estimated short-run marginal costs - American Public Power ...

A COMPARATIVE ANALYSIS OF ACTUAL LOCATIONAL 

MARGINAL PRICES IN THE PJM MARKET AND 

ESTIMATED SHORT-RUN MARGINAL COSTS: 2003-2006 

 

prepared by London Economics International LLC 

Julia Frayer, Amr Ibrahim, Serkan Bahçeci, Sanela Pecenkovic 

717 Atlantic Avenue, Unit 1A 

Boston, MA 02111 

January 31, 2007

About the Authors 

Julia Frayer, as Managing Director, co-leads the firm’s market analysis and quantitative business 

practice area, which involves economic analysis and evaluation of infrastructure assets, detailed 

modeling of electricity markets, and price forecasting. She has worked extensively in the power 

sector (covering all aspects of the value chain), as well as other infrastructure industries such as the 

natural gas sector and the water sector. On regulatory front, she has specialized in areas related to 

market power mitigation, auction design, and performance-based ratemaking. Dr. Amr Ibrahim is a 

Senior Consultant with LEI specializing in restructuring, market design, regulation, and operation of 

wholesale and retail energy markets in the US, Canada, and South America. Dr. Serkan Bahçeci is a 

Consultant with LEI specializing in empirical analysis and applied econometrics to electricity 

markets. He has worked extensively on engagements involving generation market power and 

strategic bidding. Sanela Pecenkovic is a consultant with LEI, providing research and analytical 

support to market analysis-related engagements in the power sector for markets across North 

America. 

Notice 

This study has been produced by London Economics International LLC (“LEI”), an independent consulting firm 

specializing in economic analysis for the infrastructure industries. The study was funded by American Public Power 

Association (“APPA”) and the National Rural Electric Cooperative Association (“NRECA”). APPA and NRECA make 

no warranty, express or implied, and assume no legal liability for the information in this report; nor does any party 

represent that the uses of this information will not infringe upon privately owned rights.

Executive summary 

In 2006, the American Public Power Association (“APPA”) launched the Electric Market Reform 

Initiative (“EMRI”) to investigate the challenges facing wholesale electricity markets. The first phase of 

this initiative entails a series of detailed studies of wholesale electricity markets in the United States. 

One of these studies, and the focus of this report by London Economics International LLC (“LEI”), is an 

assessment of the relationship between the actual locational marginal prices (LMPs) for energy in the 

PJM market and the short-run marginal cost (“SRMC”) of producing electricity. The main task for this 

study was to estimate the effective prices (we refer to them as 

modeled LMPs through this report) assuming generators are 

offering their output exactly at SRMCs. Once short-run marginal 

cost-based prices are estimated, they can then be compared 

against actual historical LMPs and can be used to calculate a 

price-cost markup index. 

The study involves a two-stage process: (i) a simulation-based 

estimate of prices that would result if all generators in PJM Classic 

were bidding their SRMC and (ii) a comparison of those 

simulated (modeled) LMPs to actual market clearing prices in the 

Day-Ahead energy market. 

The ultimate analytical objective of this study is to estimate the 

price-cost markup index 1 for the PJM Classic market area for the 

43-month period, January 2003 through July 2006. The “cost” 

element of the price-cost markup index is specifically the shortrun 

marginal cost of the price-setting generator, which in the 

aggregate includes all costs that are variable across output levels 

in the short run 2 , such as fuel costs, variable operations and 

maintenance expenses, and emissions allowance purchase costs. 

The definition of SRMC used in this study focuses on physical 

operating costs, whereas some practitioners may also include 

opportunity costs. Indeed, PJM, in its implicit definition of 

reasonable marginal costs for purposes of bid mitigation also 

includes an adder for the recovery of other going forward costs. 

Perfect competition requires that the 

following five parameters be fulfilled. 

• firms and consumers are price 

takers; 

• the commodity is homogenous, i.e., 

there is no product 

differentiation; 

• there is perfect and complete 

information, all firms and 

consumers know the prices set by 

all firms; 

• resources (including information) 

are perfectly mobile, so all firms 

have equal access to production 

technologies and capital; and 

• there are no barriers to entry, any 

firm may enter or exit the market 

as it wishes. 

If all the above conditions are present, 

then in such a market, prices would 

instantaneously move to an 

equilibrium, where firms make zero 

economic profits (only covering their 

costs, including their fixed and 

opportunity cost) and social welfare is 

optimized. In reality, however, many 

of the above conditions do not apply 

and therefore most markets depart to 

some degree from the theoretical 

premise of perfect competition. 

