Assessing the implications of socioeconomic diversity for low carbon technology uptake in electrical distribution networks

Highlights: • Analyse low carbon technology penetration in residential neighbourhoods. • Socioeconomic household and neighbourhood archetypes developed. • Synthetic low voltage distribution network generation and simulation. • High network upgrade and decarbonisation costs in wealthy rural networks. • Method employs open data and is transferable to other contexts. - Abstract: Adequately accounting for interactions between Low Carbon Technologies (LCTs) at the building level and the overarching energy system means capturing the granularity associated with decentralised heat and power supply in residential buildings. This paper combines dwelling/household archetypes (DHAs) combined with a mixed integer linear program to generate optimal (minimum cost) technology configurations and operation schedules for individual dwellings. These DHAs are scaled up to three socioeconomically differentiated neighbourhood clusters at the Output Area level in the UK. A synthetic distribution network generation and simulation assesses the required network upgrade costs for these clusters with different LCT penetration scenarios. Whilst the application here is to the United Kingdom (UK) setting, the method is largely based on freely available data and is therefore highly transferable to other contexts. The results show significant differences between the upgrade costs of the three analysed network types, and especially the semi-rural cluster has much higher costs. The employment of heat pumps together with photovoltaics (PV) has strong synergy effects, which can considerably reduce the network upgrade and carbon abatement costs if deployed in parallel. The determined CO₂-abatement costs also suggest that decarbonisation measures with these two technologies should focus on semi-urban neighbourhoods due to the lower cost in comparison to the semi-rural case. This shows that such a socioeconomically differentiated approach to distribution network modelling can provide useful energy policy insights.