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Comparison of four rule-based demand response control algorithms in an electrically and heat pump-heated residential building

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  • Alimohammadisagvand, Behrang
  • Jokisalo, Juha
  • Sirén, Kai

Abstract

This study aims to investigate the effect of demand response (DR) actions on energy consumption and energy cost with two alternative heat generation systems in a detached house in a cold climate. The heat generation systems are a ground source heat pump coupled with a two-storage tank system (GSHP heating system) and a water-based electric heating system coupled with a similar storage tank system (storing electric heating system). Four rule-based DR control algorithms were applied and studied for two alternative heat generation systems. Two rule-based DR control algorithms from previous study were applied and investigated for these heat generation systems based on real-time hourly electricity price and future hourly electricity prices based on the blocking-maximum subarray method. Two new rule-based DR control algorithms based on future hourly electricity prices were developed and investigated for these heat generation systems based on the sliding-maximum subarray method and moving average method. This research was implemented with the validated dynamic building simulation tool IDA ICE. The obtained results show that the maximum annual savings in the heating energy and cost take place using the rule-based DR control algorithm based on the sliding-maximum subarray method. As well, these savings are about 10% and 15%, respectively, for the GSHP heating system; and about 1% and 8%, respectively, for the storing electric heating system.

Suggested Citation

  • Alimohammadisagvand, Behrang & Jokisalo, Juha & Sirén, Kai, 2018. "Comparison of four rule-based demand response control algorithms in an electrically and heat pump-heated residential building," Applied Energy, Elsevier, vol. 209(C), pages 167-179.
  • Handle: RePEc:eee:appene:v:209:y:2018:i:c:p:167-179
    DOI: 10.1016/j.apenergy.2017.10.088
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    3. Behrang Vand & Aira Hast & Sanaz Bozorg & Zelin Li & Sanna Syri & Shuai Deng, 2019. "Consumers’ Attitudes to Support Green Energy: A Case Study in Shanghai," Energies, MDPI, Open Access Journal, vol. 12(12), pages 1-20, June.
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    5. Pallonetto, Fabiano & De Rosa, Mattia & Milano, Federico & Finn, Donal P., 2019. "Demand response algorithms for smart-grid ready residential buildings using machine learning models," Applied Energy, Elsevier, vol. 239(C), pages 1265-1282.
    6. Felten, Björn & Weber, Christoph, 2018. "The value(s) of flexible heat pumps – Assessment of technical and economic conditions," Applied Energy, Elsevier, vol. 228(C), pages 1292-1319.
    7. Janne Suhonen & Juha Jokisalo & Risto Kosonen & Ville Kauppi & Yuchen Ju & Philipp Janßen, 2020. "Demand Response Control of Space Heating in Three Different Building Types in Finland and Germany," Energies, MDPI, Open Access Journal, vol. 13(23), pages 1-35, November.
    8. 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, Open Access Journal, vol. 12(7), pages 1-25, April.
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