The Eastern New Energy (ENE) project is funded by the European Regional Development Fund (ERDF). The project aims to understand and remove barriers that prevent rapid decarbonisation, and to identify effective interventions to accelerate the transition to a low carbon economy in the East of England. The partnership is working with local authorities, industry, SMEs, and communities to explore decarbonisation options and to help develop innovative interventions. The purpose of the Eastern New Energy project was to support a collaborative project to build a stronger local energy/low carbon economy in the East of England. The Eastern New Energy region includes the four study areas of Norfolk, Suffolk, Cambridgeshire and Uttlesford.
To help get the project on track and formulate a plan for what each county council needed to understand its current energy status, Energy Systems Catapult developed a Local Energy Area Representation (LEAR) for each study area. Each study area was further split into sub-regions and modelled using the Catapult’s EnergyPath® Networks tool.
LEARs were delivered in the following areas: Norfolk, Suffolk, Cambridgeshire, Uttlesford.
As a part of the wider ENE project, we provided numerous workshops to explore specialist areas such as carbon measurement and trading estate energy service companies (ESCO).
Data Canvas workshops provided a template which was used to collectively identify existing data assets, demonstrate the value of collecting energy data, and a gap-analysis to identify where additional data collection is required. Workshops were held with KnowNow, Cambridge Electric Transport Company and an industrial estate and were very well received.
For this study, the focus is on the current energy system which is analysed using a LEAR model. LEAR is a local energy system modelling tool developed by the Catapult that uses data analysis and aspects of machine learning to pull together information on energy demand; generation (focussing on renewables); storage and distribution assets; social factors like fuel poverty; and characteristics like building design types and local geography. It enables planners and innovators to strategically decide how they might deploy and grow low carbon businesses.
The LEARs for each area have been created by collating and processing data from a variety of sources and using in-house modelling techniques. We build our understanding of local buildings by collating data from different sources and then use a whole-system approach and various modelling tools to fill in gaps. Datasets are layered to represent domestic buildings’ heating systems and building fabric.
The resulting LEAR output provides an understanding of the buildings in the local area; their annual and peak energy demands; and the energy networks that serve them. It also provides some information on the levels of employment and deprivation in the area. A LEAR report was generated summarising the results of each of the four areas.
The resulting LEAR reports provide information on some of the national datasets that are used, highlights examples and outputs from other LEAR reports and provides an insight into how other local authorities have used the data.
The outcome of the LEAR modelling also supports the wider aim of the ENE project to build an “Open-Source Data Asset” by developing a base layer of the regulated energy system. This approach was piloted in Suffolk and later expanded to analyse both Norfolk, Cambridgeshire and Uttlesford. The LEARs for all four areas support development for local low carbon economy and energy innovation which is consumer-centred and takes a whole-systems thinking approach.
The “Open-Source Data Asset” is aimed at helping stakeholders, in each area:
Each local area is unique, and the right decarbonisation strategy for every area will depend on many things from the geography, building types, energy infrastructure, energy demand, resources, urban growth plans, low carbon ambitions, and investment plans of communities and stakeholders. That is why it is important to have a deeper knowledge of the current energy system in each local area.
The individual environmental goals of each study are listed below, noting the overall legally binding target for the UK to reach Net Zero emissions by 2050 and reach the milestone of 78% reduction compared to 1990 levels by 2035 (Carbon Budget 6).
These four LEAR projects aimed to build an understanding of current energy demand in the local area by providing a baseline representation of the regulated energy system including annual and peak energy demand, building stock and locations, social metrics, electric vehicles (EV) charge points, and identify areas that would benefit from innovation and development. A LEAR is produced for selected local authority areas within the project area to provide a whole-system picture of the current local energy system and a baseline dataset from which to develop more detailed decarbonisation plans.
The Catapult developed EnergyPath® Networks (EPN), a tool that enables regional and building-level energy system modelling and analysis. Using this tool, it is possible to evaluate the impact of energy system transition scenarios, for example, the cost-optimised pathway for the energy system transition or the regional effects of a large-scale deployment of low-carbon heating.
The Local Energy Area Representation uses data and information to develop and understanding of existing local energy systems and to support an understanding of the potential impact, opportunities and benefits of new smart local energy solutions being designed and demonstrated. The current energy system is represented by collating and processing data from a variety of sources.
The key benefits of creating a LEAR report for an area include:
Visual mapping of point source emissions across an entire region by sector
This allows local authorities to prioritise decarbonisation projects and potentially collaborate with business and industry. This is with the intention to target measures collectively and to potentially utilise waste heat from industrial sites to provide low carbon heating to homes.
Mapping domestic electricity and gas demand
Delivered at Lower Super Output Area (LSOA) geographical level, this allows local authorities to target areas for wider scale projects such as low-carbon district heating which requires an anchor heating demand to be economically viable.
Mapping of low voltage and high voltage substation capacity and demands
An understanding of potential network constraints across an area is essential to energy project planning. Roll-out of low carbon technologies such as solar PV and heat pumps require sufficient capacity on the electrical network. By visualising substation capacity across an area, local authorities can avoid sinking development costs into projects that later require significant grid re-enforcement or are unable to proceed due to network constraints.
