capacity demand curve in iso-ne: responses to initial stakeholder inquiries
DESCRIPTION
Capacity Demand Curve in ISO-NE: Responses to Initial Stakeholder Inquiries. ISO New England. Samuel A. Newell Kathleen Spees Mike DeLucia Ben Housman. February 6, 2014. Table of Contents. What are the Parameter Values of the Initial Candidate Curve?. Demand Curve Parameter Values. - PowerPoint PPT PresentationTRANSCRIPT
Copyright © 2014 The Brattle Group, Inc.
PREPARED FOR
PREPARED BY
Capacity Demand Curve in ISO-NE:Responses to Initial Stakeholder Inquiries
ISO New England
Samuel A. NewellKathleen SpeesMike DeLuciaBen Housman
February 6 , 2014
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Table of ContentsWhat are the Parameter Values of the Initial Demand Curve? Slide 3
Can you Report the Percent of Draws Clearing Below NICR for Each Curve? Slide X
What are the Parameter Values of the Initial Candidate Curve? Slide 3
Can you Report the Percent of Draws Clearing Below NICR for Each Curve? Slide 4
Can You Provide Simulation Results for a Multi-Point Curve? Slide 5
How Would an Error in the Estimate of Net CONE Affect the Curve’s Performance? Slide 6
Can You Explain the Price Cap Minimum at 1x Gross CONE? Slides 7-8
What are the Parameter Values of the Initial Demand Curve in Capacity Subzones? Slide 9
Would a Flatter Curve be More Appropriate in Import-Constrained Zones? Slides 10-11
Can You Further Explain the Need for a Demand Curve in Maine? Slide 12
Can You Compare Historical Price Volatility in PJM to the Volatility in Your Simulations? Slide 13
Can You Provide a More Detailed Description of Your Simulation Modeling Approach? Slides 14-20
How Would Larger or Smaller Shocks Affect the Candidate Curve’s Performance? Slides 21-23
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What are the Parameter Values of the Initial Candidate Curve?
Demand Curve Parameter Values
Notes: LOLE lines shown in gray between 1-in-5 and 1-in-10 increase by increments of 1 (i.e. 1-in-6, 1-in-7, etc.), while lines in gray between 1-in-15 and 1-in-100 increase by increments of 10 (starting at 1-in-20).
Initial Candidate Demand Curve
Demand Curve Slope (if Net CONE = $8.3/kW-m)
Notes: MW quantities based on FCA7; due to supply elasticity, price impacts from a 100 MW shift in supply-demand would be less than the slope suggests.
Cap to Kink(Steep Section)
Kink to Foot(Flat Section)
Change in Price ($/kW-m) $10.8 $5.8Change in Quantity (MW) 1,492 2,166Slope ($/kW-m per 100 MW) $0.73 $0.27
Note: Price cap is subject to a minimum price of 1x Gross CONE.
Cap Kink Foot
Curve DefinitionPrice 2x Net CONE* 35% of Cap $0Quantity 1-in-5 LOLE 1-in-15 LOLE 1-in-100 LOLE
Corresponding Quantities in FCA7Reserve Margin 9.0% 14.1% 21.5%% of NICR 97.2% 101.8% 108.3%MW 32,053 33,545 35,711
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Price * Quantity
AverageStandard Deviation
Frequency at Cap
Average LOLE
Average Reserve Margin
Reserve Margin
Standard Deviation
Frequency Below NICR
Frequency Below
Minimum Acceptable
Average
($/kW-m) ($/kW-m) (% of draws) (%) (%) (%) (% of draws) (% of draws) ($mil)
Candidate Demand Curves
Initial Candidate Curve $8.3 $3.7 5.1% 0.100 13.1% 2.2% 28.9% 6.1% $3,309
Flatter Curve $8.3 $3.0 6.2% 0.100 13.2% 2.4% 29.7% 7.2% $3,317
Steeper Curve $8.3 $4.5 11.5% 0.100 12.9% 1.8% 26.1% 5.3% $3,316
Multi-Point Curve $8.3 $3.8 8.0% 0.100 13.1% 2.2% 29.4% 6.1% $3,295
Other Demand Curve Designs
Vertical at NICR $8.3 $5.7 28.3% 0.124 11.6% 1.2% 28.3% 6.3% $3,283
Vertical Shifted Right to Achieve 0.1 LOLE $8.3 $5.7 28.8% 0.100 12.6% 1.2% 18.4% 4.3% $3,320
Stoft LICAP $8.3 $3.0 3.0% 0.042 17.4% 2.6% 3.0% 0.2% $3,441
PJM (applied to ISO-NE) $8.3 $2.7 10.3% 0.117 12.5% 2.4% 37.6% 11.4% $3,299
NYISO (applied to ISO-NE) $8.3 $1.8 0.4% 0.112 13.0% 2.8% 35.8% 11.2% $3,308
Price Reliability
Can you Report the Percent of Draws Clearing Below NICR for Each Curve?
