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 

Impact of Energy Imbalance 

Tariff on Wind Energy 

Y. Wan, M. Milligan 

National Renewable Energy Laboratory 

B. Kirby 

Oak Ridge National Laboratory

Presented at the AWEA WindPower 2007 Conference 

Los Angeles, California 

June 3–6, 2007 

Conference Paper 

NREL/CP-500-40663 

July 2007 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
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Impact of Energy Imbalance Tariff on Wind Energy 

Y. Wan, M. Milligan 

National Renewable Energy Laboratory 

B. Kirby 

Oak Ridge National Laboratory 

Abstract 

The amount of electric energy used by customers is usually not controlled. Utility system 

operators must schedule generators to provide the electricity needed by customers. Although the 

amount of energy delivered in an hour is generally controllable with certain precision for 

conventional generators, the energy actually generated is not always exactly the same as the 

energy scheduled in an hour. The difference between energy scheduled and actually used or 

generated is the imbalance energy. The energy imbalance service tariff is designed to discipline 

the power market by promoting better scheduling and discouraging unfavorable generator 

operating practices. Because of the variable nature of wind, the difference between predicted and 

actual generated wind energy is inevitable despite progress made in wind forecasting. It is 

obvious that an energy imbalance tariff that is designed to penalize generators that intentionally 

deviate from schedule will have a negative impact on wind energy. 

In early 2007 the Federal Energy Regulatory Commission (FERC) issued Order 890, which 

adopted a tiered approach to energy and generation imbalance. This paper summarizes the results 

of a study that uses actual wind power data collected by NREL and actual hourly energy prices 

of the Midwest Independent Transmission System Operator to analyze the impact of FERC 

energy imbalance tariff on wind power. Sensitivities of the results to various components of the 

imbalance tariff, such as different deviation band widths and penalty stipulations, were simulated, 

and the effects of improving wind forecasting accuracy were also tested. 

The study shows the design of the penalty for energy deviation has a roughly 2% impact on wind 

plant revenue. Other factors studied, such as improving wind forecasting accuracy, can 

somewhat reduce but not eliminate this impact. 

Introduction 

Energy imbalance service is one of the ancillary services specified in the Open Access 

Transmission Tariff by FERC Order No. 888. The amount of energy consumed by load in a 

given time frame is variable and usually not controllable. System operators have to schedule 

generators to serve the load. While the amount of energy delivered in a given time is generally 

controllable with certain precision for conventional generators, the energy actually generated is 

not always exactly the same as the energy scheduled during the same time. The difference 

between energy scheduled and actually used or generated in an hour is the imbalanced energy 

that needs to be provided by system operators. The energy imbalance service tariff is designed to 

promote system reliability by encouraging better scheduling and by discouraging unfavorable 

operating practices, such as intentional deviation from a schedule. The energy imbalance service 

is applicable to both load and generation. However in this report this term is used exclusively for 

generation imbalance. 

 1
In early 2007 FERC issued Order 8901

, which adopted a tiered approach to energy imbalance. 

Under the FERC order the hourly energy imbalances (either over or under schedule) of less than 

or equal to 1.5% of the scheduled energy (or 2 MW, whichever is larger) will be netted out on a 

monthly basis and settled at actual incremental or decremental cost. Imbalances outside this band 

will be charged 110% of system incremental cost for the amount of energy under-delivered (the 

actual energy delivered during the hour is less than that scheduled) and will only be paid 90% of 

the system decremental cost for the amount over-delivered (the actual energy delivered during 

the hour is more than that scheduled). This is equivalent to a 10% penalty for energy imbalance 

that is outside the 1.5% deviation band. The deviation band offers operating flexibility and 

penalties discourage unfavorable generator operating practices.2

Wind energy is variable in nature. Despite the significant progress made in wind forecasting, 

some difference between predicted and actually generated hourly wind energy is inevitable. It is 

obvious that these deliberately imposed energy imbalance tariffs will have a negative impact on 

wind. This study uses actual wind power data and real-time prices to examine such impacts on 

wind plant's revenue. 

