Energy from microgeneration: sustainability and perceptions in the UK

Transcription

1 Energy from microgeneration: sustainability 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 Sciences 2014 School of Chemical Engineering and Analytical Science

2 Contents List of Figures... 7 List of Tables List of abbreviations Declaration Copyright statement Acknowledgements Chapter 1: Introduction Background and motivation Aims, objectives and novelty Alternative format of the thesis Overarching methodology References Chapter 2: Motivations and barriers associated with adopting microgeneration energy technologies in the UK Annex References Motivations and barriers associated with adopting microgeneration energy technologies in the UK Abstract Introduction Motivations and barriers Finance Environment Security of supply Uncertainty and trust Inconvenience Impact on residence Page 2 of 210

3 3. Differing perceptions within subgroups of the UK population Age Household size and ownership Social class, income and education Further discussion and conclusions Acknowledgements Appendix References Chapter 3: Investigating the importance of motivations and barriers related to microgeneration uptake in the UK Annex Investigating the importance of motivations and barriers related to microgeneration uptake in the UK Abstract Introduction UK microgeneration policy Feed-In Tariffs Renewable Heat Incentive Green Deal Microgeneration Strategy Existing research on the motivations and barriers affecting adoption Finance Environmental concerns Self-sufficiency Uncertainty and trust Inconvenience Impact on residence Differing perceptions across the UK population Methodology Page 3 of 210

4 4.1 Questionnaire design and data collection Best-worst scaling Data analysis Results Motivations for installing microgeneration Barriers to installing microgeneration Discussion Motivations for installing microgeneration Barriers to installing microgeneration FITs and the experience of adoption Conclusions Appendix Acknowledgements References Supplementary Material Chapter 4: Self-sufficiency and reducing the variability of grid electricity demand: integrating solar PV, Stirling engine CHP and battery storage Annex Self-sufficiency and reducing the variability of grid electricity demand: integrating solar PV, Stirling engine CHP and battery storage Highlights Abstract Introduction Methodology Household simulation Household electricity self-sufficiency Electricity grid demand profiles Cost-benefit analysis Results Page 4 of 210

5 3.1 Electricity self-sufficiency Variability in grid demand profiles Cost-benefit analysis Discussion Conclusions Appendix References Chapter 5: Environmental impacts of microgeneration: Integrating solar PV, Stirling engine CHP and battery storage Environmental impacts of microgeneration: Integrating solar PV, Stirling engine CHP and battery storage Abstract Introduction Methodology Goal and scope Results Environmental impacts of the PV-SECHP-battery system Comparison of results with literature Sensitivity analysis Conclusions Acknowledgements References Supplementary material Chapter 6: Conclusions and further work General conclusions Motivations and barriers Increasing self-sufficiency and flattening grid demand Environmental impacts of the PV-SECHP-battery system Study limitations Page 5 of 210

6 6.5.1 Best-worst scaling survey PV-SECHP-battery simulation, costs-benefit analysis and environmental assessment Recommendations for policy and industry Recommendations for further work Word count: 57,360 Page 6 of 210

7 List of Figures Figure 1 Increase in the number of installations from Figure 2 The percentage of each age category associated with different consideration stages (Consumer Focus, 2011) Figure 3. Decrease in capital costs of solar PV installations from (/ kwp) (CompareMySolar Ltd, 2012; DECC, 2011a; Parsons Brinckerhoff, 2012; Vaughan, 2012) 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) 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) Figure 6. An example subset of motivations taken from the best-worst scaling survey Figure 7. The year of installation for the sample of adopters and the year of rejection for the sample of rejecters Figure 8. The proportion of adopters, considerers and rejecters who have installed or considered each technology Figure 9. Hierarchical Bayes estimation of the relative importance of motivations for installing microgeneration for adopters, considerers and rejecters Figure 10. Motivation importance scores for pre- and post-2010 adopters Figure 11. Hierarchical Bayes estimation of the relative importance of barriers to installing microgeneration for adopters, considerers and rejecters Figure 12. Barrier importance scores for pre- and post 2010 adopters 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 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) Page 7 of 210

8 Figure 15. Graph of the range of PV capacities for UK installations <4 kwp and for the simulation data (DECC, 2013b) Figure 16. The percentage of imported electricity for different installed battery capacities, with 80% battery efficiency and efficient SECHP operation, averaged across all households 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) Figure 18. Daily demand profile for different quarters of the year for the reference and solar PV only systems, averaged across all households 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 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) 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) 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) Figure 23. NPV difference for each household for the base case plotted against the household annual electricity demand Figure 24. Average NPV difference for each dwelling type for the base case, relative to the reference system Figure 25. NPV difference for different lifespans of the SECHP-PV-battery system for the base case, relative to the reference system Figure 26. Annualised NPV difference for base case for different consumer discount rates Page 8 of 210

9 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) Figure 28. Average NPV across all households for the base case for different proportions of grants for the total capital cost Figure 29. The life cycle diagram of the household microgeneration system comprising solar PV, SECHP and battery storage Figure 30 The life cycle of a natural gas boiler Figure 31. Environmental impacts of the PV-SECHP-battery system in comparison with the grid electricity and gas boiler Figure 32. The contribution to environmental impacts of solar PV, SECHP, battery and electricity imports and exports Figure 33 Comparison with literature of environmental impacts of solar PV Figure 34. Comparison with literature of environmental impacts of SECHP Figure 35 Environmental impacts of the battery cell estimated in this study Figure 36. Comparison with literature of selective emissions from the life cycle of battery Figure 37. Environmental impacts for the PV-SECHP-battery system, showing the variation in impacts for different dwelling types 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 Figure 39. Effect on the environmental impacts of the efficiency of SECHP operation Figure 40. Effect on the impacts of different battery capacities Figure 41. Effect on the impacts of different battery lifespans Figure 42. Effect on the impacts of different SECHP lifespans Page 9 of 210

10 Figure 43. Effect on the impacts of different recycling rates of metals used to manufacture the microgeneration system Figure 44. Effect on the impacts of recycling of antimony used in batteries List of Tables Table 1 Summary description of various microgeneration technologies Table 2 Summary of surveys carried out related to attitudes to microgeneration Table 3 Summary of motivations and barriers associated with adopting microgeneration as found in literature Table 4 Comparison of capital costs and consumer willingness to pay (WTP) Table 5 Correlations between several demographic factors and likelihood of adoption Table 6. Motivations and barriers considered in the survey Table 7. A summary of the characteristics of the sample, showing the breakdown for adopters, considerers and rejecters 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 variance a Table 9. The simulation parameters, their units and range of values, as well as the base case values 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) Table 11. Total capital cost for different battery usable capacities Table 12. Costs associated with each operating cost component 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 Table 14. Expected lifespan and installation cost of each replacement item Page 10 of 210

