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Predictive rule-based control to activate the energy flexibility of Norwegian residential buildings: Case of an air-source heat pump and direct electric heating

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  • Clauß, John
  • Stinner, Sebastian
  • Sartori, Igor
  • Georges, Laurent

Abstract

The building energy flexibility potential of a Norwegian single-family detached house is investigated using predictive rule-based control (PRBC) and building performance simulation (using IDA ICE). Norwegian timber buildings are lightweight and four different insulation levels are considered. Both on-off and modulating air-source heat pumps are analyzed and compared to direct electric heating which is the most common heating system for Norwegian residential buildings. A detailed model for both the heat pump system and the building is implemented, a level of detail not found in previous research on building energy flexibility. The three PRBC investigated have the following objectives: reduce energy costs for heating, reduce annual CO2eq. emissions and reduce energy use for heating during peak hours. This last objective is probably the most strategic in the Norwegian context where cheap electricity is mainly produced by hydropower. The results show that the price-based control does not generate cost savings because lower electricity prices are outweighed by the increase in electricity use for heating. The implemented price-based control would create cost savings in electricity markets with higher daily fluctuations in electricity prices, such as Denmark. For the same reasons, the carbon-based control cannot reduce the yearly CO2eq. emissions due to limited daily fluctuations in the average CO2eq. intensity of the Norwegian electricity mix. On the contrary, the PRBC that reduces the energy use for heating during peak hours turns out to be very efficient, especially for direct electric heating. For air-source heat pumps, the control of the heat pump system is complex and reduces the performance of the three PRBC. Therefore, results suggest that a heat pump system should be modeled with enough detail for a proper assessment of the building energy flexibility. First, by varying temperature set-points there is a clear interaction between the prioritization of domestic hot water and the control of auxiliary heaters which increases energy use significantly. Second, the hysteresis of the heat pump control and the minimum cycle duration prevent the heat pump from stopping immediately after the PRBC requires it. Finally, the paper shows that the influence of thermal zoning, investigated here by cold bedrooms with closed doors, has a limited impact on the building energy flexibility potential and the risk of opening bedroom windows.

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  • Clauß, John & Stinner, Sebastian & Sartori, Igor & Georges, Laurent, 2019. "Predictive rule-based control to activate the energy flexibility of Norwegian residential buildings: Case of an air-source heat pump and direct electric heating," Applied Energy, Elsevier, vol. 237(C), pages 500-518.
    • Handle: RePEc:eee:appene:v:237:y:2019:i:c:p:500-518
    DOI: 10.1016/j.apenergy.2018.12.074
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    8. Carine Lausselet & Linda Ager‐Wick Ellingsen & Anders Hammer Strømman & Helge Brattebø, 2020. "A life‐cycle assessment model for zero emission neighborhoods," Journal of Industrial Ecology, Yale University, vol. 24(3), pages 500-516, June.
    9. 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).
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    12. Han, Gwangwoo & Joo, Hong-Jin & Lim, Hee-Won & An, Young-Sub & Lee, Wang-Je & Lee, Kyoung-Ho, 2023. "Data-driven heat pump operation strategy using rainbow deep reinforcement learning for significant reduction of electricity cost," Energy, Elsevier, vol. 270(C).
    13. Song, Yuguang & Xia, Mingchao & Chen, Qifang & Chen, Fangjian, 2023. "A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin," Applied Energy, Elsevier, vol. 332(C).
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    15. 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.
    16. Finck, Christian & Li, Rongling & Zeiler, Wim, 2020. "Optimal control of demand flexibility under real-time pricing for heating systems in buildings: A real-life demonstration," Applied Energy, Elsevier, vol. 263(C).
    17. Zahra Fallahi & Gregor P. Henze, 2019. "Interactive Buildings: A Review," Sustainability, MDPI, vol. 11(14), pages 1-26, July.
    18. Finck, Christian & Li, Rongling & Zeiler, Wim, 2019. "Economic model predictive control for demand flexibility of a residential building," Energy, Elsevier, vol. 176(C), pages 365-379.
    19. Zheng, Siqian & Jin, Xin & Huang, Gongsheng & Lai, Alvin CK., 2022. "Coordination of commercial prosumers with distributed demand-side flexibility in energy sharing and management system," Energy, Elsevier, vol. 248(C).
    20. D’Ettorre, F. & Banaei, M. & Ebrahimy, R. & Pourmousavi, S. Ali & Blomgren, E.M.V. & Kowalski, J. & Bohdanowicz, Z. & Łopaciuk-Gonczaryk, B. & Biele, C. & Madsen, H., 2022. "Exploiting demand-side flexibility: State-of-the-art, open issues and social perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    21. Rohde, Daniel & Knudsen, Brage Rugstad & Andresen, Trond & Nord, Natasa, 2020. "Dynamic optimization of control setpoints for an integrated heating and cooling system with thermal energy storages," Energy, Elsevier, vol. 193(C).
    22. Panagiotis Michailidis & Iakovos Michailidis & Socratis Gkelios & Elias Kosmatopoulos, 2024. "Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions," Energies, MDPI, vol. 17(3), pages 1-47, January.

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