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Milton Keynes Energy Mapping Project - Summary Report Final 30 Nov 2012 Centre for Sustainable Energy 3 St Peter’s Court Bedminster Parade Bristol BS3 4AQ Registered company no. 2219673 t. 0117 934 1400 f. 0117 934 1410 [email protected] www.cse.org.uk Registered charity no. 298740

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Page 1: Milton Keynes Energy Mapping Project Summary Report · Milton Keynes Energy Mapping Project -Summary Report . Final . 30 Nov 2012 . Centre for Sustainable Energy . 3 St Peter’s

Milton Keynes Energy Mapping Project

- Summary Report Final 30 Nov 2012

Centre for Sustainable Energy 3 St Peter’s Court Bedminster Parade Bristol BS3 4AQ Registered company no. 2219673

t. 0117 934 1400 f. 0117 934 1410 [email protected] www.cse.org.uk

Registered charity no. 298740

Page 2: Milton Keynes Energy Mapping Project Summary Report · Milton Keynes Energy Mapping Project -Summary Report . Final . 30 Nov 2012 . Centre for Sustainable Energy . 3 St Peter’s

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Contents

Executive Summary .................................................................................................................................................... 4

1 Introduction ....................................................................................................................................................... 8

1.1 Background ................................................................................................................................................ 8

1.2 Terms of reference ..................................................................................................................................... 9

2 Existing low and zero carbon technologies ........................................................................................................10

3 Potential for low and zero carbon technologies ................................................................................................12

3.1 Wind power ..............................................................................................................................................12 3.1.1 Methodology .............................................................................................................................................. 12 3.1.2 Basic constraints ......................................................................................................................................... 12 3.1.3 Additional/optional constraints ................................................................................................................ 13 3.1.4 Flood risk .................................................................................................................................................... 14 3.1.5 Aviation constraints ................................................................................................................................... 14 3.1.6 Data caveats ............................................................................................................................................... 14 3.1.7 Mapped resource ....................................................................................................................................... 14

3.2 Biomass: woodland residues ....................................................................................................................15 3.2.1 Methodology .............................................................................................................................................. 15 3.2.2 Woodland resource .................................................................................................................................... 16

3.3 Biomass: energy crops .............................................................................................................................17 3.3.1 Methodology .............................................................................................................................................. 17 3.3.2 Energy crops resource ................................................................................................................................ 18

3.4 Hydropower ..............................................................................................................................................19 3.4.1 The Environment Agency hydropower study............................................................................................. 19 3.4.2 Approximate locations of opportunities .................................................................................................... 19 3.4.3 Calculation of capacity of hydropower opportunities ............................................................................... 19

3.5 Energy from waste ....................................................................................................................................20 3.5.1 Agricultural and food waste ...................................................................................................................... 20 3.5.2 Wood waste................................................................................................................................................ 20 3.5.3 Other waste ................................................................................................................................................ 21

3.6 Microgeneration .......................................................................................................................................21 3.6.1 Microgeneration resources ........................................................................................................................ 21 3.6.2 Microgeneration - key opportunities and constraints .............................................................................. 22 3.6.3 Solar resource ............................................................................................................................................. 23

3.7 Energy efficiency .......................................................................................................................................24 3.7.1 Loft and cavity wall insulation ................................................................................................................... 24 3.7.2 Solid wall insulation ................................................................................................................................... 25 3.7.3 Other measures .......................................................................................................................................... 26

3.8 Summary ..................................................................................................................................................26

4 Milton Keynes Heat Map ...................................................................................................................................28

4.1 Introduction ..............................................................................................................................................28

4.2 Heat demand across Milton Keynes ..........................................................................................................29

4.3 Opportunities for district heating and CHP ...............................................................................................29 4.3.1 Existing development ................................................................................................................................. 29 4.3.2 New development ...................................................................................................................................... 35

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5 Energy character areas ......................................................................................................................................36

5.1 Areas of opportunity .................................................................................................................................36

5.2 GIS mapping package ................................................................................................................................36

6 Carbon reduction scenarios ...............................................................................................................................42

6.1 Introduction ..............................................................................................................................................42

6.2 The target .................................................................................................................................................42

6.3 Limitations of the model ...........................................................................................................................42

6.4 Assumptions .............................................................................................................................................43

6.5 Inclusion of grid-connected electricity in results .......................................................................................43

6.6 Scenario results ........................................................................................................................................43 6.6.1 Scenario 1: Modest measures .................................................................................................................... 43 6.6.2 Scenario 2: Energy efficiency-driven .......................................................................................................... 46 6.6.3 Scenario 3: Renewable energy-driven ....................................................................................................... 48 6.6.4 Scenario 4: High efficiency and high renewables ...................................................................................... 50

6.7 Conclusions ...............................................................................................................................................52

Appendix 1 – Maps ....................................................................................................................................................53

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Executive Summary As part of an on-going commitment to support sustainable energy strategy and spatial planning across the Borough, Milton Keynes Council has commissioned the production of a multi-layered, GIS-based Energy Map. This tool has subsequently been developed by the Centre for Sustainable Energy (CSE) in partnership with the United Sustainable Energy Agency (USEA) and consists of written outputs, spreadsheet databases and a GIS map package. The project aims to document sustainable sources of energy and their use within Milton Keynes along with the various low and zero carbon technologies currently in place and the scope for their wider implementation. Assessments of opportunities for energy efficiency in buildings, renewable energy generation and district heating have been undertaken. A range of low and zero carbon energy resources and potential opportunities has subsequently been mapped and quantified where possible along with known existing installations. Estimates have been made using appropriate methodologies tailored to each resource or technology, which typically apply a set of constraints or assumptions to result in a ‘technical’ resource. Table A1 below summarises these findings in terms of installation numbers and generation capacity where applicable. These findings have been mapped where possible in order to begin building up Energy Character Areas which aim to explore the way in which sustainable energy resources and opportunities are spatially distributed across the Borough and how they may interact. A series of pdf maps reproduced in Appendix 1 present these spatially-distributed relationships and Map A1 below illustrates how selections of the parameters considered can be combined. In addition, the GIS data supplied alongside this report can be viewed as layers to further explore interactions. In reality it is not just locational factors that need to be considered; there are also opportunities that relate to categories of buildings, development opportunities or organisations, and the report summarises key points for both in Tables 22 and 23.

In conclusion, the report presents the outcome of four different modelled scenarios for 2020 based on the resource assessments described above. A spreadsheet model was developed to model carbon reductions from a range of different sources and to compare these to the Milton Keynes area 2020 carbon reduction target of a 40% reduction relative to 2005. The approach used was to calculate the total opportunity, in terms of the maximum carbon savings which could come from each individual source, and then apply a percentage to be achieved by 2020. The 2020 target is ambitious and a high level of effort is required in all areas if the target is to be achieved. It cannot be achieved by energy efficiency alone or by use of renewable energy alone. As well as technical measures, behaviour change must be stimulated in both the domestic and commercial sectors. The transport sector should not be ignored. The scenarios here have used a relatively modest assumption for reduction of carbon emissions from transport, and a greater reduction in the transport sector would take some pressure off the other sectors. The savings achieved in each scenario are shown by category in Figure A2. The resulting per capita emissions (with and without the savings from grid-connected renewable energy) are illustrated in Figure A3.

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Existing installations

(from Table 2) Potential for technology

(summarised from Sections 3 & 4)

Measure

Approx. total no. of

identified installations

Approx. total capacity [kW]

Approx. total no. of

potential installations

Approx. total capacity [kW] Notes

Wind power (large scale) 7 14,000 45 112,500

Assumes basic, additional and flood risk constraints are applied. Small or micro scale not considered in estimate of potential. No. of potential installations refers to no. of turbines.

Wind power (med/small scale) 55 171 133 106,000

Biomass

Woodland residues

13 (wood-fired

boilers) >1,500

Variable depending on

scale 15,000

From mapped woodland resource (will also be a small additional resource from arboricultural arisings not captured in mapped resource)

Energy crops 0 0

Variable depending on

scale 39,000 Energy crops (assumes 5% utilisation of

unconstrained land area)

Hydropower 0 0 35 340 Estimated from Environment Agency hydropower study

Energy from waste

Agricultural & food waste

0 0 1 590 (elec)

Estimated using ‘Anaerobic Digestion Economic Assessment Tool V2.2’ Note – an AD plant using household food/garden waste is currently planned

Wood waste Unknown Unknown

Variable depending on

scale 13,136

Estimates from CRC site volumes of waste wood. Will include contaminated material and so limited in applications.

Sewage treatment 1 Unknown Unknown Unknown

Unlikely to be significant as most of resource already being processed by water utility in AD plant.

Other waste Unknown Unknown 1 6,000 (elec)

Assumes residual waste destined for proposed energy-from-waste plant at Old Wolverton.

Solar photovoltaics

671 (domestic)

15 (non-

domestic)

2,041 (domestic)

462 (non-

domestic)

(13,667) (20,500) Technical resource on existing building stock is vast and has not been assessed at this high level of analysis. Figures shown indicate potential on future housing development only (based on SHLAA data) for illustration. Solar thermal 670 Unknown (13,640) (34,100)

Heat pumps 8 (5 x GSHP; 3 x ASHP) >133 Significant opportunities but not

quantified Technical resource on existing and future building stock is vast and has not been assessed at this high level of analysis. Micro CHP 1 1 Significant opportunities but not

quantified

Commercial CHP & district heating 3

6,337 (elec) (heat output

unknown)

Significant opportunities but not quantified

Resource is highly site-specific and will depend on further analysis of mapped district heating opportunity areas and buildings with an identified high energy demand.

Loft insulation 12,722 n/a Significant opportunities but not quantified

Difficult to estimate potential due to lack of data.

Cavity wall insulation 14,109 n/a 42,506 n/a

Assumes properties built post-1940 are cavity wall (excl. non-trad). Potential derived by comparing the total number of cavity walled properties with those known to have insulation on construction (i.e. post 1989) or known to have been retrofitted.

Solid wall insulation Unknown n/a 8,875 n/a Assumes all solid wall properties have potential for insulation.

Window replacements 26,203 n/a Significant opportunities but not quantified

Difficult to estimate potential but considered significant as roughly only a quarter of all households have fitted replacement windows and/or boilers. Boiler replacements 24,193 n/a Significant opportunities but not

quantified

Table A1: Summary of existing and potential LZC measures

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Figure A1: Summary map of selected low and zero carbon generation resources & opportunities1

1 The map shows overall potential as per the overlay analysis described in Section 4, but also taking into account other factors as described in Table 19.

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Figure A2: Savings (tCO2) by category for each scenario

Figure A3: Resulting emissions per capita (tCO2) by scenario, with and without grid connected renewable energy

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

Domestic energy efficiency measures

Domestic behaviour change

Commercial sector energy efficiency

(measures and behaviour change)

Microgeneration, including micro-

hydro

Biomass boilers Gas from anaerobic digestion

District heating proposals

Transport savings

tCO

2

Scenario 1: Modest measures

Scenario 2: Energy efficiency driven

Scenario 3: Renewable energy driven

Scenario 4: High efficiency and high renewables

5.6 5.5

4.84.6

5.4

4.75.0

4.7

0

1

2

3

4

5

6

Per capita emissions without grid-connected RE Per capita emissions with grid-connected RE

tCO

2

Scenario 1: Modest measures

Scenario 2: Energy efficiency driven

Scenario 3: Renewable energy driven

Scenario 4: High efficiency and high renewables

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1 Introduction

1.1 Background As part of an on-going commitment to support sustainable energy strategy and spatial planning across the Borough, Milton Keynes Council has commissioned the production of a multi-layered, GIS-based Energy Map. This tool has subsequently been developed by the Centre for Sustainable Energy (CSE) in partnership with the United Sustainable Energy Agency (USEA)2

and consists of written outputs, spreadsheet databases and a GIS map package.

The project aims to document sustainable sources of energy and their use within Milton Keynes along with the range of low and zero carbon technologies currently in place and the scope for their wider implementation. Overall, the Energy Map is required to:

• Be compatible with other spatial information, i.e. to enable overlay and analysis against other GIS datasets (e.g. off-gas data, fuel poverty data, housing and master planning data);

• Provide a visual tool which can be used by local decision makers, developers and investors;

• Assist in the delivery of key strategies and plans, including MK Low Carbon Living Strategy (2010), MK Low Carbon Action Plan (2010), MK Core Strategy, MK Sustainable Community Strategy (2008) and MK Low Carbon Prospectus (2010).

Figure 1: Milton Keynes Borough study area

2 http://www.usea.org.uk/

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1.2 Terms of reference The original project tasks and outputs are shown in Table 1:

Task Requirement Output

1. Produce inventory of existing low and zero carbon (LZC) technologies within the Borough

To provide a baseline from which to monitor progress

Spreadsheet including coordinates (postcodes and house numbers) of technologies to allow map generation

2. Identify areas of opportunity for LZC technology solutions

Assess planned development opportunities across the borough to identify opportunities for LZC technologies

GIS layer identifying opportunity areas

3. Heat mapping and network analysis* Mapping of small to medium scale heat loads (1-50MWe), existing energy networks electricity, gas and district heating networks), and existing heat demands. Forecast energy network upgrades and thresholds by area to identify opportunities and constraints for LZC energy

Spreadsheet data specific to MK postcodes and point locations for heat loads and heat demand; map data of energy networks; heat map showing combined layers

4. Feasibility study for potential LZC technologies

Desk top resource and constraints analysis of each LZC technology

GIS layers and/or accompanying data highlighting resources, constraints and opportunities

5. Identify Energy Character Areas

To indicate for each part of the Borough the best energy solution

GIS layer identifying Energy Character Areas

6. Carbon Reduction Scenarios To model a number of scenarios to evaluate the effectiveness of different potential actions to achieve a 40% reduction in per capita carbon emissions by 2020, on a 2005 baseline

Written output of modelled scenario results

Table 1: Key project tasks and outputs

The actual delivered outputs of the project are as follows:

a) The following summary report detailing each task and listing data sources and assumptions; b) Spreadsheet 1 – Inventory of Existing Measures; c) Spreadsheet 2 – Areas of Opportunity; d) GIS format data and pdf maps.

*Note – information from Western Power Distribution, the local Distribution Network Operator, on existing/planned electricity networks was not available in a format to be mapped across Milton Keynes Borough. Similar data on gas distribution pipelines from Scottish Gas Networks was also unavailable at the scale required, although proxy maps of areas with/without access to mains gas are included in this report.

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2 Existing low and zero carbon technologies This section describes the accompanying spreadsheet entitled ‘Spreadsheet 1 - Inventory of Existing LZC Measures’ and the data and relevant sources therein. This data has been collated to help provide a baseline from which to monitor progress of LZC technologies within the Borough. The spreadsheet includes installed energy efficiency and renewable energy measures for private/public sector housing, public/commercial sector buildings and stand-alone installations. Individual worksheets are included which identify existing installations of each of the following LZC technologies within Milton Keynes Borough:

• Cavity wall insulation3

• Loft insulation

• Window replacements • Boiler replacements • Photovoltaic (PV) - domestic • Photovoltaic (PV) – non-domestic • Solar water heating • Wind • Biomass boilers • Micro CHP (Combined Heat and Power) • Anaerobic Digestion and waste heat • Commercial CHP & DH (Combined Heat and Power & District Heating) • Heat Pumps.

All data is collated and aggregated by each measure for the respective postcode location. The spreadsheet includes another two columns for each postcode location identifying their geographical locations in Eastings (X) and Northings (Y) which enables GIS mapping of measures. Table 2 below describes the data sources used to compile each compendium of measures. As the Low Carbon Buildings Programme has generated the majority of pre-Feed-in Tariff renewable energy installation data, the Building Research Establishment (BRE), Energy Saving Trust (EST) and Department of Energy and Climate Change (DECC) were all approached to obtain Low Carbon Buildings Programme data. It was subsequently noted that only DECC holds the installation data at this time and were not able to release this due to lack of resources and associated costs. It is thought that most of this information on renewables is captured within data gathered by Milton Keynes Council planning and development management departments. However, the limitation with this data is that it is not known which of the projects that gained planning permission were subsequently implemented, and of these, the installed capacity.

3 Note – solid wall insulation is not included as the scarcity of data is likely to significantly underestimate the number of installations.

