Solar city indicator: a methodology to predict city level PV installed capacity by combining physical capacity and socio-economic factors

Shifting to renewable sources of electricity is imperative in achieving global reductions in carbon emissions and ensuring future energy security. One technology, solar photovoltaics (PV), has begun to generate a noticeable contribution to the electricity mix in numerous countries. However, the upper limits of this contribution have not been explored in a way that combines both building-by-building solar resource appraisals with the city-scale socio-economic contexts that dictate PV uptake. This paper presents such a method, whereby a ‘Solar City Indicator’ is calculated and used to rank cities by their capacity to generate electricity from roof-mounted PV. Seven major UK cities were chosen for analysis based on available data; Dundee, Derby, Edinburgh, Glasgow, Leicester, Nottingham and Sheffield. The physical capacity of each city was established using a GIS-based methodology, exploiting digital surface models and LiDAR data, with distinct methodologies for large and small properties. Socio-economic factors (income, education, environmental consciousness, building stock and ownership) were chosen based on existing literature and correlation with current levels of PV installations. These factors were enumerated using data that was readily available across each city. Results show that Derby has the greatest potential of all the cities analysed, as it offers both good physical and socio-economic potential. In terms of physical capacity it was seen that over a 15 year payback period there are two plateaus, showing a marked difference in viability between small and large PV arrays. It was found that both the physical and socio-economic potential of a city are strongly influenced by the nature of the local building stock. This study also identifies areas where policy needs to be focused in order to encourage uptake and highlights factors limiting maximum PV uptake. While this methodology has been demonstrated using UK cities, it is equally applicable to any country where city data is available.