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MS ritgerð
Viðskiptafræði
Electric Energy Price Arbitrage
Using Battery Energy Storage
Feasibility study
Kristinn Hafliðason
Eðvald Möller
Viðskiptafræðideild
Júní 2012
Electric Energy Price Arbitrage Using Battery Energy Storage
Feasibility Study
Kristinn Hafliðason
Lokaverkefni til MS-gráðu í viðskiptafræði
Leiðbeinandi: Eðvald Möller
Viðskiptafræðideild
Félagsvísindasvið Háskóla Íslands
Júní 2012
3
Electric Energy Price Arbitrage Using Battery Energy Storage
Ritgerð þessi er 30 eininga lokaverkefni til MS prófs við
Viðskiptafræðideild, Félagsvísindasvið Háskóla Íslands.
© 2012 Kristinn Hafliðason
Ritgerðina má ekki afrita nema með leyfi höfundar.
Prentun: Háskólaprent
Reykjavík, Ísland 2012
4
Acknowledgements
Many people have contributed to the making of this thesis and their input in
whatever shape or form has been most welcomed.
First I want to thank my advisor Eðvald Möller for believing in this project and for his
theoretical input as well as practical guidance through the whole process.
My good friend Dagur Gunnarsson provided invaluable help with importing,
arranging and managing the seemingly endless data used in the research and without
his help the calculations that provide the backbone of the thesis could not have been
done. Great thanks are also owed to Birgir Jóakimsson, project manager at Promote
Iceland and a graphic design guru who assisted with the design of exhibits and pictures.
I would like to thank my employer Promote Iceland for sponsoring me through my
graduate studies, especially Jón Ásbergsson, Director for allowing me to pursue this
degree and Þórður Hilmarsson, Managing Director of Invest in Iceland for his help and
understanding during my hectic study periods.
Finally I want to thank my family for putting up with me and giving me unlimited
support, especially Hlín Kristbergsdóttir my inspiration and beacon, who showed me
remarkable patience and empathy throughout the entire process. Without her writing
this thesis would have been utterly impossible.
5
Abstract
The following thesis is a study into the feasibility of exploiting the inefficiencies of an
openly traded electricity market by buying low cost electricity storing it in electric car
batteries and reselling it to the market. To assess the feasibility a hypothetical project
with all necessary equipment, infrastructure and personnel is set up and 15 years of
daily electricity trading operation are calculated using the Discounted Cash Flow method
and Net Present Value then used to determine the feasibility of the project. 29 energy
systems on five power markets were tested and the result of the research is that it is
feasible to conduct electric energy price arbitrage on four of the twenty nine systems:
SoCal
San Diego
SP-15
Long Island
The present value of the cash generated in the operation on these systems was
higher than the value of the cash needed to set it up, i.e. it is economical to spend the
money in setting up the facilities and the operations because the cash generated by the
business more than compensates the initial investment, even with opportunity costs,
risk and time value of money taken into account.
6
Table of Contents
Acknowledgements .................................................................................................... 4
Abstract ...................................................................................................................... 5
Table of Contents ....................................................................................................... 6
List of Figures ............................................................................................................. 9
List of Tables ............................................................................................................ 10
1 Introduction ....................................................................................................... 11
2 Electricity ........................................................................................................... 14
2.1 Peak Power Problem .................................................................................. 16
2.2 Energy storage ............................................................................................ 20
2.2.1 Battery Energy Storage Systems ......................................................... 24
2.2.2 Compressed Air Energy Storage Systems ........................................... 25
2.2.3 Superconducting Magnetic Energy Storage Systems ......................... 25
2.2.4 Pumped Hydro Energy Storage Systems ............................................. 26
2.2.5 Flywheel Energy Storage Systems ...................................................... 26
2.3 Energy Storage Legacy................................................................................ 27
3 The Electric Car .................................................................................................. 30
3.1 Term ........................................................................................................... 30
3.2 History ........................................................................................................ 30
3.3 Race to popularity ...................................................................................... 31
4 Prerequisites ...................................................................................................... 34
4.1 Open electricity market .............................................................................. 34
4.2 Access to batteries ..................................................................................... 34
5 Model Limitations .............................................................................................. 36
5.1 Terminal value ............................................................................................ 36
6 Research ............................................................................................................ 37
7
6.1 Finding data ................................................................................................ 37
6.2 Arranging the data ..................................................................................... 38
6.3 Two scenarios ............................................................................................. 39
7 Financial Model ................................................................................................. 42
8 Assumptions for Financial Model ...................................................................... 47
8.1 Revenue ...................................................................................................... 47
8.2 Capital expenditure .................................................................................... 49
8.3 Operational expenditure ............................................................................ 50
8.4 Depreciation ............................................................................................... 51
8.5 Debt ............................................................................................................ 52
8.6 WACC .......................................................................................................... 52
8.7 Tax .............................................................................................................. 54
8.8 Batteries ..................................................................................................... 54
9 Results ................................................................................................................ 55
9.1 Projected ..................................................................................................... 55
9.2 Actual .......................................................................................................... 56
10 Sensitivity ....................................................................................................... 59
10.1 Single dimension analysis ........................................................................... 59
10.1.1 Spider charts ....................................................................................... 60
10.1.2 Tornado charts .................................................................................... 61
10.2 Two dimensional analysis ........................................................................... 63
10.2.1 Matrix data tables ............................................................................... 63
11 Discussion ....................................................................................................... 65
11.1 Groundwork ............................................................................................... 65
11.2 Research ..................................................................................................... 67
11.3 Limitations .................................................................................................. 68
11.4 Next steps ................................................................................................... 69
8
11.4.1 Fixing underlying cost assumptions .................................................... 69
11.4.1.1 Electric equipment contracts .......................................................... 69
11.4.1.2 Contacting CALED and ESD .............................................................. 69
11.4.1.3 EPC contract .................................................................................... 70
11.4.1.4 HR information ................................................................................ 70
11.4.1.5 Electric grid operator ...................................................................... 70
11.4.2 Designing and engineering.................................................................. 70
11.4.2.1 Computer system ............................................................................ 70
11.4.2.2 Battery system ................................................................................ 71
11.4.3 Acquiring batteries .............................................................................. 71
11.4.3.1 Partnership with auto manufacturer .............................................. 71
11.4.3.2 Recycling intermediate ................................................................... 72
11.4.3.3 Environmental incentives ................................................................ 72
11.4.3.4 Political incentives ........................................................................... 72
12 Conclusion ...................................................................................................... 73
13 References ..................................................................................................... 74
Exhibits ..................................................................................................................... 77
9
List of Figures
Figure 1 - Global Electricity Generation by Source ........................................................... 15
Figure 2 - Supply Curve for Kraftwerk Power Company ................................................... 17
Figure 3 - Supply Curve for the Town of Kraftstadt .......................................................... 17
Figure 4 - Supply and Demand Curves Matched Together ............................................... 17
Figure 5 - Surplus and Deficit of Power with 50MW Production ..................................... 18
Figure 6 - Surplus and Deficit of Power with 60MW Production ..................................... 19
Figure 7 - Surplus and Traded Power with 65 MW Production ........................................ 19
Figure 8 - Picture of a Battery Energy Storage System ..................................................... 24
Figure 9 - Diagram of a Compressed Air Storage System ................................................. 25
Figure 10 - Diagram of Superconducting Magnetic Storage System ................................ 25
Figure 11 - Diagram of Pumped Hydro Storage System .................................................... 26
Figure 12 - Diagram of a Flywheel Energy Storage System .................................................. 26
Figure 13 - Nissan Leaf Electric Car ................................................................................... 32
Figure 14 - Mitsubishi MiEV Electric Car ........................................................................... 32
Figure 15 - Ford Focus Electric Car ................................................................................... 35
Figure 16 - BMW i3 Electric Car ........................................................................................ 35
Figure 17 - Geographical location of US Power Markets ................................................. 37
Figure 19 - 2MW Storage Unit in Bluffton, Ohio .............................................................. 44
Figure 20 - 8MW Storage Unit in Johnson City, NY .......................................................... 45
Figure 21 - Spider chart for Long Island, Linear representation of each
assumption's effect on NPV of Investment .......................................................... 61
Figure 22 - Absolute Value of each Assumption's Effect on NPV of Investment ................ 62
10
List of Tables
Table 1 – All Five Markets and 29 Systems ...................................................................... 38
Table 2 ‐ Gross Margin for all 6 Systems in California ...................................................... 40
Table 3 ‐ Increase for all 6 Systems on the PJM Market .................................................. 41
Table 4 ‐ Revenue Inputs .................................................................................................. 42
Table 5 ‐ IRR and NPV of Investment ................................................................................ 42
Table 6 ‐ IRR and NPV of Equity ........................................................................................ 43
Table 7 ‐ Short‐ and Long Term Increase .......................................................................... 43
Table 8 ‐ Actual Increase on System ................................................................................. 43
Table 9 ‐ Operational Cost Inputs ..................................................................................... 44
Table 10 ‐ Investment Inputs ............................................................................................ 45
Table 11 ‐ Financing Input ................................................................................................ 45
Table 12 ‐ All Revenue Inputs ........................................................................................... 47
Table 13 ‐ Median Increase .............................................................................................. 48
Table 14 ‐ Average Increase ............................................................................................. 48
Table 15 ‐ Investment Inputs ............................................................................................ 49
Table 16 ‐ Operational Cost Inputs ................................................................................... 50
Table 17 ‐ Depreciation Inputs ......................................................................................... 51
Table 18 ‐ Debt Inputs ...................................................................................................... 52
Table 19 ‐ WACC Inputs .................................................................................................... 53
Table 20 ‐ Four Systems with Sufficient Return on Investment and Equity ..................... 55
Table 21 ‐ Six Systems with Sufficient Return on Investment .......................................... 56
Table 22‐ Twelve Systems with Sufficient Return on Investment .................................... 57
Table 23 ‐ Three PJM Systems with Actual Increase Applied for Two Years .................... 58
Table 24 ‐ Assumptions Tested for Single dimension analysis ......................................... 60
Table 25 ‐ Inputs and Corresponding Outputs for Long Island Tornado Calculations ............ 62
Table 26 ‐ Matrix Data Table Long Island with WACC and Increase in Power Prices ....... 63
Table 27 ‐ Matrix Data Table Long Island Equity with Return on Equity and Increase in
Power Prices .......................................................................................................... 64
11
1 Introduction
This thesis studies the feasibility of buying electricity on an openly traded electricity
market, storing it in electric car batteries and reselling it to the market. The approach
used for assessing the feasibility is to set up a hypothetical project with all necessary
equipment, infrastructure and personnel and then evaluate 15 years of daily electricity
trading operation with the Discounted Cash Flow (DCF) method. The Net Present Value
(NPV) of the project is calculated and if the outcome is positive the project is feasible.
The study deliberately ignores important aspects of a potential energy storage project
which will be addressed in the “Model Limitations” chapter of this thesis. The issues left
out of the study are:
Cost of batteries
Environmental impact
o Recycling of batteries
o Reduction of fossil fuel usage
Political importance
This is done for simplicity’s sake – if trading turns out to be feasible then then these
important aspects can be further investigated and quantified, if not then there is
unlikely any need to.
The idea behind this thesis is to benefit from the limitations of electricity as energy
source and the inherent inefficiencies of its market though breakthroughs in Battery
Energy Storage technology to conduct “virtual arbitrage” on the electric spot market1.
Electric energy price arbitrage exploits wholesale electricity price volatility and diurnal variability. Benefits accrue when storage is charged using low-priced off-peak energy, for sale when energy price is high (Schoenung & Eyer, 2008, p. 28).
1 The transactions are not arbitrage by definition because neither are the transactions between markets, nor do they occur at the same time. The phrase is coined because the ability to store electricity gives the possibility of a virtual risk free trading on a daily-cyclical market.
12
The business idea is to buy electricity at low prices during off-peak hours, store it in a
battery storage systems using batteries from Battery Electric Vehicles (BEV) and then
selling it again for a higher price at peak hours.
This thesis is put forward in 12 chapters. The main topic is a feasibility study on
electric arbitrage, based on energy storage. In order to investigate if it is financially
feasible to trade energy you must first determine if it is technologically possible before
the economic assumptions are applied and tested. Therefore electricity and its physical
attributes will be examined as well as previous research on energy storage and
technologies. The most widely used technologies will be studied to figure out which one
would best fit the project. By a process of elimination battery storage technology is
deemed the best and most relevant process and the lithium ion battery is chosen by
applying one of the prerequisites for the idea and by looking at the global trend of
automobile manufacturers.
The feasibility study ignores important inputs and these limitations and the reasons
for them will be explained before the research is introduced. The data gathering, the
setup and scenarios is then explained as well as all the underlying assumptions that
the results are based on. Consequently, the results are further analyzed to see how
sensitive the conclusions are to variations in the most relevant underlying
assumptions. Finally findings were discussed, a course of action recommended and
the thesis concluded.
The first chapter is an introduction to the basic idea and the structure of the thesis is
described therein. The second chapter reviews common knowledge on electricity,
where electricity itself, the history behind it and how men have gradually gained control
over harnessing it is investigated. The different types of generation methods are
explained and also the mismatch between efficient production of electricity and
electricity usage. This mismatch is the underlying precondition for the research and is
explained in the thesis as the “peak power problem”. Following the discussion of the
peak power problem a quick look is made into the different technologies man has
created to store electricity and the most common technologies were discussed to find
out which would best fit the project.
13
The topic of the third chapter is the electric car, its history and how recent trends
suggest that it will rise in popularity over the next few years.
The two basic prerequisites for this research are detailed in chapter four, as well as
how they will be fulfilled. Chapter five includes a list of the limitations of the model and
a discussion on why certain critical political and environmental aspects were
purposefully left out.
Chapter six is devoted to the research which is explained in details. The electrical
markets and the 29 systems that operate within these markets are introduced as well as
the process of data gathering and its setup. This chapter also contains a discussion on
the two scenarios that are presented in the research.
The next two chapters address the core of the feasibility study as they introduce and
explain the intricacies and conditions of the financial model. In chapter seven the model
is presented and its numerous inputs are thoroughly explained. Chapter eight, on the
other hand, deals with the assumptions of the financial model. Because the
assumptions are the pillars on which the result is based on it is of paramount
importance to explain these assumptions attentively, therefore all eight categories of
assumptions are introduced, explained and justified in this chapter.
Chapter nine includes the major findings of the study and the results of all 29
systems in both scenarios are explained in details. Chapter ten contains a sensitivity
analysis of the assumptions introduced in chapter eight. First the assumptions are
tested to find out which ones have the most influence on the outcome and then three
of the most influential ones are taken and the main results from chapter nine are tested
with two of them varying at the same time.
A discussion on the outcome of the thesis is conducted in chapter eleven. It starts
with a short and concise passage on the core findings, followed with a general
discussion of the thesis and its results. In this chapter the limitations of the model are
explained and potential action plane introduced.
Finally there is a short chapter where it is concluded that the venture is feasible on 4
of the 29 systems investigated and that additional steps should be taken to insure that
the idea will be taken forward.
14
2 Electricity
The existence of electricity is a well-known fact in the modern day life. Different
phenomena such as lightning, static electricity, and electric current do all result from
the presence and flow of electric charge that fall under our definition of electricity. The
modern man recognizes the feeling of getting a small electric shock from a door knob
when he walks on his carpet with socks on, most of us have seen lightning bolts in the
sky, and we take for granted that we can power our stereos and TV sets (and charge our
phones) from the electric socket in the wall.
But the existence of electricity has been known by men for millennia. Around 2500
BC, the ancient Egyptians spoke of the “Thunderer of the Nile” that was “the protector”
of all fish and the Greeks, Romans and Arabics also referenced these electric eels
centuries later (Moeller & Kramer, 1991). Around 600 BC Thalus of Miletus started
experimenting with static electricity and found that amber, when rubbed, would shoot
sparks and attract small (and light) objects. The term electricity, however, wasn’t
adopted into the English language until 1646, following William Gilbert’s research on
electricity and magnetism (Stewart, 2001). Further research was conducted in the
following years and in the early 18th century Alessandro Volta invented the battery and
Michael Faraday the electric motor (Kirby, 1990). In the late 19th century, following the
works of men like Tomas Alva Edison, Nikola Tesla, Werner von Siemens and Alexander
Graham Bell electricity was elevated from being a scientific research project into being
the driving force of the Second Industrial Revolution.
Today, electricity is both commonplace and necessary. The majority of the human
race is completely dependent on electricity. It powers our cities, our households and
our businesses, and most of our industries are powered by (and dependent on)
electricity. In the USA alone the electricity usage was little shy of 4 Terawatt hours (4
billion kWh) in 2009 (us energy Information Administration, 2009). To put that into
perspective the electricity used in the USA in 2009 alone could power a normal 45 watt
light bulb for 10 billion years2.
2 45 watt bulb lit continuously for a whole year uses: 45 x 8760 ≈ 400,000 Wh (400 kWh). 4,000 billion divided by 400 is 10 billion.
15
However, electricity has its limitations. It is a secondary energy source, and as such it
has to be produced or created from another source of energy. The most common ways
of producing electricity is by coal, which represents more than 40% of the world’s
electricity generation. Other methods include gas, hydro, nuclear, oil and other
renewables (in a descending order, see Fig. 1)
Once generated,
electricity is impossible to
store in its current form, it
has to be converted to a
different energy form for
storage and then
reconverted into elec-
tricity. This means that
electricity has to be
produced when it is to be
used. If one looks at the modes of electricity production one can see that a vast
majority of electricity production is performed by large scale power plants, generating
tens and often hundreds of megawatts (MW) of power. Such large plants represent
enormous capital investment with fairly low operational costs. Therefore, it is most
economical to run such facilities as close to full capacity as possible to minimize waste
and inefficiencies. It is true that capital versus operational cost inequalities are more
blatant with hydro and nuclear power plants than with fossil fuel plants. Nonetheless, it
holds true that it is most economical for all facilities to run at capacity. In short, the
supply part of the electricity market prefers stable output with minimal variations.
However, this does not hold true with the demand part of the market.
The demand part can be divided into two brackets, a) the power
intensive/dependent users and b) the general users. In the first bracket you would find
heavy industries such as metal smelting and forging, chemical industry, data storage,
etc. Quite often these industries utilize their energy almost every hour of every day of
the year (8760 hours/year) and thus are the backbone (and obvious favorites) of the
power producers. In the second bracket you find basic consumers and everyday
Figure 1 - Global Electricity Generation by Source
16
industries: supermarkets, offices, households, etc. The players in the second bracket do
not have a flat utility rate of electricity, i.e. they neither use the same amount of energy
constantly throughout the year nor within a single day. The second bracket represents
fractional (and very cyclical) demand dictated by everyday life. Because of this, power
demand spikes around 6:00-7:00 when people are waking up, turning on their lights,
toasting their bread and shaving their faces, when offices come to life and stores are
opened. And then there is another spike in the demand around 18:00-19:00 when
people come home and start cooking dinner and turning on their TV’s and computers.
2.1 Peak Power Problem
This fractional demand of electricity creates the issue that can be referred to as peak
power problem. The demand for electricity is not constant through the course of the
day or between different times of year, it fluctuates. Very little electricity is consumed
during the night when the majority of the population is asleep and activity is at a
minimum whereas the consumption shoots up during the day as the world comes alive
to create a peak in the demand. The supply, however, is kept relatively stable (for it is
most efficient to run a power plant at a stable load), and because electricity does not
store easily there has to be enough production to meet these peaks in demand.
Let’s draw a small picture of this problem with a fictional example and create the
small town of Kraftstadt with 50.000 inhabitants. The maximum energy consumption of
each individual in this town has grown from 1.000Wh a few years ago to 1.249Wh
(1,25kWh) today, i.e. during the peak hour of the day every person in Kraftstadt
consumes 1,25kWh of electricity. Let’s also assume that the people of Kraftstadt follow
a general consumption cycle of electricity, consuming between 50-60% of the max load
between the hours of 00:00 and 6:00, going up to about 80% in the morning and during
the day, and peaking over dinner with consumption around 70-80% for the TV hours of
the night. We will also create the Kraftwerk power company (located in Kraftstadt), an
old utility with a generation capacity of about 55MW of power.
Because of the recent increase in energy consumption of the townspeople, Kraftwerk
has to expand their generation capacity. Now they can supply the town with 50MW of
17
power very easily but that is not enough. But how should they go about expanding? It is
obvious from the discussion above that it is most economical for Kraftwerk to run at
close to full capacity, but what kind of inefficiencies and waste does that entail?
To visualize the
problem, the supply curve
of Kraftwerk’s power
supply can be drawn (see
Fig. 2). The current
generation is at 50MW
and as mentioned above
it is most economical for Kraftwerk to keep their output constant.
The demand curve for the town of Kraftstadt can also be drawn up (see Fig. 3). It is at a
minimum during the depth
of night, rising during the
day and reaching
maximum around dinner
time and through the TV
hours on the evening.
If these charts are
combined it is easy to see
how the supply and demand of power differs through the course of the day (see Fig
4). In the morning the stable production of power plant far exceeds the needs of the
town, while during the day and afternoon the supply and demand harmonize rather well
but late afternoon and
early evening the need for
power is far more than
the power company
produces.
But what do these
curves mean as regards
Figure 2 - Supply Curve for Kraftwerk Power Company
Figure 3 - Supply Curve for the Town of Kraftstadt
Figure 4 - Supply and Demand Curves Matched Together
18
to the inefficiencies of the system? To make it easier to comprehend one needs to look
at the areas below the respective lines. As the lines represent the supply and demand of
power (in MW), the area below them depict the energy produced and consumed (see
Fig. 5).
The area below the
blue line but still above
the red line is light blue
and represents a surplus
of power in the system
and the area under the
red line in salmon pink
represents the power that is traded in the system. Finally, the area under the red line
but above the blue line is green and represents a deficit of power in the system.
During the night Kraftwerk is generating a lot of energy with which is wasted, during
the majority of the day Kraftwerk is able to meet the demand of the people of
Kraftstadt but between the hours of 17:00 and 21:00 there is a serious lack of power to
meet the peak demand.
At the moment the people of Kraftstadt are relying on their neighboring town of
Machtburg to supply them with power during the peak hours. In addition to be hurting
their pride it is also emptying their wallets because the Machtburgers are charging them
triple for the peak power. All the while, the overproduction of Kraftwerk is 99MWh/day
(the sum of the light blue colored area) but because electricity cannot be stored, this
energy goes to waste.
Initially the board of Kraftwerk introduced a solution to the town council of Kraftstadt.
Kraftwerk would raise production capacity to 60MW and thus cover the complete
electricity demand of the day, save for the hour beginning at 18:00 (see Fig. 6).
Figure 5 - Surplus and Deficit of Power with 50MW Production
19
This solution would
save Kraftwerk substantial
investment cost and even
though they could not
supply power over the
dire peak hours they
pointed out that with this
solution (even without generation to cover the peak hours) energy wasted would
amount to 337MWh/day (blue shaded area). The town council did not like this idea one
bit, they were not going to let the Machtburgers overcharge them for energy. So a
second solution was devised, one where Kraftwerk would raise its capacity to 65ME to
cover ALL demand, including the peak hours (see Fig. 7).
The second solution
was accepted and today
Kraftwerk produces
65MW of power and is
able to supply the whole
town of Kraftstadt, even
during peak hours. This
solution results in
Kraftwerk wasting 395MWh of energy every day.
This highly simplified example explains the peak power problem. Of course power
generators are able to vary their production to some extent, and of course other factors
come into play in a major electricity market. But the principle maintains that there are
inefficiencies in the power market because of the mismatch of preferences between the
producers and off takers. This is the underlying precondition for this research. Because
there are different levels of demand for electricity through the course of the day while it
is economical to keep the supply relatively stable price swings are created on the
market. If it is possible to store electricity, one can buy it when demand is small and
prices are low and resell it at higher price when demand soars.
Figure 6 - Surplus and Deficit of Power with 60MW Production
Figure 7 - Surplus and Traded Power with 65 MW Production
20
As can be seen the electricity market has inherent inefficiencies and people have
been studying energy storage for number of years to try to mitigate and minimize these
inefficiencies. Much research has been done on the subject, and many possible
technologies have been explored and tested. These researches are giving increasingly
better results and applications are slowly getting closer to be economically viable. The
solution to cost effectively store energy therefore looks to be close at hand.
2.2 Energy storage
Storing energy is by no means novel or new, and in fact it is an inherent part of nature.
Energy is stored in all living organisms, plants convert the energy from the sun and store
it in chains of carbohydrates and humans convert the energy from carbohydrates and
store it in fat. Even for industrial or commercial applications, energy storage is ancient
and records dating back to the 11th century talk of flywheel applications (waterwheel)
for harnessing and storing energy.
In modern day vocabulary however, energy storage is referred to when energy is
collected for later use.
The fundamental idea of the energy storage is to transfer the excess of power (energy) produced by the power plant during the weak load periods to the peak periods (Ibrahim, Ilincia & Perron, 2007, p. 393).
The problem with electricity (as mentioned above) is that it is a secondary power
source and cannot be stored in its current state.
Initially electricity must be transformed into another form of storable energy (chemical, Mechanical, electrical, or potential energy) and to be transformed back when needed (Ibrahim et al., 2007, p. 393).
To understand how energy storage works it is helpful to remember that energy can
take on many forms and any of those forms can potentially be used to store electric
energy. This is exactly what scientists are doing all over the world, they are exploring
methods of converting electricity into one of the many forms for storage and then
reconverting it back to electricity.
21
The different techniques of storage can be divided into categories based on the form
of energy they exploit. One of the most elemental of energy storage is the one
mentioned in the openings of this chapter, the biological energy storage. Nature found
a way to preserve solar energy through photosynthesis, i.e. capture solar energy and
store it as hydrocarbons. This principle can be utilized in storing energy. No one has yet
figured out a way to convert sunlight directly into a storable energy form but instead
other forms of energy can be used and converted into hydrocarbons. The most common
types of Biological Energy Storage are:
Starch
Glycogen
Methanol
Ethanol
Just as biological energy storage is a key aspect of biological functioning, so too has
mechanical energy storage become a key aspect of the functioning of human society. Ever
since we have understood the causal connection between energy and its effects we have
explored ways to mechanically control and store it. In simple terms mechanical energy is
the energy associated with the motion and position of an object, and the applications of
this were discovered fairly quickly. Men found out that if they stuck a paddled wheel in a
running stream, the energy of the water would turn the wheel, which in turn could turn a
device (e.g. a millstone for wheat and corn). They also figured out that if they carried a
large (heavy) stone up and kept it at an elevated position, the position of the stone and
the velocity of its decent would release great energy when released. The most common
types of Mechanical Energy Storage are:
Compressed air energy storage
Flywheel energy storage
Hydraulic accumulator
Hydroelectric energy storage
Spring
Gravitational potential energy
Once mankind developed a little more sophistication in its approach to science a
whole new world opened up, the world of chemistry. Following in the footsteps of
22
Boyle, Lavoisier and Dalton in the 17th and 18th century, chemists started to apply
scientific method to a concentration of a set system of elements that all are composed
of atoms. Advances were rapidly made and one discovery was the chemical bonds that
keep atoms together to create elements or compounds. It was also discovered that
making or breaking these bonds involves energy that can be absorbed or evolved. This
opened up the possibility of storing energy within chemicals. The most common types
of Chemical Energy Storage are:
Hydrogen
Biofuels
Liquid nitrogen
Oxyhydrogen
Hydrogen peroxide
The 18th and 19th century also saw huge advances in the fields of electric- and
magnetic science, and in the middle of the 18th century the first electrostatic generator
was constructed. Static electricity could be stored in a jar and released when needed.
Further research into the matter revealed that an electric field could be created to store
energy. The most common types of Electrical Energy Storage are:
Capacitor
Supercapacitor
Superconducting magnetic energy storage
Parallel to research into electric energy, chemists and physicists would look into
possibilities of storing energy by combining the principles of chemistry and electricity. In
1800 Alexander Volta introduced the first battery (voltaic pile) and John F. Daniell
improved the technology with his new design (Daniell Cell). Obviously modern
technology has advanced the design and sophistication of electrochemical storage but
the principle remains, it is a device that creates current from chemical reactions (or
reversed, creates chemical reactions by a flow of current). The most common types of
Electrochemical Energy Storage are:
Batteries
Flow batteries
Fuel cells
23
The last storage technique mentioned in this thesis is thermal energy storage.
Thermal energy is the energy that dictates the temperature within a system. If a system
(say a room) is at a stable temperature, you need additional energy to
increase/decrease the temperature of an object within that system. This principle can
be applied to energy storage. If you know that you need to cool a system in the future
and you also know that energy will be scarce and expensive at that time, you can use
the abundant energy that you currently have to create ice, you then store that ice in an
insulated container until you apply it to cool the system. The most common types of
Thermal Energy Storage, apart from Ice Storage are:
Ice Storage
Molten salt
Cryogenic liquid air or nitrogen
Seasonal thermal store
Solar pond
Hot bricks
Graphite accumulator
Steam accumulator
Fireless locomotive
Eutectic system
This list of storage systems is neither detailed nor exhaustive, but is intended to give
a good indication of the vast possibilities for energy storage. But even though storage
technologies are numerous, some are better (and/or more) researched than others and
some have been applied and have been in use for decades. The most commonly studied
and commercially applied technologies are:
Battery Energy Storage (BES)
Compressed Air Energy Storage (CAES)
Superconducting Magnetic Energy Storage (SMES)
Pumped Hydro Energy Storage (PHES)
Flywheel Energy Storage (FES) systems
These are the most popular and advanced technologies for energy storage but their
characteristics and physical scope are quite different as are the applications that apply
24
to them. To take a decision on which of them best fits for electricity arbitrage a short
investigation of the technologies needs to take place.
2.2.1 Battery Energy Storage Systems
As the name suggests, BES systems consist of a number of batteries stacked together as
a unit to store energy. There are many different types of batteries (different chemistries
used for the storage process) but they can be divided into two categories:
Electrochemical batteries and Flow batteries. In this thesis the emphasis will be on
electrochemical batteries.
The electrochemical batteries comprise of electrochemical cells that use chemical
reaction to create electric current. The primary elements of these cells are the
container, the anode and cathode
(electrodes), and some sort of
electrolyte material that is in contact
with the electrodes. Within the cell
there is a chemical reaction between
the electrolyte and electrodes which
creates the current needed. During the
discharge phase, oxidation occurs when
electrically charged ions in the
electrolyte close to one of the
electrodes supply electrons and to complete the process reduction occurs with ions
near the other electrode accept electrons. To recharge the battery this process is
reversed and the electrodes are ionized.
There are numerous different chemistries used for this process, including lead-acid,
lithium-ion (Li-ion), nickel-cadmium (NiCad) and sodium/sulfur (Na/S) and others (Eyer &
Corey, 2010; Divya & Østergaard, 2008; Kinter-Meyer et al., 2010; Davidson et al., 1980;
Krupka et al, 1979).
Figure 8 - Picture of a Battery Energy Storage System
25
2.2.2 Compressed Air Energy Storage Systems
As the name suggests CAES involves
using low cost energy to compress air
that is later used to regenerate
electricity. The compressed air is
converted into electricity by a
combustion turbine, i.e. the air is
released and heated and then sent
through the turbine to generate
electricity. The storage vessel for the
compressed air varies depending on the
scale of the project. For smaller projects the air can be stored in tanks or large on-site
pipes (e.g. high-pressure natural gas transmission pipes) while in larger projects you
would need underground geologic formations, like natural gas fields, aquifers or salt
formations (Schilte, Fritelli, Holst & Huff, 2012; Agrawal et al., 2011; Eyer & Corey, 2010;
Davidson et al., 1980; Krupka et al., 1979).
2.2.3 Superconducting Magnetic Energy Storage Systems
In a SMES system energy is stored in a
magnetic field that is created by a flow
of direct current in a cryogenically
cooled superconducting coil. The system
contrives of three elements: the
cryogenically cooled refrigerator, a
superconducting coil, and a power
conducting system. If the coil is kept at a
temperature lower than its super-
conducting critical temperature the
current will not degrade and the energy can be stored indefinitely (Sander & Gehring,
2011; Eyer & Corey, 2010; Davidson et al., 1980; Krupka et al., 1979).
Figure 9 - Diagram of a Compressed Air Storage System
Figure 10 - Diagram of Superconducting Magnetic Storage System
26
2.2.4 Pumped Hydro Energy Storage Systems
The idea behind PHES is to reverse the
generating process of a hydroelectric plant
to store the energy contained within the
water’s mass. Hydro electricity is
produced by collecting water into
reservoirs and then running it through
turbines to generate electricity. In order to
have a PHS system as part of your plant,
the only addition to an ordinary hydro-
power station is a second reservoir to
collect the water running through the
turbines. At times when energy is inexpensive the turbines can be reversed to pump the
water from the lower reservoir into the elevated one. There it stays until it is feasible to
run it through the turbines again to generate electricity (Agrawal et al., 2011; Eyer &
Corey, 2010; Davidson et al., 1980; Krupka et al., 1979)
2.2.5 Flywheel Energy Storage Systems
The FES system stores energy through
the kinetic energy in rotating masses. The
mechanism of a FES system consists of a
fast spinning cylinder with shaft within an
enclosed vessel. Modern engineering
include a magnet to levitate the cylinder
and minimize friction and wear. The shaft
is connected to a generator that converts
electric energy into kinetic energy
(increases the speed of the cylinder). The
Figure 11 - Diagram of Pumped Hydro Storage System
Figure 12 - Diagram of a Flywheel Energy Storage System
27
stored energy can be converted back to electricity via the generator, slowing the
flywheel’s rotational speed (Eyer & Corey, 2010; Eyer, 2009; Davidson et al., 1980;
Krupka et al., 1979)
2.3 Energy Storage Legacy
With the introduction of new power previous production methods (wind, solar, tide,
etc.) the need for a simple solution to energy storage has become increasingly
important. The incompatibilities of the new environmentally friendly power production
and the cyclical nature of power demand creates a need for an equalizing solution on
the power grid. The new green power solutions are incapable of producing energy to
meet fluctuations in demand, their production capabilities are completely dependent
on existence of the primary power source (sun light, wind, waves, etc.). And for this
reason the systems operators and governing bodies are looking towards energy storage
as a possible solution (Srivastava, Kumar & Schulz, 2012). But although utilities and
transmission systems operators are increasingly interested in new advances in energy
storage to balance systems and fight inefficiencies, this topic has been researched and
developed for quite some time.
In 1979 the Los Alamos Scientific Laboratory of the University of California issued a
paper on energy storage technologies where
…the energy storage technologies under study included: advanced lead-acid battery, compressed air, underground pumped hydroelectric, flywheel, superconducting magnet and various thermal systems (Krupka et al., 1979, p. 2)
And later that year another paper was issued by the Central Electricity Generating
Board where the same technologies (with the addition of compressed air) were studied
(Davidson et al., 1980). It is therefore evident that the history of modern day energy
storage technologies dates back decades.
As mentioned above, the inherent inefficiencies of the electric system is what
sparks most research into energy storage. Scholars, scientists and engineers are
curious to find ways to make generation, transmission and distribution of this most
popular energy source more economical and less wasteful. To that end, every angle of
28
the problem is diagnosed. Different types of applications have been identified for
different needs, whether it is to balance the supply capacity or for voltage support or
to relief transmission congestion. Different applications apply to different problems
and this involves different types of storage solutions. The various storage technologies
are not all equally suited for all storage applications. The applications where you only
need to store small amounts of energy but need high power output for relatively short
periods of time can be thought of as “power applications”, whereas applications
where you need more energy capacity for a larger periods of time can be called
“energy applications”. The technologies that fit the former applications are SMES and
FES systems, while PHES, CAES and most BES systems are more suited for the latter
(Eyer & Corey, 2010).
In this thesis the intention is to focus on energy applications, i.e. different
technologies to store relatively large quantities of energy (75MWh) for a considerable
time (1-19 hours), and that the technology be geographically independent, i.e. that the
solution can be mobile and does not depend on geography such as rivers or old gas
fields. By a process of elimination this leaves batteries as the ideal technology, because
CAES is geographically dependent (for this scale of operations) as is PHES, and FES and
SMES is better suited for power applications.
In 2007 a study was conducted on energy storage system to explore the benefits of
relieving operational challenges on the electricity grid through energy storage. In the
study NAS batteries were used to store (a modest amount) of energy (7,2MWh), they
were fitted within a shipping container and connected to the PJM power market. The
results of the study were positive: there is a definite, quantifiable lifetime value of
storage (Nourai, 2007).
Large scale energy storage with batteries can be done, and it can be done on an
economic merit. For it to be successful there are three criteria that need to be fulfilled:
1. The storage system has to be efficient, i.e. energy losses have to be minimal (this includes charging, storing and discharging).
2. The storage system has to be durable, i.e. it has to have a long service life and maintenance has to be at minimum.
29
3. The storage system has to be inexpensive, i.e. has to represent modest capital cost relative to the operation (Prost, 2009).