The empirical analysis of this study can be characterized as 

comparing actual market dynamics to a theoretical benchmark based on the neo-classical economic 

theory of perfect competition. The basic tenets of economic theory predict that prices must equal SRMC 

under perfect competition (albeit in a hypothetical environment, because of the requirements 

1 Price-cost markup index is defined as the ratio of the difference between actual LMP and estimated SRMC over the 

actual LMP. See Section 3.6 for further details and discussion. 

2 In this context, “short run” is defined as the period of time over which the plant’s capacity and capital stock is fixed 

(i.e., prior to entire major maintenance changes or capital investments that could change the operating efficiency of 

the plant). 

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enumerated in the textbox to the right). Indeed, the complexity of the electricity industry with its 

barriers to entry and high fixed capital costs requirements may result in a situation that is not wholly 

congruent with the theory of perfect competition. Therefore, in the real world, the fact that prices will 

need to be above the short-run marginal costs so that firms can recover the minimum necessary going 

forward costs is a well accepted paradigm for energy-only markets. (Although it is important to note 

that PJM is not an energy-only market and operates other product markets, from which some 

generators can earn additional revenues.) From a policy perspective, the important question to ask is 

whether the observed markups above SRMC reported in this empirical study are adequate, too high, or 

too low to sustain the market. Although such policy applications are beyond the scope of the study, 

the results of this study provide important, statistically significant results that can be applied to think 

through and address such questions. 

Section 2 of the report provides a general overview of the PJM market place, and primarily describes 

the “Energy Market”. Section 2 also highlights the details of the other product markets that PJM 

administers. A basic understanding of these other markets, namely, “Capacity” and the “Ancillary 

Services,” is relevant to the study, because these other markets are a potential source of additional 

revenues for generators and therefore are important to understand from the prospect of analyzing the 

levels of the markups against fixed costs, and addressing questions of market efficiency, generator 

profitability, investment, and sustainability. Section 4 reviews the day-ahead locational marginal prices 

in their historical perspective within the modeled regions. Actual regional LMP is a basic component of 

the price-cost markup index; therefore it is important to understand the historical and geographical 

trends of these price series. 

Scope, methodology, and data 

As part of the scope of work, LEI proposed an analysis spanning a three and a half year timeframe 

starting from January 2003 through July 2006, coupled with a static geographical coverage area of PJM 

Classic (the original PJM service area). The selected timeframe is long enough to allow us to identify 

and study established trends in target variables. At the same time, the use of a static geographic 

designation permitted the results to be comparable across the timeframe and subject to direct contrast. 

In order to make the study computationally tractable, we selected a sample of days within each 

calendar year, representing the variations in load, seasons, and peak versus off-peak periods. Our 

sample size represents approximately 55% of the days in our timeframe leaving very small room for 

information loss that can otherwise occur with a small or biased sample. Given a sampled day, all 24 

hours are simulated so that we get a full picture of daily variation of demand and supply of electricity 

across the day. 

In order to simulate SRMC-based prices, we needed first to estimate SRMCs, and then to simulate the 

price-setting process in PJM under the assumption of SRMC-based bidding by all generators. Section 3 

describes the basic assumptions and methods used, including a description of the simulation model. 

Section 5 of this report explains how we estimated the short run marginal costs of the generating 

plants. In order to build the model, we gathered historical data that described market conditions at 

each hourly interval over the study timeframe. For example, we compiled actual load data for the 

selected PJM zones, as well as imports to and exports from the zones in question, transmission flows 

and limits through major transmission interfaces, fuel prices and other operating short run costs of the 

generating units, as well as detailed production data for major plants located in PJM Classic. We relied 

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on data in the public domain as well as general accepted, industry sources to develop the SRMC-based 

bids and other necessary assumptions for the simulation modeling, such as the technical data on 

generating plants, published in FERC Form 1, FERC 423, EIA 906, IEA 423, U.S. EPA CEMS, U.S. EPA 

Clean Air Markets unit characteristics databases, collated and provided by Velocity Suite of Global 

Energy Decisions, actual zonal load data, hourly transmission limits, interchanges (imports/exports) 

from PJM, and historical spot market fuel price data from Bloomberg. 

