Market Power Mitigation in Electricity Markets. Can We Do Better in New York? T. N. Subramaniam * February 16, 2014 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 ap- proaches to regulating market power. While the exercise of market power has received con- siderable attention in the literature, the issue of market power mitigation has attracted scant attention. 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 differ- ence 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%. Keywords: Deregulation, Market Power, Pivotal Suppliers JEL Classification Numbers: L11, L510, J50 * Department of Economics, University of Arizona, 1110 James E Rogers Way, Tucson, AZ 85721, email: tnsub- [email protected]. I thank Gautam Gowrisankaran, Ashley Langer and Derek Lemione for invaluable advice and comments throughout. I thank Stanley Reynolds and Gary Thompson for comments and suggestions. 1
Transcript
Market Power Mitigation in Electricity Markets. Can We Do Better
in New York?
T. N. Subramaniam∗
February 16, 2014
Abstract
Market power mitigation has been an integral part of wholesale electricity markets sincederegulation. In wholesale electricity markets, different regions in the US take different ap-proaches to regulating market power. While the exercise of market power has received con-siderable attention in the literature, the issue of market power mitigation has attracted scantattention. I examine the market power mitigation rules used in New York ISO (IndependentSystem Operator) and California ISO (CAISO) with respect to day-ahead and real-time energymarkets. I test whether markups associated with New York in-city generators would be lowerwith an alternative approach to mitigation, the CAISO approach. Results indicate the differ-ence in markups between these two mitigation rules is driven by the shape of residual demandcurves for suppliers. Analysis of residual demand curves faced by New York in-city suppliersshow similar markups under both mitigation rules when no one supplier is necessary to meetthe demand (i.e., when no supplier is pivotal). However, when some supplier is crucial for themarket to clear, the mitigation rule adopted by the NYISO consistently leads to higher markupsthan would the CAISO rule. This result suggest that market power episodes in New York isconfined 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%.
∗Department of Economics, University of Arizona, 1110 James E Rogers Way, Tucson, AZ 85721, email: [email protected]. I thank Gautam Gowrisankaran, Ashley Langer and Derek Lemione for invaluable adviceand comments throughout. I thank Stanley Reynolds and Gary Thompson for comments and suggestions.
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 Indepen-
dent 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.,1996).
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 deregu-
lated electricity markets now have market power mitigation mechanisms. In principle, the under-
lying 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 Pennsylvania, 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
1Substantial would mean increasing the price as high as $100 above competitive level in some cases
2
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 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.
2 Market Power and Electricity Auctions
Market power in wholesale electricity markets is fundamentally driven by lack of demand re-
sponse. Accordingly, the ability of a supplier to raise prices depends on residual demand curves.3
In figure 1, I exemplify the relationship between residual demand and market power. The top row
in figure 1 shows the demand and supply from other(rival) firms in the market. The bottom row
2Unit-level means generator-level3Residual demand curves show the level of demand response in the market in the absence of suppliers capacity
3
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 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.
Figure 1: Residual Demand Curves
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 trans-
actions 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. This practice is known as economic
withholding in electricity markets.
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3 Previous Literature in Market Power and Withholding
The issue of withholding has received much attention in CAISO4 and recently in ERCOT5 mar-
kets. 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, Fabra, 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 (1998) 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 (2003) analyzes the
ability and incentives to engage in withholding and shows that withholding incentives can be altered
by firms’ forward contract positions. Brandts, Reynolds and Schram (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
4California Independent System Operator5Electricity Reliability Council of Texas6http://www.ferc.gov/whats-new/comm-meet/2011/042111/E-18.pdf7http://pulpnetwork.blogspot.com/2007/03/in-papers-filed-in-early-2007-with.html
5
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.
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 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 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
10See Appendix for a detailed illustration on residual demand curves and pivotal firms11Conduct thresholds are usually a factor of reference price. Reference prices are historical averages of accepted
offer prices, and these are unit specific indices12Pivotal supplier test checks whether there is sufficient capacity to meet the demand when three suppliers in the
given transmission path, jointly withholds their capacity
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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 4, I 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.
Figure 4: Non Pivotal Residual Demand Curves
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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 elim-
inate 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 and slopes of the residual demand curves and marginal
cost curves. In figure 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.
Figure 5: Pivotal Residual Demand Curves
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5.3 Empirical Relevance
Table 1: Summary of Market Performace
New York California
Pivotal Prices can range from compet-itive levels to as high as $ 100above competitive level
Competitive
Non-Pivotal Prices can range from compet-itive levels to as high as $ 100above competitive level
Prices can range fromcompetitive levels to pricecaps($1000)
Table 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 2, I summarize the conditions under which one rule is preferred over the other. Table
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.
