Thermodynamic and carbon analyses of micro-generators for UK households

Energy 35 (2010) 2223–2234

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Thermodynamic and carbon analyses of micro-generators for UK households S.R. Allen a, *, G.P. Hammond a, b a b

Department of Mechanical Engineering, University of Bath, Claverton Down, Bath BA2 7AY, UK Institute for Sustainable Energy and the Environment, University of Bath, Claverton Down, Bath BA2 7AY, UK

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 August 2009 Received in revised form 1 February 2010 Accepted 5 February 2010 Available online 6 March 2010

Micro-generators have the potential to reduce carbon emissions and enhance energy security by providing heat or electricity either from renewable sources, or via the more efficient use of fossil fuels. Such potential is often, however, unquantified or unclear, and hence a thermodynamic and related carbon analysis of micro-generators for UK household energy supply has been performed. Where pertinent, the thermodynamic concept of exergy is employed alongside that of energy. Analysis begins with a description of the established methods of energy supply to, and use within, typical UK households. On these foundations a grid-tied micro-wind turbine, a grid-tied solar photovoltaic array, and a solar hot-water system are analysed. Annual outputs are estimated and contextualised against the demands of representative households. The annual energy-resource and carbon savings provided by the microgenerators are determined on the basis that they (partially) displace the established supply systems. Savings are then compared with the energy-resource and carbon-emission ‘debts’ of the micro-generators, to assess the latter’s net performance. Given appropriate installations, all three micro-generators are found to provide significant net energy and carbon benefits, confirming that all three technologies can provide net reductions in both carbon emissions and dependence on conventional energy resources. Ó 2010 Elsevier Ltd. All rights reserved.

Keywords: Micro-generation Net energy analysis Exergy analysis Residential energy use UK energy system

1. Introduction

1.2. Micro-generation and the UK’s residential sector

1.1. Background

The UK’s residential sector consists of 26 million households that account for approximately one-third of the UK’s deliveredenergy use and carbon-dioxide emissions [6,7]. A variety of studies indicate that feasible changes in the way energy is sourced and used within this sector can provide significant reductions in carbon emissions (e.g. [8–13]). On the demand side, the studies indicate that the infrastructure and end-use technologies of the housing stock can be substantially improved to enable the provision of energy services with less delivered energy (e.g. delivered gas and electricity). On the supply side, they suggest that the energy supplied to UK households can be much cleaner and less carbon intense than at present, and that micro-generators of various forms have an important role to play in achieving this. Micro-generation is defined in Section 82 of the UK’s Energy Act (2004) as the production of electricity or heat from a low-carbon source, at capacities of no more than 50 kWe or 45 kWth. It embraces a variety of technologies including micro-wind turbines, solar photovoltaic (PV) arrays, solar hot-water (SHW) systems, combined heat-and-power units, and heat pumps, each summarised by Allen et al. [14]. There is a great deal of interest in microgenerators for their potential to reduce carbon emissions and enhance energy security by providing heat or electricity either from

Energy is a fundamental part of human life, providing comfort, light, communication, transportation, and many more services to people all over the world. But most current methods of energy use entail adverse environmental impacts on a local, regional and global scale and, in many cases, involve considerable resource uncertainties [1]. In the UK, for example, energy use is based overwhelmingly on fossil fuels [2] and accounts for over 97% of emissions of carbon dioxide, the main greenhouse gas [3]. Alongside the negative environmental impacts associated with this situation there are concerns about the security of continued energy supply, since the UK is increasingly a net importer of fossil fuels [4]. These combined issues have had a significant impact on the development of energy policy in the UK, such that two of the main aims are to cut greenhouse-gas emissions by at least 80% from 1990 levels by 2050, and to maintain secure and diverse supplies of energy [5]. In order to achieve these aims, significant changes are required in the way that energy is both sourced and used. * Corresponding author. Tel.: þ44 7813874220; fax: þ44 1225 386928. E-mail address: [email protected] (S.R. Allen). 0360-5442/$ – see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2010.02.008

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renewable sources, or via the more efficient use of fossil fuels. But there are numerous barriers constraining the uptake of microgeneration [14,15] including, crucially, a lack of quantitative information regarding the performance of some technology options. For the adoption of micro-generators to be both appropriate and effective, it is vital that such quantitative information is produced regarding their energetic and carbon performance. 1.3. The present study There are a variety of techniques that provide quantitative analyses of energy-supply technologies. For a physical analysis of energetic performance, engineering thermodynamics can be applied to identify losses in the quantity and quality of energy as it is transferred and transformed within a supply system, and thus identify any potential for improvement. Thermodynamic analysis techniques can also be coupled with those arising from other disciplines to yield interdisciplinary insights as part of whole systems [16]. The present study provides a thermodynamic and related carbon analysis of three micro-generation devices – a gridtied micro-wind turbine, a grid-tied solar PV array, and an SHW system – in a UK context. It forms part of a wider and interdisciplinary ‘integrated appraisal’ process for energy-systems assessment, as described and applied by Allen et al. [17]. The present study significantly extends work published within Allen et al. [17–19] by summarising the results of the PhD thesis of Allen [20]. The latter may thus be referred to for detail beyond that given here. 2. Thermodynamic analysis of energy systems 2.1. Energy analysis Engineering thermodynamics provides a physical basis for the quantification of energy flows. The First Law indicates that energy is always conserved (e.g. [21]), and hence enables the technique of energy analysis (e.g. [17,22]) to trace energy flows back from final products or services to energy sources in their natural forms, quantifying losses at each stage. In this manner the total or ‘gross’ energy requirement of a product or service may be determined [23], a process that has many modern analogues including the calculation of the total greenhouse-gas emissions associated with an activity. While early forms of energy analysis appeared in the late 19th century and early 20th century [24,25], its current basis emerged in the 1970s with the publication of an internationallyagreed set of conventions [23]. This came at a time of increasing concern about resource depletion and scarcity, particularly in the context of the first oil price ‘shock’ of 1973 [22]. Energy analysis has since been applied, for example in the area of industrial energy management, by a variety of academics, government departments and other organisations, including the UK’s Energy Technology Support Unit (now part of AEA Technology plc) at Harwell [26]. The calculation of a gross energy requirement requires a widelydrawn and clearly-defined system boundary (the boundary to which all energy flows are traced). The system boundary defined in this research extends all the way back to the energy resources in their natural form (e.g. the coal at the mine), where those resources are quantified in terms of enthalpy. The exceptions to these specifications are nuclear fuels, which are measured as the enthalpy change they cause within the working fluid of thermal power plants; and ambient renewable inputs such as wind or solar energy, which are quantified after conversion to electrical or thermal energy. In general, these conventions are adopted here in accordance with national UK energy statistics (e.g. [2]). Further details of the defined system boundary may be found in Allen [20].

