Energy from : and perceptions in the UK

A thesis submitted to The University of Manchester for the degree of Doctor of Philosophy in the Faculty of Engineering and Physical

2014

Paul Balcombe

School of Chemical Engineering and Analytical

Paul Balcombe

Contents

List of Figures ...... 7

List of Tables ...... 10

List of abbreviations...... 11

Declaration ...... 14

Copyright statement ...... 14

Acknowledgements ...... 15

Chapter 1: Introduction ...... 16

1.1 Background and motivation ...... 16

1.1 Aims, objectives and novelty ...... 18

1.2 Alternative format of the thesis ...... 20

1.3 Overarching methodology ...... 21

1.4 References ...... 21

Chapter 2: Motivations and barriers associated with adopting microgeneration in the UK ...... 24

Annex ...... 24

References ...... 25

Motivations and barriers associated with adopting microgeneration energy technologies in the UK ...... 26

Abstract ...... 26

1. Introduction ...... 26

2. Motivations and barriers ...... 28

2.1. Finance ...... 29

2.2. Environment ...... 36

2.3. Security of supply ...... 37

2.4. Uncertainty and trust ...... 38

2.5. Inconvenience...... 39

2.6. Impact on residence ...... 40

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3. Differing perceptions within subgroups of the UK population ...... 41

3.1. Age ...... 42

3.2. Household size and ownership...... 45

3.3. Social class, income and education ...... 46

4. Further discussion and conclusions...... 47

Acknowledgements ...... 50

Appendix ...... 50

References ...... 51

Chapter 3: Investigating the importance of motivations and barriers related to microgeneration uptake in the UK ...... 59

Annex ...... 59

Investigating the importance of motivations and barriers related to microgeneration uptake in the UK ...... 61

Abstract ...... 61

1. Introduction ...... 62

2. UK microgeneration policy ...... 63

2.1 Feed-In Tariffs ...... 63

2.2 Incentive ...... 64

2.3 Green Deal...... 64

2.4 Microgeneration Strategy ...... 65

3. Existing research on the motivations and barriers affecting adoption ...... 65

3.1 Finance ...... 66

3.2 Environmental concerns ...... 66

3.3 Self-sufficiency ...... 66

3.4 Uncertainty and trust ...... 67

3.5 Inconvenience ...... 67

3.6 Impact on residence ...... 67

3.7 Differing perceptions across the UK population ...... 67

4. Methodology ...... 68

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4.1 Questionnaire design and data collection...... 68

4.2 Best-worst scaling ...... 70

4.3 Data analysis ...... 71

5. Results...... 73

5.1 Motivations for installing microgeneration ...... 75

5.2 Barriers to installing microgeneration ...... 79

6. Discussion ...... 82

6.1 Motivations for installing microgeneration ...... 83

6.2 Barriers to installing microgeneration ...... 85

6.3 FITs and the experience of adoption ...... 86

7. Conclusions ...... 87

Appendix ...... 90

Acknowledgements ...... 93

References ...... 93

Supplementary Material ...... 102

Chapter 4: Self-sufficiency and reducing the variability of grid demand: integrating solar PV, CHP and battery storage ...... 118

Annex ...... 118

Self-sufficiency and reducing the variability of grid electricity demand: integrating solar PV, Stirling engine CHP and battery storage ...... 119

Highlights ...... 119

Abstract ...... 119

1. Introduction ...... 120

2. Methodology ...... 122

2.1 Household simulation ...... 122

2.2 Household electricity self-sufficiency ...... 128

2.3 Electricity grid demand profiles ...... 128

2.4 Cost-benefit analysis...... 129

3. Results...... 133

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3.1 Electricity self-sufficiency ...... 133

3.2 Variability in grid demand profiles ...... 136

3.3 Cost-benefit analysis ...... 138

4 Discussion ...... 143

5 Conclusions ...... 146

Appendix ...... 148

References ...... 151

Chapter 5: Environmental impacts of microgeneration: Integrating solar PV, Stirling engine CHP and battery storage ...... 159

Environmental impacts of microgeneration: Integrating solar PV, Stirling engine CHP and battery storage ...... 160

Abstract ...... 160

1. Introduction ...... 161

2. Methodology ...... 162

2.1 Goal and scope ...... 162

3. Results ...... 171

3.1 Environmental impacts of the PV-SECHP-battery system ...... 172

3.2 Comparison of results with literature ...... 176

3.3 Sensitivity analysis ...... 180

4. Conclusions ...... 189

Acknowledgements ...... 191

References ...... 191

Supplementary material ...... 197

Chapter 6: Conclusions and further ...... 198

6.1 General conclusions ...... 198

6.2 Motivations and barriers ...... 199

6.3 Increasing self-sufficiency and flattening grid demand ...... 201

6.4 Environmental impacts of the PV-SECHP-battery system ...... 203

6.5 Study limitations ...... 205

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6.5.1 Best-worst scaling survey ...... 205

6.5.2 PV-SECHP-battery simulation, costs-benefit analysis and environmental assessment ...... 206

6.6 Recommendations for policy and industry ...... 207

6.7 Recommendations for further work ...... 208

Word count: 57,360

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List of Figures

Figure 1 Increase in the number of installations from 2001-2012 ...... 27

Figure 2 The percentage of each age category associated with different consideration stages (Consumer Focus, 2011) ...... 44

Figure 3. Decrease in capital costs of solar PV installations from 2010-2012 (/ kWp) (CompareMySolar Ltd, 2012; DECC, 2011a; Parsons Brinckerhoff, 2012; Vaughan, 2012) ...... 48

Figure 4 Feed-in tariff (FIT) payment rates and the number of instalations per month for solar PV retrofit installations of less than 4 kW capacity (Ofgem, 2012) ...... 49

Figure 5. Feed-in Tariff (FIT) payment rates and the number of installations per month for solar PV retrofit installations of less than 4 kW capacity modified from (Balcombe et al., 2013; DECC, 2013a) ...... 64

Figure 6. An example subset of motivations taken from the best-worst scaling survey. ... 70

Figure 7. The year of installation for the sample of adopters and the year of rejection for the sample of rejecters...... 75

Figure 8. The proportion of adopters, considerers and rejecters who have installed or considered each ...... 75

Figure 9. Hierarchical Bayes estimation of the relative importance of motivations for installing microgeneration for adopters, considerers and rejecters...... 78

Figure 10. Motivation importance scores for pre- and post-2010 adopters...... 79

Figure 11. Hierarchical Bayes estimation of the relative importance of barriers to installing microgeneration for adopters, considerers and rejecters...... 81

Figure 12. Barrier importance scores for pre- and post 2010 adopters...... 82

Figure 13. Simulation steps for the solar PV, SECHP and battery system. The boxes represent the stages and the circles indicate variables of the simulation...... 123

Figure 14. Annual gas and electricity demand for each household simulation. Vertical lines indicate UK average household gas demand and horizontal lines indicate average electricity demand (Barnes, 2013)...... 125 Page 7 of 210

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Figure 15. Graph of the range of PV capacities for UK installations <4 kWp and for the simulation data (DECC, 2013b)...... 125

Figure 16. The percentage of imported electricity for different installed battery capacities, with 80% battery efficiency and efficient SECHP operation, averaged across all households...... 135

Figure 17. Daily household demand properties for the reference system, PV only, PV + SECHP and all battery sizes, averaged across all households for 80% battery efficiency and efficient SECHP operation (where applicable)...... 137

Figure 18. Daily demand profile for different quarters of the year for the reference and solar PV only systems, averaged across all households...... 137

Figure 19. Daily demand profile in different quarters of the year for the reference and base case SECHP-PV-battery systems, averaged across all households...... 138

Figure 20. NPV difference (relative to the reference system) for PV only, PV and SECHP and SECHP-PV-battery for different battery sizes, averaged across all households for 80% battery efficiency and efficient SECHP operation (where applicable)...... 139

Figure 21. Breakdown of lifetime costs for systems with different battery capacities in comparison to the reference system, averaged across all households with 80% battery efficiency and efficient SECHP operation (where applicable)...... 140

Figure 22. Selected operating costs across different battery capacities in comparison to the reference system, averaged across all households with 80% battery efficiency and efficient SECHP operation (where applicable)...... 140

Figure 23. NPV difference for each household for the base case plotted against the household annual electricity demand...... 141

Figure 24. Average NPV difference for each dwelling type for the base case, relative to the reference system...... 141

Figure 25. NPV difference for different lifespans of the SECHP-PV-battery system for the base case, relative to the reference system...... 142

Figure 26. Annualised NPV difference for base case for different consumer discount rates...... 143

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Figure 27. Average NPV difference for the base case for different future energy cost projections. The different categories represent the source and equivalent electricity and gas price inflation rates, respectively (DECC, 2013c; Elmes, 2014; National Grid, 2012b)...... 143

Figure 28. Average NPV across all households for the base case for different proportions of grants for the total capital cost...... 145

Figure 29. The life cycle diagram of the household microgeneration system comprising solar PV, SECHP and battery storage ...... 163

Figure 30 The life cycle of a ...... 171

Figure 31. Environmental impacts of the PV-SECHP-battery system in comparison with the grid electricity and gas boiler...... 173

Figure 32. The contribution to environmental impacts of solar PV, SECHP, battery and electricity imports and exports...... 173

Figure 33 Comparison with literature of environmental impacts of solar PV ...... 177

Figure 34. Comparison with literature of environmental impacts of SECHP ...... 178

Figure 35 Environmental impacts of the battery cell estimated in this study ...... 179

Figure 36. Comparison with literature of selective emissions from the life cycle of battery...... 180

Figure 37. Environmental impacts for the PV-SECHP-battery system, showing the variation in impacts for different dwelling types ...... 182

Figure 38. The reduction in environmental impacts when replacing the conventional by the PV-SECHP-battery system, also showing the variation in impacts for different dwelling types ...... 183

Figure 39. Effect on the environmental impacts of the efficiency of SECHP operation ... 183

Figure 40. Effect on the impacts of different battery capacities...... 185

Figure 41. Effect on the impacts of different battery lifespans...... 186

Figure 42. Effect on the impacts of different SECHP lifespans...... 187

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Figure 43. Effect on the impacts of different recycling rates of metals used to manufacture the microgeneration system...... 188

Figure 44. Effect on the impacts of recycling of antimony used in batteries...... 189

List of Tables

Table 1 Summary description of various microgeneration technologies ...... 16

Table 2 Summary of surveys carried out related to attitudes to microgeneration ...... 30

Table 3 Summary of motivations and barriers associated with adopting microgeneration as found in literature ...... 33

Table 4 Comparison of capital costs and consumer willingness to pay (WTP) ...... 34

Table 5 Correlations between several demographic factors and likelihood of adoption .... 43

Table 6. Motivations and barriers considered in the survey ...... 69

Table 7. A summary of the characteristics of the sample, showing the breakdown for adopters, considerers and rejecters...... 74

Table 8. Estimates from the Hierarchical Bayes model of relative importance of each motivation and barrier for adopters, considerers and rejecters, with the standard error of the mean as a measure of variancea...... 77

Table 9. The simulation parameters, their units and range of values, as well as the base case values...... 128

Table 10. Capital cost and specification of the battery system components (Bright Green Energy Ltd., 2014; Jenkins et al., 2008; McKenna et al., 2013; Navitron Ltd., 2013)...... 130

Table 11. Total capital cost for different battery usable capacities...... 131

Table 12. Costs associated with each operating cost component...... 131

Table 13. Yearly electricity unit cost increase above inflation ordered from lowest to highest, alongside gas cost inflation rate and the source of the estimate...... 132

Table 14. Expected lifespan and installation cost of each replacement item...... 132

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Table 15. Summary of base case annual household generation and figures across the 30 simulated households...... 133

Table 16. Contribution of each energy source as a percentage of total household demand for the base case, averaged over the 30 households...... 134

Table 17. Average proportion of consumed PV and SECHP electricity for the base case, both directly and indirectly (through the battery)...... 135

Table 18. Inventory data for the manufacture of a 3 kWp solar PV, by component (Ecoinvent, 2010; Stamford and Azapagic, 2012) ...... 166

Table 19. Inventory data for the manufacture of SECHP (left) and battery (right), by component (Baxi, 2011b; Ecoinvent, 2010; Sullivan and Gaines, 2012)...... 167

Table 20. Household annual energy demand and generation by different components of the system, also showing the imports and exports of electricity...... 168

Table 21. UK electricity mix in 2013 (DECC, 2014a)...... 168

Table 22. Transport assumptions for the SECHP, solar PV and battery systems...... 170

Table 23. Inventory data for a condensing gas boiler...... 171

List of abbreviations

ADP Abiotic resource depletion potential AP Acidification potential ASHP Air source heat BWS Best-worst scaling CCGT Combined cycle gas CHP Combined heat and power DECC Department of Energy and Change EP Eutrophication potential EST FAETP Freshwater aquatic ecotoxicity potential FIT Feed-in Tariff GHG gas

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GRP Glass reinforced GSHP source GWP Global warming potential HB Heirarchical Bayes HTP Human toxicity potential iid Independent and identically distributed ISO Internation Standards Organisation LCA Life cycle assessment MAETP Marine aquatic ecotoxicity potential MCS Microgeneration Certification Scheme MNL Multi-nomial logit NPV Net-present value ODP Ozone depletion potential Ofgem Office of Gas and Electricity Markets OFT Office of fair trading POCP Photochemical ozone creation potential PV Photovoltaic RHI Renewable Heat Incentive RHPP Renewable heat premium payment RLH Root likelihood SAP Standard assessment procedure SECHP Stirling engine combined heat and power SEDBUK UK Seasonal Efficiency of Domestic TETP Terrestrial ecotoxicity potential UKERC UK energy research centre UKERC EDC Energy Database Centre VAT Value added tax WSHP source heat pump WTP Willingness to pay

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Paul Balcombe The University of Manchester

Energy from microgeneration: sustainability and perceptions in the UK Abstract Submitted for the degree of Doctor of Philosophy, October 2014

The drive to meet and targets has led the UK government to incentivise microgeneration, with 2 GW now installed, the vast majority of which is solar PV. However, this only represents 0.2% of UK energy supply and greater uptake is not guaranteed since FIT rates were cut for solar PV in 2012, reducing the financial incentive to install. Thus, other consumer motivations must be focussed on by industry and the government in order to further increase uptake. Additionally, microgeneration may be able to contribute to a sustainable and reliable UK energy mix, but such a contribution is not guaranteed. For example, there is concern that above 10 GW of installed solar PV, the electricity grid will experience balancing problems due to uncontrolled exporting to the grid. With higher intermittent solar PV generation, there will a greater load requirement on variable-load plants such as and gas generation plants. This research investigates the above issues by contributing to the question: How can microgeneration contribute further to UK climate change and energy security targets? Firstly, this research determines the consumer motivations and barriers associated with the decision whether or not to install microgeneration, in order to find ways of further improving uptake. A comprehensive literature review was carried out, followed by a survey using the ‘best-worst scaling’ approach to determine the relative importance of each motivation and barrier across existing adopters, those currently considering installing and those who have decided not to, rejecters. The most important motivations were to earn money, to increase self-sufficiency and to guard against future energy bill increases. The greatest barriers were high capital costs, not earning enough money and the risk of losing money if they moved home. Whilst the Green Deal was designed to remove the capital cost and risk of losing money barriers, it may actually increase the risk of losing money if they moved home as homebuyers are reluctant to purchase a house with an attached Green Deal loan. The desire for self-sufficiency is more important for considerers and rejecters than adopters and greater emphasis on increasing self-sufficiency could help improve uptake. Secondly, an option to increase household energy self-sufficiency whilst mitigating the grid balancing problems associated with solar PV exports was investigated: a combined solar PV, Stirling engine CHP (SECHP) and -acid battery household system was simulated and used to carry out a cost-benefit analysis and life cycle assessment compared to a conventional household system using the electricity grid and gas boiler for heating. The system provides 72% of a household’s energy demand and reduces grid demand variations by 35% with a 6 kWh battery. However, the system is only cost-effective for households with large electricity demand, 4,300 kWh/yr. If uptake of such a system is to be encouraged, it must be incentivised: a 24% capital grant would be required for the average household (£3,600). The environmental impacts of the system are reduced by 35-100% compared to the conventional system for 9 out of 11 impacts. However, depletion of elements is 42 times higher largely due to the use of antimony for the battery manufacture. Environmental benefits vary greatly across households and those with the largest energy demand achieve the greatest benefits from the system. Appropriate battery sizing is essential in order to maximise environmental benefits, with 10–20 kWh capacity being optimum for the households considered. Overall, this research has identified numerous ways to increase microgeneration uptake, but this is likely to be at a cost to the government and, ultimately, the tax payer. UK microgeneration policy over the last decade has frequently changed and created uncertainty for consumers and the industry. A more continuous, simple and transparent policy environment would provide security for both industry and consumers, allowing more stable growth in a quickly maturing market. Page 13 of 210

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Declaration

No portion of the work referred to in this thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning.

Copyright statement

The author of this thesis (including any appendices and/or schedules to this thesis) owns certain copyright or related rights in it (the “Copyright”) and s/he has given The University of Manchester certain rights to use such Copyright, including for administrative purposes.

Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has from time to time. This page must form part of any such copies made.

The ownership of certain Copyright, patents, designs, trade marks and other intellectual property (the “Intellectual Property”) and any reproductions of copyright works in the thesis, for example graphs and tables (“Reproductions”), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions.

Further information on the conditions under which disclosure, publication and commercialisation of this thesis, the Copyright and any Intellectual Property and/or Reproductions described in it may take place is available in the University IP Policy (see http://documents.manchester.ac.uk/DocuInfo.aspx?DocID=487), in any relevant Thesis restriction declarations deposited in the University Library, The University Library’s regulations (see http://www.manchester.ac.uk/library/aboutus/regulations) and in The University’s policy on Presentation of Theses

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Acknowledgements

Firstly I would like to thank my supervisors, Adisa Azapagic and Dan Rigby. They have given me excellent support, especially during the rather long teething period. I have a lot of respect for their brutal honesty and way of breaking down a problem, which I would like to emulate.

Many thanks to the Institute, who provided funding for the research and helped me to adjust to student life again with all the activities within the doctoral training centre.

I would also like to acknowledge Sawtooth Software for providing the MaxDiff software grant used to design and implement the consumer survey. Additionally, many thanks to Cathy Debenham from YouGen in helping me recruit participants for the consumer survey.

Thank you to Laurence Stamford and Harish Jeswani for spending so much of their time to teach me about life cycle assessments.

Finally, thank you very much Ximena Schmidt Rivera, who made working late nights so much more fun, productive and generally bearable. You are a little shining light.

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Chapter 1: Introduction

1.1 Background and motivation

Microgeneration technologies have been the subject of much attention over the last decade from the public press, and academia, due to rapid uptake, government incentives and their perceived environmental friendliness. In the UK, microgeneration is defined as “the small-scale production of heat and/or electricity from a low carbon source”, generating 50 kW or less of electricity and/or 45 kW of heat (HM Government, 2004). This scale of generation is suitable for installation in domestic and commercial buildings. Microgeneration technologies comprise solar thermal, ground source heat (GSHP), air source heat pumps (ASHP), water source heat pumps (WSHP), and boilers, solar photovoltaic (PV), , hydro, micro- combined heat and power (CHP) and cells. A summary description of these technologies is given in Table 1.

Table 1 Summary description of various microgeneration technologies

Microgeneration Description Fuel source type type

Solar thermal A simple heat-exchange system using solar Heat panels radiation to heat a heat-transfer fluid (in either plate-type or evacuated tubes) which in turn heats water, for , space heating or both. Ground source The relatively constant heat a few metres Geothermal Heat heat pumps below the ground supplies a small energy (GSHP) temperature differential to a heat-transfer fluid. By a compression cycle similar to that in a refrigerator, this heat is transferred to water. Air source heat Similar to ground source heat pumps, but Thermal Heat pumps (ASHP) using air as the heat source. energy from air Water source Similar to ground source heat pumps, but Thermal Heat heat pumps using a local water reservoir as the heat energy from (WSHP) source. water Biomass stoves Heat is obtained from burning forestry Heat and boilers products (e.g. logs, chips and pellets) or energy from biomass waste (e.g. agricultural etc.). biomass Solar photovoltaic excites and frees electrons to create Solar energy Electricity (PV) panels a . Wind Wind energy is used to drive exposed blades Motive force Electricity that in turn drive an electricity generator. from wind - Water from a higher level source falls to a Motive force Electricity plants lower level and the is used to from water drive a turbine attached to an electricity generator Micro combined of electricity and heat from Combustion Electricity heat and power different (e.g. natural gas, biomass, energy from and heat (CHP) plants hydrogen) and technologies (e.g. Stirling and various fuels Page 16 of 210 Chapter 1 Paul Balcombe

Microgeneration Description Fuel source Energy technology type type

engines, turbines, fuel cells). Fuel cells The of a fuel (e.g. Electrochemi Electricity hydrogen, natural gas, ) and an cal energy (and heat oxidant between two electrodes creates an from fuel if CHP) ionic charge which generates a current which reaction (e.g. is converted into electricity. hydrogen, natural gas, methane)

Global uptake of microgeneration has increased significantly over the past 10 years, in particular for solar PV, with 138 GW installed by 2013 (EPIA, 2014), caused by decreasing costs (Thretford, 2013) and various policy incentives (e.g. DECC, 2009b). Driven by the need to meet climate change and energy security targets, the UK government support the microgeneration industry by consumer incentives via the Feed-in Tariff (FIT) (NHBC Foundation, 2011) and Renewable Heat Incentive (RHI) (DECC, 2011b), as well as other non-financial directives (DECC, 2011a; DTI, 2006). Microgeneration may have the potential to contribute to climate change and energy security targets but a positive contribution is not guaranteed. This is because the environmental sustainability of microgeneration can vary significantly due to various factors, such as efficiency, manufacturing processes and local environmental conditions (Element Energy, 2008a; NHBC Foundation, 2008; Staffell et al., 2009). These factors could either increase life cycle emissions or reduce the quantity of electricity or heat generated, thereby increasing levelised greenhouse gas (GHG) emissions.

Additionally, the intermittency of microgeneration sources (e.g. wind and solar) could also reduce the security of energy supply (Brouwer et al., 2014; Grave et al., 2012; Johansson, 2013). In particular, the National Grid are concerned that greater uptake of solar PV will cause difficulty in grid operation and balancing (National Grid, 2012). More than 10 GW of grid-connected solar PV capacity would require additional regulating and ramping requirements for variable-load plants such as coal and gas plants (National Grid, 2012). This may necessitate additional variable-load plants to run at reduced capacity, or, more likely, once new capacity is installed, older plants with lower efficiency and higher environmental impacts will be used for this purpose (Gross et al., 2006; MIT, 2011; National Grid, 2012). Another potential mitigation measure is to install local small-scale battery storage, allowing consumers to utilise more locally generated electricity whilst reducing grid balancing requirements. However, this also represents additional economic and environmental costs. Thus, whilst recognising the potential of microgeneration to contribute to climate change targets and energy security, there is uncertainty around the level of contribution or role that it can play. Page 17 of 210 Chapter 1 Paul Balcombe

As a result of the emerging microgeneration industry and various UK policy incentives over the last decade, there are currently around 580,0001 microgeneration units installed in the UK, the vast majority of which are solar thermal and solar PV units (DECC, 2014; Element Energy, 2008b). However, this contributes only around 0.2% of total domestic energy supply (see Chapter 2 for further details). UKERC have suggested that microgeneration could contribute between 15% and 40% of the UK electricity supply by 2050 (UKERC, 2009), whilst DECC have set a 2020 target to achieve a 2% contribution to the total UK electricity supply for renewable installations below 5 MW2 (DECC, 2009a). If these targets are to be met, far higher uptake is required than achieved so far.

The demand for microgeneration is affected by a number of factors. This research frames these factors as motivations and barriers affecting the consumer decision whether or not to install. Such barriers are high capital costs or performance and reliability concerns, and such motivations include income through Feed-in Tariff (FIT) incentives and consumers’ desire to reduce their . In order to increase uptake, there must be either greater motivation to install, or the barriers to install must be reduced. An understanding of these motivations and barriers is important in assessing how this potential for greater uptake might be realised.

Thus, if microgeneration is to contribute to climate change and energy security goals there must be greater uptake and they must be environmentally and economically sustainable.

1.1 Aims, objectives and novelty The aim of this research has been to contribute to answering the question: can microgeneration contribute to meeting UK climate change and energy security targets, and if so, how? In order to achieve this, the following were the specific objectives of the research:

1. Determine the UK public’s perceptions of microgeneration, in order to identify improvements to products, policy and industry that could improve uptake.

2. Determine the technical, economic and environmental impacts of adding local battery storage to households with microgeneration, in particular with respect to improving household self-sufficiency and flattening household electricity grid demand.

1 This figure is estimated by adding the number of installations by 2008 from Element Energy (2008b), 110,000, and the number of installations under 50 kW capacity from DECC (2014), 470,000.

2 This is the capacity bracket within which the Feed in Tariffs apply.

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These objectives are achieved through the four papers (Chapters 2 – 5) within this thesis, which are presently summarised in terms of their objectives, research methods and contribution.

Firstly, this research determines the consumer motivations and barriers associated with the decision whether or not to install microgeneration, in order to find ways of further improving uptake. Chapter 2 identifies the current understanding of the consumer motivations and barriers associated with installing a microgeneration system through a comprehensive literature review (Balcombe et al., 2013). The research also discusses the current knowledge of the difference in perceptions across the UK population and the impacts of microgeneration policy on these motivations and barriers. Several knowledge gaps are identified in the research and recommendations for further work are provided.

Chapter 3 provides greater understanding of the motivations and barriers affecting consumers when considering installing microgeneration systems. Informed by the literature review in Chapter 2 and a set of telephone interviews, a consumer survey is conducted to determine the relative importance of each motivation and barrier in the adoption or rejection decision (Balcombe et al., 2014). In particular, the differences in perceptions between a unique sample is investigated: those who have already installed (adopters); those currently considering (considerers); and those who have decided not to install (rejecters). An assessment of the most recent UK policies affecting microgeneration adoption is made and recommendations are given with respect to policy and industry improvements in order to improve uptake further.

Chapter 4 is in part motivated by the preceding chapter: it is found that one of the most important motivations to install microgeneration is to become more self-sufficient from the electricity grid and to protect against future high energy costs. Becoming ‘more electricity self-sufficient’ is defined in this thesis as decreasing the annual household grid electricity demand. This in turn reduces the cost of importing electricity, thus protects against the risk of increasing energy costs in the future. This study identifies an option to improve uptake by increasing household self-sufficiency, whilst addressing the issue of increased grid balancing problems associated with exporting large quantities of solar PV to the UK grid. The research consists of a simulation of household energy , and cost-benefit analysis, for a unique system comprising solar PV, Stirling engine CHP (SECHP) and battery storage. The research defines: how self-sufficient such a household could become; the effect on the daily grid demand variability; and the consumer economic costs and benefits. The study also investigates the effect of different household demand patterns on the above research outputs by using 30 household electricity and demand

Page 19 of 210 Chapter 1 Paul Balcombe profiles, as well as solar PV generation profiles. The impacts of the efficiency of SECHP operation and different battery storage capacities are also determined.

Chapter 5 uses the results from the previous chapter to carry out an environmental life cycle assessment (LCA) of the PV-SECHP-battery system. Whilst the previous chapter defines an option to reduce negative grid impacts associated with microgeneration and the associated consumer economic viability, this chapter defines the environmental impacts associated with the system and compares to a standard UK household using grid electricity and a gas boiler for heating. The study determines whether such a combined system represents an environmentally sustainable option and could contribute to climate change and energy security targets. As well as being the first environmental study on this specific combination of technologies, the study adds to other studies on the environmental assessments of individual technologies by:

 determining the effect of a broad range of real household electricity and gas profiles on the environmental impacts;  quantifying the potential for inefficient CHP operation to increase environmental impacts; and  determining the impact of the use of antimony in lead-acid battery manufacture on overall environmental impacts.

1.2 Alternative format of the thesis This thesis is structured in the alternative format, following the guidelines defined by the University of Manchester. The alternative format was selected as the most suitable structure because the project scope has developed as a series of distinct sections, each with very different methodologies (i.e. one literature review, one consumer survey, one process simulation and one life cycle assessment). Furthermore, the output of each section of work in part motivates the next section and governs how the next is carried out. Thus, it was seen as helpful to the reader of this thesis to fully describe each segment of work and the results before proceeding to the next. Lastly, the author of the thesis has written and submitted each section as a journal paper progressively over the course of the PhD. Thus, chapters 2 - 5 are each written as distinct academic papers in the format that they were either published (Chapters 2, 3 and 5) or submitted for publishing (Chapter 4). These papers are followed by an overarching conclusions section (Chapter 6), which synthesises the findings of the overall research and makes recommendations for policy and industry as well as for further work.

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1.3 Overarching methodology

As briefly described in section 1.1, each study within this thesis presents a different methodological approach, which is described in detail within each chapter. The methods used are: literature review; best-worst scaling choice experiment; process system simulation; economic cost-benefit analysis; and environmental life cycle assessment. The selection of this combination of methods stems from the multi-faceted nature of the main research question. The potential contribution of microgeneration to climate change and energy security targets is dependent on a number of factors: technological capability; supply industry capability; consumer desire to install; and effective policy environment.

Thus, the studies within this thesis intend to reflect each of these factors: determining what makes consumers want to install or decide against it; analysing the impact of a technological (industrial) product on a household; and determining the potential environmental contribution. The papers analyse the past and potential future impact of industry and policy.

One of the original aims of the project was to incorporate a multi-disciplinary perspective using different methodological approaches. It is the author’s opinion that this creates a more holistic approach to the broad discussion of the role of microgeneration with respect to energy policy, climate change and energy security.

1.4 References Balcombe, P., Rigby, D. and Azapagic, A. 2013. Motivations and barriers associated with adopting microgeneration energy technologies in the UK. Renewable and Reviews, 22, 655-666. Balcombe, P., Rigby, D. and Azapagic, A. 2014. Investigating the importance of motivations and barriers related to microgeneration uptake in the UK. Applied Energy, 130, 403-418. Brouwer, A. S., van den Broek, M., Seebregts, A. and Faaij, A. 2014. Impacts of large- scale Intermittent Sources on electricity systems, and how these can be modeled. Renewable and Sustainable Energy Reviews, 33, 443-466. DECC 2009a. Impact Assessment of Feed-in Tariffs for Small-Scale, Low Carbon, . DECC (ed.). DECC 2009b. The UK Renewable Energy Strategy. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown Copyright. DECC 2011a. Microgeneration Strategy. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown Copyright.

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DECC 2011b. Renewable Heat Incentive. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown Copyright. Available: www.gov.uk/government/uploads/system/uploads/attachment_data/file/48041/138 7-renewable-heat-incentive.pdf. DECC 2014. Monthly central Feed-in Tariff Register. MARCH_2014_MONTHLY_CENTRAL_FEED- IN_TARIFF_REGISTER_STATISTICS.XLS. Microsoft Excel. London. Available: www.gov.uk/government/statistical-data-sets/monthly-central-feed-in-tariff-register- statistics. DTI 2006. The Microgeneration Strategy. DEPARTMENT OF TRADE AND INDUSTRY (ed.). London: Crown Copyright. Element Energy 2008a. The growth potential for microgeneration in England, Scotland and Wales. Element Energy 2008b. Numbers of Microgeneration Units Installed in England, Scotland, Wales and Northern Ireland. BERR (ed.). EPIA 2014. Press release: Market Report 2013. Available: www.epia.org/uploads/tx_epiapublications/Market_Report_2013_02.pdf. Grave, K., Paulus, M. and Lindenberger, D. 2012. A method for estimating security of electricity supply from intermittent sources: Scenarios for Germany until 2030. Energy Policy, 46, 193-202. Gross, R., Heptonstall, P., Anderson, D., Green, T., Leach, M. and Skea, J. 2006. The Costs and Impacts of Intermittency: An assessment of the evidence on the costs and impacts of intermittent generation on the British electricity network. UKERC (ed.). Imperial College London. HM Government 2004. Energy Act. London: Crown Copyright. www.legislation.gov.uk/ukpga/2004/20/contents. Johansson, B. 2013. Security aspects of future –A short overview. Energy, 61, 598-605. MIT 2011. Managing Large-Scale Penetration of Intermittent Renewables: An MIT Energy Initiative Symposium. MIT ENERGY INITIATIVE (ed.). , USA. National Grid 2012. Solar PV Briefing Note. DECC (ed.). London: DECC. NHBC Foundation 2008. A Review of Microgeneration and Renewable Energy Technologies. BRE (ed.). NHBC Foundation 2011. Introduction to Feed-In Tariffs. BRE (ed.). Available: www.nhbcfoundation.org/Researchpublications/IntroductiontoFeedinTariffsNF23/ta bid/437/Default.aspx: IHS BRE Press.

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Staffell, I., Baker, P., Barton, J. P., Bergman, N., Blanchard, R., Brandon, N. P., Brett, D. J. L., Hawkes, A., Infield, D., Jardine, C. N., Kelly, N., Leach, M., Matian, M., Peacock, A. D., Sudtharalingam, S. and Woodman, B. 2009. UK microgeneration. Part II: Technology overviews. Proceedings of Institution of Civil Engineers: Energy, 163, 143-165. Thretford, K. 2013. Charting the Fall of Solar Prices [Online]. The Atlantic, . Available: www.theatlantic.com/technology/archive/2013/08/charting-the-fall-of-solar- prices/278803/ [Accessed 19 September 2014]. UKERC 2009. Energy 2050. Making the transition to a secure and low-carbon : synthesis report.

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Chapter 2: Motivations and barriers associated with adopting microgeneration energy technologies in the UK

This paper was published in Renewable and Sustainable Energy Reviews in March 2013 with the following citation:

Balcombe, P., D. Rigby, and A. Azapagic, Motivations and barriers associated with adopting microgeneration energy technologies in the UK. Renewable and Sustainable Energy Reviews, 2013. 22: p. 655- 666.

The research, consisting of a comprehensive literature review and policy analysis of microgeneration uptake, was designed, implemented and written by the author of this thesis. Co-authors Rigby and Azapagic supervised the research and edited the paper prior to submission.

Annex

During the Viva examination for this thesis, it was agreed that a number of terms relating to the motivations and barriers within this paper should be clarified and further defined. The following is such clarification.

Make the household more self-sufficient/ less dependent on utility companies. This is the motivation for households to rely less upon the electricity or gas grid. In this thesis it is assumed that lowering annual household grid demand reduces dependence on the grid. It may be argued that, regardless of the quantity of annual consumption, any consumption, or even a connection to the grid, means that there is dependence. Whilst this may be the case, we consider a typical motivation is to be more self-sufficient or independent, regardless of whether they become completely self-sufficient.

Security of supply. This term is simply the title of a category of motivations discussed within this paper. Motivations and barriers were categorised in order to discuss and order them more effectively for the reader. Security of supply refers to any motivations or barriers that refer to impacting upon the availability or reliability of energy supply to the household. For example, the motivation to protect against power cuts refers to lowering impacts of power cuts thus improving reliability of energy supply.

Protect the household against power cuts. This motivation relates to desire to secure the home from power cuts by using an additional : microgeneration. We ask how Page 24 of 210 Chapter 2 Paul Balcombe important this motivation is, but the microgeneration system may not actually protect against power cuts. For example, UK households with solar PV connected to the grid will not be any more protected against power cuts than without solar PV, due to the electricity required to operate, as well as the way in which PV is electrically connected, which is governed by safety regulations (Kelly, 2013, Transition Cambridge, 2014). Regardless of the ‘rationality’ of the motivations and barriers, whether they are true or misconceptions, they still may hold importance to different respondents.

References

Kelly, G. 2013. Does solar work in a blackout? [Online]. Available: thirdsunsolar.com/does-solar-work-in-a-blackout/ [Accessed 8 Dec 2014]. Transition Cambridge. 2014. Photovoltaic energy FAQS [Online]. Available: www.transitioncambridge.org/thewiki/ttwiki/pmwiki.php?n=TTEnergy.PVFAQ [Accessed 9 Dec 2014].

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Motivations and barriers associated with adopting microgeneration energy technologies in the UK

Paul Balcombea,b,c, Dan Rigbyb and Adisa Azapagica,c* a School of Chemical Engineering and Analytical Science, The University of Manchester, M13 9PL, UK b School of Social Sciences, The University of Manchester, M13 9PL, UK c Sustainable Consumption Institute, The University of Manchester, M13 9PL, UK * Corresponding author, Tel: 0161 306 4363, Email: [email protected]

Abstract

Despite significant financial support from the UK government to stimulate adoption of microgeneration energy technologies, consumer uptake remains low. This paper analyses current understanding of motivations and barriers that affect microgeneration adoption with the aim of identifying opportunities for improving the uptake. The findings indicate that, although feed-in tariffs have increased the uptake, policies do not sufficiently address the most significant barrier – capital cost. ‘Environmental benefit’ appears to be a significant motivation to install, but there is doubt whether consumers are willing to pay extra for that. The issue is complicated by the fact that motivations and barriers differ between segments of the population, particularly with age. Younger age groups are more willing to consider installing but less frequently reach the point of installation, suggesting that other barriers such as costs prevent them from installing. Further investigation into these factors is required to understand how uptake may be increased.

Keywords: Microgeneration energy; Renewables; Consumer attitudes; Motivations and barriers.

1. Introduction

In the UK, microgeneration is defined as the generation of electricity of up to 50 kW and/or heat of up to 45 kW from a low-carbon source and includes the following technologies (HM Government, 2004):

 electricity: solar photovoltaic (PV), micro-wind, micro-hydro, micro-CHP and fuel cells;  heat: solar thermal, air source heat pumps (ASHP), ground source heat pumps (GSHP), water source heat pumps (WSHP), biomass stoves and boilers. This scale of generation is suitable for installation in domestic and non-domestic buildings, including offices, schools, shops, hotels and factories.

The UK government aims to increase the uptake of microgeneration technologies as part of its strategy to improve energy security and reduce greenhouse gas (GHG) emissions Page 26 of 210 Chapter 2 Paul Balcombe

(DECC, 2009). Given that the residential sector accounts for 30% of UK (NHBC Foundation, 2008b) and other, non-residential, buildings account for 18% (The Carbon Trust, 2009), reductions in GHG emissions within these sectors could contribute significantly to meeting the UK climate change targets.

To stimulate the adoption, the Feed-in Tariff (FIT) scheme was introduced in April 2010, significantly reducing capital payback times (EST, 2011; NHBC Foundation, 2011). The FIT scheme offers a payment for each unit of electricity generated to approved, grid- connected, electricity microgenerators of less than 5 MW capacity. There are additional payments for electricity exported back to the grid. Technologies eligible for payments are solar PV, wind, hydro, and CHP. The payment, which is guaranteed over 20-25 years (apart from CHP which is guaranteed for 10 years), is made by the energy supplier companies and their costs are recouped by increasing consumer electricity prices. Payments are different for each technology and for different capacities of installation and are based on providing a 5% return on investment. In addition, the government developed a Microgeneration Strategy to tackle non-financial barriers to greater deployment, such as uncertainties in performance and reliability, by ensuring supplier accreditation through the Microgeneration Certification Scheme (DECC, 2011b).

1,000,000 PV

CHP

Wind 100,000 Hydro

Solar Thermal

Biomass boilers 10,000 GSHP

ASHP

1,000

Number of installations of Number

100

10 2001 2003 2005 2007 2009 2011 2013 Year

Figure 1 Increase in the number of installations from 2001-2012

[Estimates based on the following sources: 2001–2005: Element Energy (2005); 2008: Element Energy (2008b); 2010-2012: DECC (2012c). For calculations, see the Appendix.]

Government support for microgeneration has helped to increase uptake, especially of solar PV, which has grown from around 3,000 installations in 2008 (Element Energy,

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2008b) to 320,0003 in 2012 (DECC, 2012c); see Figure 1. However, the uptake of other technologies has been much slower and the total contribution of microgeneration is still low, representing less than 0.2% of the final energy demand in the UK domestic sector (see the Appendix for the estimation). This suggests that there are significant barriers to adoption which must be reduced or removed if microgeneration is to contribute to UK climate change targets and energy security.

In an attempt to assist in identifying the barriers as well as motivations for adoption, this paper reviews and discusses the current understanding of different factors affecting consumers when considering installing microgeneration technologies. The paper also seeks to identify any gaps in knowledge about motivations and barriers, and makes recommendations for further research.

In total, 18 relevant studies have been found in the literature; they are summarised in Table 2. As can be seen, the majority of the studies are based in the UK and all except one () are in Europe. As also indicated in Table 2, a number of different methods of survey and analysis have been employed to elicit attitudes towards microgeneration: open ended interviews with qualitative analysis; closed format questions or rating scales with descriptive statistical analysis; closed format questions with regression analysis; and environmental valuation economic methods.

The next section reviews motivations and barriers associated with microgeneration adoption identified within the literature. This is followed by a review of how perceptions of microgeneration differ between subgroups of the UK population in Section 3. Conclusions and recommendations for further research are given in Section 4.

2. Motivations and barriers

There are many consumer motivations and barriers associated with microgeneration adoption that have been cited in the literature. They can be categorised as: finance, the environment, security of supply, uncertainty and trust; inconvenience and impact on residence. They are summarised in Table 3 and discussed below broadly in the order of their relative importance in the adoption decision as identified from the literature, although with the exception of finance and environment, there is little agreement on the importance of each motivation and barrier across the literature. Some of the motivations and barriers

3 The figure of 320,200 is derived by adding the estimated installations in 2008 (2,993) from Element Energy (2008) and the number of installations registered with Ofgem as part of the FIT scheme (DECC, 2012) by September 2012 (317,172). As the FIT register only accounts for those within the scheme, this estimation ignores any installations not in the FIT scheme that were installed after 2008. Consequently, this may be an underestimate. See also the Appendix for further details.

Page 28 of 210 Chapter 2 Paul Balcombe in Table 3 could be assigned to more than one of the categories (e.g. the requirement for planning permission could also be a financial barrier), but have been allocated to the group most closely related and are discussed below.

2.1. Finance

It is well recognised that costs are the largest barrier to microgeneration adoption (e.g. Allen et al., 2008; Claudy et al., 2010; Element Energy, 2005; Scarpa and Willis, 2010; Wee et al., 2012). Capital costs are too high for the majority of potential adopters and the payback times are too long to warrant the large investment (Scarpa and Willis, 2010). For example, Caird and Roy (2010) found in an online survey of microgeneration ‘adopters’ (545), ‘considerers’4 (314) and ‘rejecters’ (65) that the most frequently cited barriers to installing microgeneration systems were all related to cost: capital cost (86% of the respondents), long payback time (68%) and lack of grants (60%). A survey of 601 London home-owners by ORC International also found capital costs to be the most important barrier while assistance with costs was the most cited motivation (by over 75% of respondents) (Ellison, 2004).

Since 2004, there has been a VAT reduction to 5% on microgeneration products to reduce capital costs (Bergman et al., 2009). However, for microgeneration technologies besides solar PV and solar thermal, there is still a significant gap between consumers’ willingness to pay (WTP) and capital costs (Claudy et al., 2011; Scarpa and Willis, 2010). This is shown in Table 4, which indicates that the only technology for which the mean WTP was equivalent to the capital costs was solar thermal. This is not surprising as the total number of solar thermal panels at the time these surveys were carried out (2007 and 2009) far outweighed other technologies: 90,000 units compared to around 3,000 solar PV installations and less than 5,000 of other technology types (Element Energy, 2008a) (Figure 1). This has resulted in low demand for microgeneration technologies, hindering market development and preventing cost reductions associated with a maturing market (DTI, 2006).

4 Considerers were defined within the study as those considering the purchase of a microgeneration system and rejecters as those that considered but decided against purchasing.

