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03/11-12/2013 Markets Committee

03/11-12/2013 Markets Committee. Aleks Mitreski. Market Development amitreski@iso-ne.com (413) 535-4367. Final Impact Analysis Report. Energy Market Offer Flexibility. Presentation Objective .

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03/11-12/2013 Markets Committee

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  1. 03/11-12/2013 Markets Committee Aleks Mitreski Market Development amitreski@iso-ne.com (413) 535-4367 Final Impact Analysis Report Energy Market Offer Flexibility

  2. Presentation Objective • As part of the Strategic Planning Initiative the ISO reviewed the current energy market offering functionality • The New England region has become more reliant on natural gas as a results from significant investment in these types of resources • The inherent real-time fuel price uncertainty dictates a need for additional offer flexibility in the energy market • This report focuses on four observed problems and proposed enhancements • An extended discussion of the observed problems can be found in a white paper

  3. Problem Identification and Proposed Enhancement • Stale energy offers in real time Proposed solution: Introducing ability to modify offers in real time • Varying costs cannot be reflected in a single supply curve Proposed solution: Introducing hourly energy offers • Current energy offer floor can prohibit the energy price to reflect true conditions on the system (e.g., surplus generation) Proposed Solution: Introducing negative energy offers • Current Self-Scheduling practice can prevent ISO to dispatch generator across full dispatch range Proposed Solution: Self-Scheduling by using energy offers

  4. Major Initiative Impact Analysis • The scope of the proposed changes has been identified as a major initiative • In accordance with the Framework for Evaluating Major Initiatives the ISO has developed this report which performs qualitative and quantitative impact analysis for each proposed change • The analysis assumptions are a combination of ISO’s proposed approach and stakeholder feedback • A significant number of assumptions were used to simulate potential impacts, however, participant behavior may be different once the proposed changes are implemented • The analysis is best used as a guide to the scale of the impact

  5. Presentation Overview For each issue, the report discusses: • Qualitative analysis • Identification of the problem • Adverse outcomes experienced by the problem • Proposed solution • Quantitative impact analysis of the proposed solution • Summary

  6. Qualitative Analysis For Problem 1:Stale Energy offers in Real-time Solution: Introducing the ability to update energy offers in real-time

  7. Background • Participants formulate their energy supply offer in the morning of the day prior to the operating day • Fuel cost is the main component • For example, for natural-gas fueled units, the day-ahead price of natural gas is used in the formulation of the day-ahead supply offer • By noon on the day prior to the operating day, participants must submit their day-ahead supply offers • Later that day, during the Re-Offer period (16:00-18:00) participants have one final opportunity to change some financial parameters of the supply offer

  8. Problem Definition • Generator’s operating costs can change after the Re-Offer deadline of 18:00 • For example, the price of natural gas if procured during the operating day could be at a premium over the day-ahead price (which was used to formulate the energy offer) • In those instances, the price associated with the energy blocks in the supply offer no longer accurately reflect the generator’s actual operating cost • Generators that are dispatched on these “stale offers” may incur operating losses that cannot be recovered through any mechanism in the energy market

  9. Adverse Outcomes • ISO’s commitment and/or dispatch decisions can be inefficient when stale offer information is used • Participants whose cost has increased above the price used in the formulation of their supply offer are faced with a problematic decision: • Operate at a loss • Not procure gas and become unavailable • There is an operational concern because the current market rules can create environment where the best financial interest of the generator is to not follow ISO’s dispatch • This potential outcome is fundamentally contrary to good market design

  10. Proposed Enhancement cont. • Allow modification of supply offers (for generators and DARD) if submitted 30 minutes prior to the top of the hour • Self-Commitments and Self-Schedule request can be submitted on a 30 minute rolling advance timeframe • Self-Schedule request would result in offering the energy at the floor price • The reoffer capability will start during the Re-Offer Period, will be suspended during the Reserve Adequacy Analysis period, and resume afterwards to be available in real-time

