Indonesian Journal of
Electrical
Engineer
ing and
Computer Science
V
o
l. 11
, No
. 1, Ju
ly
2
018
,
pp
. 82
~89
ISSN: 2502-4752,
DOI: 10.
11591/ijeecs
.v11
.i1.pp82-89
82
Jo
urn
a
l
h
o
me
pa
ge
: http://iaescore.c
om/jo
urnals/index.php/ijeecs
Optimal Charging Schedule Coordin
ation of El
ectric Vehicl
es
in Smart Grid
W
a
n I
q
ma
l Fa
e
z
y
Wa
n
Za
ln
id
za
m
1
, H
a
s
m
aini
Moham
a
d
2
, Nur
Ashi
da Salim
3
, Hazl
ie
Mo
khlis
4
,
Zuha
ila
Ma
t
Y
a
sin
5
1,2,3,5
Faculty
of
Electrical Eng
i
n
eering
,
Univ
ersiti
Teknologi MARA, 4000 Shah
Alam, Selangor,
Malay
s
ia
4
Department of
Electrical Eng
i
n
eerin
g
,
Faculty
o
f
Engin
eering
,
University
of Malay
a
, 50603 Kuala Lumpur, Malay
s
ia
Article Info
A
B
STRAC
T
Article histo
r
y:
Received
Ja
n 20, 2018
Rev
i
sed
Mar
10
, 20
18
Accepte
d Apr 2, 2018
The incr
eas
ing
penetr
ation of el
ectr
i
c vehi
cl
e (E
V) at dis
t
ributio
n s
y
s
t
em
is
expec
t
ed in the
near future l
eadi
ng
to rising demand for power consumption.
Large sca
l
e unc
oordinat
e
d charg
i
ng dem
a
nd of E
V
s will eventual
l
y
thr
eat
ens
the saf
e
ty
op
er
ation o
f
th
e distribution n
e
tw
ork. Th
erefor
e, a
chargin
g
strateg
y
is n
eed
ed to
redu
ce the imp
act
of charg
i
ng. This
p
a
per
proposes
an
optim
al centr
al
iz
ed charging s
c
he
dule c
oordin
a
tio
n of EV to
minimize active
power losses while m
a
inta
ining t
h
e volta
ge profile at the demand
side. The
performance of
the schedule
algorithm
developed using particle swarm
optimization (P
SO) technique
is ev
alu
a
ted
at
the IE
EE-33
Bus
radia
l
distribution s
y
stem in a set time frame
of charg
i
ng period. Coor
dinated and
uncoordinated
charging schedu
le is then
com
p
a
r
ed in t
e
rm
s
of act
ive power
losses and voltage profile at diff
erent
le
v
e
l of
EV penetr
ation
co
nsidering 24
hours of load demand profile.
Results
show that the p
r
oposed
coordinated
charging s
c
hedu
le is
able
to a
c
h
ieve m
i
nim
u
m
tota
l a
c
tiv
e po
wer losses
compared to
th
e
uncoordinated
charging.
K
eyw
ords
:
Electric ve
hicle
Ch
arg
i
ng
co
ord
i
n
a
tion
Distribution sy
ste
m
Particle swarm op
ti
m
i
zatio
n
Copyright ©
201
8 Institut
e
o
f
Ad
vanced
Engin
eer
ing and S
c
i
e
nce.
All rights re
se
rve
d
.
Co
rresp
ond
i
ng
Autho
r
:
Wan Iqm
a
l Faezy
W
a
n Zalni
d
zam
,
Facu
lty of Electri
cal Engineering,
Un
i
v
ersiti Tekn
o
l
o
g
i
M
A
RA,
4
000
Sh
ah
Alam
, Selan
g
o
r,
Malaysia.
Em
a
il: iq
m
a
lfaezy94@gm
a
il.
com
1.
INTRODUCTION
Tran
sp
ort
a
t
i
o
n
sect
or i
s
am
on
g t
h
e l
a
r
g
e
s
t
cont
ributors
for e
x
cessive
carbon em
iss
i
on i
n
the
envi
ro
nm
ent
whi
c
h l
ead t
o
t
h
e de
pl
oy
m
e
nt
of el
ectric vehicle (EV) as alternative to re
duce the
envi
ro
nm
ent
a
l
l
y
dam
a
gi
ng im
pact
fr
om
conv
ent
i
onal
vehi
cl
es. H
o
we
ve
r, i
m
pact
of i
n
cre
a
si
ng
po
we
r de
m
a
nd
d
u
e
to
co
m
p
arativ
ely h
i
g
h
con
s
u
m
p
tio
n
of
EV’s b
a
tteri
es
d
u
ring
ch
arg
i
ng
gro
w
s con
c
ern
on
th
e
u
tilities. In
ad
d
ition
,
larg
e-scale p
e
n
e
t
r
atio
n
of EVs lead
to
a
p
o
t
en
tial in
crease o
n
th
e p
e
ak
lo
ad
d
e
m
a
n
d
o
f
th
e lo
cal
di
st
ri
b
u
t
i
on
ne
t
w
o
r
ks es
peci
a
l
l
y
when EV
users
pract
i
ce t
h
e unc
o
n
t
r
ol
l
e
d cha
r
gi
ng sc
hem
e
[1]
.
Theref
ore
,
several
st
u
d
i
e
s
have
been c
o
nd
uct
e
d t
o
p
r
o
pos
e sm
art
chargi
ng c
ont
rol
st
rat
e
gi
es o
f
E
V
s by
u
s
i
n
g v
a
ri
o
u
s
opt
i
m
i
zati
on t
e
chni
que
s [
2
]
,
[
3
]
t
o
re
duce
t
h
e
m
e
nt
i
oned
i
m
pact
and
i
m
prov
e t
h
e
ope
rat
i
o
n
o
f
el
ect
ri
cal
g
r
i
d
.
A
u
t
h
or
s i
n
[4
]
-
[6
] pr
opo
sed
ch
arg
i
ng
sch
e
d
u
l
es to
m
i
nimize the charging c
o
st
of
E
V
as
well as
m
i
nim
i
zi
ng
t
h
e
bu
rde
n
on di
st
ri
b
u
t
i
on net
w
o
r
k by
fi
n
d
i
n
g h
o
u
r
l
y
opt
i
m
al
char
gi
n
g
po
wer t
r
ans
f
er
as vari
abl
e
.
Ho
we
ver
,
t
h
e
pr
o
pose
d
sche
dul
es
are
q
u
es
t
i
onabl
e
si
nce
t
h
ey
l
ack t
h
e i
n
cl
usi
o
n
o
f
p
o
w
er
fl
ow
m
odel
an
d
net
w
or
k co
nst
r
ai
nt
s i
n
t
h
ei
r m
e
t
h
o
dol
ogy
.