Notes: Average prices do not account for potential reductions in the cost of capital supported by more gradual demand curves; Net CONE is assumed constant.The vertical curves have price caps at 2x Net CONE. The reported Price * Quantity is the system price multiplied by the system total quantity, and does not reflect zonal price differentials.
Simulation Results
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Can You Provide Simulation Results for a Multi-Point Curve?▀ We examined the performance of a
multi-point curve with a shape similar to our initial candidate curve
▀ The performance of a multi-point curve is very similar to a kinked curve as long as they reflect the same underlying shape (see simulation results on previous slide)
▀ A multi-point curve would increase the administrative complexity of the demand curve without providing a substantial benefit
Multi-Point Curve vs. Kinked Initial Candidate Curve
0.0x
0.5x
1.0x
1.5x
2.0x
2.5x
5% 10% 15% 20% 25%
Pric
e (%
of N
et C
ON
E)
Reserve Margin (% ICAP)
Initial Candidate Curve
Multi-Point Curve
LOLE: 1-in-5 1-in-10 1-in-15 1-in-30 1-in-100NICRMin Acceptable RM
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Price * Quantity
AverageStandard Deviation
Frequency at Cap
Average LOLE
Average RM
RM St. Dev.
Frequency Below Min. Acceptable
Frequency BelowNICR
Average
($/kW- ($/kW-m) (% of (%) (%) (%) (% of draws) (% of draws) ($mil)
Administrative Net CONE is 33% Less than True Net CONE
$8.3 $2.3 24.6% 0.188 10.5% 2.4% 28.7% 71.9% $3,231
Administrative Net CONE is Equal to True Net CONE
$8.3 $3.7 5.1% 0.100 13.1% 2.2% 6.1% 28.9% $3,309
Administrative Net CONE is 33% Greater than True Net CONE
$8.3 $4.3 1.4% 0.072 14.7% 2.2% 2.5% 14.0% $3,354
Price Reliability
How Would an Error in the Administrative Estimate of Net CONE Affect Demand Curve Performance?
▀ The administratively-determined Net CONE parameter defines the curve and is a major driver of price and reliability outcomes, so it is important that it is accurate
▀ If the administrative estimate of Net CONE were lower than the true value, the demand curve would not attract enough investment to meet the 1-in-10 reliability objective
▀ If the estimate of Net CONE were higher than the true value, the demand curve would attract more supply than needed to meet reliability objectives, and customer costs would increase
Simulated Performance if the Administrative Estimate of Net CONEis 33% Higher or Lower than True Net CONE
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$0
$5
$10
$15
$20
3% 8% 13% 18% 23%
Pric
e ($
/kW
-m)
Reserve Margin (ICAP %)
LOLE: 1-in-5 1-in-10 1-in-15 1-in-30 1-in-100NICRMin Acceptable RM
Case 2: Curve with Minimum Constraint(Cap = 1x Gross CONE)
Case 2: Curve w/o Minimum Constraint(Cap = 2x Net CONE)
Case 1: Minimum Constraint Would Not Affect Curve(Cap = 2x Net CONE)
Case 1(Recent ORTP Estimates)
Gross CONE = $11.9/kW-mE&AS Offset = $3.6/kW-mNet CONE = $8.3/kW-m
Case 2(Very High E&AS)
Gross CONE = $11.9/kW-mE&AS Offset = $8.9/kW-m
Net CONE = $3.0/kW-m
Can You Explain the Price Cap Minimumat 1x Gross CONE?
