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.
A COMPARATIVE ANALYSIS OF ACTUAL LOCATIONAL MARGINAL PRICES
IN THE PJM “CLASSIC” MARKET AREA AND ESTIMATED SHORT-RUN
MARGINAL COSTS: JANUARY 2003 – JULY 2006
prepared by London Economics International LLC
January 31, 2007
Table of Contents
1
EXECUTIVE SUMMARY ..............................................................................................................................................7
2
OVERVIEW OF THE PJM MARKETS .....................................................................................................................14
2.1
2.2
3
SUMMARY OF ANALYSIS TECHNIQUES AND INPUTS.................................................................................22
3.1
3.2
3.3
3.4
3.5
3.6
4
ENERGY MARKETS .......................................................................................................................................................15
OTHER PRODUCT MARKETS ADMINISTERED BY PJM..................................................................................................20
SCOPE OF WORK ..........................................................................................................................................................22
OVERVIEW OF STUDY ..................................................................................................................................................22
SELECTED TIMEFRAME ................................................................................................................................................23
PJM CLASSIC AND MODELED NETWORK TOPOLOGY .................................................................................................25
MODELING SOFTWARE FOR SIMULATING SRMC-BASED LMPS ................................................................................29
THE CONCEPT OF THE PRICE-COST MARKUP INDEX AND PJM’S MARKUP INDICES ...................................................30
HISTORICAL PJM DAY-AHEAD LMPS.................................................................................................................33
4.1
4.2
4.3
FORMATION OF DAY-AHEAD LMPS...........................................................................................................................33
LMP LEVELS ................................................................................................................................................................34
MARKET HEAT RATES..................................................................................................................................................36
5
ESTIMATION OF SHORT-RUN MARGINAL COSTS OF GENERATING PLANTS THROUGH
SIMULATIONS .....................................................................................................................................................................39
5.1 PERFECT COMPETITION AND SRMC ..........................................................................................................................39
5.2 HOW DO WE ESTIMATE THE SHORT-RUN MARGINAL COST OF THE MARGINAL GENERATOR?..................................40
5.3 THE COMPONENTS OF THE SHORT-RUN MARGINAL COST IN THIS STUDY .................................................................41
5.3.1
Hydro units........................................................................................................................................................44
5.3.2
Imports...............................................................................................................................................................44
6
SUMMARY OF STUDY RESULTS: THE PRICE-COST MARKUP INDEX......................................................46
6.1 SUMMARY OF DYNAMICS DURING PEAK AND OFF-PEAK PERIODS .............................................................................46
6.1.1
Statistical significance .......................................................................................................................................47
6.1.2
Peak versus off-peak Differences ........................................................................................................................48
6.1.3
Trends across time in the markup index ............................................................................................................50
6.1.4
Comparing the study results to PJM’s markup index .......................................................................................52
6.1.5
Correlation between the price-cost markup indices and regional load ...............................................................53
6.2 REGION P – PENELEC...............................................................................................................................................54
6.3 REGION M - METED AND PPL..................................................................................................................................58
6.4 REGION B – BGE AND PEPCO...................................................................................................................................62
6.5 REGION E – AECO, JCPL, PECO AND PSEG ...........................................................................................................66
6.6 REGION D – DPL ........................................................................................................................................................70
6.7 PRICE-COST MARKUP INDEX BY FUEL TYPE .................................................................................................................74
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7
CONCLUDING REMARKS .......................................................................................................................................77
8
APPENDICES ................................................................................................................................................................79
8.1 CAPACITY AND ANCILLARY SERVICES MARKETS IN PJM..........................................................................................79
8.1.1
Capacity market .................................................................................................................................................79
8.1.2
Ancillary services market...................................................................................................................................81
8.2 DETERMINING THE MODELING FORECAST ERROR ......................................................................................................84
8.3 POOLMOD AND ITS ALGORITHM, A SYNOPSIS ..........................................................................................................85
8.3.1
What is POOLMod............................................................................................................................................85
8.3.2
What POOLMod does .......................................................................................................................................85
8.3.3
POOLMod output .............................................................................................................................................85
8.3.4
Overview of Key Algorithms .............................................................................................................................86
8.4 DETAILED TABULAR SUMMARIES OF INPUTS AND ASSUMPTIONS ............................................................................88
8.4.1
Generation inputs ..............................................................................................................................................88
8.4.2
Imports...............................................................................................................................................................89
8.4.3
Demand..............................................................................................................................................................90
8.5 DETAILED TABULAR SUMMARIES OF SIMULATION OUTPUTS ...................................................................................91
8.5.1
Monthly Markup Indices and Dollar-based Markup levels...............................................................................91
8.5.2
Indicators of model forecast error.......................................................................................................................96
8.6 ANNUAL SUPPLY CURVES FOR PJM CLASSIC ............................................................................................................98
9
BIBLIOGRAPHY.........................................................................................................................................................102
Table of Figures
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 ..............................................................................................11
FIGURE 2. OVERVIEW OF PJM’S CURRENT FOOTPRINT AND EXPANSION OVER TIME ..............................................................14
FIGURE 3. MAP OF MAJOR GENERATING PLANTS IN PJM CLASSIC, SORTED BY FUEL TYPE .....................................................17
FIGURE 4. MAP OF POPULATION DENSITY IN PJM CLASSIC .....................................................................................................18
FIGURE 5. MONTHLY AVERAGE LMPS FOR PJM ZONE FROM THE RT AND DAH MARKETS, NOMINAL $/MWH (JANUARY
2000-JUNE 2006) .......................................................................................................................................................................19
FIGURE 6. COMPARISON OF THE ACTUAL LOAD FOR PJM AND LOAD DURING SAMPLED DAYS, 2005 ...................................24
FIGURE 7. TRANSMISSION ZONES WITHIN PJM CLASSIC .........................................................................................................25
FIGURE 8. KEY STATISTICS ON FLOWS ALONG PJM-MONITORED INTERNAL TRANSMISSION CONSTRAINTS, MW.................27
FIGURE 9. MAP OF PJM CLASSIC GENERATION AND MAJOR INTERNAL TRANSMISSION INTERFACES ....................................28
FIGURE 10. DIAGRAM OF THE MODELED FIVE REGION-TOPOLOGY ..........................................................................................28
FIGURE 11. SCHEMATICS OF POOLMOD IN SIMULATING THE SRMC-BASED PRICES ............................................................29
FIGURE 12. PJM’S LOAD-WEIGHTED AVERAGE MONTHLY ADJUSTED MARKUP INDEX, 2003 - 2005 .......................................31
FIGURE 13. SUMMARY STATISTICS OF HISTORICAL DAH LMPS ACROSS THE FIVE MODELED REGIONS .................................35
FIGURE 14. MONTHLY AVERAGE LMPS FOR FIVE MODELED REGIONS (JANUARY 2003-SEPTEMBER 2006) ...........................36
FIGURE 15. PERCENTAGE OF HOURS THAT A SPECIFIC FUEL TYPE HAD SET LMPS IN PJM RTO, MONTHLY AVERAGES, 2004
– 2006 ........................................................................................................................................................................................37
FIGURE 16. AVERAGE MONTHLY MARKET HEAT RATES BASED ON DAH LMPS AND SPOT NATURAL GAS PRICES (JANUARY
2003-JULY 2006), BTU/KWH ....................................................................................................................................................38
FIGURE 17. PERCENTAGE CHANGES IN ANNUAL AVERAGE MARKET HEAT RATES, BTU/KWH ..............................................38
FIGURE 18. COMPONENTS OF SRMC FOR FOUR REPRESENTATIVE PLANTS.............................................................................41
FIGURE 19. ANNUAL HEAT RATE AVERAGES USED IN MODELING, BY FUEL TYPE AND REGION, BTU/KWH ..........................43
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FIGURE 20. AVERAGE MONTHLY FUEL COSTS FOR COAL, OIL AND GAS FIRED UNITS BY REGION, $/MMBTU .......................44
FIGURE 21. DISTRIBUTION OF MONTHLY AVERAGE MARKUP INDICES FOR PEAK PERIODS BY REGION OVER STUDY
TIMEFRAME ................................................................................................................................................................................47
FIGURE 22. DISTRIBUTION OF MONTHLY AVERAGE MARKUP INDICES FOR OFF-PEAK PERIODS BY REGION OVER STUDY
TIMEFRAME ................................................................................................................................................................................48
FIGURE 23. DISTRIBUTION OF PERCENTAGE DIFFERENCES BETWEEN PEAK AND OFF-PEAK MARKUP INDICES ACROSS ENTIRE
STUDY TIMEFRAME ....................................................................................................................................................................49
FIGURE 24. DISTRIBUTION OF THE $/MWH DIFFERENCES BETWEEN PEAK AND OFF-PEAK MARKUP LEVELS ACROSS ENTIRE
STUDY TIMEFRAME ....................................................................................................................................................................49
FIGURE 25. MONTHLY AVERAGE PEAK MARKUP INDICES FOR ALL REGIONS FOR SAMPLE DAYS ACROSS STUDY TIMEFRAME50
FIGURE 26. MONTHLY AVERAGE OFF-PEAK MARKUP INDICES FOR ALL REGIONS FOR SAMPLE DAYS ACROSS STUDY
TIMEFRAME ................................................................................................................................................................................51
FIGURE 27. AVERAGES AND STANDARD DEVIATIONS FOR THE MONTHLY PEAK AND OFF-PEAK PRICE-COST MARKUP
INDICES FOR EACH REGION .......................................................................................................................................................51
FIGURE 28. LOAD-WEIGHTED MONTHLY PEAK AND OFF-PEAK PRICE-COST MARKUP INDICES FOR PJM CLASSIC.................52
FIGURE 29. MONTHLY PEAK AND OFF-PEAK PRICE-COST MARKUP INDICES PLOTTED AGAINST MONTHLY AVERAGES OF
REGIONAL LOAD ........................................................................................................................................................................53
FIGURE 30. PRICE-COST MARKUP INDICES AND 95% CONFIDENCE INTERVALS FOR ALL, PEAK AND OFF-PEAK PERIODS FOR
REGION P ACROSS SAMPLED DAYS IN JANUARY 2003 – JULY 2006 ..........................................................................................55
FIGURE 31. ACTUAL VERSUS MODELED PRICES AND THE DOLLAR VALUE OF THE DIFFERENCE IN REGION P ACROSS
SAMPLED DAYS IN JANUARY 2003 THROUGH JULY 2006, $/MWH .........................................................................................56
FIGURE 32. DISTRIBUTION OF DIFFERENCES BETWEEN ACTUAL MONTHLY AVERAGE LMP AND MODELED MONTHLY
AVERAGE LMPS (BASED ON SRMCS) FOR PEAK AND OFF-PEAK PERIODS IN REGION P .........................................................57
FIGURE 33. PRICE-COST MARKUP INDICES AND 95% CONFIDENCE INTERVALS FOR ALL, PEAK AND OFF-PEAK PERIODS FOR
REGION M ACROSS SAMPLED DAYS IN JANUARY 2003 – JULY 2006.........................................................................................59
FIGURE 34. ACTUAL VERSUS MODELED PRICES AND THE DOLLAR VALUE OF THE DIFFERENCE IN REGION M ACROSS
SAMPLED DAYS IN JANUARY 2003 THROUGH JULY 2006, $/MWH .........................................................................................60
FIGURE 35. DISTRIBUTION OF DIFFERENCES BETWEEN ACTUAL MONTHLY AVERAGE LMP AND MODELED MONTHLY
AVERAGE LMPS (BASED ON SRMCS) FOR PEAK AND OFF-PEAK PERIODS IN REGION M ........................................................61
FIGURE 36. PRICE-COST MARKUP INDICES AND 95% CONFIDENCE INTERVALS FOR ALL, PEAK AND OFF-PEAK PERIODS FOR
REGION B ACROSS SAMPLED DAYS IN JANUARY 2003 – JULY 2006 ..........................................................................................63
FIGURE 37. ACTUAL VERSUS MODELED PRICES AND THE DOLLAR VALUE OF THE DIFFERENCE IN REGION B ACROSS
SAMPLED DAYS IN JANUARY 2003 THROUGH JULY 2006, $/MWH .........................................................................................64
FIGURE 38. DISTRIBUTION OF DIFFERENCES BETWEEN ACTUAL MONTHLY AVERAGE LMP AND MODELED MONTHLY
AVERAGE LMPS (BASED ON SRMCS) FOR PEAK AND OFF-PEAK PERIODS IN REGION M ........................................................65
FIGURE 39. PRICE-COST MARKUP INDICES AND 95% CONFIDENCE INTERVALS FOR ALL, PEAK AND OFF-PEAK PERIODS FOR
REGION E ACROSS SAMPLED DAYS IN JANUARY 2003 – JULY 2006 ..........................................................................................67
FIGURE 40. ACTUAL VERSUS MODELED PRICES AND THE DOLLAR VALUE OF THE DIFFERENCE IN REGION E ACROSS
SAMPLED DAYS IN JANUARY 2003 THROUGH JULY 2006, $/MWH .........................................................................................68
FIGURE 41. DISTRIBUTION OF DIFFERENCES BETWEEN ACTUAL MONTHLY AVERAGE LMP AND MODELED MONTHLY
AVERAGE LMPS (BASED ON SRMCS) FOR PEAK AND OFF-PEAK PERIODS IN REGION E .........................................................69
FIGURE 42. PRICE-COST MARKUP INDICES AND 95% CONFIDENCE INTERVALS FOR ALL, PEAK AND OFF-PEAK PERIODS FOR
REGION D ACROSS SAMPLED DAYS IN JANUARY 2003 – JULY 2006..........................................................................................71
FIGURE 43. ACTUAL VERSUS MODELED PRICES AND THE DOLLAR VALUE OF THE DIFFERENCE IN REGION D ACROSS
SAMPLED DAYS IN JANUARY 2003 THROUGH JULY 2006, $/MWH .........................................................................................72
FIGURE 44. DISTRIBUTION OF DIFFERENCES BETWEEN ACTUAL MONTHLY AVERAGE LMP AND MODELED MONTHLY
AVERAGE LMPS (BASED ON SRMCS) FOR PEAK AND OFF-PEAK PERIODS IN REGION D.........................................................73
FIGURE 45. PRICE-COST MARKUP INDEX BY FUEL TYPE ............................................................................................................75
FIGURE 46. DIFFERENCE BETWEEN ACTUAL LMP IN ZONE E AND MODELED LMPS IN ZONE E BY FUEL TYPE ($/MWH) ...76
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FIGURE 47. MONTHLY VOLUME-WEIGHTED AVERAGE CLEARING PRICES ($/MW PER DAY) AND TOTAL VOLUME
TRANSACTED (MW) IN THE OF PJM DAILY UNFORCED CAPACITY CREDIT MARKET, JANUARY 2000 – DECEMBER 2006...80
FIGURE 48. TOTAL INSTALLED CAPACITY IN PJM CLASSIC BY REGION AND FUEL TYPE .........................................................88
FIGURE 49. AVERAGE OF LOAD FACTOR AND AVAILABILITY PERCENTAGES BY REGION AND FUEL TYPE...............................89
FIGURE 50. ANNUAL DESCRIPTIVE STATISTICS OF IMPORTS INTO PJM CLASSIC, MW............................................................89
FIGURE 51. ANNUAL DESCRIPTIVE STATISTICS OF NET LOAD (INTERNAL PLUS EXPORTS) BY REGION, MW...........................90
FIGURE 52. MARKUP LEVELS (LMP LESS SRMC-BASED PRICE) AND PRICE-COST MARKUP INDICES (%) WITH
CORRESPONDING FORECAST ERRORS IN REGION P ...................................................................................................................91
FIGURE 53. MARKUP LEVELS (LMP LESS SRMC-BASED PRICE) AND PRICE-COST MARKUP INDICES (%) WITH
CORRESPONDING FORECAST ERRORS IN REGION M..................................................................................................................92
FIGURE 54. MARKUP LEVELS (LMP LESS SRMC-BASED PRICE) AND PRICE-COST MARKUP INDICES (%) WITH
CORRESPONDING FORECAST ERRORS IN REGION B ...................................................................................................................93
FIGURE 55. MARKUP LEVELS (LMP LESS SRMC-BASED PRICE) AND PRICE-COST MARKUP INDICES (%) WITH
CORRESPONDING FORECAST ERRORS IN REGION E ...................................................................................................................94
FIGURE 56. MARKUP LEVELS (LMP LESS SRMC-BASED PRICE) AND PRICE-COST MARKUP INDICES (%) WITH
CORRESPONDING FORECAST ERRORS IN REGION D ..................................................................................................................95
FIGURE 57. RATIO OF MODELED FLOWS OVER ACTUAL HISTORICAL FLOWS THROUGH CENTRAL, EASTERN AND WESTERN
INTERFACES, MONTHLY AVERAGES...........................................................................................................................................96
FIGURE 58. RATIO OF MODELED GENERATION OVER ACTUAL HISTORICAL GENERATION FOR SELECTED BASELOAD UNITS,
MONTHLY AVERAGES ................................................................................................................................................................97
FIGURE 59. CUMULATIVE SUPPLY CURVE FOR 2003 .................................................................................................................98
FIGURE 60. CUMULATIVE SUPPLY CURVE FOR 2004 .................................................................................................................99
FIGURE 61. CUMULATIVE SUPPLY CURVE FOR 2005 ...............................................................................................................100
FIGURE 62. CUMULATIVE SUPPLY CURVE FOR 2006 ...............................................................................................................101
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1
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
Perfect competition requires that the
modeled LMPs through this report) assuming generators are
following five parameters be fulfilled.
offering their output exactly at SRMCs. Once short-run marginal
•
firms and consumers are price
cost-based prices are estimated, they can then be compared
takers;
against actual historical LMPs and can be used to calculate a
•
the commodity is homogenous, i.e.,
price-cost markup index.
there is no product
differentiation;
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 index1 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 run2, 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.
•
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 46, show that for most of the months in
the studied timeframe the price-cost markup indices, especially for peak periods, are significantly
higher than zero3, 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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be 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 average5 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 30 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
Region D
Region E
Region B
Region M
Region P
Peak
Off-Peak
All
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%
Note: 2006 is a partial calendar year (January – July 2006, only)
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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2
Overview of the PJM Markets
PJM Interconnection LLC (“PJM”) is a federally regulated regional transmission organization (“RTO”)
that is tasked with ensuring the reliable, ‘open access’ operation of the electric transmission system
within its jurisdictional footprint. PJM is responsible for the operation and the administration of the
regional wholesale electricity market. PJM, as a result of FERC oversight, also has the responsibility to
ensure that rates (prices) are “just and reasonable”.11
Originally, PJM served a geographical area covering the mid-Atlantic region, including most of
Pennsylvania, New Jersey, Delaware, Maryland, and the District of Columbia (throughout this report,
the original footprint of PJM is also referred to as “PJM Classic”). However, over the past five years, the
PJM system has expanded to parts of Kentucky, Illinois, Indiana, Michigan, North Carolina, Ohio,
Tennessee, Virginia, and West Virginia. Allegheny Power was the first addition to the original footprint
in 2002. In 2004, the service territory of Commonwealth Edison (as a result of the merger with Exelon)
was added, followed by AEP and Dayton Power & Light. In 2005, Duquesne joined, and PJM’s
membership was expanded to include Dominion Power in May 2005. Figure 2 demonstrates the
expansion of PJM’s service territory over time.
Figure 2. Overview of PJM’s current footprint and expansion over time
Source: http://www.pjm.com/planning/downloads/20060222-rtep-report-1.pdf
11
See FERC Order ¶61,292, Docket No: ER06-78-000, issued on December 20, 2005 at page 10.
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PJM in its current form as the transmission system operator and wholesale market coordinator was
established in 1997. However, the centrally coordinated nature of utility operations in the PJM area
dates back to 1927, when three utilities signed the PA-NJ Agreement to pool resources and coordinate
the operations of their control areas.12 By 1993, there were a total of eight utilities working together
within the ‘pool’ structure. These eight utilities formed the PJM Interconnection Association to
administer the power pool. In 1998, FERC approved PJM as an “Independent System Operator” and
permitted the use of locational marginal pricing to price energy and congestion-related charges for the
wholesale electricity market. PJM began operating a bid-based electricity market in 1998, and shortly
thereafter introduced a market for capacity credits. PJM held its first auction for financial transmission
rights in 1999, and that was followed by opening the first ancillary services market (regulation) and the
Day-ahead Energy market in June 2000. In 2001, PJM was conditionally approved by FERC as a RTO.
As operator of the centralized spot markets for electricity, capacity and ancillary services, PJM is
responsible for establishing the trading rules and protocols for market participants; developing and
maintaining the software, networks, and hardware necessary to run the markets; providing
independent oversight; enforcing rules and regulations; establishing market-clearing settlement prices;
facilitating the clearing and trade settlement function among market participants; and carrying out all
general administrative functions for these markets. PJM is also responsible for maintaining the integrity
of the regional power grid and for managing a regional planning process for generation expansion
needed to ensure the reliability of the electric system. Further, PJM is also responsible for demandresponse initiatives and efforts to support renewable energy. The fact that PJM is a federally regulated
RTO implies that it must act independently and impartially in conducting its responsibilities.
Currently, PJM operates four major product markets13: energy, capacity, financial transmission rights,
and the ancillary services. Generators can, if they are technically eligible, participate in these markets to
earn revenues (in the Energy, Capacity, and Ancillary Service Markets) or to hedge their congestion
costs (in the Financial Transmission Rights market). The quantitative analysis of this study is focused
primarily on prices from the Day-ahead energy market. Therefore, Section 2.1 describes the
characteristics of the energy market in detail. Section 2.2 then summarizes the other product markets,
since they provide an opportunity for generators to earn additional revenues and profits, and in some
cases, also substitute for revenues from the energy market. Appendix 8.1 contains further details on the
capacity and ancillary service markets.
2.1
Energy markets
The energy markets administered by PJM consist of the Real Time (“RT”) and Day-ahead (“DAH”)
markets. In the DAH market, PJM schedules generators to operate for the next day using a least-cost
unit commitment and economic dispatch program. As a result of the economic dispatch program, PJM
also calculates the hourly Locational Marginal Price (“LMP”) for the next operating day based on
12
See www.pjm.com/contributions/news-releases/2002/20020926-pjm-history-timeline.pdf
13
Although these markets are administered individually, they are broadly inter-related, either explicitly through
Market Rules that require participants to act in a certain way in one market as a result of their participation in
another and due to settlement and billing policies, or implicitly, because of market signals from one market that
affect prices in another.
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forecast demand, offers from generators, and bilateral transaction schedules. The RT energy markets
calculate the hourly LMPs based on actual operating conditions. The DAH market is financially
binding and establishes the basic dispatch schedule for operators. In addition a considerable portion of
the load settles through the DAH market. For example, in combination, PJM’s DAH and RT markets
have represented approximately 60% to 70%of load in all hours over recent years, with the remainder
of load provided for through bilateral transactions and self-supply.14
In setting prices in the RT market, PJM has used Locational Marginal Prices (LMPs) since April 1998
(the DAH market introduced LMPs a little later). PJM pays generators for their output based on the
LMP at the generator bus15 and charges Load Serving Entities16 (LSEs) for their consumption based on
the price at the load bus. LMPs are defined by PJM as the “cost to serve the next MW of load at a
specific location, using the lowest production costs of all available generation, while observing all
transmission limits.”17 In laymen terms, LMPs incorporate the underlying cost of generation and the
implicit costs of getting the output from that generation to load (i.e., transmission congestion). Thus,
prices for wholesale power vary by location if there are binding transmission constraints. Due to the
congestion, the lower priced generation can not access the entire market, so it is effectively exportconstrained.18 Higher priced generation on the other side of the transmission constraint is dispatched
and serves the load in the constrained areas.
In PJM, the issue has historically been how to move generation of the baseload units in the lower cost
western part of the market to serve load in the East. This problem is best illustrated by reviewing the
location of generation and load. Figure 3, classifying all major generation plants by their primary fuel
type, maps the diversity in resource mix in PJM Classic. It is noteworthy that most of the gas fired
generators (indexed by pink dots in the map) are in the east, while most of the larger coal fired plants
(indexed by green dots) are in the west. Further, Figure 4 illustrates the population density (as a proxy
for electrical load concentration) in PJM Classic, highlighting that the majority of load is on the Eastern
seaboard, far away from the low cost coal-fired generation in the west.
14
In 2005, the PJM-administered real-time and day-ahead energy markets served over 72% of all load in all hours,
while in 2004 it was over 61% of load in all hours. See 2004 State of the Market Report [2005, p. 23], and 2005 State of
the Market Report [2006, p. 94-95].
15
A bus is a physical electrical interface where two or more devices or parties share the same electric connection. This
allows power to be transferred between devices (allowing power to be shared). At a load bus, power flows from the
system to terminal nodes for consumption. At a generator bus, power flows in to the system from a generator.
16
A Load Serving Entity provides electricity to end-users or wholesale customers. It may or may not generate power
itself and buys it through the system.
17
See Locational Marginal Pricing—PJM Member Training Department, LMP-101 Training, p. 10.
