National Renewable Energy Laboratory 

Innovation for Our Energy Future 

A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 

A Primer on Wind Power for 

Utility Applications 

Y. Wan 

Technical Report 

NREL/TP-500-36230 

December 2005 
A Primer on Wind Power for 

Utility Applications 

Y. Wan 

Prepared under Task No. WER6.5003 

Technical Report 

NREL/TP-500-36230 

December 2005 

National Renewable Energy Laboratory 

1617 Cole Boulevard, Golden, Colorado 80401-3393 

303-275-3000 • www.nrel.gov 

Operated for the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337
NOTICE

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any 

warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or 

usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not 

infringe privately owned rights. Reference herein to any specific commercial product, process, or service by 

trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, 

recommendation, or favoring by the United States government or any agency thereof. The views and 

opinions of authors expressed herein do not necessarily state or reflect those of the United States 

government or any agency thereof. 

Available electronically at http://www.osti.gov/bridge

Available for a processing fee to U.S. Department of Energy 

and its contractors, in paper, from: 

U.S. Department of Energy 

Office of Scientific and Technical Information 

P.O. Box 62 

Oak Ridge, TN 37831-0062 

phone: 865.576.8401 

fax: 865.576.5728 

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from: 

U.S. Department of Commerce 

National Technical Information Service 

5285 Port Royal Road 

Springfield, VA 22161 

phone: 800.553.6847 

fax: 703.605.6900 

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/ordering.htm

Printed on paper containing at least 50% wastepaper, including 20% postconsumer waste
Table of Contents 

TABLE OF CONTENTS _________________________________________________ I

TABLE OF FIGURES ___________________________________________________II

TABLE OF TABLES ____________________________________________________II

1. INTRODUCTION _____________________________________________________1

2. STATISTICS OF WIND TIME-SERIES DATA ______________________________2

3. STATISTICS OF WIND POWER TIME-SERIES DATA _______________________4

3.1 Fluctuations of Wind Power ________________________________________________________________7

3.2 Distribution of Step Changes_______________________________________________________________10

3.3 Ramping Rates of Wind Power _____________________________________________________________11

3.4 Correlation between Wind Farms___________________________________________________________13

3.5 Aggregating Spatially Diverse Wind Plant Output _____________________________________________15

3.6 Rare Situations __________________________________________________________________________16

3.7 Generating Synthetic Wind Plant Output Time-Series__________________________________________18

4. STATISTICS OF UTILITY LOAD DATA__________________________________20

4.1 Correlation between Load and Wind Power Fluctuations _______________________________________23

5. OPERATIONAL IMPACTS OF WIND POWER ____________________________26

5.1 Transmission System Planning _____________________________________________________________26

5.2 System Reliability ________________________________________________________________________28

5.3 Regulation ______________________________________________________________________________30

5.4 Summary _______________________________________________________________________________34

6. CONCLUSIONS ____________________________________________________34

7. APPENDIX ________________________________________________________38

i
Table of Figures 

Figure 1-1. Locations of monitored wind power plants ____________________________________ 2

Figure 2-1. Sample wind speed profiles at different time intervals___________________________ 3

Figure 3-1. Single turbine output (3-second) ____________________________________________ 4

Figure 3-2. Single turbine output (1-minute average)______________________________________ 4

Figure 3-3. Three-second output of a single turbine and 11 turbines_________________________ 5

Figure 3-4. One-minute average power of a single turbine and 11 turbines ___________________ 6

Figure 3-5. Single turbine output power and step changes_________________________________ 7

Figure 3-6. Eleven-turbine output power and step changes ________________________________ 8

Figure 3-7. Output profiles of 138-turbine wind farm and 14-turbine cluster___________________ 8

Figure 3-8. Example wind power step change distribution ________________________________ 10

Figure 3-9. Wind power correlation as a function of distance______________________________ 13

Figure 3-10. Example of wind power during high wind period _____________________________ 17

Figure 3-11. Detail of turbine cut-off with 1-second power data series ______________________ 18

Figure 3-12. Probability distribution of actual and synthesized wind power series ____________ 19

Figure 4-1. Sample of 3-day high-resolution (4-second) utility load data from Xcel ____________ 21

Figure 4-2. Distribution of load and wind power step changes_____________________________ 22

Figure 4-3. Correlation coefficients of daily load and wind power step changes ______________ 24

Figure 4-4. Correlation coefficients of monthly load and wind power and step changes________ 25

Figure 5-1. Average July daily load and wind power profile _______________________________ 27

Figure 5-2. System load and wind power duration curves_________________________________ 28

Figure 5-3. Components of system load _______________________________________________ 31

Figure 5-4. Daily 4-second load and wind power time series example_______________________ 32

Figure 5-5. Correlation between ACE and interchanges __________________________________ 33

Table of Tables 

Table 2-1. Standard Deviation of Wind Speed Step Changes _______________________________ 3

Table 2-2. Sample Monthly Average Wind Speed_________________________________________ 4

Table 3-1. COV of Wind Speed, Wind Speed Cubed, and Wind Power ________________________ 5

Table 3-2. Individual Turbine Peaks and Peak of Combined Output__________________________ 6

Table 3-3. Output Variability of Single Turbine and 11 Turbines_____________________________ 7

Table 3-4. Wind Power Step Change Average Magnitude and Standard Deviation Values _______ 9

Table 3-5. Step Changes Statistics of Scaled-Up Wind Power Data _________________________ 10

Table 3-6. Cumulative Frequency of Wind Power Step Changes ___________________________ 11

Table 3-7. Wind Power 10-Minute Ramping Characteristics _______________________________ 12

Table 3-8. Distribution of Wind Power 10-Minute Ramping Rates __________________________ 12

Table 3-9. Individual Turbine Output Correlation Coefficients _____________________________ 14

Table 3-10. Effect of Aggregating Wind Farms on Step Changes___________________________ 15

Table 3-11. Effect of Aggregating Wind Farms on 10-Minute Ramping Rates _________________ 16

Table 3-12. State Transition Matrix with Hourly Average Wind Power Data___________________ 19

Table 4-1. Variance of Load and Wind Power in Different Time Scales ______________________ 21

Table 4-2. Average of Absolute Load and Wind Power Step Changes _______________________ 23

Table 4-3. Relative Frequencies of Extreme Step Changes________________________________ 23

Table 4-4. Variability of Load, Wind Power, and Net Load_________________________________ 25

Table 5-1. Example of Wind Power Impact on System Regulations _________________________ 32

ii
1. Introduction 

In the past 20 years, U.S. wind power capacity has increased continuously. With the federal tax 

credit and the emerging green market, total installed wind power capacity in the United States 

exceeds 6,000 megawatts (MW). However, the industry still faces many market barriers, some of 

which stem from utilities' lack of experience with the technology. The effects of wind power 

fluctuations on power system operations have increasingly concerned many electric utilities and 

wind power developers. 

Large-scale installation of wind power is being scrutinized more because of its unique 

characteristics. Wind power output varies with wind speed. A single wind power plant may have 

many relatively small induction generators that behave differently than conventional centralstation power plants with large synchronous generators. Utility system operators and planners 

need to understand the effects of fluctuating wind power on system regulation and stability. 

Without high-frequency wind power data and realistic wind power plant models to analyze the 

problem, utilities often rely on conservative assumptions and worst-case scenarios to make 

engineering decisions. 

To remedy the situation, the National Renewable Energy Laboratory (NREL) has undertaken a 

project to record long-term, high-resolution (1-hertz [Hz]) wind power output data from large 

wind power plants in various regions. The objective is to systematically collect actual wind 

power data from large commercial wind power plants so that wind power fluctuations, their 

frequency distribution, the effects of spatial diversity, and the ancillary services of large 

commercial wind power plants can be analyzed. It also aims to provide the industry with 

nonproprietary wind power data in different wind regimes for system planning and operating 

impact studies. 

Under NREL’s wind power plant monitoring project, data are being collected at seven locations. 

The locations and total installed capacity of the monitored plants are listed here. Figure 1-1 

shows the locations of wind power plants. 

The power data are recorded at 1 Hz. One-minute average wind speeds at hub height are also 

recorded from an on-site meteorological tower at the southwest Minnesota (SW Minn.) wind 

power plant. From the recorded 1-second and 1-minute data series, 1-minute, 10-minute, and 1-

hour time-series wind power and wind speed data are compiled for analysis. 

The industry has used a large amount of data collected for various system integration studies. 

Links to some of the reports are listed in the Appendix. This report will summarize the results of 

data analysis performed at NREL and discuss the wind power characteristics related to power 

system operation and planning. The goal is to help utility planners and operators who have not 

had experience with wind power in their systems to better understand wind power plant behavior. 

1
Figure 1-1. Locations of monitored wind power plants 

2. Statistics of Wind Time-Series Data 

Wind blows because of the atmospheric pressure differences across the surface of the earth. 

Uneven heating of the earth’s surface by the sun and heat transferred by the ocean currents create 

the pressure differences. Close to the ground, the air movement is also affected by the 

characteristics of the earth’s surface. The result of all of these factors is that wind varies all the 

time—in both speed and direction. For wind generation, speed is of particular interest because it 

has the most direct impact on the wind power plant behavior. This chapter will review the 

statistics of wind speed time-series and explain the variations of wind speed on different time 

scales. 

This report focuses on the behavior of large wind plant output, so it will only briefly discuss 

those wind attributes that directly affect short-term wind power fluctuations. The wind speed 

attributes of interest are average, standard deviation, step changes, and ramping rates over 

different time scales. The following example illustrates these attributes. 

Figure 2-1 plots wind speed time-series for several time steps: 1-second (1800 data points), 1-

minute (1440 data points), 10-minute (1440 data points), and 1-hour (720 data points). These 

wind speed traces are very similar except for the 1-second wind speed series. The 1-second data 

series appears to be “smoother” than the other three time series because the wind speed changes 

2
in such a short time interval are small. The step change (the speed differences between 

consecutive time steps) statistics of these four time series will confirm the observation. Table 2-1 

lists the standard deviation values of wind speed step changes of these four time series. Average 

step change values for these four time series are very small and are not included in the table. The 

variance of 1-second wind speed step changes is one magnitude smaller than that from the time 

series of longer time steps. This means that 1-second wind power step changes should also be 

small. 

Table 2-1. Standard Deviation of Wind Speed Step Changes 

1-second 1-minute 10-minute 1-hour 

Step Change Standard Deviation (m/sec) 0.07 0.55 0.53 0.88 

10-Minute Average Wind Speed

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

0:00

12:00

0:00

12:00

0:00

12:00

0:00

12:00

0:00

12:00

0:00

12:00

0:00

12:00

0:00

12:00

0:00

12:00

0:00

12:00

Time (hh:mm)

Wind Speed (m/sec)

Hourly Average Wind Speed

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

0 72 144 216 288 360 432 504 576 648

Hours

Wind Speed (m.sec)

1-Minute Average Wind Speed

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Time (hh:mm)

Wind Speed (m/sec)

1-Second Wind Speed

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

0 180 360 540 720 900 1080 1260 1440 1620

Seconds

Wind Speed (m/sec) 

Figure 2-1. Sample wind speed profiles at different time intervals 

Table 2-2 lists the average wind speed and its standard deviation values for a month calculated 

from four time series. Average wind speeds for these four time series are the same with only 

slightly different standard deviations. The average wind speed and its standard deviation values 

are specific to a site and the time intervals in which the data are collected. These statistics are not 

sensitive to the actual time steps of the data series. In addition, wind speed has significant 

seasonal and yearly variations. To accurately estimate power generation potential of a site, longterm measurement of wind speed at the proposed turbine hub height is needed. 

3
Table 2-2. Sample Monthly Average Wind Speed 

1-second 1-minute 10-minute 1-hour 

Average Wind Speed (m/sec) 5.69 5.69 5.69 5.69 

Standard Deviation (m/sec) 2.80 2.77 2.72 2.68 

3. Statistics of Wind Power Time-Series Data 

With wind speed fluctuating all the time, the power generated by a single turbine or the entire 

wind farm is expected to also vary continuously. Figure 3-1 shows the 3-second output power 

series from a single turbine during a 1-hour period. Figure 3-2 shows 1-minute average power 

from the same turbine for a 24-hour period. 

Single Turbine 3-second Output Power

0

100

200

300

400

500

600

12:30 12:40 12:50 13:00 13:10 13:20

Power (kW)

Figure 3-1. Single turbine output (3-second) 

Single Turbine 1-minute Average Output Power

0

100

200

300

400

500

600

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Power (kW)

Figure 3-2. Single turbine output (1-minute average) 

These two wind power profiles look similar despite the fact that one depicts 3-second 

instantaneous values over 1 hour and the other depicts 1-minute average values over a day. 

