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Large-scale heat pumps are a promising technology for the decarbonisation of heat supplied in buildings and industries, provided they operate as expected. However, common faults like fouling and unplanned downtime periods can significantly affect their performance and availability. This could limit the widespread adoption of large-scale heat pumps over other heating technologies such as gas and electric boilers. Approaches described in the literature to optimize the operation of large-scale heat pumps often lack validation under real-world conditions and do not account for performance degradation due to faults. This work demonstrates a step towards utilizing digital twins to improve the energy performance of a commercial large-scale heat pump affected by fouling. A framework was proposed based on the real-time adaptation of digital twins, where a simulation model was calibrated online based on measurements from the heat pump in operation, which was then used for set point optimization. This enabled to determine optimal intermediate pressure set points in the heat pump operating under varying levels of fouling over time. The framework was tested on different periods of heat pump operation spread over ten calendar months. The results showed that the use of online calibration rather than a single calibration decreased performance estimation errors between 3 and 17 percentage points. Moreover, the set points determined by the online-calibrated model, along with a simpler polynomial model derived from it, showed improvements in the heat pump performance by up to 3%, depending on the level of fouling. The findings of this study demonstrated the potential to extend the proposed framework using digital twins to enhance the energy efficiency of large-scale heat pumps.
Aguilera et al. (Thu,) studied this question.