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 

Analyses of Wind Energy 

Impact on WFEC System 

Operations 

Y. Wan 

National Renewable Energy Laboratory 

J.R. Liao 

Western Farmers Electric Cooperative 

Technical Report 

NREL/TP-500-37851 

August 2005 
Analyses of Wind Energy 

Impact on WFEC System 

Operations 

Y. Wan 

National Renewable Energy Laboratory 

J.R. Liao 

Western Farmers Electric Cooperative

Prepared under Task No(s). WER5.5101 

Technical Report 

NREL/TP-500-37851 

August 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
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Contents 

Abstract................................................................................................................................1 

Introduction..........................................................................................................................1 

Short-Term Wind Power Fluctuations.................................................................................2 

Short-Term Load Variations................................................................................................5 

Variability of System Load with Wind Power.....................................................................6 

Wind Power Impact on System Operations.........................................................................7 

Wind Power Impacts to ACE...............................................................................................9 

Conclusions........................................................................................................................11 

Tables 

Table 1. Statistics of 10-minute Wind Power Step Changes...............................................3 

Table 2. Statistics of Hourly Wind Power Step Changes....................................................4 

Table 3. Standard Deviations of 1-Minute Wind, Load, and Apparent Load 

Step Changes..........................................................................................................7 

Table 4. Hourly Variability with Wind Power.....................................................................7 

Table 5. Example Daily Correlation Coefficients from 1-Minute Data Series....................8 

Figures 

Figure 1. Frequency distribution of 10-minute wind power step changes...........................3 

Figure 2. Distribution of hourly wind power step changes..................................................4 

Figure 3. Profiles of 1-minute average wind power and system load..................................5 

Figure 4. Wind power and system load fluctuations details................................................5 

Figure 5. Correlation between ACE and interchanges.......................................................10 

Figure 6. CPS1 for 2003 and 2004.....................................................................................11 

Figure 7. CPS2 for 2003 and 2004.....................................................................................11 

 i 
Abstract 

Western Farmers Electric Cooperative (WFEC) is a generation and transmission Cooperative in 

Oklahoma. At the end of 2003 it added 74 megawatts (MW) of wind energy to its energy 

portfolio by purchasing the output of the Blue Canyon Wind Power Project located north of 

Lawton, Oklahoma. The wind energy has the potential to provide about 6% of WFEC’s peak 

summer energy demand. During periods of high winds and low loads, wind energy may 

represent 14% of the control area load. Conversely during periods of calm wind, wind energy 

cannot be counted upon to provide any energy to WFEC’s system. This report analyzes system

and wind energy data recorded by the WFEC control area energy management system (EMS) 

and evaluates the effects of wind energy on system operations. 

The results show that, at low penetration levels, wind energy has a very small effect on system

operations. After the addition of wind power, WFEC continues to meet the control performance

standard 1 and 2 (CPS1 and CPS2) requirements for area control error (ACE) with some

adjustments in operating procedures and reserve margin. There were no significant changes in 

the ACE that can be attributed to wind energy. The fluctuation of wind energy caused only a 

slight increase in the variability of the overall system load. The data showed that on average the 

standard deviation of the 1-minute system apparent load (system load minus wind power) is 

about 10% higher than that of the system load alone. System regulation needs are still 

dominated by short-term, random load changes. For hourly system apparent load, the increase is 

even smaller (about 4%). 

Introduction 

The Western Farmers Electric Cooperative (WFEC) is a generation and transmission cooperative 

in Oklahoma. It operates one coal-fired and four gas-fired power plants (two steam plants, one 

combined-cycle plant, and one gas turbine plant). With 279 megawatts (MW) of federal 

allocated hydro power, WFEC has a combined capacity of 1,400 MW. In 2003, the WFEC 

added 74 MW (nameplate rating) of wind power to its energy portfolio when it entered into an 

energy only purchase agreement with the Blue Canyon Wind Power Project located north of 

Lawton, Oklahoma. The project consists of 45 NEG Micon 1.65 MW turbines. The 74 MW of 

wind power represents about 6% of the WFEC system peak demand. 

