Context and background: Storage and control technologies such as household batteries, vehicle- to-grid electric vehicles, and system network platforms for metering and actuation are expected to enable a more flexible management of demand. The operational constraints of individual and combined technologies need to be understood in relation to flexibility of how, where, and when people demand energy and what this
means to demand profiles across distribution networks.
Aim: This project will analyse how existing demand-side technologies (DST) can change patterns of demand at different scales by modelling technology functionality and the implications on the temporality of practices.
Questions and methods: Activity profiles and demand flexibility will be modelled using statistical methods to capture variation under different levels of DST adoption. Modelling inputs will be based on a combination of data from case-study interviews, monitoring campaigns, and time-use diaries (both primary and secondary data). Analysis will be undertaken to address a) whether existing technologies require changes in social
practices in order to offer effective demand side management; and b) if technologies that enable flexibility actually change temporalities in what people do. Flexible demand estimates will be integrated into an existing energy system model that also incorporates models of demand-side technology operation and control.
Simulations for different levels of technology adoption will be undertaken to see: c) on what spatial scales do changing practices impact flexibility; d) what the operational limits of flexibility are for the studied technologies under identified social practices; and e) the significance of different scenarios of technology adoption to flexible demand at different spatial scales on distribution networks.
Outputs: 3 research articles covering: modelling approach and data integration; technological limitations to flexibility; and grid-spatial patterns of flexibility under technology adoption scenarios. Computational models of demand-side technology operation; demand related activity profiles integrated into energy system models; network maps of technology adoption and demand flexibility; data on demand practices of early adopters; case study data on demand practices of adopters (interviews and energy monitoring); 1 workshop on
technology adoption and demand flexibility.
October 2019 – September 2022.