ESSAYS IN MARKET POWER MITIGATION AND SUPPLY
FUNCTION EQUILIBRIUM
by
Thiagarajah Natchie Subramaniam
=
BY:
A Dissertation Submitted to the Faculty of the
DEPARTMENT OF ECONOMICS
In Partial Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
In the Graduate College
THE UNIVERSITY OF ARIZONA
2014
2
THE UNIVERSITY OF ARIZONA
GRADUATE COLLEGE
As members of the Dissertation Committee, we certify that we have read the
dissertation prepared by Thiagarajah Natchie Subramaniam, titled “Essays in Market Power Mitigation and Supply Function Equilibrium” and recommend that it
be accepted as fulfilling the dissertation requirement for the Degree of Doctor of
Philosophy.
Date: April 29, 2014
Gautam Gowrisankaran
Date: April 29, 2014
Gary D. Thompson
Date: April 29, 2014
Satheesh V. Aradhyula
Date: April 29, 2014
Stanley S. Reynolds
Date: April 29, 2014
Derek M. Lemoine
Date: April 29, 2014
Ashley A. Langer
Final approval and acceptance of this dissertation is contingent upon the candidate’s
submission of the final copies of the dissertation to the Graduate College.
Date: April 29, 2014
Dissertation Director: Gautam Gowrisankaran
Date: April 29, 2014
Dissertation Director: Gary D. Thompson
3
STATEMENT BY AUTHOR
This dissertation has been submitted in partial fulfillment of requirements for an
advanced degree at the University of Arizona and is deposited in the University
Library to be made available to borrowers under rules of the Library.
Brief quotations from this dissertation are allowable without special permission,
provided that accurate acknowledgment of source is made. This work is licensed
under the Creative Commons Attribution-No Derivative Works 3.0 United States License. To view a copy of this license, visit http://creativecommons.org/licenses/bynd/3.0/us/ or send a letter to Creative Commons, 171 Second Street, Suite 300,
San Francisco, California, 94105, USA.
SIGNED:
Thiagarajah Natchie Subramaniam
4
ACKNOWLEDGEMENTS
I would like thank my entire committee members and my family for helping me in
various ways to complete this dissertation. Writing this dissertation has been one of
the challenging experiences in my graduate career, and I thank everyone who helped
me in finishing my dissertation.
I would like to express my deepest appreciation to my advisor, Professor Gautam
Gowrisankaran for his excellent guidance, caring, patience, and most importantly
his time. I want to thank him for sharing his knowledge on electricity markets and
help me understand the big picture of my study. I am also grateful to him for his
willingness to regularly discuss progress on my study during his sabbatical.
I would like to sincerely thank assistant professor Ashley Langer, for helping
me focus on important issues in my job market paper. Her guidance, insightful
comments and lengthy discussions were paramount in completing my dissertation.
I would like to also thank assistant professor Derek Lemoine for his guidance and
his timely comments. I am deeply grateful to him for the long discussions that
helped me sort out the technical details of my work. I am grateful for guidance and
comments received from Professor Gary Thomson and Professor Reynolds throughout my dissertation work. I am indebted to the Department of Agricultural and
Resource Economics at Arizona for the graduate funding and continuous support
provided since 2007.
Last but not least, I would like to thank my parents, my brother, my wife and
my son for helping me in various ways.
5
DEDICATION
. . .Dedicated to my family
6
TABLE OF CONTENTS
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
CHAPTER 1 Market Power Mitigation in Electricity Markets
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Market Power and Electricity Auctions . . . . . . . . .
1.3 Previous Literature in Market Power and Withholding
1.4 NYISO and Locational Market Power . . . . . . . . . .
1.5 Market Power and Mitigation . . . . . . . . . . . . . .
1.5.1 Market Power Mitigation of Non-Pivotal Firms
1.5.2 Market Power Mitigation of Pivotal Firms . . .
1.5.3 Empirical Relevance . . . . . . . . . . . . . . .
1.6 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.6.1 Supplier Bid Data . . . . . . . . . . . . . . . . .
1.6.2 Marginal Cost Estimates . . . . . . . . . . . .
1.6.3 Mitigation Thresholds . . . . . . . . . . . . . .
1.7 Empirical Analysis . . . . . . . . . . . . . . . . . . . .
1.7.1 Analyzing Markup above Reference Prices . . .
1.7.2 Simulation of Market Outcomes . . . . . . . . .
1.7.3 Market Simulation Algorithm . . . . . . . . . .
1.7.4 Results from Regression Analysis . . . . . . . .
1.7.5 Market Simulation Results . . . . . . . . . . . .
1.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . .
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12
12
14
15
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30
31
32
34
35
CHAPTER 2 Supply Function Equilibrium and Power Market Outcomes
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Supply Function Equilibrium . . . . . . . . . . . . . . . . . . . . .
2.2.1 Supply Function Equillibrium Model . . . . . . . . . . . .
2.2.2 Empirical Supply Function Equilibrium . . . . . . . . . . .
2.2.3 Conservative Bidding Equilibrium . . . . . . . . . . . . . .
2.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4 Analysis of Observed Offer Curves . . . . . . . . . . . . . . . . . .
2.5 Analysis of Constructed Offers . . . . . . . . . . . . . . . . . . . .
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49
49
51
52
55
59
60
61
62
7
TABLE OF CONTENTS – Continued
2.6
2.5.1 Comparison of Constructed Offers and Actual Offers . . . . . 63
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
APPENDIX A Sample Appendix .
A.1 Reference Price Calculation
A.2 Margin Variable: Dependent
A.3 Pivotal Firms . . . . . . . .
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Variable
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For Regressions
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74
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76
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
8
LIST OF FIGURES
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
Residual Demand Curves . . . . . . . . . .
Locational Marginal Pricing . . . . . . . .
Aggregate Supply Curve . . . . . . . . . .
Non Pivotal Residual Demand Curves . . .
Pivotal Residual Demand Curves . . . . .
Mitigation Thresholds for Peaking Units .
Profit Maximization Under NYISO Rule .
Baseload vs. Peak Load Offers . . . . . .
Residual Demand Curves after Simulation
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43
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48
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
Expost Optimal Function . . . . . . . . .
Construction of Optimal Supply Curve . .
Optimal Offer . . . . . . . . . . . . . . . .
Residual Demand Curves - Simulation . .
Optimal vs. Actual Offers . . . . . . . . .
Actual vs. Conservative Offer Curves . . .
Quantity Weighted Offer Price - By Hours
Offers from Similar Units . . . . . . . . . .
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66
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A.1 Actual Offers from Selected Intermediate Load Units . . . . . . . . . 77
A.2 Load Pocket Definitions for NYC . . . . . . . . . . . . . . . . . . . . 77
A.3 Pivotal Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
9
LIST OF TABLES
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
1.10
1.11
1.12
1.13
1.14
1.15
Summary of Market Performace . . . . . . . . . . . . . .
Choice of Mitigation Regimes . . . . . . . . . . . . . . .
Generating Firms in the NYC . . . . . . . . . . . . . . .
Cost-Based Breakdown of Units in the NYC . . . . . . .
Description of Variables . . . . . . . . . . . . . . . . . .
Testing Low-Cost Peaking Unit Behavior . . . . . . . . .
Testing Low & High-Cost Peaking Unit Behavior . . . .
Testing High Cost Unit Behavior with ConEd Units . . .
Summary Statistics . . . . . . . . . . . . . . . . . . . . .
Simulation Results for the Astoria East I . . . . . . . . .
Simulation Results for the Astoria East II . . . . . . . .
Simulation Results for the Greenwood/Vernon I . . . . .
Simulation Results for the Greenwood/Vernon II . . . . .
Simulation Results with Market Structure Changed . . .
Comparision of Divested & Non-Divested Peaking Units
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36
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2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
2.10
Generating Firms in the NYC . . . . . . .
Average Markup of Suppliers . . . . . . .
Markups - Optimal Offers . . . . . . . . .
Markups - Conservative Offers . . . . . . .
Comparison of Offers- USPG (2010) . . . .
Comparison of Offers- NRG (2010) . . . .
Comparison of Offers- Ravenswood (2010)
Surplus Comparison- USPG (2010) . . . .
Surplus Comparison- NRG (2010) . . . . .
Surplus Comparison- Ravenswood (2010) .
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67
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10
ABSTRACT
Market power mitigation has been an integral part of wholesale electricity markets
since deregulation. In wholesale electricity markets, different regions in the US
take different approaches to regulating market power. While the exercise of market
power has received considerable attention in the literature, the issue of market power
mitigation has attracted scant attention. In the first chapter, I examine the market
power mitigation rules used in New York ISO (Independent System Operator) and
California ISO (CAISO) with respect to day-ahead and real-time energy markets. I
test whether markups associated with New York in-city generators would be lower
with an alternative approach to mitigation, the CAISO approach. Results indicate
the difference in markups between these two mitigation rules is driven by the shape
of residual demand curves for suppliers. Analysis of residual demand curves faced by
New York in-city suppliers show similar markups under both mitigation rules when
no one supplier is necessary to meet the demand (i.e., when no supplier is pivotal).
However, when some supplier is crucial for the market to clear, the mitigation rule
adopted by the NYISO consistently leads to higher markups than would the CAISO
rule. This result suggest that market power episodes in New York is confined to
periods where some supplier is pivotal. As a result, I find that applying the CAISOs’
mitigation rules to the New York market could lower wholesale electricity prices by
18%.
The second chapter of my dissertation focuses on supply function equilibrium. In
power markets, suppliers submit offer curves in auctions, indicating their willingness
to supply at different price levels. Although firms are allowed to submit different
offer curves for different time periods, surprisingly many firms stick to a single
offer curve for the entire day. This essentially means that firms are submitting a
single offer curve for multiple demand realizations. A suitable framework to analyze
11
such oligopolistic competition between power market suppliers is supply function
equilibrium models. Using detailed bidding data, I develop equilibrium in supply
functions by restricting supplier offers to a class of supply functions. By collating
equilibrium supply functions corresponding to different realizations of demand, I
obtain a single optimal supply function for the entire day. Then I compare the
resulting supply function with actual day-ahead offers in New York. In addition to
supply function equilibrium, I also develop a conservative bidding approach in which
each firm assumes that rivals bid at marginal costs. Results show that the supply
functions derived from equilibrium bidding model in this paper is not consistent
with actual bidding in New York. This result is mainly driven by the class of supply
functions used in this study to generate the equilibrium. Further, actual offers do
not resemble offers generated by the conservative bidding algorithm.
12
CHAPTER 1
Market Power Mitigation in Electricity Markets
1.1 Introduction
Deregulation of electricity markets began in the mid-1990s with the Federal Energy
Regulatory Commission (FERC) order that required utilities to make transmission
lines available to Independent Power Producers (IPP). The underlying principle of
deregulation is to promote competition at the wholesale electricity market in order
to reduce long run electricity prices. Deregulated markets often, in fact, still use
some regulation to reduce the exercise of market power. Despite regulatory efforts to
achieve efficiency, the potential to exercise market power has always been a subject
of debate in electricity markets (Borenstein et al. (1995)).
Against the backdrop of opportunities to engage in anti-competitive behavior,
ISOs have taken reasonable measures to mitigate conduct that would distort competitive outcomes. Most deregulated electricity markets now have market power
mitigation mechanisms. In principle, the underlying set of rules that are used to
design these mitigation mechanisms can be categorized into two groups. The first
group is comprised of Independent System Operators (ISO’s) in which market power
mitigation is based on a conduct-impact framework whereby mitigation is only triggered if the conduct of the supplier is observed to cause substantial price increases.
Two prominent ISO’s in this category are the New York ISO (NYISO) and the New
England ISO (NEISO).1
The second group is comprised of ISO’s implementing rules based on market
structure and only mitigate when a supplier becomes crucial to meet demand.
Prominent ISO’s in this category are the California ISO (CAISO) and the Pennsyl1
Substantial would mean increasing the price as high as $100 above competitive level in some
cases
13
vania, New Jersey, Maryland (PJM) Interconnection. The fundamental differences
in these two sets of rules should lead to different market outcomes depending on the
structure of the wholesale electricity market. In other words, the market structure
and the market composition stands out as important variables in choosing between
these two mitigation mechanisms.
It is surprising that market power mitigation rules in electricity markets have
received scant in the literature, given these rules artificially suppress market clearing
prices. This study is the first attempt to understand how these different market
power mitigation rules will perform in different markets. In this paper, I test whether
market outcomes in the New York electricity market would be different, with an
alternative set of rules, namely, the CAISO rules for mitigating market power based
on market structure, to mitigating market power.
