Predictive thermal relation model for synthesizing low carbon heating load profiles profiles on distribution networks

The introduction of electric heat pumps as a low carbon option for space heating offers a potential pathway for reducing the carbon emissions resulting from domestic heating demand in the UK. However, the additional power demands of heat pumps over conventional domestic loads have the potential to significantly erode network headroom, particularly at the distribution level. The uptake of this technology within the UK is currently limited and the effects of widespread adoption on distribution networks are not well characterized due to the sparse availability of operational heat pump demand data. This paper outlines a methodology for quantifying the demand impact of heat pumps on Low Voltage networks sensitive to local temperature by deriving fundamental thermal relationships from real heat pump electrical demand data. These relations can then be applied to predict demand for new studies independent of the geographic specifics of the original dataset. The strength of this model is in the ability to predict an aggregated hourly heat pump electrical demand profile that reflects local temperature conditions and intra-day usage as well as population size, thereby also accounting for diversity effects that are difficult to capture in physics based models. This work augments the usability of limited existing data by facilitating demand analysis sensitive to local temperature conditions, rather than blanket rescaling of existing customer data as has been performed in previous studies. This creates future opportunities for examining heat pump demand sensitivity for different geographic locations against existing heat pump assessments, as well as performing studies which incorporate multiple low carbon technologies connected to a Low Voltage network