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Table 2 Summary of surveys carried out related to attitudes to microgeneration

Author Year Location Sample Aims Type of survey Technologies Analysis considered 1. Brook 2003 UK 502 London Identifying the public's attitudes Face-to-face interviews Solar, wind, CHP No information Lyndhurst et (London) residents towards climate change, Both open and closed al. (2003) renewables and microgeneration ended questions 2. Fischer 2004 Germany 142 CHP Identifying the socio-demographic Postal survey with closed Fuel cell micro Using mean average (2004) owners profile of fuel cell CHP users, as ended questions and CHP responses and well as their attitudes to energy and agreement rating scales comparing to the environment and perceptions of general German public CHP. 3. Ellison 2004 UK 601 London Identifying the public's attitudes Telephone interviews Solar PV, solar Descriptive statistics (2004) (London) residents towards climate change, Both open and closed thermal, wind and cross tabulations renewables and microgeneration ended questions 4. Curry et al. 2006 UK 1,056 UK Identifying the public's attitudes Online questionnaire Solar, wind, No information (2005) residents towards climate change and closed format questions biomass renewable energy 5. Faiers and 2006 UK 43 UK 'early Investigating the difference in Kelly's repertory grid Solar thermal, Segmented sample by Neame adopters' and 350 attitudes towards solar thermal and survey solar PV innovation theory (2006) UK 'early majority' PV systems between early adopters Closed ended questions groups and the early majority 0 - 13 agreement scale 6. Jager (2006) 2006 Holland 197 Dutch solar Identify behaviour-related Closed ended questions Solar PV Univariate analysis of PV adopters motivations to installing solar PV Likert agreement scale importance of motivations against environmental awareness. Segmentation of high awareness and low awareness 7. Keirstead 2007 UK 91 UK solar PV To understand factors affecting the Semi-structured face-to- Solar PV Descriptive statistics (2007) adopters adoption decision and to identify face interviews and whether energy use behaviour closed-format posted changes after microgeneration questionnaires adoption

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Author Year Location Sample Aims Type of survey Technologies Analysis considered 8. Mahapatra 2008 Sweden 630 and 711 (two Identifying factors affecting Postal survey with closed GSHPs, biomass Mean average and surveys) Swedish homeowners' decisions to adopt ended questions and boilers responses of Gustavsson detached microgeneration systems. agreement rating scales agreement scales (2008) homeowners 9. Goto and 2009 Japan 3,431 Japanese Identifying the most important Closed ended questions Solar PV, fuel cell Multivariate regression Toshio residents factors that affect preferences for Likert agreement scale preferences for (2009) solar PV and fuel cell technologies different microgeneration systems against capital cost, operating cost, environmental benefit etc. 10. Caird and 2010 UK 545 adopters, 314 Determining the motivations and Online questionnaire Solar thermal, Descriptive statistics Roy (2010) considerers, 65 barriers associated with installing multiple choice and open GSHPs, biomass and cross tabulations rejecters heat producing microgeneration ended questions boilers technologies in households, UK 11. Claudy et al. 2010 Republic 1,010 Irish Defining the importance of socio- Telephone interview with Solar PV, solar Regression of (2010) of Ireland residents demographic factors that affect the closed ended questions thermal, wind, awareness on awareness of microgeneration. CHP, heat pumps, demographic biomass boilers information 12. Scarpa and 2010 UK 1,279 UK Estimating the WTPa for different Choice experiment Solar PV, solar Various logit models to Willis (2010) homeowners microgeneration technologies and thermal, wind regress the decision to the influence of perceptions on adopt microgeneration WTPa against capital cost, maintenance bill, energy cost, inconvenience etc 13. Sopha et al. 2010 Norway 649 Norwegian Identify factors that affect the Postal survey, closed ASHP, biomass Regression of choice of (2010) homeowners decision to to wood pellet ended questions with boilers against boilers and heat pumps from electric multiple choice and Likert socio-demographics heating agreement scale ratings and various product- and choice-related factors

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Author Year Location Sample Aims Type of survey Technologies Analysis considered 14. Warren 2010 UK 17 small-sized Determining the motivations and Semi-structured face-to- Solar PV, solar Qualitative (2010) (Camden companies barriers associated with installing face open ended thermal, wind, , London) microgeneration technologies in interviews CHP, biomass commercial buildings in Camden, boilers, ASHPs, UK GSHPs 15. Palm and 2011 Sweden 20 Swedish Determining the motivations and Half face-to-face and half Solar PV, wind Qualitative Tengvard homeowners: 9 barriers associated with installing telephone interviews (2011) adopters, 8 solar PV and wind systems in Open ended questions considerers, 3 Swedish households rejecters 16. Claudy et al. 2011 Republic 1,012 Republic of Estimating the WTPa for different Contingent valuation Solar PV, solar Bivariate probit model (2011) of Ireland Ireland microgeneration technologies and method thermal, wind, to regress the decision homeowners the importance of different factors in biomass boilers to adopt against the adopting decision various ‘innovation theory’ factors 17. Consumer 2011 UK 1,223 UK Identifying attitudes towards, Focus group discussion Solar PV, solar Descriptive statistics Focus residents (and microgeneration in terms of (12), face-to-face thermal, wind, (2011) 2,655 UK adoption and experience, and interviews (40) and online CHP, hydro, residents for the developing a profile of those at questionnaires (1,223) biomass boilers, microgeneration different stages of consideration heat pumps experience survey) 18. Leenheer 2011 Holland 2,047 Dutch Defining the factors that affect the Closed ended questions Any electricity Descriptive statistics (2011) residents motivation to install microgeneration Likert agreement scale microgeneration and a multivariate regression of the intention to adopt against environmental concern and independence from centralised energy generation a WTP: willingness to pay.

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Table 3 Summary of motivations and barriers associated with adopting microgeneration as found in literature

Motivation Barrier Financial – Save or earn money from lower fuel bills and government  Costs too much to buy/ install incentives  Can't earn enough/ save enough money – Increase the value of my home  Lose money if I moved home  High maintenance costs Environmental  Help improve the environment  Environmental benefits too small Security of supply  Protect against future higher energy costs  Wouldn't make me much more self sufficient/  Make the household more self sufficient/ less dependant on independent utility companies  Protect the household against power cuts Uncertainty and  Use an innovative/high-tech system  Home/ location not suitable trust  System performance or reliability not good enough  Energy not available when I need it  Hard to find trustworthy information/ advice  Hard to find any information/ advice  Hard to find trustworthy builders to install Inconvenience None identified  Hassle of installation  Disruption or hassle of operation  Potential requirement for planning permission

Impact on  Improve the feeling or atmosphere within my home  Take up too much space residence  Show my environmental commitment to others  The installation might damage my home  Would not look good  Neighbour disapproval/annoyance

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Since these two studies were been carried out in 2007 and 2009, however, UK demand for solar PV has significantly increased and is now the dominant technology in terms of number of installations (see Figure 1 and the Appendix). The main cause of this shift is the introduction of FITs which have reduced capital payback times and consequently increased consumers’ WTP. Additionally, global demand for solar PV has increased, reducing world market prices: UK capital costs have decreased by approximately 10% in 2010 (Gardiner et al., 2011), 30% in 2011 (Cambridge Economic Policy Associates Ltd and Parsons Brinckerhoff, 2011) and 15% in 2012 (Parsons Brinckerhoff, 2012). These capital cost reductions owing to a maturing global solar PV market have led the UK government to reduce the FIT rates by half for solar PV (from 45 to 21 p/kWh), whilst for other technologies the rates have remained stationary (DECC, 2012b).

Table 4 Comparison of capital costs and consumer willingness to pay (WTP)

Type Levelised Levelised Acceptable Year Source Cost mean WTP payback (£/ kW)a (£/ kW) time (yrs) Solar PV (2 kW) 5,319 1,416 N/A 2007 Scarpa and Willis (2010) Solar PV (3 kW) 6,383 2,069 9 2009 Claudy et al. (2011) Wind (1 kW) 4,998 1,288 N/A 2007 Scarpa and Willis (2010) Wind (5 kW) 5,830 1,685 11 2009 Claudy et al. (2011) Solar thermalb 1,575 1,920 12 2009 Claudy et al. (2011) Solar thermal 1,952 1,452 N/A 2007 Scarpa and Willis (2010) (2 kWth) Biomass boiler 1,000 489 7 2009 Claudy et al. (2011) (wood pellets)b aCosts as cited by the relevant study. bThe size and capacity of the system was not stated within the study. UK average peak capacities of 2 kW for solar thermal and 11 kW for biomass boiler have been used, as used in (Scarpa and Willis, 2010) and derived within (Element Energy, 2008b, page 11, tables 7 and 8).

There is a clear financial trade-off between capital cost and the motivation to save or earn money from lower fuel bills and FIT incentives, often represented as payback time. Prior to the introduction of FITs, payback times were too long for most consumers: 15 to 18 years for wind turbines, 8 to 53 years for solar thermal (Bergman et al., 2009; NHBC Foundation, 2008a) and 35 to 58 years for solar PV (Bergman et al., 2009; Watson et al., 2008). In comparison, Claudy et al. (2011) estimated mean payback times that were acceptable to potential adopters as nine years for solar PV, 11 years for wind and 12 years for solar thermal (see Table 4). Scarpa and Willis (2010) estimated an aggregated acceptable payback time as 3-5 years across the three microgeneration products considered. This gap between acceptable and expected payback times has been significantly reduced for some technologies due to the introduction of FITs, as well as increasing electricity and gas prices and reduced capital costs, which are discussed in section 4. For example, one study estimated a payback time of 11 years for solar PV Page 34 of 210

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(NHBC Foundation, 2011) which is much closer to the estimated acceptable payback time (see Table 4). FIT payments have clearly increased demand for microgeneration, evident from the increase in uptake, as well as a survey cited within the UK Microgeneration Strategy, stating that 40% of those who were considering adoption said they would not consider adoption without the FIT incentives (DECC, 2011b).

Another frequently cited cost-related barrier is concern about the resale value of the home. The ORC International study found that many 5 respondents expressed concern that potential future house buyers would be put off by a microgeneration installation which could lead to a decrease in house price (Ellison, 2004). Faiers and Neame (2006) also investigated whether potential adopters thought microgeneration would be a positive influence on house sales, but suggested that this was not an important issue in the decision to adopt. There is limited evidence as to the effect of solar PV installations on house prices. Two studies based in the USA find that house prices tend to increase almost proportionately to the installation cost (Dastrup et al., 2012; Hoen et al., 2011). However, only one UK study was found, which was conducted prior to the introduction of FITs in 2010 (Morris-Marsham, 2010). The results of this study were that there was a negligible positive increase in house value. There are a number of web articles on the subject (e.g. Brignall, 2012; Ecohouse Solar, 2009; Rowley, 2011), which provide conflicting conclusions. There is a concern that even if house prices increase with a solar PV installation, it still may not be enough cover the costs. This represents a risk for the future and thus can be a significant barrier for those who may move home.

As with capital cost and operational savings, potential adopters may also perceive a trade- off between capital cost and environmental benefit in their adoption decision. This is suggested by studies conducted by Brook Lyndhurst et al. (2003) and Curry et al. (2005). In the former study, respondents were asked to 'leave aside cost' whilst considering installing solar thermal or solar PV. On this basis, 23% of households were ‘very likely’ to consider it, with another 34% ‘fairly likely’. When presented with the cost implications, support fell to 4% (‘very likely’) and 70% said they were ‘certain not to’ install. Similarly, Curry et al. (2005) found that there was less support for renewable energy to help mitigate against climate change when supporters were made aware of associated costs, which prompted a small shift in support of nuclear energy. These two surveys demonstrate how capital cost can counter other motivations to install microgeneration such as operational savings, or the perceived benefit to the environment. The latter is discussed next.

5 The number of respondents was not specified within the report.

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2.2. Environment

Along with economic costs, environmental benefit appears to be a significant factor in the decision to install microgeneration (Claudy et al., 2010; Leenheer et al., 2011). Microgeneration is generally perceived to be , perhaps by its very definition as a ‘low-carbon source’ of energy. Some of the potential adopters are driven by the desire to reduce GHG emissions and most believe microgeneration will help achieve it. For example, ‘reducing emissions’ was ranked as the most important motive for purchasing a system within the Caird and Roy study (2010) and was considered an important factor in the adoption decision in a study of Dutch households by Leenheer et al. (2011).

Although for many there is a desire to be more environmentally friendly (Curry et al., 2005), a number of studies suggest that this desire does not translate into a willingness to pay extra for it (Claudy et al., 2010; Walters and Walsh, 2011; Wimberly, 2008). Many adopters might be environmentally aware, but will make a purchase decision based on cost and factors other than environmental benefit (Hack, 2006; Wimberly, 2008). For instance, Wimberly (2008) surveyed the American public on their perceptions of energy efficiency and renewable energy and found that the sample placed much more importance on saving money than on reducing their environmental impact. Another study highlights the sample’s unwillingness to reduce their environmental impact: “It is almost as if consumers are holding their noses to take medicine they perceive to taste awful but is necessary to bring the fever down” (Cogar, 2008).

Microgeneration technologies may be perceived by the public as low-carbon, but there are other associated environmental impacts that may be viewed differently. For example, a study of 49 Norwegian residents found that some respondents thought air source heat pumps would produce more indoor owing to assumed dust recirculation from a heat pump (Sopha et al., 2010). Warren (2010) also noted that participants raised concerns over the impact of biomass boilers on air quality. This study of 17 small businesses in Camden, London, also found environmental benefit and promoting a ‘green’ image for the company to be important motivations for installing microgeneration technologies (Warren, 2010).

Promoting a ‘green’ image by installing a publicly visible system such as solar panels or a is also a motive for some residential consumers (Caird and Roy, 2010; Palm and Tengvard, 2011). Palm and Tengvard (2011) surveyed Swedish households

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Chapter 2 Paul Balcombe considering the purchase of a ‘DIY install’ 6 microgeneration system. The study investigated motivations and barriers associated with purchasing these products for respondents at different stages of their decision using 20 semi-structured inductive interviews. As well as being able to visibly demonstrate environmental commitment, another significant motivation was ‘to set an example for others’ (Palm and Tengvard, 2011). Those who are motivated to visibly demonstrate their environmental commitment may want to identify themselves with a low-carbon, ‘green’ image, to use microgeneration to send an environmentally friendly message to others (Jager, 2006; Nye et al., 2010). Fischer and Sauter (2003) suggest that installing solar PV, in particular, is a clear socio- political statement, one that appeals to those with “green political orientation and postmaterialist values”.

As well as reducing GHG emissions and creating a ‘green’ image, some potential adopters are also motivated by the desire to use a low-carbon, innovative technology. Caird and Roy (2010) found when existing adopters and considerers were asked what drove them to consider microgeneration that a fifth of the sample (sample size N=859) stated that they wanted to use innovative, pioneering low-carbon technology and a fifth either worked in a field relating to energy, environment or technology, or it was a personal interest of theirs. Fischer (2004) and Leenheer et al. (2011) also suggest that those who have an affinity with technology are more likely to want to generate their own energy using microgeneration.

2.3. Security of supply

The issue of independence or security of supply in the adoption decision encapsulates the motivation for increased energy self-sufficiency, being able to reduce reliance on the gas or electricity grid and being less susceptible to future energy price increases (Claudy et al., 2010; Goto and Toshio, 2009; Jager, 2006; Leenheer et al., 2011; Palm and Tengvard, 2011; Rae and Bradley, 2012). Praetorius (2010) suggests that consumers are motivated to guard against fuel bill increases owing to an increase of 45% in UK electricity and 71% in gas bills from 2003 to 2007, which has led to increased public interest in microgeneration. Leenheer et al. (2011) identified the desire within the sample to generate own power as important in the decision to adopt microgeneration. However, there was significant focus within this survey of Dutch households on the risk of power outages,

6 ‘DIY install’ microgeneration products are designed to be installed, set up and connected by anyone, without the need for expert installers (solar PV and wind were considered in the study).

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Chapter 2 Paul Balcombe which has not been considered in any UK based research, so it is not clear if this would be as relevant to the UK public.

Jager (2006) also found that independence from centralised energy generation was an important motivation to adopt solar PV systems. A survey of 197 Dutch households with solar PV systems found that increased independence was a greater motivation for those with higher environmental awareness. In other words, the study identified a segment of the population who identify themselves as environmentally aware and desiring self- sufficiency. Palm and Tengvard (2011) also suggest that this motivation is linked to a desired environmentally-benign, self-sufficient identity, which is perhaps similar to the environmentally-friendly image mentioned in Section 2.2.

2.4. Uncertainty and trust

A frequently cited barrier to installing microgeneration systems relates to a lack of confidence that the system will perform as desired. Whilst some studies suggest potential adopters are motivated to install by confidence in performance and reliability (e.g. Caird and Roy, 2010), many studies cite barriers such as performance uncertainties (Caird and Roy, 2010; Ellison, 2004; Zahedi, 2011), uncertain payback on investment (Caird and Roy, 2010; Scarpa and Willis, 2010), uncertainty over the reliability, or even lack, of general and technical information, and uncertainty over the potential benefits of microgeneration (Ellison, 2004; Williams, 2010).

Performance and reliability uncertainties were significant barriers to adoption to 58% of rejecters within the Caird and Roy study (2010). This uncertainty also features within the Microgeneration Strategy (DECC, 2011b), which suggests that those who have not yet considered adoption lack confidence in the technologies as well as the suitability of their homes and suggest that this is an information-related barrier. This barrier develops as most consumers begin an initial investigation into microgeneration on the internet, where it may be difficult to find information they trust (DECC, 2011b).

The lack of trust in the performance and reliability of microgeneration systems has been identified in many studies (Ellison, 2004; Envirolink Northwest, 2010). The ORC International study also found that there was a lack of awareness of information and advice centres: only 35% knew that there were energy advice centres and many respondents called for more product-specific information (Ellison, 2004). Owing to the relatively low number of microgeneration installations in the UK with the exception of solar PV, perhaps there is a lack of visible examples of microgeneration systems in the public

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Chapter 2 Paul Balcombe eye, contributing to the lack of awareness, confidence and high degree of scepticism in the technologies (Williams, 2010). This is corroborated by Caird and Roy (2010) who found that potential adopters wanted to see examples of microgeneration systems on local residences and public buildings. Similarly, Scarpa and Willis (2010) found that positive perceptions or advice from friends or trusted experts increased willingness to purchase microgeneration (shown by an increase in WTP of £263 with advice from a heating engineer).

However, the difficulty in finding a trusted expert also represents a barrier to adoption (DECC, 2011b). The government Microgeneration Strategy suggests that potential adopters fear that advice from installers will not be impartial, regardless of whether they are approved by the Microgeneration Certification Scheme7 or not (DECC, 2011b).

2.5. Inconvenience

As well as finding an appropriate installer, the inconvenience of major modifications to heating or electrical systems, or to the roof or garden during installation, is also a significant barrier to adoption (Caird and Roy, 2010; Ellison, 2004; Scarpa and Willis, 2010; Wee et al., 2012). For example, installing a residential GSHP may require the garden to be dug up to install a ground heating loop (further discussed in Section 2.6). Warren’s (2010) research with potential adopters for non-domestic buildings found that there was most interest in CHP systems due to the similarity with existing boiler systems and the fact that it could be a replacement rather than an additional system. However, initial awareness of the technology was low, supporting the suggestion that there is an information-related barrier as discussed in Section 2.4.

Additionally, the perceived difference in the day-to-day use of a microgeneration system compared to an existing system is a factor in the adoption decision. Many potential adopters are put off by inconveniences such as a greater space requirement, refuelling (e.g. wood pellet boilers) and modifications to the garden (Scarpa and Willis, 2010).

A perceived increase in maintenance and the complexity associated with a system change is also a barrier to adoption. Element Energy (2008b) found that respondents, of which the majority were already considering installing microgeneration, were willing to pay an average £6 in upfront cost to negate every £1 of annual maintenance cost for heating systems. With solar PV, solar thermal and wind, this WTP rose to around £10, perhaps

7 The Microgeneration Certification Scheme is a quality assurance mechanism to set a minimum standard for microgeneration products and installations.

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Chapter 2 Paul Balcombe due to perceived complexity or ‘the unknown’ nature of these technologies. The NHBC Foundation (2008b) stresses the need for the industry to minimise additional service and maintenance responsibilities for the adopter and reduce the need for system intervention, such as refuelling, as much as possible. There are a number of warranties and insurances offered by suppliers and the REAL Assurance Scheme Code, an accreditation scheme for suppliers, stipulates a requirement for basic information on the warranties that are offered (DECC, 2011b). However, at present there is no drive from the government to regulate service and maintenance contracts with suppliers that may ease fears amongst consumers. The Microgeneration Certification Scheme regulates the quality of the product and installation, but there is no regulation of post-installation servicing or product care. The government have recognised the need to tackle this barrier of increased maintenance but have tasked the industry to provide assurances to consumers instead of providing regulation or direction (DECC, 2011b).

Additionally, a barrier to adopting microgeneration may simply be that households are generally content with their existing system and thus see the replacement of their system as a low priority since there is not enough perceived relative advantage (Element Energy, 2008a). Claudy et al. (2010) define microgeneration as a ‘resistant innovation’, since increased uptake requires potential adopters to significantly alter their daily routines and traditions, which represents an inconvenience. Alternatively, this barrier could be negligible for those who are already planning home modifications (Caird and Roy, 2010; Consumer Focus, 2011; Keirstead, 2007). Combining a microgeneration installation with other house modifications also tends to reduce costs; for example, fitting solar panels at the same time as roof modifications means the same scaffolding could be used, reducing a significant cost.

2.6. Impact on residence

Some microgeneration technologies use a significant amount of space within the home which is a barrier for some potential adopters (Brook Lyndhurst Ltd et al., 2003; Caird and Roy, 2010; Scarpa and Willis, 2010). The value of space is often significant, but will vary across different population groups as well as locations. Those living in a city, for example, where space is at a premium, may not be able to even consider a technology such as a GSHP, where horizontally-laid heating loops in particular require a lot of space. This was also confirmed by a study with owners and managers of offices within high-rise buildings, where floor space is valued highly (Warren, 2010). Respondents were generally of the opinion that GSHPs were not practicable, with not enough space for horizontal heating

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Chapter 2 Paul Balcombe loops and vertical loops were unlikely to be allowed due to underground utility lines and the underground tube system.

New-build housing which allows for, or already has fitted, microgeneration would eliminate the space issue. For this reason, legislation for new developments will begin phasing into building regulations the requirement for and connection to either household microgeneration systems or to small, low-carbon distribution networks (HM Government, 2007). The zero-carbon homes policy requires all new homes to be built with a high-energy efficiency rating and access to a low-carbon fuel source by 2016 (McLeod et al., 2012). However, the retrofitting of existing homes, of which there are over 25 million, will remain an issue and may reduce significantly the microgeneration options available for those households.

Another frequently cited barrier to installation is concern about disapproval of neighbours regarding the aesthetics of microgeneration installations (Ellison, 2004). Palm and Tengvard (2011) also found that fear of neighbour disapproval is a barrier to adoption. This may be particularly important for wind turbine installations due to the social stigma associated with their aesthetics (Ben Hoen, 2009). This barrier seems to be in contrast to the ‘demonstrating environmental commitment’ motive (Section 2.2).

In summary, as discussed in this section, there are many factors that affect the decision to install microgeneration. Additionally, there are some significant differences in the attitudes across the UK population, with many of these factors being barriers for some people, but motivations for others. The following section reviews these differences in perceptions among different societal groups.

3. Differing perceptions within subgroups of the UK population

A number of studies have attempted to find correlations between differing perceptions of microgeneration and the characteristics of the person, the household in which they reside or their experience of microgeneration (whether they are adopters, considerers or rejecters). This section gives an overview of current understanding of these differing perceptions and suggests reasons as to why demand is higher for some groups than others and what policies might improve microgeneration uptake among those who have not installed. A summary of the demographic factors and the expected correlation with the likelihood of adoption is given in Table 5.

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3.1. Age

It has been found in a number of studies that attitudes towards microgeneration differ across age groups (Consumer Focus, 2011; GfK NOP Social Research, 2006; Leenheer et al., 2011; Mahapatra and Gustavsson, 2008; Willis et al., 2011). The number of microgeneration installations is lower amongst those who are below 45 (Ellison, 2004; GfK NOP Social Research, 2006) and those above 65 years old (GfK NOP Social Research, 2006; Leenheer et al., 2011; Willis et al., 2011). This correlation has been found in several studies, where 45–64 year olds are either the most commonly aware of microgeneration (Claudy et al., 2010), have a more positive attitude towards it (GfK NOP Social Research, 2006), or are the age group most likely to install (Fischer and Sauter, 2003; Mahapatra and Gustavsson, 2008). Older age groups are less inclined to adopt new technologies such as microgeneration (Sopha et al., 2010; Willis et al., 2011), exhibiting a greater resistance if they have been using their existing system for many years (Mahapatra and Gustavsson, 2008). This is perhaps due to the security of knowing that the existing system works, combined with the uncertainty of a new, untried, system (see Section 2.5). Willis et al. (2011) find there is even ‘disutility’ for adopting microgeneration with over 65 year olds, suggesting that this age group would actually pay not to install microgeneration. They also find that there is a discontinuous relationship where adoption increases with age until retirement after which there is a significant drop in uptake.

The cause of the reduced number of installations amongst over 65 year olds could be due to their different financial position. The trade-off between high capital costs and fuel savings/ FIT incentives, described in Section 2.1, is perhaps particularly relevant for retired households. In terms of capital costs, pensioners are likely to have lower incomes than before retirement, which may reduce their willingness to invest in costly microgeneration. Conversely, it is suggested by Faiers and Neame (2006) that the decrease in income due to retirement may drive a desire for future fuel savings, to economise on expenditure, which makes an investment that reduces fuel bills more attractive. Willis et al. (2011), however, find that pensioners are actually less sensitive to change in fuel bills. This could be because pensioners are concerned about depleting their capital savings, whilst being less affected by rising energy costs (Willis et al., 2011) owing to Winter Fuel Payments for pensioners in the UK (HM Government, 2009).

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Table 5 Correlations between several demographic factors and likelihood of adoption

Demographic Correlation with adoption Source Age Inverted 'u' shaped correlation Claudy et al. (2010); Mahapatra OR increase of adoption with and Gustavsson (2008) ; Willis age until 65, with a sharp et al. (2011) decrease afterwards OR decrease of adoption with age Household size Increases with size Caird and Roy (2010); Keirstead (2007) Homeowners/ Almost all adoption is by Keirstead (2007); Fischer and tenants homeowners Sauter (2003) Family size No correlation found Ellison (2004) Social class Upper-middle class most likely Ellison (2004); Devine-Wright to adopt (2005); GfK NPT Social Research (2006) Income Adoption increases with Keirstead (2007); Sopha (2010) income OR middle income most likely to adopt Education Adoption increases with Keirstead (2007); Fischer and education Sauter (2003)

The visible increase in microgeneration installations up to retirement age indicates that there are fewer, or perhaps reduced, barriers to adoption for older working households. This may be due to higher incomes amongst older working households (see Section 3.3) or simply that there are more home owners aged 45–65 than younger age groups (see Section 3.2). Additionally, there may be more of a financial motive to install in this age group who have the capital to invest, rather than younger age groups who are more environmentally aware (Ellison, 2004) but may not have the capital.

Other studies suggest different correlations between age and microgeneration adoption. Surveys of Swedish home owners in 2004 and 2007 revealed the number planning to adopt microgeneration, particularly pellet boilers and heat pumps, decreased with age, with the exception of those aged 36–45, who were most likely to install (Mahapatra and Gustavsson, 2008). Keirstead’s (2007) study of 91 solar PV owners revealed the adopters to be generally older, with 92% being over 45. However, there was no breakdown of ages within the over 45 age group, which limits the interpretability of this finding.

Consumer Focus (2011) also conducted a survey of the UK population, which identified variation in age groups at different stages of microgeneration adoption. Their results are displayed in Figure 2 which shows the percentage of each age group considered that lies within each consideration stage of the process of adopting microgeneration. The stages included were pre-consideration, consideration, preparation and adoption. The graph indicates two clear relationships at either end of the age spectrum. Higher proportions of Page 43 of 210

Chapter 2 Paul Balcombe over 65 year olds are at the ends of the consideration scale, i.e. either they have not considered installing or they have installed. Conversely, higher proportions of the under 35 year olds are in one of the considering stages, i.e. either consideration or preparation. Both the age groups adjacent to these, the 55-64 and the 35-44, exhibit a similar relationship to their age-group neighbour, with the difference between consideration stages slightly less noticeable.

Under 35 35-44 45-54 55-64 65 or older

100% 90% 80% 70% 60% 50% 40%

Percentage (%) Percentage 30% 20% 10% 0% Pre-consideration Consideration Preparation Installed Stage Figure 2 The percentage of each age category associated with different consideration stages (Consumer Focus, 2011)

The fact that the older age groups mostly lie within the pre-consideration stage or the installed stage perhaps shows that they are either unaware or simply content with their existing system (pre-consideration), or have discovered that microgeneration is suitable for them (installed) and have experienced fewer barriers to adoption (e.g. cost or suitability to home). The younger age groups mostly lie within the central consideration stages, indicating higher awareness but that perhaps other barriers, such as cost, prevent them from installing.

To summarise, whilst there are a number of suggested correlations for the relationship between age and adoption, there is no agreement in literature. It is likely that this relationship is not straight forward and that there is a complex interplay of a range of causal factors, including house size, whether they own their residence, the level of available capital for investment, the size of house or family, or the suitability of use of microgeneration within a particular home. These aspects are discussed in the following sections.

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3.2. Household size and ownership

Adoption of microgeneration is more prevalent in larger houses (Caird and Roy, 2010; Keirstead, 2007). This may be due to a number of factors: available space, higher energy use or perhaps a higher household income (discussed further in Section 3.4). Large homes are likely to have more space to incorporate a microgeneration system. They also tend to use more energy due to larger space heating requirement, which may increase the importance of energy consumption within the home. Both of these factors potentially contribute to a greater motivation to install microgeneration. Caird and Roy (2010) found that 78% of surveyed adopters lived in a larger detached house or bungalow, as opposed to 47% considerers and 44% rejecters. Most of the considerers and rejecters in the sample resided in smaller semi-detached or terraced homes. However, the study by Claudy et al. (2010) tested for a relationship between household size and awareness of microgeneration and did not find any correlation.

Those who own and live in their own home are far more likely to install microgeneration (Fischer and Sauter, 2003; Keirstead, 2007). Keirstead (2007) found that of those who have installed solar PV, 97% owned their home, which is significantly higher than the 71% national average. Fischer and Sauter (2003) differentiate between owners of a family home, tenants and flat owners, and suggest that only family home owners have direct control over the decision to install. Family home owners also have a direct financial motivation in benefiting from fuel bill savings, as opposed to landlords and housing associations, where these savings are normally passed on to the tenant. Further, there may be more than just a financial motivation within a family home, such as to visibly demonstrate environmental commitment or to become more secure against future fuel bill increases (as discussed in previous sections). This is less likely for a landlord as the house may be merely a financial investment rather than their residence (Fischer and Sauter, 2003). Fischer (2004) conducted a survey of 142 participants of a field test in Germany, where fuel cell CHP systems were installed in their homes. They were asked about their attitudes to energy and the environment, attitudes towards technologies, in particular fuel cell CHP, and environmental behaviour. The study compared this survey with a representative survey of the German public (Hocke-Bergler and Stolle, 2003) and found that the fuel cell CHP owners had larger families and consequently larger homes, with an average of 3.15 per household as opposed to 2.59 in an average German household.

Homes with larger families may have less disposable income or lower savings to spend on microgeneration. However, Ellison (2004) finds that households with children under 16 Page 45 of 210

Chapter 2 Paul Balcombe are not significantly more or less likely to install microgeneration. Whilst available funds for an investment may be lower, the author suggests that households with younger children are less likely to move homes and so they may be more suited for a long-term investment such as microgeneration (Ellison, 2004).

Furthermore, the length of subsidies through the FIT incentives is up to 25 years, which may be a barrier to those wishing to move house before then. Standard property rights apply to microgeneration equipment installed in the property, which means that FIT payments would be transferred to the new owners and the value of these will be set by the housing market. However, this added complication and risk of low resale value may be unwelcome by those who anticipate moving house sooner than 25 years. Particularly, there may be less willingness for older generations to become locked into a long investment that may outlive them (Mahapatra and Gustavsson, 2008).

The presence of differing opinions within a household is also a barrier for some (Fischer and Sauter, 2003). Decisions made by a household can be very different to those made by an individual, after incorporating different preferences by the household members (Sopha et al., 2010).

3.3. Social class, income and education

A number of studies have found correlations between awareness or adoption of microgeneration and social class (Devine-Wright, 2007; Ellison, 2004; GfK NOP Social Research, 2006), income and education (Claudy et al., 2010; Fischer and Sauter, 2003; Keirstead, 2007). In terms of social class, there appears to be more knowledge and awareness of microgeneration in the AB or ABC1 groups (Devine-Wright, 2007; Ellison, 2004; GfK NOP Social Research, 2006). In one study, taking the example of solar PV, 28% of ABC1s stated that they knew ‘a great deal’ or ‘a fair amount’ compared to 16% of C2DEs (Ellison, 2004). Another study (Claudy et al., 2010) suggested that the most aware of microgeneration are an upper-middle class category (social class A). Keirstead (2007) also found that adopters are wealthier (40% had household incomes of greater than £50,000 pa) and have more degree-level qualifications than the national average (77% rather than 30% nationally). Similarly, Fischer and Sauter (2003) identified that those most likely to adopt microgeneration (in this case fuel cell CHP) are a high-income, highly- educated ‘academic elite’.

The causality between adoption, social class, income and education is less known. Claudy et al. (2010) suggest that high earners are more likely to install due to the high

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Chapter 2 Paul Balcombe cost and more highly educated people are more likely to adopt due to the ‘high- involvement’ nature of microgeneration, particularly in terms of investigating prior to installation and the ‘hands on’ operation for some technologies (e.g. biomass). However, there is no justification for this correlation between education and involvement and as such it is unexplained. Conversely, Fischer and Sauter (2003) suggest that income is not the reason for a greater number of installations among higher earners but instead it is due to social status and education. They also suggest that different microgeneration technologies appeal to different segments of the population: solar thermal and biomass boilers are adopted more by farmers and skilled manual workers, whereas solar PV tends to be adopted by a high earning ‘academic elite’.

On the other hand, Sopha et al. (2010) find that a higher income does not imply greater likelihood of installing a microgeneration system. The results indicate that those with a higher income were more inclined to choose an system over a wood-pellet boiler or heat pump. Instead, middle income respondents were most likely to prefer a wood pellet boiler over electric heating. The authors suggest that this occurs because middle income households lie between two barriers associated with lower and higher incomes: the former group is put off by the high capital costs whereas for high-income households, fuel cost savings is not a significant issue (Sopha et al., 2010).

4. Further discussion and conclusions

The literature discussed in this paper suggests that capital costs are the most important barrier for installing microgeneration technologies. This is because they are too high for the majority of potential adopters, as also indicated by the significant gap between potential adopters’ WTP and capital costs (see Table 4). However, FITs have modified the UK financial landscape for those considering adoption and are likely to have increased consumers’ WTP for microgeneration in the last two years, especially for solar PV. Additionally, the global solar PV market has grown significantly, leading to a drop in UK capital costs by around 50% between December 2010 and September 2012. At a levelised installed cost of approximately £2,000 / kW by September 2012 (see Figure 3), prices are now approaching those of Germany’s more mature market (approximately £1,500 / kW) (BSW Solar, 2012; Joachim et al., 2013) and are far lower than the USA (£5,500 / kW) (Joachim et al., 2013).

The reduction in costs of solar PV is illustrated in Figure 3, which shows levelised average installed UK consumer capital costs from 2006 to September 2012. These figures are installation costs for systems of less than 4 kW capacity and are collated from installation Page 47 of 210

Chapter 2 Paul Balcombe quotes (Vaughan, 2012), advice from installers and the experience of adopters (CompareMySolar Ltd, 2012; Gardiner et al., 2011)8. Within this period, UK domestic electricity prices rose by around 15%, increasing the savings made by generating electricity through PV FIT payments and resulting in higher relative financial gains from installing microgeneration. The rising electricity costs, along with high solar PV FIT rates between April 2010 and April 2012, have led to high demand for solar PV with the number of installations rising from 3,000 in 2008 to 320,000 in 2012 (see Figure 1 and Appendix). As mentioned in the introduction, this high demand as well as significantly reduced installation costs prompted the government to halve the FIT rate in April 2012 (from 45 to 21 p/kWh). The effect of this change on demand for solar PV can be seen clearly in Figure 4: a significant rise in demand prior to the reduction in FITs is followed by a sharp drop after it. Although the government recognised that the uptake of solar PV since April 2012 has been very low (DECC, 2012b; Murray-West, 2012), it still subsequently announced a further reduction of FIT payments to 16 p/kWh from August 2012 (DECC, 2012b). This is likely to hamper further the uptake of PV and other microgeneration technologies. The demand for the latter has been low anyway (see the Appendix) as their capital costs have not decreased as drastically as PV costs.

CompareMySolar Ltd, 2012 [60] DECC, 2011 [61] (2.6 kW)

Parsons Brinckerhoff, 2012 [20] (2.6 kW) Vaughan, 2012 [62] (4 kW)

DECC, 2011 [68] 2.5 kW £8,000

£7,000

£6,000

£5,000

£4,000

£3,000

£2,000

£1,000

£0

Installed levelised cost of solar PV (less than 4 4 kW) than (less PV solar of cost levelised Installed Jun-2006 Oct-2007 Feb-2009 Jul-2010 Nov-2011

Figure 3. Decrease in capital costs of solar PV installations from 2010-2012 (/ kWp) (CompareMySolar Ltd, 2012; DECC, 2011a; Parsons Brinckerhoff, 2012; Vaughan, 2012)

8 These two references give installation costs but do not give the source or derivation.

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FIT rates (p/ kWh) Installations per month 50 40,000

45 35,000 40 30,000 35 25,000 30

25 20,000

20 15,000

FIT rate (p/kWh) rate FIT 15 10,000 10

5,000 installations of Number per month 5

0 -

10 11 12

10 11 12

10 11 12

11 12

11 12

10 11 12

12 10 10 11 11 12

10 11 12

10 11 12 10 11 12

11 12

- - -

- - -

- - -

- -

- -

- - -

------

------

- -

Jul Jul Jul

Oct Oct Oct

Apr Apr Apr

Jun Jun Jun Jan Jan

Feb Feb Mar Mar

Nov Aug Sep Nov Dec Aug Sep Nov Dec Aug Sep Dec

May May May Figure 4 Feed-in tariff (FIT) payment rates and the number of instalations per month for solar PV retrofit installations of less than 4 kW capacity (Ofgem, 2012)

Consumer cost reductions are most likely to occur through market development with increased uptake (such as seen with solar PV) or through policies to reduce capital costs. Policies that could further reduce capital costs include capital grants and loans, which could be paid back with money earned through FIT payments. Capital grants can be appealing to consumers as they are clearly visible (as opposed to tax relief) and easily understandable (rather than incremental incentives) (Schroeder et al., 2011). Private loans specific to the solar PV market already exist in England, from a number of banks and microgeneration suppliers. The Italian government has gone a stage further, however, using low-interest loans which are directly paid back through FIT payments (Candelise et al., 2010).

As opposed to barriers, the most commonly identified motivations to installing microgeneration are environmental benefit and earning or saving money through incentives and reduced fuel bills. Potential adopters are driven by the desire to show others their environmental commitment and to reduce GHG emissions but there has been little research into the WTP for this in different segments of the UK population.

Attitudes towards microgeneration and rates of installation are found to vary across age groups. However, there is no agreement on the correlation within the literature. Younger age groups (under 44) typically have a higher awareness of microgeneration and are more willing to consider installing but less frequently reach the point of installation. This suggests that other factors come in to play that prevent them from installing, such as cost. Older age groups (over 65) can be segmented into two groups: those who are unaware or simply content with their existing system and those who have installed. Those of this age Page 49 of 210

Chapter 2 Paul Balcombe group who are aware and have considered microgeneration may have experienced fewer barriers to adoption, such as cost or the suitability of their home, hence they have installed.

There are a number of factors that may directly affect barriers to installation that are likely to be correlated with age, such as whether people own a house or not, the level of available capital for investment and the size of house or family. Further investigation into these factors is required to understand why there are differing perceptions across different segments of the population.

Furthermore, in many cases, the surveys conducted were limited to the inspection of descriptive statistics. Many studies have identified some factors that affect adoption but few have investigated how important different factors are. Perhaps most importantly, there has been little research into why adoption is lower for different segments of the population and the background to the motivations and barriers that affect adoption remains unclear. Thus, to help towards a better understanding of how the uptake of microgeneration could be improved, a deeper analysis is required of the importance of motivations and barriers and possible reasons that affect people’s decisions.

Acknowledgements

This work has been funded by the Sustainable Consumption Institute which is gratefully acknowledged.

Appendix

Table A.1 Estimate of total energy contribution from microgeneration (Element Energy, 2008b)

Technology No. of Capacity (kWa) Energy Energy yield per installations generation kW (2008) (MWh/year) (MWh/yr.kW) Solar PV 2,993 10,354 8,801 0.850 Micro-CHP 200-1,000 7,000 N/A N/A (mean 600) Wind 2,323 4,367 3,825 0.876 Micro-hydro 73 921 4,033 4.379 Solar thermal 97,500- 205,000-213,000 132,000-137,000 0.644 102,000 (mean 209,000) (mean 134,500) (mean 99,750) Biomass 1,400 28,000 23,961 0.856 GSHP 3,415 22,198 58,448 2.633 ASHP 169 1,146 2,892 2.524 Total 110,723 282,986 236,460 N/A a kWp for PV solar. Page 50 of 210

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Table A.2 Number of installations and capacity of microgeneration systems below 50 kW registered in the FIT database (DECC, 2012c)

No. of installations Capacity Technology (2012) (kW)a Solar PV 317,172 974,046 Micro-CHP 400 406 Wind 2,512 33,752 Micro-Hydro 171 4,037 a kWp for PV solar.

Table A.3 Estimated number of total installations, capacity and energy yield of microgeneration in the UK as of the 4th quarter 2012a

Technology No. of installations Total capacity Total energy yield (kW)b (MWh/yr) PV 320,165 984,400 836,750 CHP 1,000 7,406 N/A Wind 4,835 38,119 33,388 Hydro 244 4,958 21,711 Solar Thermal 99,750 209,000 134,500 Biomass boilers 1,400 28,000 23,961 GSHP 3,415 22,198 58,448 ASHP 169 1,146 2,892 Total 430,978 1,295,227 1,111,649 a The estimate of the microgeneration installations in the UK is assumed to be the sum of those registered with the FIT, which include those previously registered with the Renewables Obligation scheme (Table A.2) and the estimate of installations by Element Energy in 2008 (Table A.1). The total energy yield is estimated by scaling up the estimated energy yield per kW capacity (Table A.1, column 5) to estimate total capacity, shown in Table A4. b kWp for PV solar.

Table A.4 Estimation of total contribution of microgeneration to UK domestic energy consumption

Variable Value Unit Source (1) Energy from domestic 563,891,022 MWh DECC (2012a) sector

(2) Energy from 1,111,649 MWh From Table A 3 microgeneration Table (3) Energy contribution 0.197% of the UK domestic sector = (2) / (1) from final energy demand microgeneration

References

Allen, S. R., Hammond, G. P. and McManus, M. C. 2008. Prospects for and barriers to domestic micro-generation: A perspective. Applied Energy, 85, 528-544. Page 51 of 210

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Ben Hoen, R. W., Peter Cappers, Mark Thayer, Gautam Sethi, 2009. The Impact of Projects on Residential Property Values in the : A Multi-Site Hedonic Analysis. OFFICE OF ENERGY EFFICIENCY AND RENEWABLE ENERGY & US DEPARTMENT OF ENERGY (eds.). Washington, D.C.: Ernest Orlando Lawrence Berkeley National Laboratory. Bergman, N., Hawkes, A., Brett, D. J. L., Baker, P., Barton, J., Blanchard, R., Brandon, N. P., Infield, D., Jardine, C., Kelly, N., Leach, M., Matian, M., Peacock, A. D., Staffell, I., Sudtharalingam, S. and Woodman, B. 2009. UK microgeneration. Part I: Policy and behavioural aspects. Proceedings of Institution of Civil Engineers: Energy, 162, 23-36. Brignall, M. 2012. How solar panels can dim mortgage prospects [Online]. London: The Guardian,. Available: http://www.guardian.co.uk/money/2012/mar/23/solar-panels- dim-mortgage-prospects [Accessed 14 January 2013]. Brook Lyndhurst Ltd, MORI and Upstream 2003. Attitudes to renewable energy in London: public and stakeholder opinion and the scope for progress. LONDON RENEWABLES & DTI (eds.). London. legacy.london.gov.uk/mayor/environment/energy/docs/renewable_attitudes.pdf. BSW Solar. 2012. Statistic data on the German (photovoltaic) industry [Online]. Available: www.solarwirtschaft.de/fileadmin/content_files/factsheet_pv_engl.pdf [Accessed 14 January 2013]. Caird, S. and Roy, R. 2010. Adoption and use of household microgeneration heat technologies. Low Carbon Economy, 1, pp. 61–70. Cambridge Economic Policy Associates Ltd and Parsons Brinckerhoff 2011. Updates to the Feed-in Tariffs model: documentation of changes for solar PV consultation. DECC (ed.). Candelise, C., Gross, R. and Leach, M. A. 2010. Conditions for deployment in the UK: the role of policy and technical developments. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 224, 153-166. Claudy, M. C., Michelsen, C., O'Driscoll, A. and Mullen, M. R. 2010. Consumer awareness in the adoption of microgeneration technologies: An empirical investigation in the Republic of Ireland. Renewable and Sustainable Energy Reviews, 14, 2154-2160. Claudy, M. C., Michelsen, C. and O’Driscoll, A. 2011. The diffusion of microgeneration technologies – assessing the influence of perceived product characteristics on home owners' willingness to pay. Energy Policy, 39, 1459-1469. Page 52 of 210

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Cogar, D. 2008. Customer Perceptions of Green Technologies EcoPinion, Survey Report. CompareMySolar Ltd. 2012. Price of solar: Compare prices from local installers [Online]. London. Available: www.comparemysolar.co.uk/price-of-solar/ [Accessed 9 October 2012]. Consumer Focus 2011. Keeping FiT Consumers' attitudes and experiences of microgeneration. ENERGY SAVING TRUST & DECC (eds.). London. Available: www.consumerfocus.org.uk/files/2012/04/Keeping-FiT.pdf. Curry, T. E., Reiner, D. M., Figueiredo, M. A. d. and Herzog, H. J. 2005. A Survey of Public Attitudes towards Energy & Environment in Great Britain. Available: www.stanford.edu/~kcarmel/CC_BehavChange_Course/readings/Additional%20R esources/Sample%20Intervention%20Docs/Surveys/mit.pdf: Massachusetts Institute of Technology, Laboratory for Energy and the Environment. Dastrup, S. R., Graff Zivin, J., Costa, D. L. and Kahn, M. E. 2012. Understanding the Solar Home price premium: Electricity generation and “Green” social status. European Economic Review, 56, 961-973. DECC 2009. The UK Renewable Energy Strategy. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown Copyright. DECC 2011a. Feed-in tariffs scheme: consultation on Comprehensive Review Phase 1 – tariffs for solar PV. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London www.gov.uk/government/uploads/system/uploads/attachment_data/file/42834/341 6-fits-IA-solar-pv-draft.pdf: Crown copyright. DECC 2011b. Microgeneration Strategy. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown Copyright. DECC. 2012a. Energy Consumption in the UK [Online]. London. Available: www.decc.gov.uk/en/content/cms/statistics/publications/ecuk/ecuk.aspx [Accessed 14 January 2013]. DECC 2012b. Feed-in Tariffs Scheme. Government response to Consultation on Comprehensive Review Phase 2A: Solar PV cost control. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown Copyright. DECC 2012c. Monthly central Feed-in Tariff register statistics. 5920-MONTHLY- CENTRAL-FEEDIN-TARIFF-REGISTER-STATISTICS.XLS (ed.) Microsoft Excel. London: DEPARTMENT OF ENERGY AND CLIMATE CHANGE. Devine-Wright, P. 2005. Local aspects of UK renewable : exploring public beliefs and policy implications. Local Environment, 10, 57-69.