11 Table 15. Summary of base case annual household generation and consumption figures across the 30 simulated households Table 16. Contribution of each energy source as a percentage of total household demand for the base case, averaged over the 30 households Table 17. Average proportion of consumed PV and SECHP electricity for the base case, both directly and indirectly (through the battery) Table 18. Inventory data for the manufacture of a 3 kwp solar PV, by component (Ecoinvent, 2010; Stamford and Azapagic, 2012) Table 19. Inventory data for the manufacture of SECHP (left) and battery (right), by component (Baxi, 2011b; Ecoinvent, 2010; Sullivan and Gaines, 2012) Table 20. Household annual energy demand and generation by different components of the system, also showing the imports and exports of electricity Table 21. UK electricity mix in 2013 (DECC, 2014a) Table 22. Transport assumptions for the SECHP, solar PV and battery systems Table 23. Inventory data for a condensing gas boiler List of abbreviations ADP AP ASHP BWS CCGT CHP DECC EP EST FAETP FIT GHG Abiotic resource depletion potential Acidification potential Air source heat pump Best-worst scaling Combined cycle gas turbine Combined heat and power Department of Energy and Climate Change Eutrophication potential Energy Saving Trust Freshwater aquatic ecotoxicity potential Feed-in Tariff Greenhouse gas Page 11 of 210

12 GRP GSHP GWP HB HTP iid ISO LCA MAETP MCS MNL NPV ODP Ofgem OFT POCP PV RHI RHPP RLH SAP SECHP SEDBUK TETP UKERC UKERC EDC VAT WSHP WTP Glass reinforced plastic Ground source heat pump Global warming potential Heirarchical Bayes Human toxicity potential Independent and identically distributed Internation Standards Organisation Life cycle assessment Marine aquatic ecotoxicity potential Microgeneration Certification Scheme Multi-nomial logit Net-present value Ozone depletion potential Office of Gas and Electricity Markets Office of fair trading Photochemical ozone creation potential Photovoltaic Renewable Heat Incentive Renewable heat premium payment Root likelihood Standard assessment procedure Stirling engine combined heat and power UK Seasonal Efficiency of Domestic Boilers Terrestrial ecotoxicity potential UK energy research centre Energy Database Centre Value added tax Water source heat pump Willingness to pay Page 12 of 210

13 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 climate change and energy security 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 coal 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 lead-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 % 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 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

14 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 in any relevant Thesis restriction declarations deposited in the University Library, The University Library s regulations (see and in The University s policy on Presentation of Theses Page 14 of 210

15 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 Sustainable Consumption 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. Page 15 of 210

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

17 Chapter 1 Microgeneration technology type Description Fuel source Energy type Fuel cells steam engines, turbines, fuel cells). The chemical reaction of a fuel (e.g. hydrogen, natural gas, methane) and an oxidant between two electrodes creates an ionic charge which generates a current which is converted into electricity. Electrochemi cal energy from fuel reaction (e.g. hydrogen, natural gas, methane) Electricity (and heat if CHP) 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

18 Chapter 1 As a result of the emerging microgeneration industry and various UK policy incentives over the last decade, there are currently around 580,000 1 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 MW 2 (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 carbon footprint. 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, This is the capacity bracket within which the Feed in Tariffs apply. Page 18 of 210

19 Chapter 1 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 supply and demand, 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

20 Chapter 1 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. Page 20 of 210

21 Chapter 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 Motivations and barriers associated with adopting microgeneration energy technologies in the UK. Renewable and Sustainable Energy Reviews, 22, Balcombe, P., Rigby, D. and Azapagic, A Investigating the importance of motivations and barriers related to microgeneration uptake in the UK. Applied Energy, 130, Brouwer, A. S., van den Broek, M., Seebregts, A. and Faaij, A Impacts of largescale Intermittent Renewable Energy Sources on electricity systems, and how these can be modeled. Renewable and Sustainable Energy Reviews, 33, DECC 2009a. Impact Assessment of Feed-in Tariffs for Small-Scale, Low Carbon, Electricity Generation. 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. Page 21 of 210

22 Chapter 1 DECC 2011b. Renewable Heat Incentive. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown Copyright. Available: 7-renewable-heat-incentive.pdf. DECC Monthly central Feed-in Tariff Register. MARCH_2014_MONTHLY_CENTRAL_FEED- IN_TARIFF_REGISTER_STATISTICS.XLS. Microsoft Excel. London. Available: DTI 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 Press release: Market Report Available: Grave, K., Paulus, M. and Lindenberger, D A method for estimating security of electricity supply from intermittent sources: Scenarios for Germany until Energy Policy, 46, Gross, R., Heptonstall, P., Anderson, D., Green, T., Leach, M. and Skea, J 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 Energy Act. London: Crown Copyright. Johansson, B Security aspects of future renewable energy systems A short overview. Energy, 61, MIT Managing Large-Scale Penetration of Intermittent Renewables: An MIT Energy Initiative Symposium. MIT ENERGY INITIATIVE (ed.). Massachusetts, USA. National Grid Solar PV Briefing Note. DECC (ed.). London: DECC. NHBC Foundation A Review of Microgeneration and Renewable Energy Technologies. BRE (ed.). NHBC Foundation Introduction to Feed-In Tariffs. BRE (ed.). Available: bid/437/default.aspx: IHS BRE Press. Page 22 of 210

23 Chapter 1 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 UK microgeneration. Part II: Technology overviews. Proceedings of Institution of Civil Engineers: Energy, 163, Thretford, K Charting the Fall of Solar Prices [Online]. The Atlantic,. Available: [Accessed 19 September 2014]. UKERC Energy Making the transition to a secure and low-carbon energy system: synthesis report. Page 23 of 210

24 Chapter 2 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, : p 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 power source: microgeneration. We ask how Page 24 of 210

25 Chapter 2 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 Does solar work in a blackout? [Online]. Available: thirdsunsolar.com/does-solar-work-in-a-blackout/ [Accessed 8 Dec 2014]. Transition Cambridge Photovoltaic energy FAQS [Online]. Available: [Accessed 9 Dec 2014]. Page 25 of 210

26 Chapter 2 Motivations and barriers associated with adopting microgeneration energy technologies in the UK a,b,c, Dan Rigby b and Adisa Azapagic a,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: , adisa.azapagic@manchester.ac.uk 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

27 Chapter 2 (DECC, 2009). Given that the residential sector accounts for 30% of UK energy consumption (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, gridconnected, 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, anaerobic digestion and CHP. The payment, which is guaranteed over 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 100,000 Wind Hydro Number of installations 10,000 1,000 Solar Thermal Biomass boilers GSHP ASHP Year Figure 1 Increase in the number of installations from [Estimates based on the following sources: : Element Energy (2005); 2008: Element Energy (2008b); : 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, Page 27 of 210

28 Chapter b) to 320,000 3 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 (Japan) 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 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 Consequently, this may be an underestimate. See also the Appendix for further details. Page 28 of 210

29 Chapter 2 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 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. Page 29 of 210