To analyze the effects of wind energy on its system operations, WFEC collected 1-minute data 

from the EMS system. Below is a sample of the 1-minute EMS data stream which includes (1) 

time stamp, (2) wind power, (3) 1-minute sliding average area control error (ACE), (4) frequency, 

(5) system load, (6) actual interchange, (7) scheduled interchange, and (8) total on-line 

generation (wind power and other generation combined). A negative interchange value means 

power was imported into the control area. The example below shows the system was scheduled 

to import 239 MW (column 7) at 19:46 central standard time (CST), and the actual import was 

243.16 MW. 

 1 
(1) (2) (3) (4) (5) (6) (7) (8)

 ⋮

19:46:00 53.23 2.82 60.01 796.71 -243.16 -239 553.56

19:47:00 53.14 1.99 60.01 795.54 -244.14 -239 551.39

19:48:00 52.57 -2.95 60.01 797.34 -248.00 -239 549.33

19:49:00 54.56 1.82 60.01 794.91 -243.29 -239 551.63

19:50:00 55.73 5.22 60.01 792.83 -240.18 -239 552.65

19:51:00 54.82 2.96 60.02 793.22 -243.32 -239 549.90

19:52:00 56.21 1.16 60.01 793.55 -244.91 -239 548.64

19:53:00 56.95 0.88 60.02 793.73 -245.92 -239 547.81

19:54:00 55.64 2.19 60.03 793.09 -245.80 -239 547.28

19:55:00 57.86 1.73 60.01 791.19 -244.48 -239 546.71

 ⋮

In addition to 1-minute data stream from the EMS, 10-minute average wind power data series are 

also available. The 10-minute data series are from wind plant supervisory control and data 

acquisition (SCADA) system. 

Short-Term Wind Power Fluctuations 

Short-term wind power fluctuations are stochastic in nature. Wind power can be higher, lower or 

remain the same from one instant to the next. To gauge the variability of Blue Canyon wind 

power, statistics and distribution of output single step changes (the step changes are differences 

between two consecutive values of wind power in a time series) are calculated from the 10-

minute wind power data series and hourly average power data series (derived from the 10-minute 

data series). Step changes indicate wind power persistence. 

Table 1 compares the monthly standard deviation values of 10-minute wind power series from

the Blue Canyon Project, two Midwest wind power plants (Lake Benton in Minnesota and Storm

Lake in Iowa), and four Texas wind power plants (Indian Mesa, King Mountain, Trent Mesa, and 

Texas Wind Power Project [TWPP]). The average values of the monthly step changes (positive 

and negative) are nearly zero for all cases, and therefore, are not shown. Numbers in the lower 

portion of the table are the normalized standard deviation values obtained by dividing the 

standard deviation values with the nameplate rating of respective wind power plants. During 10-

minute intervals, the standard deviation of power level changes at Blue Canyon range from 3.7 

MW to 5.0 MW, or within the range of 5% to 7 % of nameplate rating. It can be seen that the 

10-minute fluctuations of Blue Canyon wind power plant are similar to that of the other wind 

power plants. When more data are included in computing, the standard deviation values of step 

changes of all large wind power plants expressed in terms of nameplate rating are remarkably 

close.

 2 
Table 1. Statistics of 10-minute Wind Power Step Changes

Blue Canyon Lake Benton Storm Lake Indian Mesa King Mtn Trent Mesa TWPP 

Stdev (MW) Stdev (MW) Stdev (MW) Stdev (MW) Stdev (MW) Stdev (MW) Stdev (MW)