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Devine-Wright, P. 2007. Reconsidering public attitudes and public acceptance of renewable energy technologies: a critical review. Published by the School of Environment and Development, University of Manchester, Oxford , Manchester M13 9PL, UK, Available: www.sed.manchester.ac.uk/research/beyond_nimbyism/. DTI 2006. The Microgeneration Strategy. DEPARTMENT OF TRADE AND INDUSTRY (ed.). London: Crown Copyright. Ecohouse Solar. 2009. Solar panels to boost property prices [Online]. WordPress & the Atahualpa WP Theme. Available: www.ecohousesolar.co.uk/http:/www.ecohousesolar.co.uk/uncategorized/solar- panels-to-boost-property-prices [Accessed 14 Jan 2013 2013]. Element Energy 2005. Potential for Microgeneration. Study and Analysis. ENERGY SAVING TRUST (ed.). London. www.berr.gov.uk/files/file27558.pdf Element Energy 2008a. The Growth Potential for Microgeneration in England, Wales and Scotland. BERR (ed.). London. Element Energy 2008b. Numbers of Microgeneration Units Installed in England, Scotland, Wales and Northern Ireland. BERR (ed.). London. Ellison, G. 2004. Renewable Energy Survey 2004 Draft summary report of findings. LONDON ASSEMBLY (ed.). London. legacy.london.gov.uk/assembly/reports/environment/power_survey_orc.pdf: ORC International. Envirolink Northwest 2010. Giving Power to People - North West of England Results and Best Practice. ENERGY SAVING TRUST (ed.). EST. 2011. Feed-in Tariff scheme [Online]. London. Available: www.energysavingtrust.org.uk/Generate-your-own-energy/Sell-your-own- energy/Feed-in-Tariff-scheme [Accessed 10 September 2012]. Faiers, A. and Neame, C. 2006. Consumer attitudes towards domestic solar power systems. Energy Policy, 34, 1797-1806. Fischer, C. 2004. Who uses innovative energy technologies, when, and why? The case of fuel cell MicroCHP. TRANSFORMATION AND INNOVATION IN POWER SYSTEMS (ed.). Freie Universität Berlin. Fischer, C. and Sauter, R. 2003. Governance for Industrial Transformation. Human Dimensions of Global Environmental Change. Berlin. Gardiner, M., White, H., Munzinger, M. and Ray, W. 2011. Low Carbon Building Programme 2006 - 2011 Final Report. DECC (ed.). London: Crown Copyright.

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GfK NOP Social Research 2006. Renewable Energy Awareness and Attitudes Research. DTI (ed.). London. webarchive.nationalarchives.gov.uk/+/http://www.dti.gov.uk/files/file29360.pdf. Goto, H. and Toshio, A. 2009. Ananalysis of residential customers’ preferences for household energy systems. IAEE European Conference in Vienna, 31 August. Hack, S. 2006. International Experiences with the Promotion of Solar Water Heaters (SWH) at Household-level. DEUTSCHE GESELLSCHAFT FÜR TECHNISCHE ZUSAMMENARBEIT (GTZ) GMBH (ed.). Mexico City. Available: www.conuee.gob.mx/work/sites/CONAE/resources/LocalContent/6942/1/IEPSWH. pdf. HM Government 2004. Energy Act. London: Crown Copyright. Available: www.legislation.gov.uk/ukpga/2004/20/contents. HM Government 2007. Energy White Paper: Meeting the Energy Challenge. DTI (ed.). London: Crown Copyright. webarchive.nationalarchives.gov.uk/20121205174605/http:/www.decc.gov.uk/asse ts/decc/publications/white_paper_07/file39387.pdf. HM Government 2009. UK Fuel Poverty Strategy Seventh Annual Progress report. DECC (ed.). Crown Copyright. Hocke-Bergler, P. and Stolle, M. 2003. Ergebnisse der Bevölkerungsumfragen und der Medienanalyse zum Thema Endlagerung radioaktiver Abfälle. http://www.akend.de/projekte/umfrage-main.htm, Anlagenband zum ITAS-Bericht. Hoen, B., Wiser, R., Cappers, P. and Thayer, M. 2011. An Analysis of the Effects of Residential Photovoltaic Energy Systems on Home Sales Prices in . ERNEST ORLANDO LAWRENCE BERKELEY NATIONAL LABORATORY (ed.). Orlando. Available: eetd.lbl.gov/ea/emp/reports/lbnl-4476e.pdf. Environmental Energy Technologies Division. Jager, W. 2006. Stimulating the diffusion of photovoltaic systems: A behavioural perspective. Energy Policy, 34, 1935-1943. Joachim, S., Galen, L. B. and Ryan, H. W. 2013. Why Are Residential PV Prices in Germany So Much Lower Than in the United States? A Scoping Analysis. Lawrence Berkeley National Laboratory. Keirstead, J. 2007. Behavioural responses to photovoltaic systems in the UK domestic sector. Energy Policy, 35, 4128-4141. Leenheer, J., de Nooij, M. and Sheikh, O. 2011. Own power: Motives of having electricity without the energy company. Energy Policy, 39, 5621-5629.

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Mahapatra, K. and Gustavsson, L. 2008. An adopter-centric approach to analyze the diffusion patterns of innovative residential heating systems in Sweden. Energy Policy, 36, 577-590. McLeod, R. S., Hopfe, C. J. and Rezgui, Y. 2012. An investigation into recent proposals for a revised definition of zero carbon homes in the UK. Energy Policy, 46, 25-35. Morris-Marsham, C. 2010. Do solar PV and solar thermal installations affect the price and saleability of domestic properties in Oxford. Degree of Master of Science Built Environment: and Engineering, UCL. Murray-West, R. 2012. Government plans to cut solar feed-in tariff [Online]. Available: http://www.telegraph.co.uk/finance/personalfinance/consumertips/household- bills/9287863/Government-plans-to-cut-solar-feed-in-tariff.html [Accessed March 2012]. NHBC Foundation 2008a. A Review of Microgeneration and Renewable Energy Technologies. BRE (ed.). NHBC Foundation 2008b. Zero carbon: what does it mean to homeowners and housebuilders? Amersham, UK. NHBC Foundation 2011. Introduction to Feed-In Tariffs. BRE (ed.). Available: www.nhbcfoundation.org/Researchpublications/IntroductiontoFeedinTariffsNF23/ta bid/437/Default.aspx: IHS BRE Press. Nye, M., Whitmarsh, L. and Foxon, T. 2010. Sociopsychological perspectives on the active roles of domestic actors in transition to a lower carbon electricity economy. Environment and Planning A, 42, 697-714. Ofgem 2012. Feed-in Tariff Payment Rate Table for Photovoltaic Eligible Installations. DECC (ed.). London. Palm, J. and Tengvard, M. 2011. Motives for and barriers to household adoption of small- scale production of electricity: examples from Sweden. Sustainability: Science, Practice, & Policy, Vol 7, pp 6-15. Parsons Brinckerhoff 2012. Solar PV cost update. DECC (ed.). London: www.pbworld.com. Praetorius, B., Martiskainen, M., Sauter, R. and Watson, J. 2010. Technological innovation systems for microgeneration in the UK and Germany - a functional analysis. Technology Analysis & Strategic Management, 22, 745 - 764. Rae, C. and Bradley, F. 2012. Energy autonomy in sustainable communities—A review of key issues. Renewable and Sustainable Energy Reviews, 16, 6497-6506. Rowley, E. 2011. Renting out roof to solar power firms could make your home harder to sell, surveyors warn [Online]. London: Telegraph Media Group Limited 2013. Page 56 of 210

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Available: www.telegraph.co.uk/finance/newsbysector/energy/8856365/Renting- out-roof-to-solar-power-firms-could-make-your-home-harder-to-sell-surveyors- warn.html [Accessed 14 January 2013 2013]. Scarpa, R. and Willis, K. 2010. Willingness-to-pay for renewable energy: Primary and discretionary choice of British households' for micro-generation technologies. Energy Economics, 32, 129-136. Schroeder, S. T., Costa, A. and Obé, E. 2011. Support schemes and ownership structures – the policy context for fuel cell based micro-combined heat and power. Journal of Power Sources, 196, 9051-9057. Sopha, B. M., Klöckner, C. A., Skjevrak, G. and Hertwich, E. G. 2010. Norwegian households’ perception of wood pellet compared to air-to-air heat pump and electric heating. Energy Policy, 38, 3744-3754. The Carbon Trust 2009. Building the future, today: Transforming the economic and carbon performance of the buildings we work in. London. Vaughan, A. 2012. Sharp drop in number of UK homes installing solar panels [Online]. Available: http://www.guardian.co.uk/environment/2012/sep/21/drop-uk-homes- solar-panels [Accessed 9 October 2012]. Walters, R. and Walsh, P. R. 2011. Examining the financial performance of micro- generation wind projects and the subsidy effect of feed-in tariffs for urban locations in the United Kingdom. Energy Policy, 39, 5167-5181. Warren, P. 2010. Uptake of Micro-generation among Small Organisations in the Camden Climate Change Alliance. Masters thesis, Durham University. Watson, J., Sauter, R., Bahaj, B., James, P., Myers, L. and Wing, R. 2008. Domestic micro-generation: Economic, regulatory and policy issues for the UK. Energy Policy, 36, 3095-3106. Wee, H.-M., Yang, W.-H., Chou, C.-W. and Padilan, M. V. 2012. Renewable energy supply chains, performance, application barriers, and strategies for further development. Renewable and Sustainable Energy Reviews, 16, 5451-5465. Williams, J. 2010. The deployment of decentralised energy systems as part of the housing growth programme in the UK. Energy Policy, 38, 7604-7613. Willis, K., Scarpa, R., Gilroy, R. and Hamza, N. 2011. Renewable energy adoption in an ageing population: Heterogeneity in preferences for micro-generation technology adoption. Energy Policy, 39, 6021-6029. Wimberly, J. 2008. Banking the Green: Customer Incentives for EE and Renewable. EcoAlign. Available: www.ecoalign.com/news/releases/banking-green-role- customer-incentives-energy-efficiency-and-renewable-energy. Page 57 of 210

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Zahedi, A. 2011. A review of drivers, benefits, and challenges in integrating renewable energy sources into electricity grid. Renewable and Sustainable Energy Reviews, 15, 4775-4779.

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Chapter 3: Investigating the importance of motivations and barriers related to microgeneration uptake in the UK

This paper was submitted to Applied Energy in November 2013 and published in June 2014 with the following citation:

Balcombe, P., D. Rigby, and A. Azapagic, Investigating the importance of motivations and barriers related to microgeneration uptake in the UK. Applied Energy, 2014. 130: p. 403-418.

This research consisted of a set of telephone interviews and surveys of UK consumers and potential consumers of microgeneration systems. The research was designed, implemented and written by the author of this thesis. Co-authors Rigby and Azapagic supervised the research and edited the paper prior to submission.

Annex

During the Viva examination for this thesis, it was agreed that clarification was required with respect to demonstrating that the survey sample size was large enough to give statistical significance. As described within the paper, a sample size of 291 was collected in order to be able to split the group and determine differences across the group. The issue of whether the sample size was appropriate for the study is multi-faceted and discussed below.

Firstly, an attempt was made to describe the demographic of the sample in order to illustrate the representativeness to the population. However, the demographic of this population, which comprises adopters, considerers and rejecters in the UK, is largely unknown amongst the wider UK population. We make comparisons to other studies of adopters, of which the demographic is very similar to ours, but not for considerers and rejecters as this is a unique sample, thus we cannot definitively say that this sample is representative.

Secondly, the sample size was large enough to ensure that the model was an acceptable fit. Root likelihood (RLH) is the measure of model fit, the value of which was deemed acceptable for both motivation and barrier sets, as described within the paper. This means that the choices exhibited in the survey by the respondents are reflected in the importance

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Chapter 3 Paul Balcombe scores for each motivation and barrier. Thus, we can say that the importance scores correctly reflect the opinions of the sample.

Thirdly, as the analysis yields the relative importance of the motivations and barriers, one cannot express the statistical significance of each individual importance score but only the significance of the difference between each pair of items. For the graphs of importance scores, we display the standard errors alongside the mean values. The standard error gives an indication of the significance in terms of the difference between importance scores: if there are no overlaps between bars of values of twice the standard error, the difference is significant to approximately 95%. Many of the differences in importance discussed are significant to 5%, others to 10%. We don’t present significance simply because there are too many pairwise comparisons to describe (with the 8 motivations and the 14 barriers). A larger sample size may have improved the significance further, especially across sub-groups and for the HB analysis. Nevertheless, the analysis has still obtained some useful insight in terms of: the most and least important motivations and barriers in the adoption decision; and differences between sub-groups such as pre-FIT and post-FIT adopters. From this insight the past impacts and future implications of the various policy incentives were discussed.

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Investigating the importance of motivations and barriers related to microgeneration uptake in the UK

Paul Balcombea,b,c, Dan Rigbyb* and Adisa Azapagica a School of Chemical Engineering and Analytical Science, The University of Manchester, M13 9PL, UK b School of Social Sciences, The University of Manchester, M13 9PL, UK c Sustainable Consumption Institute, The University of Manchester, M13 9PL, UK

*Corresponding author at: School of Social Sciences, University of Manchester, M13 9PL, UK. Tel: +44 161 275 4808

Email addresses: [email protected], [email protected], [email protected]

Abstract

Microgeneration technologies such as solar photovoltaics, solar thermal, wind and heat pumps may be able to contribute to meeting UK climate change and energy security targets, but their contribution to UK domestic energy supply remains low. This research uses a best-worst scaling survey of microgeneration adopters, considerers and rejecters (n=291) to determine the relative importance of different motivations and barriers in microgeneration (non) adoption decisions. The most important motivations are earning money from installation, increasing household and protecting against future high energy costs. Results indicate that the introduction of Feed-in Tariffs has clearly encouraged a new, more financially-motivated, group to install. Financial factors are the most important barriers and of most importance to rejecters is the prospect of losing money if they moved home. The Green Deal was introduced to reduce this barrier, but may instead exacerbate the problem as potential homebuyers are put off purchasing a home with an attached Green Deal debt. The difficulty in finding trustworthy information on microgeneration is also a major obstacle to adoption, particularly for considerers, despite efforts by the government and microgeneration interest groups to reduce this barrier. Self-sufficiency in energy is a more important motivation for those considering or having rejected installation than for adopters. Provision of accessible information and greater emphasis on household self-sufficiency in energy could help improve the uptake.

Keywords: microgeneration, motivations, barriers, feed-in tariffs, best-worst scaling, the green deal

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1. Introduction

Microgeneration is the generation of electricity and/or heat from a low carbon source (HM Government, 2004) at a scale suitable for households. For example, UK government limits microgeneration capacity to 50 kW for electricity and 45 kW for heat. The microgeneration technologies include solar photovoltaic (PV), micro-wind, micro-hydro, micro-CHP, fuel cells, solar thermal and heat pumps (air, water and ground source).

The UK government aims to increase uptake in microgeneration in order to meet climate change and renewable energy targets (DECC, 2009) and to improve energy security (DECC, 2011c). A number of incentive schemes have been implemented since 2010 and uptake has increased in particular for solar PV: from approximately 5,000 installations in 2010 to 400,000 in July 2013 and the total number of microgeneration installations was 520,000 (Balcombe et al., 2013; DECC, 2013a).

However, the overall contribution of microgeneration in the domestic sector remains low, accounting for ~0.2% of the total energy supplied to households (Balcombe et al., 2013). Significant barriers to wider adoption exist that must be overcome if microgeneration is to contribute to UK climate change and energy security targets, such as high capital costs.

Recent research into the consumer perceptions of microgeneration has identified many motivations and barriers in the adoption decision (as discussed in section 3), but their relative importance remains unknown. Therefore, this research provides new understanding and knowledge of the relative importance of various motivations and barriers and how this relative importance varies between those who adopt and those who reject microgeneration. This understanding allows recommendations to be made to policymakers and the microgeneration industry that would help increase the uptake. For these purposes, we use a sample comprising existing adopters, those who are considering installing and those who have rejected it. The specific aims of the research are to:

 identify the motivations and barriers associated with the consumer decision whether to install a microgeneration system;  elicit the relative importance of these motivations and barriers and any differences between adopters, considerers and rejecters;  identify the differentiating factors between those who adopt and those who reject installing a microgeneration system; and

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 identify improvements that could be made in policy and within the microgeneration industry as well as population segments that would be most affected.

In the next section, the paper describes the background to this research in terms of recent policies that have impacted on microgeneration uptake and section 3 gives an overview of recent research into the factors affecting consumer adoption. This is followed in section 4 by a description of the methodology. Results are presented in section 5 and a discussion which relates the research findings to microgeneration policy appears in section 6. Conclusions are drawn in section 7, including recommendations for both policy makers and microgeneration suppliers.

2. UK microgeneration policy

A number of policies have been recently implemented to remove financial barriers to microgeneration uptake: the Feed-in Tariff (FIT) (DECC, 2011c), Renewable Heat Incentive (RHI) (DECC, 2011d) and more recently the Green Deal (DECC, 2010). The Microgeneration Strategy (DECC, 2011c) also included a number of measures to remove non-financial barriers. These policy measures and their impact on uptake are described briefly below.

2.1 Feed-In Tariffs

The FIT scheme was introduced in April 2010 and offers a fixed payment to households for every unit of energy they generate by approved, electricity-generating microgeneration installations; this is paid for by the household’s electricity supplier. Depending on the technology, the tariffs were designed to give an annual return on investment of 5% (DECC, 2011a) with the payments guaranteed for 20-25 years.

Since the implementation of FITs, the global solar PV market has grown significantly, leading to a fall in UK installation costs by approximately 50% by 2012 (Balcombe et al., 2013). Over the same period, there was a 15% increase in the UK electricity price, further reducing payback times. In October 2011, the UK Government launched an emergency tariff review and proposed reducing the tariff for small solar PV by half, to 21 p/ kWh (DECC, 2011b). The short notice period given for the tariff change, approximately 6 weeks, caused much concern within the industry due to the expected rush to install before the deadline and the subsequent industry redundancies after this period (Debenham, 2013). A group of microgeneration suppliers contested this change at the UK Supreme Court and the tariff change was temporarily rescinded until April 2012 (Debenham, 2013). As predicted, there was a spike in the number of installations before, and a sharp drop in Page 63 of 210

Chapter 3 Paul Balcombe installations observed after the cut (see Figure 5). The process by which the tariff rate was changed may also have caused a degree of uncertainty or scepticism amongst potential adopters, adding to the barriers to adoption.

FIT rate (p/kWh) Number of installations per month 40,000 50

35,000 45 40 30,000 35 25,000 30 20,000 25

15,000 20

15 kWh) (p/ rate FIT 10,000 10 5,000 No. of installations per month per installations of No. 5 - 0

Figure 5. Feed-in Tariff (FIT) payment rates and the number of installations per month for solar PV retrofit installations of less than 4 kW capacity modified from (Balcombe et al., 2013; DECC, 2013a)

2.2 Renewable Heat Incentive

Renewable Heat Incentive (RHI) is an equivalent incentive to the FIT scheme but for heat generators. However, the RHI is still not available for the domestic sector – after many delays, it is expected to be implemented in Spring 2014 (Nichols, 2011; Nichols, 2013). While awaiting the RHI, the Renewable Heat Premium Payment (RHPP) has been offering a small grant since August 2011: £300 for solar thermal systems (which typically costs £5,000 to install), £850 for air source heat pumps (costing £6,000-10,000), £950 for biomass boilers (£5,000 - £12,000) and £1,250 for ground source heat pumps (£9,000 - £17,000). These grants have doubled for each technology since May 2013 (Energy Saving Trust, 2013a; Energy Saving Trust, 2013b). However, households that are connected to the central gas grid, which represent 85% of the UK housing stock (OFT, 2011), are only eligible for a solar thermal system grant. This limits the potential uptake of the scheme, reflected in the fact that since the initiation of the grant, only 9,000 new microgeneration systems have been installed (Energy Saving Trust, 2012b; Energy Saving Trust, 2013c).

2.3 Green Deal

The Green Deal, implemented in Jan 2013, facilitates loans for the capital cost of various energy efficiency measures for residences and businesses. The loans are paid back at a Page 64 of 210

Chapter 3 Paul Balcombe fixed rate with estimated fuel bill savings resulting from the improvements and are automatically added to the property’s energy bill (Dowson et al., 2012). Energy improvements such as insulation, double glazing and some forms of microgeneration can be installed by accredited installers, paid for with a loan from an accredited private loan company. Loan interest rates are 7-9% and the repayment term is 10-25 years. The improvement work must be recommended by an accredited home energy assessor and the Green Deal stipulates that the loan is permitted only if the monthly repayments are lower than the predicted fuel bill savings (DECC, 2010).

Thus, the Green Deal seeks to address the capital cost barrier and eliminate the risk of not recouping the investment as the repayment is offset against fuel bill savings. However, since the Green Deal began, uptake has been slow. The number of preliminary household assessments reached 38,000 by mid-June 2013, but very few households subsequently applied for Green Deal finance (245 applications by June 2013) and none had been implemented (DECC, 2013b).

2.4 Microgeneration Strategy

The Microgeneration Strategy (DECC, 2011c) was published in 2011 and suggests pathways for the microgeneration industry to reduce a number of non-financial barriers to greater uptake. Such barriers are concerns about performance and durability and the availability of trustworthy information and advice. In particular, the strategy outlined the task of the Microgeneration Certification Scheme (MCS) to ensure that technological and installation standards were met. The MCS is an accreditation scheme for installers and technologies, which aims to ensure that any installed product meets the required set of standards (DECC, 2011c). In order for a household to receive any of the incentives described above, the microgeneration technology and installer must be accredited.

3. Existing research on the motivations and barriers affecting adoption

Previous research has identified a number of motivations and barriers that affect adoption of microgeneration, including finance, environmental concerns, self-sufficiency, uncertainty and trust, inconvenience and impact on residence. These are reviewed briefly next, as a way of introduction to the research carried out in this work. For a more comprehensive review, see Balcombe et al. (2013).

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3.1 Finance

Capital cost has repeatedly been found to be the main barrier to installing microgeneration (e.g. Allen et al., 2008; Bergman and Eyre, 2011; Claudy et al., 2011; Element Energy, 2005; Scarpa and Willis, 2010). For many people, the capital cost is either unaffordable (Scarpa and Willis, 2010) or they cannot earn enough money from the installation to warrant the investment (Claudy et al., 2010). However, the introduction of the FITs has improved payback time and the significant increase in solar PV uptake suggests that the changing financial landscape has further motivated people to adopt. There is also concern that the installation will have a negative impact on the home value: the resale value of the home would either not increase proportionally with the capital investment, or would put off potential homebuyers such that the home value decreases. Currently, there is limited research into the effects of microgeneration on house resale value (Dastrup et al., 2012; Hoen et al., 2011; Morris-Marsham, 2010).

3.2 Environmental concerns

Many people are motivated to install by the desire to improve the environment (Claudy et al., 2011; Leenheer et al., 2011). However, a number of studies suggest that there is little desire from households to pay extra for this environmental improvement (Claudy et al., 2011; Walters and Walsh, 2011; Wimberly, 2008; Yamaguchi et al., 2013). Households may be motivated by the environmental motive to consider installing, but the decision is more often based on financial or other factors than environmental benefit (Claudy et al., 2013; Hack, 2006; Wimberly, 2008). One other environment-related motivation to install is to demonstrate environmental commitment to others via technologies which are visible outside the property, such as solar panels or wind turbines (Caird and Roy, 2010; Palm and Tengvard, 2011).

3.3 Self-sufficiency

The motivation to increase the household’s self-sufficiency in energy or independence from the central electricity grid is also important to potential adopters (Bergman et al., 2009; Jager, 2006; Palm and Tengvard, 2011). The recent increases in energy prices have also contributed to a desire to protect against future price rises (Praetorius et al., 2010). Guarding against power cuts (Praetorius et al., 2010) may also be a motivation but no UK study has considered this within their research. Recent concerns raised by the UK gas and electricity regulatory body Ofgem (Office of Gas and Electricity Markets) regarding the tightening margins between the quantity of electricity supply and demand

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Chapter 3 Paul Balcombe within the next two years (Ofgem, 2013) suggest that this motivation may become more important as the risk of power cuts increases.

3.4 Uncertainty and trust

There are also barriers to adoption relating to uncertainty over technological performance of a microgeneration system (Brook Lyndhurst Ltd et al., 2003; Caird and Roy, 2010; Ellison, 2004; Zahedi, 2011) and the suitability of their home (DECC, 2011c). Fuelling this uncertainty has been the perceived lack of reliable or trustworthy information (Allen et al., 2008; Mahapatra et al., 2013). Consumers are often unaware of information and advice centres (DECC, 2011c; Ellison, 2004) and there is also a lack of trust in suppliers and installers (DECC, 2011c), with numerous examples shared online of poor installation experiences (e.g. Taylor, 2013) or aggressive product-selling (Lonsdale, 2013; Yamaguchi et al., 2013).

3.5 Inconvenience

Installing a microgeneration system often involves major modifications to the household heating or electricity system (Scarpa and Willis, 2010; Wee et al., 2012). There may also be a change in day-to-day use of the heating/electricity system, with different technologies requiring different modes of operation, space requirement (e.g. heat pumps, biomass) or frequent refuelling (biomass boilers) (Scarpa and Willis, 2010). Other barriers include a change in maintenance requirements and complexity of the system (Element Energy, 2008).

3.6 Impact on residence

There is a space requirement associated with retrofitting households with some technologies and is a particularly significant barrier for smaller households. The zero- carbon homes initiative (HM Government, 2007) eliminates this barrier for new-build homes: by 2016 homes must either have a microgeneration installation or be connected to a district renewable energy system in order to comply (McLeod et al., 2012). However, the barrier for the 25 million existing UK homes remains. There is also an aesthetic impact on the house by installing a microgeneration system and concerns are often raised about neighbour disapproval (Ellison, 2004; Hoen et al., 2011).

3.7 Differing perceptions across the UK population

The various motivations and barriers described above may impact upon a household’s adoption decision, but the extent to which they impact upon the decision varies

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Chapter 3 Paul Balcombe considerably across the population. Although adoption is highest amongst 45–65 year olds (Ellison, 2004; GfK NOP Social Research, 2006; Leenheer et al., 2011; Willis et al., 2011), awareness is higher for under 45-year-olds and they more frequently consider installing (Consumer Focus, 2011) but less frequently install (Ellison, 2004). The correlating factors with age that affect uptake may be the level of available income for investment, house size or likelihood of moving house (Mahapatra and Gustavsson, 2009; Willis et al., 2011). A number of studies also find that there is greater adoption amongst those with higher income and a higher level of education (Bergman and Jardine, 2009; Claudy et al., 2010; Fischer and Sauter, 2003; Keirstead, 2007; Mahapatra and Gustavsson, 2009). A higher income may somewhat mitigate the capital cost barrier, but the causality between adoption and education is less clear.

4. Methodology

While the studies discussed in the previous section have identified a number of factors that affect the adoption decision, they did little to identify how important they are in the adoption decision. This is the focus of the present research which aims to identify the relative importance of consumer motivations and barriers associated with the adoption decision and to identify relative differences between adopters, considerers and rejecters across population segments. The aim is also to suggest improvements in policy and within the microgeneration industry that could help to increase uptake.

4.1 Questionnaire design and data collection

To achieve the above aims, an online survey of adopters, considerers and rejecters has been carried out using the questionnaire developed as part of this research. To help design the questionnaire, first a comprehensive list of motivations and barriers was identified through a literature review detailed in Balcombe et al. (Balcombe et al., 2013) and summarised in Section 3. Semi-structured telephone interviews were then undertaken with a sample of 12 adopters, considerers and rejecters to refine the list of motivations and barriers. The interviews lasted approximately 20 minutes and participants were asked to describe their interest in microgeneration: what motivated them, what put them off and any background information related to these factors. While these topics were followed broadly, the open nature of semi-structured interviews also allowed for new topics to be discussed, depending on what the interviewees said. As a result, eight motivations and 14 barriers were identified and included in the survey; these are listed in Table 6. The survey was carried out using the best-worst scaling (BWS) method to help elicit the relative

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Chapter 3 Paul Balcombe importance of the motivations and barriers in the adoption decision; BWS is described further below.

The survey was carried out online between October 2012 and March 2013. Recruitment was undertaken via advertisements placed on a number of websites and microgeneration forums, as well as to approximately 20 renewable energy showrooms in the UK. Leaflets were also distributed in neighbourhoods where one or more property had installed a - as these were visible from the outside the property, they indicated clearly the adopters. Based on the previous research, it was also possible that other neighbours might be considerers, motivated by their adopter-neighbours (Bollinger and Gillingham, 2012; Müller and Rode, 2013).

Respondents were asked which of the following statements applied to them: I have bought a microgeneration system (adopters); I am currently thinking about buying a microgeneration system (considerers); and I have thought about it and decided not to buy a microgeneration system at this time (rejecters). They were then asked to complete the BWS survey, which is described in the next section. The full questionnaire can be found in Supplementary material.

In total, 291 respondents completed the survey with a relatively even split between adopters (n=113), considerers (n=87) and rejecters (n=91). Their characteristics are discussed in section 5.

Table 6. Motivations and barriers considered in the survey

Motivations Barriers 1. Save or earn money from lower fuel bills and 1. Costs too much to buy/install government incentives 2. Help improve the environment 2. Can't earn enough/save enough money 3. Protect against future higher energy costs 3. Home/location not suitable 4. Make the household more self-sufficient/ less 4. Lose money if I moved home dependent on utility companies 5. Use an innovative/high-tech system 5. High maintenance costs 6. Protect the household against power cuts 6. System performance or reliability not good enough 7. Increase the value of my home 7. Energy not available when I need it 8. Show my environmental commitment to others 8. Environmental benefits too small 9. Take up too much space 10. Hassle of installation 11. Would not look good 12. Neighbour disapproval/ annoyance 13. Disruption or hassle of operation 14. Hard to find trustworthy information/ advice

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4.2 Best-worst scaling

BWS is a survey method in which respondents are asked repeatedly to select the best and worst options (in this case motivations and barriers) within a set. They make repeated pairs of best/worst choices, each set with a different combination of options shown. The choices are analysed to reveal the relative importance or preference associated with the options, based on random utility theory (see section 4.3) and the assumption that the frequency of selection of an item as best or worst indicates the strength of preference for that item (Finn and Louviere, 1992; Louviere et al., 2013).

Figure 6 shows an example of a choice task used within the survey. For items A, B, C and D, the selection of A as best and B as worst suggests that A > (C & D) > B, providing preference orderings on 5 of the 6 possible pairwise comparisons (Sawtooth Software, 2013b). Repeated choice tasks with differing motivations or barriers allow an estimate of the probability that, given a certain set of motivations, item x will be selected as best and item y as worst, from which the relative importance of each item can be inferred.

A B C D

Figure 6. An example subset of motivations taken from the best-worst scaling survey.

Respondents were asked to complete five choice tasks for motivations, each comprising four motivations, and seven choice tasks for barriers, each consisting of five barriers. The total number of times each motivation and barrier should appear for each respondent (the number of items per choice set multiplied by the number of choice sets, divided by the number of items) should normally be approximately three, in order to produce statistically significant results (Orme, 2005). Preliminary testing of the survey suggested that 12 choice sets were acceptable without resulting in respondent fatigue. The number of items per choice set is typically four or five and a study by Orme (2005) on the internal validity of such BWS experiments suggests that there is little advantage in more than five items per set. Thus, the survey was designed such that each motivation and barrier appears Page 70 of 210

Chapter 3 Paul Balcombe approximately the same number of times for all respondents across all the choice sets: an average of 2.5 times per person.

As far as we are aware, this is the first time BWS has been used to elicit consumer perceptions of microgeneration. Other studies have used open ended interviews with qualitative analysis (Palm and Tengvard, 2011; Warren, 2010), closed format questions or rating scales with descriptive statistical (e.g. Brook Lyndhurst Ltd et al., 2003; Curry et al., 2005; Ellison, 2004; Leenheer et al., 2011) or regression analysis (Fischer and Sauter, 2003); environmental valuation economic studies have used choice experiments (Scarpa and Willis, 2010; Yamaguchi et al., 2013) and the contingent valuation method (Baskaran et al., 2013; Claudy et al., 2011). The BWS methodology was selected over other methods for its suitability for eliciting importance values over large sets of independent items. Asking respondents to rank items over large sets has been shown to prompt greater likelihood of anomalous choice behaviour, hence the desire to reduce the cognitive load via small sets. The cognitive load is further reduced by only asking respondents to make judgements at the extreme (best/worst) rather than ranking all items shown (Vermeulen et al., 2010). BWS also forces the respondent to discriminate between the different items by having to select a best and worst option, thus respondents cannot simply rate each item as of ‘middling’ importance, as is the case with agreement scale methods (e.g. Likert scales). Additionally, there is no scale use bias associated with the method as respondents do not explicitly rate each motivation and barrier on an absolute scale which is vulnerable to systematic differences in respondents tendency to (not) use certain portions of the scale. BWS also avoids differences in interpretation of terms such as “very” and “quite” often used as labels in such rating scales. Finally, the random utility models estimated on BWS data yield ratio-scaled importance scores, rather than just a rank order, which provide more information and help to understand the results better.

4.3 Data analysis

As mentioned earlier, random utility theory has been used to reveal the relative importance of preferences. The importance of each motivation and barrier is expressed as follows (Louviere et al., 2013):

푈푥 = 퐼푥 + 휀푥 Equation 1 where 푈푥 is the relative importance of motivation or barrier x, Ix is the systematic element of importance (the importance level measured within the study) and 휀푥 is the unobserved error component, which accounts for internal inconsistencies in the choices. Ix is

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Chapter 3 Paul Balcombe estimated by making an assumption regarding the error terms which are independent and identically distributed (iid), i.e. they all have the same probability distribution.

The best-worst choice tasks are used to estimate the probability of each motivation or barrier being selected as best or worst, given a certain subset of motivations or barriers. Probabilities for the different pairs within the subset are then transformed into relative importance values using the multinomial logit (MNL) rule (Marti, 2012):

푒퐼푥−퐼푦 푃(푥푦|퐶) = Equation 2 퐾 퐼푗−퐼푘 ∑1 푒 where 푃(푥푦|퐶) is the probability that item (motivation or barrier) x is selected as best and item y is selected as worst within subset C; j and k are two of the non-selected items in subset C and K is the total number of pairs of items in subset C. A relative importance value Ux is estimated for every motivation and barrier except one, which is the reference value by which to measure the relative importance of the other items. In this study, the reference motivation was Show my environmental commitment to others and the reference barrier was Hard to find trustworthy information/advice.

A Hierarchical Bayes (HB) model was used to estimate individual-level importance scores. Individual-level importance scores allow us to analyse the variation of importance scores across the sample, which is an advantage over an aggregate MNL model (which yields average importance scores over the whole sample). The survey was designed and data collected and analysed using Sawtooth software: Maxdiff and CBC Hierarchical Bayes (Sawtooth Software Inc, 2013). The HB model is hierarchical as it is an iterative operation between two distinct levels of parameter estimation (Sawtooth Software, 2003). On the lower level, individual-level MNL scores are estimated from each individual’s choice sets. However, there is not enough survey data to fully estimate each parameter for each individual as this would require more choice sets for each respondent potentially resulting in a greater respondent drop-out rate. In order to fill in these information gaps, importance values and covariances are taken from a set of normal distributions from the whole sample: this is the upper level (Orme and Howell, 2009). The new estimate for the individual-level scores then allows a new estimate for the upper-level mean importance scores and covariance matrix. The number of iterations carried out is specified (20,000 in this model) and the importance scores are estimated by taking the average values over the iterations (after a ‘burn-in’ period of 10,000 iterations to negate the influence of starting values of importance scores and the covariance matrix).

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A number of covariates are used in the model in order to improve estimates of the upper- level normal distribution of importance values. If a covariate has a significant effect on the model the different covariate values significantly alter the prediction of the importance weights. Each of the 10,000 HB iterations produces an estimate of the effect of the covariate and this may be either positive (i.e. a change in the covariate from the reference value increases the importance weight) or negative (decreases the importance weight). The covariate effect is significant if over 95% of the iterations are either positive or negative (Orme and Howell, 2009).

5. Results

The sample characteristics are given in Table 7 for the total sample and the three sub- groups: adopters, considerers and rejecters. Mean responses are given alongside the standard error of the mean as a measure of the average variance within the group. Further detail in the responses can be found in the Appendix.

The demographic of the aggregate sample was similar to that of a typical adopter (Balcombe et al., 2013; Ellison, 2004; GfK NOP Social Research, 2006; Leenheer et al., 2011; Willis et al., 2011). In comparison to the UK 2011 Census data, the sample was older (54 compared to 48 years old9), educated to a higher level (60% had a Bachelor’s degree or higher, compared to 27% in England and Wales) and wealthier (median income £30,000 - £40,000 versus the UK average £26,500) (Office for National Statistics, 2013). Whilst there is little difference between the adopter and rejecter groups, considerers are far closer to the national average with a lower income (median of £20,000 - £30,000), age (51 years old) and level of education than the rest of the sample (although still twice that of the national average).

It is important to note that these three groups are not static or necessarily homogeneous in their preferences. Adopters are an aggregated group who have installed different technologies at different times and perhaps for different reasons. Figure 7 shows the distribution of the year of installation or rejection across the sample of adopters and rejecters indicating that over 75% of adopters had installed since the FITs were introduced in 2010. Figure 8 reflects the proportion of the whole sample that considered/installed each technology. The vast majority of adopters have installed a solar PV system. Some have installed solar thermal (25%, normally in addition to a solar PV system) but very few

9 This figure is derived from the 2011 Census statistics (ONS 2013) and considering only those aged 18 or over.

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Chapter 3 Paul Balcombe other technologies have been installed, which is consistent with the current number of installations of different microgeneration technologies in the UK (Balcombe et al., 2013).

The following sections detail the survey results for the motivation for and barriers to installing microgeneration in the UK.

Table 7. A summary of the characteristics of the sample, showing the breakdown for adopters, considerers and rejecters.

Total Adopters Considerers Rejecters Variable Mean Standard Mean Standard Mean Standard Mean Standard error error error error Which technology n = 291 n = 113 n = 87 n = 91 have you installed/considered?

Solar PV 0.85 0.021 0.85 0.034 0.84 0.040 0.86 0.037 Solar thermal 0.44 0.029 0.27 0.042 0.58 0.053 0.52 0.053 Micro-wind 0.18 0.023 0.062 0.023 0.30 0.049 0.22 0.044 GSHPa 0.17 0.022 0.027 0.015 0.26 0.048 0.24 0.045 ASHPb 0.13 0.020 0.12 0.030 0.20 0.043 0.088 0.030 Biomass 0.13 0.020 0.062 0.023 0.23 0.045 0.11 0.033 Micro-CHP 0.048 0.013 0 0 0.10 0.033 0.055 0.024 Micro-hydro 0.024 0.009 0.018 0.012 0.057 0.025 0 0 Income n = 282 n = 106 n = 86 n = 90 < £20,000 0.25 0.026 0.24 0.04 0.28 0.049 0.24 0.046 £20,000 - £30,000 0.17 0.022 0.16 0.036 0.22 0.045 0.13 0.036 £30,000 - £40,000 0.15 0.021 0.11 0.031 0.16 0.040 0.17 0.040 £40,000 - £50,000 0.12 0.020 0.11 0.031 0.14 0.038 0.12 0.035 £50,000 - £60,000 0.071 0.015 0.094 0.029 0.035 0.020 0.078 0.028 £60,000 - £80,000 0.10 0.018 0.10 0.030 0.058 0.025 0.14 0.037 £80,000 - £100,000 0.050 0.013 0.066 0.024 0.047 0.023 0.033 0.019 > £100,000 0.085 0.017 0.11 0.031 0.058 0.025 0.078 0.028 Gender n = 289 n = 111 n = 87 n = 91 1 = Male, 0 = Female 0.79 0.024 0.83 0.036 0.77 0.045 0.77 0.044 Age n = 264 n = 102 n = 78 n = 84 Years 53.9 0.771 55 1.04 51.2 1.56 54.9 1.46 Occupationc n = 281 n = 108 n = 85 n = 88 Employed 0.59 0.029 0.59 0.048 0.58 0.054 0.61 0.052 Retired 0.27 0.026 0.31 0.045 0.20 0.044 0.27 0.048 Student 0.032 0.011 0 0 0.071 0.028 0.034 0.019 Unemployed 0.11 0.018 0.093 0.028 0.15 0.039 0.080 0.029 Educationd n = 291 n = 113 n = 87 n = 91 Bachelor's degree (or 0.59 0.029 0.66 0.045 0.49 0.054 0.60 0.052 equiv) Master's degree (or 0.31 0.027 0.35 0.045 0.24 0.046 0.33 0.050 equiv) a b c Nine types of occupation were considered but only the types for which correlation was found are shown. d Eight education groups were considered but only the groups for which correlation was found are shown.

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0.5 Adopters 0.4 Rejecters 0.3

0.2

0.1 Sampleproportion

0 Before 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2000 Installation/ rejection year Figure 7. The year of installation for the sample of adopters and the year of rejection for the sample of rejecters.

1 0.9 0.8 Adopters 0.7 Considerers 0.6 Rejecters 0.5 0.4 0.3

Sampleproportion 0.2 0.1 0 Solar PV Thermal Wind GSHP ASHP Biomass CHP Hydro

Figure 8. The proportion of adopters, considerers and rejecters who have installed or considered each technology.

5.1 Motivations for installing microgeneration

As described in section 4.3, choice models were estimated using an HB technique to elicit importance values for motivations and barriers, the results of which are given in Table 8. The values shown are the measure of relative importance given to each motivation, whereby the sum of importance values for each group always equals 100. The sample was treated in aggregate (adopters, considerers and rejecters together) as all groups were presented with the same motivations and barriers to adoption, in order to elicit importance scores for each respondent, as shown in Table 8. The individual level scores, as well as individual root likelihood (RLH) estimates 10 (Sawtooth Software, 2009; Sawtooth Software, 2013a), were then averaged over the adopter, considerer and rejecter

10 The root likelihood is a measure of model fit, defined as the geometric mean of the probabilities of each respondent selecting each choice that they did, given the estimated model. The maximum theoretical RLH value (a perfect model fit) is 1, whilst a minimum value (with no model fit, called the null RLH) equates to the reciprocal of the number of items per choice task (Sawtooth Software 2013a). A rule of thumb for acceptance of the model is a RLH that is double the null RLH value: 0.5 for motivations [4 items per choice set: (1/4)*2=0.5] and 0.4 for barriers [5 items per choice set: (1/5)*2=0.5] (Sawtooth Software 2009).

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Chapter 3 Paul Balcombe groups to give average group scores. Figure 9 illustrates these importance scores of each motivation for each adopter, considerer and rejecter group. The error bars on each estimate represent the standard error of the mean importance scores (shown in Table 8). The model fit of the HB models (0.71–0.76 for motivation models and 0.55–0.57 for barrier models) was judged acceptable (Sawtooth Software, 2009).

The covariates used for the estimation of models on motivations were: adopters, considerers and rejecters (3 groups); income (8 groups; see Table 7); age (continuous); level of education (3 groups: no Bachelor’s degree, Bachelor’s degree, Master’s degree or equivalent); and technology adopted/considered/rejected (4 binary groups 11 (yes/no): solar PV, solar thermal, wind and ground source heat pumps). These covariates were found to significantly affect importance estimates and notable differences are described below.

As shown in Figure 9, four motivations are found to be consistently more important than the others, of which three relate to finance and independence from power companies: saving or earning money from the installation, increasing household independence and to protect against future high energy costs. The fourth top motivation, desire to help improve the environment, is consistently below these financial motivations, but its relative importance to them is variable across the three groups. For rejecters saving money from lower fuel bills is 2.3 times as important a motivation as improving the environment, but for adopters and considerers, it is only 1.4 times as important.

11 All technologies were tested as covariates during the analysis but only solar PV, solar thermal, wind and ground source heat pumps had a significant impact on the parameter estimations.

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Table 8. Estimates from the Hierarchical Bayes model of relative importance of each motivation and barrier for adopters, considerers and rejecters, with the standard error of the mean as a measure of variancea.

