By:
Ibrahem M. Hussein
Date: 12-5-2015
2015
KING FAHD UNIVERSITY OF PETROLEM AND MINERALS ELECTRICAL ENGINEERING DEPARTMENT
Power System Planning
Term-Paper
[ Sustainable Campus at KFUPM
Instructor: Dr. Ali Al
ID: 201405220
KING FAHD UNIVERSITY OF PETROLEM AND MINERALSELECTRICAL ENGINEERING DEPARTMENT
Power System Planning, EE-524
Campus at KFUPM, PV’s with EVs
Ali Al-Awami
, PV’s with EVs ]
2
Table of Contents
List of Figures………………………………………………………….…3 List of Tables ………………………………………………….…………4 Abstract …………………………………………………………….…….5 Chapter One: Introduction…………...………………………………...6
1.1 General overview ………………………………..………....……….7 1.2 Motivation for this research …………………….………..…………7 1.3 Research Questions …………………………………………..……..7 1.4 Scope of this research……………………………………………….7
Chapter Two: Energy Resources Estimation ……………………….…8 2.1 Introduction ….………………………………….………………..…8 2.2 Solar radiation ………………………………………………………8 2.2.1 Solar Radiation at KFUPM…………………………………...….8 2.3 Temperature effect .….………………...……...................................11 2.3.1 Temperature effect at KFUPM……………………………….….11 2.4 Effect of Orientation angel ………………………………….….….12 2.5 Annual Estimated solar energy ………………………….…………14 2.6 Proposed area for system installation ………………….….……….15 2.6.1 Parking slot areas…………………………………………….….15 2.6.2 Building roofs area………………....…………....……....……....16 Chapter Three: Modeling and Design…………….………………..…..18 3.1 Introduction…………………………………… ……………….......18 3.2 Component of standalone PV system………….…………….….......18 3.2.1 Storage Units………………...……..............................................19 3.2.2 Generation stations technical data………………....……….........20 3.3 Load modeling. …………………………………..……………....…20 3.3.1 Electric Vehicles …………………………………..…….............20 3.4 Charging station design ………………………………….........…....22 3.4.1 PV array output power…………………………………...…........22 3.4.2 Inverter input-output……………………………………...……...22 3.4.3 PV system modeling…………………………………...………...23 3.4.4 Battery banks modeling……………………………...….……….23 Chapter Four: Simulation Results …………………………………..…24 4.1 Load profile…………………………………………………………24 4.2 Charging stations requirements and components………………......25 4.3 Generation profile…………………………………………….…......27 4.4 Reliability analysis………………………………………..……...... 29 4.5 Cost analysis………………………………………………………...30 Chapter Four: Conclusion and Future work ………...…......................28
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List of Figures Figure 1:Oil consumption by sector [1]. ..................................................................... 6
Figure 2:Emissions in tones of CO2 per person [2]. ................................................... 6
Figure 3 :University of Dammam station. ................................................................... 8
Figure 4: KFUPM station. ......................................................................................... 8
Figure 5: Solar radiation for both metering stations. ................................................ 10
Figure 6: Radiation for both stations. ....................................................................... 11
Figure 7: Temperature measurement in both metering stations................................. 12
Figure 8: Array position with respect to angels. ........................................................ 14
Figure 9: Angels definitions, tilt and azimuth. .......................................................... 15
Figure 10: Parking-near kfupm stadium- proposed full capacity area. ...................... 15
Figure 11: One of the proposed building roofs in the campus. .................................. 16
Figure 12: A total amount of 36 similar building of B.853. ...................................... 18
Figure 13: Standalone system topology. .................................................................. 18
Figure 14: Percentage of Evs being charged at the campus for 24 hours. .................. 21
Figure 15: Battery array. .......................................................................................... 23
Figure 16: Evs load demand versus time. ................................................................. 24 Figure 17: Evs existing in each parking versus time. ................................................ 24
Figure 18: EVs power demand versus number of EVs. ............................................ 25
Figure 19: TSA versus NM. ..................................................................................... 25
Figure 20: TSA and TNM versus the needed storage. .............................................. 26 Figure 21: Total array power Vs the number of arrays and the total number of EVs. 27
Figure 22: Generating solar power per parking versus time. ..................................... 27
Figure 23: System availability versus battery capacity. ............................................ 29
Figure 24: Load duration curve of the unit storage. .................................................. 29
Figure 25: AIDI versus battery size. ........................................................................ 30
Figure 26: AIFI versus battery size. ......................................................................... 30
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List of Tables Table 1: Solar Stations data. ....................................................................................... 9 Table 2: Metering station data .................................................................................. 10 Table 3: Optimal tilt angles in seasonal bases. .......................................................... 13 Table 4: Effect of incident angle on total radiation per day. ...................................... 13 Table 5: Solar Radiation and energy estimation. ....................................................... 14 Table 6: Parking slots detailed areas. ........................................................................ 15 Table 7: Total suggested roofs area .......................................................................... 17 Table 8: Nisan Leaf-vehicle characteristic. ............................................................... 20 Table 9: Average distance followed by Evs per single day per one trip. .................... 20 Table 10: Area calculation results. ............................................................................ 26 Table 11: Charging stations specifications……………………………………...……29 Table 12: System Specifications. .............................................................................. 31
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Abstract
Electric vehicles contributes to the worlds free emissions, this is true under promote
the renewable resources to be the main source of generating power for charging
stations to satisfy the load demand of electric vehicles.
This research aims to design charging stations or power source to serve the electric
vehicles load demand at King Fahd University of Petroleum and Minerals campus,
Kingdom of Saudi Arabia. The kingdom have a very attractive solar energy resources
and can be effectively used for the purpose of our research. This plan will be mainly
applied to the free space areas of existing parking slots through the campus, the total
amount of available area will be approximated in which the system to be installed.
The system will be designed in standalone bases in which the photovoltaic’s arrays
will be responsible to satisfy the total load demand of total electric vehicles assumed
to existing in the university, as well as, the logical increment of these electric
vehicles. The implementation of this project have benefits in both, technical and
environmental aspects, that is in term of improving the methodological ways in
renewable generation and green energy resources. A basic cost estimation will be
holed to express the economical feasibility of such a project.
