OPTIMIZATION OF HYBRID RENEWABLE
ENERGY BASED ELECTRIC VEHICLE
CHARGING STATION
A thesis submitted to the
Department of Electrical and Electronic Engineering (EEE)
of
Dhaka University of Engineering & Technology, Gazipur
In partial fulfillment of the requirement for the degree of
MASTER OF SCIENCE IN ELECTRICAL AND ELECTRONIC ENGINEERING
by
Ashish Kumar Karmaker
(Student ID.: 122219- P)
DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING (EEE)
DHAKA UNIVERSITY OF ENGINEERING & TECHNOLOGY, GAZIPUR
January 2019
ii
The thesis titled “Optimization of Hybrid Renewable Energy based Electric Vehicle
Charging Station” submitted by Ashish Kumar Karmaker, Student ID: 122219-P, Session:
2012-2013, has been accepted as satisfactory in partial fulfillment of the requirement of the
degree of Master of Science in Electrical and Electronic Engineering on 14 January 2018.
Board of Examiners
1.
Dr. Md. Raju Ahmed
Professor& Head
Department of Electrical and Electronic Engineering
Dhaka University of Engineering & Technology, Gazipur
Chairman (Supervisor)
&
Member (Ex-Officio)
2.
Dr. Md. Anwarul Abedin
Professor
Department of Electrical and Electronic Engineering
Dhaka University of Engineering & Technology, Gazipur
Member
3.
Dr. Md. Saifuddin Faruk
Professor
Department of Electrical and Electronic Engineering
Dhaka University of Engineering & Technology, Gazipur
Member
4.
Dr. Masuma Akter
Associate Professor
Department of Electrical and Electronic Engineering
Dhaka University of Engineering & Technology, Gazipur
Member
5.
Dr. Mohammad Rubaiyat Tanvir Hossain
Professor
Department of Electrical and Electronic Engineering
Chittagong University of Engineering & Technology, Chittagong
Member (External)
i
Declaration
It is hereby declared that this thesis or any part of it has not been submitted elsewhere for the
award of any degree or diploma.
Signature of the candidate
------------------------------
Ashish Kumar Karmaker
(Student ID. 122219-P)
ii
Dedication
To my parents
iii
Acknowledgements
At first, I would like to express my gratitude to almighty. Then, I would like to express my
sincere gratitude to my thesis supervisor, Prof. Dr. Md. Raju Ahmed, for his continued
encouragement and support. His extreme enthusiasm toward research has motivated me
during my entire research life. I am eternally grateful for the things- both academic and
nonacademic- I have learnt from my supervisor. I will always remember the countless hours
(even in the middle of the night) we spent together discussing our research works and ideas.
I am also indebted to Prof. Dr. Md. Anwarul Abedin for his help, support, and guidelines.
My sincere gratitude and thanks to Prof. Dr. Md. Saifuddin Faruk for his valuable
guidelines throughout the research period.
Finally I would like to thanks all the faculty and staff of the EEE department for their
cooperation and motivations throughout the research.
At last, I would like to thank my father and my wife for their continuous support, love and
positive attitude towards my research life.
iv
Abstract
The sudden proliferation of Electric Vehicles (EV) like Easy Bike, Auto-Rickshaw, Electric-
Bike plays a vital role in producing energy crisis in the worldwide. As a developing country,
Bangladesh is also facing difficulties due to excess energy consumption. Although, Electric
Vehicles are opening a new dimension in the transportation sector with several benefits such
as- cheapest mode of transportation & lower greenhouse gas (GHG) emission, however the
generation of huge energy required for charging the batteries in every day is not very easy.
In addition, lack of charging station in Bangladesh hampers the time and takes higher cost
from EV owner. Owing to this reason, almost all of the EV owner takes power from the
residential connection illegally and pays the bill as the residential consumer. Thus, the
power sector is on system loss. Moreover, the non-linear characteristics of EV charger
affect the power quality by producing harmonics, voltage fluctuation and causing power
loss. To overcome the problems mentioned earlier, this research focuses on the utilization of
available renewable resources for EV charging. Bangladesh Rural Electrification Board
(BREB) and Bangladesh Power Development Board (BPDB) have already established few
solar charging stations throughout the country. These charging stations are not sufficient and
depend only on solar energy which is absent on rainy day & foggy environment. As
Bangladesh has a great potential of biogas/biomass resources, solar and biogas based
Electric Vehicle Charging Station (EVCS) would increase the effective operational hours.
This research investigates the feasibility of solar–biogas based EVCS using HOMER Pro
software. The impacts of existing EVCS on the power system network are also simulated &
analyzed by MATLAB simulation. A comparison is also demonstrated between the results
obtained from HOMER Pro software and mathematical analysis. Finally, the optimization
algorithm is developed using fuzzy logic (Mamdani and Sugeno) based on different
parameters like as output power availability, power demand, period & duration of charging.
Besides, a performance comparison is made on Mamdani and Sugeno fuzzy logic controller
after applying the same fuzzy rules to those controllers.
v
TABLE OF CONTENTS
Declaration I
Dedication II
Acknowledgements III
Abstract IV
Chapter 1 Introduction
1.1 Introduction 1
1.2 Literature Review 3
1.3 Objectives 5
1.4 Thesis Overview 6
Chapter 2 Challenges and Impacts of Electric Vehicles in Bangladesh
2.1 Introduction 7
2.2 Electric Vehicles in Bangladesh 7
2.3 Benefits and Drawbacks of Electric Vehicles 9
2.4 Charging Infrastructures in Bangladesh 10
2.5 Analysis of Challenges for Adoption of EV by Different Method 10
2.5.1 PORTER’s five forces model 10
2.5.2 PESTEL analysis 12
2.5.3 SWOT analysis 13
2.6 Challenges for Electric Vehicle adoption in Bangladesh 14
2.6.1 Shortage of power supply/load shedding 15
2.6.2 Lack of charging stations 16
2.6.3 Battery charging affects power quality issues 16
2.6.4 Battery price and capacity 16
2.6.5 High charging cost and time 17
2.6.6 Battery life time, maintenance and technology/material used 17
2.6.7 Low EV speed and Range 17
2.6.8 Frequent accident and quality of road 17
2.6.9 Lack of government support 18
2.6.10 Non-licensed vehicle 18
2.7 Impact Assessment of Electric Vehicle Charging Station 18
2.7.1 Modeling of EVCS parameters 19
2.7.2. MATLAB SIMULINK model of the EVCS 21
2.7.3 Impact Analysis 24
2.8 Policies Recommended for EV Adoption 28
2.9 Summary 30
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Chapter 3 Feasibility Assessment of Hybrid Renewable Energy Based EVCS
3.1 Introduction 32
3.2 Potential of renewable resources in Bangladesh 32
3.2.1 Solar Energy Potential in Bangladesh 32
3.2.2 Potentials of Biogas Energy Resources in Bangladesh 33
3.3 Mathematical modeling 37
3.3.1 Mathematical Model of Technical Parameters 37
3.3.2 Mathematical Model of the Economic Parameters 40
3.3.3 Modeling of the Environmental Parameters 42
3.4 System Component 44
3.5.1 Design of the Grid Connected Hybrid Renewable Energy Based EVCS 48
3.5.2 Design of EVCS using HOMER Pro Software 50
3.6 Technological Feasibility Analysis 51
3.7 Economic Feasibility Analysis 54
3.8 Environmental Feasibility of the Proposed EVCS 56
3.9 Comparison of results between HOMER analysis and mathematical analysis 59
3.10 Socio-Economic Aspects of the Proposed EVCS 60
3.11 MATLAB SIMULINK modeling for solar and biogas based generation 61
3.11 Summary 63
Chapter 4 Fuzzy Optimization of Proposed EVCS
4.1 Introduction 65
4.2 Block diagram of the proposed EVCS 66
4.3 Fuzzy Optimization Model 67
4.4 Input and output Variables 68
4.5 Optimization Algorithm 70
4.6 Fuzzy Rule Viewer 72
4.7 ANFIS (Adaptive Neuro Fuzzy Inference System) Model structure 73
4.8 Result and discussion 74
4.9 Summary 82
Chapter 5 Conclusion and Future Works
5.1 Conclusion 83
5.2 Future Works 84
References 85
Publications Related to Thesis 92
vii
LIST OF FIGURES
Fig. No. Figure Caption Page No.
Fig. 2.1 Electric Vehicles in Bangladesh 8
Fig. 2.2 Charging stations in Bangladesh: (a) Keraniganj and (b) Chandra,
Gazipur 10
Fig. 2.3 PORTER’S Five Forces Model 11
Fig. 2.4 PESTEL analysis for EV adoption challenges in Bangladesh 13
Fig. 2.5 SWOT Analysis for EV adoption 14
Fig. 2.6 Load curve [Source: Power Grid Company Bangladesh Ltd. (PGCB)] 15
Fig. 2.7 Impacts of Electric Vehicle 19
Fig. 2.8 Block diagram of an Electric Vehicle Charging Station 19
Fig. 2.9 MATLAB SIMULINK model of EVCS 22
Fig. 2.10 MATLAB SIMULINK Model for EV Battery Charger 23
Fig. 2.11 Battery Discharge characteristics 23
Fig. 2.12 Charging profile of an EVCS located in Gazipur district, Bangladesh 24
Fig. 2.13 Harmonics, when single EV (a), 3 EV (b) and 5 EV (c) is connected
at a charging station 25
Fig. 2.14 a) Input Voltage, before connecting charger 26
Fig. 2.14 b) Input Voltage, after connecting charger 27
Fig. 3.1 Solar irradiation in different cities of Bangladesh 33
Fig. 3.2 Types of MSW in Bangladesh 35
Fig. 3.3 Maximum electricity generation from biogas/biomass resources 36
Fig. 3.4 Grid Connected Hybrid Renewable Energy Based EVCS 48
Fig. 3.5 Block diagram of EVCS designed by HOMER Pro software 50
Fig. 3.6 Solar irradiation profile used in HOMER 50
Fig. 3.7 Temperature Curve at various seasons used in HOMER 51
Fig. 3.8 Daily available Biomass (Cow dung, poultry waste and MSW) used in
HOMER 51
Fig. 3.9 Load profile (daily, seasonal) used in HOMER. 52
Fig. 3.10 Annual energy production in kWh by resource type 53
Fig. 3.11 Percentage share of total generation by resources 53
Fig. 3.12 Annual cash flow by resources 55
Fig. 3.13 Payback period and life time of the proposed EVCS 56
Fig. 3.14 Carbon dioxide generation in renewable and conventional systems 57
Fig. 3.15 Comparison of CO2 emission from grid based EVCS and proposed
EVCS 58
Fig. 3.16 Solar PV based system model 62
Fig. 3.17 I-V & P-V Characteristics curve of PV based Model 63
Fig. 3.18 Biogas Generation output in m3 64
Fig. 4.1 Block Diagram of the Proposed EVCS 66
Fig. 4.2 Fuzzy (Mamdani) Optimization Model 67
viii
Fig. 4.3 a) Input variable “Power_ Availability” with membership functions
b) Input variable “Power_ Demand” with membership functions
c) Input variable “Period_of_Charging” with membership functions
d) Input variable “Duration_of_Charging” with membership functions
e) Output variable “Charging_Rate” with membership functions
68-70
Fig. 4.4 Optimization Algorithm for EVCS
72
Fig. 4.5 Fuzzy rule viewer 73
Fig. 4.6 ANFIS Model structure 74
Fig. 4.7 Surface view of charging rate, Power availability, power demand, time
of charging and duration of charging, (a), (b), (c) and (d) 75-76
Fig. 4.8 Variation of charging rate with (a) power availability (b) power
Demand (c) time of charging (d) duration of charging from Mamdani
fuzzy logic controller
77-78
Fig. 4.9 Variation of charging rate with (a) Power availability (b) Power demand
(c) time of charging (d) duration of charging from Sugeno fuzzy logic
controller
79
Fig. 4.10 Comparison of charging cost by fuzzy logic system with conventional
electricity price 81
ix
LIST OF TABLES
Table No. Table Title Page No.
Table 2.1. Specifications of Electric Vehicles 8
Table 2.2. Benefits and drawbacks of EV 9
Table 2.3 Transformer output at different EV load 27
Table 3.1 MSW generation scenarios of urban cities in Bangladesh 35
Table 3.2 Bio-waste to electricity conversion 36
Table 3.3 Specifications of the PV module (Canadian Solar Dymond CS6K-
285M-FG) 44
Table 3.4 Cost of the PV panels 45
Table 3.5 Digester size and cost according to IDCOL 45
Table 3.6 Cost and size of the biogas generator 46
Table 3.7 Technical parameters of the Suntree 10,000 TL 10 kW Converter 46
Table 3.8 Technical parameters of the 60, 038 MF-12 V, 100 Ah lead-acid Battery 47
Table 3.9 Technical specifications of an EV charger 47
Table 3.10 Calculation of the economic parameters 54
Table 3.11 CO2 emission rate on non-renewable sources 56
Table 3.12 CO2 generation by renewable energy 56
Table 3.13 Comparison of results from HOMER and Mathematical analysis 59
Table 3.14 Charging cost and monthly income summary of an EV 60
Table 4.1 (a) Charging rate variation with power availability in Mamdani &
Sugeno
(b) Charging rate variation with power demand in Mamdani & Sugeno
(c) Charging rate variation with time/period of charging in Mamdani &
Sugeno
(d) Charging rate variation with duration of charging in Mamdani &
Sugeno
80
10
1
Chapter 1
Introduction
1.1 Introduction
Bangladesh is an energy-starved country where natural gas and petroleum products are the
main sources of energy. Energy harvesting is performed significantly by transportation sector
which is 46.46% of the total petroleum consumption and 6% of the total natural gas
consumption. In addition, only 8% of the total demand of petroleum is reserved in
Bangladesh and every year about 1.2 million tons of crude oil and 2.6 million tons of refined
petroleum products need to import [1, 2]. So,the government has to pay a high amount of
budget to import these from abroad. Also, the Green House Gas (GHG) emission from
petroleum resources is a major environmental issue. Besides this scenario, GHG emissions
due to the transport sector increases significantly [3]. Growing concern about the GHG
emissions to the environment and low energy consumption facilities encourage peoples to use
Electric Vehicles (EV) which is also the economical mode of transportation [4].
The transport sector of Bangladesh plays a significant role in producing GHG emissions.
Major portion of these GHG emissions is CO2. A report published by International Energy
Agency showed that, approximately 23% of the GHG emitted to the environment comes from
transport vehicles [5]. Besides, the energy and agricultural sectors are accelerating the CO2
emission process [6]. The rapid increase in the number of transport vehicles to support the
huge population throughout the country is an alarming sign of environmental pollution as
well as fuel consumption. Moreover, the use of EVs like Auto-rickshaws and Easy Bikes
increases day by day [7]. These EVs produce less emission and no fumes. The Bangladesh
Road Transport Authority (BRTA) has no clear statistics about these types of EV [8].
However, the rapid increases of EV require approximately 500 MW power per day from the
national grid of Bangladesh [9].
Nowadays, EVs are charged in residential areas and the electricity bill is paid by residential
consumers. In that case, the power sector fails to gain any profit from charging those EVs.
