Techno-economic feasibility of retrofitting existing fuel stations with DC fast chargers integrating PV and BESS Nilanshu Ghosh KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT DEGREE PROJECT IN INDUSTRIAL MANAGEMENT, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020
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Techno-economic feasibility of
retrofitting existing fuel stations with DC fast
chargers integrating PV and BESS
Nilanshu Ghosh
KTH ROYAL INSTITUTE OF TECHNOLOGY
SCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT
DEGREE PROJECT IN INDUSTRIAL MANAGEMENT, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020
Master of Science Thesis-TRITA-ITM-EX 2020:566
Department of Energy Technology
KTH 2020
Techno-economic analysis of retrofitting existing fuel stations
with DC fast chargers along with solar PV and energy storage
with load flow analysis
Nilanshu Ghosh
Approved
Examiner
Viktoria Martin, PhD
Supervisor
Jagruti R. Thakur, PhD (KTH)
Co-Supervisor
Sivapriya Mothilal Bhagavathy, PhD (UOXF)
Industrial Supervisor
Contact person
Jagruti R. Thakur, PhD (KTH)
Abstract
The increasing number of electric vehicles (EVs) in the transport sector has rendered the conventional fuel-
based vehicles obsolete along with the fuel filling stations. With the growth in EVs, there has been an increase
in the public charging infrastructure with fast charging equipment being used to charge the EVs in least possible
time and also address the issue of ‘range anxiety’ among the EV owners. Many countries like South Korea and
Germany has seen policies being implemented to install fast chargers for EVs in existing fuel filling stations.
This study aims conduct a techno-economic feasibility to analyse the potential of implementing Electric Vehicle
Supply Equipment (EVSE) with fast charging capacity into existing fuel filling stations. The potential of using
solar photovoltaic system (PV) and battery storage systems (BESS) to reduce the load from the grid is also
explored. Scenarios are developed considering different configurations of the EVSE, PV and BESS and an in-
depth economic analysis is conducted to analyse the economic feasibility of the configurations. The impact on
the electricity grid is also analysed in this thesis by conducting a load flow analysis on the CIGRE Low voltage
network for Europe using Python.
The proposed design enables selection of techno-economically feasible configurations of EVSE, BESS and PV.
The results of the design are explained with the UK as a case study. It is observed that the configurations with
3 EVSE, BESS and 8 hours and the configuration with 3 EVSE, 1 BESS and 1 PV system for 8 hours of
operation are economically viable. The proposed design shows that though the connection cost is the dominant
factor affecting the feasibility, use of BESS with or without PV can reduce the connection cost by almost 90%
depending on the number of BESS. Load flow analysis is conducted for the different configurations of EVSE,
BESS and PV on the CIGRE LV network on Pandapower in Python. The results indicate that the existing
network needs to be reinforced to facilitate the connection of EV fast chargers into the grid. Upgrading the
network cables and increasing the slack voltage to a value of 1.05 or 1.1 Volts per unit, are the two strategies
that have been suggested in this study to prevent any undervoltage that may occur as a result of connecting the
EVSE to the electricity grid. The simulations conducted for the two strategies highlight that by implementing
these strategies into the electricity grid network, the undervoltage issues in the transmission network can be
mitigated.
Keywords: Electric vehicle charging, DC fast chargers, Load flow analysis, EV charging stations, CIGRE LV
networks
Sammanfattning
Det ökande antalet elfordon inom transportsektorn har gjort de konventionella bränslebaserade fordonen
föråldrade tillsammans med bränslepåfyllningsstationerna. Med ökningen av elbilar har det skett en ökning av
den offentliga laddningsinfrastrukturen med snabbladdningsutrustning som används för att ladda elbilarna på
åtminstone möjlig tid och också ta itu med frågan om ’range anxiety’ bland elägare. Många länder som Sydkorea
och Tyskland har sett politik införas för att installera snabbladdare för elbilar i befintliga bensinstationer.
Denna studie syftar till att genomföra en teknisk-ekonomisk genomförbarhet för att analysera potentialen för
att implementera elfordonstillförselutrustning (EVSE) med snabb laddningskapacitet i befintliga
bensinstationer. Potentialen med att använda solcellssystem (PV) och batterilagringssystem (BESS) för att
minska belastningen från nätet undersöks också. Scenarier utvecklas med beaktande av olika konfigurationer
av EVSE, PV och BESS och en djupgående ekonomisk analys genomförs för att analysera konfigurationernas
ekonomiska genomförbarhet. Effekten på elnätet analyseras också i denna avhandling genom att genomföra en
belastningsflödesanalys på CIGRE lågspänningsnät för Europa med Python.
Den föreslagna designen möjliggör val av tekno-ekonomiskt genomförbara konfigurationer av EVSE, BESS
och PV. Resultaten av designen förklaras med Storbritannien som en fallstudie. Det observeras att
konfigurationerna med 3 EVSE, BESS och 8 timmar och konfigurationen med 3 EVSE, 1 BESS och 1 PV-
system för 8 timmars drift är ekonomiskt lönsamma. Den föreslagna designen visar att även om
anslutningskostnaden är den dominerande faktorn som påverkar genomförbarheten, kan användning av BESS
med eller utan solceller minska anslutningskostnaden med nästan 90% beroende på antalet BESS.
Lastflödesanalys utförs för de olika konfigurationerna av EVSE, BESS och PV på CIGRE LV-nätverket på
Pandapower i Python. Resultaten visar att det befintliga nätverket måste förstärkas för att underlätta
anslutningen av EV-snabbladdare till nätet. Uppgradering av nätverkskablarna och ökning av spänningen till
1,05 eller 1,1 volt per enhet är de två strategier som har föreslagits i denna studie för att förhindra underspänning
som kan uppstå till följd av att EVSE ansluts till elnätet. Simuleringarna för de två strategierna lyfter fram att
genom att implementera dessa strategier i elnätet kan underspänningsfrågorna i överföringsnätet mildras.
Nyckelord: Elfordonsladdning, DC-laddare, Lastflödesanalys, EV-laddstationer, CIGRE LV-nätverk
Acknowledgement
I would like to thank my supervisor at KTH, Dr. Jagruti Thakur for the guidance and support during the entire
course of this project. I would also like to thank my Co-supervisor from the University of Oxford, Dr. Sivapriya
Mothilal Bhagavathy for sharing her knowledge and guidance without which the completion of this project
would not have been possible. Finally, I would like to thank my parents who helped me stay motivated and on
track for this thesis.
Abbreviations
BEV- Battery Electric Vehicle
BESS- Battery Energy Storage System
CAGR- Compound Annual Growth Rate
CIGRE- International Council on Large Electric systems
DC- Direct Current
DCFC- Direct Current Fast Charging
EU- European Union
EV- Electric Vehicles
EVSE- Electric Vehicle Charging Equipment
FAME- Faster Adoption and Manufacturing of Hybrid and EV
ICE- Internal Combustion Engines
IRR- Internal Rate of Return
LCOS- Levelised Cost of Storage
LV- Low Voltage
PV- Photovoltaic
Nomenclature
A1 Area of 1 kWp PV system
APV Area of the PV system installed in the fuel station
Amax Maximum usable area of the fuel station
Cbat Cost of storage
CI Installation cost of DCFC
Cins Installation cost of storage
Cc Electricity network connection cost
Cm Operation and maintenance cost
𝐶𝑢𝑝𝑓𝑟𝑜𝑛𝑡 Total upfront cost of the fuel station
Ct Annual discounted cash flow
Epv Annual energy generated by the PV system
𝐸𝑆,𝑚𝑎𝑥 Maximum battery storage capacity
Ev Energy used per vehicle
husage Hours of usage of the DCFCs
H Global Tilt Irradiance at optimal tilt angle
nDCFC Number of DCFCs that can be installed
nbat Number of storage system installed
nDCFC,max Maximum number of DCFCs that can be installed
𝑛𝑏𝑎𝑡,𝑚𝑎𝑥 Maximum number of battery units
nv Number of vehicles arriving per day at the charging station
neff Efficiency of the BESS considering the standby losses
npv Number of PV system installed
npv,max Maximum number of PV system installed
Pbat Power capacity of storage units
PDCFC Net power of the DCFCs
𝑝𝑒𝑟𝑐𝑠𝑡𝑜𝑟 Ratio of power capacity of storage units to the net power of the DCFCs
p Billing price for the customers
𝑃𝑟 Performance ratio of the solar panel
𝑃𝑉1 Annual energy generated by 1 kWp PV system
𝑃𝑉𝑐 Maximum energy capacity of the PV system
𝑃𝑉𝑠𝑖𝑧𝑒 Capacity of the PV system that is installed
r Solar panel yield
𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑎𝑛𝑛𝑢𝑎𝑙 Annual revenue
t Number of time periods
Table of Contents- Abstract ................................................................................................................................................................................ 3
Sammanfattning .................................................................................................................................................................. 4
Acknowledgement .............................................................................................................................................................. 5
Abbreviations .......................................................................................................................................................................... 6
Nomenclature .......................................................................................................................................................................... 6
1. Introduction .............................................................................................................................................................. 10
2. Research questions and objective .......................................................................................................................... 14
2.1 Research questions................................................................................................................................................. 14
2.2 Scope and limitations ............................................................................................................................................ 14
3. Literature Review ..................................................................................................................................................... 15
3.1 Implementation of circular economy ................................................................................................................. 15
3.2 State of EV charging technology ......................................................................................................................... 15
3.3 Refueling behavior of EV drivers ....................................................................................................................... 17
3.4 Impact of DCFC on the electricity grid ............................................................................................................. 18
3.5 Integration of renewables and battery storage .................................................................................................. 18
4. Methodology ............................................................................................................................................................. 20
4.1 Techno-economic analysis.................................................................................................................................... 20
4.1.1 Assumptions considered for the analyses .................................................................................................. 21
4.1.2 Calculations for Techno-economic analysis ............................................................................................... 22
4.1.3 Case Study on the UK ................................................................................................................................... 24
4.2 Load flow analysis .................................................................................................................................................. 26
4.2.1 Simulations ...................................................................................................................................................... 29
4.2.1.1 Simulation with the Industrial network ................................................................................................... 29
4.2.2.2 Simulation with the commercial subnetwork ......................................................................................... 29
5. Results and discussion ................................................................................................................................................. 30
5.1 Results of the Techno-economic analysis .......................................................................................................... 30
5.1.1 Scenarios without BESS ................................................................................................................................ 31
5.1.2 Scenarios with BESS ...................................................................................................................................... 31
5.1.3 Comparing the scenarios with and without PV ........................................................................................ 33
5.4 Results of the load flow simulations ................................................................................................................... 34
5.4.1 Analysis of the loads at the industrial subnetwork ................................................................................... 35
5.4.2 Analysis of loads at the commercial subnetwork ...................................................................................... 36
5.4.2.1 Net demand for two DCFCs .................................................................................................................... 36
5.4.2.2 Net demand for three DCFCs .................................................................................................................. 37
5.4.2.3 Net demand for four DCFCs ................................................................................................................... 38
5.4.2.4 Upgrading the electricity network by replacing the cables ................................................................... 39
5.5 Small offload tap change ....................................................................................................................................... 42
6. Discussions and future work ................................................................................................................................. 46
7. Contribution towards Sustainability...................................................................................................................... 47
8. Conclusion ................................................................................................................................................................ 48
References .......................................................................................................................................................................... 50
List of figures
Figure 1-Greenhouse gas emissions from the transport sector in (a) EU-28, 2017(b) The USA, 2018[3],[4]... 10
Figure 2: ULEV deployment in different regions (2013-2019) [9] ........................................................................... 11
Figure 3: Number of publicly available EV chargers (a): Slow chargers and (b): Fast chargers [9] ..................... 17
Figure 4: Representation of the methodology .............................................................................................................. 20
Figure 5- Single line diagram of the CIGRE LV network [64] ................................................................................. 26
Figure 6: Voltage at I2 based on scenarios when the load is applied directly (a) For 2 DCFCs (b) For 3
DCFCs (c) For 4 DCFCs................................................................................................................................................. 35
Figure 7: Voltage at I2 based on scenarios when the load is applied additional to the existing load (a) For 2
DCFCs (b) For 3 DCFCs (c) For 4 DCFCs ................................................................................................................. 36
Figure 8: Comparison of various loads at C14 for two DCFCs ................................................................................ 37
Figure 9: Comparison of various loads at C14 for three DCFCs ............................................................................. 38
Figure 10: Simulations conducted with 50 kW net demand ...................................................................................... 39
Figure 11: Simulations conducted with 100 kW net demand .................................................................................... 40
Figure 12: Simulations with 150 kW net demand ........................................................................................................ 41
Figure 13 : Simulations conducted with 200kW net demand .................................................................................... 42
Figure 14 : Simulation of CIGRE LV network for two DCFCs ............................................................................... 42
Figure 15 : Simulation of CIGRE LV network for three DCFCs ............................................................................ 43
Figure 16 : Simulation of CIGRE LV network for four DCFCs .............................................................................. 43
Figure 17 : Simulation of CIGRE LV network for two DCFCs ............................................................................... 44
Figure 18 : Simulation of CIGRE LV network for three DCFCs ............................................................................ 44
Figure 19 : Simulation of CIGRE LV network for four DCFCs .............................................................................. 45
List of tables
Table 1- Charging technologies used globally [20],[34] ............................................................................................... 16
Table 2-Technical and Economic parameters considered for calculations ............................................................. 25
Table 3- Technical specifications of the lines [64] ....................................................................................................... 27
Table 4-Loads present at the nodes ............................................................................................................................... 27
Table 5- Specifications of the cables considered for the simulation ........................................................................ 29
Table 6- Different configurations considered for the simulations............................................................................ 30
Table 7- Breakdown of different loads considered for the scenarios....................................................................... 34
1. Introduction
The global greenhouse gas (GHG) emissions have been increasing and one of the major contributors to the
emissions is the transport sector. It is estimated with the current policies, there will be 60% increment in the
CO2 emissions from the transport sector by 2050 if the current policies persist (1). Globally, the transport sector
accounted for almost 15% of the total GHG emissions(2). Within the transport sector, road transport is the
major contributor towards the total emissions released with almost 72% as observed in the European Union
(3) and 82% in the USA (4).
