united states of america before the federal energy … · 2018-02-02 · we find that miso’s...

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UNITED STATES OF AMERICA

BEFORE THE

FEDERAL ENERGY REGULATORY COMMISSION

Midcontinent ISO

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Docket No. ER17-_____-000

TESTIMONY OF DR. SAMUEL A. NEWELL, DR. KATHLEEN SPEES, AND DR. DAVID LUKE OATES

ON BEHALF OF MIDCONTINENT INDEPENDENT SYSTEM OPERATOR REGARDING THE COMPETITIVE RETAIL SOLUTION

Our names are Dr. Samuel A. Newell, Dr. Kathleen Spees, and Dr. David Luke Oates. We are employed by The Brattle Group, Drs. Newell and Spees as Principals and Dr. Oates as an Associate. On behalf of the Midcontinent Independent System Operator (MISO), we submit this affidavit presenting our evaluation of MISO’s proposed Competitive Retail Solution. Our evaluation includes probabilistic simulation analyses of likely reserve margins, reliability, and prices under the Competitive Retail Solution. We also compare the Competitive Retail Solution to an alternative proposal favored by MISO’s Independent Market Monitor (IMM) Dr. David Patton.

Our qualifications as experts derive from our extensive experience evaluating capacity markets and alternative market designs for resource adequacy. Our experience working for RTOs across North America and internationally has given us a broad perspective on the practical implications of nuanced capacity market design rules under a range of different economic and policy conditions.1 For MISO, we have worked with staff at various stages of the resource adequacy construct’s evolution to evaluate performance and recommend enhancements.2 We have also worked on a number of assignments for market participants operating within the MISO footprint,

1 We have worked with regulators, market operators, and market participants on matters related to resource

adequacy and investment incentives in PJM Interconnection, ISO New England, New York, Ontario, Alberta, California, Texas, MISO, Italy, Russia, Greece, and Western Australia. See a more comprehensive description of these engagements in our resumes, which are included as attachments to MISO’s filing letter.

2 See Section IV.G of Samuel Newell, Kathleen Spees, and Nicholas Powers, “Developing a Market Vision for MISO,” prepared for the Midcontinent ISO, January 27, 2014 and Samuel Newell, Kathleen Spees, and Attila Hajos, “Midwest ISO’s Resource Adequacy Construct,” prepared for MISO, January 19, 2010.

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which has provided us insights on how the capacity market construct may impact the business decisions and other interests of suppliers, customers, utilities, and state regulators.3

A subset of our market design work has focused on evaluating sloped demand curves for achieving different market design objectives. That experience includes: (1) PJM capacity market reviews of 2008, 2011, and 2014 that assessed market performance, including statistical simulations of that market’s Variable Resource Requirement curve;4 (2) ISO-NE demand curve design filed before the Commission in 2014;5 (3) a study on the economics of reliability for the Commission in 2013, including calculating a value-based capacity demand curve designed to procure an economically optimal quantity of capacity from a risk-neutral societal perspective;6 and (4) assistance in defining or refining the capacity market demand curves of four other international RTOs.

Dr. Newell is an economist and engineer with 18 years of experience analyzing and modeling electricity wholesale markets, the transmission system, and market rules. He earned a Ph.D. in Technology Management and Policy from the Massachusetts Institute of Technology, an M.S. in Materials Science and Engineering from Stanford University, and a B.A. in Chemistry and Physics from Harvard College. Dr. Spees is an economic consultant with expertise in wholesale electric energy, capacity, and ancillary service market design and price forecasting. She earned a Ph.D. in Engineering and Public Policy and an M.S. in Electrical and Computer Engineering from Carnegie Mellon University, and a B.S. in Mechanical Engineering and Physics from Iowa State University. Dr. Oates is a consultant with experience in wholesale electricity market modeling and policy analysis. He earned a Ph.D. in Engineering and Public Policy from Carnegie Mellon University and a B.Sc. in Engineering Physics from Queen’s University, Canada.

Complete details of our qualifications, publications, reports, and prior experiences are set forth in our resumes, listed as attachments to MISO’s filing letter.

3 For example see our analysis of MISO’s resource adequacy construct in: Kathleen Spees, Samuel Newell,

and Roger Lueken, “Enhancing the Efficiency of Resource Adequacy Planning and Procurements in the Midcontinent ISO Footprint,” November 2015.

4 See Sections IV and V of our 2008 and 2011 PJM capacity market reviews respectively, for the review of PJM’s VRR curve, in Johannes Pfeifenberger, Samuel Newell, Robert Earle, Attila Hajos, and Mariko Geronimo, “Review of PJM’s Reliability Pricing Model (RPM),” June 30, 2008; and Johannes Pfeifenberger, Samuel Newell, Kathleen Spees, Attila Hajos, and Kamen Madjarov, “Second Performance Assessment of PJM’s Reliability Pricing Model,” August 2011, (“Pfeifenberger et al. PJM Second Performance Assessment”).

5 See “Testimony of Dr. Samuel A. Newell and Dr. Kathleen Spees on Behalf of ISO New England Inc. Regarding a Forward Capacity Market Demand Curve,” Attachment to ISO New England and New England Power Pool submission before the Federal Energy Regulatory Commission, April 1, 2014, Docket ER14-1639-000, (“Newell and Spees Testimony on Behalf of ISO New England”).

6 See Section IV.B for a derivation and discussion of a value-based capacity demand curve, from Johannes P. Pfeifenberger, Kathleen Spees, Kevin Carden, and Nick Wintermantel, “Resource Adequacy Requirements: Reliability and Economic Implications,” prepared for FERC, September 2013, (“Pfeifenberger et al. FERC Resource Adequacy Report”).

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TABLE OF CONTENTS

I. Summary............................................................................................................................... 4

II. Introduction .......................................................................................................................... 8

A. Challenges to the Status Quo Design for Resource Adequacy .................................... 8

B. Objectives of MISO’s Competitive Retail Solution .................................................. 11

C. Summary of MISO’s Design Proposal and Demand Curves .................................... 12

III. Probabilistic Modeling Approach .................................................................................... 14

A. Overview of Monte Carlo Model Structure ............................................................... 15

B. Merchant Supply Offer Behavior and Equilibrium Conditions ................................ 19

C. Utility Supply Planning and Forward Offer Behavior .............................................. 20

D. Administrative Demand and Transmission Parameters ............................................ 22

E. Shocks to Supply and Demand .................................................................................. 23

F. Reliability Outcomes ................................................................................................. 25

IV. Evaluation of MISO’s Competitive Retail Solution........................................................ 26

A. How the Competitive Retail Solution Addresses MISO’s Objectives ...................... 27

B. Performance of the Competitive Retail Solution ....................................................... 28

C. Sensitivity to Utility Offer Behavior in the Forward Auction ................................... 30

D. Sensitivity to the Magnitude of Supply and Demand Shocks ................................... 32

E. Sensitivity to Net CONE and Administrative Error in Net CONE ........................... 33

F. Summary of Performance under Sensitivity Scenarios ............................................. 35

V. Demand Curve Design ....................................................................................................... 36

A. Performance Compared to a Vertical Forward Demand Curve ................................ 36

B. Conceptual Basis for Price Cap and Quantity at the Cap .......................................... 39

C. Conceptual Basis for Curve Shape, Width, and Zero Crossing Point ....................... 42

D. Performance of Alternative “Tuned” Demand Curves .............................................. 44

E. Locational Demand Curve Design ............................................................................ 48

F. Summary of Expected Performance .......................................................................... 51

VI. Evaluation of Alternative Hybrid-Prompt Proposal ...................................................... 51

A. The Effects of Resource Discrimination ................................................................... 52

B. Efficient Price Formation .......................................................................................... 53

VII. Findings and Recommendations ...................................................................................... 54

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I. SUMMARY

This testimony supports MISO’s filing of its Competitive Retail Solution, a new capacity market design to procure capacity for competitive retail loads on a three-year-forward basis. We present our evaluation of the likely performance of the Competitive Retail Solution compared to the status quo market design. We also describe our approach for developing a demand curve for the Forward Resource Auction (FRA), which MISO has adopted in its proposal.7

We find that MISO’s current capacity market design is unlikely to attract and retain sufficient merchant capacity to meet MISO’s 1-in-10 reliability standard in the long term. This is because the combination of a vertical demand curve, inelastic prompt supply curve, and low price cap will produce prices that are volatile and capped at a moderate or low level. Prices will not rise high enough on average to support new entry until the reserve margin is low enough that the market is facing frequent shortages.

MISO’s Competitive Retail Solution would resolve these problems by procuring capacity to meet competitive retail loads’ capacity needs on a three-year-forward basis according to a sloped demand curve. Our analysis indicates that the Competitive Retail Solution would support enough merchant capacity to maintain reliability both system-wide and in the import-constrained zones. We also find that the Competitive Retail Solution would reduce price volatility compared to the current market design, but the improvement in price volatility is modest. These findings are robust across a range of sensitivity assumptions. We recommend a relatively wide demand curve on a percentage basis compared to other RTOs’ capacity auctions because of the small quantity and greater structural price volatility expected in MISO’s forward auction.

We used a Monte Carlo simulation model to analyze MISO’s Competitive Retail Solution with a range of potential demand curves and under a range of modeling assumptions. Our model is similar to the one we used in filings before the Commission to support PJM’s current demand curve and ISO-NE’s original sloped demand curve.8 We model a long-term equilibrium when new merchant generation is needed to maintain resource adequacy, which is the challenge the current construct is not equipped to meet. The model recognizes that merchant generation will not enter unless average future prices can be expected to be at least the Net Cost of New Entry (Net CONE).9 It simulates the market clearing as supply, demand, and transmission conditions fluctuate, to produce distributions of prices, reserve margins, and reliability outcomes. The price distributions account for the price stability provided by the sloped demand curve and price cap. Reliability outcomes reflect the increasingly steep loss-of-load expectation at lower reserve

7 We refer to this curve as the “MISO Proposed Curve” in our results. 8 See Samuel A. Newell and Kathleen Spees, “Affidavit of Dr. Samuel A. Newell and Dr. Kathleen Spees

on Behalf of PJM Interconnection, L.L.C. Regarding Periodic Review of Variable Resource Requirement Curve Shape and Key Parameters,” Attachment to PJM Interconnection submission before the Federal Energy Regulatory Commission, September 25, 2014, Docket ER14-2940-000, (“Newell and Spees Affidavit on Behalf of PJM”) and Newell and Spees Testimony on Behalf of ISO New England.

9 The Net Cost of New Entry is the cost of building a new gas turbine unit, minus expected energy and ancillary service margins.

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margins. This analysis makes it possible to design and tune a demand curve until the distribution of simulated outcomes meets the target level of reliability while also mitigating price volatility.

Our analysis accounts for MISO’s unique circumstances with more than 90% of the load served by vertically integrated utilities that conduct resource planning. We assume utilities will continue to meet their own needs but may often have excess capacity to sell. That excess may compete in forward auctions, reducing the amount of merchant capacity needed but also imposing price volatility on the merchant portion of the market.

We evaluated the distributions of prices, reserve margins, and reliability outcomes of MISO’s proposal under a number of candidate demand curves. Our recommended curve, shown in Figure 1, reflects a balance among competing objectives: meeting the 1-in-10 reliability requirement on average, mitigating the likelihood of very low reliability outcomes, avoiding excessive over-procurement on average, and mitigating price volatility. Although this recommended curve strikes a reasonable balance among these objectives, we note that other demand curves with slightly different tradeoffs could also be considered reasonable.

The recommended curve for procuring the total amount of capacity for all competitive retail loads is shown below. It has a price cap of 1.4× Net CONE at a reserve margin corresponding to 1-in-5 loss of load expectation (LOLE), then falling linearly to a price of zero at a reserve margin 15% above the competitive retail Planning Reserve Margin Requirement (PRMR).

The price cap of 1.4× Net CONE corresponds to approximately 1.05× Gross CONE with 2016/17 Planning Resource Auction (PRA) parameters. This is somewhat higher than the 1× Gross CONE cap that MISO had asked us incorporate into the demand curve, consistent with the current prompt auction. We recommend the slightly higher price cap to help mitigate against low reliability events and against administrative error in estimating Net CONE. Though somewhat higher than MISO’s current PRA price cap, this cap is below those of ISO-NE, PJM, and NYISO and is somewhat below the price cap range of 1.5× to 2× Net CONE that we have recommended in other markets. The advantage of the lower cap is that it yields a flatter, wider curve that helps mitigate price volatility and susceptibility to the exercise of market power. We tuned the foot quantity to 115% of PRMR to ensure that the auction would procure sufficient capacity to achieve 1-in-10 on average (without assuming utilities hold any capacity beyond their own needs plus sales obligations).

MISO’s demand curves for local capacity in import-constrained Zone 4 (Illinois) and Zone 7 (Michigan) are similar to the system demand curve described above. The price cap is set at 1.4× Net CONE and 1-in-5 LOLE, and the foot quantity is at 115% of the Local Reliability Requirement (LRR). We have not recommended adopting a wider curve or a higher cap for the import-constrained zones because we have not identified evidence that locational resource adequacy is acutely challenging in these zones.

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Figure 1 MISO Proposed Forward Resource Auction Demand Curve

Sources: ISO New England, “ISO New England Installed Capacity Requirement, Local Sourcing Requirements and Capacity Requirement Values for the System‐Wide Capacity Demand Curve for the 2019/20 Capacity Commitment Period,” January 2016. PJM Interconnection, “2019–20 RPM Base Residual Auction Planning Period Parameters,” February 2016. New York Independent System Operator, “2014–2017 Demand Curve Parameters,” March 2014.

We evaluated the likely performance of the Competitive Retail Solution with the candidate demand curve compared to alternative curves and to the status quo market design. We found, not surprisingly, that the status quo design would not attract and retain enough merchant supply to maintain reliability in the long run. The Competitive Retail Solution would solve the problem. It would attract an additional 1,800 MW of merchant supply compared to the status quo, meeting achieving or exceeding the 1-in-10 reliability requirement in the system, in Illinois, and in Michigan.

The Competitive Retail Solution would also modestly improve price volatility in the forward auction by 6–15% compared to the status quo prompt auction. Volatility would remain high despite the sloped demand curve because of the swings in supply offered by the much larger utility segment of the market. This effect requires a wider demand curve than PJM or ISO-NE in order to mitigate price volatility.

A key uncertainty is the amount of excess capacity utilities will offer into the forward auctions and, relatedly, how much excess capacity they might hold and not offer due to conservative planning (the utility “buffer”). If the buffer is lower, more utility supply will offer and clear in the forward auction. This will displace merchant supply and increase price volatility because the relatively small forward auction will absorb all the supply variation from the much larger utility segment of the market. Realized reliability will be lower if utilities do not hold excess capacity in reserve. Conversely, if the buffer is higher, there is less displacement, lower price volatility, and greater reliability. We tested a range of buffer sizes, finding that reliability objectives would be achieved in all but the most extreme cases with utilities holding no buffer, leading to

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delivered system reliability slightly worse than target, at 0.13 LOLE. Under an alternative sensitivity assumption with supply/demand fluctuations at twice the base case level derived from historical data, delivered reliability did not fall below target.

MISO is less exposed to the biggest risk facing merchant-only capacity markets, which is that a demand curve might not attract enough supply to support reliability. In MISO, that risk is lower because the majority of the system meets its own needs through utility planning rather than responding to price signals. With that stabilizing weight, the Competitive Retail Solution achieves the target reliability under almost all cases under the broad range of assumptions tested.

We also compared MISO’s Competitive Retail Solution to the alternative option initially proposed by the Independent Market Monitor (IMM) and adapted by MISO into the “hybrid-prompt proposal.” The hybrid-prompt option would involve a two-stage prompt auction with a higher Stage 1 price awarded to merchant suppliers and a lower Stage 2 price awarded to regulated suppliers. We recognize that the hybrid-prompt approach might be able to achieve reliability objectives with reduced price volatility, due to its much wider demand curve that is scaled to the entire system load rather than just competitive retail load. However, we disagree with Dr. Patton’s conclusion that the prompt-hybrid proposal would achieve “efficient price formation,” and we do not believe it would be a workable design option. Our primary concern is that the resource discrimination contemplated under the two-stage auction would introduce a large deadweight loss by procuring expensive merchant capacity when cheaper utility capacity is available. For example, borrowing from an illustrative analysis presented by Dr. Patton, the hybrid-prompt auction may select a $170 merchant resource instead of a $43 utility resource to provide the same product.10 In contrast, the Competitive Retail Solution is structured to achieve reliability objectives at least cost by always procuring the lowest cost resources available. As a secondary issue, Dr. Patton’s arguments in favor of the hybrid-prompt proposal and against MISO’s Competitive Retail Solution proposal rely on what we view as an inappropriate definition of the “efficient price” that does not constitute a measure of economic efficiency such as welfare gains or deadweight loss.