▀ The Initial Candidate Demand Curve features a price cap at the greater of 2x Net CONE and 1x Gross CONE
▀ Illustrative example of how the cap would work:− Suppose the reference technology were a CC with the values
in the ORTP filing: CONE = $11.9/kW-m; E&AS = $3.6/kW-m− The 1xCONE minimum would bind if the estimated E&AS
offset were greater than 50% of Gross CONE − If the estimated E&AS offset rose to $8.9/kW-m, the price
cap would become $11.9/kW-m rather than 2x Net CONE (which would be only [$11.9 - $8.9] x 2 = $6)
− This constraint would affect the entire demand curve (not just the price cap), because the price at the kink is defined as a percentage of the price at the cap
▀ This constraint is needed to prevent the demand curve from collapsing and leading low reliability outcomes
▀ With high E&AS, the error in the administrative estimate of Net CONE increases, introducing a risk that if the administrative Net CONE is under-estimated then the true cost of new entry might exceed the price cap (in which case FCM would not be able to procure any new capacity even at the cap)
Example Curves with and without the Minimum Constraint
* These results are de-escalated from FCA9 terms (as reported in the ORTP analysis) to FCA7 terms.
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Can You Explain the Price Cap Minimumat 1x Gross CONE? (Cont.)
With Price Cap Minimum
Constraint?
Demand Curve Price Cap
Resulting LOLE
Freq. Below Min Accept.
Avg. RM Total Cost (P x Q)
$mil
Case 1: Net CONE is Low and the Administrative Estimate is AccurateAdministrative Net CONE Estimate = True Net CONE = $3.0/kW-m
With minimum Gross CONE ($11.9) 0.047 0.0% 16.2% $1,247
W/o minimum 2x Net CONE ($6.0) 0.091 0.6% 13.0% $1,199
Case 2: There is an Error in the Administrative Estimate of Net CONEAdministrative Net CONE Estimate = $3.0/kW-m, but True Net CONE = $5.0/kW-m
With minimum Gross CONE ($11.9) 0.080 1.2% 13.8% $2,022
W/o minimum 2x Net CONE ($6.0) 0.224 35.3% 9.6% $1,949
There is a risk of overprocurement with this constraint, but we continue to recommend it because it can protect reliability outcomes from the impact of errors in the administrative estimate of the E&AS offset and Net CONE
With the minimum, there would be a ~$50m cost of overprocurement in this case (relative to w/o the minimum)
But without the minimum, errors in the estimate of Net CONE could cause unacceptably low reliability
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0.0x
0.5x
1.0x
1.5x
2.0x
2.5x
2,500 3,000 3,500 4,000 4,500 5,000
Pric
e (%
of N
et C
ON
E)
Local Generation (ICAP MW)
Same Shape as System Curve
MCL
Possible Clearing Prices & Quantities
What are the Parameter Values of the Initial Candidate Demand Curve in Capacity Subzones?
Connecticut
Cap to Kink(Steep Section)
Kink to Foot(Flat Section)
Change in Price ($/kW-m) $10.8 $5.8Change in Quantity (MW) 470 682Slope ($/kW-m per 100 MW) $2.30 $0.85
NEMA
Cap to Kink(Steep Section)
Kink to Foot(Flat Section)
Change in Price ($/kW-m) $10.8 $5.8Change in Quantity (MW) 375 545Slope ($/kW-m per 100 MW) $2.88 $1.07
Maine
Cap Kink Foot
Curve DefinitionPrice 2x Net CONE 35% of Cap $0
QuantityMax of 1-in-5 LOLE or TSA
104.7% of Cap
111.4% of Cap
Corresponding Quantities in FCA7
Local + Import MW 10,089 10,559 11,240
Cap Kink Foot
Curve DefinitionPrice 2x Net CONE 35% of Cap $0
QuantityMax of 1-in-5 LOLE or TSA
104.7% of Cap
111.4% of Cap
Corresponding Quantities in FCA7
Local + Import MW 8,059 8,434 8,979
Cap to Kink(Steep Section)
Kink to Foot(Flat Section)
Change in Price ($/kW-m) $10.8 $5.8Change in Quantity (MW) 168 244Slope ($/kW-m per 100 MW) $6.45 $2.39
Notes: MW quantities based on FCA7; prices based on a Net CONE of $8.3/kW-m.
