Connor McGarry, James Dixon, Ian Elders and Stuart Galloway
There is an increasing need to decarbonise both heating and transport sectors in the UK, and the uptake of low carbon technologies (LCTs) will be central to this. The impact of LCTs on electricity network infrastructure varies both spatially and temporally, and is driven by the diversity in technology type, consumer behaviour, variable weather patterns, variation of the building stock and the incumbent network assets. In recognition of this diversity and household energy variability, LCT adoption and utilisation will be influenced by the distribution of socio-economic factors within a local area. This has the potential to impact network decision-making across different regions. As such, there is a requirement to consider socio-technical and socio-spatial dimensions when modelling LCT impact on network infrastructure. This research, presented within a UK context, demonstrates a novel high-resolution methodology that enables assessment of electrified heat and transport impact on transformer headroom using socio-economic indicators to inform the application of LCT consumption. This includes mapping of spatially linked datasets to identify relationships between consumption and social deprivation. These relationships are used as inputs to a heat pump modelling methodology that converts gas demand to equivalent electrical heat demand. This approach is compared with a generalised trial data approach to ascertain the impact of incorporating socio-economic elements. Electric vehicles are then introduced, where charging is based on socially disaggregated behaviour in the form of travel diaries showing the combined impact of different LCTs. Findings are considered from the perspective of the distribution network operator and other key stakeholders.
McGarry, C., Dixon, J., Elders, I. and Galloway, S. 2023. A high-resolution geospatial and socio-technical methodology for assessing the impact of electrified heat and transport on distribution network infrastructure. Sustainable Energy, Grids and Networks, 35: 101118. doi: 10.1016/j.segan.2023.101118Opens in a new tabOpen access
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