30 Chapter 2 Table 2 Summary of surveys carried out related to attitudes to microgeneration Author Year Location Sample Aims Type of survey Technologies considered 1. Brook 2003 UK 502 London Identifying the public's attitudes Face-to-face interviews Solar, wind, CHP Lyndhurst et (London) residents towards climate change, Both open and closed al. (2003) renewables and microgeneration ended questions 2. Fischer (2004) 3. Ellison (2004) 4. Curry et al. (2005) 5. Faiers and Neame (2006) 2004 Germany 142 fuel cell CHP owners 2004 UK (London) 601 London residents 2006 UK 1,056 UK residents 2006 UK 43 UK 'early adopters' and 350 UK 'early majority' 6. Jager (2006) 2006 Holland 197 Dutch solar PV adopters 7. Keirstead (2007) 2007 UK 91 UK solar PV adopters Identifying the socio-demographic profile of fuel cell CHP users, as well as their attitudes to energy and environment and perceptions of CHP. Identifying the public's attitudes towards climate change, renewables and microgeneration Identifying the public's attitudes towards climate change and renewable energy Investigating the difference in attitudes towards solar thermal and PV systems between early adopters and the early majority Identify behaviour-related motivations to installing solar PV To understand factors affecting the adoption decision and to identify whether energy use behaviour changes after microgeneration adoption Postal survey with closed ended questions and agreement rating scales Telephone interviews Both open and closed ended questions Online questionnaire closed format questions Kelly's repertory grid survey Closed ended questions 0-13 agreement scale Closed ended questions Likert agreement scale Semi-structured face-toface interviews and closed-format posted questionnaires Fuel cell micro CHP Solar PV, solar thermal, wind Solar, wind, biomass Solar thermal, solar PV Solar PV Solar PV Analysis No information Using mean average responses and comparing to the general German public Descriptive statistics and cross tabulations No information Segmented sample by innovation theory groups Univariate analysis of importance of motivations against environmental awareness. Segmentation of high awareness and low awareness Descriptive statistics Page 30 of 210

31 Chapter 2 Author Year Location Sample Aims Type of survey Technologies considered 8. Mahapatra 2008 Sweden 630 and 711 (two Identifying factors affecting Postal survey with closed GSHPs, biomass and surveys) Swedish homeowners' decisions to adopt ended questions and boilers Gustavsson detached microgeneration systems. agreement rating scales (2008) homeowners 9. Goto and Toshio (2009) 10. Caird and Roy (2010) 11. Claudy et al. (2010) 12. Scarpa and Willis (2010) 13. Sopha et al. (2010) 2009 Japan 3,431 Japanese residents 2010 UK 545 adopters, 314 considerers, 65 rejecters 2010 Republic of Ireland 1,010 Irish residents 2010 UK 1,279 UK homeowners 2010 Norway 649 Norwegian homeowners Identifying the most important factors that affect preferences for solar PV and fuel cell technologies Determining the motivations and barriers associated with installing heat producing microgeneration technologies in households, UK Defining the importance of sociodemographic factors that affect the awareness of microgeneration. Estimating the WTP a for different microgeneration technologies and the influence of perceptions on WTP a Identify factors that affect the decision to switch to wood pellet boilers and heat pumps from electric heating Closed ended questions Likert agreement scale Online questionnaire multiple choice and open ended questions Telephone interview with closed ended questions Choice experiment Postal survey, closed ended questions with multiple choice and Likert agreement scale ratings Solar PV, fuel cell Solar thermal, GSHPs, biomass boilers Solar PV, solar thermal, wind, CHP, heat pumps, biomass boilers Solar PV, solar thermal, wind ASHP, biomass boilers Analysis Mean average responses of agreement scales Multivariate regression preferences for different microgeneration systems against capital cost, operating cost, environmental benefit etc. Descriptive statistics and cross tabulations Regression of awareness on demographic information Various logit models to regress the decision to adopt microgeneration against capital cost, maintenance bill, energy cost, inconvenience etc Regression of choice of heating system against socio-demographics and various productand choice-related factors Page 31 of 210

32 Chapter 2 Author Year Location Sample Aims Type of survey Technologies considered 14. Warren 17 small-sized (2010) companies 15. Palm and Tengvard (2011) 16. Claudy et al. (2011) 17. Consumer Focus (2011) 18. Leenheer (2011) a WTP: willingness to pay UK (Camden, London) 2011 Sweden 20 Swedish homeowners: 9 adopters, 8 considerers, 3 rejecters 2011 Republic of Ireland 1,012 Republic of Ireland homeowners 2011 UK 1,223 UK residents (and 2,655 UK residents for the microgeneration experience survey) 2011 Holland 2,047 Dutch residents Determining the motivations and barriers associated with installing microgeneration technologies in commercial buildings in Camden, UK Determining the motivations and barriers associated with installing solar PV and wind systems in Swedish households Estimating the WTP a for different microgeneration technologies and the importance of different factors in the adopting decision Identifying attitudes towards, microgeneration in terms of adoption and experience, and developing a profile of those at different stages of consideration Defining the factors that affect the motivation to install microgeneration Semi-structured face-toface open ended interviews Half face-to-face and half telephone interviews Open ended questions Contingent valuation method Focus group discussion (12), face-to-face interviews (40) and online questionnaires (1,223) Closed ended questions Likert agreement scale Solar PV, solar thermal, wind, CHP, biomass boilers, ASHPs, GSHPs Solar PV, wind Solar PV, solar thermal, wind, biomass boilers Solar PV, solar thermal, wind, CHP, hydro, biomass boilers, heat pumps Any electricity microgeneration Analysis Qualitative Qualitative Bivariate probit model to regress the decision to adopt against various innovation theory factors Descriptive statistics Descriptive statistics and a multivariate regression of the intention to adopt against environmental concern and independence from centralised energy generation Page 32 of 210

33 Chapter 2 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 incentives Costs too much to buy/ install 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 Make the household more self sufficient/ less dependant on utility companies Protect the household against power cuts Wouldn't make me much more self sufficient/ independent Uncertainty and trust Use an innovative/high-tech system Home/ location not suitable 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 residence Improve the feeling or atmosphere within my home Show my environmental commitment to others Take up too much space The installation might damage my home Would not look good Neighbour disapproval/annoyance Page 33 of 210

34 Chapter 2 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 Cost ( / kw) a Levelised mean WTP ( / kw) Acceptable payback time (yrs) section 4. For example, one study estimated a payback time of 11 years for solar PV Page 34 of 210 Year Source Solar PV (2 kw) 5,319 1,416 N/A 2007 Scarpa and Willis (2010) Solar PV (3 kw) 6,383 2, Claudy et al. (2011) Wind (1 kw) 4,998 1,288 N/A 2007 Scarpa and Willis (2010) Wind (5 kw) 5,830 1, Claudy et al. (2011) Solar thermal b 1,575 1, Claudy et al. (2011) Solar thermal 1,952 1,452 N/A 2007 Scarpa and Willis (2010) (2 kwth) Biomass boiler (wood pellets) b 1, Claudy et al. (2011) a Costs as cited by the relevant study. b The 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

