• Banta, R. M., R. K. Newsom, J. K. Lundquist, Y. L. Pichugina, R. L. Coulter, and L. Mahrt, 2002: Nocturnal low-level jet characteristics over Kansas during CASES-99. Bound.-Layer Meteor., 105, 221252, doi:10.1023/A:1019992330866.

    • Search Google Scholar
    • Export Citation
  • Banta, R. M., Y. L. Pichugina, N. D. Kelley, B. Jonkman, and W. A. Brewer, 2008: Doppler lidar measurements of the Great Plains low-level jet: Applications to wind energy. IOP Conf. Ser.: Earth Environ. Sci., 1, 012020, doi:10.1088/1755-1315/1/1/012020.

    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., B. E. Schwartz, E. J. Szoke, and S. E. Koch, 2004: The value of wind profiler data in U.S. weather forecasting. Bull. Amer. Meteor. Soc., 85, 18711886, doi:10.1175/BAMS-85-12-1871.

    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., B. D. Jamison, W. R. Moninger, S. R. Sahm, B. Schwartz, and T. W. Schlatter, 2010: Relative short-range forecast impact from aircraft, profiler, radiosonde, VAD, GPS-PW, METAR, and mesonet observations via the RUC hourly assimilation cycle. Mon. Wea. Rev., 138, 13191343, doi:10.1175/2009MWR3097.1.

    • Search Google Scholar
    • Export Citation
  • Bianco, L., D. Gottas, and J. M. Wilczak, 2013: Implementation of a Gabor transform data quality control algorithm for UHF wind profiling radars. J. Atmos. Oceanic Technol., 30, 26972703, doi:10.1175/JTECH-D-13-00089.1.

    • Search Google Scholar
    • Export Citation
  • Bonner, W. D., 1968: Climatology of the low level jet. Mon. Wea. Rev., 96, 833850, doi:10.1175/1520-0493(1968)096<0833:COTLLJ>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bossanyi, E. A., 1985: Short-term wind prediction using Kalman filters. Wind Eng., 9, 18.

  • Chang, C.-C., and C.-J. Lin, 2011: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2, Article 27, doi:10.1145/1961189.1961199.

    • Search Google Scholar
    • Export Citation
  • Cortes, C., and V. Vapnik, 1995: Support-vector networks. Mach. Learn., 20, 273297.

  • Deppe, A. J., W. A. Gallus Jr., and E. S. Takle, 2013: A WFR ensemble for improved wind speed forecast at turbine height. Wea. Forecasting, 28, 212228, doi:10.1175/WAF-D-11-00112.1.

    • Search Google Scholar
    • Export Citation
  • EnerNex, 2011: Eastern wind integration and transmission study. Subcontract Rep. NREL/SR-550–47078, 242 pp. [Available online at www.nrel.gov/docs/fy11osti/47078.pdf.]

  • Finley, C., M. Ahlstrom, L. Sheridan, G. Brinkman, K. Orwig, G. Stark, D. Todey, and M. McMullen, 2014: The Wind Forecast Improvement Project (WFIP): A public/private partnership for improving short term wind energy forecasts and quantifying the benefits of utility operations—The northern study area. WindLogics Final Tech. Rep. to DOE, Award DE-EE0004421, 125 pp. [Available online at www.osti.gov/scitech/biblio/1129929.]

  • Freedman, J., M. Markus, and R. Penc, 2008: Analysis of West Texas wind plant ramp-up and ramp-down events. Analysis of wind generation impact on ERCOT ancillary services requirements, R. A Walling, AWS Truewind Rep., 250–278. [Available online at www.uwig.org/AttchB-ERCOT_A-S_Study_Final_Report.pdf.]

  • Freedman, J., and Coauthors, 2014: The Wind Forecast Improvement Project (WFIP): A public/private partnership for improving short term wind energy forecasts and quantifying the benefits of utility operations—The southern study area. AWS Truepower Final Tech. Rep. to DOE, Award DE-EE0004420, 107 pp. [Available online at www.osti.gov/scitech/biblio/1129905.]

  • GE Energy, 2010: Western wind and solar integration study. Prepared for NREL Subcontract Rep. SR-550–47781, 536 pp. [Available online at www.nrel.gov/docs/fy10osti/47434.pdf.]

