions




278

Figure C.28 – Bus 30 voltage for wind farm scenario A (5MW base level) and different load

conditions




this bus is different for the different load conditions.

279

Figure C.29 – Bus 2 voltage for wind farm scenario A (5MW base level) and different load

conditions


the wind farms vary. The base level of the voltage at this bus is the same for the

different load conditions.

280

C.2.1.2.3. Wind Farm Scenario B (10MW Base Level) and Different Load

Conditions

Figure C.30 – Bus 30 wind farm output for wind farm scenario B (10MW base level) and






load conditions.

281

Figure C.31 – Bus 2 generator output for wind farm scenario B (10MW base level) and different

load conditions




282

Figure C.32 – Bus 30 voltage for wind farm scenario B (10MW base level) and different load

conditions




this bus is different for the different load conditions. The voltage varies most for the

high load condition and stays within the narrowest band for the low load condition.

283

Figure C.33 – Bus 2 voltage for wind farm scenario B (10MW base level) and different load

conditions


the wind farms vary. The variations in power from the wind farms and conventional

generators are reflected in the bus voltage. The base level of the voltage at this bus is

the same for the different load conditions.

284

C.2.1.2.4. Wind Farm Scenario C (15MW Base Level) and Different Load

Conditions

Figure C.34 – Bus 30 wind farm output for wind farm scenario C (15MW base level) and






load conditions.

285

Figure C.35 – Bus 2 generator output for wind farm scenario C (15MW base level) and different

load conditions




286

Figure C.36 – Bus 30 voltage for wind farm scenario C (15MW base level) and different load

conditions






287

Figure C.37 – Bus 2 voltage for wind farm scenario C (15MW base level) and different load

conditions




the same for the different load conditions.

288

C.2.1.2.5. Wind Farm Scenario D (20MW Base Level) and Different Load

Conditions

Figure C.38 – Bus 30 wind farm output for wind farm scenario D (20MW base level) and






load conditions.

Wind Farm output starts at 20MW but input is actually higher due to losses in the

machine. Thus, moving the power input to 20MW actually reduces the power

slightly, as can be seen at the start of this simulation.

289

Figure C.39 – Bus 2 generator output for wind farm scenario D (20MW base level) and different

load conditions




290

Figure C.40 – Bus 30 voltage for wind farm scenario D (20MW base level) and different load

conditions






291

Figure C.41 – Bus 2 voltage for wind farm scenario D (20MW base level) and different load

conditions




the same for the different load conditions. The voltage variation is greatest for the

low load condition.

292

C.2.1.2.6. Low Load Condition and Different Wind Farm Scenarios

Figure C.42 – Bus 30 wind farm output for low load condition and different wind farm scenarios

(different MW base levels)

This plot shows how the different outputs from the wind farm at bus 30 vary over the

60-second simulations for the low load condition. The abrupt changes in input

power defined in the model code become overshoots and oscillations in the output

power from the wind farm. At the end of the simulation, the oscillations are smallest

for the scenario B wind farm output (10MW).

These differences in the amplitudes of oscillation were thought to be because of

interference between the different oscillations instigated by the different changes in

input power. To test this hypothesis, a further set of studies was conducted for the

low load condition with a slightly different pattern of wind power input changes.

These are described in the next section.

293

Figure C.43 – Bus 2 generator output for low load condition and different wind farm scenarios



different for the different wind farm scenarios (different wind farm output base

levels). The output changes in response to the changing outputs from the wind

farms.

294

Figure C.44 – Bus 30 voltage for low load condition and different wind farm scenarios (different

MW base levels)


farm connected – varies for the different wind farm scenarios in the low load

condition. The variations in wind farm power are reflected in the bus voltage. The

base level of the voltage at this bus is different for the different wind farm scenarios;

higher wind farm outputs result in higher bus voltages.

