Final Ver1

2015

KING FAHD UNIVERSITY OF
PETROLEM AND MINERALS
ELECTRICAL ENGINEERING DEPARTMENT
Power System Planning, EE-524

Term-Paper

KFUPM, PV’s with EVs ]
[ Sustainable Campus at KFUPM,
Instructor: Dr. Ali Al
Al-Awami

By:
Ibrahem M. Hussein
Date: 12-5-2015

ID: 201405220

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

2

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

3

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

4

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.

5

CHAPTER ONE
INTRODUCTION

1.1 General Overview
One man said - in the period from 1962 to 1986 – that “ The stone age did not end
for lack of stone, and the oil age will end long before
before the worlds runs out of oil”.
Those are the former Saudi Arabia minister of oil and minerals resources
resources.
Worlds turns into production and manufacturing, actually the world try to reduce
the resulting
ulting emissions from such process. Transportation account for considerable
amount of energy consumption, figure 1 shows oil consumption in million of barr
barrels
per day versus time, as the figure indicates the continuous increment of oil
consumption for transportation sector, it’s
it will know that these figures are
proportional to the CO2 emissions in the country.

Figure 1:Oil consumption by sector [1].

Figure 2:Emissions in tones of CO2 per person [2].

The overall CO2 emissions takes another direction in many countries, they try to
reduce the total amount of produced CO2 emissions every year, figure 2 indicate this
fact, the x-axis
axis represent the time and the y-axis
y axis represent tones of CO2 emissions per
person. The lines represent these emissions decreases with time for the most
6

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.

7

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
8

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

Insulation in

Error in

in km

Kwh/m2/day

measurements

KFUPM station

Dammam city, 8km

5.70

± 5%

Dammam

Dammam city,

5.79

± 5%

University Station

13km

Figure 3 :University of Dammam station.

Figure 4: KFUPM station.

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.
9

Table 2: Metering station data.

Date\Station
Month
Jun, 2013
Jul, 2013
Aug, 2013
Sep, 2013
Oct, 2013
Nov, 2013
Dec, 2013
Jan, 2014
Feb, 2014
Mar, 2014
Apr, 2014
May, 2014
Jun, 2014
Jul, 2014
Aug, 2014
Sep, 2014
Oct, 2014
Nov, 2014
Dec, 2014
Jan, 2015
Feb, 2015
Error

KFUPM Station in Dammam city, 8
KM
Gh KWh/m2/day Temperature
7.7
36
7.2
37
6.8
35
6.4
34
5.4
28
4
23
3.9
18
3.7
16
4.8
18
5.5
22
6.7
28
7.6
33
7.9
36
7.7
37
6.5
36
6.5
34
5
30
4
23
3.8
19
4
17
4.5
19
± 5%
±0.6 degrees C
DAMMAM_STATION

KFUPM_STATION
8

8

7

7
6
6
5

4

4

3

3
Jun, 2013
Jul, 2013
Aug, 2013
Sep, 2013
Oct, 2013
Nov, 2013
Dec, 2013
Jan, 2014
Feb, 2014
Mar, 2014
Apr, 2014
May, 2014
Jun, 2014
Jul, 2014
Aug, 2014
Sep, 2014
Oct, 2014
Nov, 2014
Dec, 2014
Jan, 2015
Feb, 2015

5

Jun, 2013
Jul, 2013
Aug, 2013
Sep, 2013
Oct, 2013
Nov, 2013
Dec, 2013
Jan, 2014
Feb, 2014
Mar, 2014
Apr, 2014
May, 2014
Jun, 2014
Jul, 2014
Aug, 2014
Sep, 2014
Oct, 2014
Nov, 2014
Dec, 2014
Jan, 2015
Feb, 2015

Radiation in kwh/m2/day

9

Dammam Station city, 10 km
Gh KWh/m2/day
Temperature
7.9
35
7
36
6.5
34
6.4
33
5.7
28
4
23
3.8
18
3.7
16
4.9
17
5.7
23
6.8
27
7.4
33
8
35
7.6
36
6.8
35
6.5
34
5.5
30
4.1
23
4
19
4.2
17
5
19
± 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
10

