Analysis of ERCOT Regulation Service

Deployments during 2011

David MaggioMarket Enhancement Task Force Meeting

3/29/2012

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Assumptions

• The analysis presented here was done using hourly average, maximum and minimum data

• For these purposes, the term “regulation deployed” refers to the total amount of regulation that was requested by ERCOT from the various QSEs and does not necessarily imply that the energy was realized

• Load and wind volatility for a given hour was calculated as the difference between the maximum and minimum value of the system load or aggregate WGR output

• It should be noted that in the context of this analysis, “deviation” does not necessarily indicate that a Market Participant is behaving incorrectly– A WGR may “deviate” from expected during periods in which the

weather is changing rapidly– A thermal generator may “deviate” from expected when shutting

down as a result of the non-optimal method in use during the analysis period

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General Statistics

• Regulation deployments were biased towards regulation up• There were a significant number of hours of only regulation

up being deployed• There was clear negative correlation between regulation

deployments and generation deviations• During hours in which regulation was deployed in a single

direction, the generator deviations seem to be in a direction that would require those deployments

Average Regulation Deployed 55Number of Hours During Which Only Reg. Up was Deployed 590

Number of Hours During Which Only Reg. Down was Deployed 178Correlation Between Average Reg. Deployed and Average Gen. Deviation -0.808Correlation Between Average Reg. Deployed and Average WGR Deviation -0.516

Correlation Between Average Reg. Deployed and Average Non-WGR Deviation -0.691Average Gen. Deviation When Only Reg. Up Deployed -274

Average Gen. Deviation When Only Reg. Down Deployed 224

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• The correlation between average reg. deployment and average gen. deviation for a given hour was -.81– This indicates a strong relationship between the two datasets– The scatter plot also indicates the correlation with a strong linear relationship

between the two datasets and the data points primarily being in the II and IV quadrants

General Statistics

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• There was a clear connection between average regulation deployed and average generator deviation

• Hours of only deploying regulation up tended to occur after hour 13– These hours are also when there was the greatest bias towards deploying reg. up – During these hours there was a tendency to under-generate, particularly during

hour 23 (when resources are typically shutting down)

• Hours of only deploying regulation down tended to occur during hours 2-6– These hour are also when there was a bias towards deploying reg. down – During these hours there was a slight tendency to over-generate

General Statistics by Hour of the Day

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• WGRs showed a consistent tendency to under-generate– The bias decreased during the evening and early night hours– This makes sense as these would be the hours when wind output is typically ramping up

• Non-WGRs showed a tendency to over-generate in the early morning hours and to under-generate after hour 12

– Over-generating around early hours lines up somewhat with when non-WGRs would typically come online

– Under-generation around hours 23-1 lines up with when generators would typically go offline

– Hour 23 seems to be a particular concern for non-WGRs

General Statistics by Hour of the Day

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Hour Ending

Number of Hours During Which Only

Reg. Up was Deployed

Number of Hours During Which Only

Reg. Down was Deployed

Average Reg. Deployed

Average Gen. Deviation

Average WGR Deviation

Average Non-WGR

Deviation

Average Load Volatility

Average Wind Volatility

1 16 4 94.9 -102.6 -42.6 -60.0 2137.8 541.42 5 13 11.8 8.4 -40.3 48.7 1449.0 398.63 12 15 -4.4 13.3 -46.0 59.3 954.6 387.54 10 13 1.2 1.5 -52.0 53.5 756.6 379.85 8 14 -14.1 1.6 -53.2 54.8 939.1 391.66 5 10 -23.5 21.3 -51.9 73.3 1980.0 377.57 9 8 59.3 -70.2 -54.6 -15.6 2318.3 388.68 23 4 46.7 -37.8 -68.7 30.9 1266.3 480.09 32 5 49.8 -42.9 -69.5 26.7 1912.6 571.4