  11. Quantitative Impact Analysis to introducing ability to update energy offers in real-time

  12. Simulation Overview • Study period January 1st 2010 – December 31st 2012 • Simulate impact to real-time energy market if participants had ability to change energy offers in real-time • Assumed energy offers of natural-gas fueled resources were proportionally modified based on the difference in price between the day-ahead and real-time price of natural gas • Using these “reoffered” energy offers the ISO “re-cleared” the real-time energy market to simulate changes to hourly LMPs, NCPC, and cost to load

  13. Calculating a Daily Change Factor • Day-ahead natural gas index prices are widely available, but real-time are not due to lack of liquidity • Individual real-time natural gas trades were obtained from Intercontinental Exchange to develop a volume weighted average index price for purchasing natural gas at the spot price during the operating day • For each operating day a “daily change factor” was calculated as: (real-time natural gas price)/(day-ahead natural gas price) • The “daily change factor” indicates the premium or discount that a participant could have paid if natural gas was procured during the operating day instead of day-ahead • For some days (e.g., weekends) real-time gas trades were not available, in which case the “daily change factor” was set to 1

  14. Daily Change Factor example For example: • Day-Ahead natural gas price for September 1 was $4/MMBtu • Real-Time natural gas price for September 1 was $5/MMBtu • For this day the “daily change factor” was calculated as: $5MMBtu/$4MMBtu = 1.25 • If Generator A offered 50MW in the day-ahead at $100 per MWh then the simulation assumed that the participant would have reoffered this generator during real-time as 50MW at $125 per MWh

  15. Comparison of Day-Ahead and Real-Time Natural Gas Prices

  16. Preparing for the Simulation • PROBE (by PowerGEM) was used as the simulation software • An initial run was performed using: • Existing generator supply offers (day-ahead submitted or modified during the Re-Offer period • Real-time load • Transmission network model for each day • The base case serves two purposes • To compare with actual settled results in order to gauge the quality of the simulation • To establish baseline LMPs, load costs, and NCPC whose changes will be observed after the simulation

  17. Preparing for the Simulation First Comparison • Comparing actual settled results with PROBE’s base case • Sanity check. Are the inputs in preparation of the simulation comparable to the actual settled values? Actual settled data Base Probe Run (using original offers to clear the market based on PROBE logic)

  18. Preparing for the Simulation cont. • The comparison exhibited some inconsistencies between the baseline PROBE run and settled LMPs, which can be attributed to: • Transmission modeling discrepancies • Real-time events or operator decisions that cannot be captured through a simulation • The simulation data results excludes any days for which the average daily LMP produced by the baseline PROBE run was 30% higher or lower than the actual settled average daily LMP • For example the simulation results only included 260 days from 2010 • In addition, this PROBE version could not handle days when there are 23 or 25 hours

  19. Performing the Simulation • The simulation used modified energy offers of natural-gas fueled generators based on the “daily change factor” • This was the only input value that changed for the simulation from the base run • PROBE re-cleared the real-time energy market to produce: • Hourly LMP (hub and zonal) • A new dispatch of resources • Additional resources may have been committed or existing resources may have been de-committed • Cost to load • A generic NCPC calculation (not using ISO’s NCPC rules)

  20. Performing the Simulation Second Comparison Base Probe Run (using original offers, but excludes days outside the +/-30% threshold) Energy Offers Modified PROBE Simulation re-clears the market using modified offers • What will be the market impacts once energy offers are modified proportionally to the “daily change factor”? • Increase or Decrease?

  21. Simulation Cost To Load Impact Results • Actual Load Cost is included as a comparison to the results produced by the base case rune by PROBE (prior to simulation) • Actual Load Cost was calculated as Hourly Sum of (RT LMPZone x RTLOZone) • During this period, on average roughly 94% of load cleared in day-ahead. In other words, only 6% of the real-time load would have been impacted by the cost increase • The last column includes results only from days when gas-price increased in real time

  22. Simulation NCPC Impact • PROBE used a generic NCPC calculation that did not consider ISO’s NCPC rules (e.g., eligibility, ISO supplemental commitments, self-scheduling)

