non-collusive market failures in the context of new ... · burden of congestion management is the...
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Master’s Thesis
Non-Collusive Market Failures in the Context of New Markets for Flexibility:
An Application of the Residual Supply Index to the Altdorfer Flexmarkt & A Review
of Historical Market Manipulation Cases
In partial fulfillment of the requirements for the degree M.Sc. Life Sciences Economics & Policy at the School of Life Sciences Weihenstephan
of the Technical University of Munich
written by
Ryan Harper Matr.Nr. 03686378
submitted to the
Chair of Energy Economy and Application Technology Technical University of Munich,
Prof. Dr.-Ing. Ulrich Wagner
In cooperation with the Forschungsstelle für Energiewirtschaft e.V.
Internally Supervised by:
Prof. Dr.-Ing. Ulrich Wagner Prof. Dr.-lng. Wolfgang Mauch
Chair of Energy Economy and Application Technology
Externally Supervised by:
Dipl.-Ing. Simon Köppl
M. Sc. Andreas Zeiselmair
Forschungstelle für Energiewirtschaft e.V.
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Abstract
The simultaneous trends of increasing intermittent renewable generation and electrification of
consumer energy demand have played a large part in the increased frequency of critical conditions in
German networks in recent years, which in turn have led to increased costs associated with returning
networks to safe operation. These congestion management costs exceeded €1.5 billion in 2017, and
are expected to further increase in coming years. One proposed solution to reduce the economic
burden of congestion management is the development of markets for flexibility such as the Altdorfer
Flexmarkt being designed by the Research Center for Energy Economics. These markets would
connect network operators with the owners of small generators or electric devices whose flexibility
could be contracted for congestion management purposes. Competition between suppliers of
flexibility can enable cost-efficient congestion management. However, markets for flexibility could be
susceptible to market power and market manipulation.
A market for flexibility vulnerable to these anti-competitive behaviors could become more
burdensome than current methods of congestion management. Identifying and preventing
conditions that could encourage such behaviors, or mitigating the damage and punishing the
offenders if prevention is impossible, will be key to ensuring markets for flexibility can become an
effective institution. This thesis develops a methodology for determining whether structural market
power is present in a market flexibility through the application of the residual supply index to a
middle voltage strand that will be part of the Altdorfer Flexmarkt. Using this method three conditions
conducive to structural market power were identified. A literature review of past cases of market
manipulation was also conducted, from which forms of manipulative behavior that could be adapted
for use in markets for flexibility were identified. Based upon these efforts, initial suggestions for
protecting markets for flexibility from anti-competitive behavior were made.
Keywords: flexibility, congestion management, market power, market manipulation
Lehrstuhl für Energiewirtschaft und AnwendungstechnikFakultät für Elektrotechnik und InformationstechnikTechnische Universität München
AufgabenstellungMaster‘s Thesis
von
Herrn HARPER, RyanMatr.-Nr. 03686378
Non-Collusive Market Failures in the Context of New Markets for Flexibility:An Application of the Residual Supply Index to the Altdorfer Flexmarkt & A Review
of Historical Market Manipulation Cases
Marktversagen im Kontext neuer Flexibilitsmärkte:Anwendung des Residual Supply Index auf den Altdorfer Flexmarkt & Untersuchung
historischer Marktmanipulationsfälle
A weil designed and implemented market for flexibility could reduce costs of congestion management faced by network operators, savngs which could result in lower fees tor consumers. PreventIng congestion can also prolong the life of existing transmission network infrastructure and reducethe urgency of expanding the grid, as weil as precludng the need to build transmission capacity to„the last Megawatt“. However, electricity markets have proven suscepUbie to anti-competitive behavior in the past. Dominant firms can exert market power to raise prices, while unscrupulous firmshave manipulated market rules to extract extra unjust payments. The regional nature of new markets for flexibility could be conducive to market power, and any new market rules could be exploited by manipulative firms. Recognizing and preventing these dangers are key to ensuring marketsfor flexibility become an effective institution rather than a costiy mistake.
The Master Thesis addresses the following research questions:
- Under what conditions, in what form, and with what consequences could market poweremerge in a market tor flexibility, and how should its presence and effect be measured?
- What opportunities for manipulation does the market design create through interactionswith related markets and its bidding and pricing methods, and how can manipulative behavior be distinguished from legal profit-maximizing behavior?
- How can anti-competitive and manipulative behavior be prevented, mitigated, or punished in the context of a market for flexibility?
The following tasks should be fulfilied in order to complete the Master‘s thesis:
- lnvestigation of manipulation possibilities in regional markets
- Identification of market power on regional smart markets based on key indicators
- Research, analysis and preparation of historical manipulation cases
- Development of prevention mechanisms to market power and manipulation
Betreuer: Prof. Dr.-lng. W. Mauch Tel.: 089 1581210
Ausgabedatum: 15.05.2019
Aufgabensteller: Prof. r.-lng. UI h Wagner
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Statement of Academic Integrity
I, Last name: Harper First name: Ryan ID No.: 03686378 hereby confirm that the attached thesis,
Non-Collusive Market Failures in the Context of New Markets for Flexibility: An Application of the Residual Supply Index to the Altdorfer Flexmarkt & A
Review of Historical Market Manipulation Cases
was written independently by me without the use of any sources or aids beyond those cited, and all passages and ideas taken from other sources are indicated in the text and given the corresponding citation. Tools provided by the institute and its staff, such as models or programs, are also listed. These tools are property of the institute or of the individual staff member. I will not use them for any work beyond the attached thesis or make them available to third parties. I agree to the further use of my work and its results (including programs produced and methods used) for research and instructional purposes. I have not previously submitted this thesis for academic credit. Munich, 14.11.2019 .................................................. Ryan Harper
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Table of Contents
Abstract ....................................................................................................................................................ii
Aufgabenstellung..................................................................................................................................... iii
Statement of Academic Integrity ............................................................................................................ iv
Table of Contents ..................................................................................................................................... v
Table of Figures ....................................................................................................................................... vi
Table of Tables........................................................................................................................................ vii
Table of Equations .................................................................................................................................. vii
1. Introduction ..................................................................................................................................... 1
1.1. C/sells and ALF ......................................................................................................................... 2
1.2. Motivation and Research Questions ....................................................................................... 2
1.3. Structure .................................................................................................................................. 4
2. The German Electricity Sector ......................................................................................................... 5
2.1. Current Structure .................................................................................................................... 5
2.1.1. Generation ....................................................................................................................... 6
2.1.2. Transmission .................................................................................................................... 6
2.1.3. Markets ............................................................................................................................ 7
2.1.4. Balancing ......................................................................................................................... 8
2.2. Trends .................................................................................................................................... 11
2.2.1. Distributed Energy Resources ....................................................................................... 11
2.2.2. Electrification ................................................................................................................. 11
2.2.3. System Effects ............................................................................................................... 12
2.3. Congestion and Current Solutions ......................................................................................... 13
3. Markets for Flexibility as a Future Solution ................................................................................... 14
3.1. Defining Flexibility ................................................................................................................. 15
3.2. Flex-Markets in the Traffic Light Concept ............................................................................. 16
3.3. The Altdorfer Flexmarkt ........................................................................................................ 17
3.4. Benefits of a Market for Flexibility ........................................................................................ 19
4. Market Power ................................................................................................................................ 20
4.1. Definition and Consequences ................................................................................................ 20
4.1.1. Monopsony & Monopoly Power ................................................................................... 21
4.2. Susceptibility of Electricity Markets ...................................................................................... 22
4.3. Exercising MP in Electricity Markets ..................................................................................... 23
4.4. Consequences ........................................................................................................................ 25
4.5. Detecting and Measuring Market Power .............................................................................. 26
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4.5.1. Structural Market Power ............................................................................................... 26
4.5.1.1. Concentration Ratio and the Herfindahl-Hirschman Index ....................................... 27
4.5.1.2. The Pivotal Supplier Indicator and the Residual Supply Index .................................. 27
4.5.2. Exercised Market Power ................................................................................................ 29
5. Analysis of Structural Market Power in ALF .................................................................................. 31
5.1. The Distribution Network in Altdorf ...................................................................................... 31
5.2. Methods ................................................................................................................................ 32
5.3. Results ................................................................................................................................... 37
5.4. Conclusions ............................................................................................................................ 45
6. Market Manipulation .................................................................................................................... 47
6.1. Market Manipulation in the California Energy Crisis ............................................................ 47
6.2. Reaction to the California Strategies ..................................................................................... 50
6.3. Cases of Market Manipulation under 18 CFR §1c2 ............................................................... 51
6.3.1. Price Manipulation ........................................................................................................ 52
6.3.2. Gaming of Market Rules ................................................................................................ 55
6.3.2.1. Increased Market Payments ...................................................................................... 55
6.3.2.2. Increased Side Payments ........................................................................................... 59
6.4. Conclusions ............................................................................................................................ 64
7. Summary and Suggestions ............................................................................................................ 67
References ............................................................................................................................................. 71
Table of Figures
Figure 1: Overarching methodology - own representation .................................................................... 4
Figure 2: Aggregated supply and demand from Sept. 30th 2018, 15:00-16:00 - /EPEX-08 18/ ............... 8
Figure 3: Balancing Groups - own representation based upon /GRAE-01 14/ ........................................ 9
Figure 4: Congestion in Zonal Markets - own representation based on /FFE-48 18/ and .................... 15
Figure 5: Physical and economic withholding - own representation based on /EWI-02 08/ and
/HILE-01 14/ .......................................................................................................................................... 24
Figure 6: Middle Voltage Network of Altdorf and the Surrounding Area ............................................. 32
Figure 7: Methods for calculating RSI values. Own Representation ..................................................... 33
Figure 8: Stylized process for calculation of effectiveness factors. /FFE-35 18/ ................................... 34
Figure 9: Strand 4008 with LV networks, line 1723, and unit type capacities ...................................... 36
Figure 10: RSI values of selected owners - largest independent owner ............................................... 37
Figure 11: RSI values of selected owners - independent owners.......................................................... 38
Figure 12: RSI Values of selected owners - PtH units aggregated ......................................................... 39
Figure 13: RSI Values of selected owners - LV PV aggregated .............................................................. 40
Figure 14: RSI values of selected owners - EVs aggregated .................................................................. 41
Figure 15: RSI values of selected owners - LV networks aggregated .................................................... 42
Figure 16: RSI values of selected owners: PtH and LV networks aggregated ....................................... 43
Figure 17: RSI values of selected owners - no PtH, independent owners ............................................. 44
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Figure 18: RSI values of selected owners - MV 1391 with decreasing available flexibility ................... 45
Figure 19:Manipulative inflation of baseline load - own representation based on /US-06 13/ ........... 56
Figure 20: Gaming of technical restrictions - own representation based on information from
/US-07 13/ ............................................................................................................................................. 62
Table of Tables
Table 1: Reserve Energy Products - own representation based on /CON-01 14/ ................................. 10
Table 2: Tools for the detection and measurement of market power - own representation based on
/HILE-01 14/ .......................................................................................................................................... 26
Table of Equations
Equation 1:Pivotal Supplier Indicator .................................................................................................... 28
Equation 2: Residual Supply Index ........................................................................................................ 28
Equation 3: Lerner Index ....................................................................................................................... 29
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1. Introduction
The German Climate Action Plan seeks to cut carbon dioxide emissions in Germany by 80 to 95
percent of 1990 levels by the year 2050. The energy sector has a major role to play in this, having
been the sector with by far the highest emissions in the reference year of 1990. Based on the targets
set out as waypoints for the year 2030, the energy sector should lower emissions to between 175
and 183 million tons of CO2 equivalent per year, an approximately 61.5% reduction from the 466
million tons emitted in 1990. In 2014 the sector had lowered emissions approximately 23% compared
to 1990, an average rate of less than 1% per year. /BMU-03 16/ In that time, 85.2 GW of new
renewable generating capacity was installed /UBA-02 18/. Between 2014 and 2018 an additional 28.9
GW were added, and renewable sources generated 37.8% of German electricity as 2019 began
/UBA-02 18/ /UBA-05 19/. However, this means more than 60% of the current German demand for
electricity is still met with electricity generated from fossil fuels or nuclear plants. Meeting the goals
of the Climate Action Plan and the ongoing nuclear phase out will require further increases in the
amount of renewable generation in Germany.
Even with the current levels of renewable generation grid congestion has become commonplace,
requiring frequent and costly curtailment of renewable generation. Originally conceptualized as an
emergency measure to be used sparingly, curtailment of renewable electricity has become a regular
feature of the German electricity system /FFR-01 17/. In 2017, 5,518 GWh of electricity was curtailed
to relieve congestion, a record amount that triggered compensation payments of approximately
€610 million. When combined with the costs of redispatch (see section 2.3) the total bill for
congestion management in 2017 came to €1.7 billion. /BNETZA-28 19/ Barring improved prevention
and management of congestion, these costs can only be expected to rise still further as more
renewable generation is integrated into the grid in efforts to decarbonize the energy sector
/UDE-01 19/.
Complicating this further is the fact that nearly all new PV generation and much of the new wind
generation is connected to low-voltage distribution networks. Not only is their transmission capacity
much lower compared to the high-voltage transmission networks, but they were also not engineered
to handle electricity flows in both directions. While the long-term solution to network congestion is
upgrading and expanding the electricity network infrastructure, this will be a relatively slow,
expensive process that faces public opposition in some areas. Additionally, if it is treated as the only
solution and transmission infrastructure is installed to “the last megawatt” – enough to handle the
highest transmission peaks that occur only rarely – grid expansion becomes an inefficient solution as
expensive infrastructure sits un- or under-used for much of the year. /ETG-01 14/ Fortunately new
technologies are being integrated into the energy system as part of the digitalization process,
enabling new market structures as alternative methods of congestion management. One such
emerging tool for congestion management is a market for flexibility.
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1.1. C/sells and ALF
In late 2016 the German Federal Ministry of Economy and Energy (Bundesministerium für Wirtschaft
und Energie – BMWi) announced the projects selected for the new funding program “Smart Energy
Showcases – Digital Agenda for the Energy Transition” (Schaufenster intelligente Energie - Digitale
Agenda für die Energiewende - SINTEG). SINTEG is intended to support large-scale projects, or
showcases, that seek to develop and test models for the future energy system. Five projects focusing
on digitalization, enabling very high penetration of renewable resources, and transferability to other
regions were selected for SINTEG funding. /BMWI-119 17/ /BMWI-07 18/
One of these five projects was C/sells – “The energy system of the future in the ‘solar arch’ in
southern Germany” (Großflächiges Schaufenster im Solarbogen Süddeutschlands). Spanning the
federal states of Bavaria, Baden-Württemberg, and Hesse, C/sells is guided by the ideas of cellularity,
participation, and diversity in its approach to achieving the goals set out by SINTEG. The Research
Center for Energy Economics e.V. (Forschungsstelle für Energywirtschft e.V. – FfE) serves both as the
regional coordinator for Bavaria and as a partner in many of the projects within C/sells. /FFE-75 17/
Of these, this thesis is primarily concerned with the Altdorfer Flexmarkt (ALF), a pilot project
designing and testing a market for flexibility services in the local distribution grid of Altdorf, a
community near the Bavarian city of Landshut (see section 2.4) /FFE-48 18/. Previous work
conducted by the FfE has been concerned with the technical details of creating this new market, such
as the design of the market and efforts to increase citizen acceptance and participation /LANG-01 18/
/LOHM-01 19/. Based upon the market structure and rules that have been developed from the
results of these previous projects, this thesis will continue the process of refining the ALF by
examining the potential for abuse of market power, opportunities for manipulation of the market,
and ways in which these market distortions can be prevented or their impacts mitigated in the
context of a flexibility market.
1.2. Motivation and Research Questions
A well designed and implemented market for flexibility can have many positive effects. If the cost of
congestion management can be reduced through competition in the flexibility market, network
operators could pass these decreased operating costs on to consumers in the form of lowered
connection fees, currently the largest portion of the household electricity price /EUOT-01 18/
/FFE-48 18/ /FFR-01 17/. Preventing congestion can prolong the life of existing transmission network
infrastructure and somewhat reduce the urgency of expanding the grid, as well as precluding the
need to build transmission capacity to „the last Megawatt” /ETG-01 14/. At the individual level, it can
offer owners of generation a new revenue stream, whether these are farms with biogas generation
and large PV panels or urban households with heat pumps /FFE-48 18/. Participating in a flexibility
market also offers citizens the chance to actively participate in the Energiewende. /FFE-48 18/
However, establishing any new market is not a simple endeavor, and electricity markets present their
own particular challenges. The collapse of the then newly-deregulated California energy market at
the beginning of this century offers a glimpse at the potential magnitude of the consequences of a
market susceptible to market power and manipulation by market participants.
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Prior to the introduction of the competitive market for electricity in California, the existing large
utility companies had been required to divest their generating capacity. When deregulation went in
into effect in April 1998, the utilities were required to purchase the electricity needed to meet
demand exclusively on the new spot market, while consumer retail rates were frozen. The market
functioned well until a variety of largely external factors began causing prices to rise in the summer
of 2000. Average prices that summer were around five times higher than the prior summer, and
rolling blackouts occurred in parts of the state. From December 2000 until May 2001 average prices
were around ten times higher than the summer of 1999, and the utilities, forced to buy electricity at
average rates approaching $400/MWh while selling it at $65/MWh, were losing about $50 million
per day. The state government became the buyer of last resort, spending $8 billion on electricity over
five months and negotiating inflated long-term contracts for electricity at rates around $3 billion per
year. While market fundamentals played a large role in increasing prices, analyses show that the
exercise of market power during times of tight supply likely increased prices an additional 33%, and
manipulative bidding strategies resulted in further unjust transfers of wealth. All told, the crisis had
an estimated economic impact of $40 - $45 billion over two years /PPIC-01 03/. /ALAY-01 04/
/MIT-01 01/ /FERC-01 03/
While a disaster on that scale is unlikely, a flexibility market with legal loopholes encouraging
manipulation, or lacking design features to prevent or mitigate the abuse of market power could
prove a more burdensome solution than the current methods of regulatory redispatch and
curtailment. As the costs of congestion management are ultimately passed from network operators
to consumers, preventing anti-competitive behavior in markets for flexibility is in the public interest
/FFR-01 17/.
This thesis will seek to answer the following research questions:
• Under what conditions, in what form, and with what consequences could market power
emerge in a new flex market? How should its presence and effect be measured?
• What opportunities for manipulation does the market design create through interactions with
related markets and its bidding and pricing methods? How can manipulative behavior be
distinguished from legal profit-maximizing behavior?
• How can anti-competitive and manipulative behavior be prevented, mitigated, or punished in
the context of a flex market?
In attempting to answer these research questions, the two types of anti-competitive behavior will be
considered separately as depicted in Figure 1 on the following page.
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Figure 1: Overarching methodology - own representation
Literature research was conducted on both types of anti-competitive behavior, both in general terms
for an understanding of the topics and state of research, and with specific regards to electricity
markets. Based upon the results of this literature research, a methodology to assess the potential for
the abuse of market power in the ALF was developed on the basis of the Residual Supply Index (RSI).
Such an analysis was carried out for one strand of the middle-voltage network in the Altdorf area.
Turning to market manipulation, a literature review of cases of manipulative behavior which were
prosecuted by American regulators was conducted. These cases were categorized, and forms of
potentially manipulative behavior that could emerge within markets for flexibility were examined
using this categorization. The results of the research into both types of anti-competitive behaviors
served to draw conclusions and offer suggestions for a more resilient market for flexibility.
1.3. Structure
The remainder of this thesis will be structured as follows. Chapter 2 will place the development of
the ALF in context by describing the current state of the German electricity market, with a focus on
the trends driving congestion and how congestion is presently dealt with. The potential of markets
for flexibility as a future tool for congestion management will be discussed in Chapter 2, and the
structure and processes of ALF will be described in more detail.
Chapter 4 will focus on market power, defining its different forms and metrics used for its detection.
This will then be placed in the context of electricity markets, including the reasons for their
susceptibility to the abuse of market power, before turning to the exercise of market power and
examples demonstrating the consequences resulting from past abuses of market power in electricity
markets.
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Chapter 5 will describe the application of the Residual Supply Index to ALF and examine the results of
this analysis. Chapter 6 will concern itself with a summary of the literature review regarding market
manipulation, beginning with the evolution of anti-manipulation legislation before moving on to
concrete examples of manipulation of electricity markets. Finally, Chapter 7 will summarize the
conclusions reached and provide a small number of suggestions for combating anti-competitive
behavior in markets for flexibility.
2. The German Electricity Sector
The electricity sector can be divided into two halves: A technical half, concerning the generation and
transmission of electricity, and an economic half containing various markets for electricity. Electricity
is generated in conventional power plants through the burning of fossil fuels, or by converting the
energy contained in renewable sources, most commonly the wind or solar radiation, into electricity.
From the source of generation, the electricity then flows through different network levels to reach
the point of consumption. The generator of a unit of electricity only sells that unit to the eventual
consumer of it in a few cases. More commonly, electricity is sold bilaterally through long-term
contracts or anonymously on different auction markets to an energy provider, who in turn re-sells
electricity at a contracted rate to end consumers. Because the storage of electricity is not yet
practical at a large scale, the generation of electricity must be forecast and adjusted to match the
desired level of consumption at all times. /GRAE-01 14/ /SCHWB-01 17/ This chapter will examine the
German electricity sector in more detail.
2.1. Current Structure
Until 1998, the German electricity sector, like that of most countries around the world at the time,
was a regulated oligopoly. Nine vertically-integrated companies controlled the generation and
transmission of around 90% of demanded electricity, which they delivered to regional suppliers and
city utilities for distribution to consumers. These companies were responsible for the remaining 10%
of generation and enjoyed regional monopolies, with all consumers obligated to purchase energy
from the supplier whose territory they lived in. In turn, there was a legal requirement for each
regional monopolist to serve all consumers within their territory. The prices consumers could be
charged were subject to regulatory approval and monitoring, and the reliability of service under this
system was very good. However, German electricity prices were relatively high compared to other
countries. As the sector matured, the oligopolists gradually expanded their transmission networks,
and increased interconnection with other European grids. This expansion, combined with
improvements in information and communication technologies, provided policymakers with the
opportunity to pursue lower energy prices through deregulation of the electricity sector. The
beginning of the deregulation process was the passage of the Energy Industry Act
(Energiewirtschaftsgesetz – EnWG) in April of 1998 /EWI-02 08/. With the goal of creating efficient
and undistorted market competition in the electricity sector in order to lower prices, the unbundling
of the electricity value chain is at the core of the law. In contrast to the previous vertically-integrated
companies, a firm is now restricted to only one of network operation, generating electricity, or
buying and reselling electricity. /VTV 01 10/ /SCHWB-01 17/
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2.1.1. Generation
In 2017, the installed generation capacity in Germany grew to equal 217.6 GW. Renewable
generation capacity represents 112.5 GW of this, while the remaining 104.1 GW is conventional
generation capacity. Among the individual federal states NRW has by far the largest share of installed
capacity, totaling 41.8 GW, of which 30.4 GW is conventional generation capacity. Bavaria holds the
second largest share of capacity at 28 GW, closely followed by Niedersachsen with 26.5 GW. Both of
these federal states feature more renewable than conventional generation capacity and the highest
and second highest amounts of renewable capacity overall. All of the net growth in 2017 can be
attributed to new installation of renewable generation units, with a net 8.3 GW of new capacity
being brought online while conventional capacity decreased by 2.5 GW. /BNETZA-28 19/
From this installed capacity, in 2017 net generation of electricity in Germany was 601.4 terawatt
hours (TWh). The trend of increasing shares of renewable generation continued, accounting for 204.8
TWh (34%) in 2017. Conventional sources accounted for 396.6 TWh (66%) of generation. The
discrepancy between proportion of capacity and proportion of generation can likely be attributed to
the intermittent nature of renewable sources and the role of conventional generators in providing
“always on” base-load power. Just five firms generated 75% of all non-renewable energy injected
into the German grid in 2017. /BNETZA-28 19/
2.1.2. Transmission
Although separating network operation from the generation and trading of electricity was the top
priority of the unbundling process, competition between network operators would be impractical
due to the amount of redundant investment in infrastructure this would require. Instead, network
operation is considered a natural monopoly and is regulated and closely monitored by the Federal
Network Agency (Bundesnetzagentur - BnetzA) /VTV-01 10/. The German electric grid can be divided
into two networks: the transmission network and the distribution network. These can be further split
into four levels, differentiated by voltage level. The transmission network operates at the highest
levels of voltage (220 or 380 kV) to enable low-loss bulk transport of electricity over long distance,
including connections to the electric grids of other countries. Only the largest generating units, such
as nuclear plants, large coal or gas fired plants, and large offshore windfarms, are connected to the
transmission network. In general, no consumers of electricity are connected to this network.
