Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 222222
Loughborough University

Centre for Renewable Energy Systems Technology (CREST)

Wind and Water Power

Resource Assessment

Mesoscale Modelling of Offshore Wind Resource

Image showing a mesoscale model simulation of wind speed over four days using

different PBL schemes and time-offsets compared with site observations.

Knowledge of the wind conditions at a potential offshore wind farm site is key in reducing investment risk. This is normally done through the use of large meteorological masts. However, the increasing scale of the turbines offshore requires higher and more expensive masts, driving interest in the use of alternatives to extend accurate assessment of the resource.

Research in CREST has looked at the use of the Weather Research and Forecasting (WRF) mesoscale model (Skamarock et al. 2008), (Janjic, 2003) to assess the wind conditions at selected sites in UK offshore waters. Specifically, the Advance Research WRF model core (ARW) is used in this work. The accuracy of the model is assessed in a number of ways: 1) Through application of several planetary boundary layer (PBL) schemes, both individually and as an ensemble; 2) through the use of time-step ensembles; 3) by the use of different timescale filters; 4) through the use of model ‘nudging’ using nearby observations.

Each model run has its boundary conditions set using output from the National Centers for Climate Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) (Saha et al. 2010). This research is therefore concerned with how well a mesoscale model can downscale global forecast analysis data. A comparison is made between model output and observations from meteorological masts at Scroby Sands off the east coast of the UK and two masts at Shell Flats off the north-west coast.

Model performance is assessed in terms of ability to predict wind speed and atmospheric stability. Recommendations are made in terms of how best to use the model for offshore wind resource prediction. Finally, a projection is made of the wind conditions at a future potential offshore wind farm site in the UK Round 3 Dogger Bank development zone. The variation in synoptic wind conditions across a large hypothetical 1.2GW wind farm in this area are also assessed including maximum expected wind speed and wind direction differences across the wind farm.

References

Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X-Y., Wang,  W. and Powers, J.G. 2008. A description of the Advanced Research WRF Version 3. NCAR/TN-475=STR, NCAR Technical Note, Mesoscale and Microscale Meteorology Division, National Center of Atmospheric Research, June 2008, 113 pp.

Janjic, Z. I., 2003: A Nonhydrostatic Model Based on a New Approach. Met. Atmos. Phy., 82, 271-285.

Saha, S. et al., 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015–1057.

 

 

The Challenge of Wind Turbine Siting Near Forests

Image showing CFD simulations of the wind speed and turbulence in the wake of a

forest canopy. Zones A-D represent different stages of the wake evolution.

In the past, wind farm developers have avoided sites containing complex terrain and atmospheric features in order to ensure bankable levels of uncertainty in their resource assessments. However, as viable sites containing low levels of complexity become less common and the pressure to meet renewable energy targets continues, sites once considered marginal are now being increasingly developed. In order to meet the standards demanded in this era of financial rigor, research activities into the flow dynamics present in these complex sites has increased.

One element of terrain complexity, which is increasingly found on or around potential wind farm sites, is the presence of forestry. Trees are living, breathing organisms which exert a considerable drag force on the wind, introduce turbulence and alter local temperature and heat flux profiles. The aggregated effect of these factors is an extremely complicated flow regime in the vicinity of forest canopies which presents a significant challenge to the micrometeorologist.

The extent of this challenge was clearly demonstrated in Brower et al. (2014) where it was shown that the presence of forestry increases modelling uncertainty by a factor of 4-5 regardless of the computational technology used. Thus, it is perhaps unsurprising that various reports have identified the effects of forestry as a priority area for wind resource assessment research. [TPWind, 2008, Sanz Rodrigo, 2010].

This research examines the possibility of using state of the art measurement and modelling techniques to understand the structure of both forest canopies and the atmosphere above. It is hoped that these technologies will provide a fuller understanding of the elements that drive the flow in these complex environments and contribute to a further tightening of the net around resource assessment uncertainty.

The work was part of the WAUDIT Marie Curie European collaboration project (PITN-GA-2009-238576) comprising 30 organisations in 8 countries working together to audit, standardise and improve industrial wind resource assessment techniques.

 

References

Brower, M.C., Vidal, J., Beaucage, P., 2014. A Performance comparison of four numerical wind flow modeling systems. Proceedings of European Wind Energy Association Annual Conference, Barcelona, March 2014.

Sanz Rodrigo J., State of the art of Wind Resource Assessment: Deliverable D7 of the Waudit project. CENER, Spain, 2010

TPWind, 2008. Strategic research development agenda. Market development strategy from 2008 to 2030. The European Wind Energy Technology Platform, Brussels