  23. Observed LMP Changes During Days when Real-Time Natural Gas price different from Day-Ahead

  24. Summary • The simulation indicates that proposed changes will cause relatively small impact to load cost • This is expected since natural gas price volatility is infrequent • However, price volatility most likely occurs during critical operational needs (e.g., cold snap, gas supply issues) • The lack of ability to modify energy offers in real time can cause significant undervaluation of the operating cost of a generator which underscores the necessity of the reoffer functionality, especially for days when gas supply is scarce: • Creates financial incentive to not follow ISO’s dispatch • Can cause the resource to become unavailable

  25. Top 10 Increase Of Natural Gas Price ($/MMBTU) • The data in the table has been rounded and masked to protect confidential information • Most of the days occur during the winter months

  26. Qualitative analysis for Problem 2:Varying cost cannot be reflected IN a single Supply curve Solution: Introducing Hourly Energy Offering of additional parameters in the Day-Ahead Energy Market and Real Time

  27. Background • Currently, participants can submit only one monotonically increasing supply curve per day • The price associated with the energy blocks is identical for all hours of the day

  28. Problem Definition • For certain resources the operating costs can vary between hours of the day (e.g., dual-fuel units, natural gas fueled units) • For example, the natural gas trading day starts at 10:00, which can cause participants to have one fuel price for hours before 10:00 and a different price for the remaining hours of a calendar day • Participants may blend the different fuels costs, or use a variation of strategies to formulate the supply curve • In any case, the price associated with the energy blocks in the Supply Offer may not accurately reflect the generator’s actual operating cost Fuel Price 1 Fuel Price 2 24:00 00:00 10:00

  29. Adverse Outcomes • Participant are faced with trade-offs when attempting to reflect intraday varying cost through a single supply curve • If the supply curve is formulated by blending costs then generators may operate at a loss if not dispatched for all hours or if dispatched more during hours when fuel cost is actually higher • If the supply curve is formulated using the highest cost then: • For some hours the energy is offered at premium than actual cost • Generator will be uncompetitive if an identical generator decides to offer using a blended rate

  30. Proposed Enhancement • Introduce the ability to vary the energy offers on an hourly basis during the day-ahead market and for the balance of the day in real time • Parameters for which hourly granularity is allowed, but only one identical value was submitted for all hours in the day ahead, would be allowed to be reoffered with varying hourly values in real time • The ability to offer these parameters on an hourly basis would be available to generators and DARDs

  31. Proposed Parameters with Hourly Granularity Generators

  32. Proposed Parameters with Hourly Granularity DARD

  33. Quantitative Impact Analysis on the introduction of Hourly offering in the day-ahead energy market And Real-Time

  34. Simulation Overview • Study period January 1st 2010 – December 31st 2012 • Simulate impact to day-ahead energy market assuming participants had the ability to vary energy offers on an hourly basis • Assume energy offers for natural-gas fueled resources for a given day will differ based on the difference in gas price for the first 10 hrs and the remaining 14hrs of an energy day (i.e., the price separation is based on natural gas trading day boundary) • Using the modified energy offers the ISO simulated changes to the hourly LMPs, payments to generators and cost to load • Similar verification like the one for the reoffer simulation (settled values versus base PROBE case) was performed for this simulation

  35. Adjusting Hourly Energy Offers Based on Appropriate Day-Ahead Natural Gas Price • The simulation data results excludes any days for which the average daily LMP produced by the baseline PROBE run was 30% higher or lower than the actual settled average daily LMP • In the simulation, the day-ahead offer was split in two parts • One set of MW-price pairs for hours 00:00 -10:00 based on the day-ahead natural gas price for those hours • Second set of MW-price pairs for hours 10:00-24:00 based on the day-ahead natural gas price for those hours

  36. Adjusting Hourly Energy Offers Based on Appropriate Day-Ahead Natural Gas Price - Example • Assume day-ahead natural gas price observed as $3/MMBTU for 0-10hr and $5/MMBTU for 10-24hrs • Assume an existing offer as: • We derive what would have been the hourly offer cost (assuming weighted average) using observed gas prices using the formula ($3 x 10hr)+($5 x 14hr))/24hr = $4.16 • In the simulation the offers are modified as: For price pairs 00:00 -10:00 For price pairs 10:00 -24:00 $70 x $3/$4.16 = $50/MWh $70 x $5/$4.16 = $84.13/MWh

