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
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
3
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.
4
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.
5
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
3
4
5
The Electric Car .................................................................................................. 30
3.1
Term ........................................................................................................... 30
3.2
History ........................................................................................................ 30
3.3
Race to popularity ...................................................................................... 31
Prerequisites ...................................................................................................... 34
4.1
Open electricity market.............................................................................. 34
4.2
Access to batteries ..................................................................................... 34
Model Limitations .............................................................................................. 36
5.1
6
Energy Storage Legacy................................................................................ 27
Terminal value ............................................................................................ 36
Research ............................................................................................................ 37
6
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
7
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
8
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
9
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 10 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 market 1.
Electric energy price arbitrage exploits wholesale electricity price volatility
and diurnal variability. Benefits accrue when storage is charged using lowpriced 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.
11
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.
12
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.
13
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 19 th 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.
14
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
reconverted
and
then
into
elec-
tricity. This means that
electricity
Figure 1 - Global Electricity Generation by Source
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
15
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
16
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
Figure 2 - Supply Curve for Kraftwerk Power Company
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
Figure 3 - Supply Curve for the Town of Kraftstadt
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 4 - Supply and Demand Curves Matched Together
17
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
Figure 5 - Surplus and Deficit of Power with 50MW Production
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).
18
This
solution
would
save Kraftwerk substantial
investment cost and even
though they could not
supply power over the
dire
Figure 6 - Surplus and Deficit of Power with 60MW Production
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
Figure 7 - Surplus and Traded Power with 65 MW Production
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.
19
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.
20
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
21
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
22
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
23
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
electrolyte
close
ions
in
the
one
of
the
Figure 8 - Picture of a Battery Energy Storage System
to
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).
24
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
Figure 9 - Diagram of a Compressed Air Storage System
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
cryogenically
three
cooled
elements:
refrigerator,
the
a
superconducting coil, and a power
conducting system. If the coil is kept at a
temperature lower than its super-
Figure 10 - Diagram of Superconducting Magnetic
Storage System
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).
25
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
produced
mass.
Hydro
electricity
by
collecting
water
is
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 hydropower station is a second reservoir to
collect the water running through the
Figure 11 - Diagram of Pumped Hydro Storage System
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
Figure 12 - Diagram of a Flywheel Energy Storage System
(increases the speed of the cylinder). The
26
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
27
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.
28
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.
29
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)
30
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
31
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
place
may
some
additional value
Figure 14 - Mitsubishi MiEV Electric Car
to the fact that
her car is environmentally friendly and has no CO2
emission. It is true the electric car has no tailpipe
Figure 13 - Nissan Leaf Electric Car
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
32
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.
33
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
34
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).
Figure 15 - Ford Focus Electric Car
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
Figure 16 - BMW i3 Electric Car
batteries for this kind of operation without it
representing the bulk of the total number of batteries on the market.
35
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, politicaland 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.
36
Research
6
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):
Figure 17 - Geographical location of US Power Markets
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
37
California
Midwest
New England
New York
PJM
Pacific G&E
Cinergy
CT
Capital
Western
SoCal
First Energy
NEMass
Hudson
Baltimore
San Diego
Illinois
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
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 “RealTime 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 2 nd,
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
38
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.
39
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 2:004:00 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 proxim ity 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
Gross margin
representation of the
Projected
Actual
2009
2010
2011
Pacific G&E
$ 826.899
$ 904.278
$ 885.983
$ 1.404.274 $ 1.618.872 $ 1.724.483
systems in California.
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
After having calculated
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
$ 756.434
$ 1.432.841 $ 1.738.897 $ 1.779.699
gross margin of all the
gross margin for all
three years for each of
$ 918.903
2009
2010
2011
Table 2 - Gross Margin for all 6 Systems in California
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
40
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
values
were
Increase
Projected
highest
Actual
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%
12,23%
12,23%
8,30%
8,30%
Pepco
Table 3 - Increase for all 6 Systems on the PJM Market
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.
41
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
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
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
Traded
Projected
IRR of Investment
15,00%
7,50%
IRR and NPV of the equity. The total cash flow
NPV of Investment
$1.000.000
$500.000
that was calculated for the whole project is
Table 5 - IRR and NPV of Investment
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).
42
The
model
provides
the
option
of
calculating for each transmission system
independently. One system is chosen and
Traded
Projected
IRR of Equity
10,00%
5,00%
NPV of Equity
$500.000
$325.000
corresponding revenue and cost numbers is
Table 6 - IRR and NPV of Equity
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).
Increase in power prices
15%
Long term increase
5%
first
Table 7 - Short- and Long Term Increase
5
yrs
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).
Increase from 2009
Grid
Increase
SP-15
10,74%
Table 8 - Actual
Increase on System
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’.
43
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
Salaries
through a clearing house, contingencies are built into
Bonus payments
the model and working capital is assumed to increase
Nr of traders
Traider accuracy
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
$75.000
2%
2
95%
Overhead
$30.000
Maintenance
$120.000
Other
$100.000
Working capital
$200.000
Selling expense
Start-up expense
0,2%
$300.000
Table 9 - Operational Cost Inputs
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
Figure 18 - 2MW Storage Unit in Bluffton, Ohio
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 10,000m 2 (1
hectare) would be ample, and easily provide space for possible future expansions.
44
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
Figure 19 - 8MW Storage Unit in
Johnson City, NY
(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
Land
with applicable laws and legislations (depending on
Building
location). The model assumes that assets can only be
Equipment
depreciated by 90%, i.e. 10% residual value must
remain in the books5
$300.000
$1.000.000
$10.000.000
Other
$300.000
Total
$11.600.000
Table 10 - Investment Inputs
The equipment will be financed with export
Leverage of equipment
70%
Equipment loan
Bank I
Germany). The model assumes that the equipment
$7.000.000
Interest rate
Payback in years
6,25%
cost will be leveraged through either the vendor
8
himself or an export financing program within the
Short term financing
Bank II
$500.000
Interest rate
8%
Payback in years
Total capital needed
financing from the country of origin (Sweden or
3
$12.100.000
Equity ratio
38%
Equity
$4.600.000
Debt
$7.500.000
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
Table 11 - Financing Input
(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.
45
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.
46
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 interviews 6, 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).
Grid to be used
Hours in/out
SP-15
3
Increase from 2009
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%
Long term increase
5%
first
5
yrs
Table 12 - All Revenue Inputs
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.
47
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
Projected
Actual
New England
28,06%
19,09%
California
5,99%
21,16%
an increase in consumer prices (or spot prices); this is
Midwest
16,43%
10,97%
New York
24,15%
-3,69%
the percentage by which the gross margin of buying
PJM
49,11%
41,13%
Overall
24,63%
17,68%
Table 14 - Average Increase
one realizes that this number does not correspond to
at lowest prices and selling at peak prices
increases over the course of one year. For the
Actual scenario
Projected
Actual
22,54%
23,37%
California
5,90%
21,30%
Midwest
18,43%
12,58%
16,33%), and for the Projected scenario the
New York
24,50%
-0,65%
respect matching numbers were 24,63% and
PJM
55,75%
46,99%
Overall
20,85%
16,33%
the average increase between the years 2009 and
2011, over all the 29 systems was 17,68% (median
20,85% (average and medium, respectively; see
New England
Table 13 - Median Increase
Tables 13 and 14).
48
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
Phasing
Cost
Land
Building
Equipment
this project will fit within 8 normal 40
Other
foot containers, so that the area needed
Total
for the batteries is about 350 m2. With
yr 1
yr 2
yr 3
$300.000
100%
0%
0%
$1.000.000
75%
25%
0%
$10.000.000
100%
0%
0%
$300.000
50%
50%
0%
$11.600.000
Table 15 - Investment Inputs
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 m 2. 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)
49
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 11 th, 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
Opex cost
Salaries
Bonus payments
Nr of traders
Traider accuracy
$75.000
pr/yr
2%
of GM
2
95%
system (see Table 16). The yearly salary is
Overhead
$30.000
pr/yr
set at $75.000. It is assumed that active
Maintenance
$120.000
pr/yr
Other
$100.000
pr/yr
traders on the utility market could be
Working capital
$200.000
for
contracted to add this responsibility to
Selling expense
0,2%
their portfolio of trading activities. The
Start-up expense
trading bonuses are then defined as a
2
yrs
of revenue
$300.000
Table 16 - Operational Cost Inputs
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.
50
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
%
Factor
4%
90%
can only be depreciated by 90%, i.e. that a 10%
Building
residual value remains. The building is depreciated
Years of depreciation
22,75
Equipment
12,5%
by 4%, a standard practice in most cases. The
Years of depreciation
7,20
equipment is depreciated by 12,5%, giving them an
Other
25%
Years of depreciation
4,10
90%
90%
8 years lifetime and other items are assumed to
Table 17 - Depreciation Inputs
have a lifetime of 4 years (see Table 17).
51
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
Leverage of equipment
Equipment loan
Bank I
Interest rate
communications,
8
Short term financing
September 8th, 2011 & Jóhann Ingi Kristjánsson,
personal
5,75%
Payback in years
Bank II
Investments,
$7.000.000
Interest rate
Davíðsson, Arion Bank, personal communication,
Akurey
70%
Payback in years
$500.000
8%
3
Table 18 - Debt Inputs
th
September 15 , 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).
52
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
Total capital needed
Equity ratio
$12.100.000
38%
Equity
$4.600.000
Debt
$7.500.000
WACC input
the work of Aswath Damodaran, Professor at Stern
CAPM
Business School, in assuming the risk premium at
Risk free rate
2,71%
Market risk premium
6,19%
6,19% (Damodaran, 2012). The beta (β) for this project
β for project
is impossible to calculate because it is a correlation
rE
20,00%
between the volatility of the project and the market.
rD
5,90%
WACC (discount rate)
10,02%
The way to calculate it is to find the correlation
between a set of returns of the project and a set of
2,79
Table 19 - WACC Inputs
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%
53
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.
54
Results 9
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 NPV equity
IRR equity
SoCal
NPV investment IRR investment
$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
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 $2.387.333 on the Baltimore system) it was still quite far from the 55 defined target rates of return for equity ($-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
NPV investment IRR investment
San Diego
Long Isl
NY
NPV equity
IRR equity
$577.330
10,77%
-$2.579.222
12,01%
$2.507.743
13,17%
-$1.434.188
15,59%
$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%
$1.136.470
11,48%
-$2.245.098
13,06%
NJ
in the projected
Table 21 - Six Systems with Sufficient Return on Investment
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,
56
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
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%
three systems with
San Diego
$10.652.938
21,81%
$3.153.378
29,26%
NP-15
$3.555.005
14,36%
-$880.149
17,35%
positive
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%
New
York,
NPV
investment,
although
had
of
and
no
system in PJM gave
a
high
enough
return on equity it
is a market worth
Table 22- Twelve Systems with Sufficient Return on Investment
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).
57
Despite
fact
Island
the
that
the
Long
produced
highest
outcome and that
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
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.
58
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
59


Trader accuracy
Maintenance



Start-up expense
Working capital
Corporate tax rate
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
Values to be tested
“Trader
accuracy”
∆ from base case
20%
assumption does not
vary by 20%, simply
because we assume
95% accuracy and a
20%
positive
variation from 95% is
WACC
Increase in power prices
Equipment
Power losses
Salaries
Trader accuracy
Maintenance
Star-up expense
Working capital
Corporate tax rate
10,02%
15%
$10.000.000
3%
$75.000
95%
$120.000
$300.000
$200.000
34%
which
is
Base
Positive
12,02%
12%
$12.000.000
4%
$90.000
90%
$144.000
$360.000
$240.000
40,8%
10,02%
15%
$10.000.000
3%
$75.000
95%
$120.000
$300.000
$200.000
34%
8,02%
18%
$8.000.000
2%
$60.000
100%
$96.000
$240.000
$160.000
27,2%
Output value
NPV of Investment
114%,
Negative
-
Table 24 - Assumptions Tested for Single dimension analysis
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).
60
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
Figure 20 - Spider chart for Long Island, Linear representation of each
assumption's effect on NPV of Investment
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
61
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
Figure 21 - Absolute Value of each Assumption's Effect on NPV of Investment
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
NPV of Investment
Corresponding Input Value
Input Variable
WACC
Increase in power prices
Equipment
Output Value
Percent
Low Output
Base Case
High Output
Low
Base
High
Swing
Swing2
12,02%
10,02%
8,02%
$4.492.557
$6.678.480
$9.330.276
$4.837.719
37,1%
12%
15%
18%
$4.731.864
$6.678.480
$8.811.187
$4.079.323
26,3%
$5.124.660
$6.678.480
$8.207.462
$3.082.803
15,0%
$12.000.000 $10.000.000 $8.000.000
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%
Table 25 - Inputs and Corresponding Outputs for Long Island Tornado Calculations
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 Swing 2”. 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
62
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
tivity analysis
are that the
NPV of the
investment is
∆ Incr. in power prices
of the sensi-
∆ WACC
10.648.459
17%
11,52%
11,02%
10,52%
10,02%
9,52%
9,02%
8,52%
10.312.626 11.028.720 11.779.789 12.567.935 13.395.408 14.264.616 15.178.132
17%
16%
9.727.677
10.421.569 11.149.302 11.912.910 12.714.569 13.556.606 14.441.510
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
Table 26 - Matrix Data Table Long Island with WACC and Increase in Power Prices
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
63
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
greatest
impact on the
overall
out-
come
and
3.153.378
∆ Increase in power prices
variables have
the
∆ Return on equity
five
23%
2.684.710
22%
3.126.157
21%
3.605.835
20%
4.127.861
19%
4.696.874
18%
5.318.103
17%
5.997.452
16,50%
15,75%
2.405.702
2.830.865
3.292.885
3.795.737
4.343.895
4.942.405
5.596.966
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
17,25%
Table 27 - Matrix Data Table Long Island Equity with Return on Equity and Increase in Power Prices
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.
64
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
65
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”.
66
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
67
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.
68
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
69
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
70
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.
71
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.
72
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.
73
13 References
Agrawal, P., Nourai, A., Markel, L., Fioravanti, R., Gordon, P., Tong, N. & Huff, G. (2011).
Characterization and assessment of novel bulk storage technologies [Sandia report].
CA: Sandia National Laboratories.
Bloomberg (2011). German government bonds. Retrieved 22nd April 2012 from
http://www.bloomberg.com/markets/rates-bonds/government-bonds/germany/
Bureau of Transportation (2011). Number of vehicles and vehicle classification [Report].
Retrieved from
http://www.bts.gov/publications/national_transportation_statistics/html/table_01
_11.html
Davidson, B. J., Glendenning, I., Harman, R. D., Hart, A. B., Maddock, B. J. Moffitt, R. D.
et al. (1980). Large-scale electrical energy storage. IEE Proceedings, 127(6), 345-385.
Damodaran, A. (2011). Implied equity risk premium (US). Retrieved 22nd April 2012 from
http://www.damodaran.com
Divya, K. C. & Østergaard, J. (2009). Battery energy storage technology for power
systems – An overview. Electric Power Systems Research, 79, 511-520.
Eyer, J. & Corey, G. (2010). Energy storage for the electricity grid: benefits and market
potential assessment guide [Sandia report]. CA: Sandia National Laboratories.
Eyer, J. (2009). Benefits from flywheel energy storage for area regulation in California –
demonstration results: A study for the DOE energy storage systems program [Sandia
report]. CA: Sandia National Laboratories.
French, C. W. (2003). The Treynor capital asset pricing model. Journal of Investment
Management, 1(2), 60-72.
Georgano, G. N. (2000). Vintage Cars 1886 to 1930. Sweden: AB Nordbok.
GreenCar Magazine. (2009). Tesla Motors Moving Quickly to Commercialization of an
Electric Car [article]. Retrieved from
http://www.greencarmagazine.net/2009/07/tesla-motors-moving-quickly-tocommercialization-of-an-electric-car/
Ibrahim, H., Illinca, A. & Perron, J. (2007). Comparison and analysis of different energy
storage techniques based on their performance index. Proceedings from IEEE
Canada Electrical Power Conference (pp. 393-398). Montreal: IEEE.
Internal Revenue Service (2009). The American Recovery and Reinvestment Act of 2009.
Retrieved from http://www.irs.gov/newsroom/article/0,,id=206875,00.html
Kirby, R. S. (1990). Engineering in History. New York: Courier Dover Publications.
74
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.
Kromer, M. A., & Heywood, J. B. (2007) Electric Powertrains: Opportunities and
Challenges in the U.S. Light-Duty Vehicle Fleet. Cambridge, Massachusetts: MIT
Laboratory for Energy and the Environment.
Krupka, M. C., Moore, J. E., Keller, W. E., Baca, G. A., Brasier, R. I. & Bennett, W. S.
(1979). Energy storage technology-environmental implications of large scale
utilization. Proceedings from Second International Alternative Energy Sources
Conference. Florida: LOS Alamos Scientific Laboratory.
Makarov, Y. V., Du, P., Kintner-Meyer, M. C. W., Jin, C. & Illian, H. F. (2012). Sizing
energy storage to accommodate high penetration of variable energy resources. IEE
Transactions on sustainable energy, 3(1), 34-40.
Miles, J., & Ezzell, J. (1980). The weighted average cost of capital, perfect capital
markets and project life: a clarification. Journal of Financial and Quantitative
Analysis, 15, 719-730.
Moller, P., & Kramer, B. (1991). "Review: Electric Fish", BioScience, 41(11), 794–6.
Nourai, A. (2007). Installation of the first distributed energy storage system (DESS) at
American electric power (AEP): A study for the DOE energy storage systems program
[Sandia report]. CA: Sandia National Laboratories.
Post, R. F. (2009). A development path to the efficient and cost-effective bulk storage of
electrical energy [report]. CA: Lawrence Livermore National Laboratory.
Sandalow, D. (Ed.). (2009). Plug-In Electric Vehicles: What Role for Washington? (1st.
edition). Washington DC: The Brookings Institution.
Sander, M. & Gehring, R. (2011). LIQHYSMES – A novel energy storage concept for
variable renewable energy sources using hydrogen and SMES. IEE Transactions on
Applied Superconductivity, 21(3), 1362-1366.
Schoenung, S. M. & Eyer, J. (2008). Benefit/cost framework for evaluating modular
energy storage: a study for the DOE energy storage systems program [Sandia
report]. CA: Sandia National Laboratories.
Schulte, R. H., Critelli, N. Holst, K. & Huff, G. (2012). Lessons from Iowa: development of
a 270 megawatt compressed air energy storage project in Midwest independent
system operator: a study for the DOE energy storage systems program [Sandia
report]. CA: Sandia National Laboratories.
Sousa, M. D., Roque, A. & Casimiro, C. (2011). Analysis and simulation of a system
connecting batteries of an electric vehicle to the electric grid. Proceedings from
75
International Conference on Power Engineering, Energy and Electrical drives (pp. 16). Malaga: IEEE.
Sperling, D. & Gordon, D. (2009). Two billion cars: driving toward sustainability. New
York: Oxford University Press.
Srivastava, A., Kumar, A. A. & Schulz, N. N. (2012). Impact of distributed generations
with energy storage devices on the electric grid. IEEE Systems Journal, 6(1), 110117.
Stewart, J. (2001). Intermediate Electromagnetic Theory. Singapore: World Scientific.
The White House (2011). State of the Union Address. Retrieved from
http://www.whitehouse.gov/the-press-office/2011/01/25/remarks-president-stateunion-address
US Energy Information Administration (2009). An Updated Annual Energy Outlook 2009
[Report]. Retrieved from
http://www.eia.doe.gov/oiaf/servicerpt/stimulus/pdf/sroiaf%282009%2903.pdf
US Department of Treasury (2011). Daily treasure long term rate data. Retrieved 22nd
April 2012 from http://www.treasury.gov/resource-center/data-chartcenter/interest-rates/Pages/TextView.aspx?data=longtermrate
Zimba, J. (2009). Force and Motion: An Illustrated Guide to Newton’s Laws. Maryland:
The Johns Hopkins University Press.
76
Exhibit 1
FERC - Division of Energy Market Oversight - www.ferc.gov
CAISO Daily Report
Tuesday July 20, 2010
Price
<=0
Day-Ahead Prices and DA Forecast Load
Hour Ending:
>=100
>=200
Forecast Error % 0-4.9% 5-9.9% >=10%
Avg. Hr $
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 Pk
Pacific 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
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
DA Forecast Load (GW)
Cleared Load
$80
Pacific G&E
SoCal Edison
San Diego G&E
NP-15
SP-15
Palo Verde
45000
$70
40000
35000
$50
30000
$40
Load (MW)
Price ($/MWh)
$60
25000
$30
20000
$20
15000
$10
$0
10000
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
Preliminary Real-Time Prices with Forecasted and Actual System Load
Hour Ending:
Avg. Hr $
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 Pk
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
31.20
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%
Actual Load
Pacific G&E
SoCal Edison
San Diego G&E
NP-15
SP-15
Palo Verde
DA Forecast
$50
45000
$45
40000
$40
Price ($/MWh)
$30
30000
$25
25000
$20
$15
20000
$10
15000
$5
$0
10000
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Hour
For archival records go to http://www.ferc.gov/market-oversight/mkt-electric/caiso/caiso-iso-archives.asp
20
21
22
23
24
Load (MW)
35000
$35
Hour
Pacific
SoCal
San
NP‐15
SP‐15
Palo
DA
Forecast
For
Avg.
Forecast
CAISO
FERC
Tuesday
Avg.