Data and Simulation 

Actual hourly average wind power data time series of a wind plant (nameplate capacity 103 MW) 

located in the Midwest region are used in this analysis. The actual hourly outputs are derived 

from the 1-second data series NREL collected at the wind plant. To produce the hourly time 

series that represents the forecasted (scheduled) hourly output which is finalized 80 minutes 

before the start of the hour (–80 minutes), the average wind power generated during the previous 

60 minutes (i.e., the period from –80 minutes to –140 minutes) was calculated. This represents a 

simple wind energy forecasting strategy based on wind persistency. The imbalance is the 

difference between the actual wind power produced and the scheduled power during the hour and 

is further separated into two categories (within the 1.5% band or without) in this analysis. The 

simulation is carried out for 2003, 2004, and 2005, and the imbalance was tallied hourly, 

monthly and yearly. 

The hourly cost data are based on Midwest Independent Transmission System Operator (MISO) 

day ahead hourly prices from April 1, 2005 to March 31, 2006 (total 12 months). The overall 

average hourly price for this data set is $44.38/MWh. The highest hourly price is $362.96/MWh 

and the lowest hourly price is -$3.61/MWh. There are actually 52 hours of negative prices in the 

yearly data set (about 0.6% of the time). Not surprisingly, the high hourly prices tend to occur 

around late afternoon (4:00 p.m. to 5:00 p.m.), early evening (between 7:0 p.m. and 10:00 p.m.) 

and morning hours (8:00 a.m. and 9:00 a.m.) while the low hourly prices tend to occur around 

early morning hours (from midnight to 7:00 a.m.). Figure 1 shows the hourly prices of two 7-day 

periods. The period from May 24 to May 30 had the lowest average hourly price at $23.61/MWh 

while the period from December 3 to December 9 had the highest average hourly price at 

$212.46/MWh. Despite the huge differences in hourly prices during these periods, the distinctive 

 

1

 FERC Docket Nos. RM05-17-000 and RM05-25-000, Order 890. 

2

 The FERC order actually contains three deviation bands for energy imbalances: less than 1.5%, between 1.5% and 

7.5%, and above 7.5%. The 90%/110% rule applies only to the second band (between 1.5% and 7.5%). Higher 

penalties are applied to the imbalances greater than 7.5% of the scheduled energy. However, wind energy is 

exempted from the third band. 

 2
pattern of daily price variation is still clear. Although the hourly price data used in the analysis 

are not the actual system prices used for settlement during the three test years, it is a reasonable 

approximation. The results are still useful in providing an insight of the impact on wind energy. 

Variations of Energy Hourly Prices

0

50

100

150

200

250

300

350

400

1 25 49 73 97 121 145

Hours

($/MWh)

December 3-9

May 24-30

Figure 1. Examples of hourly energy prices 

In this analysis, it is assumed that a wind plant operator will receive a base payment equal to 

scheduled energy multiplied by the hourly price. The hourly energy imbalance within the 

deviation band (both over- and under-delivery) will be netted out at the end of the month and 

settled at monthly average hourly prices. It can be either a payment or a charge to the wind plant 

operators depending on the sign of the netted energy (positive for over-delivery and negative for 

under-delivery). When the hourly imbalance is outside the 1.5% band, the wind plant operators 

will receive an additional payment equal to the amount of the imbalance over 1.5% multiplied by 

90% of the energy price for the hour3

 for over-delivery (i.e., the actual generation is more than 

the scheduled amount). For under-delivery (the actual generation is less than the scheduled 

amount for the hour), the wind plant operators will be charged an amount equal to the imbalance 

outside the deviation band multiplied by 110% of the energy price for the hour.4

To establish a baseline revenue for the year so we can compare the effects of different deviation 

band widths and forecasting accuracies, a theoretical maximum revenue for each test year is 

calculated by assuming the wind energy will be paid at the hourly price for actual energy 

generated. The resulting revenues can be interpreted as the revenue for the wind plant operators 

with perfect hourly forecasting (the scheduled wind power is always equal to the actual wind 

generation every hour) or no energy imbalance penalty. This number is thus independent of the 

deviation band width and 90%/110% rule (or any other similar rules). Any revenue deviation 

from this number reflects a deliberately imposed penalty on wind power because of an 

 

3

 This should be the system hourly incremental price. In this analysis the hourly energy prices are used for both the 

incremental and decremental prices. 