A
cent
r
al
i
zed c
h
a
r
gi
ng st
rat
e
gy
i
s
pr
o
pose
d
wh
ere t
h
e act
i
v
e p
o
w
e
r
of E
V
s cha
r
gi
ng i
s
co
nt
r
o
l
l
e
d by
reg
u
l
a
t
i
n
g t
h
e v
o
l
t
a
ge and
fre
que
ncy
at
connect
i
o
n
poi
nt
[
7
]
.
Th
ere are
m
a
ny
bene
fi
t
s
of
usi
n
g t
h
i
s
t
echni
que
su
ch as
re
duc
i
n
g t
h
e
v
o
l
t
a
ge
devi
at
i
o
n i
n
r
e
si
dent
i
a
l
di
st
r
i
but
i
o
n
n
e
two
r
k
s
[8
] an
d m
a
x
i
mizin
g
th
e
p
e
n
e
tratio
n of EV wit
h
v
e
h
i
cle to
gri
d
(V2
G
) cap
a
b
ility as a d
i
stribu
ted
ener
gy
r
e
so
ur
c
e
(
D
ER
)
i
n
i
s
l
a
nde
d
g
r
i
d
[
9
]
.
The
cha
r
gi
n
g
st
rat
e
gy
i
s
al
s
o
p
r
o
p
o
sed
i
n
[
10]
,
[
1
1
]
t
o
m
i
ni
m
i
ze
Evaluation Warning : The document was created with Spire.PDF for Python.
In
d
onesi
a
n
J
E
l
ec En
g &
C
o
m
p
Sci
ISS
N
:
2
5
0
2
-
47
52
Opt
i
m
al
C
har
g
i
ng
Sc
hed
u
l
e
C
oor
di
nat
i
o
n
of
El
ect
ri
c Vehi
cl
es ..
. (
W
a
n
Iq
m
a
l
F
a
ezy W
a
n
Zal
n
i
d
z
a
m)
83
the power loa
d
varia
n
ce
with st
ochastic
pl
ug-in elect
ri
c
vehi
cl
e
(PE
V
)
co
nne
ct
i
o
n
t
o
g
r
i
d
by
c
o
nsi
d
eri
n
g
V2G and load
forecasting to achieve fl
atten load profile in a distributio
n network. Resc
heduling the charging
o
f
EV in
to
mu
ltip
le ch
arg
i
ng
slo
t
s as p
r
op
o
s
ed
in
[12
]
ab
le to
p
r
od
u
c
e a
m
o
re u
n
i
fo
rm
lo
ad
p
r
o
f
i
l
e th
u
s
ens
u
ring t
h
e c
o
nnection
of
E
V
loa
d
s
does
not excee
d t
h
e l
o
adi
n
g capacit
y
at th
e lo
cal su
b
s
tation
.
Howev
e
r,
an
unc
o
o
r
d
i
n
a
t
ed ch
ar
gi
n
g
s
c
hed
u
l
i
n
g m
a
y l
ead t
o
vi
ol
at
i
on
o
f
vol
t
a
ge
p
r
o
f
i
l
e
an
d s
ubst
a
nt
i
a
l
l
y
i
n
crea
s
e
l
o
sses.
Th
us, t
h
i
s
resea
r
c
h
p
r
o
p
o
ses a c
o
o
r
di
nat
i
on
f
o
r
EV c
h
ar
ge sc
hed
u
l
i
n
g i
n
e
a
c
h c
h
ar
gi
n
g
sl
ot
s t
o
opt
i
m
al
ly
coor
di
nat
e
cha
r
ge s
c
hed
u
l
i
n
g f
o
r e
l
ect
ri
c vehi
cl
e by
consi
d
eri
n
g
m
i
nim
u
m
active p
o
we
r l
o
sse
s and
acceptable volt
age lim
it. Th
e optim
ally coordinated a
n
d uncoordinated
c
h
argi
ng sc
he
dul
e is then com
p
ared
for in
creasing
p
e
n
e
t
r
atio
n of
EVs i
n
to
t
h
e
network.
The sc
hed
u
l
i
n
g o
f
E
V
c
h
a
r
gi
ng i
s
o
p
t
i
m
i
zed
u
s
ing
th
e Particle Swarm
Op
timisatio
n
(PSO)
t
echni
q
u
e.
T
h
e
pr
oce
d
ure
o
f
t
h
e o
p
t
i
m
i
zat
i
on c
onsi
d
er
s
the technical c
h
aracteristic of
the cha
r
ging st
ation,
users
cha
r
gi
ng
beha
vi
o
r
,
2
4
-
h
o
u
r
l
o
a
d
p
r
o
f
i
l
e [1
3]
at
di
st
ri
but
i
o
n s
u
bst
a
t
i
on a
n
d t
h
e
net
w
o
r
k
co
nst
r
ai
n
t
s. Fo
r
case st
udi
es, t
h
e scal
e of t
h
e c
h
ar
gi
n
g
(C
S
)
s
t
at
i
on i
s
vari
e
d
based
o
n
t
h
e
d
i
ffere
nt
pe
net
r
at
i
on l
e
vel
o
f
EV i
n
th
e test d
i
stribu
tio
n
system
.
Prior to
th
e opti
m
isatio
n
o
f
E
V
cha
r
gi
ng, the charging cha
r
acteristic of E
V
suc
h
as t
h
e char
gi
n
g
pr
o
f
i
l
e
of t
h
e
bat
t
e
ri
es, char
gi
n
g
m
ode and
EV user
s char
gi
n
g
be
havi
or
need t
o
be i
d
e
n
t
i
f
i
e
d
[1
4]
,[
1
5
]
.
The
st
udy
anal
y
s
i
s
i
s
perf
orm
e
d o
n
di
st
ri
b
u
t
i
o
n s
y
st
em
consi
d
er
i
ng dai
l
y
l
o
ad
pr
ofi
l
e
. T
h
e ne
t
w
o
r
k
m
odel
cases are devel
ope
d
ba
sed o
n
t
h
e
dem
a
nd sc
ena
r
i
o
a
nd E
V
penet
r
at
i
on l
e
vel
.
The i
l
l
u
st
rat
i
on
of s
y
st
em
an
alysis f
r
a
m
e
w
o
r
k
is as
show
n in
Figu
r
e
1.
