Comparison of Short-term Forecast Method of Wind Power in Wind Farm

To further improve the accuracy of wind energy forecast and provide more valuable service, several wind energy forecast methods are studied comparatively in Jiugongshan Wind Farm. Based on the wind speed simulation results by the aggregative model CALMET coupled with MM5 (the model resolution is 200 m), the principle method and dynamic-statistical method are used to discuss 24-h forecast effect, with the temporal resolution set to 15 minutes. There are three kinds of principle method to discuss the effect of simulated wind speed correcting and the wind energy forecast model based on observations. The dynamic-statistical method forecast by establishing a rolling model using the simulated data of last period every day.The fine-scale simulation can obviously forecast the variation trend of wind speed. The correcting of simulated wind speed can effectively reduce the wind speed error and improve the forecasting accuracy, but it is difficult to revise the changing trend of simulated wind speed.The dynamic-statistical method is much more suitable for the complex topography mountainous terrain, and the monthly relative mean square root error is 14%—26% from July to December in 2011, which might be the result of its spontaneous adaption for terrain conditions.The wind energy forecast model based on observations is better than those based on theoretical model and can effectively reduce the forecasting error, because the wind farm environment has unique effects on the output power of fan.Furthermore, it is discovered that the wind energy forecast in southern mountain area is much more difficult than in north area. The fine-scale simulation should be used to reduce the infection of terrain; the method of simulated wind speed correcting must consider the different situation of the wind farm; the extreme weather events must be considered, and effects of different weather especially meteorological disaster such as ice-coating and thunderstorm should be deeply studied. These results enhance the service effect at Jiugongshan Wind Farm in Hubei Province, and more research should be carried out to improve the forecast accuracy.