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The Impacts of Distributed Generation on the Wider UK Energy System – Extension of the Project

Final Report to C.E.O.S.A., Defra

Restricted CommercialED 43397Issue Number 1Date 16th April 2008

Restricted – Commercial The impacts of distributed generation on the wider UK energy systems-Extension ProjectAEA/ED43397/Issue 1

ii AEA Energy & Environment

Restricted – Commercial The impacts of distributed generation on the wider UK energy systems-extension projectAEA/ED43397/Issue 1

Title Extension of the project - the impacts of distributed generation on the wider UK energy systems

Customer Defra, C.E.O.S.A.

Customer reference

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This report is the Copyright of the Department for Environment, Food and Rural Affairs (Defra) and has been prepared by AEA Technology plc under contract to Defra. The contents of this report may not be reproduced in whole or in part, nor passed to any organisation or person without the specific prior written permission of Defra. AEA Technology plc accepts no liability whatsoever to any third party for any loss or damage arising from any interpretation or use of the information contained in this report, or reliance on any views expressed therein.

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Restricted – Commercial The impacts of distributed generation on the wider UK energy systems-Extension ProjectAEA/ED43397/Issue 1

Executive summary

There has been growing interest in distributed, embedded and micro-generation over recent years, and this subject was a major theme in the Government’s Energy Review1 and the recent Energy White Paper. While the Energy Review recognised that the current centralised model for electricity generation and gas supply delivers ‘economies of scale, safety and reliability’, it also proposes that a ‘combination of new and existing technologies are making it possible to generate energy efficiently near where we use it, potentially delivering lower emissions, increased diversity of supply and in some cases lower cost’.

One of the distributed energy technologies that is closest to the market, and that has significant potential for development in the next few years, is micro-CHP (micro Combined Heat and Power). This is where a typical domestic boiler would be replaced by an integrated system, which can produce electricity as well as heat for the home. Large scale CHP systems are well established and understood, but the smaller CHP systems have not yet been exploited to their full potential, for a mixture of reasons which include the early state of development of the smaller scale technology and difficulties in selling any excess electricity back to the grid. As well as domestic applications, there is also potential to use this technology in small commercial applications where there is a significant heat demand. The objective of this research is to extend an earlier 2007 evaluation of the implications of a substantial number of distributed generation (DG) units on the UK’s electricity and gas networks, with a particular emphasis on micro-CHP. The 2007 analysis reported in the AEA/Carbon Consortium report ‘The Impacts of Distributed Generation on The Wider UK Energy System’, March 2007, flagged up a concern about the robustness of the available data, with a recommendation that the model used in the evaluations be re-run as more robust end-use information emerged; with the most useful release of data anticipated to come from the Carbon Trust (CT) field trial.

This updating work also includes estimates of carbon savings from mini-CHP in larger office / commercial premises; additional systems of 50-100kWe compared with the ‘cut-off’ limit of 50kWe in the initial 2007 study.

In order to understand the potential impacts of micro-CHP technology, we have developed and extended models that allow us to understand the cost effectiveness of these systems, the potential carbon savings (compared to central generation) that they can deliver and their likely impacts on the existing electricity and gas networks. The analysis that we carried out relies on three modelling/data sources:

An end-use demand model – the AEA CHP cost and energy and emissions savings model for CHP, which has been adapted for domestic sector use and small scale commercial sector applications;

A national electricity supply model – input from a BERR Markets model;

Quantitative assessments of the low voltage distribution system add-on costs required by higher levels of micro-generation.

The extension of the study to include the trial data from the Carbon Trust field trials, has shown that out of the 33 sites for which cost and carbon savings analysis could be carried out, only approximately half show carbon savings and these are well below the levels shown for the SIAM Report domestic profiles, which were used in the original 2007 evaluation. However, it must recognised that the situation into which the system is installed and the way it is operated is critical in determining costs to the user and in delivering carbon dioxide savings.

1 The Energy Challenge. Energy Review Report. BERR. 2006.

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The range of situations trialled in the CT assessments, clearly include domestic situations where micro-CHP cannot be effective. It is concluded that the efficiency of the micro-CHP application is strongly dependent on, not just overall heat demand, but also on the demand profile and the delivered heat to power ratio.

There is further evidence that the time of operation of the micro-CHP (determined by the time of the heat demand) can coincide with peak electricity demand from the grid, which tends to be the most expensive electricity. A significant number of installed micro-CHP systems would help to reduce these peaks and reduce the price spikes at these times. We have factored these effects into our ‘total system cost’ benefit calculations.

The main conclusions from this study are:

1. The overall results from the cost-effectiveness and carbon saving calculations for micro-CHP installations (domestic and small commercial) suggest the technology will not be cost effective to the consumer; and saves carbon in the (small) commercial sector applications and in about half of the domestic applications. When the benefits accruing for central generators are also allowed for, there are ‘total system’ cost improvements, but positive costs remain (i.e. technologies remain not cost effective) for domestic sector applications and most commercial situations.

2. The net present values (NPV) for domestic micro-CHP installations derived from the CT data all show values that are above +£1,300, with an overall average of +£2,000, indicating further losses above the marginal cost of the technology (£1,500). Capital support could in some cases change non-cost effective investments to cost-effective; however, for the majority of applications, the revenue stream will always be negative. This indicates that this technology would require ongoing support if it is to be made cost effective for the consumer i.e. a guaranteed export electricity tariff at higher prices than those for imports.

3. For mini-CHP systems (33 kWe up to 75 kWe engines) for the commercial sectors, the highest NPV value is typically +£60,000 to +£70,000 (i.e not cost effective). At a typical installed cost at about this value (£65,000 to £72,000), the level of support would need to be very high to make these applications cost-effective.

4. Alternatively, increasing the export price for customers (up to 80 per cent of the import price) slightly improves the domestic micro-CHP application’s cost-effectiveness (but still remain +ve). At this level of export price, the commercial applications of mini-CHP become slightly more cost effective, but only the applications in health sector shows a -ve NPV (i.e cost effective), with applications in the medium to large health sector buildings showing an NPV of about -£72,000.

5. Estimates of total UK system cost changes include the costs (or benefits) for the customer installing the micro-CHP units, the benefits for central generation and the likely costs for local network operators. We conclude that for an uptake of domestic micro-CHP at both the 5 and 10 million homes level, and using the average performance of micro-CHP from the CT trials, the total net cost is positive (i.e. there are overall increases in national costs), although at the 5 million homes level the total costs are less.

6. Approximately 50% of the CT trial applications save carbon and the average performance would provide a total lifetime carbon saving of 7.1 MtCO2 at the 10 million home penetration level (by 2022). This would be at a total cost to the UK in excess of £2,000/tCO2 saved.

7. For the commercial sector, whilst central generation cost savings help to balance the large CHP operating losses, total system net costs all remain positive under baseline conditions.

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Table of contents

1 Introduction 1

1.1 Technical background 2

1.2 Methodology 2

2 Past results 42.1 Domestic micro-CHP 4

2.2 Commercial sector micro-CHP 4

3 Key tasks in the update 54 Development of scenarios 9

4.1 Description of scenarios adopted 9

4.2 Technology data 10

4.3 Domestic sector data 11

4.4 Carbon Trust data 12

4.5 Service sector data 15

4.6 Latest fuel price projections 17

4.7 Export electricity prices 20

4.8 Electricity Prices and Embedded Benefit 23

4.9 Central generation carbon emissions factors - averages 23

5 Results – CT trial domestic sector 256 Results - commercial sectors 327 Total system energy savings: end-users and generators 37

7.1 End-user fuel changes 37

7.2 Central generation fuel use and total system energy changes 37

8 Total system cost changes 398.1 Domestic sector 39

8.2 Commercial sectors 43

9 Overall conclusions 46

AppendicesA. Models and other analysis adoptedB. Additional results - commercial sectorsC. Why are there some good micro- CHP applications in the CT trial

sample?

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1 Introduction

The objective of this research is to extend the 2007 evaluation of the implications of a substantial number of distributed generation (DG) units on the UK’s electricity and gas networks, with a particular emphasis on micro-CHP. The earlier analysis reported in the AEA/Carbon Consortium report ‘The Impacts of Distributed Generation on The Wider UK Energy System’, March 2007, flagged up a concern about the robustness of the available data, with a recommendation that the model used in the evaluations be re-run as more robust end-use information emerged; with the most useful release of data anticipated to come from the Carbon Trust (CT) field trial.

Following meetings with the Carbon Trust and DEFRA, the further actions and requirements were identified:

The need to obtain better information, from the CT field trials, on heating and electricity profiles from houses with micro-CHP units.

The Carbon Trust to supply 5-minute profiles to AEA (as specified by AEA for their modelling work), together with details of the housing (e.g. house size, date of construction, details of state of insulation).

The Carbon Trust to also supply a provisional figure for the working efficiency of condensing boilers (a better estimate than that used in the AEA modelling work).

AEA/Carbon Consortium to review and, as necessary, revise the modelling work and the carbon factors for electricity used by Defra, SAP, CERT etc.

To be aware of the need to conserve confidentiality, and to agree an approach between AEA and the Carbon Trust.

Other issues:

The updating work is to also include estimates of carbon savings from CHP in larger office / commercial premises (additional systems of 50-100kWe compared with the ‘cut-off’ limit of 50kWe in the initial 2007 study). There is a requirement to check the status of ‘best practice’ data for large office / commercial premises with BRE. This is required to allow an update of the size and demand profile data used in the earlier study. In the event of such data not being available from BRE, we are to use our previous estimation method to extend the service sector size range up to 100kWe. In practice, this latter approach has proved necessary.

This update report will be used for the Foresight project; noting that the reporting date for the Foresight Project has been changed to late 2008.

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1.1 Technical background

The term Distributed Generation (DG) covers the heat and electricity generated at, or near to, its point of use. These are typically small installations; and are also sometimes referred to as de-centralised energy systems or micro-generators. In the context used in this study, we have been asked to extend micro-generators at the lower end of the range (up to 100kWe), which are intended for installation in domestic (~1kWe) and small commercial premises2.

The main focus of this report is on micro-CHP applications used in a domestic or small business premises situation to provide heat for use on site and electricity, which can either be used on site or exported to the electricity grid.

A sizeable amount of work has already been published on Distributed Generation systems, both on the projected technology uptake rates and the likely barriers and issues that surround the practicalities of connecting Distributed Generation systems to the electricity grid. The issues and assumptions published in these reports have been evaluated and considered as part of the original AEA study; and a summary of these reports was provided in Appendix 1 of that report.

1.2 Methodology

The existing AEA CHP model and the macro-models developed by the Carbon Consortium3 have been used to assess the implication of the changes derived from three components:

Costs (or benefits) for the consumer i.e. the customer installing the micro-CHP system. These benefits are calculated using the AEA CHP model.

The costs or benefits for central generation, who will produce less electricity overall and will not have to meet peak demands as in the base case. These profile changes by season, are described further in Appendix A - Models and other analysis adopted. Our current measure of these benefits is through the electricity wholesale price calculation, which shows a significant reduction as micro-CHP generation increases. In our total system cost change calculation, we have carried out a ‘net present value’ calculation in which we discount the annual changes in future years total wholesale value (the total for all generation) compared with the base case (no micro-CHP) total wholesale value.

The likely costs for local network operators, with a net present value based on future discounted costs of future network re-enforcements and changes required to accommodate increasing levels of micro-CHP. In this case the cost data used is from the SIAM report and others, as described in our February 2007 report.

We have used modified electricity generation profiles to reflect the effect of different levels of penetration of micro-CHP on the central dispatch of electricity. The DTI (now BERR) ran their markets model to identify, amongst other things, the wholesale clearing price of electricity and implied generator emissions variations. This was done for dispatch profiles at 2006 and projected out to 2020. In the current work we use the same modelling data4.

