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Optimal Location and Energy Management Strategy
for EV Fast Charging Station With Integration of
Renewable Energy Sources
Fareed Ahmad
Department of Electrical Engineering
Aligarh Muslim University
Aligarh, India
fareed903@gmail.com
Imtiaz Ashraf
Department of Electrical Engineering
Aligarh Muslim University
Aligarh, India
iashraf@rediffmail.com
Atif Iqbal
Department of Electrical Engineering
Qatar University
Doha, Qatar
atif.iqbal@qu.edu.qa
Irfan Khan
Clean and Resilient Energy Systems
(CARES) Lab
Texas A&M University, USA
irfankhan@tamu.edu
Mousa Marzband
Department of Mathematics, Physics
and Electrical Engineering
Northumbria University, UK
mousa.marzband@northumbria.ac.uk
Abstract—As climate change becomes a significant concern, the
use of electric vehicles (EVs) has emerged as an effective remedy
to the pollution caused by fossil-fuel transportation. To make
the transportation sector carbon emission-free, the energy supply
sources to the EV battery should be renewable. Furthermore,
renewable energy sources have intermittent; therefore, an energy
management strategy (EMS) has been proposed to manage the
grid’s peak and off-peak power demand. The location of fast
charging stations (FCSs) and their size are critical for the
distribution network. So, in this paper, to minimize the energy
loss in the distribution system and transportation cost to reach
the FCS for the charging, optimal location and capacity of FCS
have been proposed. In addition, the FCS with Photovoltaic
energy sources (PVES) and battery energy storage system (BESS)
is proposed to manage the above problem. A novel improved
bald eagle search algorithm is proposed for the optimal solution
to locate the FCS with size. Furthermore, the IEEE-33 bus
distribution system with residential, commercial, and industrial
loads at the respective bus distribution system is suggested to place
FCSs. For this work, the direct load flow technique is employed to
analyze the load flow of the proposed IEEE 33 distribution system.
The distribution system’s active power flow and bus voltages are
investigated for three recommended cases.
Index Terms—Energy management strategy, Optimal place-
ment, Fast charging station, Electric vehicle, battery energy
storage system
I. INT ROD UC TI ON
Electrification of the transportation sector is attaining
vogue in the government sector and the automotive industry
leading to lowered carbon emissions. According to a study in
[1], EVs might reduce CO2emissions by 28% by 2030. In
practice, the overall deployment of charging networks for their
complete and fundamental electricity-supplying tasks to EVs
cannot be separated from the adoption of EVs, and studies on
their layouts should be conducted ahead of time. As a result,
the distribution system’s construction of charging stations
requires additional care. FCS is being explored for this study
since time for waiting and charging are two further hurdles
to EV adoption. Apart from the advantages of FCS, it poses
significant challenges for the electrical system, which may be
mitigated by selecting the right location and size for the FCS.
Furthermore, FCS’s position and capacity might impact EV
charging habits. As a result, energy loss and traveling costs
for charging the EV battery have been proposed as objective
functions for resolving the right location and capacity of FCS
in a distribution network.
The market for electric car charging stations is anticipated
to reach $103.6 billion between 2021 and 2028, extending
at a compound annual growth rate (CAGR) of 26.4 percent.
The market is also anticipated to expand at a 31.1 percent
CAGR from 2021 to 2028, attaining 11.6 million units [2],
[3]. The balanced mayfly method is used to calculate the
ideal placement for the charging station based on the cost of
CS development, active and reactive power loss, and voltage
variation [4]. The authors of the [5] presented a problem with
multi-objective functions for locating FCSs established on
traveling energy loss, station investment cost, and sub-station
energy loss, which was proposed using the binary lighting
search method. Furthermore, the authors in [6] installed the
FCS in the best possible position by minimizing power loss
and installation in the network. The installation cost, the cost
for power loss, and maximum voltage variation were also
proposed for the multi-objective optimization problem, and
an untried hybrid shuffled frog leap-teaching learning-based
algorithm was used to solve this issue in [7]. Furthermore, in978-1-6654-7100-8/22/$31.00 © 2022 IEEE
2022 IEEE Silchar Subsection Conference (SILCON) | 978-1-6654-7100-8/22/$31.00 ©2022 IEEE | DOI: 10.1109/SILCON55242.2022.10028897
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Fig. 1. PV power generation (p.u.)