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Measure Approx. total no. of identified installations

Approx. total capacity [kW] Data source

Cavity Wall Insulation 14,1091 n/a

• Milton Keynes Council building control data • Milton Keynes Carbon Offset Fund data • Maintenance data (Keystone database) – Milton

Keynes Council own stock

Loft Insulation 12,7221 n/a

• Milton Keynes Carbon Offset Fund data • HEED data • Maintenance data (Keystone database) – Milton

Keynes Council own stock

Window replacements 26,203 n/a

• Milton Keynes building control data • Maintenance data (Keystone database) – Milton

Keynes Council own stock

Boiler replacements 24,193 n/a

• Milton Keynes building control data • Maintenance data (Keystone database) – Milton

Keynes Council own stock

PV domestic 671 2,041 • Ofgem FIT register

PV non-domestic 15 462

• MKC – “Low Carbon Map” • Ofgem FIT register • MK Low Carbon Living2 • MKC planning and building control data

Solar water heating 670 Unknown

• MKC – “Low Carbon Map” • MK Low Carbon Living2 • MKC planning and building control data

(includes installations within new housing sites)

Wind 7 turbines (large scale) 55 turbines (med/small scale)

14,000 (large scale) 171 (med/small scale)

• MKC – “Low Carbon Map” • MK Low Carbon Living2

Biomass boilers 13 >1,500 • MKC – “Low Carbon Map” • MK Low Carbon Living2 • MKC planning and building control data

Micro CHP 1 1 • Ofgem FIT register Anaerobic Digestion and waste heat

1 Unknown • MKC – “Low Carbon Map”

Commercial CHP & district heating 3 6,337 (kWe)

Heat output unknown • MKC – “Low Carbon Map” • MKC planning and building control data

Heat Pumps 8 (5 x GSHP; 3 x ASHP) >133 • MKC – “Low Carbon Map” • MK Low Carbon Living2 • MKC planning and building control data

Table 2: Summary of identified LZC technology measures and data sources

1 Note that these figures are likely to be underestimates as they do not include properties receiving measures prior to 2005 as no reliable data exists. 2MK Low Carbon Living data from: http://www.milton-keynes.gov.uk/mklowcarbonliving/displayarticle.asp?ID=70509 (last accessed 29/11/11)

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3 Potential for low and zero carbon technologies This section describes the categories of LZC considered, evaluates their potential in Milton Keynes Borough and discusses areas of opportunity for their deployment. LZC technologies can generally be categorised as:

• Stand-alone and community-scale generation – i.e. wind power, biomass (woodland residues & energy crops), hydro, energy from waste, combined heat and power (CHP) and district heating;

• Microgeneration – i.e. solar PV, solar water heating, heat pumps, micro-hydro, micro-wind and micro-CHP; • Energy efficiency – i.e. wall/loft insulation, double glazing, high efficiency boilers etc.

In reality there is some cross-over between these categories e.g. biomass, solar PV and hydro can all be implemented at the larger stand-alone/ community-scale or as microgeneration. These LZC technologies are discussed below, with the exception of CHP and district heating, which is covered in Section 4. The accompanying spreadsheet entitled ‘Spreadsheet 2 - Areas of opportunity’ contains various data pertaining to opportunities for some of the LZC technologies described and is referenced below as appropriate.

3.1 Wind power

3.1.1 Methodology A constraints based-analysis was undertaken using a Geographic Information System (GIS). The basic principle of this analysis is to identify places where the installation of wind turbines would be impossible or unlikely, based on a set of defined constraints. The areas that remain unconstrained are those which are potentially suitable for wind development. Note: for the purposes of this analysis, large and medium scale wind is defined as in Table 3 and a set of constraints were applied sequentially in three groups: basic constraints, additional optional constraints, and flood risk. However, at time of writing it should be noted that Milton Keynes Council had very recently issued a draft Supplementary Planning Document on Wind Turbine Planning Applications which proposed guidance on separation distances i.e. buffer zones around buildings. The separation distances assumed in this report do not reflect those proposed in the SPD. Small or micro scale turbines are not included in the estimates below due to the many site-specific assessment factors that are considered to be outside the scope of this level of analysis.

Scale Capacity Hub height Rotor diameter Minimum wind speed required Large 2.5MW 85m 100m 6 m/s at hub height

Medium 800kW 50m 48m 6 m/s at hub height

Table 3: Large and medium scale wind definitions

3.1.2 Basic constraints The set of constraints in Table 4 indicates areas where it would be impossible or impractical to install a turbine:

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Constraint Data source and notes

Wind speed below 6 m/s at hub height Average annual wind speed from NOABL (Numerical Objective Analysis Boundary Layer): www.decc.gov.uk/en/content/cms/meeting_energy/ wind/windsp_databas/windsp_databas.aspx

Canals, rivers and lakes4

Milton Keynes Council data

Wooded areas

Natural England: Ancient woodland. Forestry Commission: National Inventory of Woodland and Trees and National Forest Inventory www.naturalengland.org.uk/publications/data/default.aspx www.forestry.gov.uk/datadownload

Buffer zone around residential properties

Milton Keynes Local Land and Property Gazetteer

Buffer zone around non-residential properties

Milton Keynes Local Land and Property Gazetteer

Buffer zone around 400kV electric lines5National Grid http:

www.nationalgrid.com/uk/

LandandDevelopment/DDC/GasElectricNW/ undergroundcables/shape/

Buffer zone around roads and rail lines6

Milton Keynes Council data

Ancient monuments

Milton Keynes Council data

Table 4: Basic constraints

The sizes of the buffer zones used are shown in Table 5.

Constraint Large wind Medium wind Residential properties 750m 400m Non-residential properties 400m 80m Roads and rail 150m 80m 400kV electric lines 550m 265m

Table 5: Buffer zones

3.1.3 Additional/optional constraints The following constraints indicate places where turbines could potentially operate, but may have a significant adverse impact. GIS data for each has been obtained from Milton Keynes Council. The impact of these constraints would need to be assessed on a site-specific basis.

• Country parks • Local nature reserves • Local wildlife sites • Tree preservation order groups • Wildlife corridors • Sites of Special Scientific Interest (SSSI)7

4Canals and rivers are represented in the GIS data as lines with no width and so, after referring to satellite photos as a guide, a width of 20m was assumed.

5 No GIS data is available for smaller lines, and so the location of electric lines is something to be investigated on a site-specific basis 6 Again, the GIS data shows these as lines with no width and so width has been estimated using satellite photos as a guide: 40m for the M1, 25m for major roads, and 20m for rail lines.

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3.1.4 Flood risk These indicate areas designated as having a Level 3 flood risk (data from Milton Keynes Council). Planning Policy Statement 25 (Development and Flood Risk) classes wind turbines as essential infrastructure which is vulnerable to flooding8

. Again this is something which would also have to be considered on a site-specific basis.

3.1.5 Aviation constraints NATS En Route Ltd (NERL9

) has identified areas where wind turbines could potentially interfere with operation of MOD and civil aviation radar and communications; however, this does not mean that wind development cannot go ahead in any of these areas, but that NERL must be consulted on any proposals within these areas. According to the maps they provide, turbines located in an area to the east covering around a third of Milton Keynes may affect the operation of one of their navigational aids and they should be consulted concerning proposed developments in this area. Therefore it has not been included as a constraint. The Emberton wind farm is located within the consultation zone, as are the smaller turbines at Daimler Chrysler.

3.1.6 Data caveats Any analysis is only as good as the data used, and while the best available datasets have been used for this analysis, they are not perfect representations of reality. Some notes on the data are included below: The wind speed data, from the NOABL database, comes from an air flow model which estimates the effect of topography on wind speed. It does not take into account thermally driven winds such as sea breezes or those caused by valleys. It also does not take into account the effect of surface roughness caused by different types of land surface, such as trees, crops or buildings. Wind developers always take a year's worth of on-site wind speed measurements before going ahead with a project. Data on location of woodlands is gathered by the Forestry Commission using satellite imagery. This means that sometimes areas can be classified as the wrong type of woodland, or small areas can be missed or wrongly classified as woodland. Nationwide, this dataset has a good level of accuracy but there is the possibility of small errors at local level. At this level of analysis, there are a number of site-specific factors which cannot be taken into account, such as prevailing wind direction and economics of individual projects, which mean that some of the areas identified may be found to not be suitable for wind development when a feasibility study for an individual site is undertaken. Conversely, there may be some areas which are identified as being constrained, within which, on a site specific basis, it may be possible to remove the constraints so that it became feasible to locate a turbine within the constrained area. For example, for large wind, all areas within 750m of a dwelling have been excluded in this analysis, but in some cases this buffer zone could be reduced. This would happen in the case where local topography is such that there is no impact on dwellings within the buffer distance, or where only one or two dwellings are within the buffer zone but mitigating techniques such as sound insulation, double glazing, and the planting of screening, can be used to neutralise the impact on the dwellings.

3.1.7 Mapped resource Maps 1-6 in Appendix 1 show the areas remaining for medium and large scale wind after each set of mapped constraint layer is applied as follows:

7 In addition the locations of the following protected areas were checked, using national data from Natural England, and found not to be relevant in Milton Keynes: Ramsar, Special Areas of Conservation, Special Protection Areas, National Parks, National Nature Reserves. See www.gis.naturalengland.org.uk/pubs/gis/GIS_register.asp. 8 http://www.communities.gov.uk/documents/planningandbuilding/pdf/planningpolicystatement25.pdf: see p.25, table 2D. 9 www.nats.co.uk/enviro/windfarms/nerl-self-assessment-maps/

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• Basic constraints • Basic and additional constraints • Basic, additional and flood risk constraints

Table 6 quantifies these areas in terms of approximate generation capacities and yields. Where areas are suitable for both large and medium wind, large wind has been given priority, and hence the hectare figures in Table 6 are actually lower than those indicated in Maps 1-6 (see Appendix 1 on p.46).

Level of constraints Area [ha]

Capacity [MW]

Energy yield [MWh/yr]

Large-scale wind

Unconstrained area - not incorporating optional constraints 1,433 140 318,864

With optional constraints 1,337 125 284,700

With flood risk 3 1,205 112.5 256,230

Medium-scale wind

Unconstrained area - not incorporating optional constraints 1,139 113 197,976

With optional constraints 1,085 106 185,712

With flood risk 3 1,071 106 185,712

Table 6: Wind resource at various levels of constraint

Assumptions: • Plots of land <1 ha excluded • Large wind: 36 ha needed per turbine; capacity factor = 0.26 • Medium wind: 9 ha needed per turbine; capacity factor = 0.2

3.2 Biomass: woodland residues

3.2.1 Methodology The technically available resource from sustainable management of woodland can be assessed by calculating the total area of woodland in the Milton Keynes area and using assumptions about the yield that can be obtained from sustainable management of the woodland. Two Forestry Commission datasets have been used for this analysis. The National Inventory of Woodland and Trees10 (NIWT) is produced by using satellite images to identify and classify areas of woodland11

. It classifies areas of woodland into the following categories:

• Broadleaved • Coniferous • Coppice • Coppice with standards • Mixed • Shrub • Young trees • Felled • Ground prepared for planting

The Forestry Commission has recently updated this dataset with the National Forest Inventory (NFI)12

. This dataset adds some additional areas to the NIWT but does not classify them into woodland types. As the NIWT dataset provides more information about the woodland type, this has been used as the main dataset, with additional information being obtained from the NFI.

Different types of woodland yield have different sustainable yields. This analysis uses the following yield factors:

10 http://www.forestry.gov.uk/forestry/HCOU-54PG9U, data available from http://www.forestry.gov.uk/datadownload 11 This means that there are occasional errors where patches in photographs have been erroneously identified. 12 More information available from www.forestry.gov.uk/forestry/infd-8eyjwf

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Type Yield: Tonnes per Hectare at 30% moisture13

Broadleaved

1.93

Coniferous 6.43

Coppice 3.86

Coppice with Standards 3.86

Mixed 3.86

NFI dataset 3.86

Table 7: Typical yield factors for managed woodlands

It should be noted that this is the yield arising from sustainable management, not from cutting down all the trees in the area. At its most basic level, sustainable management means removing timber at a rate that is replaced by planting and natural regeneration. The Forestry Commission data includes groups of trees within areas managed by the Parks Trust, but does not include scattered individual trees in these parks. It also does not include street trees maintained by the Council, and these two sources could potentially contribute a further large resource, some of which is already being used, as the Parks Trust sells firewood. Following discussions with the Parks Trust, broad estimates of these resources are as follows:

• Parks Trust: 800 tonnes/yr (coppice and other residues chipped on site for spreading on paths, gardens, verges etc);

• MKC landscape operations: 500 tonnes/yr (wood chipped on site); • Parks Trust Forestry Service: 1,200 tonnes/yr (firewood sales). Note – the Parks Trust also provides ~200

tonnes of woodfuel per year for biomass boilers installed at Heathrow Airport. Additional woodfuel resources are considered in Section 3.5.2 under ‘wood waste’.

3.2.2 Woodland resource Table 8 below quantifies the different types of woodland across Milton Keynes Borough. Note that not all of the types listed in Table 7 are present in the area. The location of these woodlands within Milton Keynes Borough is indicated in Map 7 (Appendix 1). Note also that the figures will capture a certain amount of arboricultural arisings but other sources such as street tree management residues will be an additional resource.

Type Area (hectares) Yield factor Total potential yield per year (tonnes, 30% moisture)

Broadleaved 603 1.93 1,164

Coniferous 416 6.43 2,676

Felled 66 n/a n/a

Mixed 46 3.86 178

Shrub 14 n/a n/a

Young trees 148 n/a n/a

Woodland (not specified) 677 3.86 2,612

Total 1,971 – 6,631

Table 8: Areas of woodland within the study area, by type

13 Plan LoCaL Biomass Woodfuel Estimator: www.planlocal.org.uk/downloads/group/further-resources

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At 30% moisture content, the energy content per tonne is around 3,500 kWh, giving a total theoretical annual resource of 23.2 GWh. Assuming an average boiler capacity factor of 0.18, this amount would result in an approximate boiler capacity of 15 MW.

3.3 Biomass: energy crops

3.3.1 Methodology This assessment looks at the potential for short rotation coppice (SRC) and miscanthus. The total technical potential resource for energy crops can be assessed by looking at the total amount of suitable agricultural land within the study area. Energy crops can potentially be grown on agricultural land of grades 1-3 (arable land). Defra provides a dataset showing the land classification across England. Constraints have been applied to exclude areas of agricultural land where energy crops are unlikely to be grown. These constraints are shown in Table 9. The Environmental Impact Assessment (Agriculture) (England) Regulations 2006 control activity on uncultivated land and semi-natural areas. According to a resource assessment methodology published by DECC in 201014

, this is likely to constrain cultivation of energy crops on permanent pasture and moorland. For this reason, available data on grassland and common land has been used as a constraint on energy crops.

Miscanthus does not grow well in exposed areas, which for the purposes of this resource assessment are defined as areas where the wind speed is above 7m/s at 10m above ground level. There is nowhere in Milton Keynes where the wind speed exceeds this level and so both miscanthus and SRC can be grown on the land identified. Wind speed has therefore not been included in the table of constraints. It should be noted that the agricultural land classification dataset does not identify farmland as such; it identifies broad areas where the land is of a specified quality, and was not intended to have a higher resolution than this. Some of these broad areas may well contain features such as lakes, golf courses and playing fields, which are not identified in the dataset. Where possible these have been accounted for using other datasets. For example, a dataset showing the location of lakes in Milton Keynes is available, so this has been used to remove areas where lakes are identified as agricultural land. Another example is that the agricultural land classification identifies urban areas but does not identify villages and hamlets. To remove these areas, the National Land and Property Gazetteer was used to identify buildings. However, it is possible that the hectare figure resulting from the analysis will be an overestimate. This is addressed further in Section 3.3.2.

14 Available from www.decc.gov.uk/assets/decc/What%20we%20do/UK%20energy%20supply/Energy%20mix/Renewable%20energy/ORED/1_20100305105045_e_@@_MethodologyfortheEnglishregions.pdf

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Constraint Details Data source

Land grade Exclude agricultural land grades 4 and 5 and urban areas Defra. Available from www.magic.gov.uk.