Adopters Considerers Rejecters

Mean Standard Mean Standard Mean Standard error error error Motivations Make the household more self-sufficient/ 23.6 1.04 27.7 0.93 26.3 0.82 less dependent on utility companies Save or earn money from lower fuel bills and 22.7 0.87 21.0 1.21 25.0 0.85 government incentives Protect against future higher energy costs 24.1 0.76 23.1 1.04 26.5 0.64 Help improve the environment 16.6 1.10 15.2 1.43 11.2 1.23 Increase the value of my home 3.7 0.64 2.3 0.55 3.0 0.62 Use an innovative/ high-tech system 2.7 0.42 2.7 0.63 1.2 0.38 Show my environmental commitment to 5.0 0.89 3.5 0.95 2.0 0.61 others Protect the household against power cuts 1.5 0.43 4.5 0.74 4.7 0.78 Root Likelihood 0.73 0.01 0.71 0.01 0.76 0.01 Barriers Costs too much to buy/ install 14.5 0.64 18.3 0.55 15.5 0.71 Hard to find trustworthy information 11.9 0.70 13.2 0.84 8.1 0.80 System performance or reliability 10.6 0.52 10.5 0.65 8.9 0.61 Can't earn enough/ save enough money 9.4 0.60 12.1 0.77 12.1 0.78 Lose money if I moved home 11.4 1.90 5.5 1.25 18.6 2.96 Home/ location not suitable 8.5 0.81 6.0 0.76 8.1 0.94 Energy not available when I need it 8.1 0.65 8.6 0.67 6.9 0.59 Hassle of installation 6.6 0.63 5.0 0.63 4.7 0.63 High maintenance costs 4.9 0.35 8.1 0.54 4.7 0.42 Environmental benefits too small 4.7 0.42 4.1 0.52 5.1 0.65 Disruption or hassle of operation 4.8 0.48 4.4 0.52 2.8 0.37 Take up too much space 1.8 0.25 1.6 0.29 2.6 0.41 Would not look good 1.3 0.30 1.6 0.43 0.9 0.25 Neighbour disapproval/ annoyance 1.5 0.37 1.1 0.29 1.0 0.27 Root Likelihood 0.55 0.01 0.57 0.01 0.57 0.01 a Individual importance scores were transformed, rescaled and averaged across each adopter, consider and rejecter group. .For each respondent, the raw scores were first zero-centred (a mean score of zero across the set of parameters) by subtracting the mean value from each parameter. Each parameter was transformed 푒푈푖 using the equation , where Ui is the zero-centred importance score and a is the number of items in 푒푈푖+푎−1 each set (4 for motivations, 5 for barriers) (Orme, 2005). Finally, the parameters were rescaled such that the summation of the parameters equals 100.

The rest of the motivations matter little relative to the top four factors, yet there are notable differences among the groups. Protection against power cuts is far more significant an issue for considers and rejecters than for adopters. Saving money from lower fuel bills is 15 times more important than such protection for adopters, but only five times more so for considerers and rejecters.

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Adopters are more motivated by the desire to show their environmental commitment to others, relative to both financial and pure environmental motivations. Hence improving the environment is only 3.3 times more important than showing that commitment to others for adopters, whilst for considerers and rejecters it is 4.3 and 5.6 times more important. Saving or earning money is 4.5 times more important than exhibiting environmental commitment for adopters whilst for rejecters it is 12.5 times more important (see Figure 9).

Make the household more self sufficient/ less dependent on utility companies

Save or earn money from lower fuel bills and government incentives

Protect against future higher energy costs

Help improve the environment

Increase the value of my home Adopters

Considerers Use an innovative/ high-tech system Rejecters

Show my environmental commitment to others

Protect the household against power cuts

0 10 20 30 Motivation importance score

Figure 9. Hierarchical Bayes estimation of the relative importance of motivations for installing microgeneration for adopters, considerers and rejecters.

Considerers are less motivated to earn money from the installation, relative to the other motivations, than rejecters and adopters. Considerers have a lower income than adopters and rejecters and the inclusion of income group as a covariate in the model shows that lower income groups (in particular household incomes of <£20,000 and £30,000-£40,000) are also 16 – 21 times less motivated to save or earn money from the installation; this is discussed further in section 6.1.

Another group significantly less motivated by earning money from the installation are adopters who installed prior to 2010, the year in which FITs were introduced. These results are shown in Figure 10 which illustrates the differences in motivation importance scores between adopters before 2010 (n=28) and from 2010 onwards (n=85). Saving or Page 78 of 210

Chapter 3 Paul Balcombe earning money was 1.7 times more important than improving the environment for later adopters, but 1.4 times less important for earlier adopters. Therefore, the introduction of FITs has created a new group of adopters who exhibit much greater financial motivations to install.

Adopters who installed prior to 2010 were also significantly more motivated by showing their environmental commitment to others. This motivation was twice as important compared to those who installed since 2010. The motivation to increase the value of their home was twice as important for the post-FIT adopters, although still relatively unimportant in the adoption decision (five times less important than the top most important motivations; see Figure 10).

Make the household more self sufficient/ less dependent on utility companies

Save or earn money from lower fuel bills and government incentives

Protect against future higher energy costs

Help improve the environment

Increase the value of my home Post 2010 Use an innovative/ high-tech system Pre 2010

Show my environmental commitment to others

Protect the household against power cuts

0 5 10 15 20 25 30 Motivation importance score Figure 10. Motivation importance scores for pre- and post-2010 adopters.

5.2 Barriers to installing microgeneration

There is a much greater variation of importance values across the different barriers than motivations, as illustrated in Figure 11 which shows the relative importance of each barrier to the sample sub-groups. The covariates used for the estimation of barriers were: adopters, considerers and rejecters (3 groups); income (8 groups; see Table 7); age (continuous); likelihood of moving home within five years (5 groups: very likely, fairly likely, no idea, fairly unlikely and very unlikely); and technology adopted/considered/rejected (6 binary variables (yes/no): solar PV, solar thermal, wind, ASHP, biomass and CHP).

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Financial barriers (high capital costs, not earning or saving enough money and the risk of losing money if moved home) were found to be the most important. For adopters and considerers, the most important barrier was the high capital cost, which was 50% more important than not earning enough money from the installation. Surprisingly, the largest barrier for rejecters was the prospect of losing money if they moved home, 60% more important than for adopters and three times more important than for considerers. The difficulty in finding trustworthy information is also a significant barrier for most and is approximately as important as not earning or saving enough money from the installation for considerers, 1.3 times more important for adopters and 1.5 times less important for rejecters. Aspects of particularly little importance for all groups were that the system would not look good and concerns about neighbour disapproval and were between 10 and 17 times less important than the capital cost barrier.

Both considerers and rejecters are significantly more put off by not saving/earning enough money than adopters: this barrier was 30% more important for considerers and rejecters than for adopters. This implies that the FITs and other financial incentives, whilst having increased uptake, have not removed these barriers from the installation decision.

Using income categories as covariates within the model shows that the two lowest income groups (<£20,000 and £20,000-£30,000) are 20–25% more put off by the capital cost barrier.

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Costs too much to buy/ install

Hard to find trustworthy information

System performance or reliability

Can't earn enough/ save enough money

Lose money if I moved home

Home/ location not suitable

Energy not available when I need it Adopters Hassle of installation Considerers

High maintenance costs Rejecters

Environmental benefits too small

Disruption or hassle of operation

Take up too much space

Would not look good

Neighbour disapproval/ annoyance

0 5 10 15 20 25 Barrier importance score Figure 11. Hierarchical Bayes estimation of the relative importance of barriers to installing microgeneration for adopters, considerers and rejecters.

Another group who were significantly put off by the risk of losing money if they moved home are post-2010 adopters (Figure 12). Relative to the most important barrier to both pre- and post-2010 adopters – high capital cost – losing money if they moved home was 4.3 times less important for pre-2010 adopters, whereas for post-2010 adopters the two barriers are of equal importance. The latter group were four times more put off by potentially losing money if they moved home than those who installed prior to 2010 (see Figure 12). More recent adopters were also far more put off by not earning or saving enough money from the installation. This is perhaps synonymous with their greater motivation to save or earn money, described in section 5.1.

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Adopters who installed before 2010 were far more concerned about system performance, energy availability and had more difficulty in finding trustworthy information. Relative to the capital cost barrier, system performance and the information barrier were approximately as important for the pre-2010 adopters, but 1.3 and 1.5 times less important for the post 2010 adopters, respectively. The problems in purchasing the system, described by adopters within the survey and during the telephone interviews, often concerned uncertainty about the potential system performance because of a lack of accessible or trustworthy information.

Costs too much to buy/ install

Hard to find trustworthy information

System performance or reliability

Can't earn enough/ save enough money

Lose money if I moved home

Home/ location not suitable

Energy not available when I need it

Hassle of installation

High maintenance costs Post 2010 Environmental benefits too small Pre 2010

Disruption or hassle of operation

Take up too much space

Would not look good

Neighbour disapproval/ annoyance

0 5 10 15 20 Barrier importance score

Figure 12. Barrier importance scores for pre- and post 2010 adopters. 6. Discussion

Having summarised and discussed some of the key findings on motivations and barriers, we now discuss the impact of past and current policies, as well as implications for future policy and the microgeneration industry.

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6.1 Motivations for installing microgeneration

The results of the survey for motivations show that adopters are significantly more motivated to help improve the environment than rejecters (see Figure 9). Previous research has found the environmental motive to be an initiator to investigate installing rather than being a decisive factor in the decision (see section 3.2). However, this study clearly identifies it as a differentiating factor between those who adopt and those who reject.

The FIT scheme has significantly increased the earning potential of electricity-generating technologies, encouraging a new, more financially-motivated, consumer group to install. As this becomes the main motivation for some to install, other financial investment products become the competition for microgeneration systems rather than other electricity sources. Such investment products include bank saving accounts, stocks, shares, bonds and property investment (DECC, 2011a). However, during the period 2010–2012, Bank of England interest rates were 0.5% (Bank of England, 2013), which in turn meant that savings accounts had low interest rates. Similarly, the property market (House Price Crash, 2013) and the stock markets were more volatile during the economic downturn. At the same time, the rate of return on a solar PV investment reached approximately 10%12 (DECC, 2011a). Thus, aside from perhaps an early mortgage repayment, solar PV represented a preferable financial investment for many. Therefore, regardless of any other motivations for installing microgeneration, solar panels may have been chosen mainly for their investment potential.

In 2013, however, the UK financial landscape started to change. Although interest rates remained low, and house prices began to increase (House Price Crash, 2013). This suggests other assets may start to compete financially more strongly with microgeneration installations.

Alongside the impacts of any improvement on rates of return on other investments, FIT rates were reduced in 2012 (roughly by a half) so that the FIT return fell to less than half of what it was previously for solar PV: ~4.5% (DECC, 2011a; DECC, 2012b). Consequently, this consumer segment (households who regard microgeneration as investment) may be lost unless the financial landscape changes again or the appeal of

12 This figure was estimated from DECC (2011d), based on the old tariff of 43.3 p/kWh. The new tariff of 21 p/kWh gives a 4.5% annual return on investment. Payments for electricity exported to the grid are not included in the estimate because the total contribution of export payments is small (~3% of income from solar PV). Although this contribution has increased with the increase of export payments from 3 to 4.5 p/ kWh, their contribution is still small.

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Chapter 3 Paul Balcombe microgeneration increases. For example, rejecters are most motivated to protect against future high energy costs and to make the household more self-sufficient in terms of energy provision (see Figure 9) so that uptake by this group may increase if these aspects improved. For instance, self-sufficiency from solar PV can be maximised using battery storage. However, this represents an additional upfront cost, which is already an important barrier to installing microgeneration. Additionally, the FIT incentives offer a sell-back price for generated electricity of 5 p/kWh, further reducing the financial viability of battery storage. Therefore, without any incentives, the uptake of batteries will remain low, in turn reducing the potential of microgeneration to benefit from the self-sufficiency motivation and by implication, from protection of future increases in energy prices. Recognising this as an issue, the German government implemented a scheme in May 2013 offering capital grants for 30% of the installation cost and low-interest loans for the remainder of the cost to increase the uptake of battery storage (Clean Technica, 2013). A similar scheme could be introduced by the UK government, following the successful implementation of the FITs, which were also imported from Germany.

A further action that would help with the uptake of is provision of clear, impartial information on batteries and their potential to improve self-sufficiency and flexibility of electricity use as well as their financial viability in conjunction with microgeneration systems. Currently, there is a lack of such information, particularly as the incentives landscape and the related financial benefits are very complex, including the FIT scheme and the Green Deal. This is compounded by the complexity of and numerous deals offered by grid electricity providers which are very confusing to the consumer (DECC, 2012a). Providing simple and clear guidance to consumers on the benefits of battery storage should therefore be a priority for suppliers and installers, in a similar manner in which FITs were promoted (e.g. Energy Saving Trust, 2012a; NHBC Foundation, 2011).

Compared to rejecters, considerers were significantly less motivated by earning money from the installation, although this is still important in the decision (see Figure 9). Perhaps as this group has a lower income, they expect lower financial gains relative to the higher- income groups. Considerers are thus likely to be less motivated by the FIT incentives than adopters and rejecters. Therefore, instead of FITs which offer higher gains but require a high initial investment, the Green Deal may be more appealing as it lowers the initial investment whilst resulting in lower financial gains (due to the payback of the loan). The potential effectiveness of the Green Deal is discussed further in the following section.

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6.2 Barriers to installing microgeneration

The results of the survey indicate that, in spite of the numerous financial incentives, the largest barriers are still high capital costs, not earning enough money and the risk of losing money if they moved home (see Figure 11). The latter, the largest barrier for rejecters, has appeared on some specialist websites (Brignall, 2012; Debenham, 2010) with a particular concern being ‘rent a roof’ schemes (Lambert, 2012; Rowley, 2011), where solar panels are owned by a third party. This is viewed as a risk to potential homebuyers as well as mortgage lenders. However, this barrier has received very little attention in the academic literature with findings on the effect of solar PV on resale value being conflicting and inconclusive: two studies on house sales in the USA find that house prices increase approximately proportionally with the capital investment of solar PV (Dastrup et al., 2012; Hoen et al., 2011), whereas one study in Oxford, UK, finds a negligible difference in house price between those with solar panels and those households without (Morris-Marsham, 2010).

The UK government has attempted to address the capital cost and house resale value barriers with the introduction of the Green Deal. The risk of losing money if moving home is reduced by the Green Deal loan as there is no risk associated with an initial outlay. However, concern has been raised that the fixed loan repayments, which stays with the home rather than the original occupants, will put off prospective house buyers resulting in a lower house price (Bachelor, 2013; Newman, 2013). If the house-buyers were lower energy users than the previous occupants, the monthly repayment (which is fixed at the start of the term) could be greater than the savings from the improvement measures, saddling the house-buyers with an additional bill (Booth and Choudhary, 2013). A survey conducted by Which? of 2,000 UK residences found that half the sample of potential house buyers would want the loan to be paid off prior to purchasing (Bachelor, 2013). One fifth of the sample said they would be put off purchasing a property if it had a Green Deal attached to it. Thus, the Green Deal may indeed exacerbate the risk of losing out financially if an adopter moved home prior to the end of the payback.

The high interest loan rate of 7–9% has also been criticised for making the deal unappealing to homeowners (Carrington, 2013; Hickman, 2013; King et al., 2013). A number of improvements to the Green Deal have been suggested by the UK Council, including to reduce the loan interest rates and to reduce council tax for homes that meet certain energy efficiency requirements (King et al., 2013). These would both further incentivise the Green Deal agreement, as well as providing on-going financial incentives for house-buyers considering purchasing a home with an attached Green Deal. Page 85 of 210

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Other barriers to adoption, particularly for pre-FIT adopters, were system performance and energy availability concerns, as well as the difficulty in finding trustworthy information. These barriers were less of a concern for more recent adopters, as well as rejecters, which may be due to the improvement measures put in place since 2010. The Microgeneration Certification Scheme provides standards for installation and there are significant quantities of technological and performance-related information from the Department of Energy and Climate Change (DECC), the Energy Saving Trust (EST), MCS and other interest groups.

In 2011, it was reported in the Microgeneration Strategy document that (DECC, 2011c, page 38, paragraph 4.2)

“Currently, most householders … struggle to identify accurate, unbiased information. In the absence of a widely recognised source of impartial advice, anecdotal evidence of previous grant programmes suggests that investment decisions could be taken based on inadequate information or even influenced by mis-selling.”

Despite the efforts to address this (by DECC, MCS, EST, etc.) finding trustworthy information was the second-most important barrier faced by considerers. The barrier was 10% more important than earning/saving enough money, 25% more important than system performance and over twice as great as the barrier posed by fear of losing money if they moved home. Clearly there remains a considerable gap between the government’s intention to provide reliable information to those considering microgeneration adoption, and the experience of these considerers. Addressing this may be one of most effective and inexpensive means of lowering barriers to greater microgeneration uptake.

6.3 FITs and the experience of adoption

In order to investigate adopters’ experiences of their microgeneration system, they were asked “If you knew what you know now at the time of deciding to install, would you do it again?” (see Supplementary material). The findings suggest that approximately 90% would (at least) probably do so. However, many solar PV adopters also experienced problems with installing. As described in section 2.1, the solar PV FIT rate change in 2012 brought about a rush to install before the payments on new installations reduced. Several respondents reported that this rush was the cause of poor quality installation.

A high proportion of considerers and rejecters have also been affected by the cuts to the solar PV FIT rates in 2012. Many were concerned that, if they were to adopt, their FIT payment may be changed. This is a false concern: FIT rate reductions only affect systems installed after the reduction date and FIT rates remain constant once the installation is

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Chapter 3 Paul Balcombe registered. This misinterpretation may be due to the complicated nature of the tariffs and the uncertainty caused by the speed and scale of the changes to the FIT rates. The regulation regarding FIT rate degression began in 2010 as a simple annual percentage reduction, but has since been amended to include various caveats: ‘corridors’, ‘triggers’ and ‘emergency adjustments’, which are controlled by DECC (Feed-in Tariffs ltd, 2013). In order to increase consumer confidence in the incentives, the regulation framework must be stable, consistent (Allen et al., 2008) and transparent. The relationship between changing the FIT rates and installation costs should be made available and updated regularly so that consumers can make informed decisions related to the return on their investment.

Adopters were also asked “Would you do anything differently now in terms of technology, installation or using the system?” Notably, six out of the seven wind turbine adopters said they would do something different, with four of these saying they would not install a wind turbine at all. The main problem experienced by wind turbine adopters was the performance, or lack of wind to generate from, suggesting they have been installed in an unsuitable location. Similarly, analysis of the effect of the covariates used within the HB model shows that those who installed or considered wind turbines, as well as air source heat pumps and biomass, were more concerned about system performance than those who installed or considered other technologies. Those who installed or considered a wind turbine viewed a lack of system performance as equally important as the capital cost barrier, compared to the sample average figure of 63% of the importance of capital cost.

The MCS issues sets of standards for the design and installation of microgeneration systems, in order to ensure installations operate as designed (DECC, 2008). As the sample of wind installations in this study is small (n=7), further investigation into the experiences of wind turbine adopters is required in order to assess the effectiveness of the MCS accreditation in this instance. It has been widely documented that the number of suitable locations for small scale wind installations is very limited in the UK (Energy Saving Trust, 2009). Poorly performing installations cause a bad public perception as well as not contributing to the household, let alone UK climate change and energy security targets.

7. Conclusions

This paper has used best-worst scaling to explore the relative importance of the motivations and barriers associated with adoption of microgeneration. Of the motivations investigated, three were consistently the most important: saving or earning money from Page 87 of 210

Chapter 3 Paul Balcombe the installation, increasing household independence and protecting against future energy costs. Half as important in the decision was the desire to help improve the environment. However, this motivation is far stronger for adopters than rejecters, suggesting it to be a key differentiating factor between those who decide to install and those who do not.

Financial barriers dominate the adoption decision: high capital costs, not earning or saving enough money and the risk of losing money if they moved home were very important to all groups. Considerers also found the difficulty in obtaining reliable information very important, 10% more so than not earning or saving enough money from the installation. The microgeneration strategy, the Microgeneration Certification Scheme and the Energy Savings Trust have all highlighted this barrier and attempted to provide reliable information in response, but despite this the barrier remains a significant one and must be addressed further. Greater provision of impartial and more transparent information and advice may represent the most cost-effective action to help increase microgeneration uptake.

There are differences in the experience of adoption across technologies, most notably with wind turbine owners, who often experienced operational problems such as a lack of wind. The Microgeneration Certification Scheme is aimed at ensuring a certain level of product and installation quality to avoid miss-selling. Further work is needed to examine the success of the scheme in ensuring acceptable wind turbine performances.

The introduction of the feed-in tariffs (FITs) has increased uptake by enabling a more financially-motivated group to install. However, the halving of solar PV FITs in 2012 reduced uptake significantly and is likely to have impacted most upon the financially- motivated consumer group. The sudden tariff cut also caused a rush to install prior to its implementation, to which many adopters attributed poor quality installations. Additionally, the complicated nature of the FIT degression mechanism has decreased consumer confidence and caused a misinterpretation of the incentives, with many fearing that if they were to install, their FIT rate might change. In order to prevent such negative consequences of tariff degression in the future, the mechanism to regulate FIT degression must be simpler, more transparent and regularly updated, allowing a more informed consumer decision.

If the uptake figures since the FIT rate reduction are to be improved, other motivations, such as the desire for energy self-sufficiency, should be focused on and publicised more clearly. Rejecters in particular are highly motivated to be self-sufficient or independent from utility companies and to protect against future energy cost increases. The recent Page 88 of 210

Chapter 3 Paul Balcombe concern over the risk of an imminent ‘energy gap’ within the next two years may further add to households’ motivation to be self-sufficient and to guard against power cuts. In order to increase uptake, the government and microgeneration industry should focus on promoting and detailing the benefits of microgeneration in relation to these aspects, or improving them by increasing the availability of energy when required. For example, microgeneration suppliers could promote the use of battery storage with solar PV and highlight the potential benefits with respect to self-sufficiency. An incentive scheme similar to the recent German capital grant scheme for battery storage would increase uptake, albeit at an additional government (and taxpayers’) cost. However, further research is required to determine the economic and environmental impacts of battery storage.

The Green Deal is intended to deal with the installation-cost barrier and the risk of losing money if moving home by providing a capital cost loan. This may appeal to considerers who have a lower income and are less motivated by earning money from incentives as well as rejecters who are most put off by the risk of losing money if they moved home – one of the largest barriers identified in this research. However, the high loan interest rates and the risk of encountering problems if the home was sold whilst the loan is still being repaid significantly limit the consumer appeal for the scheme, as demonstrated by the very low uptake rates of the scheme. The Green Deal would be more appealing if loan interest rates were more competitive and the potential negative effect of Green Deal finance on house sale prices should be investigated further. If a negative effect is identified, the barrier could be reduced by lowering council tax rates for Green Deal homes or energy efficient homes in general as is the case with tax.

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Appendix

Table A1. Summary of answers by adopters.

Variable Participants Mean Standard Do you own the system? error 1 = Yes, 0 = No 113 0.956 0.019

Installation year Before 2000 113 0.018 0.012 2004 113 0.009 0.009 2005 113 0.018 0.012 2006 113 0.044 0.019 2007 113 0.062 0.023 2008 113 0.035 0.017 2009 113 0.062 0.023 2010 113 0.13 0.032 2011 113 0.37 0.046 2012 113 0.25 0.041 Those installed since FITs have been available (2010) 113 0.75 0.041 Do/ Have you received incentives for the system? No 113 0.28 0.043 Feed-in Tariffs 113 0.65 0.045 ROCs (Renewable Obligation Certificates) 113 0.018 0.012 Grant (e.g. from the Low Carbon Buildings 113 0.088 0.027 Programme) Other (please describe briefly) 113 0.11 0.029 If you knew what you do now at the time of deciding to install, would you do it again? Definitely would 113 0.71 0.043 Probably would 113 0.19 0.037 Not sure 113 0.027 0.015 Probably not 113 0.035 0.017 Definitely not 113 0.035 0.017 Would you do anything differently? Nothing 113 0.42 0.047 Don't know 113 0.062 0.023 Yes I would change something... 113 0.52 0.047 Did you encounter any problems during the decision/installation/operation of the system? No problems 113 0.58 0.047 Problem or difficulty when buying it 113 0.11 0.029 Problem or difficulty with installing it 113 0.14 0.033 Problem or difficulty whilst using it 113 0.19 0.037 Any other problem or difficulty 113 0.20 0.038

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Table A2. Summary of answers by considerers and rejecters.

Considerers Rejecters Variable Participants Mean Standard Participants Mean Standard error error What year did you decide not to install? Before 2000 N/A N/A N/A 91 0.033 0.019 2004 N/A N/A N/A 91 0.011 0.011 2007 N/A N/A N/A 91 0.033 0.019 2008 N/A N/A N/A 91 0.022 0.015 2009 N/A N/A N/A 91 0.088 0.03 2010 N/A N/A N/A 91 0.077 0.028 2011 N/A N/A N/A 91 0.286 0.048 2012 N/A N/A N/A 91 0.451 0.052 What stage of consideration have you got to? Initial investigation 87 0.63 0.052 91 0.65 0.05 I have talked others who have installed 87 0.32 0.05 91 0.37 0.051 I have been to see a system in action 87 0.17 0.041 91 0.13 0.036 I received professional advice 87 0.18 0.042 91 0.24 0.045 I received a quote from supplier/installer 87 0.33 0.051 91 0.44 0.052 Other information 87 0.10 0.033 91 0.15 0.038 How likely are you to install? Almost definitely will 87 0.14 0.037 N/A N/A N/A Pretty likely 87 0.33 0.051 N/A N/A N/A Perhaps 87 0.45 0.054 N/A N/A N/A Pretty unlikely 87 0.069 0.027 N/A N/A N/A

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Considerers Rejecters Variable Participants Mean Standard Participants Mean Standard error error Almost definitely won't 87 0.011 0.011 N/A N/A N/A Are you familiar with the recent cuts to the solar PV FITs? Yes 77 0.67 0.054 69 0.73 0.054 Vaguely 77 0.23 0.049 69 0.20 0.049 No 77 0.091 0.033 69 0.072 0.031 Have these cuts put you off installing a system?

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Acknowledgements

The authors would like to gratefully acknowledge the Sustainable Consumption Institute for funding this research and Sawtooth Software for the grant given to use the software for designing and analysing the survey.

References

Allen, S. R., Hammond, G. P. and McManus, M. C. 2008. Prospects for and barriers to domestic micro-generation: A United Kingdom perspective. Applied Energy, 85, 528-544. Bachelor, L. 2013. Green deal debt may have to be repaid before property sold [Online]. London. Available: www.guardian.co.uk/money/2013/may/19/green-deal-debt- repaid [Accessed 31 May 2013]. Balcombe, P., Rigby, D. and Azapagic, A. 2013. Motivations and barriers associated with adopting microgeneration energy technologies in the UK. Renewable and Sustainable Energy Reviews, 22, 655-666. Bank of England 2013. Changes in bank rate, minimum lending rate, minimum band 1 dealing rate, repo rate and official bank rate. London: Bank of England. Available: www.bankofengland.co.uk/statistics/Documents/rates/baserate.pdf. Baskaran, R., Managi, S. and Bendig, M. 2013. A public perspective on the adoption of microgeneration technologies in : A multivariate probit approach. Energy Policy, 58, 177-188. Bergman, N. and Eyre, N. 2011. What role for microgeneration in a shift to a low carbon domestic energy sector in the UK? Energy Efficiency, 4, 335-353. Bergman, N., Hawkes, A., Brett, D. J. L., Baker, P., Barton, J., Blanchard, R., Brandon, N. P., Infield, D., Jardine, C., Kelly, N., Leach, M., Matian, M., Peacock, A. D., Staffell, I., Sudtharalingam, S. and Woodman, B. 2009. UK microgeneration. Part I: Policy and behavioural aspects. Proceedings of Institution of Civil Engineers: Energy, 162, 23-36. Bergman, N. and Jardine, C. 2009. Power from the People. ECI RESEARCH REPORT NO 34 (ed.) Domestic Microgeneration and the Low Carbon Buildings Programme. Available: www.eci.ox.ac.uk/publications/downloads/bergmanjardine09powerpeople.pdf. Bollinger, B. and Gillingham, K. 2012. Peer Effects in the Diffusion of Solar Photovoltaic Panels. Marketing Science, 31, 900-912.

Page 93 of 210 Chapter 3 Paul Balcombe

Booth, A. T. and Choudhary, R. 2013. Decision making under uncertainty in the retrofit analysis of the UK housing stock: Implications for the Green Deal. Energy and Buildings, 64, 292-308. Brignall, M. 2012. How solar panels can dim mortgage prospects [Online]. London: The Guardian,. Available: http://www.guardian.co.uk/money/2012/mar/23/solar-panels- dim-mortgage-prospects [Accessed 14 January 2013]. Brook Lyndhurst Ltd, MORI and Upstream 2003. Attitudes to renewable energy in London: public and stakeholder opinion and the scope for progress. LONDON RENEWABLES & DTI (eds.). London. legacy.london.gov.uk/mayor/environment/energy/docs/renewable_attitudes.pdf. Caird, S. and Roy, R. 2010. Adoption and use of household microgeneration heat technologies. Low Carbon Economy, 1, pp. 61–70. Carrington, D. 2013. Cavity wall insulations crash by 97% following green deal introduction [Online]. London. Available: www.guardian.co.uk/environment/2013/may/29/cavity-wall-insulations-crash- green-deal [Accessed 31 May 2013]. Claudy, M. C., Michelsen, C., O'Driscoll, A. and Mullen, M. R. 2010. Consumer awareness in the adoption of microgeneration technologies: An empirical investigation in the Republic of Ireland. Renewable and Sustainable Energy Reviews, 14, 2154-2160. Claudy, M. C., Michelsen, C. and O’Driscoll, A. 2011. The diffusion of microgeneration technologies – assessing the influence of perceived product characteristics on home owners' willingness to pay. Energy Policy, 39, 1459-1469. Claudy, M. C., Peterson, M. and O'Driscoll, A. 2013. Understanding the Attitude-Behavior Gap for Renewable Energy Systems Using Behavioral Reasoning Theory. Journal of Macromarketing, 1 - 15. jmk.sagepub.com/content/early/2013/04/11/0276146713481605.full.pdf+html. Clean Technica. 2013. Germany’s Energy Storage Incentive To Start May 1 [Online]. Available: cleantechnica.com/2013/04/17/germanys-energy-storage-incentive-to- start-may-1/ [Accessed 1 August 2013]. Consumer Focus 2011. Keeping FiT Consumers' attitudes and experiences of microgeneration. ENERGY SAVING TRUST & DECC (eds.). London. Available: www.consumerfocus.org.uk/files/2012/04/Keeping-FiT.pdf. Curry, T. E., Reiner, D. M., Figueiredo, M. A. d. and Herzog, H. J. 2005. A Survey of Public Attitudes towards Energy & Environment in Great Britain. Available: www.stanford.edu/~kcarmel/CC_BehavChange_Course/readings/Additional%20R esources/Sample%20Intervention%20Docs/Surveys/mit.pdf. Massachusetts Institute of Technology, Laboratory for Energy and the Environment.

Page 94 of 210 Chapter 3 Paul Balcombe

Dastrup, S. R., Graff Zivin, J., Costa, D. L. and Kahn, M. E. 2012. Understanding the Solar Home price premium: Electricity generation and “Green” social status. European Economic Review, 56, 961-973. Debenham, C. 2010. Do solar panels affect house sales? [Online]. Devon: YouGen Ltd. Available: www.yougen.co.uk/blog- entry/1546/Do+solar+panels+affect+house+sales'3F/ [Accessed 19 December 2012 2012]. Debenham, C. 2013. Legal battle over solar feed-in tariff ends in defeat for DECC [Online]. Available: www.yougen.co.uk/blog-entry/1883/Legal+battle+over+solar+feed- in+tariff+ends+in+defeat+for+DECC/ [Accessed 14 September 2013]. DECC 2008. Requirements for contractors undertaking the supply, design, installation, set to work commissioning and handover of micro and systems. Microgeneration Installation Standard. London: Crown Copyright. DECC 2009. The UK Renewable Energy Strategy. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown Copyright. DECC 2010. The Green Deal- A Summary of the Government’s Proposals. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown Copyright. Available: www.gov.uk/government/uploads/system/uploads/attachment_data/file/47978/101 0-green-deal-summary-proposals.pdf. DECC 2011a. Feed-in tariffs scheme: consultation on Comprehensive Review Phase 1 – tariffs for solar PV. DECC (ed.). London: Crown Copyright. Available: www.gov.uk/government/uploads/system/uploads/attachment_data/file/42834/341 6-fits-IA-solar-pv-draft.pdf. DECC 2011b. Feed-in Tariffs Scheme: Summary of Responses to the Fast-Track Consultation and Government Response. DEPARTMENT OF ENERGY & CLIMATE CHANGE (ed.). London: Crown copyright. DECC 2011c. Microgeneration Strategy. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown Copyright. DECC 2011d. Renewable Heat Incentive. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown copyright. Available: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/480 41/1387-renewable-heat-incentive.pdf. DECC 2012a. Reform: policy overview DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown copyright. Available: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/656 34/7090-electricity-market-reform-policy-overview-.pdf.

Page 95 of 210 Chapter 3 Paul Balcombe

DECC 2012b. Feed-in Tariffs Scheme. Government response to Consultation on Comprehensive Review Phase 2A: Solar PV cost control. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown Copyright. DECC 2013a. Monthly central Feed-in Tariff register. JULY_2013_MONTHLY_CENTRAL_FEED- IN_TARIFF_REGISTER_STATISTICS.XLS. Microsoft Excel. London. Available: www.gov.uk/government/statistical-data-sets/monthly-central-feed-in-tariff-register- statistics. DECC 2013b. Statistical release: experimental statistics. Domestic Green Deal and Energy Company Obligation in Great Britain, Monthly report. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown copyright. Available: www.gov.uk/government/uploads/system/uploads/attachment_data/file/230138/Sta tistical_Release_- _Green_Deal_and_Energy_Company_Obligation_in_Great_Britain_- _20_August_2013.pdf. Dowson, M., Poole, A., Harrison, D. and Susman, G. 2012. Domestic UK retrofit challenge: Barriers, incentives and current performance leading into the Green Deal. Energy Policy, 50, 294-305. Element Energy 2005. Potential for Microgeneration. Study and Analysis. ENERGY SAVING TRUST (ed.). London. www.berr.gov.uk/files/file27558.pdf Element Energy 2008. The Growth Potential for Microgeneration in England, Wales and Scotland. BERR (ed.). London. Ellison, G. 2004. Renewable Energy Survey 2004 Draft summary report of findings. LONDON ASSEMBLY (ed.). London. Available: legacy.london.gov.uk/assembly/reports/environment/power_survey_orc.pdf: ORC International. Energy Saving Trust 2009. Location, location, location. Domestic small-scale wind field trial report. London. Energy Saving Trust. 2012a. Generating your own energy- An overview of what's available [Online]. Available: www.energysavingtrust.org.uk/Generate-your-own- energy/Overview-of-what-s-available [Accessed July 2012]. Energy Saving Trust 2012b. Renewable Heat Premium Payment scheme: Regional statistics as at Phase 1 Closure. London. Available: www.energysavingtrust.org.uk/Publications2/Generating-energy/Regional- statistics-for-the-Renewable-Heat-Premium-Payment-scheme: Energy Saving Trust.

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Energy Saving Trust. 2013a. Choosing a Renewable Technology [Online]. London. Available: www.energysavingtrust.org.uk/Generating-energy/Choosing-a- renewable-technology [Accessed 8 July 2013]. Energy Saving Trust. 2013b. Renewable Heat Premium Payment Phase 2 [Online]. London. Available: www.energysavingtrust.org.uk/Generating-energy/Getting- money-back/Renewable-Heat-Premium-Payment-Phase-2 [Accessed 29 June 2013]. Energy Saving Trust 2013c. Renewable Heat Premium Payment scheme Phase 2 Statistics as at 18 February 2013. London. Available: www.energysavingtrust.org.uk/Publications2/Generating-energy/RHPP-Phase- Two-web-stats: Energy Saving Trust. Feed-in Tariffs ltd. 2013. Feed-in Tariffs [Online]. Wolfe Ware. Available: www.fitariffs.co.uk/eligible/levels/contingent/ [Accessed 5 September 2013]. Finn, A. and Louviere, J. J. 1992. Determining the Appropriate Response to Evidence of Public Concern: The Case of Food Safety. Journal of Public Policy & Marketing, 11, 12-25. Fischer, C. and Sauter, R. 2003. Governance for Industrial Transformation. Human Dimensions of Global Environmental Change. Berlin. GfK NOP Social Research 2006. Renewable Energy Awareness and Attitudes Research. DTI (ed.). London. Available: webarchive.nationalarchives.gov.uk/+/http://www.dti.gov.uk/files/file29360.pdf. Hack, S. 2006. International Experiences with the Promotion of Solar Water Heaters (SWH) at Household-level. DEUTSCHE GESELLSCHAFT FÜR TECHNISCHE ZUSAMMENARBEIT (GTZ) GMBH (ed.). Mexico City. Available: www.conuee.gob.mx/work/sites/CONAE/resources/LocalContent/6942/1/IEPSWH. pdf. Hickman, L. 2013. Older and disabled people 'put off' energy efficiency schemes [Online]. London. Available: www.guardian.co.uk/environment/2013/may/02/older-disabled- people-put-off-energy-efficiency [Accessed 31 May 2013]. HM Government 2004. Energy Act. London: Crown copyright www.legislation.gov.uk/ukpga/2004/20/contents. HM Government 2007. Energy White Paper: Meeting the Energy Challenge. DTI (ed.). London: Crown copyright. webarchive.nationalarchives.gov.uk/20121205174605/http:/www.decc.gov.uk/asse ts/decc/publications/white_paper_07/file39387.pdf. Hoen, B., Wiser, R., Cappers, P. and Thayer, M. 2011. An Analysis of the Effects of Residential Photovoltaic Energy Systems on Home Sales Prices in California.

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ERNEST ORLANDO LAWRENCE BERKELEY NATIONAL LABORATORY (ed.). Orlando. Available: eetd.lbl.gov/ea/emp/reports/lbnl-4476e.pdf. Environmental Energy Technologies Division. House Price Crash. 2013. Nationwide average house prices adjusted for inflation [Online]. Available: www.housepricecrash.co.uk/indices-nationwide-national-inflation.php [Accessed 8 July 2013]. Jager, W. 2006. Stimulating the diffusion of photovoltaic systems: A behavioural perspective. Energy Policy, 34, 1935-1943. Keirstead, J. 2007. Behavioural responses to photovoltaic systems in the UK domestic sector. Energy Policy, 35, 4128-4141. King, P., Cameron, J., Clare, M., Frankiewicz, J., Hindle, P., Marchant, I., Murtagh, G., Sinfield, J. and Smith, R. 2013. Open Letter Re: Ensuring success for the Green Deal and the retrofit agenda 26 June 2013. DECC (ed.). London. Available: www.google.co.uk/url?sa=t&rct=j&q=&esrc=s&frm=1&source=web&cd=1&ved=0C CwQFjAA&url=http%3A%2F%2Fwww.ukgbc.org%2Fsystem%2Ffiles%2Fprivate% 2Fdocuments%2F130626%2520Green%2520Deal%2520open%2520letter%2520- %2520Ed%2520Davey.pdf&ei=ag14UoaKI4bR0QW- p4HQCw&usg=AFQjCNF_fnV91HmTZRj26poqnLfz8FimOw&bvm=bv.55819444,d. d2k UK Green Building Council. Lambert, S. 2012. House hunters warned against buying homes with free solar panels fitted [Online]. Available: http://www.thisismoney.co.uk/money/mortgageshome/article-2130985/RICS- warns-house-hunters-buying-homes-free-solar-panels-fitted.html [Accessed 20 May 2013]. Leenheer, J., de Nooij, M. and Sheikh, O. 2011. Own power: Motives of having electricity without the energy company. Energy Policy, 39, 5621-5629. Lonsdale, S. 2013. Eco living: Beware the 'solar-panel cowboys' [Online]. Available: www.telegraph.co.uk/property/9724311/Eco-living-Beware-the-solar-panel- cowboys.html [Accessed 10 June 2013]. Louviere, J., Lings, I., Islam, T., Gudergan, S. and Flynn, T. 2013. An introduction to the application of (case 1) best–worst scaling in marketing research. International Journal of Research in Marketing, 30, 292-303. Mahapatra, K. and Gustavsson, L. 2009. Influencing Swedish homeowners to adopt system. Applied Energy, 86, 144-154. Mahapatra, K., Gustavsson, L., Haavik, T., Aabrekk, S., Svendsen, S., Vanhoutteghem, L., Paiho, S. and Ala-Juusela, M. 2013. Business models for full service energy

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renovation of single-family houses in Nordic countries. Applied Energy, 112, 1558- 1565. Marti, J. 2012. A best–worst scaling survey of adolescents' level of concern for health and non-health consequences of smoking. Social Science & Medicine, 75, 87-97. McLeod, R. S., Hopfe, C. J. and Rezgui, Y. 2012. An investigation into recent proposals for a revised definition of zero carbon homes in the UK. Energy Policy, 46, 25-35. Morris-Marsham, C. 2010. Do solar PV and solar thermal installations affect the price and saleability of domestic properties in Oxford. Degree of Master of Science Built Environment:Environmental Design and Engineering, UCL. Müller, S. and Rode, J. 2013. The adoption of photovoltaic systems in Wiesbaden, Germany. Economics of Innovation and New Technology, 22, 519-535. Newman, C. 2013. Is the Green Deal right for me? [Online]. Cornwall. Available: www.yougen.co.uk/blog-entry/2117/Is+the+Green+Deal+right+for+me'3F/ [Accessed 30 May 2013]. NHBC Foundation 2011. Introduction to Feed-In Tariffs. BRE (ed.). Available: www.nhbcfoundation.org/Researchpublications/IntroductiontoFeedinTariffsNF23/ta bid/437/Default.aspx: IHS BRE Press. Nichols, W. 2011. Green heat industry hits out at renewable heat incentive delay [Online]. Available: www.guardian.co.uk/environment/2011/jan/21/renewable-heat- incentive-delay?INTCMP=SRCH [Accessed 10 October 2012]. Nichols, W. 2013. Green heating scheme delayed again until spring 2014 [Online]. London. Available: www.guardian.co.uk/environment/2013/mar/27/green-heating- scheme-delayed-again-rhi [Accessed 27 May 2013]. Office for National Statistics. 2013. Neighbourhood Statistics- Census 2011 data [Online]. London: Crown copyright. Available: neighbourhood.statistics.gov.uk/dissemination/instanceSelection.do?JSAllowed=tr ue&Function=&%24ph=60_61&CurrentPageId=61&step=2&datasetFamilyId=2514 &instanceSelection=132828&Next.x=14&Next.y=18 [Accessed 20 April 2013]. Ofgem 2013. Electrical Capacity Assessment Report 2013. OFGEM (ed.) Report to the Secretary of State. London. www.ofgem.gov.uk/ofgem- publications/75232/electricity-capacity-assessment-report-2013.pdf: Monitoring and Analysis. OFT 2011. Off-Grid Energy: an OFT Market Study. OFFICE OF FAIR TRADING (ed.). London. www.oft.gov.uk/shared_oft/market-studies/off-grid/OFT1380.pdf. Crown Copyright.

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Orme, B. 2005. Accuracy of HB Estimation in MaxDiff Experiments. SAWTOOTH SOFTWARE, I. (ed.) Research Paper Series. Sequim, WA 98382. Available: www.sawtoothsoftware.com/download/techpap/maxdacc.pdf. Orme, B. and Howell, J. 2009. Application of Covariates Within Sawtooth Software’s CBC/HB Program: Theory and Practical Example. Research paper series, . Sequim, WA 98382: Sawtooth Software, Inc. Palm, J. and Tengvard, M. 2011. Motives for and barriers to household adoption of small- scale production of electricity: examples from Sweden. Sustainability: Science, Practice, & Policy, Vol 7, pp 6-15. Praetorius, B., Martiskainen, M., Sauter, R. and Watson, J. 2010. Technological innovation systems for microgeneration in the UK and Germany - a functional analysis. Technology Analysis & Strategic Management, 22, 745 - 764. Rowley, E. 2011. Renting out roof to solar power firms could make your home harder to sell, surveyors warn [Online]. London: Telegraph Media Group Limited 2013. Available: http://www.telegraph.co.uk/finance/newsbysector/energy/8856365/Renting-out- roof-to-solar-power-firms-could-make-your-home-harder-to-sell-surveyors- warn.html [Accessed 14 January 2013 2013]. Sawtooth Software 2003. CVA/HB Technical Paper. Technical paper series. Sequim, WA 98382. Sawtooth Software 2009. The CBC/HB System for Hierarchical Bayes Estimation Version 5.0 Technical Paper. Technical paper series. Sequim. Sawtooth Software. 2013a. Max Diff Utilities Calculation with CBC HB V5.2.8 [Online]. Available: www.sawtoothsoftware.com/forum/3084/max-diff-utilities-calculation- with-cbc-hb-v5-2-8 [Accessed 10 June 2013]. Sawtooth Software 2013b. The MaxDiff System Technical Paper Technical paper series, . Utah: Sawtooth Software, Inc. Sawtooth Software Inc. 2013. All Products, [Online]. Orem, Utah. Available: http://www.sawtoothsoftware.com/products/all-products [Accessed 11 Nov 2013]. Scarpa, R. and Willis, K. 2010. Willingness-to-pay for renewable energy: Primary and discretionary choice of British households' for micro-generation technologies. Energy Economics, 32, 129-136. Taylor, P. 2013. Sorting out a solar PV cowboy’s mess [Online]. Available: www.solarpowerportal.co.uk/guest_blog/sorting_out_a_solar_pv_cowboys_mess_ 2356 [Accessed 10 June 2013].