January 3.7 4.0 3.5 2.4 2.3 5.3 1.3

February 3.1 3.7 3.1 2.7 2.6 5.4 1.2

March 4.1 4.2 4.6 3.0 2.4 5.9 1.3

April 4.1 3.6 3.9 4.0 3.8 6.6 1.4

May 3.8 5.0 5.2 3.1 3.5 5.7 1.6

June 5.0 3.8 3.4 3.9 3.4 7.8 1.6

July 4.4 3.3 3.1 2.6 1.9 5.7 1.3

August 3.8 3.8 3.3 2.6 1.8 5.1 1.0

September 3.9 4.5 3.6 2.3 2.6 4.3 0.8

9 months 4.0 4.0 3.9 3.0 2.8 5.9 1.3

Normalized Standard Deviation 

January 5% 4% 3% 3% 3% 4% 4%

February 4% 4% 3% 3% 3% 4% 3%

March 6% 4% 4% 4% 3% 4% 4%

April 6% 4% 3% 5% 5% 4% 4%

May 5% 5% 5% 4% 5% 4% 5%

June 7% 4% 3% 5% 4% 5% 5%

July 6% 3% 3% 3% 2% 4% 4%

August 5% 4% 3% 3% 2% 3% 3%

September 5% 4% 3% 3% 3% 3% 2%

9 months 5% 4% 3% 4% 4% 4% 4%

 

Figure 1 shows the distribution of Blue Canyon wind power 10-minuste step changes. Eightyfour percent of all step changes are within ±1σ (±4 MW or ±5% of nameplate rating). Ninetynine percent of all 10-minute step changes are within ±3σ (±12 MW or ±16% of nameplate 

rating). 

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

-24 -20 -16 -12 -8 -4 0 4 8 12 16 20 24

(MW)

Frequency

± 1σ

 84%

± 2σ

 96%

± 3σ

 99%

Figure 1. Frequency distribution of 10-minute wind power step changes 

Table 2 shows the hourly wind power changes. Again numbers in the lower half of the table are 

the standard deviation values in terms of plant rating. Wind power level variation may be larger 

during longer time frames. The standard deviation of hourly wind power changes at Blue 

Canyon ranges from 6.2 MW to 8.6 MW (8% to 12% of the nameplate rating). Again there is 

little difference in the hourly statistics between Blue Canyon and other large wind power plants. 

 3 
Table 2. Statistics of Hourly Wind Power Step Changes 

Blue Canyon Lake Benton Storm Lake Indian Mesa King Mtn Trent Mesa TWPP 

Stdev (MW) Stdev (MW) Stdev (MW) Stdev (MW) Stdev (MW) Stdev (MW) Stdev (MW)

January 7.6 9.6 10.0 6.0 5.3 14.6 3.7

February 7.2 9.3 9.0 6.7 5.5 14.3 3.1

March 8.5 9.7 12.3 7.2 5.6 15.3 3.7

April 7.6 8.8 11.5 9.0 8.1 16.9 3.5

May 6.9 10.9 11.8 6.9 7.5 16.8 3.4

June 8.6 9.2 9.5 9.1 7.2 18.7 4.1

July 7.7 7.9 6.7 5.7 3.6 16.2 2.7

August 6.2 9.5 9.1 5.5 3.5 12.6 2.4

September 6.4 9.9 8.7 6.5 5.4 12.2 2.0

9 months 7.4 9.5 10.1 7.0 6.0 15.5 3.3

Normalized Standard Deviation 

January 10% 9% 9% 7% 7% 10% 11%

February 10% 9% 8% 8% 7% 10% 9%

March 11% 9% 11% 9% 7% 10% 11%

April 10% 9% 10% 11% 10% 11% 10%

May 9% 11% 10% 8% 9% 11% 10%

June 12% 9% 8% 11% 9% 13% 12%

July 10% 8% 6% 7% 5% 10% 8%

August 8% 9% 8% 7% 4% 8% 7%

September 9% 10% 8% 8% 7% 8% 6%

9 months 10% 9% 9% 9% 8% 10% 9%

Figure 2 shows the hourly wind power step changes at Blue Canyon are scattered over a wider 

range. However 98% of all hourly step changes are still within ±3σ (±22.5 MW or ±30% of 

nameplate rating). 

0

100

200

300

400

500

600

700

800

900

1000

-30.0 -22.5 -15.0 -7.5 0.0 7.5 15.0 22.5 30.0

(MW)

Frequency

± 1σ

 77%

± 2σ

 95%

± 3σ

 98%

Figure 2. Distribution of hourly wind power step changes 

 4 
Short-Term Load Variations 

A utility system load has a well-defined, predictable daily pattern that corresponds to daylight 

and routine human activities. Figure 3 plots the 1-minute average load (green trace) and wind 

power (blue trace) during a 17-hour period. The general trend of the system load—morning load 

pick-up, late evening peak and nightly load drop-off—is clear. A utility can usually forecast 

these trends fairly accurately based on experience, weather forecast, and knowledge about load 

within its service territory. The plot shows that the system load also contains a rapid-changing 

component (the zigzag in the load profile trace) that is similar to the short-term wind power 

fluctuations. These short-term fluctuations are stochastic in nature. 