Germany has around 36,000 kilometers of transmission network lines. /BMWI-13 12/
Using transformers, electricity from the transmission network is stepped down to a lower voltage as
it is fed into the distribution network. The distribution network spans a wider range of voltages, and
features both generation and consumption units. At the high voltage (HV) level (60 – 110 kV) mid-
sized conventional generation plants and large onshore windfarms or solar arrays are connected for
generation, while energy-intensive consumers such as heavy industry take electricity directly from
the grid at this level. High voltage lines are also used to transport large amounts of electricity to
densely populated areas, where it is transformed down to a medium voltage for further distribution.
7
This medium voltage (MV) level network (6 – 30 kV) receives further generation feed-in from smaller
conventional and renewable generation plants, and delivers electricity to light industry, commercial
businesses, and the utilities of smaller towns and cities. Finally, the low voltage (LV) network (230 or
400 V) delivers electricity to individual households and small businesses, and receives electricity
injected by small-scale generators such as household rooftop solar panels or communal
heating/power stations. Germany has around 1.7 million kilometers of distribution network lines,
mostly at the low-voltage level (ca. 1.1 million kilometers). /BMWI-13 12/ /BMWI-12 17/
Four companies, each with their own geographic territory, serve as transmission network operators,
while around 900 distribution network operators maintain the lower voltage levels of the electricity
grid /BNETZA-06 18/. Network operators at any level are legally required to offer equal access to
their network for all generators and traders. The previously encouraged regional monopolies of
energy suppliers or traders, then known as territorial sovereignty (Gebietshoheit) are no longer
legally protected. Registered suppliers and traders are now empowered to both buy and sell
electricity throughout the country as long as they are a member of a balancing group (Bilanzkreis) in
a given area, and both institutional and household customers are free to purchase their electricity
from any supplier they wish /VTV-01 10/.
2.1.3. Markets
The electricity that is generated, purchased by traders, and transported through the territories of the
four transmission network operators and the numerous distribution networks to be consumed in
industry, households, and businesses is today offered and purchased on the free market. The market
consists of two different types of trading: Over the Counter (OTC), or bilateral, trading, and Auction-
Based trading, including auctions covering a variety of timeframes and services. Of the two, OTC
trading is less regulated and less standardized, allowing for more flexibility in the types of products
available and the terms of trade, but carries higher transaction costs and no guarantee in case one
trading partner cannot deliver as promised. /GRAE-01 14/
The most common forms of contracts to buy or generate electricity are traded in standardized forms
in a series of auctions. On the forward market, the European Energy Exchange (EEX), contracts for
the delivery of electricity for periods ranging from one week to one year can be bought or sold up to
six years in advance. This market is used to secure long-term supply well in advance, to hedge against
the risk of price changes, and for financial speculation. The spot market or day-ahead market, the
European Power Exchange (EPEX), is used for shorter term trading. Electricity traded on this market
must be delivered for the specified hour or block of hours on the next day, with buyers and sellers
submitting the amounts they are willing to buy or sell at what price for each hour they wish to
participate in. By aggregating all buy and sell offers, the market operator is able to determine a
market clearing price for each hour that matches supply and demand. As an example, Figure 2
depicts the aggregated supply and demand offers and market clearing price and quantity for the
15:00-16:00 hour of EPEX Day-Ahead Market for Germany and Austria on September 30th, 2018.
8
Figure 2: Aggregated supply and demand from Sept. 30th 2018, 15:00-16:00 - /EPEX-08 18/
All offers to sell lower than this price and all offers to buy higher than this price are accepted, with
each offer being paid or paying the same market-clearing price. This type of auction is known as a
uniform price auction. This rewards those generators able to produce electricity at the lowest cost,
while ensuring that those consumers who value the electricity the most are able to secure it. The
market-clearing price of the day-ahead market also serves as a reference price for the forward
market and OTC trading. Last-minute trading can be carried out on the Intra-Day Market, where
contracts for delivery over periods of 15 minutes or one hour can be traded up until 45 minutes
before the time of delivery. /EPEX-05 18/ /GRAE-01 14/
2.1.4. Balancing
No matter which market a megawatt of electricity is bought or sold on, the homogenous nature of
electricity means that the parties to this transaction have no way of tracking this particular megawatt
after it is generated. The buyer has no way of confirming that the good they have purchased has
been shipped, and the seller has no way of confirming their customer took delivery of the good sold.
To help work around this problem, all market participants are required to belong to a balancing
group. Each balancing group keeps and submits a ledger of all electricity fed into and withdrawn from
the grid by its members. Generation and purchases of energy are both considered as feed-ins into a
balancing group, while sales and consumption are both considered withdrawals from the balancing
group. The ledger submitted to the transmission network operator by each balancing group must
have a net-zero balance of feed-ins and withdrawals. /CON-01 14/ /GRAE-01 14/
9
While the two trading partners will not be able to guarantee that the electricity generated and fed
into the grid by the seller is the same electricity consumed by the buyer, the balancing group ledgers
help offer some traceability to transactions. To successfully register a transaction, both parties must
include mirrored entries in their balance group ledgers when submitting them to the transmission
network operator. A stylized example of this is depicted in Figure 3. The transmission network
operator in turn measures the net energy consumption of each balancing group for every quarter
hour to confirm that the actual feed-ins and withdrawals of the balancing group match the submitted
ledger. This offers some assurance that neither party is cheating on their transaction. However, even
when all balancing groups carry out all transactions in good faith, it is rare that actual generation and
consumption of electricity in real time exactly matches the prognoses included in the ledgers
submitted to the transmission network operator. /CON-01 14/ /GRAE-01 14/
Figure 3: Balancing Groups - own representation based upon /GRAE-01 14/
At present, most consumers are not exposed to changes in the market prices for electricity, which in
turn decouples their demand for electricity from the wholesale price /STO-01 02/. Instead, short-
term demand is more dependent on the season, the temperature, the day of the week, and the time
of day than on the wholesale price /STO-01 02/. Predicting demand based on these factors is an
imperfect science, and unforeseen events can cause demand to change without warning.
Additionally, failures of conventional power plants and possible fluctuations of renewable generation
can cause changes to available supply with little warning. All of these factors regularly lead to
imbalances between feed-ins and withdrawals within balancing groups. /CON-01 14/ /GRAE-01 14/
10
The constant balance of supply and demand is critical for the stability of the electric grid, here
referring to the maintenance of a constant frequency. In the interconnected European grids, the
frequency is sought to be maintained at 50 hertz (Hz), with a small tolerance (±0.2 Hz) for variation.
To maintain this frequency, the amount of electricity being fed into the grid must equal the amount
being withdrawn from it. The frequency begins to rise when more electricity is being fed into the grid
than withdrawn from it, and begins to sink when demand exceeds supply. /CON-01 14/
Whenever a discrepancy between supply and demand occurs, network operators call upon positive
or negative reserve energy to bring the grid back into balance and prevent frequency deviations.
Reserve energy is actually made up of three products, differentiated by the speed with which they
can be called upon and the length of time for which they can be depended upon. Table __ describes
these three products, each of which is procured via an auction. Since December 2007 the four
German transmission network operators have collectively purchased all three reserve energy
products through a joint auction, as well as implementing reserves in concert with one another, in
order avoid redundant purchase of reserve energy. /CON-01 14/
Table 1: Reserve Energy Products - own representation based on /CON-01 14/
Primary
Reserve Energy
Secondary
Reserve Energy Tertiary Reserve Energy
Activation Automatic Automatic Manual
Response Time < 30 Seconds < 5 Minutes < 15 Minutes
Length of Response 0-15 Minutes 15-60 Minutes 15-60 Minutes, potentially several
hours with repeated deviations
Purpose
Stabilize
fluctuating
frequency
Restore frequency
to & maintain
frequency at 50Hz
Relieve more flexible secondary
reserve generators, Restore frequency
to & maintain frequency at 50Hz
Generators bid a capacity price, reflecting their costs to hold capacity in reserve, with those able to
do so for the lowest price being accepted by the network operators until the required amount of
reserve capacity has been acquired. All generators selected receive the price they bid price for this
capacity, which they can no longer offer into other markets. This is an example of another auction
type, the pay-as-bid auction. Those generators accepted during the capacity auction also bid a price
for any electricity generated as reserve energy by their reserved capacity. Should balancing group
deviations cause the grid frequency to deviate from 50 Hz, the transmission network operators then
activate generators, from least expensive to most expensive, as needed to return the grid frequency
to 50 Hz. Those balancing groups with imbalanced generation and consumption are then assessed a
balancing energy charge based on magnitude of the deviation from their balanced ledger. These
charges both incentivize balancing groups to attempt to accurately forecast their generation and
consumption and in turn maintain overall balance in the grid, and can help prevent market
participants from using the homogenous nature of electricity to cheat on a transaction. /GRAE-01 14/
/CON-01 14/
11
2.2. Trends
The deregulation of the electricity sector two decades ago was a period of fundamental change.
Today two trends are combining to transform the economics and structure of the current energy
system to an even greater extent: The expansion of intermittent renewable energy generation and
the electrification of many aspects of our lives.
2.2.1. Distributed Energy Resources
Both prior to and following deregulation of the electricity sector, the basic nature of electricity supply
remained largely the same, as large centralized power plants with controllable output levels
generated electricity to be distributed down through the different grid levels to industry, households,
and businesses. The proliferation of distributed energy resources, largely from renewable sources,
has introduced energy flows at the lower levels of the grid that vary based on weather conditions.
While vital to meeting goals for the reduction of CO2 emissions necessary to prevent the worst
impacts of climate change, this development presents practical challenges for the electricity system.
/FFE-48 18/
Renewable energy has received government support in Germany since 1991, originally in the form of
the Act on the Sale of Electricity to the Grid (Stromeinspeisungsgesetz). This was replaced in 2000 by
the first version of the Renewable Energy Sources Act (Erneuerbare-Energien-Gesetz – EEG), which
has since been updated several times. /SCHWB-01 17/ In 1991, 4.3 GW of renewable generation
capacity was installed in Germany. In 2001 renewable capacity had grown to 14.6 GW, and
proceeded to more than quadruple over the next decade to reach 66.7 GW in 2011. At the end of
2018 there were 118.3 GW of installed renewable generation capacity in Germany, primarily from
wind (52.6 GW) and solar (45.2 GW) generating units. /BMWI-02 18/ /UBA-05 19/
This growth is being driven by public support for the Energiewende and desire to limit the extent of
climate change /IASS-101 17/ /SCHWB-01 17/. The Climate Action Plan 2050 of the Federal Ministry
for the Environment, Nature Conservation, and Nuclear Safety (Bundesministerium für Umwelt,
Naturschutz und nukleare Sicherheit – BMU) targets a 80-95% decrease in CO2 emissions compared
to 1990 levels, with a checkpoint goal of a 55% reduction by 2030. As described in the introduction to
this thesis, the energy sector has the largest role to play in this transition, and is tasked with
achieving a comparatively large 61-62% reduction in emissions by 2030. /BMU-03 16/ With less than
40% of the energy consumed in Germany coming from renewable sources in 2018, further expansion
of renewable generation capacity can be expected as long as the goals of the Climate Action Plan
2050 remain in place /UBA-05 19/.
2.2.2. Electrification
Electricity generation is not the only sector that must decarbonize if the effects of climate change are
to be limited. Energy used by private households, and its production, represented approximately 23%
of all German energy-related CO2 emissions in 2014, or 164 million tons of CO2. 80% of these
emissions were related to heating, where oil and gas remain the dominant fuel sources.
12
In the transportation sector, which was responsible for 24% of German energy-related CO2 emissions
in 2014, the most common forms of transportation are also still largely dependent on burning fossil
fuels. 94% of these 172 million tons of CO2 emissions in the German transportation sector are the
result of road-based personal and freight traffic using vehicles with internal combustion engines.
/FFE-13 17/
Technologies exist that allow electricity to be used in place of fossil fuels in both of these sectors.
Both air-source and ground-source heat pumps can be used to heat homes and small businesses by
using ambient heat in combination with electricity in place of fossil fuels /FFE-66 18/ /FFE-88 19/.
Electric vehicles, particularly private automobiles, are also increasing in popularity /FFE-35 16/. As
technologies for electric mobility and electric heating increase their market penetration, the makeup
of the energy mix will play a key role in ensuring that these are truly “green” solutions /FFE-35 16/.
2.2.3. System Effects
Taking both of these trends into account, it becomes clear that the electricity system is facing radical
changes to both demand and supply. Increasing use of electricity for mobility and heating will
increase the consumption of electricity from the 579.9 TWh consumed in 2017, possibly as high 970
TWh per year by 2050 if electrification is pursued to its full potential /BNETZA-28 19/ /FFE-20 17/.
More problematic will be the effects on peak load. Electricity demand follows relatively predictable
daily patterns, with the highest demand occurring in the evening as people return home from work
and turn on lights, ovens, televisions, and other appliances /SCHWB-01 17/. In the future, the
addition of large fleets of electric vehicles being plugged in to charge in the evening and increased
numbers of electric heat pumps being turned on as their owners return home, will push these peak-
demand spikes even higher /FFE-35 18/ /FFE-48 18/. If electric generation is not decarbonized, the
increased total load could potentially cancel out the emissions reductions made by electrifying the
mobility and heating sectors. Larger evening demand peaks could also lead to problems if networks
are not expanded to keep up with demand /MÜL-02 18/. An electricity sector with high amounts of
renewable energy, particularly if dominated by solar power, could also struggle to cope with demand
peaks that do not line up with periods of peak generation /EUOT-01 18/.
The key benefit of distributed renewable energy generation, the ability to generate carbon-free
electricity from solar radiation and wind, is also one of the largest challenges for an energy system
with large amounts of renewable generation. The intermittent nature of solar radiation and wind
means generation occurs only when weather conditions are suitable, a fundamental departure from
conventional plants whose generation can be adjusted largely as desired to match changes in
demand /EUOT-01 18/ /SCHWB-01 17/. While supply shortages due to unfavorable weather have
been quicker to capture the public imagination, the experience of Germany has shown that the
periods of over-generation can be equally as challenging.
13
Approximately 95% of all renewable generation units are connected to the distribution network,
which was designed to handle neither energy flows in both directions nor the sheer volume of
electricity that is fed into it in times of high generation by PV or wind units /BDEW-04 17/. Because of
this, grid congestion has become more common due to high levels of simultaneous renewable feed-
in, and congestion relief has become very costly to German network operators and their customers
/UDE-01 19/. If distribution networks are not strengthened to account for electrification of demand,
times of peak load could also lead to further congestion /MÜL-02 18/.
2.3. Congestion and Current Solutions
Network congestion, as defined by /DIW-05 13/, is “… a situation in which the requested
transmission capacity exceeds the available capacity of the existing network.” (/DIW-05 13/, p.2) This
can take the form of violations of the allowed operating band for voltage, or when the current
flowing over power lines exceeds the thermal carrying capacity specific to that line /SCHWB-01 17/.
Network congestion increases the stress on the infrastructure of the electric grid, shortening its
lifespan, and in extreme cases causing equipment failure /FFE-48 18/.
§13 EnWG sets out three sequential methods that network operators may to use in order to alleviate
congestion and maintain safe operation of the electric grid. Network-based methods of congestion
relief must be implemented first, primarily switching operations to adjust power flows. If this does
not succeed, market-based measures may be used. These include redispatch, countertrading on the
intraday market, activating power plants specially designated as system-relevant network reserves,
and the use of controllable loads /FFE-48 18/. /ENWG-01 16/
The use of redispatch, which only applies to plants above a 10 MW capacity, has become increasingly
common in recent years. This involves changing the location of the energy being fed into the
transmission network to relieve congestion, without changing the overall amount of energy being
generated. The transmission network operator orders power plants on the surplus-side and
shortage-side of a congested line to respectively decrease and increase their generation to ensure
demand at the shortage location can be met without causing congestion. According to §13 EnWG,
both plants must then be compensated for foregone profits and incurred costs due to redispatch
/ENWG-01 16/. In 2017 approximately 20,439 GWh of conventionally generated electricity,
equivalent to 2.6% of national conventional generation, was subject to redispatch at a cost of
approximately 900 million Euros /BNETZA-28 19/. /FFE-48 18/ /BDEW-03 19/
Some of the support renewable generation units have received from the German government,
beyond monetary support, takes the form of preferential treatment for the electricity generated
from these units. §11 EEG and §13 EnWG prioritize the feed-in of renewable electricity over
conventionally generated electricity and exempt it from initial congestion management efforts
/EEG-01 17/ /ENWG-01 16/. According to §14 EEG only after all other network- and market-based
congestion management options have been exhausted may renewable generation be curtailed
/EEG-01 17/ /ENWG-01 16/. Known as feed-in management (Einspeisemanagement), the curtailment
of renewable electricity is categorized as an emergency measure for preserving grid stability, the last
of the three methods of congestion relief /FFE-48 18/.
14
However, the increasing integration of renewable electricity generation into the national grid is one
of the drivers of congestion, and maintaining grid stability has increasingly required the curtailment
of renewable generation /ETG-01 14/.
Approximately 5,520 GWh, or 2.9% of national renewable generation, was curtailed at a
compensation cost of €610 million in 2017 /BNETZA-28 19/. Taken together with the costs of
redispatch, the total costs of congestion management in 2017 were over €1.5 billion /BNETZA-28 19/.
As long as the expansion of renewable generation continues to outpace the expansion of the
network infrastructure, the frequency of congestion and the costs of its relief will likely continue to
increase /FFR-01 17/. More cost-effective methods of congestion management must be employed in
order to reduce this economic burden.
3. Markets for Flexibility as a Future Solution
The zonal pricing system used in nearly all European markets for electricity treats each zone within
the market, usually corresponding to countries, as a “copper plate” within which there are
considered to be no restrictions on transmission. Only the transmission constraints of
interconnections between zones are considered during the day-ahead electricity auction, leading to
uniform prices within each zone but varied prices between different zones. Ignoring the transmission
constraints within individual zones when running the auction for electricity can lead to network
congestion. /CONS-01 18/ /ETG-01 14/ Figure 4 on the following page demonstrates this in its
simplest form.
G1, a 20 MW windfarm, faces no fuel costs and can so offer its generation at a low price. Since G1’s
capacity can meet the 15 MW of demand required by D at the lowest cost, G1 would be selected in
the day-ahead market to supply all 15 MW demanded in this zone. However, while G2 was built near
D to serve its load, G1 was located where wind conditions are favorable and G1’s electricity must be
transmitted longer distances. The zonal pricing system does not consider the technical feasibility of
the lowest-cost market results when running the auction. In this case, the 10 MW maximum capacity
of the transmission line between G1 and D means that congestion would then occur when this
market result was implemented, as the constraint was not taken into account when running the
auction.
15
Figure 4: Congestion in Zonal Markets - own representation based on /FFE-48 18/ and /CONS-01 18/
As discussed in the previous chapter, such congestion is increasingly common, and is resolved using
costly regulatory measures including redispatch and feed-in management. With regard to both cost-
efficiency and the need to integrate larger amounts of renewable generation into the grid, these
methods are not practical solutions for the future. One emerging solution, made possible by
advances in information and communications technology and the beginnings of the smart meter
rollout, is the use of flexibility to provide network services, including congestion management.
/ETG-01 14/ /FFE-48 18/
3.1. Defining Flexibility
In the future, smart meters will enable to adjustment of individual devices within a generation array
or household. Three components are key to smart meter infrastructure: modern metering units,
control boxes, and smart meter gateways. Modern meters are capable of measuring the generation
and consumption of energy in real time. Control boxes allow individual units that cannot themselves
receive a signal to be adjusted from a local control unit, or by certified external actors connecting via
a smart meter gateway. With these components in place, precise measurements of load and
generation can be made and adjusted through secure communication channels. /FFE-64 18/
The flexibility of individual units or groups of units, defined in /UCM-01 18/ as “adjusting generation
and/or consumption profiles in response to external market signals” (/UCM-01 18/ p.4), can then be
used for congestion relief or other functions to support the grid. Smart meter technology, while key
for flexibility markets, is not the focus of this thesis, and more detailed information about smart
meter components can be found in /FFE-64 18/.
16
Both generators and consumers of electricity can offer flexibility services, although the specific
features of each technology effects the nature of the flexibility it can offer /EUOT-01 18/. For
instance, electric heat pumps can both decrease and increase the power they draw from the grid, if
operating below maximum capacity, while PV units can curtail their own feed in /EUOT-01 18/. To
date, flexibility has mostly been used to keep generation and load in balance through the adjustment
of conventional generators /SCHWB-01 17/. Surplus generation can be reduced via an increased load
on the demand side or decreased generation on the supply side. Conversely, generation shortages
can be alleviated by increasing generation on the supply side or decreasing load on the demand side.
Presently most flexibility in the electric grid is present on the supply side, with conventional
generators able to adjust their production as required by demand fluctuations, and through
connections with other countries /BNETZA-05 17/ /EUOT-01 18/. On the demand side, industrial
consumers with loads above 10 MW can contract with transmission network operators to adjust
their consumption if need be, and consumers in the low-voltage network can contract with
distribution network operators to allow their load to be controlled in exchange for lower network
connection fees /BNETZA-05 17/ /ENWG-01 16/. As more conventional generators are retired from
service in favor of renewable generation, their flexibility for the purpose of grid stability will be lost
/EUOT-01 18/. Current efforts seek to both increase the overall availability of flexibility, particularly
at smaller scales on the demand side to take advantage of coming electrification of heating and
mobility to maintain grid stability /FFE-36 19/ /UCM-01 18/. The role flexibility plays can also be
expanded beyond balancing generation and load to include services such as frequency balancing and
congestion management /EUOT-01 18/ /MÜL-02 18/.
Key to enabling the provision of flexibility is the ability of the generator or consumer to adjust their
consumption or generation based on external signals. The development and pending rollout of smart
meter technology, described at the beginning of this chapter, presents the opportunity to massively
expand the pool of actors with controllable generation or consumption /MÜL-02 18/. A market-
based mechanism for the provision and acquisition of flexibility can incentivize households and
generators to assist with congestion management, as well as the emergence of new business models
for specialized flexibility marketers /UCM-01 18/. By encouraging market competition between these
actors, network operators can secure the flexibility they need at a minimum cost /ETG-01 14/. This
can increase the efficiency of congestion management in comparison to the untargeted curtailment
of all renewable generation in a given area.