EV
ch
a
r
g
i
n
g
ch
ar
act
e
r
i
s
t
ic
24
ho
ur
lo
a
d
Gr
i
d
to
po
l
o
g
y
Ne
t
w
o
r
k
mo
d
e
l
ba
s
e
d
on
EV
pe
n
e
tr
a
t
i
o
n
le
v
e
l
Op
t
i
m
i
s
a
t
i
o
n
pr
oc
e
d
u
r
e
C
oor
di
na
te
d
an
d
unc
o
o
r
d
i
n
a
t
e
d
ch
ar
g
i
n
g
an
aly
s
is
an
d
co
m
p
a
r
i
s
o
n
Figure
1.
System
Analysis Fra
m
ework
2.
CENT
RALIZ
ED SMART
CHARGING: PROBLEM
FORMULAT
ION
Th
is stud
y fo
cu
ses
o
n
d
e
v
e
lop
i
ng
an
op
tim
a
l
EV cen
t
ralized
ch
arg
i
ng
strateg
y
in
th
e sm
art ch
arg
i
ng
sch
e
m
e
s wh
ich
is
d
e
term
in
ed
b
y
an in
tellig
en
t al
g
o
rith
m in
th
e sm
art distrib
u
tion
n
e
t
w
ork. Th
e
fo
rm
u
l
a
tio
n
of E
V
c
h
ar
gi
n
g
co
o
r
di
nat
i
on
i
s
devel
ope
d
b
a
sed
on a
n
ob
jective function and s
ubjected
to a series
of s
y
ste
m
co
nstrain
t
s
n
e
cessary for im
p
r
o
v
i
n
g
grid p
e
rfo
r
m
a
n
ce and
en
suring
reliab
ility.
2.
1.
Objec
t
ive Fun
c
tion
.
Th
e m
a
in
obj
ectiv
e fu
n
c
ti
o
n
o
f
th
is
proj
ect
is to m
i
n
i
miz
e
activ
e
p
o
wer lo
sses in th
e
d
i
stribu
tio
n
net
w
or
k
by
fi
n
d
i
n
g t
h
e
opt
i
m
al
sche
dul
e
co
or
di
nat
i
o
n
f
o
r t
h
e c
h
ar
gi
n
g
o
f
el
ect
ri
c vehi
cl
e
l
o
ad
. T
h
e
r
ef
or
e, t
h
e
objective
funct
i
on is
selected
as follows (1):
min
P
∑|
I
|
.R
(1
)
P
: Activ
e
p
o
wer lo
ss
I
: Br
a
n
ch
cu
rr
en
t
R
:
B
r
anc
h
i
m
pedance
ntl
:
num
ber
of
l
i
n
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
502
-47
52
I
ndo
n
e
sian
J Elec Eng
& Com
p
Sci, V
o
l. 11
,
No
.
1
,
Ju
ly
20
18
:
82
–
89
84
2.
2.
Constr
aints
There a
r
e se
ve
ral categories
of c
o
nstraints
for t
h
e op
timiza
tio
n
pro
b
l
em
wh
ich
in
clud
e
EV ch
arg
i
ng
co
nstr
ain
t
s and n
e
twor
k’
s tech
n
i
cal lim
i
t
s.
2.
2.
1.
Ch
argi
n
g
C
o
n
s
trai
n
t
s
Th
e fi
rst con
s
t
r
ain
t
is th
e limit o
n
allowab
l
e to
tal p
o
wer
d
e
man
d
of ch
argin
g
station
(CS) to
ch
arg
e
EVs at
b
u
s
k a
s
sh
ow
n i
n
E
q
uat
i
on
(
2
).
The
l
i
m
i
t
depen
d
s
on t
h
e n
u
m
b
er
of C
S
t
h
at
i
s
al
l
o
we
d t
o
be o
p
e
rat
e
d
i
n
a part
i
c
ul
a
r
penet
r
at
i
on l
e
v
e
l
of EV
s. T
h
i
s
ensu
re
t
h
e c
h
argi
ng
dem
a
nd
fo
r EV c
oul
d
be sat
i
s
fi
ed
ba
sed o
n
t
o
t
a
l
num
ber
o
f
C
S
i
n
st
al
l
e
d a
t
part
i
c
ul
a
r
bus
es. T
h
e l
i
m
it
hence i
s
:
P
,
P
,
P
,
(2
)
The cha
r
gi
ng
of E
V
i
s
m
odel
l
e
d as a cons
t
a
nt
act
i
v
e load. In orde
r to ens
u
re the effe
ctiveness of
p
o
wer system
o
p
e
ration
,
th
e
ad
d
ition
of EV lo
ad
d
e
m
a
n
d
,
EV
to
th
e lo
cal load
d
e
m
a
n
d
,
P
must not exce
e
d
t
h
e pea
k
l
o
a
d
dem
a
nd at
t
h
e
l
o
cal
di
st
ri
but
i
o
n
t
r
a
n
sf
orm
e
r
T
,
. at ev
ery
ho
ur. Th
erefo
r
e, t
h
e ceiling
limi
t
for th
e to
tal
max
i
m
u
m p
o
w
er d
e
m
a
n
d
o
f
t
h
e d
i
stribu
tion syste
m
is als
o
set as in
Equ
a
tio
n
(3) to
prev
en
t
ove
rl
oa
d
of
t
h
e
l
o
cal
di
st
ri
b
u
t
i
o
n
t
r
a
n
sf
orm
e
r.
P
is th
e lin
e l
o
ss in
t
h
e system.
P
h
EV
h
P
h
T
,
h
(3
)
2.
2.
2.
Network’s
Te
chnical Limits
Th
e
vo
ltag
e
con
s
train
t
of th
e
d
i
stribu
tio
n sy
ste
m
is co
n
s
i
d
ered b
y
setting th
e
u
p
p
e
r an
d
lo
wer limi
t
s
wh
ich
co
rrespo
nd
t
o
grid
vo
ltag
e
reg
u
l
ation li
m
i
ts typ
i
cal
l
y
set b
y
u
tiliti
es as in
Equ
a
tio
n
(4
). In
th
is
p
a
p
e
r,
th
e vo
ltag
e
li
mits are set to +/- 10
% (
V
= 0.
9p
u a
n
d
V
=1
.1
pu
)
wh
ich
is typ
i
cal of m
a
n
y
d
i
stribu
tio
n
net
w
or
k [1
6]
.
V
,
V
,
V
,
(4
)
2.
3.
D
i
st
ribut
i
on
N
e
t
w
ork Sy
stem To
po
logy
The IEEE
-3
3 bus
ra
di
al
di
st
ri
b
u
t
i
on net
w
o
r
k wi
t
h
a
t
o
t
a
l
l
o
ad o
f
3.
72
M
W
an
d 2.