18
Congestion occurs whenever one or more major constraints are violated under which the electric system operates at
its “normal” state. These technical contingencies can either be physical limits like thermal or voltage characteristics of
the transmission lines, or even specified limits to ensure system security and reliability. As the system operator is
obliged to dispatch the system differently and away from its normal state of operation (e.g., dispatching units that are
not necessarily the cheapest), additional costs will be incurred. Cost associated with operating the system differently
than its “normal state operation” are generally classified as congestion costs--see Ihrig [2002, p. 19/29]. PJM’s
definition congestion of congestion can be found at the Transmission Operations Manual,
http://www.pjm.com/contributions/pjm-manuals/pdf/m03.pdf.
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Figure 3. Map of major generating plants in PJM Classic, sorted by fuel type
Source: LEI (using Global Energy Decisions, Velocity suite).
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Figure 4. Map of population density in PJM Classic
Note: Urbanized areas shown
Source: LEI (using Global Energy Decisions, Velocity suite)
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In the DAH market, PJM receives bids and offers for energy for the next operating day up to 12:00
noon. The DAH market is then closed from 12:00 – 4:00 pm for evaluation of the offers. By 4:00 pm,
PJM posts the day-ahead LMPs and hourly schedules from the DAH. The LMPs and corresponding
hourly schedules are financially binding on generators and load. The market is re-opened for rebidding from 4:00 – 6:00 pm (so that generation that was not selected in the DAH market can submit
revised offer data for the RT market that commences at 6:00 pm.) Throughout the operating day (from
6:00 pm the day before onward until real-time), PJM continually re-evaluates and sends out individual
generation schedule updates as required. The DAH market is settled based on the scheduled hourly
quantities and day-ahead hourly prices, and the RT market is settled based on actual hourly quantity
deviations from day-ahead schedule hourly quantities and real-time prices.
Figure 5. Monthly average LMPs for PJM Zone from the RT and DAH Markets, nominal $/MWh
(January 2000-June 2006)
$90
PJM (RTO)* Zone RT LMP ($/MWh)
$80
PJM (RTO)* Zone DAH LMP ($/MWh)
Annual Moving Average
$70
$/MWh
$60
$50
$40
$30
$20
$10
PJM (RTO)* Zone RT
LMP ($/MWh)
28.06
30.90
28.23
38.07
41.88
56.93
51.11
PJM (RTO)* Zone DAH
LMP ($/MWh)
32.02
31.43
28.35
38.12
40.74
57.01
49.73
20
00
-0
20 1
00
-0
20 5
00
-0
20 9
00
-1
20 2
01
-0
20 4
01
-0
20 7
01
-1
20 0
02
-0
20 2
02
-0
20 5
02
-0
20 9
02
-1
20 2
03
-0
20 4
03
-0
20 7
03
-1
20 0
04
-0
20 2
04
-0
20 5
04
-0
20 9
04
-1
20 2
05
-0
20 4
05
-0
20 7
05
-1
20 0
06
-0
20 2
06
-0
20 5
06
-0
9
$-
Year
2000
2001
2002
2003
2004
2005
2006
* Data for DAH starts from June 2000
** Data until June 2006
*Note: This is composite of two time series: (1) PJM Zone LMP and (2) PJM RTO Zone LMP. The DAH series for PJM Zone
began to be published by PJM in June 2000, while RT PJM Zone data is available from January 2000.
Source: PJM (http://www.pjm.com/markets/jsp/lmpmonthly.jsp)
The figure above provides a snapshot of monthly average DAH and RT LMPs for the composite PJM
Zone19,20 from 2000 through 2006. Historical DAH LMPs for the PJM Classic area are discussed in
19
PJM Zone LMP is a composite of two LMP times-series: (1) PJM Zone LMP (data available from January 2000 until
September 2004) and (2) PJM RTO Zone LMP for which the data has been collected since May 2004. Both LMPs series
effectively cover the entire footprint of the PJM market (taking into consideration the expansion of the PJM market
over the same time period).
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greater detail in Section 4. Time-weighted average monthly prices for the PJM Zone have ranged from
$21/MWh to $80/MWh over the past six years, and as the trend-line shows, have steadily increased
since 2000.
2.2
Other product markets administered by PJM
As mentioned above, in addition to the energy markets, PJM administers capacity, ancillary services
and financial transmission rights markets, allowing market participants to earn additional revenues or
hedge themselves against congestion in the energy market. The existence of these markets may
significantly affect the behavior of generators in the energy market and any a priori expectations about
the level of prices necessary to sufficiently remunerate generators. Although generators have had the
ability to earn additional revenues from capacity, sales and the provision of ancillary services, these
revenues have not been a significant portion of revenue streams for baseload plants. This may change
due to the new Reliability Pricing Model (“RPM”) that PJM is planning to implement. We provide a
cursory review of PJM’s historical capacity and ancillary services markets, with more details in the
appendix in Section 8.1.
Capacity market
The historical market construct for capacity in PJM consists of daily and monthly auctions for capacity
credits. LSEs within PJM must own or acquire capacity resources to meet their capacity obligations. The
obligations (the LSE share of the market’s capacity requirement based on peak load plus a margin) may
be acquired from the periodic capacity markets administrated by PJM or self supplied (either through
their own resources or through bilateral arrangements). The clearing prices for these auctions are
further discussed in Appendix 8.1 below. Prices in the Daily Auctions in the 12 month period from
August 2005 to July 2006 averaged at $1.84/MW-day ($0.69 per kW year).
The current market design is perceived to be weak at adequately attracting additional generation
investment, particularly in locations where resources are needed most. PJM is addressing these
attributes in its alternative capacity construct, the Reliability Pricing Model (“RPM”). The original
proposal was filed with FERC on August 31, 2005, and a settlement agreement conditionally was
approved in late December 2006. The RPM design aims at aligning capacity pricing with system
reliability requirements.21 This alignment requires that the timeframe for the RPM auctions provide for
a binding forward commitment three years22 forward so as to allow new generation and transmission
resources to compete directly with incumbents. The design also utilizes a downward-sloping price
schedule for the capacity requirements (referred to as the Variable Resource Requirement, or “VRR”),
rather than the current vertical demand curve (which has often led to binary results in pricing - either
very low prices close to zero or prices at the deficiency charge). Appendix 8.1 below discusses the
characteristics of the current and proposed capacity markets in greater details.
20
Note that monthly averages mask the hourly fluctuations in the series and smoothen the graphs.
21
See www.pjm.com/documents/donwloads/presentations/pjm-rpm-tc-testimony.pdf, at page 14.
22
Originally, the forward commitment was proposed to be four years, but it was reduced to three years in September
29, 2006 settlement filing with FERC.
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Ancillary Services market
PJM procures three Ancillary Services (“AS”) products from technically eligible generators; Regulation,
Synchronized Reserve, and Black Start services. The Regulation and Synchronized Reserve products
are procured through daily auctions, like the Energy Markets, operated by PJM, in which generators
offer to provide AS and PJM, as a single buyer on behalf of all LSEs, purchases such AS.
Regulation is the service that corrects for very short-term changes in electricity use that might affect the
stability of the power. Regulation offers may be submitted only for those resources electrically within
PJM and that satisfy the resources criteria. Selected generators are paid a regulation charge in $/MWh
terms. Synchronized Reserve supplies electricity if the grid has an unexpected need for more power on
short notice (possibly as a result of a forced outage or higher than projected demand). It is provided by
eligible resources that are located electronically within the synchronized reserve zone. Generating
resources are paid spinning payments, based on a margin above operating costs plus the cost of energy
use or based on a premium on top of average LMPs during the period they are called to provide
synchronized reserves. As for the Black Start product, it is procured by PJM through bilateral
agreements (typically on an embedded cost of service bases) with qualified generators, defined as those
that are able to start without outside electrical supply.
Financial Transmission Rights and Auction Revenue Rights
PJM also operates the Financial Transmission Rights (“FTR”) and the Auction Revenue Rights (“ARR”)
markets. FTRs are hedging tools for transmission congestion that take the form of a financial contract
which entitles its holder to a stream of revenues based on the day-ahead hourly energy price
differences across a specified transmission path.23 FTRs are obtained from PJM through annual and
monthly auctions as well as through the secondary market (i.e., bilateral trading). In contrast, the ARRs
are entitlements allocated annually to Firm Transmission Service customers and provide its holders
with revenue based on the locational price difference between ARR sources and sinks determined in
the Annual FTR auction. PJM also runs monthly FTR auctions to permit bilateral transactions and trade
in any residual FTRs.
23
See www.pjm.com/services/training/downloads/pjm101part1.pdf at p. 114. FTRs have been made available to
transmission customers as a hedge against the congestion costs since the introduction of the LMP system in April
1998.
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3
3.1
Summary of analysis techniques and inputs
Scope of work
In 2006, the American Public Power Association (“APPA”) launched the Electric Market Reform
Initiative (“EMRI”)24 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, is an assessment of the relationship between the
actual locational marginal prices for energy in the PJM market and the short-run marginal cost
(“SRMC”) of producing electricity. The PJM wholesale power market, as previously described, is a
competitive bid based market, and therefore most generators are not obligated to offer to sell their
output at their SRMC. The short-run marginal costs of producing electricity are not as easy to discern
as actual LMPs, which are published continuously on the PJM website. Therefore, the pivotal task for
this study was to estimate the effective prices (we refer to them as modeled LMPs through this report)
that would have resulted in the market, taking into account actual supply and demand conditions and
transmission constraints, if generators were offering their output strictly at SRMCs.25 Once short-run
marginal cost-based prices are estimated, they can then be compared against actual LMPs.
3.2
Overview of study
The study simulated the market clearing process so that the price-setting unit can be identified and the
hypothetical LMPs determined, under a scenario where all generators offer their output at their
respective SRMCs. The dimensions of the study were chosen so that we could study a static
geographical area and consider trends over time in that market.26 We therefore chose to study the PJM
Classic portion of the PJM RTO over a three and a half year timeframe (January 1, 2003 to July 31, 2006).
This multi-year timeframe allowed for exploration of trends over time that a shorter analytical period
would not reflect. The study intentionally focuses on PJM Classic, the area with the longest ‘market’
history within the PJM RTO footprint. The use of a static geographical dimension controlled for effects
associated with PJM’s growth over time. Nonetheless, the modeling of PJM Classic incorporates all
actual interchanges with surrounding control areas directly, so that the modeling can realistically
assess the changing supply mix hour to hour. The study focuses on the DAH prices.27 We discuss each
of the dimensions of the modeling and key input assumptions further below.
24
See http://www.appanet.org/aboutappa/index.cfm?ItemNumber=17721
25
See Section 5 for a detailed explanation of SRMC and its components.
26
It is important to note that extensive tests for market definition were outside the scope of this study; therefore, the
selected market for this study was driven by the terms of reference of the client and the authors’ recommendations,
based on professional judgment and previous experience.
27
The RT and DAH markets are closely correlated, as can be seen in Figure 5 on page 19. The DAH market, due to
scheduling requirements and the implicit arbitrage process also incorporates bilateral activity. PJM notes that a
“significant portion of spot market activity represents … bilateral contracts.” See 2004 State of the Market Report,
Market Monitoring Unit [2005, p. 23] Furthermore, over time, more and more load has been bidding into the DAH
Market (2005 State of the Market Report, Market Monitoring Unit, on page 112). We therefore believe that the most
appropriate reference point for spot market prices is the DAH LMPS.
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In order to make the study realistic and comparable to actual LMPs, we used actual hourly load
(reported by PJM), actual daily spot-market or monthly delivered fuel prices (compiled from
Bloomberg and FERC data), actual hourly imports and exports (reported by PJM), and actual daily
availability of generation (derived from reported hourly production from Environmental Protection
Agency’s Continuous Emission Monitoring System).
Although running a full nodal model was intractable given the study timeframe, we nonetheless
wanted to represent the topology of the transmission network and its effect on LMPs. PJM reports data
on flows for only the key internal interfaces. Actual flows between transmission zones are not available
publicly; and data that was not already in the public domain was not made available to the study by
PJM. We therefore modeled PJM Classic as a five-region market and used actual monitored hourly
transfer limits on PJM’s key internal interfaces as the basic input for the modeled network topology.
As briefly described above, the study relied on data from several primary and secondary sources. In
addition to PJM, from which we compiled the majority of “actual” market data on load and
transmission conditions, other sources of input data include the FERC, the Energy Information
Administration (“EIA”), Bloomberg, EPA, and NERC. We relied on the Global Energy Decisions,
Velocity Suite databases that summarize these primary source data. These inputs are documented
further in Sections 5.3.2 and 8.4 of this report.
3.3
Selected timeframe
As mentioned above, the study covers over three and a half year period from January 1, 2003 to July 31,
2006. The proper time dimension of electricity markets should not be based on the trading interval (in
PJM, spot market prices change in 5-minute increments and are traded for hourly periods), but rather
on the commercial terms of typical market arrangements, which allow buyers and sellers to make their
purchases and sales across time and substitute transactions by forward transactions, and vice versa.
From our experience in PJM and other electricity markets, the proper time dimension for an electricity
market analysis is in the range of one to three years as forward markets for longer periods are not very
liquid and therefore the substitution potential (which defines the market boundary) is greatly
diminished. Furthermore, a multi-year analysis allows examination of trends in the Price-cost markup
index over time. Therefore, the overall timeframe selected for this study was intentionally three and a
half years.
POOLMod, the software used to simulate SRMC-based LMPs in this study, allows for hourly
simulations. However, an analysis of all hourly periods over the three and a half year study timeframe
would be infeasible. Therefore, the study involved simulation of 200 days per calendar year.28 The Latin
hypercube sampling method was used to arrive at a sample that provides both representative and cost
effective sampling pool (see textbox on the next page for some further background.)
28
More specifically, the sample consists of 200 days for each year of 2003, 2004, and 2005, and 128 days for the first
seven months of 2006.
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The Latin hypercube sampling method was chosen because it considers multiple criteria (e.g., months,
days, load levels) to identify a comprehensive sample.29 The selected categories used in this study
include: daily load level, months, days of the week,
and volatility of load within each day (the standard
Latin hypercube sampling
deviation of hourly loads). Further, the Latin
Latin
hypercube
sampling
generates
a
Hypercube sampling assigns an equal number of
distribution of plausible collections of parameter
days to each identified “category” in the sample.
values from a multidimensional distribution. If
Finally, the days are selected randomly using
the population has N dimensions, i.e. each draw
uniform distribution, making each day equifrom the population comes with N identifiable
probable. For this study, the imposed requirements
characteristics, and then the range of each
dimension is divided into M equally probable
of each category consisted of:
•
2 days for each percentile of actual load
levels;30
•
2 days from each percentile based on daily
load volatility;
•
either 16 or 17 days per month; and
•
28 or 29 days per each day of the week per
year (out of 52).
intervals. M sample points are then placed to
satisfy the Latin hypercube requirements; note
that this forces the number of divisions, M, to be
equal for each dimension. Also note that this
sampling scheme does not require more samples
for more dimensions; this independence is one of
the main advantages of this sampling scheme.
Another advantage is that random samples can
be taken one at a time, remembering which
samples were taken so far.
The final outcome is a sample of 200 days for each
calendar year of the analysis that is representative of load conditions and reflects seasonality, and trends
in peak vs. off-peak hours. As an example of the comprehensiveness of our sample, Figure 6 compares
the actual load duration curve for 2005, to a load duration curve composed solely of the sampled days
for the same year. They appear identical, underscoring the representativeness of the sample.
Figure 6. Comparison of the actual load for PJM and load during sampled days, 2005
Sampled 200 days
50,000
50,000
45,000
45,000
40,000
35,000
30,000
25,000
20,000
Daily Average Load (MW)
Daily Average Load (MW)
Complete year - 365 days
40,000
35,000
30,000
25,000
20,000
29
The alternative to Latin hypercube is the orthogonal sampling method. However this method is one dimensional and
therefore may produce a sample that over-represents some days (e.g., weekdays) and under-represent others (e.g.
weekends).
30
Load data was downloaded directly from PJM, see http://www.pjm.com/markets/energy-market/downloads/realtime-constraints-post.xls.
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3.4
PJM Classic and modeled network topology
PJM has been continuously expanding its footprint, particularly in the last few years, as discussed in
Section 2. The current footprint covers an area as far west as Commonwealth Edison (Northern Illinois)
and as far south as Dominion Power (Virginia). For purposes of the analysis, the geographical scope
was limited to the original footprint of PJM, referred to also as PJM Classic.
PJM Classic has 10 transmission or transmission zones that are based on the service territories of the
local electric distribution companies. These zones (from east to west) are portrayed in Figure 7. They
include: American Electric Power Co., Inc. (“AECO”), Public Service Electric and Gas Company
(“PSEG”), Jersey Central Power and Light Company (“JCPL”), Delmarva Power and Light Company
(“DPL”), PECO Energy Company (“PECO”), Metropolitan Edison Company (“METED”), PPL Electric
Utilities Corporation (“PPL”), Baltimore Gas and Electric Company (“BG&E”), Potomac Electric Power
Company (“PEPCO”), and Pennsylvania Electric Company (“PENELEC”). Actual transfer limits and
flows between zones are not publicly available from PJM.31 Due to data limitations discussed in Section
3.2, we consolidated some of these zones into sub-regions for our modeling, based on the major
internal transmission interfaces, as discussed further below.
Figure 7. Transmission zones within PJM Classic
Source: Global Energy Decisions, Velocity Suite
31
While, generally, the dynamic transfer limits a between zones are not available from PJM and the PJM does not
publish actual flows between zones, the results of CETO/CETL study are, including CETO numbers, which are
available publicly from PJM (please see http://www.pjm.com/planning/rtep-baseline-reports/downloads/2011ceto-results.pdf). CETO numbers were used to further inform (along with the location of major interfaces) the
creation of five region topology used in this study.
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Electricity in PJM typically flows from the western region towards the East where it further continues
to feed the New York market. PJM’s Regional Transmission Expansion Planning (RTEP) documents,
and annual State of the Market Report identifies constrained areas affecting LMPs. In 2005 State of the
Market Report the following transmission zones of PJM Classic had an increase in the number of
constrained hours from the previous year: PSEG, METED, BGE, PECO, PENELEC, and AECO zones.
Other zones such as DPL, JCPL, PPL and PEPCO have had a limited amount of constrained hours and
DPL has even seen a decrease in constraints during 2005. The same report also showed the increase in
constrained hours for Western and Eastern interfaces stating that “the Western Interface, on average,
affected 9,388 MW of load32” while “the Eastern Interface affected, on average, 5,940 MW of load.”33 It
was important for our modeling to capture these transmission effects as the congestion component of
LMP can be quite significant depending on the opportunity costs of the transmission constraint.
Transmission congestion is very important in determining the LMPs due to the supply-demand
balance. Power will flow from low demand-high capacity areas to (relatively) high demand-low
capacity areas. If there is transmission congestion then the high-demand-low capacity area will end up
at a higher LMP, while idle capacity waits on the other side of the congested line. The differences in
LMPs can be quite large if congestion is present and the resource mix on either side of the constraint is
different.
PJM publishes detailed hourly data on a number of key internal transmission interfaces. Figure 8, on
the next page, presents the annual average and maximum of the hourly flows (otherwise referred to as
transfers) and transfer limits for the Western, Central, and Eastern Interfaces. Both the transfers and the
transfer limits are reported on an hourly basis and fluctuate during the day. The hourly transfer limit
data was used as an input to the modeling, while the actual hourly transfers were used in the
calibration and forecast error determination process.
Once we compiled the actual data on transfers and limits, the next step in the study required the
mapping of load and generation against the major internal interfaces (Western, Central and Eastern
interfaces) within PJM Classic. The location of interfaces was approximated34 using the definitions of
interfaces provided by PJM on its website and the schematic of these interfaces published in the PJM
Manuals, as summarized in Figure 9.35 As can be observed, the major interfaces do not align perfectly
with the boundaries of the 10 transmission zones, for which PJM published hourly load data, while the
flows (transfers) and transmission limits between the 10 transmission zones are not available. We
therefore had to make a number of assumptions on categorizing load and mapping generation.
Once the location of all generation in PJM Classic was identified vis-à-vis the internal transmission
interfaces, the model combined the transmission zones into five sub-regions for modeling reflecting the
32
Quoted from PJM State of the Market Report [2005, p. 66]
33
Quoted from PJM State of the Market Report [2005, p. 63]
34
In the course of this study the communication with PJM was not forthcoming and several data inquires filed with
PJM staff were not answered in time to inform this study’s methodology before its publication.
35
PJM Online, PJM Manual 3: Transmission Operations Revision: 21 Effective Date: March 10, 2006, prepared by
System Operation Division. Accessed on November 14, 2006.
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flow of power in PJM which travels from western region to east of PJM with some of it feeding into the
south and south central area of PJM Classic. We defined the sub-regions as:
•
region “P” (PENELEC zone);
•
region “M” (69% of METED zone load and PPL zone);
•
region “B” (zones: BG&E, PEPCO and the rest of METED load);
•
region “E” (zones: AECO, PSEG, JCPL, PECO); and,
•
region “D” (DPL zone).36
The METED transmission zone (as seen in Figure 9) is divided by the Central Interface, therefore we
used recent population data in order to attribute this zone’s hourly load to the five region topology
used in the modeling. The diagram depicting the regional topology is illustrated in Figure 10 below.
Figure 8. Key statistics on flows along PJM-monitored internal transmission constraints, MW
Average
Flow
Maximum
Flow
Average
Transfer
Limit
Maximum
Transfer
Limit
Years
2003
2004
2005
2006*
2003
2004
2005
2006*
2003
2004
2005
2006*
2003
2004
2005
2006*
Western Interface
4871
5000
5154
4806
6584
6699
6731
6723
5783
5878
5869
5694
7032
6825
6876
6855
Central Interface
3446
3291
3295
2816
5291
5305
5552
4996
4407
4319
4067
3779
5561
5704
5711
5357
Eastern Interface
5161
5393
5196
4947
7246
7414
7393
6999
6087
6437
6221
6159
7772
7644
7784
7522
*Note: 2006 figures are from January 1, 2006 to July 31, 2006.
Source: http://www.pjm.com/markets/energy-market/downloads/real-time-constraints-post.xls.
36
Although PJM does not define a transmission interface (and publish transmission flow and limit details), we chose to
separate DPL from the other eastern transmission zones. As for the transmission limit we used a static 1400 MW
figure based on “Impacts of PJM RTO expansion”, Energy Security Analysis, November 2005.
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Figure 9. Map of PJM Classic generation and major internal transmission interfaces
E a s te r n
In te r fa c e
W e s te rn
In te r fa c e
C e n tr a l
In te rfa c e
Source: LEI, using Global Energy Decisions, Velocity Suite mapping software
Figure 10. Diagram of the modeled five region-topology
C e n tra l In terfa c e
E a ste rn In te rfa ce
W e ste rn In te rfa c e
PE N E LE C
M E T E D (6 9 % )
PPL
BG&E
PEPC O
M E T E D (3 1 % )
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AECO
JC PL
PSEG
PECO
DPL
3.5
Modeling software for simulating SRMC-based LMPs
The study uses LEI’s propriety POOLMod model to simulate the merit order commitment and dispatch
of the PJM Classic system, based on the inputs outlined above and described in more detail in Section 5
and Section 8.2. POOLMod operates similar to the economic security-constrained dispatch models used
by ISOs and RTOs, like PJM, to centrally dispatch and operate their respective systems. Each day is
generally independent in the simulation, with the exception of the treatment of the hydro reservoirs,
for which POOLMod can ensure that water that is not used in any one day is stored in the reservoir for
potential use the next day (subject to reservoir limits) so that water spillage is kept to a minimum
POOLMod can also operate on the presumption of user defined reservoir criteria).
Figure 11 demonstrates through a schematic the two-staged process in POOLMod. The model begins
with a commitment process which looks at availability schedule for each plant (for those without
specific availability schedule, POOLMod creates an availability profile from assumed outage rates
using a heuristic maintenance algorithm and a stochastic, random-number generator-based forced
outage assignment process), the load duration curve for the day and the SRMC for each unit (we
discuss the derivation of SRMC in Section 5). For each hour, POOLMod selects available plant to meet
demand at least cost.
Figure 11. Schematics of POOLMod in simulating the SRMC-based prices
Stage 1 - Commitment (daily)
No
Not committed
for dispatch
StageStage
2 - Dispatch
2
(hourly)
Is plant
available?
Competitive
SRMC
bidding
assumed
Yes
Schedule hydro
based on optimal
duration of operation
LMPs set equal to the
bid of the most
expensive dispatched
resource
(MW)
(MW)
Review technical
capabilities of
unitsunits
and offers
Resources dispatched based
on offer price subject to
transmission limits, as well
as unit-specific technical
constraints
Hourly
demand
Demand duration
curve plus
reserves for a
typical day
After commitment is complete, POOLMod dispatches units to meet actual hourly load, subject to their
SRMC and technical constraints. This process then determines the actual generation level of each unit
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in each hour. The most expensive unit necessary to meet local demand sets the market-clearing price. If
there are no binding transmission constraints, the same unit sets the market-clearing price for all
regions. If there is a transmission constraint, import-constrained zones will have price set by a local
unit, different from adjoining regions, and therefore there will be a divergence in market-clearing
prices.
A more detailed description of POOLMod can be found in the Appendix (Section 8.2) of this report.
3.6
The concept of the price-cost markup index and PJM’s markup indices
After deriving the SRMC-based regional prices, we computed a price-cost markup index. The price-cost
markup index is a measurement of the difference between the observed market price and the
theoretical market price under perfect competition, namely the SRMC-based simulated price in this
study. In other words the price-cost markup index is the percentage of the actual LMP attributable to
the markup over SRMC. The index can be formulated37 as:
LMP Actual − Estimated SRMC Actual LMP − Modeled LMP
=
.