Our overall conclusion is that the Competitive Retail Solution will address the reliability shortfalls anticipated under the status quo capacity market design. We expect that price volatility will improve under the Competitive Retail Solution, but that it will continue to be a challenge. We recommend that MISO periodically reassess the effectiveness and performance of the forward auction after current uncertainties are resolved, in order to confirm whether the construct is achieving the design objectives and to make adjustments as needed. In future assessments, we recommend that MISO review the demand curve parameters, market monitoring and mitigation procedures, opt-in and opt-out provisions for loads, provisions governing utility supply offers, and the potential to incorporate voluntary buy bids in the forward auction.

10 Detailed descriptions of Dr. Patton’s proposal and MISO’s adapted hybrid-prompt option are available in

the July 7, 2014 Resource Adequacy Subcommittee (RASC) meeting materials, available at https://www.misoenergy.org/Events/Pages/RASC20160714.aspx.

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II. INTRODUCTION

MISO’s filing letter highlights concerns about imminent resource adequacy shortfalls if certain at-risk capacity retires. It attributes potential shortfall to the lack of an adequate structure for meeting the resource adequacy needs in competitive retail areas.

We concur that MISO’s current construct is not likely to support sufficient merchant supply to meet the capacity needs of competitive retail states, due to the mechanism’s reliance on a non-forward auction with a vertical demand “curve” and a relatively low price cap. We anticipate that this design will not produce prices high enough to attract merchant generation investments until reliability is unacceptably low. Further, the lack of forward visibility means that the shortage might not be identified until it is too late to address through administrative intervention. Any resulting shortages for competitive retail customers could impair reliability for the entire system.

In response to these concerns, MISO has worked with stakeholders over the last year and a half to address the problem. Together they established design principles and eventually a coherent Competitive Retail Solution. The proposed Competitive Retail Solution is a three-year-forward capacity auction to procure capacity on behalf of competitive retail loads using a sloped demand curve. Merchant suppliers will generally offer their supply into the forward auction (if not contracted to a utility), and vertically-integrated utilities will be able to offer their excess capacity as well. A prompt auction will still be held just before the delivery year. The prompt auction will account for all supply and demand in the footprint just as it does now, except that competitive retail demand will be set equal to the amount of supply cleared in the forward auction.

A. CHALLENGES TO THE STATUS QUO DESIGN FOR RESOURCE ADEQUACY

Competitive retail loads are a minority of the MISO region but large enough to affect reliability if they do not procure adequate supplies. They make up approximately 10% of the regional demand and a much larger percentage in Illinois (Zone 4) and Michigan (Zone 7).11

Retail choice customers obtain their electricity from competitive retail providers. Competitive retail providers typically do not have agreements beyond a few years with their customers, who may then elect to switch to other providers. As such, the competitive retail providers do not conduct long-term resource planning and are unlikely to enter into long-term purchase agreements. Instead, they typically procure energy, capacity, and ancillary services from the wholesale market on a short-term basis, matching the terms of their contracts with customers. For capacity, they rely on supplies being available on a short-term basis through the bilateral market or through MISO’s prompt Planning Resource Auction (PRA). In Illinois, some capacity

11 See Doying testimony at p. 3.

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needs have also been met through the near-term procurement activities of the Illinois Power Agency (IPA).12

Capacity may be available in the prompt auction from either merchant suppliers with generation that is not otherwise contracted, or from vertically-integrated utilities with excess supplies.13 Historically, these sources have provided more than enough capacity to meet competitive retail demands, such that new generation was not needed to serve that demand. However, this will change as old generators retire and as load grows. Supply adequacy for competitive retail loads will depend on the wholesale market to attract merchant generation investments.14

Historical capacity prices in MISO have been too low to attract merchant generation entry, but this was an efficient result given the prevailing surplus capacity conditions. MISO capacity prices have been far below the Net Cost of New Entry (Net CONE), which is the approximate capacity payment that merchants would need to expect on average over their asset life in order to build new generation. Going forward, the key question is whether MISO’s resource adequacy construct will allow prices to rise enough to attract new generation when the surplus disappears and new capacity is needed. Capacity markets can be designed to successfully support new merchant generation investments when needed, as we have observed over the past several years in both PJM and ISO-NE.15 However, MISO’s current resource adequacy construct has several critical design differences that will render it unable to attract merchant supply until reliability is at unacceptably low levels. The shortfall will stem from the combination of three design features: (1) the non-forward timing of the Planning Resource Auction ; (2) the vertical capacity

12 For example in its 2015 procurement plan, the IPA recommended a procurement schedule for at least 50%

of the second delivery year’s forecast capacity requirement and at least 25% of the third delivery year’s forecast requirement, see Illinois Commerce Commission, “Final Order, Illinois Power Agency Petition for Approval of the 2015 IPA Procurement Plan pursuant to Section 16-111.5(d)(4) of the Public Utilities Act,” December 17, 2014, pp. 3–10, http://www.icc.illinois.gov/downloads/public/edocket/393476.pdf.

13 For example, in its 2012 capacity procurement auctions, IPA procured excess capacity from regulated utilities and public power entities including Wisconsin Public Service Corporation, Minnesota Power, Consumers Energy, Exelon, Ameren Missouri, and DTE, and uncontracted supply from IPPs including GenOn (now part of NRG), and Dynegy, see Illinois Commerce Commission, “Public Notice of Winning Bidders and Average Prices Concerning the Illinois Power Agency’s April 2012 Procurement of Capacity Products for Ameren Illinois Company,” April 10, 2012, http://www.icc.illinois.gov/downloads/public/Notice%20of%20Results%20of%20the%202012%20Ameren%20Capacity%20Products%20RFP%202012-04-10.pdf.

14 Competitive retail loads may also continue to rely on excess supplies from regulated utilities for a portion of their capacity needs, to the extent that these regulated utilities continue to maintain an excess of supply far into the future. However, competitive retail loads cannot rely exclusively on utility excess because: (a) utility excesses will not be large enough in aggregate nor in the right locations to support all competitive retail load needs, and (b) utility customers and commissions in regulated states will not likely be willing to subsidize these needs into perpetuity.

15 For example, over the past three capacity auctions, PJM attracted about 15,000 UCAP MW of gas combined-cycle (CC) units, approximately 10,000 MW of which were merchant, see PJM, “2019/2020 RPM Base Residual Auction Results,” May 2016. In ISO-NE, 800 MW of new generation cleared in 2016/17 and more than 1,400 MW cleared in 2019/20. Among these cleared new generation resources were two large merchant gas CC plants, the 674 MW Footprint Power and 725 MW CPV Towantic projects, see ISO New England, “Forward Capacity Market (FCA 10)_ Result Report,” February 2016.

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demand curve at the reliability requirement imposed in the PRA; and (3) the lower PRA price cap at 1× CONE.

Non-Forward Timing: the non-forward timeframe of the PRA renders the supply curve inelastic because investment decisions are already cast by the time of the auction. With investment costs sunk, even new generation is expected to offer at zero or near-zero. The small amount of capacity with avoidable going forward costs creates a near vertical curve at prices above zero.

Vertical Demand: A vertical demand curve may seem to properly reflect the traditional resource adequacy requirement, but it creates a structurally challenging market unlike other industries, where demand is not administratively determined and exhibits natural price elasticity. The complete lack of demand elasticity makes prices extremely volatile, especially in combination with a near-vertical supply curve. Even a small surplus drives prices close to zero, as observed in almost all historical MISO auctions.16 Then as the market encounters even a minor shortage, prices would hit the “knife edge” and jump very high or up to the price cap. With extreme price sensitivity to small changes in quantity, the market is also susceptible to the exercise of market power on a system and locational basis.

The Low Price Cap. The price cap is relatively low at 1× CONE, or only about 1.3× Net CONE. With prices reaching this cap only during shortages, and with prices near zero otherwise, average prices will remain below Net CONE unless shortages occur frequently. Merchant entry would be limited to maintain shortages so that expected future prices remain at Net CONE on a long-term average basis.

In other words, to achieve prices high enough to attract new generation investments for competitive retail loads, MISO as a whole would need to be willing to face persistent supply shortages. This is an objectionable and unsustainable outcome because: (a) the price volatility would be undesirably high for competitive retail loads (although it should not significantly affect regulated states); and (b) reliability would be unacceptably low and would very likely trigger a policy intervention by MISO or states that would undermine the competitive market. Further, the lack of accurate forward visibility into supply means that it may come as a surprise the first time the system or a local zone clears the prompt capacity auction with a shortage. At that point it may be too late to resolve the shortage. Thus, the current MISO resource adequacy design is not suited to meeting the needs of competitive retail loads and their merchant suppliers.

Some stakeholders have suggested addressing just one of the three structural challenges with the status quo, by raising the price cap to 2× CONE. Their concept is that, even if the prompt auction becomes even more volatile, such a high cap will induce market participants to hedge their exposure by contracting forward. We agree that such an approach might achieve the long-run reliability goal of the Competitive Retail Solution, but it would dramatically increase price volatility for loads that do not hedge. Its prospect for maintaining reliability might also be risky. Our concern is that load serving entities’ (LSEs’) contracts with customers are typically not more

16 The 2015/16 capacity auction result in the Illinois zone was an exception, where the high-priced supplier

offers and local resource constraints contributed to a large price spike followed by lower prices again in the subsequent year. Midcontinent ISO, “2015–16 Planning Resource Auction Results Frequently Asked Questions,” May 12, 2015, p. 2.

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than 1–2 years, and many are shorter-term than that. By the time LSEs buy capacity to meet their obligations, they will face volatile short-term conditions and there will not be enough lead time to induce a new unit to enter. Furthermore, the bilateral market will not provide as much transparency as the Competitive Retail Solution forward auction in which the whole market comes together simultaneously.

These problems have been addressed in other capacity markets that rely on merchant supply resources. All capacity markets other than MISO have improved price stability through sloped demand curves, while also incorporating higher price caps to support reliability, as we discuss further in Section V.A below.17 Both PJM and ISO-NE further mitigate capacity market price volatility and provide additional forward certainty by holding forward capacity auctions. Forward capacity supply curves are more elastic (higher and flatter) than prompt supply curves because there are more supply options available to come online before delivery. These design features suit the needs of those markets, which are dominated by competitive retail loads and merchant generation supply. In contrast, MISO’s design to date has been primarily designed to meet the needs of vertically-integrated utilities conducting resource planning which will not adequately address the needs of competitive retail areas. MISO’s unique challenge is to develop a design that accommodates the different and sometimes conflicting needs of traditionally regulated and competitive retail areas.

B. OBJECTIVES OF MISO’S COMPETITIVE RETAIL SOLUTION

The primary objective of any resource adequacy construct—indeed, its reason for being—is to maintain reliability. MISO’s existing construct threatens to fall short of maintaining reliability because it is unlikely to attract and retain enough merchant generation to meet the needs of competitive retail customers. The Competitive Retail Solution is intended to solve that problem without interfering with resource planning conducted by traditionally regulated utilities and their state commissions.

To evaluate the proposed solution, we begin with a clear definition of MISO’s design objectives. The primary objective is to meet MISO’s resource adequacy standard of enduring a load-shed event only once every 10 years. The 1-in-10 LOLE is the standard at the system level, and 1-in-10 incremental LOLE is the standard for import-constrained zones.18 We have confirmed with

17 “…the use of vertical demand curves in the FCM [ISO-NE Forward Capacity Market] presents challenges

such as increased price volatility and a susceptibility to the exercise of market power. When vertical demand curves are used, even small increases or decreases in supply can result in large changes in price, because a fixed amount of capacity must be procured. In addition, because a small decrease in supply can lead to a significantly higher price, sellers may have an incentive to withhold certain resources.” “Order Instituting Section 206,” ISO New England, Inc. and New England Power Pool Participants Committee before the Federal Energy Regulatory Commission, December 28, 2015, Docket ER14-1639-000 at 12, and “Order Accepting Tariff Revisions,” ISO New England, Inc. and New England Power Pool Participants Committee before the Federal Energy Regulatory Commission, May 30, 2014, Docket ER14-1639-000 at 29.

See also p. 7 of Newell and Spees Testimony on Behalf of ISO New England. 18 See paragraph 24 of Clair J. Moeller, “Affidavit of Clair J. Moeller on Behalf of the Midwest Independent

Transmission System Operator, Inc.,” Attachment to Midwest Independent Transmission System Continued on next page

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MISO staff that the 1-in-10 standard should be interpreted as an average that must be met on a multi-year basis, rather than an absolute minimum that must be achieved every year. This distinction matters because a capacity auction will not always procure exactly the target, but will instead procure a distribution of quantities as market conditions fluctuate. We have worked with MISO staff to identify a proxy level for the “minimum acceptable” reliability below which MISO would procure out-of-market backstop resources. While MISO does not have an explicitly-defined minimum acceptable level, we have been instructed to use 1-in-5 LOLE as a workable proxy value.

MISO’s Competitive Retail Solution aims to meet the core design objective at least cost while accommodating the different business models of merchant suppliers and traditionally regulated utilities. MISO has also articulated the following design principles to stakeholders:19

Price Formation: The price signal sent to resources making investment decisions should not suffer from the potential for substantial year-to-year volatility or the inability to efficiently recognize the marginal reliability value of incremental capacity resources.

Timing: A more efficient resource procurement price signal mechanism should account for the competitive alternatives that may be offered by new entrants in restructured competitive markets and ensure they are available in time for planning year delivery.

Preserve Existing Construct: Any changes or enhancements to the existing construct should also preserve the methods and processes that have supported most MISO LSEs’ approach to resource adequacy planning.

We take these design objectives and principles as the benchmark for evaluating MISO’s Competitive Retail Solution and designing an appropriate demand curve. We also consider a number of other criteria for evaluating market performance, including the ability to: (a) limit susceptibility to the exercise of market power; (b) limit the frequency of outcomes at the administrative price cap; and (c) perform well under a range of market conditions and changes in administrative parameters.

Several of these design objectives are inherently difficult to satisfy. In many cases we must weigh tradeoffs among competing design objectives and the competing needs of merchant suppliers, competitive retail loads, and vertically integrated utilities. Our evaluation of the Competitive Retail Solution considers these competing objectives.

C. SUMMARY OF MISO’S DESIGN PROPOSAL AND DEMAND CURVES

The Competitive Retail Solution is designed to address the reliability concerns associated with the current design, while accommodating the different needs of the competitive retail and

Continued from previous page

Operator, Inc. before the Federal Energy Regulatory Commission, July 19, 2011, Docket ER11-4081-000, (“Moeller Affidavit on Behalf of MISO”).

19 See MISO, “Competitive Retail Solution: MISO Staff Proposal,” Presentation by Resource Adequacy Subcommittee (RASC), April 14, 2016, Slide 3.

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regulated portions of the footprint. The Competitive Retail Solution will introduce a three-year-forward capacity auction to procure competitive retail load. MISO’s filing letter presents all of the details of its Competitive Retail Solution proposal; we reiterate here only the most important elements for the purposes of our evaluation. The essential elements are:

Forward Resource Auction. The Competitive Retail Solution will introduce a three-year-forward auction for procuring system and locational resource requirements on behalf of competitive retail loads. This forward timing allows enough lead time for new resources to enter, contingent on the price.

System Demand Curve. The forward auction will incorporate a downward-sloping demand curve as shown in Figure 1. The price cap is 1.4× Net CONE at a quantity consistent with 1-in-5 LOLE (or approximately 99% of the requirement). The curve falls linearly to a price of zero at a reserve margin 15% above the competitive retail loads’ requirement. The curve parameters are tuned to procure adequate supply to meet 1-in-10 LOLE on behalf of competitive retail loads as system conditions fluctuate, and assuming that utilities meet their own requirements.