All energy flows – that is, both direct and indirect flows – involved in the provision of a product or service should be included during calculation of a gross energy requirement [22,23]. In addition, all stages of a product or service’s life cycle should be considered, although in practise this is not always achievable [17] (and hence clear specification of the included/excluded stages is required). Where indirect energy requirements are calculated, lifecycle energy requirements are usually amortised over either lifetimes or total outputs in some appropriate way. The energy requirements of power station construction, for instance, can be spread over each unit of electricity subsequently generated, such that an average energy requirement of each unit of electricity can be determined. 2.2. Net energy analysis Energy analysis can be applied to determine the overall energy implications of any product or service. Of particular interest here are the implications of energy supply to the residential sector, particularly the performance of micro-generators compared to the established methods of fuel and electricity supply. For these purposes the technique of net energy analysis can be used (e.g. [22,27–29]). This is a sub-topic of energy analysis that aims to determine the net effect of a process or change within an energysupply system. Two ‘net energy’ indicators are used in this study to describe the energy consequences of various supply options, and these have analogous ‘net carbon’ indicators that are also presented here. The first net energy indicator is the energy requirement for energy (ERE), which is defined as the gross energy requirement of an energy carrier per unit of that carrier [22]. The analogous net carbon indicator is the carbon-emission factor, which refers to the total carbon-dioxide equivalent (CO2eq) emissions associated with the supply and use of each unit of energy carrier. The second net energy indicator employed here is the energy payback period. This refers to the time taken for the cumulative energy output of a generator to ‘break-even’ in terms of its gross energy requirement. Once this break-even point has been reached, the generator provides a net energy benefit for the remainder of its lifetime. Two methods are commonly employed to quantify the cumulative energy output (e.g. [30]). In the first, the output is quantified directly (e.g. electricity output of a micro-wind turbine), and the associated payback period is defined here as the simple energy payback period. The second approach is applicable when the generator displaces an existing supply system (e.g. electricity from a solar photovoltaic panel displacing electricity from the established power grid). The output of the generator is then quantified as the overall energyresource displaced (or ‘saved’) from the established system, and the associated payback period is defined here as the displaced energy payback period. Similarly, the carbon payback period can be defined as the time taken for the cumulative CO2eq savings to reach the total life-cycle emissions associated with the generator. 2.3. Thermodynamic quality and exergy analysis The thermodynamic ‘quality’ (or ‘work-potential’) of an energy carrier varies depending on the mode of its transfer or storage [31], and is generally degraded as energy is transferred and transformed within an energy-supply system. These issues are not recognised by an energy analysis because it is based solely on the First Law of Thermodynamics, which treats differing forms of energy and its transfers (e.g. work and heat) as equivalent. An exergy analysis, in contrast, incorporates both the First and Second Laws and, by recognising and quantifying differences in thermodynamic quality,

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can provide useful information regarding the optimal conversion of energy. Exergy may be defined as the maximum amount of work obtainable from a thermodynamic system when it is brought into equilibrium with its environment via reversible interactions with that environment only (definition based upon [31–33]). Since exergy is a measure of maximum work obtainable from a system, exergy differences across a system are equivalent to work transfer [31]. In contrast, exergy differences are not generally equivalent to heat transfer. For example, the thermal exergy, EQp , associated with a heat transfer at a boundary of constant temperature is determined by the maximum work, Wmax, that could be obtained via ideal conversion when using the environment as a thermal reservoir [31]:

EQp ¼ Wmax ¼ QQp

(1)

where:

Q ¼ 1

T0 Tp

(2)

Qp is heat transfer at constant temperature Tp, and T0 is the (reference) temperature of the environment. Q is the thermodynamic quality of the transfer, in this case reflecting the proportion of the heat that could be transformed into work given ideal conversion processes. Equation (2) indicates that the higher the process temperature compared to the environment, the higher the thermodynamic quality. The variation of thermodynamic quality (Q) with process temperature, for a reference temperature of 1  C [26], is shown graphically in Fig. 1. The quality of various indicative household heating demands are included for illustrative purposes. Different types of energy have different thermodynamic qualities, depending on their mode of storage [31]. A high quality indicates that through ideal conversion an energy carrier could be made fully available as work. Mechanical and electrical energy have a thermodynamic quality of one because they are readily converted into work. Solar irradiation also has a high quality, in this case approximately 0.9 ([34] in [35]). The thermodynamic quality of a fuel is the ratio of chemical exergy (ECH) to enthalpy of combustion, and is high for many residential fuels. This is shown in Table 1, where thermodynamic quality is given in terms of the fuel’s Net Calorific Value (NCV, also known as Lower Heating Value). The differences between the thermodynamic qualities of different energy carriers suggest that an exergy analysis can provide alternative insights and quantitative information to that provided by energy analysis. During the remainder of this paper, only energy values are presented (in the interests of brevity) where

Fig. 1. Thermodynamic quality of selected heat demands.

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Table 1 Typical values of Q for selected residential fuels. Fuela

NCVa GCVa Q ¼ ECH/NCVb (MJ/tonne) (MJ/tonne)

Residential house coal Fuel oil

30.5

29.0

1.06–1.10 (Different types of coal)

43.6

41.5 35.5d

1.04–1.08 (Different fuel oils and petrol) 1.04  0.5%

12.3

1.15–1.30

Natural gas; 39.4d consumedc Residential woode 13.9 a

Source: [2] p. 211. GCV – Gross Calorific Value (or Higher Heating Value). b Source: [31] p. 269. c Home produced and imported gas. This weighted average of calorific values will approximate the average for the year that householders see quoted on their gas bills. It can also be expressed as 10.948 kWh per cubic metre. d MJ per cubic metre, rather than MJ per tonne. e Average figure covering a range of possible feedstock; at 25% moisture content. On a ‘dry’ basis; 18.6 GJ per tonne.

energy and exergy quantities are similar. Insights are drawn from exergy analysis, however, where it provides complementary information regarding either residential energy provision or microgenerator performance. 3. Established methods of energy supply to UK households 3.1. Natural gas and electricity: the dominant energy carriers By providing households with alternative sources of energy, the micro-generators assessed in Section 5 can avoid the use of energy from the established energy-supply systems. To estimate the savings of energy-resource and associated CO2eq emissions that arise from this, and to give context to the micro-generator assessments in general, it is necessary to give a brief description of the predominant energy-supply systems used by the UK’s residential sector. Natural gas and electricity are the main energy carriers supplied to UK households, as shown by Fig. 2. While the proportion of electricity supplied to the sector has slowly-but-steadily increased since 1970, Fig. 2 shows that there has been a major shift from solid fuels to natural gas. By 2007, gas constituted 68% of the delivered energy used by households, while electricity accounted for 22%, oil (petroleum) 7%, solid fuels 2% and renewables and waste 1%. There are some important distinctions that need to be made between fuels and electricity. Fuels are a stored form of energy that can be used to generate heat whenever required. Work can be

Fig. 2. Delivered fuel and electricity use of the residential sector, 1970–2007 (Source: Allen [20]; adapted from BERR [6]).

