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

3

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

4

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

5

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.

8

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.

10

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

3

4

5

6

7

8

9

Jun, 2013

Jul,

2013

Aug, 2

01

3

Sep, 2

01

3

Oct

, 2013

Nov,

201

3

Dec,

201

3

Jan, 2014

Feb, 2014

Mar,

201

4

Apr,

2014

May,

201

4

Jun, 2014

Jul,

2014

Aug, 2

01

4

Sep, 2

01

4

Oct, 2

014

Nov,

201

4

Dec,

201

4

Jan, 2015

Feb, 2

015

DAMMAM_STATION

3

4

5

6

7

8

Jun, 2013

Jul,

2013

Aug

, 201

3

Sep

, 201

3

Oct

, 2013

Nov,

201

3

Dec,

201

3

Jan, 2014

Feb, 2014

Mar,

201

4

Apr, 2

014

May,

201

4

Jun, 2014

Jul,

2014

Aug

, 201

4

Sep

, 201

4

Oct

, 2014

Nov,

201

4

Dec,

201

4

Jan, 2015

Feb, 2015

KFUPM_STATION

Radia

tion in

kw

h/m

2/d

ay

Radia

tion in

kw

h/m

2/d

ay

Radia

tion in

kw

h/m

2/d

ay

11

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.

3

4

5

6

7

8

9

Jun,

201

3

Jul, 20

13

Aug, 2

013

Sep, 2

013

Oct, 2

013

Nov

, 201

3

Dec

, 201

3

Jan,

201

4

Feb, 2

014

Mar

, 201

4

Apr, 2

014

May

, 201

4

Jun,

201

4

Jul, 20

14

Aug, 2

014

Sep, 2

014

Oct, 2

014

Nov

, 201

4

Dec

, 201

4

Jan,

201

5

Feb, 2

015

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.

15

20

25

30

35

40

Jun,

201

3

Jul, 20

13

Aug, 2

013

Sep, 2

013

Oct, 2

013

Nov

, 201

3

Dec

, 201

3

Jan,

201

4

Feb, 2

014

Mar

, 201

4

Apr, 2

014

May

, 201

4

Jun,

201

4

Jul, 20

14

Aug, 2

014

Sep, 2

014

Oct, 2

014

Nov

, 201

4

Dec

, 201

4

Jan,

201

5

Feb, 2

015

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

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.alternative-energy-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].

34

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.