Local Demand Curves. The local curve will be the same shape as the system curve, but with quantity points proportional to the Local Reliability Requirement (LRR) instead of the planning reserve margin requirement (PRMR). The quantity at the cap is also defined at 1-in-5 LOLE (approximately 98% of LRR in both Zone 4 Illinois and Zone 7 Michigan).20 This curve definition accounts for infra-marginal imports into the zone up to the Capacity Import Limit (CIL) as contributing to meeting the local demand.21

Demand Participation. Consistent with the demand curves described above, participation is mandatory for competitive retail loads with MISO procuring capacity on their behalf. Competitive retail loads within a zone falling below a “materiality threshold” that is the greater of 0.5% of the system requirement or the amount of demand that, if its Planning Reserves were not procured, would have a 0.01 impact on the system-wide LOLE are not required to participate. Utilities and other load-serving entities will not be allowed to submit voluntary demand-side bids into the auction.

Supply-Side Participation. Outside the competitive retail zones, supply-side participation is voluntary, although supply clearing in one year will be obliged to offer in the following auction. Inside the competitive retail zones, supply-side participation will be subject to a Pivotal Supplier evaluation, although supply can be excused from offering under a “safe harbor” provision that allows utilities to hold sufficient supply to meet their own needs plus an additional quantity to mitigate supply and demand risks.

Planning Resource Auction. MISO will continue to conduct a prompt auction that operates the same way as it does now. The prompt auction will incorporate all supply in the MISO footprint; any supply resources already committed in the forward auction will be

20 We do not model MISO South in our Monte Carlo Analysis. See Section V.E for further discussion. 21 In implementation for auction clearing, MISO will subtract the import capability of the zone from each

quantity point in the locational demand curve. The result is a local demand curve that only considers the need for local capacity (excluding capacity imported to the zone). The local capacity price will always be equal or higher than the system-wide capacity price.

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treated as price-takers that affect the clearing results but earn no auction revenues. Demand will be a vertical demand curve at: (a) demand for utilities set at their resource requirements, plus (b) demand for competitive retail loads set at the quantity cleared in the forward auction plus any shortfalls relative to 1-in-5 reliability.

In the balance of this testimony, we explain how we have evaluated the likely performance of MISO’s Competitive Retail Solution and assisted in developing the proposed demand curve shape.

III. PROBABILISTIC MODELING APPROACH

Our analysis evaluates the Competitive Retail Solution’s ability to attract and retain enough merchant supply to meet competitive retail loads’ needs and maintain resource adequacy. Capacity scarcity could be imminent, as MISO warns based on its surveys, but we did not attempt to conduct a year-by-year forecast.22 The timing of resource adequacy concerns will depend on existing resources’ costs and how their retirement decisions may depend on future market prices. Rather than focusing on the timing of when shortages may occur, we analyze whether the Competitive Retail Solution can achieve reliability requirements by providing prices high enough to attract new generation whenever it is eventually needed. To accomplish these goals, the Competitive Retail Solution needs to support a “long-term equilibrium” with prices that are Net CONE on average as reserve margins fluctuate over an acceptable range. We recognize that new resources may not be needed for several years if existing capacity remains online and if new resources become available that cost less than new generation, as happened in PJM after implementation of the forward capacity market.23 PJM’s capacity market retained existing generation and attracted demand response, uprates, and net imports (or avoided exports) sufficient to obviate the need for new generation for almost ten years.

We evaluate the Competitive Retail Solution’s long-term performance using a Monte Carlo simulation that captures the impact of year-to-year variability in supply and demand conditions. The underlying variability in market conditions will translate to a particular distribution of price, quantity, and reliability outcomes based on the market design and demand curve shape. We then evaluate the resulting distributions relative to MISO’s design objectives.

Our Monte Carlo simulation modeling starts with the same model we have previously used to support capacity market demand curve designs on behalf of both PJM and ISO-NE.24,25 We then

22 MISO’s transmittal letter states, “Without any change, areas like Local Resource Zone (LRZ) 4 may be

short of local resource requirements by up to 1,500 megawatts as soon as 2018” (page 2); “In light of the risk of a capacity shortfall in the near term, changes must be implemented prior to the 2018/19 PRA” (page 3).

23 PJM’s capacity market retained existing generation and attracted DR, uprates, and net imports (or avoided exports) sufficient to obviate the need for new generation for almost ten years. See Section II.C. of Pfeifenberger et al. PJM Second Performance Assessment.

24 See Newell and Spees Testimony on Behalf of ISO New England. 25 See Newell and Spees Affidavit on Behalf of PJM.

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refined the model to reflect MISO’s Competitive Retail Solution market design proposal and its unique market structure incorporating both utility and merchant business models.

In this section, we describe the primary components of this model, including our characterization of utility planning, merchant supply, demand, transmission, reliability, and auction clearing. In subsequent sections, we apply this simulation approach to evaluate the likely performance of both MISO’s current resource adequacy construct and the proposed Competitive Retail Solution. We also use the model to help develop a recommended capacity demand curve.

A. OVERVIEW OF MONTE CARLO MODEL STRUCTURE

Our Monte Carlo model provides meaningful indicators of performance because its mechanics and inputs are informed by actual capacity market experience. We randomly draw 1,000 “shocks” to supply, demand, and transmission, with the shock sizes based on historical data from MISO and PJM.26 Supply curves have the shapes from MISO’s prompt capacity auctions and from PJM’s three-year-forward capacity auctions.27 Administrative demand in each location reflects the shape that we specify.

We use a locational clearing algorithm to clear the forward and prompt auctions in each draw, similar to MISO’s auction clearing process. A stylized depiction of the forward price and quantity distributions driven by supply and demand shocks is shown in Figure 2, with the intersection of supply and demand determining price and quantity distributions under a particular demand curve. We also assume economically rational new entry, with new supply added until the long-term average price equals the Net Cost of New Entry.28,29 As such, our simulations reflect long-term conditions at economic equilibrium on average, and do not reflect a forecast of outcomes over the next several years or any other particular year.

26 Each draw is independent; we do model the sequence of draws as a time series. 27 An alternative approach would have been to model new supply as a long, flat shelf on the supply curve set

at Net CONE, but that would be inconsistent with the range of offers we have observed for actual new entrants in various capacity markets, and it would artificially eliminate price volatility. Our modeling approach reflects the fact that short-run capacity supply curves are steep, resulting in structurally volatile prices, while long-run prices converge to long-run marginal costs, or Net CONE.

28 More specifically, we use an iterative process to produce model outcomes consistent with long-run equilibrium conditions. We conduct a simulation of 1,000 draws and then calculate the resulting average price applicable to merchant suppliers. If the price is below Net CONE, we remove merchant supply and re-run a new set of 1,000 draws. If the price is above Net CONE, we add merchant supply and re-run.

29 See Section III.B for a discussion of supply curve modeling.

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Figure 2 Stylized Depiction of Supply and Demand Shocks in the Monte Carlo Analysis

Note: Illustrative shocks are not intended to reflect exact shock magnitudes or locational clearing results.

To reflect the unique characteristics of MISO’s market, we refined the model we used to evaluate the PJM and ISO-NE markets. The primary updates include: (a) decomposing demand into competitive retail demand that is supplied through the forward resource auction and utility demand that is met through utility planning; (b) decomposing supply into merchant supply that enters in response to market signals and utility supply that is built under resource planning; and (c) separately modeling the forward auctions that cover only a subset of the market and the prompt auctions that include all supply and demand.

The sequence of steps that we implement in each draw of the model is as follows. The steps represent a range of times in advance of the delivery year, from more than three years ahead of the forward auction (“T – 3+”) to the prompt period (“T – 0”). Figure 3 illustrates these steps.

Utility Supply Assessment (T – 3+). Each utility assesses its net position given variations to supply, demand, and transmission. If it is short of the requirement, the utility will either build or buy sufficient capacity to meet its customers’ needs. We assume that utilities have a modest preference to build and so will only buy if bilateral capacity prices (see “Bilateral Market” below) are less than 75% of Net CONE. If the utility is long by more than a “buffer” of excess supply, it will offer capacity bilaterally or in the forward auction, as described in Section III.C. Utilities will offer capacity into the forward auction at the expected prompt price, which is forecasted using a simple relationship between prompt prices and forward system net supply. Forecasted prompt prices are at the cap if the system is short and decrease as net supply increases.

Bilateral Market (T – 3). Bids from short utilities, determined during the Utility Supply Assessment, may be met by offers from long utilities or merchant suppliers. We assume that capacity sellers are indifferent between selling bilaterally or in the forward auction when forward bilateral and auction prices converge, and that sufficient supply will be

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offered into each market to achieve price convergence when possible. Prices converge unless the market is very tight and the forward auction clears above 75% of Net CONE or the market is very long and there is no bilateral demand from short utilities. In both of these cases, supply does not sell bilaterally. Otherwise, bilateral sales help short utilities meet their needs without having to build as much.

Forward Auction (T – 3). The forward auction determines forward clearing prices and reserve margins, given the supply and demand conditions in each draw. Demand curves for competitive retail customers are imposed on a system-wide basis for the total resource needs, and within each zone to reflect locational requirements. Merchant capacity sellers offer into the forward auction according to a supply curve consistent with the shape of PJM’s forward capacity auction supply curves. Utilities offer supply at the expected prompt price.

Prompt Auction (T – 0). The prompt auction determines prompt prices and quantities and delivered reliability, given the result of the prior steps. All demand is represented by a vertical demand curve. Competitive retail demand is set at the cleared demand from the forward auction, but no less than the 1-in-5 minimum acceptable quantity. Most of the supply in the prompt auction, including supply that cleared in the forward auction, offers as a price taker. The remaining supply is offered at prices consistent with MISO’s historical planning resource auction supply curves. Utility supply that does not clear is assumed to remain online and contribute to delivered reliability.

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Figure 3 Flow Chart Illustrating Stages in Each Draw of the Monte Carlo Model

Notes: * Forward auction and bilateral market prices converge unless the forward auction clears above 75% of Net CONE or

there is no bilateral demand from short utilities.

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B. MERCHANT SUPPLY OFFER BEHAVIOR AND EQUILIBRIUM CONDITIONS

Each simulated auction incorporates a supply curve, the shape of which influences the volatility of cleared prices and quantities. A gradually-increasing, elastic supply curve will produce relatively stable prices and quantities near the reliability requirement even in the presence of shocks to supply and demand, whereas a steep supply curve will produce greater volatility.

We model different forward and prompt supply curves using data from MISO and PJM to determine the curve shapes. We use historical PJM offer prices and quantities to create eight realistic forward supply curve shapes, consistent with the supply curves from PJM’s Base Residual Auctions (BRA) for delivery years 2009/10 through 2016/17.30,31 We use historical MISO offer prices and quantities to create four prompt supply curve shapes, consistent with the supply curves from MISO’s PRAs for delivery years 2013/14 through 2016/17.32 To develop comparable supply curve shapes using data from multiple years, we escalate all offer prices to 2016 dollars. To compare shapes across supply curves with different quantities of total offers, we normalize each curve as a percentage of the total quantity offered. Smoothed versions of the resulting supply curve shapes are presented in Figure 4. The forward supply curves show greater supply elasticity than the prompt supply curves, reflecting the greater number of supply options available on a forward basis.

We reflect the lumpy nature of investments by simulating each supply curve as a collection of discrete offer blocks. Simply modeling a smooth offer curve, like one of the individual curves shown in Figure 4, might somewhat understate realized volatility in price and quantity outcomes. To derive realistically-sized offer blocks in each zone, we randomly select from actual offers in each zone from the 2016/17 MISO PRA and re-price those offers consistent with the selected smooth supply curve shape.

To simulate rational economic entry or exit by merchant generation, we increase or decrease the quantity of supply represented in the forward supply curve until the forward average clearing price over all draws is equal to Net CONE.33 With too much supply in the curve, the average price would fall below Net CONE; with too little supply, the average price would exceed Net CONE. This procedure allows us to examine the performance of each demand curve in long-run equilibrium. It also means that forward average prices will always equal Net CONE under all demand curves, although different curves will result in different average cleared quantities, reliability, and price volatility.

30 PJM data are from Johannes Pfeifenberger, Samuel Newell, Kathleen Spees, Ann Murray, and Ioanna

Karkatsouli, “Third Triennial Review of PJM’s Variable Resource Requirement Curve,” May 15 2014, (“Pfeifenberger et al. PJM Third Triennial Review”).

31 We exclude data from the initial two PJM BRAs, because those auctions were conducted on a shorter forward period and, therefore, exhibited a steeper supply curve shape than we expect in typical BRAs. We exclude data from more recent BRAs since supply curves in these auctions have been affected by the new Capacity Performance Construct.

32 Developed from MISO Planning Resource Auction results data for 2013/14 to 2016/17. 33 We horizontally scale the supply curve shapes in Figure 4 until the total supply in the curve matches the

target quantity.

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Figure 4 Forward and Prompt Supply Curve Shapes

Sources and Notes: Smoothed supply offer curves developed from PJM BRA results and MISO PRA results. Curves normalized by quantities offered below $330/MW‐day and $130/MW‐day for the PJM Forward Curves and MISO Prompt Curves, respectively, inflated to 2016/17 dollars.

Utilities do not make entry or exit decisions based on price signals from the forward auction. However, utilities offer some of the excess supply above their requirements into either the forward auction or the forward bilateral market. If utilities offer more of their excess, they will displace merchant supply. If they offer less of their excess, the forward auction will support more merchant supply.

C. UTILITY SUPPLY PLANNING AND FORWARD OFFER BEHAVIOR

Integrated utilities reflect the large majority of supply and demand in the MISO footprint, and so interactions with the merchant market will be a key determinant of how MISO’s Competitive Retail Solution will perform. To model this interaction, we begin with the premise that utilities’ current integrated planning processes will not be materially changed in response to the Competitive Retail Solution. Utilities will continue to build capacity to meet their own customers’ needs and will continue to engage in market transactions to balance year-to-year variations in their net supply positions.

Under MISO’s proposal, utilities have the option to offer their excess supply into the forward resource auction. The prices, quantities, and consistency with which utilities offer into the forward auction will have a substantial effect on how the forward auction performs. Large volumes of utility offers would displace merchant entry for competitive retail customers. High variability in the quantities of utility supply would introduce price volatility. Any excess utility supply that is not offered into the auction would help support delivered reliability, assuming the utilities keep their excess capacity online.

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We model these behaviors on a forward basis using a simplified framework of individual utilities’ decision-making with key assumptions defined in several variables. While there is not yet any empirical data on how utilities offer into the forward auction, we begin with reasonable values and then test with sensitivity analyses. We model the utilities as a collection of individual entities in each zone. Each utility experiences year-to-year variations in supply and demand, with each utility’s net supply position varying around its requirement on a three-year-forward basis. Starting from this net position, each utility engages in a forward planning process just before the forward auction, where it decides to buy, build, hold, or sell capacity. The utilities can make one of three decisions as summarized in Figure 5:

Build or Buy: Forward Net Supply Less Than Zero. If short on a forward basis, we assume that utilities will meet their resource needs by either building new supply or buying bilaterally from merchants or other utilities. We assume that utilities have a modest preference for self-supply, for example because they may have local voltage support needs that favor locating new capacity at one of their own plant sites. We assume they will buy only if bilateral market prices are below 75% of Net CONE.

Hold: Forward Net Supply Between Zero and Buffer. Utilities that are long on a forward basis may choose not to offer all of their excess supply. Since there is some uncertainty in supply and demand between the forward and prompt periods, utilities will tend to hold a “buffer” of excess supply to mitigate risks such as unanticipated plant closures or increases in demand. In our base case, we assume that each utility will observe a buffer of 100 MW, or 3% of their requirement, meaning that a utility will offer excess capacity forward only to the extent that its surplus exceeds this amount. Under this assumption, utilities offer 53% of their average excess into the FRA. We do not have empirical data to validate this assumption since MISO’s utilities have no experience with forward capacity auctions. However, we believe this assumption is reasonable, and we reviewed it with MISO stakeholders and heard no objections. We also conducted sensitivity analyses to test the implications of a large range of alternative assumptions in Section IV.C.

Sell: Forward Net Supply Above Buffer. Utilities that have excess beyond the buffer will sell bilaterally or into the forward resource auction. Utilities offer at the expected prompt price, consistent with system-wide supply-demand balance in that draw. This offer price represents the opportunity cost of selling forward rather than prompt. We assume utilities are free to offer at this economically-rational level without being subjected to market power mitigation measures that would require them to sell more or to sell at lower prices.