4
During the 1-hour period, the average wind power is 280 kW (with a standard deviation of 100 

kW). During the 24-hour period, the average wind power is 139 kW (with a standard deviation of 

82 kW). Compared to the wind speed variations during the same periods, the variability of power 

output is less than the variability of wind speed. Table 3-1 shows the coefficient of variance 

(COV, or standard deviation divided by average) for the wind speed, wind speed cubed, and 

wind power.1

 It can be seen that the COV of wind power series is less than that of wind speed 

cubed. The reason the COV of 1-minute average power and 1-minute average wind speed are the 

same is the zero and very low output values in the power data series (but not in the wind speed 

data series) that skew the series statistics. If the zero and low power (less than 10 kW) values are 

eliminated from the computation of COV, the COV for wind speed cubed and wind power would 

be 0.38 and 0.34, as shown in the row labeled modified 1-minute series. 

 

Table 3-1. COV of Wind Speed, Wind Speed Cubed, and Wind Power 

Wind Speed Wind SpeedCubed Wind Power 

3-second series 0.17 0.46 0.36 

1-minute series 0.21 0.59 0.59 

1-minute series (modified) 0.12 0.38 0.34 

Wind farms consist of tens and even hundreds of turbines. The combined output of all turbines 

shows less variability than a single turbine because of spatial diversity of wind resources. Even 

with a well-defined weather front moving over the wind farm, not all wind turbines within the 

wind farm will experience the same wind condition. Figures 3-3 and 3-4 plot 3-second and 1-

minute outputs of the single turbine and of the combined output of 11 turbines at the same site. 

Single Turbine & 11-Turbine 3-second Output Power

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

12:30 12:40 12:50 13:00 13:10 13:20

11-Turbine Power (kW)

0

100

200

300

400

500

600

700

800

900

1000

Single Turbine Power (kW)

11-turbine

1-turbine

Figure 3-3. Three-second output of a single turbine and 11 turbines 

 

1

 COV of wind speed cubed is used because available wind power is proportional to the wind speed cubed. 

5
Single Turbine & 11 Turbine 1-minute Average Output Power

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

11-turbine Power (kW)

0

100

200

300

400

500

600

700

800

900

Single Turbine Power (kW)

11-turbines

1-turbine

Figure 3-4. One-minute average power of a single turbine and 11 turbines 

Although the combined output of 11 turbines tracks the output of a single turbine closely, the 

smoothing of the overall output is still evident. During the 1-hour period, the peak output of a 

single turbine in Figures 3-1 and 3-3 is 518 kW, but the combined output of 11 turbines only has 

a peak of 4,383 kW, or 23% lower than 5,698 kW (11 x 518 kW if all 11 turbines reached the 

same peak output at the same instant). For the 24-hour period, peak 1-minute average power for 

the single turbine is 457 kW and 4,116 kW for 11 turbines. Peak outputs of the single turbine and 

the 11-turbine string occur at different time. Table 3-2 lists individual peak outputs of the 11 

turbines and the combined output of all 11 turbines. This phenomenon is very similar to the 

behaviors of individual loads, which also show the strong effect of noncoincidence. 

Table 3-2. Individual Turbine Peaks and Peak of Combined Output 

Turbine ID 3-second peak (kW) 1-minute peak (kW) 

#1 510 376 

#2 525 434 

#3 530 442 

#4 517 430 

#5 491 423 

#6 330 252 

#7 505 375 

#8 518 457 

#9 523 464 

#10 486 453 

#11 521 423 

ALL 4,383 4,116 

The COV of the combined output series is lower than the COV of the single-turbine output 

power series. Table 3-3 shows standard deviation values (σ) and COV of the 3-second and 1-

minute power series for a single turbine and 11 turbines. It also shows these values as percentage 

of the turbine and total capacity. This observation suggests that scaling up output of a single 

turbine to simulate output of a large wind farm will distort the true characteristics of wind power. 

The scaling process exaggerates the variability of wind power because it does not account for the 

wind resource diversity within the wind farm. This will be discussed in more detail later. 

6
Table 3-3. Output Variability of Single Turbine and 11 Turbines 

3-sec Data Series 1-min Data Series 

σ (kW) (% of capacity) COV σ (kW) (% of capacity) COV 

 Single turbine 100 18% 0.36 82 15% 0.59 

 11 turbines 588 10% 0.20 727 12% 0.48 

3.1 Fluctuations of Wind Power 

For a better gauge of the wind power fluctuations, wind power level step change values are 

examined at different time steps. The step changes are power level differences between two 

consecutive time steps. Because wind power level is not expected to change the same amount in 

the same direction for every time step, the step change values establish the boundaries of wind 

power ramping (rate of changes) behavior. Figure 3-5 is an example of 1-second wind power 

series in a 20-minute interval and its step change series. 

Single Turbine 3-second Output Power & Step Changes

-300

-200

-100

0

100

200

300

400

500

600

12:30 12:40 12:50 13:00 13:10 13:20

Power (kW)

Series1

Step Change

Figure 3-5. Single turbine output power and step changes 

The average value of wind power step changes during this hour is zero. This is expected because 

this power series starts and ends at about the same level, and therefore, positive step changes are 

offset by negative step changes during this period. In fact, given enough time, the average step 

change values of a wind power series of short time step are always zero or very small. Short-term 

wind power fluctuations are random. The positive and negative steps are about evenly distributed. 

The standard deviation value of step changes during this hour is 39 kW (or 7% of the turbine 

capacity). The maximum step changes values are 283 kW increase and –188 kW decrease, or 

about 51% and 34% of the single turbine capacity, respectively. 

Figure 3-6 shows the 11-tubine output and its step change series. It is obvious that step changes 

of the combined output are relatively small compared to the single-turbine output step changes. 

For the 11-turbine output step changes, its standard deviation value is 118 kW (or 2% of the total 

capacity). The maximum step changes are 427 kW increase and –421 kW decrease. They are 

higher than the extreme values of the single turbine output, but when expressed in terms of the 

total capacity, it is only about 7% of the combined capacity. This result demonstrates that the 

aggregation effect of wind turbines on the wind power is very significant, proving again the 

7
spatial diversity of wind resources. It also shows that short-term wind power fluctuations are 

relatively small. 

11-Turbine 3-second Output Power & Step Changes

-1000

0

1000

2000

3000

4000

5000

12:30 12:40 12:50 13:00 13:10 13:20

11-Turbine Power (kW)

11-turbine

Step Change

Figure 3-6. Eleven-turbine output power and step changes 

The effect of aggregating more turbines on the combined output is also evident on a much larger 

scale. The following data collected over 1 year illustrate the effect: 1-second wind power data 

from a 14-turbine string and a 138-turbine wind farm and combined wind power output data 

from several wind farms with more than 200 wind turbines2

. From the 1-second wind power data 

series, 1-minute, 10-minute, and 1-hour average wind power data series are assembled, and their 

step changes statistics are calculated. Figure 3-7 is an example of the profiles of 1-minute 

average wind power of these three data series. 

Example 1-minute Average Wind Power

0

20

40

60

80

100

120

140

160

180

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

(HH:MM)

Wind Power (MW)

14 Turbines

138 Turbines

200+ turbines

Figure 3-7. Output profiles of 138-turbine wind farm and 14-turbine cluster 

 

2

 The 14-turbine string is part of the 138-turbine wind farm. The 138-turbine wind farm is one of the wind farms 

whose outputs make up the combined wind power data. Nameplate capacities for these three wind installations are 

10.5 MW, 103.5 MW, and 242 MW. All wind farms are within the Buffalo Ridge region of southwest Minnesota. 

8
The average values of wind power step for any long time series are always very small because over time, 

the positive steps (amount of increasing wind power) tend to be balanced by the negative steps (amount of 

decreasing wind power). For a better picture of wind power step changes at different time steps, the 

average magnitude (absolute value) of step change values is calculated instead. Table 3-4 lists the average 

magnitude and the standard deviation (σ) values of wind power step changes from these three wind 

installations for different time steps. Also listed in the table are these values expressed in terms of the 

respective generating capacities to show their relative values. 

Table 3-4. Wind Power Step Change Average Magnitude and Standard Deviation Values 

14 turbines 61 turbines 138 turbines 250+ turbines 

(kW) (%) (kW) (%) (kW) (%) (kW) (%) 

1-second 

Average 41 0.4 172 0.2 148 0.1 189 0.1 

Std Deviation 56 0.5 203 0.3 203 0.2 257 0.1 

1-minute 

Average 130 1.2 612 0.8 494 0.5 730 0.3 

Std Deviation 225 2.1 1,038 1.3 849 0.8 1,486 0.6 

10-minute 

Average 329 3.1 1,658 2.1 2,243 2.2 3,713 1.5 

Std Deviation 548 5.2 2,750 3.5 3,810 3.7 6,418 2.7 

1-hour 

Average 736 7.0 3,732 4.7 6,582 6.4 12,755 5.3 

Std Deviation 1,124 10.7 5,932 7.5 10,032 9.7 19,213 7.9 

The effect of aggregation on wind power fluctuations can easily be seen from the statistics of 

wind power step changes. It is immediately clear that as the size of wind power generating 

capacity increases (with more wind turbines in the wind farm), the magnitude of step changes 

does not increase proportionally. And as a percentage of the total generating capacity, the 

magnitude of step change actually decreases when output from more turbines is included. As 

more and more wind generating capacities are installed, their combined output becomes less and 

less variable. 

Another way to show the effect of turbine aggregation is to scale up the output from a small 

group to match the output from a large group and compare their step change statistics. Table 3-5 

is an example of such an analysis. The output from the 14-turbine string was multiplied by 

factors of about 9.6 and 22.33 to make it the same as the output of the 138-turbine wind farm and 

the combined output of 250+ turbines. Compared to the actual fluctuations of outputs from large 

wind farms, the scaled-up wind power outputs would have fluctuations that are several times 

larger, especially for shorter time steps. For example, scaling 1-minute data from 14 turbines to 

match 138 turbines results in an average step size of 1,286 kW from the scaling, compared to 

494 kW from the actual 138 turbines. When 1-minute data are scaled to match 250+ turbines, the 

scaled average is 2,870 kW compared to 730 kW calculated from the actual recorded values of 

the Buffalo Ridge substation. Similar increase to the standard deviation of step changes can also 

be observed. With 1-minute data, scaling output of 14 turbines to match that of 138 turbines 

results in a scaled standard deviation of 2,218 kW, compared to actual standard deviation of 849 

kW. When we observe the impact of scaling up to 250 turbines, we see that the scaled standard 

deviation is 5,144 kW, compared to the actual value of 1,486 kW. This analysis shows that it is 

 

3

 This factor changes with time steps as peak average outputs between small and large wind farms do not maintain 

the same ratio across the four time steps. 

9
not advisable to simply multiply the output of one turbine or a group of turbines to simulate the 

total output of a larger wind power plant. The simple scaling operation will exaggerate the 

fluctuations of wind power and make fluctuation characteristics worse than the actual 

performance. 

Table 3-5. Step Changes Statistics of Scaled-Up Wind Power Data 

14 turbines X 9.6 138 turbines (actual) 14 turbines X 22.3 250+turbines (actual)

(kW) (%) (kW) (%) (kW) (%) (kW) (%) 

1-second 

Average 413 0.4 148 0.1 932 0.4 189 0.1 

Std Deviation 552 0.5 203 0.2 1,274 0.5 257 0.1 

1-minute 

Average 1,286 1.2 494 0.5 2,870 1.2 730 0.3 

Std Deviation 2,218 2.1 849 0.8 5,144 2.1 1,486 0.6 

10-minute 

Average 3,240 3.1 2,243 2.2 7,513 3.1 3,713 1.5 

Std Deviation 5,400 5.2 3,810 3.7 12,521 5.2 6,418 2.7 

1-hour 

Average 7,255 0.7 6,582 6.4 16,824 7.0 12,755 5.3 

Std Deviation 11,077 10.7 10,032 9.7 25,685 10.7 19,213 7.9 

3.2 Distribution of Step Changes 

The small standard deviation values of wind power step changes suggest that they are 

concentrated within narrow ranges. An example of step change distribution is given in Figure 3-8, 

which plots the relative frequencies of 1-second step changes for the outputs from 14-turbine, 

138-turbine, and 250+-turbine wind installations. Although it is not easy to see, calculations 

show that practically all step changes for the 1-second time step are within ±3σ. 