I approach this empirical question, starting with a detailed description of rules
that govern the market power mitigation in wholesale electricity markets. Then I
develop qualitative predictions about market performance under NYISO and CAISO
market power mitigation measures. Later, I use a detailed unit-level bid data from
the New York market and simulate market outcomes by changing the market power
mitigation rules 2 .
I find that the wholesale prices in the New York market would be 18% lower under
the CAISO mitigation rule. In the New York market, most of the market power
episodes occur when one or more suppliers are crucial to meet the demand. This
essentially means that the residual demand curve, for those indispensable suppliers,
is perfectly inelastic at some level. Therefore, suppliers subject to the NYISO rules
can increase the prices to just below mitigation thresholds. In contrast, CAISO
rule would mitigate offers from such vital suppliers to marginal cost estimates. This
reduces the wholesale prices under CAISO during pivotal periods.
Counterfactually, I also show that CAISO rule would lead to higher markups,
when a supplier faces an inelastic residual demand curve, but its capacity is not
necessary to meet the demand. In this case, market power mitigation will not
2
Unit-level means generator-level
14
be invoked under CAISO rule, and suppliers can bid up to price caps. Overall,
performance of mitigations rules are closely tied to the shape of residual demand
curves. The shape of the residual demand curve faced by suppliers in the market
is primarily determined by the aggregate supply curve in the market. For example,
a supplier in a market with abundant base load capacity is less likely to face steep
residual demand curves. Moreover, capacity additions and retirements in the market
can affect the performance of market power mitigation rule significantly.
1.2 Market Power and Electricity Auctions
Market power in wholesale electricity markets is fundamentally driven by lack of
demand response. Accordingly, the ability of a supplier to raise prices depends on
residual demand curves.3 In figure 1.1, I exemplify the relationship between residual
demand and market power. The top row in figure 1.1 shows the demand and supply
from other(rival) firms in the market. The bottom row shows the corresponding
residual demand curves for respective cases presented in the top row. I note that
the residual demand curve presented in the right panel in figure 1.1 is perfectly
inelastic at high prices. This is an extreme case where the supplier is pivotal, and
the demand cannot be met without the supplier making its capacity available. In
this case, the supplier can increase the market clearing prices to the price cap.
Among other things, the incentive to raise prices originates from the auction
format used in electricity markets. Most electricity markets use multiunit-uniform
price auctions for energy transactions in the day-ahead and real-time market. Under
uniform price auctions, winners will receive a uniform price, the market clearing
price, for supplying energy. This format of auction provides strong incentives to
raise market clearing prices because both marginal and infra-marginal units will
receive the same price regardless of marginal costs.
Suppliers that have the ability and incentive to exercise market power can raise
market clearing prices, by offering some or all of their capacity at higher prices.
3
Residual demand curves show the level of demand response in the market in the absence of
suppliers capacity
15
This practice is known as economic withholding in electricity markets.
1.3 Previous Literature in Market Power and Withholding
The issue of withholding has received much attention in CAISO4 and recently in
ERCOT5 markets. Evidence from San Diego Gas Electric Company v. Sellers of
Energy and Ancillary Services into CAISO suggest that the California generators
engaged in significant levels of withholding from the CAISO real-time energy market
over significant periods of time from January 1, 2000 through June 20, 2001. Even
after accounting for reported outages, aggregate withholding by those generators
averaged over 1000 MW per hour during on-peak hours from May-September 2000.6
In 2007, New York electricity provider Con Edison asserted that economic withholding in the wholesale market costs New York customers approximately $157
million annually.7 This is likely to be credible evidence for market power because
Con Edison owned more than half of generating capacity in New York before deregulation..
Auctions in electricity markets have received substantial attention in the literature. Green and Newbery (1992) analyze the competition in British electricity
spot market. They find evidence for high markups above marginal costs. Similarly, von der Fehr and Harbord (1993) analyze uniform price auctions and find
evidence for high average prices. Interestingly, Harvey and Hogan (2001)) argue
that ”economic withholding arises not from the exercise of market power, but from
the efficient operation of the electric system.”
Wolfram (1997) finds evidence for strategic bidding in the England and Wales
electricity pool. She finds that suppliers that are likely to be used after other suppliers, large suppliers and suppliers with large inframarginal capacity bid higher. Borenstein and Bushnell (1999) find substantial evidence for market power in CASIO when
fringe supplies are limited. Wolak et al. (2003) analyzes the ability and incentives
4
California Independent System Operator
Electricity Reliability Council of Texas
6
http://www.ferc.gov/whats-new/comm-meet/2011/042111/E-18.pdf
7
http://pulpnetwork.blogspot.com/2007/03/in-papers-filed-in-early-2007-with.html
5
16
to engage in withholding and shows that withholding incentives can be altered by
firms’ forward contract positions. Brandts et al. (2013) show how pivotal firms in
electricity markets can raise prices.
Given the opportunities to exercise market power, various methods to identify
and mitigate anti-competitive conduct is already in place. Nevertheless, only a
handful of studies look at the effect of such market power mitigation activities. Entriken and Wan (2005) study the impact of automated mitigation process in the New
York market. They find that mitigation significantly reduces market clearing prices
in cases where the prices would have reached the price cap. Kiesling and Wilson
(2007) analyze the automated mitigation process (AMP) using human subjects in
laboratory experiments. They do not find evidence for significant price reduction in
the long run compared to markets without AMP. Finally, Shawhan et al. (2011), a
recent experimental study finds evidence for reference creep, a phenomenon where
firms systematically bid high to raise the reference levels.
1.4 NYISO and Locational Market Power
New York Independent System Operator (NYISO) manages the state’s grid and
operates the wholesale electricity markets. NYISO covers approximately 10,892
miles of transmission lines with aggregate supply reaching over 38,190 MW. In 2009,
wholesale market transactions totaled more than $75 billion.8
In NYISO, gas and coal powered generators account for more than 37% of the
generation. Gas powered and combined cycle generators largely determine the market clearing prices during peak periods. Generators usually sell energy via forward
contracts to buyers, and approximately 45% of the energy produced is sold through
such contracts. These are contracts between power generating companies and load
serving entities, which can be retail electricity providers, municipally owned utilities
and cooperatives. The day-ahead market accounts for 51% of the energy transactions while the real time market accounts only 4%. Therefore, analyzing the day
8
HTTP://www.ferc.gov/industries/electric/indus-act/rto/metrics/nyiso-rto-metrics.pdf
17
ahead market could potentially provide answers to withholding questions.
NYISO is a pioneer organization that adopted locational marginal pricing (LMP)
in 1999. Under the LMP, the price of energy at each location in the NY transmission
system is equivalent to the cost of supplying the next increment of load at that
location. The LMP includes the price of energy, congestion costs and transmission
losses. The LMP will be same at every location in the grid if transmission constraints
do not bind, given losses are zero. Figure 1.2 depicts a case where two load zones
constrained by a transmission limit of 300 MW. In principle, the generators in
the west zone should be able to supply the total demand at marginal cost of $ 6.
However, transmission constraints will require the supplier in east zone to supply
100 MW at $10. Therefore, the LMP is in the east zone will be $ 10 while the LMP
in the west is $ 6.
Moreover, transmission constraints can geographically isolate one region from the
other. When transmission constraints bind, local generators will often be dispatched
in out-of-merit order from the supply schedule, to relieve congestion. This will
provide local generators within the region the ability to exercise market power.
Therefore, transmission constraints may provide incentives to markup substantially
above marginal costs. This notion of market power is referred as locational market
power, and this is the form of market power that regulators are most concerned
about in current markets.
The New York market can be geographically separated into eleven zones, and
transmission congestions can lead to different LMPs across the zones. Out of the
eleven, New York City (NYC) accounts for most of the congestion. The interfaces
bringing power into NYC are frequently congested due to high demand. Therefore,
NYC is considered as a ”load pocket” in the New York wholesale market, referring
to an area where peak demand often exceeds the transmission capacity. As a result,
generators in these load pockets will have more opportunities to exercise market
power. Dr.David Patton of Potomac Economics, an independent market monitor
to the NYISO, claims ”Vast majority of market power in the current wholesale
electricity markets is locational in some regard”( Patton (2010)).
18
Dr.Patton further claims that in the absence of transmission congestions firms’
ability to raise system-wide market clearing price is limited. This essentially means
that the aggregate supply is flat for the most part in the New York market, and
any withheld capacity will be replaced by a supplier with similar costs. Figure 1.3
illustrates a supply curve from the New York market for a peak hour, in which the
aggregate supply is flat up to 40000MW. This clearly shows that even when the
system demand is at peak, approximately 31000MW
9
firms ability to manipulate
the price across the market is extremely limited. In short, firms could exercise
market power only when the system is congested, and if the supply from the firm is
crucial to relieve congestion.
1.5 Market Power and Mitigation
The goal of the section is to identify factors that influence bidding behavior under
each of the two common types of mitigation rules. Two factors that stand out
throughout the analysis are the notion of pivotal suppliers and the shape of the
residual demand curve. I start out the section with a discussion on pivotal firms.
Pivotalness refers to an extreme case of market power where the market will
not clear without the supply of a particular firm. Such firms are called pivotal
firms in electricity markets. Brandts et al. (2013), find evidence for pivotal suppliers
exercising market power. Another way to see pivotality is through residual demand
curves in figure 1.1.10 It should be emphasized that, for pivotal firms, residual
demand curves will be inelastic above some prices.
In this section, I compare market power mitigation mechanisms adopted by
New York and California electricity markets. The market power mitigation logic
associated with New York market (Hereafter New York rule) is sometimes referred
as the conduct-impact framework. Under this framework, supply offers are mitigated
only if those offers lead to substantial price distortions. In this mitigation process,
9
All time peak for the NYISO is 33,035mw in 2006. Source: http://www.ferc.gov/marketoversight/mkt-electric/new-york.asp Accessed on 03/16/2013
10
See Appendix for a detailed illustration on residual demand curves and pivotal firms
19
supply offers that exceed preset thresholds, known as conduct thresholds, by the
regulator will be screened for potential price impacts11 .
The screening process will compare market clearing prices from the original offers
to the mitigated offers. The market clearing price using mitigated offers serves as a
benchmark for the competitive prices. All offers exceeding conduct thresholds will
be mitigated if the market clearing prices are larger than competitive prices by a
$100. This $100 threshold above the competitive price can be seen as a mitigation
threshold.
To the contrary, the market power mitigation in CAISO and PJM (Hereafter California rule) is based on the structure of the market. Under structural mitigation,
individual transmission paths are evaluated for its competitiveness using pivotal
supplier tests
12
. Failure of pivotal supplier tests would result in the mitigation of
suppliers that can relieve congestion on respective transmission paths. Although,
there are differences in how structural mitigation is implemented in CAISO and
PJM, the fundamentals behind the mitigation procedure would lead to the mitigation of pivotal suppliers in most cases. For example, in figure 1.1, only the case
presented in the rightmost panel would warrant mitigation under California rule.
Under both types of mitigation rules, offers(bids) from suppliers that warrant
mitigation would be replaced by marginal cost estimates by the regulator. These
marginal cost estimates are referred as reference prices or default bids. In the
following subsections, I describe how structural versus conduct-impact mitigation
rules affect bidding behavior.
1.5.1 Market Power Mitigation of Non-Pivotal Firms
This is a case where the firm is not essential to meet the demand. Since non-pivotal
firms are not subjected to market power mitigation under California rule, the ability
to raise prices depends on the shape of the residual demand curve. In Figure 1.4, I
11
Conduct thresholds are usually a factor of reference price. Reference prices are historical
averages of accepted offer prices, and these are unit specific indices
12
Pivotal supplier test checks whether there is sufficient capacity to meet the demand when three
suppliers in the given transmission path, jointly withholds their capacity
20
compare the ability of a firm to raise prices under two different cases. The plot in
the left panel depicts a case where the residual demand curve is inelastic. Accordingly, under New York rule, prices cannot be higher than mitigation thresholds, a
$100 above competitive levels. Supply offers leading to prices above the mitigation
threshold will result in the mitigation of offers to reference prices. However, under
California rule, the firm has no limitations in choosing the profit maximizing offer
curve. This may result in high prices above competitive levels under California rule
if residual demand curves were very inelastic.
The plot in the right panel depicts a case where the firm faces a very elastic
residual demand curve. The markups will be low and the mitigation threshold,
under New York rule, may not even bind. In this case, both New York and California
rules would lead to identical monopoly markups.