Each individual utility then produces a net supply or demand position. The net positions of each individual utility are imperfectly correlated with other utilities. The resulting net supply or demand position is expressed through the forward bilateral market and forward auction, with the aggregate utility net position affecting auction results.

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Figure 5 Utility Planning Behavior (left) and Assumed Utility Parameters by Zone (right)

Notes: Net supply and reserve margin do not correlate perfectly with gross supply because they depend on shocks to demand as well as shocks to supply.

Under our base case assumptions, the 100 MW utility buffer results in utilities offering 53% of their average excess supply.

D. ADMINISTRATIVE DEMAND AND TRANSMISSION PARAMETERS

We model administrative demand curves at both a system and zonal level in a locational clearing algorithm that minimizes capacity procurement costs subject to transmission constraints. Table 1 summarizes the demand and transmission parameters from MISO’s 2016/17 prompt auction, which we use as the base values in our modeling before applying shocks.34 As discussed in Section V.E, zonal demand curves have the same shape (on a percentage of Local Reliability Requirement basis) as the system demand curve (on a percentage of Planning Reserve Margin Requirement basis).

Sloped demand curves in the forward auction are based on competitive retail load’s share of zonal and system reliability requirements. For locational demand curves, we directly represented import limits into Zone 4 and Zone 7, pro-rated by the competitive retail share of load in those zones in the forward auction.35

34 In the forward auction, we only model competitive retail’s share of reliability requirements and import

limits, which we obtained from MISO. In the prompt auction, we model both utility and competitive retail requirements. We also used an updated Zone 4 CIL value of 8,011 MW that reflects expected future import limit.

35 We do not model export limits from these zones because these have never been binding in past auctions.

Zone 4:

Illinois

Zone 7:

Michigan

MISO North

System

Number of Utilities 2 6 30

Average Utility Size (MW) 417 3,128 3,046

Default Buffer Size (MW) 100 100 100

Utility LCR/PRMR (MW) 833 18,766 91,369

Utility Share of LCR or PRMR (%) 22% 90% 90%Individual Utility Gross Supply Shock Size (MW) 23 181 187

(%) 6% 6% 6%Aggregate Utility Gross Supply Shock Size (MW) 46 992 2,956

(%) 6% 5% 3%

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Table 1 Reliability Requirement and Import Limits Based on 2016/17 Prompt Auction

Sources and Notes: Data from MISO 2016/17 Planning Resource Auction Results. Zone 4 CIL value of 8,011 MW was used in modeling, reflecting expected future import limit.

E. SHOCKS TO SUPPLY AND DEMAND

Year-to-year variations in supply, demand, and transmission translate to a distribution of clearing prices, quantities, and delivered reliability. As in our modeling for PJM and ISO-NE, we use historical data to inform the expected variations, or “shocks”. In each case, we examined data from MISO’s four historical auctions and planning parameters as a starting point, but relied on PJM data as another data source given the relatively few data points available in MISO. Our approach to estimating the appropriate shock sizes is as follows:

Demand: We reviewed demand data from MISO’s four Planning Resource Auctions, finding that year-to-year system demand shocks were approximately 0.7% of the reliability requirement.36 Demand shocks could be driven by increases or decreases in the load forecast or LOLE modeling results. Our experience from PJM indicated that the size of demand shocks decreases with zone size. Due to the limited historical data available in MISO, we used data from PJM to capture shock sizes in the zones. Using PJM data yielded a system demand shock size of 1.0%. We model shocks to demand as normally distributed around the expected value based on the 2016/17 PRA parameters for each zone.

Supply: We reviewed supply data from MISO’s four Planning Resource Auctions to date, finding that system supply shocks were approximately 2% for the system, and ranged from

36 This value excludes MISO South. Variation in the system coincident peak demand was 0.9%. The slightly

higher variation in coincident peak compared to the reliability requirement shocks is because system PRMR and coincident peak were negatively correlated over the four PRAs.

Zone 4:

Illinois

Zone 7:

South MI

MISO North

System

Total Zonal Parameters

Planning Reserve Margin Requirement (PRMR) (MW) 10,375 22,406 101,702

Local Reliability Requirement (LRR) (MW) 11,799 24,372 n/a

Local Clearing Requirement (LCR) (MW) 3,788 20,851 n/a

Capacity Import Limit (CIL) (MW) 8,011 3,521 n/a

Competitive Retail Load Parameters

Retail Choice Share of Zonal Load (%) 78% 10% 10.2%

Share of PRMR (MW) 8,093 2,241 10,401

Share of LRR (MW) 9,203 2,437 n/a

Share of LCR (MW) 2,955 2,085 n/a

Share of CIL (MW) 6,249 352 n/a

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4–5% across zones. Conceptually, negative supply shocks reflect retirements or down-rates of existing resources, while positive shocks reflect uprates of existing resources or the entry of new resources. The size of these historical supply shocks is based on the variation in offers, rather than the variation in cleared quantities. Due to the limited historical data available in MISO, we used a fitted curve based on PJM data to capture the relationship between shock size and zone size. We assume that for each individual utility or merchant, supply shocks are normally distributed, with a standard deviation that changes with zone size.

Transmission: PJM data appeared substantially different from MISO data on transmission shocks. We therefore chose to use MISO historical prompt auction data to determine the size of shocks to the capacity import limits even though data was limited. Across all zones and years, the average shock size was 24% of the import limit. We did not observe any apparent relationship to zone size or the magnitude of the import limit. We simulate shocks to the import limit as uniformly distributed with a standard deviation of 24% of the expected import limit value based on the 2016/17 prompt auction parameters for each zone.

We summarize the size of these shocks in aggregate in Table 2 below.37 We examine the sensitivity of the proposed demand curve’s performance to alternative supply and demand shocks in Section IV.D.

Our Monte Carlo simulations implement local supply and demand shocks independently of each other. However, supply shocks to individual utilities and merchants are correlated to each other and demand shocks are correlated across zones. Events like regulation-driven retirement and varying technology costs would tend to correlate supply shocks. Similarly, demand shocks are primarily driven by weather fluctuation, which could have similar effects across zones. We use this particular correlation structure because it corresponds to what has been observed in PJM: shock sizes decrease with increasing zone size, presumably reflecting the greater diversity across larger areas. We chose correlations in supply and demand to reproduce this pattern of diminishing shock size with zone size for all combinations of utility and merchant supply and demand across the zones and system. We also tested the implications of higher correlations of utility and merchant supply across zones and found that they had little effect on overall performance.

37 We do not model MISO South in our Monte Carlo Analysis. See Section V.E for further discussion of this

topic.

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Table 2 Shocks to Supply, Demand, and Import Limits

Notes: LCR in the zones and PRMR in the MISO North System are used as proxies to reflect the local supply in long‐

run equilibrium. Standard deviation of shock to Zone 4 Import Limit based on historical Zone 4 Import Limit of 6,323 MW. For local supply, standard deviation is reported as a percentage of LCR.

F. RELIABILITY OUTCOMES

We calculate reliability outcomes for each Monte Carlo simulation draw based on reliability simulations provided by MISO. Figure 6 shows the relationship between reserve margins and LOLE and highlights that the relationship is asymmetrical, with reliability outcomes deteriorating sharply at reserve margins below the requirement (PRMR in the system and LRR in the zones) but improving only gradually at reserve margins above the requirement. An important implication of this asymmetry is that a demand curve that results in a distribution of clearing outcomes centered on the requirement with equal variance above and below the requirement will fall short of the 1-in-10 LOLE target on average.38

38 In our analyses, the average LOLE reported for a given demand curve is calculated as the average of the

LOLE at the cleared reserve margin in each individual draw, rather than the LOLE at the average cleared reserve margin across all draws.

Forward Auction

Local

Supply

Total

Requirement

(LRR or PRMR)

Import

Limit

Local

Requirement

(LCR or PRMR)

Net

Supply

(w/ CIL)

Net

Supply

(w/o CIL)

Average Parameter Value

Zone 4: Illinois (MW) 3,788 11,799 8,011 3,788 n/a n/a

Zone 7: Michigan (MW) 20,851 24,372 3,521 20,851 n/a n/a

MISO North System (MW) 101,702 101,702 n/a 101,702 n/a n/a

Standard Deviation of Shocks

Zone 4: Illinois (MW) 202 221 1,522 1,541 1,559 291

Zone 7: Michigan (MW) 1,096 359 844 919 1,416 1,154

MISO North System (MW) 3,092 1,031 n/a n/a 3,249 3,249

Standard Deviation of Shocks

Zone 4: Illinois (%) 5.3% 1.9% 19.0% 40.7% 41.2% 7.7%

Zone 7: Michigan (%) 5.3% 1.5% 24.0% 4.4% 6.8% 5.5%

MISO North System (%) 3.0% 1.0% n/a n/a 3.2% 3.2%

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Figure 6 Loss of Load Events vs. Supply in System and Zones

Sources: LOLE data provided by MISO staff.

We use this reliability and reserve margin metric to produce two different LOLE metrics from our modeling, one based on the results of the three-year-forward auction and another based on the results of the prompt auction. We calculate the forward LOLE metric based on the quantity cleared on behalf of the competitive retail loads, while assuming that other loads are exactly meeting their requirement. This implicitly assumes that vertically-integrated utilities in the footprint will never fall short of their own needs, and that if they maintain supply in excess of their own needs it cannot be used to support the reliability needs of competitive retail loads.

We calculate the prompt LOLE metric as the quantity of merchant supply committed in either the forward or prompt auction, plus the total quantity of cleared or uncleared utility supply. This implicitly assumes that merchant resources cannot be relied on to contribute to reliability unless they are compensated, but that utilities will use their own self-supply to meet reliability needs including any excess that was not committed in an auction.

IV. EVALUATION OF MISO’S COMPETITIVE RETAIL SOLUTION

In this Section, we evaluate MISO’s Competitive Retail Solution, first qualitatively assessing how it addresses MISO’s design objectives, then quantitatively analyzing its likely performance using our probabilistic simulation model. We show that, with the proposed demand curve, the Competitive Retail Solution meets MISO objectives, whereas the status quo market design does not. We find that the Competitive Retail Solution supports more merchant supply and reduces the frequency of events with very low-reliability and prices at the cap. It also improves price volatility, but only modestly because the fluctuations in quantities offered by utilities are large compared to the size of the competitive retail market.

This section also presents sensitivity analyses evaluating the performance of the Competitive Retail Solution under alternative assumptions about key uncertain variables: utility offer behavior; the magnitude of shocks to supply, demand, and import limits; and Net CONE. We show that performance is sensitive to these factors but that the Competitive Retail Solution meets or nearly meets MISO’s resource adequacy objectives under most conditions tested.

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A. HOW THE COMPETITIVE RETAIL SOLUTION ADDRESSES MISO’S OBJECTIVES

MISO’s Competitive Retail Solution would retain a prompt auction but add a three-year-forward auction to procure capacity for competitive retail loads using a sloped demand curve, as described in Section II.C. This design is well suited to meeting competitive retail loads’ needs because it meets the business needs of the merchant suppliers who are essential for serving competitive retail loads’ demand. It is very similar to the forward capacity markets used by PJM and ISO-NE to support adequate merchant supply. A key difference in MISO is the dominance of utilities surrounding the competitive retail loads, a difference that the Competitive Retail Solution recognizes in several important ways.

The Competitive Retail Solution’s three-year-forward term provides merchant suppliers adequate lead time to see when capacity is needed and to respond. In economic terms, the supply is more elastic, which supports reliability and price stability and a better functioning market than one like the status quo with inelastic supply (combined with inelastic, vertical demand). We have long observed in PJM the greater elasticity of the three-year-forward supply curve than the one-year forward curve from PJM’s incremental auctions.39 Prompt supply curves are the least elastic since most costs to be online are already sunk by the time of the delivery year. The short-term supply curves themselves can understate supply responsiveness to demand needs if suppliers have responded in advance based on forecasts, but that depends on the quality of the information available for forecasting. Forecasting cannot beat the quality of information afforded by a forward auction, where the entire market comes together at once and transparently reveals itself through offers. The advantages of a forward auction are particularly acute during times of extreme supply change and uncertainty. For example, when PJM’s coal fleet faced retrofit-or-retire decisions for complying with the EPA’s Mercury and Air Toxics Standard (MATS) around 2015, PJM’s forward capacity auctions facilitated economic tradeoffs and seamless replacement of many thousands of MW of retiring capacity without experiencing any shortages.40

The Competitive Retail Solution’s sloped demand curve provides two important benefits: first, and most obviously, its slope makes prices less sensitive to small changes in cleared supply. For example, a 100 MW change in supply would increase the price by only about $16/MW-day, compared to as much as $260/MW-day with a vertical curve if moving prices from the floor to a ceiling of 1.4× Net CONE. This makes prices less volatile, which is desirable for both buyers and sellers. It also makes the market structurally more competitive, reducing the ability and incentive for either buyers or sellers to uneconomically affect the price.

The second benefit of the demand curve is to provide prices that are high enough to support enough entry to meet reliability objectives. As discussed in Section V, we tuned the demand curve to meet reliability objectives, given all of our assumptions on the net cost of new entry, supply/demand variability, and the other parameters that affect market outcomes.

Regarding the relationship to the surrounding utilities, MISO’s Competitive Retail Solution does not interfere with utility planning. It allows utilities to offer their excess capacity into the

39 See p. 66 of Pfeifenberger et al. PJM Third Triennial Review. See also Figure 4, comparing elasticity of

forward and prompt supply curves. 40 See Pfeifenberger et al. PJM Second Performance Assessment.

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forward auction. To the extent that utilities have excess capacity they are willing to offer, they will compete with and partially displace merchant supply and thus reduce the total resource cost of meeting reliability objectives. Their offer quantities will vary over time, and this will make auction clearing prices more volatile. We and MISO recognized this effect in designing the Competitive Retail Solution demand curve “wide enough” to attract enough merchant supply despite the volatility introduced by utilities.

The Competitive Retail Solution’s treatment of forward-cleared capacity in the prompt auction makes sense given MISO’s objective to preserve the existing construct for utilities. Cleared supplies from the forward auction would be offered as zero-priced supply; and the demand from competitive retail loads would be set at the quantity cleared in the forward auction. This allows the competitive retail loads and their forward-procured supplies to flow through the prompt auction without interfering with the utilities’ participation in the prompt auction.

One aspect that we recommend considering in future reviews is whether voluntary utility buy bids could be integrated into the design. Buy bids would reduce price volatility in the forward auction by introducing additional price elasticity of demand. By allowing short utilities to absorb some of the supply offered by long utilities, the volatility introduced by long utility offers would be reduced. Allowing buy bids would also reduce costs by helping short utilities to find alternatives to building new capacity for self-supply. While short utilities can already buy bilaterally, the forward auction provides more complete information about supply, demand, and transmission and supports more efficient purchasing decisions.

We have not evaluated some other aspects of the design, including the monitoring and mitigation measures, opt-in/opt-out approaches for competitive retail demand, materiality threshold, and utility participation rules. But our modeling is consistent with these aspects of the design.

B. PERFORMANCE OF THE COMPETITIVE RETAIL SOLUTION

Our simulations indicate that MISO’s proposed solution will substantially improve average reliability over the status quo construct and that it should meet MISO’s objectives. Much of the improvement in reliability is due to 1,800 MW of additional merchant supply being supported by the richer demand curve. The right part of the demand curve prevents prices from collapsing under slight excess conditions. The slightly higher price cap also helps reduce the frequency of very low reliability events by allowing MISO to buy more capacity when supply is tight. Volatility is lower than in the status quo construct, but only slightly since the variation in supply, including utility offers, is large compared to the width of the demand curve.

In our simulations of MISO’s status quo construct, a single auction occurs immediately before the delivery year. Utility and competitive retail system load are represented using a vertical demand curve at the Planning Reserve Margin Requirement, with a price cap at the gross Cost of New Entry (CONE). Local loads are represented using vertical demand curves in each zone at the Local Clearing Requirement (LCR). Table 3 shows the results of our simulations and corroborate MISO’s concerns that the status quo falls short of reliability objectives. In the long-run, average market clearing prices rise to Net CONE in order to attract merchant supply when needed. Price volatility is evident at the system and zone levels. The standard deviation of system prices is $88/MW-day, 48% of the average price. Prices clear at the cap more than 60%

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of the time.41 While the zones have sufficient supply to cover their local requirements, the system itself is short by more than 300 MW. This leads to average LOLE of 1-in-5.2, with reliability worse than 1-in-5 more than 40% of the time.

