Australia’s Energy Transition: Balancing Competing Demands and Consumer Roles [1st ed.] 9789811561443, 9789811561450

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Australia’s Energy Transition Balancing Competing Demands and Consumer Roles Glen Currie

Australia’s Energy Transition “In this excellent book, Dr. Glen Currie, a leading expert in energy, argues in favor of policy that includes customer behavior in the system planning. He reminds us of the increasing complexity in managing electricity systems globally, and shares lessons from Australia. The relevance of Australia is that it has the world’s highest domestic PV concentration, one of the highest concentrations of domestic air-conditioning, and operates on one of the world’s most dispersed electricity grids. This book argues for energy planning to incorporate a broader set of stakeholders and to allow more innovation to counter this complexity. It is likely that there are pertinent lessons herein for electricity planners internationally.” —Professor Abbas Rajabifard is Head of Department of Infrastructure Engineering at The University of Melbourne. He is also Director of the Centre for Spatial Data Infrastructures & Land Administration (CSDILA). He was President of the GSDI Association (2009–2012), Vice Chair of Working Group 3 of the United Nations-supported Permanent Committee on GIS Infrastructure for Asia and the Pacific (PCGIAP), is a member of ICA-Spatial Data Standard Commission, and is a member of Victorian Spatial Council “Australia’s energy transition offers lessons for global energy managers. The high renewable content in Australia and stretched distribution puts pressure on stability of the networks. This book recommends policy options for the customer role in the energy transition. This is one of the more difficult processes in the transition and the discussion here is clear, relevant and urgent!” —Professor Iven Mareels, FTSE and Fellow of IEEE (USA), IFAC (Austria), KVAB (Belgium), EA (Australia), The University of Melbourne

Glen Currie

Australia’s Energy Transition Balancing Competing Demands and Consumer Roles

Glen Currie University of Melbourne Parkville, VIC, Australia

ISBN 978-981-15-6144-3 ISBN 978-981-15-6145-0 (eBook) https://doi.org/10.1007/978-981-15-6145-0 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover illustration: © Harvey Loake This Palgrave Macmillan imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Electricity systems are technically complicated and are becoming more complex as they accommodate rising social-political demands. The principal aim of this book is to improve understandings of social-political roles in the electricity system to help balance these complex demands. This book goes part of the way to understanding the Energy Transition, which is destabilising electricity systems worldwide. Choices to manage this change are not obvious, and this book is therefore designed to help guide the delicate balancing of technical and social-political demands. Seeking this balance is urgent, and solutions can only be achieved with the active participation of society and politicians. Chapter 1 introduces the reader to the burning issues and active urgency needed to properly guide the Energy Transition. Chapter 2 explains why Australia is a valid case study of relevance to other countries. The third chapter reviews political dimensions and social dynamics of the Energy Transition. In Chapter 4, an analysis of Australian household PV uptake decisions is undertaken to show the role of modelling in the Energy Transition. Chapter 5 introduces the technology and data options for the Energy Transition. The sixth chapter reviews the use of systems engineering to manage the Energy Transition, and the concluding chapter sums up the findings and outlines implications for future research. The conclusions of this book are based on a statistical analysis of the 1.6 million PV installations in Australia between 2001 and 2016 and interviews with energy leaders in Australia and Europe. Interviewees included v

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PREFACE

academics, politicians, businesses and consumer advocates. Quantitative and qualitative analysis showed a low level of confidence in Australian energy policy but a high confidence in business solutions. This book assumes that reliable two-way communication will take some time before adoption, and that local area smart grid technology can quickly improve the operation of the distribution grid without networkwide two-way communication. The Transactive Grid is therefore not the focus of this book, but the focus is on the first stage and calls this the Energy Transition. This book is written for people concerned with the electricity system and with an interest in finding solutions. It is not for a general audience. Parkville, Australia

Glen Currie, Ph.D.

Acknowledgements

To the people of Palgrave Macmillan, my colleagues at the University of Melbourne and many people in industry and government: thank you for your support, your wisdom, your research experience, your friendship, and your willingness to debate and develop the ideas in this book. Thanks to Professor Robin Evans, Professor Colin Duffield, Professor Iven Mareels, Professor Robin Batterham, Professor Frank Larkins, and Dr. David Wilson. To my wife Chloe and sons, Angus, Hayden, Max and Lachlan, thank you for supporting me. Finally, I am indebted to all those whose work I have drawn from and built upon.

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Contents

1

Why 1.1 1.2 1.3 1.4

1 3 4 5

Should We Learn About the Energy Transition? Defining the Energy Transition Grid Stability and the Energy Transition Consumer Roles in the Electricity System Why Are Consumers Creating a Problem Now and Not Before? 1.5 Grid Problems Caused by PV 1.6 Risks in This Transition? 1.7 Who Needs a Voice in the Energy Transition? 1.8 What Can We Do Better to Manage the Energy Transition? References

15 16

2

Why Focus on Australia? 2.1 Where Does Australia’s Electricity Come from? 2.2 Australian Electricity Market Structure 2.3 What Lessons Are Available from Australia? References

21 22 23 28 30

3

Political-Social Dynamic of the Energy Transition 3.1 The Political-Social Dynamic of the Energy Transition 3.2 Will Distribution Businesses Lead the Energy Transition?

33 35

7 8 9 12

40 ix

x

4

5

6

CONTENTS

3.3 Drivers of the Energy Transition? 3.4 Social and Legislative Institutions 3.5 Knowledge Management Networks? 3.6 What Did We Learn in This Chapter? References

44 46 49 52 52

Modelling Consumer Roles in the Electricity System 4.1 Choosing a Dataset to Help Understand Consumer Choices 4.2 Exploratory Modelling 4.3 Finding Influential Variables 4.4 What We Learnt from the Analysing the Solar Data by Postcode 4.5 Temporal Model of Australian PV Adoption 4.6 Method 4.7 ARIMA Modelling Results 4.8 What Did We Learn from the Australian Solar Data? References

55 56 56 58 62 64 67 70 79 81 85 86 87 89 92 94 96

Technology and Data for Improved Decision Making 5.1 Limiting PV Export to Reduce Overvoltage Problems 5.2 Smart Devices 5.3 New Inverter Technology 5.4 Remote Control of Consumer Assets 5.5 The Role of Energy Efficiency in the Transition? 5.6 Cost Reflective Network Pricing 5.7 Demand Response (DR) and Demand Management (DM) 5.8 Electric Vehicle Charging 5.9 Storage 5.10 Data, Ethics and Social Licence 5.11 What Have We Learnt About Technology and Data for Improved Decision Making? References

109 110

The Energy Transition as a System 6.1 What Needs Managing in the Energy Transition? 6.2 Framing the Energy Transition as a System

115 116 116

99 101 102 104

CONTENTS

7

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6.3 System Dynamics View of the Energy Transition 6.4 Systems Engineering 6.5 Conclusion References

118 120 126 127

Conclusion 7.1 Government Role in Increasing Electricity System Innovation 7.2 Social Licence for the Energy Transition 7.3 Risks of the Energy Transition 7.4 What Next? References

129 131 135 137 138 139

Appendix A: Interview Questionnaire

143

Appendix B: Interview Analysis Method

153

Appendix C: PV in Australia Analysis

157

References

175

Index

189

Abbreviations and Acronyms

ABM ABS ACCC AEC AEMC AEMO AER AIC APVI ATA AusNet CEC CER CitiPower CoAG Community Energy Consumer CSIRO DB DER DM

Agent-Based Modelling Australian Bureau of Statistics. Australian Government Australian Competition and Consumer Commission Australian Electoral Commission. Australian Government Australian Energy Market Commission. Australian Government Australian Energy Market Operator. Government/ Industry body Australian Energy Regulator. Australian Government Akaike Information Criterion, to compare the quality of models Australian Photovoltaic Institute Alternative Technology Association. Australian lobby group Australian Distribution Business (otherwise known as Ausnet Services) Clean Energy Council. Australian lobby group Clean Energy Regulator. Australian Government Australian Distribution Business Council of Australian Governments General Term for Community-Based Energy Systems Households and small businesses Australian Government research laboratories Distribution Business Distributed Energy Resources Demand Management xiii

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ABBREVIATIONS AND ACRONYMS

DR ENA Energy Transition Energy-action Ergon and Energex ESB EUAA EV FCAS FIT Grid kW kWh Mini grid NEG NER

Ofgem Postcode Powercor Powershop Prosumers PV PV export RIIO SEIFA Smart Meter Social licence Solar system Standards Australia Transactive Grid United Energy

Demand Response Energy Networks Association. Australian lobby group Current phase of transition of the electricity system Any significant consumer energy action Queensland Retail and Distribution Businesses Energy Security Board. Australian Government Energy Users Association of Australia not for profit Electric Vehicles Frequency Control Ancillary Services in Australian electricity market Feed-In Tariff, Money paid to consumers who export PV electricity Electricity system 1000 watts, which is a measure of power. For example, a small domestic heater can draw 1kW 1000 watts per hour, which is a measure to energy Mini electricity system with some autonomy from the grid National Energy Guarantee. Australian Law under review during 2018 National Energy Rules in Australia, such as Rule 6.1.4 which blocks the system owner from charging for reverse flows of electricity Office of Gas and Electric Markets. UK regulator Postal Areas Australian Distribution Business Australian Electricity Retail Company Active Consumers Photovoltaics. Term for all types of electrical solar systems Electricity despatched into the electricity system from a solar system Ofgem UK distribution system regulation (Revenue = Incentives + Innovation + Outputs) Socio-Economic Indexes for Areas. ABS, Australian Government Electricity metre that allows two-way communication Theoretical measure of the level of public support Electrical PV system powered by photovoltaic cells Australian Government Standards Authority Theoretical future electricity grid with higher levels of information transfer Australian Distribution Business

List of Figures

Fig. 1.1 Fig. 1.2 Fig. 1.3 Fig. 1.4 Fig. 2.1 Fig. 2.2

Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 4.1 Fig. 4.2 Fig. 5.1

Electricity price change since 2003 compared with CPI Main parts of electricity systems (South Australian Government, 2020) Percent of Australian homes with air-conditioning Household average electricity use, Australia (Tustin, Taylor, Lourey, & O’Mullane, 2012) Structure of Australian electricity system regulation Subsidy and PV system $A price per watt and PV installation total by year (APVI & ABS) Note The subsidy was calculated as the first year of feed-in tariff (FIT) income after the purchase of the PV system for a weighted average of the FIT subsidies, and the carbon subsidy is a payment for the reduction of carbon dioxide emissions. The FIT pays the owner per unit of PV electricity A school class with a sleeping schoolmaster, oil on panel painting by Jan Steen, 1672 Analysis of the interviews showing the links between the categories Priorities in energy policy (n = 46) Illustrative plot to illustrate Australia may be leading the transition Australian remoteness categorisation NSW major cities area 10, PV per month per 1000 homes Hornsdale wind farm and Tesla battery (Hornsdale Power Reserve)

2 3 6 7 24

26 34 38 38 40 64 65 102

xv

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LIST OF FIGURES

Fig. 6.1 Fig. 6.2 Fig. 6.3

Fig. 7.1 Fig. Fig. Fig. Fig. Fig.

A1 A2 A3 A4 B1

Fig. C1

Model of consumer PV sales using flows of electricity, money and power How the subsystems interact in the Australian energy system National electricity transmission lines (Source [adapted from Australian Renewable Energy Mapping Infrastructure, 2020], retrieved from https://nationalmap.gov.au/renewa bles/ on 5 June, 2020) Operational framework for public policy (adapted from Fels, 2019) Box and whisker plots of the answers Questions 1–4 Box and whisker plots of the answers to Questions 5–8 Box and whisker plots of the answers to Questions 9–12 Box and whisker plots of the answers to Questions 13–14 Analysis of the interviews showing the links between the categories. This grouping was completed using SPSS Modeler Australian PV Institute (APVI) Solar Map (Source Funded by the Australian Renewable Energy Agency, accessed from pv-map.apvi.org.au on 7 May 2020 Setting up the regression)

119 123

124 130 148 149 150 151

155

161

List of Tables

Table 2.1 Table 2.2 Table 2.3 Table 3.1 Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 4.5 Table 6.1 Table 6.2 Table A1 Table A2 Table C1 Table C2

Capacity and output by fuel source (Commonwealth of Australia, 2017) Changes in South East Queensland consumers’ appliance penetration (ENERGEX) Consumer voltage rules in Australia Quantitative survey questions mean/standard deviation (Question 1–14) Correlation between actual PV-system installations and theoretical model Sample of model comparisons using Pearson correlation RMSPE and Akaike Information Criterion (AIC)-3 models Model of PV-system adoption in Australia Correlation of the model by remoteness geographic classification Requirements for the energy transition (IEEE P1220) Functional analysis of the transition of the Australian electricity system Questionnaire Results for mean and standard deviation by country and employment type Variable choice Test results—Akaike Information Criterion (AIC) and Schwarz orientation

22 26 28 36 60 72 75 77 78 121 122 144 146 162 165

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CHAPTER 1

Why Should We Learn About the Energy Transition?

Abstract Blackouts struck South Australia on the 28 September 2016, then again on the 8 February 2017. The Australian Energy Market Operator (AEMO) Final Report on the South Australian blackouts (AEMO in Black system South Australia 28 September 2016: Final report, 2017a) made 19 recommendations and none refer to managing consumer load. They do however refer to household PV causing a low level of reactive load, and hence contributing to instability. A further shortage of energy in NSW on 10 February 2017, prompted an AEMO Incident Report (AEMO in Incident report NSW 10 February 2017 , 2017b) and again made no mention of managing consumer load. Infrastructure Australia was a government body to foresee and document these coming problems (Infrastructure Australia in Australian infrastructure audit, 2015). They saw the electricity system as robust but in audit findings 63 and 66 warned that climate policies were interfering with investment and recommended regulatory reform to reduce peak period demand and improve grid stability. Grid stability has been a focus of recent Australian energy policy (Finkel, Moses, Munro, Effeney, & OKane in Independent review into the future security of the National Electricity Market: Blueprint for the future, 2017), but the consumer has yet to be integrated in energy policy. We need a focus on this integration because the rapid uptake of air-conditioning and PV is putting pressure on grid stability and electricity prices.

© The Author(s) 2020 G. Currie, Australia’s Energy Transition, https://doi.org/10.1007/978-981-15-6145-0_1

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Keywords Balancing stakeholder demands · Complexity · Duck curve · Risk of delay · Urgency

Electricity prices have risen to a point that it is now an energy justice or social equity issue (VCOSS, 2018). The high price of electricity also makes Australia’s manufacturing industry less competitive. Energy justice is defined by Jenkins, McCauley, Heffron, Stephan and Rehner (2016) as covering energy policy, energy production and systems, energy consumption, energy activism, energy security and climate change. This book does not attempt to cover the full gamut of energy justice. The related term used in this book is social equity and is defined in this book as a fair system that equitably shares costs and benefits. The Australian electricity system faces a crisis if it continues along the current path (Millken, 2018). One cause of this is the decreasing utilisation of electricity distribution assets which is due to very low loads during the day and high loads during the evening peak (Denholm, O’Connell, Brinkman, & Jorgenson, 2015). Resultant increases in electricity prices are having negative consequences for many people in Australia and the price average shown in Fig. 1.1.

Fig. 1.1 Electricity price change since 2003 compared with CPI

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Fig. 1.2 Main parts of electricity systems (South Australian Government, 2020)

Governments are conscious of this problem of high electricity prices as citizens bring it to their attention and it often comes up in the media. The challenges for the electricity system occur in all the above four sectors, generation, transmission, distribution and retail. The sectors with most focus in this book are the distribution and retail sectors as they are poorly understood in the context of electricity systems. They have been studied in isolation, but this book brings them into the context of the operation of the whole electricity system, as they are a key part of the Energy Transition. Generation is shown in Fig. 1.2 as a coal generator and a wind generator. Other key generation sources are gas, hydro and gas. Further detail on the Australian generation mix is in Table 2.1. Distribution Business faces the customer on one side and the transmission system on the other. They are under a mix of private and public ownership and have little visibility to the customer because the customer would normally only contract with their retailer. Distribution Businesses are discussed widely in this book, because they are a key battleground for the Energy Transition.

1.1

Defining the Energy Transition

The “Old Grid” was centralised, focussed on the efficiency of generation, and offered a stable system. It began with the “War of the Currents” between the companies of Thomas Edison and George Westinghouse. Westinghouse bought Nikola Tesla’s induction motor patent in 1888 and supported the winning choice of alternating current (AC). Edison supported direct current (DC). The “Old Grid” finished with the 1992 United Nations Rio Accord (Bodansky, 1993). The Rio Accord led to the introduction of policies for low emissions generation around the world.

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The “Grid Development ” phase includes the development of control and grid management including sophisticated tariff structures which enabled the grid to absorb large-scale wind generation. It included the adoption of energy policies that recognised the role of renewable energy, as well as a stable grid. The “Energy Transition” refers to the current transition. One example of an organisation in this space is the European Go Flex Community (GoFlex, 2019). Consumers are far more active in the electricity system, and their decisions to install air-conditioning, solar PV and buy electric vehicles are all affecting the whole electricity system. The electricity system has recently moved to a phase we call in this book the Energy Transition. The Energy Transition as defined in this book is more than just the change in generation types. It relates to the current decarbonisation of generation, social change, how people use energy, policy, regulation, and the overall structure of the energy system.

1.2

Grid Stability and the Energy Transition

There is high diurnal variation in load at present caused by PV and air-conditioning which creates overvoltage, thermal overload, frequency instability and voltage instability and a worsening reliability of the electricity system. Northern Europe has grid stability problems (Menck, Heitzig, Kurths, & Schellnhuber, 2014) as has Australia (Finkel et al., 2017). The grid is not designed for the intermittent contribution of Distributed Energy Resources (DER) mixed with centralised synchronous generation. It is technically hard to schedule supply to follow rapid changes in demand. Distributed Energy Resources (DER) reduce grid stability because they lower the system inertia. This inertia is also measured by the amount of spinning plant on the electricity system. The main sources of inertia in the Australian electricity system are coal and gas generation plants. This is because wind and solar energy are normally non-inertia sources. The electricity system needs inertia to maintain the required frequency. Traditionally inertia has been provided by heavy rotating generating equipment such as used in coal-fuelled generators. Therefore, without new alternate sources of inertia, renewable generation is limited to about 50% (Finkel et al., 2017) of total instantaneous generation. An example is that Siemens Australia in 2017 proposed the addition of three 100 MVAr synchronous condensers around the South Australian grid (Parkinson,

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2018). Theoretically system inertia could be maintained by continuing to spin the generators in decommissioned fossil fuel plants (to function as capacitors), and that this might allow 80% of generation to be intermittent. From there, the use of pumped hydro or other inertia maintaining generation, we could envisage a stable system with 100% renewables. One response to increase grid stability is to increase investment in the electricity system assets and this can drive up the costs of electricity (Alexander, Wyndham, James, & McIntosh, 2017). This increase in cost is seen in the distribution system which runs between customer meters to the 66 kV end of the transmission system. Australia does not dynamically manage the distribution system, so assets are not currently able to adjust to dynamic variations in the load and system state. On the other hand, the transmission system is actively balanced by AEMO. AEMO operates eight Frequency Control Ancillary Services markets (FCAS) to help grid stability. They are mostly traded in the wholesale electricity market, but households can access the FCAS markets. This is done by companies such as Reposit Power (2018). Frequency services from consumers are technically difficult because the despatch of frequency synchronisation needs communication in and out of the consumer premises.

1.3

Consumer Roles in the Electricity System

user-centred innovation is a powerful and general phenomenon. It is rapidly growing due to continuing advances in computing and communication technologies. It is becoming both an important rival to and an important feedstock for manufacturer-centred innovation in many fields. (von Hippel, 2005)

As von Hippel points out in the above quote, the consumer will increasingly be a source of innovation. This dynamic is influencing increased supply side and the demand side variability. Demand side variability has been driven by consumer actions and expectations. Consumers now own solar generators and draw larger electricity volumes with air-conditioning and electric vehicles. On the supply side, the increasing renewables portion in the generation mix is more variable than the previous fossil generation (dependant on the wind and sun). This variability in supply at the same time as the rapid

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Fig. 1.3 Percent of Australian homes with air-conditioning

changes from air-conditioning and PV means that generation must be more responsive. Figure 1.3 shows the increase in domestic air-conditioning sales in Australia as a percentage of Australian homes from 1999 to 2016. Consumers were previously just recipients of electricity services, but now their actions with air-conditioning and PV are integral to the electricity system. Consumer energy-actions are driven by climate change awareness, interest in renewables, the reducing price of renewables, reducing price of electrical devices, social media, and an increase in consumer energy self-determination (AEMC, 2018). The consumer role in the electricity system will increase with home storage, and electric vehicle chargers. Australian consumers use a variety of electricity averages depending on climate and household appliances. Figure 1.4 shows the seasonal averages for Australian households. Figure 1.4 shows the average quarterly electricity consumption in Victoria (VIC) as about 1200 kWh, which is 1.2 MWh, and annual consumption about 5.2 MWh. By comparison, a small PV system would generate about 1 MWh and a large system about 3 MWh, so a consumer with a smaller than average load, could easily be a net exporter of electricity. Energy planners and regulators have struggled to integrate consumer roles into their modelling. This is partly due to the challenge of understanding consumer behaviour.

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Fig. 1.4 Household average electricity use, Australia (Tustin, Taylor, Lourey, & O’Mullane, 2012)

1.4 Why Are Consumers Creating a Problem Now and Not Before? Electricity system planning and design must now cope with more variability, higher peaks, and negative loads (Liu, Gao, Ma, & Li, 2015). The most significant planning and design challenges in Australia are the highly dispersed grid, the fast uptake of air-conditioning systems and high concentrations of electrical solar systems (PV). Policy has come from both state and Australian Governments which has not aligned with the electricity system technical requirements. This has led to higher electricity costs (Wood, Blowers, Griffiths, & Weisbrot, 2018). These new high concentrations of air-conditioning and PV have meant upgrades to transformers, lines, and protection systems. This addition of an air-conditioner may have seemed like adding a fridge in the past. The difference is that many air-conditioners now being

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installed are up to 10 kW. They have many times the electricity load of a fridge which is normally 1–2 kW. Air-conditioners also tend to be run all at the same time when the weather is very hot. This adds up to a considerable new load on the electricity system. To illustrate this from the infrastructure perspective, we can think about building new freeways, they tend to fill to 100% capacity in a year or so. If we build more electricity system capacity, we only use 100% for a few moments on the hottest day of the year. This was not a problem when the load was spread out, but air-conditioning has concentrated the load to the hottest times. At times when the grid capacity is increasing, then any new air-conditioner requires new powerlines and new generating capacity, which is then fully used for a few moments a year. This economically is unfair, as the cost is spread to all citizens, including the poorest citizens. This dynamic is likely to be worsened with electric vehicles with a resulting wealth transfer from the poor to the rich.

1.5

Grid Problems Caused by PV

Feeding solar into the grid can cause overvoltage. Also, as the sun goes down, the solar switches off and the evening peak begins. This switch causes very high ramp rates and causes instability (Lazar, 2016). We need policy to guide consumer actions in the direction that mitigates some of these technical issues. Even putting storage with every PV system would not stabilise the grid (Mohanan, 2019), so the solutions are complex. PV grid issues are acutely felt in Australia due to world leading PV market concentrations where some locations have PV on over 50% of building stock. Also, Australia has long distribution lines (up to 200 km) with significant drops in voltage (due to their length). Distribution lines are otherwise called feeders. The high PV concentrations and the long feeders mean the impact of PV is more severe in Australia than in other electricity systems. Excessive PV generation in a single feeder can cause a backwards flow of power and protection from this is needed as the grid is designed for single direction flow for safety reasons. The other PV grid problem is that the PV systems cut out when voltage rises above the operating range of the PV inverter (Alexander et al., 2017) and many PV owners are gaining less benefit from PV than they expected. Many Australian transformers cannot even be tapped down to accommodate

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solar, as they are above the nominal voltage during peak export of electricity from PV and below the nominated voltage at peak load times (Stringer, 2018). Overvoltage mitigation usually includes grid-reinforcement, storage, reactive power absorption by PV inverters, active transformers, active power curtailment, and demand response. Power management could also increase the PV hosting capacity of a distribution line and transformer by more than 20% (Hashemi & Østergaard, 2016). Current Australian regulation is encouraging restriction of PV connections, rather than finding solutions that integrate consumer action into their planning. Given that more active consumer roles are expected in the long term, any move to restrict PV connections works against the expected direction of the Energy Transition.

1.6

Risks in This Transition?

The Energy Transition will face barriers and risks which are explored in the perspectives below. 1.6.1

Risk of Delay

There is wide concern that delay was a leading risk for the transition of the Australian electricity system. We must execute the Energy Transition now or we will pay more later. The framing of policy and regulations by government is seen as too slow compared to the speed of technology. Government is on a 5–10-year cycle and the technology is on a 1–2-year cycle. “The evolutionary perspective on technology change offers more useful insights for policy makers….It emphasises the value of multiple approaches, the irreducible uncertainty and the power of recombination/modularity…” (Robert C. Williamson & Dana Sanchez). As Williamson notes, professional associations can become gatekeepers and become locked into the earlier paradigm. Other sources of slow response include non-market sources such as organisations that are social, cultural or governance oriented. Understanding uncertainty and the willingness to risk failure are important ways to speed policy response. Delay may occur if the consumer pays more because the electricity system becomes too complex, siloed and not fit for purpose. Errors in policy might lead to increased cost. A failure, such as a blackout, might

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cause others to leave the transition. Sources of complexity include the technology and the difficulty harnessing it to deliver on the value streams. The build-up of renewables integration capability, market flexibility and market design might be insufficient to avoid grid issues. This may also cause pushback from politics and industry. 1.6.2

Risk of Political Process Failure

There has been a lack of political leadership and political hijacking. The Australian Government was criticised as lacking commercial reality and not understanding the best way to manage distribution grids as a regulated monopoly. Politicians tend to respond to the electoral cycles and have difficulty planning for electricity systems in 50-year cycles. This is not unique in Australia. Europe is also seeing a lack of political commitment to changing energy policy. Change to energy rules takes 2–3 times the period it ought. If governments can see through changes (given the short electoral terms) bipartisan political support will still be needed. This will be difficult to achieve. Australian states have been acting to effect energy policy, but not coordinated with the Australian Government. 1.6.3

Risk of Policy Process Failure

Without a clear energy vision, good government policy is not possible. A lack of political vision contrasts with the clarity of consumers vision of low-cost electricity. The effect of PV has been huge, and coal-fired power stations are retiring but policy is not guiding this, it is mostly the market driving this. Policy bodies should have accountability and not assume a deregulated market will solve capacity issues. Energy policy should incentivise the full gamut of energy including energy efficiency, cost reduction and demand management. On the other hand, Australian policy developers have been accused of overreach and not understanding the risk management skills of businesses. Generators, retailers, and networks are in silos and not working together There is also a need to avoid technology lock-in in the policy process. If the wrong interim technologies are installed, it may stall the transition 20 years. Therefore, the rules need to be technology agnostic.

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Risk from Loss of Social Licence

This issue of social licence in Australia can be loosely defined: if you can convince Australians that something is harmful to the collective good, governments will have a stronger licence to act than they might assume. (Huntley, 2019)

There is a risk that the community will not understand the complex economic and technical needs of the electricity system and lead to a loss of social licence. The complexity of this change may be a barrier to effective communication to the community. Poorer households will need special provisions to avoid hardship or their discontent will lead to loss of social licence. If the transition is inequitable, it will erode social licence. Blackouts are another risk that was mentioned that may undermine social licence. Other issues include data-hacks, resistance from consumers to putting boxes on their houses and implementation risk. 1.6.5

Risk from Resistance from the Incumbents

Existing key institutions such as Distribution Businesses are motivated to resist change. Distribution Businesses will try to spread the costs of the initial change into their asset base and governments will tend not to resist this if they own those assets. Distribution Businesses set frameworks that block the transition. The costs of the transition would need to be paid by a diminishing revenue base as PV continues to grow, hence increasing the Distribution Businesses motivation to preserve the status quo. Right-wing lobbyists have tended to favour the incumbents, so it will be difficult to remove barriers. For example, it would be economically sensible for the fringe of grid to go to microgrids, but, both the regulation and the physical infrastructure are barriers. 1.6.6

Risk the Energy Transition Is Achieved Without Reducing Carbon Emissions

The risk of not achieving carbon emissions is low now that wind and solar are competing with the incumbent generation sources.

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But, if we do not solve for affordable interday and seasonal flexibility, there may be an undersupply of long-term flexibility which might drive up the cost of renewable integration. Batteries and demand side management can provide short term flexibility but not interday and seasonal flexibility. Interday and seasonal flexibility will need innovative technologies as fossil-based generation is phased out. The requirements for these flexibilities are currently solved with dispatchable fossil capacity that ramps up when needed. For example, hydrogen production may replace fossil systems, although the costs of hydrogen will need to reduce for commercial viability.

1.7

Who Needs a Voice in the Energy Transition?

Candidates for a voice in the Energy Transition must somehow also balance the different stakeholder interests. 1.7.1

Consumer Role in the Transition

Most consumers want secure, decarbonised energy at the best cost. Consumers have some representation in Australia through the Energy Users Association of Australia (EUAA). EUAA is a member of the AER’s Consumer Reference Group and the AEMO Reliability Panel. The consumer voice also includes hardship advocates such as the churches. Consumer technologies are driving the Energy Transition and that the gaps in the market are being filled by vendors, who are always looking for market opportunities and who will drive change with or without the government. 1.7.2

Social Equity Role in the Transition

Social equity is missing a voice in policy development. 25% of Australian homes are rented and 54% of businesses are in rented buildings. Mostly, they have been unable to adopt PV and take other energy-actions that would reduce their cost of electricity. Some local governments in Australia are working with disadvantaged people to help them access PV (Moreland Energy, 2019). The energy efficiency schemes and the renewables schemes offer no benefit to people who are renting or poor. Inequities are getting worse,

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as renters are excluded from the low cost of electricity available to homeowners who can modify their homes with energy devices such as solar. Compounding this problem is the fact that rental properties are often of a low building standard and not energy efficient. 1.7.3

Technology Role in the Transition

Policy should be technology agnostic, but policy will inevitably influence technology choices. Businesses and homeowners only will apply technology if the right policies (incentives) and social licence (norms) are in place. The mistrust of government energy policies, and distrust of energy providers due to high pricing, has left consumers inclined to “go-it-alone” and install storage or take energy-actions to increase their independence from the electricity system. Therefore, storage adoption will increase faster than it would if it were driven solely by economics. An example of technology having a role in the transition is ComEd Ltd, an electricity retailer in Illinois, USA which offer residential customers real-time pricing. It varies hourly, but unfortunately the prices are too low to incentivise much change in behaviour. 1.7.4

Economics Role in the Transition

Economics has been preeminent in Australian Government policy including energy policy for over 20 years. There have been significant problems for the Australian Government running a free-market agenda while running the Distribution Businesses as regulated monopolies. One option is to send price signals (through the regulated monopolies) to the stakeholders in the electricity system, to optimise the assets and give consumers the lowest cost and reliability. Economic efficiency is reliant on these flows of information. 1.7.5

Government and Political Roles in the Transition

Partisan policy on the topic of renewables has been harmful to Australian energy policy outcomes. There has also been difficulty in reconciling freemarket regulation with the regulated monopolies, and because ownership of Distribution Businesses in Australia is a mix of private and government.