Overall, the markup under California rule should yield monopoly markups on
residual demand when the firm is non-pivotal. As a result, California rule may lead
to very high markups when the residual demand curve is relatively inelastic. However, under New York rule, markup will always be capped at $100 above competitive
levels. This essentially means that wholesale prices would be lower under New York
rule during non-pivotal periods compared to California rule.
1.5.2 Market Power Mitigation of Pivotal Firms
The term pivotal firm means that the firm is crucial for the market to clear. Under
California rule, all offers from a pivotal firm will be mitigated to a competitive
benchmark. This would eliminate firms’ incentive to raise the market clearing prices.
Therefore, in the pivotal case California rules should always lead to competitive
outcomes.
Under New York rule, pivotality does not provide additional market power. The
firm has to choose offers curves such that the resulting prices are not greater than
mitigation thresholds. The only noticeable difference is that mitigation thresholds
may bind more often when the firm is pivotal. This depends on precise position
21
and slopes of the residual demand curves and marginal cost curves. In figure 1.5, I
provide an illustration of residual demand curves when the firm is pivotal.
For the pivotal supplier in the left panel, the mitigation threshold may not bind,
even if the firm chooses monopoly prices. In this case pivotality does not influence
markups. The case presented in the right panel is interesting. The mitigation
threshold binds for the price setting unit, at the inelastic part of the residual demand
curve. In this case, the firm will choose to bid the price setting unit at threshold
prices. This result may depend on the precise position of residual demand curves
and the marginal cost curves.
1.5.3 Empirical Relevance
Table 1.1, summarizes the performance of market power mitigation rules, in terms
of markups, under pivotal and non-pivotal cases. There does not seem to be a clear
winner between these mitigation rules as California rule leads to lower markups
when firms are pivotal, while New York rule leads to lower markups when the firms
are not pivotal. Hence, the choice between these two regimes should be determined
based on market characteristics. For example, a market that has frequent pivotal
episodes may lead to lower markups under California rule, given elastic residual
demand curves in non-pivotal periods. Similarly, New York rule would lead to lower
markups in markets with low incidence of pivotal episodes.
In table 1.2, I summarize the conditions under which one rule is preferred over
the other. Table 1.2 brings an important empirical dimension to the choice of
mitigation rule. Given the number of factors that might influence the performance
of a mitigation rule, an empirical approach is necessary to assess the performance
of these mitigation regimes in any market.
Using facts presented so far, I can develop predictions about the residual demand
curves and pivotalness in any market, using supplier offers. For example in the
New York, low-cost firms should bid closer to marginal costs if residual demand
curves are sufficiently elastic. Further, high-cost units should bid near mitigation
thresholds, $100 above reference prices, if they expect to be marginal units when
22
pivotal, see right panel in figure 1.5. Therefore, analysis of supplier offers may
provide insights into residual demand curves and pivotalness. This in turn, would
allow us to compare market performances under alternate mitigation regimes.
In a broad sense, under New York rule firms could increase the prices up to a
$100 from competitive levels. However, the ability do so depends on whether or not
mitigation thresholds bind under the New York rule. Theoretically these mitigation
thresholds should bind if residual demand curves faced by firms are inelastic. Therefore, first I need to test whether these mitigation thresholds bind for suppliers in
the New York. If residual demand curves are inelastic only during pivotal periods,
the California rule should lead lower markups over the New York rule. However,
it would be difficult to comment on market performance if residual demand curves
are inelastic during both pivotal and non-pivotal periods. Therefore, an empirical
approach is necessary to evaluate the market performance.
1.6 Data
In order to identify market power and compare the performance of market power
mitigation rules, I need data on supplier bids, and a measure of marginal costs.
I obtain data on supplier bids from the NYISOs’ Market & Operations website13 .
The challenging part is estimating marginal costs for different types of units. I
take two different approaches to estimating the marginal costs in this study. In the
first approach, I try to estimate a proxy for the marginal costs that the ISO uses
for its mitigation activities. This proxy measure is referred to as reference prices
in electricity markets. In the second approach, I estimate marginal costs using
engineering parameters.
1.6.1 Supplier Bid Data
I construct a detailed generator-level bid data using publicly-available information
from the NYISO’s market and operations website. Due to proprietary restrictions,
13
http://mis.nyiso.com/public/P-27list.htm
23
the identities of individual units are masked in data releases. Nevertheless, these
masked IDs can be linked to known generators in the New York market by cross
referencing to publicly available ISO documents. Using this method I was able to
identify 126 generators in New York City.
The focus of the study is limited to generators within New York City (NYC)
because transmission constraints in NYC can lead to severe locational market power.
In NYC, when demand exceeds 7500 megawatts, transmission constraints can limit
the flow of electricity to certain locations within the city. Firms with generators in
such constrained areas can generate substantial market power.
The bid data obtained from the ISO is at the unit level14 . In order to identify
market power, these individual units have to be linked to the plant it belongs to15 .
To be able to do this, I turn to EIA (Energy Information Administration) data sets
that provide detailed inventory of units, plants and firms.
In Table 1.3, I provide a summary of generating assets in NYC, aggregated at
the firm level. I note that approximately 95% of the generating capacity is owned
between six large firms. Out of the six firms, Consolidated Edison Co (ConEd) and
New York Power Authority (NYPA) are net buyers from the wholesale electricity
market in most periods. These net buyers from the market are less likely to engage
in anti-competitive activities that would raise market clearing prices. This brings
three independent power producers NRG marketing, Astoria Generating CO and
TC Ravenswood to the spotlight of this study.
Column 4 in table 1.3 indicates the firm that owned the generating asset before
year 2000. ConED of New York was a vertically integrated utility before year 2000
and owned almost the entire capacity within NYC. Upon deregulation ConED was
required by the Federal Energy Regulatory Commission (FERC) and New York
Public Service Commission (NYPSC) to divest its assets to promote competition.
After several rounds negotiations with FERC, 5500 megawatts of ConEd assets were
divested in 1999, and the market mitigation rights were vested to ConEd until year
14
15
Generator level. Units and generators are synonymous
In the industry, plants are sometimes referred as portfolios
24
2003.16
In table 1.4, I provide a cost-based breakdown of units in NYC. In NYC, intermediate load units are the least expensive ones and the FO2/KER units are the
most expensive ones17 . I emphasize the fact that all FO2/KER units were originally installed by the ConED, in the 1970s, and these units are identical in many
aspects. In Table 1.15, I provide a summary of FO2/KER units owned by ConED
and IPPs. These high-cost peaking units seem to be similar in many aspects across
firms.. Hence, I expect the operating costs of FO2/KER units to be similar, if not,
identical across firms.
For this study, I use day-ahead and hour-ahead bidding information from individual units-over the period 2009 to 2011. The bidding data is at the hourly level,
and the data contains over 700,000 hourly observations. Hourly load data, hourly
locational prices, and the cost of delivered fuel data is obtained from the NYISO.
The data on cost of delivered fuel varies only at monthly level.
1.6.2
Marginal Cost Estimates
In the first approach to calculate marginal costs, I try to replicate the method used
by ISOs. Accordingly, I take the average of a generators’ (units’) accepted bids over
the last ninety days to create a proxy for marginal costs. This measure, in fact, is
called the reference prices in electricity markets.18 The idea behind this measure
is that it captures the hidden costs associated with keeping the generator online,
in addition to marginal fuel costs19 . In principle, the reference price is a historical
average of accepted offers. However, in circumstances where there are not sufficient
accepted bids in the last ninety days, reference prices may be worked out using
engineering estimates. See Appendix for details on reference prices.
In the alternative approach, I use the conventional method for calculating
16
Divested assets are now owned by Astoria Generating Co, TC Ravenswood and NRG
FO2: Petroleum Oil, KER: Kerosene
18
Reference prices are also known as default bids
19
Hidden costs may include startup costs, minimum load costs, outage risks, etc. See Harvey
and Hogan (2001)
17
25
marginal cost in electricity literature( Joskow and Kahn (2001)). In which I use
data on heat content of the fuel and data on heat rates to estimate the marginal
cost. Data on delivered fuel cost is obtained from the NYISO monthly reports20 .
Heat rate data was calculated using data from Clean Air Markets Data (CAMD)
database. The CAMD provides hourly generation and heat input at the unit level,
which allows me to calculate heat rate at the unit level21 .
1.6.3 Mitigation Thresholds
Mitigation thresholds vary with competitive prices. Given the demand, competitive
prices can be obtained by substituting all supply offers with reference prices. Since
mitigation thresholds vary with competitive prices, for baseload units I cannot tie
mitigation thresholds to reference prices.
Fortunately, for peaking units I can provide an upper bound for mitigation
thresholds based on reference prices. In figure 1.6, I provide two cases where the
firm faces an inelastic residual demand curve. This means that the firms have the
ability to raise prices. In both cases, the mitigation thresholds for peaking units
cannot be larger than $100 from reference prices for the peaking units.
1.7 Empirical Analysis
This section is organized into two parts. First I provide evidence on how firms
in NYC exercise market power. To this end, I show that some units–peakers–
bid closer to mitigation thresholds. Afterwards, I simulate New York in-city market
using CAISO mitigation rules and compare market outcomes. In the simulation, I let
firms choose supply functions and compete against other firms until an equilibrium in
supply functions is reached. Finally, I change the market composition hypothetically
by introducing more peaking capacity and test market performance under NYISO
20
Heat rates are conversion factors between fuel heat content and net generation from the unit.
Heat rate measures the efficiency at which heat is converted into power
21
Interestingly CAMD also provides a way to figure out which fuel is being used in a particular
hour via emissions
26
and CAISO mitigation rules.
1.7.1 Analyzing Markup above Reference Prices
First, I check whether firms in NYC are bidding near mitigation thresholds
22
. In
order to carry out this exercise, I create a variable that measures the margin between
offers(bid prices) and the reference prices23 . If firms are systematically bidding near
mitigation thresholds, this measure would be closer to $100. See Appendix for actual
bids and the calculation of this variable.
I note that the validity of this measure depends on how precisely reference prices
are calculated. If estimated reference prices are systematically different from the
actual reference prices, the margin variable is likely to be biased. Since reference
price calculations are based on previous accepted offers from the unit, I can precisely
estimate reference prices for units that are frequently dispatched (offers accepted
and called to produce). Therefore, reference price estimates on intermediate load
units and low-cost peakers (NG Based) should be precise because these units are
frequently dispatched.
For high-cost peakers (FO2/KER based) data on accepted bids are sparse.
Therefore, ISO relies on engineering parameters of the unit to develop reference
prices. Since engineering data on units are proprietary and not available for public,
I have to use another method to calculate reference prices of high-cost peakers. A
recent data release by the Clean Air Market Data (CAMD), reports hourly generation and emissions24 at the unit level. This allows me to calculate heat rates, a
measure of heat to energy conversion, at the unit level. I use heat rates and cost
data on delivered fuel, from the ISO, to calculate marginal fuel costs for high-cost
peakers.
In the first set of tests I only use intermediate load units and low-cost peakers
(NG based) because these units are frequently dispatched. Another reason for using
22
$100 above the reference price of the price setting bid under marginal cost bidding/reference
price bidding
23
margin = (bid price − ref erence price)
24
using CO2 emissions, we can clearly identify which fuel is being used
27
only intermediate and low cost peaking units is that these units are, in most cases,
powered by natural gas. In contrast, high cost peakers either use petroleum oils or
kerosene in combination with natural gas.
In regressions, intermediate load units also serve as a control for fuel price shocks.
In electricity markets, the price of delivered fuel can be very different from spot
prices. Moreover, the EIA only reports the prices of delivered fuel at the monthly
level. This makes it difficult to control for daily fuel price shocks. Fortunately, intermediate load units in most cases offer their capacity at near marginal costs because
these units need to run for longer periods to cover fixed costs such as startup costs.25
Therefore, any changes in offers by intermediate load units should only reflect fuel
price shocks. This fact is evident from Figure 1.8, which compares offers across different types of units. Figure 1.8 shows average offer prices from intermediate load
units, low-cost peaking units and high cost peaking units belonging to Astoria Co.
and NRG Marketing. Notice the similarity in offer prices by intermediate load units
and low-cost peaking units that is largely driven fuel price shocks.
I regress the margin variable on covariates in the following random effects specification.