Preliminary
Day‐Ahead
Load
Actual
<=0
0‐4.9%
Hour
Pacific
SoCal
San
NP‐15
SP‐15
Palo
DA
Verde
Forecast
Error
archival
Hr
Error
Daily
‐
July
Hr
Real‐Time
Prices
Ending:
G&E
Edison
Diego
Verde
Forecast
(MW)
Load
>=100
5‐9.9%
Ending:
G&E
Edison
Diego
1
25,62
24,96
24,93
24,72
24,41
24,05
26,66
‐0,091
records
$
%
Report
Division
20,
$
Prices
and
Pacific
>=200
>=10%
1
37,05
36,16
36,13
36,15
35,41
34,76
Load
Forecasted
Forecast
Energy
2010 Price
of
with
DA
to
3
23,08
22,21
22,16
22,23
21,73
21,42
24,12
‐0,09
3
29,17
28,26
28,24
28,44
27,71
27,25
26,7
SoCal
go
2
26,28
25,32
25,24
25,34
24,76
24,41
25,1
‐0,09
2
32,42
31,53
31,56
31,63
30,9
30,36
(GW)
G&E
San
5
27,91
27,01
27,02
27,26
26,48
26,07
24,1
6
30,98
30,05
30,03
30,21
29,42
28,93
23,6
Diego
7
31,35
30,86
31,01
30,57
30,36
29,63
23,9
G&E
8
34,9
34,36
34,48
33,94
33,72
32,93
24,9
NP‐15
9
36,76
36,47
36,67
35,45
35,76
34,9
26,3
SP‐15
and
Load
Market
Actual
Oversight
System
‐
Load
www.ferc.gov
4
5
6
7
8
9
18,86
26,89
23,31
18,31
33,67
22,26
21,4
18,17
26,02
23,01
18,06
26,31
21,34
18,14
26,01
23,06
18,15
26,4
21,48
18,32
26,12
22,43
17,77
27,73
20,94
17,78
25,46
22,63
17,75
25,8
20,66
17,52
25,06
22,19
17,35
25,26
23,63
23,86
24,93
26,27
28,38
30,39
‐0,09
‐0,087
‐0,081
‐0,071
‐0,066
‐0,061
http://www.ferc.gov/market‐oversight/mkt‐electric/caiso/caiso‐iso‐archives.asp
4
28,59
27,56
27,58
27,91
27,03
26,63
25,1
Edison
10
41,99
29,6
29,63
32,17
28,98
28,42
32,2
‐0,059
10
41,01
40,97
41,01
39,6
40,13
39,11
28,4
Palo
11
34,04
32,04
32,04
31,16
31,31
30,71
33,93
‐0,054
11
44,1
44,64
44,49
42,58
43,63
42,55
30,4
Verde
DA
12
38,85
39,69
39,71
37,06
38,74
38,08
35,26
‐0,055
12
46,52
47,44
47,12
44,96
46,29
45,24
32,2
13
37,33
41,1
41,15
35,69
40,13
39,62
36,23
‐0,062
13
46,89
49,31
48,95
45,28
48,12
47,14
33,9
Forecast
14
38,42
40
40
36,65
39,03
38,56
37,46
‐0,063
14
49,51
51,26
50,76
47,84
49,94
49
35,3
15
34,24
43,21
43,26
32,47
42,23
41,78
38,46
‐0,067
15
57,04
58,96
58,37
55,07
57,39
56,39
36,2
16
39,94
41,87
41,97
38,25
40,84
40,4
39,14
‐0,068
16
60,76
69,44
68,77
58,48
67,61
66,47
37,5
17
40,47
42,3
42,38
38,71
41,27
40,85
39,36
‐0,066
17
67,57
69,7
68,85
65,21
67,8
66,64
38,5
18
40,47
42,1
42,07
38,83
41,05
40,59
38,8
‐0,064
18
51,48
52,99
52,33
49,75
51,64
50,74
39,1
19
37,08
38,35
38,25
35,61
37,43
36,97
37,64
‐0,067
19
50,83
51,92
51,2
49,19
50,7
49,72
39,4
20
31,25
32,01
31,94
30,05
31,3
30,85
36,28
‐0,067
20
47,69
48,46
48,2
46,17
47,42
46,45
38,8
21
31,2
31,71
31,61
29,83
31,03
30,43
36,15
‐0,07
21
47,34
47,7
47,42
45,78
46,71
45,59
37,6
22
28,07
28,46
28,43
26,82
27,9
27,28
34,94
‐0,077
22
44,87
45,53
45,36
43,45
44,62
43,49
36,3
23
33,57
30,97
30,91
30,42
30,2
29,79
31,89
‐0,081
23
39,44
39,11
38,91
38,3
38,15
37,42
36,1
24
26,28
26,06
25,94
25,35
25,44
25,1
28,76
‐0,084
24
35,54
35,03
35,1
34,6
34,31
33,6
34,9
All
31,31
31,04
31,03
29,38
30,34
29,89
All
42,49
43,11
42,9
41,16
42,14
41,29
31,9
On
34,29
34,36
34,38
31,95
33,59
33,08
28,8
On
47,41
48,75
48,44
45,83
47,62
Exhibit 2
Exhibit 2
27,07
30,9
26,39
22,33
27,58
29,55
30,55
35,54
31,82
36,71
31,13
36,13
26,35
31,02
31,91
34,52
33,1
30,72
35,14
31,69
33,27
30,34
35,02
09.07.10
10.07.10
11.07.10
12.07.10
13.07.10
14.07.10
15.07.10
16.07.10
17.07.10
18.07.10
19.07.10
20.07.10
21.07.10
22.07.10
23.07.10
24.07.10
25.07.10
26.07.10
27.07.10
28.07.10
29.07.10
30.07.10
31.07.10
25
15,59
26,21
30,49
40,02
25,42
32,94
35,29
19,5
24,93
23,74
34
38,11
42,91
31,32
29,86
30,9
29,38
35,86
30,37
24,42
12.07.10
13.07.10
14.07.10
15.07.10
16.07.10
17.07.10
18.07.10
19.07.10
20.07.10
21.07.10
22.07.10
23.07.10
24.07.10
25.07.10
26.07.10
27.07.10
28.07.10
29.07.10
30.07.10
31.07.10
09.07.10
22,35
25,61
08.07.10
11.07.10
20,93
07.07.10
10.07.10
84,4
24,2
59,21
05.07.10
06.07.10
12,19
27,04
08.07.10
04.07.10
26,07
07.07.10
25,71
16,64
06.07.10
03.07.10
16,83
05.07.10
20,9
26,1
04.07.10
28,77
21,13
03.07.10
02.07.10
16,71
02.07.10
01.07.10
12,83
0:00 ‐ 1:00
01.07.10
2010
35,57
26,86
32,33
28,62
30,79
32,29
30,19
38,57
34,14
32,91
29,61
25,24
27,96
30,41
33
29,8
28,36
27,16
16,37
3,38
10,4
18,59
20,51
26,52
26,23
33,77
‐1,34
24,99
42,53
32,05
30,81
32,35
27,04
28,92
29,82
30,65
29,09
30,29
31,72
30,06
29,81
26,83
31,56
28,21
34,82
30,25
31,19
28,74
27,96
24,78
19,91
22,66
30,32
24,95
27,13
25,17
15,61
12,21
23,46
14,22
10,18
10,27
30,65
23,81
24,7
23,71
27,25
30,87
27,28
29,84
31,75
30,42
31,38
22,16
25,11
29,08
29,77
15,84
28,43
28,04
21,16
23,67
32,36
29,47
19,56
29,91
22,72
26,41
27,4
34,99
33,99
34,01
27,45
30,13
25,41
27,61
27,28
29,95
26,95
26,94
28,46
27,83
27,1
25,06
28,24
20,24
30,34
26,42
29,1
21,58
25,94
22,77
20,09
20,99
25,83
23,86
24,93
22,8
10,15
10,02
14,91
10,11
9,24
7,02
29,76
5,06
2,29
12,91
27,82
29,14
27,07
28,42
32,42
29,63
25,99
21,34
29,14
5,58
‐1,41
‐16,27
28,6
28,2
‐22,47
14,39
27,98
32,37
3,85
23,24
25,61
30,56
31,82
27,15
27,29
28,93
21,47
28,22
22,63
26,43
24,08
24,01
18,57
19,93
27,42
26,96
24,55
19,94
27,58
18,67
24,82
25,43
26,22
21,26
22,23
16,58
15,15
14,68
24,19
16,32
17,65
16,07
8,87
8,76
10,14
7,61
5,08
3,05
16,5
28,08
8,43
27,15
15,18
29,42
26,89
26,39
34,25
30,84
27,29
18,14
29,92
‐18,52
‐8,38
‐10,84
27,03
8,07
‐28,07
1,73
27,34
22,74
‐18,32
4,44
22,93
‐14,78
‐6,86
12,43
26,91
28,54
12,49
27,91
21,21
26,49
25,42
26,53
20,7
19,32
24,54
27,07
26,34
21,77
27,02
18,67
23,25
15,51
22,71
21,3
22,59
18,88
17,72
11,2
23,05
19,88
21,67
19,57
10,96
8,29
9,03
6,69
7,72
7,96
24,72
30,39
30,82
27,45
4,02
24,06
18,1
28,63
29,4
31,7
31,43
26,01
30,51
26,32
16,5
20,2
1,72
20,34
‐0,19
‐24,48
26,24
19,51
‐7,68
16,14
23,8
‐21,04
‐19,04
34,93
36,1
‐6,9
‐19,86
28,72
27,09
29,84
30,39
29,44
30,47
13,99
26,25
30,09
29,25
27,58
30,03
28,23
15,61
11,65
26,32
23,47
26,26
23,88
24,57
9,69
23
24,69
27,75
23,33
15,17
0
6,96
6,66
10,28
12,79
5,3
12,03
10,93
19,81
15,03
26,77
24,35
30,02
34,67
31,04
33
23,06
20,85
31,66
‐6,07
20,43
25,14
27,81
‐2,4
7,38
28,22
32,53
17,14
24,74
11,06
‐29,41
12,51
38,94
34,75
4,65
4,75
26,53
29,25
30,2
32,03
30,47
30,55
11,37
17,85
29,38
26,33
29,13
31,01
29,05
0,05
1,04
29,24
25,19
23,25
21,56
14,7
‐9,94
11,63
24,83
26,45
18,03
20,34
‐10,15
0
2,04
19,66
13,34
‐11,23
33,76
29,8
27,71
22,6
36,6
28,56
29,32
37,26
30,08
32,81
18,15
30,44
17,36
‐3,56
27,51
27,87
31,86
22,51
19,52
20,93
24,72
14,11
20,32
21,92
14,71
35,58
50,19
29,14
29,63
29,12
29,02
29,54
31,08
32,02
32,27
32,12
12,57
22,42
32,02
27,35
32,37
34,48
34,26
8,35
12,37
33,37
30,64
26,63
22,78
18,39
‐5,07
19,22
25,96
30,53
24,83
27,12
‐0,01
0
9,91
22,51
10,8
26,37
168,19
33,62
30,75
29,41
30,28
20,04
29,96
35,01
32,87
31,15
26,4
34,76
19,37
16,16
29,23
40,29
33,29
25,76
17,74
‐29,29
17,41
15,14
19,63
26,68
17,87
9,34
‐19,04
23,39
14,93
‐17,71
33,48
32,29
34,87
35,4
34,87
35,67
20,8
30,42
35,67
31,33
34,77
36,67
35,77
26,98
30,26
37,79
35,29
30,68
28,41
26,58
10,75
26,45
33,84
34,9
32,58
31,37
10,18
10,19
20,25
29,41
26,33
40,25
422,99
32,8
30,46
28,41
31,92
30,31
32,15
36,11
28,28
30,9
29,63
39,69
25,71
33,12
34,12
44,71
38,74
28,26
27,17
‐23,79
26,63
21,86
23,03
28,85
15,88
18,86
6,38
8,37
24,25
‐0,03
37,05
36,02
35,81
36,71
36,8
39,3
32,83
35,11
36,85
35,77
35,79
41,01
40,19
34,94
36,99
43,12
37,92
34,85
31,66
30,61
17,24
33,56
34,57
36,32
34,76
34,93
21,27
20,93
27,07
33,03
33,55
39,56
77,24
34
30,78
29,63
34,16
28,18
37,38
38,83
29,53
32,52
32,04
42,79
29,64
38,39
43,98
44,58
42,57
31,01
29,96
10,03
28,25
28,23
28,29
26,5
24,65
27,57
‐20,36
2,81
31,21
27,56
41,64
37,12
37,1
37,44
38,03
42,04
37,51
37,08
39,87
37,51
38,8
44,49
45,31
37,96
42,31
46,14
44,32
39
34,51
33,11
26,36
34,42
37,47
36,87
37,42
37,31
30,06
30,28
32,92
36,56
35,08
222,33
121,76
43,22
35,92
32,15
43,17
31,85
41,67
57,97
30,4
35,46
39,71
47,61
30,92
45,67
138,31
49,75
47,67
32,83
30,87
31,67
41,17
31,24
29,88
27,84
25,55
31,71
‐12
21,25
32,54
31,72
45,3
38,11
39,7
38,33
40,87
46,77
38,11
39,4
45,02
38,68
44,02
47,12
49,2
44,08
46,02
49,38
46,6
40,03
35,94
34,38
27,91
36,91
39,54
40,09
41,07
40,22
32,92
30,4
34,04
36,26
35,64
59,21
338,81
45,49
33,15
34,16
39,11
32,94
49,27
49,4
32,72
34,57
41,15
46,26
34,02
54,21
47,31
407,01
46,21
42,82
31,66
32,59
35,23
31,45
27,05
27,39
26,86
29,06
13,97
20,39
35,44
29,98
49,05
40,8
44,3
41,87
44,36
49,25
41,56
42,33
45,11
43,89
47,46
48,95
50,07
47,55
50,17
69,1
47,88
41,94
36,42
36,37
28,16
35,61
39,58
39,66
40,2
40,72
31,23
32,68
34,21
36,82
36,43
49,75
269,16
44,55
34,69
37,68
43,55
42,08
53,06
44,31
32,08
32,29
40
45,64
40,2
68,09
45,3
734,08
49,2
50,57
30,3
26,88
37,03
34,97
26,11
27,5
26,05
36,75
15,83
25,38
31,65
15,86
51,83
44,55
47,52
43,46
47,17
50,65
44,98
45,98
50,59
46,6
50,41
50,76
55,06
50,95
58,51
79,56
58,05
47,91
38,3
39,37
31,03
35,29
43,14
41,75
42,94
43,23
34,49
35,49
34,46
37,94
37,44
44,73
665,61
48,44
31,89
37,61
45,86
46,24
53,94
56,78
31,43
32,63
43,26
48,17
45,3
128,29
62,47
734,08
40,17
43,82
37,52
26,7
40,25
43,35
30,85
28
26,57
34,99
21,13
26,36
37,88
34,8
55,04
48,46
51,93
46,01
50,73
53,06
48,64
50,42
55,55
51,75
56,65
58,37
82,44
57,89
80,65
97,9
68,89
51,7
41,55
41,75
33,16
39,96
47,54
47,49
48,52
49,1
40,49
39,2
41,74
45
46,13
42,15
98,19
49,17
31,75
39,57
50,52
50,8
46,06
43,22
32,29
37,47
41,97
48,31
46,62
698,38
53,65
343,91
216,23
165,74
42,23
26,75
40,61
45,12
36,07
28,02
27,1
42,51
20,16
28,75
41,14
35,89
55,12
49,06
52,79
48,62
53,02
58,38
51,35
56,94
57,86
55,99
62,54
68,77
94,15
71,78
87,2
105,55
78,1
62,78
47,41
47,37
36,43
44,36
50,9
50,28
52,4
50,14
43,39
42,41
44,84
46,91
54,56
39,28
59,04
164,32
36,7
41,18
44,54
55,55
74,75
48
37,5
36,45
42,38
47,92
46,33
84,28
67,17
165,39
84,79
269,63
37,93
28,12
55,89
58,99
39,05
30
27,45
176,23
25,12
31
41,78
32,25
55,16
48,23
52,06
47,56
52,38
59,23
53,51
56,84
57,6
53,03
60,34
68,85
89,35
83,8
87,37
96,7
78,48
63,8
48,13
47,27
38,37
44,18
49,49
49,61
50,92
49,26
46,22
46,28
45,74
46,54
50,84
36,33
42,24
267,02
33,33
35,71
42,93
38,35
47,64
51,35
43,87
35,49
42,07
47,14
46,63
48,65
45,91
46,59
77,5
48,36
33,65
29,51
43,87
41,83
35,22
29,1
26,42
48,6
22,23
31,34
34,05
30,51
53,57
42,45
49,19
42,64
48,59
50,24
51,57
51,01
50,1
48,67
50,83
52,33
59,52
60,07
77,62
78,79
64,72
49,9
39,7
40,34
39,08
40,84
44,08
44,15
45,21
44,96
43,46
47,12
43,05
41,6
39,5
34,77
49,65
31,38
153,2
31,06
40,96
43,73
49,46
50,27
43,79
36,6
38,25
43,68
42,91
45,78
41,88
188,23
54,17
50,39
30,42
27,27
33,64
37,88
30,86
28,82
26,21
46,1
11,8
30,35
28,43
28,27
49,5
40,23
48,06
42,34
46,8
49,72
48,6
49,06
48,13
46,16
48,69
51,2
51,7
52,66
53,02
65,2
55,85
47,56
37,97
37,14
36,64
38,98
41,52
42
42,31
42,51
41,8
42,86
39,03
36,97
36,7
31,4
37,46
16,53
34,58
29,55
33,86
34,56
46,97
92,78
34,42
30,97
31,94
36,06
39,63
43,41
39,42
46,61
53,49
36,04
27,4
26,58
40,81
31,6
33,13
27,34
23,83
31,6
14,22
28,55
26,22
26,05
46,7
37,33
45,08
40,22
43,92
46,67
46,37
45,32
44,02
42,77
46,77
48,2
49,02
49,73
49,49
51,65
48,39
42,01
35,75
33,95
33,08
34,79
39,16
39,24
35,3
35,49
36,55
39,78
35,7
35,2
36,46
31,81
39,82
211,31
33,49
28,99
35,13
47,1
43,39
47,43
27,91
31,2
31,61
38,56
41,54
44,66
39,93
42,82
50,38
36,36
28,15
28,39
40,11
26,31
28,97
27,46
27,11
374,43
‐19,54
28,23
25,61
26,83
47,9
37,41
47,99
41,78
44,44
48,14
46,46
46,44
44,52
39,83
46,19
47,42
49,54
47,76
48,51
48,07
46,1
42,55
35,73
34,28
36,67
36,97
39,94
39,82
38,68
36,99
39,5
38,83
39,95
36,31
36,15
31,48
34,48
42,86
32,36
27,96
33,2
40,04
38,76
50,01
30,42
29,12
28,43
34,07
38,27
38,86
35,74
40,16
50,7
37,54
29,06
27,62
38,62
29,82
31,88
25,6
27,55
43,31
16,07
30,12
27,57
31,87
47,88
36,18
39,61
37,99
38,13
42,21
41,94
40,31
39,74
35,78
43,93
45,36
46,69
44,94
46,52
46,91
43,89
38,19
35,31
31,91
33,14
36,52
37,88
37,96
37,6
42,11
34,48
34,58
37,6
39,27
35,99
31,2
202,83
37,1
33,86
31,91
35,32
33,22
34,78
41,4
34,05
24,73
30,91
29,86
32,79
31,63
30,7
48,15
47,58
32,31
33,35
32
32,99
30,93
28,1
27,55
22,15
21,83
20,83
48,23
26,73
29,43
36,25
35,49
37,53
33,92
35,77
35,96
34,64
35,85
36,23
34,96
35,58
38,91
37,77
38,22
40,15
41,4
38,13
35,02
34,84
33,8
29,63
37,69
35,74
35,74
36,59
30,58
30,2
32,7
32,73
35,18
33,22
19,2
39,11
5,44
33,73
26,45
31,66
31,62
32,3
39,7
30,87
21,17
25,94
26,3
32,9
28,19
28,98
38,58
37,66
37,88
30,19
31,51
31,76
29,46
18,27
29,24
24,18
‐5,41
201,53
19,47
64,81
28,55
34,93
32,58
35,16
31,21
33,86
33,7
32,03
36,09
34,72
32,65
34,23
35,1
34,82
35,06
35,01
37,66
34,3
33,95
32,9
29,22
25,97
33,61
33,84
34,49
34,8
25,4
26,02
27,08
26,05
28,07
26,11
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 ‐ 11:00 11:00 ‐ 12:00 12:00 ‐ 13:00 13:00 ‐ 14:00 14:00 ‐ 15:00 15:00 ‐ 16:0 16:00 ‐ 17:00 17:00 ‐ 18:00 18:00 ‐ 19:00 19:00 ‐ 20:00 20:00‐ 21:00 21:00 ‐ 22:00 22:00 ‐ 23:00 23:00 ‐ 0:00
Exhibit 3
Exhibit 3
0:00 ‐ 3:00
30,12
36,13
45,46
64,47
39,06
42,4
74,04
79,1
75,88
87,05
70,04
62,33
75,13
83,45
80,87
95,83
88,49
101,87
79,58
95,93
78,24
87,93
89,8
94,7
90,33
86,76
95,74
88,79
89,8
82,79
97,5
79,16
94,83
102,23
72,17
110,46
84,38
108,16
77,36
65,68
73,06
65,11
42,64
63,74
85,69
96,81
71,06
95,71
94,78
72,57
72,33
84,73
97,33
104
111,32
88,79
93,02
88,94
81,71
92,89
81,04
90,64
2010
01.07.10
02.07.10
03.07.10
04.07.10
05.07.10
06.07.10
07.07.10
08.07.10
09.07.10
10.07.10
11.07.10
12.07.10
13.07.10
14.07.10
15.07.10
16.07.10
17.07.10
18.07.10
19.07.10
20.07.10
21.07.10
22.07.10
23.07.10
24.07.10
25.07.10
26.07.10
27.07.10
28.07.10
29.07.10
30.07.10
31.07.10
01.07.10
02.07.10
03.07.10
04.07.10
05.07.10
06.07.10
07.07.10
08.07.10
09.07.10
10.07.10
11.07.10
12.07.10
13.07.10
14.07.10
15.07.10
16.07.10
17.07.10
18.07.10
19.07.10
20.07.10
21.07.10
22.07.10
23.07.10
24.07.10
25.07.10
26.07.10
27.07.10
28.07.10
29.07.10
30.07.10
31.07.10
79,73
94,99
103,81
87,13
57,88
90,74
74,56
79,67
43,92
80,43
70,74
41,44
15,06
83,4
85,39
29,37
61,36
65,07
82,21
68,74
86,98
92,96
98,31
96,83
84,54
92,3
85,86
65,24
59,32
55,73
95,98
20,34
24,5
31,94
48,51
30,99
34,63
64,04
69,71
65,13
80,34
58,33
55,15
64,13
76,13
71,58
86,51
82,1
89,98
67,12
87,38
71,83
81,46
84,85
87,6
77,16
74,61
84,61
81,18
82,96
75,08
90,7
1:00 ‐ 3:00
61,41
91,48
88,19
74,57
52,36
42,19
71,26
57,59
5,09
84,58
87,68
39,79
‐29,38
64,31
84,06
‐11,27
19,98
16,14
84,17
61,64
84,66
90,89
98,42
84,65
81,24
89,43
70,25
63,77
35,42
56,95
76,91
18,03
22,04
24,41
34,08
27,07
29,98
58,44
64,25
60,06
73,07
46,87
52,96
58,23
70,76
64,14
78,03
67,36
78,41
57,58
82,84
66,77
77,99
81,86
80,42
66,19
66,22
80,49
76,78
80,53
69,25
86,26
2:00 ‐ 5:00
14,1
50,57
90,3
74,51
5,92
‐5,26
72,34
43,82
‐22,15
74,62
81,56
‐8,36
‐50,73
56,61
57,35
‐6,91
6,71
13,38
89,57
65,49
84,71
92,17
96,07
83,44
72,06
82,62
47,02
67,51
41,54
63,53
70,98
23,8
23,08
20,96
26,13
17,05
35
58,97
67,07
60,89
70,24
35,57
57,44
59,34
71,08
66,03
75,25
52,59
63,68
65,57
84,63
69,29
80,14
84,12
78,21
53,24
69,74
79,98
79,89
82,76
70,93
84,85
3:00 ‐ 6:00
‐2,62
26,29
97,76
86,3
‐13,39
‐65,23
57,79
45,32
‐8,86
74,78
81,8
‐15,37
‐30,66
56,22
53,89
29,79
2,05
39,46
81,28
67,21
91,72
93,58
98,32
85,04
69,34
80,25
34,23
74,41
50,18
70,5
46,52
34,09
37,66
15,39
15,99
‐1,86
46,47
60,93
75,87
69,4
57,68
10,95
56,99
64,32
72,1
69,96
78,27
28,2
38,91
75,95
88,06
78,48
81,92
86,54
68,64
44,68
81,72
86,44
87,84
86,53
77,55
83,16
4:00 ‐ 7:00
14,01
27,38
99,99
124,06
29,05
‐35,74
56,78
61,2
23,57
76,76
75,39
2,42
19,92
80,01
54,73
68,14
6,87
75,34
81,8
67,22
97,24
92,82
101,33
87,97
71,01
87,43
41,65
74,97
71,55
76,18
18,79
36,93
52,45
18,61
6,96
‐10,16
62,63
66,19
84,73
75,48
53,85
‐5,32
57,66
68,22
76,14
79,3
88,93
25,06
24,01
91,54
95,52
89,08
82,93
91,49
66,52
37,93
93,14
92,18
94,44
91,12
85,88
84,27
5:00 ‐ 8:00
16,16
49,21
87,28
70,09
57,43
3,17
59,66
64,69
46,39
74,66
19,86
44,64
45,87
92,96
93,3
77,17
6,53
68,39
86,05
67,61
96,96
93,99
106,94
89,3
72,95
93,65
67,04
78,27
74,35
213,98
20,44
50,47
71,58
32,2
10,19
0,02
78,83
75,44
91,88
84,63
57,3
‐4,26
59,67
72,75
80,56
91,12
100,4
43,67
35,38
99,08
102,16
96,27
85,01
97,07
70,69
44,74
98,34
97,61
99,45
96,15
91,08
89,03
6:00 ‐ 9:00
11,38
68,81
60,9
37,53
63,78
48,46
77,45
62,98
51,11
68,76
‐32,15
64,43
76,53
103,89
112,87
90,86
45,72
62,44
104,89
74,18
94,86
91,23
108,38
91,43
78,91
98,8
80,42
88,92
96,22
624,94
55,39
70,68
84,95
57,23
31,12
31,44
93,42
92,17
101,75
94,37
79,23
22,92
75,58
82,85
92,16
103,85
114,28
79,62
70,27
110,22
112,16
102,93
94,45
104,54
87,95
66,2
107,09
103,94
104,13
101,76
97,85
99,55
7:00 ‐ 10:00
9,82
70,39
34,57
‐33,02
55,77
58,4
82,03
70,95
65,23
72,29
‐43,05
74,87
85,03
114,6
129,58
107,33
87,67
74,72
117,24
88,07
94,57
90,68
109,95
99,49
78,53
96,36
87,45
91,99
100,42
668,42
106,18
94,96
99
80,24
61,4
61,51
103,61
104,76
108,09
105,88
94,43
54,35
90,3
94,58
104,53
117,53
127,05
109,56
99,88
121,27
122,17
109,36
104,61
112,39
102,61
91,14
117,01
109,7
109,55
107,78
105,43
112,17
8:00 ‐ 11:00
59,25
88
32,43
‐25,98
78,14
66,08
83,19
81,2
81,33
96,05
17,91
88
92,1
128,98
139,04
216,41
117,18
86,27
130,09
101,38
98,88
88,21
132,91
111,2
90,34
109,25
90,19
97,16
110,02
621,99
302,14
104,27
105,85
94,03
81,61
84,25
112,46
113,25
113,28
111,58
104,89
71,51
98,1
102,11
113,88
128,84
138,64
125,32
116,98
134,7
132,62
118,61
111,96
121,74
111,59
108,45
128,11
115,7
112,48
112,61
111,25
123,99
9:00 ‐ 12:00
89,26
99,19
44,45
‐18,39
88,34
77,06
81,73
85,22
90,92
104,65
74,29
92,49
106,66
136,45
501,34
229,6
138,27
94,58
136,66
112,9
102,55
92,65
146,2
128,32
92,97
116,44
95,94
99,85
122,71
537,81
321,1
107,15
109,64
101,17
93,36
94,21
118,25
118,69
116,62
116,59
106,94
82,43
103,86
106,87
120,97
138,8
164,62
138,5
129,59
144,58
140,56