We consider the total system energy savings, which includes energy savings at central generation - considering both gas and coal fuel demand changes, coupled with the net effect of increased energy use compared with boilers from the application of micro-CHP. The analysis also factors-in the effect of daily emissions generation profile changes in the emission savings calculations. In our calculations we derive the cost components and total savings for two scenarios of domestic micro-CHP uptake (5 and 10 million homes by 2021). We assess the lifetime carbon saving together

2 In the UK, a micro-CHP plant is usually a CHP plant with a capacity of 5kWe or less (this is in contrast to the EC Cogeneration Directive where a micro-CHP is defined as <50kWe).3 We also include an Appendix on modelling procedures, which has been reproduced from the original 2007 report. 4 We have however, adjusted the emission factor profile to match the average annual value used by government in other policy assessments see later section 4.9.

with the CHP net present value, both derived from the AEA model. The central generation carbon savings are derived from the BERR electricity model and the generator NPV savings are calculated via a spreadsheet discounted cash flow (DCF) model (a 3.5% DCR is assumed). Network costs have been taken from the literature, and in the higher penetration case, have been assumed to be at the upper level of figures quoted. For the 5 million home penetration scenario and other ‘lower total electricity generation’ scenarios5 the low quoted network costs have been assumed.

5 As derived from the Carbon Trust field trial data.


2 Past results

2.1 Domestic micro-CHP

The February 2007 results suggest that:

For both cases of 5 and 10 million homes by 2021, the total net cost is positive (i.e. there are overall increased costs) although at the 5 million homes level, the total increased costs are less;

The overall cost effectiveness over the period 2006 to 2021 is + (plus) £81/tCO2 for the 10 million home penetration scenario and + (plus) £69/tCO2 for the lower penetration scenario;

For the micro-CHP alone, the cost effectiveness, again to 2021, derived from the AEA model is significantly less attractive at +£196/tCO2. In the overall cost effectiveness calculation, the savings in central generation improve the balance.

We also assessed the effect of improving the value of the exported electricity to a price equivalent to 80% of the import price. Under these circumstances the total net cost benefit becomes negative and the overall cost effectiveness is – (minus) £28.5/tCO2 saved.

The same set of ‘scenario’ outputs are produced in the updating work.

2.2 Commercial sector micro-CHP

The results for the commercial sector applications (less than 50 kWe) demonstrated that in only one case (medium health) the total system net costs are negative; with the overall cost effectiveness (to 2021) range from between – (minus) £24/tCO2 for the health sector up to +(plus) £81/tCO2 for education. Central generation savings (both cost and carbon) tend to balance the CHP losses. At a higher export price scenario6 all of the medium 33kWe CHP examples become more cost effective (a -ve NPV) even at lower assumed efficiency values.

6 See the original report Section 2 for a description of the scenarios used in the AEA micro-CHP model runs.

3 Key tasks in the update The objective of this updating work is to:

Introduce better information on heating and electricity demand profiles from houses with micro-CHP units, using real performance data from the CarbonTrust Micro-CHP field trials;

Include estimates of carbon savings from CHP in medium to larger office / commercial premises (suitable for CHP in the size range of 50-100kWe).

The following summarises the main tasks in the updating work:

3.1 Task 1. Define and analyze the heating and electricity profiles data from the Carbon Trust trials

On the basis of earlier meetings with Carbon Trust and a further assessment of our model requirements, we defined the data requirements from the CT trials as shown below.

THE METER / SENSOR DATA COLLECTED -CT trials data readings every 5 mins

FREQUENCY and COVERAGE -reading every 5 mins

Data required marked yellow/bold Value Units Source CommentElectricity generated by the engine Wh Meter All 30 sites To be grouped by AEA into sub-groups/ technologies etc Electricity used by the engine Wh Meter Daily 5 min data Heat Out Wh Meter Whole weeks (7 days worth): These cover the DTI daily electricity dispatch model data: Domestic Hot Water flow (in some Litres Meter 1. WC 16th April 2007 (16th to 22nd) 20th AprilExternal temperature Celsius Remote 2. WC 23rd J uly 2007 (23rd to 29th) 27th J ulyUpstairs Temperature Celsius Remote 3. WC 25th Sep 2006 (25th to 1st Oct) 28th SeptLiving room Temperature Celsius Remote 4. WC 11th Dec 2006 (11th to 17th) 13th DecFlow Temperature Celsius SensorReturn Temperature Celsius SensorStorage tank temperature (in some Celsius Sensor OTHERCold water feed temperature (in some Celsius SensorStorage tank heat (in some properties) Wh MeterFlue gas temperature Celsius SensorGas into property Litres MeterGas used by engine Litres MeterElectricity into property Wh MeterElectricity exported from property Wh Meter

Require best estimate of the operating efficiency of condensing boilers

The key requirements are shown shaded in the above table.

The analysis of this data has enabled us to provide the necessary inputs to the suite of mathematical models used in the micro-CHP evaluations for the domestic sector.

The selected weekly data sets (four seasons, seven days including weekends) from the Carbon Trust field trial data logs, cover the BERR daily electricity dispatch model data sets for the typical days of:

20th April 27th July 28th Sept 13th Dec

The data from 42 applications have been analysed by AEA and attempts were made to group these by house type sub-groups or on the basis of performance and demand variations. It became clear that


such groupings are not really possible (as described later) and thus sub-group ‘typical’ site model runs were not possible, as no typical profiles could be identified.

We have however, used the within week trial data to provide averaged household operating profiles and to also look at weekend operations.

3.2 Task 2. Define the commercial sector extended coverage and technologies

In order to update the potential uptake rates in the service sector, we requested that Defra check on the status of ‘best practice’ data for large office / commercial premises with BRE. This would allow an update of the size and demand profile data used in the earlier study. However, such data has not been available from BRE; and so we have used our previous estimation method to extend the service sector size range up to 100kWe and continue to use the demand profiles from the AEA ‘potentials model’.

CHP technologies to match the required larger size range of commercial sites have also needed to be identified and entered into the AEA CHP model.

3.3 Task 3. Update the AEA CHP model to account for the range of applications/ technologies arising

This task involved updating the AEA CHP model to include addition end-use areas in both the domestic and service sectors. The model’s application areas and technologies are shown in Table 1 (including the additional areas). A significantly larger number of applications have resulted from the Carbon Trust trial data on domestic application of micro-CHP and the extensions of the service sector to the larger demand areas.

We discuss the required modifications to the AEA CHP modelling approach later in the report.

Table 1. Original and additional application areas and technologies

kWe at Max heat

Average home Elec and Heat Profiles from Siam Report Site A 1.2

Education (Small) Site B 1.2Education (Medium) Site C 33.3Health (Small) Site D 1.2Health (Small-Med) Site E 1.2Health (Medium) Site F 33.3Retail (Small) Site G 1.2Retail (Small-Med) Site H 1.2Hotels/restaurants (Small) Site J 1.2Hotels/restaurants (Small-med) Site K 33.3Offices (Small) Site L 1.2Offices (Small- med) Site M 1.27

7 These sites have been subsequently resized to a 54 kWe CHP application.

Additional applications

kWe at Max heat

Individual CT trial sites 1.2CT trials (average) 1.2CT trials upper Standard Dev limit 1.2CT trials lower Standard Dev limit 1.2Education (Medium-large) Site P 75Retail (Medium-large) Site Q 5.5Hotels/restaurants (Medium-large) Site R 75Offices (Medium-large) Site S 70Health (Medium-large) Site T 75

All sites CT trial data has been analysed individually. However in order to make direct comparisons with the earlier study results, using the original Siam report domestic profiles, we have also identified a ‘CT trials (average)’ and two outliers:

1. A ‘CT trials upper Standard Dev limit’ (the mean value plus the standard deviation of the individual site distributions) and;

2. A ‘CT trials lower Standard Dev limit’ (the mean value minus the standard deviation of the individual site distributions).

3.4 Task 4. Update the scenarios on fuel prices and support scenarios and run the CHP model

The technology cost assessments require projections of the future price of delivered gas and electricity to the consumer, and the likely value to the customer (the supplier) of the exported electricity from the micro-CHP schemes.

The projected future values used in the update are based upon latest price forecasts made by BERR for the Energy White Paper 2007 (for the previous study we used the projections made in May 20068). These have now been updated as necessary using our existing template for the conversion of wholesale prices to delivered, by end-use category.

The support scenario used in the previous work was associated with improving the value of the exported electricity to a price equivalent to 80% of the import price. These export prices also needed to be re-assessed.

3.5 Task 5. Revise the macro cost models and develop outputs

These models consider the total system energy savings, which includes energy savings at central generation - considering both gas and coal fuel demand changes, coupled with the net effect of increased energy use compared with boilers from the application of micro-CHP.

In addition, the ‘total system’ cost model derives the separate components and their values in the net present value total system cost, as shown in the following table. These results are described in Section 8.

8 These were in the process of being updated for the Energy White Paper – Spring 2007.


Table 2: Example ‘total system’ cost data and emissions savings outputs ascribed to: the micro-CHP systems, central generation and the network

Micro-CHP lifetime carbon saving (AEA model)Generator carbon saving at 2021 (savings pa at 2021) Micro-CHP NPV (AEA model)Generator NPV (to 2035)9 Network NPV costs to 202110 Net cost to 203511

CUM generator carbon savings to 203512

Overall cost effectiveness (to 2021) in £/tCO2 saved 13

Micro-CHP cost effectiveness (to 2021) (AEA model) in £/tCO2 saved

We have produced estimates for domestic micro-CHP separately from the small-CHP costs in the commercial sector and have adopted a significant broadening of this analysis with a wider set of demand and technology data.

3.6 Task 6. Review output, cost and implied carbon savings compared with previous analysis plus reporting

This final task makes comparisons between the original analysis and the updated findings to identify any trends that may arise from the Carbon Trust trial data on domestic applications and the updating and widening of the service sector applications.

9 We assume a linear growth in CHP until 2021and therefore the benefits of the installations at 2020 will still be available out to 2035 10 Network investments will need to be made out to 2021.11 Total net costs or benefits will be available to 2035. 12 Cumulative emissions savings will be available to 2035.13 Cost effectiveness (£/tCO2) is defined as the total net cost (lifetime to 2035) divided by the lifetime carbon savings.

4 Development of scenarios4.1 Description of scenarios adopted

The models allow us to build a range of scenarios and to test the individual parameters and their effects on the whole UK system. Those scenarios considered are:

Scenario 1 Central Fuel Price – this is the normal price that the consumer would pay for gas and

electricity, and its assumed that they would receive the wholesale price for electricity sold back to the grid

A 3.5% Discount Rate is used for economic evaluation of installation and operation of the micro-CHP

Current Base - low penetration scenario, where it is assumed that there is no significant uptake of the technology.

This scenario is referred to as Run 2a in the model

Scenario 2 Central Fuel Price, as in scenario 1 above. A 6% Discount Rate is used for the economic evaluation, Current Base – low penetration as in Scenario 1 above This scenario is referred to as Run 2 in the model

Scenario 3 Central Fuel Price, A 10% Discount Rate is used for the economic evaluation, Current Base – low penetration This scenario is referred to as Run 4 in the model

Scenario 4 In this scenario, the consumer pays the normal price or tariff for gas and electricity, and

receives a tariff of 80% of the import electricity price for any electricity exported, A 3.5% Discount Rate is used for economic evaluation, Current Base – Low Penetration This scenario is referred to as run 3a in the model

Scenario 5 Export Fuel Price = 80% of the import fuel price, 6% Discount Rate, Current Base – low penetration This scenario is referred to as Run 3 in the Model

Scenario 6 Central Fuel Price, 3.5% Discount Rate, 5 million installations This scenario is referred to as Run 5a in the model

Scenario 7 Central Fuel Price, 10% Discount Rate, 5 million installations This scenario is referred to as run 5 in the model

Scenario 8 Central Fuel Price, 3.5% Discount Rate,


10 Million Homes This scenario is referred to as run 6a in the model

Scenario 9 Central Price Scenario, 10% Discount Rate, 10 Million Homes This scenario is referred to as run 6 in the model

4.2 Technology data

The following tables show the technology operating and cost data use for the domestic and commercial sector analysis in this study. These have been reviewed and updated for the wider applications to now being considered.