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

-800 -600 -400 -200 0 200 400 600 800

1-Second Wind Power Step Changes (kW)

Relative Frequency

14 turbines 138 turbines 200+ turbines

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

-80 -64 -48 -32 -16 0 16 32 48 64 80

1-Hour Wind Power Step Changes (MW)

Relative Frequency

14 turbines 138 turbines 200+ turbines

Figure 3-8. Example wind power step change distribution 

More information on wind power step change distribution is given in Table 3-6, which lists the 

cumulative frequencies of wind power step changes in terms of its standard deviation values (σ). 

For the 138-turbine wind farm (total generating capacity 103.5 MW), 98.67% of the 1-minute 

step changes are within ±3σ (±2.6 MW), or about 2.5% of farm capacity. For hourly step 

changes, 98.34% of step changes are within ±3σ (±30 MW), or about 29% of farm capacity. 

10
Table 3-6. Cumulative Frequency of Wind Power Step Changes 

14 turbines 

1-second 

(σ=42kW) 

1-minute 

(σ=225kW) 

10-minute 

(σ=548kW) 

1-hour 

(σ=1.1MW) 

 ±1σ 0.84862 0.85808 0.83122 0.78389 

 ±2σ 0.94342 0.95387 0.95466 0.94628 

 ±3σ 0.97876 0.98337 0.98447 0.98307 

 ±5σ 0.99727 0.99715 0.99677 0.99831 

> ±5σ 0.00273 0.00285 0.00323 0.00169 

138 turbines 

1-second 

(σ=154kW) 

1-minute 

(σ=849kW) 

10-minute 

(σ=3.8MW) 

1-hour 

(σ=10.0MW) 

 ±1σ 0.83681 0.83178 0.83543 0.78813 

 ±2σ 0.93999 0.95598 0.95479 0.94462 

 ±3σ 0.97842 0.98666 0.98365 0.98339 

 ±5σ 0.99693 0.99795 0.99637 0.99823 

> ±5σ 0.00307 0.00205 0.00363 0.00177 

250+ turbines 

1-second 

(σ=0.2MW) 

1-minute 

(σ=1.5MW) 

10-minute 

(σ=6.4MW) 

1-hour 

(σ=19.2MW) 

 ±1σ 0.84126 0.87624 0.84122 0.78629 

 ±2σ 0.96155 0.97424 0.95850 0.94601 

 ±3σ 0.99005 0.99253 0.98495 0.98419 

 ±5σ 0.99896 0.99816 0.99583 0.99803 

> ±5σ 0.00104 0.00184 0.00417 0.00197 

The table also shows that extreme step changes (those step changes with magnitude greater than 

5σ) increase as time step lengthens. It is expected because wind can experience bigger changes in 

longer time. For shorter time steps such as 1-second and 1-minute, the σ values are small; even 

for step changes at 5σ level, they are still only about 2% to 10% of the plant capacity. Further 

investigation suggests that none of the 1-second step change that exceeds 10% of plant capacity 

was wind speed related. For data series of longer time steps, it is not easy to determine the cause 

of large step changes. Many of these large changes were the results of abnormal network 

conditions such as forced outages within and outside of the wind farm. It is extremely unlikely 

that all of the turbines (or even a large number of turbines) in a large wind plant would develop a 

fault at the same time, causing a large power-level change. 

3.3 Ramping Rates of Wind Power 

Step change statistics define the outer boundary of the wind power fluctuations. The ramping 

(rate of change) of wind power level in a given time interval (e.g., 10 minutes or 1 hour) is also 

of interest to system operators. Wind power level changes continuously. For example, with a 1-

second wind power data series, it is calculated that the average duration for wind power to keep 

increasing or decreasing is only 2.2 seconds (with a standard deviation of 44 seconds). With a 1-

minute data series, the average duration is 2.4 minutes (standard deviation 8.4 minutes) before 

the wind power level changes its momentum (i.e., from increasing to decreasing, or vice versa).4

 

4

 Calculated from output power data of the 14-turbine string. For a 138-turbine wind farm, the average durations are 

2.2 seconds (standard deviation 37 seconds) with 1-second data series and 2.8 minutes (standard deviation 7.3 

minutes) with 1-minute data series. 

11
To analyze the ramping behavior of wind power, the apparent rate of changes of wind power 

over a fixed time interval is calculated. In this report, the apparent rate of change is the slope of 

the straight line that fits the wind power data points in a 10-minute interval. Table 3-7 lists the 

resulting ramping statistics. 

Table 3-7. Wind Power 10-Minute Ramping Characteristics 

Average Standard Deviation Max (+) Max(–) 

(kW/min) (%/min) (kW/min) (%/min) (MW/min) (%/min) (MW/min) (%/min) 

14-turbine 47 0.45% 78 0.74% 1.2 11% –1.4 -13% 

138-turbine 269 0.26% 465 0.45% 7.0 7% –12.2 -12% 

250+ turbine 438 0.18% 811 0.34% 11.3 5% –29.0 -12% 

The average and standard deviation values of 10-minute interval ramping rates for small and 

large wind farms are small. In terms of wind farm generating capacity, average ramping rates 

range from 0.45% per minute for a small wind installation to 0.18% per minute for a large wind 

installation. The maximum ramping rates, however, appear to be large. The maximum downward 

ramping rates for large wind farms exceed 12% of wind farm capacity per minute. To gain a 

better picture of wind power extreme ramping behavior, the distribution of all ramping rates are 

calculated (Table 3-8). 

Table 3-8. Distribution of Wind Power 10-Minute Ramping Rates 

< ±1σ

(78 kW/min) 

< ±2σ

(156 kW/min)

< ±3σ

(234 kW/min)

< ±4σ

(312 kW/min)

< ±5σ

(390 kW/min) 

> 5%/min 

(525 kW/min) 

 14-turbine 0.82476 0.95146 0.98305 0.99299 0.99673 0.00122 (64) 

< ±1σ

(0.5 MW/min) 

< ±2σ

(1.0 MW/min)

< ±3σ

(1.5 MW/min)

< ±4σ

(2.0 MW/min)

< ±5σ

(2.5 MW/min) 

> 5%/min 

(5.2 MW/min) 

 138-turbine 0.84047 0.95742 0.98450 0.99304 0.99619 0.00040 (21) 

< ±1σ

(0.8 MW/min) 

< ±2σ

(1.6 MW/min)

< ±3σ

(2.4 MW/min)

< ±4σ

(3.2 MW/min)

< ±5σ

(4.0 MW/min) 

> 5%/min 

(12.1 MW/min) 

 250+-turbine 0.86471 0.96779 0.98856 0.99438 0.99649 0.00044 (23) 

It can be seen that high ramping events (wind power ramping faster than 5% of plant capacity 

per minute) decrease as the number of turbines in the wind installations increases. When the 

number of wind turbines increases (larger wind farms), the distribution of ramping rates becomes 

more tightly bundled (i.e., more smaller ramping values). The last column lists the relative 

frequencies and the actual numbers of occurrences (over 1 year) of large ramping rates that are 

over 5% of capacity per minute. Further examination of the data indicates that not all of those 

very large ramping rates (over 5% capacity per minute) are the results of wind speed changes. 

Fifty-one out of the 64 occurrences of over 5%/min ramping rates from the 14-turbine data series 

are wind-speed related.5

 For the 138-turbine data series, only 6 out of the 21 occurrences are 

wind-speed related; 3 are up ramping and 3 are down ramping. For the 250-plus-turbine wind 

data series, only 1 such occurrence (down ramping) can be related to wind speed change.

 

5

 A corresponding change of wind speed was observed during those events. The causes of other events of high 

ramping rates were not clear. Some pf them are the results of forced and scheduled outages and curtailment. 

12
3.4 Correlation between Wind Farms 

Despite their close proximity, instantaneous outputs from individual turbines of a large wind 

farm are not synchronized. Physical separations and differences of local terrains cause wind 

speeds at each turbine to vary. Output power from individual turbines will vary as each turbine is 

likely operating at different states (e.g., outputs from some turbines are increasing while others 

are decreasing). The diverse outputs from many turbines will make the aggregate output from 

large numbers of turbines less volatile, especially in short time frames. However, similar wind 

conditions will eventually sweep over the entire wind farm. During longer time frames, outputs 

of individual turbines or groups of turbines should be similar and are likely to move in the same 

directions. Table 3-9 gives an example of output correlation for wind power from individual 

turbines in a small wind farm. 

The numbers in Table 3-9 are correlation coefficients of individual wind turbine output power 

step changes. The data used in the table are daily 3-second, 1-minute, 10-minute, and 1-hour 

output series from a 12-turbine string installed along a ridge line. The turbines are identified as 

#6 through #17 (data from turbine #11 are not available) in column and row headings in Table 3-

9. For 3-second data series, it can be seen that even outputs from adjacent turbines have very 

poor correlation. When turbines are farther apart, their outputs actually move in opposite 

directions at the same time (numbers in parentheses denote negative correlation coefficients). As 

time interval increases, the outputs from those turbines are more in sync. The high correlation 

coefficients for the 1-hour data series suggest that hourly outputs from these turbines are 

practically in lockstep. 

Large wind farms that are hundreds of kilometers apart experience significantly more spatial 

variations of wind resources, and their outputs should be less correlated than those among 

different turbine groups within a single plant. Their short time frame power fluctuations should 

be independent, and even their longer time frame power fluctuations are not expected to be 

correlated. 

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

3 40 200 290 490 1580

Distance between Wind Farms (km)

Correlation Coefficients

1 hour

10 minute

1 minute

1-second

Figure 3-9. Wind power correlation as a function of distance 

To examine the effect of separation on wind power output correlation, the correlation 

coefficients of output step changes from five wind farms for different time steps are calculated. 

Figure 3-9 plots the correlation coefficients vs. distances between wind farms. Wind power data 

from wind farms in the Midwest and Texas are used. The distances between these wind farms 

13
range from about 3 km (two turbine strings within the same wind farm) to about 1,580 km 

(between Midwest and Texas wind farms). 

Table 3-9. Individual Turbine Output Correlation Coefficients 

3-second #7 #8 #9 #10 #12 #13 #14 #15 #16 #17 

#6 0.019 0.002 (0.014) 0.013 (0.027) (0.002) (0.002) (0.009) (0.009) 0.015 

#7 0.000 0.012 (0.018) (0.003) 0.016 (0.005) (0.003) (0.007) (0.002) 

#8 0.023 0.005 (0.018) 0.009 (0.006) 0.001 0.015 (0.001) 

#9 0.042 (0.011) (0.010) (0.001) 0.007 (0.010) 0.008 

#10 0.003 (0.008) 0.009 (0.002) 0.019 0.006 

#12 0.014 0.001 0.013 (0.008) (0.018) 

#13 (0.023) 0.003 (0.010) 0.019 

#14 0.012 0.011 (0.005) 

#15 (0.018) (0.012) 

#16 (0.009) 

1-minute #7 #8 #9 #10 #12 #13 #14 #15 #16 #17 

#6 0.492 0.188 0.055 0.043 0.053 0.071 0.055 0.014 0.012 (0.005) 

#7 0.372 0.116 0.064 0.018 0.067 0.096 0.020 0.043 (0.001) 

#8 0.459 0.285 0.102 0.111 0.141 0.112 0.072 0.008 

#9 0.608 0.163 0.126 0.129 0.117 0.055 0.002 

#10 0.164 0.108 0.114 0.124 0.016 0.013 

#12 0.003 0.128 0.097 0.018 0.047 

#13 0.195 0.109 0.152 0.092 

#14 0.360 0.234 0.223 

#15 0.501 0.300 

#16 0.493 

10-minute #7 #8 #9 #10 #12 #13 #14 #15 #16 #17 

#6 0.767 0.619 0.555 0.485 0.425 0.362 0.418 0.309 0.400 0.377 

#7 0.842 0.659 0.558 0.423 0.373 0.462 0.357 0.493 0.502 

#8 0.835 0.685 0.572 0.441 0.449 0.406 0.519 0.486 

#9 0.903 0.671 0.443 0.568 0.530 0.571 0.514 

#10 0.750 0.497 0.624 0.595 0.619 0.550 

#12 0.538 0.578 0.570 0.597 0.433 

#13 0.691 0.596 0.615 0.553 

#14 0.769 0.716 0.666 

#15 0.831 0.776 

#16 0.848 

1-hour #7 #8 #9 #10 #12 #13 #14 #15 #16 #17 

#6 0.953 0.915 0.922 0.918 0.889 0.794 0.910 0.940 0.940 0.916 

#7 0.968 0.965 0.966 0.946 0.791 0.937 0.940 0.924 0.882 

#8 0.993 0.992 0.972 0.802 0.947 0.942 0.918 0.881 

#9 0.996 0.964 0.777 0.933 0.936 0.921 0.889 

#10 0.966 0.787 0.938 0.943 0.921 0.885 

#12 0.816 0.946 0.926 0.889 0.853 

#13 0.856 0.885 0.882 0.839 

#14 0.965 0.937 0.919 

#15 0.987 0.967 

#16 0.980 

Figure 3-9 clearly displays the temporal and spatial relationships of wind power. For very short 

time intervals (1 second to 1 minute), power outputs from all wind farms are not correlated, 

regardless of the distances that separate them. As time interval increases, outputs from nearby 

wind installations begin to show higher and higher correlation while outputs from distant wind 

14
farms are still independent from each other. This implies that the combined power output of all 

wind farms should have a lower variability than either site on its own. 

