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

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Innovation for Our Energy Future 

ERCOT Event on February 26, 2008: 

Lessons Learned 

E. Ela 

National Renewable Energy Laboratory 

B. Kirby 

Oak Ridge National Laboratory

Technical Report 

NREL/TP-500-43373 

July 2008 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
ERCOT Event on February 26, 2008: 

Lessons Learned 

E. Ela 

National Renewable Energy Laboratory 

B. Kirby 

Oak Ridge National Laboratory

Prepared under Task No. WER8.5001 

Technical Report 

NREL/TP-500-43373 

July 2008 

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Introduction/Abstract 

On February 26, 2008, the Electric Reliability Council of Texas (ERCOT) called for an 

Emergency Electric Curtailment Plan (EECP) at 18:41 due to a worsening imbalance 

between generation and load which led to a decline in system frequency. The event is of 

special interest, and was widely reported on in the press, because wind generation played 

a partial role in the event. Other load-response events, which did not involve wind 

generation, have not received similar attention. 

Three major contributions to this event include a large ramp-down of wind generation 

which started at 15:00, the unexpected loss of conventional generation, and a quicker 

than expected evening load ramp-up. Collectively these factors led to ERCOT calling on 

reserve capacity, including Loads acting as a Resource (LaaR) – large industrial and 

commercial electricity users who have agreed to allow ERCOT to curtail their electricity 

supply in exchange for economic compensation – to both increase generation and reduce 

total demand. With a more accurate generation and demand forecast, ERCOT could have 

easily scheduled additional generation to be available in advance of the evening load 

pickup and avoided the need for this emergency response. The event itself lasted less than 

two hours and no customers lost power involuntarily. 

EECP 

The EECP is called on by ERCOT when the amount of available generation is limited. 

EECP has four steps depending on the criticality of the event. Each of these steps has 

different procedures based on what actions ERCOT will carry out. Before EECP is 

called, ERCOT will usually issue an Operating Condition Notice, an Advisory, an Alert, 

and an Emergency Notice as conditions worsen. These notifications are designed to elicit 

full, available market response to increase generation supply and maintain reliability. 

Once the amount of adjusted reserves is below 2300 MW, ERCOT will enter into EECP 

Step 1. This step allows operators to use the DC ties to import as much power as 

possible into the ERCOT system and to use out-of-merit generation dispatch to bring on 

line all available additional generation that can physically respond. Step 2 is entered 

when reserves fall below 1750 MW and allows the ERCOT operators to call upon all 

responsive reserves from LaaR, request Block Load Transfers to neighbors, and strongly 

encourage conservation through media appeals. Step 3 uses ‘Emergency Interruptible 

Load Services’ to maintain system frequency at 60 Hz. The EECP Step 4 allows ERCOT 

system operators to shed firm load in 100-MW blocks if system frequency drops to 59.8 

Hz. 

The event on February 26 caused an EECP Step 2, where LaaRs were curtailed, but 

otherwise no load was turned off. 

Event Chronology 

• 15:00 – Wind generation output at 2000 MW and begins a 3.5-hour ramp down to 

360 MW at 18:30. The down ramp was 2 hours sooner and somewhat faster (8 

MW/minute vs. 5 MW/minute) than forecast the day ahead. 

1 
• 17:10 – Evening load ramp begins, increasing 3800 MW in 90 minutes, (42 

MW/minute). The evening load ramp-up began 25 minutes earlier than the shortterm hour-ahead load forecast predicted.

• 17:44 – 150 MW conventional unit trips offline (370-MW capacity).

• 18:00 – Responsive reserves rapidly declining, up-balancing energy bid stack 

depleted, up-regulation depleted, frequency declining, load increasing at 

50MW/minute.

• 18:24 – Reserves fall below 3000 MW and ERCOT issues an Advisory.

• 18:28 – ERCOT calls on non-spin service to come on-line and requests startup of 

combustion turbines.

• 18:30 – Frequency declines to 59.94 Hz.

• 18:33 – 328 MW of Responsive (spinning) Reserve deployed automatically 

because of low frequency (59.91 Hz).

• 18:41 – Responsive reserves at 1725 MW, system frequency at 59.85 Hz: ERCOT 

declares EECP Step 2.