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Fig. 3. Primary inputs for UK electricity generation (stacked area with left axis) and average conversion efficiency of those inputs to delivered electricity (line graph with right axis), 1970–2007 (Adapted from Allen [20]).

provided via the combustion of fuels, but only indirectly through the use of a heat engine of some form. Electricity, in contrast to fuels, is a process occurring simultaneously and instantaneously throughout an entire interconnected circuit, and can be readily converted into either heat or work [36]. The British electricity system has developed on the premise of ‘many loads, few sources’ [37], where a large number of end-users receive electricity generated by relatively few, large power stations [2]. In 2001, for example, the median size of coal-fired, nuclear, oilfired, and CCGT plants were 1930 MW, 1150 MW, 520 MW, and 680 MW respectively [38]. Fig. 3 shows the mixture of inputs used to generate electricity in the UK, and the majority are fossil fuels. It also shows that the conversion efficiency of inputs to useful outputs (delivered electricity) is low, at only 35%. This low efficiency is primarily a result of the low-temperature heat loss that occurs during electricity generation within the dominant ‘thermal’ power plant. Such heat losses appear particularly wasteful given that over 80% of the energy demand of UK households is in the form of lowtemperature heat. Since electricity it is not currently stored in large quantities1 it is generated more or less when it is used, and stability is currently achieved through a complex balance of generally-controllable supply with generally-uncontrollable demand via the national grid. Demand varies significantly during the day and by season – from a maximum of just over 60 GW (a winter early-evening) to a minimum of approximately 23 GW (a summer night-time) during 2007/2008 [39]. Most variation in demand is followed by coal- and gas-fired plant, while nuclear, the next most significant source of electricity, provides ‘base-load’ (its output does not generally vary) [20,39]. 3.2. Energy-resource and carbon-emission implications of fuels and electricity

electricity saving by the appropriate ERE and carbon-emission factor (Section 2.2). Allen [20] discussed the calculation of EREs and carbon-emission factors for various energy carriers used in the residential sector, and the selected values are summarised here. For natural gas and (heating) oil, collaborating (environmental) life-cycle assessment (LCA) researchers (e.g. [17]) provided data (based on [40]) with which to calculate average EREs and carbonemission factors. At the stage of delivery to households, the average unit of natural gas was found to have an ERE of 1.22 units of energy-resource (in NCV terms), and a carbon-emission factor was 0.07 kgCO2eq/MJdelivered (0.24 kgCO2eq/kWhdelivered). For oil, the equivalent ERE was 1.39 while the carbon-emission factor was 0.09 kgCO2eq/MJdelivered (0.33 kgCO2eq/kWhdelivered). These average values were deemed sufficient to assess the energy-resource and carbon savings provided by a micro-generator, because in the case of gas or oil the majority of the saving occurs at the level of the household [20]. This is consistent with a variety of other literature estimating the impact of micro-generators (e.g. [41–43]). For electricity, LCA-data (based on [2] and [40]) indicates that an average unit of electricity had an ERE of 3.1 units of energy-resource per unit delivered electricity in 2005 [18], with an associated carbon-emission factor of 0.58 kgCO2eq per kWh of delivered electricity. These values include transmission/distribution losses and the full life-cycle impacts of components within the supply system, amortised appropriately over their lifetimes (Section 2.1). While the use of average values is an approach previously taken in the literature to estimate the effect of electricity savings [44], it is problematic for the estimation of energy and carbon savings from micro-generators. This is because when instantaneous demand on the grid is reduced, for example when a household uses electricity from a micro-generator, it is specific marginal plant that reduce their output rather than the average of all generators. But although it is relatively easy to describe how a change in electricity demand causes a change in the energy-resource use and carbon emissions of the central power grid, it is by no means easy to quantify this [44]. Nevertheless, Allen [20] estimated a range for the ERE and carbonemission factor of marginal plant, on the basis of a variety of publications in the literature (e.g. [44–47]). The range for the marginal ERE was found to be 2.3–2.9, and for the marginal carbonemission factor 0.49–0.76 kgCO2eq/kWh (again including transmission/distribution losses and life-cycle impacts). These values apply for a 0.5–5% reduction in overall electricity demand from the grid [20], and they are used here to estimate the savings in energy resources and carbon emissions that accrue for each unit of gridelectricity displaced by the micro-generators (Section 5.4). The carbon-emission factors are within the broad range of other published values. DEFRA [48] have used 0.43 kgCO2eq/kWh (not including life-cycle impacts), apparently on the assumption that CCGTs are the marginal plant, while others indicate that 0.9 kgCO2eq/kWh has been used, on the assumption that marginal plant are coal-fired [49]. 4. Energy use in UK households

When a household reduces its use of fuel or electricity, for example through the use of a micro-generator, all the upstream impacts of this reduction must also be considered in order to determine the total energy-resource and CO2eq-emission savings. This can be achieved by multiplying the household’s fuel or

1 Electricity is not yet stored on a large scale due to technical and economic constraints. ‘Pumped storage hydro-electric’ stations are a currently-available large-scale storage option that uses electricity to pump water to a high level reservoir, which is later released to generate electricity at peak times. But they are not employed widely. In the UK, for example, they stored only 1% of the total electricity supply during 2005–2007 (4–5 GWh per year; [2]).

4.1. Overview The energy outputs of the micro-generators assessed in Section 5 can be placed in context through comparison with the energy demands of typical UK households. This section thus provides an analysis of energy use within the residential sector and, in particular, representative UK households. Fig. 2 showed that gas has become the dominant energy carrier supplied to the residential sector, followed by electricity. Fig. 4 now shows how these energy inputs are used, on the basis of modelling carried out by the UK’s Building Research Establishment (BRE, e.g.

S.R. Allen, G.P. Hammond / Energy 35 (2010) 2223–2234

Fig. 4. Delivered-energy carriers used for each end-use in the residential sector, 2006 (Source: Allen [20]; adapted from BERR [6]).

[50]). Gas is the dominant provider of space and water heating, accounting for 82% of the energy inputs used for these purposes. While cooking is provided by both gas and electricity, lighting and appliances are powered exclusively by electricity (the only energy carrier to be used in all four end-use categories). (Fig. 4 gives data for the residential sector as a whole, compared to a figure given by Allen et al. [14] that normalised sector-wide data against the total number of households.) 4.2. Space heating The rise of gas illustrated in Fig. 2, and its prevalence within the space heating category of Fig. 4, reflects a major shift to the use of central-heating systems that are mostly gas-fired. 91% of households used central heating in 2006, compared to 31% in 1970 [51]. In the majority of cases water heating is also provided by the centralheating boiler [52], which explains the similar prevalence of gas for water heating. The technological shift to central-heating has been a key factor in the increased energy efficiency of the average heating system, which has risen from 49% in 1970 to 74% in 2006 [51]. This has coincided with increasing levels of insulation and, all other things being equal, these two energy-efficiency improvements would have decreased the delivered-energy use of an average household. The potential savings have, however, been ‘taken back’ in the form of higher temperature (comfort) levels in households [51] that have been made possible, in part, by the increased ‘whole-house’ heating effectiveness of central-heating systems. The net result is shown in Fig. 5 where, notwithstanding the fluctuations that are driven partly by external temperature

Fig. 5. Average UK household delivered-energy use by end use, 1970–2006 (Source: Allen [20]; adapted from BERR [6]).

