thesis - gavin d. j. harper - can 'complexity science' inform the uk 'nuclear vs....

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Gavin D. J. Harper

A thesis submitted in partial fulfilment of the requirements for the degree of

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CAN ‘COMPLEXITY SCIENCE’ INFORM THE ‘NUCLEAR vs . RENEWABLES’ ENERGY DEBATE

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AUTHOR’S DECLARATION

I hereby declare that I am the sole author of this thesis. This thesis is the result of my own work and includes nothing that is the outcome of work done in collaboration, except if explicitly stated. It has not been submitted, in whole or in part, for a degree or qualification at any other university. I authorize the University of East London / Centre for Alternative Technology to lend this thesis to other institutions or individuals for the purpose of scholarly research. G A V I N D . J . H A R P E R I further authorize the University of East London / Centre for Alternative Technology to reproduce this thesis by photocopying or by other means, in total or in part, at the request of other institutions or individuals for the purpose of scholarly research. G A V I N D . J . H A R P E R


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UNIVERSITY OF EAST LONDON

&

CENTRE FOR ALTERNATIVE TECHNOLOGY

ABSTRACT

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by

Gavin D. J. Harper

A thesis presented on the application of Complexity Science to the domain of Energy

Production & Distribution. The thesis presents Complexity Science as a method of looking

at the problems of UK Energy Production & Distribution in the context of the Nuclear vs.

Renewables debate. The thesis draws on approaches from multiple domains of complexity

science and applies to the problems posed by energy production and distribution.

CAN COMPLEXITY SC IENCE INFORM THE NUCLEAR vs . RENEWABLES ENERGY DEBATE

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TABLE OF CONTENTS

Front Matter

Author’s Declaration ii

Abstract iii

Table Of Contents iv

List Of Figures xi

List Of Tables xiii

List Of Graphs xiv

Acknowledgements xv

Chapter One

Summary Of Chapter One – Introduction 1

Chapter One – Introduction 2

Statement Of The Problem 2

Background & Need 4

Why Is This Study Important 5

What Does This Study Address 8

What Is The Context Of This Study In The Field Of Complexity Science 9

Thesis Structure 10


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Conceptual Approach & Methods 11

Chapter Two

Summary Of Chapter Two – Literature Review 13

Chapter Two – Literature Review 14

Outline Of The Literature Review 14

The Nuclear vs. Renewables Debate 15

Why Not Business As Usual 16

The Case For Nuclear 17

The Case For Renewables 18

Current UK Energy Developments 19

Complex Systems Science / Complexity Overview 20

Chapter Three

Summary Of Chapter Three – Meeting Our Needs Nuclear vs. Renewables – an Overview 22

Chapter Three – Meeting Our Needs Nuclear vs. Renewables – an Overview 23

Why Not Business As Usual 23

The Argument For Nuclear 24

Public Opinion Towards Nuclear Energy 27

The Argument for Renewables 31


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Chapter Four

Summary of Chapter Four – Complex Systems Science 33

Chapter Four – Complex Systems Science 34

Introduction To Complex Systems Science 34

Defining Complex Systems 36

The Roots of Complex Systems Theory 40

Evolutionary Timeline of Complex Systems Theory 41

Examples Of Complex Systems 43

Defining Systems 45

Defining Behaviours 46

Computation And Complexity 49

Modelling or Reality? 50

How does Complex Systems Theory Differ From Mechanistic / Vitalistic Views? 52

The Science of Free Will 56

Network Dynamics 58

Criticism of Complex Systems Science 60

Chapter Five

Summary of Chapter Five - UK Energy Production And Distribution As A Complex System 62

Chapter Five - UK Energy Production And Distribution As A Complex System 63


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Energy as a Complex Adaptive System 63

Nodes & Links 67

Network Topologies 68

Regular Networks 69

Small World Networks 69

Random Networks 69

Distributed Computing to Model the UK ESI 72

Complex Socio-Technical Systems in Energy Production 73

Socio-Technical Complexity in System Behaviour 75

Observing Emergence in Energy Use Patterns 81

Inherent Complexity in “Energy Source Development” Funding 85

Unpredictability & Sustainable Innovation 89

Time Dependence of “The Energy System” 91

Entropy & Energy Systems 94

Path-Dependence and the Selection of Appropriate Technology 97

Dealing With Complex Systems 102

Organisational Complexity and the case for Centralised Governance vs. Deregulation 104

The “Complexity” Case for Deregulation 106

The Complexity of the Energy Pool 108

Chapter Six

Summary of Chapter Six – Conclusions 111


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Chapter Six – Conclusions 112

Is the Problem Complex? 112

The Complexity of Co-Dependence 115

Meeting Our Needs 117

Chapter Seven

Summary of Chapter Seven – Further Research 119

Chapter Seven – Suggested Further Research 120

Comparison of Network Topologies Using Complexity Principles 120

Stickiness in Energy Awareness 121

Using Fitness Landscapes to Plan Renewable Energy Developments 121

The search for non-linearity in energy distribution networks. 122

Failure analysis with a complex systems toolkit. 122

Using a Measure of Cyclomatic Complexity to Detect Redundancy in Energy Distribution

Networks 123

Using Complex Systems to Understand the Dynamics of Public Opinion 123

Synthetic Population Techniques in Modelling Energy Consumption 124

Chapter Eight


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Summary – Chapter Eight 125

Chapter Eight – Limitations of the Method 126

Limitations of Time and Scope 126

Limitations on Knowledge 126

Uncertainties on Pre-Existing Work 126

Limitations on Discursive Data 127

Theoretical Nature of The Thesis 127

End Matter

Bibliography 129

Glossary 138

Abbreviations 140

Vita 143

Creative Commons © License 144


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LIST OF FIGURES

Figure № Description

Chapter Three Figure 3-1 Nuclear Power Plants in Operation Worldwide Chapter Four Figure 4-1 Conceptual Map of the Axioms of Complex Systems Figure 4-2 Characterisation of the Braches of Complexity Theory and Their Roots in the Natural Sciences Figure 4-3 Diagram Showing the influences that shaped the evolution of Complex Systems Theory. Figure 4-4 An ant hill can be considered an example of a complex system. Figure 4-5 Avalanches are a dramatic demonstration of “Emergent Behaviour” on a massive scale which comes out of the action of a small number of agents in the system. Figure 4-6 Examples of Behaviours in different types of system. Figure 4-7 How Mechanistic and Vitalistic views of a system differ. Figure 4-8 Convection Cells or Raleigh-Bernard Cells observed in a heated fluid. Figure 4-9 Close-up of the spontaneous self-organisation of convection currents in a Raleigh Bernard Cell Figure 4-10 Type I & II Dynamics


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Chapter Five

Figure 5-1 Global Energy System as a Complex Adaptive System after (Lewin, 1993a)

Figure 5-2 Network Topologies as expressed by parameter p

Figure 5-3 Socio-Technical Systems Exist in the Overlap Between Social Systems and

. Technical Systems – Sharing Traits and Qualities of Both.

Figure 5-4 The Dynamic Nature of Socio Technical Systems.

Figure 5-5 Socio-Technical Complexity in the Management of Nuclear Power Stations

Figure 5-6 Chernobyl Disaster

Figure 5-7 Complex Adaptive Behaviour

Figure 5-8 Examining Micro Generation as a Complex Adaptive System

Figure 5-9 Network Diagram Showing the “Chaotic” Funding of Renewable Energy

Figure 5-10 Structure of the Electricity Industry in Great Britain in 2005,


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LIST OF TABLES

Figure № Description

Chapter Three Table 3-1 Number of Operating Nuclear Power Plants plotted against % Nuclear Share

in Electricity Generation on a Country by Country Basis Source:

International Atomic Energy Agency in (Eisenhower Institute, 2003)

Chapter Five

Table 5-1 Cross Section of Renewable Energy Development Projects in the UK from

(Walker, n.d.).

Table 5-2 Key to Figure 5-7


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LIST OF GRAPHS

Graph № Description

Chapter One Graph 1-1 Production, Exploration & Price of North Sea Gas. Graph 1-2 Generating Capacity of Major Power Producers Graph 1-3 Fuel used in Electricity Generation on an Output Basis Chapter Three Graph 3-1 Percentage contributions to the total radiation exposure of the population of Britain. Graph 3-2 World Fuel Resources Graph 3-3 Number of Operating Nuclear Power Plants plotted against % Nuclear Share in Electricity Generation on a Country by Country Basis Chapter Five Graph 5-1 Properties of networks with different p-value Graph 5-2 Bifurcation in Energy Policy


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ACKNOWLEDGMENTS

I wish to express sincerest thanks to Damian Randle my thesis supervisor, for his advice,

guidance and experience in the preparation of this thesis. Thanks are also due to Kara Millen my

tutor for the taught element of the MSc. and Mike Thompson and the rest of the AEES course

team.

My unending gratitude must also be conveyed to Dr. John C. Bullas, of the Transportation

Research Group at the University of Southampton for his unrelenting help, support and

practical advice with regards to writing my thesis. His perpetual hindsight on the thesis process

greatly aided the speed with which I was able to produce this document.

I would also like to take this opportunity to thank Professor Jeffrey Johnson from the

Department of Design and Innovation at the Open University for his generosity in time, and

the pointers he gave me during my preliminary investigations into ‘Complexity Science’, I thank

him sincerely for him enabling me to participate on his short course ‘EPSRC Taught School on

Mathematics for the Science of Complex Systems’ held at the Institute of Mathematics and its

Applications at Warwick University. I would also like to thank all of the students and staff who

attended Warwick on the 11th - 15th September 2006 and made it such a smashing week,

particular thanks to Professor Ian Stewart, Professor Robert MacKay, Dr. Bruce Westbury &

James Derbyshire.

I would also like to thank Dr. Carol Webb at Cranfield University, for her copious online

references to interesting complexity material, which yielded a wealth of interesting information,

and for the notes prepared for the EPRSC Taught Course “Complexity Science for Beginners”.

I would also like to thank the staff at Liverpool University, particularly John Lewis, & Halim

Boussabainev for their informative EPRSC Taught Course “Complexity in the Built

Environment”.


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I would like to thank the all the library staff who were incredibly helpful throughout the thesis

process, particularly, the staff of the Open University Library, Walton Hall, Milton Keynes, the

staff at University of East London Library in Stratford and the library staff of the Institute of

Engineering & Technology, Faraday House, London, for their assistance and encouragement.

Finally, I owe an amazing debt of gratitude to students past and present on the MSc.

Architecture: AEES course who never cease to inspire, and offer new perspectives and insight.


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C h a p t e r O n e

INTRODUCTION

SUMMARY

HIS CHAPTER PROVIDES AN INTRODUCTION TO THE STUDY AND SETS

OUT THE CONTEXT AND RELEVANCE OF THE STUDY AND THE

CURRENT DEVELOPMENTS IN THE SPHERE OF “UK ENERGY

PRODUCTION & D ISTRIBUTION” AND “COMPLEXITY SCIENCE”.

T


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C h a p t e r O n e

INTRODUCTION

Statement of the Problem

Following a period of national energy security, where cheap gas supplies from the North Sea,

fuelled the “Dash for Gas”, the UK is now entering a new age, where its energy future is

uncertain, and not mapped out.

Graph 1-1

Production, Exploration & Price of North Sea Gas. (The Economist, n.d.).

The Labour Government under Tony Blair seems poised to steer the UK towards a nuclear

future. Whilst other party leaders seem to be joining the green movement, at least in rhetoric,

one thing seems clear, “Nobody agrees about figures” (Jenkins, 2005).


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It could be argued that energy policy is currently in disarray with little cross-party support for

any one coherent plan (Jenkins, 2005) goes on to say “energy policy is like Victorian medicine, at

the mercy of quack remedies and snake-oil salesmen.”. At the moment, there is a fundamental

lack of structural understanding of the problem, and a need for new and innovative ways to view

the problem at hand.

Many commentators (Juniper, 2006), (Tindale, 2006), (Mc Smith, 2006a), feel that the

corridors of power only pay lip-service to the alternatives afforded by renewable energy

technologies.

In order to examine the problems posed by energy supply and distribution, it seems that the

toolkit afforded by the new science of “Complex Systems” could present a new analytical

framework in which to consider the problem of UK Energy Supply and Distribution.

This thesis seeks to address the chasm between the language used by energy experts and

environmentalists and the vocabulary of complex systems scientists by examining the energy

problem in terms of the methodology of the Complex Systems Scientist, drawing common links

between issues raised in solving the “energy problem” and the problem solving techniques of the

emerging field of “Complex Systems Science”.

The thesis sets out the problem, and the two main solutions to deal with a future “post fossil-

fuels” that is to say, the choice between “Nuclear Power” and “Renewable Energy”.

The thesis aims to explore the potential for “Complex Systems Theory” to offer a new

framework to allow advanced and critical understanding of the issues, over and above that

offered by current methods.

This thesis aims to challenge current thinking in the ‘Energy Debate’.


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Background & Need

In light of ‘Peak Oil’ and the predicted future scarcity of Fossil Fuel Resources, it is apparent

that the UK must adopt a more varied portfolio of energy supplies if we are to meet our

continued demand for energy supply. In the same way that we will meet ‘Peak Oil’, so will we

meet ‘Peak Coal’, ‘Peak Gas’ and ultimately ‘Peak Uranium’. It must therefore be realised that to

meet our energy needs for the indefinite future, there is a need to adopt coherent policy that

ensures that we can maintain quality of life, whilst not jeopardising our ability to meet our

future energy needs.

Quotes of reserves of all the resources above vary wildly, (Smale, 2004),(Society of Petroleum

Engineers, 2003),(Arnett, n.d.) Furthermore, the length of time which the reserves last for

depends largely on how long they remain economically recoverable for – this in turn depends on

the prevailing market conditions. As can be seen, this in itself is a complex problem, not easily

understood.

There is also a requirement, for the UK to reduce its Carbon Emissions – both to fulfil its role as

an international ambassador – setting an example to other developed countries, and

furthermore to meet international pressure to reduce carbon emissions as there is no

international accord on agreed limits or acceptable levels of carbon dioxide.

It is presently unclear what combination of technologies will be able to deliver this low-carbon

future, and both camps – nuclear and renewables – argue vociferously for their cause.

This problem must be considered in the wider context of the UK as a developed country, in a

world of many countries, with mixed and varied energy needs. Each country has indigenous

energy resources, and the ability to buy resources from other countries on the open market.

With this in mind, and excluding “fossil fuel resources” one thing is clear. The UK has a vast

potential renewable energy resource but no exploitable uranium deposits.


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Why Is This Study Important?

The UK’s recent energy security was built upon reliable supplies of North-Sea gas, and a

generation of CCGT power generators, however, as shown in Graph 1-1, this supply is rapidly

waning.

Graph 1-2

Generating Capacity of Major Power Producers (Department of Trade & Industry, 2006a).

We can see, looking to Graph 1-2, how since 1993, the amount of CCGT generating capacity

has increased massively, to meet the rising demand for energy, whilst the other technologies have

remained relatively stable, with coal power stations showing a decline in capacity.


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Furthermore, looking at Graph 1-2, we can gain a sense of perspective, that within the relatively

short period of just over a decade, our demand for energy has risen significantly – and this is

taking into account the decline of heavy industry in the United Kingdom, and the increases in

energy efficiency made over this period.

Graph 1-3 illustrates the extent to which we are reliant upon gas to meet our electricity needs,

and that this has not changed since the turn of the millennium.

Graph 1-3

Fuel used in Electricity Generation on an Output Basis (Department of Trade & Industry, 2006a)

Nuclear power showed some drop in output between 2000 and 2005, and “Other Fuels”

including the bulk of renewables showed some increase – however, despite the recent trend to

the contrary, this study is particularly timely as the Labour government have recently announced

the findings of their energy review – presently they seem poised to build a new generation of

nuclear reaction, to the dismay of many.


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Many contemporary commentators feel that there is a lack of clear direction from the

government, and that their energy review is not coherent, and does not provide long-term

solutions for the UK. (Juniper, 2006), (Tindale, 2006), (Mc Smith, 2006a).

Carbon emissions are an ever-pressing problem facing the international community, with lack of

agreement amongst nations, and a deficient support from nations with some of the biggest

sources of carbon emissions, notably the U.S. It is felt that considering the recent strong Anglo-

American relations, a strong U.K stance on carbon emissions, with a positive plan-of-action,

could be used to leverage the U.S. into action.

The government has been criticised for setting long term “visionary” targets, but without setting

clear, immediate and measurable targets to be implemented immediately. “…the government has

resisted the idea pushed by Friends of the Earth- and the Tories- of an annual target, arguing

that unforeseen factors, such as extreme weather or unexpectedly strong economic growth,

could mean that targets might be missed from one year to the next…” (NATTA, 2007b)

In light of this criticism, of current policy and methodology, it is felt that Complex Systems

Science may offer a unique perspective on the energy debate that differs from the orthodox view

of electricity supply, based on a view of “supply and demand” and “meeting the demand”.

There has been growing awareness, and a heightened national consciousness in the media that

we are faced by some complex decisions that need to be made in the coming years if we are to

deal with the issues of long-term power security and addressing the threat of climate-change.

It is felt that at this time, the exploration of new and innovative approaches to the problem are

particularly appropriate and that work in this area is vital.


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What Does The Study Address?

Complexity Science asks us to look at problems in a way that is holistic and takes in the many

facets of the problem, the driving notion behind the field, is to reject simplification of the

problem in favour of considering the problem as a whole.

This study is borne out of a feeling that the present approaches to dealing with the energy

problem revolve around a “numbers game” of matching supply and demand and that in

reducing the problem to a technical – “how do we produce x GJ of energy” – we lose a

fundamental understanding of the problem.

The problem of how to supply and distribute electricity with minimal environmental impact is

intractably large, we cannot hope to provide “a solution” in a thesis with only limited scope such

as this, we can only hope to scratch the surface of the project, however, it is hoped that this thesis

will prove to be a catalyst for engaging “Complex Systems Scientists” in the energy debate,

furthermore it is hoped that it will engage “Energy Professionals” with a new and powerful

toolkit.

“Complex Systems Science” is seen as a new and exciting discipline, which is currently relatively

undefined, it has gained acceptance from the scientific community, however, as a subject in its

own right, it is still in its infancy.

In terms of what this study does not address, a further explanation of the limitations of this

study is addressed at the end of this thesis.


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What Is The Context Of The Study In The Field Of Complexity Science?

Complexity Science is a new field which is rapidly being defined, as a result of it being relatively

new subject, there is some lack of common definition, with different views on “what constitutes

a complex system” coming from different complex systems scientists. This is discussed later in

this thesis in Chapter Three.

As a result of this lack of definition, there are few “clear-cut” boundaries within the subject at

present, with different methods, techniques, and domains being brought into the arena, but no

“edges” to the domains covered.

It could be argued that part of this lack of division comes from the fact that complex systems

scientists are drawn from all quarters of the “traditional” sciences. Complexity has been found in

systems that traverse traditional boundaries of “the sciences”. As such, the subject is a hot-bed of

innovative thinking and provides a good forum to discuss old problems in a new light.

Complex Systems Science aims to be universal in appeal, taking into account viewpoints from

many different disciplines – even from outside the “traditional” sciences, taking into account

work from the Social Sciences, Economics and Information Theory.