• 18:49 – ERCOT instructs 1150 MW of LaaRs to reduce consumption.

• ERCOT requested emergency power from SPP. This request was never 

successfully completed. 

• 18:52 – Frequency recovers to 60 Hz.

• 18:56 – Spinning reserve deployment ends.

• 18:59 – LaaR response reaches 1108 MW within 10 minutes (1200 MW within 12 

minutes).

• 20:08 – ERCOT ends Step 2 and enters Step 1, LaaR deployment ends and loads 

allowed to return to service.

• 20:15 – Requested and received 30 MW of emergency power from Mexico.

• 21:40 – EECP terminated.

Load Forecast 

ERCOT uses two main load forecasts when determining unit commitment and dispatch. 

The first, called the medium-term or ‘Day-Ahead’ load forecast is used to assure that 

enough generation is scheduled to be on-line to meet the hour-by-hour demand for the 

following day. The second, usually referred to as a short-term or real-time load forecast, 

is used in near real-time to adjust online capacity to meet the changing load demand on a 

more granular scale (e.g., 5 or 15 minutes). Both forecasts use similar inputs (weather 

forecast, historical load data, etc.). During the evening of February 26th, expected DayAhead demand and scheduled supply (including 1294 MW of scheduled wind) indicated 

a 1400 MW excess of generation above load. Because of this excess, ERCOT had no 

reason to procure additional reserve capacity. Figure 1 shows the Day-Ahead load 

forecast and the actual load for February 26, 2008. The Day-Ahead forecast is well above 

the actual load, except for the time immediately before and after the 18:41 reliability 

event. 

2 
Figure 1. The Day-Ahead forecast exceeded the actual load on February 26 except for the few 

minutes around the 18:41 reliability event. (Figure adapted from ERCOT Operations Report) 

One key observation is the rapid load increase from 18:00 to 18:45; more than 2,500 

MW, or about 55 MW/min. This was not an unusual ramp; morning load pickups often 

exceed 75 MW/min in ERCOT and winter evening pickups like this one often reach 50-

60 MW/min. While the February 26, 2008 load ramp itself was not extremely large, it did 

differ from the pattern seen the day before (Figure 2). Note that both days are weekdays. 

3 
Load Profiles of 2/25 and 2/26

0

5000

10000

15000

20000

25000

30000

35000

40000

12:00:00 AM 4:48:00 AM 9:36:00 AM 2:24:00 PM 7:12:00 PM 12:00:00 AM 4:48:00 AM

MW

26-Feb

25-Feb

Figure 2. The late afternoon load pattern differed significantly on February 26, 2008 from the 

pattern seen the day before. 

Figure 2 shows that the load profiles after the morning load pickup differ by quite a bit. 

As stated earlier, historical load data is a key input to the load forecaster. Once the prior 

day’s data is available (i.e., for the short-term load forecast) it is used to predict the 

upcoming load. Figure 3 shows both days’ actual load profiles along with the short-term 

load forecast (STLF) for the event day. As can be seen, the short-term load forecast 

performed very well when the event day coincided with the previous day, but did not 

when the two diverged. This was a major factor in triggering of the EECP as the load 

ramp occurred prior to the short-term load forecast prediction, more than a 3% error 

(more than 1,000 MW) resulting from the influence of the prior day’s load profile. The 

next section discusses wind energy forecasting, which unlike load forecasts, tend not to 

be correlated from one day to another and therefore rely much less on data from the prior 

day, and more on the most recent data received for the current day. 

4 
Figure 3. The short term load forecast failed to predict the February 26, 2008 ("Today") load and 

instead followed the pattern of the previous (“Yesterday”) day. (Figure adapted from ERCOT 

PDCWG presentation on March 11, 2008) 

Wind Energy Forecasting 

One portion of the incorrect amount of available capacity given by the Qualified 

Scheduling Entities (QSE) during this event (although by no means the only one) was the 

assessment of wind power availability. As has been stated a number of times following 

this event, the forecast of wind power coming from ERCOT’s contracted forecast 

provider was very accurate in its prediction. Unfortunately, this wind forecast had not yet 

been integrated into ERCOT’s system operations on February 26th. Instead, the 

scheduling of wind resources was done based on their corresponding QSE’s resource 

schedules that are given to ERCOT both a day ahead and about an hour before the 

operating hour. While the use of the forecast is an obvious lesson learned and has been 

anticipated for quite a while at ERCOT, the way by which it is used can be just as 

important as the forecast itself. 

