Identifying Local Energy Assets for Smart Local Energy Systems

By Ben Walters, Senior Modelling Analyst, Energy Systems Catapult

Ben Walters explores how local area energy modelling can inform the design of smart local energy systems.

Smart Local Energy Systems (SLES) are a new concept being developed around the UK that require multiple stakeholders to collaborate on delivering multiple outcomes across multiple systems with the goal of delivering better, more valued low carbon services to end users. These complex systems of systems may span the electricity system, heating system and transport system, while utilising digital and commercial systems to deliver a broad range of low carbon technologies and services, from grid scale batteries to domestic heat pumps.

As a result, SLES require a wide range of datasets to inform strategic plans. By analysing different datasets together, from electricity network constraints to social factors such as levels of fuel poverty, it is possible to get a much greater insight of how a range of technologies and services can best be deployed to meet energy efficiency, carbon emission targets in a cost-effective way.

The Energy Revolution Integration Service (ERIS) has been supporting a range of SLES projects within the UK Government’s Prospering from the Energy Revolution (PFER) programme to build a picture of their local areas, by providing the data and analysis to support their strategic planning activities.

Introducing the Local Energy Asset Representation (LEAR) model

LEAR is a local energy system modelling tool developed by Energy Systems Catapult that pulls together information on energy demand, generation, storage and distribution assets, social factors like fuel poverty and characteristics like building design types and local geography, using data analysis and aspects of machine learning. It enables planners and innovators to strategically decide how they might deploy and grow low carbon businesses.

We have extended the capabilities of LEAR to consider the potential of certain technologies, including:

• Identifying potential solar generation – by looking at the shape of a building’s main roof, and what direction it points;
• Identifying homes with potential for off-street parking – by identifying land associated to a building, determining which parts of this land have access to a road, and whether a standard 2.4m x 4.8m UK parking space would fit.

“The Local Energy Asset Representation has been really helpful to the SLES project in Coventry. It gives us an indication of the current scale and potential for photovoltaic solar and off-street parking, both important when considering local electricity generation and the geographical impact of electric vehicles in the project area. So it is great to see the Catapult continually developing this analysis.”

Dr Grant Wilson, Head of the Energy Data Analytics Group, University of Birmingham

West Midlands Regional Energy System Operator

Key LEAR Results

From our recent analysis covering the SLES demonstration projects across eight local authorities, we have found;

• 36% of UK homes identified as being suitable for rooftop solar, with each home on average capable of 2.5 kWp. This estimate is based on analysis of 570,467 homes across 8 local authorities.
• The percentage of homes with the potential for off-street parking varies from just 13% up to 71% across the 8 local authorities that we investigated. This shows that any roll-out of electric vehicles is going to be subject to huge regional variations in regards to being able to charge a vehicle at home overnight.

Figure 1: Snapshot of a local area with houses suitable for solar panels highlighted in yellow

Figure 2: Snapshot of the potential for off street parking to inform potential electric vehicle home charging locations

Future Energy: Plans for LEAR

Extending LEAR analysis to include satellite data to validate the assumptions made and also to capture local knowledge from surveys, for example on consumer preferences, is included in future plans for LEAR.

As well as enhancing the model’s capability, we plan to assess the outcomes from Smart Local Energy Systems SLES projects to gain a better understanding of how the systems approach can be replicated in areas with different characteristics, without starting from scratch each time. This insight is critical for the success of SLES projects, as it would form a much more cost-effective, detailed design and implementation process.

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