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.
9

Radiation in kwh/m2/day

8

7

6

5

4

Ju

n,

Ju 2 0 1
3
l
Au , 20
g, 13
Se 20
p, 13
O 20
ct 13
N o , 20
v 13
D , 20
ec 1
, 3
Ja 20
n, 1 3
Fe 20
b, 14
M 20
ar 14
,
A p 20
r, 1 4
M 20
ay 1
, 4
Ju 20
n, 14
Ju 2 0 1
4
l
Au , 20
g, 14
Se 20
p, 14
O 20
ct 14
N o , 20
v 14
D , 20
ec 1
, 4
Ja 20
n, 1 4
Fe 20
b, 15
20
15

3

DAMMAM_STATION

KFUPM_STATION

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 Jan2014. 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
11

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.
40

Temperature- KFUPM Station
Temperature- Dammam Station

Temperature in C

35

30

25

20

Ju
n,
Ju 201
l
Au , 20 3
g, 13
Se 20
p, 13
O 20
ct 13
N , 20
ov 1
D , 20 3
ec 1
, 3
Ja 20
n, 13
Fe 20
b 1
M , 20 4
ar 1
, 4
Ap 20
r, 14
M 20
ay 1
4
Ju , 20
n, 14
Ju 201
l
Au , 20 4
g, 14
Se 20
p, 14
O 20
ct 14
N , 20
ov 1
D , 20 4
ec 1
, 4
Ja 20
n, 14
Fe 20
b, 15
20
15

15

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.

12

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
Month
Jun, 2013
Jul, 2013
Aug, 2013
Sep, 2013
Oct, 2013
Nov, 2013
Dec, 2013
Jan, 2014
Feb, 2014
Mar, 2014
Apr, 2014
May, 2014
Jun, 2014
Jul, 2014
Aug, 2014
Sep, 2014
Oct, 2014
Nov, 2014
Dec, 2014
Jan, 2015
Feb, 2015
Total kwh/m2 / year

Effect of tilt angle (64 deg) kwh/m2/day
KFUPM Station
Dammam station
6.92
7.1
6.47
6.29
6.11
5.84
5.75
5.75
4.85
5.12
3.59
3.59
3.5
3.41
3.32
3.32
4.31
4.4
4.94
5.12
6.02
6.11
6.83
6.65
7.1
7.19
6.92
6.83
5.84
6.11
5.84
5.84
4.49
4.94
3.59
3.68
3.41
3.59
3.59
3.77
4.04
4.49
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
13

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.

Data
(Average
kwh/m2)
Daily insulation
in
Total energy per
year
Total energy per
day

Table 5: Solar Radiation and energy estimation.
With incident angle effect
Without incident angle effect
KFUPM Station

Dammam Station

KFUPM Station

Dammam Station

5.12

5.2

5.70

5.79

2007

2061

2328

2334

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.

14

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.
Near B.12
Near B.42
Near KFUPM Stadium
Near B.57
Near B.57 (beside B.4)
Near KFUPM mall
Near central kitchen

Expected Area
m2
22532
8826
31803
11480
1266
3648
9443

Number of
Vehicles
1609
630
2271
820
90
260
674

Diversity of
0.5
805
315
1136
410
45
130
337

Occupied area 70%
15772
6178
22262
8036
886
2553
6610

15

Near B.848 or B.853
Near B.817 - 820
Near B.822
Near B.1
Near B.14
Near B.20-18-19- a
Near B.20-18-19 - b
Sum

7163
7112
9240
8201
3670
5000
9246
138630

512
508
660
585
262
357
660
9898

256
254
330
293
131
179
330
4949

5014
4978
6468
5740
2569
3500
6472
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.

16

Figure 12: A total amount of 36 similar building of B.853.

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
Central kitchen
B.58
B.853 - 36 similar
B.1
B.3
B.16/4
B.6
B.14
B.59
B.22/23 and 24/25
B.68
Sum

Full Roof Area m2
4500
1600
37404
4500
1800
3384
2500
2550
8000
11000
3400
80638

70 % occupied area m2
3150
1120
26182
3150
1260
2368
1750
1785
5600
7700
2380
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.

17

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] :

Parking
charging
point

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.

18

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
19

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
Battery capacity
Energy per distance
Nominal distance at full capacity
Charging time

Value
24 KWh
25KWh/100 mile
117 km
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
20

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.

