1. Wind 101 – Technical Basics
Clean Energy BC 2010
Mark Green – Wind Engineer, Natural Power Consultants

2. Natural power - About us
Practical consulting and risk management for the international renewable energy industry, providing services throughout the project life-cycle
15 year track record in wind energy consultancy - established in 1995
Over 200 employees worldwide across 7 offices in 5 countries
Consultancy services provided to more than 15,000MW of projects
2,000MW where we have provided full project design & consenting
We have managed the construction of 500MW of wind energy
300MW+ of wind plant under our asset management

3. A Global Presence We have presence worldwide, including:
Scotland (head office)
British Columbia, Canada
France
Ireland
England
Wales
Chile
USA

4. Our Core Services Our core services:
Advanced resource assessment and site modelling
Development, EIA and permitting
Ecology services
Construction and geotechnical services
Site management
Operational site analysis and optimisation
360o Due-diligence

5. Wind 101 – Overview Wind: The Basics Commercial Background
Site Selection
Wind data collection
Data analysis – Long-term prediction
Wind flow modelling
Turbine layout and selection
Energy yield modelling

6. The Basics

7. The Basics: Wind
All renewable energy (except tidal and geothermal power) ultimately comes from the sun.
Uneven heating of the earth’s surface causes differences in temperature throughout the atmosphere.
Warm air, which weighs less than cold air, rises. Then cool air moves in and replaces the rising warm air.
This movement of air is what makes the wind blow. 

8. The Basics: Turbines Blades (35-55m length) Rotor (70-110m diameter)
Nacelle
Rotor Hub
Tower (60-100m high)
Transformer

9. The Basics: Wind
Maximum theoretical power in a moving fluid is defined in Watts…For wind, the power in the area swept by the turbine rotor:
P = 0.5 x rho x A x V3
Betz law: maximum of 59% of the power moving through the rotor can be captured.
Wind power in the area swept by the turbine rotor is described be this equation: P = 0.5 x rho x A x V3
where…
P is power in watts rho is air density in kg/m3
A is the swept area of the rotor in m2 V is the wind speed in meters/sec
So we can see that there is a cubic relationship between wind speed and power. If wind speed doubles, the power in the wind increases 8-fold, and likewise if the wind speed is halved, the power becomes 1/8th of what it was…this is why accurately estimating wind speed is essential!
However, not all of the energy passing through the rotor disc is available for the wind turbine to convert to energy. And this is defined by Betz’ law which says that you can only convert a maximum of 59% of the kinetic energy in the wind to mechanical energy using a wind turbine.
This is because the more kinetic energy a wind turbine pulls out of the wind, the more the wind will be slowed down as it leaves the rotor disc. If we tried to extract all the energy from the wind, the air would be reduced to a speed of zero, and the air could not leave the turbine, therfore producing no energy at all!

10. The Basics: Wind The Watt is the SI unit of power - instantaneous
Energy in the context of electricity generation is the multiplication of power in Watts and time in hours.
E.g. a 1MW turbine producing at 100% for 1 hour will produce 1MWh of energy.
However, the wind never blows 100% of the time!
The term Capacity Factor (C.F.) is used to describe the actual energy produced vs the max rated production.

11. The Basics: Commercial Background
What are the commercial drivers in performing technical analyses?:
For a wind farm to receive financial backing, lenders and developers require a robust estimate of the lifetime energy yield (GWh)
To secure wind turbines, a developer needs to demonstrate that the site conditions do not exceed the design and operational limits of the turbines
The greater the uncertainty in the yield and design predictions, the greater the risk to the lender/developer
The Basics: Commercial Background

12. The Basics: Process of Design & Analysis
Desk-based resource modelling
Short-term wind data collection
Long-term wind climate prediction
Wind flow modelling
Energy yield modelling
Uncertainty analysis

13. Site Selection

14. Site Selection: Desk Based Modelling
Used for initial site prospecting
Does not use any actual on-site wind data as an input
Instead uses a local correction model
Examples of regional mesoscale models are the Canadian Wind Atlas and the BC Wind Atlas, both are available online.
Typically of too coarse a resolution and accuracy to be applicable in absolute wind resource assessment for financing

