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THREE ESSAYS ON STRATEGIC BEHAVIOR, INFORMATION REVELATION AND RESTRUCTURING IN THE U.S. ELECTRICITY MARKETS
A dissertation presented
by
Vladlena Sabodash
to The Department of Economics
In partial fulfillment of the requirements for the degree of Doctor of Philosophy
in the field of
Economics
Northeastern University Boston, Massachusetts
September, 2010
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2THREE ESSAYS ON STRATEGIC BEHAVIOR, INFORMATION REVELATION AND
RESTRUCTURING IN THE U.S. ELECTRICITY MARKETS
by
Vladlena Sabodash
ABSTRACT OF DISSERTATION
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Economics
in the Graduate School of Arts and Sciences of Northeastern University, September, 2010
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3ABSTRACT OF DISSERTATION
This doctoral dissertation analyzes transitioning to restructured electricity markets, drivers
underlying restructuring choice in the U.S., opportunities for suppliers’ strategic behavior created by
restructuring of electricity markets, and effect of restructuring and suppliers’ strategic behavior on
wholesale and retail electricity prices. The following are abstracts of the three chapters of my doctoral
dissertation entitled “Three Essays on Strategic Behavior, Information Revelation and Restructuring in
the U.S. Electricity Markets.”
The first chapter of this dissertation, “Price Spikes in Energy Markets: “Business by Usual
Methods”or Strategic Withholding?” focuses on one phenomenon in restructured electricity markets,
called price spikes. There are two divergent explanations for occurrence of price spikes: ordinary but tight
supply and demand conditions, or abnormal behavior by suppliers. In the latter case, if suppliers alter
their behavior by refusing to bid some of their otherwise-profitable output into the market with the
purpose of raising price sufficiently so that their remaining output earns considerably more than the loss
on the withheld quantity, this represents a perverse supply shift that can cause an abnormal price spike.
This research develops an operational empirical approach for distinguishing between these two sources of
price spikes. The data used in the analysis is hourly bidding data from the New York Day-Ahead
electricity market in the summer of 2001. We use an OLS and non-linear exponential models to estimate
highly non-linear supply curves in the electricity markets. The results indicate that strategic behavior of
several large bidders, as well as two particular generators have contributed to or produced the price spikes
observed in the New York Day-Ahead electricity market in the summer of 2001. These results establish
the legitimacy of concern over strategic withholding and focus policy attention on periods of superpeak
prices and on especially large bidders.
The second chapter of this dissertation, “Effect of Information Revelation on Bidding Strategies
of Marginal Bidders in the New York Day-Ahead Electricity Market,” analyzes the effect of exchange of
and access to rivals’ private information on the pricing strategies of influential suppliers (so-called
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4marginal bidders) in the electricity markets and on the probability of those bidders being marginal. In the
majority of competitive markets exchange of cost and demand information between market participants is
not a common practice since it can create opportunities for anticompetitive behavior and cause distortion
of market equilibrium. Rules and regulations of some restructured electricity markets in the U.S.,
however, allow their participants to get access to rivals’ private information by allowing short-term
contractual agreements between bidders of electricity generation and multiple generating units
simultaneously or over time. We provide a simple theoretical model that demonstrates how access to
rivals’ cost information may alter bidding behavior of a marginal, or likely to be marginal, bidder, and
then test this model using our two unique measures of information revelation. This research uses hourly
bidding data from the New York Day-Ahead electricity market in 2006-2008. We use fixed effects panel
data model to investigate the effect of information revelation on price bids and Heckman two-step model
to estimate the probability of bidders being marginal. Estimation results reveal differences between
groups of marginal and non-marginal bidders and indicate that access to rivals’ private cost information
may cause higher marginal price bids by marginal bidders and, therefore, higher market equilibrium price.
The third chapter of this dissertation, “What Drives States’ Choice of Electricity Restructuring:
Understanding the Differences between Restructured and Non-Restructured States,” analyzes the drivers
behind states’ choice to implement restructuring and the differences in performance of restructured and
non-restructured states. Since implementation of electricity restructuring has not been mandatory in the
U.S., there are more than half of U.S. states that still operate their regulated electricity markets. Although
23 states have been restructured up to date, there has been no conclusion reached yet on whether
electricity restructuring achieved its goals of reduced costs and prices to consumers and greater market
efficiency. We use annual state level data for all 51 U.S. states from 1990-2008. The analysis involves
multiple estimations, including GLS model, two-stage least squares estimation and simultaneous
equations model with endogenous switching to account for the heterogeneity in the states’ decision to
implement restructuring or not, and for unobservable states’ characteristics. We find that significant
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5heterogeneity is indeed present in the sample between restructured and non-restructured states and that
there are substantial differences in determinants of retail electricity retail rates between the states that
implemented restructuring and those that did not. Our results indicate that as state’s restructuring choice is
not exogenously defined, results from simple OLS or GLS models without correction for endogeneity
bias, should be interpreted with care. These results are particularly important in developing states’
decisions about electricity restructuring and further restructuring reforms that keep in line with the
potential impact of restructuring on electricity prices.
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6ACKNOWLEDGEMENTS
This dissertation has been made possible through the assistance and encouragement of many
people. I would like to thank my dissertation advisor, Professor Kwoka, for his continuous inspiration,
enthusiasm and patience. I am very grateful to the other members of my thesis committee, Prof Dadkhah
and Prof Dana, for their support, encouragement and valuable critique and advice at various stages during
my progress. My dissertation committee members, as well as Prof. Wang, Prof. Sum, Prof. Morrison, and
Prof. Alper, have always served as indispensible teachers, mentors, and friends throughout my graduate
experience.
My experience at Northeastern University has been enriched considerably by my classmates, with
whom I shared all these years at graduate school, and who made this journey through graduate school
more enjoyable. I would especially like to mention Megan Gay, who has been the best studying
companion, office mate, and friend I could hope for.
My professional skills and experience were tremendously enhanced during my graduate years by
multiple invaluable opportunities of work with the American Public Power Association on various
projects and their financial support, without which my dissertation work and progress would not be
possible. I also express my gratitude to Synapse Energy Economics and its outstanding staff, in particular
Ezra Hausman, Bruce Biewald and Paul Peterson for their interest in my research, for believing in me,
their continuous support in various aspects of my student and professional life, and for the opportunity to
work with such amazing highly skilled experienced people, experts and just wonderful friendly people.
I have been strengthened by the love and support of my entire family in Russia and Israel. My
profound thanks go to my parents, who have always believed in me whether or not I gave them reason to,
and always supported me in everything even being so far away from me. I also thank my sister Olesya
who always provided me with distant support in all aspects of my life. This work is dedicated to my
family who made my years in graduate school more rewarding and enjoyable. However, with my parents
and my sister being so far away, my friends here became a part of my family and were extremely helpful
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7and supportive at all times. I would especially like to thank Julia Kopytova, Evgenia Shumilkina and
Victoria Angelatova for being such wonderful friends, for their continuous support and belief in me.
Without them this journey would not be possible and complete.
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8TABLE OF CONTENTS
Abstract 2
Acknowledgements 6
Table of Contents 8
Introduction 9
Chapter 1: Price Spikes in Energy Markets: “Business by Usual Methods”or Strategic Withholding? 12
Chapter 2: Effect of Information Revelation on Bidding Strategies of Marginal Bidders in the New York
Day-Ahead Electricity Market 56
Chapter 3: What Drives States’ Choice of Electricity Restructuring: Understanding the Differences
Between Restructured and Non-Restructured States 98
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9INTRODUCTION
In the late 1990s Massachusetts, Rhode Island and California started the process of restructuring
their electric power markets. Since that time more than 20 states began implementation of different
restructuring reforms in the electricity markets, even though some states decided to delay this process or
even completely abandoned the process of deregulation. The primary political driver for restructuring in
those early restructured states was that it would benefit consumers by reducing costs of electricity
generation and electricity prices to consumers both in the short and long run. Later, in the aftermath of
restructuring in the early restructuring adopting states and no evidence of coherent cost and price
reductions, the goal of restructuring has been changed from cost and price reduction to creation of more
efficient electricity markets through increased reliance on market forces. This doctoral dissertation
analyzes the drivers underlying restructuring choice in the U.S., opportunities for suppliers’ strategic
behavior created by restructuring of electricity markets, and effect of restructuring and suppliers’ strategic
behavior on wholesale and retail electricity prices.