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The regulator should be independent of government, and we could change the role of AEMC. In 2005, AEMC signed off on gold plating and was far too slow to halt the gold plating and has shown no accountability for what happened. Government policy for a complex system such as the electricity system should be free of politics. The long-term nature of energy policy decisions means that government should be seeking bipartisan support for long-term energy policies. Long-term energy policies should include emissions and equity and could include clear timeframes. Government is starting to act on the ACCC report (ACCC, 2018) and the Finkel Review (Finkel et al., 2017), but one improvement to the policy arm of government would be an increased appreciation of the role for business in the energy sector (asset owners and entrepreneurs) as they are skilled managers of risk and trial-and-error testing. 1.7.6

Environment

Carbon is an important part of energy policy. The NEG policy proposal tied climate and energy policy for the first time. The NEG policy was blocked after opposition to the NEG policy led to a change of Prime Minister (Hannam, 2018). Interestingly there has also been an erosion of the importance of the environment amongst young Australians in the past decade (Mission Australia, 2017) but it is still a potent issue. 1.7.7

Women in the Transition and Diversity

There are more women than men in environmental groups in Australia, and in 2018, all the key Australian energy policy and regulatory bodies were run by women and three of the four members of the AER board were female. Further, the idea of consumer empowerment and sustainability has appealed to women and there are also some women being appointed to leading roles in energy companies. Large companies are now focussing on gender bias in key appointments, but conservative politics is still male dominated. It is perceived that men have focussed on economic efficiency and technology. Women have tended to be less interested in technology, but interested in the outcomes, and what the technology enables. In fact, one woman who has experience in community energy stated: “I notice the

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women in Community Energy are the goers and blokes have been just looking on. Energy has been a blokey industry, but now the C level in AIG, Minerals Council and Energy Australia are all women, but not a lot of women at the next level”. Research on the climate-change sceptics suggests the old industrial patriarchy is fighting against the Energy Transition (Anshelm & Hultman, 2014).

1.8 What Can We Do Better to Manage the Energy Transition? The World Energy Council’s definition of energy sustainability is based on three core dimensions – energy security, energy equity, and environmental sustainability. These three goals constitute a ‘trilemma’, entailing complex interwoven links between public and private actors, governments and regulators, economic and social factors, national resources, environmental concerns, and individual behaviours.” (Wyman, 2016)

The World Energy Council definition of the energy “trilemma” confirms the need for energy policy to consider all stakeholders including consumers. Domestic use is only 26% of the total in Australia. Nevertheless, increased understanding of consumer roles can improve decisions about the electricity system, and the choices of all consumers effect commercial and industrial energy choices. Therefore, modelling the choices made by consumers we can also shine a light on the commercial and industrial electricity customers, and better manage the Energy Transition. Business and government could use this understanding to moderate grid infrastructure by coordinating grid management with consumers. The topics explored in this book: • Chapter 2: How Australia’s Energy Transition offers insights for other countries, • Chapter 3: Politics and the social dynamic of the Energy Transition, • Chapter 4: Consumer roles in the electricity transition, • Chapter 5: Technology and data to improve decision making, • Chapter 6: Systems engineering to better manage the Energy Transition.

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This book builds its arguments on evidence and suggests new policy for the Energy Transition. The evidence includes an econometric model of PV uptake in Australia and an interview process with leaders in the energy sector to explore policy options for the Energy Transition. 1.8.1

Summary

The electricity system faces increasing variability on the supply side and the demand side, with the resulting system being increasingly complex. The demand side variation is significantly affected by air-conditioning and domestic solar, and the supply side variation is significantly driven by the increasing role of large-scale renewables in the generation mix. Large-scale renewables are defined here as over 1GW generation capacity. Technical issues need to be addressed in the electricity system. These issues include control of voltage, frequency, asset utilisation, and system stability. Controls can include EV charging algorithms (Mareels et al., 2014), transformer design (Reiter, Ardani, Margolis, & Edge, 2015), demand response (Wijaya, Vasirani, & Aberer, 2014) and demand dispatch (Adika & Wang, 2014). Control of the grid is available through consumer roles but the solution though must work within a complex interplay between technical constraints (voltage, frequency, stability, and utilisation), financial relationships, political considerations, technical innovation, and the regulated structures of the Distribution Businesses. Chapter 2 explains why the Australia offers lessons for other countries.

References ACCC. (2018). Restoring electricity affordability and Australia’s competitive advantage (978 1 920702 34 2). Canberra: Australian Government. Adika, C. O., & Wang, L. (2014). Autonomous appliance scheduling for household energy management. IEEE Transactions on Smart Grid, 5(2), 673–682. AEMC. (2018, June 15). 2018 retail energy competition review, final report (RPR0007). Sydney: Australian Government. Retrieved from https://www. aemc.gov.au/sites/default/files/2018-06/Final%20Report.pdf. AEMO. (2017a). Black system South Australia 28 September 2016: Final report. http://www.aemo.com.au/-/media/Files/Electricity/NEM/Market_Not ices_and_Events/Power_System_Incident_Reports/2017/Integrated-FinalReport-SA-Black-System-28-September-2016.pdf.

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AEMO. (2017b). Incident report NSW 10 February 2017 . Retrieved from https://www.aemo.com.au/-/media/Files/Electricity/NEM/Market_Not ices_and_Events/Power_System_Incident_Reports/2017/Incident-reportNSW-10-February-2017.pdf. Alexander, D., Wyndham, J., James, G., & McIntosh, L. (2017). Networks renewed: Technical analysis. Retrieved from Sydney, Australia https://www. uts.edu.au/sites/default/files/NetworksRenewedTechnicalAnalysis.pdf. Anshelm, J., & Hultman, M. (2014). A green fatw¯a? Climate change as a threat to the masculinity of industrial modernity. NORMA: International Journal for Masculinity Studies, 9(2), 84–96. Bickerstaff, K., Walker, G., & Bulkeley, H. (2013). Energy justice in a changing climate: Social equity and low-carbon energy. London: Zed Books. Bodansky, D. (1993). The United Nations framework convention on climate change: A commentary. The Yale Journal of International Law, 18, 451. Denholm, P., O’Connell, M., Brinkman, G., & Jorgenson, J. (2015). Overgeneration from solar energy in California: A field guide to the duck chart. Golden, CO: National Renewable Energy Laboratory. Finkel, A., Moses, K., Munro, C., Effeney, T., & OKane, M. (2017). Independent review into the future security of the National Electricity Market: Blueprint for the future. Canberra: Australian Government. Retrieved from http://www. environment.gov.au/energy/publications/electricity-market-final-report. GoFlex. (2019). GoFlex community website. Retrieved from https://www.goflexcommunity.eu/. Hannam, C. L. P. (2018). ‘I haven’t left anger yet’: NEG architect slams policy ‘anarchy’, newspaper. The Age. Hashemi, S., & Østergaard, J. (2016). Methods and strategies for overvoltage prevention in low voltage distribution systems with PV. IET Renewable Power Generation, London (United Kingdom), 11(2), 205–214. Huntley, R. (2019). Listening to the nation. Quarterly Essay (73), 1. Infrastructure Australia. (2015). Australian infrastructure audit (ISBN 978-1925352-03-0). Commonwealth of Australia. Retrieved from https://infras tructureaustralia.gov.au/policy-publications/publications/Australian-Infrastru cture-Audit.aspx. Jenkins, K., McCauley, D., Heffron, R., Stephan, H., & Rehner, R. (2016). Energy justice: A conceptual review. Energy Research & Social Science, 11, 174–182. Lazar, J. (2016). Teaching the “duck” to fly (2nd ed.). Liu, J., Gao, H., Ma, Z., & Li, Y. (2015). Review and prospect of active distribution system planning. Journal of Modern Power Systems and Clean Energy, 3(4), 457. Mareels, I., de Hoog, J., Thomas, D., Brazil, M., Alpcan, T., Jayasuriya, D., … Kolluri, R. R. (2014). On making energy demand and network constraints

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compatible in the last mile of the power grid. Annual Reviews in Control, 38(2), 243–258. https://doi.org/10.1016/j.arcontrol.2014.09.007. Menck, P. J., Heitzig, J., Kurths, J., & Schellnhuber, H. J. (2014). How dead ends undermine power grid stability. Nature Communications, 5, 3969. Millken, R. (2018). Australia’s energy crisis. The Economist. Retrieved from http://www.theworldin.com/edition/2018/article/14426/australiasenergy-crisis. Mission Australia. (2017). National survey of young Australians. Sydney, Australia: Author. Retrieved from https://www.missionaustralia.com.au/ what-we-do/research-impact-policy-advocacy/youth-survey. Mohanan, V. A. V. (2019). On the impact of renewable sources on power systems. PhD, University of Melbourne. Moreland Energy. (2019). Solar for renters. Retrieved from https://www.mefl. com.au/news/solar-for-renters-2/. Parkinson, G. (2018). Cheap condensers to displace gas as renewable energy backup. Retrieved from https://reneweconomy.com.au/cheap-condensers-to-dis place-gas-as-renewable-energy-back-up-29544/. Reiter, E., Ardani, K., Margolis, R., & Edge, R. (2015). Industry perspectives on advanced inverters for US solar photovoltaic systems. Grid benefits, deployment challenges, and emerging solutions (NREL/TP-7A40-65063). Retrieved from Golden, CO https://www.nrel.gov/docs/fy15osti/65063.pdf. Reposit Power. (2018). VPP trial. Retrieved from https://repositpower.com/ news/canberra-virtual-power-plant-awarded-top-engineering-honours/. South Australian Government. (2020). South Australia’s electricity supply and market. Retrieved from https://www.sa.gov.au/topics/energy-and-env ironment/energy-supply/sas-electricity-supply-and-market. Stringer, N. (2018). Data driven exploration of voltage conditions in the low voltage network for sites with distributed solar PV . Paper presented at the Asia-Pacific Solar Research Conference. Tustin, J., Taylor, B., Lourey, M., & O’Mullane, L. (2012). Electricity bill benchmarks for residential customers. VCOSS. (2018). A fair energy market for people on low incomes submission to the interim response to the review of electricity and gas retail markets. Retrieved from Melbourne https://www.energy.vic.gov.au/__data/assets/ pdf_file/0012/123303/VCOSS-submission.pdf. von Hippel, E. (2005). Democratizing innovation: The evolving phenomenon of user innovation. Journal for Betriebswirtschaft, 55(1), 63–78. https://doi. org/10.1007/s11301-004-0002-8.

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Wijaya, T. K., Vasirani, M., & Aberer, K. (2014). When bias matters: An economic assessment of demand response baselines for residential customers. IEEE Transactions on Smart Grid, 5(4), 1755–1763. Wood, T., Blowers, D., Griffiths, K., & Weisbrot, E. (2018). Down to the wire: A sustainable electricity network for Australia. Wyman, O. (2016). World Energy Trilemma 2016. Retrieved from London https://www.worldenergy.org/assets/downloads/World-Energy-Tri lemma_full-report_2016_web.pdf.

CHAPTER 2

Why Focus on Australia?

Abstract The Australian case has international implications for energy policy and regulation. Australia has leading levels of PV, air-conditioning and a very dispersed grid. These lead to the Australian grid being difficult to manage. Australian energy system has a few other key distinctions, including being highly privatised, having followed the lead of the UK in the 1980s. The other key distinction is the federated system with state governments balancing their management of the distribution grid with the national government management of the rules. The transmission and energy flows are set by an independent body with both government and private ownership that controls the electricity market. This chapter explores how the Australian electricity system operates and aims to help other electricity system operator reflect on how the lessons from Australia might apply in their jurisdictions. Keywords Australian energy · Market dynamic · Network stability · Legislative delay

© The Author(s) 2020 G. Currie, Australia’s Energy Transition, https://doi.org/10.1007/978-981-15-6145-0_2

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2.1

Where Does Australia’s Electricity Come from?

Most electricity in Australia is sourced from coal. The generation sources are shown in Table 2.1. Coal has dominance in Australian generation at present. This is changing as more renewable energy is built. The historical reason was the abundant availability of thermal coal. This includes brown coal that was transported to the generator at a cost of $8/tonne at a time when exported black coal fetched $100/tonne at the port. Apart from the price advantage, the central coal plants were mostly built at the time of nation building by Australian Governments. Many have since been sold to private companies. The trend now is for increasing renewables in the generation mix in Australia. This is mostly wind and solar. For a decade from 2010, the predominant solar was on households, but large solar (rated over 1 Mw) is now common. A household solar system is typically between 1 and 5 Kw. This will generate about 1–5 Mwh per year. By comparison, a coal fire power station might be rated at 1 Gw and therefore generate a million times the rated power of a household PV-system and a thousand times the rated power of a large 1 MW solar system. Table 2.1 Capacity and output by fuel source (Commonwealth of Australia, 2017)

Fuel

Black coal (NSW, QLD and WA) Brown coal (Victoria) Gas (all states) Hydro (mostly NSW and Victoria) Wind (mostly SA and Victoria) Liquid Solar Battery Other

Capacity (percent of total generation)

Output (percent of total generation)

35.9

55.9

9.3

17.8

19.0 16.1

6.8 9.3

11.2

8.3

2.2 4.3 0.4 1.7

0.0 1.4 0.0 0.5

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There has been a little alignment of the energy policy in Australia with climate policy. The programs to incentivise renewables were separate to a focus on energy policy, and this has meant a conflict has emerged between the renewable generation and many of the stakeholders in the Australian electricity market.

2.2

Australian Electricity Market Structure

The Australian electricity market has a high level of private ownership and control. The Australian system is primarily privately owned and controlled. Policy interventions therefore must navigate this private sector. Privatisation started in Victoria 30 years ago and has continued apace in Australia. The Victorian and South Australian electricity systems are now fully private including generation, transmission, distribution and retail. NSW has privatised most of its distribution. Queensland has only privatised retail operations in their electricity system. Tasmania, Western Australia and Northern Territory have not started to privatise. And, finally, the Australian Capital Territory has partially privatised their distribution and retail and fully privatised their transmission (Infrastructure Australia, 2016). The regulation of the Australian electricity system is illustrated in Fig. 2.1. The COAG Energy Council is a body representing state and federal governments in Australia. The AEMC sets the energy rules, the AER enforces and AEMO abides by the rules as an operator. Also, the ACCC (Australian Consumer and Competition Commission) supervises energy law through the AER. Australian National Energy Law must be unanimously agreed by the states and then passed by the South Australian Parliament. The South Australian Parliament is used as a constitutional device to accommodate that energy constitutionally as state matter and not governed by the federal government. Unanimous agreement is slow and sometimes impossible. It takes about 18 months for a new law to be enacted. Australian electricity system regulators are increasingly recognising the change to the Energy Transition. The Demand Management Incentive Scheme (DMIS) (AER, 2017) was a major step in Australian Government policy and has been successfully taken up by a wide range of market participants. Another rule change being considered in Australia is the 5-minute rule change which will allow batteries to participate more profitably in

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Fig. 2.1 Structure of Australian electricity system regulation

the electricity market. This 5-minute rule means that the wholesale electricity price would be for 5 minutes and not 30 minutes. This is bid into the market, and the ability of wind, solar and storage owners to bid for 5 minutes is higher than their ability to bid for 30 minutes. The Energy Security Board led the fraught National Energy Guarantee (NEG) initiative. AEMO understands technical risks. AEMC oversaw the runaway asset expansion that may have wasted $A20B (Wood, Blowers, Griffiths, & Weisbrot, 2018), and the AEMC has been criticised as being overly conservative.

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By comparison, the Finkel Review was fast and effective because it was not beholden to the incumbent network and generator owners (Finkel, Moses, Munro, Effeney, & OKane, 2017). There are also excellent government financing bodies including the Clean Energy Finance Corporation (CEFC) and the Australian Renewable Energy Agency (ARENA). They are prime movers of the Energy Transition. This is because they provide important seed money as it is difficult for businesses to justify testing of energy innovations otherwise. 2.2.1

Electricity Distribution Businesses

Distribution Businesses are a key part of the market and work as regulated monopolies to serve electricity consumers for a regulated fee. They own the electricity meters at consumer sites, collect the data and send this to retail companies for customer billing. Distribution Businesses control consumer connections to the electricity system including connections of PV, electric vehicle connections and other changes to existing electricity connections. The Australian Government through the Australian Energy Regulator (AER) is constrained to ensure asset returns to Distribution Businesses. If a write-down as suggested by the Grattan Institute (Wood et al., 2018) was too unfavourable, the DBs can walk away from their contracts, so government is constrained. 2.2.2

Australian Household Consumers

Households in Australia have been installing air-conditioning at a fast rate. This has reflected the drop in the cost, but also the hot temperatures in most of Australia. Table 2.2 illustrates one example of rapid change in consumer electricity. The other household impact on the electricity system has been from PV installations. Australian feeders (distribution lines) sometimes have over 50% of connections with PV. PV is on 15% of all Australian homes (ABS, 2016). This leads to grid issues which include overvoltage, thermal overload, frequency instability and voltage instability. Rapid uptake of PV in Australia (heavy blue line in Fig. 2.2) has occurred at the same time as rapid reductions in PV prices (dotted orange line in Fig. 2.2). The PV uptake also correlated with the subsidy (green line shows carbon subsidy and one year of PV feed-in tariff).

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Table 2.2 Changes in South East Queensland consumers’ appliance penetration (ENERGEX) Appliance Air-conditioning Computer (%) Number of TV’s Dishwasher (%) Microwave ovens (%)

1999

2009

23% of homes 48 1 31 72

72% of homes (34% with more than one) 98 3 50 97

Fig. 2.2 Subsidy and PV system $A price per watt and PV installation total by year (APVI & ABS) Note The subsidy was calculated as the first year of feed-in tariff (FIT) income after the purchase of the PV system for a weighted average of the FIT subsidies, and the carbon subsidy is a payment for the reduction of carbon dioxide emissions. The FIT pays the owner per unit of PV electricity

During the years 2010–2012, the average subsidy exceeded the PV price (as shown by the orange circle in Fig. 2.2). The main spike in installations shown in the blue line occurred in 2011–2012. Therefore, consumers responded rationally to this subsidy signal of a “free” PV-system.

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Comparing PV Costs in Australia and the USA

In terms of comparing PV in Australia vs the USA, there are significantly higher labour costs in Australia, the PV subsidies in Australia are focussed on a capital subsidy and generation subsidy (nominally aligned with carbon dioxide reductions) encouraging private owners (many of them were consumers), but in the USA the subsidy is focussed on a 30% investment tax credit and accelerated depreciation (worth 10–14% of the capital) which encourages corporate ownership (Nelson & Winter, 1974). In 2016, half of the new solar in USA was in California, and 70% of new residential solar was owned by a third party, either leased to homeowners or sold under a power purchase contract (PPA). The California rate was $A0.13 per kWhr for half of the houses in California, and the other half of the houses were paid $A0.30–40 per kWhr. The PPAs were structured at a flat rate of 17c such that people use the 15c power from the grid and the rest is from their roof. 2.2.4

Electricity Connection Voltage Rules

The Australian grid delivers consumer electricity at mostly 230 V, 50 Hz as per the Australian Standards AS60038 (Standards Australia, 2012) and AS611000.3.100 (Standards Australia, 2011). These standards are statuary in only Victoria, Tasmania, South Australia, Northern Territory and Western Australia. Table 2.3 shows the consumer voltage rules in Australia. Table 2.2 shows the standard is 230–240 V and the tolerance is up to +14% above the target. AC electricity is mostly used in transmission and distribution. 2.2.5

Electricity Price

Australia has the 15th most expensive national average electricity price at around $US0.1 per kWhr. This is similar to the USA, France and the Netherlands (Dillinger, 2018). This price doubled between 2009 and 2012, which played a part in the loss of manufacturing businesses and global competitiveness. This increase in the electricity price could have been mitigated with better management of the Australian electricity system.

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Table 2.3 Consumer voltage rules in Australia Australian state

Rules documentation

Voltage rules

Western Australia

Western Australian Distribution Connections Manual 2015 (Manual) Electricity Distribution Code (“Essential Services Commission Act 2002,” 2002) Queensland Electricity Regulation (“Electricity Regulation 2016,” 2016) Electricity Distribution Code Version 9A, August 2018 (“Essential Services Commission Act 2001,” 2018) NSW Electricity Regulation and Customer Service Standards (New South Wales Legislation, 2001) Tasmanian Electricity Code 2015 (“Electricity Supply Industry Act,” 1995) Utilities (Electricity Service and Installation Rules Code) Determination 2013 (“Utilities Act 2000,” 2013)

240 V ± 6%

South Australia

Queensland

Victoria

New South Wales

Tasmania

Australian Capital Territory

2.3

230 V +10%, −6%

240 V ± 6% in 2020 moving to 230 V +6, − 2% 230 V +10%, −6%

230 V +10%, −2% mostly with some areas 230 V + 14%, −6% 230 V +10%, −6% 240 V ± 6%

What Lessons Are Available from Australia?

The Australian Energy Market Commission (AEMC) review (2012) was an important first step towards recognition of the Energy Transition in policy. It covered peak shifting, efficiency, consumers generating their own electricity and non-energy services such as ancillary services. Although they were looking to increase choice, their report was still a top-down view. This means that their paradigm was that consumers were recipients of energy services and not active participants in the electricity system. There was more understanding of consumer roles shown in a AEMO demand response trial seeking 200 MW of amelioration (AER, 2017; Dunstan, Alexander, Morris, Langham, & Jazbec, 2017) and the

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Finkel Review on energy policy (Finkel et al., 2017) which argues for integrating and rewarding consumers. The Australian Energy Market Operator (AEMO) also worked with the Energy Networks Australia or ENA (an industry body) to develop distribution system optimisation showing savings of $A415B (2017). Consumers can play a role through market innovators such as Reposit Power (2018), and these market forces are driving change in the Australian electricity system. 2.3.1

Alternate Generation Options

Wind farms in Australia generate over 40% of theoretical maximum output and the inland clear skies in the desert regions see solar delivering over 25% of theoretical maximum output. These rates of generation make these sources competitive. Also, Australian reserves include about 30% of the worlds’ thorium. Nuclear technology, although electorally unpopular, might offer an important low carbon option as well and in particular offer supply stability and inertia as we phase out carbon fuels (Buongiorno, Corradini, John, & Petti, 2018). Inertia is a measure of the spinning power that keeps the alternating current wave form at a constant frequency. There are many other options such as wave and geothermal being evaluated on large scale in Australia, so the transition to alternate generation is not constrained by renewable sources. The main issue is that coal is cheap and plentiful, and most of the capital and network connections are focussed on the coal generators. This will take time to unravel, and the closure of Hazelwood and Liddell coal generators in the past 2 years has started the wind-down of these ageing assets (Reneweconomy, 2017). Apart from the incumbency of coal, the main barrier to the Energy Transition is the change required in the transmission and distribution network outlined in Chapters 3–6 of this book. 2.3.2

Politics Are also Proving a Barrier to the Energy Transition in Australia

The past four prime ministers of Australia arguably lost their jobs due to different aspects of the Energy Transition: Rudd and Gillard due to difficulty implementing a carbon pricing scheme (CPRS), Abbott due to his reversing of the CPRS and Turnbull due to his attempt to push

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through the National Energy Guarantee (Crabb, 2019). These political travails have contrasted with the comfortable position of the states who have been able to align with voter attitudes supporting a reduction in carbon (Huntley, 2019). There have been many commitments on carbon reduction by the states, with little chance of success. 2.3.3

Summary of This Chapter

Australian energy policy is strongly appreciative of the Energy Transition (AEMO, 2015). Despite this, the Australian National Electricity Law must be passed through COAG and a complex regulatory structure, so changes to that Law tend to be slow. The Energy Transition is happening faster than changes to the National Energy Rules can be enacted. Fortunately, one rule change could allow the pricing of two-way flows of electricity and help the Energy Transition. It is Rule 6.1.4 of the National Energy Rules. Chapter 3 develops a model of the political and social dynamic of the Energy Transition.

References ABS. (2016). 2900.0—Census of population and housing. Retrieved from http:// www.abs.gov.au. AEMC. (2012). Power of choice review-giving consumers options in the way they use electricity: Final report. Sydney: Commonwealth Government. Retrieved from https://www.aemc.gov.au/sites/default/files/content/2b566f4a-3c274b9d-9ddb-1652a691d469/Final-report.pdf. AEMO. (2015). Emerging technologies information paper, national electricity forecasting report. http://www.aemo.com.au/-/media/Files/PDF/EmergingTechnologies-Information-Paper.pdf only published to AEMO website. AER. (2017). Demand management incentive scheme. Australian Energy Regulator. Retrieved from https://www.aer.gov.au/networks-pipelines/guidelinesschemes-models-reviews/demand-management-incentive-scheme-and-innova tion-allowance-mechanism/final-decision. Buongiorno, J., Corradini, M., John, P., & Petti, D. (2018). The future of nuclear energy in a carbon-constrained world. Massachusetts Institute of Technology, 26. Commonwealth of Australia. (2017). Australian energy update (ISSN 22038337). Retrieved from Canberra https://www.energy.gov.au/sites/default/ files/energy-update-report-2017.pdf.

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Crabb, A. (2019). The day that plunged Australia’s climate policy into 10 years of inertia. Retrieved from https://www.abc.net.au/news/2019-11-24/10years-of-climate-change-inertiaand-the-role-of-andrew-robb/11726072. Dillinger, J. (2018). Cost of electricity by country. Retrieved from https://www. worldatlas.com/articles/electricity-rates-around-the-world.html. Dunstan, C., Alexander, D., Morris, T., Langham, E., & Jazbec, M. (2017). Demand Management Incentives Review: Creating a level playing field for network DM in the National Electricity Market. Electricity Regulation 2016. (2016). Electricity Supply Industry Act, Office of the Tasmanian Economic Regulator (1995). Essential Services Commission Act 2002, EDC/12.1, Essential Services Commission of South Australia (2002). Essential Services Commission Act 2001, Version 9A, Essential Services Victoria, No. 62 of 2001 Stat. (2018 Amended 9 May 2013). Finkel, A., Moses, K., Munro, C., Effeney, T., & OKane, M. (2017). Independent review into the future security of the national electricity market: Blueprint for the future. Canberra: Australian Government. Retrieved from http://www. environment.gov.au/energy/publications/electricity-market-final-report. Huntley, R. (2019). Listening to the nation. Quarterly Essay (73), 1. Infrastructure Australia. (2016). Australian infrastructure audit (ISBN: 978-1925352-07-8). Commonwealth of Australia. Retrieved from https://infras tructureaustralia.gov.au/policy-publications/publications/files/Australian_I nfrastructure_Plan.pdf. Nelson, R. R., & Winter, S. G. (1974). Neoclassical vs. evolutionary theories of economic growth: Critique and prospectus. The Economic Journal, 84(336), 886–905. New South Wales Legislation. (2001). Electricity supply (general) regulation 2001. Reneweconomy. (2017). AGL bought Liddell for nothing, but what will it cost Turnbull. Retrieved from https://reneweconomy.com.au/agl-bought-liddellfor-nothing-what-will-it-cost-turnbull-14579/. Reposit Power. (2018). VPP trial. Retrieved from https://repositpower.com/ news/canberra-virtual-power-plant-awarded-top-engineering-honours/. Standards Australia. (2011). Limits–Steady state voltage limits in public electricity systems. ANSI Standard AS, 61000(3), 100. Standards Australia. (2012). Standard voltages. Sydney: Standards Australia. Utilities Act 2000. (2013). Wood, T., Blowers, D., Griffiths, K., & Weisbrot, E. (2018). Down to the wire: A sustainable electricity network for Australia.

CHAPTER 3

Political-Social Dynamic of the Energy Transition

Abstract Jan Steen’s painting in Fig. 3.1 illustrates the challenge to find coherent learning in complexity. The complexity of the electricity transition may mean failure. The rapid increase in electricity prices in Australia has taken Australia from a low cost of electricity, to amongst the highest in the world. The consequences for the economy from high electricity prices were ignored as some parts of the Australian Government enjoyed the dividends from overbuilding of assets (Wood et al. in Down to the wire: A sustainable electricity network for Australia, 2018). This chapter presents policy options drawn from interviews of energy experts. These policies are critically important to moderating electricity prices. Keywords European energy · Knowledge networks · Political-social dynamics · Transition drivers

The interview questions are: 1. Distribution companies’ role in facilitating the Energy Transition. 2. Change the rules for distribution companies to create incentive for innovation and competition. 3. Smart inverters to improve the grid? 4. PV export limits to improve the grid. 5. Autonomous load control by consumer inverters? © The Author(s) 2020 G. Currie, Australia’s Energy Transition, https://doi.org/10.1007/978-981-15-6145-0_3

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6. Remote control of consumer inverters, in critical events? 7. Demand response to reduce peak demand. 8. Network price reform to improve the grid. 9. Motivating consumers to install energy efficient devices. 10. Building code reform to reduce peak energy demand? 11. Gaining social licence for this change? 12. Will governments succeed in this Energy Transition? 13. Electric vehicle charging rules. 14. Home battery storage rules. 15. Who are the prime movers in this future system? 16. Key information networks in this future system? 17. Setting the rules for this future system, social rules and legal rules? 18. What is #1 risk in this Energy Transition? 19. What is #2 risk in this Energy Transition? 20. What relative roles (1–7) should the following have in energy policy? Diversity, Technology, Consumers, Social Equity, Economy, Environmental and Political (Fig. 3.1).

Fig. 3.1 A school class with a sleeping schoolmaster, oil on panel painting by Jan Steen, 1672

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Table 3.1 shows the means and standard deviations for answers to questions 1–14. There were no numerical answers for questions 15–20. The answers for years in the future show a higher standard deviation, partly due to the scale being 1–10 compared to 1–7 for the other questions. Other survey information is in Appendix A in the backmatter. In Australia, I interviewed 5 academic, 16 government and 20 business people. This included 5 people with ministerial responsibility, and generally people were at a CEO role or equivalent. In Europe, there were 12 interviews with academics, 6 with government people, and 12 with businesspeople. As with the Australian interviews, the roles and experience of people was high, including one ex Minister for Energy in the UK. These interviews were analysed using the method shown in Appendix B in the backmatter. The evidence from these interview results is that the Energy Transition will occur sooner in Australia, but that the prioritisation and sequencing is similar between Australia and Europe. The interview answers were grouped, and a linking of the key ideas shown in Fig. 3.2. The word chart in Fig. 3.2 shows that the interviews uncovered a focus on people, homes, humans, government, economics, policy and physics. These groupings were used to help interpret the million words captured in the interviews. The remainder of this chapter explores the political-social dynamic of the Energy Transition.

3.1 The Political-Social Dynamic of the Energy Transition The political-social dynamic of the Energy Transition is explored in terms of the roles of the electricity Distribution Businesses (DB’s), policy for innovation, the role of energy efficiency, social licence, government roles, prime movers, social and legislative institutions, knowledge management networks, risks, and who needs a voice. There is clear criticism of Australian energy policy, confidence in European energy policy and a desire to market innovation.

7

6

5

4

3

2

1

Distribution Businesses’ role in facilitating this Energy Transition? Change the rules for Distribution Businesses to create incentive for innovation and competition Smart inverters to minimise harm to the grid? PV export limits to minimise harm to the grid? Autonomous load control by consumer inverters? Remote control of consumer inverters, in critical events? Demand response to reduce peak demand? 5.8/1.6

5.0/1.7

4.8/1.9

4.1/1.8

5.0/2.0

5.4/1.6

5.9/1.0

5.7/1.4

5.4/1.7

5.5/1.7

5.7/1.4

5.8/1.5

4.9/1.5

4.6/1.7

2.7/1.8

3.4/2.5

2.8/2.3

2.0/1.9

2.9/2.4

5.5/2.9

4.7/3.0

5.9/1.6

5.0/1.9

4.8/1.7

3.9/1.9

4.9/1.4

6.0/1.3

5.9/1.5

Importance 1–7

Year 1–10

Importance 1–7

Likelihood 1–7

Europe (30 surveys)

Australia (41 surveys)

Quantitative survey questions mean/standard deviation (Question 1–14)

Mean/Standard deviation

Table 3.1

5.3/1.4

4.8/1.6

5.0/1.7

4.7/1.6

5.3/1.3

4.5/1.3

4.9/1.3

Likelihood 1–7

4.6/2.5

6.6/3.2

6.2/2.4

5.1/3.0

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Year 1–10

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12

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Network price reform to minimise harm to grid? Motivating consumers to install energy efficient devices Building code reform to reduce peak energy demand? Gaining social licence for this change? Will governments succeed in this Energy Transition? Electric vehicle charging rules Home battery rules

Mean/Standard deviation

4.8/1.9

4.5/1.9

5.7/1.6

5.6/1.8

6.2/1.2

5.6/1.6

5.7/1.6

6.0/1.7

6.1/1.5

4.1/1.8

5.3/1.4

4.2/1.7

5.4/1.2

4.6/1.6

3.1/2.1

4.8/3.2

6.3/2.9

3.0/2.3

4.9/3.4

2.9/2.3

4.6/2.7

5.0/1.9

5.7/1.3

6.4/1.2

5.6/1.2

6.0/1.5

6.1/1.0

5.9/1.4

Importance 1–7

Year 1–10

Importance 1–7

Likelihood 1–7

Europe (30 surveys)

Australia (41 surveys)

5.1/1.5

5.6/1.0

4.6/1.4

4.4/1.4

4.6/1.6

5.1/1.6

4.1/1.4

Likelihood 1–7

5.6/2.6

5.7/2.8

7.5/3.3

5.8/2.9

6.6/2.9

5.2/5.6

6.1/2.3

Year 1–10

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Fig. 3.2 Analysis of the interviews showing the links between the categories

A surprising result was that the different audiences see the risks and constraints so differently. For example, the policy planners see the distribution companies as being unwilling to change, but the distribution companies feel this should be driven by policy. Priorities for a voice in energy policy shown in Fig. 3.3.