130,28
120,08
130
118,81
117,18
138,06
123,26
117,64
121,1
116,03
135,99
10:00 ‐ 13:00
77,56
99,63
67,02
17,8
97,52
78,46
82,73
83,04
97,66
113,43
91,14
92,83
126,22
143,08
1190,84
230,92
167,97
105,14
139,51
120,86
102,32
95,2
151,68
144
106,87
125,83
103,99
103,76
133,26
729,73
331,29
109,51
111,02
102,71
98,57
98,64
124,17
124,21
121,5
122,26
107,81
87,1
110,12
110,66
129,88
152,53
198,04
154,7
142,58
154,33
146,83
141,89
129,17
140,72
127,71
124,65
146,67
132,4
123,66
131,52
123,46
146,18
11:00 ‐ 14:00
80,64
104,97
72,13
50,93
100,8
79,48
82,89
84,01
109,77
112,51
86,17
99,48
137,21
135,58
1141,09
155,08
250,59
119,52
140,07
124,41
99,49
96,23
150,49
156,27
121,26
128,52
109,45
99,73
138,48
1273,58
153,69
120
119,76
110,41
107,37
106,21
133,05
131,66
128,9
130,26
110,86
92,35
117,49
116,27
141,55
174,82
246,56
189,33
156,39
187,57
158,08
154,52
142,24
151,25
138,73
135,18
152,96
142,26
131,34
143,75
133,81
155,92
12:00 ‐ 15:00
86,55
110,67
80,49
57,12
114,25
79,72
83,52
93,03
123,44
117,89
80,33
110,05
260,13
305,6
1077,99
161,42
894,76
132,12
142,12
125,23
102,39
95,8
144,31
153,06
139,12
139,93
114,86
98,33
142,16
1032,96
136,63
138,13
129,85
121,04
117,1
118,37
142,47
143,86
139,52
141,58
119,61
100,62
128,49
127,26
162,39
205,04
283,01
226,36
180,62
231,65
177,9
169,6
154,34
164
153,34
144,97
162,09
150,92
138,09
152,24
142,07
161,99
13:00 ‐ 16:00
102,94
120,8
86,11
66,41
253,73
81,12
86,02
105,97
147,46
136,75
81,57
117,68
479,19
341,19
509,3
183,29
910,95
138,25
144,4
127,61
106,55
101,22
148
174,75
152,59
140,92
118,36
100,34
261,93
822,84
126,16
151,53
138,45
132,32
127,89
130,1
148,5
151,84
147,38
147,93
128,5
107,96
136,39
137,09
178,28
225,47
300,15
255,22
213,47
265,94
195,99
179,53
160,77
171,01
164,2
153,5
170,67
156,13
142,19
156,78
145,75
165,32
14:00 ‐ 17:00
98,65
116,97
91,09
67,51
267,34
80,97
87,12
110,34
145,94
140,37
84,38
113,81
483,73
378,52
555,89
166,73
831,31
139,58
143,37
126,42
109,41
113,66
142,57
168,45
144,7
137,99
116,46
101,78
480,51
199,47
117,76
144,9
135,05
133,63
135,81
133,07
144,36
148,53
144,04
144,47
129,38
113,88
134,98
135,24
176,48
221,3
281,04
252,19
215,65
243,02
189,95
173,71
157,69
165,56
164,79
156,43
167,85
153,99
138,82
154,04
139,74
163,85
15:00 ‐ 18:00
91,03
104,26
92,69
59,15
270,93
80,08
87,92
105,13
138,7
133,4
84,9
102
368,38
216,46
400,21
154,96
178,71
135,87
138,74
122,7
108,54
125,16
149,62
171,85
137,63
128,43
107,95
223,23
462,72
150,93
110,38
127,04
125,11
127,82
136,26
131,48
136,73
138,44
135,76
135,09
124
114,09
124,75
125,8
161,26
199,05
240,69
218,01
196,53
200,57
172,38
159,86
147,86
155,83
156,91
153,68
159,19
147,77
132,54
149,31
130,91
158,23
16:00 ‐ 19:00
84,83
88,7
90,24
48,25
126,3
76,46
85,26
99,21
111,31
118,32
83,36
91,47
134,79
185,16
281,43
127,21
137,84
129,17
126,88
112,26
103,06
122,08
194,4
144,07
116,64
117,75
96,32
221,11
314,93
129,35
102,5
112,66
113,77
117,78
129,76
121,81
122,96
122,82
125,39
124,76
114,61
108,8
111,43
113,42
139,47
168,96
195,64
180,13
162,46
160,24
151,73
146,29
137,6
142,25
145,39
146,54
146,63
139,31
125,2
142,33
120,01
149,77
17:00 ‐ 20:00
81,15
80,26
87,13
6,48
452,13
77,15
83,62
92,96
95,79
114,56
82,24
85,97
122,79
158,04
277,66
121,23
133,85
124,08
118,3
101,8
98,77
106,12
190,48
139,82
125,39
109,95
89,6
221,27
259,22
126,93
97,98
109,31
108,48
114,68
121,47
117,85
114,99
116,29
121,06
120,62
110,74
106,39
105,37
109,45
132,12
150,34
164,92
151,02
150,15
150,26
146,82
141,65
128,76
136,67
140,82
141,43
144,53
135,16
124,34
141,13
114,97
144,1
18:00 ‐ 21:00
84,75
79,4
86,9
10,75
449,34
78,49
80,4
93,98
87,73
119,54
82,59
84,61
109,94
154,57
129,59
115,09
126,93
119,44
108,69
91,98
91,29
92,75
190,22
129,12
121,7
102,19
86,5
100,43
270,7
111,76
94,69
108,6
110,78
113,25
113,19
110,53
114,59
111,58
117,02
116,98
108,28
102,89
100,14
106,79
122,75
138,38
146,63
144,52
142,43
145,25
140,98
136,89
118,38
128,28
132,07
134,77
137,02
126,49
119,99
132,68
110,92
142,48
19:00 ‐ 22:00
88,13
79,91
106,58
17,36
439,57
76,81
80,61
88,95
87,06
111,72
88,01
90,56
106,21
148,66
131,13
106,37
115,15
112,6
102,49
90,95
85,05
92,38
138,84
116,93
120,36
103,65
88,86
99,71
291,27
277,13
94,49
105,36
110,76
110,28
106,11
104,18
109,68
112,87
113,52
113,56
111,18
99,44
99,99
105,88
115,76
128,12
136,38
135,18
130,92
134
131,69
125,7
110,57
120,49
122,6
123,04
126,31
118,34
113,69
125,13
109,08
132,03
20:00‐ 23:00
89,85
119,11
97,82
238,43
59,73
73,88
82,39
78,25
90,21
103,37
91,13
92,6
107,73
135,94
126,89
95,42
98,68
103,96
90,23
85,28
75,02
95,34
131,11
105,84
104,88
100,18
86,32
99,95
85,4
276,42
81,88
95,32
102,52
96,38
94,36
90,7
98,09
108,99
108,19
107,46
107,82
88,74
94,93
103,05
107,16
116,32
125,97
121,68
118,22
119,28
119,37
113,74
103,39
110,69
112,25
108,61
111,87
107,76
103,12
112,3
104,25
119,06
21:00 ‐ 24:00
Exhibit 4
Exhibit 4
0:00 ‐ 3:00
30,12
36,13
45,46
64,47
39,06
42,4
74,04
79,1
75,88
87,05
70,04
62,33
75,13
83,45
80,87
95,83
88,49
101,87
79,58
95,93
78,24
87,93
89,8
94,7
90,33
86,76
95,74
88,79
89,8
82,79
97,5
79,16
94,83
102,23
72,17
110,46
84,38
108,16
77,36
65,68
73,06
65,11
42,64
63,74
85,69
96,81
71,06
95,71
94,78
72,57
72,33
84,73
97,33
104
111,32
88,79
93,02
88,94
81,71
92,89
81,04
90,64
2010
01.07.10
02.07.10
03.07.10
04.07.10
05.07.10
06.07.10
07.07.10
08.07.10
09.07.10
10.07.10
11.07.10
12.07.10
13.07.10
14.07.10
15.07.10
16.07.10
17.07.10
18.07.10
19.07.10
20.07.10
21.07.10
22.07.10
23.07.10
24.07.10
25.07.10
26.07.10
27.07.10
28.07.10
29.07.10
30.07.10
31.07.10
01.07.10
02.07.10
03.07.10
04.07.10
05.07.10
06.07.10
07.07.10
08.07.10
09.07.10
10.07.10
11.07.10
12.07.10
13.07.10
14.07.10
15.07.10
16.07.10
17.07.10
18.07.10
19.07.10
20.07.10
21.07.10
22.07.10
23.07.10
24.07.10
25.07.10
26.07.10
27.07.10
28.07.10
29.07.10
30.07.10
31.07.10
79,73
94,99
103,81
87,13
57,88
90,74
74,56
79,67
43,92
80,43
70,74
41,44
15,06
83,4
85,39
29,37
61,36
65,07
82,21
68,74
86,98
92,96
98,31
96,83
84,54
92,3
85,86
65,24
59,32
55,73
95,98
20,34
24,5
31,94
48,51
30,99
34,63
64,04
69,71
65,13
80,34
58,33
55,15
64,13
76,13
71,58
86,51
82,1
89,98
67,12
87,38
71,83
81,46
84,85
87,6
77,16
74,61
84,61
81,18
82,96
75,08
90,7
1:00 ‐ 3:00
61,41
91,48
88,19
74,57
52,36
42,19
71,26
57,59
5,09
84,58
87,68
39,79
‐29,38
64,31
84,06
‐11,27
19,98
16,14
84,17
61,64
84,66
90,89
98,42
84,65
81,24
89,43
70,25
63,77
35,42
56,95
76,91
18,03
22,04
24,41
34,08
27,07
29,98
58,44
64,25
60,06
73,07
46,87
52,96
58,23
70,76
64,14
78,03
67,36
78,41
57,58
82,84
66,77
77,99
81,86
80,42
66,19
66,22
80,49
76,78
80,53
69,25
86,26
2:00 ‐ 5:00
14,1
50,57
90,3
74,51
5,92
‐5,26
72,34
43,82
‐22,15
74,62
81,56
‐8,36
‐50,73
56,61
57,35
‐6,91
6,71
13,38
89,57
65,49
84,71
92,17
96,07
83,44
72,06
82,62
47,02
67,51
41,54
63,53
70,98
23,8
23,08
20,96
26,13
17,05
35
58,97
67,07
60,89
70,24
35,57
57,44
59,34
71,08
66,03
75,25
52,59
63,68
65,57
84,63
69,29
80,14
84,12
78,21
53,24
69,74
79,98
79,89
82,76
70,93
84,85
3:00 ‐ 6:00
‐2,62
26,29
97,76
86,3
‐13,39
‐65,23
57,79
45,32
‐8,86
74,78
81,8
‐15,37
‐30,66
56,22
53,89
29,79
2,05
39,46
81,28
67,21
91,72
93,58
98,32
85,04
69,34
80,25
34,23
74,41
50,18
70,5
46,52
34,09
37,66
15,39
15,99
‐1,86
46,47
60,93
75,87
69,4
57,68
10,95
56,99
64,32
72,1
69,96
78,27
28,2
38,91
75,95
88,06
78,48
81,92
86,54
68,64
44,68
81,72
86,44
87,84
86,53
77,55
83,16
4:00 ‐ 7:00
14,01
27,38
99,99
124,06
29,05
‐35,74
56,78
61,2
23,57
76,76
75,39
2,42
19,92
80,01
54,73
68,14
6,87
75,34
81,8
67,22
97,24
92,82
101,33
87,97
71,01
87,43
41,65
74,97
71,55
76,18
18,79
36,93
52,45
18,61
6,96
‐10,16
62,63
66,19
84,73
75,48
53,85
‐5,32
57,66
68,22
76,14
79,3
88,93
25,06
24,01
91,54
95,52
89,08
82,93
91,49
66,52
37,93
93,14
92,18
94,44
91,12
85,88
84,27
5:00 ‐ 8:00
16,16
49,21
87,28
70,09
57,43
3,17
59,66
64,69
46,39
74,66
19,86
44,64
45,87
92,96
93,3
77,17
6,53
68,39
86,05
67,61
96,96
93,99
106,94
89,3
72,95
93,65
67,04
78,27
74,35
213,98
20,44
50,47
71,58
32,2
10,19
0,02
78,83
75,44
91,88
84,63
57,3
‐4,26
59,67
72,75
80,56
91,12
100,4
43,67
35,38
99,08
102,16
96,27
85,01
97,07
70,69
44,74
98,34
97,61
99,45
96,15
91,08
89,03
6:00 ‐ 9:00
11,38
68,81
60,9
37,53
63,78
48,46
77,45
62,98
51,11
68,76
‐32,15
64,43
76,53
103,89
112,87
90,86
45,72
62,44
104,89
74,18
94,86
91,23
108,38
91,43
78,91
98,8
80,42
88,92
96,22
624,94
55,39
70,68
84,95
57,23
31,12
31,44
93,42
92,17
101,75
94,37
79,23
22,92
75,58
82,85
92,16
103,85
114,28
79,62
70,27
110,22
112,16
102,93
94,45
104,54
87,95
66,2
107,09
103,94
104,13
101,76
97,85
99,55
7:00 ‐ 10:00
9,82
70,39
34,57
‐33,02
55,77
58,4
82,03
70,95
65,23
72,29
‐43,05
74,87
85,03
114,6
129,58
107,33
87,67
74,72
117,24
88,07
94,57
90,68
109,95
99,49
78,53
96,36
87,45
91,99
100,42
668,42
106,18
94,96
99
80,24
61,4
61,51
103,61
104,76
108,09
105,88
94,43
54,35
90,3
94,58
104,53
117,53
127,05
109,56
99,88
121,27
122,17
109,36
104,61
112,39
102,61
91,14
117,01
109,7
109,55
107,78
105,43
112,17
8:00 ‐ 11:00
59,25
88
32,43
‐25,98
78,14
66,08
83,19
81,2
81,33
96,05
17,91
88
92,1
128,98
139,04
216,41
117,18
86,27
130,09
101,38
98,88
88,21
132,91
111,2
90,34
109,25
90,19
97,16
110,02
621,99
302,14
104,27
105,85
94,03
81,61
84,25
112,46
113,25
113,28
111,58
104,89
71,51
98,1
102,11
113,88
128,84
138,64
125,32
116,98
134,7
132,62
118,61
111,96
121,74
111,59
108,45
128,11
115,7
112,48
112,61
111,25
123,99
9:00 ‐ 12:00
89,26
99,19
44,45
‐18,39
88,34
77,06
81,73
85,22
90,92
104,65
74,29
92,49
106,66
136,45
501,34
229,6
138,27
94,58
136,66
112,9
102,55
92,65
146,2
128,32
92,97
116,44
95,94
99,85
122,71
537,81
321,1
107,15
109,64
101,17
93,36
94,21
118,25
118,69
116,62
116,59
106,94
82,43
103,86
106,87
120,97
138,8
164,62
138,5
129,59
144,58
140,56
130,28
120,08
130
118,81
117,18
138,06
123,26
117,64
121,1
116,03
135,99
10:00 ‐ 13:00
77,56
99,63
67,02
17,8
97,52
78,46
82,73
83,04
97,66
113,43
91,14
92,83
126,22
143,08
1190,84
230,92
167,97
105,14
139,51
120,86
102,32
95,2
151,68
144
106,87
125,83
103,99
103,76
133,26
729,73
331,29
109,51
111,02
102,71
98,57
98,64
124,17
124,21
121,5
122,26
107,81
87,1
110,12
110,66
129,88
152,53
198,04
154,7
142,58
154,33
146,83
141,89
129,17
140,72
127,71
124,65
146,67
132,4
123,66
131,52
123,46
146,18
11:00 ‐ 14:00
80,64
104,97
72,13
50,93
100,8
79,48
82,89
84,01
109,77
112,51
86,17
99,48
137,21
135,58
1141,09
155,08
250,59
119,52
140,07
124,41
99,49
96,23
150,49
156,27
121,26
128,52
109,45
99,73
138,48
1273,58
153,69
120
119,76
110,41
107,37
106,21
133,05
131,66
128,9
130,26
110,86
92,35
117,49
116,27
141,55
174,82
246,56
189,33
156,39
187,57
158,08
154,52
142,24
151,25
138,73
135,18
152,96
142,26
131,34
143,75
133,81
155,92
12:00 ‐ 15:00
86,55
110,67
80,49
57,12
114,25
79,72
83,52
93,03
123,44
117,89
80,33
110,05
260,13
305,6
1077,99
161,42
894,76
132,12
142,12
125,23
102,39
95,8
144,31
153,06
139,12
139,93
114,86
98,33
142,16
1032,96
136,63
138,13
129,85
121,04
117,1
118,37
142,47
143,86
139,52
141,58
119,61
100,62
128,49
127,26
162,39
205,04
283,01
226,36
180,62
231,65
177,9
169,6
154,34
164
153,34
144,97
162,09
150,92
138,09
152,24
142,07
161,99
13:00 ‐ 16:00
102,94
120,8
86,11
66,41
253,73
81,12
86,02
105,97
147,46
136,75
81,57
117,68
479,19
341,19
509,3
183,29
910,95
138,25
144,4
127,61
106,55
101,22
148
174,75
152,59
140,92
118,36
100,34
261,93
822,84
126,16
151,53
138,45
132,32
127,89
130,1
148,5
151,84
147,38
147,93
128,5
107,96
136,39
137,09
178,28
225,47
300,15
255,22
213,47
265,94
195,99
179,53
160,77
171,01
164,2
153,5
170,67
156,13
142,19
156,78
145,75
165,32
14:00 ‐ 17:00
98,65
116,97
91,09
67,51
267,34
80,97
87,12
110,34
145,94
140,37
84,38
113,81
483,73
378,52
555,89
166,73
831,31
139,58
143,37
126,42
109,41
113,66
142,57
168,45
144,7
137,99
116,46
101,78
480,51
199,47
117,76
144,9
135,05
133,63
135,81
133,07
144,36
148,53
144,04
144,47
129,38
113,88
134,98
135,24
176,48
221,3
281,04
252,19
215,65
243,02
189,95
173,71
157,69
165,56
164,79
156,43
167,85
153,99
138,82
154,04
139,74
163,85
15:00 ‐ 18:00
91,03
104,26
92,69
59,15
270,93
80,08
87,92
105,13
138,7
133,4
84,9
102
368,38
216,46
400,21
154,96
178,71
135,87
138,74
122,7
108,54
125,16
149,62
171,85
137,63
128,43
107,95
223,23
462,72
150,93
110,38
127,04
125,11
127,82
136,26
131,48
136,73
138,44
135,76
135,09
124
114,09
124,75
125,8
161,26
199,05
240,69
218,01
196,53
200,57
172,38
159,86
147,86
155,83
156,91
153,68
159,19
147,77
132,54
149,31
130,91
158,23
16:00 ‐ 19:00
84,83
88,7
90,24
48,25
126,3
76,46
85,26
99,21
111,31
118,32
83,36
91,47
134,79
185,16
281,43
127,21
137,84
129,17
126,88
112,26
103,06
122,08
194,4
144,07
116,64
117,75
96,32
221,11
314,93
129,35
102,5
112,66
113,77
117,78
129,76
121,81
122,96
122,82
125,39
124,76
114,61
108,8
111,43
113,42
139,47
168,96
195,64
180,13
162,46
160,24
151,73
146,29
137,6
142,25
145,39
146,54
146,63
139,31
125,2
142,33
120,01
149,77
17:00 ‐ 20:00
81,15
80,26
87,13
6,48
452,13
77,15
83,62
92,96
95,79
114,56
82,24
85,97
122,79
158,04
277,66
121,23
133,85
124,08
118,3
101,8
98,77
106,12
190,48
139,82
125,39
109,95
89,6
221,27
259,22
126,93
97,98
109,31
108,48
114,68
121,47
117,85
114,99
116,29
121,06
120,62
110,74
106,39
105,37
109,45
132,12
150,34
164,92
151,02
150,15
150,26
146,82
141,65
128,76
136,67
140,82
141,43
144,53
135,16
124,34
141,13
114,97
144,1
18:00 ‐ 21:00
84,75
79,4
86,9
10,75
449,34
78,49
80,4
93,98
87,73
119,54
82,59
84,61
109,94
154,57
129,59
115,09
126,93
119,44
108,69
91,98
91,29
92,75
190,22
129,12
121,7
102,19
86,5
100,43
270,7
111,76
94,69
108,6
110,78
113,25
113,19
110,53
114,59
111,58
117,02
116,98
108,28
102,89
100,14
106,79
122,75
138,38
146,63
144,52
142,43
145,25
140,98
136,89
118,38
128,28
132,07
134,77
137,02
126,49
119,99
132,68
110,92
142,48
19:00 ‐ 22:00
88,13
79,91
106,58
17,36
439,57
76,81
80,61
88,95
87,06
111,72
88,01
90,56
106,21
148,66
131,13
106,37
115,15
112,6
102,49
90,95
85,05
92,38
138,84
116,93
120,36
103,65
88,86
99,71
291,27
277,13
94,49
105,36
110,76
110,28
106,11
104,18
109,68
112,87
113,52
113,56
111,18
99,44
99,99
105,88
115,76
128,12
136,38
135,18
130,92
134
131,69
125,7
110,57
120,49
122,6
123,04
126,31
118,34
113,69
125,13
109,08
132,03
20:00‐ 23:00
89,85
119,11
97,82
238,43
59,73
73,88
82,39
78,25
90,21
103,37
91,13
92,6
107,73
135,94
126,89
95,42
98,68
103,96
90,23
85,28
75,02
95,34
131,11
105,84
104,88
100,18
86,32
99,95
85,4
276,42
81,88
95,32
102,52
96,38
94,36
90,7
98,09
108,99
108,19
107,46
107,82
88,74
94,93
103,05
107,16
116,32
125,97
121,68
118,22
119,28
119,37
113,74
103,39
110,69
112,25
108,61
111,87
107,76
103,12
112,3
104,25
119,06
21:00 ‐ 24:00
MIN
‐2,62
26,29
32,43
‐33,02
‐13,39
‐65,23
56,78
43,82
‐22,15
68,76
‐43,05
‐15,37
‐50,73
56,22
53,89
‐11,27
2,05
13,38
72,57
61,64
75,02
88,21
96,07
83,44
69,34
80,25
34,23
63,77
35,42
55,73
18,79
18,03
22,04
15,39
6,96
‐10,16
29,98
58,44
64,25
60,06
53,85
‐5,32
52,96
58,23
70,76
64,14
75,25
25,06
24,01
57,58
82,84
66,77
77,99
81,86
66,52
37,93
66,22
79,98
76,78
80,53
69,25
83,16
MAX
102,94
120,8
106,58
238,43
452,13
90,74
108,16
110,34
147,46
140,37
91,14
117,68
483,73
378,52
1190,84
230,92
910,95
139,58
144,4
127,61
109,41
125,16
194,4
174,75
152,59
140,92
118,36
223,23
480,51
1273,58
331,29
151,53
138,45
133,63
136,26
133,07
148,5
151,84
147,38
147,93
129,38
114,09
136,39
137,09
178,28
225,47
300,15
255,22
215,65
265,94
195,99
179,53
160,77
171,01
164,79
156,43
170,67
156,13
142,19
156,78
145,75
165,32
COST
$ ‐65,50 $ 657,25
$ 810,75
$ ‐825,50 $ ‐334,75 $ ‐1.630,75 $ 1.419,50
$ 1.095,50
$ ‐553,75 $ 1.719,00
$ ‐1.076,25 $ ‐384,25 $ ‐1.268,25 $ 1.405,50
$ 1.347,25
$ ‐281,75 $ 51,25
$ 334,50
$ 1.814,25
$ 1.541,00
$ 1.875,50
$ 2.205,25
$ 2.401,75
$ 2.086,00
$ 1.733,50
$ 2.006,25
$ 855,75
$ 1.594,25
$ 885,50
$ 1.393,25
$ 469,75
$ 450,75
$ 551,00
$ 384,75
$ 174,00
$ ‐254,00 $ 749,50
$ 1.461,00
$ 1.606,25
$ 1.501,50
$ 1.346,25
$ ‐133,00 $ 1.324,00
$ 1.455,75
$ 1.769,00
$ 1.603,50
$ 1.881,25
$ 626,50
$ 600,25
$ 1.439,50
$ 2.071,00
$ 1.669,25
$ 1.949,75
$ 2.046,50
$ 1.663,00
$ 948,25
$ 1.655,50
$ 1.999,50
$ 1.919,50
$ 2.013,25
$ 1.731,25
$ 2.079,00
REV
$ 2.573,50
$ 3.020,00
$ 2.664,50
$ 5.960,75
$ 11.303,25
$ 2.268,50
$ 2.704,00
$ 2.758,50
$ 3.686,50
$ 3.509,25
$ 2.278,50
$ 2.942,00
$ 12.093,25
$ 9.463,00
$ 29.771,00
$ 5.773,00
$ 22.773,75
$ 3.489,50
$ 3.610,00
$ 3.190,25
$ 2.735,25
$ 3.129,00
$ 4.860,00
$ 4.368,75
$ 3.814,75
$ 3.523,00
$ 2.959,00
$ 5.580,75
$ 12.012,75
$ 31.839,50
$ 8.282,25
$ 3.788,25
$ 3.461,25
$ 3.340,75
$ 3.406,50
$ 3.326,75
$ 3.712,50
$ 3.796,00
$ 3.684,50
$ 3.698,25
$ 3.234,50
$ 2.852,25
$ 3.409,75
$ 3.427,25
$ 4.457,00
$ 5.636,75
$ 7.503,75
$ 6.380,50
$ 5.391,25
$ 6.648,50
$ 4.899,75
$ 4.488,25
$ 4.019,25
$ 4.275,25
$ 4.119,75
$ 3.910,75
$ 4.266,75
$ 3.903,25
$ 3.554,75
$ 3.919,50
$ 3.643,75
$ 4.133,00
GM
GM month
$ 2.639,00
$ 2.362,75
$ 1.853,75
$ 6.786,25
$ 11.638,00
$ 3.899,25
$ 1.284,50
$ 1.663,00
$ 4.240,25
$ 1.790,25
$ 3.354,75
$ 3.326,25
$ 13.361,50
$ 8.057,50
$ 28.423,75
$ 6.054,75
$ 22.722,50
$ 3.155,00
$ 1.795,75
$ 1.649,25
$ 859,75
$ 923,75
$ 2.458,25
$ 2.282,75
$ 2.081,25
$ 1.516,75
$ 2.103,25
$ 3.986,50
$ 11.127,25
$ 30.446,25
$ 7.812,50 $ 195.656,25
$ 3.337,50
$ 2.910,25
$ 2.956,00
$ 3.232,50
$ 3.580,75
$ 2.963,00
$ 2.335,00
$ 2.078,25
$ 2.196,75
$ 1.888,25
$ 2.985,25
$ 2.085,75
$ 1.971,50
$ 2.688,00
$ 4.033,25
$ 5.622,50
$ 5.754,00
$ 4.791,00
$ 5.209,00
$ 2.828,75
$ 2.819,00
$ 2.069,50
$ 2.228,75
$ 2.456,75
$ 2.962,50
$ 2.611,25
$ 1.903,75
$ 1.635,25
$ 1.906,25
$ 1.912,50
$ 2.054,00 $ 90.006,75
Exhibit 5
Exhibit 5
Exhibit 6.1
