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6
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u
r
e
p
l
a
n
n
i
n
g
.
F
i
r
s
t
,
te
m
p
o
r
a
l
u
n
c
e
r
t
ai
n
t
y
i
n
ch
a
r
g
i
n
g
d
e
m
a
n
d
l
e
a
d
s
t
o
i
n
a
c
cu
r
a
t
e
f
o
r
e
c
as
t
i
n
g
a
n
d
i
n
e
f
f
i
c
ie
n
t
u
t
il
i
z
a
ti
o
n
o
f
g
r
i
d
r
e
s
o
u
r
c
e
s
[
1
]
–
[
3
]
.
S
e
c
o
n
d
,
s
p
a
t
i
a
l
i
n
t
e
r
d
e
p
e
n
d
e
n
c
i
es
a
m
o
n
g
c
h
a
r
g
i
n
g
s
t
a
t
i
o
n
s
,
r
o
a
d
n
e
tw
o
r
k
s
,
a
n
d
p
o
w
e
r
d
i
s
t
r
i
b
u
ti
o
n
s
y
s
t
e
m
s
a
r
e
o
f
t
e
n
o
v
e
r
l
o
o
k
e
d
,
r
e
s
u
l
t
i
n
g
i
n
s
u
b
o
p
t
i
m
a
l
p
l
ac
e
m
e
n
t
a
n
d
c
o
n
g
es
ti
o
n
[
4
]
,
[
5
]
.
T
h
i
r
d
,
e
x
i
s
ti
n
g
p
l
a
n
n
i
n
g
m
o
d
e
l
s
r
a
r
e
l
y
i
n
c
o
r
p
o
r
a
t
e
p
o
w
e
r
e
l
e
ct
r
o
n
i
c
s
c
o
n
s
t
r
a
i
n
ts
,
s
u
c
h
as
co
n
v
e
r
t
e
r
e
f
f
i
c
i
e
n
c
y
,
s
w
it
c
h
i
n
g
l
o
s
s
es
,
a
n
d
h
a
r
m
o
n
i
c
d
i
s
t
o
r
t
i
o
n
,
w
h
ic
h
s
i
g
n
i
f
i
ca
n
t
l
y
i
n
f
l
u
e
n
c
e
g
r
i
d
p
e
r
f
o
r
m
a
n
c
e
.
A
d
d
i
ti
o
n
a
l
l
y
,
s
y
s
te
m
-
l
e
v
e
l
a
s
p
ec
t
s
s
u
c
h
a
s
t
r
a
n
s
f
o
r
m
e
r
l
o
a
d
i
n
g
,
v
o
l
t
a
g
e
s
ta
b
i
l
it
y
,
a
n
d
r
e
n
ew
a
b
l
e
i
n
te
r
m
i
t
t
e
n
c
y
a
r
e
n
o
t
a
d
e
q
u
a
t
e
l
y
i
n
t
e
g
r
a
t
e
d
i
n
t
o
d
e
c
i
s
i
o
n
-
m
a
k
i
n
g
f
r
a
m
e
w
o
r
k
s
[
6
]
,
[
7
]
.
T
h
e
s
e
l
i
m
i
t
at
i
o
n
s
h
i
n
d
e
r
t
h
e
d
e
v
e
l
o
p
m
e
n
t
o
f
r
e
s
i
li
e
n
t
a
n
d
a
d
a
p
t
i
v
e
E
V
c
h
a
r
g
i
n
g
e
c
o
s
y
s
t
e
m
s
.
R
e
c
e
n
t
r
es
e
a
r
c
h
h
as
e
x
p
l
o
r
e
d
a
d
v
a
n
c
e
d
d
a
t
a
-
d
r
i
v
e
n
a
p
p
r
o
a
c
h
e
s
f
o
r
E
V
i
n
f
r
a
s
t
r
u
c
t
u
r
e
p
l
a
n
n
i
n
g
.
L
o
n
g
s
h
o
r
t
-
t
e
r
m
m
e
m
o
r
y
(
L
S
T
M
)
n
e
t
w
o
r
k
s
h
a
v
e
d
e
m
o
n
s
t
r
a
te
d
s
t
r
o
n
g
c
a
p
a
b
i
l
it
y
i
n
m
o
d
e
l
i
n
g
n
o
n
l
i
n
e
a
r
t
e
m
p
o
r
a
l
v
a
r
i
a
t
i
o
n
s
i
n
c
h
a
r
g
i
n
g
d
e
m
a
n
d
,
o
u
t
p
e
r
f
o
r
m
i
n
g
c
o
n
v
e
n
t
i
o
n
a
l
s
t
a
t
is
t
ic
a
l
m
o
d
e
l
s
[
8
]
,
[
9
]
.
I
n
p
a
r
a
l
l
e
l
,
g
r
a
p
h
n
e
u
r
a
l
n
e
t
w
o
r
k
s
(
GN
Ns
)
h
a
v
e
e
m
e
r
g
e
d
a
s
e
f
f
e
ct
i
v
e
t
o
o
ls
f
o
r
c
a
p
t
u
r
i
n
g
s
p
a
t
ia
l
d
e
p
e
n
d
e
n
c
i
es
i
n
t
r
a
n
s
p
o
r
t
a
ti
o
n
a
n
d
p
o
w
e
r
g
r
i
d
n
e
t
w
o
r
k
s
[
1
0
]
–
[
1
3
]
.
D
i
g
i
t
al
T
w
i
n
f
r
a
m
e
w
o
r
k
s
h
a
v
e
a
l
s
o
b
e
e
n
a
p
p
l
i
e
d
i
n
s
m
a
r
t
c
it
i
e
s
f
o
r
m
o
n
i
t
o
r
i
n
g
a
n
d
s
i
m
u
l
a
ti
o
n
o
f
e
n
e
r
g
y
s
y
s
te
m
s
[
1
4
]
–
[
1
7
]
.
