Advanced Data Assimilation for Short-term Renewable Power Prediction: a Complex Terrain Case

An advanced data assimilation method that couples the ensemble Kalman Filter and three-dimensional variational algorithm is introduced into wind power forecasting field. Various in-situ measurements from met towers and hub-height anemometers are introduced into the Weather Research and Forecasting model (WRF) to improve the forecast skills of near-surface winds. Comparing the standalone data assimilation methods, the coupled method could combine the strengths (i.e., flow-dependent structure and stable minimization) from each component and then mitigate their weaknesses (i.e. sampling errors and poor small-scale features). Such method could be more favorite for the optimal analysis of model initial conditions over a complex terrain environment with limited observations.

In this study, a series of 0~48 hour wind forecasting experiments with various data assimilation methods are conducted over a 100 MW wind farm in China with complex terrains. Preliminary results from a case study and monthly error performances will be presented.