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Vermeulen, B., Goos, P. and Vandebroek, M. 2010. Obtaining more information from conjoint experiments by best–worst choices. Computational Statistics & Data Analysis, 54, 1426-1433. Walters, R. and Walsh, P. R. 2011. Examining the financial performance of micro- generation wind projects and the subsidy effect of feed-in tariffs for urban locations in the United Kingdom. Energy Policy, 39, 5167-5181. Warren, P. 2010. Uptake of Micro-generation among Small Organisations in the Camden Climate Change Alliance. Masters thesis, Durham University. Wee, H.-M., Yang, W.-H., Chou, C.-W. and Padilan, M. V. 2012. Renewable energy supply chains, performance, application barriers, and strategies for further development. Renewable and Sustainable Energy Reviews, 16, 5451-5465. Willis, K., Scarpa, R., Gilroy, R. and Hamza, N. 2011. Renewable energy adoption in an ageing population: Heterogeneity in preferences for micro-generation technology adoption. Energy Policy, 39, 6021-6029. Wimberly, J. 2008. Banking the Green: Customer Incentives for EE and Renewable. EcoAlign. Available: www.ecoalign.com/news/releases/banking-green-role- customer-incentives-energy-efficiency-and-renewable-energy. Yamaguchi, Y., Akai, K., Shen, J., Fujimura, N., Shimoda, Y. and Saijo, T. 2013. Prediction of photovoltaic and solar water heater diffusion and evaluation of promotion policies on the basis of consumers’ choices. Applied Energy, 102, 1148- 1159. Zahedi, A. 2011. A review of drivers, benefits, and challenges in integrating renewable energy sources into electricity grid. Renewable and Sustainable Energy Reviews, 15, 4775-4779.

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Supplementary Material

The following is the questionnaire used for the survey described in this paper.

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For adopters only:

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For considerers only:

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For rejecters only:

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The following questions were seen by all adopter, considerer and rejecter groups. However the phrasing of the questions is slightly different for each group. The following questions were seen by adopters:

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Chapter 4: Self-sufficiency and reducing the variability of grid electricity demand: integrating solar PV, Stirling engine CHP and battery storage

This paper was submitted to Applied Energy for publication in November 2014 and is currently under review. The authors of the paper are Balcombe, P., D. Rigby, and A. Azapagic. The research was designed, implemented and written by the author of this thesis. Co-authors Rigby and Azapagic supervised the research and edited the paper prior to submission.

Annex

During the Viva examination for this thesis, it was agreed that clarification was required with respect to the motivation behind investigating the impacts of a combined PV-SECHP- battery system. The motivation is based on the following: to improve household electricity self-sufficiency; and to flatten household grid electricity demand. The first motivation, to improve self-sufficiency, is a benefit to the household but not necessarily from a grid perspective. Increasing self-sufficiency is typically an important motivation to install microgeneration from the household perspective. Thus, improving self-sufficiency by incorporating battery storage and a SECHP system may improve the motivation to install. However, this is not a benefit from the grid perspective. The second motivation, to flatten household grid electricity demand, is a benefit to the electricity grid but not for the household.

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Self-sufficiency and reducing the variability of grid electricity demand: integrating solar PV, Stirling engine CHP and battery storage

Paul Balcombea,b,c, Adisa Azapagica* and Dan Rigbyc a School of Chemical Engineering and Analytical Science, The University of Manchester, M13 9PL, UK b Sustainable Consumption Institute, The University of Manchester, M13 9PL, UK c School of Social Sciences, The University of Manchester, M13 9PL, UK * Corresponding author, Tel: 0161 306 4363, Email: [email protected]

Highlights

 Simulation of integrated solar PV, Stirling engine CHP and battery system  Grid demand variability significantly reduced but incentives to install required  Electricity self-sufficiency reaches 72% with a 6 kWh battery  The 6 kWh battery reduces grid ramping requirements by 35%  System only financially viable for households with electricity demand >4,300 kWh/yr

Abstract

Global uptake of solar PV has risen significantly over the past four years, motivated by increased economic feasibility and the desire for electricity self-sufficiency. However, uptake of solar PV in the UK greater than 10 GW could cause grid balancing issues. A system comprising Stirling engine combined heat and power, solar PV and battery storage (SECHP-PV-Battery) may further improve self-sufficiency, satisfying both heat and electricity demand as well as mitigating potential negative grid effects. This paper presents the results of a simulation of 30 households with different energy demand profiles using this system, in order to determine: the degree of household electricity self- sufficiency achieved; resultant UK grid demand profiles; and the consumer economic costs and benefits. Even though PV and SECHP collectively produced 30% more electricity than the average demand of 3300 kWh/yr, the results indicate that households still had to import 28% of their electricity demand from the grid with a 6 kWh battery. This work shows that SECHP is much more effective in increasing self-sufficiency than PV, with the households consuming on average 49% of electricity generated (not including battery contribution), compared to 28% for PV. The addition of a 6 kWh battery to PV and SECHP improves the grid demand profile by 28% in terms of grid demand ramp up requirement and 40% for ramp downs. However, the variability of the grid demand profile is still greater than for the conventional system comprising a standard gas boiler and Page 119 of 210 Chapter 4 Paul Balcombe electricity from the grid. These moderate improvements must be weighed against the consumer cost: with current incentives, the system is only financially beneficial for households with high electricity demand (>4300 kWh/yr), representing approximately 40% of the UK housing stock. A capital grant of 24% of the total installed cost is required to be financially viable households with average electricity demand and a comparative impact analysis between this incentive option and others to achieve grid stability, availability and reliability should be a subject of future research.

1. Introduction

Global demand for solar PV in residential dwellings has increased rapidly in the past decade, resulting in 138 GW of installed capacity by 2013 (EPIA, 2014). This has been driven by government incentives such as Feed-in Tariffs (FITs) (e.g. DECC, 2009) and the rapid reduction in manufacturing costs: PV module costs reduced by 62% between 2011 and 2013 (Thretford, 2013). In the UK, there is presently 2 GW of installed capacity (DECC, 2014). However, UK FIT rates for solar PV were cut in half in 2012, reducing the financial motivation to install and has slowed uptake significantly (Balcombe et al., 2014). If uptake is to increase again, the consumer motivation to install must be improved: a paper investigating the motivations and barriers affecting consumer adoption suggests uptake would increase further if higher levels of self-sufficiency are achieved, such as by incorporating battery storage (Balcombe et al., 2014).

Additionally, the UK National Grid has reported that the installed capacity of solar PV above 10 GW feeding into the grid would present difficulties in the operation and balancing of the electricity transmission system (National Grid, 2012a). The intermittent and diurnal nature of PV generation increases the ramping requirements of variable load power plants, such as combined cycle gas plants (Jones, 2012; National Grid, 2012a). The ramping requirements are the rates at which the electrical output of variable-load plants must change to meet demand. Furthermore, with 22 GW of uncontrolled solar PV feeding into the grid, the summer peak PV generation together with anticipated baseline generation from nuclear could exceed demand (National Grid, 2012a). It has been suggested that battery storage could be used to help towards aleviating the these grid issues (Edmunds et al., 2014; Leadbetter and Swan, 2012b; National Grid, 2012a) Whilst centralised battery storage remains unappealing owing to low energy densities and financial constraints (IEC, 2012), decentralised lead-acid battery storage local to solar PV generators is more common (Hoppmann et al., 2014). However, local battery storage represents an additional upfront cost to the consumer, which is already an important barrier for most who consider installing it (Balcombe et al., 2014). Batteries are currently not cost effective (McKenna et al., 2013), although smaller capacity systems are perhaps Page 120 of 210 Chapter 4 Paul Balcombe close to being so (Bianchi et al., 2013; Nottrott et al., 2013; Yan et al., 2014), particularly lead-acid batteries (Mulder et al., 2013). Additionally, there is a growing expectation that local battery storage will become cost effective in the near future (Platt et al., 2014; UBS Limited, 2014).

Furthermore, adding a Stirling Engine combined heat and power (SECHP) unit to a system with solar PV and battery storage would further improve the household’s electricity self-sufficiency, and reduce the required battery capacity (and cost). SECHP systems are intermittent electricity generators, only generating whilst there is a household heat demand similarly to a standard gas boiler, therefore mainly during the winter. This provides a useful contrast to solar PV, which generates most during the summer owing to higher insolation. Additionally, Stirling engine CHP systems tend to have higher heat to power (HTP) ratios than other CHP systems, approximately 6 – 7 (Baxi 2011a), which is more suited to the ratio of household heat and power demand. SECHP could deliver improved economic and environmental impacts over a gas boiler but is highly dependent on the way in which it is operated by the household (Fubara et al., 2014; Orr et al., 2011). High system efficiencies are achieved only when the system is operated for long periods as the high operation temperatures (approximately 500 °C) require startup and shutdown periods where gas is consumed but no electricity is generated (Carbon Trust, 2011; Lombardi et al., 2010; Roselli et al., 2011).

Thus, a combined household system comprising solar PV, SECHP and battery storage could help to mitigate potential grid balancing and ramping issues, whilst significantly improving household electricity self-sufficiency. A number of studies have modelled the potential for battery storage installed with microgeneration to reduce variability of household grid demand, thus mitigating grid balancing issues, finding that some degree of smoothing (10–50% reduction in grid energy demand oscillations) is possible with mid- sized batteries (3–8 kWh) (e.g. Li and Danzer, 2014; Purvins and Sumner, 2013; Riffonneau et al., 2011). Additionally, many studies have simulated different combinations of microgeneration technologies with battery storage to provide household self-sufficiency; for example, with solar PV (Castillo-Cagigal et al., 2011; Hosseini et al., 2013; Jenkins et al., 2008), SECHP (Mehleri et al., 2013; Nosrat and Pearce, 2011), fuel cells (Hosseini et al., 2013; Maclay et al., 2011; Wang et al., 2013), or wind turbines (Carmeli et al., 2012). Most studies indicate that the degree of self-sufficiency achieved is limited without very large battery capacities. To the authors’ knowledge, none has investigated the combination of solar PV, SECHP and battery storage and none has studied both self- sufficiency and grid demand smoothing effects.

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Therefore, the aim of this research is to determine the impact of using a combined solar PV, SECHP and battery household system on electricity self-sufficiency, the variability of grid demand and household economic costs. This paper presents the results of a simulation of energy supply and demand for 30 households using the PV-SECHP-battery system as well as a consumer cost-benefit analysis. In particular, the study demonstrates the effects of the following variables on the above research outputs:  the variation in household electricity and gas demand;  different battery storage capacities; and  the efficiency of SECHP operation. The work provides a greater understanding of the potential benefits and economic costs of decentralised battery storage systems to contribute to mitigating future electricity grid operation and balancing difficulties associated with increased solar PV uptake. This gives policy makers and National Grid a sound basis for deliberating on the pathways to mitigate this future risk to the grid and capital cost implications. Recommendations regarding system improvements and policy are also made.

The following section describes the methodology for the simulation. This is followed by the results of the self-sufficiency, grid demand profile and the cost-benefit analyses (Section 3). A discussion of the results relating to financial incentives is given in Section 4 and conclusions are made in Section 5.

2. Methodology

The operation of the household energy system comprising solar PV, SECHP and battery storage was simulated over a year for 30 dwellings in detached, semi-detached and terraced houses with different heat and electricity demands and solar PV generation. The simulation provides energy performance data which are then compared to a UK household using currently predominant energy sources, i.e. gas boiler for heating and electricity from the grid. The following sections describe how the simulation was carried out, the analysis of household electricity self-sufficiency, the assessment of the effect of the system on the electricity grid and the cost-benefit analysis.

2.1 Household simulation

Figure 13 gives an overview of the simulation steps. Real household energy demand and solar PV generation profiles are used for the simulation input data. Using the heat demand data with a control variable for the efficiency of operation, the SECHP operation profile is modelled. Combining this with the PV generation and electricity demand profiles allows the generation of an electricity surplus/ deficit profile for each household (and each control variable value). The battery storage can then be simulated, using the surplus/ deficit Page 122 of 210 Chapter 4 Paul Balcombe profile and defining the battery capacity and discharge efficiency variables. Various values for battery capacity and discharge efficiency are used to create a set of scenarios of battery profiles. Lastly, the electricity grid import and export profiles are generated for each scenario. A detailed description of these simulation steps is given in Section 2.1.2 and the data used to conduct the simulation is described presently.

Figure 13. Simulation steps for the solar PV, SECHP and battery system. The boxes represent the stages and the circles indicate variables of the simulation. 2.1.1 Simulation data The simulation is based on 30 household electricity and gas demand profiles from the UKERC Energy Database Centre (EDC) (BRE, 1990). The UKERC EDC is an open source database, containing data from the Milton Keynes Energy Park consisting of 94 household hourly demand profiles from 1990. Although this dataset is now 24 years old, it remains the only openly available dataset with coincident gas and electricity demand of sufficient quality to conduct a household simulation and continues to be used for energy- related simulations (e.g. Fubara et al., 2014; Kopanos et al., 2013; Parra et al., 2014). The 30 profiles were selected based on the completeness of the data set (i.e. electricity and gas profiles with at least one year’s data), to include range of detached (DH), semi- detached (SDH) and mid-terraced (MTH) house types and a broad range of electricity and gas demand profiles. In addition, three average UK household electricity profiles were also used (Wardle et al., 2013): average electricity demand profiles for typical urban, suburban and rural households replaced the UKERC EDC data for three households with similar annual electricity demands.

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Solar PV generation profiles were sourced from the open-access PVoutput.org database, a website where solar PV users can upload 5-minutely generation data (PVOutput, 2013). Data were selected based on their completeness and to be representative of UK installation capacities (DECC, 2013b) with a range of capacities of 1–4 kWp. Allocation of each PV profile to a demand profile was carried out by ranking the PV data by peak capacity and the household data by floor area, and matching the ranking numbers (assuming that greater floor area implies greater roof space availability for solar PV). A summary of the household demand and PV generation data is given in the Appendix (Table A 1).

As the simulation is based on hourly electricity and gas demand data and 5-minutely PV data, the demand data were split and assumed constant over 5 minute divisions within the hour. Thus, the simulation estimated SECHP and battery usage on a 5-minutely scale. However, the smoothing effect of using the hourly demand data, in particular for electricity demand, may have resulted in lower instantaneous power variations (Hawkes and Leach, 2005; Thomson and Infield, 2008). Higher resolution data is preferable for investigations into network voltage variations, but this is deemed acceptable for investigating the impact on the central grid as this demand is likely to be smoothed out over the large number of households that the grid supplies.

The SECHP system was modelled using data from the only SECHP system approved by the Microgeneration Certification Scheme (MCS, 2014): the Baxi Ecogen (Baxi, 2011a), with a variable output of 3.4–6.4 kW heat and 0.3–1kW electricity using 3.7–7.7 kW natural gas (Baxi, 2011b). For periods when heat demand is greater than the maximum output, 6.4 kW, an auxiliary burner is used to supply the additional requirement. This burner delivers 3.6–17.6 kWth output, consuming 3.8–19 kW natural gas (Baxi, 2011b).

Note that the 30 simulations are all specific household case studies, designed to reflect a broad range of dwelling types, demand and generation profiles. The simulation results are not necessarily representative of the UK housing stock, but an example of the potential impact such a household system would have on self-sufficiency and grid demand. In order to give some detail on the representativeness of the households, Figure 14 shows the electricity and gas demand for each household together with the UK average (low, medium and high, from Barnes, 2013). The graph shows a broad spread across the electricity demand axis and two clusters around low and high gas demands. However, to the authors’ knowledge there is no available data on the distribution of UK household demand for different dwelling types, thus the representativeness of the data is not known.

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7000

6000

High electricity demand 5000 MTH

SDH 4000 Medium electricity demand DH 3000 Low electricity demand

2000 Elec Elec demand(kWh/yr)

1000 Low gas Medium gas High gas demand demand demand 0 0 5000 10000 15000 20000 25000 30000 35000 Gas demand (kWh/yr)

Figure 14. Annual gas and electricity demand for each household simulation. Vertical lines indicate UK average household gas demand and horizontal lines indicate average electricity demand (Barnes, 2013). [DH – detached house; SDH – semi-detached house; MTH – mid-terraced house]

The PV data collected are representative of the UK PV stock as illustrated by Figure 15, showing the proportion of PV installation capacities for the UK (DECC, 2013b) and for the simulation data.

50%

45% 43% 40% 40% 35% UK installations 30% Simulation

25%

20% 20%

20%

14% 13%

15% 13%

10% 10%

10%

7% 6%

5% 3%

1% 0% 0% <1 1 - 1.5 1.5 - 2 2 - 2.5 2.5 - 3 3 - 3.5 3.5 - 4 Peak PV capacity (kW)

Figure 15. Graph of the range of PV capacities for UK installations <4 kWp and for the simulation data (DECC, 2013b).

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2.1.2 Simulation design Household heat demand were derived from the gas demand data by applying an assumed boiler efficiency of 80%, based on average UK boiler efficiencies and the UKERC EDC stated average boiler SEDBUK13 (Seasonal Efficiency of Domestic Boilers) efficiency of 75–90 (BRE, 1990; Energy Saving Trust, 2008). The operation of the SECHP system was modelled in two different ways to investigate the impact of the efficiency of operation:

1. ‘inefficient’ SECHP mode 1: Operating the SECHP system in the same way that a standard boiler is used (turned on whenever there is a heat requirement); and

2. ‘efficient’ SECHP mode 2: Operating the SECHP to deliver the total heat requirement for each day within two on/off cycles (morning and afternoon).

The study assumes a startup and shutdown sequence of 2 minutes of maximum natural gas consumption (7.7 kW gas input for 2 minutes = 0.26 kWh), where no useful energy output is generated, based on discussion with the Baxi Ecogen Technical Department (pers. comm., 18 September 2013).

Under the efficient SECHP operation (mode 2), the system is switched on twice per day: in the morning (05:00) and in the evening (16:00). These are the times with the most commonly occurring demand peaks, based on observations of the heat profile data. Therefore, this mode of operation is more efficient in terms of the ratio of useful energy output to the quantity of natural gas consumed as there are fewer startup/ shutdown cycles compared to mode 1.

The SECHP heat output for each cycle is equal to the total heat demand and the power output is equal to the maximum hourly demand for each part of the day (morning or evening), in order to maximise electricity power output during that period. The duration of the SECHP operation for each part of the day is consequently determined by:

∑ 푫풉풆풂풕 ∆풕푪푯푷 = Equation 1 푴푨푿(푷풉풆풂풕) where ΔtCHP is the duration of the SECHP operation, ∑Dheat is the total heating requirement and MAX(Pheat) is the maximum heat power requirement during the morning or evening.

The household system is operated such that the consumption of locally generated electricity is maximised, maximising self-sufficiency. Thus, when electricity generation by SECHP or solar PV coincides with demand, the electricity is consumed. When local

13 SEDBUK is the UK standard measurement of boiler efficiency, used within the UK government’s Standard Assessment Procedure (SAP) of household efficiency ratings (Energy Saving Trust, 2008).

Page 126 of 210 Chapter 4 Paul Balcombe generation exceeds demand, the battery is charged until full, at which point the surplus is exported to the grid. Likewise, when demand exceeds local generation, the battery supplies the deficit until the minimum battery capacity is reached, at which point grid electricity is imported. For each 5-minutely time point, the simulation determines the state of battery charge and the quantity of residual electricity that is imported from, or exported to, the grid.

Six different battery capacities were simulated for each household: 2, 4, 6, 10, 20 and 40 kWh, as well as a ‘no battery’ scenario which is used for comparison. These storage capacities were selected based on sizes used in similar battery simulation studies (e.g. Leadbetter and Swan, 2012a). The battery is operated to be discharged to only 50% of full capacity in order to prolong battery life, based on a conservative estimate from literature (Nottrott et al., 2013; Schmiegel and Kleine, 2014; Zucker and Hinchliffe, 2014). Thus, the usable capacity is half the total capacity: 1, 2, 3, 5, 10 and 20 kWh. This paper refers only to the usable capacity from here on.

The efficiency of the battery system was modelled by applying a constant discharge efficiency, defined as the ratio of useful energy output to energy input. In reality, battery efficiency is variable and depends upon the ambient temperature, operating voltage and state of charge (Jenkins et al., 2008; Li and Danzer, 2014; McKenna et al., 2013; Papic, 2006; Riffonneau et al., 2011; Thygesen and Karlsson, 2014). This study adopts the simpler approach of modelling power flows with a constant battery discharge efficiency (as per Castillo-Cagigal et al., 2011; Chen et al., 2012; Purvins et al., 2013; Williams et al., 2012), instead focussing on the impact of different battery capacities and the degradation of discharge efficiency over time. A number of discharge efficiency scenarios are considered for each household to reflect the broad range of efficiencies cited in literature (IEA, 2014; Ruxandra and Stroeve, 2012): 40%, 60%, 80% and 100%.

In summary, the simulation was carried out for each combination of each parameter shown in Table 9, using Stata, a database analysis and statistics software package. Two additional scenarios are also considered in this study in order to understand the contribution of each technology: a solar PV only system (with a gas boiler) and a PV with SECHP system (without the battery). Owing to the large number of parameter combinations (1440), a base case scenario was selected for analysis (as shown in Table 9). The efficient SECHP mode was selected for the base case as this offers energy efficiency and economic benefit. A battery (usable) capacity of 3 kWh was selected for the base case as this was the most cost-efficient capacity. Further, a base-case battery discharge efficiency of 80% was used as it most closely reflects battery efficiency found in

Page 127 of 210 Chapter 4 Paul Balcombe literature (Rydh and Sandén, 2005; Sullivan and Gaines, 2012; Van den Bossche et al., 2006).

Table 9. The simulation parameters, their units and range of values, as well as the base case values.

Parameter Units Values Base case Battery efficiency % 40, 60, 80, 100 80 Battery capacity kWh 1, 2, 3, 5, 10, 20 3 SECHP operation mode N/A Inefficient, efficient (0, 1) Efficient Electricity demand kWh/yr 1491 – 6276 (30 profiles) N/A Gas demand kWh/yr 7901 – 29174 (as above) N/A PV generation kWh/yr 692.2 – 4556 (as above) N/A Total no. of simulations 1440 4 x 6 x 2 x 30a N/A a The total number of simulations equals the product of the number of values for each parameter.

2.2 Household electricity self-sufficiency

The degree of household electricity self-sufficiency is defined by the proportion of demand met by local generation, i.e. not imported from the grid. Thus, the annual proportion of imported electricity is determined for each household simulation and the impact on self- sufficiency of each parameter listed in Table 9 is analysed. The individual contribution of PV, SECHP and the battery is also investigated.

2.3 Electricity grid demand profiles

The effect of the household system on the variability of grid electricity demand is determined by creating and analysing a series of daily grid demand profiles. Note, when the variability of grid electricity demand is measured here, this is from the household perspective rather than the perspective of the electricity grid. Reducing the variability of a single household’s grid electricity demand will have a negligible impact on the grid, but this is used as an example of the impact that such a combination of technologies would have on grid demand if large numbers were installed. Average demand profiles are generated for each simulation and each quarter of the year, showing the variation in grid electricity imports and exports across the course of a day. A comparison between the simulation profiles and the conventional system (grid electricity and gas boiler for heating) is made using the following profile parameters:  the mean daily demand;  the daily variation in electricity demand, from maximum to minimum;  the maximum hourly ramp up rate (i.e. maximum hourly gradient of electricity demand over time); and  the maximum hourly ramp down rate (minimum hourly gradient).

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Other studies investigating the variability of electricity demand profiles often use common statistics such as mean, standard deviation, variance and the coefficient of variation (Morley and Hazas, 2011; Purvins et al., 2013), or instead focus on the reduction in (e.g. Leadbetter and Swan, 2012a). This study creates the additional ‘ramping’ indicators (ramp up and ramp down as described above) in order to describe more intuitively the potential change in ramping duty placed on the centralised generation plants. The ‘variation’ indicator compliments the hourly ramping figures by illustrating the daily magnitude in ‘swing’ between the peak export and peak demand.

2.4 Cost-benefit analysis

A cost-benefit analysis was conducted for a 30 year period, based on the longest expected lifespan of the system component: solar PV panels. This lifespan was based on literature figures of 25-50 years (Cambridge Economic Policy Associates Ltd and Parsons Brinckerhoff, 2011).Component lifespans are described further in Section 2.4.3. Household costs were estimated based on 2013 values, thus no inflation over time was considered. The calculation comprised the summation of capital, operating and equipment replacement costs for each year. All costs and benefits (e.g. FIT incentives) considered are from the household perspective, thus no other costs/benefits (e.g. the ‘social’ benefit of reducing ) are included. The difference in net-present value (NPV) between the SECHP-PV-battery system and the conventional gas boiler and grid system was used to indicate financial feasibility: the former system is financially viable for households with an NPV difference above zero. The calculation of NPV is defined in the Appendix. The payback time and undiscounted lifetime costs were also estimated for each combination of parameter values given in Table 9. Payback time is the time it takes to pay back the capital cost of the SECHP-PV-battery system by way of lower operational (energy) costs, including the consumer discount rate. ‘Simple payback time’ is also estimated, which is the payback time without accounting for the consumer discount rate (i.e. the discount rate is zero).

The consumer discount rate used to calculate NPV and payback time was 5%, but a range of 0–50% was used to conduct a sensitivity analysis, as consumer discount rates are notoriously difficult to predict and vary significantly for different forms of investment (Chunekar and Rathi, 2012). Estimated discount rates are often based on the cost of the capital (i.e. equal to the rate of return of the best alternative investment) (Owens, 2002; Short et al., 1995); therefore, 5% was selected as a base case as this is a typical savings account interest rate (before the recession). Additionally, 5% is approximately the rate of return achieved for a solar PV system (Balcombe et al., 2014).

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2.4.1 Capital costs A range of solar PV capital costs were found, ranging from £1500 - £13,859 for capacities of 1 – 4 kWp (see Appendix, Table A 2). The installation cost was estimated based on the Parsons Brinckerhoff (2012a) ‘medium’ estimate, defined as:

푪푷푽 = £ퟏퟏퟐퟕ + £ퟏퟔퟐퟏ. 푷 Equation 2 where CPV is the installed cost (£) of the whole solar PV system and P is the rated peak capacity of the system (kWp).

Various installation costs for were also found for SECHP systems, ranging from £3500 to £10,000 (see Appendix, Table A 3 for full list). A median value of £5500 was assumed in the study. The lead-acid battery capital cost includes costs of battery cells, inverter, charge controller, cabling and installation cost, and varies with battery capacity. Table 10 shows the assumed cost for each component of the battery system, based on quoted prices from online microgeneration equipment distributors (Bright Green Energy Ltd., 2014; Navitron Ltd., 2013) as well as estimates in the literature (Jenkins et al., 2008; McKenna et al., 2013). The required number and specification of battery cells for each capacity is also included in Table 10. A battery system voltage of 24 V was assumed (Palmer and Cooper, 2012) and the required rated charge of the battery cells was estimated by:

푩 푸 = 푻 Equation 3 푽

. Where Q is the total charge required (A hr), BT is the total battery capacity (twice the usable battery capacity in this case, due to the 50% required depth of discharge) and V is the system voltage. The resultant total battery system costs were estimated between £3,180 and £8,300 and are shown in Table 11.

Table 10. Capital cost and specification of the battery system components (Bright Green Energy Ltd., 2014; Jenkins et al., 2008; McKenna et al., 2013; Navitron Ltd., 2013).

Component Specification Cost (£)

Battery cells 1 kWh 12V 90 Ahr x2 480 Battery cells 2 kWh 12V 90 Ahr x4 960 Battery cells 3 kWh 6V 460 Ahr x4 1120 Battery cells 5 kWh 12V 220 Ahr x4 2000 Battery cells 10 kWh 6V 460 Ahr x12 3360 Battery cells 20 kWh 6V 460 Ahr x20 5600 Charge controller N/A 100 Inverter 24V 3 kW 1500 Cabling N/A 100 Installation cost N/A 1000 a The multipliers under the Specification column show the number of cells needed to give the required quantity of energy storage (1 – 20 kWh usable capacity)

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Table 11. Total capital cost for different battery usable capacities.

Battery usable capacity Cost (£) (kWh)

1 3180 2 3660 3 3820 5 4700 10 6060 20 8300

2.4.2 Operating costs The net annual operating costs were estimated based on the following:

푪풐풑 = 푬 + 푮 + 푴푷푽 + 푴푪푯푷 + 푴푩 − 푭푰푻푷푽 − 푭푰푻푪푯푷 − 푭푰푻풆풙풑 Equation 4 where Cop is the total yearly operating cost, E is the electricity import cost, G is the gas import cost, MPV is the solar PV maintenance cost, MCHP is the SECHP maintenance cost,

MB is the battery maintenance cost, FITPV is the FIT earnings from the solar PV tariff,

FITCHP is the FIT earnings from the CHP tariff and FITexp is the FIT earnings from exporting unused electricity to the grid. With the exception of the electricity and gas unit costs, operating costs remain constant over the time period and the values used are listed in Table 12.

Table 12. Costs associated with each operating cost component.

Operating cost type Cost Source

Electricity (at year 0) 15 p/kWh DECC (2013a); McKinsey & Co. (2012) Gas (at year 0) 5 p/kWh DECC (2013a) Solar PV maintenance £63/yr Parsons Brinckerhoff (2012a) CHP maintenance £130/yr CEPA and Parsons Brinckerhoff (2011) Battery maintenance £50/yr Assumption FIT solar PV generation tariff 15 p/kWh Ofgem (2012) FIT SECHP generation tariff 10 p/kWh Ofgem (2012) FIT export tariff 5 p/kWh Ofgem (2012)

The solar PV maintenance cost estimates varied from £42/yr to £110/yr (Cambridge Economic Policy Associates Ltd and Parsons Brinckerhoff, 2011; Parsons Brinckerhoff, 2012a) and the figure of £63/yr was based on the ‘medium’ estimate from the Parsons Brinckerhoff cost review. The SECHP maintenance cost of £110/yr was based on the high estimate from the CEPA and PB cost review (Cambridge Economic Policy Associates Ltd and Parsons Brinckerhoff, 2011). Estimates for battery maintenance cost were not found

Page 131 of 210 Chapter 4 Paul Balcombe and an assumption of £50/yr was made. FIT tariff payments were all based on 2013 current rates (Ofgem, 2012).

The initial electricity and gas costs (Table 12) were taken from the DECC average estimates of 2013 UK domestic energy bills (DECC, 2013a). There are various projections of future electricity and gas unit costs that vary considerably, as shown in Table 13. The DECC ‘high’ annual inflation of 2.7% for electricity and 2.3% for gas were used as a base case as this was the median projection for both electricity and gas prices (DECC, 2013c). The effects of the other cost projections are included within the sensitivity analysis.

Table 13. Yearly electricity unit cost increase above inflation ordered from lowest to highest, alongside gas cost inflation rate and the source of the estimate.

Electricity cost Gas cost inflation rate inflation rate Source -0.11%* -0.48%a National Grid (2012b) 1.35%* -0.71%a DECC (2013c) low 2.12%* 0.99%a DECC (2013c) ref 2.60% 5.80% Elmes (2014) 2.7%* 2.32%a DECC (2013c) high 2.94%* 3.11%a National Grid (2012b) 3.65% N/A McKinsey & Co (2012) 5%* 5%* National Grid (2012b) a The inflation rates are derived average rates over the 30 year period, but do not reflect the shape of the cost increase over time (i.e. they are not necessarily exponential).

2.4.3 Equipment replacement costs The cost-benefit analysis was conducted for a 30 year period, which is the expected life span of the solar PV system. Other major system components must be replaced over this time. Table 14 lists major components that need replacing, their expected lifespans and cost of replacement.

Table 14. Expected lifespan and installation cost of each replacement item.

Component Lifespan Replacement Source (yr) cost (£) Solar PV inverter 11 1000 Electricians Forums (2012); Rudge (2010) Battery inverter 11 1500 Electricians Forums (2012); Rudge (2010) SECHP system 10 5500 Parsons Brinckerhoff (2012b) Battery cells 10 See Table 2 N/A

2.4.4 Disposal and residual asset value The disposal cost is dependent on the installation of a replacement system (e.g. boiler replacement services often include disposal), which is unknown. Additionally, owing to the different operational lifespans of the components, some components will still have an

Page 132 of 210 Chapter 4 Paul Balcombe asset value at the end of the 30 year period considered. For simplicity, it is assumed that there is zero net-cost to the consumer associated with disposal and asset value of the system.

2.4.5 Reference system The reference system, as previously stated, consists of a gas boiler which provides heat and electricity from the UK grid. The installation cost of the boiler is assumed to be £2500 (Carbon Trust, 2011) with an operational lifespan of 15 years (one replacement over the 30 year period considered here). No cost of connection to the electricity grid is considered, as this would be required for both household systems. Similarly, no cost of the heating distribution systems (radiators and pipework) is considered. The annual maintenance cost of the boiler is assumed to be £120 (Carbon Trust, 2011).

3. Results

The results of the simulation and analysis are presented in this section, in terms of the level of household self-sufficiency achieved (Section 3.1), the variability of grid demand (3.2) and the consumer cost-benefit analysis (3.3).

3.1 Electricity self-sufficiency

Table 15 shows a summary of the average energy demand and the generation by each technology, estimated through the simulation. On average, the total solar PV and SECHP electricity generation over a year is 30% higher than household demand. However, imports still account for 28% of electricity supply, as shown in Table 16. The reason for the high level of imports is because the generation profiles of PV and SECHP do not match the household demand profile, and the base case battery capacity is not large enough to store the excess electricity required.

Table 15. Summary of base case annual household generation and consumption figures across the 30 simulated households.

Standard Variable Mean deviation Minimum Maximum Electricity demand (kWh/yr) 3,265 1,320 1,491 6,276 Heat demand (kWh/yr) 11,773 4,943 6,321 23,339 PV generation (kWh/yr) 2,772 1,087 692 4,557 SECHP electricity generation (kWh/yr) 1,477 637 715 2,946 SECHP gas use (kWh/yr) 13,963 5,413 7,680 26,034 Battery contribution (kWh/yr) 797 126 401 958 Imported electricity (kWh/yr) 982 663 218 2,882 Exported electricity (kWh/yr) 1,965 952 136 3,662

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The contributions toward electricity supply from each source are shown in Table 16. Solar PV and SECHP make similar contributions but account for less than half of the electricity supply in total (45%). The battery storage increases consumption of household-generated electricity by 27%. This finding is similar to that of Li and Danzer (2014), who add 3.3 kWh battery storage to a household solar PV system, reducing imports by approximately 25% and exports by 10%.

Table 16. Contribution of each energy source as a percentage of total household demand for the base case, averaged over the 30 households.

Standard Source Mean Deviation Minimum Maximum Grid 27.6% 10% 9% 49% Solar PV 22.8% 5% 12% 33% SECHP 22.2% 5% 13% 31% Battery 27.4% 9% 14% 42%

Although solar PV and SECHP contributions are similar, the consumed proportion of total SECHP generation is far greater than that of solar PV: 80% vs 51%.

Table 17 shows the consumption of electricity generated by solar PV and SECHP, as a percentage of the total generation from each technology. This value is split into instantaneous consumption and consumption via the battery. Consumption from solar PV is somewhat smaller than expected: it is normally assumed that 50% of electricity generated from solar PV is consumed (McKenna and Thomson, 2013; NHBC Foundation, 2011), whereas this study shows only half of this (28%) is achieved on average, albeit with a range of 19–65% across all households. Even with battery storage, only 51% PV electricity is consumed, although this figure varies significantly with different battery capacities. Consumption from SECHP is somewhat higher: 49% is consumed instantaneously, rising to 80% with battery storage. The instantaneous SECHP consumption is broadly in line with other similar studies: Fubara et al. (2014) estimate 47– 64%, whilst Peacock and Newborough (2005; 2006) estimate 21–63%, both with similar SECHP systems. The daily generation profile of SECHP makes it much more effective in meeting demand than solar PV. SECHP generation is governed by the household heat profile, which is likely to be a closer match to the electricity demand profile than the solar PV generation profile. Thus, SECHP is more effective than solar PV for providing electricity self-sufficiency.

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Table 17. Average proportion of consumed PV and SECHP electricity for the base case, both directly and indirectly (through the battery).

Standard Source Mean Deviation Minimum Maximum Instantaneous PV consumption 27.8% 9% 19.1% 64.9% PV consumption from battery 23.4% 7% 14.5% 36.2% Total PV electricity consumption 51.2% 13% 36.1% 97% Instantaneous SECHP consumption 49.4% 11% 32.5% 66.9% CHP consumption from battery 30.8% 8% 17.6% 48.0% Total SECHP electricity consumption 80.1% 8% 62.1% 98%

Overall, there was a large variation in reliance on imported electricity across households: from 9% to 49% for the base case as shown in Table 16. This is mainly due to the large variation in electricity demand, as well as the time of use of electricity in relation to the time of local generation.

The change in battery capacity has a significant impact on the amount of electricity imported from the grid. As shown in Figure 16, increasing battery capacity to 20 kWh decreases imports to 12%. However, the reduction in imports above 5 kWh is marginal.

60%

50%

40%

30%

20% householdsupply)

10% Imported electricity Imported electricity (% of

0% 0 5 10 15 20 Battery capacity (kWh)

Figure 16. The percentage of imported electricity for different installed battery capacities, with 80% battery efficiency and efficient SECHP operation, averaged across all households.

The impact of different battery discharge efficiencies on imports is somewhat smaller than battery capacity. At 100% efficiency, the mean imports are 23% but increase to 40% with

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40% efficiency. Additionally, the SECHP operation efficiency has little impact on self- sufficiency, with an average import of 25.9% for inefficient operation (mode 1) and 27.6% for efficient operation (mode 2). This is because very similar quantities of electricity are generated in both modes of operation, albeit whilst consuming different quantities of natural gas.

Therefore, this part of the simulation suggests that whilst some degree of self-sufficiency is achieved for the base case (72% for 3 kWh battery), there are only marginal improvements above 5 kWh battery capacity. Additionally, the SECHP is far more suitable to provide electricity self-sufficiency than solar PV, due to the far better correlation between the generation profile and household demand.

3.2 Variability in grid demand profiles

In addition to reducing annual electricity imports, the SECHP-PV-battery system significantly alters the daily grid demand profile. Figure 17 summarises the average daily demand properties for the reference system, PV only, SECHP-PV-battery for all battery sizes considered. The graph clearly shows an increased daily variation in demand when PV and SECHP are added to the household: solar PV increases the maximum ramp down and ramp up rates by a factor of 2.5 and 1.6, respectively. Somewhat surprisingly, the addition of SECHP substantially increases ramping requirements even further, by 3.9 for ramp down and 2.2 for ramp up relative to the reference system. This corroborates the findings of Peacock and Newborough (2007), who suggest that the electricity grid profiles increase in variability if the SECHP system is operated as a heat-led system, as is currently the case for the Baxi Ecogen considered here. The addition of battery storage reduces ramping requirements and variation considerably: 1 kWh storage reduces ramp down by 43% and ramp up by 22% relative to the PV+SECHP scenario. This result broadly agrees with that of Purvins et al. (2013) who find that a 0.6 kWh battery reduces household grid demand variation by 35%. As battery capacity increases, grid ramping requirements and variation in demand are reduced further. Although the addition of battery storage reduces the impacts significantly compared to the PV-SECHP system without the battery, the variation is still greater than the reference system, even with a 20 kWh battery. Thus, the addition of a battery may not prevent UK grid balancing problems.

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2.50 Reference PV only 2.00 SECHP-PV only 1.91

SECHP-PV-Battery 1 kWh 1.65

SECHP-PV-Battery 2 kWh 1.53 1.42

1.50 SECHP-PV-Battery 3 kWh 1.34

SECHP-PV-Battery 5 kWh 1.22 1.10 SECHP-PV-Battery 10 kWh 1.05

1.00 SECHP-PV-Battery 20 kWh

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Figure 17. Daily household demand properties for the reference system, PV only, PV + SECHP and all battery sizes, averaged across all households for 80% battery efficiency and efficient SECHP operation (where applicable).

The effect of the negative impact of solar PV on grid demand is shown in greater detail in Figure 18. The reducing PV generation in the afternoon for Q2 and Q3, combined with increasing demand in the evening, produces a prolonged ramp up in grid import, demonstrating very clearly the increase in demand variation that concerns the National Grid (as described in section 1).

1

0.5 Q1 Reference Q2 Reference 0 Q3 Reference Q4 Reference -0.5 Q1 PV only Q2 PV only -1 Electricity Electricity imported (kW) Q3 PV only Q4 PV only -1.5 04:00 08:00 12:00 16:00 20:00 00:00 Time

Figure 18. Daily demand profile for different quarters of the year for the reference and solar PV only systems, averaged across all households.

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As shown in Figure 19, the contribution of SECHP and a battery is to decrease peak demand and peak exports, whilst adding a trough at 16:00, due to the SECHP system being switched on Although during the winter months (Q1 and Q4) the demand curve is visibly flattened, there is higher variation in demand during the summer months (Q2 and Q3). The 3 kWh battery system is unable to negate the greater summer PV generation rates, causing the sharp rise from mid-afternoon export to high evening demand.

Thus, these results show the impact of each technology on the variation in grid demand: both solar PV and SECHP significantly increase grid demand variation and the effect is cumulative when both installed, particularly during the summer months, whilst battery storage provides some reduction in grid ramping requirements.

1

0.5 Q1 Reference Q2 Reference 0 Q3 Reference Q4 Reference -0.5 Q1 Bat 3 kWh Q2 Bat 3 kWh

Electricity Electricity imported (kW) -1 Q3 Bat 3 kWh Q4 Bat 3 kWh

-1.5 04:00 08:00 12:00 16:00 20:00 00:00 Time

Figure 19. Daily demand profile in different quarters of the year for the reference and base case SECHP-PV-battery systems, averaged across all households.

3.3 Cost-benefit analysis

The results of the cost-benefit analysis consider payback time and NPV difference compared to the reference system. The results show that the payback of the base case system is achieved for 9 out of 30 households within the lifespan of the system (30 years). Further, the simple payback time, which excludes the consumer discount rate, is achieved for 17 of the households. See the summary table in the Appendix (Table A 4) for a breakdown of the cost-benefit analysis results. There is a very large variation in NPV across the 30 households, with NPV difference ranging from £8,542 to £-11,379, largely due to the varying household energy demand. Payback times range from 15 years to never paying back the investment. Those households which achieve positive NPV

Page 138 of 210 Chapter 4 Paul Balcombe difference have higher electricity demand (greater than 3,600 kWh/yr). For these high demand households, the SECHP-PV-battery system provides more electricity and heat at lower cost than the reference system, resulting in an improved operating cost reduction.

3.3.1 Factors affecting the cost benefit analysis The results suggest that only the installations without battery storage (PV only and PV with SECHP) have a positive NPV difference relative to the reference system (Figure 20). The NPV difference remains roughly constant for battery capacities of 1–3 kWh, implying that the marginal increase in capital cost is nullified by the marginal decrease in electricity import cost. At capacities above 3 kWh, the NPV decreases much more significantly, reaching £-12,077 for the largest battery capacity of 20 kWh. Thus, the addition of any- sized battery storage tends to decrease the relative NPV, which is consistent with the findings of McKenna et al. (2013). This means that, if local battery storage is seen as beneficial to the UK electricity grid, it must be incentivised to increase uptake.

£4,000 £2,973 £1,635 £2,000 £0 -£2,000 -£4,000 -£6,000 -£3,407 -£3,855 -£3,745 -£5,057 -£8,000

-£10,000 -£7,475 NPV NPV difference -£12,000 -£14,000 -£12,077

Figure 20. NPV difference (relative to the reference system) for PV only, PV and SECHP and SECHP-PV-battery for different battery sizes, averaged across all households for 80% battery efficiency and efficient SECHP operation (where applicable).

The decrease in NPV at larger battery capacities is due to the increase in capital and, in particular, equipment replacement costs. The total undiscounted lifetime cost breakdown shown in Figure 21 for different scenarios shows that the effect of increasing battery capacity on reducing operating costs is minimal: a battery capacity of 20 kWh decreases operating costs by less than 20% relative to a battery capacity of 1 kWh, whereas capital costs are 36% higher and replacement equipment costs are 65% greater.

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£80,000 Replacement £70,000 Capital £60,000 £50,000 Operating £40,000 £30,000 £20,000 Totallifetime cost £10,000 £0

Figure 21. Breakdown of lifetime costs for systems with different battery capacities in comparison to the reference system, averaged across all households with 80% battery efficiency and efficient SECHP operation (where applicable).

Operating costs are not reduced significantly by large battery capacities because these costs are dominated by gas costs, as shown in Figure 22. Although electricity cost is reduced significantly (approximately by 40% from 1 kWh to 20 kWh battery capacity), the high gas cost is over 200% of the net total operating cost (including FIT credits).

£60,000 £50,000 Elec import £40,000 £30,000 Gas £20,000 FIT- PV £10,000 FIT- CHP £0 -£10,000 FIT- export -£20,000

Lifetime operatingcost -£30,000

Figure 22. Selected operating costs across different battery capacities in comparison to the reference system, averaged across all households with 80% battery efficiency and efficient SECHP operation (where applicable).

The NPV varies significantly across the households (see Appendix, Table A 4). The main contributor to this difference is electricity demand. Figure 23 shows the NPV difference

Page 140 of 210 Chapter 4 Paul Balcombe against household electricity demand and indicates that NPV difference is significantly improved as household electricity demand increases. A household demand of above 4,300 kWh/yr would make the base case financially viable relative to the reference system. This is above the average UK household electricity demand of 3,300 kWh/yr (Barnes, 2013), but nevertheless accounts for approximately 40% of the UK housing stock (CSE, 2014). There is also a significant difference between dwelling types, with only detached houses obtaining a positive NPV difference, as shown in Figure 24. This is because larger households generally have higher energy demands, due to increased floor area and a higher average number of occupants.

