The Effects of Location and Climate Change on the Energy Consumption of Supermarkets in the UK

This thesis reports on the investigation into how climate change may affect the energy consumption in supermarkets at various locations throughout Great Britain in the 2030s. Both complete supermarkets and refrigeration systems were studied. Information on questions on climate change impact can assist supermarket owners and operators with their long term planning regarding energy users, demand and infrastructure, and add impetus to the search for adaptation and mitigation strategies. After reviewing relevant literature and evaluating different energy research tools, the fundamentals of climate change modelling were studied to understand the reliability of climate predictions. The guiding principle for selecting analysis tools was to use as simple an approach as possible which still yielded meaningful results. This led to the selection of simple regression and change point regression models for investigating whole supermarkets. This analysis was preceded by the identification of seven comparable grocery supermarkets with a good geographic spread. A refrigeration system software model was developed based on thermodynamic principles, also allowing examination of the effect of condenser fan control on energy use. As climate change forecasts have a large error margin, the research findings should be treated as indicative only. To show the range of uncertainty, different values from the predicted temperature distribution were used. These results suggested that the electricity consumption for complete supermarkets will rise by between 0.6% and 4.7%, whilst gas use decreases by between 3.3% and 24.1%. This trend agrees with other research. The estimated increase of electricity use of between 1.7% and 13% from the refrigeration model indicates that this would account for most of the electricity demand rise. Future work should include investigating the condenser fan control, as the software model predicted an energy saving potential of approximately 4.5% by the use of better control algorithms.