Table 3: Technology operating data

Gross Electricity generating capacity

Typical parasitic

load

Net Electricity generating capacity

Seasonal Gross elect efficiency

(HHV)

Seasonal H:P ratio

kW(e) % of gross kW(e) % gross (HHV)

Solid oxide fuel cell CHP (1kWe)

1.000 3.0%* 0.970 12.18% 5.50

Stirling CHP (Whispergen)(From CT Trial

Samples)

1.200 Avg 2.1%* Avg 1.175 Avg 6.56% Avg 10.70

Reciprocating engine CHP

5.500 3.0% 5.335 25.0% 2.27

Recip. engine CHP

33.300 3.0% 32.300 27.5% 1.50

Recip. engine CHP

54.000 3.0% 52.380 27.0% 1.80

Recip. engine CHP

70.000 3.0% 67.90 31.0% 1.49

Recip. engine CHP

75.000 3.0% 72.75 29.0% 1.73

Recip. engine CHP

90.000 3.0% 87.30 30.0% 1.51

* Since the WisperGen or Fuel Cells would be installed in place of a boiler and not alongside, as would be the case with the engines, and the displaced boiler has it’s own approximately equal parasitic load, the effective parasitic load is reduced to zero.

Table 4: Technology cost data

Gross Electricity generating

capacity

Micro-CHP

package supply

cost

AvailabilityAnnual

maintenance cost

System life

kW(e) £ % £ YearsSolid oxide fuel cell CHP

(1kWe)1 £3,500* 100% Equiv ** £200*** 15

Stirling CHP (Whispergen)

1.2 £2,700 100% Equiv ** £50*** 15

Recip. engine CHP 5.5 £12,000 90% £1,200 15Recip. engine CHP 33.3 £61,200 90% £2,200 15Recip. engine CHP 54 £65,700 90% £4,250 15Recip. engine CHP 70 £67,500 90% £5,800 15Recip. engine CHP 75 £72,300 90% £6,300 15Recip. engine CHP 90 £72,900 90% £7,800 15

* The fuel cell cost is a 2020 cost (2005 money) rather than a current cost to reflect the fact that the cost of fuel cells is likely to drop dramatically before they are used in CHP applications.

** The CHP availability is assumed to be effectively 100% for fuel cells and Stirling engines as they replace the boiler and it is assumed maintenance will only take a few hours during which the house/small commercial building will cool somewhat and when the unit comes back online, it will have to make up for lost heat thus running at a higher output than would usually be the case. It is therefore assumed that the overall effect in energy terms is the same as if the CHP were running continuously. The real availability is expected to be high, in the region of 98% and so any error is very small.

*** Maintenance costs for fuel cell and CHP are marginal because they replace a boiler and associated costs whereas the engines do not.

4.3 Domestic sector data

The original study used domestic electricity demand and micro-CHP generation profiles from the SIAM study data and averaged time tranche figures for the AEA CHP model. An example of the single winter profile used is shown in the following figure; other season profiles used included:

A shoulder (spring/autumn); A summer profile.


Figure 1: Electricity demand and micro-CHP generation profiles (SIAM study data) and averaged time tranche figures for the AEA CHP model - Winter

The rectangular sections superimposed on these profiles show the time tranches that were used in the original AEAT model (in total, three day tranches and two evening tranches).

4.4 Carbon Trust data

An important improvement for the current study is the use of actual household profiles from the Carbon Trust field trials. In total, data from Micro-CHP applications at 42 dwellings have been assessed although in practice, only 33 sets of relatively complete data have been provided.

The following figures show examples of micro-CHP electricity output profiles averaged over the weeks requested and containing the following days for which BERR electricity model dispatch data is available:

20th April - Spring 27th July - Summer 28th Sept - Autumn 13th Dec - Winter

In total 31 of the 33 available sets of data are plotted14 as shown below. These were grouped according to the dwelling’s size and age (Old 3 bed, new 3 bed, old 2 bed, old 4 bed and old 1 bed).

14 Remaining samples – mainly new four bedroom dwellings have not been analysed graphically. Note: Data in not consistently available for all houses over all daily periods.

Figure 2: Old 3-bed electricity generation (kWe) - 10 examples

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Figure 3: New 3-bed electricity generation (kWe) - 12 examples

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It is concluded that no clear patterns emerge for building types; and that it is therefore not possible to make the grouping or categorisations sought in the original programme of work. We have therefore decided that it is better to analyse each property separately, and to develop net cost and carbon savings calculations for each example. In this way, it is possible to look at the spread in individual dwelling results after the modelling runs to assess whether there is any significant difference between the mean values and the spreads obtained within building types.

This produces some interesting results on the inherent variations (costs and carbon saved) depending upon the way micro-CHP has been used.

4.5 Service sector data

We were asked to extend the upper limit of coverage of micro/mini CHP in the service sector from 50kWe (in the original study) to 100kWe.

In order to determine the potential for uptake in the service sector, the previously modelled profiles from the AEA Energy & Environment potentials model have been updated and evaluated in detail for schemes up to 100kWe. The typical electrical demands for suitable sites are presented in the table below.

Table 5: Distribution of the average size of electricity demand per site (kWe) by employment size groupings15 - based on annual average site electricity demand figures and a demand factor of 50%

SectorMicro (1-9

employees)(kWe)

Small (10-49 employees)

(kWe)

Medium (50-249 employees)

(kWe)

Typical demand heat: power

ratioWholesale trade and commission trade, except of motor vehicles 15 91 481 1.66

Retail trade, except of motor vehicles; repair of personal and household goods

5 23 153 0.47

Hotels and Restaurants 12 43 250 1.62

Transport, storage and communication 0.5 2 13 0.24

Offices 1 5 26 1.59

Public administration, defence and compulsory social security n.a n.a n.a 3.10

Education 7 28 113 3.21Health and social work 2 9 32 3.83

The number of sites in each category (below 100kWe) is then listed in the table below and these figures are taken forward into the model for assessing the total system costs and potential impacts.

15 Based on data from the Carbon Trust study: Modelling business sector climate change programme options, Oct 2005.


Table6: Numbers of service sector sites with a below 100kWe electricity demand by employment size groupings

Sector Micro (1-9 employees)

Small (10-49 employees)

Medium (50-249 employees)

Wholesale trade and commission trade, except of motor vehicles 51,500 15,400 >100kWeRetail trade, except of motor vehicles; repair of personal and household goods

128,600 13,900 >100kWe

Hotels and Restaurants 90,700 13,800 >100kWeTransport, storage and communication 35,600 7,900 1,400Offices 231,200 29,300 4,700Public administration, defence and compulsory social security N.A N.A N.A

Education 9,600 2,300 470Health and social work 31,800 17,700 2,300Total services 579,000 100,000 8,400

Figure 7: Total electricity demand (MWe) for varying size ranges of individual site capacities and employment size groupings

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<100 kWe <50 kWe <10 kWe <5 kWe <1 kWe

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(MW

e)

Total potential capacity (MWe)Micro (1-9 employees)Small (10-49 employees)Medium (50-249 employees)Large (250+ employees)

For sites between 0-100 kWe electricity demand, (assumed 50% load factor) and across all service sectors, there is a total demand for 6 GWe16. This falls to about 1 GWe for sites between 0-5kWe, and to only 160 MWe for all sites of less than 1KWe. Micro/Mini CHP (<100 kWe) might therefore have a technical potential of up to 6 GWe (electricity matched). Considering only those technologies of less than 5KWe might provide a potential of 1GWe.

16 This compares with a 5 GWe demand in the original study for sites between 0-50 kWe.

4.6 Latest fuel price projections

The technology cost assessments require projections of the future price of delivered gas and electricity to the consumer and the likely value to the customer (the supplier) of the exported electricity from the micro-CHP schemes.

This section provides data on the updated fuel and electricity prices (based upon the 2007 Energy White Paper assumptions) used in the latest modelling work.

We are assuming that the form of the domestic tariffs for gas and electricity supply remain as at present. The following figures show the historic movements in delivered fuel prices (gas and electricity) back to 1998 and the projections of prices out to 2020. The spread in historic prices is also shown. The values are for direct debit customers17.

The projected future values are based upon the recent price forecasts made by BERR for the EWP (2007) and are compared with values used in the earlier (2007) micro-CHP study. These projections were based on BERR results at May 200618, for the CO2 projections used as the basis for developing the National Allocation Plan for the EU Emissions Trading Scheme, Phase II. The electricity projections have not been published.19

Figure 8: Recent domestic gas prices and projections based on latest information from the BERR EWP modelling work

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17 Source of data: Latest BERR Quarterly Energy Prices Tables. 18 At the time of the original AET report, these projections were in the process of being updated for the Energy White Paper – Spring 2007.19 Private communication in connection with recent work by the Carbon Consortium to make projections of detailed sector CO2 emissions using bottom-up projection modelling.


Figure 9: Domestic gas prices and projections used in the earlier (2007) AEA domestic micro-CHP study

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Figure 10: Domestic electricity prices and projections based on latest information from the BERR EWP modelling work

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Figure 11: Domestic electricity prices and projections used in the earlier (2007) AEA domestic micro-CHP study

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The domestic price projections are made using the BERR wholesale price forecasts as a basis for the year-on-year change to domestic tariffs. There is a significant increase in the prices projected between the May 2006-based results and those from the EWP.

For small commercial applications of micro-CHP, we have been able to use a more detailed approach based on analysis carried out for the Carbon Trust, in which the relationship between wholesale prices and delivered prices to small commercial customers have been modelled.

The following tables summarise the latest (EWP) domestic price data and the projections shown in the figures above.

Table 7: Delivered domestic electricity prices and projections (p/kWh)

< - - - - - - - - - - - - - - - - - - - - - - - - Winter - - - - - - - - - - - - - - - - - - - - - - - >Enter Number of Days in Winter Period 90

< - - - - - - - -Weekdays - - - - - - - - - - > <- -Weekends - ->< - - - - - - Daytime - - - - - > Night Day Night Totals

Period Start Time 5.30 9.00 15.30 22.00 9.00 22.00 Period Finish Time 9.00 15.30 22.00 5.30 22.00 9.00

Hours in Each Period 3.30 6.30 6.30 7.30 16.30 7.30 2005 8.15 8.15 8.15 8.15 8.15 8.15 2006 8.98 8.98 8.98 8.98 8.98 8.98 2007 8.85 8.85 8.85 8.85 8.85 8.85 2008 8.73 8.73 8.73 8.73 8.73 8.73 2009 8.61 8.61 8.61 8.61 8.61 8.61 2010 8.49 8.49 8.49 8.49 8.49 8.49 2011 8.51 8.51 8.51 8.51 8.51 8.51 2012 8.53 8.53 8.53 8.53 8.53 8.53 2013 8.54 8.54 8.54 8.54 8.54 8.54 2014 8.56 8.56 8.56 8.56 8.56 8.56 2015 8.58 8.58 8.58 8.58 8.58 8.58 2016 8.60 8.60 8.60 8.60 8.60 8.60 2017 8.61 8.61 8.61 8.61 8.61 8.61 2018 8.63 8.63 8.63 8.63 8.63 8.63 2019 8.65 8.65 8.65 8.65 8.65 8.65

There are no time-of-day or seasonality effects in the delivered prices for the domestic sector. However, the AEA CHP model uses time tranches in the technology cost calculations, which are set equal in this case. Note however, the move to a half hourly modelling system as described later.