M arginit = Xit β+
X type
type
β2
Ditype +
X
load
β3load Dtload +
X X type,load
β4
Ditype Dtload +(αi +it )
type load
(1.1)
Where i indicates the unit and t indicates the time. I note that the time index for
the panel does not refer to a particular hour. Instead there are 365 × 24 unique time
indices for each year.26 type indicates whether a given unit is a low-cost peaking
unit, high-cost peaking unit or intermediate load unit. load indicates the demand
25
Further intermediate load units are not designed for sudden ramp up and ramp down of
generation. Therefore, these units usually operate at constant production levels for longer period.
26
In the data, there are hourly supply offers from generators on a daily basis. This means that
there are different offers for the same hour every day. Hence, if we use hour as the time index; it
will lead to repeated time observations within the panel
28
for electricity at the zonal level in a given hour. D denotes a dummy variable, for
example, Dtype is a dummy variable indicating the type of unit.
From these regressions, I intend to test whether firms are able to increase offer
prices on low-cost peaking units and successfully raise the market clearing prices.
In this case, I expect βtype=low cost peaker to have a coefficient larger than 100 if lowcost units were bidding closer to mitigation thresholds. My hypothesis is that if
the residual demand curve faced by the firm is sufficiently inelastic; a firm can
increase the offer prices on its marginal units, regardless of the type of marginal
unit. However, if the residual demand curve is elastic the firms’ ability to raise
prices will be limited to high cost units.
Next I turn to the high-cost peaking units. I estimate a version of the regression
specified in equation (3). I calculate the margin variable for high-cost peakers by
subtracting the marginal fuel costs27 from offer prices (bid prices). There are few
caveats in this specification. First, there is no way to control for fuel price shocks
for the high-cost peakers (KER/FO2 Based) because the control group in this specification, intermediate load units, is powered by natural gas. Fuel price shocks for
the high-peakers will only be controlled to the extent that natural gas prices and
other petroleum oil (FO2/Ker) prices move in the same direction.
Second, the actual references prices for high-cost peakers will be higher than the
marginal fuel costs. The ISO and the firm agree on having a reference price that is
higher than marginal fuel costs to cover other costs associated with production28 .
Since I use marginal fuel costs to proxy for reference prices, I expect my estimates to
be biased upwards. Regardless I estimate the regression to see if high cost peakers
are bidding closer to mitigation thresholds.
In an attempt to strengthen my analysis on high-cost peakers further, I exploit
the ConEd divestiture in the year 1999. ConEd divested its assets in 3 segments,
and each segment had multiple portfolios of intermediate load and peaking units.
Upon divestiture, ConEd was left with few intermediate load units and some high27
28
margin = bid − marginal cost. Marginal costs are calculated from the CAMD datasets
Some ISOs use a 10% adder on top of marginal costs to calculate reference prices
29
cost peaking units in NYC. There are striking similarities between peaking units
that were divested and the ones that are currently held by ConEd. These high cost
peakers match on their make, installation year,29 capacity, fuel type and heat rates.
This leads me to believe that the reference prices of divested units and the ones that
are still owned by ConEd should be similar, if not identical.
What makes this even more appealing is that ConEd is a net buyer from the electricity market. This essentially means that ConEd would prefer wholesale prices to
be low, and has no incentive to raise market clearing prices. Therefore, I exploit the
difference in offer prices between ConEd and Independent Power Producers (IPPs),
on identical high-cost peakers. I use the following random effects specification in
the estimation.
bidpriceit = Xit β+
X type
β2
type
Ditype +β3 DiConEd +
X type
β4
Ditype DtConEd +(αi +it ) (1.2)
type
There are few issues associated with this specification. First, to the extent that
CondD divested the inefficient peakers and held onto the efficient ones, the estimates
are going to be biased upwards. In order to control for the efficiency, I use heat rate
data calculated from the CAMD data sets. Further, It seems unlikely that ConEd
could have acted strategically to hold on to efficient assets, as the divestiture was
overseen by the FERC and NYPSC. Further, peakers that ConEd continue to own
are isolated stand-alone peaking units. These peaking units were not geographically
tied to other portfolios, and it could be the case the it was not profitable for IPPs
to buy these in isolation. See Table 1.15 for a comparison of divested and nondivested high-cost peaking units. If my assumptions about ConEd were true, I
should have βtype=high cost peaker,ConEd equals negative 100. This essentially means that
other suppliers, independent power producers, bid high-cost peaking units closer to
29
My discussions with ISO staff supports the notion that year of installation is highly correlated
with engineering parameters
30
mitigation thresholds.
The regressions on equation (1) and equation (2) will provide insights on how
firms bid under the NYISO mitigation regime. Fundamentally, firm behavior is
driven by the shape of the residual demand curve. If the residual demand curve is
inelastic for the most part, firms will bid substantially higher than reference prices
on all units. The regressions will allow me to form conjectures about the shapes of
the residual demand curves faced by firms in the market. However, using regressions
it is hard to comment about whether or not the mitigation thresholds were actually
binding. In addition, the principal goal of the paper is to discern market outcomes
for NYC generators under an alternative mitigation rule, the CAISO mitigation
rule. To conduct this exercise, I turn to a simulation approach where I let the firms
compete by choosing supply functions.
1.7.2 Simulation of Market Outcomes
This approach was first applied in Borenstein and Bushnell (1999) to identify market
power in the California electricity market. Borenstein and Bushnell (1999) use a
Cournot algorithm to simulate the market, where each firm responds to optimal
quantities chosen by rival firms. I use a different approach, in which I assume firms
respond to supply functions chosen by rival firms. This is a reasonable assumption
given the amount of publicly available data on supplier bids. According to Baldick
et al. (2004), supply function equilibrium models provide a reasonable representation
of electricity markets as firms are required to bid offer curves. Moreover, supply
function equilibrium models yield lower markups compared to Cournot models.
I simulate the market as a strategic game between three large suppliers in NYC.
Remaining suppliers in the market are either utilities or small firms, and assumed to
bid closer to reference prices (marginal costs). Since, NYC is not a geographically
isolated market; I need to account for imports and exports to figure out the demand
for electricity. Because the NYISO mandates that 80 % of NYCs’ demand has to
be met from the supply by firms within NYC, the amount of supply required from
in-city suppliers can be calculated net of imports.
31
The biggest challenge in this simulation is dealing with transmission congestions.
Transmission congestions can limit the amount of dispatchable supply even within
NYC. Therefore, I restrict my simulations to known sub markets(load pockets)
resulting from transmission constraints within NYC.
1.7.3
Market Simulation Algorithm
The algorithm involves two steps. First, I create the residual demand curve for a
firm in the market. In the second step, I let the firm choose the profit maximizing
supply(offer) curve. These two steps will be iterated for every firm in the market
until an equilibrium in supply functions is reached. In the process of iteration, I
impose the New York mitigation rule in the following way
1. For a given realization of demand, construct the aggregate supply curve using reference prices. And, calculate the competitive market clearing price,
Pcompetitive , under reference price bidding.
2. For a firm i in the market, discard its own supply from the aggregate supply
curve, and construct the residual demand curve
3. Grid search profit maximizing price-quantity pair for the firm i, not exceeding
NYISOs impact thresholds, see figure 1.7. This procedure will lead to marginal
cost(reference price) bidding as the residual demand curve becomes elastic
4. Reconstruct the supply curve for the firm i, by replacing the price obtained in
the previous step over its capacity as long as the price is greater than marginal
costs.
5. Reconstruct the aggregate supply curve by imposing changes to firm is offer
curve in step 4
6. For the firm j, repeat steps (1), (2), (3), (4)
7. Iterate until an equilibrium in supply functions is reached
32
8. Calculate the market clearing prices using the new supply functions
Imposing the California mitigation rule is straightforward. I check whether a
firm is pivotal, crucial to meet the demand, in the step 2, and mitigate the offers
to reference prices (marginal costs) if pivotal. When the firm is not pivotal, it
can choose the profit maximizing offer curve on its residual demand, in step 4.
This exercise of simulating the market will shed light on the performance of these
mitigation rules.
1.7.4
Results from Regression Analysis
The goal is to identify whether some units within the firm bid closer to mitigation thresholds under the NYISO mitigation rule. Table 1.6 reports the coefficients
from the specification in equation (1), which tests low-cost peaking unit behavior.
The coefficient on dummy variable Low Peak suggests that low-cost peaking units
bid closer to competitive levels. The magnitude of this coefficient shrinks significantly once I control for winter months, in column 3. In winter, electricity prices
are generally high due to high fuel prices. Moreover, the cost of delivered fuel in
winter varies substantially across plants. Therefore, the differences in magnitudes
across specifications on the dummy variable Low Peak can be explained by fuel price
changes in winter. Results from table 1.6 further lend support to the notion that
residual demand curves for suppliers in the in-city market are elastic for the most
part. This suggests that offers from low-cost peaking units would be low regardless
of the mitigation rule.
In table 1.7, I report estimated coefficients from the specification in equation (1),
with the whole data set. In this specification, the dependent variable for high-cost
peaking units are calculated somewhat differently30 Estimated coefficients on the
variable High Peak are substantially larger in columns (1)-(4). Interestingly, the
coefficients are closer to a $100, which is the upper bound for mitigation thresholds
for high-cost peaking units. However, the coefficients are consistently higher than
30
In order to calculate the dependent variable for high-cost peaking units, I subtract marginal
cost estimates from offer prices
33
$100 across specifications. This might be associated with using marginal costs to
calculate the dependent variable instead of reference prices31 . While this remains a
concern, large coefficients on High Peak permits me to comment about the shape of
the residual demand curve. It seems that the high-cost peaking units are dispatched
on the steep side of the residual demand curve. Therefore, mitigation thresholds
may frequently bind for high-cost peaking units.
In table 1.8, I report results from the specification in equation (2). This specification will compare the bidding behavior of ConED against IPPs on similar high-cost
peaking units. It is important to note that the goal of the modeling is to determine
whether IPPs offer prices are systematically different from ConED offer prices, on
similar high-cost peakers32 . The coefficient on High Peak×ConEd is significant, and
closer to negative $100 across specifications. This strongly suggests that high-cost
peaking units owned by IPPs are, in fact, bidding closer to mitigation thresholds. I
argue that the high cost units used in this specification are similar in many aspects
including engineering parameters. Therefore, any difference in offer prices should
reflect firms’ attempt to exercise market power.
The regression results provide some insights into the shapes of residual demand
curves in the in-city market. It seems that mitigation thresholds often bind for
high-cost peaking units. It could also be that high-cost peaking units are only
dispatched when the firm is pivotal. These results are consistent with the story that
residual demand curves are elastic for the most part except in pivotal periods. This
essentially means that NYC market would lead to lower markups with California
mitigation rule.
However, the conclusion about market performance under California remains
speculative. In order to comment on how California rule would perform in NYC
market, I need further evidence on markups under California rule. This can only be
achieved by simulating NYC market under California rules.
31
Reference prices are larger than marginal costs, and this may have biased the dependent
variable upwardly.
32
Independent Power Producers (IPP) that bought ConEd assets in 1998
34
1.7.5 Market Simulation Results
It is important to emphasize that market simulations were carried out for two load
pockets that are frequently congested. The term load pocket refers to a geographic
location in NYC that can become a closed market by itself when transmission constraints bind. More precisely, when transmission interfaces are congested all the
demand in the load pocket has to be met by supply within the load pocket. See
appendix for a list of NYC load pockets.
In tables 1.10-1.13, I report results from simulating the Astoria East load pocket
and Vernon-Greenwood load pocket. Results show similar market clearing prices
under both mitigation rules when the demand for electricity is less than 3500
megawatts. I note that the market clearing prices under these two regimes diverge as the demand exceeds 3500 megawatts. Not surprisingly, when prices diverge
substantially, the price setters are high-cost peaking units under New York rule.
This results can be better explained by the residual demand curves in figure 1.9.
In figure 1.9, I present residual demand curves for three large IPPs in the Astoria
East load pocket, after simulations. It is important note how residual demand curves
change in shape with increasing load levels. It should be noted that residual demand
curves become inelastic above 3500 megawatts of load. At these load levels, prices
under New York rule diverge substantially from competitive levels.
In tables 1.10-1.13 , I note that prices under New York rule increases by a $100
from competitive levels when peaking units are price setters. This permits me to
conclude that, for high-cost peaking units, mitigation thresholds bind during high
load periods. Another interesting result is that when high-cost units are prices
setters under New York rule, some firm in the market is always pivotal. This can
be further seen by lower prices under California rule when high-cost peaking units
are price setters. This result permits me to conclude that high-cost peaking units
usually operate during pivotal periods, and mitigation thresholds do bind during
such pivotal episodes.