  37. Analyzing the Impact of Using the Appropriate Day-Ahead Natural Gas Price • For most hours the difference in prices between the first 10 hour blocks and the remaining 14 hour blocks was small • The median difference was half a cent ($0.005). In other words, for all price increases, there were symmetrical price decreases

  38. Simulation Cost To Load Impact Results • Actual Load Cost is included as a comparison with the base case used in PROBE • Actual Load Cost was calculated as Hourly Sum of (DA LMPZone x DALOZone)

  39. Simulation of NCPC Impact • PROBE used a generic NCPC calculation that did not consider ISO’s NCPC rules (e.g., eligibility, ISO supplemental commitments, self-scheduling)

  40. Observed LMP Changes From the Simulation

  41. Summary • The simulation indicates that the proposed changes will cause a relatively small impact to load cost • This is expected since on average the difference in prices between the two natural gas trading days that span one energy day was small • However, for some energy days, there were instances of large spread between the respective prices of the two natural gas days • During these days, the real-time energy market clearing price can be inefficient, since participant cannot offer their true incremental cost and must average/blend the varying fuel cost

  42. Summary (cont.) For a given energy day, the natural gas price was $8/MMBTU higher for the first 10 hours of the day than the remaining 14 hours

  43. Qualitative analysis for Problem 3: energy Price not reflecting true conditions of the system Solution: Introduction of negative energy offers

  44. Background • The current energy offer floor price is $0/MWh • The energy price has reached $0/MWh on 178 hours during the 2009-2012 period • This can happen through normal dispatch, or • Administratively through Minimum Generation Emergency (“Min Gen”) event • In both circumstances the system experienced low load/surplus generation conditions

  45. Problem Definition • The current offer floor price can prohibit the energy price to reflect the true severity of surplus generation on the system • As a consequence, when energy price reaches $0/MWh, this may not be a strong enough signal that generation needs to be decreased or consumptions increased when the energy price reaches • Declaring the administrative Min Gen event procedure might be the only solution to alleviate generation surplus

  46. Adverse Outcomes • Declaring a Min Gen event is undesired because : • The LMP is administratively set at $0/MWh, and will no longer indicate the incremental cost of the next MW • Generators can be dispatched down to their Emergency Min or some generation might be de-committed • Less flexible generators (or wind resources) may desire to have a higher output than the administrative dispatch received during Minimum Generation Emergency event • Generators cannot properly price their desired dispatch output

  47. Proposed Enhancement • The ISO is proposing lowering the current offer floor price to -$150/MWH for offers and bids in the Day-Ahead and Real-Time Energy Markets • The new floor price will be available to: • Generator/DARD Offers • Load Bids • External Transactions • Virtual Transactions • Once negative offers are implemented, the actual offer data and clearing prices will indicate the need for further decrease of the floor price (i.e., will observe how many offers are submitted at the floor price)

  48. Quantitative Impact Analysis for the introduction of negative offers Simulate the increase in NCPC payments during a set of hours

  49. Simulation Overview • Study period January 1st 2009 – December 31st 2012 • Identified the hours when the hourly LMP reached $0/MWh 2009 – 59 hrs 2010 – 25 hrs 2011 – 47 hrs 2012 – 45 hrs • Simulate what would have been the increase in real-time NCPC payments if during all of those hours the LMP was: • -$25/MWh • -$75/MWh • -$150/MWh

  50. Simulation Overview • The sum of the daily NCPC paid during the days with an LMP of $0/MWh was $10,354,346 • The simulation did not account for any changes to commitment, generation dispatch, supply offers or load during those hours • For example, energy price may gradually decrease below -$0/MWh and may not be at -$25/MWh for the entire hour (or -$75MWh, -$150/MWh) • As energy price becomes negative, some generation may reduce output, which in turn may increase the energy price • No study was performed to estimate the benefit to load for charges to generation during negative energy offers

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