3M
VAR
use
d
i
n
th
is stud
y is as sho
w
n
in Figur
e 2. Th
e MVA
and
vo
ltag
e
b
a
se
v
a
lu
es ar
e 1
0
M
VA
and
12
.6
6kV
r
e
sp
ect
iv
ely
[14
]
. 10
bu
ses
in
th
is
n
e
two
r
k are rand
o
m
ly
selected
as
th
e
lo
catio
n
for th
e CS in
stallatio
n
wh
ich
are
bus 3
,
6,
10
, 1
4
,
1
9
, 2
2
,
23
, 2
5
,
29 a
n
d 3
1
. T
h
e
di
st
r
i
but
i
o
n sy
st
em
i
s
assum
e
d t
o
be occ
u
pi
ed
b
y
10
00
p
o
p
u
l
a
t
i
on
of
reside
ntial consum
ers. Each
selected
bu
s is in
stalled
b
y
a fix
n
u
m
b
e
r
o
f
CS. Th
is
nu
mb
er is later in
crease
base
d on t
h
e
p
e
net
r
at
i
o
n l
e
ve
l
of EV. A
s
t
h
e
penet
r
at
i
o
n l
e
vel
of E
V
i
n
cr
eases fr
om
20% t
o
80%
, t
h
e num
ber
of CS installed at each bus is
assum
e
d to increase from
20 to 80 stations
.
This m
a
kes a total charger i
n
stalled
i
n
t
h
e t
e
st
net
w
or
ks i
n
crease
f
r
om
20
0C
S t
o
80
0C
S
res
p
ect
i
v
el
y
.
1
2
3
4
5
6
7
8
9
1
01
11
21
31
41
51
61
71
8
26
2
7
28
2
9
30
3
1
32
3
3
19
20
21
22
23
2
4
25
13
2k
V
/
12
.6
6k
V
CS
CS
CS
CS
CS
CS
CS
CS
CS
CS
Fi
gu
re
2.
IEE
E
-3
3 B
u
s
Di
st
ri
but
i
o
n
Net
w
o
r
k Sy
st
em
Inst
a
l
l
e
d
W
i
t
h
El
ect
ri
c Ve
hi
cl
e C
h
argi
ng
St
at
i
o
n
Evaluation Warning : The document was created with Spire.PDF for Python.
In
d
onesi
a
n
J
E
l
ec En
g &
C
o
m
p
Sci
ISS
N
:
2
5
0
2
-
47
52
Opt
i
m
al
C
har
g
i
ng
Sc
hed
u
l
e
C
oor
di
nat
i
o
n
of
El
ect
ri
c Vehi
cl
es ..
. (
W
a
n
Iq
m
a
l
F
a
ezy W
a
n
Zal
n
i
d
z
a
m)
85
2.
4.
Procedure of Optimiz
a
tion Algorithm
Particle Swarm Op
timizatio
n
(P
SO) is a p
opu
latio
n-b
a
sed
o
p
tim
iza
t
i
o
n
m
e
th
od
d
e
v
e
lop
e
d by
K
e
nn
ed
y and
Eb
erh
a
r
d
t
o
opti
m
ize th
e o
b
j
e
ctiv
e f
u
n
c
tion
s
f
o
r
a co
n
tinuou
s
o
p
tim
iza
tio
n
and
co
m
b
in
ato
r
ial
problem
s
. The potential solut
i
ons called t
h
e particles (P
best) fly through the proble
m
s
p
ace in search
of t
h
e
b
e
st so
lu
tion
called
fitn
ess. Th
e
b
e
st v
a
l
u
e
ob
tain
ed b
y
an
y
p
a
rticles in the n
e
ighb
ourhoo
d is called
Gbest. At
each ti
m
e
step, each particle updates its velocity a
nd acceleration
based on the we
ightage of a random with
separate ra
ndom num
bers
being
gene
rated for acceleration toward Pb
e
s
t and
Gbest loca
tion. T
h
e proc
edure
of t
h
e P
S
O t
e
chni
que i
s
d
o
n
e i
n
M
A
TL
AB
. The c
o
m
put
at
i
o
nal
pr
o
cedu
r
e t
o
fi
nd
t
h
e opt
i
m
al char
gi
n
g
sche
dul
e co
or
d
i
nat
i
on i
s
as sho
w
n i
n
Fi
g
u
r
e
3. The
net
w
or
k dat
a
w
h
i
c
h
consi
s
t
o
f
t
h
e
l
i
n
e and b
u
s
dat
a
as
well as the 24
hours loa
d
pr
ofile are set as the initial inputs
for the al
gorithm
.
20 initial population
of
particles
whic
h re
prese
n
t the com
b
ination
pattern
of c
h
arging station ope
ration
for each
pa
rticle at the selected bus in
the stipulated
charging
slot i
s
ge
ne
rated. T
h
e
Newt
on Ra
phs
on l
o
ad fl
ow
(NRL
F) is
perform
e
d for each
in
itial p
a
rticle an
d th
e Pb
est
are liste
d
after tak
i
ng
in
t
o
acco
un
t sev
e
ral
co
n
s
t
r
ain
t
s su
ch as vo
ltag
e
limit an
d
d
i
stribu
tio
n tran
sfo
r
m
e
r p
e
ak li
m
i
t to
d
e
termin
e th
e f
easi
b
ility o
f
th
e p
a
rticles cu
rren
t
p
o
s
ition
.
Gb
est
is the
selected as the
m
i
nim
u
m
fitness am
ong t
h
e
Pbest. The
weight,
velocity and position
of eac
h
pa
rticle are
u
p
d
a
ted. Th
e new Pb
est and
Gb
est
po
sitio
n is u
p
d
a
ted
i
f
th
ey are
b
e
tter th
an
t
h
e
p
r
ev
iou
s
o
n
e
s. Th
e op
ti
m
a
l
h
ourly EV ch
arg
i
n
g
p
a
ttern
is ach
iev
e
d
after
th
e
op
tim
iza
tio
n
p
r
o
cess m
eet
th
e st
o
p
p
i
ng
criteria.
St
a
r
t
In
p
u
t
ne
t
w
o
r
k
da
t
a
an
d
24
ho
ur
s
lo
ad
p
r
o
f
ile
I
n
i
t
ia
liz
e
p
a
r
t
ic
le
po
pu
l
a
ti
o
n
Ca
l
c
u
l
a
t
e
ob
j
e
c
t
i
v
e
fu
n
c
t
i
o
n
fo
r
ea
c
h
pa
r
t
i
c
l
e
Rec
o
r
d
Pb
e
s
t
an
d
Gb
e
s
t
Up
d
a
te
pa
r
t
i
c
l
e
po
s
i
t
i
o
n
an
d
ve
l
o
c
i
t
y
Ch
e
c
k
st
o
p
p
i
n
g
c
r
it
er
ia
Ho
u
r
l
y
EV
ch
a
r
g
i
n
g
pa
tte
r
n
YE
S
NO
Fi
gu
re 3.