Protected land

Grassland types: lowland heathland, upland heathland, lowland meadows, coastal and floodplain grazing marsh, lowland dry acid grassland, lowland calcareous grassland

Natural England. Available from www.gis.naturalengland.org.uk /pubs/gis/GIS_register.asp

Protected woodland Ancient woodland Natural England. Available from www.gis.naturalengland.org.uk /pubs/gis/GIS_register.asp

Woodland National Inventory of Woodland and Trees National Forest Inventory

Forestry Commission. Available from http://www.forestry.gov.uk/datadownload

Heritage sites Ancient monuments Milton Keynes Council data

Datasets used to

reduce

overestimates in the

agricultural land

dataset

Parks and Gardens Milton Keynes Council data

Areas of housing MKC Local Land and Property Gazetteer

Lakes Milton Keynes Council data

Recreation and open space Milton Keynes Council data

Roads and railway Milton Keynes Council data

Housing expansion areas Milton Keynes Council data

Table 9: Constraints applied for energy crops

3.3.2 Energy crops resource Map 8 (Appendix 1) includes a map showing the extent of land area within Milton Keynes Borough that remains after the energy crop constraints are applied. Each hectare of this land planted with a crop could potentially yield a certain tonnage of wood fuel annually. Multiplying the land area available by this yield factor gives a total yield for the land area. Yield can vary depending on land and weather conditions, but typical yield factors are shown in Table 10 (here oven dried tonnes, which have a 0% moisture content, are used in the calculation). Appendix 2 includes a map to show the geographical spread of the energy crops resource.

Crop Yield (oven-dried tonnes) per hectare

Miscanthus 15

SRC (Short Rotation Coppice) 10

Table 10: Typical yield factors for Miscanthus and SRC

Table 11 below shows expected yields and the energy provided if energy crops were grown on specific proportions of the suitable land. At 0% moisture, energy content is around 5,000 kWh per tonne. As most agricultural land will currently be in use, either for other crops or as set-aside, establishment of energy crops would require farmers to make the decision to switch to energy crops. It is unlikely that a large proportion of the agricultural land would be switched to energy crops, at least in the short to medium term, and so small percentages of the land are used as examples to give a more realistic assessment of energy crop potential. Table 11 therefore shows yields under different land utilisation scenarios. The use of these scenarios also mitigates the risk of over-estimating the potential resource due to the agricultural land classification dataset covering some areas which are not in reality farmland. Note that the miscanthus and SRC yields are alternatives and should not be added together.

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% of available land

Hectares Yield (oven-dried tonnes) Energy MWh

Misc. SRC Misc. SRC

100% 16,389 245,840 163,893 1,229,201 819,467

5% 819 12,292 8,195 61,460 40,973

2% 328 4,917 3,278 24,584 16,389

1% 164 2,458 1,639 12,292 8,195

Table 11: Energy crop production under different land utilisation scenarios (using either miscanthus or SRC)

Assuming an average boiler capacity factor of 0.18, the miscanthus yield at 5% land-take would result in an approximate boiler capacity of 39 MW. Examples of site-specific factors which should be considered when planting energy crops are: whether the area is water-stressed, and what the potential (positive and negative) impacts are on biodiversity and protected landscapes. Note - the accompanying spreadsheet entitled ‘Spreadsheet 2 - Areas of Opportunity’ contains a ‘Biomass boiler proposed’ worksheet which lists biomass installations proposed under the Milton Keynes ‘Low Carbon Map’.

3.4 Hydropower

3.4.1 The Environment Agency hydropower study In assessing the potential for hydropower, the 2010 hydropower study by the Environment Agency has been reviewed. This study looked at hydropower potential in England and Wales and the environmental impact of developing sites (in particular the impact on fish migration)15. It looked at existing structures in rivers such as weirs and locks and identified sites which would provide 'win-win' opportunities, sites with good hydropower potential which are located in heavily modified water bodies16

3.4.2 Approximate locations of opportunities

where the ecological status of the water body could be improved by works to install a hydropower plant (for example, at the same time as installing a hydropower plant, a fish pass could be added to a weir to aid fish migration).

The locations of win-win opportunities are published in the report but the maps are very large scale. However, these locations have been transferred to GIS format for the purposes of this report. This was done by identifying by eye the approximate location from the Environment Agency study, and then locating them on a river or body of water, as identified by other GIS data and OS maps. Map 9 (Appendix 1) shows the locations of win-win opportunities, but it should be emphasised that these locations are only very approximate due to the method used to transfer them to GIS format.

3.4.3 Calculation of capacity of hydropower opportunities The potential capacity of hydropower opportunities in Milton Keynes was calculated by using the categories from the map above, assuming an average size for opportunities in this band, and then multiplying this by the number of opportunities in each band, as illustrated in Table 12. Note that because these were identified by eye from a large-scale printed map, the numbers are approximate because some points overlap, creating a cluster, and in these cases a best estimate has been made of the number of points in a cluster.

15 Opportunity and Environmental Sensitivity Mapping for Hydropower in England and Wales. See www.environment-agency.gov.uk/shell/hydropowerswf.html 16 As defined by the EU Water Framework Directive, heavily modified water bodies are those at significant risk of failing to achieve good ecological status because of modifications resulting from past engineering works.

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Number Category Assumed average size Capacity (number x assumed size)

26 0-10 kW 5 kW 130 kW

4 10-20 kW 15 kW 60 kW

5 20-50 kW 30 kW 150 kW

Total 340 kW Table 12: Calculation of micro-hydro opportunity

The total capacity of 340kW (which could deliver around 1.4GWh/yr assuming a 50% capacity factor) is very low compared to the potential of other technologies; however, hydropower should not be discounted as it is a popular technology for community energy projects and can provide associated environmental benefits, as shown by the Environment Agency Study.

3.5 Energy from waste

3.5.1 Agricultural and food waste In considering the total potential for anaerobic digestion from feedstock arisings within Milton Keynes, estimates have been made of agricultural and food waste arisings and the subsequent installed plant capacity and energy yields these could generate (Table 13). The current ‘inventory of existing measures’ spreadsheet shows one anaerobic digestion site at Pineham waste water treatment facility operated by Anglian Water. It is understood that further anaerobic digestion facilities are also planned. This facility will process all of the Borough’s food and garden waste; hence the estimates below will include arisings that are destined for this new facility.

Waste source Quantity (tonnes/yr) Potential installed capacity [MWe]

Potential electricity generation [MWh/yr]

Potential useful heat generation

[MWh/yr]

Agricultural waste 27,833 0.15 1,392 1,440

Food waste – commercial and Industrial 18,296 0.19 1,683 1,742

Food waste – Municipal Solid Waste 24,181 0.25 2,225 2,303

Total 70,310 0.59 5,300 5,485

Table 13: Technical resource for Anaerobic Digestion

Assumptions:

• Agricultural waste: only cattle waste is considered; pig slurry and poultry litter is negligible and hence not considered. [Source: June Survey of Agriculture and Horticulture; 2010. Defra17

• Food waste: Data available on England regions. Milton Keynes Commercial and Industrial waste share based on population; [Source “Survey of Commercial and Industrial Waste Arisings, 2010”; Environmental Agency/Defra]

]. 1 tonne slurry/day assumed from 30 cattle with 50% collected. [Source: Anaerobic digestion of farm and food processing residues; Good Practice Guidelines; British Biogen]. Agricultural waste biogas yield = 25m3/tonne feedstock; energy value = 23 MJ/m3 biogas. [Source: Anaerobic digestion of farm and food processing residues; Good Practice Guidelines; British Biogen];

18

• In estimating installed capacities and elec/heat generation, the ‘Anaerobic Digestion Economic Assessment Tool V2.2’ was used

. Food waste biogas yield = 46 m3/tonne feedstock; energy value = 21 MJ/m3 biogas. [Source: Anaerobic digestion of farm and food processing residues; Good Practice Guidelines; British Biogen];

19

. This assumes a parasitic heat load which decreases useful heat output.

3.5.2 Wood waste The waste wood resource is more difficult to quantify but will include some 5,918 tonnes/yr collected at Community Recycling Centres and other unquantified amounts within commercial/industrial/construction waste streams. This will typically consist of clean, untreated material mixed with that contaminated with paint, preservative, fixings and other

17 http://www.defra.gov.uk/statistics/foodfarm/landuselivestock/junesurvey/junesurveyresults/ 18 http://www.defra.gov.uk/news/2010/11/10/waste-arisings-stats/ 19 http://www.nnfcc.co.uk/tools/economic-assessment-of-anaerobic-digestion-technology-and-its-suitability-to-uk-farming-and-waste-systems-ad-cost-caculator-tool-nnfcc-10-010

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foreign materials. Whilst clean waste wood can potentially be sourced directly from saw mills, carpenters, joineries etc, a large proportion of this resource will be mixed with contaminated material in mainstream commercial and municipal solid waste streams. Due to pollution and air quality concerns contaminated waste wood is generally not suitable to be used in small or medium scale boiler installations due to the lack of suitable exhaust gas clean-up equipment; these clean-up systems are costly and tend to be viable on large scale plant only. Note – the wood resource from woodland and arboricultural arisings are considered in Section 3.2.

3.5.3 Other waste Following AD/composting and recycling activities, residual elements of Municipal Solid Waste and commercial/industrial waste can be used as a feedstock in larger scale energy-from-waste plant using advanced thermal processes. Table 14 shows the end-uses of annual household waste arisings in Milton Keynes for 2010/11. The figures show that over 52,000 tonnes of residual household waste went to landfill, a proportion of which could potentially be used for energy generation. As most commercial/industrial waste is collected by private contractors, the quantity of arisings is not known although it is generally believed to be of an equal order to the quantity of household waste arisings. Assuming 50% of residual household waste is suitable for an energy-from-waste process, a plant in the order of 2.5MW may be required.20

End-use Quantity (tonnes)

Reuse 3,225

Recycling 33,333

Composting 24,181

Other recycling & composting 644

Energy from waste 4,133

Landfilling 52,253

Total household waste 117,769

Table 14: End-use of household waste arisings in Milton Keynes 2010/1121

It is understood that a residual waste treatment plant is scheduled to be commissioned in Old Wolverton by 2016 with a capacity upwards of 60,000 tonnes/yr. This is expected to employ a number of conversion technologies and be sized around 6MW (electrical) to produce around 45 GWh/yr. Waste oil Waste oil from industrial and commercial uses and vehicles can be refined to produce refined fuel oil (RFO). It is difficult to calculate the amount of waste oil available in the UK, but the best estimates from the Environment Agency assume a figure of about 350,000 tonnes per year. Apportioning this figure in terms of population, a city the size of Milton Keynes (i.e. 0.039 x UK population) is likely to have to potential to produce about 1.5 million litres of RFO a year. This would be sufficient to provide enough heat for over 700 homes.

3.6 Microgeneration

3.6.1 Microgeneration resources The term ‘microgeneration’ generally refers to small-scale technologies that are physically integrated to buildings or are located within the curtilage of a development and are directly supplying it with energy. The most well-known microgeneration technologies include: 20 Assumes 10kt per MW elec capacity (see Table 3-5 in reference in Footnote 14) 21 From Municipal Zero Waste Management Strategy Refresh 2011, Table 7 (Milton Keynes Council)

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• Solar PV • Solar water heating • Micro/small-scale wind • Micro-hydro • Ground or air source heat pumps • Biomass heating (woodfuel) • Micro-CHP

A more formal definition of microgeneration is given under the terms of the Green Energy Act 2009 and assumed by the Government’s Microgeneration Strategy (June 2011) i.e. those installations up to 50kW for electricity and up to 300kWth (kW thermal) for heat. This definition therefore includes some micro-hydro plants, which although fall under the 50kW threshold, may not be directly associated with the supply of specific buildings. However, for the purpose of the current energy mapping study, micro-hydro is considered separately in Section 3.4 above. Most microgeneration technologies therefore tend to be for smaller-scale building-integrated applications, on both existing and new development, although certain technologies such as solar PV, biomass and hydro are also used for stand-alone or larger community-scale systems. In undertaking resource assessments, care is therefore needed to avoid double-counting certain resources such as woodfuel. For example, an amount of woodfuel could be earmarked for a CHP/district heating plant in a particular area at the same time as being allocated for microgeneration in the same area. Due to their direct link with buildings, estimating the type of microgeneration technology and generation capacities that are most likely to be deployed within a specific area over a specified timescale is a site-specific and complex task usually undertaken by specialist consultants employing detailed modelling tools set against the context of the prevailing regulatory requirements and financial viability which is beyond the scope of the current study. However, the theoretical building-integrated solar PV and solar water heating resources as indicated by the future development of an area is considered below in Section 3.6.3.

3.6.2 Microgeneration - key opportunities and constraints Table 15 sets out the key opportunities and constraints pertaining to microgeneration technologies. Alongside these, the Feed-in Tariff and Renewable Heat Incentive also act as key opportunities in terms of financial incentives for renewable technologies.

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Technology Key opportunities and constraints

Solar PV • Suited to southerly-orientated roofs or façades with minimal shading (GIS software tools are becoming increasingly

available to access suitability of individual buildings or whole areas)

• Restrictions apply on listed buildings or in conservation areas/World Heritage Sites

• Requires suitable grid-connection point (unless an off-grid system)

Solar water heating • Suited to southerly-orientated roofs with minimal shading

• Less suitable for buildings with low hot water demand e.g. offices

• Restrictions apply on listed buildings or in conservation areas/World Heritage Sites

• May be more limited on flats with separate hot water systems (lower ratio of roof space to dwellings)

• Requires hot water cylinder

• Can be particularly suited to buildings off the mains gas network (with higher existing heating fuel costs)

Micro/small scale wind • Micro-scale: potential on roofs of tall or exposed buildings with high annual average wind speeds

• Small-scale: suited to exposed sites some distance from dwellings with high annual average wind speeds

• Generally subject to similar constraints as for medium/large scale e.g. noise, visual impact etc; currently not classed as

‘Permitted Development’

• Requires suitable grid-connection point (unless an off-grid system)

• See also Section 3.1 for assessing the wind resource

Micro-hydro • Suited to water courses with suitable ‘head’ or flow characteristics, particularly former water mill sites

• Requires suitable grid-connection point (unless an off-grid system)

• See also Section 3.4 for assessing the hydro resource

Ground/air source

heat pumps

• Suited to buildings with high levels of insulation and low temperature heat distribution systems

• Ground source: suited to buildings with sufficient surrounding land area and having a suitable geology for heat

collector pipes

• Can be particularly suited to buildings off the mains gas network (with higher existing heating fuel costs)

• Particularly effective when used in conjunction with renewable electricity generation i.e. when powered with low/zero

carbon electricity

Small-scale biomass

heating (woodfuel)

• Suited to buildings with adequate space for biomass plant and fuel storage/deliveries

• Suited to buildings with access to a reliable woodfuel supply (preferably local)

• Can be particularly suited to buildings off the mains gas network (with higher existing heating fuel costs)

• Provides an opportunity for existing buildings where heating systems are due for refurbishment

• See also Section 3.2 for assessing woodfuel resource

Micro-CHP • Gas-fired domestic-scale models currently not widely market-tested or deployed. Can be more suited to households

with specific heating profiles.

• Non-domestic gas-fired small-scale CHP applications are widespread (note – technically, some fall under the definition

of microgeneration mentioned above)

Table 15: Key opportunities and constraints of microgeneration technologies

3.6.3 Solar resource The number of existing installations can be estimated from the Ofgem Central FIT (Feed-in Tariff) Register, which shows a total of 671 domestic and 17 non-domestic solar PV installations throughout Milton Keynes Borough up to December 31st 2011. Clearly the total potential for solar rooftop systems on existing buildings within the Borough is very large and could technically consider every building. However, practical opportunities will largely depend on roof area, slope and orientation, shading, building ownership, access to capital funding and revenues. More technical constraints generally apply to solar water heating such as additional space requirements for system components within a building (such as a hot water cylinder). Solar water heating is also limited to meeting the hot water demand of the building whereas PV is able to export surplus energy to the grid. However, buildings that are likely to benefit the most from solar PV installations are those that use a significant proportion of their electricity during the day, such as offices and

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commercial buildings. More PV-generated electricity can then be used directly on-site whilst offsetting electricity imports, which results in a greater financial benefit than when exporting surplus generation. The theoretical building-integrated solar PV and solar water heating resources as indicated by the existing and future development of an area can be broadly estimated using GIS techniques by considering the total roof area within a location and then considering the proportion that is appropriately orientated (e.g. a quarter of all roofs) followed by further assumptions on the extent of shading and size of system appropriate to the building type. A much finer-grained analysis can also be done using bespoke software packages that take into account actual roof area, orientation and shading.22

A coarse evaluation of the solar resource on existing buildings in an area the size of Milton Keynes Borough will be very approximate due to the many factors influencing system applications in practice. However, relative to building numbers, a much higher proportion of solar rooftop systems are likely to be installed on new developments due to sustainable construction standards and emission targets. Technically, solar technologies remain a relatively easy option for developers and currently are very likely to be part of the LZC technology mix in meeting targets. To illustrate the potential solar resource in this sector, Table 16 estimates generation capacities based on future housing requirements in the Borough as set out in the Strategic Housing Land Availability Assessment (SHLAA).