Cap Kink Foot
Curve DefinitionPrice 2x Net CONE 35% of Cap $0
Quantity97.2%
of MCL101.8% of MCL
108.3% of MCL
Corresponding Quantities in FCA7
Local MW 3,606 3,774 4,018
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Would a Flatter Curve be More Appropriate in Import-Constrained Zones?
▀ A flatter curve would help mitigate against price volatility and the exercise of market power
▀ With a flatter curve that is ½ as steep as the initial candidate local curve, the price impact of a change in supply would be substantially lower. For example, in NEMA:− To the left of the kink (from a starting price of
$10/kW-m), a 100 MW reduction in supply with the flatter curve would increase prices by $1.35/kW-m, compared to $2.61/kW-m with the initial candidate curve*
− To the right of the kink (from a starting price of $3/kW-m), a 100 MW reduction in supply with the flatter curve would increase prices by $0.24/kW-m, compared to $0.30/kW-m with the initial candidate curve*
*Notes: these illustrative examples assume Net CONE = $8.3/kW-m, and that the supply curve is shaped consistent with our core shape. If the supply curve were vertical, the price impact of a 100 MW reduction in supply to the left of the kink would be $1.44 with the flatter curve and $2.88 with the candidate curve. The price impact of a 100 MW reduction in supply to the right of the kink would be $0.54 with the flatter curve and $1.07 with the candidate curve.
Flatter Curve in NEMA
0.0x
0.5x
1.0x
1.5x
2.0x
2.5x
7,00
0
7,50
0
8,00
0
8,50
0
9,00
0
9,50
0
10,0
00
Pric
e (%
of N
et C
ON
E)
Local ICAP + Imports (MW)
LRA (1-in-10 LOLE) TSA (LSR)
Initial Candidate Local Curve
Flatter Curve(slope 1/2 as steep)
Net CONE
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Average Standard Deviation
Frequency at Cap
Average Standard Deviation
Frequency Below TSA
Average Difference from Candidate Curve
($/kW-m) ($/kW-m) (% of draws) (% of draws) ($mil/year) ($mil/year)
NEMA/BostonVertical Curve at LSR $9.6 $4.6 20.3% 8.4% 9.3% 16.8% $433 ($21)Initial Candidate Curve $9.6 $4.3 14.9% 10.5% 9.3% 11.0% $454 $0Flatter Curve $9.6 $4.1 11.1% 13.3% 9.4% 6.8% $479 $25
ConnecticutVertical Curve at LSR $9.6 $4.5 20.3% 4.7% 5.8% 13.3% $914 ($12)Initial Candidate Curve $9.6 $4.2 13.9% 5.9% 5.7% 10.3% $926 $0Flatter Curve $9.6 $3.9 8.6% 8.7% 5.8% 4.5% $961 $35
Price Cleared Quantity
(% Above LSR+TTC)
Price * Quantity
Would a Flatter Curve be More Appropriate in Import-Constrained Zones? (Cont.)
▀ A flatter, right-shifted curve would reduce price sensitivity, but it would increase customer costs− The initial candidate local curve is already right-shifted compared to vertical at LSR (to limit outcomes below TSA)− Our analysis shows that with a curve that is ½ as steep as the initial candidate curve (as shown on the prior slide),
long-run equilibrium costs would be approximately $25m/yr higher in NEMA and $35m/yr higher in CT− Customers in import-constrained zones would still be buying the same total quantity of capacity
▀ An alternative we do not consider is shifting the top of the curve to the left, because it would compromise reliability− Would increase the frequency of outcomes below TSA− “Adding money” at the bottom of the curve would not mitigate this concern much since the bottom of the local
curve is irrelevant whenever clearing prices are set by the system curve
Simulated Performance of Flatter Curves (1/2 as steep) in NEMA and CT
Notes: All simulations have initial candidate curve as the system curve, and have an average system price equal to system Net CONE. Price * Quantity results represent local prices and quantities only.