Meanwhile, these EVs are producing a huge pressure on the national grid of Bangladesh [10].
2
EV batteries may operate from a single phase or three phase supply system. Because of wide
availability of single supply points, EV chargers are connected with this system. However,
three phase supply system gives larger power with fast charging. These EV chargers are
basically power electronic converter similar to non-linear load. This non-linear characteristic
of EV charger produce harmonics in the current and affect the voltage profile of the power
network [11]. High non-linear loading can be a cause for non-linear voltage drop and thus
voltage waveform might be distorted. On the other hand, non-linear load can affect the
performances of distribution transformer by increasing power losses in the winding and
thereby reducing its power output [12]. Thus, EV chargers when integrated with the power
grid or distribution network, it hampers the power quality. When large number of chargers is
connected with the distribution networks, the power quality problem arises [13].
In order to solve the above problems regarding EV charging, it is necessary in Bangladesh to
develop sufficient charging infrastructure, their charging coordination scheme and charging
policy. In addition, to minimize the pressure on the national grid and maintain quality power
throughout the country, a cost-effective alternative approach for generating electricity is
required [14]. However, to best of my knowledge, no initiatives to design hybrid renewable
energy-based Electric Vehicle Charging Station (EVCS) from the perspective of Bangladesh
exist, which has motivated me to do the research presented herein.
Performance of an EVCS depends upon different factors such as- power availability, load
demand, battery capacity, charging cost, charging time etc. In this research, the charging cost
optimization is performed using fuzzy logic while maximizing the utilization of renewable
resources. An energy management algorithm will be developed using fuzzy if-then rules.
Several researches were performed using fuzzy logic although hybrid renewable energy
based EVCS optimization is new one. In Bangladesh, electricity tariff is different for peak
and off-peak hour. Thus, the charging time, battery capacity, power demand and power
availability are taken as the input of the fuzzy system where charging cost is the only output
of the system. This type of optimization will save the charging cost as well as maintain the
proper use of electric energy.
3
1.2 Literature Review
The rapid increase in EV market penetration around the world has promoted the energy
consumption sector as a new research area. This section mainly reviews the challenges &
impacts of existing EVCS on power grid, utilization of renewable resources and optimization
techniques etc. The sudden proliferation of EVs make the power system more vulnerable
especially in peak hour period. The major problems of the EV deployment are insufficient
charging stations, battery technology, higher charging time & cost etc. [15]. On the other
hand, non-linear EV charger affects the power system by producing harmonics, voltage
fluctuation and power loss etc. [16]. The environmental pollution regarding EV acceptance
demonstrates that, the GHG emission would be greatly reduced if the EVs are charged by
renewables rather than coal based electricity [17].
As it is known that, increasing cost of limited fossil fuel affects the electricity &
transportation sector worldwide. In addition, these two sectors are the main contributor of the
GHG emission in the world. Thus it is right time to choose renewable energy resources
instead of petroleum resources for sustainable development in Bangladesh [18]. Limited
fossil fuel and its increasing cost is a great problem for transportation and electricity
generating sectors. To promote sustainable development in urban areas as well as rural areas
it is necessary to fulfill the electricity demand. In rural areas where grid electricity is
unavailable, electricity demand can meet using renewable resources like solar, biogas, wind
etc. [19]. Renewable resources like solar, biogas/biomass, wind are available in Bangladesh
which can be used for generating electricity for charging EVs [20].
Solar energy is available in all over the country to generate electricity effectively for 5-6
hours with solar irradiation of 4 to 6.5 kWh /m2-day [21]. Since the solar radiation is absent
in rainy, foggy days and night time, thus the operational hours for charging EV is decreased.
This is the major drawback of the solar stand-alone system [22]. In Bangladesh, necessary
wind speed is limited to only coastal and off-shore areas [23].
Moreover, the biogas resources are available throughout the country irrespective of location
& time. Das et al. explored the biomass potential of Bangladesh through gasification
technology and concluded that agriculture residues, rice husks, bagasse, wheat straw, jute
stalks, maize residues, coconut shells, forest residues and Municipal Solid Waste (MSW) are
4
the main sources of biomass [24]. Biogas production from poultry waste, animal waste/cow
dung is calculated from the data obtained from the Department of Livestock Services and
Food and Agriculture Organization (FAO) statistics. MSW data is calculated from the
Bangladesh waste-related database and the website of Sustainable and Renewable Energy
Development Agency (SREDA), Bangladesh. M. S. Shah et al. estimated the 7.6775 billion
m3 biogas potential in the fiscal year of 2012-13 which can be used as bio-CNG of 5.088
billion m3 [25]. As an agricultural country, biomass energy has a great potential to strengthen
the industrial and manufacturing sectors in Bangladesh [26]. Thus, the proper use of
biogas/biomass resources for electricity generation increases the effective operation hours
compared to solar & wind potential [27].
Charging stations in Bangladesh depend on grid electricity, but in the case of off-grid remote
areas, this type of EV charging is quite impossible. So, there is a need for stand-alone hybrid
renewable power generation in Bangladesh [28]. Rural electrification projects sometimes
may fail due to a lack of attention in the financial, technical and environmental feasibility
aspects. Rahman et al. proposed a standardized approach for decision making concerned with
the extension of electricity into remote areas to evaluate the economic, technical and
environmental feasibility [29]. Design and feasibility (technical, economic and
environmental) analysis of the combination of solar PV-biogas-diesel-wind-battery for
electrification of stand-alone remote area/island is investigated by using the HOMER
software tool [30-32]. The results are related to parameters, cost of energy, net present cost,
payback period and annual cash flow [30-32]. A feasibility study regarding a solar energy-
based Electric Vehicle charging station in Shenzhen, China was analyzed by HOMER. This
proposed model can mitigate the grid energy-based problem by integrating solar energy and
can meet the large demand needed for EVs [33]. Another feasibility study was conducted
based on solar powered charging stations for EVs in the north central region of Bulgaria [34].
However, from the research conducted in [33] and [34] explored that, in the case of rainy and
foggy days, there is no alternate option for power generation. Thus it hampers the EV
charging.
Energy management algorithm for an EVCS offers maximization of renewable energy use
with minimum charging cost. A research performed on real time energy management
algorithm helps to determine EV charging cost involving renewable energy [35]. Another
research accomplished on EV charging demonstrates that, proposed model based on fuzzy
5
logic decreases the waiting time with better performance than traditional charging station
[36]. EVCS based on fuzzy inference system exhibits optimal charging or discharging rate
considering dynamic electricity tariff and battery SOC [37]. Different algorithm has taken
battery SOC and dynamic electricity tariff as the fuzzy inputs whereas charging cost
minimization is the main objectives [38-40]. However, other factors which affect the
performance of the EVCS such as- power availability, power demanded by EVs, period and
duration of charging etc. are not taken into consideration. In Bangladesh, maximum EVs are
placed for charging at peak-hour period and load shedding is happened frequently at that
time. The EV can be recharged the batteries during off-peak hour at cheaper rates while
helping to absorb excess electricity generation. Thus, it is quite difficult for effective
charging management of EVs. Besides, output power availability from the hybrid renewable
resources for EV charging should be considered in fuzzy inputs. For these reason, the output
power availability, power demand, period of charging (peak/off peak hour) and duration of
charging are considered as fuzzy inputs and charging rate as output. This type of energy
management algorithm will help to optimize charging cost with maximization of renewable
energy use.
1.3 Objectives
The main goal of this thesis is to analyze the feasibility with technical, financial,
environmental and socio-economic aspects and design of a hybrid renewable energy based
EVCS with energy management algorithm.
The following specific objectives will be taken into consideration in the present study:
i. To analyze the challenges and impacts of EVs on the power system;
ii. To identify the prospects of the solar and biogas-based hybrid power generation
scheme in Bangladesh;
iii. To analyze the technical, financial, environmental and socio-economic
feasibility of the proposed EVCS using HOMER Pro software;
iv. To design a model of proposed EVCS suitable for Bangladesh with fuzzy
optimization technique using MATLAB.
6
1.4 Thesis Overview
This thesis is divided into five chapters-
Chapter 1 provides a general introduction followed by the background, literature review,
objectives and methodology of this research.
Chapter 2 covers a brief description of the present status of EVs, charging infrastructures. In
addition, challenges of EV adoption are analyzed by three methods i.e. PORTER’s five forces
model, PESTEL analysis, SWOT analysis etc. Then, the analysis of impact of EVs in the
power sector of Bangladesh is performed using MATLAB simulation and finally, several
policies are recommended for increasing EV acceptance in the context of Bangladesh.
Chapter 3 demonstrates the feasibility assessment & design of hybrid renewable energy
based electric vehicle charging station in the context of Bangladesh. The prospects of hybrid
power generation using solar & biogas is analyzed in this section according to the
technological, financial and environmental aspects. The results obtained from HOMER pro
software is compared with the results of mathematical analysis of the proposed EVCS.
Moreover, few model of power generation are simulated using MATLAB SIMULINK based
on solar and biogas.
Chapter 4 illustrates mathematical modeling of optimization and designing of a fuzzy logic-
based energy management algorithm for EVCS. Furthermore, differentiate between the
Mamdani and Sugeno type fuzzy controller based on their performances.
Chapter 5 contains the concluding remarks followed by few suggestions on the possible
future work.
7
Chapter 2
Challenges and Impacts of Electric Vehicle Charging
Station in Bangladesh
2.1 Introduction
The sudden proliferation of the Electric Vehicles (EV) in all corners of Bangladesh brings a new
sector in the field of transportation. Although it has numerous benefits to the users but there are
lot of difficulties appeared in EV adoption. In this chapter, the challenges of EV adoption in
Bangladesh are investigated & analyzed using PORTER’s five forces model, PESTEL analysis
and SWOT analysis. In addition, the MATLAB SIMULINK model for measuring the impacts of
the existing EVCS is developed. The MATLAB model will provide results corresponding to the
power quality disturbances during EV charging. Finally, several policies are recommended for
mitigating the impacts and overcoming challenges in the context of EV adoption in Bangladesh.
2.2 Electric Vehicles in Bangladesh
Battery run EVs were first introduced in Bangladesh in 2009. These are now extensively used in
almost all corners of the country. They are three types—Easy Bikes (which carry 4–5
passengers), Auto-rickshaws (which carry 2 passengers) and Electric rickshaw vans (which carry
goods). A fully charged Easy Bike can travel approximately 80–100 km and its market price
around $1500 whereas Auto rickshaws and Electric rickshaw vans can travel 50–70 km with a
fully charged battery and price around $ 750. At first these were imported from China but now-a-
days these are produced by domestic companies. A fully charged EV can move 80–100 km per
day and it consumes 8–11 kWh per day. According to the commercial tariff, per unit cost of
electricity is $0.1225. So, the cost per km run of an electric car is approximately $ 0.0168 and the
energy consumed per km run is approximately to 0.1375 kWh. Fig. 2.1 shows the three type of
EVs which are available throughout the country.
8
Fig. 2.1: Electric Vehicles in Bangladesh
Table 2.1 shows the specifications of Electric Vehicles in Bangladesh. This type of transport is
environmentally friendly, producing almost zero fumes and noise pollution and creating less CO2
emissions. It has bought a silent revolution to the transport sector and become popular for the
cheaper fares compared to other modes of road transportation and thus increasing the happiness
among low income people, including the drivers of the vehicles.
Table 2.1. Specifications of Electric Vehicles [41]
Easy Bike Auto-Rickshaw and Electric Rickshaw Van
Power > 800 W Power >500 W
Voltage 60 V (5 batteries of 12 V
each) Voltage 36/48 V
Load bearing capacity 300–350 kg Load bearing capacity 120–160 kg
Continued trip mileage 80–100 km Continued trip mileage 50–70 km
Charging time 6–8 h/day Charging time 4–6 h/day
Power consumption 8–11 kWh Power consumption 3.0–4.5 kWh
Electric powered Easy Bikes and Auto-rickshaw vehicles are launched by some private
initiatives, and have grown a certain level of popularity in rural area and near big cities. These
types of EVs are consuming a lot of energy from the grid but there many initiatives are being
taken to reduce the pressure on the grid. However the improvements are yet to be widespread
9
because the power rickshaws have been claimed to consume a considerable amount of electrical
energy from our national power grid.
For this thesis, a survey was conducted at Trishal upazilla which is 100 km away from Dhaka
city and 20 km from Mymensingh city. Here more than 250 various types of Easy Bikes, Auto
rickshaws and Electric rickshaw vans running every day. Only 20 manually driven rickshaws
were found. Most of the Easy Bikes are battery driven and these are charged by electricity from
residential connections which is not approved by the Govt. of Bangladesh. A few of the EVs are
Compressed Natural Gas (CNG)-driven. They have to go 10 to 15 km to refueling their cylinders
with CNG. Another problem is that the CNG station takes time to refuel these EVs because there
is a huge pressure on that station. Therefore, time and energy are wasted. On the other hand, load
shedding occurs in that area very frequently every day. Thus it is difficult to charge EVs during
that period.
In these circumstances, if the facilities were built to charge the batteries using other reliable
sources at the adjacent area, it would be better for those people to run their battery driven EVs
easily. Although there are some difficulties in charging EVs, they have had a revolutionary
impact on the transportation vehicle sector, especially in sub-rural and rural areas.
2.3 Benefits and Drawbacks of EV
There are lots of benefits using EV but mass adoption of EVs may be a threatening issue for the
power sector, transport policy regulations. The benefits & drawbacks with respect to technical
and socio-economic reasons are given below in Table 2.2.
Table 2.2: Benefits and drawbacks of EV
Benefits Drawbacks
1. Cheaper to run
2. Better for the environment
3. Easy to drive
4. Energy security
1. Low range and speed
2. Lack of charging station
3. Battery need to change after few month of continuous use
4. Frequent accident etc.
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2.4 Charging Infrastructures in Bangladesh
Although huge number of battery EVs are running all over the country, the charging
infrastructures are not available in everywhere. Recently, government has established few solar
based charging stations in different corners of Bangladesh which seems very less in quantity.
The solar charging stations in Bangladesh shown in Fig. 2.2 are operated by the Bangladesh
Rural Electrification Board (BREB).
Fig. 2.2: Solar Charging stations in Bangladesh: (a) Keraniganj and (b) Chandra, Gazipur
These charging stations are established to recharge 15-20 EVs per day but due to several
drawbacks it turns into a loss project. As on rainy day and foggy environment, the solar energy is
absent and thus the EV battery recharging process is hampering. Also, at night time the solar
energy is not present, thus charging batteries at that time is quite difficult. For this reason, excess
battery requires to store solar energy which makes the system expensive. In this circumstance,
the solar charging stations should be modified including another renewable resource which is
sustainable throughout day and night period.
2.5 Analysis of Challenges for EV Adoption by Different Method
In this section, the PORTER’s Five Forces model, PESTEL analysis and SWOT analysis are
carried out in analyzing the challenges for acceptance of EVs in Bangladesh.
2.5.1 PORTER’s five forces model
PORTER’s five forces model is designed for competitive analysis of an industry/ organization.
