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ERCOT Market Credit Proposal Summary

Proposal from REMC Sanchir Dashnyam, ERCOT Market Credit Manager, outlining scenarios and formula changes related to EAL calculations for ERCOT markets. The proposal introduces new definitions and adjustments to existing formulas to enhance accuracy and stability in calculating exposure and liability. Various scenarios are discussed along with details on netting, FAF vs. RFAF, and implications on Total Potential Exposure Amount (TPEA) calculations. The goal is to improve risk assessment and financial management within the ERCOT market framework.

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ERCOT Market Credit Proposal Summary

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  1. EAL Change proposals: Proposal from REMC Sanchir Dashnyam ERCOT Market Credit Manager ERCOT Public January 19, 2024

  2. Scenario #1: Proposal from REMC Scenario 1: definitions EAL q = Max [IEL during the first 40-day period only beginning on the date that the Counter-Party commences activity in ERCOT markets, RFAF * Max {RTNLE during the previous lrq days}, FAF*RTNLF] + DFAF * DALE + Max [RTLCNS NLCD, Max {ULERTA during the previous lrq days}] + OUT q + ILE q OUTq= OIAq+ UDAAq+ UFAq+ UTAq+ CARD EAL t = Max [RFAF * Max { RTNLE during the previous lrt days}, FAF*RTNLF] + DFAF * DALE + Max [RTLCNS NLCD, Max {ULERTA during the previous lrq days}] + OUTt OUTt= OIAt+ UDAAt+ UFAt+ UTAt NLE = Total net liability extrapolated (Last 14 days RTM Initial Statement Average + Last 14 days DAM Initial Statement Average based on RTM Initial OD)*M1. Use same RTM ODs for DAM as well NLF = net liability forward = 1.5 * (7 most recent Operating days Real time estimates + 7 most recent RTM ODs day-ahead) if settleddata is available use settled else estimates – no price cap FAF = 21 future / most recent days 7 RTM Prices NLCD = (7 most recent Operating days Real time estimates + 7 most recent DAM ODs day-ahead) if settled data is available use settledelse estimates – no price cap ULE = unbilled liability extrapolated (Last 14 days RTM Initial Statement Average + Last 14 days DAM Initial Statement Average based on RTM Initial OD)*M2 - use same RTM ODs for DAM as well OD Invoice Exposure: new proposal from RMC Include:  RTM and DAM statements for the same Operating Day must be netted for every Operating Day considered in Invoice Exposure calculation (past 7 ODs and M1 forward ODs days).  For Finals and true-ups include (past 7 days as well as M1 forward days statements) Exclude: M&N securitization invoices, CRR auction invoices, miscellaneous invoices relating to $2B distributed to market for Sec N on 6/21/22, and miscellaneous invoices related Uri short pay to market on 12/19/2022 2 ERCOT Public

  3. Current EAL Formula vs. Scenarios #2 and #3 Current: EAL q= Max [IEL during the first 40-day period only beginning on the date that the Counter-Party commences activity in ERCOT markets, RFAF * Max {RTLE during the previous lrq days}, RTLF] + DFAF * DALE + Max [RTLCNS, Max {URTA during the previous lrq days}] + OUTq+ ILEq Scenario #2: EAL q = Max [IEL during the first 40-day period only beginning on the date that the Counter-Party commences activity in ERCOT markets, Max{(RFAF * Max {RTLE) during the previous lrq days}, RTLF] + DFAF * DALE + Max [RTLCNS, Max {URTA during the previous lrq days}] + OUT q + ILE q Scenario #3: EAL q = Max [IEL during the first 40-day period only beginning on the date that the Counter-Party commences activity in ERCOT markets, RFAF * Max {RTLE during the previous lrq days}, RTLF] + DFAF * DALE + Max [RTLCNS, Max {URTA during the previous lrq days}] + OUT q + ILE q Two RFAF’s: CP specific RFAF and Global RFAF Max RTLE date • CP specific RFAF = Projected Real-Time ICE Forward Average Price / Historic Real-Time Settled Average Price • Global RFAF is calculated based on existing methodology. Global RFAF is used in MCE calculations. 3 ERCOT Public