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Can You Further Explain the Need for a Demand Curve in Maine?
Capacity in Maine beyond the MCL has little reliability value▀ As discussed previously, capacity sourced in Maine has
less value than capacity sourced in system▀ Figures show marginal and cumulative value of Maine
Capacity as “delivered” to system▀ Calculated based on incremental value of Maine and
System MW in reduced MWh of unserved energy
A “Maximum” demand curve is therefore needed to prevent too much capacity from clearing in Maine▀ Without a maximum demand curve in Maine, there would
be no limit on how much capacity could clear there, which might harm system reliability
▀ For example, capacity 1,000 MW in excess of the MCL could clear in Maine, and this capacity would displace 1,000 MW capacity in the rest of the system but would not provide 1,000 MW of reliability value
The slope of our curve loosely reflects the marginal reliability value of capacity in Maine ▀ Reliability value above MCL is low but non-zero
Marginal Reliability Value of Maine Capacity
(as % of System Capacity)
“Maximum” Demand Curve(Export-Constrained Zones)
Possible Prices &
Quantities
Impossible Prices &
Quantities
0%
20%
40%
60%
80%
100%
2,500 3,500 4,500
Mar
gina
l Val
ue (%
)
Local Generation (ICAP MW)
MCL
Marginal Value
0
1,000
2,000
3,000
4,000
5,000
2,500 3,500 4,500
Cum
ulati
ve V
alue
(MW
)
Local Generation (ICAP MW)
MCL
x-y line
Cumulative Value
Cumulative Reliability Value
of Maine Capacity
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Can You Compare Historical Price Volatility in PJM to the Volatility in Your Simulations?
PJM Historical Rest-of-RTO Capacity Prices
PJM Historical Prices vs. Simulations▀ PJM historical prices are less volatile than
the prices in our simulations− PJM historical Rest-of-RTO prices from
capacity auctions held during previous 10 years (but the system has been in surplus, so average prices and price volatility are both likely below a long-run average level)
− PJM Simulated and Initial Candidate Curve prices from our Monte Carlo analysis using 1,000 draws
▀ Caveat: PJM’s curve is simulated as applied to ISO-NE. The curve points are defined using PJM’s shape proportional to New England’s NICR; the supply curve shape is from ISO-NE rather than PJM; and the supply and demand shocks are based on ISO-NE historical data. Therefore, PJM’s historical prices cannot be compared directly against the Monte Carlo simulation results.
$1.2
$3.4$3.1
$5.3
$3.3
$0.5$0.8
$3.8$4.1
$1.8
$0
$1
$2
$3
$4
$5
$6
2007
/200
8
2008
/200
9
2009
/201
0
2010
/201
1
2011
/201
2
2012
/201
3
2013
/201
4
2014
/201
5
2015
/201
6
2016
/201
7
PJM
Cap
acity
Pric
es ($
/kW
-m)
Auction Delivery Period
Standard Deviation of
PricesSample
Size($/kW-m)
Demand CurvesPJM (Actual) $1.58 10
PJM VRR applied to ISO-NE (Simulated) $2.68 1,000
Initial Candidate Curve (Simulated) $3.69 1,000
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Can You Provide a More Detailed Description of Your Simulation Modeling Approach? Overview (as discussed in our prior meeting)
▀ Adapted historical FCA and PJM offers to create a realistic supply curve shape▀ Assumed locational supply curves, demand curves, and transmission parameters
consistent with FCA 7 (as adjusted for shocks)▀ Used a locational clearing model to calculate clearing prices and quantities ▀ Simulated a distribution of 1,000 outcomes using a Monte Carlo analysis of
realistic “shocks” to supply and demand▀ The draws are independent of each other. The simulation is not a time-series
analysis, and the results from a given draw do not affect any other draws▀ Calibrated the quantity of zero-priced supply so that the average price over all
draws is equal to Net CONE
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$0
$2
$4
$6
$8
$10
$12
$14
$16
$18
$20
70% 80% 90% 100% 110%
Offe
r Pric
e ($
/kW
-m)
Quantity (% of Offers below $7/kW-m)
Smoothed Publicly-Posted PJM Supply Curves (2010/11 -2015/16)
ISO-NE FCA Supply Curves (FCA1 - FCA7)
Supply Curve Shape for Monte CarloAnalysis
Simulation Modeling ApproachSupply Curve Shape▀ The shape of the supply curve is a key determinant of
