Capacity Demand Curve in ISO-NE: Responses to Initial Stakeholder Inquiries

1. 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

2. Table of Contents

3. What are the Parameter Values of the Initial Candidate Curve? Demand Curve Parameter Values Initial Candidate Demand Curve Note: Price cap is subject to a minimum price of 1x Gross CONE. Demand Curve Slope (if Net CONE = $8.3/kW-m) Notes: MWquantities based on FCA7; due to supply elasticity, price impacts from a 100 MW shift in supply-demand would be less than the slope suggests. 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).

4. Can you Report the Percent of Draws Clearing Below NICR for Each Curve? Simulation Results 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.

5. 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

6. 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 CONE is 33% Higher or Lower than True Net CONE

7. 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.

8. Can You Explain the Price Cap Minimumat 1x Gross CONE? (Cont.) • 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 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

9. What are the Parameter Values of the Initial Candidate Demand Curve in Capacity Subzones? NEMA Connecticut Maine Notes: MWquantities based on FCA7; prices based on a Net CONE of $8.3/kW-m.

10. Would a Flatter Curve be More Appropriate in Import-Constrained Zones? • Aflatter 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

11. 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.

12. 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 Cumulative Reliability Value of Maine Capacity Marginal Reliability Value of Maine Capacity (as % of System Capacity) “Maximum” Demand Curve (Export-Constrained Zones) Impossible Prices & Quantities Possible Prices & Quantities

13. Can You Compare Historical Price Volatility in PJM to the Volatility in Your 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. PJM Historical Prices vs. Simulations PJM Historical Rest-of-RTO Capacity Prices

14. 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

15. Simulation Modeling ApproachSupply Curve Shape Supply Curve Core Shape for Simulations • 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 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 etal.) Historical offers inflated by Handy-Whitman Index.

16. Simulation Modeling ApproachSupply Curve Blockiness Example Supply Curve with Random Offer Blocks Around Core Shape • 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 Sources and Notes: The curve in this chart is a single example. 1,000 different curves are used in the simulations.

17. 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

18. Simulation Modeling ApproachSupply Shocks Supply 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

19. Simulation Modeling ApproachDemand Shocks System and Local Reliability Requirements Across FCAs 1-7 Demand 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

20. 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

21. 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.

22. 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

23. How Would Larger or Smaller Shocks Affect the Candidate Curve’s Performance? (Cont.) Initial Candidate Curve Base Case Simulated Outcomes (for reference)