The potential demand for bioenergy in residential heating applications (bio-heat) in the UK based on a market segment analysis

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The potential demand for bioenergy in residential heating applications (bio-heat) in the UK based on a market segment analysis S. Jablonski, A. Pantaleo, A. Bauen, P. Pearson, C. Panoutsou, R. Slade Imperial Centre for Energy Policy and Technology (ICEPT), Imperial College London, Exhibition Road, London SW7 2AZ, UK

art i cle info

ab st rac t

Article history:

How large is the potential demand for bio-heat in the UK? Whilst most research has

Received 12 September 2007

focused on the supply of biomass for energy production, an understanding of the potential

Received in revised form

demand is crucial to the uptake of heat from bioenergy. We have designed a systematic

11 December 2007

framework utilising market segmentation techniques to assess the potential demand for

Accepted 13 December 2007

biomass heat in the UK. First, the heat market is divided into relevant segments,

Available online 14 February 2008

characterised in terms of their final energy consumption, technological and fuel supply

Keywords: Bio-heat Market segmentation Bioenergy competitiveness Demand assessment Barriers analysis Space heating Domestic hot water Combined heat and power (CHP)

options. Second, the key technical, economic and organisational factors that affect the uptake of bioenergy in each heat segment are identified, classified and then analysed to reveal which could be strong barriers, which could be surmounted easily, and for which bioenergy heat represents an improvement compared to alternatives. The defined framework is applied to the UK residential sector. We identify provisionally the most promising market segments for bioenergy heat, and their current levels of energy demand. We find that, depending on the assumptions, the present potential demand for bio-heat in the UK residential sector ranges between 3% (conservative estimate) and 31% (optimistic estimate) of the total energy consumed in the heat market.

1.

The objective and the context

1.1.

The objective

This paper’s main objective is to present an original framework for assessing the potential demand for bioenergy heat, based on market segmentation techniques. The focus is on the principles of the framework’s design and its development in practice. We include preliminary results from applying the framework to the largest UK heat market sector, the residential sector. No single study of the potential demand for bio-heat published in the UK context or otherwise has gone beyond the sectoral level. The market segment analysis framework presented here aims to fill that gap, by focusing down on the Corresponding author. Tel.: +44 20 7594 6781; fax: +44 20 7594 9334.

E-mail address: [email protected] (S. Jablonski). 0961-9534/$ - see front matter & 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.biombioe.2007.12.013

& 2007 Elsevier Ltd. All rights reserved.

diverse market segments in sectoral markets, considering a wide range of technical, economic, behavioural and organisational factors which ultimately affect the uptake of bioenergy.

1.2.

The context

Government attempts to stimulate bio-heat in the UK have had limited success—less than 1% of the UK heat demand comes from biomass (excluding energy from waste) [1–3]. The May 2007 UK Energy White Paper [4] identifies renewable heat from biomass as an important means of reducing carbon emissions, and states that biomass will need to make a greater contribution to the UK’s share of the EU renewable energy targets. Nevertheless, and despite the existence of mature heat production technologies in the residential and

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industrial sectors, UK biomass consumption for heating has been growing slowly, when compared to biomass for electricity and for transport fuel [5]. The efficient development of bioenergy as part of a renewable policy agenda depends on the capacity to assess the potential demand for bio-heat. The International Energy Agency (IEA) Bioenergy Implementing Agreement identifies a lack of demand as the main barrier to significant growth in the uptake of heat from bioenergy (or bio-heat) [6]. It suggests that the low demand results mainly from an absence of policy instruments for renewable heating, targeted to create an enabling market environment. While UK bioenergy policy makers have emphasised the role of supply side studies of biomass resource availability to estimate potential energy production in the short and long term, less attention has been paid to the sources of bioenergy demand and how demand could be further stimulated. The Biomass Task Force Report, using such supply side approach, estimates that renewable heat could represent between 3% and 7% of the total heat consumption in the UK by 2010 and 2015, respectively, although the figures do not isolate the bioheat share [1]. Most published work on potential demand has on the other hand been either been very aggregated, not recognising sufficiently the differences within broad market segments, or case study based, focusing on specific applications and scales. Only two studies have quantified estimates of the potential bio-heat demand in the UK, now or in the future, and both have methodological limitations. While they both explore the potential contribution of biomass heat to the main UK ‘‘sectoral’’ heat markets, i.e. the residential, public/commercial, and industrial sectors [2,3], they have major limitations for policy use: (1) their hypotheses on bio-heat market penetration do not allow for the differences between heat consumers in a given market sector; and (2) the rationale behind the market shares is loosely justified, preventing an objective assessment of whether the estimate of final potential penetration is conservative or optimistic.

2.

Fig. 1 – UK heat market size and main sectors 2004 (source: UK DUKES [7]).

Fig. 2 – Overall heat fuel supply option in the UK 2004 (source: based on UK DUKES [7]).

Overview of the UK heat market

2.1. Current heat market size and main final consumption sectors The final heat market can be divided between the industrial, the service, the agricultural and the residential sectors (Fig. 1). Heat accounts for over a third of the UK’s primary energy consumption and about half of the UK’s total energy consumption by end use—815 GWh in 2004. As Fig. 1 shows most heat is consumed by the residential and industrial sectors, while the commercial and public sector represents only one seventh (14%), and the agriculture sector less than 1%.

2.2.

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Current heat fuel supply options

Most heat in the UK is produced from fossil fuels, with all renewables contributing less than 1% and district heating 1.4% of the market (Fig. 2). In fact, recent energy statistics

suggest that renewables used to generate heat in the UK are now about half of the level they were 10 years ago. This decline has been attributed to tighter emission controls on the combustion of biomass fuels and waste [8].

2.3.

Current heat technological supply options

Heat can be produced either in heat-only devices or via combined heat and power (CHP) units which also produce electricity. Over 90% of the heat produced in the UK comes from heat-only devices. In the residential and the service sectors, most of the heat is consumed for space and water heating. The corresponding technologies are boilers (and stoves), which can be designed with integral hot water energy storage or accumulator tanks. In addition, specifically designed boilers can be used in the service sector when heat is used for process needs, such as steam used in hospitals or for dry cleaning, etc. In industrial settings, heat producing

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technologies cover a much wider range, including boilers, steam generators and furnaces (ovens, heaters, kilns, etc.) [9]. CHP covers about 8% of the UK heat market [7,10]. In principle CHP could be used for any scale, from small domestic applications to very large industrial sites. At present, the industrial sector contains almost all (94%) of the available CHP installed electrical capacity. The remaining 6% corresponds to micro-CHP (about 990 installations in 2004 [11]), and ‘‘non-micro’’ community or commercial systems (about 1100 schemes, with 250 MWe/615 MWth installed [10]).

