Indonesian
J
our
nal
of
Electrical
Engineering
and
Computer
Science
V
ol.
25,
No.
1,
January
2022,
pp.
25
∼
34
ISSN:
2502-4752,
DOI:
10.11591/ijeecs.v25.i1.pp25-34
❒
25
Optimizing
the
effect
of
char
ging
electric
v
ehicles
on
distrib
ution
transf
ormer
using
demand
side
management
Swapna
Ganapaneni,
Srini
v
asa
V
arma
Pinni
Department
of
Electrical
and
Electronics
Engineering,
K
oneru
Lakshmaiah
Education
F
oundation,
Guntur
,
India
Article
Inf
o
Article
history:
Recei
v
ed
Mar
14,
2021
Re
vised
Oct
27,
2021
Accepted
No
v
26,
2021
K
eyw
ords:
Char
ging
Demand
side
management
Distrib
ution
transformer
Electric
v
ehicle
Ov
er
loading
Scheduling
ABSTRA
CT
This
paper
mainly
aims
to
present
the
demand
side
ma
nagement
(DSM)
of
electric
v
e-
hicles
(EVs)
by
considering
distrib
ution
transformer
capacity
at
a
residential
area.
The
objecti
v
e
f
unctions
are
formulate
d
to
obtain
char
ging
schedule
for
indi
vidual
o
wner
by
i)
minimizing
the
load
v
ariance
and
ii)
price
indicated
char
ging
mechanism.
Both
the
objecti
v
e
functions
prot
the
o
wner
in
the
follo
wing
w
ays:
i)
fullling
their
needs,
ii)
minimizing
o
v
erall
char
ging
cost,
iii)
lessening
the
peak
load,
and
i
v)
a
v
oiding
the
o
v
erloading
of
distrib
ution
trans
former
.
The
proposed
objecti
v
e
functions
were
w
ork
ed
on
a
residential
area
with
8
houses
each
house
with
an
EV
connected
to
a
20
kV
A
dist
rib
ution
transformer
.
The
formulations
were
tested
in
LINGO
platform-
optimization
modeling
softw
are
for
linear
,
nonlinear
,
and
inte
ger
programming.
The
results
obtained
were
compa
red
which
gi
v
es
good
insight
of
EV
load
scheduling
with-
out
actual
price
prediction.
This
is
an
open
access
article
under
the
CC
BY
-SA
license
.
Corresponding
A
uthor:
Sw
apna
Ganapaneni
Department
of
Electrical
and
Electronics
Engineering,
K
oneru
Lakshmaiah
Education
F
oundation
Green
Fields,
V
addesw
aram,
Guntur
District,
Andhra
Pradesh,
522502,
India
Email:
sw
apna@kluni
v
ersity
.in
1.
INTR
ODUCTION
Electric
v
ehicle
(EV)
is
the
one
can
satis
fy
the
need
of
future
t
ransportation
due
to
lack
of
enough
fossil
fuels
which
tops
the
demand
for
electrical
ener
gy
.
In
such
a
situation
if
char
ging
and
dischar
ging
of
EVs
are
not
handled
properly
will
o
v
erloads
the
grid.
In
this
re
g
ard
char
ging
the
EV
at
residential
places
is
a
k
e
y
issue
resulting
in
se
v
eral
technical
problems
at
the
le
v
el
of
distrib
ution
transformer
,
demand
side
management
(DSM)
is
possibly
a
good
solution.
An
o
v
ervie
w
on
the
literature
of
DSM
techniques
is
presented
here.
Rapid
increase
in
day
to
day
el
ec-
tricity
demand,
DSM
helps
to
a
v
oid
utilities
b
uilding
e
xtra
capacity
of
the
generation
by
means
of
decreasing
the
peak
demand
through
shifting
and
adjusting
customers
electricity
consumption.
DSM
mainly
in
v
olv
es
three
programs
lik
e
ef
cient
ener
gy
management
(EM),
demand
response
(DR),
ef
fecti
v
e
load
management
(ELM)
by
the
customers
[1].
Figure
1
represents
the
detailed
classication
of
DSM.
Ener
gy
management
(EM)
mainly
aims
to
reduce
ener
gy
consumption
which
automatically
minim
izes
the
ener
gy
cost.
A
good
scope
of
ener
gy
management
can
be
easily
achie
v
ed
in
v
arious
sectors
lik
e
industrial,
commercial,
agricultural
and
e
v
en
in
households
if
ener
gy
sa
ving
tips
are
follo
wed.
Proper
m
aintenance
of
boilers,
steam
systems,
compressed
air
systems,
motor
and
dri
v
e
systems,
and
lightening
aspects,
will
lo
wers
the
ener
gy
usage.
T
ime
to
time
audit
of
ener
gy;
a
w
areness
and
training
programs;
and
good
metering
and
billing
systems,
are
the
important
aspects
through
which
ef
cient
ener
gy
management
is
obtained
[1].
Demand
response
(DR)
is
one
important
polic
y
of
DSM
which
concentrates
mainly
on
the
pricing
J
ournal
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http://ijeecs.iaescor
e
.com
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26
❒
ISSN:
2502-4752
system
to
manage
the
peak
l
oad.