In contrast, residual demand curves are elastic for the most part below 3500
35
megawatts. One reason for the flat residual demand curve is the large amount
of base load (inexpensive) capacity in New York market. In order to test market
performance under a different market structure, I simulate the market by replacing
some baseload units with peaking load units. This should lead to inelastic residual
demand curves even during non-pivotal periods. In this case, I expect markups
under California rule to be higher during non-pivotal periods. Results in Table 1.14
show high markups for California rule during non-pivotal periods and high markups
under New York rule in pivotal period.
1.8 Conclusions
Many electricity markets across the US have adopted some form of market power
mitigation. Although there are differences across markets in how mitigation is implemented, the underlying set of rules fall into two broad categories. The New York
ISO and the New England ISO use a more general form of conduct-impact framework in mitigating market power. Whereas the California ISO uses a structural
form of mitigation that depends on pivotal suppliers.
The California rule focuses on mitigating extreme market power cases due to
pivotal firms while New York rule takes a uniform approach. To the extent that
market power in New York is confined to pivotal periods, a rule similar to that
of California should lead to lower markups in New York. My results from New
York market do not show any evidence of bidding above reference prices for lowcost units while high-cost peakers consistently bid closer to mitigation thresholds.
This reinforces the fact that market power in New York is confined to high demand
periods.
Moreover, during pivotal episodes these mitigation thresholds seem to be binding
for high-cost peaking units. My calculations show an estimated 18% reduction in
wholesale prices during peak periods in the New York market, with a mitigation
rule similar to that of California.
An important policy question is whether NYISO should adopt stringent mitiga-
36
tion measures similar to CAISO. The answer to this question depends on multiple
factors. Although not explicitly analyzed in this paper, the choice of mitigation rule
also depends on base load to peak load capacity in the market. With more peak
load capacity in the market, the CAISO rule may lead to very high markups during
non-pivotal periods. In a market like New York, where we expect plant retirements
in the in the near future, it is still a long shot to conclude whether or not we need
a stringent mitigation regime.
That said, market mitigation rules in electricity markets are constantly changing
as and when new issues emerge. My findings are useful for electricity markets that
are in the process of changing or re-designing mitigation frameworks.
Table 1.1: Summary of Market Performace
New York
Pivotal
Prices can range from competitive levels to as high as $
100 above competitive level
Non-Pivotal Prices can range from competitive levels to as high as $
100 above competitive level
California
Competitive
Prices can range from
competitive levels to price
caps($1000)
Table 1.2: Choice of Mitigation Regimes
Residual Demand
Inelastic
Inelastic
Elastic
Elastic
Pivotal Frequency
High
Low
High
low
Choice
Unclear
Regime 1
Regime 2
Regime 1/Regime2
37
Table 1.3: Generating Firms in the NYC
Capacity(mw)
Firm
Astoria Energy LLC
Astoria Generating Company L.P.
Calpine Energy Service LP
Consolidated Edison Co. of NY, Inc.
NRG Power Marketing LLC
New York Power Authority
TC Ravenswood
Total
Base
Load
1300
1330
1050
930
570
2070
7350
Peak
Load
1000
120
100
720
530
530
3000
Total
1300
2330
120
1150
1650
1100
2600
10350
Owned by
(in 2001)
n/a
ConED
n/a
ConED
ConED
ConED
ConED
Table 1.4: Cost-Based Breakdown of Units in the NYC
Astoria Co. NRG Ravenswood ConED
Intermediate load
1330
930
2070
1050
Peaker-NG based
690
550
340
Peaker-FO2/KER Based
310
180
180
100
Table 1.5: Description of Variables
Variable
Margin ($)
Low Peak
High Peak
ConED
Load (mw)
7k ≤ Load ≤ 10k
Load ≥ 10k
Description
This variable measures the difference between offer prices and reference prices
Dummy = 1 if the unit is low-cost (NG based) peaking unit
Dummy = 1 if the unit is high-cost (FO2/KER based) peaking unit
Dummy = 1 if the unit belongs to ConED
The demand for electricity at the zonal level
Load exceeds 7000 megawatts, but less than 10000 megawatts
Load exceeds 10000 megawatts
ISOs’ estimate on generators’ marginal cost Calculated by
Reference Prices ($/MWh)
taking the average of historical accepted offer prices from the generatorl
Marginal Cost ($/MWh)
Calculated using heat rates and delivered fuel costs
The price of the fuel used to power the prime mover
Fuel Price($ per MMBTU)
Fuel prices are at the mothly level
Pivot
Dummy = 1 if the plant is crucial to meet the demand.
Number of Units
Number of units from other suppliers in the same area
38
Table 1.6: Testing Low-Cost Peaking Unit Behavior
V ariable
RE I
RE II
RE III
1(Low P eak)
22.5***
(4.45)
22.1***
(3.98)
9.67***
(2.61)
1(7k ≤ Load ≤ 10k)
5.39***
(1.50)
5.11***
(1.43)
-2.65**
(1.02)
1(Load ≥ 10k)
6.64***
(1.62)
6.38***
(1.56)
-1.44
(1.2)
1(Low P eak) × 1(7k ≤ Load ≤ 10k)
-5.59***
(1.73)
-5.07**
(1.63)
4.62**
(1.03)
7.17**
(2.59)
7.82**
(2.50)
17.68***
(1.9)
1(P ivot) × 1(Low P eak)
-7.00***
(1.13)
-7.50***
(1.2)
-7.27***
(1.2)
F uel P rice
-1.45***
(0.29)
-1.50***
(1.24)
-0.73**
(0.17)
N umber of U nits
-0.14***
(0.01)
-0.14***
(0.01)
-0.15***
(0.01)
1(LowP eak) × 1(Load ≥ 10k)
1(W inter)
33.6***
(2.8)
1(W inter) × 1(Low P eak)
26.1***
(3.3)
Time Fixed Effects
Month Fixed Effects
Year Fixed Effects
Plant Fixed Effects
Observations
†
no
yes
yes
No
yes
yes
yes
yes
yes
yes
yes
yes
510,000
510,000
510,000
Standard errors are clustered at the unit level. Only the intermediate load
units and low-cost peakers are included in all specifications.
†† ∗∗
P < 0.05 ∗∗∗ P < 0.01
39
Table 1.7: Testing Low & High-Cost Peaking Unit Behavior
V ariable
I
II
III
1(Low P eak)
30.86***
(5.09)
5.39
(4.96)
-3.18
(2.61)
1(High P eak)
131.8***
(7.75)
114.0***
(20.59)
114.0***
(22.4)
-0.72
(1.50)
-0.92
(1.43)
-4.33**
(1.37)
1(Load ≥ 10k)
-4.23***
(1.47)
-4.39***
(1.75)
-7.79***
(1.5)
1(Low P eak) × 1(7k ≤ Load ≤ 10k)
-4.18***
(1.82)
-3.84**
(1.30)
3.44***
(1.31)
1(High P eak) × 1(7k ≤ Load ≤ 10k)
9.53***
(3.72)
9.86***
(3.68)
8.38***
(3.53)
1(LowP eak) × 1(Load ≥ 10k)
11.91***
(1.65)
12.24***
(1.12)
19.62***
(1.53)
1(HighP eak) × 1(Load ≥ 10k)
16.49**
(3.41)
16.87***
(3.34)
15.29***
(3.22)
1(P ivot) × 1(Low P eak)
-10.9***
(1.1)
-10.4***
(1.2)
-9.7***
(1.08)
1(P ivot) × 1(High P eak)
13.63***
(1.91)
13.5***
(1.8)
15.29***
(1.9)
F uel P rice
-2.47***
(0.35)
-2.19***
(0.36)
-1.87**
(0.39)
N umber of U nits
-0.16***
(0.01)
-0.16***
(0.01)
-0.16**
(0.01)
1(7k ≤ Load ≤ 10k)
1(W inter)
17.5**
(3.21)
1(W inter) × 1(Low P eak)
24.1**
(2.9)
1(W inter) × 1(High P eak)
-5.45**
(2.8)
T imeDummies
yes
yes
yes
M onthDummies
yes
yes
yes
Y earDummies
yes
yes
yes
P lantDummies
no
yes
yes
†
Standard errors are clustered at the unit level. Only the intermediate
load units and low-cost peakers are included in all specifications.
††
For high cost peakers, the dependent variable is (bid price −
marginal cost). Marginal cost were calculated from CAMD datasets.
††† ∗∗
P < 0.05 ∗∗∗ P < 0.01
40
Table 1.8: Testing High Cost Unit Behavior with ConEd Units
Variable
RE I
RE II
RE III
304***
(11.2)
316***
(11.1)
328***
(10.7)
1(High Peak)×1(CONED)
-83.4***
(0.86)
-91.4***
(0.3)
-89***
(0.8)
1(7k ≤ Load≤10k)
11.1***
(2.63)
12.5***
(2.21)
12.5***
(2.31)
1(High Peak) × 1(7k ≤ Load≤10k)
-27.2***
(4.1)
-29.7***
(3.07)
-32.1***
(3.03)
1(High Peak) × 1(7k≤ Load ≤10k)× 1(CONED)
8.68***
(2.4)
5.03***
(2.51)
5.1***
(2.5)
1(Load≥10k)
-2.3
(2.25)
-2.3
(2.77)
-2.3
(2.56)
1(High Peak) × 1(Load≥10k)
2.9
(2.6)
2.92
(2.6)
2.29
(1.8)
2.07***
(0.8)
2.07**
(0.8)
4.0***
(0.4)
1(High Peak)
1(High Peak) × 1(Load≥10k)× 1(CONED)
1(High Peak)×1(Winter)
-6.05
(10.7)
1(High Peak)×1 Winter×1(CONED)
-9.6***
(1.98)
1(High Peak) × 1(7k ≤ Load≤10k)× 1(CONED)× 1(WINTER)
17.3***
(3.2)
Time Fixed Effects
Month Fixed Effects
Year Fixed Effects
Plant Fixed Effects
Unit Fixed Effects
Observation
†
yes
yes
yes
No
No
yes
yes
yes
yes
No
yes
yes
yes
yes
No
400,000
400,000
400,000
Standard errors are clustered at the unit level. This regression only includes high peakers and
baseloads.
†† ∗∗
P < 0.05 ∗∗∗ P < 0.01
41
Table 1.9: Summary Statistics
NY C
mean
st.dev
Zonal Load (mw) 7687
Congestion ($)
-22.08
Price($)
81
Avg. Size(mw)
90.6
1290
31.03
54.6
157.1
Rest of N Y
min
max
mean
st.dev
0
11300
-227.5 73.5
-15.2 779.4
16
986
2001
-2.24
56.4
336
873
14.75
31.6
280
Table 1.10: Simulation Results for the Astoria East I
Prices($)
Demand
2500 mw
3000 mw
3500 mw
4000 mw
4500 mw
5000 mw
†
Competitive
75
80
125
130
130
250
NYISO
75
105
130
230
230
350
CAISO
75
105
125
130
130
250
Percent
0%
0%
4%
76 %
76 %
40 %
Price Setter
Base Load
Base Load
Low Peak
High Peak
High Peak
High Peak
Base load forced to bid marginal costs
Table 1.11: Simulation Results for the Astoria East II
Prices($)
Demand
2500 mw
3000 mw
3500 mw
4000 mw
4500 mw
5000 mw
†
Competitive
75
80
125
130
130
250
Base load not restricted
NYISO
125
170
205
230
230
350
CAISO
125
170
205
130
130
250
Percent
0%
0%
0%
76 %
76 %
40 %
Price Setter
Base Load
Base Load
Low Peak
High Peak
High Peak
High Peak
min
max
0
5418
-133.3 57.99
-43.2 360.1
46.5
901
42
Table 1.12: Simulation Results for the Greenwood/Vernon I
Prices($)
Demand
2500 mw
3000 mw
3500 mw
4500 mw
5000 mw
†
Competitive
65
65
65
133
160
NYISO
65
65
80
228
260
CAISO
65
65
80
136
160
Percent
0%
0%
0%
67 %
62 %
Price Setter
Base Load
Base Load
Base Load
Low Peak
High Peak
Base load forced to bid marginal costs
Table 1.13: Simulation Results for the Greenwood/Vernon II
Prices($)
Demand
2500 mw
3000 mw
3500 mw
4500 mw
5000 mw
†
Competitive
65
65
80
133
160
NYISO
150
155
175
228
260
CAISO
75
95
128
136
160
Percent
50 %
60 %
36 %
67 %
62 %
Price Setter
Base Load
Base Load
Base Load
Low Peak
High Peak
Base load not restricted
Table 1.14: Simulation Results with Market Structure Changed
Prices($)
Demand
2500 mw
3000 mw
3500 mw
4000 mw
4500 mw
5000 mw
†
Competitive
125
130
245
275
340
340
NYISO
130
230
295
335
435
440
CAISO
250
265
270
275
340
340
Composotion changed to more peak load
Percent
-92 %
-15 %
9%
21%
27 %
29 %
Price Setter
Base Load
Base Load
Base Load
Low Peak
Low Peak
High Peak
43
Table 1.15: Comparision of Divested & Non-Divested Peaking Units
Firm
Astoria Co.