3.2. Flex-Markets in the Traffic Light Concept
In the traffic light concept proposed by Business Organization for the Energy and Water Industry
(Bundesverband der Energie- und Wasserwirtschaft e.V), the market operation can be divided into
three phases, corresponding to the colors of a traffic light. The green phase represents normal
market operation, the yellow phase represents a state of forecasted congestion, and the red phase
represents a situation featuring a direct risk to system stability. Today’s primary tools for managing
congestion, redispatch and feed-in management, could only be used during the red phase to
eliminate congestion in, or just before, real time.
17
Tools that could be used during the yellow phase are designed to be implemented before time
becomes a critical factor, preventing congestion from occurring rather than eliminating it after it
occurs. /BDEW-05 18/
A flexibility market offers a market-based option in the yellow phase, ideally resulting in an
economically efficient prevention of congestion instead of the direct interventions by network
operators used today /BDEW-05 18/. As most of the new electric loads and nearly all renewable
generation units will be connected to the low-voltage distribution networks least equipped to handle
large, two-way energy flows, a flexibility market that allows the participation of these low-voltage
units will provide tools to alleviate network congestion closest to the problem /ETG-01 14/.
Distribution network operators currently lack the authority to take market-based actions during the
yellow phase, with the exception of manipulating previously contracted controllable loads
/BDEW-05 18/ /FFE-48 18/. The introduction of flexibility market will offer them a versatile new tool
for managing their networks /FFE-48 18/. The Altdorfer Flexmarkt will now be presented to provide a
more detailed description of the structure and functioning of a market for flexibility.
3.3. The Altdorfer Flexmarkt
The beginning of the C/sells project in 2017 brought with it the conception of the Altdorfer Flexmarkt
(ALF). A cooperative venture between the Forschungsstelle für Energiewirtschaft e.V. and
Bayernwerk Netz GmbH, ALF is intended to serve as a demonstration of a congestion management
tool for distribution network operators that encourages participation of small scale, local flexible
devices through low barriers for market entry. In particular, ALF is designed for managing congestion
of power lines, which occurs when the current flowing over a line exceeds the manufacturer’s
defined thermal carrying capacity. /FFE-48 18/
The market platform process functions as follows. Before the market can be utilized, information
from both the supply and demand sides must be collected. The supply side is made up of the owners
or operators of flexible devices. An owner or operator of a flexible device wishing to participate in
ALF must register each device they wish to offer on the market with the ALF platform. Two types of
registration are possible: active marketing and long-term contracting. Active marketing devices are
those with a production or consumption schedule, which serves as a baseline, from which they can
deviate to provide flexibility. Different amounts of flexibility can be offered at various times for
different prices, and these devices are often controlled by experienced owners capable of creating
technically or economically optimized flexibility offers. /FFE-48 18/
Devices registered via long-term contracting are more likely to be household devices whose
production or consumption schedule is not planned in advance. The rated power of these units is
recorded during the registration process. At the time of registration, the owner or operator may
submit additional restrictions on the use of their flexibility, for example the minimum time between
flexibility activations, and the platform then calculates the available relevant flexibility based on
these conditions when needed. The network connection point of each unit is determined in
cooperation with the network operator based on location information submitted by the unit owner.
18
ALF has been designed to minimize barriers for participation for owners of small flexible devices
without experience in electric markets. Despite this, some owners may instead choose to contract
with market actors specializing in marketing the flexibility owned by others. By obtaining the rights to
many individual devices, these aggregators can offer larger bundles of flexibility and provide a single
point of contact for the market operator, lowering transaction costs. However, aggregation
simultaneously reduces the number of market participants and increases the amount of flexibility
controlled by single firms, potentially creating a situation favorable to the exercise of market power.
/FFE-48 18/
The demand side of the market is represented by the distribution network operator. To satisfy
confidentiality requirements for network operators, ALF itself stores no network models or data
regarding load flows. Instead, a network operator wishing to use the platform creates a topological
assignment matrix. This matrix indicates whether or not each network connection point is effectively
electrically connected to each power line within the network. The connectivity to other elements
determines whether flexibility options located at a given node can have any influence on congestion
elsewhere in the network. Influence factors can then be generated for those point-line pairs that are
connected. These influence factors are generated by the network operator using a series of load-flow
simulations, featuring increasing or decreasing levels of power at the connection point in each
iteration, to measure the effects these changes have on the current of each line. Matrices containing
these influence factors are then uploaded to the flexibility platform by the network operator.
/FFE-35 18/ /FFE-48 18/ More detail about the calculation of impact factors can be found in
/FFE-35 18/.
ALF will only be initialized after the closing of the day-ahead market. Should the network operator
identify congestion in their network when conducting network simulations based upon day-ahead
market results, they submit a call for flexibility to the ALF platform. This call must include the location
of the congestion in the form of the congested network element, when and for how long the
flexibility is needed, the change in current required to prevent the congestion, and their maximum
willingness to pay. Using the topological connection matrix, the mapping function of the ALF platform
can quickly remove all flexibility options that would be unable to exert influence on the congestion
from consideration, helping optimize the market process. The influence factor matrices allow the
market operator to then determine change in current that can be achieved by each of the remaining
flexible devices based on their location relative to the congested line. The mapping function also
serves to aggregate devices offered under the long-term contracting method. As the availability of
these devices depends upon household behavior, considering all devices of a type allows the market
platform to provide a minimum available flexibility that will be available with, for instance, 95%
certainty at time t. /FFE-48 18/
A list of all devices and their effectiveness on the congested element is then constructed. This list,
together with the offer prices and any additional restrictions of each device, is passed to the
platform’s matching algorithm. The matching algorithm determines the optimized cost-efficient
contracting of flexibility for each time period of the following day. /FFE-48 18/
19
Devices with active marketing receive a new schedule, altered from their baseline to include the
contracted flexibility, and this schedule becomes binding. Devices without active marketing are
controlled by the platform via connection to their smart meter gateways. Through these adjusted
operation patterns, the forecasted network congestion can be avoided, and all devices are paid their
bid price for their role in preventing the congestion. /FFE-48 18/
3.4. Benefits of a Market for Flexibility
A successful implementation of a market for flexibility will bring many benefits for network
operators. Most obvious are the potential for costs savings compared to redispatch and curtailment,
as congestion can now be managed using competition to provide flexibility at the lowest cost. At the
distribution network level, network operators will gain their first tool for market-based congestion
management, which will also allow them to prevent congestion ahead of time rather than reacting to
critical situations. A market for flexibility can also help to bridge the time until slow, expensive
infrastructure projects can be completed by preventing critical situations and prolonging the lifespan
of existing infrastructure. Additionally, although a market for flexibility will not eliminate the need for
grid expansion, it can lessen the total cost of grid expansion by precluding the need to build
transmission capacity “to the last megawatt” – the idea of building infrastructure with the capacity to
handle rarely occurring peak flows – by instead smoothing out these peaks through flexibility
/ETG-01 14/. Infrastructure constructed for the last megawatt would otherwise be used inefficiently,
rarely utilizing its full potential. This is especially beneficial in areas that may only face congestion in
during special short-term events, where infrastructure projects would not be cost effective. Network
operators may also enjoy other less tangible benefits from the successful implementation of a
market for flexibility. These include, but are not limited to, a better knowledge of their network and
the individual units connected to it, more detailed knowledge about the behavior of units within the
network, and better relationships with the public as they are given an opportunity to participate in
the market. /FFE-48 18/
At the individual level, flexibility markets can offer owners of actively marketed units an additional
revenue stream, while many owners of devices marketed using long term contracting will gain access
to the energy market for the first time /EUOT-01 18/. Both of these groups have the chance to
actively support the Energiewende through their participation in ALF. Making their flexibility
available to manage congestion can reduce the amount of renewable electricity that is curtailed,
effectively “thrown away”, and enabling the incorporation of further renewable generation into the
energy system /FFE-48 18/. Activating these new sources of flexibility will be key to maintaining grid
stability as more conventional plants are retired, taking their ability to respond quickly to changes in
demand with them /EUOT-01 18/.
Finally, the introducing markets for flexibility can create a new role in the electricity sector. The
market operator will be a new actor, independent of the network operator in order to maintain
neutrality. They will have the opportunity to create additional value beyond collecting fees for the
operation of the market for flexibility. Their close contact with network operators and the data
generated from market operation provide excellent opportunities to provide consulting services and
other data-driven market analyses. /FFE-48 18/
20
The realization of these benefits, particularly those related to cost savings, depends on the efficient
functioning of the market for flexibility. Market distortions or failures can decrease the efficiency of a
market, in some cases to the point where a market-based solution becomes more costly than
regulation. Market power and market manipulation are two forms of market distortions that have
received particular focus in the context of energy markets, and will be discussed in more detail in the
following chapters.
4. Market Power
In a theoretically perfectly competitive market, the presence of a large number of buyers and sellers
trading a uniform good precludes the ability of any single market participant to unilaterally alter the
price to their own advantage. Should a seller attempt to charge a higher price for their output,
another participant will undercut them to capture the sale, preventing the market price from rising.
Should a buyer attempt to offer a lower price for a unit of the market good, another participant will
offer a higher price to secure the good for themselves, preventing the market price from sinking. The
market price is taken as given when participants make decisions about how much to produce or
consume. /PIRU-01 08/ /STO-01 02/
This efficient functioning of a competitive market begins to break down when the number of buyers
or sellers is small, or similarly when a small number of market participants control a large proportion
of the demand or supply. The ability to substitute the good of one party for that of another in
response to price changes is lost. In these situations, the market can no longer be described as
perfectly competitive, and the few and/or large participants are said to have market power.
/PIRU-01 08/ /STO-01 02/ This chapter will first define market power and explore its potential
consequences before exploring the problem of market power in electricity markets in more detail.
Strategies for the exercise of market power by electricity market participants will be described, as
well as methods used by economists and regulators for evaluating the presence or absence of market
power in a market and for quantifying the impact of the exercise of market power.
4.1. Definition and Consequences
Definitions of market power differ in a broader sense between economic and regulatory
perspectives, and can vary in their details between different regulatory agencies or economic
disciplines. However for the remainder of this paper, market power will be defined as in /STO-01 02/:
“The ability to profitably alter prices away from competitive levels”
/STO-01 02/ p.318
The concise nature of this definition does not label specific behaviors as abuse of market power. This
avoids circumscribing particular behaviors while implying all other behavior is acceptable, thereby
risking both false positives and false negatives, and reserves the ability to label as-yet unseen
behaviors as abuse of market power. Not linking the abuse of market power with a duration makes
this definition well-suited for use in the context of electricity markets.
21
High prices for a small number of hours can lead to tens of millions of dollars in extra costs, and high
prices for a small number of months can lead to hundreds of millions of dollars in extra costs
/HILE-01 14/. This makes the “sustained basis” or “significant period of time” clauses of other
definitions dangerous loopholes that could let abusive behavior go unpunished. On the other hand,
this change in price need not be large. The Bundeskartellamt estimates that a price increase of
1€/MWH in the German wholesale electricity market would lead to increased costs of more than 500
Million Euros if maintained over an entire year /BKARTELL-01 15/. The profitability requirement is
also important, as outages of large plants that caused prices to rise but led to significant lost
revenues for the owner could falsely be labeled abuse of market power, regardless of the reason for
the shutdown /HILE-01 14/. /STO-01 02/
4.1.1. Monopsony & Monopoly Power
As the number of market participants falls, or as participants capture increasing shares of the market,
a market can be said to be increasingly concentrated. With increasing concentration of a market, the
market power of the remaining market participants also increases. At its most extreme, the process
of concentration can continue until there is only a single participant left in their respective market
role. As the supply side of a market becomes increasingly concentrated, it is described as an oligopoly
once dominated by a small number of large suppliers, and a monopoly in the extreme case of a single
supplier controlling the entire market supply. When a few large buyers dominate the demand side of
a market it is described as an oligopsony, while monopsony refers to a market with demand entirely
controlled by a single buyer. /PIRU-01 08/
The mere status of being an oligopolist, or even a monopolist, is not illegal. Indeed, innovation and
technological progress can lead to a firm becoming dominant due to a superior product or service,
and making achieving this success illegal would remove a major incentive for competition. Instead, it
is the use of this dominant status to harm other firms or impede competition that is forbidden. Thus,
it is important to distinguish between the potential for a firm to exert market power, or structural
market power, and the actual abuse of market power, or exercised market power /HILE-01 14/.
/PIRU-01 08/
The market power available to buyers and sellers in concentrated markets is referred to as
monopsony and monopoly power respectively /STO-01 02/. This thesis will focus upon monopoly
power, despite using the more general term market power. The power to alter the market price
away from competitive levels essentially stems from the lack of options available to the opposite
parties in a concentrated market. While a supplier that raises their price in a competitive market will
simply lead to customers switching to another supplier offering at a lower price, the limited or
nonexistent alternative suppliers in an oligopoly or monopoly forces customers to either accept the
higher price or forgo consuming the good. /PIRU-01 08/
22
Even in oligopolies and oligopsonies the structural potential for market power does not guarantee
that it will be used by participants to alter prices away from competitive levels. As long as the
remaining firms continue to compete aggressively amongst one another, each trying to capture
market share at the expense of the others, prices can continue to remain at or near the prices typical
of a competitive market.
In the case of a monopolist no competitor that could undercut a price hike by the monopolist and
take market share at the monopolist’s expense exists. This absence forces consumers unwilling or
unable to go without the product to accept the monopolist’s price rather than purchasing from a
different supplier. In turn, this gives the monopolist complete power to set the market price in order
to maximize their own profits. /PIRU-01 08/
4.2. Susceptibility of Electricity Markets
Since the world’s electricity markets began moving from regulated monopolies to competitive
markets, preventing the abuse of market power has been a main concern of governments and energy
market regulators /DICE-01 14/ /GCR-01 18/. Lower consumer electricity bills was one of the major
selling points for the privatization of the power sector, but a market subjected to the abuse of
market power by the former monopolists, still in control of a majority of their generating assets,
could lead to higher electricity prices instead /DUV-01 07/.
Electricity markets exhibit several features that set them apart from other commodity markets and
make them particularly vulnerable to the abuse of market power. Chief among these is the
requirement that generation and load within the grid must constantly be held in balance, with severe
consequences for failing to maintain this balancing act. While shortages in other commodity markets
may result in consumers having to go without a good, allowing this to happen in electricity markets
leaves the entire electric grid in danger of collapse. The inability to store electricity economically in
large quantities exacerbates both the challenge of holding the grid in balance and the ability of
suppliers to exert market power in order to extract high prices. Until large-scale storage technologies
become economically viable, buying electricity while prices are low to save for a later time of need
will not be an option to blunt the effects of market power. /HILE-01 14/ /STO-01 02/
Just as the use of stored electricity will someday provide a supply-side substitute for electricity from
a dominant market player, increasing the responsiveness of consumers to electricity prices will
eventually allow the demand side to play a role in minimizing the effects of market power. However
historically, and still to this day, nearly all final consumers of electricity are insulated from fluctuating
wholesale prices of electricity, leading to extremely inelastic aggregate demand. In order to maintain
system security, the grid operator must procure electricity at essentially any price the dominant
supplier wishes to ask in order to meet what amounts to a fixed short-term level of demand.
/HILE-01 14/ /STO-01 02/
These characteristics of electricity markets combine to simplify the abuse of market power by
dominant suppliers when compared to other markets. Additionally, they create possibilities for price-
setting abuse of market power even by suppliers that would normally be unable to do so.
23
In periods of peak demand, a non-dominant supplier may find that their generation is now required
in order to maintain the stability of the grid. /DICE-01 14/
The same can happen under conditions that reduce supply, for example low hydropower production
due to drought. The impossibility of storing previously-generated electricity for consumption in such
a period and the non-responsive nature of demand together leave no options as a substitute for the
generation of even a non-dominant supplier, providing them with situation-specific market power.
/DICE-01 14/
A final characteristic of electricity markets can result in a typically non-dominant supplier wielding
market power: transmission congestion. All electricity bought and sold on the market must flow
through the transmission and distribution networks to reach the loads it is intended to serve. If the
amount of electricity attempting to flow over a given line or transformer exceeds the safe operating
range of that element, it is said to be congested /SCHWB-01 17/. The safe operating range of an
element is determined by its physical characteristics, and operating outside of this range causes
increased wear and tear or even failure of the element /FFE-48 18/. When transmission becomes
congested, electricity from other geographic areas can be prevented from flowing over the
congested elements to reach loads, effectively splitting the area off into a separate market
/HILE-01 14/. If no options are available to relieve the congestion and once again allow competitively
priced electricity to flow into the area, any suppliers located within the area find themselves with the
ability to alter the price to their own benefit. Even when the market, when considered as a whole, is
perfectly competitive, such transmission constraints can give suppliers within the constrained area
significant local market power /HILE-01 14/.
4.3. Exercising MP in Electricity Markets
There are two common methods used by generators to exercise market power in electricity markets:
physical and economic withholding. Physical withholding is a strategy that involves decreasing the
amount of generation bid into the market, despite prevailing market prices providing the opportunity
to profitably sell this generation. Whether capacity is withheld simply by not being offered into the
market, being de-rated to a lower maximum generation level, or declared out of service, the goal is
to cause the merit order to shift to the left. This results in a unit with higher operating costs, and
therefore higher bids into the market, becoming the marginal unit and setting a higher market price.
/DUV-01 07//HILE-01 14//STO-01 02/
Similarly to physical withholding, economic withholding is also a strategy used by dominant firms to
exercise their market power and cause the market price to be set by a generating unit with higher
operating costs. Unlike physical withholding, in the economic withholding strategy generation is still
offered into the market, but at high price intended to remove the capacity from the merit order.
Figure 5 visualizes simple examples of both physical and economic withholding.
/DUV-01 07//HILE-01 14//STO-01 02/
24
Figure 5: Physical and economic withholding - own representation based on /EWI-02 08/ and /HILE-01 14/
In Figure 5, the market is dominated by a large supplier (yellow) which controls generating units A, B,
and C. Two units are owned by small independent suppliers (blue). Whether the dominant firm
exerts its market power by withholding capacity physically or economically, the result is the same:
The higher-cost unit E enters the merit order to replace the withheld unit B and becomes the
marginal generator, thus setting the new higher market price P2. Both physical and economic
withholding are only profitable if the revenue lost by withholding generation from the market is
exceeded by the increased revenues generated by capacity remaining in the market at the new
higher price /EWI-02 08/. In Figure 5 the additional revenue earned by the dominant supplier from
the large unit A outweighs the revenue lost by withholding unit B.
In a competitive market, or if a supplier could not exert market power, the physical and economic
withholding strategies would not be profitable, as the withdrawn capacity would simply reduce the
owner’s market share, and therefore revenue, without affecting the market price /HILE-01 14/.
Markets with pay-as-bid settlement mechanisms also impair the abuse of market power, as
withholding a single unit does not affect the price received by remaining units, but can lead to other
inefficiencies that may offset this benefit /STO-01 02/ /EWI-02 08/.
The ability to exert market power and network congestion are also tied to one another. In some
situations, network congestion can introduce market power into an otherwise competitive market. If
sufficient electricity to serve load in a geographic area cannot be delivered there due to limited
transmission capacities, any generators located within this area gain the ability to exert market
power. All remaining load can only be met by generation within the area, effectively making
competition with generators outside of the area irrelevant.
25
If only a small number of generators are located in this area, they will face little to no competition,
and can use this so-called local market power to increase prices. Additionally, if the residual load is
large relative to the available generation, market power can emerge even given a higher number of
generators in the area. Network congestion redefines the relevant market, which can bring market
power with it. /HILE-01 14/
In other situations, market power can introduce network congestion into an otherwise uncongested
network. Firms able to exert market power can cause network congestion, using a withholding
strategy, in order to raise prices. Withholding the generation of a unit can be used to force electricity
from other areas to serve local demand. If located in an area with limited transmission capacity, this
can be used to strategically create congestion and alter the nodal or zonal price. Just as with normal
withholding strategies, this congestion strategy becomes profitable when the increased revenue of
the remaining units outweighs the lost revenue from the withheld unit. /HILE-01 14/
4.4. Consequences
The most noticeable consequence, and indeed the goal of the abuse of market power is additional
revenue due to higher prices. Although the dominant supplier is responsible for the increased price,
they are the only firm that bears the cost, in the form of lost revenue via withholding, of causing the
price increase. Under a uniform price settlement mechanism, all other suppliers stand to benefit
from the increased prices proportionally to their sales. Because the increased prices are the result of
withholding, rather than higher costs leading to higher supply bids, the additional revenue represents
pure profit. The results of withholding can be more profitable for those firms not engaging in
withholding than for the dominant supplier, as their capacity in the market has remained the same
and is receiving the new market price while the dominant supplier receives nothing for their withheld
plant. Thus, market power can be expected to only be exercised when the dominant supplier
anticipates the price increase will lead to higher revenues from their remaining generation sufficient
to outweigh lost revenue due to withholding. /STO-01 02/
The counterpart to increased profits for suppliers is increased costs for buyers of electricity. As most
households or other customers of electricity retailers are not directly exposed to the market prices
for electricity, they will not reduce their demand for electricity in the face of higher market prices.
Retailers will be saddled with higher costs as they meet their obligations for the same level of
demand, and will in turn pass these costs on to their customers with a rate increase in the next
contractual period. While this may finally cause some final consumers to adjust their consumption,
the price signal comes far too late to mitigate the abuse of market power in the short run. Large
industrial consumers which purchase their required electricity directly on the wholesale market will
be forced to either accept higher operating costs and lower profit margins, or to curtail their
production. /STO-01 02/
A second consequence of the abuse of market power is a loss of economic efficiency in the market
/STO-01 02/. Withholding generation that could otherwise have been profitably operated at the
prevailing market price leads to a higher-cost plant entering the merit order /EWI-02 08/.
26
This results in a deviation from cost-efficient production, nullifying the main benefit of competitive
electricity markets: least-cost generation of electricity. The withholding of capacity leads to lower
levels of electricity being generated at a constant price, creating a shortage, or requires a higher price
to maintain the same level of generation. Such a price change has nothing to do with the market
fundamentals, and sends false signals regarding the state of the market and incentives for
investment /STO01 02/ /HILE-01 14/.
4.5. Detecting and Measuring Market Power
A wide variety of methods have been developed to both analyze markets for their potential
vulnerability to the abuse of market power, and to examine market outcomes for evidence of the
exercise of market power. Tools from both of these categories can be used for long-term analysis to
examine how a market structure has changed or whether the market is delivering efficient outcomes,
as well as for short-term analysis of competition levels and bidding behavior under particular
conditions. /HILE-01 14/ /DUV-01 07/ Table 2 displays several methods for detecting and measuring
market power, which will be described in more detail in the following section.