  • Giebel, G., and G. Kariniotakis, 2007: Best practice in short-term forecasting—A users guide. European Wind Energy Conf. and Exhibition, Milan, Italy, European Wind Energy Association, BT2.2. [Available online at www.ewea.org/ewec2007/allfiles2/156_Ewec2007fullpaper.pdf.]

  • IEC, 2005: Wind turbines—Part 1: Design requirements. International Electrotechnical Commission IEC 61400-1, 179 pp. [Available online at http://webstore.iec.ch/preview/info_iec61400-1%7Bed3.0%7Den.pdf.]

  • Kelly, N. D., M. Shirazi, D. Jager, S. Wilde, J. Adams, M. Buhl, P. Sullivan, and E. Patton, 2004: Lamar low-level jet program interim report. NREL/TP-500–34593. [Available online at www.nrel.gov/docs/fy04osti/34593.pdf.]

  • Mahoney, W. P., and Coauthors, 2012: A wind power forecasting system to optimize grid integration. IEEE Trans. Sustainable Energy, 3, 670682, doi:10.1109/TSTE.2012.2201758.

    • Search Google Scholar
    • Export Citation
  • Makarov, Y. V., Z. Huang, P. V. Etingov, J. Ma, R. T. Guttromson, K. Subbarao, and B. B. Chakrabarti, 2010: Incorporating wind generation and load forecast uncertainties into power grid operations. Pacific Northwest National Laboratory Rep. PNNL-19189, 169 pp. [Available online at www.pnl.gov/main/publications/external/technical_reports/PNNL-19189.pdf.]

  • Manobianco, J., J. W. Zack, and G. E. Taylor, 1996: Workstation-based real-time mesoscale modeling designed for weather support to operations at the Kennedy Space Center and Cape Canaveral Air Station. Bull. Amer. Meteor. Soc., 77, 653672, doi:10.1175/1520-0477(1996)077<0653:WBRTMM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Marquis, M., J. Wilczak, M. Ahlstrom, J. Sharp, A. Stern, J. C. Smith, and S. Calvert, 2011: Forecasting the wind to reach significant penetration levels of wind energy. Bull. Amer. Meteor. Soc., 92, 11591171, doi:10.1175/2011BAMS3033.1.

    • Search Google Scholar
    • Export Citation
  • Monteiro, C., R. Bessa, V. Miranda, A. Botterud, J. Wang, and G. Conzelmann, 2009: Wind power forecasting: State-of-the-art 2009. Argonne National Laboratory Rep. ANL/DIS-10–1, 198 pp. [Available online at www.dis.anl.gov/pubs/65613.pdf.]

  • Sisterson, D. L., and P. Frenzen, 1978: Nocturnal boundary-layer wind maxima and the problem of wind power assessment. Environ. Sci. Technol., 12, 218221, doi:10.1021/es60138a014.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the Advanced Research WRF version 2. NCAR Tech NCAR/TN–468+STR, 88 pp. [Available online at http://www2.mmm.ucar.edu/wrf/users/docs/arw_v2.pdf.]

  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN–475+STR, 113 pp. [Available online at http://www2.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.]

  • Storm, B., and S. Basu, 2010: The WRF model forecast-derived low-level wind shear climatology over the United States Great Plains. Energies, 3, 258276, doi:10.3390/en3020258.

    • Search Google Scholar
    • Export Citation
  • Storm, B., J. Dudhia, S. Basu, A. Swift, and I. Giammanco, 2009: Evaluation of the Weather Research and Forecasting Model on forecasting low-level jets: Implications for wind energy. Wind Energy, 12, 8190, doi:10.1002/we.288.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 71837192, doi:10.1029/2000JD900719.

    • Search Google Scholar
    • Export Citation
  • Wendell, L. L., H. L. Wegley, and M. G. Verholek, 1978: Report from a working group meeting on wind forecasts for WECS operation. Pacific National Laboratory Rep. PNL-2513, 14 pp. + appendixes, doi:10.2172/6548011.

