Verification of the Validity of WRF Model for Wind Resource Assessment in Wind Farm Pre-feasibility Studies

With offshore wind becoming a key source of renewable energy there exists a requirement for the acquisition of meteorological information at the sites allocated for development. Installation of a conventional, static, meteorological mast is costly. Multiple masts are required to obtain data at several positions in a large offshore wind farm, which further increases the cost of gathering such data. A structure that has mobility for relocation about the site has the potential to reduce costs whilst improving data capture coverage. As such, an instrumentation platform in the form of a floating structure which can be moved easily is desirable. This work discusses the development of a low-motion, lightweight, floating platform with tunable motion response as a basis for a repositionable meteorological measurement station. Wind speed and direction measurements are acquired at a range of heights in the atmosphere through the use of a pulsed Lidar (light detection and ranging) system. The motions of the platform have been analyzed both numerically and experimentally, and the performance of the platform in a range of seas is good.

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Applying Satellite Data Assimilation to Wind Simulation of Coastal Wind Farms in Guangdong, China

Remote Sensing ◽

10.3390/rs12060973 ◽

2020 ◽

Vol 12 (6) ◽

pp. 973

Author(s):

Wenqing Xu ◽

Like Ning ◽

Yong Luo

Keyword(s):

Wind Speed ◽

Data Assimilation ◽

Satellite Data ◽

Surface Wind ◽

Wrf Model ◽

Wind Farms ◽

Resource Assessment ◽

Near Surface ◽

Wind Resource Assessment ◽

Wind Resource

With the development of the wind power industry in China, accurate simulation of near-surface wind plays an important role in wind-resource assessment. Numerical weather prediction (NWP) models have been widely used to simulate the near-surface wind speed. By combining the Weather Research and Forecast (WRF) model with the Three-dimensional variation (3DVar) data assimilation system, our work applied satellite data assimilation to the wind resource assessment tasks of coastal wind farms in Guangdong, China. We compared the simulation results with wind speed observation data from seven wind observation towers in the Guangdong coastal area, and the results showed that satellite data assimilation with the WRF model can significantly reduce the root-mean-square error (RMSE) and improve the index of agreement (IA) and correlation coefficient (R). In different months and at different height layers (10, 50, and 70 m), the Root-Mean-Square Error (RMSE) can be reduced by a range of 0–0.8 m/s from 2.5–4 m/s of the original results, the IA can be increased by a range of 0–0.2 from 0.5–0.8 of the original results, and the R can be increased by a range of 0–0.3 from 0.2–0.7 of the original results. The results of the wind speed Weibull distribution show that, after data assimilation was used, the WRF model was able to simulate the distribution of wind speed more accurately. Based on the numerical simulation, our work proposes a combined wind resource evaluation approach of numerical modeling and data assimilation, which will benefit the wind power assessment of wind farms.

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