Figure 1-Greenhouse gas emissions from the transport sector in (a) EU-28, 2017(b) The USA, 2018(3),(4)
To reduce the amount of the greenhouse gas (GHG) emissions in the transport sector, it is mandatory that
strategies are developed to improve the vehicles on road. The strategies may include the following-
Use of better alternate fuel which emit less emissions on combustion
Use of low emission vehicles in the transport sector
There is a global trend to promote sustainable modes of transport which has resulted in increasing use of Ultra
Low Emission Vehicles (ULEVs), as shown in Figure 1. ULEVs are the vehicles that emit less than 75g of
CO2/km and include different classes of vehicles namely Battery electric Vehicles (BEV), electric range-
extender vehicles and Plug-in Hybrid vehicles (PHEVs) (5). The different classes of the ULEVs can be
described as following-
Battery Electric Vehicles (BEVs)- BEV is a class of electric vehicle which is operated on electricity and
has a battery system supporting the electric motors which drive the vehicle. These vehicles have an on-
board charger which when connected to an external charger can be used to charge the battery system.
Electric range extender vehicles- These are the class of vehicles installed with an auxiliary power unit
which is used to charge the electric motors which increase the operational range of the vehicles.
Plug-in Hybrid Vehicles (PHEVs)- PHEVs are the vehicles which use a conventional internal
combustion engine and battery-powered electric motor.
82%
9%
2% 2%
5%
RoadtransportCivil Aviation
Navigation
Railway
Other72%
14%
13%
1%
Germany was the market leader in the EU with almost 60,000 new vehicles closely followed by France with
46,554 in 2018 (6). Most of the EVs registered in the UK and Sweden are PHEV whereas BEVs are preferred
in France and China(7). However, the number of BEVs almost doubled in 2019 in the UK post the introduction
of changes in grant available for the purchase of Electric Vehicles(EVs) (8). In developing countries except for
China, growth of EV is still in its nascent stage owing to various reasons like availability of technology, electricity
access, high, upfront costs, lack of policy initiatives etc.
Figure 2: ULEV deployment in different regions (2013-2019) (9)
This trend in the increase in the EVs on road can be contributed to the various policies introduced to put a cap
on the GHG emissions and promote electrification of the transport sector. The 2020 climate and energy
package introduced in the Eu aimed to reduce the emissions by setting three major targets 20% reduction in
GHG emissions from 1990 levels, 20% of the total energy in the EU from renewables and 20% improvement
in the energy efficiency (10). The EU has also put in legislation to reduce the GHG emissions by almost 40%
by 2030 in accordance with the Paris agreement (11). In the US as well, the federal government has established
policies to promote the growth of electric vehicles. The American Recovery and Reinvestment Act of 2009
provided tax credits to retrofit conventional fuel operated vehicles with battery systems and for purchasing new
EVs as well (12).
With the increasing penetration of EVs into the transport sector, we encounter new challenges with the
charging requirements for the vehicles, its availability, and its impact on the electricity network. The common
types of charging points available are-
AC Mode 2 Home- Used in homes, these charging points has a power output of 11 kW and can provide
1-2 kilometers per 10 minutes of charge.
AC Mode 2 Commercial- Used for private or public and in workplaces, these charging points have a
power output up to 19.4 kW and can provide 3.2 kilometers per 10 minutes of charge.
AC Mode 3 Fast Charging- Used for private or public applications with a power output of 22 kW or
43 kW and provides 21 kilometers per 10 minutes of charge.
DC fast charging (standard)- Used for public or private applications with a power output in the range
of 20-50 kW and provided 64 kilometers per 10 minutes of charge.
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Num
ber
of
UL
EV
Europe ULEV
US ULEV
Other ULEV
DC high power fast charging- Used for public applications with a power output in the range of 100-
400 kW and provide 90 kilometers per 10 minutes of charge.
There are different charging strategies, that are used by the EV owners to charge their vehicles. The most
commonly used charging strategy is to charge the EVs at home overnight and the second strategy employed is
to use a public charging station at work or a fast charger and then charge the vehicles during the day. EV owners
prefer to charge their vehicles overnight at home and this charging strategy is traditionally known as “Dumb
charging”. In dumb charging, the electric vehicle is connected to the charger and the battery system is allowed
to charge to its complete capacity. Another charging strategy employed while charging the electric vehicles is
“Smart charging” which involves the charging station owner to monitor the EV charging and optimize the
energy consumption (13). Studies have shown that smart charging is better than dumb charging and is also
more commonly used charging strategy in the current times. Smart charging strategy is more commonly used
in public places that give the flexibility to the owners, to book a charging slot and allow the owners to remotely
monitor the process (14). Most commonly used EV chargers are either home chargers or the publicly available
EV chargers. The public chargers are most commonly available at charging stations set up by various
companies, or chargers at workplace or any other public hotspot with the option to park EVs and then charge
them. Public chargers typically make use of smart chargers which give the EV owners the flexibility to choose
the time slot for charging their vehicles and can remotely monitor the progress on charging and also the ease
of paying using online gateways. However, the major challenges faced during public charging is the long waiting
times at the charging stations. Introduction of fast charging in public charging stations reduces the waiting times
and addresses the problem of “range anxiety” encountered by the EV owners. The fast charging is capable of
flattening the aggregate demand curve and is a more suitable option for public charging (15). In recent surveys,
faster charging and increased availability of charge points has been two major factors chosen by EV drivers
that would improve their charging experience (16). However, installing DCFCs in public is limited by the high
connection cost incurred while connecting the DCFCs to the electricity grid network (17). DCFCs also has the
impact on the distribution network and owing to this factor, DCFC based charging station are required to have
a separate sub-station to meet the energy demand of the charging station. To make the use of DCFC in public
hotspots economically more profitable, the reliance on the electricity grid can be reduced by integrating a
renewable energy source in the charging station. Solar energy based Photovoltaic Systems (PV) are most suitable
to be used in an EV charging station (18). Apart from the high connection costs, due to the spikes in power
usage, demand charges also tend to be high which is a barrier for the installation of EV charging stations (19).
With the inclusion of DCFCs, these spikes tend to increase further which limits the use of DCFCs in the public
EV charging stations. However, using a renewable energy generation method like solar PV along with an energy
storage system can reduce the dependency on the grid electricity, flattening the demand spikes and preventing
any demand charges as a result (20), (21). The use of renewable energy systems in EV charging stations results
in making the charging stations more sustainable in the countries which have less integration of renewables in
their energy mix. The countries with higher levels of irradiance will be able to generate excess electricity using
the PV system which can be sold back to the grid and hence render the usage of PV into the charging station
more economically feasible.
This rise in the demand of public charging stations requires the construction of new stations to keep up with
the growth of EVs. This would mean utilisation of more material and resources to complete the construction
and installation of the EV charging stations which may not be sustainable. Traditional fast chargers (22kW-
43kW) used in the commercial spots tend to have higher charging times which often results in queuing which
is not preferred by the EV owners. To make the option of public chargers more lucrative for the EV owners,
fast chargers (50-150 kW) are installed in public charging spots which give the owners less queuing time
motivating the EV owners to use public charging stations. To have a more sustainable solution, the existing
infrastructure of the fuel stations can be retrofitted to suit the charging needs of the EVs and to reduce any
queuing and fulfill the drivers’ expectation of faster charging, DC fast chargers (DCFCs) can be implemented
in the fuel stations. This system is being implemented in various countries like South Korea and Germany. In
Germany, under the 2020 COVID-19 relief stimulus package, it has been made mandatory for all the fuel
stations in Germany to retrofit their facility to integrate EV charging solutions (22). Fuel stations are suited for
the EV charging needs, as the EV owners are familiar with their location and it usually is the trend for the
drivers to refuel their cars at familiar fuel stations. Retrofitting the fuel stations also are in compliance with the
principles of circular economy promoting the concepts of reducing waste and pollution and reusing the existing
products and materials. However, installing multiple DCFCs can have a major impact on the grid electricity
with the short-term peak loads. A power flow analysis can be conducted to study these impacts and developing
strategies to make the power grid more resilient towards the peak loads as a results pf the DCFCs. This thesis
conducts a techno-economic feasibility study to implement DCFCs into existing fuel stations’ infrastructure
and studying the impact of the DCFCs on the electricity grid network. PV systems and BESS are also integrated
into the charging stations and their feasibility is also analysed.
Figure 3: Proposed design for retrofitting
Gas Station EV Charging station
with DCFC Electric Vehicles
Grid Electricity
Storage unit Solar PV
2. Research questions and objective
The objective of the thesis is to study the potential of using the existing infrastructures of fuel filling stations
to implement EV fast charging considering solar PV and energy storage alongwith the study on the network
through load flow analysis.
2.1 Research questions
What is the economic feasibility of retrofitting the existing fuel stations with DCFCs?
What is the impact on the electricity grid network caused due to the installation of the DCFCs?
What is the potential of augmenting PV and Battery storage along with the DCFCs in the charging
station?
2.2 Scope and limitations
The thesis considers 50 kW DCFC and the number of EVSE at a charging station is constrained to 4 given the
high connections costs. The methodology developed can be used for any fuel station irrespective of location.
The charger type used is CCS1 and the vehicle for the study used is KIA e-Niro. The load flow analysis is
conducted using the CIGRE low voltage European network and the load is studied at Industrial and
Commercial nodes. The data considered in this study for the economic analysis namely PV prices, connection
costs, discount rate and inflation factor are taken for the region of UK.
1
1Combined Charging System (CCS) is a connector type developed by the Society of Automotive Engineers (SAE).
CCS supports both AC slow charging and DC fast charging (20). In EU, for fast charging applications, CCS is the
most commonly used connector type in public charging stations (19).
3. Literature Review
Transport sector is one of the major contributors towards the greenhouse gas emissions. Transportation
accounts for a total 28% of the emissions in the US and 27% of the total emissions in Europe (23,24). However,
studies point out that electrifying the transport sector alone cannot have significant effect on the reduction of
the overall greenhouse gas emissions (25). The authors arrive to the conclusion that decarbonizing the power
generation is the key to successful reduction of the greenhouse gas emissions. In the U.S, the electricity grid is
integrating more renewable energy in the mix to have a cleaner power generation and promote electrification
in the transportation sector (26).
3.1 Implementation of circular economy
The global shift moving towards sustainability, the concept of circular economy is introduced which focusses
on reusing the resources at disposal, reusing the established infrastructures, and reducing waste from the system.