35 Chapter 2 (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 tradeoff 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. Page 35 of 210

36 Chapter 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 environmentally friendly, 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 carbon dioxide 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 air pollution 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 wind turbine is also a motive for some residential consumers (Caird and Roy, 2010; Palm and Tengvard, 2011). Palm and Tengvard (2011) surveyed Swedish households Page 36 of 210

37 Chapter 2 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 sociopolitical 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 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). Page 37 of 210

38 Chapter 2 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 selfsufficiency. 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 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 Page 38 of 210

39 Chapter 2 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 Scheme 7 or not (DECC, 2011b) 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. Page 39 of 210

40 Chapter 2 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 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 Page 40 of 210

41 Chapter 2 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 efficient energy use 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. Page 41 of 210

42 Chapter 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 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). Page 42 of 210

43 Chapter 2 Table 5 Correlations between several demographic factors and likelihood of adoption Demographic Correlation with adoption Source Age Inverted 'u' shaped correlation OR increase of adoption with age until 65, with a sharp decrease afterwards OR decrease of adoption with age Page 43 of 210 Claudy et al. (2010); Mahapatra and Gustavsson (2008) ; Willis et al. (2011) Household size Increases with size Caird and Roy (2010); Keirstead (2007) Homeowners/ tenants Almost all adoption is by homeowners Family size No correlation found Ellison (2004) Social class Income Education Upper-middle class most likely to adopt Adoption increases with income OR middle income most likely to adopt Adoption increases with education Keirstead (2007); Fischer and Sauter (2003) Ellison (2004); Devine-Wright (2005); GfK NPT Social Research (2006) Keirstead (2007); Sopha (2010) Keirstead (2007); Fischer and 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 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

44 Chapter 2 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 and the 35-44, exhibit a similar relationship to their age-group neighbour, with the difference between consideration stages slightly less noticeable. Under or older 100% 90% 80% Percentage (%) 70% 60% 50% 40% 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. Page 44 of 210

45 Chapter 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

46 Chapter 2 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) 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, highlyeducated 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 Page 46 of 210

47 Chapter 2 cost and more highly educated people are more likely to adopt due to the highinvolvement 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 electric heating 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 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 These figures are installation costs for systems of less than 4 kw capacity and are collated from installation Page 47 of 210

48 Installed levelised cost of solar PV (less than 4 kw) Chapter 2 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] Parsons Brinckerhoff, 2012 [20] (2.6 kw) DECC, 2011 [61] (2.6 kw) Vaughan, 2012 [62] (4 kw) 8,000 DECC, 2011 [68] 2.5 kw 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 Jun-2006 Oct-2007 Feb-2009 Jul-2010 Nov-2011 Figure 3. Decrease in capital costs of solar PV installations from (/ 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. Page 48 of 210

49 Apr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Jan-11 Feb-11 Mar-11 Apr-11 May-11 Jun-11 Jul-11 Aug-11 Sep-11 Oct-11 Nov-11 Dec-11 Jan-12 Feb-12 Mar-12 Apr-12 May-12 Jun-12 Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12 FIT rate (p/kwh) Number of installations per month Chapter 2 50 FIT rates (p/ kwh) Installations per month 40, , , , , , , , 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

50 Chapter 2 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 installations (2008) Capacity (kw a ) Page 50 of 210 Energy generation (MWh/year) Energy yield per kw (MWh/yr. kw) Solar PV 2,993 10,354 8, Micro-CHP 200-1,000 7,000 N/A N/A (mean 600) Wind 2,323 4,367 3, Micro-hydro , Solar thermal 97, ,000 (mean 99,750) 205, ,000 (mean 209,000) 132, ,000 (mean 134,500) Biomass 1,400 28,000 23, GSHP 3,415 22,198 58, ASHP 169 1,146 2, Total 110, , ,460 N/A a kwp for PV solar.

51 Chapter 2 Table A.2 Number of installations and capacity of microgeneration systems below 50 kw registered in the FIT database (DECC, 2012c) Technology No. of installations Capacity (2012) (kw) a Solar PV 317, ,046 Micro-CHP 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 2012 a Technology No. of installations Total capacity Total energy yield (kw) b (MWh/yr) PV 320, , ,750 CHP 1,000 7,406 N/A Wind 4,835 38,119 33,388 Hydro 244 4,958 21,711 Solar Thermal 99, , ,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 microgeneration (3) Energy contribution from microgeneration 1,111,649 MWh From Table A 3 Table 0.197% of the UK domestic sector = (2) / (1) final energy demand References Allen, S. R., Hammond, G. P. and McManus, M. C Prospects for and barriers to domestic micro-generation: A United Kingdom perspective. Applied Energy, 85, Page 51 of 210

52 Chapter 2 Ben Hoen, R. W., Peter Cappers, Mark Thayer, Gautam Sethi, The Impact of Wind Power Projects on Residential Property Values in the United States: 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 UK microgeneration. Part I: Policy and behavioural aspects. Proceedings of Institution of Civil Engineers: Energy, 162, Brignall, M How solar panels can dim mortgage prospects [Online]. London: The Guardian,. Available: [Accessed 14 January 2013]. Brook Lyndhurst Ltd, MORI and Upstream 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 Statistic data on the German Solar power (photovoltaic) industry [Online]. Available: [Accessed 14 January 2013]. Caird, S. and Roy, R Adoption and use of household microgeneration heat technologies. Low Carbon Economy, 1, pp Cambridge Economic Policy Associates Ltd and Parsons Brinckerhoff Updates to the Feed-in Tariffs model: documentation of changes for solar PV consultation. DECC (ed.). Candelise, C., Gross, R. and Leach, M. A Conditions for photovoltaics 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, Claudy, M. C., Michelsen, C., O'Driscoll, A. and Mullen, M. R Consumer awareness in the adoption of microgeneration technologies: An empirical investigation in the Republic of Ireland. Renewable and Sustainable Energy Reviews, 14, Claudy, M. C., Michelsen, C. and O Driscoll, A The diffusion of microgeneration technologies assessing the influence of perceived product characteristics on home owners' willingness to pay. Energy Policy, 39, Page 52 of 210

53 Chapter 2 Cogar, D Customer Perceptions of Green Technologies EcoPinion, Survey Report. CompareMySolar Ltd Price of solar: Compare prices from local installers [Online]. London. Available: [Accessed 9 October 2012]. Consumer Focus Keeping FiT Consumers' attitudes and experiences of microgeneration. ENERGY SAVING TRUST & DECC (eds.). London. Available: Curry, T. E., Reiner, D. M., Figueiredo, M. A. d. and Herzog, H. J A Survey of Public Attitudes towards Energy & Environment in Great Britain. Available: 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 Understanding the Solar Home price premium: Electricity generation and Green social status. European Economic Review, 56, DECC 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 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: [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 MONTHLY- CENTRAL-FEEDIN-TARIFF-REGISTER-STATISTICS.XLS (ed.) Microsoft Excel. London: DEPARTMENT OF ENERGY AND CLIMATE CHANGE. Devine-Wright, P Local aspects of UK renewable energy development: exploring public beliefs and policy implications. Local Environment, 10, Page 53 of 210

