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er
atio
n
al
co
s
ts
an
d
im
p
r
o
v
in
g
ef
f
icien
cy
[
1
6
]
-
[
1
9
]
.
Dee
p
r
ein
f
o
r
ce
m
en
t
lea
r
n
in
g
ap
p
r
o
ac
h
es
h
av
e
b
ee
n
a
p
p
lied
to
o
p
tim
ize
en
er
g
y
d
is
tr
ib
u
tio
n
i
n
s
m
ar
t
g
r
id
s
,
ac
h
iev
in
g
s
ig
n
if
ican
t
im
p
r
o
v
em
en
ts
in
p
o
wer
u
tili
za
tio
n
[
2
0
]
.
T
h
e
g
r
o
win
g
ad
o
p
tio
n
o
f
AI
-
d
r
i
v
en
tech
n
iq
u
es
u
n
d
er
s
co
r
es
th
e
n
ee
d
f
o
r
co
n
tin
u
ed
r
esear
c
h
in
to
th
eir
ap
p
licatio
n
s
in
r
ea
l
-
tim
e
en
er
g
y
m
an
ag
e
m
en
t sy
s
tem
s
[
2
1
]
.
Desp
ite
th
ese
ad
v
an
ce
m
en
ts
,
ch
allen
g
es
p
er
s
is
t
in
im
p
le
m
en
tin
g
cy
b
e
r
s
ec
u
r
ity
m
ea
s
u
r
es,
b
atter
y
tech
n
o
lo
g
ies,
an
d
ch
a
r
g
in
g
in
f
r
astru
ctu
r
e
f
o
r
elec
tr
ic
m
o
b
ilit
y
.
R
esear
ch
h
as
h
ig
h
lig
h
ted
th
e
v
u
ln
er
a
b
ilit
ies
in
s
m
ar
t
g
r
id
co
m
m
u
n
icatio
n
s
,
em
p
h
asizin
g
t
h
e
im
p
o
r
tan
ce
o
f
e
n
cr
y
p
tio
n
a
n
d
au
th
e
n
ticatio
n
m
ec
h
an
is
m
s
to
p
r
ev
en
t
c
y
b
er
th
r
ea
ts
[
2
2
]
-
[
2
3
]
.
R
ev
iews
o
n
wir
eless
p
o
wer
tr
an
s
f
er
s
y
s
tem
s
f
o
r
E
V
c
h
ar
g
in
g
h
av
e
id
en
tifie
d
th
e
n
ee
d
f
o
r
s
tan
d
ar
d
izatio
n
a
n
d
ef
f
icien
c
y
im
p
r
o
v
em
e
n
ts
[
2
4
]
.
T
h
ese
f
in
d
i
n
g
s
in
d
icate
t
h
at
wh
ile
s
ig
n
if
ican
t
p
r
o
g
r
ess
h
as
b
ee
n
m
ad
e,
f
u
r
t
h
er
r
esear
ch
is
n
ee
d
e
d
to
ad
d
r
e
s
s
th
e
tech
n
ical
an
d
in
f
r
astru
c
tu
r
al
ch
allen
g
es
in
lar
g
e
-
s
ca
le
E
V
in
teg
r
atio
n
[
1
1
]
,
[
1
6
]
,
[
2
0
]
,
[
2
5
]
.
T
h
is
r
esear
ch
b
u
ild
s
u
p
o
n
ex
i
s
tin
g
liter
atu
r
e
b
y
p
r
o
p
o
s
in
g
an
o
p
tim
ized
f
r
am
ewo
r
k
th
at
in
teg
r
ates
ad
v
an
ce
d
m
ac
h
in
e
lear
n
in
g
a
lg
o
r
ith
m
s
with
r
e
n
ewa
b
le
en
er
g
y
-
b
ased
E
V
ch
a
r
g
in
g
s
y
s
tem
s
.
B
y
co
m
p
ar
i
n
g
co
n
v
en
tio
n
al
o
p
tim
izatio
n
te
ch
n
iq
u
es
with
AI
-
d
r
iv
en
ap
p
r
o
ac
h
es,
th
e
s
tu
d
y
aim
s
t
o
en
h
an
ce
e
n
er
g
y
ef
f
icien
cy
,
r
ed
u
ce
ch
ar
g
in
g
t
im
e,
an
d
im
p
r
o
v
e
p
o
wer
g
r
i
d
s
tab
ilit
y
.