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Annual
Revenues
COGS
GM
190.810
113.866
76.944
157.178
85.916
71.261
114.222
64.567
49.655
95.875
65.079
30.796
155.947
84.288
71.659
151.251
78.485
72.766
209.778
86.216
123.562
183.364
78.633
104.731
135.530
73.912
61.618
97.049
62.815
34.234
111.432
77.112
34.320
185.224
118.284
66.940
1.787.658
989.173
798.485
Revenues
COGS
GM
185.389
113.607
71.782
151.535
83.779
67.756
109.937
62.635
47.302
109.079
66.577
42.502
143.982
82.773
61.208
144.898
77.317
67.582
196.915
85.204
111.711
178.895
77.613
101.282
134.888
73.946
60.942
93.950
63.274
30.676
107.723
77.943
29.780
177.819
117.313
60.506
1.735.009
981.980
753.029
Revenues
COGS
GM
184.116
112.416
71.700
145.880
83.437
62.443
107.735
61.637
46.098
87.341
66.090
21.251
141.808
80.427
61.381
144.244
77.202
67.042
196.762
84.984
111.779
176.045
77.240
98.805
134.866
73.972
60.894
94.426
63.335
31.091
106.727
77.115
29.612
175.804
115.828
59.977
1.695.754
973.681
722.073
Revenues
COGS
GM
176.443
115.381
61.063
139.328
90.542
48.786
103.570
62.299
41.271
83.758
64.179
19.579
134.929
78.128
56.802
135.537
76.102
59.435
186.750
82.911
103.840
174.364
75.377
98.987
132.853
73.538
59.316
91.022
62.128
28.894
103.435
75.908
27.527
170.726
113.059
57.667
1.632.717
969.550
663.167
Revenues
COGS
GM
186.971
112.958
74.013
151.781
84.581
67.200
110.660
63.251
47.409
92.611
67.257
25.354
144.889
83.489
61.400
141.875
78.216
63.659
197.933
87.883
110.050
174.886
80.148
94.738
135.209
74.160
61.050
94.280
63.428
30.852
108.366
78.260
30.105
179.246
118.156
61.090
1.718.706
991.786
726.920
Revenues
COGS
GM
152.556
84.549
68.007
96.463
63.489
32.974
118.672
60.689
57.984
116.321
44.860
71.460
90.710
44.572
46.139
148.450
49.223
99.227
112.372
48.870
63.502
110.332
43.529
66.803
134.923
43.547
91.377
193.246
58.801
134.444
170.928
60.845
110.084
118.069
55.791
62.278
1.563.043
658.765
904.278
Revenues
COGS
GM
195.721
76.664
119.057
142.467
61.884
80.584
145.030
58.475
86.555
127.039
41.786
85.252
88.030
30.429
57.601
138.835
46.692
92.144
158.965
47.002
111.963
158.475
41.269
117.206
158.325
40.680
117.645
200.390
53.633
146.758
171.997
53.136
118.861
127.110
53.669
73.441
1.812.384
605.318
1.207.066
Revenues
COGS
GM
155.351
75.554
79.797
101.270
61.091
40.180
136.771
59.189
77.582
122.107
41.867
80.240
93.946
31.156
62.791
140.900
45.514
95.386
157.136
47.100
110.036
155.611
41.003
114.608
149.829
40.259
109.571
196.350
53.337
143.014
218.550
52.573
165.977
258.003
53.477
204.526
1.885.824
602.118
1.283.706
Revenues
COGS
GM
147.243
83.767
63.476
93.826
62.314
31.512
114.844
59.067
55.777
113.045
43.940
69.105
86.007
44.942
41.065
137.743
47.353
90.390
105.842
47.255
58.587
106.271
42.510
63.761
128.930
42.782
86.148
187.615
57.667
129.949
164.487
59.802
104.685
113.978
54.735
59.244
1.499.830
646.132
853.698
Revenues
COGS
GM
183.083
75.146
107.938
131.992
60.614
71.377
137.904
57.645
80.259
123.393
41.087
82.306
85.624
29.648
55.977
136.116
45.728
90.388
153.438
46.141
107.297
152.264
40.540
111.724
148.116
40.013
108.103
197.074
52.710
144.364
169.593
52.178
117.415
134.387
52.780
81.607
1.752.984
594.228
1.158.756
Revenues
COGS
GM
139.813
72.541
67.272
98.826
58.937
39.890
111.661
57.299
54.362
96.080
50.394
45.686
95.538
32.574
62.965
133.531
45.947
87.585
150.910
45.568
105.343
148.857
40.404
108.453
137.633
44.904
92.729
176.014
52.623
123.391
139.129
50.895
88.234
91.599
48.607
42.993
1.519.592
600.689
918.903
Revenues
COGS
GM
115.495
56.998
58.497
99.101
58.285
40.816
86.984
50.035
36.949
44.005
19.830
24.174
102.569
42.474
60.095
120.567
43.560
77.007
162.862
44.756
118.106
136.677
40.154
96.523
98.529
28.864
69.665
76.675
41.661
35.014
97.767
46.691
51.076
115.116
58.087
57.029
1.256.346
531.394
724.952
Revenues
COGS
GM
141.126
60.622
80.504
102.076
59.550
42.526
86.952
52.541
34.411
43.924
19.565
24.360
96.361
43.646
52.716
120.383
45.963
74.421
172.224
45.506
126.718
142.174
42.031
100.143
99.803
28.032
71.770
77.348
46.955
30.393
95.650
50.116
45.534
113.313
54.310
59.003
1.291.335
548.836
742.499
Revenues
COGS
GM
109.648
47.649
61.999
94.391
48.704
45.687
76.201
39.086
37.116
43.167
15.007
28.160
100.108
37.931
62.177
137.622
29.833
107.789
168.275
41.348
126.927
132.487
37.549
94.938
82.292
13.704
68.588
66.920
35.823
31.097
74.229
35.982
38.248
95.595
31.010
64.585
1.180.935
413.625
767.310
Revenues
COGS
GM
105.702
56.779
48.923
100.943
60.730
40.213
86.743
54.403
32.340
42.797
19.559
23.238
101.793
53.409
48.385
125.785
51.025
74.760
156.994
47.315
109.680
143.787
42.912
100.875
100.360
29.034
71.327
84.997
59.346
25.651
103.418
54.846
48.572
119.809
60.114
59.694
1.273.127
589.470
683.657
Revenues
COGS
GM
112.896
42.041
70.855
111.714
48.133
63.582
78.918
31.524
47.394
44.700
11.055
33.645
88.219
39.778
48.441
97.328
18.451
78.878
131.789
27.670
104.119
138.539
29.096
109.443
79.756
3.231
76.525
84.932
21.115
63.817
105.432
21.013
84.420
115.484
31.405
84.078
1.189.706
324.510
865.196
Revenues
COGS
GM
113.747
44.117
69.630
100.457
50.667
49.790
86.248
40.475
45.773
42.863
14.021
28.843
103.997
32.237
71.760
98.995
19.612
79.383
154.048
35.176
118.872
131.056
34.699
96.356
74.949
5.783
69.167
79.823
28.470
51.354
106.974
26.076
80.898
139.392
32.756
106.636
1.232.549
364.087
868.462
Revenues
COGS
GM
217.458
99.151
118.307
134.735
84.581
50.154
115.308
59.671
55.637
53.957
29.392
24.565
97.282
52.622
44.661
121.530
76.533
44.997
185.631
94.727
90.903
159.269
79.249
80.021
123.016
66.503
56.513
99.445
30.961
68.484
109.027
66.533
42.494
194.025
125.011
69.014
1.501.656
798.400
703.256
Revenues
COGS
GM
208.625
94.765
113.860
141.787
80.002
61.785
118.885
60.656
58.229
54.714
30.423
24.291
93.826
51.947
41.880
208.082
78.210
129.871
214.441
97.050
117.391
186.903
80.865
106.038
161.203
67.908
93.295
109.983
24.388
85.594
154.458
107.125
47.333
200.590
122.321
78.269
1.699.038
788.533
910.505
Revenues
COGS
GM
339.084
111.670
227.414
192.265
85.842
106.424
149.096
64.051
85.045
64.220
37.307
26.913
147.427
52.663
94.764
265.180
79.488
185.692
302.119
100.659
201.460
253.113
83.920
169.194
177.888
68.116
109.771
138.734
29.039
109.695
210.630
126.878
83.752
287.806
133.903
153.904
2.316.932
846.656
1.470.276
Revenues
COGS
GM
216.338
95.153
121.186
159.069
80.552
78.516
125.857
60.890
64.967
61.698
30.498
31.200
152.435
52.033
100.402
245.126
79.942
165.184
256.078
101.852
154.227
174.280
82.808
91.472
179.889
67.966
111.924
117.065
32.184
84.882
173.716
117.753
55.963
223.039
122.448
100.591
1.910.875
806.325
1.104.550
Revenues
COGS
GM
150.569
74.692
75.877
78.478
55.196
23.282
79.596
53.400
26.196
41.554
28.041
13.513
69.414
44.829
24.585
106.279
76.701
29.578
159.234
91.887
67.348
142.367
78.084
64.284
111.098
65.670
45.429
89.908
28.881
61.028
108.166
75.569
32.597
161.323
93.803
67.520
1.189.821
691.182
498.639
Revenues
COGS
GM
132.454
64.692
67.763
83.465
58.782
24.683
78.147
50.412
27.736
43.905
27.731
16.174
82.472
45.086
37.386
102.348
70.072
32.277
161.595
86.982
74.613
141.485
72.898
68.587
103.315
60.282
43.034
87.997
22.151
65.846
104.921
76.966
27.955
136.946
87.625
49.321
1.154.131
646.711
507.420
Revenues
COGS
GM
155.506
94.989
60.517
126.482
72.582
53.901
109.796
63.500
46.296
110.146
58.111
52.035
105.048
45.108
59.940
156.343
59.176
97.167
224.553
68.610
155.944
182.225
66.987
115.238
149.640
57.063
92.578
109.198
59.657
49.541
115.127
68.452
46.674
161.583
86.897
74.686
1.705.648
801.131
904.517
Revenues
COGS
GM
193.578
99.897
93.681
150.011
78.002
72.009
123.543
66.239
57.305
125.969
60.489
65.480
129.202
47.561
81.642
196.273
63.775
132.498
298.474
73.534
224.939
240.222
70.508
169.714
233.106
59.560
173.546
133.734
62.815
70.919
138.776
70.036
68.740
194.361
93.963
100.398
2.157.249
846.377
1.310.872
Commonwealth
Revenues
COGS
GM
120.736
58.856
61.880
105.893
56.287
49.606
89.556
40.549
49.007
80.827
19.294
61.533
74.694
30.640
44.054
126.838
42.679
84.159
164.198
50.529
113.669
147.609
48.455
99.154
81.394
17.493
63.901
79.292
34.049
45.244
83.166
20.150
63.016
90.989
39.237
51.753
1.245.192
458.217
786.976
Revenues
COGS
GM
186.036
98.537
87.500
145.140
78.026
67.114
112.569
66.156
46.413
132.475
60.545
71.931
121.308
47.968
73.340
211.533
63.225
148.308
256.466
73.267
183.200
208.635
69.006
139.629
201.495
58.838
142.657
129.661
64.111
65.550
118.577
68.176
50.401
185.029
93.404
91.625
2.008.925
841.256
1.167.669
Revenues
COGS
GM
178.982
103.341
75.641
137.968
76.449
61.519
125.780
66.163
59.617
118.910
60.252
58.658
121.236
47.362
73.874
170.968
65.703
105.265
291.455
74.660
216.795
198.373
70.350
128.023
155.502
60.702
94.801
113.772
60.182
53.590
127.433
68.195
59.237
194.058
94.697
99.361
1.934.437
848.056
1.086.382
Revenues
COGS
GM
0
0
0
0
0
0
0
0
0
0
0
0
31.676
12.598
19.079
196.348
63.268
133.081
286.852
73.159
213.693
220.709
69.866
150.843
221.013
58.977
162.036
131.602
62.021
69.581
141.681
68.110
73.572
196.730
94.771
101.959
1.426.611
502.768
923.843
CT
NEMass
NH
ME
Internal
Pacific G&E
SoCal
San Diego
NP-15
SP-15
Palo Verde
Cinergy
First Energy
Illinois
Michigan
Minnesota
Wisconsin
Capital
Hudson
Long Isl
NY
North
West
Western
Baltimore
Dominion
NJ
Pepco
Exhibit 6.2
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Annual
Revenues
COGS
GM
205.208
101.277
103.931
165.256
81.042
84.214
127.843
57.230
70.614
105.982
61.253
44.730
187.018
76.139
110.879
178.343
73.456
104.887
232.415
83.918
148.498
204.279
77.702
126.578
169.809
71.384
98.425
106.118
60.850
45.268
126.393
74.555
51.838
196.491
108.415
88.076
2.005.155
927.217
1.077.938
Revenues
COGS
GM
198.176
99.800
98.376
157.588
79.017
78.571
122.015
56.098
65.917
119.508
59.740
59.768
174.394
74.680
99.714
171.175
72.603
98.571
222.025
82.780
139.245
197.757
76.564
121.194
169.583
71.206
98.378
102.509
61.287
41.222
123.003
74.713
48.291
189.053
106.808
82.245
1.946.787
915.294
1.031.493
Revenues
COGS
GM
196.646
98.646
98.000
152.637
78.191
74.447
120.354
55.360
64.994
94.800
58.539
36.261
170.955
74.294
96.661
169.569
72.294
97.275
221.452
82.616
138.836
196.950
76.169
120.781
165.267
71.272
93.996
103.240
61.340
41.900
122.085
74.005
48.080
187.042
105.530
81.513
1.900.998
908.253
992.745
Revenues
COGS
GM
195.694
97.138
98.556
157.383
75.548
81.835
129.563
55.352
74.211
89.250
56.099
33.151
159.351
71.639
87.713
164.376
70.629
93.747
210.757
80.279
130.478
190.457
74.116
116.342
161.140
70.116
91.024
100.149
60.263
39.886
118.743
72.602
46.141
181.598
102.893
78.705
1.858.460
886.671
971.789
Revenues
COGS
GM
198.679
100.414
98.265
157.941
79.756
78.185
122.861
56.595
66.266
100.170
60.239
39.932
177.740
75.574
102.166
172.836
73.177
99.659
224.146
84.628
139.518
198.966
78.075
120.891
166.538
71.494
95.044
103.225
61.437
41.788
123.846
75.072
48.774
190.659
107.828
82.831
1.937.607
924.288
1.013.320
Revenues
COGS
GM
165.090
76.996
88.095
114.725
57.467
57.258
123.760
54.015
69.745
150.043
39.227
110.816
179.897
13.711
166.186
289.345
-6.357
295.702
130.922
23.877
107.045
129.228
33.394
95.834
169.803
33.150
136.653
219.567
55.974
163.593
210.747
53.852
156.894
206.510
35.459
171.051
2.089.636
470.764
1.618.872
Revenues
COGS
GM
248.924
70.527
178.398
209.884
57.004
152.880
161.515
52.650
108.865
159.096
38.341
120.755
161.781
11.813
149.969
278.912
-6.181
285.093
241.445
23.314
218.131
195.159
31.678
163.481
180.080
31.319
148.761
235.682
51.661
184.021
217.077
48.989
168.088
240.571
33.653
206.918
2.530.127
444.768
2.085.359
Revenues
COGS
GM
164.736
68.795
95.941
136.390
56.465
79.925
147.505
52.774
94.731
175.734
38.278
137.457
162.871
11.638
151.233
282.144
-5.991
288.134
212.370
23.282
189.088
192.605
31.606
160.999
171.913
30.690
141.223
240.842
51.095
189.747
257.375
48.647
208.728
420.712
33.719
386.993
2.565.196
440.997
2.124.199
Revenues
COGS
GM
160.178
76.097
84.081
110.403
56.542
53.861
119.996
53.057
66.939
145.491
38.427
107.065
175.125
13.180
161.946
271.530
-6.501
278.031
124.141
22.881
101.260
123.349
32.533
90.816
162.636
32.547
130.089
216.948
54.956
161.992
203.573
52.945
150.628
200.723
34.739
165.985
2.014.094
461.402
1.552.692
Revenues
COGS
GM
226.666
69.020
157.646
187.924
55.885
132.039
152.235
51.908
100.328
154.979
37.849
117.130
157.390
11.507
145.884
271.732
-6.025
277.757
223.883
22.868
201.015
188.877
31.123
157.754
170.202
30.796
139.406
234.315
50.732
183.583
210.614
48.035
162.579
232.299
33.268
199.031
2.411.116
436.965
1.974.152
Revenues
COGS
GM
151.180
61.493
89.687
119.484
54.330
65.154
124.916
46.173
78.744
141.933
36.342
105.591
155.673
11.347
144.326
269.569
-6.165
275.734
224.875
22.378
202.497
184.559
30.308
154.251
162.408
29.758
132.650
223.726
49.095
174.631
190.123
41.314
148.810
175.053
8.232
166.821
2.123.501
384.604
1.738.897
Revenues
COGS
GM
134.434
52.192
82.242
116.044
53.509
62.536
110.336
45.092
65.244
55.409
17.910
37.499
128.152
38.213
89.939
142.492
40.295
102.197
183.490
43.170
140.321
169.142
39.608
129.534
117.529
16.415
101.114
92.157
37.203
54.954
110.402
42.431
67.971
133.761
52.266
81.496
1.493.348
478.302
1.015.046
Revenues
COGS
GM
164.243
54.378
109.866
118.421
54.651
63.770
111.108
46.030
65.078
56.695
17.342
39.353
127.746
37.846
89.900
143.887
41.246
102.641
192.095
41.935
150.160
174.150
38.600
135.551
122.644
13.185
109.459
96.789
39.332
57.457
110.438
43.095
67.344
130.828
41.716
89.112
1.549.045
469.353
1.079.692
Revenues
COGS
GM
128.977
43.739
85.238
112.401
44.296
68.104
98.290
27.615
70.675
52.833
10.441
42.392
134.088
31.190
102.898
166.116
21.272
144.844
192.552
39.391
153.161
165.072
36.940
128.132
98.579
-1.628
100.206
81.012
22.782
58.230
89.572
2.321
87.252
117.677
-626
118.303
1.437.168
277.732
1.159.436
Revenues
COGS
GM
130.977
48.189
82.789
119.180
55.016
64.164
113.760
47.097
66.664
55.807
16.157
39.650
147.048
42.382
104.666
150.078
43.211
106.868
177.153
45.616
131.537
178.182
42.161
136.022
128.943
16.251
112.692
131.473
37.776
93.697
126.915
45.078
81.837
139.878
48.541
91.337
1.599.396
487.475
1.111.921
Revenues
COGS
GM
130.611
32.440
98.171
126.746
44.261
82.485
96.003
20.452
75.551
53.619
4.027
49.592
121.389
26.665
94.724
107.645
4.754
102.892
152.721
21.195
131.527
162.863
21.579
141.284
94.571
-11.237
105.809
99.889
3.771
96.118
126.171
10.005
116.166
129.491
27.019
102.473
1.401.720
204.930
1.196.790
Revenues
COGS
GM
122.190
34.699
87.492
105.225
48.020
57.205
98.220
34.951
63.269
53.075
7.302
45.774
130.118
22.843
107.275
113.423
827
112.596
172.227
26.312
145.914
153.583
24.729
128.854
92.672
-9.842
102.514
87.470
14.985
72.485
118.076
16.267
101.810
155.969
26.551
129.418
1.402.247
247.642
1.154.606
Revenues
COGS
GM
262.251
54.971
207.280
221.130
57.084
164.046
150.723
44.164
106.558
66.328
23.732
42.596
122.026
45.546
76.481
151.856
56.810
95.046
215.668
89.091
126.577
181.437
64.070
117.367
150.735
59.243
91.492
103.275
12.373
90.902
171.278
98.542
72.736
258.627
88.222
170.405
1.884.056
595.305
1.288.751
Revenues
COGS
GM
246.899
55.052
191.847
196.331
56.951
139.380
154.539
44.784
109.755
68.234
24.465
43.769
119.988
44.308
75.680
282.162
64.621
217.541
267.244
91.363
175.881
206.423
71.942
134.481
197.823
61.267
136.556
114.949
13.515
101.435
192.442
106.928
85.514
262.269
89.430
172.839
2.116.861
617.697
1.499.164
Revenues
COGS
GM
405.175
70.915
334.260
239.352
60.807
178.545
180.378
48.714
131.664
88.096
26.098
61.998
192.659
46.515
146.144
330.536
70.403
260.133
343.023
95.804
247.220
310.580
76.920
233.660
228.132
62.106
166.026
155.858
14.058
141.801
256.489
129.147
127.342
347.591
95.419
252.172
2.821.378
667.756
2.153.623
Revenues
COGS
GM
258.434
56.228
202.206
212.422
57.988
154.435
170.114
46.476
123.639
74.808
25.522
49.287
165.267
45.764
119.503
324.619
72.170
252.449
313.370
95.088
218.283
198.576
73.793
124.783
225.569
61.401
164.167
125.244
13.474
111.769
214.492
117.929
96.563
290.988
91.674
199.314
2.359.411
639.576
1.719.836
Revenues
COGS
GM
195.470
36.555
158.915
111.795
28.479
83.317
106.051
31.369
74.682
56.034
19.951
36.084
92.144
37.296
54.849
137.977
49.456
88.521
189.394
85.165
104.229
162.725
62.180
100.545
134.808
57.845
76.964
96.143
10.761
85.382
134.797
35.935
98.862
200.229
69.607
130.622
1.482.771
488.662
994.109
Revenues
COGS
GM
173.282
31.352
141.930
113.164
40.868
72.296
103.921
34.835
69.086
57.694
21.992
35.702
94.113
38.265
55.848
133.501
45.585
87.916
189.406
80.589
108.817
160.051
59.057
100.994
117.556
52.781
64.775
94.043
11.984
82.059
128.230
75.615
52.615
173.802
70.275
103.528
1.410.533
487.580
922.953
Revenues
COGS
GM
178.916
79.227
99.689
133.155
69.784
63.371
127.907
61.980
65.927
124.273
56.780
67.493
126.478
42.483
83.995
182.941
58.773
124.169
254.711
68.017
186.694
205.445
66.042
139.403
167.621
55.928
111.693
121.084
55.819
65.265
126.019
62.748
63.271
182.519
77.203
105.315
1.931.069
754.784
1.176.285
Revenues
COGS
GM
230.557
88.731
141.826
159.889
75.001
84.889
141.217
64.521
76.696
144.567
58.727
85.840
155.948
44.977
110.971
246.800
62.980
183.821
345.154
72.723
272.432
266.640
69.340
197.300
257.140
58.590
198.550
155.095
56.594
98.501
151.985
65.478
86.507
224.819
83.786
141.033
2.479.812
801.446
1.678.365
Commonwealth
Revenues
COGS
GM
135.495
54.003
81.492
113.448
52.895
60.554
100.730
28.608
72.122
91.866
9.252
82.614
93.020
23.237
69.783
135.601
39.174
96.427
183.685
47.301
136.384
167.403
47.125
120.278
93.214
11.112
82.103
86.523
26.131
60.393
89.227
6.576
82.652
102.148