4

 Id. 

 3
inadvertent forecasting inaccuracy. Table 1 lists the maximum revenues and average per kWh 

prices along with the actual annual wind generation and total scheduled energy (simulated) for 

the three test years. 

Table 1 shows that the simple persistence method tends to over-forecast the wind power. For the 

3 test years, the scheduled wind energy is higher than the actual wind generation, ranging from 

0.5% to 1.4% higher. The table also shows that even with the identical hourly prices, natural 

variations of wind energy production from year to year will result in different annual average 

kWh prices. The average prices calculated from the actual wind generation are also lower than 

the average hourly price of the hourly price data set itself. With the maximum revenue based on 

actual production, the average prices for the 3 test years range from 4.291¢/kWh to 4.436¢/kWh 

—all of which are lower than the 4.438¢/kWh price calculated from data set itself. It appears that 

the distribution of actual wind energy generation does not match the distribution of hourly prices. 

The wind at this location tends to produce more electricity during low price periods than during 

high price periods. 

Table 1. Maximum Revenue Case 

2003 2004 2005 

Wind Generation (MWh) 

Actual 313,771 244,655 283,378 

Scheduled (from simulation) 313,803 244,667 283,418 

% Increase 1.0% 0.5% 1.4% 

Max Revenue ($000), Actual 13,918 10,548 12,161 

Average Unit Price (¢/kWh) 4.436 4.312 4.291 

Max Revenue ($000), Scheduled 13,855 10,530 12,097 

Average Unit Price (¢/kWh) 4.415 4.304 4.268 

% Change over Actual (0.47%) (0.19%) (0.54%) 

The table also shows that despite the fact that the total scheduled energy (based on the 

persistence method) for the year is slightly higher than the actual production of the year, the 

maximum revenues based on the total scheduled energy (i.e., wind be paid for the scheduled 

energy at the hourly price) are less than those based on actual production. For example using 

2003 data, total actual hourly production of 313,771 MWh will generate a revenue of 

$13,917,627 while the total scheduled hourly energy of 313,803 MWh will only generate 

$13,855,497. 

More energy (scheduled) resulting in less revenue highlights the linkage between the hours of 

high price and the hours when wind is producing electricity, but more factors are in play here. A 

brief discussion is provided below. 

Actual revenues are consistently higher than forecasted in this case because of the nature of the 

persistence forecast, the typical daily wind pattern in this region, and the nature of power price 

fluctuations. Figure 2 provides a stylized example to help explain the phenomena. The example 

is stylized to emphasize the differences. Power price (dark blue) follows a typical daily load 

pattern with an afternoon peak. Wind output (green) shows a similar pattern but typically peaks 

about 5 hours later than load and price. As is not uncommon, the wind pattern does not perfectly 

match the daily load or price pattern and in fact tends to provide more energy during off-peak 

hours. In this case, the wind pattern lags the daily price pattern by a few hours. Because the 

persistence forecast (dotted blue) lags the actual wind output, the forecasted wind production is 

 4
even farther off-peak than the actual wind production; the persistence forecast understates the 

value of wind generation. The wind forecast error energy (dashed red) is symmetric with equal 

over- and under-production but the value of the error energy (purple) is not symmetric. The net 

area under the purple curve is positive. Actual wind overproduction tends to happen at a time 

when power prices are high (afternoon) so the wind plant gets paid 90% of a high price for the 

forecast error energy. Wind underproduction tends to happen in the early morning when energy 

is cheap so even though the wind generator has to pay 110% of the market price for the forecast 

error this is at a relatively low price time. 