Table 2: Tools for the detection and measurement of market power - own representation based on /HILE-01 14/
Analysis of Potential Analysis of Outcomes
Long-Term Analysis
Concentration Ratio
Herfindahl-Hirschman-Index
Pivotal & Residual Supply
Index
Competitive Benchmark
Analysis
Bid Comparisons
Short-Term Analysis
Pivotal & Residual Supply
Index
Bid Screening
Outage Audits & Analysis
Residual Demand Analysis
4.5.1. Structural Market Power
A potential for the abuse of market power that exists due to the structure of the market is known as
structural market power. Methods used to analyze structural market power can be used for both
long-term and short-term analysis. Some can only describe the state of the market as a whole, while
others can also be used to assess individual companies. These metrics cannot, however, predict
whether or not market power will be exerted or determine after market closure whether or not
market power was exerted. Despite these limitations, it is still useful to be able to determine
whether a market is vulnerable to the abuse of market power. /DUV-01 07/ /HILE-01 14/
27
4.5.1.1. Concentration Ratio and the Herfindahl-Hirschman Index
Concentration Ratios are widely used to measure the degree to which a selected number, n, of the
largest suppliers control the chosen industry. The market shares of the n largest suppliers are simply
added together, and calculated as a percent of the total market supply. The higher the concentration
ratio, the more likely it is that one or more of these n firms can exercise market power. Different
sources consider different concentration ratios as the threshold for structural market power for
different numbers of firms. /HILE-01 14/
Another commonly used metric for examining market structures for the potential for abuse of
market power is the Herfindahl-Hirschman Index (HHI). While the concentration ratio measures only
the market share of the n largest firms in a given market, the HHI includes all suppliers in the market.
The market share of each supplier in the market is squared, and these values are then summed to
produce the HHI value. More highly concentrated markets are represented by increasing values, up
to a value of 10,000, representing a complete monopoly. Both a decrease in the number of suppliers
in the market and increases in market share of individual firms will increase the HHI. Based upon the
HHI value, a market is classified as unconcentrated, moderately concentrated, or highly
concentrated. Similarly to the concentration ratio, exact thresholds for each category vary depending
on the industry being examined and the concerns of the relevant regulator. /HILE-01 14/
Past experiences have shown that the concentration ratio and the HHI are of limited value in
predicting the potential for the abuse of market power in electricity markets. Both the concentration
ratio and the HHI are static indicators, only including information on the supply side of the market for
a single point in time. In electricity markets where demand fluctuates both hourly and seasonally, the
potential ability of a firm to exercise market power can change from month to month, day-to-day, or
even between different quarter-hour market clearing periods within the same hour. When the buffer
between total generation capacity and electricity demand shrinks during peak load hours, during
periods in which transmission congestion limits the market size, or due to plant outages, suppliers
with small market shares may suddenly find themselves able to exercise market power as they
become temporarily necessary to meet demand. Both the concentration ratio and the HHI do not
reflect such changes. /HILE-01 14/
4.5.1.2. The Pivotal Supplier Indicator and the Residual Supply Index
Both the Pivotal Supplier Indicator (PSI) and Residual Supply Index (RSI) were designed to overcome
the shortcomings of the concentration ratio and the HHI and serve as methods for assessing the
potential for the abuse of market power specifically in electricity markets. The PSI was defined in
1999, creating a binary measure equal to one if a supplier’s capacity is necessary to meet demand at
a chosen point in time, and equal to zero if demand can be met without the supplier in question.
When a supplier’s capacity is necessary to meet demand, they are said to be pivotal. Although it
must be recalculated for each time period to be examined, the results can be combined to describe
the share of the time in a larger time period that a supplier is pivotal to meeting demand, and
therefore potentially able to exercise market power. Equation 1 depicts the inequality used to
calculate the binary PSI. /HILE-01 14/
28
Equation 1:Pivotal Supplier Indicator
𝐶𝑥 > ∑ 𝐶𝑖 − 𝐿 𝑛
1
Where:
Cx = generation capacity of the supplier under examination
Ci = generation capacity of supplier i=1..n
L = load
Building upon the PSI, the California Independent System Operator (CAISO) developed the Residual
Supply Index during the initial deregulation of the California electricity market at the end of the
1990’s. It has been shown to be a significant predictor of price markups over competitive prices.
Equation 2 depicts the equation used to calculate the RSI. /FERC-01 02/ /DICE-01 14/
Equation 2: Residual Supply Index
𝑅𝑆𝐼𝑥 = ∑ 𝐶𝑖
𝑛1 − 𝐶𝑥
𝐿
where:
RSIx = RSI of the supplier under examination
Ci = generation capacity of supplier i=1..n
Cx = generation capacity of the supplier under examination
L = load
By subtracting the generation of the supplier being examined (supplier x) from total available
capacity, the RSI value represents the supply situation that would exist if supplier x chose to withhold
their generation from the market. Dividing this available supply by the demand for electricity in a
chosen time period results in the RSI value of supplier x in that time period. The RSI value is a single
number representing the percent of load that can be met without the capacity of the supplier x.
/DICE-01 14/ /HILE-01 14/
Similar to the PSI, the RSI value can indicate whether a supplier is pivotal (RSI < 1) or not (RSI ≥ 1), but
offers more nuance than the binary PSI /DICE-01 14/. A supplier nearly pivotal to system stability
would not be flagged by the PSI, but the RSI value would reveal how close to this threshold the
supplier is /HILE-01 14/. The CAISO found that hours with an RSI value of 1.2 (120% of load can be
met without the capacity of supplier x) or larger had market clearing prices approximately equivalent
to a benchmarked competitive price /FERC-01 02/. In hours with RSI values between 1.2 and 1, CAISO
observed that non-pivotal suppliers were still able to exercise market power and raise prices
somewhat, but to a lesser extent than in situations where a supplier is pivotal /FERC-01 02/.
A conspicuous result from a residual supply index analysis or any of the other indicators described in
this chapter can be a useful sign that the potential for the exercise of market power exists. The
supplier or the entire market should be examined more closely after market closure to determine
whether the abuse of market power has taken place. Those tools used to assess market outcomes in
or to identify the exercise of market power or quantify its impact will now be described in more
detail.
29
4.5.2. Exercised Market Power
As stated in section 4.1.1, a firm finding itself in the position to exercise market power has not yet
done anything illegal. On the contrary, this may simply be a sign of exceptional innovation,
technological superiority, or superior management. Moreover, price spikes in electricity markets are
not always indicative of a supplier exercising market power, and a plant being withheld from the
market does not imply anti-competitive intent /STO-01 02/. It is thus important to have tools that
can be used to analyze market outcomes and the conduct of dominant companies in order to
determine whether their behavior constitutes the abuse of market power or not.
• Lerner Index
In competitive markets firms are expected to bid the marginal cost of producing their last unit of
output /STO-01 02/. This is the lowest price they can receive for this unit without losing money by
producing it /STO-01 02/. Given increasing marginal cost structures this ensures a profit for the
previous units, produced at lower marginal costs, should this bid set the market price. This bidding
behavior increases the chances of being under the market clearing price, and therefore earning a
profit on all units, if the market price is set by a higher bid. For firms without market power, bidding
over marginal cost only increases the chance that they will not be selected in an auction and lose
revenue. /PIRU-01 08/
For dominant firms, bidding over marginal cost can serve as one way to exercise their market power,
raise the market price, and increase their profits. The Lerner Index (LI) is described in Equation 3. This
index is used to compare bids or market prices to marginal costs in order to determine if and by how
much a bid exceeds marginal cost. If the firm under examination is the marginal firm, the Lerner
Index then measures by how much the market price has been raised above the marginal cost of the
marginal supplier. /HILE-01 14/ /STO-01 02/
Equation 3: Lerner Index
𝐿𝑥 =𝑃 − 𝐶
𝑃
where:
𝐿𝑥 = 𝐿𝑒𝑟𝑛𝑒𝑟 𝐼𝑛𝑑𝑒𝑥 𝑜𝑓 𝑓𝑖𝑟𝑚 𝑥
P = bid price
C = marginal cost
The Lerner Index will be equal to zero in a perfectly competitive market, and quantify the effect the
exercise of market power has had when used to raise prices. Marginal costs of individual generators
are normally private data not readily available to outside parties, making it difficult to accurately
calculate any markups. While fuel costs and heat rates can be used to approximate variable costs,
opportunity costs and non-quantifiable costs are very difficult to incorporate.
30
Additional complications with using this index arise during periods of peak demand, when the market
price can rise above the marginal cost of the most expensive generator even in a competitive market.
As an alternative to comparing bids to estimates of marginal cost, current bids can be compared to
reference bids. A company’s bidding behavior from periods where the market was known to be
competitive can be used to identify behavioral changes that go beyond shifting market fundamentals
or established acceptable thresholds. /HILE-01 14/
• Net Revenue Benchmark Analysis
While the Lerner Index is focused on short term profits, the net revenue benchmark analysis takes a
long-term perspective of a firm’s earnings and costs. While high revenues do not mean that market
power is being exercised, if they are significantly higher than estimated long-run costs this can
indicate the potential for unjust profits earned via market power. Additionally, if revenues can be
demonstrated to be below the level required for a return on the investment involved in constructing
new generation there will be no new entrants into the market, a state which can lead to higher prices
and a potential increase in market concentration. This could be indicative of firms suppressing prices
to prevent new competitors from entering the market. /HILE-01 14/
• Withholding Analysis
Given that withholding generating capacity from the market is the most common strategy for the
exercise of market power in electricity markets, examining cases in which generating units did not
feed electricity into the market can uncover evidence of the exercise of market power. Of particular
interest are instances in which generating capacity could have profitably offered electricity into the
market but did not, sometimes termed a “missed opportunity”. A profit-maximizing, price-taking
competitive firm would be expected to sell energy in any case in which it is technically able to do so
and in which the sale would be expected to be profitable. Not doing so can indicate the exercise of
market power. /HILE-01 14/ /STO-01 02/
Recall from section __ that withholding generation capacity from the market can be achieved via
either physical or economic withholding. Economic withholding involves bidding capacity into the
market at prices above its marginal cost, so that it will not be selected at all or so that lower amounts
of generation will be called. The extent of economic withholding can be estimated by calculating the
difference between the amount of production that would be profitable to produce with the unit at
the prevailing market price and the amount actually produced. A positive difference between
profitable and actual production indicates potential withholding. Determining what is profitable to
produce involves estimations of variable costs or comparisons with known competitive bids from the
unit. These estimations and comparisons can be both difficult and imprecise due to the difficulty in
obtaining accurate information about a firm’s costs. However, when coupled with structural analysis
of the market and consideration of a firm’s incentives to withhold capacity, an output gap can be
enough to warrant closer investigation. /HILE-01 14/
31
Physical withholding can be achieved through the derating of a generator’s capacity to zero,
indicating it is not available to provide any electricity, or to a level below its technically feasible
maximum capacity. Unplanned deratings of generators can occur in perfectly competitive markets as
well, and should not be taken as evidence of the exercise of market power alone. However
observable patterns of outages and statistical analysis of outage rates can both be used to identify
periods of suspicious behavior. Identifying a relationship between periods with outages and periods
with increased profitability of a company’s portfolio can be stronger evidence of the exercise of
market power. /HILE-01 14/
It is clear that a wide variety of methods can be used to assess markets for electricity to determine
whether market power is present or has been used. When considering markets for flexibility, such as
the Altdorfer Flexmarkt, analysis is currently limited to those methods and metrics used to examine
structural. ALF has yet to begin operation, meaning no bid data or market results are available for
analysis to determine whether market power was exerted and with what consequences.
Of the different methods for detecting structural market power considered, the RSI seems the best
suited for use in examining ALF. Designed specifically for investigating electric markets, it avoids the
one-dimensional nature of the concentration ratio and HHI. The ability to include changes in both
supply and demand in a single metric is decisively important for the examination of a market in which
each of these market forces can vary based on time of day or weather to an even larger extent than
other energy markets. Having selected a tool for the analysis of structural market power in ALF, the
next chapter will describe the methods and results of this analysis.
5. Analysis of Structural Market Power in ALF
At the time of writing, the field test of the Altdorfer Flexmarkt has not yet begun. Until bid data from
this field test is available, any analysis of market power in ALF is limited to analyzing structural
market power. In this chapter, a process for the application of the Residual Supply Index to the
Altdorfer Flexmarket will be described and the results of its application presented. This analysis will
focus on changes in structural market power based primarily on the state of unit aggregation in the
market, with consideration also given to situations that could limit unit availability.
5.1. The Distribution Network in Altdorf
The distribution network serving Altdorf and the surrounding area is made up of eight middle-voltage
strands, supplying 173 connected low-voltage networks. One of these eight middle-voltage strands,
strand 40008, and the ten low-voltage networks it supplies will be the focus of this analysis. Strand
40008 was chosen due to the detailed knowledge available regarding the flexible devices connected
to the strand. The topology of each of the ten low-voltage networks has been mapped, and the
location, unit type, and rated power of all units currently capable of participating in ALF are known.
32
In the Altdorf area, four types of units currently capable of providing flexibility are present in the low-
voltage networks. These are photovoltaic (PV) units, power-to-heat (PtH) units (heat pumps and
electric storage heaters), electric vehicles (EV), and electric storage units installed with PV units
/FFE-48 18/. However, as storage units currently cannot feed electricity back into the grid, using
them to store the electricity generated by their associated PV units is functionally equivalent to the
curtailment of PV units in regard to flexibility, and they will not be considered in this analysis. PV
units can be curtailed to reduce the amount of electricity fed into the grid. When connected to the
grid EVs can charge, increasing the amount of electricity taken from the grid, or cease charging for
the opposite effect. Similarly, PtH units can be activated to increase the amount of electricity taken
from the grid or deactivated to reduce their load. In addition to these low-voltage units, strand
40008 features nine PV units connected to the middle voltage network.
The middle voltage network of the Altdorf area is depicted in Figure 6, with strand 40008 circled for
visibility. The detailed knowledge of the network and the units within it makes it possible to assign
ownership of individual units in changing constellations, enabling an analysis of market power based
on changes in aggregation levels. The unit-level detail will allow this analysis to reflect reality more
closely than would be possible working with aggregated networks.
Figure 6: Middle Voltage Network of Altdorf and the Surrounding Area
5.2. Methods
The scenarios described in /FFE-23 19/ depict different levels of increased penetration of flexible
units perceived as realistic development pathways to future situations for the Altdorf networks. The
network developments modeled under the “Market-Driven Prosumer” (Prosument am Markt - PAM)
scenario, which features strong expansion of installed PV capacity and high levels of electrification
among consumers, were used for this analysis /FFE-23 19/. The increased numbers of all types of
units the scenario brings with it offer many options for different states of unit aggregation.
33
In a concurrent thesis being written at the FfE, these scenarios are being used with the FfE’s GridSim
network simulation tool to predict the nature and location of future network congestion.
Additionally, these simulations will create prognoses of the amounts of flexibility available in various
congestion situations and how this varies over multiple time steps. The analysis presented here is
less concerned with the technical aspects of determining specific amounts of flexibility available in a
given situation or the effects of time and consumption patterns on the availability of flexibility.
Instead, it will develop and demonstrate a process for assessing the potential for the abuse of market
power, which can be applied to simulated or real market situations and eventually directly integrated
into the ALF market platform.
As stated at the beginning of this chapter, the RSI will be used for this analysis. Recall from Equation
2 that three components are necessary for the calculation of the RSI: total supply, supply controlled
by the firm under examination, and demand. Figure 7 depicts the methods used to calculate the
supply side of the RSI. After calculating the total available supply, a range of demand levels from 0 to
the maximum available supply was generated. The remainder of this section will describe the steps
used to obtain the results to be presented.
Figure 7: Methods for calculating RSI values. Own Representation
Starting from lists of all connection points, generated during simulations of Strand 40008 under the
PAM scenario, information about the individual units was extracted and stored in a matrix. Unit type
and unit rated power were recorded to determine the type and amount of flexibility a unit could
provide. Unit location was recorded in the form of middle-voltage connection point, low voltage
connection point, and to differentiate each unit at a single connection point, household connection
point number. These three values were combined to create an identification number unique to each
household connection point. While all units were previously associated only with a low-voltage
connection point, some of which featured multiple units of a single type, they could now be assigned
a specific owner using this identification number, or owner ID.
34
The middle-voltage node to which the low-voltage network containing the unit in question is
connected allows each unit to be matched to an effectiveness factor based on its location.
The definition of the relevant market to be considered is a decisive factor in the analysis of structural
market power when calculating indicators such as the HHI and RSI. In markets for physical electricity,
this involves defining the geographic limitations of the market, determining which firms can
participate in it, and adjusting the market share of these firms to account for generation already
committed to long-term contracts or other markets. /HILE-01 14/ All of these factors can affect the
calculation of the RSI value, as both the total available supply and the supply of the generator in
question must be considered. However, in the context of a market for flexibility, these numbers
cannot be taken directly from the rated power of the individual units.
The ability of a unit to influence congestion of a given network element is dependent upon its
physical location and the network topology /FFE-48 18/. This determines not only whether a unit can
be considered part of the relevant market, but also the effective flexibility the unit can offer. Units
not electrically connected to the problem element, or whose location would make their contribution
to alleviating congestion negligible, can be excluded from the relevant market.
A method to calculate the effectiveness of a unit on network congestion at a given location using
linearization has been previously developed at the FFE. After the generation of a topological
assignment matrix, which uses binary notation to describe the connections between different nodes
within a network, load-flow simulations are carried out to set reference levels for current at each
network element. Additional load or generation, representing flexible units, is then added to the grid
at a chosen point, and the changes to current during a new load-flow simulation are recorded. This is
repeated over a range of increased load or generation for all flexibility locations and problem
elements. These values are then linearized, resulting in a regression coefficient and intercept that
can be used to calculate the change in current at the problem element given a change in power at
each location in the network. /FFE-35 18/ A simplified version of this process is depicted in Figure 8,
while more details can be found in /FFE-35 18/.
Figure 8: Stylized process for calculation of effectiveness factors. /FFE-35 18/
35
The regression coefficient and intercept will now be collectively referred to as the effectiveness
factor. Based upon the rated power of each individual unit and its location within strand 40008, the
effectiveness factor associated with its location can be used to calculate the effective flexibility of the
unit at any congested element. Here the use of the term effective flexibility in this thesis must be
defined.
The term flexibility normally refers to a change in the active power of a unit, but the result of the
multiplication of a unit’s rated power and its effectiveness factor represents a change in current at a
specific location that results from a change in power /MÜL-02 18/ /FFE-35 18/. The term effective
flexibility will refer to the conversion of this change in current into the equivalent amount of power
that would be required to cause this change in a 20,000 V middle voltage network with an
effectiveness factor of 1. In other terms, the rated power of the unit is adjusted to account for its
ability to influence congestion at the defined location.
The borders of the relevant market can now be limited to those nodes topologically connected to
the problem element. Within this relevant market, the total available effective flexibility can be
found by summing the effective flexibility of all available units.
Combining the unit-specific information extracted previously with the effectiveness factors for Strand
40008, the effective flexibility of each unit on congestion at a chosen network element can now be
calculated. Using the owner ID generated for each unit, the effective flexibility of all units belonging
to the same household can be summed to give the total effective flexibility the household can offer
into ALF. With the ability to calculate the available effective flexibility of each owner, initially a
household, the supply side of the RSI equation can now be calculated. After calculating the total
available flexibility, a range of demand levels from 0 to the maximum available flexibility was
generated.
Manipulating the owner ID numbers allows for the creation of different aggregation scenarios or
degrees of aggregation, ranging from the household-specific starting point up to a single aggregator
controlling every unit in the network. Aggregation of individual low-voltage networks and type-
specific aggregation were the focus of this analysis. In addition to allowing for type-specific
aggregation, the unit type ID numbers enabled the creation of different supply situations featuring
the inclusion or elimination of different types of units. These reflect different seasonal variations,
weather conditions, or human behavior patterns that all can affect unit availability and should be
considered when examining the structure of the market.
All results presented here concern RSI values based upon available effective flexibility in the case of
the overloading of line 1723 due to large power flows in the direction of the transformer. Line 1723 is
the second line after the HV/MV transformer in strand 40008. The positions of line 1723, the ten low-
voltage networks of strand 40008, and middle-voltage node 1391 (the only node in the strand
featuring only a middle-voltage unit) can be seen in Figure 9.
36
Figure 9: Strand 4008 with LV networks, line 1723, and unit type capacities
Line 1723 was chosen because it features the highest amount of available effective flexibility within
the strand 40008. All 725 units located within strand 4008 can influence congestion here. This
essentially creates a liquid market for flexibility at line 1723. Although market power can be expected
to be more of a problem in illiquid markets, the choice of line 1723 allows the effects of aggregation
on market power within a liquid market to be examined.
When all units are assigned an owner based on the household number associated with the unit, the
725 units are spread over 443 independent owners. All units are assumed to be operated binarily –
either operating at their full capacity or not at all. The analysis performed is static, with no change in
congestion or availability being taken into account, although different starting availabilities are
considered based on seasonal or time-of-day differences.
With all units available, the effective flexibility available at line 1723 is 6.38 MW. A situation with
both congestion at line 1723 and high unit availability could take the form of a sunny afternoon on a
weekend in spring. The simultaneous feed-in of all PV units could cause congestion at line 1723 as
this power flows to the transformer, necessitating the curtailment of generation or increased load to
relieve the congestion. EVs are likely to be present and available for flexible charging as it is not a
workday, and cool temperatures could allow PtH units to be activated without violating an owner’s
chosen comfort restrictions.
37
5.3. Results
The RSI curve of the largest independent owner, a 3.1 MW PV unit that will be referred to as MV
1391, is depicted in Figure 10. This, and all graphics depicting RSI curves in this thesis, are own
representations of calculations carried out using the methodology described in the previous section.
As similar graphics will be used throughout this section, this simple version featuring only a single
owner will be used as a brief guide for their interpretation.
The X-Axis depicts the demand for flexibility in the form of the magnitude of the congestion. This can
also be described as the reduction in current, recalculated into terms of power, flowing over line
1723 required to return it to safe operating levels. The values of the RSI curve found on the Y-Axis
indicate the proportion of the demanded flexibility that can be obtained without the flexibility
controlled by the owner in question. The dotted horizontal lines indicate the thresholds for RSI values
of 1.2 and 1. An RSI value below 1 indicates that the congestion cannot be relieved through ALF
without the flexibility controlled by the owner in question. This makes the owner in question a
pivotal supplier capable of exercising market power /DICE-01 14/. The 1.2-threshold is used because
CAISO found a statistically significant relationship between RSI values above 1.2 and competitive
prices in their analysis of the CAISO spot market /FERC-01 02/. Values between 1 and 1.2 indicate a
market situation in which the owner in question can likely exert some influence on the price, but to a
lesser extent than when pivotal /HILE-01 14/. The dashed vertical lines descending from the
intersection of the RSI curve and the horizontal lines are intended to serve as a visual aid to help
determine the level of demand at which an owner’s RSI value equals 1.2 and 1.
Figure 10: RSI values of selected owners - largest independent owner
MV 1391 is the largest owner when all units are operated independently and has an effective
flexibility at line 1723 of 1.78 MW. Subtracting this from the total available effective flexibility of 6.38
MW leaves 4.6 MW of available effective flexibility.
38
Based on the findings of CAISO, as long as this available flexibility is larger than 120% of demanded
flexibility MV1391 should not be able to exercise any significant market power, and the market price
can be expected be functionally equivalent to that of a perfectly competitive market /HILE-01 14/. If
all units are available and controlled by independent owners, MV 1391 has an RSI value below the
1.2-threshold for all congestion larger than 3.84 MW. In the case of congestion above 4.6 MW, MV
1391 becomes a pivotal supplier. Figure 11 compares the RSI curve of MV 1391 to the second-largest
(MV-4001), third-largest (MV 4011), 44th-largest (representing the last of the top 10% of owners),
and the smallest independent owners.