The transmission constraints data (thermal limits and actual historical flows between transmission 

zones), which is crucial to determine the network topology, is not readily available publicly and was 

not provided by PJM. PJM, in contrast to other ISOs, also does not decompose its LMPs into an energy 

and congestion component (instead, PJM provides shadow-prices of constrained elements). Therefore, 

we had to rely on data on actual flows and limits on the major interfaces to construct our model’s 

topology assumptions. The availability of data and PJM’s own practices to monitor only the major 

interfaces led us to model PJM Classic as a five-region network. 

Summary of results 

Our results, which are discussed in Section 6, starting at page 43, show that for most of the months in 

the studied timeframe the price-cost markup indices, especially for peak periods, are significantly 

higher than zero 3 , indicating that actual average LMPs were higher than he modeled LMPs (which are 

based on estimated SRMC bidding). This is not surprising given the realities of market dynamics. PJM 

has acknowledged that it also believes that there is a positive markup above SRMC embedded in actual 

LMPs. However, the results of this study are interesting for the more subtle observations that are not 

apparent in PJM’s markup indices. For example, the index levels vary significantly based on location 

(region), and time of day (peak versus off-peak) as well as across time. As expected, the peak period 

markup indices were almost always higher than the off-peak period markup indices for each region. 

This is intuitive given the differences in supply-demand balance during peak versus off-peak periods, 

and the types of resources that are price-setting as we move from off-peak demand levels to peak 

demand levels (and peaking resources’ need for above-marginal cost bidding to recover going forward 

costs). 

Monthly markup indices for each region are quite volatile – standard deviations of indices for each 

region and year are close to half of the average indices for all periods and almost the same magnitude 

as the actual indices for the off-peak periods. 4 For example, the average monthly markup index in the 

Delmarva peninsula area (region D) for peak periods is 10% with a standard deviation of 5%, and for 

off-peak periods the average is 6%, again with a 5% standard deviation over the study timeframe. In 

another example, the average monthly markup index in the service area of Pennsylvania Electric 

Company (region P) is 14% and 5% for peak and off-peak periods, respectively, both with a standard 

deviation of 4%. This volatility has a number of important implications. First, there is no apparent 

trend which correlates markup index levels with seasons or months. Therefore, other variables need to 

3 In statistical jargon, most indices are statistically significant at the chosen 95% confidence level. 

4 In statistics, volatility of a variable is sometimes measured by the ratio of standard deviation and mean. If that ratio is 

higher than one third, the variable is considered “volatile” in statistical conventions, although volatility is an ordinal 

concept, used to compare variables rather than labeling them. 

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e explored to understand or explain the timing and levels of markup indices. Leaving aside potential 

structural changes in the market, since the magnitudes of standard deviations can be so close to the 

averages, any estimation of the markup index value outside of the studied timeframe (i.e. extrapolation 

to the future) faces a high degree of uncertainty. Therefore, we cannot conclude whether such markups 

are going to be repeated in the future from this historical backcast. 

Figure 1, below, portrays the range of monthly average 5 price-cost markup indices for different periods 

across each of the years in our study timeframe (based on the sample days) and across modeled regions 

in PJM Classic. 6 The difference between the market price and the short run marginal cost of the 

generating unit is called the price-cost markup, and the markup divided by the market price is called 

the price-cost markup index. Price-cost markup is measured in dollar terms, so it is difficult to compare 

two markup values, if the underlying prices are different. The price-cost markup index, in contrast, is 

free of units, and so allows temporal or across markets comparisons even though the underlying prices 

are different. 7 We have focused on the markup index in this report, but have also augmented the 

discussions in Section 6 with the dollar-denominated markup levels (or differences) between actual 

LMPs and modeled LMPs, based on SRMC-bidding. 

The figure below shows the variation between peak and off-peak periods as well as a snapshot of the 

differences across regions within the same year. As discussed above and elaborated further in Section 6 

of this report, these types of trends are (at least partially) explained by the supply and demand 

conditions within each region and across time. However, further analysis is required in order to 

robustly and exhaustively document the causes of explanatory variables of observed markups. 

The average on-peak monthly indices by region rarely exceed 20% over the study timeframe, with the 

range more typically below 10%, as seen below. Taking into account the forecast error of the model, 

some months’ results become statistically insignificant at a 95% confidence level, and those are 

demarcated in the figure below with an asterisk. 8 

5 Throughout the report, unless it is explicitly stated otherwise, each period (hour, day, etc.) is weighed equally in 

calculating monthly averages, consistent with the Latin hypercube sampling method. 