LMP Actual
Actual LMP
In cases, where the actual price is equal to the SRMC (as represented by the modeled LMP), the markup
index would be zero. A positive index value (greater than 0) indicates that actual LMPs exceeded the
estimated SRMC-based price. While a negative index value (less than 0) suggests that actual LMPs
were lower than the estimated SRMC-based prices.
The calculation of a markup index is a standard component of PJM’s annual State of the Market
Reports. In PJM’s analysis, however, the markup index is calculated for the marginal unit or units that
set the LMP in every 5-minute interval across the entire RTO footprint, and the markup is then loadweighted over time and by geographical location.38 Furthermore, PJM’s markup index is based on cost
submissions made by generators rather than independently calculated SRMC. Therefore, PJM’s
markup index results are based on what the generators report. Indeed, PJM acknowledges that it
believes that generators routinely incorporate a premium above their costs, since the Market Rules have
institutionalized a 10% adder.39 The probable presence of the adder in reported costs artificially reduces
37
Note that the price-cost markup index does not measure the difference between price and cost expressed as a percent
of cost (markup over cost). Instead, the index is defined as the markup over price and measures the difference
between price and cost expressed as a percent of price. We follow PJM’s (and other ISO’s) formulation of the pricecost markup index, which is an adaptation of Lerner’s index--used to measure the market power of a monopolist in
economic literature, and is the more conventional form used by economists.
38
See 2005 State of the Market Report, p. 83. PJM explains load weighting as follows “For example if a marginal unit
with mark up index of 0.50 set the LMP for 3,000 MW of load in an interval and a second marginal unit with a
markup index of 0.01 set the LMP for 27,000 MW of load, the weighted average markup index for the interval would
be 0.06.”
39
See 2005 State of the Market Report, p. 84. The 10% adder aims at mitigating the uncertainty associated with the
calculation of marginal costs for the actual range of units in PJM. PJM had also rationalized this 10% adder as a
means for generators to reflect the cost of capacity—see http://www.pjm.com/markets/marketmonitor/downloads/mmu-presentations/20050418-euci-rpm-market-power-migration.pdf.
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the markup index calculated by PJM; therefore, PJM has also started to report an “Adjusted Markup
Index,” which backs out 10% from reported costs before calculating the actual price-cost markup index.
Figure 12. PJM’s load-weighted average monthly adjusted markup index, 2003 - 2005
15%
2003
2004
2005
10%
5%
ov
em
be
r
D
ec
em
be
r
N
ct
ob
er
O
Se
pt
em
be
r
us
t
A
ug
Ju
ly
e
Ju
n
M
ay
pr
il
A
ar
ch
M
ry
Fe
br
ua
Ja
nu
ar
y
0%
-5%
Source: PJM, State of the Market Reports for 2003, 2004, and 2005.
It is useful to understand the results of PJM’s analysis prior to considering the results of this study.
Figure 12 above summarizes the load-weighted average monthly adjusted markup indices that PJM
published for the calendar years 2003, 2004, and 2005. In 2003, PJM’s Adjusted Markup Index across all
hours ranged between 10% in the months of January, June, July, August, and September to 15% in
February. For 2004, PJM’s Adjusted Markup Index ranged from approximately 4% in the month of
December to 12% in April. In 2005, the index ranged from approximately negative 1% in November to
9% in March.40 It is important to keep in mind that PJM calculates its markup indices for its entire
system and therefore the numbers noted above encompass a changing footprint; and, therefore, the
results are not strictly comparable from one year to the next. Similarly, they are not comparable to the
analysis performed in this study, which focused on the PJM Classic geographical region.
It should be noted that there are several other differences in inputs between PJM’s analogies and that
which is presented in this study, and therefore we would expect divergence in results. Since we do not
have access to the same extensive dataset as PJM on each unit’s technical performance or the actual
40
A negative value for the Markup Index—that is MC is greater than P—is plausible according to PJM given that PJM’s
calculations rely on submitted cost data from generators and generators may provide their cost data inclusive of an
adder up to the 10 percent. Therefore the markup index can be negative if the marginal unit’s offer price is between
cost and cost plus 10 percent. See 2005 State of the Market Report, p. 84, footnote 44. However, the adjusted markup
index, which is what we discuss above, is supposed to account for this implicit adder. Nevertheless, some negative
markups observed in PJM’s calculations for 2005 for the adjusted markup index. It is possible that under some
circumstances, generators may offer below their strict SRMC in order to manage technical ramping and operating
constraints. For example, a coal-fired plant may want to be dispatched even in low demand hours in order to avoid
shut down and start up costs and may therefore rationally adjust his bid in low demand hours to ensure that he will
not be instructed to shut down.
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generators whose offers set the price, our analysis will differ from PJM’s calculations. Furthermore, as
discussed above, we are not modeling every transmission element in the simulations. Notwithstanding
the fact that the 200-day sample was carefully chosen to cover all dimensions of daily variation in load
and is quite rich in its representation, it does not represent 100% of all hours (which is the basis for
PJM’s calculations). On the other hand, the analysis in this study is positively distinct from that of PJM
in several areas. First, this study uses simulated SRMC of generators, derived independently of cost
submissions made by generators. Moreover, this study will present results on a regional basis and also
by peak and off-peak periods, so that locational and time-based effects can be analyzed.
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4
Historical PJM Day-Ahead LMPs
As noted in the previous section, there are effectively two components to the price-cost markup index –
the actual DAH prices and the estimated prices based on SRMCs. This section of the report provides an
overview of actual DAH prices for the selected geographical area over the timeframe of the study.
Understanding the historical trend in LMPs is pertinent to this study since actual LMPs are one of the
components of the markup index. It is also important to understand the process of bidding and the
basis for the formation of actual LMPs as compared to the simulation process. Although PJM allows for
different types of offers and participation in the market, the modeling done in this study assumes
effectively that every resource participates in the spot DAH market and that self-scheduled offers are
slotted in the supply curve optimally based on their opportunity costs, and not on the basis of any
financial arrangement or contract price. Although this is an extrapolation of real world dynamics,
given the potential for arbitrage between spot and bilateral markets, and the relative freedom with
which resources can enter into hedge contracts, it is not unreasonable to assume that self-scheduled
offers are positioned in a way that optimizes the revenue of the resource as if it was bidding directly in
the market.
4.1
Formation of Day-ahead LMPs
In April of 1998, PJM introduced nodal energy pricing into its market (with offers by generators at
cost), followed by LMPs in a bid-based auction system a year later. The spot markets administered by
PJM are voluntary, in that generators can also choose to sell bilaterally or even in other control
areas/markets (although, even bilateral transactions are reported to PJM so that they can efficiently
coordinate the operation of the system). The LMP is based on the least expensive offer or bid needed to
meet or clear the next increment of demand.
As stated in Section 3, this study focuses on the DAH LMPs which are financially binding and typically
cover a large portion of load. PJM accepts three types of financially binding generation offers in the
DAH market: 41
•
•
•
Self-Scheduled: supply of a fixed block of MW that must run from a specific unit, or its
minimum amount with a dispatchable component above that minimum.
Generator Offer: offer, accompanied by the corresponding prices, to supply a schedule of MW
from a specific unit.
Increment Offer: financial offer by any market participant agreeing to supply agreed upon
amount of MW at (or above) a given price.
In the same market three types of bids are used (bids are the LSEs’ requests to buy electricity):
•
•
41
Fixed-Demand Bid: bid to purchase a fixed quantity of energy, regardless of price levels.
Price-Sensitive Bid: bid to purchase a specified quantity of energy up to a specified LMP.
See PJM’s State of the Market Reports (2004 and 2005). See also PJM manuals for more details http://www.pjm.com/
contributions/pjm-manuals/manuals.html
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•
Decrement Bid: similar to the price-Sensitive Bid, this is a financial bid to purchase a specified
quantity of energy up to a specified LMP; however, this type of bid can be submitted by any
market participant.
PJM’s LMP price is composed of two elements: the marginal cost at that node (energy price) and the
marginal cost of congestion on the transmission system.42 Differences in LMPs between the point of
receipt and the point of delivery (e.g., source and sink) are an indication of the price of transmission
congestion between those points. The LMP, in short, is the minimum cost of providing one additional,
i.e. marginal, unit of electricity at a specific location given the transmission limits.
4.2
LMP levels
In order to encompass the different characteristics of the load (200 days in the sample), the study
focuses on modeling ‘five regions’ rather than individual LMP nodes. As mentioned in Section 3.4, the
five regions in question are regions P, M, B, E, and D which represent combinations of the 10
transmission zones which comprise PJM Classic. Below, Figure 13 and Figure 14 summarize the key
statistics for historical DAH LMPs in these five regions, based on load-weighted hourly averages of the
DAH LMPs reported by PJM for the ten transmission zones. From the graph below in Figure 14, it can
be seen that the prices in the five regions are moving in synch, although prices in region P (PENELEC
zone) are generally lower in some periods than the regional averages in the rest of PJM Classic.
The system represented by these regions is a summer peaking system, with higher prices occurring
during the summer months. In the second half of 2006, price levels have started a downward trend
after the sharp increase in the second half of 2005. The rise in energy prices across all markets in 2005
has generally been attributed to the rise in natural gas prices, especially during the last quarter of 2005.
The relationship between fuel prices and LMP is important to understand because fuel prices are a
major factor of both the actual LMPs and modeled LMPs in the markup index. In order to appreciate
the impact of fuel prices on actual LMPs, the next section discusses briefly the trends observed in
“market heat rates” which represent actual LMPs adjusted for fuel costs.
42
PJM does not incorporate transmission losses in its LMP historically, but is planning to do so beginning June 1, 2007.
PJM was requested to institute marginal losses component of LMPs in its market operations and billing activities by
FERC in Order 115 (FERC ¶ 61, 132) in response to Docket No. EL06-55-000. The initial date for implementing this
FERC Order was October, 2 2006 but with FERC’s approval, PJM has pushed the implementation’s deadline to June
1, 2007.
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Figure 13. Summary statistics of historical DAH LMPs across the five modeled regions
Regions
P
M
B
E
D
2003
Average price ($/MWh)
Standard deviation
Average on peak price ($/MWh)
Standard deviation (on peak)
Average off peak price ($/MWh)
Standard deviation (off peak)
2004
Average price ($/MWh)
Standard deviation
Average on peak price ($/MWh)
Standard deviation (on peak)
Average off peak price ($/MWh)
Standard deviation (off peak)
2005
Average price ($/MWh)
Standard deviation
Average on peak price ($/MWh)
Standard deviation (on peak)
Average off peak price ($/MWh)
Standard deviation (off peak)
2006
Average price ($/MWh)
Standard deviation
Average on peak price ($/MWh)
Standard deviation (on peak)
Average off peak price ($/MWh)
Standard deviation (off peak)
$
$
$
$
$
$
38.0
20.5
47.3
19.1
29.6
18.0
$
$
$
$
$
$
38.0
20.9
48.3
19.3
28.7
17.6
$
$
$
$
$
$
38.6
22.1
48.7
20.9
29.5
18.9
$
$
$
$
$
$
39.8
21.1
50.4
19.2
30.2
18.0
$
$
$
$
$
$
40.1
21.8
50.7
20.1
30.5
18.6
$
$
$
$
$
$
40.8
15.1
49.4
12.1
32.9
13.0
$
$
$
$
$
$
42.5
15.9
51.6
12.7
34.2
13.8
$
$
$
$
$
$
43.4
16.8
52.6
13.8
35.0
14.9
$
$
$
$
$
$
46.5
18.7
57.6
15.0
36.3
15.8
$
$
$
$
$
$
45.0
17.4
54.7
13.6
36.2
15.7
$
$
$
$
$
$
56.7
28.0
71.1
27.8
43.7
20.8
$
$
$
$
$
$
64.3
34.5
81.0
35.2
49.1
25.7
$
$
$
$
$
$
67.4
37.3
83.7
38.6
52.6
29.2
$
$
$
$
$
$
67.5
35.3
85.6
35.1
51.1
26.3
$
$
$
$
$
$
67.1
35.3
84.7
35.2
51.1
26.7
$
$
$
$
$
$
49.8
22.6
60.8
24.5
39.8
14.9
$
$
$
$
$
$
56.6
29.9
69.4
34.2
44.8
18.8
$
$
$
$
$
$
62.6
35.0
75.5
40.5
50.8
23.5
$
$
$
$
$
$
57.6
29.4
70.5
33.2
45.7
18.7
$
$
$
$
$
$
57.7
30.7
70.6
35.2
45.9
19.4
Note: On peak hours are from 7:00 AM to 11:00 PM (the hour ending 0800 to the hour ending 2300) at the prevailing time
zone. Peak days are Monday through Friday (excluding holidays). Off-peak hours are from midnight to 7:00 AM (the hour
ending 0100 to the hour ending 0700) and 11:00 PM to midnight (the hour ending 2400) during the weekdays and all hours on
weekends and North American Electric Reliability Council holidays.
Source: LEI, based on published LMP data from PJM.
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Figure 14. Monthly average LMPs for five modeled regions (January 2003-September 2006)
100
95
P
M
B
90
E
D
Trendline
85
80
75
70
$/MWh
65
60
55
50
45
40
35
30
25
1/
1
/2
3/ 003
1/
2
5/ 003
1/
2
7/ 003
1/
20
03
9/
1/
11 200
3
/1
/2
1/ 003
1/
2
3/ 004
1/
2
5/ 004
1/
20
04
7/
1/
20
9/ 04
1/
11 200
4
/1
/2
00
1/
4
1/
20
0
3/
1/ 5
20
5/ 05
1/
2
7/ 005
1/
20
05
9/
1/
2
0
11
/1 0 5
/2
00
1/
5
1/
20
0
3/
1/ 6
2
5/ 006
1/
20
7/ 06
1/
20
06
9/
1/
20
06
20
Note: The trendline is based on a rolling annual average.
Source: LEI calculations based on PJM data.
4.3
Market heat rates
Some portion of the change in LMPs can be explained by fuel price trends, since fuel costs are a major
component of costs of producing energy. As an example, Figure 15, below, demonstrates the
percentage of time that specific fuel-fired units have set prices in PJM in 2004 through 2006 (note that
this data is directly taken from PJM and therefore includes the impact of the expanding footprint on
over the years). The data is aggregated and averaged by load at each hour across the entire PJM
footprint, and thus overlooks the geographical differences in resource mix and load, as demonstrated
by Figure 3 and Figure 4 on pages 17 and 18. The price setting fuel types in the coal-rich western PJM
and the gas-rich eastern PJM would be different in many periods. Nevertheless, Figure 15 does indicate
the diversity of the price-setting generation in PJM.
In order to better understand the trends in LMPs, we can index LMPs to fuel prices, and look at market
heat rates (in MMBtu/kWh terms) rather than market prices (in $/MWh). Understanding market heat
rates is also useful since it provides insight to the market conditions and bidding levels. One can
compare the market heat rate to actual technical heat rates to analyze what type of resource is marginal
or price setting and how it is bidding. For example, if we know that in a particular period, a gas-fired
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combined cycle unit was price-setting, we can compare that hour’s market heat rates against the
technical heat rate for that technology. If the market heat rate is higher than the appropriate or
corresponding technical heat rate (adjusted for variable O&M costs, etc.), then one can infer that the
price-setting resource was bidding above its marginal costs and that resulting LMPs will incorporate a
markup above SRMC.
Figure 15. Percentage of hours that a specific fuel type had set LMPs in PJM RTO, monthly averages,
2004 – 2006
100%
Coal
90%
Natural Gas
80%
Heavy Oil
Light Oil
70%
Other
60%
50%
40%
30%
20%
10%
-0
6
Ju
l
-0
5
N
ov
-0
5
Ja
n06
M
ar
-0
6
M
ay
-0
6
Se
p
-0
5
Ju
l
ay
-0
5
5
M
4
-0
5
ar
-0
M
Ja
n
N
ov
-0
-0
4
Se
p
-0
4
Ju
l
4
ay
-0
4
M
ar
-0
M
Ja
n
-0
4
0%
Source: PJM http://www.pjm.com/markets/jsp/marg-fuel-type-data.jsp
Since the gas prices are the most volatile and since gas-fired units, after coal, are most prominent price
setting fuel in PJM, it is useful to gauge the impact of natural gas prices on LMPs by calculating a gasfired market heat rate. This market heat rate would show a measure of LMPs, normalized for the rise
and fall of natural gas prices. In order to calculate implied market heat rate for PJM Classic, we took the
daily regional LMPs (averaged from hourly LMPs) for the five modeled regions and divided their
values by the daily spot prices of natural gas (as used in the modeling).43 The figure below shows the
resulting market heat rates for five regions modeled in this study. The monthly average market heat
rates are mostly in the range of 6,000 Btu/kWh to 7,000 Btu/KWh. Most notable spikes in the heat rates
appear to be in the period from May until November of 2005 and again in the second half of 2006,
43
In contrast to the modeling, which is based on 200-day samples, the LMP and market heat rate data presented above
is for the entire year.
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rising above 10,000 Btu/kWh. Prior to the second half of 2005, the market heat rates hovered just below
8,000 Btu/kWh.
Figure 16. Average monthly market heat rates based on DAH LMPs and spot natural gas prices
(January 2003-July 2006), Btu/kWh
11,000
P
E
B
M
D
10,000
Btu/KWh
9,000
8,000
7,000
6,000
5,000
1/
1/
2
3/ 003
1/
2
5/ 003
1/
2
7/ 003
1/
20
9/ 03
1/
11 200
3
/1
/2
0
0
1/
3
1/
20
0
3/
4
1/
20
0
5/
4
1/
20
7/ 04
1/
20
9/ 04
1/
11 200
4
/1
/2
0
1/ 04
1/
2
3/ 005
1/
2
5/ 005
1/
2
7/ 005
1/
20
9/ 05
1/
11 200
5
/1
/2
0
0
1/
5
1/
20
3/ 06
1/
2
5/ 006
1/
2
7/ 006
1/
20
06
4,000
As observed in Figure 14, DAH LMPs have generally risen from 2003. However, the calculated market
heat rates remained more stable (and in some instances in certain regions even declined from one year
to the next, as seen in Figure 17, below).
Figure 17. Percentage changes in annual average market heat rates, Btu/kWh
Region P
Region M
Region B
Region E
Region D
2004
0%
8%
1%
10%
9%
2005
-5%
4%
6%
-1%
2%
2006
6%
2%
8%
2%
0%
Note: average for 2006 is partial year (until July 2006)
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5
Estimation of short-run marginal costs of generating plants through
simulations
This section provides a short description of the theoretical and practical aspects of deriving SRMC for
the modeling. As background, we begin with a brief introduction to perfect competition, the economic
theory which describes the relationship between SRMC and prices, and we then focus on the
components of SRMC on a theoretical and practical level.
5.1
Perfect competition and SRMC
Classic economic theory describes perfect competition as a model, under which economic efficiency
and optimal allocation of resources are achieved if market price (P) is set equal SRMC. There are a
number of prerequisites44 for perfect competition, which are not realistic and do not allow the premise
of the underlying economic theory to hold in real life. Nonetheless, the model of perfect competition is
important to understand because it is the structural foundation for the price-cost markup index. Under
perfect competition, producers are expected to cover their short run and long run production costs.
Producers that cannot produce at that price level will incur losses and eventually exit the market,
leaving room for more efficient firms. On the other hand, producers that can produce at less than P will
make supra-economic profits. This higher level of profits will attract new entrants to the market; the
increased supply will cause downward pressure on price and restore profits to their normal levels
under perfect competition. It is noteworthy that normal profits are guaranteed at an output level at
which P = SRMC, as average cost is inclusive of return on investment and other fixed costs. It is also
important to highlight that as SRMC is a measure of the change in cost because of change in output, it
excludes fixed costs; fixed costs do not change with the change in output level.
The expectation that electricity generators shall offer their output at the SRMC of their production
presumes that electricity markets fit the theoretical paradigm of perfect competition. However,
electricity markets, albeit producing a seemingly homogenous product (electricity), may not completely
fit the characteristics of perfect competition theory. First, barriers to entry are high in the short to
medium term; considerable investments (substantial fixed costs) are needed to attain economies of
scale. With high fixed costs to attain scale economies, average cost of generation will also be sensitive to
plant utilization levels, and accordingly the premise of ease of exit is also challenged. Furthermore,
suppliers are not homogenous given that electricity cannot be stored, and a mix of resources (e.g.,
baseload, shoulder, and peaking units) with different cost curves is needed to provide optimal service,
especially since electricity is consumed instantaneously and storage of the product is extremely
expensive.
As competition force generators to offer their output at rates close to marginal costs, it is plausible that
the market clearing price as determined by the last unit dispatched in certain periods of the day may
not be high enough to cover fixed costs of some of the generators, particularly in an energy only
markets. Other product markets, e.g., capacity and ancillary services, could help in recouping some of
the fixed costs. However, clearing prices of capacity credits have generally been low, and ancillary
44
The prerequisites include homogeneity of products, price taking behavior of market participants, perfect and
complete information, and free entry and exit.
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services markets are not available to all units, because of technical considerations, and occasionally are
substitutes for – rather than supplements of – energy market revenues.
5.2
How do we estimate the short-run marginal cost of the marginal generator?
In the electricity industry, the major components of SRMC include cost of fuels and costs associated
with operation, maintenance and administrative (“OM&A”) expenses that change with the level of
output.45 Variable OM&A costs include periodic inspection, replacement, and repair of system
components (i.e., filters, desulfurizer, etc.) since the timing and frequency of such maintenance is
dependent on output and utilization levels, as well as consumables (i.e., water, limestone, etc.). Fixed
costs are not included, neither are they recovered, through SRMC measures.
PJM Manual 15 is dedicated to defining the standard methodologies that are recognized by PJM as
appropriate for determining various cost components for use whenever producers are required to
provide to PJM cost-based rates.46 As expected, these standards are related to Fuel Cost, and Operating
and Maintenance Costs.47 Fuel costs, according to Manual 15, vary with generation type. In its most
general form, it is inclusive of the sum of the basic fuel cost, applicable other fuel-related costs, the
maintenance adder, and SO2 and NOx emission allowance costs.48 As for Operating, Maintenance and
Administrative costs, they also vary by generation type, however, generally driven by FERC Accounts
512, 513, 530, 531, and 553 which are generally related to maintenance expenses for the different types
of generation plants.49
In addition to fuel costs and variable operations and maintenance costs, it is important that variable
emissions or environmental costs or surcharges are also part of a generator’s SRMC.50 Manual 15
45
As for Operating, Maintenance and Administrative costs, they also vary by generation type, however, generally
driven by FERC Accounts 512, 513, 530, 531, and 553 which are generally related to maintenance expenses to the
different types of generation plants.
46
See Manual 15, Revision 07 [2006, p. 4].
47
The coverage of Manual 15 is larger than the guidelines for fuels and for O&M costs. It encompasses guidelines for
developing heat rates, no-load costs, performance factor guidelines, and start cost guidelines. Additionally, Manual
15 provides additional cost guidelines for providing the services of synchronized reserves, and regulation. The
Manual also provide guidelines for “opportunity cost” which may be necessary cost component under certain
circumstances like ability to produce energy is affected if a unit is providing regulation, or for a must run unit in
response to transmission constraint and the unit has only a limited number of available annual run hours.
48
This is for fossil steam and diesel generation units. For combustion turbines, the maintenance adder is included
directly with the individual operating cost components on a $/hour basis. There are no SO2 and NOx emission
allowance costs for nuclear units. See Manual 15, p. 13.
49
FERC Accounts 512 and 513 pertain to maintenance for boiler plant, and of electric plant, respectively. Accounts 530
and 531 pertain to maintenance of reactor plant, and of electric plant in nuclear units. Account 553 is maintenance of
generation plant in other types of power generation.
50
The emissions generated by electricity generators include NOx and SO2 that are also the two most important factors
causing acid rain and air pollution. The Clean Air Act (CAA) requires each state to develop State Implementation
Plans (“SIPs”) that contain control measures and strategies used to attain and maintain the national air quality
standards. Title IV of the Clean Air Act set a goal of reducing annual SO2 emissions by 10 million tons below 1980
levels. To achieve these reductions, the law required a two-phase tightening of the restrictions placed on fossil fuelfired power plants. Phase I began in 1995 and Phase II in year 2000. Furthermore, the Ozone Transport Commission
(“OTC”) developed a multi-state cap and trade program to control NOx emissions and to address the regional
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explains that for combustion turbines, diesels and fossil-fired units that requires emission allowances to
operate, SO2 and NOx allowance costs are part of the total fuel-related costs. Regarding the opportunity
cost, PJM has claimed that one can include it as part of SRMC, but because PJM does not do so in its
markup indices, we do not include it in SRMC calculations.
Figure 18. Components of SRMC for four representative plants
$/MWh
$90
$80
Environmental costs
$70
$60
Fuel Costs
$50
Variable O&M Costs
$40
$30
$20
$10
$0
Nuclear
Coal
CCGT
Gas fired peaker
Figure 18, above, shows the components of SRMC for four different types of plants in PJM Classic,
based on the inputs in our modeling. For this figure, the variable O&M costs and the environmental
costs are averaged across types; however, for the modeling, we used plant specific estimates based on
reported O&M costs. For the chart in Figure 18, we also use average annual 2006 heat rates across all
units in the database for that technology and average monthly fuel prices in July 2006.