500

550

600

650

700

750

5:006:007:008:009:00

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

Time (hh:mm)

System Load (MW)

0

50

100

150

200

250

Wind Power (MW)

Figure 3. Profiles of 1-minute average wind power and system load

To show the rapid fluctuations of load and wind power in detail, Figure 4 plots the 1-minute 

average load and wind power for a three-hour period. It is obvious that the system load and wind 

power short-term changes are very similar. 

580

600

620

640

660

680

8:008:108:208:308:408:509:009:109:209:309:409:50

10:0010:1010:2010:3010:4010:50

Time (hh:mm)

System Load (MW)

0

30

60

90

120

150

Wind Power (MW)

Figure 4. Wind power and system load fluctuations details 

 5 
During the 17-hour period, wind power step changes are within the range of 7.5 MW and –5.4 

MW with an average value of 0.0 MW and a standard deviation value of 1.3 MW. The system

load exhibits a higher volatility than the wind power. During the same period the system load 

step changes vary between 24.0 MW and –20.0 MW. The system load step changes have an 

average value of 0.1 MW and a standard deviation value of 2.4 MW. 

While the data show there is positive but weak correlation between the short-term fluctuations of

system load and wind power, they are two independent events. The daily correlation coefficients 

between 1-minute step changes of load and wind power ranges from 0.03 to 0.22. This suggests 

that wind power tends to move in the same direction as the system load. From a system operations point 

of view, this is a desirable situation. However, this specific phenomena could be associated with the 

limited available data. There is no reason for the short-term fluctuations of wind power and 

system load to be related. More short-term data should provide a clearer picture. 

Variability of System Load with Wind Power 

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 

normal operations. 

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

fluctuations of both system load and wind power. One way to gauge the effect of wind power on 

system regulation requirement is to examine the variability of wind power, system load and the 

apparent load (i.e., load minus wind power)1

. The fluctuations of the apparent load are the 

combination of load and wind power fluctuations, and they represent the regulation requirements 

to the system after wind power is added. Table 3 lists the standard deviation values of wind 

power (σW), system load (σL ) and apparent load step changes (σA ) and their extreme values 

from a 1-minute data series.2

Table 3 shows that the apparent load has a higher variability than the system load. The apparent 

load is smaller in magnitude than the system load, but its step changes generally have larger 

standard deviation values. Although this indicates that wind power causes the variability of the 

apparent load to increase, which in turn increases the system regulation requirement, the 

increases are relatively moderate. Compared to the step changes of the system load alone, the 

step changes of the apparent load have a standard deviation value that ranges from no change to 

26% larger. The average increase is about 10%. The small changes in both maximum positive 

and negative step change values also suggest that little additional regulation is required by wind 

power. As shown in the table, the addition of wind power only slightly increases the extreme 

values of apparent load step changes. 

 

1

 Here wind power is treated as a negative load because WFEC does not regulate the Blue Canyon output. 

Whenever wind power is available, the rest of the control area generating units will see a reduced load. 2

 The average values are not listed because they all are very small or zero, and reveal no information. 

 6 
Table 3. Standard Deviations of 1-Minute Wind, Load, and Apparent Load Step Changes 

Standard Deviation (MW) Maximum (+) Step Change (MW) Maximum (-) Step Change (MW) 

Wind Step 

σW

Load Step 

σL

Apparent 

Load Step 

σA

% change Wind Step Load Step

Apparent 

Load Step Wind Step Load Step 

Apparent 

Load Step

0.8 1.7 1.7 – 4.7 14.0 14.0 (4.5)† (9.9) (9.9)

1.3 1.6 1.9 19% 4.9 7.2 6.1 (4.3) (6.0) (6.4)

1.3 2.7 2.8 4% 4.2 44.3 42.9 (5.6) (40.4) (40.2)