Fig. 3.3 Priorities in energy policy (n = 46)

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Figure 3.3 shows the top ranked issues are the consumer, the economic aspects, social equity, and environment. It is notable that diversity and technology were low as they have high profiles in the media. 3.1.1

Agreement Between Australia and Europe

There is a surprising alignment between Australia and Europe on both the importance and the likelihood of success of the Energy Transition. The most important measures include the engagement of Distribution Businesses in this change and cost reflective pricing. Pricing incentives are needed to direct consumer behaviour, but we should avoid radical tariff reform. Some policies should be easy. PV export limits, high-technology inverters, and storage. However, it will be harder to achieve Distribution Businesses adapting to the Energy Transition. And it is very unlikely that we will achieve cost reflective pricing. There is caution about the active role of Distribution Businesses. We will need storage rules by the year 2022 and cost reflective pricing three years later. PV export limits and high technology inverters are available for immediate rollout. 3.1.2

Agreement Between Academics, Business and Government

The alignment between the views of academic, business and government employees is surprising. The evidence for the transition being 1–3 years earlier in Australia is illustrated in Fig. 3.4. Figure 3.4 shows the results for the question about gaining social licence for this change. The blue boxes show the spread of the answers for Australia (AU) and Europe (EU) for the importance of social licence. The orange and pink boxes show the rating for likelihood of success of social licence (note that this is rated lower in Europe than Australia). Then the green boxes are the years in the future, until the social licence is expected to be in place, and the Australian mean (where the X is shown in the box) is 3 years. The European mean is 6 years. This result suggests Australia is 3 years ahead in the transition is surprising as the European policy process is ahead of Australia (especially in the UK, Netherlands and Germany). In general, EU Energy policy

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Fig. 3.4 Illustrative plot to illustrate Australia may be leading the transition

is focussed on building consumer roles in the electricity system (Siddi, 2017). The main argument for the Australian transition being ahead lies in the high concentration of PV in Australia, the highly dispersed grid, and the current problems with stability. The high concentration of PV, the dispersed grid, and the current stability problems all provide a burning platform to encourage a faster transition. The public and media discourse in Australia has been high. Government control is not effective, and the establishment of incentives is an important key. The free market is seen as leading the transition in Australia, but policy is seen as leading in Europe. This conclusion aligns with the EU perspective being highly government centric, whereas the market is central in the Australian system.

3.2

Will Distribution Businesses Lead the Energy Transition?

Policy options for the transition to consumer roles in the electricity system will involve Distribution Businesses as they control the key constrained assets. The four perspectives below explore the role in the transition of Distribution Businesses. These cover innovation, ROA, asset write-downs and threats to their core business. Changing the rules to encourage innovation and competition will involve many business stakeholders. Business stakeholders include consumers who are businesses, consumers who are individuals, governments, electricity retailers, electricity Distribution

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Businesses, metering companies, transmission suppliers, the vendors of equipment to consumers and network operators. Technology could integrate individual energy users with their grid, with two-way communications to the market operator. This could enable distributed energy and could encourage innovation in automation of thermostats and home management system. The three perspectives below explore the role in the transition of innovation and competition. They include economics, management of overvoltage, and rule changes that are needed. 3.2.1

Innovation in the Distribution Businesses

Distribution Businesses are starting to innovate. For example, Powercor puts consumers off-grid in some cases to manage fire risk. But on the other hand, Distribution Businesses are regulated monopolies and are highly controlled and rules block them from investing on the consumer side of the meter. These are known as ring-fencing rules and are to control monopolistic behaviour by network owners. Distribution Business innovation may increase but other parties will need to fight for price reduction as Distribution Businesses may not deliver price reductions. The Distribution Businesses’ regulation must change to send a price signal that increases asset utilisation as current regulatory settings for the “asset utilisation measure” are problematic, as it is a measure of the flow in one direction, while the electricity system will need flows in two directions. In 2017, there was an AEMC ruling that Distribution Businesses must disclose their overloaded transformers. For example, this is shown in the 5-year plan for Ausgrid (2018). Half the Australian retail bill is distribution and transmission, so there is ample room for innovation in this sector to deliver lower prices. Part of this is could be through higher utilisation of their assets. The Australian Competition and Consumer Commission (the peak government body for competition regulation) is trying to incentivise Distribution Businesses to become more consumer centric (ACCC, 2018). Many of their 56 recommendations effect Distribution Businesses and they claim a pathway to a 20–25% retail electricity price reduction. Innovation will be difficult as Distribution Businesses are highly regulated businesses. The lack of innovation is worsened if governments are owners as they have been gaining rich dividends from the status quo

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(Wood, Carter, & Harrison, 2014). Anecdotally, the private networks in Victoria and South Australia are more innovative than other states where governments continue to own networks. Europe does not see the same distribution constraints from PV and air-conditioning faced in Australia, but their grid operators are starting to innovate (Schubert, Pollak, & Kreutler, 2016). In the UK, the Ofgem regulation known as RIIO (Ofgem, 2010) has incentivised UK Distribution Businesses to move to the Energy Transition. RIIO is example of outcome focussed regulation for Distribution Businesses. By comparison, the Australian regulatory regime is not outcome focussed; it is input focussed and rewards Distribution Businesses for assets. Distribution Businesses have been enjoying a return on assets (ROA) of up to 11%. Their returns are in the form of a guaranteed return on assets which is a common way to manage a regulated monopoly. A return of 11% is high for infrastructure returns and this is now under threat as governments push for lower returns to the Distribution Businesses due to soaring electricity prices. 3.2.2

Distribution Businesses Face Asset Write-Downs

Write-downs of the asset value of Distribution Businesses are a threat due to overbuilding of network assets. Wood et al. (2018) argue for a writedown of $A20B on network assets to reduce electricity costs. The way this would reduce costs, is that the owners of the networks would not be then entitled to regulated returns on that $20B of assets. The returns for distribution grid owners are eroding as governments seek better pricing for citizens. The desire of governments to see prices drop creates a burning platform for distribution grid owners to look for new sources of income. Stakeholders do not want to strand grid assets, and this creates an incentive for the system to move to dynamic grid operation. Dynamic grid operation means that the electricity system would not just be one way on a fixed price per unit of electricity. The electricity would flow both ways and would change in price for various times and places. To do this, one possible change is peer-to-peer electricity trading (Gassmann, Frankenberger, & Csik, 2013).

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Threats to Core Business of Distribution Businesses

Grid defection was considered a threat around 2010, but at the time of this survey the threat had receded. Threats include increasing instability from EV uptake, the deterioration of DB return on assets, the rise of new businesses acting as intermediaries with consumers (known as aggregators), the consumer drive for energy independence and pressures from government. Another threat is that Distribution Businesses operate in a highly regulated space, so their response to consumer roles in the electricity system will be very conservative. The conservatism of the Distribution Businesses is partly due to the high reliability hurdles for Distribution Businesses which means they receive zero payment if service levels are missed. Regulations currently mean that Distribution Businesses cannot charge for electricity exports from customers (as stated in Rule 6.1.4 of the National Energy Rules). The National Energy Law sits above the National Energy Rules and requires a political process for change. All changes to National Energy Law must first be agreed by all Australian states, and then be passed by the South Australian Parliament. This structure to sign off new rules is because under the Australian constitution, electricity is a state issue. Any shift of the role of Distribution Businesses should consider the risk of deregulating a regulated monopoly and it should be noted that there is some argument that the electricity market may benefit from maintaining a monopoly regulated distribution market. 3.2.4

Economics of PV Development

Economic benefits of distributed solar (Bushnell, 2015) include: spatial diversification; closer to load, so less line loss; delayed distribution upgrade costs; reduced transmission costs; free real estate on rooftops; resilience of power supply; efficiency pressure on regulated utility. Costs of solar (Bushnell, 2015) include: higher cost of low-scale deployments; non-optimised deployment; not controllable/curtailable; reverse flow on distribution system; net metering is used mostly for solar. Bushnell is correct in theory, but solar overvoltage on distribution lines is causing a significant economic cost. Solar overvoltage occurs in parts of the electricity network on sunny days. This could be improved by incentivising more storage. Pricing for

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PV and storage is now low enough to encourage more sales. Aggregators may also emerge to work with consumers on solving overvoltage problems, but the short-term solution is to use PV export limits.

3.3

Drivers of the Energy Transition?

There are a number of entities driving a transition. The most important driver is known as a prime mover. Hughes introduced the concept of prime movers, or drivers of change in his book “The evolution of large technological systems ” (Hughes, 1987) which explores the early development of the electricity system. The prime movers will be the parties that create incentives for consumer action and may be the consumer, their agents, or the vendors of products to consumers. Possible prime movers of the Energy Transition in Australia are consumers, utilities including Distribution Businesses, government, community energy, vendors of PV and batteries and the lobbyists such as the CEC and the Energy Networks Association (ENA). The consumer is an important prime mover and to a great extent is leading the change. 3.3.1

The Consumer as Prime Mover

Consumers take energy-actions, drive the transition through their product purchases and purchases from energy retailers and innovators such as Reposit Power. Consumers may be willing to work with business to manage the transition, and the dynamic will be both top down and bottom up. Corporates, retail vendors and people concerned about climate change and air quality will all have a voice. The consumer role as a prime mover is driven by consumer desire for control, cost savings and reliability. Consumers also have power as they are also voters. 3.3.2

Utilities Including Distribution Businesses as Prime Movers

Some Distribution Businesses are prime movers in this transition. For example, the AusNet Yackandandah project (Ausgrid, 2017), the AusNet pilot at Mooroolbark (Ausnet, 2018), and the United Energy VPP trial in the Mornington Peninsula (GreenSync, 2016). These projects confer roles as prime mover to these Distribution Businesses even though they are government-funded projects.

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3.3.3

45

Government as Prime Mover

Australian Government regulatory and energy operating entities are prime movers including the Energy Security Board, AEMO, the AEMC and the AER. There has been a lack of clear policy outcomes and political leadership is needed to bring the networks and others along. There is a role for government to set the parameters for business to solve the problem. Government will need to fund as has been seen in January 2020 (Visentin, 2020). But the government focus should be climate/security/cost and leave the business decisions to business to avoid problems like the pink batts debacle (Lewis, 2010). In Europe, the governmental prime movers are Distribution Businesses and national entities such as National Grid and the UK Office of Gas and Electric Markets (Ofgem). 3.3.4

Aggregators and New Business Entrants as Prime Movers

New businesses have a role in challenging the incumbents (Reposit Power, 2018). The innovators are people who think outside the current structures and includes universities, the Distribution Businesses, the retailers other emerging businesses. One type of new business is aggregators, who service groups of customers without making a broad offer to all customers (in this way they aggregate). The cost of PV is low and EV and batteries are becoming increasingly viable. Increasing sales of solar, EV and batteries will increase the role for vendors who may become prime movers. This may be countered by the problem that suppliers face complexity in dealing with state-based legislation. The state governments are also prime movers with the control of air-conditioners in South Australia and the smart meter program in Victoria. 3.3.5

Community Groups as Prime Movers

Communities are important prime movers. Forms of these communities include, local councils, leaders in communities, and Australian not for profits such as the Zen group at Noosa, Sandford Community, C4CE and CVGA.

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The community groups are innovators, people who want to see a change quickly, willing to try new models and are identifying and lobbying for needed rule changes.

3.4

Social and Legislative Institutions

Institutions are defined as both social and legislative (Jacobsson & Johnson, 2000). The government, utilities, and NGO’s influencing views on green purchasing are the key institutions. These institutions are the source of soft influence such as social rules and hard influence such as laws and regulations. The broad community sets social rules. They set the rules for the relationships between consumers, businesses, politicians and electorate. The perspectives below explore the role in the transition of social and legislative institutions. 3.4.1

Government and Industry Influence on Social Rule Setting

Some social rules will be set by governments through policy and propaganda. For example, in Victoria there is a percentage-based concession for concession electricity customers and in South Australia it is a fixed dollar amount. Both are social rules set by the social expectations of social equity and fairness. Social rules include privacy, and information protection (security) which are both covered in legislation but are primarily social rules. The operating businesses have a strong voice in what rules are set, so a lot of the regulations are fed up to the government. Distributors set some rules, and the big renewables operators are setting some of the rules. The peak lobbyists for the energy business in Australia are the ENA, EUAA and AIG. It is deeply technical reform process, so the community needs specialist lobby groups like the Grattan Institute, and Energy Networks Australia (and Energy Networks in the UK) to interpret the needs of their constituents. The AEMC needs more power. AEMO, Energy Security Board (ESB) and AER are performing well. The other national rule setter is Standards Australia with AS4777 which regulates all inverters for grid connected solar, and other important electricity system standards. Australia needs a new institution to lead the policy change to optimise distribution assets. Their argument is that AEMO and the AER are not

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well structured to do this as they have focussed previously on running the National Electricity Market which is at the transmission grid level. Australia would need a new exchange manager to manage settlements for distribution services. Australia regulatory rule setting is moving too slowly for the rate of changes in the electricity system. It is too early to set rules as the political will does not exist. It is more about conversation and finding and rewarding best practice for the next couple of years. In any case, specialists will be needed to design the Energy Transition. 3.4.2

Not for Profit Advisors, Lobbyists, Academics, and Community

Specialists will include energy associations such as ENA and CSIRO. The transition cannot be defined just in terms of environment, cost and equity but must include technical aspects. Many technical solutions are not being heard and the role of the technologist in the Energy Transition is not clear. Past Energy Transitions show the importance of considering the consumer and that the planning should allow technology and the social setting to coevolve (Grübler, Naki´cenovi´c, & Victor, 1999). Energy policy with social, consumer and non-government perspectives is complex. There is an urgent need for technical input into energy policy to best integrate the complexity. There have been efforts to set social rules by local governments and a couple of state governments, but the bulk of the change effort currently rests on the shoulders of individuals. Therefore, the social and legislative institutions all derive their voice from consumers. 3.4.3

Consumer Voices Are Being Heard in Legislative Rule Setting

Voters are demanding the attention of their elected members on the price of energy and to respond to climate change. The result is a strong political view on pricing issues and support for renewable energy. Neither of these can deliver lower prices unless they are aligned with the operation of the electricity system. The peak body in Australia for consumers is the Electricity Consumers Australia. Other credible consumer advocates include the Consumer Action Law Centre and the Alterative Technology Association (ATA). Community energy groups also have a consumer voice in the legislative rule setting. For example, the Central Victorian Greenhouse Alliance

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(CVGA) is working with private developers on sector specific forums run by the Victorian Government. National Consumer Roundtable on Energy, Vincent de Paul and, Brotherhood of St Lawrence all offers an energy social equity voice. 3.4.4

Legislative Rules

The AEMC wrote an excellent report on Power of Choice, battery policy, disadvantaged communities and changes to regulation for consumers (AEMC, 2017). The government report “Restoring electricity affordability and Australia’s competitive advantage” shows an excellent framework (ACCC, 2018) and builds on the reform foundations put forward in the 2017 Finkel Review (Finkel, Moses, Munro, Effeney, & OKane, 2017). Australian Government energy regulators and the Distribution Businesses are comfortable with the current governance system; however, a simplification of the governance in Australia may be possible. In long term, with higher penetration of renewable technologies with zero marginal cost, electricity system managers may need to look at a new market mechanism. New legislative rules need to be built on the existing rules for consumers, competition, privacy, and the National Energy Rules. Governments can set up a framework for social rules for the Energy Transition and industry can fill the gaps. For example, an energy industry working group in 1990 worked from a government framework to produce a short-term electricity future trading market. The government maintained a veto, to ensure the companies did not make the rules too complex. The local rules such as PV feed-in tariffs are important drivers, so need to be considered in the setting of national rules. The Australian Energy Regulator (AER) is the lead developer of legislative rules for the grid and must work with historical policy frameworks, the huge sunk costs and the ideological debate about climate and the best sources of energy. The market itself is sending confusing signals to the AER with the rapid uptake of solar, and air-conditioning.

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Knowledge Management Networks?

The Energy Transition will require the development of information and knowledge networks. Government cannot run these knowledge networks alone and they need to include a broad range of stakeholders (Jacobsson & Bergek, 2004). This is discussed in this section. 3.5.1

Defining Knowledge Management Networks

There needs to be some centre of knowledge developed to manage this transition. There is a support for the role of Alan Finkel who is seen as independent and not driven by politics or business interest. Some lobbyists such as ENA could have a say and we need a voice for independent competent people, from places like CSIRO and universities. The knowledge networks are any information network that allows consumers to take part effectively and cheaply, and that helps them. There is also a role for peer-to-peer information and business can also provide services, such as the market place developed by GreenSync Pty Ltd known as “dex” (GreenSync, 2019). 3.5.2

A Safe Voice that Monitors and Guides the Quality of the Knowledge

Definitions of the honest broker are widely divergent. Government has a role. Local councils know how to get greater economic developments in local areas, but the leadership needs to be from State and Australian Government, who are getting better at it. ARENA has a balanced voice on renewable energy. Frydenberg, Kerry Schott, Claire Savage (Deputy Chair of Energy Security Board) and the COAG Energy council are safe voices. COAG is seen as a poor decisionmaking body but there is an effective informal stakeholder meeting before the COAG Energy meeting. Also, a new government body was suggested to offer prompt information. Community-based initiatives have a role. ENOVA Energy in Byron Bay has been set up to keep money in that community. Not for profits are honest brokers and include the Grattan Institute and the Clean Energy Council (CEC). Other honest brokers include: Reneweconomy, Embark, universities, Moreland Energy Foundation; and the Alternate Technology Association.

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Business voices can be honest brokers in the context of conferences (Solar Council and All Energy), private conversations between businesses, and industry organisations such as CEC and Energy Networks Australia and their conferences. In Europe, there is a wide sharing of best practises via the National Regulatory Networks (https://eeueuropa.eu/national-regulatory-author ities-energy-europe/) and there are formal connections between innovative companies working in several Member States. The European Commission process brings scientists together with policymakers and regulators. 3.5.3

Data Has a Role in the Knowledge Management System

A knowledge management system does not need to manage data, but data should be generated to enable good decisions. Smart meter data is key, as well as visibility of network constraints and flexibility resource registers. Distribution network data is important. At present, businesses control a lot of user data and the Energy Transition needs transparency of user data and the regulators need to ensure businesses use this for pricing. For example, data by each transformer, by housing classification and on local congestion. Retail pricing (offers to customers) is an important form of data. The retail pricing for electricity in Australia is not transparent, and there is no government monitoring of prices. Businesses that try to monitor and guide customers to cheaper electricity are facing opacity as to what the current electricity pricing tables are for each electricity retailer. The creation of a new independent data management organisation was suggested. The suggestion is that the new independent organisation takes the consumer information, aggregates it, and overcomes the privacy issues. Data would sit in government and distribution business computers, and this would be governed by privacy and ownership rules. There is a proposed system to serve data up to consumers in the State of Victoria. Society is moving into a data-driven economy. Consumers should know how their data is being used and that privacy is adhered to and need to have access to their data.

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Data Has a Role in Building a Market System

Data needs to be used to guide regulatory control and operation of the system. Australia needs a data system to connect both system control and grid planning. At present, AEMO is blind to what happens behind the meter. Data and electricity will be intertwined. Information, totally democratised, will need trusted sites, and government management of data safety. Examples include the AEMO-Energy Live website (2019), the Victorian Price Comparison website Energy Compare (2019) and the Victorian Energy Data Hub (2018). IT companies will be important players because they excel in managing large databases and innovating in the analysis of the data. Companies actively pursuing this data include Microsoft, IBM, and Google. To enable distribution optimisation, it is important that data is freely available, transparent, disaggregated and consumers can access their own data. Consumers accessing their own data will enable innovation. 3.5.5

Information Dissemination

The role of social media is strong in the transition of the Australian electricity system and information dissemination also occurs through: media; community energy groups; local community groups; local councils; Distribution Businesses; local government; state government; Australian Government; not for profits (such as ATA); lobbyists (such as ENA); and change agent organisations. State governments have a role in sharing energy data. For example, government tariff comparison websites (Victorian Government, 2019). The Australian Government also has a role such as the office of the Australian Chief Scientist Alan Finkel, and in Europe Ofgem would suit information dissemination as well as universities as the only non-biased parties. There could be vital information shared through the industry associations such as the industry association, Energy Networks Australia (ENA) who are widely perceived as honest brokers of information. The government also has a key role.

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3.6

What Did We Learn in This Chapter?

The transition is happening in both Australia and Europe and the priorities are similar, but the Australian view is that the change will occur 2–3 years sooner in Australia. In terms of who should have a voice in setting energy policy, the top ranked are the consumer, the economic aspects, social equity, and environment. Diversity and technology were low despite the media focus on these two areas. And finally, there is a transition process, which calls out for government leadership, but independent information sources. The next chapter shows how statistics can give valuable insights into the Energy Transition.

References ACCC. (2018). Restoring electricity affordability and Australia’s competitive advantage (978 1 920702 34 2). Canberra: Australian Government. AEMC. (2017). Power of choice review. Canberra: Australian Government. Retrieved from http://www.aemc.gov.au/getattachment/2b566f4a3c27-4b9d-9ddb-1652a691d469/Final-report.aspx. AEMO. (2019). Energy live. Retrieved from http://energylive.aemo.com.au/. Ausgrid. (2017). Australia’s first community mini grid launched in Yackandandah. Retrieved from https://www.ausnetservices.com.au/Misc-Pages/ Links/About-Us/News-Room/News-Room-2017/Australias-first-commun ity-mini-grid-launched-in-Yackandandah. Ausgrid. (2018). Ausgrid regulatory response for 2019–24. Retrieved from http://energyconsumersaustralia.com.au/wp-content/uploads/Ausgrid-Reg ulatory-proposal-2019-24-Submission-to-the-AER-Issues-Paper.pdf. Ausnet. (2018). Mooroolbark mini grid project. Retrieved from https://www.aus netservices.com.au/Community/Mooroolbark-Mini-Grid-Project. Bushnell, S. B. J. (2015). The US electricity industry after 20 years of restructuring. Annual Review of Economics, 7 (1), 437–463,408. Finkel, A., Moses, K., Munro, C., Effeney, T., & OKane, M. (2017). Independent review into the future security of the National Electricity Market: Blueprint for the future. Canberra: Australian Government. Retrieved from http://www. environment.gov.au/energy/publications/electricity-market-final-report. Gassmann, O., Frankenberger, K., & Csik, M. (2013). The St. Gallen business model navigator.

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GreenSync. (2016). GreenSync partners with United Energy for landmark asset deferral project. Retrieved from https://greensync.com/news/greensync-par tners-united-energy-landmark-asset-deferral-project/. GreenSync. (2019). “Dex” website link on GreenSync corporate website. Retrieved from https://dex.energy. Grübler, A., Naki´cenovi´c, N., & Victor, D. G. (1999). Dynamics of energy technologies and global change. Energy Policy, 27 (5), 247–280. Hughes, T. P. (1987). The evolution of large technological systems. In The social construction of technological systems: New directions in the sociology and history of technology, 51–82. Jacobsson, S., & Bergek, A. (2004). Transforming the energy sector: The evolution of technological systems in renewable energy technology. Industrial and Corporate Change, 13(5), 815–849. https://doi.org/10.1093/icc/dth032. Jacobsson, S., & Johnson, A. (2000). The diffusion of renewable energy technology: An analytical framework and key issues for research. Energy Policy, 28(9), 625–640. Lewis, C. (2010). The home insulation program policy debacle: Haste makes waste. Public Policy, 5(2), 83. Ofgem, R. (2010). A new way to regulate energy networks. Final Decision, Office of Gas and Electricity Markets, London, 25. Reposit Power. (2018). VPP trial. Retrieved from https://repositpower.com/ news/canberra-virtual-power-plant-awarded-top-engineering-honours/. Siddi, M. (2017). Energy policy of the European Union (Vol. 55). New York: Blackwell. Schubert, S. R., Pollak, J., & Kreutler, M. (2016). Energy policy of the European Union. London: Palgrave Macmillan. Victorian Government. (2018). Energy data hub, executive summary. Retrieved from https://www.energy.vic.gov.au/__data/assets/pdf_file/0021/121827/ L1_executive-summary_3.pdf. Victorian Government. (2019). Energy compare. Retrieved from https://www. energy.gov.au/victorian-energy-compare. Visentin, L. (2020). Massive green deal. Retrieved from https://www.theage. com.au/politics/nsw/massive-green-deal-nsw-environment-minister-spruiks2-billion-energy-package-20200131-p53wgg.html. Wood, T., Blowers, D., Griffiths, K., & Weisbrot, E. (2018). Down to the wire: A sustainable electricity network for Australia. Wood, T., Carter, L., & Harrison, C. (2014). Fair pricing for power. Melbourne, VIC, Australia: Grattan Institute Melbourne.

CHAPTER 4

Modelling Consumer Roles in the Electricity System

Abstract The consumer role has been difficult to map to Energy System planning due to the difficulty of measuring it. Research methodology was a discovery and enquiry into the question of behaviour in response to energy policy. There was a wealth of relevant data available and the experiment assessed what correlations there are between that data and the adoption of solar. As this experiment was on an entire population there was no way to assess it on in a controlled experiment, but the quantity of data meant there is a high confidence in the result. The method was: sourcing data; exploratory modelling of PV adoption by postcode; method of analysis; regression to identify influential variables; spatial regression; what we learnt from analysing the solar data by postcode; temporal modelling to forecast consumer actions; autoregressive integrated moving average (ARIMA) modelling results; and then testing the ARIMA model. The ARIMA model is similar to the autoregressive moving average (ARMA) model and is powerful for time-series modelling. This chapter shows how this modelling successfully forecast PV uptake in Australia. Keywords ARIMA model · PV · Spatial modelling · Temporal modelling

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4.1 Choosing a Dataset to Help Understand Consumer Choices The PV dataset is available and may help with understanding of consumer choices with Electric Vehicles and home storage in the future. Postcode level PV data is available for the 1.6 million houses in Australia that have installed PV. The Clean Energy Council (2018) is a department of the Australian Government and has been collecting data on household PV purchases by postcode, by month, since 2001 as per the Renewable Energy (Electricity) Act (Australian_Government, 2001). Another key dataset chose was Census data from the Australian Bureau of Statistics (ABS, 2016). The Census collects socio-economic measures of households. The Census used for the exploratory spatial analysis was the 2016 Census. For the temporal ARIMA modelling, the 2001, 2006, 2011 and 2016 Census data was used. The main source for Census data was a website managed by the ABS (Table Builder Pro). The ABS database that was used was Structure of Dwelling (STRD) for measures of houses, Equivalised Total Household Income (HIED) for income measures and Tenure Type (TEND) for home ownership measures. Electricity use and electricity cost were also measured by the ABS between 2010 and 2012 and this was also used in this research. The ABS also generates SEIFA Indexes used in this book. The SEIFA Index used in this book is the Disadvantage/Advantage Index. Another Government dataset used was the electoral result data recorded by the Australian Electoral Commission (AEC). The AEC publishes election results by voting booth. The high-quality data available for this research lends itself to statistical analysis. It is high quality because is primarily sourced from government surveys and data collection.

4.2

Exploratory Modelling

The PV market in Australia was chosen as a case study for analysis as there is ample rich data available. Over 1.6 million homes in Australia have registered for a PV subsidy, which means they have supplied their postcode and the month of installation.

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The average postcode in Australia has 8900 people, but many rural postcodes have small populations. Social measures for Australian postcodes are available from the Australian Government Census which runs every five years. The statistical analysis of postcode data was chosen for the exploratory modelling as it suited a study of national patterns. Postcode data is otherwise known as district data. The following three studies of PV uptake using district data were found in the literature: Boulaire, Higgins, Foliente, and McNamara (2014) studied PV in NSW Australia; Adil and Ko (2016) explored PV policy issues in the USA; and Kwan (2012) analysed PV uptake by ZIP code in the USA using the 2000 census, electricity cost and subsidy. The exploratory modelling choices were to use stepwise regression, neural network analysis and spatial analysis. There was a decision not to use agent-based analysis, socio-technical analysis nor power systems modelling. Stepwise regression allows the testing of a wide range of drivers (Efroymson, 1960). The aim of this stepwise regression is to find the most influential variables. Care must be taken due to the known criticisms of stepwise regression, and it is not assumed that complete information is available. The key points are: temporal variables will not be included in the stepwise regression; postcode data is available for the PV installations and for the Australian Bureau of Statistics social data, but some data is calculated by aggregating data from other levels; a neural network check can be used to verify the stepwise regression results; and spatial tests explore how each district interacts with districts around it (neighbourhood effect). Factor analysis and principal component analysis were both considered, but stepwise regression with correlation testing was chosen as the suitable method. The steps for this methodology were: collation and formatting the 1.6 million PV installations against their postcode location; collate and format the associated social data from the Australian census by postcode; stepwise regression to assess for social-measure links to PV adoption; run a check on stepwise regression using neural network analysis; and test the spatial regression. A limitation to this method is that different results can differ due to the arbitrary allocation of postcode boundaries. The problem of differing results is known as the “Modifiable Areal Unit Problem” (Openshaw & Openshaw, 1984).

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4.3

Finding Influential Variables

The goal of this regression is to select the three most influential variables on the adoption of PV from the 36 variables assessed. Four or five variables may deliver a stronger model, so they are also considered. In the first step, many of the 36 variables were above the VIF cut-off of 10. Each of the 36 variables was assessed separately to check their effect on the other variables. Some have high VIF results such as: broadband = 118; drove car to work = 57; no Internet = 30; dial up Internet = 36; moved in the past 5 years = 43; no kids = 153 and these were immediately removed from the regression. The ratio of “protest votes ” was calculated from the sum of primary votes for minor parties. The voting data was at Polling Booth level and was joined to the postcode level, but the regression showed it was not correlated with PV adoption. Greens Political Party voting (the percent of people in the suburb who had voted for the Greens Political Party) made only a minor impact on the correlation. The indicator for bedrooms was originally included for the a priori reason that more bedrooms should mean more suitable roof area for PV per house, but it only had a minor impact on the correlation and was then excluded. The regressions showed a consistent negative correlation between Australian PV adoption and “urban density”. The negative correlation to density is not surprising as it corresponds to the outer suburbs having a higher rate of PV installations, which was already shown in the visualisation step. Percentage of home ownership has been used in previous research, by Higgins, McNamara, and Foliente (2014), but the variable for the percentage of homes with mortgages gave stronger correlations and was retained in this regression process. The stronger correlation to mortgages (than home ownership) was a surprising result and may relate to how PV adoption relates to income. 4.3.1

Testing the Effect of Income

A negative correlation to income was found and may be caused by the higher rates of Australian PV adoption in outer suburbs (where average incomes are lower) and mask the true effect of income on PV purchase

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decisions. Therefore, “income” was not included in further tests and the model kept high results. A negative correlation between PV adoption and income levels has been noted previously in work of Green_Energy_Trading (2014). Their paper concluded there were four PV adoption drivers: 1. Level of home ownership. 2. Suitability of buildings (number of detached and semi-detached dwellings). 3. Relative importance of energy bills (families that spend a higher % of their incomes on energy). 4. The level of new home and renovation activity (home builders sometimes offer PV in new home packages). Of these Green Energy Trading results, two of their variables are used in the temporal ARIMA testing later in this chapter. The two variables are: 1. Relative importance of energy bills and 2. Percent of new homes. Percent of income spent on electricity (Uniting Care, 2015) was tested at the state level (not postcode) and it marginally improved the regression results. 4.3.2

The Three Variables Selected as Having the Strongest Influence on PV Adoption

This regression concluded that the three influential variables on the adoption of PV are: percentage of owned or mortgaged (sourced from the ABS); percentage of separate homes (sourced from the ABS); and percentage of solar-hot-water (sourced from the CER). Table 4.1 shows correlations by State for the 50% of most wealthy households and 100% of households. Apart from South Australia, the results show a strong correlation between Australian PV adoption and the three variables:

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Table 4.1 Correlation between actual PV-system installations and theoretical model State

N=

New South Wales Victoria Queensland South Australia Western Australia Tasmania ACT

240 313 111 93 183 19 20

Correlation 50% wealthiest postcodes R2 R2 R2 R2 R2 R2 R2

= = = = = = =

0.74 0.74 0.76 0.51 0.80 0.76 0.73

N=

Correlation all postcodes

584 640 390 286 286 104 20

R2 R2 R2 R2 R2 R2 R2

= = = = = = =

0.60 0.61 0.63 0.53 0.74 0.67 0.73

1. % owned or mortgaged. 2. % separate home. 3. % solar-hot-water. There is no information to explain the difference in South Australia. Of interest is the correlation between PV and solar-hot-water. Correlation to previous solar-hot-water installations may be significant, as it suggests the model of PV adoption may also work for other energy-actions, such as EV and storage. 4.3.3

Testing Stepwise Regression Results

Economic validity was considered by checking that all the variables align to our understanding of what would influence Australian PV adoption. Other variables may have been missed in the analysis, for example consumer sentiment (not available in this data set) as the correlations were high but not perfect. The hysteresis test for residual normality was close to meeting the goal of a random error with a mean of zero. The test of the residual showed the residual is not random but is highly clustered which suggests another unknown variable is having an effect.