5.3
The components of the short-run marginal cost in this study
In practice and in this study, the short-run marginal cost of each unit was considered separately to
address issues specific to each unit such as the location, technology, age and ownership, although the
same formula with applicable components was used across all plants:
SRMC = Fuel costs + Variable O&M costs + Environmental costs
transport of ozone in the Northeast. This market-based program, called the NOx Budget Program, sets a regional
“budget” on NOx emissions from power plants and other large combustion sources during the “ozone season” (May
to September). In PJM, emission costs are an integral part of the parameters taken into consideration in RTEP. See
“Market Efficiency Measurements in the Regional Transmission Planning Process”, Regional Planning Process
Working Group, September 2005.
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In order to populate the formula above, we mainly used data in the public domain, published in FERC
Form 1, FERC 423, EIA 906, IEA 423, U.S. EPA CEMS, U.S. EPA Clean Air Markets unit characteristics
databases, collated and provided by Velocity Suite of Global Energy Decisions.
The short-marginal cost for a non-hydro unit is determined hourly by each unit’s annual heat rate
(MMBtu/MWh) multiplied by the delivered fuel cost ($/MMBtu) plus the hourly variable O&M costs
($/MWh) and if applicable, estimated allowance purchase costs ($/MWh). Variable O&M costs are
averaged over the timeframe and considered as a static component of the short-run marginal costs over
the modeling timeframe. Variable O&M costs, excluding emissions costs, are based on reported
variable O&M figures by unit/plant over a multi-year timeframe and refined by Global Energy
Decisions51, and include the following where applicable:
•
operations supervision;
•
coolants and water expenditures;
•
pumped storage expenses;
•
equipment expenses;
•
steam expenses from other sources;
•
expenses of transferred steam;
•
electric expenses;
•
miscellaneous steam power expenses;
•
maintenance supervision and engineering;
•
structures maintenance;
•
boiler maintenance;
•
maintenance of reservoirs;
•
maintenance of electric plant; and
• miscellaneous maintenance.
Because they are averages of multiple years, we held them constant over the timeframe and considered
them a static component of the short-run marginal costs. All other components of SRMC changed, as
frequently as daily (gas and oil fuel prices), monthly (emissions costs and coal prices), and annually
(heat rates).
We have used annual average heat rates52 for each unit, based on actual production and fuel burn
levels, as reported by the Velocity Suite of Global Energy Decisions, which changed over our modeling
timeframe depending on the quality of fuel and fluctuating production levels. Although the heat rates
51
See “Better Model Inputs: Estimating Fixed and Variable O&M Costs”, Global Energy Decisions white paper, available at
http://services.energyvelocity.com/registrationservice/getdocument.aspx?, and “That is our forecast and we are
sticking to it”, Global Energy Decisions report, available at http://www.globalenergy.com/BR05/BR05-thats-ourforecast.pdf.
52
An alternative would be to use the fully loaded heat rates instead of averages. Units can only reach their fully loaded
heat rate when they are running at full capacity. For most of the units, running below full capacity, their actual
attained heat rate is higher than the hypothetical fully loaded heat rate. Many units do not run at full capacity
throughout the year so using the fully loaded heat rate is not optimal.
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were specific to each unit, for illustrative purposes, the annual heat rate averages by fuel type and
region in the period 2003 – 2006 are summarized in Figure 19.
Figure 19. Annual heat rate averages used in modeling, by fuel type and region, Btu/kWh
Coal
Gas
Heavy Oil
Light Oil
Uranium
2003
2004
2005
2006
2003
2004
2005
2006
2003
2004
2005
2006
2003
2004
2005
2006
2003
2004
2005
2006
BGE & PEPCO
10,870
10,984
10,985
11,163
12,801
12,840
12,920
13,034
12,868
12,148
12,424
15,099
15,924
16,102
15,704
15,292
10,867
10,867
10,867
10,867
DPL
11,325
11,246
10,930
10,884
10,321
9,885
10,239
10,392
11,965
11,792
11,394
11,588
11,280
11,270
11,542
11,283
EAST METED & PPL PENELEC
10,074
10,997
11,397
10,060
10,913
11,335
10,063
10,730
11,438
10,097
11,143
11,480
13,436
10,143
10,471
13,794
10,165
10,686
13,124
10,202
11,534
13,103
10,423
11,361
14,335
11,000
13,649
11,341
14,269
10,771
15,067
11,474
12,688
13,609
11,190
12,891
13,609
11,190
12,740
13,609
11,252
12,490
13,609
11,252
10,593
10,614
10,593
10,614
10,593
10,614
10,208
8,406
Source: Global Energy Decisions, Velocity Suite
In order to control for the high volatility in the fuel prices, we compiled daily spot fuel prices for
natural gas and residual (heavy) and distillate (light) fuel oil. We used actual delivered costs for the
coal units. The fuel costs for coal units were based on the monthly average delivered fuel costs for each
plant as reported by Global Energy Decisions. For gas fired units, we used the daily prices of the closest
natural gas hub, or an index of hub prices, if the unit is located between two major gas pipelines. Figure
20 shows the average monthly fuel costs by region.
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Figure 20. Average monthly fuel costs for coal, oil and gas fired units by region, $/MMBtu
$/MMBtu
$18
$16
$14
$12
Light Oil
Heavy Oil
Coal
NG
$10
$8
$6
$4
$2
Ja
nM 03
ar
M 03
ay
-0
3
Ju
l-0
Se 3
pN 03
ov
-0
Ja 3
nM 04
ar
M 04
ay
-0
4
Ju
l-0
Se 4
pN 04
ov
-0
Ja 4
nM 05
ar
M 05
ay
-0
5
Ju
l-0
Se 5
pN 05
ov
-0
Ja 5
nM 06
ar
M 06
ay
-0
6
Ju
l-0
6
$0
Sources: Bloomberg for gas and oil, Global Energy Decisions for actual plant-level coal delivery costs
5.3.1
Hydro units
POOLMod dispatches hydro units using a specific algorithm – described in Section 8.3 – which
involves shadow-pricing. The hydro unit takes on the short-run marginal cost characteristics of the unit
that it displaces, based on its energy and availability qualities. In short, the offer prices for hydro units
are not predetermined by calculating short-run marginal cost proxies, as they are for thermal units.
Instead, a shadow price mechanism, based on the thermal unit offers, is used, given the monthly
energy schedules and capacities for each hydro unit. Monthly energy schedules were based on actual
monthly production data submitted by hydroelectric operators in EIA Form 906.
5.3.2
Imports
Imports into PJM Classic are coming from Allegheny Power Systems (“APS”) and First Energy (“FE”)
control areas into PENELEC (region P), and Virginia Power (“VAP”) control area into BGE (region B).
Imports from (and exports to) New York ISO are flowing into PENELEC (region P) and the composite
region E, using a 12% and 88% ratio breakdown, respectively.53 We have gathered the actual hourly
53
The breakdown figures are based on our phone interview with a NY ISO representative.
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flows at each interface, and imposed that quantity schedule of imports and exports onto the dispatch,54
combined with the corresponding hourly regional price55 for the injection region through our
timeframe.56 In order to simulate the actual supply-demand condition at each hour, POOLMod
dispatches the hourly imports at their actual quantity using actual hourly DAH LMPs for that region.
We are therefore considering the impact of interchanges with external markets in the simulations.
54
As discussed in Section 8.4.3 exports were incorporated into the demand schedules (as exports are viewed by
generators as additional demand on the system).
55
The relevant regional price was used as the hourly price for all imports, rather than the price at the interchange node,
because our modeling topology is not considering all transmission elements on the system. Actual imports would not
have been scheduled if they were not economic; and therefore, the use of the regional price is a proxy for this
economic decision-making process.
56
Source for the data is PJM ESuite, available at https://esuite.pjm.com/mui/index.htm.
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6
Summary of study results: the price-cost markup index
The modeling results indicate that over the majority of months of the study horizon, average peak and
off-peak monthly LMPs were higher than the estimated short-run marginal cost for the price-setting
generator in PJM Classic. Throughout this section of the report we refer to the markup index, which, as
we described in Section 3.6 is the difference between the average monthly actual LMP and the average
monthly modeled LMP (based on SRMC bidding) divided by the actual LMP. In other words, a 5%
markup index value translates to the difference between actual and modeled LMP being 5% of the
actual LMP. We also document the dollar-based markup values, which are the differences between
actual and modeled LMPs. While Section 6.1 below provides a broad overview, Sections 6.2 through 6.6
delve deeper into the results for each region. Section 8.5 of the Appendix provides detailed tabular
results by region for reference purposes.
6.1
Summary of dynamics during peak and off-peak periods
During peak periods for the sampled days, the average monthly57
price-cost markup index ranged from 4% to 25% over the PJM Classic
area, with the highest statistically significant average monthly
markup of 25% in region P in 2005 and the lowest statistically
significant markup of 5% in regions B and M in 2004 and 2005,
respectively.58 Region P and to a certain extent Region B have the
highest markup indices, especially for the overall (all hours) markup
index (as seen in the line graphs in Figure 25 on page 50). Figure 21, on
the next page, shows the distribution of monthly average peak period
markups over the January 2003 through July 2006 timeframe in the top
graph and the distribution of statistically significant monthly average
peak period markups over the January 2003 through July 2006
timeframe in the bottom graph. As can be seen from this figure (as well
as Figure 25 on page 50), region P has a propensity for higher overall
markups on-peak, while region M is on the lower end of the scale.
Helpful statistics terminology
• statistical significance means
there is statistical evidence
that a random variable is
different than zero.
• standard deviation is a
measure of variation from the
arithmetic mean, specifically
it is the square root of the
variance, which is a measure
of a variable’s statistical
dispersion, indicating how its
possible values are spread
around the expected value.
• volatility refers to the
degree of unpredictable
change over time of a certain
variable; it is commonly
measured by standard
deviation.
• forecast error is the difference
between the actual and
estimated values of a
variable.
The off-peak period markups (as seen in the distribution plots in Figure
22 on page 48 and in the time-series lien graphs in Figure 26 on page
51) show a similar distribution pattern across regions to the peak period trends. Region P and region D
both have higher overall markups off-peak, especially seen in the statistically significant results, while
region M, again, has lower markups.
57
Although the simulation modeling was done on an hourly basis, the modeling was carried out only for a subset of
days in each month. Therefore, the methodological approach followed in this study focused on capturing statistical
significance of monthly peak and off-peak periods (based on sampled days) such an approach is consistent with the
sampling approach, which was designed to represent peak and off-peak periods in each month, as well as the full
spectrum of load conditions across the year.
58
If a monthly result is statistically insignificant, it effectively means that it is no different from a 0% markup index
value.
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Figure 21. Distribution of monthly average markup indices for peak periods by region over study
timeframe
25
20
15
Frequency
10
5
0
Peak markup
index, %
20% +
20%
15% 15%
10% 0%
5% - 1
%
0% - 5
P
M
B
E
D
Region
25
20
15
Frequency
10
5
0
Peak markup
index, %
20% +
20%
15% 15%
10% 0%
5% - 1
%
0% - 5
P
M
B
E
D
Region
Top graph shows entire range of results, and bottom graph shows only statistically significant results.
6.1.1
Statistical significance
The results briefly described above and represented in the graphs on the next few pages indicate a
price-cost markup index as high as 25% for peak periods (in June 2004) in DPL, and as low as -3% for
off-peak periods (in July 2005) in PENELEC (although the results for July 2005 are not statistically
different from a 0% markup, as we discuss below). A negative markup, i.e. generators bidding lower
than their SRMC, is possible, as discussed earlier in footnote 40. Baseload units, for which the start up
costs can be significant, may rationally choose to bid slightly below their SRMC during low demand
levels in order to ensure that they remain online, with the expectation of recovering the losses when
demand goes up. However, in the modeling, the negative markups derived from the simulations are
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generally statistically insignificant at 95% confidence level. In other words, although a negative markup
is reported, it is not different from a 0% markup based on the forecast error59 of the modeling.
Figure 22. Distribution of monthly average markup indices for off-peak periods by region over
study timeframe
30
25
20
15
Frequency
10
5
0
Off-peak markup
index, %
20% +
20%
15% 15%
10% 0%
5% - 1
%
0% - 5
P
M
B
E
D
Region
25
20
15
10
Frequency
5
0
Off-peak markup
index, %
20% +
20%
15% 15%
10% 0%
5% - 1
%
0% - 5
P
M
B
E
D
Region
Top graph shows entire range of results, and bottom graph shows only statistically significant ones.
6.1.2
Peak versus off-peak Differences
The spread between the peak and off-peak markup indices is the highest in Region P, in contrast to the
other regions, where the peak and off-peak indices follow each other very closely. The figures below
demonstrate the distribution of the peak versus off-peak spread based on difference in the percentage
markups as well as the $/MWh difference in the peak versus off-peak indices.
59
See Appendix 8.2 for a detailed explanation of forecast error calculations.
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Figure 23. Distribution of percentage differences between peak and off-peak markup indices across
entire study timeframe
30
25
20
Frequency
15
10
5
-
Difference of peak
and off-peak
markup indices, %
+
15% %
- 15
10% - 10%
5%
5%
0% - 0%
P
M
B
E
D
Region
Figure 24. Distribution of the $/MWh differences between peak and off-peak markup levels across
entire study timeframe
30
25
20
15
Frequency
10
5
-
Difference of peak
and off-peak
markups, $/MWh
$10 + 0
1
$7 - $ - $7
$4
4
$0 - $ 0
-$
P
M
B
E
D
Region
The spread between the peak and off-peak indices is partially a reflection of the market dynamics in
each region, such as the mix of supply resources and demand variability. Some regions will have vastly
different resources setting price off-peak versus peak due to supply-demand balance (for example,
coal-based resources off-peak and gas-fired resources on-peak), which could result in different bidding
dynamics for purposes of fixed cost recovery. In addition, PENELEC is a large exporter to other
regions of PJM Classic, and therefore, its regional LMPs are influenced by the frequency of congestion
and the impact that it has on price-setting.
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6.1.3
Trends across time in the markup index
The line graphs in Figure 25 below indicate that the price-cost markup indices in peak periods move
quite differently through time across the regions (the coincidence rate is very low), but generally all the
peak period indices have upwards trends, climbing from 5% to 10% range in early 2003 to 10% to 15%
range in the summer of 2006. The off-peak indices (in Figure 26), exhibit more aligned movements
across regions, with dramatic increases in the monthly off-peak indices in early 2004 and mid 2005 for
all five regions.
Figure 25. Monthly average peak markup indices for all regions for sample days across study
timeframe
25%
Peak
20%
15%
P
M
B
E
D
10%
5%
0%
Ja
n0
M 3
ar
-0
M 3
ay
-0
3
Ju
l-0
3
Se
p0
N 3
ov
-0
3
Ja
n0
M 4
ar
-0
M 4
ay
-0
4
Ju
l-0
Se 4
p0
N 4
ov
-0
4
Ja
n0
M 5
ar
-0
M 5
ay
-0
5
Ju
l-0
Se 5
p0
N 5
ov
-0
5
Ja
n0
M 6
ar
-0
M 6
ay
-0
6
Ju
l-0
6
-5%
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Figure 26. Monthly average off-peak markup indices for all regions for sample days across study
timeframe
25%
Off-peak
20%
15%
P
M
B
E
D
10%
5%
0%
Ja
n0
M 3
ar
-0
M 3
ay
-0
3
Ju
l-0
Se 3
p0
N 3
ov
-0
3
Ja
n04
M
ar
-0
M 4
ay
-0
4
Ju
l-0
Se 4
p0
N 4
ov
-0
4
Ja
n0
M 5
ar
-0
M 5
ay
-0
5
Ju
l-0
Se 5
p0
N 5
ov
-0
5
Ja
n0
M 6
ar
-0
M 6
ay
-0
6
Ju
l-0
6
-5%
While there is volatility in actual level of the markup indices, the standard deviations against the
average levels indicate that the results are fairly consistent. If we look at the monthly markup indices
for each region, both the peak and the off-peak indices are quite volatile, taking fairly different values
from month to month within a wide range; the monthly average values for the regional peak indices
range between 2% and 25% and the monthly averages for the off-peak indices range between -3% and
16%. As a simple measure of volatility, standard deviations of markup indices for each region (see
Figure 27) are quite high compared to the average levels. We observe that the standard deviation
(which measures the change in the markup indices from month to month) is close to half of the average
levels for peak periods and almost the same magnitude as the monthly markup levels for the off-peak
periods. However, despite volatility, the average markup values lie within a fairly constant band:
monthly average markup values exceeding 20% are quite rare as can be seen from the charts in Figure
21 through Figure 25.
Figure 27. Averages and standard deviations for the monthly peak and off-peak price-cost markup
indices for each region
Peak index
Off-peak index
P
M
B
E
D
Average
14%
9%
9%
10%
10%
Standard Deviation
4%
4%
3%
4%
5%
Average
5%
4%
5%
5%
6%
Standard Deviation
4%
3%
3%
3%
5%
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6.1.4
Comparing the study results to PJM’s markup index
Figure 28, below, shows the load-weighted averages of peak and off-peak price-cost markup indices for
PJM Classic for the sampled days analyzed in each month from January 2003 to July 2006. The figure is
presented only for illustrative purposes, since it involves averaging over regional results during
periods of market bifurcation (due to transmission constraints), and therefore ignores one of the core
objectives of the study, which is to analyze the markups and derive the markup indices on a more
specific inter-temporal and location dimension.60 We therefore discuss the results for each year and
region separately in the sections below.
Figure 28. Load-weighted monthly peak and off-peak price-cost markup indices for PJM Classic
14%
12%
Peak
Off-Peak
10%
8%
6%
4%
2%
0%
Ja
n0
M 3
ar
-0
M 3
ay
-0
3
Ju
l-0
3
Se
p0
N 3
ov
-0
3
Ja
n04
M
ar
-0
M 4
ay
-0
4
Ju
l-0
4
Se
p04
N
ov
-0
4
Ja
n05
M
ar
-0
M 5
ay
-0
5
Ju
l-0
5
Se
p0
N 5
ov
-0
5
Ja
n0
M 6
ar
-0
M 6
ay
-0
6
Ju
l-0
6
-2%
Nonetheless, the graph above is instructive for purposes of illustrating the benefits of this study’s
approach versus the methodology imposed by PJM in their annual Markup Indices. PJM, in computing
its Markup Indices, uses load weighting to represent their entire market footprint and does not
decompose the results to major sub-markets. However, a number-by-number comparison of the
markup levels in the above figure to PJM’s markup calculations as discussed in Section 3.6 is not
appropriate. Over many periods of the study time horizon, PJM’s Markup Index values are calculated
for an area larger than PJM Classic due to the growing footprint. Moreover, PJM simply does not report
separate values for monthly peak and off-peak periods. Most importantly, as we discussed in Section
3.6, PJM uses reported cost data rather than an independent estimate of SRMCs to do its computations.
60
Although PJM Market Monitoring Unit reports a single price-cost markup index value for all of the PJM, the loadweighted average does not allow for analysis of the effect of transmission congestion, or the structural differences
between peak and off-peak hours. This study allows for analysis of the markups by region and peak versus off-peak
periods.
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6.1.5
Correlation between the price-cost markup indices and regional load
In order to analyze the relationship between the price-cost markup index and the demand for
electricity, we looked at the correlations between peak and off-peak markup indices and corresponding
peak and off-peak load levels (averaged monthly) in each of our five regions. Figure 29, below, shows
the scatter diagram of ten markup index-regional load pairs. As expected, the points are clustered
across the horizontal axis, measuring the regional load, since regional average load levels are quite
different across regions and quite similar across months. On the vertical axis (measuring the markup
index) though there are is no clustering. Moreover, there are no visible relationship between the load
level for a region and the corresponding markup index. The correlation coefficients are exceedingly
close to zero, ranging from -19% to 28%.61
Figure 29. Monthly peak and off-peak price-cost markup indices plotted against monthly averages
of regional load
30%
P - Peak
B - Off-peak
Price-cost markup index (%)
25%
P - Off-peak
E - Peak
M - Peak
E - Off-peak
M - Off-peak
D - Peak
B - Peak
D - Off-peak
20%
15%
10%
5%
0%
-5%
0
5,000
10,000
15,000
Regional load (MW)
20,000
25,000
30,000
The lack of correlation seems counter intuitive, considering the expectancy of increased levels of
competition when load levels go up,62 suggesting that the behaviors of market participants in PJM are
reasonably complex and cannot be explained by a simple factor such as load.
61
The correlation coefficients between the daily (as opposed to monthly) price-cost markup indices and daily regional
load averages are closer to zero, because of the increased level of noise.
62
Keep in mind that, algebraically even if prices or differences of prices and SRMCs are closely correlated with load,
the index values, which are independent of units, need not to be.
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6.2
Region P – PENELEC
PENELEC, our region P, is characterized by relatively low demand and high output (in terms of
generation from local power plants), and historically has the lowest actual LMPs in PJM Classic (see
Figure 14 on page 36 for a summary of actual LMP trends). Region P also has the lowest SRMC because
of this resource mix. In contrast, the markup indices are not lower compared to the other regions (as
was indicated in the distribution graphs on pages 47 and 48).
The monthly forecast errors, calculated for each month, region and for peak and off-peak periods and
presented in the Appendix, are on average 6% for region P, the highest being 10%. Any markup, which
is higher than its corresponding forecast error is statistically significant on a 95% confidence level.63 In
other words, we can say with a 95% confidence that there was a positive markup in that period. The
figure on the next page shows the price-cost markup indices and their confidence intervals for peak,
off-peak and all periods for each month in our timeframe for region P. The first, top-left panel gives all
three indices together, while the remaining three show the 95% confidence bands around the markup
indices. These bands are defined by the lowest and the highest values that markup indices can take by
a 95% probability. When the band cuts across or below the 0% line for a month then that month’s
corresponding index value is statistically insignificant and is not meaningfully different from a 0%
markup in statistical terms. We can conclude that the peak index for region P is significant for all
months in our timeframe, the overall (all hours) index is not significant in September 2004 and the three
summer months of 2005 and the off-peak index is not significant in 20 months out of the total 43.
We have to stress that these results on significance depend on the chosen level of confidence.64
Furthermore, the confidence interval is bi-directional. The upper end of the band describes the
potential for higher markup indices than those reported by the average in the computation (this
monthly average value is presented in the top left panel of the figure on the next page). For example,
the results represented in Figure 30 suggest that in January 2003, the peak index value falls within the
range of 7% to 16% with 95% probability, while the off-peak index value falls within the range of 3%
and 16% with the same confidence level. In July 2006, on the other hand, the off-peak index value falls
within the range of -7% and 6% with 95% probability. Since the 95% confidence band includes a 0%
markup for that month, we can not claim that the index value is technically different than zero with a
95% probability; hence, we categorize that month’s result as statistically insignificant.
As it can be observed in the top left panel of Figure 30 on page 55, there is a significant level of
volatility in the monthly index values but linear trend lines for all three of our indices (all hours, peak,
and off-peak) is close to horizontal if we consider the entire timeframe, suggesting that there is no
significant upward or downward trend across time in the markup levels.
63
In other words, if the markup for a region and month is X% and the corresponding forecast error is Y% then the
actual markup is larger than (X-Y)% and smaller than (X+Y)% with a 95% confidence level. (X-Y, X+Y) is also known
as the 95% confidence interval.
64
At 80% confidence level off-peak index is insignificant only in 6 months instead of 20, and the other two indices are
always significant throughout the timeframe. Going in the other direction, at a 99% confidence level, the number of
months that our indices are statistically significant declines, e.g. there is only 2 months that the off-peak markup
index for region P is significant at 99% confidence level for only two months over the study timeframe.