  4. TPEA for Scenario#1,#2,#3 vs Current: Market, Load & Gen, Load 4 ERCOT Public

  5. Market TPEA – Winter storm Elliott 5 ERCOT Public

  6. Market TPEA – Summer 2023 6 ERCOT Public

  7. Scenario #1: (a) Netting, (b) FAF vs RFAF, (c) Application of FAF EAL t = Max [RFAF * Max { RTNLE during the previous lrt days}, FAF*RTNLF] + DFAF * DALE + Max [RTLCNS NLCD, Max {ULERTA during the previous lrq days}] + OUTt • on RTM Initial OD)*M1. Use same RTM ODs for DAM as well • NLF = net liability forward = 1.5 * NLCD • NLCD = (7 most recent Operating days Real time estimates + 7 most recent DAM ODs day-ahead) if settled data is available use settled else estimates – no price cap • FAF = 21 future / most recent days 7 RTM Prices • ULE = unbilled liability extrapolated (Last 14 days RTM Initial Statement Average + Last 14 days DAM Initial Statement Average based on RTM Initial OD)*M2 - use same RTM ODs for DAM as well NLE = Total net liability extrapolated (Last 14 days RTM Initial Statement Average + Last 14 days DAM Initial Statement Average based  TPEA is driven mostly by Max NLE.  Scenario #1 avoids the “double top” and provides a gradual increase in TPE in both coming into the high volatility period as well as coming out. The shape of the TPE graph is more gradual and does not have “up & down” feature of the current TPE formula.  FAF is generally not as volatile as the existing RFAF. Thus, when applied against NLF it doesn’t overtake Max NLE. 7 ERCOT Public

  8. Example : Load & Gen MP - Summer 2023 Scenario #1 looks at Max NLE during the previous lrq days, thus, it does solve the problem of RFAF going below 1 and artificially depressing exposures. On the other hand, the TPEA under Scenario #1 will tend to “sit” there until the Max rolls off. • • 8 ERCOT Public

  9. Example: Load MP - Summer 2023 9 ERCOT Public

  10. RFAF RFAF 12.00 11.00 2/12/2021, 10.61 10.00 9.00 8.00 7.00 7/14/2023, 6.09 6.00 2/1/2022, 5.37 8/9/2023, 5.33 5.00 4.00 3.00 2.00 1.00 7/26/2022, 0.64 3/15/2022, 0.62 7/9/2023, 0.62 1/11/2023, 0.52 9/12/2023, 0.17 2/24/2021, 0.01 0.00 RFAF RFAF by its definition is very volatile: RFAF peaked during winter storm Uri at almost 11 then went to 0.01 on 2/24/21. Most recently it hit 6 in July 2023, which was the highest since Feb 2021. Also, it went as low as 0.15 on 9/13/23, which was the lowest since Uri. The fact that, RFAF goes down below 1 means TPE is essentially mechanically lowered once historical settled prices roll off from the denominator. • • 10 ERCOT Public

  11. FAF vs RFAF vs Average Settled prices – Winter storm Elliott Going into the event, both factors are about the same. After the event, FAF normalizes faster since the denominator is based on the most recent 7 day prices vs. RFAF’s 14 days settled prices (5 day lag). Also, RFAF stays at the “bottom” longer than FAF, which recovers faster. Overall, FAF seems to be more “sensitive”. • • • 11 ERCOT Public

  12. FAF vs RFAF vs Average Settled prices – Summer 2023 The spike in June was not captured by ICE futures. FAF appears to be not as volatile as RFAF. The reason for the higher volatility for RFAF is due to the denominator using 14-day historical settled prices. Due to shorter window for FAF’s denominator (last 7 days), it tends to be less volatile. • • • 12 ERCOT Public

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