Table 1 – Scales of application covered in the study Scale reference Micro Small Medium Large

3.2. 2.4.

Heat only technologies

Combined heat and power technologies

o50 kWth 50–500 kWth 500 kWth–5 MWth 450 MWth

o50 kWe 50–500 kWe 500 kWe–5 MWe 450 MWe

Defining the potential bioenergy demand

Current bioenergy penetration in the UK heat market

Biomass penetration is currently less than 1% of the overall UK heat demand, with domestic and industrial use of wood being the main contributors [10]. While there are numerous examples of biomass use in boilers (mostly solid woody biomass), mainly heat-only units, but also in combined heat and power units (CHP) in the UK, as yet there are no official statistics on bio-heat use in the UK. The Renewable Energy Association (REA) has recently made an attempt at publishing a map of British bioenergy, which presented Britain’s anaerobic digestion, energy from waste and non-domestic wood heat-only installations with their respective capacities, along with dedicated biomass CHP plants locations and capacities. In total, it is estimated that biomass (in the form of straw or wood products) contributes approximately 6.31 TWh per year to the non-residential heat market [10]. It has also been estimated that there are about 150 residential biomass boilers using pellets (corresponding to an installed capacity of less than 10 MWth—assuming that the pellet boilers have individual capacities ranging between 15 and 50 kWth). The REA map suggests that the total electric capacity of all biomass CHP schemes in Britain is about 88 MWe (excluding sewage gas), which corresponds to about 1.5% of the CHP electrical capacity in the UK. Most of the available data on biomass heat-only plants concern non-domestic wood heating projects, with an estimated installed thermal capacity of 126 MWth [12].

In this study the bioenergy potential is defined as the potential final energy yearly consumption in the form of heat, produced from biomass (measured in GWh). Underpinning this study is the need for an adapted definition of the bioenergy potential demand, to fill a clear methodological gap. The literature contains several definitions of ‘‘bioenergy potential’’ (notably [13–17]). Because such definitions were usually derived from analyses of the potential for renewable energy sources, however, they tend to consider the potential as purely a supply-driven concept. The ‘‘total potential bioenergy demand’’ can be thought of as the intersection between the technical potential, the economic potential and the implementation potential, with corresponding demand-focused definitions (adapted from [2,3,16]). The technical potential represents that fraction of the market which can be supplied by bio-heat, assuming current and future levels of advancement in biomass heat producing technologies. The economic potential represents the fraction of the technical potential which can be produced at economically profitable levels and so offered to the market. The implementation potential represents the fraction of the economic potential that can be implemented within a certain timeframe, taking into account institutional and social constraints and policy incentives. In this study the focus is put on the technical and economic potential, which are quantitatively estimated. The implementation potential is only qualitatively discussed via the corresponding key factors and their effect on market segments.

3.3.

3.

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Market segmentation

Market segment analysis methodology

3.1. Overview of the key steps of the proposed methodology Our work focuses on demand and analyses (by market segment) the relative attractiveness and competitiveness of biomass-based heat and/or CHP, when compared to energy produced with fossil fuels. Both small and large scales of energy producing installations are covered, with the boundaries indicated in Table 1. There are four key steps in defining and estimating potential bioenergy demand: (1) market segmentation, (2) identification of key factors, (3) interaction of the key factors with the market segments, (4) estimation of potential demand and scenario formulation (Fig. 3).

‘‘Market segmentation’’ can be defined as the division of a market into homogeneous groups of customers, each reacting differently to promotion, communication, pricing and other variables of the marketing mix [18]. Market segmentation is a concept which has long been discussed in the marketing literature [19], and became a central topic of discussion among marketing and research circles in the 1960s [20]. Generally it is applied when designing the ‘‘optimal’’ marketing mix for a company, aiming at increasing their sales and/or market shares, and has only recently started to be used by the public sector or NGOs [21] for policy making. Market segmentation is used in this study as a tool to highlight where bioenergy heat and power applications appear most promising in the UK, and to quantify the bioenergy demand potential (based on the 2004 data).

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MARKET SEGMENTATION Segmentation of the heat market and determination of each sub-segment’s characteristics in terms of: User needs User types Geographical area KEY FACTORS X Identification of the key factors which can affect (positively or negatively) the uptake of bioenergy technologies for heat: Technical factors Economical factors … Organisational factors Factor Y (environmental, social, … behavioural, etc.)

In the context of each sub-segment X, determination of whether each key factor Y provides an opportunity or a barrier for the use of a bioenergy system

PROMISING SUB-SEGMENTS and MARKET PENETRATION HYPOTHESES In comparing the results obtained for various market sub-segments it is possible to identify the subsegments that appear the most suitable for bioenergy heat applications. This data guides the formulation of hypotheses on bioenergy penetration of the market Fig. 3 – Approach for market segment analysis and market penetration hypotheses.

Several varieties of market segmentation have been popular in the history of the technique [22–24] but it is generally acknowledged that the number of market segments to be formed (and their typologies) depend more on judgement than on some rigid rule. In principle, many variables could be used for market segmentation. They comprise easy to determine demographic factors as well as variables relating to user behaviour or customer preferences [18]. Two key criteria for the definition of appropriate market segments are recurrent in the literature: (i) ‘‘revealing, meaningful or relevant’’, and (ii) ‘‘operational, actionable, applicable, or feasible’’ [18,20,22,25]. The market segments in this study were designed to respond to the influence of ‘‘key factors’’ that either promote or hinder the uptake of bioenergy, and two sets of segmenting dimensions were used. This was done in order to ensure that the segments obtained would be meaningful and operational: (1) a set mirroring the statistical data commonly available on the heat market at the sectoral level; and (2) a set reflecting the positive or negative influence of the key factors on the uptake of bioenergy at the project level.

