The model domain configuration of real-time ensemble FDDA and forecasting system for the wind farms located in the east portion of Northwest China. The grid sizes are 24.3, 8.1, 2.7 and 2.7 km. The terrain height is shown in color shades.
The purpose of this work is to develop an optimal WRF- and MM5- based realtime ensemble 4D data assimilation and forecasting system, which would be capable of supporting the operation and power integration of wind farms at State Grid Corporation of China (SGCC).
The wind power industry is one of the fastest developing energy industries with a total capacity of 41 GW installed worldwide in 2011, more than half of which is from China. China’s total installed wind power capacity has doubled in five consecutive years during 2006-2011, jumping to rank #1 for total installed wind power capacity globally. The lightning fast development of wind power technology imposes great stress on how to safely and economically integrate wind power into the electric grid. Currently, many wind companies are facing the “bottlenecks” during the process of integration, and it is realized that accurate weather prediction and precise spatial analysis of mesoscale weather events are crucial to wind energy managements. In collaboration with the scientists at China Electric Power Research Institute (CEPRI), which is a subsidiary of SGCC conducting comprehensive research on electric power managements, a WRF- and MM5 based realtime four-dimensional data assimilation (FDDA) and forecasting system is being developed at NCAR for supporting wind power prediction and integration in China.
The wind rose plots, and the color denotes the wind speeds. The shade denotes the terrain height; (b) 10m mean wind vectors, the mean temperature shown in colorful shades, and the sea level pressure in thick green contours; and (c) The temperature/wind/moisture profiles at a sample wind farm located at (42.95N, 88.55E).
The collaborative effort is to develop an optimal wind/power forecasting system for supporting SGCC wind energy integration and operation across China. The first phase of the project aims to implement two realtime ensemble FDDA wind modeling systems based on the existing realtime FDDA technologies developed at NCAR for two SGCC transmission system operators (TSOs) in China: (1) Xinjiang autonomous region of China and (2) the east portion of the Northwest China. Both TSOs have large wind power capacity installed. The project will also implements the NCAR advanced statistical post-processing technologies to improve the realtime ensemble FDDA wind forecasts for the wind farms in the two TSOs. This ensemble post-processing technology includes an analog-based Kalman Filter model forecast bias correction scheme (ANKF), and a quantile regression (QR) based ensemble calibration algorithm that calibrates the ensemble probabilistic (spread) prediction. Our approach is, first, the forecasts from each ensemble member are bias-corrected using ANKF for each wind farm at each forecast range. Then, the bias corrected ensemble forecasts are input to the QR ensemble calibration module to produce the final calibrated wind forecasts.