The increased effort into research and development on lithium ion batteries gives
hope that these criteria can be fulfilled. The technology for storing electricity in
batteries is rapidly improving and in large parts it is due to the increased focus on the
electric car. Batteries are getting smaller, more powerful and cheaper. But although the
technology is driven towards the electrification of the common car there are numerous
other applications for this new technology. One application is to utilize the storage
possibilities on a large scale.
The notion of using electric car batteries to fight inefficiencies of the electrical
systems has been studied. Electric cars are plugged in while not in use and the condition
of the electricity market dictates whether the car buys electricity (and charges) or sells
electricity to the market (discharges), allowing energy markets to be regulated and
demand peaks mitigated to some extent. This way “peak shaving” can be obtained
through the sophistication of the grid (Sousa, Roque & Casimiro, 2011). But there is
another application, to use the batteries to utilize the variance between demand and
supply of electricity and conduct arbitrage on the electrical market.
The idea in this research is to collect batteries into “battery islands” where relatively
large quantities of energy (75MWh) can be stored for a considerable time (1-19 hours).
The batteries are charged during the low cost, off-peak hours and then discharged at
peak hours for a much higher price. But what types of batteries would be best suited for
such an operation? The answer can be found within a global trend fueled by
environmental awareness, the electrification of the common car.
30
3 The Electric Car
3.1 Term
There are many types of vehicles that use electricity for propulsion, and therefore have
claim for the name ‘electric car’. In this thesis the term will, however, be assigned only
to high performance cars that derive their power from an a on-board battery pack, and
not small, low performance vehicles like golf carts or electric wheelchairs; solar
powered cars that use photo voltaic technology to convert sunlight into electricity; or
hybrid cars that merge internal combustion engine and electric powered propulsion.
3.2 History
The electric car was big in the late 19th and early 20th century, and in fact it was the
preferred choice of automobile. Electricity was the driving force of the second industrial
revolution and its popularity as an energy source extended outside the home and
factories. The electric car was also considered simpler and more enjoyable to use, it had
neither the noise nor smell of a gasoline car, it did not vibrate and shake, there was no
changing of gears (one of the most difficult part of operating a gasoline car of that
time), and it did not require the manual effort of cranking of the engine to start
(Sperling & Gordon, 2009; Sandalow, ed., 2009)
However, with Ford Motor Company’s new methods of production the cost of a
gasoline car was slashed to less than half of that of an electric one and advances in
internal combustion engines, gave them increased range, quicker refilling time and
made them more enjoyable to ride overall. (Sperling & Gordon, 2009; Sandalow, ed.,
2009; Georgano, 2000)
In the oil crises of the 70’s and 80’s the eyes of the world quickly gazed toward the electric
car, but only for a brief moment. And it wasn’t until the global downturn in the early 2000’s
when car makers steered away from fuel-inefficient SUV’s toward smaller cars. Recently the
concerns about environmental impact are playing a bigger role and eyes have shifted
toward the electric car again (Sperling & Gordon, 2009; Sandalow, ed., 2009)
31
3.3 Race to popularity
The debate on whether car ownership is a necessity or luxury is ongoing but fact
remains that car ownership is widespread in the USA, with more than 137 million
passenger cars on the streets in 2008 (Bureau of Transportation, 2011). It is therefore
safe to say that cars are a commodity.
The typical consumer goes through a cost/benefit analysis when he purchases a
commodity. More often than not he does not realize that such an analysis has taken
place, he simply buys what he is used to buying and the benefits of getting something
that he knows, plus the benefit of not having to bother with the alternatives outweigh
the cost of having to evaluate other options. But when it comes to cars the analysis
tends to be more thorough. And if the electric car is to be able to substitute the gasoline
car it will have to win this cost/benefit comparison.
One of the most critical factors in the auto industry’s fight for the consumer is the
distance the car can travel with its potential purchaser. Traditional car makers (those
focusing on gasoline) have conceded that look and feel can be matched with electric
cars but the range cannot, and are therefore highlighting this fact to their advantage. As
with a typical internal combustion engine vehicle, the range of an electric car is a
determined by four factors:
Energy source
Weight of vehicle
Resistance
Performance demand of driver
The last three factors are simply limitations governed by the laws of physics.
Newton’s 1st law of motion tells us that the speed (or velocity) of an object remains
constant until a force acts upon it and the 2nd law says that the acceleration of an object
is directly proportional to the force acting on it and inversely to its mass (the heavier the
object the bigger the force needed).
Applying these laws one sees that heavy vehicle depletes its energy faster driving
from A to B, than in a lighter one (all other things kept equal) and a car with high
resistance has a harder time propelling forward and therefore requires more energy
32
than a car with lower resistance. The performance constraint is also dictated by the law
of motion because a driving style of rapid acceleration demands more energy than a
constant speed type of driving (Zimba, 2009).
In the race toward wide-spread commercial electric car it is relatively easy to keep
the last three factors on par with the internal combustion one. The weight is similar
because the battery pack replaces a heavy engine and the car manufacturers are
looking at new alloys and carbon fiber for the body in white (BIW). Both car types are
built on comparable bodies and chassis so the wind resistance and tire friction should
be very similar (or the same). And the performance demand is subject to the individual
driver which will act the same in both car types, a fast driving teenager that drives the
car to its maximum can travel less distance with the energy stored in the car than a
veteran truck driver. And will do so regardless of the type of car.
The first factor, the energy source, is most difficult and will determine the outcome
of the race. It seems that battery technology is on the right track. Electric car makers
today usually focus on lithium based batteries for energy storage and major advances
have been made in this area with today’s electric vehicles are sporting ranges in excess
of 300km pr/charge (GreenCar Magazine, 2009). As previously mentioned governments
(local and national) are assisting private entities in research and development of
batteries and major car manufacturers have announced their version of an electric car
for the coming years (see Fig. 15 and 16).
To win the race toward commercialization the
electric car will also have to overcome a bigger
challenge, the cost. It can be argued that a typical
consumer may
place some
additional value
to the fact that
her car is environmentally friendly and has no CO2
emission. It is true the electric car has no tailpipe
pollution and depending on the power source used
to generate the electricity it emits less than half, down to a quarter, of the CO2 of an
Figure 14 - Mitsubishi MiEV Electric Car
Figure 13 - Nissan Leaf Electric Car
33
conventional gasoline powered car (Kromer & Haywood, 2007), but nonetheless, the
first thing that affects consumer is the direct cost, i.e. how much she has to pay. Today
the price of an electric car is considerably higher than of a gasoline car, primarily
because of the high cost of electric batteries. To address this issue, both private and
governmental entities are pouring money into research and development of batteries to
enhance their capabilities and reduce the cost, with the aim of market parity in the
coming years. Governments around the world are also spending considerable money in
promoting electric cars, including incentives of up to $7,500 for buyers of electric
vehicle (IRS, 2011). New advances are also being made in the production of batteries
and production costs are gradually shrinking, making new batteries better and cheaper.
The outlook for the electric vehicle looks promising and if all goes as planned there
will be more than one million electric cars on the streets within a few years. And with
each car comes a battery and with each battery comes possibilities.
34
4 Prerequisites
In order to operate a project that uses battery storage for electric arbitrage many
obstacles have to be overcome and numerous conditions have to be met. One obvious
condition is that energy storage is technologically possible, another is the condition of
economics.
The technological requirements were addressed in chapters two and three. It has
been established that energy storage is possible and has been done, and if the results
from the research prove promising a more thorough investigation into the technological
aspects are in order. As for the commercial and economic concerns, they will be
addressed in the research. There are, however, two basic prerequisites that need to be
fulfilled:
There needs to be access to an open electricity market
There needs to be access to a sufficient number of batteries
4.1 Open electricity market
For the project to be successful one has to be able to connect to the electricity
transmission system, buy of it at one price during a particular time and sell into it at
another time. This demands the following of the electric system and its market:
The transmission system has to be open, i.e. anyone can connect to the system and negotiate purchase out of it / sale into it (given the necessary competencies and licenses)
The market for prices on the system must be open, i.e. one can negotiate purchases/sales in real time (with delayed delivery)
The market for prices on the system must be active, i.e. it follows the laws of supply and demand, making off-peak electricity cheaper than peak
The US electrical markets and the transmission systems on them fulfill all
the conditions above.
4.2 Access to batteries
Based on the abovementioned trend in the auto industry it is assumed that electric car
batteries will be used for energy storage. The size of project defined in this thesis will
35
need roughly 5.000 batteries so it is obvious that quite a few electric cars would have to
be on the streets to accommodate such a large number of batteries.
Today’s outlook is that a considerable amount of
battery electric vehicles will be in operation within
few years, as many large car manufacturers (e.g.
Mitsubishi, Nissan, BMW, Renault, and Ford) have
announced their electric vehicles and proposed
rollout in the coming years (see Fig. 13 and 14).
The US government has also been vocal about
their support toward the electric car and president
Obama announced in his State of the Union Address
in January 2011 plans to see 1 million electric vehicles
on American streets in 2015 (The White House,
2011). Therefore we believe there will be enough
batteries for this kind of operation without it
representing the bulk of the total number of batteries on the market.
Figure 16 - BMW i3 Electric Car
Figure 15 - Ford Focus Electric Car
36
5 Model Limitations
With the two prerequisites met, it is possible to calculate the commercial viability and
the economic benefits of trading electricity on an open market. The viability is
ascertained through a feasibility study that uses a DCF method to calculate the NPV of a
project that trades electricity for 15 years.
As mentioned in chapter one, this research ignores a few important aspects of a
potential energy storage project. The research is conducted only to determine if electric
energy price arbitrage is profitable based on the operation of such project including all
operation cost, investment cost of equipment, financing cost and depreciation. The
study, however, neither includes the cost of batteries nor the political and
environmental impact of the operation.
These omissions are purposely done for the sake of simplicity. This thesis is a
feasibility study and not a business plan. If the results of the feasibility study are positive
and it turns out that electric arbitrage is feasible then steps can be taken to create a
business plan around the idea with the focus of setting up and running an actual
business. Such a business plan will have to take into account acquisition of car batteries,
and various environmental- and political impacts such as the benefits of reducing CO2,
stabilizing the electric grid and the disposal of the batteries.
All these issues can be incorporated into a business plan in many inventive ways that
create value for both the project and its customers, but a positive outcome from this
feasibility study needs to be in place for this work to take place (otherwise it will be in
vain).
5.1 Terminal value
Because of the abovementioned limitations it was decided not to calculate terminal
value in this study. The goal was to determine if the cash flow from an electric arbitrage
would suffice to cover the investment (and opportunity) cost of setting up such an
operation. And lacking important cost and benefits inputs related to batteries, political-
and environmental aspects, it did not seem right to attribute a perpetual growth to the
substantial cash flow the operation produces without including part of the basis of what
constitutes a going concern project.
37
6 Research
6.1 Finding data
The aim of this study is to determine if it is feasible to actively trade electricity on an
electricity market in the United States. The Federal Energy Regulatory Commission
(FERC) is an independent agency that regulates the interstate transmission of electricity,
natural gas and oil. It also collects real time data on day to day electricity prices. All data
for this research was collected from the FERC’s website3.
The electrical power market in the USA is broken down into ten independent
markets (see Fig. 17):
Of the ten markets, FERC has collected detailed and comprehensive data on five of
them, and then issued the data reports for the years 2009, 2010 and 2011:
California (CAISO)
Midwest (MISO)
New England (ISO-NE)
New York (NYISO)
PJM
There are several independent transmission systems that are traded within each
market. The five markets that were the focus of this research had 29 systems
between them:
3 http://www.ferc.gov
Figure 17 - Geographical location of US Power Markets
38
The data for these five markets was found in monthly reports. Each report gave a
daily overview for each of the transmission system for that month and each day was
broken down to 24 individual one hour intervals, giving prices quoted for each hour of
the day. The reports had two sets of pricing information, “Day-Ahead Prices” and “Real-
Time Prices”. The Day-Ahead prices are prices that are forecasted the previous day and
Real-Time prices are the actual prices that were traded (see Exhibit 1).
6.2 Arranging the data
All the information was exported into Excel, one month at a time (see Exhibit 2) and
then rearranged for each transmission system (see Exhibit 3).
The aim of the project is to investigate the feasibility of buying electricity when the
price is low and reselling it to the market at peak prices. It was decided to fix the
amount of energy to 75MWh, i.e. buying and selling 75MWh at different times. After
consulting with electrical engineers at Orkuveita Reykjavíkur (Guðleifur Kristmundsson
& Þorsteinn Sigurjónsson, Orkuveita Reykjavíkur, personal communication, March 2nd,
2011) and VJI Engineering (Eymundur Sigurðsson, VJI Engineering, personal
communication, March 3rd, 2011) it was decided to limit the power load to 25MW. A
power load of 25MW offers less expensive equipment solutions and this was further
confirmed at a meeting at Smith & Norland, the Siemens distributers in Iceland (Ólafur
Marel Kjartansson & Ólaf Sveinsson, Smith&Norland, personal communication, March
11th, 2011). With a power load at 25MW it is obvious that in order to achieve 75MWh it
is necessary to buy power for three hours (at full load) and sell it for another three
hours. With these basic conditions in mind the sum of each consecutive three hour
California Midwest New England New York PJM
Pacific G&E Cinergy CT Capital Western
SoCal First Energy NEMass Hudson Baltimore
San Diego Ill inois NH Long Island Commonwealth
NP-15 Michigan ME NY Dominion
SP-15 Minnesota Internal North NJ
Palo Verde Wisconsin West Pepco
Table 1 – All Five Markets and 29 Systems
39
period in the day was calculated, i.e. from 0:00 – 2:00, 1:00-3:00, 2:00-4:00, etc. Each
day was treaded as standalone and therefore calculations were made on the cost
between days (e.g. 23:00-1:00). This gave 22 unique sums for each day (see Exhibit 4).
These sums were calculated for both sets of prices provided by the daily reports. The
“Day-Ahead Prices” sums were called “Projected” and “Real-Time Prices” sums were
called “Actual”. The lowest and highest sums were then found4.
6.3 Two scenarios
Having two sets of numbers, the projections (best guess) of what prices would be
the following day and the actual prices of that day gave the opportunity to calculate
outcomes with and without the presence of intellect (human or artificial). In order to
do that, two scenarios were created. One called Actual; being the theoretical
maximum (always buying at the lowest possible and selling at the highest possible
price) the other called Projected; being the intellectual minimum (relying purely on
day-ahead predictions). And even though the fundamental principle of the two
scenarios is the same the approach for calculating the outcomes for Projected and
Actual are very different.
In the Actual scenario the lowest of the 22 daily sums were multiplied with 25 (MW)
to get the price of buying 75MWh of electricity at the lowest possible cost for that day
(25MW x 3 hours = 75MWh). The same was done with the highest price, thus getting
the highest possible revenue for the day. Subtracting the cost from the revenue gave
the maximum gross margin of the day. This was done for every day of every month (see
Exhibit 5). The daily gross margin was then used to calculate the gross margin for each
month and consequently each year. The same calculation was done for all transmission
systems and the data compiled to a simple overview (see Exhibit 6).
The calculation for Projected was more complicated. The “Day-Ahead Prices” is a
base for what the market projects will be next day prices. The idea was to use these
4 There was no constraint in the calculations that the lowest sum had to incur earlier in the day
than the highest sum, but a sample check revealed that this was the case nonetheless in more than 99% of the time.
40
forecasts and apply them to the actual numbers. By finding the lowest (and highest)
three consecutive hours of the “Day-Ahead Prices” it was possible to mirror them to
the actual prices, i.e. buying and selling at real prices on the hours the market
predicted would be the best. If the “Day-Ahead Prices” were lowest between 2:00-
4:00 and highest between 18:00-20:00, energy would be bought at 2:00-4:00 and
sold at 18:00-20:00 irrespective of the actual prices.
The major difference between the applications of the two scenarios is that for
Actual you would require human intelligence monitoring and reacting to the market
in real time. Traders would have to be hired who would strive toward the theoretical
maximum of always buying at the lowest possible price and selling at the highest.
Ideally the compensation structure for these traders would reflect the proximity of
the actual trading to that theoretical maximum goal, i.e. the closer you get to the
maximum the higher your compensation. The Projected scenario is the simplest
setup where the equipment is merely connected to a computer that buys and sells
solely on predictions from the day before. This scenario assumes no intelligence.
With revenue, cost and gross margin numbers for all years on each of the
transmission systems calculated, the data sets could then be merged into a single
model. Each year was considered unique and each system was filed individually,
giving a total of 86 individual sub datasets (see Exhibit 7). The numbers were then
arranged in an overview with numbers for Projected scenario and Actual scenario in
separate columns, and top five values for gross margin and increase percentage
were highlighted to enable visual comparison.
Table 2 gives a visual
representation of the
gross margin of all the
systems in California.
After having calculated
gross margin for all
three years for each of
the 29 systems, the highest results were identified and shaded (different shades of blue
for the Projected scenario and red for the Actual scenario). When focusing on the Actual
2009 2010 2011 2009 2010 2011
Pacific G&E 826.899$ 904.278$ 885.983$ 1.404.274$ 1.618.872$ 1.724.483$
SoCal 954.318$ 1.207.066$ 961.644$ 1.643.958$ 2.085.359$ 1.934.894$
San Diego 958.977$ 1.283.706$ 1.123.566$ 1.815.862$ 2.124.199$ 2.391.214$
NP-15 777.676$ 853.698$ 813.826$ 1.369.337$ 1.552.692$ 1.640.471$
SP-15 947.408$ 1.158.756$ 930.238$ 1.739.558$ 1.974.152$ 1.926.366$
Palo Verde 700.196$ 918.903$ 756.434$ 1.432.841$ 1.738.897$ 1.779.699$
Gross margin
Projected Actual
Table 2 - Gross Margin for all 6 Systems in California
41
scenario it is interesting to see that in the years 2010 and 2011 four of the five highest
outcomes were in California.
The increase in gross margin for all the 29 systems was also calculated for each
scenario and five
highest values were
identified and shaded
(light green for the
Projected scenario,
green for the Actual
scenario. Table 3
depicts the percentage increase in gross margin for the PJM market and just as the
gross margin numbers are dominated by the California market, the increase in gross
margin is nowhere higher than in the North East with four of the five highest (in both
scenarios) outcomes.
09 to '10 10 to '11 Overall 09 to '10 10 to '11 Overall
Western 41,70% 5,97% 50,16% 42,35% 0,03% 42,38%
Baltimore 69,50% -1,44% 67,06% 64,12% -5,13% 55,71%
Commonwealth 28,37% 2,03% 30,97% 25,26% 0,55% 25,94%
Dominion 63,76% -1,47% 61,35% 65,78% -8,55% 51,60%
NJ 59,70% 8,25% 72,87% 56,54% 4,05% 62,87%
Pepco 12,23% 12,23% 8,30% 8,30%
Increase
Projected Actual
Table 3 - Increase for all 6 Systems on the PJM Market
42
7 Financial Model
In this study the DCF analysis was used to value the project. The model calculates free
cash flow from buying electricity at low prices, storing it in electric car batteries and
reselling it at peak prices. It is assumed that trading occurs every day of the year and
that transmission losses are incurred. The model projects 15 years of operation.
The model uses the historical data and projects it into the future based on a set of
assumptions. The assumptions inputs are all located on a single sheet for the user of the
model to manipulate at will (see Exhibit 8). All changes made to the assumption inputs
will immediately be incorporated into the model and the outcome will change
accordingly. An example of the revenue inputs can be seen in Table 4.
The outcome of this model is two sets of numbers. The
first set consists of two numbers, the Internal Rate of
Return (IRR) and the NPV of the investment as a whole.
To arrive at these numbers the total cash flow of each
year is calculated and is then discounted against the
entire investment of the project. The goal is to find the
cash available to repay loans, pay out as dividends and/or plow back into the
project. And because the objective is to use this cash to service both debt and equity
it is discounted with the weighted average cost of capital (WACC), i.e. the cost of
both debt and equity. These two numbers are referred to as “IRR of Investment” and
“NPV of Investment” (see Table 5).
The second set of numbers consists of the
IRR and NPV of the equity. The total cash flow
that was calculated for the whole project is
taken and the cost of servicing loans is
deducted. The resulting cash flow is then discounted against the equity contributions of
the project. The goal is to find the cash available to pay out as dividends or plow back
into the project. In this instance, because the objective is to find the cash earmarked for
equity it is discounted with the cost of equity. These two numbers are referred to as
“IRR of equity” and “NPV of equity” (see Table 6).
Grid to be used -
Hours in/out 3
MW pr/hour in 25
Power losses 3,00%
MW pr/hour out 24,25
Table 4 - Revenue Inputs
Traded Projected
IRR of Investment 15,00% 7,50%
NPV of Investment $1.000.000 $500.000
Table 5 - IRR and NPV of Investment
43
The model provides the option of
calculating for each transmission system
independently. One system is chosen and
corresponding revenue and cost numbers is
pulled from the data set and used as a base for the projections. The mode allows for
two kinds of growth, short term and long term. The user denotes the increase
percentage and the length of the short term period (in years) and then appoints the
long term growth percentage (see Table 7).
Revenue and cost will rise according to the
short term growth rate, for the determined
number of years, and then at the long term
rate for the remainder of the 15 years. When a transmission system
is chosen as a base for calculations, its corresponding increase
numbers appears on the screen. This is done so that the user can
better determine the appropriate increase number (see Table 8).
The two different set of data Actual and Projected are used to create two
independent sets of outcome. The Actual data assumes that traders use insight and
intelligence to achieve results close to the “theoretical maximum” and the model allows
the user to predict the accuracy level. All deviation from the “theoretical maximum” is
treated as cost of goods sold and comes as a deduction to the gross margin. This is done
to maintain logic in the compensation system for the traders. The traders receive salary
for their work but also bonuses based on performance. The bonuses are tied to the
gross margin, i.e. the higher the gross margin the higher the bonuses. By tying the
accuracy level of the traders with cost of goods sold it creates an increased incentive for
results as a lower accuracy decreases the gross margin which in turn decreases bonuess.
The Projected data assumes no intelligence and therefore neither salary (and bonus
payments) nor accuracy costs are included in the calculations.
The operational costs for the project, aside from salaries and bonuses are overhead,
maintenance, selling expense and ‘other expenses’.
Traded Projected
IRR of Equity 10,00% 5,00%
NPV of Equity $500.000 $325.000
Table 6 - IRR and NPV of Equity
Increase in power prices 15% first 5 yrs
Long term increase 5%
Table 7 - Short- and Long Term Increase
Grid Increase
SP-15 10,74%
Increase from 2009
Table 8 - Actual Increase on System
44
Overhead is assumed to be minimal as the only thing needed is an office for the
traders with telephone- and internet connection, computers, mobile- and landline
phones, and appropriate furniture and amenities. Maintenance of the electric system
will be outsourced to a third party on a long term contract, terminable by both parties
once a year.
Selling expense is assumed to be modest, because all trading is done online in real
time. However, traders need to have the possibility to develop and maintain their
network of business relations and therefore a small ratio of revenues is allocated as
selling expense. Other expenses are for any kind of contingencies that might occur
when running a business. A lump sum is also allocated as startup cost, and is intended
to cover unexpected costs that often pile up when starting a business. Even though the
settlement system for online electrical trading is done
through a clearing house, contingencies are built into
the model and working capital is assumed to increase
for a defined number of years (determined by the
user). It is assumed that initial capital raised will
finance the first year of working capital, and internally
generated cash flow will service the consequent
years. All inputs for operation costs can be modified
by the user of the model (see Table 9).
Capital expenditures for the project will
consist of acquisition of land (buying or
leasing), constructing (or buying) a building,
and purchasing electrical equipment. The
piece of land needed under the project is
modest in size. The batteries can easily fit
into a few shipping containers and the
electrical equipment is of a similar size. Figures 18 and 19 depict a 2MW and 8MW
battery storage units (respectively) currently found in the USA. A land of 10,000m2 (1
hectare) would be ample, and easily provide space for possible future expansions.
Salaries $75.000
Bonus payments 2%
Nr of traders 2
Traider accuracy 95%
Overhead $30.000
Maintenance $120.000
Other $100.000
Working capital $200.000
Selling expense 0,2%
Start-up expense $300.000
Table 9 - Operational Cost Inputs
Figure 18 - 2MW Storage Unit in Bluffton, Ohio
45
The purchase of electrical equipment would take
place either through local distributors or directly
through vendors. The leading European electrical
equipment providers ABB and Siemens both have
readymade solutions, on shelf, at $10,000,000 with lead
time of 12 to 24 months. The need of shelter for both
batteries and electrical equipment depends on location
(local weather conditions) and would consist of a simple
structure. The batteries and electrical equipment together cover about 500m2, so a 700m2
house would be erected, giving room for storage and maintenance facilities. The model
allows for additional unforeseen capital expenditure of $300.000 (see Table 10).
The fixed assets will be depreciated in accordance
with applicable laws and legislations (depending on
location). The model assumes that assets can only be
depreciated by 90%, i.e. 10% residual value must
remain in the books5
The equipment will be financed with export
financing from the country of origin (Sweden or
Germany). The model assumes that the equipment
cost will be leveraged through either the vendor
himself or an export financing program within the
vendor’s home country, at local rates plus a premium.
A smaller, short term loan of $500,000 to cover the
initial outflow of cash is also included in the model.
Interest rates and payback years are adjustable by the
user of the model (see Table 11).
Income taxes are levied on Earnings before Tax
(EBT) with losses carried over for a maximum of 10
years. Property tax is calculated on all real estate, i.e. both land and building.
5 This is done in accordance with Generally Accepted Accounting Principles (GAAP) and European accounting legislation.
Land $300.000
Building $1.000.000
Equipment $10.000.000
Other $300.000
Total $11.600.000
Table 10 - Investment Inputs
Leverage of equipment 70%
Equipment loan
Bank I $7.000.000
Interest rate 6,25%
Payback in years 8
Short term financing
Bank II $500.000
Interest rate 8%
Payback in years 3
Total capital needed $12.100.000
Equity ratio 38%
Equity $4.600.000
Debt $7.500.000
Table 11 - Financing Input
Figure 19 - 8MW Storage Unit in Johnson City, NY
46
All in all, the total capital needed for the project is $12,100,000, with $7,500,000
as debt and $4,600,000 provided as equity. This corresponds to an equity ratio of
roughly 38%.
The model calculates the cost of equity using the Capital Asset Pricing Model (CAPM;
French, 2003) and derives a discount rate for the project by applying the Weighted
Average Cost of Capital (WACC; Miles & Ezzel, 1980) method. Both rates are used to
calculate the NPV of the investment. The WACC is used to discount the leveraged
project, i.e. the cash flow that can be used to service debt and pay out dividends. The
cost of equity is used to discount the cash flow after debt has been serviced.
47
8 Assumptions for Financial Model
A financial model is only as strong as the assumptions it is built on, and therefore it is
imperative to scrutinize each assumption thoroughly. The model is set up with 39
different assumptions that fall into 8 different categories. The categories are:
Revenue
Capital expenditure
Operational expenditure
Depreciation
Debt
WACC
Tax
Batteries
A decision on each different assumption was arrived at through a rigorous process.
Information on each point was gathered through interviews6, the above mentioned
research, and the author’s experience and common sense. The data was then matched
to real examples and the results double checked with industry experts (see Exhibit 9).
8.1 Revenue
Hours in/out is the only assumption in the whole model that is fixed and cannot be
altered by the user. After meetings with electrical engineers and distributors of the
electrical equipment it was decided
to divide the flow of total electricity
purchased/sold over 3 hours. This is
done to decrease the strain on the
batteries and to some extent the
electrical equipment (see Table 12).
6 Interviews were conducted with academics in finance, economics, electrical engineering and
chemistry and professionals from energy companies, electrical engineering consultancies, financial institutions and consulting firms.
Grid to be used SP-15
Hours in/out 3
MW pr/hour in 25 Grid Increase
Power losses 3,00% SP-15 10,74%
MW pr/hour out 24,25
Increase in power prices 15% first 5 yrs
Long term increase 5%
Increase from 2009
Table 12 - All Revenue Inputs
48
MW hours in/out was arrived at following the advice from electrical experts at
Orkuveita Reykjavikur and VJI Engineering. To have a sizeable operation, it was the aim
to have at least 25GWh of energy traded per year. To achieve this minimum
requirements one would have to have at least 22,83 MW of installed power (keeping in
mind the fixed precondition of 3 hours in/out).
25GWh = 25.000MWh. The goal is to buy/sell 3 hours of energy every day of the year (3h/day * 365 days= 1095 hours). To arrive at the necessary power load, the total amount of energy is divided by the total hours bought/sold:
22,83MW of power is needed to trade 25GWh of energy pr/year, this number was
then rounded up to 25MW of installed power, resulting in 27,375GWh of energy
traded per year.
In this model power losses are assumed 3% in the model based on the Icelandic grid
operator Landsnet which publishes 3% as their power losses. The same level of
sophistication is assumed with American operators.
Increase in power prices dictates how much revenues and COGS increase between
years, i.e. the percentage by which the previous year will increase. The model assumes a
15% increase. This looks high, but it is imperative that
one realizes that this number does not correspond to
an increase in consumer prices (or spot prices); this is
the percentage by which the gross margin of buying
at lowest prices and selling at peak prices
increases over the course of one year. For the
Actual scenario
the average increase between the years 2009 and
2011, over all the 29 systems was 17,68% (median
16,33%), and for the Projected scenario the
respect matching numbers were 24,63% and
20,85% (average and medium, respectively; see
Tables 13 and 14).
Projected Actual
New England 28,06% 19,09%
California 5,99% 21,16%
Midwest 16,43% 10,97%
New York 24,15% -3,69%
PJM 49,11% 41,13%
Overall 24,63% 17,68%
Table 14 - Average Increase
Projected Actual
New England 22,54% 23,37%
California 5,90% 21,30%
Midwest 18,43% 12,58%
New York 24,50% -0,65%
PJM 55,75% 46,99%
Overall 20,85% 16,33%
Table 13 - Median Increase
49
The next assumption deals with the length of the short term power price growth, i.e.
it determines how many years the short term growth will last. After consulting with
experts in corporate banking at Arion Bank it was agreed that 5 years would be an
appropriate period.
Finally the long term increase is set at 5%, which represents roughly one third of the
average increase between 2009 and 2011.
8.2 Capital expenditure
There are two sets of assumptions in this category. First the prices of the assets are
assumed and second, the speed of acquisition (or construction; see Table 15).
The project assumes a purchase of 1
hectare of land. This will be ample for
current operations as well as any future
expansions. The batteries needed for
this project will fit within 8 normal 40
foot containers, so that the area needed
for the batteries is about 350 m2. With
the electrical equipment allocated 150 m2 of space the total area of building would be
500 m2. Provided that the utility ratio of the land is 50% (maximum half of the land can
be occupied for building) the maximum size of buildings on the land is 5.000 m2. This
means that the operations can expand ten times without further acquisition of land.
The price of land is assumed to be $300.000 pr/hectare, which is on par with prices of
industrial land in Iceland. The land is assumed to be purchased all at once (100%
phasing during first year).
The building would be a simple construction, large enough to shelter batteries and
electrical equipment, plus a small storage space and maintenance facility. The model
assumes $1.000.000 as the cost of the building, the cost of a simple shell house with
specialized wiring and utilities is assumed to be $1,666 pr/m2 and a size around 600 m2.
The building will be erected in year 1 and finished in year two, 75% phasing in first year)
Cost yr 1 yr 2 yr 3
Land $300.000 100% 0% 0%
Building $1.000.000 75% 25% 0%
Equipment $10.000.000 100% 0% 0%
Other $300.000 50% 50% 0%
Total $11.600.000
Phasing
Table 15 - Investment Inputs
50
Equipment is assumed to cost $10.000.000. This number was quoted at a meeting
with an electrical engineering expert (Eymundur Sigurðsson, personal communication,
March 3rd, 2011) and then confirmed by the distributers of Siemens in Iceland (Ólafur
M. Kjartansson & Ólaf Sveinsson, personal communications, March 11th, 2011). All the
equipment will be installed during first year.
The model allows for unforeseen additional capital expenditure of $300.000, half of
which is allocated to first year, the other half to the second.
8.3 Operational expenditure
The first four assumptions regarding
operational expenditure are associated
with the professionals that buy and sell
electricity out of and into the electric
system (see Table 16). The yearly salary is
set at $75.000. It is assumed that active
traders on the utility market could be
contracted to add this responsibility to
their portfolio of trading activities. The
trading bonuses are then defined as a
percentage of gross margin, thus tying the effectiveness of their trading directly to the
compensation they receive. Based on the analysis a 2% bonus would match the
traders’ salaries after three years on the most profitable systems. The model assumes
two traders to be employed, due to the fact that electricity will be traded 24 hours of
the day, every day of the year. Finally the model predicts on the accuracy of the
traders. The benchmark is always the defined “theoretical maximum”, i.e. buying at
the lowest possible price and selling at the highest and the traders will strive to reach
as high a percentage of that maximum as possible. 95% is believed to be a reachable
goal, based on the fact that a simple algorithm with no integrated intelligence (based
on day-ahead projections) achieved 65% accuracy.
Opex cost
Salaries $75.000 pr/yr
Bonus payments 2% of GM
Nr of traders 2
Traider accuracy 95%
Overhead $30.000 pr/yr
Maintenance $120.000 pr/yr
Other $100.000 pr/yr
Working capital $200.000 for 2 yrs
Selling expense 0,2% of revenue
Start-up expense $300.000
Table 16 - Operational Cost Inputs
51
Overhead is set at $30.000 a year. The overhead consists of running computers,
telephones and internet connections. This amount is to cover these costs in addition to
licenses and access to the electricity market exchange where the electricity is traded.
Maintenance on the electrical equipment and building will be outsourced. We assume
to pay $10.000 a month to a service provider that offers full maintenance service.
There is a build-in contingency of $100.000 for operational cost. This will cover any
other expenditure that derives directly from operating the business.
We assume that working capital needs to be funded for a set number of years. It is
assumed that $200.000 will be the addition to the working capital for the first two
years. In the first year it will be funded with equity, but in the second with internally
generated cash flow.
The traders will have a budget to secure deals and maintain normal customer
relations. The budget is set as 0,2% of revenues.
A leeway of $300.000 is given for any unforeseen circumstances and additional cost
of starting up the business during the initial year.
8.4 Depreciation
It is assumed that Building, Equipment and Other
can only be depreciated by 90%, i.e. that a 10%
residual value remains. The building is depreciated
by 4%, a standard practice in most cases. The
equipment is depreciated by 12,5%, giving them an
8 years lifetime and other items are assumed to
have a lifetime of 4 years (see Table 17).
% Factor
Building 4% 90%
Years of depreciation 22,75
Equipment 12,5% 90%
Years of depreciation 7,20
Other 25% 90%
Years of depreciation 4,10
Table 17 - Depreciation Inputs
52
8.5 Debt
The model assumes that 70% of the equipment cost
will be financed with an export credit loan offered in
both Germany (Siemens) and Sweden (ABB). Experts
assume the cost of this loan to be the rate of local 10
year treasury bonds plus a premium (Erlendur
Davíðsson, Arion Bank, personal communication,
September 8th, 2011 & Jóhann Ingi Kristjánsson,
Akurey Investments, personal communications,
September 15th, 2011). In the model the German 10
year bond (1,75%; Bloomberg, 2012) with a 400 point premium is assumed, or 5,75%
interest rate. The length of the loan is directly tied to the depreciation of the
collateralized asset, and therefore, it is assumed that it will be paid back in 8 years (see
Table 18).
An additional short term loan of $500.000 will be taken to finance initial burning of
cash. It is assumed to carry 8% interests and be paid up in 3 years, when the business
generates enough cash to easily sustain its operation.
The equity ratio is set at 38.02% which corresponds with the required equity
capital of $4.600.000.
8.6 WACC
In order to discount the cash flow from the project and arrive at a meaningful NPV of
the investment it is necessary to calculate the cost of the equity for the project, as well
as the cost of debt (see Table 19).
Leverage of equipment 70%
Equipment loan
Bank I $7.000.000
Interest rate 5,75%
Payback in years 8
Short term financing
Bank II $500.000
Interest rate 8%
Payback in years 3
Table 18 - Debt Inputs
53
In the model we assumed the risk free rate to be the
long term (more than 10 years) US treasury bills, 2,71%
(US Department of Treasury, 2012) and we mirrored a
standard practice of many corporate bankers in using
the work of Aswath Damodaran, Professor at Stern
Business School, in assuming the risk premium at
6,19% (Damodaran, 2012). The beta (β) for this project
is impossible to calculate because it is a correlation
between the volatility of the project and the market.