54 Chapter 2 Devine-Wright, P 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 Road, Manchester M13 9PL, UK, Available: DTI The Microgeneration Strategy. DEPARTMENT OF TRADE AND INDUSTRY (ed.). London: Crown Copyright. Ecohouse Solar Solar panels to boost property prices [Online]. WordPress & the Atahualpa WP Theme. Available: [Accessed 14 Jan ]. Element Energy Potential for Microgeneration. Study and Analysis. ENERGY SAVING TRUST (ed.). London. 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 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 Giving Power to People - North West of England Results and Best Practice. ENERGY SAVING TRUST (ed.). EST Feed-in Tariff scheme [Online]. London. Available: [Accessed 10 September 2012]. Faiers, A. and Neame, C Consumer attitudes towards domestic solar power systems. Energy Policy, 34, Fischer, C 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 Governance for Industrial Transformation. Human Dimensions of Global Environmental Change. Berlin. Gardiner, M., White, H., Munzinger, M. and Ray, W Low Carbon Building Programme Final Report. DECC (ed.). London: Crown Copyright. Page 54 of 210

55 Chapter 2 GfK NOP Social Research Renewable Energy Awareness and Attitudes Research. DTI (ed.). London. webarchive.nationalarchives.gov.uk/+/ Goto, H. and Toshio, A Ananalysis of residential customers preferences for household energy systems. IAEE European Conference in Vienna, 31 August. Hack, S International Experiences with the Promotion of Solar Water Heaters (SWH) at Household-level. DEUTSCHE GESELLSCHAFT FÜR TECHNISCHE ZUSAMMENARBEIT (GTZ) GMBH (ed.). Mexico City. Available: pdf. HM Government Energy Act. London: Crown Copyright. Available: HM Government Energy White Paper: Meeting the Energy Challenge. DTI (ed.). London: Crown Copyright. webarchive.nationalarchives.gov.uk/ / ts/decc/publications/white_paper_07/file39387.pdf. HM Government UK Fuel Poverty Strategy Seventh Annual Progress report. DECC (ed.). Crown Copyright. Hocke-Bergler, P. and Stolle, M Ergebnisse der Bevölkerungsumfragen und der Medienanalyse zum Thema Endlagerung radioaktiver Abfälle. Anlagenband zum ITAS-Bericht. Hoen, B., Wiser, R., Cappers, P. and Thayer, M An Analysis of the Effects of Residential Photovoltaic Energy Systems on Home Sales Prices in California. ERNEST ORLANDO LAWRENCE BERKELEY NATIONAL LABORATORY (ed.). Orlando. Available: eetd.lbl.gov/ea/emp/reports/lbnl-4476e.pdf. Environmental Energy Technologies Division. Jager, W Stimulating the diffusion of photovoltaic systems: A behavioural perspective. Energy Policy, 34, Joachim, S., Galen, L. B. and Ryan, H. W Why Are Residential PV Prices in Germany So Much Lower Than in the United States? A Scoping Analysis. Lawrence Berkeley National Laboratory. Keirstead, J Behavioural responses to photovoltaic systems in the UK domestic sector. Energy Policy, 35, Leenheer, J., de Nooij, M. and Sheikh, O Own power: Motives of having electricity without the energy company. Energy Policy, 39, Page 55 of 210

56 Chapter 2 Mahapatra, K. and Gustavsson, L An adopter-centric approach to analyze the diffusion patterns of innovative residential heating systems in Sweden. Energy Policy, 36, McLeod, R. S., Hopfe, C. J. and Rezgui, Y An investigation into recent proposals for a revised definition of zero carbon homes in the UK. Energy Policy, 46, Morris-Marsham, C 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. Murray-West, R Government plans to cut solar feed-in tariff [Online]. Available: [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 Introduction to Feed-In Tariffs. BRE (ed.). Available: bid/437/default.aspx: IHS BRE Press. Nye, M., Whitmarsh, L. and Foxon, T Sociopsychological perspectives on the active roles of domestic actors in transition to a lower carbon electricity economy. Environment and Planning A, 42, Ofgem Feed-in Tariff Payment Rate Table for Photovoltaic Eligible Installations. DECC (ed.). London. Palm, J. and Tengvard, M Motives for and barriers to household adoption of smallscale production of electricity: examples from Sweden. Sustainability: Science, Practice, & Policy, Vol 7, pp Parsons Brinckerhoff Solar PV cost update. DECC (ed.). London: Praetorius, B., Martiskainen, M., Sauter, R. and Watson, J Technological innovation systems for microgeneration in the UK and Germany - a functional analysis. Technology Analysis & Strategic Management, 22, Rae, C. and Bradley, F Energy autonomy in sustainable communities A review of key issues. Renewable and Sustainable Energy Reviews, 16, Rowley, E Renting out roof to solar power firms could make your home harder to sell, surveyors warn [Online]. London: Telegraph Media Group Limited Page 56 of 210

57 Chapter 2 Available: [Accessed 14 January ]. Scarpa, R. and Willis, K Willingness-to-pay for renewable energy: Primary and discretionary choice of British households' for micro-generation technologies. Energy Economics, 32, Schroeder, S. T., Costa, A. and Obé, E Support schemes and ownership structures the policy context for fuel cell based micro-combined heat and power. Journal of Power Sources, 196, Sopha, B. M., Klöckner, C. A., Skjevrak, G. and Hertwich, E. G Norwegian households perception of wood pellet stove compared to air-to-air heat pump and electric heating. Energy Policy, 38, The Carbon Trust Building the future, today: Transforming the economic and carbon performance of the buildings we work in. London. Vaughan, A Sharp drop in number of UK homes installing solar panels [Online]. Available: [Accessed 9 October 2012]. Walters, R. and Walsh, P. R Examining the financial performance of microgeneration wind projects and the subsidy effect of feed-in tariffs for urban locations in the United Kingdom. Energy Policy, 39, Warren, P 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 Domestic micro-generation: Economic, regulatory and policy issues for the UK. Energy Policy, 36, Wee, H.-M., Yang, W.-H., Chou, C.-W. and Padilan, M. V Renewable energy supply chains, performance, application barriers, and strategies for further development. Renewable and Sustainable Energy Reviews, 16, Williams, J The deployment of decentralised energy systems as part of the housing growth programme in the UK. Energy Policy, 38, Willis, K., Scarpa, R., Gilroy, R. and Hamza, N Renewable energy adoption in an ageing population: Heterogeneity in preferences for micro-generation technology adoption. Energy Policy, 39, Wimberly, J Banking the Green: Customer Incentives for EE and Renewable. EcoAlign. Available: Page 57 of 210

