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

• Published in: Applied energy Vol. 304; p. 117663
• Main Authors: Johnson, R.C., Royapoor, M., Mayfield, M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.12.2021
• Subjects: Building simulation; Electrified heating; Heat pumps; Heating system control; Building simulation; Heat pumps; Electrified heating; Heating system control
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2021.117663

•A dynamic, multi-zone model for heat pump demand profile simulation is presented.•The model is capable of rapid simulation at 5 s resolution.•The model is suited to batch simulation of multiple building profiles.•An interface that allows control logic modification is provided.•Results are consistent when compared to real data and equivalent EnergyPlus models. 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.