High resolution modelling of multi-energy domestic demand profiles

Increasing interest in low carbon electro-thermal heating technologies, such as electric heat pumps and combined heat and power units, may in the near future significantly change the character of domestic electricity and gas demand profiles. To understand the impact of adoption of such technologies it is necessary to appreciate fully the multi-energy aspects of such technologies, and indeed of any other domestic energy service demands. Such holistic treatment is necessary to fully capture the effects of coincidence in various energy service demands within and across dwellings. To that end this paper introduces and details a high resolution domestic multi-energy model comprised of physically based space heating, domestic hot water (DHW), cooking and electrical appliance models. Given the potential for thermal storage in the heating system and building elements particular attention is given to the space heating model, which includes building, heat emitter, buffer tank and heating unit models, capturing the relevant inter-temporal dependencies, heat transfer rates and system inertia. The power and interest of the presented model is demonstrated through exploration of a selection of various possible applications, including amongst others: effect of input energy/heating technology substitution on consumption profiles of different energy vectors, effect of thermal inertia, and investigation of dwelling diversity. Additionally the importance of high granularity modelling is demonstrated. The presented model can be utilised by many actors involved in energy systems for various purposes. For example electricity distribution network operators for modelling of network peak demand, demand aggregators for estimation of potential demand side flexibility, government agencies for assessing incentive scheme costs, electricity retailers for understanding the impact of electro-thermal technology adoption upon their demand portfolio or potential heat network operators for understanding the demand for heat in an area.