Mapping the service areas of Great Britain's electrical infrastructure for whole systems energy decarbonisation

Electrification of presently fossil fuel-based heating and transport is recognised as a likely pathway to a net-zero energy system. However, this vector shifting will involve a significantly increased demand on the electrical network. Therefore, it is important to understand the geography of the electrical network in order to accurately anticipate where these extra demands will occur and their impact on peak demand, to in turn determine if network reinforcement or other flexibility solutions would be required.

The area served by the same piece of electrical infrastructure, such as a medium voltage substation (also known as a primary) can be visualised by a polygon drawn around all the properties which are normally connected to that substation. This is a valuable addition to the energy data landscape which can enable analysis of local area-based decarbonisation scenarios. For the first time, our research group has compiled the shapefiles from the six separate regional Distribution Network Operator companies in Great Britain, into a single map and made the data available for public download on Zenodo (it has been downloaded over 150 times as of January 2024)[1].  The methods of deriving these boundaries also differ by region (most use a Voronoi polygon algorithm), so they are critically contrasted.

The main benefit of opening this data is to allow the open modelling community and other stakeholders to conduct their own analysis and develop use cases with a geographical unit (the primary substation) which is relevant to the energy network, rather than an administrative or political boundary for which lots of energy datasets are currently aggregated to. In one example of these use cases, we have used open government data on annual domestic energy consumption to determine the mean domestic gas consumption for each of the 4436 primary substations in Great Britain, and place them in a decile. This gives insight into the scale of energy required to be provided through that part of the electrical network rather than the gas network, should heat be electrified to varying degrees. As a fundamental dataset and combined with network monitoring data, our output could ultimately enable advanced models such as digital twins, with applications for near and long-term energy forecasting could be used for system planning.

[1] https://zenodo.org/records/8335354