The way to calculate it is to find the correlation
between a set of returns of the project and a set of
returns of the market, i.e. find out how much the two change together. So, instead of
trying to chase the beta, we decided to fix the rate of equity. The normal expected
(desired) return of a risky, startup project is 20%, i.e. an investor would need a return of
20% to invest in the project. With the rate of equity fixed at 20%, the beta coefficient
turns out to be 2,79
Applying the CAPM formula:
re = Return on Equity rf = Risk free rate rmrp = Market risk premium
We fix the at 20% and solve for the β:
The cost of debt is simply the weighted average of the rates on the loans assumed, or
5,90%, and with an equity ratio of 38% and a tax rate of 34%, we arrive at a cost of
capital for the project at 10,21%
Total capital needed $12.100.000
Equity ratio 38%
Equity $4.600.000
Debt $7.500.000
WACC input
CAPM
Risk free rate 2,71%
Market risk premium 6,19%
β for project 2,79
rE 20,00%
rD 5,90%
WACC (discount rate) 10,02%
Table 19 - WACC Inputs
54
Simply applying the WACC formula:
We get:
8.7 Tax
The model assumes 34% income tax. In 2011 the marginal tax rate in the USA produced
a flat 34% tax rate on income between $335.000 to $10.000.000. The projected income
of the project falls within these limits. Taxes are calculated on EBT (earnings before tax)
and losses are carried over for up to 10 year. The model also calculates 1.65% property
tax on book value of building and land.
8.8 Batteries
The battery calculations are only to give the user a sense of physical scope of the
project, i.e. how much space it will require. Ford Motor Company will introduce their
new electric car, Ford Focus Electric in 2012. This car will have a battery pack that holds
21kWh of electricity. If it is assumed that these batteries will be used, and that the
recharge of these batteries has dropped to 60% by the time received by this project
5.435 batteries would be needed to store 75MWh of electricity and they would fit easily
into 8 normal 40 foot containers.
55
9 Results
The results of the research are that the project in this form is only to some extent
feasible. It should, however, be taken further and a thorough investigation of the most
critical assumptions should be conducted. It is also evident that some markets are more
ideal for this kind of venture than others and that four of the 29 electrical systems
should be focused on and investigated further (see Exhibit 10).
The systems in question are: SoCal, San Diego, SP‐15 and Long Island. Those
were the only four systems that produced sufficient return on both investment and
equity (see Table 20).
The research was
setup around two
different scenarios, one
where no intelligence
was applied in buying
and selling, a day‐ahead projections given by the market was used blindly (Projected); and
another where human intelligence was used to actively trade on the market, using real
time data to identify the best possible time to buy and sell (Actual).
9.1 Projected This scenario turns out not to be feasible given the assumptions we applied (see
Exhibit 11). None of the 29 systems achieved the required 20% return of equity
investment and only in six systems (San Diego, Long Island, NY, Commonwealth, NJ
and Pepco) was the investment’s IRR above the required rate of 10,02% (see Table
21). In fact, the average rate of return for the 29 systems was only 6,00% and 5,40% of
project and equity (respectively), and the average NPV was $‐2.806.973 for the
investment and $‐4.643.864 for equity. One market did stand out, PJM, but even
though the returns there were dramatically better than the other markets (NPV of
investment was $2.387.333 on the Baltimore system) it was still quite far from the
NPV investment IRR investment NPV equity IRR equity
SoCal $6.336.742 17,45% $721.157 22,15%
San Diego $10.652.938 21,81% $3.153.378 29,26%
SP‐15 $6.280.126 17,39% $688.778 22,06%
Long Isl $6.682.143 17,82% $918.692 22,74%
Table 20 ‐ Four Systems with Sufficient Return on Investment and Equity
56
defined target rates of return for equity ($-1.510.370 was the NPV of equity for the
Baltimore system).
The accuracy
in the day-ahead
projections was
only about 65%
(on average), i.e.
the gross margin
in the projected
scenario was only about 65% of the theoretical max gross margin. All markets had a
trading surplus in all years (2009, 2010 and 2011), and in fact all months in all years were
traded in a surplus, in all of the 29 markets. However, there were quite a few days that
resulted in a trading loss, sometimes in excess of $7.000. The biggest problem with the
day-ahead projection was that they were completely unable to predict extraordinary
activity on the market, i.e. large spikes/drops in power prices. This was often costly, not
only because the model missed these opportunities but often times because it traded on
the wrong side of it (sold at negative prices, bought at souring prices).
In this form and with the given assumptions this scenario should not be
pursued. Nevertheless, with the extensive and detailed data available a more
sophisticated algorithm can easily be created, one that learns from its mistakes
and is fed real time data from the market. The scenario is not feasible, but should
not be totally disregarded.
9.2 Actual
The results from the second scenario were somewhat more positive, although with the
given assumptions the idea is only feasible on specific systems (see Exhibit 12). 12 of
the 29 systems had an IRR of investment higher than the required 10,02% but only 4 of
them achieved a positive NPV of equity. The average rate of return for all 29 systems
was 9,14% and 9,98% of investment and equity (respectively) and the average NPV was
$-349.145 for the investment and $-3.245.572 for equity. Again California stands out,
NPV investment IRR investment NPV equity IRR equity
San Diego $577.330 10,77% -$2.579.222 12,01%
Long Isl $2.507.743 13,17% -$1.434.188 15,59%
NY $747.116 10,98% -$2.477.387 12,33%
Baltimore $2.378.333 13,02% -$1.510.370 15,35%
Dominion $861.000 11,13% -$2.409.444 12,55%
NJ $1.136.470 11,48% -$2.245.098 13,06%
Table 21 - Six Systems with Sufficient Return on Investment
57
with New York and PJM showing some potential. In California all systems resulted in a
positive NPV of investment and half of them (SoCal, San Diego and SP-15) also had a
positive value of equity, in fact the average IRR of equity in the whole market of
California was 1,55% over the required rate of 20%. Both the New York and PJM
markets had three systems that traded with a positive NPV of investment, but only in
the Long Island system in New York was the IRR of equity above the 20% mark. The
single highest score of all the systems (by far) was in San Diego, with a NPV of
investment of $10.652.938 and an IRR of 21,81%, and the comparable numbers for
equity were $3.153.378 and 29,26% (see Table 22).
The PJM
market, as well as
New York, had
three systems with
positive NPV of
investment, and
although no
system in PJM gave
a high enough
return on equity it
is a market worth
looking at. PJM was the market with considerably largest increase in gross margin
between the years 2009 and 2011. As mentioned in the Assumptions for financial model
chapter, the increase in gross margin is the percentage by which the gross margin of
buying at lowest prices and selling at peak prices increases over the course of one year
and the average for all markets was 25,39% (median 18,91%). In the six systems within
the PJM market this average was roughly 47,7% and the three systems what showed
positive NPV of investment (Baltimore, Dominion and NJ) had 55,71%, 51,60% and
62,87% increase in their gross margin between the years 2009 and 2011 (respectively).
If these increase numbers are applied to the model (for only 2 years) and then the long
term 5% growth rate for the consecutive 13 years both systems would show quite
different results (see Table 23).
NPV investment IRR investment NPV equity IRR equity
Pacific G&E $4.348.581 15,27% -$419.696 18,74%
SoCal $6.336.742 17,45% $721.157 22,15%
San Diego $10.652.938 21,81% $3.153.378 29,26%
NP-15 $3.555.005 14,36% -$880.149 17,35%
SP-15 $6.280.126 17,39% $688.778 22,06%
Palo Verde $4.934.785 15,93% -$80.624 19,76%
Hudson $464.058 10,61% -$2.707.849 11,76%
Long Isl $6.682.143 17,82% $918.692 22,74%
NY $2.710.526 13,38% -$1.372.855 15,85%
Baltimore $2.498.939 13,12% -$1.497.413 15,47%
Dominion $1.031.242 11,33% -$2.367.274 12,81%
NJ $1.132.928 11,46% -$2.306.608 13,00%
Table 22- Twelve Systems with Sufficient Return on Investment
58
Despite the
fact that Long
Island produced
the highest
outcome and that
Baltimore, Dominion and NJ had huge growth between 2009 and 2011, the California
market turns out to be most feasible. All systems in that market had a positive NPV of
investment and 3 systems had a positive NPV for equity as well, and the calculated
growth rate was healthy 31,68% and 17,7% in SoCal and San Diego (respectively).
The results from the research were that it is feasible to conduct electrical trading on 4
of the 29 systems, SoCal, San Diego, SP-15 and Long Island. Based on these findings some
further calculations were made on the four systems to assess the vulnerability of the
outcome and to identify the effect a variance in the assumptions have on the results.
NPV investment IRR investment NPV equity IRR equity
Baltimore $9.000.955 20,55% $2.419.914 27,49%
Dominion $6.032.938 17,40% $739.096 22,33%
NJ $8.797.337 20,30% $2.281.847 27,02%
Table 23 - - Three PJM Systems with Actual Increase Applied for Two Years
59
10 Sensitivity
The results of the research are that electrical arbitrage is feasible on four systems if the
assumptions the model is based on hold true. To assess the level of depth to the results
and how strong a foundation they are built on, a sensitivity analysis was made on these
underlying assumptions. First a single dimension analysis was made to gauge what
assumptions are most vulnerable to variation, i.e. change to what assumptions have the
most severe effect to the change of the outcome. Once the assumptions had been
assessed and the most critical ones identified, a two dimensional analysis was
performed. The most critical assumptions were tested interacting with each other (and
not only as stand-alone) and the effect on the outcome calculated.
For the single dimension analysis two types of output were created, a “Spider chart”
and a “Tornado chart”, and based on the results from that analysis a two dimensional
“two-variable matrix data table” was created .
The analysis was carried out on the basis of both the investment as a whole as well
as the equity part of it, and thus the same set of calculations was made for both NPV of
investment and NPV of equity.
10.1 Single dimension analysis
The reason this analysis is called single dimension is because only one variable is tested
at a time, all other assumptions are kept at their initial value.
For the single dimension analysis ten assumptions were tested:
Cost of Investment/Equity
Increase in power prices
Equipment
Power losses
Salaries
Trader accuracy
Maintenance
Start-up expense
Working capital
Corporate tax rate
60
As mentioned above both “Spider charts” and “Tornado charts” are the output of a
single dimension analysis. It means that they are the results of a set of tests where a
single assumption at a time is tested, keeping all other assumptions fixed. In our
analysis the 10 assumptions were tested with a variation of 20% to both sides, i.e. each
assumption was treated as a variable and given extreme values of 20% better and 20%
worse. Thus we get an output of three data points for each of the variable tested. E.g.
when the first variable (Cost of Investment/Equity) was tested, all the other variables
were fixed (i.e. they kept their initial value). Then the NPV was calculated with three
values of Cost of Investment/Equity, a) the initial value (Base Case); b) 20% lower value
(Best Case); and c) 20% higher value (Worst Case). This gave three different outcomes
for NPV. The same calculations were made for all other assumptions.
10.1.1 Spider charts
The 10 assumptions that were thought to be the most influential on the outcome were
chosen, and a worst and a best case was assigned (see Table 24).
Obviously the
“Trader accuracy”
assumption does not
vary by 20%, simply
because we assume
95% accuracy and a
20% positive
variation from 95% is
114%, which is
impossible!
Therefore, 100% accuracy was assigned as best case and 90% as worst case.
The end result is a linear representation of each variable changing from negative to
positive (two data points on each side of the center) keeping all other variables
constant. The output of the calculation is a graphical depiction of how much effect each
assumption has on the outcome (see fig. 17).
Values to be tested
∆ from base case 20%
Negative Base Positive
WACC 10,02% 12,02% 10,02% 8,02%
Increase in power prices 15% 12% 15% 18%
Equipment $10.000.000 $12.000.000 $10.000.000 $8.000.000
Power losses 3% 4% 3% 2%
Salaries $75.000 $90.000 $75.000 $60.000
Trader accuracy 95% 90% 95% 100%
Maintenance $120.000 $144.000 $120.000 $96.000
Star-up expense $300.000 $360.000 $300.000 $240.000
Working capital $200.000 $240.000 $200.000 $160.000
Corporate tax rate 34% 40,8% 34% 27,2%
Output value
NPV of Investment -
Table 24 - Assumptions Tested for Single dimension analysis
61
The lines represent
each assumption and
the steeper the slope
of the line, the bigger
effect the assumption
has on the outcome.
Based on the pictures
it was evident that five
of the assumptions had
considerable effect on
the outcome (large incline) while the effect of the other five was limited.
This calculation was made for NPV of investment and equity on all 4 systems, 8 sets
of calculations in total. All 8 spider charts were uniform and indicated that there were
potentially five assumptions that had major effect on the outcome, i.e. had the steepest
incline (see Exhibit 13):
Discount rate (WACC or cost of equity)
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
To find out if all these assumptions had the same effect on the outcome, or if there
was a level to their influence a tornado charts were created. This helped with gauging
the scope and the volume of the influence each of the assumptions had on the results.
10.1.2 Tornado charts
Like the Spider charts, Tornado charts are an output of a single dimension analysis and
the same principle is applied to both methods, i.e. varying one assumption while fixing
all the others. The Tornado is a visual representation but unlike the Spider it gives a
better sense of the volume of financial variation the change in each assumption has on
the outcome. So to investigate further whether the five most influential assumptions
Figure 20 - Spider chart for Long Island, Linear representation of each assumption's effect on NPV of Investment
62
had the same effect on the outcome, Tornado charts was created to see the absolute
number effect on the end result (see Fig. 18).
The calculation for a
tornado chart is the same
as for a spider chart, but
the tornado chart
visualizes the change in
absolute numbers of the
outcome, instead of the
relative change. The
tornado chart confirmed
the results of the spider
chart. There are five variables that have the greatest effect on the outcome, but the
tornado also calculates how the difference between the expected value and the certain
equivalent is affected by the uncertainty of a specific input variable, i.e. how much each
variable influences the outcome based on the uncertainty of the input (see Table. 25).
The table shows the three values each assumption is given (base, worst- and best
case) and gives the corresponding number value of the outcome if all other assumptions
are kept fixed at their base value. The table also shows how much the outcome varies
between the two extremes, i.e. how much the outcome “swings” between the worst
case and the best. Finally the table shows how much influence each assumption has on
the outcome based on its uncertainty in a metric called “Percent Swing2”. The results in
all eight calculation sets were uniform, the Percent Swing2 indicated that there were
three assumptions that were the most relevant and had the largest impact on the
Figure 21 - Absolute Value of each Assumption's Effect on NPV of Investment
Percent
Input Variable Low Output Base Case High Output Low Base High Swing Swing2
WACC 12,02% 10,02% 8,02% $4.492.557 $6.678.480 $9.330.276 $4.837.719 37,1%
Increase in power prices 12% 15% 18% $4.731.864 $6.678.480 $8.811.187 $4.079.323 26,3%
Equipment $12.000.000 $10.000.000 $8.000.000 $5.124.660 $6.678.480 $8.207.462 $3.082.803 15,0%
Trader accuracy 90% 95% 100% $5.189.258 $6.678.480 $8.155.547 $2.966.289 13,9%
Corporate tax rate 41% 34% 27% $5.601.841 $6.678.480 $7.755.119 $2.153.278 7,3%
NPV of Investment
Corresponding Input Value Output Value
Table 25 - Inputs and Corresponding Outputs for Long Island Tornado Calculations
63
outcome (even though the order of the second and third assumption alternated
between power systems). The most influential assumptions were:
Discount rate (WACC or cost of equity)
Increase in power prices
Equipment
Knowing that three assumptions had by far the most effect on the outcome a two
dimensional analysis was performed. This allowed us to see how the change in two
assumptions at the same time would affect the outcome.
10.2 Two dimensional analysis
10.2.1 Matrix data tables
Having determined the three most significant assumptions a two dimensional matrix data
tables were created to see the effect changes in two of the variables at the same time
have on the outcome. Three tables were created for each of the four systems for the total
investment and three for equity, resulting in 24 individual tables (see Exhibit 14).
The variables are arranged with a range of values from bad to good with the base
value in the middle, e.g. “Increase in power prices” were arranged with 15% in the
middle with 10% on the “bad” end and 20% on the “good” one. One variable is arranged
vertically the other horizontally, thus creating a matrix. The outcome is then calculated
based on the matching value of both variables (see Table. 26)
The results
of the sensi-
tivity analysis
are that the
NPV of the
investment is
not particularly sensitive to changes in any of the assumptions while the NPV of equity
is much more vulnerable to change.
The one dimensional analysis exposed the order in which the assumptions affect the
outcome, both calculated for the total investment as well as equity. When calculated for
10.648.459 11,52% 11,02% 10,52% 10,02% 9,52% 9,02% 8,52%
17% 10.312.626 11.028.720 11.779.789 12.567.935 13.395.408 14.264.616 15.178.132
17% 9.727.677 10.421.569 11.149.302 11.912.910 12.714.569 13.556.606 14.441.510
16% 9.156.283 9.828.523 10.533.499 11.273.178 12.049.663 12.865.204 13.722.208
15% 8.598.198 9.249.325 9.932.112 10.648.459 11.400.399 12.190.106 13.019.908
14% 8.053.178 8.683.721 9.344.875 10.038.476 10.766.488 11.531.012 12.334.296
14% 7.520.981 8.131.460 8.771.527 9.442.956 10.147.645 10.887.623 11.665.061
13% 7.001.369 7.592.293 8.211.809 8.861.630 9.543.590 10.259.646 11.011.897
∆ WACC
∆ In
cr. in p
ow
er p
rices
Table 26 - Matrix Data Table Long Island with WACC and Increase in Power Prices
64
total investment the “WACC” (discount rate) is by far the most influential variable with
“Increase in power prices” a dominant runner up. When it comes to calculations for equity
the front runner is also “Return on equity” but the runner up is “Equipment” in all but one
system (San Diego) where “Increase in power prices” edges in front (see Table 27).
It is evident
that five
variables have
the greatest
impact on the
overall out-
come and
three of them play a crucial role. However, in both cases (project and equity) the sensitivity
of the outcome is small enough to sustain a change of 10% in two assumptions at the same
time, and for the NPV of investment it can easily sustain 15% negative variation in any two
of the three most influential variables at the same time.
3.153.378 23% 22% 21% 20% 19% 18% 17%
17,25% 2.684.710 3.126.157 3.605.835 4.127.861 4.696.874 5.318.103 5.997.452
16,50% 2.405.702 2.830.865 3.292.885 3.795.737 4.343.895 4.942.405 5.596.966
15,75% 2.132.746 2.542.019 2.986.810 3.470.952 3.998.764 4.575.110 5.205.490
15,00% 1.865.735 2.259.504 2.687.486 3.153.378 3.661.341 4.216.069 4.822.862
14,25% 1.604.565 1.983.209 2.394.795 2.842.884 3.331.488 3.865.131 4.448.923
13,50% 1.349.130 1.713.022 2.108.616 2.539.344 3.009.069 3.522.151 4.083.514
12,75% 1.099.328 1.448.834 1.828.833 2.242.631 2.693.949 3.186.984 3.726.480
∆ Return on equity
∆ In
crease
in p
ow
er p
rices
Table 27 - Matrix Data Table Long Island Equity with Return on Equity and Increase in Power Prices
65
11 Discussion
In this thesis the feasibility of buying electricity on an openly traded electricity market,
storing it in electric car batteries and reselling it to the market was investigated. The
result of the research is that it is feasible to conduct electric energy price arbitrage on
four of the twenty nine systems tested:
SoCal
San Diego
SP-15
Long Island
The present value of the cash generated in the operation on these systems was
higher than the value of the cash needed to set it up, i.e. it is economical to spend the
money in setting up the facilities and the operations because the cash generated by the
business more than compensates the initial investment, even with opportunity costs,
risk and time value of money taken into account.
These findings suggest that a further investigation should be conducted on the
conditions of those four systems, a thorough due diligence on the assumptions of the
model should be made and a business plan created.
11.1 Groundwork
The research is based on a simple idea: to exploit the inherent inefficiencies of an
electrical energy system to conduct electrical arbitrage. It is understood that for this to
be possible a basic understanding of electricity and its physical attributes, challenges
and opportunities, is paramount. The thesis, therefore, started out by taking a look at
electricity itself, the history behind it and how men have gradually gained control over
harnessing it. The different types of generation methods were reviewed and also the
mismatch between the efficient production of electricity and the demand for it, i.e. the
“peak power problem”, which is the underlying precondition for the research.
Having established that there are inherent inefficiencies in the electric market and
that the volatility has diurnal cycles, solutions to store electricity to exploit these
inefficiencies were investigated. It turns out that energy storage has been conducted for
66
decades and after exploring the most commonly used technologies a decision was made
to focus on BES systems. The decision was taken by a process of elimination, batteries
were the only option that served the quantity and mobility we needed, i.e. was a
geographically independent energy application.
To decide what types of batteries would best suit the project recent technology
advances and potential accessibility in the future were taken into account. The latest
trends within the auto industry and the electrification of the common car made lithium
ion batteries a logical choice. The reason was twofold:
Considerable corporate and official resources (money and brain power) are being allocated to finding new advances in Li-ion battery technology
I.e. lithium ion batteries are good and steadily improving, and the second reason was
because they fulfill the second prerequisites for the project (access).
There were two other prerequisites that needed to be fulfilled in order to test the
economics of the project once all the all the technological issues have been adequately
dealt with (i.e. having determined that energy storage was indeed possible):
Access to an open electricity market
Access to a sufficient number of batteries
A short look at the US electrical markets suffices to satisfy the first premise, but to
answer the second one a better look at the electric car and the plans of major auto
manufacturers was needed. Many large car manufacturers have announced their
electric vehicles for rollout in the coming years and the US government plans to have
over a million electric cars on the streets by 2015. Most of these electric cars will be
powered by lithium-ion batteries, hence the increase in number of batteries will be
dramatic and thus the access to them.
Having defined the technological scope of the project and with all the prerequisites
satisfied a feasibility study was conducted on a 15 years operation. The calculations
were based on a free cash flow method, but no terminal value was assigned at the end
of the 15 years. The reason for the absence of terminal value calculations are explained
in chapter 4, “Model Limitations”.
67
11.2 Research
After consulting with industry experts it was decided to investigate the feasibility of
trading 75MWh of energy using 25MW equipment. This means that the operation must
buy and sell 3 hours of electricity each day on a continuous cycle, i.e. energy has to be
bought and sold for 3 consecutive hours. Each day was treaded as isolated (you could
not buy/sell between two days) and thus 22 unique cost data for 75MWh of energy
each day was calculated.
Detailed, hourly price data was collected from five of the ten independent electrical
US markets for the years 2009, 2010 and 2011, and uploaded into our model. Each
market had 5-6 electricity systems resulting in 29 systems, and each system was treated
as independent. The data collected contained two sets of information, projected prices
(estimations from the previous day) and actual prices. The data was used to construct
two scenarios:
Projected – where the day old estimates were used to buy electricity (representing theoretical minimum)
Actual – where energy was bought at lowest prices and sold at highest (representing theoretical maximum)
Having two sets of 22 unique cost data made it possible to calculate cost and
revenue of buying and selling energy in two different scenarios. In the Projected
scenario, energy was bought at the time the predicted price would be lowest and sold
at a time predicted as being highest giving a theoretical minimum, because no
intelligence was applied only extrapolation of the previous day’s data. In the Actual
scenario, energy was bought at the cheapest price and sold at the highest, thus
providing the theoretical maximum. The accuracy of the projections turned out to be
about 65%, i.e. trading on predictions gave 65% lower gross margin than was the
theoretical maximum.
With all the data arranged and cost, revenues and gross margin of each day
calculated it could be imported into the model. The model calculates NPV and IRR of a
15 year project, based on 39 different assumptions in eight categories. Each
assumption was arrived at through a rigorous process of research, interviews,
experience and common sense and then verified by industry experts. Applying all the
68
assumptions to the data gave results for all the 29 systems. The results were then
categorized into two categories
Investment – results applying to the whole project, i.e. cash that can be used to pay debt, distribute as dividends and/or plowed back into the project
Equity – results applying to the equity of the project, i.e. cash that can be used to distribute as dividends and/or plowed back into the project
NPV and IRR was calculated for both categories and the results were that 12 systems
had positive NPV in the first category but only four of systems had positive NPV of
equity. These four systems were investigated further to ascertain how sensitive the
outcome was to change in the most important underlying assumptions. Three sets of
analysis were done and it turns out that in the investment category the outcome of all
of the four systems can sustain considerable negative variation in two of the most
critical assumptions at the same time In the equity category the results are strong
enough to handle mild negative variation in two assumptions simultaneously. The
results are therefore that four systems show clear signs of arbitrage feasibility and
should be further investigated.
11.3 Limitations
Because this research is conducted only to determine if electric energy price arbitrage is
feasible based on the operation of such a project, it ignores important aspects of a
potential energy storage venture. The study does not include the acquisition cost of
batteries, the political and/or environmental impact of the operation (such as the
benefits of reducing CO2, stabilizing the electric grid, and disposing of the batteries), and
because of the absence of these important factors a terminal value is not assigned at
the end of the 15 year operation.
This was done for the sake of simplicity. The thesis is a feasibility study and not a
business plan and because a positive outcome of a feasibility study is needed to justify
the efforts of a full blown business plan it was therefore, decided to conduct such a
study first. The positive outcome of this research, however, begs the question of what
steps should be taken next.
69
11.4 Next steps
Because it is feasible to conduct electrical arbitrage on four of the systems investigated
in the research it would be a terrible waste not to investigate those systems further and
see if the assumptions hold true and profit can be made from such a venture. A number
of things would have to be done before the theoretical idea becomes a physical project,
but because we have investigated the feasibility of the venture we can justify the effort
of examining it further. The next steps would fall into three categories:
Fixing the underlying cost assumptions
Designing and engineering
Acquiring batteries
11.4.1 Fixing underlying cost assumptions
For the first category a number of steps can be taken to get a firmer grip on the
parameters of the operation. Most of these steps involve interaction with a person or
company in order to create trust, build a relationship and secure a contract. A few
examples of possible steps include:
11.4.1.1 Electric equipment contracts
Electric equipment manufacturers should be identified and engaged. The large
suppliers Siemens, ABB and GE would be an obvious choice as well as other European
and East-Asian companies. The goal here would be a signed memorandum of
understanding (MOU) or letter of intent (LOI) on price, conditions and financing of
equipment for the project. If the companies are unable (or unwilling) to assist in
financing local state regimes, e.g. European export credit or the China Development
Bank should be approached.
11.4.1.2 Contacting CALED and ESD
The California Association for Local Economic Development (CALED) and the Empire
State Development (ESD) should be contacted. They are economic development
agencies for the state of California and New York (respectively) and would be a
valuable ally in preparing and managing the setup of a project in their states. They
possess valuable information and statistics on local business environment and
70
economic affairs and have connections into local businesses as well as governmental
agencies and institutions.
11.4.1.3 EPC contract
EPC contractors should be contracted to oversee the entire engineering, procurement
and construction of the physical project. The local Icelandic engineering firms should
initially be approached as they could provide assistance and (perhaps) act as
subcontractor. CALED and ESD could assist with finding a EPC contractor in their
respective areas.
11.4.1.4 HR information
Headhunting agencies and local traders should be visited and interviewed to get a
better idea of price and the salary structure of electricity traders. CALED and ESD could
provide valuable assistance on this issue.
11.4.1.5 Electric grid operator
The electrical grid operator in California should be approached and a MOU should be
signed. It is imperative to understand the rules, regulations, limitations and conditions
there regarding operation on the California- and New York electric grid. Information on
grid security, power losses and incentives for non-fossil solutions would provide
important information to the assumptions. The Icelandic grid operator Landsnet, and
CALED and ESD could assist with identifying the appropriate contact(s).
11.4.2 Designing and engineering
Parallel to establishing business relationships with potential vendors, partners and
customers and gathering information into the business model, considerable work can
be done on the designing and engineering front, e.g.:
11.4.2.1 Computer system
A computer system can be designed that applies artificial intelligence to the historical
data in order to better predict future prices. In this thesis the simplest possible
algorithm is used to extrapolate when to buy and when to sell based on historical
data, and this simple algorithm resulted in 65% accuracy. A computer engineer should
be able to come up with a program that learns from its mistakes in order to increase
71
the accuracy. This could easily be done in a graduate level academic setting, as a
project or thesis.
11.4.2.2 Battery system
The interaction between thousands of batteries and the interconnection of an
integrated battery system needs to studied and designed. The chemical and physical
properties of the batteries need to be understood, e.g. charge and discharge time,
durability, lifetime, effect of expedite charging, etc., as well as the limitations and
challenges of having thousands of batteries interconnected. Such a study is also ideal
for academic work on a graduate level.
11.4.3 Acquiring batteries
The final category deals with how to acquire the batteries, who should bear the cost
and how it should be split. A number of possible solutions are available for tying
environmental- and political aspects into the equation as well as focusing on the purely
commercial aspect.
11.4.3.1 Partnership with auto manufacturer
A partnership with car manufacturer would provide considerable value to both parties
as it would provide the venture with the batteries for storage while providing the auto
manufacturer ways to relieve perceived risk, thus lowering the risk for the buyer,
making the electric car more sellable.
One of the challenges the car manufacturers face when introducing the electric car is
the psychological barrier the consumer has to overcome regarding the perception of
maintenance. It is a common perception that an electric car demands extensive
maintenance and replacing the battery pack every few years acts as a deterrent from
purchasing such vehicle.
Partnership can supply a solution, for by partnering with an energy storage
project the auto manufacturer can offer deals on new cars, where the buyer would
be able to return the used battery and be replaced with a new one. The auto
company will offset the cost of this service through the increased sales and the
revenues of the electricity arbitrage.
72
11.4.3.2 Recycling intermediate
One business model would be to act as an intermediate between the consumer and
the recycling plant. An owner of an electric car needs to replace the primary battery of
his car every few years, because the performance demand on cars are such that when
the car’s range has dropped below a certain point the battery of that car will be
replaced. These replaced batteries are not unusable and even though they are unfit
for cars they still have 60-70% of their charge left. An intermediate would collect
these batteries, use them while they still maintain charge and then have them
recycled. This venture would provide an additional value to the community because it
would be responsible for collecting used batteries and recycling them, and not letting
them be thrown away. There is a possibility of an environmental incentive as well as a
political backing for such a project.
11.4.3.3 Environmental incentives
The project set up for electric arbitrage is inherently CO2 friendly. It exploits the cyclical
nature of power prices and by doing so it evens out the imbalances on the grid (reduces
the spikes), which otherwise would be met with by increasing output from fossil fuel
power plants. Evening out the spikes means reduction of CO2 on the electric grid and for
such projects you can get environmental incentives in California and New York.
11.4.3.4 Political incentives
One of the bigger issues in US politics is energy security as Americans are by far the
largest consumers of energy in the world. Their electric grid is however old and
outdated and because this project acts as a regulator on the electric system
(reducing imbalance) there is a possibility of getting political incentives in the name
of energy security.
Of course this list is by no means exhaustive, but it gives a good indication of the
possibilities of an energy storage project. And once the project preparation reaches
this stage with all these important aspects already included into the model, terminal
value must be assigned at the end of the operation horizon for a comprehensive
valuation of the project.
73
12 Conclusion
Electric price arbitrage using battery energy storage is feasible and the idea should
be pursued further.
The research in this thesis demonstrates the feasibility of conducting energy
arbitrage on four of the 29 systems studied. Each of those four systems produced
positive NPV for the total investment as well as the equity part of it, based on
calculations from historical data. The results were tested and all systems could
withstand considerable negative variation on two of the most important assumptions
simultaneously without producing negative NPV.
Having established the feasibility, considerable effort should be put into actualizing
the underlying idea. The four performing systems should be further investigated and
the other 25 discarded. The grid operators on the four systems should be contacted as
well as local economic development agencies. Financial- and human capital should be
allocated to fixing the underlying assumptions and designing the necessary battery- and
computer systems as well as creating an extensive business plan, which includes
acquisition of batteries, political importance and environmental impact.
74
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Kintner-Meyer, M. C., Subbarao, K., Prakash Kumar, N., Bandyopadhyay, G., Finley, C., Koritarov, V. S. et al. (2010). The role of energy storage in commercial buildings: A preliminary report. USA: Pacific northwest National Laboratory.
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Exhibit 1
<=0 >=100 >=200
0-4.9% 5-9.9% >=10%
Hour Ending: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 All On PkPacific G&E 37.05 32.42 29.17 28.59 27.91 30.98 31.35 34.90 36.76 41.01 44.10 46.52 46.89 49.51 57.04 60.76 67.57 51.48 50.83 47.69 47.34 44.87 39.44 35.54 42.49 47.41
SoCal Edison 36.16 31.53 28.26 27.56 27.01 30.05 30.86 34.36 36.47 40.97 44.64 47.44 49.31 51.26 58.96 69.44 69.70 52.99 51.92 48.46 47.70 45.53 39.11 35.03 43.11 48.75
San Diego G&E 36.13 31.56 28.24 27.58 27.02 30.03 31.01 34.48 36.67 41.01 44.49 47.12 48.95 50.76 58.37 68.77 68.85 52.33 51.20 48.20 47.42 45.36 38.91 35.10 42.90 48.44
NP-15 36.15 31.63 28.44 27.91 27.26 30.21 30.57 33.94 35.45 39.60 42.58 44.96 45.28 47.84 55.07 58.48 65.21 49.75 49.19 46.17 45.78 43.45 38.30 34.60 41.16 45.83
SP-15 35.41 30.90 27.71 27.03 26.48 29.42 30.36 33.72 35.76 40.13 43.63 46.29 48.12 49.94 57.39 67.61 67.80 51.64 50.70 47.42 46.71 44.62 38.15 34.31 42.14 47.62
Palo Verde 34.76 30.36 27.25 26.63 26.07 28.93 29.63 32.93 34.90 39.11 42.55 45.24 47.14 49.00 56.39 66.47 66.64 50.74 49.72 46.45 45.59 43.49 37.42 33.60 41.29 46.62
DA Forecast Load (GW) 26.7 25.1 24.1 23.6 23.9 24.9 26.3 28.4 30.4 32.2 33.9 35.3 36.2 37.5 38.5 39.1 39.4 38.8 37.6 36.3 36.1 34.9 31.9 28.8
Hour Ending: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 All On PkPacific G&E 25.62 26.28 23.08 22.26 18.86 26.89 23.31 18.31 33.67 41.99 34.04 38.85 37.33 38.42 34.24 39.94 40.47 40.47 37.08 31.25 31.20 28.07 33.57 26.28 31.31 34.29
SoCal Edison 24.96 25.32 22.21 21.40 18.17 26.02 23.01 18.06 26.31 29.60 32.04 39.69 41.10 40.00 43.21 41.87 42.30 42.10 38.35 32.01 31.71 28.46 30.97 26.06 31.04 34.36
San Diego G&E 24.93 25.24 22.16 21.34 18.14 26.01 23.06 18.15 26.40 29.63 32.04 39.71 41.15 40.00 43.26 41.97 42.38 42.07 38.25 31.94 31.61 28.43 30.91 25.94 31.03 34.38
NP-15 24.72 25.34 22.23 21.48 18.32 26.12 22.43 17.77 27.73 32.17 31.16 37.06 35.69 36.65 32.47 38.25 38.71 38.83 35.61 30.05 29.83 26.82 30.42 25.35 29.38 31.95
SP-15 24.41 24.76 21.73 20.94 17.78 25.46 22.63 17.75 25.80 28.98 31.31 38.74 40.13 39.03 42.23 40.84 41.27 41.05 37.43 31.30 31.03 27.90 30.20 25.44 30.34 33.59
Palo Verde 24.05 24.41 21.42 20.66 17.52 25.06 22.19 17.35 25.26 28.42 30.71 38.08 39.62 38.56 41.78 40.40 40.85 40.59 36.97 30.85 30.43 27.28 29.79 25.10 29.89 33.08
DA Forecast (GW) 26.66 25.10 24.12 23.63 23.86 24.93 26.27 28.38 30.39 32.20 33.93 35.26 36.23 37.46 38.46 39.14 39.36 38.80 37.64 36.28 36.15 34.94 31.89 28.76
Forecast Error % -9.1% -9.0% -9.0% -9.0% -8.7% -8.1% -7.1% -6.6% -6.1% -5.9% -5.4% -5.5% -6.2% -6.3% -6.7% -6.8% -6.6% -6.4% -6.7% -6.7% -7.0% -7.7% -8.1% -8.4%
For archival records go to http://www.ferc.gov/market-oversight/mkt-electric/caiso/caiso-iso-archives.asp
Avg. Hr $
Forecast Error %
CAISO Daily ReportFERC - Division of Energy Market Oversight - www.ferc.gov
PriceTuesday July 20, 2010
Avg. Hr $
Preliminary Real-Time Prices with Forecasted and Actual System Load
Day-Ahead Prices and DA Forecast Load
$0
$10
$20
$30
$40
$50
$60
$70
$80
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
Pric
e ($
/MW
h)
10000
15000
20000
25000
30000
35000
40000
45000
Load
(MW
)
Cleared Load Pacific G&E SoCal Edison San Diego G&E NP-15 SP-15 Palo Verde
$0
$5
$10
$15
$20
$25
$30
$35
$40
$45
$50
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Hour
Pric
e ($
/MW
h)
10000
15000
20000
25000
30000
35000
40000
45000
Load
(MW
)Actual Load Pacific G&E SoCal Edison San Diego G&E NP-15 SP-15 Palo Verde DA Forecast
Exhibit 2
Load
(MW)
Actual
Load
Pacific
G&E
SoCal
Ediso
nSan
Diego
G&E
NP‐15
SP‐15
Palo
Verde
DAForecast
<=0
>=10
0>=20
00‐4.9%
5‐9.9%
>=10
%Ho
urEnding:
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
All
On
Pacific
G&E
37,05
32,42
29,17
28,59
27,91
30,98
31,35
34,9
36,76
41,01
44,1
46,52
46,89
49,51
57,04
60,76
67,57
51,48
50,83
47,69
47,34
44,87
39,44
35,54
42,49
47,41
SoCal
Ediso
n36
,16
31,53
28,26
27,56
27,01
30,05
30,86
34,36
36,47
40,97
44,64
47,44
49,31
51,26
58,96
69,44
69,7
52,99
51,92
48,46
47,7
45,53
39,11
35,03
43,11
48,75
San
Diego
36,13
31,56
28,24
27,58
27,02
30,03
31,01
34,48
36,67
41,01
44,49
47,12
48,95
50,76
58,37
68,77
68,85
52,33
51,2
48,2
47,42
45,36
38,91
35,1
42,9
48,44
NP‐15
36,15
31,63
28,44
27,91
27,26
30,21
30,57
33,94
35,45
39,6
42,58
44,96
45,28
47,84
55,07
58,48
65,21
49,75
49,19
46,17
45,78
43,45
38,3
34,6
41,16
45,83
SP‐15
35,41
30,9
27,71
27,03
26,48
29,42
30,36
33,72
35,76
40,13
43,63
46,29
48,12
49,94
57,39
67,61
67,8
51,64
50,7
47,42
46,71
44,62
38,15
34,31
42,14
47,62
Palo
Verde
34,76
30,36
27,25
26,63
26,07
28,93
29,63
32,93
34,9
39,11
42,55
45,24
47,14
4956
,39
66,47
66,64
50,74
49,72
46,45
45,59
43,49
37,42
33,6
41,29
DAForecast
Load
(GW)
26,7
25,1
24,1
23,6
23,9
24,9
26,3
28,4
30,4
32,2
33,9
35,3
36,2
37,5
38,5
39,1
39,4
38,8
37,6
36,3
36,1
34,9
31,9
28,8
Hour
Ending:
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
All
On
Pacific
G&E
25,62
26,28
23,08
22,26
18,86
26,89
23,31
18,31
33,67
41,99
34,04
38,85
37,33
38,42
34,24
39,94
40,47
40,47
37,08
31,25
31,2
28,07
33,57
26,28
31,31
34,29
SoCal
Ediso
n24
,96
25,32
22,21
21,4
18,17
26,02
23,01
18,06
26,31
29,6
32,04
39,69
41,1
4043
,21
41,87
42,3
42,1
38,35
32,01
31,71
28,46
30,97
26,06
31,04
34,36
San
Diego
24,93
25,24
22,16
21,34
18,14
26,01
23,06
18,15
26,4
29,63
32,04
39,71
41,15
4043
,26
41,97
42,38
42,07
38,25
31,94
31,61
28,43
30,91
25,94
31,03
34,38
NP‐15
24,72
25,34
22,23
21,48
18,32
26,12
22,43
17,77
27,73
32,17
31,16
37,06
35,69
36,65
32,47
38,25
38,71
38,83
35,61
30,05
29,83
26,82
30,42
25,35
29,38
31,95
SP‐15
24,41
24,76
21,73
20,94
17,78
25,46
22,63
17,75
25,8
28,98
31,31
38,74
40,13
39,03
42,23
40,84
41,27
41,05
37,43
31,3
31,03
27,9
30,2
25,44
30,34
33,59
Palo
Verde
24,05
24,41
21,42
20,66
17,52
25,06
22,19
17,35
25,26
28,42
30,71
38,08
39,62
38,56
41,78
40,4
40,85
40,59
36,97
30,85
30,43
27,28
29,79
25,1
29,89
33,08
DAForecast
26,66
25,1
24,12
23,63
23,86
24,93
26,27
28,38
30,39
32,2
33,93
35,26
36,23
37,46
38,46
39,14
39,36
38,8
37,64
36,28
36,15
34,94
31,89
28,76
Forecast
Error
‐0,091
‐0,09
‐0,09
‐0,09
‐0,087
‐0,081
‐0,071
‐0,066
‐0,061
‐0,059
‐0,054
‐0,055
‐0,062
‐0,063
‐0,067
‐0,068
‐0,066
‐0,06 4
‐0,067
‐0,067
‐0,07
‐0,077
‐0,081
‐0,084
For
archival
records
goto
http://w
ww.fe
rc.gov/m
arket‐oversig
ht/m
kt‐electric/caiso/caiso‐iso‐archives.asp
Avg.