Table 8: Delivered domestic gas prices and projections (p/kWh) –

Winter Spring Summer AutumnDomestic user

2005 1.96 1.96 1.96 1.962006 2.28 2.28 2.28 2.282007 2.01 2.01 2.01 2.012008 2.00 2.00 2.00 2.002009 1.99 1.99 1.99 1.992010 1.98 1.98 1.98 1.982011 1.95 1.95 1.95 1.952012 1.91 1.91 1.91 1.912013 1.87 1.87 1.87 1.872014 1.84 1.84 1.84 1.842015 1.80 1.80 1.80 1.802016 1.81 1.81 1.81 1.812017 1.83 1.83 1.83 1.832018 1.84 1.84 1.84 1.842019 1.85 1.85 1.85 1.852020 1.87 1.87 1.87 1.87

Again there are no time-of-day or seasonality effects in the delivered prices for the domestic sector

4.7 Export electricity prices

The actual value of the exported electricity from micro-CHP schemes will depend upon the time of day (different for ‘business’ and ‘non-business’ days) and the season. Determining appropriate export electricity prices using ‘market clearing data’ is only really relevant for large CHP operators (normally utilities). For small operators, tariff based contracts are normally used. Nevertheless, to determine the overall benefits of distributed generation, the daily varying ‘system average price’ is likely to be the best estimate of the prices displaced by electricity exported; the ‘embedded’ benefit value resulting from local generation also needs to be added.

In the earlier 2007 study, we used the current and projected value of export electricity calculated for the time tranche breakdown used in the AEA CHP model. These profiles were calculated from monthly wholesale price data from the Spectron Power Index website, and using system half hourly profiles for the different seasons provided by Elexon, to assess the average within day, and between season, variations.

In the current analysis, and particularly in view of the significant variations between example households, we have decided to retain the half hourly market price data and to carry out the cost and savings analysis using half hourly data. This enhances the accuracy, and reduces the difficulty in trying to establish typical time tranches for the analysis periods, which clearly vary considerably between example applications (see section 4.4).

It does however, also mean that we are now dealing with significantly higher volumes of data.

A further issue here is the need to establish typical prices and profiles under recent circumstances of rapidly varying market wholesale prices. The following figure shows the ‘time weighted’ half hourly average market price by quarters over a period back to the start of 200520. The more usual seasonality variations are not so apparent i.e. higher winter costs are not evident for 2007; and there is a particularly high autumn 2007 price.

To project price profiles forward to 2020 for the micro-CHP cost evaluation, we have used the 2006 profile data for ‘business’ and ‘non-business’ days (weekend) and seasons, as it appears the recent 20 The 2008 winter quarter is for Dec 2007 and Jan 2008 only.

most typical ‘present’ price profile; and projected this forward on the base of the average annual wholesale price projections. In other word, the shape of the daily profiles is the same as at 2006, but escalated for 2020 by the annual wholesale price escalation assessed by BERR21. In the previous study we used the 2005 profiles, but in that case to define average prices within defined time tranches, rather than for half hourly based profiles.

Figure 12: Quarterly electricity wholesale prices – from Elexon data analysis compared with BERR annual wholesale averages

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The average 2006 daily price profiles for the four seasons and for ‘business’ and ‘non-business’ days are shown in the following four figures.

Figure 13: Winter 2006 average system price: £51.8/MWh

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nbd

21 Private communication from the BERR modelling team.


Figure 14: Spring 2006 average system price: £43.7/MWh

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Figure 15: Summer 2006 average system price: £36.1/MWh

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Figure 16: Autumn 2006 average system price: £30.1/MWh

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These price profiles have been taken from the average daily profile data from Elexon, and demonstrate the large ‘within-day’ half hourly variations normally seen. All values have been deflated to 2005 prices.

The following Figure shows the historical and latest (EWP) projected electricity wholesale prices (real 2005) obtained from BERR. The real value escalation between 2006 and 2020 (+3.3%) has been use to escalate the system price profiles at 2006 (above) for 2020 profiles.

Figure 17: BERR wholesale price projections

38

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2000 2005 2010 2015 2020 2025

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including CO2 costs

4.8 Electricity Prices and Embedded Benefit

Embedded benefits are most likely to be available to large and medium scale CHP schemes in industry. For these, the total embedded benefit may be high – justifying the process of negotiation with a supplier. Hence this issue is likely to be relevant only for the large and medium scale industrial schemes.

There is an argument for the micro-CHP to also avoid some of these charges, as the electricity from micro-generation will be used locally, or if exported to the low voltage system, will more than likely be used by the property next door (or very nearby). This will mitigate to some extent the transmission losses (though transmission losses tend to be greater in the low voltage network than in the high voltage network). There will of course be a greater need for balancing the network, and network operators and distributors are likely to have concerns because of the intermittent nature of this generation, so these charges will not wholly be avoided.

In the scenarios run by the model, we have assumed that the user will receive the wholesale electricity price for any electricity sold back to the grid, and an alternative scenario, where the domestic customer would receive a standard tariff of 80% of their import electricity price tariff.

4.9 Central generation carbon emissions factors - averages

Our modelling method, using the AEA CHP model, allows for variations in the central electricity generation factor with both time-of-day and season. This provides a more accurate assessment of the potential CO2 savings from widespread uptake of micro-CHP by taking account of the periods in which central electricity generation is being displaced.

In the earlier study the emission profile used was based upon the BERR electricity markets model, and in general, the averaged values did not agree with carbon factors used in other policy assessment areas by Defra, SAP, CERT etc.


A review has therefore been necessary; and a revision in the modelling work of the carbon factors required to bring the policy cost assessments (£/tCO2 saved) more into line with other evaluation policy areas.

The 2006 average emissions factor used in the current analysis is 0.523 tCO2 per MWh22 and the following table shows how this varies according to the CHP penetration scenario used, 5million homes and 10 million homes at 2020, using re-calibrated BERR markets model projections.

Table 9: Average CO2 emissions (tCO2 per MWh) of electricity generated (2020) for different micro-CHP penetration scenarios

Emission factor tCO2/MWh

Zero penetration

5 million homes

10 million homes

2020 0.520 0.514 0.505 For comparison, the BERR electricity markets model for 2020 has a total average ‘ weighted’ emission factor of 3.80 tCO2/MWh across the days used in the profile calculations. With the 10 million homes (by 2020) scenario, this reduced to 3.69 tCO2/MWh.

Example daily emission factor profiles and the scenario effects were provided in the original 2007 report, Section 5.2.1 - National central generation effects.

22 Communication from Defra.

5 Results – CT trial domestic sectorThe following figures shows the results from the revised AEAT CHP model for the set of scenarios listed previously in Section 4 – Development of Scenarios.

Results are provided for the individual sites ‘lifetime carbon savings’ and ‘total net present value’ for groups of the scenarios. Comparisons are also made with the original study results, base on the Siam Report domestic profile data, and updated results for the Siam profiles, using the latest fuel price and emissions factor data. This latter comparison demonstrates the trend of carbon savings and cost effectiveness from the changes/updates in fuel price projections and emissions factors.

In total, 42 trial sites data is available; however complete data is only available for 33 of these as shown in the figures.

The figure sub-headings make comparisons between the trial average results and Siam based results (new and original 2007). The sub-headings also show the standard deviation or coefficient of variation of the distribution of the CT trial results across the 33 available results. We have also used this statistical data to identify a ‘CT trials (average)’ and two outliers, a ‘CT trials upper Standard Dev limit’ and a ‘CT trials lower Standard Dev limit’ as described earlier, and have used them in Section 7 to evaluate the total system cost changes.

It is evident in all cases that the CT trial results provide a significantly worse view of the potential carbon savings and cost effectiveness of micro-CHP.

Figure 18: Scenario 1 Central Fuel Price, 3.5%, 6% and 10% Discount Rate, Current Base - low penetration scenario (referred to as Run 2a, 2 and 4 in the model)

Lifetime carbon saving (the same for all scenario 1 runs); average: - (minus) 0.19 tCO2 (SD:+- 2.7); new result (Siam): +8.65 tCO2; original result (Siam): +3.29 tCO2

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Approximately half of the sites show carbon savings (indicated as + (plus) in the figures) and these are generally below the levels shown for the Siam profiles. From the Siam results, it is apparent that the effect of the revised emission factors is to increase the lifetime carbon savings.


The following figure in this set shows a histogram of the distribution of the lifetime carbon savings; and compares the savings values with the original Siam savings. As from the previous figure it is evident that the CT trial data gives a significantly worse view of the potential carbon savings; with in around half the cases no carbon being saved.

Lifetime carbon saving for individual household distribution (tCO2)

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Orginal analysis savings; 3.29tCO2

We consider the characteristics of those sample households, which provide actual carbon savings, as compared with those that don’t, further in Appendix C.

The following three figures show the net present value for the base case assessment (Scenario 1) at differing discount rates: 3.5%, 6% and 10%. NPVs are positive in all cases. The model results show non-cost effective examples as +ve NPV values.

Net present value 3.5% discount rate (Run 2a); average: + £1,970 (CV: +-15%); new result (Siam): + £849; original result (Siam): +£1,137

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Net present value 6.0% discount rate (Run 2); average: + £1,938 (CV: +-13%); new result (Siam): + £952; original result (Siam): +£1,205

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Net present value 10.0% discount rate (Run 4); average: + £1,846 (CV: +-11%); new result (Siam): + £1,072; original result (Siam): +£1,284

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The following set of two figures show the effect on NPV of increasing the value of the export electricity price to 80% of the purchase price at 3.5% and 6% DCR. The effect is relatively small in all cases23.

Figure 19: Scenario 4 Export Fuel Price = 80% of import fuel price, 3.5% and 6.0% Discount Rate, Current Base – Low Penetration (referred to as run 3a and run 3 in the model)

Net present value 3.5% discount rate (Run 3a); average: + £2,000 (CV: +-14%); new result (Siam): + £843; original result (Siam): +£91

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Net present value 6.0% discount rate (Run 3); average: + £1,938 (CV: +-12%); new result (Siam): + £947; original result (Siam): +£319

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Increasing the level of installations of CHP affects the emission factor of electricity generation (see previous section 4.9).

The following set of figures show the emissions savings and NPV values for the two scenario cases of 5 million installations and 10 million installations by 2020.

23 Reflecting a lower export level from the CT trial micro-CHP.

Figure 20: Scenario 6 Central Fuel Price, 3.5% and 6.0% Discount Rate, 5 million installations (referred to as Run 5a and Run 5 in the model)

Lifetime carbon saving; average: - (minus) 0.49 tCO2 (SD: +-2.7); new result (Siam): 7.93 tCO2; original result (Siam): 2.10 tCO2. Note: the AEAT model shows carbon savings as +ve values.

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Net present value 10.0% discount rate (Run 5); average: - £1,910 (CV: +-11%); new result (Siam): + £1072; original result (Siam): +£943

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Figure 21: Scenario 8 Central Fuel Price, 3.5% and 6.0% Discount Rate, 10 million installations (referred to as Run 6a and Run 6 in the model)

Lifetime carbon saving; average: - (minus) 0.69 tCO2 (SD: +-2.6); new result (Siam): 7.61 tCO2; original result (Siam): 1.62 tCO2

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Net present value 3.5% discount rate (Run 6a); average: + £2,065 (CV: +-15%); new result (Siam): - £849; original result (Siam): -£620

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Net present value 10.0% discount rate (Run 6); average: + £1,909 (CV: +-11%); new result (Siam): - £1072; original result (Siam): -£943

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6 Results - commercial sectorsThe following sets of tables show the lifetime carbon savings and NPV for the commercial sector sites described in Section 3 - Key tasks in the update; including those additional commercial application identified, up to 100kW. These results are calculated with the original AEAT model, which for the original 2007 study also include the domestic sector Siam analysis. For completeness, we have retained the original average home profile from the Siam report, for comparison (top row).