Table 2: Choice of Mitigation Regimes
Residual Demand Pivotal Frequency Choice
Inelastic High UnclearInelastic Low Regime 1Elastic High Regime 2Elastic low Regime 1/Regime2
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
12
be marginal units when pivotal, see right panel in figure 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.
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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.
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, 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 transmis-
sion 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 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
13http://mis.nyiso.com/public/P-27list.htm14Generator level. Units and generators are synonymous15In the industry, plants are sometimes referred as portfolios
14
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 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
2003.16
In table 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 9, 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.
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
16Divested assets are now owned by Astoria Generating Co, TC Ravenswood and NRG17FO2: Petroleum Oil, KER: Kerosene18Reference prices are also known as default bids19Hidden costs may include startup costs, minimum load costs, outage risks, etc. See Harvey and Hogan (2001)
15
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 marginal cost in
electricity literature (Kahn et al. [1997]). 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.
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 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.
Figure 6: Mitigation Thresholds for Peaking Units
20Heat rates are conversion factors between fuel heat content and net generation from the unit. Heat rate measuresthe efficiency at which heat is converted into power
21Interestingly CAMD also provides a way to figure out which fuel is being used in a particular hour via emissions
16
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
and CAISO mitigation rules.
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.
22$100 above the reference price of the price setting bid under marginal cost bidding/reference price bidding23margin = (bid price− reference price)24using CO2 emissions, we can clearly identify which fuel is being used
17
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 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 8, which compares offers across different types of units.
Figure 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.
Marginit = Xitβ +∑
type
βtype2 Dtypei +
∑
load
βload3 Dloadt +
∑
type
∑
load
βtype,load4 Dtypei Dload
t + (αi + εit) (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 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
25Further 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.
26In the data, there are hourly supply offers from generators on a daily basis. This means that there are differentoffers for the same hour every day. Hence, if we use hour as the time index; it will lead to repeated time observationswithin the panel
18
βtype=low cost peaker to have a coefficient larger than 100 if low-cost 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 high-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
27margin = bid−marginal cost. Marginal costs are calculated from the CAMD datasets28Some ISOs use a 10% adder on top of marginal costs to calculate reference prices29My discussions with ISO staff supports the notion that year of installation is highly correlated with engineering
parameters
19
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.
bid priceit = Xitβ +∑
type
βtype2 Dtypei + β3D
ConEdi +
∑
type
βtype4 Dtypei DConEd
t + (αi + εit) (2)
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 9 for a comparison of divested and non-divested 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 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.
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7.2 Simulation of Market Outcomes
This approach was first applied in Borenstein and Bushnell (2003) to identify market power
in the California electricity market. Borenstein and Bushnell (1998) 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.
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.
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 bid-
ding.
21
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 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
8. Calculate the market clearing prices using the new supply functions
Figure 7: Profit Maximization Under NYISO Rule
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.
22
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 7 reports the coefficients from the specification in equation
(1), which tests low-cost peaking unit behavior. The coefficient on dummy variable Low Peak sug-
gests 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 7
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 8, 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 $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 10, 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
30In order to calculate the dependent variable for high-cost peaking units, I subtract marginal cost estimates fromoffer prices
31Reference prices are larger than marginal costs, and this may have biased the dependent variable upwardly.32Independent Power Producers (IPP) that bought ConEd assets in 1998
23
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.
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 table (11-14), 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 9.
In figure 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
24
megawatts of load. At these load levels, prices under New York rule diverge substantially from
competitive levels.
In tables 11-14, 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 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 15 show high
markups for California rule during non-pivotal periods and high markups under New York rule in
pivotal period.
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 low-cost units while high-cost peakers consistently bid closer to mitigation thresholds. This
25
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 mitigation 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.