Figure 11: RSI values of selected owners - independent owners
The three independent owners with the smallest RSI values, and therefore the first owners that will
be able to exert market power, are all PV units connected to the middle voltage network. This is not
surprising given that generating units connected to the MV network tend to be larger. This,
combined with the fact that they are considered more likely to be operated by practiced marketers
with the ability to forecast production and actively bid into markets, suggests that such units should
receive more attention during the market monitoring process /FFE-48 18/. The gap between MV
1391 and the second-largest owner, MV 4001, spans 1.28 MW between their 1.2-thresholds,
particularly large compared to the differences between all other owners. Just 0.17 MW separate the
1.2-thresholds of the second-largest and smallest owners. No other owner is likely to be able to exert
market power until well after MV 1391 has become pivotal, with MV 4001 reaching the 1.2-threshold
at 5.12 MW. Given congestion requiring a power flow change larger than 5.3 MW, all 443 owners
could be expected to be able to exert some degree of market power.
Making a first adjustment to the state of aggregation while maintaining the availability of all units, all
units of a given type will now be allocated to a single owner. This strategy of focusing on aggregating
units of a single type is one potential business model that could be pursued by firms seeking to enter
markets for flexibility. First the unit type with the least available flexibility, PtH units, will be
aggregated under a single owner. The RSI curves for this situation can be seen in Figure 12.
39
Figure 12: RSI Values of selected owners - PtH units aggregated
With the total available effective flexibility remaining the same, and no changes to the amount of
flexibility controlled by MV 1391, its status as largest owner and its threshold values remain
unchanged. The aggregation of the PtH units has halved the gap between the 1.2-thresholds of MV
1391 and the second largest owner, now the PtH aggregator.
Unlike in the unaggregated scenario, the PtH aggregator can now likely exercise market power
before MV 1391 becomes pivotal. MV 4001, a 366 kW PV unit and the second largest owner in the
previous, unaggregated, scenario, is now the third largest owner. The largest independent owner
among the low voltage networks remains owner 3266, controlling a 141 kW PV unit connected to the
LV 4001 network.
Altering the aggregated unit type to LV PV units instead of PtH units still leaves MV 1391
unchallenged as the dominant supplier. However, aggregating the LV PV units leads to a larger
aggregator, now controlling units representing 1.34 MW of effective flexibility instead of the 1 MW
of effective flexibility available using the aggregated PtH units. The corresponding shift to the left of
the RSI curve can be seen in Figure 13.
40
Figure 13: RSI Values of selected owners - LV PV aggregated
The aggregator will now reach the 1.2-threshold at 4.2 MW and becomes the second pivotal supplier
at congestion of 5.03 MW, just before MV 4001 reaches the 1.2-threshold. As the unit controlled by
owner 3266, the previous largest LV owner, has been contracted by the aggregator, the largest LV
owner is now owner 11182. They control a PtH unit with an effective flexibility of 50 kW, reaching
the 1.2-threshold at congestion of 5.28 MW, well after both MV 1391 and the aggregator become
pivotal suppliers. Should an aggregator acquire control of all MV PV units, instead of only the LV PV
units, they would control 3.13 MW of effective flexibility. The RSI values of such an aggregator would
cross the 1.2-threshold if congestion exceeded 2.7 MW, and the aggregator would become pivotal if
congestion exceeded 3.24 MW.
Aggregating all 288 EVs present in strand 40008 while all other units are operated independently
creates the largest aggregator yet. Simultaneous activation of their combined capacity of 3.2 MW in
this congestion situation can reduce the power flow at line 1723 by 1.79 MW. This is just larger than
the effective flexibility of MV 1391, resulting in RSI values slightly lower than those of MV 1391. This
can be seen in Figure 14 on the following page.
The 1.2-threshold of an aggregator controlling all EVs in strand 40008 is 3.82 MW, and this
aggregator becomes pivotal when demand is above 4.58 MW. Of the three variations on a single,
type-specific aggregator, this permutation creates the situation most likely to lead to an abuse of
market power. The market is now dominated by two very large suppliers, which together control
more than 50% of available flexibility. Meanwhile, MV 4001 continues to remain the third largest
supplier, with owner 3266 once again the largest independent LV supplier.
41
Figure 14: RSI values of selected owners - EVs aggregated
Adding additional type-specific aggregators to the market and does not alter the RSI values of other
owners, but rather increases the concentration of the market. In its most extreme form, all LV units
of each type were allocated to a different aggregator. This situation results in four very large market
participants controlling 5.9 MW of the 6.38 MW of total available effective flexibility, a CR4 of 93%.
However, as the total available flexibility has not changed, the RSI values of each of these market
participants remains unchanged from the scenarios in which only their single unit type was
aggregated. All market participants cross their respective 1.2-thresholds before any single participant
becomes pivotal. Barring explicit or tacit collusion, the market price is likely to remain competitive
while the demand for flexibility remains below approximately 3.8 MW, although such a highly
concentrated market does make collusion more likely /PIRU-01 08/.
Aggregating multiple unit types together creates market conditions still more conducive to the
emergence of structural market power. The largest permutation of an aggregator controlling all units
of two different types, one combining all EFZ and LV PV units, would control 3.13 MW of effective
flexibility. This aggregator would cross the 1.2-threshold at 2.7 MW of demanded flexibility and
would become pivotal at 3.25 MW, before any other market participant reached their 1.2-threshold.
A single aggregator controlling all LV units, representing an effective flexibility of 4.14 MW, would
reach their 1.2-threshold at 1.9 MW of demanded flexibility and become pivotal at 2.24 MW.
In addition to aggregation based on unit type, another possible business model among aggregators is
acquiring control of all units in a geographic or topological area. In this analysis, units within the same
low voltage network, regardless of unit type, were assigned to a single aggregator to examine the
effects of this strategy on structural market power. Figure 15 depicts the RSI curves of the two largest
aggregated LV networks alongside the RSI curve of MV 1391.
42
Figure 15: RSI values of selected owners - LV networks aggregated
Driven primarily by the 145 EVs located in this network, the aggregated units of LV 4011 can achieve
a current change of approximately 1.64 MW, slightly less than the 1.78 MW of effective flexibility
offered by MV 1391. Continuing to hold all units available for an available effective flexibility of 6.38
MW, LV 4011 has a 1.2-threshold of 3.95 MW of demanded flexibility and becomes pivotal when
congestion exceeds 4.73 MW. As in previous scenarios, MV 1391 crosses the 1.2-threshold at 3.84
MW of demanded flexibility and becomes pivotal at 4.6 MW.
The two aggregation strategies described so far, aggregation by unit type and aggregation by
location, could be implemented concurrently in the same market. Figure 16, on the following page,
depicts the RSI curves of the four largest owners should PtH units be aggregated by type while the
remaining LV units are aggregated by location. This decreases the number of units aggregated by LV
4011 and LV 4005, pushing their thresholds slightly higher, and replacing LV 4005 with the PtH
aggregator as the third largest owner.
Having held the total available effective flexibility constant until now by making all units available has
highlighted the dominant position held by MV 1391. Working under the assumption that units
connected to the middle voltage network are operated by firms with enough know-how to avoid
being forced to rely upon an aggregator to market their capacity in the market for flexibility, MV
1391 was always operated independently /FFE-48 18/. Thus, with the effective flexibility controlled
by MV 1391 never fluctuating, the RSI values remained constant as well.
Among the different single type-specific aggregation scenarios MV 1391 was only surpassed as the
largest supplier when every unit of the most numerous type of low-voltage unit, EVs, was controlled
by a single aggregator and simultaneously available.
43
In addition to large single units and the aggregation of numerous LV units, aggregators controlling
large proportions of available units, for example in the form of multiple types of units or the most
populous LV networks, are also capable of dominating the market.
Figure 16: RSI values of selected owners: PtH and LV networks aggregated
Two changes could lead to different RSI values for MV 1391: A change in owned effective flexibility or
a change in total available effective flexibility. Given the earlier assumption regarding the expertise of
firms operating MV units, a change to the own effective flexibility is perhaps more likely to take place
by becoming an aggregator rather than becoming part of another firm’s portfolio. Increasing the
effective flexibility controlled by the largest supplier would allow them to exert market power at
lower levels of demand. Meanwhile, given the assumption that all units operate at full capacity when
available, total effective capacity will only differ if units become unavailable.
To bring about an initial shift in unit availability, the congestion scenario will be reimagined. Although
the cause of congestion will remain high simultaneous feed-in from PV units, also maintaining their
availability, Power-to-Heat units will be removed from consideration. On a hot, sunny, summer
weekend all EVs may still be present at home and available for flexible charging, but PtH units can no
longer be activated because of the hot weather and comfort restrictions submitted during
registration. Without PtH units, the total effective flexibility available in strand 40008 decreases to
5.37 MW. Figure 17 depicts the RSI values of the three largest flexibility owners when no PtH units
are available and none of the remaining units have been aggregated.
44
Figure 17: RSI values of selected owners - no PtH, independent owners
The three largest independent owners from the Aggregated PtH scenario, MV 1391, MV 4001, and
Owner 3266, remain the largest owners in a scenario without PtH units, but their RSI values have all
decreased. The 1-threshold of each of the three owners is now 1 MW lower, while each
1.2-threshold declined by 0.84 MW. These changes correspond to the total effective flexibility of the
now-absent PtH units and this value divided by 1.2, respectively. By decreasing the size of the
relevant market, the relative importance of the effective flexibility of all remaining units increases.
Large owners benefit the most from this, as they can now exert market power at lower levels of
demand.
The larger the amount of effective flexibility removed from the market, the sooner a large supplier
can exert market power. Figure 18 demonstrates this, depicting the RSI curves of MV 1391 in
multiple supply scenarios. The RSI curve furthest to the right, indicating that the ability to exert
market power will only emerge at higher levels of demanded flexibility, is the familiar curve of MV
1391 when all units are available.
Immediately to its left is the curve seen in the preceding figure, Figure 17, depicting the RSI values of
MV 1391 when PtH units are removed from available supply. The next curve to the left represents
RSI values if, instead of PtH units, all EVs were removed from available supply, and the final curve a
situation in which PV units are the only unit type available. Weather- and comfort-based restrictions
make the complete removal of each individual unit type possible, although it is perhaps more likely
that their supply would be limited rather than eliminated. Regardless of the extent of the supply
limitation, situations with reduced available flexibility lead to dominant suppliers being able to exert
market power at lower levels of demand.
45
Figure 18: RSI values of selected owners - MV 1391 with decreasing available flexibility
5.4. Conclusions
Returning to the research question regarding market power, three conditions for the emergence of
market power in a market for flexibility can be identified:
• Concentrated ownership of available flexibility
• Limited available flexibility
• Peak demand for flexibility
Examples of each of these three conditions can be observed in the RSI analysis of ALF. Concentrated
ownership of available flexibility can take the form of comparatively large individual units
representing significant proportions of available flexibility, as seen in the case of MV 1391, or
through the aggregation of many smaller units. This second form of concentrated ownership is
particularly relevant if aggregators engage in business models aimed at controlling either the most
important unit type in a particular market, or the geographic or network areas that feature to large
supplies of effective flexibility.
In the market limited to strand 40008, these two forms of concentrated ownership through
aggregation are represented by the aggregation of EVs and the aggregation of all units within LV
network 4001. All three forms of concentrated ownership lead to a similar consequence: a dominant
supplier able to exert market power and raise prices at lower levels of demanded flexibility than any
other market participant.
46
Limited available flexibility can also take multiple forms. First among these is a simple lack of
widespread participation in a market for flexibility. If the impending launch of the market is not well-
publicized in advance, or if the incentives to participate in the market are not viewed by owners of
small-scale flexibility, the new market could be largely limited to participants already active in the
electric industry. This could occur as such insiders would be more likely to learn of the market launch
and control suitable units, and are also likely to understand the benefits available to themselves
possible with a dominant market position.
However, even a market with satisfactory participation by independent households does not
guarantee a competitive outcome. In a second form, unit availability can be restricted because of
weather, the comfort-based restrictions chosen by owners, or the use patterns of different unit
types. In both of these forms of limited availability the flexibility of remaining suppliers takes on
more importance to meeting demand, increasing the likelihood of the emergence of market power.
This is particularly relevant if the available flexibility of a dominant supplier is not affected by the
supply limitation, demonstrating that either of these conditions for the emergence of market power
can be exacerbated by the other.
A third form of limited available flexibility can occur when the location of congestion limits the size of
the relevant market. Recall that the effectiveness of all units varies based upon their location in
relation to the congested element. In the case of line 1723 depicted throughout this chapter, all units
in strand 40008 were able to help relieve congestion. On the other hand, MV 1391 sits at the other
end of the strand, and is the only unit able to provide flexibility for congestion relief on several lines
between the unit and the next LV network. If any of these lines were to become congested, MV 1391
would gain local market power because of its location.
The third and final condition for the emergence of market power demonstrated in this analysis is a
situation of peak demanded flexibility. This could occur in for short times, for example in the form of
severe congestion during seasonal events, or over longer periods such as the time between the
connection of new PV arrays to the grid and the expansion of the relevant grid infrastructure
/FFE-48 18/. As could be seen in the RSI graphics that included small independent suppliers, when
demanded flexibility reached very high levels, even owners controlling a single small unit could be
expected to have the ability to exert market power. Such situations also guarantee pivotal supplier
status to any dominant suppliers, and serve to enhance the previous two conditions for the
emergence of market power.
With regards to the different scenarios evaluated in the course of this analysis, congestion at line
1723 seems unlikely to result in the emergence of market power. Line 1723 is capable of safe
operation at levels of current up to 417 A. In the middle voltage grid of Altdorf, which is operated at
20,000 volts, congestion will only emerge when 8.3 MW of electricity or more must be transported
over line 1723. In the most restrictive supply situation, with only PV units available to provide
flexibility, a further 1.5 MW of power would be necessary to reach the 1.2-threshold of the largest
supplier, MV 1391. Even under the assumption of future increased unit penetration of the PAM
scenario, it is unlikely that power flows over line 1723 will reach this level of 9.8 MW.
47
However, this analysis was concerned with a single line within one of the eight middle voltage
strands in Altdorf, and should not be considered evidence that ALF, or even only strand 40008 is
immune to the abuse of market power.
In the analysis described in this chapter, a method for the application of the residual supply index to
a market for flexibility for the detection of structural market power has been demonstrated. The
strengths of this method include its ability to reflect changes in the supply and demand of flexibility
and different constellations of unit ownership, as well as the ease of interpreting the output value in
relation to clearly defined thresholds. Furthermore, the method demonstrated here requires no
additional data beyond that which will already be available to the ALF platform. Once the market
begins operation, the suitability of this method for predicting market power can be tested, and the
relevant thresholds can be adapted to better fit the nature of the market over time. Until that time,
further work can be performed to adapt this method to take technical restrictions, such as the state-
of-charge of electric vehicles, or comfort-based restrictions, such as minimum acceptable
temperatures for PtH units, into account.
6. Market Manipulation
While the attention of energy market regulators focused largely on monitoring and mitigating the
abuse of market power in the first years following the deregulation of energy markets, increased
focus was given to preventing the manipulation of energy markets in the aftermath of the California
Energy Crisis at the turn of the millennium /GCR-01 18/. Existing antitrust legislation or competition
law at the time was not conducive to preventing or prosecuting manipulative behavior in energy
markets /FERC-02 16/. This chapter will first describe the manipulative strategies that led to energy
market manipulation laws being promulgated in the United States and Europe after the shortcomings
of the then-current laws were realized, before reviewing and discussing examples of market
manipulation in electricity markets, and finally analyzing the relevance of the manipulative strategies
and responses of the involved authorities to the Altdorfer Flexmarkt.
6.1. Market Manipulation in the California Energy Crisis
The California Energy Crisis, which was briefly outlined in the introduction to this thesis, played a
major role in bringing market manipulation to the attention of both regulators and the general public
/GCR-01 18/. A great deal has been written about the crisis, and many sources examine it in much
more detail than is feasible in this paper. However, the manipulative strategies used during the
California Energy Crisis shaped anti-manipulation laws written following the crisis and will be
examined here because of their relevance /FERC-02 16/. These strategies have come to be associated
with Enron, a now-bankrupt energy trading company whose internal memos first brought the
strategies to light. However, the US Federal Energy Regulatory Committee (FERC) investigated more
than 130 market participants, and initiated legal proceedings against 43 of them, related to market
manipulation /FERC-01 03/.
48
Only one strategy seems to have been unique to Enron: selling non-firm energy (energy not backed
by ancillary services) as firm energy. Non-firm energy would be purchased outside of California and
imported into the state as firm energy, allowing Enron to avoid the cost of purchasing operating
reserves that must accompany firm energy. By misrepresenting this energy when selling it into the
California market, Enron was able to earn unjust profits by fraudulently avoiding costs. The remaining
strategies have all come to be known by the names used to refer to them by the traders, which were
revealed in the Enron memos, and will be used here to assist in differentiating each strategy.
/FERC-01 03/
The strategy known variously as “False Import”, “Ricochet”, or “Megawatt Laundering” was found to
have taken unfair advantage of market rules. A Californian firm would schedule power on the day-
ahead market as an export and sale to an out-of-state firm. In exchange for a “parking” fee, this firm
would schedule the sale of an equal amount of power back to the Californian firm, creating the
appearance of an import transaction. The Californian firm could then sell their own generation as
imported power on the real-time market above the price cap. No power was actually exported from
or imported into the state, but the Californian firm was now able to take advantage of a rule that
allowed imported energy to be offered at prices over the price cap for domestic energy in the real-
time market. By falsely representing their domestic generation as an import, the firms using this
strategy unjustly received prices above the market rate for the product they provided. /FERC-01 03/
At the time, the CAISO markets used a zonal pricing system, meaning that market results could lead
to network congestion. In such a case, congestion relief payments were given to market participants
for decreasing flows of electricity in the congested direction or increasing flows in the opposite
direction, also known as counterflows. Four different strategies targeted the collection of these
congestion relief payments: “Cutting Non-Firm”, “Wheel Out”, “Load Shift”, and “Death Star”.
“Cutting Non-Firm” refers to scheduling a counterflow of non-firm power without the intent to ever
deliver the power, and in some cases without even the ability to deliver the power. After the
congestion relief payment was received at the close of the hour-ahead market the scheduled power
flow would be cancelled by the manipulative firm, earning revenue without providing any congestion
relief. /FERC-01 03/
“Wheel Out” also involved scheduled counterflows being cut, but in this case by the ISO. When the
ISO took interconnection lines at the border of their service area out of service, their capacity in the
system would be adjusted to zero, but the software used would still accept schedules including
power flows across this line. In real-time, the ISO cancelled all schedules that used this point, but any
congestion relief payments received from the day-ahead or hour-ahead congestion relief process
could be kept. Firms could take advantage of market notices regarding out of service lines and in-
house forecasts of transmission usage to target these lines. Transactions would be scheduled over
the line in order to create congestion due to its derated capacity of zero. In conjunction with this,
additional transactions over the same line would be scheduled in the other direction. These
counterflows allowed firms to collect congestion relief payments while knowing the schedule would
be cut and they would not need to generate any electricity. /FERC-01 03/
49
“Load Shift” targeted both congestion relief payments and revenues from Firm Transmission Rights
(FTR). By falsely overscheduling load in one of California’s zones while falsely under-scheduling load
in another connected zone, a market participant could increase congestion in the direction of the
overscheduled zone. By later reverting to the true distribution of their contracted loads, a
counterflow towards the under-scheduled zone was created and the market participant could
receive congestion relief payments. The artificial congestion also increased the congestion charges
assessed to those market participants wishing to transport energy across a congested element. These
charges were then paid to any firm that owns FTRs for the congested direction, offering additional
revenue for this strategy. /FERC-01 03/
The final congestion-related strategy, “Death Star” involved collecting congestion relief payments
without providing a physical power flow by using a circular schedule that extended beyond the CAISO
control area /FERC-02 16/. By scheduling a congestion-relieving import at congested point A, the
market participant received a congestion relief payment. If they also exported an equal amount of
power at point B and reserved transmission from point B back to point A outside of the area
controlled by the ISO, a loop was created and no net power flow occurred. A profit was earned for no
effort as long as the congestion relief payment was larger than the cost of reserving transmission. All
four of these strategies involved the submission of false schedules to the ISO, earning congestion
relief payments without providing the energy to relieve congestion in the cases of “Cutting Non-
Firm”, “Wheel Out”, and “Death Star”, and receiving congestion relief payments for relieving
congestion artificially created through their own false schedule in the case of “Load Shift”.
/FERC-01 03/
A pair of strategies used in the market for ancillary services also involved false representations. Paper
trading of ancillary services, or “Get Shorty”, describes a market participant selling ancillary services
in the day-ahead market before buying them back at a lower price in the real-time market. A key
difference between normal arbitrage of price differences and this paper trading is that the market
participant would not have been able to provide the ancillary services in this case had they been
unable to buy back their obligation. In a variation of this strategy known as “Double Selling”, the
market participant does control resources able to provide the ancillary services sold in the day-ahead
ancillary services market, but later sells the same resources in the hour-ahead energy market, leaving
them unable to provide the contracted ancillary services if called. The auction for ancillary services
required that the specific unit that would provide them be identified, and in cases of paper trading
unavailable units were falsely represented as available. Units in the Double Selling strategy may have
been available at the time of sale, but could not have provided the service for which they had been
contracted should they have been called, and so also received a payment for a service they could not
provide. /FERC-01 03/
Although individual strategies involved different products and methods, common themes can be
found among the California strategies. At least one of misrepresentation of a product or transaction,
the targeting of side payments, and collecting payments for services that were not or could not be
provided can be observed in every strategy described here, while some strategies involved
combinations of all of the above.
50
These strategies did not come to light until well after the end of the crisis, when Enron’s lawyers
provided on FERC with memoranda detailing the strategies on May 6th, 2002 /FERC-01 03/. The
variety and scope of the strategies and the acknowledgment that all regulatory bodies had been
unable to even detect them, much less stop and effectively penalize the companies using them,
played an important role in shifting FERC’s priorities and shaping the rules made in reaction to the
California Energy Crisis /FERC-02 16/.
6.2. Reaction to the California Strategies
In the years since the California Energy Crisis, regulatory agencies around the world have evolved to
react to the potential harms that market manipulation can bring. Both the US Federal Energy
Regulatory Committee and the European Agency for the Cooperation of Energy Regulators (ACER)
now have laws prohibiting manipulation of energy markets. Both agencies have brought these laws
to bear to punish instances of market manipulation, with FERC in particular having prosecuted
several high-profile cases that resulted in major financial penalties. At the time of writing, only two
cases prosecuted by ACER in relation to electricity markets had been finalized. This thesis will instead
be concerned only with those cases pursued by FERC. Before examining the details of these cases,
the anti-manipulation regulations of FERC will be introduced.
Until the aftermath of the California Energy Crisis, the various laws and regulation of the US Federal
Energy Regulatory Committee (FERC) neither precisely defined nor specifically prohibited market
manipulation. Further hampering their potential to discourage manipulative behavior, FERC’s ability
to assess penalties was restricted to a maximum level of $11,000 per day. The Energy Policy Act of
2005 (EPAct 2005) brought with it an explicit Anti-Manipulation Rule, as well as the ability to assess
significant penalties for violating this new rule /UOT-01 12/. /FERC-02 16/ The EPAct 2005 Anti-
Manipulation Rule will hereafter be referred to as 18 CFR §1c2, after its location in the Code of
Federal Regulations, and is as follows:
§1c.2 Prohibition of electric energy market manipulation. (a) It shall be unlawful for any entity, directly or indirectly, in connection with the purchase or sale of electric energy or the purchase or sale of transmission services subject to the jurisdiction of the Commission,
(1) To use or employ any device, scheme, or artifice to defraud,
(2) To make any untrue statement of a material fact or to omit to state a material fact necessary in order to make the statements made, in the light of the circumstances under which they were made, not misleading, or
(3) To engage in any act, practice, or course of business that operates or would operate as a fraud or deceit upon any entity.
