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

Office of Energy Efficiency & Renewable Energy 

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Conference Paper The Impact of Wind Energy NREL/CP-500-38061 on Hourly Load Following May 2005 

Requirements: An Hourly 

and Seasonal Analysis 

Preprint 

A. Krich and M. Milligan, Consultant 

National Renewable Energy Laboratory 

To be presented at WINDPOWER 2005 

Denver, Colorado 

May 15-18, 2005 

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

Michael Milligan, Consultant 

National Wind Technology Center 

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401 

Abstract 

The impacts of wind energy on the power system grid can be decomposed into several 

time scales that include regulation, load following, and unit commitment. Techniques for 

evaluating the impacts on these time scales are still evolving, and as wind energy 

becomes a larger part of the electricity supply, valuable experience will be gained that 

will help refine these methods. Studies that estimated the impact of wind in the load 

following time scale found differing results and costs, ranging from near zero to 

approximately $2.50/megawatt-hour (MWh). Part of the reason for these differences is 

the different interpretation of the impacts that would be allocated to this ancillary service. 

Because of the low correlation between changes in load and wind, long-term analyses of 

the load following impact of wind may find low impacts. During the daily load cycle, 

there is a tremendous variability in load following requirements in systems without wind. 

When significant levels of wind generation are added to the resource mix, relatively small 

changes in wind output can complicate the task of balancing the system during periods of 

large load swings. This paper analyzes the load following impacts of wind by segregating 

these critical time periods of the day and separating the analysis by season. The analysis 

compares wind generation at geographically dispersed sites to wind generation based 

primarily at a single site, and for a large penetration of wind (more than 20% wind 

capacity to peak load). 

Load-Following Analysis 

A 2000 study by Milligan and Factor [2] identified a number of scenarios for 

geographically distributing 1,600 megawatts (MW) of wind capacity among 12 locations 

in Iowa, optimized for either system reliability or economic benefit. A nameplate wind 

capacity of 1600 MW was chosen because this represented 9% of Iowa’s predicted 

electrical load (energy) in 2015, and the state was considering a 10% Renewable 

Portfolio Standard (RPS) in this timeframe. 

An hourly energy output was calculated for each of the wind farms in these scenarios, 

based on wind speed data and turbine power curves. The wind speeds were collected at a 

height of 50 m in 1997, as this year was very close to the long-term norm, and were 

scaled to match a 4-year average. 

Matching hourly load data were then collected from the Mid-Continent Area Power Pool, 

representing the Iowa load. As the wind power fluctuates, the load also fluctuates, and the 

1
two are not significantly correlated. During some hours the wind output and load will 

both increase or decrease, and at other hours they will move in opposite directions. At 

times when the load and wind fluctuations are moving in the same direction, they cancel 

each other out. It is thus not necessary to balance every fluctuation in wind output by 

ramping dispatchable generators to an equal and opposite extent. It is only necessary to 

balance out the difference between the load and wind fluctuations to achieve a total 

system balance. 

In this study, the change in load from one hour to the next is considered the hourly ramp 

rate. Any fluctuations within the hour are ignored. As in [1], this study defines the hourly 

load following requirement as 

Rt = Lt – Lt-1 (1) 

Where L is the hourly load at hour t, and R is the load following requirement in the hour 

t. Once wind is added into the system, the load following requirements are defined as 

Rwt = (Lt – Wt) – (Lt-1 – Wt-1) (2) 

Where Rwt is the load following requirement in the hour t for the system with wind and 

W is the wind production at hour t, making the terms in parentheses the load net of wind 

in the respective hour. 

In 2003, Milligan [1] used eight of the scenarios identified in the 2000 study to examine 

their load following impacts. Equations (1) and (2) were used to calculate Rt and Rwt for 

each hour of the year for each of these eight scenarios. 

The load following effects were identified on an annual basis and compared between the 

different scenarios. The more geographically dispersed scenarios tended to have a lesser 

system impact than the ones with wind power based primarily at a single site. 

Two of these scenarios are further examined in this study. Case A was chosen because it 

represented one of the most geographically dispersed of the eight cases and had one of 

the lowest impacts on load following demands. Case B was chosen because it represented 

the least geographically dispersed of the eight cases and had the highest impacts on load 

following demands. This gives an indication of a reasonable range for these impacts 

among well-sited wind farms. 

Table 1 shows the geographic dispersion of the wind power in each of these cases, and 

Figure 1 shows the monthly average wind generation. 