CHAPTER ONE
1.1 General Overview
One man said - in the period from 1962 to 1986
for lack of stone, and the oil age will end long bef
Those are the former Saudi Arabia minister of oil and minerals resources
Worlds turns into production and
the resulting emissions from such process. Transportation account for
amount of energy consumption, figure 1 shows oil
per day versus time, as the figure indicates the
consumption for transportation sector, it
proportional to the CO2 emissions in the country.
Figure
Figure
The overall CO2 emissions takes another direction in many countries, they try to
reduce the total amount of produced CO
fact, the x-axis represent the time and the y
person. The lines represent these emissions decreases with time for the most
INTRODUCTION
period from 1962 to 1986 – that “ The stone age did not end
for lack of stone, and the oil age will end long before the worlds runs out of oil”.
Saudi Arabia minister of oil and minerals resources
Worlds turns into production and manufacturing, actually the world try to reduce
ulting emissions from such process. Transportation account for
amount of energy consumption, figure 1 shows oil consumption in million of barr
versus time, as the figure indicates the continuous increment
consumption for transportation sector, it’s will know that these figures are
emissions in the country.
Figure 1:Oil consumption by sector [1].
Figure 2:Emissions in tones of CO2 per person [2].
emissions takes another direction in many countries, they try to
reduce the total amount of produced CO2 emissions every year, figure 2 indicate this
axis represent the time and the y-axis represent tones of CO
represent these emissions decreases with time for the most
6
that “ The stone age did not end
ore the worlds runs out of oil”.
Saudi Arabia minister of oil and minerals resources.
manufacturing, actually the world try to reduce
ulting emissions from such process. Transportation account for considerable
million of barrels
increment of oil
will know that these figures are
emissions takes another direction in many countries, they try to
emissions every year, figure 2 indicate this
axis represent tones of CO2 emissions per
represent these emissions decreases with time for the most
7
countries, this due to many reasons, one of them is the use of renewable energy
resources for generating power.
1.2 Motivation for this research The location of kingdom of Saudi Arabia (KSA) allow this country to have viable
solar energy resources, the ability to generate power from solar resources along with
sufficient radiation flux promote this land to accommodate this green energy source
along with oil production.
As we going far through this research, the report chapters will reveal the widely
useful applicable ideas such as our idea to develop the sustainable campus at KFUPM,
our main objectives are to construct an long term plan to make best use of solar
energy recourses and designing of an sustainable charging stations to integrate the
electrical vehicles (EVs) in standalone topology. This idea is not really new, it’s
actually hold in many countries and university campuses.
1.3 Research questions To design an effective standalone power source mainly from renewable energy
resources, a lot of questions should be suggested and most of them must be answered.
1. What is the value of solar radiation available at KFUPM campus?
a. How much solar energy available at KFUPM campus through the year?
b. What is the impact of whether fluctuation on the generated power from
photovoltaic (PV) modules?
c. What is the expected yearly load and load profile for our system?
2. Is this designed solar micro-grid in standalone topology have the ability to sustain
the load demand or EVs in daily bases?
3. What is the reliability level of using such this system and it’s economical
feasibility to apply?
1.4 Scope of this research 1. It will be assumed that all electric vehicles have the same rate of charge and
consume the same amount of energy according to an selected electric vehicle
model, one of the popular electric vehicles which is Nissan leaf, the car
specification will be described in the next chapters.
2. The solar radiation will be assumed to have constant value through our simulation
and calculation.
3. The suggested area for installing the PV arrays and charging stations are
suggested by the author and an actual measurements was performed throughout
the university existing parking slots using Google maps. The suggested total area
are extended to include some of building roofs, an detailed description will be
provided in the next chapters about this topic.
4. The total maximum number of EVs that can be available in the university parking
can be estimated, and an diversity factor will be assumed to cover the verity of
actual existing vehicles in the parking.
5. There are another trends and aspects that will be provided and covered in the
related chapters.
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CHAPTER TWO ENERGY RESOURCES ESTIMATION Energy Resources Estimation
2.1 Introduction Planning for renewable energy (RE) based system needs to estimate the amount of
energy it may expect to gain for specific RE source, the design itself should
considered to be in standalone topology for multi generation RE sources or a specified
source, for example, to use PV system alone or integrated with wind farms. In this
research, our efforts will be concentrated to design the system based on solar power
and in standalone topology to sustain our EVs load demand.
2.2 Solar Radiation
The earth surface receives solar energy from sun. This solar energy called
radiation, the radiation travels from the sun to earth surface and it will face many
obstacles in the earth atmosphere, such as the atoms, this will decompose the radiation
into multi-components such as direct, diffusive and reflected radiation, they defined
as the following, [3]:
Direct radiation: It’s sometimes called beam radiation, which describe the solar
radiation travelling in straight line from sun to the earth surface.
Diffuse radiation: It’s describes the radiation that has been scattered by the molecules
and particles of the atmosphere, but still reaching the earth surface.
Reflected radiation: It’s the radiation has been reflected from the earth surface.
There is another important term to define which is the global insulation, it’s
referred to the total radiation reaching the earth surface or the sum of the three
components we defined at any particular time. Global horizontal radiation have the
same definition, but deal with radiation hitting a horizontal surface [3].
2.2.1 Solar Radiation at KFUPM
Planning for EVs and PVs require an study for solar resources available at the
university campus, KFUPM location with latitude of 26o.18 North. According to [4],
the highest radiation area located in the region or coordinate of 30o North and 30o
South, Saudi Arabia in general has 2300 wh/m2 solar insulation which in turn
contribute to the solar power to being one of the most efficient renewable resources in
the country. This will motivate us to continue with system design hence this region
considered to have rich radiation intensity.
In this research, the radiation data was obtained from atlas of renewable energy
(ARE), it’s an Saudi institute with partnership with many responsible universities and
research institute [5]. The obtained data duration for one and half year in monthly
average bases starting from Jun-2013 to Feb 2015. To reduce the error as possible as
we can, we were got data from two measurement substations in Dammam city, as
indicated in Table 1, based on these stations data, the total amount of average monthly
9
insulation can be determined and used in our calculation. Table 1 indicate the distance
from each substation to the university campus, also these distances were measured by
using Google map as shown in figure 3 and figure 4 below: Table 1: Solar Stations data.