However, the studies indicate that the implementation of circular economy is still in its nascent stages with the
exception of China (27). In China, the push towards circular economy has been since 2005 with various policies
being implemented to cope with the energy and environment crisis (28). The European circular economy
package was implemented in the EU to promote sustainable growth in various sectors (29). In Europe, research
suggests that implementation of circular economy could generate a net economic gain of 1.8 trillion euros per
year by 2030 (30). Existing fuel stations will become redundant with the countries setting up targets of going
100% EVs on road. The abandoned structure of the gas stations can be used to implement the charging stations
for the EVs. The location of fuel stations align with the needs of the EV charging stations (31), so to eliminate
waste, the existing infrastructure of the fuel stations can be used to implement DCFCs. Many companies in
various regions of the world has already started implementing EV chargers at the existing gas stations to
promote electromobility (32,33).
3.2 State of EV charging technology
EVs, when compared to traditional Internal Combustion Engine (ICE) vehicles, have a shorter driving range
and longer refueling time. This restricts the owners to make the transition to EVs. Developing the EV charging
infrastructure with fast charging capacity would aid in increasing the number of EVs on the road. The
International Electrotechnical Commission (IEC) 61851-1 Committee on Electric vehicle conductive charging
system has defined the various modes of charging available for EVs (20). The mode of charging is defined
based on the type of power received by the EV, level of voltage, presence or absence of grounding and control
lines and the presence of device protection. Table 1 shows the charger types by country, its power rating and
application.
Table 1- Charging technologies used globally (20),(34)
The commonly used charging strategy for EVs are either dumb charging or smart charging. Various studies
have been conducted to compare the two charging strategies and analyse the impact on the electricity grid
network. In (36), the authors develop various scenarios with different numbers of EVs integrated into the
grid and analyse the impact on the electricity grid network. The results of this study highlight that the peak
load only increases by 11% when compared to an 85% increment in the peak load when dumb charging is
considered.
Charging Type Power Rating Application Voltage Level Presence of
grounding
and device
protection
UK Mode 2(AC) 3kW (13A) Residential 110-480V IEEE standard
makes it
mandatory for
all DC fast
chargers to
implement
device
protection and
grounding for
the EV
chargers.(35)
Mode 2(AC) 7.4 kW(32A) Industrial 110-480V
Mode 3(AC) 3.7 kW(16A) Residential 110-480V
Mode 3(AC) 7.4 kW(32A) Residential 110-480V
Mode 4(DC) 50 kW Public and Commercial 480+V
US Level 1 (AC) 1.44 kW(12A) Residential and Commercial 110-120V
Level 2(AC) 3.3 kW(15A) Private and Public 208-240V
DCFC 50-100 kW Commercial and Public 200-600V
Europe Level 1 (AC) 1.4 kW Residential and Workplace 110-480V
Level 2(AC) 7.2 kW Residential and Workplace 110-480V
Level 3 (DC) 50 kW Public parking places 480+V
Figure 4: Number of publicly available EV chargers (a): Slow chargers and (b): Fast chargers (9)
The increase in EVs in the global market pose a challenge for the charging infrastructure. Most of the public
charging stations installed in 2019, in Europe and the US were slow chargers. In 2019, 264k fast chargers were
available globally for public charging, 80% out of which were installed in China (9). In Europe, 164k charging
points were available out of which only 15k were fast charging type (>22 kW) (37). In developing countries,
growth of EV is still in its nascent stage owing to various reasons like availability of technology, electricity
access, high, upfront costs, lack of policy initiatives etc. Other than China, the growth in EV is meagre in other
nations. EVs occupy less than 1% of total cars owned in India in 2019 (38). Faster Adoption and Manufacturing
of Hybrid and EV (FAME) scheme was launched in India in 2015 which aimed at providing subsidies for EV
comprising of 2 or 3 wheelers, hybrid, e-cars and buses. This scheme was also extended to include charging
infrastructure as well. Total funds allotted for the scheme was INR 100 billion (1.3 billion USD). As a result of
this scheme, there is a projected Compound Annual Growth Rate (CAGR) of 53.6% in the number of EV in
the duration of 2019-2030 in India (39).
3.3 Refueling behavior of EV drivers
An analysis of the refueling behavior of gasoline-car drivers shows that almost three-quarters of the drivers
refueled on the way or to the way to their homes(40). The authors concluded that considering the refueling
pattern of the drivers, refueling stations should be close to the households as the drivers tend to use familiar
fueling stations which are near their households. For the EVs as well, drivers prefer to refuel close to home or
work and prefer to charge their vehicles N-route origin to destination (41). Given the refueling behavior of the
drivers, the authors in (31) consider gas stations as an ideal location for installation of a public charging station.
The authors consider Beijing as a case study and identified 698 petrol stations that are suitable for 200 kW fast-
charging locations. The authors of (42) use travel information from 48 households and investigate the demand
for charging over a range of 80, 100 and 120 miles in Sacramento, US. The authors highlight the importance of
DCFC in public charging infrastructure and the proximity to the residence for shorter travel distance. In the
81%
3%
5%2%1%
1%2% 0% 5%
China
Japan
United States
United Kingdom
Germany
France
Norway
Netherlands
Other
Publicly accessible fast chargers264 000
50%
4%11%
4%
6%
4%
1%
8%
12%
Publicly accessible slow chargers598 000
study performed in (43), the need for level 3 charging (50 kW-150 kW range) to provide a reasonable level of
service and to reduce the social cost is emphasized. For the purpose of publicly available EV charging, the
capacities of the charging station is taken in the range of 3.7 kW to 50 kW (44). The potential of a fuel-station
based charging using 7 to 37 kW AC chargers is discussed in (45) as it fits the consumer habit of refueling and
reduces the “range anxiety”. The authors highlight that there will be long waiting times at such charging stations
and consider this option as unrealistic.
3.4 Impact of DCFC on the electricity grid
Connecting DCFC with the electricity grid has the major concern of disrupting the distribution network. Many
studies have been conducted to study the impact, DCFCs may have on the distribution network. Hydro-
Quebec, a generation, transmission, and distribution company based in Quebec conducted tests to analyse the
impact of using DCFCs on their distribution network. The results indicate lower levels of voltage in the power
quality tests conducted when compared to the standard limits. However, these tests are conducted in a
laboratory and only the distribution network of Hydro-Quebec is taken into consideration (46). Studies have
also been conducted to study the impact on the electricity grid by charging by taking into consideration some
of the widely used EVs. The authors in (47) set up a fast charging station offering power of up to 150 kW and
tested a variety of vehicles to observe the impact on the electricity grid. The results showcased that the fast
charging station imposes a huge burden on the electricity grid due to high peak loads. The impact of the DCFCs
has been studied on the low voltage (LV) distribution networks as well. In the region of New Zealand, in-line
chargers have been used to study the impact on the electricity grid (48). However, the study lacks the use of
CHAdeMO and the CCS based chargers as the authors consider them not to be used as widely as the in-line
chargers. But in the present time, CCS and CHAdeMO are the most used connector types for fast and rapid
charging.
3.5 Integration of renewables and battery storage
Multiple vehicles charging at 50-100 kW (output power range of DCFCs) can cause a significant increase in the
peak demand and impact the power grid negatively(20). Integration of an energy storage solution can reduce
this stress on the grid and a prototype of a fast-charging station and an ESS which reduced the impact on the
main distribution grid due to peak demand is developed in (20). The authors conduct an experimental evaluation
of the impact of the combination of 50 kW DCFC and 16 kWh battery storage on the grid and conclude that
the combination resulted in almost ‘zero’ impact on the grid, proving the technical feasibility of such a
configuration. The impact on the electricity grid after installing BESS to facilitate the EV charging stations was
analysed for Beijing, China (49). The results showcased that the use of BESS reduces the reliance on the
electricity grid and a better peak to valley equilibrium for EV charging was obtained. To assess the economic
feasibility of using EV charging infrastructure, business model has been developed and three different scenarios
are considered in the study viz. 3.7 kW for home chargers, 22 kW for public hotspots and 50 kW DCFC along
the highways in (50). The authors state that the frequency of use of DCFC needs to be increased to make the
scenario more economically profitable. The frequency of DCFC used can be increased by implementing them
into public hotspots. DCFC used for public charging is still preferred along the highways and only medium or
slow chargers are used at public hotspots (50). To integrate the EV charging into public hotspots, an EV
charging station was integrated into an existing gas station in Spain (51). 50 kW fast charger is used for the
charging station along with 22 kW medium and 3.7 kW slow charging points. The authors highlight that fast
charging EVSE can be a problem for weak distribution grids. PV system and BESS can be used to reduce
demand from the grid. The authors also mention that integrating BESS into the charging stations can
economically benefit the owner of the charging station as well as the local distribution system operator.
However, economic analysis to show the profitability of using charging stations in existing gas stations is
missing.
The integration of renewable energy systems along with the BESS units can further reduce the dependency on
the electricity grid to fulfil the demand from the EV charging stations. Among all the renewable energy systems
available, solar energy-based PV systems are most flexible with EV charging stations (18). To optimally size the
PV system along with the BESS for the EV charging stations, an algorithm has been developed considering the
constraints imposed by the electricity grid into consideration (52). However, the economic feasibility of using
BESS and PV system has not been taken into consideration in the study.
From the literature study it can be concluded that the possibility of retrofitting a fuel station to utilize as an EV
charging station has been discussed, but a detailed analysis on the electricity distribution grid by installing DCFC
and also implementing PV and energy storage in the charging station has not been performed. This thesis aims
at performing a detailed techno-economic analysis of installing the DCFCs and its impact on the electricity grid
network. Furthermore, PV and BESS are implemented into the charging stations to augment the use of DCFCs
and various scenarios are developed to study the configurations of DCFCs, PV and BESS.
4. Methodology
The methodology of the thesis is conducted in two parts. Techno-economic analysis of installing DCFCs in
the charging station along with PV and BESS system is conducted in the first part. The load flow analysis to
study the impact on a CIGRE Low Voltage (LV) European network is conducted in the second part.
Figure 5: Representation of the methodology
4.1 Techno-economic analysis
In the first part of the methodology, the techno-economic analysis is conducted to analyse the feasibility of
retrofitting a fuel station with DCFCs along with PV and BESS. PV and BESS are implemented in the fuel
station to study the impact on the grid electricity. A standard vehicle was considered and the battery capacity
growth, as observed over the years in various regions of the world, was considered as 50 kWh. The details of
the assumptions are presented in the following section.
Figure 4: Average battery capacity in EVs(8)
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
2011 2012 2013 2014 2015 2016 2017 2018
Bat
tery
Cap
acit
y(k
Wh)
North America Japan, India and Korea China Europe
Initial study
and literature
review
Model for techno-
economic analysis Case Study
Without
Storage With Storage
and PV
Region of
UK
Load flow
Analysis
4.1.1 Assumptions considered for the analyses
1. The EVSE used for this analysis is a 50 kW CCS type fast charger. An example of such a charger is
ABB Terra 54HV, specifications of which are as shown in Table 2
Table 2-Technical specifications of ABB Terra 54HV
Charging Standard CCS
Maximum output power 50 kW
Output Voltage 150-920 V(DC)
Maximum output current 125 A(DC)
Efficiency 94%
Protection rating IP54
Charging Session 1 DC session
2. EV battery capacity is 50 kWh
Table 3: Technical specifications of KIA e-Niro
Parameter Value
Battery Storage capacity 67.1 kWh
Fast-charge port
Combined Charging System
(CCS)
Fast charge Power (max) 77 kW DC
Average power 45 kW DC
Charging time (SoC 0.1 to 0.8)
CCS (50 kW DC) 63 min
CCS (175 kW DC) 44 min
CCS (350 kW DC) 44 min
3. The EV is assumed to arrive at an SoC of 0.2 and would reach an SoC of 0.8 by the end of the charging
session.
4. The duration of the charging session calculated using the specifications in Table 3, is 1 hour.
5. The amount of energy required to charge is calculated as 34 kWh.
6. Degradation losses of the battery are ignored.
7. There is no queuing of vehicles in the fuel station at any given point of time.
8. It is assumed that there will be around fifteen minutes between the departure and arrival of cars to the
charging station. This time interval will be used to charge the BESS at full capacity from grid electricity if
required.
9. BESS efficiency is considered to be around 90%
10. Lifetime is 10 years.
11. The capacity of the PV system is considered equal to the capacity of the BESS used as no feed-in tariff
is available in the UK.
12. The hours of usage (husage) of newly installed DCFCs based on typical studies are about 4 hours a day
and reaches up to a maximum of 8 hours a day.