0:00 4:00 8:00 12:00 16:00 20:00 0:00

Energy Price Wind Output

Wind Forecast Forecast Error

Forecast Error Payment

Figure 2. Actual payments exceed forecasted because of the wind pattern and the nature of 

persistence forecasts. 

This extra revenue may not be considered a bonus for the wind generator. It is the forecast error 

that results in the Max Revenue Scheduled being below the Max Revenue Actual. In fact, an 

improved forecast would increase both numbers because it would reduce the 10% out-of-band 

penalty as well as reduce the extent to which the forecast error overstates the off-peak nature of 

the wind production. Note that if the wind pattern had high production early in the day, before 

the load and price picked up, the persistence forecast would tend to move the forecasted 

production into the peak and would overstate the value of the wind. Actual revenue would be 

below forecasted. 

After establishing the baseline, test cases were run with FERC energy imbalance rules and with 

variations of rules such as different deviation band widths and improved forecasting accuracy. 

To simulate the improvement in forecasting (scheduling), the hourly imbalance from the 

persistence method is artificially reduced by a fixed ratio. For example, to simulate a 15% 

improvement over the simple persistence method, the hourly imbalance from the persistence 

method is multiplied by a factor of 0.85 and the scheduled energy for the hour is adjusted to 

reflect the reduced imbalance. The industry experience has shown that actual forecasting 

accuracy with sophisticated numerical models and real time weather data and wind plant outputs 

 5
is 5% to 25% better than the persistence method. Forecasting improvements of 15% and 25% are 

simulated in this analysis. 

Summary of Simulation Results 

. The FERC Rule (Base Case) 

ith the ±1.5% deviation band and 90%/110% rule for imbalance energy outside the band, 

he 

and 

Table 2. Base Case (±1.5% Deviation Band and with 90%/110% Rule) 

1

W

annual wind plant revenues will be reduced. Table 2 lists the simulation results. On average t

wind plant revenues will be decreased by about 1.9% compared to the maximum revenues with 

no deviation band (for the 3 test years of 2003, 2004, and 2005, the percentages range from 

1.71% to 2.11%). Table 3 shows the monthly average hourly prices, monthly netted energy, 

costs (payment or charge) associated with the in-band netting for the 3 test years. 

±1.5% Deviation Band 2003 2004 2005 

Imbalance (MWh) 

Total Over-Delivery 32,71 2 32,67

T

Revenue ($000) 

Base Pay 13,85 10,53

 P

Sum

1 1

Unit Pri

) )

1 32,59 2

Inside Band

otal Un

7,053 

(3

6,790 

(3

6,981 

der-Delivery 2,743) 2,605) (32,712) 

Inside Band (7,074) (6,936) (7,106) 

ment 5 0 12,098

Outside Band ayment @90% 1,047 1,004 1,026 

Outside Band Charge @110% (1,222) (1,203) (1,183) 

 of Inside Band Monthly netting (0)* 

3,6

(6) 

0,3

(6) 

Net (Monthly Netting) 11,9

ce (¢/kWh

80 25 35 

) 

Net (Monthly Netting) 4.220 4.212 

% Change over

4.360 

 Max Revenue Case (1.71% (2.11%) (1.86%

*small negative amount less than 0 (see ) 

Table 3. Summary of Monthly Imbalance within ±1.5% Deviation Band 

$1,000.0 Table 2

 2003 2004 2005 

Average 

(

Month

Im

yment Month

Im

yment Month

Im

yment 

Hourly 

Price 

$/MWh) 

ly Pa

Net 

balance 

(MWh) 

or 

(Charge) 

($) 

ly Pa

Net 

balance 

(MWh) 

or 

(Charge) 

($) 

ly Pa

Net 

balance 

(MWh) 

or 

(Charge) 

($) 

Jan 37.98 7 272 (25) (942) (43) (1,614) 

Feb 40.52 (12) (506) 40 1

(1

(1,225) 

(37) (1

(32) (1 (

(1,436) 

y 44.38 ( (125) 