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Test PV Installation Correlation to Previous Solar-Hot-Water Installation

The Pearson correlation between PV and solar-hot-water in Australia (n = 1098) is 0.571 which suggests that the PV modelling results may apply to other consumer energy-actions such as demand management, P2P electricity sales and home storage (batteries). 4.3.5

Testing the Effect of Income and Urban Density on PV Uptake

Next, the effects of income and urban density were tested. The three variables selected by the stepwise regression were the percent of homes that were owned (%owned), the percent of homes that were separate (%separate house) and percent of homes with solar-hot-water (%hot water). Income related variables were added (Density and Income). The variables chosen to test the effect of income and urban density were: 1. Average bedrooms (This is the average number of bedrooms in this postcode. This may be an indicator of income). 2. %separate homes (This is the ratio of separate houses versus all dwellings including apartments. Chosen by stepwise regression). 3. Density (This is the area of land per house. This is from ABS and is the urban density measure). 4. %solar-hot-water (This is the ratio of homes in the postcode with solar-hot-water. Chosen by stepwise regression). 5. Income (This is the strongest link to income. It is average income for the postcode). 6. %owned (This is the percent of homes in the postcode that are owned not rented. Chosen by stepwise regression). Testing on income and urban density included tests on each Australian state, the total for all Australia, the wealthiest 20% of Australian postcodes, the wealthiest 75% of Australian postcodes, the highest density communities, mid-density communities and country areas. The following results show the income and urban density effects:

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1. Removing the 20% most wealthy suburbs gave an R 2 for Australia of 0.37. 2. Removing inner-suburban areas or the wealthiest 10% of postcodes or the wealthiest 20% of postcodes did not improve results. 3. A test on all suburbs with income above the first quartile suburbs gave an R 2 for Australia of 0.51. The state of Victoria was 0.71. 4. Further testing included removing the income variable, which gave R 2 of 0.56. 5. It was discovered that only taking the wealthiest 50% of postcodes mean the R 2 increased to 0.601. The conclusion of this test is that higher income is negatively related to PV. Ownership and urban density are also negatively related. Income and urban density have a negative relationship to PV uptake, and this corresponds to the work by Green_Energy_Trading (2014). The conclusion of further testing of PV uptake concluded it is negatively related to the rate of Greens voters, so this relationship is significant, but not expected. The inner-suburban voters are a stronghold for the Greens party, but there is a low rate of adoption of PV in the inner suburbs where most Greens supporters live.

4.4

What We Learnt from the Analysing the Solar Data by Postcode

The contribution is the identification of variables that can be used in temporal modelling of Australian PV adoption. The conclusions were: • The most significant variables are income, percent of separate housing, percent of homes with solar-hot-water, and percent of homes that were owned or had a mortgage. • Adoption is higher if there is lower urban density. • There is a relationship between PV adoption and solar-hot-water adoption. • PV adoption is more likely if electricity cost is a higher percent of income.

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Causality is inconclusive at this stage. The spatial regression showed that a variable for “income” should be a fourth variable. The impact of adding income is shown in Appendix A in the backmatter. The data from the 1.6 million installations of PV on Australian homes and the Australian Census data have allowed a thorough assessment of the patterns of PV adoption in Australia. 4.4.1

PV Adoption in Australia Correlates with Income, Separate Housing, Home Ownership and Solar-Hot-Water

The regression suggests that leading drivers of PV adoption by postcode include the four variables chosen are: • • • •

Income (sourced from the ABS). Percent of separate housing (sourced from the ABS). Percent of homes with solar-hot-water (sourced from the CER). Percent of homes with that are owned or under mortgage (sourced from the ABS).

4.4.2

Patterns of PV Are Different in Different Australian States

The spatial patterns showed differences in different states which were due to policy and housing type. For example, the 10 kW PV feed-in tariff (FIT) in NSW favoured rural areas. The different Brisbane pattern reflects the larger houses in inner Brisbane relative to other cities and may be was useful in building an understanding of PV uptake. The focus on urban density led to the experimental design of using remoteness areas to cluster Australian geographic areas into 32 areas which is used in the temporal regression. 4.4.3

There Is a Correlation Between Solar-Hot-Water Adoption and PV Adoption That Suggests Other Energy-Actions Will Be Correlated

The correlation between solar-hot-water adoption and PV adoption was demonstrated in this research. There is some evidence that this model will be applicable for other changes in the electricity system such as home battery systems, energy efficiency investment and other energy-actions.

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4.5

Temporal Model of Australian PV Adoption

The spatial study showed the different patterns between urban and rural PV purchases. This verified the validity of using a remoteness categorisation. Using the remoteness categorisation reduced the areas from 2300 postcodes to 32 remoteness areas which consequently improves the ARIMA model by reducing dimensionality (Fig. 4.1). In the remoteness categories, major cities are x0, inner regional x1, outer regional x2, remote x3, very remote x4. Therefore, for example, Fig. 4.2 shows the Northern Territory as solely remote and very remote. The other states are less remote than that and include at least regional status. Temporal testing in this section develops dynamic aspects of the data such as price and subsidy changes. The temporal analysis is of Australian

Fig. 4.1 Australian remoteness categorisation

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Fig. 4.2 NSW major cities area 10, PV per month per 1000 homes

PV installations over the past decade and develops a predictive element to seek clues to future behaviour. This section includes temporal modelling to increase our understanding of consumer roles in the electricity system and offer a tool for policymakers and vendors. The aim is to answer, “Are there Government policy options to manage consumer roles in the electricity system?” Temporal modelling will assist in understanding these consumer roles. The ARIMA modelling used the same data as the spatial regression, with the addition of temporal variables. A temporal analysis of 32 geographic areas, using monthly data for 11 years, was conducted using ARIMA time series (temporal) modelling. Time series or temporal econometrics is a field of statistics with wide application in economics and business planning. The specific goals of this ARIMA modelling were:

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• Finding the drivers of PV adoption and • Finding whether there are other unknown drivers of Australian PV such as the consumer decision process (psychology) and the media. 4.5.1

Choosing Temporal ARIMA and Granger Causal Methods

Autoregressive integrated moving average (ARIMA) modelling (Box & Jenkins, 1970) is a generalisation of ARMA modelling and the integrated part of the model can be used to reduce non-stationarity. ARIMA was chosen for this modelling as this data has a lack of stationarity and the integration function of ARIMA ameliorates these non-stationary trends. The Granger Causal method will be used to identify effective policy options (Granger, 1969). Granger Causality is popular amongst economists looking to predict sales behaviour such as the PV data used in this book. There has been some criticism of the Granger Causal method, and Toda and Yamamoto (1995) offer tips to manage these constraints. Stern (2011) also offers useful insights on Granger Causal in research on energy and economic growth in Sweden over 150 years. Eichler (2012) notes that “Granger’s definition covers only direct causal relationships ”. The modelling in this book uses direct variables so Eichler’s constraint does not apply. Bayesian Hierarchical Modelling (BHM) was considered for this book to test relationships between spatial areas. It was dropped from consideration after testing did not discover useful spatial patterns. It is applicable for models in which three drivers are data, and one a hidden process. Cressie and Wikle (2015) suggest the use of Berliner’s three-stage factorisation (Berliner, 1996). There is no literature on BHM in socio-technical modelling, but this may suit further research, as there are elements of the PV purchase decision that are hidden. The complexity of spatial-temporal regression is enormous due to the unlimited directionality of space, so it was trialled and eliminated from this research. Cybernetics suggests modelling a complex dynamic system with mathematical connections between human responses and the outcome (Wiener, 1948). Further research could use dynamic system modelling of the Energy Transition for the whole electricity system.

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Method

The aim is to predict sales of PV in Australia as well as characterising its role in the electricity system and informing policy. Stock and Watson (1993) suggest four uses for multivariate time series models: describing and summarising; forecasting; quantifying something about the economy; to advise policymakers. The modelling in this chapter will address all four modes. The method for the ARIMA modelling was: • Data acquisition and preparation: Create temporal dataset in monthly intervals using a straight-line average for each of the 32 regions of Australia using ARCMAP and SPSS. • Select the top 6 variables: Use ARIMA to select variables and test the model on the 32 regions (using the remoteness categorisation) of Australia using SPSS Modeler 18.1. • Use the 6 variables in ARIMA model and select the top 7 models: Select models using Akaike Information Criterion (AIC) and Root Mean Squared Percent Error (RMSPE). 4.6.1

Data Acquisition and Preparation

The software used included ARCMAP (ESRI, 2016) and IBM SPSS Statistics 22 (IBM, 2015), and IBM SPSS Modeler 18.1 (IBM, 2015). A temporal data set was created with monthly intervals for 32 regions of Australia defined by the remoteness categorisation (Pink, 2011). The data set was loaded on IBM SPSS Modeler 18.1 (IBM, 2015). ARIMA modelling was in SPSS Modeler 18.1, and its graphical imagery was useful in showing a flow for the modelling. Iterative modelling was also easy due to the visualisation and interfaces in this software. The choice of 119 months to June 2017 was to cover to the most recent data at the time of the experiment in 2017, and to include the most active times in the PV market to help the regression highlight dependencies. This included activity in the sales of PV but also the government policy changes, which influenced the market. The structuring of the data included transformation and data configuration using SPSS Modeler. • ABS data was converted from 5-year data to monthly by taking a straight-line average.

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• The AEC (Australian Government) election data for 5 elections was also converted to monthly data using a straight-line average as a percent of population. • The APVI cost of PV was converted from annual data to monthly data via a straight-line average, the feed-in tariff at time of installation was used (at the state level) and the price of PV installs by state by year converted to monthly figures after taking a straight-line average across the year; • A variable for electricity bill stress was synthesised on the basis that the retail electricity price shifted from about 10c/kWh to 30c/kWh between 2001 and 2017 (a 300% increase). The 300% change was applied to the ratio between the 2012 electricity use and Income with the starting point of the ratio being 2016 Income. This was a compromise to synthesise a variable for electricity bill stress because 2012 electricity use survey by the ABS is the only national electricity use measure. • The postcode variable was used to calculate the remoteness area and state levels were also used in the ARIMA modelling. The variables chosen for the temporal model (with source shown in the bracket) were: 1. Solar (CER). PV units by remoteness area by month. 2. %Separate house (ABS). Number of separate houses by remoteness area by month. 3. %Owned (ABS). Percentage of homes owned by remoteness area by month. 4. km2 (ABS). Average square km per house. 5. Income over $A2000 (ABS). Ratio of homes with income over $A2000/week by month. 6. Income (ABS). Average income by remoteness area by month. 7. Average-use-electricity (AER, ACIL and Hugh Saddler ABS). Average household electricity use by remoteness area by month (discussion below). 8. Price-electricity (ABS). Average electricity price by state by month (discussion below). 9. Price-solar-system (APVI and BNEF). The pricing was drawn from the APVI (APVI, 2017) and Bloomberg New Energy Finance

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article on Australian household solar (2018). These were converted to monthly figures with a straight-line average. 10. Carbon subsidy (APVI). FIT rates. It is sourced from the APVI report for the International Energy Agency, PVPS Programme (APVI, 2016). 11. Cost-solar-minus-subsidy (APVI). Financial variable encompasses the price of PV minus one year of PV Feed-In-tariffs and minus Carbon Certificates. 12. Liberal-votes, Labour-votes and Green-votes. Ratio of total votes for Liberals, Labour and Greens by month (AEC). The AEC releases data on the past Australian elections, which are down to the electoral booth level. The electoral booth areas are smaller than postcodes and can be reformatted to fit postcode data polygons. These were then converted to monthly figures via straight line averaging. 13. Business conditions (NAB). A monthly figure by state that is generated by the National Australia Bank. 14. FIT (APVI). PV feed-in tariff (FIT) is sourced from the APVI (2017). 15. Date. A temporal tag in Month-Year format (for the 119 months to June 2017). 16. State (ABS). A spatial tag. 17. Remoteness area (ABS). A spatial tag. 4.6.2

Electricity Use Variable

The electricity use variable was expected to be important. To synthesise this, the ABS electricity consumption benchmark data from 2010 (ABS, 2011) was converted to monthly data for 11 years and averaged up to the 32 remoteness areas using a weighted average (on number of dwellings). Data was not available for NT, so the data for NT was drawn from the ACIL calculation of NT electricity (ACIL, 2015). The average use of electricity varied significantly in different areas of Australia and includes a decrease in average electricity use between 2011 and 2014 (ACIL, 2015).

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4.6.3

Electricity-Price Variable

Average electricity-price data for each of the areas for the years 2010– 2012 was acquired from the Australian Bureau of Statistics (ABS, 2011) and averaged up to the 32 remoteness areas using a weighted average on number of dwellings. Then monthly extrapolations from those figures over the 11 years 2006–2017 were calculated using data from Hugh Saddler (2013), the AER (2018) and ACIL (2015). From 2003 to 2006, the electricity price rose at the same rate as the inflation for all goods (CPI) but since 2006, it had risen sharply (ABS, 2018).

4.7

ARIMA Modelling Results

The ARIMA modelling process followed these steps: • Select the top 6 variables as drivers of Australian PV adoption. • Select the top 7 models by minimising AIC and RMSPE. • Analyse patterns in the top 6 models and through testing create a strong model. • Test the temporal model. 4.7.1

Selecting the Top 6 Variables as Temporal Drivers of PV Adoption

Testing included eighteen potential drivers of Australian PV adoption. The maximum-lag setting was 12 lags (one year). Variables with no meaningful results in each of the 32 areas were cut. Six key variables (with their source in brackets) selected by iterative testing were: • Business conditions (National Australia Bank). A very high-quality data set, with monthly figures by state. A consumer conditions variable would have been stronger, but there was none available at this detail level. • PV feed-in tariff (APVI). Reflects state government settings. • Carbon subsidy (APVI). Reflects Australian Government settings. • Price-electricity (ABS). Based on Australian Bureau of Statistics government run surveys.

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• Price-PV-system (APVI and BNEF). The source was annual prices, which were extrapolated to monthly. • Average-use-electricity (ABS). Based on Australian Bureau of Statistics government run surveys. Testing on the average amount of electricity used (Average-useelectricity) showed it as very influential. This is an interesting result and may prove significant. The data for the Business conditions variable from the NAB Economics Unit also proved influential. 4.7.2

Select the Top Models Using Combinations of the Selected Variables

The six variables chosen were tested in a variety of configurations to find the more effective models. A variety of time periods were tested to increase understanding of the data. Three timescales were tested: August 2006 to January 2012; January 2012 to January 2017; August 2006 to June 2017. After these tests, 119 months to June 2017 was chosen. The best seven models are listed in Table 4.2. Then AIC and RMSPE were used to compare models (Akaike, 1974). The AIC compares different models of the same subject (the same geographic area) but the AIC test for statistical inferencing has wide acceptance for time series modelling. Lower AIC indicates a better model even when the number is negative. The AIC testing including the effect of different variables on the geographic areas that had shown poor results in the first stage of modelling (areas 11 and 44) as follows: • The area with remoteness code 11 (Fig. 4.1) gave the highest AIC for 6 variables but gave a similar AIC with 5 variables (Business conditions, FIT, Carbon subsidy, Price-electricity, Average-useelectricity) and • The area with remoteness code 44 (Fig. 4.1) gave the lowest AIC with 5 variables. One variable was removed by combining the variable for the price of PV and the subsidy variable, as they both added together to make the total prices faced by the consumer. There would normally have been no

Date To June 2017

July 2006 to June 2017

To March 2014

To March 2014

6VAR

5VAR no PV$

5VAR no Business Conditions

5VAR no PV$

FIT, Carbon subsidy, Price-electricity, Price-PV-system, Average-use-electricity Business conditions, FIT, Carbon subsidy, Price-electricity, Average-use-electricity

Business conditions, FIT, Carbon subsidy, Price-electricity, Price-PV-system, Average-use-electricity Business conditions, FIT, Carbon subsidy, Price-electricity, Average-use-electricity

Variables

Sample of model comparisons using Pearson correlation

Model name

Table 4.2

Model performs well, with all R 2 over 0.95 except areas 73, and 74

Area 64 (remote Tasmania) with R 2 of 0.5 and area 63 is poor as well with R 2 of 0.76. Area 14 (remote NSW) only Price of electricity was significant Model performs well, with all R 2 over 0.95 except areas 73 and 74

All 32 remoteness areas have R 2 over 0.88

Comment

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Date To June 2017

To January 2012

To June 2017

Model name

5VAR no PV$

5VAR no PV$

4VAR

Business conditions, FIT, Carbon subsidy, Price-electricity

Business conditions, FIT, Carbon subsidy and the Price-electricity, Average-use-electricity Business conditions, FIT, Carbon subsidy, Price-electricity, Average-use-electricity

Variables Model performs well, with all R 2 over 0.95, except area 13, 14, 23, 33, 34, 44, 54, 63, 64, 73, 74 Model performs well, with only R 2 below 0.95 being areas 23, 63, 73, 74. Also the model gives a high AIC rating. Some areas do not have any equation, namely 43, 44, 73, 74, and only one for following: area 63 solar lag 4, area 31 solar % lag 4, area 34 Price-electricity lag 11, and area 12 Price-electricity lag 7, 21 solar lag 10, and 22 had solar lag 3 and lag 10 Model performs moderately well, with all inner urban and outer urban areas with R 2 above 0.87, Low R 2 scores for all rural and remote areas and means they should not be mapped this wa

Comment

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distinction between the two prices at the time of sale. The new variable name is Price-PV-system. The use of AIC shows in the three examples in Table 4.3. Changing the model does not always affect AIC and not all geographic areas of Australia have responded to the same model changes. For example, NSW outer regional area with remoteness code 12 had the same AIC for the first two models shown. 4.7.3

Analyse Patterns in the Top 6 Models

The third and last step in the ARIMA modelling was to analyse patterns in the top 6 models and through testing create a strong model. The search for patterns was iterative and intuitive. Each model was tested on each of the 32 geographic areas of Australia. Each ARIMA regression generated hundreds of possible lagged variables which was far more than desired for the final model. The method to select amongst these was to compare the different models and build an understanding of the more important components. Comparison of the models was done by analysis of the frequency and size of coefficient of variables using charts of the outputs from each model. The most significant variables are: • Price-electricity. • Business conditions. • Price-PV-system including subsidy. Further comparison of the models led to dropping price-electricity and focussing on the other variables including the auto regressive elements for a lag of 1 month and lag of 12 months. The chosen model explains about 2/3 of the cause of the PV purchase (Granger, 1969). The model is shown in Table 4.4. The three variables Price-PV-system, Business conditions and FIT are all logical inputs to the adoption decision. The lag periods are interesting and may related to the period it takes between a cost rise and the price rising for consumers.

10 11 12 13 14 20 21 22 23 30 31 32 33 34 40 41

Remoteness code

Variables

0.94 0.55 0.42 0.42 0.56 0.3 0.12 0.27 0.69 0.38 0.33 1.54 4.22 0.8 1.32 6.91

RMSPE

RMSPE 1.02 0.71 0.42 0.32 0.56 0.3 0.12 0.27 0.53 0.38 0.33 1.54 4.22 0.8 1.32 6.91

−306 −219 −212 −265 −241 −220 −311 −311 −85 −270 −291 −167 −149 −247 −142 −141

−284 −287 −212 −264 −241 −220 −311 −311 −170 −270 −291 −167 −149 −247 −142 −141

AIC

Business conditions, FIT, Carbon subsidy, Price-electricity, Price-PV-system, Average-use-electricity

AIC

Business conditions, FIT, Carbon subsidy, Price-electricity, Average-use-electricity

RMSPE and Akaike Information Criterion (AIC)-3 models

NSW major cites NSW inner regional NSW outer regional NSW remote NSW very remote VIC major cites VIC inner regional VIC outer regional VIC remote QLD major cites QLD inner regional QLD outer regional QLD remote QLD very remote SA major cites SA inner regional

Area

Table 4.3

0.93 0.71 0.42 0.91 0.56 0.72 0.16 0.27 1.03 0.88 0.99 2.76 13.89 0.8 1.32 7.36

RMSPE

(continued)

−326.02 −287.92 −212.76 −144 −241.84 −203.87 −280.6 −311.63 −67.52 −221.5 −243.71 −128.41 −110.53 −247.31 −142.45 −161.97

AIC

FIT, Carbon subsidy, Price-electricity, Price-PV-system, Average-use-electricity

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42 43 44 50 51 52 53 54 61 62 63 64 72 73 74 80

Remoteness code

Variables

(continued)

SA outer regional SA remote SA very remote WA major cites WA inner regional WA outer regional WA remote WA very remote TAS inner regional TAS outer regional TAS remote TAS very remote NT outer regional NT remote NT very remote ACT major cities

Area

Table 4.3

1.13 0.74 1.66 0.64 2.97 0.29 0.22 0.55 0.44 0.31 1.38 1.85 0.26 3.34 47.35 0.14

RMSPE

RMSPE 0.85 0.96 2.16 0.64 1.75 0.29 0.22 0.55 0.44 0.31 1.38 1.85 0.29 1.73 5.6 0.14

−196 −295 −63 −304 −366 −322 −277 −107 −193 −196 −139 −132 −314 −37 −144 −278

−197 −248 −104 −304 −285 −322 −277 −107 −193 −196 −139 −132 −281 −93 −297 −278

AIC

Business conditions, FIT, Carbon subsidy, Price-electricity, Price-PV-system, Average-use-electricity

AIC

Business conditions, FIT, Carbon subsidy, Price-electricity, Average-use-electricity

1.75 1.4 2.16 0.64 6.16 0.29 0.24 0.84 0.58 0.24 1.38 1.68 1.3 2.51 42.89 0.3

RMSPE

−173.25 −183.09 −104.24 −304.66 −396.89 −322.44 −216.77 −50.27 −188.7 −224.55 −139.05 −75.79 −191.14 −10.19 −116.75 −212.32

AIC

FIT, Carbon subsidy, Price-electricity, Price-PV-system, Average-use-electricity

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Table 4.4 Model of PV-system adoption in Australia Variable name

Model elements

PV Price-PV-system Business conditions FIT PV

PV Autocorrelation PV price in $A (inclusive of subsidy) NAB Business Conditions Index PV feed-in tariff in $A cents PV Autocorrelation

4.7.4

Lag in months

Weighting

1 4 8 8 12

0.4 (0.01) 0.02 0.05 0.3

Testing the Temporal Model

Diagnostic checks are applied in SPSS Modeler to reveal inadequacies in the fitted model and to suggest suitable modifications. These included inspection of the residuals and autocorrelations (Box & Jenkins, 1970). The goal of these checks was that econometric hurdles should be satisfied, and the models satisfied these tests. Tests in SPSS Modeler also show a Granger Causality for the model using the function “Temporal Causal Modelling Causality”. These tests showed Price-PV-system, Business conditions and FIT were all Granger Causal. They account for 2/3 of cause so one or more other unknown drivers are causal. Another perspective on cause and effect is from Sims (1980) who was critical of reliance on macroeconomic variables with strong assumptions about dynamic natures, and ignored previous actions affecting the current action. The chosen model was strongly autocorrelated, so Sims’ criticism of causality is not relevant to this case. Stationarity was not satisfied but the integrated aspect of ARIMA meant the transforming of inputs made no difference to the modelling. The R 2 for the model is listed in Table 4.5. The three variables tested were price-electricity, business conditions measure, and price-PV-system including subsidy. The predictive relevance of these input variables is strong. The variables Price-PV-system, Business conditions and FIT are all logical drivers of PV adoption. Developing conceptual definitions for the model variables is not required as they are specific indicators and all financial measures in Australian currency. The histogram of the residuals for the model showed adequate normality, and Shapiro-Wilk checks were used in this modelling.

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Table 4.5 Correlation of the model by remoteness geographic classification Area NSW major cites NSW inner regional NSW outer regional NSW remote NSW very remote VIC major cites VIC inner regional VIC outer regional VIC remote QLD major cites QLD inner regional QLD outer regional QLD remote QLD very remote SA major cites SA inner regional SA outer regional SA remote SA very remote WA major cites WA inner regional WA outer regional WA remote WA very remote TAS inner regional TAS outer regional TAS remote TAS very remote NT outer regional NT remote NT very remote ACT major cities

Area code 10 11 12 13 14 20 21 22 23 30 31 32 33 34 40 41 42 43 44 50 51 52 53 54 61 62 63 64 72 73 74 80

Pearson correlation per month for 119 months to June 2017 0.89 0.90 0.89 0.95 0.77 0.79 0.82 0.78 0.81 0.91 0.92 0.85 0.68 0.58 0.92 0.95 0.90 0.92 0.74 0.91 0.94 0.88 0.78 0.79 0.88 0.89 0.74 0.99 0.90 0.92 0.68 0.84

Control variable options were considered, but this econometric model does not have any useful control variables due to the fluctuations in the economic inputs to the consumer behaviour. The Durbin–Watson test for collinearity was checked for the key variables and it was concluded the variables in this model are not collinear: manufacturers/vendors set the price of the PV-system independent of

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the other variables; Government tries to set the subsidy to respond to price but their decision is independent; and business conditions are an independent measure of economic activity. The relevance and significance of the relationships is considered high. This because the costs, FIT and the economic environment (measured by Business Conditions) are all relevant to PV adoption. Results for the model for the 32 different areas of Australia appear in Appendix C in the backmatter. A sample of the output for the city area of Sydney is shown in Fig. 4.2. For this region and all urban areas, the model performed well and had an R 2 in the range of 0.8–0.95. By comparison, the R 2 in country areas was typically 0.7–0.8. Application of the model in policy was considered. Government energy policy is focussed on security and reliability of energy, the price of energy and climate-change policy. The ability to better understand consumer energy-actions would contribute to all three. The ability of the model to be predictive is useful for policy development, and this model suggests a confidence in its prediction for 3–6 months into the future. There are periods where the simulation diverges from the measured value, and these are during rapid changes in the input variables. This means the model fares well in stable conditions but not during rapid changes in the inputs.

4.8 What Did We Learn from the Australian Solar Data? The ARIMA modelling method allowed the use of dynamic temporal data such as economic confidence and pricing data that would otherwise be difficult to analyse. The tool used was SPSS Modeler (IBM, 2017). The ARIMA process used the software IBM SPSS Modeler (IBM, 2017) and used the function temporal causal modelling. In the context of temporal causal modelling, the term causal refers to Granger Causality. Granger Causality is used carefully as it is recognised that it will not be causal in all circumstances. Temporal causal modelling builds an autoregressive time series model for each target and includes only those inputs that have a causal relationship with the target. This approach differs from traditional time series modelling which needs specification of predictors (IBM, 2017). The outputs for the finally selected model were run for all 32 areas of Australia. The veracity of the model is strong as the data set was complex, comprehensive and accurate. The only one of the three variables that was

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extrapolated was the annual PV price which was converted to monthly values via a straight-line average. The data was highly variable, as PV prices came down quickly and governments changed subsidies often. The variability of the variables increased the fidelity of inputs to the modelling with a resulting benefit to the strength of the chosen model. The chosen model offers clues to the behaviour of consumers with other energy-actions and builds on the evidence that PV and solar-hotwater are correlated. The link between PV adoption and other energyactions is also shown in the work of Graziano and Gillingham (2014) which is now in a PV location service from Google (2018). The econometric view of the ARIMA model also suggests that PV consumers respond to norms and incentive (Lindbeck, 1997). The strong element of autoregression shown in the model suggests the norm effect (that people will keep doing what they normally do). The incentives shown to have effect are FIT, subsidy and the price. The aim of this research was to offer control to governments for the rate of adoption of PV and other energy-actions such as storage. This chapter has shown evidence of: • Links between PV purchases and solar-hot-water purchases. • Links between PV purchases and policy changes. • Links between PV purchases and economic changes. In the spatial regression key variables were • • • •

Income Percent of separate housing Percent of homes with solar-hot-water Percent of home ownership

The patterns of PV were different in different Australian state This reflected the state-based PV policies and helped uncover the trends shown in the ARIMA model. Links between PV purchases and solar-hot-water purchases There was a correlation found between PV and the solar-hot-water adoption which may be the most significant finding as it may help understand future household energy device adoption such as storage devices.

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The temporal ARIMA model for PV adoption in Australia is: Variable name

Model elements

Lag in months

Weighting

PV PV Autocorrelation Price-PV-system PV price in $A (inclusive of subsidy) Business conditions NAB Business Conditions Index FIT PV feed-in tariff in $A cents PV PV Autocorrelation

1 4 8 8 12

0.4 (0.01) 0.02 0.05 0.3

4.8.1

Price, Subsidy, Business Conditions and Earlier PV Installations Are Granger Causal on PV

The model of PV adoption showed the price including subsidy, business conditions and FIT were Granger Causal. In summary, the model does not describe all behaviour and there is a missing driver or multiple drivers. One candidate for a missing driver is public sentiment which is influenced by politicians, media, and public discourse. Methods to explore this in future research could include Twitter analysis, real estate advertisements and online blog analysis. The next chapter, Chapter 5, explores the use of technology and data in the Energy Transition.

References ABS. (2011, March). 4602.0.55.001-Environmental issues: Energy use and conservation tables. Canberra: Australian Government. Retrieved from http://www. abs.gov.au/AUSSTATS/[email protected]/DetailsPage/4602.0.55.001Mar%202011? OpenDocument. ABS. (2016). 2900.0—Census of population and housing. Retrieved from http:// www.abs.gov.au. ABS. (2018). Energy prices—The story behind rising costs. Parliamentary Handbook: Australian Government. Retrieved from http://www.aph.gov.au/ About_Parliament/Parliamentary_Departments/Parliamentary_Library/ pubs/BriefingBook44p/EnergyPrices. ACIL. (2015). Electricity bill benchmarks, a report to the Australian Energy Regulator. Retrieved from https://www.aer.gov.au/system/files/ACIL%20A llen_%20Electricity%20Benchmarks_final%20report%20v2%20-%20Revised% 20March%202015.PDF.