Source Location, distance
in km
Insulation in
Kwh/m2/day
Error in
measurements
KFUPM station Dammam city, 8km 5.70 ± 5%
Dammam
University Station
Dammam city,
13km
5.79 ± 5%
The data resolutions are in average monthly bases, it’s considered to be an
accepted data recourse hence our objective is to plan for an long term project, and
these data are needed to make an initial estimation to the total energy production.
Table 2 below indicates the data obtained for each metering station, it’s consist of
measurements of global horizontal insulation (GHR), as well as, the average
temperature over that period. GHR are plotted for both metering stations as shown in
figure 5.
Figure 3 :University of Dammam station.
Figure 4: KFUPM station.
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Table 2: Metering station data.
Date\Station KFUPM Station in Dammam city, 8
KM Dammam Station city, 10 km
Month Gh KWh/m2/day Temperature Gh KWh/m2/day Temperature
Jun, 2013 7.7 36 7.9 35
Jul, 2013 7.2 37 7 36
Aug, 2013 6.8 35 6.5 34
Sep, 2013 6.4 34 6.4 33
Oct, 2013 5.4 28 5.7 28
Nov, 2013 4 23 4 23
Dec, 2013 3.9 18 3.8 18
Jan, 2014 3.7 16 3.7 16
Feb, 2014 4.8 18 4.9 17
Mar, 2014 5.5 22 5.7 23
Apr, 2014 6.7 28 6.8 27
May, 2014 7.6 33 7.4 33
Jun, 2014 7.9 36 8 35
Jul, 2014 7.7 37 7.6 36
Aug, 2014 6.5 36 6.8 35
Sep, 2014 6.5 34 6.5 34
Oct, 2014 5 30 5.5 30
Nov, 2014 4 23 4.1 23
Dec, 2014 3.8 19 4 19
Jan, 2015 4 17 4.2 17
Feb, 2015 4.5 19 5 19
Error ± 5% ±0.6 degrees C ± 5% ±0.6 degrees C
Figure 5: Solar radiation for both metering stations.
The curves shape of radiation for both metering stations seems to be very similar,
the reason of that is the distance between these two stations is small, essentially if the
reading values is correct, then both of them must gives the same estimated radiation
value, to show the difference between these two curves, figure 6 represent the plot of
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Radia
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Radia
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Radia
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both curves on the same axis, as expected, there are small differences between both
curves, which insure that our data is correct and we can follow with our work.
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DAMMAM_STATION KFUPM_STATION
Ra
dia
tio
n i
n k
wh
/m2
/da
y
Figure 6: Radiation for both stations.
The radiation curves seems to be repeated in the mid and at the beginning of the
second year within the same pattern, the radiation seems to have higher values in
summer months and it’s also decreases in winter months, a maximum value of 8
kwh/m2/day obtained in July-2014 and an minimum value of 3.7 kwh/m2/day in Jan-
2014. In the next sections, we will draw the same conclusion on temperature
measurements for both metering station.
2.3 Temperature Effect
The output of the PV module if affected by surrounding ambient temperature. The
cell model which is the basic building unit of the PV module consist of diode
elements in the equivalent circuit, the current through the diode depends on the
surrounding temperature and thus the PV module as well. There is an term called
temperature coefficient which is an measure of how much the output of an PV module
reduced by the effect of the ambient temperature. the PV module comes with
specification measured under standard testing conditions (STC) which are 1000 W/m2
irradiance and 25o ambient temperature. In an actual site, the measured parameters
differ from STC specifications, for example, the output of an PV module of 250 Watt
and at 25o ambient temperature tested under STC will differ if the temperature now is
30o, assume an temperature coefficient of -0.44 C, then the actual output under STC is
250 – 0.44*(30-25) =244.5 W. Form the last discussion, we can draw the conclusion
of needing an module with lower temperature coefficient hence the temperature in the
kingdom is relatively high to reduce the amount of power losses of the PV module.
2.3.1 Temperature measurement at KFUPM
The metering stations at both, KFUPM and Dammam stations also provide average
temperature measurements for the ambient temperature, these values are available in
12
table 2 in daily average bases, figure 7 represent the average temperature plots for
both stations, the temperature also have seasonal minimum and maximum peaks, the
maximum peak observed is on summer, the average maximum temperature is 37 C in
July-2014 and the minimum average temperature is 16 C in Jan-2014.
The temperature effect will not be considered for this planning project and we will
consider the selection of an lower temperature coefficient modules as enough guiding
for project simplifications and planning process.
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Temperature- KFUPM StationTemperature- Dammam Station
Te
mp
era
ture
in
C
Figure 7: Temperature measurement in both metering stations.
2.4 Effect of orientation angle
The way we fix the PV array and its orientation angle with respect to the horizontal
are differ from one location to another, the PV panel specified by two angles called
tilt and azimuth, the tilt defined as the angle of the PV array with respect to the
horizontal surface. The azimuth angel defined with respect to an reference direction,
i.e., the South. 00 angel for azimuth mean that the array facing North direction, an
angel of 90o mean that the array facing the west direction [6], figure 8 and figure 9
represent the physically meaning of these angles in term of directions and array
position.
Figure 8: Array position with respect to angels. Figure 9: Angels definitions, tilt and azimuth.
13
According to [7], for Saudi Arabia and khoubar city, the optimal tilt angel have
different values according to the year seasons, there is an best angel definition for
each season on an average bases, table 3 below indicates these different angels
according to each season and it’s total average per year.
Table 3: Optimal tilt angles in seasonal bases.
Season Correspond tilt angle in degree (o)
Winter 40
Spring\ Autumn 64
Summer 88
Average value 64 (26 with H)
For simplicity in modeling and recall that our objective is for long term planning
for this project, we can depend on an average seasonal tilt angel of 64o in our
calculations, Table 4 display the results obtained after considering the tilt angel effect
on our calculations.
Table 4: Effect of incident angle on total radiation per day.