In contrast, MISO’s Competitive Retail Solution proposal meets the 1-in-10 LOLE standard on average on a forward basis and surpasses the standard on a prompt basis. As described in Section V, we tuned our proposed forward demand curve to achieve 1-in-10 average LOLE on a forward basis, assuming utilities hold no excess capacity beyond what they need to meet their own customers’ needs, except what they commit in the forward auction. This assumption is necessary to ensure that competitive retail loads procure enough capacity to meet their own needs (more or less, along the demand curve) without planning to lean on utility excess that they do not purchase. However, we also report prompt LOLE, which corresponds to realized, delivered reliability, and it does account for any excess capacity the utilities hold beyond what they sell or need for their own needs. Under our base case assumptions where utilities with more than 3% excess capacity can sell their excess down to a 3% buffer, but they hold on to all other excess capacity, prompt LOLE is 0.054 days per year, substantially better than the 0.1 target. The frequency of events below 1-in-5 drops from 41% under the status quo, to 2% under MISO’s proposal (see the “Prompt Auction” panel, corresponding to delivered reliability).

Locational reliability needs are met easily, with incremental LOLE (due to local shortages when the system is not short) at 0.018–0.055, much better than the 0.1 target. It is less challenging to meet incremental locational LOLE targets in MISO than in other markets we have studied primarily because the local requirements are less stringent. For example, PJM requires that zones achieve a more stringent 1-in-25 LOLE incremental to the system.42 Additionally, in MISO the cost of new generation is similar across zones. In the 2016/17 prompt auction, Net CONE varied by an average of 5% across the zones.43 Zones only need to price-separate from the system occasionally to incentivize generation to locate in the zones rather than outside. Shortfalls would occur only 15% of the time, just enough to achieve a 5% price premium on average. If less capacity were added to the zones, local prices would exceed local Net CONE on average, and more would have incentive to enter.

One metric that our simulations suggest that the Competitive Retail Solution improves only modestly over the status quo is price volatility. Price volatility in the Competitive Retail Solution’s forward market, as measured by the standard deviation of prices, decreases to $83/MW-day from $88/MW-day under the status quo prompt auction. The change is modest because of utility offers. The variation in offer levels is large compared to the width of the supply and demand curves. This maintains high volatility in spite of the Competitive Retail Solution’s improvements in the elasticity of supply and demand.

41 In the zones, frequency at the cap includes occasions when the system clears at its cap. 42 See Newell and Spees Affidavit on Behalf of PJM. 43 MISO, “2016/2017 Planning Resource Auction Results,” April 15, 2016.

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Table 3 Performance of Status Quo Compared to MISO’s Competitive Retail Solution

Notes: LOLE in Zones 4 and 7 is incremental to system LOLE. “Total Supply” forward includes merchant and utility cleared quantities. “Total Supply” prompt includes cleared merchant and all utility supply.

Average forward price converges to Net CONE under MISO’s proposal, and average prompt price converges to Net CONE under Status Quo.

C. SENSITIVITY TO UTILITY OFFER BEHAVIOR IN THE FORWARD AUCTION

Section III.C described the utility “buffer” assumption, representing the minimum amount of excess supply utilities have to have in order to offer capacity into the forward auction. In our base case, we assume that each utility will observe a buffer of 100 MW, or 3% of their requirement.

The size of the utility buffer is uncertain, and it affects price volatility and reliability. With a low buffer, utilities offer substantial supply into the forward auction, leading to lower reliability since their capacity is offered (and displaces merchant capacity) rather than being held back as insurance. Price volatility is also greater because the auctions absorb all of the net supply fluctuations utilities experience. With a large buffer, utilities offer little, displacing less merchant capacity and leading to higher reliability and lower price volatility.

Recognizing the uncertainty and importance of this parameter, we tested the sensitivity of Competitive Retail Solution performance to the size of the assumed buffer. We tested a wide range of values, from no buffer, to a 3% buffer (our base case), to a 7% buffer. All cases assumed the same shocks to utilities’ net supplies, so all had the same amount of excess capacity, but the amount offered into the forward auction varied. With no buffer, utilities offered all of their excess supply. With 3% buffer, utilities offered 53% of their excess supply on average. And with a 7% buffer, utilities offered only 24% of their excess supply on average, as shown in Table 4.

Price Quantity Reliability

Average Standard

Deviation

Frequency

at Cap

Frequency

of Price

Separation

Requirement

(LCR or

PRMR)

Merchant

Supply

Cleared

Utility

Supply

Cleared

Total

Supply

Excess

(Deficit)

above

LCR or

PRMR

Standard

Deviation

of Excess

(Deficit)

LOLE Frequency

Below

LCR or

PRMR

Frequency

Below

1‐in‐5

($/MW‐d) ($/MW‐d) (% of years) (% of years) (MW) (MW) (MW) (MW) (MW) (MW) (events/yr) (% of years) (% of years)

Status Quo

Zone 4: Illinois $195 $85 67% 20% 11,789 4,378 968 5,351 1,570 1,383 0.067 16% 10%

Zone 7: Michigan $195 $81 65% 25% 24,347 2,243 19,220 21,560 729 875 0.056 13% 0%

MISO North System $185 $88 62% n/a 101,651 7,510 93,250 101,346 (305) 1,799 0.191 62% 41%

MISO Proposed Curve

Forward Auction

Zone 4: Illinois $195 $72 39% 16% 9,195 4,856 65 4,920 1,971 1,300 0.018 4% 2%

Zone 7: Michigan $195 $71 39% 16% 2,435 2,375 313 2,689 606 639 0.055 3% 0%

MISO North System $185 $83 39% n/a 10,396 9,294 1,220 10,514 118 762 0.106 42% 9%Prompt Auction

Zone 4: Illinois $114 $79 15% 12% 11,789 4,860 953 5,824 2,043 1,371 0.015 4% 1%

Zone 7: Michigan $122 $77 15% 21% 24,347 2,390 19,115 21,696 865 843 0.044 2% 0%

MISO North System $106 $79 13% n/a 101,651 9,360 92,567 103,078 1,426 1,492 0.054 15% 2%

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Table 4 Utility Buffer Size Scenarios

Source: Brattle Monte Carlo simulation model.

Table 5 shows the simulated sensitivity of Competitive Retail Solution performance to these alternative assumptions about the assumed utility buffer. The conclusion is that performance is sensitive to the utility buffer, but not terribly so. At worst, with no buffer, delivered reliability is worse than target, at 0.13 LOLE on average. Given this finding, we recommend proceeding with MISO’s recommended curve but to later review the utility buffer assumption and its implications for the demand curve as a part of MISO’s quadrennial review process.

When utilities hold no buffer, they offer additional supply into the forward auction, reducing the need for merchant resources. In fact, each additional MW of utility supply displaces more than 1 MW of merchant supply on average. Utility supply is highly variable, and drives down average clearing prices more than the equivalent amount of merchant supply. In order to bring average prices to Net CONE, additional merchant supply must exit. Our simulation results indicate that, with no buffer, utility supply cleared in the forward auction increases by 1,070 MW, while merchant supply decreases by nearly 1,350 MW. This reduction in total supply worsens forward LOLE to 0.14 and prompt LOLE to 0.13.44

With a 200 MW buffer, more merchant supply can be supported by the market. While the additional supply improves reliability, the improvement is small due to the asymmetry of the LOLE curve (see Figure 6). Simulation results show that, with a 200 MW buffer, cleared utility supply decreases by 660 MW and cleared merchant supply increases by 810 MW. This increase in total supply improves forward LOLE to 0.09 and prompt LOLE to 0.03.

44 The forward LOLE metric does not account for any non-offered utility supply in excess of the

requirement, regardless of the value of the buffer. Forward LOLE degrades to 0.13 in the no utility buffer scenario because each additional MW of utility supply, with its high volatility, displaces more than 1 MW of merchant supply.

Buffer

MW

Buffer as % of

Utility Size

Offered Quantity

as % of Utility

Excess

No Buffer 0 0% 100%

Base Case 100 3% 53%

200 MW Buffer 200 7% 24%

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Table 5 Sensitivity of Performance to a Range of Utility Offer Behaviors (“Buffers”)

Notes:

“Total Supply” forward includes merchant and utility cleared quantities. “Total Supply” prompt includes cleared merchant and all utility supply.

Average price converges to Net CONE in forward auction. The “MISO Proposed Curve” has a buffer of 100 MW.

D. SENSITIVITY TO THE MAGNITUDE OF SUPPLY AND DEMAND SHOCKS

One of the key assumptions affecting the market’s performance is the magnitude of shocks to supply, demand, and import limits. Section III described the distribution of shocks we used to calibrate our proposed demand curve with other sections illustrating the proposed curve’s performance under those assumptions.

Although we have a strong historical basis for these assumed shock sizes, future shocks could be larger or smaller on average, either because of random variation or because of fundamental changes to the factors that give rise to shocks. For example, shock sizes may be impacted by future changes to environmental rules, business cycles, load forecast methods, or LOLE study methods. Or, market dynamics could differ from past dynamics, such that supply responds more (or less) readily to shocks, producing smaller (or larger) net shocks.

To test the robustness of the proposed curve to changes in the magnitude of shocks, we implement two sensitivity analyses in which the shock distributions are 2× and ½× our base values. The standard deviations of system-wide supply minus demand in the “Larger Shock” and “Smaller Shock” sensitivities are 6,498 MW and 1,625 MW, respectively, compared to 3,249 MW in the Base Case.

Table 6 shows the results of these sensitivity scenarios. As expected, we find that with smaller shocks, price and reserve margin volatility are reduced. Forward LOLE drops to 0.081 events per year, exceeding the 1-in-10 LOLE target by a small amount. With larger shocks, price and reserve margin volatility are greater, and forward LOLE rises to 0.176 events per year, falling short of the reliability target. While these tests show that reliability is sensitive to shock size, the magnitude of the change in shock size is large. The low reliability under the Larger Shock case is driven by the draws representing the 17% most extreme shortage events, in which the system


Average Standard

Deviation

Frequency

at Cap

Requirement

(PRMR)

Merchant

Supply

Cleared

Utility

Supply

Cleared

Total

Supply

Excess

(Deficit)

above

PRMR

Standard

Deviation

of Excess

(Deficit)

LOLE Frequency

Below

PRMR

Frequency

Below

1‐in‐5

($/MW‐d) ($/MW‐d) (% of years) (MW) (MW) (MW) (MW) (MW) (MW) (events/yr) (% of years) (% of years)

Forward Auction

MISO Proposed Curve $185 $83 39% 10,396 9,294 1,220 10,514 118 762 0.106 42% 9%

No Buffer $185 $88 46% 10,396 7,944 2,292 10,236 (160) 1,083 0.142 49% 26%

200 MW Buffer $185 $69 23% 10,396 10,107 557 10,664 268 536 0.090 30% 1%

Prompt Auction

MISO Proposed Curve $106 $79 13% 101,651 9,360 92,567 103,078 1,426 1,491 0.054 15% 2%

No Buffer $190 $69 46% 101,651 8,076 93,436 101,777 126 1,532 0.134 48% 25%

200 MW Buffer $63 $65 3% 101,651 10,094 91,899 103,846 2,195 1,549 0.031 4% 0%

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averages 8,000 MW short, compared to 3,600 MW short in the Base Case. We believe the reality will be much closer to the Base Case than the sensitivities, barring a large permanent change to underlying supply and demand fundamentals.

Table 6 Sensitivity of Performance to Magnitude of Supply and Demand Shocks

Notes:

“Total Supply” forward includes merchant and utility cleared quantities. Total Supply prompt includes cleared merchant and all utility supply.

Average price converges to Net CONE in forward auction.

E. SENSITIVITY TO NET CONE AND ADMINISTRATIVE ERROR IN NET CONE

Prices on the demand curve are indexed to Net CONE, so the curve will adjust as Net CONE estimates change over time. For this reason, if energy prices decrease and the energy market provide a smaller proportion of the incentives necessary to invest, then the administrative Net CONE and capacity demand curve will increase even if CONE stays the same. Similarly, when energy prices increase, the demand curve will decrease providing approximately the same investment incentives overall.

However, the curve shape was fine-tuned based on the current value of Net CONE, so it is possible that future adjusted curves will not meet resource adequacy objectives exactly. To test the robustness of the curve to changes in Net CONE, we present simulation analyses with Net CONE varying from 80% to 120% of the base value. We find that the curve continues to meet resource adequacy targets almost exactly, for modest changes of 20%, as shown in Table 7.

The above sensitivities reflect changes in Net CONE under the assumption of no estimation error. In other words, they assume that MISO’s administrative estimate of Net CONE accurately represents the true Net CONE that developers need to earn in order to enter. However, estimation error is inevitable even in a careful analysis due to uncertainties in every component of the Net CONE estimate; for example: (a) the identification of an appropriate reference technology; (b) estimation of the capital and fixed operations and maintenance (O&M) costs; (c) translation of those costs into an appropriately levelized value consistent with developers’ cost of capital, long-term views about the market, and assumed economic life; and (d) estimation of net energy and ancillary services (E&AS) revenues on a 3-year forward basis.


Average Standard

Deviation

Frequency

at Cap

Requirement

(PRMR)

Merchant

Supply

Cleared

Utility

Supply

Cleared

Total

Supply

Excess

(Deficit)

above

PRMR

Standard

Deviation

of Excess

(Deficit)

LOLE Frequency

Below

PRMR

Frequency

Below

1‐in‐5


Forward Auction


2x Shocks $164 $102 43% 10,396 7,223 2,945 10,168 (223) 1,486 0.176 45% 31%1/2x Shocks $185 $51 7% 10,396 10,454 298 10,752 353 349 0.081 14% 0%

Prompt Auction

MISO Proposed Curve $106 $79 13% 101,651 9,360 92,567 103,078 1,426 1,491 0.054 15% 2%2x Shocks $113 $98 28% 101,651 7,414 94,268 103,420 1,819 3,245 0.101 29% 16%

1/2x Shocks $93 $59 0% 101,651 10,455 91,589 103,003 1,326 784 0.043 1% 0%

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If the administrative estimate of Net CONE understates true Net CONE, the demand curve would be lower than needed to meet the reliability objectives. Supply would still enter and set prices at the true Net CONE, but the cleared quantity and reliability would be below target. Conversely, overstated Net CONE would attract excess supply as suppliers continued entering until average prices equal the true Net CONE. Customers would not have to pay significantly higher prices, but they would have to buy a greater quantity that has diminishing value.

We test the robustness of the market’s performance to Net CONE estimation errors, under a range of true Net CONE values with over- or underestimated administrative Net CONE as summarized in Table 7. We hold true Net CONE at the base value of $185/MW-day, always adjusting supply until the long-term average price across simulation draws equals to that value no matter what the demand curve. We then vary the administrative Net CONE estimate by ±20%, representing estimation error.45

Net CONE estimation errors can have a substantial impact on reliability outcomes. Reliability impacts of estimation errors are asymmetric with respect to positive and negative estimation errors because LOLE rises steeply as reserve margins fall below target (see Figure 6 in Section III.F above). A 20% underestimate of Net CONE worsens forward LOLE by 0.16, to 0.265 events per year, whereas an overestimate improves it by only 0.025 (to 0.081 events/yr). Administrative underestimation of Net CONE also increases the frequency of years with reliability worse than 1-in-5 from 9% to 56%.

While a demand curve with a higher price cap could mitigate the reliability impact of underestimation, MISO’s choice to use a lower price cap reflects a balance of objectives. The reliability impacts of underestimating Net CONE are not as severe as in other markets. Even with a 20% underestimate of Net CONE, delivered (prompt) reliability only deteriorates to 0.127 events per year due to the bolstering of reliability by utilities. Additionally, MISO will update its Net CONE estimates annually and will periodically perform an extensive review of market performance. Given these procedures, it is quite unlikely that a 20% error in Net CONE would persist indefinitely.

We recommend that MISO consider, in its next review, applying a minimum of 1× CONE to the price cap to reduce the risk of under-procuring when high E&AS revenues and low Net CONE would otherwise collapse the demand curve. This constraint on the cap supports the entire curve, since the curve is a straight line from the cap to the zero crossing point. The minimum cap prevents Net CONE estimation errors from reducing reliability.