15. Site Selection: Desk Based Modelling

16. Site Selection: Desk Based Modelling

17. Site Selection: Desk Based Modelling

18. Site Selection– Constraints
Economic Considerations:
Distance to transmission
Transmission capacity
Site access
Constructability
Wind speed
Technical constraints:
Forestry, topography, obstacles
Public rights of way, Parks
Microwaves/Telecommunication links, other Infrastructure (pipelines, etc.)
Ecology, Hydrology, Archaeology
Noise
Setbacks from other windfarms
Visual impact / Landscape / Shadow flicker

19. Site Selection– Constraints

20. Site Selection

21. Wind data collection

22. Wind Data Collection Why are on-site measurements required?
Provide an accurate representation of the wind regime of the site and its viability
Highlight localised wind flow issues
Reducing prediction uncertainty
Measurement locations must be representative of turbine locations:
Topographically
Altitude
Exposure

23. Wind Data Collection Duration and density of masts:
Ideally, a “known point “ within 2km of every prediction location (depends on size and topography of wind farm)
Particularly complex locations should be further investigated with additional monitoring/modelling
12 month minimum campaign

24. Wind Data Collection

25. Wind Data Collection Prop Vane - (measures wind speed and direction)
Wind vane
(measures wind direction)
Cup anemometer
(measures wind speed)

26. Wind Data Collection
To achieve an industry best practice 0.5% deficit in wind speed or less: R r
Cylindrical mast:
For a mast with diameter, d, and boom with diameter, D:
r/d > 8.5
R/D > 12
Lattice mast
For a mast with face length, L, and low porosity:
r/L > 5.7

27. Wind Data Collection Masts At least 2/3rds of hub height
Cup anemometers at 3 or 4 heights (for shear and turbulence profiles)
Collect 10 minute average speed, direction, SD, gusts, temperature, pressure,
Instruments
Vector, NRG, Thies, RM Young ...
Calibrated instruments (MEASNET wind tunnel)
Mounting adhering to best practice
Consider a mix of instruments

28. Wind Data Collection

29. Wind Data Collection Remote sensing is another option:
Ground based wind data collection
LIDAR and SODAR
Measure up to ~200m height
Very useful for wind characteristics (shear, Ti) and for additional known points in complex flow
Replacing masts in many applications
LIDAR data is validated for project finance use

30. Wind Data Collection transmitted light LASER
DETECTOR
TARGET
transmitted light
scattered and received light
(with Doppler frequency shift)
local oscillator
(reference beam)

31. Wind Data Collection

32. Wind Data Review

33. Wind Data Review – Review & Processing
Perform quality checks on the data
Instrument continuity
Mast integrity (boom slippage)
Tower/instrument shadow
Shear profile
Turbulence
Icing affected data

34. Wind Data Review – Review & Processing
Process and review the raw data recorded (Excel / Windographer / WAsP)

35. Wind Data Review – Long Term Prediction
Site data collection will result in an onsite time series dataset of typically 1-2 years duration
However, the wind farm annual energy yield prediction must be valid for the long-term mean annual average
Wind farm life is years
We must therefore adjust the short-term site data to make it representative of the long-term mean annual wind climate

36. Wind Data Review – Long Term Prediction
Main tool to achieve the long term correlation is MCP:
MEASURE wind speed and direction at the wind farm site
CORRELATE between the wind farm site data and wind data from a suitable long-term reference weather station (Environment Canada station)
PREDICT the long-term wind climate at the site
The keys to MCP are:
Establishing good correlations
Consistency of measurement

37. Wind Data Review – Reference Stations

38. Wind flow modelling

39. Wind Flow Modelling
The data analysis and MCP process results in a prediction of the long-term mean annual wind climate (frequency of speed and direction)
This data is valid only at the height and location of the principal site anemometer (s) dataset used in the analysis
The turbines in the wind farm will be situated across the project area
The wind climate will vary across the site with changes in exposure, topography, surface roughness
The wind climate must therefore be extrapolated horizontally and vertically to the hub-height of all turbines within in the wind farm

40. Wind Flow Modelling WAsP/Ms-Micro flow model
Simple, quick, easy to run
Assume flow is always attached (i.e. no turbulence)
This severely limits their use in complex flow environments (steep slopes/forests) – can lead to significant model errors
Simple flow models are being replaced by advanced 3D computational fluid dynamics (CFD) models (such as Ventos)
Designed to deal specifically with complex terrain and forestry
Complex, computationally demanding, require expert use
Applicable also in determining areas of flow disturbance – the wind quality – for turbine micro-siting

41. Wind Flow ModelLing: Complex Flow
What causes complex flow?
Forestry
Terrain
Obstacles
Complex flow impacts wind flow quality
Flow parameters that define the wind quality :
Wind shear
Turbulence
In-flow angle

