1

Integrating Wind into the Transmission Grid

Michael C Brower, PhDAWS Truewind LLCAlbany, New York

mbrower@awstruewind.com

2

About AWS Truewind• Providing integrated consulting services to the

wind industry

• Responsible for the Irish Wind Atlas (with ESBI, initiated by SEI)

• Forecasting for 2000+ MW of wind plant in US and Europe

• Conducted wind integration studies in US

3

Time Scales – Electric Power

• Regulation: seconds to minutes

• Load following: minutes to hours

• Unit commitment: hours to days

• Reliability: months to years

4

Time Scales – Wind

5

53 MW Capacity

Wind and Wind Plant VariabilityNot the Same

+33%

+10%

6

Propagation of Gusts Through a Wind Farm

7

Mean Change in Power vsNumber of Turbines at Flat

Rock

8

Spatial Diversity of Turbine Output

Correlation coefficient of power change for different average times over the distance

From Ernst et al, 1999

9

Typical 4-Hr PIRP Forecast PerformanceSan Gorgonio Pass, California - May 2003

Reported vs Forecasted Hourly Energy Output

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

110%

Date (Label is 1 AM PDT)

Reported 4-Hr Forecast

Wind Forecasting

10

Forecast Accuracy Vs Time

11

Forecast Accuracy Vs Output

3-Hour Ahead Forecasts

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

0% 20% 40% 60% 80% 100% 120%

Forecast Output (% of Capacity)

Mea

n A

bsol

ute

Err

or (

% o

f C

apac

ity)

Central US

San Gorgonio

12

New York Integration Study

• Evaluating 3300 MW of wind on a 33,000 MW system

• Time scales from seconds to days

• AWS Truewind provided wind data

• GE PSEC performing grid analysis (from AGC to day-ahead scheduling)

13

NY Study: The Challenge

• How to simulate the behavior of 3300 MW of wind with little site data?– Must capture spatial and temporal

correlations– Met stations often not in windy areas and

exhibit wrong diurnal pattern

• Solution: Mesoscale modeling

14

NY Study: Tasks• Selected 33 potential project sites with

50-300 MW capacity• Used a mesoscale weather model to

simulate hourly wind speed, direction, temperature for 5 continuous years

• Sampled 1-min and 1-sec data to synthesize sub-hourly fluctuations

• Created statistical model to synthesize plant forecasts – based on actual forecasts

15

16

17

Forecasting

0

50

100

150

200

250

300

350

-0.8

-0.6

-0.4

-0.2 0

0.2

0.4

0.6

0.8

Error Distribution

18

Validation of Dynamic BehaviorMean Absolute Deviation

0.000

0.050

0.100

0.150

0.200

0.250

0 2 4 6 8 10 12 14Hours Ahead

Per

cen

t o

f R

ated

Cap

acit

y

MADISON TURBINE

MADISON PLANT

SIM POINT

SIM 11.5MW PLANT

SIM 50 MW PLANT

19

0

500

1000

1500

2000

2500

3000

0 60 120 180

9/14/2002 6:00 8/2/2001 5:00 5/25/2002 19:00 10/20/2003 16:00 4/12/2002 13:00

“Extreme” Wind Events

20

0

5000

10000

15000

20000

6:00:00 7:00:00 8:00:00

-500.00

0.00

500.00

Total NY Load September Wind AGC MW w / Load OnlyAGC MW w / Load & Wind Rate Limited MW w / Load Only Rate Limited MW w / Load & Wind

“Extreme” Event System Response

21

Conclusions• Wind, turbine, and wind plant variability are

not the same

• The more spatial diversity, the less temporal variability

• Mesoscale modeling provides a powerful tool for analyzing scenarios of large wind penetration