DR
denotes
“changes
in
electric
usage
by
end-use
customers
from
their
normal
consumption
patterns
in
response
to
changes
in
the
price
of
electricity
o
v
er
time,
or
to
incenti
v
e
payments
designed
to
induce
lo
wer
electricity
use
at
times
of
high
wholesale
mark
et
prices
or
when
system
reliability
is
jeopardized”
[2].
Figure
1.
Classication
of
demand
side
management
techniques
T
w
o
major
classication
are
done
in
the
DR
programs
based
on
the
pricing
system
are
t
ime-based
pricing
system
(TBPS)
and
incenti
v
e-based
pricing
system
(IBPS)
[3].
Direct
load
control
(DLC),
interrupt-
ible/curtailable
service
(I/CS),
emer
genc
y
demand
response
program
(EDRP),
capacity
management
(CM),
demand
bidding
(DB),
ancillary
s
ervice
mark
et
(ASM)
are
classied
under
IBPS
whereas
time
of
use
(T
oU),
real
time
pricing
(R
TP),
critical
peak
pricing
(CPP)
are
cate
gorized
under
TBPS.
–
Direct
load
control
(DLC):
DLC
is
one
approach
of
DR
where
customer’
s
loads
are
shuts
do
wn
by
the
utilities
remotely
on
short
notice
for
reliability
problems.
This
is
mainly
e
x
ecuted
on
small
consumers
lik
e
residential
and
small
commercial
customer
[4].
–
Interruptible/curtailable
service
(I/CS):
It
is
the
program
where
customers
on
curtail
of
their
equipment
gets
a
discount
or
credited
on
their
bill
when
the
system
is
under
contingenc
y
and
if
the
y
do
not
agree
to
curtail,
the
y
are
penalized.
–
Emer
genc
y
demand
response
program
(EDRP):
When
an
e
v
ent
occurs,
emer
genc
y
DR
is
a
usual
program
to
implement.
In
the
case
of
reliability
e
v
ents
EDRP
of
fers
incenti
v
es
to
customers
for
reducing
their
loads
and
cannot
be
penalized
for
not
curtailing
their
load
because
the
prices
are
pre-specied
[5].
–
Capacity
ma
n
a
gement
(CM):
I
t
is
a
demand
side
resource,
during
contingencies
it
commits
to
reduce
pre
specied
amount
of
load
and
penalizes
the
participants
if
the
y
do
not
c
u
r
tail
the
load
on
instructions.
Customers
obliging
the
instructions
are
guaranteed
to
recei
v
e
payments
in
e
xchange.
–
T
oU:
Prices
are
set
in
adv
ance
b
ut
dif
fers
depending
on
the
ti
mes
of
the
day
and
will
not
reect
an
y
adjustments
to
the
actual
conditions
of
the
system.
Consumers
will
not
ha
v
e
an
y
incenti
v
es
for
reduced
consumption
in
electricity
during
peak
periods
and
hourly
metering
is
not
required.
–
R
TP:
It
is
also
termed
as
dynamic
pricing
as
prices
v
aries
with
real
time
conditions
and
reects
the
actual
phenomenon
of
the
system
by
pro
viding
best
information
about
the
po
wer
a
v
ailable
at
a
location.
Ener
gy
consumption
should
be
measured
on
hourly
basis
as
it
is
char
ged
appropriately
,
and
customers
are
of
fered
with
incenti
v
es
for
their
reduced
consumption
of
ener
gy
during
peak
periods.
–
CPP:
This
is
a
dynamic
pricing
scheme
where
fe
w
peak
hours
are
cha
r
ged
with
high
prices
to
reduce
peak
demand
and
other
time
periods
are
char
ged
with
normal
prices,
there
by
permits
the
customers
to
minimize
their
o
v
erall
ener
gy
bill.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
25,
No.
1,
January
2022:
25–34
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
27
Ef
fecti
v
e
load
management
(ELM)
techniques
are
the
one
where
utility
tries
to
reduce
the
peak
con-
sumption
by
the
subsequent
approaches
lik
e
peak
clipping,
v
alle
y
lling,
load
shifting,
strate
gic
conserv
ation,
strate
gic
load
gro
wth,
e
xible
load
shape
[6].
–
Peak
clipping:
It
is
the
w
ay
where
load
is
reduced
during
peak
time
by
means
of
DLC
lik
e
shutting
do
wn
the
equipment
of
the
consumer
.
This
method
will
not
greatly
inuence
the
entire
load
curv
e
b
ut
reects
in
reduction
of
load
during
peak
period.
–
V
alle
y
lling:
It
encourages
ener
gy
consum
ption
during
of
f
peak
hours
by
the
customers
o
v
er
of
fering
in-
centi
v
es
lik
e
allo
wing
them
to
pay
lo
w
tarif
f
for
changing
their
schedule
to
of
f
peak
hours,
and
discounts.
–
Load
shifting:
This
method
aims
to
shift
the
load
during
peak
hours
to
period
where
load
is
lessened
ho
we
v
er
o
v
erall
demand
remains
constant
in
this
phenomenon.