Table 1 - MW wind power at each site for Case A and B showing relative geographic dispersion 

Algona Alta Esther Forest Radcliffe Sibley 

Case A 200 400 500 50 100 350 

Case B 0 1300 200 0 100 0 

2 
0 

100 

200 

300 

400 

500 

600 

700 

Jan

Feb

Mar

Apr

May

Jun 

Jul

Aug

Sep

Oct

Nov

Dec 

Average Wind Generation (MW)

Case A 

Case B 

Figure 1 - Monthly wind generation 

To get an idea of the impact of wind generation on load following requirements, [1] 

looked at the statistical distribution of the load following requirements over the entire 

year. Figure 2 and Figure 3 show the annual statistical distribution of hourly load 

following demands for the no-wind scenario (load only) and for the scenario of wind 

energy subtracted from the load (combined) for Cases A and B. Table 2 summarizes the 

differences, comparing the standard deviation of the load-only distribution, the standard 

deviation of the combined distribution, and the difference between the two cases. As 

compared with the no-wind scenario, Case A adds 20 MW, or 10.6%, to the standard 

deviation of load following demands, while Case B adds 32 MW, or 17.1%. Although 

these are noticeable increases to the load following variability, they are still only 1%-2% 

of the nameplate capacity of the wind farms. 

3
0 

200 

400 

600 

800 

<=-650

-650<x<=-600-600<x<=-550-550<x<=-500-500<x<=-450

-450<x<=400

-400<x<=-350-350<x<=-300-300<x<=-250-250<x<=-200-200<x<=-150-150<x<=-100

-100<x<=-50

-50<x<=0

0<x<=50

50<x<=100

100<x<=150150<x<=200200<x<=250250<x<=300300<x<=350350<x<=400400<x<=450450<x<=500500<x<=550550<x<=600600<x<=650

>650 

MW 

Frequency (count/year) 

i

Load Following Distribution 

1 000 

1 200 

1 400 

1 600 

Load C ount 

Comb ned Count 

Figure 2 - Statistical distribution of load following requirements: Case A 

0 

200 

400 

600 

800 

<=-650

-650<x<=-600-600<x<=-550-550<x<=-500-500<x<=-450

-450<x<=400

-400<x<=-350-350<x<=-300-300<x<=-250-250<x<=-200-200<x<=-150-150<x<=-100

-100<x<=-50

-50<x<=0

0<x<=50

50<x<=100

100<x<=150150<x<=200200<x<=250250<x<=300300<x<=350350<x<=400400<x<=450450<x<=500500<x<=550550<x<=600600<x<=650

>650 

MW 

Frequency (count/year) 

i

Load Following Distribution 

1 000 

1 200 

1 400 

1 600 

Load C ount 

Comb ned Count 

Figure 3 - Statistical distribution of load following requirements: Case B 

Table 2 - Statistics of annual load following requirements 

Case A 

St Dev Load Only 187.5 

St Dev Load – Wind 205.5 

Incremental Increase 20 

Case B 

187.5 

219.5 

32 

Although looking at this data on an annual basis gives an indication of the average 

system impacts, it overlooks the trends of both the load and wind variations between 

seasons and across periods of the day. The Iowa electrical demand shows immense 

4
5

systematic variation throughout the day and between seasons, while Iowa’s wind 

variability, although sensitive to seasonal and diurnal differences, is not so systematic. 

Figure 4 illustrates the impact of each of the two cases on load following requirements 

for the year. The upper panel of the graph shows Case B, the non-geographically disperse 

case, and the bottom panel shows the disperse generation Case A. The upper time series 

is the combined ramping requirements from load and wind, and the bottom series 

represents the load alone. Because the graphs show all hours of the year, it is difficult to 

discern the details. However, by examining the bands surrounding ±500 MW, we can 

observe that wind does have an impact on the hourly load following requirements, and 

this effect is more pronounced in Case A than in Case B. We can also observe that the 

impact of 1,600 MW of wind in the 7,042-MW system does not increase the load 

following requirement anywhere near the rated capacity of the wind generation. 

However, this coarse analysis does not provide sufficient information about wind’s effect 

on the system. For that we must break down the data set into meaningful time periods, 

which is the goal of this paper. 

-1000

-500

0

500

1000

Load Ramp (MW)

0 2000 4000 6000 8000

Hour of Year

-1000

-500

0

500

1000

T op: Net wind and load (*not* geographically disperse). Bottom : Load only

Iowa: 22.7% P enetration

-1000

-500

0

500

1000

Load Ramp (MW)

0 2000 4000 6000 8000

H our of Year

-1000

-500

0

500

1000

T op: N et wind and load (geographically disperse). Bottom : Load only

Iow a: 22.7% Pene tration

Figure 4 - Impact of 1,600 MW wind in the 7,042-MW Iowa (projected) system for the year 
For the purposes of this study, the year was divided into seasons in which the monthly 

load profiles displayed a correlation of over 0.90. This resulted in five seasons and a 

minimum correlation of 0.96: 

• Early spring – February and March 

• Late spring – April and May 

• Summer – June, July, and August 

• Fall – September, October 

• Winter – November, December, and January 

Figure 5 shows the average daily load profiles for each of the five seasons designated in 

this study. The system load is highest in the summer and lowest in the late spring. The 

peak load of 7,042 MW occurs on July 25 in the hour ending at 5 pm, while the average 

at this time is only 5,371 MW. The late spring load is the lowest, with its peak of 4,863 

MW occurring on April 9 in the hour ending at 9 am, while the average late spring peak 

of 4,264 MW falls in the hour ending at 11 am. The most pronounced difference in the 

load shape is during the evening period of 4 pm to 9 pm. In the fall, this period is rather 

flat, whereas the winter shows a sharp peak. 