3.4. Selection and classification of key factors of bioenergy uptake The uptake of bio-heat is influenced (mostly at the project level) by a set of ‘‘key factors’’ characterising along which

dimensions bio-heat (and its associated technologies) is perceived as ‘‘special’’ or ‘‘different’’ from the alternative means (fuels and/or technologies) of providing heat. The key factors are classified into three categories (technical, economic and organisational factors), in line with which dimension of potential they mostly seem to affect. Thus the key technical factors show where biomass technologies can or cannot fulfil a heat demand for technical reasons. Because some key factors will be ‘‘showstoppers’’, they will limit the technical potential via ‘‘hard’’ constraints on bioenergy penetration. The economic key factors show where bio-heat can be potentially competitive with heat produced by other sources. The applications where bio-heat is a profitable means of obtaining heat (without the actual policy incentives in place) thus define the economic potential. Finally, the key organisational factors are additional constraints or drivers which can influence the economic potential either positively or negatively and thereby affect the implementation potential.

3.5. Interaction of the key factors with market segments: qualitative and quantitative assessments The study used both qualitative and a quantitative tools to assess the attractiveness of market segments for bioenergy. In theory, each segment can be assessed against a series of key factors (using a ‘‘positive/negative/neutral’’ rating), thereby

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leading to the identification of the most or least ‘‘attractive’’ for bioenergy. However whilst most factors can be measured in terms of economic impact, some of the technical and organisational key factors are not always tractable to quantitative analysis, and so have been excluded from the majority of past analyses, even though at the project level they might have been very significant [26]. The qualitative assessment of market segments is in effect a multi-criteria analysis, where the interaction between a market segment and a key factor is rated via a qualitative scoring code (‘‘+/++ ¼ good’’, ‘‘/ ¼ bad’’). The number of pluses and minuses can provide an indicator of the relative attractiveness of one market segment versus another. As a complement to the qualitative analysis, a Discounted Cash Flow (DCF) model was used to provide an indicative financial assessment of the biomass energy provision in the various market segments. Revenues come from heat sales (in the case of heat plants), and/or electricity sales (in the case of CHP plants). As there is no traded market for heat, the price of heat is based on the cost of heat produced by the displaced fuels. The model’s output is used to chart the profitability index (PI) of a given biomass plant, against an index representing the ratio of biomass fuel price to the alternative fossil fuel price (both in GBP/MWh). This ratio is transformed into an index where the base case tested by the model (i.e. the default value) corresponds to value 1. This is equivalent to a sensitivity analysis on biomass price for each market segment. The PI is computed by PI ¼ ðNPV þ Inv0 Þ=Inv0, where NPV is the net present value of the project and Inv0 is the initial capital investment (corrected by the capital grant as relevant). Finally, the results obtained in terms of PI are closely linked to the data inputs, and their uncertainties. The price of biomass is in fact not a deterministic variable, and its stochastic components should be incorporated in the analysis. Correlations to the price of fossil fuels are likely to emerge as well. These important considerations could be included in future developments of the analysis, but have been considered beyond the scope of this paper.

3.6. Formulation of hypotheses on potential bioenergy demand using the qualitative and quantitative assessments The results of the qualitative and quantitative analyses are used to explore three scenarios of bio-heat potential: (i) a conservative (or low) estimate, (ii) a middle estimate, and (iii) an optimistic (or high) estimate (see principles in Table 2). The interaction of key factors with the various market segments provides an indication of where the technical, economic and implementation potential demand is limited or could be larger. However, the actual estimates of these potentials (in particular the technical potential) require a set of context-related additional assumptions to be able to quantify them. Coupling potentials to scenarios is one way of making these additional assumptions transparent [27]. The conservative case corresponds to the base case. It is the closest to the present situation shown in the qualitative and quantitative assessment results. All potential technical showstoppers are strong constraints on penetration, and the other constraints are strongly applied as well. In addition, the

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Table 2 – Principles of construction of conservative, middle- and optimistic scenarios for estimating potential bio-heat demand in the market segment analysis Technical potential Conservative scenario All potential technical showstoppers are real constraints Other technical barriers reduce the potential by half on affected segments Middle scenario Potential technical showstoppers limit the potential in half of the affected segments Other technical barriers reduce the potential by 25% on affected segments

Optimistic scenario Potential technical showstoppers limit the potential in 25% of the affected segments Other technical barriers do not reduce the technical potential

Economic potential

The economic potential is strictly limited for applications where PI41

The economic potential is expanded to include, on the top of all applications where PI41: K 50% of market segments where a 15% biomass price reduction (or fossil fuel price increase) can make biomass applications profitable

The economic potential is expanded to include, on the top of all applications where PI41: K All applications where a 15% biomass price reduction (or fossil fuel price increase) can make biomass applications profitable K 50% of market segments where 30% biomass price reduction (or fossil price increase) can make biomass applications profitable

quantitative assessment results are assumed deterministic and only applications with PI higher than 1 can penetrate the market. In the middle and optimistic cases, we change the hypotheses to progressively make the constraints less stringent, thereby allowing the penetration to be potentially higher. It is assumed that the potential technical showstoppers can be surmounted in some cases, as for the other barriers. Finally from an economic point of view, applications with PI lower than 1 (down to a certain limit level) can penetrate the market.

4. Application to the UK residential heat market: preliminary results and policy implications 4.1.

UK residential heat market segmentation

The segmentation of the residential heat market is undertaken at the macro and at the micro level. At the ‘‘macro’’

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level (Fig. 4), the segmenting dimensions correspond to the end user categories as defined and documented in the statistics [28]. At the ‘‘micro’’ level, the segmenting dimensions are based on parameters which will determine whether bioenergy is at an advantage or disadvantage when compared to alternative energy producing systems. The values taken by the parameters in different market segments correspond to an additional set of hypotheses, which can be represented as a possibility ‘‘tree’’ (Fig. 5 and Fig. 6). The various branches (A–G) correspond to the market non-geographic segments that were assessed both qualitatively and quantitatively. In parallel to these cases, it is possible to introduce the technological option of using CHP (as opposed to a heat-only technology).

Fig. 4 – UK residential heat macro market segments (source: Domestic Energy Fact File 2006 [28]).

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Similar branches/market segments are created and are named A*, B*, etc. until G* (the * identifying the CHP option). The size of the different micro market segments is estimated from the statistical information available for the macro market segments (as indicated in Table 3), linking the two segmentation levels, and ultimately ensuring that the ranking of ‘‘bioenergy attractiveness’’ will be traceable to particular activities for which demand data are commonly available.

4.2.