–
Strate
gic
conserv
ation:
It
mainly
dri
v
es
to
bring
do
wn
seasonal
ener
gy
consumption
by
encouraging
consumers
to
w
ards
the
use
of
ef
cient
de
vices
and
appliances,
decreasing
w
astage
of
ener
gy
.
It
also
of
fers
incenti
v
es
to
consumers
who
adopts
technological
changes
in
their
usage.
–
Strate
gic
load
gro
wth:
It
mainly
tries
to
control
seasonal
increase
in
ener
gy
consumption.
The
dealership
emplo
ys
intelligent
systems,
ef
fecti
v
e
de
vices
and
more
viable
sources
of
ener
gy
to
reach
their
goals.
–
Fle
xible
load
shape:
It
includes
set
of
acti
vities
and
inte
grated
planning
between
concessionary
and
the
customer
render
ing
to
the
requirement
of
the
moment.
Consumer
loads
are
modeled
with
the
help
of
load
limiting
equipment
such
that
there
will
not
be
much
de
viation
in
the
actual
load
and
will
not
disturb
security
issues.
Upgrading
the
distrib
ution
transformer
with
penetration
of
EV
in
the
distrib
ution
system
is
a
cost
e
x-
pensi
v
e
and
unplanned
char
ging
of
these
EVs
may
cause
the
grid
o
v
erloading.
Therefore,
a
strate
gy
DR
is
applied
to
a
v
oid
o
v
erloading
of
transformers
by
considering
the
priority
of
each
indi
vidual
home
and
con
v
e-
nience
preference
setting.
Ho
we
v
er
,
impact
of
v
arying
price
signals
is
not
considered
in
applying
DR
[7].
Real
time
optimal
scheduling
of
a
battery
ener
gy
storage
system
is
proposed
to
reduce
peak
load
of
a
b
uilding
as
an
DSM
technique
to
reduce
cost
of
electrical
ener
gy
in
[8]
and
i
n
t
e
gration
of
r
ene
w
able
ener
gy
sources
are
not
considered.
Microgrid
grid
resources
were
inte
grated
to
the
Indian
distrib
ution
system
and
ha
v
e
been
scheduled
to
reduce
dependenc
y
on
main
grid
and
on
the
other
hand
peak
loads
were
managed
by
means
of
e
xible
load
shaping
which
is
a
tool
of
DSM
minimizes
the
customer’
s
dissatisf
action
e
v
en
diminished
the
operation
cost
of
micro
grid
[9].
PEVs
char
ging
control
is
done
based
on
a
ne
w
distrib
uted
random-access
approach
which
does
not
need
centralized
control
and
can
be
e
x
ecuted
in
real
time.
The
w
ork
dif
fers
from
the
e
xisting
methodologies
as
it
considers
the
historical
data
to
coordinate
smart
agents
rather
than
R
TP
[10].
Multi
objecti
v
e
formulations
were
done
in
[11]
to
minimize
total
ener
gy
generation
and
cost
associated
for
implementing
DSM
such
that
ener
gy
planning
w
as
done
in
a
decentralized
manner
,
PEVs
char
ging
is
shifted
result
ed
in
reduction
of
total
emissions
and
sa
vings
in
cost.
Scheduling
of
PEVs
at
a
b
uilding
g
arage
to
reduce
the
peak
load
and
ener
gy
cost
is
achie
v
ed
in
[12]
by
formulating
an
optimization
model
which
minimizes
the
square
of
the
Euclidean
distance.
Similarly
,
in
a
decentralized
system,
non-cooperati
v
e
g
ame
approach
is
follo
wed
for
obtaining
char
ging
and
dischar
ging
schedules
of
the
batteries
and
a
distrib
uted
algorithm
w
as
de
v
eloped
where
the
total
ener
gy
char
ging
cost
of
a
PEV
is
minimized.
Ho
we
v
er
,
a
pricing
mechanism
for
v
ehicle
to
b
uilding
model
is
not
proposed,
impact
of
dis-
char
ging
process
of
battery
on
its
life
is
not
e
v
aluated,
rene
w
able
ener
gy
sources
inte
gration
is
not
considered.
Dynamic
pricing
mechanism
is
one
of
the
possible
solutions
to
achie
v
e
DSM
w
as
w
ork
ed
out
in
[13]
e
v
aluated
the
ef
fects
of
herding
unusual
participation
of
customers,
laziness
of
customers,
and
dif
ferent
usage
group
of
customers.
Minimum
size
of
the
ener
gy
storage
system
is
proposed
in
Plug
in
electric
v
ehicle
char
ging
station
supported
by
rene
w
able
ener
gy
sources
[14].
Quadratic
programming
(QP)
and
m
ulti
agent
system
(MAS)
approaches
were
discussed
and
com-
pared
by
Mets
et
al.
in
[15]
reduced
the
peak
load
and
v
ariability
in
the
load
of
a
distrib
ution
grid.
MAS
pro
v
ed
to
be
the
best
ho
we
v
er
QP
results
gi
v
e
more
optimal
solutions.
Ho
we
v
er
,
EVs
char
ged
at
of
f
peak
time
can
be
helped
to
dischar
ge
their
ener
gy
during
peak
periods
back
to
the
grid
and
v
ehicles
arri
ving
randomly
to
char
ge
at
the
w
orkplace
are
not
considered.