295


MW base levels)

This plot shows how the voltage at bus 2 – the swing bus – varies for the different

wind farm scenarios in the low load condition. The variations in power from the

wind farms and conventional generators are reflected in the bus voltage. The base

level of the voltage at this bus is the same for the different wind farm scenarios.

C.2.1.2.7. Low Load Condition and Different Wind Farm Scenarios with

Different Power Input Pattern

In the previous section it was noted that the power from a wind farm displayed

oscillations of different amplitudes for different wind farm scenarios. These

differences were thought to be because of interference between the different

oscillations instigated by the different changes in input power. To test this

hypothesis, a further set of studies was conducted for the low load condition with a

slightly different pattern of wind power input changes.

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The oscillations appear to have a period of approximately six seconds so the power

input pattern was altered by delaying the final ramp up by three seconds. Thus, the

new power input pattern was as shown in Table C.11.

Table C.11 – New pattern of input power variations

Time Period Input Power Variations 0 to 5 seconds Power held at its initial value 5 to 10 seconds Power ramped up to 100% of MVA base 10 to 20 seconds Power held at 100% of MVA base 20 to 30 seconds Power ramped down to zero 30 to 43 seconds Power held at zero 43 to 48 seconds Power ramped back up to its initial value After 48 seconds Power held at its value

Figure C.46 – Bus 30 wind farm output for low load condition and different wind farm scenarios


This plot shows that with the final ramp in input power delayed by three seconds,

there is constructive rather than destructive interference of the power output

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waveforms resulting in oscillations of much greater amplitude. In fact, the additive

effects are enough in scenario d to drive the machine power and rotor current

magnitude high enough that the protection is operated and the machine trips.

Figure C.47 – Bus 2 generator output for low load condition and different wind farm scenarios


This plot shows how the generator at the swing bus responds to the varying power

from the wind farms and adjusts when wind farms trip in scenario d.

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MW base levels) and the adjusted pattern of input power

This plot shows how the voltage at bus 30 collapses in scenario d when the wind

farm trips due to excessive rotor current magnitude, which itself is due to

constructive interference between the oscillations instigated by changes in the input

power.

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MW base levels) and the adjusted pattern of input power

This plot shows how the voltage at bus 2 is affected by the loss of wind farms caused

by the constructive interference of oscillations in power.

This issue of interference between the oscillations established by changes in input

power is worthy of further investigation but was not examined any further in these

studies.

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C.2.1.2.8. Medium Load Condition and Different Wind Farm Scenarios

Figure C.50 – Bus 30 wind farm output for medium load condition and different wind farm

scenarios (different MW base levels)


60-second simulations for the medium load condition. The abrupt changes in input


power from the wind farm. At the end of the simulation, the oscillations are smallest

for the scenario B wind farm output (10MW). This is thought to be because the

oscillations instigated by upward and downward changes in input power cancel each

other out; and scenario B has the best balance of upward and downward changes.

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Figure C.51 – Bus 2 generator output for medium load condition and different wind farm





farms.

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Figure C.52 – Bus 30 voltage for medium load condition and different wind farm scenarios



farm connected – varies for the different wind farm scenarios in the medium load




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Figure C.53 – Bus 2 voltage for medium load condition and different wind farm scenarios



wind farm scenarios in the medium load condition. The variations in power from the



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C.2.1.2.9. High Load Condition and Different Wind Farm Scenarios

Figure C.54 – Bus 30 wind farm output for high load condition and different wind farm



60-second simulations for the high load condition. The abrupt changes in input


power from the wind farm.

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Figure C.55 – Bus 2 generator output for high load condition and different wind farm scenarios





farms.

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Figure C.56 – Bus 30 voltage for high load condition and different wind farm scenarios



farm connected – varies for the different wind farm scenarios in the high load




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Figure C.57 – Bus 2 voltage for high load condition and different wind farm scenarios (different

MW base levels)


wind farm scenarios in the high load condition. The variations in power from the



Documents

Colin Foote PhD Thesis March 2007