demand curve performance. A more elastic supply curve will result in more stable prices and quantities near the reliability requirement even in the presence of shocks to supply and demand
▀ We adapted historical FCA and PJM offers to create a realistic supply curve shape. The price floors that were in effect in FCAs 1-7, meaning that we observed no supplier offers that would have been made at prices below the floor. Therefore, supply curves from PJM are used as a proxy to construct the portion of the supply curve shape at prices below the floor prices in FCAs 1-7
▀ To construct a single composite shape from the individual historical supply curves, we first normalize each curve in terms of the percent of offers made below $7/kW-m. This normalization price was chosen because it resulted in relatively consistent shapes across the individual curves. We then combine the normalized individual curves into the composite shape by taking the average quantity at each price level
▀ The composite supply curve is relatively steep, especially at prices greater than $5/kW-m. While it is difficult to project the shape of future supply curves, we believe this is a reasonable approach based on the information available from historical auctions
Supply Curve Core Shape for Simulations
Sources and Notes: Historical ISO-NE FCA supply curves provided by ISO-NE.PJM supply curves from The Second Performance Assessment of PJM’s Reliability Pricing Model (2011, Pfeifenberger et al.)Historical offers inflated by Handy-Whitman Index.
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$0
$2
$4
$6
$8
$10
$12
$14
$16
$18
$20
28,000 30,000 32,000 34,000 36,000 38,000
$/kW
-m
MW
Offer Curve with Individual Offer Blocks
Supply Curve Core Shape
Simulation Modeling ApproachSupply Curve Blockiness▀ After constructing the composite core shape,
we fit individual offer blocks onto it to represent a realistic amount of “blockiness” in offer sizes. Simply modeling a smooth offer curve would slightly understate volatility in price and quantity outcomes (especially in smaller zones)
▀ Individual block sizes are derived from a random selection of cleared resources in FCA7 resources
▀ We shuffle offer block MW and prices stochastically, while maintaining a shape consistent with historical observation
▀ 1,000 individual blocky supply curves (each consistent with the core shape) are used in the Monte Carlo simulations to avoid skewed outcome distributions driven by a single large block at a constant price
Example Supply Curve with Random Offer Blocks Around Core Shape
Sources and Notes: The curve in this chart is a single example. 1,000 different curves are used in the simulations.
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0.0x
0.5x
1.0x
1.5x
2.0x
2.5x
24,000 26,000 28,000 30,000 32,000 34,000 36,000
Pric
e (%
of N
et C
ON
E)
MW
Distribution of QuantityOutcomes
Supply ShocksDemand Shocks
Distribution of Price Outcomes
Simulation Modeling ApproachShocks to Supply and Demand
To simulate a realistic distribution of price, quantity, and reliability outcomes, we include upward and downward shocks to both supply and demand, with the magnitude of the shocks based on historical observation
Stylized Depiction of Supply and Demand Shocks
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Simulation Modeling ApproachSupply ShocksSupply Shocks▀ Objective is to simulate realistic upward and
downward “shocks” to the supply curve, which might be driven by retirements, low-priced entry of new resources, or expanded interties
Approach▀ Assume that supply shocks are normally
distributed, with a standard deviation equal to the standard deviation of the quantity of offers made below the price cap across FCAs 1-7
▀ Shocks are implemented independently for each zone
▀ With historical data limited to just 7 auctions, entry or exit decisions in a single auction can drive much of observed variation in smaller zones
− Exit of Salem Harbor from NEMA in FCA5− Entry of Kleen, Devon peakers, and
Middletown peakers in CT in FCA 2
Offer Quantities by Zone Across FCAs 1-7
0
5,000
10,000
15,000
20,000
25,000
FCA1 FCA2 FCA3 FCA4 FCA5 FCA6 FCA7
Offe
rs B
