Future Ready

Neighbourhood Green

Project Data

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Neighbourhood Green will explore what normal domestic loads and After Diversity Maximum Demand (ADMD) for heating technologies will be in the future, so that networks can be planned and managed appropriately.

What is the project about?

New technologies connecting to electricity networks in the delivery of Net Zero will lead to increased domestic demand and a change in diversity factors and load profiles. The trial is exploring what normal domestic loads and After Diversity Maximum Demand (ADMD) will be in the future for heating technologies, so that networks can be planned and managed appropriately. If this is not done in a timely manner, it puts UK Power Network’s aim to ‘touch the network once’ at risk.

How we’re doing it

We are working collaboratively with Energy Systems Catapult (ESC) to monitor data from homes across the UK with electric heating (heat pumps, electric heaters etc.) and other Low Carbon Technologies (EV, batteries, PVs etc.) in different weather conditions. These data will be analysed to understand the load profile of different heating technologies coupled with other LCTs. They will also be used to perform network testing in the PNDC lab in different network topologies (rural, urban etc.).

What makes it innovative

There is currently no industry standard view on diversity factors for heat, and the ENA does not have a view either, including through the relevant working groups.

The understanding of the clustering effect of all low carbon technologies (LCTs) is low.

The potential of flexibility is not fully explored, especially for LCT clusters.

What we’re learning

The objective of the project is to understand the impact of Low Carbon Technology clusters (electric heating technologies and others) on different network topologies (rural, urban) and provide insight into the potential DNO/DSO response to manage these loads in the most cost-effective and inclusive manner.

A key output of the project will be a proposal for an industry-standard view on diversity factors for heat based on the project learnings.

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