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Temporal aggregation: time use methodologies applied to residential electricity demand

Torriti, J. ORCID: https://orcid.org/0000-0003-0569-039X (2020) Temporal aggregation: time use methodologies applied to residential electricity demand. Utilities Policy, 64. 101039. ISSN 0957-1787

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To link to this item DOI: 10.1016/j.jup.2020.101039

Abstract/Summary

Understanding what constitutes peaks and identifying areas of effective load shifting intervention becomes vital to the balancing of demand and supply of electricity. Whilst there is information about the aggregate level of consumption of electricity, little is known about residential peak demand and what levels of flexibility might be available. Specifically, methodologies linking people’s activities and residential electricity load profiles are typically under-investigated. The overall aim of this paper is to introduce methodologies which capture the variation in sequences of activities taking place at times of peak electricity demand. The paper introduces a set of analytical tools which can be deployed when examining time use survey data in energy demand research. It presents the state of the art with modelling load profiles based on time use data and design methodological modifications to improve modelling around peak periods. It is demonstrated how the methodologies presented in the paper can be applied to specific understanding of distributional effects of Time of Use tariffs. The paper discusses issues associated with validation between synthetic data, survey data and electricity metered data and concludes with policy implications and some observations for future research.

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary centres and themes > Energy Research
Science > School of the Built Environment > Energy and Environmental Engineering group
ID Code:79651
Uncontrolled Keywords:Demand Side Response; Electricity demand; Load profiles; Markov chains; Residential energy demand; Time of Use tariffs; Time use
Additional Information:Special Issue 'The Challenges of Temporal and Spatial Aggregation: Modelling and Policy Implications'
Publisher:Elsevier

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