Hr$
Forecast
Error
%CA
ISO
Daily
Repo
rtFERC
‐Divisio
nof
Energy
Market
Oversight
‐www.fe
rc.gov
Tuesday
July
20,
2010
Price
Avg.
Hr$
Prelim
inary
Real‐Tim
ePrices
with
Forecasted
and
Actual
System
Load
Day‐Ah
ead
Prices
and
DAForecast
Load
Exhibit 2
Exhibit 3
2010
0:00
‐ 1:00
1:00
‐ 2:00
2:00
‐ 3:00
3:00
‐ 4:00
4:00
‐ 5:00
5:00
‐ 6:00
6:00
‐ 7:00
7:00
‐ 8:00
8:00
‐ 9:00
9:00
‐ 10
:00
10:00 ‐ 1
1:00
11:00 ‐ 1
2:00
12:00 ‐ 1
3:00
13:00 ‐ 1
4:00
14:00 ‐ 1
5:00
15:00 ‐ 1
6:0
16:00 ‐ 1
7:00
17:00 ‐ 1
8:00
18:00 ‐ 1
9:00
19:00 ‐ 2
0:00
20:00‐ 21:00
21:00 ‐ 2
2:00
22:00 ‐ 2
3:00
23:00 ‐ 0
:00
01.07.10
12,83
10,27
7,02
3,05
7,96
12,79
13,34
10,8
26,33
33,55
35,08
35,64
36,43
37,44
46,13
54,56
50,84
39,5
36,7
36,46
36,15
35,99
33,22
26,11
02.07.10
16,71
10,18
9,24
5,08
7,72
10,28
19,66
22,51
29,41
33,03
36,56
36,26
36,82
37,94
4546
,91
46,54
41,6
36,97
35,2
36,31
39,27
35,18
28,07
03.07.10
21,13
14,22
10,11
7,61
6,69
6,66
2,04
9,91
20,25
27,07
32,92
34,04
34,21
34,46
41,74
44,84
45,74
43,05
39,03
35,7
39,95
37,6
32,73
26,05
04.07.10
26,1
23,46
14,91
10,14
9,03
6,96
00
10,19
20,93
30,28
30,4
32,68
35,49
39,2
42,41
46,28
47,12
42,86
39,78
38,83
34,58
32,7
27,08
05.07.10
16,83
12,21
10,02
8,76
8,29
0‐10,15
‐0,01
10,18
21,27
30,06
32,92
31,23
34,49
40,49
43,39
46,22
43,46
41,8
36,55
39,5
34,48
30,2
26,02
06.07.10
16,64
15,61
10,15
8,87
10,96
15,17
20,34
27,12
31,37
34,93
37,31
40,22
40,72
43,23
49,1
50,14
49,26
44,96
42,51
35,49
36,99
42,11
30,58
25,4
07.07.10
26,07
25,17
22,8
16,07
19,57
23,33
18,03
24,83
32,58
34,76
37,42
41,07
40,2
42,94
48,52
52,4
50,92
45,21
42,31
35,3
38,68
37,6
36,59
34,8
08.07.10
27,04
27,13
24,93
17,65
21,67
27,75
26,45
30,53
34,9
36,32
36,87
40,09
39,66
41,75
47,49
50,28
49,61
44,15
4239
,24
39,82
37,96
35,74
34,49
09.07.10
27,07
24,95
23,86
16,32
19,88
24,69
24,83
25,96
33,84
34,57
37,47
39,54
39,58
43,14
47,54
50,9
49,49
44,08
41,52
39,16
39,94
37,88
35,74
33,84
10.07.10
30,9
30,32
25,83
24,19
23,05
2311
,63
19,22
26,45
33,56
34,42
36,91
35,61
35,29
39,96
44,36
44,18
40,84
38,98
34,79
36,97
36,52
37,69
33,61
11.07.10
26,39
22,66
20,99
14,68
11,2
9,69
‐9,94
‐5,07
10,75
17,24
26,36
27,91
28,16
31,03
33,16
36,43
38,37
39,08
36,64
33,08
36,67
33,14
29,63
25,97
12.07.10
22,33
19,91
20,09
15,15
17,72
24,57
14,7
18,39
26,58
30,61
33,11
34,38
36,37
39,37
41,75
47,37
47,27
40,34
37,14
33,95
34,28
31,91
33,8
29,22
13.07.10
27,58
24,78
22,77
16,58
18,88
23,88
21,56
22,78
28,41
31,66
34,51
35,94
36,42
38,3
41,55
47,41
48,13
39,7
37,97
35,75
35,73
35,31
34,84
32,9
14.07.10
29,55
27,96
25,94
22,23
22,59
26,26
23,25
26,63
30,68
34,85
3940
,03
41,94
47,91
51,7
62,78
63,8
49,9
47,56
42,01
42,55
38,19
35,02
33,95
15.07.10
30,55
28,74
21,58
21,26
21,3
23,47
25,19
30,64
35,29
37,92
44,32
46,6
47,88
58,05
68,89
78,1
78,48
64,72
55,85
48,39
46,1
43,89
38,13
34,3
16.07.10
35,54
31,19
29,1
26,22
22,71
26,32
29,24
33,37
37,79
43,12
46,14
49,38
69,1
79,56
97,9
105,55
96,7
78,79
65,2
51,65
48,07
46,91
41,4
37,66
17.07.10
31,82
30,25
26,42
25,43
15,51
11,65
1,04
12,37
30,26
36,99
42,31
46,02
50,17
58,51
80,65
87,2
87,37
77,62
53,02
49,49
48,51
46,52
40,15
35,01
18.07.10
36,71
34,82
30,34
24,82
23,25
15,61
0,05
8,35
26,98
34,94
37,96
44,08
47,55
50,95
57,89
71,78
83,8
60,07
52,66
49,73
47,76
44,94
38,22
35,06
19.07.10
31,13
28,21
20,24
18,67
18,67
28,23
29,05
34,26
35,77
40,19
45,31
49,2
50,07
55,06
82,44
94,15
89,35
59,52
51,7
49,02
49,54
46,69
37,77
34,82
20.07.10
36,13
31,56
28,24
27,58
27,02
30,03
31,01
34,48
36,67
41,01
44,49
47,12
48,95
50,76
58,37
68,77
68,85
52,33
51,2
48,2
47,42
45,36
38,91
35,1
21.07.10
26,35
26,83
25,06
19,94
21,77
27,58
29,13
32,37
34,77
35,79
38,8
44,02
47,46
50,41
56,65
62,54
60,34
50,83
48,69
46,77
46,19
43,93
35,58
34,23
22.07.10
31,02
29,81
27,1
24,55
26,34
29,25
26,33
27,35
31,33
35,77
37,51
38,68
43,89
46,6
51,75
55,99
53,03
48,67
46,16
42,77
39,83
35,78
34,96
32,65
23.07.10
31,91
30,06
27,83
26,96
27,07
30,09
29,38
32,02
35,67
36,85
39,87
45,02
45,11
50,59
55,55
57,86
57,6
50,1
48,13
44,02
44,52
39,74
36,23
34,72
24.07.10
34,52
31,72
28,46
27,42
24,54
26,25
17,85
22,42
30,42
35,11
37,08
39,4
42,33
45,98
50,42
56,94
56,84
51,01
49,06
45,32
46,44
40,31
35,85
36,09
25.07.10
33,1
30,29
26,94
19,93
19,32
13,99
11,37
12,57
20,8
32,83
37,51
38,11
41,56
44,98
48,64
51,35
53,51
51,57
48,6
46,37
46,46
41,94
34,64
32,03
26.07.10
30,72
29,09
26,95
18,57
20,7
30,47
30,55
32,12
35,67
39,3
42,04
46,77
49,25
50,65
53,06
58,38
59,23
50,24
49,72
46,67
48,14
42,21
35,96
33,7
27.07.10
35,14
30,65
29,95
24,01
26,53
29,44
30,47
32,27
34,87
36,8
38,03
40,87
44,36
47,17
50,73
53,02
52,38
48,59
46,8
43,92
44,44
38,13
35,77
33,86
28.07.10
31,69
29,82
27,28
24,08
25,42
30,39
32,03
32,02
35,4
36,71
37,44
38,33
41,87
43,46
46,01
48,62
47,56
42,64
42,34
40,22
41,78
37,99
33,92
31,21
29.07.10
33,27
28,92
27,61
26,43
26,49
29,84
30,2
31,08
34,87
35,81
37,1
39,7
44,3
47,52
51,93
52,79
52,06
49,19
48,06
45,08
47,99
39,61
37,53
35,16
30.07.10
30,34
27,04
25,41
22,63
21,21
27,09
29,25
29,54
32,29
36,02
37,12
38,11
40,8
44,55
48,46
49,06
48,23
42,45
40,23
37,33
37,41
36,18
35,49
32,58
31.07.10
35,02
32,35
30,13
28,22
27,91
28,72
26,53
29,02
33,48
37,05
41,64
45,3
49,05
51,83
55,04
55,12
55,16
53,57
49,5
46,7
47,9
47,88
36,25
34,93
01.07.10
20,9
30,81
27,45
21,47
12,49
‐19,86
4,75
29,12
‐17,71
‐0,03
27,56
31,72
29,98
15,86
34,8
35,89
32,25
30,51
28,27
26,05
26,83
31,87
29,43
28,55
02.07.10
28,77
32,05
34,01
28,93
28,54
‐6,9
4,65
29,63
14,93
24,25
31,21
32,54
35,44
31,65
37,88
41,14
41,78
34,05
28,43
26,22
25,61
27,57
26,73
64,81
03.07.10
25,71
42,53
33,99
27,29
26,91
36,1
34,75
29,14
23,39
8,37
2,81
21,25
20,39
25,38
26,36
28,75
3131
,34
30,35
28,55
28,23
30,12
48,23
19,47
04.07.10
12,19
24,99
34,99
27,15
12,43
34,93
38,94
50,19
‐19,04
6,38
‐20,36
‐12
13,97
15,83
21,13
20,16
25,12
22,23
11,8
14,22
‐19,54
16,07
20,83
201,53
05.07.10
84,4
‐1,34
27,4
31,82
‐6,86
‐19,04
12,51
35,58
9,34
18,86
27,57
31,71
29,06
36,75
34,99
42,51
176,23
48,6
46,1
31,6
374,43
43,31
21,83
‐5,41
06.07.10
24,2
33,77
26,41
30,56
‐14,78
‐21,04
‐29,41
14,71
17,87
15,88
24,65
25,55
26,86
26,05
26,57
27,1
27,45
26,42
26,21
23,83
27,11
27,55
22,15
24,18
07.07.10
59,21
26,23
22,72
25,61
22,93
23,8
11,06
21,92
26,68
28,85
26,5
27,84
27,39
27,5
2828
,02
3029
,128
,82
27,34
27,46
25,6
27,55
29,24
08.07.10
20,93
26,52
29,91
23,24
4,44
16,14
24,74
20,32
19,63
23,03
28,29
29,88
27,05
26,11
30,85
36,07
39,05
35,22
30,86
33,13
28,97
31,88
28,1
18,27
09.07.10
25,61
20,51
19,56
3,85
‐18,32
‐7,68
17,14
14,11
15,14
21,86
28,23
31,24
31,45
34,97
43,35
45,12
58,99
41,83
37,88
31,6
26,31
29,82
30,93
29,46
10.07.10
2518
,59
29,47
32,37
22,74
19,51
32,53
24,72
17,41
26,63
28,25
41,17
35,23
37,03
40,25
40,61
55,89
43,87
33,64
40,81
40,11
38,62
32,99
31,76
11.07.10
22,35
10,4
32,36
27,98
27,34
26,24
28,22
20,93
‐29,29
‐23,79
10,03
31,67
32,59
26,88
26,7
26,75
28,12
29,51
27,27
26,58
28,39
27,62
3231
,51
12.07.10
15,59
3,38
23,67
14,39
1,73
‐24,48
7,38
19,52
17,74
27,17
29,96
30,87
31,66
30,3
37,52
42,23
37,93
33,65
30,42
27,4
28,15
29,06
33,35
30,19
13.07.10
26,21
16,37
21,16
‐22,47
‐28,07
‐0,19
‐2,4
22,51
25,76
28,26
31,01
32,83
42,82
50,57
43,82
165,74
269,63
48,36
50,39
36,04
36,36
37,54
32,31
37,88
14.07.10
30,49
27,16
28,04
28,2
8,07
20,34
27,81
31,86
33,29
38,74
42,57
47,67
46,21
49,2
40,17
216,23
84,79
77,5
54,17
53,49
50,38
50,7
47,58
37,66
15.07.10
40,02
28,36
28,43
28,6
27,03
1,72
25,14
27,87
40,29
44,71
44,58
49,75
407,01
734,08
734,08
343,91
165,39
46,59
188,23
46,61
42,82
40,16
48,15
38,58
16.07.10
25,42
29,8
15,84
‐16,27
‐10,84
20,2
20,43
27,51
29,23
34,12
43,98
138,31
47,31
45,3
62,47
53,65
67,17
45,91
41,88
39,42
39,93
35,74
30,7
28,98
17.07.10
32,94
3329
,77
‐1,41
‐8,38
16,5
‐6,07
‐3,56
16,16
33,12
38,39
45,67
54,21
68,09
128,29
698,38
84,28
48,65
45,78
43,41
44,66
38,86
31,63
28,19
18.07.10
35,29
30,41
29,08
5,58
‐18,52
26,32
31,66
17,36
19,37
25,71
29,64
30,92
34,02
40,2
45,3
46,62
46,33
46,63
42,91
39,63
41,54
38,27
32,79
32,9
19.07.10
19,5
27,96
25,11
29,14
29,92
30,51
20,85
30,44
34,76
39,69
42,79
47,61
46,26
45,64
48,17
48,31
47,92
47,14
43,68
36,06
38,56
34,07
29,86
26,3
20.07.10
24,93
25,24
22,16
21,34
18,14
26,01
23,06
18,15
26,4
29,63
32,04
39,71
41,15
4043
,26
41,97
42,38
42,07
38,25
31,94
31,61
28,43
30,91
25,94
21.07.10
23,74
29,61
31,38
25,99
27,29
31,43
3332
,81
31,15
30,9
32,52
35,46
34,57
32,29
32,63
37,47
36,45
35,49
36,6
30,97
31,2
29,12
24,73
21,17
22.07.10
3432
,91
30,42
29,63
30,84
31,7
31,04
30,08
32,87
28,28
29,53
30,4
32,72
32,08
31,43
32,29
37,5
43,87
43,79
34,42
27,91
30,42
34,05
30,87
23.07.10
38,11
34,14
31,75
32,42
34,25
29,4
34,67
37,26
35,01
36,11
38,83
57,97
49,4
44,31
56,78
43,22
4851
,35
50,27
92,78
47,43
50,01
41,4
39,7
24.07.10
42,91
38,57
29,84
28,42
26,39
28,63
30,02
29,32
29,96
32,15
37,38
41,67
49,27
53,06
53,94
46,06
74,75
47,64
49,46
46,97
43,39
38,76
34,78
32,3
25.07.10
31,32
30,19
27,28
27,07
26,89
18,1
24,35
28,56
20,04
30,31
28,18
31,85
32,94
42,08
46,24
50,8
55,55
38,35
43,73
34,56
47,1
40,04
33,22
31,62
26.07.10
29,86
32,29
30,87
29,14
29,42
24,06
26,77
36,6
30,28
31,92
34,16
43,17
39,11
43,55
45,86
50,52
44,54
42,93
40,96
33,86
35,13
33,2
35,32
31,66
27.07.10
30,9
30,79
27,25
27,82
15,18
4,02
15,03
22,6
29,41
28,41
29,63
32,15
34,16
37,68
37,61
39,57
41,18
35,71
31,06
29,55
28,99
27,96
31,91
26,45
28.07.10
29,38
28,62
23,71
12,91
27,15
27,45
19,81
27,71
30,75
30,46
30,78
35,92
33,15
34,69
31,89
31,75
36,7
33,33
153,2
34,58
33,49
32,36
33,86
33,73
29.07.10
35,86
32,33
24,7
2,29
8,43
30,82
10,93
29,8
33,62
32,8
3443
,22
45,49
44,55
48,44
49,17
164,32
267,02
31,38
16,53
211,31
42,86
37,1
5,44
30.07.10
30,37
26,86
23,81
5,06
28,08
30,39
12,03
33,76
168,19
422,99
77,24
121,76
338,81
269,16
665,61
98,19
59,04
42,24
49,65
37,46
39,82
34,48
202,83
39,11
31.07.10
24,42
35,57
30,65
29,76
16,5
24,72
5,3
‐11,23
26,37
40,25
39,56
222,33
59,21
49,75
44,73
42,15
39,28
36,33
34,77
31,4
31,81
31,48
31,2
19,2
Exhibit 3
Exhibit 4
2010
0:00
‐ 3:00
1:00
‐ 3:00
2:00
‐ 5:00
3:00
‐ 6:00
4:00
‐ 7:00
5:00
‐ 8:00
6:00
‐ 9:00
7:00
‐ 10
:00
8:00
‐ 11
:00
9:00
‐ 12
:00
10:00 ‐ 1
3:00
11:00 ‐ 1
4:00
12:00 ‐ 1
5:00
13:00 ‐ 1
6:00
14:00 ‐ 1
7:00
15:00 ‐ 1
8:00
16:00 ‐ 1
9:00
17:00 ‐ 2
0:00
18:00 ‐ 2
1:00
19:00 ‐ 2
2:00
20:00‐ 23:00
21:00 ‐ 2
4:00
01.07.10
30,12
20,34
18,03
23,8
34,09
36,93
50,47
70,68
94,96
104,27
107,15
109,51
120
138,13
151,53
144,9
127,04
112,66
109,31
108,6
105,36
95,32
02.07.10
36,13
24,5
22,04
23,08
37,66
52,45
71,58
84,95
9910
5,85
109,64
111,02
119,76
129,85
138,45
135,05
125,11
113,77
108,48
110,78
110,76
102,52
03.07.10
45,46
31,94
24,41
20,96
15,39
18,61
32,2
57,23
80,24
94,03
101,17
102,71
110,41
121,04
132,32
133,63
127,82
117,78
114,68
113,25
110,28
96,38
04.07.10
64,47
48,51
34,08
26,13
15,99
6,96
10,19
31,12
61,4
81,61
93,36
98,57
107,37
117,1
127,89
135,81
136,26
129,76
121,47
113,19
106,11
94,36
05.07.10
39,06
30,99
27,07
17,05
‐1,86
‐10,16
0,02
31,44
61,51
84,25
94,21
98,64
106,21
118,37
130,1
133,07
131,48
121,81
117,85
110,53
104,18
90,7
06.07.10
42,4
34,63
29,98
3546
,47
62,63
78,83
93,42
103,61
112,46
118,25
124,17
133,05
142,47
148,5
144,36
136,73
122,96
114,99
114,59
109,68
98,09
07.07.10
74,04
64,04
58,44
58,97
60,93
66,19
75,44
92,17
104,76
113,25
118,69
124,21
131,66
143,86
151,84
148,53
138,44
122,82
116,29
111,58
112,87
108,99
08.07.10
79,1
69,71
64,25
67,07
75,87
84,73
91,88
101,75
108,09
113,28
116,62
121,5
128,9
139,52
147,38
144,04
135,76
125,39
121,06
117,02
113,52
108,19
09.07.10
75,88
65,13
60,06
60,89
69,4
75,48
84,63
94,37
105,88
111,58
116,59
122,26
130,26
141,58
147,93
144,47
135,09
124,76
120,62
116,98
113,56
107,46
10.07.10
87,05
80,34
73,07
70,24
57,68
53,85
57,3
79,23
94,43
104,89
106,94
107,81
110,86
119,61
128,5
129,38
124
114,61
110,74
108,28
111,18
107,82
11.07.10
70,04
58,33
46,87
35,57
10,95
‐5,32
‐4,26
22,92
54,35
71,51
82,43
87,1
92,35
100,62
107,96
113,88
114,09
108,8
106,39
102,89
99,44
88,74
12.07.10
62,33
55,15
52,96
57,44
56,99
57,66
59,67
75,58
90,3
98,1
103,86
110,12
117,49
128,49
136,39
134,98
124,75
111,43
105,37
100,14
99,99
94,93
13.07.10
75,13
64,13
58,23
59,34
64,32
68,22
72,75
82,85
94,58
102,11
106,87
110,66
116,27
127,26
137,09
135,24
125,8
113,42
109,45
106,79
105,88
103,05
14.07.10
83,45
76,13
70,76
71,08
72,1
76,14
80,56
92,16
104,53
113,88
120,97
129,88
141,55
162,39
178,28
176,48
161,26
139,47
132,12
122,75
115,76
107,16
15.07.10
80,87
71,58
64,14
66,03
69,96
79,3
91,12
103,85
117,53
128,84
138,8
152,53
174,82
205,04
225,47
221,3
199,05
168,96
150,34
138,38
128,12
116,32
16.07.10
95,83
86,51
78,03
75,25
78,27
88,93
100,4
114,28
127,05
138,64
164,62
198,04
246,56
283,01
300,15
281,04
240,69
195,64
164,92
146,63
136,38
125,97
17.07.10
88,49
82,1
67,36
52,59
28,2
25,06
43,67
79,62
109,56
125,32
138,5
154,7
189,33
226,36
255,22
252,19
218,01
180,13
151,02
144,52
135,18
121,68
18.07.10
101,87
89,98
78,41
63,68
38,91
24,01
35,38
70,27
99,88
116,98
129,59
142,58
156,39
180,62
213,47
215,65
196,53
162,46
150,15
142,43
130,92
118,22
19.07.10
79,58
67,12
57,58
65,57
75,95
91,54
99,08
110,22
121,27
134,7
144,58
154,33
187,57
231,65
265,94
243,02
200,57
160,24
150,26
145,25
134
119,28
20.07.10
95,93
87,38
82,84
84,63
88,06
95,52
102,16
112,16
122,17
132,62
140,56
146,83
158,08
177,9
195,99
189,95
172,38
151,73
146,82
140,98
131,69
119,37
21.07.10
78,24
71,83
66,77
69,29
78,48
89,08
96,27
102,93
109,36
118,61
130,28
141,89
154,52
169,6
179,53
173,71
159,86
146,29
141,65
136,89
125,7
113,74
22.07.10
87,93
81,46
77,99
80,14
81,92
82,93
85,01
94,45
104,61
111,96
120,08
129,17
142,24
154,34
160,77
157,69
147,86
137,6
128,76
118,38
110,57
103,39
23.07.10
89,8
84,85
81,86
84,12
86,54
91,49
97,07
104,54
112,39
121,74
130
140,72
151,25
164
171,01
165,56
155,83
142,25
136,67
128,28
120,49
110,69
24.07.10
94,7
87,6
80,42
78,21
68,64
66,52
70,69
87,95
102,61
111,59
118,81
127,71
138,73
153,34
164,2
164,79
156,91
145,39
140,82
132,07
122,6
112,25
25.07.10
90,33
77,16
66,19
53,24
44,68
37,93
44,74
66,2
91,14
108,45
117,18
124,65
135,18
144,97
153,5
156,43
153,68
146,54
141,43
134,77
123,04
108,61
26.07.10
86,76
74,61
66,22
69,74
81,72
93,14
98,34
107,09
117,01
128,11
138,06
146,67
152,96
162,09
170,67
167,85
159,19
146,63
144,53
137,02
126,31
111,87
27.07.10
95,74
84,61
80,49
79,98
86,44
92,18
97,61
103,94
109,7
115,7
123,26
132,4
142,26
150,92
156,13
153,99
147,77
139,31
135,16
126,49
118,34
107,76
28.07.10
88,79
81,18
76,78
79,89
87,84
94,44
99,45
104,13
109,55
112,48
117,64
123,66
131,34
138,09
142,19
138,82
132,54
125,2
124,34
119,99
113,69
103,12
29.07.10
89,8
82,96
80,53
82,76
86,53
91,12
96,15
101,76
107,78
112,61
121,1
131,52
143,75
152,24
156,78
154,04
149,31
142,33
141,13
132,68
125,13
112,3
30.07.10
82,79
75,08
69,25
70,93
77,55
85,88
91,08
97,85
105,43
111,25
116,03
123,46
133,81
142,07
145,75
139,74
130,91
120,01
114,97
110,92
109,08
104,25
31.07.10
97,5
90,7
86,26
84,85
83,16
84,27
89,03
99,55
112,17
123,99
135,99
146,18
155,92
161,99
165,32
163,85
158,23
149,77
144,1
142,48
132,03
119,06
01.07.10
79,16
79,73
61,41
14,1
‐2,62
14,01
16,16
11,38
9,82
59,25
89,26
77,56
80,64
86,55
102,94
98,65
91,03
84,83
81,15
84,75
88,13
89,85
02.07.10
94,83
94,99
91,48
50,57
26,29
27,38
49,21
68,81
70,39
8899
,19
99,63
104,97
110,67
120,8
116,97
104,26
88,7
80,26
79,4
79,91
119,11
03.07.10
102,23
103,81
88,19
90,3
97,76
99,99
87,28
60,9
34,57
32,43
44,45
67,02
72,13
80,49
86,11
91,09
92,69
90,24
87,13
86,9
106,58
97,82
04.07.10
72,17
87,13
74,57
74,51
86,3
124,06
70,09
37,53
‐33,02
‐25,98
‐18,39
17,8
50,93
57,12
66,41
67,51
59,15
48,25
6,48
10,75
17,36
238,43
05.07.10
110,46
57,88
52,36
5,92
‐13,39
29,05
57,43
63,78
55,77
78,14
88,34
97,52
100,8
114,25
253,73
267,34
270,93
126,3
452,13
449,34
439,57
59,73
06.07.10
84,38
90,74
42,19
‐5,26
‐65,23
‐35,74
3,17
48,46
58,4
66,08
77,06
78,46
79,48
79,72
81,12
80,97
80,08
76,46
77,15
78,49
76,81
73,88
07.07.10
108,16
74,56
71,26
72,34
57,79
56,78
59,66
77,45
82,03
83,19
81,73
82,73
82,89
83,52
86,02
87,12
87,92
85,26
83,62
80,4
80,61
82,39
08.07.10
77,36
79,67
57,59
43,82
45,32
61,2
64,69
62,98
70,95
81,2
85,22
83,04
84,01
93,03
105,97
110,34
105,13
99,21
92,96
93,98
88,95
78,25
09.07.10
65,68
43,92
5,09
‐22,15
‐8,86
23,57
46,39
51,11
65,23
81,33
90,92
97,66
109,77
123,44
147,46
145,94
138,7
111,31
95,79
87,73
87,06
90,21
10.07.10
73,06
80,43
84,58
74,62
74,78
76,76
74,66
68,76
72,29
96,05
104,65
113,43
112,51
117,89
136,75
140,37
133,4
118,32
114,56
119,54
111,72
103,37
11.07.10
65,11
70,74
87,68
81,56
81,8
75,39
19,86
‐32,15
‐43,05
17,91
74,29
91,14
86,17
80,33
81,57
84,38
84,9
83,36
82,24
82,59
88,01
91,13
12.07.10
42,64
41,44
39,79
‐8,36
‐15,37
2,42
44,64
64,43
74,87
8892
,49
92,83
99,48
110,05
117,68
113,81
102
91,47
85,97
84,61
90,56
92,6
13.07.10
63,74
15,06
‐29,38
‐50,73
‐30,66
19,92
45,87
76,53
85,03
92,1
106,66
126,22
137,21
260,13
479,19
483,73
368,38
134,79
122,79
109,94
106,21
107,73
14.07.10
85,69
83,4
64,31
56,61
56,22
80,01
92,96
103,89
114,6
128,98
136,45
143,08
135,58
305,6
341,19
378,52
216,46
185,16
158,04
154,57
148,66
135,94
15.07.10
96,81
85,39
84,06
57,35
53,89
54,73
93,3
112,87
129,58
139,04
501,34
1190
,84
1141
,09
1077
,99
509,3
555,89
400,21
281,43
277,66
129,59
131,13
126,89
16.07.10
71,06
29,37
‐11,27
‐6,91
29,79
68,14
77,17
90,86
107,33
216,41
229,6
230,92
155,08
161,42
183,29
166,73
154,96
127,21
121,23
115,09
106,37
95,42
17.07.10
95,71
61,36
19,98
6,71
2,05
6,87
6,53
45,72
87,67
117,18
138,27
167,97
250,59
894,76
910,95
831,31
178,71
137,84
133,85
126,93
115,15
98,68
18.07.10
94,78
65,07
16,14
13,38
39,46
75,34
68,39
62,44
74,72
86,27
94,58
105,14
119,52
132,12
138,25
139,58
135,87
129,17
124,08
119,44
112,6
103,96
19.07.10
72,57
82,21
84,17
89,57
81,28
81,8
86,05
104,89
117,24
130,09
136,66
139,51
140,07
142,12
144,4
143,37
138,74
126,88
118,3
108,69
102,49
90,23
20.07.10
72,33
68,74
61,64
65,49
67,21
67,22
67,61
74,18
88,07
101,38
112,9
120,86
124,41
125,23
127,61
126,42
122,7
112,26
101,8
91,98
90,95
85,28
21.07.10
84,73
86,98
84,66
84,71
91,72
97,24
96,96
94,86
94,57
98,88
102,55
102,32
99,49
102,39
106,55
109,41
108,54
103,06
98,77
91,29
85,05
75,02
22.07.10
97,33
92,96
90,89
92,17
93,58
92,82
93,99
91,23
90,68
88,21
92,65
95,2
96,23
95,8
101,22
113,66
125,16
122,08
106,12
92,75
92,38
95,34
23.07.10
104
98,31
98,42
96,07
98,32
101,33
106,94
108,38
109,95
132,91
146,2
151,68
150,49
144,31
148
142,57
149,62
194,4
190,48
190,22
138,84
131,11
24.07.10
111,32
96,83
84,65
83,44
85,04
87,97
89,3
91,43
99,49
111,2
128,32
144
156,27
153,06
174,75
168,45
171,85
144,07
139,82
129,12
116,93
105,84
25.07.10
88,79
84,54
81,24
72,06
69,34
71,01
72,95
78,91
78,53
90,34
92,97
106,87
121,26
139,12
152,59
144,7
137,63
116,64
125,39
121,7
120,36
104,88
26.07.10
93,02
92,3
89,43
82,62
80,25
87,43
93,65
98,8
96,36
109,25
116,44
125,83
128,52
139,93
140,92
137,99
128,43
117,75
109,95
102,19
103,65
100,18
27.07.10
88,94
85,86
70,25
47,02
34,23
41,65
67,04
80,42
87,45
90,19
95,94
103,99
109,45
114,86
118,36
116,46
107,95
96,32
89,6
86,5
88,86
86,32
28.07.10
81,71
65,24
63,77
67,51
74,41
74,97
78,27
88,92
91,99
97,16
99,85
103,76
99,73
98,33
100,34
101,78
223,23
221,11
221,27
100,43
99,71
99,95
29.07.10
92,89
59,32
35,42
41,54
50,18
71,55
74,35
96,22
100,42
110,02
122,71
133,26
138,48
142,16
261,93
480,51
462,72
314,93
259,22
270,7
291,27
85,4
30.07.10
81,04
55,73
56,95
63,53
70,5
76,18
213,98
624,94
668,42
621,99
537,81
729,73
1273
,58
1032
,96
822,84
199,47
150,93
129,35
126,93
111,76
277,13
276,42
31.07.10
90,64
95,98
76,91
70,98
46,52
18,79
20,44
55,39
106,18
302,14
321,1
331,29
153,69
136,63
126,16
117,76
110,38
102,5
97,98
94,69
94,49
81,88
Exhibit 4
Exhibit 5
2010
0:00
‐ 3:00
1:00
‐ 3:00
2:00
‐ 5:00
3:00
‐ 6:00
4:00
‐ 7:00
5:00
‐ 8:00
6:00
‐ 9:00
7:00
‐ 10
:00
8:00
‐ 11
:00
9:00
‐ 12
:00
10:00 ‐ 1
3:00
11:00 ‐ 1
4:00
12:00 ‐ 1
5:00
13:00 ‐ 1
6:00
14:00 ‐ 1
7:00
15:00 ‐ 1
8:00
16:00 ‐ 1
9:00
17:00 ‐ 2
0:00
18:00 ‐ 2
1:00
19:00 ‐ 2
2:00
20:00‐ 23:00
21:00 ‐ 2
4:00
MIN
MAX
COST
REV
GMGM
mon
th01
.07.10
30,12
20,34
18,03
23,8
34,09
36,93
50,47
70,68
94,96
104,27
107,15
109,51
120
138,13
151,53
144,9
127,04
112,66
109,31
108,6
105,36
95,32
18,03
151,53
450,75
$
3.78
8,25
$
3.33
7,50
$
02.07.10
36,13
24,5
22,04
23,08
37,66
52,45
71,58
84,95
9910
5,85
109,64
111,02
119,76
129,85
138,45
135,05
125,11
113,77
108,48
110,78
110,76
102,52
22,04
138,45
551,00
$
3.46
1,25
$
2.91
0,25
$
03.07.10
45,46
31,94
24,41
20,96
15,39
18,61
32,2
57,23
80,24
94,03
101,17
102,71
110,41
121,04
132,32
133,63
127,82
117,78
114,68
113,25
110,28
96,38
15,39
133,63
384,75
$
3.34
0,75
$
2.95
6,0 0
$
04.07.10
64,47
48,51
34,08
26,13
15,99
6,96
10,19
31,12
61,4
81,61
93,36
98,57
107,37
117,1
127,89
135,81
136,26
129,76
121,47
113,19
106,11
94,36
6,96
136,26
174,00
$
3.40
6,50
$
3.23
2,5 0
$
05.07.10
39,06
30,99
27,07
17,05
‐1,86
‐10,16
0,02
31,44
61,51
84,25
94,21
98,64
106,21
118,37
130,1
133,07
131,48
121,81
117,85
110,53
104,18
90,7
‐10,16
133,07
‐254
,00
$
3.32
6,75
$
3.58
0,75
$
06.07.10
42,4
34,63
29,98
3546
,47
62,63
78,83
93,42
103,61
112,46
118,25
124,17
133,05
142,47
148,5
144,36
136,73
122,96
114,99
114,59
109,68
98,09
29,98
148,5
749,50
$
3.71
2,50
$
2.96
3,0 0
$
07.07.10
74,04
64,04
58,44
58,97
60,93
66,19
75,44
92,17
104,76
113,25
118,69
124,21
131,66
143,86
151,84
148,53
138,44
122,82
116,29
111,58
112,87
108,99
58,44
151,84
1.46
1,00
$
3.79
6,00
$
2.33
5,00
$
08.07.10
79,1
69,71
64,25
67,07
75,87
84,73
91,88
101,75
108,09
113,28
116,62
121,5
128,9
139,52
147,38
144,04
135,76
125,39
121,06
117,02
113,52
108,19
64,25
147,38
1.60
6,25
$
3.68
4,50
$
2.07
8,25
$
09.07.10
75,88
65,13
60,06
60,89
69,4
75,48
84,63
94,37
105,88
111,58
116,59
122,26
130,26
141,58
147,93
144,47
135,09
124,76
120,62
116,98
113,56
107,46
60,06
147,93
1.50
1,50
$
3.69
8,25
$
2.19
6,75
$
10.07.10
87,05
80,34
73,07
70,24
57,68
53,85
57,3
79,23
94,43
104,89
106,94
107,81
110,86
119,61
128,5
129,38
124
114,61
110,74
108,28
111,18
107,82
53,85
129,38
1.34
6,25
$
3.23
4,50
$
1.88
8,25
$
11.07.10
70,04
58,33
46,87
35,57
10,95
‐5,32
‐4,26
22,92
54,35
71,51
82,43
87,1
92,35
100,62
107,96
113,88
114,09
108,8
106,39
102,89
99,44
88,74
‐5,32
114,09
‐133
,00
$
2.85
2,25
$
2.98
5,25
$
12.07.10
62,33
55,15
52,96
57,44
56,99
57,66
59,67
75,58
90,3
98,1
103,86
110,12
117,49
128,49
136,39
134,98
124,75
111,43
105,37
100,14
99,99
94,93
52,96
136,39
1.32
4,00
$
3.40
9,75
$
2.08
5,75
$
13.07.10
75,13
64,13
58,23
59,34
64,32
68,22
72,75
82,85
94,58
102,11
106,87
110,66
116,27
127,26
137,09
135,24
125,8
113,42
109,45
106,79
105,88
103,05
58,23
137,09
1.45
5,75
$
3.42
7,25
$
1.97
1,5 0
$
14.07.10
83,45
76,13
70,76
71,08
72,1
76,14
80,56
92,16
104,53
113,88
120,97
129,88
141,55
162,39
178,28
176,48
161,26
139,47
132,12
122,75
115,76
107,16
70,76
178,28
1.76
9,00
$
4.45
7,00
$
2.68
8,00
$
15.07.10
80,87
71,58
64,14
66,03
69,96
79,3
91,12
103,85
117,53
128,84
138,8
152,53
174,82
205,04
225,47
221,3
199,05
168,96
150,34
138,38
128,12
116,32
64,14
225,47
1.60
3,50
$
5.63
6,75
$
4.03
3,25
$
16.07.10
95,83
86,51
78,03
75,25
78,27
88,93
100,4
114,28
127,05
138,64
164,62
198,04
246,56
283,01
300,15
281,04
240,69
195,64
164,92
146,63
136,38
125,97
75,25
300,15
1.88
1,25
$
7.50
3,75
$
5.62
2,50
$
17.07.10
88,49
82,1
67,36
52,59
28,2
25,06
43,67
79,62
109,56
125,32
138,5
154,7
189,33
226,36
255,22
252,19
218,01
180,13
151,02
144,52
135,18
121,68
25,06
255,22
626,50
$
6.38
0,50
$
5.75
4,0 0
$
18.07.10
101,87
89,98
78,41
63,68
38,91
24,01
35,38
70,27
99,88
116,98
129,59
142,58
156,39
180,62
213,47
215,65
196,53
162,4 6
150,15
142,43
130,92
118,22
24,01
215,65
600,25
$
5.39
1,25
$
4.79
1,00
$
19.07.10
79,58
67,12
57,58
65,57
75,95
91,54
99,08
110,22
121,27
134,7
144,58
154,33
187,57
231,65
265,94
243,02
200,57
160,24
150,26
145,25
134
119,28
57,58
265,94
1.43
9,50
$
6.64
8,50
$
5.20
9,0 0
$
20.07.10
95,93
87,38
82,84
84,63
88,06
95,52
102,16
112,1 6
122,17
132,62
140,56
146,83
158,08
177,9
195,99
189,95
172,38
151,73
146,82
140,98
131,69
119,37
82,84
195,99
2.07
1,00
$
4.89
9,75
$
2.82
8,75
$
21.07.10
78,24
71,83
66,77
69,29
78,48
89,08
96,27
102,93
109,36
118,61
130,28
141,89
154,52
169,6
179,53
173,71
159,86
146,29
141,65
136,89
125,7
113,74
66,77
179,53
1.66
9,25
$
4.48
8,25
$
2.81
9,0 0
$
22.07.10
87,93
81,46
77,99
80,14
81,92
82,93
85,01
94,45
104,61
111,96
120,08
129,17
142,24
154,34
160,77
157,69
147,86
137,6
128,76
118,38
110,57
103,39
77,99
160,77
1.94
9,75
$
4.01
9,25
$
2.06
9,50
$
23.07.10
89,8
84,85
81,86
84,12
86,54
91,49
97,07
104,5 4
112,39
121,74
130
140,72
151,25
164
171,01
165,56
155,83
142,25
136,67
128,28
120,49
110,69
81,86
171,01
2.04
6,50
$
4.27
5,25
$
2.22
8,75
$
24.07.10
94,7
87,6
80,42
78,21
68,64
66,52
70,69
87,95
102,61
111,59
118,81
127,71
138,73
153,34
164,2
164,79
156,91
145,39
140,82
132,07
122,6
112,25
66,52
164,79
1.66
3,00
$
4.11
9,75
$
2.45
6,75
$
25.07.10
90,33
77,16
66,19
53,24
44,68
37,93
44,74
66,2
91,14
108,45
117,18
124,65
135,18
144,97
153,5
156,43
153,68
146,54
141,43
134,77
123,04
108,61
37,93
156,43
948,25
$
3.91
0,75
$
2.96
2,50
$
26.07.10
86,76
74,61
66,22
69,74
81,72
93,14
98,34
107,09
117,01
128,11
138,06
146,67
152,96
162,09
170,67
167,85
159,19
146,63
144,53
137,02
126,31
111,87
66,22
170,67
1.65
5,50
$
4.26
6,75
$
2.61
1,25
$
27.07.10
95,74
84,61
80,49
79,98
86,44
92,18
97,61
103,9 4
109,7
115,7
123,26
132,4
142,26
150,92
156,13
153,99
147,77
139,31
135,16
126,49
118,34
107,76
79,98
156,13
1.99
9,50
$
3.90
3,25
$
1.90
3,75
$
28.07.10
88,79
81,18
76,78
79,89
87,84
94,44
99,45
104,13
109,55
112,48
117,64
123,66
131,34
138,09
142,19
138,82
132,54
125,2
124,34
119,99
113,69
103,12
76,78
142,19
1.91
9,50
$
3.55
4,75
$
1.63
5,25
$
29.07.10
89,8
82,96
80,53
82,76
86,53
91,12
96,15
101,7 6
107,78
112,61
121,1
131,52
143,75
152,24
156,78
154,04
149,31
142,33
141,13
132,68
125,13
112,3
80,53
156,78
2.01
3,25
$
3.91
9,50
$
1.90
6,25
$