Five competitive forces are used for this modeling where three forces (i.e. entrants, substitutes,
and established rivals etc.) are horizontal and two forces (i.e. supplier power and buyer power)
are vertical. This model framework had been developed by Michael E. Porter in the year of
11
1979. This model expresses the profitability of the industry and the behavior of competitive
forces for market analysis. This model is used in electric vehicle market competitiveness in a
research [42]. Fig. 2.3 shows the PORTER’s five forces model for analyzing barriers in the EV
adoption.
Fig. 2.3: PORTER’S Five Forces Model
The PORTER’s five forces model provides an analysis regarding different threats for EV adoption in
Bangladesh which are given in below.
A. Threat of Entrants: Entry of new entrant into the electric vehicle market makes it
unprofitable. Due to lack of charging infrastructure and incentives for electric vehicle, it is
barrier for entering into electric vehicle market.
B. Threat of Rivalry: Electric vehicle has to compete with the established model of
conventional car industries. The more industries in the electric vehicle market make it
competitive for market penetration.
12
C. Threat of Substitutes: Different new modeled vehicles are running in roads now-a-days
such as- CNG and LPG. These vehicles are displayed as a substitute of electric vehicles.
D. Buyer Power: Electric vehicle in Bangladesh like easy bike, auto rickshaw is cheaper than
other conventional cars. However, for long range electric vehicle it is expensive for new
buyers. Although in Bangladesh these light battery electric vehicles are not expensive, but
after sales, repair & maintenance services are very poor. Battery lifetime is also a major
concern for new buyers. Because, expensive EV batteries are need to change after 1 year.
E. Supplier Power: High technology cost and lack of manufacturing industries is the hindrance
of supplier. Although in Bangladesh, many industries are engaged to manufacture electric
vehicles & their accessories, thus it will be strong factor for EV adoption. Lack of
government policy for Electric vehicle industries, the supplier have to face different problem
regarding electric vehicles supplying.
2.5.2 PESTEL analysis
A PESTEL (Political, Economic, Social, Technological, Environmental and Legal) analysis is a
framework or tool that is used to analyze and monitor the marketing environmental factors of an
organization. The results obtained from PESTEL analysis can be used to determine the threats &
weakness of the organization in SWOT analysis. This analysis consists of the following
directions- political, economic, social, technological, environmental and legal. Macro
environmental analysis of EV market was performed in a research using PESTEL method and it
showed that, the retired batteries may be used to overcome the cost of EV penetration [43]. The
challenges for EV acceptance in Bangladesh by PESTEL analysis are shown in Fig. 2.4.
13
Fig. 2.4.: PESTEL analysis for challenges of EV adoption in Bangladesh.
2.5.3 SWOT analysis
SWOT (Strength, Weakness, Opportunities and Threats) analysis is a strategic planning system
that can be used for categorizing strengths, weakness, opportunities and threats of electric
vehicle diffusion in Bangladesh. In this thesis, the SWOT analysis is executed to know the
challenges and then describe how these challenges can be overwhelmed. The SWOT analysis for
the EV adoption is shown in Fig. 2.5.
14
Fig. 2.5: SWOT Analysis for EV Adoption.
2.6 Challenges for Electric Vehicle Adoption in Bangladesh
Mass adoption of EVs is eventually reliant on consumer’s readiness to purchase the technology.
Mainly the EV consumers count on several factors when taking a decision to accept EVs. It
includes price, range, robust and battery life. Although the numbers of EV are increasing, it will
not be sustainable and profitable if the several factors are keeping untreated. Analyzing the EV
adoption by different method i.e. PORTER’s five forces model, PESTEL analysis and SWOT
analysis, the several main factors are found as the threats & hindrance in Bangladesh.
Strengths
•Environment friendly due to less GHG emission
• Low running and maintenance cost
•Reduce the dependence on foreign oil imports
•Easy to drive
• Energy efficient transportation system
Weakness
•Limited range and low speed vehicle
• High charging time
•Battery need to change
•Low consumer awareness
• Frequent road accident
Threats
•Lack of government support
•Available compitors in electrc vehicle manufacturers
• Lack of charging stations
• Insufficient spare parts compared to the conventional vehicles
• Non-licensed vehicle
• Poor roads & Traffic conditions
Opportunities
• Increase employment opportunities
• Availabiity of skilled labor at low cost
• Improvement in battery technology
• Integration of renewable resources for electric vehicle charging
• Rising fuel cost
•Greater opportunities for research and developemnt
• Rising awareness of environmental factors for sustainable development
15
2.6.1 Shortage of power supply/load shedding
Bangladesh has a great electricity demand where only 67% of the people get electricity access.
According to the BPDB, there are 25,26,594 electricity consumer has serve demand of 10,958
MW power on 30 June 2018 [44]. The difference between maximum demand and the supply, the
load shedding occurs in different areas of Bangladesh. Also, the power loss due to the auxiliary
use at generating station, transmission & distribution networks is around 9%. Thus the supply
and demand cannot fulfill and thus load shedding arises. The daily load curve for Bangladesh
electricity sector is given in Fig. 2.6. The load curve shows that, the load increases in the peak
hour period (5 P.M. to 11 P.M.) whereas it was minimum at off-peak hour (11 P.M. to 5 P.M.).
However, the increased electricity demand causes by EVs add an extra pressure to the grid
especially at peak-hour period. It creates a great problem for minimizing demand and load-
shedding occurs. Electric auto-rickshaw and tri-cycle mainly run at villages, upazilla and small
cities in Bangladesh. In these places, load shedding occurs frequently in a day. Thus, the
shortage of power supply hampers the EV charging.
Fig. 2.6: Load curve [Source: Power Grid Company Bangladesh Ltd. (PGCB)].
16
2.6.2 Lack of charging stations
One of the major difficulties for EV adoption is insufficient charging infrastructure. To promote
EV adoption, there should have a lot of charging stations throughout the all corners of
Bangladesh. However, these charging stations are very unsatisfactory. Almost all the charging
stations are private and they have taken higher rate for EV charging. Also, the national grid is
under pressure for such type of EV charging demand especially at peak hour [45]. There is a
problem of finding free space for EV charging anytime due to long queue. Thus, the EV owner
has to wait. It kills the time which also drops the income of the EV driver. Recently there is a
trend in power sector to establish some public charging stations throughout the country. For an
example, BREB established 6 EVCS based on solar energy in Gazipur, Dhaka, Sylhet and
Chittagong. DPDC also established 21 kW solar EVCS in Keranigonj, Dhaka. These are the
positive signs and hope for the charging stations in Bangladesh.
2.6.3 Power quality issues due to battery charging
Battery is the main component of an EV from where it takes the required power. The AC power
is converted into DC power using a converter/charger which is a non-linear load. This nonlinear
load affects the power system by producing various problems such as- harmonics, current and
voltage unbalance, voltage sag and swelling, flickers and phase shifting [46]. When EVs are
connected for charging their batteries in a network, it produces harmonics and voltage & current
fluctuation. Thus the power quality falls and it’s a hindrance of EV penetration in Bangladesh
[47].
2.6.4 Battery price and capacity
There are several types of battery lead-acid, lithium ion, Ni-Cd, Zn/air, Ni-Zn, Ni-MH, Na/S
batteries. In Bangladesh, lead acid batteries are popular due to its low cost. Although lead acid
batteries have a number of drawbacks such as- it cannot discharge more than 20% of the rated
capacity, low power density, heavy weight, lower life cycle etc. However, lithium ion batteries
are advantageous over lead acid batteries such as- high power density, long life time, good
performance at higher temperature etc.[48]. Lithium ion batteries have few disadvantages like
high cost, recharging takes large time. Ni-Cd battery has long battery life, fully dischargeable,
recyclable but it’s costlier in case electric vehicles. Another problem of using Ni-Cd battery, it
17
pollutes the environment when not disposed properly. NiMH batteries have double energy
density than lead acid battery.
2.6.5 High charging cost and time
EV adoption depends on another great factors that are cost & hours of charging. In Bangladesh,
everyday $1.5 to $1.875 is required for full charging of electric auto-rickshaw/easy bike. There
are also different tariffs for EV charging which is increasing every year. In addition, this
charging takes 6 to 8 hours daily. Thus, it’s a big problem that deals with the shrinkage of the EV
adoption.
2.6.6 Battery life time, maintenance and technology/material used
Mass adoption of EV depends upon the battery life time, costing and maintenance. These
batteries are very much costly and life time is not so long. Thus the EV owner has to change it
periodically. EV adoption can be increased if the battery technology & their performances are
improved [49]. Although some of the batteries can recover their performance by maintenance.
2.6.7 Low EV speed and Range
Most of the electric vehicle running in Bangladesh has a problem regarding speed. People are
eagerly waiting for the techno logy which can solve these problems. EVs in Bangladesh have a
speed on average of 20 km/hour. This low speed deteriorates the chance of mass EV deployment
in Bangladesh. The range problems in Electric Vehicle discourage it to use. Almost, all Electric
Vehicles have range on average 60-80 km on full charging. Thus, after traveling this distance it
requires to recharge the batteries. So, long distance traveling would be hampered using these
EVs.
2.6.8 Frequent accident and quality of road
Now-a-days, it is seen everywhere in Bangladesh that due to EVs road accident happens
frequently. As there is no separate road for these vehicles exists and the EV drivers are not much
experienced in driving, road accident happening every day.
EV requires smooth and healthy road for economic transportation. As it is sure that maximum
portion of the EVs are used in rural areas where road transportation system are not healthy with a
18
lot of disturbances. Thus, mass EV penetration would require high quality road for safe and
smooth running.
2.6.9 Lack of government support
According to the government of Bangladesh, using of EV is prohibited and government
discourages to use these EVs. As these EV consume a huge amount of electricity, it is a burden
on the power sector. EV has no clear database and registration procedure. However in recent
observation, there are number of EV charging station running in different corners of Bangladesh
on behalf of the power sector. Thus the lack of government support, it is difficult to deploy EVs
in a higher penetration [50].
2.6.10 Non-licensed vehicle
There are no rules for registering EV in BRTA. But there should be a legal framework for
promoting sustainable environment through energy efficient method of EV use. The rules and
regulations for EV licensing should be designed by technical and some other means as like
motorized vehicle. As the electric vehicle is unlicensed vehicle as per government indication, it
acts as an obstacle for EV penetration in Bangladesh [51].
2.7 Impact Assessment of Electric Vehicle Charging Station
As the EV load increases rapidly, thus the impact of EVs should be analyzed. The impact of
mass EV penetration on power system is expressed in Fig. 2.7 below. Although EV penetration
has cheapest transportation system, lower GHG emission facility, smart grid facilities. But
negative impacts on power system network are very much significant.
Power quality is the ability of a power grid network to supply a sustainable and clean power
supply with sinusoidal wave shape, noise free within the standard limit of voltage & current
harmonics. Harmonics, voltage sag/swelling are the common problems related to power quality.
EV chargers are the components that causing these problems when connected with grid. The EV
adoption in Bangladesh, is not only provides its negative impacts but also have some positive
impacts. Here in this thesis, I have discussed only the negative impacts of EV penetration on
power system.
19
Fig. 2.7: Impacts of Electric Vehicle.
2.7.1 Modeling of EVCS parameters
EVCS consists of the following parts basically- transformer, rectifier and converter etc. Fig. 2.8
shows a block diagram of an EVCS which comprises transformer, rectifier and converter.
Basically, rectifier and converter make a charger which used for EV charging.
Fig. 2.8: Block diagram of an Electric Vehicle Charging Station.
As a non-linear load, EV charger produces harmonics, low voltage profile and power loss in
distribution transformer. In Bangladesh, for EV charging level 2 type AC charging scheme is
used where maximum current rating is 16 A and maximum power rating is 3.3 kW. Most of the
EVs have power ranges from 0.5 kW to 1 kW and all of them use single phase 240 V, 50 Hz
20
supply system. In this section, mathematical modeling is developed for harmonics, voltage
profile and transformer overloading due to EV charging.
A. Power demand
Electric Vehicle battery takes charge from the power distribution system. The increased power
demand affects the stability of the system due to non-linearity. The power demand by an EV can
be expressed as
D
BattEV
T
SOCSOCCP
)(* minmax (2.1)
where CBatt is the battery capacity, TD is the duration of charging. Battery SOC is a factor
whether the EV takes high or small power. The gross power demand of the EVs is the
summation of individual power demand of all EVs which likely signifies as
N
N
EVGross PP1
(2.2)
B. Harmonics
The rise in high frequency components of voltage and current with compared to fundamental
frequency is defined as harmonics. Harmonics distorts the voltage & current waveforms and
thereby affecting power quality. It can be measured by total harmonic distortion (THD) of
current & voltage.
%1001
2
2
I
I
THD
N
n
n
i (2.3)
%1001
2
2
V
V
THD
N
n
n
v (2.4)
Equation (3) & (4) express the Total Harmonic Distortion (THD) for current and voltage
respectively. For slow charging THDi, THDv will be less than the fast charging. Thus, the EV
with low SOC will have a great chance to produce harmonics.
21
C. Voltage profile
The low voltage profile becomes a threatening issue induced by EV charging. Voltage stability
refers to the ability that the power network being stable after the sudden increase or decrease in
the loads. EV loads take large amount of power at a very short duration. Thus, voltage profile
will be degraded and grid will be unstable.
D. Transformer performance
Mass deployment of EVs creates an additional stress on distribution transformers and their life
cycles. Another problem is that, the EV charging rate should be limited per day and charging
stations should keep far away from transformer for reducing power loss. Harmonic current is
responsible for occurring load losses in transformer whereas harmonic voltage incurs no load
loss. Due to these harmonic losses, heating is increased relative to the pure sinusoidal wave. This
harmonic withstand capability can be measured by a factor called k- factor. The equ. expresses
K-factor as
2
1
2 ][R
nN
n I
InfactorK
, (2.5)
where In is the current related to nth harmonic and IR is the rated load current. The presence of
harmonics causes overheating in the transformer. Thus, the transformer should be selected
according to the withstand capability at higher harmonic current for non-linear loading [52].
2.7.2. MATLAB SIMULINK model of EVCS
When EVs are connected to the utility grid for recharging the batteries, it would hamper the
power quality. In this paper, the impacts of EVCS on utility grid are analyzed using MATLAB
SIMULINK model shown in Fig. 2.9. In this modeling, the three phase source is used as utility
grid and the battery ratings are taken as the EVs running in Bangladesh. Charger consist mainly a
rectifier and a DC-DC converter circuit. Switched Mode Power Supply (SMPS) based battery
charger provides constant voltage constant current for charging batteries.
22
Fig. 2.9: MATLAB SIMULINK model of EVCS.
A. Battery Charger model
Typical switched model power supply or battery charger incorporates a front-end AC to DC
rectifier for producing unregulated DC voltage. A high frequency chopper (IGBT/ MOSFET)
then chops the input DC voltage according to the duty cycle. After that, a high frequency
transformer isolates, step down and converts square wave DC to square wave AC output. It is
then rectified and filtered to produce ripple free smooth DC output voltage. Pulse width
modulation technique is generally employed in this conversion. Fig. 2.10 shows the Switching
mode power supply based battery charger model of designed in MATLAB SIMULINK.