58 Chapter 2 Zahedi, A A review of drivers, benefits, and challenges in integrating renewable energy sources into electricity grid. Renewable and Sustainable Energy Reviews, 15, Page 58 of 210

59 Chapter 3 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, : p 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 Page 59 of 210

60 Chapter 3 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. Page 60 of 210

61 Chapter 3 Investigating the importance of motivations and barriers related to microgeneration uptake in the UK a,b,c, Dan Rigby b * and Adisa Azapagic a 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: addresses: paul.balcombe@postgrad.manchester.ac.uk, dan.rigby@manchester.ac.uk, adisa.azapagic@manchester.ac.uk 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 energy independence 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 Page 61 of 210

62 Chapter 3 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 Page 62 of 210

63 Chapter 3 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 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

64 No. of installations per month FIT rate (p/ kwh) Chapter 3 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. 40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000 - FIT rate (p/kwh) Number of installations per month 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

65 Chapter 3 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 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). Page 65 of 210

66 Chapter 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 Page 66 of 210

67 Chapter 3 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 zerocarbon 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 Page 67 of 210

68 Chapter 3 considerably across the population. Although adoption is highest amongst 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 Page 68 of 210

69 Chapter 3 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 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 solar panel - 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 Page 69 of 210

70 Chapter 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

71 Chapter 3 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): U x = I x + ε x Equation 1 where U x is the relative importance of motivation or barrier x, I x is the systematic element of importance (the importance level measured within the study) and ε x is the unobserved error component, which accounts for internal inconsistencies in the choices. I x is Page 71 of 210

72 Chapter 3 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): P(xy C) = ei x Iy K e I j I k 1 Equation 2 where P(xy C) 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 U x 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). Page 72 of 210

73 Chapter 3 A number of covariates are used in the model in order to improve estimates of the upperlevel 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 subgroups: 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 old 9 ), 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 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. Page 73 of 210

74 Chapter 3 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 error Mean Standard error Mean Standard error Mean Standard error Which technology have you installed/considered? n = 291 n = 113 n = 87 n = 91 Solar PV Solar thermal Micro-wind GSHP a ASHP b Biomass Micro-CHP Micro-hydro Income n = 282 n = 106 n = 86 n = 90 < 20, ,000-30, ,000-40, ,000-50, ,000-60, ,000-80, , , > 100, Gender n = 289 n = 111 n = 87 n = 91 1 = Male, 0 = Female Age n = 264 n = 102 n = 78 n = 84 Years Occupation c n = 281 n = 108 n = 85 n = 88 Employed Retired Student Unemployed Education d n = 291 n = 113 n = 87 n = 91 Bachelor's degree (or equiv) Master's degree (or equiv) a Ground source heat pump b Air source heat pump 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. Page 74 of 210

75 Sample proportion Sample proportion Chapter Adopters Rejecters 0 Before Installation/ rejection year Figure 7. The year of installation for the sample of adopters and the year of rejection for the sample of rejecters Adopters Considerers Rejecters 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). Page 75 of 210

76 Chapter 3 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 ( for motivation models and 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. Page 76 of 210

77 Chapter 3 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 variance a. Motivations Mean Adopters Considerers Rejecters Standard error Mean Standard error Mean Standard error 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 Use an innovative/ high-tech system Show my environmental commitment to others Protect the household against power cuts Root Likelihood Barriers 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 Environmental benefits too small Disruption or hassle of operation Take up too much space Would not look good Neighbour disapproval/ annoyance Root Likelihood 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 e U i using the equation e U, where Ui is the zero-centred importance score and a is the number of items in i+a 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. Page 77 of 210

78 Chapter 3 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 Use an innovative/ high-tech system Adopters Considerers Rejecters Show my environmental commitment to others Protect the household against power cuts 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 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

79 Chapter 3 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 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 Use an innovative/ high-tech system Post 2010 Pre 2010 Show my environmental commitment to others Protect the household against power cuts 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). Page 79 of 210

80 Chapter 3 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. Page 80 of 210

81 Chapter 3 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 Adopters Considerers Rejecters Environmental benefits too small Disruption or hassle of operation Take up too much space Would not look good Neighbour disapproval/ annoyance 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 Barrier importance score Page 81 of 210

82 Chapter 3 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 Environmental benefits too small Post 2010 Pre 2010 Disruption or hassle of operation Take up too much space Would not look good Neighbour disapproval/ annoyance 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. Page 82 of 210

83 Chapter 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 , 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, economic growth 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. Page 83 of 210

84 Chapter 3 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 energy storage 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 electricity pricing 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 higherincome 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. Page 84 of 210

85 Chapter 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 Green Building 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

86 Chapter 3 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 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 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 Page 86 of 210

87 Chapter 3 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

88 Chapter 3 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 financiallymotivated 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

89 Chapter 3 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 vehicle tax. Page 89 of 210

90 Chapter 3 Appendix Table A1. Summary of answers by adopters. Variable Participants Mean Standard Do you own the system? error 1 = Yes, 0 = No Installation year Before Those installed since FITs have been available (2010) Do/ Have you received incentives for the system? No Feed-in Tariffs ROCs (Renewable Obligation Certificates) Grant (e.g. from the Low Carbon Buildings Programme) Other (please describe briefly) If you knew what you do now at the time of deciding to install, would you do it again? Definitely would Probably would Not sure Probably not Definitely not Would you do anything differently? Nothing Don't know Yes I would change something Did you encounter any problems during the decision/installation/operation of the system? No problems Problem or difficulty when buying it Problem or difficulty with installing it Problem or difficulty whilst using it Any other problem or difficulty Page 90 of 210

91 Chapter 3 Table A2. Summary of answers by considerers and rejecters. Considerers Variable Participants Mean Standard error What year did you decide not to install? Rejecters Participants Mean Standard error Before 2000 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A What stage of consideration have you got to? Initial investigation I have talked others who have installed I have been to see a system in action I received professional advice I received a quote from supplier/installer Other information How likely are you to install? Almost definitely will N/A N/A N/A Pretty likely N/A N/A N/A Perhaps N/A N/A N/A Pretty unlikely N/A N/A N/A Page 91 of 210

92 Chapter 3 Considerers Rejecters Variable Participants Mean Standard Participants Mean Standard error error Almost definitely won't N/A N/A N/A Are you familiar with the recent cuts to the solar PV FITs? Yes Vaguely No Have these cuts put you off installing a system? Page 92 of 210