F
u
r
t
h
e
r
m
o
r
e
,
o
p
t
i
m
i
z
a
ti
o
n
t
e
c
h
n
i
q
u
e
s
s
u
c
h
a
s
g
e
n
e
t
i
c
a
l
g
o
r
i
t
h
m
s
(
G
A
)
,
p
a
r
ti
c
l
e
s
w
a
r
m
o
p
t
i
m
i
z
at
i
o
n
(
PS
O
)
,
a
n
d
r
e
i
n
f
o
r
c
e
m
e
n
t
l
e
a
r
n
i
n
g
(
R
L
)
h
a
v
e
b
ee
n
wi
d
e
ly
u
s
e
d
f
o
r
c
h
a
r
g
i
n
g
s
t
a
ti
o
n
p
l
a
c
e
m
e
n
t
a
n
d
o
p
e
r
a
t
i
o
n
a
l
c
o
n
t
r
o
l
[
1
8
]
,
[
1
9
]
.
H
o
w
e
v
e
r
,
m
o
s
t
e
x
i
s
t
i
n
g
s
t
u
d
ie
s
a
d
d
r
e
s
s
t
h
e
s
e
c
o
m
p
o
n
e
n
ts
i
n
i
s
o
l
at
i
o
n
r
a
t
h
e
r
t
h
a
n
w
i
t
h
i
n
a
u
n
i
f
i
e
d
f
r
a
m
e
w
o
r
k
.
A
l
t
h
o
u
g
h
p
r
i
o
r
w
o
r
k
s
h
a
v
e
m
a
d
e
s
i
g
n
i
f
i
c
a
n
t
c
o
n
t
r
i
b
u
ti
o
n
s
,
s
e
v
e
r
a
l
c
r
i
ti
c
a
l
g
a
p
s
r
e
m
a
i
n
.
T
h
e
r
e
i
s
no
u
n
i
f
i
e
d
f
r
a
m
e
w
o
r
k
t
h
a
t
s
i
m
u
l
t
a
n
e
o
u
s
l
y
i
n
t
e
g
r
a
te
s
s
p
at
i
o
t
e
m
p
o
r
a
l
f
o
r
e
c
a
s
t
i
n
g
(
L
S
T
M)
,
s
p
a
t
i
a
l
n
e
t
w
o
r
k
i
n
t
e
l
li
g
e
n
c
e
(
G
NN
)
,
a
n
d
r
e
s
il
i
en
c
e
-
d
r
i
v
e
n
o
p
t
i
m
i
z
a
ti
o
n
w
i
t
h
i
n
a
D
T
e
c
o
s
y
s
t
e
m
[
2
0
]
,
[
2
1
]
.
E
x
i
s
t
i
n
g
s
t
u
d
i
es
a
ls
o
l
a
c
k
i
n
t
e
g
r
a
t
i
o
n
wi
t
h
p
o
w
e
r
e
le
c
t
r
o
n
i
c
s
-
aw
a
r
e
m
o
d
e
ls
,
i
g
n
o
r
i
n
g
t
h
e
i
m
p
a
c
t
o
f
c
o
n
v
e
r
t
e
r
t
o
p
o
l
o
g
i
e
s
,
h
a
r
m
o
n
i
c
d
i
s
t
o
r
t
i
o
n
,
a
n
d
e
f
f
i
c
ie
n
c
y
o
n
g
r
i
d
s
t
a
b
il
i
t
y
.
M
o
r
e
o
v
e
r
,
E
V
d
r
i
v
e
-
c
y
c
l
e
c
h
a
r
a
ct
e
r
is
t
i
cs
,
s
u
c
h
a
s
f
as
t
-
c
h
a
r
g
i
n
g
b
e
h
a
v
i
o
r
a
n
d
b
a
t
t
e
r
y
c
o
n
s
t
r
a
i
n
t
s
,
a
r
e
r
a
r
e
l
y
i
n
c
o
r
p
o
r
a
t
e
d
i
n
t
o
i
n
f
r
a
s
t
r
u
c
t
u
r
e
p
l
a
n
n
i
n
g
.
P
r
a
c
t
i
c
al
d
e
p
l
o
y
m
e
n
t
c
h
a
l
l
e
n
g
e
s
—
i
n
cl
u
d
i
n
g
r
e
a
l
-
tim
e
d
a
t
a
s
y
n
c
h
r
o
n
i
z
a
t
i
o
n
,
I
o
T
c
o
m
m
u
n
i
c
a
t
i
o
n
l
a
t
e
n
c
y
,
in
t
e
r
o
p
e
r
a
b
i
l
i
t
y
w
i
t
h
S
C
A
D
A/
E
M
S
,
a
n
d
c
y
b
e
r
s
e
cu
r
i
t
y
—
a
r
e
al
s
o
i
n
s
u
f
f
i
c
ie
n
t
l
y
a
d
d
r
e
s
s
e
d
[
2
2
]
–
[
2
5
]
.
T
h
e
s
e
g
a
p
s
li
m
i
t
t
h
e
a
p
p
l
i
c
a
b
i
li
t
y
o
f
c
u
r
r
e
n
t
a
p
p
r
o
a
ch
e
s
i
n
r
e
a
l
-
w
o
r
l
d
s
m
a
r
t
g
r
i
d
e
n
v
i
r
o
n
m
e
n
t
s
.
T
o
a
d
d
r
e
s
s
t
h
e
a
b
o
v
e
c
h
al
l
e
n
g
e
s
,
t
h
i
s
p
a
p
e
r
p
r
o
p
o
s
e
s
a
n
o
v
e
l
c
i
t
y
-
s
c
a
l
e
s
p
a
ti
o
t
e
m
p
o
r
a
l
D
T
f
r
a
m
e
w
o
r
k
f
o
r
E
V
c
h
a
r
g
i
n
g
i
n
f
r
a
s
t
r
u
c
t
u
r
e
p
l
a
n
n
i
n
g
a
n
d
o
p
e
r
a
t
i
o
n
.