As part of restructuring reforms, generation component of electricity infrastructure has been
separated from transmission and distribution stages. In the restructured market, consumers still buy
regulated component from the local regulated distribution company, but are free to choose a competitive
retail provider of generation services. Overall, restructuring of the electricity markets is considered to be
one of the most complex due to the unique characteristics of the good produced in the market. At least
until recently, it was considered that electricity cannot be stored, which makes balancing of the supply
and demand in the real-time more complex than for a storable good. This non-storability, or limited
storability, of electricity may lead to significant volatility in the spot market prices. In addition, limited
ability of suppliers and consumers to respond to dramatic changes in the real-time prices may result in
significant price spikes during the hours of peak demand, when even a slight increase in demand produces
market-clearing prices far in excess of the average electricity price. Therefore, in the competitive
electricity market, where market-clearing price reflects marginal cost of the supplier that clears the
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10market, consumers face much more price volatility resulting from scarcity conditions and fuel price
fluctuations. In other words, these unique characteristics of electricity create a lot of room for price
manipulations and strategic behavior by electricity producers in unregulated electricity markets.
One such phenomenon in restructured energy markets - a period in which market price rises
suddenly, markedly, unexpectedly, and temporarily, called “price spikes” - is the focus of the first essay
of this dissertation. Some participants and/or observers of electricity markets say that price spikes are
necessary since this is the only way for high-cost “peaking” generating units to recover their costs, which
would not be the case in regulated cost-of-service electricity markets.
However, there are two divergent explanations for occurrence of price spikes. One explanation
emphasizes the role of ordinary demand or supply determinants. When those determining factors take on
unusual values - for example, due to summertime heat or transport limits - equilibrium price may well rise
to unusually, but appropriately, high levels. The alternative explanation involves changes in the suppliers’
behavior itself that underlies the supply function. That is, if suppliers alter their behavior by refusing to
bid some of their otherwise-profitable output into the market with the purpose of raising price sufficiently
so that their remaining output earns considerably more than the loss on the withheld quantity, this
represents a perverse supply shift that can cause an abnormal price spike. This strategy–essentially
unilateral withholding–has consistently been viewed by the FTC as outside the reach of the antitrust laws,
leaving the agency without a remedy for such behavior.
Transition to restructured electricity markets has also been accompanied by creation of day-ahead
and real-time markets for energy where energy is bought and sold by market participants through the
uniform clearing auctions. In these auctions determination of market-clearing price is left to the free
market forces through competitive bids from buyers and sellers of energy. In the majority of competitive
markets exchange of cost and demand information between market participants is not a common practice
since it can create opportunities for anticompetitive behavior and cause distortion of market equilibrium.
Rules and regulations of some restructured electricity markets in the U.S., however, allow their
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11participants to get access to rivals’ private information by allowing short-term contractual agreements
between bidders of electricity generation and multiple generating units simultaneously or over time. As a
result of such contractual arrangements bidders can get nearly contemporaneous information about
operations and costs of rivals’ multiple generating units. Such access to private rivals’ cost information,
which became possible as market transitioned to wholesale and retail competition, and its effect on
pricing behavior of market participants is the focus of the second essay in this dissertation.
These opportunities for strategic behavior through price manipulations or acquiring access to
rival’s private information became available as electricity markets transitioned to restructuring and the
prices were no longer determined by the cost of service but were left to the market forces. However, since
implementation of electricity restructuring has not been mandatory in the U.S., there are still more than
half of U.S. states that operate their regulated electricity markets. Drivers behind states’ choice to
implement restructuring and the differences in performance of restructured and non-restructured states are
investigated in the third essay of this dissertation.
Overall, although the primary goals of restructuring were to benefit consumers through reduced
costs and prices and to create efficient electricity markets through greater reliance on market forces,
restructuring experience varied substantially from state to state; there still has been no conclusion reached
on whether electricity restructuring achieved its goals. Together with greater reliance on competitive
market forces, restructuring created greater opportunities for strategic behavior and exercise of market
power, which, if not mitigated, can distort competitive market equilibrium. While moving away from
regulation and market intervention, electricity restructuring still requires a lot of attention and policy
action to prevent anticompetitive outcomes. This doctoral dissertation sheds some light on the effect of
electricity restructuring, opportunities for strategic behavior, and potential anticompetitive outcomes that
result from it.
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12CHAPTER 1: PRICE SPIKES IN ENERGY MARKETS: “BUSINESS BY USUAL METHODS”
OR STRATEGIC WITHHOLDING? (Co-authored with John Kwoka)
The authors gratefully acknowledge helpful discussions and comments from Tim Brennan,
Kamran Dadkhah, Diana Moss, Machiel Mulder, Marcel Vermeulen, and personnel from the New York
ISO Market Services. All opinions and remaining errors in this paper are the sole responsibility of the
authors.
I. INTRODUCTION
The antitrust case against Standard Oil case involved a number of allegedly anticompetitive
practices by that company, ranging from industrial espionage to discriminatory rebating. In its opinion
the Supreme Court sought not only to determine Standard Oil’s culpability but also to provide guidance
for the distinction between tough but legally unobjectionable behavior and true anticompetitive practices.
Its criterion for the latter was “acts and dealings wholly inconsistent with the theory that they were made
with the single conception of advancing the development of business power by usual methods.”1 This
standard has been echoed in subsequent judicial rulings and served as the basis for economic analyses of
various business practices.
Several specific practices of Standard Oil, including predatory pricing practices and price
manipulation, were taken as evidence of its antitrust liability. In the century since the Standard Oil
decision, these practices have persisted in energy markets. Crude oil extraction, refining, transport, and
marketing have been the subjects of repeated antitrust inquiries into alleged instances of price distortion
directed at both rivals and customers. Similar allegations have recently arisen in other energy markets,
including natural gas, electricity, and automotive gasoline. The frequency of these allegations suggests
that these industries are either uniquely subject to suspect pricing practices, or uniquely subject to policy
scrutiny for their pricing practices, or perhaps some combination of the two.
1 U.S. v. Standard Oil Company of New Jersey et al., 221 U.S. 76 (1911).
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13This paper is concerned with one such phenomenon in energy markets, namely, “price spikes.”
We define a price spike as a period in which market price rises suddenly, markedly, unexpectedly, and
temporarily. That is, in the context of otherwise fairly steady and predictable prices, with little or no
warning or indication, price rises rapidly to very unusual if not unprecedented levels, and then rapidly
reverts to something like its prior level. We develop two arguments: First, we demonstrate that it is not a
coincidence that price spikes occur so frequently in energy markets. We analyze conditions giving rise to
price spikes, and observe that crude oil, natural gas, gasoline, and electricity markets all have
characteristics that make them especially vulnerable to them. Secondly, we develop an operational
empirical approach for distinguishing between a price spike that is in fact the result of abnormal behavior
by suppliers, as opposed to ordinary but tight supply and demand conditions. This latter exercise is in the
spirit of Standard Oil in that we seek to identify “acts and dealings wholly inconsistent
with…business…by usual methods.” We apply this approach to the New York State electricity market.
While other papers have examined strategic behavior and strategic withholding in electricity, ours
differs in two major respects. First, we focus on the reduction of quantity offered to the market under
conditions of strong demand as the defining characteristic of strategic withholding. No economic theory
explains or predicts such a leftward shift in supply under these circumstances. Secondly, we do not treat
all bidders and generators participating in the auction as homogeneous. Rather, we distinguish among
bidders and generators based on certain load and price characteristics that the theory of withholding
predicts should result in different behavior. Our analysis of the data confirms those predictions.
