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Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations

机译:使用WRF高分辨率模拟对复杂地形上的实际陆上风电场进行风能预测

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Regional meteorological models are becoming a generalized tool for wind resource forecasting, due to their capacity to simulate local flow dynamics impacting wind farm production. This study focuses on the case of production forecast and validation for a real onshore wind farm using high horizontal and vertical resolution WRF (Weather Research and Forecasting) model simulations. The wind farm is located in Galicia, in the northwest of Spain, in a complex region with high wind resource. Utilizing the Fitch scheme, specific for wind farms, a period of one year is simulated with a daily operational forecasting setup. Power and wind predictions are obtained and compared with real data at each wind turbine hub, provided by the management company. Results show that WRF yields good wind power operational predictions for this kind of wind farms, due to a good representation of the planetary boundary layer behaviour of the region and the good performance of the Fitch scheme under these conditions. The best mean annual error (MAE) obtained is 1.87 m/s for wind speed and 14.75% for wind power. By comparing experiments with and without Fitch scheme, we estimate wind resource losses in the area due to the wake disturbances. The mean annual wake or environmental footprint of the farm extends for several kilometres in the southwest-northeast direction of the prevailing winds, with resource losses of 0.5% even at 17 km from the turbines. (C) 2018 Elsevier Ltd. All rights reserved.
机译:由于区域气象模型具有模拟影响风电场生产的局部流量动态的能力,因此正成为风资源预测的通用工具。这项研究的重点是使用高水平和垂直分辨率WRF(天气研究和预测)模型模拟​​对实际陆上风电场进行生产预测和验证的情况。该风电场位于西班牙西北部加利西亚的一个风能资源丰富的复杂地区。利用针对风电场的Fitch方案,通过每天的运行预测设置模拟了一年的时间。获取功率和风能预测,并将其与管理公司提供的每个风力涡轮机枢纽处的实际数据进行比较。结果表明,WRF对此类风电场产生了良好的风能运行预测,这是由于该区域的行星边界层行为的良好表示以及Fitch方案在这些条件下的良好性能。获得的最佳年平均误差(MAE)对于风速为1.87 m / s,对于风电为14.75%。通过比较使用和不使用Fitch方案的实验,我们估计了由于尾流扰动引起的该地区的风资源损失。该农场的年平均尾迹或环境足迹沿盛行风的西南-东北方向延伸了几公里,即使距涡轮机17公里,其资源损失也仅为0.5%。 (C)2018 Elsevier Ltd.保留所有权利。

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