One of the criticisms of “Complexity Science” is that some view elements of it as a “rebranding”

of known principles of various disciplines subsumed under a “new umbrella”. This is a fair and

valid judgement, as certainly the interdisciplinary nature of Complexity Science means that the

subject takes in factors of many other schools and integrates them into a new subject area.

However, the subject lays no claim to defining a completely new arena,

It is felt that due to the Complex Nature of many “Sustainable & Environmental Problems”,

Complex Systems Theory offers and effective framework for understanding these problems.


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Thesis Structure

In this thesis, I will outline why Complexity Science is a good tool for us to use to look at the

problems associated with UK energy production and distribution.

I will explore some of the methods that are applicable to the problem and develop a preliminary

toolkit of ideas that can be developed to tackle the problems faced by “UK Energy Production

and Distribution”. This does not claim to be a comprehensive toolkit, which covers all aspects of

the problem – rather, it is an initial foray into marrying the worlds of the “Complex Systems

Scientist” and “Energy Scientist.”

Each chapter opens with an introductory page, which aims to “signpost” the salient points

covered within that chapter covered within a summary.

The thesis opens with a review of the literature I have looked over the course of this study; it

takes in the body of literature relating to current developments in “Complexity Science” as well

as the current developments in the ambit of UK Energy Production and Distribution.

Complex Systems Science is then explored. One of the difficulties of composing the chapter is

that there is a lack of lucidity in what is meant by “complex systems” and whilst the definitions

centre on common themes, there is some disparity in the accepted definitions of the subject. In

order to support the views held, the historical context of the subject is given, with a clear

roadmap of the “roots” of Complexity Science and some criticism of the theory to offer a

counter-argument.

The next chapter is designed to explore UK Energy Production and Distribution, in the context

of the Nuclear vs. Renewables Energy Debate, by looking at how we have arrived the network

that we now know today, the historical context which has led to the infrastructure developing in

the way that it has and some postulations as to what direction the network is heading in. At all


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times, the assumptions are grounded in fact and “crystal ball gazing” has been avoided at all

costs, with the focus being on near-term technologies that are mature and ripe for

implementation, rather than unproven technologies on the horizon. The chapter aims to

concentrate on the current context of the debate, rather than the historical context.

Chapter Four then begins to apply some of the ideas from complexity to the energy problem,

looking for application for ideas from complexity to the current situation. The approach taken

is mainly theoretical, with the aim being to establish common ground that can be built upon in

future studies, and developed into a more practical “applied” theory of “The Energy Problem”

viewed with the toolkit of Complexity.

Conclusions are then drawn about whether “Complexity Theory” can be applied to any of the

questions that we face when examining the “Energy Problem”. The chapter takes an overall view

of whether there is merit in the pursuit of this subject, rather than focussing on specific themes.

Further Research is then examined, with some themes encountered during the course of this

study being expanded upon., and developed to form brief proposals for future research

opportunities.

The limitations of the thesis are then examined in Chapter Seven, with a critical look at this

study and some of the lessons learned from it.


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Conceptual Approach & Methods

This thesis has been approached as a desk study, from the point of view of an MSc. Student

interested in new ways of understanding the energy debate.

The thesis was undertaken, with knowledge of the energy debate, interest in “Complexity

Science”, but no formal training in this area.

Over the course of this thesis, the author has attended a number of EPRSC funded schools on

complexity science, conducted a thorough review of the literature surrounding “complexity” and

spoken to many experts in the area.

The approach to this thesis, has been to research ideas from the field of “Complexity Science”

and attempt to ‘marry them’ with some of the issues in the energy debate. Where particular

phenomena encountered during the energy debate were encountered which might be

considered “complex”, attempts were made to find “Complexity Science” explanations for these

phenomena.

During the investigations into ‘Complexity Science’, it was found that there were a number of

examples of where complex systems had been applied to previous problems, where an analogy

could be drawn between that problem and one that was faced in the energy debate – in this

manner, bridges can be built between situations where complexity science has been successfully

applied to other problems and the problems faced in the “energy debate”.


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C h a p t e r T w o

LITERATURE REVIEW

SUMMARY

HE FOLLOWING CHAPTER IS A CRITICAL REVIEW OF THE LITERATURE

ENCOUNTERED DURING THE COURSE OF THIS STUDY . IT AIMS TO SET

OUT THE FOUNDATION UPON WHICH THE AUTHOR BUILDS , AND

ADDRESSES THE LACK OF SPECIFIC LITERATURE ON “ENERGY AND

COMPLEXITY” BY BUILDING BRIDGES BETWEEN THE LITERATURE THAT IS

AVAILABLE IN THE FIELD OF “COMPLEX SYSTEMS SCIENCE” AND THE

LITERATURE CONCERNING “ENERGY PRODUCTION & D ISTRIBUTION”. THE

CHAPTER ALSO EVALUATES THE CURRENT INFORMATION ON THE STATE OF

THE “NUCLEAR VS . RENEWABLES” ENERGY DEBATE .

T


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C h a p t e r T w o

LITERATURE REVIEW

Outline of the Literature Review

In this thesis, I will be exploring the Nuclear vs. Renewable energy debate through the lens of

complexity science. This has led me to a literature review which can be delineated into a number

of sections.

• The UK Nuclear vs. Renewables Debate

o This section explores only the “current news” relating to this argument; it is

assumed that the reader is familiar with the historical context of the Nuclear vs.

Renewables Debate.

• Current UK Energy Developments

o This section focuses on current and proposed government policy and the

strategic roadmap for UK energy systems.

• Complex Systems Science / Complexity Overview

o This section looks at Complex Systems Science, the current body of work in the

subject and where it is likely to go in the near-term.

• Specific Complexity Methods applied to Energy

o This section begins to explore some specific methods and techniques within

“Complexity Science” which could be applied to the “Energy problem” and

“Nuclear vs. Renewables” debate.


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Why not “Business as Usual”?

This section aims to set the context of the study and why the argument is “Nuclear vs.

Renewables” and not “Nuclear vs. Fossil Fuels vs. Renewables”. In this study, we have specifically

excluded fossil fuels from the remit of our argument, looking only at “carbon free” fuels –

renewables and nuclear power.

The case against oil is supported by arguments drawn from (Fells, 2002), furthermore, the

decline in North Sea oil and gas is corroborated by (Stephen, 2005). (Arnett, n.d.) also made

interesting reading, when taking into consideration how long the current “Business as Usual”

approach will last for. (Society of Petroleum Engineers, 2003) has produced detailed estimates of

future reserves of oil and gas, however, the neutrality of this source could be debated and should

be viewed critically.

(National Science Foundation, 2005), (Kolbert, 2005), (Argiri and Birol, 1996), (Tyndall

Centre, 2006) all provide very sound arguments, based on the consequences of climate change,

as to why “Business as Usual” cannot continue.

(Anon, 2006d) provides a sound financial case for why present dependency on carbon fuels

cannot be maintained, as it is important to consider the economic as well as technical argument.

(Pool, 2006) provides a contrast, exploring clean coal – a fossil fuel dependent technology which

has the possibility to deliver a lower carbon future.

(Chandler, 2004) lights the way for the future, illuminating options for a future where we are

not dependent on carbon based fuels. This leads us on to the next section, where we will look at

the literature supporting the spectrum of solutions for a carbon-free future.


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The Nuclear vs. Renewables Debate

The Nuclear vs. Renewables energy debate has been covered from both sides in extensive detail

in the literature. In this thesis, the debate has been approached from the perspective of the

current arguments and state of play. There is a broad spectrum of different views, from those

advocating a wholly nuclear future, to those advocating a wholly renewable future, and any

number of variations in between.

(Whittington, 2002) provides quite a well-balanced overview of a variety of different approaches

to reducing carbon emissions in electricity generation.

“Transforming Electricity”, (Patterson, 1999), provides a very solid introduction to how our

energy generating network has evolved to its present form. The author favours renewables,

however, the arguments that he puts forward are clear and well structured, and both nuclear &

fossil fuels are covered in exceptional depth and clarity.

The two books accompanying the Open University Course “T206 – Energy for a Sustainable

Future” (Boyle et al., 2003) & (Boyle, 2004) are both very clear, well written introductions to

their field, the books provide clear explanation of the issues encountered during the debate, and

were also a useful source of references – providing a stepping stone to a wealth of further

material.

“Introduction to Energy”, by (Cassedy and Grossman, 1998) is a well rounded introduction to

energy, also covering social, political and environmental perspectives on the energy debate, the

book covers both nuclear and renewable energy sensitively.

“Power Surge” by (Flavin and Lenssen, 1995), looks at the future, and collates information

gathered from dialogue with leaders of industry, technology advocates and a wide cross-section

of experts to forecast options for our energy future, the authors come from the influential

“Worldwatch Institute”,


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Case for Nuclear

(Boyle et al., 2003) provided a thorough an balanced introduction to the Nuclear debate, with

good introduction to nuclear technologies, and the politics and sociological issues surrounding

the implementation of nuclear technology. The book thoroughly covers the “historical debate”

with nuclear power, and “looks to the future”, however, this book was written at a time when

nuclear power looked as if it had been abandoned as a future option by the government, and so

does not cover very recent policy changes that have been made.

The Block 1 guide to the Open University course T206 (Open University, 2006), was helpful in

signposting a number of other publications on both sides of the argument, for and against. This

publication pointed me towards such polarised views at opposite ends of the spectrum as

(BNFL, 2007) & (Greenpeace, 2007), (Homepage, 2007), formerly the Uranium Institute.

(Open University, 2006) also pointed me in the direction of a two very good books by

(Grimston and Beck, 2000) & (Grimston and Beck, 2002) both of which take a critical look at

the future of nuclea energy as a provider of clean energy for the future.

When examining the case for a Nuclear future, I considered (Kidd, 2006), however, the bias in

this source must carefully be considered as Stephen Kidd is Director of Strategy and Research at

the World Nuclear Association, despite this fact, I found the paper to put forward a well-

balanced argument.

(van der Zwaan, 2002) has produced a particularly compelling argument for a ten-fold increase

in nuclear power in the paper “Nuclear Energy: Tenfold Expansion or Phase-Out?” there are

parallels between the arguments in this paper and (Grimston and Beck, 2002), both supporting

the view that we must either commit fully or phase-out entirely – the “double or quits”

argument.


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The Case for Renewables

The case for Renewables is also given thorough coverage in (Boyle et al., 2003) and more

thoroughly in the title devoted to Renewable Energy, of the same name (Boyle, 2004) both

books coming from the Open University course T206. These books provided a thorough

grounding in the technology, social issues and politics, and signposted a variety of further

material.

(Heiman, 2004) provides compelling cases for Renewable Energy, although the book is quite

one sided, and does not provide the balanced arguments of some other publications.

(Afgan, 1998) is quite a balanced paper from the Journal “Renewable & Sustainable Energy

Reviews 2”. On “Sustainable Energy Development”.

The paper by (Salameh, 2003) in Applied Energy explores whether Renewable Energy

Technologies can bridge the energy gap, and makes for interesting reading.

The “War on Terror”, seems to be a very current, hotly debated political topic, which in recent

years seems to have taken centre-stage amongst a range of issues. In the UK, we see increasingly

home office policy, and foreign policy geared towards this “War on Terror”, (Asmus, 2001)

argues that support for distributed renewables comes from this unlikely quarter.

(Herring, 2005) provides a coverage of the opposition to the nuclear debate from an alternative

angle, he challenges existing wisdom about the opposition to nuclear power, and asserts a range

of different factors.


19


Current UK Energy Developments

For current reading on the state of the UK’s energy stance, current developments in the sphere

of nuclear and renewable energies and policy decisions made by government, I turned to the

Institute of Mechanical Engineers “Professional Engineering” (Anon, 2006d, Anon, 2006f,

Anon, 2006b, Anon, 2006a, Anon, 2006e, Anon, 2006g, Cunningham, 2006, Sampson, 2006)

magazine, the Institute of Engineering and Technologies “IET Review”, and preceding the IET,

the Institute of Electrical Engineers “IEE Review”. (Pool, 2006)

For energy in the built environment, I turned to Building Services Journal, the Journal of the

Chartered Institute of Building Services Engineers. (Burton, 2006, Warwicker, 2006)

It was observed that for Professional Journals, the difference between date of submission and

date of publication varied between several months and a year. Upon commencement of this

thesis, it was noted that there were a number of developments in UK energy, notably decisions

by politicians, which would take significant time to filter through the machinations of the

academic community.

There is a delicate balance between finding information that is academically rigorous and

finding information that is current and up-to-date. Due to the dynamic nature of the energy

debate in the UK, it was my feeling that current content was vital to making this thesis relevant

in the wider context. I am grateful to my supervisor, Damian Randle, for suggesting the

“RENEW” publication from the Energy & Environment Research Unit at the Open

University, which has yielded a wealth of good information.

Energy Statistics are taken from (Department of Trade & Industry, 2006b) & (Department of

Trade & Industry, 2006a).


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Complex Systems Science / Complexity Overview

I looked at a number of books on Complexity as background reading in order to give me a

“bird’s eye view” of the subject, several authors have addressed the subject comprehensively,

writing about the subject accessibly in a manner that is easily comprehensible for the newcomer.

There are a vast array of “popular science” publications relating to “Complexity” and “Chaos

Theory”, these provided a good general introduction to the subject, covering a large breadth of

information about the current research into complexity, quickly, as a newcomer to the field I

found (Lewin, 1993b) to be particularly helpful.

Another good introduction to complexity came from (Waldrop, 1992), a well thought of

author in the area.

John H. Holland is well respected in the field of Complexity, he is known in some circles by his

moniker “Mr. Emergence” (Holland, 1996, Holland, 1999, Holland, 1992) all provided a very

sound introduction to complexity.

(Morowitz, 2004) provides an interesting introduction to the phenomena of emergence and

emergent behaviour, in an easy read introduction.

I also looked at the work of Stuart Kauffman, whilst he was working with the Santa Fe Institute,

(Kauffman, 1996, Kauffman, 1993), his work on self-organisation, being particularly interesting.

I looked at (Prigogine, 1997, Prigogine, 2003), and in particular Ilya Prigogine’s work on

dissipative structures, for which he won the Nobel Prize.

(Batty, 2005) is particularly relevant for the built environment, modelling complex phenomena

using Cellular Automata.


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I found Jeff Johnson’s current work at the Open University to be of particular interest, (Scott et

al., 2002, Johnson, 2002a, Johnson, 2000, Johnson, 2002b).

The EPRSC have funded a number of UK residential schools on aspects of Complexity Science,

I found information from these schools to be particularly helpful, particularly (Webb, 2006) &

(Mitleton-Kelly, 2003).

(Cohen and Stewart, 1995) provides an interesting read as to how the idea of “Chaos” theory

gradually collapsed and was subsumed by Complexity Theory.


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C h a p t e r T h r e e

WHY IS THE NUCLEAR VS.

RENEWABLES DEBATE A GOOD

CANDIDATE FOR COMPLEX SYSTEMS

ANALYSIS?

SUMMARY

HIS CHAPTER IS INTENDED COVER THE CURRENT NUCLEAR VS .

RENEWABLES DEBATE AND EXAMINES THE MAIN BODY OF THE

ARGUMENTS PUT FORWARD BY BOTH CAMPS . THE CHAPTER SETS THE

SCENE FOR AN EXAMINATION OF THE ISSUES ENCOUNTERED USING THE

TOOLKIT OF “COMPLEXITY SCIENCE”.

THE CHAPTER OPENS WITH A STATEMENT OF WHY NOT “BUSINESS AS USUAL”

T


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C h a p t e r T h r e e

WHY IS THE NUCLEAR VS. RENEWABLESDEBATE A CANDIDATE FOR COMPLEX SYSTEMS ANALYSIS?

Why not “Business as Usual?”

In the introduction to this thesis, we examined the rise of Natural Gas, as a Primary Energy

feedstock for the production of electricity in this country. It is now widely accepted that fossil

fuel resources are finite, and that we must look to alternative solutions to move forward into a

sustainable future.

(Fells, 2002) states that in the medium to long term, world supplies of natural gas will go into

decline, furthermore, as this thesis is UK-centric, it is noted that Fells states that North Sea

supplies are already in dramatic decline. This is a view corroborated by (Stephen, 2005).

The cost of producing fossil fuels appears to be rising dramatically (Anon, 2006d) as resources

become harder and harder to exploit. The UK is faced with an energy shortfall in the medium to

long term which cannot be addressed by carrying on as we are.

(Fells, 2002) argues that the solutions to the problems posed by global warming and security of

energy supply are not mutually exclusive. The proponents for nuclear power and renewable

energy, both argue that these forms of energy reduce carbon emissions, and solve the problems

with the security of supply associated with fossil fuels.

There is a growing body of contemporary commentators (Chandler, 2004) who believe that

within the next 50 years, we can cure our addiction to fossil fuels.


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The Argument for Nuclear

(Kidd, 2006) cites the resurgence in interest in Nuclear Technology to be as a result of:

• Rising fossil fuel prices

• Doubts about energy security

• Worries about greenhouse gas emissions from fossil fuel burning

In his paper, (Kidd, 2006) acknowledges that Nuclear issues are suddenly back in the public eye

after a period of relative unimportance.

Kidd argues that the feeling in energy circles was that nuclear was “past its prime, indeed

suffering a slow and elongated death”.

(Fells, 2002) cites that ‘responsible organizations’ – the World Energy Council and

International Energy Agency state that only 25% of world energy demand could be provided by

renewables by 2050. Fells goes on to state that “perversely, the advocates of renewable energy are

often opposed to nuclear power, which is the other major, carbon dioxide free energy producer”

however, Fells’ viewpoint seemingly ignores the inherent embodied carbon in the construction

and decommissioning of nuclear plant, the carbon inherent in the extraction and transportation

of nuclear fuel.

However, even in (van der Zwaan, 2002), which provides a compelling case for the ten-fold

expansion of nuclear energy to mitigate carbon emissions, it is noted “Nuclear energy, however,

can be no panacea for the problem of global warming. Even with a massive expansion, nuclear

energy should be complemented by drastic fossil fuel decarbonisation measures or the

development of renewable energy resources.”.


25


Graph 3-1

Percentage contributions to the total radiation exposure of the population of Britain.(Hodgson, 1997a)

Also, there is complexity inherent in the difference between public perception as to the dangers

of radiation, and the “real” dangers that are presented by nuclear power. As can be seen in

(Hodgson, 1997b), the percentage contribution of radiation exposure from “man made” nuclear

emissions and that from generation, is only a small percentage of the total exposure.

However, many would counter this argument by saying that although when functioning

correctly, nuclear power should produce no deleterious effects, in the event of an accident, the

reverse is often the case.

It must be considered, that following the Chernobyl disaster, an area twice the size of Britain

(Anon, 2006c) was contaminated with thousands of villages being made uninhabitable.


26


Graph 3-2

World Fuel Resources (Hodgson, 1997a)

Next it must be considered if Britain did switch to nuclear power, how long would the world’s

uranium deposits last? Graph 3-2 above from (Hodgson, 1997b) shows world fuel resources, it

can be seen, that if the world switched to Nuclear power, there is less GTOE of Uranium –

when this is used in thermal reactors, then there are known deposits of natural gas. However,

proponents for nuclear power, argue that “fast breeder” reactors are the answer as they extract

more energy from the same amount of fuel.