The results for the scenario 1 – ‘Current base - low penetration’, are shown below (model run 2a: 3.5% DCR, run 2: 6% DCR and run 4: 10% DCR, together with a comparison of scenario 4 - Export fuel price = 80% of import fuel price at 3.5% DCR (referred to as run 3a in the model). This shows the effect of an increased value of export electricity price. The remaining scenario runs listed previously in Section 4 – Development of Scenarios, are provided in Appendix B.

All of the applications save carbon; however, only in one case – health (medium-large) and with the export fuel price equal to 80% of import fuel price, is the NPV -ve (negative) i.e. stand alone cost effective.

Table 10: Scenario 1 Central Fuel Price, 3.5% Discount Rate, Current Base - low penetration scenario (referred to as Run 2a in the model)

No. of installat-ions

MWe at Max heat

Net Present Value

IRRLifetime Carbon Saving (tCO2)

Scheme cost of carbon saving

(£NPV/tCO2)

Discount Rate

Average home Elec and Heat Profiles from Siam Report

1.20 +£849 None 8.6 98 3.5%

Education (Small) 9,650 1.20 +£1,495 None 16.0 94 3.5%Education (Medium) 2,315 33.30 +£61,575 None 316.5 195 3.5%

Education (Medium-large) 428 75.00 +£60,233 None 1,052.0 57 3.5%

Health (Small) 31,760 1.20 +£2,346 None 9.1 257 3.5%Health (Small-Med) 17,740 1.20 +£669 None 23.0 29 3.5%

Health (Medium) 2,250 33.30 +£59,778 None 459.0 130 3.5%Health (Medium-

large) 180 75.00 +£39,313 None 1,684.8 23 3.5%

Retail (Small) 128,560 1.20 +£2,919 None 3.5 836 3.5%Retail (Small-Med) 13,880 1.20 +£1,782 None 14.2 126 3.5%

Retail (Medium-large) 923 5.50 +£14,734 None 102.8 143 3.5%

Hotels/restaurants (Small) 90,695 1.20 +£1,052 None 21.0 50 3.5%

Hotels/restaurants (Small-med) 13,780 33.30 +£52,126 None 321.7 162 3.5%

Hotels/restaurants (Medium-large) 831 75.00 +£70,173 None 904.3 78 3.5%

Offices (Small) 29,315 1.20 +£2,395 None 8.7 274 3.5%Offices (Small-

med) 4,655 54.00 +£62,802 None 535.7 117 3.5%

Offices (Very Small) 231,205 1.20 +£3,154 None 1.2 2,601 3.5%

Offices (Medium-large) 680 70.00 +£92,233 None 511.8 180 3.5%


Table 11 : Scenario 2 Central Fuel Price, 6% Discount Rate, Current Base – low penetration (referred to as Run 2 in the model)


MWe at Max heat

Net Present Value



(£NPV/tCO2)

Discount Rate


1.20 +£952 None 8.6 110 6.0%

Education (Small) 9,650 1.20 +£1,689 None 16.0 106 6.0%Education (Medium) 2,315 33.30 +£61,763 None 316.5 195 6.0%Education (Medium-

large) 428 75.00 +£62,479 None 1,052.0 59 6.0%

Health (Small) 31,760 1.20 +£2,404 None 9.1 264 6.0%Health (Small-Med) 17,740 1.20 -£995 None 23.0 43 6.0%


large) 180 75.00 +£45,085 None 1,684.8 27 6.0%


Retail (Medium-large) 923 5.50 +£14,342 None 102.8 140 6.0%Hotels/restaurants

(Small) 90,695 1.20 +£1,317 None 21.0 63 6.0%



Offices (Small) 29,315 1.20 +£2,445 None 8.7 280 6.0%Offices (Small- med) 4,655 54.00 +£63,482 None 535.7 119 6.0%Offices (Very Small) 231,205 1.20 +£3,083 None 1.2 2,543 6.0%


Table 12: Scenario 3 Central Fuel Price, 10% Discount Rate, Current Base – low penetration (referred to as Run 4 in the model)


MWe at Max heat

Net Present Value


Scheme cost of carbon

saving (£NPV/tCO2)

Discount Rate


1.20 +£1,072 None 8.6 124 10.0%


large) 428 75.00 +£65,014 None 1,052.0 62 10.0%

Health (Small) 31,760 1.20 +£2,472 None 9.1 271 10.0%Health (Small-Med) 17,740 1.20 +£1,374 None 23.0 60 10.0%


large) 180 75.00 +£51,660 None 1,684.8 31 10.0%



(Small) 90,695 1.20 +£1,625 None 21.0 77 10.0%






Table 13: Scenario 4 Export Fuel Price = 80% of import fuel price, 3.5% Discount Rate, Current Base – Low Penetration (referred to as run 3a in the model)


MWe at Max heat

Net Present Value



(£NPV/tCO2)

Discount Rate


1.20 +£843 None 8.6 98 3.5%


large) 428 75.00 +£29,491 None 1,052.0 28 3.5%


Health (Medium) 2,250 33.30 +£9,266 1.40% 459.0 20 3.5%Health (Medium-

large) 180 75.00 - (minus) £71,895 14.75% 1,684.8 -43 3.5%



(Small) 90,695 1.20 +£1,052 None 21.0 50 3.5%





7 Total system energy savings: end-users and generators

In this section we consider the total system energy savings, which includes energy savings at central generation - considering both gas and coal fuel demand changes, coupled with the net effect of increased energy use compared with boilers from the application of micro-CHP. We have only considered these changes in the application of domestic micro-CHP and do not include small-scale commercial CHP.

7.1 End-user fuel changes

The AEA CHP model generates the additional gas consumption figures for the average CT micro-CHP trial site compared with a new boiler; this is equivalent to 2.53 MWh pa (average) +-0.55 MWh pa (standard deviation of all of the 32 trial sites). If we scale this additional average gas consumption nationally to scenario penetration levels of 5 million and 10 million homes, the total additional gas used would be equivalent to 45.5 PJ pa for 5 million homes and approximately 91 PJ pa at the 10 million homes level.

Table 14 : Total addition gas use for the two micro-CHP penetration scenarios

Gas PJ pa 5 million homes

10 million homes

Increase against new boiler 45.5 91.0

7.2 Central generation fuel use and total system energy changes

Increasing levels of micro-CHP reduce the demand for a central generation and this in turn reduces fossil fuel requirements. The following table summarises the total coal and gas demands for the base case (no micro-CHP) and for the 5 and 10 million homes scenarios, using projected 2020 generation figures.

Table 15: Comparison of the coal and gas use by central generation for increasing micro-CHP generation (2020)

Base case

5 million homes Reduction

10 million homes Reduction

Coal PJ 433 417 17 403 31Gas PJ 1823 1,739 84 1,655 168Total PJ 2,256 2,155 101 2,058 199

As expected there is a reduction in total fuel demand - the effect of oil generation is negligible, which amounts to approximately 9 per cent for the 10 million homes scenario. The largest reduction is from gas firing with a reduction of over 9 per cent gas requirement for the 10 million homes scenario. By comparison, the reduction in coal fuel requirement is 7 per cent, but at a much lower level of overall use.

The net effect of the increased domestic gas use and the reduction in central generation fuel requirements is summarised in a following table, where it can be seen that there is a reduction in both gas and coal use (77 PJ and 31 PJ respectively, for 10 million homes). The overall balance is therefore a net savings of 108PJ in primary fuel (56 PJ pa for a 5 million penetration level).

The choice on micro-CHP is likely to be made between this and a new boiler at the end of the useful life of an existing boiler; hence these savings estimate will be valid under those circumstances, and not where a boiler is replaced before the end of its useful life; with larger potential savings compared with a conventional boiler.


Table 16 : Total system national energy savings resulting from the penetration of differing levels of micro-CHP

PJ pa 5 million home reduction

10 million home

reductionCoal generation reduction 17 31Gas generation reduction against new boilers 39 77

8 Total system cost changesWe have considered system cost changes derived from three components:

Costs (or benefits) for the consumer i.e. the customer installing the micro-CHP system. These benefits are summarised above in Sections 6 and 7;

The costs or benefits for central generation, who will produce less electricity overall and will not have to meet peak demands as in the base case. These profile changes by season, are described further in Appendix A - Models and other analysis adopted. Our current measure of these benefits is through the electricity wholesale price calculation (£/MWh), which shows a significant reduction as micro-CHP generation increases. In our total system cost change calculation we have carried out a net present value calculation in which we discount the annual changes in future years total wholesale value (the total for all generation) compared with the base case (no micro-CHP) total wholesale value24.

The likely costs for local network operators, where again we have calculated a net present value based on future discounted costs of future network re-enforcements and changes required to accommodate increasing levels of micro-CHP. In this case we have used the cost data from the SIAM report and others, as reported previously in the 2007 report, section 4. These refer to maximum likely costs, and we have assumed a linear build-up to 2021 (a 15 year time horizon has been used).

The components and values in the net present value total system costs are shown in the following tables where we have produced estimates for domestic micro-CHP separately from the small-CHP costs in the commercial sector.

We have not attempted to factor into the calculation the effects of avoided investments in future generation plant or grid or networks. The analysis to date is based upon the current situation in the central generation sector, and we have thus not considered future investment requirements and the possibility to avoid these with increased distributed generation.

8.1 Domestic sector

The first two sets of tables present the cost components and totals for the two scenarios of domestic micro-CHP uptake base on the domestic CT trial data averages. Each table shows the lifetime carbon saving derived from the AEA model together with the CHP net present value again from the AEA model. The central generation carbon savings have been derived from the BERR electricity markets model25 and the generator NPV savings are calculated via a spreadsheet discounted cash flow (DCF) model (a 3.5% DCR is assumed). Network costs have been taken from the literature, and in the higher penetration case, have been assumed to be at the upper level of figures quoted. For the 5 million home penetration scenario the low quoted network costs have been assumed. In reality we are probably over estimating these costs as even at the upper limits of assumed penetration we are only reducing central generation by approximately 9 per cent of total dispatch. The costs used have been described as appropriate at significantly higher levels of distributed generation.

For both cases based on the CT averaged results, the total net cost is positive26 (i.e. there are overall increased costs) although at the 5 million homes level, the total costs are less. In both cases, using the CT averaged results, carbon is saved when the positive contribution from generator carbon savings is taken into account. Reduced carbon levels are 7.1 MtCO2 for the 10 million home scenario and 3.55 MtCO2 for the 5 million home scenario These savings differ from the previous results using the SIAM report domestic profiles, where saving were much higher.

24 The effect on central generation is still based on the 2007 BERR assessment, assuming the house-hold demand and export profile according to the SIAM Report25 Emissions savings are generally accounted for in the end-use CHP calculations (the AEA model); however, this uses an emission factor based on the low CHP penetration scenario. Generator carbon savings reflect the savings from the reducing average emissions factor as CHP penetration rises. The ‘CUM generator carbon savings’ need to be added to the ‘micro-CHP lifetime carbon saving’ to derive the total lifetime carbon savings used in calculating the ‘overall cost effectiveness’ figure. 26 We adopt the normal convention of assuming a positive net cost represents a cost increase.