3.5 Aggregating Spatially Diverse Wind Plant Output 

Wind power fluctuations are a function of wind speed variations, which are similar everywhere. 

Consequently wind power step change characteristics are very similar for wind farms from 

different regions. When the average and standard deviations of step changes are expressed in 

terms of wind farm generating capacity, they are remarkably close in value. Because the wind 

power step changes are random and independent, the combined wind power from more turbines 

and wind farms will be less volatile. Table 3-10 lists step change statistics (absolute average and 

standard deviation) from other large wind farms identified by the numbers of turbines and total 

nameplate capacity. Six wind farms are included in Table 3-10. The 138-turbine and 151-turbine 

wind farms are located in the Midwest. The distance between them is about 200 km. The next 

four wind farms are in Texas. The distances between these four wind farms range from 40 km to 

490 km. The distances between wind farms in the Midwest and Texas range from 1,200 km to 

1,590 km. The number in parentheses next to a quantity is its value expressed as a percentage of 

the wind farm generating capacity. 

Table 3-10. Effect of Aggregating Wind Farms on Step Changes 

1-second 1-minute 10-minute 1-hour 

Avg (kW) Stdev (kW) Avg (kW) Stdev (kW) Avg (MW) Stdev (MW) Avg (MW) Stdev (MW)

138-turbine (104 MW) 87 (0.08) 154 (0.14) 481 (0.5) 657 (0.6) 2.2 (2.1) 2.9 (2.8) 6.7 (6.5) 7.7 (7.4) 

151-turbine (113 MW) 66 (0.06) 121 (0.11) 457 (0.4) 748 (0.7) 2.2 (1.9) 3.2 (2.8) 6.6 (5.8) 7.8 (6.9) 

2 combined (217 MW) 120 (0.06) 201 (0.09) 709 (0.3) 976 (0.5) 3.4 (1.6) 4.2 (1.9) 10.7 (4.9) 11.2 (5.2) 

112-turbine (35 MW) 22 (0.06) 37 (0.11) 272 (0.8) 473 (1.4) 0.7 (2.1) 1.2 (3.4) 2.0 (5.7) 2.8 (8.0) 

61-turbine (79 MW) 107 (0.13) 119 (0.15) 606 (0.8) 838 (1.1) 1.6 (2.1) 2.2 (2.8) 3.7 (4.7) 4.6 (5.8) 

125-turbine (83 MW) 87 (0.11) 100 (0.12) 401 (0.5) 694 (0.8) 1.6 (1.9) 2.6 (3.2) 4.0 (4.8) 5.4 (6.5) 

100-turbine (150 MW) 85 (0.06) 133 (0.09) 821 (0.5) 1194 (0.8) 3.2 (2.1) 4.7 (3.1) 9.1 (6.1) 11.7 (7.8) 

4 combined (347 MW) 172 (0.05) 190 (0.05) 1241 (0.4) 1531 (0.4) 4.5 (1.3) 5.5 (1.6) 12.9 (3.7) 13.7 (4.0) 

All combined (564 MW) 219 (0.04) 224 (0.04) 1508 (0.3) 1767 (0.3) 6.1 (1.1) 6.5 (1.2) 17.7 (3.1) 16.5 (2.9) 

Two points are easily noticeable from Table 3-10. First, the step change statistics from the data 

series of the same time step are consistent for all wind farms. Second, large plants (with more 

turbines) tend to fluctuate less (relative to total installed capacity) in their outputs than do the 

smaller plants. When outputs from two or more wind farms are combined, the resulting power 

series are relatively less variable than individual wind farms. The average and standard deviation 

values of step changes as a percentage of total capacity are always smaller than individual wind 

farms. This relationship holds true for all wind farms. It appears that output fluctuations of wind 

power plants are determined more by the size of the plant. The types of turbines and locations of 

the plants have less influence. 

Aggregating outputs of many turbines also reduces the ramping rates. Table 3-11 lists the 

statistics of 10-minute ramping rates for these wind farms and their combined outputs. It is very 

clear that large wind farms and aggregated wind power will have lower ramping rates (as a 

percentage of wind farm capacity). Aggregated wind power output also has less extreme ramping 

rates. For example, the output of all wind farms combined for this report (the last row of Table 3-

15
11) has an extreme ramping rate of about 15 MW/min, or about 2.7% of capacity (total) per 

minute—far less than the extreme ramping rates of 5% per minute seen from individual wind 

farms. In fact, the data show that there are only four occurrences of ramping rates exceeding 

2.5%/min during a 12-month period. 

Table 3-11. Effect of Aggregating Wind Farms on 10-Minute Ramping Rates 

Average Standard Deviation Max (+) Max(–) 

(kW/min) (%/min) (kW/min) (%/min) (MW/min) (MW/min) 

138-turbine (104 MW) 269 0.26% 465 0.45% 7.0 –12.2 

151-turbine (113 MW) 248 0.22% 429 0.39% 8.3 –12.9 

2 combined (217 MW) 394 0.18% 635 0.29% 9.3 –12.9 

112-turbine (35 MW) 100 0.29% 171 0.49% 3.5 –4.4 

61-turbine (79 MW) 241 0.30% 402 0.51% 7.5 –5.7 

125-turbine (83 MW) 212 0.26% 403 0.49% 8.8 –6.4 

100-turbine (150 MW) 397 0.26% 713 0.48% 12.8 –15.6 

4 combined (347 MW) 576 0.175 916 0.26% 12.0 –16.1 

All combined (564 MW) 752 0.13% 1120 0.20% 15.0 –15.3 

The result is significant to system planning and operating. As more and more wind generating 

plants over a wider area are integrated into the grid, spatial diversity of the wind resources will 

make the overall wind power much less volatile than the output from any individual wind farm. 

It also points out that when studying high wind power penetration scenarios, it is imperative to 

make sure the wind resource spatial diversity is considered in assembling the wind power series. 

3.6 Rare Situations 

The analyses of 1-second, 1-minute, and hourly wind power series show that the majority of 

wind power fluctuations are limited in narrow ranges and their average values are relatively 

small. However, with strong winds sweeping across the plant, the output power can increase 

quickly, as indicated by the extreme values of wind power step changes and ramping rates. If the 

ramping-up rate needs to be controlled during such an event, the plant operators can change the 

ramping-up rate by temporarily stopping some of the turbines. 

Another concern about big wind power change is that power from large wind plants will drop 

from full capacity to zero very quickly. It has been surmised that during certain weather events, 

the continuously increasing wind speed will be over the turbine cut-off speed at some point and 

cause all the turbines to shut down after they have been operating at full capacity. Such an event 

may burden the power system considerably as it tries to bring up generation reserves to 

compensate for the sudden loss of a large amount of wind power. Grid disturbances or equipment 

malfunctions can certainly cause the entire wind power plant to trip off line in a very short time. 

However, other than such forced outage events, the data collected so far have not shown any 

evidence of high wind causing all turbines within a plant to reach cut-off state at the same time. 

High wind will exceed wind turbine cut-off speed and cause individual turbines to shut down, 

but not the entire wind power plant. 

16
0

10

20

30

40

50

60

70

80

90

100

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Time (hh:mm)

Power (MW)

0

2

4

6

8

10

12

14

16

18

20

Wind Speed (m/sec)

30-turbine 39-turbine 14-turbine

55-turbine Total Wind Speed

Figure 3-10. Example of wind power during high wind period 

Figure 3-10 is an example of strong winds that exceeded turbine cut-off speed and caused output 

to drop. In Figure 3-10, 1-minute average powers from four turbine strings6 and the total wind 

farm are plotted along with the reference wind speed at the site7 for a 24-hour period. It shows 

that the plant had been generating at full capacity (about 100 MW) since 3:00 a.m. with 

continuously increasing wind. Shortly after 12:00 noon, the output power began to drop, and at 

about 15:00 when wind speed appeared to be at peak, the output power dropped to the lowest 

point for the day. After 15:00, wind speed began to decrease, but the output power of the wind 

plant began to increase (with some fluctuations closely related to the recorded wind speed). At 

about 17:00, the plant finally generated full power and remained at that level for the rest of the 

day. 

The resolution in Figure 3-10 does not provide enough information about plant operations during 

a high-wind period. Figure 3-11 plots 1-second output power from the four turbine groups during 

the 3-hour period from 12:00 noon to 15:00. The step changes in the turbine group output traces 

are about 750 kW in magnitude (average) and within 2 seconds (2 data points). The fact that 

there are down-steps and up-steps during this period provides further evidence of wind 

diversities within the plant. These step changes show the shutdown of individual turbines during 

this high-wind period. They also show that all turbines do not shut down at the same instant, 

although some did appear to occur in rapid succession. The largest 1-second power drop during 

this 3-hour period was 1.4 MW (two turbines shut down at the same time), but the average 

ramping was only 293 kW/s (less than 0.3% of total capacity per second). Using 1-minute power 

data for this period, the average ramping rate was only 586 kW/min with the magnitude of the 

maximum ramping at 2.2 MW/min. Figure 3-10 offers clear evidence that output from a large 

plant does not drop from full power to zero rapidly because of a very strong wind that exceeds 

turbine cut-off wind speed. Physical separation and local terrain will cause variations in wind 

 

6

 These four turbine strings of 14, 30, 39, and 5 turbines make up the wind farm. Output data from each string are 

recorded separately. 

7

 The wind speeds were recorded by an anemometer mounted on a pole about 4 m above ground inside a fenced area. 

Although not hub-height wind speeds, they nevertheless give good indications of the wind conditions at the site. 

17
speed at individual wind turbines, and therefore all turbines will not be at the same operating 

status. 

0

5

10

15

20

25

30

35

40

45

12:0012:1012:2012:3012:4012:5013:0013:1013:2013:3013:4013:5014:0014:1014:2014:3014:4014:50

Time (hh:mm)

Output Power (MW)

30-turbine 39-turbine

14-turbine 55-turbine

Figure 3-11. Detail of turbine cut-off with 1-second power data series 

3.7 Generating Synthetic Wind Plant Output Time-Series 

The intermittent nature of wind power and the limited control options on wind power plants 

make it difficult to study the wind power impact on system operations. The results will vary with 

each specific wind power used in the study. One of the accepted approaches to this problem is to 

perform a Monte Carlo type (probabilistic) simulation, in which a large number of wind power 

conditions are used. The statistics of all the results are then compiled and evaluated. For this 

method to be valid, a large number of independent wind power time series is required. With a 

large data requirement and the limited availability of actual wind power data series, it may be 

necessary to synthesize wind power data series for the probabilistic type of analysis. 

A commonly used method for synthesizing wind power data series is the Markov method.8 This 

method uses the probability distribution calculated from available actual wind power data series 

and a random number generator to create a new time series. A state transition matrix is first 

calculated from the available wind power time series. Table 3-12 is an example of a state 

transition matrix derived from hourly wind power data series. Only 10 states are used in Table 3-

12 for clarity (they easily display on the page). The numbers in the matrix are probabilities that 

wind power will change from one hour to the next. For example, if the wind power for the 

current hour is at 30% of the wind farm capacity (within the range of 20% and 30%), the 

probability that it will stay at the same level is 0.4252 while the probability that it will jump to 

70% is 0.0089. The probabilities across any row should add up to 1. A similar matrix can be 

constructed for other time series and with more states (power levels) for better resolution (e.g., 

0.1% for 1-minute data series). 

