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Assessing the impact of real-time price visualization on residential electricity consumption, costs, and carbon emissions

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  • Nilsson, Anders
  • Stoll, Pia
  • Brandt, Nils

Abstract

The development of smart grid projects, with demand side management as an integral part, has led to an increased interest of households’ willingness to react to different types of demand response programs. This paper presents a pilot study assessing the impact of real-time price visualization on residential electricity consumption, and its effects on electricity costs and carbon (CO2eq) emissions. We analyze changes in electricity consumption based on a test group and a reference group of 12 households, respectively. To allow for analysis on load shift impact on CO2eq emissions, hourly dynamic CO2eq intensity of the Swedish electricity grid mix is calculated, using electricity generation data, trading data, and fuel-type specific emission factors. The results suggest that, on average, the test households shifted roughly 5% of their total daily electricity consumption from peak hours (of high electricity price) to off-peak hours (of low electricity price) as an effect of real-time price visualization. However, due to the mechanisms of the Swedish electricity market, with a negative relation between spot price and CO2eq intensity, the load shift led to a split effect; electricity costs modestly decreased while CO2eq emissions increased. In addition, any indication of the contribution of real-time spot price visualization to a reduction in overall household electricity consumption level could not be found, as the relative difference in consumption level between the test households and the reference households remained constant during both the baseline period and the test period.

Suggested Citation

  • Nilsson, Anders & Stoll, Pia & Brandt, Nils, 2017. "Assessing the impact of real-time price visualization on residential electricity consumption, costs, and carbon emissions," Resources, Conservation & Recycling, Elsevier, vol. 124(C), pages 152-161.
    • Handle: RePEc:eee:recore:v:124:y:2017:i:c:p:152-161
    DOI: 10.1016/j.resconrec.2015.10.007
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    References listed on IDEAS

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    1. Bartusch, Cajsa & Alvehag, Karin, 2014. "Further exploring the potential of residential demand response programs in electricity distribution," Applied Energy, Elsevier, vol. 125(C), pages 39-59.
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    4. Nilsson, Andreas & Bergstad, Cecilia Jakobsson & Thuvander, Liane & Andersson, David & Andersson, Kristin & Meiling, Pär, 2014. "Effects of continuous feedback on households’ electricity consumption: Potentials and barriers," Applied Energy, Elsevier, vol. 122(C), pages 17-23.
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    Cited by:

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    2. Madler, Jochen & Harding, Sebastian & Weibelzahl, Martin, 2023. "A multi-agent model of urban microgrids: Assessing the effects of energy-market shocks using real-world data," Applied Energy, Elsevier, vol. 343(C).
    3. Guangxu Li & Lingyu Wang & Jie Hu, 2023. "Integration with Visual Perception—Research on the Usability of a Data Visualization Interface Layout in Zero-Carbon Parks Based on Eye-Tracking Technology," Sustainability, MDPI, vol. 15(14), pages 1-14, July.
    4. Chin Hui Hao & Presley K. Wesseh & David Iheke Okorie & Hermas Abudu, 2023. "Implications of Growing Wind and Solar Penetration in Retail Electricity Markets with Gradual Demand Response," Energies, MDPI, vol. 16(23), pages 1-15, December.
    5. Du, Limin & Guo, Jin & Wei, Chu, 2017. "Impact of information feedback on residential electricity demand in China," Resources, Conservation & Recycling, Elsevier, vol. 125(C), pages 324-334.
    6. Ibrahim Alsaidan & Mohd Bilal & Muhannad Alaraj & Mohammad Rizwan & Fahad M. Almasoudi, 2023. "A Novel EA-Based Techno–Economic Analysis of Charging System for Electric Vehicles: A Case Study of Qassim Region, Saudi Arabia," Mathematics, MDPI, vol. 11(9), pages 1-31, April.

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