45 We specify the sensitivities by fixing true Net CONE rather than fixing administrative Net CONE so that

performance metrics are more directly comparable with the base case.

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Table 7 Sensitivity of Performance to Net CONE and Administrative Error in Net CONE

Notes: “Total Supply” forward includes merchant and utility cleared quantities. “Total Supply” prompt includes cleared merchant and all utility supply.


F. SUMMARY OF PERFORMANCE UNDER SENSITIVITY SCENARIOS

We believe that the forward market and demand curve proposed by MISO in this docket are well designed and strike an appropriate balance among competing design objectives. The design will perform well under the range of market conditions MISO may face in the future. Table 8 summarizes this performance as discussed in the prior sections, under varying utility buffers, assumed shock sizes, Net CONE levels, and administrative errors to Net CONE.

Reliability is robust across most assumptions we examined, but price volatility remains a challenge. Reliability is most sensitive to utility forward offer behavior, reflected in the utility buffer, and to the sizes of shocks to demand, supply, and import limits. If utilities offer without any buffer, or if shock sizes are twice as large as historically observed, reliability may suffer modestly. A larger risk could be posed by persistently underestimating Net CONE, but this risk can be mitigated by examining the accuracy of Net CONE estimates in future quadrennial reviews.

Overall, we believe the market and demand curve will perform well over the coming years.


Average Standard

Deviation

Frequency

at Cap

Requirement

(PRMR)

Merchant

Supply

Cleared

Utility

Supply

Cleared

Total

Supply

Excess

(Deficit)

above

PRMR

Standard

Deviation

of Excess

(Deficit)

LOLE Frequency

Below

PRMR

Frequency

Below

1‐in‐5


Forward Auction


20% Lower Net CONE $148 $66 38% 10,396 9,302 1,197 10,500 104 786 0.108 42% 10%

20% Higher Net CONE $223 $100 40% 10,396 9,275 1,222 10,497 101 766 0.108 44% 10%

20% Admin Under‐Estimate $186 $49 76% 10,396 8,112 1,128 9,240 (1,156) 1,273 0.265 77% 56%

20% Admin Over‐Estimate $185 $100 19% 10,396 9,657 1,174 10,832 436 594 0.081 23% 2%

Prompt Auction


20% Lower Net CONE $104 $78 13% 101,651 9,402 92,528 103,120 1,468 1,500 0.053 15% 2%

20% Higher Net CONE $107 $80 14% 101,651 9,332 92,581 103,044 1,393 1,493 0.055 15% 2%

20% Admin Under‐Estimate $157 $94 45% 101,651 8,157 93,099 101,979 328 1,702 0.127 47% 24%

20% Admin Over‐Estimate $90 $68 5% 101,651 9,777 92,363 103,426 1,774 1,410 0.040 6% 0%

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Table 8 Sensitivity of Performance to a Range of Model Assumptions

Notes: “Total Supply” forward includes merchant and utility cleared quantities. “Total Supply” prompt includes cleared

merchant and all utility supply. Average price converges to Net CONE in forward auction. The “MISO Proposed Curve” has a buffer of 100 MW.

V. DEMAND CURVE DESIGN

With input from MISO staff and stakeholders, we constructed a range of potential demand curves and evaluated their performance. The final proposed curve is consistent with the primary design objectives of meeting a 1-in-10 reliability standard on behalf of competitive retail loads and accommodating the interactions between utility and merchant business models in the same footprint. The curve also reflects a balance of tradeoffs among other competing design objectives and stakeholder priorities including low price volatility, low risk of very poor reliability events, and treating utility and competitive retail loads equitably.

A. PERFORMANCE COMPARED TO A VERTICAL FORWARD DEMAND CURVE

As a starting point for this analysis, we compare the performance of MISO’s proposed curve to a vertical curve with the same price cap, both in the forward auction. As summarized in Figure 7 and Table 9, we compare expected price volatility and reliability performance of: (a) a vertical curve at the reliability requirement; (b) a vertical curve that is right-shifted sufficiently to achieve the 1-in-10 reliability standard on behalf of competitive retail loads; and (c) MISO’s proposed curve. The primary advantages of a vertical demand curve would be simplicity and continuity


Average Standard

Deviation

Frequency

at Cap

Requirement

(PRMR)

Merchant

Supply

Cleared

Utility

Supply

Cleared

Total

Supply

Excess

(Deficit)

above

PRMR

Standard

Deviation

of Excess

(Deficit)

LOLE Frequency

Below

PRMR

Frequency

Below

1‐in‐5


Forward Auction


No Buffer $185 $88 46% 10,396 7,944 2,292 10,236 (160) 1,083 0.142 49% 26%

200 MW Buffer $185 $69 23% 10,396 10,107 557 10,664 268 536 0.090 30% 1%2x Shocks $164 $102 43% 10,396 7,223 2,945 10,168 (223) 1,486 0.176 45% 31%

1/2x Shocks $185 $51 7% 10,396 10,454 298 10,752 353 349 0.081 14% 0%20% Lower Net CONE $148 $66 38% 10,396 9,302 1,197 10,500 104 786 0.108 42% 10%

20% Higher Net CONE $223 $100 40% 10,396 9,275 1,222 10,497 101 766 0.108 44% 10%

20% Admin Under‐Estimate $186 $49 76% 10,396 8,112 1,128 9,240 (1,156) 1,273 0.265 77% 56%20% Admin Over‐Estimate $185 $100 19% 10,396 9,657 1,174 10,832 436 594 0.081 23% 2%

Prompt Auction


No Buffer $190 $69 46% 101,651 8,076 93,436 101,777 126 1,532 0.134 48% 25%

200 MW Buffer $63 $65 3% 101,651 10,094 91,899 103,846 2,195 1,549 0.031 4% 0%

2x Shocks $113 $98 28% 101,651 7,414 94,268 103,420 1,819 3,245 0.101 29% 16%1/2x Shocks $93 $59 0% 101,651 10,455 91,589 103,003 1,326 784 0.043 1% 0%

20% Lower Net CONE $104 $78 13% 101,651 9,402 92,528 103,120 1,468 1,500 0.053 15% 2%

20% Higher Net CONE $107 $80 14% 101,651 9,332 92,581 103,044 1,393 1,493 0.055 15% 2%20% Admin Under‐Estimate $157 $94 45% 101,651 8,157 93,099 101,979 328 1,702 0.127 47% 24%

20% Admin Over‐Estimate $90 $68 5% 101,651 9,777 92,363 103,426 1,774 1,410 0.040 6% 0%

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with the status quo market design. We compare these advantages to the substantial disadvantages such as price volatility and reliability performance.

Figure 7 MISO Proposed Curve Compared to Vertical Demand Curve

Sources and Notes: The light blue vertical curve achieves 1‐in‐10 LOLE on a forward basis under our base case modeling

assumptions.

Consistent with our and others’ previous findings, our simulations show that a vertical demand curve would lead to higher price volatility than a sloped curve. This volatile pricing outcome is a consequence of combining steep system and local supply curves with vertical demand curves, allowing a small supply reduction (or demand increase) to cause prices to spike quickly from moderate levels to the cap. In equilibrium conditions with prices at Net CONE on average, we estimate that the vertical curve would experience frequent shortage conditions with prices at the cap more than half of the time. Under MISO’s proposed demand curve, we estimate that price volatility would decrease by 13% and price-cap events would decrease by 30%.

This improvement in price volatility is not as large as the improvement we have observed in other forward capacity markets for two reasons. First, MISO’s forward competitive retail auction is smaller in megawatt terms, so that modest supply-demand fluctuations will create proportionally greater price changes. Second, the forward competitive retail auction may experience larger year-to-year swings in offered quantity based on utilities’ offer behavior.

Another important consideration is that a vertical demand curve at the requirement would not achieve the 1-in-10 reliability standard on behalf of competitive retail customers. The auction would clear exactly the requirement most of the time, but would fall short any time prices reached the cap. It would never clear additional supply even when additional supply was

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available at low cost. On average, we estimate that the auction would clear 533 MW short of the competitive retail loads’ reliability requirements. Translating that shortage to a system-wide reliability impact, this would produce forward LOLE of 0.155 or 1-in-6.4 LOLE.46 The reliability implications of this shortfall may be less significant in MISO than in a full merchant market because utilities may retain an additional “buffer” of excess supply that is not offered into the forward auction. If the buffer is as large as we assume in our base case, that excess utility supply will help support reliability in the prompt year. However, if utilities hold less buffer, the forward shortfall for competitive retail loads would persist into the prompt year, creating prompt reliability concerns (see also Section IV.C on the impact of buffer assumptions).

The forward shortfall also has equity and cross-subsidy implications. If the forward resource auction allows for a shortfall in supply for competitive retail loads, it implies that those customers are allowed to lean on excess utility supplies without purchasing them. This type of cross-subsidization is not consistent with MISO’s design objectives. We therefore proposed a demand curve that requires competitive retail loads to procure enough supply to meet their resource adequacy requirements without leaning on utility supply.

To achieve reliability and self-sufficiency (including purchases from utilities) on behalf of retail-choice loads, a vertical demand curve would need to be right-shifted by 7%. This would not improve price volatility, however. Moreover, prices would be at the cap whenever reserve margins are less than 7% beyond the traditional target. Competitive loads would almost certainly object to paying such high prices when capacity does not appear to be scarce.

MISO’s proposed downward-sloping curve has much better pricing characteristics. It is tuned to achieve reliability and self-sufficiency on behalf of competitive retail customers, with higher prices when supply is tight and lower prices otherwise. Price volatility would be slightly lower.

46 As discussed in Section III.F, we calculate a forward LOLE metric that is based on the results of the

forward auction assuming utilities meet their requirement, but no more.

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Table 9 Performance of MISO Proposed Curve Compared to Vertical Forward Demand Curve


merchant and all utility supply. Average price converges to Net CONE in forward auction.

B. CONCEPTUAL BASIS FOR PRICE CAP AND QUANTITY AT THE CAP

The price and quantity at the cap strongly affect the reliability and price volatility performance of a capacity market demand curve. We describe here the considerations for establishing the quantity at the cap and the tradeoffs between higher and lower caps. Table 10 summarizes MISO’s proposed price and quantity at the cap compared to the demand curves in other capacity markets.

In our view, the most important reference point when establishing a quantity at the price cap is the “minimum acceptable” quantity at which MISO might initiate out-of-market intervention to sustain reliability. A demand curve with a price cap at or above the “minimum acceptable quantity” will ensure that MISO purchases all in-market supply options before making any out-of-market backstop procurements. Although MISO does not have an explicitly-defined minimum acceptable quantity that would trigger intervention, we have worked with MISO staff to identify the 1-in-5 level as a reasonable proxy that can be used to establish the quantity at the cap. This is consistent with the analytical basis for the capacity demand curves in ISO-NE and PJM, which have minimum acceptable quantities near 1-in-5 LOLE.47

47 Quantity at the cap in ISO-NE is 98.1% of the requirement, compared to a 1-in-5 quantity of 97%. The

location of this point is determined by ISO-NE’s Marginal Reliability Impact curve. In PJM, the 1-in-5 quantity corresponds to 98.8% of the requirement and the price cap is right-shifted by 1%. See p. 49 of Pfeifenberger et al. PJM Third Triennial Review.


Average Standard

Deviation

Frequency

at Cap

Requirement

(PRMR)

Merchant

Supply

Cleared

Utility

Supply

Cleared

Total

Supply

Excess

(Deficit)

above

PRMR

Standard

Deviation

of Excess

(Deficit)

LOLE Frequency

Below

PRMR

Frequency

Below

1‐in‐5


Forward Auction

Status Quo n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a

Forward Vertical at Requirement $185 $95 55% 10,396 8,762 1,100 9,862 (533) 536 0.155 86% 21%


Forward Vertical, Right‐Shifted $185 $96 57% 10,396 9,425 1,122 10,546 150 552 0.098 33% 5%

Prompt Auction

Status Quo $185 $88 62% 101,651 7,510 93,250 101,346 (305) 1,799 0.191 62% 41%

Forward Vertical at Requirement $124 $90 25% 101,651 8,915 92,574 102,575 924 1,398 0.074 25% 6%


Forward Vertical, Right‐Shifted $128 $90 27% 101,651 9,583 92,619 103,232 1,580 1,379 0.046 11% 1%

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Table 10 Price Cap and Quantity at the Cap Compared to Other Markets

Price Cap Quantity at Cap Minimum Acceptable (% of Requirement)

MISO Proposed Curve 1.4× Net CONE 99% of Requirement 1‐in‐5 LOLE

ISO New England 1.6× Net CONE 98% of Requirement 1‐in‐5 LOLE

PJM Interconnection Max(1.5× Net CONE, 1× Gross CONE)

99.8% of Requirement 99.1% of Requirement

New York ISO 1.5× Gross CONE 62%‐90% of Requirement n/a

Great Britain 1.5× Net CONE 97% of Requirement n/a

Western Australia 1.6× Net CONE 100% of Requirement 100% of Requirement

Sources: ISO New England, “ISO New England Installed Capacity Requirement, Local Sourcing Requirements and Capacity Requirement Values for the System‐Wide Capacity Demand Curve for the 2019/20 Capacity Commitment Period," January 2016. PJM Interconnection, “2019–20 RPM Base Residual Auction Planning Period Parameters,” February 2016. New York Independent System Operator, “2014–2017 Demand Curve Parameters,” March 2014. Ofgem, “Annual Report on the Operation of the Capacity Market in 2015,” June 6, 2016. Public Utilities Office, “Position Paper on Reforms to the Reserve Capacity Mechanism,” December 3, 2015.

Establishing the price cap requires tradeoffs. The price cap needs to be high enough to offset low prices during surplus market conditions and enable investors to earn Net CONE on average. A high price cap reduces the risk of extreme shortages requiring out-of-market backstop procurement and mitigates the risk that the true Net CONE faced by developers is higher than the administrative Net CONE used to set demand curve parameters. For the same level of average reliability, a high price cap also implies a steep, narrow demand curve. Such a curve achieves low variability in cleared quantities and limits procurement of supply in excess of the requirement, but produces higher price volatility. In contrast, the primary advantages of maintaining a lower price cap are mitigating price volatility and the exercise of market power. Balancing these considerations, other markets have adopted price caps in the range of 1.5–2.0× Net CONE, as summarized in Table 10. This is also the range that we have typically recommended in other capacity markets.

For MISO, we are recommending a curve with a somewhat lower price cap of 1.4× Net CONE. Although we maintain our view that a higher price cap range is typically preferred, we acknowledge several considerations in MISO’s context that suggest a lower price cap may be more appropriate than in other markets:

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Strong stakeholder aversion. Most stakeholders, other than merchant suppliers, do not want to increase the price cap above the current level of 1× CONE that currently applies to the prompt auction.

Consistency between forward and prompt price caps. A higher forward price cap would increase the discrepancy between the forward and prompt auction structures and clearing outcomes under the same market conditions.

Greater price volatility challenges. We expect that addressing price volatility will be more challenging in MISO’s forward auction than in other capacity markets due to its small size and the potentially large impact of utility offers. As discussed further in the following section, a lower price cap is consistent with a wider curve that will better mitigate this additional volatility.

Low E&AS offset now and in the near future. Currently, low energy prices in MISO are producing relatively low E&AS offsets, meaning that the uncertainty in Net CONE is less than under conditions with higher E&AS offsets.48 This reduces the likelihood that the true Net CONE faced by developers falls above the administrative Net CONE estimated by MISO.

Lower reliability impact from supply shortage conditions. For fully merchant capacity markets, the reliability impacts of procurement shortfalls are much greater than in the MISO footprint. A large shortfall on behalf of competitive retail loads translates to only a modest shortfall for the footprint (and in some cases no shortfall once considering the utility buffer). Therefore the need for a higher price cap to protect against extreme shortage events is less compelling in MISO than in PJM, NYISO, or ISO-NE.

As a final design feature, both ISO-NE and PJM have adopted a minimum price cap of 1.0× CONE in case their usual Net CONE-based price caps fall below that level. The minimum prevents the entire demand curve from collapsing to near zero if energy margins are very high (which would mean that administrative Net CONE is very small). This mechanism is particularly advantageous in conditions of high and uncertain E&AS offsets; in those conditions there is a greater risk of underestimating Net CONE and procuring insufficient incremental supply. We do not recommend that MISO adopt a minimum at this time given the low E&AS conditions, but we recommend re-examining this issue in the future.

