| Summary: | Over the last decade, increasing numbers of multi-national corporations, public institutions and individual property owners have become interested in installing solar photovoltaics and small wind turbines. To best inform this broad range of actors, this research aims to assess the financial viability of such investments across broad city regions whilst maintaining accuracy at individual properties. Publicly available digital representations of urban surfaces are central to meeting this aim because they can be used to assess the area, slope and orientation of potential solar photovoltaic (PV) installation sites and to define how vertical wind profiles are altered by urban areas. A first study utilised digital surface models (DSMs) across seven UK cities to assess the roof spaces available for solar PV and also incorporated socio-economic factors to define the propensity for cities to install the technology. Despite changes to financial incentives that had recently occurred, the technologies remained viable at a very large number of locations and could theoretically meet large percentages (16% to 43%) of the cities’ electricity demands. The accuracy of slope, orientation and available area estimation in roof geometry modelling was then improved through the development of a neighbouring buildings method. In 87% of 536 validated results, the method identified the correct roof shape and roof slope was estimated to a mean absolute error of 3.76° when compared to 182 measured roofs. Work was then undertaken to improve solar insolation modelling. A radiative transfer model was created that incorporated shading based on DSM data. It estimated the power output of 17 solar PV installations across four UK cities with +2.62% mean percentage error when its 2013 insolation estimates were converted to power outputs using a 0.8 performance ratio. The validation data showed that the RTS model outperformed the market-leading esri ArcMap solar radiation software which incurred a -15.97% mean percentage error. This method was then adapted to be deployable on a city scale and predicted solar insolation with a mean percentage error of -4.39% despite the process being made far more computationally efficient. A method to estimate long-term average wind speeds for urban areas was then developed that produced results of comparable accuracy to an existing model but with considerably reduced computational demand and complexity in deployment. The mean absolute error inwind speed estimation was just 1.75% greater using the simplified methodology than the existing model. Finally, the improved modelling of roof geometries, solar insolation and long-term mean wind speed were brought together to evaluate the city-scale potential for solar PV and small to medium wind microgeneration. The research has shown that wind and solar PV microgeneration at sites that pay back within nine years could theoretically meet 88.5% of annual domestic electricity demand in the city of Leeds, or would be the equivalent of providing electricity to 300,319 homes. Current financial contexts were used to define a baseline scenario from which hypothetical changes to a variety of factors influencing microgeneration viability were investigated. When the costs and revenues were defined from a pessimistic, but still realistic, perspective the percentage of the study area’s electricity demand that could theoretically be met by wind and solar PV microgeneration fell to 0.1%. This suggests that government policy will continue to play a key role in the future growth of UK wind and solar PV deployment.
|
|---|
