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Wind Resource AnalysisWind Resource Analysis

An Introductory OverviewAn Introductory Overview

MGA/NWCC Midwestern Wind Energy: Moving It to MarketsJuly 30, 2008

Detroit, Michigan

Mark Ahlstrom

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WindLogics BackgroundWindLogics Background

Founded 1989 - supercomputing background

Atmospheric modeling and visualizationUS Air Force Operational Weather SquadronsIsraeli Air Force Operational Forecasting SystemHarvard University Air Quality ModelingDOE Real-time Wind Field MonitoringNASA Meteorological Data Assimilation

Experience in fine-scale forecasting systems

Applied these advanced modeling and analysis technologies to wind energy since 2002

Subsidiary of FPL Energy since September 2006

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One Day of WeatherOne Day of Weather

Inner grid from July 4, 2003 - 5 minute resolution at 30 min/secShowing wind vectors (90 m AGL) and cloud water/precipitation isosurface

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Atmospheric ComplexityAtmospheric ComplexityThe atmosphere is so complexThe atmosphere is so complex…… So how does this work?So how does this work?

Solar Radiation

Moisture Fluxes

Turbulence

Evaporation

Convection

Condensation

SurfaceHeat

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Gridded 3D Weather DataGridded 3D Weather DataIntegrates all available data sources, from the surface to the upper atmosphere, into a unified and physically consistent state of all grid cells at a given point in time.

Over 160 weather variables collected from:

• Surface / METAR station data• Oceanographic buoys• Ship reports• Aircraft (over 14,000 ACARS/day)• NOAA 405 MHz profilers• Boundary-layer (915 MHz) profilers• Rawinsondes (balloon soundings)• Reconnaissance dropwinsonde• RASS virtual temperatures• SSM/I precipitable water• GPS total precipitable water• GOES precipitable water• GOES cloud-top pressure• GOES high-density vis. cloud drift wind• GOES IR cloud drift winds• GOES cloud drift winds• VAD winds: WSR-88D NEXRAD radars

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Meteorological ModelsMeteorological ModelsNumerical gridded representation of the laws of physics

Conservation relationsMassEnergyMomentumWater, etc.

Physical processesRadiationTurbulenceSoil/ocean interactions, etc.

Use lots of fast computersPartial differential equationsGridpoint difference valuesStep all points through time using very small steps (a few seconds per step)

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Nesting Modeling TechniquesNesting Modeling TechniquesModeling Modeling ““fills the gapsfills the gaps”” in both space & timein both space & time

Outer Grids

Inner Grids

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Averaging of LongAveraging of Long--term Resultsterm Results

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Variability in the DayVariability in the Day

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Variability over YearsVariability over Years

(Annual Energy (Annual Energy -- 19721972––2002)2002)

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Influence of Local TerrainInfluence of Local Terrain

30m Grid (5x6 km)

Example showing wind speed in color, wind direction as streamlines.

Data Sources:Data Sources:• WindLogics Archive• Local Test Towers• Hi-Res. Terrain / Land Cover

Process:Process:• Detailed Windfield Modeling

Result: Result: • 30 meter grid • 50 meter hub height

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Influence of HeightInfluence of HeightProduction estimate in GWh per year at multiple heightsProduction estimate in GWh per year at multiple heights

50m Height50m Height

30m Grid (5x6 km)

80m Height80m Height

30m Grid (5x6 km)

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Complexity of Wind EnergyComplexity of Wind Energy

Location & terrain make big differencePower in the wind is proportional to the cube of wind speed, so great value in optimizing location, layout & heightMany characteristics to consider

Shear (speed increase with height)Diurnal & seasonal patternsLong-term interannual variability

Planning, financing & operating issuesA large investment with a 25-year timelineVariability on many time scalesImplications for utility operations

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ChallengesChallenges

50 m

80 m

100 m

Can’t afford a full gridded array of met

towers – one or two will have to

suffice

Measuring above 50

meters is too expensiveMet Tower Wind Turbine Airport Anemometer

Can’t wait forever – one or two years

measurement will have to

sufficeAnemometers are

never perfect…

Data Gaps, Icing,

Calibration, Spares…

Airport Anemometers are too low, blocked

by trees and vegetation…

Airport Anemometers are

too far away…

7 or 8 years is NOT long-term

data…

Diurnal patterns don’t match hub

height

Challenges of distance, height, time & space

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Risk Uncovered in Long Term AnalysisRisk Uncovered in Long Term Analysis

Normalized Speed vs. Year

1st Year of Operations

Facility Planning

Phase

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Avoiding RiskAvoiding Risk

Seek All Available Data

Apply Best Technologies

Invest Today for Future Returns

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Available DataAvailable Data

On Site “Met Tower” DataNational/Global DataRegional Weather DataRegional Tower DataModel-based Data

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Data Integration is the KeyData Integration is the KeyTower Data for Point AccuracyModel-based Wind Data for 3D UnderstandingUse Models as a Foundation for Integration

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The Snapshot Prior to ConstructionThe Snapshot Prior to ConstructionUnderstanding the resource, variability & riskUnderstanding the resource, variability & risk

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The Continuous Process & TimelineThe Continuous Process & TimelineReg

ional Pro

spec

ting

Met. Tower

Placem

ent

Site A

sses

smen

t

Financia

l Due D

iligen

ce

•Enhan

ced Tower

Data

Projec

t Enginee

ring

Constructi

on

•Hourly

Wea

ther Forec

ast

•Virt

ual Tower™

•Turb

ine Sele

ction

•Micr

o Siting

Operatio

ns

•Long-te

rm Finan

cial F

orecas

t

•Main

tenan

ce Plan

ner

•Plan

ning and W

eather

Forecas

t

Project TimelineWind Maps/GISInitial StudiesSite Visits

Detailed Maps/GISPreliminary Layouts Forecasting

Operational Assessment

Long Term NormalizationMicrositing

Capacity Factor Predictions

Data CollectionData Analysis

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Questions & DiscussionQuestions & Discussion

Time series showing forecast with wind speed

and cloud cover

Mark Ahlstrom, CEO651.556.4262

mark@WindLogics.com

www.WindLogics.com