30.07.10
82,79
75,08
69,25
70,93
77,55
85,88
91,08
97,85
105,43
111,25
116,03
123,46
133,81
142,07
145,75
139,74
130,91
120,01
114,97
110,92
109,08
104,25
69,25
145,75
1.73
1,25
$
3.64
3,75
$
1.91
2,5 0
$
31.07.10
97,5
90,7
86,26
84,85
83,16
84,27
89,03
99,55
112,17
123,99
135,99
146,18
155,92
161,99
165,32
163,85
158,23
149,77
144,1
142,48
132,03
119,0 6
83,16
165,32
2.07
9,00
$
4.13
3,00
$
2.05
4,00
$
90.006
,75
$
01.07.10
79,16
79,73
61,41
14,1
‐2,62
14,01
16,16
11,38
9,82
59,25
89,26
77,56
80,64
86,55
102,94
98,65
91,03
84,83
81,15
84,75
88,13
89,85
‐2,62
102,94
‐65,50
$
2.57
3,50
$
2.63
9,00
$
02.07.10
94,83
94,99
91,48
50,57
26,29
27,38
49,21
68,81
70,39
8899
,19
99,63
104,97
110,67
120,8
116,97
104,26
88,7
80,26
79,4
79,91
119,11
26,29
120,8
657,25
$
3.02
0,00
$
2.36
2,75
$
03.07.10
102,23
103,81
88,19
90,3
97,76
99,99
87,28
60,9
34,57
32,43
44,45
67,02
72,13
80,49
86,11
91,09
92,69
90,24
87,13
86,9
106,58
97,82
32,43
106,58
810,75
$
2.66
4,50
$
1.85
3,75
$
04.07.10
72,17
87,13
74,57
74,51
86,3
124,06
70,09
37,53
‐33,02
‐25,98
‐18,39
17,8
50,93
57,12
66,41
67,51
59,15
48,25
6,48
10,75
17,36
238,43
‐33,02
238,43
‐825
,50
$
5.96
0,75
$
6.78
6,25
$
05.07.10
110,46
57,88
52,36
5,92
‐13,39
29,05
57,43
63,78
55,77
78,14
88,34
97,52
100,8
114,25
253,73
267,34
270,93
126,3
452,13
449,34
439,57
59,73
‐13,39
452,13
‐334
,75
$
11.303
,25
$
11.638
,00
$
06.07.10
84,38
90,74
42,19
‐5,26
‐65,23
‐35,74
3,17
48,46
58,4
66,08
77,06
78,46
79,48
79,72
81,12
80,97
80,08
76,46
77,15
78,49
76,81
73,88
‐65,23
90,74
‐1.630
,75
$
2.26
8,50
$
3.89
9,25
$
07.07.10
108,1 6
74,56
71,26
72,34
57,79
56,78
59,66
77,45
82,03
83,19
81,73
82,73
82,89
83,52
86,02
87,12
87,92
85,26
83,62
80,4
80,61
82,39
56,78
108,16
1.41
9,50
$
2.70
4,00
$
1.28
4,50
$
08.07.10
77,36
79,67
57,59
43,82
45,32
61,2
64,69
62,98
70,95
81,2
85,22
83,04
84,01
93,03
105,97
110,34
105,13
99,21
92,96
93,98
88,95
78,25
43,82
110,34
1.09
5,50
$
2.75
8,50
$
1.66
3,0 0
$
09.07.10
65,68
43,92
5,09
‐22,15
‐8,86
23,57
46,39
51,11
65,23
81,33
90,92
97,66
109,77
123,44
147,46
145,94
138,7
111,31
95,79
87,73
87,06
90,21
‐22,15
147,46
‐553
,75
$
3.68
6,50
$
4.24
0,25
$
10.07.10
73,06
80,43
84,58
74,62
74,78
76,76
74,66
68,76
72,29
96,05
104,65
113,43
112,51
117,89
136,75
140,37
133,4
118,32
114,56
119,54
111,72
103,37
68,76
140,37
1.71
9,00
$
3.50
9,25
$
1.79
0,25
$
11.07.10
65,11
70,74
87,68
81,56
81,8
75,39
19,86
‐32,15
‐43,05
17,91
74,29
91,14
86,17
80,33
81,57
84,38
84,9
83,36
82,24
82,59
88,01
91,13
‐43,05
91,14
‐1.076
,25
$
2.27
8,50
$
3.35
4,75
$
12.07.10
42,64
41,44
39,79
‐8,36
‐15,37
2,42
44,64
64,43
74,87
8892
,49
92,83
99,48
110,05
117,68
113,81
102
91,47
85,97
84,61
90,56
92,6
‐15,37
117,68
‐384
,25
$
2.94
2,00
$
3.32
6,25
$
13.07.10
63,74
15,06
‐29,38
‐50,73
‐30,66
19,92
45,87
76,53
85,03
92,1
106,66
126,22
137,21
260,13
479,19
483,73
368,38
134,79
122,79
109,94
106,21
107,73
‐50,73
483,73
‐1.268
,25
$
12.093
,25
$
13.361
,50
$
14.07.10
85,69
83,4
64,31
56,61
56,22
80,01
92,96
103,89
114,6
128,98
136,45
143,08
135,58
305,6
341,19
378,52
216,46
185,16
158,04
154,57
148,66
135,94
56,22
378,52
1.40
5,50
$
9.46
3,00
$
8.05
7,5 0
$
15.07.10
96,81
85,39
84,06
57,35
53,89
54,73
93,3
112,87
129,58
139,04
501,34
1190
,84
1141
,09
1077
,99
509,3
555,89
400,21
281,43
277,66
129,59
131,13
126,89
53,89
1190
,84
1.34
7,25
$
29.771
,00
$
28.423
,75
$
16.07.10
71,06
29,37
‐11,27
‐6,91
29,79
68,14
77,17
90,86
107,33
216,41
229,6
230,92
155,08
161,42
183,29
166,73
154,96
127,21
121,23
115,09
106,37
95,42
‐11,27
230,92
‐281
,75
$
5.77
3,00
$
6.05
4,75
$
17.07.10
95,71
61,36
19,98
6,71
2,05
6,87
6,53
45,72
87,67
117,18
138,27
167,97
250,59
894,76
910,95
831,31
178,71
137,84
133,85
126,93
115,15
98,68
2,05
910,95
51,25
$
22.773
,75
$
22.722
,50
$
18.07.10
94,78
65,07
16,14
13,38
39,46
75,34
68,39
62,44
74,72
86,27
94,58
105,14
119,52
132,12
138,25
139,58
135,87
129,17
124,08
119,44
112,6
103,96
13,38
139,58
334,50
$
3.48
9,50
$
3.15
5,00
$
19.07.10
72,57
82,21
84,17
89,57
81,28
81,8
86,05
104,89
117,24
130,09
136,66
139,51
140,07
142,12
144,4
143,37
138,74
126,88
118,3
108,69
102,49
90,23
72,57
144,4
1.81
4,25
$
3.61
0,00
$
1.79
5,75
$
20.07.10
72,33
68,74
61,64
65,49
67,21
67,22
67,61
74,18
88,07
101,38
112,9
120,86
124,41
125,23
127,61
126,42
122,7
112,26
101,8
91,98
90,95
85,28
61,64
127,61
1.54
1,00
$
3.19
0,25
$
1.64
9,25
$
21.07.10
84,73
86,98
84,66
84,71
91,72
97,24
96,96
94,86
94,57
98,88
102,55
102,32
99,49
102,39
106,55
109,41
108,54
103,06
98,77
91,29
85,05
75,02
75,02
109,41
1.87
5,50
$
2.73
5,25
$
859,75
$
22.07.10
97,33
92,96
90,89
92,17
93,58
92,82
93,99
91,23
90,68
88,21
92,65
95,2
96,23
95,8
101,22
113,66
125,16
122,08
106,12
92,75
92,38
95,34
88,21
125,16
2.20
5,25
$
3.12
9,00
$
923,75
$
23.07.10
104
98,31
98,42
96,07
98,32
101,33
106,94
108,38
109,95
132,91
146,2
151,68
150,49
144,31
148
142,57
149,62
194,4
190,48
190,22
138,84
131,11
96,07
194,4
2.40
1,75
$
4.86
0,00
$
2.45
8,25
$
24.07.10
111,32
96,83
84,65
83,44
85,04
87,97
89,3
91,43
99,49
111,2
128,32
144
156,27
153,06
174,75
168,45
171,85
144,07
139,82
129,12
116,93
105,84
83,44
174,75
2.08
6,00
$
4.36
8,75
$
2.28
2,75
$
25.07.10
88,79
84,54
81,24
72,06
69,34
71,01
72,95
78,91
78,53
90,34
92,97
106,87
121,26
139,12
152,59
144,7
137,63
116,64
125,39
121,7
120,36
104,88
69,34
152,59
1.73
3,50
$
3.81
4,75
$
2.08
1,25
$
26.07.10
93,02
92,3
89,43
82,62
80,25
87,43
93,65
98,8
96,36
109,25
116,44
125,83
128,52
139,93
140,92
137,99
128,43
117,75
109,95
102,19
103,65
100,18
80,25
140,92
2.00
6,25
$
3.52
3,00
$
1.51
6,75
$
27.07.10
88,94
85,86
70,25
47,02
34,23
41,65
67,04
80,42
87,45
90,19
95,94
103,99
109,45
114,86
118,36
116,46
107,95
96,32
89,6
86,5
88,86
86,32
34,23
118,36
855,75
$
2.95
9,00
$
2.10
3,25
$
28.07.10
81,71
65,24
63,77
67,51
74,41
74,97
78,27
88,92
91,99
97,16
99,85
103,76
99,73
98,33
100,34
101,78
223,23
221,11
221,27
100,43
99,71
99,95
63,77
223,23
1.59
4,25
$
5.58
0,75
$
3.98
6,50
$
29.07.10
92,89
59,32
35,42
41,54
50,18
71,55
74,35
96,22
100,42
110,02
122,71
133,26
138,48
142,16
261,93
480,51
462,72
314,93
259,22
270,7
291,27
85,4
35,42
480,51
885,50
$
12.012
,75
$
11.127
,25
$
30.07.10
81,04
55,73
56,95
63,53
70,5
76,18
213,98
624,9 4
668,42
621,99
537,81
729,73
1273
,58
1032
,96
822,84
199,47
150,93
129,35
126,93
111,76
277,13
276,42
55,73
1273
,58
1.39
3,25
$
31.839
,50
$
30.446
,25
$
31.07.10
90,64
95,98
76,91
70,98
46,52
18,79
20,44
55,39
106,18
302,14
321,1
331,29
153,69
136,63
126,16
117,76
110,38
102,5
97,98
94,69
94,49
81,88
18,79
331,29
469,75
$
8.28
2,25
$
7.81
2,50
$
195.65
6,25
$
Exhibit 5
Exhibit 6.1Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
CT
Revenues 190.810 157.178 114.222 95.875 155.947 151.251 209.778 183.364 135.530 97.049 111.432 185.224 1.787.658
COGS 113.866 85.916 64.567 65.079 84.288 78.485 86.216 78.633 73.912 62.815 77.112 118.284 989.173
GM 76.944 71.261 49.655 30.796 71.659 72.766 123.562 104.731 61.618 34.234 34.320 66.940 798.485
NEMass
Revenues 185.389 151.535 109.937 109.079 143.982 144.898 196.915 178.895 134.888 93.950 107.723 177.819 1.735.009
COGS 113.607 83.779 62.635 66.577 82.773 77.317 85.204 77.613 73.946 63.274 77.943 117.313 981.980
GM 71.782 67.756 47.302 42.502 61.208 67.582 111.711 101.282 60.942 30.676 29.780 60.506 753.029
NH
Revenues 184.116 145.880 107.735 87.341 141.808 144.244 196.762 176.045 134.866 94.426 106.727 175.804 1.695.754
COGS 112.416 83.437 61.637 66.090 80.427 77.202 84.984 77.240 73.972 63.335 77.115 115.828 973.681
GM 71.700 62.443 46.098 21.251 61.381 67.042 111.779 98.805 60.894 31.091 29.612 59.977 722.073
ME
Revenues 176.443 139.328 103.570 83.758 134.929 135.537 186.750 174.364 132.853 91.022 103.435 170.726 1.632.717
COGS 115.381 90.542 62.299 64.179 78.128 76.102 82.911 75.377 73.538 62.128 75.908 113.059 969.550
GM 61.063 48.786 41.271 19.579 56.802 59.435 103.840 98.987 59.316 28.894 27.527 57.667 663.167
Internal
Revenues 186.971 151.781 110.660 92.611 144.889 141.875 197.933 174.886 135.209 94.280 108.366 179.246 1.718.706
COGS 112.958 84.581 63.251 67.257 83.489 78.216 87.883 80.148 74.160 63.428 78.260 118.156 991.786
GM 74.013 67.200 47.409 25.354 61.400 63.659 110.050 94.738 61.050 30.852 30.105 61.090 726.920
Pacific G&E
Revenues 152.556 96.463 118.672 116.321 90.710 148.450 112.372 110.332 134.923 193.246 170.928 118.069 1.563.043
COGS 84.549 63.489 60.689 44.860 44.572 49.223 48.870 43.529 43.547 58.801 60.845 55.791 658.765
GM 68.007 32.974 57.984 71.460 46.139 99.227 63.502 66.803 91.377 134.444 110.084 62.278 904.278
SoCal
Revenues 195.721 142.467 145.030 127.039 88.030 138.835 158.965 158.475 158.325 200.390 171.997 127.110 1.812.384
COGS 76.664 61.884 58.475 41.786 30.429 46.692 47.002 41.269 40.680 53.633 53.136 53.669 605.318
GM 119.057 80.584 86.555 85.252 57.601 92.144 111.963 117.206 117.645 146.758 118.861 73.441 1.207.066
San Diego
Revenues 155.351 101.270 136.771 122.107 93.946 140.900 157.136 155.611 149.829 196.350 218.550 258.003 1.885.824
COGS 75.554 61.091 59.189 41.867 31.156 45.514 47.100 41.003 40.259 53.337 52.573 53.477 602.118
GM 79.797 40.180 77.582 80.240 62.791 95.386 110.036 114.608 109.571 143.014 165.977 204.526 1.283.706
NP-15
Revenues 147.243 93.826 114.844 113.045 86.007 137.743 105.842 106.271 128.930 187.615 164.487 113.978 1.499.830
COGS 83.767 62.314 59.067 43.940 44.942 47.353 47.255 42.510 42.782 57.667 59.802 54.735 646.132
GM 63.476 31.512 55.777 69.105 41.065 90.390 58.587 63.761 86.148 129.949 104.685 59.244 853.698
SP-15
Revenues 183.083 131.992 137.904 123.393 85.624 136.116 153.438 152.264 148.116 197.074 169.593 134.387 1.752.984
COGS 75.146 60.614 57.645 41.087 29.648 45.728 46.141 40.540 40.013 52.710 52.178 52.780 594.228
GM 107.938 71.377 80.259 82.306 55.977 90.388 107.297 111.724 108.103 144.364 117.415 81.607 1.158.756
Palo Verde
Revenues 139.813 98.826 111.661 96.080 95.538 133.531 150.910 148.857 137.633 176.014 139.129 91.599 1.519.592
COGS 72.541 58.937 57.299 50.394 32.574 45.947 45.568 40.404 44.904 52.623 50.895 48.607 600.689
GM 67.272 39.890 54.362 45.686 62.965 87.585 105.343 108.453 92.729 123.391 88.234 42.993 918.903
Cinergy
Revenues 115.495 99.101 86.984 44.005 102.569 120.567 162.862 136.677 98.529 76.675 97.767 115.116 1.256.346
COGS 56.998 58.285 50.035 19.830 42.474 43.560 44.756 40.154 28.864 41.661 46.691 58.087 531.394
GM 58.497 40.816 36.949 24.174 60.095 77.007 118.106 96.523 69.665 35.014 51.076 57.029 724.952
First Energy
Revenues 141.126 102.076 86.952 43.924 96.361 120.383 172.224 142.174 99.803 77.348 95.650 113.313 1.291.335
COGS 60.622 59.550 52.541 19.565 43.646 45.963 45.506 42.031 28.032 46.955 50.116 54.310 548.836
GM 80.504 42.526 34.411 24.360 52.716 74.421 126.718 100.143 71.770 30.393 45.534 59.003 742.499
Illinois
Revenues 109.648 94.391 76.201 43.167 100.108 137.622 168.275 132.487 82.292 66.920 74.229 95.595 1.180.935
COGS 47.649 48.704 39.086 15.007 37.931 29.833 41.348 37.549 13.704 35.823 35.982 31.010 413.625
GM 61.999 45.687 37.116 28.160 62.177 107.789 126.927 94.938 68.588 31.097 38.248 64.585 767.310
Michigan
Revenues 105.702 100.943 86.743 42.797 101.793 125.785 156.994 143.787 100.360 84.997 103.418 119.809 1.273.127
COGS 56.779 60.730 54.403 19.559 53.409 51.025 47.315 42.912 29.034 59.346 54.846 60.114 589.470
GM 48.923 40.213 32.340 23.238 48.385 74.760 109.680 100.875 71.327 25.651 48.572 59.694 683.657
Minnesota
Revenues 112.896 111.714 78.918 44.700 88.219 97.328 131.789 138.539 79.756 84.932 105.432 115.484 1.189.706
COGS 42.041 48.133 31.524 11.055 39.778 18.451 27.670 29.096 3.231 21.115 21.013 31.405 324.510
GM 70.855 63.582 47.394 33.645 48.441 78.878 104.119 109.443 76.525 63.817 84.420 84.078 865.196
Wisconsin
Revenues 113.747 100.457 86.248 42.863 103.997 98.995 154.048 131.056 74.949 79.823 106.974 139.392 1.232.549
COGS 44.117 50.667 40.475 14.021 32.237 19.612 35.176 34.699 5.783 28.470 26.076 32.756 364.087
GM 69.630 49.790 45.773 28.843 71.760 79.383 118.872 96.356 69.167 51.354 80.898 106.636 868.462
Capital
Revenues 217.458 134.735 115.308 53.957 97.282 121.530 185.631 159.269 123.016 99.445 109.027 194.025 1.501.656
COGS 99.151 84.581 59.671 29.392 52.622 76.533 94.727 79.249 66.503 30.961 66.533 125.011 798.400
GM 118.307 50.154 55.637 24.565 44.661 44.997 90.903 80.021 56.513 68.484 42.494 69.014 703.256
Hudson
Revenues 208.625 141.787 118.885 54.714 93.826 208.082 214.441 186.903 161.203 109.983 154.458 200.590 1.699.038
COGS 94.765 80.002 60.656 30.423 51.947 78.210 97.050 80.865 67.908 24.388 107.125 122.321 788.533
GM 113.860 61.785 58.229 24.291 41.880 129.871 117.391 106.038 93.295 85.594 47.333 78.269 910.505
Long Isl
Revenues 339.084 192.265 149.096 64.220 147.427 265.180 302.119 253.113 177.888 138.734 210.630 287.806 2.316.932
COGS 111.670 85.842 64.051 37.307 52.663 79.488 100.659 83.920 68.116 29.039 126.878 133.903 846.656
GM 227.414 106.424 85.045 26.913 94.764 185.692 201.460 169.194 109.771 109.695 83.752 153.904 1.470.276
NY
Revenues 216.338 159.069 125.857 61.698 152.435 245.126 256.078 174.280 179.889 117.065 173.716 223.039 1.910.875
COGS 95.153 80.552 60.890 30.498 52.033 79.942 101.852 82.808 67.966 32.184 117.753 122.448 806.325
GM 121.186 78.516 64.967 31.200 100.402 165.184 154.227 91.472 111.924 84.882 55.963 100.591 1.104.550
North
Revenues 150.569 78.478 79.596 41.554 69.414 106.279 159.234 142.367 111.098 89.908 108.166 161.323 1.189.821
COGS 74.692 55.196 53.400 28.041 44.829 76.701 91.887 78.084 65.670 28.881 75.569 93.803 691.182
GM 75.877 23.282 26.196 13.513 24.585 29.578 67.348 64.284 45.429 61.028 32.597 67.520 498.639
West
Revenues 132.454 83.465 78.147 43.905 82.472 102.348 161.595 141.485 103.315 87.997 104.921 136.946 1.154.131
COGS 64.692 58.782 50.412 27.731 45.086 70.072 86.982 72.898 60.282 22.151 76.966 87.625 646.711
GM 67.763 24.683 27.736 16.174 37.386 32.277 74.613 68.587 43.034 65.846 27.955 49.321 507.420
Western
Revenues 155.506 126.482 109.796 110.146 105.048 156.343 224.553 182.225 149.640 109.198 115.127 161.583 1.705.648
COGS 94.989 72.582 63.500 58.111 45.108 59.176 68.610 66.987 57.063 59.657 68.452 86.897 801.131
GM 60.517 53.901 46.296 52.035 59.940 97.167 155.944 115.238 92.578 49.541 46.674 74.686 904.517
Baltimore
Revenues 193.578 150.011 123.543 125.969 129.202 196.273 298.474 240.222 233.106 133.734 138.776 194.361 2.157.249
COGS 99.897 78.002 66.239 60.489 47.561 63.775 73.534 70.508 59.560 62.815 70.036 93.963 846.377
GM 93.681 72.009 57.305 65.480 81.642 132.498 224.939 169.714 173.546 70.919 68.740 100.398 1.310.872
Commonwealth
Revenues 120.736 105.893 89.556 80.827 74.694 126.838 164.198 147.609 81.394 79.292 83.166 90.989 1.245.192
COGS 58.856 56.287 40.549 19.294 30.640 42.679 50.529 48.455 17.493 34.049 20.150 39.237 458.217
GM 61.880 49.606 49.007 61.533 44.054 84.159 113.669 99.154 63.901 45.244 63.016 51.753 786.976
Dominion
Revenues 186.036 145.140 112.569 132.475 121.308 211.533 256.466 208.635 201.495 129.661 118.577 185.029 2.008.925
COGS 98.537 78.026 66.156 60.545 47.968 63.225 73.267 69.006 58.838 64.111 68.176 93.404 841.256
GM 87.500 67.114 46.413 71.931 73.340 148.308 183.200 139.629 142.657 65.550 50.401 91.625 1.167.669
NJ
Revenues 178.982 137.968 125.780 118.910 121.236 170.968 291.455 198.373 155.502 113.772 127.433 194.058 1.934.437
COGS 103.341 76.449 66.163 60.252 47.362 65.703 74.660 70.350 60.702 60.182 68.195 94.697 848.056
GM 75.641 61.519 59.617 58.658 73.874 105.265 216.795 128.023 94.801 53.590 59.237 99.361 1.086.382
Pepco
Revenues 0 0 0 0 31.676 196.348 286.852 220.709 221.013 131.602 141.681 196.730 1.426.611
COGS 0 0 0 0 12.598 63.268 73.159 69.866 58.977 62.021 68.110 94.771 502.768
GM 0 0 0 0 19.079 133.081 213.693 150.843 162.036 69.581 73.572 101.959 923.843
Exhibit 6.2
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
CT
Revenues 205.208 165.256 127.843 105.982 187.018 178.343 232.415 204.279 169.809 106.118 126.393 196.491 2.005.155
COGS 101.277 81.042 57.230 61.253 76.139 73.456 83.918 77.702 71.384 60.850 74.555 108.415 927.217
GM 103.931 84.214 70.614 44.730 110.879 104.887 148.498 126.578 98.425 45.268 51.838 88.076 1.077.938
NEMass
Revenues 198.176 157.588 122.015 119.508 174.394 171.175 222.025 197.757 169.583 102.509 123.003 189.053 1.946.787
COGS 99.800 79.017 56.098 59.740 74.680 72.603 82.780 76.564 71.206 61.287 74.713 106.808 915.294
GM 98.376 78.571 65.917 59.768 99.714 98.571 139.245 121.194 98.378 41.222 48.291 82.245 1.031.493
NH
Revenues 196.646 152.637 120.354 94.800 170.955 169.569 221.452 196.950 165.267 103.240 122.085 187.042 1.900.998
COGS 98.646 78.191 55.360 58.539 74.294 72.294 82.616 76.169 71.272 61.340 74.005 105.530 908.253
GM 98.000 74.447 64.994 36.261 96.661 97.275 138.836 120.781 93.996 41.900 48.080 81.513 992.745
ME
Revenues 195.694 157.383 129.563 89.250 159.351 164.376 210.757 190.457 161.140 100.149 118.743 181.598 1.858.460
COGS 97.138 75.548 55.352 56.099 71.639 70.629 80.279 74.116 70.116 60.263 72.602 102.893 886.671
GM 98.556 81.835 74.211 33.151 87.713 93.747 130.478 116.342 91.024 39.886 46.141 78.705 971.789
Internal
Revenues 198.679 157.941 122.861 100.170 177.740 172.836 224.146 198.966 166.538 103.225 123.846 190.659 1.937.607
COGS 100.414 79.756 56.595 60.239 75.574 73.177 84.628 78.075 71.494 61.437 75.072 107.828 924.288
GM 98.265 78.185 66.266 39.932 102.166 99.659 139.518 120.891 95.044 41.788 48.774 82.831 1.013.320
Pacific G&E
Revenues 165.090 114.725 123.760 150.043 179.897 289.345 130.922 129.228 169.803 219.567 210.747 206.510 2.089.636
COGS 76.996 57.467 54.015 39.227 13.711 -6.357 23.877 33.394 33.150 55.974 53.852 35.459 470.764
GM 88.095 57.258 69.745 110.816 166.186 295.702 107.045 95.834 136.653 163.593 156.894 171.051 1.618.872
SoCal
Revenues 248.924 209.884 161.515 159.096 161.781 278.912 241.445 195.159 180.080 235.682 217.077 240.571 2.530.127
COGS 70.527 57.004 52.650 38.341 11.813 -6.181 23.314 31.678 31.319 51.661 48.989 33.653 444.768
GM 178.398 152.880 108.865 120.755 149.969 285.093 218.131 163.481 148.761 184.021 168.088 206.918 2.085.359
San Diego
Revenues 164.736 136.390 147.505 175.734 162.871 282.144 212.370 192.605 171.913 240.842 257.375 420.712 2.565.196
COGS 68.795 56.465 52.774 38.278 11.638 -5.991 23.282 31.606 30.690 51.095 48.647 33.719 440.997
GM 95.941 79.925 94.731 137.457 151.233 288.134 189.088 160.999 141.223 189.747 208.728 386.993 2.124.199
NP-15
Revenues 160.178 110.403 119.996 145.491 175.125 271.530 124.141 123.349 162.636 216.948 203.573 200.723 2.014.094
COGS 76.097 56.542 53.057 38.427 13.180 -6.501 22.881 32.533 32.547 54.956 52.945 34.739 461.402
GM 84.081 53.861 66.939 107.065 161.946 278.031 101.260 90.816 130.089 161.992 150.628 165.985 1.552.692
SP-15
Revenues 226.666 187.924 152.235 154.979 157.390 271.732 223.883 188.877 170.202 234.315 210.614 232.299 2.411.116
COGS 69.020 55.885 51.908 37.849 11.507 -6.025 22.868 31.123 30.796 50.732 48.035 33.268 436.965
GM 157.646 132.039 100.328 117.130 145.884 277.757 201.015 157.754 139.406 183.583 162.579 199.031 1.974.152
Palo Verde
Revenues 151.180 119.484 124.916 141.933 155.673 269.569 224.875 184.559 162.408 223.726 190.123 175.053 2.123.501
COGS 61.493 54.330 46.173 36.342 11.347 -6.165 22.378 30.308 29.758 49.095 41.314 8.232 384.604
GM 89.687 65.154 78.744 105.591 144.326 275.734 202.497 154.251 132.650 174.631 148.810 166.821 1.738.897
Cinergy
Revenues 134.434 116.044 110.336 55.409 128.152 142.492 183.490 169.142 117.529 92.157 110.402 133.761 1.493.348
COGS 52.192 53.509 45.092 17.910 38.213 40.295 43.170 39.608 16.415 37.203 42.431 52.266 478.302
GM 82.242 62.536 65.244 37.499 89.939 102.197 140.321 129.534 101.114 54.954 67.971 81.496 1.015.046
First Energy
Revenues 164.243 118.421 111.108 56.695 127.746 143.887 192.095 174.150 122.644 96.789 110.438 130.828 1.549.045
COGS 54.378 54.651 46.030 17.342 37.846 41.246 41.935 38.600 13.185 39.332 43.095 41.716 469.353
GM 109.866 63.770 65.078 39.353 89.900 102.641 150.160 135.551 109.459 57.457 67.344 89.112 1.079.692
Illinois
Revenues 128.977 112.401 98.290 52.833 134.088 166.116 192.552 165.072 98.579 81.012 89.572 117.677 1.437.168
COGS 43.739 44.296 27.615 10.441 31.190 21.272 39.391 36.940 -1.628 22.782 2.321 -626 277.732
GM 85.238 68.104 70.675 42.392 102.898 144.844 153.161 128.132 100.206 58.230 87.252 118.303 1.159.436
Michigan
Revenues 130.977 119.180 113.760 55.807 147.048 150.078 177.153 178.182 128.943 131.473 126.915 139.878 1.599.396
COGS 48.189 55.016 47.097 16.157 42.382 43.211 45.616 42.161 16.251 37.776 45.078 48.541 487.475
GM 82.789 64.164 66.664 39.650 104.666 106.868 131.537 136.022 112.692 93.697 81.837 91.337 1.111.921
Minnesota
Revenues 130.611 126.746 96.003 53.619 121.389 107.645 152.721 162.863 94.571 99.889 126.171 129.491 1.401.720
COGS 32.440 44.261 20.452 4.027 26.665 4.754 21.195 21.579 -11.237 3.771 10.005 27.019 204.930
GM 98.171 82.485 75.551 49.592 94.724 102.892 131.527 141.284 105.809 96.118 116.166 102.473 1.196.790
Wisconsin
Revenues 122.190 105.225 98.220 53.075 130.118 113.423 172.227 153.583 92.672 87.470 118.076 155.969 1.402.247
COGS 34.699 48.020 34.951 7.302 22.843 827 26.312 24.729 -9.842 14.985 16.267 26.551 247.642
GM 87.492 57.205 63.269 45.774 107.275 112.596 145.914 128.854 102.514 72.485 101.810 129.418 1.154.606
Capital
Revenues 262.251 221.130 150.723 66.328 122.026 151.856 215.668 181.437 150.735 103.275 171.278 258.627 1.884.056
COGS 54.971 57.084 44.164 23.732 45.546 56.810 89.091 64.070 59.243 12.373 98.542 88.222 595.305
GM 207.280 164.046 106.558 42.596 76.481 95.046 126.577 117.367 91.492 90.902 72.736 170.405 1.288.751
Hudson
Revenues 246.899 196.331 154.539 68.234 119.988 282.162 267.244 206.423 197.823 114.949 192.442 262.269 2.116.861
COGS 55.052 56.951 44.784 24.465 44.308 64.621 91.363 71.942 61.267 13.515 106.928 89.430 617.697
GM 191.847 139.380 109.755 43.769 75.680 217.541 175.881 134.481 136.556 101.435 85.514 172.839 1.499.164
Long Isl
Revenues 405.175 239.352 180.378 88.096 192.659 330.536 343.023 310.580 228.132 155.858 256.489 347.591 2.821.378
COGS 70.915 60.807 48.714 26.098 46.515 70.403 95.804 76.920 62.106 14.058 129.147 95.419 667.756
GM 334.260 178.545 131.664 61.998 146.144 260.133 247.220 233.660 166.026 141.801 127.342 252.172 2.153.623
NY
Revenues 258.434 212.422 170.114 74.808 165.267 324.619 313.370 198.576 225.569 125.244 214.492 290.988 2.359.411
COGS 56.228 57.988 46.476 25.522 45.764 72.170 95.088 73.793 61.401 13.474 117.929 91.674 639.576
GM 202.206 154.435 123.639 49.287 119.503 252.449 218.283 124.783 164.167 111.769 96.563 199.314 1.719.836
North
Revenues 195.470 111.795 106.051 56.034 92.144 137.977 189.394 162.725 134.808 96.143 134.797 200.229 1.482.771
COGS 36.555 28.479 31.369 19.951 37.296 49.456 85.165 62.180 57.845 10.761 35.935 69.607 488.662
GM 158.915 83.317 74.682 36.084 54.849 88.521 104.229 100.545 76.964 85.382 98.862 130.622 994.109
West
Revenues 173.282 113.164 103.921 57.694 94.113 133.501 189.406 160.051 117.556 94.043 128.230 173.802 1.410.533
COGS 31.352 40.868 34.835 21.992 38.265 45.585 80.589 59.057 52.781 11.984 75.615 70.275 487.580
GM 141.930 72.296 69.086 35.702 55.848 87.916 108.817 100.994 64.775 82.059 52.615 103.528 922.953
Western
Revenues 178.916 133.155 127.907 124.273 126.478 182.941 254.711 205.445 167.621 121.084 126.019 182.519 1.931.069
COGS 79.227 69.784 61.980 56.780 42.483 58.773 68.017 66.042 55.928 55.819 62.748 77.203 754.784
GM 99.689 63.371 65.927 67.493 83.995 124.169 186.694 139.403 111.693 65.265 63.271 105.315 1.176.285
Baltimore
Revenues 230.557 159.889 141.217 144.567 155.948 246.800 345.154 266.640 257.140 155.095 151.985 224.819 2.479.812
COGS 88.731 75.001 64.521 58.727 44.977 62.980 72.723 69.340 58.590 56.594 65.478 83.786 801.446
GM 141.826 84.889 76.696 85.840 110.971 183.821 272.432 197.300 198.550 98.501 86.507 141.033 1.678.365
Commonwealth
Revenues 135.495 113.448 100.730 91.866 93.020 135.601 183.685 167.403 93.214 86.523 89.227 102.148 1.392.361
COGS 54.003 52.895 28.608 9.252 23.237 39.174 47.301 47.125 11.112 26.131 6.576 28.584 373.996
GM 81.492 60.554 72.122 82.614 69.783 96.427 136.384 120.278 82.103 60.393 82.652 73.564 1.018.364
Dominion
Revenues 236.760 158.328 130.918 159.849 155.834 253.662 313.361 231.685 220.634 151.835 133.364 213.515 2.359.745
COGS 84.223 73.080 61.923 57.502 44.212 62.031 72.012 67.660 57.392 61.029 60.912 83.020 784.994
GM 152.538 85.248 68.996 102.346 111.622 191.631 241.349 164.025 163.242 90.807 72.452 130.495 1.574.751
NJ
Revenues 210.055 146.181 138.108 128.378 144.232 206.063 335.086 221.871 177.108 122.626 144.717 215.404 2.189.830
COGS 85.195 74.587 64.638 58.586 44.426 63.239 73.517 69.143 59.000 55.892 65.118 81.023 794.360
GM 124.859 71.595 73.471 69.793 99.806 142.825 261.569 152.728 118.109 66.734 79.599 134.382 1.395.470
Pepco
Revenues 0 0 0 0 35.133 239.446 332.709 247.423 245.319 154.040 152.712 226.683 1.633.464
COGS 0 0 0 0 12.236 62.339 72.135 68.612 58.054 55.008 64.974 83.533 476.891
GM 0 0 0 0 22.897 177.107 260.574 178.811 187.265 99.032 87.738 143.151 1.156.574
Exhi
bit 7
Proj
ecte
dAc
tual
2009
2010
2011
2009
2010
2011
09 to
'10
10 to
'11
Ove
rall
09 to
'10
10 to
'11
Ove
rall
2009
2010
2011
New
Eng
land
28,0
6%19
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CT57
2.62
3$
79
8.48
5$
72
0.33
0$
78
1.34
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938
$
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2.39
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37,9
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27,0
1%73
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3$
87
9.56
2$
38
,92%
-14,
63%
18,6
0%25
,39%
-3,1
6%21
,43%
71,7
6%79
,50%
70,0
9%N
ew Y
ork
24,1
5%-3
,69%
ME
382.
