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-bybuilding 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.

An assessment of roof-mounted PV capacity over a local region can be accurately calculated by established roof segmentation algorithms using high-resolution light detection and ranging (LiDAR) datasets. However, over larger city regions often only low-resolution LiDAR data is available where such algorithms prove unreliable for small rooftops. A methodology optimised for low-resolution LiDAR datasets is presented, where small and large buildings are considered separately. The roof segmentation algorithm for small buildings, which are typically residential properties, assigns a roof profile to each building from a catalogue of common profiles after identifying LiDAR points within the building footprint. Large buildings, such as warehouses, offer a more diverse range of roof profiles but geometric features are generally large, so a direct approach is taken to segmentation where each LiDAR point within the building footprint contributes a separate roof segment. The methodology is demonstrated by application to the city region of Leeds, UK. Validation by comparison to aerial photography indicates that the assignment of an appropriate roof profile to a small building is correct in 81% of cases.Keywords: PV capacity; PV output; LiDAR; Roof profile; Solar resource; City region IntroductionPhotovoltaics (PV) are viewed as a key climate change mitigation technology. To achieve this potential will require the large scale installation of PV, either on rooftops or as ground mounted arrays [1]. Installing highly distributed PV within city environments, such as on building rooftops and facades, locates electricity generation close to electricity end use, reducing the requirement for modifications to the electricity distribution network and minimizing transmission losses. Roof mounted PV also avoids the cost and competition for land, and the possible social and environmental impacts associated with large arrays of ground mounted panels [2]. An accurate assessment of the potential roof-mounted PV capacity in city regions is an essential component for establishing regional and national carbon reduction policies and informing investment decisions [3]. However, such assessments are not straightforward because of the range in size, orientation, pitch, and geometric complexity typically found in roof profiles.Previously reported methods to calculate the potential PV capacity over a city region include image analysis of geometrically-corrected high-resolution aerial photography [4,5], statistical approaches based on correlations between building class, population, and roof profile [6][7][8], and roof profile reconstruction from light detection and ranging (LiDAR) point clouds [9][10][11][12][13][14][15][16][17][18][19][20].Methods that utilise LiDAR data usually employ an error-minimising plane-fitting algorithm that divides each roof in to an arbitrary set of planes, which are referred to as roof segments. While such methods report high accuracy for large geometrically simple roofs, such as warehouses, they invariably requir...

6This article describes a method to model roof geometries from widely available low-resolution (2 m 7 horizontal) Light Detection and Ranging (LiDAR) datasets for application on a city wide scale. The 8 model provides roof area, orientation, and slope, appropriate for predictions of solar technology 9 performance, being of value to national and regional policy makers in addition to investors and 10 individuals appraising the viability of specific sites. Where present, similar buildings are grouped 11 together based on proximity and building footprint dimensions. LiDAR data from all the buildings in a 12 group is combined to construct a shared high-resolution LiDAR dataset. The best-fit roof shape is 13 then selected from a catalogue of common roof shapes and assigned to all buildings in that group. 14 Method validation was completed by comparing the model output to a ground-based survey of 169 15 buildings and aerial photographs of 536 buildings, all located in Leeds, UK. The method correctly 16 identifies roof shape in 87% of cases and the modelled roof slope has a mean absolute error of 3.76°. 17 These performance figures are only possible when segmentation, similar building grouping and ridge 18 repositioning algorithms are used. 19

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