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