Table 17: Central case27 total system cost changes for domestic CT trial data average - 10 million by 2022 using the projected 2022 generation mix

– total carbon saving amounts to: 7.1 MtCO2

Micro-CHP lifetime carbon saving (AEA model) -1.90 MtCO2Generator carbon saving at 2022 0.60 MtCO2 pa Micro-CHP NPV (AEA model) +15,661 £M Generator NPV (to 2035) -1,292 £M Network NPV costs to 2022 +167 £M Net cost to 2035 +14,536 £MCUM generator carbon savings to 2035 + 9.0 MtCO2 Overall cost effectiveness (to 2022) +2,050 £/tCO2Micro-CHP cost effectiveness (to 2022) (AEA model) No carbon

saving

Table 18: Central case total system cost changes for domestic CT trial data average - 5 million by 2022 using the projected 2022 generation mix


Micro-CHP lifetime carbon saving (AEA model) -0.95 MtCO2Generator carbon saving at 2022 0.30 MtCO2 pa Micro-CHP NPV (AEA model) +7,830 £M Generator NPV (to 2035) - 646 £M Network NPV costs to 2022 +167 £M Net cost to 2035 +7,352 £MCUM generator carbon savings to 2035 +4.5 MtCO2 Overall cost effectiveness (to 2022) +2,070 £/tCO2Micro-CHP cost effectiveness (to 2022) (AEA model) No carbon

saving

27 See section 4.1 for a description of the scenarios used in the AEAT micro-CHP model runs.

In the following two tables, we compare savings results from the Siam report data on domestic profiles with results using CT trial data, which uses NPV and CO2 savings values at the upper limit of the standard deviation of the spread in trial results (i.e. we assume an NPV value which is the total sample average plus the standard deviation of the distribution, and a carbon saving value which likewise, is the sum of the average plus the SD of the carbon losses/savings distribution). This ‘upper SD limit’ sample site has an NPV value of +(plus) £1,670 and saves a small amount of carbon (2.53 tCO2 over the lifetime). By comparison, the Siam Report site profile has an NPV value of +(plus) £849 and saves 8.6 tCO2 over the lifetime.

The following tables show that both of these cases provide an overall carbon saving (including generator savings), of 18.8 MtCO2 (CT upper SD limit case) and 53.2tCO2 (from previous Siam data). However, neither offers a net cost benefit (-ve NPV value) when also including generator savings28.

Table 19: Central case total system cost changes for domestic (CT upper SD limit)- 5 million by 2022 using the projected 2022 generation mix

– total carbon savings amounts to: 18.8 MtCO2

Micro-CHP lifetime carbon saving (AEA model) +12.65 MtCO2Generator carbon saving at 2022 0.41 MtCO2 pa Micro-CHP NPV (AEA model) +6,628 £M Generator NPV (to 2035) -878 £M Network NPV costs to 2022 +167 £M Net cost to 2035 +5,917 £MCUM generator carbon savings to 2035 + 6.1 MtCO2 Overall cost effectiveness (to 2022) +315 £/tCO2Micro-CHP cost effectiveness (to 2022) (AEA model) +525 £/tCO2

Table 20: Central case total system cost changes for domestic from previous Siam data - 5 million by 2022 using the projected 2022 generation mix


Micro-CHP lifetime carbon saving (AEA model) +43.24 MtCO2Generator carbon saving at 2022 +0.67 MtCO2 pa Micro-CHP NPV (AEA model) +3,374 £M Generator NPV (to 2035) -1,434 £M Network NPV costs to 2022 +167 £M Net cost to 2035 +2,106 £MCUM generator carbon savings to 2035 +10.0 MtCO2 Overall cost effectiveness (to 2022) +39.6 £/tCO2Micro-CHP cost effectiveness (to 2022) (AEA model) +78.0 £/tCO2

28 The Carbon Trust trials suggest that in practice, there may not be 5 million suitable homes, as micro-CHP can only save carbon in large houses with a high heat demand.


We have already pointed out that our estimates of network support costs are probably too high and for the following table we have therefore assumed them to be zero, and have recalculated the total system costs for the 10 million homes scenario using the CT trial data average. In this case, the total net cost figure is still positive at + (plus) £14,370 million compared with + (plus) £14,535 million when network costs are included. As before, no carbon is saved using the CT trial data average.

Table 21: Central case29 total system cost changes for domestic CT trial data average - 10 million by 2022 (assuming no significant grid change costs)


Micro-CHP lifetime carbon saving (AEA model) -1.90 MtCO2Generator carbon saving at 2022 0.60 MtCO2 pa Micro-CHP NPV (AEA model) +15,661 £M Generator NPV (to 2035) -1,292 £M Network NPV costs to 2022 nil £M Net cost to 2035 +14,369 £MCUM generator carbon savings to 2035 +9.0 MtCO2 Overall cost effectiveness (to 2022) +2,025 £/tCO2Micro-CHP cost effectiveness (to 2022) (AEA model) No carbon

saving

The following table shows the effect of improving the value of the exported electricity to a price equivalent to 80% of the import price. Under these circumstances the total net cost is still positive at + (plus) £14,020 million.

Table 22: Total system cost for domestic CT trial data average - 10 million by 2022 assuming an export price equivalent to 80% of import price


Micro-CHP lifetime carbon saving (AEA model) -1.90 MtCO2Generator carbon saving at 2022 0.60 MtCO2 pa Micro-CHP NPV (AEA model) +15,144 £M Generator NPV (to 2035) -1,292 £M Network NPV costs to 2022 +167 £M Net cost to 2035 +14,019 £MCUM generator carbon savings to 2035 +9.0 MtCO2 Overall cost effectiveness (to 2022) +1,977 £/tCO2Micro-CHP cost effectiveness (to 2022) (AEA model) No carbon

saving

29 See Section 2 for a description of the scenarios used in the AEAT micro-CHP model runs

8.2 Commercial sectors

The next six tables show the total system cost calculations for education, the health sector, hotels and restaurants, offices and retail. In these sectors we have only analysed those larger size sites where CHP is potentially more cost-effective - each using a 33kWe engine, or larger. For the smaller demand sites, where Stirling engine micro-CHP systems are more appropriate, the applications are very much less cost-effective per unit of capacity - see the previous individual CHP results in this section 6. To be consistent with our treatment of the domestic sector, we have assumed network reinforcement costs pro-rated from the costs quoted in the current literature and use in the domestic sector modelling (expressed as costs per units to of electricity displaced). The generator savings have similarly been derived by extrapolating results from the BERR electricity markets model.

Table 23: Central case total system cost changes for the education sector (medium 33kWe engine application, no of sites = 2,300) using the projected 2022 generation mix


Mini-CHP lifetime carbon saving (AEA model) +0.73 MtCO2Generator carbon saving at 2022 +0.023 MtCO2 pa Mini-CHP NPV (AEA model) +113.3 £M Generator NPV (to 2035) -50 £MNetwork NPV costs to 2022 +12 £M Net cost to 2035 +76 £MCUM generator carbon savings to 2035 +0.35 MtCO2 Overall cost effectiveness (to 2022) +70.3 £/tCO2

Micro-CHP cost effectiveness (to 2022) (AEA model) +154.6 £/tCO2

Table 24: Central case total system cost changes for the medium health sector (medium 33kWe engine application, no of sites = 2,250) using the projected 2022 generation mix




Table 25: Central case total system cost changes for hotels/restaurants (Small to medium 33kWe engine application, no of sites = 13,800) using the projected 2022 generation mix



Mini-CHP lifetime carbon saving (AEA model) +4.43 MtCO2Generator carbon saving at 2022 +0.13 MtCO2 pa Mini-CHP NPV (AEA model) +570.8 £M Generator NPV (to 2035) -275 £MNetwork NPV costs to 2022 +68 £M Net cost benefit to 2035 +363 £MCUM generator carbon savings to 2035 +1.92 MtCO2 Overall cost effectiveness (to 2022) +15.6 £/tCO2


Table 26: Central case total system cost changes for hotels/restaurants (Medium to large 75kWe engine application, no of sites = 830) using the projected 2022 generation mix


Mini-CHP lifetime carbon saving (AEA model) +0.75 MtCO2Generator carbon saving at 2022 +0.01 MtCO2 pa Mini-CHP NPV (AEA model) +46.3 £M Generator NPV savings (to 2035) -29 £MNetwork NPV costs to 2022 +3 £M Net cost benefit to 2035 +21 £MCUM generator carbon savings to 2035 +0.2 MtCO2 Overall cost effectiveness (to 2022) +6.1 £/tCO2


Table 27: Central case total system cost changes for offices (Small to medium 54kWe engine application, no of sites = 4,650) using the projected 2022 generation mix




Table 28: Central case total system cost changes for retail (Medium to large 5.5kWe engine application, no of sites = 920) using the projected 2022 generation mix




The results demonstrate that in all cases the total system net costs are positive; with the overall cost effectiveness (to 2022) range from between +(plus) £6/tCO2 for hotels/restaurants (medium to large), up to +£70/tCO2 for the education sector. Central generation savings (both cost and carbon) tend to balance the CHP losses. At a higher export price scenario30 all of the 33kWe, and larger, CHP examples become more cost effective, with a –ve (minus) NPV for education (medium) and health (medium), but with the other sector examples remaining positive.

We assume that only a fraction of the coverage of sites in the smaller demand categories is appropriate for mini-CHP. The uptake used in calculating the above carbon and cost savings are the number of sites shown in the table headings, which is all of the technical potential in the larger size categories relevant for the 33kWe, and larger, systems. As described earlier, we have not included the smaller demand sites, where Stirling engine micro-CHP systems are more appropriate and applications are even less cost-effective.

30 See Section 4 for a description of the scenarios used in the AEA micro-CHP model runs


9 Overall conclusionsIn this study we have modelled the performance of micro- and mini- (up to 100kWe) systems against conventional heating systems so that we can compare the costs and the possible emissions savings.

The extension of this study to include the trial data from the Carbon Trust field trials has had the overall effect of providing a significantly worse view of the potential carbon savings and cost effectiveness of micro-CHP. Out of the 33 sites for which a cost and carbon savings analysis has been carried out, only five sites show carbon savings and these are well below the levels shown for the Siam domestic profiles, which were used in the original 2007 evaluation. From additional Siam data based analysis it is also apparent that the effect of the revised emission factors used in this latest study is to increase lifetime carbon savings.

However, we must recognised that the situation into which the system is installed and the way it is operated is critical in determining costs to the user and in delivering the carbon dioxide savings. Micro-CHP tends to be less effective in situations where there is either a low, or an intermittent heat demand, and works more effectively where there is a high or constant heat demand. The range of situations trialled in the CT assessments clearly include domestic situations were micro-CHP cannot be effective.

Appendix C clarifies why there are some good micro-CHP applications in the CT trial sample. It concludes that the efficiency of the micro-CHP application is strongly dependent on, not just overall heat demand, but also on the way in which that demand is called for.

There is further evidence that the time of operation of the micro-CHP (determined by the time of the heat demand) can coincide with peak electricity demand from the grid, which tends to be the most expensive electricity. A significant number of installed micro-CHP systems would help to reduce these peaks and reduce the prices spikes at these times. We have factored these effects into our ‘total system cost’ benefit calculations.

1. The overall results from the cost-effectiveness and carbon saving calculations for micro-CHP installations (domestic and small commercial) suggest the technology will not to be cost effective to the consumer; and saves carbon in the (small) commercial sector and in about half of the domestic situations. When the benefits accruing for central generators are also allowed for, there are ‘total system’ cost benefits from a small number of commercial sector applications, but positive cost increases remain for domestic sector applications and most commercial situations.

2. The net present values for domestic micro-CHP installations derived from the CT data all show values that are above +£1,300, with an overall average of +£2,000, indicating further losses above the marginal cost of the technology (£1,500). Capital support could change non-cost effective investments to cost-effective; however, for the majority of applications, the revenue stream will always be negative.

3. For mini-CHP systems (33 kWe up to 75 kWe engines) for the commercial sectors, the highest NPV value is typically £60,000 to £70,000. At a typical installed cost at about this value (£65,000 to £72,000), the level of support would need to be very high to make these applications cost-effective.