Technology No of dwellings Total installed capacity (MW)

Total generation (MWh/yr)

Solar PV 27,285

20.5 14,785

Solar water heating 34.1 17,394

Table 16: Solar technical resource for new residential development

Assumptions: • Assumes 50% of dwellings have roofs suitable for 1.5kW PV systems; 850kWh/kW/yr yield with 15% additional shading. • Assumes 50% of dwellings have roofs suitable for 2.5kW solar water heating systems; 600kWh/kW/yr yield with 15% additional shading.

Note - the accompanying spreadsheet entitled ‘Spreadsheet 2 - Areas of Opportunity’ contains a ‘Solar PV proposed’ worksheet which lists solar PV installations proposed under the Milton Keynes ‘Low Carbon Map’.

3.7 Energy efficiency

3.7.1 Loft and cavity wall insulation The accompanying spread sheet entitled ‘Spreadsheet 2 - Areas of opportunity’ contains a worksheet which lists individual property addresses from the Milton Keynes Carbon Offset Fund (COF) database where the potential for insulation is known as the occupant has expressed an interest in either loft or cavity wall insulation. The ‘CWI potential’ worksheet also indicates the potential for cavity wall insulation by estimating the difference at ward level between all houses with cavity walls, and those known to have had post construction cavity wall insulation installed. This difference is expressed as a percentage of total households in each ward. Wards with a high proportion of houses built in the last 20 years are likely to have cavity walls filled at construction; therefore the most modern estates will have little if any potential. This needs to be taken into account when looking at the worksheet. The ‘CWI potential (2)’ worksheet includes an alternative analysis carried out at ward level by taking all housing and subtracting those houses constructed before 1940 (most likely to be of solid wall construction) those built after 1989 (when building regulations required cavity walls to be insulated) and those houses where data shows cavity wall insulation has been installed or where the property was constructed using non-traditional methods (no cavity walls).

22 see Bristol’s Solar Map: www.bristol.gov.uk/page/solar-energy

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This left a residual data set of properties with the potential for cavity wall insulation and is thought to be a more reliable estimate than the method detailed above. Please note that data on cavity wall insulation installed post construction is not a complete data set of all such properties. Data is available for properties receiving post construction cavity wall insulation through CERT and EEC funded schemes and the Milton Keynes Carbon Offset Fund. Reliable data does not exist for properties receiving cavity wall insulation before this (2005). Map 10 (Appendix 1) indicates this potential and the geographical spread by mapping by ward the percentage of filled cavities relative to the total number of dwellings thought to have cavity walls. This analysis suggests that the potential for cavity wall insulation is highest in Bradwell, Linford North, Stony Stratford and Wolverton – these correspond to the wards in red on Map 10 which indicate those wards having only 21%-30% of all cavity-walled dwellings thought to have insulation. In terms of dwelling numbers, wards with more than 3,000 potential unfilled cavities are Campbell Park, Bradwell, Newport Pagnell and Stony Stratford. Using this method, the total number of properties with potential for cavity wall insulation is estimated to be about 42,500. However, it should be borne in mind that this figure includes properties receiving cavity wall insulation before 2005 for which no reliable information is obtainable. It is therefore recommended that any subsequent analysis looks at relative proportions of unfilled cavities rather than absolute numbers. The potential for loft insulation measures has not been estimated due to lack of reliable data, although this is thought to be significant. Known installed loft insulation measures number at 12,024 (based on HEED data), with 9,031 having been identified through the Carbon Offset Fund. Additional data received in Nov 2012 from MKC regarding loft insulation measures for 2012 indicates that a further 698 installations were undertaken, making a total of 12,722. As with cavity wall insulation, these numbers may not include older installations that pre-date the relevant datasets.

3.7.2 Solid wall insulation The accompanying spreadsheet entitled ‘Spreadsheet 2 - Areas of Opportunity’ contains a ‘Solid wall potential’ worksheet which lists the percentage of households in each ward having solid walls, the large majority of which will have no retrofit insulation. This is reproduced in Table 17 below. Also listed is the percentage of off-gas properties in each ward (see Map 11, Appendix 1) where insulation measures will be comparatively more viable due to increased savings on higher cost heating fuels. By ranking the product of these two percentages an ‘index’ is created which can be used to identify those wards with most potential for solid wall insulation. Examples of the latter are Sherington and Olney. Two other indicators that can be used to help identify priority areas for energy efficiency measures are Indices of Deprivation and fuel poverty, shown in Maps 12 and 13 (Appendix 1) respectively.

Ward Name Total households Solid Walled Off Gas Solid wall % Off gas % Index HTT

Bletchley and Fenny Stratford 5,120 850 224 16.6% 4.4% 0.007

Bradwell 5,168 142 86 2.7% 1.7% 0.000

Campbell Park 6,078 6 418 0.1% 6.9% 0.000

Danesborough 1,830 581 199 31.7% 10.9% 0.035

Denbigh 3,268 0 91 0.0% 2.8% 0.000

Eaton Manor 3,196 2 110 0.1% 3.4% 0.000

Emerson Valley 5,486 186 670 3.4% 12.2% 0.004

Furzton 3,673 2 308 0.1% 8.4% 0.000

Hanslope Park 1,699 333 259 19.6% 15.3% 0.030

Linford North 3,557 50 37 1.4% 1.0% 0.000

Linford South 3,560 60 293 1.7% 8.2% 0.001

Loughton Park 5,567 302 484 5.4% 8.7% 0.005

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Ward Name Total households Solid Walled Off Gas Solid wall % Off gas % Index HTT

Middleton 3,346 117 663 3.5% 19.8% 0.007

Newport Pagnell North 2,922 131 143 4.5% 4.9% 0.002

Newport Pagnell South 3,237 732 107 22.6% 3.3% 0.007

Olney 3,539 1,297 459 36.6% 13.0% 0.048

Sherington 1,667 604 1,030 36.2% 61.8% 0.224

Stantonbury 3,873 56 24 1.4% 0.6% 0.000

Stony Stratford 6,546 1,079 668 16.5% 10.2% 0.017

Walton Park 5,861 2 376 0.0% 6.4% 0.000

Whaddon 3,508 39 23 1.1% 0.6% 0.000

Wolverton 4,920 2,299 104 46.7% 2.1% 0.010

Woughton 4,412 5 146 0.1% 3.3% 0.000

Total 92,033 8,875 6,921 – – –

Table 17: Solid wall insulation potential (Note – ‘Index HTT’ is the product of ‘Solid wall %’ and ‘Off gas %’)

3.7.3 Other measures The accompanying spreadsheet entitled ‘Inventory of existing LZC technologies’ contains a list of known window and boiler replacements according to information from council stock housing databases and the development management process i.e. Building Regulations and Planning. Map 14 (Appendix 1) shows the location of Council stock improvements in terms of regeneration areas. Identifying further opportunities using this data is somewhat limited. However, as the built age of developments is known as well as the proportion of those properties in each development with new boilers and/or windows, some initial targeting of measures maybe possible. Roughly a quarter of all households have fitted replacement windows and/or boilers. There is therefore significant scope to improve housing stock energy efficiency.

3.8 Summary Table 18 summarises the assessment of low and zero carbon measures as set out in Sections 3 and 4. Note – refer to the relevant chapter section for further data sources and assumptions.

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Existing installations

(from Table 2) Potential for technology

(summarised from Sections 3 & 4)

Measure

Approx. total no. of

identified installations

Approx. total capacity [kW]

Approx. total no. of

potential installations

Approx. total capacity [kW] Notes

Wind power (large scale) 7 14,000 45 112,500

Assumes basic, additional and flood risk constraints are applied. Small or micro scale not considered in estimate of potential. No. of potential installations refers to no. of turbines.

Wind power (med/small scale) 55 171 133 106,000

Biomass

Woodland residues

13 (wood-fired

boilers) >1,500

Variable depending on

scale 15,000

From mapped woodland resource (will also be a small additional resource from arboricultural arisings not captured in mapped resource)

Energy crops 0 0

Variable depending on

scale 39,000 Energy crops (assumes 5% utilisation of

unconstrained land area)

Hydropower 0 0 35 340 Estimated from Environment Agency hydropower study

Energy from waste

Agricultural & food waste

0 0 1 590 (elec)

Estimated using ‘Anaerobic Digestion Economic Assessment Tool V2.2’ Note – an AD plant using household food/garden waste is currently planned

Wood waste Unknown Unknown

Variable depending on

scale 13,136

Estimates from CRC site volumes of waste wood. Will include contaminated material and so limited in applications.

Sewage treatment 1 Unknown Unknown Unknown

Unlikely to be significant as most of resource already being processed by water utility in AD plant.

Other waste Unknown Unknown 1 6,000 (elec)

Assumes residual waste destined for proposed energy-from-waste plant at Old Wolverton.

Solar photovoltaics

671 (domestic)

15 (non-

domestic)

2,041 (domestic)

462 (non-

domestic)

(13,667) (20,500) Technical resource on existing building stock is vast and has not been assessed at this high level of analysis. Figures shown indicate potential on future housing development only (based on SHLAA data) for illustration. Solar thermal 670 Unknown (13,640) (34,100)

Heat pumps 8 (5 x GSHP; 3 x ASHP) >133 Significant opportunities but not

quantified Technical resource on existing and future building stock is vast and has not been assessed at this high level of analysis. Micro CHP 1 1 Significant opportunities but not

quantified

Commercial CHP & district heating 3

6,337 (elec) (heat output

unknown)

Significant opportunities but not quantified

Resource is highly site-specific and will depend on further analysis of mapped district heating opportunity areas and buildings with an identified high energy demand.

Loft insulation 12,722 n/a Significant opportunities but not quantified

Difficult to estimate potential due to lack of data.

Cavity wall insulation 14,109 n/a 42,506 n/a

Assumes properties built post-1940 are cavity wall (excl. non-trad). Potential derived by comparing the total number of cavity walled properties with those known to have insulation on construction (i.e. post 1989) or known to have been retrofitted.

Solid wall insulation Unknown n/a 8,875 n/a Assumes all solid wall properties have potential for insulation.

Window replacements 26,203 n/a Significant opportunities but not quantified

Difficult to estimate potential but considered significant as roughly only a quarter of all households have fitted replacement windows and/or boilers. Boiler replacements 24,193 n/a Significant opportunities but not

quantified

Table 18 – summary of existing and potential LZC measures

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4 Milton Keynes Heat Map

4.1 Introduction CSE has produced the National Heat Map for the Department of Energy and Climate Change23

. The National Heat Map shows heat demand across England at a range of scales from national to local. Behind the heat map is a database of modelled heat demand for every address in the country (and actual heat demand for buildings which have Display Energy Certificates). This enables users to locate and investigate areas of high heat demand which may be suitable for district heating.

The purpose of the map is to support planning and deployment of local low-carbon energy projects in England, by providing publicly accessible high-resolution web-based maps of heat demand by area. The most useful way to visualise heat map data is in the form of a heat demand density layer. This shows heat demand per unit of land area (typically kWh heat / square metre). Areas with high concentrations of heat demand have higher spatial density values. This is intuitively easy to understand when seen on a map - Figure 2 shows an example heat density map24

overlaid on the address points from which it originates. The address points are scaled so that those with higher heat demand are represented by larger points. Heat density (the coloured base-map) is shown from blue to red, with blue areas being low density and red areas high density. Areas in which there are more and/or larger point heat demands close together, have higher heat densities.

Figure 2: Example of heat density map

23 See http://ceo.decc.gov.uk/nationalheatmap/ 24 This is an example area which is not within Milton Keynes

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4.2 Heat demand across Milton Keynes In this report different heat density maps are shown (and will also be provided in the GIS package – see Section 5.2). A point of note when looking at the maps illustrating this section of the analysis is that for a given location, the value for heat density will vary depending on the parameters used for the density calculation. This is because the heat density is calculated using a volume-preserving form of weighted average over a radius around each location on the map. Larger radii are typically used for larger scale, less detailed maps. Conversely, smaller radii are used on smaller scale, more detailed maps. As the level of detail increases, overall heat demand is constrained into smaller areas, so the density values naturally increase. Therefore, the relative heat density of different parts of an area is more important than the absolute figure. Maps 15 to 17 (Appendix 1) show all heat demand, residential heat demand and non-residential heat demand across the whole of Milton Keynes, using the same legend categories so that they can be compared. Values up to 5 kWh per square metre have been excluded and each legend category is the same width (5kWh).

Overall, as can be seen in Map 15, heat demand is concentrated in central Milton Keynes, as would be expected. There are also areas of high heat demand in Wolverton and Stony Stratford to the west, Newport Pagnell and Olney to the north, Kingston to the east and Bletchley to the south. Residential heat demand (Map 16) is more spread out, with no pockets of very high demand comparable to the overall heat demand map. Residential heat demand is mostly concentrated in the southern half of Milton Keynes, with pockets in Olney, Hanslope and Sherington. Non-residential heat demand (Map 17) is more concentrated, with the two highest areas of demand being central Milton Keynes (centred on the area around Saxon Gate) and the industrial area of Kingston.

Maps 18-20 show the same heat demand sources as above, with the same legend categories, but zoomed in to the central part of the borough. Each figure shows the same location. In these maps it is easier to see the contour effect produced by the heat density calculation.

Maps 21-23 also show the central area of Milton Keynes, but the view is more detailed because the parameters for the density calculation have been changed. In the previous figures, heat demand from individual buildings was smoothed out over quite a large area to give a contoured effect, showing the areas of highest heat demand. In the following figures, the smoothing happens over a smaller area, with the result being a more fine-grained picture where demand from individual buildings, or at least blocks, is visible. The heat density maps can be presented in many different ways. In the following figures, heat demand is divided into deciles by land area, so the width of each legend category varies, but each represents 10% of the land area. This means that each colour in the legend appears an equal number of times across the whole of Milton Keynes, so that there are more red areas in these maps than in the previous figures. The three figures all show the same location; only the types of heat demand (all, residential and non-residential) change.

4.3 Opportunities for district heating and CHP

4.3.1 Existing development District heating networks can source their heat from a range of energy plant technologies which may generate heat only, combined heat and power (CHP) or combined cooling, heat and power (CCHP). CHP plants combined with district heating can be an efficient way to provide energy25

25 For more information see DECC’s CHP Focus website and Site Assessment Tool:

, but the relative yields of heat and power are critical factors in economic plant operation. In particular, the proportion of generated heat that can be used (i.e. sold) is vital

http://chp.decc.gov.uk/cms/

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to consider during the design of CHP plant, hence the importance of establishing the potential for district heating. CHP plants not supplying district heating networks have potential for high energy use buildings, where imported mains electricity can be offset through cheap on-site generation and where there is an on-site use for the associated heat generation. CHP often uses natural gas, in which case it is an efficient but not renewable energy technology. When a renewable fuel source is used, typically woodfuel, CHP becomes a renewable energy technology. Gas-fired CHP is well-used in industry, but there are few biomass CHP schemes in the UK. The remainder of this section concerns assessing heat loads for district heating by use of the National Heat Map. There is an existing district heating system installed in central Milton Keynes, with a capacity of 3MW serving 800 homes and 30 shops and offices. There is a second proposed installation in the Oakgrove development, which would serve over 1,000 new homes. Milton Keynes Council has also identified other clusters of buildings (see ‘District heating proposed 2’ in the accompanying ‘Spreadsheet 2 – Areas of Opportunity’), which could form anchor loads in district heating systems, in the centre and south of the borough.

