Local Heat and Energy Efficiency Strategies (LHEES): phase 1 pilots - technical evaluation

Findings from the technical evaluation of the first phase of LHEES pilots, in which 12 local authorities participated between September 2017 and March 2018.

4. Options appraisal

4.1. Alignment with council policy priorities

Once the baseline data for the areas under consideration has been agreed and established, the next step in developing the LHEES was to assess the options for energy efficiency and heat decarbonisation.

The starting point for this should always be the policy priorities for the local authority and, as has been noted, these were taken into account prior to commencing data collection. The key local authority level documents that were considered for the pilot projects included:.

  • Local Outcomes Improvement Plan
  • Local Development Plan
  • Local Housing Strategy
  • Fuel poverty strategy
  • Sustainability, energy and Climate Change Strategies

The LHEES overlaps with a number of areas of council responsibility and it is important that the options appraisal is consistent with council priorities.

4.2. Energy efficiency opportunities

4.2.1. Fabric first

In determining the LHEES, priority is given to so-called 'fabric first' options – that is, improvements to the energy efficiency performance of the building that reduces the overall energy requirements. This can be seen as a low regrets option as, regardless of any subsequent heat decarbonisation, reducing energy demand will always assist in meeting the goals of the LHEES.

4.2.2. Domestic properties

For every building in the area under consideration we evaluated the potential for the following:

  • Loft insulation
  • Wall insulation (cavity, internal and external)

Information on glazing was available for some of the local authorities but energy savings potential was not assessed for this measure as adding double glazing tends to not increase the SAP score much[2], and is not effective unless there is wall and loft insulation also installed. It is also very difficult to make a generic assessment of glazing requirements for a particular property.

Installation costs, and savings in annual running costs and CO2 emissions per insulation measure were based on property type, using data from the Energy Saving Trust (EST) (last update April 2018).

4.2.3. Non-domestic properties

Analysis of the non-domestic properties is more complex than the domestic. This is due to the much wider range of building types, nature of ownership, and, primarily, the limited data on the non-domestic stock. In the development of the pilot LHEES we evaluated the potential for the following:

  • Roof insulation
  • External wall cladding
  • Improved controls
  • Improved user behaviour
  • Air tightness improvements
  • Glazing improvements
  • HVAC insulation
  • Boiler replacement
  • Heat recovery
  • Low-flow hot water
  • Destratification

For the, very small, number of buildings with swimming pools we also considered pool backwash optimisation.

Non-domestic buildings which had a building type assigned to them had their types pushed into the CIBSE TM46 building categories. This was not possible for non-domestic building where the building type was unknown. There are 29 typical building types and by sorting the buildings into these categories, the energy performance of the building can be benchmarked. Unfortunately, this approach is limited by the number of buildings that had an assigned type. On average across the eight study areas for which Atkins carried out the Options Appraisal, only 41% of non-domestic properties had an assigned building type, for the individual areas these range from 15% - 66%. It is worth noting that the original Assessor's data may have better information on building types than the Scotland's Heat Map data used as the basis for these pilots. When the Scotland's Heat Map data was originally populated from the Assessor's data the lack of linkage to UPRN meant it was not possible to fully incorporate the building type data that had been collected. Revisiting this could prevent the need to re-survey in order to close the data gap for non-domestic building types.

4.2.4. Commentary

For the domestic properties, there was good information available in the data sets which enabled a robust analysis of energy efficiency opportunities. However, for many councils, the 'low hanging fruit' of loft insulation has already been undertaken and for social housing it is often the case that cavity wall insulation measures have also been undertaken. As LHEES progress other measures, such as External Wall Insulation may need to be considered for a wider group of properties.

Unlike domestic properties available data-sets do not typically provide sufficient information on non-domestic fabric and services nature / condition / age to support the same level of confidence in the analysis. Broad averages have to be applied across the building stock, based on the perception of the typical condition and servicing solution of different types of buildings.

Reflecting the greater complexity of non-domestic buildings, and the fact that many buildings are not owned by the business that occupies them, controls measures and user behaviour interventions have been considered to be the priority measures across most building types, along with fabric measures, on the basis that it can be much more expensive and challenging to add fabric efficiency measures to non-domestic buildings, and controls measures can be effective in reducing energy wastage.

As noted in section 2.3, the data analysis and measure identification for non-domestic buildings is significantly constrained by the available data sets. We consider that the methodology that has been developed could be very useful for councils to form strategies in an efficient and auditable manner but only if significant improvements in the available data are made.

In the pilot studies, Dumfries and Galloway council physically surveyed the buildings which enabled building types to be assigned. However, that area was a small village with only about 100 properties – for larger areas the time required to perform such a survey would be significant. Even having carried out the survey, we were still unable to fully assess energy efficiency improvements for about half of the properties as the fuel type was not known (see section 2.3 for commentary on this).