Fl
o
w
chart
of
t
h
e PS
O
Tec
h
ni
q
u
e
3.
R
E
SU
LTS AN
D ANA
LY
SIS
The
opt
i
m
i
zation
o
f
E
V
c
h
ar
gi
n
g
sc
hed
u
l
e
coo
r
di
nat
i
o
n i
s
real
i
zed
by
usi
ng t
h
e P
S
O t
e
c
hni
que
. T
h
e
coo
r
di
nat
e
d a
n
d u
n
co
or
di
nat
e
d sm
art
chargi
ng sc
hed
u
l
e
are consi
d
ere
d
as the case studies. Inc
r
easing
market
penet
r
ation le
vel is analyze
d
for eve
r
y charging sc
he
dule. The im
pa
ct of each chargi
ng sc
hedul
e
with
di
ffe
re
nt
l
e
vel
of m
a
rket
pene
t
r
at
i
on o
n
t
h
e t
e
st
sy
st
em
is e
v
alu
a
ted
i
n
term o
f
to
tal syst
e
m
lo
sses an
d
v
o
ltage
pr
ofi
l
e
.
3.
1.
Unc
oor
di
na
te
d ch
argi
n
g
The c
h
ar
gi
n
g
s
c
hed
u
l
e
i
n
t
h
i
s
st
udy
c
o
nsi
s
t
s
of
f
o
u
r
c
h
ar
gi
n
g
sl
ot
s
.
Eac
h
c
h
ar
gi
n
g
sl
ot
ha
s a d
u
r
at
i
o
n
of
4 h
o
u
r
s t
o
f
u
l
l
y
charge t
h
e part
i
c
ul
ar
nu
m
b
er of EV
in the selected bus. In
t
h
e unc
oo
r
d
i
n
at
ed
c
h
a
r
gi
ng
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
502
-47
52
I
ndo
n
e
sian
J Elec Eng
& Com
p
Sci, V
o
l. 11
,
No
.
1
,
Ju
ly
20
18
:
82
–
89
86
each c
h
arging
slot is accomm
odated with
1/4
of t
h
e to
tal
charging l
o
ad. Figure
4 s
h
ows the
24-hour active
powe
r loss
of t
h
e system
for t
h
is sce
n
ari
o
.
Fi
gu
re
4.
U
n
c
o
or
di
nat
e
d Sm
art
C
h
ar
gi
n
g
24
-
h
o
u
r
A
c
t
i
v
e P
o
wer
Lo
ss
For
al
l
of
t
h
e E
V
penet
r
at
i
on l
e
vel
,
t
h
e
hi
ghe
st
l
o
ss rec
o
rde
d
i
s
at
t
h
e
st
art
of c
h
a
r
gi
ng
at
17
0
0
whi
c
h
ar
e 2
1
9
.
13
4kW
, 24
7.233
kW
, 27
7.988
kW
an
d 3
1
1
.
481
kW
fo
r
20
%,
40
%,
60
%
and
8
0
% p
e
n
e
t
r
atio
n
lev
e
l
r
e
sp
ectiv
ely. Th
e b
a
se case lo
ss fo
r
th
is p
a
r
ticu
l
ar
ho
ur
is 1
9
3
.
9
11kW
. Th
e p
e
n
e
tr
ation
o
f
EV
at 8
0
% lev
e
l
shows
an inc
r
e
a
se of active
powe
r l
o
sses m
o
re
tha
n
50%
f
r
om
t
h
e base
c
a
se wi
t
h
o
u
t
E
V
penet
r
at
i
o
n
.
Sin
ce th
e ch
aracteristic o
f
t
h
e test syste
m
ex
h
i
b
its th
at
b
u
s 18
h
a
v
e
th
e lo
west
vo
ltag
e
in
th
e
b
a
si
c
case, t
h
e
v
o
l
t
a
ge
dr
o
p
due
t
o
EV l
o
adi
n
g
i
s
m
o
re si
gni
fi
ca
nt
com
p
are
d
t
o
t
h
e
ot
he
r
b
u
se
s as s
h
ow
n i
n
Fi
gu
re
5. T
h
e
r
ef
ore
,
t
h
e
vol
t
a
ge
d
r
o
p
at
t
h
i
s
b
u
s
i
s
anal
y
zed.
T
h
ere
i
s
n
o
c
h
argi
ng
occ
u
r
f
r
om
09
0
0
t
o
16
0
0
,
th
erefore th
e vo
ltag
e
is always
m
a
in
t
a
i
n
ed abo
v
e
0.
91
p.
u.
As t
h
e ch
ar
gi
ng
of E
V
st
art
s
at
17
00
, i
t
sho
w
s
a
d
r
op
o
f
v
o
ltage fo
r all cases. Th
e vo
ltag
e
is main
tain
ed
ab
ove
0.
90
p.
u. at
al
l
ch
arging time for 20% and 40%
EV
. Vo
ltag
e
dr
op
b
e
l
o
w
0
.
90
p.u. du
r
i
n
g
the f
i
r
s
t ch
arg
i
ng
ho
ur
,
1
700
to
18
00
and
incr
ease abo
v
e
0.90
p.u.
after 180
0
for 6
0
%
EV. Howev
e
r, a sign
i
f
ican
t vo
ltag
e
d
r
op
is id
en
tified
for 80
% EV case b
y
which
th
e
vol
t
a
ge al
way
s
rem
a
i
n
s bel
o
w 0.
9
0
p
.
u
.
f
r
o
m
170
0 t
o
2
3
00
bef
o
re i
t
st
art
s
t
o
i
n
creas
e for t
h
e rest
of t
h
e
ch
arg
i
ng
tim
e. Th
e l
o
w
e
st
vo
ltag
e
m
a
g
n
itu
d
e
is 0.892
p.u. r
e
co
rd
ed
at
1
700 fo
r
80
% EV
.
Fi
gu
re
5.
U
n
c
o
or
di
nat
e
d Sm
art
C
h
ar
gi
n
g
24
-
h
o
u
r
V
o
l
t
a
ge
P
r
o
f
i
l
e
at
B
u
s
1
8
0,000
50,000
100,000
150,000
200,000
250,000
300,000
350,000
16:00
19:00
22:00
1:00
4:00
7:00
10:00
13:00
Loss (
k
W)
Hour(h)
0%
EV
20%
EV
40%
EV
60%
EV
80%
EV
0,89
0
0,89
5
0,90
0
0,90
5
0,91
0
0,91
5
0,92
0
0,92
5
0,93
0
0,93
5
16:0
0
19:0
0
22:0
0
1:00
4:00
7:00
10:0
0
13:0
0
Voltage(p.u)
Hour(h)
0%
EV
20%
EV
40%
EV
60%
EV
80%
EV
Evaluation Warning : The document was created with Spire.PDF for Python.