Identification of key factors

Table 4 presents the list of key factors expected to influence bioenergy uptake in the UK residential heat sector, and shows whether each factor is included in the qualitative and/or quantitative analysis. It is based on a thorough literature review of the drivers and barriers to energy efficiency, renewable energy technologies, and bioenergy. In addition a total of 17 interviews were conducted between August and November 2006 for the purposes of this work, covering stakeholders in various trade associations, government agencies, power companies and other commercial companies dealing with biomass supply or biomass technical equipment. Each key factor describes one aspect that influences the biomass energy technical, economic or organisational potential in the UK, and helps inform the decision as to whether the bioenergy project is likely to be viable. The factors are not put in any order of priority—their relative weights depend on the UK market segment and the specific bioenergy project investor or final energy consumer. Such weighting can better inform the qualitative assessment, but for practical reasons, lay beyond the scope of the present study.

Residential sector Heat

Scale

Capacity factor

Third party

Micro

Small

Low CF

Low CF

No 1/3 P

No 1/3 P

1/3 P

No 1/3 P

1/3 P

No 1/3 P

1/3 P

B

C

D

E

F

G

A

Medium

Large

Medium CF

Low CF

Medium CF

Geographic market segmenting dimensions

Fig. 5 – Segmentation tree for the UK residential heat sector (non-geographic/locational).

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Non-geographic segmenting dimensions

Development characterisation

New / retrofit

Rural

Area characterisation

Displaced fossil fuel (main)

Proximity to gas grid

Natural gas

Yes

No

Oil / coal

Yes

No

Urban

Natural gas

Electricity

Yes

No

Yes

No

Oil / coal

Yes

No

Electricity

Yes

No

Fig. 6 – Tree of segmentation for the UK residential heat sector (geographic/locational).

Table 3 – Linking the micro and macro segmenting dimensions in the residential heat sector Micro segmentation branch

Macro description (which can be linked to energy consumption statistics)

Estimated micro market segment size 2004 (in GWh)

A A*

Individual house or flat, bungalow

268,385 1.2

B B*

Big individual house/mansion, small apartment building, small private district heating with a few dwellings

168,484 18.6

C C*

Medium apartment building, medium residential-only private district heating

535 31

D D*

Big country estate or property (for residential purpose)

35,483 40

E E*

Medium district heating system with mixed users (including commercial/ service activities)

535 31

F F*

Medium district heating system with mixed users (including commercial/ service activities)

535 31

G G*

Large district heating system with mixed users (including commercial/ service activities)

1593 535

Note: * indicates the option using CHP technology.

4.3. Qualitative assessment of the UK residential heat market The values taken by the key factors for each market segment are aggregated to provide a summary view of the results in Table 5, which can be used to identify key opportunities for bioenergy uptake, as well as potential showstoppers in the UK residential heat sector. The qualitative analysis results suggest that the three main drivers for bioenergy uptake in the UK residential heat sector are the potential for carbon displacement (especially if oil or

coal is displaced), employment creation in rural areas, and the social acceptability of bio-heat for smaller scale heat plants local to the consumer or the community. The qualitative analysis also suggests that additional drivers which can play a role at larger scales are policies which support the development of renewable energy by setting mandatory targets for buildings (such as the creation of requirements for new developments above a threshold of x square metres should incorporate a certain percentage of predicted energy requirements from renewable on-site sources).

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Table 4 – List of key factors and references used for their characterisation

Technical key factors and references used Technology availability (reliability and maturity) [29,30] Heat/power characteristics [31,32] System response time [33] Fuel supply constraints (quantity)—technical management of the logistics of supply [34] Fuel supply constraints (quality) [35] Space availability (energy system and fuel storage/handling/delivery) [36]

Qualitative analysis

Quantitative analysis

x x x x x x

(x)

Economic key factors and references used Technical efficiency of the system [35,37] Dual fuel/co-firing option Capital costs [2,36]

x x x x x x (x) (x) x x (x) (x) x x x x x x

Eligibility for capital grants and other benefits [1, 38–40] Biomass versus fossil fuel price [1] Operating and maintenance costs Grid connection costs [10] Licensing costs (generation, distribution and supply) [10] Electricity buy-back tariff/export reward Cost of electricity (from the grid)-value of auto-consumed electricity Eligibility for/revenues from net metering option [41] Eligibility for/revenues from ‘‘embedded benefits’’ [10] Eligibility for/revenues from renewable obligation certificates [10,39] Eligibility for/revenues from carbon trading [42] Eligibility for/revenues from levy exemption certificates [43] Access to/cost of capital-discount rate [39,44] Eligibility for/revenues from climate change levy/CCL exemption [43] Eligibility for/revenues from enhanced capital allowances [45] Organisational key factors and references used Potential for carbon displacement [46] Employment creation [47] Social acceptability (tradition, confidence with biomass fuel, conversion technology) [44,48] Amenity issues (fuel delivery and energy production) [44] Organisational capability (skilled personnel availability, know-how) and management of complexity [44] Incumbent fuel infrastructure availability Strategic consideration: fuel security of supply [46] Strategic consideration: biomass fuel price stability [49] Policies and legislation [1,50,51] Administrative issues: planning [10,52] Administrative issues: grid connection [10] Power export option [10]

x x x x x x x x x x x x

(x)

(x)

x: Key factor included in the (qualitative or quantitative) analysis, (X): Key factor indirectly included in the (qualitative or quantitative) analysis.

Perhaps not surprisingly, since most of the UK population lives in urban areas [53], space availability (for the energy plant, and the fuel storage, handling and delivery) is the most important technical barrier suggested by the qualitative analysis. The main organisational barriers in the residential sector are the availability of the current fossil fuel infrastructure (especially proximity to the natural gas grid) and the lack of organisational capability if no ‘‘expert’’ third party is involved. Additional serious barriers are specific to CHP technologies. They relate to the heat and power characteristics of the demand (since residential applications mostly have low capacity factors which are not very suitable to CHP), and the difficulties of obtaining planning permission or grid connection (and in some cases to have the option to export power to the grid).

The results of the qualitative analysis can also be analysed segment by segment. For the residential sector, the results suggest that the branches C–F (medium-scale applications) are the best suited for bio-heat applications when compared to the others. This is especially true for rural applications where a third party is involved (for instance district heating operated by an Energy Service Company), but is less so for new developments in urban areas and where no third party is involved. For smaller scale branches (A and B) many factors are neutral, but there still remain more barriers than drivers at present. Finally, whereas larger scale applications (branch G) have strong drivers, the sourcing of the required quantity of biomass fuel starts to be an issue. For CHP applications (branches A*, B*, C*, D*, E*, F* and G*) the picture is gloomy overall, reflecting the difficulties faced by decentralised power production in the UK at all scales.