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Adil, A. M., & Ko, Y. (2016). Socio-technical evolution of Decentralized Energy Systems: A critical review and implications for urban planning and policy. Renewable and Sustainable Energy Reviews, 57, 1025–1037. https://doi.org/ 10.1016/j.rser.2015.12.079. AER. (2018, June 22). National Electricity Market electricity consumption. Retrieved from https://www.aer.gov.au/wholesale-markets/wholesale-statis tics/national-electricity-market-electricity-consumption. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. APVI. (2016). Solar PV Maps and Tools. Retrieved from http://pv-map.apvi. org.au/. APVI. (2017). Solar PV Maps and Tools. Retrieved from http://pv-map.apvi. org.au/. Berliner, L. M. (1996). Hierarchical Bayesian time series models. In Maximum entropy and Bayesian methods (pp. 15–22). New York: Springer. Boulaire, F., Higgins, A., Foliente, G., & McNamara, C. (2014). Statistical modelling of district-level residential electricity use in NSW, Australia. Sustainability Science, 9(1), 77–88. Box, G. E., & Jenkins, G. M. (1970). Time series analysis forecasting and control. Madison: Department of Statistics, Wisconsin University. CER. (2018). Postcode data for small-scale installations. Clean Energy Regulator (CER): Australian Government. Retrieved from http://www.cleanenergyregu lator.gov.au/RET/Forms-and-resources/Postcode-data-for-small-scale-instal lations. Cressie, N., & Wikle, C. K. (2015). Statistics for spatio-temporal data. Chichester, UK: Wiley. Efroymson, M. (1960). Multiple regression analysis. In Mathematical methods for digital computers (pp. 191–203). New York: Wiley. Eichler, M. (2012). Causal inference in time series analysis. In Causality: Statistical perspectives and applications (pp. 327–354). Chichester, UK: Wiley. ESRI. (2016).ArcGIS Desktop: Release 10.4.1. Environmental Systems Research Institute. Google. (2018). Google Sunroof . Retrieved from https://www.google.com/get/ sunroof#p=0. Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 37, 424–438. Graziano, M., & Gillingham, K. (2014). Spatial patterns of solar photovoltaic system adoption: The influence of neighbors and the built environment. Journal of Economic Geography, 15(4), 815–839. https://doi.org/10.1093/ jeg/lbu036.

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Green Energy Trading. (2014). Postcode and income distribution of solar. REC Agents Report. Retrieved from http://www.recagents.asn.au/wp-content/upl oads/2014/04/GET-Postcode-report-for-RAA-April-2014.pdf. Higgins, A., McNamara, C., & Foliente, G. (2014). Modelling future uptake of solar photo-voltaics and water heaters under different government incentives. Technological Forecasting and Social Change, 83, 142–155. IBM. (2015).IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp. IBM. (2017).IBM SPSS Modeler, Version 18.1. Armonk, NY: IBM Corp. Kwan, C. L. (2012). Influence of local environmental, social, economic and political variables on the spatial distribution of residential solar PV arrays across the United States. Energy Policy, 47, 332–344. https://doi.org/10.1016/j.enpol. 2012.04.074. Lindbeck, A. (1997). Incentives and social norms in household behavior. The American Economic Review, 87 (2), 370–377. Openshaw, S., & Openshaw, S. (1984). The modifiable areal unit problem. Norwich: Geobooks. Pink, B. (2011). Australian statistical geography standard (ASGS): Volume 5– Remoteness structure. Canberra: Australian Bureau of Statistics. Renewable Energy (Electricity) Act 2001, Section 3. Canberra, (2001). Saddler, H. (2013). Why is electricity consumption decreasing in Australia? Retrieved from https://theconversation.com/why-is-electricity-consumptiondecreasing-in-australia-20998. Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 48, 1-48. Stern, D. I. (2011). From correlation to Granger causality. Crawford School Research Paper. Stock, J. H., & Watson, M. W. (1993). Introduction to ‘Business cycles, indicators and forecasting’. In Business cycles, indicators and forecasting (pp. 1–10). Chicago: University of Chicago Press. Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1), 225–250. Uniting Care. (2015, February). Submission to the Australian Energy Regulator (AER). Response to Electricity Distribution Business, regulatory proposals for 2015–20, from SA power networks, Electricity Distribution Business. Retrieved from https://www.unitingcare.org.au/media-publications/submissions/res ponse-to-electricity-distribution-business-regulatory-proposals-for-2015-20from-sa-power-networks-electricity-distribution-business. Wiener, N. (1948). Cybernetics: Or control and communication in the animal and the machine. Cambridge: MIT Press.

CHAPTER 5

Technology and Data for Improved Decision Making

Abstract This chapter explores data and technology that will allow electricity to flow to and from consumers (in and out of the electricity grid) and control voltage and frequency. Smart controllers can do this control and may help grid balancing through control of devices such as airconditioners; demand response; batteries; phase shifting inverters (solar, battery and air-conditioning); and electric vehicle (EV) charging settings. Dynamic grid control, smart meter rollouts and ethical management of data are explored. In Australia and Europe, we agree on the general direction of the Energy Transition and both support some government intervention but recommend freeing up incentive and allowing the market to respond. Australia and Europe see the consumer at the centre of this transition with their purchases of solar, storage and EV, and both want to see a greater role of the consumer in energy policy and optimisation of the system. This chapter covers PV export limits, cost reflective network pricing, demand response and demand management, new inverter technology, smart devices, EV, storage and management of data. The reason for starting with PV export limits is that is the most active of the DB actions at present, as well as being contentious. The other discussions explore options for the coming decade of the Energy Transition. Keywords Capacity market · Data · Inverters · Smart meters · Technology

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5.1 Limiting PV Export to Reduce Overvoltage Problems The high concentration of PV in Australia has accentuated the problem of overvoltage and backwards flow on the distribution grid. The most constrained grids are where the electricity distribution line and the firstlevel transformer are constrained. This is usually on long electricity distribution lines (feeders) with few consumers. High concentrations of PV seldom occur in Europe, so few Europeans see control for overvoltage and backwards flow as important. One exception in Europe is Germany. Ideally, any limit on the PV export from households would be dynamic and recognise grid constraints. The aim should be to minimise the application of limits. The four perspectives below explore the role in the transition of PV export limits. These include the issue of communication to the stakeholders, alternates to limiting PV export, how PV limits misalign with the Energy Transition and how the limits could be used. 5.1.1

Communicating PV Export Limits

PV vendors and investors are often working in the dark on whether they will face PV output limits so a clearer process for rolling out PV export limits is needed. PV export limits already exist in areas of Australia. For example, Powercor has been limiting PV exports broadly and Ergon is limiting commercial consumers. There are also grid export limits being used in Germany, which also has a high penetration of PV on distribution networks. 5.1.2

Alternates to PV Export Limits

The PV export limits are a blunt instrument and there may be a temptation for Distribution Businesses to adopt them instead of better solutions. For example, some Australian Distribution Businesses are using other technical options to allow reverse power flow. More sophisticated management of the grid such as variable setting (tap-change) transformers ameliorates system constraints and requires less PV export limits.

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PV Export Limits Do Not Fit with the Energy Transition

PV export limits work against the Energy Transition and felt that society does not want the networks to refuse connections. PV export limits are a Distribution Business centric solution that does not meet other goals of environment and community desire for control of their electricity system. PV export limits are a short-term fix that will be needed less as Australia makes the Energy Transition. In fact, PV export limits work against a smart grid and they embody the static view of the grid and do not help design the electricity system with smart controls, storage and optimisation. 5.1.4

PV Export Limits as a Short-Term Fix, Not a Long-Term Solution

PV export limits are not suitable for the long term but are important in the short term. The long-term solution lies with consumers being given a choice to select their output based on price signals in the distribution services market. Dynamic PV export limits could respond to distribution system constraints as well as transmission constraints. With the right incentive, the DBs could reduce the need for PV export limits. Dynamic PV export pricing currently being used in Queensland is a start towards dynamic limits.

5.2

Smart Devices

Smart device capability lies in inverters today unused. The inverter control technology available in inverters at present includes very sophisticated controllers which can manage power settings. Smart meters are not usually set up to control power settings, so that the inverter is a powerful tool for low voltage grid management. The first step is for policy to drive the required controls, algorithms and communication systems into use. This could improve frequency management, capacity management, voltage support and load factor correction. Examples of testing of this include: Mooroolbark (Ausnet, 2018) where the local electricity system is given some autonomy from the grid, and a Virtual Power Plant (VPP) in Canberra, focussed on control of household batteries (Reposit Power, 2018a).

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5.2.1

Smart Inverters and Virtual Synchronous Generators (VSGs)

Inverters are used for PV, air-conditioning, EV and battery systems to convert direct current to alternating current. Options for inverter control defined in an NREL report (Reiter, Ardani, Margolis, & Edge, 2015) include: connect and disconnect, adjustable peak generation, adjustable power factor, Volt-VAR mode to adjust reactive power, frequency ridethrough and voltage ride-through to set voltage parameters governing inverter connection shutoff. These inverter control options include the capability to manage grid issues by PV output curtailment or the redirection of PV output to storage. Inverters can also autonomously monitor voltage and set output voltage with a learning algorithm (Ganu et al., 2013). Synchronous control of inverters to create Virtual Synchronous Generators (VSG) is under development. VSG systems would need to have a very robust synchronising signal to lock the inverter frequencies, so the VSG system is not workable at present, as any under-capacity in storage, for example, would run the risk of voltage drop and a resultant risk to grid stability. 5.2.2

Smart Meters

Electricity will flow to and from consumers (in and out of the electricity grid), and the voltage and frequency will need control. Smart meters can do this control and may help grid balancing with air-conditioners, demand response, batteries, phase shifting inverters and electric vehicle charge settings. Dynamic grid control would be possible in Victoria due to the universal smart meter rollout. There is little ability for dynamic control in other Australian states due to their low concentration of smart meters. The role of the smart meter is that it offers a low-cost communication and verification hub. The despatch of demand response may be through another channel, such as mobile phone messages, but smart meters are best for measurement of the response. Policy addressing smart meters is beginning to reflect the rise of the Internet of Things (IoT) (AEMC, 2017). Sharing voltage and other information from smart meters does create a privacy risk as the information can show when a person is home or using specific devices in their home. There are ways to protect privacy

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by masking the outgoing signal (Reinhardt, Egarter, Konstantinou, & Christin, 2015). Also, there are ways to mask the individual signals but still draw useful network data (Tong, Sun, & Sun, 2015). 5.2.3

Communication for the Energy Transition

Communication is a key issue for the Energy Transition. The IEEE standard for interconnections of distributed generators is 1547–2003, and there is a broad adoption of this IEEE standard. Frequency matching and voltage regulation need some form of communication. Control can be offered by inverters (solar, battery and EV) using solid-state electronics to control frequency (Gungor et al., 2011). 5.2.4

Artificial Intelligence (AI)

The implementation of AI in energy systems has been slow due to the naturally conservative nature of the system operators. This is changing the power electronics in inverters and smart meters (as per above) including AI to analyse data with regression and classification. Google’s company DeepMind has been using AI for control of their data centre cooling systems, for example, and forecasting to optimise market participation for intermittent renewables (Gao, 2014). There is also a synergy between communications technology development and AI, as networks of sensors (otherwise known as IoT) can easily send data back to a central processor for AI analysis and reporting (used widely in industry for preventative maintenance and machine sensing).

5.3

New Inverter Technology

Air-conditioning, PV-systems, storage systems and EV charging all use inverters that could help the grid if they could be integrated into grid operation to optimise the use of grid assets. Inverters could moderate their imposition on the line with voltage set points close to the current grid voltage. Moderating inverter output helps protect transformers from reverse current and the problem of overvoltage events cutting out PV inverters. These are known as autonomous controllers, but really are just set points for each inverter that learns over time their optimal setting (Ganu et al., 2013).

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Energy policy could strategically drive the introduction of grid-friendly inverters. Australia is ready for such a transition (Nelson, 2016). The most recent demonstration of Australia’s readiness is outlined in the Finkel Review (Finkel, Moses, Munro, Effeney, & OKane, 2017) which states, “Consumers are at the heart of our energy system. The actions of consumers will be harnessed to improve the reliability and security of the electricity system and keep costs down”. The regulation of inverters is likely to offer a cost benefit to consumers as it reduces the need to upgrade lines and inverters, and hence will lower costs. The technology for inverters to manage PV grid issues is too uncertain for fixed regulation, but there is a possibility for open architecture in the inverter programming that will allow some degree of future proofing. There is comfort that the standards process will give adequate guidance without a push from government. Inverter technology can automatically respond to network conditions, and distribution system overload can be helped through battery storage of PV, load reduction or load switching. The main reason this is not used currently is that network operators are not sure how to manage this disaggregated resource, and it is not yet clear what settings are best for the grid. Inverters can already respond to network conditions using neural networks and learning systems. Smart inverters might smooth demand using storage and demand management, and their capabilities will increasingly tie to other devices such as smart meters. 5.3.1

How to Implement New Inverter Technologies?

A future where domestic inverters deliver grid services would require an update to standards and would also need a technology pathway that synchronises network and inverter technology. The stakeholders in this synchronisation would include consumers, industry clients, industry supply chain and retailers. The pathway might see firstly government working to influence the standards change, then the technology pathway is developed by small business and government working together, and finally, the retailers help sell the change. Inverters should moderate their imposition on the grid (with voltage set points close to the current grid voltage). Better control of inverter output voltage will help reduce the cut-out of PV due to overvoltage. Voltage control built into inverters is coming in an update to AS4777 (the standard for Australian PV inverters). AS4777 includes a droop setting

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to respond to frequency rise (by reducing PV output) and can support voltage if there is a battery in the system. The recruitment of consumers is tricky, and updating standards is a minimal risk and low-cost transition. Industry can lead this standards development. For example, the Clean Energy Council (CEC) industry body has managed a successful standards regime for PV inverters (AS4777). There is little cost to do this. Future proofing is possible if, for example, new inverter standards required that they be programmable and compatible with the network. An example of the use of smart inverters is in South Australia where some consumers have agreed that their distribution company can partially shut down their air-conditioning compressor with a four-stage control (for under and over voltage control). The South Australian agreement is that these homes receive lower electricity costs, and in Queensland, the Distribution Businesses control air-conditioning and pool pumps (Energex, 2019). These controls of inverters are considered important innovations as they can be targeted to areas where the electricity system is constrained. 5.3.2

Data Flows from Inverters and Sensors

Data delivered to electricity consumers will allow them to alter the way they use technology such as EV and to save electricity costs. Smart charging of an electric vehicle could save the system over $A1000 per year (NERA, 2014). More information can motivate consumers and give them control over their electricity and allow them to sell power or sell demand response. Consumers will see the grid as more reliable and offering more value if they have information (Buchanan, Russo, & Anderson, 2015). An increase in this information will improve customers’ buying and electricity use choices. Data flows from sensors and artificial intelligence are playing an increasing role in grid management. The electricity system can be optimised through appliance load control. Appliance tracking using energy signatures is available which has no need for plug level hardware but the drawback of privacy risk.

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5.4

Remote Control of Consumer Assets

There are claims that the only way to optimise operations includes remote control of consumer assets. Remote control for critical events is reasonable. A more extensive use of remote control of consumer devices might come later when energy is reframed as a service in 10–20 years. The primary reason for remote control is to minimise electricity distribution system issues. The right incentives are needed, and people should be able to opt in and be incentivised. Examples of remote control are the South Australian and Queensland examples described in Sect. 5.3 where the electricity supplier had remote control of customer assets. 5.4.1

Remote Control with Modern Technology

Devices already can take remote control signals, but very few jurisdictions are using this capability. Exceptions include France and Switzerland where water heaters can be remotely controlled. Virtually, all air-conditioners on the market can be switched remotely but are not being switched remotely at present. Where they have been turned on it is where the electricity supplier was struggling with overloaded network systems (Energex, 2019). Innovative business structures could reward the user for amelioration at times of overload. Users could reduce their load on the grid through battery storage of PV output or load reduction, but the highest value services would be on-call to match the network’s requirement. On-call services would need remote control. 5.4.2

Remote Control of Customer Assets

Remote control has been in the form of hot water switching for years. Energex has exercised direct load control of pool pumps in the past decade and has been incentivising consumers to accept this control (Energex, 2014). South Australia has been successfully running control of air-conditioning by agreement with consumers. The South Australian arrangement is turning off aircon compressors for 15 minutes an hour, and in exchange, these people receive electricity at a cheaper rate. The need for remote control is primarily in network overload situations, which are usually short-lived and infrequent. Distribution Businesses should have the ability to control the device if there is a problem on

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the grid. The 2016 South Australian blackout was caused partly because of zero load due to solar. This was a problem because the negative load from the solar contribution contributed to the instability instead of the households acting like a huge capacitor to smooth the instability. Remote control to turn off PV in such a grid instability would be another useful application of remote control. Consumer energy devices can feasibly contribute to frequency control and voltage support, but cannot protect against voltage collapse (voltage stability) which is the domain of the large generators. The distribution grid is too weak to contribute on that scale. 5.4.3

The Effect on Consumers of Remote Control

An opt-in system of remote control is preferable to mandated control. People should not be forced to disconnect, but if they have signed into a contract to do it then that is fine. If remote control was used too indiscriminately, there may be a risk of people who might die from heat effects. On the other hand, most homeowners will not even turn off their second fridge. Unless remote control is well explained, consumers will push back. There are risks that Distribution Businesses will use remote control in excess to manage their distribution grid; therefore, minimum performance standards might be useful. There would need to be at least a public discussion on remote control by Distribution Businesses. It is a bit “old world” to have central control, although frequency control is vital. The Distribution Businesses are the only parties that know how to optimise the grid, so should have remote control as needed. 5.4.4

Integration of Remote Control into the Electricity Market

Remote control could be incentivised in the same way as other market services, but this would need a regulatory framework due to the need for careful protection of consumer rights. Storage can be set up to address grid overload by injecting power into the grid and could work with remote control. The energy rules prevent distribution companies owning storage on the customer side, but customers could agree to hand over control of their battery to the distribution company for money. There are also theoretical models for electric vehicle (EV) batteries to contribute, but they are not designed for this mode of operation, so EV batteries are unlikely to participate.

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Remote control is being used in an Australian VPP trial (Reposit Power, 2018a) but there are no standards yet. By comparison, Germany has standards for droop, dead-band and frequency response. A remote control framework for Australia is being written and trials began in 2019. 5.4.5

Rewarding Consumer Device Switching

For consumers to participate in the market, there would need to be a 10–15 second response, and it would be hard to validate. A solution for this might be to have it incentivised via a market like the FCAS market in Australia. A sophisticated market would open roles for aggregators which are companies that have a group of customers whose electricity and electricity services can be aggregated and traded on the wholesale market. Aggregators are likely to do the trading rather than consumers.

5.5 The Role of Energy Efficiency in the Transition? The seven perspectives below explore the role in the transition of energy efficiency. These include a definition, importance, actual actions, the role within the transition, the role of the building code and the importance of the building code. 5.5.1

Defining Energy Efficiency

We can change out of old appliances with more efficient ones, building renovations and eliminating standby power. Energy efficiency can also contribute to load deferral with heating/cooling, water heaters, washing machines, dishwashers and clothes dryers. There is an opportunity to regulate the new appliances and reward people to upgrade equipment. An example of rewards for upgrading equipment is seen in Australia in the NABERS (Australian Government, 2019) real estate energy rating scheme for commercial properties. Energy efficiency is the cheapest intervention according to Enkvist, Nauclér, and Rosander (2007), followed by DM and batteries. Storage has become cheaper in the years since Enkvist et al. did their research. Energy efficiency in devices and the built environment includes thermal inertia in buildings, demand response and demand dispatch. One example of the application of energy efficiency is Reunion Island which has been

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modelled (Bouckaert, Mazauric, & Maïzi, 2014) to use energy efficiency and renewable energy to make the island self-sufficient. Energy efficiency is important but difficult to apply. Our experience in Australia is that we cannot motivate people to implement energy efficiency. Deployment works best through regulation in building codes and product standards. Australia has a minimum energy performance standard for whitegoods efficiency and a range of Australian and state-level energy efficiency rating schemes. For example, incentive schemes have been successful and have led to the installation of 30–40 million LED lights in Victoria, a state with six million inhabitants (Victorian Government, 2007). Europe is leader in energy efficiency as defined in their Energy Efficiency Directive (EU, 2018). 5.5.2

Building Codes Are Important

Buildings are a major source of carbon dioxide during construction and operation, and many buildings operate for up to 50 years (Kelly, 2009). The World Green Building Council has a goal that all building stock would be converted to Net Zero Emissions by 2050. This goal is ambitious, but is aligned with research that energy efficiency is the lowest cost form of carbon reduction (Enkvist et al., 2007). Building efficiency should be achievable in new buildings but is much more difficult for retrofit. It is not expensive to design new buildings with smart air-conditioning and thermal efficiency. To achieve this, alignment is needed between all levels of government as currently in Australia there is poor monitoring of self-assessable codes and the building code for the tropics has resulted in higher costs for the grid. Overall, the practice of building houses that rely on air-conditioning is causing problems for the Australian grid. And finally, governments must lead the building code change, as others cannot do this. 5.5.3

Building Codes Are Being Used but Spread Over Multiple Levels of Government

Building code reform is happening but results have been mixed. Building code reform is important and easy to implement. There are political worries about its effect on the cost of housing. In fact, consumers are not normally motivated to improve their building efficiency, and when a

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consumer buys a new home, they put more focus on the benchtop and energy efficiency comes as an afterthought. Australia regulates building codes at both national and state levels. State government control includes Victoria which has a 6-star rating on all new homes, and in the Australian Capital Territory (ACT) when a person sells a house, the rules require them to supply an energy rating. Given that building codes are spread over multiple levels of government, their reform is exceedingly difficult. By comparison, the European Commission has the Buildings Directive (2010) which includes an obligation on building suppliers and distribution operators to ensure 1.5% energy savings per year.

5.6

Cost Reflective Network Pricing

The electricity system is running most of the time at a fraction of its maximum capacity. It is only during peak periods that the networks near their capacity. The price would ideally function as an incentive for customers to reduce their usage during peak periods, but this is not usually the case. The price to the customer in most cases is not related to how much they use the network during peak periods. Many have called for more dynamic pricing (Faruqui and Lessem 2012; Schweppe, Caramanis, & Tabors 1985). Despite this, there is little support for changing networks to dynamic pricing and price reform is seen as autocratic and takes too long to set. The view is that it is better to set up a distribution service market to set pricing. Some key challenges with cost reflective pricing include: it is difficult to reflect the cost of building networks in any pricing model; dynamic pricing down to lower level transformers is very difficult to measure; congestion results in such high real-time prices that would “frighten” customers; and pricing for reverse flow depends on whether that reverse flow helps the system or overloads the system. In one scenario, if your neighbours all have EV charging, then your transformer is overloaded. You would then pay a high price for any use of the network, even though the constraint is due to your neighbours. This would be unfair.

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Cost Reflective Pricing and Capacity Pricing

Consumers respond to peak reduction via technology better than via pricing, and most consumers will not opt-in, so there may need to be some mandated rollout. The Australian state of Victoria could do cost reflective pricing due to its universal smart meter rollout, and Ergon in Queensland has time variable PV feed-in tariffs. France and Germany are innovative in incentivising behaviour that helps the grid. For example, there are capacity markets in Germany. Sonnen batteries (a storage product vendor in Germany) trade in that market, offering a 15-year power purchase agreement (PPA) to retail customers. 5.6.2

Cost Reflective Pricing Is Important but Difficult to Implement

Cost reflective network pricing is unpopular with consumers, and there has been resistance to change in this area. Economists would love to see locational pricing as more efficient than subsidies. Locational pricing is unlikely to happen because of the social contract between rural and urban Australians to subsidise services to rural areas. On the other hand, temporal network pricing is more likely. An example of temporal network pricing is a 2017 trial of demand management pricing run by AEMO on behalf of the Australian Government (AER, 2017; Dunstan, Alexander, Morris, Langham, & Jazbec, 2017). The tender for this trial has seen retailers and other participants bidding on demand response. For example, Powershop, which is a retailer, responded to this tender. During the first 6 months, Powershop sent three text messages to their retail consumers that offered a set reduction in load for $A10 for each successful reduction. Another response to the tender was from the CitiPower & Powercor, which responded by setting their voltage to 209 volts for 2 hours. This shows that the market can innovate when the government sets the rules. 5.6.3

Social Barriers to Cost Reflective Pricing

People will still want to run their air-conditioning on hot days and will be unresponsive. Selling demand tariffs to consumers is difficult so a targeted approach would be safer, such as targeting consumers who are motivated

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to participate in the energy market. Any change to cost reflective network pricing would need to be automatic to avoid increasing the complexity of the current system. To improve the operation of the grid, there would need to be an incentive signal that changes people’s behaviour. If price was high enough to do this during network peaks, a social barrier would emerge that is a barrier to this policy because a true price (reflecting a fair proportion of the cost) would be extremely high (perhaps 100 × the normal price). 5.6.4

Technical Process of Implementing Cost Reflective Pricing

Cost reflective pricing would be suited to reward the owners of batteries because they can export and charge their batteries at a time that suits the grid and can benefit the grid. This could be done with no impact on amenity for the consumer. Temporal network pricing could then provide the signal for these flows of electricity (to and from the batteries). The benefits of cost reflective pricing might include optimised distribution grid capital decisions, optimised regulation, as more information is available, smart charging of EV, and better decisions on purchase and use of technology such as air-conditioning, and PV. The pairing of certain load profiles with network tariffs would give consumers the signal for investment in their energy devices. The price signal could reward people for reducing large and sudden changes in generation or load, reward self-consumption of renewable energy (which would use the network less) and reward peer-to-peer storage services that only use the local grid. Despite the above market dynamic, the introduction of cost reflective pricing is likely to be led by Distribution Businesses which need the right incentive to introduce the technology for dynamic management of their assets, and currently, they do not have the right economic incentives. Regulations allow DBs to implement cost reflective pricing, but they have been slow to adopt this. 5.6.5

Cost Reflective Pricing as Part of the Energy Transition

To achieve optimisation of the distribution network, the network tariffs could use dynamic temporal pricing. Problems with fairness are a big barrier to pricing reform so rules would be needed that allow differential pricing that is fair to each consumer group.

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Government price reform is too autocratic and takes too long to set. It would be better to set up markets for the distribution system (but to do this slowly). The political resistance to dynamic tariffs is strong as politicians are not technical and are sensitive to the voluble demands of their non-technical constituents, who see variable tariffs as costing them more than fixed tariffs. Cost reflective pricing would be very difficult to implement so a step along the way could be to set cheaper pricing for the transformers (and feeders) that are not under constraint, so the system owner then sorts out their efficient operation and focusses their dynamic pricing on the constrained part of the network.

5.7 Demand Response (DR) and Demand Management (DM) The demand referred to here is the demand for electricity. These fields of DR and DM are customer changes to reduce their load on the system and are to reduce load at the time when the electricity system is overloaded or constrained. The Demand Management Incentive Scheme (DMIS) is an important first step in the introduction of system scale demand management (DM) into Australia (AER, 2017). The impacts of DR and DM are felt in peak reduction and can be used to reduce the need for additional network assets. They can also be used to increase stability in locations with a lack of inertia (rotating) generation. One Australian state with a lack of inertia is South Australia. 5.7.1

Demand Management

Demand management means actions which result in sustained reductions in energy use. Action on demand management has been high in South East Queensland for over the past decade due to the fast population growth and electricity use outstripping the energy infrastructure. Energex is the distribution company for this area, and their 2014 report on demand management offers many valuable insights into options (Energex, 2014). The wide availability of electronics to allow consumers to participate in demand management is a recent phenomenon. An example in Australia is the business Reposit Power (Reposit Power, 2018a) whose

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customers engage in the wholesale electricity market through their intermediary or aggregator (Reposit Power), and therefore, they will reduce load when the price is high, which correlates with system constraints. 5.7.2

Where Demand Management Is Happening

DM is already happening in the USA and a few other countries, although on a small scale. The DMIS (AER, 2017) is focussed on wholesale constraints and not on distribution constraints. There were outages in 2016–2017, which motivate AEMO to quickly bring the DMIS trial online soon after the AER release. AEMO tendered the DMIS trial (AER, 2017), and it was implemented surprisingly fast during 2018 and has been successful. Intervention is needed as the current process is proving expensive (AEMO, 2015). A systems view to address this is shown in “Sociotechnical evolution of Decentralized Electricity systems: A critical review and implications for urban planning and policy” (Adil & Ko, 2016). The most progressive countries in Europe on DM and DR are in the UK under the RIIO rules (Ofgem, 2010), and DR in France has developed well and has been tightly regulated. 5.7.3

Consumer Participation in Demand Response and Demand Management

Consumer roles in the electricity system are challenging to communicate. People understand differing prices for air tickets but not energy services. There may be solutions found through further research in behavioural economics. In Australia, most peak reduction will be achieved through regulation and technology and that DM and DR will not create enough change. People will not respond to signals but may participate in automated schemes with the right incentives, education and technology. 5.7.4

Demand Response (DR) and Demand Management (DM) in the Energy Transition

Some interesting models are emerging in Australia where consumers are rewarded for demand response. Two Australian companies operating in this sector include Reposit Power Pty Ltd and GreenSync Pty Ltd. Where

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there is a financial incentive, businesses and consumers will be open to playing a role. This is likely to require a distribution services market. DM and DR would need to be with consumer agreement and would require functioning markets with price signals. The current DMIS program in Australia does nothing to ameliorate distribution constraints. The distribution market that would allow market incentives for DM and DR might be started in 8 years in 2026 according to my research.

5.8

Electric Vehicle Charging

The question about EV charging was met with disinterest in Australia but seen as important in Europe due to the increasing commitments from European governments to phase in EV and the expected huge effect on the grid. This reflects the low sales of EV in Australia. Methods of EV charging times being set by a network were explored by de Hoog, Alpcan, Brazil, Thomas, and Mareels (2015). Controls for EV charging are likely to be very important as Morgan Stanley forecasts that EV charging is set to double electricity peaks (2017). 5.8.1

Electric Vehicle Charging Rules Are Happening

50% of auto sales in Norway are EV but only 0.1% in Australia. Australia has yet to develop a framework for allowing EVs onto the grid. A framework for EV charging can be like other network connections which are controlled by Distribution Businesses. Pricing for electricity can follow time-of-use pricing, but there may need to be special time-of-charge agreements to minimise the EV impact on grid operation. Smart charging is needed, or the grid will fail (de Hoog et al., 2015). Distribution Businesses hope that EVs can prop up demand to increase asset utilisation, but Australia needs to be cautious backing EV as it may overload the grid and with minor impact on emissions. The lack of interest in policy for EV charging is not supported by the literature, and grid benefits from this type of regulation are evidenced in the literature (Mareels et al., 2014). They conclude that arrangements for sharing network assets can increase the number of EVs in a community 2–3 times, the number possible with uncoordinated grid access.

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5.9

Storage

The Tesla battery storage plant at Hornsdale in South Australia takes part in the wholesale ancillary services market and has delivered healthy profits to its owners, who arbitrage the battery with their adjacent wind farm (Fig. 5.1). The fast response of large batteries has been proven over 30 years ago, and the first large-scale plant in Berlin has been in operation since 1987. Storage has a key role in reducing grid issues and reverse flows (Liu, McArthur, & Lee, 2016). Storage is important because it offers a solution to the intermittency problems of renewables, can smooth peaks, offer fast frequency response and load shift. Storage also offers opportunities to Distribution Businesses, to smooth power, manage peaks and for frequency regulation. Costs for storage are expected to further reduce (Schmidt, Hawkes, Gambhir, & Staffell, 2017), and cost of installing PV and storage

Fig. 5.1 Hornsdale wind farm and Tesla battery (Hornsdale Power Reserve)

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(together) is close to competitive (Lai & McCulloch, 2017). PV and battery packages are being marketed to Australian consumers with a 7-year payback, which includes the Australian 30% carbon subsidy on PV. 5.9.1

Storage Is Happening Now

Currently, there are about 24,000 household batteries (the type connected to the switchboard outside) in Australia, but this is expected to rise to 700,000 by 2027. There is huge variation in their payback period because of differing electricity plans, differing use patterns, differing load devices and what load devices are planned in future. As the storage technology matures, it will be taken up at an increasing rate, so rules are expected to be needed urgently. Storage rules will need to consider suitable network pricing and control over when grid charging can occur and at what rate. Battery sharing is getting extensive academic interest (Kittner, Lill, & Kammen, 2017). The network centric orchestration of storage is technically possible, and for improved grid control (including the management of PV grid issues), consumers may accept controls on their household batteries if the energy plan rewards them for this imposition (Ochoa, Pilo, Keane, Cuffe, & Pisano, 2016). Storage is a key part of current VPP tests in Australia (GreenSync, 2017) and one test successfully automated despatch of storage (ARENA, 2018). The EU is reviewing regulations in Europe for distribution stability and will focus on solving distribution problems at the national level (not at the EU level). Under the EU rules, Distribution Service Operators (DSOs) cannot invest in storage or generation. European policy encourages the building of consumer actions into distribution grid planning. There is a current debate in the EU on whether to pursue a flexibility market (buy the stability on market) or use tariffs to encourage PV and EV. 5.9.2

The Role of Storage in the Energy Transition

Policy development and regulation of storage are beginning in Australia with the AEMO register for batteries (AEMC, 2018). There is a current rollout of 30,000 batteries in South Australia, which should motivate more regulation and policy development (South Australia Government, 2020).