£10,000

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£0

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NPV NPV difference -£10,000

-£15,000 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Annual electricity demand (kWh)

Figure 23. NPV difference for each household for the base case plotted against the household annual electricity demand.

£4,000 1,175 £2,000

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-£6,000 NPV NPV difference -£8,000 -6,649

-£10,000 -10,140 -£12,000 Detached Semi- detached Mid-terraced

Figure 24. Average NPV difference for each dwelling type for the base case, relative to the reference system.

The impact of SECHP efficiency on the cost is also substantial, as previously suggested by The Carbon Trust (2011). This study estimates that an inefficient operation decreases

Page 141 of 210 Chapter 4 Paul Balcombe the NPV difference a factor of two in the base case (-£6,900 compared to -£3,600 for the efficient operation). Thus, operating the SECHP efficiently will save a significant amount of money for the household. More efficient gas usage reduces operating costs significantly as gas cost represents such a high proportion of the total operating costs and a higher quantity of electricity is generated.

3.3.2 Sensitivity analysis As seen in Figure 21, equipment replacement costs represent a large proportion of the undiscounted lifetime cost (25%–40%). The SECHP system contributes the most to replacement costs: 40%–65%. At 20 kWh battery capacity, the replacement of battery cells becomes the highest cost. This study assumes an operating life of 10 years for both SECHP and battery cells, resulting in two replacements each for the 30 year period considered. However, other estimates of SECHP lifespan are 8–15 years (Parsons Brinckerhoff, 2012b), which would mean replacing between 1–3 times, and the lifespan of the battery cells also varies widely (Dufo-López et al., 2014; Jenkins et al., 2008).

As the SECHP has such a large replacement cost (£5,500), the effect of prolonging or shortening its lifespan is large, as shown in Figure 25. If it lasted for 15 years, the SECHP- PV-battery system would approach financial feasibility. The lifetime of the battery cells is also important, although less so than the SECHP system, except for the largest battery capacities. Shortening the lifespan to 5 years decreases the NPV difference by 46% but a 15 year lifespan is only marginally different (15%) to a 10 year lifespan.

£1,000 £0 -£1,000 -£2,000 -£3,000 -£909 -£4,000 -£3,036 -£5,000 -£3,579 -£3,579 NPV NPV difference -£6,000 -£7,000 -£5,243 -£8,000 -£5,959 8 10 15 5 10 15 SECHP life (yr) Battery life (yr)

Figure 25. NPV difference for different lifespans of the SECHP-PV-battery system for the base case, relative to the reference system.

The impact of different consumer discount rates on the NPV of the household system relative to the reference system is stark, as shown in Figure 26. As the operational savings of the household system are discounted at a higher rate, the impact of the higher capital cost becomes stronger, increasing the financial gap between the system and the Page 142 of 210 Chapter 4 Paul Balcombe reference system. Note that the NPV difference becomes positive for the average household at discount rates of 3% and below.

£6,000 £4,000 £2,000 £0 -£2,000 0% 10% 20% 30% 40% 50%

-£4,000 NPV NPV difference -£6,000 -£8,000 -£10,000 -£12,000 Discount rate

Figure 26. Annualised NPV difference for base case for different consumer discount rates.

As previously mentioned, the system is more financially viable for households with higher electricity demand. Thus, as electricity and gas prices increase over the 30 period, the relative operational savings of the system increase. Eight future cost projections were used to estimate the effect that could have on NPV; these results are shown in Figure 27. The only cost projection that comes close to producing a positive NPV difference is the highest, a 5% year on year increase in both electricity and gas over 30 years.

£2,000

£0

-£2,000

-£4,000

NPV difference NPV -£6,000

-£8,000 National No change DECC low DECC ref Elmes DECC high National National Grid Slow Grid Gone Grid progression green Accelerated growth -0.11%, - 0%, 0% 1.35%, - 2.12%, 2.6%, 5.8% 2.7%, 2.32% 2.94%, 5%, 5% 0.48% 0.71% 0.99% 3.11%

Figure 27. Average NPV difference for the base case for different future energy cost projections. The different categories represent the source and equivalent electricity and gas price inflation rates, respectively (DECC, 2013c; Elmes, 2014; National Grid, 2012b).

4 Discussion

The results have shown that the SECHP-PV-battery system provides some reduction in the variability of the grid demand relative to households with solar PV only or with PV and SECHP without battery storage. Households with both PV and SECHP exhibit even Page 143 of 210 Chapter 4 Paul Balcombe greater import ramp downs (59%) and ramp ups (36%) than PV only. Whilst SECHP electricity generation more closely coincides with household demand, there is still an excess in electricity generation from SECHP that causes increased variability. The addition of a 1 kWh battery store reduces these ramp downs by 63% and ramp ups by 22% and greater reductions occur with increasing battery capacity. Thus, battery storage offers an option to mitigate the intermittency-related impacts associated with microgeneration. However, this reduction in demand variability is limited: even a 20 kWh battery system is still worse than the reference system. Additionally, the overall level of electricity self- sufficiency achieved with this system is limited to approximately 70% (with 30% of electricity imported) for a 3 kWh battery.

Clearly, a larger capacity battery system provides greater benefits, both in reducing variation in grid demand and increasing household self-sufficiency. However, battery capital and replacement costs increase linearly with increasing capacity owing to their modular nature (doubling the number of battery cells doubles the capacity), whereas the marginal benefit decays. Even with small battery capacities (3 kWh), the household system is only financially feasible for households with high electricity demand, above 4,300 kWh/yr. This minimum electricity demand for which the system is viable increases to 4,500 kWh/yr for a 5 kWh battery, 5,000 kWh/yr for 10 kWh and 5,900 kWh/yr for 20 kWh. The total undiscounted lifetime costs are very similar between the household system and the reference system, but equipment costs (i.e. both capital and replacement) contribute to 70% of the total costs in the base case, compared to 11% for the reference system. The high replacement costs associated with the base case are due to the expected short lifespan of the SECHP unit and battery cells.

This system is not currently financially viable for the majority of UK households (60% of households have electricity demand lower than 4,300 kWh). In order for it to become financially appealing to the majority of consumers, capital (and replacement) costs must be reduced or gas and electricity costs must increase substantially. A capital grant was applied to the cost-benefit calculation in order to determine at which point the base case system becomes financially viable across the households studied. Figure 28 shows the average NPV difference relative to the reference case across all households for different levels of capital cost grant (as a proportion of the total installed cost). The error bars show the mean standard error for each value, indicating the range of ‘financial cross-over’ points across the households. The figure shows that, assuming a consumer discount rate of 5%, a 24% capital grant is required in order to make the SECHP-PV-battery system financially beneficial to the average household with an electricity demand of 3,300 kWh/yr. This is equal to £3690, close to the cost of the 2 kWh battery (£3660). It is important to

Page 144 of 210 Chapter 4 Paul Balcombe note that this average household from the simulation is not necessarily representative of the average UK household: whilst the annual electricity demand is similar (3,265 kWh/yr vs. UK average 3,300 kWh/yr), the average gas demand across households in this study was lower than the UK average: 14,700 kWh/yr compared to 16,500 kWh/yr (Barnes, 2013). However, households with higher heat demand are likely to benefit more from the PV-SECHP-battery system, therefore this estimated grant requirement is a conservative estimate for the average UK household. Additionally, 17% of British homes are not heated by mains gas (Baker, 2011), meaning they are unsuitable for this PV-SECHP-battery system and are thus excluded from the findings of this study.

£14,000 £12,000 £10,000 £8,000 £6,000 £4,000

£2,000 Present Present Value

- £0

-£2,000 Net -£4,000 -£6,000 0% 20% 40% 60% 80% 100% Capital cost grant

Figure 28. Average NPV across all households for the base case for different proportions of grants for the total capital cost.

There are currently no incentives available in the UK for household battery storage. In fact, Germany is currently the only country to subsidise small-scale battery storage (Parkinson, 2013a), whilst Japan and California are subsidising larger scale storage (1.3 GW target by 2020 for California, various multi-megawatt facilities in Japan) (Glick, 2013; Parkinson, 2013b; Wesoff, 2014). In 2013, the German government committed 25m Euro towards capital grants for battery storage systems, applicable to households that have already installed a solar PV system of less than 30 kWp capacity (Trainer, 2013). The incentive offers up to 660 Euro/kWp of solar PV capacity installed and is aimed at mitigating the country’s electricity grid balancing problems, which is expected once 40% of renewable electricity generation is reached (Parkinson, 2013a). The motivation for battery incentives from the grid operation perspective was for households to become ‘more decoupled’ from the grid (Parkinson 2013a) and to smoothen out the peaks and troughs in demand that occur for households with PV (BSW Solar 2013). This would reduce the required capacity of the central generators, as well as ‘saving power line capacities’ and thus reduce cost (BSW Solar 2013). Similarly to the case of solar PV (Balcombe et al., 2013), it is expected that the grants and low interest loans will increase demand for battery storage and trigger Page 145 of 210 Chapter 4 Paul Balcombe global manufacturing cost reductions (Weiss, 2013). Indeed, a number of interest groups are suggesting that battery costs will decrease dramatically in the near future and transform the to greater decentralisation (Platt et al., 2014; UBS Limited, 2014).

One other option to incentivise battery storage with microgeneration systems is to eliminate the export tariff associated with the FIT incentives. Currently, microgeneration owners are paid 5 p/kWh for every unit of electricity exported to the grid, in addition to the standard FIT tariff. If this was reduced, or the gap between importing and exporting costs increased, there would be a greater financial incentive to maximise consumption of the locally generated electricity (Balcombe et al., 2014; McKenna and Thomson, 2013).

If battery storage is to be incentivised in the short term, an economic impact assessment must compare other options to provide grid stability, reliability and availability. Such options are to increase the capacity of centralised variable-load generation, such as by gas and coal power, to provide greater interconnection of electricity with neighbouring countries, or to limit the feeding of solar PV electricity into the grid using local terminals and smart meters. Each of these options carries a large cost burden and, in the case of limiting solar PV feeding in to the grid, reducing the contribution of renewable electricity generation. The latter may negatively impact upon the UK 2020 renewables target of 15% (DECC, 2009). An impact analysis for each of these options is required to determine the best option, which must include environmental and energy security-related impacts in addition to cost.

5 Conclusions

Results have shown that even with solar PV, SECHP and battery storage, on average 28% of electricity demand still has to be met by imports from the grid, even though the average combined generation from solar PV and SECHP across all simulations was 4,190 kWh/yr, 30% greater than the average household electricity demand. Battery capacities above 5 kWh provide only marginal improvements in self-sufficiency relative to their large cost.

Consumption of electricity generated by solar PV is somewhat smaller than is typically assumed in literature: 28% as opposed to 50%, compared to 49% from SECHP. The SECHP generation profile is far more suitable to achieve self-sufficiency owing to the better correlation between the generation and household demand profiles.

The impact on the grid demand profile of a PV installation and a PV with SECHP without a battery is stark, drastically increasing the variation, ramp up and ramp down in daily grid demand. Battery storage reduces ramping down rates by 40% and ramping up by 28% for

Page 146 of 210 Chapter 4 Paul Balcombe a 3 kWh capacity. Thus, battery storage offers an option to mitigate PV grid balancing problems. However, the profiles are still not an improvement on the reference system even with large battery capacities of 20 kWh, which carries a high capital cost.

The base case SECHP-PV-battery system is only financially viable for those with an electricity demand above 4,300 kWh/yr, which encompasses 40% of UK households. This is much higher than the average demand of 3,300 kWh/yr. The capital and replacement costs of the battery cells and SECHP system had the largest impact on the financial viability of the system. Because of this, the financial impacts were highly sensitive to the assumed lifespan of these components, as well as the assumed consumer discount rate. Operating the SECHP more efficiently (continuous operation rather than frequent on/off cycles) was shown to be significantly more efficient and cost-effective.

With a capital cost grant equal to a small battery (2 kWh), the system would be financially feasible for the average household and would provide significant benefits in terms of grid balancing, equivalent to a reduction in ramp ups and downs of 28% and 40%, respectively. Small battery storage systems are the subject of increasing attention in global energy policy owing to the rapid rise in renewable electricity generation and so capital costs may be reduced in the near-term future. A UK capital cost grant for batteries applicable to households with microgeneration installations would serve to increase demand could help to reduce manufacturing costs with a maturing market. A comparative impact analysis between this option and others to achieve grid stability, availability and reliability should be a subject of future research.

Another option to provide greater motivation for microgeneration owners to install batteries is to reduce or eliminate the Feed-in Tariff (FIT) electricity export rate. This would create a greater price differential between importing and exporting electricity and would serve to promote greater consumption of self-generated electricity. In the longer term, this price differential is likely to increase anyway considering the current projections of high future grid electricity costs.

Finally, whilst the PV-SECHP-battery system provides benefits relating to grid balancing and household self-sufficiency, the associated environmental impacts and potential contribution to climate change targets is thus far unknown. The social benefit (or otherwise) of GHG emissions reduction was not included within the cost-benefit analysis, but if such an effect were to be monetised and included, the results may be significantly improved upon. Further research into the environmental, economic and social impacts associated with this system must be carried out in order to ensure the drive towards a sustainable energy supply is maintained.

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Appendix

The formula for estimating NPV is the following:

푪 푵푷푽 = ∑풕=ퟑퟎ 풕 Equation 5 풕=ퟎ (ퟏ+풊)풕 where Ct is the total cost associated with year t and i is the consumer discount rate. The total cost Ct was estimated by:

푪풕 = 푪풄풂풑 풕 + 푪풐풑 풕 + 푪풓풆풑 풕 Equation 6 where Ccap t is the capital cost, Cop t is the operating cost and Crep t is the equipment replacement cost, all associated with year t. The difference in NPV between the household system and the reference system is used as the indicator of financial performance:

횫푵푷푽 = 푵푷푽 − 푵푷푽풓 Equation 7 where NPVr is the NPV of the reference system.

Table A 1. Summary profile data associated with each household, including dwelling type, floor area, gas demand, electricity demand, PV capacity and PV generation.

Floor Gas Electricity PV PV Household Dwelling area demand demand capacity generation ID type (m2) (kWh/yr) (kWh/yr) (kW) (kWh/yr) 1 DH 183.9 29,173 6,276 4 3,896 2 DH 139.1 11,573 3,880 4 3,729 3 DH 139.1 12,064 3,888 4 4,224 4 DH 136.1 25,270 4,611 4 2,959 5 DH 136.1 20,616 5,851 4 3,201 6 DH 134.7 16,247 3,665 4 4,130 7 DH 128 22,931 4,773 4 3,167 8 DH 128 24,088 4,710 4 4,368 9 DH 128 20,130 4,493 4 4,557 10 DH 125.1 28,423 4,053 3.9 3,779 11 DH 104.8 20,687 5,637 3.8 4,135 12 DH 76.2 14,034 2,305 3.5 3,448 13 DH 76.2 11,320 2,550 3.4 3,037 14 DH 76.2 12,812 2,999 3.4 3,251 15 SDH 74.3 11,848 3,821 3.3 3,178 16 SDH 74.3 11,929 2,870 3.3 2,907 17 SDH 74.3 9,371 2,155 3 2,806 18 SDH 74.3 10,385 2,042 3 2,993 19 MTH 68.8 7,901 2,976 2.8 1,563 Page 148 of 210 Chapter 4 Paul Balcombe

Floor Gas Electricity PV PV Household Dwelling area demand demand capacity generation ID type (m2) (kWh/yr) (kWh/yr) (kW) (kWh/yr) 20 MTH 68.8 10,904 2,365 2.6 1,875 21 SDH 64.8 13,395 1,903 2.5 2,281 22 SDH 64.8 10,229 1,895 2.4 1,771 23 SDH 64.8 9,081 3,530 2.2 2,269 24 SDH 64.8 9,655 2,355 2.2 1,700 25 SDH 64.8 10,642 2,054 2.1 1,387 26 SDH 62.8 15,876 2,436 2 1,762 27 SDH 62.8 12,233 2,017 1.8 1,467 28 MTH 60.3 10,280 1,700 1.6 1,450 29 MTH 60.3 8,790 2,659 1.5 692 30 MTH 60.3 9,585 1,491 1.1 1,169

Table A 2. Low, medium and high capital cost estimates for a set of solar PV battery capacities (Cambridge Economic Policy Associates Ltd and Parsons Brinckerhoff, 2011).

Solar PV Low cost Medium cost High cost capacity (£) (£) (£) (kW) 1 1500 2748 5096 2 2400 4369 8017 3 3300 5990 10938 4 4200 7611 13859

Table A 3. Various estimates of SECHP installed capital cost, alongside the source of the estimate.

Installation Source cost (£)

3500 Low estimate: (Cambridge Economic Policy Associates Ltd and Parsons Brinckerhoff, 2011) 5000 (Carbon Trust, 2011) 5000 Low estimate: (Parsons Brinckerhoff, 2012) 5500 High estimate: (Cambridge Economic Policy Associates Ltd and Parsons Brinckerhoff, 2011) 6500 Conversation with distribution company 7500 Medium estimate: (Parsons Brinckerhoff, 2012) 10000 High estimate: (Parsons Brinckerhoff, 2012)

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Table A 4. A summary of the base case costs for each household, in descending order for NPV difference (relative to the reference system). Elec demand = annual electricity demand; PV cap = PV capacity; Cap cost = installed capital cost; Ref op cost = reference operating cost; NPV diff = NPV difference.

Elec PV Cap Op Ref op Simple HH demand cap cost cost cost NPV Payback payback ID (kWh/yr) (kW) (£) (£/yr) (£/yr) diffa (£) time (yr) time (yr) 1 6,276 4 16,931 50,308 110,717 8,542 15 8 10 4,053 4 16,801 38,641 93,939 6,186 16 9 11 5,637 4 16,607 35,367 87,633 4,817 17 9 8 4,710 4 16,931 37,104 88,831 4,296 17 9 9 4,493 4 16,931 28,194 78,616 3,632 18 15 7 4,773 4 16,866 40,264 86,706 1,609 20 15 6 3,665 4 16,866 18,295 64,408 1,510 20 15 4 4,611 4 16,923 44,957 90,767 1,235 27 15 5 5,851 4 16,915 45,281 88,926 188 30 15 3 3,888 4 16,931 14,189 56,690 -344 None 16 2 3,880 4 16,931 18,364 55,554 -3,000 None 18 12 2,305 3.5 16,121 14,835 50,269 -3,060 None 18 15 3,821 3.3 15,845 21,288 55,758 -3,340 None 18 14 2,999 3.4 16,023 18,395 52,295 -3,750 None 19 16 2,870 3.3 15,780 18,271 49,465 -4,907 None 28 26 2,436 2 13,689 28,761 55,231 -5,249 None None 13 2,550 3.4 16,023 15,347 45,941 -5,411 None 29 18 2,042 3 15,310 11,541 40,421 -5,549 None 30 23 3,530 2.2 14,046 21,159 47,668 -5,582 None None 21 1,903 2.5 14,435 20,412 46,124 -6,294 None None 17 2,155 3 15,310 11,597 38,955 -6,331 None None 24 2,355 2.2 14,013 20,155 40,943 -8,401 None None 27 2,017 1.8 13,365 25,658 44,332 -8,807 None None 20 2,365 2.6 14,637 22,991 43,770 -9,010 None None 22 1,895 2.4 14,337 18,957 39,079 -9,023 None None 28 1,700 1.6 12,976 20,849 37,863 -9,247 None None 25 2,054 2.1 13,819 23,200 41,072 -9,652 None None 30 1,491 1.1 12,230 21,072 34,907 -10,088 None None 19 2,976 2.8 14,999 23,666 41,296 -10,974 None None 29 2,659 1.5 12,879 28,436 41,099 -11,379 None None a NPV difference estimated as the NPV of the base case minus the reference NPV.

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References

Baker, W. 2011. Off-gas consumers Information on households without mains gas heating. London: Consumer Focus. Balcombe, P., Rigby, D. and Azapagic, A. 2013. Motivations and barriers associated with adopting microgeneration energy technologies in the UK. Renewable and Sustainable Energy Reviews, 22, 655-666. Balcombe, P., Rigby, D. and Azapagic, A. 2014. Investigating the importance of motivations and barriers related to microgeneration uptake in the UK. Applied Energy, 130, 403-418. Barnes, N. 2013. Review of typical domestic consumption values [Online]. Ofgem. Available: www.ofgem.gov.uk/ofgem-publications/39337/review-typical-domestic- consumption-values.pdf [Accessed 10 Jan 2014]. Baxi 2011a. Ecogen: The Baxi Ecogen Dual Energy System. Warwick. Baxi 2011b. Installation & Servicing Instructions: Ecogen 24/1.0 Gas Fired Wall Mounted and Power Generator. Warwick. Bianchi, M., De Pascale, A. and Melino, F. 2013. Performance analysis of an integrated CHP system with thermal and Electric Energy Storage for residential application. Applied Energy, 112, 928-938. BRE. 1990. Domestic (low energy) buildings - hourly gas and electricity consumption [Online]. UKERC: Building Research Establishment. Available: data.ukedc.rl.ac.uk/cgi-bin/dataset_catalogue/catalogue.cgi.py [Accessed 5 March 2013]. Bright Green Energy Ltd. 2014. Off Grid Power Store, [Online]. Available: www.offgridpowerstore.com/default.asp [Accessed 01 December 2013]. BSW Solar. 2013. Information on support measures for solar power storage systems [Online]. Available: http://www.solarwirtschaft.de/fileadmin/media/pdf/infopaper_energy_storage.pdf [Accessed 9 June 2014] Cambridge Economic Policy Associates Ltd and Parsons Brinckerhoff 2011. Updates to the Feed-in Tariffs model: Documentation of changes made for non-PV technologies. London: DECC Carbon Trust 2011. Micro-CHP Accelerator Final Report March 2011. London. Carmeli, M. S., Castelli-Dezza, F., Mauri, M., Marchegiani, G. and Rosati, D. 2012. Control strategies and configurations of hybrid systems. Renewable Energy, 41, 294-305. Castillo-Cagigal, M., Gutiérrez, A., Monasterio-Huelin, F., Caamaño-Martín, E., Masa, D. and Jiménez-Leube, J. 2011. A semi-distributed electric demand-side Page 151 of 210 Chapter 4 Paul Balcombe

management system with PV generation for self-consumption enhancement. Energy Conversion and Management, 52, 2659-2666. Chen, X. P., Wang, Y. D., Yu, H. D., Wu, D. W., Li, Y. and Roskilly, A. P. 2012. A domestic CHP system with hybrid storage. Energy and Buildings, 55, 361-368. Chunekar, A. and Rathi, S. S. 2012. Discount Rates and Energy Efficiency Discussion Paper. Prayas Energy Group. CSE. 2014. Energy consumption data (domestic) [Online]. Bristol: Centre for Sustainable Energy. Available: www.cse.org.uk/resources/open-data/domestic-energy- consumption-data [Accessed 10 September 2014]. DECC 2009. The UK Renewable Energy Strategy. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown Copyright. DECC. 2013a. Annual domestic energy bills (QEP 2.2.3) [Online]. Statistical data set: Crown Copyright. Available: www.gov.uk/government/statistical-data-sets/annual- domestic-energy-price-statistics [Accessed 1 February 2014]. DECC 2013b. Monthly central Feed-in Tariff register. JULY_2013_MONTHLY_CENTRAL_FEED- IN_TARIFF_REGISTER_STATISTICS.XLS. Microsoft Excel. London. Available: www.gov.uk/government/statistical-data-sets/monthly-central-feed-in-tariff-register- statistics: DEPARTMENT OF ENERGY AND CLIMATE CHANGE. DECC. 2013c. Updated energy and emissions projections: 2013 [Online]. Crown Copyright. Available: www.gov.uk/government/publications/updated-energy-and- emissions-projections-2013 [Accessed 01 December 2013]. DECC 2014. Monthly central Feed-in Tariff register. JULY_2013_MONTHLY_CENTRAL_FEED- IN_TARIFF_REGISTER_STATISTICS.XLS. Microsoft Excel. London. Available: www.gov.uk/government/statistical-data-sets/monthly-central-feed-in-tariff-register- statistics. Dufo-López, R., Lujano-Rojas, J. M. and Bernal-Agustín, J. L. 2014. Comparison of different lead–acid battery lifetime prediction models for use in simulation of stand- alone photovoltaic systems. Applied Energy, 115, 242-253. Edmunds, R. K., Cockerill, T. T., Foxon, T. J., Ingham, D. B. and Pourkashanian, M. 2014. Technical benefits of energy storage and electricity interconnections in future British power systems. Energy, 70, 577-587. Electricians Forums. 2012. Inverter Lifespan [Online]. Available: www.electriciansforums.co.uk/photovoltaic-solar-panels-green-energy- forum/63240-inverter-lifespan.html [Accessed 12 Dec 2013].

Page 152 of 210 Chapter 4 Paul Balcombe

Elmes, S. 2014. The Future of Energy Bills: getting to an inflation rate for domestic energy [Online]. Available: www.solarblogger.net/2014/01/the-future-of-energy-bills.html [Accessed 01 February 2014]. Energy Saving Trust 2008. Domestic heat by gas: boiler systems- guidance for installers and specifiers. Available: www.energysavingtrust.org.uk/Publications2/Housing- professionals/Heating-systems/Domestic-heating-by-gas-boiler-systems-2008- edition. EPIA 2014. Press release: Market Report 2013. Available: www.epia.org/uploads/tx_epiapublications/Market_Report_2013_02.pdf. Fubara, T. C., Cecelja, F. and Yang, A. 2014. Modelling and selection of micro-CHP systems for domestic energy supply: The dimension of network-wide consumption. Applied Energy, 114, 327-334. Glick, D. 2013. Inside California’s new energy storage mandate [Online]. GreenBiz Group. Available: www.greenbiz.com/blog/2013/12/11/inside-california-energy-storage- mandate [Accessed 15 April 2014]. Hawkes, A. and Leach, M. 2005. Impacts of temporal precision in optimisation modelling of micro-Combined Heat and Power. Energy, 30, 1759-1779. Hoppmann, J., Volland, J., Schmidt, T. S. and Hoffmann, V. H. 2014. The economic viability of battery storage for residential solar photovoltaic systems – A review and a simulation model. Renewable and Sustainable Energy Reviews, 39, 1101-1118. Hosseini, M., Dincer, I. and Rosen, M. A. 2013. Hybrid solar–fuel cell combined heat and power systems for residential applications: Energy and exergy analyses. Journal of Power Sources, 221, 372-380. IEA 2014. Technology Roadmap: Energy storage. INTERNATIONAL ENERGY AGENCY (ed.). Paris, France. IEC 2012. Grid integration of large-capacity Renewable Energy sources and use of large- capacity Electrical Energy Storage. White Paper. Geneva: International Electrotechnical Commission. Jenkins, D. P., Fletcher, J. and Kane, D. 2008. Model for evaluating impact of battery storage on microgeneration systems in dwellings. Energy Conversion and Management, 49, 2413-2424. Jones, B. 2012. Study on the impact of Photovoltaic (PV) generation on peak demand. NETWORK PLANNING AND DEVELOPMENT BRANCH (ed.). Western power. Kopanos, G. M., Georgiadis, M. C. and Pistikopoulos, E. N. 2013. Energy production planning of a network of micro combined heat and power generators. Applied Energy, 102, 1522-1534.

Page 153 of 210 Chapter 4 Paul Balcombe

Leadbetter, J. and Swan, L. 2012a. Battery storage system for residential electricity peak demand shaving. Energy and Buildings, 55, 685-692. Leadbetter, J. and Swan, L. G. 2012b. Selection of battery technology to support grid- integrated renewable electricity. Journal of Power Sources, 216, 376-386. Li, J. and Danzer, M. A. 2014. Optimal charge control strategies for stationary photovoltaic battery systems. Journal of Power Sources, 258, 365-373. Lombardi, K., Ugursal, V. I. and Beausoleil-Morrison, I. 2010. Proposed improvements to a model for characterizing the electrical and performance of Stirling engine micro-cogeneration devices based upon experimental observations. Applied Energy, 87, 3271-3282. Maclay, J. D., Brouwer, J. and Samuelsen, G. S. 2011. Experimental results for hybrid energy storage systems coupled to photovoltaic generation in residential applications. International Journal of Hydrogen Energy, 36, 12130-12140. McKenna, E., McManus, M., Cooper, S. and Thomson, M. 2013. Economic and environmental impact of lead-acid batteries in grid-connected domestic PV systems. Applied Energy, 104, 239-249. McKenna, E. and Thomson, M. 2013. Photovoltaic metering configurations, feed-in tariffs and the variable effective electricity prices that result. Renewable Power Generation, IET, 7, 235-245. McKinsey & Co 2012. Capturing the full electricity efficiency potential of the U.K. DECC (ed.). Crown Copyright. MCS. 2014. The Microgeneration Certification Scheme [Online]. Available: www.microgenerationcertification.org/ [Accessed 14 July 2014 ]. Mehleri, E. D., Sarimveis, H., Markatos, N. C. and Papageorgiou, L. G. 2013. Optimal design and operation of distributed energy systems: Application to Greek residential sector. Renewable Energy, 51, 331-342. Morley, J. and Hazas, M. The significance of difference: Understanding variation in household energy consumption. Energy efficiency first: The foundation of a low- carbon society, 2011. ECEE 2011 Summer Study. Mulder, G., Six, D., Claessens, B., Broes, T., Omar, N. and Mierlo, J. V. 2013. The dimensioning of PV-battery systems depending on the incentive and selling price conditions. Applied Energy, 111, 1126-1135. National Grid 2012a. Solar PV Briefing Note. DECC (ed.). London: DECC. National Grid 2012b. UK Future Energy Scenarios. UK gas and electricity transmission. Navitron Ltd. 2013. Navitron [Online]. Available: www.navitron.org.uk [Accessed 1 Dec 2013].

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NHBC Foundation 2011. Introduction to Feed-In Tariffs. BRE (ed.). Available: www.nhbcfoundation.org/Researchpublications/IntroductiontoFeedinTariffsNF23/ta bid/437/Default.aspx: IHS BRE Press. Nosrat, A. and Pearce, J. M. 2011. Dispatch strategy and model for hybrid photovoltaic and trigeneration power systems. Applied Energy, 88, 3270-3276. Nottrott, A., Kleissl, J. and Washom, B. 2013. Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems. Renewable Energy, 55, 230-240. Ofgem 2012. Feed-in Tariff Payment Rate Table for Photovoltaic Eligible Installations. DECC (ed.). London. Orr, G., Dennish, T., Summerfield, I. and Purcell, F. 2011. SEAI Commercial micro-CHP Field Trial Report. THE SUSTAINABLE ENERGY AUTHORITY OF IRELAND (ed.). Dublin. Owens, G. 2002. Economic & Financial Evaluation of Renewable Energy Projects. OFFICE OF ENERGY ENVIRONMENT AND TECHNOLOGY (ed.) Best Practices Guide. Alternative Energy Development. Palmer, J. and Cooper, I. 2012. Housing Energy Fact File 2012: energy use in homes. DECC (ed.). London: Crown Copyright. Papic, I. 2006. Simulation model for discharging a lead-acid battery energy storage system for load leveling. Energy Conversion, IEEE Transactions on, 21, 608-615. Parkinson, G. 2013a. Germany finances major push into home battery storage for solar [Online]. Renew Economy. Available: reneweconomy.com.au/2013/germany- finances-major-push-into-home-battery-storage-for-solar-58041 [Accessed 10 April 2014]. Parkinson, G. 2013b. Japanese Energy Giants Rush Into Storage as Solar Booms [Online]. Greentech Media. Available: www.greentechmedia.com/articles/read/japanese-energy-giants-rush-into-storage- as-solar-booms [Accessed 15 April 2014]. Parra, D., Gillott, M. and Walker, G. S. 2014. The role of hydrogen in achieving the decarbonization targets for the UK domestic sector. International Journal of Hydrogen Energy, 39, 4158-4169. Parsons Brinckerhoff 2012a. Solar PV cost update. DECC (ed.). London: www.pbworld.com. Parsons Brinckerhoff 2012b. Update of non-PV data for Feed In Tariff. DECC (ed.). Available: www.pbworld.com. Peacock, A. D. and Newborough, M. 2005. Impact of micro-CHP systems on domestic sector CO2 emissions. Applied Thermal Engineering, 25, 2653-2676.

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Peacock, A. D. and Newborough, M. 2006. Impact of micro-combined heat-and-power systems on energy flows in the UK electricity supply industry. Energy, 31, 1804- 1818. Peacock, A. D. and Newborough, M. 2007. Controlling micro-CHP systems to modulate electrical load profiles. Energy, 32, 1093-1103. Platt, R., Williams, J., Pardoe, A. and Straw, W. 2014. A new approach to electricity markets: How new, disruptive technologies change everything. London. Available: www.ippr.org/assets/media/publications/pdf/new-approach-electricity- markets_Sep2014.pdf: Institute for Public Policy Research. Purvins, A., Papaioannou, I. T. and Debarberis, L. 2013. Application of battery-based storage systems in household-demand smoothening in electricity-distribution grids. Energy Conversion and Management, 65, 272-284. Purvins, A. and Sumner, M. 2013. Optimal management of stationary -ion battery system in electricity distribution grids. Journal of Power Sources, 242, 742-755. PVOutput. 2013. PVOutput.org [Online]. Available: www.pvoutput.org [Accessed 01 December 2013]. Riffonneau, Y., Bacha, S., Barruel, F. and Ploix, S. 2011. Optimal power flow management for grid connected PV systems with batteries. IEEE Transactions on Sustainable Energy, 2, 309-320. Roselli, C., Sasso, M., Sibilio, S. and Tzscheutschler, P. 2011. Experimental analysis of microcogenerators based on different prime movers. Energy and Buildings, 43, 796-804. Rudge, C. 2010. Inverters for solar PV panels: your questions answered [Online]. Available: www.yougen.co.uk/blog- entry/1516/Inverters+for+solar+PV+panels%273A+your+questions+answered/ [Accessed 12 Dec 2013]. Ruxandra, Y. H. and Stroeve, V. P. 2012. Report on Solar Energy Storage Methods and Life Cycle Assessment: Final project reportm Energy Research and Development Division. CALIFORNIA SOLAR ENERGY COLLABORATIVE (ed.). California: California Energy Commission. Rydh, C. J. and Sandén, B. A. 2005. Energy analysis of batteries in photovoltaic systems. Part I: Performance and energy requirements. Energy Conversion and Management, 46, 1957-1979. Schmiegel, A. U. and Kleine, A. 2014. Optimized Operation Strategies for PV Storages Systems Yield Limitations, Optimized Battery Configuration and the Benefit of a Perfect Forecast. Energy Procedia, 46, 104-113.

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Short, W., Packey, D. J. and Holt, T. 1995. A Manual for the Economic Evaluation of Energy Efficiency and Renewable Energy Technologies Golden, Colorado: National Renewable Energy Laboratory. Sullivan, J. L. and Gaines, L. 2012. Status of life cycle inventories for batteries. Energy Conversion and Management, 58, 134-148. Thomson, M. and Infield, D. 2008. Modelling the impact of micro-combined heat and power generators on electricity distribution networks. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 222, 697-706. Thretford, K. 2013. Charting the Fall of Solar Prices [Online]. The Atlantic, . Available: www.theatlantic.com/technology/archive/2013/08/charting-the-fall-of-solar- prices/278803/ [Accessed 19 September 2014]. Thygesen, R. and Karlsson, B. 2014. Simulation and analysis of a solar assisted heat pump system with two different storage types for high levels of PV electricity self- consumption. Solar Energy, 103, 19-27. Trainer, T. 2013. 100% Renewable supply? Comments on the reply by Jacobson and Delucchi to the critique by Trainer. Energy Policy, 57, 634-640. UBS Limited 2014. Will solar, batteries and electric cars re-shape the electricity system? Q-Series®: Global Utilities, Autos & Chemicals. Available: www.qualenergia.it/sites/default/files/articolo-doc/ues45625.pdf. Van den Bossche, P., Vergels, F., Van Mierlo, J., Matheys, J. and Van Autenboer, W. 2006. SUBAT: An assessment of sustainable battery technology. Journal of Power Sources, 162, 913-919. Wang, Y., Ronilaya, F., Chen, X. and Roskilly, A. P. 2013. Modelling and simulation of a distributed power generation system with energy storage to meet dynamic household electricity demand. Applied Thermal Engineering, 50, 523-535. Wardle, R., Barteczko-Hibbert, C., Miller, D. and Sidebotham, E. 2013. Initial Load Profiles from CLNR Intervention Trials. (Northeast) Limited: Durham University. Weiss, J. 2013. Could 2014 be the year of the battery? [Online]. Energetics Ltd. Available: www.energetics.com.au/insights/latest-news/climate-change-matters/electricity- storage-battery-solar-pv-renewable [Accessed 8 April 2014]. Wesoff, E. 2014. Another 40MW of Grid-Scale Energy Storage in the California Pipeline [Online]. Greentech Media. Available: www.greentechmedia.com/articles/read/Another-40-MW-of-Grid-Scale-Energy- Storage-in-the-California-Pipeline [Accessed 15 April 2014]. Williams, C. J. C., Binder, J. O. and Kelm, T. Demand side management through heat pumps, thermal storage and battery storage to increase local self-consumption

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and grid compatibility of PV systems. Innovative Technologies (ISGT Europe), 2012 3rd IEEE PES International Conference and Exhibition on, 14-17 Oct. 2012 2012. 1-6. Yan, X., Zhang, X., Chen, H., Xu, Y. and Tan, C. 2014. Techno-economic and social analysis of energy storage for commercial buildings. Energy Conversion and Management, 78, 125-136. Zucker, A. and Hinchliffe, T. 2014. Optimum sizing of PV-attached electricity storage according to power market signals – A case study for Germany and Italy. Applied Energy, 127, 141-155.

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Chapter 5: Environmental impacts of microgeneration: Integrating solar PV, Stirling engine CHP and battery storage

This paper was submitted to Applied Energy in July 2014 and is now published with the following citation:

Balcombe, P., Rigby, D. & Azapagic, A. 2015. Environmental impacts of microgeneration: Integrating solar PV, Stirling engine CHP and battery storage. Applied Energy, 139, 245-259

The research consists of an environmental life cycle assessment of a household energy supply system comprising solar PV, Stirling engine CHP and lead-acid battery storage. The research was designed, implemented and written by the author of this thesis. Co- authors Rigby and Azapagic supervised the research and edited the paper prior to submission.

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Environmental impacts of microgeneration: Integrating solar PV, Stirling engine CHP and battery storage

Paul Balcombea,b,c, Dan Rigbyb and Adisa Azapagica,c* a School of Chemical Engineering and Analytical Science, The University of Manchester, M13 9PL, UK b School of Social Sciences, The University of Manchester, M13 9PL, UK c Sustainable Consumption Institute, The University of Manchester, M13 9PL, UK * Corresponding author, Tel: 0161 306 4363, Email: [email protected]

Abstract

Rapid increase in household solar PV uptake has caused concerns regarding intermittent exports of electricity to the grid and related balancing problems. A microgeneration system combining solar PV, combined heat and power plant (CHP) and battery storage could potentially mitigate these problems whilst improving household energy self-sufficiency. This research examines if this could also lead to lower environmental impacts compared to conventional supply of electricity and heat. Life cycle assessment has been carried out for these purposes simulating daily and seasonal energy demand of different household types. The results suggest that the impacts are reduced by 35–100% compared to electricity from the grid and heat from gas boilers. The exception is depletion of elements which is 42 times higher owing to the antimony used for battery manufacture. There is a large variation in impacts with household energy demand, with higher consumption resulting in a far greater reduction in impacts compared to the conventional supply. CHP inefficiency caused by user maloperation can decrease the environmental benefits of the system significantly; for example, the global warming potential increases by 17%. This highlights the need for consumer information and training to ensure maximum environmental benefits of microgeneration. Appropriate battery sizing is essential with the 10–20 kWh batteries providing greatest environmental benefits. However, any reduction in impacts from battery storage is heavily dependent on the assumptions for system credits for electricity export to the grid. Effective management of the battery operation is also required to maximise the battery lifetime: a reduction from 10 to five years increases depletion of elements by 45% and acidification by 32%. Increasing the recycling of metals from 0-100% reduces the impacts from 46-179%. If 90% of antimony is recycled, the depletion of elements is reduced by three times compared to the use of virgin antimony. However, this impact is still 12 times higher than for the conventional system owing to the use of other metals in the system.

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Keywords: Microgeneration; solar PV; Stirling engine CHP; battery storage; life cycle assessment; environmental impacts

1. Introduction

The uptake of solar PV has been growing rapidly over the past few years, driven largely by the need to reduce greenhouse gas emissions from energy generation. By the end of 2013, the global installed capacity of solar PV reached 138 GW, with 37 GW added in 2013 alone, a 35% increase on the previous year (EPIA, 2014). However, this has led to concerns related to intermittency of supply and grid balancing. For example, the UK National Grid warned that a penetration of PV higher than 10 GW (equivalent to the uptake by 10% of UK households) will exacerbate problems with grid balancing and that the uncontrolled exporting of PV electricity would make it unreliable, requiring rapid ramping up and down of load-following generators such as coal and gas plants (National Grid, 2012). This may also necessitate the installation of additional load-following plants to run at reduced capacity, in order to meet the greater variation in supply (National Grid, 2012). Or, more likely, once new capacity is installed, older plants with lower efficiency and higher environmental impacts will be used at a reduced capacity (Gross et al., 2006; MIT, 2011). The construction of more capacity alongside lower-efficiency operation would result in higher environmental (and economic) impacts (Trainer, 2013) which are typically not accounted for when considering intermittent renewable energy generation.

One of the solutions proposed for dealing with these grid issues is coupling solar PV with battery storage which could potentially help to reduce uncontrolled exports and prevent the balancing and ramping problems (Leadbetter and Swan, 2012; Li and Danzer, 2014; Papic, 2006). Battery storage would also help to improve household self-sufficiency of energy supply, an important motivation for installing microgeneration technologies (Balcombe et al., 2014b), by allowing them to use electricity when needed rather than when generated. A recent study (Balcombe et al., 2014a) demonstrated that coupling solar PV and battery storage with a Stirling engine combined heat and power plant (SECHP) would help further towards improving self-sufficiency of supply. Like a standard gas boiler, SECHP is fuelled by natural gas to provide heat and co-generate electricity. Its daily electricity generation profile is likely to match household electricity demand more closely than solar PV, as the system only generates electricity when there is a heat demand, that is, when the residents are likely to be at home. However, as the system is heat-led, this applies only in the winter months so that the PV is still needed during summer. Additionally, the SECHP efficiency, and consequently its environmental impacts, depend greatly on how the system is operated (Carbon Trust, 2011; Orr et al., 2011). As it needs time and fuel to reach the operating temperature (~500 °C), frequent switching on Page 161 of 210 Chapter 5 Paul Balcombe and off reduces its efficiency (Carbon Trust, 2011; Lombardi et al., 2010; Roselli et al., 2011) - there is currently little information on how the environmental impacts are affected by its operation. Furthermore, it has been suggested that SECHP is only suitable for large households with higher energy demands (Carbon Trust, 2011), but the effect of different demand profiles on environmental impacts has not been investigated yet.

Similarly, it is unclear how the impacts associated with the production and use of batteries would affect the environmental performance of an integrated PV-SECHP-battery system in comparison with the conventional supply of electricity from the grid and heat from a boiler. Although the environmental impacts of batteries have been reported previously (McKenna et al., 2013; McManus, 2012; Rydh, 1999; Sullivan and Gaines, 2012), only a few studies considered their use with solar PV (García-Valverde et al., 2009; Kaldellis et al., 2010; McKenna et al., 2013; et al., 1998), finding the impacts to be unfavourable compared to solar PV only (Kaldellis et al., 2010; McKenna et al., 2013) or highly dependent on the battery capacity (Watt et al., 1998). Several researchers also considered the impacts of individual technologies such as solar PV (e.g. Alsema, 2000; Fthenakis et al., 2008; Stamford and Azapagic, 2012) and SECHP (Greening, 2013; Pehnt, 2008), but none investigated the impacts associated with the integrated system comprising all three technologies and considering its dynamic operation with respect to daily and seasonal energy demand.

Therefore, the aim of this research is to evaluate the life cycle environmental impacts of such a system installed in a household and compare it to conventional electricity and heat supply. For these purposes, demand profiles in different household types have been simulated (Balcombe et al., 2014a), considering different SECHP operating efficiencies as well as battery capacities. The impacts have been estimated using the life cycle assessment (LCA) methodology detailed in the next section. The results are presented in section 3 and discussed in section 4 with the conclusions drawn in section 5.

2. Methodology

The LCA study follows the ISO 14040/14044 methodology (ISO, 2006a; ISO, 2006b). The following sections define the goal and scope of the study together with the data and assumptions.

2.1 Goal and scope

The main goal of the study is to determine the environmental impacts associated with an integrated solar PV, SECHP and battery storage system installed in a household and compare it to the impacts from a conventional supply of electricity from the grid and heat

Page 162 of 210 Chapter 5 Paul Balcombe from a domestic boiler. A further goal is to determine the effect on environmental impacts of the following parameters:

 variation in the daily and seasonal electricity and gas demand in different households;  efficiency of CHP operation related to the way it is operated by the user; and  different battery capacity.

As shown in Figure 29, the scope of the study is from cradle to grave, comprising the following life cycle stages:

 extraction and processing of raw materials;  manufacture of solar PV, SECHP and battery;  installation of the system in a household;  operation and maintenance over the lifetime of the system;  waste disposal and recycling at the end of life of system components; and  all transportation.

