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ERCOT

ERCOT. LRS Precision Analysis PWG Presentation June 28, 2006. Options for Determining Round Two Sample Size Increases. Option 1:

kristen-haynes
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  1. ERCOT LRS Precision Analysis PWG Presentation June 28, 2006

  2. Options for Determining Round Two Sample Size Increases • Option 1: • Determine minimum sample size increase needed to obtain ±10% Accuracy at 90% Confidence for a selected percent of intervals for the year independently for each Profile Type / Weather Zone Combination • Option 2: • Determine minimum sample size increase needed to obtain ±10% Accuracy at 90% Confidence for a selected percent of intervals such that all Profile Type / Weather Zone Combinations meet the accuracy target in all selected interval • Option 3: • Determine minimum sample size increase needed to obtain ±10% Accuracy at 90% Confidence for enough intervals to account for a selected percent of the MWh for each Profile Type / Weather Zone Combination • Option 4: • Determine minimum sample size increase needed to obtain ±10% Accuracy at 90% Confidence for enough intervals to account for a selected percent of the dollars (ΣMWh * MCPE) for each Profile Type / Weather Zone Combination • Option 5: • Iteratively allocate increments of 10 sample points to the Profile Type / Weather Zone Combination which produces the most gain in terms of reducing MWh estimation error • Option 6: • Iteratively allocate increments of 10 sample points to the Profile Type / Weather Zone Combination which produces the most gain in terms of reducing Dollar estimation error

  3. Option 1 • To obtain ±10% Accuracy at 90% Confidence By Profile Type and Weather Zone - Independent of Interval Minimum Sample Size Increase Required For example: to obtain ±10% Accuracy at 90% Confidence for 50% of the intervals for BUSHILF_COAST would require a sample size increase of 10 points

  4. Option 1 • To obtain ±10% Accuracy at 90% Confidence By Profile Type and Weather Zone - Independent of Interval Minimum Sample Size Increase Required

  5. Option 1 • To obtain ±10% Accuracy at 90% Confidence By Profile Type and Weather Zone - Independent of Interval Increase of Sample Size

  6. Option 1 • To obtain ±10% Accuracy at 90% Confidence By Profile Type - Independent of Interval For example: to obtain ±10% Accuracy at 90% Confidence for 50% of the intervals for all Profile Type/Weather Zone combinations would require a sample size increase of 5,358 points

  7. Option 2 • To obtain ±10% Accuracy at 90% Confidence for all Profile Types and Weather Zones - Within Each Interval For example: to obtain ±10% Accuracy at 90% Confidence for 50% of the intervals such that all Profile Type/Weather Zone combinations within those intervals would achieve that level of accuracy would require a sample size increase of 750 points for BUSHILF and 2,267 points for BUSMEDLF

  8. Option 2 • To obtain ±10% Accuracy at 90% Confidence for all Profile Types and Weather Zones - Within Each Interval For example: to obtain ±10% Accuracy at 90% Confidence for 50% of the intervals such that all Profile Type/Weather Zone combinations within those intervals would achieve that level of accuracywould require a sample size increase of 5,703 points for BUSLOLF and 7,047 points for BUSNODEM

  9. Option 2 • To obtain ±10% Accuracy at 90% Confidence for all Profile Types and Weather Zones - Within Each Interval

  10. Option 2 • To obtain ±10% Accuracy at 90% Confidence for all Profile Types - Within Each Interval For example: to obtain ±10% Accuracy at 90% Confidence for 1% of the intervals such that all Profile Type/Weather Zone combinations within those intervals would require a sample size increase of 7,749 points

  11. Option 3 • To obtain ±10% Accuracy at 90% Confidence for Intervals Accounting for Selected Percents of the MWH within Each Profile Type and Weather Zone For example: to obtain ±10% Accuracy at 90% Confidence for intervals accounting for 50% of the BUSMEDLF MWh would require asample size increase of 170 points

  12. Option 3 • To obtain ±10% Accuracy at 90% Confidence for Intervals Accounting for Selected Percents of the MWH within Each Profile Type and Weather Zone

  13. Option 3 • To obtain ±10% Accuracy at 90% Confidence for Intervals Accounting for Selected Percents of the MWH within Each Profile Type and Weather Zone