ConED
NRG
Ravenswood TC
†
Installed On
1971
1971
1970
1969
Average Type
Fuel
Heat Rate
Size
19.3 Single Cycle FO2/KER
14.72
24.8 Single Cycle FO2/KER
15.5
26.4 Single Cycle FO2/KER
15.7
25.2 Single Cycle NG/FO2
12.2
Reported heat rates (MMBTU/MWh) correspond to FO2 usage except for TC Ravenswood
Figure 1.1: Residual Demand Curves
44
Figure 1.2: Locational Marginal Pricing
Figure 1.3: Aggregate Supply Curve
45
Figure 1.4: Non Pivotal Residual Demand Curves
Figure 1.5: Pivotal Residual Demand Curves
Figure 1.6: Mitigation Thresholds for Peaking Units
46
Figure 1.7: Profit Maximization Under NYISO Rule
47
Figure 1.8: Baseload vs. Peak Load Offers
48
Figure 1.9: Residual Demand Curves after Simulation
49
CHAPTER 2
Supply Function Equilibrium and Power Market Outcomes
2.1 Introduction
In deregulated electricity markets, suppliers engage in oligopolistic competition to
supply energy. These suppliers submit offer curves (price-quantity pairs) in dayahead and real-time markets that will clear via uniform price auctions. Starting
from Green and Newbery (1992) many studies have used supply function equilibria
to study oligopolistic competition in electricity markets. Hortasu and Puller (2008)
develop equilibrium bidding strategies, using a class of additively separable supply
functions, and find that actual offer curves submitted in the ERCOT spot markets
are consistent with their equilibrium.1
In this study, I ask the question of whether oligopolistic competition in the New
York day-ahead market can be supported by a supply function equilibrium model
generated from a particular class of supply functions. Unlike the spot market,
suppliers in the day ahead market need to submit offer curves well in advance.
Although suppliers are allowed to submit different offer curves for different hours in
the day-ahead market, surprisingly suppliers tend to stick with the same offer curve
for the entire day. This is only possible if the supplier submits an offer curve that
maximizes profit under multiple demand realizations. Theoratically, the supplier has
to submit an offer curve through price-quantity pairs that would maximize profit
under variety of demand realizations. A realistic choice to model such oligopolistic
competition is supply function equilibrium models.
In this study, I compare the day-ahead offer curves submitted by firms in New
York to benchmark offers derived from the supply function equilibrium model devel1
Hortasu and Puller (2008) assume that the supply functions are additively separable in contract quantities and price
50
oped in this study. The class of supply functions used in this model will impose the
firms to bid the optimal offer, as long as the optimal offer is higher than marginal
costs. Therefore, as in other supply function equilibrium models; different demand
realizations will lead to different equilibrium supply functions.2 It is quite a challenge to create a single optimal supply function for the whole day. To overcome this
problem, first I create multiple equilibrium supply functions for various demand
realizations, for every firm in the market. Next, for every unit of a given firm, I
identify the highest sustainable bid among those equilibrium supply functions. This
way I will have a single optimal supply function for an entire day. It should be noted
that this is a unit-wise bidding approach, where the firm chooses a single offer over
the entire capacity of the unit.
In addition to optimal supply functions developed by supply function equilibrium
model, I also develop a conservative bidding algorithm to obtain a conservative
supply function. In this, rival firms are assumed to bid marginal costs for any
demand realization. This framework assumes that shocks to demand will only shift
the residual demand curves in a parallel way. This allows creating a single supply
function for the whole day by using the Hortasu and Puller (2008) approach. Finally,
I compare the conservative offers generated with actual offers in the day ahead
market.
Results show that the supply function equilibrium generated in this study is
not consistent with actual offers submitted in the New York market. The offers
generated from supply function equilibrium are on average higher than actual offers
for peaking units. The conservative offers show some resemblance to actual offers.
However, careful analysis suggests that firms in New York do not bid conservatively.
The actual offers fall in between conservative offers and offers generated by supply
function equilibrium model in this study.
In section 2.2, I provide details on supply function equilibrium and propose an
2
I use the term equilibrium supply function to identify the supply functions from SFE model.
Equillibirum supply functions will vary with demand. I use the term optimal supply function to
identify a supply function obtained by collating the equilibrium supply functions. There will be a
single optimal supply function for every day in the sample
51
empirical approach to generate the equilibrium. Also in section 2.2, I discuss the
assumptions and limitations in my model in relation to Hortasu and Puller (2008).
Section 2.3, focuses on data and observed patterns in actual offers from the New
York day-ahead market. Finally, in section 2.5, I compare actual offers from the
New york market with optimal offers developed in this study.
2.2 Supply Function Equilibrium
Supply function equilibrium (SFE) models for oligopolistic competition were originally developed by Klemperer and Meyer (1989). The idea behind this approach
allows firms to choose supply functions in multi-unit auctions. Unlike Cournot or
Bertrand models, firms in SFE model can choose price and quantity simultaneously
by choosing supply functions.
SFE models have been widely used in electricity markets since Green and Newbery (1992). Many studies have looked into the existence of SFE under various
assumptions. Rudkevich (2003) and Baldick and Hogan (2006) provide details on
the existence of SFE. Further, Baldick et al. (2004) provide insights on parameterizing supply functions for SFE models.
A general critique on SFE models is the class of supply functions chosen to
generate equilibrium. Rudkevich (2003) use linear supply functions and prove the
existence of SFE. Hortasu and Puller (2008) use additively separable, still linear,
supply functions to form their equilibrium. Depending on the choice of supply
functions, SFE models can result in different equilibria. Therefore, the choice of
supply functions is critical to forming an equilibrium.
In addition to how supply functions are parameterized, the SFE is also susceptible to other factors such as contract quantities. Suppliers in the electricity markets
to date rely heavily on long term contracts to recoup their investment. Newbery
(1998) and Green (1996) study how contract quantities can alter outcomes under
SFE models. To this end, Hortasu and Puller (2008) theoretically show the effect
of contract quantities on SFE under linear (additively separable) supply functions.
52
A common problem in SFE models is the presence of multiple equilibria. Holmberg (2008) provide conditions that would guarantee a unique equilibrium. Delgado
(2005) show that the issues of multiple equilibria can be eliminated via coalition
proof Nash equilibrium. In general, a unique SFE equilibrium is guaranteed only
under symmetric cost assumptions. Nevertheless, Genc and Reynolds (2011) provide
conditions under which asymmetric equilibrium exists. Further, Genc and Reynolds
(2011) characterize a symmetric supply function equilibrium when firms are capacity
constrained.
In this study, I develop an empirical supply function equilibrium, for a class
of supply functions. I calculate the optimal price quantity pairs using an iterative
Courtnot algorithm. Upon finding the optimal price quantity pair, I restrict firms
to bid optimal price over the capacity as long as marginal costs are lower and bid
marginal costs beyond that. .
2.2.1 Supply Function Equillibrium Model
In this section I set up a general framework to study supply function equilibrium
with a model similar to Green and Newbery (1992). Under SFE models, firms
maximize profit by choosing supply functions, which is sometimes referred as price
quantity pairs. I denote the supply function of firm i by qi (p), where p is the market
clearing price in a given hour. Consistent with the previous literature, I assume
demand, D(p, ), as a function of market clearing price with a random shock to it.
Firms maximize the profit, πi = pqi − C(qi )qi , where C(qi ) is the cost function
πi = p[D(p, ) − qj ] − C(qi )[D(p, ) − qj ]
(2.1)
In equation (1), qj is the total rival supply in the market. Therefore, D(p, ) − qj
00
is the residual demand, RD, faced by firm i. If we assume Dp = 0 as in Klemperer
and Meyer (1989), the demand curve can be written as:
D(p, ) = D(p) + (2.2)
53
Equation (2) ensures that shocks to the demand will only shift the residual
demand,[D(p, ) − qj ], in a parallel fashion. Solving equation (2) gives the following
optimality condition:3
qi
∂qj
=
+ Dp
∂p
p − C 0 (qi )
(2.3)
Dp is the derivative of demand with respect to price, ( ∂D
). Rearanging terms in
∂p
equation 3 will result in the following markup equation:
p − C 0 (qi ) =
qi
−( ∂D
∂p
In the above markup equation, ( ∂D
−
∂p
dqj
)
dp
−
dqj
)
dp
(2.4)
is nothing but the derivative of the
residual demand. Therefore, in SFE models, markups can be calculated if there is a
way to estimate the slope of the residual demand curve. Authors who have studied
SFE models have used different assumptions on supply functions, to facilitate the
calculation of residual demand slopes. For example, Puller and Hortacsu (2005) use
the following supply function:
qi (p, QCi ) = αi (p) + βi (QCi )
(2.5)
Where QCi denotes the amount of quantity contracted. The class of supply
functions in equation (5) is linear and additively separable in contract quantities,
QCi . With further restrictions on the supply function, Puller and Hortacsu (2005)
arrive at the following optimality condition:4
0
p − Ci (qi (p, QCi )) =
qi(p,QCi, ) − QCi
0
−RDi (p)
(2.6)
The difference between optimality conditions in equations (4) and (6) is that
the latter accounts for QCi , contract quantities. Futrher, the optimality condition
in equation (6) provides a platform to empirically construct the ex-post optimal
3
4
See Green and Newberry (1992) for proof
Puller and Hortacsu assume that the supply function is additively separable in p and QC
54
supply curves. In order to create ex-post optimal curves, one has to compute RD0 ,
slope of the residual demand, at different realization of residual demand curves.
Interestingly, with additive separability of supply functions, a single realization of
residual demand curve is sufficient to calculate RD0 for all realizations.5 Hence,
Puller and Hortacsu(PH) was able to solve qi in equation (6) for multiple demand
realizations.
From an empirical perspective, PH start from creating the residual demand
curve from observed rival offers at respective price levels. The additive separability
assumption ensures that uncertainty will only shift residual demand curves in a parallel way. Therefore, PH was able to shift the residual demand curve and identify
optimal bids points for varying demand levels. Finally, PH trace out the ex-post
optimal function through optimal bid points identified, by shifting the residual demand curve. This method has been the closest empirical approximation of supply
of function equilibrium in the literature so far.
Figure 2.1 shows the construction of ex-post optimal function in PH. Point A in
figure 1, refers to the optimal bid point under the realization of residual demand 1
(RD1). Similarly, Point B refers to the optimal bid point under the realization RD
2. It should be noted that RD 2 is a parallel shift of RD 1 because demand shocks
will only shift the demand curve, with additive separability assumption in equation
(2).
The PH method is ideal to evaluate the spot market outcomes.6 However, extending PH method to the day-ahead market may pose a few challenges. In the
day-ahead market, the bidding occurs well in advance, which leaves the firm with
some uncertainty about the demand. In that case how can one construct the residual
demand curve before observing the rival offers? Further, during a 24 hour window,
demand can fluctuate between several thousand megawatts. Therefore, residual demand curves can be very different in the peak hours as opposed to off-peak hours.
5
Puller and Hortacsu (2007) specification assumes that uncertainty/random shocks will only
shift residual demand curves, and does not pivot. Therefore, from a single realization of residual
demand, one can shift calculate the slope of residual demand, RD0 for any realization
6
balancing market in EROCT
55
Even if residual demand curves were known a priori, the challenge is to construct a
single optimal bidding curve for the whole 24 hour window.
One might argue that, if all firms bid the same supply function for the entire day,
the shape of the residual demand curve should be same for the entire day. Given all
firms bid the same offer curve, as demand changes the residual demand curve should
only shift in parallel ways. However, different units will be online at different hours
within a day, so even if offers were unchanged the shape of the residual demand
curve will change.
