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

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

Analyses of Wind Energy Impact 

on WFEC System Operations 

Preprint 

Y. Wan 

National Renewable Energy Laboratory 

J.R. Liao 

Western Farmers Electric Cooperative

To be presented at the European Wind Energy Conference 

Athens, Greece 

February 27 – March 2, 2006 

Conference Paper 

NREL/CP-500-39477 

March 2006 
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Analyses of Wind Energy Impact on WFEC System Operations 

Yih-huei Wan∗

National Renewable Energy laboratory/National Wind Technology Center 

1617 Cole Boulevard/MS3811 

Golden, CO 80401, USA 

yih-huei_wan@nrel.gov

(303) 384-7025 

James R. Liao 

Western Farmers Electric Cooperative 

701 NE 7th Street 

Anadarko, Oklahoma, 73005 

j_liao@wfec.com

(405) 247-4286 

Summary 

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 power to its energy portfolio by purchasing the output of the Blue Canyon 

Wind Power Project located north of Lawton, Oklahoma. The wind plant has the potential to provide about 6% of 

WFEC’s peak summer demand. During periods of high winds and low loads, wind power may reach 16% of the control 

area load. The National Renewable Energy Laboratory (NREL) worked with WFEC to analyze the impact of wind power 

on WFEC system operations through the 1-minute system data stream (load, total generation, scheduled interchange, 

actual interchange, frequency, and ACE) collected from the energy management system (EMS). The results show that, at 

such a penetration level, wind power has a very small effect on system operations. The fluctuations of wind power caused 

only a slight increase in the variability of the system apparent load (system load minus wind power). 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. Actual data show that there was 

virtually no correlation between system ACE and the fluctuations of wind power, and no significant changes in the ACE 

that could be attributed to changes of wind power. System regulation needs are still dominated by short-term, random load 

changes. 

 

∗ Employees of the Midwest Research Institute under Contract No. DE-AC36-99GO10337 with the U.S. Dept. 

of Energy have authored this work. The United States Government retains, and the publisher, by accepting the 

article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, 

irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, 

for the United States Government purposes. 

1 
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. 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 8% higher than that of the system load alone. 

System regulation needs are still dominated by short-term, random load changes. 

I. Introduction 

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

below is a sample of the 1-minute EMS data stream that 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 scheduled to import 239 MW (column 7) at 19:46 

central standard time (CST), and the actual import was 243.16 MW. 

Table 1. Sample Data Stream from WFEC EMS 

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

Time Wind Power ACE Freq. Load Actual Interchange Scheduled Interchange Total Gen. 

19:46:00 53.23 2.82 60.01 796.71 -243.16 -239.00 553.56 

19:47:00 53.14 1.99 60.01 795.54 -244.14 -239.00 551.39 

19:48:00 52.57 -2.95 60.01 797.34 -248.00 -239.00 549.33 

19:49:00 54.56 1.82 60.01 794.91 -243.29 -239.00 551.63 

19:50:00 55.73 5.22 60.01 792.83 -240.18 -239.00 552.65 

19:51:00 54.82 2.96 60.02 793.22 -243.32 -239.00 549.90 

19:52:00 56.21 1.16 60.01 793.55 -244.91 -239.00 548.64 

19:53:00 56.95 0.88 60.02 793.73 -245.92 -239.00 547.81 

19:54:00 55.64 2.19 60.03 793.09 -245.80 -239.00 547.28 

19:55:00 57.86 1.73 60.01 791.19 -244.48 -239.00 546.71 

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. 

II. Short-Term Wind Power Fluctuations 

Short-term wind power fluctuations are stochastic in nature. 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). 

Table 2 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. 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 the calculation, the 

standard deviation values of step changes of all large wind power plants expressed in terms of nameplate rating are 

remarkably close [1]. 

