The Value of Mesoscale Numerical Weather Prediction in Wind Energy Resource AssessmentMark Stoelinga

25. Windenergietage 08. - 10. November 2016 Potsdam

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Vaisala Overview

Vaisala is a weather company started by

Finnish Professor Vilho Väisälä in the 1930s

1500+ employees, 30 offices worldwide

Primary business areas

Weather (includes Energy)

Controlled Environments

Vaisala Energy services

Measurement systems

(incl. Triton, global lightning data)

3TIER information services

(incl. Forecasting, Assessment)

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Met campaign

Steps in estimating long-term net energy production

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Met CampaignMet Data Analysis

Wind Flow Modeling

Long-Term Climate

Adjustment

Long-Term Gross Energy

Production Loss Estimation

Uncertainty Estimation

Final Long-Term Net Energy Production

P-level

(Capacity

Factor) 1-year 10-year

Gross-P50 41.6% 41.6%

Net-P50 32.1% 32.1%

Net-P75 29.8% 29.8%

Net-P95 26.4% 26.6%

Net-P99 24.0% 24.3%

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Met campaign

Steps in estimating long-term net energy production

11/13/20165

Met CampaignMet Data Analysis

Wind Flow Modeling

Long-Term Climate

Adjustment

Long-Term Gross Energy

Production Loss Estimation

Uncertainty Estimation

Final Long-Term Net Energy Production

P-level

(Capacity

Factor) 1-year 10-year

Gross-P50 41.6% 41.6%

Net-P50 32.1% 32.1%

Net-P75 29.8% 29.8%

Net-P95 26.4% 26.6%

Net-P99 24.0% 24.3%

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The standard engineering approach to wind flow modeling

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Linear flow models

(Jackson-Hunt, aka

WAsP)

Computational Fluid

Dynamics (CFD)

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Enter the meteorologist!

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You see an engineering problem.

I see a meteorology problem.

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And what is the primary forecasting tool for a meteorologist?

Numerical Weather Prediction (NWP)

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Benefits of Mesoscale (or Regional) Numerical Weather Prediction Models

Not “just a weather

forecast model”

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Benefits of Mesoscale (or Regional) Numerical Weather Prediction Models

Not “just a weather

forecast model”

Wind is weather

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Benefits of Mesoscale (or Regional) Numerical Weather Prediction Models

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Not “just a weather

forecast model”

Wind is weather

Scale “telescoping”

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Benefits of Mesoscale (or Regional) Numerical Weather Prediction Models

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Not “just a weather

forecast model”

Wind is weather

Scale “telescoping”

“Informed” by global

reanalysis datasets

(e.g., ERA-Interim,

MERRA, MERRA2,

CFSR, JRA-55)

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Benefits of Mesoscale (or Regional) Numerical Weather Prediction Models

Not “just a weather

forecast model”

Wind is weather

Scale “telescoping”

“Informed” by global

reanalysis datasets

(e.g., ERA-Interim,

MERRA, MERRA2,

CFSR, JRA-55)

Time evolving

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Page © Vaisala

Benefits of Mesoscale (or Regional) Numerical Weather Prediction Models

Not “just a weather

forecast model”

Wind is weather

Scale “telescoping”

“Informed” by global

reanalysis datasets

(e.g., ERA-Interim,

MERRA, MERRA2,

CFSR, JRA-55)

Time evolving

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Synthetic long-term

reference time series

Time series loss

estimates

Time series for

financial modeling

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Caveats

“Legal limit”: 1 km resolution

Must couple with microscale model

Linear flow Mass conserving CFD LES

Microscale model must be informed with accurate terrain and

roughness data at the turbine scale.

More expensive supercomputer needed

Models aren’t perfect

Global reanalysis datasets aren’t perfect

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Validation

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Validation: Synthetic reference time series

Horizon (EDPR, 2011)

validation study

23 met towers across

US

8 conventional off-site

reference stations

NWP-based synthetic

reference produced at

each tower

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Mean Absolute Error

of 5 year mean

Mean A

bsolu

te E

rror

Training Months

Conventional Off-site NWP Synthetic

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Validation: Spatial wind flow modeling

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5.0 kmBlack dots: turbine locations

Blue sites: met mast data provided

Red sites: “blind” met mast

(data withheld for validation)

Circled sites: largest average absolute error

among participants (high bias in both cases)

Less complex terrain More complex terrain

AWEA Wind Flow Modeling Experiment (2013)

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Validation: Spatial wind flow modeling

1

Vaisala

(NWP Modeling)

AWEA Wind Flow Modeling Experiment (2013)

Less Complex Terrain Site

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1

AWEA Wind Flow Modeling Experiment (2013)

Validation: Spatial wind flow modeling

Vaisala

(NWP Modeling)

More Complex Terrain Site

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Mesoscale modeling

methods had the

least bias and the

smallest errors on

average, at both the

non-complex and

complex terrain sites.

Non-complex

AWEA Wind Flow Modeling Experiment (2013)

Validation: Spatial wind flow modeling

Complex

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Round-robin tower experiments: Modeling errors

versus distance from met tower

Validation: Spatial wind flow modeling

Data from one tower are combined with

modeling results to make a “blind

prediction” of wind speed at other towers

Speed-up ratios computed, errors

calculated.

Process is repeated for each individual

met tower

Two studies:

Vaisala internal validation study (2016)

– Mesoscale NWP methodology

– 210 met tower pairs

Clerc et al. (2012) (RES Study)

– Linear flow model (Jackson-Hunt,

similar to WAsP)

– 557 met tower pairs

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Distance between met towers (km)

Round-robin tower experiments: Modeling errors

versus distance from met tower

Validation: Spatial wind flow modeling

Individual prediction errors (Vaisala/NWP)

Enclosing 1 std. dev. of errors (Vaisala/NWP)

Enclosing 1 std. dev. of errors (RES/linear flow)

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Distance between met towers (km)

Individual prediction errors (Vaisala/NWP)

Enclosing 1 std. dev. of errors (Vaisala/NWP)

Enclosing 1 std. dev. of errors (RES/linear flow)

Round-robin tower experiments: Modeling errors

versus distance from met tower

Validation: Spatial wind flow modeling

?

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• AWS Truepower (AWEA WindPower, 2014) Comparison Study

• 144 met tower pairs

• linear flow (“JH”); CFD (“RANS-CFD”); and NWP (“NWP-MC”)

Validation: Spatial wind flow modeling

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Summary

Mesoscale Numerical Weather Prediction is a

meteorological approach to spatial wind flow modeling

The value of NWP is that it simulates global to regional-

scale weather and climate phenomena outside the project

boundary, that produce important spatial patterns of wind

resource inside the project boundary

This enhanced skill really emerges beyond ~5 km from the

met tower

NWP can also be used, in conjunction with reanalysis

datasets, to produce high-quality, synthetic long-term

reference time series

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