Impact of Spatio-Climatic Variability on Environment-Hosted Land-Based Renewables: Microclimates

Lead Research Organisation: Loughborough University
Department Name: Electronic, Electrical & Systems Enginee


Many current or projected future land-based renewable energy schemes are highly dependent on very localised climatic conditions, especially in regions of complex terrain. For example, mean wind speed, which is the determining factor in assessing the viability of wind farms, varies considerably over distances no greater than the size of a typical farm. Variations in the productivity of bio-energy crops also occur on similar spatial scales. This localised climatic variation will lead to significant differences in response of the landscape in hosting land-based renewables (LBR) and without better understanding could compromise our ability to deploy LBR to maximise environmental and energy gains. Currently climate prediction models operate at much coarser scales than are required for renewable energy applications. The required downscaling of climate data is achieved using a variety of empirical techniques, the reliability of which decreases as the complexity of the terrain increases. In this project, we will use newly emerging techniques of very high resolution nested numerical modelling, taken from the field of numerical weather prediction, to develop a micro-climate model, which will be able to make climate predictions locally down to scales of less than one kilometre. We will conduct validation experiments for the new model at wind farm and bio-energy crop sites. The model will be applied to the problems of (i) predicting the effect of a wind farm on soil carbon sequestration on an upland site, thus addressing the question of carbon payback time for wind farm schemes and (ii) for predicting local yield variations of bio-energy crops. Extremely high resolution numerical modelling of the effect of wind turbines on each other and on the air-land exchanges will be undertaken using a computational fluid dynamics model (CFD). The project will provide a new tool for climate impact prediction at the local scale and will provide new insight into the detailed physical, bio-physical and geochemical processes affecting the resilience and adaptation of sensitive (often upland) environments when hosting LBR.
Description This project provided the means to test and tune the use of CFD in the prediction of wind conditions close to the ground. It showed that predictions of wind speed could be made close to the ground (2m) which is the limit of applicability for such models in rough terrain. In addition, better understanding of forest canopy models and actuator discs for wake predictions within a CFD code where achieved.
Exploitation Route In understanding the impact of wind farms on underlying land, e.g. impact on crops.
Sectors Agriculture, Food and Drink,Energy,Environment

Description Input to industry short course in wind energy
Geographic Reach Multiple continents/international 
Policy Influence Type Influenced training of practitioners or researchers
Impact Better planning of wind farms. Better knowledge of the performance and impact of wind farms.
Description Building Research Establishment 
Organisation Building Research Establishment
Country United Kingdom 
Sector Private 
Start Year 2003
Description E.ON 
Organisation E ON
Department E ON UK
Country United Kingdom 
Sector Private 
PI Contribution Working with E.ON to better predict wake losses in wind farms.
Collaborator Contribution Working with E.ON to better predict wake losses in wind farms.
Impact Development on simplified wake model. Best practice for setting wake model in CFD code