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Interestingly, the grid problem for EV is much simpler than storage because the charging point for EV is a fixed load, while storage is responding to PV and load. The only way to get home storage to soak up excess PV is to financially incentivise it. I do not think it can be done via rules, we will need pricing reform and must avoid over-regulating the battery. Batteries can act as a load and as a source of electricity (generator) so are covered by different regulations with the National Electricity Rules at present. There is also a different regulation for batteries inside or outside the meter. Storage needs to be controlled by the Distribution Business to reliably be used to avoid network upgrades and reduce total system cost. Unfortunately, this means the participation of domestic batteries in grid optimisation will be difficult to achieve.

5.10

Data, Ethics and Social Licence

There is a current turf battle in Australia between the retailers and the Distribution Businesses on rollout of electricity meters that allow two-way communication (smart meters) and management of the associated data. The Distribution Businesses have the advantage that consumers have no choice of network supplier and have an ability to work with the data they hold at present. Changes would be needed for Distribution Businesses to share that data to allow others to take part in the solution. There are some signs of this change beginning in the current regulatory proposals for Australian Distribution Businesses for the years 2019–2024. One example is Ausgrid which has extensive consumer engagement proposed (Ausgrid, 2018). Change in the grid increases complexity. Flows of consumer data could help Distribution Businesses manage this complexity. Third-party aggregators are likely to be key players in this flow of data, as consumers are ill equipped and uninterested in complex data deals. Government should control the data. The Victorian government has proposed a data repository of all smart grid data, which may lead to a market emerging. The Distribution Businesses are highly regulated, which is incompatible with innovation, but they are a key participant as Distribution Businesses have privileged knowledge about the network. That said, consumers should not be forced to go to the Distribution Business

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because it will restrict market innovation and the signals are bad, as getting access to consumer data from the Distribution Businesses is currently a clumsy process. 5.10.1

Market for Distribution Services

A market for distribution services is now more likely because of the availability of smart meter data and the controllability of consumer inverters. Markets can allow users to have choice in their grid access. For example, a choice for a higher level of network service might have a higher price. A market would set these prices in a similar system to the Frequency Control Ancillary Services (FCAS) market, but Rule 6.1.4 of the National Energy Rules would need to change to allow pricing of exports from customers. Uber redefined the taxi market and there may be a similar redefinition in the electricity market. Distribution grids are increasingly facing overloading from distributed generation and loads such as air-conditioning. Both can be moderated by changing loads and generation to avoid expensive network extension. There are many smart grid mechanisms that rely on communications, and a system with comprehensive communications and control is defined as a Transactive Grid. 5.10.2

Ethics of Gathering Data About Our Electricity Use

As our electricity use includes information about our whereabouts and activities in our homes, the issue of ethics becomes preeminent. We can consider the work of John Stuart Mill Mill (1859) regarding the social contract. The social contract discussed by John Stuart Mill argues that we must manage the democratic subsystems with the required respect for individual’s goals, aspirations and self-actualisation. Mill’s work intersects with the work on democratic structure offered by de Tocqueville on democratic structure. Their work makes it clear that policy should accommodate diverse views to build a robust society. The goals, aspirations and self-actualisation of individuals should balance with the perspective of the community. Technology and longterm contracts with individuals will be the best way to deliver this balance, as individuals have no way to optimise the grid. This may mean the individual pays for a more expensive inverter but may then contract for lower cost electricity.

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The individual will need to lose some autonomy to avoid socialisation of their network costs. At present, in Australia, connection of air-conditioning, solar and other devices creates a cross-subsidisation of network costs by the whole of society (Wood, Carter, & Harrison, 2014). 5.10.3

Distribution Business (DB) Use of Data

Distribution Business regulation faces a conflict between monopoly regulation and market competition. Monopoly regulation tends to stymy innovation, and markets do not easily work within highly regulated monopolies. One development in Australia is that many Distribution Businesses set up market entities in 2018. In the UK, RIIO regulation (Ofgem, 2010) has been successful in establishing innovation in their Distribution Businesses. Distribution Businesses face network constraints but tend not to take part in energy efficiency and leave it to the retailers. Retailers then have their own conflict between selling more electricity and improving the efficiency of their consumers. Regulation of Distribution Business pricing is a key energy policy area. Dynamic network pricing can drive down transaction cost, and Faruqui and Lessem (2012) argue that real-time pricing and fast response technologies reduced peak load in New York City by 13–16%. This included smart appliances, batteries, other energy storage and home energy controllers. One idea on Distribution Business network pricing from the Netherlands (Enexis Holding) suggests that consumers are offered a band of maximum PV input and maximum draw on the grid, and a separate and higher tariff for any electricity outside this band. The goal of this is to reward flattening of load on the distribution system. Another key regulatory challenge is how to maintain investment signals for the electricity system owners. In fact, fossil generation assets are becoming unviable in many markets around the world and may close sooner than regulators would prefer and create instability in the electricity system. Pricing has in the past sent suitable signals to keep the market in balance and allowed investors to choose what electricity system assets will return healthy profit returns over 50+ years. Such a long-time period means that the price signals need to be clear and steady for the investor to have confidence that the investment is sound. The changes in the electricity system described in this book mean that the electricity

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price is deteriorating when renewable generation comes online making the fossil generation uncompetitive and investment signals weaker. 5.10.4

Aggregators and Data Freedom

Distribution Businesses cannot invest directly on the consumer side due to the ring-fencing rules, so the space is open for new market participants such as unregulated arms of the Distribution Businesses and aggregators. Australia needs an equivalent market for distribution services to the FCAS market. Amongst aggregators and market innovators is the Australian company Reposit Power. Commercial and industrial electricity consumers in Australia mostly buy electricity through brokers. These consumers have a low understanding of the electricity market and have opted to buy on price from a broker, so the emergence of sophisticated aggregators may deliver better value to these consumers. Consumer solutions will vary widely but the market is increasingly providing optimisation services. Retailers are motivated to retain consumers through increasing services to their consumers (Carbontrack, 2019). There may be a trend to microgrids structured as community energy systems. There are 30 community energy organisations in the Australian state of Victoria alone, and this is a strong source of innovation. Ownership of raw electricity data in Australia is not possible under property law. The data only becomes property when someone has added value to it. The privacy law also dictates the consumer has control of this data. After the smart meter rollout in Victoria, the Distribution Businesses handed over the data for free. CitiPower & Powercor, and United Energy are two Distribution Businesses (Distribution Businesses) now passing real-time data to consumers via a government platform. The intent in Victoria is to allow the Distribution Businesses to benefit from this data, but the retailers in other states are fighting for control of smart meter data as they can see a strong value proposition. There is a need for freedom of data flows to engender a market to optimise the low voltage electricity system, and the need for this market includes voltage, frequency, electricity and network constraint data. If the data is only viewed as balancing supply and demand, a mere fraction of the economic value will be extracted and further rises in electricity prices can be expected. Information traded in this market would benefit from blockchain or similar encoding to allow the generation of many mini

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contracts between consumers, Distribution Businesses and retailers and other interested parties such as aggregators. Freedom of data will allow the market to work to benefit society. All the system actors can optimise their decisions, and decisions will be evidence driven. The counterargument to this freedom of data is that privacy needs to be carefully managed. The European Privacy Law (European Commission, 2018) may have significant negative impact on the development of markets from consumer electricity data in Europe. 5.10.5

Business Perspective of Increased Data

If governments free up trade and data flow, consumers can expect lower prices. Consumers should see equitable pricing that rewards their grid friendly innovations and sends signals to drive better decisions on solar, air-conditioning, EV and storage (Xia et al., 2014). Information delivered to electricity consumers would allow them to alter the way they use technology to save electricity costs. Smart charging of an electric vehicle (EV) could save over $A1000 per year (NERA, 2014). Consumers would see the grid as more reliable and offering more value if they have information about their electricity use (Buchanan et al., 2015). Individuals with technologies such as solar, electricity storage, mobile phones and network-connected fridges need to choose their investments. They need data to manage their electricity-related buying and electricity-related choices. It is widely understood that the last mile of the telecommunications system is highly profitable (Dingwall, 1995). Similarly, the last mile of the electricity system offers a profit opportunity and has led to a rush of new electricity retail companies in Australia. There may be new markets including secondary markets, brokers, integrators and aggregators. The markets can support competition, which should drive prices down. Consumers may have access to more information about their electricity system. More information can motivate consumers and give them control over their electricity and allow them to sell power or sell services such as demand response. Reliability standards are currently set up to protect against blackouts (at a cost) but increasing costs of generation and increasing network costs may lead to further increases in electricity prices unless meaningful change in regulation is achieved to optimise the network assets.

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Deployment of Increased Data Flows

Deployment of the Energy Transition would ideally match constraints in the grid. Matching constraints in the grid would need collaboration with the grid operator. Grid operators would be most likely to focus on transformers under stress. Control of consumer assets may reduce PV overvoltage or undervoltage from air-conditioning. Voltage control built into inverters can help homeowners by bringing down the voltage, so the consumer gets more real power, and less apparent or reactive power. Testing of this type of voltage reduction shows a power saving of up to 2.5% (Faruqui, Arritt, & Sergici, 2017). This testing also found that this could increase the life of consumer devices from an average of nine years to an average of 12 years. Voltage control could help balance the electricity system, and tests on VPP’s are underway (Reposit Power, 2018b). Storage offers a voltage control opportunity to avoid the Solar Inverter Lockout due to overvoltage by controlling the voltage level, and voltage fluctuations that can damage equipment can be moderated. The Australian Standard AS4777 requires PV inverters to have reactive power capability, but this capability is not yet being used. There is significant opportunity to harness this reactive power capability. Incumbents may resist the transition, but they are motivated also to change. Kern and Smith (2008) give the example of the restructuring of the Dutch electricity system. The Dutch program was managed as a transition management program, and the work by Kern and Smith show that the goals were crushed by the incumbents (the existing energy utilities) who succeeded in stacking the transition committees with their people and blocking change.

5.11 What Have We Learnt About Technology and Data for Improved Decision Making? We should aim for changes to Distribution Businesses, inverter regulation, PV export limits, cost reflective pricing and the implementation of more storage. We face upward pressure on prices as we maintain reliability standards in the face of increasing variability from consumer load and renewables generation. The management of consumer load will require sophisticated engagement with consumers that links network constraints

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to consumer loads in a way that reduces the constraints. The reduction of constraints will require the creation of a capacity market as the energy-only market creates no incentive for the networks to respond to constraints. This will require a far stronger role for technology and data in the Energy Transition. 5.11.1

Policy to Align Technology and Data for the Energy Transition Is Urgent

The current system was designed to meet peaks with ample spare capacity, but the peak load has increased at a time that the total electrical sales have reduced, so less electricity is sold but in shorter bursts. This means that the supply side of the electricity system is becoming more difficult to control as variable renewable energy is integrated (wind and PV fluctuate with the weather). The next chapter explores the use of systems methodology to design the Energy Transition.

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NERA. (2014). Efficiency of tariffs for current and emerging technologies. Retrieved from https://www.aemc.gov.au/sites/default/files/content/f50 1b892-e2e0-4318-a1d1-93ad011e02c7/NERA-Economic-Consulting-%E2% 80%93-Current-and-emerging-technologies.PDF. Accessed 19 October 2018. Ochoa, L. N., Pilo, F., Keane, A., Cuffe, P., & Pisano, G. (2016). Embracing an adaptable, flexible posture: Ensuring that future European distribution networks are ready for more active roles. IEEE Power and Energy Magazine, IEEE New York, 14(5), 16–28. https://doi.org/10.1109/mpe.2016. 2579478. Ofgem, R. (2010). A new way to regulate energy networks. Final Decision, Office of Gas and Electricity Markets, London, 25. Reinhardt, A., Egarter, D., Konstantinou, G., & Christin, D. (2015). Worried about privacy? Let your PV converter cover your electricity consumption fingerprints. Paper presented at the Smart Grid Communications (SmartGridComm), 2015 IEEE International Conference. Reiter, E., Ardani, K., Margolis, R., & Edge, R. (2015). Industry perspectives on advanced inverters for US solar photovoltaic systems. Grid benefits, deployment challenges, and emerging solutions (NREL/TP-7A40-65063). Retrieved from Golden, CO https://www.nrel.gov/docs/fy15osti/65063.pdf. Reposit Power. (2018a). VPP trial. Retrieved from https://repositpower.com/ news/canberra-virtual-power-plant-awarded-top-engineering-honours/. Reposit Power. (2018b). Company website. Retrieved from https://repositpower. com. Schmidt, O., Hawkes, A., Gambhir, A., & Staffell, I. (2017). The future cost of electrical energy storage based on experience rates. Nature Energy, 2, 17110. https://doi.org/10.1038/nenergy.2017.110. Schweppe, F. C., Caramanis, M. C., & Tabors, R. D. (1985). Evaluation of spot price based electricity rates. IEEE Transactions on Power Apparatus and Systems (7), 1644–1655. South Australia Government. (2020). South Australia’s home battery scheme. Retrieved from https://homebatteryscheme.sa.gov.au/. Tong, Y., Sun, J., & Sun, K. (2015). Privacy-preserving spectral estimation in smart grid. Paper presented at the Smart Grid Communications (SmartGridComm), 2015 IEEE International Conference. Victorian Energy Efficiency Target Act. (2007). Wood, T., Carter, L., & Harrison, C. (2014). Fair pricing for power. Melbourne, Australia: Grattan Institute Melbourne. Xia, L., Mareels, I., Alpcan, T., Brazil, M., de Hoog, J., & Thomas, D. A. (2014, 19–22 February). A distributed electric vehicle charging management algorithm using only local measurements. Paper presented at the ISGT 2014.

CHAPTER 6

The Energy Transition as a System

Abstract As George Bernard Shaw suggested, “the possibilities are numerous once we decide to act and not react”. Indeed, we need to proactively manage the Energy Transition, and not just react to it. This chapter proposes a method for this management. The electricity system is the most complex machine ever built and it is struggling. The current system was designed to meet peaks with ample spare capacity, but the peak load has increased at a time that the total electrical sales have reduced, so less electricity is sold, but in peakier bursts. The supply side of the electricity system is also becoming more difficult to control as variable renewable energy is integrated (wind and PV fluctuate with the weather). We need to flatten the load curve, and align it to renewables generation, while maintaining grid stability. There are strong political headwinds against a more proactive management of the Energy Transition. Our chosen method must be robust to withstand these political headwinds. Systems approaches are useful in managing transitions because they can accommodate dynamic and complex behaviour, and this chapter sets out how systems analysis might be used to manage the Energy Transition. Key Words Complexity · Systems · System dynamics · Systems engineering

© The Author(s) 2020 G. Currie, Australia’s Energy Transition, https://doi.org/10.1007/978-981-15-6145-0_6

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6.1 What Needs Managing in the Energy Transition? The objective of energy policy must be to deliver energy that is: reliable; has lower carbon content; and lower cost (Wyman, 2016). Governments to increase consumer roles in the electricity system through the power of open data that encourages innovation. The use of strategic short-term use of PV export limits is important, but we should also incentivise smart appliances, batteries, other energy storage, home energy controllers. This incentivised storage is critically important as it is through storage that consumers can provide valuable services to the network and gain “status ” as having a meaningful role. And some type of financing support will need to be established, which could include infrastructure funds, grant schemes, tax credits, loan guarantees, or superannuation (pension) funds. The Energy Transition must also fit into the existing energy market structure. Any effort to improve the grid can only succeed if the entire system of risks and societal pressures are considered.

6.2

Framing the Energy Transition as a System

The Energy Transition should be designed to be resilient, and adapt to keep its potency (Althaus, Bridgman, & Davis, 2013). Dynamic systems modelling can use learning systems to facilitate this adaption (Checkland, 1981). Human systems are dynamic and hard to anticipate, and Checkland suggests a reiteration of the modelling process to give a chance to correct for the errors. Checkland’s method considers what is being transformed (P), how “I ” want to transform it (Q), and why “I ” want to transform it (R) (Checkland, 2000). Therefore, to put the argument of this book in Checkland’s PQR method: P = energy policy Q = because current policy is encouraging consumers to break not mend the system R = because good energy policy keeps prices lower than they would be otherwise Another tool that could be explored is collaborative planning. Innes and Booher (1999) offer a useful framework which is a system, and a learning

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process. Innes and Booher argue that the more the learning process is integrated in policy, then the more chance that capital is wisely spent, and there is optimisation of resources. Argyris introduced Double Loop Learning in organisations (1977) and shows that learning systems must have a structure that encourages learning. Senge took this further where he connected the idea of organisational learning with systems thinking (1990). Senge offers a way for an organisation to work towards a goal in a complex system by using five disciplines: systems thinking; personal mastery; mental models; shared vision; team learning; iterative modelling and interventions. Organisational learning comes with the encouragement of individual creativity, distributed problem solving, and expectations are clear. This supports the argument that energy policy could be better designed as a dynamic system. The Victorian Government has utilised dynamic systems in policy development (Sposito, Faggian, & Pettit, 2008). Energy policy could best be developed by considering its dynamic and evolutionary systemic interrelations with technology, other policy, regulations and society. The concept of evolutionary systems in economics calls for structures that integrate emergent or dynamic behaviour. The book “An evolutionary theory of economic change:” (Nelson & Winter, 1982) argues for the use of evolutionary rather than neoclassical theory. Their argument builds on Weiner, Popper, Beer, Simon and Checkland in the following ways: the relationship between man and machines is relevant to the development of a socio-technical system view of consumer roles (Wiener, 1948); the complexity of the distribution system and user systems is increasing with the development of new technology such as electric vehicles, home storage and home automation and statistical validation is important (Popper & Weiss, 1959); organisational cybernetics is relevant to developing a system for consumer roles in the electricity system (Beer, 1984); a system can be decomposed, and the sections of the system can exist separately (Simon, 1962); and Checkland’s iterative modelling (1981) is ideal for the evolutionary view of the transition process to consumer roles in the electricity system. Academia also has used evolutionary modelling of the electricity system (Ochoa, Pilo, Keane, Cuffe, & Pisano, 2016).

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6.3 System Dynamics View of the Energy Transition System dynamics can be used for elements of the Energy Transition management plan, as can temporal models such as the ARIMA model of the PV-system in Chapter 4. 6.3.1

System Dynamics Theoretical Model of PV Sales

Figure 6.1 shows all flows are in money, and there is a monetisation of the information flows to allow the process to balance. The focus relates solely to the solar purchase process and does not show other flows related to the purchase of non-solar electricity. The model in Fig. 6.1 is the customer subsystem of the electricity system. The underlying driver of the system is the customer utility and there are two other key drivers. Vendor offers (which are in turn driven by the economy, technology innovation, technology and pricing), and money (in turn linked to the economy). There are other drivers such as Distribution Businesses, electricity retailers and governments (including regulators), but these effects are subsumed in the three drivers of customer utility, vendor offers and money. The model in Fig. 6.1 has significant lags which include the lagged information collection start to decision to buy (technical knowledge might precede economic knowledge by 6 months); the lag from decision to buy to buying action; and the lag from buying action to installation completion. Relevant to these time lags, Simon developed a concept of time vs knowledge (as a trade-off). This analysis does not explore this trade-off as the prima facie flow of information is used and valued (Simon, 1962). There is value in the build-up of knowledge in the consumer, before and during the solar purchase process. The media have a role, and the information gains its value as it is converted into a decision about installing solar. There is value that is then paid for the original information as the solar system generates electricity that is of value to the homeowner. Payoff from the purchase is also intrinsic in the pride and social prestige for the homeowner. Systems dynamics, learning systems, policy as a system and evolutionary modelling are all tools that might be used to proactively manage

THE ENERGY TRANSITION AS A SYSTEM

Fig. 6.1 Model of consumer PV sales using flows of electricity, money and power

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the Energy Transition. In terms of a framework for action, we should first consider systems engineering.

6.4

Systems Engineering

The development of systems engineering had a strong drive from the military-industrial complex in the USA in 1960s which saw the development of a military specification (DOD, 1969). The further development of a commercial focussed specification called IEEE P1220 (Table 6.1) is of more interest to this analysis (Schmidt, 1993). The development of IEEE P1220 was motivated by the realisation that commercial operators operate with far more complexity, so it offers a sophisticated feedback system and can offer a framework to proactively manage the Energy Transition. The IEEE P1220 definition of requirements in Table 6.1 is a reminder of the complexity. Firstly, we can consider the physical architecture. 6.4.1

Linking the Physical Architecture to the Functional Requirements

IEEE P1220 calls for a functional analysis (an action analysis, so all functions are verbs) and a connection to the physical form. This is shown in Table 6.2. The theoretical example in Table 6.2 is for illustration only and would need an extensive research to complete. 6.4.2

Physical System Breakdown

IEEE P1220 calls for a physical system breakdown to understand scope. To consider how the subsystems interact we must first define a boundary. Figure 6.2 suggests we include the finance sector, the government, and all customers. That would be most people in Australia within the system and would be too ambitious. In any case, Fig. 6.2 might offer clues for the relationship of the stakeholders. Figure 6.2 shows the complementary roles of state and federal government. State governments manage the politically messy area of customers. The federal government would like to have more influence with customers and during 2019 their Energy Minister met with electricity retailers to ask them to lower prices (there was no compulsion).

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Table 6.1 Requirements for the energy transition (IEEE P1220) IEEE P1220 definition of requirements

Energy transition

Customer expectations

Reasonable cost (no increase over current prices), Reliable electricity (failure rate limited by political tolerance), Lower emissions (defined by political tolerance), Customer voice (defined by political tolerance) Cost of capital, the limits of political tolerance and the inertia of the system Cost of capital, attractiveness of international capital to invest in the Australian energy system, the value of $A, and consumer confidence Various speeds of transition to higher renewables, and realisation of the Australian opportunity for low carbon energy Price of electricity, Reliability metrics, Emissions Meaningful customer engagement Electricity system customers, owners, regulators, managers and carbon emissions Electricity bills are an interface. The regulation of electricity system actor is the interface between government and the electricity system. Investment decisions are the interface between the financiers in the electricity system and the system The utilisation environment is defined by Newton and Faraday and does not venture outside their definitions Any capital decision has a 30–60-year life cycle, and policy has a 5–10-year life cycle Voltage and frequency stability, and system redundancy As defined by voltage standards (Fig. 2.1) Constant 24/7 with diurnal variation with a summer peak, morning peak and evening peak, and decreasing load in the middle of the day

Project and enterprise constraints External constraints

Operational scenarios

Measure of effectiveness (MOE’s)

System boundaries Interfaces

Utilisation environments

Life cycle Functional requirements Performance requirements Modes of operation

(continued)

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Table 6.1 (continued) IEEE P1220 definition of requirements

Energy transition

Technical performance measures

As defined by IEEE, Australian Standards and Australian Safety regulation Wires, generators, switches, controllers, transformers, and all electrical devices are part of the physical character Use, control, management and investment all involve human decisions

Physical characteristics

Human system integration

Table 6.2 Functional analysis of the transition of the Australian electricity system Physical location of the function and performance allocation First Level: Basic Functional Requirement Second Level Breakdown:

Third Level Decomposition: Third Level: Showing decomposition of the satisfying function for reasonable cost Third Level: Showing decomposition of the for deliver reliability function Third Level: Showing decomposition of the reducing emissions function

Third Level: Showing decomposition of the hearing customers function

Electrify Satisfy stakeholders Reducing cost (less than current prices), Deliver reliability (failure rate limited by political tolerance), Reducing emissions (defined by political tolerance), Hearing customers (defined by political tolerance) Physical location of the function and performance allocation Generation, marketing, and regulation need to reduce their cost due to the increase in poles and wires cost 90% is allocated to poles and wires, none to generation, and 10% to regulation 50% allocated to regulation, as the flow of decisions and money will be guided by the regulation. 50% to poles and wires as their action will enable lower emissions 50% marketing as the customer will interface mostly here, and 50% regulation

The next part of the IEEE P1220 physical system breakdown is to consider the system in space which is shown in Fig. 6.1. The major generation nodes are shown with blue dots. 90% of customers are customers

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Fig. 6.2 How the subsystems interact in the Australian energy system

on the east coast, but the political process gives strong political influence to the remaining 10% of customers. Figure 6.3 illustrates the transmission lines in Australia with the remainder of much of the land connected with distribution lines which highlights the dispersed grid, which drives the instability and high cost of upgrade. Next, we break the physical components into subcomponents: generation (coal private; coal government owned; hydro private; hydro government owned; wind; solar; diesel; gas), transmission: (NEM in all states other than Western Australia and Northern Territory and SWIS in Western Australia), distribution (private; government owned) and customers (consumers; commercial; industry; retail). 6.4.3

Synthesis

The synthesis in IEEE P1220 links the requirements to the physical system. For this, we can consider the work of Peter Senge in The Fifth Discipline (1990) which suggests: systems thinking; personal mastery;

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Fig. 6.3 National electricity transmission lines (Source [adapted from Australian Renewable Energy Mapping Infrastructure, 2020], retrieved from https://nation almap.gov.au/renewables/ on 5 June, 2020)

mental models; shared vision; team learning. Senge argues that systems thinking challenges reductionist thinking and is the better way to manage complex tasks. The development of these five behaviours will require leadership bourn of a deep personal conviction of the value of system thinking. This is a huge challenge for most people, who have been practicing reductionism. One change that gives hope we can achieve the transition of the Australian electricity system is that the Australian Curriculum changed in 2008 and the first graduates of that more systemic view of the world are entering the workforce now. Change can come from the within an organisation (Senge, 1990) so these new employees may be an important asset for the transition, in particular as they are more likely to be advocates of increasing renewable percentages which is a key tenant of the transition. This suggests that in the context of the transition, all the key drivers of the system must exercise these 5 behaviours. The most potent parties

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in this process are the distribution companies and the government regulators. This is unlikely at present due to the animosity between the distribution companies and the government regulators. The dynamic complexity of the transition of the Australian electricity system means that there is a need for: high-level thinking, reflection feedback systems, teams learning, a shared vision and shared understandings. These might be possible if the energy system transition was depoliticised, both on the level of party politics and state and federal relations. This is almost impossible to imagine but, the one opportunity is for is a marketdriven innovation, and that the Australian electricity system could be reformed with a single regulatory change to allow distribution companies to charge for electricity that flows back up their wires (National Energy Rule 6.1.4). With that one change it might be possible then for the distribution companies to act with systems thinking, personal mastery, mental models, a shared vision and team learning. They could each separately do this and work with their industry bodies such as the Energy Networks Australia (Energy Networks, 2019) to share best practice. The government role would be then to set up measurement and control. 6.4.4

Measurement and Control of the Energy Transition

Various parts of the transition are interrelated which makes measurement and control difficult to define. The requirements analysis and the functional analysis will give opportunities for control. There are control opportunities with the final form of the transition but that intervention at that stage will certainly be more expensive than in the design phase. One example of exercising control over the specification process is where government uses industry committees to develop new processes, with government holding a veto, but not restricting innovation. Each of the system components changed will require an allocation of resources and this resource can be measured and controlled. The rollout of time of use (ToU) network pricing requires budget for regulatory checking, pricing, communication and implementation. Even though ToU network pricing has no physical asset associated there is a huge amount of resource required to make a change, and any change introduces risk.

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A framework for the associated costs of the transition could be agreed between distribution companies and the government, as the distribution company returns are regulated. If this framework follows the current price agreement process the transition will take too long, so the pricing rules for distribution companies needs reform. Therefore, following the above argument, only two key actions are priorities: a relaxing of National Energy Rule 6.1.4 and the reform of pricing rules for distribution companies to encourage innovation. These changes would need to also recognise that any innovation has costs associated with the strategic oversight, analysis and implementation, and the pricing of strategic oversight and analysis will be difficult.

6.5

Conclusion

IEEE P1220 offers a system engineering framework for the Energy Transition that we recommend. It can manage the relationships between householders, utilities, and government (Chunlin & Chan, 2015) and flows of electricity, money and power as follows: consumers may receive cross subsidy from other consumers; may buy devices; may change their behaviour; may pursue home automation; may pursue high airconditioning; may be disadvantaged, and outside the norm, utilities are motivated to work to maximise profits within the regulated monopoly control of the government, and government sets policy and can establish incentive payments. This is shown as a system dynamic in Fig. 6.2. The complexity of the Energy Transition is increasing with the development of new technology such as electric vehicles, home storage and home automation (Chunlin & Chan, 2015). Water system transitions have been extensively studied and may offer valuable lessons (Bawden, Ison, Macadam, Packham, & Valentine, 1985). The development of a systems view of agriculture was at a time the central engineering view of the agricultural irrigation system gave way to a privatised model with a stronger voice for the users, which is the core argument of this book. Policies will need to engender innovation, focus on data transparency, and market design. Significant regulatory change is needed to keep prices from rising further and evidentiary research can drive this reform.

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References Althaus, C., Bridgman, P., & Davis, G. (2013). The Australian policy handbook. Crows Nest, NSW: Allen & Unwin. Argyris, C. (1977). Double loop learning in organizations. Harvard Business Review, 55, 115–125. Australian Government. (2015). National electricity transmission lines. Australian Government. Retrieved from https://data.gov.au/dataset/ds-ga-1185c97cc042-be90-e053-12a3070a969b/details?q. Bawden, R. J., Ison, R. L., Macadam, R. D., Packham, R. G., & Valentine, I. (1985).A research paradigm for systems agriculture. Beer, S. (1984). The viable system model: Its provenance, development, methodology and pathology. Journal of the Operational Research Society, 35(1), 7–25. Checkland, P. (1981). Systems thinking, systems practice. Chichester, UK: Wiley. Checkland, P. (2000). Soft systems methodology: A thirty year retrospective. Systems Research & Behavioral Science, 17, 11–58. Chunlin, G., & Chan, C. C. (2015). Whole-system thinking, development control, key barriers and promotion mechanism for EV development. Journal of Modern Power Systems and Clean Energy, 3(2), 160–169. DOD, U. (1969). MIL-STD-499 (USAF) system engineering management. Washington, DC: US Department of Defense. Energy Networks. (2019). Retrieved from https://www.energynetworks. com.au/. Innes, J. E., & Booher, D. E. (1999). Consensus building and complex adaptive systems: A framework for evaluating collaborative planning. Journal of the American Planning Association, 65(4), 412–423. Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Cambridge, MA: Belknap Press of Harvard University Press. Ochoa, L. N., Pilo, F., Keane, A., Cuffe, P., & Pisano, G. (2016). Embracing an adaptable, flexible posture: Ensuring that future European distribution networks are ready for more active roles. IEEE Power and Energy Magazine, IEEE New York, 14(5), 16–28. https://doi.org/10.1109/mpe.2016. 2579478. Popper, K. R., & Weiss, G. (1959). The logic of scientific discovery. Physics Today, 12, 53. Schmidt, R. (1993). IEEE p1220-standard for system engineering-a commercial standard for improving competitiveness. Paper presented at the [1993 Proceedings] AIAA/IEEE Digital Avionics Systems Conference. Senge, P. M. (1990). The fifth discipline: The art and practice of the learning organization. New York: Crown Pub. Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106(6), 467–482.

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Sposito, V., Faggian, R., & Pettit, C. (2008). Systems thinking and scientific research. Victoria: Victorian Government. Wiener, N. (1948). Cybernetics; or control and communication in the animal and the machine. Wyman, O. (2016). World Energy Trilemma 2016. Retrieved from London https://www.worldenergy.org/assets/downloads/World-Energy-Tri lemma_full-report_2016_web.pdf.