28.584
73.564
1.392.361
373.996
1.018.364
Revenues
COGS
GM
236.760
84.223
152.538
158.328
73.080
85.248
130.918
61.923
68.996
159.849
57.502
102.346
155.834
44.212
111.622
253.662
62.031
191.631
313.361
72.012
241.349
231.685
67.660
164.025
220.634
57.392
163.242
151.835
61.029
90.807
133.364
60.912
72.452
213.515
83.020
130.495
2.359.745
784.994
1.574.751
Revenues
COGS
GM
210.055
85.195
124.859
146.181
74.587
71.595
138.108
64.638
73.471
128.378
58.586
69.793
144.232
44.426
99.806
206.063
63.239
142.825
335.086
73.517
261.569
221.871
69.143
152.728
177.108
59.000
118.109
122.626
55.892
66.734
144.717
65.118
79.599
215.404
81.023
134.382
2.189.830
794.360
1.395.470
Revenues
COGS
GM
0
0
0
0
0
0
0
0
0
0
0
0
35.133
12.236
22.897
239.446
62.339
177.107
332.709
72.135
260.574
247.423
68.612
178.811
245.319
58.054
187.265
154.040
55.008
99.032
152.712
64.974
87.738
226.683
83.533
143.151
1.633.464
476.891
1.156.574
CT
NEMass
NH
ME
Internal
Pacific G&E
SoCal
San Diego
NP-15
SP-15
Palo Verde
Cinergy
First Energy
Illinois
Michigan
Minnesota
Wisconsin
Capital
Hudson
Long Isl
NY
North
West
Western
Baltimore
Dominion
NJ
Pepco
Capital
Hudson
Long Isl
NY
North
West
$
$
$
$
$
2009
$
566.093
$
724.742
$ 1.109.670
$
863.426
$
318.142
$
449.844
Western
Baltimore
Commonwealth
Dominion
NJ
Pepco
$
$
$
$
$
$
Cinergy
First Energy
Illinois
Michigan
Minnesota
Wisconsin
2009
638.345
773.357
613.056
713.027
680.271
$
$
$
$
$
$
2009
$
905.059
$
960.807
$ 1.009.842
$ 1.023.623
$ 1.133.226
$
920.639
2011
958.517
1.291.984
802.946
1.150.486
1.175.992
1.036.823
2009
$
826.357
$ 1.022.661
$
813.030
$
949.918
$
891.464
Gross margin
$
$
$
$
$
$
2009
1.386.061
1.411.918
1.887.644
1.605.326
803.135
1.135.105
Gross margin
2011
713.266
756.196
695.872
720.505
825.387
744.940
2009
781.347
734.474
724.350
857.280
718.005
2009
1.404.274
1.643.958
1.815.862
1.369.337
1.739.558
1.432.841
Gross margin
2011
$
627.067
$
952.585
$ 1.304.475
$ 1.140.159
$
476.351
$
465.001
Projected
2010
$
904.517 $
$ 1.310.872 $
$
786.976 $
$ 1.167.669 $
$ 1.086.382 $
$
923.843 $
Projected
2010
$
703.256
$
910.505
$ 1.470.276
$ 1.104.550
$
498.639
$
507.420
2009
574.790 $
601.357 $
617.060 $
578.457 $
787.256 $
698.994 $
$
$
$
$
$
Gross margin
2011
720.330
639.609
616.462
583.298
636.363
Gross margin
2011
$
885.983
$
961.644
$ 1.123.566
$
813.826
$
930.238
$
756.434
$
$
$
$
$
Projected
2010
724.952 $
742.499 $
767.310 $
683.657 $
865.196 $
868.462 $
2009
826.899
954.318
958.977
777.676
947.408
700.196
$
$
$
$
$
$
Projected
2010
$
904.278
$ 1.207.066
$ 1.283.706
$
853.698
$ 1.158.756
$
918.903
Pacific G&E
SoCal
San Diego
NP-15
SP-15
Palo Verde
$
$
$
$
$
$
$
$
$
$
CT
NEMass
NH
ME
Internal
2009
572.623
529.262
519.790
382.498
519.301
Projected
2010
798.485
753.029
722.073
663.167
726.920
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
Actual
2010
1.176.285 $
1.678.365 $
1.018.364 $
1.574.751 $
1.395.470 $
1.156.574 $
Actual
2010
1.288.751
1.499.164
2.153.623
1.719.836
994.109
922.953
Actual
2010
1.015.046
1.079.692
1.159.436
1.111.921
1.196.790
1.154.606
Actual
2010
1.618.872
2.085.359
2.124.199
1.552.692
1.974.152
1.738.897
Actual
2010
1.077.938
1.031.493
908.253
971.789
1.013.320
2011
1.176.588
1.592.336
1.023.922
1.440.092
1.451.925
1.252.593
2011
1.149.846
1.384.702
2.037.131
1.615.317
934.269
816.588
2011
1.026.001
1.101.873
1.081.120
1.144.285
1.188.479
1.049.870
2011
1.724.483
1.934.894
2.391.214
1.640.471
1.926.366
1.779.699
2011
992.396
906.130
879.562
851.767
892.247
09 to '10
41,70%
69,50%
28,37%
63,76%
59,70%
09 to '10
24,23%
25,63%
32,50%
27,93%
56,73%
12,80%
09 to '10
26,12%
23,47%
24,35%
18,19%
9,90%
24,24%
09 to '10
9,36%
26,48%
33,86%
9,78%
22,31%
31,24%
09 to '10
39,44%
42,28%
38,92%
73,38%
39,98%
Projected
10 to '11
5,97%
-1,44%
2,03%
-1,47%
8,25%
12,23%
Projected
10 to '11
-10,83%
4,62%
-11,28%
3,22%
-4,47%
-8,36%
Projected
10 to '11
-1,61%
1,84%
-9,31%
5,39%
-4,60%
-14,22%
Projected
10 to '11
-2,02%
-20,33%
-12,47%
-4,67%
-19,72%
-17,68%
Projected
10 to '11
-9,79%
-15,06%
-14,63%
-12,04%
-12,46%
09 to '10
37,96%
40,44%
25,39%
13,36%
41,13%
09 to '10
15,28%
26,85%
16,98%
13,39%
13,49%
21,36%
09 to '10
12,15%
12,37%
14,81%
8,63%
5,61%
25,41%
09 to '10
-7,02%
6,18%
14,09%
7,13%
23,78%
-18,69%
Overall
50,16%
67,06%
30,97%
61,35%
72,87%
12,23%
09 to '10
42,35%
64,12%
25,26%
65,78%
56,54%
Increase
Overall
10,77%
31,44%
17,56%
32,05%
49,73%
3,37%
Increase
Overall
24,09%
25,75%
12,77%
24,56%
4,84%
6,57%
Increase
Overall
7,15%
0,77%
17,16%
4,65%
-1,81%
8,03%
Increase
Overall
25,79%
20,85%
18,60%
52,50%
22,54%
Increase
Actual
10 to '11
0,03%
-5,13%
0,55%
-8,55%
4,05%
8,30%
Actual
10 to '11
-10,78%
-7,64%
-5,41%
-6,08%
-6,02%
-11,52%
Actual
10 to '11
1,08%
2,05%
-6,75%
2,91%
-0,69%
-9,07%
Actual
10 to '11
6,52%
-7,22%
12,57%
5,65%
-2,42%
2,35%
Actual
10 to '11
-7,94%
-12,15%
-3,16%
-12,35%
-11,95%
Overall
42,38%
55,71%
25,94%
51,60%
62,87%
8,30%
Overall
-17,04%
-1,93%
7,92%
0,62%
16,33%
-28,06%
Overall
13,36%
14,68%
7,06%
11,79%
4,88%
14,04%
Overall
22,80%
17,70%
31,68%
19,80%
10,74%
24,21%
Overall
27,01%
23,37%
21,43%
-0,64%
24,27%

2009
2010
2011
77,25%
76,90%
81,47%
75,62%
78,10%
81,14%
75,40%
77,28%
78,42%
75,06%
74,15%
79,89%
76,31%
77,85%
81,00%
79,88%
82,77%
75,60%
77,45%
80,64%

2009
2010
2011
40,84%
54,57%
54,53%
51,33%
60,73%
68,79%
58,79%
68,27%
64,03%
53,79%
64,22%
70,58%
39,61%
50,16%
50,99%
39,63%
54,98%
56,94%
48,63%
59,67%
62,27%

2009
2010
2011
63,51%
71,42%
69,52%
62,59%
68,77%
68,63%
61,10%
66,18%
64,37%
56,51%
61,48%
62,97%
69,47%
72,29%
69,45%
75,92%
75,22%
70,96%
65,12%
68,79%
67,27%

2009
2010
2011
58,88%
55,86%
51,38%
58,05%
57,88%
49,70%
52,81%
60,43%
46,99%
56,79%
54,98%
49,61%
54,46%
58,70%
48,29%
48,87%
52,84%
42,50%
54,20%
56,97%
47,42%

2009
2010
2011
73,29%
74,08%
72,58%
72,06%
73,00%
70,59%
71,76%
79,50%
70,09%
44,62%
68,24%
68,48%
72,33%
71,74%
71,32%
66,81%
73,31%
70,61%
New England
California
Midwest
New York
PJM
Overall
New England
California
Midwest
New York
PJM
Overall
Median Increase
Projected Actual
22,54%
23,37%
5,90%
21,30%
18,43%
12,58%
24,50%
-0,65%
55,75%
46,99%
20,85%
16,33%
Average Increase
Projected Actual
28,06%
19,09%
5,99%
21,16%
16,43%
10,97%
24,15%
-3,69%
49,11%
41,13%
24,63%
17,68%
Exhibit 7
Exhibit 7
Exhibit 8
Revenue input
Debt input
Grid to be used
-
Hours in/out
3
MW pr/hour in
Power losses
MW pr/hour out
Leverage of equipment
0
Grid
Increase
0,00%
-
0,00%
Equipment loan
Bank I
0
Increase in power prices
0%
Long term increase
0%
Interest rate
first
0
yrs
Payback in years
Phasing
Cost
$0
$0
$0
$0
$0
$0
0%
0
Traider accuracy
Overhead
Maintenance
Other
Working capital
Selling expense
Start-up expense
0%
$0
$0
$0
$0
0,0%
$0
0
yr 2
yr 3
100%
100%
100%
100%
0%
0%
0%
0%
0%
0%
0%
0%
pr/yr
of GM
pr/yr
pr/yr
pr/yr
for
of revenue
0
yrs
Bank II
$0
Interest rate
0%
Payback in years
yr 1
Opex input
Opex cost
Salaries
Bonus payments
Nr of traders
$0
0,00%
Short term financing
Capex input
Land
Building
Equipment
Other
Total
0%
Increase from 2009
0
Total capital needed
Equity ratio
Equity
Debt
$0
0%
$0
$0
WACC input
CAPM
Risk free rate
Market risk premium
β for project
0,00%
0,00%
0,00
rE
0,00%
rD
0,00%
WACC (discount rate)
0,00%
Tax input
Corporate tax
Property Tax
0%
0,00%
IRR of Investment
NPV of Investment
Traded
0,00%
$0
Projected
0,00%
$0
IRR of Equity
NPV of Equity
0,00%
$0
0,00%
$0
Depreciation input
Building
Years of depreciation
Equipment
Years of depreciation
Other
Years of depreciation
%
0%
0,00
0,0%
0,00
0%
0,00
Factor
0%
0%
0%
Battery input
Type of batteries used
Recharge of batteries
# of batteries
# of containers
Ford
0%
0
0
Exhibit 9
Revenue input
Grid to be used
Hours in/out
MW pr/hour in
Debt input
Leverage of equipment
North
25
Grid
Increase
Power losses
3,00%
North
16,33%
MW pr/hour out
24,25
Increase in power prices
15%
Long term increase
5%
Equipment loan
Bank I
Interest rate
first
5
yrs
Payback in years
Bank II
Interest rate
Phasing
Cost
$300.000
$1.000.000
$10.000.000
$300.000
$11.600.000
$75.000
2%
2
Traider accuracy
Overhead
Maintenance
Other
Working capital
Selling expense
Start-up expense
95%
$30.000
$120.000
$100.000
$200.000
0,2%
$300.000
yr 2
yr 3
100%
75%
100%
50%
0%
25%
0%
50%
0%
0%
0%
0%
pr/yr
of GM
pr/yr
pr/yr
pr/yr
for
of revenue
Payback in years
yr 1
Opex input
Opex cost
Salaries
Bonus payments
Nr of traders
$7.000.000
5,75%
8
Short term financing
Capex input
Land
Building
Equipment
Other
Total
70%
Increase from 2009
3
2
yrs
$500.000
8%
3
Total capital needed
Equity ratio
Equity
Debt
$12.100.000
38%
$4.600.000
$7.500.000
WACC input
CAPM
Risk free rate
Market risk premium
β for project
2,71%
6,19%
2,79
rE
20,00%
rD
5,90%
WACC (discount rate)
10,02%
Tax input
Corporate tax
Property Tax
34%
1,65%
Depreciation input
Building
Years of depreciation
Equipment
Years of depreciation
Other
Years of depreciation
%
4%
22,75
12,5%
7,20
25%
4,10
Factor
90%
90%
90%
IRR of Investment
NPV of Investment
Traded
4,47%
-$4.017.060
Projected
-0,11%
-$6.910.815
IRR of Equity
NPV of Equity
3,21%
-$5.438.057
-2,67%
-$7.165.756
Battery input
Type of batteries used
Recharge of batteries
# of batteries
# of containers
Ford
60%
5.435
8
21,81%
14,36%
17,39%
15,93%
17,82%
13,38%
$6.336.742
$10.652.938
$3.555.005
$6.280.126
$4.934.785
$6.018.029
-$2.925.111
-$2.143.507
-$2.168.704
-$1.673.290
-$925.593
-$2.478.872
-$2.052.513
-$1.873.520
$464.058
$6.682.143
SoCal
San Diego
NP-15
SP-15
Palo Verde
Average
Cinergy
First Energy
Illinois
Michigan
Minnesota
Wisconsin
Average
Capital
Hudson
Long Isl
7,90%
13,12%
6,19%
11,33%
11,46%
-$5.288.532
-$220.397
-$1.598.662
$2.498.939
-$2.824.231
$1.031.242
$1.132.928
-$731.081
-$81.811
-$183.648
North
West
Average
Western
Baltimore
Commonwealth
Dominion
NJ
Pepco
Average
Average Over All
9,43%
9,84%
9,06%
9,39%
2,56%
4,47%
$2.710.526
-$4.017.060
NY
10,61%
7,52%
7,26%
6,68%
8,80%
7,79%
7,11%
7,15%
6,05%
17,04%
17,45%
15,27%
$4.348.581
Pacific G&E
3,41%
3,62%
-$4.725.992
2,81%
-$4.581.550
-$5.123.409
ME
3,23%
Average
-$4.842.509
NH
3,65%
5,00%
IRR project
Internal
-$4.561.893
NEMass
NPV project
-$3.653.948
CT
-$3.138.160
-$3.043.556
-$3.429.183
-$2.306.608
-$2.367.274
-$4.704.663
-$1.497.413
-$3.956.198
-$3.157.889
-$6.223.597
-$5.438.057
-$1.372.855
$918.692
-$2.707.849
-$4.123.667
-$4.233.205
-$4.492.716
-$3.546.582
-$4.001.669
-$4.303.522
-$4.288.169
-$4.766.573
$530.474
-$80.624
$688.778
-$880.149
$3.153.378
$721.157
-$419.696
-$5.786.625
-$5.875.661
-$6.121.467
-$5.947.728
-$5.774.164
-$5.214.107
NPV equity
Traded
10,33%
10,71%
9,54%
13,00%
12,81%
5,54%
15,47%
7,90%
10,27%
0,71%
3,21%
15,85%
22,74%
11,76%
7,37%
7,02%
6,21%
9,17%
7,75%
6,81%
6,86%
5,35%
21,55%
19,76%
22,06%
17,35%
29,26%
22,15%
18,74%
2,10%
1,82%
1,04%
1,59%
2,14%
3,92%
IRR equity
NPV project
-$2.655.773
-$42.947
-$383.778
$1.136.470
$861.000
-$2.998.933
$2.378.333
-$1.250.775
-$2.849.640
-$7.063.705
-$6.910.815
$747.116
$2.507.743
-$1.319.568
-$5.058.612
-$3.692.846
-$3.661.828
-$2.737.985
-$3.951.091
-$4.235.445
-$3.535.757
-$4.034.970
-$1.774.226
-$3.528.881
-$1.559.048
-$2.872.006
$577.330
-$1.207.131
-$2.055.618
-$4.919.204
-$4.949.847
-$5.587.274
-$5.186.484
-$4.911.106
-$3.961.310
6,22%
9,89%
9,51%
11,48%
11,13%
5,88%
13,02%
8,34%
5,78%
-0,37%
-0,11%
10,98%
13,17%
8,25%
2,78%
4,85%
4,90%
6,25%
4,47%
4,04%
5,09%
4,34%
7,58%
5,10%
7,92%
6,06%
10,77%
8,40%
7,23%
2,99%
2,94%
1,95%
2,58%
3,00%
4,45%
IRR project
-$4.551.356
-$2.959.771
-$3.159.305
-$2.245.098
-$2.409.444
-$4.750.920
-$1.510.370
-$3.683.489
-$4.679.969
-$7.256.665
-$7.165.756
-$2.477.387
-$1.434.188
-$3.725.404
-$6.020.411
-$5.177.552
-$5.157.961
-$4.590.777
-$5.336.366
-$5.511.742
-$5.080.369
-$5.388.098
-$4.005.081
-$5.076.149
-$3.871.318
-$4.673.025
-$2.579.222
-$3.656.897
-$4.173.875
-$5.934.410
-$5.953.139
-$6.347.565
-$6.099.501
-$5.929.177
-$5.342.668
NPV equity
Projected
5,69%
10,79%
10,19%
13,06%
12,55%
5,09%
15,35%
8,53%
5,27%
-2,99%
-2,67%
12,33%
15,59%
8,39%
0,95%
3,70%
3,76%
5,61%
3,18%
2,61%
4,02%
3,01%
7,48%
4,03%
7,93%
5,35%
12,01%
8,61%
6,96%
1,23%
1,17%
-0,12%
0,69%
1,24%
3,16%
IRR equity
Exhibit 10
Exhibit 10
Exhibit 11
Projected
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%
$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%
$747.116
10,98%
-$2.477.387
12,33%
-$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%
San Diego
NY
North
$861.000
11,13%
-$2.409.444
12,55%
NJ
Dominion
$1.136.470
11,48%
-$2.245.098
13,06%
Pepco
-$383.778
9,51%
-$3.159.305
10,19%
-$42.947
9,89%
-$2.959.771
10,79%
-$2.655.773
6,22%
-$4.551.356
5,69%
Average
Average Over All
Exhibit 12
Traded
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%
-$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%
Minnesota
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%
Exhibit 13.1
SensIt
Spider Analysis
Long Island
Input Variable
WACC
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
Salaries
Maintenance
Working capital
Start-up expense
Power losses
Corresponding Input Value
Low Output
Base Case
High Output
12,25%
10,21%
8,17%
12%
15%
18%
$12.000.000
$10.000.000
$8.000.000
90%
95%
100%
41%
34%
27%
$90.000
$75.000
$60.000
$144.000
$120.000
$96.000
$240.000
$200.000
$160.000
$360.000
$300.000
$240.000
4%
3%
2%
Low %
120%
80%
120%
94,7%
120%
120%
120%
120%
120%
120%
Input Value as % of Base
Base %
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
High %
80%
120%
80%
105,3%
80%
80%
80%
80%
80%
80%
Low
$4.270.995
$4.535.788
$4.897.667
$4.984.179
$5.396.527
$6.301.775
$6.332.447
$6.405.258
$6.415.891
$6.455.134
NPV of Investment
Output Value
Base
$6.455.134
$6.455.134
$6.455.134
$6.455.134
$6.455.134
$6.455.134
$6.455.134
$6.455.134
$6.455.134
$6.455.134
High
$9.112.327
$8.557.543
$7.987.476
$7.913.789
$7.513.741
$6.607.540
$6.577.292
$6.504.199
$6.493.990
$6.455.134
Swing
$4.841.332
$4.021.755
$3.089.808
$2.929.610
$2.117.214
$305.765
$244.845
$98.941
$78.099
$0
$10
Millions
$9
WACC
NPV of Investment
$8
Increase in power prices
Equipment
$7
Trader accuracy
Corporate tax rate
$6
Salaries
Maintenance
$5
Working capital
Start-up expense
$4
Power losses
$3
75%
85%
95%
105%
Input Value as % of Base Case
115%
125%
Exhibit 13.2
SensIt
Spider Analysis
SP-15
Input Variable
WACC
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
Salaries
Maintenance
Working capital
Start-up expense
Power losses
Corresponding Input Value
Low Output
Base Case
High Output
12,25%
10,21%
8,17%
12%
15%
18%
$12.000.000
$10.000.000
$8.000.000
90%
95%
100%
41%
34%
27%
$90.000
$75.000
$60.000
$144.000
$120.000
$96.000
$240.000
$200.000
$160.000
$360.000
$300.000
$240.000
4%
3%
2%
Low %
120%
80%
120%
94,7%
120%
120%
120%
120%
120%
120%
Input Value as % of Base
Base %
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
High %
80%
120%
80%
105,3%
80%
80%
80%
80%
80%
80%
Low
$3.922.064
$4.180.642
$4.495.919
$4.989.302
$5.038.186
$5.904.769
$5.935.441
$6.008.251
$6.018.885
$6.058.127
NPV of Investment
Output Value
Base
$6.058.127
$6.058.127
$6.058.127
$6.058.127
$6.058.127
$6.058.127
$6.058.127
$6.058.127
$6.058.127
$6.058.127
High
$8.656.513
$8.118.343
$7.594.103
$7.121.969
$7.078.068
$6.211.485
$6.180.814
$6.108.003
$6.097.370
$6.058.127
Swing
$4.734.449
$3.937.701
$3.098.184
$2.132.667
$2.039.882
$306.717
$245.373
$99.752
$78.485
$0
$10
Millions
$9
WACC
NPV of Investment
$8
Increase in power prices
Equipment
$7
Trader accuracy
Corporate tax rate
$6
Salaries
Maintenance
$5
Working capital
Start-up expense
$4
Power losses
$3
75%
85%
95%
105%
Input Value as % of Base Case
115%
125%
Exhibit 13.3
SensIt
Spider Analysis
San Diego
Corresponding Input Value
Low Output
Base Case
High Output
12,25%
10,21%
8,17%
12%
15%
18%
$12.000.000
$10.000.000
$8.000.000
41%
34%
27%
90%
95%
100%
$90.000
$75.000
$60.000
$144.000
$120.000
$96.000
$240.000
$200.000
$160.000
$360.000
$300.000
$240.000
4%
3%
2%
Input Variable
WACC
Increase in power prices
Equipment
Corporate tax rate
Trader accuracy
Salaries
Maintenance
Working capital
Start-up expense
Power losses
Low %
120%
80%
120%
120,0%
95%
120%
120%
120%
120%
120%
Input Value as % of Base
Base %
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
High %
80%
120%
80%
80,0%
105%
80%
80%
80%
80%
80%
Low
$7.706.314
$8.054.570
$8.851.681
$8.922.311
$9.102.218
$10.225.703
$10.255.640
$10.329.648
$10.339.382
$10.375.388
NPV of Investment
Output Value
Base
$10.375.388
$10.375.388
$10.375.388
$10.375.388
$10.375.388
$10.375.388
$10.375.388
$10.375.388
$10.375.388
$10.375.388
High
$13.625.580
$12.928.480
$11.887.566
$11.828.466
$11.647.956
$10.525.074
$10.495.137
$10.421.129
$10.411.395
$10.375.388
Swing
$5.919.265
$4.873.910
$3.035.885
$2.906.154
$2.545.737
$299.370
$239.496
$91.480
$72.013
$0
$15
Millions
$14
NPV of Investment
$13
WACC
Increase in power prices
$12
Equipment
$11
Corporate tax rate
Trader accuracy
$10
Salaries
Maintenance
$9
Working capital
$8
Start-up expense
Power losses
$7
$6
75%
85%
95%
105%
Input Value as % of Base Case
115%
125%
Exhibit 13.4
SensIt
Spider Analysis
SoCal
Corresponding Input Value
Low Output
Base Case
High Output
12,25%
10,21%
8,17%
12%
15%
18%
$12.000.000
$10.000.000
$8.000.000
90%
95%
100%
41%
34%
27%
$90.000
$75.000
$60.000
$144.000
$120.000
$96.000
$240.000
$200.000
$160.000
$360.000
$300.000
$240.000
4%
3%
2%
Input Variable
WACC
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
Salaries
Maintenance
Working capital
Start-up expense
Power losses
Low %
120%
80%
120%
94,7%
120%
120%
120%
120%
120%
120%