The next section of this paper sets out some examples of price spikes in energy markets and how
these have been previously evaluated. Section III develops the economics of price spikes, demonstrates
the distinguishing features of abnormal spikes, and explains the special vulnerability of energy markets to
such spikes. The subsequent section describes the New York State wholesale electricity market, sets out
our empirical approach, and tests that approach on periods of alleged price spikes in 2001. Our test finds
evidence of behavior inconsistent with “normal” bidding practices on one of three suspect days in the
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14month of August 2001, corroborating concerns about strategic withholding and providing support for this
analytical approach.
II. PRICE SPIKES IN ENERGY MARKETS
While price manipulation of various forms certainly dates back to the days of Standard Oil and
before, price spikes in energy markets began to occur with considerable frequency during the 1990s as
these markets were freed from regulatory constraints. This section recounts a number of these
experiences, which illuminate the key distinctions we later analyze.
One of the first prominent examples of price spikes involved wholesale electricity markets in the
Midwest during the summer of 1998. In late June, the price of wholesale power, normally about $30 to
$50 per MWH, shot up and briefly reached $7,500. A report by the Federal Energy Regulatory
Commission (FERC, 1998) attributed this episode to four exogenous factors--unusually hot weather,
equipment outages, transmission congestion, and retail price inflexibility. It also noted the possible
contribution by traders and buyers inexperienced in the newly formed electricity market,2 although many
other observers suspected price manipulation. Indeed, during the following summer with most of the cited
factors absent, the wholesale price spiked again, briefly reaching $10,000.
Similar price spikes in electricity began to occur in other regions of the country in 1999 and 2000.
The most famous example was California, where in the summer and fall of 2000, prices suddenly rose
from $30-35 per MWH to as much as $750. In that winter, with demand at its seasonal low, persistent
unexplained supply shortages caused price to average $260 to $400 for several months starting in
December, and even at such prices, outright shortages required rolling blackouts throughout the state.
2 A report by the private utility American Electric Power (cited in Michaels and Ellig (1999)) repeated this explanation and sought to allay further concerns by assigning probabilities of such an event ever recurring again. It assessed the probability of comparable weather at 0.3 percent and the probability of similar outages at 1.5 percent, implying the likelihood of both together at less than 1 in 22,000. As the text notes, another spike occurred the following year.
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15The persistent chaotic pricing resulted in financial disaster for consumers and distribution companies,
eventually leading to state intervention simply in order to maintain minimal operation of the market.3
The California experience has been analyzed endlessly and countless causes held responsible for
its problems–hot weather, reduced hydro flow, unanticipated outages, fixed retail rates, transmission
congestion, environmental regulations, excessive reliance on the spot market, etc.4 But another important
cause is now widely acknowledged, namely, that the market had been subject to strategic bidding and
trading practices, exploiting weaknesses of the regulatory system to raise price far beyond market
fundamentals. One of the methods involved the refusal of a supplier to bid some of its otherwise-
profitable output into the market with the purpose of raising price sufficiently so that its remaining output
earns considerably more than the loss on the withheld quantity (FERC, 2003; Weaver, 2004). We shall
analyze this conduct - unilateral withholding - in detail below.
Several other examples of price spikes in energy markets also occurred during this period.
Automotive gasoline prices in Chicago increased from $1.85 per gallon on May 30, 2000, to $2.13 on
June 20, before falling back to $1.57 on July 24. In Milwaukee, the price rose from $1.74 to $2.02, then
reverting to $1.48 by late July. A Federal Trade Commission investigation and report on this episode
concluded that there had been no collusion or other antitrust violation by refiners. Rather, it pointed to
high capacity utilization, some unusual supply disruptions, industry miscalculation, and some
independent, profit-maximizing decisions by refiners. The Report did, however, acknowledge that during
the crucial time period, one firm with substantial inventories chose to
limit[] its response because selling extra supply would have pushed down prices and thereby reduced the profitability of its existing ...sales. An executive of this company made clear that he would rather sell less gasoline and earn a higher margin on each gallon sold than sell more gasoline and earn a lower margin.5
3 The persistence of this abnormal price distinguishes this case, which might be termed a price shock rather than a price spike. 4 See, among many other sources, GAO (2002), Borenstein et al (2002), and Puller (2007).
5 FTC, 2001, p. 21.
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16 This strategy–essentially unilateral withholding–has consistently been viewed by the FTC as
outside the reach of the antitrust laws, leaving the agency without a remedy for such behavior.
The New York state electricity market experienced price spikes in 2000 and again in 2001.
Unusually hot weather in May of 2000 set the stage for wholesale price to reach $1300 per MWH over
the course of several days. The New York State Department of Public Service staff issued a report on this
experience acknowledging the likely problem of unilateral withholding but not proposing any remedy.6
A 2001 policy paper by the NY State Electric and Gas Corporation cautioned that the “large amounts of
generation available but unscheduled” was a harbinger of trouble for the state ISO.7 And indeed in 2001,
particularly in the month of August, much larger price spikes occurred. We shall postpone further
discussion of this latter experience until Section IV, where it is the focus of our empirical work.
Price spikes continued to appear in various energy markets. In February, 2003, the benchmark
price for natural gas at a major production point climbed from $8 to $16 per MMBtu in a single day and
then to $22 the following day. Price at the New York City consumption point briefly reached $40 per
MMBtu on February 25, then receded, and spiked again on February 28. The Federal Energy Regulatory
Commission concluded that this price increase was due to cold weather, pipeline and storage limits, and
illiquid (i.e., thin) markets, that is, “normal” factors rather than withholding.8
Automotive gasoline markets continued to experience unusual price movements. The Federal
Trade Commission investigated spikes in Arizona, Atlanta, the mid-Atlantic, and the western states in
2003-2004, in each case concluding that the causes were underlying costs or temporary tightness of
supply rather than market manipulation (Kovacic, 2004). Others disagreed. The Consumer Federation of
America cited evidence that refiners were (CFA, p. 32)
6 State of New York Department of Public Service, 2000 7 New York State Electric and Gas Corporation, 2001, p. 14. 8 FERC, 2003, p. 1.
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17[r]elying on ... existing plant and equipment to the greatest possible extent, even if that ultimately meant curtailing output of certain refined product...openly questioning the once-universal imperative of a refinery not ‘going short’–that is, not having enough product to meet market demand. The price spikes in these examples differ in many respects from each other. From our perspective
what is important is the fact that there are two divergent explanations for their occurrence. One
explanation emphasizes the role of ordinary demand or supply determinants. When those determining
factors take on unusual values - for example, due to summertime heat or transport limits - equilibrium
price may well rise to unusually, but appropriately, high levels. The alternative explanation involves
changes not in the determinants of ordinary supply (e.g., electricity usage during hot weather), but rather
in the behavior itself that underlies the supply function. That is, if suppliers alter their behavior by
reducing their offer quantities as price rises, this represents a perverse supply shift that can cause an
abnormal price spike.
The analysis that follows is based on this key distinction - whether price spikes result from
normal market behavior in unusual circumstances, or from abnormal behavior designed to exploit market
conditions. The latter, we would argue, is inconsistent with the Standard Oil criterion of “business…by
usual methods” since no ordinary profit-maximizing firm offers less output as market demand shifts to the
right and price is expected to increase.
III. THE THEORY OF PRICE SPIKES
This section sets out a simple theory of price spikes in a fashion that distinguishes between
natural causes and abnormal supplier behavior. We develop the theory in the context of electricity
markets, where this issue has attracted much attention, and also because we shall examine an episode of
price spike in electricity for our empirical test. The issues and analysis, however, are quite general. We
note some alternative methods of analyzing price spikes, and conclude with the key implications for our
approach.
A. A SIMPLE MODEL OF “NATURAL” PRICE SPIKES
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18A standard illustration of a wholesale electric power market is depicted in Figure 1. The key
characteristics are (a) a horizontal marginal cost curve up to some fixed capacity where unit costs increase
sharply, and (b) a very inelastic demand. Each of these characteristics deserves comment. Evidence is
overwhelming that electricity demand is quite inelastic in the short run, and not much more elastic in the
long run. Indeed, Independent System Operators and Regional Transmission Organizations, which now
cover approximately one-half of the country, essentially define forward demand as completely inelastic
since they take day-ahead load to be a fixed quantity and then acquire power to meet that load. This
assumption is embodied in the vertical demand D0 in Figure 1.
