Development of a wind power ramp forecasting system via meteorological pattern analysis

Ramp phenomena caused by abrupt changes in wind speed may confound the stable operation of correlated electrical power supply systems, yet accurate numerical predictions are challenging, as the wind is affected by complex interactions between large-scale weather patterns and local geographical conditions. Further, optimal numerical weather prediction (NWP) methods and physics schemes vary as a function of weather patterns. The present study proposed a new real-time wind power ramp forecast framework based on the flexible selection of optimal NWP models, which were derived via principal component analysis (PCA). The novelty of this analysis lies in that statistical methods were employed for NWP optimization, compared with their more conventional use during an NWP postprocessing. Here, a weather pattern was classified by PCA using outcomes from the global-scale prediction models, and the optimum regional NWP system settings were acquired according to the weather patterns for further wind field dynamical downscaling. The performance of the developed prediction system was verified with wind power at wind turbine hub-heights for three areas in eastern Japan, and the Critical Success Index (CSI) indicated an improvement of prediction accuracy over benchmark predictions by <= 0.184 for ramp-up events and <= 0.127 for ramp-down events (both observed in Tohoku area). Higher CSI values were consistently seen in three wind farm areas, indicative of the improvement in detection probability for actual ramp events compared with benchmark.

Automated summary

This study proposed a new real-time wind power ramp forecast framework based on PCA for optimal NWP model selection, utilizing statistical methods for NWP optimization to improve detection probability for actual ramp events.