Input Value as % of Base
Base %
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
High %
80%
120%
80%
105,3%
80%
80%
80%
80%
80%
80%
Low
$3.971.204
$4.230.658
$4.552.498
$5.028.027
$5.088.652
$5.960.680
$5.991.351
$6.064.162
$6.074.796
$6.114.038
NPV of Investment
Output Value
Base
$6.114.038
$6.114.038
$6.114.038
$6.114.038
$6.114.038
$6.114.038
$6.114.038
$6.114.038
$6.114.038
$6.114.038
High
$8.720.706
$8.180.196
$7.649.502
$7.194.396
$7.139.425
$6.267.396
$6.236.725
$6.163.914
$6.153.281
$6.114.038
Swing
$4.749.501
$3.949.538
$3.097.004
$2.166.369
$2.050.773
$306.717
$245.373
$99.752
$78.485
$0
$10
Millions
$9
WACC
NPV of Investment
$8
Increase in power prices
Equipment
$7
Trader accuracy
Corporate tax rate
$6
Salaries
Maintenance
$5
Working capital
Start-up expense
$4
Power losses
$3
75%
85%
95%
105%
Input Value as % of Base Case
115%
125%
Exhibit 13.5
SensIt
Spider Analysis
Long Island Equity
Input Variable
Cost of Equity
Equipment
Increase in power prices
Trader accuracy
Corporate tax rate
Salaries
Start-up expense
Working capital
Maintenance
Power losses
Corresponding Input Value
Low Output
Base Case
High Output
24,00%
20,00%
16,00%
$12.000.000
$10.000.000
$8.000.000
12%
15%
18%
90%
95%
100%
41%
34%
27%
$90.000
$75.000
$60.000
$360.000
$300.000
$240.000
$240.000
$200.000
$160.000
$144.000
$120.000
$96.000
4%
3%
2%
Low %
120%
120%
80%
94,7%
120%
120%
120%
120%
120%
120%
Input Value as % of Base
Base %
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
High %
80%
80%
120%
105,3%
80%
80%
80%
80%
80%
80%
Low
-$355.999
-$295.731
-$86.688
$66.833
$444.938
$819.389
$826.152
$835.780
$839.250
$918.692
NPV of Investment
Output Value
Base
$918.692
$918.692
$918.692
$918.692
$918.692
$918.692
$918.692
$918.692
$918.692
$918.692
High
$2.734.378
$2.098.583
$2.006.438
$1.753.409
$1.392.447
$1.016.669
$1.010.694
$1.000.476
$997.398
$918.692
Swing
$3.090.376
$2.394.315
$2.093.125
$1.686.576
$947.509
$197.280
$184.542
$164.696
$158.149
$0
$3,0
Millions
Cost of Equity
$2,5
Equipment
Increase in power prices
$2,0
NPV of Equity
Trader accuracy
Corporate tax rate
$1,5
Salaries
$1,0
Start-up expense
Working capital
$0,5
Maintenance
Power losses
$0,0
70%
80%
90%
100%
-$0,5
-$1,0
Input Value as % of Base Case
110%
120%
130%
Exhibit 13.6
SensIt
Spider Analysis
SP-15 Equity
Input Variable
Cost of Equity
Equipment
Increase in power prices
Trader accuracy
Corporate tax rate
Salaries
Start-up expense
Working capital
Maintenance
Power losses
Corresponding Input Value
Low Output
Base Case
High Output
24,00%
20,00%
16,00%
$12.000.000
$10.000.000
$8.000.000
12%
15%
18%
90%
95%
100%
41%
34%
27%
$90.000
$75.000
$60.000
$360.000
$300.000
$240.000
$240.000
$200.000
$160.000
$144.000
$120.000
$96.000
4%
3%
2%
Low %
120%
120%
80%
94,7%
120%
120%
120%
120%
120%
120%
Input Value as % of Base
Base %
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
High %
80%
80%
120%
105,3%
80%
80%
80%
80%
80%
80%
Low
-$548.622
-$531.716
-$294.675
$69.799
$235.400
$589.474
$596.238
$605.865
$609.335
$688.778
NPV of Investment
Output Value
Base
$688.778
$688.778
$688.778
$688.778
$688.778
$688.778
$688.778
$688.778
$688.778
$688.778
High
$2.453.044
$1.873.734
$1.757.900
$1.300.809
$1.142.156
$788.081
$781.318
$771.691
$768.221
$688.778
Swing
$3.001.665
$2.405.450
$2.052.576
$1.231.010
$906.756
$198.607
$185.081
$165.825
$158.886
$0
$3,0
Millions
Cost of Equity
$2,5
Equipment
Increase in power prices
$2,0
NPV of Equity
Trader accuracy
Corporate tax rate
$1,5
Salaries
$1,0
Start-up expense
Working capital
$0,5
Maintenance
Power losses
$0,0
70%
80%
90%
100%
-$0,5
-$1,0
Input Value as % of Base Case
110%
120%
130%
Exhibit 13.7
SensIt
Spider Analysis
San Diego Equity
Input Variable
Cost of Equity
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
Salaries
Start-up expense
Working capital
Maintenance
Power losses
Corresponding Input Value
Low Output
Base Case
High Output
24,00%
20,00%
16,00%
12%
15%
18%
$12.000.000
$10.000.000
$8.000.000
90%
95%
100%
41%
34%
27%
$90.000
$75.000
$60.000
$360.000
$300.000
$240.000
$240.000
$200.000
$160.000
$144.000
$120.000
$96.000
4%
3%
2%
Low %
120%
80%
120%
94,7%
120%
120%
120%
120%
120%
120%
Input Value as % of Base
Base %
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
High %
80%
120%
80%
105,3%
80%
80%
80%
80%
80%
80%
Low
$1.502.892
$1.952.621
$1.986.447
$2.432.708
$2.464.177
$3.059.387
$3.065.557
$3.076.497
$3.078.185
$3.153.378
NPV of Investment
Output Value
Base
$3.153.378
$3.153.378
$3.153.378
$3.153.378
$3.153.378
$3.153.378
$3.153.378
$3.153.378
$3.153.378
$3.153.378
High
$5.487.720
$4.467.458
$4.302.449
$3.873.132
$3.842.579
$3.247.369
$3.241.199
$3.230.259
$3.228.571
$3.153.378
Swing
$3.984.828
$2.514.836
$2.316.002
$1.440.424
$1.378.402
$187.982
$175.643
$153.762
$150.386
$0
$6,0
Millions
Cost of Equity
Increase in power prices
$5,0
Equipment
NPV of Equity
Trader accuracy
Corporate tax rate
$4,0
Salaries
Start-up expense
Working capital
$3,0
Maintenance
Power losses
$2,0
$1,0
70%
80%
90%
100%
Input Value as % of Base Case
110%
120%
130%
Exhibit 13.8
SensIt
Spider Analysis
SoCal Equity
Input Variable
Cost of Equity
Equipment
Increase in power prices
Trader accuracy
Corporate tax rate
Salaries
Start-up expense
Working capital
Maintenance
Power losses
Corresponding Input Value
Low Output
Base Case
High Output
24,00%
20,00%
16,00%
$12.000.000
$10.000.000
$8.000.000
12%
15%
18%
90%
95%
100%
41%
34%
27%
$90.000
$75.000
$60.000
$360.000
$300.000
$240.000
$240.000
$200.000
$160.000
$144.000
$120.000
$96.000
4%
3%
2%
Low %
120%
120%
80%
94,7%
120%
120%
120%
120%
120%
120%
Input Value as % of Base
Base %
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
High %
80%
80%
120%
105,3%
80%
80%
80%
80%
80%
80%
Low
-$521.494
-$498.482
-$265.384
$92.226
$264.909
$621.854
$628.617
$638.244
$641.714
$721.157
NPV of Investment
Output Value
Base
$721.157
$721.157
$721.157
$721.157
$721.157
$721.157
$721.157
$721.157
$721.157
$721.157
High
$2.492.664
$1.905.400
$1.792.902
$1.342.209
$1.177.405
$820.461
$813.697
$804.070
$800.600
$721.157
Swing
$3.014.159
$2.403.881
$2.058.286
$1.249.982
$912.496
$198.607
$185.081
$165.825
$158.886
$0
$3,0
Millions
Cost of Equity
$2,5
Equipment
Increase in power prices
$2,0
NPV of Equity
Trader accuracy
Corporate tax rate
$1,5
Salaries
$1,0
Start-up expense
Working capital
$0,5
Maintenance
Power losses
$0,0
70%
80%
90%
100%
-$0,5
-$1,0
Input Value as % of Base Case
110%
120%
130%
Exhibit 14.1
SensIt 1.45
Long Island
Tornado Analysis
Input Variable
WACC
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
Salaries
Maintenance
Working capital
Start‐up expense
Power losses
Corresponding Input Value
Low Output
Base Case
High Output
12,02%
10,02%
8,02%
12%
15%
18%
$12.000.000
$10.000.000
$8.000.000
90%
95%
100%
41%
34%
27%
$90.000
$75.000
$60.000
$144.000
$120.000
$96.000
$240.000
$200.000
$160.000
$360.000
$300.000
$240.000
4%
3%
2%
Low
$4.492.557
$4.731.864
$5.124.660
$5.189.258
$5.601.841
$6.523.579
$6.554.559
$6.628.562
$6.639.248
$6.678.480
NPV of Investment
Output Value
Base
$6.678.480
$6.678.480
$6.678.480
$6.678.480
$6.678.480
$6.678.480
$6.678.480
$6.678.480
$6.678.480
$6.678.480
High
$9.330.276
$8.811.187
$8.207.462
$8.155.547
$7.755.119
$6.832.439
$6.801.878
$6.727.597
$6.717.329
$6.678.480
Percent
Swing^2
37,1%
26,3%
15,0%
13,9%
7,3%
0,2%
0,1%
0,0%
0,0%
0,0%
Swing
$4.837.719
$4.079.323
$3.082.803
$2.966.289
$2.153.278
$308.860
$247.318
$99.035
$78.081
$0
Long Island
WACC
8,02%
12,02%
Increase in power prices
18%
12%
Equipment
$8.000.000 $12.000.000 Trader accuracy
100%
90%
Corporate tax rate
27%
41%
Salaries
$90.000 $60.000 Maintenance
$144.000 $96.000 Working capital
$240.000 $160.000 Start‐up expense
$360.000 $240.000 Power losses
4%
$3,5
$4,5
$5,5
2%
$6,5
$7,5
$8,5
$9,5
Millions
NPV of Investment
Exhibit 14.2
SensIt 1.45
SP‐15
Tornado Analysis
Input Variable
WACC
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
Salaries
Maintenance
Working capital
Start‐up expense
Power losses
Corresponding Input Value
Low Output
Base Case
High Output
12,02%
10,02%
8,02%
12%
15%
18%
$12.000.000
$10.000.000
$8.000.000
90%
95%
100%
41%
34%
27%
$90.000
$75.000
$60.000
$144.000
$120.000
$96.000
$240.000
$200.000
$160.000
$360.000
$300.000
$240.000
4%
3%
2%
Low
$4.138.762
$4.372.382
$4.718.029
$5.194.444
$5.239.107
$6.121.642
$6.152.622
$6.226.625
$6.237.312
$6.276.543
NPV of Investment
Output Value
Base
$6.276.543
$6.276.543
$6.276.543
$6.276.543
$6.276.543
$6.276.543
$6.276.543
$6.276.543
$6.276.543
$6.276.543
High
$8.869.625
$8.366.353
$7.809.117
$7.353.716
$7.313.979
$6.431.443
$6.400.463
$6.326.461
$6.315.774
$6.276.543
Swing
$4.730.863
$3.993.971
$3.091.088
$2.159.272
$2.074.873
$309.801
$247.841
$99.836
$78.463
$0
Percent
Swing^2
39,2%
28,0%
16,8%
8,2%
7,5%
0,2%
0,1%
0,0%
0,0%
0,0%
SP‐15
WACC
8,02%
12,02%
Increase in power prices
18%
12%
Equipment
$8.000.000 $12.000.000 Trader accuracy
100%
90%
Corporate tax rate
27%
41%
Salaries
$90.000 $60.000 Maintenance
$144.000 $96.000 Working capital
$240.000 $160.000 Start‐up expense
$360.000 $240.000 Power losses
4%
$3,5
$4,5
$5,5
2%
$6,5
$7,5
$8,5
$9,5
Millions
NPV of Investment
Exhibit 14.3
SensIt 1.45
San Diego
Tornado Analysis
Input Variable
WACC
Increase in power prices
Equipment
Corporate tax rate
Trader accuracy
Salaries
Maintenance
Working capital
Start‐up expense
Power losses
Corresponding Input Value
Low Output
Base Case
High Output
12,02%
10,02%
8,02%
12%
15%
18%
$12.000.000
$10.000.000
$8.000.000
41%
34%
27%
90%
95%
100%
$90.000
$75.000
$60.000
$144.000
$120.000
$96.000
$240.000
$200.000
$160.000
$360.000
$300.000
$240.000
4%
3%
2%
Low
$7.976.952
$8.294.232
$9.127.997
$9.172.094
$9.358.999
$10.497.186
$10.527.441
$10.602.625
$10.612.423
$10.648.459
NPV of Investment
Output Value
Base
$10.648.459
$10.648.459
$10.648.459
$10.648.459
$10.648.459
$10.648.459
$10.648.459
$10.648.459
$10.648.459
$10.648.459
High
$13.892.296
$13.238.536
$12.157.549
$12.124.823
$11.937.323
$10.799.732
$10.769.477
$10.694.293
$10.684.495
$10.648.459
Swing
$5.915.344
$4.944.303
$3.029.552
$2.952.729
$2.578.324
$302.546
$242.037
$91.669
$72.072
$0
Percent
Swing^2
41,6%
29,1%
10,9%
10,4%
7,9%
0,1%
0,1%
0,0%
0,0%
0,0%
San Diego
WACC
8,02%
12,02%
Increase in power prices
18%
12%
Equipment
$12.000.000 Corporate tax rate
41%
Trader accuracy
$8.000.000 27%
100%
90%
Salaries
$90.000 $60.000 Maintenance
$144.000 $96.000 Working capital
$240.000 $160.000 Start‐up expense
$360.000 $240.000 Power losses
4%
$7
$8
$9
$10
2%
$11
$12
$13
$14
Millions
NPV of Investment
Exhibit 14.4
SensIt 1.45
So Cal
Tornado Analysis
Input Variable
WACC
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
Salaries
Maintenance
Working capital
Start‐up expense
Power losses
Corresponding Input Value
Low Output
Base Case
High Output
12,02%
10,02%
8,02%
12%
15%
18%
$12.000.000
$10.000.000
$8.000.000
90%
95%
100%
41%
34%
27%
$90.000
$75.000
$60.000
$144.000
$120.000
$96.000
$240.000
$200.000
$160.000
$360.000
$300.000
$240.000
4%
3%
2%
Low
$4.188.587
$4.423.009
$4.775.295
$5.233.650
$5.290.191
$6.178.248
$6.209.228
$6.283.230
$6.293.917
$6.333.148
NPV of Investment
Output Value
Base
$6.333.148
$6.333.148
$6.333.148
$6.333.148
$6.333.148
$6.333.148
$6.333.148
$6.333.148
$6.333.148
$6.333.148
High
$8.934.499
$8.429.000
$7.865.217
$7.427.059
$7.376.105
$6.488.049
$6.457.069
$6.383.066
$6.372.380
$6.333.148
Swing
$4.745.912
$4.005.991
$3.089.921
$2.193.409
$2.085.915
$309.801
$247.841
$99.836
$78.463
$0
Percent
Swing^2
39,2%
27,9%
16,6%
8,4%
7,6%
0,2%
0,1%
0,0%
0,0%
0,0%
So Cal
WACC
12,02%
Increase in power prices
8,02%
12%
Equipment
18%
$12.000.000 Trader accuracy
$8.000.000 90%
Corporate tax rate
100%
41%
27%
Salaries
$90.000 $60.000 Maintenance
$144.000 $96.000 Working capital
$240.000 $160.000 Start‐up expense
$360.000 $240.000 Power losses
4%
$3,5
$4,5
$5,5
2%
$6,5
$7,5
$8,5
$9,5
Millions
NPV of Investment
Exhibit 14.5
SensIt 1.45
Long Island Equity
Tornado Analysis
Input Variable
Return on Equity
Equipment
Increase in power prices
Trader accuracy
Corporate tax rate
Salaries
Start‐up expense
Working capital
Maintenance
Power losses
Corresponding Input Value
Low Output
Base Case
High Output
24,00%
20,00%
16,00%
$12.000.000
$10.000.000
$8.000.000
12%
15%
18%
90%
95%
100%
41%
34%
27%
$90.000
$75.000
$60.000
$360.000
$300.000
$240.000
$240.000
$200.000
$160.000
$144.000
$120.000
$96.000
4%
3%
2%
Low
‐$355.999
‐$295.731
‐$86.688
$66.833
$444.938
$819.389
$826.152
$835.780
$839.250
$918.692
NPV of Investment
Output Value
Base
$918.692
$918.692
$918.692
$918.692
$918.692
$918.692
$918.692
$918.692
$918.692
$918.692
High
$2.734.378
$2.098.583
$2.006.438
$1.753.409
$1.392.447
$1.016.669
$1.010.694
$1.000.476
$997.398
$918.692
Percent
Swing^2
40,6%
24,4%
18,6%
12,1%
3,8%
0,2%
0,1%
0,1%
0,1%
0,0%
Swing
$3.090.376
$2.394.315
$2.093.125
$1.686.576
$947.509
$197.280
$184.542
$164.696
$158.149
$0
Long Island Equity
Return on Equity
Equipment
16,00%
24,00%
$8.000.000 $12.000.000 Increase in power prices
18%
12%
Trader accuracy
100%
90%
Corporate tax rate
27%
41%
Salaries
$90.000 $60.000 Start‐up expense
$360.000 $240.000 Working capital
$240.000 $160.000 Maintenance
$144.000 $96.000 Power losses
4%
‐$1,0
‐$0,5
$0,0
$0,5
2%
$1,0
$1,5
$2,0
$2,5
$3,0
Millions
NPV of Investment
Exhibit 14.6
SensIt 1.45
SP‐15 Equity
Tornado Analysis
Input Variable
Return on Equity
Equipment
Increase in power prices
Trader accuracy
Corporate tax rate
Salaries
Start‐up expense
Working capital
Maintenance
Power losses
Corresponding Input Value
Low Output
Base Case
High Output
24,00%
20,00%
16,00%
$12.000.000
$10.000.000
$8.000.000
12%
15%
18%
90%
95%
100%
41%
34%
27%
$90.000
$75.000
$60.000
$360.000
$300.000
$240.000
$240.000
$200.000
$160.000
$144.000
$120.000
$96.000
4%
3%
2%
NPV of Investment
Output Value
Base
$688.778
$688.778
$688.778
$688.778
$688.778
$688.778
$688.778
$688.778
$688.778
$688.778
Low
‐$548.622
‐$531.716
‐$294.675
$69.799
$235.400
$589.474
$596.238
$605.865
$609.335
$688.778
High
$2.453.044
$1.873.734
$1.757.900
$1.300.809
$1.142.156
$788.081
$781.318
$771.691
$768.221
$688.778
Percent
Swing^2
42,0%
26,9%
19,6%
7,1%
3,8%
0,2%
0,2%
0,1%
0,1%
0,0%
Swing
$3.001.665
$2.405.450
$2.052.576
$1.231.010
$906.756
$198.607
$185.081
$165.825
$158.886
$0
SP‐15 Equity
Return on Equity
16,00%
24,00%
Equipment $12.000.000 Increase in power prices
$8.000.000 18%
12%
Trader accuracy
100%
90%
Corporate tax rate
27%
41%
Salaries
$90.000 $60.000 Start‐up expense
$360.000 $240.000 Working capital
$240.000 $160.000 Maintenance
$144.000 $96.000 Power losses
‐$1,0
4%
‐$0,5
$0,0
$0,5
2%
$1,0
$1,5
$2,0
$2,5
$3,0
Millions
NPV of Investment
Exhibit 14.7
SensIt 1.45
San Diego Equity
Tornado Analysis
Input Variable
Return on Equity
Increase in power prices
Equipment
Trader accuracy
Corporate tax rate
Salaries
Start‐up expense
Working capital
Maintenance
Power losses
Corresponding Input Value
Low Output
Base Case
High Output
24,00%
20,00%
16,00%
12%
15%
18%
$12.000.000
$10.000.000
$8.000.000
90%
95%
100%
41%
34%
27%
$90.000
$75.000
$60.000
$360.000
$300.000
$240.000
$240.000
$200.000
$160.000
$144.000
$120.000
$96.000
4%
3%
2%
Low
$1.502.892
$1.952.621
$1.986.447
$2.432.708
$2.464.177
$3.059.387
$3.065.557
$3.076.497
$3.078.185
$3.153.378
NPV of Investment
Output Value
Base
$3.153.378
$3.153.378
$3.153.378
$3.153.378
$3.153.378
$3.153.378
$3.153.378
$3.153.378
$3.153.378
$3.153.378
High
$5.487.720
$4.467.458
$4.302.449
$3.873.132
$3.842.579
$3.247.369
$3.241.199
$3.230.259
$3.228.571
$3.153.378
Swing
$3.984.828
$2.514.836
$2.316.002
$1.440.424
$1.378.402
$187.982
$175.643
$153.762
$150.386
$0
Percent
Swing^2
50,2%
20,0%
16,9%
6,6%
6,0%
0,1%
0,1%
0,1%
0,1%
0,0%
San Diego Equity
Return on Equity
16,00%
24,00%
Increase in power prices
18%
12%
Equipment
$8.000.000 $12.000.000 Trader accuracy
90%
100%
Corporate tax rate
41%
27%
Salaries
$90.000 $60.000 Start‐up expense
$360.000 $240.000 Working capital
$240.000 $160.000 Maintenance
$144.000 $96.000 Power losses
4%
$1,0
$1,5
$2,0
$2,5
$3,0
2%
$3,5
$4,0
$4,5
$5,0
$5,5
$6,0
Millions
NPV of Investment
Exhibit 14.8
SensIt 1.45
So Cal Equity
Tornado Analysis
Input Variable
WACC
Equipment
Increase in power prices
Trader accuracy
Corporate tax rate
Salaries
Start‐up expense
Working capital
Maintenance
Power losses
Low Output
24,00%
$12.000.000
12%
90%
41%
$90.000
$360.000
$240.000
$144.000
4%
Corresponding Input Value
Base Case
High Output
20,00%
16,00%
$10.000.000
$8.000.000
15%
18%
95%
100%
34%
27%
$75.000
$60.000
$300.000
$240.000
$200.000
$160.000
$120.000
$96.000
3%
2%
Low
‐$521.494
‐$498.482
‐$265.384
$92.226
$264.909
$621.854
$628.617
$638.244
$641.714
$721.157
NPV of Investment
Output Value
Base
$721.157
$721.157
$721.157
$721.157
$721.157
$721.157
$721.157
$721.157
$721.157
$721.157
High
$2.492.664
$1.905.400
$1.792.902
$1.342.209
$1.177.405
$820.461
$813.697
$804.070
$800.600
$721.157
Percent
Swing^2
42,0%
26,7%
19,6%
7,2%
3,9%
0,2%
0,2%
0,1%
0,1%
0,0%
Swing
$3.014.159
$2.403.881
$2.058.286
$1.249.982
$912.496
$198.607
$185.081
$165.825
$158.886
$0
So Cal Equity
WACC
Equipment
24,00%
16,00%
$8.000.000 $12.000.000 Increase in power prices
12%
Trader accuracy
18%
90%
Corporate tax rate
100%
41%
27%
Salaries
$90.000 $60.000 Start‐up expense
$360.000 $240.000 Working capital
$240.000 $160.000 Maintenance
$144.000 $96.000 Power losses
4%
‐$1,0
‐$0,5
$0,0
$0,5
2%
$1,0
$1,5
$2,0
$2,5
$3,0
Millions