4.1.2 Calculations for Techno-economic analysis
This study focuses on using the area of the existing fuel stations to install DCFC. The average area of the fuel
stations was found out from the area of the gas stations in the US. The area of the fuel station is considered as
1200 m2 for this study. The number of DCFC that can be installed in the fuel station is limited by the number
of bays available in the fuel station that can be used to install the charger.
The steps of the proposed methodology for the re-design of the fuel station is as follows:
1. The number of DCFCs that can be installed on a fuel station is physically limited by the space available
at the station. If there are 4 bays in the fuel station, it is possible to have 4 DCFCs. This gives the
maximum number of DCFCs that can be installed (nDCFC,max).
2. As the idea is to repurpose the existing fuel station without additional infrastructure cost, it is proposed
to use the fuel storage tanks as battery storage area. Thus, the net storage that can be installed in a
station is almost limited by the volume of storage pre-existing in the station. The maximum battery
storage (𝐸𝑆,𝑚𝑎𝑥 𝑖𝑛 𝑘𝑊ℎ) that can be installed is given by
𝐸𝑆,𝑚𝑎𝑥 = 𝑒𝑛𝑒𝑟𝑔𝑦 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝐿𝑖𝑡ℎ𝑖𝑢𝑚 𝑖𝑜𝑛 𝑏𝑎𝑡𝑡𝑒𝑟𝑦 (𝑘𝑊ℎ 𝑚3) ×⁄ 𝑣𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑠𝑡𝑜𝑟𝑎𝑔𝑒 (𝑚3) (1)
The maximum number of battery units (𝑛𝑏𝑎𝑡,𝑚𝑎𝑥) is given by
𝑛𝑏𝑎𝑡,𝑚𝑎𝑥 =𝐸𝑆,𝑚𝑎𝑥
𝐸𝑏𝑎𝑡 (2)
Where, 𝐸𝑏𝑎𝑡 is the energy capacity of a single battery storage unit in kWh.
3. The annual energy generated by a 1 kWp PV system (𝑃𝑉1𝑖𝑛 𝑘𝑊ℎ) is calculated using the PVWatts®
Calculator [32] considering the average annual solar irradiance value in the region.
4. The nominal capacity of the installed PV system is calculated (𝑃𝑉𝑠𝑖𝑧𝑒 𝑖𝑛 𝑘𝑊𝑝) using the annual
energy generated by the PV system (𝐸𝑝𝑣 𝑖𝑛 𝑘𝑊ℎ) and the annual energy generated by a 1kWp PV
system (𝑃𝑉1𝑖𝑛 𝑘𝑊ℎ) as shown in equation (3)
𝑃𝑉𝑠𝑖𝑧𝑒 =𝐸𝑃𝑉×1 𝑘𝑊𝑝
𝑃𝑉1 (3)
5. PV system can be integrated into the charging station to reduce the dependency on the grid electricity.
The capacity of the PV system (𝑃𝑉𝑐 𝑖𝑛 𝑘𝑊ℎ) installed is considered equal to the maximum battery
storage (𝐸𝑆,𝑚𝑎𝑥 𝑖𝑛 𝑘𝑊ℎ) and is limited by the maximum usable area in the fuel station (𝐴𝑚𝑎𝑥). The
area of the PV system (𝐴𝑃𝑉 𝑖𝑛 𝑚2) can be calculated using (4) where (𝐴1 𝑖𝑛 𝑚2) is the area of the PV
system for 1 kWp capacity and (𝑃𝑉𝑠𝑖𝑧𝑒 𝑖𝑛 𝑘𝑊𝑝) is the capacity of the PV system installed. The area of
the PV system should be less than the maximum usable area of the fuel station.
𝐴𝑃𝑉 =𝐴1×𝑃𝑉𝑠𝑖𝑧𝑒
1 𝑘𝑊𝑝< 𝐴𝑚𝑎𝑥 (4)
PV system can be integrated into the charging station to reduce the dependency on the grid electricity.
The energy capacity of the PV system (𝑃𝑉𝑐 𝑖𝑛 𝑘𝑊ℎ) installed is considered equal to the maximum
battery storage (𝐸𝑆,𝑚𝑎𝑥 𝑖𝑛 𝑘𝑊ℎ) to avoid any loss in energy.
6. Calculation of the upfront cost: If there are nDCFC DCFCs , nbat storage systems installed and npv PV
modules installed, the total upfront cost of the fuel station repurposing will include the cost of the
DCFC (CDCFC), cost of storage (Cbat) (if any), installation cost of DCFC (CI), installation cost of storage
(Cins), electricity network connection cost (Cc), cost of solar PV (Cpv) as shown in equation (4)
𝐶𝑢𝑝𝑓𝑟𝑜𝑛𝑡 = 𝑛𝐷𝐶𝐹𝐶 × 𝐶𝐷𝐶𝐹𝐶 + 𝑛𝑏𝑎𝑡 × 𝐶𝑏𝑎𝑡 + 𝐶𝐼 + 𝑛𝑏𝑎𝑡 × 𝐶𝑖𝑛𝑠 + 𝐶𝑐 + 𝑛𝑝𝑣 × 𝐶𝑝𝑣 (5)
where all the costs are in pounds. The connection cost is dependent on the net demand at the point of
connection to the grid and it increases with increase in net demand. However, this increase is non-
linear and may rise steeply after a knee point reflecting the need to upgrade the system and/or to have
an additional local transformer. Typical values of this connection cost across Europe and the US are
given in (17).
7. Calculation of annual revenue: The annual revenue depends on the energy used per vehicle (Ev in
kWh), the number of vehicles arriving per day (nv) and billing rate of the customers. If customers are
billed at ‘p’ pence/kWh, the annual revenue can be calculated using
𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝐴𝑛𝑛𝑢𝑎𝑙 = 𝑛𝑣 × 𝐸𝑣 × 𝑝 × 365 (6)
8. Calculation of annual energy cost: The annual energy cost will be dependent on the net energy
consumed at the point of connection. If there is no storage, this energy will be the same as the energy
supplied to the customers. If there is storage, a percentage of the energy supplied would come from
the storage. It can be safely considered that the ratio of energy supplied from the battery will be equal
to the ratio of power capacity of storage units (Pbat in kW) to the net power of the DCFCs (PDCFC in
kW). The percentage of energy utilized from the BESS is dependent on the availability of the BESS
and the time needed to charge the BESS. It is calculated using eqn. (7) where the percentage of
utilization is limited to 50% to prevent complete discharge of the BESS.
𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑟𝑎𝑡𝑖𝑜 =𝑛𝑏𝑎𝑡×𝑃𝑏𝑎𝑡
𝑛𝐷𝐶𝐹𝐶×𝑃𝐷𝐶𝐹𝐶 (7)
𝑝𝑒𝑟𝑐𝑠𝑡𝑜𝑟 = { 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑟𝑎𝑡𝑖𝑜, 𝑖𝑓 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑟𝑎𝑡𝑖𝑜 < 0.5
0.5, 𝑖𝑓 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑟𝑎𝑡𝑖𝑜 ≥ 0.5 (8)
The annual energy cost (𝐶𝐴𝐸) can thus be calculated using the following equation:
Annual Energy Cost-
𝐶𝐴𝐸 = [𝑛𝑣 × {𝑝 × 𝐸𝑣 × (1 − 𝑝𝑒𝑟𝑐𝑠𝑡𝑜𝑟) + 𝑝 ×𝐸𝑣
𝑛𝑒𝑓𝑓∗ 𝑝𝑒𝑟𝑐𝑠𝑡𝑜𝑟} − 𝑝 × 𝑛𝑝𝑣] × 365 (9)
Where, (neff ) is the efficiency of the BESS considering the standby losses
Since there is no feed-in tariff available for solar PV, the cost of the electricity generated using the
PV system is equivalent to the billing price ‘p’ pence/kWh.
9. Calculation of annual operation and maintenance cost: The annual maintenance cost (Cm) is
roughly around 10% of the capital cost of the DCFC (17). This includes the software maintenance and
updates as well. The operation and maintenance cost for the solar PV is considered as 1.5% of the
capital cost for the PV system. There are no operation and maintenance charges associated with the
storage system.
10. Calculation of Net present value (NPV): NPV is used to calculate the discounted cash flows over
10 years to analyse the economic viability of the considered system. NPV can be calculated using (7)
where Ct is the net cash flow during the period t and r is the discount rate. The cash flow Ct is calculated
using (8), where Ct is the yearly discounted cash flow. All the cost parameters are in pounds.
𝑁𝑃𝑉 = ∑𝐶𝑡
(1+𝑟)𝑡10𝑡=1 − 𝐶𝑢𝑝𝑓𝑟𝑜𝑛𝑡 (10)
𝐶𝑡 = 𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑎𝑛𝑛𝑢𝑎𝑙 − (𝐶𝐴𝐸 + 𝐶𝑚) (11)
11. Calculation of Internal rate of return (IRR): IRR is the interest rate r at which the NPV becomes
zero. IRR can be calculated using (9)
𝐼𝑅𝑅 𝑖𝑠 𝑟 𝑎𝑡 𝑤ℎ𝑖𝑐ℎ ∑𝐶𝑡
(1+𝑟)𝑡𝑇𝑡=1 − 𝐶𝑢𝑝𝑓𝑟𝑜𝑛𝑡 = 0 (12)
12. Calculation of Discounted Payback Period (DPP): DPP is the number of years taken to break
even the initial investment by using future discounted cash flows. DPP can be calculated using (10)
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑦𝑒𝑎𝑟𝑠 𝑎𝑡 𝑤ℎ𝑖𝑐ℎ ∑𝐶𝑡
(1+𝑟)𝑡𝑇𝑡=1 = 𝐶𝑢𝑝𝑓𝑟𝑜𝑛𝑡 (13)
13. Repeat steps 3-10 for all the potential combinations of hours of operation (e.g. for 4, 6 and 8 hours of
operation per day), number of DCFCs (1 to nDCFC,max) , number of battery storage systems (1 to nbat,max)
and number of solar PV systems (1 to npv,max).
14. Selection of techno-economically feasible combination of the number of DCFCs and number of
batteries: The most profitable scenario can be identified using NPV and IRR.
4.1.3 Case Study on the UK
A case study of a typical fuel station in the UK is considered and it will be able to accommodate 4 DCFCs. The
underground storage capacity of fuel station is 35,000 Gallons (~132 m3)(53). If we consider the typical energy
density of Lithium-ion battery (280 kWh/m3), this implies that we can have around 36.96 MWh (54). The
scenarios considered for the analysis are:
1. One DCFC without storage
2. Two DCFCs without and with storage
3. Two DCFCs with storage and PV system
4. Three DCFCs without and with storage
5. Three DCFCs with storage and PV system
6. Four DCFCs without and with storage
7. Four DCFCs with storage and PV
The configurations considered for the analysis can be described as “n1Cn2Sn3h”, where n1 represents the number
of DCFCs and ranges from 1 to 4, n2 represents the number of BESS and ranges from 0 to 3 and n3 represents
the number of hours of operations per day which can be 4, 6 or 8.
Considering the typical connection capacity of less than 69 kW in the UK (55), the size of a single battery unit
is limited to 50 kWh with a maximum charging power of 50 kW. As this power is the same as the power rating
of a DCFC, the maximum number of batteries (𝑛𝑏𝑎𝑡 ) is considered as 𝑛𝐷𝐶𝐹𝐶-1 for the different scenarios
listed above.
The technical and economical parameters considered for the analysis are given in table 3.
Table 2-Technical and Economic parameters considered for calculations
Sr No. Parameter Value Unit Source
Economical
1 Discount factor 3.5 % (56)
2 EV Charge price 31.2 p/kWh (Table 4)
3 Commercial electricity price 13.5 p/kWh (57)
4 Price of EVSE unit 28,502 £ (58)
5 Price of Tesla Powerwall 27,400 £ (59)
6 Annual maintenance cost for first two years for EVSE 3,030 £ (17)
7 Annual maintenance after two years for EVSE 3,330 £ (17)
8 Cost Escalation rate 2.8 %
Technical
8 Maximum capacity of grid 69 kW (55)
9 Installation cost of EVSE 16,800 £ (17)
10 Efficiency of charging 90 %
11 Size of the BESS used 50 kWh
12 Standby Losses for the BESS 10 % (60)
13 Capital cost of PV system installed 1.077 £/wp (61)
14 Operation and Maintenance cost for PV (percentage
of capital cost)
1.5 % (62)
15 Annual PV degradation 0.5 % (63)
The price to charge an EV is calculated using the average price from the companies in the UK as described in
Table 4.