10 25 15 28.3 -25.8 -52.7 26.9 2111.1 507.311 14 6 33.7 -39.1 -52.3 13.3 2097.4 491.112 18 6 62.5 -77.8 -57.8 -20.0 1944.9 427.013 22 1 60.8 -79.1 -50.0 -29.2 1708.9 385.714 35 9 70.0 -81.5 -40.6 -40.9 1525.0 351.915 42 10 71.3 -83.4 -34.4 -49.0 1286.3 379.216 37 8 65.4 -68.7 -30.4 -42.6 976.5 373.017 45 8 68.8 -66.2 -31.3 -34.9 632.5 390.518 38 3 83.5 -83.5 -41.1 -42.5 1397.4 444.419 26 2 57.0 -47.0 -26.2 -20.8 1777.7 465.520 31 10 54.2 -41.8 -15.1 -26.6 1444.5 539.321 45 5 92.4 -71.7 -14.8 -56.9 1141.0 514.022 28 4 87.0 -52.6 -2.3 -50.3 2356.9 535.923 36 3 160.1 -125.8 -8.5 -117.2 3147.1 531.824 28 2 107.5 -64.8 -28.6 -36.2 3107.7 557.0

General Statistics by Hour of the Day

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General Statistics by Month

• The connection between average regulation deployed and average generator deviation again looks clear

• Hours of only deploying regulation up tended to occur during April, June, and July– These months are also when there was the greatest bias towards deploying regulation up – During these months there was a tendency to under-generate across all resources

• Hours of only deploying regulation down tended to occur during January and February

– Next slide shows that months where there was number of hours in which only regulation down deployed were also the months that non-WGR generators tended to over-generate

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General Statistics by Month

• Generators as a whole showed a consistent tendency to under-generate– WGRs were the primary driver in this tendency for the beginning of the year– WGRs seemed to have a particularly high tendency to under-generate in April and

June

• Tendency of WGRs to under-generate has seemed to improve since July– Improvements in the method for sending HSL telemetry may have helped

• Non-WGRs did have months in which there was a tendency to over-generate, in particular January and February

– These months generally had more hours in which only regulation down was deployed9

General Statistics by Month

Month

Number of Hours During Which Only

Reg. Up was Deployed

Number of Hours During Which Only

Reg. Down was Deployed

Average Reg. Deployed

Average Gen. Deviation

Average WGR Deviation

Average Non-WGR

Deviation

Average Load Volatility

Average Wind Volatility

1 39 45 19.6 -14.3 -21.7 7.4 1330.7 447.52 41 36 35.4 -35.8 -59.6 23.8 1342.2 473.43 57 7 63.1 -42.6 -57.0 14.4 1196.9 520.64 74 8 86.6 -87.8 -93.1 5.3 1509.3 552.65 42 11 55.9 -55.6 -41.4 -14.1 1704.7 515.26 119 4 130.1 -142.3 -89.0 -53.3 2254.0 501.27 73 3 93.9 -95.1 -36.9 -58.2 2367.4 395.98 36 7 49.2 -31.7 -12.0 -21.8 2511.5 395.39 30 22 32.4 -29.9 -15.7 -14.2 2166.1 371.5

10 22 16 21.1 -19.9 -23.2 3.3 1461.6 389.511 27 8 47.4 -36.9 -18.3 -18.6 1118.8 358.512 30 11 27.9 -17.5 -18.3 0.7 1199.6 485.6

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Observations

• This analysis refers to generator “deviation,” however this does not necessarily indicate that a Market Participant is behaving incorrectly– Wind ramps, unit trips, the shut-down and start-up process

• There is a strong negative correlation between generator deviation and regulation bias– Both WGRs and non-WGRs contributed significantly to the overall

deviation– WGRs were shown to consistently under-generate through most hours

of the day for most months • Method for updating the telemetered HSL would have contributed to the

deviation for the first several months after Go-Live implying that the regulation bias may have improved as a secondary benefit of the improvements have been made to HSL telemetry

– Non-WGR resource deviation varied by hour of the day with a tendency to over-generate during the early morning and under-generate during the second half of the day

– Non-WGR resources also showed a greater tendency to under-generate during the summer months, particularly June and July

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Observations

• There did not appear to be any significant correlation between regulation bias and load volatility– Dispatch seems to be accounting for the 5 minute load changes

relatively well

• There does appear to be a connection between only deploying regulation in a single direction and WGR output, in particular during prolonged wind ramps

• The largest regulation bias is in the up direction was during the end of the day when non-WGR resources are likely going offline

• The implementation of NPRR 348 should improve regulation bias during the hours when resources are typically shutting down or starting up– The NPRR is scheduled for implementation during the summer of 2012

• The plan is to work with stakeholder working groups to discuss possible ways to make improvements

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