2 
Table 2. 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)

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 

October 4.4 4.7 3.4 3.2 3.1 6.1 0.9 

November 3.4 3.2 3.0 3.1 3.1 5.5 1.4 

6 months 4.1 3.9 3.3 3.0 2.8 5.9 1.2 

Normalized Standard Deviation 

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% 

October 6% 5% 3% 4% 4% 4% 3% 

November 5% 3% 3% 4% 4% 4% 4% 

6 months 6% 4% 3% 4% 4% 4% 3% 

 

Figure 1 shows the distribution of Blue Canyon wind power 10-minute step changes. Eighty-four percent of all step 

changes are within ±1σ (±4 MW or ±5% of nameplate rating). Ninety-nine percent of all 10-minute step changes are 

within ±3σ (±12 MW or ±16% of nameplate rating). Table 3 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 variations are larger in 

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 step change statistics between Blue 

Canyon and other large wind power plants. 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). 

III. Short-Term Load Variations 

The 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) for a day. 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 

predict 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 zigzags in the load profile 

trace) that is similar to the short-term wind power fluctuations. These short-term fluctuations are stochastic in nature. To 

show the rapid fluctuations of load and wind power in detail, Fig. 4 plots the 1-minute average load and wind power for a 

four-hour period. The system load and wind power short-term changes are very similar. 

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

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

Size of Step Changes (MW)

± 1σ

 84%

± 2σ

 96%

± 3σ

 99%

Frequency

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

Sizes of Step changes (MW)

± 1σ

 77%

± 2σ

 95%

± 3σ

 98%

Frequency

Figure 1. Frequency distribution of 10-minute wind 

power step changes 

Figure 2. Distribution of hourly wind power step 

changes 

3 
Table 3. 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)

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 

October 8.0 8.7 9.2 8.1 6.6 15.1 2.0 

November 7.2 8.9 8.8 7.8 7.0 13.4 3.4 

6 months 7.4 9.1 8.7 7.2 5.9 15.1 2.9 

Normalized Standard Deviation 

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% 

October 11% 8% 8% 10% 8% 10% 6% 

November 10% 9% 8% 9% 9% 9% 10% 

6 months 10% 9% 8% 9% 7% 10% 8% 

During the 24-hour period, wind power step changes are within the range of 3.7 MW and –3.9 MW. It has an average 

value of 0.0 MW and a standard deviation value of 0.8 MW. The system load exhibits a higher volatility than the wind 

power. During the same period, the system load step changes vary between 6.4 MW and –7.2 MW. The system load step 

changes have an average value of 0.0 MW and a standard deviation value of 1.9 MW. 

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

power, they are practically two independent events. The daily correlation coefficients between 1-minute step changes of 

load and wind power ranges from –0.02 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. 

0

200

400

600

800

1000

1200

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)

Load (kW)

0

20

40

60

80

100

120

Wind Power (kW)

Wind Power

Load

700

820

940

1060

10:00 11:00 12:00 13:00

Time (hh:mm)

Load (kW)

0

24

48

72

Wind Power (kW)

Wind Power

Load

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

system load 

Figure 4. Wind power and system load fluctuations 

details 

IV. Variability of System Load and Wind Power 

The electric system responds to the short-term load fluctuations by adjusting the outputs of designated online generating 

units that 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 the 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). The wind power is treated as 

4 
a negative load in this analysis 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. 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 4 lists the monthly standard deviation values of wind power (σW), system load (σL ), and apparent 

load step changes (σA ) and their extreme values from the 1-minute data series. The average values are not listed because 

they are very small or zeros. 

