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When modelling the environmental and economic aspects of meeting a given heat and power demand with a combination of combined heat and power (CHP) and grid power, it is common to use a coarse temporal precision such as 1-h demand blocks in heat and power demand data. This may be appropriate for larger applications where demand is reasonably smooth, but becomes questionable for applications where demand exhibits substantial volatility such as for a single residential dwelling—an important potential market for the commercialisation of small-scale fuel cells and other micro-CHP. Choice of temporal precision is also influenced by the relative ease in obtaining coarse data, their compatibility with available energy price data, and avoidance of computational overheads when data sets expand. The thesis of this paper is that use of such coarse temporal precision leads to averaging effects that result in …