A
micro
grid
consis
ting
of
wind,
photo
v
oltaic
generation,
utilized
the
stationary
plug
in
h
ybrid
electric
v
ehicles
by
de
v
eloping
a
n
optimal
schedule
for
char
ging
them
to
support
the
dynamic
nature
of
rene
w
able
resources
is
proposed
in
[16].
A
no
v
el
algorithm
is
proposed
to
char
ge
lar
ge
EV
eet
by
predicting
their
load
day
a
head
satisfying
grid
constraints,
the
indi
vidual
requirements
of
the
customer
Optimizing
the
ef
fect
of
c
har
ging
electric
vehicles
on
distrib
ution
tr
ansformer
using
...
(Swapna
Ganapaneni)
Evaluation Warning : The document was created with Spire.PDF for Python.
28
❒
ISSN:
2502-4752
lik
e
arri
v
al
and
departure
times,
by
minimizing
their
o
v
erall
char
ging
cost,
de
v
eloping
their
indi
vidual
plans
in
[17].
P
article
sw
arm
optimization
is
implemented
to
optimally
schedule
the
EVs
in
a
coordinated
manner
and
minimized
the
acti
v
e
po
wer
losses
compared
to
uncoordinated
char
ging
of
EVs
for
an
IEEE-33
b
us
radial
system
[18].
A
meta
heuristic
algorithm
used
as
optimization
algorithm
in
[19]
to
optimize
demand
side
of
en-
hance
time
of
use
(ET
OU)
pricing
for
a
commercial
load
demand
and
signicantly
analyzed
that
the
technique
shifted
the
maximum
demand
from
peak
time
to
of
f
peak
time
which
mi
nimized
the
cost
of
electricity
.
Impacts
of
EV
technology
and
ho
w
the
y
help
the
w
orld’
s
gro
wing
demand
for
ener
gy
is
demonstrated
in
[20].
Increase
in
number
of
EVs
gro
ws
demand
for
electricity
and
to
a
v
oid
interruptions
in
the
grid,
PV
in-
te
gration
with
EV
char
ging
station
is
presented
thoroughly
in
[21].
Ener
gy
controller
for
micro
grid
is
designed
in
[22]
to
de
v
elop
char
ging
and
dischar
ging
schedules
of
EVs
by
absorbing
o
v
er
produced
electricity
with
the
inte
gration
of
rene
w
able
ener
gy
and
sha
v
es
the
peak
load
of
the
micro
grid.
This
paper
introduced
an
objecti
v
e
function
to
minimize
the
load
v
ariance
and
price
indicated
char
ging
mechanism
for
controlled
scheduling
of
EVs
during
v
alle
y
hours.
Finally
results
obtained
in
both
the
methods
were
compared
and
highlighted
the
bes
t
solution.
The
paper
is
outlined
as
follo
ws:
i)
Section
2
consists
of
problem
formulation,
modelling
of
EV
load;
ii)
Section
3
consists
of
tw
o
objecti
v
e
functions
formulations;
and
iii)
Section
4
summarizes
the
results
by
comparing
both
the
scheduling
schemes.
2.
PR
OBLEM
FORMULA
TION
The
residential
area
under
consideration
is
serv
ed
by
a
20
kV
A
distrib
ution
transformer
from
the
grid.
It
is
ha
ving
8
houses
and
each
house
with
an
EV
as
sho
wn
in
Figure
2.
Each
household
load
is
the
po
wer
consumed
for
lighting,
air
conditioner
,
w
ashing
machine,
and
w
ater
hea
ter
.
and
not
including
the
EV
load.
The
household
load
prole
is
adopted
from
[23]
which
is
in
f
act
considered
from
the
website
of
electric
reliability
council
of
T
e
xas
(ERCO
T),
a
South-Central
T
e
xas
residential
area.
Basic
household
load
prole
of
a
day
is
sho
wn
in
Figure
3.
2.1.
Stochastic
EV
load
modelling
As
EVs
char
ging
adds
e
xtra
load
to
the
distrib
ution
transformer
there
is
a
need
to
kno
w
about
their
daily
tra
v
elling
distances,
esti
mating
initial
state
of
char
ge
(SoC),
starting
time
of
char
ging
to
balance
the
ener
gy
and
to
a
v
oid
upgrading
the
e
xisting
transformer
.
T
o
account
uncertainties
in
the
beha
vior
of
EV
load,
probabilistic
distrib
ution
functions
are
used
to
estimate
arri
v
al
time
of
the
v
ehicle,
distance
tra
v
elled,
initial
SoC
and
time
required
to
char
ge
its
battery
.
As
p
e
r
national
househol
d
tra
v
el
surv
e
y
(NHTS)
2009
report
which
pro
vides
complete
details
of
transportation
in
US,
daily
distance
tra
v
elled
by
an
EV
follo
ws
Lognormal
distrib
ution
and
arri
v
al
time
of
the
v
ehicle
follo
ws
Gaussian
distrib
ution
functions.