Table 5 – Summary results of qualitative analysis for the UK residential heat sector Market segments

Key factors 3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

Res-A

Individual house or flat, bungalow

J

J

J

J



J/+

J

+

J



J

J

J

x

x



J

J



J/+

J

J

J



x

J

J



J



Big individual house/ mansion, small apartment building, small private district heating with a few dwellings Medium apartment building, medium residential-only private district heating Big country estate or property (for residential purpose)

J

J

J

J



J/+

J/+

+

J



x

J

J

J

x

x





J

J



J/+

J/+

J

J



/  /  /  / 

x



/ / J / / J / / J / / J

x

J

J







J

J

J

J

J

J

+/++

J/+

+

/J

J

x

J/+

J

/+

J

x

x

J



J

J

J

J

+/++

J/+

J

/J

J

/  / 

x

J/+

J

/+





J

J

J

J

J

J

+/++

J/+

+

/J



J

J

/+

J

x

x



J

J

J

+/++

J/+

J

/J



x

J

J

/+





J

Medium district heating system with mixed users (including commercial/service activities) Medium district heating system with mixed users (including commercial/service activities) Large district heating system with mixed users (including commercial/service activities)

J

J

J

J

J

J

+/++

J/+

J

/J

J

x

J/+

J

/+

J

x

x

J



J

J

J

J

+/++

J/+



/J

J

/  /  /  / 

x

J

/ / J / / J

x

J/+

J

/+





J

J

J

J

J

J

J

+/++

J/+

J

/J



x

J

J

/+

J

x

x

J



J

J

J

J

+/++

J/+



/J



/  / 

x

J

J

/+





J

J

J

J



J

J

+/++

J/

J

J/+

+/++

J

x

x



/  / 

x

+/++

/  / 

J

++ J/ ++

x

J/+

+/++





J

Res-A* Res-B Res-B*

Res-C Res-C*

Res-D Res-D* Res-E Res-E*

Res-F Res-F*

Res-G Res-G*

J



J



J

J

J

18

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Technical key factors 1 Technology availability (reliability and maturity) 2 Heat/power characteristics 3 System response time 4 Fuel supply constraints (quantity)-technical management of the logistics of supply 5 Fuel supply constraints (quality) 6 Space availability (energy system and fuel storage/handling/delivery) Organisational key factors 7 Potential for carbon displacement 8 Employment creation 9 Social acceptability (tradition, confidence with biomass fuel, conversion technology) 10 Amenity issues (fuel delivery and energy production) 11 Organisational capability (skilled personel availability, know-how) 12 Incumbent fuel infrastructure availability 13 Strategic consideration: fuel security of supply 14 Strategic consideration: biomass fuel price stability 15 Policies and legislation 16 Administrative issues: planning 17 Administrative issues: grid connection 18 Power export option Key factor effects J The factor is neutral ++ The factor provides a strong opportunity for bioenergy uptake + The factor provides an opportunity for bioenergy uptake  The factor provides a barrier for bioenergy uptake   The factor provides a barrier for bioenergy uptake, with a risk of being a showstopper x The key factor is not relevant or not assessed

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Table 5 (continued)

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It is important to note that this analysis of the heat market corresponds to the situation at present and that, based on evolutions in the policy, technology developments or changes in the economic framework, a key factor could switch from being a barrier to a driver and vice versa.

4.4. Quantitative assessment of the UK residential heat market The economic model thus allows an analysis of which market segments are potentially the most profitable under the present policy and price level conditions, and based on the economic parameters assumptions in Tables 6–9. For each market segment A (A*) to G (G*) the quantitative assessment provides a snapshot of the competitiveness of bioenergy, depending on the fossil fuel displaced and on the biomass used (for example, Fig. 7 representing branch A in the analysis). In particular, we can test the sensitivity of various market segments (and biomass chains) to changes in key economic parameters. In this study, we focus on the price of biomass as a key variable determining profitability in each market segment. Table 10 summarises the results obtained for each market segment in terms of the profitability index (PI), and what change in the price of biomass would be required to make the biomass option profitable (based on the sensitivity macro built in the model). We find that in UK residential heat market segments, the displacement of oil (as opposed to natural gas) gives better results in terms of PI, and when woodchips technologies are available, using woodchips gives better results than pellets.

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Overall the displacement of oil by biomass (bio-oil or woodchips) appears profitable in most market segments, and in the segments where it is not, lower biomass prices by 15–30% would make bioenergy competitive. Such reductions in biomass (woodchip) prices are achievable in some cases, especially when biomass is produced locally and/or from residues (as it was mentioned during our interviews, notably [33]). The displacement of natural gas by biomass (woodchips) has a higher PI in CHP rather than heat-only units, but (with the current input data into the economic model) is only economic in branch G* (i.e. large-scale CHP units), where ROCs and carbon credits (from the EU Emission Trading Scheme) can be cumulated. In other market segments, prices of biomass (woodchips) lower by 15–50% would be necessary to make bioenergy competitive. It is theoretically possible that in cases where large amounts of biomass are purchased (e.g. by a third party), lower biomass costs might be negotiated, pushing bioenergy closer to profitability (when compared to natural gas). However such reductions in prices have not been documented so far.

4.5. Formulation of hypotheses on bio-heat potential demand in the UK residential sector (2004 basis) Based on the results of the qualitative and quantitative assessments in each market segment of the UK residential heat sector, the technical and economic potentials can be estimated in the three different scenarios defined. The results are presented in Table 11. In the conservative scenario, the technical potential is about 27 TWh yearly (i.e. 6% of the market), because of the strong technical constraints posed by space availability,

Table 6 – Key characteristic of the residential heat sub-segments tested by the quantitative model Market segments characteristics

A

B

B*

C, D, E, F

C*, D*, E*, F*

G

G*

Micro Heat-only 10 kW

Small Heat-only 50 kW

Medium Heat-only 1500 kW

Large CHP 10 MWe/ 30 MWth 65% Woodchips

Displaced fossil fuel

30% Woodchips/ pellets/bio-oil Natural gas/oil

Medium CHP 500 kWe/ 1.5 MWth 40%/65% Woodchips

Large Heat-only 10 MW

30% Woodchips/ pellets/bio-oil Natural gas/oil

Small CHP 100 kWe/ 200 kWth 60% Woodchips

Debt level ROC eligibilityb Emissions trading Displaced plant type

0% No No Heat-only

0% No No Heat-only

Scale Plant type Size Capacity factora Biomass fuel

Natural gas/oil 0% Yes No Heat-only/ CHP

40%/65% Woodchips/ pellets Natural gas/oil 50% No No Heat-only

Natural gas/oil 50% Yes No Heat-only/ CHP

65% Woodchips/ pellets Natural gas/oil 50% No No Heat-only

Natural gas/oil 50% Yes Yes Heat-only/ CHP

a The capacity factor here corresponds to the ratio of the energy generated during the year by the (installed capacity  8760 h). In the case of CHP, both the thermal and the power capacity factors are equal. b ROC: Renewable Obligation Certificate.