Table 3-Price for charging EV
Company Cost
Ecotricity 30p/kWh
Engenie 36p/kWh
Genie Point 30p/kWh
Instavolt 35p/kWh
Shell Recharge 25p/kWh
Average 31.2p/kWh
4.2 Load flow analysis
Load flow analysis was conducted using Pandapower in Python for a CIGRE European LV network. To
conduct a power flow analysis, Python has two options namely Python for Power System Analysis (PyPSA)
and Pandapower. Pandapower was chosen for the analysis as PyPSA lacks features like modelling of switches
and three-winding transformers, short circuit calculations and state estimation. The CIGRE LV network is
designed in Pandapower using the benchmark values (64). The designed CIGRE network is validated against
the default CIGRE LV network present in pandapower and after running the load flow analysis the voltage
calculated at the nodes are found to be approximately equal. The single line diagram of the CIGRE LV network
is presented in Figure 4. CIGRE network has three subnetworks namely residential, commercial, and industrial.
In Figure 4, the nodes of the residential subnetwork start with the letter ‘R’ , the nodes of the industrial
subnetwork start with the letter ‘I’ and the nodes of the commercial subnetwork start with the letter ‘C’. The
system frequency of the CIGRE network is 50 Hz and the safe voltage limits for the voltage in the transmission
network +/-10% (27).
Figure 6- Single line diagram of the CIGRE LV network (64)
System specifications of the CIGRE LV network are-
Transformers- Three 20/0.4 kV transformers between R0-R1, C0-C1 and I0-I1.
Switches- Three switches represented by S1, S2 and S3
Lines- The nodes in the residential and industrial subnetworks are connected using underground lines
and the nodes in the commercial subnetwork are connected with overhead lines. The letters ‘UG’ refers
to the underground lines and the letters ‘OH’ refers to the overhead lines.
Table 3- Technical specifications of the lines (64)
Line type Resistance CIGRE (ohm/km) Reactance CIGRE (ohm/km)
UG1 0.162 0.0832
UG2 0.2647 0.0823
UG3 0.822 0.0847
OH1 0.4917 0.2847
OH2 1.3207 0.321
OH3 2.0167 0.3343
Loads- The real power and the apparent power present at the various residential, commercial and
industrial loads are listed in Table 5.
Table 4-Loads present at the nodes
Load Bus Real power (MW) Apparent power (MVAR)
Load R1 2 0.19 0.06245
Load R11 12 0.01425 0.004684
Load R15 16 0.0494 0.016237
Load R16 17 0.05225 0.017174
Load R17 18 0.03325 0.010929
Load R18 19 0.04465 0.014676
Load C1 24 0.108 0.052307
Load C12 35 0.018 0.008718
Load C13 36 0.018 0.008718
Load C14 37 0.0225 0.010897
Load C17 40 0.0225 0.010897
Load C18 41 0.0072 0.003487
Load C19 42 0.0144 0.006974
Load C20 43 0.0072 0.003487
Load I2 22 0.085 0.052678
To conduct the load flow analysis, various scenarios were considered calculating the demand that will be fulfilled
by the electricity grid. The scenario with one DCFC is not considered, as one DCFC without any storage will
be utilizing 50 kW from the grid which is under the maximum allowed limit of 69 kW limit of the electricity
grid. The scenarios developed are described as-
Scenario with two DCFCs-
1. Two DCFCs and one BESS (2C1S)- One of the DCFCs is connected to the electricity grid
and the BESS provides electricity to the other DCFC.
2. Two DCFCs and two BESS(2C2S)- This scenario is considered with one of the BESS charged
which supply electricity to the DCFC and the other BESS is discharged which uses electricity
from the grid to get charged. The electricity grid is also used to supply electricity to the second
DCFC.
3. Two DCFCs, one BESS and one PV system(2C1S1P)- In this scenario, solar PV system
generates electricity which is used to charge the BESS. The demand of the two DCFCs is met
by the BESS and the electricity grid.
Scenario with three DCFCs-
1. Three DCFCs and one BESS(3C1S)- One of the DCFCs is connected to the BESS and the electricity
grid supplies electricity to the other DCFCs.
2. Three DCFCs and two BESS(3C2S)- In this scenario, one of the BESS units is being charged using
the grid electricity and the other two BESS units provide electricity to the DCFCs. The third DCFC
gets the electricity supply directly from the grid electricity.
3. Three DCFCs, one BESS and one PV system(3C1S1P)- In this scenario, one of the BESS supplies
electricity to the DCFC. The electricity grid supplies to the other two DCFCs and the PV system is
used to charge the BESS.
4. Three DCFCs, two BESS and two PV system(3C2S2P)- In this scenario, the electricity generated by
the PV systems is used to charge the BESS. The power to the DCFC is supplied from the BESS or the
electricity grid.
Scenario with four DCFCs-
1. Four DCFCs and one BESS(4C1S)- One of the DCFCs is connected to the electricity grid
and the BESS provides electricity to the other DCFCs.
2. Four DCFCs and two BESS(4C2S) - In this scenario, two of the BESS units is being charged
using the grid electricity and the other two BESS units provide electricity to the DCFCs. The
remaining DCFCs gets the electricity supply directly from the grid electricity.
3. Four DCFCs and three BESS(4C3S)- In this scenario, three of the DCFCs are supplied
electricity from the BESS and the electricity grid is used to supply electricity to the fourth
DCFC.
4. Four DCFCs and four BESS(4C2S)- In this scenario, the electricity supplied is from the BESS
and the electricity grid is used to charge the BESS when they are discharged and supply
electricity to the DCFCS when the BESS are discharged.
5. Four DCFCs, one BESS and one PV system(4C1S1P)- In this scenario, the BESS supplies
electricity to one of the DCFCs. The electricity grid supplies to the remaining three DCFCs
and the PV system is used to charge the BESS.
6. Four DCFCs, two BESS and two PV system(4C2S2P)- In this scenario, the electricity
generated by the PV systems is used to charge the BESS. The power to the DCFC is supplied
from the BESS or the electricity grid.
7. Four DCFCs, three BESS and three PV systems(4C3S3P)- In this scenario, the electricity from
the PV system is used to charge the BESS units. Three of the DCFCs are supplied electricity
from the BESS and the electricity grid is used to charge the remaining DCFC and when the
BESS units are discharged, then the grid electricity is supplied to the DCFCs.
4.2.1 Simulations
The simulations in Python pandapower were run using the loads mentioned in Table 6. The net demand from
the electricity grid is used as the load to be integrated into the CIGRE LV network at the Industrial subnetwork
and the commercial subnetwork. The commercial and the Industrial subnetworks are chosen, as the location
of the fuel stations are typically found near the substations for either industrial or commercial subnetwork. The
loads added to the CIGRE network are added on top of the existing loads to get an analysis on the robustness
and the reliability of the network with the additional load additional to the existing loads. The results from the
load flow analysis was checked for any undervoltage at the bus network. The upper limit is taken as +10% and
the lower limit as -10% from the ideal slack voltage per unit i.e. 1.
4.2.1.1 Simulation with the Industrial network
The load at the Industrial subnetwork is applied at the bus I2 (Figure 4), and then the load flow analysis is
conducted for this configuration. In an industrial subnetwork, the distance from the substation to the final
node of demand varies as the To analyse the impact of the distance with the load, the length of the line
connecting the buses I1 and I2 is varied. The load at the node I2 in the first simulation is added directly and in
the second simulation, the load is added over the existing load at I2.
4.2.2.2 Simulation with the commercial subnetwork
The loads were then added to the commercial subnetwork at the bus C14. The first set of simulations are carried
out with adding the load to the node C14 directly. The second set of simulations are run with the load added
over the existing load at C14 to check the robustness and reliability of the CIGRE network.
The voltage at the different buses of the network is observed and mitigation strategies are suggested to prevent
undervoltage at the connection points. Two mitigation strategies are considered-
Upgrade the transmission lines of the network with cables having less resistance. The technical
specifications of the cables considered based on the IEC 60502-1(65) standards are listed in Table 7.
Small offload tap change to change the voltage setting to a higher value of 1.05 and 1.1.
Table 5- Specifications of the cables considered for the simulation
Line thickness(mm2) Resistance(ohm/km) Reactance(ohm/km)
10 2.24 0.119
16 1.41 0.112
25 0.889 0.106
35 0.641 0.101
50 0.473 0.101
95 0.326 0.0975
120 0.188 0.0939
Based on the standard technical specifications mentioned in Table 4, OH1 is selected with the maximum
thickness and least resistance and OH3 with less thickness and higher resistance. The cables are changed to
analyse the impact on the voltage per unit at the buses. The various configurations of the wires used are
presented in Table 8-
Table 6- Different configurations considered for the simulations
Thickness of cable(mm2)
Simulation 1 Simulation 2 Simulation 3
OH1 10 25 35
OH2 16 35 50
OH3 50 95 120
5. Results and discussion
5.1 Results of the Techno-economic analysis
The annual energy demand of installing the different DCFCs are calculated based on maximum number of
DCFCs that can be installed in the given area of the fuel station. The underground capacity of the fuel stations
is used to install the BESS. Based on the scenarios, techno-economic analysis was conducted for the different
scenarios described in section 3.3. The scenarios with the same number of EVSE and the number of storage
units were ignored as such scenarios do not provide any technical or economic advantages.
Figure 6: For scenarios with and without BESS (a): NPV for different hours of operation (b): IRR for different hours of
operation (c): DPP for different hours of operation
Figure 6(a), 6(b), 6(c) shows NPV, IRR and DPP respectively for all the scenarios considered for the analysis.
The size of the circles represents the magnitude of the NPV for a given configuration. The positive value of
NPV (Figure 6(a)) indicates an economically viable configuration, but this should also be considered along with
the values of IRR (Figure 6(b)) and DPP (Figure 6(c)).
5.1.1 Scenarios without BESS
The results from the scenarios in which the DCFCs are connected to the grid are presented in this section.
The first scenario (1C0S4h) has an NPV of £15k with IRR of 9% and a DPP of 8.25 years. This indicates that,
if an investor invests in only one DCFC, the system is feasible, owing to very low connection charges and
associated costs. The return on investment increases with an increasing number of hours of operations leading
to a DPP of less than 5 years for 8 hours of operation.
In the second scenario, the configuration 2C0S4h has an NPV of £36k with IRR of 10% and DPP of 8.5 years
at 4 hours of operation. As the number of DCFC increases to two, the NPV increases when compared to the
scenario with just one DCFC. For the configuration 2C0S8h, with the increase in the hours of operation, the
NPV increases to a value of 222,190 and the IRR becomes 36% with a payback period of 3 years.
In the third scenario, the configuration 3C0S4h has an NPV of £51k with IRR of 10% and a DPP of 8.4 years
for 4 hours of operation. With the increasing number of operations, the configuration becomes economically
more feasible with a higher NPV and IRR and a lower DPP. For 8 hours of operation the NPV value is £330k
with an IRR of 35% and a payback period of 4.8 years.
The fourth scenario is considered with four DCFC with no storage, one storage, two storage units and three
storage (BESS) units. For the configuration 4C0S4h when the daily hours of operation is low, the NPV is
negative indicating that this configuration is not economically viable. This is due to the high connection cost
involved in connecting 4 DCFC to the electricity grid. However, as the hours of operation increase, the
configuration 4C0S8h has an NPV value of £334k and an IRR of 21% with a discounted payback period of 5
years.
5.1.2 Scenarios with BESS
For second scenario, the configuration 2C1S4h was found out to be economically feasible but with a low NPV
of £900 and IRR of 4% and a DPP value of greater than 10 years. With an increase in the hours of operation,
the NPV value becomes more positive as observed from the configuration 2C1S8h which has an NPV value
of £179k, IRR of 25% and a DPP of 6 years. This scenario is economically viable for all hours of operation
and as the daily hours of operation increase, the DPP reduces and the NPV increases.
In the third scenario, for the configuration 3C1S4h when 4 hours of daily operation, is considered the NPV has
a very low value of £16k with an IRR of just 5% and the payback period is not achievable for the period of
analysis. This is due to the higher cost of storage and lower revenue generated because of the shorter hours of
operation. The configuration 3C1S6h is economically feasible with an NPV of £152k and IRR of 18% and a
payback period of 6.5 years. The configuration 3C2S8h has a highly positive NPV of £252k with an IRR of
21% and a payback period of fewer than 6 years. Even with the increased upfront costs due to the BESS
installed, the revenue generated is sufficient to ensure positive cash flow.