In
d
onesi
a
n
J
E
l
ec En
g &
C
o
m
p
Sci
ISS
N
:
2
5
0
2
-
47
52
Opt
i
m
al
C
har
g
i
ng
Sc
hed
u
l
e
C
oor
di
nat
i
o
n
of
El
ect
ri
c Vehi
cl
es ..
. (
W
a
n
Iq
m
a
l
F
a
ezy W
a
n
Zal
n
i
d
z
a
m)
87
3.
2.
Co
ordi
n
a
ted Smar
t Ch
argi
ng
The c
o
ordi
nat
e
d sm
art charging m
eans that each slot of charging
will allow only a
coordinated
num
ber
of
EV
t
o
t
a
l
cha
r
gi
ng
l
o
ad
rat
h
er t
h
an al
l
o
cat
i
n
g a
fi
xat
i
o
n
of
1/
4
of t
h
e t
o
t
a
l
c
h
ar
gi
n
g
l
o
ad
i
n
e
v
er
y
char
gi
n
g
sl
ot
a
s
i
n
t
h
e u
n
co
or
di
nat
e
d c
h
ar
gi
ng
. B
a
sed
on T
a
bl
e 1, t
h
e c
o
o
r
di
nat
i
on
of C
S
i
s
do
ne f
o
r t
h
e 40%
,
60
% an
d
80%
of E
V
penet
r
at
i
on. T
h
e
20
%EV
have ac
com
m
odat
e
al
l t
h
e m
i
nim
u
m
char
ger
s
re
qu
i
r
ed
f
o
r
ev
ery ch
arg
i
ng
slo
t
. Th
erefo
r
e, th
is p
e
n
e
t
r
atio
n
le
v
e
l is n
o
t
con
s
i
d
ered
fo
r co
ord
i
n
a
tio
n
.
Th
e 40
% u
n
til
80%EV
cases
show
that bus 3,19
a
n
d
23 allow m
o
re CS
to operate duri
ng the firs
t
and seco
nd c
h
ar
g
i
ng sl
ot
w
h
ile
b
u
s
25
,2
9,10
and
14
allo
w
m
o
r
e
C
S
to
o
p
e
r
a
te
du
r
i
n
g
t
h
e th
i
r
d and
th
e
fo
ur
th
ch
arg
i
ng
sl
ot. For
40%EV,
bus
6,31 a
n
d 22 c
ould accomm
odate
m
o
re CS
ope
ration duri
ng
the first and se
cond c
h
a
r
gi
ng
slot.
Howev
e
r, as th
e p
e
n
e
tration
lev
e
l in
crease up
to
80
%,
thos
e buses
only allow m
o
re CS to operate duri
ng the
t
h
i
r
d a
nd f
o
urt
h
cha
r
gi
ng sl
ot
. The cha
r
gi
ng
coo
r
di
nat
i
o
n o
f
C
S
i
s
great
l
y
i
n
fl
ue
nce
d
by
t
h
e l
o
ad
pr
ofi
l
e
.
The
fi
rst
a
n
d
sec
o
n
d
c
h
ar
gi
n
g
sl
ot
i
s
al
l
o
cat
ed
fr
om
170
0 t
o
0
0
0
0
w
h
i
c
h
i
s
i
n
t
h
e
peri
od
o
f
hi
g
h
er
l
o
a
d
de
m
a
nd
co
m
p
ared
to
t
h
e o
t
h
e
r two
sl
ots. Th
erefo
r
e, as p
e
n
e
t
r
atio
n
of EV i
n
crease,
m
o
re
ch
arg
i
n
g
activ
ities are sh
ifted
to
th
e
slo
t
s in between
00
00
t
o
09
00
b
ecau
s
e
o
f
lower l
o
ad de
m
a
n
d
d
u
ring
t
h
is tim
e in
terv
al.
Tabl
e
1. C
o
o
r
d
i
nat
e
d Sm
art
C
h
ar
gi
n
g
Sche
d
u
le for CS
Ope
r
ation at Selected B
u
ses
Penetr
ation
level
Slot
Nu
m
b
er
of allowable CS oper
a
tion at selected bus
Total CS
Bus
3
Bus
5
Bus
22
Bus
29
Bus
31
Bus
6
Bus
10
Bus
19
Bus
14
Bus
23
1
16
11
6
8
7
7
6
16
6
8
91
40%
2
5
9
5
7
18
18
6
5
8
15
96
3
13
9
18
11
10
7
13
9
8
5
103
4
6
11
11
14
5
8
15
10
18
12
110
1
19
9
11
8
5
14
6 12
6 25
115
60%
2
20
20
21
13
14
16
21
31
5
9
170
3
12
22
6
15
12
22
6 12
16
8
131
4
9
9
22
24
29
8
27
5
33
18
184
1
38
9
11
6
6
24
18
20
5
42
179
80%
2
7
26
38
8
11
12
11
35
33
22
203
3
28
24
19
36
43
16
41
14
6
7
234
4
7
21
12
30
20
28
10
11
36
9
184
Fi
gu
re
6 sh
o
w
s t
h
e l
o
ss
p
r
o
f
i
l
e
for
co
or
di
na
t
e
d cha
r
gi
ng i
n
24
h
o
u
r
s.
The
hi
g
h
est
l
o
ss
r
ecor
d
e
d
f
o
r
each penetration levels are 237.9
17kW, 258.821kW
and
289.730kW
for 40%,
60%
and 80%EV respectively.
Thi
s
s
h
ows
a
r
e
duct
i
o
n i
n
t
h
e
act
i
v
e
po
wer
l
o
sses c
o
m
p
ared t
o
t
h
e
u
n
c
o
or
di
nat
e
d c
h
ar
gi
n
g
sc
he
dul
e
fo
r al
l
th
e p
e
n
e
t
r
atio
n lev
e
ls. Th
e com
p
ariso
n
in
to
tal d
a
ily
lo
sses b
e
tween
th
e coo
r
d
i
n
a
ted
and
u
n
c
oo
rd
in
ated
sm
art
ch
arg
i
ng
is sho
w
n
i
n
Tab
l
e
2
.
Th
e coo
r
d
i
nated
ch
arg
i
ng
sch
e
d
u
l
e cou
l
d r
e
du
ce t
h
e lo
sses b
y
4
.
7
97kW
an
d
4
.