Distance
tra
v
elled
by
most
of
the
people
is
around
20-25
miles
a
day
and
more
than
half
of
the
people
tra
v
el
less
than
30
miles/day
[23].
The
tra
v
elled
dist
ance
in
miles
per
day
can
be
approximated
by
Lognormal
distrib
ution
gi
v
en
by
(1)
with
mean
(
µ
)
of
3.37
and
standard
de
viation
(
σ
)
of
0.5
and
it
is
sho
wn
in
Figure
4.
F
dist
(
d
)
=
1
dσ
√
2
π
e
−
(
l
nd
−
µ
)
/
2
σ
2
for
d>
0
(1)
%
of
soc
j
=
[1
−
(
E
C
∗
d
j
)
/C
bat
]
∗
100
(2)
Based
on
the
distance
tra
v
elled
initial
state
of
char
ge
(SoC)
of
all
EVs
can
be
estimated
as
follo
wing
from
(2).
Where
soc
j
is
the
initial
soc
of
j
th
v
ehicle.
d
j
is
the
distance
tra
v
elled
in
miles
by
j
th
v
ehicle.
E
c
is
the
ener
gy
consumed
in
kWh/
miles.
C
bat
is
the
battery
capacity
in
kWh.
The
EV
model
considered
in
this
study
is
Niss
an
Leaf
2016
model,
a
car
which
solely
runs
on
electricity
with
24
kWh
battery
capacity
and
ha
ving
0.28
kWh/miles
consumption.
Ener
gy
still
required
and
time
needed
to
char
ge
the
battery
can
be
obtained
as
from
(3)
and
(4).
E
r
eq
j
=
soc
f
−
soc
j
η
∗
C
bat
(3)
Where
E
r
eq
j
ener
gy
required
to
ll
j
th
v
ehicle’
s
battery
in
kWh,
η
is
the
ef
cienc
y
of
the
char
ger
which
is
considered
as
0.95,
S
oC
f
is
the
nal
SoC
to
be
attained
by
the
end
of
the
char
ging
and
(4),
C
time
=
E
r
eq
j
/P
(4)
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
25,
No.
1,
January
2022:
25–34
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
29
where
C
time
is
the
char
ging
time
required
to
char
ge
battery
in
Hours,
P
is
the
output
po
wer
of
the
char
ger
in
kW
.
Since
it
is
assumed
char
ging
EVs
is
done
at
residential
area
from
[24],
[25],
le
v
el
1
char
ging
output
po
wer
is
1.44
kW
(120
V
,12
A)
and
for
le
v
el
2
it
is
3.3
kW
(240
V
,14
A)
are
considered.
Most
of
the
household
o
wners
char
ge
their
EVs
after
the
y
return
home
from
w
ork
at
16:00
to
21:00
according
to
NHTS
2009
report,
the
randomness
in
connecting
EVs
to
start
char
ging
follo
ws
Gaussian
distrib
ution
as
gi
v
en
in
(6)
with
a
mean
(
η
)
of
17:00
and
standard
de
viation
(
σ
)
of
2.28.
The
distrib
ution
function
is
described
as
follo
ws
and
the
distrib
ution
curv
e
for
arri
v
al
time
of
the
EV
is
sho
wn
in
Figure
5.
F
A
(
t
)
=
1
σ
√
2
π
e
−
(
t
−
µ
)
/
2
σ
2
,
for
0
<t<
24
(5)
Figure
2.
Residential
area
serv
ed
by
distrib
ution
transformer
under
study
Figure
3.
Household
load
prole
of
a
day
T
o
come
up
with
randomness
in
the
arri
v
al
time
and
distance
tra
v
elled
by
the
EVs
random
function
is
applied
to
their
probability
distrib
ution
function
such
that
arri
v
al
ti
me
and
distance
tra
v
elled
by
each
EV
are
obtained.
The
uncontrolled
load
curv
e
which
include
EVs
along
with
household
load,
household
load
and
EV
load
are
sho
wn
in
Figure
6
and
Figure
7
respecti
v
ely
for
le
v
el
1
and
le
v
el
2
char
ging.
The
uncontrolled
load
results
from
household
load
and
EVs
load
when
e
v
ery
household
o
wner
connects
their
EV
immediately
the
y
arri
v
e
home.
It
is
observ
ed
that
from
Figure
5
and
Figure
6
due
to
uncontrolled
char
ging
of
EVs
distrib
ution
transformer
is
o
v
erloaded
for
4
hours
about
25%
in
le
v
el
1
whereas
for
almost
2
hours
to
50%
in
le
v
el
2
char
ging.
Optimizing
the
ef
fect
of
c
har
ging
electric
vehicles
on
distrib
ution
tr
ansformer
using
...
(Swapna
Ganapaneni)
Evaluation Warning : The document was created with Spire.PDF for Python.
30
❒
ISSN:
2502-4752
Figure
4.
Distrib
ution
of
distance
tra
v
elled
in
miles
Figure
5.
Distrib
ution
of
arri
v
al
time
of
EVs
Figure
6.
Uncontrolled
char
ging
of
EVs
o
v
erloading
distrib
ution
transformer
in
le
v
el
1
char
ging
Figure
7.