T
h
e
k
e
y
c
o
n
t
r
i
b
u
t
i
o
n
s
a
r
e
:
i)
First
u
n
if
ied
in
teg
r
atio
n
of
L
STM
–
GNN
f
u
s
io
n
with
r
esil
ien
ce
-
d
r
iv
e
n
GA
–
PSO
–
Dee
p
R
ein
f
o
r
ce
m
en
t
L
ea
r
n
in
g
o
p
tim
izatio
n
with
in
a
DT
ar
ch
itectu
r
e
f
o
r
E
V
in
f
r
a
s
tr
u
ctu
r
e.
ii)
Dev
elo
p
m
en
t
o
f
a
s
p
atio
tem
p
o
r
al
f
o
r
ec
asti
n
g
m
o
d
el
th
at
jo
in
tly
ca
p
tu
r
es
tem
p
o
r
al
d
em
an
d
d
y
n
a
m
ics
an
d
s
p
atial
n
etwo
r
k
d
ep
e
n
d
en
cies [
2
6
]
,
[
2
7
]
.
iii)
I
n
co
r
p
o
r
atio
n
o
f
p
o
wer
elec
tr
o
n
ics
-
awa
r
e
co
n
s
tr
ain
ts
,
in
clu
d
in
g
PC
S
ef
f
icien
cy
,
s
witch
in
g
lo
s
s
es,
an
d
h
ar
m
o
n
ic
d
is
to
r
tio
n
,
in
t
o
in
f
r
a
s
tr
u
ctu
r
e
o
p
tim
izatio
n
.
iv
)
I
n
teg
r
atio
n
o
f
en
er
g
y
s
y
s
tem
m
etr
ics
,
s
u
ch
as
tr
an
s
f
o
r
m
er
lo
ad
in
g
,
v
o
ltag
e
d
ev
iatio
n
,
a
n
d
r
e
n
ewa
b
le
en
er
g
y
v
ar
iab
ilit
y
[
2
8
]
.
v)
I
n
clu
s
io
n
o
f
E
V
d
r
iv
e
-
c
y
cle
ch
ar
ac
ter
is
tics
(
f
ast
c
h
ar
g
in
g
,
r
e
g
en
er
ativ
e
b
e
h
av
io
r
,
b
atter
y
lim
its
)
t
o
en
h
an
ce
m
o
d
elin
g
r
ea
lis
m
.
v
i)
Desig
n
o
f
a
r
esil
ien
ce
-
d
r
iv
en
m
u
lti
-
s
ce
n
ar
io
ev
alu
atio
n
f
r
a
m
ewo
r
k
ca
p
ab
le
o
f
h
an
d
lin
g
o
u
tag
es,
d
em
an
d
s
u
r
g
es,
an
d
r
e
n
ewa
b
le
in
ter
m
it
ten
cy
[
2
9
]
.
v
ii)
Pro
v
is
io
n
o
f
a
d
e
p
lo
y
m
e
n
t
-
o
r
ien
ted
DT
ar
c
h
itectu
r
e
,
c
o
n
s
id
er
in
g
I
o
T
d
ata
s
tr
ea
m
s
,
c
o
m
m
u
n
icatio
n
laten
cy
,
an
d
in
ter
o
p
e
r
ab
ilit
y
w
ith
s
m
ar
t g
r
id
p
latf
o
r
m
s
[
3
0
]
.
T
h
e
p
r
o
p
o
s
e
d
f
r
a
m
e
w
o
r
k
a
d
v
a
n
c
e
s
t
h
e
s
t
a
t
e
-
of
-
t
h
e
-
a
r
t
b
y
b
r
i
d
g
i
n
g
a
r
t
i
f
i
ci
a
l
i
n
t
el
l
i
g
en
c
e
,
p
o
w
e
r
e
l
e
c
t
r
o
n
i
cs
,
a
n
d
e
n
e
r
g
y
s
y
s
t
em
s
e
n
g
i
n
e
e
r
i
n
g
i
n
t
o
a
s
i
n
g
l
e
c
o
h
e
s
i
v
e
p
l
at
f
o
r
m
.
I
t
e
n
a
b
l
es
r
e
a
l
-
t
i
m
e
p
r
e
d
i
ct
i
v
e
c
o
n
t
r
o
l
,
i
m
p
r
o
v
e
s
g
r
i
d
s
ta
b
i
l
ity
,
a
n
d
e
n
h
a
n
c
e
s
i
n
f
r
a
s
t
r
u
ct
u
r
e
r
e
s
il
i
e
n
c
e
u
n
d
e
r
u
n
c
e
r
t
a
i
n
o
p
e
r
a
t
i
n
g
c
o
n
d
i
t
i
o
n
s
.
F
r
o
m
a
p
r
a
c
t
i
c
a
l
p
e
r
s
p
e
ct
i
v
e
,
t
h
e
D
T
s
u
p
p
o
r
t
s
d
a
t
a
-
d
r
i
v
e
n
d
e
c
i
s
i
o
n
-
m
a
k
i
n
g
f
o
r
u
t
i
l
i
ti
es
,
u
r
b
a
n
p
l
a
n
n
e
r
s
,
a
n
d
p
o
l
i
c
y
m
a
k
e
r
s
,
f
a
ci
l
it
a
t
i
n
g
c
o
s
t
-
e
f
f
e
c
t
i
v
e
a
n
d
s
u
s
t
ai
n
a
b
l
e
E
V
i
n
f
r
a
s
t
r
u
c
t
u
r
e
e
x
p
a
n
s
i
o
n
.
F
u
r
th
e
r
m
o
r
e
,
t
h
e
s
t
u
d
y
c
o
n
t
r
i
b
u
t
e
s
t
o
b
r
o
a
d
e
r
e
n
e
r
g
y
t
r
a
n
s
i
ti
o
n
g
o
a
l
s
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y
e
n
a
b
l
i
n
g
r
e
n
e
w
a
b
l
e
-
i
n
t
e
g
r
at
e
d
c
h
a
r
g
i
n
g
h
u
b
s
,
s
m
a
r
t
c
i
t
y
d
e
v
e
l
o
p
m
e
n
t
,
a
n
d
l
a
r
g
e
-
s
ca
l
e
E
V
a
d
o
p
t
i
o
n
,
p
a
r
t
i
c
u
la
r
l
y
i
n
r
a
p
i
d
l
y
u
r
b
a
n
i
z
i
n
g
r
e
g
i
o
n
s
.
T
h
e
p
r
o
p
o
s
e
d
a
p
p
r
o
a
c
h
p
r
o
v
i
d
e
s
a
s
c
al
a
b
l
e
f
o
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n
d
a
t
i
o
n
f
o
r
n
e
x
t
-
g
e
n
e
r
a
t
i
o
n
i
n
t
e
l
li
g
e
n
t
tr
a
n
s
p
o
r
t
a
t
i
o
n
a
n
d
e
n
e
r
g
y
s
y
s
t
em
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i
Sy
s
t
I
SS
N:
2088
-
8
6
9
4
S
p
a
tio
temp
o
r
a
l
d
ig
ita
l tw
in
fo
r
city
-
s
ca
le
E
V
ch
a
r
g
in
g
in
fr
a
s
tr
u
ctu
r
e
u
s
in
g
…
(
Dee
p
a
S
o
ma
s
u
n
d
a
r
a
m
)
2273
2.