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Table 7 – Summary of main assumptions used in the quantitative model, for parameters independent from the market segment considered Parameter name

Unit

Value

Notes on data source/rationale

Nb of years

15

Nb of years

0

Debt payback time/debt term Depreciation lifetime

Years

15

Years

15

Disbursing time of equity investment Cost of debt

Years

1

%

8%

Beta of the industry/ indicator of systematic risk

Ratio

0.64

Market risk premium

%

4.8%

Risk free interest rate

%

4.4%

Price of ROCs

GBP MWh1

35

Price of LECs

GBP MWh1

3.5

GBP tCO1 2 avoided

13.2

This assumption is in line with the other studies on bioenergy competitiveness [2,54] Has an influence on the cash flow discounting, by shifting the debt and equity recovery. Here we simplify the analysis at first by choosing 0 A common assumption is to consider the economic life of the plant/boiler, the debt term and the depreciation lifetime equal A common assumption is to consider the economic life of the plant/boiler, the debt term and the depreciation lifetime equal Has an influence on the cash flows. Here we assume that all equity is disbursed in year 0 Corresponds to the interest on debt. We assume here it is 8%, in line with the Bank of England rates in 2005–2006 This indicator is available for different industries in various countries on Damodaran online webpage [55]. Here the ‘‘Energy-Alternate Sources’’ value in the betaEurope.xls file is used (Unlevered Beta corrected for cash) This indicator is available for different countries on Damodaran online webpage [55]. Here the UK value in the ctryprem.xls file is used This rate can be determined via the interest rate of government bonds of a length equivalent to the investment useful life. Here we use the Annual Average Yield from British Government Securities, 10-year nominal par yield of 4.4% (value at 31/12/ 2005, available on the Bank of England website [56]) Average price obtained in the non-fossil fuel obligation auction done in October 2006 [57]: 35 GBP/MWh Based on Green Energy website [58], which suggests a value of 3.5 GBP/MWh of power 20 EUR/tCO2 is commonly used for accounting purposes in the industry (and converted into GBP) (based on interview by authors [49])

Economic life of the biomass plant/boiler Construction period length

Price of carbon credits

Table 8 – Economic parameters for biomass and alternative fuel plants Parameter name Biomass plant economic parameters Biomass heat-only woodchips Plant thermal conversion efficiency Unitary investment costs (including equipment, installation and other) Annual O&M costs as a percentage of the total investment costs Biomass heat-only pellets Plant thermal conversion efficiency Unitary investment costs (including equipment, installation and other) Annual O&M costs as a percentage of the total investment costs Biomass heat-only bio-oil Plant thermal conversion efficiency Unitary investment costs (including equipment, installation and other)

Unit

Value

Notes on data source/rationale

%

85%

Based on literature review and interviews by the authors 2006

GBP kW1 th

200–685

Based on literature review and interviews by the authors 2007

%

2–3%

Average values from case studies in Europe, including the cost of staff for large plants

%

88–92%

Based on literature review and interviews by the authors 2006

GBP kW1 th

140–480

Assumed to be 30% less investment costs than for woodchips technology

%

2–3%

Assumed identical than for woodchip technology

%

85%

GBP kW1 th

52.5–157.5

In this case it is assumed that the equipment is the same than for heating oil equipment Minor changes are needed to adapt the boiler to bio-oil. It is assumed that the capital costs are only +5% of what a heating oil boiler costs

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Table 8 (continued )

Parameter name Annual O&M costs as a percentage of the total investment costs Biomass CHP-solid combustion Plant thermal conversion efficiency Plant electric conversion efficiency Unitary investment costs (including equipment, installation and other) Annual O&M costs as a percentage of the total investment costs Selling price of electricity to the grid Alternative plant economic parameters Heat-only natural gas Plant thermal conversion efficiency Unitary investment costs (including equipment, installation and other) Annual O&M costs as a percentage of the total investment costs CHP-Natural Gas Plant thermal conversion efficiency Plant electric conversion efficiency Unitary investment costs (including equipment, installation and other) Annual O&M costs as a percentage of the total investment costs Heat-only oil Plant thermal conversion efficiency Unitary investment costs (including equipment, installation and other) Annual O&M costs as a percentage of the total investment costs

Unit

Value

Notes on data source/rationale

%

1.5–2.3%

It is assumed that the O&M costs are the same than for a heating oil boiler

%

40–60%

Based on literature review and interviews by the authors 2006

%

16–25%

Based on literature review and interviews by the authors 2006

GBP kWe1

1467–1800

Data based on literature review for different sizes and technologies by the authors 2006

%

4–10%

Average values from case studies, including additives, staff, global service

GBP MWh1

40–60

Average values of electricity sold to the grid in UK (local electricity distributor or NGT)

%

88–94%

Based on literature review and interviews by the authors 2006

GBP kW1 th

50–120

Based on discussion with DTI and Ashden Awards seminar briefing paper [59]

%

1.5–2.3%

Assumed to be 25% less than for biomass heat only plants

%

45–55%

Based on literature review and interviews by the authors 2006

%

25–40%

GBP kWe1

400–1500

Median value of different technologies relevant to the scale considered and the fuel chosen among: diesel engine, natural gas engine, steam turbine, gas turbine, and micro-turbine Based on IPA Energy Consulting study [60] which cites a 2000 survey of cogeneration European manufacturers

%

3–7.5%

Assumed to be 25% less than for biomass CHP with gasification or solid combustion technology

