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Wind

Projects supervised by: Professor Alison Tomlin, Professor William Gale, Dr Rolf Crook and Dr Catherine Bale

Researchers: David Allen, James Gooding, Victoria Hoolohan, Kelly Marsh, Shemaiah Weekes

The UK is the windiest country in Europe, meaning that wind energy has the potential to contribute significantly towards our energy needs as a clean and renewable technology. In addition to large-scale onshore and offshore wind farms, small- to medium-scale wind energy is one technology that can be used to help achieve the UK’s target of 15% of energy from renewables by 2020. In order to maximize both the financial benefits and potential carbon savings, it is vital that wind turbines are carefully located. Currently, there are challenges in accurately determining the wind resource, particularly in urban areas and regions of complex topography. While long-term direct measurements may not be always be financially viable, indirect models may be too coarse to fully resolve small-scale local features resulting in inaccurate wind speed predictions.

The DTC and Wind Energy

Research at the DTC aims to address these issues and produce improved tools for wind resource prediction.

Ideally, wind speeds would be measured at a site for at least twelve months before installing a turbine. Often, this is not financially and practically viable and does not account for inter-annual variations in the wind resource. A measure-correlate-predict (MCP) model is proposed to alleviate these problems. Wind speeds are measured at a site for a short period of time and this data is correlated to longer-term wind speed data at a nearby reference site. Future predictions can then be made based on the correlation between the sites and long-term historical weather records. Work is being undertaken to reduce the length of the direct measurement period in order to make the MCP approach more accessible to small and medium sized wind installations.

Another wind speed modelling project entails the multi-scale modelling of the wind resource across a city. A quasi-empirical model will be applied on a neighborhood by neighbourhood scale to provide an estimation of annual mean wind speeds at every property in the study area. The results of this research will then be translated to clear, accessible maps leading to a publically available tool for investigating the potential for renewable energy technologies, or to aid local councils in their decisions for optimal placement of wind turbines.

This research contains elements which are transferable to the prediction of other renewable resources including solar energy and is linked to ongoing work related to the implementation of a ‘smart’ electricity grid which will be required as renewables gain a greater penetration into the electricity system.