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AEIC Annual Load Research Conference September 12, 2006 - Reno, NV

AEIC Annual Load Research Conference September 12, 2006 - Reno, NV. ERCOT Residential Profile ID Assignments – Dealing with Assignment Accuracy and Migration Presented By: Diana Ott Carl Raish. Overview. ERCOT Settlement highlights Residential Annual Validation

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AEIC Annual Load Research Conference September 12, 2006 - Reno, NV

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  1. AEIC Annual Load Research ConferenceSeptember 12, 2006 - Reno, NV ERCOT Residential Profile ID Assignments – Dealing with Assignment Accuracy and Migration Presented By: Diana Ott Carl Raish

  2. Overview • ERCOT Settlement highlights • Residential Annual Validation • Heating Fuel Type Residential Survey • Impact of Miss-Assignment of Residential Load Profile ID Assignment • New Residential Algorithm • Q & A

  3. Settlement • ERCOT requires a fifteen (15) minute settlement interval • Vast majority of Customers do not have this level of granularity. • Profiles are created using adjusted static models • Models are dependent on season, day of week, time of day and weather • Backcasted Profiles are generated the day following a trade day and used for all settlements (initial, final and true-up) • Load Profiling: • Converts monthly NIDR reads to fifteen (15) minute intervals • Enables the accounting of energy usage in settlements • Allows the participation of these Customers in the retail market (reduces barrier to entry)

  4. 3 Load Profile Groups, 9 Segments Residential (2) Low-Winter Ratio (Non-electric Heat) High-Winter Ratio (Electric Heat) Non-Metered (2) Lighting (Street Lights) Flat (Traffic Signals) Business (5) Low Load Factor Medium Load Factor High Load Factor Non-Demand IDR Default

  5. 8 ERCOT Weather Zones Stars represent the location for the 20 ERCOT Weather Stations for each Weather Zone

  6. Annual Validation of Profile Assignments • ERCOT in conjunction with Profiling Working Group establishes the rules for Profile ID assignment and publishes in the form of a Decision Tree on the ERCOT website • Annual Validation is a process established by the Market to annually review and update Profile ID assignments based on the rules defined in the Decision Tree • Historically, May 1 thru April 31 meter reads were used to determine the Annual Validation assignment. The process normally began in June and completed in January.

  7. History of Residential Annual Validation • Oct. 2001 Initial Validation • Profile IDs were assigned by TDSPs prior to Market Open • Validation started in 2001 and was not completed until Sept. 2002 • 2002 Annual Validation • Not performed due to 2001 Initial Validation still in progress • PWG sub team changed methodology from using billing month to usage month • 2003 Annual Validation • Large volume of migrations (1.5 million out of 4.9 million ESIIDs) • 2004 Annual Validation • Large volumes of changes were identified (1.0 million out of 5.4 million ESIIDs) • Annual Validation suspended to allow time to improve assignment process • 2005 Annual Validation • Some methodology changes were identified which still resulted in large volumes of migrations (0.5 million out of 5.1 million ESIIDs) • Market delayed sending in transactions and ultimately decided to only send in a subset of changes identified

  8. Residential Assignment Rules2001 - 2004 • Winter Ratio >=1.5 RESHIWR • Winter Ratio < 1.5 RESLOWR * Round to two decimal places Where ADUsedec = Average Daily Use in the December Usage Month, ADUsejan = Average Daily Use in the January Usage Month, ADUsefeb = Average Daily Use in the February Usage Month, FallBase = minimum ADUse for the Usage Months of October and November SpringBase = minimum ADUse for the Usage Months of March and April.

  9. Preliminary Residential Assignment Rules for Annual Validation 2005 • Do not replace a non-default assignment with a default assignment • Apply Dead-Bands • RESHIWR goes to RESLOWR if WR ≤ 1.0 • RESLOWR goes to RESHIWR if WR > 1.8 • Dead-Bands do not apply if currently a default assignment • kWh Minimums • WR numerator ≤ 20 then assign RESLOWR

  10. Additional Profile Assignment Improvement Ideas • Use a statistical approach to correlate premise usage to profile usage. • Use a residential survey to obtain the necessary data to relate usage patterns to heating system type. • More accurately account for weather variations • Account for periods of low/no occupancy • Move calculation responsibility to ERCOT from TDSPs • Change time period for submission of assignment change transactions • During the original October/November timeframe for submitting changes, the RESHIWR and RESLOWR profiles are significantly different • RESHIWR and RESLOWR profiles are quite similar during the summer months

  11. Residential Heating & Fuel Type Survey • Design: • 41,000 bilingual survey forms mailed • Stratified by Weather Zone and Profile Type • 2,563 RESHIWR per Weather Zone • 2,562 RESLOWR per Weather Zone • Response • Survey responses were identified to allow connecting the response to usage history • 4,669 responses as of 09/30/2005 • 11.4% response rate

