Systems modelling of the socio-technical aspects of residential electricity use and network peak demand

Lewis, Jim, Mengersen, Kerrie, Buys, Laurie, Vine, Desley, Bell, John M., Morris, Peter, & Ledwich, Gerard (2015) Systems modelling of the socio-technical aspects of residential electricity use and network peak demand. PLoS ONE, 10(7), e0134086.

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Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price,managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tick box interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.

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ID Code: 87802
Item Type: Journal Article
Refereed: Yes
Additional Information: ARC project and funding details
Keywords: network peak demand, electricity, residential electricity use, complex system model, systems modelling, Bayesian network modelling, socio-technical system
DOI: 10.1371/journal.pone.0134086
ISSN: 1932-6203
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Stochastic Analysis and Modelling (010406)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Electrical and Electronic Engineering not elsewhere classified (090699)
Australian and New Zealand Standard Research Classification > STUDIES IN HUMAN SOCIETY (160000) > SOCIOLOGY (160800) > Sociology and Social Studies of Science and Technology (160808)
Divisions: Current > Research Centres > ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)
Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > Schools > School of Design
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Creative Industries Faculty
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 Lewis et al.
Copyright Statement: This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Deposited On: 28 Sep 2015 01:00
Last Modified: 26 Feb 2016 05:27

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