elow
Pric
e Ca
p (M
W)
Rest-of-Pool (Standard Deviation 327 MW)
CT (Standard Deviation = 486 MW)
ME (Standard Deviation = 148 MW)
NEMA (Standard Deviation = 387 MW)
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Simulation Modeling ApproachDemand ShocksDemand Shocks▀ Objective is to simulate realistic upward and
downward “shocks” to demand (i.e. to NICR), which might be driven by increases or decreases in the load forecast and LOLE modeling
Approach▀ Assume shocks to supply and demand are
independent▀ Assume that demand shocks are normally
distributed with standard deviation equal to the standard deviation in NICR across FCAs 1-7− Shocks to local demand (LSR and MCL) modeled
in the same way▀ Change in LSR in FCA4 is a major driver of CT &
NEMA results
System and Local Reliability RequirementsAcross FCAs 1-7
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
FCA1 FCA2 FCA3 FCA4 FCA5 FCA6 FCA7
Relia
bilit
y Re
quire
men
t (M
W)
System NICR (Standard Deviation = 567 MW)
CT LSR (Standard Deviation = 387 MW)
ME MCL (Standard Deviation = 287 MW)
NEMA LSR (Standard Deviation = 567 MW
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$0
$3
$6
$9
$12
$15
10,000 15,000 20,000 25,000 30,000 35,000O
ffer P
rice
($/k
W-m
)
Supply (MW)
ShockBlock
SmartBlock
ShapeBlock
Simulation Modeling ApproachNormalization: Average Clearing Prices = Net CONE
▀ The quantity of zero-priced supply modeled for each demand curve is calibrated so that the average clearing price over all draws is equal to Net CONE− For example, too much zero-priced supply would result
in an average price below Net CONE, while too little supply would result in a price above Net CONE
− This normalization allows us to examine the performance of each demand curve in a long-term equilibrium state
▀ The block of zero-priced supply used for this normalization is shown as the “Smart Block” in the figure to the right− The quantity of supply in the smart block is held
constant across individual draws, but is slightly different across demand curves. For example, with Stoft’s right-shifted curve, more supply is needed in the smart block than with our Initial Candidate curve (if the same smart block was used to model both curves, then clearing prices with Stoft’s curve would be higher than with our Initial Candidate Curve)
− In contrast to the smart block, the quantity of the shock block varies with each draw to generate “shocks” to the supply curve (as described in prior slides)
Supply Curve Components in Monte Carlo Simulations
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How Would Larger or Smaller Shocks to Supply and Demand Affect the Candidate Curve’s Performance?
▀ With larger shocks, price and reserve margin volatility would be greater, and reliability would fall short of the 1-in-10 LOLE target
▀ With smaller shocks, price and reserve margin volatility would be reduced, and reliability would exceed the 1-in-10 LOLE target
Simulated Performance with Larger and Smaller Shocks to Supply and Demand
Notes: In the sensitivity cases, the shocks to both supply and demand are 50% larger than (or 50% smaller than) the base case shocks.
Price * Quantity
AverageStandard Deviation
Frequency at Cap
Average LOLE
Average Reserve Margin
Reserve Margin
Standard Deviation
Frequency Below NICR
Frequency Below
Minimum Acceptable
Average
($/kW-m) ($/kW-m) (% of draws) (%) (%) (%) (% of draws) (% of draws) ($mil)
Shocks 50% Smaller than Base Case $8.3 $2.3 0.3% 0.092 13.0% 1.2% 22.5% 0.4% $3,313
Base Case $8.3 $3.7 5.1% 0.100 13.1% 2.2% 28.9% 6.1% $3,309
Shocks 50% Larger than Base Case $8.3 $4.5 11.0% 0.122 13.2% 3.2% 32.0% 12.6% $3,301
Price Reliability
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How Would Larger or Smaller Shocks Affect the Candidate Curve’s Performance? (Cont.)
Initial Candidate Curve Simulated Outcomes with Shocks 50% Larger than Base Case
Initial Candidate Curve Simulated Outcomes with Shocks 50% Smaller than Base Case
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How Would Larger or Smaller Shocks Affect the Candidate Curve’s Performance? (Cont.)
Initial Candidate Curve Base Case Simulated Outcomes (for reference)