[8], the distribution systems with V2G capabilities, a power
loss reduction is formulated as the objective function to place
the FCS. The suggested situation is solved by the grey wolf
and whale optimization techniques. Three goals are used
to construct the challenge: power loss, voltage profile, EV
charging costs, and load providing costs, according to [9].
The differential evolution approach was employed to tackle
the concern mentioned above.
The paper’s objective is to place the FCSs at the appro-
priate position with capacity to minimize the energy loss and
traveling cost for charging the EV battery. Furthermore, EV
charging increases the power demand from the grid, and if the
centralized power generation is based on fossil fuels, then there
are no environmental advantages of EVs for society, as many
researchers claim. Therefore, PV power generation is proposed
with a charging station, which has two benefits: it reduces the
extra power loss in the distribution system and benefits the
environment. Furthermore, to manage the difference between
peak to off-peak power demand, an energy management strat-
egy is proposed using a battery energy storage system with
charging stations.
II. METHODOLOGY
A. Solar Power Distributed Generation
PV that has been designed and installed at the same location
of charging station are also evaluated. PV integration in the
system can lower grid stress, improve the voltage profile,
and minimize system energy losses. The temperature and
insolation data are borrowed from [10]. The PV source’s power
generation is expressed by (1). Fig. 1 shows solar PV panels’
average yearly power output per unit.
PP V = 0.995 ×η×Ar ×IPV ×(Tm−Tref )(1)
where, ηis efficiency of canopy, Ais the area required
in m2,IP V is radiance in kW/m2,Tmand Tr ef are the
temperature which is measured at location and reference which
is 25◦C, respectively.
B. Electric Load Modeling
For the purpose of calculating overall energy consumption,
the energy needs for the buses must be modeled. As a result,
the load pattern for a full day is obtained from the [10], the
Fig. 2. Different types of electrical load in per unit
residential, commercial and industrial load curve are shown in
Fig. 2. The IEEE-33 bus system is demonstrated in Fig. 3.
C. Objective function
For the optimal position and capacity of fast charging station
in the electrical system, the optimization issue are modeled by
considering the energy loss and travelling cost as objective
functions. Therefore, this paper have considered the electrical
distribution network and transportation network both for the
placement of FCS.
1) Energy loss cost: Energy loss of the electrical distribu-
tion system depends on the current flow in lines, and after the
integration of the charging load, the line current will be more.
Furthermore, the EV load is depends on the number of EV
connected to the FCS as shown in 4. Therefore, the energy
loss of the network is created as an objective function for the
deployment of FCS, which is expressed in (2),(3) and (4).
Eloss =
24
X
t=1
P(t)
loss ×∆t(2)
P(t)
loss =
Nl
X
l=1
(I(t)
l)2×Rl(3)
I(t)
l=U(t)
p−U(t)
q
Rl+jXl
, p = 1,2...Nn;q= 1,2...Nn;p=q(4)
where, P(t)
loss is the power loss at time t,I(t)
lis the current in
lline at time t,Rland Xlare the resistance and reactance of
lth line respectively, Upand Uqare the voltage at pth and qth
buses, respectively.
f1
i=Elossi−Elossmin
Elossmax −Elossmin (5)
2) Travelling cost for charging: The distance from the
charging demand point to the closest charging station deter-
mines how much it will cost to travel to charge an EV battery.
Therefore, energy consumption is established as an objective
function for the best FCS position and size when going from
the charging demand point to the closest FCS. The length of
jth EV to ith FCS is determined as (6)
lij =diag(yi)×dij (6)
where yiis a binary vector for the placement of FCS, if yi= 1,
FCS will be placed at defined bus and yi= 0, FCS will not
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Fig. 3. Single line presentation of IEEE-33 electrical distribution network
be placed, dij is the traveling distance from the demand point
to CS and in (6), by using yia matrix is formulated. Later,
the traveling distance to the nearest FCS from EVican be
calculated using (7).
Lj min =min(lij )(7)
Travelling cost (TC) from jth EV charging point to closest
CS is given in the (8).
T Cj=PEC ×SE×Lj min (8)
where, PEC is the $/kWh cost of energy , SEis the energy
required per km to travel. the value of TC can be normalised
using (9) as:
f2
j=T Cj−T Cmin
T Cmax −T C min (9)
The simple method to optimize the multi-objective function
is to formulate the proposed objective function into a single
objective function. Furthermore, the formulated optimization
problem can be minimized/maximized by providing the ap-
propriate weight coefficient for each objective, as shown in
(10).
f=min
α
NF CS
X
i=1 f1
i/NFC S +β
NEV
X
j=1 f2
j/NDP
(10)
where, NF CS is the number of FCS at the electrical network,
NDP is the charging demand point, αand βare the positive
coefficient.