,602 10 410 

Mar 34.41 (51) ,769) (91) (3,116) 18 612 

Apr 38.92 74 2,886 1 36 

(1

(24) (915) 

May 28.81 (22) (622) (4) 27) (43) 

Jun 44.32 16 696 ,639) (25) (1,128) 

Jul 55.88 (2) (121) 52 2,882 20 1,126 

Aug 46.82 (1) (55) 23 1,059 4 

3

208 

Sep 42.87 9 371 23 992 1

2

8 ,614 

Oct 45.33 ,442) (61) ,787) (10) (449) 

Nov 42.92 (20) (843)

1,002 

(38) (1,622) (33) 

Dec 73.14 14 (28) (2,063) (38) (2,765) 

Yearl (21) (132) 146) (5,725) (5,562) 

 6
2. Sensitivity to Deviation Bandwidth 

o see how much change to the potential wind plant revenue would occur under a wider 

ected 

 case, 

Table 4. 10% Deviation Band with 90%/110% Rule 

±10% De 005 

T

deviation band, the same simulation was performed using a ±10% deviation band. As exp

the net revenue for the wind plant operators would increase, but only slightly (about 0.29% 

higher). However, they are still less than the base cases—ranging from 1.42% to 1.85% less.

Table 4 lists the results. Both the charge for under-delivery and additional payment for overdelivery decrease as expected. The imbalance energy within the deviation band increases 

significantly as do the annual net amounts that are settled at the end of each month. In this

the monthly net imbalance energy within the deviation band is always negative (see Table 5 for 

the monthly netted energy and net charges). 

viation Band 2003 2004 2

Imbalance (MWh) 

Total Over-Delivery 32,71 2 32,67

T

Revenue ($000) 

Base Pay

 P

Sum

13 10 11

Unit Pri

) 

C

1 32,59 2

Inside Band

otal Un

10,953 9,496 

(3

10,532 

der-Delivery (32,743) 2,605) (32,712) 

Inside Band (13,249) (11,858) (12,914) 

ment 13,855 10,530 12,098 

Outside Band ayment @90% 886 898 891 

Outside Band Charge @110% (922) (972) (912) 

 of Inside Band Monthly netting (99) 

,

(103) (103) 

Net (Monthly Netting)

ce (¢/k

720 ,353 ,974 

Wh) 

Net (Monthly Netting) 4.232

% Cha

4.373 4.225 

nge over Base Case (1.42% (1.85%) (1.54%)

hange over the ±1.5% Case 0.29% 0.27% 0.32% 

Table 5. Summary of Monthly Imbalance with ±10% Deviation Band 

 2003 2004 2005 

Average 

(

Month

Im

yment Month

Im

yment Month

Im

yment 

Hourly 

Price 

$/MWh) 

ly Pa

Net 

balance 

(MWh) 

or 

(Charge) 

($) 

ly Pa

Net 

balance 

(MWh) 

or 

(Charge) 

($) 

ly Pa

Net 

balance 

(MWh) 

or 

(Charge) 

($) 

Jan 37.98 (209) (7,944) (260) (9,879) (185) (7,029) 

Feb 40.52 (244) (9,900) (113) (4,506) (154) (6,222) 

Mar 34.41 (277) (9,547) (314) (

(

(

( (

y 44.38 (2,296) (2 ( (2,381) (

10,811) (157) (5,402) 

Apr 38.92 (50) 

(

(1,959) (144) (5,606) (303) 11,780) 

May 28.81 238) (6,857) (183) (5,270) (339) (9,763) 

Jun 44.32 (106) (4,694) (221) (9,808) (199) (8,800) 

Jul 55.88 (163) (9,125) (60) 

(

(3,371) (92) 

(

(5,130) 

Aug 46.82 (104) (4,886) 148) (6,918) 112) (5,257) 

Sep 42.87 (232) (9,937) (167) (7,178) (135) (5,785) 

Oct 45.33 (206) (9,361) (283) 12,832) (156) (7,080) 

Nov 42.92 (295) 12,672) (257) (11,021) (308) 13,207) 

Dec 73.14 (169) (12,389) (211) (15,452) (243) (17,754) 

Yearl (99,271) ,362) 102,652) 103,209) 

When the deviation band is further increased to ±20%, the revenues continue trending upward 

(see Table 6)—about 0.67% more than that in the ±1.5% case. This exercise confirms the idea 

that wider deviation band is better than narrower one, but the improvement is only marginal. 