1.0 1.4 1.6 14% 4.2 8.7 7.5 (5.4) (4.3) (5.8)

0.8 1.6 1.7 6% 3.4 4.4 4.9 (3.8) (13.0) (13.0)

0.5 1.3 1.3 – 3.2 5.0 4.7 (3.4) (5.2) (4.9)

0.6 1.3 1.4 8% 3.0 5.5 5.5 (3.5) (4.5) (4.6)

1.1 1.6 1.8 13% 3.7 5.7 5.8 (4.0) (5.2) (7.0)

1.4 1.7 2.1 24% 3.4 5.0 5.1 (5.6) (15.8) (16.8)

0.8 1.4 1.5 7% 3.8 6.1 6.0 (4.0) (6.0) (7.0)

1.1 2.9 3.0 3% 4.7 49.9 49.3 (4.5) (50.3) (50.4)

1.0 1.9 2.1 11% 7.2 23.5 23.5 (7.3) (25.1) (25.1)

1.8 2.9 3.3 14% 6.3 36.1 37.3 (7.0) (37.1) (38.8)

1.0 1.5 1.7 13% 4.5 5.4 6.5 (4.6) (6.2) (8.2)

1.3 2.4 2.7 13% 7.5 24.0 24.5 (5.4) (20.0) (20.5)

1.7 1.9 2.4 26% 7.1 12.6 14.0 (5.5) (13.5) (14.0)

1.2 1.7 2.0 18% 3.1 6.1 7.8 (3.9) (6.7) (6.9)

1.1 5.3 5.4 2% 5.5 90.7 91.5 (5.2) (94.0) (92.3)

0.8 17.2 17.2 – 3.4 382.8 383.1 (3.6) (383.8) (383.2)

0.4 1.5 1.5 – 3.3 6.9 7.6 (3.4) (5.3) (4.8)

†parentheses denote negative numbers 

Table 4 shows the hourly step change statistics for wind power, system load, and apparent load. 

It shows that the addition of wind power causes only a slight increase in the hourly variability of 

the apparent load (4%). The differences in apparent load step change extreme values are also 

relatively small (about 11%). 

Table 4. Hourly Variability with Wind Power 

Wind Step Changes

(MW) 

Load Step Changes

(MW) 

Apparent Load Step Changes

(MW) % Change 

Standard Deviation 7.3 30.3 31.6 4%

Max (+) Step 31.6 90.5 99.5 10% 

Max (–) Step (20.7) (81.2) (90.1) 11% 

Wind Power Impact on System Operations 

To see how system operations are affected by the fluctuations of wind power, the correlation 

between the wind power step changes and various other system parameters are computed and 

examined. Table 5 lists the daily correlation coefficients calculated from 1-minute data series. 

Column (1) shows the correlation coefficients between wind power step changes and actual 

interchange step changes. Column (2) shows the correlation coefficients between wind power 

step changes and control area net online generation (i.e., total generation minus wind power). 

Column (3) shows the correlation coefficients between system load step changes and actual 

interchange step changes. Column (4) shows the correlation coefficients between system load 

 7 
step changes and net on line generation. Column (5) shows the correlation coefficients between 

changes in apparent load and actual interchange.3

 Column (6) shows the correlation coefficients 

between changes in apparent load and net online generation, and (7) shows the correlation 

coefficients between wind power step changes and ACE.4 Column (8) shows the correlation 

coefficients between system load step changes and ACE, and column (9) the differences between 

scheduled and actual interchanges and the ACE. 

It should be noted that the numbers in Table 5 are numerical results of mechanic computations 

performed on random series (such as wind power step changes and system load step changes) of 

limited length. The absolute values of these numbers are therefore less important than their 

relative relations and the underlining pattern they display. 