  12. Questions from Residential Survey pertinent to Electric Heat Analysis • What classification best fits this address? (Check only one box) •  Single-Family Dwelling •  Multi-Family Dwelling (Duplex, Apartments, etc.) •  Other (Please skip the remaining questions and disregard the survey.) • What is the primary type of home heating used at this residence? (Check only one box) •  Electricity •  Natural gas or bottled gas (propane/butane) •  Other or not sure • Have you added central electric cooling or heating in the last 2 years? (Check all that apply) •  Yes, I added central air conditioning •  Yes, I added central electric heating • No •  Not sure • What is the approximate age of your residence? (Check only one box) •  Less than 5 years •  5 – 15 years •  16 – 30 years •  More than 30 years •  Not sure

  13. Number of Survey Responses by WZone & Profile Type 700 600 350 333 HIWR 289 HIWR 500 HIWR 252 231 HIWR 268 205 HIWR 400 HIWR HIWR 176 HIWR 300 371 365 200 355 325 309 303 LOWR LOWR LOWR 275 262 LOWR LOWR LOWR LOWR LOWR 100 0 COAST EAST FWEST NCENT NORTH SCENT SOUTH WEST Residential Heating & Fuel Type Survey 8.5% 12.6% 10.5% 9.9% 13.6% 11.3% 10.6% 14.1% Return rate by WZone

  14. Residential Heating & Fuel Type Survey Electricity Natural/Bottled Gas Other/Not Sure

  15. Residential Heating & Fuel Type Survey Overall 14% misclassified Overall 30% misclassified Electricity Natural/Bottled Gas Other/Not Sure

  16. Residential Heating & Fuel Type Survey Survey indicates low rate of heating system type change

  17. Residential Heating & Fuel Type Survey Primary Home Heat % for Homes Less than 5 years

  18. Residential Heating & Fuel Type Survey • Performed visual inspection of usage patterns for each survey response • 4,630 responses indicated either a “Single-Family Dwelling” or “Multi-Family Dwelling” and a primary home heating type of either “Electricity” or “Natural gas or bottled gas (propane/butane) • 673 (14.5%) responses to the home heating type were deemed invalid by examination of their seasonal usage pattern • 3,957 (85%) responses were used to develop an improved Profile Type classification algorithm

  19. Survey Response Validation - Electric Heat Example Note:Summer usage values omitted for clarity

  20. What we found out from the Survey • Saturation of Electric Heat varied considerably across weather zones • Saturation of Electric Heat was inconsistent with breakdown between RESHIWR and RESLOWR • 30% of Survey responders reporting Electric Heat were assigned to RESLOWR • 14% of Survey responders reporting No Electric Heat were assigned to RESHIWR • There is very little year-to-year change in heating system fuel actually occurring • The percent of newer homes using electric heat varies considerably across weather zones (37% Coast – 84 South %)

  21. Why Does Assignment Accuracy Matter? • Profile assignment errors create two types of load profile estimation errors • Assignment of billing kWh to the days within the billing period • (RESHIWR assigns more kWh than RESLOWR to cold days) • Assignment of daily kWh to the intervals within the day • (RESHIWR assigns more kWh to morning intervals)

  22. Daily kWh as a Percent of Monthly kWh ReslowrReshiwr Daily kWh Pct Trade Day

  23. Residential Profile Comparison - FWEST Reshiwr vs. Reslowr

  24. Findings and Next Steps • ERCOT’s Profile ID Assignment process has resulted in unacceptably high migration rates • Dead - bands would reduce migration but could do more harm than good in terms of assignment accuracy • The impact of Profile ID miss-assignment is significant at the ESIID level • Undertake an effort to develop a new and improved assignment process with a goal of reducing migration and improving accuracy • More improvements are needed

  25. Classification Algorithm Overview • Use Residential Survey response data in conjunction with responder usage data to build an algorithm to predict heating fuel • Use regression between actual meter readings for a premise and the RESHIWR and RESLOWR profile kWh for the same time periods • Use reads during shoulder and winter months for several (4.5) years • Omit reads during periods of very low use (no/low occupancy) • Omit outlier reads and require some reads to exceed a minimum kWh/day threshold in order to assign RESHIWR • Assign the better fitting profile to the ESIID

  26. Classification Algorithm Development • For each ESI ID with a survey response usage values were selected from Lodestar for the January 2002 – September 2005 time period • Each usage value was converted into units of kWh/day and any read covering a period longer than 44 days was dropped • Each usage value was classified as a winter or shoulder reading • Only shoulder and winter readings were used in the analysis • Winter/Shoulder: start > September 20 and stop < May 11 • Winter: start > November 15 and stop < March 15 • Shoulder: all others • Usage values were screened for high and low outlier usage values

  27. Classification Algorithm Development • For each ESI ID compute a mean and standard deviation of the kWh/day values for the winter and shoulder readings and use these to “normalize” each usage value • Usage value dropped if: • Z > 3 and kWh/day > 100 • Z > 3.5 • Z < -2 • kWh/day < 5 Low Occupancy Outliers