It is worth noting that due to extensive and ongoing government supported programmes, to improve domestic building energy use, this has provided a valuable source of energy related data. Consideration could be given to investigate if more use can be made of existing datasets to model energy options where data is not available. More analysis is suggested to consider opportunities to create potential links between domestic and non-domestic datasets such as where building characteristics are likely to be similar; an example would be where a small shop is below a flat and thereby within the same building.

Additionally, more work is encouraged to find ways of using more granular non-domestic data while balancing concerns of business sensitivity and national climate targets.

4.3. Heat decarbonisation options

4.3.1. Measures for individual buildings

As with energy efficiency measures, we divided our analysis into domestic and non-domestic sectors. For the domestic sector we considered the following measures:

Heat pumps

The property type (houses, not flats) and main fuel type (all but mains gas) were used to asses if a heat pump would be a possible heat decarbonisation measure for a property. Properties that were listed buildings were excluded from this selection. Given the likely heat load of a single domestic building and the space requirements for installation of Ground Source Heat Pumps the heat pump analysis for domestic buildings was predicated on Air Source Heat Pumps. Given that the analysis was predicated on an individual building basis there is a question in terms of how communal heat pumps should be considered. This was done in the pilots for blocks of flats but not for, say, a grouping of detached properties.

Solar thermal

Although solar thermal technology does not usually provide space heating, it will reduce the demands for domestic hot water. Solar PV was excluded from this analysis as the (present) focus of LHEES has been directed away from the generation and storage of electricity by householders. However, given the strong synergies with the core focus of LHEES it is recommended that solar PV and electricity storage is considered as part of LHEES development in the future.

For the non-domestic sector, the following individual building measures were considered:

Heat Pumps

Heat pumps achieve significant decarbonisation by using electricity from a decarbonised grid in lieu of natural gas or heating oil, and by providing a far superior efficiency compared to conventional electric or electric storage heating. Three different types of heat pump system were considered for deployment for the larger non-domestic buildings:

Air Source Heat Pumps (ASHP)

Air Source Heat Pumps are available in smaller sizes that the other two options, thus making them the best match to smaller heat demands, with a minimum peak heat demand of 4 kW. As this heat pump is exposed to ambient temperatures, it does not suit particularly exposed areas where the temperature frequently drops well below zero or locations near the coast with saline-laden air.

Water Source Heat Pumps (WSHP)

Water Source Heat Pumps must be located near a water source. The cost for this technology increases the further away from a water source the heat demand is. Suitability for a WSHP was based on a peak load of 350 kW or over, and within 200 m of a river source. This is a rule of thumb suitable for initial screening but further analysis would be required to properly determine viability.

Ground Source Heat Pumps (GSHP)

This technology requires space near-by for the ground loops or boreholes. This can exclude city centre areas that are densely populated and, even for detached properties with a decent sized surrounding green space, installation would be highly disruptive. A threshold was been set of a minimum peak heat demand of 50 kW required for this technology to be considered.


This technology requires space for a woodchip / wood pellet store and room for deliveries to be made. As such, localised installations in dense high streets are much more challenging. It is much less likely to be possible to locate in an Air Quality Management Area (AQMA).

Solar Thermal

Solar thermal is theoretically an option for non-domestic buildings, producing hot water. However, it is noted that market trends suggest that where there is space on a roof to mount solar panels, it is more economically sensible to install solar PV for electricity generation. Taking this into account in conjunction with the comments made relating to solar thermal for domestic properties, solar thermal was not considered for non-domestic properties in the analysis.

4.3.2. District heating Methodology

There are two key steps when identifying a potential district heating network; identifying anchor loads and assessing the heat density of the area.

Anchor Loads

Anchor loads are significant heat demands that have high potential to be one of the first connected demands on a network; these are critical to making a network economically viable. For the majority of councils in the pilot we set a minimum anchor load heat demand of 100 MWh/a (based on experience from other studies and projects that Atkins has been involved in) and a further requirement that the building be publicly owned. The minimum heat demand is somewhat arbitrary but serves as a sensible initial screening level. In terms of building ownership, the drivers for looking at public buildings were:

  • A district heating network predicated on publicly owned buildings is far more easily 'actionable' than a network that relies on private sector buildings. It means that public sector buildings or social housing can provide revenue certainty for any developer of a heat network which is a critical consideration in terms of actually getting a scheme implemented. There is good support for this approach if one considers that the majority of district heating schemes in the UK were initially supported by public sector anchor loads even if some have now grown significantly from that base.
  • Typically, the data available for public buildings is more complete than for private buildings enabling better analysis to be conducted.