Even the configuration 4C1S4h is not economically feasible as operational hours are less and the annual revenue
generated cannot compensate for the upfront costs involved for the installation of DCFC and the BESS. The
configuration 4C2S4h had a slightly positive NPV of £1700 and an IRR value of 1% but the DPP is not
achievable during the period of analysis, rendering the configuration economically infeasible. But as the daily
hours of operation increase, the system becomes economically viable. 4C1S8h and 4C2S8h have highly positive
NPV of £290k and £358k and the IRR rate is 18% and 24% respectively. The DPP for the configurations is
also 6.6 years and 5.9 years respectively. For the configuration with 3 battery storage units, the scenario 4C3S4h
is economically infeasible with a negative cash flow value. The price of the BESS increases the upfront costs
and the annual revenue generated is not sufficient to compensate for the increased upfront costs. However,
with the increase in daily hours of operation the configurations 4C3S6h and 4C3S8h become economically
viable with a highly positive NPV £152k and £330k and IRR of 13% and 21% respectively. The DPP is 7.5
years and 5.5 years respectively.
Figure 7: For scenarios with PV and BESS (a): NPV for different hours of operation (b): IRR with PV for different hours of
operation (c): DPP with PV for different hours of operation
The techno-economic analysis was also conducted for the different scenarios after integrating solar PV along
with the EVSE. The configurations with PV are defined as “n1Cn2Sn2Pn3h”. The total capacities of PV units
installed in the charging station is considered equal to the number of BESS installed. The positive values of the
NPV (Figure 7(a)) highlights the economic feasibility of the scenario but should be considered with IRR (Figure
7(b)) and the DPP (Figure 7(c)).
For the first scenario with PV, the NPV is observed to be negative for 4 hours of operation which indicates
that the system is economically infeasible when the hours of operations are less. This is because of the high
investment cost for the PV system. When two DCFC with one storage and one PV system is considered, it is
observed that the configuration 2C1S16h has a highly positive NPV of £81k with an IRR of 10% and a payback
period of 8.5 years. For 8 hours of operation, the configuration 2C1S1P8h has highly positive NPV of £170k
with IRR of 15% and DPP of 6.3 years when 8 hours of operation is considered. It can be observed that with
the increase in the hours of operation, this scenario becomes economically more viable.
For the second scenario with PV, it is observed that the configuration 3C1S1P4h is economically infeasible as
it has a negative NPV value. However, when the hours of operation are increased, the configuration 3C1S1P6h
has a positive NPV value of £143k indicating that it is economically feasible with an IRR of 12% and a DPP
of 7.3 years. With an increasing number of PV systems installed along with the BESS units for the configuration
3C2S2P6h, the NPV reduces to £101k and the IRR reduces to 8% and the DPP increases to 8 years. The
configuration 3C1S1P8h has a highly positive NPV of £235k with IRR of 19% and a DPP of 5 years when 8
hours of operation is considered. As the number of PV units increases, the IRR value reduces to 13% for
3C2S2P8h and the DPP increases to 7.5 years. This is due to the increasing investment cost owing to both the
BESS units and the PV units. For 6 hours of operation, The configuration 3C2S2P6h which has 2 BESS units
and 2 PV systems for 6 hours of operation has a lower NPV and IRR value than the configuration 3C1S1P6h
with 1 BESS unit and 1 PV system for 6 hours of operation. Similarly, 3C2S2P8h with 2 BESS units and 2 PV
system installed for 8 hours of operation has a lower NPV and IRR value when compared to the configuration
3C1S1P8h which has just 1 BESS unit and 1 PV system for 8 hours of operation which highlights the fact that
with the increase in the number of PV and the BESS units, the higher investment cost results in a lower NPV
and IRR value.
The third scenario with PV is considered with four DCFCs with no storage, one storage, two storage units and
three storage (BESS) units. The configuration with one storage unit and one PV unit, 4C1S1P4h is economically
infeasible with a negative NPV value. Similarly, the configuration 4C2S2P4h and 4C3S3P4h are also
economically infeasible as the investment cost is high which cannot be compensated with less hours of
operation. However, when the hours of operation are increased, the configurations 4C1S1P6h, 4C2S2P6h and
4C3S3P6h have positive NPV values of £100k, £162k, £127k with IRR of 8%, 10% and 7% with DPP of 9.6
years, 8.5 years and 9.6 years respectively indicating that the scenario becomes economically viable with an
increase in the hours of operation. 4C1S1P8h has a highly positive NPV value of £282k with an IRR of 14%
and a discounted payback period of 7.7 years. As the hours of operation is increased to 8, it can be observed
that with the configurations 4C2S2P8h and 4C3S3P8h, the NPV value is highly positive £341k and £305k, IRR
values of 15% and 12% and the DPP value of 6.2 years and 7.3 years, respectively. The configuration 4C1S1P8h
has a lower IRR value then the configuration 4C2S2P8h and similarly 4C1S1P6h has a lower IRR value when
compared to the configuration 4C2S2P6h. This is due to the high connection cost incurred by connecting 4
DCFC with the electricity grid and the integration of only one BESS unit and one PV system.
5.1.3 Comparing the scenarios with and without PV
The results indicate an increase in the DPP and a decrease in the IRR when solar PV is installed in the charging
station in all the cases. This trend is due to the additional investment cost incurred due to the PV system
installed. But the additional PV system reduces the reliance on the electricity grid and would also avoid the high
connection cost incurred while connecting DCFC to the electricity grid. With a suitable feed-in tariff, the
additional electricity generated by the PV system can be sold to the electricity grid increasing the profitability
of installed the PV system.
It is observed that the operational hours play a critical role in the success of the proposed configurations as it
leads to increased revenue. With the increasing number of DCFCs, having storage will be a more feasible
option, as it would lead to reduces grid reliability and improved economic indicators. However, with a greater
number of DCFCs, the connection cost will increase further and hence, an optimal combinatory needs to be
considered based on the desired objective of the stakeholder. The present analysis aims to assess the dynamics
between economic indicators given the combination of DCFC and storages for observing the feasibility trend
for the proposed design of retrofitting the fuel station.
The results indicate that if the number of DCFCs is increased beyond 4, a similar trend in the economic
profitability will be observed. With an increase in the number of DCFCs, there will be a higher annual revenue
which will ensure shorter payback periods and a positive NPV but at increased investment and connection
costs. Also, the integration of storage would become more profitable if other revenue streams to bank upon
the BESS are explored like participation in the frequency regulation market or ancillary services market. The
introduction of dynamic pricing would lead to benefits of demand response which can be availed by storage
through the BESS as it can be charged in off-peak hours to increase the revenue generated. The policies
introduced by the government will play a vital role in the growth of EV infrastructure. The government of the
UK has already introduced funds to facilitate stakeholders with high connection costs. Also, apart from this,
the availability of grid capacity will play a huge role in the transition of a fuel station to an EV charging station.
In the future, this design can be further developed with the availability of high-quality data. Nonetheless, the
proposed design gives an idea about the feasibility of retrofitting the existing fuel station to an EV station given
the present technical constraints and economic parameters. This would pave the way for sustainable expansion
of EV transportation with efficient asset utilization, thereby contributing to circular energy systems.
5.4 Results of the load flow simulations
The scenarios considered for the load flow analysis is considered based on the scenarios developed in the
section 4.2. The scenarios are configured as the same notation explained in the section 4.2, where “n1Cn2S”
represents the scenarios with only DCFCs and BESS denoted by letters “C” and “S” respectively. The numbers
n1 and n2 represent the numbers of DCFCs and BESS units present in the configuration. The scenarios with PV
are represented by the notation “n1Cn2Sn3P” where the letter “P” denotes solar PV and the number n3 denotes
the number of PV present. The detailed description of the scenarios is listed in Table 7.
Table 7- Breakdown of different loads considered for the scenarios
Scenario
name
Number of
DCFC
DCFC
demand(kW)
Demand supplied
from BESS (kW)
Demand supplied
from the PV (kW)
Net Demand from the
electricity grid (kW)
2C0S 2 100 0 0 100
2C1S 2 100 50 0 50
2C1S1P 2 100 50 50 50
3C0S 3 150 0 0 150
3C1S 3 150 50 0 100
3C2S 3 150 100 0 50
3C1S1P 3 150 50 50 100
3C2S2P 3 150 100 100 50
4C0S 4 200 200 0 200
4C1S 4 200 50 0 150
4C2S 4 200 100 0 100
4C3S 4 200 150 0 100
4C1S1P 4 200 50 50 150
4C2S2P 4 200 100 100 100
4C3S3P 4 200 150 150 100
5.4.1 Analysis of the loads at the industrial subnetwork
The net demand from the electricity grid from the Table 7, are used for the simulations where the net demand
from the scenarios are applied as load at the industrial subnetwork of the CIGRE LV network. The loads are
applied at the bus I2 of the network and the results are then analysed to study the variation of the voltage at I2
with the variation of distance between the buses I1 and I2. Two cases are considered
Case 1- The loads are applied to the bus I2 directly
Case 2- The loads are applied at bus I2 in addition to the existing load present at that bus.
Figure 7: Voltage at I2 based on scenarios when the load is applied directly (a) For 2 DCFCs (b) For 3 DCFCs (c) For 4 DCFCs
Figure 6 represents the results of Case 1, where the loads are applied directly to the bus I2. For the various
scenarios, it can be observed that a lower voltage is observed with the increase in the distances between the
buses I1 and I2. Some of the lines overlap in Figure 6, as some of the scenarios presented have the same net
demand from the electricity grid. It can be observed that, even with different DCFCs, if the demand from the
electricity grid is same, a same load flow profile is observed.
0.88
0.9
0.92
0.94
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0.98
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0 0.2 0.4 0.6 0.8 1 1.2
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2C0S
2C1S
2C1S1P
0.84
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4C0S
4C1S
4C2S
4C3S
4C1S1P
4C2S2P
4C3S3P
0.86
0.88
0.9
0.92
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Distance in km
3C0S
3C1S
3C2S
3C1S1P
3C2S2P
Figure 8: Voltage at I2 based on scenarios when the load is applied additional to the existing load (a) For 2 DCFCs (b) For 3 DCFCs (c)
For 4 DCFCs
Figure 7 represents the results of Case 2, where the loads are applied additional to the existing loads at bus I2.
For the various scenarios, it can be observed that the voltage per unit value decreases with an increase in the
distances between the buses I1 and I2. Some of the lines overlap in Figure 6, as some of the scenarios presented
have the same net demand from the electricity grid. It can be observed that, even with different DCFCs, if the
demand from the electricity grid is same, a same load flow profile is observed.
5.4.2 Analysis of loads at the commercial subnetwork
The net demand from the various scenarios as listed in Table 7 are designed as loads at the bus C14 of the
CIGRE LV network to analyse the impact on the network. The load flow analysis is carried out by varying the
net demand from the scenarios and the impact on the voltage per unit is observed and compared against the
voltage per unit at C14 for the default load value.
5.4.2.1 Net demand for two DCFCs
The net demand is applied at the node C14 additional to the default load present at that node. The results
highlight that as the peak demand increases, there is an undervoltage observed at the node C14. The scenarios
2C1S and 2C1S1P have the same net demand, hence their voltage profile overlaps in Figure 8. It can also be
0.88
0.89
0.9
0.91
0.92
0.93
0.94
0.95
0.96
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2C0S
2C1S
2C1S1P
0.86
0.87
0.88
0.89
0.9
0.91
0.92
0.93
0.94
0.95
0.96
0 0.1 0.2 0.3 0.4
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Distance in km
3C0S
3C1S
3C2S
3C1S1P
3C2S2P
0.85
0.86
0.87
0.88
0.89
0.9
0.91
0.92
0.93
0.94
0 0.1 0.2 0.3
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Distance in km
4C0S4C1S4C2S4C3S4C1S1P4C2S2P4C3S3P
observed that, with a renewable energy generation, the voltage per unit value is closer to the safe voltage limit
value.
Figure 9: Comparison of various loads at C14 for two DCFCs
5.4.2.2 Net demand for three DCFCs
The net demand from three DCFCs is applied at the node C14 additional to the default load present at that
node. The results highlight that as the peak demand increases, there is an undervoltage observed at the node
C14. Also, with the increase in demand posed by the higher number of DCFCs, the variation from the default
load value is much more prominent with a significant undervoltage. Some of the scenarios have the same net
demand, hence their voltage profile overlaps in Figure 9. In case of three DCFCs, the impact of adding a
renewable power generation system is more evident, as the voltage value is much closer to the safe voltage
limits when two PV system are used as compared to the scenario with just one PV system.