3) Constraint: Voltage constraint: As a result of the charg-
ing load, all bus voltage will change. As a result, as indicated
in (11) for electrical network dependability, some specific re-
strictions are imposed on each bus of the distribution network.
Vmin < Vi< V max where i = 1,2,3...Nb (11)
Current flow constraint: Each branch of the electrical network
has a maximum and minimum current limit running through
it in order to monitor its temperature, as indicated in (12).
Imin < Ik< Imax where k = 1,2,3...N (12)
Active and reactive power constraints: The distribution sys-
tem’s power equality boundaries were applied for 24 hours,
viewing the energy demands of the FCS, the bus load, the
energy taken from the PV source, and the grid.
Number of FCS: The number of charging stations, as given in
(13), is subject to a maximum.
NB
X
i=1
Xi≥NCS (13)
4) Energy management strategy using battery energy stor-
age system: After the distribution system has integrated EV
load, the peak power demand from the grid is increased
because, in most cases, the peak power demand of the distri-
bution system coincides with EV load demand. The PV power
generation is only available during the daytime, and during
day-times, the power demand from the grid is flat; therefore,
to manage the peak power demand of the system, a battery
energy storage system (BESS) is proposed. Furthermore, in this
paper, the load curve has three types of load, peak load, flat
load, and off-peak load. Moreover, during off-peak or valley
load, the BESS will be charged from the grid power, and during
peak power demand, the BESS will be discharged.
III. PROP OS ED OPTIMIZATION TECHNIQUE
Classical optimization algorithms might yield the best so-
lution for unconstrained maxima and minima continuous and
differential functions. Classical approaches, on the other hand,
have limited practical use since they need neither continuous
nor differential objectives. As a result, complex optimization
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approaches are utilized to implement the optimal results for
the defined problem.
A. An Improved Version of Bald Eagle Search Technique
The improved bald eagle search (IBES) approach is based
on the earlier BES method and influenced bald eagle behavior
during hunting [11]. Space selection, searching, and swooping
in on the prey are all part of the hunting strategy.
Space selection: Using Eq. (14), the bald randomly establishes
the space using the formerly searched knowledge.
qn,j =qb+β×r(qmean −qj)(14)
Somewhat of a predefined weight as in the earliest BES
technique, a refreshed parameter, βis utilized to address
position changes and may be propagated using Eq. (15).
β=(1.5×(Maxit −t+ 1))
(Maxit)(15)
This proposed parameter affects eagles’ bald location and
boosts the IBES technique’s exploration and exploitation. r is a
real number which lie from 0 to 1. qnand qbindicate the new
and best search. qmean shows that the eagles have absorbed
all the details from the last search.
Stage searching: The eagles travel in a circular pattern to
speed up their search for prey in the chosen area. Eq. (16),
Eq. (17)- Eq. (20) are used to adjust the eagle’s position at
this moment.
qj,n =qj+n(j)×(qj−qj+1)qb+m(j)×r(qj−qmean )(16)
x(j) = mr(j)
max|mr|, n(j) = nr(j)
max|nr|(17)
mr(j) = r(j)×sin(δ(j)), nr(j) = r(j)×cos(δ(j)) (18)
δ(j) = β×π×rand (19)
r(j) = δ(j)×R×rand (20)
where, βlie between 5 and 10, and R value lie between 0.5
and 2.
Stage swooping: In this step, as mentioned in Eq. (21)-
Eq. (24), the eagles start to swing from an ideal search stance
towards their prey.
qj,n =rand×qb+m1(j)(qj−c1×qmean)+n1(j)(qj−c2×qb)
(21)
m1(j) = mr(j)
max|mr|, n1(j) = nr(j)
max|nr|(22)
mr(j) = r(j)×sinh(δ(j)), nr(j) = r(j)×cosh(δ(j)) (23)
δ(j) = β×π×rand, r(j) = δ(j)(24)
Where, c1and c2both lie between 1 to 2.
IV. RES ULTS
To illustrate the intended work for installing FCS in the
electrical network. It is supposed that 600 EVs are charged
each day in the suggested region, and the power flow analysis
is carried out using the IEEE 33 bus system. Furthermore,
12 demand points are generated randomly, assuming that each
demand point has 50 electric vehicles traveling to charge the
battery. In addition, the possible location of the charging station
and charging demand point is created randomly in MATLAB to
calculate the traveled distance for charging by the EV users.