498
$
663.
167
$
583.
298
$
857.
280
$
971.
789
$
851.
767
$
73,3
8%-1
2,04
%52
,50%
13,3
6%-1
2,35
%-0
,64%
44,6
2%68
,24%
68,4
8%PJ
M49
,11%
41,1
3%In
tern
al51
9.30
1$
72
6.92
0$
63
6.36
3$
71
8.00
5$
1.
013.
320
$
89
2.24
7$
39
,98%
-12,
46%
22,5
4%41
,13%
-11,
95%
24,2
7%72
,33%
71,7
4%71
,32%
Ove
rall
24,6
3%17
,68%
66,8
1%73
,31%
70,6
1%
Proj
ecte
dAc
tual
2009
2010
2011
2009
2010
2011
09 to
'10
10 to
'11
Ove
rall
09 to
'10
10 to
'11
Ove
rall
2009
2010
2011
New
Eng
land
22,5
4%23
,37%
Paci
fic G
&E
826.
899
$
904.
278
$
885.
983
$
1.40
4.27
4$
1.61
8.87
2$
1.72
4.48
3$
9,36
%-2
,02%
7,15
%15
,28%
6,52
%22
,80%
58,8
8%55
,86%
51,3
8%Ca
lifor
nia
5,90
%21
,30%
SoCa
l95
4.31
8$
1.
207.
066
$
96
1.64
4$
1.
643.
958
$
2.
085.
359
$
1.
934.
894
$
26
,48%
-20,
33%
0,77
%26
,85%
-7,2
2%17
,70%
58,0
5%57
,88%
49,7
0%M
idw
est
18,4
3%12
,58%
San
Dieg
o95
8.97
7$
1.
283.
706
$
1.
123.
566
$
1.
815.
862
$
2.
124.
199
$
2.
391.
214
$
33
,86%
-12,
47%
17,1
6%16
,98%
12,5
7%31
,68%
52,8
1%60
,43%
46,9
9%N
ew Y
ork
24,5
0%-0
,65%
NP-
1577
7.67
6$
85
3.69
8$
81
3.82
6$
1.
369.
337
$
1.
552.
692
$
1.
640.
471
$
9,
78%
-4,6
7%4,
65%
13,3
9%5,
65%
19,8
0%56
,79%
54,9
8%49
,61%
PJM
55,7
5%46
,99%
SP-1
594
7.40
8$
1.
158.
756
$
93
0.23
8$
1.
739.
558
$
1.
974.
152
$
1.
926.
366
$
22
,31%
-19,
72%
-1,8
1%13
,49%
-2,4
2%10
,74%
54,4
6%58
,70%
48,2
9%O
vera
ll20
,85%
16,3
3%Pa
lo V
erde
700.
196
$
918.
903
$
756.
434
$
1.43
2.84
1$
1.73
8.89
7$
1.77
9.69
9$
31,2
4%-1
7,68
%8,
03%
21,3
6%2,
35%
24,2
1%48
,87%
52,8
4%42
,50%
54,2
0%56
,97%
47,4
2%
2009
2010
2011
2009
2010
2011
09 to
'10
10 to
'11
Ove
rall
09 to
'10
10 to
'11
Ove
rall
2009
2010
2011
Cine
rgy
574.
790
$
724.
952
$
713.
266
$
905.
059
$
1.01
5.04
6$
1.02
6.00
1$
26,1
2%-1
,61%
24,0
9%12
,15%
1,08
%13
,36%
63,5
1%71
,42%
69,5
2%Fi
rst E
nerg
y60
1.35
7$
74
2.49
9$
75
6.19
6$
96
0.80
7$
1.
079.
692
$
1.
101.
873
$
23
,47%
1,84
%25
,75%
12,3
7%2,
05%
14,6
8%62
,59%
68,7
7%68
,63%
Illin
ois
617.
060
$
767.
310
$
695.
872
$
1.00
9.84
2$
1.15
9.43
6$
1.08
1.12
0$
24,3
5%-9
,31%
12,7
7%14
,81%
-6,7
5%7,
06%
61,1
0%66
,18%
64,3
7%M
ichi
gan
578.
457
$
683.
657
$
720.
505
$
1.02
3.62
3$
1.11
1.92
1$
1.14
4.28
5$
18,1
9%5,
39%
24,5
6%8,
63%
2,91
%11
,79%
56,5
1%61
,48%
62,9
7%M
inne
sota
787.
256
$
865.
196
$
825.
387
$
1.13
3.22
6$
1.19
6.79
0$
1.18
8.47
9$
9,90
%-4
,60%
4,84
%5,
61%
-0,6
9%4,
88%
69,4
7%72
,29%
69,4
5%W
iscon
sin69
8.99
4$
86
8.46
2$
74
4.94
0$
92
0.63
9$
1.
154.
606
$
1.
049.
870
$
24
,24%
-14,
22%
6,57
%25
,41%
-9,0
7%14
,04%
75,9
2%75
,22%
70,9
6%65
,12%
68,7
9%67
,27%
2009
2010
2011
2009
2010
2011
09 to
'10
10 to
'11
Ove
rall
09 to
'10
10 to
'11
Ove
rall
2009
2010
2011
Capi
tal
566.
093
$
703.
256
$
627.
067
$
1.38
6.06
1$
1.28
8.75
1$
1.14
9.84
6$
24,2
3%-1
0,83
%10
,77%
-7,0
2%-1
0,78
%-1
7,04
%40
,84%
54,5
7%54
,53%
Huds
on72
4.74
2$
91
0.50
5$
95
2.58
5$
1.
411.
918
$
1.
499.
164
$
1.
384.
702
$
25
,63%
4,62
%31
,44%
6,18
%-7
,64%
-1,9
3%51
,33%
60,7
3%68
,79%
Long
Isl
1.10
9.67
0$
1.47
0.27
6$
1.30
4.47
5$
1.88
7.64
4$
2.15
3.62
3$
2.03
7.13
1$
32,5
0%-1
1,28
%17
,56%
14,0
9%-5
,41%
7,92
%58
,79%
68,2
7%64
,03%
NY
863.
426
$
1.10
4.55
0$
1.14
0.15
9$
1.60
5.32
6$
1.71
9.83
6$
1.61
5.31
7$
27,9
3%3,
22%
32,0
5%7,
13%
-6,0
8%0,
62%
53,7
9%64
,22%
70,5
8%N
orth
318.
142
$
498.
639
$
476.
351
$
803.
135
$
994.
109
$
934.
269
$
56,7
3%-4
,47%
49,7
3%23
,78%
-6,0
2%16
,33%
39,6
1%50
,16%
50,9
9%W
est
449.
844
$
507.
420
$
465.
001
$
1.13
5.10
5$
922.
953
$
816.
588
$
12,8
0%-8
,36%
3,37
%-1
8,69
%-1
1,52
%-2
8,06
%39
,63%
54,9
8%56
,94%
48,6
3%59
,67%
62,2
7%
2009
2010
2011
2009
2010
2011
09 to
'10
10 to
'11
Ove
rall
09 to
'10
10 to
'11
Ove
rall
2009
2010
2011
Wes
tern
638.
345
$
904.
517
$
958.
517
$
826.
357
$
1.17
6.28
5$
1.17
6.58
8$
41,7
0%5,
97%
50,1
6%42
,35%
0,03
%42
,38%
77,2
5%76
,90%
81,4
7%Ba
ltim
ore
773.
357
$
1.31
0.87
2$
1.29
1.98
4$
1.02
2.66
1$
1.67
8.36
5$
1.59
2.33
6$
69,5
0%-1
,44%
67,0
6%64
,12%
-5,1
3%55
,71%
75,6
2%78
,10%
81,1
4%Co
mm
onw
ealth
613.
056
$
786.
976
$
802.
946
$
813.
030
$
1.01
8.36
4$
1.02
3.92
2$
28,3
7%2,
03%
30,9
7%25
,26%
0,55
%25
,94%
75,4
0%77
,28%
78,4
2%Do
min
ion
713.
027
$
1.16
7.66
9$
1.15
0.48
6$
949.
918
$
1.57
4.75
1$
1.44
0.09
2$
63,7
6%-1
,47%
61,3
5%65
,78%
-8,5
5%51
,60%
75,0
6%74
,15%
79,8
9%N
J68
0.27
1$
1.
086.
382
$
1.
175.
992
$
89
1.46
4$
1.
395.
470
$
1.
451.
925
$
59
,70%
8,25
%72
,87%
56,5
4%4,
05%
62,8
7%76
,31%
77,8
5%81
,00%
Pepc
o92
3.84
3$
1.
036.
823
$
1.
156.
574
$
1.
252.
593
$
12
,23%
12,2
3%8,
30%
8,30
%79
,88%
82,7
7%75
,60%
77,4
5%80
,64%
Gr
oss m
argi
nIn
crea
sePr
ojec
ted
Actu
alPr
ojec
ted
Actu
al
Gros
s mar
gin
Incr
ease
Proj
ecte
dAc
tual
Proj
ecte
dAc
tual
Gros
s mar
gin
Incr
ease
Proj
ecte
dAc
tual
Proj
ecte
dAc
tual
Gros
s mar
gin
Incr
ease
Med
ian
Incr
ease
Proj
ecte
dAc
tual
Proj
ecte
dAc
tual
Gros
s mar
gin
Incr
ease
Aver
age
Incr
ease
Proj
ecte
dAc
tual
Proj
ecte
dAc
tual
Exhibit 7
Exhibit 8
Revenue input Debt input
Grid to be used - Leverage of equipment 0%
Hours in/out 3
MW pr/hour in 0 Grid Increase Equipment loan
Power losses 0,00% - 0,00% Bank I $0
MW pr/hour out 0 Interest rate 0,00%
Increase in power prices 0% first 0 yrs Payback in years 0
Long term increase 0% Short term financing
Bank II $0
Capex input Interest rate 0%
Payback in years 0
Cost yr 1 yr 2 yr 3
Land $0 100% 0% 0% Total capital needed $0
Building $0 100% 0% 0% Equity ratio 0%
Equipment $0 100% 0% 0% Equity $0
Other $0 100% 0% 0% Debt $0
Total $0
WACC input
Opex input CAPM
Risk free rate 0,00%
Opex cost Market risk premium 0,00%
Salaries $0 pr/yr β for project 0,00
Bonus payments 0% of GM
Nr of traders 0 rE 0,00%
Traider accuracy 0% rD 0,00%
Overhead $0 pr/yr
Maintenance $0 pr/yr WACC (discount rate) 0,00%
Other $0 pr/yr
Working capital $0 for 0 yrs Tax input
Selling expense 0,0% of revenue
Start-up expense $0 Corporate tax 0%
Property Tax 0,00%
Depreciation input
% Factor
Building 0% 0% Traded Projected
Years of depreciation 0,00 IRR of Investment 0,00% 0,00%
Equipment 0,0% 0% NPV of Investment $0 $0
Years of depreciation 0,00
Other 0% 0% IRR of Equity 0,00% 0,00%
Years of depreciation 0,00 NPV of Equity $0 $0
Battery input
Type of batteries used Ford
Recharge of batteries 0%
# of batteries 0
# of containers 0
Increase from 2009
Phasing
Exhibit 9
Revenue input Debt input
Grid to be used North Leverage of equipment 70%
Hours in/out 3
MW pr/hour in 25 Grid Increase Equipment loan
Power losses 3,00% North 16,33% Bank I $7.000.000
MW pr/hour out 24,25 Interest rate 5,75%
Increase in power prices 15% first 5 yrs Payback in years 8
Long term increase 5% Short term financing
Bank II $500.000
Capex input Interest rate 8%
Payback in years 3
Cost yr 1 yr 2 yr 3
Land $300.000 100% 0% 0% Total capital needed $12.100.000
Building $1.000.000 75% 25% 0% Equity ratio 38%
Equipment $10.000.000 100% 0% 0% Equity $4.600.000
Other $300.000 50% 50% 0% Debt $7.500.000
Total $11.600.000
WACC input
Opex input CAPM
Risk free rate 2,71%
Opex cost Market risk premium 6,19%
Salaries $75.000 pr/yr β for project 2,79
Bonus payments 2% of GM
Nr of traders 2 rE 20,00%
Traider accuracy 95% rD 5,90%
Overhead $30.000 pr/yr
Maintenance $120.000 pr/yr WACC (discount rate) 10,02%
Other $100.000 pr/yr
Working capital $200.000 for 2 yrs Tax input
Selling expense 0,2% of revenue
Start-up expense $300.000 Corporate tax 34%
Property Tax 1,65%
Depreciation input
% Factor
Building 4% 90% Traded Projected
Years of depreciation 22,75 IRR of Investment 4,47% -0,11%
Equipment 12,5% 90% NPV of Investment -$4.017.060 -$6.910.815
Years of depreciation 7,20
Other 25% 90% IRR of Equity 3,21% -2,67%
Years of depreciation 4,10 NPV of Equity -$5.438.057 -$7.165.756
Battery input
Type of batteries used Ford
Recharge of batteries 60%
# of batteries 5.435
# of containers 8
Increase from 2009
Phasing
Exhi
bit 1
0
NPV
pro
ject
IRR
proj
ect
NPV
equ
ityIR
R eq
uity
NPV
pro
ject
IRR
proj
ect
NPV
equ
ityIR
R eq
uity
CT-$
3.65
3.94
8 5,
00%
-$5.
214.
107
3,92
%-$
3.96
1.31
0 4,
45%
-$5.
342.
668
3,16
%
NEM
ass
-$4.
561.
893
3,65
%-$
5.77
4.16
4 2,
14%
-$4.
911.
106
3,00
%-$
5.92
9.17
7 1,
24%
NH
-$4.
842.
509
3,23
%-$
5.94
7.72
8 1,
59%
-$5.
186.
484
2,58
%-$
6.09
9.50
1 0,
69%
ME
-$5.
123.
409
2,81
%-$
6.12
1.46
7 1,
04%
-$5.
587.
274
1,95
%-$
6.34
7.56
5 -0
,12%
Inte
rnal
-$4.
725.
992
3,41
%-$
5.87
5.66
1 1,
82%
-$4.
949.
847
2,94
%-$
5.95
3.13
9 1,
17%
Aver
age
-$4.
581.
550
3,62
%-$
5.78
6.62
5 2,
10%
-$4.
919.
204
2,99
%-$
5.93
4.41
0 1,
23%
Paci
fic G
&E
$4.3
48.5
8115
,27%
-$41
9.69
6 18
,74%
-$2.
055.
618
7,23
%-$
4.17
3.87
5 6,
96%
SoCa
l$6
.336
.742
17,4
5%$7
21.1
5722
,15%
-$1.
207.
131
8,40
%-$
3.65
6.89
7 8,
61%
San
Dieg
o$1
0.65
2.93
821
,81%
$3.1
53.3
7829
,26%
$577
.330
10,7
7%-$
2.57
9.22
2 12
,01%
NP-
15$3
.555
.005
14,3
6%-$
880.
149
17,3
5%-$
2.87
2.00
6 6,
06%
-$4.
673.
025
5,35
%
SP-1
5$6
.280
.126
17,3
9%$6
88.7
7822
,06%
-$1.
559.
048
7,92
%-$
3.87
1.31
8 7,
93%
Palo
Ver
de$4
.934
.785
15,9
3%-$
80.6
24
19,7
6%-$
3.52
8.88
1 5,
10%
-$5.
076.
149
4,03
%
Aver
age
$6.0
18.0
2917
,04%
$530
.474
21,5
5%-$
1.77
4.22
6 7,
58%
-$4.
005.
081
7,48
%
Cine
rgy
-$2.
925.
111
6,05
%-$
4.76
6.57
3 5,
35%
-$4.
034.
970
4,34
%-$
5.38
8.09
8 3,
01%
Firs
t Ene
rgy
-$2.
143.
507
7,15
%-$
4.28
8.16
9 6,
86%
-$3.
535.
757
5,09
%-$
5.08
0.36
9 4,
02%
Illin
ois
-$2.
168.
704
7,11
%-$
4.30
3.52
2 6,
81%
-$4.
235.
445
4,04
%-$
5.51
1.74
2 2,
61%
Mic
higa
n-$
1.67
3.29
0 7,
79%
-$4.
001.
669
7,75
%-$
3.95
1.09
1 4,
47%
-$5.
336.
366
3,18
%
Min
neso
ta-$
925.
593
8,80
%-$
3.54
6.58
2 9,
17%
-$2.
737.
985
6,25
%-$
4.59
0.77
7 5,
61%
Wisc
onsin
-$2.
478.
872
6,68
%-$
4.49
2.71
6 6,
21%
-$3.
661.
828
4,90
%-$
5.15
7.96
1 3,
76%
Aver
age
-$2.
052.
513
7,26
%-$
4.23
3.20
5 7,
02%
-$3.
692.
846
4,85
%-$
5.17
7.55
2 3,
70%
Capi
tal
-$1.
873.
520
7,52
%-$
4.12
3.66
7 7,
37%
-$5.
058.
612
2,78
%-$
6.02
0.41
1 0,
95%
Huds
on$4
64.0
5810
,61%
-$2.
707.
849
11,7
6%-$
1.31
9.56
8 8,
25%
-$3.
725.
404
8,39
%
Long
Isl
$6.6
82.1
4317
,82%
$918
.692
22,7
4%$2
.507
.743
13,1
7%-$
1.43
4.18
8 15
,59%
NY
$2.7
10.5
2613
,38%
-$1.
372.
855
15,8
5%$7
47.1
1610
,98%
-$2.
477.
387
12,3
3%
Nor
th-$
4.01
7.06
0 4,
47%
-$5.
438.
057
3,21
%-$
6.91
0.81
5 -0
,11%
-$7.
165.
756
-2,6
7%
Wes
t-$
5.28
8.53
2 2,
56%
-$6.
223.
597
0,71
%-$
7.06
3.70
5 -0
,37%
-$7.
256.
665
-2,9
9%
Aver
age
-$22
0.39
7 9,
39%
-$3.
157.
889
10,2
7%-$
2.84
9.64
0 5,
78%
-$4.
679.
969
5,27
%
Wes
tern
-$1.
598.
662
7,90
%-$
3.95
6.19
8 7,
90%
-$1.
250.
775
8,34
%-$
3.68
3.48
9 8,
53%
Balti
mor
e$2
.498
.939
13,1
2%-$
1.49
7.41
3 15
,47%
$2.3
78.3
3313
,02%
-$1.
510.
370
15,3
5%
Com
mon
wea
lth-$
2.82
4.23
1 6,
19%
-$4.
704.
663
5,54
%-$
2.99
8.93
3 5,
88%
-$4.
750.
920
5,09
%
Dom
inio
n$1
.031
.242
11,3
3%-$
2.36
7.27
4 12
,81%
$861
.000
11,1
3%-$
2.40
9.44
4 12
,55%
NJ
$1.1
32.9
2811
,46%
-$2.
306.
608
13,0
0%$1
.136
.470
11,4
8%-$
2.24
5.09
8 13
,06%
Pepc
o-$
731.
081
9,06
%-$
3.42
9.18
3 9,
54%
-$38
3.77
8 9,
51%
-$3.
159.
305
10,1
9%
Aver
age
-$81
.811
9,
84%
-$3.
043.
556
10,7
1%-$
42.9
47
9,89
%-$
2.95
9.77
1 10
,79%
Aver
age
Ove
r All
-$18
3.64
8 9,
43%
-$3.
138.
160
10,3
3%-$
2.65
5.77
3 6,
22%
-$4.
551.
