The Wind Resource: Prospecting for Good Sites

Transcription

1 The Wind Resource: Prospecting for Good Sites Bruce Bailey, President AWS Truewind, LLC 255 Fuller Road Albany, NY

2 Talk Topics Causes of Wind Resource Impacts on Project Viability Siting Linkages to Resource Assessment Wind Resource Definition Energy Production Prediction Uncertainty Analysis Due Diligence Objectives For Financing

3 What Causes Wind? Uneven heating of the earth s surface Daily heating and cooling cycles Earth s rotation Weather systems track and intensity Position of jet stream Local influences sea breezes, slope winds, channeling through valleys, etc.

4 Establishing Project Viability Wind Resources Determine: Project Location & Size Tower Height Turbine Selection & Layout Energy Production» annual, seasonal» on- & off-peak» capacity credit Cost of Energy/Cash Flow Warranty Terms Size of Emissions Credits The wind energy industry is more demanding of wind speed accuracy than any other industry.

5 Power in the Wind (W/m 2 ) = 1/2 x air density x swept rotor area x (wind speed) 3 ρ A V 3 Density = P/(RxT) P - pressure (Pa) R - specific gas constant (287 J/kgK) T - air temperature (K) Area = π r 2 Instantaneous Speed (not mean speed) kg/m 3 m 2 m/s

6 Summary of Wind Resource Planning Steps Identify Attractive Candidate Sites Collect >1 yr Wind Data Using Tall Towers Adjust Data for Height and for Long-Term Climatic Conditions Use Model to Extrapolate Measurements to All Proposed Wind Turbine Locations Predict Energy Output From Turbines Quantify Uncertainties

7 Siting

8 Siting Main Objective: Find and design viable wind project sites Main Attributes: Adequate winds Access to transmission Permit approval reasonably attainable Sufficient land area for target project size» acres per MW for arrays» 8 12 MW per mile for single row on ridgeline

9 Siting Attributes Winds» Minimum Class 4 desired (>7 hub height) for wind farms Transmission» distance, voltage, excess capacity Permit approval» land use compatibility» public acceptance» visual, noise, and bird/bat impacts are leading issues Land area» economies of scale with larger project size» number of landowners

10 Siting Tools Wind Maps & Other Regional Resource Data Topographic Maps Transmission Line Maps & Databases Property Maps Geographic Information Systems (GIS) and associated data layers Local Wind Map Showing Transmission and Road Overlays Old vs. New Wind Maps of the Dakotas

11 Modern Wind Maps Old and new wind maps of the Dakotas Source: NREL utilize mesoscale numerical weather models high spatial resolution ( m grid = 3-10 acre squares) simulate land/sea breezes, low level jets, channeling give wind speed estimates at multiple heights extensively validated std error typically 4-7% GIS compatible reduce development risks

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13 Wind data projections available at a 200 m grid resolution statewide

14 Measurement

15 Sources of Wind Resource Info Existing Data (surface & upper air)» usually not where needed» use limited to general impressions» potentially misleading Modeling/Mapping» integrates wind data with terrain, surface roughness & other features New Measurements» site specific using towers & other measurement systems

16 How and What To Measure Anemometers, Vanes, Data Loggers, Masts Measured Parameters» wind speed, direction, temperature» 1-3 second sampling; 10-min or hourly recording Derived Parameters» wind shear, turbulence intensity, air density Multiple measurement heights» best to measure at hub height» can use shorter masts by using wind shear derived from two other heights to extrapolate speeds to hub height Multiple tower locations, especially in complex terrain Specialty measurements of growing importance» Sodar, vertical velocity & turbulence in complex terrain

17 Typical Monitoring Tower Heights up to 60 m Tubular pole supported by guy wires Installed in 1-2 days without concrete using 3 people Solar powered; cellular data communications