These are areas and loads which are already identified. The National Heat Map can be used to identify other areas of existing heat demand which might be suitable for district heating – this is described below. One of the main constraints to district heating is the need to identify a sufficient heat demand density. Urban areas with high population densities offer the most potential. The civil works associated with laying heat mains and establishing connections to individual buildings are expensive; high heat densities mean shorter pipe runs and therefore lower costs. District heating schemes are also cheaper in new developments due to the lower cost of civil works on new sites. Linear heat density is the critical factor in heat distribution economics, but this can only be calculated at the stage when a route has been defined. A route can only be defined when the participant buildings have been identified, and so when searching a large area for opportunities, a different approach must be taken. As a proxy for linear heat density, spatial heat density can be used to find areas most likely to contain high concentrations of heat demand. As well as looking for areas of high heat demand, the heat map can be used to identify other characteristics which might make an area suitable for district heating. Two key characteristics are the presence of anchor loads and large domestic loads, normally large blocks of social housing. Anchor heat loads are high, stable sources of heat demand. For example, a large hotel or hospital consumes a high amount of heat, and the heat used does not vary much during a day. This is a useful load to 'anchor' a district heating system around because it can provide a large proportion of the initial customer base required to justify the upfront cost of the investment. Other smaller sources of heat demand can be added on around this anchor load. Large public sector heat loads (e.g. council offices, council-owned leisure centres) are particularly good as anchor loads, as development of district heating is often public sector-driven. To illustrate an approach in looking at opportunities within Milton Keynes, the following three criteria have been applied to the heat map to find potential areas of search for district heating (DH) opportunities: areas of high heat demand, areas with anchor loads and areas with large domestic loads (i.e. blocks of flats):

• Areas within the top 10% of heat demand, where heat demand is smoothed over a relatively wide area; • Areas within 200m of an anchor load. These are types of buildings which are likely to have relatively high and

stable heat demands and/or be in sectors more likely to participate in heat distribution projects. These categories are from the National Heat Map:

o Hotels o Health (hospitals, health centres, etc) o Education (schools, colleges) o 'Recreational' buildings (leisure centres, gyms, etc) o Government buildings (e.g. local authority offices) o Public buildings (buildings with a floor area of over 1,000sq m that are occupied (in part or in whole)

by public authorities or institutions providing public services, which are frequently visited by the

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public and must therefore have a display energy certificate). This includes local authority-owned leisure centres;

• Areas within 200m of residential blocks or estates with a combined annual heat demand of more than 100,000kWh per year (these could in theory be single dwellings but in practice only blocks of flats tend to have heat demand this high).

Places which meet at least one of these criteria are shown in Map 24 (Appendix 1). The existing and proposed extents for district heating (as shown in Map 24) are also shown. Note that the existing district heating in the central business district coincides with an area identified by the above criteria, but the proposed district heating (Oakgrove) does not coincide with them. This is because this system will cover a new development so the heat demand does not yet exist, and therefore it is not shown on the map. The point heat loads which were previously identified are also shown, and these fall within the areas identified by the above criteria.

Map 24 shows the situation where only one of the three criteria is met. When the criteria are tightened so that at least two of the three criteria are applicable, the identified areas reduce. These are shown in Map 25 (Appendix 1). The locations of the top 50 heat loads to be found within these areas are also shown; this illustrates which areas have the highest loads. These loads are also listed in Table 20. The main potential DH areas as identified by this process are described in Table 19, with a brief assessment of whether they appear to be suitable for district heating, and if they do, the types of loads to be found. They are shown the order of north to south. As would be expected, they are concentrated in the Central Milton Keynes area, with further concentrations in Eagleston, Kingston, and Bletchley.

Potential DH areas Description

Olney Several small anchor loads such as schools, health centres and a museum, but the distance between them means they are unlikely to be suitable for district heating.

Newport Pagnell A good number of potential anchor loads including schools, Middleton swimming pool, police station, fire station, community centres and health centres.

Wolverton Anchor loads include schools, Milton Keynes Museum, Town Hall, Health Centre.

Stony Stratford Several small anchor loads such as schools, but these are spread out in such a way that district heating is unlikely to be feasible here.

Linford Wood / Neath Hill

There are a number of reasonably sized heat loads in the area although they may be spaced too far apart for district heating to be feasible. There are several anchor loads (including three schools) although each one is relatively small.

Central Milton Keynes This is the largest area and the location of the existing district heating system. There are a number of large loads located here, including John Lewis and XScape, and so the existing district heating system could be extended to include them.

Kingston / Brinklow This is an industrial area with several premises with high heat demand. There are few anchor loads and no domestic premises.

Eaglestone / Netherfield

This is the location of Milton Keynes General Hospital as well as the Redway School and Buckland Lodge Sheltered Housing. Langland Combined School is nearby. These all belong to the set of point loads that were already identified as having potential for district heating, while the hospital is also the second highest heat consumer in the borough.

Bletchley The two patches in the south in the Bletchley area contain some large housing blocks and a large number of anchor loads.

Table 19: Potential DH areas identified by the overlay analysis

Table 20 below shows the top 50 heat loads within these areas26

.

26 Where some of the address details are unknown, this is due to gaps in the underlying address database which is used.

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Potential DH areas (see Table 19) Name Description Postcode Street

Modelled or actual GWh

BLETCHLEY BLETCHLEY LEISURE CENTRE BLETCHLEY LEISURE CENTRE MK2 2HQ PRINCES WAY ACTUAL

3.13

BLETCHLEY UNKNOWN OTHER MK2 3HX BARTON ROAD MODELLED 1.35

BLETCHLEY MK TWO BUSINESS CENTRE BUSINESS UNITS AND PREMISES MK2 3HU BARTON ROAD MODELLED

1.07

BLETCHLEY UNKNOWN SHOPS & OTHER RETAIL OUTLETS MK2 2AZ WHARFSIDE MODELLED 0.89

BLETCHLEY 27 THE CONCOURSE, BRUNEL CENTRE SUPERSTORE AND PREMISES MK2 2JS LOCKE ROAD MODELLED

0.72

BLETCHLEY COOPERATIVE SOCIETY SHOP AND PREMISES MK2 2DT QUEENSWAY MODELLED 0.68

CENTRAL MILTON KEYNES JOHN LEWIS PARTNERSHIP SHOP AND PREMISES MK9 3EP FIELD WALK MODELLED

3.87

CENTRAL MILTON KEYNES SANTANDER HOUSE OFFICES AND PREMISES MK9 1AN GRAFTON GATE H5 TO H6 MODELLED 2.92

CENTRAL MILTON KEYNES UNKNOWN SHOP AND PREMISES MK9 3PD SUNSET WALK MODELLED 2.48

CENTRAL MILTON KEYNES UNKNOWN OFFICES AND PREMISES MK9 3DB AVEBURY BOULEVARD MODELLED 2.09

CENTRAL MILTON KEYNES UNKNOWN SHOP AND PREMISES MK9 3GA MIDSUMMER PLACE MODELLED 2.08

CENTRAL MILTON KEYNES SAINSBURY PLC SUPERSTORE AND PREMISES MK9 2FW WITAN GATE EAST MODELLED 1.89

CENTRAL MILTON KEYNES SNOWDOME

MK9 3XS MARLBOROUGH GATE MODELLED 1.56

CENTRAL MILTON KEYNES VIRGIN ACTIVE, XSCAPE BUILDING

HEALTH AND FITNESS CENTRE & PREMISES MK9 3XS MARLBOROUGH GATE MODELLED

1.36

CENTRAL MILTON KEYNES UNKNOWN SUPERSTORE AND PREMISES MK9 3NN AVEBURY BOULEVARD MODELLED 1.21

CENTRAL MILTON KEYNES UNKNOWN SHOP AND PREMISES MK9 3BE SILBURY ARCADE MODELLED 1.14

CENTRAL MILTON KEYNES UNKNOWN SHOP AND PREMISES MK9 3DJ ACORN WALK MODELLED 1.09

CENTRAL MILTON KEYNES UNKNOWN RESTAURANT AND PREMISES MK9 3PU SAVOY CRESCENT MODELLED 1.07

CENTRAL MILTON KEYNES UNKNOWN SHOP AND PREMISES MK9 3AH CROWN WALK MODELLED 1.02

CENTRAL MILTON KEYNES

MILTON KEYNES COUNCIL, SAXON COURT MILTON KEYNES COUNCIL MK9 3HS AVEBURY BOULEVARD ACTUAL

0.83

CENTRAL MILTON KEYNES BANNANTYNES FITNESS LEISURE CENTRE AND PREMISES MK9 3DB AVEBURY BOULEVARD MODELLED 0.83

CENTRAL MILTON KEYNES UNKNOWN SUPERSTORE AND PREMISES MK9 3NJ MIDSUMMER BOULEVARD MODELLED

0.82

CENTRAL MILTON KEYNES NORTH EAST, EXCHANGE HOUSE CBX1 OFFICES AND PREMISES MK9 2EA

MIDSUMMER BOULEVARD MODELLED

0.77

CENTRAL MILTON KEYNES BOUVERIE SQUIRE RESTAURANT AND PREMISES MK9 1EA MIDSUMMER BOULEVARD MODELLED

0.76

CENTRAL MILTON KEYNES UNKNOWN RESTAURANT AND PREMISES MK9 3PT GARRICK WALK MODELLED 0.67

CENTRAL MILTON KEYNES UNKNOWN RESTAURANT AND PREMISES MK9 2EA MIDSUMMER BOULEVARD MODELLED

0.67

CENTRAL MILTON KEYNES MILTON KEYNES BOROUGH COUNCIL

MILTON KEYNES BOROUGH COUNCIL MK9 3EJ SAXON GATE EAST ACTUAL

0.66

CENTRAL MILTON KEYNES H M REVENUE & CUSTOMS H M REVENUE & CUSTOMS MK9 1NG SILBURY BOULEVARD ACTUAL

0.63

CENTRAL MILTON KEYNES UNKNOWN RESTAURANT AND PREMISES MK9 3PU SAVOY CRESCENT MODELLED 0.60

CENTRAL MILTON KEYNES

FIRST SECOND AND THIRD FLOORS, EXCHANGE HOUSE CBX11 OFFICES AND PREMISES MK9 2EA

MIDSUMMER BOULEVARD MODELLED

0.59

EAGLESTON / NETHERFIELD MILTON KEYNES GENERAL HOSPITAL MILTON KEYNES HOSPITAL MK6 5LD STANDING WAY ACTUAL

16.51

EAGLESTON / NETHERFIELD REDWAY SCHOOL REDWAY SCHOOL MK6 4HG FARMBOROUGH ACTUAL 0.73

EAGLESTON / NETHERFIELD EAGLESTONE HEALTH CENTRE EAGLESTONE HEALTH CENTRE MK6 5AZ STANDING WAY ACTUAL

0.70

EAGLESTON / NETHERFIELD

MILTON KEYNES COMMUNITY N H S TRIST

MK6 5NG STANDING WAY ACTUAL

0.65

KINGSTON / BRINKLOW TESCO SUPERSTORE AND PREMISES MK10 0AH WINCHESTER CIRCLE MODELLED 2.37

KINGSTON / BRINKLOW AMSCAN LIMITED WAREHOUSE AND PREMISES MK10 0DA BRUDENELL DRIVE MODELLED 2.18

KINGSTON / BRINKLOW UNITS 1- 2, STRATUS PARK WAREHOUSE AND PREMISES MK10 0DA BRUDENELL DRIVE MODELLED

1.58

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Potential DH areas (see Table 19) Name Description Postcode Street

Modelled or actual GWh

KINGSTON / BRINKLOW UNITS 5 AND 6, STRATUS PARK WAREHOUSE AND PREMISES MK10 0DA BRUDENELL DRIVE MODELLED

1.08

KINGSTON / BRINKLOW

MILTON KEYNES SCHOOL OF GYMNASTICS

GYMNASIUM FITNESS CENTRE AND PREMISES MK10 0BA WINCHESTER CIRCLE MODELLED

0.94

KINGSTON / BRINKLOW UNITS 7 TO 9, STRATUS PARK WAREHOUSE AND PREMISES MK10 0DA BRUDENELL DRIVE MODELLED

0.85

KINGSTON / BRINKLOW UNKNOWN RETAIL WAREHOUSE AND PREMISES MK10 0BA WINCHESTER CIRCLE MODELLED

0.73

LINFORD WOOD / NEATH HILL MARLBOROUGH COURT N H S DIRECT CALL CENTRE MK14 6DY SUNRISE PARKWAY ACTUAL

0.92

LINFORD WOOD / NEATH HILL SCORPIO HOUSE OFFICES AND PREMISES MK14 6LY ROCKINGHAM DRIVE MODELLED

0.87

LINFORD WOOD / NEATH HILL LIBRA HOUSE OFFICES AND PREMISES MK14 6PH SUNRISE PARKWAY MODELLED

0.76

LINFORD WOOD / NEATH HILL MARLBOROUGH COURT OFFICES AND PREMISES MK14 6DY SUNRISE PARKWAY MODELLED

0.72

NEWPORT PAGNELL OUSEDALE SCHOOL OUSEDALE SCHOOL MK16 0BJ THE GROVE ACTUAL 1.24

WOLVERTON ELECTROLUX LIMITED WAREHOUSE AND PREMISES MK12 5LJ MCCONNELL DRIVE MODELLED 1.33

WOLVERTON TESCO SUPERSTORE AND PREMISES MK12 5RJ STRATFORD ROAD MODELLED 0.96

Table 20: Top 50 heat loads in potential DH areas For information, the overall top 50 heat loads in Milton Keynes Borough as identified by the National Heat Map are shown in Table 21. Those in bold with shading are located in the potential DH areas identified by the overlay analysis. Their locations are illustrated in Map 26 (Appendix 1). Most of the borough's top heat loads are not within the potential DH areas. In most cases this will be because they are not near to other high heat loads or anchor loads that could be joined together to form a district heating network.

Name Description Postcode Street Locality

Modelled or actual GWh

OPEN UNIVERSITY THE OPEN UNIVERSITY MK7 6AA WALTON DRIVE WALTON HALL ACTUAL 20.43 MILTON KEYNES GENERAL HOSPITAL MILTON KEYNES HOSPITAL MK6 5LD STANDING WAY EAGLESTONE ACTUAL 15.60 XI CENTRE KINGSTON GATEWAY WAREHOUSES & WHOLESALERS MK10 0BU WHITEHALL AVENUE KINGSTON MODELLED 12.51

H M PRISON WOODHILL H M PRISON MK4 4DA WISEWOOD ROAD WOODHILL ACTUAL 10.83

NORTHFIELD APEX WAREHOUSE AND PREMISES MK15 0DB NORTHFIELD DRIVE NORTHFIELD MODELLED 7.93

TESCO STORES LIMITED WAREHOUSE AND PREMISES MK1 1QA BLETCHAM WAY V7 TO A5 FENNY LOCK MODELLED 6.84

DAVID LLOYD TENNIS CENTRE

HEALTH AND FITNESS CENTRE AND PREMISES MK15 0DL LIVINGSTONE DRIVE NEWLANDS MODELLED 5.49

UNKNOWN FACTORIES & MANUFACTURING MK19 7NF TATHALL END HANSLOPE MODELLED 5.31

UNKNOWN FACTORIES & MANUFACTURING MK19 7NF TATHALL END HANSLOPE MODELLED 5.31

3 NORTH HOUSE FACTORIES & MANUFACTURING MK1 1SW BOND AVENUE BLETCHLEY MODELLED 5.28 TIBBETT AND BRITTEN GROUP PLC WAREHOUSE AND PREMISES MK6 4AG MERTON DRIVE REDMOOR MODELLED 5.28

DAIMLER CHRYSLER UK LTD WAREHOUSE, OFFICE AND PREMISES MK15 8BA DELAWARE DRIVE TONGWELL MODELLED 4.59

ARVATO SCM LTD WAREHOUSE, OFFICE AND PREMISES MK10 0AT CHIPPENHAM DRIVE KINGSTON MODELLED 4.30

JOHN LEWIS PLC BLAKELANDS 2 WAREHOUSE AND PREMISES MK14 5BT YEOMANS DRIVE BLAKELANDS MODELLED 4.02

JOHN LEWIS PARTNERSHIP SHOP AND PREMISES MK9 3EP FIELD WALK CENTRAL MILTON KEYNES MODELLED 3.87

CADBURY DHL WAREHOUSE AND PREMISES MK4 4BX PENDEEN CRESCENT SNELSHALL EAST MODELLED 3.87

GRAND UNION HOUSE WAREHOUSE, OFFICE AND PREMISES MK12 5PT

OLD WOLVERTON ROAD OLD WOLVERTON MODELLED 3.86

STANTONBURY CAMPUS STANTONBURY CAMPUS MK14 6BN PURBECK STANTONBURY ACTUAL 3.63

ANKER INTERNATIONAL WAREHOUSE AND PREMISES MK16 9PX HOWARD WAY NEWPORT PAGNELL MODELLED 3.41

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Name Description Postcode Street Locality Modelled or actual GWh