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variations [51], the use of delivered energy for space heating in the average UK household has remained broadly constant since 1970. The trend of increasing temperatures within households – from roughly 12–14  C in 1970 to 16–18  C in 2006 [51] – is expected to saturate in the approximate region 18–21  C [10,51]. Given such saturation, together with the considerable scope for continued energy-efficiency improvements, the heating load of an average household could decrease in the future. Estimates summarised by the UK Government [53], for example, indicate that feasible improvements for existing buildings could reduce the average load by roughly one-third, to 32 GJ/yr. By comparison, the UK Government [53] indicates that current new-builds require 7 GJ/yr while houses built under the ‘Building a Greener Future’ initiative could have loads approaching zero. But while these significant ‘newbuild’ improvements are valuable on a house-by-house basis, the need for improvements in the existing stock remains paramount, since it is estimated that approximately 66–80% of the housing stock of 2050 are already standing [12,54]. 4.3. Water heating Fig. 5 indicates that the average household of 2006 used approximately 18 GJ of delivered energy per year for water heating – a reduction of 18% since 1970. This reduction was partly due to the decrease in average household occupancy from three people per house in 1970 to 2.4 per house in 2006 [6], since hot-water use is determined primarily by the number of occupants [55]. On a percapita basis, delivered-energy demand for water heating has remained broadly constant since 1970 [20]. This implies that the quantity of water used per person has increased, since efficiency improvements have been made in terms of heating-system efficiency and hot-water tank insulation [51]. The UK Government [53] expect water-heating loads to remain constant in the near future, even for new homes moving towards the 2016 ‘zero-carbon’ target [56]. This suggests a slight increase in future hot-water use since average efficiencies (e.g. of boilers) are improving. 4.4. End-uses of electricity As indicated previously, electricity is a very different energy carrier to the fuels that provide the majority of the residential sector’s space and water heating. Like fuels, electricity can be used to supply low-quality heating, but it can also conveniently provide a wide array of other energy services including refrigeration, illumination, communication, and entertainment. On the basis of a variety of sources, Allen et al. [57] gave an estimated breakdown of how electricity has been used in the residential sector, as summarised in Fig. 6. This indicates that the residential sector’s electricity consumption has grown by approximately 50% since 1970, and that this increase has been driven by a growing array of appliances, particularly consumer electronics and ICT in recent years. Average household (as opposed to the sector as a whole) electricity usage has remained broadly constant in recent years, at 16 GJ/yr (4500 kWh/yr) between 1996 and 2006. The type of tariff used by a household has a significant effect on mean usage. The mean ‘Economy Seven’ household, which often involves the use of night-storage heaters during night-time hours, used 22 GJ/yr (6200 kWh/yr) in 2006 [58]. This is over 50% larger than the mean ‘standard’ tariff household, which used 14 GJ/yr (4000 kWh/yr) in the same year [58]. Hawkes and Leach [43] and BRE [59] indicate that the distribution of household electricity usage has a strong positive skew, and hence the mean value resides above the majority of the sample due to a minority of high-usage households. Of the two sources, the BRE [59] report had a significantly larger sample

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Fig. 6. Residential electricity consumption by end-use category (Adapted from Allen et al. [57], which was, in turn, based on the modelling of DEFRA’s ‘Market Transformation Programme’ and the Building Research Establishment (both in [6]), together with the work of Leach et al. [88] and Evans and Herring [63]).

size (of more than 7000 English households compared to 60), and Allen [20] thus selected the BRE’s modal range of 11–14 GJ/yr (3000–4000 kWh/yr) as representative of a typical household’s electricity use. It is difficult to estimate how the trend in annual household electricity consumption will develop, particularly because of the fast-moving nature of some categories set out in Fig. 6. Nevertheless, the recent projections by the UK Government’s Market Transformation Programme [42] suggest that average household usage could remain broadly constant (under a ‘reference scenario’) or even fall (under a ‘feasible product policy’ (P1), scenario) out to 2020 [20].

4.5. The thermodynamics of an illustrative UK-household energy system The discussion thus far, similar to many concerning energy supply and use, has been based on the First Law of Thermodynamics (‘energy is always conserved’). While the First Law enables energy losses and efficiencies to be determined, it does not account for variations in the thermodynamic quality (potential to do work) of energy carriers, and thus provides an incomplete assessment of thermodynamic performance. The preceding analysis of energy use has also focused at the stage of delivered energy – the quantity of fuel or electricity used by households to provide desired energy services to the householder. The stage of delivered energy is illustrated in Fig. 7, alongside other stages that can be defined between the harnessing of naturally-occurring energy resources and the delivery of energy services to end-users. In this sub-section an exergy analysis of an illustrative UK-household energy system is presented to gain insights into the thermodynamic quality of energy conversions and the upstream and downstream aspects of delivered energy. Since the majority of UK households use gas-fired centralheating systems to provide both space and water heating, such a system has been assumed for this illustrative example. The energy efficiency of the heating system (from inputs to outputs of the boiler) is assumed to be 74%, estimated by Utley and Shorrock [51] to be the sector-average for 2006, while the heating-process temperature was taken as 328 K (55  C) according to Reistad [60]. Cooking is assumed to be electrically powered, to have an energy efficiency of 80% [26], and to have a process temperature of 394 K (121  C) [60]. In the case of heat transfer at constant temperature the exergy efficiency is directly proportional to the energy efficiency (e.g. [1])

Fig. 7. Terminology for describing energy use (Source: Allen [20], adapted from Haldi and Favrat [89] and BERR [2]).

and thus dependent only upon the ratio of process temperature to reference temperature:

j¼



1

 T0 h Tp

(3)

j is the exergy efficiency, h is the energy efficiency, Tp is the process temperature, and T0 is the reference temperature. The latter is taken as 272 K (1  C), a common winter outside design temperature for the UK [26]. Exergy efficiencies for household heating processes were calculated via Eq. (3) from the previously quoted energy efficiencies and temperatures. Energy and exergy efficiencies were used to calculate the useful energy and exergy output of various end-use technologies, and the results are summarised in Fig. 8. Delivered exergy was assumed to be equal to delivered energy because these quantities are similar for both natural gas and electricity (Section 2.3). Fig. 8 also shows the overall quantity of (upstream) energy-resource required for each end-use category. These overall values were obtained by multiplying delivered energies by the appropriate energy requirement for energy (Section 2.2). Exergy values were assumed to equal energy values, because the energy-resource inputs are dominated by fossil fuels. Van Gool [61] indicated that gas boilers have a low exergy efficiency compared to their energy efficiency, and Fig. 8 quantifies this for the example UK household. While the energy efficiency of the boiler is 74%, its exergy efficiency is only 13%. The significant difference between energy and exergy efficiencies is due to the nature of the energy conversion during the heating process. A large proportion of the fuel input to a boiler is converted into useful