These stages and the individual technologies are described in turn below assuming the system to be installed in a household in the UK. The functional unit is defined as the annual heat and electricity demand of a household.

Extraction & processing of raw materials

T T T

Solar PV manufacture SECHP manufacture Battery manufacture

T T T

Fuel extraction & processing

Electricity Installation generation

Operation T Maintenance (electricity & heat generation)

T

Waste recycling/disposal

Figure 29. The life cycle diagram of the household microgeneration system comprising solar PV, SECHP and battery storage

[T: transport]

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2.1.1 Solar PV manufacture A multi-crystalline panel mounted on a slanted roof is considered to represent a typical UK installation (DECC, 2013; Ofgem, 2014). An average panel size of 3 kWp is assumed; the effect of different panel sizes (1.1-4 kWp), based on the available roof- space of different households, is explored later in the paper as part of the sensitivity analysis. The inventory data for the 3 kWp panel are shown in Table 18. Life cycle inventory data for the manufacture of solar PV have been sourced from the Ecoinvent database V2.2 (Ecoinvent, 2010), assuming that the panels are produced in Europe.

2.1.2 SECHP manufacture The type of the SECHP considered in the study is Baxi Ecogen, the only system accredited by the UK Microgeneration Certification Scheme (2014). It is manufactured in the UK with a capacity of 1 kWe and 6.4 kWth. The inventory data are given in Table 19 and have been obtained mainly from the manufacturer (Baxi, 2011b), with missing data sourced from Ecoinvent (2010).

The SECHP requires an auxiliary burner of 18 kW to supplement the heat generation; this is included in the system boundary. However, the ancillary household heating components, such as pipework, radiators and hot water tank, are not considered as these are also required for gas boiler with which the system is being compared.

2.1.3 Battery manufacture A lead-acid battery is chosen for consideration here as the most common type (Sullivan and Gaines, 2012). The average battery capacity, defined as the maximum quantity of electricity stored, is assumed at 6 kWh with a range of other battery sizes (2–40 kWh) considered within the sensitivity analysis. The inventory data are given in Table 19 for the average-size battery, assumed to be produced in the UK. A charge controller would normally be required to operate the battery efficiently, but no data were found so that this component is not included in the study. Average composition of the battery cell was taken from Sullivan and Gaines (2012), based on the composition of lead-acid batteries for (w/w): lead 69%, water 18%, sulphuric acid 11%, polypropylene 4%, glass fibre 4% and antimony 1%. The energy consumption for the manufacture is assumed at 13 MJ/kg battery cell, of which 65% is electricity and the rest heat from natural gas (Rydh and Sandén, 2005); the weight of the battery is 156 kg (Bright Green Energy Ltd., 2014; Navitron Ltd., 2013).

2.1.4 Installation and operation of the PV-SECHP-battery system For the installation, only transport of the energy units to the household has been considered as detailed in section 2.1.6.

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To obtain energy generation profiles, the system operation was simulated for 30 real households with differing electricity and heat demands over the course of a year, considering demand in 5-minute intervals at different times of the day and year. Energy demand according to the size of the house has also been considered for three main types of house in the UK: detached, semi-detached and terraced. The simulation data used to estimate the impacts are summarised in Table 20 with further details given in Supplementary material; a detailed description of the simulation and the results is available in Balcombe et al. (2014a). In the base case, average figures across all 30 households have been used for LCA modelling. The influence on the impacts of the variability of household demand is explored in the sensitivity analysis in the latter parts of the paper.

In the simulation model (Balcombe et al., 2014a), the PV-SECHP-battery system is assumed to be operated to maximise household self-sufficiency of energy supply, as follows. The SECHP unit supplies all the heat demand (Table 20), also co-generating electricity to meet a proportion of the demand, together with the solar PV. When the total electricity generation from the SECHP and solar PV exceeds demand, residual electricity is stored in the battery. If the battery is at full capacity, the residual electricity is exported to the grid and the system credited for the equivalent avoided impacts. When the total electricity generation from SECHP and solar PV is lower than the demand, it is supplemented by electricity from the battery. If there is insufficient capacity in the battery, the electricity shortfall is met by grid imports (Table 20). The average UK electricity grid mix used for LCA modelling is given in Table 21.

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Table 18. Inventory data for the manufacture of a 3 kWp solar PV, by component (Ecoinvent, 2010; Stamford and Azapagic, 2012)

Energy and materials Value Unit Energy and materials Value Unit PV panel manufacture (22.8 m2) Inverter manufacture (2.5 kW) Electricity, medium voltage 387 MJ Electricity, medium voltage 76.3 MJ Heat, natural gas 123 MJ Steel, low alloy 9.8 kg PV cell 21.3 m2 5.5 kg Solar glass 230 kg Corrugated board 2.5 kg Water 485 l Aluminium 1.4 kg Aluminium alloy 60 kg Inductor 0.35 kg Corrugated board 25 kg Capacitor, film, through-hole mounting 0.34 kg Ethylvinylacetate foil 22.8 kg Polystyrene foam 0.3 kg Polyethylene terephthalate 8.5 kg Capacitor, electrolyte type 0.26 kg Glass fibre reinforced plastic 4.3 kg Connector 0.24 kg Silicone 2.8 kg Printed wiring board 0.22 m2 Copper 2.6 kg Polyethylene 0.06 kg Polyvinylfluoride film 2.5 kg Diode 0.047 kg Acetone 0.3 kg Transistor 0.038 kg Brazing 0.2 kg 0.028 kg Propanol 0.19 kg Capacitor, Tantalum 0.023 kg 0.049 kg Polyvinylchloride 0.01 kg Vinyl acetate 0.037 kg Styrene-acrylonitrile 0.01 kg Lubricating oil 0.037 kg Resistor 0.005 kg 0.0037 kg Electric installation Mounting frame manufacture (23.5 m2) High density polyethylene 17.61 kg Aluminium 64.6 kg Copper 14.7 kg Steel, low alloy 34.2 kg Polyvinylchloride 2.13 kg Corrugated board 3.04 kg Steel, low alloy 0.86 kg Polystyrene 0.16 kg Nylon 0.23 kg High density polyethylene 0.03 kg Polycarbonate 0.2 kg Zinc 0.04 kg Brass 0.02 kg Epoxy resin 0.002 kg

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Table 19. Inventory data for the manufacture of SECHP (left) and battery (right), by component (Baxi, 2011b; Ecoinvent, 2010; Sullivan and Gaines, 2012).

Energy and materials Value Unit Energy and materials Value Unit SECHP manufacture Battery manufacture (6 kWh) Electricity, medium voltage 147 MJ Electricity, medium voltage 1318.2 MJ Electricity, low voltage 32 MJ Heat, natural gas 709.8 MJ Heat from natural gas 295 MJ Lead 107.64 kg Heat from light fuel oil 66 MJ Water 28.08 l Water 94 l Sulphuric acid 17.16 kg Iron 33 kg Polypropylene 6.24 kg Steel 30 kg Glass fibre 6.24 kg Chromium steel 6.8 kg Antimony 1.56 kg Copper 0.99 kg Inverter manufacture (2.5 kW) Aluminium 0.53 kg Electricity, medium voltage 76.3 MJ Tin 0.057 kg Steel, low alloy 9.8 kg Lead 0.026 kg Copper 5.51 kg Nickel 0.013 kg Corrugated board 2.5 kg Zinc 0.0088 kg Aluminium 1.4 kg High density polyethylene 2.8 kg Inductor 0.351 kg Polyvinylchloride 0.26 kg Capacitor 0.341 kg Ceramic tiles 0.11 kg Polystyrene foam 0.3 kg Rock wool 2.6 kg Capacitor 0.256 kg Auxiliary boiler manufacture Connector 0.237 kg Electricity, medium voltage 74 MJ Printed wiring board 0.2246 m2 Heat from natural gas 119 MJ Polyethylene 0.06 kg Heat from light fuel oil 63 MJ Diode 0.047 kg Water 46 l Transistor 0.038 kg Steel 29 kg Integrated circuit 0.028 kg Aluminium 1.9 kg Capacitor 0.023 kg Chromium steel 1.3 kg Polyvinylchloride 0.01 kg Brazing solder 1.0 kg Styrene-acrylonitrile 0.01 kg Copper 0.77 kg Resistor 0.005 kg Brass 0.013 kg Wiring Rock wool 2.0 kg Copper 20 kg Alkyd paint 0.32 kg Polyvinylchloride 10 kg Corrugated board 1.3 kg High density polyethylene 0.23 kg

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Table 20. Household annual energy demand and generation by different components of the system, also showing the imports and exports of electricity.

Minimum Average Maximum (kWh/yr) (kWh/yr) (kWh/yr) Energy demand Electricity 1,491 3,265 6,276 Heat 6,321 14,716 23,339 Energy generation Solar PVa (electricity) 692 2,772b 4,557 SECHP (electricity)b 715 1,477 2,946 SECHP (heat)b 6,321 14,716 23,339 Battery storage 401 797 958 Electricity imported from the grid 218 982 2,882 Electricity exported to the grid 36 1,965 3,433 a Solar PV capacity: 1.1-4 kWp (average: 3 kWp). b Average annual generation per household: (2,772 kWh.yr-1)/(3 kWp) = 924 kWh/kWp.yr

Table 21. UK electricity mix in 2013 (DECC, 2014a).

Source Contribution Coal 37 (%) Gas 28 Nuclear 19 Onshore wind and solar PV 6a 5 Offshore wind 3 Hydro (natural flow) 1 Oil 1 Total 100 a Only aggregated data are available. It is assumed that 90% is from wind and 10% from solar PV.

Based on the simulation and the actual PV generation data (Balcombe et al., 2014a), the efficiency of solar PV is estimated at 13.2% and average annual generation at 924 kWh/kWp.yr. This compares to the UK average solar PV performance of 840 kWh/kW.yr (NHBC Foundation, 2011). Using the same simulation model, the average efficiency of SECHP operation is found to be 94.7%, including start-ups and shut-downs. The influence on the impacts of lower and higher efficiencies (72.9-96.5%) is explored in the sensitivity analysis.

To generate heat and electricity, SECHP uses 1.19 MJ of natural gas per MJ heat output and 0.05 kWh of electricity per kWh electricity generated (Baxi, 2011a). It is assumed to be serviced annually, with steel parts being replaced at the rate of 1% per year (Ecoinvent, 2010). The battery is also serviced yearly to top-up the evaporated water (50% of the original amount). Its average efficiency over the lifetime is assumed at 80% (Rydh and Sandén, 2005; Sullivan and Gaines, 2012; Van den Bossche et al., 2006).

The operational lifetime of the solar panels is assumed at 30 years and 10 years for the SECHP and the battery (Cambridge Economic Policy Associates Ltd and Parsons Page 168 of 210

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Brinckerhoff, 2011). The inverter for both the PV and battery has the lifetime of 11 years (Electricians Forums, 2012; Rudge, 2010) and the wiring 30 years.

2.1.5 Waste disposal and recycling At the end of life, metal components are assumed to be recycled according to the global recycling rates as follows: aluminium 91%, copper 41%, iron and steel 62% and lead 94% (Ayris and Zakar, 2009; Copper Alliance, 2014; Dahlströma et al., 2004; International Aluminium Institute, 2009; World Steel Association, 2009). The system has been credited for displacing the equivalent quantity of virgin material used to manufacture the components (see Table 18 and Table 19 for the quantities). Battery cells are recycled at 100% as this is required by UK law (HM Government, 2009). For every 1000 kg of waste batteries, 650 kg of secondary lead (94% of lead input) and 71 kg of sulphuric acid (65%) is recovered (Fisher et al., 2006); the system has been credited for both. All other battery components are assumed to be landfilled. The recycling of antimony is not considered because of lack of data but its potential effect on the impacts is discussed in section 3.3.6. The quantity of tin and nickel is very small (<0.1%) so that their end-of-life management has not been considered. are incinerated with energy recovery (and system credits) while rock wool and ceramics are landfilled (Ecoinvent, 2010).

2.1.6 Transport The assumptions made for transport of raw materials and components as well as maintenance and recycling are summarised in Table 22. The life cycle inventory data for transport have been sourced from Ecoinvent (2010).

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Table 22. Transport assumptions for the SECHP, solar PV and battery systems.

System Stage Transport mode Distance (km) Solar PV Materials (panels) Freight rail 600 Lorry (>16t) 100 Materials (wiring and controllers) Lorry (20 - 28t) 100 Freight rail 200 Materials (inverter) Transoceanic freight ship 2000 Lorry (20 - 28t) 100 Freight rail 200 Materials (mounting frame) Freight rail 200 Lorry (>16t) 50 Van (<3.5t) 100 Manufacture Lorry (>16t) 500 Installation Van (<3.5t) 100 SECHP Materials Freight rail 200 Lorry (>16t) 200 Manufacture Lorry (>16t) 200 Installation Passenger car 200 Maintenance Passenger car 200 Battery Materials Lorry (>16t) 200 Materials (Inverter) Transoceanic freight ship 2000 Freight rail 200 Lorry (>16t) 100 Manufacture Lorry (>16t) 200 Maintenance Passenger car 200 Metals recycling Sorting Freight rail 200 Lorry (>16t) 100 Recycling Freight rail 200 Lorry (>16t) 100

2.1.7 Conventional system Most UK households (>99%) use electricity from the grid (England, 2014; Rosen, 2014) and heat from natural gas boilers (83%) (Baker, 2011) so that these options are considered for comparison with the PV-SECHP-battery system. The UK electricity grid mix used to estimate the LCA impacts is given in Table 21; the life cycle inventory data for the individual electricity sources have been sourced from Ecoinvent. The grid is included in the system boundary.

The life cycle of the gas boiler is outlined in Figure 30 and the inventory data are detailed in Table 23. As shown in the figure, all the stages from ‘cradle to grave’ are considered, from extraction of raw materials and fuels, construction, operation and maintenance of the boiler to end-of-life . The life cycle inventory data are sourced from Ecoinvent, adapted for the UK energy mix to reflect the fact that the boiler is manufactured in the UK. A condensing boiler with an efficiency of 90% (Energy Saving Trust, 2008) and the lifetime at 15 years is assumed. At the end of life, the individual components are either

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Chapter 5 Paul Balcombe recycled or landfilled, following the same assumptions for metals, plastics, rock wool and ceramics as for the PV-SECHP-battery system (see section 2.1.5).

Extraction & processing of raw materials

T

Natural gas Boiler manufacture extraction & processing

T

Natural gas supply Operation

T

Waste recycling/disposal

Figure 30 The life cycle of a natural gas boiler.

Table 23. Inventory data for a condensing gas boiler.

Energy/materials Value Unit

Manufacture Natural gas 472 MJ Electricity, medium voltage 294 MJ Light fuel oil 249 MJ Water 182 l Steel low alloy 115 kg Aluminium 7.5 kg Chromium steel 5 kg Corrugated board 5 kg Brazing solder 4 kg Copper 3.03 kg Alkyd paint 1.25 kg High density polyethylene 0.9 kg Brass 0.05 kg Installation Transport (van, <3.5 t) 200 km Operation Natural gas 1.1 MJ/MJ heat 3. Results

GaBi v6 (PE International, 2013) has been used to model the system and the CML 2001 method (April 2013 update) (CML, 2013; Guinée JB, 2004) to estimate the impacts. The following impact categories are considered: abiotic resource depletion elements (ADP elements), abiotic resource depletion fossil (ADP fossil), acidification potential (AP), eutrophication potential (EP), fresh water aquatic ecotoxicity potential (FAETP), global

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Chapter 5 Paul Balcombe warming potential (GWP), human toxicity potential (HTP), marine aquatic ecotoxicity potential (MAETP), ozone depletion potential (ODP), photochemical oxidation creation potential (POCP) and terrestrial ecotoxicity potential (TETP).

In the following sections, first the environmental impacts associated with the PV-SECHP- battery system are presented and compared with the conventional grid electricity-gas boiler system. This is followed by a discussion and comparison with the literature of individual results for the solar PV, SECHP and battery. The effect on impacts of different household types and energy demand is described in section 3.3, followed by an investigation of the influence of SECHP operation efficiency. The effects of different battery sizes are discussed subsequently, followed by different battery and SECHP lifespans as well as metal recycling rates.

3.1 Environmental impacts of the PV-SECHP-battery system

The environmental impacts associated with the system are compared to the conventional system (grid electricity and heat from gas boiler) in Figure 31. The results for the PV- SECHP-battery system include electricity imports and exports. The system has been credited for the latter for avoiding the impacts by not using the equivalent amount of electricity from the grid. The UK electricity mix has been assumed for the credits (see section 2.1.4). The system credits are shown in Figure 32, also indicating the contribution of each system component to the total impacts.

Overall, the microgeneration system has significantly (35-100%) lower impacts than the conventional energy supply for nine out of 11 categories. The greatest difference is found for the TETP which is negative for the PV-SECHP-battery system (-0.09 kg DCB eq./yr) because of the system credits for electricity exports. By comparison, this impact for the conventional system is equal to 36.6 kg DCB eq./yr; all other toxicity-related as well as other impacts are also much lower for the microgeneration system. For example, the AP is 13 times lower and the GWP by 40%. The ODP is approximately the same for both systems. However, depletion of elements is 42 times higher for the microgeneration system as discussed below.

As shown in Figure 32, the system component contributing most to the impacts is the SECHP, particularly for the ADP fossil (82%), GWP (78%), ODP (67%) and POCP (52%). The battery is a major contributor only to ADP elements (85%) while the contribution of solar PV is relatively small across the impacts, ranging from 3% for ADP fossil to 27% for the HTP.

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100 PV-SECHP-battery system 83.60 94.21 Conventional system (grid electricity

80 and gas boiler heat) 68.40

60 57.09

50.31

49.13

44.62 37.44

40 36.64

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0 0.09 -20 - ADP ADP AP x 0.1 EP x 0.1 FAETP x GWP x HTP x MAETP ODP x POCP x TETP elements fossil (kg SO2 (kg PO4 10 (kg 100 (kg 100 (kg x 100 (t 10 (mg 0.01 (kg (kg DCB x 0.01 (GJ/yr) eq./yr) eq./yr) DCB CO2 DCB DCB R11 C2H4 eq./yr) (kg Sb eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr)

Figure 31. Environmental impacts of the PV-SECHP-battery system in comparison with the grid electricity and gas boiler.

[All impacts correspond to the average household annual electricity and heat demand. Credits for electricity exports are included, assuming the UK electricity grid. Some impacts have been scaled to fit. To obtain the original value, multiply the value in the graph with the factor shown against relevant impact. ADP elements: Abiotic depletion of elements; ADP fossil: Abiotic depletion of fossil fuels; AP: Acidification potential; EP: Eutrophication potential; FAETP: Fresh water aquatic ecotoxicity potential; GWP: Global warming potential; HTP: Human toxicity potential; MAETP: Marine aquatic ecotoxicity potential; ODP: Ozone layer depletion potential; POCP: Photochemical ozone creation potential; TETP: Terrestrial ecotoxicity potential.]

100% Electricity imports Solar PV 80% Battery SECHP Electricity exports 60%

40%

20%

0%

-20%

-40%

-60% ADP ADP AP (kg EP (kg FAETP GWP (kg HTP (kg MAETP ODP POCP TETP elements fossil SO2 eq.)PO4 eq.) (kg DCB CO2 eq.) DCB (t DCB (mg R11 (kg (kg DCB (kg Sb (GJ) eq.) eq.) eq.) eq.) C2H4 eq.) eq.) eq.)

Figure 32. The contribution to environmental impacts of solar PV, SECHP, battery and electricity imports and exports.

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Abiotic resource depletion (elements): The reason for a 42-fold increase in this impact for the PV-SECHP-battery compared to the conventional system (0.18 kg Sb eq./yr vs 4 g Sb eq./yr) is the use of antimony in the batteries (1% of the cell weight) which contributes 80% to the total depletion of elements. Solar PV is the next main contributor with 14% (Figure 32), owing to the use of silver within the metallisation paste coating on the solar cells. The avoided impact from exports of household-generated electricity to the grid is minimal (- 1%).

Abiotic resource depletion (fossil): This impact (44.6 GJ/yr with the credits for electricity exports or 58.2 GJ/yr without the credits) is largely caused by natural gas extraction, used for the SECHP operation (44 GJ/yr). A certain amount of coal and gas is also depleted (7.54 GJ/yr) through the use of electricity imported from the grid by the household but this is made up by the credit to the system from the electricity export which saves twice as much fossil resources (13.6 GJ/yr). Overall, the PV-SECHP-battery system reduces this impact by 35% relative to the conventional system because of the reduction in electricity imports.

Acidification potential: The total impact is 0.735 kg SO2 eq./yr including the avoided impact from electricity exports. As mentioned earlier, this is 13 times lower than for the conventional system (9.4 kg SO2 eq.). Although electricity imports contribute 2.6 kg SO2 eq./yr, the impact is reduced by 4.6 kg/yr SO2 eq. through electricity exports owing to the avoidance of SO2 and NOx emissions. SECHP adds 1.3 kg SO2 eq./yr while solar PV and the battery each contribute approximately 0.7 kg SO2 eq./yr, primarily from NOx and SO2 emissions associated with steel, copper, aluminium, lead and silicon production.

Eutrophication potential: Similar to the AP, electricity generation from coal dominates this impact estimated at 0.9 kg PO4 eq./yr with the export credits (Figure 31). Phosphate emissions to fresh water from coal generation for electricity imports cause 0.97 kg PO4 eq. but the system saves 1.7 kg PO4 eq./yr through electricity exports. As indicated in Figure 32, the remainder of the impact comes from the manufacture of solar PV (0.53 kg PO4 eq./yr), battery (0.5 kg PO4 eq./yr) and SECHP (0.66 kg PO4 eq./yr). This is due to coal electricity used for their manufacture as well as phosphate leaching from the disposal of sulphide tailings in the beneficiation process of lead, copper, antimony, zinc, silver and nickel. In total, the microgeneration system reduces the EP by a factor of four relative to the conventional energy supply.

Fresh water aquatic ecotoxicity potential: The coal electricity is again a large contributor to FAETP, estimated at 174.2 kg DCB eq./yr of which 159.2 kg DCB eq./yr is from imported Page 174 of 210

Chapter 5 Paul Balcombe electricity. This is due to discharges of heavy metals to fresh water associated with the coal life cycle. Heavy-metal emissions from the battery’s life cycle contribute 118 kg DCB eq./yr, from solar PV 114 kg DCB eq./yr and from SECHP 69 kg DCB eq./yr. However, the system is also credited for the avoided impact of 286 kg DCB eq./yr for exporting the electricity. Overall, the FAETP is three times lower than for the conventional system.

Global warming potential: CO2 emissions from the combustion of natural gas in the

SECHP contribute 78% of the GWP of 2,967 kg CO2 eq./yr. The remainder is from combustion of coal and natural gas during generation of grid electricity. The electricity exports save 1137 kg CO2 eq./yr but more than half of this saving is lost through the imports (633 kg CO2 eq./yr). Nevertheless, the GWP is still 41% lower than for the conventional system.

Human toxicity potential: The HTP is contributed almost equally by the emissions of heavy metals associated with life cycles of grid electricity (249 kg DCB eq./yr), battery (290 kg DCB eq./yr), SECHP (212 kg DCB eq./yr) and solar PV (281 kg DCB eq./yr). However, their total impact is almost halved trough the electricity exports (448 kg DCB eq./yr) to yield the overall HTP of 585 kg DCB eq./yr. This represents a 40% reduction relative to the conventional energy supply.

Marine aquatic ecotoxicity potential: This impact is reduced significantly because of the electricity exports: from 1620 t without system credits to 250 t DBC eq./yr with the credits. This is 11 times lower than for the conventional system. The main contributors to the MAETP are emissions of HF to air (397 t DCB eq./yr) and beryllium to fresh water (169 t DCB eq.) from the life cycle of grid electricity as well as heavy metal emissions to fresh water from the life cycles of solar PV (345 t DCB eq.), battery (337 t DCB eq.) and SECHP (175 t DCB eq.).

Ozone depletion potential: The life cycle of natural gas is the main cause of the ODP, estimated at 0.16 g R11 eq. for the PV-SECHP-battery system. Specifically, emissions of Halon 1211, used for natural gas station and as a fire retardant in natural gas pipelines (Classen et al., 2009), causes approximately 68% of the impact. The remainder is mainly from halogenated emissions during the production of tetrafluoroethylene used in PV cell manufacture. As can be seen from Figure 31, both systems have a similar ODP – although the amount of natural gas used in the SECHP is slightly higher than in the gas boiler (1.19 vs 1.1 MJ/MJ heat), system credits for the electricity exports reduce the impact from the microgeneration system to make it almost equal to that of the conventional energy supply. Page 175 of 210

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Photochemical oxidation creation potential: The total POCP is estimated at 0.49 kg C2H4 eq./yr, the majority of which is due to hydrocarbon emissions from natural gas used in the

SECHP (0.4 kg C2H4 eq./yr) with the rest being from the life cycle of grid electricity (0.15

C2H4 eq./yr) and the production of the PV cells (0.13 C2H4 eq./yr). The credits for electricity exports save 0.28 kg C2H4 eq./yr, so that the overall impact is 41% lower than for the conventional system.

Terrestrial ecotoxicity potential: As mentioned earlier, this impact is negative (-0.09 kg DCB eq./yr) because of the avoided grid electricity imports. This compares very favourably with 36.6 kg DCB eq./yr for the conventional system. The TETP originates from chromium emissions to soil from the electricity distribution network (10.4 kg DCB eq. for electricity imports, -18.7 kg DCB eq. for exports). Another major contributor is the chromium emission to air from the production of steel for the SECHP unit (4.8 kg DCB eq.).

3.2 Comparison of results with literature

No other studies have investigated an integrated PV-SECHP-battery system, so that comparison of results at the system level is not possible. Instead, the results obtained for the individual technologies comprising the system are compared to those found in the literature.

3.2.1 Solar PV Only one other LCA study was found in the literature for the same type of solar PV (multi- panel mounted on a slanted roof) for UK conditions (Stamford and Azapagic, 2012); these results are compared to the current study in Figure 33. As indicated, per kWh of electricity generated, the impacts estimated in the present study are on average 25% lower, ranging from 16% lower ADP elements to 32% lower MAETP. These differences are mainly due to the different assumptions in the two studies. For example, the annual electricity generation in the current work is estimated at 924 kWh/kWp.yr based on the household simulation data (see section 2.1.4 and Table 20); in the study by Stamford and Azapagic (2012), the assumed generation of 750 kWh/kWp.yr is 20% lower. Furthermore, the assumed lifespans are different: 30 years for the PV panel and 11 years for the inverter (Electricians Forums, 2012; Rudge, 2010) in this study as opposed to 35 and 15 years, respectively in Stamford and Azapagic (2012). Finally, the current work assumes the use of virgin materials for the PV manufacture and credits the system for their recycling at the end of life, whereas the other study assumes recycled materials in the inputs but no credits for recycling.

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12

Stamford and Azapagic [22]* This study

10.5 10.2

10 9.3

8.8 7.8

8 7.2

6.9

6.2 5.6

6 5.6

4.7 4.1

4 3.7

2.7

2.5

1.9

1.8

1.7

1.3 1.3

2 1.2 1.0

0 ADP ADP fossil AP x 0.1 EP x 0.01 FAETP x GWP x 10 HTP x 0.1 MAETP x ODP x POCP x TETP x elements x 0.1 (g SO2 (g PO4 10 (g DCB (g CO2 (kg DCB 100 (kg 0.01 (mg 0.01 (g 0.1 (g (mg Sb (MJ/kWh) eq./kWh) eq./kWh) eq./kWh) eq./kWh) eq./kWh) DCB R11 C2H4 DCB eq./kWh) eq./kWh) eq./kWh) eq./kWh) eq./kWh)

Figure 33 Comparison with literature of environmental impacts of solar PV

[*The results reported in Stamford and Azapagic are for the global mix of solar PV technologies but here only the results for multi-crystalline silicon panels are shown to enable comparison with the current study which considers this type of panels.]

3.2.2 SECHP Two studies estimated the environmental impacts associated with SECHP, one based in the UK and another in Germany (Greening, 2013; Pehnt, 2008). While the former considered all the LCA impacts as the current study, the latter only reported the results for the GWP and AP.

The results are compared to those estimated by Greening (2013) in Figure 34 for the functional unit of 1 kWh of electricity generated; the credits for heat generation are not considered. The capacity of SECHP in both studies is 1 kWe. As can be seen in the figure, the average relative difference in the results is 35%, ranging from 6% difference for the GWP to 75% for the ODP. This is due to the different system boundaries and the assumptions made in the two studies. First, unlike this study, Greening did not consider the influence of the varying household demand and the way in which the unit is operated. Further, the mass of the materials in the SECHP unit is lower in this study, 115 kg compared to 175 kg in Greening (2013), resulting in lower impacts from the manufacture of materials. Greening’s study also included a 200 litre water tank, mainly consisting of glass-reinforced plastic (GRP) and steel (80 kg each). This was not considered in the present work as the same water tank is required for the gas boiler within the conventional energy system. On the other hand, an auxiliary boiler unit within the SECHP system has been considered here but not by Greening. Moreover, the Greening study also included

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40 kg of steel pipework that was excluded from the system boundaries here. Finally, Greening considered the UK grid electricity mix of 2009 while the present study is based on the 2013 mix with a higher proportion of coal (37% vs 28%) and lower contribution from gas (28% vs 45%) electricity. Nevertheless, despite these numerous differences in the assumptions, the results still fall within the same order of magnitude.

The study by Pehnt (2008) considered a slightly smaller SECHP unit than here (0.8 kWe vs 1 kWe), based in Germany. The author credited the system for heat generation, so to enable a comparison, the results obtained in the current study have been recalculated to include the heat credits. Despite the difference in the geographical location, the results in

Pehnt (2008) and here are relatively close, respectively: 0.5 vs 0.2 kg CO2 eq./kWh for the

GWP and 0.3 vs 0.25 g SO2 eq./kWh for the AP.

60 This study

49.40 46.96

50 44.98 41.60

40

32.61 32.62

32.15

31.80 27.04

30 26.44

22.60

21.81

20.53 18.27

20 16.00

14.38

14.11

11.88

8.91

8.10 6.50 10 6.24

0 ADP ADP fossil AP x 0.1 EP x 0.01 FAETP (t GWP x HTP x MAETP x ODP x POCP x TETP x elements (MJ/kWh) (g SO2 (g PO4 DCB 0.1 (kg 0.01 (kg 10 (kg 0.01 (mg 0.01 (g 0.1 (g x 0.1 (mg eq./kWh) eq./kWh) eq./kWh) CO2 DCB DCB R11 C2H4 DCB Sb eq./kWh) eq./kWh) eq./kWh) eq./kWh) eq./kWh) eq./kWh) eq./kWh)

Figure 34. Comparison with literature of environmental impacts of SECHP

[SECHP capacity: 1 kWe. All impacts expressed per kWh electricity generated. The credits for heat generation are not considered.]

3.2.3 Battery The life cycle impacts of the battery estimated here are shown in Figure 35, expressed per 1 kg of the battery cell, as in many other LCA studies (2012). However, it is not possible to compare the results directly with the literature because of the methodological differences with the existing studies. For example, Sullivan and Gaines (2012) reviewed 12 existing LCA studies of batteries but reported only life cycle air emissions rather than the impacts. Therefore, to enable comparison, Figure 36 shows the life cycle emissions estimated in the present study together with the data in Sullivan and Gaines (2012). It can be seen that all values are within the range reported in these studies, with the exception of the which is 2.5 times greater. This may be due to the assumed ratio of virgin and Page 178 of 210

Chapter 5 Paul Balcombe secondary lead used for batteries: the CO emissions in this study are mainly caused by secondary lead (72%). The primary lead production process emits less CO (Classen et al., 2009) but Sullivan and Gaines (2012) do not specify the percentage of lead assumed in different studies that they reviewed so that it is not possible to discuss this difference in more detail.

Another study (2012) estimated the impacts but used the ReCiPe instead of the CML method applied here to estimate the impacts. Therefore, the only impact that can be compared between the two studies is the GWP as the methodology for its estimation is the same in both methods. There, the GWP is estimated at 0.9 kg CO2 eq./kg battery, compared to 2.55 kg CO2 eq. here. It is not possible to discern the reasons for this difference owing to a lack of detail in the other study. For example, the energy used for battery manufacture is not specified so that it is not known what assumptions were made. Additionally, the impacts associated with antimony extraction and production were not considered which could have affected the results.

10 9.32 9 8.11 8 7 6 5 4.20 4 3.32 2.68 2.55 3 2.19 2.14 1.57 2 1.15 1.42 1 0 ADP ADP AP x 0.01 EP x 0.01 FAETP GWP (kg HTP (kg MAETP (t ODP x POCP (g TETP x elements fossil x 10 (kg SO2 (kg PO4 (kg DCB CO2 DCB eq. DCB 0.1 (mg C2H2 0.01 (kg (g Sb (MJ/kg) eq. kg) eq./kg) eq./kg) eq./kg) kg) eq./kg) R11 eq./kg) DCB eq./kg) eq./kg) eq./kg)

Figure 35 Environmental impacts of the battery cell estimated in this study

[Functional unit: 1 kg of battery. System boundary: from cradle to grave.]

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25 Sullivan and Gaines (high) [15] 22 This study 20 Sullivan and Gaines (low) [15]

15.0 15 14.9

11 9.2 10 10.0 7.9 8.7 9 4.6 6.6 6.2 6.4 5 5.5 1.65 2.8 2.4 2 2.1 0.7 0.8 0.002 1.1 0 0 NMVOC x CO (g/kg) NOx (g/kg) PM (g/kg) SOx (g/kg) CH4 (g/kg) N2O x 0.01 CO2 (kg/kg) 0.1 (g/kg) (g/kg)

Figure 36. Comparison with literature of selective emissions from the life cycle of battery.

[The functional unit: 1 kg of battery cell. System boundaries: from cradle to gate.]

3.3 Sensitivity analysis

To investigate the effect of various assumptions on environmental impacts, a sensitivity analysis has been carried out for the following parameters:

 variation in demand and generation profiles of different households;  efficiency of SECHP and its lifespan;  battery capacity and lifespan;  metal recycling rates; and  antimony recycling.

These are discussed in turn below.

3.3.1 Variation in demand and generation profiles As mentioned in section 2.1.4, the energy demand profiles of 30 real households have been simulated in 5-minute intervals over a period of one year with the estimated ranges given in Table 20. These data have been used to estimate the environmental impacts for each household. For these purposes, the following parameters have been considered in the simulation model:

 different type of dwelling: detached (DH), semi-detached (SDH) and terraced (TH) house;

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 different capacity of solar PV (1.1-4 kWp) and the corresponding sizes of the panel and the mounting frame;  electricity generation by each solar PV panel;  heat and electricity generation by SECHP;  the electricity imported and exported by each household.

The results are compared for each type of dwelling in Figure 37. On average, detached houses have the highest impacts because of the highest energy demand. However, there is a great deal of variation in the impacts across the different households owing to the large difference in the amount of electricity exported, ranging from 36 to 3,433 kWh/yr. In the Since the detached households export the most electricity owing to the larger roof area and the related PV capacities as well as greater exports from SECHP generation, they receive larger credits for the avoided impacts, in the best case leading to negative values for the AP, EP, FAETP, HTP, MAETP and TETP. There is also a large variation in the ADP fossil, GWP, ODP and POCP owing to the variation in heat demand and the fact that the SECHP operation dominates these impacts. Depletion of elements varies much less because it is caused by the variation in the solar PV size, which is comparatively small (1.1–4 kWp).

Compared to the conventional energy system, the largest average reduction across all the impacts (73%) is found for the dwellings with the highest energy demand, i.e. the detached houses (Figure 38). The reduction of the semi-detached and terraced houses is 58% and 32%, respectively. The exception is again depletion of elements which is higher for microgeneration than for the conventional system for all the household types.

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100

DH 55.4

80 55.6 SDH 49.1

36.6 TH

41.0 0.6

60 27.3

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-40

-60 ADP ADP AP x 10 EP x 10 FAETP x GWP x HTP x MAETP ODP x POCP x TETP elements fossil (kg SO2 (kg PO4 0.1 (kg 10 (t 0.01 (kg x 0.01 (t 100 (g 100 (kg (kg DCB x 100 (kg (GJ) eq.) eq.) DCB eq.)CO2 eq.)DCB eq.)DCB eq.) R11 eq.) C2H4 eq.) Sb eq.) eq.)

Figure 37. Environmental impacts for the PV-SECHP-battery system, showing the variation in impacts for different dwelling types

[DH: detached house; SDH: semi-detached house; TH: terraced house.]

3.3.2 Efficiency of SECHP operation Two modes of SECHP operation have been simulated:

 an inefficient operation where the SECHP is turned on whenever there is a heat demand throughout the day; and  an efficient operation where the system is only turned on twice per day for a more prolonged period.

Figure 39 shows that the efficient SECHP operation reduces the depletion of fossil fuels and the GWP by 17%, ODP by 12% and POCP by 11% compared to the inefficient operation because these impacts are caused by the combustion of natural gas during SECHP operation. All other impacts are also reduced but to a lesser extent as they are largely due to the materials used to manufacture the equipment or the quantity of electricity imported and exported.

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200%

150%

100%

50%

0%

-50% Reduction in impactsReductionin

-100%

TH TH TH TH TH TH TH TH TH TH TH

DH DH DH DH DH DH DH DH DH DH DH

SDH SDH SDH SDH SDH SDH SDH SDH SDH SDH SDH ADP ADP AP EP FAETP GWP HTP MAETP ODP POCP TETP elements fossil x 100

Figure 38. The reduction in environmental impacts when replacing the conventional energy supply by the PV-SECHP-battery system, also showing the variation in impacts for different dwelling types

[DH: detached house; SDH: semi-detached house; TH: terraced house. Conventional system: grid electricity and natural gas boiler. The relative difference relates to the annual household energy demand.]

100 Efficient operation of SECHP

Inefficient operation of SECHP 83.6

80 94.2 Conventional system

68.4

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0.1

0.3 - -20 - ADP ADP AP x 0.1 EP x 0.1 FAETP x GWP x HTP x MAETP ODP x POCP x TETP elements fossil (kg SO2 (kg PO4 10 (kg 100 (kg 100 (kg x 100 (t 10 (mg 0.01 (kg (kg DCB x 0.01 (GJ) eq.) eq.) DCB eq.)CO2 eq.)DCB eq.)DCB eq.) R11 eq.) C2H4 eq.) (kg Sb eq.) eq.)

Figure 39. Effect on the environmental impacts of the efficiency of SECHP operation

[Inefficient operation: SECHP turned on several times during the day; efficient operation: SECHP turned on only two times during the day. Conventional system: grid electricity and natural gas boiler.]

3.3.3 Battery capacity To determine the effect of the battery size on the environmental impacts, the following battery capacities have been considered: 2, 4, 6, 10, 20 and 40 kWh. The impacts are

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Chapter 5 Paul Balcombe also compared to the case where there is no battery storage in the system. The results in Figure 40a indicate that, when the system is credited for electricity exports, the total impacts from the microgeneration system are lower than for the conventional system for all battery capacities. The only exceptions to this are the ADP elements and ODP. For the case with no battery, some impacts are negative, notably the AP, MAETP and TETP. However, every increase in battery capacity to higher impacts. The reason for this is partly the assumptions made with respect to crediting the electricity exports. Whilst the battery reduces electricity imports and their associated environmental impacts, it also reduces the avoided burden associated with exporting excess electricity. Furthermore, owing to the inherent round-trip inefficiency of battery storage, assumed at 80% (Rydh and Sandén, 2005; Sullivan and Gaines, 2012; Van den Bossche et al., 2006), the reduction in exports is always greater than the reduction in imports. This means that for every 1 kWh battery charge which would otherwise be exported without a battery, only 0.8 kWh is discharged to offset the imports.

It is also interesting to notice that the effect of the battery size on the impacts changes when the credits for electricity exports are excluded. As shown in Figure 40b, the impacts generally reduce with increasing battery capacity, until it reaches 10 kWh, after which they start to increase. This is because smaller batteries are unable to store enough energy to significantly reduce the electricity imports, whereas larger batteries are over-sized such that their additional capacity is not utilised for large periods the year. However, there are some exceptions to this trend. The EP, FAETP, HTP and POCP increase slightly when a 2 kWh battery is added to the system, compared to the case where the battery is not used. This is because, for small battery sizes, the reduction in imports is not enough to counter the impacts from the additional components required for the battery (e.g. inverter, copper wiring). A further exception is depletion of elements which increases with the battery size because a reduction in electricity imports has a negligible effect on this impact, whereas an increase in battery size and, therefore, the impacts from its manufacture, has a significant effect. Overall, all the impacts but ADP elements and ODP from the microgeneration system are still lower than for the conventional energy supply, regardless of the battery size; this was also the case when the system was credited for the electricity exports, as discussed above.

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100 0 kWh 2 kWh 80 4 kWh 6 kWh (base case) 10 kWh 20 kWh 60 40 kWh Conventional system

40

20

0

-20 ADP ADP AP x 0.1 EP x 0.1 FAETP x GWP x HTP x MAETP ODP x POCP x TETP elements fossil (kg SO2 (kg PO4 10 (kg 100 (kg 100 (kg x 100 (t 10 (mg 0.01 (kg (kg DCB x 0.01 (GJ/yr) eq./yr) eq./yr) DCB CO2 DCB DCB R11 C2H4 eq./yr) (kg Sb eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr)

a) With system credits for electricity exports to the grid

100 0 kWh 2 kWh 4 kWh 6 kWh (base case) 80 10 kWh 20 kWh 40 kWh Conventional system 60

40

20

0 ADP ADP AP x 0.1 EP x 0.1 FAETP x GWP x HTP x MAETP ODP x POCP x TETP elements fossil (kg SO2 (kg PO4 10 (kg 100 (kg 100 (kg x 100 (t 10 (mg 0.01 (kg (kg DCB x 0.01 (GJ/yr) eq./yr) eq./yr) DCB CO2 DCB DCB R11 C2H4 eq./yr) (kg Sb eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr)

b) Without system credits for electricity exports to the grid

Figure 40. Effect on the impacts of different battery capacities.

[The base case considered in the rest of the paper assumes a 6 kWh capacity.]

3.3.4 Battery and SECHP lifespans Battery cell lifetime varies widely depending on its application and operation (Dufo-López et al., 2014; Jenkins et al., 2008). Much less is known about the lifespan of SECHP as it is a relatively immature technology with less than 500 installations in the UK (DECC, 2014b). Therefore, this section considers how the impacts may be affected if the lifetime of both technologies is varied between 5–15 years (Dufo-López et al., 2014; Jenkins et al., 2008; Parsons Brinckerhoff, 2012) compared to 10 years considered in the base case.

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Figure 41 indicates that if the battery cells only last for five instead of 10 years, all impacts go up, with most significant increases noticed for depletion of elements (45%), AP (32%), MAETP (34%) and TETP (three times). Increasing the battery lifespan to 15 years results in a more moderate improvement in the impacts: ADP elements by 27%, AP by 15%, MAETP by 17% and TETP by two times. However, for all the lifetimes the impacts still remain significantly lower than for the conventional system, with the exception of the ADP elements, as before.

The effect of the SECHP’s lifetime is smaller than for the battery but affects the same impacts as the battery lifespan. As shown in Figure 42, reducing the lifespan from 10 to 5 years reduces the AP, FAETP and HTP by 17% and MAETP by 22%; however, the reduction in TETP is much more significant (37 times). Increasing the lifespan from 10 to 15 years yields small improvements, around 7% for the above impacts except for the TETP which is 14 times lower. Again, as for the battery, the impacts from the microgeneration system remain significantly lower than from the conventional energy supply, regardless of the SECHP lifetime (with the exception of ADP elements, as in the base case).

Battery lifespan: 5 years 120 Battery lifespan: 8 years

Battery lifespan: 10 years (base case) 97.1 100 94.2 Battery lifespan: 15 years

Conventional system 83.6

80

68.4

65.0

60.1

58.5

57.1 56.3

60 51.3

50.3

49.7

49.1

48.4

44.8

45.1

44.6

44.4

32.2

37.4 36.6

40 21.3

30.1

29.8

29.7

29.5

26.3

21.6

18.5

17.7

17.4

16.0

15.7

15.5

15.5

15.4

15.3

12.8

11.1 10.8

20 8.2

9.7

9.3

8.7

7.3

6.2

3.8

2.8

2.5

2.1

0.4 0.2

0

0.0

0.1

0.2 - -20 - ADP ADP AP x 0.1 EP x 0.1 FAETP x GWP x HTP x MAETP ODP x POCP x TETP elements fossil (kg SO2 (kg PO4 10 (kg 100 (kg 10 (kg x 100 (t 10 (mg 0.01 (kg (kg DCB x 0.01 (GJ/yr) eq./yr) eq./yr) DCB CO2 DCB DCB R11 C2H4 eq./yr) (kg Sb eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr)

Figure 41. Effect on the impacts of different battery lifespans.