NPV of Investment
Exhibit 15.1
Variation
System used
WACC
Increase
Equipment
10%
Long Isl
10,02%
15%
10.000.000
Long Island
∆ Increase in power prices
6.678.480
18,75%
18,00%
17,25%
16,50%
15,75%
15,00%
14,25%
13,50%
12,75%
12,00%
11,25%
12,53%
6.248.369
5.779.383
5.321.079
4.873.264
4.435.749
4.007.031
3.587.001
3.176.740
2.776.067
2.384.805
2.002.778
12,02%
6.816.366
6.330.047
5.854.834
5.390.527
4.936.928
4.492.557
4.057.295
3.632.188
3.217.046
2.811.686
2.415.923
11,52%
7.411.477
6.906.957
6.413.988
5.932.362
5.461.874
5.001.066
4.549.810
4.109.113
3.678.779
3.258.616
2.848.432
11,02%
8.035.305
7.511.661
7.000.038
6.500.217
6.011.986
5.533.910
5.065.852
4.608.778
4.162.485
3.726.771
3.301.439
10,52%
8.689.560
8.145.815
7.614.584
7.095.639
6.588.758
6.092.533
5.606.815
5.132.529
4.669.463
4.217.407
3.776.154
6.678.480
7.500.000
8.000.000
8.500.000
9.000.000
9.500.000
10.000.000
10.500.000
11.000.000
11.500.000
12.000.000
12.500.000
12,53%
5.967.076
5.578.210
5.185.744
4.793.278
4.400.812
4.007.031
3.608.351
3.209.672
2.810.993
2.407.586
2.001.612
12,02%
6.442.685
6.055.719
5.665.250
5.274.781
4.884.312
4.492.557
4.096.016
3.699.475
3.302.934
2.901.751
2.498.050
11,52%
6.941.025
6.556.006
6.167.585
5.779.163
5.390.742
5.001.066
4.606.720
4.212.374
3.818.029
3.419.134
3.017.771
11,02%
7.463.441
7.080.417
6.694.096
6.307.775
5.921.453
5.533.910
5.141.819
4.749.728
4.357.636
3.961.095
3.562.139
10,52%
8.011.367
7.630.391
7.246.223
6.862.056
6.477.888
6.092.533
5.702.758
5.312.982
4.923.207
4.529.086
4.132.608
13,50%
7.596.356
7.215.243
6.833.286
6.451.328
6.069.203
5.681.807
5.294.410
4.907.013
4.514.511
4.120.584
3.726.657
∆ WACC
10,02%
9.376.071
8.811.187
8.259.336
7.720.281
7.193.789
6.678.480
6.174.192
5.681.807
5.201.104
4.731.864
4.273.872
9,52%
10.096.791
9.509.668
8.936.122
8.375.909
7.828.787
7.293.399
6.769.575
6.258.150
5.758.893
5.271.577
4.795.976
9,02%
10.853.813
10.243.279
9.646.899
9.064.416
8.495.577
7.939.057
7.394.670
6.863.205
6.344.420
5.838.079
5.343.945
8,52%
11.649.373
11.014.185
10.393.758
9.787.823
9.196.116
8.617.342
8.051.301
7.498.733
6.959.386
6.433.011
5.919.363
8,02%
12.485.868
11.824.704
11.178.941
10.548.297
9.932.497
9.330.276
8.741.422
8.166.621
7.605.610
7.058.130
6.523.924
7,52%
13.365.866
12.677.320
12.004.848
11.348.158
10.706.962
10.080.027
9.467.125
8.868.889
8.285.044
7.715.317
7.159.443
9,52%
9.189.998
8.813.275
8.433.585
8.053.894
7.674.204
7.293.399
6.908.447
6.523.494
6.138.541
5.749.474
5.358.174
9,02%
9.824.101
9.449.587
9.072.223
8.694.860
8.317.496
7.939.057
7.556.616
7.174.174
6.791.733
6.405.304
6.016.710
8,52%
10.490.525
10.118.278
9.743.303
9.368.327
8.993.352
8.617.342
8.237.482
7.857.622
7.477.761
7.094.047
6.708.240
8,02%
11.191.283
10.821.364
10.448.840
10.076.316
9.703.792
9.330.276
8.953.069
8.575.862
8.198.655
7.817.735
7.434.801
7,52%
11.928.532
11.561.001
11.190.994
10.820.988
10.450.981
10.080.027
9.705.547
9.331.068
8.956.588
8.578.546
8.198.571
∆ Increase in power prices
14,25%
15,00%
15,75%
8.085.409
8.586.339
9.099.371
7.705.412
8.207.462
8.721.440
7.323.454
7.825.505
8.339.662
6.941.496
7.443.547
7.957.705
6.559.538
7.061.589
7.575.747
6.174.192
6.678.480
7.193.789
5.786.795
6.291.083
6.807.499
5.399.398
5.903.686
6.420.102
5.010.572
5.516.290
6.032.705
4.616.644
5.124.660
5.644.854
4.222.717
4.730.733
5.250.927
16,50%
9.624.732
9.246.801
8.866.154
8.484.196
8.102.238
7.720.281
7.336.269
6.948.872
6.561.475
6.174.079
5.783.530
17,25%
10.162.652
9.784.720
9.405.209
9.023.251
8.641.293
8.259.336
7.877.378
7.490.227
7.102.830
6.715.433
6.328.037
18,00%
10.713.363
10.335.431
9.957.060
9.575.103
9.193.145
8.811.187
8.429.230
8.044.399
7.657.002
7.269.605
6.882.209
18,75%
11.277.100
10.899.169
10.521.238
10.139.986
9.758.029
9.376.071
8.994.113
8.611.624
8.224.227
7.836.831
7.449.434
Long Island
∆ Equipment
∆ WACC
10,02%
8.586.339
8.207.462
7.825.505
7.443.547
7.061.589
6.678.480
6.291.083
5.903.686
5.516.290
5.124.660
4.730.733
Long Island
∆ Equipment
6.678.480
7.500.000
8.000.000
8.500.000
9.000.000
9.500.000
10.000.000
10.500.000
11.000.000
11.500.000
12.000.000
12.500.000
11,25%
6.195.794
5.813.837
5.431.879
5.048.665
4.661.269
4.273.872
3.883.710
3.489.783
3.095.855
2.697.685
2.296.428
12,00%
6.651.631
6.269.673
5.887.715
5.505.758
5.119.261
4.731.864
4.344.467
3.951.298
3.557.371
3.163.444
2.763.130
12,75%
7.118.695
6.736.737
6.354.779
5.972.821
5.588.500
5.201.104
4.813.707
4.424.111
4.030.184
3.636.257
3.241.228
Exhibit 15.2
Variation
System used
WACC
Increase
Equipment
10%
SP‐15
10,02%
15%
10.000.000
SP‐15
∆ Increase in power prices
6.276.543
18,75%
18,00%
17,25%
16,50%
15,75%
15,00%
14,25%
13,50%
12,75%
12,00%
11,25%
12,53%
5.860.479
5.401.721
4.953.412
4.515.015
4.084.496
3.663.892
3.253.023
2.851.709
2.459.775
2.077.047
1.702.755
12,02%
6.415.926
5.940.214
5.475.365
5.020.843
4.574.649
4.138.762
3.712.993
3.297.157
2.891.069
2.494.550
2.106.833
11,52%
6.997.875
6.504.358
6.022.140
5.550.686
5.088.031
4.636.095
4.194.681
3.763.595
3.342.647
2.931.647
2.529.836
11,02%
7.607.890
7.095.666
6.595.201
6.105.957
5.626.009
5.157.213
4.699.362
4.252.257
3.815.698
3.389.487
2.972.870
10,52%
8.247.642
7.715.756
7.196.110
6.688.168
6.190.044
5.703.526
5.228.401
4.764.459
4.311.492
3.869.295
3.437.118
∆ WACC
10,02%
8.918.917
8.366.353
7.826.537
7.298.934
6.781.696
6.276.543
5.783.253
5.301.607
4.831.388
4.372.382
3.923.845
6.276.543
7.500.000
8.000.000
8.500.000
9.000.000
9.500.000
10.000.000
10.500.000
11.000.000
11.500.000
12.000.000
12.500.000
12,53%
5.630.899
5.239.174
4.846.708
4.454.242
4.061.776
3.663.892
3.265.213
2.866.533
2.465.177
2.059.204
1.653.231
12,02%
6.095.681
5.705.933
5.315.464
4.924.995
4.534.526
4.138.762
3.742.220
3.345.679
2.946.510
2.542.809
2.139.108
11,52%
6.582.667
6.194.947
5.806.525
5.418.104
5.029.683
4.636.095
4.241.749
3.847.403
3.450.482
3.049.119
2.647.756
11,02%
7.093.172
6.707.530
6.321.208
5.934.887
5.548.566
5.157.213
4.765.121
4.373.030
3.978.419
3.579.463
3.180.507
10,52%
7.628.596
7.245.086
6.860.919
6.476.751
6.092.584
5.703.526
5.313.750
4.923.975
4.531.739
4.135.261
3.738.782
∆ WACC
10,02%
8.190.440
7.809.117
7.427.159
7.045.202
6.663.244
6.276.543
5.889.146
5.501.749
5.111.956
4.718.029
4.324.102
13,50%
7.220.496
6.838.538
6.456.580
6.074.623
5.689.004
5.301.607
4.914.210
4.523.706
4.129.779
3.735.852
3.340.690
9,52%
9.623.625
9.049.307
8.488.270
7.939.980
7.402.633
6.877.876
6.365.476
5.865.205
5.376.837
4.900.149
4.434.401
9,02%
10.363.810
9.766.592
9.183.218
8.613.154
8.054.641
7.509.248
6.976.734
6.456.860
5.949.390
5.454.092
4.970.232
8,52%
11.141.657
10.520.323
9.913.427
9.320.433
8.739.629
8.172.506
7.618.811
7.078.294
6.550.710
6.035.815
5.532.883
8,02%
11.959.508
11.312.764
10.681.084
10.063.932
9.459.643
8.869.625
8.293.613
7.731.349
7.182.573
6.647.033
6.124.010
7,52%
12.819.870
12.146.341
11.488.536
10.845.917
10.216.872
9.602.721
9.003.186
8.417.997
7.846.885
7.289.585
6.745.385
9,52%
8.780.306
8.401.226
8.021.536
7.641.845
7.262.155
6.877.876
6.492.923
6.107.970
5.720.688
5.329.388
4.938.088
9,02%
9.399.908
9.023.131
8.645.767
8.268.404
7.891.040
7.509.248
7.126.807
6.744.366
6.359.666
5.971.072
5.582.479
8,52%
10.051.081
9.676.668
9.301.692
8.926.717
8.551.741
8.172.506
7.792.646
7.412.786
7.030.743
6.644.937
6.259.131
8,02%
10.735.792
10.363.805
9.991.281
9.618.757
9.246.233
8.869.625
8.492.418
8.115.210
7.735.901
7.352.967
6.970.032
7,52%
11.456.145
11.086.648
10.716.641
10.346.635
9.976.628
9.602.721
9.228.241
8.853.761
8.477.264
8.097.289
7.717.314
∆ Increase in power prices
14,25%
15,00%
15,75%
7.699.973
8.190.440
8.692.283
7.318.016
7.809.117
8.312.061
6.936.058
7.427.159
7.930.103
6.554.100
7.045.202
7.548.146
6.170.650
6.663.244
7.166.188
5.783.253
6.276.543
6.781.696
5.395.856
5.889.146
6.394.299
5.008.460
5.501.749
6.006.902
4.615.020
5.111.956
5.619.506
4.221.093
4.718.029
5.226.878
3.827.166
4.324.102
4.832.951
16,50%
9.206.186
8.827.070
8.445.112
8.063.154
7.681.197
7.298.934
6.911.537
6.524.140
6.136.743
5.747.865
5.353.937
17,25%
9.732.374
9.354.368
8.972.411
8.590.453
8.208.495
7.826.537
7.441.085
7.053.688
6.666.291
6.278.895
5.887.286
18,00%
10.271.074
9.893.143
9.512.226
9.130.269
8.748.311
8.366.353
7.983.170
7.595.774
7.208.377
6.820.980
6.433.225
18,75%
10.822.517
10.444.585
10.064.790
9.682.832
9.300.875
8.918.917
8.536.959
8.150.628
7.763.231
7.375.834
6.988.438
SP‐15
∆ Equipment
SP‐15
∆ Equipment
6.276.543
7.500.000
8.000.000
8.500.000
9.000.000
9.500.000
10.000.000
10.500.000
11.000.000
11.500.000
12.000.000
12.500.000
11,25%
5.849.653
5.467.696
5.085.738
4.699.172
4.311.776
3.923.845
3.529.918
3.135.991
2.739.976
2.338.719
1.937.463
12,00%
6.295.548
5.913.590
5.531.633
5.147.176
4.759.779
4.372.382
3.981.368
3.587.441
3.193.514
2.795.242
2.393.986
12,75%
6.752.425
6.370.468
5.988.510
5.606.181
5.218.785
4.831.388
4.443.869
4.049.942
3.656.015
3.262.088
2.861.657
Exhibit 15.3
Variation
System used
WACC
Increase
Equipment
10%
San Diego
10,02%
15%
10.000.000
San Diego
∆ Increase in power prices
10.648.459
18,75%
18,00%
17,25%
16,50%
15,75%
15,00%
14,25%
13,50%
12,75%
12,00%
11,25%
12,53%
10.103.518
9.534.064
8.977.610
8.433.921
7.902.767
7.383.921
6.877.156
6.382.252
5.898.987
5.427.147
4.966.516
12,02%
10.797.228
10.206.624
9.629.536
9.065.721
8.514.939
7.976.952
7.451.526
6.938.429
6.437.434
5.948.315
5.470.848
11,52%
11.524.194
10.911.380
10.312.626
9.727.677
9.156.283
8.598.198
8.053.178
7.520.981
7.001.369
6.494.108
5.998.966
11,02%
12.286.385
11.650.238
11.028.720
10.421.569
9.828.523
9.249.325
8.683.721
8.131.460
7.592.293
7.065.976
6.552.267
10,52%
13.085.904
12.425.231
11.779.789
11.149.302
10.533.499
9.932.112
9.344.875
8.771.527
8.211.809
7.665.466
7.132.246
10.648.459
7.500.000
8.000.000
8.500.000
9.000.000
9.500.000
10.000.000
10.500.000
11.000.000
11.500.000
12.000.000
12.500.000
12,53%
9.316.910
8.931.819
8.546.728
8.159.444
7.771.683
7.383.921
6.996.159
6.607.599
6.215.133
5.822.667
5.430.201
12,02%
9.900.775
9.517.470
9.134.165
8.748.736
8.362.844
7.976.952
7.591.060
7.204.391
6.813.922
6.423.453
6.032.984
11,52%
10.512.626
10.131.152
9.749.678
9.366.150
8.982.174
8.598.198
8.214.222
7.829.492
7.441.071
7.052.649
6.664.228
11,02%
11.154.121
10.774.524
10.394.927
10.013.348
9.631.337
9.249.325
8.867.314
8.484.571
8.098.250
7.711.928
7.325.607
10,52%
11.827.029
11.449.358
11.071.687
10.692.106
10.312.109
9.932.112
9.552.115
9.171.410
8.787.243
8.403.075
8.018.908
13,50%
11.328.654
10.952.958
10.576.750
10.198.819
9.820.887
9.442.956
9.065.025
8.683.688
8.301.730
7.919.772
7.537.815
∆ WACC
10,02%
13.924.999
13.238.536
12.567.935
11.912.910
11.273.178
10.648.459
10.038.476
9.442.956
8.861.630
8.294.232
7.740.500
9,52%
14.806.074
14.092.477
13.395.408
12.714.569
12.049.663
11.400.399
10.766.488
10.147.645
9.543.590
8.954.044
8.378.734
9,02%
15.731.699
14.989.543
14.264.616
13.556.606
12.865.204
12.190.106
11.531.012
10.887.623
10.259.646
9.646.792
9.048.775
8,52%
16.704.628
15.932.396
15.178.132
14.441.510
13.722.208
13.019.908
12.334.296
11.665.061
11.011.897
10.374.501
9.752.576
8,02%
17.727.808
16.923.888
16.138.716
15.371.950
14.623.254
13.892.296
13.178.747
12.482.284
11.802.587
11.139.339
10.492.229
7,52%
18.804.395
17.967.074
17.149.321
16.350.782
15.571.104
14.809.939
14.066.946
13.341.785
12.634.122
11.943.627
11.269.974
9,52%
13.274.793
12.901.123
12.527.454
12.152.024
11.776.212
11.400.399
11.024.586
10.648.115
10.268.425
9.888.735
9.509.044
9,02%
14.053.838
13.682.248
13.310.657
12.937.385
12.563.746
12.190.106
11.816.467
11.442.196
11.064.832
10.687.469
10.310.105
8,52%
14.872.698
14.503.242
14.133.786
13.762.726
13.391.317
13.019.908
12.648.499
12.276.485
11.901.509
11.526.534
11.151.558
8,02%
15.733.855
15.366.590
14.999.325
14.630.537
14.261.416
13.892.296
13.523.176
13.153.478
12.780.954
12.408.430
12.035.906
7,52%
16.639.969
16.274.954
15.909.938
15.543.481
15.176.710
14.809.939
14.443.168
14.075.848
13.705.841
13.335.835
12.965.828
∆ Increase in power prices
14,25%
15,00%
15,75%
11.923.718
12.533.246
13.157.509
11.548.022
12.157.549
12.781.813
11.172.270
11.781.853
12.406.117
10.794.339
11.404.322
12.029.041
10.416.407
11.026.390
11.651.110
10.038.476
10.648.459
11.273.178
9.660.544
10.270.528
10.895.247
9.280.565
9.891.913
10.517.316
8.898.608
9.509.955
10.136.045
8.516.650
9.127.997
9.754.087
8.134.692
8.746.040
9.372.129
16,50%
13.796.785
13.421.089
13.045.393
12.668.773
12.290.842
11.912.910
11.534.979
11.157.048
10.777.154
10.395.196
10.013.238
17,25%
14.451.354
14.075.658
13.699.962
13.323.798
12.945.866
12.567.935
12.190.004
11.812.072
11.433.561
11.051.603
10.669.646
18,00%
15.121.499
14.745.803
14.370.107
13.994.398
13.616.467
13.238.536
12.860.604
12.482.673
12.104.741
11.723.593
11.341.635
18,75%
15.807.506
15.431.810
15.056.114
14.680.418
14.302.931
13.924.999
13.547.068
13.169.136
12.791.205
12.411.452
12.029.494
San Diego
∆ Equipment
∆ WACC
10,02%
12.533.246
12.157.549
11.781.853
11.404.322
11.026.390
10.648.459
10.270.528
9.891.913
9.509.955
9.127.997
8.746.040
San Diego
∆ Equipment
10.648.459
7.500.000
8.000.000
8.500.000
9.000.000
9.500.000
10.000.000
10.500.000
11.000.000
11.500.000
12.000.000
12.500.000
11,25%
9.627.566
9.251.869
8.874.294
8.496.363
8.118.431
7.740.500
7.359.152
6.977.195
6.595.237
6.212.847
5.825.451
12,00%
10.180.842
9.805.146
9.428.027
9.050.095
8.672.164
8.294.232
7.914.224
7.532.267
7.150.309
6.768.351
6.383.148
12,75%
10.747.784
10.372.088
9.995.424
9.617.493
9.239.562
8.861.630
8.482.968
8.101.010
7.719.052
7.337.095
6.954.541
Exhibit 15.4
Variation
System used
WACC
Increase
Equipment
10%
SoCal
10,02%
15%
10.000.000
SoCal
∆ Increase in power prices
6.333.148
18,75%
18,00%
17,25%
16,50%
15,75%
15,00%
14,25%
13,50%
12,75%
12,00%
11,25%
12,53%
5.915.106
5.454.908
5.005.191
4.565.767
4.134.141
3.712.217
3.300.057
2.897.484
2.504.319
2.120.389
1.745.520
12,02%
6.472.321
5.995.115
5.528.806
5.073.199
4.625.843
4.188.587
3.761.481
3.344.339
2.936.977
2.539.213
2.150.866
11,52%
7.056.123
6.561.057
6.077.325
5.604.724
5.140.850
4.687.494
4.244.694
3.812.255
3.389.985
2.977.695
2.575.197
11,02%
7.668.083
7.154.251
6.652.215
6.161.760
5.680.532
5.210.263
4.750.976
4.302.467
3.864.536
3.436.987
3.019.625
10,52%
8.309.878
7.776.321
7.255.044
6.745.823
6.246.356
5.758.310
5.281.694
4.816.295
4.361.906
3.918.320
3.485.336
∆ WACC
10,02%
8.983.299
8.429.000
7.887.489
7.358.535
6.839.887
6.333.148
5.838.310
5.355.151
4.883.456
4.423.009
3.973.599
6.333.148
7.500.000
8.000.000
8.500.000
9.000.000
9.500.000
10.000.000
10.500.000
11.000.000
11.500.000
12.000.000
12.500.000
12,53%
5.678.243
5.286.921
4.894.455
4.501.989
4.109.523
3.712.217
3.313.537
2.914.858
2.514.240
2.108.267
1.702.294
12,02%
6.144.550
5.755.194
5.364.725
4.974.256
4.583.787
4.188.587
3.792.046
3.395.505
2.997.060
2.593.359
2.189.658
11,52%
6.633.135
6.245.795
5.857.374
5.468.952
5.080.531
4.687.494
4.293.148
3.898.803
3.502.592
3.101.228
2.699.865
11,02%
7.145.317
6.760.044
6.373.723
5.987.401
5.601.080
5.210.263
4.818.172
4.426.081
4.032.165
3.633.209
3.234.252
10,52%
7.682.503
7.299.349
6.915.182
6.531.014
6.146.847
5.758.310
5.368.535
4.978.759
4.587.202
4.190.724
3.794.245
∆ WACC
10,02%
8.246.195
7.865.217
7.483.259
7.101.301
6.719.343
6.333.148
5.945.752
5.558.355
5.169.222
4.775.295
4.381.368
13,50%
7.273.548
6.891.590
6.509.632
6.127.675
5.742.548
5.355.151
4.967.754
4.577.889
4.183.961
3.790.034
3.395.650
9,52%
9.690.262
9.114.140
8.551.342
8.001.627
7.462.800
6.936.394
6.422.386
5.920.544
5.430.642
4.952.457
4.485.768
9,02%
10.432.818
9.833.724
9.248.519
8.676.951
8.116.884
7.569.779
7.035.593
6.514.086
6.005.023
5.508.169
5.023.295
8,52%
11.213.159
10.589.874
9.981.072
9.386.492
8.804.057
8.235.153
7.679.719
7.137.505
6.608.264
6.091.752
5.587.729
8,02%
12.033.636
11.384.861
10.751.198
10.132.371
9.526.370
8.934.499
8.356.679
7.792.649
7.242.150
6.704.928
6.180.733
7,52%
12.896.764
12.221.119
11.561.249
10.916.864
10.286.020
9.669.940
9.068.523
8.481.497
7.908.592
7.349.541
6.804.083
9,52%
8.838.003
8.459.256
8.079.565
7.699.875
7.320.184
6.936.394
6.551.442
6.166.489
5.779.849
5.388.549
4.997.250
9,02%
9.459.647
9.083.189
8.705.826
8.328.462
7.951.098
7.569.779
7.187.337
6.804.896
6.420.820
6.032.226
5.643.632
8,52%
10.112.969
9.738.861
9.363.885
8.988.910
8.613.934
8.235.153
7.855.293
7.475.433
7.093.992
6.708.186
6.322.379
8,02%
10.799.940
10.428.243
10.055.719
9.683.195
9.310.671
8.934.499
8.557.292
8.180.085
7.801.355
7.418.421
7.035.486
7,52%
11.522.672
11.153.452
10.783.445
10.413.438
10.043.432
9.669.940
9.295.461
8.920.981
8.545.040
8.165.065
7.785.090
∆ Increase in power prices
14,25%
15,00%
15,75%
7.754.531
8.246.195
8.749.614
7.372.573
7.865.217
8.369.740
6.990.616
7.483.259
7.987.782
6.608.658
7.101.301
7.605.824
6.225.706
6.719.343
7.223.867
5.838.310
6.333.148
6.839.887
5.450.913
5.945.752
6.452.490
5.063.516
5.558.355
6.065.094
4.670.726
5.169.222
5.677.697
4.276.799
4.775.295
5.285.742
3.882.872
4.381.368
4.891.815
16,50%
9.265.131
8.886.366
8.504.408
8.122.450
7.740.493
7.358.535
6.971.352
6.583.956
6.196.559
5.808.365
5.414.438
17,25%
9.792.971
9.415.039
9.033.362
8.651.405
8.269.447
7.887.489
7.502.563
7.115.166
6.727.770
6.340.373
5.949.461
18,00%
10.333.362
9.955.431
9.574.873
9.192.915
8.810.958
8.429.000
8.046.351
7.658.954
7.271.558
6.884.161
6.496.764
18,75%
10.886.536
10.508.605
10.129.172
9.747.214
9.365.256