The lead acid battery is used in almost all the EVs in Bangladesh. These are 48/60 V. Each
battery unit consist 4 or 5 battery where each battery rating is 12 V and 20 Ah.
23
Fig. 2.10: MATLAB SIMULINK Model for EV Battery Charger.
Battery discharge characteristic is shown in Fig. 2.11. When the battery discharges the maximum
ampere-hour, the value of voltage reached to zero. It means SOC of the battery going down. Also
for different current rating the discharge phenomenon of the battery is shown in Fig. 2.11.
Fig. 2.11: Battery discharge characteristics.
24
2.7.3 Impact Analysis
Growing popularity of Electric Vehicles due to several positive impacts is admirable but its
detrimental impacts on the grid power quality cannot be neglected. To analyze the impacts, in
this research MATLAB based SIMULINK model for EVCS connected with utility grid is
demonstrated.
A. Increased power demand
The increased electricity demand causes by electric vehicles add an extra pressure to the grid.
The daily load curve for Bangladesh power sector is given in Fig. 2.6. The charging profile for
EVs in a charging station is shown in Fig. 2.12. This graph indicates that, at the time of peak
hour, the demand of EV charging increases. Thus, for mass penetration of EV leads to the huge
demand during peak hour in all corners of Bangladesh.
Fig. 2.12.: Charging profile of an EVCS located in Gazipur district, Bangladesh
The increased power demand can be a cause of load-shedding and also hampers the power
quality. If the EV charging is scheduled and maintain strictly at peak and off-peak period, then
the problem arises with power demand will be minimized.
0
2
4
6
8
10
12
14
6 A.M. 8 A.M. 10 A.M.12 A.M. 2 P.M. 4 P.M. 6 P.M. 8 P.M. 10 P.M. 12 P.M. 2 A.M. 4 A.M.
Load
in
kW
Hour
25
B. Harmonics disturbance
Harmonics are the disturbances of a power system. EV charger is non-linear load and when it
connected in the power system then it generates harmonics. As the EV charger normally
connected at the power distribution network for charging, the aggregated effects of harmonics
can be threat for the whole power system.
(a)
(b)
(c)
Fig. 2.13 (a),(b) & (c): Harmonics, when single EV (a), 3 EV (b) and 5 EV (c) is connected at a
charging station.
26
According to the IEEE Standard 519-1992 Harmonics level, the value of THD should not exceed
5% for ensuring power quality. In this analysis, it is found that, the harmonics level is greater
than the accepted level for power quality. In the MATLAB Simulink modeling, the harmonics
generated at the different ratio of EV charging is shown in Fig. 2.13 (a), (b) & (c). The THD
value is different because in this experiment, I have used different categories of battery with
different rating.
C. Voltage Disturbances
Voltage at the distribution end also reduces when multiple EV chargers are connected. The
overloading due to large number of EVs causes this problem. The voltage profile variation
before connecting EV charger and after connecting EV charger is shown in Fig. 2.14 (a) & (b).
Fig. 2.14 (b) shows that, the voltage is affected by harmonics disturbance compared to the
voltage without connection of EV chargers in Fig. 2.14 (a). In the Fig. 2.14 (b), it is seen that
voltage fluctuation occurs with harmonic disturbances.
Fig. 2.14 (a): Input Voltage, before connecting charger
27
Fig. 2.14 (b): Input Voltage, after connecting charger
D. Transformer power loss
Clustered EV charging can be a cause of transformer overloading and thereby increasing the
power loss. The overloading scenario of a distribution transformer obtained from MATLAB
simulation with different EV load is shown in Table 2.3.
Table 2.3: Transformer output at different EV load
The transformer power loss due to harmonic effects can be minimized by selecting transformer
with higher k-factor. More the EVs connected with the distribution transformer, the losses will
be more and thereby the efficiency of the power system decreased.
Output kVA under Rated current Output kVA under Harmonic current
200 191.80 (1 EV)
200 188.75 (3 EV)
200 185.45 (5 EV)
28
2.8 Policies Recommended for EV adoption
Although electric vehicle has a huge demand of electricity and most of the times power sector
falls into a problem with shortcomings of profit. In other cases, the electric vehicle has several
positive impacts on environment, fossil fuel reduction, improved socio-economic status of EV
owner and decreases the unemployment. To become sustainable in power sector, it is very urgent
to increase the EV penetration in Bangladesh. Analyzing challenges of EV adoption, authors
suggest few recommendations for increasing it. The recommendations are given below.
i. At first, EV i.e. Auto-rickshaw, Electric bike, Electric bi-cycle, Electric tri-cycle needs
registration in the national website. When it is completed then the total number of charging
station required in different corners of Bangladesh can be calculated easily. Charging rate of
EV should be selected according to the energy consumption.
ii. As an environment friendly vehicle and cheapest mode of transportation, government should
prioritize this vehicle. It can be done by applying no tax on accessories of the EVs,
establishing more charging infrastructures. Locations of charging stations need to be
situated at a suitable place where transportation is easy & known for all. Also charging
stations should be far away from distribution transformers for avoiding power loss &
distortion.
iii. As the maximum number of EV chargers are connected at grid simultaneously, it affects the
power quality issues by producing harmonics, voltage fluctuation etc. Coordinate charging
scheme can be helpful for charging EV to reduce power quality problems.
iv. To cut pressure on the national grid via EV charging, the huge potentiality of renewable
resources such as- solar, biogas and wind should be utilized. Few Solar based Charging
stations are already established in different corners of Bangladesh by the government. These
charging stations are designed to charge 20 to 30 EVs per day. The Government has set a
tariff $0.5 to $0.625 for recharging Easy bike and Electric rickshaw respectively. As the
solar can provide power only at day time and is absent in cloudy & foggy days, thus hybrid
29
system (such as solar, biogas or solar, wind) is mandatory for sustainable production of
electricity for EV charging [53].
v. Vehicle to Grid (V2G) technology is required to EV management for proper utilization of
renewable resources and also for improving efficiency. Thus excess energy will be used
effectively.
vi. The EV owner should use updated battery technology. Also, battery management system
should be used to monitor SOC when charging for avoiding overcharge. In Bangladesh,
maximum EV owner uses lead-acid batteries. However, lithium ion batteries are more
efficient and have higher life cycle than lead acid batteries. In addition, lead acid batteries
are more vulnerable to the environment due to disposal of lead. Thus, it is necessary to use
lithium ion batteries instead of lead acid batteries.
vii. The used batteries of EVs can be used for backup and load leveling purposes. The used
batteries when no longer usable in EVs, their residual capacity still has significant value.
During off-peak hour, the excess electricity generation can be stored using these batteries. In
addition, the EV owner earns some extra money by selling these batteries. The battery
recycling policy developed by the government will help environmentally and financially.
viii. Grid based charging station produces more CO2 emission than charging station based on
renewable energy [54]. Thus, the in case of considering lower GHG emission by EVCS
renewable based charging station is necessary for increasing sustainable EV adoption.
ix. Training facilities should be provided by the government institution to the EV driver for
better performance and safe driving management. For this purpose, BRTA can arrange
training & workshop facilities.
30
x. In case of highway, there should be separate lane for electric vehicles to decreasing frequent
road accident. Also, the quality of the road especially for rural areas should be improved.
Due to awkward, low quality & damaged road causes road accident and more energy
consumption for EVs.
xi. The government should encourage establishing more research center on these transportation
vehicle for improving technology. Such type of research center can help to extend range of
EVs as well as enhancing battery capacity.
xii. Finally, awareness should be grown up by publishing the environmental benefits of using
EVs can help more adoption in Bangladesh.
2.8 Summary
As the EVs acceptance is growing in rapid manner and it induces several disturbances to the
power quality, thus it is an important issue now-a-days. The government and respective planning
section is very much worried about this EV adoption. In this scientific era, this type of problems
should be overcome by technical advancement. In this chapter, the different model for analyzing
challenges are developed and found that, numerous factors are behind for this EV adoption. Main
problems are found by such analysis as: no governmental policy, high charging cost, lack of
charging infrastructures, high charging time and less investment to this sector makes EV
adoption difficult to the user.
The increased power demand due to the EV charging makes power sector to be more lagging
beyond the power generation. As the load shedding occurs basically at evening to mid night
period (peak hour) and maximum EV is gone to the charging station at that time. EVs battery can
be able to supply power up to 8-10 hours and their range is also limited i.e. 70-100 km. So, if an
EV starts running from morning hour, it will go for next charging at evening hour. Thus, the
scheduled charging especially at off peak hour is required for EVs. It will improve the
performance as well as reduce the demand at peak hour.
31
Mathematical modeling for EVCS is also developed in this chapter which demonstrates the
significance of the EVCS parameters. The MATLAB Simulink model showed that, the power
quality factors i.e. harmonics, voltage fluctuation (sag/swelling) and transformer power loss due
to EV charging is a threatening issue. This simulation shows that, the integration of one EV
charger creates THD = 4.82%, for 3 EV charger, THD = 12.35% and for 5 EV chargers, THD =
19.69%. In addition, if the number of EV chargers is connected to the utility grid, it produces
voltage fluctuations with significant power loss in distribution transformer which is given in this
chapter.
32
Chapter 3
Feasibility Assessment of Hybrid Renewable Energy
Based EVCS
3.1 Introduction
Electric Vehicle Charging Station (EVCS) basically connected with the utility grid for battery
charging purposes. However due to sharp increasing in the EVs worldwide, it becomes a threat
to the power sector. On the other hand, Bangladesh has a great potential of renewable resources
like solar, biogas, wind etc. As the wind energy resources are not available throughout the
country, in this research only solar and biogas based hybrid generation is taken to analyze. In this
chapter, the prospect of hybrid power generation through solar & biogas/biomass resources is
analyzed. Then, the feasibility assessment of the hybrid renewable energy based EVCS is
performed according to the technological, financial and environmental aspects using HOMER
Pro software. The comparison of the results obtained from HOMER Pro software and
mathematical analysis are shown in this chapter. In addition, socio-economic aspects of this type
of proposed EVCS is demonstrated. Finally MATLAB Simulink based PV and biogas based
Electricity generation scheme is formed and described.
3.2 Potential of Renewable Resources in Bangladesh
In this research, two types of renewable energy resources are proposed for the purpose of
electricity generation, i.e., solar and biogas.
3.2.1 Solar Energy Potential in Bangladesh
Bangladesh has a geographical position from 20°34′N to 26°38′N and from 88°01′E to 92°41′E.
Variation of the solar insolation ranges from 4 to 6.5 kWh/(m2·day) and the average insolation is
5 kWh/(m2·day). Consequently, during winter and summer time, the average temperatures are 20
°C and 27.75 °C, respectively. In this research, the NASA monthly average global radiation chart
has been used to estimate the solar system capability. The solar radiation is maximum during the
months of March–April and minimum during December–January. A study on the daily sunlight
33
hours in Bangladesh suggested that 7–10 h of daily solar radiation is available in Bangladesh but
due to rainfall, cloudy and foggy environments, this is reduced further by 54% and finally it is
4.6 h daily [55].
Fig. 3.1 shows that Rajshahi is identified as the highest solar intensity region vis-a-vis Sylhet
being the lowest. Due to having enough resources, solar energy is proposed in this research to
establish a solar powered electric vehicle charging station that is a crying need to meet the
increasing demand for electricity.
Fig. 3.1: Solar irradiation in different cities of Bangladesh [55].
In Bangladesh, 40% of the rural population has no access to electricity. To provide electricity by
using renewable solar energy to households, the government introduced a scheme called Solar
Home System (SHS). The Govt. of Bangladesh is working towards ensuring 100% electricity
access by 2021 with the SHS program. A solar power plant of 15 MW is already installed in
Bangladesh and the government is committed to producing 10% of the total power generation
from renewable energy by 2021 [56].
3.2.2 Potential of Biogas Energy Resources in Bangladesh
Biogas refers to a renewable energy source consisting of gas produced by the biological
breakdown of organic biodegradable materials in the absence of oxygen. Suitable raw materials
include biomass, human waste, animal waste, poultry droppings, Municipal Solid Waste (MSW),
green waste, plant materials, crops, etc.
It typically consists of methane (CH4: 50%–75%), carbon dioxide (CO2: 25%–50%), nitrogen
(N2: 0%–10%), hydrogen (H2: 0%–1%) and hydrogen sulfide (H2S: 0%–3%). It may contain
34
small amounts of oxygen (O2: 0%–0.5%) and moisture, etc. The gases contained in biogas like
methane, hydrogen, and carbon monoxide (CO) can be further combusted or oxidized with
oxygen in a combustion chamber. The released energy allows the use of biogas as a fuel. This
biogas can be used for some heating applications as well as for electricity generation. In
Bangladesh, there are three main sources of biogas: poultry waste from the poultry industry,
municipal solid waste and animal waste.
A. Poultry Waste
According to the Bangladesh Division of Livestock Statistics annual report on livestock for the
fiscal year 2015–2016, there are more than 53,000 poultry farms in different regions of the
country. The number of poultry farms is increasing rapidly due to their perceived profitability.
They serve the purpose of egg and meat production and also produce fertilizer and fish feed. The
wastes from poultry can be a good source of bio-energy.
A study conducted by the U.N. Food and Agriculture Organization (FAO) in 2013 suggested that
in Bangladesh there are approximately 245 million chickens and 46 million ducks which produce
12.9 million tons of waste per day [57]. This poultry waste could be collected and converted into
biogas. The extracts from the biogas plants could be an additional source of fertilizer and fish
feed.
B. Animal Waste
Animal waste in the form of cattle or buffalo dung can be used as a source of biogas. According
to a study conducted by the Bangladesh Livestock Department, there are 24 million cows and 1.5
million buffalos which produce 102.6 million tons of waste per day [58]. After collecting this
waste it can be utilized as a biogas source and extracts from the plant can be used as a good
fertilizer.
C. Municipal Solid Waste
Municipal solid waste (MSW) is also referred to as trash or garbage, refuse or rubbish. These are
the everyday items discarded by the public after use. MSW contains mainly food waste, paper,
cardboards, textiles, plastics, glass/metal/ceramics, etc. The MSW produced in Bangladesh are
classified as given below in Fig. 3.2.
Per capita waste generation in a day is about 0.5 kg. In Dhaka city, the daily waste production
capacity is 4200 tons. Major six cities produce waste per day is equal to 7890 tons. In addition,
35
the rate of waste generation is increasing rapidly. Thus, the extrapolation result shows that, it will
be 47,064 tons per day by 2025 which is shown in Table 3.1.
Fig. 3.2: Types of MSW in Bangladesh [59]
Table 3.1: MSW generation scenarios of urban cities in Bangladesh [59]
Year Urban
Population
% of total
Population
Waste
Generation Rate
Total Waste
Generation/Day (Ton)
1991 20,872,204 20.15 0.49 9873.5
2001 28,808,477 23.39 0.5 11,695
2004 32,765,152 25.08 0.5 16,382
2015 54,983,919 34.20 0.5 27,492
2025 (Projected) 78,440,000 40.00 0.6 47,064
These wastes can be used as a source of biogas to generate electricity. Firstly, waste is collected
from different areas, next these wastes are fed to a combustion chamber and burnt. This burning
produces steam which turns a turbine to generate electricity. These wastes have high potential as
MSW
Domestic waste
Industrial
waste
Medical waste
Biodegradable
(Food waste)
Non-Biodegradable
(Polythene, plastics)
Hazardous
(Benzene)
Non-
hazardous
(Fuel oil)
Hazardous
(Blood/syringe)
36
an important source for recovering energy and meeting the electricity demand. Table 3.2 shows
the maximum amount of electricity generation from different types of bio-waste.