Date\Station Effect of tilt angle (64 deg) kwh/m2/day
Month KFUPM Station Dammam station
Jun, 2013 6.92 7.1
Jul, 2013 6.47 6.29
Aug, 2013 6.11 5.84
Sep, 2013 5.75 5.75
Oct, 2013 4.85 5.12
Nov, 2013 3.59 3.59
Dec, 2013 3.5 3.41
Jan, 2014 3.32 3.32
Feb, 2014 4.31 4.4
Mar, 2014 4.94 5.12
Apr, 2014 6.02 6.11
May, 2014 6.83 6.65
Jun, 2014 7.1 7.19
Jul, 2014 6.92 6.83
Aug, 2014 5.84 6.11
Sep, 2014 5.84 5.84
Oct, 2014 4.49 4.94
Nov, 2014 3.59 3.68
Dec, 2014 3.41 3.59
Jan, 2015 3.59 3.77
Feb, 2015 4.04 4.49
Total kwh/m2 / year 2007 2062
The given data in table 2 are for global horizontal insulation or radiation, it’s
notice that the values of insulation per day decreases as while the effect of
14
incident angle taken into account. However, this is not true in general, hence our
effort to select an optimal angle will maximize the total radiation given per day,
but the values decreases as we notice from table 4, that’s because of using an fixed
angle while the earth actually moving. We will consider these values as the worst
scenario for our planning purpose of this project.
2.5 Annual estimated solar energy
Using table 4, we can sum the solar insulation over one year to get the total
energy per unit meter square, this can be done by assume an constant insulation
during all the days of the month and multiply each value by 30 then sum the total
insulation.
After we few steps of calculation, we get 2007 and 2062 kwh per meter square
per year. As expected, both stations total sum are near to each other. If we take for
example, the expected value for KFUPM station is 2007 kwh/m2/year. Assume an
optimistic efficiency of an PV module of 19%, then the total energy per unit area
can be simply obtained by multiplying both numbers which yield 381
kwh/m2/year or an one meter square will produce 381 kwh in yearly bases, this
number is obtained taking into account the effect of incident angle on the PV array
of the system. Table 5 summarize the total obtained solar energy in yearly and
daily bases for the same PV module efficiency.
Table 5: Solar Radiation and energy estimation.
Data
(Average
kwh/m2)
With incident angle effect Without incident angle effect
KFUPM Station Dammam Station KFUPM Station Dammam Station
Daily insulation
in 5.12 5.2 5.70 5.79
Total energy per
year 2007 2061 2328 2334
Total energy per
day 0.9728 0.988 1.083 1.1001
Hence our design efforts concern with designing an charging station for EVs, we
can get an initial picture about the load impact, assume an average electric vehicle
consumes 4.6 kwh per day ( the next chapter explain how this number obtained)
which is the average commuter distance required energy per one way trip, assume the
same PV module efficiency then, (19% * 5.2 kwh/m2 ) / 4.6 kwh = 0.21 times
charging per meter square, or the car will charged from 0 to 20 % per one meter
square per day, if we have 5 meters square of PV modules, then the car will be
charged from 0 to 100% per day. The next chapters will show that these numbers are
not really exact numbers and need.
15
2.6 Proposed area for installing the system
As it mentioned an chapter one, the proposed area will be the parking area slots,
thanks for Google map which allow us to make an extensive scan for the university
campus looking for parking slots locations and it’s corresponding area, as well as, to
make benefit of any additional suggested area for our project, the proposed area
locations are suggested by the author and can be changed according to the
requirements of the project. The proposed land divided into two main categories, the
parking slots land and some of building roofs. These two options to place the PV
arrays which are to place it on the parking open space or on the top of roof building.
Covering the parking areas will make the stuff and student benefit that by providing
shade from the vehicles. Figure 10 represents an map for on the parking captured
from Google map, the area shaded by red color which is the 100% proposed area for
installing the PV arrays. However, an assumption on the total occupied area will be
made in the next section.
Figure 10: Parking-near kfupm stadium- proposed full capacity area.
More details can be found on appendix A for more information about the parking
maps and total occupied areas. The categories of the proposed area will be discussed
in the next section.
2.6.1 Parking slots area
After an extensive searching through the university campus, it found that there are
14 parking slots with an total approximated area of 138630 m2 . These parking are
distributed throughout the university campus in many locations, table 6 below
indicates these parking slots and it’s corresponding areas. Table 6: Parking slots detailed areas.
Parking near building/ NO.
Expected Area m2
Number of Vehicles
Diversity of 0.5
Occupied area - 70%
Near B.12 22532 1609 805 15772
Near B.42 8826 630 315 6178
Near KFUPM Stadium 31803 2271 1136 22262
Near B.57 11480 820 410 8036
Near B.57 (beside B.4) 1266 90 45 886
Near KFUPM mall 3648 260 130 2553
Near central kitchen 9443 674 337 6610
16
Near B.848 or B.853 7163 512 256 5014
Near B.817 - 820 7112 508 254 4978
Near B.822 9240 660 330 6468
Near B.1 8201 585 293 5740
Near B.14 3670 262 131 2569
Near B.20-18-19- a 5000 357 179 3500
Near B.20-18-19 - b 9246 660 330 6472
Sum 138630 9898 4949 97041
Each parking is mentioned according to the nearest building located around the
parking, the largest parking is located near KFUPM stadium with 31803 m2. But what
about the maximum capacity of each parking? As an estimation, it will be
approximated that each vehicle occupy 14 m2 for typical sedan vehicle ( 4 meter
length and 3.5 width), based on this approximated number, the maximum capacity
number of each parking vehicles can be approximated as indicated in table 6.
However, it’s rarely to all the parking on its full capacity in daily bases, an 0.5
diversity factor will be assumed as a fair fraction for the total number of vehicles. In
the same manner, it can be assumed that the PV array will not occupying the hole
space of the parking so, assume that they will be installed on 70% of the total area of
each parking, the results for these calculations are shown in table 6.
2.6.2 Building roofs area
While the search process was performed for parking slots spaces, we notice that
there are a lot of clear roofs available in the campus, the author suggest an total
amount of 11 building and one of them (B.853) have an 36 similar copy in the campus
, they could be considered as backup plan or an alternative solution if the existing
parking areas can’t satisfy the load demand in standalone bases, figure 12 indicates
one of these building, there are about an 36 almost identical building to that one
shown in figure 11 and it’s shown in figure 12.
Figure 11: One of the proposed building roofs in the campus.
