Why Numerical Mesoscale Modeling Eastern Wind Integration and

Analysis of Mesoscale Model Data for Wind Integration

Marc Schwartz, Dennis Elliott, Debra Lew, Dave Corbus, George Scott, Steve Haymes, Yih Huei Wan

National Renewable Energy Laboratory • WINDPOWER 2009

Western Wind and Solar Integration

Western Wind and Solar Integration Study

for Wind Integration Studies? Transmission Study (EWITS)

Control areas:

1) Arizona Public

Service

2) El Paso 

3) Nevada Power 

4) Public Service

of New Mexico

5) Sierra Pacific 

6) Salt River Project

7) Tristate

8) Tucson

Study (WWSIS)

1. Supports examination of implications of 

national 20% wind vision

2. Provides input to integration and 

transmission studies for operational impact 

of large penetrations of wind on the grid

3. Generates consistent wind speed and 

power plant output time series data sets

Ti i hi di i

Study area 

includes:

• PJM

• Midwest ISO

• Mid-Continent 

Area Power Pool

• Southwest 

Power Pool 8) Tucson

9) Xcel

10) Western Area

Power Administration

– Time series capture geographic diversity 

issues for:

• Resource planning

• System operations

• Transmission expansion analyses

• TVA

• New York ISO

• Other interested 

parties

Mesoscale Model Data Sets Evaluated at NREL Wind plant locations and size

EWITS

Eastern Wind Integration and 

Transmission Study

WWSIS

Western Wind and Solar 

Integration Study

Produced by: AWS Truewind 3TIER

Horizontal resolution 2km 1 arc-minute (~1.5km)

Temporal resolution 10 minutes

Period 2004-2006

Wind speed output 5 heights

Wind plant output 80m, 100m 100m

Wind plant

representation

Amalgamated grid cells used for onshore 

wind plants, and individual grid cells 

sed for offshore ind plants

Individual grid points 

represent a wind plant

• Each point that 

represents a wind 

plant location 

corresponds to

an area of 1

arc-minute or 

approximately

1.5 km by 1.5 km

• The installed 

capacity for

each point is

30 megawatts

• Each wind power 

plant location is 

a grouping of 

2 km by 2 km 

grid points

• The installed 

capacity for each 

grid point 

contained within 

a wind plant is 

20 megawatts

Model and Observed Wind Speeds

at One EWITS Validation Tower

representation used for offshore wind plants p p

1326 onshore wind plants of various 

sizes (100 MW to 1500 MW); 4948 

offshore grid points of 20 MW each

32043 individual grid

points of 30 MW each

Validation

Kansas

Validation Conclusions

• EWITS and WWSIS data sets developed using different 

numerical computer models 

– EWITS used MASS model, part of AWS Truewind’s

Comparison of Observed and 

Modeled (WWSIS) Changes in 

Power from Texas Wind Plants

20%

Distribution of Hourly Step Changes

(2006)

Design of Mesoscale Data Sets Design of EWITS and WWSIS Future Work for Mesoscale Data Sets

Diurnal Monthly

p

MesoMap@ system

– WWSIS used WRF model as employed by 3TIER

• Both models were optimized using comparisons between raw

model data and measurement from tall towers

• Greatest uncertainty in model data occurs:

– In complex terrain (e.g. downslope acceleration)

– Where wind flows are thermally driven (e.g. land-sea breeze)

– Where there are strong flows near the top of the boundary 

layer (e.g. low-level jets) 

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

-250.0 -200.0 -150.0 -100.0 -50.0 0.0 50.0 100.0 150.0 200.0

Relative Frequency

∆P (MW)

Actual

Meso-scale

Design of Mesoscale Data Sets Mesoscale Data Sets

 Data sets designed to:

− Provide a robust and consistent data

set for modeling studies of integrating 

significant amounts of wind in

balancing areas

• Used for “what if we had wind turbines 

here” type of modeling studies

− Cover the same time period as load

Future Work for Mesoscale Data Sets

 Tall tower measurement campaign to increase understanding of

boundary layer and validate mesoscale model data

 Validation of mesoscale wind speed time series

− Diurnal

− Seasonal

 Comparison of overlap areas of EWITS and WWSIS data

 Standardization of data set characteristics

 Data sets are not designed to:

− Predict distribution, location, and 

size of future wind plants across

the U.S.

− Predict the long-term power 

production from a particular

wind plant

− Predict future wind generation 

The information contained in this poster is subject to a government license | WINDPOWER 2009 | Chicago, IL | May 4–7, 2009 | PO-500-45203

NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

data in the studies

− Recreate historical climate and weather 

data to enable historic modeling of winds 

• Analyzed in conjunction with load, 

hydro and other climate/weather 

related data 

− Wind plant shapes

• Series of individual grid points versus amalgamated grid cells

− Horizontal resolution of grid cells and wind plant installed capacity

− Protocol for converting wind speeds to wind plant production

• Calculation of losses

• Appropriate IEC turbine class based on wind speed

levels from a state or region

− Replace updated state wind 

resource maps

− Be the only basis for investment

in wind development