Figure 5 - Seasonal daily load profiles 

In a summer-peaking region such as Iowa, which is most constrained during this season, 

20 MW of increased variation due to wind will have a greater operating impact during the 

summer months than winter months. If the increased variation due to wind in summer 

were much higher than that of winter on a summer-peaking grid such as this one, the 

average annual impact may under-represent the true impact on the system during some 

times of the year and overestimate at other times. 

Similarly, if the increased variation from wind were to occur mainly during the morning 

hours of 6 am to 8 am when the load following ability of the system is most constrained, 

6

Seasonal Daily Load Profiles

3000.0

3500.0

4000.0

4500.0

5000.0

5500.0

0 5 10 15 20 25

Hour of Day Ending In --

System Load (MW)

Early Spring

Late Spring

Summer

Fall

Winter
it would weigh more heavily on the system than if it were to fall mainly during the 

overnight lows from 2 am to 5 am when little ramping is needed. However, it is also 

possible (or even likely) that at high wind penetrations, high wind output at night could 

cause minimum loading problems if units run at minimum load. 

This is actually the opposite of the Iowa data results. Figure 6 shows the statistical 

distribution of hourly load following demand for the load-only and combined scenarios in 

March and in August for Case A, while Figure 7 shows the same for Case B. Table 3 

shows a summary of these distribution statistics. In both cases we can see that the 

variation of load following requirements is much greater in August than in March and 

that the increased variation due to the addition of wind is much greater in March than in 

August. The average hourly wind generation during the month of August is 

approximately 1/3 of the hourly wind generation in March, which contributes to wind’s 

smaller impact on system variability during the summer. 

March Load Follo w ing D istribution 

0 

20 

40 

60 

80 

100 

120 

140 

160 

<=-650 

-650<x<=- 600

-600<x<=- 550 

-550<x<=-500

-500<x<=-450 

-450<x<=400

-400<x<=-350 

-350<x<=-300-300<x<=-250 -250<x<=-200 -200<x<=-150 -150<x<=-100 

-100<x<=-50 

-50<x<=0 

0<x<=50 

50<x<=100 

100<x<=150 150<x<=200

200<x<=250 

250<x<=300

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350<x<=400

400<x<=450 450<x<=500 

500<x<=550 

550<x<=600

600<x<=650

>650 

MW 

Frequency (Count/ 

Load Only 

Combined 

August Load Follow ing D istribution 

0 

20 

40 

60 

80 

100 

120 

140 

160 

<=-650 

-6 50< x<=-6 00 -6 00< x<= -5 50 -5 50< x<= -5 00 -5 00< x<= -4 50 

-4 50< x <= 40 0 

-4 00< x<= -3 50 -350<x<=-3 00 -3 00< x <= -2 50 -250<x<=-200 -200<x<=-150 -150<x<=-100 

-1 00< x<=-5 0 

-50<x<=0 

0<x<=50 

50 <x < =10 0 

100<x<=150 150<x<=200 200<x<=250 250<x<=300 300<x<=350 350<x<=400 400<x<=450 450<x<=500 500<x<=550 550<x<=600 600<x<=650 

>650 

MW 

Freque nc y (Count/ 

Load Only 

Combined 

Figure 6 - March and August load following distributions for Case A 

March Load Follo w ing D istribution 

0 

20 

40 

60 

80 

100 

120 

140 

160 

<=-650 

-6 50< x<=-6 00 -6 00< x<= -5 50 -5 50< x<= -5 00 -5 00< x<=-4 50 

-4 50<x<=40 0 

-4 00<x <= -3 50 -3 50< x<= -3 00 -3 00< x<=-2 50 -2 50<x <= -2 00 -2 00<x <= -1 50 -1 50<x<=-1 00 

-1 00<x<=-5 0 

-5 0< x< = 0 

0<x<=50 

50 <x< =1 00 

100<x<=150 150<x<=200 200<x<=250 250<x<=300 300<x<=350 350<x<=400 400<x<=450 450<x<=500 500<x<=550 550<x<=600 600<x<=650 

>650 

MW 

Frequency (Count/ 

Load Only 

Combined 

August Load Follow ing D istribution 

0 

20 

40 

60 

80 

100 

120 

140 

160 

<=-650 

-600<x<=-550 -500<x<=-450 

-400<x<=-350 -300<x<=- 250 

-200<x<=-150 

-100<x<=-50 

0<x<=50 

100<x<=150 

200<x<=250 

300<x<=350 

400<x<=450 

500<x<=550 

600<x<=650 

MW 

Freque ncy (C ount/ 

Load Only 

Combined 

Figure 7 - March and August load following distributions for Case B 

Table 3 – Statistics of load following requirement in March and August for Cases A and B 