Table 3.2: Bio-waste to electricity conversion
Waste
Type
Waste produced
per day (kg)
Biogas produced
per day (m3)
* Maximum electricity
production per day (kW)
Poultry 12,900,000 954,600 1344.50
Cow dung 102,600,000 3,488,400 4913.24
MSW 27,492,000 2,089,392 2942.81
* Maximum amount of electricity generation.
kW
WE
B
BWP
. , (3.1)
where W, BW and BkW stands for total waste in kg, biogas production per kg of waste and biogas
required for 1 kW electricity generation, respectively. It is assumed that for poultry waste &
MSW biogas production per kg of waste is 0.074 m3 and for cow dung biogas produced per kg is
0.034 m3. It is also assumed that biogas required for 1 kW electricity generation is 0.71 m
3. Fig.
3.3 shows the percentage share of maximum electricity generation in Bangladesh from biogas
resources like MSW, poultry and cow dung.
Fig. 3.3: Maximum electricity generation from biogas/biomass resources.
Poultry 15%
Animal waste 53%
MSW 32%
37
3.3 Mathematical Modeling
This section describes modeling of technical, economic and environmental parameters related to
the Electric Vehicle Charging Station.
3.3.1 Mathematical Model of Technical Parameters
A. Power demand by an Electric Vehicle
Power consumption of an EV depends on the distance travelled, battery capacity and the mode of
driving. An electric vehicle consumes power which can be calculated as below
d kD
K EP
T
, (3.2)
where, Kd is the number of kilometers driven, Ek is the energy required per kilometer and T is the
time required to charge the vehicle battery. T is the difference between the departure and arrival
time of the EV at a charging station. T depends on the SOC of the vehicle’s battery. Power
demand of an electric vehicle can be represented by using battery capacity, SOC and its charging
time [43]:
max(SOC SOC)batD
QP
T
, (3.3)
where Qbat indicates the battery capacity, SOCmax is the upper limit of the battery SOC and T is
charging duration. Therefore, power demanded by the Nth
electric vehicle will be:
1
N
D
i
P P
(3.4)
B. PV Array Output
The PV array output is determined by equ. (3.5):
PVP SI A , (3.5)
where SI is the average annual solar insolation in kWh/m2, η is the PV module efficiency and A
is the surface area of the proposed solar PV system [33]. If the temperature of the PV cells
increases, the output power drops. This is taken into consideration by the use of a temperature
de-rating factor, ηt for calculating PV system efficiency.
38
1 [ ( )]t C ST T , (3.6)
( )[1 ( )]CPV t C S
S
GP W T T
G , (3.7)
where W is the rated capacity of the PV panel in kW, β is the temperature coefficient (%/°C), GC
is the solar insolation in practical conditions, GS is the solar insolation at Standard testing
conditions in kW/m2. TC and TS are the temperature of the PV cell at current conditions and
standard conditions respectively in °C [33].
C. Digester Size and Biogas Production
Digester size is an important factor for biogas plants. The volume of the digester can be
determined by as
d i RV S T , (3.8)
where Vd is the digester volume, Si is the daily substrate input and TR is the retention time in
days. Retention time depends upon the digesting temperature. It will be at least 40 days for a
simple biogas plant but in practical scenarios it can be 60–80 days. An extra-long retention time
will increase the gas yield by 40%. The substrate input depends on how much water has to be
added to the substrate in order to arrive at a solids content of 4–8%.
iS B W , (3.9)
where B and W stand for biomass and amount of water added, respectively. The mixing ratio for
animal waste/poultry droppings and water amounts is between 1:3 and 2:1 for a biogas plant.
Biogas generation per day, G (m3 /day), is calculated on the basis of the specific gas yield, Gy of
the substrate and the daily substrate input Si. The biogas production, G in m3 /day can be
calculated as:
G = VS × Gy (solid); VS is the volatile solid content
G = B × Gy (moist mass); B is the weight of the moisture mass
G = Number of LSU × Gy (species); LSU is the standard gas yield values per livestock unit.
Biogas yield from a digester depends on the temperature and retention time. Thus, it can be
represented by using the equ. (3.10) given below:
39
( , ) ( , )y R RG T T m f T T , (3.10)
where m = mass of the volatile solids content, f(T, TR) = Function of digester temperature T and
retention time TR [60].
The ratio of the daily total solid input or volatile solid input, Si and the digester volume, Vd is
called as digester loading, LD.
d
iD
V
SL ; (3.11)
D. Gas Storage Design
Gas storage is used in the biogas plant to hold the biogas. Its size depends on the rate of
generation and consumption. It must be designed as so that it can cover the maximum
consumption rate (>Vg1) and can hold the gas for the longest period with zero consumption
(>Vg2). For safety margin, 15% should be added to the original size as indicated in equ. (3.12)
given below [60].
),max(15.121 ggg VVV , (3.12)
The ratio of the volume of digester and gasholder is important for designing a biogas plant.
Typically it is 3:1 and 10:1.
E. Biogas Generator Output
The total power generated by the biogas system consists of Biogas generator 1, Biogas generator
2 and Biogas generator 3:
Bio poultry Cowdung MSWP P P P 0.71
PW CD MSWG G G , (3.13)
where GPW, GCD and GMSW are the amount of biogas produced from the poultry waste, cow dung
and MSW, respectively.
F. Battery Modeling
In this system, two types of batteries are used - one is the lead acid battery for storing the surplus
energy generated by the system and another one is the vehicle battery. Charging time and
charging rate both depend on the state of charge (SOC) of the battery. The charge level of the
vehicle battery can be described by two ways. One is SOC and another is Depth of Discharge
(DOD). If a battery is fully charged, it is called 100% SOC. when it is fully discharged, it is said
that the cell has 100% DOD. For maximum battery life cycle, the 100% DOD situation must be
40
avoided. In this model, the SOC is taken as 20% and maximum DOD is assumed as 80%. The
battery energy can be calculated by using equ. (3.14):
0
SOC d
t
Battery bat batQ V I t , (3.14)
where SOC is the initial charge of the battery [20].
State of charge (SOC) of a battery:
SOC
,max
100%bat
bat
QB
Q , (3.15)
The initial SOC of the battery can be considered as a random variable depending on the distance
travelled by an EV is as -
2
2
(ln )
2
d
1( ; ; ) .e
2Π
d
d
d
d d
d
f dd
, (3.16)
where d (distance travelled)> 0, μd is the mean distance and σd is the standard deviation of the
random variable [61]. The battery initial SOC can be expressed by using distance travelled by the
EV and its maximum travelling distance range:
max
1d
Ed
; 0 < d < dmax (3.17)
G. Converter Modeling
A bi-directional converter is used to convert from DC power into AC and vice-versa. In this
proposed model, only PV output DC power is converted to AC power by the converter. The
output power of the converter is given by:
AC Conv DCP P . (3.18)
The converter efficiency taken into consideration in the proposed system is 97%.
3.3. 2 Mathematical Model of the Economic Parameters
Mathematical modeling of economic parameters such as cost of energy, net present value,
internal rate of return, expected payback period and profitability index is used in this research.
41
A. Cost of Energy (COE)
It is the average cost per kWh electricity production in a system. The ratio of annual cost of
energy production to the total energy production/year gives the value of COE. HOMER uses the
following equation for calculating COE:
COE Annual
Served
C
E
(( ) / (1 ) )NPVCR n
TL Sold
F i r r T
E E
, (3.19)
where FCR is the capital recovery factor, in is the nominal interest rate, r is the annual inflation
rate, T is the project life time, NPV is the net present value, ETL is the total load served
(kWh/year) and ESold is the energy sold to the grid (kWh/year) [33]. Levelized Cost of Energy
(LCOE) depends on several factors such as plant size, capital cost, the solar radiation and
biomass collection, lifetime, O&M cost, capital recovery factor and degradation of the modules
used, etc.
B. Net Present Value
This is a measurement of profit determined by subtracting the present values of cash outflows
(including initial cost) from the present values of cash inflows over a period of time. The net
present value can be calculated as-
0
Nt
1tt
t CC
NPV
r)(1
, (3.20)
where Ct is the net cash inflow during the period t, C0 is the initial investment or capital cost, r is
the discount rate and t is the time period in years [34].
C. Internal Rate of Return (IRR)
It is a discount rate that makes the net present value (NPV) of all cash flows from a particular
project equal to zero. When the NPV is equal to zero, then the IRR will be determined by using
the formula:
1 2 3 40 1 2 3 4
NPV 0 ......(1 ) (1 ) (1 ) (1 ) (1 )
t t t t ti
T
C C C C CC
r r r r r
, (3.21)
where value of ‘r’ will be equal to the IRR.
42
D. Expected Payback Period
Payback period is an important parameter for project selection. It is the time after the total
project cost will be vanished by project cash inflows. It indicates that the project will be
profitable after this period.
& Re .
inf
EBPBCap O M pl
Cash low
C C C
C
, (3.22)
where CCap ,CO&M and CRepl, Ccashinflow are the capital cost, O&M cost, replacement cost and
annual cash inflows respectively. It should always less than the project lifetime, T for a feasible
and profitable project.
E. Profitability Index (PI)
The profitability index plays a vital role for a project to determine if one should proceed with an
investment or not. The profitability index rule states that if the profitability index or ratio is
greater than 1, the project is profitable and may receive the green signal to proceed. In contrast, if
the profitability ratio or index is below, the project should be rejected:
inf
. & Re .
PI Cash low
Cap O M pl
T C
C C C
, (3.23)
where T = lifetime of a project in years.
PIEPBP
T , (3.24)
If EPBP < T the project is feasible. If EPBP ≥ T, then the project will be a failure and thus it
should not proceed.
3.3.3 Modeling of the Environmental Parameters
Environmental feasibility is assessed by calculating the total GHG gases emitted from a power
plant. These gases are CO2, SO2, NO and other pollutants, which increase environmental
pollution endangering human life and putting wildlife at risk. In this paper, CO2 emissions from
the hybrid renewable energy-based EVCS are calculated. Emission factor and total CO2
emissions from the charging station are the important parameters for this feasibility test.
43
A. Emission Factor
It represents a value that attempts to relate the quantity of pollutants (i.e., GHG gases) released to
the environment per unit generation of power. It is usually expressed in kg/kWh:
Emission, (1 )RE P EF , (3.25)
where P is the total generation capacity, EF is the emission factor, ηR is the overall pollutant
reduction efficiency of the system [62].
B. Life Cycle Emission Factor
i &GWP ( )
LCEFi Ci Oi Di B C
i
Net
E E E E E
P
, (3.26)
where i is the type of GHG; GWP is the global warming potential factor for each GHG; EF =
direct emissions caused by the combustion of fossil fuels; EC = emissions regarding construction
of the plant; EO = emissions at O&M works; ED = emissions caused by decommissioning the
plant; EB&C = emissions caused by the battery storage and charging apparatus; PNet = net output
of electricity during the lifetime of the system.
Total CO2 emission in a renewable energy based power plant can be calculated as-
.&..,2,2,,222 , recyclingconstBattBattBatttdthermal
d t
i
i
Total COCapCOEdCOCapiCOCO
(3.27)
Here, dCO2 is the direct emissions caused by fuel combustion; iCO2 is the annualized indirect
emissions from the system; Capi is the capacity of the plant, Ethermal is the energy produced at
time t of day d; CO2 Batt is the CO2 emissions produced by the battery; CapBatt is the capacity of
the battery and CO2, Batt is the CO2 generated by the battery construction & recycling processes
[63]. In a hybrid renewable energy-based EVCS, CO2 generation per kWh energy should be less
than the CO2 generation from the conventional power generating system. Otherwise,
environmental feasibility result will be negative and the proposed model fail or be rejected.
The batteries normally used in the EVs are lead acid, lithium ion type. Although these batteries
do not responsible for emit GHG emission but the manufacturing/ recycling process of these
44
batteries contribute to the GHG emission a lot. That means as the more EVs are penetrated, the
GHG emissions are more.
Another point of consideration is that the extracts generated from the biogas system should be
utilized properly. They can be in the form of solid ash or fly ash. Proper use of these extracts can
reduce the pollution regarding waste generated from the hybrid system.
3.4 System Component
The major components for the hybrid renewable energy powered EVCS are the solar
photovoltaic (PV) module, digester, biogas generator, converter, battery, hybrid charge controller
and electric vehicle charging apparatus. For our technical, economic and environmental
feasibility study by using HOMER, it is necessary to know the number of units to be used,
capital cost, O&M cost, replacement cost, lifetime, operating hours, biomass and solar resources.
3.4.1 PV Module
A solar PV module consists of a number of solar cells connected in series and parallel. Four
types of solar cells: mono-crystalline, poly-crystalline, thin film and amorphous silicon are
available nowadays. In this project, mono-crystalline solar cells are used due to their higher
efficiency (14%–20%) and the fact they require less space. Electrical parameters are taken from
the standard test conditions at air mass AM 1.5, irradiance 1000 W/m2, cell temperature 25 °C.
The specifications of solar PV module are given in Table 3.3.
Table 3.3: Specifications of the PV module (Canadian Solar Dymond CS6K-285M-FG)
Characteristics Value
Maximum Power (Pmax) 275 W
Voltage at Maximum Power (Vmp) 31.3 V
Current at Maximum Power (Imp) 8.8 A
Open Circuit Voltage (Voc) 38.3 V
Short Circuit Current (Isc) 9.31 A
Panel Efficiency (ηp) 16.72%
Power Tolerance +2%
Dimension of the Panel 1658 × 992 × 5.8 mm
Operating Temperature Range −45 °C–85 °C
45
At 1000 W/m2 irradiance, solar module can produce = Efficiency × Irradiance × Panel Area =
0.1672 × 1000 × (1.658 × 0.992) = 275 W. The number of modules required for a 10 kW system
= 37.
Table 3.4 shows different types of cost and ratings of the PV panels.
Table 3.4: Cost of the PV panels
Capital Cost $10,000
Replacement Cost $5000
O&M Cost/Year $1.00
Life Time 25 years
De-Rating Factor 80%
3.4.2 Digester
The digester is the main component for biogas production. The animal waste is supplied to the
digester with proper mixing of water. It will only start producing biogas after effective
combustion. The size of the digester depends on the retention time and substrate input of the
daily waste supply. The amount of substrate input is the sum of water and the biomass in cubic
meters. In this system, three digesters are required for different wastes. Ratings of the biogas
generator according to the input waste material cow dung, poultry waste and municipal solid
waste are 4 kW, 4 kW and 2 kW, respectively. Digester size and cost according to the data taken
from IDCOL is given in Table 3.5.
