00:00

Analysis of Forward Adjustment Factors in ERCOT Credit Estimation

The analysis focuses on the potential "gating" of forward adjustment factors (FAF) to provide certainty around capital requirements during high price events in the ERCOT market. By considering real-time FAF values and historical trends, the study explores the implications of setting maximum and minimum FAF values. Suggestions include incorporating max values for clarity on collateral requirements and maintaining a min value below 1.0 only if lookback values change. The findings underline the importance of adjusting FAF dynamics to mitigate risks and ensure market stability.

deleonardo
Download Presentation

Analysis of Forward Adjustment Factors in ERCOT Credit Estimation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Analysis of Forward Adjustment Factors Brenden Sager, Austin Energy, Chair 18 April 2024 1

  2. Purpose Purpose • ERCOT Credit Estimate Aggregate Liability presentations included discussions on possible “gating” of forward adjustment factors • Max values could provide certainty around capital requirements during high price events • Minimum value could provide credit assurance when FAF “haircuts” invoice exposure at values < 1.0, especially after high pricing events • However, FAF’s below < 1 are the only protection against persistent high collateral requirements from lookbacks unaligned with pricing and invoices • To date, analysis focused on EAL/TPE in various scenarios versus aggregate invoice exposure • Need to consider collateral posting which also includes discretionary collateral and CRR locks – might be hard to back out these effects

  3. FAF definition FAF definition • Simplified formula: RFAF x Max 40-day RT invoice history • Analysis focuses on Real Time FAF’s since both RFAF and DFAF highly correlated (coefficient = 0.87) • RFAF more prominent in EAL calculation, applied to max value of Real Time daily history (lookback) • Fixing max/min values for FAF more straightforward implementation compared EAL adjustment proposals (which are not mutually exclusive)

  4. FAF Data Summary FAF Data Summary min 0.01 2/7/2018 max 10.61 4/9/2024 N 2254 2500 FAF Distribution 2000 700 600 1500 500 1000 400 500 300 0 200 0 2 4 6 8 10 12 100 0

  5. More Data Summary More Data Summary • FAF rising by roughly .073 per year • 45% of data between 1.0 and 1.5; 31% below 1.0 • Volatility increasing annually • Higher and more frequent pricing events in data midpoint starting in 2021 • 10 RFAF’s > 4.0 (98thpercentile) before 2021 and 34 after • Uri had highest RFAF at 10.61

  6. RFAF Percentiles and Max Value RFAF Percentiles and Max Value FAF 2 2.5 3.25 5.25 obs > 117 45 11 4 N-tile 0.90 0.95 0.97 0.99 Wgt Avg > FAF level 4.215 5.425 5.819 8.119 • CFSG members proposed including max value for RFAF to provide more certainty around maximum collateral requirements during pricing events • ERCOT uses percentile and distributional analytic methods for other operational needs in protocols • Above method uses a weighted average of values greater than FAF at observed n-tile • For instance, at 99thpercentile only 21/2254 observations = RFAF of 5.25 • Observation weighted average of the values > 5.25 = 8.119, only four obs > than this (max 10.61)

  7. RFAF time series and periods where < 1.0 RFAF time series and periods where < 1.0 7

  8. RFAF history and minimums RFAF history and minimums • We observed that RFAF can fall below 1.0 and usually does so after a dramatic high price event, 31% of data < 1.0 • As high prices roll off but settlement prices remain high, calculation may cause (unintended?) FAF of < 1.0 • Given nature of calc this effectively haircuts the maximum invoice exposure, possibly below actual which is a risk to the market and undermines intent of methodology • Uri a particularly notable case where RFAF went from 10.61 to 0.44 in eight days then remained at 0.01 for several days thereafter Uri RFAF 12.00 10.00 8.00 6.00 4.00 2.00 0.00 8

  9. RFAF history and invoice history RFAF history and invoice history • Protocols propose a Look-back period for RTM seeking max invoice exposure for 40 days ensure there is enough collateral from CP to cover default and mass transition event • While FAF method calls more collateral when forwards rise against most recent SPP’s – lookbacks maintain elevated collateral levels well past an event (40 days posting max invoice vs 2-day event) • As shown below, FAF < 1.0 effectively adjusts down the lookback to a “reasonable” amount • Can we calibrate lookback/min FAF? … Max (current RTLE, FAF adjusted RTLE) Look-back period for RTM to find the maximum of RTLE or URTA for all QSEs represented by the Counter-Party if any of the QSEs represented by the Counter-Party represent either Load or generation. 40 lrq Days Days lrq Price MW Hours Dollars FAF RTLE $ 40 2500 16 $ 1,600,000 1 $ 1,600,000 $ 5,000 5000 16 $ 400,000,000 0.01 $ 4,000,000 9

  10. Conclusion Conclusion • CFSG should consider max values of 8.5 for FAF’s • Would add clarity for short term collateral requirements • CFSG should consider a min value of FAF’s < 1 only if there are changes to the lookback value • If lookback not changed, then current method is preferable • Fixing max/min values for FAF and/or lookbacks more straightforward implementation compared complex EAL formula adjustment proposals (which are not mutually exclusive) 12

  11. Questions?

More Related