Dispersed Case Non-Dispersed Case 

March August March August 

St Dev Load Only 166 204 166 204 

St Dev Load – Wind 195 211 218 220 

Incremental Increase 29 7 52 16 

Figure 8 shows the statistical distribution of load following demand for the hours of 3 am 

to 4 am and 5 pm to 6pm for the load only and combined scenarios for Case A, and 

Figure 9 shows the same for Case B. Table 4 shows a summary of these distribution 

7 
statistics. In both cases we can see that the variation of load following requirements is 

much greater from 5 pm to 6 pm than from 3 am to 4 am and that the increased variation 

due to the addition of wind is much greater in the early morning hour than the evening 

hour. 

0 

30 

60 

90 

120 

150 

180 

210 

MW 

/ ) 

ly 

0 

30 

60 

90 

MW 

( ) 

ly 

Load Following Distribution from 3am-4am annually 

-10000<x<=-700 

-700<x<=-650 -650<x<=-600 -600<x<=-550 -550<x<=-500 -500<x<=-450 

-450<x<=400 

-400<x<=-350 -350<x<=-300 -300<x<=-250 -250<x<=-200 -200<x<=-150 -150<x<=-100 

-100<x<=-50 

-50<x<=0 

0<x<=50 

50<x<=100 

100<x<=150 150<x<=200 200<x<=250 250<x<=300 300<x<=350 350<x<=400 400<x<=450 450<x<=500 500<x<=550 550<x<=600 600<x<=650 650<x<=700 

700<x<=10000 

Frequency (Count Yr

Load On

Load-Wind 

Load Follow ing Distribution from 5pm-6pm Annually 

120 

150 

180 

210 

-10000<x<=-

-700<x<=-650 -650<x<=-600 -600<x<=-550 -550<x<=-500 -500<x<=-450 

-450<x<=400 

-400<x<=-350 -350<x<=-300 -300<x<=-250 -250<x<=-200 -200<x<=-150 -150<x<=-100 

-100<x<=-50 

-50<x<=0 

0<x<=50 

50<x<=100 

100<x<=150 150<x<=200 200<x<=250 250<x<=300 300<x<=350 350<x<=400 400<x<=450 450<x<=500 500<x<=550 550<x<=600 600<x<=650 650<x<=700 

700<x<=10000 

Frequency Count/Yr

Load On

Load-WInd 

Figure 8 - Load following distributions for hour ending at 4 am and hour ending at 6 pm for Case A 

0 

30 

60 

90 

120 

150 

180 

210 

MW 

/ ) 

ly 

0 

30 

60 

90 

MW 

/ ) 

ly 

Load Following Distribution from 3am-4am Annually 

-10000<x<=-

-700<x<=-650 -650<x<=-600 -600<x<=-550 -550<x<=-500 -500<x<=-450 

-450<x<=400 

-400<x<=-350 -350<x<=-300 -300<x<=-250 -250<x<=-200 -200<x<=-150 -150<x<=-100 

-100<x<=-50 

-50<x<=0 

0<x<=50 

50<x<=100 

100<x<=150 150<x<=200 200<x<=250 250<x<=300 300<x<=350 350<x<=400 400<x<=450 450<x<=500 500<x<=550 550<x<=600 600<x<=650 650<x<=700 

700<x<=10000 

Frequency (Count yr

Load On

Load-Wind 

Loa d Follow ing Distribution from 5pm -6pm Annually 

120 

150 

180 

210 

-10000<x<=-

-700<x<=-650 -650<x<=-600 -600<x<=-550 -550<x<=-500 -500<x<=-450 

-450<x<=400 

-400<x<=-350 -350<x<=-300 -300<x<=-250 -250<x<=-200 -200<x<=-150 -150<x<=-100 

-100<x<=-50 

-50<x<=0 

0<x<=50 

50<x<=100 

100<x<=150 150<x<=200 200<x<=250 250<x<=300 300<x<=350 350<x<=400 400<x<=450 450<x<=500 500<x<=550 550<x<=600 600<x<=650 650<x<=700 

700<x<=10000 

Frequency (Count Yr

Load On

Load-WInd 

Figure 9 - Load following distributions for hour ending at 4 am and hour ending at 6 pm for Case B 

Table 4 - Statistics of load following requirement in the hours ending at 4 am and 6 pm for Cases A 

and B 

Case A Case B 

3 am - 4 am 5 pm - 6 pm 3 am - 4 am 5 pm - 6 pm 

St Dev Load Only 43 169 43 169 

St Dev Load – Wind 87 177 122 193 

Incremental Increase 44 8 79 24 

Similar to the August distribution as compared with March, the distribution from 5 pm to 