MEIDEN EUROPE LIMITED WAREHOUSE AND PREMISES MK7 8BN BRADBOURNE DRIVE TILBROOK MODELLED 3.33

WAITROSE LTD WAREHOUSE AND PREMISES MK10 0BG BRANSWORTH AVENUE BRINKLOW MODELLED 3.19

BLETCHLEY LEISURE CENTRE BLETCHLEY LEISURE CENTRE MK2 2HQ PRINCES WAY BLETCHLEY ACTUAL 3.13 JOHN LEWIS DISTRIBUTION CENTRE WAREHOUSE AND PREMISES

MK17 8EW FEN STREET FEN FARM MODELLED 3.03

SANTANDER HOUSE OFFICES AND PREMISES MK9 1AN GRAFTON GATE H5 TO H6

CENTRAL MILTON KEYNES MODELLED 2.92

SCHUECO INTERNATIONAL K G

WAREHOUSE, OFFICE AND PREMISES MK10 0AL WHITEHALL AVENUE KINGSTON MODELLED 2.88

UNKNOWN SHOP AND PREMISES MK9 3PD SUNSET WALK CENTRAL MILTON KEYNES MODELLED 2.48

XI CENTRE KINGSTON GATEWAY WAREHOUSE AND PREMISES MK10 0BU WHITEHALL AVENUE KINGSTON MODELLED 2.39

TESCO SUPERSTORE AND PREMISES MK10 0AH WINCHESTER CIRCLE KINGSTON MODELLED 2.37

IKEA RETAIL WAREHOUSE AND PREMISES MK1 1QB BLETCHAM WAY V4 - V6 TO V7 DENBIGH NORTH MODELLED 2.35

ASDA SUPERSTORE AND PREMISES MK1 1QB BLETCHAM WAY V4 - V6 TO V7 DENBIGH NORTH MODELLED 2.32

RIVER ISLAND CLOTHING COMPANY WAREHOUSE AND PREMISES

MK15 8HG DELAWARE DRIVE TONGWELL MODELLED 2.24

UNISYS LOGISTICS CENTRE WAREHOUSE AND PREMISES MK15 0YS TONGWELL STREET H5 TO H6 FOX MMILNE MODELLED 2.22

AMSCAN LIMITED WAREHOUSE AND PREMISES MK10 0DA BRUDENELL DRIVE BRINKLOW MODELLED 2.18

FORMER BINATONE TELECOM PLC WAREHOUSE AND PREMISES MK1 1LG DAWSON ROAD BLETCHLEY MODELLED 2.14 ORION MEDIA MARKETING LTD WAREHOUSE AND PREMISES MK15 0YS EMERALD GATE FOX MILNE MODELLED 2.13

UNKNOWN OFFICES AND PREMISES MK9 3DB AVEBURY BOULEVARD CENTRAL MILTON KEYNES MODELLED 2.09

UNKNOWN SHOP AND PREMISES MK9 3GA MIDSUMMER PLACE CENTRAL MILTON KEYNES MODELLED 2.08

FORMER LAING HOMES WAREHOUSE AND PREMISES MK8 8LF GARAMONDE DRIVE WYMBUSH MODELLED 2.00

ABBEY NATIONAL PLC COMPUTER CENTRE AND PREMISES MK5 6LA CHALKDELL DRIVE SHENLEY WOOD MODELLED 1.96

SAINSBURY PLC SUPERSTORE AND PREMISES MK9 2FW WITAN GATE EAST CENTRAL MILTON KEYNES MODELLED 1.89

WATERLINE LTD WAREHOUSE AND PREMISES MK16 9QA

NORTH CRAWLEY ROAD NEWPORT PAGNELL MODELLED 1.77

SUZUKI GB PLC WAREHOUSE AND PREMISES MK4 4AE STEINBECK CRESCENT SNELSHALL WEST MODELLED 1.76

MILTON CAMPUS OFFICES AND PREMISES MK15 0YS EMERALD GATE FOX MILNE MODELLED 1.71

SECURE TRAINING CENTRE SECURE TRAINING CENTRE MK5 6AJ CHALGROVE FIELD OAKHILL ACTUAL 1.65

FORMER OFFICE WORLD WAREHOUSE AND PREMISES MK10 0DF HARDING ROAD BRINKLOW MODELLED 1.65

STRATUS PARK WAREHOUSE AND PREMISES MK10 0DA BRUDENELL DRIVE BRINKLOW MODELLED 1.58

SNOWDOME LEISURE MK9 3XS MARLBOROUGH GATE CENTRAL MILTON KEYNES MODELLED 1.56

LEON SCHOOL SCHOOL MK2 3HQ FERN GROVE BLETCHLEY ACTUAL 1.53

TESCO SUPERSTORE AND PREMISES MK1 1DD WATLING STREET BLETCHLEY MODELLED 1.52

Table 21: Top 50 heat loads in Milton Keynes Borough as identified by the National Heat Map

Further analysis of point heat loads and potential DH areas can be undertaken using the Heat Map on a site-by-site basis for specific areas of interest. This will then provide a basis on which to examine discreet areas of potential and begin considering heat main route options, strategic link-up options and overall viability. It is important to note that this process is designed as a way of quickly identifying areas which are most likely to be suitable for district heating. On investigating a specific area it may be found that only part of it is suitable, or it could be that it makes sense for the area to be extended. The identified areas shown on the map should not be treated as fixed boundaries. For example, consider a large heat load such as a leisure centre. The overlay analysis process takes that heat load and draws a radius of 200m around the building to create a zone which is within 200m of an anchor

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load. Imagine there is a block of flats 300m away. A radius of 200m is also drawn around the block of flats to create a zone which is within 200m of a residential block. When the overlay analysis is performed with these two criteria, there is an overlap between the two zones, and this overlap is what remains as an identified area - however, the two buildings which created the zones are left outside of the resulting area. The identified area is therefore a central point of overlap of opportunities rather than being a boundary to an area of opportunity. In practice, because many buildings influence the creation of a zone for each individual criterion, there are not a large number left on the outside of the identified areas. Map 27 (Appendix 1) shows what the identified areas look like when they are extended by adding a 200m buffer.

4.3.2 New development In addition to the opportunities for CHP/district heating presented by existing development, the potential within future development should also be considered. The co-location of existing and new development within district heating schemes can help to diversify the heat load (i.e. promote a more constant demand for heat) and help to optimise plant design. It is also clearly easier to incorporate larger scale infrastructure projects such as district heating networks within new developments, which can then potentially be expanded to serve additional heat loads. Like the heat mapping exercise on existing development described above, spatial heat density is also an important indicator of viability for district heating within new development. For a district heating scheme, civil works around the laying of heat mains and establishing connections to individual buildings is expensive; high heat densities therefore mean shorter pipe runs and lower costs. Urban areas with high population density offer most potential for district heating schemes. Viability is very much site-specific, but for residential areas a housing density of at least 40-50 dwellings per hectare is generally found to be a minimum threshold for viability, with significant CO2 savings generally occurring at scales above 50 homes, and with economies of scale meaning that communities of over 500 homes are often considered most appropriate for these kinds of schemes. Maps 24-26 in Appendix 1 also indicate the location of two main Expansion Areas in Milton Keynes: the Western Expansion Area and Eastern Expansion Area.27

It is expected these will comprise up to 6,000 and 4,000 dwellings respectively with additional employment land, schools and community facilities. Character areas within these proposals include ‘City Street Environs’, and ‘Urban Core’ zones which are expected to incorporate average housing densities of 50 and 40 dwellings per hectare respectively. These scales and densities suggest that CHP/district heating may have potential. Detailed site layouts and the proximity of larger non-domestic loads to dwellings will need to be assessed to confirm this, although the inclusion of ‘flexible ground floors’ to promote mixed use development will improve viability in terms of heat load profiles.

The phasing of large developments can present challenges to district heating schemes as the system needs to be able to adapt and accommodate future heat loads as they come on line. Existing buildings situated within or close to new developments which are considering district heating can offer significant benefits in that they can act as district heating ‘anchor’ points around which new systems could be established. The inclusion of large public sector ‘anchor load’ sites such as social housing schemes, universities and local authority buildings can be particularly beneficial. In terms of linking heat networks with existing development, Map 28 indicates that the Eastern Expansion Area holds most potential, being located adjacent to an identified cluster of development with potential for district heating.

27 See www.miltonkeynespartnership.info/development_control/

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5 Energy character areas

5.1 Areas of opportunity This section summarises the areas of opportunity for LZC technologies that have been identified from the wider mapping work. It is at this stage that data from the various project tasks can now be integrated in order to begin building up Energy Character Areas. The general concept of Energy Character Areas concerns the way in which sustainable energy resources and LZC technology opportunities are spatially distributed across the Borough and how they may interact. However, in assessing opportunities it is not just locational factors that need to be considered and for the purposes of this study two broad types are considered:

1. Opportunities that relate to categories of buildings, development opportunities or organisations; and

2. Opportunities that relate to the LZC technologies themselves – some of these relate to specific locations where resources for LZC technologies are known to arise whilst others are more generic.

Table 22 and Table 23 below summarises these opportunity areas and the relevant data sources and assumptions. By considering these factors and by reviewing the Energy Character Areas that the GIS mapping package reveals, it is hoped that users will be able to build up a picture of the opportunities that may exist in locations or within categories of particular interest. Specific areas of note are shown on Map 10, which indicates the potential for cavity wall insulation in dwellings and Map 28, which indicates the opportunities for selected low or zero carbon energy generation. Key points to note for the latter are as follows:

• Overall, there is a clearly defined split between the characteristics of rural and urban areas;

• Urban areas see the best potential for district heating, while rural areas see best potential for wind and energy crops;

• Through their proximity to woodland in general, rural areas have clear potential for woodfuel heating; however with the establishment of adequate woodfuel supply chains and an ‘economic’ catchment area28

• Both urban and rural areas contain potential micro-hydro opportunities;

likely to encompass the borough and parts of neighbouring local authorities (e.g. 20-40km radius), woodfuel installations within urban areas may also be viable, particularly at larger scales such as district heating schemes where any fuel transport impacts may be offset by bulk purchasing;

• The Eastern Expansion Area is adjacent to a developed area with potential for district heating, suggesting that heat networks could potentially serve both areas.

5.2 GIS mapping package It is anticipated that Council officers and potentially members of the public will ultimately have access to the GIS data produced from this study. CSE has therefore supplied the data in ESRI ArcGIS format which the Council intends to convert to MapInfo for viewing on its internal/external GIS viewers. The user will then be able to select the GIS data layers of interest and view them across the Borough at large scale, or examine in more detail at specific locations.

28 An economic catchment area in this case refers to the area defined by the radius within which it is economically and environmentally viable to transport woodfuel from source to end-user.

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Area of opportunity (Type 1)

Opportunities Description Assumptions Data sources & spreadsheet reference

Large housing sites

• CHP/District Heating • Micro-scale or community-scale

renewables 29

• Greater building fabric improvement and emissions reduction than current building regulations

• Tattenhoe Park – 1,284 new homes • Oakgrove – 1,000 homes • Western expansion area – 6,500 homes

• Scale of developments is potentially suitable for

CHP/District Heating • New build offers design-stage potential for

building-integrated renewables and energy efficiency (e.g. passive solar design) – more flexibility

• If district heating is not feasible smaller community-scale or micro-scale renewable heating (or micro-CHP) can be considered

• MKC planning data - expansion areas (MKC

GIS data) • Spreadsheet Ref: “Large Housing Sites” within

‘Spreadsheet 2 - Areas of Opportunity’ workbook.

Smaller housing sites

• Micro-scale or community-scale renewables

• Greater building fabric improvement and emissions reduction than current building regulations

• Includes on-going developments and proposed

developments which have been granted planning permissions

• Only developments bigger than 24 units were taken into consideration. Where a development falls under both growth area (expansion area) and active housing area; for clarity, they were only categorised under growth area.

• Mostly small scale with biggest site having 280

units. • Generally beyond further planning influence

but still may be opportunities to work with or encourage developers to focus more on LZC technologies.

• MKC SHLAA data • MKC panning data – Active Housing (MKC GIS

data) • Spreadsheet Ref: “Smaller Housing Sites”

within ‘Spreadsheet 2 - Areas of Opportunity’ workbook.

Regeneration sites

• Non-traditional wall insulation • Roof insulation • Window double glazing • District Heating • Retrofit RE e.g. solar PV & SWH

• Lakes Estate – 851 homes. • Netherfield – 1,077 homes. • Fishermead • Beanhill • Tinkers Bridge • Fullers Slade – 295 homes

• Irrespective of measures taken in individual

dwellings; opportunities were identified for regeneration sites as whole

• Feasibility studies30

• MK Low Carbon Map (source - Martin Davies MKC)

• No spreadsheet reference; covered in GIS file format

Houses with solid walls

• External or internal solid wall insulation

• Houses with uninsulated solid walls

• Assumed all solid wall houses have solid wall

insulation potential

• CSE/Fuel Poverty Indicator • Spreadsheet Ref: “Solid wall potential” within

‘Spreadsheet 2 - Areas of Opportunity’ workbook.

29 Community-scale renewables in this case refers to shared systems such as a biomass boiler or roof-mounted solar PV system serving a block of flats 30 Low-Carbon Social Housing: A FEASIBILITY STUDY OF ENVIRONMENTAL REGENERATION IN THE LAKES ESTATE – Milton Keynes Council November 2010, USEA Low-Carbon Social Housing: A FEASIBILITY STUDY OF ENVIRONMENTAL REGENERATION IN MILTON KEYNES – March 2009, USEA

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Area of opportunity (Type 1)

Opportunities Description Assumptions Data sources & spreadsheet reference

Houses with cavity walls

• Cavity wall insulation

• Identifies cavity wall insulation according to individual wards

• Assumes properties built post-1940 are cavity

wall. • Potential was derived by comparing the total

number of cavity walled properties with those known to have insulation on construction (i.e. post 1989) or known to have been retrofitted.

• MKC building control data • 2011 census data on number of households

per each ward • Spreadsheet Ref: “CWI potential” within

‘Spreadsheet 2 - Areas of Opportunity’ workbook.

Council own stock (Housing)

• Building fabric energy efficiency opportunities

• District Heating • Micro-scale or community-scale

renewables

• This is full council won stock including

regeneration sites (focus is given to regeneration sites specifically )

• All council-owned stock shows potential for

further improvements and/or on-site generation

• MKC data – Active Housing (accessed through

MKC GIS officer) • No spreadsheet reference; covered in GIS file

format

Key public buildings

• Anchor loads for District Heating • Energy efficiency; improved

building fabric, heating systems and appliances

• Large/Micro-scale renewables • CHP generation • Waste heat

• Top 54 public buildings (as per CO2 emissions

from Display Energy Certificates) • Leisure and sport centres • Includes both government author buildings and

buildings with large public access

• Public sector more likely to access funding and

adopt technologies due to sector sustainability targets, high public profile, potentially long contractual options (e.g. for district heating), etc.

• Display Energy Certificate data (CSE) • Spreadsheet Ref: “Key public buildings”

within ‘Spreadsheet 2 - Areas of Opportunity’ workbook.

High energy users (commercial)

• Building/process energy efficiency, improved building fabric, heating systems and appliances

• Anchor loads for District Heating • CHP generation • Larger-scale renewables • Waste heat

• Mainly manufacturing/food processing sector.

Also includes bigger premises with warehouses etc

• Includes third sector high energy users

• High energy consumers within the borough

considered – CRC-driven incentive to cut emissions

• Head offices of national facilities excluded as their energy footprint in MK borough is less significant

• Type of opportunity depends on relative consumption of heat & electricity, type of site process, scale of site etc.

• CRC registered participants list (Environment

Agency) • Internet search and then data validation of

manufacturing/food processing industries/large distribution centres/warehouses within MK

• Spreadsheet Ref: “High energy users commercial” within ‘Spreadsheet 2 - Areas of Opportunity’ workbook.