17
Table 7 summarize the results obtained for building roofs area with 70% occupied
roof area hence to keep some space within the building roofs. More details can be
found on appendix A for more information about the building maps and total
occupied areas. Table 7: Total suggested roofs area
Building Name Full Roof Area m2 70 % occupied area m2
Central kitchen 4500 3150
B.58 1600 1120
B.853 - 36 similar 37404 26182
B.1 4500 3150
B.3 1800 1260
B.16/4 3384 2368
B.6 2500 1750
B.14 2550 1785
B.59 8000 5600
B.22/23 and 24/25 11000 7700
B.68 3400 2380
Sum 80638 56446
It can be notice that an total amount of 80638 m2 of roofs area is obtained, its
implement 58% of the total parking area at full capacity and with 70% of total
capacity. This is an considerable amount of area and should be taken into account for
designing purposes, the total area can be extended if more building added as an target
to install the PV arrays.
Figure 12: A total amount of 36 similar building of B.853.
18
CHAPTER THREE
MODELING AND DESIGN 3.1 Introduction The system will be designed in standalone topology, and it will not be connected to
the public grid, we can say it’s looks like a small micro-grid for KFUPM campus
serving the electric vehicles or the load requirements.
The application of this system will be limited to serve the EVs demand or to be
more precise, the batteries of the EVs. The system will provide AC power to the EVs,
according to [8], there are four mode of charging the electrical vehicles, the fourth
mode is the fast DC charging mode, we will not consider this mode in our design and
thus, we will concentrate on the other charging modes.
3.2 Component of Standalone PV system
System design depends on system configuration or topology, and components,
these component are shown in figure 13. The components of standalone PV system
will be as the following [9] :
Figure 13: Standalone system topology.
1. PV modules: An array consist of multiple strings that connected in parallel to
form an array, it’s the source of power in PV system, the basic building unit of an
array is the PV module with its specified characteristic.
Parking charging
point
19
2. Storage : It’s almost one of the most important part of any standalone PV system,
it’s the source of continuity in service and considered to be the backup source to
store the electrical energy during the sun day and release this energy according to
demand when required.
3. Inverter : An inverter is used to convert the DC output ( current of voltage) into
AC output ( current or voltage). It’s an important part of the system to charge the
EVs.
4. Charge controller : This is an multifunction component, perform monitoring of
battery status of charge, provide the charging conditions such as overcharge and
undercharge limits and provide the maximum power point tracking power from
the PV array.
5. Loads : In our case, the loads are the electric vehicles existing in the university
campus. An discussion about the load behavior will be assigned in the next
sections.
6. Cables, connectors and installation equipments. The system design will be
conducted through an large area compared to an small house or residential small
load, as proposed in the sections before, the area over which the system is
suggested to be installed is large. As the system size increase, the losses overall
the system is increases too so, an considerable amount of power loss will be
dropped from the total production of the PV arrays due to connection points and
junctions losses, an 2 % losses model for these factor considered as fair fraction
and an practical percentage and will be considered in this project [10].
3.2.1 Storage units
Batteries are the preferred choice to store energy for system architects [11]. There
are wide range of battery kinds, for purpose of system design and planning, a choice
of commercial battery type will be preferable choice hence the system will required a
lot of battery banks with respect to the total load to compensate for system continuity.
For long term system planning which related to technical specifications of the storage
unit, it will be assumed that they have an constant capacity and efficiency
characteristic throughout the system design, an typical value for battery capacity of 85
% considered to be accepted in practical design issues. Another important
characteristic of the battery which is the temperature, as the temperature increase, the
battery capacity increases as well, which considered to be a positive effect. On the
other hand, it will decrease the life time of the battery. However, the temperature
effect will not be considered in this research hence it will be assumed to have a
constant characteristic behavior.
For simulation purposes, the battery will be assumed to have an full charge capacity
at starting of the time and implemented by one big mass for the total system. This
20
assumption is valid hence our design based on long term planning and to have an
initial estimation for what we have in term of total energy and for how much of time
to serve the load requirements.
3.3 Load Modeling
For the purpose of simulation balancing of thee system energy, the load profile for
our load which the electric vehicles must be specified, this research will propose two
load profiles and one of the will be used in simulation process but first, the load
modeling for the electrical vehicles will be proposed.
3.3.1 Electric Vehicles
Mainly, the load will be the electric vehicles existing in the university campus, to
study the load behavior, the electric vehicle type should be specified, there are a wide
range of electric vehicles kinds in the markets, this research will select an average EV
type such as Nissan Leaf. This EV has an average specifications and can be used for
design purposes, the vehicle specifications is indicated in table 8 below: Table 8: Nisan Leaf-vehicle characteristic.
Vehicle characteristic Value
Battery capacity 24 KWh
Energy per distance 25KWh/100 mile
Nominal distance at full capacity 117 km
Charging time 5 hours
One of the major factors regarding the amount of energy required for each vehicle
during a normal day, is the total distance travelled by the vehicle. In the university
campus, there are the employee, graduate and undergraduate students, part-time
student..etc. those different classes have different distance distribution which they
follow each day. Table 9 indicate a proposed distance distribution that followed by the
Evs per one way trip during a normal working day for the university people, the
commuter distance is assumed to be from the university to Dammam city with
maximum distance of 40 Km for those whom living outside the university and 15 km
for whom living inside the university. Table 9: Average distance followed by Evs per single day per one trip.
User type Distance
University Employee 40 km
Resident grad/undergraduate 15 km
grad/undergraduate 40 km
Average 31.6 km
From table 8, Nissan Leaf needs 25 KWh per 100 mile which equivalent to
0.15625 kWh per km. Then, the total energy capacity needed per one electrical
vehicle per one way trip is 0.15625 KWh/km *31.6 km = 4.6 KWh. Which is the
21
average energy capacity needed per one electric vehicle per a day. Knowing that the
vehicle needs five hours to being fully charged then, the power required to charge the
vehicle up to 4.6 KWh is 4.6 divided by 5 which is 0.92 KW.
Using those initial numbers, with 4.6 KWh energy required by the EV and with
total approximated number of vehicles of 5000 as indicated in chapter two, then the
total energy required at full capacity is 5000*4.6 KWh = 23 MWh per a day. Also, the
average total insulation per a day is 2.5 KWh/m2 (assume 26 degree incident angel),
then assume an optimistic efficiency of 16% for the PV module and total surface area
of 138630 m2, then the total average obtained energy per a day is 16% * 2.5 * 138630
m2 = 55 MWh per a day. However, through this chapter and the following one, it will
be shown that those numbers are not true, many factors should be taken into account
through the simulation process.