6 pm is shorter and wider than the distribution from 3 am to 4 am. However, the 

distribution from 5 pm to 6 pm differs from the August distribution in that a clear double 

peak is visible, a result of the changing sunset time throughout the year and the different 

load shapes noted in Figure 5. If the distribution from 5 pm to 6 pm is further broken 

down by seasons, as shown in Figure 10 for Case A and Figure 11 for Case B, it becomes 

clear why it is necessary to examine the load following impacts on both a time-of-day 

and a seasonal basis simultaneously. The variation of load following from 5 pm to 6 pm 

in the summer and in the winter is roughly half of what it is for this hour considered 

throughout the entire year. As shown in Table 5, the increase in variation due to the 

addition of wind is higher when this hour is broken down by season than when looked at 

throughout the year. This higher value more accurately reflects the variations managed by 

the grid operators. If the hour were looked at only on an annual basis, the impact of wind 

for this hour would be grossly underestimated. 

8
) ll i i -

0 

5 

10 

15 

20 

25 

30 

700 

) 

ly 

Wi ( ) l i

0 

5 

10 

15 

20 

25 

700 

( ) 

ly 

Summer (Jun, Jul, Aug Load Fo owng Dstribution from 5pm 6pm 

-10000<x<=-

-700<x<=-650 -650<x<=-600 -600<x<=-550 -550<x<=-500 -500<x<=-450 

-450<x<=400 

-400<x<=-350 -350<x<=-300 -300<x<=-250 -250<x<=-200 -200<x<=-150 -150<x<=-100 

-100<x<=-50 

-50<x<=0 

0<x<=50 

50<x<=100 

100<x<=150 150<x<=200 200<x<=250 250<x<=300 300<x<=350 350<x<=400 400<x<=450 450<x<=500 500<x<=550 550<x<=600 600<x<=650 650<x<=700 

700<x<=10000 

Ramp Rate (MW

Frequency 

Load On

Load - WInd 

nter Nov, Dec, Jan Load Fol ow ng Distribution from 5pm- 6pm 

-10000<x<=-

-700<x<=-650 -650<x<=-600 -600<x<=-550 -550<x<=-500 -500<x<=-450 

-450<x<=400 

-400<x<=-350 -350<x<=-300 -300<x<=-250 -250<x<=-200 -200<x<=-150 -150<x<=-100 

-100<x<=-50 

-50<x<=0 

0<x<=50 

50<x<=100 

100<x<=150 150<x<=200 200<x<=250 250<x<=300 300<x<=350 350<x<=400 400<x<=450 450<x<=500 500<x<=550 550<x<=600 600<x<=650 650<x<=700 

700<x<=10000 

Ramp Rate MW

Frequency 

Load On

Load - WInd 

Figure 10 - Load following distributions for hour ending at 6 pm during the summer and winter for 

Case A 

( l ll i -

0 

5 

10 

15 

20 

25 

30 

700 

) 

ly 

Wi ( ) ll i i i

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5 

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( ) 

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Summer Jun, Ju , Aug) Load Fo owing Distribut on from 5pm 6pm 

-10000<x<=-

-700<x<=-650 -650<x<=-600 -600<x<=-550 -550<x<=-500 -500<x<=-450 

-450<x<=400 

-400<x<=-350 -350<x<=-300 -300<x<=-250 -250<x<=-200 -200<x<=-150 -150<x<=-100 

-100<x<=-50 

-50<x<=0 

0<x<=50 

50<x<=100 

100<x<=150 150<x<=200 200<x<=250 250<x<=300 300<x<=350 350<x<=400 400<x<=450 450<x<=500 500<x<=550 550<x<=600 600<x<=650 650<x<=700 

700<x<=10000 

Ramp Rate (MW

Frequency 

Load On

Load - WInd 

nter Nov, Dec, Jan Load Fo ow ng Dstribut on from 5pm - 6pm 

-10000<x<=-

-700<x<=-650 -650<x<=-600 -600<x<=-550 -550<x<=-500 -500<x<=-450 

-450<x<=400 

-400<x<=-350 -350<x<=-300 -300<x<=-250 -250<x<=-200 -200<x<=-150 -150<x<=-100 

-100<x<=-50 

-50<x<=0 

0<x<=50 

50<x<=100 

100<x<=150 150<x<=200 200<x<=250 250<x<=300 300<x<=350 350<x<=400 400<x<=450 450<x<=500 500<x<=550 550<x<=600 600<x<=650 650<x<=700 

700<x<=10000 

Ramp Rate MW

Frequency 

Load On

Load - WInd 

Figure 11 - Load following distributions for hour ending at 6 pm during the summer and winter for 

Case B 

Table 5 - Statistics of load following requirement for hour ending at 6 pm in the summer and winter 

for Cases A and B 

Case A Case B 

St Dev Load Only 

St Dev Load – Wind 

Incremental Increase 

Summer 

86 

104 

18 

Winter 

90 

118 

28 

Summer 

86 

139 

53 

Winter 

90 

126 

36 

Table 5 shows another surprising result. Whereas in Case A the increased variation in 

winter is greater than in summer, the two are reversed in Case B. In all previous 

examples, the season or hour with the greater variation in the load-only scenario has 

consistently experienced less of a variation increase when wind is added. It is unclear 

why this reversal occurs between Cases A and B, although it is possible that it is related 

to the small difference in load following variation between the two seasons at this hour. 

