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Realistic Nudging through ICT Pipelines to Help Improve Energy Self-Consumption for Management in Energy Communities

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Listed:
  • Haicheng Ling

    (Univ. Grenoble Alpes, CNRS, Grenoble INP, G2Elab, 38000 Grenoble, France
    Enogrid, 38100 Grenoble, France)

  • Pierre-Yves Massé

    (Enogrid, 38100 Grenoble, France)

  • Thibault Rihet

    (Enogrid, 38100 Grenoble, France)

  • Frédéric Wurtz

    (Univ. Grenoble Alpes, CNRS, Grenoble INP, G2Elab, 38000 Grenoble, France)

Abstract

Taking full advantage of the potentialities of renewable energies implies overcoming several specific challenges. Here, we address matching an intermittent energy supply with household demand through a nudging approach. Indeed, for households endowed with solar panels, aligning energy consumption with production may be challenging. Therefore, the aim of this study is to introduce two information and communication technology (ICT) nudging pipelines aimed at helping households integrated in energy communities with solar panels to improve their self-consumption rates, and to evaluate their efficiency on semi-real data. Our pipelines use information available in real-world settings for efficient management. They identify “green periods”, where households are encouraged to consume energy with incitation through nudging signals. We evaluate the efficiency of our pipelines on a simulation environment using semi-real data, based on well-known consumption datasets. Results show that they are efficient, compared to an optimal but unrealistic pipeline with access to complete information. They also show that there is a sweet spot for production, for which nudging is most efficient, and that a few green periods are enough to obtain significant improvements.

Suggested Citation

  • Haicheng Ling & Pierre-Yves Massé & Thibault Rihet & Frédéric Wurtz, 2023. "Realistic Nudging through ICT Pipelines to Help Improve Energy Self-Consumption for Management in Energy Communities," Energies, MDPI, vol. 16(13), pages 1-24, July.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5105-:d:1185122
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