However, this argument is not just about a simple transition to a different technology – as “fast

breeder” reactors must operate “super critically”, and furthermore, there are concerns that this

technology produces nuclear waste suitable for use in weapons, and could encourage weapons

proliferation.


27


Public Opinion Towards Nuclear Energy

Public opinion towards nuclear energy has changed over time. Nuclear power came into vogue

on the crest of the “White Heat of Technology” in the 1960’s, with the promise of “power too

cheap to meter”. However, consumer illusions about the actual cost of nuclear energy have

shattered, additionally, a number of high-profile nuclear accidents internationally, (Three-Mile

Island, Chernobyl) dented nuclear power’s image.

Current research from an ICM public opinion poll, (NATTA, 2007a), seems to suggest that

“58% of people asked thought that nuclear plants were safe, but 50% said they would be very

concerned if one was to be built near them.”

In a recent YouGov poll, the following was found:

“The expansion of nuclear energy was supported by 40% of people asked, up from 34% while

opposition to nuclear energy dropped to 37%, compared to 46% against in 2005. 68% said they

would support new nuclear power stations if they were part of a wider programme that also

included investment in renewables, but 44% felt that nuclear would create unacceptable dangers

for future generations.”

Whilst nuclear energy does not enjoy strong support from all quarters, there is a sizeable lobby

who believe that nuclear power can offer us a sustainable future, however, the arguments

involved are complex, and there is a lack of concord internationally.

Looking close to home, within the European Union, opinion is split, some countries such as

Lithuania and France are nearly wholly dependent on nuclear energy, whereas some have

decided to phase out all future nuclear development.


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Figure 3-1

Nuclear Power Plants in Operation Worldwide Source: (New Internationalist, 2005)


29


Country № of Plants % Power from Nuclear Plants

Argentina 2 8% Armenia 1 35% Belgium 7 58% Brazil 2 4% Bulgaria 6 42% Canada 14 13% China 3 1% Czech Republic 5 20% Finland 4 31% France 59 77% Germany 19 31% Hungary 4 39% India 14 4% Japan 54 34% South Korea 16 39% Lithuania 2 78% Mexico 2 4% Netherlands 1 4% Pakistan 2 3% Romania 1 11% Russian Federation 30 15% Slovak Republic 6 53% Slovenia 1 39% South Africa 2 7% Spain 9 29% Sweden 11 44% Switzerland 5 36% Ukraine 13 46% United Kingdom 33 23% United States 103 20%

Table 3-1

Number of Operating Nuclear Power Plants plotted against % Nuclear Share in Electricity Generation on a Country by Country Basis Source: International Atomic Energy Agency in (Eisenhower Institute, 2003)


30


Graph 3-3

Number of Operating Nuclear Power Plants plotted against % Nuclear Share in Electricity Generation on a Country by Country Basis Source: (New Internationalist, 2005)

Looking at Figure 3-1, Table 3-1 and Graph 3-3, it can be seen that the scale of the problem is

truly global – Britains position must be considered against a backdrop of ever changing global

economics. Many point to the “two elephants in the room” of India and China – it was recently

highlighted by Tony Blair, that even if Britain were “completely to shut down”, the emissions

saved would be made up for by China and India’s growth in the next two years.

At the moment, Britain is in a ‘reasonably comfortable’ position, in that whilst we have nuclear

power, we are not wholly dependent on it – only 23% of our electricity needs being met through

nuclear. It can be seen therefore how other countries are reliant to a much greater extent on

nuclear, so a transition to other forms of generation might affect Britain less, than say “France”

which as can be seen in Graph 3-3 meets most of its power need through nuclear power.

The recent decision to move Britain towards a nuclear future is a very bold step, however, many

would argue it is a step in the wrong direction.


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The Argument for Renewables

One Tony Blair, then Shadow energy minister reported during a standing committee for the

Electricity Bill in 1989 “The one thing that can be foreseen is that the cost of disposing of

nuclear waste, reprocessing and decommissioning of nuclear power stations will go on escalating.

It is the one element in the entire equation that can be predicted with any certainty”. (Private

Eye, 2006)

Tony Blair now appears to have backtracked on this earlier statement and believes that nuclear

power is the way forward for the UK, however, he is not without opposition. Greenpeace

(NATTA, 2007a) have lodged papers with the high court, saying that they do not believe that

the public was offered the fair consultation that they were originally promised by the

government on the matter.

In a predominantly pro-nuclear paper, (van der Zwaan, 2002) cites the most persistent obstacles

to the adoption of nuclear energy:

• Radioactive Waste

• Nuclear Proliferation

• Reactor Accidents

• The Global Context

Whilst this thesis exclusively deals with the Nuclear vs. Renewables debate in the context of the

United Kingdom, it is important that we consider for the wider context, the implications this

study has worldwide.


32


Whilst many people, in principle, are ‘for’ the adoption of Renewable Energy technologies, there

is still debate as to which technology will be able to deliver sufficient energy to meet our needs.

There are economies of scale to be gained in generating energy on a massive scale – and many

would advocate large centralised renewable energy schemes – such as offshore windfarms or

onshore solar arrays on a large scale. However, there is also another side to the Renewable

Energy coin, that of de-centralised generation. Many advocate the use of building integrated

renewables to meet our needs, however, detractors argue the case that they could not meet our

present energy needs, and especially in urban areas, there is not enough renewable energy

resource to meet our needs locally.

At the moments, arguments about “large scale” renewables deployment are academic, as

renewable energy currently makes up such a small amount of our total generated energy

(NATTA, 2006), however, if we increasingly move towards a renewables-based energy

portfolio, then this concern will increase. At the moment there is a lack of certainty, however, if

we are to commit to any one solution, we need to know how this will affect the network, and

what changes need to be made to accommodate this.

It can be seen however, that the adopting a renewables based portfolio involves much more than

just a “technical solution”, it also requires a massive amount of social capital to achieve these

aims, and many would argue that a renewables based future only becomes economically

achievable with some degree of “power down” incorporated into the strategy.

The choice between two conflicting energy ideologies is a hard one, not a problem which can be

easily solved, or agreed on by all. The complex arguments involved in this debate continue to

rage on – and as they do so, we are slowly burning away the fossil fuel “safety net” of known

reserves of energy, whilst slowly choking ourselves.

Can complexity help us answer some of these questions?


33


C h a p t e r F o u r

WHAT IS COMPLEX SYSTEMS SCIENCE?

SUMMARY

OMPLEX SYSTEMS SCIENCE IS INTRODUCED IN THIS CHAPTER . IT AIMS

TO PROVIDE SOME OF THE FOUNDATIONS OF THE SUBJECT ,

CLARIFICATION AND DEFINITION OF “WHAT IS A COMPLEX SYSTEM”

AND A BIRDS-EYE PERSPECTIVE OF THE ROOTS OF “COMPLEX SYSTEMS

SCIENCE”. THE CHAPTER PROVIDES AN INTRODUCTION TO SYSTEMS THINKING

BEFORE DELINEATING TRADITIONAL “SYSTEMS THINKING” AND “COMPLEX

SYSTEMS THINKING” . THE WORDS “COMPLEXITY THEORY”, “COMPLEX

SYSTEMS THEORY”, “COMPLEXITY SCIENCE” AND “COMPLEX SYSTEMS

SCIENCE” ARE USED INTERCHANGEABLY .

C


34


C h a p t e r F o u r

COMPLEX SYSTEMS SCIENCE

“In science, there is only physics; the rest is stamp collecting”

Lord Rutherford

Introduction to Complex Systems Science

Complex Systems Science is one of a number of names given to the emerging field of the study of

“Complex Systems”, also known as “Complexity Theory”, “Complex Systems Theory” or

“Complexity Science”, the emerging field aims to give better understanding of systems, and the

behaviour of those systems which can be defined as complex.

Some prefer the wording “theory” as the field is new, cutting edge and relatively undefined, others

prefer the title “science” – in some way lending a certain credence to the brave new ideas of

complexity. Either/or these terms are used interchangeably in this thesis.

This is not the only instance of a “lack of definition” in Complex Systems Science, there is a lack

of concord between experts in the field and the “Complex Systems” community as to an exact and

precise definition, but this is hardly surprising given the breadth of subject matter that the field

aims to assimilate. The various practitioners of “Complex Systems” have devised their own set of

rules or criteria which a complex system should fulfil, but in part because of the diverse nature of

the subject matter, it is hard to devise a “one-size-fits-all” measure of complexity.

It is widely accepted that there are a vast variety of different systems which have structural features

which can be classed as “complex” and that these may exhibit greater or lesser degrees of

“complex” behaviour.


35


“Complexity as a phenomenon is omnipresent in natural, social, business, artificial, engineered or

hybrid systems. Cells, organisms, the ecosystem, companies, supply networks, markets, societies,

governments, cities, regions, countries, large scale software and hardware systems, the Internet, all

are examples of complex systems. Despite this omnipresence there is no commonly accepted, crisp

and robust definition or classification of complex systems and one might ask why we would

expect commonalities among such systems despite their obvious differences." (ACM Ubiquity

Magazine, 2006)

One way of differentiating a “complex” system from a “simple” system, is that if we consider

“simple systems”, the constituent parts have a very fixed, static relationships to one another, which

are non co-dependent, in a complex system, there is a greater degree of connectivity between the

component parts – this results in a system where it is harder to predict how the system will react

when one of it’s variables is perturbed.

This statement in (Barrow, 1993), could equally apply to Complexity.

"If we define a religion to be a system of thought that contains unprovable statements, so it

contains an element of faith, then Gödel has taught us that not only is mathematics a religion but

it is the only religion able to prove itself to be one."

In the same way that number systems and mathematics are a “constructed” series of rules which

help to explain the physical world, so the tenets of “complexity” have been constructed to explain

certain phenomena. The arguments against complexity will be covered later, however as Johann

Wolfgang von Goethe said:

"Daring ideas are like chessmen moved forward; they may be beaten, but they may start a winning

game."


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Defining Complex Systems

There is a patent lack of definition of what constitutes a “Complex System” in the literature.

Experts agree on a series of common themes, however, all “repackage” these themes, depending on

their own particular slant on the subject.

The “Conceptual Map” below in Figure 4-1 aims to map some of the key concepts of “Complex

Systems Theory” and form a picture of the key axioms of the concept.

Figure 4-1

Conceptual Map of the Axioms of Complex Systems

To understand the diversity of research into “Complex Systems”, we need to take a step back, and

look at the “roots” of the theory in the “Physical Sciences”. The diagram on the following page,

Figure 4-2, from (Mitleton-Kelly, 2003), maps out some of the areas of research into Complexity

Theory and finds generic characteristics in those complex systems.


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Figure 14-2

Characterisation of the Braches of Complexity Theory and Their Roots In The Natural Sciences (Mitleton-Kelly, 2003)

It be seen, that ‘Complexity’ which at first sight might seem a “narrow” field, in fact encompasses

elements from all the “traditional” sciences, as well as ideas from the “social sciences” and

positions itself as a truly multi-disciplinary subject.

This seems to make good sense, as in the problems that we face, the “lines that we draw around

them” to delineate certain aspects of the problem, and assign them to experts are truly ‘arbitrary’.

By dividing the ‘energy problem’ into a technical problem this, a social problem that and a waste

management problem the other, we are adding whimsical boundaries that only serve to

“compartmentalise” thinking in relation to the problem, clouding our view of the “gestalt”.


38


In “Complexification”, John Casti’s definition of a complex system states that it must possess one

or more of the following properties (Casti, 1994)

1. Catastrophic – A small variation in the initial conditions of the system give range to

dramatically different outcomes.

2. Chaotic – The output of the system cannot be predicted using deterministic rules.

3. Irreducible – The system cannot be further decomposed into simpler parts without losing

information about the system.

4. Emergent – The properties of the whole are greater than the sum of its parts.

Another definition of the salient features of a Complex System is put forward by John Holland, a

revered complex systems scientist, known in some circles as “Mr. Emergence”, he defines the

features of Complex Adaptive Systems as (Holland, 1996):

• Many agents acting in parallel in an environment produced by it’s interactions

with other agents in the system; because the agent is constantly acting and

reacting to the other agent’s actions, nothing in it’s environment is fixed.

• Control is highly dispersed, therefore any coherent behaviour there might be in

the system has to arise from competition and co-operation amongst the agesnts

themselves.

• Many levels of organisation, agents at one level serving as building blocks for the

next level up.

• Constant rearrangement of the building blocks as a result of learning, experience,

evolution, adaption.


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• All anticipate the future to some degree, making attempts at prediction on the

basis of models of their environment.

• All have niches they can exploit, filling up one niche often opening up new ones

that can be exploited.

• They never reach equilibrium.

• They can improve on some dimensions, but never optimise.

• The richness of the interactions within the system allows the system as a whole to

undergo spontaneous self organisation.

When we consider the “energy debate” we can see that problems of these kinds are faced at all

levels of the problem. Indeed, the debate is so large, that presently, we can only make sense of it by

rationalising the whole problem to “one specific niche” that we deal with in isolation.

However, by looking at each simple argument in turn, we ignore the effect that it is having on the

wider system. An example would be, in wind turbine planning, by looking at each case on its

merits, we neglect to see what effect the decision taken could have on the wider public opinion

towards wind turbines, setting legal precedents e.t.c.. However, by granting blanket planning

permission, we would risk ostracising local communities and turning public opinion away from

turbines en masse. This illustrates this conflict between a “local” view of the world, and a “global”

view of the world.

“The most important lesson of complexity theory is the demonstration of the diversity of

phenomena that can arise through the reaction of simple components”

(Pippinger in (Flood, 1988)


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The Roots of Complex Systems Theory

As ‘Complex Systems Theory’ per se is considered a “new” area of research, it is important to see

the roots and foundations of the theory, as this helps us to understand how it has arrived at its

present form, and may possibly help us to predict a future trajectory for where the subject is likely

to head.

By understanding the history of Complex Systems Theory, we can see that it is the evolution of a

number of other different theoretical approaches which have been honed and worked up over

time. By having it’s foundations in other approaches to systems based problems, we can see that

Complex Systems Theory – whilst being “new” in fact contains methods, techniques and ideas

from a vast body of other supporting theories and approaches.

In part, this has helped me to understand, that whilst originally looking for a ‘radically different’

solution to the problem, Complex Systems only provides a framework within which to apply lots

of ‘old’ techniques, united together in one umbrella. As such, whilst looking for revolutionary new

understanding, I have come across a lot of pre-existing ideas, some of which are already

understood and talked about by people in ‘the Energy Debate’ albeit, not under the banner of

Complex Systems.

The ‘Visual Maths Institute’ have quite a good timeline for the development of complex systems

theory, (Abraham, 2002c) which has been reprinted overleaf. It can be seen how the umbrella

subject which we now call “Complex Systems Theory” encompasses many ideas which have gone

before, unifying them under a single subject which takes in diverse strands from many unrelated

fields and united them within a common framework.

The next couple of sections aim to illustrate how the theory has grown, and from which fields it

draws ideological support.


41


Evolutionary Timeline of Complex Systems Theory

• Dynamical systems theory founded by Poincare circa 1882

• Politicometrics created by Lewis Frye Richardson circa 1920

• The Gestalt theory was created in Berlin around 1930

• Concept of Homeostasis introduced by W. B. Cannon circa 1932 or perhaps earlier by Claude Bernard circa 1850

• Cybernetics created by Norbert Wiener ca 1942

o The idea was diffused by the Macy Conference in 1946

o Wiener's book was published in 1948

• General Systems Theory wascreated by Von Bertalanffy around 1950

• The concept of System dynamics founded by Jay Forrester ca 1950

• Morphogenesis founded by Alan Turing ca 1952

• Homeokinetics founded by Arthur Iberall ca 1950

• Cellular automaton created by John von Neumann ca 1952

• Homeorhesis introduced by C. H. Waddington ca 1957

• Theoretical biology confs 1966, 1967, 1968, 1970

• Catastrophe theory created by Rene Thom ca 1966

• Limits to Growth, the Club of Rome, 1972

• General Evolution Theory due to Ervin Laszlo ca 1985


42


Figure 4-3

Diagram Showing the influences that shaped the evolution of Complex Systems Theory. Redrawn from (Abraham, 2002a)


43


Examples of Complex Systems

To illustrate the breadth and diversity of phenomena which can be classified as “complex

systems”, I am going to take a number of radically different examples of systems and illustrate how

complexity has aided their understanding.

Ant Hills as Complex Systems

Consider for example an “ant hill” as a complex system:

Figure 4-4

An ant hill can be considered an example of a complex system Image Courtesy: (Dyson, 2005)

An ant hill is an “emergent property” of the synchronised behaviour of a large number of smaller

agents actions coming together to produce an emergent property – the construction of an ant hill,

which would not be expected from looking at the behaviour of each individual agent, or ant. Such

a “complex system” could be seen to straddle the disciplines of “biological science” and


44


“management science”. A biologist would argue one case for the behaviour of the ants leading to

this emergent global structure of the anthill, a management scientist might look at the co-

operation of the ants in a different way from a management perspective.

Avalanches as a Complex System

Figure 4-5

Avalanches are a dramatic demonstration of “Emergent Behaviour” on a massive scale which comes out of the action of a small number of agents in the system. Image Courtesy: (Cemagref-Grenoble, n.d.)

Avalanches are freak, unexpected events which are hard to predict and have a massive impact on

the “system” of snow accumulating on a mountain.

Models, with their roots in complexity theory have been used successfully to examine why

avalanches occur; rule based cellular-automata, where each cell represents a piece of granular

material, and rules dictate how the cells stack and topple.

However, (Wiesenfeld, 2001) argues that there is little correlation between the behaviour of these

models and the “real world” arguing that these are “toy models”.


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Defining Systems

We use the word “System” in everyday language to refer to a number of entities that are

associated, we call our local group of planets “The Solar System”, a Radio, CD and Amplifier can

be readily identified as a “HiFi System”, so we can group related entities together that interact

with one another, and classify them as a “system”.

We can classify phenomena that occur as systems, whereby a system consists of a group of parts

that interact with one another.

We can observe different types of systems around us in every day life, we see computer systems,

where a group of components work together to provide computing power; production systems,

where a group of workers work together to produce a part; biological systems, where biological

entities work together to reproduce; mechanical systems, where a group of parts work together to

provide a mechanical function… the list of systems that we can classify is virtually endless.

We can classify an Energy Production System as a collection of components which work together

for the purpose of “producing” energy. (No energy is in fact produced, merely transferred from

one form to another) In this thesis, we will be referring to “Energy Production Systems” in the

context of equipment used to generate electricity.

We can also classify an Energy Distribution System as a collection of components which work

together for the purpose of moving electricity from one place to another. In this thesis we will be

referring to “Energy Distribution Systems” in the context of equipment used to “move” electricity

from one place to another.

We cannot treat these systems in isolation as the method used to produce electricity will dictate

the type of distribution network and if there is a fixed distribution network, it may affect the way

that energy generating capacity can be added to the network.


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Defining Behaviours

If we look at these systems, and the interactions between the components, we can see that there

are often constraints on the way that these components interact. These constraints may be

imposed by the components themselves, or they may arise as a result of external influences on the

system. These constraints dictate the collective behaviour of the components in the system.