Except for bus 1, every bus, as demonstrated in this study,
might be a possible FCS location. The direct approach is
utilized for power flow study in the electrical network. It is
assumed that the connector capacity is 50 kW, and the EV
load is constant.
In this article, three case studies are suggested for the
placement of FCS through minimizing the energy loss and
transportation loss cost. In case study 1 (CS-1): the optimal
location and capacity of FCS is proposed whereas in case
study 2 (CS-2): the optimal siting and sizing of FCS with
PVES is proposed. Further, in case study 3 CS-3, the energy
management stratgy is proposed to minimised the peak demand
from the grid.
In CS-1: By lowering the energy loss of the electrical
network and the cost of traveling, the best location and size
for the FCS may be achieved. Other restrictions that are
considered while framing the issue include the voltage of
every bus, the current flow through lines, and the power
balance of the electrical network. When the recommended
solution is applied, bus numbers 2, 19, and 21 are in the best
locations, with corresponding capacities of 250, 150, and 200
kW. Additionally, by solving the optimization issue to achieve
the siting and size solution of FCS in expressed electrical
system, 2585.1565 kWh energy loss, 0.95619 for bus 18th
lowest voltage, and 0.9980 for bus two highest voltage are
obtained.
For CS-2: By reducing the electrical network’s energy loss
and traveling costs, the best location and size for the FCS
are taken care of. Furthermore, to reduce energy loss in the
electrical network, PV energy sources have been built precisely
where the FCS is, and the size of the PVES is equal to
half of the demand for EVs. After applying the optimization
method to the given issue, bus numbers 2, 19, and 21 are
the optimal placements, with capacities of 250, 150, and 200
kW, respectively. Additionally, 2564.3981 kWh energy loss,
0.95619 for bus 18th lowest voltage, and 0.9980 for bus two
highest voltage are achieved by addressing the optimization
issue to reach the FCS placement and size solution in the
electrical network. The grid’s active power consumption for
each scenario has been depicted in Fig. 4.
For CS-3: Energy management strategies have been applied
using battery energy storage systems to reduce the difference
between peak and off-peak power demand from the grid. The
BESS is placed with FCS to lower the energy loss, and the size
of BESS is half of the capacity of FCS, which are 125, 75,
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Fig. 4. Active Power drawn from the Grid
TABLE I
OPTIMAL LOC ATION A ND CA PCI TY O F FCS WITH ENERGY LOSS , MI N. VOLTAG E AND MA X. VO LTAGE
-Base case CS-1 CS-2 CS-3
Energy loss (kWh) 2472.2396 2585.1565 2564.3981 2565.9901
Vmin 0.95667 for bus 18 0.95619 for bus 18 0.95619 for bus 18 0.95594 for bus 18
Vmax 0.99846 for bus 2 0.9980 for bus 2 0.9980 for bus 2 0.99776 for bus 2
FCS locations - 2,19,21 2,19,21 2,19,21
Ncon - 5,3,4 5,3,4 5,3,4
FCS Capacity (kW) - 250,150,200 250,150,200 250,150,200
Fig. 5. Variation of 19th bus voltage presentation
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and 100 kW, respectively. Additionally, 2565.9901 kWh energy
loss, 0.95594 for bus 18th lowest voltage, and 0.99776 for bus
two highest voltage are achieved by solving the optimization
issue to reach the FCS placement and capacity solution in the
electrical network. Furthermore, by applying the EMS, peak
power demand from the grid is reduced from 4034 kW to 3701
kW, whereas off-peak power demand is increased from 1843
kW to 2146 kW. The voltage variation of 19 bus of IEEE-33
electrical network are expressed in Fig. 5 for 24 hours.
V. CONCLUSION
The study suggests an ideal location and size problem for a
fast-charging station that uses PV energy sources and a battery
energy storage system in the electrical distribution network.
In addition to ensuring voltage stability and power quality,
it provided a revolutionary method for placing fast-charging
stations with the least transportation expense and electrical
energy loss. Using a new, updated bald eagle search algorithm,
which is a very effective and potent instrument to acquire
the best outcomes, the presented optimization problem is
tackled. This study also discusses the three scenarios when the
offered results explore the optimization problem. The electrical
network saw the less energy loss once FCS was installed at the
appropriate position and size, with EV customers paying the
least for a trip to charged batteries. The scientists hope their
work will eventually help EVs integrate into the grid, lessen
carbon emissions, and persuade merchants to invest in FCS
construction.
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