M
E
T
H
O
D
2
.
1
.
Resea
rc
h
des
ig
n/a
pp
ro
a
ch
T
h
is
s
tu
d
y
ad
o
p
ts
a
s
im
u
latio
n
-
d
r
iv
e
n
,
d
ata
-
ce
n
tr
ic,
an
d
s
y
s
tem
-
lev
el
m
o
d
elin
g
a
p
p
r
o
ac
h
to
d
esig
n
an
d
ev
alu
ate
a
s
p
atio
tem
p
o
r
al
d
ig
ital
twin
(
SDT)
f
o
r
city
-
s
ca
le
E
V
ch
ar
g
in
g
in
f
r
astru
ctu
r
e.
T
h
e
f
r
am
ewo
r
k
in
teg
r
ates
d
ee
p
lear
n
in
g
-
b
ase
d
f
o
r
ec
asti
n
g
(
L
STM
–
GNN
f
u
s
io
n
)
with
h
y
b
r
id
o
p
tim
izati
o
n
(
GA
–
PSO
–
d
ee
p
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
)
an
d
p
o
wer
elec
tr
o
n
ics
-
awa
r
e
c
o
n
s
tr
ain
ts
with
in
a
u
n
if
ie
d
c
y
b
e
r
–
p
h
y
s
ical
s
y
s
tem
.
Fig
u
r
e
1
s
h
o
ws
th
e
AI
-
d
r
iv
e
n
DT
f
o
r
city
-
s
ca
le
E
V
ch
a
r
g
in
g
in
f
r
astru
ctu
r
e
.
T
h
e
ch
o
s
en
d
esig
n
is
ap
p
r
o
p
r
iate
b
ec
au
s
e
E
V
ch
a
r
g
in
g
in
f
r
astru
ctu
r
e
in
v
o
lv
es
c
o
m
p
lex
n
o
n
l
in
ea
r
in
ter
ac
tio
n
s
ac
r
o
s
s
tim
e
(
d
em
an
d
v
ar
iatio
n
)
,
s
p
ac
e
(
n
etwo
r
k
to
p
o
l
o
g
y
)
,
an
d
s
y
s
tem
lay
er
s
(
g
r
id
,
m
o
b
ilit
y
,
an
d
c
o
n
v
e
r
ter
s
)
.
A
DT
e
n
ab
les
co
n
tin
u
o
u
s
s
y
n
ch
r
o
n
izatio
n
b
etwe
en
r
ea
l
-
wo
r
ld
d
ata
an
d
s
im
u
latio
n
,
allo
win
g
s
ce
n
ar
io
-
b
ased
ev
al
u
atio
n
an
d
ad
a
p
tiv
e
d
ec
is
io
n
-
m
ak
in
g
u
n
d
er
u
n
ce
r
t
ain
ty
.
Fig
u
r
e
1
.
Sh
o
ws th
e
AI
-
d
r
i
v
en
d
ig
ital twin
f
o
r
city
-
s
ca
le
E
V
ch
ar
g
in
g
in
f
r
astru
ctu
r
e
2
.
2
.
M
a
t
er
ia
ls
/da
t
a
s
o
urce
s
:
da
t
a
s
et
des
cr
iptio
n a
nd
prep
ro
ce
s
s
ing
T
h
e
s
tu
d
y
em
p
lo
y
s
a
r
ea
lis
tic
u
r
b
an
-
s
ca
le
d
ataset
co
m
p
r
is
in
g
E
V
ch
ar
g
in
g
d
em
a
n
d
,
tr
a
f
f
i
c
m
o
b
ilit
y
,
r
o
ad
n
etwo
r
k
t
o
p
o
lo
g
y
,
p
o
we
r
d
is
tr
ib
u
tio
n
n
etwo
r
k
in
f
o
r
m
atio
n
,
an
d
r
en
ewa
b
le
en
er
g
y
g
en
er
atio
n
p
r
o
f
iles
.
T
h
e
d
ataset
in
clu
d
es
1
0
0
ca
n
d
id
ate
ch
ar
g
i
n
g
l
o
ca
tio
n
s
with
h
o
u
r
ly
d
ata
r
eso
lu
tio
n
(
8
7
6
0
s
am
p
les/
y
ea
r
)
.
Key
f
ea
tu
r
es
in
clu
d
e
ch
ar
g
in
g
d
e
m
an
d
,
tr
af
f
ic
d
e
n
s
ity
,
g
r
id
ca
p
ac
ity
,
an
d
r
en
ewa
b
le
en
er
g
y
o
u
tp
u
t.
Prio
r
to
an
aly
s
is
,
th
e
d
ata
ar
e
n
o
r
m
alize
d
u
s
in
g
Min
–
Ma
x
s
ca
li
n
g
,
an
d
m
is
s
in
g
v
alu
es
ar
e
h
an
d
led
th
r
o
u
g
h
in
ter
p
o
latio
n
.
T
em
p
o
r
al
s
m
o
o
th
in
g
is
a
p
p
lied
to
r
ed
u
ce
f
lu
ctu
atio
n
s
,
wh
ile
g
r
ap
h
s
tr
u
ctu
r
es
ar
e
c
o
n
s
tr
u
cte
d
u
s
in
g
ad
jace
n
cy
m
atr
ices b
ased
o
n
g
eo
g
r
ap
h
ical
an
d
elec
tr
ic
al
co
n
n
ec
tiv
ity
.
Ad
d
itio
n
al
f
ea
tu
r
es su
ch
as p
ea
k
-
d
em
an
d
in
d
icato
r
s
,
s
ea
s
o
n
al
p
atter
n
s
,
an
d
lo
ad
g
r
o
wth
f
a
cto
r
s
ar
e
ex
tr
ac
ted
to
im
p
r
o
v
e
f
o
r
ec
asti
n
g
an
d
o
p
tim
izatio
n
p
er
f
o
r
m
an
ce
.