In this paper, I develop an equilibrium bidding model using the supply function equilibrium model. More precisely, this equilibrium bidding model will allow
constructing residual demand curves for any realization of the demand. In order to
construct a single offer curve for the whole day, I develop a unit-wise optimal bidding
approach, described later in the empirical analysis.7 I also develop a conservative
bidding algorithm in which each firm assumes that the rivals are bidding at marginal
costs. Finally, I compare actual offers from the New York market to optimal offers
generated by SFE and offers generated by conservative bidding approach.
2.2.2 Empirical Supply Function Equilibrium
Unlike Hortasu and Puller (2008), I try to develop an equilibrium bidding model
by only using marginal costs. More precisely, I will not use the observed bids to
generate the supply function equilibrium. Further, this method does not rely on any
theoretical assumption about residual demand curves. I take a complete empirical
approach to developing the bidding equilibrium. However, this method has its own
caveats mainly due to data limitations.
First, marginal cost estimates are calculated from averages of historical accepted
offers. These estimates may not reflect the true marginal costs in some cases.8 I
provide a detailed discussion on how marginal costs are calculated in appendix. Second, lack of information on actual contract quantities is a setback to this empirical
7
8
a correct term would be ”portfolio-wise” because I impose similar units to bid the same offer
if firms were consistently under/over bidding, marginal cost estimates will be biased
56
attempt to generate supply function equilibrium. Since I do not include information on contract quantities, any equilibrium generated will be biased towards an
aggressive bidding equilibrium.
Further, the class of supply functions I propose may lead to multiple equilibria
at some demand levels. In this empirical approach, I restrict firms to bid a uniform
price, the optimal price, over its capacity as along as the optimal price exceeds
marginal costs. In the range where marginal costs are higher than optimal price,
I impose the firm to bid marginal costs. Consequently, the non-linearity of supply
functions does lead to multiple equilibria at some demand levels. This problem of
multiple equilibria is less severe as I restrict the bid space according to existing
mitigation rules in the New York market.9
Implementation of Empirical SFE Equilibrium
I use a two-step approach to creating a single optimal supply function for the whole
day. First step involves generating an equilibrium supply function for a given level
of demand. In the second step, I collate multiple supply functions corresponding to
multiple demand periods to form a single optimal supply function for the day.10
To generate equilibrium supply functions, I extend the method proposed
by Borenstein and Bushnell (1999). Their approach is based on a Cournot algorithm where each firm will choose the optimal price-quantity pairs iteratively in
response to rival optimal offers, until equilibrium is obtained. I deviate from this
approach slightly by choosing supply functions through optimal price-quantity pairs.
The starting point of this iterative approach is to construct the residual demand
curve for every firm in the market, assuming that the rivals bid at marginal costs.
It should be noted that I use marginal costs to begin with instead of observed rival
9
existing mitigation rules in New York City are stringent such that suppliers cannot raise offers
substantially above marginal costs
10
to be consistent I use the term equilibrium supply function for the supply function generated
by SFE for a given demand level. I note that there will be different equilibrium supply functions
for different demand levels. I use the term optimal supply function for the supply curve obtained
by collating equilibrium supply functions. Therefore, there will be one optimal supply function for
every 24 hour window
57
offers. Next, I choose a random firm from the market and trace out the optimal
supply function through its profit maximizing point, on its residual demand curve.
Once the firm chooses the supply function, the residual demand curves for other
firms will be updated. Then I move onto another firm and choose an optimal supply
function through its profit maximizing price-quantity pair. This process will be
iterated until a fixed point in supply functions is reached for all firms.11 Below are
the steps involved in generating supply functions for a given demand level with
existing mitigation rules in New York:
1. For a given realization of demand, I construct the aggregate supply curve using
reference prices.
2. For a firm i in the market, I discard its own supply from the aggregate supply
curve, and construct the residual demand curve
3. Grid search profit maximizing price-quantity pair for the firm i, not exceeding
mitigation thresholds. This procedure will lead to marginal cost(reference
price) bidding as the residual demand curve becomes elastic
4. Reconstruct the supply curve for the firm i, by replacing the price obtained in
the previous step over its capacity as long as the price is greater than marginal
costs.
5. Reconstruct the aggregate supply curve by imposing changes to firm i’s offer
curve in step 4
6. For the firm j, repeat steps (1), (2), (3), (4)
7. Iterate until an equilibrium in supply functions is reached
The fixed point solution would give equilibrium supply functions for every firm in
the market. Therefore, it is straight forward to construct residual demand curves
faced by firms at the equilibrium. However, it is somewhat challenging to construct
a single optimal supply function for all 24 hours of the day.
11
see Chapter 1 for details on simulation
58
Collating Equilibrium Supply Functions to form Optimal Supply Functions
I propose a portfolio-wise bidding approach where the firm will submit a single offer
over the entire capacity on similar units.12 For example, a firm with a baseload unit
and a portfolio of similar peaking units will bid a single offer on its peaking units.
This aggregation of capacity may not be realistic in some cases where the firm would
want to bid different offers on similar type of units.13 However, it is not unusual
for firms to bid the same offer on similar types of units in the presence of sufficient
rival capacity. To this effect, in section 3, I show evidence for similar/identical offers
from units with similar cost structures.
In figure 2.2, I show how these different equilibrium supply functions can be
collated to form a single optimal supply function for the day. In that the top row
shows residual demand curves and marginal costs curves. In the middle row, I show
the equilibrium supply functions corresponding to residual demand curves in the
top row. In the bottom row, I gather all supply curves and identify the maximum
sustainable bid, at the unit level, to form a single supply schedule.
In figure 2.2 (a), I depict a low demand case in which it is optimal for the
firm to bid marginal costs on its baseload. Not surprisingly, in low demand levels
where only baseload would operate, residual demand curves are flat for the most
part. Therefore, it is always safe to bid marginal costs on baseload. Another reason
for bidding marginal costs on baseload is startup cost and ramp costs. I show an
extreme demand case in figure 4(d) where the most expensive unit in the fleet is in
dispatch. Under such cases, it is optimal for the firm to bid the maximum allowed
by the regulators as the residual demand curves are perfectly inelastic. Therefore,
it somewhat straight forward to identify the optimal offers in most expensive and
least expensive units.
Figures in 2.2(b) and 2.2(c) are problematic cases because the optimal offer on
12
I use the term portfolio to refer the set of similar units with identical marginal costs
Things like startup costs and outage risks can force the firm to bid differently on similar type
of units
13
59
this unit will depend on the residual demand curve. In this case, I use the least
among the optimal prices for this unit under all realizations of demand. However,
I acknowledge that this may not be desirable from a theoratical perspective. For
example in the case presented in Figure 2.3, I choose the lowest optimal bid P2,
corresponding to residual demand curve RD 2. Although, there is not enough theory
to support the notion why the firm would choose the lowest sustainable offer, it
guarantees a single offer for the entire portfolio. The alternative approach here is
to fit a linear curve via price points p2 and p3 over the capacity of the unit. This
would guarantee an increasing offer curve over the entire capacity. Nevertheless,
this method was not implemented in this study.
2.2.3 Conservative Bidding Equilibrium
In addition to the optimal supply function, I also develop a conservative bidding
algorithm. This approach is similar to Hortasu and Puller (2008) except I use
marginal costs to construct rival offers. The implicit assumption in this framework
is that the firm responds to marginal costs of rivals, not actual offers. Although
this may not reflect actual bidding in the market, this will clearly show firms ability
to raise offers given rivals bid at marginal costs. Then it would be interesting to
compare actual offers with conservative offers to see if firms are only bidding high
when there is absolutely no risk of undercutting. An important contribution of
conservative bidding curve is that, if firms were bidding below conservative offers it
should indicate high amounts of bilateral contracts.
To make this framework consistent with Hortasu and Puller (2008), I assume the
complete rival capacity is available at any given time. This essentially means that
the shape of the residual demand curve is unchanged in a given 24 hour window.14
Therefore residual curve demand curves will only shift as demand changes and do
not pivot. Since this framework is similar to PH and I can shift the residual demand
curve to identify optimal bid points for any realization of demand. Consequently, I
14
marginal costs will only change on a day to day basis as fuel price changes. This implies that
the residual demand curves will also change on a day to day basis
60
can trace the conservative supply function through the profit maximizing bid points
at different demand levels. It should be emphasized that this method will result in
a single supply curve for the 24 hour window. This method does not require a single
offer at the portfolio level.
Although I do not expect firms to bid conservatively on its entire fleet, intuitively
firms should at least bid conservatively on its baseload capacity. The conservative
bidding equilibrium will serve as a benchmark to gauge the extent of market power.
This would be a lower bound indicator for market power, as there is no threat of
undercutting in conservative equilibrium. In a previous study, I find that firms only
markup on their peaking units. Based on that, I expect the conservative bidding
equilibrium to indicate significant markups above marginal costs on peaking units.
2.3 Data
I use day-ahead bidding data from the New York market. I focus on the period
from April 2010 to September 2010. In order to trace out optimal supply functions,
I need data on marginal costs. As a preliminary estimate, I use marginal fuel costs
instead of marginal costs. Marginal fuel costs can be calculated from heat rates
and the cost of delivered fuel.15 I estimate heat rates using unit level data reported
in the air markets program website. Cost of delivered fuel is obtained from New
York Independent System Operator monthly reports. Since this method provides a
single estimate for marginal cost for the entire capacity of the unit, this may not
be accurate for large base load/ intermediate units. For such units I use reference
prices from chapter 116 . Reference prices are regulators’ estimate of a units’ marginal
costs. These estimates are historical averages of accepted offers at the unit level.
This estimate will be calculated for every 10mw interval of a units’ capacity.
An important caveat of using reference prices is that it can prevent the identification of quantities contracted using the Hortasu and Puller (2008) method. In that,
15
Since I do not include emission permit costs,overhead and maintenance cost, I call this marginal
fuel costs
16
90 days moving average of accepted offers at the unit level
61
they use the amount of quantity bid below marginal costs as contracted, consistent
with their theoretical model. If firms were consistently bidding below marginal costs
it should lead to reference prices below actual marginal costs. Therefore, the firm
does not have to bid below reference prices, which is already less than marginal
costs. Therefore, I do not observe actual offers below reference prices for the most
part in the sample.
I restrict the focus of this study to suppliers located in New York City. All generating assets inside New York City are owned between seven firms. From a simulation
perspective, fewer firms will reduce the computation burden significantly. Moreover,
these firms will only supply electricity inside New York City, and eliminates the need
to account for exports. In table 2.1, I provide a summary of generating units in New
York, aggregated at the firm level.
Out of the seven firms reported in table 1, some are net buyers from the market
during most periods. Since net buyers bid at their marginal costs to keep the prices
low, I exclude analyzing offer curves from such net buyers/utilities. Consequently,
I focus on three large independent power producers (IPPs) in the market.
2.4 Analysis of Observed Offer Curves
In this section, I describe certain features of observed offer curves from the New York
market. Further, I provide insights into how actual data relates to assumptions in
section 2.2. In table 2.2, I report the average markup above marginal costs for
three large independent power producers in the New York market. In table 2.2, low
peak refers to peaking units that use natural gas and high peak refers to peaking
units that use petroleum oils/kerosene. As expected the margins on base load units
are low for all three firms. Base load units usually have large startup costs and
ramp up/down costs; once online they need be operating for long periods to cover
the costs. Therefore, base load units maintain a constant level of generation by
offering capacity at marginal costs. In contrast, peaking units are dispatched only
under tight supply conditions. Firms with significant market power may choose to
62
markup on peaking units. It should be noted that the average markup for peaking
units in table 2.2 exceeds $ 100 in some cases.
While fixed costs associated with base load operation may prevent such units
from changing the offers within a day, it is not clear why peaking units should stick
to a single offer. In Figure 2.8, I show quantity-weighted average bid prices from base
load and peak load units on July 10.17 There is absolutely no change in submitted
offers across time periods. This type of bidding is puzzling when suppliers are given
the freedom to bid different offers curves at different time periods within a day. The
objective of the study is to replicate the actual offers curves using a supply function
equilibrium model.
In figure 2.9, I provide a graphical illustration of how firms bid on similar type
of units.18 It should be noted that the offers from similar units belonging to NRG
and Astoria differ only in decimals. This pattern holds true for most days in the
sample. Therefore, imposing firms to bid the same offer on similar type of units, in
section 2.2, is a reasonable approximation of actual bidding behavior.
2.5 Analysis of Constructed Offers
In this section, I focus on optimal supply functions and conservative supply functions constructed using methods described in section 2.2. In tables 2.3 and 2.4, I
report markups obtained from constructed offers. Interestingly, both optimal and
conservative supply functions lead to same level of markups on baseload units. This
suggests that firms face very elastic residual demand curves at low demand levels.
