Open AccessThe Wind Forecast Improvement Project (WFIP): A Public–Private Partnership Addressing Wind Energy Forecast Needs
Author(s) -
James M. Wilczak,
Catherine A. Finley,
Jeff Freedman,
J. D. Cline,
L. Bianco,
Joseph B. Olson,
Irina V. Djalalova,
Lindsay Sheridan,
Mark Ahlstrom,
John Manobianco,
John Zack,
Jacob R. Carley,
Stanley G. Benjamin,
R. L. Coulter,
Larry K. Berg,
Jeffrey D. Mirocha,
K. L. Clawson,
Edward Natenberg,
Melinda Marquis
Publication year - 2015
Publication title -
bulletin of the american meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.367
H-Index - 197
eISSN - 1520-0477
pISSN - 0003-0007
DOI - 10.1175/bams-d-14-00107.1
Subject(s) - meteorology , environmental science , numerical weather prediction , wind power , global forecast system , weather research and forecasting model , wind speed , anemometer , model output statistics , wind power forecasting , national weather service , engineering , power (physics) , geography , electric power system , physics , quantum mechanics , electrical engineering
The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models and, second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 nacelle anemometers. Results demonstrate that a substantial reduction (12%–5% for forecast hours 1–12) in power RMSE was achieved from the combination of improved numerical weather prediction models and assimilation of new observations, equivalent to the previous decade’s worth of improvements found for low-level winds in NOAA/National Weather Service (NWS) operational weather forecast models. Data-denial experiments run over select periods of time demonstrate that up to a 6% improvement came from the new observations. Ensemble forecasts developed by the private sector partners also produced significant improvements in power production and ramp prediction. Based on the success of WFIP, DOE is planning follow-on field programs.