2
34kW
fo
r 40
% and
6
0
%
EV
resp
ectiv
ely.
Fi
gu
re
6.
C
o
or
di
nat
e
d
sm
art
char
gi
n
g
2
4
-
h
o
u
r
act
i
v
e
p
o
we
r
l
o
ss
0,000
50,000
100,000
150,000
200,000
250,000
300,000
350,000
16:00
19:00
22:00
1:00
4:00
7:00
10:00
13:00
Loss(
kW)
Hour(h)
0%
EV
20%
EV
40%
EV
60%
EV
80%
EV
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
502
-47
52
I
ndo
n
e
sian
J Elec Eng
& Com
p
Sci, V
o
l. 11
,
No
.
1
,
Ju
ly
20
18
:
82
–
89
88
Tabl
e
2. C
o
m
p
ari
s
o
n
bet
w
ee
n
u
n
co
o
r
di
nat
e
d
an
d c
o
o
r
di
nat
e
d c
h
ar
gi
n
g
Penetr
ation
level (%)
Total active powe
r
losses in a day(kW)
Loss
r
e
duction(
kW
)
Uncoor
dinated
Coor
dinated
0% 4018.
9
0
0
4018.
9
0
0
0
40%
4740.
6
5
0
4735.
8
5
3
4.
797
60%
5161.
5
6
7
5157.
3
3
3
4.
234
80%
5625.
8
9
3
5624.
1
0
1
1.
792
Fi
gu
re 7 s
h
o
w
s t
h
e vol
t
a
ge
p
r
o
f
i
l
e
at
bus 1
8
i
n
t
h
e co
or
di
nat
e
d ch
ar
gi
n
g
sched
u
l
e
. B
o
t
h
4
0
% an
d
6
0
% EV
p
e
n
e
tr
atio
n
lev
e
l sh
ow
s th
at the vo
ltag
e
is ma
in
tain
ed above 0
.
90
p.u. at
all h
o
u
r
s.
Th
e lo
w
e
st
vol
t
a
ge
rec
o
rd
ed
i
s
0.
9
0
1
p
.
u
.
at
08
0
0
fo
r 60
%EV.
Fig
u
r
e
7
.
Coo
r
d
i
n
a
ted 24-
hour
Vo
ltag
e
Prof
ile at Bu
s
1
8
4.
CO
NCL
USI
O
N
In a
di
st
ri
b
u
t
i
o
n sy
st
em
wi
t
h
hi
g
h
l
e
vel
s
o
f
EV pe
net
r
at
i
o
n
,
u
n
co
or
di
nat
e
d ve
hi
cl
e bat
t
e
ry
char
gi
n
g
may i
m
p
o
s
e su
b
s
tan
tial in
cre
m
en
tal lo
ad
s
to
d
i
stri
b
u
tion
tr
an
sf
or
m
e
r
s
, cau
se vo
ltag
e
r
e
gu
latio
n
pr
ob
lem
s
,
and c
o
nsi
d
e
r
ab
l
y
i
n
crease sy
st
em
l
o
sses. Thi
s
pape
r p
r
op
os
es an o
p
t
i
m
al
EV cha
r
gi
n
g
s
c
hed
u
l
e
co
or
di
nat
i
o
n
usi
n
g P
S
O
al
go
ri
t
h
m
s
. The
si
m
u
l
a
ti
on re
sul
t
s
f
o
r
IE
E
E
-3
3 B
u
s
di
st
ri
b
u
t
i
o
n
sy
st
em
are prese
n
t
e
d a
n
d
com
p
ared
wi
t
h
unc
o
o
r
d
i
n
at
e
d
and c
o
or
di
nat
e
d cha
r
gi
n
g
sc
hed
u
l
e
. T
h
e
pr
op
ose
d
P
S
O al
go
ri
t
h
m
appr
o
ach i
s
v
a
lid
ated
b
y
co
m
p
arin
g
its so
lu
tion
s
at
d
i
fferen
t EV
p
e
netratio
n
lev
e
ls. Th
e
PSO algo
rith
m
sch
e
du
l
e
th
e
charging activities by determining th
e
be
st com
b
ination of CS
ope
rat
i
on
for
10 sel
ected buses for each
ti
m
e
slo
t
. Th
e
resu
lts i
n
d
i
cat
e th
at th
e to
tal d
a
ily po
we
r
losses a
r
e
grea
tly affected by
the c
o
m
b
ination
of
di
ffe
re
nt
char
gi
n
g
dem
a
nd
at
di
ffe
rent
b
u
ses. T
h
e
r
ef
or
e, i
t
i
s
very
i
m
port
a
nt
t
o
consi
d
er t
h
e c
h
ar
gi
n
g
co
ord
i
n
a
tion
of th
e
g
r
id for
main
tain
in
g
t
h
e electrical syste
m
secu
rity.
Resu
lts sh
ow t
h
at
th
e
activ
e p
o
wer
lo
sses ar
e r
e
duced
w
h
en
an
op
ti
m
a
l ch
ar
g
i
ng
co
or
d
i
n
a
tio
n
o
f
CS is pr
oposed
co
m
p
ar
ed
t
o
th
e
un
coor
d
i
n
a
ted
c
h
a
r
g
i
ng
s
c
en
ar
io
f
o
r
ev
e
r
y ca
s
e
of
EV
p
e
ne
tr
a
tio
n
.
T
h
e fu
tur
e
wor
k
fo
r th
is
r
e
s
e
ar
ch
s
h
ou
ld
fo
cu
s
on
the
new a
p
pr
oach
of
opt
i
m
i
z
i
ng t
h
e sche
dul
e c
o
o
r
di
nat
i
o
n f
o
r t
h
e EV c
h
a
r
gi
n
g
dem
a
nd.
Pro
b
l
e
m
form
ul
at
i
o
n
sho
u
l
d
be
f
o
c
u
si
ng
o
n
t
h
e
de
cent
r
al
i
zed c
h
a
r
gi
ng
w
h
i
c
h
co
nsi
d
e
r
se
veral
aspect
s s
u
c
h
a
s
ra
nd
om
con
n
ect
i
o
n
of E
V
t
o
t
h
e
g
r
i
d
a
nd
va
ri
abl
e
pri
c
i
n
g sc
he
m
e
for t
h
e cha
r
gi
ng sc
he
dul
e
opt
i
m
i
zati
on.
In a
d
di
t
i
on,
ra
nd
om
co
nn
ection
o
f
EV t
o
th
e grid
an
d th
e
dece
nt
ral
i
zed c
h
ar
gi
n
g
s
h
o
u
l
d
be c
o
nsi
d
e
r
ed
i
n
t
h
e
p
r
o
b
l
e
m
form
ul
at
i
on
wh
en
op
ti
m
i
zi
n
g
th
e sch
e
du
le.