%

85%

GBP kW1 th

50–150

Based on DTI study on the economics of biomass [2], published along the UK biomass strategy Based on DTI study on the economics of biomass [2], published along the UK biomass strategy

%

1.5–2.3%

Assumed to be the same than for gas heat-only boiler

Table 9 – Economic parameters for biomass and alternative fuels Parameter name Biomass fuel parameters Woodchips Moisture content Biomass lower heating value (LHV)

Unit

Value

Notes on data source/rationale

%

30%

MJ kg1

12

Based on Carbon Trust study [54], and interviews with stakeholders Calculation based on moisture content, based on data from the European Biomass Industry Association website [61]

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Table 9 (continued )

Parameter name

Unit

Value

Notes on data source/rationale

GBP t1

60

Assumptions based on various data from National Energy Foundation website [62], the Carbon Trust study [54], and interviews with stakeholders

%

11%

Biomass lower heating value (LHV)

MJ kg1

16

Unitary biomass cost

GBP t1

150

Based on Carbon Trust study [54], and interviews with stakeholders Based on data from European Biomass Industry Association website [61] Assumptions based on various data from National Energy Foundation website [62], the Carbon Trust study [54], and interviews with stakeholders

MJ kg1

37

GBP t1

300

GBP MWh1 GBP MWh1

17.6–28 13.6–16.5

Data from Table 5.10.1-45.10.3-data 2005 [64] Data from Table 3.1.4-data 2005 (excluding climate change levy) [64]

GBP MWh1

10.15

Data from Table 3.2.1-data 2005 [64]

GBP MWh1 tCO2 MWh1

1.5 0.22

HM Customs and Excise website [65] Based on the Carbon Trust study on the economics of bioenergy [54]

GBP MWh1 GBP MWh1

35 20.5–24.1

GBP MWh1

21.7

Based National Energy Foundation website [62] Data from Table 3.1.4-data 2005-average of data for Gas Oil and Heavy Fuel Oil (excluding climate change levy) [64] Data from Table 3.2.1-data 2005 [64]

GBP MWh1

0

tCO2 MWh1

0.3

GBP MWh1

68.8–106

GBP MWh1

36.3–47.4

tCO2 MWh1

0.43

Unitary biomass cost

Pellets Moisture content

Bio-oil (vegetable oil)a Biomass lower heating value (LHV)

Unitary biomass cost

Alternative fuel parameters Gas-displaced fuel price Status of the gas buyer/energy producer: domestic Status of the gas buyer/energy producer: non domestic (i.e. commercial or industrial, or public and tertiary sector excluding major power producers) Status of the gas buyer/energy producer: non domestic/major power producer CCL rates (not adjusted for inflation) CO2 emission factors for alternative fuel

Oil- displaced fuel price Status of the oil buyer/energy producer: domestic Status of the oil buyer/energy producer: non domestic (i.e. commercial or industrial, or public and tertiary sector excluding major power producers) Status of the oil buyer/energy producer: non domestic/major power producer CCL rates (not adjusted for inflation) CO2 emission factors for alternative fuel

Electricity- from UK grid Status of the electricity buyer/energy producer: domestic Status of the electricity buyer/energy producer: non-domestic (i.e. commercial or industrial, or public and tertiary sector excluding major power producers) CO2 emission factor for power brought from the UK grid

Based on data on the calorific value of different vegetal bio-oils (olive oil, palm sterine, refined palm oil, etc.) which can be used as bio-diesel [63] Assumption of about 450 EUR t1 as average value of bio-diesel, which computes to about 12 EUR GJ1, in line with the values for different biodiesel on the European Biomass Industry Association website [61]

HM Customs and Excise website 2006 (oil is not taxable for levy purposes) [65] Based on Carbon Trust study on the economics of bioenergy [54]

Data from table 5.6.1-45.6.3-data 2005 (including taxes) [64] Data from Table 5.4.1-45.4.4-data 2005 (including taxes) [64] The Carbon Trust website mentions this as the value used by DEFRA to ensure a consistent base on which to measure savings [66]

a

The bio-oil considered here corresponds to crude bio-oil from vegetable oleaginous crops (such as olive oil). It is not pyrolysis oil (which could be another biomass option but was excluded from the analysis at this stage). The price considered is only indicative and will be highly affected by the cost variations of raw biomass, while the LHV is similar to that of bio-diesel.

heat/power ratios in CHP segments and supply logistics. The economic potential is about 12 TWh yearly, because of the large share of heat coming from natural gas at present, whose

displacement by biomass is often very costly. In total the conservative potential demand for residential bio-heat is about 2.6% of the corresponding market.

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Fig. 7 – Variation of profitability index for various biomass chains in branch A—micro scale heat-only plants.

In the middle scenario, the technical potential is much larger (about 252 TWh) mainly due to the assumption that space availability becomes a showstopper constraint in only half of the urban installations in the three biggest market segments. The economic potential is also larger than in the conservative case but the displacement of natural gas by biomass is still largely unprofitable in the three largest market segments, limiting the total potential demand to about 40 TWh (or 8.3% of the market). In the optimistic scenario, the technical potential approaches the maximum theoretically possible and reaches about 370 TWh. Such large technical potential is linked to the fact that the technical constraints are assumed to be overcome in most installations even in urban dwellings. The economic potential is much higher as well (about 145 TWh or 30.5% of the market). This is mainly due to the assumption that with appropriate incentives or information, 50% of natural gas installations in branches A, B and D would have operating costs high enough for people to switch to biomass installations (which in the present conditions remains very optimistic).

4.6.