Multi story car parks

• Voltage optimisation • Improved lighting • PV arrays

• Theatre District MSCP • Food Centre MSCP • Sainsbury’s MSCP • Point MSCP • Midsummer Place MSCP

• Assumed each car park poses similar

opportunities for LZC technologies • Viability depends on existing types of lighting

and hours of use

• MKC website and web search

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Area of opportunity (Type 1)

Opportunities Description Assumptions Data sources & spreadsheet reference

Other notable buildings

• Energy efficiency; improved building fabric, heating systems and appliances

• Anchor loads for District Heating • CHP generation • Larger-scale renewables

• These include: swimming pools, leisure centres,

hotels, council offices, sheltered housing, schools & Further Education Institutes

• Assumed all these facilities pose specific

opportunities for LZC technologies • Type of opportunity depends on relative

consumption of heat & electricity, type and scale of building activity

• MKC data – Active Housing (MKC GIS data) • No spreadsheet reference; covered in GIS file

format

Table 22: Data sources and assumptions for LZC technology areas of opportunity (Type 1)

Area of opportunity (Type 2)

Opportunities Description Assumptions Data sources & spreadsheet reference

Wind power

• Approx. 112MW (256GWh/yr) capacity for large-scale wind after certain constraints applied

• Approx. 106MW (186GWh/yr) capacity for medium-scale wind after certain constraints applied

• Opportunity areas identified across Milton

Keynes Borough as GIS layers for large-scale (2.5MW) and medium-scale (800kW) turbines

• Example maps included in Section 3.1

• Various – see Section 3.1

• Various – see Section 3.1 • No spreadsheet reference; covered in GIS file

format

Biomass (woodland residues)

• Sustainable technical resource of approx. 6,631 tonnes woodfuel/yr from woodlands representing around 23GWh/yr, or 15MW installed boiler capacity

• Woodland areas of different types identified

across Milton Keynes Borough as GIS layers • Example maps included in Section 3.2

• Various – see Section 3.2

• Various – see Section 3.2 • No spreadsheet reference; covered in GIS file

format

Biomass (energy crops)

• Miscanthus: assuming 5% land-take of potentially suitable land, approx. 12,292 tonnes woodfuel/yr representing around 61.5GWh/yr, or 39MW installed boiler capacity

• Slightly lower yields if SRC assumed

• Areas with potential for miscanthus or SRC

identified across Milton Keynes Borough as GIS layers

• Example maps included in Section 3.3

• Various – see Section 3.3

• Various – see Section 3.3 • No spreadsheet reference; covered in GIS file

format

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Area of opportunity (Type 2)

Opportunities Description Assumptions Data sources & spreadsheet reference

Hydro power • Approx. 35 potential sites for plant

up to 50kW totaling 340kW or 1.4GWh/yr at 50% capacity factor

• Sites identified by EA study as ‘win-win” sites

in regard of hydropower potential and environmental impact

• Average plant sizes of 5kW, 15kW and 30kW

assumed for each of the three size ranges considered in EA study

• Environment Agency Hydropower Study • No spreadsheet reference; covered in GIS file

format

Waste

• Approx. 5,300MWh/yr electricity and 5,485MWh/yr useful heat generation potentially available from anaerobic digestion. (Plant MW capacity will vary depending on design).

• Significant quantities of residual waste currently going to landfill, some of which potentially available for energy recovery

• AD resource derived from national waste

statistics at local authority level on food and agricultural waste arisings

• Residual waste is that remaining after composting/AD and recycling processes

• [Note - 6MW plant proposed at Old Wolverton to extract energy from residual waste]

• Various – see Section 3.5. Assumes plant

operated as combined heat and power (CHP)

• Various – see Section 3.5 • Spreadsheet reference: “AD&Waste heat”

within ‘Spreadsheet 2 - Areas of Opportunity’ workbook.

Microgeneration

• Resource difficult to quantify but many opportunities depending on type of microgeneration and building application – see Table 22

• Solar PV and SWH technical potential on new development estimated as 20.5MW and 34.1MW (15GWh/yr and 17GWh/yr) respectively

• Various building types suited to particular

microgeneration technologies

• Various – see Section 3.6

• Various – see Section 3.6 • No specific spreadsheet reference but

relevant to most categories in ‘Spreadsheet 2 - Areas of Opportunity’ workbook .

Energy efficiency

• Resource difficult to quantify but many opportunities depending on type of building and measure – see Table 22

Potential for loft, cavity wall and solid wall insulation measures indicated through various means including:

• % of cavity-walled dwellings by ward and mapping of known CWI installations

• % of solid-walled dwellings by ward and mapping of known SWI installations

• Mapping of off-gas areas, indices of multiple deprivation and instances of fuel poverty

• Mapping of double glazing installations & boiler replacements within Council stock

• Identification of key public buildings and commercial high energy users

• See Section 3.7

• See Section 3.7 • Spreadsheet reference: ‘Loft and Cavity COF’,

‘CWI potential’ ‘Solid wall potential’, key public buildings’ and ‘High energy users commercial’, in ‘Spreadsheet 2 - Areas of Opportunity’ workbook

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Area of opportunity (Type 2)

Opportunities Description Assumptions Data sources & spreadsheet reference

CHP & district heating

Resource difficult to quantify but potential opportunities identified by:

• Identified areas of high heat demand density relative to the whole Borough

• Identified top 50 buildings in terms of heat demand

• (for CHP) – identified commercial high energy users

• Opportunities through co-location with new development and Expansion Areas

• Use of the National Heat Map tool to identify

areas of high heat demand density and potential anchor loads or buildings with high energy use

• Provides a basis on which to examine discreet areas of potential for district heating and begin considering heat main route options and strategic link-ups

• See Section 4

• See Section 4 • Spreadsheet reference: ‘key public buildings’,

‘High energy users commercial’, ‘District heating proposed 1’, ‘District heating proposed 2’, and ‘large housing sites’ in ‘Spreadsheet 2 - Areas of Opportunity’ workbook

Table 23: Data sources and assumptions for LZC technology areas of opportunity (Type 2)

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6 Carbon reduction scenarios

6.1 Introduction The purpose of this section is to describe the outcome of four different modelled scenarios for 2020 based on the resource assessments described in this report. A spreadsheet model was developed to model carbon reductions from a range of different sources and compare these to the 2020 carbon reduction target of a 40% reduction relative to 2005. The approach used was to calculate the total opportunity, in terms of the maximum carbon savings which could come from each individual source, and then apply a percentage to be achieved by 2020. For example, the total savings opportunity for cavity wall insulation is assessed as 27,830 tonnes of CO2. This is based on an assessment of the number of cavity walled properties yet to be insulated. For a scenario we might assume that 75% of the unfilled cavity walls can be insulated by 2020, and so the savings by 2020 from this source would be 0.75 x 27,830 = 20,873 tCO2. The sources of carbon savings included in the model are:

• Cavity wall insulation • Loft insulation • Solid wall insulation • Boiler replacement • Domestic sector behaviour change • Commercial and industrial sector efficiency • Solar PV • Solar thermal • Heat pumps • Biomass (woodfuel and energy crops) - used in CHP and boilers • Micro hydro • Energy from waste at Old Wolverton (grid-connected) • Large wind (grid-connected) • Medium wind (grid-connected) • District heating • Savings in transport emissions

Where no maximum savings for a source have been assessed (for example transport emissions), a percentage reduction on emissions has been applied, relative to the most recent year for which data is available.

6.2 The target Milton Keynes has a target to reduce per capita carbon emissions by 40% over 2005-2020. In 2005 per capita carbon emissions were 7.9 tCO2, meaning that the target level of per capita emissions in 2020 is 4.7 tCO2. As significant population increase is expected over the period, this equates to an absolute tonnage reduction of less than 40%. The equivalent tonnage reduction is 368,000 tCO2, but modelling here will concentrate on per capita tonnages rather than the overall tonnages for Milton Keynes.

6.3 Limitations of the model The spreadsheet model does not take into account the following:

• Potential decarbonisation of grid electricity nationally. Depending on the future mix of electricity generation sources nationally, 1 kWh of electricity consumed in 2020 may have a higher or lower content relative to today (in fact, the carbon content of electricity changes moment-to-moment as

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the mix of generation changes, and an average annual factor is used for carbon emissions reporting). If electricity is significantly lower-carbon in 2020 than it is currently, then emissions from electricity will be lower, but so will the carbon savings which come from reducing electricity consumption. As there is uncertainty around the future carbon content of electricity, this has not been incorporated into the model - in effect, the model assumes that electricity will have the same carbon content in 2020 as it does now;

• Potential for background emissions increases. In the absence of policies to reduce carbon emissions, emissions would most likely not stay static but would rise with economic growth. National Government uses some assumptions about business as usual (i.e. absence of policy) emissions increases for their emissions projections, but these have not been incorporated here because a projection of business as usual emissions in Milton Keynes in 2020 is beyond the scope of this work. However, emissions from expected population increases are included in the model and one of the scenario inputs is the level of per capita emissions expected from new housing to accommodate this. It can be reasonably expected that the level of emissions as a result of increased population would be lower than the current per capita emissions due to new housing being significantly more efficient than the current housing stock.

6.4 Assumptions The model necessarily contains a high number of assumptions and these are all set out in the spreadsheet, which has been provided to Milton Keynes along with this report.

6.5 Inclusion of grid-connected electricity in results Milton Keynes' target for 2020 is based on the local authority dataset produced by the Department of Energy and Climate Change (formerly known as NI186). The methodology for producing this dataset uses an emissions factor for grid electricity derived from the average annual mix of generation types nationally. This means that the installation of, for example, a large wind farm in a local authority area does not affect that area's local carbon emissions as measured by the DECC local authority dataset (instead, because the wind farm contributes to a lower carbon content for national grid electricity, the effect of the installation in terms of carbon emissions is shared out between electricity consumers nationally, meaning a very small effect locally). This is an issue in that local effort to install renewable electricity projects is not sufficiently recognised by the DECC local authority emissions dataset. For this reason, the spreadsheet model used here includes an option to count the savings from grid-connected generation towards the per-capita emissions target. Although this would not be reflected in the DECC local authority emissions statistics, it represent a contribution by Milton Keynes to national carbon emissions reduction and so it can be argued that it should be included when achievement of the target is measured. Each scenario description below includes results including and not including savings from grid-connected electricity.

6.6 Scenario results

6.6.1 Scenario 1: Modest measures The emissions reduction target is very ambitious and will require significant effort in many areas. The first scenario is designed to demonstrate the scale of the challenge by using settings which reflect a realistic level of installations if significant effort is made in a number of areas but without the size of the target being borne in mind. The settings, resulting installed capacities and energy reduction, and carbon savings, are shown in Table 24, Table 25 and Table 26.

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Source Setting Description Emissions per capita new housing in MK tCO2

4.7 This is the level of per capita emissions required from everyone to meet the 2020 target; using this figure for population increase means that the effect of increasing population is neutral.

Cavity wall insulation 80% Percentage of opportunity (= currently un-insulated cavity walls) realised

Loft insulation 60% Percentage of opportunity (= lofts which are currently under-insulated) realised

Solid wall insulation 10% Percentage of opportunity realised Boiler replacement 60% Percentage of opportunity realised (=boilers

currently in categories D-G which are replaced with A-rated ones)

Domestic sector behaviour change - electricity

5% Percentage of opportunity realised additional to other measures)

Domestic sector behaviour change - gas 5% Reduction in consumption relative to 2010 (additional to other measures)

Commercial and industrial sector efficiency- electricity

10% Reduction in consumption relative to 2010 additional to other measures)

Commercial and industrial sector efficiency- gas

10% Reduction in consumption relative to 2010 additional to other measures)

Percentage of suitable homes installing solar PV

25%

Percentage of suitable homes installing solar thermal

0% Solar thermal is not included because solar PV has greater carbon reduction due to displacing electricity

Solar PV: Non domestic 20% Percentage of suitable properties installing Heat pumps 10% Percentage of opportunity (off-gas homes)

realised Energy crops - miscanthus 2% Percentage of total agricultural land given to

miscanthus Energy crops - SRC 2% Percentage of total agricultural land given to SRC Sustainable woodfuel 10% Percentage of theoretical technical resource

harvested Biomass CHP 0% Percentage of biomass output used for CHP Micro hydro 5% Percentage of opportunity realised Energy from waste 100% Percentage of waste resource utilised Large wind - constraints set to be used Basic constraints See wind resource assessment section Large wind - percentage of opportunity 10% Proportion of theoretical resource harnessed Medium wind - constraints set to be used Basic constraints See wind resource assessment section Medium wind - percentage of opportunity 0% Proportion of theoretical resource harnessed District heating cluster 1 Yes Four proposed district heating clusters are

outlined in the 'Areas of Opportunity' spreadsheet provided alongside this report

District heating cluster 2 No District heating cluster 3 No District heating cluster 4 Yes Transport 5% Reduction in local transport emissions relative to

2009 Table 24: Settings for scenario 1

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Measure Installed capacity Cavity wall insulation 34,005 Homes Loft insulation 25,504 Homes Solid wall insulation 888 Homes Boiler replacement 17,170 Homes Solar PV domestic 38 MWe Solar thermal domestic 0 Homes Solar PV non-domestic 5 MWe Heat pumps 692 Homes Biomass CHP (non-domestic) 0 Heat demand met annually, MWh Biomass boilers (domestic and non-domestic) 24

MWt

Micro-hydro 1.49 MW Energy from waste 6.0 MWe Large wind 14.0 MWe Medium wind 0 MWe District heating 25,766 Heat demand met annually, MWh

Table 25: Installed capacity in 2020 under scenario 1

Source Carbon saving tCO2 Domestic energy efficiency measures 40,725 Domestic behaviour change 22,228 Commercial sector energy efficiency (measures and behaviour change) 61,893 Microgeneration, including micro-hydro 9,730 Biomass CHP 0 Biomass boilers 10,024 District heating proposals 6,225 Transport savings 19,782

TOTAL SAVINGS 170,607 Emissions increase resulting from population increase 250,510

Resulting 2020 emissions tCO2 1,640,443 Resulting per capita emissions 5.7

Additional savings from grid-connected renewable energy 44,307 Resulting 2020 emissions tCO2 1,596,136

Resulting per capita emissions tCO2 5.5 Table 26: Annual savings in 2020 under scenario 1, with and without inclusion of grid-connected renewable energy

In this scenario, biomass boilers are favoured over biomass CHP because the model contains an assumption that biomass CHP is installed in on-gas areas and so are reducing emissions from combustion of gas, which is relatively low-carbon, while biomass-boilers are installed in off-gas areas, replacing higher-carbon fuels like oil. This means that for a given amount of biomass, higher savings are achieved from biomass boilers (in later scenarios, both technologies are used, because a much higher energy crop resource is assumed). Likewise, solar thermal does not fare well relative to solar PV because solar PV displaces electricity, which is more carbon-intensive than gas or oil and so solar PV gives higher carbon savings. As the same roofs are suitable for solar PV or solar thermal it makes sense under these assumptions to use solar PV on available roofs.

Even with grid-connected renewable energy included, per capita emissions are almost three-quarters of a tonne higher than required to meet the target, meaning at the aggregate level the target is overshot by more than 230,000 tCO2. In the remaining three scenarios, the settings are chosen so that the target is met.

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6.6.2 Scenario 2: Energy efficiency-driven In this scenario, the renewable energy settings are left as in scenario 1 (modest measures), but the energy efficiency settings are increased so that the target is met. Table 27, Table 28, and Table 29 show the settings for this scenario, the resulting installed capacities (or heat demands met where appropriate), and the resulting savings in 2020. Source Setting Description Emissions per capita new housing in MK tCO2

3.0

This is the level of per capita emissions required from everyone to meet the 2020 target; this is lower than the average per capita level required to meet the target, meaning that new housing must be very efficient.