  • Werth, D., R. Kurzeja, N. Dias, G. Zhang, H. Duarte, M. Fischer, M. Parker, and M. Leclerc, 2011: The simulation of the southern Great Plains nocturnal boundary layer and the low-level jet with a high-resolution mesoscale atmospheric model. J. Appl. Meteor. Climatol., 50, 14971513, doi:10.1175/2011JAMC2272.1.

    • Search Google Scholar
    • Export Citation
  • Wilczak, J. M., and Coauthors, 1995: Contamination of wind profiler data by migrating birds: Characteristics of corrupted data and potential solutions. J. Atmos. Oceanic Technol., 12, 449–467, doi:10.1175/1520-0426(1995)012<0449:COWPDB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wilczak, J. M., L. Bianco, J. Olson, I. Djalalova, J. Carley, S. Benjamin, and M. Marquis, 2014: The Wind Forecast Improvement Project (WFIP): A public/private partnership for improving short term wind energy forecasts and quantifying the benefits of utility operations. NOAA Final Tech. Rep. to DOE, Award DE-EE0003080, 162 pp. [Available online at http://energy.gov/sites/prod/files/2014/05/f15/wfipandnoaafinalreport.pdf.]

  • Wu, W.-S., R. J. Purser, and D. F. Parrish, 2002: Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev., 130, 29052916, doi:10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Xue, M., K. K. Droegemeier, and V. Wong, 2000: The Advanced Regional Prediction System (ARPS)—A multiscale nonhydrostatic atmospheric simulation and prediction tool. Part I: Model dynamics and verification. Meteor. Atmos. Phys., 75, 161193, doi:10.1007/s007030070003.

    • Search Google Scholar
    • Export Citation
  • Xue, M., and Coauthors, 2001: The Advanced Regional Prediction System (ARPS)—A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part II: Model physics and applications. Meteor. Atmos. Phys., 76, 143165, doi:10.1007/s007030170027.

    • Search Google Scholar
    • Export Citation
  • Zack, J. W., S. H. Young, and E. J. Natenberg, 2011: Evaluation of wind ramp forecasts from an initial version of a rapid update dynamical-statistical ramp prediction system. Proc. Second Conf. on Weather, Climate, and the New Energy Economy, Seattle WA, Amer. Meteor. Soc., 781. [Available online at https://ams.confex.com/ams/91Annual/webprogram/Paper186686.html.]

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1159 646 72
PDF Downloads 600 319 44

The Wind Forecast Improvement Project (WFIP): A Public–Private Partnership Addressing Wind Energy Forecast Needs

View More View Less
  • 1 NOAA/Earth System Research Laboratory, Boulder, Colorado
  • | 2 WindLogics Inc., St. Paul, Minnesota
  • | 3 AWS Truepower, Albany, New York
  • | 4 DOE/Energy Efficiency and Renewable Energy, Washington, D.C.
  • | 5 University of Colorado/CIRES, Boulder, Colorado
  • | 6 WindLogics Inc., St. Paul, Minnesota
  • | 7 MESO, Inc., Troy, New York
  • | 8 IM Systems Group, NOAA/National Weather Service, College Park, Maryland
  • | 9 NOAA/Earth System Research Laboratory, Boulder, Colorado
  • | 10 DOE/Argonne National Laboratory, Lemont, Illinois
  • | 11 DOE/Pacific Northwest National Laboratory, Richland, Washington
  • | 12 DOE/Lawrence Livermore National Laboratory, Livermore, California
  • | 13 NOAA/Air Resources Laboratory, Idaho Falls, Idaho
  • | 14 MESO, Inc., Troy, New York
  • | 15 NOAA/Earth System Research Laboratory, Boulder, Colorado
Restricted access

Abstract

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.

ADDITIONAL AFFILIATION: Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, New York

CORRESPONDING AUTHOR: Dr. James M. Wilczak, NOAA/Earth System Research Laboratory, 325 Broadway, Mail Stop PSD3, Boulder, CO 80305, E-mail: james.m.wilczak@noaa.gov

Abstract

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.

ADDITIONAL AFFILIATION: Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, New York

CORRESPONDING AUTHOR: Dr. James M. Wilczak, NOAA/Earth System Research Laboratory, 325 Broadway, Mail Stop PSD3, Boulder, CO 80305, E-mail: james.m.wilczak@noaa.gov
Save