T
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
o
lo
g
y
f
o
llo
ws a
s
y
s
tem
atic
m
u
lti
-
s
tag
e
f
r
am
ewo
r
k
:
Step
1
:
Data
p
r
ep
ar
atio
n
-
C
o
llect
E
V
m
o
b
ilit
y
,
g
r
id
,
an
d
r
en
ewa
b
le
en
e
r
g
y
d
atasets
.
-
Per
f
o
r
m
p
r
ep
r
o
ce
s
s
in
g
an
d
c
o
n
s
tr
u
ct
th
e
g
r
a
p
h
n
etwo
r
k
G
=
(
V,
E)
Step
2
: D
em
an
d
f
o
r
ec
asti
n
g
-
Use L
STM
f
o
r
tem
p
o
r
al
p
r
ed
i
ctio
n
an
d
GNN
f
o
r
s
p
atial
d
ep
en
d
en
cy
an
aly
s
is
.
-
Fu
s
e
o
u
tp
u
ts
to
g
en
e
r
ate
a
u
n
if
ied
s
y
s
tem
s
tate.
Step
3
: D
ig
ital
twin
m
o
d
ellin
g
-
Dev
elo
p
a
r
ea
l
-
tim
e
d
ig
ital twin
u
s
in
g
s
y
s
tem
s
tates {
Dt,
Gt,
R
t,
C
t}
.
Step
4
: O
p
tim
izatio
n
-
Ap
p
ly
GA
f
o
r
ch
ar
g
in
g
s
tatio
n
p
lace
m
en
t,
PS
O
f
o
r
ca
p
ac
ity
allo
ca
tio
n
,
an
d
DR
L
f
o
r
r
ea
l
-
ti
m
e
o
p
er
atio
n
al
c
o
n
tr
o
l.
Step
5
: Po
wer
elec
tr
o
n
ics in
te
g
r
atio
n
-
Mo
d
el
ch
ar
g
er
p
o
wer
co
n
v
er
s
io
n
s
y
s
tem
s
co
n
s
id
er
in
g
e
f
f
icie
n
cy
,
s
witch
in
g
lo
s
s
es,
an
d
T
H
D
co
n
s
tr
ain
ts
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
6
9
4
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
,
Vo
l.
1
7
,
No
.
3
,
Sep
tem
b
er
20
2
6
:
2271
-
2
2
8
0
2274
Step
6
: Sim
u
latio
n
an
d
ev
alu
at
io
n
-
An
aly
ze
p
ea
k
d
em
an
d
,
g
r
i
d
o
u
tag
e,
r
en
ewa
b
le
v
ar
iab
ilit
y
,
an
d
h
ig
h
E
V
p
en
etr
atio
n
s
ce
n
ar
i
o
s
.
-
E
v
alu
ate
f
o
r
ec
asti
n
g
ac
cu
r
ac
y
,
o
p
er
atio
n
al
c
o
s
t,
r
esil
ien
ce
,
a
n
d
g
r
i
d
p
er
f
o
r
m
a
n
ce
.
2
.
3
.
M
o
dels
/
a
lg
o
rit
hm
s
/t
ec
hn
iq
ues
2
.
3
.
1
.
Dig
it
a
l t
win a
rc
hite
ct
u
re
a
nd
s
y
s
t
em
m
o
delin
g
T
h
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
is
d
ev
elo
p
e
d
as
a
city
-
s
ca
le
DT
th
at
m
ir
r
o
r
s
th
e
p
h
y
s
ical
E
V
ch
ar
g
in
g
ec
o
s
y
s
tem
,
in
clu
d
in
g
elec
tr
ic
v
eh
icles,
ch
ar
g
i
n
g
s
tatio
n
s
,
r
en
ewa
b
le
en
er
g
y
s
o
u
r
ce
s
,
an
d
p
o
wer
g
r
id
co
m
p
o
n
en
ts
.
T
h
e
s
y
s
tem
s
tate
at
tim
e
t
is
r
ep
r
esen
ted
as
(
1
)
.
t
=
{
D
t
,
G
t
,
R
t
,
C
t
}
,
(
1
)
W
h
er
e
D
t
d
en
o
tes
c
h
ar
g
in
g
d
em
an
d
,
G
t
r
ep
r
esen
ts
g
r
i
d
co
n
s
tr
ain
ts
s
u
ch
as
tr
an
s
f
o
r
m
er
an
d
f
ee
d
er
lim
its
,
R
t
ca
p
tu
r
es
r
en
ewa
b
le
e
n
er
g
y
a
v
ailab
ilit
y
,
an
d
C
t
d
escr
ib
es
c
h
ar
g
er
o
cc
u
p
a
n
cy
a
n
d
q
u
eu
e
s
tates.
T
h
e
DT
co
n
tin
u
o
u
s
ly
s
y
n
ch
r
o
n
izes
r
e
al
-
tim
e
an
d
h
is
to
r
ical
d
ata,
en
ab
lin
g
s
ce
n
ar
io
-
b
ased
s
im
u
lat
io
n
s
an
d
p
r
e
d
ictiv
e
o
p
tim
izatio
n
u
n
d
er
u
n
ce
r
tain
o
p
er
atin
g
c
o
n
d
itio
n
s
.
2
.
3
.
2
.
Sp
a
t
io
t
e
m
po
ra
l dem
a
n
d f
o
re
ca
s
t
ing
us
ing
L
S
T
M
T
em
p
o
r
al
v
ar
iatio
n
s
i
n
E
V
ch
ar
g
in
g
d
em
an
d
a
r
e
f
o
r
ec
ast
u
s
in
g
a
lo
n
g
s
h
o
r
t
-
te
r
m
m
e
m
o
r
y
(
L
STM
)
n
etwo
r
k
.