Linear Heat Density

Linear heat density is a measure of heat load per meter of district heating pipework. In short, this is an approximation of how much revenue a branch of a network can generate for a given capital cost. This is a useful approximation for identifying areas where a district heating network may be viable. The linear heat densities chosen to be reviewed are 4 MWh/m and 7 MWh/m. Any areas where there are overlapping or large radii for the 4 MWh/m should be considered for a district heating network, with the areas covered by the 7 MWh/m being of particular interest. The lower threshold of 4 MWh/m has been chosen due to this being the typical lowest value for a network to be economically viable, as the heat sales over the lifetime period (20+ years) need to payback the CAPEX investment of the infrastructure.

The linear heat density radii have been applied to the anchor loads, an example is shown in the figure below for the Dundee study area of Lochee. Where a pipe route can intersect multiple radii, this would generate a network with relatively high linear density.

Figure 4-1 - Linear heat density analysis example

Figure 4-1 - Linear heat density analysis example

Network Identification and Routing

By using the linear heat density of the anchor loads, it is possible to draw an initial network boundary and pipe route. In this study, the pipes have been routed along the roads to limit disruption to houses and gardens. An identified network for the Lochee area, Dundee is shown below.

Figure 4-2 - Network identification and routing example

Figure 4-2 - Network identification and routing example

4.3.3. Commentary

In terms of the analysis for individual buildings, and for the non-domestic sector in particular, we again observe that the methodology developed should be beneficial but that development of an LHEES with robust consideration of heat decarbonisation options for all individual buildings will only be possible with improved data on the non-domestic side.

It is interesting to note that across all of the pilots, we did not identify any immediately implementable district heating networks by employing the core methodology outlined above. However, by considering a large private sector heat consumer we were able to identify a potential network in Clackmannanshire and we were also able to identify an opportunity for an expansion to the existing heat network in Aberdeen.

A key reason for this is that we looked at district heating from the perspective of a carbon reduction measure and therefore did not consider gas fired CHP as a fuel source for district heating even though, in many cases, this would be the most economic option. There is an argument that district heating could be developed initially using gas fired CHP and then transitioned to lower carbon fuel options but this was not the approach employed in this particular work.

It could be argued that the areas selected for the pilots were not necessarily the most likely to generate opportunities for district heating. In general, the pilot areas were either very rural (and hence lacking the heat density to make a heating network viable) or mains gas connected (and thereby not priority in terms of Scottish Government policy). District heating has been proven to be a strong option for tower blocks and campus schemes (for example, hospitals and universities) but the pilots either did not include these features or, where they did, the district heating opportunities had already been realised.

Notwithstanding the above, we would observe that a significant driver for viability of a district heating scheme is the availability of existing unused heat such as a waste heat source (for example from an Energy from Waste or Industrial facility). The key reason for this is that for retrofit district heating projects it is hard to make the economics work (in comparison to mains gas) if one has to generate all the heat required – a source of free / low-cost heat is a significant advantage. As the methodology we used was driven purely from a heat demand standpoint these potential heat sources were not considered. We would recommend that, for future LHEES, that analysis of district heating is driven not just from the demand side but also from the heat supply side. This is likely to be a more robust methodology and more successful in identifying potential district heating opportunities.

Clearly improvement of the building fabric and the amount heat demands are reduced can materially impact heat required in any district heating system. This is important to consider in the evaluation and options stages of developing an LHEES, and in future regular revisions.

4.4. Tools and techniques

For the pilots, we utilised both Excel for data storage and processing and Geographic Information System (GIS) for spatial analysis, with some data analysis using Feature Manipulation Engine to assist in the interpretation and explanation of the data. All were required to create and analyse the data. Having different ways of analysing and understanding the data is important. Both data and images aid understanding. Many of the councils also have some level of GIS capability. This approach was generally well received by the councils but one council with a particular strength in GIS noted that they would have preferred a more spatially driven approach. This approach would likely take longer to initially set-up but would likely allow better visualisation of options and would work well with a zonal approach to implementation. It does however require specialist software and resource with the ability to use that software.

From our experiences on the pilot, we would observe that the work was much faster where Scottish Government enabled coordination of the underlying data, such as the Scotland Heat Map and Home Analytics. The work has highlighted further areas where data could be improved. It would be useful if Scottish Government could look at further opportunities for central coordination of data and data sets, coordinating national collection and integration of data. This could add efficiencies to the time of data collection for LHEES and enable standardised tools and approaches to be developed. National coordination of data also releases local capacity for performing the data analysis and interpretation and for more delivery based work. There are ongoing local skills challenges that government could consider supporting; for example even within the subset of councils taking part in the phase 1 pilot, there was significant disparity in their preferences in terms of software and their capability and resourcing in the use of specialist software such as GIS.



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