  14. Option 3 • To obtain ±10% Accuracy at 90% Confidence for Intervals Accounting for Selected Percents of the MWH within Each Profile Type For example: to obtain ±10% Accuracy at 90% Confidence for intervals accounting for 50% of the MWh for each of the Profile Type / Weather Zone combinations would require a sample size increase of 4,322 points Continues on next slide

  15. Option 3 • To obtain ±10% Accuracy at 90% Confidence for Intervals Accounting for Selected Percents of the MWH within Each Profile Type

  16. Option 4 • To obtain ±10% Accuracy at 90% Confidence for Intervals Accounting for Selected Percents of the dollars within each Profile Type & Weather Zone Note: Dollars = Σ (MWh * MCPE)

  17. Option 4 • To obtain ±10% Accuracy at 90% Confidence for Intervals Accounting for Selected Percents of the dollars within each Profile Type & Weather Zone

  18. Option 4 • To obtain ±10% Accuracy at 90% Confidence for Intervals Accounting for Selected Percents of the dollars within each Profile Type & Weather Zone

  19. Option 4 • To obtain ±10% Accuracy at 90% Confidence for Intervals Accounting for Selected Percents of the dollars within each Profile Type For example: to obtain ±10% Accuracy at 90% Confidence for intervals accounting for 50% of the Dollars for each of the Profile Type / Weather Zone combinations would require a sample size increase of 3,673 points Continues on next slide

  20. Option 4 • To obtain ±10% Accuracy at 90% Confidence for Intervals Accounting for Selected Percents of the dollars within each Profile Type

  21. Precision vs Sample Size • Increasing sample size has a diminishing return on precision improvement • Error Ratio (thus Precision improvement) varies across Profile Types / Weather Zones and across intervals • Thus the impact of adding sample points varies by Profile Type and Weather Zone

  22. Class Level MWH & Dollars Not all Profile Type / Weather Zone Combinations are created equal in either MWh or Total Dollars (ΣMWh * MCPE) !

  23. Class Level MWH & Dollars Not all Profile Type / Weather Zone Combinations are created equal in either MWh or Total Dollars (ΣMWh * MCPE) !

  24. Class Level MWH & Dollars Not all Profile Type / Weather Zone Combinations are created equal in either MWh or Total Dollars (ΣMWh * MCPE) !

  25. Class Level MWH & Dollars • Totals from previous three slides. Is accuracy more important for RESLOWR (41% of Dollars) than for BUSNODEM (2.1% of Dollars)?

  26. Class Level MWH & Dollars - Descending Order by Dollars Top 4 classes account for 49% of the MWh and 51% of the dollars * Note: Dollars = Σ (MWh * MCPE) Continues on next slide

  27. Class Level MWH & Dollars - Descending Order by Dollars * Dollars = Annual MWh * MCPE Continues on next slide

  28. Class Level MWH & Dollars - Descending Order by Dollars * Dollars = Annual MWh * MCPE Continues on next slide

  29. Options 5 & 6 • These options iteratively allocate increments of 10 sample points to the next Profile Type / Weather Zone Combination in order to produce the most gain in • reducing MWh (Option 5) estimation error (Precision × MWh) summed across all intervals • reducing Dollar (option 6) estimation error (Precision × Dollars) summed across all intervals • The allocations are based on • The MWh (or Dollars) associated with each of the Profile Type / Weather Zone combinations in each interval • The Error ratio in each interval for each Profile Type / Weather Zone combination • The cumulative number of sample points allocated by preceding iterations (including the original sample size) • The precision improvement that would be realized by adding 10 sample points, and the diminishing return on that improvement

  30. Option 5 – MWh Error Reduction Optimization Cumulative sample sizes are shown in increments of 1,000; they were determined iteratively in increments of 10 sample points as additions to the current sample size.

  31. Option 6 – Dollar Error Reduction Optimization

  32. Option 5 with Collapsed Profiles/Weather Zones Collapse classes to reduce the number for which the few or no additional sample points are required. BUSNODEM would no additional points.

  33. Option 6 with Collapsed Profiles/Weather Zones

  34. Conclusions and Follow-up Analysis • The iterative sample point allocation process has some intuitive appeal • Seems to allocate sample points where they do the most good • Maximizes UFE reduction • However, • How does UFE allocation affect the final accuracy? • If the iterative allocation process is used, will we end up with more or less accurate estimates when they are adjusted for UFE? • ERCOT will try some Monte Carlo simulations to explore this question

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