REFERE
NC
ES
[1]
Mukherjee J. C.
and Gupta A., “A Re
view of Ch
arge Schedu
ling
of Elec
tric Vehicles in Smart Grid,”
IEEE Sy
ste
m
s
Journal,
vo
l/issu
e: 9(4)
, pp
. 1541
-1553, 2015
.
[2]
Tan K. M. and
R
a
machandaramurth
y
V. K., “Integration
of
electr
ic veh
i
cles in
smart gr
id: A rev
i
ew
onvehicle
to grid
technolo
g
ies and
optimization
techniqu
es,”
Ren
e
wable an
d Sustainable Energy Reviews,
vol. 53
, pp
. 720
-
732, 2016
.
0,89
0
0,89
5
0,90
0
0,90
5
0,91
0
0,91
5
0,92
0
0,92
5
0,93
0
0,93
5
16:0
0
19:0
0
22:0
0
1:00
4:00
7:00
10:0
0
13:0
0
Voltage (p.u)
Hour(h)
0%
EV
20%
EV
40%
EV
60%
EV
80%
EV
Evaluation Warning : The document was created with Spire.PDF for Python.
In
d
onesi
a
n
J
E
l
ec En
g &
C
o
m
p
Sci
ISS
N
:
2
5
0
2
-
47
52
Opt
i
m
al
C
har
g
i
ng
Sc
hed
u
l
e
C
oor
di
nat
i
o
n
of
El
ect
ri
c Vehi
cl
es ..
. (
W
a
n
Iq
m
a
l
F
a
ezy W
a
n
Zal
n
i
d
z
a
m)
89
[3]
Gong X. and Lin T., “Optimal
Decision-makin
g
on Charging
of Electric Vehicles,”
TELKOMNIKA Indonesia
n
Journal of Electr
ical Engineerin
g,
vol/issue: 12(
4), pp
. 2431-243
8, 2014
.
[4]
Ahmad M. R.
and Othman M. M., “Optimal Charging
Strateg
y
for Plug-in
Hy
br
id Electr
i
c Vehicle Using
Evolution
a
r
y
Algorithm,”
I
EEE
8
th
Internation
a
l Power
Engin
eering and
Optimization Confer
ence (
PEOCO)
.
Langkawi
, pp
. 5
57-562, 2014
.
[5]
Celli G. and Ghiani E.
, “
P
artic
le Swarm
Opti
m
i
zation for
m
i
nim
i
zing the bu
rden of elec
tric
vehic
l
es in acti
v
e
distribution
networks,”
I
EEE Po
wer and En
ergy
Society G
e
neral
Meeting
, pp. 1-7
,
2012
.
[6]
Sortomme E. and E. Sharkawi M.
A.
, “
O
pti
m
a
l charg
i
ng s
t
rateg
i
es
fo
r un
idirectional veh
i
cle-
to-grid,”
I
E
EE
Transactions on
Smart Grid
, vol/issue: 2(1), pp. 1
19-126, 2011
.
[7]
Marra F. and Yang G. Y., “Imp
rovement of local voltage in f
eeders with
pho
tovoltaic using electric vehicles
,”
IEEE Transactio
ns on Power
Sys
t
em,
vo
l/issue: 2
8
(3), pp
. 3515-3
516, 2013
.
[8]
Knezovi
ć
K
.
an
d Marinelli M., “Phase-wise enhanced vo
lt
age
support from electr
i
c v
e
hicles in a Danish low
-
voltag
e
distr
i
bution grid,”
Ele
c
tri
c
Pow
e
r Syst
em
Research,
vol. 1
40, pp
. 274-283
, 2016.
[9]
Pillai J
.
R.
and B
.
Jensen B., “Vehicl
e
-to-Grid
for
islanded power
s
y
stem
operation
in Bornholm
,
”
I
EEE Power
and
Ener
gy So
ci
ety
Gener
a
l Me
et
ing
.
Pr
ov
iden
ce
,
pp
. 1-8, 2010.
[10]
Jian L. and Zh
eng Y., “Optimal scheduling for
vehicle-
to-gr
i
d
operation with stocha
stic conn
ection of plug-in
ele
c
tri
c
v
e
hic
l
es
to sm
art grid
,”
Applied
Energy,
v
o
l. 146
, pp
. 150-
161, 2015
.
[11]
Tan K. M. and Ramachandar
a
murth
y
V.
K., “
M
inim
ization of
Load Varianc
e
in Power
Grids-Investiga
tion on
Optimal Vehicle-to-Grid Schedu
ling,”
En
er
gies
,
v
o
l/issue: 1
0
(11),
pp. 1-21
, 2017
.
[12]
Putrus G. A.
an
d Suwanapingkarl
P., “Impact of
electr
i
c vehicles on power d
i
str
i
bution n
e
twork
s
,”
IEEE Vehicle
Power and
Propulsion Conferen
ce. Dearborn
, p
p
. 827-831
, 200
9.
[13]
Fofana G. H. and Zhang Y
.,
“
E
le
ctri
c Vehi
cl
e
Lith
ium
Ion Batt
eries
Therm
a
l
Managem
e
nt
,”
TE
LKOMNIKA
Indonesian Jour
nal of El
ectrical Engineering,
vol/issue: 12(3)
, pp
. 2414-2421, 201
4.
[14]
Luo H. and L
i
F., “
A
Method fo
r Ele
c
tri
c
Vehi
cl
e Owner
ship Forecast Consid
eri
ng Different
Ec
onom
ic Factors,”
TE
LKOMNIKA
Te
le
c
o
mmunic
a
tion Com
puting
Electronics
and
Control,
vo
l/issue: 11(4)
, pp
. 223
9-2246, 2013
.
[15]
Gia I. K.
and J
a
m
i
an J
.
J
., “
O
pt
im
um distribution network oper
a
tion consid
ering
distributed gen
e
ration mode of
operations and sa
fe
ty
ma
rgi
n
,”
IET Renewable
Power Gene
ration Power Generation,
vol/issue: 10(8), pp. 1049-
1058, 2016
.
[16]
Deilami S.
and
Masoum A. S.,
“Real-T
im
e Coo
r
dination
of Plu
g
-In Ele
c
tr
ic Ve
hicl
e Charg
i
ng i
n
Sm
art Grids t
o
Minimize Power Losses and I
m
p
r
ove Voltage Pr
ofile,”
IEEE Transaction Smart Grid,
vol/issue: 2(3), pp. 456-467,
2011.
Evaluation Warning : The document was created with Spire.PDF for Python.