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
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1.05B
us
0
Bus
R0
Bus
R1
Bus
R2
Bus
R3
Bus
R4
Bus
R5
Bus
R6
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R7
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I0
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C2
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C3
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C4
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C5
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C6
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C7
Bus
C8
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C9
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C10
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C11
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C14
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C15
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C16
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C17
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C18
Bus
C19
Bus
C20
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Bus number
Default CIGREvalues
2C0S
2C1S
2C1S1P
Figure 10: Comparison of various loads at C14 for three DCFCs
5.4.2.3 Net demand for four DCFCs
The net demand from four DCFCs is applied at the node C14 additional to the default load present at that
node. A similar trend is observed when compared to the scenarios with three and two DCFCS. With an increase
in the peak net demand, a severe undervoltage is observed. This is due to the high load of 200 kW which is
applied additional to the existing load. With a solar PV installed, the voltage value is closer to the safe voltage
limits when compared to the configurations without the presence of the solar PV.
From the simulations conducted for the various scenarios with two, three and four DCFCs, it is evident that
the additional load caused due to the DCFCs have severe impact on the grid electricity network and needs to
be mitigated to ensure the smooth functioning of the DCFCs when connected to the electricity grid. The
following two steps are considered to mitigate the impact on the grid electricity network-
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
1.05
Bus
0
Bus
R0
Bus
R1
Bus
R2
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R3
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R4
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R5
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R6
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R7
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C6
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C7
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C15
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C16
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C17
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C18
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C19
Bus
C20
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Bus number
Default CIGRE values
3C0S
3C1S
3C2S
3C1S1P
3C2S2P
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
1.05
Bus
0
Bus
R0
Bus
R1
Bus
R2
Bus
R3
Bus
R4
Bus
R5
Bus
R6
Bus
R7
Bus
R8
Bus
R9
Bus
R10
Bus
R11
Bus
R12
Bus
R13
Bus
R14
Bus
R15
Bus
R16
Bus
R17
Bus
R18
Bus
I0
Bus
I1
Bus
I2
Bus
C0
Bus
C1
Bus
C2
Bus
C3
Bus
C4
Bus
C5
Bus
C6
Bus
C7
Bus
C8
Bus
C9
Bus
C10
Bus
C11
Bus
C12
Bus
C13
Bus
C14
Bus
C15
Bus
C16
Bus
C17
Bus
C18
Bus
C19
Bus
C20
Vo
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Bus number
Default CIGRE values
4C0S
4C1S
4C2S
4C3S
4C1S1P
4C2S2P
4C3S3P
Upgrading the existing electricity network with cables of higher thickness and lower resistance values.
Small offload tap changes at the substations to change the voltage per unit supplied to the grid network.
Simulations are carried out in Python to analyse both the methods to mitigate the undervoltage observed at the
C14 bus of the CIGRE LV network. The results of the simulations are presented in the following sections.
5.4.2.4 Upgrading the electricity network by replacing the cables
From the scenarios mentioned in Table 7, the net demands observed from the various scenarios are 50, 100,
150 and 200 kW. Considering these net demands, the overhead cables used in the default CIGRE LV
network are replaced with the cables as mentioned in Table 6. All the cables considered for the simulation are
in accordance with the IEC standards.
Case 1- 50 kW net demand at C14
The simulations were conducted with the cables of 50 , 16 and 10 mm2 thickness as simulation 1, cables of
thickness 95, 35 and 25 mm2 is considered for simulation 2 and cables of thickness 120, 50 and 35 mm2 for
simulation 3 for the overhead cables. The results clearly highlighted that as the thickness of the cables increase,
the voltage per unit shifts from undervoltage to the safe voltage limits of +/-10% from the ideal value of 1.
Figure 11: Simulations conducted with 50 kW net demand
Case 2- 100 kW net demand at C14
The simulations were conducted with the cables of 50 , 16 and 10 mm2 thickness as simulation 1, cables of
thickness 95, 35 and 25 mm2 is considered for simulation 2 and cables of thickness 120, 50 and 35 mm2 for
simulation 3 for the overhead cables for this scenario as well. A more severe undervoltage is observed as the
load increases to 100 kW when compared to the scenario with 50 kW. It is expected as with an increase in load
there tends to be a decrease in the voltage per unit value at bus. The results clearly highlighted that as the
thickness of the cables increase, the voltage per unit shifts from undervoltage to the safe voltage limits of +/-
10% from the ideal value of 1.
0.800
0.850
0.900
0.950
1.000
1.050
Bus0
BusR1
BusR3
BusR5
BusR7
BusR9
BusR11
BusR13
BusR15
BusR17
BusI0
BusI2
BusC1
BusC3
BusC5
BusC7
BusC9
BusC11
BusC13
BusC15
BusC17
BusC19
Vo
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Bus number
50,16 and 10 mm2
95,35 and 25 mm2
120,50 and 35 mm2
Figure 12: Simulations conducted with 100 kW net demand
Case 3- 150 kW net demand at C14
The simulations were conducted with the cables of 50 , 16 and 10 mm2 thickness as simulation 1, cables of
thickness 95, 35 and 25 mm2 is considered for simulation 2 and cables of thickness 120, 50 and 35 mm2 for
simulation 3 for the overhead cables for this scenario as well. A more severe undervoltage is observed as the
load increases to 150 kW when compared to the scenarios with 50 kW and 100 kW in accordance with the
trend. The results clearly highlighted that as the thickness of the cables increase, the voltage per unit shifts
from undervoltage to values closer to the safe voltage limits of +/-10% from the ideal value of 1.
0.800
0.850
0.900
0.950
1.000
1.050
Bus
0
Bus
R0
Bus
R1
Bus
R2
Bus
R3
Bus
R4
Bus
R5
Bus
R6
Bus
R7
Bus
R8
Bus
R9
Bus
R10
Bus
R11
Bus
R12
Bus
R13
Bus
R14
Bus
R15
Bus
R16
Bus
R17
Bus
R18
Bus
I0
Bus
I1
Bus
I2
Bus
C0
Bus
C1
Bus
C2
Bus
C3
Bus
C4
Bus
C5
Bus
C6
Bus
C7
Bus
C8
Bus
C9
Bus
C10
Bus
C11
Bus
C12
Bus
C13
Bus
C14
Bus
C15
Bus
C16
Bus
C17
Bus
C18
Bus
C19
Bus
C20
Vo
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Bus Number
50,16 and 10 mm2
95,35 and 25 mm2
120, 50 and 35 mm2
Figure 13: Simulations with 150 kW net demand
Case 4- 200 kW net demand at C14
The simulations were conducted with the cables of 50 , 16 and 10 mm2 thickness as simulation 1, cables of
thickness 95, 35 and 25 mm2 is considered for simulation 2 and cables of thickness 120, 50 and 35 mm2 for
simulation 3 for the overhead cables for this scenario as well. A severe undervoltage is observed as the load
increases to 200 kW when compared to the scenarios with 50 kW, 100 kW and 200 kW in accordance with the
trend. The results clearly highlighted that as the thickness of the cables increase, the voltage per unit shifts
from undervoltage to values closer to the safe voltage limits of +/-10% from the ideal value of 1. It can also
be observed that even with the increased thickness of the cables, the final voltage per unit values are still outside
the safe limits of the voltage per unit values, it is because of the presence of a 200 kW load additional to the
existing load present at the node. Even then, varying the thickness of the cables managed to bring the
undervoltage much closer to the safe limits.
0.700
0.750
0.800
0.850
0.900
0.950
1.000
1.050
Bus
0
Bus
R0
Bus
R1
Bus
R2
Bus
R3
Bus
R4
Bus
R5
Bus
R6
Bus
R7
Bus
R8
Bus
R9
Bus
R10
Bus
R11
Bus
R12
Bus
R13
Bus
R14
Bus
R15
Bus
R16
Bus
R17
Bus
R18
Bus
I0
Bus
I1
Bus
I2
Bus
C0
Bus
C1
Bus
C2
Bus
C3
Bus
C4
Bus
C5
Bus
C6
Bus
C7
Bus
C8
Bus
C9
Bus
C10
Bus
C11
Bus
C12
Bus
C13
Bus
C14
Bus
C15
Bus
C16
Bus
C17
Bus
C18
Bus
C19
Bus
C20
Vo
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Bus number
50,16 and 10 mm2
95, 35 and 25 mm2
120, 50 and 35 mm2
Figure 14 : Simulations conducted with 200kW net demand
5.5 Small offload tap change
The substations have the option to vary the voltage supplied to the grid using the tap changer mechanism.
The impact of having a higher slack voltage of 1.05 and 1.1 on the CIGRE LV network.
Load flow analysis is conducted for the scenarios with a slack voltage value of 1.05. The results observed
highlight that, with a higher slack voltage the scenarios are closer to the safe voltage limits.
Figure 15 : Simulation of CIGRE LV network for two DCFCs
0.600
0.650
0.700
0.750
0.800
0.850
0.900
0.950
1.000
1.050
Bus
0B
us
R0
Bus
R1
Bus
R2
Bus
R3
Bus
R4
Bus
R5
Bus
R6
Bus
R7
Bus
R8
Bus
R9
Bus
R10
Bus
R11
Bus
R12
Bus
R13
Bus
R14
Bus
R15
Bus
R16
Bus
R17
Bus
R18
Bus
I0B
us
I1B
us
I2B
us
C0
Bus
C1
Bus
C2
Bus
C3
Bus
C4
Bus
C5
Bus
C6
Bus
C7
Bus
C8
Bus
C9
Bus
C10
Bus
C11
Bus
C12
Bus
C13
Bus
C14
Bus
C15
Bus
C16
Bus
C17
Bus
C18
Bus
C19
Bus
C20
Vo
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Bus number
50, 16 and 10 mm2
95, 35 and 25 mm2
120, 50 and 35 mm2
0.85
0.9
0.95
1
1.05
1.1
Bus
0B
us
R0
Bus
R1
Bus
R2
Bus
R3
Bus
R4
Bus
R5
Bus
R6
Bus
R7
Bus
R8
Bus
R9
Bus
R10
Bus
R11
Bus
R12
Bus
R13
Bus
R14
Bus
R15
Bus
R16
Bus
R17
Bus
R18
Bus
I0B
us
I1B
us
I2B
us
C0
Bus
C1
Bus
C2
Bus
C3
Bus
C4
Bus
C5
Bus
C6
Bus
C7
Bus
C8
Bus
C9
Bus
C10
Bus
C11
Bus
C12
Bus
C13
Bus
C14
Bus
C15
Bus
C16
Bus
C17
Bus
C18
Bus
C19
Bus
C20
Vo
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Bus number
2C0S
2C1S
2C1S1P
Figure 16 : Simulation of CIGRE LV network for three DCFCs
Figure 17 : Simulation of CIGRE LV network for four DCFCs
It is observed from the Figures 14, 15 and 16, that with the change in the slack voltage, there is a reduction in
the undervoltage value and the voltage per unit value observed at the connection point is closer to the safe
voltage limits. In the cases two and three DCFCs, it can be clearly observed that with the addition of the PV
and BESS along with a change in the slack voltage, the issue of undervoltage can be clearly mitigated and the
scenarios designed vale a voltage per unit value withing the safe voltage limits.
Load flow analysis is conducted for the scenarios with a slack voltage value of 1.1. The results observed
highlight that, with a higher slack voltage the scenarios are closer to the safe voltage limits.