CHAPTER 7

Conclusion

Abstract The previous chapter demonstrated that this research offers an enhanced understanding of consumer roles in the electricity system and concluded that policy for the transition is urgent and should include social equity and systems design. This chapter aims to review the key contributions of this book and outline implications for future research. If current trends continue, then consumers will have an increasing role in the electricity system. Their engagement in the electricity system is likely to involve some form of trading or financial rewards, and this would help optimise the last “mile” of the distribution grid (Mareels et al. in Annual Reviews in Control 38: 243–258, 2014). This optimisation will begin about 2026. This optimisation would mean that consumers would have roles in the electricity system and such roles will be recognised in regulation and policy. There are policy changes needed. Policy would need to overturn equilibrium economics (Ackerman et al. in The Flawed Foundations of General Equilibrium Theory: Critical Essays on Economic Theory. Routledge, London, 2004) and align with Schumpeter (From Capitalism, Socialism and Democracy. Harper and Brothers, New York, 1942). Schumpeter argues for evolutionary economics to encourage innovation and accepted associated “creative destruction” and “entrepreneurial rent”. If we apply this to the work of Moore (Creating public value: Strategic management in government: Harvard university press, 1995), we can implement public value, through an authorising environment (democratic process) and operating capability (the bureaucracy) (Fig. 7.1). We can use Moore’s framework to drive dynamic policy © The Author(s) 2020 G. Currie, Australia’s Energy Transition, https://doi.org/10.1007/978-981-15-6145-0_7

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in a learning system (see Chapter 6 of this book) and to take heed of social fairness (see Sect. 7.3). I disagree with Schumpeter’s view of the need for innovation policy to focus on larger companies (Backhouse in Italian Economic Journal 1: 139–153, 2015) as small businesses must also be encouraged to innovate and then sell their innovations to companies that have the capital to exploit the idea. Keywords Framework · Government · Policy · Social licence · Risk · Transition

Figure 7.1 shows the flow from the authorising environment (the policy development) which flows both direct to public value and via operating capability to public value. This gives a “how to” but defining the public value will in my view need to be strongly aligned with Schumpeterian innovation.

Authorising environment Public value

Operating capability

Fig. 7.1 Operational framework for public policy (adapted from Fels, 2019)

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Government Role in Increasing Electricity System Innovation

Government should not pick winners but should guide the development of rules to encourage innovation. There is some support for Energy Transition innovation from the ARENA and the Clean Energy Finance Corporation or CEFC (Corporation, 2020), but the level of innovation needs to be many times the amount that ARENA and CEFC can support. The USA has a strong tax concession for business investment, and a similar change in Australia to turbo-charge innovation is needed. One example of such investment is the private investment in Evie Pty Ltd which is developing electric vehicle changing stations across Australia (ARENA, 2019). There is no technology government needs to pick. The market will deliver solutions when there is support for innovation. For example, rather than picking a winner and investing government money into additional transmission interconnects (ABC, 2020) or Snowy 2.0 (Coorey, 2019), the question is how to ramp up industry innovation while managing social licence and risks. Hence, this last chapter explores the government role in Sect. 7.1, social licence in Sect. 7.2 and the risks in Sect. 7.3. The Council of Australian Governments (COAG) Energy Council is the penultimate political forum for Australian energy policy but is focussed on large-scale systems. Consumers could technically offer storage and fast frequency response in the future, but many problems need to be solved before that can occur. Inverters being sold now are likely to be out of service before the grid operator can dispatch them for fast frequency response. A market for distribution services in Australia is unlikely before 2026. The reason for this slow change is that there has never been optimisation of distribution assets. The transmission assets have been optimised, but the distribution assets are built to connect customers under a distinct set of rules that are not focussed on optimisation. The creation of a market for distribution services will require multifaceted innovation. 7.1.1

Increasing the Government Role

The National Energy Guarantee (NEG) was an Australian Government policy under consideration in August 2018, but shelved due to a change in the prime minister. The three goals proposed by the NEG were to meet

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the Australian Government UN resolution to reduce carbon emissions by 26%, set up a mechanism for managing energy and give regulators the power to control reliability and emissions. The Australian Government must design an Energy Transition that optimises the system, but government leadership of the energy transformation is very difficult to achieve in a federated system such as Australia. The Energy Transition could be governed as laissez-faire (where there may be difficulties in the Energy Transition due to the lack of direction), autocratic (where the participation of private investment might flee the system) or preferably a balance between government control, private industry and user incentives, where we might achieve an optimal through information flows to provide evidence for government policy. This book argues for a balance between government rules and the market control of the transition. A balance between government control, private industry and user incentives should create an important market dynamic for the Energy Transition. Market innovations for load reduction, voltage and frequency and other grid services can be delivered by secondary markets with brokers, integrators and aggregators. The markets would support competition, which should drive prices down. Successful market initiatives in Australia include: Evie Pty Ltd program to roll out electric vehicle charging stations (ARENA, 2019), GreenSync “dex” (2019) program is an example that may offer transparency, Ergon’s website that shows constrained areas of their network (Ergon, 2018), and the AER is negotiating changes to the regulatory investment test for Distribution Businesses to require more open competition (AER, 2018). In Europe, government progress towards the Energy Transition is strongest in the Netherlands, UK and Germany. The UK RIOO model is considered the best in Europe, and European distribution regulation is national and governed by 28 national regulators in Europe. RIIO (Revenue = Incentives + Innovation + Outputs) is Ofgem’s performance-based framework. 7.1.2

Government Policy Tools

The research for this book explored four transition dynamics: neighbourhood effect (how each district interacts with districts around it); policy effect; price effect; and linking between energy-actions. These are available to government to manage the Energy Transition.

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Lovins’ Energy Strategy: the road not taken (1976) is a valuable book on the challenge of energy policy. Some relevant literature on energy policy design includes electricity market design for the prosumer era including psychology (Parag & Sovacool, 2016), ideas on dynamic management of distribution grids (Ochoa, Pilo, Keane, Cuffe, & Pisano, 2016), regulation for the electricity system transition (Nelson, 2016), an EU policy overview (Dudin, Frolova, Sidorenko, Pogrebinskaya, & Nikishina, 2017), policy sequencing towards decarbonisation (Meckling, Sterner, & Wagner, 2017), socio-technical modelling of electricity systems (Adil & Ko, 2016) and a systems view of Australian energy policy (Newell, Marsh, & Sharma, 2011). The Australian Government has a large policy arm, but politics is getting in the way of good energy policy. The replacement of the Australian Prime Minister on 24 August 2018 was partly about the differing political views on energy policy (Knaus, 2018). Big-ticket items such as the sale of Liddell Coal Power Plant (Reneweconomy, 2017) and Snowy Hydro 2.0 (Hydro. 2018) are politically attractive. In contrast, there is a political fear of the complexity of electricity customers. Therefore, the Australian Government tasks the state governments to handle most electricity customer regulation. State governments manage consumers through energy comparison sites such as the Victorian Government site (Victorian Government, 2019), energy ombudsmen, which manage hardship cases, and set policies such as PV subsidy. 7.1.3

Framework for the Energy Transition

The electricity system is being led by consumer energy-actions, and government control is limited. Research on the dynamic behaviour at the interface between consumers and the distribution DBs has been scarce. The free market will drive Australian energy policy and not the government. The nature of a transition is that it is dynamic, and dynamic systems modelling is complex. A valuable perspective is Peter Checkland’s “Systems thinking, systems practice” (Checkland, 1981) which introduced social systems thinking. Another systems view to energy policy is offered by J. Liu, Gao, Ma and Li (2015) which tackles dynamic planning. Another is Althaus, Bridgman, and Davis (2013) who offer a view of the policy dynamic that comes from the policy development cycle which reflects the policy goal as

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being a balance between many stakeholder inputs and good governance. Finally, excellent work on the Energy Transition in Australia comes from the government research laboratory CSIRO and the Energy Networks Australia or ENA (CSIRO, 2017). The Energy Transition could be possible in about 10 years. The benefit might be the $A40B savings modelled by the ENA and CSIRO (Graham & Bartley, 2013). The current stakeholders in the Australian electricity system will tend to resist change, so frameworks for the Energy Transition should include some help for innovators, DSO and other Energy Transition businesses. There will be success, but it will not be due to governments. Consumers are leading this. It will happen whether the government wants to play or not, because the consumer controls it. The government will tend to fail due to inherent conflicts. It will be consumer-led demand for clean energy technologies that will be leading the transition. 7.1.4

Government Policy Options to Manage the Transition in the Electricity System

The drivers of Australian PV adoption include price, subsidy, business conditions and tariff income. These four variables explain two-thirds of the drive leading to PV purchases in the model. This model of Australian PV adoption over the past decade can be used to understand consumer behaviours and be an input to policy and business. There is also some evidence that this model will be applicable for other energy innovations such as home battery systems because there is a correlation between the adoption of PV and solar-hot-water. The limitation of these results is that the social and economic conditions in Australia are similar but not easily generalised internationally. On the other hand, the research methodology could be generalised internationally. 7.1.5

Policy Options to Address PV Overvoltage

The third contribution offers policy options for managing PV overvoltage. This contribution draws on the statistical evidence of the government policy options being price, business conditions, subsidy and tariff income, and the consensus of energy leaders. This contribution offers a framework for addressing the current issue of PV overvoltage and offers a

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tool of immediate value to energy policy. It shows the steps, timing, risks and opportunities for addressing PV overvoltage. My research suggested: • Price, subsidy, business conditions and earlier PV installations are Granger Causal support this contribution that a transition framework can be planned. • Australia will transition 2–3 years earlier than Europe because consumer incentives are more potent than government direction, and Australia has more extensive consumer innovation through the free market. • Social equity is a critical problem; diversity (including gender) may be significant; consumers are the primary actors; and nongovernment actors are a leading information source. • Policy change is urgent: social equity and network charge cross-subsidies are important policy considerations, and systemic behaviours exist and could improve market design.

7.2

Social Licence for the Energy Transition

Social licence is defined here as a measure of public support for an issue. One of the leading social geographers in Australia is Rebecca Huntley whose view is that Australians offer governments licence if there is a sense that people will generally benefit from a policy (Huntley, 2019). Social licence is needed for the Energy Transition, as consumers will need to agree to take part. Social licence is particularly important to this research. Social licence as part of the transition means changing people’s mindsets and this is difficult. At the end of the day, the only thing the average person cares about is their bill. 7.2.1

Distribution Businesses Investing in Social Licence

Distribution Businesses do not consult properly with consumers, they are not seeking a consensus view, and they are not bringing people on the journey. Consumers are also most interested in their bill and will only challenge the Distribution Business if something is adversely affecting them.

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7.2.2

Government Investment in Social Licence

Governments have a key role in building social licence. Using Australian water consumption as an example, consumers knew there were both regulations and incentives in place, and behaviour changed. Domestic water use in Melbourne between 1994 and 2012 halved to 155 litres per person (Low et al., 2015). This was due to a water shortage which gave the government social licence for surprisingly strong constraints on consumer water use (Huntley, 2019). The development of social licence for the Energy Transition will be about consumer behaviour and uptake of technologies. The European Commission (EC) has minimal direct communication with consumers. The European Commission energy directives are included in national law (not EU law) and their role is hidden from the consumer. Therefore, in Europe, the maintenance of social licence is driven by state and national governments. 7.2.3

Social Licence Role in the Energy Transition

Consumers in Australia are far less homogenous than 20 years ago, so the development of social licence is difficult. Consumers have a low trust in electricity businesses given the price hikes and perceived gouging and gold plating in recent years (Wood, 2017) so social licence should be taken very seriously. It needs to be social engineering, not forcing. Social licence is about actions controlled by consumers, such as energy efficiency, or installing solar, batteries and EV. People who rent and cannot adapt houses and those who are too poor to install PV are being left behind in the Energy Transition (Wood, Carter, & Harrison, 2014). If too many people are left behind, then the Energy Transition will be unsustainable, so social equity should be a high priority to maintain social licence. Some community energy initiatives have been a social equity success such as the Moreland Energy PV program for renters (Moreland Energy, 2019). Victoria has, for example, over 30 community energy groups, some of which also have a social equity focus.

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Social Equity and Network Charge Cross-Subsidisation Are Important Policy Considerations

PV and air-conditioning are both cross-subsidised. The Productivity Commission of Australia reported (2013) that all non-PV consumers were paying $A60 a year for the network that supports PV consumers, and air-conditioners created $A2500 of network costs for each connection.

7.3

Risks of the Energy Transition

Transition risks discussed include: whether networks agree to this flexibility, whether the technology argument can penetrate the bureaucracy, how long this might take and whether the different businesses work together on the transition. This transition will require careful coordination and should include influential lobby organisations. Politically, there may be benefit from leveraging the ethical argument that current regulatory settings are transferring cost to social housing, renters and other vulnerable consumers and to the rest of the society. Social licence may become damaged by poor social equity. The wealth transfer from non-PV households to PV households will total $A14B up to 2030, and as there are 1.6 M PV households in Australia, this is approximately $A10,000 per PV household (Wood et al., 2014). The installation of a 5 kW air-conditioner creates a socialised cost impost on the electricity networks of $A1200–1550, which is paid back at a rate of $A53.40 per year via retail electricity rates (Wood et al., 2014). This benefit paid to households with PV has not been available to people in rental homes and poorer households. They are paying this subsidy to richer households. To continue to socialise network costs when consumers connect solar, EV chargers, batteries and air-conditioners is unethical and irrational. The lockout of renters may be changing as the Australian state of Victoria has passed a new law which allows tenants to make changes to their residence (“Residential Tenancies Amendment Bill,” 2018) and one local council has released a new PV offer for renters (Moreland Energy, 2019). Other transition risks include the way existing renewable policies can act as a barrier to the transition because grid connection rules allow damage to the grid. Another risk is that the inflexible electricity tariffs are a barrier as they do not align consumer behaviour with the needs of the grid. The Distribution Businesses are paid under rules that measure

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inputs, not outputs, and network charges are not tailored to the individual connection and do not therefore incentivise the efficient use of the electricity network. Current policy settings do not optimise the electricity system. This reflects the nature of the relationships between stakeholders and flows of electricity, money and power (Chunlin & Chan, 2015). The primary risk is the inertia of the current system, which is inherently conservative, partly due to the complexity of the system. The emerging voltage and frequency problems (Finkel, Moses, Munro, Effeney, & OKane, 2017) and the deteriorating investment case for fossil generators mean the transition will need careful control. The other huge risk is the party political and state/federal disagreement on the transition. It is for this last reason this book argues that a focus of transition activity should be distribution companies and not the government.

7.4

What Next?

Avoid picking winners and set up a process for driving productivity. The energy innovations from ARENA and CEFC will not deliver volume of innovation needed. The only chance of delivering the scale of investment and innovation is through a structural change to tax or investment rules. There have been Australian Research and Development Tax subsidies (Australian Government, 2020), but the scale needs to be more on the scale of the broad tax credits businesses see in the USA (US Government, 2020). Building an innovation culture in Australia is urgent. From 1995 to 2017 Australia fell from 57th to 93rd from 1995 to 2017 in an innovation ranking of all countries (Hausmann, 2019). This innovation must be balanced with a focus on regulatory overhaul and a strong hand from government. For example, a key barrier to the emergence of a competitive market is Rule 6.1.4 of the National Energy Rules. Rule 6.1.4 limits networks from charging for customer exports. Social equity is a critical problem needing policy attention and it may provide the legitimacy for the government to push for this transition. Consumers are the primary actors in the transition: The consumer is seen as the primary actor through their adoption of technologies and through their voice on environment, cost and equity. And there is a key role for non-government actors as leading information sources.

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Independent actors are the trusted voices and not the government. Energy Networks Australia and Energy Networks in the UK are the most important independent information sources at present in the Energy Transition. Non-government actors are more likely to respond flexibly and innovatively and are likely to lead information dissemination (Liu et al., 2015). So, in conclusion, we need governments that will work within their operating capability to drive policy that is driving public value with a preeminent role of innovation. That public value can only be determined by iterative learning conversations, and we can use this research as a starting point. Let us continue the conversation!

References ABC. (2020). Electricity interconnector between SA and NSW has ‘robust’ business case, energy regulator finds. Retrieved from https://www.abc.net.au/ news/2020-01-24/electricity-interconnector-sa-nsw-economic-benefit-dow nplayed/11897002. Ackerman, F., Nadal, A., & Gallagher, K. P. (2004). The flawed foundations of general equilibrium theory: Critical essays on economic theory. London: Routledge. Adil, A. M., & Ko, Y. (2016). Socio-technical evolution of Decentralized Energy Systems: A critical review and implications for urban planning and policy. Renewable and Sustainable Energy Reviews, 57, 1025–1037. https://doi.org/ 10.1016/j.rser.2015.12.079. AER. (2018). Improving guidance to support cost-benefit analysis in network investment. Australian Energy Regulator. Retrieved from https://www.aer. gov.au/system/files/D18-98442%20AER%20-%20Fact%20Sheet%20-%20D raft%20RIT%20application%20guidelines%20-%2027%20July%202018.pdf. Althaus, C., Bridgman, P., & Davis, G. (2013). The Australian policy handbook. Crows Nest, NSW: Allen & Unwin. ARENA. (2019). Ultra fast highway charging network for electric vehicles. Retrieved from https://arena.gov.au/news/ultra-fast-highway-charging-net work-for-electric-vehicles/. Australian Government. (2020). Research and development tax concession. Retrieved from https://www.ato.gov.au/Business/Research-and-develo pment-tax-concession/. Backhouse, R. E. (2015). Samuelson, Keynes and the search for a general theory of economics. Italian Economic Journal, 1(1), 139–153. Checkland, P. (1981). Systems thinking, systems practice: John Wiley.

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Chunlin, G., & Chan, C. C. (2015). Whole-system thinking, development control, key barriers and promotion mechanism for EV development. Journal of Modern Power Systems and Clean Energy, 3(2), 160–169. Clean Energy Finance Corporation. (2020). Retrieved from https://www.cefc. com.au/. Coorey, P. (2019, February 26). Scott Morrison climate plan: Tony Abbott’s direct action, Malcolm Turnbull’s Snowy 2.0. Australian Financial Review. Retrieved from https://www.afr.com/news/scott-morrison-climate-plantony-abbotts-direct-action-malcolm-turnbulls-snowy-20-20190225-h1bnyf. CSIRO. (2017). Electricity network transformation roadmap, final report. Melbourne: Energy Networks. Retrieved from https://www.energynetworks. com.au/sites/default/files/entr_final_report_april_2017.pdf. Dudin, M. N., Frolova, E. E., Sidorenko, V. N., Pogrebinskaya, E. A., & Nikishina, I. V. (2017). Energy policy of the European Union: Challenges and possible development paths. International Journal of Energy Economics and Policy, 7 (3), 294–299. Ergon. (2018). Network capacity map. Retrieved from https://www.ergon.com. au/network/contractors-and-industry/developers-toolkit/network-capacitymap. Fels, A. (2019). Tough customer: Chasing a better deal for battlers. Melbourne: Melbourne University Press. Finkel, A., Moses, K., Munro, C., Effeney, T., & OKane, M. (2017). Independent review into the future security of the National Electricity Market: Blueprint for the future. Canberra: Australian Government. Retrieved from http://www. environment.gov.au/energy/publications/electricity-market-final-report. Graham, P., & Bartley, N. (2013). Change and choice: The future grid Forum’s analysis of Australia’s potential electricity pathways to 2050. CSIRO. GreenSync. (2019). "dex" website link on GreenSync corporate website. Retrieved from https://dex.energy. Hausmann, R. (2019). Retrieved from http://atlas.cid.harvard.edu/. Huntley, R. (2019). Listening to the nation. Quarterly Essay (73), 1. Knaus, C. (2018). The Liberal party is self-destructing over energy. Here’s what you need to know. Retrieved from https://www.theguardian.com/australianews/2018/aug/20/liberal-party-self-destructing-national-energy-guaran tee-malcolm-turnbull-what-you-need-to-know. Liu, J., Gao, H., Ma, Z., & Li, Y. (2015). Review and prospect of active distribution system planning. Journal of Modern Power Systems and Clean Energy, 3(4), 457. Lovins, A. B. (1976). Energy strategy: The road not taken. Foreign Affairs, 55, 65. Low, K. G., Grant, S. B., Hamilton, A. J., Gan, K., Saphores, J. D., Arora, M., & Feldman, D. L. (2015). Fighting drought with innovation: Melbourne’s

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response to the Millennium drought in Southeast Australia. Wiley Interdisciplinary Reviews: Water, 2(4), 315–328. Mareels, I., de Hoog, J., Thomas, D., Brazil, M., Alpcan, T., Jayasuriya, D., & Kolluri, R. R. (2014). On making energy demand and network constraints compatible in the last mile of the power grid. Annual Reviews in Control, 38(2), 243–258. https://doi.org/10.1016/j.arcontrol.2014.09.007. Meckling, J., Sterner, T., & Wagner, G. (2017). Policy sequencing toward decarbonization. Nature Energy, 2(12), 918–922. https://doi.org/10.1038/s41 560-017-0025-8. Moore, M. H. (1995). Creating public value: Strategic management in government. Cambridge: Harvard University Press. Moreland Energy. (2019). Solar for renters. Retrieved from https://www.mefl. com.au/news/solar-for-renters-2/. Nelson, T. (2016). Redesigning a 20th century regulatory framework to deliver 21st century energy technology. Journal of Bioeconomics, 19, 1–18. Newell, B., Marsh, D. M., & Sharma, D. (2011). Enhancing the resilience of the Australian National Electricity Market: Taking a systems approach in policy development. Ecology and Society, 16(2), 15. Ochoa, L. N., Pilo, F., Keane, A., Cuffe, P., & Pisano, G. (2016). Embracing an adaptable, flexible posture: Ensuring that future European distribution networks are ready for more active roles. IEEE Power and Energy Magazine, IEEE New York, 14(5), 16–28. https://doi.org/10.1109/mpe.2016. 2579478. Parag, Y., & Sovacool, B. K. (2016). Electricity market design for the prosumer era. Nature Energy, 1, 16032. https://doi.org/10.1038/nenergy.2016.32. Reneweconomy. (2017). AGL bought Liddell for nothing, but what will it cost Turnbull. Retrieved from https://reneweconomy.com.au/agl-bought-liddellfor-nothing-what-will-it-cost-turnbull-14579/. Residential Tenancies Amendment Bill, Victorian Government (2018, 9 August). Schumpeter, J. A. (1942). From capitalism, socialism and democracy. New York: Harper and Brothers. Snowy Hydro. (2018). Snowy 2.0. Retrieved from https://www.snowyhydro. com.au/our-scheme/snowy20/about-snowy-2-0-2/. US Government. (2020). Tax credits and deductions. Retrieved from https:// www.usa.gov/tax-benefits. Victorian Government. (2019). Energy compare. Retrieved from https://www. energy.gov.au/victorian-energy-compare. Wood, T. (2017). How to cut power bills and build a more sustainable electricity network. Retrieved from https://grattan.edu.au/how-to-cut-power-bills-andbuild-a-more-sustainable-electricity-network/. Wood, T., Carter, L., & Harrison, C. (2014). Fair pricing for power. Melbourne, Australia: Grattan Institute Melbourne.

Appendix A: Interview Questionnaire

See Table A1 for a copy of the survey used in this research. There were 100 energy experts chosen for the Australian interviews for their leading roles and deep knowledge of the Australian energy system. By comparison, the European field was constrained to referrals. The people who were interviewed all met the criteria that they have a comprehensive understanding of consumer roles in the electricity system. This included academics, government employees, Distribution Business employees, small business owners and one industry lobbyer. The names have been removed as there are constraints on privacy. Australian and European Survey Breakdown by Employment Type The following breakdown showed some variation by employment type but the number of surveys of academics and government employees was too small to have high confidence in the results. Table A2 is the mean and standard deviation for each question in the Australian survey and each question in the European survey. The ranges for questions of Importance and Likelihood of Success were 1–7, whereas the question asking “years in future this will start”, is on a range of 1– 10 years. Therefore, the means and standard deviations of the answers to “years in the future” are higher. Figures A1, A2, A3 and A4 show box and whisker charts of the survey answers for the fourteen policy questions. These box and whisker charts © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 G. Currie, Australia’s Energy Transition, https://doi.org/10.1007/978-981-15-6145-0

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Questionnaire

Distribution companies’ role in facilitating this Energy Transition? Change the rules for distribution companies to create incentive for innovation & competition Smart inverters to improve the grid? PV export limits to improve the grid? Autonomous load controL by consumer inverters? Remote control of consumer inverters, in critical events? Demand response to reduce peak demand? Network price reform to improve the grid? Motivating consumers to install energy-efficient devices Building code reform to reduce peak energy demand? Gaining social licence for this change? Will governments succeed in this Energy Transition?

Transition to consumer roles in the electricity system

Table A1 Importance? 1–7

How likely are we to succeed? 1-low chance 7–certain

# of years in future 1–10

Comment and Barriers

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Electric vehicle charging rules Home battery storage rules Who are the prime movers in this future system? Key information networks in this future system? Setting the rules for this future system, social rules, and legal rules? What is #1 risk in this Energy Transition? What is #2 risk in this Energy Transition? What relative roles (1–7) should the following have in energy policy? Diversity, Technology, Consumers, Social Equity, Economy, Environmental and Political

Transition to consumer roles in the electricity system

Importance? 1–7

How likely are we to succeed? 1-low chance 7–certain

# of years in future 1–10

Comment and Barriers

APPENDIX A: INTERVIEW QUESTIONNAIRE

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Business

Govt/Policy

Academic

Business

Govt/Policy

Likelihood of Academic

Business

Govt/Policy

Academic

Business

Govt/Policy

Importance Academic

AU mean AU Std Dev AU mean AU Std Dev AU mean AU Std Dev EU mean EU Std Dev EU mean EU Std Dev EU mean EU Std Dev Success AU mean AU Std Dev AU mean AU Std Dev AU mean AU Std Dev EU mean EU Std Dev EU mean EU Std Dev EU mean 5.1 1.6 5.0 1.5 4.4 1.8 4.6 1.5 4.9 1.5 4.9

6.0 0.7 6.3 0.7 5.8 1.2 5.7 2.1 6.2 0.8 6.1 1.2

Q1

4.9 2.0 4.5 1.9 5.0 1.4 3.8 1.1 4.1 1.1 5.3

6.6 0.9 4.6 1.9 5.4 1.6 6.2 1.4 6.1 1.3 5.8 1.2

Q2

6.1 1.5 6.0 0.9 5.7 1.7 5.1 1.6 5.2 1.3 5.3

5.2 1.8 5.0 2.1 4.9 2.1 4.3 1.5 5.5 1.5 5.3 1.1

Q3

5.9 1.5 5.8 1.6 5.6 1.4 4.7 1.5 4.9 1.3 4.2

4.6 2.1 3.9 2.0 4.1 1.8 3.5 1.9 3.8 2.7 4.3 1.7

Q4

5.1 2.3 6.3 0.8 5.5 1.7 5.5 1.4 5.4 1.5 4.3

5.2 1.3 5.3 1.9 4.5 2.0 5.0 1.6 5.2 2.4 4.5 1.4

Q5

5.6 1.3 5.2 1.9 5.4 1.8 4.8 2.0 4.8 1.6 4.6

5.0 1.2 4.8 2.1 5.1 1.6 4.8 1.8 5.7 2.3 4.9 2.0

Q6

5.6 1.4 6.3 0.8 5.6 1.5 5.4 1.1 5.2 1.4 5.3

6.4 0.5 5.5 1.9 5.8 1.6 5.8 1.4 5.0 2.8 6.5 0.5

Q7

4.7 1.9 5.0 1.7 4.5 1.6 4.0 1.2 4.4 1.3 3.8

6.2 0.8 5.1 1.9 5.8 1.6 6.0 1.7 5.7 1.5 5.8 1.1

Q8

5.3 1.1 5.7 0.5 5.4 1.4 5.0 1.5 5.3 1.4 4.9

6.0 1.2 5.8 1.7 5.5 1.7 5.6 1.0 6.5 0.5 6.3 1.1

Q9

4.4 2.2 4.3 1.5 4.1 1.7 4.3 1.8 4.4 1.6 4.8

6.2 1.3 6.3 1.2 6.1 1.2 5.6 1.9 6.2 1.3 6.3 1.2

Q10

Results for mean and standard deviation by country and employment type

Mean and Standard Dev

Table A2

6.3 0.8 5.3 0.8 5.0 1.6 5.0 1.7 4.7 1.5 3.9

5.6 1.1 6.0 1.4 5.4 2.0 5.3 1.5 6.0 1.3 5.8 1.0

Q11

3.9 1.8 4.8 2.4 4.0 1.7 4.7 1.8 4.6 1.5 4.3

4.6 1.8 6.6 0.7 5.6 1.7 6.3 1.4 6.0 1.1 6.7 1.2

Q12

6.6 0.5 6.5 0.5 5.9 1.8 5.8 0.8 5.6 0.9 5.5

5.2 1.9 4.9 2.0 4.3 1.9 5.8 0.8 6.3 0.8 5.3 1.8

Q13

6.1 0.9 6.7 0.5 5.8 2.0 4.9 1.6 5.3 1.6 4.9

5.8 0.8 4.6 1.8 4.6 2.1 5.2 2.0 4.2 2.7 5.3 1.4

Q14

146 APPENDIX A: INTERVIEW QUESTIONNAIRE

1.4 4.0 2.5 4.0 3.0 2.3 2.1 5.8 2.9 5.6 2.7 6.2 2.8

1.3 5.4 2.9 6.3 2.7 5.3 3.1 5.9 2.2 5.5 2.9 4.8 2.1

EU Std Dev 1.3 Years in the Future This Will Start Academic AU mean 5.4 AU Std Dev 2.7 Govt/Policy AU mean 4.3 AU Std Dev 3.8 Business AU mean 4.7 AU Std Dev 3.0 Academic EU mean 5.3 EU Std Dev 2.9 Govt/Policy EU mean 5.5 EU Std Dev 2.5 Business EU mean 6.4 EU Std Dev 3.3

Q3

Q2

Q1

Mean and Standard Dev

2.0 1.0 3.4 3.2 1.6 1.4 6.0 3.4 5.0 3.4 5.4 2.2

1.8

Q4

4.0 2.5 3.8 2.5 2.4 2.2 5.0 2.0 5.4 2.1 7.3 2.5

1.6

Q5

3.8 1.3 4.8 2.9 3.0 2.6 5.8 3.4 5.7 3.4 8.0 2.3

1.6

Q6

3.8 1.6 3.4 2.1 2.3 1.7 5.2 2.4 4.7 2.3 4.5 2.9

1.4

Q7

5.0 3.1 4.6 2.9 4.5 2.7 6.3 2.8 6.4 2.5 5.8 2.1

1.5

Q8

3.8 2.8 3.6 3.2 2.6 1.9 5.6 8.3 4.8 6.9 5.8 3.0

1.8

Q9

5.8 4.1 5.5 3.5 4.5 3.4 7.4 2.9 6.5 3.3 6.7 2.1

1.7

Q10

4.4 3.8 3.0 2.2 2.8 2.1 4.8 3.0 5.6 2.9 6.1 3.0

1.3

Q11

9.0 2.2 4.5 2.6 6.5 3.2 6.7 3.6 7.3 3.7 7.9 2.7

1.3

Q12

4.4 3.8 4.8 3.1 5.1 3.6 5.4 3.1 5.5 2.7 5.8 2.9

1.1

Q13

2.8 1.8 2.8 1.6 3.2 2.4 5.3 2.7 5.1 2.9 6.3 2.0

1.4

Q14

APPENDIX A: INTERVIEW QUESTIONNAIRE

147

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APPENDIX A: INTERVIEW QUESTIONNAIRE

Fig. A1

Box and whisker plots of the answers Questions 1–4

APPENDIX A: INTERVIEW QUESTIONNAIRE

Fig. A2

Box and whisker plots of the answers to Questions 5–8

149

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APPENDIX A: INTERVIEW QUESTIONNAIRE

Fig. A3

Box and whisker plots of the answers to Questions 9–12

APPENDIX A: INTERVIEW QUESTIONNAIRE

Fig. A4

151

Box and whisker plots of the answers to Questions 13–14

show the mean marked with “X” and the box encompasses the central half of the results. The “whiskers” show the reach of 1.5 times the interquartile range. Additional dots outside the whiskers show answers that are outliers.