Cavity wall insulation 100%

Percentage of opportunity (= currently un-insulated cavity walls) realised

Loft insulation 100%

Percentage of opportunity (= lofts which are currently under-insulated) realised

Solid wall insulation 25% Percentage of opportunity realised Boiler replacement

80%

Percentage of opportunity realised (=boilers currently in categories D-G which are replaced with A-rated ones)

Domestic sector behaviour change - electricity 15%

Percentage of opportunity realised additional to other measures)

Domestic sector behaviour change - gas 10%

Reduction in consumption relative to 2010 (additional to other measures)

Commercial and industrial sector efficiency- electricity 25%

Reduction in consumption relative to 2010 additional to other measures)

Commercial and industrial sector efficiency- gas 25%

Reduction in consumption relative to 2010 additional to other measures)

Percentage of suitable homes installing solar PV 25%

Percentage of suitable homes installing solar thermal

0%

Solar thermal is not included because solar PV has greater carbon reduction due to displacing electricity

Solar PV: Non domestic 20% Percentage of suitable properties installing Heat pumps

10% Percentage of opportunity (off-gas homes) realised

Energy crops - miscanthus 2%

Percentage of total agricultural land given to miscanthus

Energy crops - SRC 2% Percentage of total agricultural land given to SRC Sustainable woodfuel

10% Percentage of theoretical technical resource harvested

Biomass CHP 0% Percentage of biomass output used for CHP Micro hydro 5% Percentage of opportunity realised Energy from waste 100% Percentage of waste resource utilised Large wind - constraints set to be used Basic constraints See wind resource assessment section Large wind - percentage of opportunity 10% Proportion of theoretical resource harnessed Medium wind - constraints set to be used Basic constraints See wind resource assessment section Medium wind - percentage of opportunity 0% Proportion of theoretical resource harnessed District heating cluster 1 Yes Four proposed district heating clusters are

outlined in the 'Areas of Opportunity' spreadsheet provided alongside this report

District heating cluster 2 No District heating cluster 3 No District heating cluster 4 Yes Transport

5% Reduction in local transport emissions relative to 2009

Table 27: Settings for scenario 2

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Measure Installed capacity Cavity wall insulation 42,506 Homes Loft insulation 42,506 Homes Solid wall insulation 2,219 Homes Boiler replacement 22,893 Homes Solar PV domestic 38 MWe Solar thermal domestic 0 Homes Solar PV non-domestic 5 MWe Heat pumps 692 Homes Biomass CHP (non-domestic) 0 Heat demand met annually, MWh Biomass boilers (domestic and non-domestic) 24

MWt

Micro-hydro 1.49 MW Energy from waste 6.0 MWe Large wind 14.0 MWe Medium wind 0 MWe District heating 25,766 Heat demand met annually, MWh

Table 28: Installed capacity in 2020 under scenario 2

Source Carbon saving tCO2 Domestic energy efficiency measures 55,883 Domestic behaviour change 54,635 Commercial sector energy efficiency (measures and behaviour change) 154,732 Microgeneration, including micro-hydro 9,730 Biomass CHP 0 Biomass boilers 10,024 District heating proposals 6,225 Transport savings 19,782

TOTAL SAVINGS 311,012 31

Emissions increase resulting from population increase

159,900 Resulting 2020 emissions tCO2 1,409,428 Resulting per capita emissions 4.9

Additional savings from grid-connected renewable energy 44,307 Resulting 2020 emissions tCO2 1,365,122 32

Resulting per capita emissions tCO2

4.7 Table 29: Annual savings in 2020 under scenario 2, with and without inclusion of grid-connected renewable energy

In scenario 2 the per capita target is met even without direct savings from grid-connected electricity being included. However, this is only achieved by taking the maximum savings from cavity wall insulation and loft insulation, very high rates of boiler replacement and solid wall insulation in 25% of solid wall homes. The assumptions about additional savings from behaviour change in the domestic and non-domestic sectors are much higher than in scenario 1, and the per-capita emissions of new housing is assumed to be low (lower than the overall target per capita emissions), meaning highly efficient new homes.

31 Does not sum due to rounding 32 Does not sum due to rounding

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6.6.3 Scenario 3: Renewable energy-driven In this scenario we start with the same settings as scenario 1 (modest measures) and then increase the renewable energy settings until the target is met. In this case, the only way to meet the target is to use figures which are too high to be realistic in the timescales considered. Table 30, Table 31 and Table 32 below show a combination of settings which meet the target when savings from grid-connected renewable energy are included directly. Source Setting Description Emissions per capita new housing in MK tCO2

4.7

This is the level of per capita emissions required from everyone to meet the 2020 target; using this figure for population increase means that the effect of increasing population is neutral.

Cavity wall insulation 80%

Percentage of opportunity (= currently un-insulated cavity walls) realised

Loft insulation 60%

Percentage of opportunity (= lofts which are currently under-insulated) realised

Solid wall insulation 10% Percentage of opportunity realised Boiler replacement

60%

Percentage of opportunity realised (=boilers currently in categories D-G which are replaced with A-rated ones)

Domestic sector behaviour change - electricity 5%

Percentage of opportunity realised additional to other measures)

Domestic sector behaviour change - gas 5%

Reduction in consumption relative to 2010 (additional to other measures)

Commercial and industrial sector efficiency- electricity 10%

Reduction in consumption relative to 2010 additional to other measures)

Commercial and industrial sector efficiency- gas 10%

Reduction in consumption relative to 2010 additional to other measures)

Percentage of suitable homes installing solar PV 100%

Percentage of suitable homes installing solar thermal

0%

Solar thermal is not included because solar PV has greater carbon reduction due to displacing electricity

Solar PV: Non domestic 100% Percentage of suitable properties installing Heat pumps

40% Percentage of opportunity (off-gas homes) realised

Energy crops - miscanthus 10%

Percentage of total agricultural land given to miscanthus

Energy crops - SRC 10% Percentage of total agricultural land given to SRC Sustainable woodfuel

50% Percentage of theoretical technical resource harvested

Biomass CHP 5% Percentage of biomass output used for CHP Micro hydro 5% Percentage of opportunity realised Energy from waste 100% Percentage of waste resource utilised Large wind - constraints set to be used Basic constraints See wind resource assessment section Large wind - percentage of opportunity 60% Proportion of theoretical resource harnessed Medium wind - constraints set to be used Basic constraints See wind resource assessment section Medium wind - percentage of opportunity 51% Proportion of theoretical resource harnessed District heating cluster 1 Yes Four proposed district heating clusters are

outlined in the 'Areas of Opportunity' spreadsheet provided alongside this report

District heating cluster 2 Yes District heating cluster 3 Yes District heating cluster 4 Yes Transport

5% Reduction in local transport emissions relative to 2009

Table 30: Settings for scenario 3

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Measure Installed capacity Cavity wall insulation 34,005 Homes Loft insulation 25,504 Homes Solid wall insulation 888 Homes Boiler replacement 17,170 Homes Solar PV domestic 150 MWe Solar thermal domestic 0 Homes Solar PV non-domestic 26 MWe Heat pumps 2,768 Homes Biomass CHP (non-domestic) 9,416 Heat demand met annually, MWh Biomass boilers (domestic and non-domestic)

113 MWth

Micro-hydro 1.49 MW Energy from waste 6.0 MWe Large wind 84.0 MWe Medium wind 57.6 MWe District heating 28,934 Heat demand met annually, MWh

Table 31: Installed capacity in 2020 under scenario 3

Source Carbon saving tCO2 Domestic energy efficiency measures 40,725 Domestic behaviour change 22,228 Commercial sector energy efficiency (measures and behaviour change) 61,893 Microgeneration, including micro-hydro 40,792 Biomass CHP 2,275 Biomass boilers 47,614 District heating proposals 6,991 Transport savings 19,782

TOTAL SAVINGS 242,300 Emissions increase resulting from population increase 250,510

Resulting 2020 emissions tCO2 1,568,750 Resulting per capita emissions 5.4

Additional savings from grid-connected renewable energy 191,599 Resulting 2020 emissions tCO2 1,377,151

Resulting per capita emissions tCO2 4.7 Table 32: Annual savings in 2020 under scenario 3, with and without inclusion of grid-connected renewable energy

In this scenario very high levels of renewable energy installations were required to meet the target. The energy crops, PV and wind figures are all technically possible but are highly unlikely to be achieved within the next eight years.

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6.6.4 Scenario 4: High efficiency and high renewables This scenario attempts to find a balance between the very high levels of energy efficiency in scenario 2 and the extremely high levels of renewables installations in scenario 3. The settings, installed capacities and savings are show in Table 33, Table 34 and Table 35 respectively. Source Setting Description Emissions per capita new housing in MK tCO2 3.5

Cavity wall insulation 90%

Percentage of opportunity (= currently un-insulated cavity walls) realised

Loft insulation 80%

Percentage of opportunity (= lofts which are currently under-insulated) realised

Solid wall insulation 30% Percentage of opportunity realised Boiler replacement

80%

Percentage of opportunity realised (=boilers currently in categories D-G which are replaced with A-rated ones)

Domestic sector behaviour change - electricity 10%

Percentage of opportunity realised additional to other measures)

Domestic sector behaviour change - gas 8%

Reduction in consumption relative to 2010 (additional to other measures)

Commercial and industrial sector efficiency- electricity 20%

Reduction in consumption relative to 2010 additional to other measures)

Commercial and industrial sector efficiency- gas 20%

Reduction in consumption relative to 2010 additional to other measures)

Percentage of suitable homes installing solar PV 60%

Percentage of suitable homes installing solar thermal

0%

Solar thermal is not included because solar PV has greater carbon reduction due to displacing electricity

Solar PV: Non domestic 60% Percentage of suitable properties installing Heat pumps

5% Percentage of opportunity (off-gas homes) realised

Energy crops - miscanthus 3.0%

Percentage of total agricultural land given to miscanthus

Energy crops - SRC 3.0% Percentage of total agricultural land given to SRC Sustainable woodfuel

20% Percentage of theoretical technical resource harvested

Biomass CHP 5% Percentage of biomass output used for CHP Micro hydro 5% Percentage of opportunity realised Energy from waste 100% Percentage of waste resource utilised Large wind - constraints set to be used Basic constraints See wind resource assessment section Large wind - percentage of opportunity 20% Proportion of theoretical resource harnessed Medium wind - constraints set to be used Basic constraints See wind resource assessment section Medium wind - percentage of opportunity 20% Proportion of theoretical resource harnessed District heating cluster 1 Yes Four proposed district heating clusters are

outlined in the 'Areas of Opportunity' spreadsheet provided alongside this report

District heating cluster 2 Yes District heating cluster 3 Yes District heating cluster 4 Yes Transport

5% Reduction in local transport emissions relative to 2009

Table 33: Settings for scenario 4

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Measure Installed capacity Cavity wall insulation 38,225 Homes Loft insulation 34,005 Homes Solid wall insulation 2,663 Homes Boiler replacement 22,893 Homes Solar PV domestic 90 MWe Solar thermal domestic 0 Homes Solar PV non-domestic 16 MWe Heat pumps 346 Homes Biomass CHP (non-domestic) 2,873 Heat demand met annually, MWh Biomass boilers (domestic and non-domestic)

35 MWt

Micro-hydro 1.49 MW Energy from waste 6.0 MWe Large wind 28.0 MWe Medium wind 22.6 MWe District heating 28,934 Heat demand met annually, MWh

Table 34: Installed capacity in 2020 under scenario 4

Source Carbon saving tCO2 Domestic energy efficiency measures 53,085 Domestic behaviour change 39,637 Commercial sector energy efficiency (measures and behaviour change) 123,786 Microgeneration, including micro-hydro 23,425 Biomass CHP 695 Biomass boilers 14,539 District heating proposals 6,991 Transport savings 19,782

TOTAL SAVINGS 281,938 33

Emissions increase resulting from population increase

186,550 Resulting 2020 emissions tCO2 1,465,152 Resulting per capita emissions 5.1

Additional savings from grid-connected renewable energy 88,864 Resulting 2020 emissions tCO2 1,376,288

Resulting per capita emissions tCO2 4.7 Table 35: Annual savings in 2020 under scenario 4, with and without inclusion of grid-connected renewable energy

A high level of effort is required in many areas but this scenario appears to be more realistic than scenarios 2 or 3.

33 Does not sum due to rounding

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6.7 Conclusions The 2020 target is ambitious and a high level of effort is required in all areas if the target is to be achieved. It cannot be achieved by energy efficiency alone or by use of renewable energy alone. As well as technical measures, behaviour change must be stimulated in both the domestic and commercial sectors. The transport sector should not be ignored. The scenarios here have used a relatively modest assumption for reduction of carbon emissions from transport, and a greater reduction in the transport sector would take some pressure off of the other sectors. The savings achieved in each scenario are shown by category in Figure 3. The resulting per capita emissions (with and without the savings from grid-connected renewable energy) are illustrated in Figure 4.

Figure 3: Savings (tCO2) by category for each scenario

Figure 4: Resulting emissions per capita (tCO2) by scenario, with and without grid connected renewable energy

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

Domestic energy efficiency measures

Domestic behaviour change

Commercial sector energy efficiency

(measures and behaviour change)

Microgeneration, including micro-

hydro

Biomass CHP Biomass boilers District heating proposals

Transport savings

tCO

2

Scenario 1: Modest measures

Scenario 2: Energy efficiency driven

Scenario 3: Renewable energy driven

Scenario 4: High efficiency and high renewables

5.75.5

4.94.7

5.4

4.7

5.1

4.7

0

1

2

3

4

5

6

Per capita emissions without grid-connected RE Per capita emissions with grid-connected RE

tCO

2

Scenario 1: Modest measures

Scenario 2: Energy efficiency driven

Scenario 3: Renewable energy driven

Scenario 4: High efficiency and high renewables

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Appendix 1 – Maps Map 1 Medium wind basic constraints

Map 2 Medium wind additional constraints

Map 3 Medium wind constraints and flood risk

Map 4 Large wind basic constraints

Map 5 Large wind additional constraints

Map 6 Large wind constraints and flood risk

Map 7 Woodland resource map

Map 8 Energy crop resource map

Map 9 Micro-hydro resource

Map 10 Estimated filled cavity wall dwellings, as a percentage of all cavity wall dwellings, by ward

Map 11 Extent of mains gas distribution network

Map 12 Indices of Deprivation by income score

Map 13 Incidents of fuel poverty as a proportion of total households

Map 14 Council stock improvements and regeneration areas

Map 15 All heat demand

Map 16 Residential heat demand

Map 17 Non-residential heat demand

Map 18 Central Milton Keynes, all heat demand

Map 19 Central Milton Keynes, residential heat demand

Map 20 Central Milton Keynes, non-residential heat demand

Map 21 Central Milton Keynes, detailed view of all heat demand

Map 22 Central Milton Keynes, detailed view of residential heat demand

Map 23 Central Milton Keynes, detailed view of non-residential heat demand

Map 24 Potential DH areas: areas meeting at least one of the following three criteria: top 10% of heat demand; within 200m of an anchor load; within 200m of a large domestic load. Existing, proposed and potential district heating shown.

Map 25 Potential DH areas: areas meeting at least two of the following criteria: top 10% of heat demand; within 200m of an anchor load; within 200m of a large domestic load. Also shows the 50 highest heat loads in these areas.

Map 26 Potential DH areas with locations of the top 50 heat loads in Milton Keynes

Map 27 Potential DH areas with 200m buffer

Map 28 Summary map of selected low and zero carbon energy generation resources and opportunities

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Map 1 – Medium wind basic constraints

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Map 2 – Medium wind additional constraints

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Map 3 – Medium wind constraints and flood risk

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Map 4 – Large wind basic constraints

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Map 5 – Large wind additional constraints

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Map 6 – Large wind constraints and flood risk

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Map 7 – Woodland resource map

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Map 8 – Energy crop resource map

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Map 9 – Micro-hydro resource

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Map 10 – Estimated filled cavity wall dwellings, as a percentage of all cavity wall dwellings, by ward

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Map 11 – Extent of mains gas distribution network

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Map 12 – Indices of Deprivation by income score

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Map 13 – Incidents of fuel poverty as a proportion of total households

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Map 14 – Council stock improvements and regeneration areas

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Map 15 – All heat demand

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© Crown copyright and database right [2012]: Ordnance Survey 100019593

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Map 16 – Residential heat demand

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© Crown copyright and database right [2012]: Ordnance Survey 100019593

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Map 17 – Non-residential heat demand

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© Crown copyright and database right [2012]: Ordnance Survey 100019593

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Map 18 – Central Milton Keynes, all heat demand

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Map 19 – Central Milton Keynes, residential heat demand

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© Crown copyright and database right [2012]: Ordnance Survey 100019593

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Map 20 – Central Milton Keynes, non-residential heat demand

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© Crown copyright and database right [2012]: Ordnance Survey 100019593

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Map 21 – Central Milton Keynes, detailed view of all heat demand

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© Crown copyright and database right [2012]: Ordnance Survey 100019593

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Map 22 – Central Milton Keynes, detailed view of residential heat demand

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© Crown copyright and database right [2012]: Ordnance Survey 100019593

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Map 23 – Central Milton Keynes, detailed view of non-residential heat demand

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© Crown copyright and database right [2012]: Ordnance Survey 100019593

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Map 24 – Potential DH areas: areas meeting at least one of the three criteria: top 10% of heat demand; within 200m of an anchor load; within 200m of a large domestic load. Existing, proposed and potential district heating shown.

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Map 25 – Potential DH areas: areas meeting at least two of the following criteria: top 10% of heat demand; within 200m of an anchor load; within 200m of a large domestic load. Also shows the 50 highest heat loads in these areas.

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Map 26 – Locations of the top 50 heat loads in Milton Keynes

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Map 27 – Identified areas with 200m buffer

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Map 28 – Summary map of selected low and zero carbon generation resources & opportunities

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