/FERC-02 16/ p.7
Violations of 18 CFR §1c2 were made punishable with penalties of up to $1,000,000 per violation per
day /UOT-01 12/.
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When taking the California Strategies as examples, the fraudulent nature of the conduct is very clear.
Several strategies involved selling products as something other than their true nature, whether that
was non-firm energy being sold as firm energy or electricity being generated in California being sold
as imported electricity. Others involved collecting payments for services never rendered or that could
not have been rendered if called upon, such as the congestion relief payments for power flows that
never occurred or the paper trading of ancillary services.
FERC investigated more than 100 cases of potential manipulation in electricity and gas markets in the
first decade after 18 CFR §1c2 went into effect. In considering the details of each case to determine
whether fraudulent conduct was involved, the nature of conduct was determined in the context of
the case at hand. Three main factors were vital to making a judgement on the particular behavior in
question: purpose, profitability, and market fundamentals. /FERC-02 16/
Purpose can refer to the motivation of a participant to make a trade, with “any action, transaction, or
conspiracy for the purpose of impairing, obstructing, or defeating a well-functioning market”
(/FERC-02 16/, p.11) being considered fraudulent, or to conducting trades not consistent with the
purpose intended for the product being traded. Unprofitable trading is a generally unavoidable part
of participating in a market, and on its own is not enough to be considered manipulation. However
when such trading becomes a regular occurrence or follows a pattern, FERC has been able to link it to
positions in other markets that unjustly benefited as a result /GCR-01 18/. A related notion is the
consideration of market fundamentals. FERC considers supply and demand to be the proper price
signals used to determine competitive market behavior, and behavior that seems to be based off of
other considerations can provide a hint of fraudulent behavior. A number of manipulation cases
prosecuted by FERC will now be examined in more detail, demonstrating the application of these
factors and the variety of behaviors that can be considered manipulative. /FERC-02 16/
6.3. Cases of Market Manipulation under 18 CFR §1c2
The cases of market manipulation that have been prosecuted since 2005 in the United States can be
broken into two broad categories: price manipulation and gaming of market rules /GCR-01 18/.
Although both categories seek to increase profits for the manipulative firm, they differ in the method
used to extract these additional profits. The strategies involving price manipulation were designed to
move the settlement price for the target product away from competitive levels in order to benefit
the manipulative firm’s position. While manipulative strategies seeking to influence market prices
may share a common goal with dominant firms exercising market power, they do not require a
dominant position to be profitable. The gaming strategies, unlike the strategies for price
manipulation, did not actively seek to alter the market price. Instead, they sought to increase the
payments received at the competitive price, or sought to increase the receipt of out-of-market
payments (side payments). In this section groups of similar cases of manipulation will be examined.
The strategies used will be described, and the fraudulent and manipulative aspects identified.
52
6.3.1. Price Manipulation
Strategies used to manipulate market prices will be considered first. Ten cases are included here. The
majority of these were prosecuted by FERC for violations of 18 CFR §1c2 in various American
markets, but two cases prosecuted by the Danish regulator after referral from ACER are also
examined. The strategies used by the manipulative firms were intended to benefit prices of financial
swap positions, congestion revenue rights, and zonal market prices for physical electricity.
• Financial Swap Positions
FERC has prosecuted and negotiated settlements in three major cases involving benefited trading in
related financial markets since 2005, one each in New York ISO (NYISO), Midcontinent ISO (MISO),
and CAISO control areas /BBPF-01 12/ /US-01 14/ /US-104 12/. Although the precise details vary
slightly from case to case based on the rules of the different ISO markets and the financial swap
products available to trade, the core strategy of the three firms was the same. A single case will be
described in more detail to serve as a representative example.
The example case involves trading of physical electricity in the CAISO control area and neighboring
markets between November 2006 and December 2008 in order to increase the profitability of
related financial positions held by the firm in question. Three products were involved in carrying out
this strategy: “Fixed-for-Floating” (FFF) financial swaps, contracts for physical power at index, and
day-ahead physical electricity. FFF swaps are a purely financial transaction in which the trading
parties exchange payments without involving any delivery of electricity. Each day for the duration of
the contract, a buyer pays a fixed price for the FFF contract, and receives the day-ahead index price,
while the seller pays this daily index price and receives the fixed price. /BBPF-01 12/
Physical power at index is electricity for delivery that is bought and sold at the daily index price. Both
FFF swaps and these physical contracts can correspond to a variety of time scales. In this case,
monthly contracts for both products were the primary duration involved. The monthly physical
power contracts used in this case entail the purchase or sale, depending on the holder’s position, of
electricity each day for a given month. The transaction is settled at the resulting daily index price of
each day. Similarly, a monthly FFF contract repeats the transaction every day using the same fixed
price and the floating daily index price. The daily index price at which both the FFF and physical
contracts settled was determined by calculating the volume-weighted average price of all
transactions for day-ahead physical electricity. /BBPF-01 12/
The starting point for the strategy used by the manipulating firm was acquiring a significant number
of FFF swaps, known as a long position if the firm was a net buyer and a short position if the firm was
a net seller of FFF contracts. Firms with a long position in FFF swaps benefit from a higher index price,
while firms with a short position benefit from a lower index price. Moving the daily index price in the
direction that benefited their FFF position was the goal of the manipulating firm. In order to do so,
after establishing their position in monthly FFF swaps, they would establish an opposite position in
monthly contracts for physical power at index. The market rules of the CAISO at the time required
energy traders that did not serve load or control generation, a category that includes the firm in
question, to hold a net-zero position in physical markets each day.
53
The firm’s position for physical power at index thus created an obligation to zero out this transaction
each day through trading in the direction of their FFF position. /BBPF-01 12/
To illustrate, if the firm acquired a long position in monthly FFF swaps they would also acquire a short
position in monthly contracts for physical power at index, usually of a smaller magnitude than the
financial position. To fulfill the requirement to bring their physical position to net-zero, the firm
would then have to purchase day-ahead physical power, and did so at above-average prices. While
these transactions usually resulted in net losses for their physical trading, the additional higher-
priced trading volume would lead to a higher index price than would have resulted without their
activity, and therefore gains in value for the larger FFF positions. /BBPF-01 12/
In months identified by FERC as alleged manipulation periods, due to noticeably different trading
behavior and statistically significant differences in profit and loss margins, this firm’s daily physical
trading resulted in losses of over $4 million. In the same 35 months, the estimated profits from FFF
swaps were $34.9 million, while other market participants are estimated to have lost $139.3 million
due to the manipulation of index prices. The firm and FERC reached a settlement in 2017 resulting in
$70 million in fines and the repayment of $35 million in unjust profits /BARC-01 17/. In the cases in
the NYISO and MISO control areas the involved firms and traders were similarly judged to have
repeatedly made trades of physical energy products they knew would result in losses, in order to
manipulate the prices that determined the value of their financial swap positions /US-02 14/
/US-104 12/. These two cases ended in settlements in 2012 and 2014, and resulted in fines totaling
$138.25 million and the repayment of $120 million in unjust profits /US-01 14/ /US-104 12/.
/BBPF-01 12/
• Congestion Revenue Rights
Another group of cases involving benefited trading in related markets centers on congestion
revenues and transmission rights. Five firms have agreed to settlements or been served notice of a
proposed penalty for strategies that sought to influence congestion in order to increase profits or
avoid losses for congestion revenue rights (CRR), or to increase the prices received for their physical
transactions. A CRR is a financial instrument that can be purchased from the ISO. Each CRR has a
source node and a sink node, and represents flows from the source to the sink. When there is
congestion in this direction, the owner of the CRR receives a payment. When the congestion is in the
other direction though, the owner is assessed a fee. /US-105 12/
Two of the CRR cases, both of which occurred in the CAISO control area, involved a trader who had
previously helped to design the software used by CAISO to optimize the CRR market and bid
allocations. In January 2010, his employer held a Congestion Revenue Rights (CRR) position at the
Silver Peak intertie linking the CAISO and the Sierra Pacific Power Company control areas. Sourced
within CAISO and sunk at Silver Peak, this CRR position would be profitable when export congestion
occurred at Silver Peak, while import congestion would result in losses. /US-105 12/
A derating of the intertie by the ISO prevented import flows and limited export flows, but did not also
prevent bidding and scheduling over the intertie. Import congestion occurred as a result, and the
firm accordingly lost money on the CRR position.
54
In the remaining days of the month, the trader began exporting power over Silver Peak, successfully
eliminating the import congestion that had caused their CRR losses, and even causing beneficial
export congestion for a small number of hours. The export transactions were never profitable and
were executed only to benefit the CRR position. /US-105 12/
Three years later, and now employed by a different trading firm, the same trader responsible for
implementing the strategy used to benefit the CRR position at the Silver Peak intertie in 2010 was
involved in a nearly identical case. A derating of the Cragview intertie in the export direction led to
losses of over $30,000 to the firm’s CRR position there in a two-hour period. To avoid losses
estimated at $1.2 million during the next three-day derating period, the trader used his experience
from the Silver Peak case and successfully offered a small amount of power for import at Cragview
for $1/MWh. This resulted in a net import flow at the intertie, eliminating the export congestion. The
firm lost $1,000 on the physical transactions but avoided more than $240,000 in CRR losses over the
course of this single day. While working for both companies, the trader in question was not involved
in the trading of physical electricity under normal circumstances, and only did so to influence his CRR
revenues. /US-01 19/
In two further cases, the manipulating firms used virtual transactions to benefit their CRR positions.
Virtual trading is a purely financial transaction, buying or selling electricity in the day-ahead market
before doing the reverse in the real time market in an attempt to profit from price changes. Despite
not being linked to any physical power flows, high volumes of such transactions can still affect
congestion enough to have an impact on CRR revenues /US-02 14/. One of these two firms acted
similarly to the firms in the two previous examples, using virtual trading to eliminate congestion that
was harming their CRR position. Both firms also actively sought to create or worsen congestion in
order to increase the value of the CRR positions they held. Both accepted losses from their virtual
trading in order to secure profitable conditions for the CRR positions they held. Neither could provide
plausible support for claims that their trades were meant to profit from arbitrage between the day-
ahead and real time markets, the intended purpose of such virtual transactions. /US-03 15/
/US-02 14/
A final firm was penalized for using a variation of the Californian “Wheel Out” strategy in order to
benefit their physical imports by reducing congestion at the importing node. This node had the
lowest cost of transmission from their power plant into the CAISO area, but congestion frequently
lowered both the amount of power able to be imported here and the price received for this power.
In an attempt to raise both the quantity and price of imports it could schedule over the intertie, the
firm scheduled imports to the CAISO area at a different node simultaneously with exports at the
target intertie, which it designated as a wheeling through transaction, or power being moved
through the control area to a load elsewhere. However, in the next step, transmission was reserved
outside of the CAISO area leading from the target intertie to the import intertie, creating a loop flow
and precluding the need for any power to actually flow. The exports at the target node would
eliminate congestion there, returning the price to a higher level and allowing more imports to be
scheduled. /US-106 12/
55
In all of these cases, the trades were judged to constitute cross-product manipulation in violation of
18 CFR §1c2. The trades were not made in response to supply and demand in order to achieve a
profit, but rather to benefit the value of a different product. FERC considers this injection of false
information into the market in question, which impairs the proper functioning of the market. Several
of the strategies used here were also determined to have caused harm to other market participants.
Penalties of $9.6 million and disgorgements of $5.2 million of unjust profits have been agreed on in
settlements between four firms and FERC, with another $6.8 million in penalties and $1.2 million in
disgorgements pending. /US-106 12/ /US-02 13/ /US-02 14/ /US-01 18/ /US-01 19/
6.3.2. Gaming of Market Rules
The cases involving gaming of market rules considered here were prosecuted, as were the cases
involving price manipulation, as violations of 18 CFR §1c2. As stated in the introduction to this
chapter however, the two categories of strategies differ in the overarching method for increasing
profits: manipulating prices or manipulating payments. Having already examined the strategies used
to manipulate prices, attention will now be given to the manipulative strategies used to game the
market rules and extract additional payments. These cases can divided into two further categories:
gaming behavior targeting increased market payments, and gaming behavior targeting out-of-market
payments, or side payments.
6.3.2.1. Increased Market Payments
In August 2013 FERC published orders assessing penalties to three firms and one individual in
connection with two cases of manipulation in demand response markets. Both cases centered
around the same strategy of inflating the baseline energy consumption of paper mills to earn
additional payments in the PJM’s Day-Ahead Load Response Program (DALRP). In the DALRP program
participants offered a load reduction from their baseline load at a chosen price for the peak hours
(7 A.M. – 6 P.M.) of the next business day. Those offers that cleared the market were obligated to
reduce their load by at least the offered amount the next day and would receive the real-time market
price for the load reduced, or pay this price per missing MWh if they did not meet their obligation.
The baselines used to measure the size of the load reduction were initially set by measuring the
average load of the resource during normal operation over the peak hours of the five business days
after being enrolled in DALRP. The baselines were then continually adjusted on a rolling basis using
peak-hour load from each business day on which a resource did not clear the DALRP market, and so
was assumed to be operating normally. /US-05 13/ /US-06 13/ /US-03 13/
Both paper mills employed the same strategy to obtain payments for demand reduction without
actually reducing load, though in one case the strategy was developed and implemented internally
while in the other it was recommended and implemented by a consulting firm’s representative. The
first step in this strategy was to make electricity consumption appear higher during the five-day
period used to establish the initial baseline. During normal operation both paper mills had nearly
constant loads 24 hours per day on business days, and each met at least a portion of this load using
on-site generation.
56
In order to create the appearance of a higher baseline electricity consumption, both paper mills
curtailed their own generation and purchased electricity from the grid during the baseline-setting
hours, before returning to generating their own electricity shortly after peak hours ended. As a
result, the normal electricity consumption of the two plants respectively appeared approximately 5
MW and approximately 35 MW higher than true normal consumption. A stylized example of this
consumption pattern at the larger paper mill is depicted in Figure 19. /US-05 13/ /US-06 13/
/US-03 13/
Figure 19:Manipulative inflation of baseline load - own representation based on /US-06 13/
After the baseline-setting period, the mills returned their generators to the normal levels of
production throughout the day. As this electricity was generated on-site instead of being drawn from
the grid, this appeared to PJM as reduced load. The paper mills could then begin offering load
reductions through the DALRP program, and would be paid for the difference between their false
baselines and actual load without any deviation from their normal operations. /US-05 13/ /US-06 13/
/US-03 13/
The paper mills then implemented the second step of the manipulative strategy. Normally the
baseline load would be adjusted daily based on measured load after the baseline-setting period, but
days on which a resource reduced its load through the DALRP program would be excluded from this
calculation. In order to maintain their false baselines, the paper mills needed to ensure that their
load reduction offers were accepted every day. /US-05 13/ /US-06 13/ /US-03 13/
57
To minimize the risk of their offers not being accepted both mills bid the minimum allowed offer
price daily, a bidding strategy made possible because they faced no costs or lost revenues from load
reductions seeing as they were not actually reducing load. This strategy succeeded, and PJM paid the
two paper mills approximately $3.78 million for daily “phantom load reductions”. Both paper mills,
the consulting firm, and the employee of said firm responsible for developing, recommending, and
implementing the scheme were fined by FERC based on their misrepresentations of willingness and
ability to reduce load in order to receive payments for a service never delivered. The fines totaled
$27 million, and an additional order required the repayment of unjust profits totaling $3.38 million.
/US-05 13/ /US-06 13/ /US-03 13/
Two further firms have also received fines from FERC because of payments received in demand
response markets for services not provided. Concerning the first firm, the Senior Vice President (SVP)
of a curtailment service provider (CSP) was found to have manipulated two different demand
response markets within PJM that he had earlier helped to design. In the Synchronized Reserve
Market, resources that clear the market must respond to calls to reduce their load within ten
minutes. A CSP is responsible for offering resources into the market, notifying the resources of a call,
and verifying their compliance. The SVP offered a number of the CSP’s contracted resources into the
market at times they reported not being available for load reduction, as well as offering a resource
into the market after it was no longer under contract with the CSP. None of these resources could
have provided demand response in case of a call. Additionally, in nine separate instances, resources
contracted with the company did not reduce their load when instructed because the SVP purposely
neglected to inform the resources of the load reduction call. In each of these instances the SVP also
failed to submit data to PJM demonstrating resource compliance, and only responded to
notifications of this failure after repeated attempts. In the second demand response market, known
as Interruptible Load for Reliability, the SVP overstated the load reduction capabilities of 52
resources he registered for the program in 2007. The next year, the SVP registered more than 100
resources for ILR before being authorized by the resource to do so, or without confirming availability
and willingness to participate. While the majority did later agree to participate, 27 did not authorize
their participation at any point. The CSP was fined $500,000 and required to repay $2.25 million in
unjust profits, and the SVP was fined $50,000 for the combination of violations. /US-01 10/
In the case of the second firm, another CSP, the manipulation also took place in the ILR program.
When resources are registered for the ILR program, the CSP submits the amount of load reduction
the specific resource can commit to providing given a two-hour notice. The CSP then receives
capacity payments based on its resources, as well as payments for load actually reduced. The load
reduction capability in question in this case totaled 4.6 MW, 3.6 MW of which could be provided by
operating two 1.8 MW generators instead of drawing power from the grid. However, prior to the
registration deadline the resource informed the CSP that the two generators could not be operated
simultaneously due to a technical defect, effectively limiting their load reduction capability to 2.8
MW. /US-04 13/
58
The CSP nonetheless registered the resource with a 4.6 MW load reduction capability, and during the
mandatory test event prior to the beginning of the 2009/2010 delivery year arranged for a one-time
manual bypass of the technical defect which prevented simultaneous operation of the generators. By
misrepresenting the load reduction capabilities of the resource, the CSP received extra capacity
revenues for the second 1.8 MW of load reduction that it could not realistically provide. No load
reduction events were triggered in the 2009/2010 delivery year, preventing the CSP from earning
additional payments for load actually reduced, and meaning the misrepresentation of the resource’s
capability did not have any actual negative effects on grid stability. FERC fined the CSP $780,000,
required a repayment of $20,726 in unjust profits, and obtained a commitment from the CSP to
invest $500,000 in demand response technologies for PJM customers. /US-04 13/
In a gaming case reminiscent of the paper trading of ancillary services that occurred during the
California Energy Crisis, FERC reached a settlement with a firm operating in the ISO-NE control area
concerning payments for services not rendered. The firm in question controlled two peaking units
that it registered in the ISO-NE capacity market, receiving a monthly payment in exchange for the
obligation to offer the electricity produced by the units into the ISO-NE energy markets. Participating
in the capacity market also required the firm to schedule any non-emergency periods of
unavailability in advance with the ISO, and notify the ISO of all planned and unplanned outages. On
three separate occasions, ranging from several days to several months in length, the firm removed a
generating unit from service for planned maintenance without having scheduled said maintenance
with the ISO. Not notifying the ISO that the unit was unavailable, the firm continued offering the
unit’s generating capacity into the energy market during all three occasions. As a result, the out-of-
service unit was eventually dispatched to produce electricity during all three maintenance periods. In
response to each of these dispatches, the firm declared a forced outage until the end of the planned
maintenance period, a contingency only intended for plants unavailable due to emergencies or other
unforeseen circumstances. The first two instances of manipulation initially went undetected, and the
firm was able to collect full capacity payments. During the third instance, an employee inadvertently
disclosed to the ISO that the unit was already out of service for planned maintenance. The
investigation sparked by this revelation led to a negotiated settlement between FERC and the firm,
requiring the repayment of all revenues received for the units during the outages, totaling
approximately $336,000. FERC found this firm’s behavior to be a fraudulent misrepresentation of the
availability of the generating units designed to deceive ISO-NE into making capacity payments
despite the generating units being unable to produce electricity if dispatched. /US-01 11/
59
6.3.2.2. Increased Side Payments
Gaming strategies targeting increased side payments involve bidding strategies designed to secure
additional revenue from various types of payments made to firms by market operators separate from
payments based on the market price.
• Up To Congestion Transactions
One family of manipulative strategies targeting side payments rather than market payments are the
Up To Congestion Transactions that were used in the PJM market in the summer of 2010. Originally
intended as a method to hedge against the risk of price changes between day-ahead and real-time
markets, by the summer of 2010 up-to congestion (UTC) transactions were also commonly used by
financial traders as virtual transactions to arbitrage price differences. When conducting a UTC trade,
the trader “buys the spread” by paying PJM the difference in the local marginal prices of source node
A and sink node B in the day-ahead market.
After the close of the real-time market the next day, PJM pays the trader the real-time difference
between nodes A and B, earning the trader a profit if prices have diverged and incurring a loss if
prices have converged. Each UTC trade also brought transaction costs with it, most notably the
requirement to reserve transmission. This transmission did not need to geographically match the
nodes chosen for the UTC trade, only the volume (in MWh) of the trade. To minimize transaction
costs most traders reserved transmission from a PJM node to a MISO node, because such
reservations were exempt from reservation fees. /US-01 15/ /US-02 15/
One component of PJM locational marginal prices are line loss fees, incorporated into the price to
reflect the true costs of electricity at a location after transmission losses are taken into account. The
method PJM uses to calculate line loss fees results in payments to PJM larger than the true amount
of line losses, a pool of money known as the marginal loss surplus. When more energy is fed into the
grid, the marginal loss surplus is also larger. In the summer of 2010 PJM redistributed the marginal
loss surplus to all market participants that had paid to reserve transmission for their transactions
based on in a process known as Marginal Loss Surplus Allocation (MLSA). These side payments were
made proportionally to the amount of transmission reserved, and became the target for the
manipulating parties. /US-01 15/ /US-02 15/
The manipulative UTC strategies originated in late 2009 and early 2010, when a single trader noticed
that his legitimate UTC trades, attempting to arbitrage spread changes, had begun receiving MLSA
payments in October of 2009 if he had paid to reserve transmission for a trade. Whereas he had
sought out small, reliably predictable spread movements during his trading activities since 2005, he
began changing his strategy to target node pairs whose LMPs would move together, leading to
minimal changes in price between the DA and RT markets. Such small spreads carried little risk of
losing money on the UTC transaction itself, but were equally unlikely to be a profitable arbitrage
opportunity. The trader could use these low risk trades to reserve large volumes of paid transmission
and profitably collect MLSA payments at times when these side payments would exceed transaction
costs. /US-01 15/
60
At the end of May 2010, the trader altered the strategy again to eliminate all risks. UTC trades for the
spread from node A to node B were now paired with trades for the spread from node B to node A,
offsetting any gains or losses from spread movements. This type of trading now guaranteed a loss
after factoring in transaction costs, but the risk free nature of a net-zero transaction allowed the
volume of the trades to increase, more paid transmission to be reserved, and larger MLSA payments
to be captured. /US-01 15/
Transmission reservations were made over a public platform, and other traders began to notice the
large volumes of transmission being reserved throughout June 2010. Some were able to deduce the
strategy being used, and adopted it themselves in July 2010. In addition to the type of offsetting
trades already being used, these new manipulators also took advantage of certain node pairs used
for import and export pricing that were guaranteed to have no price changes between the DA and RT
markets. /US-01 15/ /US-02 15/ /US-02 16/
This volume-based UTC trading cost PJM $17 Million in additional MLSA payments in July 2010 alone.