18 Raising the Tower

19 Final Touches / Sensor Orientation

20 Wind Resource Assessment Handbook Fundamentals for Conducting a Successful Monitoring Program Published by NREL» fy97/22223.pdf Peer reviewed Technical & comprehensive Topics include:» Siting tools» Measurement instrumentation» Installation» Operation & maintenance» Data collection & handling» Data validation & reporting» Costs & labor requirements WIND RESOURCE ASSESSMENT HANDBOOK Fundamentals For Conducting A Successful Monitoring Program Prepared By: AWS Scientific, Inc. 255 Fuller Road Albany, NY NREL Subcontract No. TAT April 1997 Prepared for: National Renewable Energy Laboratory 1617 Cole Boulevard Golden, CO 80401

21 Data Analysis Wind Speed (m/s) Hours/ Year , , , , Wind Speed (m/s) Speed Frequency Distribution Turbine Output (kw) GE 1.5xle MW Wind Speed (m /s) Wind Turbine Power Curve: Output As a Function of Speed Wind Direction Rose

22 Wind Shear The change in horizontal wind speed with height Wind Shear is important when extrapolating wind speed data from a met. mast that is shorter than the intended hub height of the turbine A function of wind speed, surface roughness (may vary with wind direction), and atmospheric stability (changes from day to night) Wind shear exponents are higher at low wind speeds, above rough surfaces, and during stable conditions Z 2 = 80 m V 2 = 7.7 m/s V 1 = 7.0 m/s Z 1 = 50 m Typical exponent (α) values:» : water/beach» : gently rolling farmland» : forests/mountains α = Log 10 [V 2 /V 1 ] Log 10 [Z 2 /Z 1 ] Wind Shear Profile α V 2 = V 1 (Z 2 /Z 1 ) α

23 Predicting Long-Term Wind Conditions From Short-Term Measurements Measure - Correlate - Predict Technique Measure one year of data on-site using a tall tower Correlate with one or more regional climate reference stations» Need high r 2» Reference station must have long-term stability» Upper-air rawinsonde data may be better than other sources for correlation purposes Predict long-term (7+ yrs) wind characteristics at project site Project Site 60 m Wind Speed (m/s) Airport C Regression y = x R = Airport B Regression y = x R 2 = Airport A Regression y = x R 2 = Airport A Airport B Airport C Reference Station Mean Wind Speed (m/s) This plot compares a site s hourly data with three regional airport stations. A multiple regression resulted in an r 2 of 0.92.

24 Energy Prediction For Wind Farm

25 Sample Energy Production Calculation Turbine Output (kw) Turbine Power Curve GE 1.5xle MW Wind Speed (m /s) GE 1.5xle (Altitude Adjusted) WS (m/s) Output (kwh) Wind Speed Frequency Distribution cut in 3 1, , , , , , , , , , , , , , , ,828.3 Wind Speed (m/s) 19 3,061.0 cut out 20 1, INPUTS 39 On-Site Turbines Meter Weibull C Gross Output per Turbine 3,698 MWh/yr Weibull K at 50m Net Energy per Turbine 3,258 MWh/yr Turbine Capacity (MW) Number of Turbines 39 Gross Plant Production 144,220 MWh/yr Net Plant Production 127,048 MWh/yr Net Capacity Factor 24.8% Probability (hours/year)

26 Energy Production Projection Multiply wind speed frequency distribution data (annual hours per 0.5 or 1.0 m/s speed bin) by turbine power curve output values (for same speed bins)» Power curve must be adjusted for site air density Sum the product of all speed bins for the total gross energy production (MWh) Determine production loss factors and their magnitude» Wakes, availability, electrical, blade soiling/icing, high wind hysteresis, cold temperatures» Cumulative losses are typically 10-15% Deduct losses to calculate net energy production Determine net production for different probability levels (P75, P90, P95, etc.) based on uncertainty analysis

27 Micrositing Predicting Wind Conditions at Every Turbine, and Optimizing Turbine Locations Software tools (WindFarmer, WindFarm, WindPro) are available to optimize the location and performance of wind turbines, once the wind resource within a project area is defined.

28 Optimization Tools Turbine Noise Emissions Wind Resource Mapping & Turbine Energy Production Photosimulations

29 Conclusions The wind resource drives project viability. Wind conditions are site-specific and time/height variable. Accuracy is crucial. Wind resource assessment programs must be designed to maximize accuracy. Combination of measurement and modeling techniques gives the most reliable result. Know the uncertainties and incorporate into decision making. Good financing terms depend on it.