Transcript WIND FLOW MODELLING

Wind 101 – Technical Basics
Clean Energy BC 2010
Mark Green – Wind Engineer, Natural Power Consultants
NATURAL POWER - ABOUT US
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Practical consulting and risk management for the international renewable
energy industry, providing services throughout the project life-cycle
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15 year track record in wind energy consultancy - established in 1995
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Over 200 employees worldwide across 7 offices in 5 countries
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Consultancy services provided to more than 15,000MW of projects
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2,000MW where we have provided full project design & consenting
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We have managed the construction of 500MW of wind energy
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300MW+ of wind plant under our asset management
A GLOBAL PRESENCE
We have presence worldwide, including:
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Scotland (head office)
British Columbia, Canada
France
Ireland
England
Wales
Chile
USA
OUR CORE SERVICES
Our core services:
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Advanced resource assessment and site modelling
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Development, EIA and permitting
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Ecology services
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Construction and geotechnical services
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Site management
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Operational site analysis and optimisation
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360o Due-diligence
WIND 101 – OVERVIEW
1.
Wind: The Basics
2.
Commercial Background
3.
Site Selection
4.
Wind data collection
5.
Data analysis – Long-term prediction
6.
Wind flow modelling
7.
Turbine layout and selection
8.
Energy yield modelling
The Basics
THE BASICS: WIND
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All renewable energy (except tidal and geothermal
power) ultimately comes from the sun.
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Uneven heating of the earth’s surface causes
differences in temperature throughout the
atmosphere.
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Warm air, which weighs less than cold air, rises. Then
cool air moves in and replaces the rising warm air.
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This movement of air is what makes the wind blow.
THE BASICS: TURBINES
Blades (35-55m length)
Rotor (70-110m diameter)
Nacelle
Rotor Hub
Tower (60-100m high)
Transformer
THE BASICS: WIND
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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
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Betz law: maximum of 59% of the power moving through the
rotor can be captured.
THE BASICS: WIND
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The Watt is the SI unit of power - instantaneous
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Energy in the context of electricity generation is the
multiplication of power in Watts and time in hours.
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E.g. a 1MW turbine producing at 100% for 1 hour will produce
1MWh of energy.
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However, the wind never blows 100% of the time!
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The term Capacity Factor (C.F.) is used to describe the actual
energy produced vs the max rated production.
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: PROCESS OF DESIGN & ANALYSIS
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Desk-based resource modelling
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Short-term wind data collection
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Long-term wind climate prediction
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Wind flow modelling
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Energy yield modelling
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Uncertainty analysis
Site Selection
SITE SELECTION: DESK BASED MODELLING
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Used for initial site prospecting
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Does not use any actual on-site wind data as an input
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Instead uses a local correction model
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Examples of regional mesoscale models are the Canadian Wind Atlas and the BC
Wind Atlas, both are available online.
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Typically of too coarse a resolution and accuracy to be applicable in absolute wind
resource assessment for financing
SITE SELECTION: DESK BASED MODELLING
SITE SELECTION: DESK BASED MODELLING
SITE SELECTION: DESK BASED MODELLING
SITE SELECTION– CONSTRAINTS
Economic Considerations:
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Distance to transmission
Transmission capacity
Site access
Constructability
Wind speed
Technical constraints:
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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
SITE SELECTION– CONSTRAINTS
SITE SELECTION
Wind data collection
WIND DATA COLLECTION
Why are on-site measurements required?
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Provide an accurate representation of the wind regime of the
site and its viability
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Highlight localised wind flow issues
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Reducing prediction uncertainty
Measurement locations must be representative of turbine locations:
• Topographically
• Altitude
• Exposure
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
WIND DATA COLLECTION
WIND DATA COLLECTION
Prop Vane -
Wind vane
(measures wind direction)
Cup anemometer
(measures wind speed)
(measures wind speed
and direction)
WIND DATA COLLECTION
To achieve an industry best practice 0.5% deficit in
wind speed or less:
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Cylindrical mast:
– For a mast with diameter, d, and
boom with diameter, D:
• r/d > 8.5
• R/D > 12
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Lattice mast
– For a mast with face length, L,
and low porosity:
• r/L > 5.7
• R/D > 12
R
r
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
WIND DATA COLLECTION
WIND DATA COLLECTION
Remote sensing is another option:
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Ground based wind data collection
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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
WIND DATA COLLECTION
transmitted light
LASER
local oscillator
(reference beam)
DETECTOR
scattered and
received light
(with Doppler
frequency shift)
TARGET
WIND DATA COLLECTION
Wind Data Review
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
WIND DATA REVIEW – REVIEW & PROCESSING
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Process and review the raw data recorded (Excel / Windographer / WAsP)
WIND DATA REVIEW – LONG TERM PREDICTION
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Site data collection will result in an onsite time series dataset of typically 1-2 years
duration
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However, the wind farm annual energy yield prediction must be valid for the longterm mean annual average
– Wind farm life is 20-25 years
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We must therefore adjust the short-term site data to make it representative of the
long-term mean annual wind climate
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 longterm 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
WIND DATA REVIEW – REFERENCE STATIONS
Wind flow modelling
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
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
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
WIND FLOW MODELLING: COMPLEX FLOW
WIND FLOW MODELLING: SHEAR
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
WIND FLOW MODELLING: TURBULENCE
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 % @15m/s (Class
A/B/C)
WIND FLOW MODELLING: INFLOW ANGLE
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
WIND FLOW MODELLING: TURBULENCE
WIND FLOW MODELLING: RECIRCULATION
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
TURBINE LAYOUT AND SELECTION
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
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
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)
Energy Yield Modelling, Losses &
Uncertainty
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
Power (kW)
x
Time (hours)
=
Annual Energy (kWh)
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
ENERGY YIELD MODELLING: LOSSES
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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
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.
ENERGY YIELD MODELLING: LOSSES
ENERGY YIELD MODELLING: LOSSES
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 timeperiods
ENERGY YIELD MODELLING: UNCERTAINTY
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
The End.
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Thanks!