To further represent the variation from both time of day and seasonal differences 

simultaneously, Figure 12 shows the maximum, average plus a standard deviation, 

average minus a standard deviation, and minimum ramp rates by season for each hour of 

the day with and without wind for Case A. For all hours of all seasons, the standard 

deviation band is widened with the addition of wind, as is the range between the 

maximum and minimum hourly ramp rate. The same is true of Case B. This suggests that 

at no time can it be expected that the addition of wind power will decrease the variability 

in ramp rates needed from the conventional generators. 

9
-800

-600

-400

-200

0

200

400

600

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2 0 2 1 2 2 2 3 2 4

Load Only

Load - W ind

-800

-600

-400

-200

0

200

400

600

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2 0 2 1 2 2 2 3 2 4

Load Only

Load - W ind

-800

-600

-400

-200

0

200

400

600

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2 0 2 1 2 2 2 3 2 4

Load Only

Load - W ind

Early Spring Ramp Rates Early Spring Ramp Rates (Feb, M ar)

-800 

-600 

-400 

-200 

0 

200 

400 

600 

800 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2 0 2 1 2 2 2 3 2 4 

Ramp Rate (MW) 

Hour of Day 

Load Only 

L o ad - W in d 

Ramp Rate (MW)

Hour of Day

- 8 0 0 

- 6 0 0 

- 4 0 0 

- 2 0 0 

0 

200 

400 

600 

800 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2 0 2 1 2 2 2 3 2 4 

Late Spring Ram p Rates (F e b , M ar) 

Ramp Rate (MW) 

Hour of Day 

Load Only 

L o a d - W in d 

Late Spring Ram p Rates (Feb, M ar)

Ramp Rate (MW)

Hour of Day

Sum mer Ramp Rates Sum mer Ramp Rates (Jun, Jul, A ug) (Jun, Jul, Aug)

-800 

-600 

-400 

-200 

0 

200 

400 

600 

800 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2 0 2 1 2 2 2 3 2 4 

Ramp Rate (MW) 

Hour of Day 

Load Only 

L o ad - W in d 

Ramp Rate (MW)

Hour of Day

10
-800

-600

-400

-200

0

200

400

600

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Load Only

Load - W ind

-800

-600

-400

-200

0

200

400

600

800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Load Only

Load - W ind

)

-800 

-600 

-400 

-200 

0 

200 

400 

600 

800 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 

F a ll R a m p R a te s (S e p , O c t

Ramp Rate (MW) 

H o u r o f D a y 

L o a d O n ly 

L o a d ­ W in d 

Fall Ramp Rates (Sep, Oct

Ramp Rate (MW)

Hour of Day

-800 

-600 

-400 

-200 

0 

200 

400 

600 

800 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 

W in te r R a m p R a te s ( N o v , D e c , J a n ) 

Ramp Rate (MW) 

H o u r o f D a y 

L o a d O n l y 

L o a d ­ W in d 

W inter Ram p Rates (Nov, Dec, Jan)

Ramp Rate (MW)

Hour of Day

Figure 12 - Maximum, average plus a standard deviation, average minus a standard deviation, and 

minimum ramp rates for each hour of the day by season with and without wind for Case A. The 

thick band is the average +/- the standard deviation, the whiskers are the max and min. 

Between the hours and seasons, the amount by which the standard deviation and range 

are increased and how the average ramping value shifts change. However, from these 

charts alone it is difficult to pinpoint the amount of the variability increase and any trends 

in how much and at what times it increases. 

To assist in identification of these trends, the average and maximum increase in both 

standard deviation and range for each season are shown in Figure 13 and Figure 14. For 

Case A, there is a clear trend that appears in the standard deviation increase. The two 

spring seasons have a significantly higher incremental increase when wind is added when 

11
compared with the other seasons. This pattern is still visible in Case B and the range 

increase. 