356
5,69
%
Trad
edPr
ojec
ted
Exhibit 10
Exhibit 11
NPV project IRR project NPV equity IRR equity
CT -$3.961.310 4,45% -$5.342.668 3,16%
NEMass -$4.911.106 3,00% -$5.929.177 1,24%
NH -$5.186.484 2,58% -$6.099.501 0,69%
ME -$5.587.274 1,95% -$6.347.565 -0,12%
Internal -$4.949.847 2,94% -$5.953.139 1,17%
Average -$4.919.204 2,99% -$5.934.410 1,23%
Pacific G&E -$2.055.618 7,23% -$4.173.875 6,96%
SoCal -$1.207.131 8,40% -$3.656.897 8,61%
San Diego $577.330 10,77% -$2.579.222 12,01%
NP-15 -$2.872.006 6,06% -$4.673.025 5,35%
SP-15 -$1.559.048 7,92% -$3.871.318 7,93%
Palo Verde -$3.528.881 5,10% -$5.076.149 4,03%
Average -$1.774.226 7,58% -$4.005.081 7,48%
Cinergy -$4.034.970 4,34% -$5.388.098 3,01%
First Energy -$3.535.757 5,09% -$5.080.369 4,02%
Illinois -$4.235.445 4,04% -$5.511.742 2,61%
Michigan -$3.951.091 4,47% -$5.336.366 3,18%
Minnesota -$2.737.985 6,25% -$4.590.777 5,61%
Wisconsin -$3.661.828 4,90% -$5.157.961 3,76%
Average -$3.692.846 4,85% -$5.177.552 3,70%
Capital -$5.058.612 2,78% -$6.020.411 0,95%
Hudson -$1.319.568 8,25% -$3.725.404 8,39%
Long Isl $2.507.743 13,17% -$1.434.188 15,59%
NY $747.116 10,98% -$2.477.387 12,33%
North -$6.910.815 -0,11% -$7.165.756 -2,67%
West -$7.063.705 -0,37% -$7.256.665 -2,99%
Average -$2.849.640 5,78% -$4.679.969 5,27%
Western -$1.250.775 8,34% -$3.683.489 8,53%
Baltimore $2.378.333 13,02% -$1.510.370 15,35%
Commonwealth -$2.998.933 5,88% -$4.750.920 5,09%
Dominion $861.000 11,13% -$2.409.444 12,55%
NJ $1.136.470 11,48% -$2.245.098 13,06%
Pepco -$383.778 9,51% -$3.159.305 10,19%
Average -$42.947 9,89% -$2.959.771 10,79%
Average Over All -$2.655.773 6,22% -$4.551.356 5,69%
Projected
Exhibit 12
NPV project IRR project NPV equity IRR equity
CT -$3.653.948 5,00% -$5.214.107 3,92%
NEMass -$4.561.893 3,65% -$5.774.164 2,14%
NH -$4.842.509 3,23% -$5.947.728 1,59%
ME -$5.123.409 2,81% -$6.121.467 1,04%
Internal -$4.725.992 3,41% -$5.875.661 1,82%
Average -$4.581.550 3,62% -$5.786.625 2,10%
Pacific G&E $4.348.581 15,27% -$419.696 18,74%
SoCal $6.336.742 17,45% $721.157 22,15%
San Diego $10.652.938 21,81% $3.153.378 29,26%
NP-15 $3.555.005 14,36% -$880.149 17,35%
SP-15 $6.280.126 17,39% $688.778 22,06%
Palo Verde $4.934.785 15,93% -$80.624 19,76%
Average $6.018.029 17,04% $530.474 21,55%
Cinergy -$2.925.111 6,05% -$4.766.573 5,35%
First Energy -$2.143.507 7,15% -$4.288.169 6,86%
Illinois -$2.168.704 7,11% -$4.303.522 6,81%
Michigan -$1.673.290 7,79% -$4.001.669 7,75%
Minnesota -$925.593 8,80% -$3.546.582 9,17%
Wisconsin -$2.478.872 6,68% -$4.492.716 6,21%
Average -$2.052.513 7,26% -$4.233.205 7,02%
Capital -$1.873.520 7,52% -$4.123.667 7,37%
Hudson $464.058 10,61% -$2.707.849 11,76%
Long Isl $6.682.143 17,82% $918.692 22,74%
NY $2.710.526 13,38% -$1.372.855 15,85%
North -$4.017.060 4,47% -$5.438.057 3,21%
West -$5.288.532 2,56% -$6.223.597 0,71%
Average -$220.397 9,39% -$3.157.889 10,27%
Western -$1.598.662 7,90% -$3.956.198 7,90%
Baltimore $2.498.939 13,12% -$1.497.413 15,47%
Commonwealth -$2.824.231 6,19% -$4.704.663 5,54%
Dominion $1.031.242 11,33% -$2.367.274 12,81%
NJ $1.132.928 11,46% -$2.306.608 13,00%
Pepco -$731.081 9,06% -$3.429.183 9,54%
Average -$81.811 9,84% -$3.043.556 10,71%
Average Over All -$183.648 9,43% -$3.138.160 10,33%
Traded
Exhibit 13.1
SensIt
Spider Analysis
NPV of Investment
Input Variable Low Output Base Case High Output Low % Base % High % Low Base High Swing
WACC 12,25% 10,21% 8,17% 120% 100% 80% $4.270.995 $6.455.134 $9.112.327 $4.841.332
Increase in power prices 12% 15% 18% 80% 100% 120% $4.535.788 $6.455.134 $8.557.543 $4.021.755
Equipment $12.000.000 $10.000.000 $8.000.000 120% 100% 80% $4.897.667 $6.455.134 $7.987.476 $3.089.808
Trader accuracy 90% 95% 100% 94,7% 100% 105,3% $4.984.179 $6.455.134 $7.913.789 $2.929.610
Corporate tax rate 41% 34% 27% 120% 100% 80% $5.396.527 $6.455.134 $7.513.741 $2.117.214
Salaries $90.000 $75.000 $60.000 120% 100% 80% $6.301.775 $6.455.134 $6.607.540 $305.765
Maintenance $144.000 $120.000 $96.000 120% 100% 80% $6.332.447 $6.455.134 $6.577.292 $244.845
Working capital $240.000 $200.000 $160.000 120% 100% 80% $6.405.258 $6.455.134 $6.504.199 $98.941
Start-up expense $360.000 $300.000 $240.000 120% 100% 80% $6.415.891 $6.455.134 $6.493.990 $78.099
Power losses 4% 3% 2% 120% 100% 80% $6.455.134 $6.455.134 $6.455.134 $0
Corresponding Input Value Input Value as % of Base Output Value
Long Island
$3
$4
$5
$6
$7
$8
$9
$10
75% 85% 95% 105% 115% 125%
NP
V o
f In
ve
stm
en
t
Input Value as % of Base Case
WACC
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
Salaries
Maintenance
Working capital
Start-up expense
Power losses
MillionsMillions
Exhibit 13.2
SensIt
Spider Analysis
NPV of Investment
Input Variable Low Output Base Case High Output Low % Base % High % Low Base High Swing
WACC 12,25% 10,21% 8,17% 120% 100% 80% $3.922.064 $6.058.127 $8.656.513 $4.734.449
Increase in power prices 12% 15% 18% 80% 100% 120% $4.180.642 $6.058.127 $8.118.343 $3.937.701
Equipment $12.000.000 $10.000.000 $8.000.000 120% 100% 80% $4.495.919 $6.058.127 $7.594.103 $3.098.184
Trader accuracy 90% 95% 100% 94,7% 100% 105,3% $4.989.302 $6.058.127 $7.121.969 $2.132.667
Corporate tax rate 41% 34% 27% 120% 100% 80% $5.038.186 $6.058.127 $7.078.068 $2.039.882
Salaries $90.000 $75.000 $60.000 120% 100% 80% $5.904.769 $6.058.127 $6.211.485 $306.717
Maintenance $144.000 $120.000 $96.000 120% 100% 80% $5.935.441 $6.058.127 $6.180.814 $245.373
Working capital $240.000 $200.000 $160.000 120% 100% 80% $6.008.251 $6.058.127 $6.108.003 $99.752
Start-up expense $360.000 $300.000 $240.000 120% 100% 80% $6.018.885 $6.058.127 $6.097.370 $78.485
Power losses 4% 3% 2% 120% 100% 80% $6.058.127 $6.058.127 $6.058.127 $0
Input Value as % of Base Output ValueCorresponding Input Value
SP-15
$3
$4
$5
$6
$7
$8
$9
$10
75% 85% 95% 105% 115% 125%
NP
V o
f In
ve
stm
en
t
Input Value as % of Base Case
WACC
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
Salaries
Maintenance
Working capital
Start-up expense
Power losses
Millions
Exhibit 13.3
SensIt
Spider Analysis
NPV of Investment
Input Variable Low Output Base Case High Output Low % Base % High % Low Base High Swing
WACC 12,25% 10,21% 8,17% 120% 100% 80% $7.706.314 $10.375.388 $13.625.580 $5.919.265
Increase in power prices 12% 15% 18% 80% 100% 120% $8.054.570 $10.375.388 $12.928.480 $4.873.910
Equipment $12.000.000 $10.000.000 $8.000.000 120% 100% 80% $8.851.681 $10.375.388 $11.887.566 $3.035.885
Corporate tax rate 41% 34% 27% 120,0% 100% 80,0% $8.922.311 $10.375.388 $11.828.466 $2.906.154
Trader accuracy 90% 95% 100% 95% 100% 105% $9.102.218 $10.375.388 $11.647.956 $2.545.737
Salaries $90.000 $75.000 $60.000 120% 100% 80% $10.225.703 $10.375.388 $10.525.074 $299.370
Maintenance $144.000 $120.000 $96.000 120% 100% 80% $10.255.640 $10.375.388 $10.495.137 $239.496
Working capital $240.000 $200.000 $160.000 120% 100% 80% $10.329.648 $10.375.388 $10.421.129 $91.480
Start-up expense $360.000 $300.000 $240.000 120% 100% 80% $10.339.382 $10.375.388 $10.411.395 $72.013
Power losses 4% 3% 2% 120% 100% 80% $10.375.388 $10.375.388 $10.375.388 $0
Corresponding Input Value Input Value as % of Base Output Value
San Diego
$6
$7
$8
$9
$10
$11
$12
$13
$14
$15
75% 85% 95% 105% 115% 125%
NP
V o
f In
ve
stm
en
t
Millions
Input Value as % of Base Case
WACC
Increase in power prices
Equipment
Corporate tax rate
Trader accuracy
Salaries
Maintenance
Working capital
Start-up expense
Power losses
Exhibit 13.4
SensIt
Spider Analysis
NPV of Investment
Input Variable Low Output Base Case High Output Low % Base % High % Low Base High Swing
WACC 12,25% 10,21% 8,17% 120% 100% 80% $3.971.204 $6.114.038 $8.720.706 $4.749.501
Increase in power prices 12% 15% 18% 80% 100% 120% $4.230.658 $6.114.038 $8.180.196 $3.949.538
Equipment $12.000.000 $10.000.000 $8.000.000 120% 100% 80% $4.552.498 $6.114.038 $7.649.502 $3.097.004
Trader accuracy 90% 95% 100% 94,7% 100% 105,3% $5.028.027 $6.114.038 $7.194.396 $2.166.369
Corporate tax rate 41% 34% 27% 120% 100% 80% $5.088.652 $6.114.038 $7.139.425 $2.050.773
Salaries $90.000 $75.000 $60.000 120% 100% 80% $5.960.680 $6.114.038 $6.267.396 $306.717
Maintenance $144.000 $120.000 $96.000 120% 100% 80% $5.991.351 $6.114.038 $6.236.725 $245.373
Working capital $240.000 $200.000 $160.000 120% 100% 80% $6.064.162 $6.114.038 $6.163.914 $99.752
Start-up expense $360.000 $300.000 $240.000 120% 100% 80% $6.074.796 $6.114.038 $6.153.281 $78.485
Power losses 4% 3% 2% 120% 100% 80% $6.114.038 $6.114.038 $6.114.038 $0
Input Value as % of Base Output ValueCorresponding Input Value
SoCal
$3
$4
$5
$6
$7
$8
$9
$10
75% 85% 95% 105% 115% 125%
NP
V o
f In
ve
stm
en
t
Input Value as % of Base Case
WACC
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
Salaries
Maintenance
Working capital
Start-up expense
Power losses
Millions
Exhibit 13.5
SensIt
Spider Analysis
Input Variable Low Output Base Case High Output Low % Base % High % Low Base High Swing
Cost of Equity 24,00% 20,00% 16,00% 120% 100% 80% -$355.999 $918.692 $2.734.378 $3.090.376
Equipment $12.000.000 $10.000.000 $8.000.000 120% 100% 80% -$295.731 $918.692 $2.098.583 $2.394.315
Increase in power prices 12% 15% 18% 80% 100% 120% -$86.688 $918.692 $2.006.438 $2.093.125
Trader accuracy 90% 95% 100% 94,7% 100% 105,3% $66.833 $918.692 $1.753.409 $1.686.576
Corporate tax rate 41% 34% 27% 120% 100% 80% $444.938 $918.692 $1.392.447 $947.509
Salaries $90.000 $75.000 $60.000 120% 100% 80% $819.389 $918.692 $1.016.669 $197.280
Start-up expense $360.000 $300.000 $240.000 120% 100% 80% $826.152 $918.692 $1.010.694 $184.542
Working capital $240.000 $200.000 $160.000 120% 100% 80% $835.780 $918.692 $1.000.476 $164.696
Maintenance $144.000 $120.000 $96.000 120% 100% 80% $839.250 $918.692 $997.398 $158.149
Power losses 4% 3% 2% 120% 100% 80% $918.692 $918.692 $918.692 $0
Corresponding Input Value Input Value as % of Base Output Value
NPV of Investment
Long Island Equity
-$1,0
-$0,5
$0,0
$0,5
$1,0
$1,5
$2,0
$2,5
$3,0
70% 80% 90% 100% 110% 120% 130%
NP
V o
f E
qu
ity
Input Value as % of Base Case
Cost of Equity
Equipment
Increase in power prices
Trader accuracy
Corporate tax rate
Salaries
Start-up expense
Working capital
Maintenance
Power losses
MillionsMillions
Exhibit 13.6
SensIt
Spider Analysis
Input Variable Low Output Base Case High Output Low % Base % High % Low Base High Swing
Cost of Equity 24,00% 20,00% 16,00% 120% 100% 80% -$548.622 $688.778 $2.453.044 $3.001.665
Equipment $12.000.000 $10.000.000 $8.000.000 120% 100% 80% -$531.716 $688.778 $1.873.734 $2.405.450
Increase in power prices 12% 15% 18% 80% 100% 120% -$294.675 $688.778 $1.757.900 $2.052.576
Trader accuracy 90% 95% 100% 94,7% 100% 105,3% $69.799 $688.778 $1.300.809 $1.231.010
Corporate tax rate 41% 34% 27% 120% 100% 80% $235.400 $688.778 $1.142.156 $906.756
Salaries $90.000 $75.000 $60.000 120% 100% 80% $589.474 $688.778 $788.081 $198.607
Start-up expense $360.000 $300.000 $240.000 120% 100% 80% $596.238 $688.778 $781.318 $185.081
Working capital $240.000 $200.000 $160.000 120% 100% 80% $605.865 $688.778 $771.691 $165.825
Maintenance $144.000 $120.000 $96.000 120% 100% 80% $609.335 $688.778 $768.221 $158.886
Power losses 4% 3% 2% 120% 100% 80% $688.778 $688.778 $688.778 $0
NPV of Investment
Corresponding Input Value Input Value as % of Base Output Value
SP-15 Equity
-$1,0
-$0,5
$0,0
$0,5
$1,0
$1,5
$2,0
$2,5
$3,0
70% 80% 90% 100% 110% 120% 130%
NP
V o
f E
qu
ity
Input Value as % of Base Case
Cost of Equity
Equipment
Increase in power prices
Trader accuracy
Corporate tax rate
Salaries
Start-up expense
Working capital
Maintenance
Power losses
MillionsMillions
Exhibit 13.7
SensIt
Spider Analysis
Input Variable Low Output Base Case High Output Low % Base % High % Low Base High Swing
Cost of Equity 24,00% 20,00% 16,00% 120% 100% 80% $1.502.892 $3.153.378 $5.487.720 $3.984.828
Increase in power prices 12% 15% 18% 80% 100% 120% $1.952.621 $3.153.378 $4.467.458 $2.514.836
Equipment $12.000.000 $10.000.000 $8.000.000 120% 100% 80% $1.986.447 $3.153.378 $4.302.449 $2.316.002
Trader accuracy 90% 95% 100% 94,7% 100% 105,3% $2.432.708 $3.153.378 $3.873.132 $1.440.424
Corporate tax rate 41% 34% 27% 120% 100% 80% $2.464.177 $3.153.378 $3.842.579 $1.378.402
Salaries $90.000 $75.000 $60.000 120% 100% 80% $3.059.387 $3.153.378 $3.247.369 $187.982
Start-up expense $360.000 $300.000 $240.000 120% 100% 80% $3.065.557 $3.153.378 $3.241.199 $175.643
Working capital $240.000 $200.000 $160.000 120% 100% 80% $3.076.497 $3.153.378 $3.230.259 $153.762
Maintenance $144.000 $120.000 $96.000 120% 100% 80% $3.078.185 $3.153.378 $3.228.571 $150.386
Power losses 4% 3% 2% 120% 100% 80% $3.153.378 $3.153.378 $3.153.378 $0
Corresponding Input Value Input Value as % of Base Output Value
NPV of Investment
San Diego Equity
$1,0
$2,0
$3,0
$4,0
$5,0
$6,0
70% 80% 90% 100% 110% 120% 130%
NP
V o
f E
qu
ity
Input Value as % of Base Case
Cost of Equity
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
Salaries
Start-up expense
Working capital
Maintenance
Power losses
MillionsMillions
Exhibit 13.8
SensIt
Spider Analysis
Input Variable Low Output Base Case High Output Low % Base % High % Low Base High Swing
Cost of Equity 24,00% 20,00% 16,00% 120% 100% 80% -$521.494 $721.157 $2.492.664 $3.014.159
Equipment $12.000.000 $10.000.000 $8.000.000 120% 100% 80% -$498.482 $721.157 $1.905.400 $2.403.881
Increase in power prices 12% 15% 18% 80% 100% 120% -$265.384 $721.157 $1.792.902 $2.058.286
Trader accuracy 90% 95% 100% 94,7% 100% 105,3% $92.226 $721.157 $1.342.209 $1.249.982
Corporate tax rate 41% 34% 27% 120% 100% 80% $264.909 $721.157 $1.177.405 $912.496
Salaries $90.000 $75.000 $60.000 120% 100% 80% $621.854 $721.157 $820.461 $198.607
Start-up expense $360.000 $300.000 $240.000 120% 100% 80% $628.617 $721.157 $813.697 $185.081
Working capital $240.000 $200.000 $160.000 120% 100% 80% $638.244 $721.157 $804.070 $165.825
Maintenance $144.000 $120.000 $96.000 120% 100% 80% $641.714 $721.157 $800.600 $158.886
Power losses 4% 3% 2% 120% 100% 80% $721.157 $721.157 $721.157 $0
NPV of Investment
Corresponding Input Value Input Value as % of Base Output Value
SoCal Equity
-$1,0
-$0,5
$0,0
$0,5
$1,0
$1,5
$2,0
$2,5
$3,0
70% 80% 90% 100% 110% 120% 130%
NP
V o
f E
qu
ity
Input Value as % of Base Case
Cost of Equity
Equipment
Increase in power prices
Trader accuracy
Corporate tax rate
Salaries
Start-up expense
Working capital
Maintenance
Power losses
MillionsMillions
Exhibit 14.1
SensIt 1.45Long IslandTornado Analysis
PercentInput Variable Low Output Base Case High Output Low Base High Swing Swing^2WACC 12,02% 10,02% 8,02% $4.492.557 $6.678.480 $9.330.276 $4.837.719 37,1%Increase in power prices 12% 15% 18% $4.731.864 $6.678.480 $8.811.187 $4.079.323 26,3%Equipment $12.000.000 $10.000.000 $8.000.000 $5.124.660 $6.678.480 $8.207.462 $3.082.803 15,0%Trader accuracy 90% 95% 100% $5.189.258 $6.678.480 $8.155.547 $2.966.289 13,9%Corporate tax rate 41% 34% 27% $5.601.841 $6.678.480 $7.755.119 $2.153.278 7,3%Salaries $90.000 $75.000 $60.000 $6.523.579 $6.678.480 $6.832.439 $308.860 0,2%Maintenance $144.000 $120.000 $96.000 $6.554.559 $6.678.480 $6.801.878 $247.318 0,1%Working capital $240.000 $200.000 $160.000 $6.628.562 $6.678.480 $6.727.597 $99.035 0,0%Start‐up expense $360.000 $300.000 $240.000 $6.639.248 $6.678.480 $6.717.329 $78.081 0,0%Power losses 4% 3% 2% $6.678.480 $6.678.480 $6.678.480 $0 0,0%
Corresponding Input Value Output ValueNPV of Investment
12,02%
12%
$12.000.000
90%
41%
$90.000
$144.000
$240.000
$360.000
4%
8,02%
18%
$8.000.000
100%
27%
$60.000
$96.000
$160.000
$240.000
2%
$3,5 $4,5 $5,5 $6,5 $7,5 $8,5 $9,5
WACC
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
Salaries
Maintenance
Working capital
Start‐up expense
Power losses
NPV of Investment
Millions
Long Island
Exhibit 14.2
SensIt 1.45SP‐15Tornado Analysis
PercentInput Variable Low Output Base Case High Output Low Base High Swing Swing^2WACC 12,02% 10,02% 8,02% $4.138.762 $6.276.543 $8.869.625 $4.730.863 39,2%Increase in power prices 12% 15% 18% $4.372.382 $6.276.543 $8.366.353 $3.993.971 28,0%Equipment $12.000.000 $10.000.000 $8.000.000 $4.718.029 $6.276.543 $7.809.117 $3.091.088 16,8%Trader accuracy 90% 95% 100% $5.194.444 $6.276.543 $7.353.716 $2.159.272 8,2%Corporate tax rate 41% 34% 27% $5.239.107 $6.276.543 $7.313.979 $2.074.873 7,5%Salaries $90.000 $75.000 $60.000 $6.121.642 $6.276.543 $6.431.443 $309.801 0,2%Maintenance $144.000 $120.000 $96.000 $6.152.622 $6.276.543 $6.400.463 $247.841 0,1%Working capital $240.000 $200.000 $160.000 $6.226.625 $6.276.543 $6.326.461 $99.836 0,0%Start‐up expense $360.000 $300.000 $240.000 $6.237.312 $6.276.543 $6.315.774 $78.463 0,0%Power losses 4% 3% 2% $6.276.543 $6.276.543 $6.276.543 $0 0,0%
Corresponding Input Value Output ValueNPV of Investment
12,02%
12%
$12.000.000
90%
41%
$90.000
$144.000
$240.000
$360.000
4%
8,02%
18%
$8.000.000
100%
27%
$60.000
$96.000
$160.000
$240.000
2%
$3,5 $4,5 $5,5 $6,5 $7,5 $8,5 $9,5
WACC
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
Salaries
Maintenance
Working capital
Start‐up expense
Power losses
NPV of Investment
Millions
SP‐15
Exhibit 14.3
SensIt 1.45San DiegoTornado Analysis
PercentInput Variable Low Output Base Case High Output Low Base High Swing Swing^2WACC 12,02% 10,02% 8,02% $7.976.952 $10.648.459 $13.892.296 $5.915.344 41,6%Increase in power prices 12% 15% 18% $8.294.232 $10.648.459 $13.238.536 $4.944.303 29,1%Equipment $12.000.000 $10.000.000 $8.000.000 $9.127.997 $10.648.459 $12.157.549 $3.029.552 10,9%Corporate tax rate 41% 34% 27% $9.172.094 $10.648.459 $12.124.823 $2.952.729 10,4%Trader accuracy 90% 95% 100% $9.358.999 $10.648.459 $11.937.323 $2.578.324 7,9%Salaries $90.000 $75.000 $60.000 $10.497.186 $10.648.459 $10.799.732 $302.546 0,1%Maintenance $144.000 $120.000 $96.000 $10.527.441 $10.648.459 $10.769.477 $242.037 0,1%Working capital $240.000 $200.000 $160.000 $10.602.625 $10.648.459 $10.694.293 $91.669 0,0%Start‐up expense $360.000 $300.000 $240.000 $10.612.423 $10.648.459 $10.684.495 $72.072 0,0%Power losses 4% 3% 2% $10.648.459 $10.648.459 $10.648.459 $0 0,0%
Corresponding Input Value Output ValueNPV of Investment
12,02%
12%
$12.000.000
41%
90%
$90.000
$144.000
$240.000
$360.000
4%
8,02%
18%
$8.000.000
27%
100%
$60.000
$96.000
$160.000
$240.000
2%
$7 $8 $9 $10 $11 $12 $13 $14
WACC
Increase in power prices
Equipment
Corporate tax rate
Trader accuracy
Salaries
Maintenance
Working capital
Start‐up expense
Power losses
NPV of Investment
Millions
San Diego
Exhibit 14.4
SensIt 1.45So CalTornado Analysis
PercentInput Variable Low Output Base Case High Output Low Base High Swing Swing^2WACC 12,02% 10,02% 8,02% $4.188.587 $6.333.148 $8.934.499 $4.745.912 39,2%Increase in power prices 12% 15% 18% $4.423.009 $6.333.148 $8.429.000 $4.005.991 27,9%Equipment $12.000.000 $10.000.000 $8.000.000 $4.775.295 $6.333.148 $7.865.217 $3.089.921 16,6%Trader accuracy 90% 95% 100% $5.233.650 $6.333.148 $7.427.059 $2.193.409 8,4%Corporate tax rate 41% 34% 27% $5.290.191 $6.333.148 $7.376.105 $2.085.915 7,6%Salaries $90.000 $75.000 $60.000 $6.178.248 $6.333.148 $6.488.049 $309.801 0,2%Maintenance $144.000 $120.000 $96.000 $6.209.228 $6.333.148 $6.457.069 $247.841 0,1%Working capital $240.000 $200.000 $160.000 $6.283.230 $6.333.148 $6.383.066 $99.836 0,0%Start‐up expense $360.000 $300.000 $240.000 $6.293.917 $6.333.148 $6.372.380 $78.463 0,0%Power losses 4% 3% 2% $6.333.148 $6.333.148 $6.333.148 $0 0,0%
NPV of InvestmentCorresponding Input Value Output Value
12,02%
12%
$12.000.000
90%
41%
$90.000
$144.000
$240.000
$360.000
4%
8,02%
18%
$8.000.000
100%
27%
$60.000
$96.000
$160.000
$240.000
2%
$3,5 $4,5 $5,5 $6,5 $7,5 $8,5 $9,5
WACC
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
Salaries
Maintenance
Working capital
Start‐up expense
Power losses
NPV of Investment
Millions
So Cal
Exhibit 14.5
SensIt 1.45Long Island EquityTornado Analysis
PercentInput Variable Low Output Base Case High Output Low Base High Swing Swing^2Return on Equity 24,00% 20,00% 16,00% ‐$355.999 $918.692 $2.734.378 $3.090.376 40,6%Equipment $12.000.000 $10.000.000 $8.000.000 ‐$295.731 $918.692 $2.098.583 $2.394.315 24,4%Increase in power prices 12% 15% 18% ‐$86.688 $918.692 $2.006.438 $2.093.125 18,6%Trader accuracy 90% 95% 100% $66.833 $918.692 $1.753.409 $1.686.576 12,1%Corporate tax rate 41% 34% 27% $444.938 $918.692 $1.392.447 $947.509 3,8%Salaries $90.000 $75.000 $60.000 $819.389 $918.692 $1.016.669 $197.280 0,2%Start‐up expense $360.000 $300.000 $240.000 $826.152 $918.692 $1.010.694 $184.542 0,1%Working capital $240.000 $200.000 $160.000 $835.780 $918.692 $1.000.476 $164.696 0,1%Maintenance $144.000 $120.000 $96.000 $839.250 $918.692 $997.398 $158.149 0,1%Power losses 4% 3% 2% $918.692 $918.692 $918.692 $0 0,0%
NPV of InvestmentCorresponding Input Value Output Value
24,00%
$12.000.000
12%
90%
41%
$90.000
$360.000
$240.000
$144.000
4%
16,00%
$8.000.000
18%
100%
27%
$60.000
$240.000
$160.000
$96.000
2%
‐$1,0 ‐$0,5 $0,0 $0,5 $1,0 $1,5 $2,0 $2,5 $3,0
Return on Equity
Equipment
Increase in power prices
Trader accuracy
Corporate tax rate
Salaries
Start‐up expense
Working capital
Maintenance
Power losses
NPV of Investment
Millions
Long Island Equity
Exhibit 14.6
SensIt 1.45SP‐15 EquityTornado Analysis
PercentInput Variable Low Output Base Case High Output Low Base High Swing Swing^2Return on Equity 24,00% 20,00% 16,00% ‐$548.622 $688.778 $2.453.044 $3.001.665 42,0%Equipment $12.000.000 $10.000.000 $8.000.000 ‐$531.716 $688.778 $1.873.734 $2.405.450 26,9%Increase in power prices 12% 15% 18% ‐$294.675 $688.778 $1.757.900 $2.052.576 19,6%Trader accuracy 90% 95% 100% $69.799 $688.778 $1.300.809 $1.231.010 7,1%Corporate tax rate 41% 34% 27% $235.400 $688.778 $1.142.156 $906.756 3,8%Salaries $90.000 $75.000 $60.000 $589.474 $688.778 $788.081 $198.607 0,2%Start‐up expense $360.000 $300.000 $240.000 $596.238 $688.778 $781.318 $185.081 0,2%Working capital $240.000 $200.000 $160.000 $605.865 $688.778 $771.691 $165.825 0,1%Maintenance $144.000 $120.000 $96.000 $609.335 $688.778 $768.221 $158.886 0,1%Power losses 4% 3% 2% $688.778 $688.778 $688.778 $0 0,0%
NPV of InvestmentCorresponding Input Value Output Value
24,00%
$12.000.000
12%
90%
41%
$90.000
$360.000
$240.000
$144.000
4%
16,00%
$8.000.000
18%
100%
27%
$60.000
$240.000
$160.000
$96.000
2%
‐$1,0 ‐$0,5 $0,0 $0,5 $1,0 $1,5 $2,0 $2,5 $3,0
Return on Equity
Equipment
Increase in power prices
Trader accuracy
Corporate tax rate
Salaries
Start‐up expense
Working capital
Maintenance
Power losses
NPV of Investment
Millions
SP‐15 Equity
Exhibit 14.7
SensIt 1.45San Diego EquityTornado Analysis
PercentInput Variable Low Output Base Case High Output Low Base High Swing Swing^2Return on Equity 24,00% 20,00% 16,00% $1.502.892 $3.153.378 $5.487.720 $3.984.828 50,2%Increase in power prices 12% 15% 18% $1.952.621 $3.153.378 $4.467.458 $2.514.836 20,0%Equipment $12.000.000 $10.000.000 $8.000.000 $1.986.447 $3.153.378 $4.302.449 $2.316.002 16,9%Trader accuracy 90% 95% 100% $2.432.708 $3.153.378 $3.873.132 $1.440.424 6,6%Corporate tax rate 41% 34% 27% $2.464.177 $3.153.378 $3.842.579 $1.378.402 6,0%Salaries $90.000 $75.000 $60.000 $3.059.387 $3.153.378 $3.247.369 $187.982 0,1%Start‐up expense $360.000 $300.000 $240.000 $3.065.557 $3.153.378 $3.241.199 $175.643 0,1%Working capital $240.000 $200.000 $160.000 $3.076.497 $3.153.378 $3.230.259 $153.762 0,1%Maintenance $144.000 $120.000 $96.000 $3.078.185 $3.153.378 $3.228.571 $150.386 0,1%Power losses 4% 3% 2% $3.153.378 $3.153.378 $3.153.378 $0 0,0%
Corresponding Input Value Output ValueNPV of Investment
24,00%
12%
$12.000.000
90%
41%
$90.000
$360.000
$240.000
$144.000
4%
16,00%
18%
$8.000.000
100%
27%
$60.000
$240.000
$160.000
$96.000
2%
$1,0 $1,5 $2,0 $2,5 $3,0 $3,5 $4,0 $4,5 $5,0 $5,5 $6,0
Return on Equity
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
Salaries
Start‐up expense
Working capital
Maintenance
Power losses
NPV of Investment
Millions
San Diego Equity
Exhibit 14.8
SensIt 1.45So Cal EquityTornado Analysis
PercentInput Variable Low Output Base Case High Output Low Base High Swing Swing^2WACC 24,00% 20,00% 16,00% ‐$521.494 $721.157 $2.492.664 $3.014.159 42,0%Equipment $12.000.000 $10.000.000 $8.000.000 ‐$498.482 $721.157 $1.905.400 $2.403.881 26,7%Increase in power prices 12% 15% 18% ‐$265.384 $721.157 $1.792.902 $2.058.286 19,6%Trader accuracy 90% 95% 100% $92.226 $721.157 $1.342.209 $1.249.982 7,2%Corporate tax rate 41% 34% 27% $264.909 $721.157 $1.177.405 $912.496 3,9%Salaries $90.000 $75.000 $60.000 $621.854 $721.157 $820.461 $198.607 0,2%Start‐up expense $360.000 $300.000 $240.000 $628.617 $721.157 $813.697 $185.081 0,2%Working capital $240.000 $200.000 $160.000 $638.244 $721.157 $804.070 $165.825 0,1%Maintenance $144.000 $120.000 $96.000 $641.714 $721.157 $800.600 $158.886 0,1%Power losses 4% 3% 2% $721.157 $721.157 $721.157 $0 0,0%
Corresponding Input Value Output ValueNPV of Investment
24,00%
$12.000.000
12%
90%
41%
$90.000
$360.000
$240.000
$144.000
4%
16,00%
$8.000.000
18%
100%
27%
$60.000
$240.000
$160.000
$96.000
2%
‐$1,0 ‐$0,5 $0,0 $0,5 $1,0 $1,5 $2,0 $2,5 $3,0
WACC
Equipment
Increase in power prices
Trader accuracy
Corporate tax rate
Salaries
Start‐up expense
Working capital
Maintenance
Power losses
NPV of Investment
Millions
So Cal Equity
Exhibit 15.1
Variation 10%
System used Long IslWACC 10,02%
Increase 15%
Equipment 10.000.000
6.678.480 12,53% 12,02% 11,52% 11,02% 10,52% 10,02% 9,52% 9,02% 8,52% 8,02% 7,52%
18,75% 6.248.369 6.816.366 7.411.477 8.035.305 8.689.560 9.376.071 10.096.791 10.853.813 11.649.373 12.485.868 13.365.86618,00% 5.779.383 6.330.047 6.906.957 7.511.661 8.145.815 8.811.187 9.509.668 10.243.279 11.014.185 11.824.704 12.677.32017,25% 5.321.079 5.854.834 6.413.988 7.000.038 7.614.584 8.259.336 8.936.122 9.646.899 10.393.758 11.178.941 12.004.84816,50% 4.873.264 5.390.527 5.932.362 6.500.217 7.095.639 7.720.281 8.375.909 9.064.416 9.787.823 10.548.297 11.348.15815,75% 4.435.749 4.936.928 5.461.874 6.011.986 6.588.758 7.193.789 7.828.787 8.495.577 9.196.116 9.932.497 10.706.96215,00% 4.007.031 4.492.557 5.001.066 5.533.910 6.092.533 6.678.480 7.293.399 7.939.057 8.617.342 9.330.276 10.080.02714,25% 3.587.001 4.057.295 4.549.810 5.065.852 5.606.815 6.174.192 6.769.575 7.394.670 8.051.301 8.741.422 9.467.12513,50% 3.176.740 3.632.188 4.109.113 4.608.778 5.132.529 5.681.807 6.258.150 6.863.205 7.498.733 8.166.621 8.868.88912,75% 2.776.067 3.217.046 3.678.779 4.162.485 4.669.463 5.201.104 5.758.893 6.344.420 6.959.386 7.605.610 8.285.04412,00% 2.384.805 2.811.686 3.258.616 3.726.771 4.217.407 4.731.864 5.271.577 5.838.079 6.433.011 7.058.130 7.715.31711,25% 2.002.778 2.415.923 2.848.432 3.301.439 3.776.154 4.273.872 4.795.976 5.343.945 5.919.363 6.523.924 7.159.443
6.678.480 12,53% 12,02% 11,52% 11,02% 10,52% 10,02% 9,52% 9,02% 8,52% 8,02% 7,52%
7.500.000 5.967.076 6.442.685 6.941.025 7.463.441 8.011.367 8.586.339 9.189.998 9.824.101 10.490.525 11.191.283 11.928.5328.000.000 5.578.210 6.055.719 6.556.006 7.080.417 7.630.391 8.207.462 8.813.275 9.449.587 10.118.278 10.821.364 11.561.0018.500.000 5.185.744 5.665.250 6.167.585 6.694.096 7.246.223 7.825.505 8.433.585 9.072.223 9.743.303 10.448.840 11.190.9949.000.000 4.793.278 5.274.781 5.779.163 6.307.775 6.862.056 7.443.547 8.053.894 8.694.860 9.368.327 10.076.316 10.820.9889.500.000 4.400.812 4.884.312 5.390.742 5.921.453 6.477.888 7.061.589 7.674.204 8.317.496 8.993.352 9.703.792 10.450.981
10.000.000 4.007.031 4.492.557 5.001.066 5.533.910 6.092.533 6.678.480 7.293.399 7.939.057 8.617.342 9.330.276 10.080.02710.500.000 3.608.351 4.096.016 4.606.720 5.141.819 5.702.758 6.291.083 6.908.447 7.556.616 8.237.482 8.953.069 9.705.54711.000.000 3.209.672 3.699.475 4.212.374 4.749.728 5.312.982 5.903.686 6.523.494 7.174.174 7.857.622 8.575.862 9.331.06811.500.000 2.810.993 3.302.934 3.818.029 4.357.636 4.923.207 5.516.290 6.138.541 6.791.733 7.477.761 8.198.655 8.956.58812.000.000 2.407.586 2.901.751 3.419.134 3.961.095 4.529.086 5.124.660 5.749.474 6.405.304 7.094.047 7.817.735 8.578.54612.500.000 2.001.612 2.498.050 3.017.771 3.562.139 4.132.608 4.730.733 5.358.174 6.016.710 6.708.240 7.434.801 8.198.571
6.678.480 11,25% 12,00% 12,75% 13,50% 14,25% 15,00% 15,75% 16,50% 17,25% 18,00% 18,75%
7.500.000 6.195.794 6.651.631 7.118.695 7.596.356 8.085.409 8.586.339 9.099.371 9.624.732 10.162.652 10.713.363 11.277.1008.000.000 5.813.837 6.269.673 6.736.737 7.215.243 7.705.412 8.207.462 8.721.440 9.246.801 9.784.720 10.335.431 10.899.1698.500.000 5.431.879 5.887.715 6.354.779 6.833.286 7.323.454 7.825.505 8.339.662 8.866.154 9.405.209 9.957.060 10.521.2389.000.000 5.048.665 5.505.758 5.972.821 6.451.328 6.941.496 7.443.547 7.957.705 8.484.196 9.023.251 9.575.103 10.139.9869.500.000 4.661.269 5.119.261 5.588.500 6.069.203 6.559.538 7.061.589 7.575.747 8.102.238 8.641.293 9.193.145 9.758.029
10.000.000 4.273.872 4.731.864 5.201.104 5.681.807 6.174.192 6.678.480 7.193.789 7.720.281 8.259.336 8.811.187 9.376.07110.500.000 3.883.710 4.344.467 4.813.707 5.294.410 5.786.795 6.291.083 6.807.499 7.336.269 7.877.378 8.429.230 8.994.11311.000.000 3.489.783 3.951.298 4.424.111 4.907.013 5.399.398 5.903.686 6.420.102 6.948.872 7.490.227 8.044.399 8.611.62411.500.000 3.095.855 3.557.371 4.030.184 4.514.511 5.010.572 5.516.290 6.032.705 6.561.475 7.102.830 7.657.002 8.224.22712.000.000 2.697.685 3.163.444 3.636.257 4.120.584 4.616.644 5.124.660 5.644.854 6.174.079 6.715.433 7.269.605 7.836.83112.500.000 2.296.428 2.763.130 3.241.228 3.726.657 4.222.717 4.730.733 5.250.927 5.783.530 6.328.037 6.882.209 7.449.434
Long Island
Long Island∆ WACC
∆ Increase in power prices
∆ Equipmen
t∆ Eq
uipmen
t
Long Island
∆ WACC
∆ Increase in p
ower p
rices
Exhibit 15.2
Variation 10%
System used SP‐15WACC 10,02%
Increase 15%
Equipment 10.000.000
6.276.543 12,53% 12,02% 11,52% 11,02% 10,52% 10,02% 9,52% 9,02% 8,52% 8,02% 7,52%