Table 5. Example Daily Correlation Coefficients from 1-Minute Data Series 

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Wind 

Power 

Changes 

Wind 

Power 

Changes 

Load 

Changes 

Load 

Changes

Apparent 

Load 

Changes 

Apparent

Load 

Changes

Wind 

Power 

Changes

Load 

Changes 

Actual & 

Schedule 

Interchange

Differences

Actual 

Interchange 

Changes 

Net 

Gen 

Changes 

Actual 

Interchange

Changes 

Net 

Gen 

Changes

Actual 

Interchange

Changes 

Net 

Gen 

Changes

ACE ACE ACE 

(0.13) (0.02) 0.40 0.23 0.45 0.23 0.04 (0.20) 0.93

(0.33) 0.01 0.40 0.05 0.59 0.03 0.14 (0.05) 0.95

(0.19) (0.11) 0.70 0.05 0.75 0.10 0.14 (0.27) 0.88

(0.26) (0.00)* 0.33 0.11 0.46 0.10 0.13 (0.10) 0.80

(0.17) (0.00)* 0.31 0.10 0.37 0.10 0.05 (0.18) 0.99

(0.05) (0.05) 0.45 0.14 0.48 0.16 0.04 (0.19) 0.83

(0.12) (0.10) 0.54 0.04 0.57 0.09 0.03 (0.13) 0.98

(0.34) (0.04) 0.55 (0.01) 0.68 0.02 0.09 (0.09) 0.95

(0.37) (0.08) 0.50 0.10 0.68 0.15 0.11 (0.04) 0.95

(0.19) (0.08) 0.51 0.05 0.57 0.09 0.14 (0.06) 0.92

(0.21) (0.10) 0.79 0.01 0.83 0.05 0.07 (0.08) 0.96

(0.19) (0.19) 0.64 0.09 0.67 0.17 0.13 (0.07) 0.97

(0.41) (0.03) 0.69 0.09 0.84 0.09 0.14 (0.15) 0.97

(0.20) (0.13) 0.44 0.10 0.50 0.16 0.10 (0.12) 0.95

(0.35) (0.04) 0.66 0.14 0.77 0.15 0.07 (0.15) 0.97

(0.47) (0.03) 0.50 0.13 0.73 0.13 0.16 (0.19) 0.90

(0.39) (0.02) 0.44 0.20 0.59 0.18 0.12 (0.22) 0.97

(0.15) (0.05) 0.90 0.05 0.92 0.07 0.03 (0.20) 0.99

(0.01) (0.06) 1.00* 0.01 1.00* 0.02 0.05 (0.69) 0.80

(0.00)* (0.03) 0.52 0.19 0.53 0.21 (0.02) (0.28) 0.89

(0.16) (0.05) 0.87 0.09 0.90 0.05 0.09 (0.24) 0.94

*Due to rounding 

As shown, there is a negative correlation between wind power step changes and actual 

interchange step changes. The daily correlation coefficients range from 0.00 to –0.47 and the 

 

3

 Here the concept of negative load for wind power is used. Because the system operators do not schedule wind 

power and all output from Blue Canyon is accepted, the presence of wind power will result in a reduction of 

apparent system load that is seen by other generators. The apparent load thus is the differences between system load 

and wind power. 4

 Actual 1-minute average ACE values, not the differences between two consecutive ACE values, were used in 

computing the correlation coefficients in Table 5, because ACE itself is derived from the differences between actual 

and scheduled interchanges. 

 8 
overall correlation coefficient for the entire period is –0.16 (column 1). The correlation between 

wind power changes and net online generation is also negative. The overall correlation

coefficient is –0.05 (column 2). The smaller correlation coefficients are associated with low 

wind power days, and the larger correlation coefficients with high wind power days. The wind 

power and actual interchange move in opposite directions. When wind power increases, the 

actual interchange tends to decrease (i.e., less power into the control area).as does the outputs 

from other online generators. This is an expected outcome. When more power becomes 

available within the control area (i.e., an increase in wind power), both online generation and 

power flow into the control area will decrease to maintain the balance between load and 

generation. The correlation between wind power changes and net online generation is weaker 

than the correlation between wind power changes and actual interchange. It suggests that the 

frequency response of the entire grid is faster than that of a single control area. In this case it 

appears that wind power fluctuations are mostly taken up by the grid in the form of higher 

interchange variability. 