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15%
10%
10%
5%
5%
-10%
Off-Peak Markup index
30%
25%
20%
20%
15%
15%
10%
10%
5%
5%
0%
0%
-5%
Peak periods
-10%
Se
p03
N
ov
-0
3
Ja
n04
M
ar
-0
M 4
ay
-0
4
Ju
l -0
4
Se
p04
N
ov
-0
4
Ja
n05
M
ar
-0
5
M
ay
-0
5
Ju
l -0
5
Se
p05
N
ov
-0
5
Ja
n06
M
ar
-0
6
M
ay
-0
6
Ju
l -0
6
15%
3
20%
-0
3
20%
ay
-0
25%
Ju
l
25%
M
Peak Markup index
Ja
n03
M
ar
-0
M 3
ay
-0
3
Ju
l-0
3
Se
p03
N
ov
-0
3
Ja
n04
M
ar
-0
M 4
ay
-0
4
Ju
l-0
4
Se
p04
N
ov
-0
4
Ja
n05
M
ar
-0
M 5
ay
-0
5
Ju
l-0
5
Se
p05
N
ov
-0
5
Ja
n06
M
ar
-0
M 6
ay
-0
6
Ju
l-0
6
30%
-0
3
30%
ar
-5%
Ja
Overall Markup index
M
03
ar
-0
M 3
ay
-0
3
Ju
l-0
3
Se
p03
N
ov
-0
3
Ja
n04
M
ar
-0
M 4
ay
-0
4
Ju
l-0
4
Se
p04
N
ov
-0
4
Ja
n05
M
ar
-0
M 5
ay
-0
5
Ju
l-0
5
Se
p05
N
ov
-0
5
Ja
n06
M
ar
-0
M 6
ay
-0
6
Ju
l-0
6
Ja
n-
M
0%
n03
03
ar
-0
M 3
ay
-0
3
Ju
l-0
3
Se
p03
N
ov
-0
3
Ja
n04
M
ar
-0
M 4
ay
-0
4
Ju
l-0
4
Se
p04
N
ov
-0
4
Ja
n05
M
ar
-0
5
M
ay
-0
5
Ju
l-0
5
Se
p05
N
ov
-0
5
Ja
n06
M
ar
-0
M 6
ay
-0
6
Ju
l-0
6
Ja
n-
M
Figure 30. Price-cost markup indices and 95% confidence intervals for all, peak and off-peak periods for region P across sampled
days in January 2003 – July 2006
0%
-5%
-10%
All periods
30%
25%
-5%
-10%
Off-peak periods
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Nevertheless, there is an observable upward shift in late 2005 and early 2006 in the peak index and a
downward shift in the off-peak index over this same period. Indeed the monthly average off-peak
index reaches negative values (which are effectively 0%, when we factor in the forecast error of the
model) for some months in late 2005 and 2006. This inverse relationship is most likely an indication of
the counterbalancing effect of peak versus off-peak spreads. In order to capture expected high peak
prices, baseload units make an additional effort to ensure that they will be available to operate in the
coming peak periods by bidding low during off-peak, and sometimes bearing some short-run marginal
costs in anticipation for higher profits in the peak hours. Related to that, the spread between the peak
index and the off-peak index is higher in region P, as compared to the other regions.
It is also interesting to look at the deviation in the dollar levels per MWh between actual regional LMPs
and the modeled LMPs (which represent the simulated SRMC-based prices). The dollar difference
allows us to directly observe the financial implications of the markups. Figure 31 shows the average
actual LMPs and average modeled LMPs for each month (for only the sampled days) and the $/MWh
difference, i.e. the dollar-based markup, for each month. For Region P, the monthly average difference
for all hours has an increasing trend line over time, with an overall range of $2/MWh to $9/MWh from
January 2003 through July 2006, as seen in the figure below.
Figure 31. Actual versus modeled prices and the dollar value of the difference in region P across
sampled days in January 2003 through July 2006, $/MWh
$10
$100
$80
Difference
Actual - All hours
Modeled - All hours
$9
$8
$7
$70
$6
$60
$5
$4
$50
$3
$40
$2
$30
$1
$-
Ja
n-
03
A
pr
-0
3
Ju
l-0
3
O
ct
-0
3
Ja
n04
A
pr
-0
4
Ju
l-0
4
O
ct
-0
4
Ja
n05
A
pr
-0
5
Ju
l-0
5
O
ct
-0
5
Ja
n06
A
pr
-0
6
Ju
l-0
6
$20
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Difference ($.MWh)
Actual and modeled prices ($/MWh)
$90
There is a strong correlation, as can be expected, between modeled and actual LMPs for PENELEC
(92% over modeling timeframe). Notably, however, the correlation between LMPs and the difference
(i.e., dollar-based markup) is relatively high as well. In other words, as LMPs (both actual and the
modeled LMPs, based on SRMC-bidding) rise, we have estimated larger differences between actual
and modeled LMPs (for example, the correlation coefficient between actual average monthly LMPs and
the monthly average markup is 57% and the correlation coefficient between modeled average monthly
LMPs and the monthly average markup is 67%). Therefore, although the trend over time has been for
higher dollar markups (especially in late 2005 and 2006), those differences are commensurate with
rising fuel costs in the market.
Figure 32 shows the distribution of the dollar values of differences between the monthly averages of
actual LMP and the modeled SRMC for region P at peak and off-peak periods. The differences in peak
periods are larger than the differences in off-peak periods, as seen in the figure below, with most of the
peak period resulting in a difference of greater than $4/MWh between actual LMPs and modeled
LMPs, while most of the differences in off-peak periods lie below $2/MWh.
Figure 32. Distribution of differences between actual monthly average LMP and modeled monthly
average LMPs (based on SRMCs) for peak and off-peak periods in region P
Frequency of occurrence over timeframe
30
Off-peak
25
Peak
20
15
10
5
0
< $0
$0 - $2
$2 - $4
$4 - $6
$6 - $8
$8 - $10
>$10
Difference (Actual LMPs – Modeled LMPs, Monthly Averages), $/MWh
- 57 London Economics International LLC
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6.3
Region M - METED and PPL
Region M is made up of the PPL zone, combined with a big part of METED zone (69% of the
population), lying to the north of the Central interface. Region M therefore lies in the middle of the
Western, Central and Eastern interfaces, connecting the relatively low-cost western resources in the
west with the high load in the east. As described in Section 4, actual average LMPs in region M were
higher than that of region P, but lower than the other three modeled regions, as can be expected given
this region’s resource mix and the typical profile of flows from Western PJM to Eastern PJM.
Figure 33 shows the monthly price-cost markup indices for peak, off-peak, and all periods for region M.
The top-left panel shows the three indices together (for all periods, including those that are not
statistically significant), while the other panels show the 95% confidence intervals for each index. The
average monthly markup index for all periods in region M averages around the 5% level, with the
exception of summer 2005, where it reaches a high of 14%. The peak and off-peak indices are also close
to the overall index, indicating that the bidding behaviors across peak and off-peak periods are not
very different, in contrast to what was observed in the latter part of the modeling timeframe in region
P. In short, except the period from October 2004 to May 2005, all three indices are relatively low as
compared to other regions and generally exhibit less variability between months across time.
Moreover, all three indices increased during the summer months of 2004 and 2005 but stayed lower in
other months, including the summer of 2006.
The confidence intervals, shown in Figure 33, present the upper and lower limits that index values can
take with a 95% probability. For instance, we can say with a 95% confidence that in January 2003, the
monthly average of the peak markup index would lie somewhere between 5% and 16%. The months
with markups in region M that are lower than those of region P in that same month (e.g. May through
July 2004), are typically insignificant at 95% confidence level (in other words, equivalent to a 0%
markup, statistically speaking). The peak index is insignificant for 10 out of the 43 months while the
off-peak index is insignificant for 28 months of the modeling timeframe. The greater frequency of
statistical insignificance in the results for region M than other regions suggests that the price-setting
generators for this region’s LMPs are bidding closer to their short-run marginal costs, especially in the
off-peak periods, as compared to other regions.65 In addition, the spread between the peak and off-peak
markup indices is smaller compared to the spread in markup indices for region P.
65
Note that the forecast errors in region M are not very different from that of region P, which is demonstrated by the
width of the confidence intervals.
- 58 London Economics International LLC
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ay
-0
-0
3
3
5%
5%
0%
0%
-5%
-10%
30%
25%
20%
15%
15%
10%
10%
5%
5%
0%
0%
-5%
-10%
Ju
l-0
3
Se
p03
N
ov
-0
3
Ja
n04
M
ar
-0
4
M
ay
-0
4
Ju
l-0
4
Se
p04
N
ov
-0
4
Ja
n05
M
ar
-0
5
M
ay
-0
5
Ju
l-0
5
Se
p05
N
ov
-0
5
Ja
n06
M
ar
-0
M 6
ay
-0
6
Ju
l-0
6
10%
3
10%
3
15%
ay
-0
15%
M
Peak Markup index
-0
3
20%
ar
-0
Overall Markup index
M
25%
Ja
n
Ja
n0
M 3
ar
M 03
ay
-0
3
Ju
l-0
3
Se
pN 03
ov
-0
Ja 3
n0
M 4
ar
-0
M 4
ay
-0
4
Ju
l-0
Se 4
pN 04
ov
-0
Ja 4
nM 05
ar
M 05
ay
-0
5
Ju
l-0
Se 5
pN 05
ov
-0
Ja 5
nM 06
ar
M 06
ay
-0
6
Ju
l-0
6
30%
Ja
n03
M
ar
-0
M 3
ay
-0
3
Ju
l-0
3
Se
p03
N
ov
-0
3
Ja
n04
M
ar
-0
M 4
ay
-0
4
Ju
l-0
4
Se
p04
N
ov
-0
4
Ja
n05
M
ar
-0
M 5
ay
-0
5
Ju
l-0
5
Se
p05
N
ov
-0
5
Ja
n06
M
ar
-0
M 6
ay
-0
6
Ju
l-0
6
3
Ju
l-0
3
Se
p03
N
ov
-0
3
Ja
n04
M
ar
-0
4
M
ay
-0
4
Ju
l-0
4
Se
p04
N
ov
-0
4
Ja
n05
M
ar
-0
5
M
ay
-0
5
Ju
l-0
5
Se
p05
N
ov
-0
5
Ja
n06
M
ar
-0
6
M
ay
-0
6
Ju
l-0
6
M
n0
ar
Ja
M
Figure 33. Price-cost markup indices and 95% confidence intervals for all, peak and off-peak periods for region M across sampled
days in January 2003 – July 2006
30%
25%
Off-Peak Markup index
20%
-5%
-10%
All periods
30%
25%
Off-peak periods
20%
Peak periods
-5%
-10%
- 59 London Economics International LLC
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The difference between actual LMPs and modeled LMPs (representing SRMC-bidding), i.e., the dollarbased markup, is increasing through the timeframe for region M, reaching as high as $8/MWh in late
2005, as Figure 34 illustrates. Both the actual and modeled LMPs are also increasing through time: the
$30/MWh average monthly actual LMP level, observed throughout 2003, rises to levels in the range of
$50 to $80 per MWh on average in late 2005 and 2006.
Figure 34. Actual versus modeled prices and the dollar value of the difference in region M across
sampled days in January 2003 through July 2006, $/MWh
$100
$80
Difference
$8
Actual - All hours
Modeled - All hours
$7
$6
$70
$5
$60
$4
$50
$3
$40
Difference ($/MWh)
$2
$30
$1
$20
$-
Ja
n03
A
pr
-0
3
Ju
l-0
3
O
ct
-0
3
Ja
n04
A
pr
-0
4
Ju
l-0
4
O
ct
-0
4
Ja
n05
A
pr
-0
5
Ju
l-0
5
O
ct
-0
5
Ja
n06
A
pr
-0
6
Ju
l-0
6
Actual and modeled prices ($/MWh)
$90
$9
Figure 35 shows the distribution of the dollar values of differences between the monthly averages of
actual LMP and the modeled SRMC for region M at peak and off-peak periods. As in region P, the
differences in peak periods are larger than the differences in off-peak periods, though the differences in
peak periods are distributed between $0/MWh and $10/MWh, most of the differences in off-peak
periods lie below $2/MWh, similar to region P.
- 60 London Economics International LLC
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Figure 35. Distribution of differences between actual monthly average LMP and modeled monthly
average LMPs (based on SRMCs) for peak and off-peak periods in region M
Frequency of occurrence over timeframe
25
Off-peak
20
Peak
15
10
5
0
< $0
$0 - $2
$2 - $4
$4 - $6
$6 - $8
$8 - $10
>$10
Difference (Actual LMPs – Modeled LMPs, Monthly Averages), $/MWh
- 61 London Economics International LLC
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6.4
Region B – BGE and PEPCO
The transmission zones of BGE and PEPCO, along with the remaining portion of METED (31% of the
population) to the south of the Central interface makeup region B. The load-weighted actual historical
LMPs in region B are higher than the bordering regions P and M. Nonetheless, based on historical
actual flows, we know that region B sends power to its northeast neighbor (region E), where the load is
more concentrated and the local generation is more expensive.
Figure 36, shows the price-cost markup indices and their confidence intervals for peak, off-peak and all
periods for each month in our timeframe for region B. The first panel gives all three indices together,
while the remaining three graphs show the 95% confidence bands around the markup indices. These
bands are defined by the lowest and the highest values that markup indices can take at a 95%
probability.
As seen in the figure on the next page, the three price-cost markup indices move very closely together,
starting around the 5% level in 2003, slowly climbing towards 10% in late 2005. The 2006 levels, at least
for the off-peak index, decline, breaking the upward trend. In terms of significance at the 95%
confidence level, the summer months in all four years have comparatively lower markup indices (to the
point of being statistically insignificant or equivalent to 0% markup) and the off-peak index is also
usually statistically insignificant, e.g. equivalent to 0%. The period from fall of 2005 through early 2006
has the highest values for the markup indices, with the peak period monthly markup index climbing
towards (a statistically significant) 15% in those months. Since the forecast error levels are quite similar
to other regions, the statistical significance of the indices are mainly determined by their magnitudes,
compared to the other regions. For example, we can with a 95% confidence, say that in January 2003,
the peak markup index would be between 5% and 13%.
Similar to region M, the spread between the peak and off-peak indices is smaller compared to the same
spread for region P, again suggesting that the bidding behaviors of price-setting market participants in
this region were similar in peak and off-peak periods, resulting in similar markup index values
(probably the result of more consistency across price-setting resources, as compared to region P).
- 62 London Economics International LLC
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Ja
n03
ar
-0
3
M
ay
-0
3
Ju
l-0
3
Se
p03
N
ov
-0
3
Ja
n04
M
ar
-0
4
M
ay
-0
4
Ju
l-0
4
Se
p04
N
ov
-0
4
Ja
n05
M
ar
-0
M 5
ay
-0
5
Ju
l-0
5
Se
p05
N
ov
-0
5
Ja
n06
M
ar
-0
M 6
ay
-0
6
Ju
l-0
6
All periods
15%
15%
10%
10%
5%
5%
0%
0%
-5%
-5%
-10%
-10%
30%
25%
20%
15%
15%
10%
10%
5%
5%
0%
0%
-5%
-10%
-0
M 3
ay
-0
3
Ju
l-0
3
Se
p03
N
ov
-0
3
Ja
n04
M
ar
-0
4
M
ay
-0
4
Ju
l-0
4
Se
p04
N
ov
-0
4
Ja
n05
M
ar
-0
M 5
ay
-0
5
Ju
l-0
5
Se
p05
N
ov
-0
5
Ja
n06
M
ar
-0
6
M
ay
-0
6
Ju
l-0
6
20%
Overall Markup index
Ja
n03
M
ar
-0
M 3
ay
-0
3
Ju
l-0
3
Se
p03
N
ov
-0
3
Ja
n04
M
ar
-0
M 4
ay
-0
4
Ju
l-0
4
Se
p04
N
ov
-0
4
Ja
n05
M
ar
-0
M 5
ay
-0
5
Ju
l-0
5
Se
p05
N
ov
-0
5
Ja
n06
M
ar
-0
M 6
ay
-0
6
Ju
l-0
6
25%
M
ar
Ja
n03
M
ar
-0
M 3
ay
-0
3
Ju
l-0
3
Se
p03
N
ov
-0
3
Ja
n04
M
ar
-0
M 4
ay
-0
4
Ju
l-0
4
Se
p0
N 4
ov
-0
4
Ja
n05
M
ar
-0
M 5
ay
-0
5
Ju
l-0
5
Se
p0
N 5
ov
-0
5
Ja
n06
M
ar
-0
M 6
ay
-0
6
Ju
l-0
6
30%
Ja
n03
M
Figure 36. Price-cost markup indices and 95% confidence intervals for all, peak and off-peak periods for region B across sampled
days in January 2003 – July 2006
30%
Peak Markup index
25%
Off-Peak Markup index
20%
30%
25%
Off-peak periods
20%
Peak periods
-5%
-10%
- 63 London Economics International LLC
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The monthly average differences in the actual LMPs and modeled LMPs (based on SRMC) for Region B
were quite low, around $2/MWh level throughout 2003, but rise to the $5/MWh level in 2004 and early
2005. The rising trend continues, with the dollar-based markups (i.e., the difference between actual and
modeled LMPs) surpassing the $10/MWh level in late 2005. The 2006 figures are lower than those in
late 2005, but still are quite high compared to the 2003 and 2004 differences estimated for region B. It is
important to note, however, that in terms of statistical significance, all three indices (peak, off-peak, and
all hours) in early 2003 turn out to be statistically insignificant (i.e., equivalent to a 0% markup index
value) due to their lower values, and therefore the difference in early 2003 cannot technically be proven
to be different than $0/MWh.66 While the peak index stays statistically significant for the rest of the
timeframe with two exceptions, the off-peak index fluctuates in and out of being significant from 2003
through 2005 and stays statistically insignificant for most of 2006.
Figure 37. Actual versus modeled prices and the dollar value of the difference in region B across
sampled days in January 2003 through July 2006, $/MWh
$100
$14
Actual - All hours
$12
Modeled - All hours
$80
$10
$70
$8
$60
$6
$50
$4
$40
$l-0
Ju
-0
A
pr
n0
Ja
3
O
ct
-0
3
Ja
n04
A
pr
-0
4
Ju
l-0
4
O
ct
-0
4
Ja
n05
A
pr
-0
5
Ju
l-0
5
O
ct
-0
5
Ja
n06
A
pr
-0
6
Ju
l-0
6
$20
3
$2
3
$30
Difference ($/MWh)
Actual and modeled prices ($/MWh)
Difference
$90
Figure 38 shows the distribution of the dollar values of differences between the monthly averages of
actual LMPs and the modeled LMPs for region B at peak and off-peak periods. Similar to region M, the
66
In other words, there is no statistical evidence to prove that the LMP-SRMC difference is different than zero.
- 64 London Economics International LLC
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differences in peak periods are distributed between $0/MWh and $10/MWh, and most of the
differences in off-peak periods lie below $4/MWh.
Figure 38. Distribution of differences between actual monthly average LMP and modeled monthly
average LMPs (based on SRMCs) for peak and off-peak periods in region M
Frequency of occurrence over timeframe
25
Off-peak
20
Peak
15
10
5
0
< $0
$0 - $2
$2 - $4
$4 - $6
$6 - $8
$8 - $10
Difference (Actual LMPs – Modeled LMPs, Monthly Averages), $/MWh
- 65 London Economics International LLC
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>$10
6.5
Region E – AECO, JCPL, PECO and PSEG
Region E consists of the transmission zones to the east of the Eastern interface, with the exception of
Delmarva peninsula (which has been isolated into its own region in the study). Region E is our largest
region both in terms of load and capacity. It is important to also note that the proportion of gas-fired
peaking units to total installed capacity is highest in region E as compared to the other regions,
especially the western ones with coal-fired resources. As a result of the supply-demand conditions,
load weighted LMPs in region E are one of the highest within PJM Classic.
Figure 39, shows the price-cost markup indices and their confidence intervals for peak, off-peak and all
periods for each month in our timeframe for region E. The top-left panel gives all three markup indices
together, while the other three panels show the 95% confidence bands around each of the markup
indices. These bands are defined by the lowest and the highest values that monthly average markup
indices can take at a 95% probability based on the forecast error in the modeling. For instance, the peak
index for July 2006 for region E would take a value between 10% and 17% at a 95% confidence level.
The trends in the markup indices as well as the levels of the markups themselves are quite similar to
that of region B. The indices start low in early 2003 and – though fluctuating a little – increase towards
15% over time. A significant difference is that in region E the monthly indices stay close to the 10%
mark for a longer period of time and are more frequently statistically significant. All three indices
declined in 2006 in region E, approaching their 2003 values. The peak index stays significant
throughout the timeframe with the exception of four months in 2003 (namely, January, February, April
and December). The off-peak markup index and the markup index for all periods are statistically
insignificant at the beginning and the end of the modeling timeframe, but statistically significant
during most of 2004 and 2005. It is also worth noting the climbing trend in the upper bound of the 95%
confidence level for this region – for example, in late 2004, the confidence bands climb upwards of 20%
for all indices for region E. Furthermore, the results of the study suggest that the spread between the
peak and off-peak indices, is smaller as compared to the spread in markup indices for region P, but
similar to the results observed for regions M and B.
- 66 London Economics International LLC
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www.londoneconomics.com
ay
-0
3
3
-10%
30%
25%
20%
15%
10%
10%
5%
5%
0%
0%
-5%
-10%
Se
p03
N
ov
-0
3
Ja
n04
M
ar
-0
M 4
ay
-0
4
Ju
l -0
4
Se
p04
N
ov
-0
4
Ja
n05
M
ar
-0
5
M
ay
-0
5
Ju
l -0
5
Se
p05
N
ov
-0
5
Ja
n06
M
ar
-0
6
M
ay
-0
6
Ju
l -0
6
5%
3
5%
-0
3
10%
ay
-0
10%
Ju
l
15%
M
-5%
03
15%
-0
3
0%
n-
Off-Peak Markup index
ar
20%
Ja
Overall Markup index
M
30%
Ja
n03
M
ar
-0
M 3
ay
-0
3
Ju
l-0
3
Se
p03
N
ov
-0
3
Ja
n04
M
ar
-0
M 4
ay
-0
4
Ju
l-0
4
Se
p04
N
ov
-0
4
Ja
n05
M
ar
-0
M 5
ay
-0
5
Ju
l-0
5
Se
p05
N
ov
-0
5
Ja
n06
M
ar
-0
M 6
ay
-0
6
Ju
l-0
6
Ja
n0
M 3
ar
-0
M 3
ay
-0
3
Ju
l-0
Se 3
pN 03
ov
-0
Ja 3
nM 04
ar
M 04
ay
-0
4
Ju
l-0
Se 4
pN 04
ov
-0
Ja 4
nM 05
ar
M 05
ay
-0
5
Ju
l-0
Se 5
pN 05
ov
-0
Ja 5
nM 06
ar
M 06
ay
-0
6
Ju
l-0
6
25%
Ju
l-0
3
Se
p03
N
ov
-0
3
Ja
n04
M
ar
-0
4
M
ay
-0
4
Ju
l-0
4
Se
p04
N
ov
-0
4
Ja
n05
M
ar
-0
5
M
ay
-0
5
Ju
l-0
5
Se
p05
N
ov
-0
5
Ja
n06
M
ar
-0
M 6
ay
-0
6
Ju
l-0
6
M
-0
3
ar
-0
Ja
n
M
Figure 39. Price-cost markup indices and 95% confidence intervals for all, peak and off-peak periods for region E across sampled
days in January 2003 – July 2006
30%
Peak Markup index
25%
20%
0%
-5%
-10%
All periods
30%
25%
Off-peak periods
20%
15%
Peak periods
-5%
-10%
- 67 London Economics International LLC
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The monthly average differences in the actual LMPs and modeled LMPs (based on SRMC) in region E
have a strong upwards trend until the end of 2005 where they reach approximately $14/MWh, the
highest monthly dollar-based markup value across all regions and timeframes in the study. Similar to
the other regions, there is a decline in the monthly average differences between actual and modeled
LMPs in 2006 for region E as compared to the differences at the end of 2005.
Figure 40. Actual versus modeled prices and the dollar value of the difference in region E across
sampled days in January 2003 through July 2006, $/MWh
$100
$14
$80
Actual - All hours
$12
Modeled - All hours
$10
$70
$8
$60
$6
$50
Difference ($/MWh)
$4
$40
$30
$2
$20
$-
Ja
n03
A
pr
-0
3
Ju
l-0
3
O
ct
-0
3
Ja
n04
A
pr
-0
4
Ju
l-0
4
O
ct
-0
4
Ja
n05
A
pr
-0
5
Ju
l-0
5
O
ct
-0
5
Ja
n06
A
pr
-0
6
Ju
l-0
6
Actual and modeled prices ($/MWh)
Difference
$90
Figure 41 shows the distribution of the dollar values of differences between the monthly averages of
actual LMPs and the modeled LMPs for region E at peak and off-peak periods. Similar to regions M
and B, the differences in peak periods are distributed between $0/MWh and $10/MWh, and most of
the differences in off-peak periods lie below $4/MWh.
- 68 London Economics International LLC
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Figure 41. Distribution of differences between actual monthly average LMP and modeled monthly
average LMPs (based on SRMCs) for peak and off-peak periods in region E
Frequency of occurrence over timeframe
25
Off-peak
20
Peak
15
10
5
0
< $0
$0 - $2
$2 - $4
$4 - $6
$6 - $8
$8 - $10
Difference (Actual LMPs – Modeled LMPs, Monthly Averages), $/MWh
- 69 London Economics International LLC
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>$10
6.6
Region D – DPL
The Delmarva peninsula, which is quite isolated in terms of transmission lines, makes up our region D.
The only connection between region D with the rest of PJM Classic is through region E. Because of the
character of this transmission interconnection (prior to recent upgrades), region D has had a history of
congestion problems.67 Though, region D is very small compared to the neighboring region E in terms
of load and installed capacity, it is a net importer of power from region E, and because of the power
flow dynamics, it has the highest average actual LMPs in PJM Classic during the timeframe of our
study.
Figure 42, shows the price-cost markup indices and their confidence intervals for peak, off-peak and all
periods for each month in our timeframe for region D. The first panel gives the three indices together,
while the other panels show the 95% confidence bands around the corresponding markup indices.68
These bands are defined by the lowest and the highest values that markup indices can take by a 95%
probability. The confidence intervals, shown in Figure 42, present the upper and lower limits that index
values can take with a 95% probability. For instance, the peak index, with 95% probability, would take
a value between 2% and 11% in January 2003.
The markup indices for DPL exhibit a different trend from the other regions studied. For many months
for the overall index (all hours) and the off-peak index, the results are statistically insignificant, i.e.,
equivalent to 0% markup. We observe the highest markup index values for region D in the spring of
2005. In particular, all three price-cost markup indices are well above 10% in 2005, except for the
summer months. The spread between the peak and off-peak indices, similar to the ones for regions M,
B and E, are smaller compared to the peak versus off-peak spread for region P.