42. Wind Flow ModeLling: Complex Flow

43. Wind Flow Modelling: Shear
Variation of horizontal wind speed with height
Characterised by log or power law profile
Effects :
Increased fatigue loading
Reduced power output
Values :
Power law exponent ≤ 0.3

44. Wind Flow Modelling: Turbulence
The formation of eddies and vortices (transient)
Characterised by turbulence intensity (TI%)
Effects :
Reduced power output
Increased fatigue loading
Values : IEC limit ≈ 12 – 16 (Class A/B/C)

45. Wind Flow Modelling: Inflow Angle
Deviation of the directional component of the wind velocity from the turbine rotor axis in the vertical plane.
Effects:
Reduced power output
Increased fatigue loading
Values:
θ ≤ 8° (±) θ

46. Wind Flow Modelling: Turbulence

47. Wind Flow Modelling: Recirculation

48. Wind Flow Modelling: Mitigation
Forestry felling or management options
Scenario modelling with Ventos CFD flow model
Potential improvements in wind quality and resource
Sector-wise curtailment
Preserve turbine integrity
Maximise availability/energy in “clean” sectors
Maintenance and repair strategy
Target maintenance and repair by turbine and component

49. Turbine Layout and Selection

50. Turbine Selection and Layout Design
Wind farm should be designed to meet physical and technical constraints whilst utilising the maximum potential from the wind
Other optimisation criteria:
Inter-turbine spacing (4-8 rotor diameters / circular or elliptical). Much greater offshore
Hub height
Proximity to trees (> 50 x tree height) – optimal not always practical
Proximity to noise sensitive properties - allowable noise limit in BC - 40dBA at night
Maximise energy output

51. Turbine Selection: Classification
Wind turbines are certified for different site conditions according to international standards (IEC/GL/DNV)
Principle criteria are:
Average wind speed
Maximum 50-year return 3 second gust
Ambient site turbulence
Vertical wind shear and inflow angle
Temperature ranges
Suitable turbine selection is necessary for warranty and economic optimisation

52. Turbine Selection: Classification
Sites defined as either:
Class I: Most severe site wind climate
Class II: Moderate site wind climate
Class III: Least severe site climate
Sub-category for ambient site turbulence at 15m/s (A/B/C)

53. Energy Yield Modelling, Losses & Uncertainty

54. Energy Yield Modelling
The basic principles…..
Take the “instantaneous” turbine power curve (power in kW)
Combine with a wind speed frequency data for the location (time in hrs)
Calculate the generated electricity yield (energy in kWh) for the time period
Apply losses x
Power (kW)
Time (hours)
Annual Energy (kWh) =

55. Energy Yield Modelling
Typically performed in wind farm design software
WAsP/WindFarmer/WindFarm/WindPro/OpenWind
Output is an “ideal” mean annual energy yield value for each turbine
Losses to apply
Production losses
Array losses due to turbine wake interaction

56. Energy Yield Modelling: Losses
Losses are applied for a range of energy production issues:
Turbine availability (~3-5%) - Estimated or based on warranty
Grid availability (<1%) - Estimated
Electrical losses (~1-4%) – Calculated to metering point
Blade performance (<1%) – Estimated – site dependant
Icing, degradation
Control losses (~1%) – Estimated/calculated - site/turbine dependant
Curtailment losses (grid restriction, noise, shadow) – site dependant

57. Energy Yield Modelling: Losses
Array Losses:
Often most significant loss in a large wind farm arrays
Wind turbines create a disturbance downwind as kinetic energy in the wind is converted to mechanical energy by the rotor – the “wake”
In the turbine wake, wind velocity generally decreases and turbulence increases.

58. Energy Yield Modelling: Losses

59. Energy Yield Modelling: Losses

60. Energy Yield Modelling: Uncertainty
All stages of the modelling process have uncertainties associated with them:
Data collection
Long-term correlation
Wind flow modelling
Wake modelling
Loss prediction
We must also account for the natural variability of wind over different time-periods

61. Energy Yield Modelling: Uncertainty

62. Energy Yield Modelling: Uncertainty
How to reduce uncertainty:
Give a high priority to quality on-site data collection and checking
Collect as long a data-set as possible
High density data collection – numerous points and heights
Use an appropriate flow model for the site
Reference data – careful selection of station and reference period to ensure consistency, veracity and applicability

63. The End Thanks! 63