Giv
e
n
an
in
p
u
t seq
u
e
n
ce
{
x
t
−
n
,
…
,
x
t
}
,
th
e
L
STM
u
p
d
ates its
in
ter
n
al
s
tates a
s
(
2
)
.
f
t
=
σ
(
W
f
[
h
t
−
1
,
x
t
]
+
b
f
)
,
i
t
=
σ
(
W
i
[
h
t
−
1
,
x
t
]
+
b
i
)
,
c
̃
t
=
ta
n
h
(
W
c
[
h
t
−
1
,
x
t
]
+
b
c
)
,
c
t
=
f
t
⊙
c
t
−
1
+
i
t
⊙
c
̃
t
,
h
t
=
o
t
⊙
ta
n
h
(
c
t
)
,
(
2
)
W
h
er
e
f
t
,
i
t
,
an
d
o
t
d
en
o
te
f
o
r
g
et,
in
p
u
t,
an
d
o
u
tp
u
t
g
ates
r
esp
ec
tiv
ely
.
T
h
e
p
r
ed
icted
c
h
ar
g
in
g
d
em
a
n
d
is
co
m
p
u
ted
as
(
3
)
.
D
̂
t
+
1
=
W
o
h
t
+
b
o
.
(
3
)
T
h
is
f
o
r
m
u
latio
n
ef
f
ec
tiv
ely
c
ap
tu
r
es p
ea
k
,
o
f
f
-
p
ea
k
,
an
d
s
e
aso
n
al
d
em
an
d
v
ar
iatio
n
s
.
2
.
3
.
3
.
Sp
a
t
ia
l dependency
m
o
delin
g
us
ing
g
ra
ph
ne
ura
l net
wo
rk
s
Sp
atial
in
ter
ac
tio
n
s
am
o
n
g
ch
ar
g
in
g
s
tatio
n
s
a
n
d
g
r
id
c
o
m
p
o
n
en
ts
ar
e
m
o
d
eled
u
s
in
g
a
g
r
ap
h
n
e
u
r
al
n
etwo
r
k
(
GNN)
.
T
h
e
u
r
b
a
n
c
h
ar
g
in
g
n
etwo
r
k
is
r
ep
r
esen
ted
as
a
g
r
ap
h
=
(
,
ℰ
)
,
wh
er
e
n
o
d
es
r
ep
r
esen
t
ch
ar
g
in
g
s
tatio
n
s
an
d
g
r
i
d
ass
ets,
an
d
ed
g
es
ℰ
d
en
o
te
elec
tr
i
ca
l
o
r
m
o
b
ilit
y
co
n
n
ec
tiv
it
y
.
No
d
e
em
b
ed
d
in
g
s
ar
e
u
p
d
ate
d
u
s
in
g
(
4
)
.
h
v
(
k
+
1
)
=
σ
(
W
(
k
)
h
v
(
k
)
+
∑
1
c
vu
u
∈
(
v
)
W
n
(
k
)
h
u
(
k
)
)
,
(
4
)
W
h
er
e
(
v
)
d
en
o
tes
th
e
n
eig
h
b
o
r
h
o
o
d
o
f
n
o
d
e
v
,
an
d
c
vu
is
a
n
o
r
m
aliza
tio
n
co
n
s
tan
t.
T
h
is
f
o
r
m
u
latio
n
ca
p
tu
r
es c
o
n
g
esti
o
n
p
r
o
p
ag
ati
o
n
an
d
s
p
atial
lo
ad
co
r
r
elatio
n
s
ac
r
o
s
s
th
e
n
etwo
r
k
.
2
.
3
.
4
.
L
ST
M
–
G
NN
f
us
io
n f
o
r
DT
s
t
a
t
e
predict
io
n
T
o
jo
in
tly
m
o
d
el
tem
p
o
r
al
an
d
s
p
atial
d
y
n
am
ics,
th
e
o
u
tp
u
t
s
o
f
L
STM
an
d
GNN
m
o
d
u
les
ar
e
f
u
s
ed
to
o
b
tain
a
u
n
if
ied
DT
s
tate
r
ep
r
esen
tatio
n
:
Z
t
=
α
h
t
L
S
T
M
+
(
1
−
α
)
h
t
GNN
,
(
5
)
wh
er
e
α
∈
[
0
,
1
]
co
n
tr
o
ls
th
e
r
elativ
e
im
p
o
r
tan
ce
o
f
tem
p
o
r
al
an
d
s
p
atial
f
ea
tu
r
es.
T
h
e
f
u
s
ed
s
tate
Z
t
s
er
v
es
as
th
e
in
p
u
t
to
th
e
o
p
tim
izatio
n
a
n
d
co
n
tr
o
l
lay
er
s
,
en
a
b
lin
g
ac
cu
r
ate
p
r
ed
ictio
n
o
f
c
o
n
g
esti
o
n
h
o
ts
p
o
ts
an
d
g
r
id
s
tr
ess
.
2
.
3
.
5
.
M
ulti
-
O
bje
ct
iv
e
o
ptimiza
t
io
n a
nd
re
info
rc
em
ent
le
a
rning
Op
tim
al
s
tatio
n
p
lace
m
en
t
an
d
ch
ar
g
i
n
g
co
n
tr
o
l
a
r
e
f
o
r
m
u
lated
as
a
m
u
lti
-
o
b
jectiv
e
o
p
t
im
izatio
n
p
r
o
b
lem
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i
Sy
s
t
I
SS
N:
2088
-
8
6
9
4
S
p
a
tio
temp
o
r
a
l
d
ig
ita
l tw
in
fo
r
city
-
s
ca
le
E
V
ch
a
r
g
in
g
in
fr
a
s
tr
u
ctu
r
e
u
s
in
g
…
(
Dee
p
a
S
o
ma
s
u
n
d
a
r
a
m
)
2275
min
{
f
1
=
C
ins
tall
+
C
ope
r
,
f
2
=
∑
d
ij
,
f
3
=
∑
ma
x
(
0
,
L
g
−
L
g
m
ax
)
(
6
)
s
u
b
ject
to
(
7
)
.
D
i
≤
C
i
m
ax
,
L
g
≤
L
g
m
ax
,
(
7
)
W
h
er
e
d
ij
r
ep
r
esen
ts
u
s
er
tr
av
e
l
d
is
tan
ce
,
an
d
L
g
is
g
r
id
lo
ad
in
g
.