Step changes in actual interchange have a positive and strong correlation with changes of system

load. The correlation coefficients range from 0.31 to 1.00 (rounded up from 0.995). The overall 

correlation coefficient is 0.87 (column 3). The correlation between changes in load and changes 

in net online generation is also positive, but weaker; its correlation coefficients range from –

0.005 to 0.23 with an overall correlation coefficients of 0.09 (column 4). These results show that 

when load increases, both online generation and the amount of power imported increase to meet 

the additional demand. The relative strength of the correlation coefficients between load changes 

and changes in actual interchange and the correlation coefficients between load changes and 

changes in net online generation suggests that interchange follows the change of system load 

more closely. Online generation within the control area also responds to the changes of system

load fluctuations, but to a lesser degree than does the interchange. This is similar to responses of 

the online generation and actual interchange to the changes in wind power discussed in item (1) 

above. The grid is more responsive to changes of control area load. 

It is clear that load fluctuations cause more changes in the interchange than do the wind power 

fluctuations. Load fluctuations also have more influence on control area net online generation 

than do the wind power fluctuations. Therefore, the changes in apparent load and actual 

interchange are highly correlated (column 5). The correlation coefficients between changes in 

apparent load and actual interchange range from 0.45 to 1.00 (rounded up fro, 0.996) with an 

overall value of 0.90. Compared to the strong correlation between changes in apparent load and 

actual interchange, the correlation between changes in apparent load and control area net online 

generation is almost nonexistent (column 6). 

Wind Power Impacts to ACE

The efficiency of the system regulation function is measured by ACE statistics. The available 

data show that wind power fluctuations have minimal influence on ACE for the WFEC control 

area. The correlation between system load changes and ACE (overall correlation coefficient of –

0.24; column 8) is stronger than the correlation between wind power changes and ACE (overall 

correlation coefficients of 0.09; column 7). This result reinforces the idea that load fluctuations 

have a greater impact on system operations than do wind power fluctuations. The positive 

 9 
correlation coefficients between wind power step changes and ACE mean both quantities move 

in the same direction, i.e., increases in wind power tend to associate with positive ACE values 

and decreases in wind power tend to associate with negative ACE values. The negative 

correlation coefficients between system load changes and ACE are just the opposite—increases 

in system load tend to associate with negative ACE values and vise versa.5

ACE is derived from the differences between actual and scheduled interchanges, and therefore, a 

strong correlation between these two is expected. Correlation coefficients calculated from the 

WFEC 1-minute data series confirms this relationship. The correlation coefficients between 

ACE and differences of actual and scheduled interchanges range from 0.80 to 0.99 with an 

overall value of 0.90 (Column 9 in Table 5). 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 when there were 

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. Figures 5 is an example 

of such event. It plots 1-minute actual and scheduled interchanges and their differences, 

-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. Correlation between ACE and interchanges 

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. 

All the control areas are required to meet the Control Performance Standard CPS1 and CPS2 

requirements. CPS1 measures the long-term impact of ACE on the health of the interconnection 

 

5

 According to industry convention, positive ACE indicates power going out and the negative ACE indicates power 

flowing into the control area. 

 10 
in terms of frequency. CPS2 measures the short-term excursions (10 minute average) of ACE 

against a predefined limit for each control area.6

The CPS1 and CPS2 graphs for 2003 and 2004 for the WFEC control area are shown in Figures 

6 and 7. Although the graphs show that a downward trend of CPS1 before the Blue Canyon 

wind power plant came on-line in December 2003, it is evident that the CPS1 deterioration 

accelerated in early 2004 after wind power was included into the control area. Uncertainty of 

wind power availability affected the unit commitment decisions made by the system operators 

and worsened CPS1. With more experiences, WFEC system operators began to take corrective 

actions7

 in March of 2004 and the CPS1 eventually returned to the level before wind power was 

added. There is very little difference on CPS2 before and after wind power. 