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described via the concept of degree days (e.g. [63,64]), and water heating, a service that may be described as a volume and temperature of hot water used by the householder. Allen [20] undertook such an energy-service calculation for water heating because it was directly relevant to the SHW assessment that is discussed in the following section. 5. Micro-generator assessments 5.1. Background

Fig. 8. Useful energy and exergy derived from upstream energy flows for a representative household in 2006 (Source: Allen [20]).

energy in the form of hot water, and hence the boiler has a high energy efficiency. But this conversion process degrades the energy flow, as indicated by Fig. 1, and hence the associated exergy efficiency is low. The majority of the exergy destruction is inherent to the process of converting the gas input into the heat output of a boiler – it is the result of the intrinsic irreversibilities associated with both combustion and heat transfer [20,31,32]. This means that there is limited scope for improving the thermodynamic performance of boiler-based heating systems, which in turn suggests that entirely different technologies and processes are needed to achieve significant exergetic improvements. Electrically-driven heat pumps, for example, use a relatively small proportion of highquality electricity to extract and upgrade low-quality ambient environmental heat for household heating. By achieving a more effective match between the quality of the supply and the quality of the demand (e.g. [62]) heat pumps can reduce the exergy destruction associated with residential heating, and hence reduce the quantity of required delivered energy. As discussed in Section 3, electricity is a far more resourceintensive energy carrier to produce and deliver to households than fuels such as gas. Fig. 8 highlights this by showing, for example, that a greater quantity of original energy-resource is used for lighting and appliances than for water heating in a typical household. The useful energy for lighting and appliances, as depicted in Fig. 8, was based on the assumption that their energy and exergy efficiencies are both 5% [26]. Hammond and Stapleton [26] took the efficiencies of incandescent lightbulbs as being representative of the whole end-use category. However, there are a wide variety of electricity end-uses (as depicted in Fig. 6) and these will have varying energy and exergy efficiencies. Further work is therefore desirable to disaggregate the ‘lights and appliances’ category, and refine the energy and exergy efficiencies accordingly. A further shortcoming of Fig. 8 and the associated analysis is that, although the frame of reference has been extended downstream beyond the level of delivered energy, it is still truncated at the stage of the useful energy output of end-use technologies rather than extending all the way to the energy services actually desired by the householder. Consequently, there remain energy conversions and/or transfers that are still to be considered for a full assessment of residential energy use. In the case of the gas-fired boiler, for example, Fig. 8 quantifies the boiler’s heat output but ignores subsequent energy flows and losses. The boiler is used for both space heating, which provides an energy service that may be

Previous sections have discussed the established methods of energy supply to, and use within, UK households, and thus provided a foundation for the micro-generator assessments presented here. Three micro-generators are analysed in terms of their thermodynamic and carbon performance: a grid-tied micro-wind turbine (diameter 1.7 m, rated power 600 W at 12 m/s); a grid-tied solar photovoltaic array (15 m2, 2.1 kWp mono-crystalline silicon); and a solar hot-water system (2.8 m2 flat-plate collector, direct-feed system), all described in [17] and [20]. The micro-wind turbine and SHW system are specific, commercially-available units, and the results are not, therefore, necessarily representative of these device-types in general. In contrast, the solar photovoltaic (PV) system is a generic mono-crystalline silicon system. There are three stages to the following analysis. First, the microgenerator output estimations are presented with a focus on annual values, before being contextualised against typical household energy demands. Second, the estimated energy-resource and carbon savings that accrue through use of the micro-generators are given. Finally, data regarding the embodied energy and carbon of the micro-generators are incorporated, enabling a net energy and carbon analysis to be undertaken. 5.2. Estimated annual outputs in context Fig. 9 presents the estimated annual outputs of the three microgenerators in both energy and exergy terms. The energy-output estimations update those of Allen et al. [17,18], while the exergyoutput estimations are additional to those publications. The methodology employed to estimate the micro-wind turbine outputs was described by Allen et al. [18] and in further detail by Allen [20]. Outputs were estimated for a selection of eighteen ‘open’ (well-exposed, mostly rural) UK locations and eight ‘urban’ locations, on the basis of a dataset of hourly-average wind speeds provided by the UK’s Meteorological Office [65]. In accordance with Allen [20], the height of the bars on Fig. 9 represent the mean output estimation for the micro-wind turbine, while the 5th

Fig. 9. Summary of estimated micro-generator annual outputs (Source: Allen [20]).

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percentile of estimates is the lower limit of the error bar and the 95th percentile is the upper limit. In the case of the solar PV system, the height of the bar is the mean output given by Suri et al. [66] for the UK, while the range given was judged to be representative on the basis of both Suri et al. [66] and a recent UK domestic PV field trial published by DTI [67]. In the case of the SHW system, the estimation methodology has been partially described by Allen et al. [19] and more fully by Allen [20]. There is no ‘mean’ estimate for SHW, and the bar instead represents the median of the overall range denoted by the error bars. Fig. 9 shows that the largest annual output is provided by the solar PV system (4.7–7.2 GJe/yr with a mean of 6.1 GJe/yr), followed by SHW (1.9–3.5 GJth/yr) and micro-wind (0.7–3.4 GJe/yr with a mean of 1.8 GJe/yr). The PV system is physically large and relatively energy intensive to manufacture, however, and hence in net energy terms (Section 5.5) its high output is tempered by its large embodied energy requirements [20]. The exergy output of the micro-generators is also given in Fig. 9. Since electricity has a thermodynamic quality of one (Section 2.3), the exergy of the micro-wind turbine and solar PV system outputs are equal to their energy outputs. In contrast, the output of the SHW system is low-temperature hot water, which has a low workpotential (thermodynamic quality) and hence low exergy. This information must be interpreted with care. Firstly, the system boundary is different in the two cases. Electricity is an intermediate energy carrier that is later employed within the household to provide energy services, whereas the hot water output the SHW system is the energy service actually provided to the householder. Direct comparison between the outputs of the two forms of microgenerator is not, therefore, valid. If electricity is subsequently used to provide water heating, for example, there will be losses in both the quantity and quality of the energy flow reaching the end-user. In addition, although the exergy efficiency of the SHW system is low (because high-quality solar irradiation is converted into lowtemperature hot water), the solar input itself is free, renewable and environmentally benign. In the context of carbon-dioxide emissions and energy security, this poor exergetic performance is less of a concern than that exhibited by the established fossil fuel based heating systems employed in the majority of households (Fig. 8). This distinction indicates that thermodynamic assessments – whether in terms of energy or exergy analysis – require supplementary insights. Other available techniques that can be applied in parallel to a thermodynamic analysis include environmental life-cycle assessment and economic cost-benefit analysis (e.g. [17]). A more detailed account of the exergy analysis of the SHW system, as well as of energy use within UK households, may be found in Allen [20]. Allen et al. [18] observed that micro-wind outputs are sensitive to the surrounding terrain, and this is also indicated by Fig. 9 since there is a significant difference between ‘open’ and ‘urban’ outputs. The power output of a wind turbine is proportional to the cube of the wind speed, and wind speed increases with height while turbulent effects decrease. It is thus important to mount microwind turbines as high as possible and as clear as possible from wind shadowing or rough terrain. Solar technologies are less sensitive to location. Installation on south-east to south-west facing roofs is preferable to maximise the solar resource [20], as is the avoidance of shading effects, but local terrain is generally less important than in the case of wind turbines. While Fig. 9 suggests that the micro-wind turbine would be unsuccessful in an urban terrain, the solar results apply to any terrain given appropriate installation on a typical UK roof. The potential market for the micro-wind turbine assessed here is therefore smaller than for the solar technologies, since the majority of the UK population resides in urban environments. (The U.N. estimates the proportion of