Most scheduling software applications have a built-in sanity check so that resource 

capabilities are not misrepresented. This might include taking the metered output of 

generation at the time the application initializes, as well as the response rate of that 

generator to determine how much actual capacity is possible. For wind generation, one 

can say it has essentially no response rate since it cannot respond to dispatch orders (wind 

generators do have some capability of downward response, however). Therefore, the 

value of the wind generator’s capability is assumed based on its generation output during 

the initialization of scheduling. This is effectively a persistence-based forecast that has 

5 
almost no cost of implementation. This type of assessment works very well for flatter 

periods, but decreases in effectiveness for larger ramping periods like the one during this 

event. 

Most state-of-the-art short-term wind energy forecasts build upon this idea by taking 

recent data and using more sophisticated mathematical techniques to produce a forecast 

of the wind energy. This is very similar to the short-term load forecast, where a major 

predictor is past behavior. This causes a characteristic error of “delay.” The forecast tends 

to trail reality as it is strongly influenced by a persistence forecast. Alternatively, a DayAhead wind energy forecast may have the correct profile of the wind production, but 

shifted in time forward or backward. This would also give larger errors when the wind 

production is moving in fast ramps before or after it was expected. In both instances, it is 

up to the system operator to prepare for the wind event, and by looking at both the 

forecast and actual generation data, act appropriately. 

Some of the more advanced short-term wind energy forecasts will use offsite 

observations to get signals about the upcoming ramping behavior. With the ideal scenario 

of having multiple offsite observations all being a radius of an hour away, an oncoming 

ramp could be more accurately forecast an hour ahead of time. 

Setting aside the algorithms that come up with a wind energy forecast, the forecast itself 

needs to be presented to the system operators in a way that allows them to prepare for 

large changes in wind generation. This may include using an appropriate advisory time 

frame, as well as an assessment of the variability inside a normal forecasting time frame. 

Since day-ahead unit commitment is an evaluation of the entire day, a full-day wind 

forecast is usually used in conjunction with a full-day load forecast. During real-time 

operation, system operators still need to see a few hours ahead, and be able to prepare for 

large increases and decreases of wind generation since it may be possible that slower 

start-up units may have to be turned on to accommodate lost generation. It can also give 

efficient solutions based on the duration of a wind power increase or decrease. For 

example, let’s assume the wind forecast predicted that during the February 26th event, 

the wind power decreased in hour 18, but by 19:30, the wind generation began to increase 

to where it had been previously. The system operator in this case would know that it 

might make more sense to have some quick start units with shorter minimum run times 

on during this time, rather than to bring up a unit with a long minimum run-time and most 

likely, a high startup cost. 

Figure 4 shows the hour-ahead resource plan for all wind given by the QSEs, the wind 

forecast from the forecast provider to be used in the future ERCOT nodal system 

(forecast uses an 80% exceedance level), and the actual wind generation output. 

6 
Figure 4. Comparison of wind generation, wind forecast, and wind power resource plans (figure from 

ERCOT Operations Report, http://www.ercot.com/meetings/ros/keydocs/2008/0313/07._ERCOT_

OPERATIONS_REPORT_EECP022608_public.doc) 

Comparing the forecast and actual data for the 16:00 hour, one would say that the 

forecast is fairly accurate. However, one could also argue that by using the 16:00 

forecast, it is actually close to 300 MW less than the actual production at exactly 16:00. 