3.3.2 Proposed Load Profile
The load profile is a representation of how the load change over the time, it’s gives
an indication about the power requirement, for example, the peak daily load. The first
proposed load profile is to assume that the load is constant over the time, actually this
is the most easiest behavior and the most incorrect description, it’s well known that
the load is changing with time and depends on the consumer classes. In this research,
another proposed load profile rather than the constant will be used, figure 14 indicate
this load profile per a single day per each parking.
Figure 14: Percentage of Evs being charged at the campus for 24 hours.
For a regular day, the student vehicles are assumed to be in the parking from the
last night and fully charged, at the next morning, the employee and the non- resident
student are coming early to their work and lectures respectively, they will be assumed
to implement 30% of the total parking. The total charging vehicles then start to
decrease hence the vehicle itself coming with nearly full capacity of charge and they
being full charged until 2 pm. However, this load profile assumes late coming people
and still have 15 % from 2 pm to 4 pm, then the number of vehicles decreases to 10 %
0 5 10 15 20 250
5
10
15
20
25
30
35
40
45
50
Time in Hours, starting from 1 which is 8- AM morning
Perc
enta
ge %
of
EV
s e
xis
ting in c
am
pu
s t
o c
harg
e
22
at 5 pm. In addition, this load profile assumes that the resident student will have the
dominant load from 10 pm to 2 am, hence they all will plug their vehicles to be
charged, this will require 5 hours until 2 am and then the curve will drop down again.
For each parking, there will be a load profile depends on the percentage of the total
Evs being charged for each interval, the simulation will consist of these load profiles
for parking alone.
3.4 Charging Station Design and Modeling
In this section, the design and modeling for each parking slot will be performed.
This will include the calculation of total parking array power taking into consideration
the temperature effect and the dirt of PV module surface, inverter efficiency, wiring
efficiency and battery efficiency. Also, the manufacture tolerance will be presented.
3.4.1 PV Array Output Power
The array module output power is given in equation (1) which include the effect of
all the last parameters [10].
�� =�
������ × ������� × �����× ���(1)
������� = ����� × ����� × �����(2)
����� = 1 − �(����� − ����)(3)
Where:
Pv: Is the array output power.
E: The total energy demand.
������: The total efficiency due to wiring, battery and inverter.
�������: The total losses due to dirt, temperature and manufacture tolerance.
PSI: Is the power under standard conditions.
����� is the cell temperature and ���� is the standard test condition temperature.
� is the temperature coefficient of the PV module.
The efficiency of the inverter, wiring and battery will be assumed as 0.95, 0.98 and
0.85 respectively. The cell temperature is the average temperature plus 25 degree, the
average temperature will be assumed to have 28 degree as indicated in chapter two.
3.4.2 Inverter Input-Output Voltage
It will be assumed that three phase inverter will be used, the input to out relation of
three phase inverter is given in equation (4), [4].
��� =2√2 × ���
√3 × ��(4)
Where Vll is the line to line voltage and Ma is the modulation index, it’s range
between zero and one. It will be assumed 0.85 in the design process. The line to line
voltage for three phase system is 400 volt, the DC voltage correspond to this value
after substituting in equation (2) is 726 volt. This value will be used in the next
section to find the maximum number of modules.
23
3.4.3 PV System Modeling
The modeling of PV system will include the total number of required modules
(NM) in each array parking slot, the string voltage (SV) and string power (SP). In
addition, the number of strings (NS) and number of arrays (NA) for each parking will
be calculated. Finally, the total surface area (TSA) required by the PV modules for
each parking will be obtained along with the total number of modules (TNM) and the
total number of inverters (TI). These calculation is done using the following
equations:
�� =���
����(5)
�� = �� × ����(6)
�� = �� × ����(7)
�� =����������
��(8)
��� = �� × �� × ��(9)
�� =��
�������������������(10)
Where ���� and ���� is the PV module maximum voltage and power respectively.
The PV module which used in the simulation process is available in the appendix A.
3.4.4 Battery Banks Design and Modeling.
The total energy storage for each parking which referring to the battery size will be
estimated using equation (11) as the following:
� =���������� × �
��� × �����(11)
Where C is the total energy stored, DOD is the depth of discharge of the battery and it
will be assumed 80% in the simulation process, the battery efficiency is 85%. The
total of backup days which refers to the number of days that the battery supply power
in case of no output power from PV system, as well as, the evening period where we
have no sun. in practical systems, the battery also connected in array configuration,
it’s an practical to put limitation on the battery arrays. In this design process, the
number of battery strings is three and each string contain six battery modules.
Regarding the battery type, there is a type called Vanadium Redox Flow batteries, it’s
design for large scale storage of electricity for PV and wind energy storage, their
amper-hour reach more than 1000 Ah. However, in this design, the battery amper-
hour is 410. A battery array shown in figure 15 having two string and four modules.
Figure 15: Battery array.
24
CHAPTER 4
SIMULATION RESULTS This chapter will contain the simulation results for the simulated 14 parking slot through KFUPM campus. The simulation will focus on load profile analysis, charging stations requirements and it’s components and reliability analysis to the system all through 48 working period.
4.1 Load profile
The load profile obtained for each parking is shown in figure 16, as expected, it’s looks like the proposed load profile in figure 14. The maximum load is for parking number 3, hence this parking have the largest occupied area as well as the number of vehicles.
Figure 16: Evs load demand versus time.
Consequently, the number of existing vehicles for each time period is shown in figure 17, notice that the assumed load profile never have the load to be 100% or the parking being occupied 100%.
Figure 17: Evs existing in each parking versus time.
The load demand for Evs is increased as the number of Evs increases too, figure 18 indicate the power demand by EVs versus the number of EVs, this figure is an
8 10 12 14 16 18 20 22 240
1
2
3
4
5x 10
5
Time in Hours, starting from 8- AM morning
EV
s P
ow
er
Dem
and in W
1
2
3
4
5
6
7
8
9
10
11
12
13
14
8 10 12 14 16 18 20 22 240
100
200
300
400
500
Time in Hours, starting from 8- AM morning
Num
ber
of
exis
ting v
ehic
les
1
2
3
4
5
6
7
8
9
10
11
12
13
14
25
extension of figure 16 which gives an clear picture about the power demand. The total energy demand of EVs per a day reach about 19.7MWh correspond to a total number of 4951 EV.