people living in ‘urban’ UK environments as 90%; [68]). Fig. 9 also suggests, however, that the micro-wind turbine will perform well in open environments, and indeed it will be seen later that in certain cases the open micro-wind turbine offers the best net energy and carbon performance of all three technologies. The minimum, mean and maximum output estimations for the open turbine (Fig. 9) correspond to capacity factors of 4%, 9% and 18% respectively. Although these values are relatively low compared to those of large onshore wind turbines (UK average of 28%; [69] in [70]), it should be noted that large turbines are generally installed in optimal locations in the west, north and coastal areas of the UK [20]. In contrast, the micro-wind turbine locations used in this study have a broad geographical spread across the UK, reflecting the broad distribution of residential areas. The solar PV estimates (Fig. 9) are based upon a combination of field trial results and relatively well-established modelling. The corresponding SHW estimates are based on field trials, although these trials did not involve real households but rather an experimental setup that mimicked simplistic household demand. The results of a larger and ongoing field trial of SHW in real UK households will aid validation of the SHW estimates of Fig. 9 [71]. The micro-wind estimates were based on a methodology designed by Allen et al. [18] and require further validation. Allen [20] provided a crude and tentative corroboration of the urban estimates through comparison with the results of a small field trial [72], but the results of a larger, forthcoming trial will aid more robust validation [73].

5.3. Comparing supply and demand Allen [20] compared the mean annual output estimations of the solar PV system and micro-wind turbine with the various mean annual electricity demands outlined in Section 4.4. He also compared modal outputs with modal demands. These comparisons indicate that the average solar PV power output is equivalent to 27–57% of average household electricity usage. While these values may be taken as ‘typical’ proportions, the wider range of minimum and maximum solar PV outputs (Fig. 9) is the equivalent of 21–67% of average annual demands. Similarly, the average (mean or modal) open micro-wind turbine output was found to be the equivalent of 5–13% of an average (mean or modal) household’s annual electricity demand, compared to 2–5% for the urban turbine. Given the minimum and maximum ‘open’ micro-wind outputs, the percentage range increases to 3–31% of average demands, while the minimum and maximum ‘urban’ outputs represent 1–9% of average demands. For both the solar PV system and the micro-wind turbine, it is highly unlikely that all of the electricity generated will be used by the householder, since in the assumed grid-tied format electricity will be exported whenever supply exceeds demand (such export is sometimes referred to as ‘spill-over’). The results of Bahaj and James [74] indicate that 50% could be a typical export proportion for solar PV (with 70% as a maximum and 25% as a minimum), which agrees with the Energy Saving Trust et al. [75] figure of 50% as being representative for PV. The modelling work of Peacock et al. [70] suggests that export will be in the region 33–55% for the micro-wind turbine, depending on the household’s demand profile. BEAMA [76] logged the export of 6 small wind turbines (average rated capacity ¼ 14.6 kW) and found an export rate of 49%. The export of electricity onto the network has a variety of technical and economic implications, which would become more significant if a large number of power micro-generators are installed on households. These implications are beyond the scope of this study, but further information may be found in [75,77,78], and in the research programme summarised by Burt el al. [37].

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The SHW outputs shown in Fig. 9 (1.9–3.5 GJth/yr) are small compared to the estimated 18 GJ/yr of delivered energy used by the average UK household (Section 4.3). The system boundaries are, however, different in the two cases. There are a variety of losses after the stage of delivered energy that makes its quantity considerably larger than that of the hot water ultimately provided to the end-user. These losses include conversion losses within the heating system (e.g. boiler), and heat losses during the storage and distribution of the hot water. On the basis of [55], Allen [20] estimated that the final hot water demand of an average, 2.4-person UK household is 6.1 GJth/yr, rising to 8.4 GJth/yr for a larger, 4-person household. This range of hot water demands underlies the output estimations given in Fig. 9, and the associated solar fractions (the proportion of demand satisfied by SHW) were estimated as 28– 52%. Further detail of these estimations may be found in Allen [20], where it is seen that they broadly agree with other literature regarding SHW systems in the UK (e.g. [79]). All solar hot water is used within the household, and hence the use of the SHW system does not impinge on the external, established supply systems in the same way that electricity micro-generators may do.

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displace, or their overall resource savings. The relative performance of the SHW system has thereby increased substantially, since it is credited with the energy losses suffered by the auxiliary system. Also in contrast to Fig. 9, exergy parameters would now be similar (if they were shown), since the savings are of fuels or electricity within thermodynamic qualities of approximately one. Table 2 indicates that the greatest annual energy-resource and carbon savings are enabled by solar PV, which would be expected since it has a large electricity output and because the established electricity system is relatively energy and carbon intensive. The SHW system displaces the most energy and carbon when installed alongside an electrical immersion heater, and the least when installed alongside a gas boiler (since the latter is the most resource-efficient auxiliary system). The carbon savings shown in Table 2 are significant. It should be noted that the residential sector’s 26 million households [6] emitted 42 million tonnes of CO2eq in 2004 [7] – an average of 1600 kgCO2eq per household per year. The carbon savings enabled by the mean open wind turbine are, for example, approximately 15–23% of this value.