Uncontrolled
char
ging
of
EVs
o
v
erloading
distrib
ution
transformer
in
le
v
el
2
char
ging
3.
OPTIMIZA
TION
PR
OBLEMS
FOR
CONTR
OLLED
CHARGING
OF
EVS
In
this
section
tw
o
objecti
v
e
function
are
designed
based
on
DSM
methodologies
to
control
the
char
g-
ing
of
EVs
such
that
optimal
schedule
of
EVs
are
obtained
which
allo
ws
distrib
ution
transformer
to
operate
within
its
capacity
limits.
3.1.
Method
1:
Minimizing
load
v
ariance
The
idea
behind
this
objecti
v
e
function
is
to
reduce
load
during
peak
hours,
atten
the
load
curv
e
which
minimizes
the
v
ariance
of
ener
gy
required
in
a
day
.
Minimizing
the
dif
ference
of
load
between
of
f
peak
hours
and
peak
hours
helps
the
distrib
ution
transformer
to
function
at
high
ef
cienc
y
.
Appropriate
Scheduling
of
EVs
achie
v
es
this
objecti
v
e
more
easily
.
The
objecti
v
e
formulation
of
DSM
can
be
e
xpressed
as
follo
ws
and
EV
ij
is
the
optimization
v
ariable.
M
inimiz
e
τ
−
24
X
i
=1
8
X
j
=1
(
H
ij
+
E
V
ij
)
2
(6)
Where
τ
is
the
total
load
prole
to
be
met
in
a
day
in
kW
.
H
ij
is
the
j
th
home
load
prole
at
i
th
time
period
in
kW
.
E
V
ij
is
the
char
ging
po
wer
of
the
j
th
v
ehicle
at
i
th
time
period
in
kW
.
and
τ
=
8
X
j
=1
24
X
i
=1
H
ij
+
E
r
eq
j
!
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
25,
No.
1,
January
2022:
25–34
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
31
Subjected
to
follo
wing
constraints,
∀
j
soc
j
+
td
X
i
=
ta
E
V
ij
∗
η
≤
E
r
eq
j
∀
i
and
∀
j
0
≤
E
V
ij
≤
E
max
j
∀
i
8
X
j
=1
(
H
ij
+
E
V
ij
)
≤
P
tr
ans
where
S
oc
j
is
the
e
xisting
soc
of
the
j
th
v
ehicle
before
it
connects
for
char
ging
in
kW
.
η
is
the
ef
cienc
y
of
the
char
ger
which
is
considered
as
0.95.
E
r
eq
j
char
ging
po
wer
needed
to
ll
j
th
v
ehicle’
s
battery
in
kW
.
E
max
j
is
the
po
wer
rating
of
the
char
ger
in
kW
.
P
tr
ans
transformer
load
in
kW
.
ta,td
are
arri
v
al
and
departure
times
respecti
v
ely
.
3.2.
Method
2:
Price
indicated
char
ging
mechanism
The
char
ging
price
of
the
EVs
mainly
depends
on
the
uctuations
in
the
load.
Prior
information
about
electricity
price
helps
consumers
to
shift
their
load
from
peak
period
to
of
f
peak
period.
The
main
aim
of
this
objecti
v
e
is
to
shift
load
from
peak
hours
to
of
f
peak
hours
with
the
help
of
a
price
indicator
.
Thus,
this
objecti
v
e
function
schedules
the
char
ging
of
EVs
more
optimally
without
kno
wledge
of
real
time
prices
such
that
it
minimizes
the
char
ging
cost
of
the
EVs
and
pre
v
ents
distrib
ution
transformer
getting
o
v
erloaded.
Therefore,
the
objecti
v
e
function
is
(7),
M
I
N
24
X
i
=1
E
i
+
24
X
i
=1
8
X
j
=1
P
ij
∗
C
i
(7)
where
C
i
=
δ
i
/δ
T
and
E
i
is
the
total
household
load
prole
at
i
th
hour
in
kW
.
P
ij
is
the
char
ging
po
wer
of
the
j
th
v
ehicle
at
i
th
time
period
in
kW
.
C
i
is
the
price
indicator
which
is
the
ratio
of
household
load
at
i
th
period
to
the
a
v
erage
household
load
of
the
day
.
δ
i
is
the
household
load
at
i
th
period
in
kW
.
δ
T
is
the
a
v
erage
household
load
of
the
day
in
kW
.
4.
SIMULA
TION
RESUL
TS
This
section
presents
the
results
of
the
proposed
optimization
problems
in
tw
o
dif
ferent
char
ging
le
v
els.
The
proposed
objecti
v
es
are
e
v
aluated
in
LINGO
platform.
The
aim
is
to
achie
v
e
char
ging
plan
of
each
v
ehicle
while
satisfying
the
capacity
of
the
distrib
ution
transformer
capacity
,
minimizing
the
total
cost
of
char
ging
EVs
and
lling
the
batteries
to
90%
of
the
SoC.
The
optimization
is
performed
on
a
residential
area
with
household
load,
EV
load
obtained
from
stochastic
modelling
and
the
total
load
on
distrib
ution
transformer
based
on
uncontrolled
char
ging
of
EVs.