 

8

 McNerney, G. and Veers, P., “A Markov Method for simulating Non-Gaussian Wind Speed Time Series,” Sandia 

National Laboratory, SAND84-1277, January 1985. 

18
Table 3-12. State Transition Matrix with Hourly Average Wind Power Data 

10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

10% 0.8567 0.1199 0.0167 0.0043 0.0019 0.0005 0 0 0 0 

20% 0.2196 0.4947 0.1975 0.0697 0.0115 0.0026 0.0018 0.0026 0 0 

30% 0.0446 0.2418 0.4252 0.2091 0.0456 0.0228 0.0089 0.0020 0 0 

40% 0.0051 0.0803 0.2764 0.3439 0.1771 0.0764 0.0306 0.0089 0.0013 0 

50% 0.0033 0.0130 0.1317 0.2114 0.3171 0.1805 0.0959 0.0309 0.0130 0.0033 

60% 0 0.0089 0.0302 0.1032 0.2402 0.3132 0.1833 0.0890 0.0249 0.0071 

70% 0 0.0038 0.0076 0.0440 0.1033 0.2447 0.2849 0.2237 0.0803 0.0076 

80% 0 0 0.0073 0.0054 0.0345 0.0726 0.2341 0.3593 0.2523 0.0345 

90% 0 0 0 0.0026 0.0128 0.0217 0.0562 0.1801 0.6066 0.1201 

100% 0 0 0 0 0 0.0028 0.0028 0.0212 0.1487 0.8244 

With an initial state (power level) Pi, the next state Pj in the time series is created by first 

generating a random number r that is uniformly distributed between 0 and 1, then the next state j 

is such that ∑∑ , where s =

−

=

<≤

j

j

ij

j

j

ij srs

0

1

0

ij is the probability (row i, column j) in the state transition 

matrix. Here is a numeric example: suppose the initial state is 50% and a random number is 

0.473; using Table 3-12, the next state will be 50% ( ). The same process 

is repeated until the end of the desired time series. 

∑ ∑

=

=

=

=

<≤

50

10

50

40

10

50 473.0

j

j

j

j

j

j s s

With different initial states and random number series, any number of wind power time series 

can be synthesized. The resulting time series will have average and standard deviation values 

that are similar to the original data series used to construct the state transition matrix. Figure 3-12 

shows the probability distribution of actual hourly wind power series (8,760 data points) and the 

synthesized hourly time series (87,600 data points). 

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0 10 20 30 40 50 60 70 80 90 10

Wind Power (MW)

Relative Frequency

Actual

Synthized

0

Figure 3-12. Probability distribution of actual and synthesized wind power series 

Several issues with this method require additional attention. First and most critical, the process of 

assembling state transition matrix destroys the chronological information of the actual wind 

power time series. For example, if the actual wind power output exhibits a diurnal or seasonal 

pattern, the synthesizing process will not be able to capture any of it. If it is determined that the 

wind resources have distinguishable diurnal or seasonal patterns, additional state transition 

matrices will need to be constructed, and the process of synthesizing wind power series becomes 

19
time-dependent. Second, the step change and ramping statistics of the resulting data series are 

only representative to the periods when actual data are available to compute the state transition 

matrix. Unless very long series are produced, the synthesized series may miss few but significant 

extreme conditions that can occur. Third, the total energy of the synthesized time series may not 

match the desired level. If total energy is important to the study, an additional process needs to 

be performed (carefully) to reconcile the differences. And finally, the computational effort 

increases significantly when a finer resolution of states is desired. 

There is another approach to produce a time series of output power from many wind power 

plants within a large area. This approach has been used in a study sponsored by the Utility Wind 

Interest Group (UWIG).9

 Using sophisticated meteorological models and historical 

meteorological data, it can re-create the weather conditions for the region of interest and for the 

selected period. The wind speed time series are then extracted from the model and converted to 

wind power time series. This method has several advantages. Outputs from any number of wind 

power plants can be produced at high resolution. It re-creates the weather conditions that drove 

the wind turbines, and as a result, produces realistic wind power time series. It does not depend 

on probability to capture extreme wind conditions, which may not be representative of the actual 

situations. However, this approach required substantial computational resources. 

4. Statistics of Utility Load Data 

Utility system load generally has a well-defined and predictable daily pattern because it 

corresponds to a daily cycle of routine human activities. However, this predictable pattern does 

not mean the utility load is deterministic at any given moment. Utility load is the aggregation of 

electricity usages by all of its customers. Fluctuations of any single load are usually random 

process (e.g., turning an appliance or a building air conditioning system on and off). Despite a 

clear daily profile, short time frame load changes are still random. Figure 4-1 is a sample of 

high-resolution utility daily load (4-second data rate) for three consecutive days. The overall 

daily profile is almost identical. It is also clear that from one time step to the next (4-second 

interval in this graph), the load can experience large and random changes. The utility load can 

also differ significantly from one day to the next at the same hour and the same minute (over 400 

MW in this example). Seventeen days of 4-second high-resolution load data of Xcel Energy are 

available (April 12 to April 28, 2004). 

Table 4-1 lists the step change statistics of utility load during the 17-day period for different time 

scales. Also included in the table are step change statistics of wind power recorded at Buffalo 

Ridge substation (total 240 MW wind generating capacity) during the same 17 days. The 

variance (standard deviation value of step changes) of wind power is about one or two 

magnitudes smaller than the variance of load. This is expected because wind power is much 

smaller than the load (The peak load during this period is 6,011 MW, and the peak wind output is 

185 MW). However, when both are expressed in terms of their respective peak values, the 

differences become less dramatic. The 4-second and 1-minute step changes for the load and wind 

power are comparable when they are expressed as a percentage of their respective peak values. 

Higher variability of wind power begins to show only in a longer time frame. The average 

 

9 EnerNex Corp. and Wind Logics, Inc. (2004). Wind Integration Study – Final Report. Xcel Energy and 

the Minnesota Department of Commerce

20
magnitude of hourly wind power step changed is 7.1% of its peak value while the average 

magnitude of hourly load step changes is only 2.5% of the peak load. 

3000

3500

4000

4500

5000

5500

6000

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Load (MW)

Figure 4-1. Sample of 3-day high-resolution (4-second) utility load data from Xcel 

Figure 4-2 shows the distribution of load and wind power step changes for the four time steps 

whose standard deviation values are listed in Table 4-1 (4-second, 1-minute, 10-minute, and 1-

hour). The hourly load and wind step change distribution plots are based on 3 years of hourly 

data. It can be seen that in general, the short-time changes of load are confined in a narrower 

range than that of the wind power, but the situation reverses in a longer-time frame. For example, 

84% of 4-second load step changes are within ±1σ (±10.8 MW), and only 80% of the wind 

power step changes are within ±1σ (±0.3 MW). However, while 80% of hourly wind power step 

changes are within ±1σ (±18.6 MW), only 71% of hourly load step changes are within ±1σ

(±218.2 MW). It can also be observed that about 99% of all wind power and load step changes 

are within their respective ±3σ ranges. 

Table 4-1. Variance of Load and Wind Power in Different Time Scales 

Load Step Changes Wind Power Step Changes 

Avg (MW) % of peak Stdev (MW) % of peak Avg (MW) % of peak Stdev (MW) % of peak 

4-second 6.6 0.1 10.8 0.2 0.2 0.1 0.3 0.2 

1-minute 8.8 0.1 19.2 0.3 0.8 0.4 1.4 0.8 

10-minute 35.1 0.6 48.0 0.8 4.0 2.1 6.3 3.4 

1-hour 152.4 2.5 218.2 3.7 13.2 7.1 18.7 10.1 

To see how much step changes can be expected, the absolute value of step changes similar to 

those shown in Section 2 is computed for load and wind power for different time intervals, and 

the results are listed in Table 4-2. It can be seen that in 4 seconds, the average difference for load 

is 6.6 MW, or 0.1% of its peak. It is very similar to that of wind power changes in 4 seconds (0.2 

MW, or 0.1% of its peak). 

21
4-second Load Step Change Distribution

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

-0.83% -0.67% -0.50% -0.33% -0.17% 0.00% 0.17% 0.33% 0.50% 0.67% 0.83%

% of Peak Load

Relative Frequency

± 1σ 84%

± 2σ 97%

± 3σ 99.2%

4-second Wind Power Step Change Distribution

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

-0.83% -0.56% -0.28% 0.00% 0.28% 0.56% 0.83%

% of Peak Wind Power

Relative Frequency

± 1σ 80%

± 2σ 94%

± 3σ 98.8%

1-minute Load Step Change Distribution

0.00

0.02

0.04

0.06

0.08

0.10

0.12

-2.0% -1.5% -1.0% -0.5% 0.0% 0.5% 1.0% 1.5% 2.0%

% of Peak Load

Relative Frequency

±1σ 90%

±2σ 98%

±3σ 99.7%

1-minute Wind Power Step Change Distribution

0.00

0.05

0.10

0.15

0.20

0.25

-2.0% -1.5% -1.0% -0.5% 0.0% 0.5% 1.0% 1.5% 2.0%

% of Peak Wind Power

Relative Frequency

±1σ 88%

±2σ 97%

±3σ 98.6%

10-minute Load Step Change Distribution

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

-10.0% -7.5% -5.0% -2.5% 0.0% 2.5% 5.0% 7.5% 10.0%

% of Peak Load

Relative Frequency

±1σ 76%

±2σ 95%

±3σ 99%

10-minute Wind Power Step Change Distribution

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

-10.0% -7.5% -5.0% -2.5% 0.0% 2.5% 5.0% 7.5% 10.0%

% of Peak Wind Power

Relative Frequency

±1σ 80% 

±2σ 95%

±3σ 98%

Hourly Load Step Change Distribution

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

-33.3% -26.7% -20.0% -13.3% -6.7% 0.0% 6.7% 13.3% 20.0% 26.7% 33.3%

% of Peak Load

Relative Frequency

±2σ 92%

±1σ 71%

±3σ 98.6%

Hourly Wind Power Step Change Distribution

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

-33.8% -26.3% -18.8% -11.3% -3.8% 3.8% 11.3% 18.8% 26.3% 33.8%

% of Peak Wind Power

Relative Frequency

±2σ 95%

±1σ 80%

±3σ 99%

Figure 4-2. Distribution of load and wind power step changes 

22
Table 4-2. Average of Absolute Load and Wind Power Step Changes 

Load Step Changes Wind Power Step Changes 

Average (MW) % of peak Average (MW) % of peak 

4-second 6.6 0.1% 0.2 0.1% 

1-minute 8.8 0.1% 0.8 0.4% 

10-minute 35.1 0.6% 4.0 2.1% 

1-hour 177.1 2.9% 12.1 6.5% 

The reason the average values of step changes for all time steps are zero or very small is also 

clear from Figure 4-2. These distribution plots are all symmetric: the numbers of positive and 

negative steps are about equal, and therefore their averages are about zero. The exception is the 

long-term hourly load step changes. Although the average values of long-term hourly step 

changes are still very small, the numbers of positive load step changes are less than the numbers 

of negative step changes. The daily load profiles of Figure 4-1 explained the slightly skewed 

hourly load step change distribution plot. Figure 4-1 shows that the morning load pick-ups are at 

a higher rate (steeper slope) than the evening load drop-off. It takes fewer time steps to reach 

daily peak load than the time for the load to decrease from its peak to its daily low. 

Figure 4-2 also suggests that the differences between short-term fluctuations of utility load and 

wind power are minor. Occurrences of extreme step change values are the major differences 

between the distributions of load and wind power step changes. Table 4-3 lists the cumulative 

frequencies of step changes less than ±1σ and ±3σ. It also includes the relative frequencies of 

step changes greater than ±5σ. Higher portions of wind power fluctuations are within ±1σ, 

indicating wind power has more small step changes than load. However, the distribution of wind 

power step changes have longer tails. Over 99% of all load step changes are within ±3σ, but less 

for wind power step changes. The differences between load and wind power extreme step 

changes are significant, especially for hourly step changes. Available data show that wind power 

can start up and reach over 90% of its generating capacity in an hour, and wind power can also 

decrease such an amount in an hour because of wind speed changes. Load will not experience 

such a large change in normal conditions.10

Table 4-3. Relative Frequencies of Extreme Step Changes 

Load Step Changes Wind Power Step Changes 

< ±1σ < ±3σ > ±5σ < ±1σ < ±3σ > ±5σ

4-second 0.83534 0.99185 0.00074 0.87206 0.99093 0.00108 

1-minute 0.90418 0.99732 0.00118 0.87716 0.98615 0.00337 

10-minute 0.76355 0.99117 0.00123 0.80200 0.98243 0.00245 

1-hour 0.73601 0.99639 0.00005 0.79861 0.98597 0.00114 

4.1 Correlation between Load and Wind Power Fluctuations 

The electric system responds to the short-term load fluctuations by adjusting the outputs of some 

of its online generating units, which can change its output quickly and are under automatic 

generation control (AGC). This function, called regulation, helps a control area maintain its 

interchange schedule, support its system frequency, and balance its generation and load under 

 

10 Some large loads such as an arc furnace can change between no load and full load in minutes. For a control area, 

the aggregated load does not exhibit such a big change. 

