Kris ShresthaJames Belanger
Judith CurryJake Mittelman
Phillippe BeaucageJeff Freedman
John Zack
Medium Range Wind PowerForecasts for Texas
Increases of wind penetration into energy grids has created the need for forecast information beyond a few days
Operational Wind Power ForecastingExtended range wind power forecasts for Texas
Stabilize energy cost and supply Natural gas trading and sales Maintenance scheduling Maximizing grid integration
Datasets for Forecast Verifications
• Model: medium-range forecasts– ECMWF ENS (VarEPS)
• 3-hourly / day 1-6, 0.250°• 6-hourly / day 7-10, 0.250°• 6-hourly / day 11-15, 0.500°
– ECMWF HRES (deterministic)• 3 to 6-hourly / 10 days, 0.125°
• Observations– ERCOT wind power generation
• 15 mins, regional average– ECMWF operational analyses, 100 m winds
• 6-hourly, 0.125°– Wind Forecast Improvement Project (WFIP)
SODAR• 10 min, 7 stations in Texas
Spatial Resolutions & SODAR Locations
WFIP SODAR locations
Power Conversion
Wind speed is converted to output power by a standard curve and scaled to % of rated limit (1.5 MW).
Example Wind Speed ForecastDiurnal cycle: grid level
Diurnal cycle: regional average
SODAR vs ECMWF Diurnal Variability
6 hour interval
• Evaluation of power forecasts over three spatial domains (out to 10 days)– Single grid cell (1/4°) – Individual regions – ERCOT average
Power Forecast Verification
Single Cell (1/4°): Cleburne WFIP SODAR– 22 Jul to 13 Oct, 2012
analyses
sodar
Power Forecast Verification: Regions in TexasAverage of Multiple Cells – Weather Regions– 13 Mar to 13 Oct, 2012– ECMWF ensemble mean vs. ECMWF analyses
ERCOT avg
Power Forecast Verification: All of ERCOTERCOT Regional Average– 13 Mar to 13 Oct, 2012– ECMWF ens mean vs. ECMWF analyses, ERCOT power
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persistence
Reduced corr. vs ERCOT datacompared to analysesresults from assumptions about power curve, and lack of power-weighted regionalaverage
Ensemble mean starts tooutperform deterministic~120 hours
Towards increasing prediction skill:
statistical post processing
deterministic
ensemble mean
climatology
adjusted
Challenge to statistical post-processing using reforecasts: •Need gridded historical wind data set at Hub height (80-100 m) with similar data quality as verification data set
Mean Bias Root Mean Square Error
Summary• Regionally averaged wind power shows useful prediction skill
at the medium range• Climatological and real time hub height wind data is needed
to optimize the statistical post processing• Accurate simulation of regional power generation requires
power weighted averaging and power curves