93 Chapter 3 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 Prospects for and barriers to domestic micro-generation: A United Kingdom perspective. Applied Energy, 85, Bachelor, L Green deal debt may have to be repaid before property sold [Online]. London. Available: [Accessed 31 May 2013]. Balcombe, P., Rigby, D. and Azapagic, A Motivations and barriers associated with adopting microgeneration energy technologies in the UK. Renewable and Sustainable Energy Reviews, 22, Bank of England Changes in bank rate, minimum lending rate, minimum band 1 dealing rate, repo rate and official bank rate. London: Bank of England. Available: Baskaran, R., Managi, S. and Bendig, M A public perspective on the adoption of microgeneration technologies in New Zealand: A multivariate probit approach. Energy Policy, 58, Bergman, N. and Eyre, N What role for microgeneration in a shift to a low carbon domestic energy sector in the UK? Energy Efficiency, 4, 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 UK microgeneration. Part I: Policy and behavioural aspects. Proceedings of Institution of Civil Engineers: Energy, 162, Bergman, N. and Jardine, C Power from the People. ECI RESEARCH REPORT NO 34 (ed.) Domestic Microgeneration and the Low Carbon Buildings Programme. Available: Bollinger, B. and Gillingham, K Peer Effects in the Diffusion of Solar Photovoltaic Panels. Marketing Science, 31, Page 93 of 210

94 Chapter 3 Booth, A. T. and Choudhary, R Decision making under uncertainty in the retrofit analysis of the UK housing stock: Implications for the Green Deal. Energy and Buildings, 64, Brignall, M How solar panels can dim mortgage prospects [Online]. London: The Guardian,. Available: [Accessed 14 January 2013]. Brook Lyndhurst Ltd, MORI and Upstream 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 Adoption and use of household microgeneration heat technologies. Low Carbon Economy, 1, pp Carrington, D Cavity wall insulations crash by 97% following green deal introduction [Online]. London. Available: [Accessed 31 May 2013]. Claudy, M. C., Michelsen, C., O'Driscoll, A. and Mullen, M. R Consumer awareness in the adoption of microgeneration technologies: An empirical investigation in the Republic of Ireland. Renewable and Sustainable Energy Reviews, 14, Claudy, M. C., Michelsen, C. and O Driscoll, A The diffusion of microgeneration technologies assessing the influence of perceived product characteristics on home owners' willingness to pay. Energy Policy, 39, Claudy, M. C., Peterson, M. and O'Driscoll, A Understanding the Attitude-Behavior Gap for Renewable Energy Systems Using Behavioral Reasoning Theory. Journal of Macromarketing, jmk.sagepub.com/content/early/2013/04/11/ full.pdf+html. Clean Technica Germany s Energy Storage Incentive To Start May 1 [Online]. Available: cleantechnica.com/2013/04/17/germanys-energy-storage-incentive-tostart-may-1/ [Accessed 1 August 2013]. Consumer Focus Keeping FiT Consumers' attitudes and experiences of microgeneration. ENERGY SAVING TRUST & DECC (eds.). London. Available: Curry, T. E., Reiner, D. M., Figueiredo, M. A. d. and Herzog, H. J A Survey of Public Attitudes towards Energy & Environment in Great Britain. Available: esources/sample%20intervention%20docs/surveys/mit.pdf. Massachusetts Institute of Technology, Laboratory for Energy and the Environment. Page 94 of 210

95 Chapter 3 Dastrup, S. R., Graff Zivin, J., Costa, D. L. and Kahn, M. E Understanding the Solar Home price premium: Electricity generation and Green social status. European Economic Review, 56, Debenham, C Do solar panels affect house sales? [Online]. Devon: YouGen Ltd. Available: [Accessed 19 December ]. Debenham, C Legal battle over solar feed-in tariff ends in defeat for DECC [Online]. Available: [Accessed 14 September 2013]. DECC Requirements for contractors undertaking the supply, design, installation, set to work commissioning and handover of micro and small wind turbine systems. Microgeneration Installation Standard. London: Crown Copyright. DECC The UK Renewable Energy Strategy. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown Copyright. DECC The Green Deal- A Summary of the Government s Proposals. DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown Copyright. Available: 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: 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: 41/1387-renewable-heat-incentive.pdf. DECC 2012a. Electricity Market Reform: policy overview DEPARTMENT OF ENERGY AND CLIMATE CHANGE (ed.). London: Crown copyright. Available: 34/7090-electricity-market-reform-policy-overview-.pdf. Page 95 of 210

96 Chapter 3 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: 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: tistical_release_- _Green_Deal_and_Energy_Company_Obligation_in_Great_Britain_- _20_August_2013.pdf. Dowson, M., Poole, A., Harrison, D. and Susman, G Domestic UK retrofit challenge: Barriers, incentives and current performance leading into the Green Deal. Energy Policy, 50, Element Energy Potential for Microgeneration. Study and Analysis. ENERGY SAVING TRUST (ed.). London. Element Energy The Growth Potential for Microgeneration in England, Wales and Scotland. BERR (ed.). London. Ellison, G 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 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: [Accessed July 2012]. Energy Saving Trust 2012b. Renewable Heat Premium Payment scheme: Regional statistics as at Phase 1 Closure. London. Available: Energy Saving Trust. Page 96 of 210

97 Chapter 3 Energy Saving Trust. 2013a. Choosing a Renewable Technology [Online]. London. Available: [Accessed 8 July 2013]. Energy Saving Trust. 2013b. Renewable Heat Premium Payment Phase 2 [Online]. London. Available: [Accessed 29 June 2013]. Energy Saving Trust 2013c. Renewable Heat Premium Payment scheme Phase 2 Statistics as at 18 February London. Available: Two-web-stats: Energy Saving Trust. Feed-in Tariffs ltd Feed-in Tariffs [Online]. Wolfe Ware. Available: [Accessed 5 September 2013]. Finn, A. and Louviere, J. J Determining the Appropriate Response to Evidence of Public Concern: The Case of Food Safety. Journal of Public Policy & Marketing, 11, Fischer, C. and Sauter, R Governance for Industrial Transformation. Human Dimensions of Global Environmental Change. Berlin. GfK NOP Social Research Renewable Energy Awareness and Attitudes Research. DTI (ed.). London. Available: webarchive.nationalarchives.gov.uk/+/ Hack, S International Experiences with the Promotion of Solar Water Heaters (SWH) at Household-level. DEUTSCHE GESELLSCHAFT FÜR TECHNISCHE ZUSAMMENARBEIT (GTZ) GMBH (ed.). Mexico City. Available: pdf. Hickman, L Older and disabled people 'put off' energy efficiency schemes [Online]. London. Available: [Accessed 31 May 2013]. HM Government Energy Act. London: Crown copyright HM Government Energy White Paper: Meeting the Energy Challenge. DTI (ed.). London: Crown copyright. webarchive.nationalarchives.gov.uk/ / ts/decc/publications/white_paper_07/file39387.pdf. Hoen, B., Wiser, R., Cappers, P. and Thayer, M An Analysis of the Effects of Residential Photovoltaic Energy Systems on Home Sales Prices in California. Page 97 of 210