6 See Section 3.4 for a detailed discussion of modeling PJM Classic topology. In short, PJM Classic has ten transmission 

zones based on the service areas of the local electric distribution companies. Given the transmission constraints in 

PJM Classic, we grouped them in five regions as follows: Region P: Pennsylvania Electric Company (“PENELEC”); 

Region M: 69% of the load of Metropolitan Edison Company (“METED”) and PPL Electric Utilities Corporation 

(“PPL”); Region B: the remaining part of METED, Baltimore Gas and Electric Company (“BG&E”) and Potomac 

Electric Power Company (“PEPCO”); Region E: American Electric Power Co., Inc. (“AECO”), Public Service Electric 

and Gas Company (“PSEG”), Jersey Central Power and Light Company (“JCPL”) and PECO Energy Company 

(“PECO”); and Region D: Delmarva Power and Light Company (“DPL”). 

7 For example, consider two hypothetical electricity markets, one with a $100/MWh market price and a $90/MWh 

SRMC; and the other with $50/MWh price and $40/MWh SRMC. The price-cost markups in both markets are 

$10/MWh but the price-cost markup indices would be 10% and 20%, respectively (L.C. Section 3.6 at page 27 for the 

formula and discussion on price-cost markup index.). 

8 Note that Figure 1 only shows the range of markup indices with minimum and maximum values; detailed discussion 

of the statistical significance of monthly index values can be found in Section 6. 

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Figure 1. Annual ranges of the monthly averages of the price-cost markup indices for peak, off-peak 

and all periods across the modeled regions in PJM Classic 

Peak Off-Peak All 

Note: 2006 is a partial calendar year (January – July 2006, only) 

Region D Region E Region B Region M Region P 

2003 8% to 16% 2%* to 8% 7% to 12% 

2004 9% to 20% 1%* to 16% 6% to 15% 

2005 8% to 25% -3%* to 14% 3%* to 19% 

2006 13% to 23% -1%* to 4%* 7% to 13% 

2003 2%* to 13% 0%* to 5%* 1%* to 8% 

2004 2%* to 15% 0%* to 9% 1%* to 13% 

2005 6% to 15% -1%* to 12% 2%* to 14% 

2006 7% to 12% 0%* to 5%* 4% to 7% 

2003 4%* to 10% 0%* to 8% 3%* to 7%* 

2004 5% to 12% 1%* to 11% 3% to 10% 

2005 7% to 17% -1%* to 11% 6% to 13% 

2006 7% to 16% 1%* to 6% 5% to 11% 

2003 2%* to 14% -1%* to 5%* 2%* to 6% 

2004 7% to 17% 3%* to 14% 5%* to 16% 

2005 8% to 19% 3%* to 14% 7% to 13% 

2006 5% to 15% 1%* to 4%* 3% to 10% 

2003 1%* to 15% -1%* to 4%* 0%* to 8% 

2004 1%* to 17% 0%* to 13% 1%* to 15% 

2005 8% to 26% 2%* to 14% 5%* to 19% 

2006 8% to 16% 0%* to 5%* 4%* to 11% 

At the same time, due to the forecast error, which is region and time-specific, the average values that 

are computed in the study (and reported below) may in fact be higher (or lower) because at a 95% 

confidence level; there will be a higher bound (as well as a lower bound). In other words, a 11% 

markup index value for peak periods in January 2003 for region M with a 6% forecast error, 

corresponds to a confidence interval between 5% and 17%, hence we conclude the index in January 

2003 for region M would take a value between 5% and 17% with a 95% probability (confidence level). 

The index value for the same month and region for off-peak periods is 2.5% and the forecast error is 

5.5%, giving a confidence interval between -3% and 8%. Since the resulting confidence interval 

straddles 0%, we must conclude that off-peak index is not significantly different than zero (or 

statistically insignificant), and we cannot claim that there was a positive markup in that month for offpeak 

periods or average. 

Consideration of potential modeling critiques 

Modeling historical events in a region as large as PJM is a difficult task in and of itself, but is further 

complicated by the confidentiality or unavailability of primary data on transmission, technical 

operating constraints and generation plant characteristics over time. Because of the unavailability of 

detailed data on the transmission system (namely thermal transfer limits between the ten transmission 

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zones in PJM Classic and congestion cost decomposition of LMPs), we simplified the topology and 

modeled a five region system within PJM Classic (plus all the interconnections with external markets 

and regions outside PJM Classic). This is an abstraction of the real world, as we are not modeling 

directly the internal congestion within each region or the entire nodal system. Nevertheless, we are 

calibrating generation and inter-regional flows to actual levels in order to offset the effects of this 

simplifying assumption. The magnitude of any possible bias is further minimized within each of our 

regions since we determine the boundaries of the regions carefully using the physical layout of the 

major transmission interfaces vis-à-vis generation and historical zonal price analysis (i.e., the zones 

with closer prices are grouped together in the same region). In summary, with careful preparation of 

regional designation points and calibration of modeling results, we believe that the simplifying 

assumption on transmission topology had limited effect on modeling results and the loss of 

information was minimized. 