Figure 18: EVs power demand versus number of EVs.
4.2 Charging Stations Requirements and Components
Using the equations discussed in chapter 3, the design process is performed to obtain the system requirements for each parking station, those components such as the number of PV modules per string, NS, SV, SP, array power, NA, TNM..etc. all those results are summarized in table 11. From those figures, clear picture can be drawn about the system topology and configuration. Each station is design in standalone basis and can be built independently. Some of table 11 results will be discussed here through plotting those number versus each other. Figure 19 below indicate the total surface area required by the PV modules, the relation is linear as expected. As the total number of the PV modules increases, the total surface area is increases as well.
Figure 19: TSA versus NM.
The total surface area required for the total charging stations is 107952 m2, this area will be occupied by the PV modules, table 10 below indicate the required area in m2 per each parking (the fourth column) versus the total measured area and the 70% of the total occupied area. The majority obtained areas are less than the 70% of the total measured area per parking, as well as, less than the total measured area, it’s a good indication for system design.
0 50 100 150 200 250 300 350 400 450 5000
1
2
3
4
5x 10
5
Number of existing vehicles
Pow
er
dem
and b
y E
Vs in W
0 2000 4000 6000 8000 10000 12000 14000 160000
0.5
1
1.5
2
2.5x 10
4
Total number of PV modules "Sanyo"
Tota
l S
urf
ace a
rea in m
2
26
Table 10: Area calculation results.
Parking near building/ NO. Expected Area m2 Occupied area - 70% Required Area m2
Near B.12 22532 15772 17391
Near B.42 8826 6178 6787
Near KFUPM Stadium 31803 22262 24602
Near B.57 11480 8036 8908
Near B.57 (beside B.4) 1266 886 1060
Near KFUPM mall 3648 2553 2969
Near central kitchen 9443 6610 7423
Near B.848 or B.853 7163 5014 5514
Near B.817 - 820 7112 4978 5514
Near B.822 9240 6468 7211
Near B.1 8201 5740 6363
Near B.14 3670 2569 2969
Near B.20-18-19- a 5000 3500 4030
Near B.20-18-19 - b 9246 6472 7211
Sum 138630 97041 107952
The relation between the total surface area, number of PV modules and the needed storage for each parking is shown in figure 20 below.
Figure 20: TSA and TNM versus the needed storage.
The amount of the needed storage is calculated to support the EVs demand in the time slot between 4 pm to the next 8 am morning. It’s clearly that the amount of energy to be stored is increasing as the parking area getting larger. However, the same relation will obtained for the total generated power by the PV modules. Figure 21 present the total generated solar power as a function of the total EVs and the number of PV arrays, it’s well known that each parking will need one or more arrays to satisfy the load demand, the number of PV arrays increases with increasing in the total number of EVs which results in larger amount of the generating PV power.
00.5
11.5
2
x 104
0
1
2
3
x 104
0
1
2
3
x 106
Total number of PV modules "Sanyo"Total Surface area in m2
Tota
l S
tora
ge
27
Figure 21: Total array power versus the number of arrays and the total number of EVs.
4.3 Generation Profile
The parking’s total solar generation is indicated in figure 22, each with the corresponding parking number. As expected, the highest generated power is coming from parking number 3 which have the largest surface area, the generation reach about 3.4 MW at 11 am. Parking number 5 hav e the lowest generating power of 0.2 MW also at 11 am. The total generating capacity at peak radiation time is about 15 MW.
Figure 22: Generating solar power per parking versus time.
0200
400600
8001000
1200
0
50
100
1500
1
2
3
4
x 106
Number of EVsNumber of PV Arrays
Arr
ay P
ow
er
in W
8 10 12 14 16 18 20 22 240
0.5
1
1.5
2
2.5
3
3.5x 10
6
Time in Hours, starting from 8- AM morning
Pow
er
genra
tion in e
ach p
ark
ing
1
2
3
4
5
6
7
8
9
10
11
12
13
14
28
P. N
um
/data
NM
SVSP
P array *E6
NS
NA
TNM
TSA * E4
TW * E5 kg
NI
Storage
wh
SVB
SESAES
NAB
E B su
pplie
# veh
117
7434080
2.44698
8211152
1.73912.0695
101633000
362490
4482037
1306400805
217
7434080
0.95758
324352
0.67870.8076
4639400
362490
4482015
511520315
317
7434080
3.4538
11615776
2.46022.9276
142300000
362490
4482052
18400001136
417
7434080
1.24628
425712
0.89081.06
5832600
362490
4482019
666080410
517
7434080
0.13688
5680
0.1060.1262
192000
362490
448203
7360045
617
7434080
0.39518
141904
0.29690.3533
2266800
362490
448206
213440130
717
7434080
1.02448
354760
0.74230.8833
5685400
362490
4482016
548320337
817
7434080
0.77818
263536
0.55140.6562
4519800
362490
4482012
415840256
917
7434080
0.77218
263536
0.55140.6562
4515200
362490
4482012
412160254
1017
7434080
1.00318
344624
0.72110.8581
5671600
362490
4482015
537280330
1117
7434080
0.89068
304080
0.63630.7571
4593400
362490
4482014
474720293
1217
7434080
0.39828
141904
0.29690.3533
2266800
362490
448206
213440131
1317
7434080
0.54418
192584
0.4030.4795
3363400
362490
448209
290720179
1417
7434080
1.00318
344624
0.72110.8581
5671600
362490
4482015
537280330
Table 11: Charging stations specifications
29
4.4 Reliability Analysis
The system topology is in standalone configuration, the solar power is available during the sunny days and will be unavailable during the overcast days, as well as, the evening period. In this case, the storage unit’s is needed to compensate for those periods of no sun. in this research, reliability analysis is performed in term of availability, average interruption frequency index (AIFI) and average interruption duration index (AIDI). The simulation is performed using 85% battery efficiency and considering the charging rate of the battery as well. Figure 22 represent the availability of the system versus a range of battery capacity sizes for 48 simulation period, as expected, the availability without battery storage is too small, its reach 0.2 which correspond to 9.6 hours of outage. However, the availability problem can be solved by integrating a storage units to the system. Stating from zero battery capacity and up to 23MWh of total energy storage unit, the system availability is became unity, the availability start to fluctuate up to 20MWh storage unit, then the availability start to increase gradually up to one.