CPS1 Performance

100%

110%

120%

130%

140%

150%

160%

170%

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

2003 CPS1

CPS2 Performance

40%

60%

80%

100%

120%

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

2003 CPS2

2004 CPS2

2004 CPS1

Figure 6. CPS1 for 2003 and 2004 Figure 7. CPS2 for 2003 and 2004 

Short-term wind power fluctuations can be accommodated by additional spinning reserve and 

regulation margin. The uncertainty of wind power availability complicates the day-ahead 

resources scheduling and hour-ahead adjustment processes, which determine the available 

spinning reserve and regulation margin. Better wind power forecasting can help improve the 

system performance. WFEC is working with Blue Canyon Wind Plant operators to improve 

wind power forecasting. Researchers are currently conducting additional research to determine 

the sufficient but economic amount of spinning reserve and regulation needed to compensate for 

wind power fluctuations that considers the uncertainty of wind power forecasting.8

Conclusions 

The available data show that wind generation has less impact on system regulation requirements 

than system load. At low penetration, the impact of wind power on system operations is small. 

Analyses indicate that the magnitudes of short-term changes in system load are greater than that 

of wind power, and consequently changes in system load dominate the control area operations. 

 

6

 The minimum performance standard is 100% for CPS1 and 90% for CPS2. 7

 Including paying closer attention to ACE movement and developing new empirical formula for generator control. 8 For example: K. Methaprayoon, et. al., “An Integration of ANN Wind Power Estimation into UC Considering the 

Forecasting Uncertainty,” http://www.ewh.ieee.org/soc/ias/icps2005/ 

 11 
Changes in wind power only had small influence on actual interchange and online generation. 

Furthermore the correlation between the ACE and changes of system load is much stronger than 

the correlation between the ACE and wind power changes. The available data show that almost 

all high ACE values are caused by big changes in interchange schedule. 

The generating rating of Blue Canyon Wind Power Project is about 6% of WFEC’s peak load, 

and about 14% during light load periods. At such levels, the data show that on average the 

fluctuations of wind power only increase the short-time frame variability of system apparent load 

by 10%. For longer-time frame the increase in system apparent load variability is even less. 

WFEC CPS1 and CPS2 statistics before and after of wind power was added to the control area 

confirm that the wind power impacts on system operations are small and manageable. Although 

compliance with CPS1 showed an initial deterioration (but still within minimum performance 

standard), it recovered to its pre-wind level after operators gained more experience and made 

some adjustment in operation procedures. There was very little change in CPS2 compliance. 

It is clear that at low penetration level wind variations have much less impact on system

regulation requirements than that of load variations. System operators may still consider wind 

generation harder to manage because it is easier for operators to predict system load than wind 

power. The operators are more experienced with load forecasting than wind power forecasting 

and they feel comfortable using load forecasting information in system operations. However, as 

demonstrated by WFEC CPS1 statistics, operators learn to manage the variability of wind and 

can maintain satisfactory system performance. Good wind power forecasting is obviously of 

high value to system operators. Equally import are specific operating procedures on how to use 

wind power forecasting in control area operations.

 12 
F1147-E(12/2004) 

REPORT DOCUMENTATION PAGE Form Approved 

OMB No. 0704-0188

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PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

August 2005 

2. REPORT TYPE

Technical Report 

3. DATES COVERED (From - To)

2003 - 2005 

5a. CONTRACT NUMBER

DE-AC36-99-GO10337 

5b. GRANT NUMBER

4. TITLE AND SUBTITLE

Analyses of Wind Energy Impact on WFEC System Operations 

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

NREL/TP-500-37851 

5e. TASK NUMBER

WER5.5101 

6. AUTHOR(S)

Y. Wan and J.R. Liao 

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-500-37851 

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 

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Springfield, VA 22161 

13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

Western Farmers Electric Cooperative (WFEC) is a generation and transmission Cooperative in Oklahoma. At the 

end of 2003 it added 74 megawatts (MW) of wind energy to its energy portfolio by purchasing the output of the Blue 

Canyon Wind Power Project located north of Lawton, Oklahoma. The wind energy has the potential to provide about 

6% of WFEC’s peak summer energy demand. During periods of high winds and low loads, wind energy may 

represent 14% of the control area load. Conversely during periods of calm wind, wind energy cannot be counted 

upon to provide any energy to WFEC’s system. This report analyzes system and wind energy data recorded by the 

WFEC control area energy management system (EMS) and evaluates the effects of wind energy on system 

operations. 

15. SUBJECT TERMS

wind energy generation; transmission; Western Farmers Electric Cooperative 

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Unclassified 

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