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A multi-zone, fast solving, rapidly reconfigurable building and electrified heating system model for generation of control dependent heat pump power demand profiles

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Listed:
  • Johnson, R.C.
  • Royapoor, M.
  • Mayfield, M.

Abstract

The electrification of heating is expected to grow in the UK domestic sector, and this has increased interest in the effects that this may have on low and high voltage network operation. However, Electrified heating profiles that alter with control decisions can only be obtained from dedicated building modelling that energy system modellers do not usually have the expertise to perform, yet these are required for meaningful studies. This work outlines a novel method for modelling air source and ground source heat pump power demand profiles using a multi-zone physics based building modelling framework with building fabric, thermohydraulic, and air flow subsystems. The novel setup framework allows detailed building layout, fabric and control properties to be assigned by analysts with no prior building modelling expertise. Once fully assigned, the building model can be used to generate heat pump power demand profiles at sub minute resolution. Upon testing, a single daily run of the model could be executed in 17 s. The model was then validated against real life test house data, under various control and weather conditions. A small relative error (typically within 10%) was observed between modelled and actual cycle lengths, and modelled and actual heat and electricity demands. Due to its rapid solution rate, the model is of significant value to energy efficiency and distribution network studies, where large demand profile sets that are sensitive to detailed retrofit and control considerations are often essential. The model has been made openly available.

Suggested Citation

  • Johnson, R.C. & Royapoor, M. & Mayfield, M., 2021. "A multi-zone, fast solving, rapidly reconfigurable building and electrified heating system model for generation of control dependent heat pump power demand profiles," Applied Energy, Elsevier, vol. 304(C).
  • Handle: RePEc:eee:appene:v:304:y:2021:i:c:s0306261921010266
    DOI: 10.1016/j.apenergy.2021.117663
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    References listed on IDEAS

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    1. Wu, Wei & Skye, Harrison M. & Domanski, Piotr A., 2018. "Selecting HVAC systems to achieve comfortable and cost-effective residential net-zero energy buildings," Applied Energy, Elsevier, vol. 212(C), pages 577-591.
    2. Navarro-Espinosa, Alejandro & Mancarella, Pierluigi, 2014. "Probabilistic modeling and assessment of the impact of electric heat pumps on low voltage distribution networks," Applied Energy, Elsevier, vol. 127(C), pages 249-266.
    3. Aboelsood Zidan & Hossam A. Gabbar, 2016. "DG Mix and Energy Storage Units for Optimal Planning of Self-Sufficient Micro Energy Grids," Energies, MDPI, vol. 9(8), pages 1-18, August.
    4. McKenna, Eoghan & Thomson, Murray, 2016. "High-resolution stochastic integrated thermal–electrical domestic demand model," Applied Energy, Elsevier, vol. 165(C), pages 445-461.
    5. Johra, Hicham & Heiselberg, Per, 2017. "Influence of internal thermal mass on the indoor thermal dynamics and integration of phase change materials in furniture for building energy storage: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 19-32.
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