The shape of supply response S0 is governed by two considerations. First, baseload plants–
nuclear and large fossil fuel–run at low and essentially constant marginal cost set by the price of fuel and
the plants’ thermal efficiency. Such plants account for the large fraction of total power along the
horizontal range of the supply curve in Figure 1. Second, generation plants have relatively fixed
capacities, so that at or near their rated capacity their supply schedules rise rapidly, even becoming
vertical as shown in Figure 1. The small nonlinear range near capacity reflects the fact that capacity is not
quite a point value, but supply response does diminish rapidly as capacity is approached.
Figure 1 captures the intersection of these demand and supply curves, giving rise to a price P0
essentially equal to the marginal cost of baseload units. Demand may shift over the hourly, weekly, and
seasonal cycle, ranging between D1 and D2, but P0 would continue to prevail as a result of normal
competitive forces. It is important to note that a “natural” price spike could occur with this underlying
structure if demand temporarily shifts far to the right, say, to D3. Then a price such as P3 would arise,
simply reflecting unusually strong demand pressing on fixed available capacity.
Such conditions are virtually inevitable in many energy markets. Demand for many forms of
energy is quite volatile, and production capacity is often largely fixed. Random events such as an
unexpectedly severe summer heat wave can shift demand as shown and result in a price spike. Similarly,
an unexpected reduction in supply due to an unanticipated outage of a major generator or sudden
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19congestion on a transmission line might cause the supply curve S0 to shift leftward. Then even if demand
remained unchanged, price would rise in that same fashion as described by the previous example of a
price spike.
B. A SIMPLE MODEL OF “ABNORMAL” PRICE SPIKES
In contrast to the “normal” price spikes described above, it may also be the case that high price
results from changes in supplier behavior itself, behavior designed to exploit particular market
circumstances. In electricity markets the relevant circumstances involve periods when demand presses on
available capacity, and so expected price already exceeds normal levels. Generators can then create or
enhance scarcity by bidding into the market amounts that are smaller than their quantity offers when
demand and expected price are lower. Offering smaller quantities when price is higher involves a
divergence from normal supply behavior, and we take such a shift as the defining characteristic of
abnormal price spikes.
The strategy of offering a smaller quantity when demand is expected to be high is termed
unilateral withholding or strategic withholding. This strategy may take several specific forms,9 but for our
purposes the relevant features can be illustrated with a straightforward adaptation of the previous
analysis.10 We begin with that illustration and then provide the more general analytical demonstration.
To begin, suppose one firm supplying the market shown in Figure 2 has two plants of identical
size, denoted X1 and X2. (Their placement in Figure 2 will be explained shortly.) The remainder of the
supply sector can be structured in any manner whatsoever, from a single other firm to a highly fragmented
industry, but output from the other producers is assumed not to respond to the output choice of the firm in
question.11 Market demand is completely inelastic and either intersects the supply curve on its (short)
9 Moss (2006) distinguishes several variants, including physical withholding or simply shutting down a generator, economic withholding by bidding above marginal cost, out of merit order dispatch intended to create transmission congestion, and strategic withholding exploiting market dominance. We return to these distinctions below. 10 This framework is adapted from Kwoka (2000). 11 We shall see the importance of this assumption later on.
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20rising segment or lies no farther than the quantity X = X1 = X2 to the left of the point where the marginal
cost schedule ceases to be horizontal. As a result, initial price is given by P1.
For simplicity we begin by assuming that each of the two plants either produces exactly at
capacity or shuts down and produces nothing at all. Thus, the firm decides on plant-level output of either
X or 0, and total two-plant output takes on one of three values: 2X, X, or 0. The question we wish to
address is whether the two-plant firm can profitably withhold output from one of its plants.12 To
determine this, we need only compare its profits with two-plant production vs. profits with only one plant
in operation.
Two-plant profits at initial price P1 are given by [2 X (P1 - C)], where C is marginal cost. These
profits are shown as areas A and B above X1 and X2, respectively. The magnitudes of such profits are
clearly a function of the initial price-cost margin, (and would be zero if the initial position of demand
resembles that in Figure 1). The alternative for this firm is to withhold output from inframarginal
capacity, such as the plant producing X2. This reduction in supply can be represented by a leftward shift of
the overall supply curve in the amount X2, from S1 to S2. This will in turn cause price to rise from P1 to P2,
an amount dependent on the elasticity of supply across the range of withheld output.
For unilateral withholding to be rational for the firm in question, it must gain more in profit on its
remaining plant in service than it loses on the withheld plant. In Figure 2, that gain is shown as area D,
above the quantity X1 from the plant that continues to operate. The loss is given by B, the profit
previously earned by the now-shutdown plant. A comparison of these two areas implies that net profits
will increase when the following inequality is satisfied:
X1 (P2 - P1) > X2 (P1 - C) (1)
12 Obviously, it cannot profitably withhold output from both, since the yet higher price would only benefit other producers. The considerations relevant to this trade-off can be found in Wolfram (1998).
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21Since by assumption in this example the two plants are identical, this inequality is satisfied and unilateral
withholding profitable so long as the price increase (P2 - P1) exceeds the initial price-cost margin (P1 - C).
This simple example demonstrates how withholding may be unilaterally profitable. It is, of
course, true that any one firm would prefer that some other firm assume the burden of output reduction so
that it could free ride on the resulting price increase, but it is nonetheless in its own independent interest
and therefore predictably will occur. No conspiracy or cooperation with other firms is necessary. It is also
important to recognize that the size of the withholding firm need not be especially large. In this example
the firm in question needs only to have one plant whose quantity withdrawal is sufficient to produce the
requisite price increase. Clearly, this is a value considerably less than the market share that usually
triggers concern over market power.
C. A GENERAL MODEL OF STRATEGIC WITHHOLDING AND PRICE SPIKES
We can generalize this result and gain further insight as follows. Instead of assuming a two-
identical-plant firm making an all-or-nothing production decision for each plant, we allow the firm to
make a continuous output choice up to its total capacity from all plants. Firm profit is given by
π = P q – c q (2)
where q denotes its output and c is constant unit cost.13 P is the price that results from aggregate quantity
offers by all firms (“supply”) intersecting fixed-quantity demand, or put differently, the price on the offer
curve at exogenously fixed demand quantity. For present purposes the price function P can usefully be
written as P(Qs) = P(q + r), where Qs is the total quantity offers by all firms—the simple sum of this
firm’s quantity offer q and that of remaining suppliers r. Differentiating this expression with respect to q
gives the following profit-maximizing condition:
d π /dq = q (dP/dq) + P - c = 0 (3)
13 The assumption of constant unit cost c is a useful simplification. Unit cost would differ if the firm in question represented marginal capacity, but even then the cost effect is likely to be secondary. For an approach that allows for some cost variation, see Joskow and Kahn (2002)
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22Despite the apparent similarity, it should be stressed that this expression is not the usual first-
order condition: dP/dq does not represent movement along the demand curve, since quantity demanded is
by assumption not responsive to price. Rather, dP/dq measures the effect on equilibrium price when this
particular firm reduces its offer quantity q, thereby shifting the supply curve to the left. This term can
usefully be written out as follows:
(dP/dQs) (dQs/dq) = (dP/dQs) (d[q+r]/dq) = (dP/dQs) (1 + dr/dq) (4)
Here dP/dQs denotes the effect on equilibrium price resulting from different possible total offer
quantities Qs. Total Qs changes both with this firm’s supply q and also with any output changes in
response by other firms. We continue to assume dr/dq = 0, that is, other firms hold their offer quantities
constant.