67
See Section 3.4 for more details on the congestion issue of region D.
68
The forecast errors are calculated in a slightly different manner for region D because of data availability. For all other
regions we have access to detailed hourly transmission flow data (transfers and transfer limits) among the regions.
For region D, however, due to data unavailability we used the static transfer limit of the transmission lines
connecting region D to region E instead of hourly transfer limits for modeling. As discussed in Section 8.2, flow
variables are used as control variables. Lacking the data for transmission flows, the only control variables used for
region D’s forecast error are the generation levels of individual plants.
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15%
15%
10%
10%
5%
5%
0%
0%
-5%
-5%
-10%
-10%
Off-peak periods
30%
30%
25%
25%
20%
20%
15%
15%
10%
10%
5%
5%
0%
0%
-5%
-10%
ar
-0
3
ay
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3
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3
Ja
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ay
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4
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M
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5
Ja
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M
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6
M
ay
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6
Ju
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20%
M
Overall Markup index
M
25%
Ja
n03
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M
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M 3
ay
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3
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3
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M 4
ay
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4
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4
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ay
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5
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5
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M
ar
-0
M 6
ay
-0
6
Ju
l-0
6
30%
Ja
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M
ar
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M 3
ay
-0
3
Ju
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3
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N
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-0
3
Ja
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ar
-0
M 4
ay
-0
4
Ju
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4
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-0
5
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ay
-0
5
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ar
-0
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ay
-0
6
Ju
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ar
-0
M 3
ay
-0
3
Ju
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3
Ja
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ar
-0
M 4
ay
-0
4
Ju
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-0
4
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M 5
ay
-0
5
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-0
5
Ja
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ar
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M 6
ay
-0
6
Ju
l-0
6
Figure 42. Price-cost markup indices and 95% confidence intervals for all, peak and off-peak periods for region D across sampled
days in January 2003 – July 2006
30%
Peak Markup index
25%
Off-Peak Markup index
20%
All periods
Peak periods
-5%
-10%
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The monthly average differences in the actual LMPs and modeled LMPs (based on SRMC) in Region D
have a strong upwards trend until January 2006 where they reach $12/MWh. Similar to the other
regions, there is a decline in the monthly average differences between actual and modeled LMPs in
2006 for region D as compared to the differences at the end of 2005.
Figure 43. Actual versus modeled prices and the dollar value of the difference in region D across
sampled days in January 2003 through July 2006, $/MWh
$100
$14
$80
Actual - All hours
$12
Modeled - All hours
$10
$70
$8
$60
$6
$50
$4
$40
$2
$20
$-
Ja
n03
A
pr
-0
3
Ju
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3
O
ct
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3
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A
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4
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ct
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pr
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6
Ju
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6
$30
Difference ($/MWh)
Actual and modeled prices ($/MWh)
Difference
$90
Figure 41 shows the distribution of the dollar values of differences between the monthly averages of
actual LMPs and the modeled LMPs for region D at peak and off-peak periods. Similar to regions M
and B, the differences in peak periods are distributed between $0/MWh and $11/MWh, and most of
the differences in off-peak periods lie below $4/MWh.
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Figure 44. Distribution of differences between actual monthly average LMP and modeled monthly
average LMPs (based on SRMCs) for peak and off-peak periods in region D
Frequency of occurrence over timeframe
25
Off-peak
20
Peak
15
10
5
0
< $0
$0 - $2
$2 - $4
$4 - $6
$6 - $8
$8 - $10
>$10
Difference (Actual LMPs – Modeled LMPs, Monthly Averages), $/MWh
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6.7
Price-cost markup index by fuel type
It would also be instructive to look at the relationship between markups and estimated profits.
However, we do not have information on the actual price-setting unit in any given hour in a given zone
or region as that is not released by PJM. PJM does publish the fuel type of the price-setting units across
PJM’s entire footprint. Although this information is inferior to the identification of price-setting units
by location, we can nevertheless construct a general representation of the relationship between the
monthly averages of price-cost markup indices and price-setting fuel types. Although, calculating the
price-cost markup index by fuel type is less relevant for purposes of economic market definition, which
restricts substitutability over product, geography and time dimensions, it still is helpful allowing for
analysis and interpretation of how the markups effect notional profits, since we can look at different
broad classes of technology, which are generally situated at different parts of the supply curve.
In order to determine the markup index by fuel type, we used PJM’s hourly marginal fuel dataset (the
same dataset that supports Figure 15). Since PJM publishes the marginal units for all of PJM’s footprint,
rather than for each region or zone, it is not 100% comparable to the simulations of the price-cost
markup completed in this study. Nevertheless, for illustrative purposes, we combined the PJM
marginal fuel dataset with our region E’s actual and modeled prices69, in order to gauge the potential
markup by fuel type, as a proxy for plant or portfolio-level markups. The cumulative markup levels
over a period of time (in $/MWh per period), adjusted for operating profiles (i.e., capacity factors) can
be converted into gross profits net of variable costs, a proxy for EBITDA70 and can then be compared to
fixed costs, enabling conclusions to be drawn about profitability levels. We chose region E in this
exercise since it has the largest load and installed capacity among our regions and is therefore more
likely to have price-setting generation on-peak because of congested transmission lines.
For each fuel type, we looked at the hours where that fuel was price-setting, and only for those hours
where we calculated the average markup (i.e., only for the sample days). Figure 45 presents the
markup indices for all periods studied across the four major types of fuel, while Figure 46 presents the
same periods across the four major types of fuel but substitutes the difference between the actual and
modeled LMPs (for region E) instead of the price-cost markup index.
Inspection of Figure 45 shows that the values are consistent with what we see in regional markup index
averages in terms of trends: the markup levels (above modeled LMPs, which represent SRMCs) start
lower early in the timeline, reach their peak levels during 2004, and then decline in 2006. Units with
lower fuel costs and probably lower SRMC, specifically those using coal and heavy oil fuel, have higher
price-cost markup index values compared to the light oil and natural gas burning units (the average
markup index for coal fired units over the course of sample periods in the study timeframe is 6.3%,
while the same statistic for gas fired units is 5.5%). A possible explanation is that when a baseload
(coal-fired) unit is price-setting then it is likely offering on the basis of the market-wide opportunity
cost, which may be equal to the cost of a combined-cycle gas fired unit and therefore the offers are
69
Region E, consisting of four zones, has the highest installed capacity as well as the load in our topology. Therefore,
for purposes of illustration, we used region E’s actual and modeled LMPs in this analysis.
70
EBITDA stands for Earnings before Interest, Taxes, Depreciation, and Amortization, a standard financial measure of
profitability, or more specifically, for the cash earnings that may be applied to interest and debt retirement.
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increased to equate LMPs with the SRMCs of a gas-fired unit rather than the strict SRMCs of the coal
unit.
In addition, the small difference in the markup indices between coal and gas can possibly be explained
by the differences in how SRMCs were estimated. In contrast to other types of fuel for coal we wanted
to represent the unique transportation costs of the fuel to each coal plant, since coal plants cannot easily
realize arbitrage opportunities in the spot market due to the difficulty of physically selling coal back to
the market if it is already delivered and stockpiled. We have used the delivered costs of fuel as
reported by Global Energy Decisions’ Energy Velocity database, which diverge from a plant’s
perceived average costs or opportunity cost because of the differences in inventory accounting by each
plant and their approach to trading around their coal contract. Nevertheless, we believe the issues in
the fuel cost data for coal plants do not make a big difference on the overall regional markup index
results.
Figure 46 plots the same information as Figure 45 but substitutes the markup index value against the
difference between the actual historical LMP and the modeled SRMCs (in $/MWh terms). The results
from Figure 46 are quite similar to the trends observed in Figure 45, on a qualitative basis. When the
baseload fuel types, coal and heavy oil, are price-setting, we observe higher markups in $ per MWh
terms as compared to the periods when we see light oil and natural gas fired units price-setting.
Figure 45. Price-cost markup index by fuel type
16%
COAL
14%
HEAVY OIL
12%
LIGHT OIL
NG
10%
8%
6%
4%
2%
0%
Ja
n04
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ar
-0
4
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ay
-0
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ay
-0
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-0
6
M
ay
-0
6
Ju
l -0
6
-2%
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Figure 46. Difference between actual LMP in zone E and modeled LMPs in zone E by fuel type
($/MWh)
$7
COAL
$6
$5
HEAVY OIL
LIGHT OIL
NG
$4
$3
$2
$1
$0
Ja
n04
M
ar
-0
4
M
ay
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4
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ay
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-0
6
M
ay
-0
6
Ju
l-0
6
-$1
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7
Concluding remarks
Our results show that for most of the months in the studied timeframe the price-cost markup indices,
especially for peak periods are positive and statistically significant at a 95% confidence level, indicating
that price-setting generation in PJM Classic had typically bid greater than their short-run marginal
costs over the 2003-2006 timeframe. Since price-cost markup index is unit free, it is also independent of
the fluctuations of load. The correlation coefficients between the monthly markup indices and the
monthly average loads for each region are very close to zero, suggesting no important correlation
between the two, which dispels the common hypothesis regarding higher markup indices during high
load conditions as discussed in Section 6.1.5, but also raises questions about whether markups are
justified during lower demand conditions. Based on the results summarized in the previous section, we
see that the markup indices vary considerably across time and across modeled regions of PJM Classic.
The regional diversity in our results highlights the importance of considering the markup index on a
locational basis and demonstrates how averaging to the market-wide footprint can camouflage
statistically significant disparities between regions within PJM. We have not investigated in detail the
reason for the observed trends in the markup indices as it was outside the scope of this study, but the
empirical results we have reported can serve as a foundation for further study.
Our results are based on a model of PJM Classic, but using as much information as possible that
describes actual system conditions over the study timeframe. For example, we used historical data on
hourly zonal load, hourly imports to and exports from PJM Classic, hourly transfer flows and limits
across Eastern, Central and Western transmission interfaces, and hourly generation levels of individual
plants that submit data to CEMS database. We also relied on daily actual fuel prices, daily allowance
prices, monthly historical emissions by plant, monthly hydroelectric generation, and annual historical
reported heat rates and O&M costs by plant/unit.
In spite of all this information, we did not have access to all the data that we would have liked to have
had and that led to certain abstractions in our modeling as compared to real life. For example, we did
not have actual transmission congestion data beyond the key internal interfaces described above. We
also did not have access to plant-specific operating constraints and other technical and operational
constraints faced by PJM. In order to take into account these realities of system operation, we used
industry standard assumptions coupled with rigorous and extensive calibration of the modeling to
those parameters for which we had granular data. In addition, we modeled only the selected days
within each year of our study timeframe. The sampling was a necessity in order to make the study
feasible. However, because of the relative speed of the modeling software (which was the direct result
of employing a regional network topology rather than a full nodal model), we were able to simulate
approximately 55% of all days within the study timeframe. Though sampling almost always leads to
some information leakage, our sample size is very extensive – sufficiently in excess of the statistical
requirements for making accurate representations of a population – and the technique that we utilized
for sampling is based on best practice approaches in econometrics and ensures that the sample
represents the population as closely as possible. Altogether, given the low forecast error figures we
calculated for each month and region, we conclude that our backcasting model is exceedingly accurate
at simulating historical conditions. Therefore, for each region and month in the timeframe, the
estimated 95% confidence intervals for price-cost markup indices are robust and permit qualitative
inference and comparison on the scales of the indices.
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There are many potential implications of the empirical analysis summarized in this report. At a very
basic level, the study does find that there are positive, statistically significant markups above estimated
short run marginal costs for many periods over the January 2003 through July 2006 timeframe in the
PJM Classic market area. These observations can help answer fundamental questions about the
efficiency of the market design, the presence of market failures (such as market power) and the
adequacy of the price signal for long term investment. It is well recognized that prices strictly above
SRMC are not a sufficient indicator that markets are not competitive. For example, FERC’s long
standing tradition with respect to market power and market-based rates states that it is rational to
allow an adder on top of embedded or marginal costs. Indeed, the classic definition of market power
requires that we ask the following question: are the price levels above competitive levels and are they
profitable, substantial, and sustainable? On the flip side, are price levels sufficient to ensure a
sustainable industry? From a practical perspective, SRMC-bidding by all generators in an energy-only
market would not sustain or motivate peaking-only plants, which are very important to maintaining
system reliability in the face of uncertain demand. It should also be pointed out that whether peaking
units recover their economic costs becomes an empirical question that is beyond the scope of this study.
In order to pass judgments on market design and market efficiencies, further analysis is required,
because the empirical aspects of these policy questions (i.e., whether peaking units recover their
economic costs) were beyond the scope of this study. For example, if one were to analyze the efficiency
of LMPs for signaling investment, another layer of quantitative analysis must be performed. First, the
levels of the price-cost markup indices from the Day-ahead Energy Market must be translated into a
measure of profitability. Next, these energy profits must be included with the other revenue streams
available to generators, and then compared to the going forward costs of the industry, otherwise
known as long run costs. In effect, in the long run “all” cost components are variable, in order to
ensure fixed cost recovery. The estimate of such costs is a challenging exercise as it undoubtedly
requires some forward-looking judgment. For example, one would need to consider the type of supply
mix that leads to a sustainable, yet dynamic, industry in the longer term. One must also consider the
long term reliability of the system and the future shape of the supply curve, i.e., what is a reasonable
profit for different plants along the supply curve (from baseload to peaking) given the risk profile of
deregulated revenue streams? Do market design choices, which have their own set of criteria, influence
the reference point for judging sufficiency of the price signal (e.g., do we study only the necessary plant
at the margin or do we analyze the entire spectrum of plants along the supply curve)? How does this
profit benchmark change with time given macroeconomic conditions, capital markets, and the unique
investment demands of this sector (lumpy and time-critical capital expansions)? These are but a few of
the questions that are raised by the results of this empirical study.
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8
Appendices
8.1
Capacity and Ancillary Services markets in PJM
This Appendix extends the description of the Overview of the PJM Markets with specific emphasis on
the Capacity Markets, and the “Ancillary Service Markets (“AS”). Eligible generators participating in
these markets do see the opportunity to increase their revenue streams; an opportunity that may assist
in offsetting some of their costs.
8.1.1
Capacity market
As discussed in Section 2.2, PJM administers a capacity market in which participants are allowed to
buy and sell capacity credits; PJM was the first to implement this market in the US. The current market
construct consists of daily and monthly auction for capacity credits. These auctions should allow the
LSEs within PJM that do not self supply their capacity to acquire the resources necessary to meet their
capacity obligations. These obligations (i.e., the LSE share of the market’s capacity requirement based
on peak load plus a margin) may be acquired from the periodic capacity markets administrated by PJM
or self supplied (either through own resources or through bilateral arrangements).71 LSEs that do not
meet their requirements are subject to penalty – the capacity deficiency rate – for each MW of capacity
that LSEs are lacking vis-à-vis their obligation, which is currently set at $160 per MW-day, adjusted for
the equivalent demand forced outage rate (“EFORd”).72 Generators within PJM and, under certain
conditions, generators external to PJM, can sell their capacity in this market, although they are then
obligated to offer the commensurate energy from that contracted capacity into the PJM DAH market.
One of the characteristics of the market is that the requirement or obligation on LSEs is a fixed number
(the LSE share of the market’s capacity requirement based on peak load plus a margin, a total that
remains fixed for 12 months), irrespective of supply conditions and market-clearing prices for capacity
credits, which results in a vertical demand curve for the market. This, in turn, leads to a binary
outcome: if the amount of the installed capacity in PJM is below the overall requirement, the price rises
quickly to the penalty charge; and if the amount of capacity is above requirement, the price rapidly
approaches zero. Another characteristic of the current and historical PJM capacity market is its postage
stamp structure – with the exception of the Northern Illinois area and PJM Classic plus Allegheny
Power control area for some period of time, there has effectively been one clearing price for the entire
PJM market. Such a market design feature assumes universal deliverability potential and ignores
transmission constraints - a generator anywhere within PJM is deemed capable to deliver anywhere
within PJM.
For illustrative purposes, the figure below summarizes the monthly average price (volume weighted)
and total volume transacted in the Daily Unforced Capacity Credit auctions over the period 2000
through 2006.73 Clearing prices have generally declined from the 2000-200174 timeframe while volumes
71
LSEs may also reduce their capacity obligations by participating in demand-side response programs. See 2005 State of
the Market Report—Market Monitoring Unit [2006, p. 35].
72
See PJM, Schedule 11, Second Revised Rate Schedule FERC No. 27.
73
Monthly auctions generally clear a much smaller amount of capacity in the aggregate, although prices tend to be
higher.
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have increased (commensurate with the growing PJM footprint). Over the timeframe of this study,
capacity prices in the daily auctions on an annual average basis have ranged from $0.15/MW-day
(2005) to a high of $17.21/MW-day (in 2004, due to high prices in June and July). The clearing prices in
2006 averaged over the year at $1.96/MW-day.
Figure 47. Monthly volume-weighted average clearing prices ($/MW per day) and total volume
transacted (MW) in the of PJM Daily Unforced Capacity Credit Market, January 2000 – December
2006
$160
Sum of MW Cleared
120,000
$140
100,000
$120
80,000
$100
$80
60,000
$60
40,000
$40
20,000
Total MW Cleared in Monthly Auctions (MW)
Volume-weighted Monthly Average Capacity Credit
Price from Daily Auctions ($/MW-Day)
Volume-weighted Price from Daily Auctions
$20
-
Ja
nM 00
ay
-0
Se 0
p0
Ja 0
nM 01
ay
-0
Se 1
p0
Ja 1
nM 02
ay
-0
Se 2
p0
Ja 2
nM 03
ay
-0
Se 3
p0
Ja 3
nM 04
ay
-0
Se 4
p0
Ja 4
nM 05
ay
-0
Se 5
p0
Ja 5
nM 06
ay
-0
Se 6
p06
$-
Year-Month
Source: http://www.pjm.com/pub/capacity_credit_market/downloads/daily.csv
In acknowledgement of some of the weaknesses of the current market design for capacity, PJM filed
with FERC an alternative market design on August 31, 2005, known as the Reliability Pricing Model
(“RPM”). The proposal was further amended through a settlement process with PJM stakeholders, and
a settlement agreement on behalf of numerous specified parties was filed at FERC on September 29,
74
As can be seen in the figure above, in 2000 and 2001, average monthly prices exceeded the deficiency rate for a
number of months (this was due to a mandatory buy bids, which were discontinued after June 1, 2001). In addition,
the Market Monitoring Unit found evidence of physical and economic withholding in the capacity credit auctions in
2001. See PJM, Market Monitoring Unit, Report to the Pennsylvania Public Utility Commission, Capacity Market
Questions, November 2001.
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2006 (and conditionally approved by FERC on December 21, 2006).75 The RPM design aims at aligning
capacity pricing with system reliability requirements.76 This alignment requires that the timeframe for
the RPM auctions provide for a binding forward commitment three year77 forward so as to allow new
generation and transmission resources to compete directly with incumbents. The forward timeframe
also aligns the RPM with the Regional Transmission Expansion Planning (“RTEP”) Process, which
provides an advance notice on resource adequacy and also is used to coordinate capacity addition with
any needed transmission system expansion or reinforcement.78
One of the other major objectives of RPM is to address the inadequacy of the “one price fits all”
capacity credit markets and to allow for pricing to reflect local constraints and the deliverability of
generating capacity into different areas of PJM. The “Locational Capacity Pricing” component is a
departure from the currently used universal deliverability concept discussed above. The PJM proposal
would establish up to 23 capacity zones (referred to as Locational Deliverability Areas or “LDAs”).
Each LSE would be required to procure resources that were deemed to be able to deliver energy to that
LSE’s zone in light of transmission constraints.
RPM also attempts to combat the vertical demand problem observed in the capacity markets to date by
utilizing a downward-sloping price schedule for the reserve requirement (referred to as the Variable
Resource Requirement or “VRR”). The VRR sets the price schedule for the auction based on estimated
costs of new entry less the expected energy and ancillary services revenues for a hypothetical resource.
On behalf of PJM, Professor Hobbs conducted a study that simulated a long-run dynamics of the VRR
curve. The study’s findings showed that the VRR curve is likely to yield levels of total consumer costs
at $82/peak kW/year.79 The VRR is expected to improve the stability of the price signal, while the
forward-looking nature of RPM is expected to provide an opportunity for developers to secure
medium-term contracts, which are typically needed to get new investment financed, and the locational
pricing element geared towards replacing non-market solutions (like reliability must-run contracts)
with market-oriented price signals.
8.1.2
Ancillary services market
Ancillary services (“AS”) are an important component of system operations and help PJM achieve
reliable operation of the transmission system. PJM procures three AS products from technically eligible
providers; these products include the Regulation, Synchronized Reserve, and Black Start services. The
Regulation and Synchronized Reserve products are procured through daily auctions, like the Energy
75
See Docket Nos. EL05-148 and ER05-1410, September 29, 2006.
76
See www.pjm.com/documents/donwloads/presentations/pjm-rpm-tc-testimony.pdf p. 14.
77
Originally, the forward commitment was proposed to be four years, but it was reduced to three years in September
29, 2006 settlement filing with FERC.
78
PJM expects that this forward commitment period of 3 years will provide an adequate period for the market to
compare alternative solutions among the possible generation resource additions, potential demand response and/or
transmission investment. The competition of alternatives against incumbent resources is also expected to help
address market power issues.
79
Settlement Agreement and Explanatory Statement of the Settling Parties Resolving all Issues in PJM Interconnection
L.L.C., Docket Nos. ER05-1410-000 and 001, and EL05-148-000 and 001 (pgs. 8-9).
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Markets, operated by PJM, in which generators offer to provide AS and PJM, as a single buyer on
behalf of all LSEs, purchases such AS. PJM uses a least cost, energy constrained disptach to optimize
between these AS and energy. The Black Start product is procured by PJM through bilateral
agreements with qualified generators. PJM allocates the cost of procurement of AS to all LSEs.
Regulation is the service that corrects for very short-term changes in electricity use that might affect the
stability of the power. Regulation offers may be submitted only for those resources electrically within
PJM and satisfy the resources criteria. For example, the generation resources must have a governor
capable of Automatic Generation Control (“AGC”), be able to receive and respond to signals from the
RTO (i.e., have appropriate telemetry and interconnection with the PJM control center), as well
demonstrate minimum performance standards as set by PJM (including satisfactory performance on
dynamic evaluations).80 Regulation requirements are determined in whole MW for the entire day, and
the single requirement for PJM is equal to the 1% of the forecast peak load for the day.81 Demand for
Regulation varies by region, for example in 2005, Mid-Atlantic region’s average regulation requirement
was 434 MW per hour and for the region covering PJM Classic plus Allegheny, the hourly requirement
averaged 517 MW.82 The supply is commonly measured by the level that is both offered to the market
on an hourly basis and is eligible to participate in the market on hourly basis. Because regulation is tied
to the opportunity costs of producing energy, the price varies considerably depending on the supplydemand balance and the underlying cost of energy, although PJM Market Rules cap the offer price to
$100/MWh. As an example, in 2005, in the Mid-Atlantic region, the daily average regulation clearing
prices ranged from less than $20/MWh to as high as $115/MWh.83
Synchronized Reserve supplies electricity if the grid has an unexpected need for more power on short
notice (possibly as a result of a forced outage or higher than projected demand). It is provided by
eligible resources that are located electronically within the synchronized reserve zone (there are
currently three reserve zones over the entire PJM service territory), and satisfy the eligibility criteria as
determined by whether the resources shall participate as Tier 1 or Tier 2. Tier 1 is comprised of all those
resources on line following economic dispatch and able to ramp up form their current output in
response to a synchronized reserve event within 10 minutes or demand resources capable of reducing
load with 10 minutes. Tier 2 consists of additional capacity that is synchronized to the grid and
operating at a point that deviates from economic dispatch and therefore has the capacity to provide
additional spinning synchronized reserve in response to signal from PJM.84 The average requirements
for synchronized reserves in 2005 totaled around 1,800 MW among the different regions in PJM, based
on the size of system contingencies and NERC requirements.
Tier 1 resources are paid when the resources respond to an identified spinning event and are paid
spinning payments that are equal to the integrated increase in MW output above economic dispatch
80
See PJM Manual 11: Scheduling Operations, Revision 29 [ 2006, p. 51].
81
An exception is PJM’s Mid-Atlantic region which has different regulation requirements for on-peak and off-peak.
82
See 2005 State of the Market Report [2006, p. 225].
83
The resource merit order price ($/MWh) is equal to the resource regulation offer + estimated resource opportunity
cost per MWh of capability, hence the possibility of exceeding the offer price cap. See Manual 11, page 54.
84
Manual 11, p.60/62.
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from each generator over the length of the spinning event multiplied by the spinning energy
premium85 less the hourly integrated LMP. Tier 2 units providing synchronized reserves are
compensated whether or not they are actually called on to provide supply and their compensation is
market driven, subject to the PJM Market Rules.86
Black Start service allows for system restoration in the unlikely event that the entire grid would lose
power. Black start units are defined as those that are able to start without outside electrical supply.
Generation owners providing this service must commit for at least two years to provide the service.
Compensation for such service is cost based and related to the revenue requirement for the units
providing the service plus 10% incentive. 87
85
The spinning energy premium is the average of the five minute LMPs during the spinning event plus $50/MWh.