At the end of July, PJM began investigating the large amounts of transmission being reserved after
other participants filed a complaint based on suspicions of transmission capacity hoarding, as they
were unable to reserve transmission for their own transactions. As a result, FERC began investigating
the market conduct of seven entities with abnormally large transmission reservations associated with
UTC trades. The volume-based UTC trades were deemed to violate 18 CFR §1c2 based on five
indicators of manipulation: Trading inconsistent with supply and demand, marked difference
between manipulative and non-manipulative trades, evidence of intent, uneconomic trading, and
implausible explanations. Six companies and five individuals were fined a total of $83.85 million and
forced to disgorge an additional $10.15 million in unjust profits in a series of court decisions and
settlements finalized between 2013 and 2016. /US-01 15/ /US-02 15/ /US-02 16/
• Bid Cost Recovery Payments
In one of the largest settlements ever reached by FERC, in July 2013 the regulator settled a case
involving multiple strategies used by a single firm to target increased side payments in the CAISO and
MISO markets between September 2010 and November 2012. As a result of bankruptcies during the
financial crisis of 2008, the firm in question acquired rental agreements to around 4000 MW of
generation in the CAISO market and 545 MW in the MISO market. These agreements committed the
firm to pay around $170 million in rental fees annually, starting in January 2011. All of the plants
subject to the agreements were typically out of the money at then-average market prices, and so the
firm sought additional revenue streams in order to turn a profit. /US-07 13/
A variety of side payments, make-whole payments, and compensations for exceptional dispatches,
which will be collectively referred to here as Bid Cost Recovery (BCR) payments, were the revenue
streams the firm chose to target. The market operator issued BCR payments to units it committed in
the day-ahead market, but whose operation would not be profitable at the real-time market price.
Offers from a firm to produce energy include two components relevant to the strategies at issue in
this case. One is the minimum load cost, or the total price for running the unit at its lowest reliable
operating level (Pmin). Market participants were allowed to submit Pmin prices for a unit up to twice
the estimated actual costs of operating the unit at that level.
61
Committing a unit in the day-ahead market obligated the market operator to pay the unit its Pmin
rate, even if this was above the market price, as a form of BCR. The other component is incremental
energy payments, which are the prices per additional MWh for electricity produced above the Pmin
level up to the maximum output level. /US-07 13/
One of the strategies used by the firm in question combined these two components. The submitted
Pmin rate would be submitted at the maximum allowed level for the unit, which was approximately
three times the average market price at the time. This would be coupled with a negative incremental
energy payment price set to the price floor, so that the average price of the combined offer would be
attractive enough to be scheduled in the day-ahead market. /US-07 13/
A firm scheduled to produce electricity above its Pmin level also has the option to buy back this
electricity in the real-time market, down to its Pmin level. When scheduled, the firm in question
would do just that. This allowed the firm to take advantage of a modifier in the BCR formula, the
result of which was the ability to collect full market price for its minimum load in addition to the
Pmin price it was already guaranteed as a BCR payment. This strategy to collect two sets of payments
for the same energy transformed what would have loss of $7.6 million over a seven-month period
into a profit of $27 million via the BCR payments. /US-07 13/
The next strategy took advantage of two additional practices of the market operator: self-schedules
and operation constraints. A self-schedule was a signal from a firm to the market operator that the
firm would accept any price set by the market for their generation, and was treated by the market
software as a negative bid beyond the price floor. Such bids were more attractive to the software
than any normally submitted bid. Firms also submitted the technical operating constraints of their
generating units to the market operator, which took these into consideration when scheduling units.
Relevant among these constraints for this case are the rate at which a unit can adjust the amount of
energy being generated, or the ramp rate, and the shortest amount of time for which the generator
can be scheduled, or the minimum run time. /US-07 13/
After submitting the ramping rates of its generating units, the firm in question submitted a self-
schedule in a chosen hour followed by high bids in the next two hours, and repeated the pattern in
the next three-hour block. Based on the ramp rate, which limited the ability of the plant to adjust its
production in the hours between the self-schedules, the market software committed the firm for
these hours in order to enable the self-schedules and paid the firm at the high price they bid, instead
of the market rate. This bidding strategy is depicted in Figure 20. /US-07 13/
62
Figure 20: Gaming of technical restrictions - own representation based on information from /US-07 13/
Another variation of this strategy was possible because the market software used in the CAISO
market could not “look ahead” to the next day when processing the day-ahead market. The firm in
question took advantage of this, submitting negative bids at the price floor for the last hour of day
one, which the market software accepted when clearing the market. On day one, bids at the price
ceiling for the first two hours of day two were also made, but not accepted. However, to respect the
ramping rates, the market software then committed the firm’s plant for the first two hours of day
two. In accordance with the BCR rules, during these hours the firm was compensated at the price bid,
more than 80 times the market price of these hours. /US-07 13/
The firm also attempted to use both of these strategies in the MISO control area. After receiving a
commitment for the end of day one based on negative Pmin and incremental energy prices and a
four-hour minimum run time, the firm increased the minimum run time to 20 hours in its bids for day
two, and raised its Pmin and incremental energy price bids to the maximum allowed level. Manual
intervention by MISO staff cancelled the commitments made by the market software, preventing the
firm from being paid more than 20 times its costs. The firm’s attempt to capture BCR payments on
the basis of its ramp rate using alternating high and low bids was even less successful. MISO staff
upheld the software’s unit commitments made due to the ramping rate, but blocked the BCR
payments for these hours and caused the firm to lose approximately $140,000 on the day.
/US-07 13/
Two additional strategies, both variations on the same idea, took advantage of the fact that units
were required to be at an output level well above Pmin in order to provide ancillary services. One
such ancillary service was “reg down”, which allowed the market operator to make small downward
adjustments to the production of a unit to contribute to decreasing the amount of energy in the grid.
63
The firm in question submitted self-schedule bids to provide reg down in the ancillary services
market, but its bids in the day-ahead energy market were more than double the average market
price. These energy bids did not clear the market, leaving the firm scheduled to produce no energy.
In order to honor the self-schedule in the ancillary market and enable the market operator to use the
offered reg down service, the market software awarded the energy bids and paid them as bid.
/US-07 13/
FERC found that the bidding behavior of this firm was designed to appear economic to the ISO
market software in order to secure payments above market prices. The bids made were not based on
the underlying market fundamentals, but instead intended to ensure the receipt of side payments.
To create the situations that triggered side payments, bids were repeatedly made despite the
knowledge that they would be unprofitable. These bids also affected the merit order, altering market
price and congestion values away from the results that would have occurred under normal
competitive conditions, interfering with the proper functioning of the market. Due to this behavior,
payments to the firm were inflated despite no additional benefit being provided to the ISO by the
firm. In the settlement agreed between FERC and the firm in question, the firm was fined $285
million and ordered to repay an additional $125 million in unjust profits. /US-07 13/
• Reliability Must-Run Payments
A final case involved a firm seeking to increase side payments occurred in the ISO-NE control area
during the summer of 2010. The firm in question controlled a dual-fuel power plant, able to generate
electricity by burning either natural gas or fuel oil. The plant’s location often led to the ISO
dispatching it to run to support grid stability, despite the prices of its offers to produce leaving it out
of the merit order. To ensure such plants essential for grid stability are not operating at a loss when
dispatched out of merit, the ISO issues them reliability payments equal to the difference between the
market price and their offer price. /US-04 15/
Two attributes of the firm’s plant influenced the reliability payments it stood to receive. First, it was
important for grid stability due to its location, giving it a degree of local market power. In order to
mitigate the market power of plants such as this one, the ISO capped their reliability payments at
110% of the estimated fuel costs and variable operation & maintenance costs. Second was the
plant’s dual-fuel nature. Dual-fuel plants indicated which type of fuel they would burn when making
an offer into the day-ahead market, information that was used by the ISO to estimate costs when
issuing reliability payments. /US-04 15/
During the summer of 2010, fuel oil was more expensive than natural gas. During some of the hottest
days of the summer, when demand for electricity would be near its peak, the plant in question could
be almost guaranteed of being dispatched for reliability. Claiming to be unable to obtain natural gas
due to pipeline disruptions, the firm regularly indicated in its day-ahead offers that it would be
burning fuel oil. The market results led to a reliability dispatch, as anticipated, and the reliability
payments were then calculated based on the costs of burning fuel oil. The firm was in fact burning
less-expensive natural gas, seeking to increase the profits it could earn from the reliability payments
by increasing the margin between true costs and compensated costs.
64
The market monitor began investigating the firm’s conduct before the payments were issued, and
discovered that the plant had in fact been burning natural gas. The case was referred to FERC, which
assessed penalties of $5 million to the firm and $50,000 to an employee for misrepresentation and
omission of facts. The ISO also recalculated the reliability payments to reflect the true fuel costs,
preventing almost $3 million in additional, fraudulent, payments from being made. Following this
case, rule changes required stricter documentation of fuel types used when collecting reliability
payments. /US-04 15/
6.4. Conclusions
It is the nature of market manipulation to be unpredictable. If this were not true, then the
manipulative behavior of the California Energy Crisis and the more recent cases described here could
have been foreseen and prevented. While it will thus be impossible to predict all forms of
manipulative behavior that could emerge in a market for flexibility, history provides two sets of tools
to assist in protecting markets for flexibility. Several of the strategies employed to manipulate
markets detailed in this chapter could be applied with minimal alteration to a market for flexibility, or
indicate vulnerabilities that could be exploited in such a market. For those strategies which do not
resemble behavior of previous cases, the criteria used by regulators to determine the fraudulent
nature of past strategies can serve as a method for assessing the legitimacy of conduct in the future.
Among the historic strategies, several could be used in a market for flexibility with little or no
adaptation. First of these is the use of operating constraints or technical limitations to extract
additional payments. In addition to the technical details of each unit being decisive to its ability to
provide flexibility, flexibility owners will also have the ability to submit comfort-based restrictions on
the use of their devices in the ALF platform /FFE-48 18/. Clever operators may be able to use these
restrictions to their advantage. Although no side payments such as those targeted in /US-07 13/ exist
in ALF at present, such restrictions could, for example, be exploited to strategically limit available
flexibility and raise prices.
The inflated baselines used to extract inflated payments from demand response programs could also
potentially be seen in markets for flexibility. Falsifying a unit’s original schedule to create the
appearance of a larger flexibility contribution is one form this could take. Using artificially inflated
schedules in order to create the appearance of imminent congestion and collect payments for its
relief, an adaptation of the Californian “Load Shift” strategy, is another alternative.
As demonstrated by the actions of the CSPs in the other load response cases, for instance registering
unauthorized units or failing to carry out demand response instructions, caution must be exercised
when third parties serve as go-betweens for market operators and resource owners. In the context of
markets for flexibility, care should be taken when creating market rules to limit opportunities for
manipulation by aggregators. The CSP case involving the SVP of the company detailed in /US-01 10/
and the pair of congestion revenue cases detailed in /US-02 13/ and /US-01 19/ also serve to
illustrate a more general vulnerability of markets: the use of insider knowledge by individuals
previously responsible for developing a market to exploit gaps in the rules.
65
The cases pursued by regulators since 2005 present not only examples of manipulative behavior, but
also the reasons these behaviors were found to be fraudulent. Without the ability to foresee the
specific forms manipulative behavior will take in a market for flexibility, even having identified
potential vulnerabilities, these criteria can serve as useful guidelines for assessing the behavior of
market participants. The three criteria given at the beginning of this chapter have been expanded
upon with examples that offer more specific guidance, and are joined by consequences that can also
imply manipulative behavior has occurred /FERC-02 16/.
Many of the cases were considered fraudulent because they injected false information into the
marketplace. This often took the form of offers or bids whose purpose was not to get the best price,
but rather to either move the price to benefit other positions or enable the receipt of other
payments. Not only were such bids often uneconomic, but they provided other market participants
with a distorted view of how the traded product was valued by the market. False information also
took the form of misrepresentation, for example the import of power falsely designated as wheeling
through transactions, false claims about the willingness or ability to provide load reductions, or
burning one fuel while claiming another. Manipulative behavior commonly led, beyond unjust profits
for the manipulator, to inefficient market outcomes. Among these can be counted artificial prices,
altered merit orders, excessive out-of-market payments, and harm to other market participants.
Such outcomes can serve as evidence of manipulative behavior, especially if coupled with changes to
normal operation by firms or individuals.
Although it is yet to be seen what types of manipulative behavior emerge in markets for flexibility,
these criteria can be used to evaluate the behavior market participants that fall under suspicion. One
example of behavior that has been shown to be possible in markets for flexibility is the Increase-
Decrease (IncDec) game /HESA-01 18/. It has been observed in the original failed California market,
and in the redispatch market of Great Britain, where an investigation into the behavior based on
claims of abuse of market power was abandoned due to poor prospects for building a successful case
/OFGEM-02 09/ /STO-01 98/ /ALAY-01 04/ /LECG-01 10/. IncDec gaming has not yet been prosecuted
as a form of market manipulation, and as such was not included in the review conducted in this
thesis. However, it can serve as a demonstration for the application of the above criteria for
fraudulent behavior to a market for flexibility. The following example of IncDec gaming is most
closely based upon market structure and the events in Great Britain.
When day-ahead market outcomes led to congestion and prevented demand in an area from being
met by cheaper imported power, generators not profitable to run at the day-ahead market price
were able to submit bids to the Balancing Mechanism. Through the Balancing Mechanism they could
be called to ensure demand is met in the shortage area. This is a form of market-based redispatch,
and functions very similarly to markets for flexibility despite the differences in terminology
/HIRT-01 19/. Generators in the surplus area also made bids to buy back their obligation to produce,
but focus will remain here on the shortage area for reasons of brevity. /LECG-01 10/
66
When all firms behave competitively in the market for flexibility, the remaining demand will be met
by those generators with the lowest costs that are not yet producing electricity. The price they
receive from the market for flexibility will, by necessity, be higher than the day-ahead market price,
else they would already be running profitably. The IncDec game begins when the congestion can be
predicted prior to the day-ahead market, causing changes to behavior in this market. Knowing the
flexibility market price will be higher than the day-ahead market price, generators withhold their
capacity from the day-ahead market in order to increase their revenues through participation in the
flexibility market. Assuming firms know the level of unmet demand and the approximate marginal
price of the market for flexibility, or can infer them based upon previous market results, they can
adjust their bidding in the market for flexibility accordingly. Generators previously bidding
competitively in the day-ahead market can now strategically bid just under previously observed or
estimated clearing prices of the flexibility market. /HIRT-01 19/ /LECG-01 10/
This change in bidding behavior has multiple consequences. The magnitude of congestion is likely to
increase due to withholding in the day-ahead market, as less energy is being generated in the area
already suffering a shortage. This withholding is also likely to cause the day-ahead market price to
increase via changes in the merit order caused by the absence of low-cost plant. The increased
revenues earned in the flexibility market by the firms playing the IncDec game come in the form of
higher costs to the network operator, and ultimately the consumers. While the clearing price of the
flexibility market will likely remain the same, the increased amount of generation that must be
procured through this market increases the costs of congestion management. /HIRT-01 19/
/LECG-01 10/
Beginning application of the indicators of manipulative behavior with the consequences of the IncDec
game, market participants, in this case the network operator and consumers, have been harmed by
the higher costs. These costs increased because the behavior of the gaming firms increased the
amount of congestion, representing a larger threat to network stability. The same withholding
behavior that increased the magnitude of congestion also harmed the efficiency of the day-ahead
market, requiring higher cost units to meet demand when lower costs units could have profitably
done so. Withholding generation when it would otherwise be profitable can be seen as uneconomic
bidding that would be unprofitable if the predicted congestion does not materialize.
Although not detailed here, the IncDec bidding of firms in the surplus area would lead to direct losses
without congestion (see /HIRT-01 19/). Such uneconomic bidding has been equated with the
injection of false information into the marketplace in several of the previous manipulation cases
described in this chapter. As such, an argument can be made that the IncDec bidding behavior can be
considered a form of market manipulation. Whether this argument would be accepted by regulators
is uncertain, but should serve as a demonstration of the application of indicators of manipulative
behavior to markets for flexibility.
67
7. Summary and Suggestions
Having examined the potential forms market power and market manipulation could take in a market
for flexibility, some attention must be given to possible methods for inoculating such a market
against the harms these anti-competitive behaviors can cause. In the case of market power, three
conditions conducive to its emergence in a market for flexibility have been identified. Meanwhile,
examinations of past cases of market manipulation have highlighted the diverse forms such behavior
can take, as well as factors which set such behaviors about from legitimate conduct and which can be
used for their identification. A mixture of economic and regulatory tools can be suggested in order to
prevent, mitigate, and sanction these anti-competitive behaviors.
Beginning with market power, the conclusions reached in the respective chapters will briefly be
recounted before discussing suggestions applicable for combating each anti-competitive behavior.
Three conditions which could occur in markets for flexibility were found to be favorable for the
emergence of the ability to exercise market power: situations exhibiting concentrated ownership of
available flexibility, situations featuring limited amounts of available flexibility, and situations in
which demand for flexibility reaches peak levels.
Concentrated ownership of available flexibility may be seen when large individual units make up
large proportions of available flexibility or when aggregators gain control of sufficient numbers of
smaller units to represent a large proportion of available flexibility. Three scenarios likely to limit the
available flexibility are low levels of registration and participation in the market, weather conditions
that restrict available flexibility for technical or comfort-related reasons, and congestion of network
elements which can be influenced by few flexible units. Both of these two conditions can be
worsened by situations of peak demand. Concentration of ownership can create a dominant firm
that will be the first with the ability to exert market power, while limited available flexibility increases
the potential of all suppliers remaining in the market to gain a dominant position. Peak demand
increases the number of firms able to exert market power, and ensures that any dominant firms have
the ability to do so. Analyzing a given market period to determine whether market power should be
an immediate concern can be done using the methods described in section __, although this will not
predict whether the potential market power will indeed be used, or what its financial impact may be.
Turning to market manipulation, two quotes sum up the challenge of preventing manipulative
behavior. The first of these is “[t]he methods and techniques of manipulation are limited only by the
ingenuity of man.” (/FERC-02 16/, p. 16) All manipulative behavior will be impossible to predict,
particularly before the market begins operator and interactions between participants can be
observed /FERC-02 16/. Previous cases of market manipulation can however serve as useful starting
points when attempting to anticipate how firms could seek to manipulate markets for flexibility. Both
specific behaviors that were used in past manipulations and could be adapted for use in markets for
flexibility, as well as vulnerable features that are less dependent upon specific behaviors, could be
identified. Strategic use of technical restrictions were employed to manipulate energy markets, and
such schemes could be altered to take advantage of comfort-based restrictions on flexible units that
can be set by their owners.
68
As flexibility is the alteration of consumption or generation away from a starting level, manipulative
participants in a market for flexibility could attempt to inflate their baseline values in pursuit of
additional profits. Third party aggregators of flexibility seeking additional revenues could distort
communication between the flexible units they are responsible for marketing and the marker
operator. Finally, although not a specific strategy in of itself, individuals involved in designing markets
for flexibility will likely possess detailed knowledge of the market platform that could be used to form
new manipulative strategies.
The second quote comes from the legal proceedings concerning manipulation of energy markets
during the California Energy Crisis. Asserting that behaviors not explicitly forbidden could also be
deemed manipulative, FERC wrote:
“Enron (and others) would demand that a regulatory agency have the prescience to include in
a rate schedule all specific misconduct in which a particular market participant could
conceivably engage. That standard is unrealistic and would render regulatory agencies
impotent to address newly conceived misconduct and allow them only to pursue, to phrase it
simply, last year’s misconduct – essentially, to continually fight the last war and deny the
capability to fight the present or next one.”
/FERC-02 16/, p. 17
Simply creating a list of forbidden behaviors based upon manipulative schemes observed in the past
will not only fail to prevent further manipulation, but would also effectively give a free pass to those
firms able to conceive of a new form of manipulation /FERC-02 16/. Even those behaviors expressly
forbidden may also still be engaged in, such as the wash trades used in the UTC manipulations in the
PJM described in /US-01 15/.
The criteria used to assess the nature of behavior in past cases can be used in markets for flexibility
to determine whether previously known manipulative behaviors are present or whether a new
bidding strategy is legitimate or manipulative. Actions taken for a purpose other than fundamentals-
based competition can indicate manipulative behavior is taking place, as can the use of a market
instrument for a purpose other than that for which it was designed. Trading behavior that is
repeatedly unprofitable, yet continues despite these losses, is often a sign that an ulterior motive is
being pursued. Injecting false information into the marketplace through offers that harm the own
firm, or by misrepresenting other information concerning a firms actions in the market, can indicate
that a firm is attempting a manipulative strategy. Related to unprofitable behavior are attempts to
capture side payments through strategic bidding, rather than bidding based upon market forces in
attempts to obtain the best price in the market. /FERC-02 16/
Economic, regulatory, and technical tools are available to prevent, mitigate, and sanction the two
types of anti-competitive behavior discussed in the previous chapters. Two of the conditions for the
emergence of market power, concentrated ownership of available flexibility and limited available
flexibility, can be partially prevented via incentives. First and foremost among these is ensuring that
participation in the market for flexibility is attractive enough to create a liquid market. Increasing the
number of market participants will serve in almost all cases to reduce the potential for structural
market power.
69
If aggregators are reaching sizes deemed critical for the emergence of market power, as measured
by the RSI, incentives for units to engage in long-term contracts with the market operator should be
improved. By ensuring, weather- and comfort-based restrictions permitting, access for the market
operator to flexibility not controlled by the dominant firm, this firm will be forced to behave in a
more competitive manner. Increasing the size of this pool of contracted flexibility strengthens this
effect. Frequent occurrences of conditions limiting available supply can also be combated by
increasing incentives for market entry by those unit types unaffected by the problematic condition.
Should structural market power be unavoidable, for instance in periods of peak demand, its effects
may still be mitigated. One strategy for doing so with some success is the automatic bid mitigation
process adopted by the Public Utility Commission of Texas (PUCT). In situations where demand
requires the purchase of all available balancing energy offered, the market software takes a price
that would lead to 95% of offers being accepted and multiplies this by 1.5 to obtain a mitigated
clearing price. If the mitigated clearing price is higher than what the unmitigated clearing price would
be, no action is taken. However, if the top 5% of offers would cause the unmitigated clearing price to
be higher, they will be paid as bid while the remaining participants will receive the mitigated clearing
price. This prevents opportunistic high offers from causing excessive economic harm by setting the
market price, while ensuring the remaining offers receive rents appropriate for a situation of tight
supply. It also avoids interfering with the market when all firms are facing high costs. As an additional
form of social pressure, the identities of the firms making the excessively high offers are published,
opening them to negative publicity and potential regulatory attention. Alternatively, the PJM uses a
system that caps the bids of the three largest suppliers at 110% of estimated costs should their
combined capacity be pivotal in a given situation /CRA-01 17/. /PUC-01 04/
Either of these methods could be adapted for use in a market for flexibility using an RSI-based trigger
for mitigation. Any firm (or combination of firms) falling below, for example, the 1.2-threshold used
in chapter _ of this thesis could have their bids capped by the market software at a chosen level.
Including some allowance for scarcity rents when the situation demands, such as in the PUCT
method, will maintain price signals important for encouraging market entry while preventing price
gouging by dominant firms. The social pressure aspect of the PCUT method could have an additional
effect in a smaller regional market such as ALF. Beyond social sanctioning, economic punishments
must be of a relevant level. If the gains for exerting market power outweigh any possible
punishment, common sense suggests that firms would not hesitate to take advantage of this
opportunity. The repayments of profits earned through illicit behavior, combined with additional
penalties for both firms and individual actors, as seen in many of the manipulation cases in chapter 6,
offers an example of a relevant form of sanctioning.
