Wind Resource Predictions within Urban Environments

Predicting the wind resource in urban areas is notoriously difficult due to the complex effect of urban surfaces upon the atmospheric boundary layer and the deflection of wind around individual buildings. Without sufficiently accurate methods of predicting this resource, there is a danger that turbines will be installed at locations that are neither environmentally or financially viable, and this is counterproductive as well as damaging to the reputation of the wind energy industry. Viable sites may also be overlooked, leading to the under-exploitation of small scale wind within city regions. In this work, we develop semi-empirical models for predicting wind resource at the neighbourhood scale across a number of UK cities. Detailed mapping of surface aerodynamic parameters is first achieved based on high resolution building data derived from LiDAR measurements. A 3 stage scaling process based on logarithmic wind profiles is then used to estimate above roof mean wind speeds and turbulence. We compare predictions with measured mean wind speeds at 21 different sites, ranging from two-story suburban properties to medium-rise city-centre buildings and high-rise blocks of flats. The accuracy of predictions is seen to improve when building height variability and wind directional effects are fully taken into account. The methodology is then used to map the mean wind resource and excess energy content present within turbulent fluctuations across several UK cities including Leeds, London and Edinburgh. Overall it is demonstrated that significant potential exists for the deployment of turbines within UK cities when sited optimally.