Table 3.5: Digester size and cost according to IDCOL
Type of
Biomass
Biomass
Required(kg)
Digester
Size(m3)
Operating
Hours/Day
Cost of the
Digester
Cow Dung 130 4.8
10–12 $650
Poultry Waste 68 4.8 10–12 $650
MSW 50 3.2 7–8 $537.5
3.4.3 Biogas Generator
In this research, three biogas generators are required for the power generation. Generators are
driven by the biogas from the digester where the average operating hours will be 8–10. The
46
O&M cost is minimized by selling fertilizer which comes from the digester after biogas
production. The rating of the biogas generator, different cost, life time is mentioned in Table 3.6
below.
Table 3.6: Cost and size of the biogas generator
Generator Rating Capital
Cost
Replacement
Cost
O&M
Cost/Year Lifetime
4 kW (Bio 1) $2000 $1000 $500 5 years
4 kW (Bio 2) $2000 $1000 $500 5 years
2 kW (Bio 3) $1000 $800 $100 5 years
3.4.4 Converter
A bidirectional converter is used for converting DC power into AC power and vice versa. It is
connected to the AC bus. In the proposed system, all the biogas generators AC output (4 + 4+ 2
= 10 kW) is connected to the AC bus. The parameters regarding selection of a 10 kW converter
for the proposed system is shown in Table 3.7.
Table 3.7: Technical parameters of the Suntree 10,000 TL 10 kW Converter
Parameter Value
Efficiency & Capacity 98% & 95%
Output Voltage Range & Current 360–440 V & 15.2 A
Max. Output Power 10 kW
MPPT Voltage Range 250–800 V
THD <3% (at nominal Pout)
Dimensions (W × L × H) 470 mm × 185 mm × 585 mm
Net Weight 35 kg
However, the PV array (10 kW) output is connected to the DC bus. Most electric vehicles are
charged by AC systems. Therefore, to convert the DC output from the PV array a 10 kW
converter is needed for an optimum solution. The capital, replacement and O&M cost are
assumed as $2000, $1000 and $2.00, respectively, and the lifetime is 20 years.
47
3.4.5 Battery
Energy from the proposed hybrid system is stored by using a battery bank or in the batteries used
in the electric vehicles. For charging purposes, the electric vehicle is connected with the AC bus
through a charger. In this research, a generic 1 kWh lead acid battery is used. The technical
parameters of a lead acid battery are shown in Table 3.8 below. The battery lifespan of an EV is
affected by different factors such a high temperature, dirty environment, low driving, defects and
driving habits, etc. The battery lifetime decreases with the increase in the operating temperature.
Table 3.8: Technical parameters of the 60, 038 MF-12 V, 100 Ah lead-acid Battery
Characteristics Value
Battery material Lead acid
Nominal voltage & capacity 12 V & 81–100 Ah
Total weight 24 kg
Dimensions 350 mm × 175 mm × 190 mm
Lifetime 5–8 years
3.4.6 Charging Apparatus for Electric Vehicles
In the proposed charging station, there will be several points to recharge the vehicle battery from
the AC voltage. The cost of the charger is assumed around $50 and the average durability of the
charger is 2–4 years. Table 3.9 shows the technical specifications of an EV charger.
Table 3.9: Technical specifications of an EV charger
Characteristics Value
Charging Voltages 48/60/72 V
Charging Current Range 3–8 A
Input Voltage 180–250 V AC
Battery Capacity 10–100 Ah
Maximum Charging Power 1000 W
Efficiency 98%
Weight 2.2 kg
Dimension 260 mm × 140 mm × 130 mm
48
3.5.1 Design of the Grid Connected Hybrid Renewable Energy Based EVCS
Figure 3.4 shows a grid connected hybrid powered Electric Vehicle Charging Station where the
PV panel produces 10 kW power whose maximum power point is tracked by a MPPT. Three
biogas generators run by biogas resources obtained from animal waste (cow dung), poultry waste
and municipal solid waste (MSW), respectively.
Fig. 3.4: Grid Connected Hybrid Renewable Energy Based EVCS
A charge controller regulates the voltage and current into the batteries. When the batteries are
charged fully then it stops charging and sends the excess power to the converter. A bidirectional
converter is used to convert the DC into AC. If the hybrid power is unavailable, the power comes
from the national grid, especially during the off peak period. Excess energy can also be sold to
the national grid. Electric vehicles are charged from the AC bus through a charging apparatus.
49
3.5.2 Design of EVCS using HOMER Pro Software
HOMER is a free software application developed by the National Renewable Energy Laboratory
in the United States. This software application is used to design and evaluate technically and
financially the options for off-grid and on-grid power systems for remote, stand-alone and
distributed generation applications. The HOMER (Hybrid Optimization of Multiple Energy
Resources) model greatly simplifies the task of designing hybrid renewable micro-grid, whether
remote or attached to a larger grid. HOMER's optimization and sensitivity analysis algorithms
allow you to evaluate the economic and technical feasibility of a large number of technology
options and to account for variations in technology costs, electric load, and energy resource
availability.
Figure 3.5 shows the block diagram of EVCS designed by HOMER Pro software. In this
demonstration, solar PV module CS6K-285M-FG (10 kW), Biogas generator Bio1, Bio 2, Bio 3
(10 kW), deferrable electrical load (88 kWh/day), electric load (11.26kWh/day), converter (10
kW) and battery storage red T15-75 is used. This model uses the solar irradiation and
temperature data from the NASA surface meteorology department.
Fig. 3.5: Block diagram of EVCS designed by HOMER Pro software.
50
The solar irradiation and temperature curve are given in Fig. 3.6 and Fig. 3.7 respectively. Fig.
3.7 shows the temperature varies from 200C to 35
0C in Bangladesh. The average solar irradiation
is assumed as 4.95 kWh/m2/day.
Fig. 3.6: Solar irradiation profile used in HOMER.
The temperature variation at different season is given in Fig. 3.7. Temperature affects the solar
output energy production. In addition, a research performed on biogas production showed that,
the 220C to 35
0C temperature acts as a catalyst of the biogas generation because it boosts up the
biogas generation. In Bangladesh, the usually temperature varies from 250C to 35
0C except cold
season.
Fig. 3.7: Temperature Curve at various seasons used in HOMER.
The biomasses available in Bangladesh are cow dung, poultry waste and MSW which are
mentioned earlier in this research. The average value of biomass collection per day from these
51
resources is approximately 250 kg/day. These resources are very cheap and found everywhere in
Bangladesh. The available biomass in ton/day is shown in Fig. 3.8 which is used in HOMER Pro
software.
Fig. 3.8: Daily available Biomass (Cow dung, poultry waste and MSW) used in HOMER.
3.6 Technological Feasibility Analysis
This research deals with the utilization of available renewable energy resources like solar and
biogas for the purpose of charging EVs in Bangladesh. The proposed EVCS consists of a PV
module, and three biogas generators for electric power generation. The HOMER Pro
optimization software tool is used for designing and analysis of the economic analysis and
sensitivity analysis. The average solar irradiation obtained from NASA is 4.95 kWh/(m2·day).
The daily, monthly and yearly load profile is shown in Fig. 3.9 where the daily estimated load is
assumed as 99.26 kWh. The load varies during different seasons in Bangladesh. Every day 15–
20 EVs can be a reasonable load for the charging station. Charging hours are inversely related to
the state of charge (SOC) of the vehicles’ batteries. In the proposed design, the electric power is
supplied by the biogas generators when solar energy is unavailable to increase the effective
operational hours to 8–10 h. The hybrid renewable energy generated by the PV module and
biogas generators is supplied to recharge the EVs’ batteries.
The annual energy production scenario by resource type is shown in Fig. 3.10. Total energy
produced in a year by the system is approximately 40,170 kWh. The generated electricity then
applied to EV charger.
52
Fig. 3.9: Load profile (daily, seasonal) used in HOMER.
Every day 15–20 EVs can be recharged from this charging station according to the SOC of the
vehicle battery. According to the configuration, this EVCS is capable of charging 5 EVs
simultaneously. So, it is considered that all the EVs will arrive at different time interval in the
charging station not altogether. In the case of holidays, when vehicle consumption decreases,
excess energy can be sold to the national grid through vehicle to grid (V2G) technology. There is
also an option for purchasing electricity from the grid in the case of rainy days or during periods
of less biomass collection. The electricity generated by proposed hybrid power generation
scheme is then applied to EV charger which converts the power into constant DC Voltage.
Fig. 3.10: Annual energy production in kWh by resource type
kW
h g
ener
ati
on
53
In the hybrid renewable energy based charging station, the PV module, biogas generator 1, 2 and
3 contribute 39.41%, 23.65%, 25.26% and 11.66% of the total power generation, respectively, as
shown in Fig. 3.11.
Fig. 3.11: Percentage share of total generation by resources.
3.7 Economic Feasibility Analysis
The solar PV module of 10 kW and total generation of 10 kW from biogas resources are
expressed in financial terms in this section. The total cost of installation and replacement gives
the Net Present Cost (NPC) and O&M cost, respectively. The return of the investment is
assessed by the terms payback period and annual cash flow summary. Finally the Profitability
Index (PI) is used to determine the feasibility study of the proposed charging station.
It is assumed that an electric vehicle is used 26 days/month and consumes an average of 12 kWh
daily for traveling 80 km, so 0.15 kWh electric power is required per km run. The monthly
electric bill for charging an electric vehicle is about $38.12. Solar radiation is available for
producing electricity only for 5 to 6 h a day. Thus, the solar energy can generate electric power
only during those hours, whereas, biogas generators can be used 6–8 h daily. Calculation of
economic parameters is shown in Table 3.10.
54
0
200
400
600
800
1000
1200
1400
1600
1800
2000
Cash
-Flo
w i
n U
SD
PV Bio1 Bio2 Bio3
Table 3.10: Economic parameters obtained from HOMER Pro software
Component
Name & Size
Active
Hours
Energy
Production
(kWh/Year)
Lifetime
(Years)
Annual
Cash-
Flow
Payback
Period
(Year)
Profitability
Index (PI)
Solar PV
(10kW) 5–6 15,350 25 $1880 10.1
>1
Biogas Gen.
(4 kW) 6–8 10,220 5 $1252 3.0
Biogas Gen.
(4 kW) 6–8 9,490 5 $1162 3.10
Biogas Gen.
(2 kW) 6–8 5,110 5 $626 3.72
In Fig. 3.12, the annual cash flow according to resource type is shown where PV is the highest
contributor to the energy generation and Bio 1, Bio 2 and Bio 3 are the next. For this charging
station, the value of the profitability index is greater than one, which indicates the project is
financially feasible.
Fig. 3.12: Annual cash flow by resources.
The net present cost (NPC) for the 10 kW PV, two 4 kW biogas generators, a 2 kW biogas
generator, 10 kW converter, 10 kWh lead acid battery system and charging assemblies for five
55
electric vehicles is $56,202 which is economically feasible. The operating cost of the proposed
design is found as $2,630 and the levelized Cost of Energy/kWh is found as $0.1302. The O&M
cost is significantly lower in case of the proposed EVCS as there is no use of fuel like diesel and
gas. An additional advantage of the proposed system is that the extracts obtained from the biogas
generators can be used as fish feed and fertilizer which helps to minimize the O&M cost.
The cost of energy in the proposed EVCS is less than that of a conventional grid-based charging
station. Although it is difficult to bear the initial cost of installation of such an EVCS, it will be a
profitable option after the payback period. In this system, a solar PV and three biogas generators
give the payback period of 10.1 years, 3.0 years, 3.10 years and 3.72 years, respectively. In Fig.
3.12, it is clear that the payback period is very much less than the lifespan of the system and that
indicates the system will be a profitable option for its developer.
Fig. 3.13: Payback period and life time of the proposed EVCS.
3.8 Environmental Feasibility of the Proposed EVCS
Electric vehicles have several key benefits, including reduction of GHG emissions, fumes and
noise pollution which are hazardous to the environment. The American Council for an Energy
Efficient Economy conducted a study which predicted that GHG emissions from electric vehicles
will be greater if the EVs are charged by the power generated from the coal-fired power plant
0
5
10
15
20
25
30
PV Bio 1 (4 kW) Bio 2 (4 kW) Bio 3 (2 kW)
Lifetime
Payback period
56
[38]. Table 3.11 and Table 3.12 show the CO2 emission rate by non-renewable sources and
renewable sources respectively [64].
Table 3.11: CO2 emission rate on non-renewable sources
Type of Fuel CO2 Emission (g/kWh)
Coal 800 to 1050
Natural Gas 469 to 600
Diesel 570 to 650
Furnace Oil 640 to 765
Table 3.12: CO2 generation by renewable energy
Type of Technology CO2 Emission (g/kWh)
Biomass-Dedicated 130 to 420
Solar PV-Utility Scale 18 to 180
Solar PV-Rooftop 26 to 60
Geothermal 6.0 to 79
Concentrated Solar Power 8.8 to 63
Wind Offshore 8.0 to 35
Wind Onshore 7.0 to 56
Nuclear 3.7 to 110
Hydropower 1.0 to 22
Figure 3.14 represents that the non-renewable sources produce higher CO2 than renewable
energy sources. These scenarios encourage the establishment of hybrid renewable energy-based
power plants.
57
Fig. 3.14: Carbon dioxide generation in renewable and conventional systems
Local power plants in Bangladesh produce approximately 0.64 kg/kWh of carbon dioxide [65].
Thus for 0.15 kWh energy, there will be CO2 emissions of 96 g, but for the same amount of
power generation, the proposed EVCS will produce 33.30 g of CO2. An issue related to non-
renewable power generation is the usage of land and water. It hampers the wildlife, affects the
rivers and sea water by disposing of the residues from the power generating stations. Renewable
energy based system requires less land and water use. Also it does not hamper the wildlife. But
biogas based plant requires more land space and water. However, in case of the proposed EVCS,
the effective use of the land space decreases due to use it with solar system for same amount of
power. The biogas resources such as poultry waste, MSW and cow dung, are very noxious to
human health. Electrical energy can be produced by utilizing these potential wastes. Byproduct
from the biogas plant can be used as fertilizer and fish feed, thus the environmental pollution is
significantly reduced. The EVCS can be a promising window to explore new dimensions in
engineering business especially for the unemployed strata of the society. It can improve the
socio-economic standards of the EV drivers and the engineers who work for the development of
the hybrid renewable energy based EVCS.
0
100
200
300
400
500
600
700
800
900
1000
CO
2 e
mis
sion
in
g/k
Wh
58
Fig. 3.15: Comparison of CO2 emission from grid based EVCS and proposed EVCS.
Environmental benefits of the proposed EVCS are the reduction of carbon dioxide emissions and
other pollutants. In this system, the amount of CO2 emission found for generating1 kWh energy
is about 220 g, whereas a conventional system produces 640 g of CO2. That means there is a
significant reduction of CO2 emission (65.62%) by this system which validates the project to be
of a sustainable environmental standard. Figure 3.15 shows the comparison of the CO2 emission
from the grid based EVCS and biogas based EVCS. In the proposed EVCS, yearly CO2 emission
is about 8,837.40 kg whereas for the same demand grid based charging station produces
25708.80 kg of CO2.