Completely preventing all market manipulation is unlikely to be possible. However, clearly defining
both those behaviors which are to be explicitly prohibited and notions of what is to be considered
acceptable or desired conduct can perhaps help to limit manipulative behavior. Not only does this
remove any excuse for carrying out the forbidden behaviors, it provides a starting point for judging
behaviors based upon their purpose in relation to the intended use of the market for flexibility.
70
Ideally, market rules should be designed or adjusted to make honest market behavior the most
profitable course of action, a design philosophy known as incentive-compatible market design
/STO-01 02/.
While putting firms on notice about what will not be tolerated is a good start, ensuring that they
remain honest is also necessary. The best way of doing so, and simultaneously helping to keep
market power in check, is ensuring that the market is adequately monitored. The knowledge that
behavior is being watched may be enough to discourage some attempts at manipulation, particularly
if coupled with threat of significant sanctions as described previously. An active market monitor can
also immediately mitigate the impact of manipulative bidding, as seen when MISO staff prevented
the issuance of side payments in /US-07 13/ on the day the manipulation was attempted. Ensuring
that market results and bidding behavior can be assessed before payments are issued can also
prevent manipulation that initially went unnoticed from causing harm. This was demonstrated when
reliability payments were adjusted before settlement because of investigations in /US-04 15/, while
the “Cutting Non-Firm” strategy employed during the California Energy Crisis demonstrates the
potential for mischief if this is not done /FERC-01 03/.
The market monitor could take several forms. Incorporating it within a network operator would allow
access to large amounts of data, but as network operators will be consumers in the context of
markets for flexibility this could lead to conflicts of interest. Including the monitor within the
Bundesnetzagentur would fit well with the agency’s other tasks and benefit from their experience,
but potentially leaves the market monitor open to political influence. Creating a market monitoring
division within the market operator, should this be an independent entity when the market begins
operation, or engaging the services of an independent monitoring firm as in several US markets may
also be potential solutions, but would directly increase the costs of market operation. Whatever form
it may take, the monitor must be guaranteed access to sufficient data from the market operator and
market participants to adequately analyze market outcomes for evidence of anti-competitive
behavior. If this is detected, the monitor will also need either the power to take action itself or the
ability to cause a meaningful response from the regulator in response to its observations if market
participants are to be expected to take it seriously. /IEEE-01 03/ /HILE-01 14/
Implementing a market monitor at the beginning of market operations offers an additional
advantage. Observing developments within the market and the adjustments of participants behavior
and bidding strategies to differing conditions over time will ensure that a strong learning effect is
present. Anti-competitive behavior, particularly manipulation, will evolve over time with the
maturing of the market. A monitor with a finger on the pulse of the market will be well positioned to
react to such adaptations rather than playing catch-up like other regulators have been forced to do in
the past. If markets for flexibility can be kept free of anti-competitive behavior, they can play an
important role in creating the efficient, sustainable, and decentralized energy system of the future.
71
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Ausgefertigt am 2012-10-31; Washington D.C.:
US Federal Energy Regulatory Commission,
2012.
BDEW-04 17 Schwencke, Tilman: Erneuerbare Energien und
das EEG: Zahlen, Fakten, Grafiken (2017) -
Anlagen, installierte Leistung, Stromerzeugung,
EEG-Auszahlungen, Marktintegration der
Erneuerbaren Energien, EEG-Auszahlungen und
regionale Verteilung der EEG-Anlagen. Berlin:
BDEW, 2017
BDEW-05 18 BDEW: Arbeitsgrundlagen
Marktkommunikation - Standards zur
Modellierung von Marktprozessen im
Energiemarkt. Berlin: BDEW Bundesverband der
Energie- und Wasserwirtschaft e.V., 2018.
BDEW-03 19 Durchführung und Abrechnung von Redispatch-
Maßnahmen. Ausgefertigt am 2019-05-21;
Berlin: BDEW Bundesverband der Energie- und
Wasserwirtschaft e.V., 2019.
BKARTELL-01 15 Aktenzeichen: B8-78/13 ; P-65/14 -
Oberlandesgericht Düsseldorf bestätigt
Kartellrechtswidrigkeit der Entgeltregelung der
"Irsching-Verträge". Bonn: Bundeskartellamt,
2015
BMU-03 16 Klimaschutzplan 2050 - Klimaschutzpolitische
Grundsätze und Ziele der Bundesregierung.
Berlin: Bundesministerium für Umwelt,
Naturschutz und nukleare Sicherheit, 2016.
BMWI-13 12 Das deutsche Stromnetz. Bundesministerium
für Wirtschaft und Energie (BMWi), Berlin,
2012.
BMWI-119 17 Bundesministerium für Wirtschaft und Energie
(BMWi): Schaufenster intelligente Energie -
Digitale Agenda für die Energiewende -
Informationen zum SINTEG-Programm. Berlin:
Bundesministerium für Wirtschaft und Energie
(BMWi), 2017.
72
BMWI-12 17 Ein Stromnetz für die Energiewende. Berlin:
Bundesministerium für Wirtschaft und Energie
(BMWi), 2017
BMWI-02 18 Zeitreihen zur Entwicklung der erneuerbaren
Energien in Deutschland unter Verwendung von
Daten der Arbeitsgruppe Erneuerbare Energien-
Statistik (AGEE-Stat) - Stand März 2018; Berlin:
Bundesministerium für Wirtschaft und Energie
(BMWi), 2018.
BMWI-07 18 SINTEG – Schaufenster intelligente Energie - Ein
Programm zur Förderung von
Schaufensterregionen für die
Energieversorgung der Zukunft. Berlin:
Bundesministerium für Wirtschaft und Energie
(BMWi), 2018.
BNETZA-05 17 Flexibilität im Stromversorgungssystem -
Bestandsaufnahme, Hemmnisse und Ansätze
zur verbesserten Erschließung von Flexibilität -
Diskussionspapier Stand 03. April 2017. Bonn:
Bundesnetzagentur, 2017
BNETZA-06 18 Übersicht Stromnetzbetreiber - Stand
09.05.2018; Bonn: Bundesnetzagentur für
Elektrizität, Gas, Telekommunikation, Post und
Eisenbahnen (BNetzA), 2018.
BNETZA-28 19 Monitoringbericht 2018: Stand 29. Mai 2019 -
Monitoringbericht gemäß § 63 Abs. 3 i. V. m. §
35 EnWG und § 48 Abs. 3 i. V. m. § 53 Abs. 3
GWBStand: 29. Mai 2019. Bonn:
Bundesnetzagentur, 2019.
CON-01 14 Consentec GmbH: Beschreibung von
Regelleistungskonzepten und
Regelleistungsmarkt. Aachen: 50Hertz
Transmission GmbH, 2014
CONS-01 18 Maurer, C.; Zimmer, C.; Hirtz, L.: Nodale und
zonale Strompreissysteme im Vergleich. Studie
im Auftrag des BMWi. consentec, neon: Berlin:
2018.
CRA-01 17 Cramton, Peter: Electricity Market Design. In:
Oxford Review of Economic Policy Volume 33,
No. 4. Maryland, USA: University of Maryland,
2017.
DICE-01 14 Bataille, Marc et al.: Screening Instruments for
Monitoring Market Power in Wholesale
Electricity Markets - Lessons from Applications
in Germany. Düsseldorf: Düsseldorf Institute for
Competition Economics, 2014.
DIW-05 13 Kunz, Friedrich; Zerrahn, Alexander: The Benefit
of Coordinating Congestion Management in
73
Germany. Berlin: DIW - Deutsches Institut für
Wirtschaftsforschung, 2013
DUV-01 07 Lang, Christoph: Marktmacht und
Marktmachtmessung im deutschen
Großhandelsmarkt für Strom. Wiesbaden:
Deutscher Universitäts-Verlag, 2007
EEG-01 17 Gesetz für den Ausbau erneuerbarer Energien
(Erneuerbare-Energien-Gesetz - EEG 2017).
Berlin: Bundesregierung Deutschland, 2017
ENWG-01 16 Gesetz über die Elektrizitäts- und
Gasversorgung (Energiewirtschaftsgesetz -
EnWG) - Zuletzt geändert durch Art. 9 G v.
19.2.2016 I 254. Berlin: Bundesrepublik
Deutschland, 2016
EPEX-05 18 EPEX: Trading on EPEX SPOT. Paris: EPEX SPOT
SE, 2018.
EPEX-08 18 EPEX Spot: EPEX Spot DE-AT 30.09.18 15:00.
2018.
ETG-01 14 Apel, Rolf; Berg, Volkmar; Fey, Bernhard;
Geschermann, Kilian; Glaunsinger, Wolfgang;
von Scheven, Alexander; Störzer, Martin;
Wanzek, Siegfried: Regionalisierte
Flexibilitätsmärkte - Marktbasierte Nutzung von
regionalen Flexibilitätsoptionen als Baustein zur
erfolgreichen Integration von erneuerbaren
Energien in die Verteilnetze. Frankfurt am Main:
Energietechnische Gesellschaft im VDE (ETG),
2014
EUOT-01 18 Minniti, Simone et al.: Local Markets for
Flexibility Trading: Key Stages and Enablers.
Eindhoven: Eindhoven University of
Technology, 2018.
EWI-02 08 Zoettl, Gregor; Grimm, Veronika Dr.; Ockenfels,
Axel Prof. Dr.: Strommarktdesign -
Preisbildungsmechanismus im
Auktionsverfahren für Stromstundenkontrakte
an der EEX. Köln: Energiewirtschaftliches
Institut an der Universität zu Köln (EWI), 2008
FERC-01 02 Sheffrin, Anjali: Predicting Market Power Using
the Residual Supply Index. In: FERC Market
Monitoring Workshop; Washington D.C.:
California Independent System Operator, 2002.
FERC-01 03 Final Report on Price Manipulation In Western
Markets - Part 2. Washington D.C.: US Federal
Energy Regulatory Commission, 2003.
FERC-02 16 Staff White Paper on Anti-Market Manipulation
Enforcement Efforts Ten Years after EPAct
2005. Washington D.C.: US Federal Energy
Regulatory Commission, 2016.
74
FFE-35 16 Wagner, Ulrich; Mauch, Wolfgang; Dronia,
Miachael; Estermann, Thomas; Fischhaber,
Sebastian; Fattler, Steffen; Böing, Felix:
Faktencheck zur Elektromobilität und
Marktprämie in: et - Energiewirtschaftliche
Tagesfragen 66. Jg. (2016) Heft 9. München:
Forschungsstelle für Energiewirtschaft e.V.,
2016
FFE-13 17 Rasch, M.; Regett, A.; Pichlmaier, S.; Conrad, J.;
Greif, S.; Guminski, A.; Rouyrre, E.; Orthofer, C.;
Zipperle, T.: Eine anwendungsorientierte
Emissionsbilanz - Kosteneffiziente und
sektorenübergreifende Dekarbonisierung des
Energiesystems in: BWK Ausgabe 03/2017, S.
38-42. Düsseldorf: Verein Deutscher Ingenieure
(VDI), 2017
FFE-20 17 Guminski, Andrej; von Roon, Serafin: Transition
Towards an “All-electric World” - Developing a
Merit-Order of Electrification for the German
Energy System in: 10. Internationale
Energiewirtschaftstagung an der TU Wien.
Wien, Österreich: Technische Universität Wien,
2017
FFE-75 17 Köppl, Simon; Estermann, Thomas; Zeiselmair,
Andreas: Laufendes Projekt: C/sells –
Großflächiges Schaufenster im Solarbogen
Süddeutschlands in: www.ffe.de/csells.
München: Forschungsstelle für
Energiewirtschaft e.V. (FfE), 2017
FFE-35 18 Estermann, Thomas et al.: Approach to
determine the effect of local flexibility options
within the framework of a smart market
platform in: 8th Solar Integration Workshop.
Stockholm: Energynautics GmbH, 2018.
FFE-48 18 Zeiselmair, Andreas et al.: Altdorfer Flexmarkt
(ALF) - Konzeptbeschreibung, Zielsetzung,
Funktionsweise und Prozesse des Altdorfer
Flexmarkts. München: Forschungsstelle für
Energiewirtschaft e.V., 2018.
FFE-64 18 Bogensperger, Alexander; Estermann, Thomas;
Samweber, Florian; Köppl, Simon; Müller,
Mathias; Zeiselmair, Andreas, Wohlschlager,
Daniela: Smart Meter - Umfeld, Technik,
Mehrwert. München: Forschungsstelle für
Energiewirtschaft e.V., 2018.
FFE-66 18 Corradini, Roger; Conrad, Jochen; Greif, Simon:
Wärmewende im Heizungskeller. In:
Energiewirtschaftliche Tagesfragen (et)
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03/2018. Berlin: EW Medien und Kongresse
GmbH, 2018.
FFE-23 19 Wohlschlager, Daniela et al.: Bottom-up-
Szenarien am Beispiel des Altdorfer Flexmarkts.
In: ew - Magazin für Energiewirtschaft 3/2019.
Berlin: EW Medien und Kongresse GmbH, 2019.
FFE-36 19 Zeiselmair, Andreas et al.: Netzdienlicher
Handel als Element des zellulären
Energiesystems am Beispiel des Altdorfer
Flexmarkts (ALF) - 11. Internationale
Energiewirtschaftstagung (IEWT). Wien:
Technische Universität Wien, 2019.
FFE-88 19 Greif, Simon et al.: Wärmepumpen – Schlüssel
zur Wärmewende?. In: Energiewirtschaftliche
Tagesfragen (et) 05/2019. Berlin: EW Medien
und Kongresse GmbH, 2019.
FFR-01 17 Fattler, Steffen et al.: Einspeisemanagement –
Auf der Suche nach den Ursachen. München:
Forschungsstelle für Energiewirtschaft e. V.,
2017.
GCR-01 18 Cohen, Robin et al.: Economic Evidence of
Market Power and Market Manipulation in
Energy Markets. London: Global Competition
Review, 2018.
GRAE-01 14 Graeber, Dietmar Richard Dr.: Handel mit Strom
aus erneuerbaren Energien. Stuttgart: Springer
Gabler, 2014
HESA-01 18 Hesamzadeh, Mohammad Reza et al.:
Simulation and Evaluation of Zonal Electricity
Market Designs. Cambridge: University of
Cambridge, 2018.
HILE-01 14 Twomey, Paul et al.: A Review of the
Monitoring of Market Power - The Possible
Roles of TSOs in Monitoring for Market Power
Issues in Congested Transmission Systems.
Cambridge: University of Cambridge, 2014.
HIRT-01 19 Hirth, Lion et al.: Market-Based Redispatch in
Zonal Electricity Markets - Inc-Dec Gaming as a
Consequence of Inconsistent Power Market
Design (not Market Power). Berlin: Neon Neue
Energieökonomik GmbH (Neon), 2019.
IASS-101 17 Setton, Daniela et al.: Soziales
Nachhaltigkeitsbarometer der Energiewende
2017. Potsdam: Institute for Advanced
Sustainability Studies (IASS), 2017.
IEEE-01 03 Rahimi, Farrokh A. et al.: Effective Market
Monitoring in Deregulated Electricity Markets.
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no. 2. New York, New York: Institute of
Electrical and Electronics Engineers, 2003.
LANG-01 18 Lang, Christina: Development of an Auction
Design for a Flexibility Market Platform.
Masterarbeit. Herausgegeben durch die
Technische Universität München, betreut durch
die Forschungsstelle für Energiewirtschaft e. V.:
München, 2018.
LECG-01 10 Perekhodtsev, Dmitri et al.: UK Transmission
Congestion Problem: Causes and Solutions.
Emeryville: LECG Consulting, 2010.
LOHM-01 19 Lohmüller, Miriam: Active Citizen Involvement
in Intelligent Energy Systems - An Evaluation of
Acceptance and Participation for Flexibility
Markets in the Case Study Region Altdorf.
Masterarbeit. Herausgegeben durch die
Technische Universität München, betreut durch
die Forschungsstelle für Energiewirtschaft e.V.:
München, 2019.
MIT-01 01 Joskow, Paul L.: California's Electricity Crisis. In:
Oxford Review of Economic Policy Volume 17,
No. 3. Cambridge, USA: Massachusetts Institute
of Technology, 2001.
MÜL-02 18 Müller, Mathias et al.: Dezentrale Flexibilität für
lokale Netzdienstleistungen - Eine Einordnung
des Flexibilitätsbegriffs als Grundlage für die
Konzipierung einer Flexibilitätsplattform in
C/sells. In: BWK - Das Energie-Fachmagazin
6/2018. Düsseldorf: Verein Deutscher
Ingenieure (VDI), 2018.
OFGEM-02 09 Wright, Andrew: Competition Act Investigation
into Scottish Power and Scottish & Southern
Energy. London: The Office of Gas and
Electricity Markets, 2009.
PIRU-01 08 Pindyck, Robert; Rubinfeld, Daniel:
Mikroökonomie. München: Pearson Studium,
2008 ISBN:9783827371645
PPIC-01 03 Weare, Christopher: The California Electricity
Crisis: Causes and Policy Options. San Francisco,
California: Public Policy Institute of California,
2003.
PUC-01 04 Hurlbut, David et al.: Protecting the Market
from "Hockey Stick" Pricing - How the Public
Utility Commission of Texas is Dealing with
Potential Price Gouging. In: The Electricity
Journal April/2004. Austin, USA: Public Utility
Commission of Texas, 2004.
SCHWB-01 17 Schwab, Adolf: Elektroenergiesysteme.
Karlsruhe: Springer, 2017.
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STO-01 02 Stoft, Steven: Power System Economics -
Designing Markets for Electricity. Piscataway,
New Jersey: IEEE Press, 2002.
STO-01 98 Stoft, Steven: Gaming Intra-Zonal Congestion in
California. Berkeley: Lawrence Berkeley
National Laboratory, 1998.
UBA-02 18 Zeitreihen zur Entwicklung der erneuerbaren
Energien in Deutschland unter Verwendung von
Daten der Arbeitsgruppe Erneuerbare Energien-
Statistik (AGEE-Stat). Dessau-Roßlau:
Umweltbundesamt, Fachgebiet I 2.5 -
Energieversorgung und -daten, 2018.
UBA-05 19 Erneuerbare Energien in Deutschland - Daten
zur Entwicklung im Jahr 2018. Dessau-Roßlau:
Umweltbundesamt, 2019.
UCM-01 18 Esmat, Ayman et al.: Distribution-Level
Flexibility Market for Congestion Management.
Madrid: Universidad Carlos III de Madrid, 2018.
UDE-01 19 Höckner, Jonas et al.: Market distortions in
flexibility markets caused by renewable
subsidies – The case for side payments. Essen:
Universität Duisburg-Essen, 2019.
UOT-01 12 Ledgerwood, Shaun et al.: A Comparison of
Anti-Manipulation Rules in U.S. and EU
Electricity and Natural Gas Markets - A Proposal
for a Common Standard. In: Energy Law Journal
Volume 33, No. 1. Washington D.C.: The Energy
Bar Association, University of Tulsa College of
Law, 2012.
US-01 10 North America Power Partners - 133 FERC ¶
61,089 . Ausgefertigt am 2010-10-28;
Washington D.C.: US Federal Energy Regulatory
Commission, 2010.
US-01 11 In re Holyoke Gas and Electric Department - 137
FERC ¶ 61,159. Ausgefertigt am 2011-11-29;
Washington D.C.: US Federal Energy Regulatory
Commission, 2011.
US-104 12 Constellation Energy Commodities Group, Inc. -
138 FERC ¶ 61,168. Ausgefertigt am 2012-03-
09; Washington D.C.: US Federal Energy
Regulatory Commission, 2012.
US-105 12 Deutsche Bank Energy Trading, LLC - 140 FERC ¶
61,178. Ausgefertigt am 2012-09-05;
Washington D.C.: US Federal Energy Regulatory
Commission, 2012.
US-106 12 Gila River Power, LLC - 141 FERC ¶ 61,136.
Ausgefertigt am 2012-11-19; Washington D.C.:
US Federal Energy Regulatory Commission,
2012.
78
US-02 13 Deutsche Bank Energy Trading, LLC - 142 FERC ¶
61,056. Ausgefertigt am 2013-01-22;
Washington D.C.: US Federal Energy Regulatory
Commission, 2013.
US-03 13 Competitive Energy Services, LLC - 144 FERC ¶
61,163 . Ausgefertigt am 2013-08-29;
Washington D.C.: US Federal Energy Regulatory
Commission, 2013.
US-04 13 Enerwise Global Technologies, Inc. - 143 FERC ¶
61,218 . Ausgefertigt am 2013-06-07;
Washington D.C.: US Federal Energy Regulatory
Commission, 2013.
US-05 13 Lincoln Paper and Tissue, LLC - 144 FERC ¶
61,162 . Ausgefertigt am 2013-08-29;
Washington D.C.: US Federal Energy Regulatory
Commission, 2013.
US-06 13 Rumford Paper Company - 142 FERC ¶ 61,218 .
Ausgefertigt am 2013-03-22; Washington D.C.:
US Federal Energy Regulatory Commission,
2013.
US-07 13 In Re Make-Whole Payments and Related
Bidding Strategies - 144 FERC ¶ 61,068 .
Ausgefertigt am 2013-07-30; Washington D.C.:
US Federal Energy Regulatory Commission,
2013.
US-01 14 MISO Cinergy Hub Transactions - 149 FERC ¶
61,278. Ausgefertigt am 2014-12-30;
Washington D.C.: US Federal Energy Regulatory
Commission, 2014.
US-02 14 MISO Virtual and FTR Trading - 146 FERC ¶
61,072. Ausgefertigt am 2014-02-07;
Washington D.C.: US Federal Energy Regulatory
Commission, 2014.
US-01 15 Houlian Chen - 151 FERC ¶ 61,179 . Ausgefertigt
am 2015-05-29; Washington D.C.: US Federal
Energy Regulatory Commission, 2015.
US-02 15 City Power Marketing, LLC - 152 FERC ¶ 61,012 .
Ausgefertigt am 2015-07-02; Washington D.C.:
US Federal Energy Regulatory Commission,
2015.
US-03 15 ETRACOM LLC and Michael Rosenberg - 153
FERC ¶ 61,314. Ausgefertigt am 2015-12-16;
Washington D.C.: US Federal Energy Regulatory
Commission, 2015.
US-04 15 Maxim Power Corporation, Maxim Power
(USA), Inc. Ausgefertigt am 2015-05-01;
Washington D.C.: US Federal Energy Regulatory
Commission, 2015.
79
US-02 16 Coaltrain Energy, L.P. - 155 FERC ¶ 61,204 .
Ausgefertigt am 2016-05-27; Washington D.C.:
US Federal Energy Regulatory Commission,
2016.
US-01 18 ETRACOM LLC and Michael Rosenberg - 163
FERC ¶ 61,022. Ausgefertigt am 2018-04-10;
Washington D.C.: US Federal Energy Regulatory
Commission, 2018.
US-01 19 Vitol Inc. and Federico Corteggiano - 168 FERC ¶
61,013. Ausgefertigt am 2019-07-10;
Washington D.C.: US Federal Energy Regulatory
Commission, 2019.
VTV-01 10 Heuck, Klaus; Dettmann, Klaus-Dieter; Schulz,
Detlef: Elektrische Energieversorgung -
Erzeugung, Übertragung und Verteilung
elektrischer Energie für Studium und Praxis.
Wiesbaden: Vierweg+Teubner Verlag, 2010