Substituting, we can rewrite (2) as follows:
-q (dP/dQs) = P –c. (5)
Dividing by P, we obtain
m = s W (6)
In this expression, m is the initial price-cost margin (P – c)/P, s is the post-withholding market
share of this firm (q/Q), and W is the elasticity of price from output withholding (dP/P)/(dq/q). It is
readily apparent that W is the reciprocal of the supply elasticity. The reason for this is simply that the
price effect of a quantity shift is zero when supply elasticity is infinite, but the price effect increases as
supply elasticity falls. Mathematically, too, W = 1/E, and so equation (5) can be written
s = m E (7)
Condition (7) states that unilateral withholding is profitable up to the point where the firm’s
market share equals the product of the price-cost margin and the elasticity of supply.14 Thus, in the
extreme case where pre-withholding margin is zero, there are no lost profits from withholding so that any
14 It is most natural to interpret this as pre-withholding margin and post-withholding share. Pre-withholding output matters as well, but its role is subsumed in supply elasticity.
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23post-withholding output yields a profit gain. On the other hand, if the market is on, or moves to, the zero-
elasticity portion of the supply curve, the induced price rise becomes limitless and withholding is
profitable at any level of final output.
Table 1 provides some sample calculations of the necessary post-withholding market shares s2,
based on various possible values of m and E. Margins from 0 to .50 are examined against a range of
supply elasticities from zero to 50.15 Table 1 indicates that if m = 0 or E = 0, the necessary levels of post-
withholding output and share are trivial. But if pre-withholding margins are positive so that withholding
entails a profit sacrifice, the post-withholding share required for profitability involves a trade-off. For
market operation at elasticities such as 0.1, the necessary post-withholding share is only 5 percent even
for a fairly large pre-withholding margin of .50. Even with somewhat larger supply elasticities,
withholding often remains profitable. For example, if E = 0.5 and pre-withholding margin is 20 percent, a
10 percent post-withholding share suffices. And if E = 1.0, a 10 percent pre-withholding margin still
requires only a 10 percent post-withholding share.
The table does indicate, however, that where supply elasticity is greater, profitable withholding is
considerably more difficult. Indeed, withholding is unlikely to be a profitable strategy where E takes on
values of 5.0 or greater. As noted previously, however, such supply elasticities characterize the interval
well short of capacity.
D. OTHER ANALYSES OF PRICE SPIKES
A few other studies have examined price spikes, typically with a methodology oriented toward a
broad analysis of market power in electricity. Prominent among such studies are Borenstein et al (2002)
and Joskow and Kahn (2002), both of which focus on the 2001 California experience. The basic
methodology for each of these is similar and involves a comparison of actual price to a counterfactual
15 With respect to E, one study reports data implying that elasticity of supply to California’s Power Exchange is about 50.0 for output up to about 70 percent of capacity. For 70 – 80 percent, elasticity falls to just over 1.0. For 80-90 percent of capacity, elasticity equals 0.3, and above 90 percent, it is 0.1. See Joskow and Kahn (2002), Wolfram (1998) and Puller (2007).
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24competitive price based on calculated marginal costs of the marginal generation plant. After allowing for
other factors, both studies find evidence of market power, especially during peak periods, although by
itself this does not establish whether the mechanism was withholding. Joskow and Kahn cast some light
on this latter question in their examination of what they term the “output gap,” the difference between
generators’ production capacity that would be profitable to use and that which in fact they did use. Again
allowing for other factors, they find significant amounts of undispatched, and unexplained, power during
peak hours.16
Boddeus (2008) extends and applies this approach to the Dutch electricity market. He calculates
“unloaded but profitable capacity” per firm, a concept similar to the output gap, and then regresses
variable on two explanatory variables --the residual supply index (a measure of the pivotalness of the
firm’s supply) and an index of “cheap” capacity held by the firm. The hypotheses are that the more
crucial is the firm’s supply to price determination, and also the more cheap inframarginal capacity it
holds, the more likely it is to withhold output. His statistical tests confirm that is the case. He then tests
for a relationship between the wholesale market price and this unloaded but profitable capacity, and finds
confirmation that firms indeed do influence price by holding their output off the market.
Other work on strategic withholding and on price spikes exists, including that based on supply
function equilibria and that emphasizing the purely statistical properties of price movements (Knittel and
Roberts (2005), Siefert and Uhrig-Homburg (2006)).17 Most of these approaches to the problem face
methodological challenges. Those that require determination of marginal costs encounter either
accounting or econometric issues. Generator unit comparisons must resolve questions of seasonal outages 16 See Harvey and Hogan (2001) for comments and criticisms of Joskow and Kahn. The previously-cited New York State DPS Report had performed a similar comparison of offer quantities under different load conditions, relying upon nonpublic cost data. 17 Boddeus provides a convenient summary of much of this work. Mention should also be made of an effort by the Dutch Competition Authority to identify withholding by examining the dispatch inefficiency of the market, that is, the extent to which higher cost units are dispatched while lower cost units are available. After this paper was completed, we became aware of work by Zhang et al (2007), which examines the same price experience in the New York ISO but focuses on the price choices of small, presumably marginal generators. Our theory guides us elsewhere, but with results that are not inconsistent.
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25for maintenance. Counterfactual pricing models require assumptions about demand elasticity. Models
dependent on Cournot or other particular behavior seem suspect. The need for nonpublic data makes any
of these approaches problematic. Below, we offer a methodology that, while itself imperfect, avoids many
of these problems.
E. CONCLUDING OBSERVATIONS ON WITHHOLDING
We conclude this section with two observations. First, unilateral withholding has been shown to
be profitable when demand presses on available fixed supply. Under these conditions an abrupt shift in
supplier behavior can create price spikes that differ from the price - no matter how high - that results from
ordinary supply and demand equilibrium. It is this abrupt behavioral shift by suppliers that constitutes the
distinguishing characteristic of strategic withholding and price spikes, and it is this characteristic that
forms the basis for our empirical test below.
Secondly, this analysis also provides an explanation as to why electricity, natural gas, and
automotive gasoline markets exhibit price spikes with unusual frequency. The reason is simply that each
of these is characterized by relatively fixed capacity and demand that is both inelastic and subject to large
shifts, periodically approaching or reaching that capacity. In electricity, generation capacity is very close
to a fixed quantity: No amount of additional fuel (the variable input) can elicit more output once capacity
has been reached. In petroleum refining, the refineries serving a region establish an essentially fixed
maximum output defined by their rated capacities. Natural gas transport is limited by pipeline capacity
constraints, which when reached can fragment markets and make supply exceedingly inelastic. Since
these energy markets are generally characterized by low demand elasticity and considerable volatility in
demand, the preconditions for profitable withholding are often met.
We now examine one of these price spike experiences, with a view to distinguishing strategic
behavior vs. normal price volatility as the cause.
IV. PRICE SPIKES IN NEW YORK
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26This section analyzes the New York State wholesale electricity market, and in particular, its price
spike experience in 2001. We begin by describing that market, followed by our data, methodology, and
results.
A. THE NEW YORK WHOLESALE ELECTRICITY MARKET
The New York Independent System Operator grew out of the New York Power Pool and began
formal operation of its electricity market in late 1999. The ISO administers a day-ahead market as well as
a real-time market, both operating as uniform price auctions of offers (bids) necessary to satisfy
administratively determined requirements. The day-ahead market encompasses approximately 95 percent
of trades. The remaining real-time trades resolve any supply-demand discrepancies at the actual hour. The
entire state is divided into 11 geographic load zones, within each of which the same price obtains. Price
may on occasion differ between zones due to transmission constraints that prevent a common price from
emerging.
The day-ahead market–the focus of our attention–operates as follows: The New York ISO
announces its load forecast, in the form of a fixed MW quantity for each hour, one day in advance. That
forecast is based on known patterns of use (day-night, day of week, etc.), adjusted for such factors as
expected weather. Bidders, which either own or have under contract one or more generating units, submit
bids consisting of up to six price-quantity combinations for each generation unit, so that, for example, a
particular generating unit might be offered at $10 per MW up to 50 MW, and also at $50 up to 100 MW,
etc., for a specific day-ahead hour. 18 The NYISO uses a sophisticated computer algorithm with
information about transmission conditions and constraints to create an aggregate offer curve and
determine a uniform market-clearing price for each hour for the entire ISO or if transmission and other
18 Bidders can submit their offers in either a block or curve format. We use a modified form of the technique described in Siddiqui et al (2004) to convert block offers to the smooth curve format. Neither the contracts nor the identity of the bidder is available to the public, although the bids themselves are available with a six-month lag. In each hour, no generator can be offered by more than one bidder, but any generator can be offered by different bidders over time.