18,75% 5.860.479 6.415.926 6.997.875 7.607.890 8.247.642 8.918.917 9.623.625 10.363.810 11.141.657 11.959.508 12.819.87018,00% 5.401.721 5.940.214 6.504.358 7.095.666 7.715.756 8.366.353 9.049.307 9.766.592 10.520.323 11.312.764 12.146.34117,25% 4.953.412 5.475.365 6.022.140 6.595.201 7.196.110 7.826.537 8.488.270 9.183.218 9.913.427 10.681.084 11.488.53616,50% 4.515.015 5.020.843 5.550.686 6.105.957 6.688.168 7.298.934 7.939.980 8.613.154 9.320.433 10.063.932 10.845.91715,75% 4.084.496 4.574.649 5.088.031 5.626.009 6.190.044 6.781.696 7.402.633 8.054.641 8.739.629 9.459.643 10.216.87215,00% 3.663.892 4.138.762 4.636.095 5.157.213 5.703.526 6.276.543 6.877.876 7.509.248 8.172.506 8.869.625 9.602.72114,25% 3.253.023 3.712.993 4.194.681 4.699.362 5.228.401 5.783.253 6.365.476 6.976.734 7.618.811 8.293.613 9.003.18613,50% 2.851.709 3.297.157 3.763.595 4.252.257 4.764.459 5.301.607 5.865.205 6.456.860 7.078.294 7.731.349 8.417.99712,75% 2.459.775 2.891.069 3.342.647 3.815.698 4.311.492 4.831.388 5.376.837 5.949.390 6.550.710 7.182.573 7.846.88512,00% 2.077.047 2.494.550 2.931.647 3.389.487 3.869.295 4.372.382 4.900.149 5.454.092 6.035.815 6.647.033 7.289.58511,25% 1.702.755 2.106.833 2.529.836 2.972.870 3.437.118 3.923.845 4.434.401 4.970.232 5.532.883 6.124.010 6.745.385
6.276.543 12,53% 12,02% 11,52% 11,02% 10,52% 10,02% 9,52% 9,02% 8,52% 8,02% 7,52%
7.500.000 5.630.899 6.095.681 6.582.667 7.093.172 7.628.596 8.190.440 8.780.306 9.399.908 10.051.081 10.735.792 11.456.1458.000.000 5.239.174 5.705.933 6.194.947 6.707.530 7.245.086 7.809.117 8.401.226 9.023.131 9.676.668 10.363.805 11.086.6488.500.000 4.846.708 5.315.464 5.806.525 6.321.208 6.860.919 7.427.159 8.021.536 8.645.767 9.301.692 9.991.281 10.716.6419.000.000 4.454.242 4.924.995 5.418.104 5.934.887 6.476.751 7.045.202 7.641.845 8.268.404 8.926.717 9.618.757 10.346.6359.500.000 4.061.776 4.534.526 5.029.683 5.548.566 6.092.584 6.663.244 7.262.155 7.891.040 8.551.741 9.246.233 9.976.628
10.000.000 3.663.892 4.138.762 4.636.095 5.157.213 5.703.526 6.276.543 6.877.876 7.509.248 8.172.506 8.869.625 9.602.72110.500.000 3.265.213 3.742.220 4.241.749 4.765.121 5.313.750 5.889.146 6.492.923 7.126.807 7.792.646 8.492.418 9.228.24111.000.000 2.866.533 3.345.679 3.847.403 4.373.030 4.923.975 5.501.749 6.107.970 6.744.366 7.412.786 8.115.210 8.853.76111.500.000 2.465.177 2.946.510 3.450.482 3.978.419 4.531.739 5.111.956 5.720.688 6.359.666 7.030.743 7.735.901 8.477.26412.000.000 2.059.204 2.542.809 3.049.119 3.579.463 4.135.261 4.718.029 5.329.388 5.971.072 6.644.937 7.352.967 8.097.28912.500.000 1.653.231 2.139.108 2.647.756 3.180.507 3.738.782 4.324.102 4.938.088 5.582.479 6.259.131 6.970.032 7.717.314
6.276.543 11,25% 12,00% 12,75% 13,50% 14,25% 15,00% 15,75% 16,50% 17,25% 18,00% 18,75%
7.500.000 5.849.653 6.295.548 6.752.425 7.220.496 7.699.973 8.190.440 8.692.283 9.206.186 9.732.374 10.271.074 10.822.5178.000.000 5.467.696 5.913.590 6.370.468 6.838.538 7.318.016 7.809.117 8.312.061 8.827.070 9.354.368 9.893.143 10.444.5858.500.000 5.085.738 5.531.633 5.988.510 6.456.580 6.936.058 7.427.159 7.930.103 8.445.112 8.972.411 9.512.226 10.064.7909.000.000 4.699.172 5.147.176 5.606.181 6.074.623 6.554.100 7.045.202 7.548.146 8.063.154 8.590.453 9.130.269 9.682.8329.500.000 4.311.776 4.759.779 5.218.785 5.689.004 6.170.650 6.663.244 7.166.188 7.681.197 8.208.495 8.748.311 9.300.875
10.000.000 3.923.845 4.372.382 4.831.388 5.301.607 5.783.253 6.276.543 6.781.696 7.298.934 7.826.537 8.366.353 8.918.91710.500.000 3.529.918 3.981.368 4.443.869 4.914.210 5.395.856 5.889.146 6.394.299 6.911.537 7.441.085 7.983.170 8.536.95911.000.000 3.135.991 3.587.441 4.049.942 4.523.706 5.008.460 5.501.749 6.006.902 6.524.140 7.053.688 7.595.774 8.150.62811.500.000 2.739.976 3.193.514 3.656.015 4.129.779 4.615.020 5.111.956 5.619.506 6.136.743 6.666.291 7.208.377 7.763.23112.000.000 2.338.719 2.795.242 3.262.088 3.735.852 4.221.093 4.718.029 5.226.878 5.747.865 6.278.895 6.820.980 7.375.83412.500.000 1.937.463 2.393.986 2.861.657 3.340.690 3.827.166 4.324.102 4.832.951 5.353.937 5.887.286 6.433.225 6.988.438
SP‐15∆ Increase in power prices
∆ Equipmen
t∆ Eq
uipmen
t
SP‐15∆ WACC
∆ Increase in p
ower p
rices
SP‐15∆ WACC
Exhibit 15.3
Variation 10%
System used San DiegoWACC 10,02%
Increase 15%
Equipment 10.000.000
10.648.459 12,53% 12,02% 11,52% 11,02% 10,52% 10,02% 9,52% 9,02% 8,52% 8,02% 7,52%
18,75% 10.103.518 10.797.228 11.524.194 12.286.385 13.085.904 13.924.999 14.806.074 15.731.699 16.704.628 17.727.808 18.804.39518,00% 9.534.064 10.206.624 10.911.380 11.650.238 12.425.231 13.238.536 14.092.477 14.989.543 15.932.396 16.923.888 17.967.07417,25% 8.977.610 9.629.536 10.312.626 11.028.720 11.779.789 12.567.935 13.395.408 14.264.616 15.178.132 16.138.716 17.149.32116,50% 8.433.921 9.065.721 9.727.677 10.421.569 11.149.302 11.912.910 12.714.569 13.556.606 14.441.510 15.371.950 16.350.78215,75% 7.902.767 8.514.939 9.156.283 9.828.523 10.533.499 11.273.178 12.049.663 12.865.204 13.722.208 14.623.254 15.571.10415,00% 7.383.921 7.976.952 8.598.198 9.249.325 9.932.112 10.648.459 11.400.399 12.190.106 13.019.908 13.892.296 14.809.93914,25% 6.877.156 7.451.526 8.053.178 8.683.721 9.344.875 10.038.476 10.766.488 11.531.012 12.334.296 13.178.747 14.066.94613,50% 6.382.252 6.938.429 7.520.981 8.131.460 8.771.527 9.442.956 10.147.645 10.887.623 11.665.061 12.482.284 13.341.78512,75% 5.898.987 6.437.434 7.001.369 7.592.293 8.211.809 8.861.630 9.543.590 10.259.646 11.011.897 11.802.587 12.634.12212,00% 5.427.147 5.948.315 6.494.108 7.065.976 7.665.466 8.294.232 8.954.044 9.646.792 10.374.501 11.139.339 11.943.62711,25% 4.966.516 5.470.848 5.998.966 6.552.267 7.132.246 7.740.500 8.378.734 9.048.775 9.752.576 10.492.229 11.269.974
10.648.459 12,53% 12,02% 11,52% 11,02% 10,52% 10,02% 9,52% 9,02% 8,52% 8,02% 7,52%
7.500.000 9.316.910 9.900.775 10.512.626 11.154.121 11.827.029 12.533.246 13.274.793 14.053.838 14.872.698 15.733.855 16.639.9698.000.000 8.931.819 9.517.470 10.131.152 10.774.524 11.449.358 12.157.549 12.901.123 13.682.248 14.503.242 15.366.590 16.274.9548.500.000 8.546.728 9.134.165 9.749.678 10.394.927 11.071.687 11.781.853 12.527.454 13.310.657 14.133.786 14.999.325 15.909.9389.000.000 8.159.444 8.748.736 9.366.150 10.013.348 10.692.106 11.404.322 12.152.024 12.937.385 13.762.726 14.630.537 15.543.4819.500.000 7.771.683 8.362.844 8.982.174 9.631.337 10.312.109 11.026.390 11.776.212 12.563.746 13.391.317 14.261.416 15.176.710
10.000.000 7.383.921 7.976.952 8.598.198 9.249.325 9.932.112 10.648.459 11.400.399 12.190.106 13.019.908 13.892.296 14.809.93910.500.000 6.996.159 7.591.060 8.214.222 8.867.314 9.552.115 10.270.528 11.024.586 11.816.467 12.648.499 13.523.176 14.443.16811.000.000 6.607.599 7.204.391 7.829.492 8.484.571 9.171.410 9.891.913 10.648.115 11.442.196 12.276.485 13.153.478 14.075.84811.500.000 6.215.133 6.813.922 7.441.071 8.098.250 8.787.243 9.509.955 10.268.425 11.064.832 11.901.509 12.780.954 13.705.84112.000.000 5.822.667 6.423.453 7.052.649 7.711.928 8.403.075 9.127.997 9.888.735 10.687.469 11.526.534 12.408.430 13.335.83512.500.000 5.430.201 6.032.984 6.664.228 7.325.607 8.018.908 8.746.040 9.509.044 10.310.105 11.151.558 12.035.906 12.965.828
10.648.459 11,25% 12,00% 12,75% 13,50% 14,25% 15,00% 15,75% 16,50% 17,25% 18,00% 18,75%
7.500.000 9.627.566 10.180.842 10.747.784 11.328.654 11.923.718 12.533.246 13.157.509 13.796.785 14.451.354 15.121.499 15.807.5068.000.000 9.251.869 9.805.146 10.372.088 10.952.958 11.548.022 12.157.549 12.781.813 13.421.089 14.075.658 14.745.803 15.431.8108.500.000 8.874.294 9.428.027 9.995.424 10.576.750 11.172.270 11.781.853 12.406.117 13.045.393 13.699.962 14.370.107 15.056.1149.000.000 8.496.363 9.050.095 9.617.493 10.198.819 10.794.339 11.404.322 12.029.041 12.668.773 13.323.798 13.994.398 14.680.4189.500.000 8.118.431 8.672.164 9.239.562 9.820.887 10.416.407 11.026.390 11.651.110 12.290.842 12.945.866 13.616.467 14.302.931
10.000.000 7.740.500 8.294.232 8.861.630 9.442.956 10.038.476 10.648.459 11.273.178 11.912.910 12.567.935 13.238.536 13.924.99910.500.000 7.359.152 7.914.224 8.482.968 9.065.025 9.660.544 10.270.528 10.895.247 11.534.979 12.190.004 12.860.604 13.547.06811.000.000 6.977.195 7.532.267 8.101.010 8.683.688 9.280.565 9.891.913 10.517.316 11.157.048 11.812.072 12.482.673 13.169.13611.500.000 6.595.237 7.150.309 7.719.052 8.301.730 8.898.608 9.509.955 10.136.045 10.777.154 11.433.561 12.104.741 12.791.20512.000.000 6.212.847 6.768.351 7.337.095 7.919.772 8.516.650 9.127.997 9.754.087 10.395.196 11.051.603 11.723.593 12.411.45212.500.000 5.825.451 6.383.148 6.954.541 7.537.815 8.134.692 8.746.040 9.372.129 10.013.238 10.669.646 11.341.635 12.029.494
San Diego∆ Increase in power prices
∆ Equipmen
t∆ Eq
uipmen
t
San Diego∆ WACC
∆ Increase in p
ower p
rices
San Diego∆ WACC
Exhibit 15.4
Variation 10%
System used SoCal
WACC 10,02%
Increase 15%
Equipment 10.000.000
6.333.148 12,53% 12,02% 11,52% 11,02% 10,52% 10,02% 9,52% 9,02% 8,52% 8,02% 7,52%
18,75% 5.915.106 6.472.321 7.056.123 7.668.083 8.309.878 8.983.299 9.690.262 10.432.818 11.213.159 12.033.636 12.896.76418,00% 5.454.908 5.995.115 6.561.057 7.154.251 7.776.321 8.429.000 9.114.140 9.833.724 10.589.874 11.384.861 12.221.11917,25% 5.005.191 5.528.806 6.077.325 6.652.215 7.255.044 7.887.489 8.551.342 9.248.519 9.981.072 10.751.198 11.561.24916,50% 4.565.767 5.073.199 5.604.724 6.161.760 6.745.823 7.358.535 8.001.627 8.676.951 9.386.492 10.132.371 10.916.86415,75% 4.134.141 4.625.843 5.140.850 5.680.532 6.246.356 6.839.887 7.462.800 8.116.884 8.804.057 9.526.370 10.286.02015,00% 3.712.217 4.188.587 4.687.494 5.210.263 5.758.310 6.333.148 6.936.394 7.569.779 8.235.153 8.934.499 9.669.94014,25% 3.300.057 3.761.481 4.244.694 4.750.976 5.281.694 5.838.310 6.422.386 7.035.593 7.679.719 8.356.679 9.068.52313,50% 2.897.484 3.344.339 3.812.255 4.302.467 4.816.295 5.355.151 5.920.544 6.514.086 7.137.505 7.792.649 8.481.49712,75% 2.504.319 2.936.977 3.389.985 3.864.536 4.361.906 4.883.456 5.430.642 6.005.023 6.608.264 7.242.150 7.908.59212,00% 2.120.389 2.539.213 2.977.695 3.436.987 3.918.320 4.423.009 4.952.457 5.508.169 6.091.752 6.704.928 7.349.54111,25% 1.745.520 2.150.866 2.575.197 3.019.625 3.485.336 3.973.599 4.485.768 5.023.295 5.587.729 6.180.733 6.804.083
6.333.148 12,53% 12,02% 11,52% 11,02% 10,52% 10,02% 9,52% 9,02% 8,52% 8,02% 7,52%
7.500.000 5.678.243 6.144.550 6.633.135 7.145.317 7.682.503 8.246.195 8.838.003 9.459.647 10.112.969 10.799.940 11.522.6728.000.000 5.286.921 5.755.194 6.245.795 6.760.044 7.299.349 7.865.217 8.459.256 9.083.189 9.738.861 10.428.243 11.153.4528.500.000 4.894.455 5.364.725 5.857.374 6.373.723 6.915.182 7.483.259 8.079.565 8.705.826 9.363.885 10.055.719 10.783.4459.000.000 4.501.989 4.974.256 5.468.952 5.987.401 6.531.014 7.101.301 7.699.875 8.328.462 8.988.910 9.683.195 10.413.4389.500.000 4.109.523 4.583.787 5.080.531 5.601.080 6.146.847 6.719.343 7.320.184 7.951.098 8.613.934 9.310.671 10.043.432
10.000.000 3.712.217 4.188.587 4.687.494 5.210.263 5.758.310 6.333.148 6.936.394 7.569.779 8.235.153 8.934.499 9.669.94010.500.000 3.313.537 3.792.046 4.293.148 4.818.172 5.368.535 5.945.752 6.551.442 7.187.337 7.855.293 8.557.292 9.295.46111.000.000 2.914.858 3.395.505 3.898.803 4.426.081 4.978.759 5.558.355 6.166.489 6.804.896 7.475.433 8.180.085 8.920.98111.500.000 2.514.240 2.997.060 3.502.592 4.032.165 4.587.202 5.169.222 5.779.849 6.420.820 7.093.992 7.801.355 8.545.04012.000.000 2.108.267 2.593.359 3.101.228 3.633.209 4.190.724 4.775.295 5.388.549 6.032.226 6.708.186 7.418.421 8.165.06512.500.000 1.702.294 2.189.658 2.699.865 3.234.252 3.794.245 4.381.368 4.997.250 5.643.632 6.322.379 7.035.486 7.785.090
6.333.148 11,25% 12,00% 12,75% 13,50% 14,25% 15,00% 15,75% 16,50% 17,25% 18,00% 18,75%
7.500.000 5.898.401 6.345.696 6.804.007 7.273.548 7.754.531 8.246.195 8.749.614 9.265.131 9.792.971 10.333.362 10.886.5368.000.000 5.516.443 5.963.738 6.422.050 6.891.590 7.372.573 7.865.217 8.369.740 8.886.366 9.415.039 9.955.431 10.508.6058.500.000 5.134.486 5.581.780 6.040.092 6.509.632 6.990.616 7.483.259 7.987.782 8.504.408 9.033.362 9.574.873 10.129.1729.000.000 4.748.392 5.197.802 5.658.134 6.127.675 6.608.658 7.101.301 7.605.824 8.122.450 8.651.405 9.192.915 9.747.2149.500.000 4.360.995 4.810.405 5.270.852 5.742.548 6.225.706 6.719.343 7.223.867 7.740.493 8.269.447 8.810.958 9.365.256
10.000.000 3.973.599 4.423.009 4.883.456 5.355.151 5.838.310 6.333.148 6.839.887 7.358.535 7.887.489 8.429.000 8.983.29910.500.000 3.579.743 4.032.610 4.496.059 4.967.754 5.450.913 5.945.752 6.452.490 6.971.352 7.502.563 8.046.351 8.601.34111.000.000 3.185.816 3.638.683 4.102.637 4.577.889 5.063.516 5.558.355 6.065.094 6.583.956 7.115.166 7.658.954 8.215.55111.500.000 2.790.530 3.244.756 3.708.710 4.183.961 4.670.726 5.169.222 5.677.697 6.196.559 6.727.770 7.271.558 7.828.15412.000.000 2.389.273 2.847.229 3.314.783 3.790.034 4.276.799 4.775.295 5.285.742 5.808.365 6.340.373 6.884.161 7.440.75712.500.000 1.988.016 2.445.973 2.915.113 3.395.650 3.882.872 4.381.368 4.891.815 5.414.438 5.949.461 6.496.764 7.053.360
SoCal∆ Increase in power prices
∆ Equipmen
t∆ Eq
uipmen
t
SoCal∆ WACC
∆ Increase in p
ower p
rices
SoCal∆ WACC
Exhibit 15.5
Variation 10%
System used Long IslReturn on equity 20,00%
Increase 15%
Equipment 10.000.000
918.692 25% 24% 23% 22% 21% 20% 19% 18% 17% 16% 15%
18,75% 425.192 739.501 1.080.487 1.450.957 1.854.057 2.293.331 2.772.763 3.296.854 3.870.683 4.500.001 5.191.32418,00% 208.865 511.215 839.271 1.195.739 1.583.656 2.006.438 2.467.931 2.972.476 3.524.976 4.130.979 4.796.77517,25% -2.900 287.777 603.212 946.014 1.319.112 1.725.799 2.169.786 2.655.259 3.186.946 3.770.200 4.411.08916,50% -210.182 69.102 372.220 701.686 1.060.322 1.451.305 1.878.210 2.345.074 2.856.456 3.417.518 4.034.11015,75% -413.059 -144.893 146.206 462.659 807.186 1.182.847 1.593.088 2.041.799 2.533.374 3.072.788 3.665.68115,00% -613.331 -355.999 -76.610 227.167 557.952 918.692 1.312.707 1.743.744 2.216.036 2.734.378 3.304.20314,25% -810.977 -564.199 -296.216 -4.785 312.618 658.830 1.037.047 1.450.879 1.904.401 2.402.233 2.949.60813,50% -1.004.414 -767.931 -511.074 -231.683 72.670 404.714 767.528 1.164.583 1.599.806 2.077.642 2.603.13112,75% -1.193.717 -967.275 -721.267 -453.616 -161.989 156.243 504.038 884.739 1.302.123 1.760.468 2.264.62412,00% -1.378.960 -1.162.308 -926.879 -670.676 -391.454 -86.688 246.469 611.229 1.011.226 1.450.574 1.933.94011,25% -1.560.215 -1.353.109 -1.127.993 -882.948 -615.819 -324.178 -5.289 343.935 726.989 1.147.826 1.610.934
918.692 25% 24% 23% 22% 21% 20% 19% 18% 17% 16% 15%
7.500.000 791.664 1.060.667 1.352.322 1.668.999 2.013.364 2.388.411 2.797.510 3.244.463 3.733.566 4.269.677 4.858.3028.000.000 515.203 781.800 1.070.923 1.384.936 1.726.495 2.098.583 2.504.562 2.948.221 3.433.843 3.966.273 4.551.0028.500.000 233.501 497.778 784.463 1.095.912 1.434.772 1.804.017 2.206.997 2.647.493 3.129.773 3.658.671 4.239.6639.000.000 -48.202 213.755 498.003 806.888 1.143.049 1.509.450 1.909.432 2.346.764 2.825.703 3.351.069 3.928.3259.500.000 -329.904 -70.267 211.543 517.864 851.325 1.214.883 1.611.868 2.046.036 2.521.634 3.043.468 3.616.986
10.000.000 -613.331 -355.999 -76.610 227.167 557.952 918.692 1.312.707 1.743.744 2.216.036 2.734.378 3.304.20310.500.000 -903.177 -648.094 -371.062 -69.755 258.438 616.455 1.007.607 1.435.632 1.904.752 2.419.748 2.986.04411.000.000 -1.193.024 -940.190 -665.514 -366.677 -41.077 314.217 702.507 1.127.520 1.593.467 2.105.119 2.667.88511.500.000 -1.482.870 -1.232.285 -959.965 -663.599 -340.591 11.980 397.407 819.407 1.282.182 1.790.489 2.349.72512.000.000 -1.778.296 -1.529.956 -1.259.981 -966.065 -645.620 -295.731 86.885 505.937 965.617 1.470.672 2.026.48712.500.000 -2.076.749 -1.830.653 -1.563.016 -1.271.539 -953.641 -606.413 -226.580 189.559 646.186 1.148.039 1.700.493
918.692 11,25% 12,00% 12,75% 13,50% 14,25% 15,00% 15,75% 16,50% 17,25% 18,00% 18,75%
7.500.000 1.165.769 1.400.218 1.640.080 1.884.153 2.133.427 2.388.411 2.649.209 2.915.929 3.188.676 3.467.560 3.752.6908.000.000 871.202 1.105.652 1.345.513 1.590.887 1.841.876 2.098.583 2.360.836 2.627.555 2.900.303 3.179.187 3.464.3178.500.000 576.635 811.085 1.050.946 1.296.321 1.547.310 1.804.017 2.066.546 2.335.005 2.609.499 2.890.138 3.175.9449.000.000 280.297 516.518 756.380 1.001.754 1.252.743 1.509.450 1.771.980 2.040.438 2.314.932 2.595.571 2.882.4649.500.000 -21.940 215.550 458.480 706.952 958.176 1.214.883 1.477.413 1.745.872 2.020.366 2.301.004 2.587.897
10.000.000 -324.178 -86.688 156.243 404.714 658.830 918.692 1.182.847 1.451.305 1.725.799 2.006.438 2.293.33110.500.000 -629.991 -388.925 -145.995 102.477 356.592 616.455 882.169 1.153.841 1.431.232 1.711.871 1.998.76411.000.000 -940.673 -698.627 -451.076 -199.760 54.355 314.217 579.932 851.604 1.129.341 1.413.252 1.703.44811.500.000 -1.251.354 -1.009.308 -761.757 -508.599 -249.731 11.980 277.694 549.366 827.104 1.111.015 1.401.21012.000.000 -1.567.067 -1.319.990 -1.072.439 -819.281 -560.413 -295.731 -25.130 247.129 524.866 808.778 1.098.97312.500.000 -1.886.438 -1.638.244 -1.384.427 -1.129.962 -871.095 -606.413 -335.812 -59.185 222.629 506.540 796.735
∆ Equipmen
t
Long Island Equity∆ Return on equity
∆ Increase in p
ower p
rices
Long Island Equity∆ Return on equity
Long Island Equity∆ Increase in power prices
∆ Equipmen
t
Exhibit 15.6
Variation 10%
System used SP‐15Return on equity 20,00%
Increase 15%
Equipment 10.000.000
688.778 25% 24% 23% 22% 21% 20% 19% 18% 17% 16% 15%
18,75% 223.034 528.353 859.656 1.219.677 1.611.486 2.038.536 2.504.716 3.014.413 3.572.582 4.184.832 4.857.52018,00% 11.425 305.046 623.701 970.026 1.346.982 1.757.900 2.206.532 2.697.110 3.234.415 3.823.858 4.471.57517,25% -195.722 86.481 392.790 725.747 1.088.207 1.483.382 1.914.890 2.386.811 2.903.757 3.470.948 4.094.30216,50% -398.940 -127.878 166.387 486.303 834.624 1.214.444 1.629.250 2.082.977 2.580.070 3.125.563 3.725.16115,75% -600.730 -340.514 -57.973 249.253 583.815 948.697 1.347.258 1.783.289 2.261.077 2.785.471 3.361.97215,00% -798.255 -548.622 -277.519 17.326 338.468 688.778 1.071.493 1.490.266 1.949.225 2.453.044 3.007.02114,25% -991.589 -752.281 -492.336 -209.567 98.484 434.583 801.845 1.203.788 1.644.387 2.128.143 2.660.15913,50% -1.180.808 -951.570 -702.508 -431.516 -136.231 186.010 538.204 923.737 1.346.435 1.810.631 2.321.23912,75% -1.365.983 -1.146.566 -908.116 -648.610 -365.772 -57.043 280.461 649.996 1.055.244 1.500.374 1.990.11512,00% -1.547.186 -1.337.346 -1.109.244 -860.935 -590.232 -294.675 28.509 382.451 770.691 1.197.239 1.666.64311,25% -1.725.191 -1.524.689 -1.306.673 -1.069.277 -810.399 -527.676 -218.442 120.311 491.987 900.440 1.350.041
688.778 25% 24% 23% 22% 21% 20% 19% 18% 17% 16% 15%
7.500.000 616.280 877.468 1.160.708 1.468.313 1.802.879 2.167.325 2.564.937 2.999.423 3.474.970 3.996.316 4.568.8308.000.000 335.657 594.508 875.290 1.180.311 1.512.155 1.873.734 2.268.323 2.699.619 3.171.796 3.689.579 4.258.3248.500.000 53.955 310.485 588.830 891.286 1.220.432 1.579.167 1.970.759 2.398.891 2.867.726 3.381.978 3.946.9859.000.000 -227.748 26.463 302.370 602.262 928.709 1.284.601 1.673.194 2.098.162 2.563.657 3.074.376 3.635.6479.500.000 -509.450 -257.559 15.910 313.238 636.986 990.034 1.375.629 1.797.434 2.259.587 2.766.774 3.324.308
10.000.000 -798.255 -548.622 -277.519 17.326 338.468 688.778 1.071.493 1.490.266 1.949.225 2.453.044 3.007.02110.500.000 -1.088.101 -840.717 -571.971 -279.596 38.953 386.540 766.393 1.182.154 1.637.941 2.138.414 2.688.86211.000.000 -1.377.947 -1.132.813 -866.423 -576.518 -260.562 84.303 461.293 874.042 1.326.656 1.823.785 2.370.70311.500.000 -1.670.953 -1.428.066 -1.164.025 -876.579 -563.198 -221.034 153.123 562.896 1.012.381 1.506.218 2.049.66712.000.000 -1.969.406 -1.728.762 -1.467.060 -1.182.053 -871.219 -531.716 -160.342 246.518 692.950 1.183.585 1.723.67312.500.000 -2.267.858 -2.029.458 -1.770.094 -1.487.527 -1.179.239 -842.397 -473.806 -69.860 373.519 860.952 1.397.679
688.778 11,25% 12,00% 12,75% 13,50% 14,25% 15,00% 15,75% 16,50% 17,25% 18,00% 18,75%
7.500.000 967.688 1.197.024 1.431.655 1.671.677 1.917.192 2.167.325 2.422.435 2.683.337 2.950.136 3.222.938 3.501.8498.000.000 673.122 902.458 1.137.088 1.377.111 1.622.626 1.873.734 2.130.538 2.393.141 2.661.649 2.934.565 3.213.4768.500.000 378.555 607.891 842.521 1.082.544 1.328.059 1.579.167 1.835.971 2.098.575 2.367.082 2.641.600 2.922.2369.000.000 77.489 309.800 547.432 787.977 1.033.492 1.284.601 1.541.405 1.804.008 2.072.516 2.347.034 2.627.6699.500.000 -224.748 7.562 245.194 488.247 736.820 990.034 1.246.838 1.509.441 1.777.949 2.052.467 2.333.103
10.000.000 -527.676 -294.675 -57.043 186.010 434.583 688.778 948.697 1.214.444 1.483.382 1.757.900 2.038.53610.500.000 -838.357 -601.591 -359.439 -116.228 132.345 386.540 646.460 912.207 1.183.887 1.461.606 1.743.96911.000.000 -1.149.039 -912.272 -670.120 -422.483 -169.892 84.303 344.222 609.969 881.649 1.159.368 1.443.23511.500.000 -1.462.196 -1.222.954 -980.802 -733.165 -479.943 -221.034 41.985 307.732 579.412 857.131 1.140.99712.000.000 -1.781.566 -1.538.786 -1.291.483 -1.043.846 -790.625 -531.716 -267.016 3.578 277.174 554.893 838.76012.500.000 -2.100.937 -1.858.156 -1.609.875 -1.355.992 -1.101.306 -842.397 -577.698 -307.104 -30.510 252.192 536.522
∆ Equipmen
t
SP‐15 Equity∆ Return on equity
∆ Increase in p
ower p
rices
SP‐15 Equity∆ Return on equity
∆ Equipmen
t
SP‐15 Equity∆ Increase in power prices
Exhibit 15.7
Variation 10%
System used San DiegoReturn on equity 20,00%
Increase 15%
Equipment 10.000.000
3.153.378 25% 24% 23% 22% 21% 20% 19% 18% 17% 16% 15%
18,75% 2.418.793 2.823.233 3.261.314 3.736.542 4.252.851 4.814.661 5.426.947 6.095.319 6.826.111 7.626.487 8.504.56418,00% 2.157.745 2.547.583 2.969.877 3.428.011 3.925.782 4.467.458 5.057.844 5.702.356 6.407.112 7.179.030 8.025.95017,25% 1.902.241 2.277.826 2.684.710 3.126.157 3.605.835 4.127.861 4.696.874 5.318.103 5.997.452 6.741.602 7.558.12216,50% 1.652.187 2.013.859 2.405.702 2.830.865 3.292.885 3.795.737 4.343.895 4.942.405 5.596.966 6.314.025 7.100.88715,75% 1.407.486 1.755.581 2.132.746 2.542.019 2.986.810 3.470.952 3.998.764 4.575.110 5.205.490 5.896.123 6.654.05515,00% 1.168.046 1.502.892 1.865.735 2.259.504 2.687.486 3.153.378 3.661.341 4.216.069 4.822.862 5.487.720 6.217.43914,25% 933.773 1.255.693 1.604.565 1.983.209 2.394.795 2.842.884 3.331.488 3.865.131 4.448.923 5.088.646 5.790.85313,50% 704.576 1.013.887 1.349.130 1.713.022 2.108.616 2.539.344 3.009.069 3.522.151 4.083.514 4.698.730 5.374.11312,75% 480.366 777.378 1.099.328 1.448.834 1.828.833 2.242.631 2.693.949 3.186.984 3.726.480 4.317.804 4.967.03912,00% 261.052 546.070 855.058 1.190.535 1.555.329 1.952.621 2.385.994 2.859.487 3.377.668 3.945.703 4.569.45211,25% 46.546 319.869 616.219 938.019 1.287.991 1.669.193 2.085.072 2.539.518 3.036.924 3.582.263 4.181.175
3.153.378 25% 24% 23% 22% 21% 20% 19% 18% 17% 16% 15%
7.500.000 2.532.896 2.880.140 3.256.021 3.663.512 4.105.947 4.587.072 5.111.103 5.682.792 6.307.504 6.991.306 7.741.0698.000.000 2.262.364 2.607.068 2.980.280 3.384.962 3.824.440 4.302.449 4.823.195 5.391.417 6.012.468 6.692.400 7.438.0718.500.000 1.991.831 2.333.997 2.704.539 3.106.413 3.542.933 4.017.826 4.535.286 5.100.042 5.717.431 6.393.495 7.135.0739.000.000 1.717.751 2.057.465 2.425.427 2.824.586 3.258.246 3.730.124 4.244.403 4.805.800 5.419.642 6.091.953 6.829.5609.500.000 1.442.898 1.780.178 2.145.581 2.542.045 2.972.866 3.441.751 3.952.872 4.510.934 5.121.252 5.789.836 6.523.500
10.000.000 1.168.046 1.502.892 1.865.735 2.259.504 2.687.486 3.153.378 3.661.341 4.216.069 4.822.862 5.487.720 6.217.43910.500.000 893.193 1.225.606 1.585.889 1.976.963 2.402.106 2.865.005 3.369.810 3.921.203 4.524.473 5.185.604 5.911.37811.000.000 617.177 947.177 1.304.921 1.693.322 2.115.650 2.575.581 3.077.255 3.625.342 4.225.119 4.882.556 5.604.42211.500.000 335.475 663.155 1.018.461 1.404.298 1.823.927 2.281.014 2.779.690 3.324.614 3.921.049 4.574.955 5.293.08312.000.000 53.772 379.132 732.001 1.115.274 1.532.203 1.986.447 2.482.125 3.023.885 3.616.980 4.267.353 4.981.74512.500.000 -227.930 95.110 445.541 826.250 1.240.480 1.691.881 2.184.561 2.723.157 3.312.910 3.959.751 4.670.406
3.153.378 11,25% 12,00% 12,75% 13,50% 14,25% 15,00% 15,75% 16,50% 17,25% 18,00% 18,75%
7.500.000 3.106.711 3.389.375 3.678.619 3.974.567 4.277.343 4.587.072 4.903.881 5.227.901 5.559.260 5.898.092 6.244.5318.000.000 2.822.088 3.104.752 3.393.997 3.689.945 3.992.720 4.302.449 4.619.259 4.943.278 5.274.638 5.613.470 5.959.9088.500.000 2.534.312 2.817.741 3.107.750 3.404.463 3.708.003 4.017.826 4.334.636 4.658.655 4.990.015 5.328.847 5.675.2859.000.000 2.245.939 2.529.368 2.819.377 3.116.090 3.419.630 3.730.124 4.047.699 4.372.483 4.704.607 5.044.204 5.390.6629.500.000 1.957.566 2.240.995 2.531.004 2.827.717 3.131.257 3.441.751 3.759.325 4.084.110 4.416.234 4.755.831 5.103.034
10.000.000 1.669.193 1.952.621 2.242.631 2.539.344 2.842.884 3.153.378 3.470.952 3.795.737 4.127.861 4.467.458 4.814.66110.500.000 1.375.565 1.661.054 1.953.133 2.250.971 2.554.511 2.865.005 3.182.579 3.507.363 3.839.488 4.179.085 4.526.28811.000.000 1.080.998 1.366.487 1.658.567 1.957.359 2.262.988 2.575.581 2.894.206 3.218.990 3.551.115 3.890.712 4.237.91511.500.000 786.432 1.071.921 1.364.000 1.662.792 1.968.422 2.281.014 2.600.696 2.927.598 3.261.850 3.602.339 3.949.54212.000.000 491.256 777.354 1.069.434 1.368.226 1.673.855 1.986.447 2.306.130 2.633.032 2.967.283 3.309.017 3.658.36612.500.000 189.018 478.210 774.026 1.073.659 1.379.288 1.691.881 2.011.563 2.338.465 2.672.717 3.014.450 3.363.800
∆ Equipmen
t
San Diego Equity∆ Return on equity
∆ Increase in p
ower p
rices
San Diego Equity∆ Return on equity
∆ Equipmen
t
San Diego Equity∆ Increase in power prices
Exhibit 15.8
Variation 10%
System used SoCal
Return on equity 20,00%
Increase 15%
Equipment 10.000.000
721.157 25% 24% 23% 22% 21% 20% 19% 18% 17% 16% 15%
18,75% 251.504 558.090 890.756 1.252.248 1.645.647 2.074.419 2.542.466 3.054.189 3.614.564 4.229.218 4.904.53018,00% 39.231 334.081 654.060 1.001.813 1.380.313 1.792.902 2.243.346 2.735.890 3.275.335 3.867.110 4.517.37417,25% -168.567 114.830 422.424 756.767 1.120.726 1.517.522 1.950.787 2.424.617 2.943.639 3.513.092 4.138.91516,50% -371.964 -99.747 195.760 517.017 866.785 1.248.172 1.664.675 2.120.245 2.619.342 3.167.019 3.769.00015,75% -574.066 -312.733 -28.989 279.533 615.496 981.892 1.382.094 1.819.907 2.299.631 2.826.136 3.404.93915,00% -772.212 -521.494 -249.225 46.878 369.378 721.157 1.105.464 1.525.964 1.986.801 2.492.664 3.048.87414,25% -966.153 -725.793 -464.716 -180.727 128.641 466.164 834.969 1.238.586 1.681.005 2.166.743 2.700.92313,50% -1.155.966 -925.708 -675.548 -403.373 -106.811 216.810 570.500 957.656 1.382.118 1.848.235 2.360.93812,75% -1.341.722 -1.121.316 -881.802 -621.149 -337.073 -27.006 311.948 683.055 1.090.013 1.537.004 2.028.77412,00% -1.523.494 -1.312.695 -1.083.561 -834.140 -562.238 -265.384 59.204 414.670 804.566 1.232.917 1.704.28711,25% -1.701.353 -1.499.921 -1.280.907 -1.042.435 -782.399 -498.424 -187.836 152.385 525.655 935.842 1.387.333
721.157 25% 24% 23% 22% 21% 20% 19% 18% 17% 16% 15%
7.500.000 640.980 903.268 1.187.693 1.496.576 1.832.522 2.198.460 2.597.691 3.033.933 3.511.389 4.034.814 4.609.5978.000.000 360.943 620.884 902.842 1.209.128 1.542.341 1.905.400 2.301.593 2.734.630 3.208.700 3.728.546 4.299.5428.500.000 79.241 336.862 616.382 920.104 1.250.618 1.610.833 2.004.028 2.433.902 2.904.631 3.420.945 3.988.2039.000.000 -202.462 52.840 329.922 631.080 958.895 1.316.267 1.706.464 2.133.173 2.600.561 3.113.343 3.676.8659.500.000 -484.164 -231.182 43.461 342.056 667.171 1.021.700 1.408.899 1.832.445 2.296.491 2.805.741 3.365.526
10.000.000 -772.212 -521.494 -249.225 46.878 369.378 721.157 1.105.464 1.525.964 1.986.801 2.492.664 3.048.87410.500.000 -1.062.058 -813.590 -543.677 -250.044 69.863 418.920 800.364 1.217.852 1.675.516 2.178.035 2.730.71411.000.000 -1.351.904 -1.105.686 -838.129 -546.966 -229.651 116.682 495.264 909.740 1.364.231 1.863.405 2.412.55511.500.000 -1.644.039 -1.400.068 -1.134.862 -846.161 -531.427 -187.800 187.940 599.430 1.050.781 1.546.648 2.092.31312.000.000 -1.942.491 -1.700.764 -1.437.897 -1.151.635 -839.447 -498.482 -125.524 283.052 731.350 1.224.016 1.766.31912.500.000 -2.240.944 -2.001.460 -1.740.931 -1.457.109 -1.147.468 -809.163 -438.989 -33.326 411.919 901.383 1.440.325
721.157 11,25% 12,00% 12,75% 13,50% 14,25% 15,00% 15,75% 16,50% 17,25% 18,00% 18,75%
7.500.000 995.584 1.225.640 1.461.007 1.701.784 1.948.070 2.198.460 2.454.372 2.716.094 2.983.730 3.257.388 3.537.1768.000.000 701.018 931.074 1.166.441 1.407.217 1.653.503 1.905.400 2.163.010 2.426.438 2.695.357 2.969.015 3.248.8038.500.000 406.451 636.507 871.874 1.112.650 1.358.936 1.610.833 1.868.444 2.131.872 2.401.222 2.676.602 2.958.1199.000.000 106.051 339.091 577.307 818.084 1.064.370 1.316.267 1.573.877 1.837.305 2.106.656 2.382.035 2.663.5539.500.000 -196.186 36.853 275.232 519.048 768.401 1.021.700 1.279.310 1.542.738 1.812.089 2.087.469 2.368.986
10.000.000 -498.424 -265.384 -27.006 216.810 466.164 721.157 981.892 1.248.172 1.517.522 1.792.902 2.074.41910.500.000 -809.013 -571.503 -329.243 -85.427 163.926 418.920 679.655 946.236 1.218.769 1.497.361 1.779.85311.000.000 -1.119.695 -882.184 -639.272 -390.857 -138.311 116.682 377.417 643.999 916.532 1.195.123 1.479.88111.500.000 -1.431.987 -1.192.866 -949.953 -701.539 -447.522 -187.800 75.180 341.761 614.294 892.886 1.177.64312.000.000 -1.751.358 -1.507.815 -1.260.635 -1.012.221 -758.204 -498.482 -232.951 38.492 312.057 590.648 875.40612.500.000 -2.070.729 -1.827.186 -1.578.125 -1.323.445 -1.068.885 -809.163 -543.633 -272.189 5.274 288.411 573.168
∆ Equipmen
t
SoCal Equity∆ Return on equity
∆ Increase in p
ower p
rices
SoCal Equity∆ Return on equity
∆ Equipmen
t
SoCal Equity∆ Increase in power prices