During unit commitment decisions, especially those made day-ahead, system operators 

are typically not largely worried about the dispatch of the generation as long as there is 

enough capacity and reserve able to be adjusted when needed in real-time. That being 

said, this forecast is extremely useful in knowing that commitments should be chosen 

based on about 1200 MW of wind generation for the 16:00 hour. However, when 

operators are trying to match generation to load as closely as possible during real-time 

operation, 300 MW is a pretty big deviation. Especially during faster wind ramping 

periods, an hourly average sometimes cannot give the entire story of what the wind is 

doing for that entire time period. To mitigate this situation, there are a few things that 

system operators can do. First, by requesting shorter time periods for the wind forecast 

power averages from the forecast provider when solving economic dispatch (e.g., 15 min 

forecast averages) you can get a more accurate result that more closely aligns with the 

timeframes for available load forecasts and balancing energy schedules. Second, as 

discussed before, by updating the forecasts with the most-recent meter readings from the 

wind plants, this can also give more frequent and accurate predictions. Third, a 

probabilistic approach to wind ramp forecasting can be used to better represent the 

likelihood of such events, and also account for the timing error of the Day-Ahead 

forecast. Lastly, many state-of-the-art forecasting applications have the capability to 

7 
produce a prediction of the amount of variability in the wind along with the predicted 

value itself (e.g., a comparison of the 50% value vs. an 80% value can do this). This 

information can let the operator know an hour ahead of time if he needs to have enough 

load following or regulation reserve flexibility in case the wind does start varying away 

from its average value. Research targeted at giving operators a better idea of variability 

inside a time period, as well as error probability, is ongoing. 

Large Wind Ramps 

The February 26, 2008 drop in wind generation was large but relatively slow for a power 

system reliability event, taking three hours to drop 1,500 MW. This is typical for large 

wind events. A similar 1,500 MW drop in wind generation occurred a little over a year 

earlier, on February 24, 2007, and took two hours. The geographic diversity inherent as 

larger amounts of wind generation are added to the power system limit the speed that 

wind events can propagate across the wind generation fleet. “Analysis of West Texas 

Wind Plant Ramp-up and Ramp-down Events” (AWS Truewind, January 28, 2008) 

reports that ERCOT can expect less than one 2,800-MW, 30-minute drop in wind output 

(93 MW/minute) per year with a 15,000-MW wind fleet. Two to four 1,300-MW, 30- 

minute increases or decreases can be expected (43 MW/minute). Large ramp events are 

limited even with very large amounts of wind generation and are still in the same size 

range with normal load movements. 

Large wind ramps (Figure 4) are not comparable as reliability events with instantaneous 

generation failures. Conventional generation contingencies require dedicated spinning 

reserves and immediate response (2300 MW of ERCOT Responsive Reserve Service). 

Multi-hour wind ramps give the system operator time to utilize market responses, load 

response, supplemental reserves, or non-spinning reserves. All of these options are much 

cheaper than maintaining and deploying spinning reserves required for conventional 

generation. 

Load Response 

The fact that ERCOT used 1200 MW of LaaR on February 26, 2008 as part of its event 

response is not unusual. ERCOT is very advanced and makes greater use of voluntary 

load response than any other region in the country. This benefits the power system and 

the responsive load. Loads compete to sell this service to ERCOT. ERCOT used 

responsive load as non-spinning reserve (30-minute response) 63 times in 2007 alone. 

The average load response was 1137 MW and lasted just under three and a half hours. 

The largest deployment was 1354 MW and the longest lasted six hours and 45 minutes. 

LaaRs provide spinning reserve (10-minute response) about ten times per year. The 

February 26, 2008 use of responsive load was neither exceptionally large nor 

exceptionally long (see http://www.ercot.com/mktinfo/services/nsd/archive.html). 

Summary of Lessons Learned 

There were a number of different measures that collectively led to the EECP event in 

ERCOT. In comparing the two load forecasts used for this day, it can be seen that there 

may be a need for an alternative load forecast methodology for days when the historical 

data inputs do not align with the present day. Using a load profile from a similar 

historical day instead of the most recent day may give some better predictions. The use 

8 
9 

and application of wind forecasts is also a good lesson here. In particular, utilities and 

RTOs need to evaluate the best way to predict wind ramp events, and based on the event, 

determine the different ways to adjust the system (including allowing for timing errors). 

It is important to note that wind ramps of this duration are very different than 

contingency events involving instantaneous loss of generation or transmission. Lastly, it 

is important to understand and recognize the considerable benefits of using interruptible 

load as a resource for system reliability in these types of situations. 
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The event analyzed in this paper is of special interest, and was widely reported on in the press, because wind 

generation played a partial role in the event. 

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