5.5. Net energy and carbon analysis 5.4. Estimated annual energy-resource and carbon savings The micro-generators assessed in the present study can (partially) displace the established supply systems, and hence ‘save’ energy and carbon from those systems. The overall saving associated with each unit of displaced fuel or electricity is reflected by the appropriate energy requirement for energy and carbon-emission factor values from Section 3.2. In the case of electricity, Section 3.2 highlighted the need to use marginal values when estimating savings. The research underlying both this and the following section updates previous work [17–19] by using ‘marginal’ rather than ‘average’ values. Table 2 shows the estimated fuel and/or electricity savings provided to the household by the micro-generators, together with the associated overall energy-resource and carbon-emission savings. Mean savings are given where mean outputs were available from Fig. 9. In the case of the power micro-generators, the electricity saving is simply the output as given in Fig. 9 (any exported electricity is assumed to be used locally with no distribution losses). It was assumed that the SHW system is installed alongside a modern auxiliary heating system. Boiler efficiencies were taken as 86% in the case of gas and 85% in the case of oil on the basis of [80,81], whereas the immersion heater is assumed to have a conversion efficiency (from electricity to heat within the storage tank) of 100%. The electricity usage of boilers was estimated in accordance with [82], while storage and pipework losses were estimated in accordance with [83]. The estimation procedure is described in full by Allen [20]. When discussing the energy outputs of the micro-generators, it was noted that the system boundary was different for the SHW system compared to the electricity micro-generators. This difference is resolved in Table 2, since the micro-generators are compared in terms of either the fuel and/or electricity they

A major source of interest in micro-generation technologies is their potential to reduce carbon-dioxide emissions and enhance energy security by reducing use of, and dependence upon, the established, fossil fuel dominated methods of energy supply. For micro-generators to achieve this, they must save more energy and carbon than that ‘embodied’ within the devices themselves. Embodied energy values were calculated by collaborating LCA researchers [17–19], and were found to be 4930 MJNCV for the microwind turbine [17,18], 79 400 MJNCV for the solar PV system [17], and 10 100 MJNCV for the SHW system [19]. The associated embodied carbon values were found to be 280 kgCO2eq, 3760 kgCO2eq, and 462 kgCO2eq respectively. The LCA-data lacked information regarding the maintenance and disposal of the micro-generators (with the exception of solar PV, which includes a replacement inverter halfway through its 25 year lifetime), and hence they were omitted from the quoted values [17,19]. As a result, the embodied energy and carbon values can be described as including the ‘cradleto-site’ life-cycle stages of the micro-generators. The ‘simple’ energy payback periods of Table 3 show that the cumulative output of the micro-generators breaks even against their embodied energy ‘debts’ within their lifetimes, with the exception of the poorest-performing urban micro-wind turbine. The ‘displaced energy payback period’ and corresponding ‘carbon payback period’ account for the micro-generator output in terms of the savings of energy and carbon from the established energysupply system. Once these paybacks have been reached, a microgenerator will provide net energy-resource and carbon savings compared to a situation in which it had not been used. The displaced energy paybacks and carbon paybacks shown in Table 3 are all within – and mostly well within – the estimated lifetimes of the micro-generators, indicating that they will indeed provide a net

Table 2 Annual energy and carbon savings attributable to the micro-generators. Micro-generator

Key

Open micro-wind Urban micro-wind Solar PV SHW (gas boiler) SHW (oil boiler) SHW (elec. heater)

Overall Overall Overall Overall Overall Overall

range (Mean) range (Mean) range (Mean) range range range

Delivered elec. displacement (GJe/yr)

Delivered fuel displacement (GJNCV/yr)

Overall energy-resource saving (GJNCV/yr)

Carbon saving (kgCO2eq/yr)

0.7–3.4 (1.8) 0.2–0.9 (0.6) 4.7–7.2 (6.1) 0.15–0.20 0.27–0.35 2.6–4.6

– – – 3.1–5.5 3.2–5.8 –

1.6–9.8 (4.0–5.1) 0.55–2.7 (1.3–1.7) 11–21 (14–18) 4.1–7.3 5.1–9.1 5.7–13

95–714 (238–369) 33–200 (79–122) 640–1500 (830–1300) 230–415 340–610 340–940

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Table 3 Energy and carbon payback periods of the micro-generators. Micro-generator

Estimated lifetime (years)

Key

Open micro-wind Urban micro-wind Solar PV SHW (gas boiler) SHW (oil boiler) SHW (elec. heater)

15 15 25 25 25 25

Overall Overall Overall Overall Overall Overall

a

Payback periods

range Mean) range (Mean) range (Mean) range range range

Simple EPPa (years)

Displaced EPPa (years)

CO2eq emissions (years)

1.5–7.1 (2.8) 5.2–20 (8.5) 11–17 (13) 2.9–5.2 2.9–5.2 2.9–5.2

0.5–3.1 (1.0–1.2) 1.8–8.9 (2.9–3.7) 3.8–7.4 (4.5–5.6) 1.4–2.5 1.1–2.0 0.8–1.8

0.4–2.9 (0.8–1.2) 1.4–8.5 (2.3–3.5) 2.5–5.9 (2.9–4.5) 1.1–2.0 0.8–1.4 0.5–1.4

Energy payback period.

energy and carbon benefit. The fastest payback is achieved by the ‘open’ micro-wind turbine, which in the best case pays back within approximately half a year. The shortest payback for the SHW system occurs when it is installed alongside an electrical immersion heater, and the longest is when installed with a gas-fired boiler. In all these cases the paybacks are within 3.1 years (and sometimes much shorter). While the solar PV array was found above to enable the greatest energy-resource and carbon savings, its larger embodied energy and carbon cause generally-longer payback periods – in the range 2.5–7.4 years. Once the microgenerators have paid back (or ‘broken even’), the annual savings shown in Table 2 will begin to accrue as a net benefit, and thus the micro-generators will provide significant net savings over the course of their (varying) lifetimes. Allen [20] reviewed a range of other literature concerning the net energy and/or carbon performance of micro-generation technologies [49,84–87], and found that the technology types in general provide positive net energy benefits. The evidence-base is still relatively small, and there are problems comparing studies where underlying assumptions and system boundaries are different, but all three micro-generator types examined here were found to provide net energy and carbon benefits given appropriate UK installations. However, while the micro-generators assessed can reduce the use of the established systems they cannot provide a direct substitute for them, at least given current configurations. 6. Concluding remarks The current study has presented thermodynamic and related carbon analyses of three micro-generation technologies in a UK context: a SHW system, a micro-wind turbine, and a solar PV system. These analyses involve comparisons with the established and predominant methods of energy supply to, and use within, typical UK households. Where pertinent during the study, the thermodynamic concept of exergy has been employed alongside that of energy. During the contextual thermodynamic analysis of an illustrative UK household’s energy system, for instance, the present study has indicated that while an average UK boiler has a relatively high (First Law) energy efficiency of 74%, its exergy efficiency is only 13%. This poor performance is due to the significant exergy destruction – the significant loss of work-potential – associated with the conversion of high-quality fuels into low-temperature (and hence low-quality) heat within households. As most of the exergy destruction is intrinsic to the energy-conversion process and hence unavoidable, totally different heating technologies are required to enable significantly improved thermodynamic performance. Within the micro-generator analyses it has been estimated that the SHW system would meet 28–52% of a typical household’s annual hot water demand. When displacing a modern gas boiler, an oil boiler, or an electrical immersion heater, the SHW system would displace 2.6–6.2 GJ of delivered fuel or electricity and thus save 230–940 kg/CO2eq per year. The exergetic performance of SHW is