23
normal operations. For longer time frame load variations (hourly and daily load profiles), the 

system will adjust the set points of online generators and start up and shut down additional 

generators to meet the demand. This is called load following. 

When wind power is added to a utility system control area, the system must respond to 

fluctuations of both system load and wind power. Determining how wind power will affect 

system regulation and load following performance requires detailed modeling (system load and 

generators) and complicated calculations. System generation mix and wind resources are all 

important factors affecting the outcome. A dedicated, system-specific study of wind power 

impact on system operations is beyond the scope of this report. However, by examining the 

correlation between load and wind power, it is possible to gauge the extent of the impact.11

Two types of correlation between load and wind power are of interest: the correlation between 

step changes of load and wind power and the correlation between overall load and wind power 

levels. Short-term fluctuations of load and wind power affect system regulation. A high positive 

correlation between load and wind power step changes (both load and wind power move in the 

same direction) means the wind power will aggravate the system regulation requirement while a 

high negative correlation (load and wind power move in opposite directions) will alleviate it. 

Generator output at a given time is affected by the load and the amount of wind power. If wind 

power is consistently high during high load periods (high positive correlation), the wind power 

will have high capacity value to the system because it reduces the generation capacity needed to 

meet the load. On the other hand, if wind power is low during high load periods (high negative 

correlation), the capacity value of wind power is small. 

(0.30)

(0.25)

(0.20)

(0.15)

(0.10)

(0.05)

0.00

0.05

0.10

0.15

0.20

0.25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Correlation Coefficients

4-sec step 1-min step 10-min step

Figure 4-3. Correlation coefficients of daily load and wind power step changes 

It has been shown that short-term power fluctuations from even adjacent wind turbines are not 

correlated. There is no reason for the short-term fluctuations of wind power and system load to 

be related. Available data show that indeed the load and wind power fluctuations are independent. 

Figure 4-3 plots the daily correlation coefficients between wind power step changes and load 

step changes during a 17-day period for three time steps: 4-second, 1-minute, and 10-minute. 

 

11 For example, Hirst, E. (2001). “Interaction of Wind Farms with bulk Power Operations and Markets” prepared for 

the Project for Sustainable FERC Energy Policy, September 2001. 

24
Figure 4-4 plots the monthly correlation coefficients of hourly load and wind power and the 

monthly correlation coefficients of hourly load and wind power step changes. 

(0.20)

(0.15)

(0.10)

(0.05)

0.00

0.05

0.10

0.15

0.20

0.25

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avg

Correlation Coefficient

Load & Wind Power Load & Wind Power Step Changes

Figure 4-4. Correlation coefficients of monthly load and wind power and step changes 

These correlation coefficients are small, especially for the 4-second time series. The correlation 

coefficients for the 10-minute time series are larger, but there is no clear pattern as to which 

direction they will move, indicating the relationship is not only weak but also random. The 

results for the 1-hour time series are similar. It is clear that for this case (Xcel Energy system 

load and wind power from a region within its control area), wind power impact on the system 

regulation and load following is symmetrical (i.e., the wind power may reduce regulation and 

load following requirements at one time and it will increase such requirements at the other). 

To put the relationship between system load and wind power in better perspective, we need to 

take their sizes into consideration. Table 4-4 lists the step change standard deviation values of 

system load, wind power, and the net load (load minus wind power)12 for the four time series. 

Table 4-4. Variability of Load, Wind Power, and Net Load 

Step Change Standard Deviations Load (MW) Wind Power (MW) Net Load (MW) % change 

4-second 10.8 0.3 10.8 0.0% 

1-minute 19.2 1.4 19.3 0.3% 

10-minute 48.0 6.3 48.5 1.0% 

1-hour 220.6 18.7 221.4 0.3% 

Because of the low level of wind power (relative to the magnitude of system load), the variability 

of load before and after wind power is very small. System load variations overwhelm the wind 

power variations. These statistics do not imply that the impact of wind power on system 

regulation and load following is negligible. Wind power can experience more large magnitude 

step changes than load, and the system must cope with those large changes no matter how 

infrequently they occur. However, these statistics suggest that the impact should be very 

moderate. 

 

12 Here wind power is treated as a negative load because utilities generally do not dispatch wind generation. 

Whenever wind power is available, the rest of the control area generating units will see a reduced load. 

25
5. Operational Impacts of Wind Power 

The operational impacts of wind power actually cover concerns that span the entire range of time 

scales relevant to utility system operations. The issues range from long-term transmission and 

generation planning to progressively shorter time frame concerns such as annual and seasonal 

plant maintenance schedules, weekly unit commitment, hourly load following, minute-to-minute 

regulation, operating reserves, second-to-second frequency control, and sub-second system 

stability. Conventional utility planning and analytical tools have been adopted with wind power 

as an add-in to evaluating the impacts. Although the efforts have yielded valuable insight, many 

of those approaches are still too new to be established as standard utility practices. 

In this section, wind power impacts to transmission planning, system reliability, and regulation 

are discussed. Through the description of current practices, issues related to the unique 

characteristics of wind power in analyzing system impacts will be highlighted. 

5.1 Transmission System Planning 

Conventional load flow analysis is the first step in the process of evaluating and planning the 

transmission system. This analysis is used to determine the maximum power output at the 

prospective wind power plant sites that can be accommodated by the existing transmission 

system and what additional facilities may be required. The process is straightforward, but careful 

considerations must be given to the selection of input data for the load flow program. Utilities 

usually construct load flow model based on summer and winter peak demand conditions when 

the majority of generators are producing full output and the transmission grid is stressed the most. 

This method is used so that fatal flaws of the transmission system can be more easily detected. 

However, the wind power plants may not produce full output during system peak demand 

periods. For example, Figure 5-1 shows the average hourly profile of utility system load and two 

Midwest wind power plants for the month of July. The average hourly load and wind power in 

the figure are normalized by their respective annual peak values so that they are shown on the 

same scale. 

26
0%

20%

40%

60%

80%

100%

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Hour of Day

Wind Power 1

Wind Power 2

Load

% of Peak Load/Wind Power

Figure 5-1. Average July daily load and wind power profile 

The load profile has the familiar afternoon peak of a typical utility, but the wind power profiles 

show low output during afternoon and early evening hours. This type of wind power profile is 

not limited to wind power plants in the Midwest. The available data show that wind power plants 

in Texas, Oklahoma, and the Northwest also behave similarly. The wind resources are better in 

the winter season than in the summer season for the entire middle part of the continental United 

States. While utility system demand is near or at its annual peak during the afternoon hours, the 

wind power plants only generate at 30% or less of their annual peak output. Furthermore, 

available data show that wind power plants generate infrequently at peak level. Figure 5-2 is a 

sample of utility annual load duration curve and wind power annual hourly output duration curve. 

For the example in Figure 5-2, outputs of the wind power plant were over 90% of its annual peak 

only 8.5% of the time (744 hours). The time during which the load was over 90% of its annual 

peak value is even less (1.6% or 136 hours). The hours during which both system load and wind 

power are at their respective peak (or near peak) are very rare. In fact, for this particular example, 

it never occurred. For the Midwest wind power plants, peak production happens in the winter 

and spring (late October through early April), while the first 200 hours of system peak load occur 

in summer months (July, August, and September). 

27
0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 720 1440 2160 2880 3600 4320 5040 5760 6480 7200 7920 8640

Time (Hours)

% of Peak

Wind Power

Load

Figure 5-2. System load and wind power duration curves 

It can be argued that using the utility system model of peak demand and wind power plant 

nameplate capacity in standard load flow analysis does not reflect the actual impact of wind 

power on the transmission system. A planning process with this approach will result in a very 

conservative system with large safety margins as far as wind power is concerned. A more 

realistic approach is to examine the long-term wind power data and determine the more likely 

wind power levels for the specific season. 

5.2 System Reliability 

The purpose of system reliability analysis with wind power is to determine the impact of wind 

power on the system’s ability to maintain the desired reliability level. Various metrics gauge 

system reliability, and they are usually presented in terms of the probability that the planned total 

generating capacity will not be able to meet the projected system load. The most common 

measure is loss of load expectation (LOLE), expressed in days per year or days per 10 years. 

Utilities rely on sophisticated models and computer software to calculate reliability indices such 

as LOLE because of the complexity of multiple states associated with multiple generators and 

varying load levels. The effective load carrying capability of wind power is another index often 

calculated with the reliability model to see how wind power can contribute to meet the system 

demand. 

Wind power can be included in the standard utility reliability models to calculate system 

reliability indices. The process is straightforward. Conventional power plants are usually 

represented in the model by multiple states (output levels), each associated with a forced outage 

rate (a probability). The forced outage rates for conventional power plants are derived from longterm plant statistics. However, this approach is not applicable to wind power. If a particular wind 

28
power plant is expected to have an annual capacity factor of 40%, it cannot be simply assigned a 

60% forced output rate in the calculation process. The output of a wind power plant is not a 

simple binary process, and it is not totally random. Wind power has definite seasonal and 

sometimes diurnal patterns. Wind power plants having the same capacity factor but different 

daily and seasonal output profiles could have significantly different capacity values to a utility 

system. Treating wind power as conventional power plants in the model and assigning an 

equivalent forced outage rate to it do not capture the unique characteristics of wind power plants. 

One approach is to use a wind power time series to modify system load; i.e., reducing system 

load by the wind power amount and calculating the reliability index. The change of system 

reliability indices before and after the introduction of wind power represents the capacity 

contribution from wind power. It is obvious that the results of such analyses depend heavily on 

the wind power data. Using different wind power time series that are representative of the yearly, 

seasonal, and diurnal variation of wind power, a wind power plant’s contribution to system 

reliability index can be estimated. This approach can be time-consuming because a large number 

of simulation runs (Monte Carlo method) and long-term wind power data are needed to produce 

a reasonable estimate that captures the variation of wind power. 

To remedy the shortcoming of the above conventional approach for not recognizing the 

probabilistic nature of the power variations from wind plants, an alternative has been proposed.13 

For wind power plants under consideration, an effective forced-outage rate for a selected time 

period is calculated. Depending on the seasonal and diurnal characteristics of wind regime, the 

length of the selected time period can be adjusted so that the calculated effective forced-outage 

rate is based on an appropriate time scale. Although this approach represents a significant 

improvement in computational efficiency over the Monte Carlo method, it still requires full-scale 

reliability simulations. 

A less vigorous but faster way to estimate wind power capacity value for a system is to evaluate 

wind power production during a system peak demand period. While a detailed reliability model 

calculates system reliability indices with yearly load time series, the resulting system reliability 

indices are mainly influenced by the top 200 or so load hours of the year. For summer peaking 

utilities, these top 200 load hours almost exclusively occurred during the summer season. 

Analyzing wind power during the summer peak or wind peak demand season gives an 

approximation to the wind power capacity value. In actual application of this method, a daily 

window of several hours in the peak demand season is chosen and the wind power during these 

windows is averaged. A comprehensive wind power study for the state of New York found that 

this method could produce results close to that of a full-scale method14 with the available wind 

power data. 

It should be noted that this method provides approximate results because of the uncertainties of 

system load and wind power production. Although utilities recognize the conditions under which 

the system demand will peak (e.g., average daily temperature over 90°F five days in a row), they 

 

13 Milligan, M. (2001). “A Sliding Window Technique for Calculating System LOLP Contribution of Wind Power 

Plants,” presented at the 2001 AWEA WindPower Conference, Washington, DC, June 4-7, 2001. 

14 The New York State Energy Research and Development Authority (2005). The Effect of Integrating Wind Power 

on Transmission System Planning, Reliability, and Operations Phase 2 Report: System Performance Evaluation. 

29
still do not know in advance when such or other comparable conditions will occur. The 

approximation method can only provide an initial estimation of wind power potential. Analyses 

using standard reliability model are preferred, and long-term wind power data are needed to 

increase the confidence level of the results from either method. 