Here
total
load
represents
the
sum
of
household
load
and
EVs
load.
Figure
6
and
Figure
7
presents
the
20
kV
A
distrib
ution
transformer
o
v
erloading
condition
for
le
v
el
1
and
le
v
el
2
respecti
v
ely
for
uncontroll
ed
char
ging
of
EVs.
Figure
8
and
Figure
9
for
le
v
el
1
and
le
v
el
2
respecti
v
ely
sho
ws
the
same
after
implementing
the
optimization
for
method
1
alle
viating
the
problem
of
distrib
ution
transformer
o
v
erload
condition
while
satisfying
the
EV
o
wner
requirements
lik
e
arri
v
al
time,
departure
time
and
ensuring
that
the
battery
reaches
90%
of
SoC.
The
optimization
problem
in
method
2
i.e.
Price
indicated
char
ging
mechanism
is
analysed
on
le
v
el
2
char
ging
as
more
po
wer
is
required
to
char
ge
EVs
in
less
duration
when
compared
to
le
v
el
1
and
resulted
in
signicant
peaks
as
sho
wn
in
Figure
7.
The
optimization
results
sho
w
that
EVs
load
is
scheduled
such
that
the
total
load
curv
e
is
well
belo
w
within
the
rated
capacity
of
distrib
ution
transformer
which
minimises
the
o
v
erall
cost
of
the
electricity
.
Ho
we
v
er
of
f-peak
hours
from
1:00
to
8:00
are
turned
out
to
be
peak
hours
which
is
treated
as
price
indicated
uncontrolled
char
ging
as
sho
wn
in
Figure
10.
From
the
household
load
prole
of
a
day
sho
wn
in
Figure
3
of
f
peak
hours
are
from
1:00
to
10:00.
Ov
erall
char
ging
cost
of
EVs
are
further
minimised
by
slightly
adjusting
the
departure
time
of
v
ehicles
up
to
10:00
am.
Figure
11
sho
ws
the
price
indicated
controlled
char
ging
of
EVs.
Optimizing
the
ef
fect
of
c
har
ging
electric
vehicles
on
distrib
ution
tr
ansformer
using
...
(Swapna
Ganapaneni)
Evaluation Warning : The document was created with Spire.PDF for Python.
32
❒
ISSN:
2502-4752
Figure
8.
Controlled
char
ging
of
EVs
in
le
v
el-1
alle
viating
o
v
erload
condition
of
distrib
ution
transformer
after
minimising
load
v
ariance
Figure
9.
Controlled
char
ging
of
EVs
in
le
v
el-2
alle
viating
o
v
erload
condition
of
distrib
ution
transformer
after
minimising
load
v
ariance
Figure
10.
Price
indicated
uncontrolled
char
ging
mechanism
for
le
v
el
2
char
ging
of
EVs
Figure
11.
Price
indicated
controlled
char
ging
mechanism
for
le
v
el
2
char
ging
of
EVs
4.1.
Comparison
of
pr
oposed
optimization
methods
f
or
le
v
el
2
char
ging
The
household
load,
EV
load
and
total
load
of
the
distrib
ution
transforme
r
when
EVs
are
connected
in
le
v
el
2
char
ging
mode
based
on
uncontrolled
char
ging,
minimising
load
v
ariance
and
price
indicated
char
ging
mechanisms
are
presented
in
the
Figure
12
and
Figure
13
respecti
v
ely
.
After
performing
the
proposed
opti-
mization
methods
for
DSM,
without
disturbing
non
EV
load
i
.e
household
load
by
proper
management
of
EVs,
distrib
ution
transformer
o
v
erloading
problem
is
solv
ed.
From
the
Figure
12
it
can
be
observ
ed
that
alone
considering
household
load
is
well
belo
w
the
l
imits
of
the
transformer
b
ut
the
total
load
of
transformer
along
with
household
load
when
EVs
are
connected
in
un-
controlled
manner
o
v
erloaded
the
distrib
ution
transformer
from
100
to
150%
of
its
capacity
for
almost
3
hours
during
peak
hours
in
the
night.
So,
to
minimize
the
load
uctuations
and
peak
load
the
abo
v
e
implemented
optimizations
resulted
in
total
load
of
controlled
char
ging
of
EVs,
total
load
of
price
indicated
uncontrolled
char
ging
of
EVs,
total
load
of
price
indicated
controlled
char
ging
of
EVs.
Out
of
which
price
indicated
con-
trolled
char
ging
of
EVs
balanced
the
system
v
ery
well
in
terms
of
the
total
load
on
the
transformer
and
in
minimizing
the
char
ging
price
of
the
EVs.
In
price
indicated
uncontrolled
char
ging
though
the
total
load
on
distrib
ution
transformer
is
well
up
to
the
capacity
of
transformer
,
of
f
peak
hours
are
loaded
to
full
e
xtent
of
the
transformer
capacity
which
may
result
in
increase
of
char
ging
prices
of
EVs
compared
to
price
indicated
controlled
char
ging.
So,
e
v
en
though
there
is
a
slight
violation
in
the
departure
times
of
v
ehicles
price
indicated
controlled
char
ging
seems
to
be
best
when
compared
to
controlled
and
price
indicated
uncontrolled
char
ging.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
25,
No.