Percentage % of EVs existing in campus to charge

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.
50
45
40
35
30
25
20
15
10
5
0

0

5
10
15
20
Time in Hours, starting from 1 which is 8- AM morning

25

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 %
21

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)
) (3)
=1− (
−
×

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 ×
=
(4)
√3 ×
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.

22

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 amperhour is 410. A battery array shown in figure 15 having two string and four modules.

Figure 15: Battery array.

23

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.
5

EVs Power Demand in W

5

x 10

4

3

2

1

0

8

10

12
14
16
18
20
Time in Hours, starting from 8- AM morning

22

24

1
2
3
4
5
6
7
8
9
10
11
12
13
14

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%.

Number of existing vehicles

500

400

300

200

100

0

8

10

12
14
16
18
20
Time in Hours, starting from 8- AM morning

22

24

1
2
3
4
5
6
7
8
9
10
11
12
13
14

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
24

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.
5

x 10

Power demand by EVs in W

5

4

3

2

1

0

0

50

100

150

200
250
300
350
Number of existing vehicles

400

450

500

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.
4

Total Surface area in m2

2.5

x 10

2

1.5

1

0.5

0

0

2000

4000
6000
8000
10000
12000
Total number of PV modules "Sanyo"

14000

16000

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.

25

Table 10: Area calculation results.

Expected Area m2 Occupied area - 70%
Required Area m2
22532
15772
17391
8826
6178
6787
31803
22262
24602
11480
8036
8908
1266
886
1060
3648
2553
2969
9443
6610
7423
7163
5014
5514
7112
4978
5514
9240
6468
7211
8201
5740
6363
3670
2569
2969
5000
3500
4030
9246
6472
7211
138630
97041
107952

Parking near building/ NO.
Near B.12
Near B.42
Near KFUPM Stadium
Near B.57
Near B.57 (beside B.4)
Near KFUPM mall
Near central kitchen
Near B.848 or B.853
Near B.817 - 820
Near B.822
Near B.1
Near B.14
Near B.20-18-19- a
Near B.20-18-19 - b
Sum

The relation between the total surface area, number of PV modules and the needed
storage for each parking is shown in figure 20 below.
6

x 10

Total Storage

3
2
1
0
3
2
4

1

1

x 10
Total Surface area in m2

0

0

1.5

2

4
0.5
x 10
Total number of PV modules "Sanyo"

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.

26

6

x 10

Array Power in W

4
3
2
1

0
150
100
50
0
Number of PV Arrays

0

200

800

600

400

1000

1200

Number of EVs

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.
6

Power genration in each parking

3.5

x 10

3
2.5
2
1.5
1
0.5
0

8

10

12
14
16
18
20
Time in Hours, starting from 8- AM morning

22

24

1
2
3
4
5
6
7
8
9
10
11
12
13
14

Figure 22: Generating solar power per parking versus time.