Also, note that markups obtained from optimal offers are higher than conservative
offers on peaking units. It is important to emphasize that, markups obtained from
conservative offers are substantially higher for high-peakers in table 4. It appears
that these units can exercise market power in tight supply conditions.
17
baseload units usually bid in multiple steps. Therefore, a quantity-weighted average would
best represent the offer price per megawatt
18
Units with same marginal costs
63
I acknowledge that the lack of information on long-term contracts might be
inflating markup estimates on optimal supply functions. However, I argue that
quantities contracted should have little or no impact in the markup of peaking
units. Most likely, firms will be operating beyond their contracted quantity when
peaking units are called onto produce. This should entice firms to bid high on their
peaking units.
Furthermore, transmission constraints can influence markups. In this study,
I treat New York City as a single entity, and generate optimal supply functions
assuming no transmission constraints within New York City. In reality, transmission
constraints can isolate units belonging to the same firm into different sub-markets.
It is possible that units belonging to the same firm facing different residual demand
curves in sub-markets. Therefore, not including transmission constraints in this
study, should decrease markups on optimal supply functions.
2.5.1 Comparison of Constructed Offers and Actual Offers
In this section, I compare the actual offers with optimal offers generated via supply
function equilibrium model. In Figure 2.5, I show actual offers and optimal supply
functions for three independent power producers on July 16, 2010. Not suprisingly,
the optimal offers on baseload units are not that different from marginal costs.
Despite some closeness on baseload offers, visual inspection clearly shows that actual
offers are not consistent with optimal offers generated by SFE, in this study. This
pattern holds true for most days in the sample. Next I turn to conservative supply
functions generated using the method proposed in section 2.2.
In Figure 2.6, I show conservative offers and actual offers side by side. There
seems to be some similarity between conservative offers and actual offers on base
load units and low peak units. However, visual inspection is not sufficient to draw
conclusions about closeness. I use a reasonable metric to evaluate the closeness
across the sample. I calculate the absolute difference of actual offers from optimal
offers and conservative offers in the sample.
In tables 2.5 through 2.7, I compare the average of absolute difference of actual
64
offers from optimal and conservative offers. The average was taken over the period
from June 2010 to August 2010. This metric takes a smaller value on baseload units
belonging to USPG and NRG. This means that these firms bid closer to benchmark
offers generated by both optimal supply functions and conservative supply functions,
on baseload units.
As it turns out the optimal offers and conservative offers on baseload units are
not that different from marginal costs on these units. This indicates that firms are
facing elastic residual demand curves at low demand levels. This fact is evident
from low markups on baseload units in table 2.2. However, even with favorable
conditions to exercise market power, firms are unlikely to markup substantially on
baseload units due to hidden costs associated with base load operation.19 I cannot
rule out the possibility that bilateral contracts on baseload capacity keeping down
the offers in these units.
The absolute difference metric on peaking units are large and does not lead to
intuitive results. It only indicates that the optimal supply functions developed in
the study are not consistent with actual offers in the day ahead market.
Next, I calculate producer surplus at different demand levels under optimal bidding, conservative bidding and actual bidding. In order to calculate the surplus, I
estimate the market clearing prices under optimal bidding, conservative bidding and
actual bidding for various demand levels. In tables 2.8 through 2.10, I report the
percentage of surplus achieved by actual bidding in comparison to optimal and conservative bidding. As expected, surplus numbers provide no evidence of consistency
between actual offers and equilibrium offers generated in this study.
2.6 Discussion
In this study, I propose an empirical approach to generating supply function equilibrium. This is the first known attempt to replicate day-ahead offers in electricity
markets using a supply function equilibrium. The challenging part of this study is
19
Hidden costs include startup cost, rampup costs, outage risks
65
generating a single optimal supply function for the entire day. In order to create
optimal supply functions, I make several assumptions on firm behavior.
First, I assume that firms bid similarly on identical units. This assumption may
not hold in the presence of fixed costs and outage risks. There are costs involved
in operating at maximum capacity of the portfolio; therefore it is reasonable for
firms to bid differently on similar units that will be dispatched later. Second, the
class of supply functions used in this study may be a suspect for much of the results
driven from the model. Finally, I use reference prices instead of actual marginal
cost for baseload units. This prevents me from identifying the quantities contracted
from actual offers using Hortasu and Puller (2008) method. Not having the contract
quantities in the model is certainly a caveat to this study. This might lead to inflated
offers at the equilibrium.
Anthor important factor that may influence the results in this study is transmission constraints. Firms usually have their generating assets distributed across
strategic locations. Transmisison constraint can often geographically isolate such
locations and create sub-markets. Therefore, when transmission constraints bind,
different generating assets belonging to the same firm may face different residual demand curves. This suggests that the optimal supply functions have to be generated
at the submarket level, or even at the plant level.
The actual day-ahead offers from the New York market do not resemble offers
generated from the supply function equilibrium model. To be precise, actual offers
are not closer to the supply function equilibrium generated in this study. It may well
be that the supply function equilibrium generated in this study is not a sustainable
equilibrium. The equilibrium in this study depends critically on how I fit optimal
supply functions through profit maximizing price-quantity pairs. Therefore, future
line of research is to experiment with different classes of supply functions.
Moreover, visually conservative bidding equilibrium looks somewhat similar to
actual bidding. However, careful analysis suggests that firms only bid conservatively
on their baseload units. Interstingly, even conservative offers lead to significant
markup on peaking units. This relates to a previous study of mine, where I find
66
evidence for firms bidding substantially above marginal costs on peaking units. This
is indicative of the fact that firms in New York can exercise market power in periods
where peaking units called on to produce.
Figure 2.1: Expost Optimal Function
67
Figure 2.2: Construction of Optimal Supply Curve
Table 2.1: Generating Firms in the NYC
Capacity(mw)
Firm
Astoria Energy LLC
Astoria Generating Company L.P.
Calpine Energy Service LP
Consolidated Edison Co. of NY, Inc.
NRG Power Marketing LLC
New York Power Authority
TC Ravenswood
Total
Base
Load
1300
1330
1050
930
570
2070
7350
Peak
Load
1000
120
100
720
530
530
3000
Total
1300
2330
120
1150
1650
1100
2600
10350
68
Figure 2.3: Optimal Offer
Table 2.2: Average Markup of Suppliers
Type
Base Load
Low Peak
High Peak
Astoria
$ 1.2
$ 51.4
$124
NRG
$4
$ 13
$ 136
Ravenswood
$ 4.6
$ 81
n/a
Table 2.3: Markups - Optimal Offers
Astoria
Firm
Base Load
Low Peak
High Peak
(Optimal-mc)
$5
$102
$110
NRG
(Optimal-mc)
$5
$103
$106
Ravenswood
(Optimal-mc)
$29
$204
n/a
Table 2.4: Markups - Conservative Offers
Astoria
Firm
Base Load
Low Peak
High Peak
(Optimal-mc)
$5
$68
$104
NRG
(Optimal-mc)
$5
$17
$95
Ravenswood
(Optimal-mc)
$10
$28
n/a
69
Figure 2.4: Residual Demand Curves - Simulation
Table 2.5: Comparison of Offers- USPG (2010)
June
Firm
Base Load
Low Peak
High Peak
July
August
(Actual-EPO)
(Actual-Cons)
(Actual-EPO)
(Actual-Cons)
(Actual-EPO)
(Actual-Cons)
$ 2.5
$ 59
$ 16
$5.8
$32
$36
$ 4.4
$ 58
$ 22
$9.2
$54
$ 37
$9.2
$112
$30
$14.2
$95
$41
70
Table 2.6: Comparison of Offers- NRG (2010)
June
Firm
Base Load
Low Peak
High Peak
July
August
(Actual-EPO)
(Actual-Cons)
(Actual-EPO)
(Actual-Cons)
(Actual-EPO)
(Actual-Cons)
$ 6.5
$ 95
$ 102
$4.7
$ 4.2
$175
$ 4.9
$ 96
$ 106
$5.1
$ 2.4
$ 19
$3.6
$101
$112
$8.6
$5.3
$22
Table 2.7: Comparison of Offers- Ravenswood (2010)
June
Firm
Base Load
Low Peak
July
August
(Actual-EPO)
(Actual-Cons)
(Actual-EPO)
(Actual-Cons)
(Actual-EPO)
(Actual-Cons)
$ 70
$ 94
$42
$ 44
$ 97
$ 111
$36
$ 36
$87
$116
$44
$49
Table 2.8: Surplus Comparison- USPG (2010)
June
Firm
9000mw
11000mw
July
August
(Actua/EPO)
(Actual/Cons)
(Actual/EPO)
(Actual/Cons)
(Actual/EPO)
(Actual/Cons)
60 %
70 %
47 %
81%
60 %
72 %
46%
83%
65 %
78%
50 %
87 %
Table 2.9: Surplus Comparison- NRG (2010)
June
Firm
9000mw
11000mw
July
August
(Actua/EPO)
(Actual/Cons)
(Actual/EPO)
(Actual/Cons)
(Actual/EPO)
(Actual/Cons)
60 %
71 %
42 %
86%
60 %
77 %
43%
86%
66 %
77%
41 %
86 %
Table 2.10: Surplus Comparison- Ravenswood (2010)
June
Firm
9000mw
11000mw
(Actua/EPO)
74 %
80 %
(Actual/Cons)
58 %
90%
July
(Actual/EPO)
73 %
81 %
(Actual/Cons)
57%
89%
August
(Actual/EPO)
73 %
81%
(Actual/Cons)
57 %
89 %
71
Figure 2.5: Optimal vs. Actual Offers
72
Figure 2.6: Actual vs. Conservative Offer Curves
73
Figure 2.7: Quantity Weighted Offer Price - By Hours
Figure 2.8: Offers from Similar Units
74
APPENDIX A
Sample Appendix
A.1 Reference Price Calculation
Following is a excerpt from the NYISOs’ market services tariff, attachment H:
“1. The lower of the mean or the median of a Generators accepted Bids or
Bid components, in hour beginning 6 to hour beginning 21 but excluding weekend
and designated holiday hours, in competitive periods over the most recent 90 day
period for which the necessary input data are available to the ISOs reference level
calculation systems, adjusted for changes in fuel prices
2. using the mean of the LBMP at the Generators location during the lowestpriced 50 percent of the hours that the Generator was dispatched over the most recent
90 day period for which the necessary LBMP data are available
3. A level determined in consultation with the Market Party based on engineering
estimates”
I calculate the reference prices by taking the average of accepted bids over the
last 90 days and weighing with appropriate fuel prices. If sufficient data on accepted
bids are not available, I use the locational based market price in place of the accepted
bids. There are two caveats to this calculation. First, the fuel prices used to weight
the average is at the monthly level. Therefore, day to day fluctuations in fuel price
will not be reflected in my references prices. Second, for some high cost units that are
not frequently accepted, the ISO uses engineering estimates to calculate reference
prices. Since I do not have access to such engineering estimates, I can not calculate
the reference prices for these units.
75
A.2 Margin Variable: Dependent Variable For Regressions
Calculating this measure is straightforward for peaking units that submit offers in
single blocks. In this case, I take the offer price and subtract the reference price to
obtain the margin variable. However, for intermediate load units that submit bids in
multiple blocks I take the difference of average offers from average reference prices.
In so far as the intermediate load units offer the whole capacity around the same
price, this variable should not pose any problems.See figure A.1 for actual offers
from intermediate load units.
Load pockets are areas of the system where the transmission capability is not
adequate to import capacity from other parts of the system and demand is met by
relying on local generation1 . The NYISO identifies the following nine load pockets
in NYC.
• Astoria East
• Astoria West/Queensbridge
• Astoria West/Queensbridge/Vernon
• Greenwood/Vernon
• Greenwood/Staten Island
• Staten Island
• East River
• 138Kv
• Sprainbrook/Dunwoodie
See figure A.2 for NYC load pocket definitions.
1
http://www.iso-ne.com/support/training/glossary/index-p4.html
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A.3 Pivotal Firms
Pivotality refers to an extreme case of market power where the market demand will
not be met without the supply of a particular firm, and that firm is referred as pivotal
firm. Figure A.3 illustrates pivotalness in a duopoly market under different scenarios,
and notice that when the firm is pivotal the residual demand curve becomes perfectly
inelastic at pivotal quantity.
77
Figure A.1: Actual Offers from Selected Intermediate Load Units
Figure A.2: Load Pocket Definitions for NYC
78
Figure A.3: Pivotal Firms
79
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