4. Alternatively, increasing the export price for customers paid by suppliers (up to 80 per cent of the import price) makes the domestic micro-CHP applications only slightly more cost-effective (but still remains +ve). At this level of export price, the commercial applications of mini-CHP become more cost effective, but only the health sector shows a -ve NPV. For medium to large heath, the NPV is -£72,000.

5. Estimates of total UK system cost changes include the costs (or benefits) for the customer installing the micro-CHP system, the benefits for central generation and the likely costs for local network operators. We conclude that for an uptake of domestic micro-CHP at both the

5 and 10 million homes level, and using the average performance of micro-CHP from the CT trials, the total net cost is positive (i.e. there are overall increases in national costs), although at the 5 million homes level the total costs are less. Approximately 50% of the CT trial applications save carbon and the average performance would provide a total lifetime carbon saving of 7.1 MtCO2 at the 10 million home penetration level (by 2022) at a total cost in excess of £2,000/tCO2 saved.

6. Our assumptions about the likely costs for local network operators are probably on the high side, as even at the upper limits of assumed penetration, we are reducing central generation by only 9 per cent of total dispatch. The costs we have used have been described as appropriate at significantly higher levels of distributed generation. Removing these costs completely, the total net cost figures are still positive and the overall effect on costs is relatively immaterial.

7. For the commercial sector, whilst central generation cost savings help to balance the large CHP operating losses, total system net costs all remain positive under baseline conditions.


Appendix A. Models and other analysis adopted Overview and objectives

There have been numerous recent assessments of the costs and benefits of distributed micro-generation (see earlier section on recent studies and their conclusions). These provide estimates of potential level of future penetration (for the scenario development), micro-generation technology and operating data, costs of the technologies and the likely local distribution changes to accommodate higher levels of penetration. The analysis considered here, takes data from these previous studies and amalgamates them into a total system cost analysis. This also includes estimates of the effects on the centralised UK electricity generation system including costs and generation plant dispatch changes. Later parts of this section describe the individual elements of the analysis procedure and the final sub-section describes the interrelationships between the various elements of the analysis procedure.

In summary, the analysis is based on three modelling/data sources:

A national electricity supply model – including input from a BERR Electricity Markets model;

An end-use demand model – the AEA CHP cost and energy and emissions savings model, which has been adapted for domestic sector use and same scale commercial sector applications;

Quantitative assessments of the low voltage distribution system add-on costs required by higher levels of micro-generation.

The objective of this current analysis is to extend cost considerations to also include estimates of the effect on central generators (insofar as their cost reductions are reflected in lower wholesales prices, through the removal of the demand for peak price electricity), to arrive at a total system evaluation. Data is from the SIAM report and EST/element energy report and various other sources. This area of cost and technical consideration in the literature is controversial, and although we have reviewed this work, the costs we have adopted in the total system cost model probably needs further input.

We also consider in more detail than perhaps hitherto, the effects on CO2 emissions, using hourly emission profile information from central generation, and compare results with savings obtained from annual averaged figures, which is the normal approach.

Electricity supply and demand balance model – including the role of the BERR Energy Markets model

This process estimates the national electricity generation emission factor and wholesale electricity price movements across typical days and weights the savings from micro-generation accordingly. This provides better estimates of the CO2 emissions savings and the upstream generation cost savings (reflected in the wholesale price of electricity) achieved by distributed micro-generation. It is probable that the ‘demand profile weighted’ cost and emissions savings calculated will be higher than estimates using averaged yearly data, because of the relatively higher electricity costs and emissions factor at peak demand times31.

The following diagram shows the interrelationships between the various elements of this part of the analysis.

31 The high emissions factor at peak demand times is explored further. Historical generation dispatch data suggests that coal plant is the marginal technology run to meet peak demand. Discussions with BERR suggest that last winter (2005/2006) CCGT plant was also operated at the margin to meet peaks. This, if prevalent, would reduce CO2 savings significantly. We discuss BERR model results in the results section.

National electricity supply model –input from the DTI Markets model

Base case ‘without micro-generation’demand profile

With modified micro generation profiles

The base case is available in the DTI supply model. Extrapolations will be required to 2030+

Scenarios for the penetration of micro-generation modify the demand profile. We will need to make extrapolations between DTI model results, to reduce the number of runs necessary

Typical demand profiles are required for summer and winter business days, and for spring and autumn (the ‘shoulder’ seasons) days.

Understanding the effects on central generation of a maximum micro-generation penetration scenario is a priority; an intermediate scenario is also useful to improve the extrapolations for different penetration rates.

We needed to minimise the BERR model runs. Once the marginal (peak) generation plant is identified (currently and at 2015 (+)) and also with the demand peak removed), it is possible to generate a simple spreadsheet model to make estimates of other lower penetration scenarios.

Outputs

The first two outputs below feed in to the AEA CHP cost and energy and emissions savings model – see the description of the demand model

1. The profile of the CO2 emissions factor (CO2 per MWh generated) by time of day and season.2. Changes in electricity wholesale price (£ per MWh) by time of day and season.3. The electricity generating mix and marginal (peaking) plant.

The investigations have looked at the effects of domestic micro-CHP systems on central generation demand and the consequences on the above three factors. BERR supplied us with one years’ worth of central generation daily profile data, between 1st April 2006 and projected out to 31st March 2007 and we have modified these according to two levels of penetration of domestic micro-CHP: 5 million homes and 10 million homes using micro-CHP electricity production profile data from the SIAM report32.

In the figures below we show the latest results (using the CT trial data) for some example daily profiles of electricity demand where we have not only considered two penetration scenarios, but also the seasonal output effects of domestic micro-CHP. The difference between these profiles and those using the earlier SIAM report domestic profiles (see the 2007Report) is that averaging across all trial results leads to a ‘smoothing’ of the central generation demand reduction across the day, rather than at peak demand times; although peak demand reductions are still clearly evident in winter and in spring.

The original SIAM central generation profiles have be re-run in the BERR markets model to assess emissions and wholesale price changes and the marginal generation technologies displaced. Results are reported in Section 5. This analysis has also been extended to consider projections out to 2020, which are more relevant to the assessment of future micro-CHP.

32 This assumes the application of a 1.05kWe capacity micro-CHP system and provides output profiles for winter, summer and the two ‘shoulder’ seasons. There are significant variations in output across the days and across the seasons. We have not been able to modify the original BERR analysis for the current study with the CT trial data.


Figure: January days - ‘base case’ against a 5million home penetration scenario

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Figure: April days - ‘base case’ against a 5million home penetration scenario

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Figure: August days - ‘base case’ against a 5million home penetration scenario

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6-Aug-05

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There are clear differences between the seasons arising from not only the base case (non-micro-CHP) central generation profile, but also the different micro-CHP electricity output profiles for the different seasons, which displaces the central generation. The effect in summer is very small even at the 10 million home penetration level.

AEA CHP cost and energy and emissions savings model

This model is used to identify the cost of operating micro-generation and the associated energy and emissions savings. It is used in conjunction with the supply and demand balance model (see the previous description) to produce estimates on the ‘total system’ cost savings and total energy and emissions savings. The model operates with time profiles, but not at the level of disaggregation possible with the supply and demand balance model (hourly). We therefore need to select the time profile tranches carefully to achieve the most accurate estimates of savings. We also estimate the accuracy of the outputs using a hourly/half-hourly spreadsheet based ‘cross check’ calculation.

The model is based upon an existing CHP policy assessment model developed by AEA for a Carbon Trust study 33. Policy cost analysis elements have been removed and the model simplified to provide

33 Study of the Carbon Savings and Economics of CHP Schemes -Yet to be published.

net cost, energy and emissions savings estimates without the complexities necessary to evaluate larger CHP systems i.e. complex maintenance schedules, rebuilds, tandem boiler operations.

The diagram shows the inputs into this model.

CHP cost, energy and emissions savings model –AEAT

The AEAT model takes account of daily/seasonal demand profiles and fuel price profiles (import and export electricity prices and gas seasonal prices).

It calculates a discounted net cost of operating the CHP system and compares it with the net cost of a base case operation. It also calculates total energy and emissions for both cases. The differences are the benefits or savings from adopting CHP

Micro-CHP technical, operational and cost data – to define the net operating cost, energy and emissions levels

Demand operating data – heat and electricity profiles –to identify the base case costs and the energy and emissionsrequirements

Typical emission profiles and electricity cost profiles data from the DTI Markets model (plus extrapolations)

The model breaks down the daily profile into 3 daytime tranches for a typical business day, and one night-time tranche. Non-business days (weekends) have one daytime tranche and one night time. This is repeated over the four seasons.

A change in the electricity generation CO2 emission factor and electricity price is allowed for each of the above time tranches.

A variation in the gas price is allowed for each season.

The operation of the micro-CHP scheme can be altered to assess the effects of different operating modes as required by some of the scenarios.

Outputs

The total net cost using a discounted cash flow analysis of the CHP scheme and the base case (non-CHP situation); the difference is the cost benefit (or the additional cost).

The energy use and emissions from the CHP scheme and the base case; the difference is the

energy/emissions savings.

The model is not intended to determine levels of cost effective potential and hence likely penetration rates, or to investigate policy measures and the resulting benefits. It’s use here is only to determine the costs of the micro-CHP application(s) at the levels of penetration determined by the scenarios. However, it does inform on the appropriateness of certain scenarios.

Results are provided in Section 5.


Cross-check spread sheet analysis

The AEA ‘end-use’ CHP cost, energy and emissions savings model breaks down the daily profile into 3 daytime tranches for business days, and one night-time tranche. Non-business days have one daytime tranche and one night-time.

The check calculations made here are intended to compare outputs from the AEA model with more detailed sample calculations made with hourly/half hourly daily profiles.

The analysis flow is as follows.

AEAT end-use CHP cost and emissions savings model

National electricity supply model – DTI Markets model

Hourly/half hourly emissions factor profiles Spreadsheet based

check calculations to obtain:- Daily average wholesale electricity price changes- Daily average national generation emissions savingsHourly/half hourly

electricity price profiles

Elexon system buy / system sell price informationhttp://www.elexon.co.uk/marketdata/PricingData/SBPSSPNIV/default.aspx

Sample calculation hourly/half hourly electricity demand profiles

Compare between

The hourly/half hourly emissions factor profiles and electricity price profiles are derived from the BERR Markets model. This data is used to develop the ‘block’ time tranche profiles used in the AEA end-use CHP model.

The check calculations take the hourly/half hourly emissions factor and wholesale price profiles and multiply them by the profile of micro-CHP output to derive the ‘demand profile weighted’ average wholesale price saving and annual emissions saving (from the reduced electricity supply from central generation). These values are checked against the same calculations; however, this time using the averaged time tranche values from the AEA model. This provides an estimate of the error associated with reducing the time profile detail in the calculations. We also compare the above average wholesale price saving and annual emissions saving with values derived using annual average calculations.

Low voltage distribution costs - to support increased levels of small scale distributed generation

This analysis will develop cost data associated with activities to support a stable low voltage distribution systems under the new embedded generation circumstances, which can lead to voltage rise, reverse power flow, and other network fault issues.

It is based upon other studies, which indicate that costs might range from:

£150m-£240m to mitigate voltage rise, £60m-£650m to mitigate reverse power flow, and £2.5b (DNO estimate) to mitigate all network issues.34

34 BERR Embedded Generation Working Group Report, 12 January 2001

Linking data and model results to final outputs

The diagram below shows the interrelationships between the various elements of the analysis procedure described earlier in this section.