Observing these behaviours that occur in systems is interesting – we can draw comparisons

between different types of behaviours in different types of system. We can identify common

features between different types of behaviour in different types of system, that allow us to make

analogies between the ways that different systems behave.

The analogy of “water in pipes” is often used as an analogy by physics teachers to describe the flow

of electrons in a circuit. There are commonalities between the components of a hydraulic system

and an electrical system – pumps can represent batteries, valves can represent resistances and pipes

can represent wires.

When we look at the behaviours of the system, there are further comparisons that can be drawn,

the pressure representing voltage, area of the pipe representing current. Then we can say that

multiplying the pressure by the area, we can obtain the flow rate of the water. Back to our

electrical analogy volts x amps = watts. We can develop this analogy further to think of the

amount of water moved over a given amount of time to be similar to the measure of watt-hours,

the amount of power consumed.

Thus, using a simple example, it has been illustrated that analogies can successfully be drawn

between different types of system and different behaviours that occur within this system.


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Taking a look at Figure from (Bar-Yam, n.d) we see a range of systems, all fundamentally different

in nature. Physical systems, traditionally considered within the “domain” of the physicist,

Biological systems, traditionally considered as the “domain” of the biologist and social systems, the

preserve of the social scientist. However, “Complexity Science” seems to find unifying

characteristics in the “complex traits” that these systems share.

We see that the different types of system exhibit a range of behaviours, all of which are totally

different. Whilst the types of system are fundamentally different, we can see that there is a

resemblance between the different types of behaviours these systems exhibit – which leads us to

realise that there could be structural similarity in the inherent complexity of different types of

system, even if the systems appear to have little in common.

Figure 4-6

Examples of Behaviours in different types of system. Redrawn from:(Bar-Yam, n.d, Bar-Yam, n.d.)


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This is one of the tenets of “Complex Systems Science”, that there are certain systems that we can

define as “Complex” and there are certain behaviours that we can observe that are common to all

or some complex systems - that observations of behaviour in one system may be applied to

another.

In the lexis of complex systems scientists, we call these observations that transcend classes of

system “traversal questions”.

Thus an understanding of one type of system, for example, the spread of disease might help us to

understand a completely different type of phenomenon, for example the spread of innovation.

So, how do we differentiate between “traditional science” and its marriage with “systems

thinking” and the brave new world of “Complex Systems Science”?

In “Beyond Reductionism”, (Appenzeller, 1999) states that most would not subscribe to the idea

that everything boils down to “A question of physics”, however, we need methods, tools and

metrics to quantify the phenomena around us – ‘Science’ currently provides us with an objective,

quantitive way of reducing phenomena down to simple linear relationships that can be more

easily understood.

Some systems can be described very well by “existing” science. The motion of a simple pendulum

in a vacuum can be described admirably to a good degree of accuracy. However, in the “real

world” consider that the pendulum has air resistance to contend with, that there is friction on the

bearings of the principle, that the length of the pendulum expands and contracts with changes in

temperature and soon it is realised that the problem is more complex that at first thought.


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Computation & Complexity

Such systems present problems when we try to model them. Our ability to model a system is a

function of the computing power which is available to us. With improvements in technology year

upon year, computers are gaining ability to model ever complex phenomena in ever greater detail.

Therefore the term “computationally impossible” is somewhat a misnomer, as many things that

have been thought in the past impossible to calculate suddenly become possible with the advent of

new technology.

As a famous illustration of a problem considered “computationally too complex” for the day, take

for example, a system which by today’s standards is “relatively simple” – the “German Enigma

Machine” (Sebag-Montefiore, 2004), (Hinsley and Stripp, 2001), used during the war could have

been considered to be “relatively complex” as the letter output was the consequence of a number

of inter-related and not immediately obvious systems which worked together with great

interconnectivity to produce a scrambled output.

Before the computing power was developed to “crack the complex code” of the Enigma machine,

the task was tackled by a system involving trial and error and a process of elimination. However,

Alan Turing and Tommy Flowers developed a “computer” – a milestone in computing history

which was able to “decrypt” the Enigma codes by a process of computation – the problem now

appeared “simple” when the technology was made available.

Therefore, when discussing the complexity inherent in today’s energy distribution system, we can

only look at the “computational complexity” in terms of today’s computing power and the

resources available to us when assessing if the problem is “too complex” to compute using

“standard science”.


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Modelling or Reality?

Next we need to look at to what level of accuracy do we want to learn about the system under

analysis, to what extent do we want to “model” the system, and can complexity science reveal any

more detail than conventional modelling techniques.

When we want to understand something we commonly construct “models” which help us to

explain the phenomena under observation. Architects make “models” of buildings which are

representative of the building before actually “constructing” the building. Maths is another form

of “model” which helps us to understanding quantitive phenomena.

Does “complexity science” just provide us with a “different” model of the system under

examination, or does it provide us with a “better” model with more detail.

There are arguments on both sides of the fence for complexity science models, (Wiesenfeld, 2001)

argues that cellular automata models for avalanches are “toy models”, however, in other spheres of

application, say climate modelling, cellular automate have been applied to good effect. Cellular

automata also help us to understand traffic flows better than conventional models which

analogise traffic to say the flow of water in pipes.

There is sufficient evidence in the literature, to lead us to believe that there are instances when

some types of “Complex System Model” can yield good results, however, these vary from

application to application, and each model can only be evaluated on it’s individual merits.

Bringing this back to an energy perspective, if I connect a bulb to a battery using some wire, with a

switch to form a circuit, I can model this behaviour quite well. If I want to take a “birds eye view”

of the overall systems behaviour I know that as long as there is power in the battery, when I turn

the switch on, the bulb will illuminate as long as there is a complete circuit.

If I want to look at the detail, I can use science to analyse the circuit effectively. Ohm’s law will tell

me the amount of resistance in the wires, using the simple relationship between volts, amps and


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watts, I can tell the amount of power that is flowing through the circuit, I can look at the circuit

in terms of the electrons which are flowing past a given point at any one time. I know that the

resistance of the incandescent bulb changes as it’s filament heats up – this will affect the current

that flows. In all, science describes this system admirably.

In a mechanistic view of the world, according to (Lewin, 1993a), the simple local interactions are

viewed as giving rise to larger phenomena, termed “the Emergent Global Structure”. This is a

“bottom up” way of viewing systems.

Using the above example, a mechanistic view would involve looking at the detail of the circuit in

terms of Ohm’s Law, the amount of power flowing, the “Low Level Detail”.

By contrast, in a vitalistic view of the world, the larger system picture is considered, as stated in

(Lewin, 1993a), as giving rise to the smaller phenomena which occur at a local level.

Returning to our electrical example, a vitalistic view would take in the basic understanding of the

system – the “global properties” that most people understand – that when I turn the switch on,

the light bulb lights as long as there is power.


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How does Complex Systems Theory Differ From Mechanistic / Vitalistic Views?

‘Complex Systems Theory/Science’ aims to explain complex phenomena, by addressing the

holistic nature of systems and the co-dependence of the local interactions on the “global

phenomena” and that also the “global phenomena” affect the local interactions.

Figure 4-7

How Mechanistic and Vitalistic views of a system differ. Redrawn from (Lewin, 1993b)

Looking back at our electrical example, a mechanistic view, analysing the flow of electrons, doesn’t

really tell us anything about the “behaviour” of the system – that the bulb lights as long as there is

a complete circuit and power in the battery: neither does the vitalistic view that the bulb lights

when we press the switch really tell us about what is going on. In this simple conceptual example,


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someone with a basic knowledge of physics could quite reasonably understand both the

mechanistic and vitalistic views of the system – and hence have a very good understanding of the

entire workings of the system.

(Letiche, 2000) states that “Complexity theory can be conceptualised as a collection of new anti-

mechanistic metaphors stressing process and emergence”.

It could be argued that our current metaphors for discussing energy production and distribution

process are mechanistic and do not take into account the full complexity of the problem – as a

result we are losing a fundamental understanding of our energy production and distribution

systems as we fail to take in the full complexity of the “energy system” which encompasses political

and social dimensions in addition to the “technical complexity”.

Furthermore, there are a number of instances where the views offered by complex systems appear

to contradict what has become “accepted science”. As an example of this, the idea of “self-

organisation” seems to be at odds with the “Second Law of Thermodynamics” – Clausius’

definition being “The entropy of an isolated system not in equilibrium will tend to increase over

time, approaching a maximum value at equilibrium.”.

Figure 4-8

Convection Cells or Raleigh-Bernard Cells observed in a heated fluid. (Berg, n.d.)


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Figure 4-9

Closeup of the spontaneous self-organisation of convection currents in a Raleigh Bernard Cell. (Berg, n.d.)

However, there are observed physical behaviour, such as behaviour observed in the Bénard cell,

where a system which should apparently be heading towards “greater entropy” exhibits behaviour

of self-organisation. It appears that “out of the order” comes chaos. Chaos theory, subsumed by

Complex Systems Theory seems to suggest that there are behaviours that can be observed under

the umbrella “self organisation”, where out of apparent chaos, order becomes apparent and the

system produces some sort of consistency from the inconsistent.

Furthermore, if we take a “birds eye view” of the energy production and distribution systems, we

lose an understanding of how cumulative interactions at the local level add up to “system-wide

global behaviours” – and if we only examine the system at a local level, we lose an understanding


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of the bigger picture. What is needed is a multi-scalar view of the problem, that takes the system-

wide perspective into account, whilst not underestimating the effect on the system of local

behaviours.

Translating this into terms relevant to the energy debate – if we spend all our time looking at

meeting demand on a national level from large scale infrastructure, we may lose sight of what can

be done on a local level to solve the problem using “resource-efficient solutions”. If however, we

concentrate all our efforts to addressing local problems, we could lose sight of the “national”, even

“global” scale of the problem.

It needs to be appreciated that if we accept that our energy systems are complex, then we accept

that they are multi-scalar (Johnson, 2002a).

Constructing models that traverse different scales of interpretation is very hard, however

Complexity takes this multi-scalar nature of complex systems into account – and with this

understanding enhanced models can be created.


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The Science of Free Will

The view of traditional deterministic Newtonian mechanics is underscored by a comment made

by the scientist Laplace (cited in (Underwood, 2003), that “If he knew the current state of all the

molecules in the universe he would be able to predict the future of the universe for ever (and, by

extension, the whole of the history of the universe up until now)”.

In an energy distribution network as complex as we have in the UK, it is computationally

impossible to calculate the status of all of the items and interactions that comprise the energy

distribution network at any one time, furthermore, this view negates the fact that human agents

interact with the system affecting it’s behaviour. Science, struggles to explain this free-will

interaction of people with the system. As soon as we bring “people into the equation”, we have to

introduce “social science” and marry the two disciplines.

Even as early as Voltaire, (as cited in (Underwood, 2003) it was realised that this would mean that

“free will” was a meaningless concept, and thus could not be so. He summed it up “It would be

very singular that all nature and all the starts should obey eternal laws, and that there should be

one animal five feet tall which, despite these laws, could always act as suited his caprice. It would

act by chance and we know that chance is nothing. We have invented this word to express the

known effect of any unknown cause”.

It is this free will that is hard to quantify, yet essential to understand in an energy production and

distribution system. If we examine a scenario where power is produced by renewables then we are

at the “free will” of the weather to produce power. Similarly, the operatives of a nuclear power

station have “free will” to act as individual agents in a larger system. Then at the distribution and

supply chain end of the system, the users and consumers have “free will” to use and consume

energy as they see fit.

It is the belief of complex systems scientists that these “rogue elements” in the system cannot be

easily understood and quantified using a conventional reductionist approach, and that further


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understanding and comprehension of the system is to be gained when we accept the system to be

“non-linear” and examine the system using appropriate metaphors and analytical tools.

This is a view supported by (Letiche, 2000) who goes on to say that “[Complex Systems Science]

can be interpreted to lead both to radical process thinking and to scientific realism”.

(Underwood, 2003) highlights that it does not necessarily mean that deterministic rules do not

hold up, but that simply the amount of calculation and processing power required is too immense

and that by generalising we often lose understanding of the whole.

Complexity is seen as the “New Science” by many researchers working in the field, Ilya Prigogine,

is cited in (Underwood, 2003) as believing that a “mechanistic and deterministic world-view … is

being replaced by a new paradigm”


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Network Dynamics

If we are to describe the changes that occur in a system, we need a vocabulary to describe the type

of changes that occur and how they affect the system. We can refer to the changing nature of

systems as the “dynamics” that occur within the system.

How though, can we classify these dynamics?

In his lecture “…” to the Mathematics for Complexity Science Summer School held at Warwick

University 11th - 15th September 2006, Professor Jeff Johnson distinguished between two different

types of “dynamics” in Complex Systems, where “dynamic” is used to refer to a change in the state

of the system.

Figure 4-10

Type I & II Dynamics As Defined By Prof. Jeff Johnson


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He distinguished between Type I & Type II dynamics, where:

Type I Dynamics

In a type I dynamic, the change is where an movement of “traffic” occurs between the nodes of a

network. The “traffic” could take the form of movement of cars or trains in a transport system,

fluid in a hydraulic system or data in a computer network system.

In an energy network, an example of a Type I dynamic would be a flow of electricity from the

North to the South of the country through the National Grid.

Type II Dynamics

In a type II dynamic, the change is where the network’s configuration in some way changes as a

result of change of interconnection or an addition or subtraction of extra nodes to the system.

The “change” could take the form of adding a new road in a transport system, adding an

additional valve in a hydraulic system, or adding an additional server in a computer network

system.

In an energy network, examples of a Type II dynamic would be the addition of a new power

station, connection of a new user or rerouting of a grid connection.


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Criticism of Complex Systems Science

However, Complexity Theory is not without it’s criticism. (Monceri, 2005) asserts that

Complexity Theory relies on “reductionist assumptions” as much as any other science, and whilst

it pertains to be a science of the gestalt, even complexity must reduce these phenomena to simpler

terms in order to makes sense and understanding of them.

(Fuller, n.d.) asserts that “[Complexity does not have] a well-established set of principles that are

commonly agreed to form a rigorous basis for the development of the discipline. [However]

There are ranges of positions, theories, principles and pragmatics attached to [Complexity]”.

Another criticism of complexity is that our understanding of anything is subjective, and reliant on

the way that we frame the problem under examination. Our perception of the world influences

the way that we examine it, so any theory is a representation of the way we construct our view of

the world – not the world itself. (Pahl-Wostl, 2003)illuminates this “Even seemingly objective

scientific facts are embedded in a context of values and interpretation”. The examples (Pahl-

Wostl, 2003) cites as being famous are “the ozone hole” where measurements which eventually

proved the ozone holes existence were initially discarded as “…unplausible measurement errors…”,

another example cited is that of “chaotic behaviour in the pattern formation of chemical

experiments”, where the little-understood chaotic interactions were dismissed as “failed

experiments” and “impurities”. Thus it can be seen that we “socially construct” the sciences,

accepting measurements that seem to fit the particular model we are working with, and discarding

those which do not.

Perhaps an alternative understanding of “Complex Systems Theory” is that offered by (Fuller,

n.d.) that “[Complexity is a] function of human understanding rather than the phenomena

themselves.”. Fuller cites (Shackley et al., 1996) in saying that by studying a complex system

“nothing is any more complex than it was”, just that we view it differently.


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(Pahl-Wostl, 2003) cites (Shackley et al., 1996) as saying “Given the intellectual excitement

surrounding the new ideas on complexity, it is easy to overlook the fact that the apparent

simplicity of the past was often more a function of the constraints put on the frame of the issue

or problem at hand, both conceptually and in policy making, than it was a reflection of any

inherent properties.”.

In considering the Energy Debate, it is important to maintain the perspective that Complexity

acts as “additional information” about the problem, and the systems and behaviours under

examination – rather than making our existing models redundant. (Fuller, n.d.) does not

discout their usefulness, but offers the following words of guidance. “Complexity theory should

be seen as informing an understanding of dynamics, not of substantive knowledge. The causes

of specific instances of behaviour in a domain cannot be discovered from the metaphors of

complexity. However, devices of complex adaptive systems research, in particular simulation,

may assist the identification of causality or influence.”

(Fuller, n.d.) goes on to cite (Cohen and Stewart, 1995), where in their critique of the collapse

of “Chaos Theory” and its subsumption by an overall theory of “Complexity” - “The Collapse

of Chaos: Discovering Simplicity in a Complex World”, (Cohen and Stewart, 1995) criticism of

“Complexity” revolves around the fact that whilst an initial set of conditions and a set of rules

governing how those conditions will evolve will produce one set of outcomes, so could a different

set of initial conditions and rules. One of the characteristics of Complex Systems is stated that

they can be dependent upon their initial conditions, yet (Cohen and Stewart, 1995) cite

examples where the behaviour of a complex system, in effect “erases” its history.

From this evaluation, it is important to bear in mind that “Complexity” is just another

framework for putting phenomena that occur in the physical world into a framework that we

can conceptualise and understand, and that the picture we form of the system will always be

distorted by the lens through which we view it. Our aim therefore, should be to find the most

transparent lens.


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C h a p t e r F i v e

UK ENERGY PRODUCTION AND

DISTRIBUTION AS A COMPLEX SYSTEM

SUMMARY

N THIS CHAPTER , WE BEGIN TO APPLY THE FOUNDATIONS OF

“COMPLEXITY THEORY” TO THE UK ENERGY PRODUCTION AND

D ISTRIBUTION SYSTEM . SPECIFIC TECHNIQUES AND CONCEPTS ARE

DRAWN FROM THE ARMOURY OF THE COMPLEX SYSTEMS SCIENTIST , AND

APPLIED TO THE ENERGY PROBLEM .

I


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C h a p t e r F i v e

UK ENERGY PRODUCTION AS A COMPLEX SYSTEM

Energy as a Complex Adaptive System

Using the model of the “Complex Adaptive System” introduced in the last chapter, we can see

how users affect the “Global” behaviour of the system, and how the “Global” behaviour of the

system affects the users actions. This co-dependence makes for rich feedback loops introducing

complexity in the system.

The diagram overleaf (5-1) is redrawn from Figure 4-7, showing how the state of the global

energy market is dependent on the consumers that constitute it, and the consumer’s use of

energy is dependent on the global energy market.

It is not enough to evaluate the system from the point of view of the user – or the overall market

as the two are co-dependent. We can see how external variables influence this marketplace, and

how the marketplace in turn has a bearing on those outside variables.

If we understand the Global Energy Marketplace, in which the UK sits, as a Complex Adaptive

System, we realise that we cannot “control” the system, and that even a small perturbation of

one of the variables can have dramatic effects, as complex adaptive systems cannot be controlled

– but if we understand them, we can aim to “steer” them more effectively.

At the moment, there is disarray in “Global” energy policy, with national approaches to climate

change, fuel security and future planning varying significantly.


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Figure 5-1

Global Energy System as a Complex Adaptive System after (Lewin, 1993a)

Considering UK Energy Production and Distribution as a Complex System, we can expect it to

exhibit the classes of behaviour characteristic of complex systems as defined by (Casti, 1994).

If we consider the classes of behaviour one by one and in-turn look at how we can see complexity

in the UK system of energy production and distribution, we can see that many of these classes of

behaviour can be observed in the system on a variety of levels. The issues surrounding UK

Energy Production and Distribution are so large, encompassing facets of technology, society,

politics and environment, that a technical, sociological, political or environmental approach will

never be able to address the scale of the problem in isolation. What is needed is a coherent,

integrated approach that takes into account all of these facets.