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and ‘‘middle’’ cases. In practice the penetration of bio-heat will also be influenced by the key organisational factors. While this analysis has not been done for this paper, it will be developed in the next steps of the research. Because of the present UK residential heat market structure, the most significant limitations on expansion occur when the three largest market segments (corresponding to decentralised micro, small and medium heat-only boilers) are positively or negatively affected by the key factors. In order for the technical potential to be significantly larger (and reach 50% of the market), policies would need to target the development of biomass technologies not only adapted to rural settings, but also urban-friendly systems where space requirements are minimal. In these conditions, pellet boilers would seem primary candidates to be promoted for driving the expansion of the bio-heat sector in the residential market as long as enough space is available. Another technical option (although not investigated in this paper) could be to use biogas injected in the natural gas grid in a natural gas boiler. In addition, significant increases in the economic potential could only occur if the competitiveness of bioenergy against natural gas were targeted. In testing the model’s sensitivity, we see that by managing to make half of natural gas installations economically attractive for users to switch to biomass, the economic potential might increase very strongly from 10% to 30% of the market. Such a change in the heating sector could be reached with a sharp rise in the prices of fossil heating fuels and electricity and/or by a financial support programme. Because the profitability gap is the highest for the displacement of natural gas (when compared with other fossil fuels), however, it is likely that making biomass profitable in such cases would incur high costs of design and implementation. Finally, if the market structure were to change in the near or long-term future, the estimates of potential would change as well. In particular, the attractiveness (both technically and economically) of district heating appears an unexploited potential in the UK context. Policies could promote the development of district heating and cogeneration in urban areas of the UK to increase the bio-heat potential. Because of the low number of planned new-build, however, most of the growth would have to take place in retrofit installations. This seems unlikely, except if micro-CHP technologies were to develop strongly, both technically and economically. Assuming most of the newly build were to use district heating and/or CHP by 2020, the UK heat market structure remains very similar to that of the present, making the penetration of bio-heat very difficult. While the approach presented here enables the effect of policies to be examined qualitatively, no indication of overall costs of policy interventions is provided in this paper. Policy implications are to be assessed in more detail and depth in a further stage in the research.

Discussion of the results

Our results of potential bio-heat demand for the UK are in line with the FES study [3], where the technical and economic potentials by 2010 are estimated to be respectively of 174 and 35 TWh. This places the FES study between our ‘‘conservative’’

5.

Conclusions

In this paper, we have presented a new and systematic framework for analysing potential bioenergy demand via

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Table 10 – Summary results of quantitative analysis for the UK residential heat sector Branch

Alternative fuel

Profitability index performance Besta (biomass fuel)

A B C D E F G A* B* C* D* E* F* G*

a b c

Oil Natural Oil Natural Oil Natural Oil Natural Oil Natural Oil Natural Oil Natural Oil Natural Oil Natural Oil Natural Oil Natural Oil Natural Oil Natural Oil Natural

Gas Gas Gas Gas Gas Gas Gas

Biomass price change to reach PI ¼ 1

Worstb (biomass fuel)

1.31 (bio-oil) 0.17 (pellets) 0.36 (bio-oil) o0 1.31 (bio-oil) 0.26 (pellets) 0 (woodchips) o0 0.53 (woodchips) o0 0.11 (woodchips) o0 1.21 (woodchips) 0.45 (pellets) o0 o0 0.71 (woodchips) o0 0.05 (woodchips) o0 1.98 (woodchips) 0.69 (pellets) o0 o0 0.59 (woodchips) o0 o0 o0 Not Available (not modelled)

– 30% – At least 50% 30% At least 50% – 50% (woodchips) 15% 45% – 40% (woodchips) 15% 45% – – – 45% 30% 50% – 50% – 30% – 15% – –

Gas Gas Gas Gas Gas Gas Gas

1.1 (woodchips)c 0.45 (woodchips)c 0.68 (woodchips)c 0.44 (woodchips)c 1.19 (woodchips)c 0.41 (woodchips)c 1.06 (woodchips)c 0.69 (woodchips)c 2.11 (woodchips)c 0.87 (woodchips)c 1.18 (woodchips)c 1.05 (woodchips)c

By ‘‘best’’ it is meant ‘‘the highest result in terms of PI’’ among the different biomass options (woodchips, pellets, bio-oil). By ‘‘worst’’ it is meant ‘‘the lowest result in terms of PI’’ among the different biomass options. There is only one value because only the case of woodchips was modelled for CHP options.

Table 11 – Results of the technical and economic potentials by market segment (in GWh) market segmentation analysis. We have also successfully demonstrated and tested the approach through an empirical application to the 2004 residential heat sector in the UK. Our analysis assumes that not all demand segments of a market react in the same way to a given policy and economic environment. We have shown it is possible to divide the market into relevant segments, which within a segment, are homogeneously impacted by a series of identified key factors to bioenergy uptake, but react differently from another segment to the same list of key factors. Based on qualitative and quantitative assessments, it is possible to describe how the segments exhibit different levels of technical, economic and organisational potentials of bioenergy penetration (on the demand side). We consequently formulated hypotheses about the potential bioenergy demand which include greater scrutiny of diverse market segments in sectoral markets. In addition, our approach has highlighted how policy instruments can differentially influence the uptake in certain market segments by tackling specific drivers

Branch

Technical potential (GWh)

Conservative scenario A B C D E F G A* B* C* D* E* F* G* Total % of total market

Economic potential (GWh)

13,419 8424 535 1774 535 535 1593 0.0 0.0 0.0 0.0 16 16 267

6710 4212 0.0 887 0.0 80 0.0 0.0 0.0 0.0 0.0 0.8 0.8 267

27,115

12,158 2.6%

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Table 11 (continued )

Branch

Technical potential (GWh)

Economic potential (GWh)

140,902 88,454 535 18,629 535 535 1593 0 9 16 20 24 24 401

22,122 13,887 0.0 2925 40 80 119 0.0 0.5 0.0 1.0 1.2 12 401

251,676

39,590 8.3%

207,999 130,575 535 27,499 535 535 1593 0.0 14 24 30 31 31 535

120,431 19,586 40 4125 80 80 239 0.0 0.7 0.6 1.5 31 31 535

369,936

145,182 30.5%

Middle scenario A B C D E F G A* B* C* D* E* F* G* Total % of total market Optimistic scenario A B C D E F G A* B* C* D* E* F* G* Total % of total market

Note: Present penetration of the residential heat market by biomass is less than 1%.

or barriers. As noted, however, at this stage in the research the analysis of policy implications is still largely qualitative. We intend to develop further the framework presented here, partly through on-going research within the TSECBIOSYS project. Plans include (inter alia) the application of the framework to other sectoral case studies, in particular the public and commercial, and the industrial sectors in the UK, but also the residential heat sector of another European country. The outputs of the framework will also be examined (and tested) by using a normative approach based on a UK-MARKAL model which focuses on the heat sector.

Acknowledgements The UK Natural Environment Research Council (NERC) is supporting the 3.5 year TSEC-BIOSYS academic research

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project: ‘‘A whole systems approach to analysing bioenergy demand and supply: Mobilising the long-term potential of bioenergy.’’ (Grant reference number: NE/C516279/1: http:// www.tsec-biosys.ac.uk/).

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