Visual analysis of fuel poverty and deprivation
LEAR utilises national datasets to visualise deprivation and fuel poverty across an area. When layered with Energy Performance Certificate (EPC) ratings and property type this allows local authorities to more target pilot projects to lower heat cost through low carbon solutions.
In collaboration with our stakeholders, the Catapult is developing tools and methodologies that can help projects move closer to real-world implementation of concepts and demonstrators. We are providing a consistent and coherent analysis of the maturity, risks, and benefits of the smart local energy systems. As the programme progresses, we are also capturing lessons and insights from the programme, making these accessible to those who can influence change.
The LEAR tool is just the first step on the roadmap to a wider Local Area Energy Plan (LAEP); a LAEP simulates future energy scenarios for the study area and provides the most cost-effective pathway to achieving Net Zero emissions.
Another long-term goal for some of the project partners is to develop a “Digital Twin” of the region. For reference, the Catapult’s definitions are as follows:
Digital Model
A digital representation of physical system or object. For example, a network infrastructure map which utilises data from a fixed point in time.
Digital Shadow
A digital model which integrates automated one-way data flow from the physical system or object. For example, a network infrastructure map which pulls data from the system to dynamically update inventory, asset state and constraints.
Digital Twin
A digital model which integrates automated, two-way data flow between the model and physical object or system. Where making a change to one can change the other. For example, a control centre network infrastructure map which displays real time system needs and enables engineers to call upon assets to mitigate issues.
Therefore, the “Open Data Asset” which this project will develop could be classified as a digital model of the region. The Catapult aims to work with project partners to understand and support the long-term goal of a digital twin.
A LEAR can be used in many ways to benefit a region’s transition to Net Zero.
Norfolk are planning on using the LEAR data to:
Alex Templeton of UK Community Works CIC said that the Cambridgeshire LEAR was recommended “based on interest from Cambridge County Council and positive feedback from Uttlesford, Suffolk and Norfolk”. Alex also stated that the LEARs for Suffolk and Norfolk, as delivered by the Catapult under the ENE Suffolk Data Trust project, have:
Suffolk
Figures 1 and 2 show that LSOAs with high percentage of buildings with an EPC rating of E and below, fell into off-gas areas. These maps highlight areas where building fabric upgrades would be best suited. Furthermore, identifying dwellings off the gas network can help to focus a heat pump roll-out programme. Reduces the risk of competing heating vectors (Hydrogen or heat networks, for example) being a more financially viable option in the future.
Figure 1 – Buildings on or off the gas network in the Suffolk Central sub-region.
Figure 2 – Percentage of Buildings with EPC rated E or below in the Suffolk Central sub-region.
The map in Figure 3 displays large individual emission point sources in the sub-region using the National Atmospheric Emissions Inventory. As well as CO2, this data shows air pollutants, heavy metals, and base cations, and greenhouse gases (GHGs). It should be noted that this dataset is for fixed emission sources only, and that non-fixed emissions such as those from road traffic are not included. These sites can provide an indication where prioritisation for hydrogen may be required to decarbonise industrial processes, locations for potential heat sources for local district heating networks, or areas where local air quality considerations may need to be considered.
Figure 3 – Individual emission sources identified by the National Atmospheric Emissions Inventory (NAEI) across the Suffolk East sub-region.
Norfolk
Figure 4 shows the distribution of estimated peak and annual energy consumption for domestic buildings across the Norwich and South Norfolk sub-region.
Figure 4 – Annual domestic demand for electricity and gas within the Norwich and South Norfolk sub-region.
Figure 5 shows the levels of fuel poverty within the sub-region; comparing the energy demand to the fuel poverty highlights the areas that may benefit from building fabric upgrades, namely, insulation upgrades.
Figure 5 – Fuel poverty levels in the Norwich and South Norfolk sub-region.
Cambridgeshire
Figure 6 provides information within the area for suitability for domestic Solar PV; the footprint and orientation of all dwellings in the sub-region were analysed to calculate the potential generating capacity of rooftop solar PV panels would offer. The results were aggregated to a 200m radius to identify places ideal for mass deployment – the top 3 areas within Huntingdonshire are provided here.
Figure 6 – Top 3 most concentrated areas eligible for rooftop Solar PV within Huntingdonshire.
Figure 7 shows the topmost concentrated area with 293 dwellings eligible for solar PV, with a combined capacity of 958kW. The satellite image in Figure 8 within that rooftop solar is already present in the area. This already indicates a willingness to participate in decarbonisation or renewable projects.
Figure 7 – Most concentrated area of dwellings with eligibility for rooftop solar PV within Huntingdonshire.
Figure 8 – Satellite image of most concentrated area showing dwellings with rooftop solar PV within Huntingdonshire.
Uttlesford
Ordnance Survey Mastermap Topography and Land Registry INSPIRE polygons have been used to identify houses which have space for off-street parking. This is done by attempting to fit a standard UK space of 4.8m x 2.4m in the owned area between the house and its nearest road. This helps identify homes that may be able to charge an EV on a driveway, and areas that will require alternative charging solutions for on-street parking.
As a purely spatial exercise, this analysis did not consider local planning constraints. Therefore, it is not a replacement for a detailed feasibility study but instead gives an indication of the potential for development and the areas whereby this may be suitable.
Figure 9 – Percentage of dwellings with off-street parking on each road within Uttlesford.
Local Area Energy Planning: The Time and Place is Now
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