03/11-12/2013 Markets Committee

03/11-12/2013 MARKETS COMMITTEE

Aleks MitreskiMARKET DEVELOPMENT

[email protected] (413) 535-4367

Final Impact Analysis Report

Energy Market Offer Flexibility

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

2

Problem Identification and Proposed Enhancement

1. Stale energy offers in real timeProposed solution: Introducing ability to modify offers in real time

2. Varying costs cannot be reflected in a single supply curveProposed solution: Introducing hourly energy offers

3. 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

4. Current Self-Scheduling practice can prevent ISO to dispatch generator across full dispatch rangeProposed Solution: Self-Scheduling by using energy offers

3

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

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Presentation Overview

For each issue, the report discusses:

1. Qualitative analysisa) Identification of the problemb) Adverse outcomes experienced by the problem

2. Proposed solution3. Quantitative impact analysis of the proposed

solution4. Summary

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QUALITATIVE ANALYSIS FOR PROBLEM 1:STALE ENERGY OFFERS IN REAL-TIMESolution: Introducing the ability to update energy offers in real-time

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

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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

8

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

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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

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QUANTITATIVE IMPACT ANALYSIS TO INTRODUCING ABILITY TO UPDATE ENERGY OFFERS IN REAL-TIME

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

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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

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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

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

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YearDays

ConsideredDays with

Gas Trades

Days RT gas price increased

Avg. Daily increase

Max Daily Increase

Days RT gas price

decreasedAvg. Daily Decrease

Max Daily Decrease

2010 365 235 169 5% 56% 66 -5% -24%2011 365 250 139 7% 210% 111 -6% -43%2012 365 251 139 10% 131% 112 -8% -39%

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

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Preparing for the Simulation

• 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?

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Actual settled data

Base Probe Run(using original offers to clear the market

based on PROBE logic)

First Comparison

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

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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)

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Performing the Simulation

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Base Probe Run(using original offers,

but excludes days outside the +/-30%

threshold)

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?

Second Comparison

Energy Offers Modified

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

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YearDays Included in Simulation

Days RT price

increased

Days RT price

decreased

Days RT price

unchanged

Actual Settled

Load Cost (millions)

PROBE Load Cost

Before Simulatio

n (millions)

PROBE Load Cost Increase

After Simulation-

all days(millions)

Simulation Cost

Increase

PROBE Load Cost Increase -only days RT

price increased (millions)

2010 259 108 50 101 $4,735 $5,111 $13 0.25% $66

2011 214 74 65 75 $3,660 $3,905 $17 0.43% $77

2012 219 86 58 75 $2,735 $3,106 $41 1.32% $88

Simulation NCPC Impact

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YearDays Included in Simulation

Days RT price increased

Days RT price decreased

Days RT price

unchangedActual NCPC

Paid (millions)

PROBE NCPC Before

Simulation (millions)

PROBE NCPC

Increase After


NCPC Increase

2010 259 108 50 101 $47 $59.5 $0.7 1.16%

2011 214 74 65 75 $35.6 $50 $2 4.12%

2012 219 86 58 75 $31.5 $55 $0.4 0.74%

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

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

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YearTotal Hours

Hours Included in Simulation

Hours with gas price change

Min LMP Change

Median LMP Change

Max LMP Change

2010 8,688 6,429 4,086 -$41.2 $0.33 $164.3

2011 8,687 6,380 4,326 -$53.3 $0.02 $141.5

2012 8,688 6,483 4,441 -$108.3 $0.14 $50.9

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

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Top 10 Increase Of Natural Gas Price ($/MMBTU)

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Date Increase in

RT gas price

System Load

Average Temperatu

re (F)XXXX 200% 19,000 20sXXXX 60% 20,000 20sXXXX 130% 17,000 60sXXXX 50% 18,000 30sXXXX 50% 19,000 40sXXXX 35% 20,000 20sXXXX 50% 17,000 30sXXXX 20% 20,000 20sXXXX 50% 18,000 20sXXXX 10% 21,000 0

• The data in the table has been rounded and masked to protect confidential information

• Most of the days occur during the winter months

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

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

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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

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00:00 10:00 24:00

Fuel Price 1 Fuel Price 2

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

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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

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Proposed Parameters with Hourly Granularity

Generators

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Parameter Name UnitCurrent

GranularityProposed

Granularity

Incremental Offer (i.e. MW- Price pairs)