low because it converts high-quality solar energy into low-quality, low-temperature hot water. It has been highlighted, however, that this output is the energy service provided to the end-user, in contrast to the interim energy carrier (electricity) provided by the micro-wind turbine or solar PV system. Of greater relevance in the context of carbon emissions and energy security, SHW reduces the large, non-renewable, and mostly unavoidable exergy destruction that occurs within the established (gas, oil or electricity) heating systems that dominate the UK housing stock. The micro-wind turbine and solar PV system analysed here displace the need for electricity from the established power grid. Of the sample locations considered, the average ‘open’ micro-wind turbine provided the equivalent of 5–13% of a typical UK household’s annual electricity usage, and would save 238–369 kg/CO2eq per year (overall estimation range: 3–31% and 95–714 kg/CO2eq per year). In the urban locations, the average ‘urban’ turbine provided the equivalent of 2–5% of a typical household’s electricity usage and would save 70–122 kg/CO2eq per year (overall estimation range: 1–9% and 33–220 kg/CO2eq per year). The average solar PV system is estimated to provide the equivalent of 27–57% of average usage and save 830–1300 kg/CO2eq per year (overall range: 640–1500 kg/CO2eq per year). For all the above cases, it is unlikely that all the electricity will be used by the household, since supply is unlikely to match demand at all times. Literature cited suggests that 33–55% of the micro-wind turbine output would be exported to the grid, while approximately 50% (range: 25–70%) will be exported in the case of PV. There are a range of technical and economic implications of such export, although these were outside the scope of the current study. Power micro-generators displace marginal centralised generation plant, and it has been argued that this should be considered during any estimation of the energy and carbon savings provided by such systems. It is difficult, however, to quantify marginal energy and carbon savings because these are influenced by a large and complex system. The carbon-emission factors used here are within the range of other published marginal values, but further work is clearly needed on the marginal savings provided by micro-generation. Such work should incorporate consideration of the likely non-linear effects of increasing penetrations of micro-generators. Net energy and carbon analyses can indicate whether or not proposed technologies are likely to help meet the UK’s energy policy goals of enhancing energy security and reducing carbon emissions. Given appropriate installation, all three microgenerators examined here will repay their energy-resource and carbon-emission ‘debts’ well within their lifetimes when displacing the established supply systems. After the break-even points have been reached annual savings will begin to accrue as net energy or carbon benefits, and hence the micro-generators would provide significant net savings over their whole life cycles. Energy systems involve many complex socio-economic, political and technical interactions, all of which were outside the scope of the present study. However, this research forms part of a wider contribution to the investigation of micro-generation and other distributed-energy technologies. The thermodynamic analyses

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reported here form part of a three-part, ‘integrated appraisal’ methodology described and applied by Allen et al. [17]. This, in turn, is an element of the research programme being carried out by the UK Engineering and Physical Sciences Research Council funded ‘SUPERGEN 3 Highly Distributed Power Systems Consortium’, of which a number of publications have been brought together by Burt et al. [37]. Acknowledgements The authors’ research on the technology assessment of distributed-energy generation systems was supported by a research grant awarded by the UK Engineering and Physical Sciences Research Council (EPSRC) (under Grant GR/T28836/01) to Professor Hammond and Dr Winnett as part of the SUPERGEN 3 ‘Highly Distributed Power Systems’ Consortium. This is coordinated by Professors Graeme Burt and David Infield of the Institute for Energy and Environment at the University of Strathclyde. The authors are grateful for the interchange made possible with a range of academic and industrial partners under this SUPERGEN programme. Furthermore, they would like to thank their colleagues, Adrian Winnett, Marcelle McManus, Craig Jones, and Hassan Harajli, and also the various co-operating micro-generation companies for their collaboration and for the provision of data. References [1] Hammond GP. Engineering sustainability: thermodynamics, energy systems, and the environment. International Journal of Energy Research 2004;28(7): 613–39. [2] BERR. Digest of United Kingdom energy statistics 2008. London: Department for Business, Enterprise and Regulatory Reform; 2008. [3] Emissions of carbon dioxide, methane and nitrous oxide by NC source catergory, fuel type and end user. Available from:. DEFRA http://www.defra.gov.uk/ environment/statistics/globatmos/index.htm; 2008 [accessed 2.11.08]. [4] DTI. Meeting the energy challenge – a white paper on energy. London: The Stationary Office Limited; 2007. [5] DECC. Heat and energy saving strategy: consultation. London: Department of Energy and Climate Change; 2009. [6] BERR. Energy consumption in the United Kingdom. London: Department for Business, Enterprise and Regulatory Reform; 2008. Although the report was published in July 2002, the consumption tables were updated in July 2008. [7] DEFRA. e-Digest of environmental statistics. Available from:. Department for Environment, Food and Rural Affairs http://www.defra.gov.uk/environment/ statistics/index.htm; 2007 [accessed 23.8.07]. [8] Johnston D, Lowe R, Bell M. An exploration of the technical feasibility of achieving CO2 emission reductions in excess of 60% within the UK housing stock by the year 2050. Energy Policy 2005;33(13):1643–59. [9] Shorrock LD, Henderson J, Utley JI. Reducing carbon emissions from the UK housing stock. Garston, Watford: Building Research Establishment; 2005. [10] Boardman B, Darby S, Killip G, Hinnells M, Jardine CN, Palmer J, et al. 40% house. Oxford: Environmental Change Institute; 2005. [11] Natarajan S, Levermore GJ. Domestic futures–which way to a low-carbon housing stock? Energy Policy 2007;35(11):5728–36. [12] Boardman B. Home truths: a low-carbon Strategy to reduce UK housing emissions by 80% by 2050. Oxford: Environmental Change Institute; 2007. [13] Ekins P, Skea J. UKERC energy 2050. London: UKERC; 2009. [14] Allen SR, Hammond GP, McManus MC. Prospects for and barriers to domestic micro-generation: a United Kingdom perspective. Applied Energy 2008;85(6): 528–44. [15] Dti, OFGEM. Review of distributed generation. London: Department of Trade and Industry; 2007. [16] Hammond GP, Winnett AB. Interdisciplinary perspectives on environmental appraisal and valuation techniques. Proc. Instn Civil Engrs: Waste and Resource Managament 2006;159(3):117–30. [17] Allen SR, Hammond GP, Harajli HA, Jones CI, McManus MC, Winnett AB. Integrated appraisal of micro-generators: methods and applications. Proc. Instn Civil Engrs: Energy 2008;161(2):73–86. [18] Allen SR, Hammond GP, McManus MC. Energy analysis and environmental life cycle assessment of a micro-wind turbine. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 2008;222(7):669–84. [19] Allen SR, Hammond GP, Harajli HA, McManus MC, Winnett AB. Integrated appraisal of a solar hot water system. Energy: available online 16 December: doi: doi:10.1016/j.energy.2009.11.018. [20] Allen SR. Micro-generation for UK households: thermodynamic and related analyses: Thesis (PhD). University of Bath; 2009.

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