Total system cost benefit; also for the different actors: end-user, generators and others (distribution etc)

Total system energy savings at: end-use and generators (including information on capacity requirement and marginal generating plant)

Total system emissions changes including: increased end-user emissions and national generation reductions

Low voltage distribution costs – SIAM report and EST report

AEAT end-use CHP cost and energy and emissions savings model

National electricity supply model – DTI Markets model (plus extrapolations)

Distribution effects

End-use

Generation effects

emissions profiles and electricity cost profiles

Sample cross check calculations on generation cost and emissions benefits using more detailed profiles

Micro-CHP operating and cost data –AEAT, SIAM report and EST (element energy) report

The key outputs are shown on the right of the above diagram and include:

A breakdown of total system cost changes for the main actors: end-users, the generators and distributors (DNOs);

Total system energy savings and the resulting CO2 emissions savings

In summary, these outputs are based upon three modelling/data sources:

A national electricity supply model – including output from the BERR Markets model –the detail of which, is yet to be fully defined;

An end-use demand model – the AEA CHP cost and energy and emissions savings model; Quantitative assessments of low voltage distribution costs required from higher levels of

micro-generation from the SIAM report and EST/element energy report (+ others).

The AEA cost and energy and emissions savings model takes inputs from the BERR markets model (extrapolations are required to minimise the BERR runs) and from micro and mini-CHP operating and cost data from AEA, the SIAM and the EST (element energy) reports (+ others). More details of the individual models and analysis procedures have been provided in the separate descriptions.

There is also an ‘accuracy’ cross checking procedure to assess the likely uncertainty from using the profile breakdown allowed in the AEA cost and energy and emissions savings model, compared with hourly/half-hourly profile data. These are sample calculations only – see the earlier section on cross check calculations.


Appendix B. Additional results - commercial sectors

This appendix provides the AEAT commercial sector model results for the remaining scenario (not shown in Section 6) as listed previously in Section 4 – Development of Scenarios.

These include:

Scenario 5 Export Fuel Price = 80% of the import fuel price, 6% Discount Rate, Current Base – low penetration This scenario is referred to as Run 3 in the Model

Scenario 6 Central Fuel Price, 3.5% Discount Rate, 5 million installations This scenario is referred to as Run 5a in the model

Scenario 7 Central Fuel Price, 10% Discount Rate, 5 million installations This scenario is referred to as run 5 in the model

Scenario 8 Central Fuel Price, 3.5% Discount Rate, 10 Million Homes This scenario is referred to as run 6a in the model

Scenario 9 Central Price Scenario, 10% Discount Rate, 10 Million Homes This scenario is referred to as run 6 in the model

Table 1: Scenario 5 Export Fuel Price = 80% of the import fuel price, 6% Discount Rate, Current Base – low penetration (referred to as Run 3 in the Model)


MWe at Max heat

Net Present Value



saving (£NPV/tCO2)

Discount Rate


1.20 +£947 None 8.6 110 6.0%


large) 428 75.00 +£36,574 None 1,052.0 35 6.0%


Health (Medium) 2,250 33.30 +£17,885 1.40% 459.0 39 6.0%Health (Medium-

large) 180 75.00 - (minus) £48,591 14.75% 1,684.8 -29 6.0%



(Small) 90,695 1.20 +£1,317 None 21.0 63 6.0%






Table 2: Scenario 6 Central Fuel Price, 3.5% Discount Rate, 5 million Homes (referred to as Run 5a in the model)


MWe at Max heat

Net Present Value



saving (£NPV/tCO2)

Discount Rate


1.20 +£849 None 7.9 107 3.5%


large) 428 75.00 +£60,233 None 945.5 64 3.5%



large) 180 75.00 +£39,313 None 1,507.5 26 3.5%



(Small) 90,695 1.20 +£1,052 None 18.5 57 3.5%





Table 3 : Scenario 7 Central Fuel Price, 10% Discount Rate, 5 million Homes (referred to as run 5 in the model)


MWe at Max heat

Net Present Value

IRR

Lifetime

Carbon Saving (tCO2)


saving (£NPV/tCO2)

Discount Rate


1.20 +£1,072 None 7.9 135 10.0%


large) 428 75.00 +£65,014 None 945.5 69 10.0%



large) 180 75.00 +£51,660 None 1,507.5 34 10.0%



(Small) 90,695 1.20 +£1,625 None 18.5 88 10.0%






Table 4: Scenario 8 Central Fuel Price, 3.5% Discount Rate, 10 Million Homes (referred to as run 6a in the model)


MWe at Max heat

Net Present Value

IRR



saving (£NPV/tCO2)

Discount Rate


1.20 +£849 None 7.6 112 3.5%


large) 428 75.00 +£60,233 None 896.0 67 3.5%



large) 180 75.00 +£39,313 None 1,449.4 27 3.5%

Hotels/restaurants (Medium-large) 128,560 75.00 +£70,173 None 772.4 91 3.5%

Retail (Small) 13,880 1.20 +£2,919 None 3.0 968 3.5%Retail (Small-Med) 923 1.20 +£1,782 None 12.1 147 3.5%

Retail (Medium-large) 90,695 5.50 +£14,734 None 87.3 169 3.5%Hotels/restaurants

(Small) 13,780 1.20 +£1,052 None 17.6 60 3.5%

Hotels/restaurants (Small-med) 831 33.30 +£52,126 None 242.4 215 3.5%



Table 5: Scenario 9 Central Price Scenario, 10% Discount Rate, 10 Million Homes (referred to as run 6 in the model)


MWe at Max heat

Net Present Value

IRR



(£NPV/tCO2)

Discount Rate


1.20 +£1,072 None 7.6 141 10.0%


large) 428 75.00 +£65,014 None 896.0 73 10.0%



large) 180 75.00 +£51,660 None 1,449.4 36 10.0%



(Small) 90,695 1.20 +£1,625 None 17.6 93 10.0%






Appendix C. Why are there some good micro- CHP applications in the CT trial sample?

The domestic micro-CHP CT trail applications do not save CO2 in any of the 2-bed houses and only some of the 3 bed and 4 bed houses.

The table below shows houses ranked in order of CO2 saving.

Energy Price Forecast Scenario EUETS Scenario PlantComparison Penetration and Carbon Displacement Forecast Scenario DCR

Central Central

CHP and new boilers/with supplementary burner v new boilers if

applicable (see site spec inputs column AU

Current Base (Low pen) 6.00%

Site House Type Annual Heat Load Lifetime Carbon Saving NPVMWh TCO2 £

88AFU 100Yr old 3Bed 10.601 5.291 -£1,44776QUA 1Bed Quaker Meeting House 12.147 3.341 -£1,46469BRU 24Yr old 4Bed 14.858 3.126 -£1,348117NIE 40Yr old 3Bed 12.531 2.952 -£1,70673CPA 46Yr old 3Bed 8.525 2.849 -£1,59082JWA 20Yr old 4Bed 9.452 1.839 -£1,61477PL1 2Yr old 4Bed 9.162 1.773 -£1,631107NIE 40Yr old 3Bed 9.148 1.579 -£1,838118NIE 80Yr old 2Bed 9.702 1.575 -£1,860101NIE 45Yr old 3Bed 7.293 1.412 -£2,11881PL66 2Yr old 4Bed 10.865 1.280 -£1,59860PL248 1Yr old 3Bed 10.002 0.856 -£1,73589PL237 1Yr old 3Bed 6.541 0.781 -£1,999112NIE 100Yr old 3Bed 7.833 0.464 -£1,75175BBI 75Yr old 3Bed 4.888 0.368 -£1,83272SSE 47Yr old 3Bed 6.791 0.278 -£1,79174WAL 215Yr old 2Bed 8.083 -0.032 -£1,882114NIE 100Yr old 2Bed 8.267 -0.236 -£1,81797PL243 1Yr old 3Bed 8.009 -0.332 -£1,91693PL305 1Yr old 3Bed 5.286 -0.414 -£1,981113NIE 100Yr old 2Bed 8.140 -0.444 -£1,93792PL240 1Yr old 3Bed 5.427 -0.678 -£1,997115NIE 100Yr old 2Bed 6.804 -0.943 -£1,971102NIE 35Yr old 3Bed 7.736 -0.957 -£1,96387PL230 1Yr old 3Bed 5.930 -1.168 -£2,029111NIE 10Yr old 3Bed 9.962 -1.255 -£1,91791PL239 1Yr old 3Bed 9.644 -1.352 -£1,923116NIE 100Yr old 2Bed 9.464 -1.789 -£1,90898PL246 1Yr old 3Bed 4.521 -3.803 -£2,25694PL306 1Yr old 3Bed 2.771 -4.612 -£2,34385PL228 1Yr old 3Bed 5.950 -5.485 -£2,33496PL308 1Yr old 3Bed 7.039 -5.925 -£2,37195PL307 1Yr old 3Bed 4.792 -6.610 -£2,373

As the operations of the CHP is heat led, the CO2 saving is generally higher in larger older houses with higher annual heat loads where the CHP achieves higher average power outputs and therefore seasonal efficiencies. However it can be seen that this is not always the case with CO2 saving not always strictly in order of heat load. This may be due to other factors such as how erratic the heat load is, leading to high cycling and lower operating efficiency of the CHP.

For example, house 75BBI (the 5th highest CO2 saving house example above), a 75 year old, 3-bed house has an annual heat load of just 4.9MWh but the micro-CHP saved CO2 here. In contrast, house 111NIE, a 10 year-old 3-bed house has an annual heat load of 10.0MWh but did not provide CO2 savings. This may be explained by comparing the demand profiles below.

House 75BBI Profile and results

Housename House Type

75BBI HeatHeat Power

Avg Demand (kW) 0.558 0.055Annual Demand kWh 4888 481

Penetration and Carbon Menu

Energy Price Forecast Scenario

Plant Comparison NPV Net Present CO2

Saving

Current Base (Low pen) Central

CHP and new boilers v new

boiler only-£1,832 0.368

Annual CHP Profile

0.0000.2000.4000.6000.8001.0001.2001.4001.6001.8002.0002.2002.4002.6002.8003.0003.2003.4003.6003.8004.0004.200

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dev

Month

Ener

gy A

vg k

W

0%5%10%15%20%25%30%35%40%45%50%55%60%65%70%75%80%85%90%

Effic

ienc

y

CHP Heat OutputAvg kW

Gross ElecGeneration Avg kW

Tot eff

Power eff

House 111NIE Profile and results

Housename House Type

110NIE HeatHeat Power

Avg Demand (kW) 1.137 0.089Annual Demand kWh 9962 778

Penetration and Carbon Menu

Energy Price Forecast Scenario

Plant Comparison NPV Net Present CO2

Saving

Current Base (Low pen) Central

CHP and new boilers v new

boiler only-£1,917 -1.255

Annual CHP Profile

0.0000.2000.4000.6000.8001.0001.2001.4001.6001.8002.0002.2002.4002.6002.8003.0003.2003.4003.6003.8004.0004.200

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dev

Month

Ener

gy A

vg k

W

0%5%10%15%20%25%30%35%40%45%50%55%60%65%70%75%80%85%90%

Effic

ienc

y

CHP Heat OutputAvg kW

Gross ElecGeneration Avg kW

Tot eff

Power eff


Looking at the monthly profiles it can be seen that in all seasons, the heat load is higher for house 118NIE and yet the CHP operates less efficiently in winter, where the heat demand for all sites and therefore generation is at it’s highest; and also where the carbon factors are high.

If the weekly and daily profiles differences are examined in more detail from the selected profiles below, it can be seen that 111NIE has a more steady, often lower load but for more hours; whereas 75BBI has a more peaky demand, but this may mean 111NIE calls on heat often, but with a lot of modulation. So the CHP operates often, but with low efficiency, whereas it gets a less frequent, but better load in 77BBI.

Clearly, the efficiency of the micro-CHP application is strongly dependent on, not just overall heat demand, but also the way in which that demand is called for.

Weekly profile differences between 75BBI (saves carbon) and 111NIE (does not save carbon)

75BBI 75Yr old 3BedHeat Power

Avg Demand (kW) 0.558 0.055Annual Heat Demand kWh 4888 481

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Daily example profile differences between 75BBI (saves carbon) and 111NIE (does not save carbon)

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