Figure 23: System availability versus battery capacity.
Figure 24: Load duration curve of the unit storage.
By plotting the load duration curve (LDC) of the storage unit, the battery will be in full charging capacity for about 5 hours per two days. The minimum battery stored energy is 5MWh. The battery capacity considered to have an acceptable size hence the EVs energy demand per a day is 4.6 KWh, and hence we have 5000 EVs, the approximated total energy demand is 23MWh which is near the battery size, notice that the needed storage period is from 4 pm to the next 8 am morning, so the battery size is divided by two days. Another reliability index which is the AIDI, its
0 0.5 1 1.5 2 2.5
x 107
0
0.2
0.4
0.6
0.8
1
X: 2.3e+007
Y: 1
Battery Size in KWh
Availa
bili
ty
0 5 10 15 20 25 30 35 40 45 500
0.5
1
1.5
2
2.5x 10
7
Time in hours
Batt
ery
Capacity in K
Wh
30
correspond to average duration of an outage as seen by load point or the charging station in our case. As figure 24 indicate, it’s start to fluctuate due to battery storage size increment and then, the curve is converge to zero which correspond to unity availability. The average interruption frequency index which indicate how frequent the load is interrupted is shown in figure 25, AIFI is also start to decrease as the battery size increase with some fluctuation as the availability and AIDI curves.
Figure 25: AIDI versus battery size.
Figure 26: AIFI versus battery size.
4.5 Simplified Cost Analysis
In this short study, the total cost of the installed system will be determined, the source of cost values is the United State Department of energy (USDE). According to the USDE, the average initial cost of solar energy in $/ KWh is about 4 plus 0.02 $/kwh operating cost (2014) which include the maintenance, equipment replacement ..etc. once the system starting generating electricity. The initial cost include PV modules in standard form, inverters, and other hardware cost, and labor and other non-hardware costs. 4.5.1 System Specification in Cost Calculation
Table 12 below indicate the total specification taken into consideration as discussed in the last chapters to determine the total system cost.
0 0.5 1 1.5 2 2.5
x 107
0
5
10
15
20
Battery Size in KWh
AID
I
0 0.5 1 1.5 2 2.5
x 107
0.5
0.55
0.6
0.65
0.7
0.75
Battery Size in KWh
AIF
I
31
Table 12: System Specifications.
What to include? Cost Total PV generating power 15MW
Module type Standard Array type Fixed
Total system losses 14% Tilt angle 26
Azimuth angel 298 DC-AC conversion ratio 1.1
Inverter efficiency 98% Total initial cost 4$/kwh Operating cost 0.02$/kwh
Table 14, indicates the total cost determined in monthly basis, which relate the total system specifications, the overall system cost is about 1.7 million dollar which is the summation of the costs over each year month.
Table 14: Cost detailed calculations.
Month AC System
Output(kWh) Solar Radiation (kWh/m^2/d)
DC array Output (kWh) Value ($)
1 1221790.75 3.32772636 1254809.75 96,154.93
2 1435249.875 4.36098576 1469159.125 112,954.17
3 1766619.625 4.85966063 1809785.75 139,032.96
4 2003430.75 5.87568998 2049775 157,670.00
5 2464345.5 7.191113 2518346 193,943.99
6 2424671.25 7.4010129 2477609.5 190,821.63
7 2335966.25 6.98638725 2387322 183,840.54
8 2206053.75 6.61414242 2255298.25 173,616.43
9 1881944.125 5.77784824 1925412.25 148,109.00
10 1605064.875 4.65315437 1644615.375 126,318.61
11 1210119.375 3.50963974 1243822.25 95,236.39
12 1089660.75 2.98379374 1121585.125 85,756.30
Total 21644916.88 63.54115439 22157540.38 1703454.95 The results cost per kilo watt hour after those calculations is 0.17 $/kwh. Compared to the grid energy cost in KSA which is 0.07 S/kwh [12], it’s about 2.5 times the cost of the grid.
32
Chapter 4 Conclusion and Future Work
This research represented the analyses and discussion about designing a solar
charging stations configured in standalone topology for KFUPM, KSA. The design
start from gathering the required information to construct an initial estimation about
the system feasibility, then a detailed simulation is performed to determine the system
requirement to constructed which include the major components used to implement
this system. Mainly, the charging stations, as well as, the PV arrays will be installed
in the university parking slots areas. A proposed area space is proposed for this
research, a comparison between the required area and the available one was made, in
all parking areas, the required one is less than the available space and this is
considered to be good indication. Finally, reliability analysis was performed to the
system to determined the power availability during two working days. In addition,
simplified cost analysis was performed to estimate the project total cost and cost per
kilo-watt of power. It can be concluded that without storage units, the system can’t
sustain the load in the period of having no sun radiation. However, the system can
sustain itself with sufficient battery capacity.
The most important results.
From the simulation results in this report, the following can be concluded:
1. The system sustainability is obtained in case of having sufficient storage units.
2. The reliability analysis results was accepted considering the storage units.
3. The cost was calculated, based on the information provided by U.S department
of energy, the cost is about 2.5 larger than the current cost in KSA for
commercial KWh.
Recommendation can be made in this project such as:
1. The assumption of having 5000 EV at one time is not valid, there is must of
having logical increment in the EVs ending with 5000 EV at the end period, so
it can be guesses that this analysis was made in the worst case bases.
2. Perform 14 years or more cash flow study, that mean each year, one parking
will be build each year, this will make the problem more practical.
Future and Current work:
Currently, we are in the stage of performing the analysis in yearly bases, that will be
done by modeling the load in yearly bases and using temporal simulation.
33
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[3] A. Watson and D. E. Watson, "ftexploring," 2015. [Online]. Available: http://www.ftexploring.com/solar-energy/direct-and-diffuse-radiation.htm#fn2. [Accessed May 2015].
[4] Book, 2014. [Online].
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APPENDIX A All the Data required in this project is available in a CD attached with this report, including references, figures, area details, solar data, the main results..etc. CD include:
All the Area Details will be submitted by CD in the end of the report.
Data sheet for PV module is also included in the CD.
References.
Matlab Codes
Excel sheets of calculations.
Presentation.
The report.