3.9 Comparison of Results between HOMER Analysis and Mathematical
Analysis
HOMER analysis gives the result based on input renewable resources however the loss
associated with the EV charging accessories are not taken into consideration. But in case of
mathematical analysis, the technological, financial and environmental parameters are taken into
account for finding proper results. The charger efficiency is assumed as per the technical
specifications given by the EV charger.
On the other hand, the financial parameters such as COE, NPC, operating cost, payback period,
profitability index are analyzed in HOMER provides the result only for generating power from
the resources. However, the mathematical analysis considers the charging equipment cost and
infrastructures for charging 5 EVs simultaneously. Thus the financial parameter varies from the
0 5000 10000 15000 20000 25000 30000
Grid based EVCS
(kg/yr)
Proposed EVCS
(kg/yr)
CO2 emission in kg
59
HOMER results.
Different researches performed on emissions associated with the EV charging signifies that, EV
charging process contribute in a small percent of GHG emission [66-67]. In addition, the battery
storage system helps to reduce CO2 emission showed in a research [68]. Materials used in the
battery also responsible for GHG emission. Moreover, EV battery receives power through
charger from utility grid during low or zero power generation from the proposed project. It
would be a source of GHG emission. Research performed on this issue demonstrate that,
electricity mix (fossil plus renewables) used for EV charging emits more GHG than renewable
resources based generation [69].
Table 3.13: Comparison of results from HOMER and Mathematical analysis
Parameters HOMER Analysis Mathematical Analysis
Technological
parameter
kWh generation 40,170 39,366.60
Financial
Parameter
COE $0.1302 $0.1469
NPC $56,202 $56,860
Operating cost $2,720 $1,350
Payback period
(year)
PV – 10.10 Bio1 – 3.00
Bio2 – 3.10 Bio3– 3.72
PV – 10.14 Bio1 – 3.05
Bio2 – 3.14 Bio3 – 3.75
Profitability
Index
2.02 1.98
Environmental
Parameter
CO2
emission/year
8,837.40 kg 10,750.85 kg
Moreover, battery production processes are responsible for GHG emission. Lead acid batteries
used in the EV produce SO2 whereas lithium ion batteries produce CO2 during charging process.
Table 3.13 represents the comparison of results obtained from HOMER and Mathematical
60
analysis. Actually the drawbacks of the HOMER system are found which needs to correct for
more viable and practical application.
3.10 Socio-Economic Aspects of the Proposed EVCS
Electric Vehicles are now very promising due to their several benefits mentioned earlier. In a
country like Bangladesh, there are different environmental and socio-economic factors behind
the rising popularity of electric vehicles. Electric vehicles such as Easy Bikes, Auto-rickshaws
and Electric rickshaw vans have high potential of reducing emissions, improving air quality in
both urban and rural areas and increasing the income level of the lower class people. An Easy
Bike driver in Bangladesh can easily earn approximately $18–$25 where the energy consumption
cost of this car is only $1–$1.25 daily, so the living standard of an unemployed person would be
upgraded due to electric car adaptation.
The mass introduction of Easy Bikes could lead to a revolution in rural transport. It would
increase the income rate of the rickshaw pullers and curtail their transportation time and physical
labor down to half. However, the demand for charging stations is increasing rapidly. Charging
cost of an Easy Bike is around $45 per month. It will be lower than the present cost if the EVs
are charged by the proposed charging station. Table 3.14 shows the summary of the charging
cost in a grid-based system and the proposed EVCS-based system. The monthly savings and
monthly income by using this EVCS for an EV driver are given in Table 3.14. In the proposed
charging station, there will be low O&M cost and resources are available, so utilizing these
resources can provide an excellent opportunity to reduce pressure on the grid as well as the
environment and improving life standards. Thus, the system loss for recharging electric vehicle
will be reduced and government power sector will benefit a lot.
Table 3.14: Charging cost and monthly income summary of an EV
Electric
Vehicle
Charging Cost/Month
(Grid based EVCS)
Charging Cost/Month
(Proposed EVCS)
Monthly
Savings
Easy Bike $44 $26.25 $17.75
Auto-Rickshaw $28 $15.00 $12.50
61
3.11 MATLAB SIMULINK Modeling for Solar and Biogas Based Generation
Electricity generation for the purpose of EV charging is obtained by the hybridization of solar
and biogas resources. In this section, the MATLAB SIMULINK model of the stand alone solar
PV and Biogas system is given. The effective output from the both system can be a cost-
effective, energy efficient and environment friendly method to reduce the power crisis for EV
charging.
Fig. 3.16: Solar PV based system model.
Figure 3.9 shows the PV based electricity generation scheme where three PV module is added up
for electricity generation. The irradiance is considered as 1000 W/m2 and temperature varies
from 250C to 45
0C for this simulation. Three PV module is added up for electricity generation. In
62
this modeling, the PV module named as 1 soltech 1STH 350 Wh is chosen. The combined output
of the model shown in Fig. 3.16 which exhibits current and power variation with respect to
system voltage.
Solar Cell I-V Characteristics Curves are basically a graphical representation of the operation of
a solar cell or module summarizing the relationship between the current and voltage at the
existing conditions of irradiance and temperature shown in Fig. 3.17. I-V curves provide the
information required to configure a solar system so that it can operate as close to its optimal peak
power point (MPP) as possible. The intensity of the solar radiation (insolation) that hits the cell
controls the current (I), while the increases in the temperature of the solar cell reduce the voltage
(V). Another curve P-V shows the Power versus Voltage for a PV module in Fig. 3.17.
Fig. 3.17: I-V & P-V Characteristics curve of PV based Model.
Biogas generation comprises from the Cow dung, poultry waste and MSW which will be
collected from the surroundings. The simulink model for digester size and corresponding biogas
generation is shown in below. The value of cow dung, poultry waste and MSW varies from
month to month. Thus a variation in input has been chosen as per the biomass collection of each
63
month. Figure 3. 9.2 shows the biogas generation scheme using cow_dung, poultry_waste and
MSW as the inputs. Depending on the biomass/biogas resources collection, the output biogas
generation is carried out and finally this biogas is used to generate electricity.
The biogas production from the wastes as cow dung, poulrty droppings and MSW are used as the
inputs in MATLAB SIMULINK. The simulation results shows the variation of biogas generation
with respect to input wastes. Also, the digester size is found from the simulation. The digester
size and corresponding biogas generation plotted against month is displayed in Fig. 3. 18 where
the maximum digester size is found 39.86 m3 and minimum is 35.25 m
3.
Fig. 3.18: Biogas Generation output in m3.
3.12 Summary
This chapter firstly identified the solar and biogas/biomass potential in Bangladesh. It concludes
that, use of solar resources can meet up the existing demand as well as future demand. The
government also recognize the solar potential and already use it in the different charging station
for EV battery charging. However, in case of rainy/foggy day and night time, the stand alone PV
system can not able to generate electricity. Thus, in this research I have incorporate biogas
resources which will increase effective operational hours. The first portion of this chapter also
64
determines that, cow dung, poultry waste and MSW can be a good source for electricity
generation and it is possible to generate 4913.24 kW, 1344.50 kW and 2942.81 kW electricty per
day respectively.
The mathematical modeling of the hybrid renewable power generation based EVCS is described
in this chapter which includes different parameters like technological, financial and
environmental parameter. Then, different components specification for designing a hybrid
renewable based EVCS i.e. solar photovoltaic (PV) module, digester, biogas generator,
converter, battery, hybrid charge controller and electric vehicle charging apparatus are analyzed
according to the market availabilities and price.
HOMER Pro software is used for designing the proposed EVCS. According to the technological
view of point, it is found that 40,170 kWh electric energy can be produced by this proposed
method. Moreover, every day 15–20 EVs can be recharged from this charging station according
to the SOC of the vehicle battery. In case of financial feasibility, getting the profitability index
greater than unity and payback period less than the life time indicate the proposed EVCS is
feasible. The COE is found as $0.1302. The proposed EVCS yearly emits CO2 which is about
8,837.40 kg whereas for the same demand grid based charging station produces 25708.80 kg of
CO2. The difference in the HOMER result and the mathematical analysis responds to the several
factors associated with the EV charging which are expressed in a table.
In the context of Bangladesh, proposed EVCS can save monthly $12.50-$17.75 of the EV
owners who recharges their batteries into this EVCS instead of grid connected EVCS.
Finally MATLAB SIMULINK based PV and Biogas generation scheme shows that, the
proposed EVCS is feasible and can serve the EV owners efficiently.
65
Chapter 4
Fuzzy Optimization of Proposed EVCS
4.1 Introduction
Optimization algorithm is usually designed for maximizing or minimizing one or more objective
function. In the previous chapter, the use of solar and biogas resources for power generation to
recharge the EV batteries is found feasible according to the technological, financial and
environmental aspects. Thus, in the present chapter fuzzy logic based algorithm is developed for
minimization of charging cost while maximizing the use of renewable resources. MATLAB
based fuzzy logic controller is divided into two major categories: Mamdani and Sugeno. In this
chapter, firstly the input & output variable and the corresponding membership functions are
defined. Then the algorithm is designed based on if-then rules in fuzzy inference system. The
results obtained from Mamdani and Sugeno type fuzzy logic controller are compared in the final
section of this chapter.
4.2 Block Diagram of the Proposed EVCS
The block diagram of the proposed EVCS is shown in Fig. 4.1 where solar energy and
biomass/biogas resources (waste) are the input of the proposed EVCS. Solar power is found in
the form of DC and it needs to convert in AC using converter. Biogas forms from the digester
and the resultant is converted into electricity by using biogas generator. The combined output
from the solar and biogas system gives the output power generation. The output power
generation of the proposed EVCS depends upon the input resources as-
),( wsfPout , (4.1)
Where s is the solar irradiation and w is the waste input. Solar energy is found only on 9:00 AM
to 3:00 PM. Also, the solar resources is absent in the rainy and foggy days. In that case biogas
energy offers an alternate way to produce electricity. In addition, the biogas system provides
slurry which is used as fertilizer and fish feed. The power demanded by the EVs when greater
66
than output power generation, then the difference power comes from the utility grid. However, in
case of excess generation the extra power supply will be given to the residential connection. The
overall power demanded by EVCS is then fulfill the energy demand. In this case a fuzzy logic
controller will be employed to optimize the charging cost for different EVs at different time
period & duration. Output generated power availability, power demand of EVs, period &
duration of charging is taken as input variable and charging rate is the output variable.
Fig. 4.1: Block diagram of the proposed EVCS.
67
4.3 Fuzzy Optimization Model
In this optimization model, MATLAB based fuzzy logic scheme is used. Both Mamdani &
Sugeno type fuzzy inference model is used in this proposed EVCS for obtaining optimized
charging rate at various input conditions. Centroid based defuzzification technique is employed
in this model. Output power availability, Power demanded by EVs, period and duration of
charging are the input parameters of fuzzy model and charging rate is the output parameter. The
fuzzy (Mamdani) optimization model shows input & output variables in Fig. 4.2.
Fig. 4.2: Fuzzy (Mamdani) optimization model
4.4 Input and Output Variables
Power availability of the EVCS depends on the input renewable resources i.e. solar and biogas. If
the solar and biogas resources are sufficient then the output power generation will be maximum
and thus the power availability will be high. On the other hand, when solar energy is absent then
the generated power is low. Thus, the input parameter power availability is categorized by three
categories as the membership function (Less, Normal and High).
68
Fig. 4.3 (a): Input variable “Power_ Availability” with membership functions.
Power demanded by an EV depends on the battery capacity and SOC. In this model, the
membership functions of “Power_Demand” are as “Very_Low”, “Low”, “Medium”, “Large”
and “Very_Large”. As the number of EV comes in the charging station with different battery
capacity and SOC, thus the power demand varies. For 0 - 4 kW the membership function defined
as “Very_Low”, 4 - 8 kW is “Low”, 8 - 12 kW is “Medium”, 12 - 16 kW is “Large” and 16 - 20
kW is “Very_Large”.
Fig. 4.3 (b): Input variable “Power_ Demand” with membership functions.
In Bangladesh, peak hour is defined as 5:00 PM to 11: 00 PM and off-peak hour is the time
between 11:00 PM to 5:00 PM. For this reason, in this proposed model membership functions of
the “Period_ of_ Charging” are “peak_hour” and “off-peak_hour”.
69
Fig. 4.3 (c): Input variable “Period_of_Charging” with membership functions.
Electric vehicles in Bangladesh mainly recharge their batteries in night time. But few of them are
charged at day time also. Normally the easy bike and auto-rickshaw battery takes 8-10 hours for
full charging. But the EVs come at charging station with above the minimum SOC, requires less
time to recharge. Thus, the “Duration_ of_ charging” has membership functions as “Short”,
“Average”, “Big”. For 0-3 hours charging is considered as “short”, 3-6 hours is “Average” and
6-10 hours is “Big”.
Fig. 4.3 (d): Input variable “Duration_ of_Charging” with membership functions.
Charging rate is the output variable which is used in designing fuzzy system. It has membership
functions declared as “Very_Small”, “Small”, “Moderate”, “High” and “Extra_High”.
70
Fig.4.3 (e): Output variable “Charging_Rate” with membership functions.
4.5 Optimization Algorithm
The main objective of the optimization algorithm is to minimize charging rate while keeping the
utilization of renewable resources maximum. The objective function of the proposed model is as
follows:
}{ argingchCMinimize (4.2)
While renewable resources should be utilized as maximum. This objective function works under
few constraints as follows:
maxmin SOCSOCSOC i ; (4.3)
giD PtPP )( ; (4.4)
21 ttt ; (4.5)
The generated power, Pg depends only on the availability of renewable resources. This is also a
cause of varying electrcity price in the proposed EVCS. The power demand of the EV depends
on the SOC and battery capacity. In this model, the minimum SOC is taken as 20% where
maximum SOC is 80%. The generated available power should be less than or eqaul to the power
demand of the EVCS. In that case the renewable resources will be used as maximum. The
duration of charging is the time to recharge the batteries. Equation (4.6) shows the duration of
71
charging of EV. The period of charging should be in between t1 and t2. Minimum duration of
charging, t1 is assumed as 3 hours whereas maximum charging time, t2 is taken as 10 hours.
2
1
t
t
waitingarrivaldepartureD TTTT (4.6)
The period of charging is divided into two parts- peak_hour, off_peak hour. Equation (4.7)
displays the time/period of charging of EV.
(4.7)
The power demanded by an EV can be expressed as
(4.8)
The overall charging cost is the function of the four input parameters such as- power availability,
power demand, period and duration of charging. Thus, it can be represented by equ. (4.9).
2
1
)(),(
t
tt
DC dttrtiPF
(4.9)
where r(t) is the actual charging rate for an EV. Charging time varies from t1 to t2. If t is equal to
the TC, then r(t) will be maximum. Also, if the power generation is smaller within the proposed
EVCS, the extra demand will be fulfilled by the utility grid. In that case the price will be highest.
The definite integral of the r(t) is constant over the time between t1 and t2. So,
})(
{}{1
minmax
N
i D
CDC
T
SOCSOCBMinimizePMinimizeFMinimize
(4.10)
.