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27constraints bind for individual zones. That price in turn determines which units will be called upon and
what price will obtain.
Like all ISOs, the NYISO has had in place measures to mitigate market power, defined (in
language reminiscent of Standard Oil) as “conduct that…is significantly inconsistent with competitive
behavior” (NYISO, 2008b, p. 12-15). These measures are triggered by various types of possible conduct
by bidders and generators, including physical withholding (failure to offer output), economic withholding
(offering output at unjustifiably high prices), and uneconomic production (over-producing so as to create
system constraints). Criteria for each are set out in considerable detail.
In its first full year of operation, the New York Day-Ahead market experienced significant price
spikes. As shown in Figure 3, price was relatively well-behaved until August, when on three successive
high-demand days it rose from its normal $40-45 per MWH to $194, $917, and $180 per MWH. It is this
experience that is analyzed further below.
B. ASSUMPTIONS AND DATA FOR THIS ANALYSIS
Our method of analysis is intended to permit judgments about price spikes without reliance upon
nonpublic cost or other data. It depends simply on the difference between the quantity offered when
demand is very strong and that under more typical demand conditions. Our key proposition is that a
reduction in offer quantity under peak forecast conditions represents a divergence from normal profit-
maximizing business behavior. Such behavior is presumed suspect and warrants further investigation.
This criterion and approach require both some simple and likely unobjectionable assumptions, together
with others that may be more questionable. The latter will require further analysis before final judgment
can be passed on the overall merits of this approach, and so the present exercise should be interpreted as
an initial effort. The simple assumptions are as follows:
(1) Load forecast is common knowledge, represents the market’s ex ante best estimate of day-
ahead price, and is the basis of bid decisions. This assumption seems unobjectionable since load forecast
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28is publicized by the ISO, and does indeed represent the quantity the ISO is committed to acquiring in the
day-ahead market, even though the real-time market serves to reconcile actual demand with supply offers.
(2) There are no generator outages due to scheduled maintenance during the summer months. In
fact, the New York ISO requires generators to submit their outage schedules for approval, and will permit
outages only if the otherwise available capacity meets forecasted load.19 As a practical matter, no
scheduled outages are approved for summer months, so by focusing on summertime data we avoid
difficult determinations about the causes of outages that other studies have had to contend with.
More debatable assumptions that we also make in this preliminary analysis include the following:
(3) New York State constitutes a single integrated market. This assumes away any transmission
constraints that might subdivide the overall market into load zones with substantially different prices.
Consistent with this assumption, the ISO compiles a statewide price that we take as indicative of, if not a
precise measure of, all zonal prices. The more disaggregated zonal case would require analysis beyond
the scope of this inquiry.
(4) Supply arises from generators within the state of New York. Put differently, imports of power
either must be de minimus or do not vary greatly over the relevant time. Data show that imports constitute
less than 5 percent of total consumption, an amount that does not seem so large as to undermine our
approach.
The core data for this analysis consist of bid price data by hour by bidder and by generating unit
for the months of July and August, 2001. This focus allows us to examine supply behavior in the overall
market, by groups of bidders, by individual bidders, and by individual generators under a range of
demand conditions up to and including the spike periods in early August. These two months cover the
period of summertime demand and hence operation of the full set of generators, but it avoids the period of
scheduled outages. Relative to the approach of inferring or estimating the frequency of planned outages,
simply focusing on a period with no such outages seems preferable. 19 Based on email exchange with representative of NYISO. Data on scheduled maintenance for 2008 confirm that no such maintenance occurred in the months of June, July, and August (NYISO, 2008a).
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29These bid data are publicly available on the NYISO website with a six-month lag. There are a
total of 125 active bidders and 307 generating units–each identified by ID numbers but not names—that
participated in the day-ahead auction during this two-month period. Although the ultimate owners of
these units cannot be determined, that does not matter for most of our analysis. The actual data consist of
one or more price/quantity bids per generation unit as described above. As the first stage of our analysis,
we compile these into the actual offer curves of bidder and each generator as well as the overall market
offer curve, for each of 1488 hours in July and August, 2001.
C. ANALYZING PRICE SPIKES IN NEW YORK
Our methodology involves examination of differences in the quantity offers in periods when
demand is expected to approach capacity vs. more normal periods during the two summer months of
2001. We identify high-demand periods ex ante as those when the ISO load forecast is at or near its
maximum, and ex post as those when prices spiked. Inspection of the data indicates a maximum load
forecast of 32,008 MW at 2 PM, August 9, which is also the hour when price reached $917 per MWH.
Table 2 reports the hours, load forecasts, and prices for all of what we shall term the peak hours in 2001.
We begin by analyzing aggregate market offers and then focus on large generators.
1. Aggregate Analysis
We construct aggregate offer curves for each of the 1488 hours. Figure 4 displays one such offer
curve, that for an hour that was “normal” in all respects–both load and price were squarely within the
range of summertime experience in 2001. This hour, 2 pm on July 19, will henceforth be treated as the
benchmark hour.20 As is evident, generators’ overall price/quantity bids map out a textbook supply
curve.21 We are interested in comparing this offer curve to that for peak hours. If at peak hours aggregate
20 Other possible benchmark hours give exactly the same results. We shall discuss choice of this hour as a benchmark further below. 21 Note that since bidders offer price-quantities based on the generation units they own or control, aggregating bidders would result in the same curve as this aggregation of bids by generating units.
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30quantity offers are smaller, or equivalently if price is higher for any quantity, that would represent a shift
of the offer curve in a direction consistent with strategic withholding.
To test this formally, we employ a two-step statistical procedure. The first step consists of fitting
the nonlinear relationship between price bid P and bid quantity Q for each hour. After examining various
functional forms, we find that the nonlinear exponential model provides the best overall fit:22
P = a · bQ (8)
In this nonlinear equation the parameter a is the vertical intercept–effectively, the bid price of baseload
plants. This parameter will reflect their low marginal costs, but also perhaps their must-run status or even
generating units’ determination that they be called upon to run at any market price.23 Our hypothesis
focuses on the second parameter, b, which captures the curvature of the offer curve. A larger value of b
denotes a more sharply rising curve, that is, a higher offer price at any quantity, as would be expected
under strategic withholding.
Using nonlinear least squares regression, we estimate equation (8) for each of 1488 hours.
Typical of the results are those for our benchmark hour, as follows (standard errors in parentheses):
P = (1.92·10-7) · 1.001Q (9) (4.97·10-8) (7.73·10-6)
Both parameters a and b are estimated quite precisely, and the overall relationship displays a
very high R2 = .98, implying a very good overall fit. Inspection reveals, however, that the fit is less good
along the upper tail of the offer curve, where prediction errors are more sizeable (though not systematic).
This is unfortunate since it is in this region that withholding behavior should be most apparent. We
22 Our choice of this model over, say, the log-linear model, is grounded in the fact that about one third of our observations on price/quantity bid pairs have zero price values. These are more readily handled in the exponential than the log-linear model. 23 That is, some generating units bid their output in at very low, even zero, price. While this ensures they will not be the marginal generation unit, it also guarantees they will be called upon to produce their offer quantity at whatever (higher) price arises.
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31proceed nonetheless with our intended statistical test, and then develop some alternative methods of
examining the data.
We compile the estimates of the curvature coefficient b for all 1488 hours. Next we regress those
estimated values against two measures of peak demand–hourly forecasted loads, and the actual market-
clearing price. We hypothesize a higher value of b when load and/or price is greater, which would
indicate a leftward/upward shift of the offer curve under the conditions of high load and/or price. The
results of these regressions are reported in Table 3. Column (a), however, indicates a negative relationship
between LOAD and b, implying that total offer quantity at any price in fact increases with load forecast.
This indicates a normal supply response by generators to expected demand shifts. Additional regressions
testing for breaks or nonlinearities fail to detect evidence of withholding behavior based on load forecast.