1,
January
2022:
25–34
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
33
Ho
we
v
er
,
without
ha
ving
the
kno
wledge
of
price
uctuations,
not
disturbing
the
household
load
while
satisfy-
ing
EV
o
wner’
s
requirements
EVs
load
is
scheduled
in
an
optimal
manner
by
adopting
the
transformer
limits
in
minimizing
the
load
v
ariance
optimization
i.e
method
1.
Therefore,
out
of
the
three
cases
if
price
is
not
a
constraint
controlled
char
ging
of
EVs,
Price
indicated
uncontrolled
char
ging
yields
the
good
solution
to
the
residential
area
as
well
as
to
the
distrib
ution
grid
follo
wing
all
their
requirements
e
v
en
optimizing
the
cost
of
char
ging
EVs
to
most
possible
e
xtent.
Similarly
,
if
there
is
minor
e
xibility
considered
in
departure
time
of
EVs
abo
v
e
all
price
indicated
controlled
char
ging
gi
v
es
the
best
solution
obe
ying
the
limits
on
the
transformer
as
well
as
minimizing
o
v
erall
char
ging
cost
of
EVs
further
proceeded
to
satisfy
the
EV
o
wner
requirements.
Figure
12.
Household
load,
comparison
of
EV
load
of
uncontrolled,
controlled,
price
indicated
uncontrolled,
price
indicated
controlled
for
le
v
el
2
char
ging
of
EVs
Figure
13.
Household
load,
comparison
of
total
load
of
uncontrolled,
controlled,
price
indicated
uncontrolled,
price
indicated
controlled
for
le
v
el
2
char
ging
of
EVs
5.
CONCLUSION
This
paper
rstly
presented
the
methodology
to
model
the
stochastic
EV
load
and
total
load
at
a
res-
idential
area
including
EV
load
is
calculated.
As
uncontrolled
char
ging
of
EVs
resulted
in
o
v
erloading
of
distrib
ution
transformer
,
demand
side
management
techniques
are
implemented.
The
proposed
optimization
methods
alle
viated
the
problem
of
o
v
erloading
transformer
.
T
otal
load
on
the
distrib
ution
transformer
is
com-
pared
in
all
the
approaches.
It
is
observ
ed
that
method
one
minimized
the
load
uctuations
by
shifting
EV
load
from
peak
hours
to
of
f
peak
hours
and
method
tw
o
is
implemented
where
EVs
load
is
scheduled
during
lo
w
price
hours
and
no
kno
wledge
on
uctuations
in
real
time
price
is
needed.
Both
methods
satised
the
constraint
on
the
transformer
capacity
,
a
v
oided
peak
load
on
the
system,
minimized
the
char
ging
cost
of
EVs
and
sched-
uled
them
within
the
gi
v
en
time
limit.
W
e
also
presented
the
price
indicated
controlled
char
ging
mechanism
which
further
optimized
the
char
ging
price
of
EVs
with
slight
de
viation
in
departure
times.
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BIOGRAPHIES
OF
A
UTHORS
Mrs.
Ganapaneni
Swapna
w
orking
a
s
Assistant
professor
in
the
department
of
Electrical
and
Electronics
Engineering
at
K
L
Deeme
d
to
be
uni
v
ersity(KLEF)
in
the
area
of
Po
wer
systems
control
and
Automation.
She
obtained
her
B.T
ech
and
M.T
ech
de
gree
from
JNTU
Kakinada
and
currently
pursuing
PhD
in
KLEF
.
She
is
ha
ving
nine
years
of
teaching
e
xperience.
Her
research
interests
include
po
wer
systems
dere
gulation
and
optimal
char
ging
of
Electric
v
ehicles
i
n
smart
grid.
She
published
7
a
rticles
in
SCI
and
Scopus
inde
x
ed
journals.
She
is
af
liated
wit
h
IEEE
as
student
member
and
A
CM
as
professional
member
.
She
can
be
contacted
at
email:
sw
apna@kluni
v
ersity
.in.
Dr
.
Pinni
Srini
v
asa
V
arma
completed
his
M.
T
ech.
from
JNTU
Hyderabad.
He
has
completed
his
Ph.D.
from
JNTU
Anantapur
.
His
areas
of
research
are
Po
wer
System
Dere
gulation
and
Po
wer
Syste
m
Reliability
.
He
has
published
50
research
papers
in
v
arious
international
journals.
He
has
written
a
te
xtbook
on
Po
wer
Syste
m
Dere
gulation
and
is
publ
ished
by
Lambe
rt
publishers.
He
has
published
2
patents
in
the
area
of
Po
wer
Systems.
No
w
,
he
is
w
orking
as
Associate
Professor
in
EEE
Dept.,
K
L
Uni
v
ersity
,
Guntur
,
Andhra
Pradesh,
India.
At
present
Dr
.
P
S
V
arma
is
serving
as
Associate
Dean
RD,
KLEF
.
He
can
be
contacted
at
email:
pinni
v
arma@kluni
v
ersity
.in.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
25,
No.
1,
January
2022:
25–34
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