l ing l ing ll wi

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

i i

l i l i ll wi

0 

20 

40 

60 

80 

i

ear y spr ate spr sum m er fa nter 

Incremental Increase in R amp R ate S t D ev due to Add tion of W nd 

A verage Inc rease 

M ax Inc reas e 

ear y spr ng ate spr ng summer fa nter 

100 

120 

140 

Incremental Increase in R amp R ate S t D ev due to Addit on of W ind 

A verage Increas e 

M ax Inc rease 

Figure 13 - Average and maximum incremental increase in load following standard deviation due to 

the addition of wind for each season for Case A and Case B 

ing l i wi

0 l ll wi

0 

iti i

early spr ate spr ng summer fall nter 

100 

200 

300 

400 

500 

600 

Incremental Increase in R ange due to Addition of W ind 

A verage Increase 

M ax Inc reas e 

early spring ate spring s um m er fa nter 

100 

200 

300 

400 

500 

600 

700 

800 

900 

1000 

Incremental Increase in R ange due to Add on of W nd 

A verage Inc rease 

M ax Increas e 

Figure 14 - Average and maximum incremental increase in load following range due to the addition 

of wind for each season for Case A and Case B 

The incremental increase to the load following standard deviation and range are 

indicative of how much ramping variability can be attributed to wind. However, a more 

accurate allocation of variability to wind can be found by using a vector allocation 

method that was first developed by Kirby and Hirst [3] at Oak Ridge National Laboratory 

(ORNL). This method recognizes positive, negative, or zero correlation, in addition to 

being independent of the level of aggregation and the order in which each variable is 

introduced. The ORNL vector allocation of load following variability to wind can be 

calculated as 

LFw = (σw

2

 - σL

2

 + σT

2

)/(2σT) (3) 

Where LFw is the load following allocation to wind, σw is the standard deviation of the 

wind, σL is the standard deviation of the load, and σT is the total or combined standard 

deviation. 

This metric was developed to allocation regulation to non-conformant loads, and it has 

been used extensively to allocate regulation impacts of load and wind. Because regulation 

is a capacity service, the allocation method splits the variability between two or more 

signals and makes no assumption regarding the correlation between the two signals. 

When high penetrations of wind are added to the generation supply, the operator will 

need to re-evaluate the level of load following reserves (LFR) necessary to keep the 

12
13

system in balance. Techniques for estimating this impact on wind are not yet well 

defined. However, we believe that the use of the Oak Ridge allocation method can 

provide insight into wind’s impact on the system. 

Figure 15 shows the load following allocation to wind through the ORNL vector 

allocation method for each season for Cases A and B. Although the allocation values are 

different from the incremental increases seen in Figure 13 and Figure 14, the same trend 

is seen. In both cases, the variation allocation to wind is greatest in the spring seasons. 

L F w -- W in d 's A llo c a tio n o f L o a d F o llo w in g Im p a c t b y O R N L V e c to r 

A llo c a tio n M e th o d

0

1 0

2 0

3 0

4 0

5 0

6 0

7 0

8 0

early spring late spring su m m er fall winter

MW

L F w -- W in d 's A llo c a tio n o f L o a d F o llo w in g Im p a c t b y O R N L V e c to r 

A llo c a tio n M e th o d

0

2 0

4 0

6 0

8 0

100

120

e a rly sp rin g la te sp rin g su m m e r fa ll win te r

MW

Figure 15 - LFw - Portion of load following impact allocated to wind by ORNL vector allocation 

method for each season for Case A and Case B 

Figure 16 breaks the allocation down by hour of day for each of the seasons for Case A. 

Shown along with the allocation to wind is the ramp rate at each hour in the no-wind 

scenario. These charts depict a rough trend: for most periods when the load-only scenario 

ramps strongly, the allocation of variability to the wind is low. 

Information such as this can be used along with wind and load forecasts to help system 

operators determine the level of LFR required on the system. The analysis presented here 

represents a perfect hindsight view of the load following impact of wind. In practice, 

system operators must ensure that sufficient flexible generation is available at all times. 

Ensuring sufficient generation is online when needed is determined in the unitcommitment time frame. This process can vary significantly across different regions, 

depending on established practice and market structure. In systems with significant 

amounts of wind generation, determining the required LFR will depend on load forecasts 

and wind forecasts and will be subject to some error. Statistical analyses of the historical 

behavior of the system, such as that presented here, can help establish bounds around 

hourly load following requirements. We expect that specific methods will undergo 

development and improvement as system operators obtain more experience with 

moderate to large wind penetrations. It is unlikely that a one-size-fits-all approach will 

work because of the differences in generation assets and capabilities. 
LFw

Load Only Ramp Rate

LFw

Load Only Ramp Rate

Early Spring Load Following Variability Allocation to W ind Early Spring Load Following Variability Allocation to Wind

120 

LFw 

Load Only Ramp Rate 

400 

LFw (MW)LFw (MW) LFw (MW)LFw (MW)

100 

80 

60 

40 

20 

0 

Hour of Day Hour of Day

Late Spring Load Following Variability Allocation to W ind Late Spring Load Following Variability Allocation to Wind

LFw 

Load Only Ramp Rate 

120 400

80 

60 

40 

20 

0 

1 13 35

100 

Hour of Day Hour of Day

300 

5

7 79 9

11 1113 131517192123 

1517192123 

200 

100 

0 

-100 

-200 

-300 

-400 

300 

200 

100 

0 

-100 

-200 

-300 

-400 

Ramp Rate (MW) Ramp Rate (MW) Ramp Rate (MW) Ramp Rate (MW)

