Energy Prediction Uncertainty Assessment for Wind Power Project - PowerPoint PPT Presentation

Energy Prediction Uncertainty Assessment for Wind Power Project Using Monte Carlo Analysis Qi Jianan, Manoj Kumar Singh

Parsons Brinckerhoff • Parson Brinckerhoff – >14,000 people – 150 offices – six continents • PB Power Asia – 500 people – Most major Asian cities • PB Power Wind – Asia-Pacific region since early 1990s – All phases of wind power development

An example project in India � 25 units of 2MW turbine � Hub height: 80 m � 1 wind masts at 65 m height � 12 months’ site measurement � Reference wind data –NCAR/MERRA � Net Energy output: 120 GWh per year 50% confidence level (P50) Investors and banks need P75, P90, P95 or even P99. This can be done through uncertainty analysis

Uncertainty sources Sources of uncertainty in predicted wind farm performance � Uncertainty � Wind measurement � Cross-correlation � Extrapolation to hub height � Wind flow/wake model � Loss factor assumptions � Variability � Inter-annual wind speed variation � Turbine performance, etc

Uncertainty- measurement � Sensor type, class � Calibrations � Mounting arrangement on the mast � IEC 61400-12-1 as a guideline The measurement made at the example project is satisfactory 2% uncertainty

Uncertainty – cross correlation � High quality reference data is important to Overall Correlation 20 mean_Ref_match Traces Linear Fit reduce uncertainty Zero Fit 15 Measured Site (m/s) � The quality of the correlation depends on 10 � Distance from the site to the reference 5 � Measurement height of site & reference 0 0 5 10 15 20 � Complexity of the site terrain Reference Site (m/s) Overall Correlation � 20 Correlation quality can be judged from R 2 mean_Ref_match Traces Linear Fit Zero Fit 15 Measured Site (m/s) The cross-correlation for the example 10 project is acceptable 5 3% uncertainty 0 0 5 10 15 20 25 Reference Site (m/s)

Uncertainty- vertical extrapolation (shear) � If no hub height measurement present, extrapolation is needed � Wind shear varies at different wind speed, direction and time � Parsons Brinckerhoff uses shear matrix to do the vertical extrapolation Vertical extrapolation from 65 m to 80 m for the example project 2% uncertainty Wind Shear Profile 40 30 Height (mAGL) 20 10 0 6 6.5 7 7.5 8 8.5 Wind speed (m/s) Corrected wind speeds Wind shear profile Uncorrected wind speeds

Variability – inter-annual wind speed � Annual mean wind speed exhibit yearly variation � Vary from site to site and can be estimated from long-term data � Proportional to length and quality of reference data For the example project, the inter-annual variation is 7% uncertainty!! Annual Averages 6 Average Wind Speed (m/s) 5.75 5.5 5.25 5 4.75 4.5 4.25 4 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Years

Uncertainty – wind flow model � Resolution of map � Site complexity � Ground cover � Distance from the mast to the turbines � Size of the project (No. of turbines) For the example project, the wind flow/wake model is estimated 8% uncertainty

Monte Carlo uncertainty analysis � Stochastic/random method � Applicable to most probability distribution � Sufficient sample to achieve convergence � Final uncertainty obtained from actual energy distributions from random simulations � Allows for non-linear model Parsons Brinckerhoff uses Monte Carlo ! Source: http://www.vertex42.com/ExcelArticles/

Results Uncertainty source Uncertainty Correlation uncertainty 3% Example project in India Anemometer calibration 2% Shear 2% Wind speed annual variation 7% Wind flow model 8% � 5000 iteration � Results-normal distribution � Estimate P99, P90, P75 P90- 105 GWh per year for 20 years period

What happens if… Uncertainty source Uncertainty Correlation uncertainty 3% Example project in India Anemometer calibration 3% Shear 2% Wind speed annual variation 7% Wind flow model 12% P90 Change from 105 GWh to 101 GWh !! � 4% revenue drop if sell the project using P90 4% drop � Less debt size and lower IRR for debt financing

Any Questions? Thank you!! Further information please contact: Danny Qi Manoj Kumar Singh Senior Wind Engineer Senior Wind Engineer qi.danny@pbworld.com singh.manoj @pbworld.com