MW, $/MWh Daily Hourly

No-Load Fee $/hr Daily Hourly

Cold Start-Up Fee $/start Daily Hourly

Intermediate Start-Up Fee $/start Daily Hourly

Hot Start-Up Fee $/start Daily Hourly

Use Offer Slope Select Daily Hourly

Cold Notification Time hh:mm Daily Hourly

Intermediate Notification Time hh:mm Daily Hourly

Hot Notification Time hh:mm Daily Hourly

Cold Start-Up Time hh:mm Daily Hourly

Intermediate Start-Up Time hh:mm Daily Hourly

Hot Start-Up Time hh:mm Daily Hourly

Ramp Rate/MW pairs MW/min Daily Hourly

Offered Claim 10 MW Daily Hourly

Offered Claim 30 MW Daily Hourly

Proposed Parameters with Hourly Granularity

DARD

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Parameter Name UnitCurrent

GranularityProposed

Granularity

Incremental Offer (i.e. MW- Price pairs)

MW, $/MWh Daily Hourly

Use Offer Slope Select Daily Hourly

QUANTITATIVE IMPACT ANALYSIS ON THE INTRODUCTION OF HOURLY OFFERING IN THE DAY-AHEAD ENERGY MARKET AND REAL-TIME

Simulation Overview


• 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

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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

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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

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Energy Block

Quantity(MW)

Price ($/MWh)

1 50 $70

Energy Block

Quantity(MW)

Price ($/MWh)

1 50 $50.48

Energy Block

Quantity(MW)

Price ($/MWh)

1 50 $84.13

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

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Simulation Cost To Load Impact Results

38

YearDays Included in

Simulation

Actual Settled Load Cost (millions)

PROBE Load Cost Before


PROBE Load Cost Increase

After Simulation(millions)

Simulation Cost

Increase

2010 362 $6,114 $6,148 $6 0.12%

2011 332 $5,435 $5,507 $10 0.22%

2012 328 $4,046 $3,540 $0.76 0.05%

• 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)

Simulation of NCPC Impact

39

YearTotal Days Considered

Actual NCPC Paid(millions)

PROBE NCPC Before


PROBE NCPC Increase After

SimulationCost

Increase

2010 362 $7 $10.3 -$32,506 -0.3%

2011 332 $8 $8.8 $43,572 0.5%

2012 328 $16.5 $8.7 $181,887 2.1%

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

Observed LMP Changes From the Simulation

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Year

Hours Included in Simulation

Min LMP Change

Median LMP Change

Maximum LMP Change

2010 8,687 -$33 $0 $32

2011 8,704 -$43 $0 $26

2012 8,633 -$27 $0 $23

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

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Summary (cont.)

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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

QUALITATIVE ANALYSIS FOR PROBLEM 3: ENERGY PRICE NOT REFLECTING TRUE CONDITIONS OF THE SYSTEMSolution: Introduction of negative energy offers

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

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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

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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

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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)

47

QUANTITATIVE IMPACT ANALYSIS FOR THE INTRODUCTION OF NEGATIVE OFFERSSimulate the increase in NCPC payments during a set of hours

Simulation Overview


• Identified the hours when the hourly LMP reached $0/MWh2009 – 59 hrs2010 – 25 hrs2011 – 47 hrs2012 – 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

49

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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Simulation Results

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Summary

• The introduction of negative offers will enable the energy price to better reflect the true conditions on the system when surplus generation/low load condition exists and provide the appropriate incentives

• Generation that has cleared in the day-ahead has the incentive to deviate from its schedule and not generate (its deviation will be credit given the negative energy price)

• Surplus generation conditions can be addressed through economic dispatch instead of administrative actions during Min Gen events

• Non-flexible resources (e.g., nuclear) will have the opportunity to price their desired dispatch output during these conditions

52

QUALITATIVE ANALYSIS FORPROBLEM 4: CURRENT SELF-SCHEDULING PRACTICE CAN PREVENT ISO TO ACCESS FULL DISPATCH RANGE OF A GENERATORReforming Self-Scheduling to be achieved through energy offers

Problem Definition

• The Economic Min parameter is the lowest output at which a generator is dispatched during normal dispatch conditions

• Currently, participants can arbitrarily re-declare the Economic Min parameter at a higher value for Self-Scheduling purposes

• These actions prevents the ISO from accessing the nominal dispatchable range of a generator (unless Min Gen event is declared)

• There is dispatchable range on the system to balance supply and demand (i.e., no true emergency), but the ISO must use administrative steps during Min Gen warnings/events to access this dispatch range

54

Adverse Outcomes

• During surplus generation conditions, ISO must initiate administrative actions during Min Gen warning to cancel Self-Scheduled generators without day-ahead position