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Scott, Johnson and Frizelle (2002) say the following about the new light that complex systems

can shed on old problems.

“If we believe these explanations (and it is early days yet) then we will have

improved our understanding of these issues;

• The thinking currently employed to manage these issues must be

reassessed in the light of the new understanding we have gained

through our use of complexity science;

• We may want to employ different techniques, measure different aspects

of the system behaviour, or consider different systems designs for the

whole or its parts”


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If the complexity science evangelists are correct, then ‘Complexity’ could lead to a

fundamentally different view of the energy debate. At the moment, there is complex

interplay between public opinion, the needs of industry, our increasing energy

requirements, the need to protect the environment, coupled with the fact that the

United Kingdom sits as one country in the midst of a number of international players.

This problem is not a simple one!


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Nodes & Links

We can define an energy distribution system as a number of nodes interconnected by a number

of links. Using the terminology of graph theory, we could describe our network graph as a

number of vertices connected by a number of edges. These nodes could take a number of forms:

• “Power Station”

• Renewable Energy Generator

• Storage Facility (Pumped Storage / Hydrogen / Battery Bank e.t.c)

• Transformer

• End User

Kauffman (1995) uses the terminology of “buttons” and “threads” to represent the nodes and

their interconnections. Kauffman defines the amount of interconnection between “buttons” as

a ratio of “threads:buttons”.

Kauffman goes on to assert that this ratio is critical to the functioning of the system, and that

there is a point at which an increase in the ratio can cause a phase transition in the system with

the resulting phase transition being catastrophic.

We can apply this rule to the design of power distribution systems by realising that our power

distribution systems derive their stability and behaviour from the amount of interconnection

between nodes.

The type of mix of energy providers that we choose in the future will inevitably shape the

topology of the network and the ratio of “nodes to links” and how that structure evolves.


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Network Topologies

The efficiency of a “system” is determined by the topography and layout of the system – how the

components interact. In this section, we will take Complex Systems Theory ideas from (Cowan,

2004, Frenken, 2006) concerning social networks and the spread of innovation, and apply them

to Energy Distribution Networks. This again illustrates the traversal nature of complex systems

theory and the application of theory from one domain, to another – completely different in

scope.

Congruent with the definition of system, above, the efficiency of an energy distribution network

is determined by the components of the system and the topology and layout of the network - the

way in which they are arranged.

In a traditional centralised generation system, power is generated centrally, and is transmitted

long distances to point of use. Moving to the paradigm of de-centralised localised generation, it

can be observed that the ideal is for power to be generated locally and used locally as far as

possible with some “balancing” provided by means of storage and/or a “grid” albeit with a

slimmer, leaner infrastructure required.

If electricity has to travel long distances, there will be losses incurred as a result of resistance in

the transmission network, however, sometimes long-distance transmission is required to provide

a restorative “balancing” effect to the system.

In examining network topologies, I again turned to complexity science to look for definition of

network complexity.

Clearly these two schema for distributing electricity result in radically different network

topologies – can complexity theory provide any analysis of these differing network

configurations?


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In (Frenken, 2006), (Watts, 1998) is quoted as classifying three ideal types of network

structures, illustrated in Figure 5-2, these structures are “idealised” structures, which are

representative of different structural features in different types of network. Clearly, there is a

wide gap between “theoretical” networks, and applying this to “real” networks, however, this

serves as a model to aid understanding.

Figure 5-2

Network Topologies as expressed by parameter p (Cowan, 2004) in (Frenken, 2006)

Regular Networks

In a regular network, agents interact with their nearest neighbours, but no other agents. This

gives rise to a regular pattern.

Small World Networks

In a small world network, most agents will interacts with other agents that are in their vicinity,

however, some agents will interact with other agents that are further afield and not their

neighbours.


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Random Networks

In a random network, an agent will interact with any other agent, with no discrimination of

whether that agent is nearby.

The letter “p” was assigned to the degree or order or randomness in the network. Thus “0”

would represent a wholly ordered network and “1” would thus represent a wholly random

network. (Cowan, 2004, Frenken, 2006) plots a graph of the “average path length”, that is to say

the average time taken to get from any node to any other node against “p” as well as the level of

“cliquishness”, that is to say “local interconnection”, where “0” represents a high level of local

interconnection and “1” represents a high level of local interconnection. The results are

interesting and shown in Figure 5-1.

Graph 5-1

Properties of networks with different p-value from (Cowan, 2004) in (Frenken, 2006).


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We can observe that “Regular” networks possess a high degree of local interconnection, but with

the downside of a high average path length. “Random” networks, by contrast, possess a low

degree of local interconnection – but also a low average path length.

What is interesting, is when we examine the intermediate case of a “small world network”. As

the analogy is drawn from social networks, a small world network is defined as one where the

agents in small communities communicate effectively between themselves, but with some

sharing and dissemination of “network traffic” between groups – over longer distances.

Curiously, this arrangement, where the optimum “p” is taken to be 0.09 results in the combined

qualities of a high degree of local interconnection, and a low average path length.

I would argue that the concept of a centralized electricity production network is nearer the p=1

of a “random network”, whereas a future reality with a high degree of locally embedded

renewables – with some long distance links is nearer to the “small world network” of p=0.09

with its inherent desirable properties.

In fact, I would argue that we should be designing energy distribution networks as far as possible

to meet the conditions of “small world networks” as they give rise to desirable properties.

What we can learn from this complex systems analysis of networks, is that if we move towards a

future scenario of totally decentralised generation, with only localised power links, then we

could be moving towards the “Regular” p=0 topology, with its long average path length. This

would result in high power loss and an inefficient network topology. Thus it can be seen that

some form of “grid” is desirable in our future energy scenario, and that neither a wholly de-

centralised schema or wholly centralised plan is desirable but a balance of both properties is.


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Distributed Computing to Model the UK ESI

In projects that involve a lot of calculation, one method of processing the information is to use

“distributed” or parallel computing, where the problem is broken into “cells”. Each cell is

considered to be a “packet” of work, and is sent via the internet to a user running an application

program which processes the packet of information.

Such a process is analogous to bees working on a honeycomb – each bee doing the work required

to build a single hexagonal cell at a time – with the eventual result that a much larger

honeycomb emerges.

Such an approach shares ideas with the “Cellular Automata” encountered in “Complexity

Science” where a cells state is determined by its neighbouring cells states and the rule for that

cell.

Such a model of the UK ESI running in real time, could give a very in-depth understanding of

how the network functions. At the moment, questions still remain unanswered about the ability

of the grid to absorb renewable energy resources without detrimental effect. With a powerful

model such as this, it would be possible to run complex simulations, varying the characteristics

of the plant supplying the electricity, and seeing how this impacts upon the network.

When we take into account the fact that the system just to track “customers and suppliers –

who is supplied by who”, was considered “complex” in 1998 (Patterson, 1999), we begin to

realise the scale of the problem, and that a “Complex Toolkit” will probably need to be

employed.

Such work is beyond the scope of this thesis, suffice it to say that “cellular automata” and

“synthetic population modelling” seem to provide good tools for achieving a good model.


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Complex Socio-Technical Systems in Energy Production

When examining the complexity of Energy Production (and Distribution and Use) it is

important that we understand our system in the context of “humans in the loop”.

Our energy production and distribution system is without any shadow of a doubt, a socio-

technical system. We can see in the Venn diagram illustrated in Figure that our Socio-Technical

system exists in the overlap between technical systems and social systems.

Figure 5-3

Socio-Technical Systems Exist in the Overlap Between Social Systems and Technical Systems – Sharing Traits and Qualities of Both.

Humans are involved at all stages of the energy production and distribution process to varying

degrees. Human error is recognised as a mechanism for failure in many systems where humans

are “in the loop” – airline crashes for example can often be attributed to the failure of a socio-

technical system, rather than just equipment failure. By the same token, the Chernobyl nuclear

disaster was the result of bad system design, but also the failure of the human operators to

manage the system correctly. This will be explored later.


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It is important to note the dynamic symbiotic relationship between elements of a Social

System and elements of a Technical System when co-existing as a Socio-Technical system.

Any change by the Social System has an effect on the Technical System and creates a

feedback which then has an effect on the Social System. Returning to our definition of a

complex system, we can see that indeed, assessing our socio-technical system against the

features of complex systems:

- They contain feedback loops

- The contain socio-cognitive complexity

Figure 5-4

The Dynamic Nature of Socio Technical Systems.

Rognin, L., Salembier, P. & Zouinar, M., (2000) explore the notion of complex socio-technical

systems in the monitoring and control of nuclear power stations. Their work is empirically

based on the study of work practises; it is their study into the regulation of nuclear power

stations as a socio-technical system that is particularly interesting to us.


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Socio – Technical Complexity in System Behaviour

There are transient events that occur which are as a result of social events that occur.

A dramatic example of this is following “Coronation Street” or “Eastenders” when the nation

switch on their kettles. This event is as a result of complex phenomena, and could not be

explained by a simple linear relationship; we understand and appreciate this phenomenon – we

do not need “Complexity Science” to observe this behaviour. However, this behaviour is

indicative that there could be a wide range of other phenomena, which we do not yet recognise,

or understand, that are as a result of social action, not just technology.

Furthermore, moving from the “user end” of the ESI, to the “production side”, the way that

plant is designed affects the way that the operators interact with it. A device such as a wind-

turbine, or a PV panel, requires very little “user interaction” to produce electricity once installed,

there is not therefore much socio-technical complexity inherent. However, a large centralised

plant such as a nuclear or coal fired power station requires a large degree of interaction from a

large number of people in order to function successfully.

(Rognin et al., 1998) state that the understanding of complexity is key to “error-tolerant design”

in “safety critical work systems”, many of our “large centralised” energy systems are “safety

critical” and so an understanding of the socio-technical complexity will ensure safety.

Perhaps there is an argument for trying to reduce the socio technical complexity of our energy

generating systems? As seen in Chapter Three, a large number of nuclear power disasters can be

traced to “human error” or failure of a socio-technical machine, whether that be incoreect

maintenance, inherent procedure or otherwise. By contrast, renewables do not have this

inherent complexity.


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Using Figure 5-3 taken from Figure 6, Rognin, L., Salembier, P. & Zouinar, M., (2000), we can

see the inherent complexity in a even a simple operation in a Nuclear Power Plant.

In this case study, they looked at the process of a Mechanic opening a valve remotely in a nuclear

power station, whilst a Supervisor and Controller monitored a panel to see the reaction.

Figure 5-5

Socio-Technical Complexity in the Management of Nuclear Power Stations, Case Study redrawn from Figure 6, Rognin, L., Salembier, P. & Zouinar, M., (2000)

This is just one socio-technical process amongst many complex interlinked process that are

occurring simultaneously. Failure of any one of these process could lead to degradation of system

performance, or at worst a nuclear disaster.

If we analyse the failure mechanism of the “Chernobyl” reactor, shown Figure 5-4 we can see

that the failure was of a “socio-technical system”, not just a “technical” equipment failure.

“At 1:22 a.m. on April 26, 1986, a Soviet reactor crew carelessly turned off the safety systems of

the Chernobyl Unit 4 nuclear reactor to perform an unauthorized safety test.

Within 36 seconds the reactor surged out of control, and a steam explosion pierced the roof.

Deprived of coolant, 150 tons of uranium fuel melted into lava that oozed into the basement of

the reactor.


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A second, hydrogen explosion ignited blocks of graphite, rocketing a hot plume of radioactive

particles a mile into the sky. the explosion was so powerful that it blew the 2 million pound

concrete lid of the reactor into the air. For three weeks the fire spread out of control, sprinkling

iodine-131 and other nuclides as far as Scandinavia, Italy, and Britain.”

Source Unknown

Figure 5-6

Chernobyl Disaster, [Online Image] Accessed on 29 / 07 / 06 at: http://en.wikipedia.org/wiki/Image:Chernobyl_Disaster.jpg

Failure of a Socio Technical System?


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Similarly, the threat posed by terrorism to large centralised generation could be viewed through

the lens of complexity science as a complex socio-technical system where a small number of

agents (terrorists) have the potential to cause a dramatic effect on the parameters of the system

as a whole. Asmus explores this notion in his 2001 paper, however, he does not relate this

concept to the notion of complexity, however, when considering the butterfly effect, how a

seemingly small action can produce a large action elsewhere, it can be verified once again that

our energy production and distribution system possesses the identifying traits of a complex

system.

At all levels of the ‘energy system’ people are involved, and their judgement affects and colours

the performance of the system and how that system works and functions.

(Batten, 2004) has examined the Socio-Technical complexity inherent in the bidding systems

used for Electricity Markets, focusing on Australia’s “National Electricity Market” system for

matching supply and demand, which in very broad terms could be compared to the system used

for matching suppliers of electricity to demand in the UK post-CEGB.

There are elements of “game-theory” which can be used to inform the means by which suppliers

bid for the right to produce electricity.


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Based on the work of (Lewin, 1993a) and (Johnson, 2002b), (Andrus, 2005) produced the

diagram shown below “Complex Adaptive Behaviour”.

Figure 5-7

Complex Adaptive Behaviour

Using this Figure as a schema for examining Micro-Generation as a Complex System, we can

derive the following diagram which helps us to relate the Complex Adaptive Behaviour that

could occur in a more “renewables oriented” society to the concept of “Complex Adaptive

Systems”

Here self-organisation happens at a local level, as systems adapt to the amount of renewable

energy resource available – it can be seen that when “rich feedback loops” are created, by

providing information to occupants about the amount of energy available, they adapt their

behaviour to suit the resources. The grid is then used to provide “balancing action”, and the

behaviour arises not out of central control, but out of local responsibility and “de-centralised

intelligence”.


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Figure 5-8

Examining Micro Generation as a Complex Adaptive System


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Observing Emergence in Energy Use Patterns

Complexity theory demonstrates how it can be seen how the collective effect of a number of

smaller agents acting in synchronicity can add up to a much larger cumulative effect. This is the

principle of emergence – which is one of the ‘tell-tale’ identifying features of a complex system.

Emergent behaviour can come clearly in to view, when we analyse local and national energy use

patterns.

There are a number of external factors which can affect the energy use of a large number of

agents within the system simultaneously. Some of these might be seasonal – a change in

temperature will cause residential consumers of energy to switch on the heating, industrial users

of energy to need to heat their plant and factories, and commercial users of energy to warm their

offices in the day. Conversely, the heat of the summer causes a rise in the commercial sector’s

consumption of energy as offices simultaneously turn on their air conditioning.

An example of emergent behaviour causing a failure in the “Energy Distribution Complex

System”, was recently observed in the summer of 2006 in London where the high temperatures

synchronised the simultaneous consumption of energy by a large group of users, as offices turned

on the air conditioning to deal with the searing heat, retail businesses turned up drinks chillers.

Other factors are not driven by external environmental stimuli, but are man-made.

Observable phenomena occur where it can be seen that the behaviour of large groups of agents is

synchronised by way of collective social phenomena which affects all of the agents within that

social phenomena. If this behaviour involves the consumption of energy in some way, then the

behaviour has a direct effect on their energy use patterns – synchronised consumption of

electricity by a large number of people simultaneously has consequences for the provision of

supply.


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This was noted even as far back as the 1930’s, when the ESI in the North East of England, grew

aware of the need to increase their generation in anticipation of “Gracie Fields” singing on the

radio (Hannah, 1979). This followed a detailed study by the CEB in the North East into the

social patterns and habits of the inhabitants of the cities to be joined by the new “National

Gridiron”, (Hannah, 1979).

This behaviour is still observed clearly today, popular soaps, viewed by millions, orchestrate the

masses to simultaneously consume energy at the same time – this effect is exacerbated by the fact

that competing programmes are often broadcast at the same time – whilst they compete for

viewers, they synchronise their viewers to consume energy in harmony.

(Boyle et al., 2003) observes this:

“The growth of radio and television has produced its own problems by

increasingly synchronising the behaviour of large numbers of people. A mass

rush for the electric kettle at the end of a popular TV show can produce an

increase in national electricity demand of over 2GW in a matter of minutes.”

If we wish to look for an idiosyncratic example of this behaviour, (BBC NEWS, 1998) informs

us:


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“EastEnders beat its ITV rival Coronation Street by three to one in Sunday's

head-to-head, according to power surges recorded by the National Grid. Initial

figures seem to show that the hour-long EastEnders' episode proved more

popular with soap fans, said a National Grid spokesman. There was a 900

megawatt surge of power at the end of the special edition of the BBC soap as

millions of viewers went to put the kettle on an increase of a third on its usual

figure.”

This causes a very direct and measurable problem for our energy production and distribution

system to deal with – finding solutions to this emergent behaviour poses very real challenges

both for renewable and nuclear sources of energy as neither technology is good at coming “on

line” on demand.

If neither technology is good at managing this emergent behaviour from a “supply side”, then in

order to manage the system effectively, it would seem that we have a number of options:

• Accept a diminished QoS, less continuity of supply and more frequent blackouts &

brownouts.

• Energy Storage (Pumped Storage, Fuel Cells, Batteries e.t.c.)

• Demand Side Management

In our present electricity supply arrangement, a “buffer” for peak-loads is provided by “spinning

reserve”, however, if we accept a future without fossil-fuel generated power, then we will have to

look at alternatives to meet our “peak” demand needs, or manage that peak.


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It is known that as the amount of renewable energy in a supply system increases, in order to

maintain QoS, there needs to be some element of “storage” in order to provide a buffer for the

intermittency of renewable energy sources. However, perhaps the alternative scenario of a largely

nuclear powered ESI needs further consideration to see how quickly the network could adapt to

demands.

It will be interesting to observe with the spread of New Media, the Internet and “TV On

Demand” how the collective behaviours of large groups of people changes. (Harper, 2005)

These technologies permit users to access information at a time of their convenience – rather

than dictating a viewing time as concurrently.

The removal of the necessity to broadcast simultaneously to an audience as a result of the

limitations of broadcasting technology removes the element of central control; rather than the

collective behaviour of a large number of agents being controlled centrally, the agents are given

greater freedom to make their own decisions as to when they access media. (Harper, 2005). This

appears to me to be a validation of Professor Bar-Yam’s (n.d) hypothesis, that we are moving

from civilizations which were previously centrally governed, and hierarchical in structure, to

networked societies where agents interact freely with one another.

It could be argued that this will not necessarily lead to the demise of the emergent behaviour

observed, rather that it will lead to different types of emergence – possibly which are harder to

predict as a result of the increased complexity of interaction between agents and the media.

Whilst the ability to access some events and forms of entertainment can be de-centralised and

hence de-synchronised, there are some events which will always be live and will result in

unavoidable synchronisation of energy network user behaviour.


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Inherent Complexity In ‘Energy Source Development’ Funding

One of the themes of this thesis has been the postulate that it is essential to manage the

complexity at all stages of the energy production and distribution system in order to provide a

successful, sustainable solution.

In this next section, I will contrast the ways that Nuclear Energy & Renewable Energy

Developments are funded and argue that the success or failure of developing an energy source is

largely dependent on the complexity of the funding stream.