  28. Classification Algorithm Development Usage Screening Examples: Usage less than 5 kWh/day dropped

  29. Classification Algorithm Development Usage Screening Examples: Usage with Z < -2.00 dropped

  30. Classification Algorithm Development Usage Screening Results • 1,006 ESI IDs (21.7%) with one or more usage values screened • 2,414 usage values were screened out • 1,825 usage values screened out because < 5 kWh/day • If an ESI ID had fewer than 3 winter readings or fewer than 3 shoulder readings it was classified as “RESLOWD” (Residential Low Winter Ratio Default) and was not used for fine tuning the algorithm

  31. Classification Algorithm Development Algorithm Basics • If an ESI ID has (and uses) electric heating, then the winter and shoulder usage values for that premise should be more similar to the RESHIWR profile kWh than to the RESLOWR profile kWh • The profile kWh for a day reflects the weather conditions associated with that day in the specific weather zone as well as the day type (day-of-week/holiday) and season of the year • To perform the comparison for an ESI ID, the profile kWh is summed across the intervals for the days in each of its meter reading periods (shoulder and winter months only)

  32. Classification Algorithm Development Algorithm Basics • For each fall-winter-spring time period e.g., fall 2004 – spring 2005 the profile kWh is scaled to equal the sum of the ESI ID’s meter kWh for that time period • The correlation between the actual metered kWh and the scaled profile kWh for those readings is computed for each ESI ID • The R2 correlation is determined with a weighted linear regression analysis with no intercept term • Each reading is weighted as follows: Shoulder reading weight = 1 Winter reading weight = Winter reading weight = 1 if RESHIWR kWh < RESLOWR kWh • The weighting process associates more importance with winter readings for which the RESHIWR kWh is greater than the RESLOWR kWh

  33. ESIIDReshiwr Reslowr New Algorithm Improvement Example Note: New Algorithm improvement results from using multiple years of usage values

  34. Classification Algorithm Development

  35. Classification Algorithm Development Algorithm - Classification Rules • If the highest winter reading kWh/day is less than 15 kWh/day then assign “RESLOWR” • If R2RESHIWR > 0.60 and R2RESHIWR > R2RESLOWR then assign “RESHIWR” • If the number of readings available > 9 and R2RESHIWR > 0.90 and (R2RESHIWR + 0.010) > R2RESLOWR and Winter Max kWh/day > 50 then assign “RESHIWR” • If the number of readings available > 9 and R2RESHIWR > 0.95 and (R2RESHIWR + 0.015) > R2RESLOWR and Winter Max kWh/day > 60 then assign “RESHIWR” • Otherwise assign “RESLOWR”

  36. Classification Algorithm Development Algorithm – Rules Fine Tuning • Algorithm fine tuning was an iterative process to tune each classification criterion on the previous slide individually • Each classification criterion was adjusted to minimize misclassification error based on validated survey responses • For each iteration, misclassified ESI IDs were examined graphically to assess the accuracy of the Profile Type assignment and to establish new criteria • When the fine tuning was complete 184 (4.6%) validated survey responses regarding heating system type were different than the algorithm classification … most had usage patterns which were ambiguous

  37. ESIIDReshiwr Reslowr Survey and Algorithm Agree on Classification DailykWh Survey and algorithm both indicated electric heat, “RESHIWR”

  38. ESIIDReshiwr Reslowr Survey and Algorithm Disagree on Classification DailykWh Survey said electric heat, algorithm said gas

  39. Classification Algorithm Results • For the final version of the algorithm 3,773 (95.4%) validated survey responses regarding heating system type agreed with the algorithm classification Definitely not electric heat!

  40. Applying Algorithm to Annual Validation 2005 • 62% of the 578,572 AV 2005 Profile Type changes agreed with the algorithm classification • Changes to RESHIWR were significantly more accurate (78.4%) than changes to RESLOWR (43.5%) • Accuracy of the changes by weather zone ranged from a low of 59.8% in the SOUTH zone to a high of 68.8% in the EAST zone • The Residential population would have had somewhat more accurate Profile Type assignments as a result of conducting AV 2005 (81.4% vs. 78.7%) • The market decided to allow only changes which were in agreement with the algorithm (358,000 changes were submitted)

  41. Residential Changes for Annual Validation 2006 New algorithm adopted by market for AV2006, Calculation responsibility shifted to ERCOT

  42. Year 1 to Year 2 Migration Estimates 4.4% NonDefault Year to Year Migration Estimates 3.6% Estimates of Future Load Profile ID Migrations • Estimates below reflect migrations for 2006 if the new algorithm had been used exclusively for 2005 Annual Validation • The estimated migration rates are an indicator of what can be expected for year-to-year migration starting in 2007 • Estimates were developed from a sample of every 25th ESIID

  43. Conclusions • The survey successfully provided data necessary to build a classification algorithm for electric heating and establish its accuracy. • The classification algorithm at 96% accuracy was a significant improvement over the winter ratio method • The improved accuracy will lead to assignment stability • Profile assignments and shapes are in a feedback loop and improve each other • The new algorithm uses load profile shapes to make profile assignments • With updated load research analysis based on the new assignments, more accurate load profile shapes will be developed as a result of a more homogeneous population • The more accurate load profile shapes should lead to better assignments • ERCOT has completed load research analysis using the new profile assignments and is developing new profile models based on those latest estimates

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