5.3 Regulation 

As discussed in Section 4, utility load changes continuously. Although utilities can predict the 

daily and seasonal load profiles fairly accurately, short-term (minutes and seconds) load changes 

are random and not predictable. Operation or control of the system requires that generators be 

dispatched and their outputs adjusted to maintain a balance of total generation (generally not 

controlled by utilities) and total load (most are under utility control) all the time. A small 

deviation of system frequency from the nominal 60 Hz is an indication of how well the system is 

operated or controlled. Control performance of each system is judged by the statistics of control 

area error (ACE), which must comply with North American Electric Reliability Council (NERC) 

standards CPS1 and CPS2. 

Regulation in this report is the process of adjusting generators in response to shorter-term load 

changes (sub-hourly load fluctuations) while maintaining a near-constant 60-Hz system 

frequency. This function is performed automatically through the governor function of large 

generators and by the automatic generation control (AGC) subsystem of utility’s Energy 

Management System (EMS). The generator’s governor function responds to frequency changes 

immediately. AGC will adjust generator output set-points at a slower pace (every 4-6 seconds up 

to 2 minutes). Utilities normally reserve enough generator ramping capability under AGC to 

cope with the random load fluctuations. 

When wind power is integrated into utility system’s generation portfolio, its impact on system 

regulation needs to be analyzed through statistics derived from long-term data because of the 

stochastic nature of short-time frame wind power fluctuations and load changes. These data 

requirements have necessitated that analyses of wind generation impact on system regulation be 

performed in an after-the-fact fashion.15 This is really not much different from the standard 

utility planning studies that are based on projected future load. Because wind power variations 

are within a narrow range and their long-term statistics property remain fairly constant, the 

analyses with historical data provide valuable insight on the effects, and the results serve as a 

good indicator to future performance. 

The method for a statistical analysis of system regulation was explained by Hirst and Kirby.16 It 

starts by decomposing high-resolution system load into three components: a base load 

component that is constant over the studying period, a ramping (trending) component during the 

period, and a randomly fluctuating component. A 4-second load time series for an hour is used as 

an example to illustrate the concept in Figure 5-3. The actual load (purple trace) during the hour 

is separated into a constant 4,400-MW base load (scale shown on the left side), a ramping 

component is a near-constant 9.6 MW/min (scale on the right side), and a fluctuation component 

 

15 Hirst, E. and Kirby, B. (1998). “Separating and Measuring the Regulation and Load Following Ancillary Service.” 16 Hirst, E. and Kirby, B. (2000). “What Is the Correct Time-Averaging Period for the Regulation Ancillary 

Service?” 

30
varies between –24 MW to 43 MW (scale on the right side). The data in the figure satisfy the 

relation below. 

Actual load = Base component + Ramping component + fluctuation component17

The system schedules enough generators on line to meet the base load. The ramping component 

is served by EMS load following function by gradually adjusting the set points of the generators. 

The fluctuation component is served by AGC, which changes generator output quickly. 

3800

4000

4200

4400

4600

4800

5000

6:00 6:20 6:40

Time

MW

-200

0

200

400

600

800

1000

MW

Actual load

Base Component

Ramping Component

Fluctuation Component

Figure 5-3. Components of system load 

A properly applied decomposing process will result in a fluctuation component time series that 

has zero or very near zero average values, indicating the regulation is basically capacity service 

with no energy component. The standard deviation value of the time series can be related to the 

control area’s regulation requirements. When wind power is added into the system, the same 

algorithm can be applied to a new load time series consisting of the differences of load and wind 

power (load minus wind power). The differences in standard deviation values between these two 

cases provide the information to estimate the wind power impact on regulations. 

 

17 The base component should be the average load during this period. In Figure 5-3 it is taken as minimum load 

value during the hour to make the trace easier to see. The ramping component is obtained by taking differences of 

actual load and base load through a low-pass filter. Careful consideration should be given to choose a proper cut-off 

frequency for the low-pass filter to be used. If the cut-off frequency is too high, then the ramping component will 

have too much of the fluctuation component. If the cut-off frequency is too low, then some of the ramping 

component (load following) will become part of the fluctuation component. When using a simple low-pass filter of 

rolling average, a window of 2 minutes (previous fifteen 4-second data points and fourteen 4-second data points 

after) appears to be adequate. 

31
An example of the 4-second resolution load and wind power time series of 1 day is shown in 

Figure 5-4. Also shown in Figure 5-4 is the time series of net load (load minus wind power). 

Applying the above algorithm to the data, the standard deviation value of the fluctuation 

components are calculated and listed in Table 5-1. The example demonstrates that wind power 

affects the system regulation requirements slightly. Compared to the load fluctuation component, 

there is only a 0.8% increase in the net load fluctuation component (standard deviation). This is 

the expected result because short-term load variations and wind power variations have very low 

correlation (as shown in Section 4.1), especially for a system with a low penetration level of 

wind power (5,544 MW peak load and 145 MW peak wind power). 

0

1000

2000

3000

4000

5000

6000

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

Time of Day (hh:mm)

Load (MW)

0

100

200

300

400

500

600

Wind Power (MW)

Load

Wind Power

Load - Wind Power

Figure 5-4. Daily 4-second load and wind power time series example 

Table 5-1. Example of Wind Power Impact on System Regulations 

Load Fluctuation Wind Fluctuation Net Load Fluctuation 

Standard Dev. (MW) 9.99 0.77 10.07 

3 x Standard Dev. (MW) 29.97 2.31 30.20 

Maximum (+) (MW) 76.70 6.93 78.03 

Maximum (–) (MW) (58.60) (8.35) (58.41) 

% within ±3σ 98.35 98.99 98.38 

Results of this technique provide the range of regulation requirements when wind power is added 

into the system. It allows planners and operators to estimate wind power impact on system 

regulation requirements and make adjustments to operating procedures to integrate wind power 

into routine operations. However, it should be noted that although this gives a good indication of 

the system regulation requirements after wind power is added, the fluctuation component is not 

the real system ACE, and it does not show CPS1 and CPS2 compliance of the system. ACE is a 

function of system frequency18 and the differences of scheduled and actual interchanges, but the 

 

18 More precisely, the combined frequency response of system load and generation. 

32
above technique does not take frequency response of load and generation and interchange 

schedule into consideration. 

Available data from one control area in Oklahoma show that at a low penetration level, the wind 

power fluctuations have minimal influence on ACE.19 The correlation between system load 

changes and ACE is stronger than the correlation between wind power changes and ACE. This 

result suggests strongly that load fluctuations have a greater impact on system operations than do 

wind power fluctuations. Further examination of the data revealed that the majority of the large 

ACE values (positive and negative) have no relation to either system load changes or wind 

power changes. Large ACE values occurred during periods with large inter-hour interchange 

schedule changes. The available data have not shown that small short-term wind power 

fluctuations have any noticeable effect on ACE. Figure 5-5 is an example of such an event. It 

plots 1-minute actual and scheduled interchanges and their differences, 1-minute ACE values, 

and 1-minute step change values of wind power and system load. The figure clearly shows that 

the ACE has no correlation with either load or wind fluctuations. The ACE tracks the differences 

between actual interchange and scheduled interchange and becomes very large during periods 

when the interchange schedule takes a large step change and the generators within the control 

area have not had time to bring the actual interchange to the new designated level. 

-60

0

60

120

180

240

300

20:3020:4020:5021:0021:1021:2021:3021:4021:5022:0022:1022:2022:3022:4022:5023:0023:1023:20

(MW)

Scheduled Interchange

Actual Interchange

Diff. between Actual & 

Scheduled Interchanges

ACE

Load Fluctuations Wind Fluctuations

Figure 5-5. Correlation between ACE and interchanges 

Sudden changes of wind power do impact control area ACE behavior. However, other than 

forced and scheduled outages, wind power plants seldom experience sudden large changes (as 

discussed in Sections 3.2 and 3.2). CPS1 and CPS2 compliance are based on long-term ACE 

statistics. Infrequent large swings of wind power may not influence long-term CPS1 and CPS2 

statistics very much. To study control area ACE performance after wind power is added to the 

system, the simulation software must simulate frequency characteristics of load and generation. 

It also must input a realistic interchange schedule. Commercial software can be used to simulate 

control area ACE behavior. This task is not commonly performed because many utilities only 

 

19 In this case the wind power is about 6% of the control area’s peak summer demand. During periods of high winds 

and low loads, wind energy may represent 14% of the control area load. 

33
have relatively small wind generation in their systems. As total wind generation capacity 

continues to increase and begins to impact grid operations in some areas, the need to better 

understand the ACE performance and CPS1 and CPS2 compliance will increase. 

5.4 Summary 

In general, wind power impact on system operations can be analyzed with standard utility 

software. Minor modifications of the procedure may be required to account for the unique 

characteristics of wind power. Some situations may require a new approach. A number of 

detailed studies have been performed. Because the utilities’ experience with wind power is still 

limited (compared to operating experience with conventional power plants such as hydro and 

thermal units), trial and error seems to be a necessary process in developing robust techniques to 

analyze the interaction between wind power and system operations. However, one conclusion 

becomes clear from these studies: for low penetration of wind power in a utility system, the 

system appears to be capable of managing the impact of wind power. 

At the same time, these studies point out the need to develop better models for wind power plants 

and collect long-term wind power data that capture the realistic behavior of a wind plant. This 

information establishes a planning boundary of expected performance under normal and rare 

conditions. Together with system data (load, generator output, frequency, interchange schedule, 

etc.) and valid wind plant models, an accurate account of the wind power impact on system 

operations can be obtained. 

6. Conclusions 

The analyses of large quantities of high-resolution wind power data series have demonstrated 

several important aspects of wind power. 

1. Wind power fluctuations are concentrated within a narrow range. The average values of 

second-by-second changes of wind power are less than 0.1% of the wind power plant 

capacity. Maximum 1-second changes in power level are about 1% of the wind power plant 

capacity, and they occur infrequently. The average 1-minute step change of wind power is 

only about 0.3%~1.0% of the wind power plant capacity. Higher values are for smaller wind 

power plants and lower values are for larger wind power plants. Compared to the natural 

fluctuations of system loads, these short-term fluctuations are too small to have any impact 

on the system operations. The magnitudes of the largest 1-minute power level changes can 

reach 30% of the wind power plant capacity, but those extreme values appear to be caused by 

outages of the plant or the grid, and not the wind speed fluctuations. 

The short-term wind power data also highlight a unique attribute of wind power plants. 

Unlike other types of large power plants that can lose all generation capacity because of 

internal faults, malfunction of a single turbine will not cause all turbines in a wind power 

plant to trip off line at once. On this basis, the reliability of a wind power plant is actually 

higher than that of a large conventional power plant. The system can lose all the generation 

of a wind power plant when the line connecting to it trips because of a fault. 

34
For 10-minute intervals, the average magnitudes of wind power step changes vary around 2% 

of the wind power plant capacity. For a 100-MW wind power plant, the average absolute 

values of 10-minute power level changes are around 2 MW. This value is smaller than the 

average magnitude of load forecasting errors expressed in terms of total system load.20 More 

than 98% of all 10-minute step changes are within the range of ±3σ (±11.4 MW or about 

±11% of plant nameplate capacity). The data show that sizes of the wind power plants and 

turbine types have little effect on 10-minute step change statistics. The 10-minute interval 

maximum power level changes are only slightly bigger than the corresponding values of the 

1-minute interval. 

Wind power can change substantially in the hourly time frame, but even hourly step changes 

of wind power are still confined in a relatively narrow range. More than 98% of all hourly 

step changes are of a magnitude that is within the range of ±3σ (±30 MW of ±29% of the 

plant nameplate capacity). The average magnitudes of hourly step changes are still relatively 

small, ranging from 4.5% to 6.4% of wind plant capacity. These percentage values are 

calculated with the name-plate capacity of the respective wind power plants. The differences 

in the average hourly step changes magnitudes expressed as a percentage of the wind plant 

capacity will be smaller if the actual achieved maximum power output is used to calculate the 

percentage values. It shows that the operations of wind power plants in different regions and 

with different types of turbines are very similar. The fluctuations are a function of short-term 

wind speed variations, and this wind characteristic is likely to be the same in all of the 

locations analyzed so far. Large wind power plants (with more turbines) tend to have fewer 

variations in their output. 