86
See 2005 State of the Market Report, p. 280/281. The spinning offer price for a unit cannot be greater than the unit’s
operating and maintenance cost plus $7.5 per MWh margin. The market clearing price consists of the offer price plus
the cost of energy use and the unit’s opportunity cost. The average clearing prices in 2005 was around $15 per MWh
for an approximate average hourly quantity of around 400 MW.
87
See PJM, Black Start Service Business rules, Revision 2, June 2002. Notably, the average generator payment has been
quoted to be above $1,000 per MW per year for the 142 eligible black start units with on–site power sources (see PJM
News, December 2, 2002).
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8.2
Determining the modeling forecast error
In order to make a scientific assessment of our results, we used the flows through transmission
interfaces and generation of the individual plants as control variables.88 We then compared the hourly
historical data for our control variables against the corresponding POOLMod output to calibrate the
model and, finally, to compute the modeling forecast error. The intuition is that the degree of success
of a modeling exercise can be measured by the distance between its forecasted output and the actual
realized values of selected control variables.
Given the two sets of control variables (flow and generation), there are specific variables that produce
actual and modeled values: the three major transmission interfaces, Eastern, Central and Western
interfaces89 together determine the performance of the model in terms of general supply-demand
dispatch and power flows. Around 140 generation plants, that submit their actual hourly production
levels to U.S. Environmental Protection Agency’s Continuous Emissions Monitoring Systems (CEMS)
database90,91 for each year from 2003 to 2006, determine the model’s performance with respect to
committing and dispatching generation in each region.
The difference between the actual value and the modeled value of a control variable is a stochastic
variable. Assuming those differences have the same underlying probability distribution function and
applying the Central Limit Theorem, we conclude that the average of differences for each variable is
normally distributed. Given that fact, and after figuring the parameters of the normal distribution
(mean and variance, as given by the sample mean and variance) for each variable, we standardized92
the distributions of all variables in order to make distributions comparable and unit free. Determining
the confidence intervals for a two-tailed confidence level (95% in our study – a well established number
in the econometric literature) is then straightforward. Note that since all distributions are standard
normal, the confidence intervals are in the same units.
In determining the final forecast error, which actually is given by the two-tailed confidence interval, we
treated the set of flow variables equally (equal weights in averaging) with the set of generation
variables. For each region we considered the relevant transmission interfaces and the local generation
units. The three flow variables are also treated equally where relevant, since flow is a macro variable
determined as a result of behaviors of many plants and demand in different regions. The generation
variables are weighted by their total annual generation levels since large baseload plants can affect the
market outcomes on a grater scale/frequency than small peaking units. The estimated forecast errors
range between 3% and 7% for peak and off-peak indices, as shown in Section 8.5.2.
88
See Section 8.5.2 for tabulated results for the control variables.
89
See Figure 8 for the key statistics and source of information.
90
Available at http://www.epa.gov/ttn/emc/cem.html.
91
There are over 250 plants in PJM Classic, and approximately 140 plants (the numbers change throughout the
timeframe due to retirements and new entry) submit hourly production to CEMS. Hydro units, thermal units with
less than 25 MW capacity, and some others (cogen) are exempt from CEMS.
92
For example, we converted the normal distributions to standard normal distribution. A normal distribution with
mean µ and variance σ2 can be converted to standard normal (mean 0 and variance 1) by subtracting µ and dividing
by σ.
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8.3
POOLMod and its algorithm, a synopsis
In addition to the brief exposition of POOLMod in Section 5.2, the following appendix provides a
synopsis of this model.
8.3.1
What is POOLMod
POOLMod simulates merit order commitment and dispatch in power systems, and uses the results of
the simulation to forecast or to backcast (as in the case of this study) locational marginal prices. The
model was originally developed by London Economics in 1988 to forecast prices in the England and
Wales (E&W) power pool. The multiple region version was developed for the national market trial
operating in the SE Australian system. Since the time, the model has been refined continuously to bring
it into uniformity with North American power markets and provide for more flexibility for simulation
modeling.
8.3.2
What POOLMod does
POOLMod uses an hour by hour merit order dispatch approach. Within each generating period,
POOLMod selects available plant in the merit order to meet demand at least cost. This is a two-part
process:
•
determination of units that will be committed (that is, they will generate in the hour,
even if only at their minimum output); and
•
dispatch of unit in the hour, which determines the precise level of output of each
committed unit.
Plant availability is dealt with via a stochastic approach or based on a user-inputted schedule, and
POOLMod will then select available plant with the lowest offer price to satisfy demand. Offer prices
can be characterized based on costs or the model can calibrate offer prices using a game theoretic
approach (by profit-maximizing of select portfolios and taking into account contracts and strategic
bidding potential, as well as competitors’’ response). The model can operate over a period from 1 to 25
years. The detailed treatment of each hour means POOLMod can model the temporal components of
the generating resources such as start up costs, peak and off-peak periods, units with sophisticated
non-linear costs and part-loading.
8.3.3
POOLMod output
POOLMod produces a range of information relating to station performance and pool prices; this
includes:
•
system marginal price (SMP), which is equivalent to the LMP for each hour of the year
and each sub-region defined by the modeled transmission topology;
•
identification of the price-setting unit;
•
hourly generation from selected units, stations or portfolios of stations;
•
transmission congestion analysis and inter-regional flows;
•
indicators of financial performance of each station in the database; and
•
detailed information on regional balances, constraints, commitment and dispatch for
single specified days.
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POOLMod has also been tailored to work with other modeling tools that London Economics has
developed, including various capacity pricing models, scarcity rent projection tools (for evaluating call
options), and retail settlement account models (for pricing retail contracts).
8.3.4
Overview of Key Algorithms
8.3.4.1
plant availability
Where POOLMod does not have details of actual plant availability algorithms are used to determine
which plants are available on any given day. There are two elements to this: planned maintenance, and
forced outages.
Planned maintenance assumes that maintenance of plant is scheduled in an intelligent way by
generators and system operators so as to ensure that the ability to meet demand is not endangered by
necessary engineering work. Thus, while maintenance periods are initially selected at random, they are
subject to a series of heuristics that attempt to ensure a rational pattern of planned outages. These
heuristics include keeping the reserve plant margin at a minimum defined level (which can be varied
with the season), keeping the changes in system capacity smooth, and keeping the average size of
available plant relatively constant93. In a multi-regional system POOLMod pays attention to
maintenance on a regional basis so as to not create transmission constraints through imbalanced
allocation of planned outages.
Forced outages, by definition, are unplanned and are therefore allocated strictly randomly.
8.3.4.2
commitment and dispatch
The primary difference between the commitment and dispatch phases of POOLMod is that the former
looks at the day as a whole whereas the latter looks at individual hours independently. The reason for
this is that engineering and commercial considerations may result in a plant choosing a different
running schedule when considering the day as a whole than it would if it were simply asked to bid into
the market on an hour-by-hour basis.
8.3.4.3
thermal commitment
With thermal plant the primary consideration is the physical limitations of the plant. Larger and older
plants tend to be expensive to start and stop. They may have limitations on how long they must run
once turned on, and how long they need to remain inactive once turned off. POOLMod’s thermal
commitment algorithm takes this into account. As a result baseload plants may be committed in
periods when it would be strictly uneconomic for them to run because it would even more uneconomic
to turn them off for just a few hours. Equally mid-merit plants may be passed over for commitment
because the peak that they are being asked to meet is shorter than their optimal minimum running
time.
93
This is primarily a proxy of maintaining a constant mix of generation types, as different technologies tend to result in
different optimal sizes for plants.
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8.3.4.4
hydro commitment
With hydro commitment the considerations are primarily economic. A hydro plant may not have
sufficient water to run at full output for the entire day. It is therefore left with a choice of running all
day at a reduced output, or for fewer hours at full output. Because prices vary during the day, the
correct economic decision is to always run at full output and chose those hours to coincide with
maximum prices. The hydro commitment algorithm therefore looks at the day as a whole and chooses
to commit the plant when it is most advantageous for it to run, subject to the constraint that it must use
as much of its water as possible.
Running in this way generally means that hydro units take a place in the merit order that is well above
that indicated by their physical costs. In practice they have to do this by tailoring their bid prices to
ensure that they run when they wish to do so. POOLMod assumes that they are optimal in achieving
their desired running pattern, and then sets their bid price to be just less than that of the cheapest
thermal unit that they have effectively displaced. This is known as “shadow pricing”.
It is rarely likely that a hydro plant will have precisely the amount of water available for it to be able to
run at full output for an integral number of hours. Rather than partload the plant for one hour to use
up the water, POOLMod uses the plant’s reservoir to store or reclaim water from one day to the next so
as to allow it to run at full output as often as possible.
8.3.4.5
dispatch
In contrast to commitment, the dispatch process treats each hour independently and chooses plants
solely on the basis of their bids. The only exception to this is that all plant that has been committed for
that hour must run at least at its minimum stable generation. In rare instances (where a baseload plant
is partloaded through an overnight trough) this can lead to it being dispatched out of merit. By default
POOLMod does not allow plant that is dispatched out of strict economic merit order to set the system
price (as is the case with RMRs in PJM).
8.3.4.6
transmission constraints
In a multi-region system such as that used for this study transmission constraints are a key feature of
commitment, dispatch and price setting. POOLMod does not allow plant to be committed or
dispatched if doing so would result in flows that would breach transmission limits.
Market price is normally set for the system as a whole, but if a region, or group of regions, is
constrained by being unable to import any additional power then the price for that region or group of
regions can only be set by plant operating within that region or group of regions.
Transmission constraints also affect the choice of shadow prices for hydro plant. A thermal plant it only
a legitimate shadow price target if the output it produces can be freely displaced by the hydro output.
If a transmission constraint blocks this then a more expensive plant that is not so constrained is chosen.
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8.4
Detailed Tabular Summaries of Inputs and Assumptions
A brief introduction to our inputs and assumptions is provided in the text: in Section 3.4 for the
assumed topology of PJM Classic and the transmission constraints between the defined regions, and in
Section 5.3 for the components of the short-run marginal costs. In this section, we provide information
and descriptive statistics on the remaining inputs and assumptions.
8.4.1
Generation inputs
As briefly described in Section 5.3, above, we mainly used data in the public domain, published at
FERC Form 1, FERC 423, EIA 906, IEA 423, US EPA CEMS, US EPA Clean Air Markets unit
characteristics databases, collated and provided by Velocity Suite of Global Energy Decisions. Figure
48, below, shows the distribution of installed capacity across regions and fuel types. Tough there is a
significant nuclear presence in PJM Classic, coal and gas units’ have the highest capacities. The Eastern
PJM Classic, which has the highest load, also has the highest installed capacity, specifically in natural
gas and nuclear capacity.
Figure 48. Total installed capacity in PJM Classic by region and fuel type
Coal
region
B
5,180
D
1,454
E
6,354
M
4,917
P
3,664
Total 21,569
Heavy
Oil
1,859
606
2,178
1,640
6,283
Light Natural
Nuclear Water Renew Other
Oil
Gas
2,037
797
3,117
641
94
6,686
2,585
1,820
14,908
2,532
467
22,311
1,780
414
8,835
3,170
2,032
172
532
3,149
13,785
9
89
49
147
127
1
408
177
6
720
Total
13,981
4,679
37,841
13,337
4,812
74,651
Source: Global Energy Decisions, Velocity Suite
The availability of each thermal unit is determined daily. For units that submit their output to CEMS
database, we went through their actual production data to determine whether they were available or
not. If there is no reported activity, i.e. the output numbers are zero for a 24-hour window, then we
assumed the unit is unavailable. For the baseload units, such as nuclear units, if the reported output
numbers are positive but significantly lower than their annual average, we lowered the availability
percentages accordingly, in order to account for partloading for large segments of production capacity.
Our methodology results in a very close approximation of the behavior of the baseload units, but is
subject to a forecast error for mid-merit and peaking units, since they are more likely to be unavailable
for shorter periods than 24 hours and may be partially unavailable. We compared the availability
frequency to actual load factors reported on an annual basis, and to the extent that the availability
schedule from CEMS was understating the actual load factor, we trued up the schedule to more closely
match actual performance. In addition, some units do not report data to CEMS. In order to resolve that
issue, we looked at the average maintenance and forced outage rates for each type of unit, based on
size, technology, fuel, as provided by NERC’s GADS data. We allowed POOLMod to determine
optimal maintenance based on a target for the year (using class averages from GADS) and assigned
forced outages on a randomized basis based on the class averages from GADS.
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Figure 49. Average of load factor and availability percentages by region and fuel type
Load factor
Availability
Load factor
D
Availability
Load factor
E
Availability
Load factor
M
Availability
Load factor
P
Availability
Load factor
Total
Availability
B
8.4.2
Coal
70%
89%
64%
85%
82%
89%
74%
89%
72%
93%
75%
89%
Heavy OilLight Oil Natural Gas Nuclear Water Renew
7%
0%
1%
90%
4%
53%
83%
78%
89%
96%
17%
3%
5%
66%
93%
37%
2%
1%
3%
93%
24%
94%
58%
90%
67%
94%
96%
94%
12%
1%
7%
91%
37%
21%
36%
96%
65%
91%
96%
93%
1%
3%
12%
61%
96%
53%
96%
91%
8%
2%
3%
92%
21%
65%
57%
91%
68%
93%
96%
92%
Other
94%
94%
95%
95%
94%
94%
92%
94%
95%
95%
93%
94%
Total
27%
83%
15%
84%
31%
85%
44%
89%
33%
87%
31%
86%
Imports
As discussed in Section 5.3.2, imports are also used on an hourly basis to mimic the historical
fluctuations. Figure 35, below, shows the decline in actual import levels from FE and VAP over our
timeframe, as the prices in the importing regions increased and raised the opportunity costs of trades.
For the better part of 2005 and 2006, the flow of energy is actually reversed and PJM Classic started to
export energy to those two control areas. In order to account for the exports, we simply add the exports
into the regional load, since the exported amounts affect the way internal load is served.
Figure 50. Annual descriptive statistics of imports into PJM Classic, MW
APS
FE
NYIS
VAP
Average
Maximum
Minimum
Average
Maximum
Minimum
Average
Maximum
Minimum
Average
Maximum
Minimum
2003
2,366
3,503
2
1,422
3,069
2
250
1,140
0
702
3,104
0
2004
2,971
4,256
10
1,354
3,005
0
578
2,965
0
555
1,715
0
2005
3,274
4,408
231
126
1,600
0
591
2,598
0
429
1,130
0
Source: PJM, available at https://esuite.pjm.com/mui/index.htm.
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2006
3,204
4,342
198
2
345
0
334
1,397
0
325
955
0
8.4.3
Demand
The summary information for load in our regions is provided below in Figure 51. As noted earlier these
figures are adjusted for exports from each region, so they are higher than the internal load. The internal
load figures are based on PJM raw data at the transmission zone level.
Figure 51. Annual descriptive statistics of net load (internal plus exports) by region, MW
B
D
E
M
P
Average
Maximum
Minimum
Average
Maximum
Minimum
Average
Maximum
Minimum
Average
Maximum
Minimum
Average
Maximum
Minimum
2003
8,113
14,160
3,745
2,182
3,670
1,115
8,209
14,002
3,207
5,744
8,818
3,350
1,909
3,077
1,145
2004
8,490
14,750
3,481
2,190
3,636
1,303
8,490
14,750
3,481
5,844
9,291
3,433
1,972
2,842
1,017
2005
8,798
15,302
5,018
2,270
4,174
1,306
8,798
15,302
5,018
6,052
9,161
3,473
2,237
4,705
1,279
2006
8,548
15,233
5,038
2,189
4,114
1,289
8,548
15,233
5,038
6,006
9,473
3,387
2,253
3,229
1,315
Source: Internal load data from PJM, available at https://esuite.pjm.com/mui/index.htm.
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8.5
8.5.1
Detailed Tabular Summaries of Simulation Outputs
Monthly Markup Indices and Dollar-based Markup levels
Figures 51 to 55 show the price-cost markup indices, the corresponding forecast errors (at 95%
confidence level) and markup levels in dollar terms for our five regions. If the markup index is less
than the forecast error then that month’s result is deemed to be statistically insignificant.
Figure 52. Markup levels (LMP less SRMC-based price) and Price-Cost Markup Indices (%) with
corresponding forecast errors in region P
PE
N
EL
EC
Overall
Jan-03
Feb-03
Mar-03
Apr-03
May-03
Jun-03
Jul-03
Aug-03
Sep-03
Oct-03
Nov-03
Dec-03
Jan-04
Feb-04
Mar-04
Apr-04
May-04
Jun-04
Jul-04
Aug-04
Sep-04
Oct-04
Nov-04
Dec-04
Jan-05
Feb-05
Mar-05
Apr-05
May-05
Jun-05
Jul-05
Aug-05
Sep-05
Oct-05
Nov-05
Dec-05
Jan-06
Feb-06
Mar-06
Apr-06
May-06
Jun-06
Jul-06
Markup
index
9%
9%
12%
12%
7%
12%
10%
12%
9%
8%
8%
10%
13%
10%
13%
11%
7%
8%
6%
11%
5%
15%
9%
7%
9%
8%
9%
12%
19%
5%
3%
7%
8%
14%
9%
10%
10%
13%
8%
10%
10%
12%
7%
Peak
Forecast
error
7%
4%
8%
6%
