Solar City Indicator: A methodology to predict city level PV installed capacity by combining physical capacity and socio-economic factors

Abstract

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.

Barriers to the adoption of photovoltaic systems: The state of the art

“…The limited roof space on tenements results in less electricity output compared with the output from cities that have large properties. This is a limitation that is difficult to address by a policy mechanism [57].…”

“…In this case, the potential adopters prefer direct subsidies instead of low-interest loans. Such a mismatch between demand and policy measures can also be observed in the case of some UK cities, where the policy measures do not correspond to the socioeconomic factors [57]. Huenteler et al [50] analyzed market development paths in Japan and Germany and subsequently deduced policy recommendations for Japan.…”

Methodology for the assessment of PV capacity over a city region using low-resolution LiDAR data and application to the City of Leeds (UK)

“…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].…”

“…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 require high-resolution LiDAR data to achieve accurate results for small buildings, such as residential properties, with inherently more complex roof profiles.…”

A Novel Geographical Information Systems Framework to Characterize Photovoltaic Deployment in the UK: Initial Evidence

“…More recently, work by Gooding et al [11] began to consider the importance of spatial disaggregation of the UK PV deployment. Their approach estimated the feasible PV potential of different UK cities, based upon solar resource (e.g., irradiation and appropriate roof-area) and socio-economic factors, such as income, where higher income was considered as a variable that would increase a household's ability to install PV.…”