• Login
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    Multilayer modeling of adoption dynamics in energy demand management. 
    •   QMRO Home
    • School of Mathematical Sciences
    • Mathematics
    • Multilayer modeling of adoption dynamics in energy demand management.
    •   QMRO Home
    • School of Mathematical Sciences
    • Mathematics
    • Multilayer modeling of adoption dynamics in energy demand management.
    ‌
    ‌

    Browse

    All of QMROCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    ‌
    ‌

    Administrators only

    Login
    ‌
    ‌

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Multilayer modeling of adoption dynamics in energy demand management.

    View/Open
    Accepted version (4.001Mb)
    Volume
    30
    Pagination
    013153 - ?
    DOI
    10.1063/1.5122313
    Journal
    Chaos
    Issue
    1
    Metadata
    Show full item record
    Abstract
    Due to the emergence of new technologies, the whole electricity system is undergoing transformations on a scale and pace never observed before. The decentralization of energy resources and the smart grid have forced utility services to rethink their relationships with customers. Demand response (DR) seeks to adjust the demand for power instead of adjusting the supply. However, DR business models rely on customer participation and can only be effective when large numbers of customers in close geographic vicinity, e.g., connected to the same transformer, opt in. Here, we introduce a model for the dynamics of service adoption on a two-layer multiplex network: the layer of social interactions among customers and the power-grid layer connecting the households. While the adoption process-based on peer-to-peer communication-runs on the social layer, the time-dependent recovery rate of the nodes depends on the states of their neighbors on the power-grid layer, making an infected node surrounded by infectious ones less keen to recover. Numerical simulations of the model on synthetic and real-world networks show that a strong local influence of the customers' actions leads to a discontinuous transition where either none or all the nodes in the network are infected, depending on the infection rate and social pressure to adopt. We find that clusters of early adopters act as points of high local pressure, helping maintaining adopters, and facilitating the eventual adoption of all nodes. This suggests direct marketing strategies on how to efficiently establish and maintain new technologies such as DR schemes.
    Authors
    Iacopini, I; Schäfer, B; Arcaute, E; Beck, C; Latora, V
    URI
    https://qmro.qmul.ac.uk/xmlui/handle/123456789/62661
    Collections
    • Mathematics [772]
    Language
    eng
    Licence information
    This is a pre-copyedited, author-produced version of an article accepted for publication in Chaos: an interdisciplinary journal of nonlinear science following peer review.
    Copyright statements
    © AIP Publishing 2020
    Twitter iconFollow QMUL on Twitter
    Twitter iconFollow QM Research
    Online on twitter
    Facebook iconLike us on Facebook
    • Site Map
    • Privacy and cookies
    • Disclaimer
    • Accessibility
    • Contacts
    • Intranet
    • Current students

    Modern Slavery Statement

    Queen Mary University of London
    Mile End Road
    London E1 4NS
    Tel: +44 (0)20 7882 5555

    © Queen Mary University of London.