As in other markets, we expect that the price cap will be a key determinant of demand curve performance and recommend re-examining its parameters periodically. In particular, we recommend that future reviews re-examine: (a) whether the price cap should be increased to 1.5 to 2.0 times Net CONE; (b) whether the inconsistency in price caps between the forward and prompt auctions has introduced any performance concerns; and (c) whether a minimum price should be adopted similar to that in PJM and ISO-NE.

48 2013–15 average E&AS offsets for CTs in the MISO North region were $60/MW-day (See 2015 MISO

State of the Market Report), compared to $150/MW-day in the PEPCO region in Eastern PJM (See PJM 2019/20 auction parameters). Low E&AS offsets reduce uncertainty in Net CONE. See Appendix A.3 of Pfeifenberger et al. PJM Third Triennial Review.

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C. CONCEPTUAL BASIS FOR CURVE SHAPE, WIDTH, AND ZERO CROSSING POINT

The price cap, overall shape, width, and zero crossing point fully define a demand curve. The “shape” refers to a straight line versus a curve, and it influences price and quantity variability. The “width” refers to the increase in supply quantity corresponding to a drop in prices from the cap to zero. The “zero crossing point” refers to the maximum quantity that the auction will clear when supply is cheap and plentiful.

Specifying these demand curve parameters is an exercise in balancing tradeoffs and selecting from among a range of workable options. Market operators have made different choices about the demand curves most suited to their circumstances. Figure 8 and Table 11 compare MISO’s proposed curve with curves used in other capacity markets.

Figure 8 MISO Proposed Curve Compared to Other Markets’ Demand Curves

Sources: ISO New England, “ISO New England Installed Capacity Requirement, Local Sourcing Requirements and Capacity

Requirement Values for the System‐Wide Capacity Demand Curve for the 2019/20 Capacity Commitment Period," January 2016.

PJM Interconnection, “2019–20 RPM Base Residual Auction Planning Period Parameters,” February 2016. New York Independent System Operator, “2014–2017 Demand Curve Parameters,” March 2014. Ofgem, “Annual Report on the Operation of the Capacity Market in 2015,” June 6, 2016. Public Utilities Office, “Position Paper on Reforms to the Reserve Capacity Mechanism,” December 3, 2015.

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In terms of shape, the four main options for a sloped demand curve are: (1) straight-line (prior ISO-NE, NYISO), (2) convex (PJM, ISO-NE, Western Australia proposed), or (3) concave (prior PJM, Great Britain). A straight-line curve is simple and has good price volatility performance. A convex curve is theoretically appealing because it is more proportional to the marginal economic and reliability value of capacity. A convex curve can also create very strong price signals in shortage conditions. A concave curve performs best in terms of mitigating price volatility, but worst in terms of reliability. In our view, straight-line or convex curves strike the most favorable balance among the tradeoffs. For MISO, we recommend a straight-line curve primarily because it is simpler and has slightly better price volatility performance than a convex curve.

The width of a demand curve is another important determinant of price volatility and reliability performance. Flatter and wider demand curves imply lower price volatility, while steeper and narrower curves imply lower quantity uncertainty. Steeper curves are also more susceptible to the exercise of market power.

There are several guidelines for the necessary width of the demand curve. Ideally, a demand curve should be wide enough that standard year-to-year variations in load forecast, reliability requirement, supply, and transmission do not cause excessive changes in price. In the case of MISO’s forward market, the curve should be able to absorb variability in merchant supply and competitive retail load, as well as absorb the variability in any utility excess offered into the market. In MISO North, we estimate a standard deviation in net supply offered into the forward auction (forward total offers – competitive retail load) of 1,920 MW if utilities offer all of their excess supply, or 1,420 MW if utilities hold a buffer consistent with our base case assumptions. As another guideline, the curve should not be so steep that the entry of just one plant (approximately 600 MW) would suppress prices to near zero for many years; similarly, the exit of just one plant should not cause prices to rise to the cap. Wider curves can be advantageous in small markets or zones and in non-forward markets such as NYISO. Finally, the demand curve should not be so wide that cleared quantities can far exceed traditional reserve margin targets and introduce the potential for excess cost.

Our recommended curve in MISO has a width of 16% of the requirement, 1,700 MW, or approximately three times the size of a typical new gas CC plant. This is wider on a percentage basis than in the larger markets of PJM, Great Britain, and ISO-NE, but smaller than in NYISO and Western Australia. The wider proportional curve will partly mitigate the relatively high volatility in MISO’s market due to its small size and the presence of large and variable utility offers. On an absolute megawatt basis, the curve is much steeper than the curves of other capacity markets, and only modestly larger than the expected size of the net supply shocks. An even wider curve would be needed to significantly improve price volatility performance, but this would introduce the potential for larger over-procurements on behalf of competitive retail loads.

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Table 11 Shape, Width, and Zero Crossing Point Compared to Other Markets

Shape Width Width Foot

(% of Requirement) (MW) (% of Requirement)

MISO Proposed Curve Straight‐line 16.0% 1,661 115%

ISO New England Convex 11.7% 3,990 110%

PJM Interconnection Convex 7.7% 236–13,213 108%

New York ISO Straight‐line 22%–56% 3,203–7,802 112%

Great Britain Concave 6.7% 3,000 103%

Western Australia Convex 15%–20% 680–910 118%

Sources: ISO New England, “ISO New England Installed Capacity Requirement, Local Sourcing Requirements and Capacity Requirement Values for the System‐Wide Capacity Demand Curve for the 2019/20 Capacity Commitment Period," January 2016. PJM Interconnection, “2019–20 RPM Base Residual Auction Planning Period Parameters,” February 2016. New York Independent System Operator, “2014–2017 Demand Curve Parameters,” March 2014. Ofgem, “Annual Report on the Operation of the Capacity Market in 2015,” June 6, 2016. Public Utilities Office, “Position Paper on Reforms to the Reserve Capacity Mechanism,” December 3, 2015.

D. PERFORMANCE OF ALTERNATIVE “TUNED” DEMAND CURVES

We now use probabilistic simulation results to provide a quantitative basis for comparing the tradeoffs among different demand curve options. We compare demand curves over a range of price caps from 1.0× to 2.0× CONE, or the entire range suggested by MISO and stakeholders, as shown in Figure 9. To ensure that each curve meets the primary design objectives, we tuned the zero-crossing point to achieve LOLE of 1-in-10 on behalf of competitive retail customers. All of the curves have a quantity at the cap corresponding to 1-in-5 LOLE. For reference, we also compare to a vertical demand curve, right-shifted to meet 1-in-10 described in Section V.A. Table 12 summarizes the performance of MISO’s proposed curve compared to each of the alternatives.

All of the curves have good reliability performance by definition because they were tuned to meet the reliability objectives. The shape of the curves varies substantially however. Curves with higher price caps have a steeper shape, lower quantity uncertainty, and consequently higher price volatility. Curves with lower price caps, including MISO’s proposed curve, have wider shapes, higher quantity variability, and lower price volatility. For the curve with a 1.0× CONE cap, we also examined a variation truncated at 115% of the requirement to limit the maximum possible over-procurement quantity.

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Figure 9 MISO Proposed Curve Compared to Alternative “Tuned” Demand Curves

Notes: All curves have been “tuned” to achieve 1‐in‐10 LOLE on a forward basis under our base case modeling assumptions.

Price volatility performance varies widely across the options. Forward price volatility ranges from a standard deviation of $73/MW-day with a cap of 1.0× CONE to $149/MW-day with a cap of 2.0× CONE. MISO’s proposed curve is at the low end of the range in terms of price volatility due to the lower cap and wider shape. We view the rapid increase in price volatility even at a modestly higher price cap of 1.7× Net CONE as being a substantial downside to performance. The 1.7× Net CONE price cap shows higher volatility than the vertical demand curve that maintains a 1.4× Net CONE cap. Given MISO’s uniquely challenging price volatility context, we view a lower-end price cap as being more important than in other markets.

Consumer interests and state regulators are also concerned about the customer cost implications of each curve. Table 13 summarizes competitive retail customers’ costs simulated under each of these tuned demand curves and compared to the status quo. The table reports customer costs from the forward auction, prompt auction, and in total across both auctions. All of the tuned curves show increased customer costs of approximately $10–$18 million per year or 1.8–2.5% higher capacity costs than the (unreliable) status quo, with the exception of the right-shifted vertical forward curve, which has costs that are 7.1% ($51 million per year) higher than the status quo. This higher cost is necessary to support the larger quantity of supply needed to achieve resource adequacy objectives. We also note that the status quo cost estimate is provided for illustrative purposes only and understates the customer costs because it does not consider the out-of-market backstop procurements that would need to be pursued to maintain reliability in the status quo.

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Table 12 Performance of MISO Proposed Curve Compared to Alternative “Tuned” Curves


merchant and all utility supply. Average price converges to Net CONE in forward auction. “MISO Proposed Curve” has price cap at 1.4× Net CONE.

Curves with higher caps have somewhat higher customer costs from the forward auction because they procure more supply during shortage conditions when capacity is expensive. But these higher forward auction costs are offset by lower prompt auction costs since prompt auction prices and quantities procured on behalf of competitive retail customers are lower. Summing these offsetting factors, most of the tuned curves have nearly identical costs, within +/-1%, or smaller than calculation error in our modeling. The exception is the right-shifted vertical forward curve, which requires customers to buy extra supply at high prices and noticeably increases costs. Customer cost differences would be larger in the short run or in circumstances of administrative error in Net CONE. The wider demand curves will produce a larger deviations in procured quantity (and therefore in realized costs) than steeper curves.

Balancing these considerations, we recommend that MISO adopt a price cap of 1.4× Net CONE in the forward auction, as discussed above. This is somewhat below the range we have recommended in other markets dominated by merchant resources, but higher than the 1× CONE currently used in the prompt auction. We recommend this lower cap in MISO primarily because of the uniquely challenging and uncertain price volatility context, and because of the less significant reliability impact if underestimating administrative Net CONE. As discussed above, we recommend re-evaluating the price cap and associated demand curve shape in quadrennial reviews after MISO gains additional information regarding how the market performs.


Average Standard

Deviation

Frequency

at Cap

Requirement

(PRMR)

Merchant

Supply

Cleared

Utility

Supply

Cleared

Total

Supply

Excess

(Deficit)

above

PRMR

Standard

Deviation

of Excess

(Deficit)

LOLE Frequency

Below

PRMR

Frequency

Below

1‐in‐5


Forward Auction

Cap at 1x CONE $186 $73 40% 10,396 9,318 1,256 10,574 178 884 0.106 44% 10%

Cap at 1x CONE Truncated $185 $74 40% 10,396 9,308 1,233 10,541 145 832 0.107 44% 11%

MISO Proposed Curve $185 $83 39% 10,396 9,294 1,220 10,514 118 762 0.106 42% 9%Forward Vertical, Right‐Shifted $185 $96 57% 10,396 9,425 1,122 10,546 150 552 0.098 33% 5%Cap at 1.7x Net CONE $186 $109 25% 10,396 9,393 1,119 10,512 116 476 0.099 33% 3%Cap at 2x CONE $185 $149 9% 10,396 9,510 997 10,507 111 306 0.096 31% 0%

Prompt Auction

Cap at 1x CONE $107 $80 14% 101,651 9,364 92,636 103,115 1,463 1,557 0.055 16% 2%

Cap at 1x CONE Truncated $107 $80 14% 101,651 9,353 92,615 103,106 1,454 1,545 0.055 16% 2%

MISO Proposed Curve $106 $79 13% 101,651 9,360 92,567 103,078 1,426 1,492 0.054 15% 2%Forward Vertical, Right‐Shifted $128 $90 27% 101,651 9,583 92,619 103,232 1,580 1,379 0.046 11% 1%Cap at 1.7x Net CONE $94 $73 7% 101,651 9,565 92,284 103,153 1,501 1,354 0.047 9% 1%Cap at 2x CONE $86 $66 3% 101,651 9,865 91,946 103,236 1,585 1,275 0.041 3% 0%

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Table 13 Costs to Competitive Retail Loads Under Status Quo and the Competitive Retail Solution

Notes: Zonal costs are calculated as (Zone LCR × Zone Clearing Price) + (Zone PRMR – Zone LCR) × System Clearing Price), adjusted for clearing short or long of the requirement along the demand curve. The system cost is the sum of the zonal costs.

System Competitive Retail Cost

Average

Price

Price

Volatility Zone 4 Zone 7 System

($/MW‐d) ($/MW‐d) ($mil/year) ($mil/year) ($mil/year)

Forward Auction

Status Quo n/a n/a n/a n/a n/a

Cap at 1x CONE $186 $73 $558 $157 $715

Cap at 1x CONE Truncated $185 $74 $557 $157 $715

MISO Proposed Curve $185 $83 $555 $157 $712

Forward Vertical, Right‐Shifted $185 $96 $566 $160 $726

Cap at 1.7x Net CONE $186 $109 $559 $158 $717

Cap at 2x CONE $185 $149 $565 $160 $725

Prompt Auction

Status Quo $185 $88 $557 $158 $714

Cap at 1x CONE $107 $80 $14 $4 $17

Cap at 1x CONE Truncated $107 $80 $14 $4 $17

MISO Proposed Curve $106 $79 $12 $3 $16

Forward Vertical, Right‐Shifted $128 $90 $30 $9 $39

Cap at 1.7x Net CONE $94 $73 $6 $2 $7

Cap at 2x CONE $86 $66 $2 $0 $2

Total

Status Quo n/a n/a $557 $158 $714

Cap at 1x CONE n/a n/a $571 $161 $732

Cap at 1x CONE Truncated n/a n/a $571 $161 $732

MISO Proposed Curve n/a n/a $568 $160 $728

Forward Vertical, Right‐Shifted n/a n/a $597 $169 $765

Cap at 1.7x Net CONE n/a n/a $565 $160 $724

Cap at 2x CONE n/a n/a $567 $160 $727

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E. LOCATIONAL DEMAND CURVE DESIGN

MISO’s current capacity market accounts for locational resource adequacy needs by imposing vertical demand curves for potentially import-constrained areas. MISO proposes to implement a sloped locational demand curve in the forward auction for those zones that have competitive retail demand greater than a materiality threshold. Currently, only Zone 4 Illinois and Zone 7 Michigan meet the criteria for being modeled in the forward competitive retail auction. Both zones have generally been net importers in MISO’s capacity auctions, driven by short-term supply and demand fundamentals. In a long-run equilibrium, a zone will be import constrained only if suppliers in that location face a higher Net CONE to develop resources in that zone than in the remainder of the system.

Import-constrained zones can be subject to several challenges that need to be considered in locational demand curve design. Due to their smaller size, transmission-constrained zones often face greater variability in supply and demand, relative to zone size, than the system as a whole. Import-constrained zones also face a significant additional driver of variability in net supply: the import limit itself.49 Some import-constrained zones, particularly those located in urban centers, also have substantially higher investment costs for incremental resource development compared to the surrounding footprint. In some systems, these factors increase locational price volatility, the risk of supply shortfalls, and low-reliability events. Smaller zones also tend to be less structurally competitive and more susceptible to the exercise of market power.

As a result of these considerations, locational demand curves may need to be wider or have higher price caps than the system curve in order to support investment and maintain reliability. In PJM, we recommended that locational curves have both a higher price cap and a minimum curve width equal to 25% of the import limit.50

Most of these challenges appear to be less concerning in MISO compared to other capacity markets. Although the competitive retail zones are exposed to proportionally larger shocks to net supply and are potentially susceptible to the exercise of locational market power, the relatively wide system demand curve with a low price cap helps to mitigate price volatility and susceptibility to exercise of market power. Net CONE in Illinois and Michigan do not appear to be substantially higher than the rest of the system, suggesting that the zone will price separate from the system only infrequently.51 In Michigan (and to a much lesser extent in Illinois), substantial utility, municipal, and co-operative self-supply will help to mitigate low-reliability

49 The average standard deviation of import limits across all zones and all years in MISO’s PRA is

approximately 24% of the average import limit. 50 See p. 101 of Pfeifenberger et al. PJM Third Triennial Review. 51 According to MISO’s CONE filing for the 2016/17 auction, CONE varied from $246/MW-day in Zone 10

to $264/MW-day in Zone 5, corresponding to a standard deviation of $6/MW-day, 2% of the average value. See Attachment B to “Re: Filing of Midcontinent Independent System Operator, Inc. Regarding LRZ CONE Calculation,” Filing of Midcontinent Independent System Operator, Inc. before the Federal Energy Regulatory Commission, September 16, 2015, Docket ER15-1736-0000. According to the 2015 State of the Market Report, Net CONE ranged from $200/MW-day in the Central region to $223/MW-day in the North region, corresponding to a standard deviation of $10/MW-day, or 5%. See p. 7 of Potomac Economics, “2015 State of the Market Report for the MISO Electricity Markets,” June 2016.