The funding of Energy Development is particularly relevant at this time – potentially a turning

point – where the government’s energy review moots a paradigm shift in the funding of nuclear

power – with the onus being transferred, at least in principle, from the public to the private

pocket.

Nuclear Energy in the United Kingdom has had a long history of government funding. From

inception, nuclear power has been heavily and directly subsidised by government – the initial

development of the civil nuclear programme in the UK developed out of a desire to produce

weapons grade atomic fuels – civil nuclear power’s past is inextricably linked with the

developments of nuclear weaponry.

The immense cost of developing nuclear power meant that the responsibility for funding fell to

government.

In some respects, the safety concerns associated with nuclear power almost guarantee

government intervention where market forces cannot meet the fiscal demands imposed by the

long process of decommissioning; as it is in the public interest that the regulation of nuclear

plant is kept within strict limits – lack of funding could result in a catastrophe which would

have massive repercussions for the public at large.


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By contrast, Renewable Energy Technologies, are relatively new to the scene, and government

approaches to funding have been mixed and varied – united by one common factor – all seem to

be widely criticised.

(Walker, n.d.) observes that the infrastructure of support for community renewable energy

programmes in the UK is “complex and chaotic”. The observations noted are that:

• multiple support and resource programmes, run by different agencies with different

objectives

• no coordinating strategy

• no definition of ‘community’

• no measurable overall target

This can be contrasted to the funding approach for nuclear energy, which by comparison is:

• simple and straightforward

• co-ordinated by a single agency / entity

• clear definition of who - will carry out works / maintenance / decommissioning /

derives benefit

• well-defined target / output

The flip-side of this approach to renewable energy funding according to (Walker, n.d.) is that it

“…allowed creative, self definition, interpretation and flexible partnership working with

multiple and differentiated outcomes…” it could be argued that whilst to some degree this was


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desirable to the fledgling renewables industry, that this approach would be highly inappropriate

for nuclear energy.

Programme/Network Funding Source Managed By

Community Renewables Initiative DTI Countryside Agency

Clear Skies DTI BRE

SCHRI Scottish Executive EST, H&IE

CAFÉ DEFRA & Others EST/CSE

Community Energy DEFRA & Others EST/Carbon Trust

[EST PV programme/innovation DTI EST

Energy 4 All Baywind Coop/Coop Soc Baywind

Renewable Energy Investment Club Countryside Council for Wales/EU Dulas

Solar Clubs Various Charity Environ/CSE

Ashden Awards Ashden Trust Ashden Trust

Energy 21 Network Various Energy 21

Private Sector Community Power Powergen Powergen

Government led

NGO/ Charityled

Table 5-1

Cross Section of Renewable Energy Development Projects in the UK from (Walker, n.d.).

In this instance, the Complexity inherent is unwelcome, indeed there seems to be redundancy

and duplication in the system which transfers incentives for renewable energy from government

to ‘programme’.

This can be contrasted to the very simple mechanism by which government has funded nuclear

power in the past.

However, it is interesting to note, that the current government tend to favour arrangements

between industry and government – so called “public private partnerships”.

If we do see a new generation of nuclear power, it will be interesting to examine the complexity

in the way that these ventures are funded, certainly, there has been criticism from many quarters

in the way that other large public projects – schools and hospitals notably – have been funded.


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Figure 5-9

Network Diagram Showing the “Chaotic” Funding of Renewable Energy adapted from (Walker, n.d.).

1. DTI

2. Scottish Executive

3. DEFRA

4. BRE

5. Countryside Agency

6. EST

7. Carbon Trust

8. CSE

9. H&IE

10. Clear Skies

11. CRI

12. SCHRI

13. CAFE

14. Community Energy

15. EST PV

Table 5-2

Key to Figure 5-7 from (Walker, n.d.)


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Unpredictability & Sustainable Innovation

In the “Complexity Science for Beginners” summer school held at Liverpool University, there

was discussion about “fitness landscapes” as a measure of the suitability of an innovation, whilst

this subject deserves a much fuller exploration (Covered in “further research”), it is worth

mentioning what complexity tells us about innovation, and how “disruptive innovations” can be

very hard to forecast:

“The information revolution provides excellent examples… Some of the most

famous stories of famous foresight centre on managers and board members at

companies like IBM and Intel who were unable to grasp the world-changing

potential of their own products. IBM leaders one thought that a handful of

computers would suffice for the entire world. The Intel board of directors

discourages the first proposals to develop a microprocessor. The National

Science Foundation has remarked that its panel of distinguished information

technology scientists and engineers is consistent in it’s unwillingness to predict

the future…

…some industry leaders were frank enough to say – two years after the deluge –

that they saw the first effective Web-browser, Mosaic, as an inconsequential

toy.”

(Axelrod. R, 1999)


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We see in (Lewin, 1993a) that local interactions and global interactions are co-dependent. We

need look no further than UK energy distribution for evidence of this fact. As the global price of

oil, gas, coal and uranium changes so does the price of energy which in turn affects consumer

demand at the local level. Consumer demand for energy at the local level in turn has a

cumulative effect and adds up to global changes in the price of these resources. Thus we can see

that this system is ripe for analysis using the toolkit of complex systems science.

These examples from the technology industry show, how perilously hard it is to predict

innovation, and how even the opinion of the best experts on the field can be shown to be

incredibly wrong within a couple of years.

Applied to sustainable innovation, we can see that in such a complex marketplace, it is foolish to

speculate to what degree a technology can become dominant, as it can be shown by historical

precedent, that factors can easily conspire to produce unexpected successes and radical

innovation.

At the moment, the uptake of renewable energy technologies may seem slow, however, there are

some interesting signs – with companies like “Nanosolar” gearing up to MASSIVELY produce

“printed” solar cells in volume. Such “disruptive innovation”, could greatly reshape the energy

landscape. As could equally, a radical innovation in the nuclear industry.

One thing that can be seen with complex systems, is that “crystal ball gazing” is futile, (Frenken,

2006) and that innovation can be very hard to predict.


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Time-Dependence of “The Energy System”

One of the identifying features of “Complex Systems” is that there evolution is seen as time

dependent – the history of a system having a great influence on the “future” of the system.

(Webb, 2006) cites Professor Peter Allen’s metaphor of a branching tree, where the “branches

form through a mixture of chance and necessity”. (Webb, 2006) elaborates that Professor

Allen’s metaphor extends thus “when a system is near to the branching point, it is relatively

unstable”, and because of this “small chance disturbances can be decisive in nudging it onto one

branch rather than another”, adding “in this way we find that history is made up of successive

phases of relatively predictable development ‘along’ a particular branch, separated by moments

of instability and real change, during which the system is laid down by some rather

indeterminate chance events which push it onto one or another branch.”

Relating this to the context of UK energy, it can be seen how we have been following a phase of

relatively predictable development, using fossil fuels – this is as a result of their availability. It

could be considered that we are coming close to the “branching point” and that as a result, the

instability of the ‘decision making’ within the energy system is to be anticipated if we concur

with Allen’s metaphor.

The next section of his argument must be paid heed to though – the fact that “small chance

disturbances can be decisive in nudging it onto one branch rather than another” – and it is this

which we must elaborate upon, that during a period of instability in a complex system, which we

are currently undergoing in the energy debate, due to the increased instability, smaller factors

can affect the eventual path of the system.

If we believe that the energy system is “complex”, then we accept that it is time-dependent –

which means that decisions taken now could radically alter the future direction of the system.


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One argument from the “renewables lobby”, is that building renewable energy infrastructure on

a scale required to achieve world energy supply from renewables, is going to require a massive

input of energy to begin with. The argument runs, that before “totally exhausting” finite energy

supplies, we should begin to build renewable energy infrastructure, whilst we still have the

energy to do so.

This would seem to corroborate the time-dependent aspect of complex systems. In the lexicon

of the complex systems scientist, we could say that we have reached a “bifurcation”, where at the

moment; our ideas about what direction to head in are oscillating between a nuclear future, a

renewables future or a mixed portfolio with a combination of both “in-between”.

We can illustrate this choice using a bifurcation diagram:

Graph 5-2

Bifurcation in future energy policy.

The bifurcation diagram is commonly used in complex systems science to show how a system

can take one or two paths once it reaches a period of instability. At the moment the system has

enjoyed stability down one particular pathway as a result of cheap, readily available fossil fuels,


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now we are faced with a choice between two possible routes or a solution that arrives somewhere

in between. However, complexity seems to indicate that in the long run, the system will settle at

either one of these stable states – nuclear or renewables, and that whilst the solution comprises a

mixture of two different states, the system can be considered in oscillation.

When we consider our long term energy prospects, we can look back to see periods of our past

that were dominated by the relative dominance of one source of energy. It could be argued that

before the industrial revolution wood was the dominant fuel source, replaced by coal during the

industrial revolution – which fired the steam engines that powered this period of change. With

the dominance of the internal combustion engine, oil rose to prominence, and in recent years,

there has been the drive towards natural gas. Whilst at no period in our past have we ever settled

for “one” particular energy source, it can be seen that each has enjoyed periods of relative

“dominance”, each could be considered a stable steady state that persists until the next event

which destabilises this period of dominance.

It would be interesting to see, if we adopted a nuclear future, how long this period of stability

lasted, in the past, we have witnessed the passing of the golden age of nuclear energy, where a

couple of high-profile disasters changed public opinion against the technology dramatically.

(Herring, 2005) challenges conventional wisdom about the backlash against nuclear energy,

arguing that opposition did not suddenly come in the 1970’s as is conventionally assumed, but

instead notes a body of evidence stretching back to the 1950’s against nuclear power – and it was

in fact this opposition that grew into the greater “anti-nuclear” movement in the 70’s.

(Herring, 2005) argues that wind-power could easily undergo the same backlash in the twenty-

first century where “Utopian views initially dominate society, but over time, there is a wavering

of support as promoting institutions fail to deliver the expected benefits then suddenly there is a

shift in ideology”. This could be characterised in Complexity, as a bifurcation, with oscillation

between two states, followed by a shift to one state.


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Entropy and Energy Systems The second law of thermodynamics is commonly stated using Rudolf Clausius wording:

“The entropy of an isolated system not in equilibrium will tend to increase over time,

approaching a maximum value at equilibrium.”

This states that all systems are heading towards a state of equilibrium – that the boundaries

between gradients of temperature, pressure and different physical states are heading towards a

homogenous mass. That coherent bodies of energy over time disaggregate and diffuse into their

surrounding environment. Like ice cubes in warm water the boundaries between the two

physical states slowly blur to become one homogenous mass.

In the energy production systems we see in the world, it could be viewed that by extracting finite

resources, and “burning” them, whether that be through a process of oxidisation, or by nuclear

decomposition, to produce heat, we are taking concentrated forms of energy, and releasing the

energy embodied in the chemical and atomic bonds – and by a process of distribution and

consumption, dissipating this energy into the environment.

Could therefore, the contrasting feature of “Renewable Energy” be, that we are taking energy

that would otherwise remain “diffused” into the world around us, and taking that “disordered”

energy, and turning it into a very regular, ordered form of energy, electricity.

It was said by Sir Arthur Stanley Eddington in The Nature of the Physical World

“The law that entropy always increases, holds, I think, the supreme position among the laws of

Nature. If someone points out to you that your pet theory of the universe is in disagreement

with Maxwell's equations — then so much the worse for Maxwell's equations. If it is found to be

contradicted by observation — well, these experimentalists do bungle things sometimes. But if

your theory is found to be against the second law of thermodynamics I can give you no hope;

there is nothing for it but to collapse in deepest humiliation.”


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Ilya Prigogine, winner of the 1977 Nobel Prize for Chemistry coined the term “dissipative

structure”, to explain systems which a property – be that energy, information or another

property, diffuses through, however, Prigogine’s work is considered interesting for complex

systems scientists as it seems to suggest that there are certain types of complex system, which

should appear to “descend into entropy”, however, on various scales of examination, it can be

argued that the systems “create” order out of chaos.

(Roper, n.d.) gives a thorough explanation of how entropy and complexity are related.

Entropy cannot decrease in a closed system – the universe is a closed system, however, in an

“open” system, local entropy can decrease,

Roper states: “To keep the increase of entropy of a system to a low value, make the system as

open as possible.” and goes on to assert “The last statement is the one of most importance in

trying to achieve sustainable development. In a development context the "system" should be

defined in the usual way as the part designed and built by humans and include the environment

to which it is unavoidably open. To have low-entropy (sustainable) development it is necessary

to redefine the "system" to be designed and built to include as much of the environment as

possible.”.

By making our energy production and distribution systems “open” to receive energy from the

environment (renewables) we are taking ‘diffuse’ energy from the environment, and converting

it to a more ordered form of energy on a local level. This is contrasted to fossil fuels, and nuclear

reactions, where we are taking an “ordered” resource, and diffusing it to “disorder” releasing

useful energy in the process.


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(Roper, n.d.) asserts that for sustainable development, “What is needed is low entropy and high

complexity”, he goes on to assert that we learn from Biology that “highly complex systems

manage themselves, when one part of the system fails, another takes up the slack.”

Although (Roper, n.d.) goes on to stress the limits of this low-entropy / high complexity

approach. “A system of human development cannot completely mimic the low-entropy/high-complexity of

biological systems, because it has the goal of providing goods and services for the benefit of humans, not the

entire biosphere. A low-complexity development might provide short-term human benefits, but usually

deleterious long-term effects for humans and the rest of the biosphere.”

It could be argued, that a renewable energy based generation system could be argued to fit into

the “low entropy / high complexity” as there would inevitably be more wind turbines installed

as they produce less power per unit than large centralised nuclear, which could be argued to be

“high entropy / high complexity” by comparison, as resources are required in the process of

producing energy, and these “concentrated” resources are extracted and “degraded” to a lower,

more diffuse form in the process of extracting energy. We are left with nuclear waste as an

entropic end product of this process.

Thus looking at entropy from a complex systems perspective, renewable resources would appear

to fit within Roper’s classification of “low entropy / high complexity” as the way forward for

sustainable development.


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Path Dependence and the Selection of Appropriate Technology

In Complexity Theory, it is argued that “Complex Systems” are path-dependant – that is to say,

small variations at a point in time about the systems future, can dramatically effect the future

trajectory of that system. Examples can be seen in ‘rival’ consumer products, where a tiny margin

of support for one technology, can often give that technology a sufficient edge to dominate in

the marketplace and crush the opposition. At the moment, in the marketplace we could look at

the competing “HD-DVD” and “Blu-Ray” formats of video disc, and speculate that small leads

on either format now can create a “path dependence” which ultimately leads to that format

becoming dominant.

Brian Arthur (Arthur, 1995, Arthur, 1990) argues the case solidly, that in fact the “assumed”

negative feedback in the market, could in fact be a fallacy. The example he cites is of the VHS vs.

Betamax videocassette format war. Arthur argues the case for a complex-systems understanding

of the economic problem, where a “small” change in initial conditions had dramatic influence

on the ultimate outcome.

Relating this to the energy debate, it could be argued that “small” gains made now, by either of

the competing technologies – nuclear or renewables, could lead to their dominance in the

makeup of the UK’s energy portfolio.

The economic viability of an energy source is considered within the framework of the dominant

economic theory of the time. The financial rewards and merits of any technology are considered

using the tools of the prevailing theory.

In some respects, “Economics” adds another dimensionality to the decision making process.

A model whereby the “market decides” the suitability of a technology – and the “market” is left

to decide on the appropriate technology relies on the assumptions behind market-driven


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economics being correct. There is a sizeable enough body of people arguing against conventional

economic theory to warrant examining this in further detail.

Brian Arthur argues that “conventional economic theory” is based on an incorrect assumption –

that there are inherent negative feedback loops within the market, which provide a “balancing”

effect. This “assumed” inherent negative feedback in the system, nurtures stability within the

system, and leads to equilibrium outcomes which are predictable.

Arthur argues that far from there being “balancing” mechanisms in the system, many economic

systems follow a “virtuous circle of self-reinforcing growth”.

The following quotation from Arthur, illustrates how far from balancing mechanisms of supply

and demand and competition intervening in the race for dominance of a format, a small early

lead by one format eventually gave rise to the eventual outcome.

“The VCR market started out with two competing formats selling at about the

same price: VHS and Beta. Each format could realise increasing returns as its

market share increased: large numbers of VHS recorders would encourage video

outlets to stock more pre-recorded tapes in VHS format, thereby enhancing the

value of owning a VHS recorder and leading more people to buy one. (The

same would, of course, be true for Beta-format players.) In this way, a small gain

in market share would improve the competitive position of one system and help

it further increase its lead. ... Increasing returns on early gains eventually tilted

the competition toward VHS: it accumulated enough of an advantage to take

virtually the entire VCR market.”


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Arthur describes this self-reinforcing positive feedback as “path dependence” in economic

models. We can see clearly how the small gain made by VHS in the early days of the competition

between formats, led to the growth of pre-recorded cassettes, video recorder engineers all of

which created a more stable position for VHS which ultimately reinforced and cemented its lead

in the market.

If we consider the rivalry between competing energy generation technologies and their bid for

dominance, we can draw similar analogies between the “formats wars” that have taken place

between various ‘proprietary standards’ in the world of video media – with one becoming the

‘standard’, and the current debates about what technology is suitable to meet our energy needs

Nuclear generation technology has a rich history of central-government funding. Whilst it has

not achieved the main-stream dominance in the United Kingdom comparable to fossil fuel

resources, it has recently received a boost by the governments recent change in energy policy.

Early in this government’s first term, in the 1990’s, the government asserted that nuclear power

was not a viable energy option (Boyle et al., 2003) . Indeed, when the state-owned electricity

monopoly was sold to the market, city investors refused point blank to take on the responsibility

of Britain’s fleet of nuclear reactors, however, now there is increasing support from the

government, and it appears clear that there is more support in the commercial world for nuclear

energy – indeed if a new fleet of nuclear reactors are built, the government assert that these will

be privately funded.

How can we explain the change of direction on so many levels from so many different

stakeholder groups? One explanation could be put forward by “path-dependency”. Once some

initial support is shown by a large group in one particular direction, positive reinforcing

feedback loops begin to create a ‘path-dependency’ in this direction.

If the government begins to show some support for this technology, private investors know that

the government are open to developing and supporting this technology – with the rich history


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of state support for nuclear power, the private investors speculate this could be a good

investment, then large companies start to put forward their plans for nuclear power stations –

and suddenly with this self-reinforcing body of interest, the idea gains momentum very quickly.

I would argue, that because of the large “unit size” of nuclear power generation, and because of

the amount of infrastructure required to support such an enterprise, nuclear power

development is very “path dependent”. Once a commitment is made to nuclear power, facilities

must be built to transport and manage the fuel and waste from these power stations. Once these

facilities are established, it becomes cheaper for the “next” nuclear power station to use these

facilities as the “fixed” costs of building the infrastructure have already been met, it is just the

“variable” costs of running the plant that increase as more stations make use of them.

By contrast, I would argue that ‘renewable energy technologies’, are not very path dependent.

They have a small “unit size” and they do not require a large amount of infrastructure to support

them. As a result, this may explain the ‘relatively’ slow development of renewable energy

technologies, and why they account for a small proportion of our electricity supply.

Another argument would be, that at the level we are currently adopting renewable energy

technologies, they do not have to be very “path dependent”, because of the small unit size, we

can build such systems in small volume or large volume, by contrast, nuclear requires the

commitment and path dependency because of the large unit size involved, and because of the

supporting facilities to manage fuel and waste.