Appendix B: Interview Analysis Method

The first step was to format the interview text into an unidentified format and grouping the answers for each question together. The second step was to load the interview text into SPSS Modeler Text Analysis which uses text-link analysis. Text-link analysis first finds the patterns in the words and then relationships between the concepts (IBM, 2017). The algorithm finds words that occur frequently together, and because there are a high number of combinations, the process includes the manual elimination of less frequent combinations to focus on the high-frequency combinations. The model was set up with “interview comments” as the input file, the type indicates the file content, and the model is indicated with C1. The third step was to run the SPSS Text Analysis and build categories and concepts. The initial run generated 30 categories which were: • • • • • • • • •

AEMO Assets Business fields Change Commercial Consumers Control Customer Economics

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 G. Currie, Australia’s Energy Transition, https://doi.org/10.1007/978-981-15-6145-0

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• • • • • • • • • • • • • • • • • • • • •

APPENDIX B: INTERVIEW ANALYSIS METHOD

Finance Geographical location Government Grid Homes Human resources Human settlements Measuring instrument Mechanisms Networks Occupation Parties People Physics Policy Politics Pricing Service Society Technology Working

The fourth step was to work with the 1782 categories that the automatic text analysis algorithm generated. The fifth step was to view the relationships between the categories and the concepts to develop an understanding of the more important categories and concepts (Fig. B1). The sixth and last step was to use the text analysis above to create insights into the data. Future Research Further research on this area is needed, as there are many unresolved questions to guide the next steps in the transition to consumer roles in the electricity system. The unknowns and dynamic aspects include consumer changes and technology changes. The dynamic character of this area means that a focus on dynamic systems is highly relevant.

APPENDIX B: INTERVIEW ANALYSIS METHOD

155

Fig. B1 Analysis of the interviews showing the links between the categories. This grouping was completed using SPSS Modeler

1. Test the PV adoption model electric vehicle charging, storage, energy efficiency policy and demand response A straightforward test of the model in Chapter 4 on other datasets. Gaining access to suitable data will be difficult. 2. Adding sentiment measures to the PV adoption model Adding a measure of sentiment is considered a good method of increasing the accuracy of the PV adoption model. Twitter comments on PV for the years 2010–2012 are available and could be used to measure the relationship between sentiment and PV purchases. Technical discussions on the Australian website Whirlpool (https:// forums.whirlpool.net.au) could also be used to measure the influence of information about PV on adoption. Another data set that could be explored exists in real estate advertisements. The pattern that real estate agents advertise properties with words like “solar” and “PV” could give a window into social change.

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3. Dynamic modelling of the electricity system (using a socio-technical framework) The inclusion of political pressure, media and public discourse measures could increase the adaptability and transferability of the system model.

Appendix C: PV in Australia Analysis

Variables Tested The government variables used in this research are listed below. The government department is shown in the brackets. 1. Average income (ABS). 2. Percent of homes with income over $A2000/week (ABS). Chosen to assess income effect. 3. Number of bedrooms per house (ABS). This has 5 levels as percentiles of all houses. 4. Votes Greens/Liberals/Labor (AEC). A measure of Greens and protest votes outside the two major political parties. 5. Percent of dwellings owned (ABS). This is all dwellings including apartments. 6. Brick structure percent (ABS). 7. Education level (ABS). A measure of # years of education. 8. Number of children per house (ABS). 9. Retired percentage of total population (ABS). 10. Average age (ABS). 11. Remoteness (ABS). With 5 levels of density. 12. Solar insolation-the amount of solar energy/m2 (CSIRO). A measure of how much energy PV generates.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 G. Currie, Australia’s Energy Transition, https://doi.org/10.1007/978-981-15-6145-0

157

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13. SEIFA Advantage and Disadvantage Index (ABS). A socioeconomic measure. 14. Average Mortgage payments up to $A5000/month (ABS). 15. Percent of homes under mortgage (ABS). 16. % solar-hot-water and heat pumps (CER). 17. Average number of cars per house (ABS). 18. Average number of people per house (ABS). 19. Percent of households that take their car to work (ABS). 20. Transport to work: walk, bike, car, truck, none (ABS). 21. Not working at time of Census (ABS) . 22. The whole family moved in the past 5 years (ABS). 23. No Internet, broadband and dial-up Internet (ABS). 24. Average rent below $A650 per week (ABS). 25. Percent of homes where rent is over $A650 per week (ABS). 26. Percent of homes inhabited by homosexual couples (ABS). 27. Percent of homes inhabited by couples (ABS). 28. Percent of homes that are separate houses (ABS). 29. Density of dwellings (ABS). 30. Electricity cost per household (ABS). 31. Electricity use per house (kWhr) (ABS). 32. Change in urban density (ABS). Calculated as the ratio between the 2011 Census and 2016 Census and was to detect how this influenced PV uptake in these areas. There were also three variables selected from non-government sources, and their source is shown in brackets. 1. NAB Business Conditions Index (National Australia Bank (NAB)). NAB is motivated to ensure the integrity of their Business Conditions Index due to its high profile. 2. APVI PV price measure (Australian Photovoltaics Industry Association [APVI]). The APVI is a credible not-for-profit organisation dedicated to the PV industry. Pricing of PV is a national average for each year. 3. Electricity stress by household, electricity cost/income (Uniting Church). The Uniting Church report was drawn from the ABS, so this is also a government source.

APPENDIX C: PV IN AUSTRALIA ANALYSIS

159

Method of Solar Data Analysis This analysis focusses on the manipulation of the 1.6 million PV sales in Australia from 2001 to 2016. The chosen methods of manipulating that data are stepwise regression and ARIMA temporal regression. The specific goals are: • Finding the drivers of PV adoption in Australia that might be government policy options. • Finding patterns of PV in different Australian states. • Finding whether there is a correlation between solar-hot-water adoption and PV adoption. To discover some government policy options, the data from the PV sales in Australia in the past decade is combined with the rich socio-economic data from the Australia Bureau of Statistics (ABS). The method is data preparation, visualisation, setting up and executing regression, and finally testing spatial regression. • Data preparation: The data assembly is important in that the 1.6 million PV installations constitute a large dataset, and the choices of how to manage this data will affect all subsequent testing. • Visualisation. • Setting up the regression. • Regression analysis: It employs the well-recognised technique of linear regression and is the first step in understanding what government policy options may exist. • Spatial regression: It tests whether there is evidence of a neighbourhood effect (Graziano & Gillingham, 2014). Data Preparation PV installation data used in this book is from 2001 to mid-2017 and is linked to the address where the PV panels were registered. The address is generally where they are installed but some are away from the installation address in post office boxes. The PV installation data was then matched with Census data which is reported by postcode boundaries. The data is

160

APPENDIX C: PV IN AUSTRALIA ANALYSIS

robust due to the high quality and broad reach of data collection by the Australian Government. Over that period, there was a gradual increase in PV-system size from 1kW to 3kW average for homes. The size of systems was ignored in this research and only the instances of PV adoption were used. Visualisation Visualisation of Australian maps of PV installation concentration showed the high concentration of PV installations around the fringes of capital cities and low concentrations in inner areas of cities. This also showed there were higher concentrations in rural NSW than in other states, which aligned with an early NSW feed-in tariff for up to 10kW. Systems of that size do not normally fit on domestic rooves, so installations favoured rural areas. There was a strong outer suburban take-up in all major capital cities except Brisbane. The difference in PV density in inner Brisbane may be due to the large homes which are more likely to suit PV installation. Figure C1 shows the percentage of households with PV in each postcode in Melbourne, Victoria, which was typical of all the capital cities apart from Brisbane. The darker colours show higher concentrations of PV, and the purple areas show over 40% of homes in those suburbs having PV. The higher concentration in the outer fringe may be due to higher rates of PV on new dwellings (Green Energy Trading, 2014). The first stage of stepwise regression was without spatial positioning. It used the list of postcodes with no spatial data attached, but postcodes usually adjoin the next postcode in the numerical sequence, so there is an element of spatial grouping of the data. Stepwise regression aimed to find drivers of Australian PV purchases, so a wide choice of thirty-six variables was assessed. The selection of variables was influenced by the work of Higgins et al. (2014). Higgins et al. nominate the drivers of Australian PV adoption as annual savings in energy costs, upfront cost, household income, familiarity, socio-economic status, dwelling density and votes for the Greens Political Party. Higgins et al. also included a measure for savings in energy costs and upfront costs which are also used in the temporal modelling in this research. There were additional variables added in this research to explore the suitability of the house rooves, household ownership, previous purchases of solar-hot-water, percent of income spent on electricity and a range of

APPENDIX C: PV IN AUSTRALIA ANALYSIS

161

Fig. C1 Australian PV Institute (APVI) Solar Map (Source Funded by the Australian Renewable Energy Agency, accessed from pv-map.apvi.org.au on 7 May 2020 Setting up the regression)

162

APPENDIX C: PV IN AUSTRALIA ANALYSIS

Table C1

1 2 3

Variable choice

Research question

Variables to test

Is government policy causal in the household adoption of PV in Australia? Can a model of domestic PV adoption forecast future adoption? Is adoption of PV more likely if electricity cost is a higher percent of income?

Variables selected by analysis

4

Will adoption be higher if there is lower urban density? Higher adoption may be linked to the need to drive to work or the number of bedrooms

5

Are people who have bought energy technology previously are more likely to buy again? Does environmental concern lead to PV adoption?

6

7

8

Can high-speed Internet broadband adoption help awareness of PV technology and cause higher uptake? What is the relationship between income and PV uptake?

Variables selected by analysis Average income, average number of cars, average income below $A2000/week and income over $A2000/week (from the ABS). Subsidy from government. Percentage of income spent on electricity? Average number of bedrooms, separate house, percent of dwellings owned, percent rented, percent under mortgage, average number of people in the house, car driven to work and density of dwellings (from the ABS) Heat pumps (for hot water) and solar-hot-water (from the CER) Voted for Greens (AEC). Bike to work, average number of children and walked to work (from the ABS) No Internet, broadband Internet and dial-up Internet (from the ABS) Unemployed/retired, SEIFA Income, mortgage payments, % rent, % rent over $A650 and the number of cars per house (from the ABS)

Note AEC, ABS and CER are all departments of the Australian Government. The AEC is the Australian Electoral Commission, the ABS is the Australian Bureau of Statistics, and the CER is the Clean Energy Regulator. SEIFA measures of social and economic status are from the ABS

other socio-economic measures. The variables choice flows from research questions as shown in Table C1. Regression Analysis Method The regression method: • Firstly load the data into SPSS Statistics 22 (IBM, 2015);

APPENDIX C: PV IN AUSTRALIA ANALYSIS

163

• Secondly match the postcode district data for the number of PV installations up to 30 June 2017 thirty-six variables (the goal is to find variables that have the power to influence the PV purchase decision using stepwise regression [Pearson, 1901]); • Thirdly test the smaller set of variables using an iterative process to check the potency of the different variables to predict PV uptake by postcode (industry experience and the general knowledge of the author guided this testing and variables were eliminated if their Variance Inflation Factor (VIF) measured above 10); and • Fourthly test a range of geographic areas with linear regression to decide the effect of urban vs rural areas. For the above data set elements, the following detailed steps were used: • Input PV purchase totals by postcode on 30 June 2017. • Input 36 variables for each postcode in Australia including number of dwellings per area. • Check data sets. • Label the continuous variables separately to the categorical. • Create variable for rate of population change between 2011 and 2016 Census. • Clustering check using SPSS Statistics to test for suburb grouping. • Decide leading variables using stepwise regression using SPSS Statistics with scree plot checks and test for Pearson Correlation over 0.5. Note that this analysis is not spatial and treats all observations as independent and • Linear regression on the selected variables using SPSS Statistics, per state and other groupings to test the effect of the chosen variables. Spatial Regression Method The spatial regression method was as follows: • Visualise the data using the software tool ARCMAP. Load data into ARCMAP and check visually. • Link the PV installation data and the (ABS, 2016) Census data in Microsoft Access and then upload this to ARCMAP (ESRI, 2016) and make spatial joins (the projection for ARCMAP was EPSG

164

•

•

•

•

•

APPENDIX C: PV IN AUSTRALIA ANALYSIS

4326 which is the same as GCS_WGS_1984 which is a data frame in decimal degrees) and use visual analysis to seek anomalies. The complexity in the modelling now rises significantly. Spatial tests for relationships between neighbourhoods by calculating a spatial weights measure for each postcode. The spatial weights measure is an average density of PV in neighbouring suburbs and uses Tobler’s First Law of Geography that the adjacent areas are dependent (Tobler, 1970). This neighbourhood effect in the diffusion of PV is established in the literature (Bollinger & Gillingham, 2012). Decide to apply the Spatial Error or Spatial Lag test. The reason for this choice is because the data violates the assumptions of independence due to spatial clustering. The method to cope with this violation of independence is to calculate the global Moran’s I (Moran, 1950) and then use the Lagrange multiplier test (Anselin, 1988) to decide whether to use the Spatial Lag or Spatial Error Model. The test is for error, and the model with an error below 0.05% was chosen. An OLS regression was then run with Spatial Weights test using the algorithm chosen in the last step (either Lag or Error). In running the spatial regression, the Variance Inflation Factor (VIF) test was used to test for collinearity. The method was to include variables with VIF below 10 and run a stepwise VIF test to check for collinearity. Spatial analysis focused on the first-order neighbours and spatial autocorrelation was used. The K-means spatial analysis alternative was chosen, which uses a process of centring the data in each postcode to a centroid. Test the data using spatial-temporal regression. Data configuration for the spatial-temporal regression was for 196 time steps and over the 2300 postcode area. The PV installations are each a separate event, so there were 1.6 million lines of data. Spatial Regression

SPSS (IBM, 2015) was used as the regression software tool and the data was joined and then loaded into ARCMAP (ESRI, 2016). The boundary for this regression was PV data collected between the year 2001 and mid2017. The geographic constraint is continental Australia.

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165

Creating a Spatial Weights Matrix Next, a Spatial Weights Matrix (SWM) was developed which is a measure of spatial effect of the neighbouring postcodes. The software used was GeoDa (Anselin, Ibnu, & Youngihn, 2006) with the following methods: • Firstly, set GeoDa on Queen Contiguity at level 1 (to focus on the first-order neighbours) and then run an ordinary least squares regression to test whether Spatial Lag or Spatial Error Model are significant. • Secondly, when both models were found to be significant, the Robust Test showed the Spatial Error Model should be selected. This corresponds to an earlier assumption that the Spatial Error Model would be used, as there are unobserved social drivers. The Spatial Error Model is also useful to deal with non-random elements in the error term. • Thirdly, spatial regression testing in GeoDa (using the Spatial Error Model) showed both the Schwartz orientation and the Akaike information coefficient (AIC) decreased, see Table C2. Following Anselin et al. (2006), the Spatial Error Model was then chosen. Therefore, the spatial weight values from the Spatial Weights Matrix (using the Spatial Error Model) were allocated to each postcode for the spatial regression.

Table C2 Test results—Akaike Information Criterion (AIC) and Schwarz orientation

Coefficients

OLS

Separate House Solar-hot-water Ownership/mortgage R-Squared Akaike info criterion Schwarz orientation

14.66 0.481

*Significance at p < .001

0.68 2540 2553

Spatial Error Model 11.14 0.46 0.81 2371 2384

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APPENDIX C: PV IN AUSTRALIA ANALYSIS

Spatial Regression The spatial regression used the variables from the exploratory modelling: • • • •

Percentage of homes that are owned or under mortgage. Percentage of separate homes. Income and Percentage of homes with solar-hot-water.

All variables were joined in Access using the 2016 postcode list using the following steps: • /DIV? Errors occurred where the PV installations were in new postcodes that had not yet faced a census, so those errors were manually cleared. In further testing of the model, this step was not required. • Manually keyed data required careful checking each time the data was transferred in and out of Excel and Access and • Migratory and non-Australian postcode codes were ignored. The spatial regression in SPSS used VIF to select key variables, and a stepwise VIF test was used to check for collinearity and the result was that no collinearity problems existed. The spatial regression confirmed the four variables: income, percent of separate housing, percent of homes with solar-hot-water and percent of homes that were owned or had a mortgage. Note that a variable for “income” has been added from the list of three variables in the stepwise regression. These four variables will then be used in the temporal ARIMA modelling. Clustering was then tested using spatial regression and gave no valid results. This ran against the results in other research by Graziano and Gillingham (2014) which showed a neighbourhood effect. Spatial-Temporal Regression A further spatial-temporal regression process was run in SPSS. For this, it was assumed that the data was non-stationary. Non-stationary means that the mean shifts with time. Data configured for the spatial-temporal regression was for 196 time steps and over the 2300 postcode areas, and this was set up in Excel using a Visual Basic subprogram. The test was only on the spatial patterns of diffusion. Only one variable (Australian

APPENDIX C: PV IN AUSTRALIA ANALYSIS

167

PV adoption by month by postcode) was tested in the spatial-temporal regression due to complexity of spatial-temporal regression. The spatialtemporal regression showed no significant result, and a decision was made not to pursue this further and to instead use ARIMA modelling which tests temporal but not spatial dimensions. 32 ARIMA Model Outputs The ARIMA process in Chapter 3 used the software IBM SPSS Modeler (IBM, 2017) and used the function temporal causal modelling. In the context of temporal causal modelling, the term causal refers to Granger Causality. Granger Causality is used carefully as it is recognised that it will not be causal in all circumstances. Temporal causal modelling builds an autoregressive time series model for each target and includes only those inputs that have a causal relationship with the target. This approach differs from traditional time series modelling which needs specification of predictors (IBM, 2017). The outputs for the finally selected model were run for all 32 areas of Australia (Pink, 2011), and the resulting graphs are shown below.

Measured 73

Simulated 73

10 5 0

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH

Sydney City Area 10

-5 MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

Measured 12

Simulated 12

15 10 5 0 -5

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH

NSW Outer Suburbs Area 12

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH -5 0

0

-5

0 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

0

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH -10

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH

168 APPENDIX C: PV IN AUSTRALIA ANALYSIS

NSW Rural Area 13

30 Measured 13

Measured 14

Measured 20

Measured 21

Simulated 13

20

10

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

NSW Remote Area 14

15 Simulated 14

10

5

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

VIC City Area 20

10 Simulated 20

5

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

VIC Inner Suburbs Area 21

Simulated 21

10

5

-5

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH -5 0

0

0 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

-5

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH 0

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH

APPENDIX C: PV IN AUSTRALIA ANALYSIS

Measured 22

Measured 23

Measured 30

Measured 31

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

169

VIC Outer Suburbs Area 22 Simulated 22

10

5

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

VIC Rural Area 23

15 Simulated 23

10

5

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

QLD City Area 30

15 Simulated 30

10

5

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

QLD Inner Suburbs Area 31

15 Simulated 31

10

5

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH 0

-2 0

0 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

-2

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

0

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH

170 APPENDIX C: PV IN AUSTRALIA ANALYSIS

QLD Outer Suburbs Area 32 Measured 32

Measured 33

Measured 34

Measured 11

Simulated 32

15

10

5

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

QLD Rural Area 33 Simulated 33

8

6

4

2

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

QLD Remote Area 34

6 Simulated 34

4

2

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

Sydney Inner Surburbs Area 11

10 Simulated 11

5

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH 0

-2

-5 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

-5

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

0

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH

APPENDIX C: PV IN AUSTRALIA ANALYSIS

Measured 51

Measured 52

Measured 53

Measured 54

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

171

WA Inner Suburbs Area 51

15 Simulated 51

10

5

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

WA Outer Suburbs Area 52

10 Simulated 52

5

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

WA Rural Area 53

6 Simulated 53

4

2

0

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

WA Remote Area 54

10 Simulated 54

5

0

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH -2 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

-2

-10

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH

172 APPENDIX C: PV IN AUSTRALIA ANALYSIS

TAS Inner Suburbs Area 61

6 Measured 61

Measured 62

Measured 63

Measured 64

Simulated 61

4

2

0

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

TAS Outer Suburbs Area 62

6 Simulated 62

4

2

0

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

TAS Rural Area 63

4 Simulated 63

2

0

-2 MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

TAS Remote Area 64

30 Simulated 64

20

10

0

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

APPENDIX C: PV IN AUSTRALIA ANALYSIS

173

Measured 73

Simulated 73

10 5 0 -5

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH

Northern Territory Rural Area 73

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

Measured 74

Simulated 74

2 1

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

0 -1 MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

Australian Capital Territory Area 80 Measured 80

Simulated 80

20 10 0 -10

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH

Northern Territory Remote Area 74

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

Measured 72

Simulated 72

3 2 1 0 -1

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117

SOLAR INSTALLATIONS PER 1000 HOMES PER MONTH

Northern Territory City and Urban Area 72

MONTHS BETWEEN AUGUST 2007 AND AUGUST 2017

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Index

A Academic, 35, 39, 103, 143, 146 AEMO-Energy Live, 51 Aggregators, 43–45, 94, 100, 104, 107, 108, 132 Akaike Information Criterion (AIC), 67, 70, 71, 75, 165 Algorithms, 16, 87 Alterative Technology Association (ATA), 47, 49 Appliances, 6, 94, 106, 116 Argyris, C., 117 Artificial Intelligence (AI), 89, 91 AS4777, 46, 90, 109 Asset write-downs, 40, 42 AusNet, 44, 87 Australia and Europe, 36, 39, 52, 85 Australian Bureau of Statistics (ABS), 56, 57, 70, 71, 162 Australian Capital Territory, 23, 28, 96 Australian Competition and Consumer Commission (ACCC), 41

Australian Electoral Commission (AEC), 56, 69, 162 Australian Energy Market Commission (AEMC), 28 Australian Energy Market Operator (AEMO), 29 Australian Energy Regulator (AER), 25, 48 Australian Government, 7, 10, 13, 22, 23, 25, 45, 48, 49, 51, 56, 57, 68, 70, 97, 131–133, 160, 162 Australian Photovoltaic Institute (APVI), 26, 68, 69, 158, 161 Australian Renewable Energy Agency (ARENA), 25, 161 Autonomous load control, 33, 35, 144 B Backwards flows, 86 Batteries, 12, 23, 44, 45, 61, 85, 87, 88, 93, 94, 97, 98, 102–104, 106, 116, 136, 137 Beer, S., 117

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 G. Currie, Australia’s Energy Transition, https://doi.org/10.1007/978-981-15-6145-0

189

190

INDEX

Blackouts, 11, 108 Broker, 49–51, 107, 108, 132 Brotherhood of St Lawrence, 48 Building code reform, 34, 37, 95, 144 Building codes, 95, 96 Buildings Directive, 96 Business conditions, 69–77, 81 C C4CE, 45 Carbon subsidy, 25, 26, 103 Census, 56, 57, 63, 159, 163, 166 Central Victorian Greenhouse Alliance (CVGA), 45, 48 Checkland, P., 116, 117, 133 CitiPower, 97, 107 Clean Energy Council (CEC), 49, 56, 91 Clean Energy Finance Corporation (CEFC), 25, 131 Clean Energy Regulator (CER), 59, 63, 162 Climate policy, 23 Coal, 3, 4, 10, 22, 29 Community Energy, 14, 15, 47, 51, 107, 136 Competition, 33, 35, 40, 41, 48, 106, 108, 132, 144 Consumer, 4–6, 8–15, 25–29, 39, 40, 43, 44, 48, 65, 80, 87, 90, 91, 94, 100, 101, 103, 104, 107–109, 116–118, 126, 131, 134–137 Consumer Action Law Centre, 47 Consumer role, 6, 9, 12, 15, 16, 28, 43, 44, 55, 65, 116, 117, 129, 143, 154 Controls, 16, 87, 91, 101, 103, 134 Cost reflective pricing, 39, 96–99, 109 Council of Australian Governments (COAG), 131 CSIRO, 47, 49, 134

D Data, 25, 50, 51, 55–58, 64–67, 69, 79, 80, 85, 89, 91, 104, 105, 107, 108, 110, 116 Data flow, 91, 107, 108 Demand Management (DM), 61, 85, 90, 97, 99, 100 Demand Management Incentive Scheme (DMIS), 23, 99–101 Demand Response (DR), 9, 16, 28, 34, 35, 85, 88, 91, 94, 97, 100 de Paul, Vincent, 48 De Tocqueville, Alexis, 105 Dispersed grid, 7, 21, 40, 123 Distributed Energy Resources (DER), 4 Distribution, 2, 3, 5, 8, 9, 23, 27, 29, 38, 41, 42, 46, 50, 86, 87, 90, 92, 93, 96, 98, 99, 101, 103, 105, 106, 123, 125, 126, 129, 131, 138 Distribution Business (DB), 3, 11, 13, 25, 36, 39–45, 48, 51, 86, 87, 91–93, 98, 101, 102, 104, 106–109, 118, 132, 135, 137, 143 Distribution Service Operator (DSO), 103, 134 Distribution services, 47, 101, 105, 107, 131 Double Loop Learning, 117 Durbin Watson test, 79 Dynamic grid operation, 42 E Economics, 13, 36, 41, 65, 100, 117, 129 Electricity Consumers Australia, 47 Electricity costs, 7, 42, 91, 108 Electricity peak, 101 Electricity price, 2, 3, 24, 27, 41, 42, 68, 70, 107, 108

INDEX

Electricity retailers, 40, 118, 120 Electric vehicle charging, 34, 37, 85, 101, 132, 145 Electric Vehicles (EV), 4–6, 8, 25, 56, 85, 91, 96, 98, 101, 108, 117, 126 Empowerment, 14 Energex, 91, 92, 99 Energy-action, 6, 12, 13, 44, 60, 61, 63, 79, 80, 132, 133 Energy Compare, 51 Energy efficiency, 10, 12, 36, 63, 94–96, 106, 136 Energy efficient devices, 34, 37, 144 Energy Networks Association (ENA)., 44 Energy Networks Australia, 29, 46, 50, 51, 125, 134, 139 Energy security, 2, 15 Energy Security Board (ESB), 24, 45, 46 Energy Transition, 3, 4, 9, 11, 12, 15, 16, 23, 25, 28–30, 36, 39, 42, 44, 47, 49, 50, 52, 66, 85–87, 89, 109, 110, 115, 116, 118, 120–122, 126, 131, 132, 134–136, 139 Energy Users Association of Australia (EUAA), 12 Equilibrium economics, 129 Ergon, 86, 97, 132 Ethics, 104, 105 European Commission (EC), 50, 96, 136 Evie Pty Ltd, 131, 132 Evolutionary, 9, 117, 118, 129 Evolutionary economics, 129 Exploratory modelling, 55–57, 166

F Feeders, 8, 25, 86, 99

191

Feed-in Tariff (FIT), 26, 63, 71, 74–77, 79–81, 160 Finkel, Alan., 4, 14, 25, 29, 48, 49, 51, 90, 138 Finkel Review, 14, 25, 29, 48, 90 Flexibility, 10, 12, 50, 103, 137 Frequency, 4, 5, 16, 29, 74, 85, 87–89, 91, 93, 94, 102, 107, 131, 132, 138, 153 Frequency Control Ancillary Services (FCAS), 5, 94, 105, 107 Frequency instability, 4, 25

G Gas, 3 Gender, 14, 135 Generation, 3–5, 8, 11, 12, 16, 22, 23, 27, 29, 88, 98, 99, 103, 105–109, 115, 122, 123 Geothermal, 29 Governments, 3, 7, 10–12, 15, 22, 23, 40–42, 45–48, 51, 80, 95, 101, 108, 116, 118, 120, 133–136, 139 Greens, 58, 62, 157, 160 GreenSync, 44, 49, 103, 132 Grid Development, 4

H High technology inverters, 39 Home battery storage, 34, 145 Home ownership, 56, 58, 59, 63, 80 Hornsdale, 102 Household batteries, 87, 103 Hydro, 3

I IEEE, 89, 120, 122, 123, 126 IEEE P1220, 120–122

192

INDEX

Income, 42, 56, 58, 59, 61–63, 68, 80, 134, 160, 162, 166 Inertia, 4, 5, 29, 94, 99, 121, 138 Information networks, 34, 145 Innovation, 16, 25, 36, 40, 41, 51, 91, 104–108, 116, 118, 125, 126, 129–132, 134, 138 Institutions, 11, 36, 46, 47 Investment, 5, 27, 63, 98, 106–108, 121, 122, 131, 132, 138

L Legal rules, 34, 145 Liddell Coal Power Plant, 133 Load factor correction, 87 Lobby, 46, 137

M Market, 5, 8–10, 12, 13, 23–25, 29, 36, 40, 41, 43, 48, 49, 56, 67, 85, 87, 89, 92–94, 96–108, 110, 116, 125, 126, 131–133, 135, 138 Metering, 41, 43 Microgrid, 11, 107 Mill, John Stuart, 105 Monopoly, 10, 42, 43, 106, 126

N NABERS, 94 National Consumer Roundtable on Energy, 48 National Energy Guarantee (NEG), 24, 30, 131 National Energy Rules (NER), 30, 43, 48, 105, 138 NEG policy, 14 Network price reform, 34, 37, 144 Network pricing, 85, 96–98, 103, 106, 125

Northern Territory, 23, 27, 123 NREL, 88 NSW, 23, 57, 63, 65, 74, 160 Nuclear, 29

O Office of Gas and Electric Markets (Ofgem), 45 Old Grid, 3 Ombudsmen, 133 Optimisation, 29, 51, 85, 87, 98, 104, 107, 117, 129, 131 Overvoltage, 4, 8, 9, 25, 41, 43, 86, 89, 90, 109, 134, 135

P Peer-to-peer, 42, 98 Photovoltaics (PV), 4, 6–9, 12, 21, 25–27, 39, 40, 42, 44, 45, 48, 56–58, 60, 62, 63, 67, 78, 80, 86, 90, 103, 118, 134, 137, 157, 159, 160 Popper, K.R., 117 Postcode, 55–59, 61–64, 68, 69, 159, 160, 163–166 Powercor, 41, 86, 97, 107 Power of Choice, 48 Power purchase agreement (PPA), 97 Powershop, 97 Price-PV-system, 71, 72, 74–77, 81 Prime Minister, 14, 29, 131, 133 Prime movers, 25, 34, 35, 44, 45, 145 Privacy, 46, 48, 50, 88, 91, 107, 108, 143 Private ownership, 23 Productivity, 138 Productivity Commission, 137 Prosumers, 133 Public ownership, 3

INDEX

PV export limits, 36, 39, 85–87, 109, 116, 144

Q Queensland, 23, 28, 60, 87, 91, 92, 97, 99

R Regulated monopoly, 10, 42, 43, 126 Remote control, 34, 36, 92–94, 144 Renewable, 4–6, 10, 12, 13, 16, 22, 23, 29, 46–49, 89, 95, 98, 102, 107, 109, 110, 115, 121, 124, 137 Rental, 13, 137 Reposit Power, 5, 29, 44, 99, 107 RIIO, 42, 100, 106, 132 Ring-fencing, 41, 107

S Schumpeter, J.A., 129, 130 Security, 45, 46, 79, 90 SEIFA, 56, 162 Sensor, 89, 91 Shaw, George Bernard, 115 Simon, H.A., 117, 118 Smart device, 85, 87 Smart inverters, 35, 88, 90, 91, 144 Smart meter, 45, 85, 87–90, 97, 104, 105, 107 Snowy, 131 Social equity, 2, 12, 39, 46, 52, 129, 135–138 Social licence, 11, 13, 36, 39, 131, 135–137 Social rules, 34, 46–48, 145 Socio-technical, 57, 66, 117, 133 South Australia, 27, 28, 42, 45, 46, 59, 60, 91, 92, 99, 102, 103 Stationarity, 66, 77

193

Stepwise regression, 57, 60, 61, 159, 160, 163, 166 Storage, 6, 8, 9, 13, 24, 39, 43, 60, 61, 80, 85, 87–90, 92–94, 97, 98, 102–104, 106, 108, 109, 116, 117, 126, 131 Sustainability, 14, 15 Synchronous condensers, 4 Systems engineering, 120

T Tariffs, 97–99, 103, 137 Tasmania, 23, 27, 28, 60 Technology, 9, 10, 13, 14, 29, 39, 41, 47, 85, 87, 89–91, 97, 98, 100, 103, 105, 108, 110, 117, 118, 131, 137 Temporal Causal Modelling, 79, 167 Tesla, 102 Thermal overload, 4, 25 Thermostats, 41 Thorium, 29 Transformers, 7–9, 41, 86, 89, 96, 99, 109

U UK, 39, 42, 46, 100, 106, 132, 139 UN, 132 United Energy, 44, 107 USA, 13, 27, 57, 131, 138

V Variability, 5, 7, 16, 80, 109 Victoria, 6, 23, 27, 28, 42, 45, 46, 50, 60, 88, 95–97, 107, 136, 137 Victorian Energy Data Hub, 51 Virtual Power Plant (VPP), 87, 103, 109

194

INDEX

Virtual Synchronous Generators (VSG), 88 Voltage instability, 4, 25 W Wave, 29 Weiner, N., 117 Western Australia, 23, 27, 28, 60, 123

Wind, 3 Wind farms, 29 Women, 14, 15 World Energy Council, 15

Z Zen group, 45