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