Modeling, Analysis,Modeling, Analysis,andand

Risk AssessmentRisk Assessment

Kansas Renewable Energy & Energy Efficiency Conference

September 26, 2007Topeka, Kansas

Grant Brohm – grantb@WindLogics.com

Technology & RenewablesTechnology & Renewables

WindLogics BackgroundWindLogics Background

Founded 1989 - supercomputing background

Atmospheric modeling and visualization US Air Force Operational Weather Squadrons Israeli Air Force Operational Forecasting System Harvard University Air Quality Modeling DOE Real-time Wind Field Monitoring NASA 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

WindLogics TodayWindLogics Today

46 people focused on 3 things:46 people focused on 3 things:1. Wind Resource – over 700 studies

completed2. Wind Variability – 40-year analysis standard3. Wind Forecasting – currently ~5000 MW

Grand Rapids Sciences CenterGrand Rapids Sciences Center Ph.D. atmospheric sciences team for R&D 150 processors & 36 terabytes of storage Data center with NOAAport Satellite System

Saint Paul Operations CenterSaint Paul Operations Center Meteorology and GIS production, Sales,

Operations 400 processors & 90 terabytes of storage Data center with NOAAport Satellite System

Solar Radiation

Moisture Fluxes

Turbulence

Evaporation

Convection

Condensation

SurfaceHeat

Atmospheric ComplexityAtmospheric ComplexityThe atmosphere is a dynamic and complex space…The atmosphere is a dynamic and complex space…

Example - Patterns over the Example - Patterns over the DayDay

Complexity of Wind EnergyComplexity of Wind Energy

Location & terrain make big difference

Power in the wind is proportional to the cube of wind speed, so great value in optimizing location, layout & height

Many characteristics to consider Shear (speed increase with height)

Diurnal & seasonal patterns

Long-term interannual variability

Planning, financing & operating issues

A large investment with a 25-year timeline

Variability on many time scales

Implications for utility operations

Integrated Wind Integrated Wind UnderstandingUnderstanding

Taking advantage of all available data:

1. Meteorological tower data and other on-site weather measurements

2. Use best available “gridded” archives of real weather data from government agencies

– Actual recorded weather data from many sources– Typically used to initialize weather forecast models

3. Add the best available high-resolution topography and land cover information

4. Properly apply meteorological models and wind field models - integrating data over space and time

5. Analyze long-term variation and the financial impact on your specific situation

6. Use wind forecasting to minimize cost and operating impacts & maximize revenues

Solar Radiation

Moisture Fluxes

Turbulence

Evaporation

Convection

Condensation

SurfaceHeat

The atmosphere is so complex… So how does this work?The atmosphere is so complex… So how does this work?

Atmospheric ComplexityAtmospheric Complexity

Gridded 3D Weather DataGridded 3D Weather Data

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

Integrates 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.

Meteorological ModelsMeteorological Models

Numerical gridded representation of the laws of physics

Conservation relations Mass Energy Momentum Water, etc.

Physical processes Radiation Turbulence Soil/ocean interactions, etc.

Use lots of fast computers Partial differential equations Gridpoint difference values Step all points through time

using very small steps (a few seconds per step)

Wind vectors at 90 m and precipitation rate on outer grid at 6 Wind vectors at 90 m and precipitation rate on outer grid at 6 hr/sechr/sec

March 2003

Month of March 2003 Note the Historic Front

Range snow storm (March 17-19, 2003)

Modeling from Weather Data Modeling from Weather Data ArchivesArchives

Results over Large AreasResults over Large Areas

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

30m Grid (5x6 km)

Understanding Project Sites in Understanding Project Sites in DetailDetail

Production estimate in GWh per year at multiple Production estimate in GWh per year at multiple heightsheights

80m Height80m Height

30m Grid (5x6 km)

50m Height50m Height

30m Grid (5x6 km)

Gross Annual ProductionGross Annual Production

Variability over YearsVariability over Years

(Annual Energy - 1972–2002)(Annual Energy - 1972–2002)

Long-Term Wind Speed Long-Term Wind Speed VariationsVariations

A fairly low variability site, Annual Std. Dev. ~ 3.5% – yet thechoice of 8-year period can affect energy projection by ~20%

Site Assessment ResultsSite Assessment Results

Understanding the resource, variability & riskUnderstanding the resource, variability & risk

ConclusionsConclusions

Benefits of Modeling? Allows us to determine wind regime (and its drivers) over project area Can be completed faster than traditional measurement (4-6 weeks) Best if modeling is integrated with met tower or other on-site data Provides a method for moving more efficiently through development cycle

Important Risk Analysis Components? Wind resource analysis should incorporate long-term data for meaningful

correlation and prediction Potential climate cycles and trends should be identified Long-term data needed for more accurate P-values (sensitivity)

Key Concepts? Need understanding of long-term wind variability profile to best anticipate

wind farm production Best to use integrated approach (models, met towers, archived data,

multiple correlation techniques) for most error-proof expected wind production baseline

Time series showing forecast with wind speed and cloud

cover

Grant Brohm, Sales651.556.4279

grantb@WindLogics.com

www.WindLogics.com

WindLogics Inc.WindLogics Inc.