14
LFw

Load Only Ramp Rate

LFw

Load Only Ramp Rate

Summer Load Following Variability Allocation to Wind

120 

LFw 

Load Only Ramp Rate 

500 

LFw (MW)LFw (MW) LFw (MW)LFw (MW)

60 

40 

20 

0 

100 

1

Hour of Day Hour of Day

Fall Load Following Variability Allocation to Wind

1

LFw 

Load Only Ramp Rate 

120 400

100 

80 

Hour of Day Hour of Day

300 

200 

400 

80 

60 

40 

20 

0 

3 35 57 79 9

11 1113 1315 1517 1719 1921 2123 23 

200 

100 

0 

-100 

-200 

-300 

-400 

300 

100 

0 

-100 

-200 

-300 

-400 

-500 

Ramp Rate (MW) Ramp Rate (MW) Ramp Rate (MW) Ramp Rate (MW)

15
LFw

Load Only Ramp Rate

W inter L oad Follow ing V ariability A llocation to W ind Winter Load Following Variability Allocation to Wind

120 

LFw 

L o ad O n ly R am p R ate 

400 

100 300 

200 

100 

0 

-100 

-200 

-300 

-400 

Ramp Rate (MW) Ramp Rate (MW)

LFw (MW)LFw (MW)

80 

60 

40 

20 

0 

13579

11131517192123 

H our of D ay Hour of Day

Figure 16 - Load following allocation to wind by ORNL vector allocation method by hour of the day 

for each season is shown in the bars correlated with the left scale. The load-only ramp rate is shown 

in the line correlated with the right scale 

Conclusions 

Large penetrations of wind energy can have a significant impact on system ramping 

requirements in the hourly time scale. This impact varies based on time of day and time 

of year, but at no time does this reach the rated capability of the wind generation. This 

impact can be calculated explicitly, and the allocation of the variability can be estimated. 

Analyses of actual system performance in systems with wind can help system operators 

plan for sufficient flexible generation during times it will be needed. The approach 

presented here provides a look at potential system requirements for hourly ramping. 

Although wind has an impact on load following requirements, these requirements during 

the critical morning ramp-up and evening ramp-down is driven largely by load. To put 

this in a more operational framework, wind and load forecasting must be considered, and 

the determination of required LFR will be subject to these and other uncertainties. Wind 

generation that is installed across a broad geographic area will have a smaller impact on 

ramp requirements than if all or most of the wind capacity is at a single location. These 

results may not be robust to other regions. 

16
References 

[1] Milligan, M. Wind Power Plants and System Operation in the Hourly Time 

Domain. WINDPOWER 2003. 2003. Austin, Texas: American Wind Energy 

Association. 

[2] Milligan, M.R.; Factor, T. Optimizing the Geographic Distribution of Wind Plants 

in Iowa for Maximum Economic Benefit and Reliability. Wind Engineering. Vol 24, No 

4, 2000; pp 271-290. 

[3] Kirby, B.; Hirst, E. Customer Specific Metrics for the Regulation and Load 

Following Ancillary Services. 2000. Oak Ridge National Laboratory: Oak Ridge, TN. 

17
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1. REPORT DATE (DD-MM-YYYY) 

May 2005 

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3. DATES COVERED (From - To) 

4. TITLE AND SUBTITLE 

The Impact of Wind Energy on Hourly Load Following 

Requirements: An Hourly and Seasonal Analysis: Preprint 

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DE-AC36-99-GO10337 

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A. Krich and M. Milligan 

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14. ABSTRACT (Maximum 200 Words) 

The impacts of wind energy on the power system grid can be decomposed into several time scales that include 

regulation, load following, and unit commitment. Techniques for evaluating the impacts on these time scales are still 

evolving, and as wind energy becomes a larger part of the electricity supply, valuable experience will be gained that 

will help refine these methods. Studies that estimated the impact of wind in the load following time scale found 

differing results and costs, ranging from near zero to approximately $2.50/megawatt-hour (MWh). Part of the reason 

for these differences is the different interpretation of the impacts that would be allocated to this ancillary service. 

Because of the low correlation between changes in load and wind, long-term analyses of the load following impact of 

wind may find low impacts. During the daily load cycle, there is a tremendous variability in load following 

requirements in systems without wind. When significant levels of wind generation are added to the resource mix, 

relatively small changes in wind output can complicate the task of balancing the system during periods of large load 

swings. This paper analyzes the load following impacts of wind by segregating these critical time periods of the day 

and separating the analysis by season. The analysis compares wind generation at geographically dispersed sites to 

wind generation based primarily at a single site, and for a large penetration of wind (more than 20% wind capacity to 

peak load). 

15. SUBJECT TERMS 

wind energy; wind plant; wind farm; capacity; grid; regulation; load following; unit commitment; hourly energy 

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