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Evaluation Method for the Hourly Average CO 2eq. Intensity of the Electricity Mix and Its Application to the Demand Response of Residential Heating

Author

Listed:
  • John Clauß

    (Norwegian University of Science and Technology NTNU, Department of Energy and Process Engineering, Kolbjørn Hejes vei 1a, 7491 Trondheim, Norway)

  • Sebastian Stinner

    (Norwegian University of Science and Technology NTNU, Department of Energy and Process Engineering, Kolbjørn Hejes vei 1a, 7491 Trondheim, Norway)

  • Christian Solli

    (NTNU, Property Division, Høgskoleringen 8, 7034 Trondheim, Norway)

  • Karen Byskov Lindberg

    (NTNU, Department of Electric Power Engineering, 7491 Trondheim, Norway
    SINTEF Building and Infrastructure, Pb 124 Blindern, 0314 Oslo, Norway)

  • Henrik Madsen

    (DTU Technical University of Denmark, DTU Compute, Asmussens Allé, Building 303B, 2800 Kgs. Lyngby, Denmark
    Research Center on Zero Emission Neighbourhoods in Smart Cities, Trondheim 7491, Norway)

  • Laurent Georges

    (Norwegian University of Science and Technology NTNU, Department of Energy and Process Engineering, Kolbjørn Hejes vei 1a, 7491 Trondheim, Norway
    Research Center on Zero Emission Neighbourhoods in Smart Cities, Trondheim 7491, Norway)

Abstract

This work introduces a generic methodology to determine the hourly average CO 2eq. intensity of the electricity mix of a bidding zone. The proposed method is based on the logic of input–output models and avails the balance between electricity generation and demand. The methodology also takes into account electricity trading between bidding zones and time-varying CO 2eq. intensities of the electricity traded. The paper shows that it is essential to take into account electricity imports and their varying CO 2eq. intensities for the evaluation of the CO 2eq. intensity in Scandinavian bidding zones. Generally, the average CO 2eq. intensity of the Norwegian electricity mix increases during times of electricity imports since the average CO 2eq. intensity is normally low because electricity is mainly generated from hydropower. Among other applications, the CO 2eq. intensity can be used as a penalty signal in predictive controls of building energy systems since ENTSO-E provides 72 h forecasts of electricity generation. Therefore, as a second contribution, the demand response potential for heating a single-family residential building based on the hourly average CO 2eq. intensity of six Scandinavian bidding zones is investigated. Predictive rule-based controls are implemented into a building performance simulation tool (here IDA ICE) to study the influence that the daily fluctuations of the CO 2eq. intensity signal have on the potential overall emission savings. The results show that control strategies based on the CO 2eq. intensity can achieve emission reductions, if daily fluctuations of the CO 2eq. intensity are large enough to compensate for the increased electricity use due to load shifting. Furthermore, the results reveal that price-based control strategies usually lead to increased overall emissions for the Scandinavian bidding zones as the operation is shifted to nighttime, when cheap carbon-intensive electricity is imported from the continental European power grid.

Suggested Citation

  • John Clauß & Sebastian Stinner & Christian Solli & Karen Byskov Lindberg & Henrik Madsen & Laurent Georges, 2019. "Evaluation Method for the Hourly Average CO 2eq. Intensity of the Electricity Mix and Its Application to the Demand Response of Residential Heating," Energies, MDPI, vol. 12(7), pages 1-25, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1345-:d:220922
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    References listed on IDEAS

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    Cited by:

    1. Sam Hamels, 2021. "CO 2 Intensities and Primary Energy Factors in the Future European Electricity System," Energies, MDPI, vol. 14(8), pages 1-30, April.
    2. Hamels, Sam & Himpe, Eline & Laverge, Jelle & Delghust, Marc & Van den Brande, Kjartan & Janssens, Arnold & Albrecht, Johan, 2021. "The use of primary energy factors and CO2 intensities for electricity in the European context - A systematic methodological review and critical evaluation of the contemporary literature," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    3. Leerbeck, Kenneth & Bacher, Peder & Junker, Rune Grønborg & Goranović, Goran & Corradi, Olivier & Ebrahimy, Razgar & Tveit, Anna & Madsen, Henrik, 2020. "Short-term forecasting of CO2 emission intensity in power grids by machine learning," Applied Energy, Elsevier, vol. 277(C).
    4. Thorvaldsen, Kasper Emil & Korpås, Magnus & Lindberg, Karen Byskov & Farahmand, Hossein, 2021. "A stochastic operational planning model for a zero emission building with emission compensation," Applied Energy, Elsevier, vol. 302(C).
    5. Fleschutz, Markus & Bohlayer, Markus & Braun, Marco & Henze, Gregor & Murphy, Michael D., 2021. "The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices," Applied Energy, Elsevier, vol. 295(C).

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