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.