Wind Resource Assessment


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  • power law should be carefully employed since it is not a physical representation of the surface layer and does not describe the flow nearest to the ground very well (i.e. should only be used for heights above the roughness elements where the flow is free)
  • Wind Resource Assessment

    1. 1. Wind Resource Assessment Wind Farm Development, Design, Operation and Maintenance EPIC Course January 27-29, 2010, Edmonton Don McKay, ORTECH Power
    2. 2. Wind Resource Assessment <ul><li>How Wind is Generated </li></ul><ul><li>Wind Atlas </li></ul><ul><li>Wind Speed Characteristics </li></ul><ul><li>Accessing the Resource </li></ul><ul><li>Project Assessment </li></ul><ul><li>Energy Estimation </li></ul><ul><li>Uncertainty </li></ul>
    3. 3. How wind is generated
    4. 4. Canadian Wind Atlas
    5. 6. <ul><li>Power available in the wind is </li></ul><ul><li>P = ½ ρ π R 2 v 3 </li></ul><ul><li>Power is very sensitive to wind speed </li></ul><ul><li>-> Accurate wind speed measurements are critical </li></ul>
    6. 7. Example <ul><li>Wind speed of </li></ul><ul><li>8 m/s vs 8.2 m/s </li></ul><ul><li>= 2.5% difference in wind speed </li></ul><ul><li>= 7.7% increase in power, theoretically </li></ul><ul><li>= 5% increase in power, realistically </li></ul>
    7. 8. Example (cont’d) <ul><li>For a 100 MW wind farm, this could mean </li></ul><ul><ul><ul><ul><li>300 GWh/yr vs 315 GWh/yr </li></ul></ul></ul></ul><ul><li>= difference of $1,500,000 (assuming $0.10/kWh) </li></ul>
    8. 9. Example (cont’d) <ul><li>Moral: WRA program designed for maximum accuracy is critical to the success of your wind farm </li></ul><ul><li>Overpredict: impacts shareholders, credibility </li></ul><ul><li>Underpredict: impacts financing opportunities </li></ul>
    9. 10. Wind Resource Assessment (WRA) <ul><li>Measure wind speed and wind system </li></ul><ul><li>Correlate to long-term reference station </li></ul><ul><li>Predict long-term wind speed distribution at the site </li></ul><ul><li>Wind flow modelling </li></ul><ul><li>Micrositing </li></ul><ul><li>Annual Energy Yield prediction </li></ul>
    10. 11. Measure -Correlate-Predict <ul><li>Measure wind data </li></ul><ul><ul><li>Install met tower with wind monitoring instruments </li></ul></ul><ul><ul><li>Collect wind data </li></ul></ul><ul><ul><li>QC/QA wind data </li></ul></ul><ul><ul><li>Analyse wind data </li></ul></ul>
    11. 12. (DEWI) Meteorological Mast (Wind monitoring tower)
    12. 13. R. M. Young Wind Monitor Features Propeller-type anemometer with fuselage and tail wind vane Rugged design for use in a variety of climates worldwide Manufactured by R. M. Young Specifications Wind Speed Range: 0-134 mph (0-60 m/s) Accuracy: ±0.6 mph (0.3 m/s) Starting threshold: 2.2 mph (1.0 m/s) Gust survival: 220 mph (100 m/s) Wind Direction Range: 0-360° mechanical, 355° electrical (5° open) Accuracy: ±3° Starting threshold at 10° displacement: 2.2 mph (1.1 m/s)
    13. 14. CR800 Measurement and Control Datalogger Features Ideal datalogger where only a few sensors will be measured Stores 4 Mbytes of data and programming in SRAM Data format is table Operating system: PakBus® Software support offered in LoggerNet or PC400 (full-featured) or ShortCut (programming) Detachable keyboard/display, the CR1000KD, can be carried to multiple stations Supports Modbus protocol, SDI-12 protocol, and SDM devices Specifications Analog inputs: 6 single-ended or 3 differential, individually configured Pulse counters: 2 Switched voltage excitations: 2 Control/digital ports: 4 Scan rate: 100 Hz Analog voltage resolution: to 0.33 µV A/D bits: 13
    14. 15. <ul><li>Neutral conditions -> logarithmic wind profile (<100m) </li></ul>Logarithmic Wind Profile Height above surface (m)