• Additionally, Min Gen events may have to be declared in order to dispatch generators below the Economic Min parameter to the Emergency Min– This is undesirable for some generators which may have poor

emissions or inefficient heat rate

• The ISO does not have an operational understanding of the lowest output of a generator under normal dispatch conditions

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QUANTITATIVE IMPACT ANALYSIS FOR THE SELF-SCHEDULING CHANGESDetermine if the Min Gen Events and Warnings would have been eliminated if generators used their “true” Economic Min

Simulation Overview


• Identify the hours when the ISO issued a Minimum Generation Emergency (“Min Gen”) Warning or Event

• Calculate the amount of observed voluntary/ISO imposed reduction of the Economic Min during these events– For example a reductions of Economic Min may occur due to

cancelation of Self-Schedule by the ISO– The participant may redeclare a lower Economic Min in order to

operate at a lower output

• A second analysis looked at the back down capability that would have been available if a lowest historical Economic Min was used during the Min Gen warnings and events

57

Observed Changes to Economic Min during Min Gen Warnings and Events

• For each day that a Min Gen Warning was declared a resource’s baseline Economic Min was determined – The resource’s Economic Min in use 2 hours prior to the declaration of

a Min Gen Warning

• Additionally, the lowest Economic Min of each resource during the hours of a Min Gen Warning or Event was captured

(baseline Economic Min - lowest Economic Min) = observed Economic Min decrease

• The sum of each resource's Economic Min decrease provided the cumulative back down capability for that event

58

Determining Back Down Capability - Example

• At 02:00 Generator A has an Economic Min of 150MW

• At 04:00 the ISO declares a Min Gen Warning

• At 04:30 the participant re-declares the Economic Min of the resource to 100MW

• At 05:00 the ISO cancels the Min Gen Warning

• No other re-declarations occurred during this timeframe

• The actual Economic Min decrease for this resource is calculated as 150MW – 100MW = 50MW

• If there were 10 identical generators that all redeclared like Generator A then the total Economic Min redeclaration during this event is calculated as 500MW

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Voluntary/ISO Imposed Economic Min Re-declaration Results

• In 2012, 18 Min Gen events and 124 Min Gen warning were declared

• Average observed decrease in Economic Min was calculated as 493 MW– Median 303MW, Max 2,345 MW, Min -645 MW– Roughly half of the Min Gen Warnings would have been avoided– None Min Gen Events would have been declared

• Excluding unavailable and non dispatchable resources, the average observed decrease in Economic Min was calculated as 219.5MW– Median 136MW, Max 1096MW, Min -603MW– Roughly one third of the Min Gen Warnings would have been avoided– Only 4 Min Gen Events would have been declared

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Simulating System Conditions If Historical Low Economic Min was used during Min Gen Warnings & Events

• A second simulation was performed to determine what would have been the back down dispatch capability if historic lowest Economic Min was used during all hours

• A historic Economic Min was determined using the lowest non-zero offered Economic Min value for a resource in the day-ahead energy market (Jan 2009 – Dec 2012)

• Individual Resource back down capability per each day when Min Gen warning or event occurred was calculated as:

(baseline Economic Min - historic Economic Min)

• The sum of the individual back down capability provided the cumulative back down capability per day when Min Gen event or warning occurred

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Determining Back Down Capability - Example

• At 02:00 Generator A has an Economic Min of 150MW• At 04:00 the ISO declares a Min Gen Warning• At 04:30 the participant re-declares the Economic Min of the resource to

100MW• At 05:00 the ISO cancels the Min Gen Warning• No other re-declarations occurred during this timeframe• During the Jan 2009-Dec 2012 period, the lowest Economic Min offered

for this resource was 60MW• The additional back down capability for this resource is calculated as

150MW – 60MW = 90MW• If there were 10 identical generators that all redeclared like Generator A

then the total additional back down capability during this event would have been calculated as 900MW

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Back Down capability using historical Economic Min

• In 2012 there were 124 days when a Min Gen warning occurred

• The average back down capability was calculated at 2,476 MW– Median 2,294MW, Max 4,415MW, Min 1,023 MW

• In other words, if the historical Economic Min was offered for resources then there would not have been a need to declare a Min Gen event or warning during 2012

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Summary

• The proposed Self-Scheduling changes should decrease the instance of Min Gen warnings and events, which in turn will decrease the need of administrative (i.e., inefficient) pricing and dispatch of generators to their Emergency Min

• Participants will continue to have the ability to Self-Schedule resources through the price of their energy offers (instead of current physical constrain to the dispatch range of the generator)

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Documents

03/11-12/2013 Markets Committee