Table 4. 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 

June 1.5 2.2 2.6 18% 20.5 64.8 64.8 -19.0 -111.2 -112.7 

July 1.2 2.3 2.6 13% 25.7 144.4 144.7 -19.5 -52.1 -55.5 

August 1.1 3.2 3.3 3% 21.4 77.7 77.4 -44.8 -81.6 -81.8 

September 1.0 2.7 2.8 4% 15.7 71.3 70.9 -12.9 -68.9 -68.8 

October 1.1 1.5 1.7 13% 7.2 9.9 10.6 -7.3 -13.0 -13.0 

November 1.1 1.8 2.1 17% 7.5 33.2 34.2 -7.3 -20.0 -20.5 

6-month 1.1 2.6 2.8 8% 25.7 144.4 144.7 -44.8 -111.2 -112.7 

Table 4 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 is 3% to 18% larger. The average increase is about 

8%. The small changes in both maximum positive and negative step change values also suggest that relatively 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. 

Table 5 lists 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 (about 3%). The differences in 

apparent load step change extreme values are also relatively small. 

Table 5. Hourly Variability with Wind Power 

Wind Step Changes 

(MW) 

Load Step Changes 

(MW) 

Apparent Load Step Changes 

(MW) % Change 

Standard Deviation 6.8 45.0 46.3 3% 

Max (+) Step 56.3 97.5 109.6 12% 

Max (–) Step -41.8 -116.8 -143.8 23% 

V. 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 6 lists the correlation coefficients 

calculated from 6-month, 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 step changes and 

net online generation. Column (5) shows the correlation coefficients between changes in apparent load and actual 

interchange. 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. 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 6 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 

5 
display. Nevertheless, the correlation coefficient values in Table 6 confirm the expected interactions between wind power 

and system operations under normal conditions. 

Table 6. 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 

Overall -0.22 -0.03 0.59 0.08 0.60 0.08 0.08 -0.15 0.97 

Range of daily values 

Max. 0.14 0.33 0.91 0.24 0.92 0.23 0.36 0.05 1.00* 

Min. -0.64 -0.20 0.21 -0.11 0.25 -0.14 -0.06 -0.37 0.80 

 *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 0.14 to –0.64 and the overall correlation coefficient for the entire period is –0.22 

(column 1). The wind power and actual interchange move in opposite directions. The correlation between wind power 

changes and net online generation is also negative, but small (the overall correlation coefficient is only –0.03). 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 because 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 shows 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. 

Changes in actual interchange are strongly related to changes of system load. The overall correlation coefficient is 0.59 

(column 3) with daily values varying between 0.21 and 0.91. The correlation between changes in load and changes in net 

online generation is also positive, but much weaker; its overall correlation coefficient is only 0.08 (column 4) with its 

daily values ranging from –0.11 to 0.24. These results show that when load increases, both online generation and the 

amount of power imported increase to meet the additional demand, but the control area’s interchange tracks the short-term 

fluctuations of system load within the control area more closely than does the control area’s net online generation. This is 

similar to responses of the online generation and actual interchange to the changes in wind power discussed above. 

It is clear that load fluctuations cause more variability 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. The 

strong correlation between the changes in apparent load and actual interchange (column 5) and the weak correlation 

between changes in apparent load and control area net online generation (column 6) reinforces this idea. 

VI. 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 –0.15; column 8) is stronger than the correlation between wind power changes 

and ACE (overall correlation coefficients of 0.08; column 7). This result again shows that load fluctuations have a greater 

impact on system operations than do wind power fluctuations. The negative correlation coefficients between system load 

changes and ACE mean an increase in system load (a positive step value) tends to associate with negative ACE values 

(power flows into the control area) and vise versa.1

 The positive correlation coefficients between wind power step changes 

and ACE are just the opposite—increases in wind power tend to associate with positive ACE values. 

 

1

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

power flowing into the control area. 

6 
The differences between actual and scheduled interchanges are the major driver of a control area’s ACE [1]. 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 1.00 with an overall value of 0.97 

(Column 9 in Table 5). However, 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. 

-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 

Figure 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. The situation shown in Fig. 5 is not a special case. In fact the intra-hour interchange schedule changes are the major 

cause of large ACE values. The consequence is that large ACE values occur during the period 10 minutes before and 10 

minutes after the hour. Figure 6 plots the frequency of large ACE values (in this case an ACE magnitude of 29 MW or 

larger) against the time of such occurrence during a 6-month period. The concentration of large ACE values around the 

start of the hour is clear. In fact the available data show that only one of the more than 6,600 occurrences of large ACE 

values was directly caused by wind power change. 