27

2

1

17

17

17

743

743

743

743

4080

4080

4080

4080

4080

0.3951

0.1368

1.2462

3.453

0.9575

2.4469

8

8

8

8

8

8

8

26

35

14

5

42

116

32

82

3536

3536

4760

1904

680

5712

15776

4352

11152

0.7211

0.5514

0.5514

0.7423

0.2969

0.106

0.8908

2.4602

0.6787

1.7391

0.7571

0.8581

0.6562

0.6562

0.8833

0.3533

0.1262

1.06

2.9276

0.8076

2.0695

2

4

5

4

4

5

2

1

5

14

4

10

363400

266800

593400

671600

515200

519800

685400

266800

92000

832600

2300000

639400

1633000

36

36

36

36

36

36

36

36

36

36

36

36

36

36

2490

2490

2490

2490

2490

2490

2490

2490

2490

2490

2490

2490

2490

2490

44820

44820

44820

44820

44820

44820

44820

44820

44820

44820

44820

44820

44820

44820

15

9

6

14

15

12

12

16

6

3

19

52

15

37

537280

290720

213440

474720

537280

412160

415840

548320

213440

73600

666080

1840000

511520

1306400

330

179

131

293

330

254

256

337

130

45

410

1136

315

805

EBsupplie #veh

3
17
743
4080
1.0244
8
26

4624

0.6363

0.3533

3

671600

NAB

4
17
743
4080
0.7781
8
34

4080

0.2969

0.4795

5

AES

5
17
743
4080
0.7721
8

30

1904

0.403

0.8581

SES

6
17
743
4080
1.0031
8

14

2584

0.7211

StoragewhSVB

7
17
743
4080
0.8906
8

19

4624

TSA*E4 TW*E5kg NI

8
17
743
4080
0.3982

8

34

TNM

9
17
743
4080
0.5441

8

NA

10
17
743
4080
1.0031

Parray*E6 NS

11
17
743
4080

SP

12
17
743

SV

13
17

P. Num/dataNM

14

Table 11: Charging stations
specifications

28

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.
X: 2.3e+007
Y: 1

1

Availability

0.8
0.6
0.4
0.2
0

0

0.5

1
1.5
Battery Size in KWh

2

2.5
7

x 10

Figure 23: System availability versus battery capacity.
7

Battery Capacity in KWh

2.5

x 10

2

1.5

1

0.5

0

0

5

10

15

20

25
30
Time in hours

35

40

45

50

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
29

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.
20

AIDI

15

10

5

0

0

0.5

1
1.5
Battery Size in KWh

2

2.5
7

x 10

Figure 25: AIDI versus battery size.
0.75

0.7

AIFI

0.65

0.6

0.55

0.5

0

0.5

1
1.5
Battery Size in KWh

2

2.5
7

x 10

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.

30

Table 12: System Specifications.

What to include?
Total PV generating power
Module type
Array type
Total system losses
Tilt angle
Azimuth angel
DC-AC conversion ratio
Inverter efficiency
Total initial cost
Operating cost

Cost
15MW
Standard
Fixed
14%
26
298
1.1
98%
4$/kwh
0.02$/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
1
2
3
4
5
6
7
8
9
10
11
12
Total

AC System
Output(kWh)
1221790.75
1435249.875
1766619.625
2003430.75
2464345.5
2424671.25
2335966.25
2206053.75
1881944.125
1605064.875
1210119.375
1089660.75
21644916.88

Solar Radiation
(kWh/m^2/d)
3.32772636
4.36098576
4.85966063
5.87568998
7.191113
7.4010129
6.98638725
6.61414242
5.77784824
4.65315437
3.50963974
2.98379374
63.54115439

DC array Output
(kWh)
1254809.75
1469159.125
1809785.75
2049775
2518346
2477609.5
2387322
2255298.25
1925412.25
1644615.375
1243822.25
1121585.125
22157540.38

Value ($)
96,154.93
112,954.17
139,032.96
157,670.00
193,943.99
190,821.63
183,840.54
173,616.43
148,109.00
126,318.61
95,236.39
85,756.30
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.

31

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.
32

References:
[1] D. Gately, N. Al‐Yousef and H. M. H. Al‐Sheikh., "The Rapid Growth of
Domestic Oil Consumption in Saudi Arabia, and the Opportunity Cost of Oil
Exports Foregone.," KSA, 2001.
[2] A. G. D. o. Environment, "International emissions data from Climate Change
Authority.," Australian emissions data from Australian Government Department
of Environment, 2015.
[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].
[5] "Renewable Resource Atlas, KSA," [Online]. Available:
https://rratlas.kacare.gov.sa/RRMMDataPortal/Order/Security/NoAccess.
[Accessed May 2015].
[6] N. S. Narayan, "Solar Charging Station for Light Electric Vehicles," Delft
University., 2013.
[7] "Solar Electricity," [Online]. Available:
http://solarelectricityhandbook.com/solar-angle-calculator.html. [Accessed May
2015].
[8] "Wikipedia," [Online]. Available: http://en.wikipedia.org/wiki/Charging_station.
[Accessed May 2015].
[9] "Alternative Energy Tutorials," [Online]. Available: http://www.alternativeenergy-tutorials.com/images/stories/solar/alt23.gif. [Accessed May 2015].
[10] I. M., I. U.H. and A. H., "Design Of An Off Grid Photovoltaic System: A Case
Study Of Government Technical College,Wudil, Kano State.,"
INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY, vol. 2, no.
12, 2013.
[11] D. G. f. Sonnenenergie, Planning and Installing Photovoltaic Systems., Taylor &
Francis, 2008.
[12] "Gov. KSA, Electricity and Cogeneration regulation authority.," [Online].
Available: http://www.ecra.gov.sa/tariff170.aspx. [Accessed May 2015].

33

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.

34