 7
Table 6. 20% Deviation Band with 90%/110% Rule 

±20% Deviation Band 2003 2004 2005 

Imbalance (MWh) 

Total Over-Delivery 32,71 32,59 32,67

T

Revenue ($000) 

Base Pay

 P

Sum

13 10 12

Un 

C

1 2 2 

Inside Band

otal Un

15,856 13,787 15,149 

der-Delivery (32,743) (32,605) (32,712) 

Inside Band (20,350) (18,690) (20,023) 

ment 13,855 10,530 12,098

Outside Band ayment @90% 686 730 713 

Outside Band Charge @110% (587) (643) (581) 

 of Inside Band Monthly netting (196) (213) (212) 

Net (Monthly Netting)

it Cost (¢/kW

,759 ,403 ,017 

h) 

Net (Monthly Netting)

% Cha

4.385 4.252 4.241 

nge over Base Case (1.14%) (1.37%) (1.18%) 

hange over the ±1.5% Case 0.57% 0.76% 0.69% 

. Sensitivity to Improved Forecast Accuracy 

he effects of improved wind power forecasting and scheduling is more noticeable. To simulate 

 of 

t 

Table 7. 15% Forecasting Improvement and ±1.5% Deviation Band with 90%/110% Rule 

3

T

the scheduling improvement, the hourly imbalance from the persistence method is artificially 

reduced by a fixed ratio. A two-step process is used to get the desired outcome. To simulate a 

15% improvement in forecasting accuracy over the simple persistency method, the hourly 

imbalance from the simple persistence method is first multiplied by 0.85 and then the value

the scheduled energy for the hour is modified to reflect reduced imbalance. Table 7 lists the tes

case for 15% improvement in wind forecasting (and scheduling) over the persistence. The out-ofband (±1.5%) imbalance energy decreased 18.5%, but the increase in net revenue is small (about 

0.3% more than the base case). 

±1.5% Deviation Band 2003 2004 2005 

Wind Generation (MWh) 

Actual 313,7 5 283,3

Sch

Imbalance (MWh) 

tal Ov

T ( ( (

Revenue ($000) 

10,53 12,10

 Payment @

Sum

13 10 11

Un

he ±1.5% C

71 244,65 78 

eduled 313,798 244,665 283,412 

To er-Delivery 27,804 27,703 27,771

Inside Band 

otal Un

6,889 

2

6,636 

2

6,822 

der-Delivery 7,832) 7,714) 27,805) 

Inside Band (6,910) (6,775) (6,932) 

Base 13,865 3 7 

Outside Band 90% 854 819 837 

Outside Band Charge @110% (996) (981) (963) 

 of Inside Band Monthly netting (0)* 

,7

(6) 

,3

(5) 

Net ,9

it Cost (¢/kWh) 

22 66 76 

Net 4.373 4.237 4.226 

% Change over t ase 0.30% 0.40% 0.33% 

*small negative amou than $1,0

able 8 lists the results of the test case of a 25% improvement in forecasting accuracy. 

and 

%. 

nt less 00.00 

T

Compared to the base case, the improvement in wind forecasting decreases the out-of-b

energy by more than 30%. However, the net revenue is only increased by a very modest 0.6

 8
Table 8. 25% Forecasting Improvement and ±1.5% Deviation Band with 90%/110% Rule 

±1.5% Deviation Band 2003 2004 2005 

Wind Generation (MWh) 

Actual 313,7 5 283,3

Sch

Imbalance (MWh) 

tal Ov

T ( ( (

Revenue ($000) 