    15. 16. Power Law Profile - use wind speed measurements at two heights to find α - then use a to calculate wind speed at hub height α is the power law exponent (wind shear exponent) u R is the wind speed at height z r
    16. 17. Wind Speed Frequency Distribution
    17. 18. Wind Direction Distribution (Wind Rose)
    18. 19. Measure- Correlate-Predict <ul><li>Correlate to long-term reference stations </li></ul><ul><ul><li>12 months of measured site data recommended </li></ul></ul><ul><ul><li>Find appropriate long-term reference stations </li></ul></ul><ul><ul><li>Determine correlation between site data and reference stations for a concurrent period </li></ul></ul><ul><ul><li>Determine long-term wind data set for site </li></ul></ul><ul><li>Predict long-term windspeed distribution at the site (i.e. at the location of the met mast) </li></ul>
    19. 20. Long term observation of wind speed
    20. 21. Wind flow modelling <ul><li>Input predicted long-term wind data into wind flow model (e.g. WAsP) </li></ul><ul><li>Input digital terrain data (topographic data, vegetation data) </li></ul><ul><li>Output wind flow map over site </li></ul>
    21. 23. (DEWI) Typical Wind Speed Map
    22. 24. Micrositing <ul><li>Purpose: design turbine layout optimized on energy production, minimized on wake losses </li></ul><ul><li>Input wind flow map </li></ul><ul><li>Input terrain data </li></ul><ul><li>Input site constraints </li></ul><ul><ul><li>Site boundary </li></ul></ul><ul><ul><li>Setbacks for: roads, buildings, environmental constraints (wetlands, migratory routes), wooded areas, water bodies/courses </li></ul></ul><ul><ul><li>Noise restrictions </li></ul></ul><ul><ul><li>Visual impact </li></ul></ul><ul><li>Input number of turbines, turbine specs, turbine constraints </li></ul><ul><ul><li>Nameplate capacity, hub height, rotor diameter </li></ul></ul><ul><ul><li>Power curve </li></ul></ul><ul><ul><li>Thrust coefficient </li></ul></ul><ul><ul><li>RPM </li></ul></ul><ul><ul><li>Maximum slope for turbine </li></ul></ul><ul><ul><li>Turbine spacing </li></ul></ul>
    23. 25. Basic Parameters for IEC – WTG classes
    24. 26. Power Curve
    25. 27. Turbine Layout
    26. 28. Annual Energy Yield Prediction <ul><li>Ideal Energy Yield </li></ul><ul><li>Gross Energy Yield, adjusted for </li></ul><ul><ul><li>Topography </li></ul></ul><ul><ul><li>Roughness </li></ul></ul><ul><ul><li>wake effect </li></ul></ul><ul><ul><li>air density </li></ul></ul><ul><ul><li>high wind speed hysteresis </li></ul></ul><ul><li>Net Energy Yield, adjusted for </li></ul><ul><ul><li>Production losses </li></ul></ul><ul><li>Capacity Factor </li></ul>
    27. 29. Production Losses <ul><li>WTG availability </li></ul><ul><li>Planned maintenance </li></ul><ul><li>Icing & cold temperature related losses </li></ul><ul><li>Grid & substation </li></ul><ul><li>Grid availability </li></ul><ul><li>Blade soiling </li></ul><ul><li>High wind hysteresis </li></ul>
    28. 30. Example – Annual Energy Yield Prediction for 100 MW wind park <ul><li>Ideal energy yield: 867.8 GWh/yr </li></ul><ul><li>Gross energy yield: 350 GWh/yr </li></ul><ul><li>Production Losses: 10% </li></ul><ul><li>Net energy yield: 315 GWh/yr </li></ul><ul><li>Capacity Factor: 36% </li></ul>
    29. 31. P50 <ul><li>Previous example: </li></ul><ul><ul><li>Annual Energy Yield = 315 GWh/yr = P50 </li></ul></ul><ul><li>P50: statistical mean or the probability that this value will be exceeded 50% </li></ul><ul><li>Actual annual energy production will vary from the P50 in direct proportion to the uncertainty </li></ul>
    30. 32. Uncertainties
    31. 33. Conclusions <ul><li>Project viability depends on the wind resource </li></ul><ul><li>Wind conditions are site specific </li></ul><ul><li>Wind data vary with time and height </li></ul><ul><li>Accuracy is critical </li></ul><ul><li>Carefully assess uncertainties </li></ul><ul><li>Good financing terms depends on a WRA program that has been designed for maximum accuracy </li></ul>
    32. 34. Questions?