Abstract
Demand-side flexibility has been suggested as a tool for peak demand reduction and large-scale integration of low-carbon electricity sources. Deeper insight into the activities and energy services performed in households could help to understand the scope and limitations of demand-side flexibility. Measuring and Evaluating Time- and Energy-use Relationships (METER) is a 5-year, UK-based research project and the first study to collect activity data and electricity use in parallel at this scale. We present statistical analyses of these new data, including more than 6250 activities reported by 450 individuals in 173 households, and their relationship to electricity use patterns. We use a regularization technique to select influential variables in regression models of average electricity use over a day and of discretionary use across 4-h time periods to compare intra-day variations. We find that dwelling and appliance variables show the strongest associations to average electricity consumption and can explain 49% of the variance in mean daily usage. For models of 4-h average “de-minned” consumption, we find that activity variables are consistently influential, both in terms of coefficient magnitudes and contributions to increased model explanatory power. Activities relating to food preparation and eating, household chores, and recreation show the strongest associations. We conclude that occupant activity data can advance our understanding of the temporal characteristics of electricity demand and inform approaches to shift or reduce it. We stress the importance of considering consumption as a function of time of day, and we use our findings to argue that a more nuanced understanding of this relationship can yield useful insights for residential demand flexibility.
Highlights
The residential sector is the largest end user of electricity in the UK, accounting for 45% of total consumption in 2017 (BEIS 2018)
The paper is organized as follows: In the BLiterature review^ section, we present a brief review of the flexibility and household electricity modeling literature, including both determinants of household electricity consumption and the use of time-use data in energy modeling
Following Mckenna et al.’s (2017) point that the activity taxonomy developed for Harmonized European Time-Use Studies (HETUS) does not differentiate between Benergy-intensive^ and Blowenergy^ activities, we address this limitation by repeating the analysis described below including only activities we designate as Benergy-intensive.^ Table 3 presents a list of these 26 activities and their descriptive statistics
Summary
The residential sector is the largest end user of electricity in the UK, accounting for 45% of total consumption in 2017 (BEIS 2018). As evidenced by the share of generation from renewables increasing to over 29% in 2017, the UK’s electricity system is increasingly low-carbon, yet much more ambition is needed to achieve national climate goals in the decades (BEIS 2018). The UK’s 2017 Clean Growth Strategy sets forth a set of policies and proposals to further accelerate the deployment of low-carbon energy while maintaining increased economic growth (BEIS 2017). The BSmart systems plan,^ aims to help consumers use energy more flexibly. Energy storage will support integration efforts as the UK pursues rapid deployment of renewable energy, but demand flexibility can reduce the cost of integration and storage requirements for low-carbon energy systems (National Infrastructure Commission 2016)
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