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events. Finally, MISO has less stringent locational reliability objectives than other capacity markets, which also reduces the need for locally-sourced supply.52 As a result, we have not seen zonal reliability challenges in our modeling. Locational reliability objectives are achieved if the same system demand curve is applied to the zones.

The shape of our recommended locational demand curve is the same as for the system. The quantity at the cap corresponds to LOLE of 1-in-5 and the quantity at the foot corresponds to 115% of LRR. The quantity corresponding to 1-in-5 LOLE is 98.1% of LRR in Zone 4 and 98.3% of LRR in Zone 7, compared to 99.0% of PRMR for the system. The foot quantity is 115% of LRR across both zones. Each zonal curve has approximately the same width when expressed as a percentage of LRR, but has a different width when expressed in MW terms or as a percentage of LCR. Local curves defined as a percentage of LRR are more likely to achieve consistent reliability outcomes across zones than curves defined as a percentage of LCR. In zones with large import limits, large percentage changes in the local supply have a small effect on total supply, including imports, available to meet local demand. In zones with small import limits, changes in local supply have a much greater effect. Changes in quantity relative to LRR are therefore strongly related to changes in reliability, while changes in quantity relative to LCR are not. Table 14 summarizes the local demand curve quantities in each zone in MW and percentage quantities relative to LRR and LCR.

Table 14 Local Demand Curve Parameters

Notes:

MW quantities calculated using values from MISO’s historical 2016/17 PRA results with total CIL of 8,011 in Zone 4.

Table 15 reports zonal simulations results with MISO’s proposed curve applied to the system and zones. The simulations show strong reliability performance on a forward and prompt basis, in both Illinois and Michigan. Based on these findings and the absence of other key reasons for developing different local curves in other markets, we do not recommend any differences between the locational and system curve. However, we do recommend that locational issues be re-evaluated again in future quadrennial reviews.

52 MISO requires 1-in-10 LOLE in the zones, compared to 1-in-25 in PJM. See paragraphs 24–26 of the

Moeller Affidavit on Behalf of MISO and p. viii of Pfeifenberger et al. PJM Third Triennial Review.

Zone 4 Zone 7

Cap Foot Cap Foot

Local Reliability Requirement (LRR) (%) 98% 115% 98% 115%

(MW) 9,029 10,584 2,397 2,803

Local Clearing Requirement (LCR) (%) 94% 147% 98% 118%

(MW) 2,781 4,335 2,045 2,451

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Table 15 Performance of MISO’s Competitive Retail Solution for Locational Reliability

Notes: LOLE in Zones 4 and 7 is incremental to system LOLE.

“Total Supply” forward includes merchant and utility cleared quantities. “Total Supply” prompt includes cleared merchant and all utility supply.


Some stakeholders have questioned our decision not to model MISO South. We have several reasons for this decision. Our objective is to evaluate the performance of MISO’s Competitive Retail Solution, and there is no competitive retail load in the South. Supply in the South can only affect the Competitive Retail Solution to the extent that it flows North, helping to meet competitive retail need and potentially introducing variability into the market. The ability of supply to flow from South to North is limited by available transmission capability, which is reflected in MISO’s capacity auction using the Sub-Regional Export Constraint. The constraint has historically allowed only limited Southern supply to flow North.

We could, in principle, have included MISO South in the model to capture the limited effect of Southern supply. However, a couple of factors made that impractical. Limited data history and an ongoing dispute regarding the South to North export limit created challenges for characterizing supply variability and export capability from the South. Uncertainty in MISO’s approach to congestion and infeasibility risk allocation prevented us from evaluating how Southern utilities might offer into the forward auction.

Instead of attempting to model the South directly, we conducted sensitivity analyses accounting for the supply variability that MISO South might introduce. Our sensitivity analyses cover a much broader range of variability than MISO South could create, since the Sub-Regional Export Constraint limits variability that can be introduced from the South. The export constraint implies that the maximum year-to-year change in exports from South to North is the value of the constraint itself. When utilities in the South are particularly long, much of their excess would not be able to flow North. Depending on the value of the export constraint going forward, exports may also be constrained when Southern utilities are only slightly long. If the export limit were always binding, the South would introduce no variability at all. In fact, it likely introduces some additional variability, but much less than an equivalent amount of supply without an export constraint.


Average Standard

Deviation

Frequency

at Cap

Frequency

of Price

Separation

Requirement

(LCR or

PRMR)

Merchant

Supply

Cleared

Utility

Supply

Cleared

Total

Supply

Excess

(Deficit)

above

LCR or

PRMR

Standard

Deviation

of Excess

(Deficit)

LOLE Frequency

Below LCR

or PRMR

Frequency

Below

1‐in‐5

($/MW‐d) ($/MW‐d) (% of years) (% of years) (MW) (MW) (MW) (MW) (MW) (MW) (events/yr) (% of years) (% of years)

Forward Auction

Zone 4: Illinois $195 $72 39% 16% 2,949 4,856 65 4,920 1,971 1,300 0.018 4% 2%

Zone 7: Michigan $195 $71 39% 16% 2,083 2,375 313 2,689 606 639 0.055 3% 0%

MISO North $185 $83 39% n/a 10,396 9,294 1,220 10,514 118 762 0.106 42% 9%

Prompt Auction

Zone 4: Illinois $114 $79 15% 12% 3,781 4,860 953 5,824 2,043 1,371 0.015 4% 1%

Zone 7: Michigan $122 $77 15% 21% 20,831 2,390 19,115 21,696 865 843 0.044 2% 0%

MISO North $106 $79 13% n/a 101,651 9,360 92,567 103,078 1,426 1,492 0.054 15% 2%

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In future quadrennial reviews, MISO may wish to consider re-evaluating these considerations (and other assumptions we have made to evaluate a new market) as they gain experience observing market behavior.

F. SUMMARY OF EXPECTED PERFORMANCE

MISO proposed, based on our recommendations, a demand curve reflecting a balance of conflicting objectives. The price cap is lower, and the curve proportionally wider, than the curves adopted in most other capacity markets primarily because of the greater price volatility challenges that we expect in MISO. The primary tradeoff is that the proportionally wider and flatter curve is more susceptible to sustained over- or under-procurement if administrative Net CONE is over- or underestimated. We believe that this curve reflects an appropriate balance of objectives given MISO’s unique circumstances and challenges. While we made reasonable assumptions about utility offers in the forward auction and evaluated MISO’s proposal under a broad range of potential offer behaviors, we have yet to observe actual behavior and performance. We therefore recommend that the curve be re-evaluated in the future, particularly in terms of the level of the price cap, the width, and the need for a minimum on the price cap.

VI. EVALUATION OF ALTERNATIVE HYBRID-PROMPT PROPOSAL

MISO and stakeholders have also considered an alternative to the Competitive Retail Solution that was originally proposed by Dr. David Patton and developed further by MISO into the “hybrid-prompt” option. The prompt-hybrid alternative contemplates a non-forward auction with a two-stage approach to auction clearing and pricing, as follows:

Stage 1: Clear Merchant Supply at a Higher Price. A MISO system-wide demand curve would be defined that would be proportional to the marginal reliability value of capacity. A price would be determined based on the intersection of this demand curve with the system wide (regulated plus merchant) supply curve. Merchant suppliers clearing against this curve would earn the stage 1 price.

Stage 2: Clear Regulated Supply at a Lower Price. All merchant supply cleared in Stage 1 would be treated as price-takers in stage 2, although utility supply would be maintained as offered. The revised system supply curve would be cleared against the system-wide PRMR. Utility resources clearing in stage 2 would be paid the much lower stage 2 price.

Dr. Patton has offered a number of arguments supporting his view that the hybrid-prompt proposal is a superior option for addressing the concerns in the competitive retail areas.53 We agree with several points that Dr. Patton has made, including that: (a) the hybrid-prompt proposal would attract and retain more merchant supply than the status quo; (b) the parameters of the demand curve could be “tuned” to meet reliability objectives; (c) there are theoretical merits to

53 Detailed descriptions of Dr. Patton’s proposal and arguments and MISO’s adapted hybrid-prompt option

are available in the July 7, 2014 Resource Adequacy Subcommittee (RASC) meeting materials, available at https://www.misoenergy.org/Events/Pages/RASC20160714.aspx.

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developing a demand curve that is proportional to the marginal reliability or economic value of capacity; and the wider demand curve proposed for the hybrid-prompt auction would help mitigate price volatility.54

However, we disagree with Dr. Patton’s conclusion that the hybrid-prompt proposal would be a workable solution or an economically efficient one. Our conclusions differ from Dr. Patton’s for two primary reasons, as we discuss further below:

First, we are concerned that the resource discrimination contemplated under the two-stage auction clearing would introduce a large deadweight loss as well as administrative challenges.

Second, Dr. Patton’s arguments in favor of the hybrid-prompt proposal and against MISO’s Competitive Retail Solution proposal rely on an inappropriate definition of the “efficient price” that does not constitute a measure of economic efficiency such as welfare gains or deadweight loss.

Regarding mitigating price volatility, it is true that the wider demand curve could help. The curve is wider than MISO’s proposal on a MW basis because it is scaled to the entire system load rather than just competitive retail load. However, since the utilities do not want to face a variable resource requirement, the competitive retail loads would have to absorb any excess capacity cleared in the auction. For example, a 3% excess on the system-wide demand curve would amount to a 30% excess procured by competitive retail loads. To prevent such an unacceptable result, MISO would have to constrain the quantity of competitive supply that can clear. MISO would similarly have to constrain supply from clearing significantly below target. These constraints could send prices to extremes when binding, thus undoing some of the price volatility mitigation benefits of the wider curve. Price volatility would be further exacerbated by the prompt timing of the auction and its effect on the supply curve. Prompt supply curves will be less elastic than a forward supply curve because supply options are constricted on that timeframe and any investment costs are sunk.

A. THE EFFECTS OF RESOURCE DISCRIMINATION

First, we address the concerns introduced by resource discrimination. Dr. Patton has conducted a series of simulations suggesting that the stage 1 price is likely to be much higher than the stage 2 price. In his examples, the stage 1 price was $140 to $220/MW-day, or 1.5 to 46 times higher than the stage 2 price of $3 to $150/MW-day.

The economic effect of price discrimination is to pay a high-cost merchant resource to provide a MW of capacity when a low-cost resource was available. Taking mid-range prices from Dr. Patton’s analysis as an example, the auction would select a merchant resource with a $170/MW-

54 We have discussed the merits of capacity demand curves proportional to marginal economic value and/or

marginal reliability value in other contexts. See p. 47 of Pfeifenberger et al. PJM Third Triennial Review and pp. 91-94 of Johannes P. Pfeifenberger, Kathleen Spees, Kevin Carden, and Nick Wintermantel, “Resource Adequacy Requirements: Reliability and Economic Implications,” prepared for FERC, September 2013, (“Pfeifenberger et al. FERC Resource Adequacy Report”).

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day avoidable going-forward cost. For example, this could be composed of existing capacity with very high net ongoing fixed costs, retrofits or uprates to existing resources, or new demand response. At the same time, the stage 2 auction would not clear a utility-owned resource with a $43/MW-day avoidable going-forward cost. This would likely cause the $170 resource to enter or continue to operate while the $43 withdraws. The $127 difference is a clear deadweight loss. Or if the $43 resource continues to operate but is not counted as capacity, total resource costs will have increased by $170 while slightly enhancing reliability.

Dr. Patton and others have suggested that the utility resource’s costs are actually higher than their offers suggest, with their costs being recovered outside of the capacity market, through rates imposed on the utility’s captive ratepayers. We acknowledge this possibility, but it is more of an accounting concept, not an economic concept that recognizes sunk costs as sunk. Once the utility has excess capacity for whatever reason—for example, because it opted to build an efficient-scale combined-cycle plant that temporarily exceeds its needs rather than a costlier series of smaller aeroderivative combustion turbines—the cost of taking on a capacity supply obligation is quite low.

Resource discrimination might cause another kind of distortion as well: market participants would have large economic incentives to reclassify resources in any way they could imagine, for example by changing ownership or contracting status. This could even induce high dollar-value transactions to reclassify resources as merchant while serving no underlying business purpose other than capturing the higher capacity price. Many resources would be in a “gray” area not neatly fitting into either the merchant or utility classification, for example in cases of IPP resources under contract, demand response programs, jointly-owned assets, or utilities’ affiliated merchant companies. MISO could face substantial administrative challenges in correctly classifying resources and would likely have to litigate disputed classifications.

B. EFFICIENT PRICE FORMATION

Second, regarding the concept of an “efficient price” Dr. Patton uses a different, and in our view imprecise, definition of economic efficiency to conclude that the hybrid prompt proposal is most efficient. Dr. Patton posits that a demand curve proportional to the marginal reliability value of capacity across the MISO footprint represents the efficient price that must be paid to merchant resources under all circumstances. In his view, any deviations from that payment level represent an economic inefficiency.

We disagree with this characterization. The price difference in question does not translate into any traditional measure of economic efficiency such as welfare gains or deadweight loss. A demand curve expressing the marginal economic value of capacity might express economic efficiency and deadweight loss, but a curve proportional to marginal reliability value does not. Instead, it represents an administrative representation of the capacity prices needed to achieve a particular reliability value. At a 1-in-10 reliability standard, these capacity prices typically

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exceed the economic value of incremental capacity.55 Thus, the inefficiency posited by Dr. Patton is not well defined in economic terms.

Dr. Patton might nevertheless assert that our collective willingness to pay for reliability at the 1-in-10 standard implies that the marginal reliability value curve can be interpreted as a marginal economic value curve. However, even if that were true, it would also be true that the curve would be subject to substantial uncertainties that cloud any measure of deadweight loss against that curve. Uncertainties include the LOLE modeling assumptions, regarding weather, generation availability, imports, and emergency operations, which can shift the curve several percentage points left or right.56 Other uncertainties surround the cost of capacity assumed to translate reliability value into implied economic value. (Admittedly, these uncertainties also affect parameters in MISO’s resource adequacy construct, but MISO has to pick something as a basis for its standards, and stakeholders have accepted them. It is something else to use them as a basis for measuring economic welfare).

In contrast, the deadweight loss induced by resource discrimination under the prompt-hybrid proposal is well-defined and certain, and it would be substantial. High-cost merchant supply investments would be pursued while low-cost utility resources would retire or mothball or simply not be counted. No such concerns exist in MISO’s forward proposal. It will not cause deadweight loss or incentives to reclassify resources because it does not discriminate between resources based on its owner’s business model. It will always procure the least-cost resources that are available and will therefore reduce the costs of attaining the prevailing reserve margin.

VII. FINDINGS AND RECOMMENDATIONS

Our analysis provides additional evidence confirming our expectations that MISO’s status quo resource adequacy construct will not support the resource adequacy needs of competitive retail customers within the footprint on a long-run basis. We find that MISO’s proposed Competitive Retail Solution and associated demand curve will mitigate or resolve these reliability concerns by supporting incremental merchant supply compared to the status quo design. We also find that MISO’s proposal will modestly improve price volatility, but we expect that price volatility will continue to be a significant challenge.

The reliability and especially the price volatility that we estimate are uncertain, with the largest uncertain driver being the participation of utilities in the forward auction. We therefore recommend that MISO re-evaluate the design in future years after observing utility behavior in the forward auctions. This information would provide a better basis upon which to adjust the parameters of our recommended demand curve, particularly with respect to the price cap and width. We also recommend that future reviews evaluate the performance of monitoring and mitigation procedures, opt-in and opt-out provisions for loads, provisions governing utility supply offers, and the potential to incorporate voluntary buy bids in the forward auction.

55 See pp. 47 – 49 of the Pfeifenberger et al. FERC Resource Adequacy Report. 56 See Section III.B of the Pfeifenberger et al. FERC Resource Adequacy Report.

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