It could be asserted, that to achieve a similar proportion of renewable energy technology in our

ESI as there is currently, say nuclear or natural gas, we would need to commit to a similar scale of

“path dependence” as there is currently with nuclear technology. The costs of renewable energy

equipment will fall with economies of scale. Perhaps if the same “commitment” (read “path

dependency”) were made to developing renewable energy production facilities, as would be

committed to decommissioning for a major nuclear programme, then a direction in this path


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would be clearly established, and the development of such technologies would be self-

reinforcing.

In summary, the path-dependence inherent in the adoption of one or another of competing

technologies, can lead to one technology achieving early dominance at a stage where it would

not appear clear what technology will finally become dominant. This is as a result of the self-

reinforcing positive feedback loops inherent in a market-driven economy. At the moment we

have reached a bifurcation in energy policy, and it is important to note that decisions taken now

at the very early stages of planning our energy future post-fossil fuels, could have a very real

bearing on the future development of one or another technology.


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Dealing With Complex Systems

Scott, Johnson & Frizelle assert that there are two main ways of dealing with the dilemmas that

complex systems present:

• Simplify the system – remove the complexity and it ceases to be a complex system.

• Control the system – controlling the complex system allows it to be kept within

acceptable parameters.

If we look at these two approaches in turn, it should quickly become apparent that because of

the large number of agents that are inherent in any power production and distribution system

we can only simplify the system so far.

Historically, power has been produced by a small number of large centralised generating plants –

this relies on a constant supply of high-density energy – whether this be in the form of fossil

fuels or nuclear energy.

If we move towards a new paradigm where the availability of fossil fuel resources is scarce, then

in consideration of the first approach – simplifying the system - we are faced with two distinct

and clear options:

• Implement large centralised nuclear generation – this would have the effect of

simplifying the system.

• Move towards a diverse energy portfolio based on renewables – this would have the

effect of adding complexity to the system due to the fact that power is no longer

produced en bloc, instead the reality is the distributed nature of renewable energy.

There are a number of reasons why nuclear energy is not seen as desirable.


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Whilst simplifying the system does have the effect of “lessening the incidence and effects of

catastrophic and chaotic behaviour” (Scott, Johnson & Frizelle 2002) the simplification of the

system cannot be seen as a total solution to the control of the complex system. The individual

nodes gain an increased importance in respect of the whole system and so the behaviour of the

system is more vulnerable to a change at any of those nodes.

(Asmus, 2001) builds a strong case for the security of a distributed renewables schema, whilst

looking at the vulnerability of centralised production to a terrorist threat.

If we accept the latter option, a diverse energy portfolio, as being more desirable, then the option

that we are left with to keep the complex system within bounds is increased control measures.

At the moment, the control measures in the grid are largely “centralised” the load is monitored,

and balanced by centrally switching on extra plant to compensate. This entails having a

“spinning reserve” of fossil fuel plant, which is kept running ready to power up.

However, does complexity support the argument of a large amount of “local” intelligence which

monitors, evaluates and acts on a local level, with this “centralised” intelligence replaced by

localised units acting and responding to changing conditions on the network.

Such a system would require a massive paradigm shift in the way we think about how the

network is run, however, could this localised intelligence better manage the prospect of a grid

with a greater degree of renewables?


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Organisational Complexity and the case for Centralised Governance vs. Deregulation

The UK Electricity Production and Distribution network was part of a wider global movement

to unbundle the centralised monopolies of nationalised electricity production and distribution

arrangements to private companies that would compete for business – introducing competition

into the marketplace.

The way that the system fundamentally changed with deregulation must be understood not just

in terms of the “technical” change of how the system was managed and run, but also in terms of

how this change of paradigm affects the dynamics within the system.

Whilst deregulation is seen by many as “putting profits first and investments second”, there is no

denying, that the addition of competition adds an element of “chaos” to the system that is not

present with a central monopoly body controlling the whole system.

This new “business” network is fundamentally dynamic in nature, being fundamentally rich in

“Type II” dynamics. Conceptualising the fiscal network between these interlinked business

elements as a series of “largely static” nodes and “reconfigurable links”, we can see that in a free-

market privatised energy supply system, the players within that system can “reconfigure links”

more freely than in a static “state monopoly”. To illustrate this in an oversimplified manner: in a

state monopoly, one entity produces the energy, one entity distributes the energy and the

consumer has the option to buy from one supplier.

In a privatised energy supply industry, there are multiple suppliers each bidding into the “pool”,

the “pool” can buy from any of these suppliers – as a result, every time to “pool” decides on from

whom to buy the electricity, fiscal links are dynamically established between the pool and those

whom electricity is being brought from, this will be dealt with in the next section.

Looking at the diagram in Figure , we can conceptualise the UK ESI as one large network, which

we can reduce down to having two types of coupling – “physical” – that is to say wires between


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“this bit of infrastructure and that bit of infrastructure”, and “fiscal” – with business links and

relationships between customers, distributors, suppliers and “the pool”.

Every time a customer changes energy supplier, they are “rewiring” a link from “their node” to a

“different suppliers node”. Every time a supplier successfully bids to supply a given amount of

energy to the grid, they are creating a temporary link between the “node” of their business, and

the “pool of nodes” of the electricity suppliers. The nodes of the physical infrastructure of “high

voltage distribution”, and the “local distribution” network are slower to change – “physical”

rewiring taking longer than “virtual” rewiring, however, over time as the energy generation

network evolves, the “physical” network will slowly begin to change.

Figure 5-10

Structure of the Electricity Industry in Great Britain in 2005, (Department of Trade & Industry, 2006a)


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The “Complexity” Case For Deregulation

By deregulating the ESI, it becomes harder to understand the interactions taking place in the

system as a result of the increased complexity of the system. An element of “internal” economics

is “externalised” by making the electricity supply market competitive.

However, complexity shows us that unexpected behaviour can arise when systems are in

transition from “simple” systems, that we can easily understand, to “complex” ones. By

introducing extra complexity into the system, we are introducing more feedback. Within each

REC, there is internal feedback through the internal management structure. Within each

electricity supplier, there is a decision making process that takes into account costs, inputs,

outputs and regulatory obligations that the company is subject to, bidding a “price per unit” into

the grid.

One of the arguments for deregulation, is that state-owned industries, and monopolies in

general tend to “stagnate”, and introducing competition encourages innovation, and new ways

of dealing with problems. The “market” selects the most efficient solution on the grounds of

‘cost and economics’, which are a function of efficiency, and the regulator provides any necessary

intervention, to keep the market within “check” and acceptable parameters. (Webb, 2006)

Looking at the change from state-run monopoly to privately owned business, we can see how

there is now much more information within the system that needs to change hands – however –

by giving private companies additional autonomy, much of this information is managed locally,

and an “output” provided in the form of a bid to the grid to supply power, a price to customers

to supply energy, trading of ROC’s e.t.c., the “market” managing this emergent behaviour.

Complex systems theory tells us that we cannot ‘control’ systems, but we can introduce

‘perturbations’ to variables of the systems, and attempt to try and influence them, even if we

can’t control them directly.


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The Renewables Obligation can be seen as one way that we can attempt to “perturb” the system.

As with any form of regulation, we can see that we cannot “control the system” – we are only

introducing an incentive/disincentive for the company to meet that standard. In some cases, a

company might evaluate that the penalty for doing/not doing something is less that the cost to

meet the regulation. As such, the market adapts to meet the demand – the regulation being a

stimulus to the system rather than a “controlling hand”.

When we look at “emergent behaviour” in market-driven systems, we can see that the market

often produces “emergent behaviour” in the form of unexpected solutions to problems posed by

regulatory bodies. Citing the example in (Patterson, 1999) of the Clean Air Act Amendments in

the US – introduced to cap the amount of sulphur produced, trading was introduced as a

method for the market to balance its costs and produce an efficient solution. A market price was

established for “1 tonne of avoided sulphur emissions”. Initial predictions were that each tonne

of sulphur would cost $180-$900 USD to remove (Patterson, 1999). However, the market

acted – and “emergence” can clearly be seen – as in 1996 after trading was introduced, the cost

to remove a tonne of sulphur had fallen to under £100 USD per tonne (Patterson, 1999).

It could be argued that here was witnessed not emergent behaviour – but initial “overly

pessimistic” quotes about how much it cost to remove the sulphur in the first place, in an

attempt to dissuade the regulators from introducing such sanctions.

However, as can be seen, introducing the element of “competition” introduces more complexity

– but in some instances this complexity can lead to more efficient solutions spontaneously

emerging from the system.

This is the argument already used by those whose ideological viewpoint supports “privatisation”

of state-owned industry, however, complexity seems to provide some framework within which

to understand this behaviour.


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The Complexity of the Energy Pool

The way in which the load on the electricity supply system is met by the suppliers, has

undergone a radical change during the transition from state-owned monopoly to privatised

companies.

Patterson (1999) emphasises that in the majority of countries where privatisation took place

and assets were transferred from government to private interests, nothing particularly radical

happened – however, Patterson delineates the UK as being different in that “genuinely radical

innovation” accompanied the restructuring – the way in which electricity was bought and sold

from the pool.

The way in which it is decided which plant to use to meet the needs of the electricity pool, is

same in most of the world, and follows the schema under the CEGB.

In the days of the Central Electricity Generating Board, CEGB, plants were rated on a “merit

index” according to their variable running costs. Plant is “switched on” and its power added to

the pool according to the load on the network. This load is forecast the day before.

The order in which plant is switched on in most of the world, and also in the UK pre-

privatisation, is decided by the order of cost of running plant. (Patterson 1999) this is a fairly

“simple” system to understand – it is logical, and not really “complex”.

Large generators, nuclear and large coal stations, have a large initial “fixed” cost to build them

and maintain Patterson (1999) however, they are cheaper to run and so the “variable” costs are

cheaper. This means that this plant can be run continuously whilst smaller, less efficient

generators can be switched on and off as load dictates to match that load. Again, this is simple to

understand.


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With the privatisation of the ESI, it can be seen that this model would hold true for a transfer

from a state owned monopoly to a privately owned monopoly, where for example, one entity

was in charge of all of the generating capacity. (Patterson 1999)

However, in the UK, Patterson cites “genuine innovation”. It could be asserted that this

innovation arose as a result of the extra complexity being introduced into the system.

In the UK, the ESI was not sold to a single franchise, but to multiple operating companies. As a

result, the way that the system previously functioned with a “merit order” no longer held true.

In the new system, generating companies submit a bid for the minimum price that they will run

their generators for the next day. These bids are all submitted at 11am on the day before the

power is due to be generated. The day is divided into half an hour timeframes or slots over which

time the forecast load is calculated, and the bid selected from the lowest price up to the cheapest

bid inclusive, that will cover the load for that slot.

This in itself is a complex marketplace, it can be seen that the characteristic styles that different

energy sources generate energy and their output characteristics – when they can generate, how

long for, at what cost, affects their ability to compete within this marketplace.

Also, this marketplace as a system, affects the way in which agents within that system are able to

compete.

On top of this, there is additional complexity that aside from the main electricity pool, there are

also private contracts between producers of electricity and large consumers of electricity. This

additional complexity was ‘phased in’ 1992 and 1994 when users of loads of more than 1MW

and 100kW (Patterson 1999) respectively were allowed to choose freely their electricity

producer. Additional complexity was introduced to the system from September 1998, when

region by region, consumers were allowed to choose their own electricity supplier.


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Can this be considered complex? In 1998, the Office for Electricity Regulation considered that

the software just to “track the relationships” of suppliers and householders was not sufficiently

tested due to the complexity involved in this task Patterson (1999). If we want to gain more

meaningful analysis of this system, beyond simply knowing “who supplies who” then we will

require more in depth analysis tools, certainly the initial teething problems with the

introduction of this software – wildly miscalculated estimated bills suggest that it could be

agreed that the scale of this task at the time, was on the “edge” of the amount of complexity that

could be managed successfully.

It should also be borne in mind that the two metrics of exchange within this marketplace are

money and electricity – perhaps this does not truly reflect the inputs and outputs of the system,

‘externalising’ costs such as waste products and natural resources.

Whilst the obligation for renewables, and obligations for emissions are dealt with under separate

trading systems, perhaps there is room for an integrated trading system – introducing further

complexity, where the requirement for renewables, acceptable carbon emissions, and acceptable

amounts of nuclear waste production were governed under the same marketplace on a half hour

by half hour basis? Perhaps this would lead to more emergent behaviour from the network and

innovative solutions to reduce waste?

There is additional complexity to be observed as a result of the peculiarities of the system and

technical limitations of the plant. Nuclear power is not a particularly flexible provider of power,

such stations work best when run continuously with a low variable running cost. As a result,

nuclear power generators bid into the pool at a cost of 0p per unit. This ensures that they are

always scheduled. It can be seen here, how the design of the complex system, shapes how agents

interact with that system – the bidding at “0p” is not reflective of the variable costs of running

the station, it is a value that is chosen to manipulate how the system operates.


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C h a p t e r S i x

CONCLUSION

SUMMARY

N THIS CHAPTER , WE WILL EXAMINE THE FINDINGS FROM THE PREVIOUS

CHAPTER AND DRAW SOME CONCLUSIONS AS TO THE EXTENT THAT

“COMPLEX SYSTEMS SCIENCE” CAN INFORM DECISIONS TAKEN IN

RELATION TO THE “NUCLEAR VS . RENEWABLES” ARGUMENT . THIS IS AS PART

OF A WIDER DISCUSSION ABOUT HOW “COMPLEX SYSTEMS SCIENCE” CAN

HELP US WITH PROBLEMS RELATING TO ENERGY .

I


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C h a p t e r S i x

CONCLUSION

Undoubtedly, there continue to be many unanswered questions and debates which continue to

rage in regards to the energy debate. If these issues were easy to understand, and it were easy to

come to a consensus, then perhaps the system could be considered “simple”, however, as long as

the debate continues and agreement is not reached, then the systems can be considered to be in a

state of flux and so is “complex”.

The fact that within the past 100 years, our means of providing electricity has changed so

dramatically through so many different types of system, technologies, ways of generating and

managing that generation seems to suggest that the next 100 years will bring with it similar

change.

Is the Problem Complex?

We live in a world of ever increasing population. If statistics are believed, and growth continues

– failing a massive disaster or pandemic – the number of people on the planet will increase. (U.S.

Census Bureau, 2006). Taking this issue back down to a UK level, it is apparent, that the UK

population is going to increase for the foreseeable future. (National Statistics, 2006).

Energy is essential for life – so it should immediately be apparent that if each of these future

users of energy consumes the same amount of energy as a “2006 UK Citizen”, our need for

energy will increase. As our need for energy increases, so does the problem we face.

If we look at every consumer of energy as a “node” in a network of energy production,

distribution and consumption, we can see that that network is growing by the day.


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We also face the problem of generating this energy cleanly, as our government has acknowledged

the dire consequences that climate change could wreak, and the need to generate energy cleanly

with the minimum of CO2 emissions. (NATTA, 2007c)

The UK sits as an agent in an international network of countries, all of whom are vying for their

share of the available energy in the Global Energy Market. The UK’s position is complex, as in

addition to having to meet its own energy needs, it faces the problem of having to be an

international ambassador for “good practise” in energy, setting an example for other developed

countries, in particular the U.S., and as an agent within the European Union. Reconciling the

goals of meeting ones own needs, without compromising the needs of others, and whilst at the

same time setting an example for how other should consume energy is a complex problem.

Supplying the UK with power becomes a more complex problem, as a function of the increasing

magnitude of the problem – namely that demand for power is increasing, and that available

supplies of “cheap and dirty” power are dwindling.

Our energy production and distribution system will change in the coming years. How

significant this change will be is debatable. There is not a consensus on what level of renewable

energy can be accommodated into the present infrastructure whilst maintaining the same level

of system integrity, without having to provide other forms of “balancing”, or by compensating

for intermittency with “spinning reserve” in conventional thermal plants.

Techniques such as the network analysis tools used by Complexity Scientists show promise for

being successfully applied in the sphere of energy. Tools developed to look at the data on I.T.

networks, or the traffic on roads could be used to examine the flow of energy in a system.


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The question posed by this study was:

CCAANN ‘‘ CCOOMMPPLLEEXX IITTYY SSCCIIEENNCCEE ’’ IINNFFOORRMM TTHHEE

NNUUCCLLEEAARR VVSS .. RREENNEEWWAABBLLEESS EENNEERRGGYY DDEEBBAATTEE ??

In the course of this study, I have come to the conclusion that the many interconnected systems

that we can coin the “Energy Production and Distribution Network” are complex, and based on

the successful application of techniques that I have seen throughout the course of this study, I

believe that there is the potential for a similar toolkit to be applied to the energy problem.

The mix of energy sources that we choose will ultimately have a bearing on the Complexity of

the system, so by inference, Complexity can inform this debate.


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The Complexity of Co-Dependence

It is important to recognise the co-dependence of all the nations of the world on one another.

As an example, when looking at manufactured goods in the United Kingdom, historically, from

the “Industrial Revolution” onwards, the UK was an industrial powerhouse, producing goods

not only for itself, but also for the world.

The UK is in transition, we produce less manufactured goods in the UK, with labour in other

countries proving far cheaper, with the result that we import more goods now than ever before.

When we import goods, we are also importing “embodied energy”, and if the countries we

import our goods from use “dirtier” forms of generation, then we must take on board that we are

adding to the environmental burden of producing energy “by proxy”.

Complex Systems Science teaches us to look at the whole, and consider the web of

interconnected relations; therefore, it is not simply enough to draw a boundary line around the

UK as a “system” we must take into account where that system overlaps with others, and

consider the inputs and outputs to that system.

Clouds of radiation, atmospheric carbon dioxide and rising sea levels do not respect national

boundaries, therefore it could be argued that restricting the scope of the study to the UK does

not embrace the tenet of interconnectivity expressed by complexity.

A disaster in a nuclear power station abroad, has the potential to affect the UK, this was seen

with the Chernobyl disaster, where the cloud of radiation spread to affect other countries (BBC,

2004).

(Mc Smith, 2006b) illuminates this lasting impact on the UK: “The Department of Health has

admitted that more than 200,000 sheep are grazing on land contaminated by fallout from the


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explosion at the Ukrainian nuclear plant 1,500 miles away. Emergency orders still apply to 355

Welsh farms, 11 in Scotland and nine in England as a result of the catastrophe in April 1986.”

Therefore, if we are not prepared to accept the purported dangers of nuclear power, we must act

as an ambassador in the International Community to help other countries reduce their

dependency on Nuclear Power. We saw in Chapter Three, how France and Lithuania –

relatively near neighbours are incredibly dependent upon nuclear power. With such nuclear

giants on our doorsteps, if we accept that there are risks with nuclear power that are

unacceptable, then we are at a similar danger – if a major disaster occurred – than if we retain

nuclear power in the UK.

Similarly if the UK does not act as an ambassador for clean energy, it must bear the

consequences inaction in the carbon emissions of other countries of the world.

We can see from Complex Systems, that the way change propagates through a system, depends

on their being adequate connectivity. The European Union provides a level of “connectedness”

in the policy of it’s member states. Through this mechanism, change has been effected and

allowed to spread through the system; Lithuania’s last nuclear power plant (the biggest of the

Chernobyl RBMK type) is due to be closed in 2009 as a result of the change brought into being

by Lithuania joining the EU.