Alternatively, we examine the relationship between the supply response measure b and actual
market-clearing price MCP. This approach diverges from our effort to relate bid behavior to ex ante
demand forecasts, but focuses instead on bid behavior based on the realization of price as the best
estimate of the predicted price in future hours. The results of the regression of the supply curvature
parameter on MCP for all 1488 observations are reported in column (b) of Table 3. Again, a negative
overall relationship between the two emerges. In contrast to LOAD, however, the data reveal a different
relationship between b and MCP above and below a breakpoint for MCP. The results of dividing the
sample at a MCP of $200/MWH that point and estimating the relationship for MCP < 200 and then for
MCP > 200 are shown in columns (c) and (d), respectively. The regression coefficient continues to be
negative for the large majority of observations on market-clearing price below $200/MWH (column (c)),
but for the nine hours with very high prices there is in fact a positive and significant association (column
(d)). This latter result implies an inward shift of the offer curve under peak conditions, the kind of supply
response indicative of strategic withholding.
These observations for MCP > 200 are nine consecutive hours on the single day August 9. As
previously shown in Table 2, market-clearing prices for those hours exceeded the previous maximum
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32value of $184/MWH, which had been set on the prior day. There is no evidence of offer-reductions on
either August 8 or 10, despite quite high loads and prices on both of those days. Withholding appears to
be limited to the singularly unusual circumstances of August 9.24
2. Individual Bidder Analysis of Prices and Quantities
The theory underlying withholding behavior involves overall output changes by bidders offering
a set of generating units at peak demand. Since each bidder makes price and output bids on behalf of one
or more generating units, theory predicts that strategic withholding is most likely a decision made by a
bidder seeking to maximize its total profits from all the generation capacity it holds, rather than from a
single generating unit. Our next step is therefore to compare the supply behavior of individual bidders at
peak vs. nonpeak hours. We hypothesize that some of these bidders, most likely large ones, offer a lesser
quantity under peak demand conditions in order to cause price to spike. This section analyzes the data to
determine whether this is the case.
Analysis of data on bidding practices is made difficult by the fact that the rights to represent a
generator in the ISO auction can transfer from one bidder to another on a short-term basis. This makes
comparisons over time of supply behavior for many individual bidders difficult since they do not offer the
same units at all hours. We can nonetheless cast some light on our hypothesis by examining the subset of
bidders that have multiple generating units (typically, more than 5) while offering a relatively consistent
set of units over time (typically, at most one or two units changing between peak and nonpeak hours). Of
the 125 bidders in our data set, about fifteen meet these criteria. For each of these bidders, we construct
aggregate offer curves for the peak hour of 2 pm on August 9, 2001, and compare to these same bidders’
offer curves for several nonpeak hours one, two, and three weeks earlier.
What we observe is illustrated in Figures 5.a and 5.b. There are in fact two seemingly different
types of supply behavior at peak hours in the data. Figure 5.b depicts a leftward-shift of the offer curve
24 The load forecasts for the peak hours on August 9 were extremely high, although hours on other days also had very high loads. The correlation between forecasted load and actual market-clearing price is .48, which is significant but hardly overwhelming.
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33relative to nonpeak supply, much like the theoretical construct of strategic withholding discussed above.
But the data also reveal an alternative strategy involving a similar shift in the offer curve (leftward or,
equivalently, upward) for most quantities, but prices that are actually less than in non-withholding hours
at very large quantities. This depiction, shown in Figure 5.a, is commonly called “economic
withholding,” and suggests that offer quantities are repriced (upward) in contrast to the quantity reduction
behavior central to our analysis of withholding. Thus, with the possible exception of the highest
quantities in one bidding strategy, we can conclude that indeed there is evidence of reductions in offer
quantities at peak hours.25
Next we seek to understand how the strategy involving a lower price for some quantities may
nonetheless be profit-maximizing to the firm. We note preliminarily that most bidders develop a daily -
or sometimes even weekly - pricing strategy for their entire set of generating units. Relative to hourly
pricing, this presumably conserves on transactions costs while seeking maximum expected profit over the
daily load cycle. Demand fluctuates over the day in a fairly predictable manner, so that each bidder can
project its residual load across the cycle and chooses its overall - and fixed - supply accordingly. A
correctly chosen set of offer quantities and prices will result in high (if not maximum) profits as demand
shifts, achieving substantial profits without the need to alter the offer hour by hour.
The result of this strategy can be illustrated with the data in Table 2, which shows that the
market-clearing price exceeded $200/MWH during nine hours of August 9, 2001, and was below
$200/MWH the rest of the time. Knowing this, a bidder with a withholding strategy over the 24-hour
period similar to that illustrated in Figure 5.a can maximize profits during fifteen hours when the market-
clearing price is below $200/MWH. On the other hand, a bidder with a persistent strategy similar to the
one depicted in Figure 5.b chooses to maximize its profits during the nine hours with the market-clearing
price above $200.
25 Given that these bidders are only a subset, this exercise should be interpreted as preliminary.
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34While this explains how each strategy can maximize expected profit, the question remains: what
determines whether a bidder adopts the strategy described in Figure 5.a or 5.b? A closer look at the
generation portfolios of bidders of both types sheds light on this issue. The key is to recall that any bidder
not only needs to reduce quantity in order to raise price, but also to continue to supply sufficient quantity
at the elevated price to increase its total profit. The need to continue to supply generation requires that all
the bidders’ low-cost base-load generation is dispatched. Bidders, however, have very different amounts
of base-load generation. A bidder with mostly base-load generation may withhold only some production
at the end of the supply curve, corresponding to the output with highest level of marginal costs and
resulting in a “leftward” shift of its offer curve. On the other hand, a company with a little or no base-
load generation (and therefore an offer curve that rises almost immediately) may find it profitable to
withhold production from the early ranges of the supply curve, causing an apparent “upward” shift. This
is precisely what the data on the generation portfolios of two types of bidders and their price-quantity bid
pairs reveal.
What still remains to be explained is why a bidder like that shown in Figure 5.a offers its largest
quantities at lower prices at peak load times. An example suggests the reason. On August 9, 2001, there
were 9 hours with market-clearing prices above $200/MWH and 15 hours with market-clearing prices
below this value. The strategy of the bidder in question was to raise its price during the hours with lower
market-clearing prices, but where price was expected to be higher, it lowered its peak offer price in order
to ensure dispatch of all its generation and thereby earn high profit. Bidders of this type avoid
withholding during the expected super-peak hours and instead free-ride on other bidders whose lower-
cost generation portfolios will induce them to withhold.
These observations tie together two considerations: different types of bidders (based on their
generation portfolio) and differences in bidding strategies. Each bidder chooses its price-quantity offers
based on its mix of generation and marginal costs, as well as on transactions and information costs.
Although there may seem to be two types of price-quantity offers, each in fact promotes high prices while
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35ensuring dispatch of the bidder’s generation units. This explanation contrasts with the literature that has
generally treated “physical withholding” and “economic withholding” as two different strategies,
somehow exogenously determined or chosen. Our view is that these are simply different manifestations
of the same strategy, adapted to different exogenous conditions.26
Finally, it is noteworthy that there is a great deal of consistency within each of these two types of
bidders with respect to their individual generating units. Unit offer curves for each bidder at peak hour
vs. nonpeak hours closely resemble each other, and are necessarily mirrored in the aggregate offer curve
for each type of bidder. Figures 6.a and 6.b illustrate these supply behaviors. Figure 6.a shows that most
generators represented in the auction by a bidder of the type illustrated in Figure 5.a offer higher price
bids at peak hours for the majority, or even all, of their output. Figure 6.b demonstrates that generators of
the type shown in Figure 5.b offer the same price bids on peak and nonpeak hours for the majority of the
output range and raise price bids at peak hours only for the highest output offers. Clearly there is
considerably consistency among generating units controlled by a single bidder, even though bidders
pursue different withholding strategies to manipulate price to their advantage.