0

5

10

15

20

25

30

35

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

Frequency

Figure 6. Frequency and time of occurrence of large ACE values 

7 
All the control areas are required to meet the Control Performance Standard CPS1 and CPS2 requirements [1]. CPS1 

measures the long-term impact of ACE on the health of the interconnection in terms of frequency. CPS2 measures the 

short-term excursions (10 minute average) of ACE against a predefined limit for each control area.2

 The CPS1 and CPS2 

graphs for 2003 and 2004 for the WFEC control area are shown in Figs 7 and 8. 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 actions3

 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 7. CPS1 for 2003 and 2004 Figure 8. CPS2 for 2003 and 2004 

VII. Conclusions 

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

periods it may approach 14% of the load. 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 8%. For a longer-time frame, the increase in 

system apparent load variability is even less. 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. 

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. Changes in wind power only had small influence on actual interchange 

and online generation. The correlation between the ACE and changes of system load is stronger than the correlation 

between the ACE and wind power changes. Furthermore, the data show that almost all high ACE values are caused by big 

changes in interchange schedule. 

WFEC CPS1 and CPS2 statistics before and after 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. 

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. Longer term wind power variation may 

also affect control area load following operations. Better wind power forecasting can help improve the system 

performance. WFEC is working with Blue Canyon Wind Plant operators to improve wind power forecasting. Actually, 

two issues need to be addressed. One is to improve the accuracy of wind power forecasting, and the other is how system 

operators will incorporate the information into scheduling and operating decision processes. The effect of wind power 

forecasting on operations will be analyzed in future studies. Researchers are also conducting additional research to 

determine optimal operating reserve under the uncertainty of wind power forecasting. 

 

2

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

3

 Including paying closer attention to ACE movement and developing new empirical formula for generator 

control. 

8 
It is clear that at low penetration levels, 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. 

 

References 

1. Wan, Y. H. (2004). Wind Power Plant Behaviors: Analyses of Long-Term Wind Power Data. 66 pp.; NREL Report No. 

CP-500-36551. http://www.nrel.gov/docs/fy04osti/36551.pdf. 

2. Control Criteria Task Force, Performance Subcommittee, North American Electric Reliability Council (1996). Control 

Performance Standard and Disturbance Control Standard Frequently Asked Questions. 

ftp://www.nerc.com/pub/sys/all_updl/oc/rs/cpsdcsfaq.doc

3. K. Methaprayoon, et. al., “An Integration of ANN Wind Power Estimation into UC Considering the Forecasting 

Uncertainty,” presented at IEEE Industrial & Commercial Power Systems Technical Conference, Saratoga Springs, NY. 

May 8-11, 2005. 

9 
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4. TITLE AND SUBTITLE

Analyses of Wind Energy Impact on WFEC System Operations: 

Preprint 

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NREL/CP-500-39477 

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Y. Wan and J.R. Liao 

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National Renewable Energy Laboratory 

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Golden, CO 80401-3393 

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14. ABSTRACT (Maximum 200 Words)

The National Renewable Energy Laboratory (NREL) worked with the Western Farmers Electric Cooperative (WFEC), 

a generation and transmission Cooperative in Oklahoma, to analyze the impact of wind power from the Blue Canyon 

Wind Power Project located north of Lawton, Oklahoma, on WFEC system operations. The analysis showed that, at 

such a penetration level, wind power has a very small effect on system operations. The fluctuations of wind power 

caused only a slight increase in the variability of the system apparent load (system load minus wind power). 

15. SUBJECT TERMS

wind energy; utility grid integration; Western Farmers Electric Cooperative; Oklahoma; Blue Canyon Wind Power 

Project 

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