8.983.299
8.601.341
8.215.551
7.828.154
7.440.757
7.053.360
SoCal
∆ Equipment
SoCal
∆ Equipment
6.333.148
7.500.000
8.000.000
8.500.000
9.000.000
9.500.000
10.000.000
10.500.000
11.000.000
11.500.000
12.000.000
12.500.000
11,25%
5.898.401
5.516.443
5.134.486
4.748.392
4.360.995
3.973.599
3.579.743
3.185.816
2.790.530
2.389.273
1.988.016
12,00%
6.345.696
5.963.738
5.581.780
5.197.802
4.810.405
4.423.009
4.032.610
3.638.683
3.244.756
2.847.229
2.445.973
12,75%
6.804.007
6.422.050
6.040.092
5.658.134
5.270.852
4.883.456
4.496.059
4.102.637
3.708.710
3.314.783
2.915.113
Exhibit 15.5
Variation
System used
Return on equity
Increase
Equipment
10%
Long Isl
20,00%
15%
10.000.000
Long Island Equity
∆ Increase in power prices
918.692
18,75%
18,00%
17,25%
16,50%
15,75%
15,00%
14,25%
13,50%
12,75%
12,00%
11,25%
25%
425.192
208.865
-2.900
-210.182
-413.059
-613.331
-810.977
-1.004.414
-1.193.717
-1.378.960
-1.560.215
24%
739.501
511.215
287.777
69.102
-144.893
-355.999
-564.199
-767.931
-967.275
-1.162.308
-1.353.109
23%
1.080.487
839.271
603.212
372.220
146.206
-76.610
-296.216
-511.074
-721.267
-926.879
-1.127.993
22%
1.450.957
1.195.739
946.014
701.686
462.659
227.167
-4.785
-231.683
-453.616
-670.676
-882.948
21%
1.854.057
1.583.656
1.319.112
1.060.322
807.186
557.952
312.618
72.670
-161.989
-391.454
-615.819
918.692
7.500.000
8.000.000
8.500.000
9.000.000
9.500.000
10.000.000
10.500.000
11.000.000
11.500.000
12.000.000
12.500.000
25%
791.664
515.203
233.501
-48.202
-329.904
-613.331
-903.177
-1.193.024
-1.482.870
-1.778.296
-2.076.749
24%
1.060.667
781.800
497.778
213.755
-70.267
-355.999
-648.094
-940.190
-1.232.285
-1.529.956
-1.830.653
23%
1.352.322
1.070.923
784.463
498.003
211.543
-76.610
-371.062
-665.514
-959.965
-1.259.981
-1.563.016
22%
1.668.999
1.384.936
1.095.912
806.888
517.864
227.167
-69.755
-366.677
-663.599
-966.065
-1.271.539
21%
2.013.364
1.726.495
1.434.772
1.143.049
851.325
557.952
258.438
-41.077
-340.591
-645.620
-953.641
13,50%
1.884.153
1.590.887
1.296.321
1.001.754
706.952
404.714
102.477
-199.760
-508.599
-819.281
-1.129.962
∆ Return on equity
20%
2.293.331
2.006.438
1.725.799
1.451.305
1.182.847
918.692
658.830
404.714
156.243
-86.688
-324.178
19%
2.772.763
2.467.931
2.169.786
1.878.210
1.593.088
1.312.707
1.037.047
767.528
504.038
246.469
-5.289
18%
3.296.854
2.972.476
2.655.259
2.345.074
2.041.799
1.743.744
1.450.879
1.164.583
884.739
611.229
343.935
17%
3.870.683
3.524.976
3.186.946
2.856.456
2.533.374
2.216.036
1.904.401
1.599.806
1.302.123
1.011.226
726.989
16%
4.500.001
4.130.979
3.770.200
3.417.518
3.072.788
2.734.378
2.402.233
2.077.642
1.760.468
1.450.574
1.147.826
15%
5.191.324
4.796.775
4.411.089
4.034.110
3.665.681
3.304.203
2.949.608
2.603.131
2.264.624
1.933.940
1.610.934
19%
2.797.510
2.504.562
2.206.997
1.909.432
1.611.868
1.312.707
1.007.607
702.507
397.407
86.885
-226.580
18%
3.244.463
2.948.221
2.647.493
2.346.764
2.046.036
1.743.744
1.435.632
1.127.520
819.407
505.937
189.559
17%
3.733.566
3.433.843
3.129.773
2.825.703
2.521.634
2.216.036
1.904.752
1.593.467
1.282.182
965.617
646.186
16%
4.269.677
3.966.273
3.658.671
3.351.069
3.043.468
2.734.378
2.419.748
2.105.119
1.790.489
1.470.672
1.148.039
15%
4.858.302
4.551.002
4.239.663
3.928.325
3.616.986
3.304.203
2.986.044
2.667.885
2.349.725
2.026.487
1.700.493
∆ Increase in power prices
14,25%
15,00%
15,75%
2.133.427
2.388.411
2.649.209
1.841.876
2.098.583
2.360.836
1.547.310
1.804.017
2.066.546
1.252.743
1.509.450
1.771.980
958.176
1.214.883
1.477.413
658.830
918.692
1.182.847
356.592
616.455
882.169
54.355
314.217
579.932
-249.731
11.980
277.694
-560.413
-295.731
-25.130
-871.095
-606.413
-335.812
16,50%
2.915.929
2.627.555
2.335.005
2.040.438
1.745.872
1.451.305
1.153.841
851.604
549.366
247.129
-59.185
17,25%
3.188.676
2.900.303
2.609.499
2.314.932
2.020.366
1.725.799
1.431.232
1.129.341
827.104
524.866
222.629
18,00%
3.467.560
3.179.187
2.890.138
2.595.571
2.301.004
2.006.438
1.711.871
1.413.252
1.111.015
808.778
506.540
18,75%
3.752.690
3.464.317
3.175.944
2.882.464
2.587.897
2.293.331
1.998.764
1.703.448
1.401.210
1.098.973
796.735
Long Island Equity
∆ Equipment
∆ Return on equity
20%
2.388.411
2.098.583
1.804.017
1.509.450
1.214.883
918.692
616.455
314.217
11.980
-295.731
-606.413
Long Island Equity
∆ Equipment
918.692
7.500.000
8.000.000
8.500.000
9.000.000
9.500.000
10.000.000
10.500.000
11.000.000
11.500.000
12.000.000
12.500.000
11,25%
1.165.769
871.202
576.635
280.297
-21.940
-324.178
-629.991
-940.673
-1.251.354
-1.567.067
-1.886.438
12,00%
1.400.218
1.105.652
811.085
516.518
215.550
-86.688
-388.925
-698.627
-1.009.308
-1.319.990
-1.638.244
12,75%
1.640.080
1.345.513
1.050.946
756.380
458.480
156.243
-145.995
-451.076
-761.757
-1.072.439
-1.384.427
Exhibit 15.6
Variation
System used
Return on equity
Increase
Equipment
10%
SP‐15
20,00%
15%
10.000.000
SP‐15 Equity
∆ Increase in power prices
688.778
18,75%
18,00%
17,25%
16,50%
15,75%
15,00%
14,25%
13,50%
12,75%
12,00%
11,25%
25%
223.034
11.425
-195.722
-398.940
-600.730
-798.255
-991.589
-1.180.808
-1.365.983
-1.547.186
-1.725.191
24%
528.353
305.046
86.481
-127.878
-340.514
-548.622
-752.281
-951.570
-1.146.566
-1.337.346
-1.524.689
23%
859.656
623.701
392.790
166.387
-57.973
-277.519
-492.336
-702.508
-908.116
-1.109.244
-1.306.673
22%
1.219.677
970.026
725.747
486.303
249.253
17.326
-209.567
-431.516
-648.610
-860.935
-1.069.277
21%
1.611.486
1.346.982
1.088.207
834.624
583.815
338.468
98.484
-136.231
-365.772
-590.232
-810.399
∆ Return on equity
20%
2.038.536
1.757.900
1.483.382
1.214.444
948.697
688.778
434.583
186.010
-57.043
-294.675
-527.676
688.778
7.500.000
8.000.000
8.500.000
9.000.000
9.500.000
10.000.000
10.500.000
11.000.000
11.500.000
12.000.000
12.500.000
25%
616.280
335.657
53.955
-227.748
-509.450
-798.255
-1.088.101
-1.377.947
-1.670.953
-1.969.406
-2.267.858
24%
877.468
594.508
310.485
26.463
-257.559
-548.622
-840.717
-1.132.813
-1.428.066
-1.728.762
-2.029.458
23%
1.160.708
875.290
588.830
302.370
15.910
-277.519
-571.971
-866.423
-1.164.025
-1.467.060
-1.770.094
22%
1.468.313
1.180.311
891.286
602.262
313.238
17.326
-279.596
-576.518
-876.579
-1.182.053
-1.487.527
21%
1.802.879
1.512.155
1.220.432
928.709
636.986
338.468
38.953
-260.562
-563.198
-871.219
-1.179.239
∆ Return on equity
20%
2.167.325
1.873.734
1.579.167
1.284.601
990.034
688.778
386.540
84.303
-221.034
-531.716
-842.397
13,50%
1.671.677
1.377.111
1.082.544
787.977
488.247
186.010
-116.228
-422.483
-733.165
-1.043.846
-1.355.992
19%
2.504.716
2.206.532
1.914.890
1.629.250
1.347.258
1.071.493
801.845
538.204
280.461
28.509
-218.442
18%
3.014.413
2.697.110
2.386.811
2.082.977
1.783.289
1.490.266
1.203.788
923.737
649.996
382.451
120.311
17%
3.572.582
3.234.415
2.903.757
2.580.070
2.261.077
1.949.225
1.644.387
1.346.435
1.055.244
770.691
491.987
16%
4.184.832
3.823.858
3.470.948
3.125.563
2.785.471
2.453.044
2.128.143
1.810.631
1.500.374
1.197.239
900.440
15%
4.857.520
4.471.575
4.094.302
3.725.161
3.361.972
3.007.021
2.660.159
2.321.239
1.990.115
1.666.643
1.350.041
19%
2.564.937
2.268.323
1.970.759
1.673.194
1.375.629
1.071.493
766.393
461.293
153.123
-160.342
-473.806
18%
2.999.423
2.699.619
2.398.891
2.098.162
1.797.434
1.490.266
1.182.154
874.042
562.896
246.518
-69.860
17%
3.474.970
3.171.796
2.867.726
2.563.657
2.259.587
1.949.225
1.637.941
1.326.656
1.012.381
692.950
373.519
16%
3.996.316
3.689.579
3.381.978
3.074.376
2.766.774
2.453.044
2.138.414
1.823.785
1.506.218
1.183.585
860.952
15%
4.568.830
4.258.324
3.946.985
3.635.647
3.324.308
3.007.021
2.688.862
2.370.703
2.049.667
1.723.673
1.397.679
∆ Increase in power prices
14,25%
15,00%
15,75%
1.917.192
2.167.325
2.422.435
1.622.626
1.873.734
2.130.538
1.328.059
1.579.167
1.835.971
1.033.492
1.284.601
1.541.405
736.820
990.034
1.246.838
434.583
688.778
948.697
132.345
386.540
646.460
-169.892
84.303
344.222
-479.943
-221.034
41.985
-790.625
-531.716
-267.016
-1.101.306
-842.397
-577.698
16,50%
2.683.337
2.393.141
2.098.575
1.804.008
1.509.441
1.214.444
912.207
609.969
307.732
3.578
-307.104
17,25%
2.950.136
2.661.649
2.367.082
2.072.516
1.777.949
1.483.382
1.183.887
881.649
579.412
277.174
-30.510
18,00%
3.222.938
2.934.565
2.641.600
2.347.034
2.052.467
1.757.900
1.461.606
1.159.368
857.131
554.893
252.192
18,75%
3.501.849
3.213.476
2.922.236
2.627.669
2.333.103
2.038.536
1.743.969
1.443.235
1.140.997
838.760
536.522
SP‐15 Equity
∆ Equipment
SP‐15 Equity
∆ Equipment
688.778
7.500.000
8.000.000
8.500.000
9.000.000
9.500.000
10.000.000
10.500.000
11.000.000
11.500.000
12.000.000
12.500.000
11,25%
967.688
673.122
378.555
77.489
-224.748
-527.676
-838.357
-1.149.039
-1.462.196
-1.781.566
-2.100.937
12,00%
1.197.024
902.458
607.891
309.800
7.562
-294.675
-601.591
-912.272
-1.222.954
-1.538.786
-1.858.156
12,75%
1.431.655
1.137.088
842.521
547.432
245.194
-57.043
-359.439
-670.120
-980.802
-1.291.483
-1.609.875
Exhibit 15.7
Variation
System used
Return on equity
Increase
Equipment
10%
San Diego
20,00%
15%
10.000.000
San Diego Equity
∆ Increase in power prices
3.153.378
18,75%
18,00%
17,25%
16,50%
15,75%
15,00%
14,25%
13,50%
12,75%
12,00%
11,25%
25%
2.418.793
2.157.745
1.902.241
1.652.187
1.407.486
1.168.046
933.773
704.576
480.366
261.052
46.546
24%
2.823.233
2.547.583
2.277.826
2.013.859
1.755.581
1.502.892
1.255.693
1.013.887
777.378
546.070
319.869
23%
3.261.314
2.969.877
2.684.710
2.405.702
2.132.746
1.865.735
1.604.565
1.349.130
1.099.328
855.058
616.219
22%
3.736.542
3.428.011
3.126.157
2.830.865
2.542.019
2.259.504
1.983.209
1.713.022
1.448.834
1.190.535
938.019
21%
4.252.851
3.925.782
3.605.835
3.292.885
2.986.810
2.687.486
2.394.795
2.108.616
1.828.833
1.555.329
1.287.991
3.153.378
7.500.000
8.000.000
8.500.000
9.000.000
9.500.000
10.000.000
10.500.000
11.000.000
11.500.000
12.000.000
12.500.000
25%
2.532.896
2.262.364
1.991.831
1.717.751
1.442.898
1.168.046
893.193
617.177
335.475
53.772
-227.930
24%
2.880.140
2.607.068
2.333.997
2.057.465
1.780.178
1.502.892
1.225.606
947.177
663.155
379.132
95.110
23%
3.256.021
2.980.280
2.704.539
2.425.427
2.145.581
1.865.735
1.585.889
1.304.921
1.018.461
732.001
445.541
22%
3.663.512
3.384.962
3.106.413
2.824.586
2.542.045
2.259.504
1.976.963
1.693.322
1.404.298
1.115.274
826.250
21%
4.105.947
3.824.440
3.542.933
3.258.246
2.972.866
2.687.486
2.402.106
2.115.650
1.823.927
1.532.203
1.240.480
13,50%
3.974.567
3.689.945
3.404.463
3.116.090
2.827.717
2.539.344
2.250.971
1.957.359
1.662.792
1.368.226
1.073.659
∆ Return on equity
20%
4.814.661
4.467.458
4.127.861
3.795.737
3.470.952
3.153.378
2.842.884
2.539.344
2.242.631
1.952.621
1.669.193
19%
5.426.947
5.057.844
4.696.874
4.343.895
3.998.764
3.661.341
3.331.488
3.009.069
2.693.949
2.385.994
2.085.072
18%
6.095.319
5.702.356
5.318.103
4.942.405
4.575.110
4.216.069
3.865.131
3.522.151
3.186.984
2.859.487
2.539.518
17%
6.826.111
6.407.112
5.997.452
5.596.966
5.205.490
4.822.862
4.448.923
4.083.514
3.726.480
3.377.668
3.036.924
16%
7.626.487
7.179.030
6.741.602
6.314.025
5.896.123
5.487.720
5.088.646
4.698.730
4.317.804
3.945.703
3.582.263
15%
8.504.564
8.025.950
7.558.122
7.100.887
6.654.055
6.217.439
5.790.853
5.374.113
4.967.039
4.569.452
4.181.175
19%
5.111.103
4.823.195
4.535.286
4.244.403
3.952.872
3.661.341
3.369.810
3.077.255
2.779.690
2.482.125
2.184.561
18%
5.682.792
5.391.417
5.100.042
4.805.800
4.510.934
4.216.069
3.921.203
3.625.342
3.324.614
3.023.885
2.723.157
17%
6.307.504
6.012.468
5.717.431
5.419.642
5.121.252
4.822.862
4.524.473
4.225.119
3.921.049
3.616.980
3.312.910
16%
6.991.306
6.692.400
6.393.495
6.091.953
5.789.836
5.487.720
5.185.604
4.882.556
4.574.955
4.267.353
3.959.751
15%
7.741.069
7.438.071
7.135.073
6.829.560
6.523.500
6.217.439
5.911.378
5.604.422
5.293.083
4.981.745
4.670.406
∆ Increase in power prices
14,25%
15,00%
15,75%
4.277.343
4.587.072
4.903.881
3.992.720
4.302.449
4.619.259
3.708.003
4.017.826
4.334.636
3.419.630
3.730.124
4.047.699
3.131.257
3.441.751
3.759.325
2.842.884
3.153.378
3.470.952
2.554.511
2.865.005
3.182.579
2.262.988
2.575.581
2.894.206
1.968.422
2.281.014
2.600.696
1.673.855
1.986.447
2.306.130
1.379.288
1.691.881
2.011.563
16,50%
5.227.901
4.943.278
4.658.655
4.372.483
4.084.110
3.795.737
3.507.363
3.218.990
2.927.598
2.633.032
2.338.465
17,25%
5.559.260
5.274.638
4.990.015
4.704.607
4.416.234
4.127.861
3.839.488
3.551.115
3.261.850
2.967.283
2.672.717
18,00%
5.898.092
5.613.470
5.328.847
5.044.204
4.755.831
4.467.458
4.179.085
3.890.712
3.602.339
3.309.017
3.014.450
18,75%
6.244.531
5.959.908
5.675.285
5.390.662
5.103.034
4.814.661
4.526.288
4.237.915
3.949.542
3.658.366
3.363.800
San Diego Equity
∆ Equipment
∆ Return on equity
20%
4.587.072
4.302.449
4.017.826
3.730.124
3.441.751
3.153.378
2.865.005
2.575.581
2.281.014
1.986.447
1.691.881
San Diego Equity
∆ Equipment
3.153.378
7.500.000
8.000.000
8.500.000
9.000.000
9.500.000
10.000.000
10.500.000
11.000.000
11.500.000
12.000.000
12.500.000
11,25%
3.106.711
2.822.088
2.534.312
2.245.939
1.957.566
1.669.193
1.375.565
1.080.998
786.432
491.256
189.018
12,00%
3.389.375
3.104.752
2.817.741
2.529.368
2.240.995
1.952.621
1.661.054
1.366.487
1.071.921
777.354
478.210
12,75%
3.678.619
3.393.997
3.107.750
2.819.377
2.531.004
2.242.631
1.953.133
1.658.567
1.364.000
1.069.434
774.026
Exhibit 15.8
Variation
System used
Return on equity
Increase
Equipment
10%
SoCal
20,00%
15%
10.000.000
SoCal Equity
∆ Increase in power prices
721.157
18,75%
18,00%
17,25%
16,50%
15,75%
15,00%
14,25%
13,50%
12,75%
12,00%
11,25%
25%
251.504
39.231
-168.567
-371.964
-574.066
-772.212
-966.153
-1.155.966
-1.341.722
-1.523.494
-1.701.353
24%
558.090
334.081
114.830
-99.747
-312.733
-521.494
-725.793
-925.708
-1.121.316
-1.312.695
-1.499.921
23%
890.756
654.060
422.424
195.760
-28.989
-249.225
-464.716
-675.548
-881.802
-1.083.561
-1.280.907
22%
1.252.248
1.001.813
756.767
517.017
279.533
46.878
-180.727
-403.373
-621.149
-834.140
-1.042.435
21%
1.645.647
1.380.313
1.120.726
866.785
615.496
369.378
128.641
-106.811
-337.073
-562.238
-782.399
∆ Return on equity
20%
2.074.419
1.792.902
1.517.522
1.248.172
981.892
721.157
466.164
216.810
-27.006
-265.384
-498.424
721.157
7.500.000
8.000.000
8.500.000
9.000.000
9.500.000
10.000.000
10.500.000
11.000.000
11.500.000
12.000.000
12.500.000
25%
640.980
360.943
79.241
-202.462
-484.164
-772.212
-1.062.058
-1.351.904
-1.644.039
-1.942.491
-2.240.944
24%
903.268
620.884
336.862
52.840
-231.182
-521.494
-813.590
-1.105.686
-1.400.068
-1.700.764
-2.001.460
23%
1.187.693
902.842
616.382
329.922
43.461
-249.225
-543.677
-838.129
-1.134.862
-1.437.897
-1.740.931
22%
1.496.576
1.209.128
920.104
631.080
342.056
46.878
-250.044
-546.966
-846.161
-1.151.635
-1.457.109
21%
1.832.522
1.542.341
1.250.618
958.895
667.171
369.378
69.863
-229.651
-531.427
-839.447
-1.147.468
∆ Return on equity
20%
2.198.460
1.905.400
1.610.833
1.316.267
1.021.700
721.157
418.920
116.682
-187.800
-498.482
-809.163
13,50%
1.701.784
1.407.217
1.112.650
818.084
519.048
216.810
-85.427
-390.857
-701.539
-1.012.221
-1.323.445
19%
2.542.466
2.243.346
1.950.787
1.664.675
1.382.094
1.105.464
834.969
570.500
311.948
59.204
-187.836
18%
3.054.189
2.735.890
2.424.617
2.120.245
1.819.907
1.525.964
1.238.586
957.656
683.055
414.670
152.385
17%
3.614.564
3.275.335
2.943.639
2.619.342
2.299.631
1.986.801
1.681.005
1.382.118
1.090.013
804.566
525.655
16%
4.229.218
3.867.110
3.513.092
3.167.019
2.826.136
2.492.664
2.166.743
1.848.235
1.537.004
1.232.917
935.842
15%
4.904.530
4.517.374
4.138.915
3.769.000
3.404.939
3.048.874
2.700.923
2.360.938
2.028.774
1.704.287
1.387.333
19%
2.597.691
2.301.593
2.004.028
1.706.464
1.408.899
1.105.464
800.364
495.264
187.940
-125.524
-438.989
18%
3.033.933
2.734.630
2.433.902
2.133.173
1.832.445
1.525.964
1.217.852
909.740
599.430
283.052
-33.326
17%
3.511.389
3.208.700
2.904.631
2.600.561
2.296.491
1.986.801
1.675.516
1.364.231
1.050.781
731.350
411.919
16%
4.034.814
3.728.546
3.420.945
3.113.343
2.805.741
2.492.664
2.178.035
1.863.405
1.546.648
1.224.016
901.383
15%
4.609.597
4.299.542
3.988.203
3.676.865
3.365.526
3.048.874
2.730.714
2.412.555
2.092.313
1.766.319
1.440.325
∆ Increase in power prices
14,25%
15,00%
15,75%
1.948.070
2.198.460
2.454.372
1.653.503
1.905.400
2.163.010
1.358.936
1.610.833
1.868.444
1.064.370
1.316.267
1.573.877
768.401
1.021.700
1.279.310
466.164
721.157
981.892
163.926
418.920
679.655
-138.311
116.682
377.417
-447.522
-187.800
75.180
-758.204
-498.482
-232.951
-1.068.885
-809.163
-543.633
16,50%
2.716.094
2.426.438
2.131.872
1.837.305
1.542.738
1.248.172
946.236
643.999
341.761
38.492
-272.189
17,25%
2.983.730
2.695.357
2.401.222
2.106.656
1.812.089
1.517.522
1.218.769
916.532
614.294
312.057
5.274
18,00%
3.257.388
2.969.015
2.676.602
2.382.035
2.087.469
1.792.902
1.497.361
1.195.123
892.886
590.648
288.411
18,75%
3.537.176
3.248.803
2.958.119
2.663.553
2.368.986
2.074.419
1.779.853
1.479.881
1.177.643
875.406
573.168
SoCal Equity
∆ Equipment
SoCal Equity
∆ Equipment
721.157
7.500.000
8.000.000
8.500.000
9.000.000
9.500.000
10.000.000
10.500.000
11.000.000
11.500.000
12.000.000
12.500.000
11,25%
995.584
701.018
406.451
106.051
-196.186
-498.424
-809.013
-1.119.695
-1.431.987
-1.751.358
-2.070.729
12,00%
1.225.640
931.074
636.507
339.091
36.853
-265.384
-571.503
-882.184
-1.192.866
-1.507.815
-1.827.186
12,75%
1.461.007
1.166.441
871.874
577.307
275.232
-27.006
-329.243
-639.272
-949.953
-1.260.635
-1.578.125