10,53 12,11

 Payment @

Sum

13 10 12

Un

he ±1.5% C

71 244,65 78 

eduled 313,795 244,664 283,408 

To er-Delivery 24,533 24,444 24,504

Inside Band 

otal Un

6,755 

2

6,504 

2

6,682 

der-Delivery 4,558) 4,454) 24,534) 

Inside Band (6,770) (6,641) (6,783) 

Base 13,871 5 3 

Outside Band 90% 726 697 712 

Outside Band Charge @110% (847) (834) (818) 

 of Inside Band Monthly netting 0 

,75

(6) 

,3

(5) 

Net ,00

it Cost (¢/kWh) 

0 93 4 

Net 4.382 4.248 4.236 

% Change over t ase 0.50% 0.66% 0.57% 

iscussions and Conclusions 

he analyses have shown that the FERC final rule of energy imbalance will have a noticeable 

he reason is not difficult to understand. The most critical factor for a wind plant’s revenue is its 

. 

h. 

 should be noted that in this analysis the hourly wind power schedule based on a simple 

lute 

y 

casting 

e a 

 

D

T

impact on the revenue of a wind plant. With the MISO hourly price data and actual hourly wind

production data, the rule would reduce the revenue by about 1.9% compared to an “ideal” 

scenario. With the 90%/110% stipulation in place, changing the deviation band width and 

forecasting accuracy will not have much of an effect on the result. 

T

actual generation (how much and when it is generated), which determines the gross revenue. The 

highest scheduling penalty under the 90%/110% rule is about 10%.5 Improvement in forecasting 

accuracy and wider deviation bands only address 10% of the revenue (under the 90%/110% rule)

Any forecasting strategy will likely end up with a very small imbalance energy over a year. (For 

shorter periods, the variation could be larger, but our simulation is focused on annual revenue.) 

The effect of different forecasting methods will place more or less energy within the deviation 

band that will be netted out at the end of the month, but those imbalances are small to begin wit

Consequently, the adjustment on the gross revenue tends to be relatively small regardless of the 

forecasting strategy and deviation band. 

It

persistent forecasting method actually produces very good results. The monthly mean abso

errors (MAE)6

, a common metric to gauge the performance of wind power forecasting 

methodologies, of this approach are in the 7% to 8% range. The simulated 15% accurac

improvement further decreases the monthly MAE to about 6%. The key to such good fore

performance is the assumption that the hourly wind power forecasting was produced only 80 

minutes before the hour. If the market rules or actual wind power forecasting does not produc

5

 In an extremely simplified case the maximum penalty under the 90%/110% rule will be 10%. For example, if the 

forecasted hourly energy is constantly over the actual generation for every hour of the year, and the hourly price is 

constant for the entire year, the revenue would be 10% lower than that without the 90%/110% rule. 

6

 Mean absolute error is the average magnitude of differences between the actual and scheduled wind power over a 

specified period. It is usually expressed as a percentage of wind plant installed capacity. 

 9
result similar to that in this analysis, the impact of the FERC energy imbalance rule will be even 

more prominent. 

The analysis showed that the actual penalty for wind energy under the FERC energy imbalance 

rule is not large. It can be argued that this is a reasonable price for wind power plants to pay in 

order to promote accurate scheduling and maintain system reliability. However wind is not 

capable of intentionally deviating from the schedule and a small penalty on wind power cannot 

change the nature of wind power. 

 10
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July 2007 

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DE-AC36-99-GO10337 

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4. TITLE AND SUBTITLE

Impact of Energy Imbalance Tariff on Wind Energy 

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NREL/CP-500-40663 

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WER77002 

6. AUTHOR(S)

Y. Wan, M. Milligan, and B. Kirby 

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National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401-3393 

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This paper summarizes the results of a study that uses actual wind power data and actual energy prices to analyze 

the impact of an energy imbalance tariff imposed by the Federal Energy Regulatory Commission on wind power. 

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wind energy; systems integration; utility; energy generaton; energy regulation 

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