26
Table 3: Generating Firms in the NYC
Capacity(mw)
Firm BaseLoad
PeakLoad
Total Owned by(in 2001)
Astoria Energy LLC 1300 1300 n/a
Astoria Generating Company L.P. 1330 1000 2330 ConED
Calpine Energy Service LP 120 120 n/a
Consolidated Edison Co. of NY, Inc. 1050 100 1150 ConED
NRG Power Marketing LLC 930 720 1650 ConED
New York Power Authority 570 530 1100 ConED
TC Ravenswood 2070 530 2600 ConED
Total 7350 3000 10350
Table 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 5: Description of Variables
Variable Description
Margin ($) This variable measures the difference between offer prices and reference prices
Low Peak Dummy = 1 if the unit is low-cost (NG based) peaking unit
High Peak Dummy = 1 if the unit is high-cost (FO2/KER based) peaking unit
ConED Dummy = 1 if the unit belongs to ConED
Load (mw) The demand for electricity at the zonal level
7k ≤ Load ≤ 10k Load exceeds 7000 megawatts, but less than 10000 megawatts
Load ≥ 10k Load exceeds 10000 megawatts
Reference Prices ($/MWh)ISOs’ estimate on generators’ marginal cost Calculated by
taking the average of historical accepted offer prices from the generatorl
Marginal Cost ($/MWh) Calculated using heat rates and delivered fuel costs
Fuel Price($ per MMBTU)The price of the fuel used to power the prime mover
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
Number of Units -0.14*** -0.14*** -0.15*** -0.15***
(0.01) (0.01) (0.01) (0.01)
1(Winter) 33.6*** 33.6***
(2.8) (2.3)
1(Winter) × 1(Low Peak) 26.1*** 27.2***
(3.3) (2.3)
Time Fixed Effects yes yes yes yes
Month Fixed Effects yes yes yes yes
Year Fixed Effects yes yes yes yes
Plant Fixed Effects No yes yes yes
Unit Fixed Effects No No No yes
Observations 510, 000 510, 000 510, 000 510, 000
† Standard errors are clustered at the unit level. Only the intermediate load units andlow-cost peakers are included in all specifications.†† ∗∗P < 0.05 ∗∗∗P < 0.01
30
Table 8: Testing Low & High-Cost Peaking Unit Behavior
† Standard errors are clustered at the unit level. Only the intermediate loadunits and low-cost peakers are included in all specifications.†† For high cost peakers, the dependent variable is (bidprice−marginalcost).
Marginal cost were calculated from CAMD datasets.††† ∗∗P < 0.05 ∗∗∗P < 0.01
31
Table 9: Comparision of Divested & Non-Divested Peaking Units
Firm Installed On AverageSize
Type Fuel Heat Rate
Astoria Co. 1971 19.3 Single Cycle FO2/KER 14.72
ConED 1971 24.8 Single Cycle FO2/KER 15.5
NRG 1970 26.4 Single Cycle FO2/KER 15.7
Ravenswood TC 1969 25.2 Single Cycle NG/FO2 12.2
† Reported heat rates (MMBTU/MWh) correspond to FO2 usage except for TC Ravenswood
32
Table 10: Testing High-Cost Unit Behavior with ConEd Units
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Appendices
A Reference Price Calculation
Following methods are listed in the NYISOs’ attachement H in the market servicestariff:
”the order of preference subject to the existence of sufficient data:1. The lower of the mean or the median of a Generators accepted Bids or Bid com-
ponents, in hour beginning 6 to hour beginning 21 but excluding weekend and designatedholiday hours, in competitive periods over the most recent 90 day period for which the nec-essary input data are available to the ISOs reference level calculation systems, adjusted forchanges in fuel prices
2. using the mean of the LBMP at the Generators location during the lowest-priced 50percent of the hours that the Generator was dispatched over the most recent 90 day periodfor which the necessary LBMP data are available
3.A level determined in consultation with the Market Party based on engineering esti-mates”
I calculate the reference prices by taking the average of accepted bids over the last 90days and weighing with appropriate fuel prices. If sufficient data on accepted bids are notavailable, I use the locational based market price in place of the accepted bids. There aretwo caveats to this calculation. First, the fuel prices used to weight the average is at themonthly level. Therefore, day to day fluctuations in fuel price will not be reflected in myreferences prices. Second, for some high cost units that are not frequently accepted, theISO uses engineering estimates to calculate reference prices. Since I do not have access tosuch engineering estimates, I can not calculate the reference prices for these units.
B Margin Variable: Dependent Variable For Regressions
Calculating this measure is straightforward for peaking units that submit offers in sin-gle blocks. In this case, I take the offer price and subtract the reference price to obtainthe margin variable. However, for intermediate load units that submit bids in multipleblocks I take the difference of average offers from average reference prices. In so far asthe intermediate load units offer the whole capacity around the same price, this variableshould not pose any problems.See figure A.1 for actual offers from intermediate load units.
Figure A.1: Actual Offers from Selected Intermediate Load Units
C Load Pockets
Load pockets are areas of the system where the transmission capability is not adequateto import capacity from other parts of the system and demand is met by relying on localgeneration1. The NYISO identifies the following nine load pockets in NYC.
Pivotality refers to an extreme case of market power where the market demand willnot be met without the supply of a particular firm, and that firm is referred as pivotalfirm. Figure A.3 illustrates pivotalness in a duopoly market under different scenarios, andnotice that when the firm is pivotal the residual demand curve becomes perfectly inelasticat pivotal quantity.