98 Chapter 3 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 Nationwide average house prices adjusted for inflation [Online]. Available: [Accessed 8 July 2013]. Jager, W Stimulating the diffusion of photovoltaic systems: A behavioural perspective. Energy Policy, 34, Keirstead, J Behavioural responses to photovoltaic systems in the UK domestic sector. Energy Policy, 35, King, P., Cameron, J., Clare, M., Frankiewicz, J., Hindle, P., Marchant, I., Murtagh, G., Sinfield, J. and Smith, R Open Letter Re: Ensuring success for the Green Deal and the retrofit agenda 26 June DECC (ed.). London. Available: CwQFjAA&url=http%3A%2F%2Fwww.ukgbc.org%2Fsystem%2Ffiles%2Fprivate% 2Fdocuments%2F130626%2520Green%2520Deal%2520open%2520letter%2520- %2520Ed%2520Davey.pdf&ei=ag14UoaKI4bR0QWp4HQCw&usg=AFQjCNF_fnV91HmTZRj26poqnLfz8FimOw&bvm=bv ,d. d2k UK Green Building Council. Lambert, S House hunters warned against buying homes with free solar panels fitted [Online]. Available: [Accessed 20 May 2013]. Leenheer, J., de Nooij, M. and Sheikh, O Own power: Motives of having electricity without the energy company. Energy Policy, 39, Lonsdale, S Eco living: Beware the 'solar-panel cowboys' [Online]. Available: [Accessed 10 June 2013]. Louviere, J., Lings, I., Islam, T., Gudergan, S. and Flynn, T An introduction to the application of (case 1) best worst scaling in marketing research. International Journal of Research in Marketing, 30, Mahapatra, K. and Gustavsson, L Influencing Swedish homeowners to adopt district heating system. Applied Energy, 86, Mahapatra, K., Gustavsson, L., Haavik, T., Aabrekk, S., Svendsen, S., Vanhoutteghem, L., Paiho, S. and Ala-Juusela, M Business models for full service energy Page 98 of 210

99 Chapter 3 renovation of single-family houses in Nordic countries. Applied Energy, 112, Marti, J A best worst scaling survey of adolescents' level of concern for health and non-health consequences of smoking. Social Science & Medicine, 75, McLeod, R. S., Hopfe, C. J. and Rezgui, Y An investigation into recent proposals for a revised definition of zero carbon homes in the UK. Energy Policy, 46, Morris-Marsham, C 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 The adoption of photovoltaic systems in Wiesbaden, Germany. Economics of Innovation and New Technology, 22, Newman, C Is the Green Deal right for me? [Online]. Cornwall. Available: [Accessed 30 May 2013]. NHBC Foundation Introduction to Feed-In Tariffs. BRE (ed.). Available: bid/437/default.aspx: IHS BRE Press. Nichols, W Green heat industry hits out at renewable heat incentive delay [Online]. Available: [Accessed 10 October 2012]. Nichols, W Green heating scheme delayed again until spring 2014 [Online]. London. Available: [Accessed 27 May 2013]. Office for National Statistics 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 Electrical Capacity Assessment Report OFGEM (ed.) Report to the Secretary of State. London. Energy Market Monitoring and Analysis. OFT Off-Grid Energy: an OFT Market Study. OFFICE OF FAIR TRADING (ed.). London. Crown Copyright. Page 99 of 210

100 Chapter 3 Orme, B Accuracy of HB Estimation in MaxDiff Experiments. SAWTOOTH SOFTWARE, I. (ed.) Research Paper Series. Sequim, WA Available: Orme, B. and Howell, J 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 Motives for and barriers to household adoption of smallscale production of electricity: examples from Sweden. Sustainability: Science, Practice, & Policy, Vol 7, pp Praetorius, B., Martiskainen, M., Sauter, R. and Watson, J Technological innovation systems for microgeneration in the UK and Germany - a functional analysis. Technology Analysis & Strategic Management, 22, Rowley, E Renting out roof to solar power firms could make your home harder to sell, surveyors warn [Online]. London: Telegraph Media Group Limited Available: [Accessed 14 January ]. Sawtooth Software CVA/HB Technical Paper. Technical paper series. Sequim, WA Sawtooth Software 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: [Accessed 10 June 2013]. Sawtooth Software 2013b. The MaxDiff System Technical Paper Technical paper series,. Utah: Sawtooth Software, Inc. Sawtooth Software Inc All Products, [Online]. Orem, Utah. Available: [Accessed 11 Nov 2013]. Scarpa, R. and Willis, K Willingness-to-pay for renewable energy: Primary and discretionary choice of British households' for micro-generation technologies. Energy Economics, 32, Taylor, P Sorting out a solar PV cowboy s mess [Online]. Available: [Accessed 10 June 2013]. Page 100 of 210

101 Chapter 3 Vermeulen, B., Goos, P. and Vandebroek, M Obtaining more information from conjoint experiments by best worst choices. Computational Statistics & Data Analysis, 54, Walters, R. and Walsh, P. R Examining the financial performance of microgeneration wind projects and the subsidy effect of feed-in tariffs for urban locations in the United Kingdom. Energy Policy, 39, Warren, P 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 Renewable energy supply chains, performance, application barriers, and strategies for further development. Renewable and Sustainable Energy Reviews, 16, Willis, K., Scarpa, R., Gilroy, R. and Hamza, N Renewable energy adoption in an ageing population: Heterogeneity in preferences for micro-generation technology adoption. Energy Policy, 39, Wimberly, J Banking the Green: Customer Incentives for EE and Renewable. EcoAlign. Available: Yamaguchi, Y., Akai, K., Shen, J., Fujimura, N., Shimoda, Y. and Saijo, T Prediction of photovoltaic and solar water heater diffusion and evaluation of promotion policies on the basis of consumers choices. Applied Energy, 102, Zahedi, A A review of drivers, benefits, and challenges in integrating renewable energy sources into electricity grid. Renewable and Sustainable Energy Reviews, 15, Page 101 of 210

102 Chapter 3 Supplementary Material The following is the questionnaire used for the survey described in this paper. Page 102 of 210

103 Chapter 3 For adopters only: Page 103 of 210

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108 Chapter 3 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: Page 108 of 210

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118 Chapter 4 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-SECHPbattery 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. Page 118 of 210

119 Chapter 4 Self-sufficiency and reducing the variability of grid electricity demand: integrating solar PV, Stirling engine CHP and battery storage a,b,c, Adisa Azapagic a * and Dan Rigby c 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: , adisa.azapagic@manchester.ac.uk 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 Abstract kwh/yr 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 selfsufficiency 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

120 Chapter 4 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

121 Chapter 4 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 midsized 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 selfsufficiency and grid demand smoothing effects. Page 121 of 210

122 Chapter 4 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 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

123 Chapter 4 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 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 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 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 energyrelated 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), semidetached (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. Page 123 of 210