2. The maximum magnitudes of hourly wind power level changes can be significant. With 

favorable wind, a wind power plant can start up and reach its full output in 1 hour. A wind 

power plant can shut down just as quickly. The largest monthly maximum hourly step 

changes are in the range of 80% of the wind plant capacity. Those large step changes were 

actually caused by outages outside of the wind power plants. Excluding the grid-outageinduced large step changes, the largest power level change of a wind power plant was about 

70% of the wind plant capacity in 1 hour during a 12-month period. These changes occur 

infrequently. The step changes statistics show that even with the outages, wind power plants 

do not drop from full capacity to zero in a period of 1 hour or less. The rate of power increase 

of a wind power plant can be modified with a proper control strategy that limits the number 

of wind turbines that are allowed to start simultaneously. The rapid decrease of wind power 

is difficult to remedy. Better wind forecasting can help mitigate the impact on system 

operation. 

3. Multiplying the output of single or small numbers of turbines to emulate the output of a large 

wind power plant does not provide realistic results. The output from a large wind power plant 

with many turbines will fluctuate less than the output from a single turbine or a small group 

of turbines. The spatial and temporal variations of wind speed within a large wind power 

plant make the combined output less variable. The effect of simple scaling up is equivalent to 

force the outputs of all turbines in a large wind power plant in sync, and it exaggerates the 

fluctuations of the output power. The actual data prove that such a practice, sometimes 

 

20 Hirst, E. “Integrating Wind Energy with the BPA Power System: Preliminary Study,” September 2002. 

35
performed out of necessity because of lack of actual data, is not advisable. It also emphasizes 

that one cannot simply extrapolate the data from a single anemometer to the wind power 

plant output because the resulting wind power plant output will have much worse fluctuation 

characteristics. 

The approach of using meteorological models and archived weather data to simulate weather 

conditions and wind power may avoid these problems. If real wind power speed and power 

data are not available, the weather model can be used to produce realistic output power series 

from many wind power plants over a large area. 

4. Wind power profiles are location specific. The data show that different regions have different 

daily, monthly, and seasonal wind power profiles. Wind power data from one region can be 

used to predict fluctuation behavior from second-to-second up to hourly, but they are not 

good indicators to daily and monthly performance of wind power plants in other regions. 

Utility system load follows predictable daily, weekly (weekdays and weekends), and 

seasonal (summer cooling, winter heating, on-peak, off-peak) patterns. With long-term wind 

power data, it is possible to assemble seasonal average output profiles for the region, but they 

may not be applicable to other regions. In addition, the available data do not show that wind 

power has any distinctive weekday and weekend profiles. 

Utility planners usually compile some representative load profiles for system analysis and 

planning. A different approach may be necessary with wind power. The stochastic nature of 

wind power series makes it difficult to compile representative daily or seasonal profiles for a 

specific region. The critical issue is that the wind power profiles capture the characteristic 

wind power variations. Again, this issue could be addressed with the weather model 

approach. 

5. Capacity value or capacity credit of a generating plant to a utility power system is a very 

important measure in system planning and operation. There are different ways to assess the 

capacity credit of a power plant. For planning purposes, it is typically calculated using a 

reliability model that simulates the electricity production under various conditions. In 

operations, actual output from a power plant during system peak load period is sometimes 

used to represent the capacity credit. Long statistics of power plant output, especially hydro 

units, are often the basis for such methods. Regardless of what methods are used to assess the 

capacity credit of power plants, the results depend highly on the individual system 

characteristics such as load profiles, generation mix, and maintenance practice. 

Wind plant capacity credit can be calculated with standard utility methods. Researchers at 

NREL and other institutes studied this issue.21 Their results show that despite the intermittent 

nature of wind, wind power can contribute to enhance the system reliability and thus has 

capacity value. However, wind power capacity credit is more site specific than conventional 

generating technologies. In addition to system attributes (load, generation mix, and other 

operating practices), the capacity credit of wind power also strongly depends on wind 

resources specific to the location of the wind power plant. Using wind resource data or wind 

 

21 Milligan, M.; Porter, K. (2005). Determining the Capacity Value of Wind: A Survey of Methods and 

Implementation; Preprint. 30 pp.; NREL Report No. CP-500-38062. 

36
power data from other locations to assess wind power capacity credit introduces high 

uncertainty. 

It is clear that the short-term fluctuations of a wind power plant are very small (about 0.1% of 

plant capacity for a 1-second data series and less than 1% for 1-minute data series). They are 

about the same percentage values as the system load variations. Ramping of wind power is also 

small. Average ramping rate for a wind farm is less than 0.5% of plant capacity per minute. In 

extremely rare situations, wind power can ramp 10% of its capacity per minute. The entire wind 

farm never shuts down simultaneously because of wind speed changes. 

At low wind power penetration level, the load is much larger than the wind power, and its 

fluctuations dominate the short-time frame system regulation. For those systems, the small 

variations of short-time frame wind power are well within the range of normal system load 

variations. The addition of wind power may or may not appreciably increase system regulation 

duties, depending on the correlation between the wind fluctuations and load fluctuations. The 

impact of wind power on system operation becomes noticeable in a 10-minute or longer time 

frame. Wind power variations are higher than that of the load when both are expressed as a 

percentage of their respective capacity. Wind power also has a higher probability of large step 

change values in longer time intervals. If such large wind power fluctuations can be forecasted 

with confidence, it will be a tremendous help to system operators. 

The data also suggest that output fluctuations are influenced mainly by the size of the wind 

power plants. Differences in turbine types and plant locations play much lesser roles in 

determining the step changes of wind power. For two wind power plants of the same installed 

capacity, their step change statistics will be very similar. 

A corollary to the observed wind power temporal and spatial diversity is the cautionary note on 

the attempts to simply scale wind power up or down from available wind power data and wind 

speed data. The actual wind power data have shown that simply scaling the output of a small 

wind plant to match the expected output (either power or total energy) of a large wind power 

plant will exacerbate the fluctuation of wind power. Scaling down the output of a large wind 

power plant to match the expected output of a smaller wind power plant will have the opposite 

effect. Simply extrapolating from a single anemometer reading to the output of a wind power 

plant will produce an even worse effect. The resulting wind power profiles from either approach 

will not demonstrate the true behavior of the intended wind power plant. 

With a multi-year high-resolution wind power data series, wind power plants can be analyzed 

with the conventional utility operating study models (chronological and probabilistic) to evaluate 

their capacity contribution and effect on system production cost. Additional wind power time 

series of desired variation characteristics can be generated by judicially applying the state 

transition matrix of available wind power data series or with the weather model approach. 

37
7. Appendix 

List of Related Studies 

UWIG Operating Impacts Study & Related Operating Impacts Activities. 

http://www.uwig.org/operatingimpacts.html

The New York State Energy Research and Development Authority (2005). The Effects of Integrating 

Wind Power on Transmission System Planning, Reliability, and Operations Report on Phase 2: System 

Performance Evaluation. 

Milligan, M.; Porter, K. (2005). Determining the Capacity Value of Wind: A Survey of Methods and 

Implementation; Preprint. 30 pp.; NREL Report No. CP-500-38062. 

EnerNex Corp. and Wind Logics, Inc. (2004). Wind Integration Study – Final Report. Xcel Energy and 

the Minnesota Department of Commerce. 

Smith, J., DeMeo, E., Parsons, B., Milligan, M. (2004). Wind Power Impacts on Electric Power System 

Operating Costs: Summary and Perspective on Work to Date. Global Windpower 2004 Conference 

Proceedings (CD-ROM), 28-31 March 2004, Chicago, Illinois. Washington, DC: American Wind Energy 

Association; Omni Press; 10 pp.; NREL Report No. CP-500-38015. 

Milligan, M.; Kirby, B.; Jackson, K.; Shiu, H.; Makarov, Y.; Hawkins, D. (2004). California RPS 

Integration Study: Phase I Summary and Results; Preprint. 32 pp.; NREL Report No. CP-500-35947. 

Wan, Y. (2004). Wind Power Plant Behaviors: Analyses of Long-Term Wind Power Data.. 66 pp.; NREL 

Report No. CP-500-36551. 

Wan, Y.; Milligan, M.; Parsons, B. (2003). Output Power Correlation Between Adjacent Wind Power 

Plants. Journal of Solar Energy Engineering. Transactions of the ASME. Vol. 125(4), November 2003; pp. 

551-555; NREL Report No. JA-500-33519. 

Wan, Y., Parsons, B., Milligan, M. (2003). Output Power Correlation Between Nearby Wind Power 

Plants. Collection of the 2003 ASME Wind Energy Symposium Technical Papers at the 41st AIAA 

Aerospace Sciences Meeting and Exhibit, 6-9 January 2003, Reno, Nevada. New York: American 

Institute of Aeronautics and Astronautics, Inc. (AIAA) and American Society of Mechanical Engineers 

(ASME); pp. 351-356; NREL Report No. CP-500-32726. 

Wan, Y. and Bucaneg, D. (2002). “Short-Term Fluctuations of Large Wind Power Plants,” NREL CP500-30745. 

Hirst, E. (2001). “Interaction of Wind Farms with bulk Power Operations and Markets.” Prepared for the 

Project for Sustainable FERC Energy Policy, September 2001. 

Milligan, M. (2001). “A Sliding Window Technique for Calculating System LOLP Contribution of Wind 

Power Plants,” presented at the 2001 AWEA WindPower Conference, Washington, DC, June 4-7, 2001. 

Hirst, E. and Kirby, B. (2001). Measuring Generator Performance in Providing Regulation and LoadFollowing Ancillary Services; Oak Ridge National Laboratory, ORNL/TM-2000/383. 

38
Milligan, M. R. (2001). Chronological Reliability Model to Assess Operating Reserve Allocation to Wind 

Power Plants: Preprint. 7 pp.; NREL Report No. CP-500-30490. 

Kirby, B. and Hirst, E. (2000). Customer-Specific Metrics for the Regulation and Load-Following 

Ancillary Services; Oak Ridge National Laboratory, ORNL/CON-474. 

39
F1147-E(12/2004) 

REPORT DOCUMENTATION PAGE Form Approved 

OMB No. 0704-0188

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, 

gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this 

collection of information, including suggestions for reducing the burden, to Department of Defense, Executive Services and Communications Directorate (0704-0188). Respondents 

should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a 

currently valid OMB control number. 

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

December 2005 

2. REPORT TYPE

Technical Report 

3. DATES COVERED (From - To)

 

5a. CONTRACT NUMBER

DE-AC36-99-GO10337 

5b. GRANT NUMBER

4. TITLE AND SUBTITLE

A Primer on Wind Power for Utility Applications 

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

NREL/TP-500-36230 

5e. TASK NUMBER

WER6 5003 

6. AUTHOR(S)

Y. Wan 

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401-3393 

8. PERFORMING ORGANIZATION 

REPORT NUMBER

NREL/TP-500-36230 

10. SPONSOR/MONITOR'S ACRONYM(S)

NREL 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

11. SPONSORING/MONITORING 

AGENCY REPORT NUMBER

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service 

U.S. Department of Commerce 

5285 Port Royal Road 

Springfield, VA 22161 

13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

The wind industry still faces many market barriers, some of which stem from utilities' lack of experience with the 

technology. Utility system operators and planners need to understand the effects of fluctuating wind power on system 

regulation and stability. Without high-frequency wind power data and realistic wind power plant models to analyze the 

problem, utilities often rely on conservative assumptions and worst-case scenarios to make engineering decisions. 

To remedy the situation, the National Renewable Energy Laboratory (NREL) has undertaken a project to record longterm, high-resolution (1-hertz [Hz]) wind power output data from large wind power plants in various regions. The 

objective is to systematically collect actual wind power data from large commercial wind power plants so that wind 

power fluctuations, their frequency distribution, the effects of spatial diversity, and the ancillary services of large 

commercial wind power plants can be analyzed. It also aims to provide the industry with nonproprietary wind power 

data in different wind regimes for system planning and operating impact studies. This report will summarize the 

results of data analysis performed at NREL and discuss the wind power characteristics related to power system 

operation and planning. 

15. SUBJECT TERMS

wind energy; wind turbines; wind plants; wind farms; utility; utilities; system operators; system planners 

16. SECURITY CLASSIFICATION OF: 19a. NAME OF RESPONSIBLE PERSON 

a. REPORT 

Unclassified 

b. ABSTRACT 

Unclassified

c. THIS PAGE 

Unclassified

17. LIMITATION 

OF ABSTRACT

UL 

18. NUMBER 

OF PAGES 

19b. TELEPHONE NUMBER (Include area code)

Standard Form 298 (Rev. 8/98) 

Prescribed by ANSI Std. Z39.18