This has brought some small degree of uniformity to energy policy, removing complexity at a

legislative level. We can see how complexity has been “removed” at a technical level from the

system by the harmonisation of voltage and frequency throughout member states.

It can be seen that the connectedness of issues at all levels must be appreciated, however,

paraphrasing (Shackley et al., 1996), “nothing becomes any more or less connected”, just that we

can understand better the effects of what connectivity and coupling exists within the system.


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Meeting Our Needs

There have emerged two distinct ways to deal with this problem. The “centralised” nuclear

camp, and the renewables camp which can be further subdivided into those advocating large

scale centralised renewable generation

If we accept a future energy scenario where we rely on a diverse mix of energy produced from a

variety of sources, as seems almost inevitable, then it seems only logical that the system of UK

Energy Production and Distribution will become increasingly complex as a system. We are

adding more “nodes” to the network, in the form of power generation devices, and we relinquish

“central control” in favour of distributed, self-regulating and ordering devices.

This thesis does not have the depth to quantify how exactly these future changes to the system

will affect the “complexity” of the system, this is room for further study, however, this thesis can

firmly assert that by changing the nature of the system, the inherent complexity will be altered,

and that this is worthy of further study.

The limitations of “Complexity Theory” are apparent throughout the course of this study, the

lack of definition and agreement as to the “global definition” of Complex Systems, more

specifically the boundary between the “Complex” and the “Non-Complex” was a barrier to

understanding, however, this did not detract from the fact that when ignoring the overall

definition of “Complexity”, there are certain techniques that are stated to fall within the

umbrella of “Complexity” which show significant promise when targeted at certain types of

problem.

It is the belief of the author that we can use the extended repertoire of tools that complexity

science gives us to manage the inevitable complexity that will ensue as our dynamic energy

system changes.


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By understanding the very essence of complexity, we can aim to capture quintessential elements

of the system which are lost or neglected with conventional approaches. However, it is the

conclusion of this study, that Complexity must be used as a complimentary tool to inform the

debate, and whilst it shows promise it does not offer a panacea to all problems faced in the

debate. Conventional approaches to the problem, complimented by an understanding of

Complexity could yield greater understanding.

By examining UK Energy Production and Distribution in the context of Complex Systems, we

see that “…the approach [provides] a richer more coherent system picture, and new analytic

functionality” (Johnson, Scott & Frizelle)

One particular area that has been identified where complexity shows particular promise, is in the

overlap of “technical” and “social” issues, represented by the concept of “Socio-Technical

Systems”. It is felt by the author that whilst there is a very good understanding of “individual

issues” in the energy debate, there is a lack of integration of these issues into a coherent system

picture.


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C h a p t e r S e v e n

SUGGESTED FURTHER RESEARCH

SUMMARY

URING THE PROCESS OF WRITING THIS THESIS , MANY THEMES AND

TOPICS HAVE BEEN EXPLORED . THIS SECTION EXPLORES HOW SOME

OF THOSE THEMES COULD BE DEVELOPED FURTHER IN FUTURE

STUDIES TO YIELD FRUITFUL RESEARCH . THIS SECTION AIMS TO BRING SOME

CONTINUITY TO THIS STUDY IN THE HOPE THAT THE SUBJECT MATTER

EXPLORED MAY BE RESEARCHED FURTHER .

D


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C h a p t e r S e v e n

SUGGESTED FURTHER RESEARCH

My research into this topic has concluded that Complexity Science has the potential to be an

effective lens through which to view the problem of UK Energy Production and Distribution.

I believe I have begun to define the domain of UK Energy Production and Distribution in the

field of Complexity Science within the scope of this thesis, I believe I have also found a great

many avenues to pursue which will lead to a wealth of interesting, and relevant further research

on the topic.

My research into the subject area has led me to suggest the following research areas which could

prove fruitful and interesting.

Comparison of Network Topologies Using Complexity Principles

The idea that the topology of a network greatly influences its behaviour has been demonstrated

through numerous different examples in Complexity Science. It can be seen how the analysis of

telecommunications networks, using the ideas of small-world and scale-free networks advanced

by Complex Systems theory differ from the traditional models of network theory. This has the

potential to provide a new theoretical framework for the design of power distribution

infrastructure. The challenge now comes in relating the theory to a practical method that can be

used in the real world.


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Stickiness in Energy Awareness

Many people are familiar with the ideas of how to save energy, yet the gap between

understanding and implementation seems a challenge to bridge. “Stickiness” is one of the factors

cited by Complex Systems Scientists as being an “attractor” which makes a situation resistant to

change, or prone to change. Examples of Stickiness range from ‘Sticky’ advertising devices

leading to successful advertising campaigns”, to ‘Sticky’ neurons in the brain” leading to human

evolution. Possible future research could involve “how to make the concepts of ‘Energy

Awareness’ ‘sticky’ to ensure their promotion amongst the populace.

Using Fitness Landscapes to Plan Renewable Energy Developments

In Biology, fitness landscapes are used to look at the relationship between different species, and

their reproductive success in different environments. The replication rate of the species is

question is known as the “fitness” and this value can be plotted as a height on a landscape.

Similar species are nearby on the landscape, and dissimilar species far away. The idea was put

forward by Sewell Wright in 1932. Complex Systems Scientists have expanded upon this idea

from Biology, by looking at “Fitness” in different types of system. Fitness landscapes can be used

to provide information about the success or failure of different innovations, how stocks and

shares will perform, and inform the discussion of other phenomena not directly related to

biology.

Fitness landscapes could be used to map suitable sites for renewable energy technologies, by

assigning values to “environmental factors” such as “wind resource” and “solar resource”, then

using fitness landscapes to determine where these technologies are likely to achieve success, then,

the ability of


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The search for non-linearity in energy distribution networks.

Although my research in this thesis seems to conclude that there is some non-linearity inherent

in the energy production and distribution system, it would make for exciting research to identify

and quantify the sources of that non-linearity, and understand it better. This would lead to

better understanding of to what extent the system is “complex”.

Failure analysis with a complex systems toolkit.

A complex systems toolkit has been used to model weak nodes, and the potential for failure in

various network structures. Transport networks, and data networks have both been scrutinised,

to analyse what nodes are particularly vulnerable to attack, and by what mechanism networks

fail. Dan Braha from the New England Complex Systems Institute, spoke at the “Complexity in

the Built Environment” course run at Liverpool University in 2006, about how the internet,

and air-transport networks had been modelled using a Complex Systems simulation, to find out

which “nodes” (in the case of air-transport these would be airports and in the case of the internet

these would mean servers) were particularly vulnerable to attack under “unspecified random

order of attack” (what you might call an “act of god” on the system) and targeted attacks on

calculated nodes of the system. The systems were then analysed to find how many “attack

attempts” were required before the system failed, and lost it’s ability to re route. It would be

interesting to apply a similar methodology to the Energy Production and Distribution Network.

Some concern has been voiced that a centralised generation structure, with key loci of

importance is particularly vulnerable to terrorist attack. Renewable energy systems by contrast,

are often seen as having distributed control – however – it is important to acknowledge that in

large wind farms, centralised solar power stations (as envisaged for warmer climes than the UK)

renewable energy generation is also concentrated into a fairly central area. However, it is


123


plausible to say that different forms of power generation, will result in different structures of

networks – and that tools from Complex Systems, which have been used to analyse other

networks, might be equally applicable to energy distribution networks.

Using a Measure of Cyclomatic Complexity to Detect Redundancy in Energy

Distribution Networks

Cyclomatic complexity is a measure commonly used in software engineering to detect the

number of “loops” in a program. The program’s structure is represented as a directed network

diagram, which is then tabulated into a matrix, mathematical calculations are then performed

on that matrix to derive a measure of the cyclomatic (that is to say ‘number of cycles that can be

taken through the program) complexity. If an energy network were to be examined in this way,

the figures would provide a measure of the number of routes which could be taken through the

network. This in turn would bear some abstract relation to the redundancy of the network.

Further investigation is needed to see if this could be a useful measure for energy distribution

networks.

Using Complex Systems to Understand the Dynamics of Public Opinion

In a democracy, the politicians are elected by the populace, and the politicians make decisions on

behalf of the populace as to the future direction of the company. Public opinion is a social

phenomenon – there are many factors that can influence the dissemination of an idea or

concept, and its general acceptance or otherwise by the population. Following conversation with

Dr. Erika Calvo of the University of East London, it appears that Complexity is a good tool

which can help us understand complex social phenomena.

There are arguments on both sides of the energy debate, which put forward a coherent argument

for the different technologies. There is also a great degree of misinformation and a lack of


124


understanding. Certain myths propagate, and appear to have the property of “stickiness” which

keep them embedded within the populations psyche.

A greater understanding of public opinion on the debate could lead to new methods of sharing

information and encouraging “energy efficient” and “environmentally friendly” views in the

population whilst dispelling myths.

Synthetic Population Techniques in Modelling Energy Consumption

One of the themes explored in the EPRS(Shackley et al., 1996)C Taught Course “Mathematics

in the Science of Complex Systems”, was the use of “complex systems” techniques in traffic

simulation, and the understanding of system wide behaviour. Traffic, can be visualised as data

traffic, car traffic and this concept could further be expanded to encompass the concept of

“energy” as “instantaneous traffic” on a complex network.

One of the techniques talked about by Professor Jeffrey Johnson of the Open University, was

the use of “synthetic population” as a means of more accurately simulating the flow of traffic in a

transport system. A “virtual population” is created in the model, where population

characteristics are assigned to physical locations interconnected by roads. For example, a physical

location with the property of “school” would have a high flow of traffic at the start and close of

the school day, whereas a physical location with the property of “factory” would have a high flow

of traffic and the beginning and end of the factory shifts.

A similar approach could be used to model the flow of energy on a network, by assigning values

that correlate with when energy is used by the “synthetic population” in different physical

location. Similarly a “synthetic climate” model could interact with the renewable energy nodes

in such a simulation.


125


C h a p t e r E i g h t

LIMITATIONS OF THE METHOD

SUMMARY

N THIS CHAPTER , WE WILL LOOK AT SOME OF THE LIMITATIONS OF THE

METHOD USED IN THIS THESIS , WITH A DISCUSSION OF THE MTHODS

USED TO APPROACH THE PROBLEM AND HOW SOME OF THE THEMES

COULD BE EXPANDED UPON IN A MORE DETAILED STUDY .

I


126


C h a p t e r E i g h t

LIMITATIONS OF THE METHOD

Limitations of Time and Scope

The study is limited by scope in the fact that it is a Masters’ thesis, fulfilling the component of

60 Postgraduate Credits and not a longer work. As such has, the work has been constrained in

length and span. The work was conducted over the period of six months; as such there was little

time to develop the embryonic theories put forward in this thesis into practical, working

solutions. A longer period of research would yield a more thorough development of some of the

ideas contained within this thesis.

Limitations on Knowledge

The thesis was approached with knowledge of “Renewable Energy Technologies” and “The

Energy Debate” gleaned from the taught element of the course, previous study by the author

towards a BSc. (Hons.) Technology with the Open University and work on the Alternative

Energy Strategy 2007 being developed at the Centre for Alternative Technology, however, the

author approached this thesis as a newcomer to “complexity theory”, and as such, the “learning

curve” has been steep. Whilst a thorough review of the literature has been undertaken, the

author lays no claim to having prior study in the field of Complexity, however, during the thesis

writing period, the author attended a number of courses on Complexity run by the EPRSC. As

these courses “punctuated” the period of study, the author’s ideas about complexity evolved over

the period of the study.

Uncertainties on Pre-Existing Work

The lack of clarity as to what constitutes a “complex system” initially proved to be a barrier to

understanding, the broad an diverse nature of the subject matter encompassed by complexity


127


proved to be a hindrance when trying to find relevant examples to support the work. There

remains vagaries which are yet to be refined relating to what elements of the UK ESI are

“complex” and what can be understood adequately using existing technique.

This question would only be answered by developing the “toolkit” proposed into a practical

application of theory, and by making empirical test and comparisons, to see whether “Complex

Systems Science” can provide more accurate explanation of the phenomena encountered in the

“Energy Debate”.

Limitations on Discursive Data

Following a thorough review of literature, it appeared there was little pre-existing data on the

marriage of Complex Systems to the Energy Debate. This is to be expected, as this thesis was

seen by the author to be a largely “explorative” project, as it was believed that this avenue had not

been investigated previously. As a result, the arguments presented in Chapter Five are

constructed from the intersection of “Complexity Theory” and “The Energy Debate” as it

appears apparent to the author, however, as the author is new to Complexity, these ideas may

presently be of limited depth.

Theoretical Nature of The Thesis

The thesis has been conducted as a desk-study based on the pre- existing information produced

by experts in both fields and in finding synergies between the two subject areas, this thesis has

staked out a new area of fruitful research.

As a result of the nature of the study, and the time limitations imposed, there has not been

sufficient development of the theoretical ideas expressed within this thesis into practical

solutions – although that is not to say that given time and further research this could not be

accomplished.


128


Complexity Theory has been successfully applied to develop more effective management

strategies for companies, better technical solutions to engineering problems, if we accept that the

UK ESI is a Complex System, then there is no reason that Complex Systems theory cannot

enjoy comparative success in this sphere, however, within this study there has not been room to

expand these themes into “working proposals”.

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GLOSSARY

Adaptation. A change in a system that allows it to survive, and perform more efficiently within its operating environment.

Agent. In the context of this thesis, an agent is any component or entity of an energy production or distribution system, whether that is an item of plant or equipment, a user or any other item that can either accept stimulus from the system or can provoke a response in the system or both of the above.

Algorithmic Complexity. A The measure of how complex a problem is, by defining the smallest program or set of instructions which can form a complete picture of the problem in its entirety.

Blackout. Power outage, power cut, power failure. The condition that arises when an energy supply and distribution network fails to deliver power to the customer. The reasons are many, it could be as a result of

Brownout. In contrast to a blackout, a brownout is a reduction in the amount of powr

Butterfly Effect. The butterfly effect is the name given to a phenomenon in chaos theory where a seemingly unrelated small effect gives rise to a much larger effect – commonly illustrated by the example of a butterfly flapping it’s wings on one side of the world giving rise to a hurricane on the other side of the world. The example illustrates the chaotic nature of complex systems and how a change in a seemingly unrelated variable can give rise to a large change in the behaviour of the system.

Chaos Theory. Synonym for ‘Dynamical Systems Theory’ (Abraham, 2002b)

Chaotic. A property of a system which exhibits behaviour that cannot be predicted or governed by deterministic rules. See ‘Dynamical Systems Theory’

Closed System. A closed system is one where energy may not enter or leave the boundary of the system under examination.

Complex System. The term “Complex System” is somewhat vacuous, as there are so many definitions of complexity. However, the class of systems which we consider “complex” all have some traits in common, which allow us to draw comparisons between different systems. Complexity traverses the traditional disciplines of the sciences, and as such complex systems scientists look at behaviours of systems and underlying structures rather than looking at the system from a point of view of a specific discipline.


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Dynamical System. In a dynamical system, the present state of the system depends on the past history of the system. The system is “dynamic” in nature, and therefore subject to change over time.

Emergent behaviour. A behaviour that emerges from the complex interactions of a large number of simpler behaviours and cannot be defined by the sum of the definition of the simpler behaviours.

Emergent properties. Emergent properties are those which arise out of a systems “emergent behaviour”.

Irreducible. A property of a system which exhibits behaviour which means that the system cannot be reduced into rules that define its constituent parts without losing information about the system. (Casti, 1994)

N-ary relations. Complex systems are multidimensional by nature – that is to say they have a large amount of variables which are interrelated.

Non-Linear. Simple systems follow linear relations which can be described simply mathematically. However, non-linear systems behaviour cannot be expressed by adding the behaviour of the systems constituent descriptors.

Office of Gas & Electricity Markets (OFGEM) – ESI regulator since 1999.

Open System. An open system is one where energy may enter or leave the system under description.

Spinning Reserve. In order to provide QoS and meet peak demands, a number of fossil fuel power stations are often kept on “spinning reserve” where sufficient steam is raised in order to keep their turbines turning –without any load from the generator or power being produced. When extra power is required by the network, the generator is engaged – the turbine now turns at full power. “Spinning reserve” allows a power station to react to changing conditions much faster, than had the power station had to “start from cold”.

Traversal Questions. Complex Systems Scientists believe that there are commonalities between different behaviours that are observed in Complex Systems, and that there are questions that can be asked about one type of behaviour in one type of system that will hold true for other types of system, thus “traversing” the boundaries of the definition of that system and applying to more than one type of system.


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ABBREVIATIONS

AC. Alternating Current

BBC. British Broadcasting Corporation

BRE. Building Research Establishment

CAFÉ. Community Action for Energy

CCGT. Combined Cycle Gas Turbine

CEGB. Central Electricity Generating Board

CHP. Combined Heat & Power

CSE. Centre for Sustainable Energy

DC. Direct Current

DEFRA. Department of Environment, Food & Rural Affairs

DETR. Department of Environment, Transport & Regions

DNO. Distribution Network Operator

DSM. Demand Side Management

DTI. Department of Trade and Industry

EdF. Electricité de France

EGWG. Embedded Generation Working Group

ESI. Electricity Supply Industry


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EST. Energy Saving Trust

EU. European Union

GW. Gigawatt

H&IE. Highlands & Islands Enterprise

HV. High Voltage

HVDC. High Voltage Direct Current

IEA. International Energy Agency

IGCC. Integrated Gasification Combined Cycle

kV. Kilovolts

kWh. Kilowatt Hour

MW. Megawatt

NETA. New Electricity Trading Arrangements

NFFO. Non Fossil Fuel Obligation

NGC. National Grid Company

NSI. North Sea Interconnector

OFGEM. Office of Gas & Electricity Markets

POST. Parliamentary Office of Science & Technology

PV. Photovoltaic


- 142 -


QoS. Quality of Supply

REC’s. Regional Electricity Companies

RO. Renewables Obligation

ROC’s. Renewables Obligation Certificates

SCHRI. Scottish Community & Household Renewables Initiative

SE. Scottish Executive

SO. System Operator

TO. Transmission asset Owner


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VITA

GAVIN D.J.HARPER

Gavin D.J.Harper was born, the son of Cheryl Dawn Harper and Geoffrey Richard Harper

on the 11th of December 1986 in Harold Wood, Essex.

He was educated at the Coopers’ Company & Coborn School in Upminster, Essex. Leaving

at 16, with a complement of 11 GCSE’s, he started his BSc. (Hons.) Technology, with the

Open University, shortly afterwards.

He simultaneously studied A-Level Design & Technology at Palmers College, Grays, Essex

and City & Guilds Level 4 Computer Aided Design at the Centre for Engineering &

Manufacturing Excellence, Rainham, Essex.

He is the author of a number of books “50 Awesome Auto Projects for the Evil Genius”,

“Build Your Own Car PC”, “Model Rocket Projects for the Evil Genius”, “Solar Energy

Projects for the Evil Genius” and “Domestic Solar Energy”.

His work has featured in the Journal “Science”, the Independent newspaper, MAKE:

magazine and “The Daily Kos” blog amongst others.


- 144 -


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