Smart scheduling of household appliances to decarbonise domestic energy consumption

Kitty Stacpoole, Hongjian Sun, Jing Jiang

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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    Abstract

    Demand side response (DSR) and the inter-connectivity of smart technologies will be essential to transform and revolutionize the way consumers engage with the energy industry. The carbon intensity of electricity varies throughout the day as a result of emissions released during generation. These fluctuations in carbon intensity are predicted to increase due to increased penetration of variable generation sources. This paper proposes a novel insight into how reductions in domestic emissions can be achieved, through the scheduling of certain wet appliances to optimally manage low carbon electricity. An appliance detecting and scheduling algorithm is presented and results are generated using real demand data, electricity generation and carbon intensity values. Reductions were achieved from the variations in grid carbon intensity and the availability of solar generation from a household photovoltaic (PV) supply.
    Original languageEnglish
    Title of host publication2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops 2019)
    Subtitle of host publicationChangchun, China 11 – 13 August 2019
    Place of PublicationPiscataway, NJ
    PublisherIEEE
    Pages216-221
    Number of pages6
    ISBN (Electronic)9781728107387, 9781728107370
    ISBN (Print)9781728107394
    DOIs
    Publication statusPublished - Aug 2019
    Event8th IEEE/CIC International Conference on Communications in China - Changchun, China
    Duration: 11 Aug 201913 Aug 2019
    https://iccc2019.ieee-iccc.org/

    Conference

    Conference8th IEEE/CIC International Conference on Communications in China
    Abbreviated titleICCC2019
    Country/TerritoryChina
    CityChangchun
    Period11/08/1913/08/19
    Internet address

    Keywords

    • Carbon dioxide
    • Carbon
    • Mathematical model
    • Washing machines
    • Arrays
    • Renewable energy sources

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