5%
6%
7%
6%
5%
6%
3%
5%
7%
5%
5%
4%
5%
6%
4%
5%
7%
3%
5%
5%
5%
6%
6%
6%
5%
6%
5%
8%
5%
9%
3%
3%
5%
3%
6%
6%
6%
7%
7%
Markup
$4.11
$5.00
$6.95
$4.61
$2.36
$3.92
$3.52
$4.32
$2.68
$2.12
$2.17
$3.61
$7.04
$4.41
$5.23
$4.76
$3.04
$2.92
$2.58
$4.09
$1.97
$5.62
$3.41
$2.65
$4.11
$3.08
$4.23
$5.64
$9.02
$2.23
$1.65
$4.98
$6.03
$9.44
$4.80
$7.48
$4.68
$6.24
$4.37
$4.88
$4.04
$5.08
$3.97
Markup
index
11%
12%
18%
16%
9%
16%
14%
15%
12%
13%
10%
14%
11%
14%
19%
15%
10%
12%
10%
20%
10%
19%
10%
9%
8%
10%
13%
11%
25%
8%
8%
14%
13%
15%
15%
15%
17%
23%
13%
21%
16%
20%
15%
Forecast
error
4%
6%
6%
6%
3%
2%
4%
6%
5%
8%
4%
5%
6%
5%
6%
7%
7%
6%
5%
6%
4%
7%
6%
5%
6%
5%
5%
5%
7%
6%
4%
7%
4%
8%
6%
5%
5%
6%
4%
7%
6%
5%
4%
Off-Peak
Markup
$6.06
$7.90
$12.51
$7.91
$3.90
$7.21
$6.68
$7.73
$4.54
$4.66
$3.36
$5.98
$6.52
$7.24
$8.83
$7.34
$5.90
$6.30
$4.70
$8.75
$4.33
$8.63
$4.91
$4.04
$3.96
$4.58
$6.99
$5.78
$13.92
$5.03
$5.75
$13.16
$10.89
$11.29
$10.06
$12.99
$9.63
$12.29
$7.75
$12.09
$8.06
$11.72
$10.75
Markup
index
7%
6%
5%
8%
5%
8%
5%
7%
7%
2%
6%
6%
16%
6%
8%
8%
3%
4%
4%
3%
1%
12%
8%
6%
11%
5%
5%
14%
14%
0%
-3%
-1%
5%
13%
1%
4%
1%
2%
4%
0%
4%
3%
-1%
- 91 London Economics International LLC
717 Atlantic Avenue, Unit 1A
Boston, MA 02111
www.londoneconomics.com
Forecast
error
5%
5%
6%
5%
6%
5%
6%
6%
5%
5%
3%
4%
6%
6%
6%
7%
4%
3%
6%
7%
3%
8%
4%
4%
7%
6%
3%
7%
7%
4%
4%
6%
6%
7%
9%
6%
4%
7%
4%
8%
4%
6%
6%
Markup
$2.44
$2.60
$2.37
$2.21
$1.23
$1.64
$1.31
$2.02
$1.40
$0.45
$1.30
$1.72
$7.33
$2.23
$2.50
$2.84
$0.98
$0.97
$1.19
$0.91
$0.24
$3.59
$2.34
$1.83
$4.36
$1.83
$2.18
$5.28
$5.53
-$0.01
-$1.19
-$0.28
$2.75
$7.91
$0.60
$2.55
$0.31
$0.88
$1.63
-$0.20
$1.23
$1.04
-$0.43
Figure 53. Markup levels (LMP less SRMC-based price) and Price-Cost Markup Indices (%) with
corresponding forecast errors in region M
M
ET
E
& D
PP
L
Overall
Jan-03
Feb-03
Mar-03
Apr-03
May-03
Jun-03
Jul-03
Aug-03
Sep-03
Oct-03
Nov-03
Dec-03
Jan-04
Feb-04
Mar-04
Apr-04
May-04
Jun-04
Jul-04
Aug-04
Sep-04
Oct-04
Nov-04
Dec-04
Jan-05
Feb-05
Mar-05
Apr-05
May-05
Jun-05
Jul-05
Aug-05
Sep-05
Oct-05
Nov-05
Dec-05
Jan-06
Feb-06
Mar-06
Apr-06
May-06
Jun-06
Jul-06
Markup
index
7%
6%
7%
1%
5%
3%
2%
5%
5%
3%
6%
8%
8%
7%
7%
9%
2%
1%
4%
6%
7%
12%
11%
13%
14%
12%
14%
8%
10%
5%
2%
5%
5%
5%
7%
9%
4%
6%
5%
7%
6%
5%
6%
Forecast
error
6%
7%
5%
4%
7%
6%
4%
5%
6%
4%
6%
7%
4%
7%
5%
6%
6%
8%
7%
6%
7%
6%
3%
7%
5%
5%
3%
3%
7%
4%
6%
7%
4%
3%
7%
3%
4%
4%
6%
4%
7%
5%
3%
Peak
Markup
$3.11
$3.25
$3.77
$0.33
$1.59
$1.18
$0.92
$2.06
$1.43
$0.90
$1.62
$2.87
$4.42
$2.92
$2.90
$3.95
$0.71
$0.47
$1.87
$2.49
$2.79
$4.75
$4.41
$5.81
$6.95
$5.01
$7.04
$3.67
$4.00
$3.12
$1.33
$4.28
$4.15
$3.77
$4.73
$7.79
$2.45
$3.75
$2.85
$3.37
$2.48
$2.60
$3.95
Markup
index
10%
13%
9%
2%
7%
4%
4%
8%
6%
5%
7%
11%
10%
7%
8%
10%
2%
2%
5%
9%
12%
15%
14%
15%
15%
13%
15%
9%
14%
7%
6%
9%
8%
5%
10%
11%
9%
8%
9%
12%
7%
7%
9%
Forecast
error
5%
6%
4%
3%
7%
3%
5%
6%
4%
6%
2%
6%
9%
6%
4%
5%
4%
4%
5%
5%
6%
7%
4%
5%
4%
6%
5%
4%
5%
7%
7%
7%
4%
7%
6%
3%
6%
7%
5%
5%
7%
7%
3%
Off-Peak
Markup
$5.36
$7.94
$5.86
$0.86
$2.82
$2.00
$2.23
$4.14
$2.24
$1.76
$2.62
$4.76
$6.19
$3.70
$3.93
$4.87
$0.95
$0.92
$2.82
$4.37
$5.38
$7.32
$6.84
$7.91
$8.88
$6.05
$8.90
$5.13
$6.84
$5.47
$5.03
$10.17
$7.94
$5.37
$7.08
$10.16
$5.65
$4.89
$5.27
$7.09
$3.76
$4.75
$8.40
- 92 London Economics International LLC
717 Atlantic Avenue, Unit 1A
Boston, MA 02111
www.londoneconomics.com
Markup
index
3%
1%
5%
0%
3%
3%
0%
2%
4%
1%
4%
5%
5%
6%
6%
9%
1%
0%
3%
4%
2%
9%
8%
9%
12%
9%
12%
7%
6%
3%
-1%
1%
3%
4%
4%
8%
0%
5%
2%
2%
5%
3%
2%
Forecast
error
5%
5%
6%
5%
8%
6%
8%
6%
4%
6%
6%
6%
5%
7%
4%
6%
4%
6%
5%
5%
6%
5%
6%
8%
6%
4%
5%
6%
5%
4%
3%
5%
7%
6%
6%
5%
6%
3%
5%
8%
6%
5%
6%
Markup
$1.28
$0.34
$2.32
-$0.07
$0.78
$0.59
-$0.05
$0.54
$0.77
$0.23
$0.77
$1.38
$2.55
$2.07
$1.99
$3.12
$0.42
$0.03
$1.11
$1.42
$0.62
$2.73
$2.43
$3.73
$5.50
$3.64
$5.31
$2.70
$2.14
$1.41
-$0.50
$0.83
$1.62
$2.49
$2.34
$6.32
-$0.06
$2.54
$1.18
$0.63
$1.60
$1.19
$1.08
Figure 54. Markup levels (LMP less SRMC-based price) and Price-Cost Markup Indices (%) with
corresponding forecast errors in region B
BG
E
PE &
PC
O
Overall
Jan-03
Feb-03
Mar-03
Apr-03
May-03
Jun-03
Jul-03
Aug-03
Sep-03
Oct-03
Nov-03
Dec-03
Jan-04
Feb-04
Mar-04
Apr-04
May-04
Jun-04
Jul-04
Aug-04
Sep-04
Oct-04
Nov-04
Dec-04
Jan-05
Feb-05
Mar-05
Apr-05
May-05
Jun-05
Jul-05
Aug-05
Sep-05
Oct-05
Nov-05
Dec-05
Jan-06
Feb-06
Mar-06
Apr-06
May-06
Jun-06
Jul-06
Markup
index
8%
7%
3%
4%
4%
3%
4%
3%
7%
4%
4%
5%
11%
9%
9%
10%
5%
5%
9%
6%
3%
11%
6%
7%
12%
7%
13%
11%
5%
6%
8%
8%
8%
12%
8%
13%
11%
6%
8%
8%
6%
6%
5%
Forecast
error
7%
7%
7%
6%
5%
6%
3%
8%
6%
5%
7%
4%
5%
8%
6%
5%
3%
4%
6%
8%
5%
5%
5%
6%
5%
7%
5%
5%
2%
3%
7%
7%
6%
9%
6%
3%
5%
6%
8%
6%
6%
7%
8%
Peak
Markup
$2.38
$1.71
$3.61
-$0.02
$2.32
$1.21
-$0.86
$3.75
$2.70
-$0.13
$0.45
$3.11
$9.03
$0.93
$1.26
$3.43
$2.92
$2.48
$2.46
$4.05
$1.09
$4.06
$2.03
$2.04
$3.42
$0.12
-$3.04
-$0.22
$0.96
$3.19
$5.17
$14.17
$9.02
$13.32
$9.33
$11.30
$6.96
$6.98
$6.09
$3.96
$2.93
$1.34
$1.66
Markup
index
8%
10%
4%
6%
5%
6%
5%
6%
8%
4%
5%
7%
12%
10%
11%
11%
6%
9%
10%
7%
2%
11%
8%
11%
11%
7%
13%
11%
7%
12%
10%
9%
9%
15%
9%
14%
16%
7%
13%
14%
10%
7%
9%
Forecast
error
4%
5%
5%
5%
8%
4%
5%
7%
4%
6%
3%
4%
4%
8%
3%
7%
4%
6%
7%
7%
6%
4%
9%
7%
5%
5%
3%
6%
6%
4%
5%
5%
4%
5%
5%
6%
3%
4%
8%
5%
5%
5%
5%
Off-Peak
Markup
$4.26
$6.06
$2.57
$0.80
$2.15
$2.78
$2.72
$2.98
$2.97
$1.50
$1.82
$3.12
$7.85
$1.43
$5.12
$3.91
$3.54
$4.95
$6.09
$3.95
$1.15
$5.17
$0.74
$1.83
$6.46
$3.70
$7.14
$4.59
$7.29
$9.43
$9.76
$10.54
$9.61
$15.56
$6.88
$13.56
$10.44
$5.07
$8.26
$8.20
$5.48
$5.23
$8.86
- 93 London Economics International LLC
717 Atlantic Avenue, Unit 1A
Boston, MA 02111
www.londoneconomics.com
Markup
index
8%
4%
3%
3%
2%
1%
3%
0%
5%
5%
3%
3%
10%
8%
7%
9%
4%
1%
7%
5%
3%
12%
5%
4%
12%
6%
12%
10%
5%
-1%
5%
8%
8%
8%
7%
12%
6%
5%
4%
1%
1%
4%
2%
Forecast
error
4%
5%
8%
6%
5%
5%
6%
7%
7%
6%
3%
7%
3%
5%
5%
7%
6%
5%
8%
6%
4%
6%
4%
5%
4%
6%
3%
4%
5%
6%
4%
4%
6%
8%
6%
5%
6%
8%
3%
6%
5%
3%
6%
Markup
$3.38
$1.43
$1.48
$0.83
$0.55
$0.24
$0.79
-$0.13
$0.98
$0.89
$0.58
$0.97
$6.01
$3.17
$2.13
$3.04
$1.51
$0.22
$5.73
$4.10
$1.09
$3.63
$3.16
$1.49
$5.51
$2.56
$5.48
$4.26
$4.48
-$0.55
$3.23
$8.07
$8.58
$5.72
$5.47
$10.60
$3.24
$9.52
$2.04
$0.56
$0.33
$1.82
$1.11
Figure 55. Markup levels (LMP less SRMC-based price) and Price-Cost Markup Indices (%) with
corresponding forecast errors in region E
EA
ST
Overall
Jan-03
Feb-03
Mar-03
Apr-03
May-03
Jun-03
Jul-03
Aug-03
Sep-03
Oct-03
Nov-03
Dec-03
Jan-04
Feb-04
Mar-04
Apr-04
May-04
Jun-04
Jul-04
Aug-04
Sep-04
Oct-04
Nov-04
Dec-04
Jan-05
Feb-05
Mar-05
Apr-05
May-05
Jun-05
Jul-05
Aug-05
Sep-05
Oct-05
Nov-05
Dec-05
Jan-06
Feb-06
Mar-06
Apr-06
May-06
Jun-06
Jul-06
Markup
index
4%
3%
5%
2%
5%
6%
9%
6%
6%
5%
5%
4%
9%
10%
6%
12%
9%
9%
5%
6%
7%
16%
10%
10%
9%
9%
11%
9%
7%
9%
7%
9%
13%
16%
8%
7%
4%
3%
6%
8%
6%
6%
10%
Forecast
error
7%
6%
6%
7%
7%
7%
7%
6%
4%
5%
4%
7%
5%
7%
6%
5%
6%
6%
5%
5%
4%
6%
6%
4%
5%
4%
7%
3%
3%
7%
4%
5%
6%
6%
3%
5%
4%
6%
5%
6%
6%
7%
3%
Peak
Markup
$1.58
$1.56
$2.91
$1.12
$2.18
$2.36
$4.62
$3.72
$2.52
$1.85
$1.76
$1.44
$4.95
$4.38
$2.13
$5.59
$4.73
$5.15
$8.89
$8.39
$3.37
$6.39
$9.95
$8.52
$8.86
$5.90
$7.35
$6.91
$4.88
$5.94
$5.69
$9.36
$10.94
$14.17
$7.38
$7.22
$7.17
$1.89
$3.64
$4.46
$2.98
$3.26
$7.97
Markup
index
4%
2%
6%
3%
8%
8%
14%
13%
11%
7%
7%
4%
9%
12%
9%
14%
10%
13%
7%
7%
11%
17%
13%
10%
10%
11%
13%
11%
9%
12%
10%
14%
12%
19%
9%
8%
7%
5%
10%
13%
9%
8%
15%
Forecast
error
4%
6%
3%
6%
5%
6%
6%
7%
4%
5%
7%
7%
2%
5%
5%
4%
4%
6%
4%
5%
5%
2%
5%
5%
4%
6%
6%
5%
6%
6%
5%
7%
5%
7%
5%
5%
5%
5%
6%
5%
4%
4%
4%
Off-Peak
Markup
$1.77
$1.53
$3.97
$1.53
$3.54
$3.82
$8.03
$7.32
$4.88
$2.91
$2.62
$1.69
$5.99
$6.17
$1.12
$7.16
$6.55
$8.46
$10.32
$11.13
$5.61
$8.37
$12.31
$12.44
$12.23
$5.70
$8.01
$6.70
$4.60
$9.22
$9.47
$16.25
$12.29
$19.55
$7.28
$8.30
$12.13
$3.16
$6.31
$7.58
$4.97
$5.44
$13.29
- 94 London Economics International LLC
717 Atlantic Avenue, Unit 1A
Boston, MA 02111
www.londoneconomics.com
Markup
index
3%
4%
3%
2%
3%
4%
5%
-1%
0%
2%
4%
4%
7%
7%
3%
11%
7%
6%
3%
5%
4%
14%
8%
10%
8%
7%
8%
7%
6%
5%
2%
4%
14%
13%
7%
7%
1%
2%
2%
3%
3%
4%
4%
Forecast
error
7%
5%
8%
2%
5%
8%
3%
4%
4%
5%
7%
4%
5%
7%
6%
5%
3%
5%
4%
6%
5%
6%
5%
6%
5%
3%
5%
9%
7%
5%
6%
7%
9%
3%
4%
3%
3%
9%
3%
4%
4%
6%
5%
Markup
$1.13
$1.62
$1.56
$0.44
$0.73
$0.93
$1.58
-$0.27
$0.09
$0.37
$0.86
$1.20
$3.54
$2.73
$3.65
$3.81
$2.66
$1.60
$6.35
$5.89
$1.36
$4.44
$7.77
$4.52
$5.19
$5.90
$6.88
$6.90
$5.92
$2.09
$1.30
$2.35
$9.55
$9.05
$7.58
$6.77
$0.45
$0.89
$1.10
$1.13
$1.09
$1.29
$2.11
Figure 56. Markup levels (LMP less SRMC-based price) and Price-Cost Markup Indices (%) with
corresponding forecast errors in region D
D
PL
Overall
Jan-03
Feb-03
Mar-03
Apr-03
May-03
Jun-03
Jul-03
Aug-03
Sep-03
Oct-03
Nov-03
Dec-03
Jan-04
Feb-04
Mar-04
Apr-04
May-04
Jun-04
Jul-04
Aug-04
Sep-04
Oct-04
Nov-04
Dec-04
Jan-05
Feb-05
Mar-05
Apr-05
May-05
Jun-05
Jul-05
Aug-05
Sep-05
Oct-05
Nov-05
Dec-05
Jan-06
Feb-06
Mar-06
Apr-06
May-06
Jun-06
Jul-06
Markup
index
4%
4%
0%
4%
7%
4%
8%
6%
7%
5%
5%
4%
9%
8%
5%
10%
1%
2%
8%
6%
3%
12%
12%
16%
19%
12%
13%
14%
13%
5%
6%
10%
11%
15%
11%
12%
9%
4%
6%
6%
4%
5%
10%
Forecast
error
7%
6%
3%
6%
3%
5%
4%
5%
7%
5%
4%
8%
4%
5%
5%
7%
6%
3%
7%
5%
3%
4%
6%
6%
4%
6%
6%
7%
6%
8%
8%
5%
5%
6%
8%
4%
5%
5%
6%
5%
3%
7%
3%
Peak
Markup
$1.93
$1.99
$0.07
$1.32
$2.77
$1.49
$4.80
$3.54
$2.80
$1.45
$1.68
$1.39
$5.38
$3.69
$1.99
$4.33
$0.57
$1.42
$3.22
$2.98
$1.65
$4.85
$5.14
$9.01
$11.75
$5.68
$7.27
$7.45
$5.73
$4.06
$4.95
$10.68
$9.58
$13.49
$6.61
$11.45
$5.70
$2.21
$3.74
$3.76
$2.36
$3.39
$8.70
Markup
index
6%
4%
1%
3%
12%
4%
15%
13%
11%
4%
7%
4%
10%
10%
4%
10%
1%
4%
6%
8%
6%
11%
12%
17%
26%
15%
13%
15%
15%
10%
9%
15%
11%
18%
8%
15%
16%
5%
10%
12%
8%
9%
17%
Forecast
error
4%
6%
5%
4%
7%
5%
4%
7%
4%
7%
6%
6%
8%
3%
2%
6%
7%
6%
6%
4%
4%
6%
5%
5%
3%
8%
7%
6%
5%
4%
5%
8%
4%
6%
6%
3%
8%
3%
4%
6%
3%
6%
6%
Off-Peak
Markup
$3.02
$2.67
$0.82
$1.57
$5.13
$2.20
$8.73
$7.39
$4.75
$1.67
$2.59
$1.86
$6.79
$4.74
$1.77
$5.04
$0.55
$2.09
$3.39
$4.24
$3.02
$5.54
$6.45
$11.11
$16.92
$7.78
$7.76
$8.55
$7.34
$7.64
$8.74
$17.66
$11.69
$18.86
$6.49
$14.89
$10.28
$3.32
$6.16
$7.03
$4.21
$5.99
$15.38
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Markup
index
2%
3%
0%
4%
1%
4%
1%
-1%
4%
4%
4%
3%
8%
7%
6%
10%
2%
0%
10%
6%
1%
13%
11%
13%
12%
9%
14%
14%
11%
1%
2%
5%
12%
11%
13%
9%
3%
3%
3%
1%
0%
1%
5%
Forecast
error
4%
8%
7%
7%
4%
6%
5%
5%
2%
5%
7%
4%
5%
5%
6%
4%
6%
7%
7%
6%
6%
7%
6%
7%
2%
7%
5%
5%
6%
7%
4%
5%
6%
5%
4%
6%
4%
6%
5%
9%
4%
6%
6%
Markup
$0.73
$1.10
-$0.18
$1.17
$0.24
$0.85
$0.43
-$0.35
$0.96
$0.98
$0.94
$1.00
$4.05
$2.49
$2.11
$3.61
$0.61
$0.13
$3.27
$2.11
$0.43
$4.09
$3.82
$6.27
$6.08
$3.43
$6.34
$6.27
$4.10
$0.27
$1.21
$3.20
$7.85
$7.46
$6.88
$7.37
$1.47
$1.73
$1.36
$0.26
$0.03
$0.39
$2.22
8.5.2
Indicators of model forecast error
Figures 56 and 57 show the comparison of POOLMod output (flows and generation) with the actual
historical figures for these parameters. The forecast errors are calculated based on these comparisons.
Figure 57. Ratio of modeled flows over actual historical flows through Central, Eastern and Western
interfaces, monthly averages
ALL
OFF-PEAK
PEAK
Central Eastern Western Central Eastern Western Central Eastern Western
Jan-03
Feb-03
Mar-03
Apr-03
May-03
Jun-03
Jul-03
Aug-03
Sep-03
Oct-03
Nov-03
Dec-03
Jan-04
Feb-04
Mar-04
Apr-04
May-04
Jun-04
Jul-04
Aug-04
Sep-04
Oct-04
Nov-04
Dec-04
Jan-05
Feb-05
Mar-05
Apr-05
May-05
Jun-05
Jul-05
Aug-05
Sep-05
Oct-05
Nov-05
Dec-05
Jan-06
Feb-06
Mar-06
Apr-06
May-06
Jun-06
100%
95%
98%
101%
101%
100%
97%
95%
100%
103%
101%
97%
101%
95%
97%
101%
102%
100%
98%
98%
102%
104%
102%
103%
102%
103%
94%
100%
103%
99%
104%
103%
100%
96%
96%
102%
104%
96%
100%
94%
100%
95%
Jul-06 101%
105%
104%
94%
99%
96%
104%
97%
95%
95%
103%
95%
97%
96%
100%
96%
104%
97%
96%
103%
97%
98%
96%
105%
102%
103%
101%
104%
101%
101%
98%
98%
103%
100%
105%
102%
103%
101%
103%
104%
105%
103%
99%
99%
96%
101%
101%
96%
103%
102%
103%
105%
101%
103%
100%
99%
97%
96%
101%
101%
100%
103%
95%
104%
101%
103%
104%
97%
105%
95%
100%
98%
102%
104%
99%
100%
105%
99%
105%
95%
97%
97%
101%
100%
95%
102%
102%
94%
96%
103%
95%
100%
96%
101%
104%
103%
103%
101%
99%
102%
95%
96%
101%
99%
103%
101%
102%
104%
97%
96%
98%
96%
101%
100%
97%
99%
103%
98%
103%
101%
98%
102%
101%
104%
98%
95%
99%
95%
101%
103%
100%
95%
102%
100%
95%
101%
96%
102%
99%
98%
102%
100%
95%
100%
95%
100%
97%
98%
101%
97%
105%
105%
97%
101%
99%
98%
99%
103%
98%
96%
96%
98%
102%
101%
98%
103%
95%
103%
104%
101%
94%
99%
96%
98%
95%
98%
98%
104%
103%
103%
95%
102%
103%
99%
103%
96%
104%
101%
105%
106%
106%
100%
104%
104%
99%
99%
98%
105%
105%
94%
98%
102%
105%
101%
100%
95%
105%
101%
94%
99%
103%
99%
96%
105%
96%
101%
104%
102%
94%
100%
99%
94%
94%
102%
99%
105%
104%
95%
93%
98%
95%
105%
95%
103%
102%
105%
97%
106%
98%
96%
94%
99%
106%
103%
100%
93%
105%
98%
97%
95%
95%
98%
96%
103%
98%
99%
100%
100%
97%
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105%
97%
104%
104%
101%
95%
104%
96%
104%
104%
105%
101%
99%
95%
98%
105%
106%
95%
97%
95%
98%
102%
103%
96%
94%
100%
98%
99%
101%
96%
106%
95%
100%
102%
102%
106%
99%
103%
99%
101%
106%
94%
99%
101%
102%
105%
103%
101%
95%
97%
103%
104%
101%
95%
102%
106%
103%
103%
102%
101%
103%
98%
94%
95%
104%
101%
94%
102%
97%
102%
98%
100%
94%
101%
98%
98%
94%
103%
94%
98%
94%
98%
94%
104%
105%
99%
Figure 58. Ratio of modeled generation over actual historical generation for selected baseload units,
monthly averages
Jan-03
Feb-03
Mar-03
Apr-03
May-03
Jun-03
Jul-03
Aug-03
Sep-03
Oct-03
Nov-03
Dec-03
Jan-04
Feb-04
Mar-04
Apr-04
May-04
Jun-04
Jul-04
Aug-04
Sep-04
Oct-04
Nov-04
Dec-04
Jan-05
Feb-05
Mar-05
Apr-05
May-05
Jun-05
Jul-05
Aug-05
Sep-05
Oct-05
Nov-05
Dec-05
Jan-06
Feb-06
Mar-06
Apr-06
May-06
Jun-06
Nuclear
plant in B
99%
101%
96%
103%
99%
99%
100%
97%
100%
102%
101%
103%
102%
102%
98%
103%
102%
98%
100%
97%
99%
101%
98%
102%
102%
101%
100%
100%
102%
101%
102%
101%
100%
101%
99%
102%
101%
98%
98%
100%
101%
101%
Gas plant
in B
95%
100%
100%
90%
89%
98%
100%
96%
104%
103%
92%
103%
94%
94%
89%
87%
98%
105%
97%
95%
105%
106%
102%
96%
93%
102%
108%
106%
149%
90%
85%
90%
82%
75%
104%
100%
94%
98%
94%
99%
108%
106%
Coal plant Oil plant in Coal plant Coal plant
in B
D
in P
in D
97%
87%
102%
103%
97%
109%
101%
100%
107%
86%
100%
102%
97%
117%
101%
96%
118%
96%
103%
119%
94%
90%
102%
90%
92%
99%
99%
101%
99%
84%
102%
97%
99%
106%
101%
98%
101%
72%
101%
86%
88%
136%
98%
88%
101%
130%
99%
100%
92%
102%
103%
100%
103%
94%
101%
94%
101%
36%
102%
102%
101%
7%
99%
99%
97%
72%
97%
97%
100%
111%
102%
97%
104%
95%
101%
97%
101%
113%
99%
95%
102%
125%
101%
103%
102%
12%
98%
98%
95%
151%
100%
100%
101%
95%
98%
95%
94%
73%
99%
92%
99%
49%
100%
102%
102%
31%
104%
81%
101%
3%
100%
100%
78%
22%
104%
102%
99%
81%
102%
101%
101%
59%
102%
98%
91%
96%
100%
102%
84%
58%
95%
97%
87%
40%
102%
93%
87%
246%
101%
99%
93%
58%
97%
94%
104%
192%
101%
97%
91%
109%
101%
94%
102%
127%
102%
85%
86%
0%
103%
95%
100%
48%
104%
88%
100%
102%
98%
102%
Nuclear
plant in E
101%
101%
100%
99%
102%
100%
98%
98%
101%
98%
100%
99%
102%
100%
101%
100%
98%
100%
101%
101%
102%
100%
98%
99%
99%
99%
104%
101%
100%
100%
101%
102%
101%
102%
98%
102%
98%
101%
99%
102%
101%
100%
Coal plant Coal plant Oil plant in Coal plant
Nuclear
in E
in M
B
in P
plant in M
104%
99%
105%
97%
99%
99%
100%
99%
103%
99%
93%
102%
101%
98%
97%
96%
100%
94%
90%
98%
0%
100%
96%
95%
99%
98%
104%
96%
103%
99%
97%
103%
96%
100%
102%
96%
101%
99%
103%
100%
96%
103%
95%
95%
102%
95%
99%
104%
98%
99%
97%
101%
102%
100%
101%
92%
98%
96%
95%
102%
96%
99%
104%
99%
101%
93%
102%
102%
102%
100%
104%
78%
101%
101%
101%
95%
83%
118%
95%
101%
103%
105%
105%
100%
101%
100%
101%
100%
92%
102%
99%
103%
101%
95%
99%
103%
94%
97%
103%
101%
98%
95%
98%
101%
102%
101%
323%
95%
96%
102%
84%
101%
107%
99%
101%
92%
102%
97%
101%
99%
92%
86%
96%
100%
99%
58%
80%
87%
95%
96%
103%
105%
84%
98%
113%
99%
94%
113%
81%
98%
93%
100%
102%
105%
99%
86%
77%
87%
98%
101%
95%
88%
95%
99%
99%
101%
91%
106%
100%
100%
99%
80%
102%
88%
99%
98%
29%
103%
72%
101%
68%
56%
105%
87%
101%
96%
90%
87%
100%
100%
99%
103%
66%
89%
100%
100%
100%
80%
89%
100%
100%
98%
76%
94%
99%
100%
101%
70%
104%
101%
97%
102%
80%
88%
99%
93%
101%
95%
93%
99%
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8.6
Annual Supply Curves for PJM Classic
Figure 59. Cumulative supply curve for 2003
Cumulative Regional Supply Curve 2003
200
Avg. Demand
(32,764 MW)
175
Peak Demand
(54,918 MW)
Min. Demand
(20,386 MW)
Marginal Cost (MC) $/MWh
150
125
100
75
50
25
B
D
E
M
P
0
-
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
Cumulative DNC (MW) - unadjusted for transmission constraints and availability
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90,000
100,000
Figure 60. Cumulative supply curve for 2004
Cumulative Regional Supply Curve 2004
225
Avg. Demand
(33,231 MW)
200
Peak Demand (52,816
MW)
Min. Demand
(20,562 MW)
Marginal Cost (MC) $/MWh
175
150
125
100
75
50
B
25
D
E
M
P
0
-
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
Cumulative DNC (MW) - unadjusted for transmission constraints and availability
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90,000
100,000
Figure 61. Cumulative supply curve for 2005
Cumulative Regional Supply Curve 2005
325
B
300
D
E
M
P
Avg. Demand
(34,475 MW)
Peak Demand
(58,896 MW)
275
Min. Demand
(21,756 MW)
Marginal Cost (MC) $/MWh
250
225
200
175
150
125
100
75
50
25
0
-
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
Cumulative DNC (MW) - unadjusted for transmission constraints and availability
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90,000
100,000
Figure 62. Cumulative supply curve for 2006
Cumulative Regional Supply Curve 2006
250
B
D
E
M
P
225
Peak Demand
(60,494 MW)
Min. Demand
(20,851 MW)
200
Marginal Cost (MC) $/MWh
Avg. Demand
(33,694 MW)
175
150
125
100
75
50
25
0
-
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
Cumulative DNC (MW) - unadjusted for transmission constraints and availability
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90,000
100,000
9
Bibliography
2003 State of the Market Report, Market Monitoring Unit. PJM, 2004
2004 State of the Market Report, Market Monitoring Unit. PJM, 2005
2005 State of the Market Report, Market Monitoring Unit. PJM, 2006
Baumel, W.; Panzar, J. & Willig R. “Contestable Markets and the Theory of Industry Structure”. The
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774-780.
Berg, S., and Teschirhart, J. “Natural Monopoly Regulation, Principles and practice”. Cambridge
Surveys of Economic Literature. Cambridge University Press, 1988.
FERC -- Docket Nos. EL05-148 and ER05-1410, September 29, 2006.
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http://www.pjm.com/markets/ancillary/downloads/blackstart-buss-rules.pdf
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Ihrig, A “Congestion in the ISO-NE England Electricity markets”. A thesis submitted impartial
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Elektrotechnik” of Damstage University of Technology, Germany. University of Illinois at
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“Impacts of the RTO Market Expansion”— Report prepared by ESAI, November 2005
Kahn, A. “The Economics of Regulation, Principles and Institutions”. The MIT Press, Cambridge, MA,
USA, 1990.
Koutsoyiannis, A. “Modern Economics”. John Wiley & Sons, New York, 1975.
“Market Efficiency Measurements in the Regional Transmission Planning Process”, Regional Planning
Process Working Group, Sep. 2005
MacKie-Mason, J., Varian, H. “Pricing Congestible Network Resources”. Department of Economics,
University of Michigan, 1994.
PJM Manual 11: Scheduling Operations, Revision 29, 2006
PJM Manual 15: Cost Development Guidelines, Revision 07, 2006.
“Survey of capacity product posting, unbundled capacity attributes, and trading platforms in use in
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PJM, Market Monitoring Unit, Report to the Pennsylvania Public Utility Commission, Capacity Market
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717 Atlantic Avenue, Unit 1A
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www.londoneconomics.com