Fo
r
r
ea
l
-
tim
e
d
ec
is
io
n
-
m
ak
in
g
,
a
Dee
p
R
ein
f
o
r
ce
m
en
t L
ea
r
n
in
g
(
DR
L
)
ag
en
t m
a
x
im
izes c
u
m
u
lativ
e
r
ewa
r
d
:
R
t
=
−
(
λ
1
C
t
+
λ
2
W
t
+
λ
3
O
t
)
,
(
8
)
wh
er
e
C
t
is
o
p
er
atio
n
al
co
s
t,
W
t
is
waitin
g
tim
e,
an
d
O
t
d
en
o
tes
o
v
er
lo
ad
p
e
n
alties.
Po
licy
p
ar
am
eter
s
ar
e
u
p
d
ated
as
(
9
)
.
θ
←
θ
+
η
∇
θ
[
R
t
]
.
(
9
)
2
.
3
.
6
.
Resili
ence
ev
a
lua
t
io
n a
nd
s
ce
na
rio
-
ba
s
ed
a
na
ly
s
i
s
Sy
s
tem
r
esil
ien
ce
is
q
u
an
tifie
d
u
s
in
g
a
s
er
v
ice
co
n
tin
u
ity
in
d
ex
(
1
0
)
.
R
=
∑
D
t
s
e
r
ve
d
T
t
=
1
∑
D
t
r
e
que
s
ted
T
t
=
1
.
(
1
0
)
Mu
ltip
le
d
is
tu
r
b
an
ce
s
ce
n
ar
io
s
,
in
clu
d
in
g
g
r
id
o
u
tag
es,
r
en
e
wab
le
in
ter
m
itten
cy
,
an
d
s
u
d
d
en
d
em
an
d
s
u
r
g
es
,
ar
e
ev
alu
ated
with
in
th
e
DT
.
T
h
e
f
r
am
ewo
r
k
d
y
n
am
ically
ad
ap
ts
o
p
er
atio
n
al
d
ec
is
io
n
s
to
m
ain
tain
h
ig
h
s
er
v
ice
av
ailab
ilit
y
,
en
s
u
r
in
g
r
o
b
u
s
t a
n
d
r
esil
ien
t E
V
ch
ar
g
in
g
in
f
r
astru
ctu
r
e.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
p
r
esen
ts
a
co
m
p
r
eh
en
s
iv
e
ev
alu
atio
n
o
f
t
h
e
p
r
o
p
o
s
ed
AI
-
d
r
iv
en
DT
f
r
am
ewo
r
k
f
o
r
city
-
s
ca
le
E
V
ch
ar
g
in
g
in
f
r
astru
c
tu
r
e.
T
h
e
p
er
f
o
r
m
a
n
ce
o
f
s
p
atio
tem
p
o
r
al
d
em
a
n
d
f
o
r
ec
asti
n
g
,
o
p
tim
izatio
n
,
r
esil
ien
ce
en
h
an
ce
m
e
n
t,
an
d
r
en
ewa
b
le
in
teg
r
atio
n
is
a
n
aly
ze
d
u
n
d
er
r
ea
lis
tic
o
p
e
r
atio
n
al
s
ce
n
ar
io
s
.
C
o
m
p
ar
ativ
e
an
d
s
en
s
itiv
ity
an
aly
s
es
f
u
r
th
er
d
em
o
n
s
tr
ate
th
e
r
o
b
u
s
tn
ess
,
s
ca
lab
ilit
y
,
an
d
d
ec
is
io
n
-
m
a
k
in
g
ef
f
ec
tiv
en
ess
o
f
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
ag
ain
s
t c
o
n
v
en
tio
n
al
p
lan
n
in
g
tech
n
iq
u
es.
3
.
1
.
De
m
a
nd
f
o
re
ca
s
t
ing
per
f
o
rm
a
nce
(
L
ST
M
–
G
NN
F
us
i
o
n)
T
h
e
L
STM
–
GNN
h
y
b
r
id
m
o
d
el
d
em
o
n
s
tr
ates
s
tr
o
n
g
ca
p
ab
ilit
y
in
ca
p
tu
r
in
g
b
o
th
th
e
tem
p
o
r
al
ev
o
lu
tio
n
an
d
s
p
atial
d
ep
en
d
e
n
cies
o
f
E
V
ch
ar
g
in
g
d
em
an
d
ac
r
o
s
s
th
e
city
.
T
h
e
L
STM
m
o
d
u
le
ef
f
ec
tiv
ely
lear
n
s
d
aily
,
h
o
u
r
l
y
,
an
d
p
ea
k
–
o
f
f
-
p
ea
k
f
lu
ctu
atio
n
s
,
wh
il
e
th
e
GNN
ca
p
tu
r
es
r
elatio
n
s
h
ip
s
am
o
n
g
s
tatio
n
s
in
f
lu
en
ce
d
b
y
r
o
ad
to
p
o
lo
g
y
,
m
o
b
ilit
y
b
e
h
av
io
r
,
an
d
g
r
id
c
o
n
n
ec
tiv
ity
.
Fo
r
ec
asti
n
g
ac
cu
r
ac
y
is
r
ef
lecte
d
in
a
lo
w
MSE
(
≈
4
.
2
k
W
²)
,
in
d
ic
atin
g
clo
s
e
alig
n
m
en
t
b
etwe
en
p
r
ed
icted
a
n
d
m
ea
s
u
r
ed
v
alu
es.
Statio
n
S4
ex
h
ib
ited
th
e
m
ax
im
u
m
d
em
a
n
d
d
u
e
to
h
ig
h
tr
af
f
ic
d
en
s
ity
an
d
co
m
m
er
cial
lan
d
u
s
e,
p
e
ak
in
g
at
~1
2
0
k
W
d
u
r
in
g
e
v
en
in
g
h
o
u
r
s
.
Fro
m
an
en
er
g
y
s
y
s
tem
s
p
er
s
p
ec
tiv
e,
ac
cu
r
ate
f
o
r
ec
asti
n
g
en
ab
les
p
r
o
ac
tiv
e
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h
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g
s
tatio
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3.
3
.
Resili
ence
a
na
ly
s
is
un
de
r
m
ulti
-
s
ce
na
rio
g
rid st
re
s
s
co
nd
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ns
R
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n
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er
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ess
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ly
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ter
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el
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s
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t
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o
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m
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with
in
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s
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ig
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r
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4
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h
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h
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ce
n
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in
d
ex
.
3
.
4
.