0.8
0.85
0.9
0.95
1
1.05
1.1
Bus
0B
us
R0
Bus
R1
Bus
R2
Bus
R3
Bus
R4
Bus
R5
Bus
R6
Bus
R7
Bus
R8
Bus
R9
Bus
R10
Bus
R11
Bus
R12
Bus
R13
Bus
R14
Bus
R15
Bus
R16
Bus
R17
Bus
R18
Bus
I0B
us
I1B
us
I2B
us
C0
Bus
C1
Bus
C2
Bus
C3
Bus
C4
Bus
C5
Bus
C6
Bus
C7
Bus
C8
Bus
C9
Bus
C10
Bus
C11
Bus
C12
Bus
C13
Bus
C14
Bus
C15
Bus
C16
Bus
C17
Bus
C18
Bus
C19
Bus
C20
Vo
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Bus number
3C0S
3C1S
3C2S
3C1S1P
3C2S2P
0.7
0.75
0.8
0.85
0.9
0.95
1
1.05
1.1
Bus
0B
us
R0
Bus
R1
Bus
R2
Bus
R3
Bus
R4
Bus
R5
Bus
R6
Bus
R7
Bus
R8
Bus
R9
Bus
R10
Bus
R11
Bus
R12
Bus
R13
Bus
R14
Bus
R15
Bus
R16
Bus
R17
Bus
R18
Bus
I0B
us
I1B
us
I2B
us
C0
Bus
C1
Bus
C2
Bus
C3
Bus
C4
Bus
C5
Bus
C6
Bus
C7
Bus
C8
Bus
C9
Bus
C10
Bus
C11
Bus
C12
Bus
C13
Bus
C14
Bus
C15
Bus
C16
Bus
C17
Bus
C18
Bus
C19
Bus
C20
Vo
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Bus number
4C0S4C1S4C2S4C3S4C1S1P4C2S2P4C3S3P
Figure 18 : Simulation of CIGRE LV network for two DCFCs
Figure 19 : Simulation of CIGRE LV network for three DCFCs
0.85
0.9
0.95
1
1.05
1.1
1.15
Bus
0B
us
R0
Bus
R1
Bus
R2
Bus
R3
Bus
R4
Bus
R5
Bus
R6
Bus
R7
Bus
R8
Bus
R9
Bus
R10
Bus
R11
Bus
R12
Bus
R13
Bus
R14
Bus
R15
Bus
R16
Bus
R17
Bus
R18
Bus
I0B
us
I1B
us
I2B
us
C0
Bus
C1
Bus
C2
Bus
C3
Bus
C4
Bus
C5
Bus
C6
Bus
C7
Bus
C8
Bus
C9
Bus
C10
Bus
C11
Bus
C12
Bus
C13
Bus
C14
Bus
C15
Bus
C16
Bus
C17
Bus
C18
Bus
C19
Bus
C20
Vo
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Bus number
2C0S
2C1S
2C1S1P
0.8
0.85
0.9
0.95
1
1.05
1.1
1.15
Bus
0B
us
R0
Bus
R1
Bus
R2
Bus
R3
Bus
R4
Bus
R5
Bus
R6
Bus
R7
Bus
R8
Bus
R9
Bus
R10
Bus
R11
Bus
R12
Bus
R13
Bus
R14
Bus
R15
Bus
R16
Bus
R17
Bus
R18
Bus
I0B
us
I1B
us
I2B
us
C0
Bus
C1
Bus
C2
Bus
C3
Bus
C4
Bus
C5
Bus
C6
Bus
C7
Bus
C8
Bus
C9
Bus
C10
Bus
C11
Bus
C12
Bus
C13
Bus
C14
Bus
C15
Bus
C16
Bus
C17
Bus
C18
Bus
C19
Bus
C20
Vo
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Bus number
3C0S
3C1S
3C2S
3C1S1P
3C2S2P
Figure 20 : Simulation of CIGRE LV network for four DCFCs
The simulations conducted after increasing the slack voltage to 1.1 Volts per unit, a similar trend is observed
as that with 1.05 Volts per unit. After increasing the slack voltage to 1.1 Volts per unit, some of the scenarios
become technically feasible as they are under the safe voltage limits. The impact of PV and BESS is quite
evident from these simulations, as the trend clearly highlights that with a higher number of PV and BESS units,
the scenarios encounter no undervoltage and the voltage per unit value at the connection points is under the
safe limits.
0.7
0.75
0.8
0.85
0.9
0.95
1
1.05
1.1
1.15
Bus
0B
us
R0
Bus
R1
Bus
R2
Bus
R3
Bus
R4
Bus
R5
Bus
R6
Bus
R7
Bus
R8
Bus
R9
Bus
R10
Bus
R11
Bus
R12
Bus
R13
Bus
R14
Bus
R15
Bus
R16
Bus
R17
Bus
R18
Bus
I0B
us
I1B
us
I2B
us
C0
Bus
C1
Bus
C2
Bus
C3
Bus
C4
Bus
C5
Bus
C6
Bus
C7
Bus
C8
Bus
C9
Bus
C10
Bus
C11
Bus
C12
Bus
C13
Bus
C14
Bus
C15
Bus
C16
Bus
C17
Bus
C18
Bus
C19
Bus
C20
Vo
ltag
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nit
Bus number
4C0S4C1S4C2S4C3S4C1S1P4C2S2P4C3S3P
6. Discussions and future work In this thesis, a techno-economic analysis is conducted to repurpose the existing fuel stations into EV
charging stations. For the economic analysis, three economic tools namely NPV, IRR and DPP are used
and the hours of operation of the charging station is taken into consideration. The high connection cost
incurred while upgrading the electricity grid is one of the factors that effects the economic feasibility. Solar
PV and BESS is also used for the charging stations to reduce the reliance on the grid electricity and produce
cleaner energy. DC fast chargers are considered for the charging station to reduce the waiting time for the
EV owners and to address the issue of ‘range anxiety’. However, the use of DC fast chargers in not so
prominent within the urban areas as they tend to impact the grid electricity. A technical analysis is
conducted in this study using the CIGRE European LV network to analyse the impact on the grid electricity
and two mitigation strategies are suggested to prevent any undervoltage that may occur.
The hours of operation of the charging station implemented in the existing fuel station can be improved
based on the number of EVs and the capacity of the EVSE installed in the charging stations. The BESS
used in the charging stations can be incentivized to be used for other applications thus improving the
economic feasibility of the model.
For the future work, peak hours of operation for the charging station can be considered optimizing the
charging duration and developing a more detailed model. Load flow analysis can be conducted for more
networks to substantiate the results. A Geographic Information System (GIS) analysis can be conducted
to augment the information on transformers, network lines and substations.
7. Contribution towards Sustainability Sustainability has three aspects namely, economic development, social development, and environmental
protection (66). To promote sustainability, the focus of this study is to present a techno-economic feasibility
to install EV charging stations into the existing fuel stations. With the integration of the EVs into the
transport sector, the greenhouse gas emissions can be reduced. To keep up with the increase in the EVs it
is essential to install more charging stations. The repurposing of the fuel stations into charging stations will
be in accordance with the trend of increasing EVs into the transport sector providing an economic solution
for the stakeholders. This will also motivate the gasoline vehicle owners to switch to EVs which will reduce
the greenhouse gas emissions from the transport sector. This study is in accordance with four of the
Sustainable Development Goals (SDGs) set by the United Nations namely, affordable and clean energy,
industry, innovation and infrastructure, sustainable cities and communities and climate action.
The implementation of EV charging stations in the existing infrastructure of the fuel stations promotes
circular economy by reusing the existing resources. The techno-economic feasibility conducted in this study
aims to present a model that can be used by stakeholders to develop EV charging stations to promote the
use of EVs over the traditional gasoline driven vehicles. A solution is suggested in this study to repurpose
the existing fuel stations and to convert them into useful infrastructure for the future. The solution
provided presents an in-depth economic analysis of the feasibility of charging stations with DC fast chargers
and for different hours of operations. The use of DC fast chargers present a challenge to be integrated with
the electricity grid, so a detailed technical analysis is presented in which the DC fast chargers can be
implemented into the electricity grid and in case of overloading, two mitigation strategies have been
suggested. Currently DC fast chargers are currently used along the highways and with this strategy a solution
is presented to install DC fast chargers into the urban areas. This would ensure lesser waiting times for the
EV owners as they will be able to charge their vehicles while they are commuting to work or returning
home. The use of public DC fast chargers will also mitigate the need for the EV owners to charge their
vehicles overnight thus reducing the load on the commercial subnetwork. With the implementation of PV
and BESS, the reliance on the grid electricity can be reduced and cleaner energy can be produced for the
charging stations.
8. Conclusion
The number of EVs on the road has been increasing as the transition in the transport sector is being made
for a more sustainable future. With the increasing EV, there is an increase in the demand for public charging
stations. Studies have shown that drivers prefer no waiting time while using a public charging station to
charge their EVs. DCFC can charge the battery of an EV in a shorter duration of time than that of a slow
or a fast charger. Though ultra-rapid chargers can provide even shorter charging times than DCFC, their
initial upfront cost and electricity connection cost is significantly higher and so are the adverse impacts on
the electricity network. Therefore, DCFC is considered as a preferred option for public charging stations.
With a reducing number of conventional vehicles, the demand for petrol/diesel/gas will reduce rendering
the existing fuel stations nonfunctional. The existing fuel stations are already familiar with the customers,
are typically conveniently located and the service area of the gas stations ensures no obstruction in the
traffic flow. To keep pace with the transition to EVs, existing fuel station infrastructure can be repurposed
as charging stations for EVs.
A techno-economic analysis-based method is proposed to design the refurbishment of an existing fuel
station. The method is applied to the UK as a case study, the results of which are discussed in detail in
Section 4. A positive NPV value indicates that the system is economically profitable. The results indicate
that with an increase in hours of operation per day, the NPV becomes more positive and the discounted
payback period reduces as well. The most economic configuration is 3C1S8h with an IRR 28% and DPP
of 4.2 years for the present study of UK. With the integration of Solar PV into the system, the configuration
3C1S1P8h is the most suitable configuration with an IRR of 19% and DPP of 5 years. The electricity
network connection cost negatively affects the economic feasibility of the system as the number of DCFCs
increase beyond four. This is due to the need for the upgrade of the electricity network beyond the current
capacity of the network, the costs of which are partially passed on to the installer of the DCFC. The usage
of storage along with the DCFC will help reduce the connection cost, earn more revenue in the future by
participating in local energy markets, enable utilization of cheaper time of use of flexible tariffs while also
limiting the impact on the electricity network. The installation of PV will reduce the dependency on the
electricity grid and reduce the yearly fuel cost generated by using the electricity grid. With a suitable feed-
in tariff, the excess electricity generated from the solar panels can be sold to the grid making the use of PV
economically more profitable.
Technical feasibility of connecting DCFCs to an electricity grid network is also analysed in this thesis by
conducting a load flow analysis using a CIGRE LV network. It is observed that the net demand of the
additional DCFCs tend to cause an undervoltage at the points where they are connected to the transmission
system. With the integration of PV and BESS units, the dependency on the grid electricity will be reduced
and voltage per unit values at the connection points is closer to the safe limits. To mitigate this impact on
the grid network, two strategies are proposed and simulations are carried out on the CIGRE network. The
first strategy proposed is to upgrade the network cables of the distribution network. Since the commercial
sub-network of the CIGRE LV network uses overhead cables, it should be relatively easier to replace them
when compared to the underground cables. From the results of the different scenarios, it is observed that
with an increase in the thickness of the cables, the voltage per unit value gets closer to the safe limits. The
second strategy proposed is to change the slack voltage value of the transmission line to a higher value of
1.05 Volts per unit or 1.1 Volts per unit. With an increase in the slack voltage, there can be some
disturbances in the network, but it will be a more cost-effective option when compared to replacing the
cables. Even with this strategy, it is observed that by increasing the slack voltage, the voltage per unit
observed at the connection point is closer to the safe limits of the voltage. Based on the economic feasibility
and the grid network data, either one of the two strategies can be employed to mitigate any undervoltage
in the electricity network.
From the results of the techno-economic analysis and the load flow analysis conducted in this thesis, it is
evident that integrating a renewable energy system with the EV charging station reduces the reliance on
the grid electricity hence reducing the costs incurred due to grid upgradation occurred by connecting DCFC
to the electricity grid. The repurposing of fuel stations is more sustainable as the existing infrastructure is
used and there is no additional construction needed for the charging stations which aims to promote
circular economy. The existing gas stations are ideally located to implement EV fast charging stations and
policies are being developed in countries like South Korea and Germany to repurpose the existing gas
stations to install EV fast chargers. It maximizes the utilization of existing assets and minimizes the
investment costs that may have incurred due to construction of any new infrastructure. Policies and
investment funds from the government will attract more stakeholders to invest into the DCFC installed
public charging stations. The transition to local energy markets, the enabling policies, market regulations
and the demand for sustainable transportation would also further facilitate the growth of EV charging
station with storage solar PV.
References
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2. Global Emissions | Center for Climate and Energy Solutions [Internet]. [cited 2020 Aug 17]. Available from: https://www.c2es.org/content/international-emissions/
3. Greenhouse gas emissions from transport in Europe — European Environment Agency [Internet]. [cited 2020 Aug 17]. Available from: https://www.eea.europa.eu/data-and-maps/indicators/transport-emissions-of-greenhouse-gases/transport-emissions-of-greenhouse-gases-12
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