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100 SECHP lifespan: 5 years 94.2

SECHP lifespan: 10 years (base case) 83.6 80 SECHP lifespan: 15 years

68.4 Conventional system

60 57.1

51.14

50.3

49.13

48.5

45.04

44.62

44.5 37.4

40 36.6

29.99

29.67

29.6

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21.23

17.75

17.69

17.7

17.42

16.1

15.67

15.45 15.4

20 15.3

10.13

9.7

9.27

9.0

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7.04

6.8

5.85

5.4

3.33

3.20

2.50

2.3 0.4

0

1.2

- 0.09 -20 - ADP ADP AP x 0.1 EP x 0.1 FAETP x GWP x HTP x MAETP ODP x POCP x TETP x elements fossil (kg SO2 (kg PO4 10 (kg 100 (kg 100 (kg x 100 (t 10 (mg 0.01 (kg 0.1 (kg x 0.01 (GJ/yr) eq./yr) eq./yr) DCB CO2 DCB DCB R11 C2H4 DCB (kg Sb eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr)

Figure 42. Effect on the impacts of different SECHP lifespans. 3.3.5 Metal recycling rates In this study, global recycling rates of metals have been assumed at the end of life of the system components (see section 2.1.5). However, it is not known if and at what rate they will be recycled in the future. Therefore, this section examines the effect on the total impacts for two extreme cases: no recycling and 100% recycling of metals. The results in Figure 43 show that the most affected impacts are the toxicity-related categories as well as acidification and eutrophication. Increasing the recycling rate from no recycling to 100% recycling reduces these impacts from 46% for the EP to 179% for the TETP, increasing the relative difference between the microgeneration and conventional system in favour of the former. Depletion of elements and fossil fuels is unaffected by the recycling rates of metals because these impacts are dominated by the extraction of antimony and natural gas. The effect of recycling the former is examined next.

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Global recycling rates (base case)

100 No recycling 94.2 100% recycling 83.6

80 Conventional system

68.4 57.1

60 53.3

50.3

49.1

47.2

45.2

44.6

44.5 37.4

40 36.6

30.3

29.7

29.5

26.3

25.6

17.8

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15.5

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15.4 15.3

20 12.1

11.3

10.3

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3.4

2.5

0.5 0.4

0

0.9

- 12.2

-20 - ADP ADP AP x 0.1 EP x 0.1 FAETP x GWP x HTP x MAETP ODP x POCP x TETP x elements fossil (kg SO2 (kg PO4 10 (kg 100 (kg 100 (kg x 100 (t 10 (mg 0.01 (kg 0.1 (kg x 0.01 (GJ/yr) eq./yr) eq./yr) DCB CO2 DCB DCB R11 C2H4 DCB (kg Sb eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr)

Figure 43. Effect on the impacts of different recycling rates of metals used to manufacture the microgeneration system. 3.3.6 Antimony recycling As shown in section 3.1, antimony used within the battery cell contributes 80% to the total depletion of elements from the microgeneration system, which is 42 times higher than for the conventional energy system. This is due to the assumption that virgin antimony is used for the manufacture of batteries (1.56 kg per 6 kWh battery; see Table 19) since no data were available to suggest that recycled antimony is used to manufacture batteries. However, it has been reported (2006) that around 90% of antimony is recovered during the battery recycling process; indeed, the main source of secondary antimony is from battery recycling (Classen et al., 2009). Therefore, this section considers the effect of 90% recycled antimony being used to manufacture batteries instead of using it elsewhere.

The results in Figure 44 show that the depletion of elements and TETP for the whole microgeneration system are three and two times lower, respectively, compared to the system using only virgin antimony. Some other impacts are also reduced, including the EP by 12%, FAETP by 16% and MAETP by 31%. However, depletion of elements is still 12 times higher than for the conventional system owing to the contribution from the remaining antimony that is not recycled (10%) as well as other metals used within the system. Even if antimony was completely eliminated from the batteries which is envisaged to occur by 2020 (Carlin, 2006), the depletion of elements from the use of silver in the metallisation paste for solar PV panels is still five times higher than for the conventional system.

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100 Virgin antimony (base case) 90% recycled antimony 83.6 80 94.2

68.4 Conventional system

60 57.1

50.3

49.1

48.9

44.6

44.6 37.4

40 36.6

29.7

29.7

26.3

17.7

17.4

15.5

15.4 15.3

20 14.7

9.7

9.3

8.2

7.3

7.0

5.8

5.4

5.1

2.5

1.7 0.4

0

0.9

1.9 - -20 - ADP ADP AP x 10 EP x 10 FAETP x GWP x HTP x MAETP ODP x POCP x TETP elements fossil x (kg SO2 (kg PO4 0.1 (kg 0.01 (kg 0.01 (kg x 0.01 (t 0.1 (mg 10 (kg (kg DCB x 100 (kg 0.1 eq./yr) eq./yr) DCB CO2 DCB DCB R11 C2H4 eq./yr) Sb (GJ/yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) eq./yr) Figure 44. Effect on the impacts of recycling of antimony used in batteries. 4. Conclusions

This study has estimated the life cycle environmental impacts of a household microgeneration system comprising solar PV, SECHP and battery storage, generating heat and electricity. The results have been compared to conventional electricity supply from the grid and heat from a natural gas boiler. Overall, the microgeneration system provides significant improvements in all environmental impacts compared to the conventional energy supply, ranging from 35% for depletion of fossil fuels to 100% for terrestrial ecotoxicity. The exception to this is depletion of elements which is 42 times higher, caused largely (85%) by the antimony used in batteries.

Natural gas used for SECHP is the main contributor to depletion of fossil fuels and global warming (80%) as well as ozone layer depletion (62%) and creation of photochemical oxidants (44%). Electricity generation from coal also contributes significantly to marine ecotoxicity and acidification (40%), eutrophication (29%) and fresh water ecotoxicity (26%).

The results show a large variation in environmental impacts across households with different energy demand. The system studied is particularly suited for detached households with typically higher demand, where there are significant reductions in impacts; for example, acidification is reduced by 104%, eutrophication by 88% and global warming by 53% compared to the conventional system. The system installed in the smallest (terraced) households also has lower impacts than the conventional supply, but the reduction in impacts is much smaller; for example, acidification is reduced by 62% and global warming by 35%. However, these reductions in impacts are predicated on the Page 189 of 210

Chapter 5 Paul Balcombe system credits for the exported electricity. Since the contribution of coal in the UK grid electricity is currently high and is expected to go down, the benefit of the microgeneration system over the conventional supply will be reduced, particularly for households with lower energy demand.

The environmental impacts are also affected by the way in which the SECHP system is operated. In particular, global warming is reduced by 17% if the system is operated more efficiently, depletion of ozone layer and fossil fuels by 12% and 17%, respectively, and creation of photochemical oxidants by 11%. This highlights the need for providing information and appropriate training for consumers to maximise the environmental benefits of the microgeneration system. There is also a financial gain associated with higher operational efficiencies, which benefits the consumer.

The results show that improvements associated with adding battery storage are sensitive to the system credits for electricity exports. When credits are included, the addition of any battery storage leads to higher environmental impacts owing to the inherent round-trip inefficiency of battery storage: the quantity of avoided electricity imports is always lower than the avoided exports. When the credits for electricity exports are excluded, the battery performs favourably for all impacts, with the exception of depletion of elements. However, the greatest environmental benefits occur for mid-sized batteries: the addition of a small (2 kWh) battery does not reduce the imports enough to offset the impacts of the battery manufacture. Likewise, battery capacities above 20 kWh provide little extra benefit. Thus, the correct sizing of battery storage is essential, not only for the environmental impacts but also for costs reasons.

The lifespan of both battery cells and SECHP has a large effect on environmental impacts: a decrease in SECHP lifespan from 10 to five years results in an increase in acidification, fresh water and human toxicity (all by 17%) as well as marine aquatic ecotoxicity (22%) and terrestrial ecotoxicity (37 times). Likewise, a decrease in lifespan of battery cells from 10 to five years results in increases for all impacts, including depletion of elements (45%), acidification (32%) and terrestrial ecotoxicity (three times). Therefore, using effective control systems to maximise the battery cell lifespan would increase the environmental benefits from the microgeneration system. Nevertheless, even for the lowest lifetimes, all impacts are still lower than for the conventional system, except for depletion of elements.

Increasing metal recycling rates from zero to 100% reduces a number of impacts, including acidification (by 56%), eutrophication (46%), fresh water ecotoxicity (60%), human toxicity (58%), marine (91%) and terrestrial ecotoxicity (179%). If 90% of recycled Page 190 of 210

Chapter 5 Paul Balcombe antimony is used in batteries, the depletion of elements and TETP are three and two times lower, respectively, compared to the system using only virgin antimony. However, the depletion of elements is still 12 times higher than for the conventional system because of the use of other metals in the system. Even if the use of antimony was eliminated altogether, this category would still be five times greater because of the materials used for the solar PV.

Acknowledgements

This work has been funded by the Sustainable Consumption Institute at the University of Manchester and UK Engineering and Physical Sciences Research Council, EPSRC (Grant no. EP/K011820/1). The authors gratefully acknowledge this funding. We are also grateful to Dr Laurence Stamford and Dr Harish Jeswani for their advice and help with data collection.

References

Alsema, E. A. 2000. Environmental life cycle assessment of solar home systems. Department of Science Technology and Society Utrecht.: Utrecht University, NL Ayris, L. and Zakar, S. 2009. Management of non-aggregate waste. CONSTRUCTION RESOURCES AND WASTE PLATFORM (ed.). London: BRE. Baker, W. 2011. Off-gas consumers Information on households without mains gas heating. London: Consumer Focus. Balcombe, P., Azapagic, A. and Rigby, D. 2014a. Self-sufficiency and reducing the variability of grid electricity demand: integrating solar PV, Stirling engine CHP and battery storage. (paper in progress) University of Manchester. Balcombe, P., Rigby, D. and Azapagic, A. 2014b. Investigating the importance of motivations and barriers related to microgeneration uptake in the UK. Applied Energy, In Press. Baxi 2011a. Ecogen: The Baxi Ecogen Dual Energy System. Warwick. Baxi 2011b. Installation & Servicing Instructions: Ecogen 24/1.0 Gas Fired Wall Mounted Condensing Boiler and Power Generator. Warwick. Bright Green Energy Ltd. 2014. Off Grid Power Store, [Online]. Available: www.offgridpowerstore.com/default.asp [Accessed 01 December 2013]. Cambridge Economic Policy Associates Ltd and Parsons Brinckerhoff 2011. Updates to the Feed-in Tariffs model: Documentation of changes made for non-PV technologies. London: DECC

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Carbon Trust 2011. Micro-CHP Accelerator Final Report March 2011. London: The Carbon Trust 2011. Carlin, J. F. 2006. Antimony Recycling in the United States in 2000. U.S. DEPARTMENT OF THE INTERIOR (ed.) Flow studies for recycling metal commodities in the United States. U.S Geological survey circular 1196-Q. Classen, M., Althaus, H.-J., Blaser, S., Scharnhorst, W., Tuchschmid, M., Jungbluth, N. and Emmenegger, M. F. 2009. Life Cycle Inventories of Metals. HANS-JORG ALTHAUS (ed.) Final report Ecoinvent data v2.1. EMPA Dubendorf, CH: Swiss Centre for Life Cycle Inventories. CML. 2013. CML-IA Characterisation Factors- April 2013 update [Online]. Institute of Environmental Sciences, Universiteit Leiden. Available: cml.leiden.edu/software/data-cmlia.html [Accessed 26 June 2014]. Copper Alliance. 2014. Physics – Copper Recycling and Sustainability [Online]. Available: www.copperalliance.eu/education-and-careers/educational-programmes/physics- copper-recycling-and-sustainability [Accessed 29 June 2014]. Dahlströma, K., Ekins, P., He, J., Davis, J. and Clift, R. 2004. Iron, steel and aluminium in the UK: material flows and their economic dimensions. POLICY STUDIES INSTITUTE (ed.). London: Centre for Environmental Strategy, University of Surrey. DECC 2013. Monthly central Feed-in Tariff register. JULY_2013_MONTHLY_CENTRAL_FEED- IN_TARIFF_REGISTER_STATISTICS.XLS (ed.) Microsoft Excel. London. Available: www.gov.uk/government/statistical-data-sets/monthly-central-feed-in- tariff-register-statistics. DECC 2014a. Fuel used in electricity generation and electricity supplied (ET 5.1). MS Excel Spreadsheet. Available: www.gov.uk/government/publications/electricity- section-5-energy-trends Crown Copyright. DECC 2014b. Monthly central Feed-in Tariff register. JULY_2013_MONTHLY_CENTRAL_FEED- IN_TARIFF_REGISTER_STATISTICS.XLS. Microsoft Excel. London. Available: www.gov.uk/government/statistical-data-sets/monthly-central-feed-in-tariff-register- statistics. Dufo-López, R., Lujano-Rojas, J. M. and Bernal-Agustín, J. L. 2014. Comparison of different lead–acid battery lifetime prediction models for use in simulation of stand- alone photovoltaic systems. Applied Energy, 115, 242-253. Ecoinvent 2010. Ecoinvent v2.2 database. Dubendorf, Switzerland: Swiss Centre for Life Cycle Inventories. Page 192 of 210

Chapter 5 Paul Balcombe

Electricians Forums. 2012. Inverter Lifespan [Online]. Available: www.electriciansforums.co.uk/photovoltaic-solar-panels-green-energy- forum/63240-inverter-lifespan.html [Accessed 12 Dec 2013]. Energy Saving Trust 2008. Domestic heat by gas: boiler systems- guidance for installers and specifiers. www.energysavingtrust.org.uk/Publications2/Housing- professionals/Heating-systems/Domestic-heating-by-gas-boiler-systems-2008- edition. England, R. 2014. Going off-grid [Online]. Resource Media Limited,: Resource Media Limited,. Available: www.resource.uk.com/article/Interviews/Going_offgrid#.U81Oh7HRknM [Accessed 21 July 2014 ]. EPIA 2014. Press release: Market Report 2013. www.epia.org/uploads/tx_epiapublications/Market_Report_2013_02.pdf. Fisher, K., Wallén, E., Laenen, P. P. and Collins, M. 2006. Battery Waste Management Life Cycle Assessment. MANAGEMENT, E. R. (ed.). DEFRA. Fthenakis, V. M., Kim, H. C. and Alsema, E. 2008. Emissions from Photovoltaic Life Cycles. Environmental Science & Technology, 42, 2168-2174. García-Valverde, R., Miguel, C., Martínez-Béjar, R. and Urbina, A. 2009. Life cycle assessment study of a 4.2 kWp stand-alone . Solar Energy, 83, 1434-1445. Greening, B. 2013. Life cycle environmental and economic sustainability assessment of micro-generation technologies in the UK domestic sector. Doctor of Philosophy, University of Manchester. Gross, R., Heptonstall, P., Anderson, D., Green, T., Leach, M. and Skea, J. 2006. The Costs and Impacts of Intermittency: An assessment of the evidence on the costs and impacts of intermittent generation on the British electricity network. UKERC (ed.). Imperial College London. Guinée JB, G. M., Heijungs R, Huppes G, Kleijn R, de Koning A, van Oers L, Wegener Sleeswijk A, Suh S, Udo de Haes HA, de Bruijn H, van Duin R, Huijbregts MAJ, 2004. Handbook on Life Cycle Assessment: Operational Guide to the ISO Standards, Dordrecht, Kluwer Academic Publishers. HM Government 2009. The Waste Batteries and Accumulators Regulations 2009. No. 890 London: Crown Copyright. Available: http://www.legislation.gov.uk/uksi/2009/890/contents/made. International Aluminium Institute 2009. Global Aluminium Recycling: A Cornerstone of . ORGANISATION OF EUROPEAN ALUMINIUM Page 193 of 210

Chapter 5 Paul Balcombe

REFINERS AND REMELTERS (ed.). London: The Global Aluminium Recycling Committee. ISO 2006a. ISO 14040-environmental management - life cycle assessment - principles and framework. Geneva ISO 2006b. ISO 14044-environmental management - life cycle assessment - requirements and guidelines. Geneva. Jenkins, D. P., Fletcher, J. and Kane, D. 2008. Model for evaluating impact of battery storage on microgeneration systems in dwellings. Energy Conversion and Management, 49, 2413-2424. Kaldellis, J. K., Zafirakis, D. and Kondili, E. 2010. Energy pay-back period analysis of stand-alone photovoltaic systems. Renewable Energy, 35, 1444-1454. Leadbetter, J. and Swan, L. G. 2012. Selection of battery technology to support grid- integrated renewable electricity. Journal of Power Sources, 216, 376-386. Li, J. and Danzer, M. A. 2014. Optimal charge control strategies for stationary photovoltaic battery systems. Journal of Power Sources, 258, 365-373. Lombardi, K., Ugursal, V. I. and Beausoleil-Morrison, I. 2010. Proposed improvements to a model for characterizing the electrical and thermal energy performance of Stirling engine micro-cogeneration devices based upon experimental observations. Applied Energy, 87, 3271-3282. McKenna, E., McManus, M., Cooper, S. and Thomson, M. 2013. Economic and environmental impact of lead-acid batteries in grid-connected domestic PV systems. Applied Energy, 104, 239-249. McManus, M. C. 2012. Environmental consequences of the use of batteries in low carbon systems: The impact of battery production. Applied Energy, 93, 288-295. MCS. 2014. The Microgeneration Certification Scheme [Online]. Available: www.microgenerationcertification.org/ [Accessed 14 July 2014 ]. MIT 2011. Managing Large-Scale Penetration of Intermittent Renewables: An MIT Energy Initiative Symposium. MIT ENERGY INITIATIVE (ed.). Massachusetts, USA. National Grid 2012. Solar PV Briefing Note. DECC (ed.). London: DECC. Navitron Ltd. 2013. Navitron [Online]. Available: www.navitron.org.uk [Accessed 1 Dec 2013]. NHBC Foundation 2011. Introduction to Feed-In Tariffs. BRE (ed.). www.nhbcfoundation.org/Researchpublications/IntroductiontoFeedinTariffsNF23/ta bid/437/Default.aspx: IHS BRE Press.

Page 194 of 210

Chapter 5 Paul Balcombe

Ofgem. 2014. Feed-in Tariff Installation Report [Online]. Available: www.ofgem.gov.uk/publications-and-updates/feed-tariff-installation-report-31- march-2014 [Accessed 10 May 2014]. Orr, G., Dennish, T., Summerfield, I. and Purcell, F. 2011. SEAI Commercial micro-CHP Field Trial Report. THE SUSTAINABLE ENERGY AUTHORITY OF IRELAND (ed.). Dublin. Papic, I. 2006. Simulation model for discharging a lead-acid battery energy storage system for load leveling. Energy Conversion, IEEE Transactions on, 21, 608-615. Parsons Brinckerhoff 2012. Update of non-PV data for Feed In Tariff. DECC (ed.). www.pbworld.com. PE International 2013. GaBi V.6. PE INTERNATIONAL (ed.). Stuttgart, Echterdingen. Pehnt, M. 2008. Environmental impacts of distributed energy systems--The case of micro cogeneration. Environmental Science & Policy, 11, 25-37. Roselli, C., Sasso, M., Sibilio, S. and Tzscheutschler, P. 2011. Experimental analysis of microcogenerators based on different prime movers. Energy and Buildings, 43, 796-804. Rosen, N. 2014. Off-grid living: it's time to take back the power from the energy companies [Online]. Guardian News and Media Limited. Available: www.theguardian.com/lifeandstyle/2014/apr/11/power-energy-companies [Accessed 21 July 2014]. Rudge, C. 2010. Inverters for solar PV panels: your questions answered [Online]. Available: www.yougen.co.uk/blog- entry/1516/Inverters+for+solar+PV+panels%273A+your+questions+answered/ [Accessed 12 Dec 2013]. Rydh, C. J. 1999. Environmental assessment of vanadium redox and lead-acid batteries for stationary energy storage. Journal of Power Sources, 80, 21-29. Rydh, C. J. and Sandén, B. A. 2005. Energy analysis of batteries in photovoltaic systems. Part I: Performance and energy requirements. Energy Conversion and Management, 46, 1957-1979. Stamford, L. and Azapagic, A. 2012. Life cycle sustainability assessment of electricity options for the UK. International Journal of Energy Research, 36, 1263-1290. Sullivan, J. L. and Gaines, L. 2012. Status of life cycle inventories for batteries. Energy Conversion and Management, 58, 134-148. Trainer, T. 2013. 100% Renewable supply? Comments on the reply by Jacobson and Delucchi to the critique by Trainer. Energy Policy, 57, 634-640.

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Van den Bossche, P., Vergels, F., Van Mierlo, J., Matheys, J. and Van Autenboer, W. 2006. SUBAT: An assessment of sustainable battery technology. Journal of Power Sources, 162, 913-919. Watt, M. E., Johnson, A. J., Ellis, M. and Outhred, H. R. 1998. Life-cycle air emissions from PV power systems. Progress in Photovoltaics: Research and Applications, 6, 127-136. World Steel Association. 2009. Fact Sheet: the three Rs of sustainable steel [Online]. worldsteel.org. Available: www.steel.org/en/Sustainability/~/media/Files/SMDI/Sustainability/3rs.ashx [Accessed 29 June 2014].

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Supplementary material

Table S1: Annual energy demand and generation profiles for different dwelling types

Household Dwelling Solar PV Solar PV Electricity Electricity Gas use SECHPa type capacity generation imports exports (kWh/yr) electricity (kW) (kWh/yr) (kWh/yr) (kWh/yr) generation (kWh/yr) 1 DHb 4 3896 2342 2689 26034 2946 2 DH 4 3729 1506 2486 11515 1363 3 DH 4 4224 1127 2607 11879 1360 4 DH 4 2959 1346 2019 23797 2564 5 DH 4 3201 2882 2219 19374 2222 6 DH 4 4130 576 2590 15793 1772 7 DH 4 3167 1512 2017 21261 2328 8 DH 4 4368 1747 3391 22322 2206 9 DH 4 4557 1247 3103 19755 2015 10 DH 3.9 3779 1152 3433 24470 2786 11 DH 3.8 4135 1871 2484 20243 2339 12 DH 3.5 3448 218 2615 13546 1470 13 DH 3.4 3037 595 2102 11120 1243 14 DH 3.4 3251 844 2311 12580 1439 15 SDHc 3.3 3178 1321 1638 11434 1160 16 SDH 3.3 2907 703 1650 11369 1116 17 SDH 3 2806 324 1812 9320 1045 18 SDH 3 2993 254 2136 10228 1129 19 THd 2.8 1563 1380 527 7680 715 20 TH 2.6 1875 725 967 10579 902 21 SDH 2.5 2281 243 1716 12745 1278 22 SDH 2.4 1771 369 1166 10043 1102 23 SDH 2.2 2269 1220 707 8899 937 24 SDH 2.2 1700 544 712 9512 1039 25 SDH 2.1 1387 564 887 10459 1150 26 SDH 2 1762 590 834 13559 1102 27 SDH 1.8 1467 504 750 11592 977 28 TH 1.6 1450 237 683 10016 873 29 TH 1.5 692 1215 36 8472 887 30 TH 1.1 1169 310 686 9287 848 a SECHP capacity: 1 kWe, 6.4 kWth b Detached house c Semi-detached house d Terraced house

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Chapter 6: Conclusions and further work

This work has investigated a number of issues surrounding the development of microgeneration, in terms of the technology, the industry and policy, in order to determine how microgeneration could better contribute to UK climate change and energy security targets. A number of conclusions and recommendations for policy, industry and further work have been made with the aim of increasing uptake and identifying an environmentally sustainable option to mitigate against grid balancing problems associated with intermittent microgeneration.

Firstly, an investigation into how greater uptake could be achieved was carried out. Focussing on the consumer motivations and barriers affecting adoption, a comprehensive literature review and UK policy analysis was conducted (see Chapter 2). This research was used to inform a best-worst scaling survey of microgeneration consumers and potential consumers to determine the relative importance of each motivation and barrier affecting the adoption decision, as well as differences across the population (see Chapter 3).

Secondly, the impact of intermittent microgeneration on energy security was investigated by conducting a simulation of household energy supply and demand using a combined solar PV, Stirling engine CHP (SECHP) and battery storage system. The effect on daily grid demand variation, as well as the level of household electricity self-sufficiency was determined (Chapter 4). The beneficial effects of the system with respect to flattening grid demand and increasing household self-sufficiency were analysed against the economic and environmental impacts, by a cost-benefit analysis (Chapter 4) and environmental life cycle assessment (Chapter 5). This chapter synthesises the conclusions and recommendations made in each of the previous chapters within the thesis.

6.1 General conclusions Overall, this research finds numerous ways in which uptake could be increased for different microgeneration technologies in order to increase contribution to the energy supply mix. However, this is likely to be at cost to the government and ultimately, to tax payers (or utility bill payers). Currently, the consumer cost is still prohibitive for many. A return on investment of 5% for the majority of microgeneration technologies is achievable due to the Feed-in Tariffs (FITs) and Renewable Heat Incentive (RHI). However, high capital cost is still a barrier and consumer discount rates may well be far higher than 5%, thus rendering the investment unappealing.

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UK microgeneration policy over the last decade has frequently changed and created uncertainty for consumers and the industry. For example, the introduction of the FITs increased uptake particularly for solar PV but the tariff degression mechanism has created a ‘boom-bust’, unstable industry, whilst some consumers perceive the frequent changes as a risk to their financial investment. In the face of a quickly maturing microgeneration market, a more continuous, simple and transparent policy environment would provide security for industry and consumers alike and allow more stable industrial growth.

Additionally, certain environmental and technical impacts of mass uptake of microgeneration are not necessarily an improvement on more conventional household energy systems. For example:

 Microgeneration systems may provide an improvement in the global warming potential, but other environmental impacts such as depletion of elements can be significantly worse. Careful attention to all potential impacts must be paid to ensure that the improvement of the global warming potential is not carried out at the expense of other impacts.  Microgeneration clearly improves energy security in the sense that there is a greater use of UK resources and more efficient use of fossil fuels (for example, Stirling engine CHP). However, energy security is multi-faceted and greater uptake of the intermittent technologies such as solar PV could cause grid balancing issues and lower grid stability and reliability. Thus, contribution to improving energy security is not guaranteed unless mitigation measures are taken to ensure grid availability and reliability are maintained, for example with increased battery storage or increased variable-load capacity in the electricity grid.

6.2 Motivations and barriers The research into the motivations and barriers associated with microgeneration adoption points to the following conclusions:

 The results of the survey showed that the greatest motivations to install were to earn money from the incentives, to increase household self-sufficiency and to protect against future high energy costs. Whilst the motivation to earn money from the installation is well documented in literature, the high importance of self- sufficiency and protecting against future energy costs is a new finding.  The motivation to improve the environment is well cited in literature but the survey showed this to be only half as important as the other motivations stated above.

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However, this motivation was a key differentiator between the adopter, considerer and rejecter groups: it was much more important for adopters than for rejecters.  The greatest barriers to installing microgeneration are high capital costs, not earning or saving enough money from the installation and the risk of losing money if they moved home. Surprisingly, the risk of losing money if they moved home was the largest barrier to adoption for rejecters.  Considerers also found the difficulty in obtaining reliable information very important, 10% more so than not earning or saving enough money from the installation. Many attempts have been made to provide such information by organisations such as the Microgeneration Certification Scheme and the Energy Savings Trust but evidently there remains a barrier.  The Green Deal is designed to remove the capital cost barrier by providing loans for the installation cost. The risk of losing money if they moved home would also be eliminated due to the lack of initial outlay. This could particularly suit considerers, who tend to have lower incomes and are less motivated by earning money from the installation. However, thus far the scheme has experienced very low uptake rates, which may be due to the high interest loan rates and the concern of complications associated with house sales. This incentive would be far more appealing if loan rates were lower or equal to standard mortgage rates. The potential negative effect of Green Deal finance on house sales must be investigated further in order to determine the best solution to eliminate this additional barrier.  The introduction of FITs in 2010 has greatly increased the operating cost savings associated with microgeneration relative to conventional household electricity grid and gas boiler heating systems. The survey showed that the FITs have enabled a more financially motivated group to install. The rapid increase in uptake for solar PV, combined with a global decrease in capital cost of 50% between December 2010 and September 2012, resulted in the UK government halving the FIT rate in April 2012. This rate cut is likely to have impacted most upon this financially- motivated group. The FIT cut caused a sharp increase in installations prior to the FIT rate cut and low demand after it. The rush to install beforehand resulted in a number of documented poor quality installations.  The mechanism with which FIT rates reduce over time is complicated and has decreased consumer confidence and may have caused a misinterpretation: many fear that if they were to install, their FIT rate might change. The FIT rate degression mechanism should serve to increase consumer confidence by Page 200 of 210

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providing simple, transparent and regular updates to allow more informed consumer decisions.  In order to further improve uptake in the wake of the FIT cuts, other motivations should be focused on and publicised more clearly. For example, rejecters are particularly motivated by the desire for self-sufficiency and to protect against future high energy costs. This motivation is only set to increase further given the high levels of publicity surrounding the imminent ‘energy gap’ within the next few years. The government and microgeneration industry should focus on increasing availability of energy when required, for example by using battery storage with solar PV and highlight the potential benefits to consumers. Thus, the next phase of the project investigated the potential for battery storage to improve household self-sufficiency by conducting simulations of 30 households using a combined solar PV, SECHP and battery storage system, the conclusions of which are discussed presently.

6.3 Increasing self-sufficiency and flattening grid demand The conclusions of the research into the impact of a combined solar PV, SECHP and battery storage system on increasing household self-sufficiency and flattening grid electricity demand are the following:  The results of the simulation of a combined solar PV, SECHP and battery system showed that on average households were 72% electricity self-sufficient. Despite the combined generation from PV and SECHP being 30% more than annual household demand, the base case 6 kWh battery was not of sufficient capacity to match supply with demand.  Battery capacities above 10 kWh for such a combined household system only provide marginal improvement, relative to their large capital cost. In order to become more than 88% self-sufficient, battery capacities above 40 kWh are required.  In comparison to the solar PV system, the SECHP is far more suitable to provide household self-sufficiency, as 57% of the electricity generated was consumed, as opposed to 28% for solar PV. This is due to the electricity supply profile of SECHP matching demand far better than the PV generation profile.  The very low household consumption of PV electricity generated (28%) is contrary to 50% often assumed in literature.  The simulation results also demonstrated the National Grid concerns about solar PV increasing household grid demand variability. A solar PV installation increases

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hourly grid ramping up by 60% and ramping down by 150%. Furthermore, adding a SECHP to the household further exacerbates the problem: ramping up is 2.2 times higher and ramping down 3.9 times greater than for the conventional electricity grid and gas boiler heating system.  The incorporation of a battery reduces grid demand variability significantly: ramping up is reduced by 28% and ramping down by 40% for a 6 kWh battery relative to the system consisting of only PV and SECHP. Therefore local battery storage represents an option to mitigate the grid balancing problems expected with high uptake of solar PV. However, even with the largest battery capacity considered in this study (40 kWh), the grid demand profile was still 38% more variable than that of the conventional system (grid electricity and gas boiler), in terms of the hourly demand ramping. Thus, the contribution of battery storage to grid variation reduction without appropriate automated operational battery control would be limited. Additionally, there would be various trade-offs to be made, not least relating to economic and environmental costs.  For the base case with a 6 kWh battery, only nine out of the 30 households achieved a positive net-present value (NPV) relative to the conventional household system because they had the largest electricity demand.  Therefore, the system is on average financially feasible for households with an electricity demand above 4,300 kWh/yr, which is some way above the UK average 3,300 kWh/yr. For those households with lower demand, the large installation and equipment replacements costs outweigh the operational savings associated with reduced grid electricity demand.  If this system is to be encouraged in order to mitigate negative grid balancing effects, financial incentives would be required. A capital cost grant of 24% is required on average to make the base case system financially feasible, equivalent to £3,600. This is comparable to incentives for battery systems recently offered by the German government for households with solar PV. Local battery systems are the subject of increased attention in global energy policy due to their potential to mitigate grid variability issues and as such are expected to reduce in cost significantly, similarly to the recent reduction in solar PV costs. Thus, although this system represents some benefit to the grid, financial incentives would be required and this cost to the government (and tax payer) must be assessed against other grid balancing options.  Another way in which battery storage with microgeneration could be incentivised is to increase the difference between electricity import cost and the FIT export tariff. Page 202 of 210

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Currently the FIT export tariff is 5 p/kWh: if this was eliminated such that no credit is given, or if grid electricity import prices were to increase, there would be a greater incentive for households to maximise their consumption of locally generated electricity. As grid electricity prices are expected to increase in the medium-term, local battery storage should become more financially attractive over time anyway. However, the reduction in export tariff will make microgeneration systems less financially appealing and may reduce uptake. Thus, any financial incentives to maximise consumption of locally generation electricity must be carefully considered and should not reduce the financial appeal of microgeneration overall.  Due to the large equipment costs, the cost-effectiveness is highly dependent on the achieved lifespan of the SECHP and battery cells.  Additionally, operating the SECHP efficiently with fewer on/off cycles significantly improves the cost-effectiveness of the system due to the large annual cost of natural gas.

6.4 Environmental impacts of the PV-SECHP-battery system The conclusions of the research into the life cycle environmental impacts of the household system comprising solar PV, SECHP and battery are presented below.

 The life cycle environmental impacts of the combined household system represent significant improvements compared to the conventional electricity grid and gas boiler heating system for nine out of the 11 environmental impacts considered. Savings range from 35% for depletion to 100% to terrestrial ecotoxicity. The exceptions to this are ozone depletion potential, which is approximately the same as the conventional system, and the depletion of elements which is 42 times greater. The latter is largely due to the use of antimony within the lead-acid battery manufacture.  As well as the use of antimony, the main hotspots in the life cycle are the use of natural gas for SECHP operation on global warming (contributing 80% of the total impact), ozone layer depletion (62%) and photochemical oxidant creation (44%), as well as the use of coal within the electricity grid generation mix on marine ecotoxicity and acidification (40%), eutrophication (29%) and fresh water ecotoxicity (26%).  However, the reductions in impacts are highly dependent on the credits for the large quantity of electricity that is exported, due to the avoidance of using an electricity mix with high coal generation proportion. If the proportion of coal- Page 203 of 210

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generated electricity were to reduce then the environmental benefits of the household system would be reduced.  Similarly to the cost-benefit analysis, the environmental improvement relative to the conventional system is highly dependent on the household electricity and gas demand and is most beneficial to those with high demand.  In particular, detached households have the largest reductions in impact, whereas terraced houses have the lowest reductions, albeit still an improvement on the conventional system for most impacts.  Similarly to the cost-benefit analysis, the environmental benefits are significantly affected by the efficiency of SECHP operation. If households were to operate the SECHP more efficiently, global warming is reduced by 17%, ozone layer depletion is reduced by 12% and fossil fuel depletion is reduced by 17%. The economic and environmental gain by efficient household operation of the SECHP is significant and highlights the requirement to provide information and training on how to use the SECHP for the greatest benefit.  The environmental benefit of incorporating battery storage is highly dependent on the assumptions made regarding electricity export credits. When credits are accounted for, any sized battery decreases the environmental benefit, due to the inherent inefficiency of battery systems. When export credits are discounted however, the results show that there is an optimum battery capacity that produces the lowest impacts.  Small batteries (less than 4 kWh) do not store enough energy to offset the impacts associated with battery manufacture and large batteries (above 20 kWh) are not utilised enough to warrant the additional manufacturing impacts. Thus, appropriate sizing of battery systems for each household are essential in order to maximise environmental benefits.  Similarly to the cost-benefit analysis, the achieved lifespans of the SECHP and battery cells have a large effect on environmental impacts. In particular if the SECHP system were to only last 5 years instead of the assumed 10 years, acidification, fresh water toxicity and human ecotoxicity would all increase by 17%, marine ecotoxicity by 22% and terrestrial ecotoxicity by 37 times. Consequently, the use of effective control systems that maximise the operational life of the system is vital to ensuring environmental benefits are achieved.  The recycling rates of the metals used to manufacture the system also have a large effect on the environmental impacts. Increasing recycling rates from zero to 100% reduces impacts such as acification (56%), eutrophication (46%) and the Page 204 of 210

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toxicities (58 – 179%), demonstrating the importance of recycling and the use of secondary material during production.  There was little data on the use of secondary antimony in the battery cell manufacture, but if 90% secondary antimony were used, the depletion of elements reduces by 3 times. However, this is still 12 times higher than the conventional system due to the contribution from solar PV to heavy metal depletion.

6.5 Study limitations Upon reflection of the main studies carried out for this thesis, there are a number of limitations or areas for improvement. This section details a critique of the thesis papers, first describing the survey paper (Chapter 3) and then the simulation, cost-benefit and environmental assessment (Chapters 4 and 5).

6.5.1 Best-worst scaling survey

One potentially significant source of error in the survey was due to respondent fatigue. As the best-worst scaling survey involves numerous repetitions of choice-sets, this can become arduous for respondents. The survey was piloted on a number of subjects prior to publishing and this problem was indeed identified: the number of choice sets was reduced from 15 to 12 per respondent. However, a few comments were appended to the survey regarding the repetitiveness and length of the survey. It is possible that after a certain number of choice sets, respondents reduce cognitive load and choose more unselectively. The impact of this on the choice model is likely to be an increased random error component across respondents, decreasing the fit of the model. However, as described the model fit was acceptable.

Another comment that was appended to a few surveys was that a number of the choice tasks were very difficult to complete: there were occasions where none of the options within a choice task were important at all, so the selection of most or least important was arbitrary. Although this may have increased respondent fatigue or frustration, this should not have impacted negatively on the results: these items would all have low importance scores assuming that the selection of most and least important was approximately evenly spread across the motivations and barriers from these choice sets.

The phrasing of questions regarding concerns about FIT payments may have been ‘leading’ questions. There was a suggestion from the telephone interviews that there was concern that people may lose money if FIT payments were reduced after they had installed. This is not correct, as once an installation is registered, the FIT rate remains

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Chapter 6 Paul Balcombe constant. However, to investigate further, a question was introduced within in the survey. Considerer and rejecter respondents were first asked whether the recent change to FIT payments affected their decision to install. If they selected an affirmative answer, they were then asked to select whether this was because: it made the installation less financially attractive; they were concerned that once they installed the FIT rates would change again; or due to another reason (they were asked to specify). Because a number of options were available, both a true and false statement, it was considered a fair question. However, the inclusion of a false statement may have affected the respondents’ judgement.

The survey sample size was 291 and, as described in Chapter 3, enabled segregation of the group whilst maintaining an acceptable model fit. Both time and cost restraints restricted the sample size, but a larger group would have allowed greater analysis of heterogeneity in preferences across the sample, for example by age, or by considerers who are different stages of consideration.

6.5.2 PV-SECHP-battery simulation, costs-benefit analysis and environmental assessment

The simulation and thus the economic and environmental analyses, were primarily limited by data availability. The household demand data, as described in Chapter 4, was 20 years old, thus not necessarily truly representative of current household demand. There was a lack of available data whilst this study was being carried out, however since then a number of studies have been published by DECC using smart meter data which may be suitable. This would enable both more recent data to be used, as well as more households, which could result in a true representation of the UK housing stock. However, the study described in this thesis served as a set of case studies, or examples of the effect of such a system on a variety of household demands. Thus, a contribution to knowledge was still made.

The simulation used a number of assumptions regarding the operation of the technologies, which simplified or restricted the model. For example, the model used a constant average battery discharge efficiency. Real battery efficiency changes over time and is dependent on a number of factors, such as ambient temperature, state of charge, rate of discharge and voltage. Thus, the power flows across the system are likely to be slightly different and may impact upon the level of self-sufficiency achieved, as well as the degree of dampening of grid demand variation. The assumptions were made in order to limit the size

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Chapter 6 Paul Balcombe of the model, which was large due to the number of technologies, the number of houses studied (30) and the time period considered (one year with 5 minutely intervals).

Additionally, the operation of the SECHP system was modelled on data from the Baxi Ecogen. Some of this data was incomplete and assumptions had to be made. For example, the time required to start producing electricity after start-up was not available. The assumption of 2 minutes start-up time was based on discussion with the Baxi technical department. The real monitoring of a SECHP system in operation would allow better assumptions to be made and further increase the legitimacy of the simulation.

Lastly, in some cases the time resolution of the data could have been improved. Some of the demand profiles were hourly records, which has the effect of smoothening out the more dynamic aspects of demand. For example turning on a kettle would result in a demand spike over a matter of seconds. The collection of higher resolution data, such as 5-minutely, would be preferable to create a more realistic requirement for the electricity supply technologies.

6.6 Recommendations for policy and industry Based on this research, the following recommendations for policy and industry can be made:  Evidently there remains a barrier in terms of providing more accessible and transparent information and advice on microgeneration products and installations. Further investigation into how best to reduce this barrier is required. Reducing this barrier may prove to be the most cost-efficient way to increase microgeneration adoption.  It is evident that the Green Deal would be far more appealing to microgeneration consumers if loan rates were more competitive; for example, if they were lower or equal to standard mortgage rates. The UK government should consider the impacts of decreasing the loan rates associated with Green Deal finance.  The potential negative effect of Green Deal finance on house sales is largely unknown and must be investigated further in order to determine the best solution to eliminate this barrier.  The UK government should review the simplicity and transparency of the FIT rate degression mechanism.  In order to improve further microgeneration uptake in the wake of the FIT cuts, other motivations should be focused on and publicised more clearly. For example, rejecters are particularly motivated by the desire for self-sufficiency and to protect

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against future high energy costs. This motivation is only set to increase further given the high levels of publicity surrounding the imminent ‘energy gap’ within the next few years. The government and microgeneration industry could consider implementing policies and/ or products that maximise self-sufficiency and protection from future bill increases. One option would be to highlight the potential benefits of using battery storage with solar PV.  One option to reduce the grid variability effects of significant quantities of solar PV would be to incentivise local battery storage. An assessment comparing this option against other options to provide the same mitigation, such as increasing the variable-load capacity of the central grid, is required in order to determine the most environmentally and economically effective solution. The suitability of an incentive scheme similar to that offered by the German government for small battery systems should be investigated further.  Another option to incentivise battery storage could be to increase the cost difference between exporting and importing electricity. The UK government should investigate the potential this, for example by eliminating the FIT export tariffs. However, such a measure must ensure that the financial appeal of microgeneration is not significantly worsened.  Appropriate battery storage sizing to suit household demand patterns is essential to ensure environmental and economic benefits. If battery systems were to be incentivised, product and installation guidelines such as those within the Microgeneration Certification Scheme should be produced.  The economic and environmental gain by more efficient household operation of the SECHP is significant and highlights the need for installers, manufacturers and distributers to provide information and training on how to use the SECHP for the greatest benefit.  The incorporation by manufacturers or suppliers of effective control systems that maximise the operational life of battery storage, as well as SECHP, is vital to ensuring that environmental benefits are achieved.

6.7 Recommendations for further work These studies have yielded the following recommendations for further work:  The risk of losing money if moving home is the largest barrier to installing for rejecters and further investigation into the impact of installed microgeneration systems on house sale prices is required in order to dispel or confirm this

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perceived consumer risk. Additionally, the impact of Green Deal finance on house sale prices should be investigated.  Although the study detailed in Chapter 3 determines the relative importance of different motivations to install microgeneration, the consumer willingness to pay (WTP) for an improvement in the potential benefits of microgeneration (e.g. level of self-sufficiency and level of financial protection against future cost increases) is unknown. A few studies have estimated WTP for selected microgeneration attributes (Claudy et al., 2011; Scarpa and Willis, 2010), however this study on motivations and barriers could inform the inclusion of different product attributes (e.g. level of self-sufficiency, level of financial protection against future cost increases) to provide information to industry and policy on how to improve microgeneration products to best meet consumer demand.  The discount rate that consumers apply to microgeneration investment decisions is unknown and should be investigated further in order to provide more detail on the future microgeneration markets. Additionally, discount rates are likely to vary significantly across the population and a valuation study to elicit such heterogeneity would provide valuable information for predicting uptake rates.  With the incorporation of local battery storage, there is potential to reduce the capacity of solar PV installations as more PV electricity can be used via the battery. Further research could investigate the ability of households to meet demand with different solar PV and battery sizes and any trade-offs between environmental, economic and electricity grid impacts could be optimised.  Further investigation into the recycling impacts of antimony and the current proportion of secondary material used in lead-acid batteries is required. The use of antimony in lead-acid batteries causes large depletion of elements and recycling offers significant benefits. However, the environmental impacts of the antimony recycling process is thus far unknown.  The inventories used to estimate the environmental impacts associated with natural gas distribution must be updated. Currently the Ecoinvent database for natural gas transmission includes a large quantity of halon emissions associated with leakage from compressor station coolant and fire retardant. This halon release contributes to very large ozone depletion which may no longer be applicable: production of halon ceased in 1994 due to the Montreal Protocol and many companies have phased out its usage, including the company from which this data was sourced. Determining an up to date inventory for gas distribution, including

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compressor and fire retardants, would provide more accurate life cycle assessment models.  The environmental impacts associated with solar PV exporting intermittently into the grid are currently unknown. The impacts stem from increased ramping requirements of variable-load plants, resulting in a greater capacity requirement and a lower efficiency of operation: hence the requirement to investigate the use of battery storage. Estimating the effect on grid emissions of adding different quantities of solar PV (or wind) electricity to the grid mix is required to understand the comparative benefits, or otherwise, of battery storage.  The relative environmental benefit of the SECHP system compared to the conventional gas boiler and electricity grid system is largely due to the high emissions associated with the electricity grid, mostly attributable to the large coal fraction in the electricity generation mix. An investigation into the diminishing relative advantages of SECHP as the electricity grid is decarbonised in the future would give valuable information on the future feasibility of SECHP to contribute to climate change targets.

This research has contributed to understanding how microgeneration could contribute further to UK climate change and energy security targets. Specifically, the studies make recommendations aiming to increase uptake, mitigate grid balancing problems and maximise the efficiency of technologies considered. There is much potential for microgeneration to contribute to these, but to achieve this there must be more stable growth in the industry, which should stem from stable and transparent energy policy. Additionally, whilst microgeneration may be beneficial to climate change and energy security targets, other impacts, particularly environmental, may well be worse than conventional energy supply systems: care must be taken so that we do not replace one problem with another.

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