3. Generator Group Analysis
The analysis of generation portfolios of individual bidders revealed different behavior patterns
among generators of different sizes. Thus, we next examine more closely the supply behavior of different
size segments of the population of 307 generators. Theory suggests that it is large generators that offer a
lesser quantity under peak demand conditions. We have no hypothesis regarding small and medium size
generators: In principle they might aid the withholding strategy by contracting, but it seems more likely
that they hold output constant or actually increase their quantity offers in response to others’ contraction
and the prospects of higher price.27
26 Indeed, to the extent that the common distinction between physical and economic withholding seems to rest on whether the supply curve is said to shift leftward or upward, the difference seems unpersuasive.
27 Recall our earlier assumption that smaller producers’ output remained unchanged. While common to such analyses, there is no reason to accept that assumption.
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36To test these predictions, we compute each generator’s maximum quantity offer for each hour in
July and August, calling this result their “size.”28 We then rank all generators by their average size
across all hours and identify a breakpoint that distinguishes “large” from “small and medium” generators.
The breakpoint that clearly emerges is at 485 MW. Above this point there are 19 large generators, with
the next considerably smaller at 390 MW. The remaining 288 generators will simply be called “others” or
“small and medium size generators.” Each of these two groups accounts for approximately one-half of
total offer quantity.29
Table 4 reports the supply responses of these two groups according to market clearing price MCP.
These data reveal that the 19 large generators collectively offered 15,760 MW for prices in the range of
$150-175 and 15,726 MW for MCP between $175 and $200, but only 15,527 MW for MCP > $200. This
decrease of 233 MW relative to their maximum represents a 1.5 % supply reduction. While hardly
overwhelming, it is not consistent with normal business practices, which would involve either an increase
in offer quantity as price rises (as is the case at lower prices for these generators), or at least a fixed
quantity if these generators faced a capacity constraint at 15,726-15,760 MW. The fact that large
generators offered smaller quantities to the ISO as peak price was approached is behavior consistent with
strategic withholding.
These data also allow testing the behavior of small and medium size bidders, which the literature
generally assumes is unchanged in the face of price spikes. We find, however, that across this same range
of prices, small and medium generators actually expand their offer quantities much as they did at lower
prices. For MCP between $150 and $175, they offered 17,394 MW, whereas for prices above $200, their
28 There are in fact two methods of calculating such an average–across all 1488 hours, and across hours where the generator bid any positive quantity. While the former is in some sense a better measure of their average size, the complete absence of a generator during periods of high demand must be the result of some factor not relevant for purposes of determining its typical size in the NYISO, for example, delivery of power to another market, etc. Henceforth we shall focus on the set of hours with positive quantity offers. 29 We have also examined the data distinguishing between small and medium size generators, but without important differences.
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37quantity offers rose to 17,765 MW. This behavior implies that small and medium size generators behave
more like competitive firms, increasing their output in response to the prospect of higher price. Indeed,
their 371 MW increase more than fully offsets the decrease by large generators, resulting in a small
positive change in total offer quantity at peak hours. This helps to explain our earlier result indicating a
total quantity offer no smaller at peak loads, but it would seem that that result occurs despite efforts by the
large generators to rein in quantity. Without the output reduction by the large generators, total output
would presumably have been larger yet, implying that large generator withholding indeed did contribute
to the price spike episode.
4. Individual Generator Analysis of Prices and Quantities
Our final approach to the issue of price spikes and strategic withholding is to examine the
behavior of a few large individual generating units within bidders’ generation sets. The largest
generators’ offer quantities for the peak price hour of 2 pm on August 9 are shown in Table 5, together
with the corresponding quantities for the benchmark hour (which is typical of their summertime offer
quantities). The striking fact that emerges is that among the group of nineteen large generators, ten are
found to hold output constant. Seven others alter their offers in the narrow range +16 to -10 MW, which
are trivial relative to their own or the market’s total offer quantities. Essentially all of the aggregate output
reduction derives from two large generators, those labeled generator K and generator O.
Generator K offered at least 670 MW for the most of the summer of 2001, and between 690 and
697 MW consistently for all hours of August 7 and August 8, and for hours up until 2 am on August 9. At
that point its quantity offer dropped to 350 MW for the following 24 consecutive hours–a decline of 340-
347 MW. It then rose to the range of 500-530 MW up until August 20 at which time it resumed its
previous level of about 700 MW. Its offer prices cast further light on its strategy. On August 8 up until 12
noon, it bid 530 MW at price bid $0 (ensuring it would be called upon), 645 at $58 per MW, and 690 at
$160. Starting at noon and for the next twelve hours, it offered only two blocks—645 MW at price bid
$0, and 690 MW at $160. This ensured that a minimum of 645 MW would operate, and more if price
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38exceeded $160 (which it did for four hours). Starting at 4 am on August 9, it bid its lower quantity of
350 MW in at a price bid of $0 and continued to bid a price of zero for the next 48 hours (half of which
involved quantities of 350 MW and half 530 MW). Its low bids ensured that its reduced output would be
called upon and receive the high market-clearing price.
The other generator - Generator O - also employed a strategy involving an initially greater
quantity at a very high bid price. Its quantity offer averaged about 600 MW for July and early August,
but at 4 am on August 8 it reduced its offer to 400 MW and 24 hours later to 350 MW, for a total decline
of 250 MW. It was not until August 15 that it resumed its offer of 600 MW to the market. Its pricing
strategy differed from that pursued by Generator K. Up until 4 am on August 8, Generator O offered six
blocks of output at the following sequence of prices: (34, 37, 77,30 500, 700, 1000). Only the first three
blocks were likely to be called upon; the latter were being made available only if prices happened to
spike.
Beginning at 4 am on August 8, this generator dropped the last three price-quantity blocks
altogether, reducing its total quantity offer to 400 MW and then to 350 MW. It simultaneously repriced
the three initial blocks to (42,31 100, 200), up from (34, 37, 77), and held to that strategy even as market-
clearing prices fluctuated between $33 and $917/MWH. The effect was that it was called upon for
varying amounts of output, ranging from none (for early morning hours on August 8 through 11) to 350
MW when that was its maximum offer (on August 9), but never the full 400 MW (which was offered at a
price of $200 at times where market-clearing price was lower than $200/MWH). By contrast, recall that
Generator K dropped its offer price to zero when it truncated output so as to ensure it would be called
upon.
D. CONCLUSIONS REGARDING NEW YORK PRICE SPIKES
30 This number varied in eight hours.
31 Or $43.
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39The New York ISO experienced very unusual price movements in August 2001. We have found
that several large bidders, as well as two particular generators behaved in ways that appear to have
contributed to or produced the price spike that occurred on August 9. We have focused on bidders and
generators that might have engaged in physical withholding - a reduction in offer quantity - but economic
withholding - repricing output - also appears to be at work. The use of this latter strategy helps explain
the modest reduction in total offer quantity in the market even at the peak price. Repricing reinforces
physical withholding by helping to ensure a higher market-clearing price, but as we have seen the
generator using economic withholding risks that some of its output is not called upon and hence does not
benefit from that higher price.
V. SUMMARY OBSERVATIONS
In the Standard Oil case, the Supreme Court sought to identify anticompetitive and illegal
behavior by articulating a standard of consistency with “business by usual methods.” While imprecise,
this standard does prove helpful in analyzing energy markets that have experienced price spikes: It
directs attention to supply reductions in the face of expected high demand and price - a phenomenon that
differs from normal market behavior and is thus competitively suspect. The 2001 experience of the
NYISO provides support for this interpretation. It illustrates how price spikes due to strategic
withholding by some suppliers can be distinguished from large price increases resulting from ordinary
demand and supply behavior, albeit under conditions of extreme volatility.
While our test is as yet imperfect, taken at face value the results are of considerable substantive
interest. They document the fact that strategic withholding did indeed occur in the NYISO during the
summer of 2001. We also find that strategic withholding was responsible for unusually high prices only
during the superpeak period consisting of a few hours on one day. Other hours on that day as well as high
prices on the surrounding days all seem to be consistent with equilibrium prices under conditions of very
tight supply. Moreover, the analysis focuses attention on a very small number of large suppliers that were
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40uniquely responsible for the withholding. Other suppliers either held their offer quantities constant or, in
the case of less sizeable bidders, actually increased their supply.
These results have implications for both regulatory and antitrust analysis of electricity markets.
They es