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Control of Heat Pumps with CO 2 Emission Intensity Forecasts

Author

Listed:
  • Kenneth Leerbeck

    (Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark)

  • Peder Bacher

    (Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark)

  • Rune Grønborg Junker

    (Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark)

  • Anna Tveit

    (Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark)

  • Olivier Corradi

    (Tomorrow (Tmrow IVS), Njalsgade, 2300 Copenhagen, Denmark)

  • Henrik Madsen

    (Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark
    Faculty of Architecture and Design, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway)

  • Razgar Ebrahimy

    (Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark)

Abstract

An optimized heat pump control for building heating was developed for minimizing CO 2 emissions from related electrical power generation. The control is using weather and CO 2 emission forecasts as inputs to a Model Predictive Control (MPC)—a multivariate control algorithm using a dynamic process model, constraints and a cost function to be minimized. In a simulation study, the control was applied using weather and power grid conditions during a full-year period in 2017–2018 for the power bidding zone DK2 (East, Denmark). Two scenarios were studied; one with a family house and one with an office building. The buildings were dimensioned based on standards and building codes/regulations. The main results are measured as the CO 2 emission savings relative to a classical thermostatic control. Note that this only measures the gain achieved using the MPC control, that is, the energy flexibility, not the absolute savings. The results show that around 16% of savings could have been achieved during the period in well-insulated new buildings with floor heating. Further, a sensitivity analysis was carried out to evaluate the effect of various building properties, for example, level of insulation and thermal capacity. Danish building codes from 1977 and forward were used as benchmarks for insulation levels. It was shown that both insulation and thermal mass influence the achievable flexibility savings, especially for floor heating. Buildings that comply with building codes later than 1979 could provide flexibility emission savings of around 10%, while buildings that comply with earlier codes provided savings in the range of 0–5% depending on the heating system and thermal mass.

Suggested Citation

  • Kenneth Leerbeck & Peder Bacher & Rune Grønborg Junker & Anna Tveit & Olivier Corradi & Henrik Madsen & Razgar Ebrahimy, 2020. "Control of Heat Pumps with CO 2 Emission Intensity Forecasts," Energies, MDPI, vol. 13(11), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2851-:d:366911
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    References listed on IDEAS

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    1. Junker, Rune Grønborg & Azar, Armin Ghasem & Lopes, Rui Amaral & Lindberg, Karen Byskov & Reynders, Glenn & Relan, Rishi & Madsen, Henrik, 2018. "Characterizing the energy flexibility of buildings and districts," Applied Energy, Elsevier, vol. 225(C), pages 175-182.
    2. Oldewurtel, Frauke & Sturzenegger, David & Morari, Manfred, 2013. "Importance of occupancy information for building climate control," Applied Energy, Elsevier, vol. 101(C), pages 521-532.
    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. Lund, H. & Mathiesen, B.V., 2009. "Energy system analysis of 100% renewable energy systems—The case of Denmark in years 2030 and 2050," Energy, Elsevier, vol. 34(5), pages 524-531.
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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. Miroslav Variny & Dominika Jediná & Miroslav Rimár & Ján Kizek & Marianna Kšiňanová, 2021. "Cutting Oxygen Production-Related Greenhouse Gas Emissions by Improved Compression Heat Management in a Cryogenic Air Separation Unit," IJERPH, MDPI, vol. 18(19), pages 1-32, October.
    4. Wang, Jing & Munankarmi, Prateek & Maguire, Jeff & Shi, Chengnan & Zuo, Wangda & Roberts, David & Jin, Xin, 2022. "Carbon emission responsive building control: A case study with an all-electric residential community in a cold climate," Applied Energy, Elsevier, vol. 314(C).
    5. Gasser, Jan & Cai, Hanmin & Karagiannopoulos, Stavros & Heer, Philipp & Hug, Gabriela, 2021. "Predictive energy management of residential buildings while self-reporting flexibility envelope," Applied Energy, Elsevier, vol. 288(C).
    6. 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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