G
rid lo
a
d beha
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r
a
nd
re
newa
ble int
eg
ra
t
io
n o
utc
o
m
es
T
h
e
in
teg
r
atio
n
o
f
r
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to
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s
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d
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en
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r
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s
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if
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p
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y
a
p
p
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ately
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en
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u
r
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5
s
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ly
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ly
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atter
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T
h
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p
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d
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D
T
f
r
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eth
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.
Fig
u
r
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6
s
h
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th
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AI
v
s
co
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v
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tio
n
al
tr
av
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d
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R
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lts
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ican
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im
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r
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ch
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in
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3
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6
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t
ra
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m
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Sen
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im
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s
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itiv
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I
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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Vo
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No
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3
,
Sep
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b
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20
2
6
:
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2
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2278
AUTHO
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r
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.
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fr
o
m
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a
m
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lai
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in
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h
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d
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re
e
fro
m
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a
th
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a
b
a
m
a
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n
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s
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tern
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sy
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h
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c
a
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c
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tac
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m
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so
m
s1
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3
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h
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n
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k
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m
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n
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h
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se
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irc
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is
th
e
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k
s.
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in
terd
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ip
li
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ry
in
tere
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lu
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y
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p
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m
m
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o
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g
s
(Io
T).
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h
a
s
p
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b
li
sh
e
d
re
se
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rc
h
a
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in
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ls.
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c
a
n
b
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c
o
n
tac
ted
a
t
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m
a
il
:
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m
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.
c
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.
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Tec
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0
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.
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.
d
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re
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in
p
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s
with
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m
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ig
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ll
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f
E
n
g
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,
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k
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d
a
,
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k
in
a
d
a
in
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n
d
P
h
.
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d
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re
e
i
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1
fro
m
J.N.T
.
Un
iv
e
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,
Ka
k
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n
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d
a
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I
n
d
ia
.
He
is
c
u
rre
n
tl
y
wo
r
k
in
g
a
s
a
n
a
ss
istan
t
p
ro
fe
ss
o
r
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th
e
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e
c
tri
c
a
l
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n
d
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e
c
tro
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ics
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g
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g
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p
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n
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ll
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o
f
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g
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g
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k
in
a
d
a
,
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k
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d
a
,
Ka
k
in
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d
a
.
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a
re
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s
o
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in
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ig
h
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tag
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,
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n
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e
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m
s,
d
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u
ti
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m
s,
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n
d
e
lec
tri
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v
e
h
icle
s
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
jay
e
e
e
jn
tu
@
g
m
a
il
.
c
o
m
.
Dr
.
S
a
b
a
r
im
u
th
u
M
u
th
u
s
a
m
y
re
c
e
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e
d
h
is
b
a
c
h
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l
o
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s
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l
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n
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n
g
i
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rin
g
fr
o
m
An
n
a
Un
iv
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,
C
h
e
n
n
a
i,
i
n
2
0
0
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a
n
d
a
p
o
stg
ra
d
u
a
te
d
e
g
re
e
i
n
Ap
p
li
e
d
El
e
c
tro
n
ics
fro
m
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n
a
U
n
iv
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rsit
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C
h
e
n
n
a
i,
In
d
ia,
i
n
2
0
1
2
.
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c
o
m
p
lete
d
h
is
P
h
.
D
i
n
th
e
field
o
f
El
e
c
tri
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Ve
h
icle
F
a
st
Ch
a
rg
e
rs
fro
m
An
n
a
Un
iv
e
rsit
y
,
Ch
e
n
n
a
i,
I
n
d
ia,
in
2
0
2
3
.
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h
a
s
b
e
e
n
wo
rk
in
g
a
s
a
n
a
ss
o
c
iate
p
r
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ss
o
r
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t
th
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De
p
a
rtme
n
t
o
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i
n
K
o
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g
u
En
g
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n
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g
Co
ll
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g
e
sin
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e
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0
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s
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e
d
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r
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h
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tex
t
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s,
a
n
d
m
o
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th
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5
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p
a
p
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in
in
tern
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t
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a
l
jo
u
r
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a
ls.
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h
a
s
c
o
n
d
u
c
te
d
m
o
re
th
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n
six
sp
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s
o
re
d
p
r
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ra
m
s
fu
n
d
e
d
b
y
M
NRE,
DST
,
DBT
,
a
n
d
IS
RO.
He
h
a
s
re
c
e
iv
e
d
th
e
b
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st
fa
c
u
lt
y
o
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th
e
y
e
a
r
fro
m
th
e
sa
m
e
in
stit
u
ti
o
n
i
n
2
0
1
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.
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h
a
s
a
n
e
x
p
e
rien
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e
o
f
m
o
re
t
h
a
n
a
d
e
c
a
d
e
in
t
h
e
field
o
f
e
lec
tri
c
v
e
h
icle
s,
fa
st
c
h
a
rg
e
rs,
p
o
we
r
q
u
a
li
ty
,
a
n
d
p
o
we
r
e
lec
tro
n
ics
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
e
e
e
sa
b
a
ri@g
m
a
il
.
c
o
m
.
K
.
Vin
o
th
re
c
e
iv
e
d
th
e
B.
E.
d
e
g
re
e
in
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tri
c
a
l
a
n
d
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lec
tro
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ics
e
n
g
in
e
e
rin
g
fro
m
th
e
M
.
I.
E
.
T.
E
n
g
i
n
e
e
rin
g
C
o
ll
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g
e
,
Bh
a
ra
th
id
a
sa
n
Un
i
v
e
rsity
,
Ti
r
u
c
h
irap
p
a
ll
i,
In
d
ia,
i
n
2
0
0
2
,
a
n
d
t
h
e
m
a
ste
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d
e
g
re
e
in
p
o
w
e
r
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lec
tro
n
ics
a
n
d
d
riv
e
s,
S
AST
RA
Un
iv
e
rsity
,
Th
a
n
jav
u
r,
In
d
ia,
i
n
2
0
0
6
,
a
n
d
th
e
P
h
.
D.
d
e
g
re
e
fro
m
An
n
a
Un
iv
e
rsity
,
in
2
0
2
1
.
He
is
c
u
rre
n
tl
y
w
o
rk
i
n
g
a
s
a
P
ro
fe
ss
o
r
with
t
h
e
De
p
a
rt
m
e
n
t
o
f
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
in
e
e
rin
g
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