N
i D
CD
T
SOCSOCBP
1
minmax )(
pmpmT
pmpmTT
peak
peakoff
C 115;
511;
72
Figure 4.4 shows the optimization algorithm using fuzzy logic controller for the proposed EVCS.
Fig. 4.4: Optimization Algorithm for EVCS.
4.6 Fuzzy Rule Viewer
Figure 4.5 shows the fuzzy rule viewer diagram where if-then rules are emloyed for obtaining the
optimized charging rate. 92 rules are used in this mamdani based fuzzy algorithm and
corresponding output i.e. charging rate can be seen here for each and individual rule.
73
Fig. 4.5: Fuzzy rule viewer
4.7 ANFIS (Adaptive Neuro Fuzzy Inference System) Model Structure
The Adaptive Neuro Fuzzy Inference System (ANFIS) model is obtained from sugeno fuzzy
logic controller where number of input is 4 and output is 1. The membership functions of each
input are defined by user. Sugeno Fuzzy logic controller when integrated with the neural
network, then it is termed as ANFIS model. In this model, 54 rules are used and each time
controller provides an output. The output variable is defined by rules used in Sugeno logic
74
controller.
Fig. 4.6: ANFIS Model structure.
4.8 Result & Discussion
Electric Vehicle Charging Station optimization for minimizing charging cost is the main purpose
of this research. In the present energy scenario of Bangladesh, charging electric vehicles leads to
a huge consumption with increasing system loss. EV owners are also in a big trouble for being
less number of charging stations throughout the country. On the other hand, charging cost is very
high when the electric vehicles are charged from the commercial line. Due to solve these
problems, fuzzy optimization technique is employed in this research. Fuzzy “if-then” rule-based
strategy is used in this optimization system. The membership functions are defined according to
the data for the current battery electric vehicles of Bangladesh. The surface view of the charging
75
rate, power availability, power demand, time of charging and duration of charging is shown in
Fig. 4.7 (a) & (b).
(a) Charging rate with respect to power availability and duration of charging.
(b) Charging rate with respect to power availability and time/period of charging.
76
(c) Charging rate with respect to power demand and duration of charging.
(d) Charging rate with respect to power demand and time/period of charging
Fig. 4.7: Surface view of charging rate, Power availability, power demand, time of
charging and duration of charging, (a), (b), (c) and (d).
77
The variation of charging rate with power availability, power demand, time/period of charging
and duration of charging are expressed in Fig. 4.8 (a), (b), (c) & (d).
When the available power generation at
maximum i,e, the membership function
is “High”(16-20 kW), then the charging
rate according to Mamdani FLC is 0.33.
Thus, using actual electricity tariff for
the EV consumer in Bangladesh, it
would be BDT. 3.05. However, at less
generation period i.e. at 0-5 kW, the
charging rate is found 0.522 which
means the tariff would be near about
BDT. 4.83. Thus, it is economical to
charge the EVs in the proposed charging
station during the highest generation.
(a) Variation of charging rate with power availability
The variation of charging rate with
power demand is shown in Fig. 4.8
(b). Here, it is seen that, during the
power demand is very low(0-5 kW),
the degree of charging rate is near
about 0.12. Thus, the charging cost
would be equal to BDT. 1.11 which
seems very small. However, during
high power demand (15-20 kW), the
degree of charging rate is near about
0.7 i.e. BDT. 6.47, which is expensive.
(b) Variation of charging rate with power demand.
78
During peak hour the electricity tariff is
higher due to having large demand. If the EV
takes place for charging in this period it
would charges higher rate. Figure 4.8 (c)
shows that, charging rate is higher in the time
between peak-hour and lower in off peak-
hour. At peak hour the charging rate is about
BDT.6.66.
(c) Variation of charging rate with time/period of charging.
Charging duration depends upon battery
capacity, SOC. If the duration of charging
becomes higher, the charging cost would be
expensive according to the fuzzy rules.
However, if this charging is performed on off
peak hour, the charging cost will be lower.
Figure 4.8 (d) shows the variation of charging
rate at peak and off peak hour with difference
in charging duration.
(d) Variation of charging rate with duration of charging.
Fig. 4.8: Variation of charging rate with (a) power availability (b) power demand (c) time of
charging (d) duration of charging from Mamdani Fuzzy logic controller.
79
(a) (b)
(c) (d)
Fig. 4.9: Variation of charging rate with (a) Power availability (b) Power demand (c) time of
charging (d) duration of charging from Sugeno Fuzzy logic controller.
Figure 4.9 shows the variation of charging rate with four input variables in a sugeno fuzzy logic
controller. As like Mamdani, Sugeno logic controller also optimizes charging cost with respect to
four input variables.
80
Comparison between the output charging rate obtained from Mamdani and Sugeno type fuzzy
logic controller is given in Table 4.1 for different input variables.
Table 4.1 (a): Charging rate variation with power availability in Mamdani & Sugeno
Power_ availability
Membership
function Range (kW)
Charging_ Rate (Degree of
membership function)
Mamdani Sugeno
Low 0-7 0.52 0.5
Medium 7-14 0.5-0.32 0.5-0.11
High 14-20 0.32 0.01
Table 4.1 (b): Charging rate variation with power demand in Mamdani & Sugeno
Power_ Demand
Membership
function Range (kW)
Charging_ Rate
Mamdani Sugeno
Very low 0-4 0.12 0.48
Low 4-8 0.12-0.15 0.48-0.5
Medium 8-12 0.5 0.5-0.7
High 12-16 0.5-0.7 0.99-1.0
Very High 16-20 0.7 1.0
Table 4.1 (c): Charging rate variation with time/period of charging in Mamdani & Sugeno
Time of charging
Membership
function Range (Hour)
Charging_ Rate
Mamdani Sugeno
Off Peak-hour 0-17 0.5-0.52 0.5
Peak hour 17-23 0.72 1.0
Table 4.1 (d): Charging rate variation with duration of charging in Mamdani & Sugeno
Duration of
charging
Membership
function Range (Hour)
Charging_ Rate
Mamdani Sugeno
Low 0-3 0.33-0.35 0.04-0.05
Medium 3-6 0.45-0.50 0.1-0.5
High 6-10 0.51 0.52
81
Electric vehicle charging affects the power network by consuming huge energy. If the EV
charging is performed on peak hour, it will surely provide a negative impact on the power
system. Otherwise, electric vehicle charging at off-peak hour can be beneficial for the power
system and also for the consumers. In Bangladesh, off-peak period are between 11 pm to 5 pm
and peak hours are between 5 pm to 11 pm. Due to having less power demand in the off peak
hour, the charging rate will be lesser than the peak hour charging rate. In the proposed fuzzy
based EVCS, the charging rate for peak hour is taken greater than the off peak hour.
The conventional EVCS takes on average $1.5 to $1.875 for charging an EV. According to the
electricity tariff determined by BERC (Bangladesh Energy Regulatory Commission), the battery
charging rate is $0.09625/ kWh (BDT.7.70/kWh) of electricity consumption. However,
optimization of proposed EVCS based on hybrid renewable resources offers different tariff for
difference in time, duration, power demand and availability of the power generated by the EVCS
itself. In this proposed method, the charging cost is optimized which is shown in Fig.4.8.
Conventional charging station has maximum charging rate per kWh $0.09625 (BDT. 7.70)
whereas the fuzzy logic based EVCS offers $0.0925 (BDT. 7.40) at peak hour conditions. During
off-peak hour proposed EVCS will take $0.073 - $ 0.07375 (BDT. 5.84-5.90) per kWh.
Fig. 4.10: Comparison of charging rate by fuzzy logic system with conventional electricity price.
0
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8.00PM
10.00PM
12.00PM
2.00AM
4.00AM
6.00AM
8.00AM
10.00AM
12.00AM
2.00PM
4.00PM
Charging Cost (Existing Tariff in BDT.)
Charging Cost (Fuzzy optimized tariff in BDT.)
Hour
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arg
ing
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DT
.)
82
This type of optimization motivates the EV consumer to charge the EV batteries on off-peak
hours which will further reduces the demand at the peak hour period. In addition, at day time
solar energy is available and charging from solar energy resources will maximize the renewable
energy utilization and cut the excess battery requirement for the EVCS.
4.9 Summary
Optimization algorithm for the proposed EVCS is developed in this chapter for minimizing the
charging rate while maximizing the use of renewable resources under subjected to few
constraints. In this chapter, firstly fuzzy logic based algorithm is designed using four input
parameters such as- power availability, power demand, time/period of charging and duration of
charging. All these parameters are assumed in different categories called membership functions.
Also, in case peak/off peak hour, the charging rate is different in Bangladesh and the tendency of
EV charging in peak hour causes a great harm to the power sector. Thus, in this logic it is
recommended to place high charging rate for this period. Power availability is a function of
renewable resources because the output power availability depends upon the solar and biogas
resources. In the absence of solar resources, only biogas can help to provide electricity. Thus the
charging rate of will be high for less availability of power generation.
Mathematical modeling for optimization is analyzed in this chapter. This optimization algorithm
is used in both Mamdani and Sugeno fuzzy logic scheme and finally a comparison is established
which demonstrate that, the Mamdani is best suited for optimization of the proposed EVCS.
The conventional grid connected charging station has maximum charging rate per kWh $0.09625
(BDT 7.70) whereas the fuzzy logic based EVCS offers $0.925 (BDT. 7.40) at peak hour period.
Also, for off-peak hour conventional EVCS takes $0.09625 (BDT. 7.70) per kWh. However, the
proposed EVCS will take $0.073 - $ 0.07375 (BDT 5.84-5.90) per kWh at off-peak hours.
This type of optimization will inspire the EV owner to charge the batteries at off peak hour
which further cut the demand at peak hour period. In addition to this, during day time solar
energy is available. Thus, the charging at day time corresponds to the maximum utilization of
renewable resources with minimum battery backup for the proposed EVCS. Use of Mamdani
fuzzy logic controller scheme will be a potential optimizer for minimizing charging cost with
maximizing renewable energy utilization.
83
Chapter 5
Conclusions and Future works
5.1 Conclusion
Growing popularity of Electric Vehicles opens a new sector in the field of transportation. It is an
user & environment friendly and cost-effective mode of transportation. The whole research
aimed to design, develop and optimization of Electric Vehicle Charging Station using solar and
biogas/biomass resources.
This research identifies the main challenges of the EV adoption in Bangladesh by PORTER’s
five forces model, PESTEL and SWOT analysis such as- lack of charging infrastructure, high
charging cost & time, no governmental policy, less investment and technological barriers etc.
Finally, the negative impacts of the existing EVCS onto the utility grid, distribution station by
affects power quality which is analyzed by MATLAB SIMULINK. It is found that, EV chargers
are non-linear load and produces harmonics, sag/swelling and transformer power loss. Moreover
the increased demand causes a great harm to the power sector and accelerates the load shedding
process. This research shows that how the large number of electric vehicle charging station
affects the power quality of Bangladesh. In order to establish the efficient charging infrastructure
throughout the country, several policies are recommended in this research after analyzing
different barriers.
In chapter 3 determines the solar and biogas potential throughout the country and showed that
cow dung, poultry waste and MSW can be a good renewable resources for electricity generation
which can meet the existing and future demand. As the transportation fuel and electricity
generating fuel is limited in stock, thus it is necessary to incorporate renewable resources.
Moreover, the proposed hybrid generation based EVCS is found feasible in case of
technological, financial and environmental benefits. The proposed method produces less GHG
than other grid connected EVCS. It reduces up to 65.62% CO2 employing solar and biogas
resources. Another benefit is, the proposed EVCS produces slurry that can be used in agricultural
field and fish feed. The O & M cost of the proposed plant can be minimized by selling the slurry
as a fertilizer or fish feed. Besides a comparison is made between the results obtained from
HOMER analysis and mathematical analysis. The socio-economic aspects of the EV adoption
84
also are analyzed in this research. In addition, identification of great potentiality of renewable
resources and from these the possible electricity generation is obtained. However, to cut the huge
pressure on the national grid and make the EV sector a profitable one, effective utilization of
available renewable resources can be a great option.
Main part of this research which demonstrates that fuzzy optimization algorithm based on the
power availability, power demand, duration & period of charging determines the optimum
charging rate for the EV charging. By using the same algorithm for both the Mamdani and
Sugeno fuzzy logic controller, it is found that the optimization results found from the Mamdani
controller is more cost-effective and efficient. For sustainable development of the country’s
power sector and also the transportation sector this research will bring a new hope and contribute
to the national development.
5.2 Future Works
This research mainly identified the renewable resources and tested the feasibility of these
resources for electric vehicle charging. Only the solar and biogas resources are assumed to
exploit in this hybrid generation system. However, in future experimental investigation will be
carried out if sufficient funding is found. The environmental affects upon acceptance of Electric
Vehicle charging infrastructure all over the country will be analyzed which also be a good option
for researchers.
The optimization algorithm developed in this system is based on fuzzy logic. In future, the
proposed hybrid renewable based EVCS will be optimized in particle swarm optimization (PSO),
genetic algorithm (GA) and also by the combination of GA-PSO algorithm for better results.
Comparison among these algorithms for obtaining better result could be an interesting point to
the researchers. Also the implementation of the best optimizer among these methods can help us
to build a sustainable charging infrastructure worldwide.
Finally, a scheme will be designed as the bidirectional energy transfer facility like smart grid for
EVCS that is called V2G (Vehicle to Grid) technology. During blackout and peak hour period,
the EVs will transfer energy to the utility grid by using the scheme which will be the future
work.
85
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Publications Related to Thesis
Journal Papers:
1. AK Karmaker, MR Ahmed, MA Hossain, and MM Sikder. “Feasibility assessment &
design of hybrid renewable energy based electric vehicle charging station in
Bangladesh.” Sustainable Cities and Society 39 (2018): 189-202.
2. Ashish Kumar Karmaker, Md Mijanur Rahman, Md Alamgir Hossain, and Md Raju
Ahmed, “Exploitation of Biogas Resources for Electric Vehicle Charging in
Bangladesh”, is under review at International journal of Energy & Environment.
Conference Papers:
1. Ashish Kumar Karmaker and Md. Raju Ahmed, “Techno-economic & environmental
feasibility analysis of solar-biogas based electric vehicle charging station in Bangladesh”
accepted & Presented in IEEE conference of EECCMC at Vellore, Tamil Nadu, India
2018.
2. Ashish Kumar Karmaker, Sujit Roy, and Md. Raju Ahmed, “Analysis of the impact of
Electric Vehicle Charging on Power Quality issues.” accepted in IEEE Conference on
ECCE, 2019, CUET, Cox’s Bazar, Bangladesh.
3. Md. Raju Ahmed and Ashish Kumar Karmaker “Challenges for Electric Vehicle
Adoption in Bangladesh.” accepted in IEEE Conference on ECCE, 2019, CUET, Cox’s
Bazar, Bangladesh.
4. Ashish Kumar Karmaker, Md. Raju Ahmed, “Fuzzy logic based Electric Vehicle
Charging Station Optimization in Bangladesh”, Submitted in IEEE conference on IC4
ME2-2019, Rajshahi University, Bangladesh.