A second simplification in the modeling relates to the generating unit characteristics. The key 

parameters were typically estimated hourly (for example, availability) or daily (spot fuel prices). 

However, we have also used monthly data (for hydroelectric production) or in some cases annual 

averages (for example, heat rates of individual plants) in our model. For plants where hourly 

generation data is unavailable, we have used generic technology based outage rates from North 

American Reliability Corporation’s Generating Availability Data System (GADS) 9 to project availability 

across the year. Though use of averages and generic industry data is not optimal, the operating 

parameters and technical characteristics for which we used monthly or annual data inputs do not 

fluctuate much typically and are not major drivers of the results. 

One possible critique of the modeling is that we did not model every day in the selected timeframe but 

only the selected days. The process of sampling almost always leads to some information leakage. In 

order to minimize problems, we used a very large sample size of 55%, and a robust statistical sampling 

technique to ensure that the sampled days represent the entire timeframe as closely as possible. In 

addition, we reviewed the days which were not sampled to understand if they had exhibited patterns 

of bias because they were outliers we did not cover. Based on an ex post examination of the load and 

LMP data, we have not excluded interesting periods, such as the hours with the lowest or highest 

actual LMPs. 

One other issue is the technical operating constraints faced by PJM controllers and system operators, 

which we did not take into account due to data unavailability. Many dynamic operating constraints, 

however, usually produce localized results and do not affect the average LMPs across the day or the 

regional aggregates. We did not account for certain plant-level operating parameters (for example, we 

did not consider ramp rates explicitly, although our proprietary power market simulation program, 

POOLMod, 10 does have an ability to optimize dispatch through low-demand periods and so forth). In 

addition, partial outages and certain fixed costs (such as start up costs and no load heat costs) were not 

incorporated in the modeling, as we felt that they were already represented in the calibration process or 

were outside the scope of our definition of SRMC. Lastly, we also did not take into account actual 

9 See http://www.nerc.com/~gads/ for more information on GADS. 

10 See Section 8.3 for a detailed description of POOLMod. 

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hourly hydro production (because data at such a granular level is not available) but rather let 

POOLMod schedule the hydro subject to a daily energy budget and profit maximizing rules. 

There is also a methodological difference between our simulation modeling (and selection of the pricesetting 

unit) and PJM’s mechanism in determining the LMPs. PJM’s Security Constrained Economic 

Dispatch (“SCED”) procedures use shadow prices – effectively the lowest undispatched bid price – in 

setting actual LMPs, while our proprietary simulation model looks at the highest dispatched price to 

set the modeled LMP. Theoretically, the difference between those two can be quite high, if the supply 

stack has big gaps. However, in a market as large as PJM Classic, the gaps in the supply stack tend to 

be smaller and thus negligible. Figure 59 through Figure 62 in Section 8.6 present the cumulative 

supply curves and demonstrate the fact that the gaps are indeed small. 

How to interpret the results 

Every model, by definition, is an abstraction of complex, real systems; and therefore the results from 

any modeling require careful consideration, interpretation, and application to real world problems and 

policymaking. The fact that prices may depart from SRMC is not itself unusual or an indication of 

market dysfunction. Rather, it is a signal that further study and analysis is necessary before conclusions 

can be drawn about the efficiency of the market system in PJM. A closer look at the extent to which 

LMPs exceed marginal costs and an analysis of other income streams’ contribution to fixed cost 

recovery is warranted to better understand how the markups relate to price levels necessary to 

motivate investment, and how the overall price levels relate to the long run marginal cost of the sector, 

or the break-even cost that is necessary to motivate investment and security of supply. Do markups 

exceed commercially reasonable profit levels for the sector and are they sustainable over a significant 

period of time, without erosion by new entrants Do these price-cost markups indicate patterns in 

bidding behavior that demonstrate the potential for market power This study does not attempt to 

answer all these complex questions, but rather provides the reader with the empirical basis for further 

examination. 

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