T
h
e
f
in
d
in
g
s
o
f
th
is
r
esear
ch
will
co
n
tr
ib
u
te
to
d
ev
el
o
p
in
g
m
o
r
e
s
u
s
tain
ab
le
an
d
in
tellig
en
t
en
er
g
y
m
an
ag
e
m
en
t
s
y
s
tem
s
th
at
s
u
p
p
o
r
t
th
e
wid
esp
r
ea
d
ad
o
p
tio
n
o
f
elec
tr
i
c
m
o
b
ilit
y
[
2
2
]
,
[
2
4
]
,
[
2
6
]
.
2.
M
E
T
H
O
D
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
o
lo
g
y
p
r
esen
ts
a
co
m
p
r
eh
en
s
iv
e,
m
u
lti
-
p
h
ase
f
r
am
ewo
r
k
f
o
r
o
p
tim
izin
g
en
er
g
y
m
an
ag
e
m
en
t
in
E
V
c
h
ar
g
in
g
s
y
s
tem
s
u
s
in
g
ad
v
an
ce
d
ML
tech
n
iq
u
es.
T
h
e
ap
p
r
o
ac
h
is
d
esig
n
e
d
to
en
h
an
ce
ch
ar
g
in
g
ef
f
icien
c
y
,
in
teg
r
ate
r
e
n
ewa
b
le
e
n
er
g
y
s
o
u
r
ce
s
,
a
n
d
m
ain
tain
g
r
id
s
tab
ilit
y
in
a
r
ea
l
-
tim
e
en
v
ir
o
n
m
en
t.
T
h
e
m
eth
o
d
o
lo
g
y
is
s
tr
u
ctu
r
ed
in
to
f
iv
e
m
ajo
r
p
h
ases
:
2
.
1
.
Da
t
a
c
o
llect
io
n a
nd
prepro
ce
s
s
ing
I
n
th
e
in
itial
p
h
ase,
r
ea
l
-
wo
r
l
d
d
atasets
ar
e
g
ath
er
e
d
f
r
o
m
p
u
b
licly
av
ailab
le
s
o
u
r
ce
s
,
s
m
ar
t
g
r
i
d
test
b
ed
s
,
an
d
ex
p
e
r
im
en
tal
s
etu
p
s
.
T
h
ese
d
atasets
in
clu
d
e:
−
E
V
ch
ar
g
in
g
p
atter
n
s
(
c
h
ar
g
in
g
d
u
r
atio
n
,
p
o
wer
co
n
s
u
m
p
tio
n
,
s
tatio
n
u
s
ag
e
f
r
eq
u
e
n
cy
)
−
Gr
id
lo
ad
f
l
u
ctu
atio
n
s
(
v
o
ltag
e,
f
r
eq
u
en
cy
,
p
h
ase)
−
R
en
ewa
b
le
en
er
g
y
g
en
er
atio
n
m
etr
ics (
s
o
lar
ir
r
ad
ian
ce
,
win
d
s
p
ee
d
,
wea
th
er
d
ata)
T
o
en
s
u
r
e
r
o
b
u
s
tn
ess
an
d
r
eliab
ilit
y
,
th
e
d
ata
u
n
d
er
g
o
es a
p
r
ep
r
o
ce
s
s
in
g
s
tag
e
th
at
in
v
o
lv
e
s
:
−
Data
c
lean
in
g
:
r
em
o
v
al
o
f
o
u
tl
ier
s
,
m
is
s
in
g
v
alu
es,
an
d
er
r
o
n
eo
u
s
r
ea
d
in
g
s
−
No
r
m
aliza
tio
n
:
s
ca
lin
g
f
ea
tu
r
e
s
b
etwe
en
0
an
d
1
to
im
p
r
o
v
e
ML
tr
ain
in
g
p
e
r
f
o
r
m
an
ce
−
L
ab
elin
g
:
a
n
n
o
tatin
g
d
ata
f
o
r
s
u
p
er
v
is
ed
lear
n
in
g
task
s
−
B
en
ch
m
ar
k
in
g
:
in
co
r
p
o
r
atio
n
o
f
h
is
to
r
ical
d
ata
f
r
o
m
co
n
v
en
tio
n
al
c
h
ar
g
in
g
s
tatio
n
s
f
o
r
p
e
r
f
o
r
m
an
ce
co
m
p
ar
is
o
n
2
.
2
.
M
a
chine le
a
rning
m
o
de
l selec
t
io
n a
nd
t
ra
ini
ng
T
h
is
p
h
ase
in
v
o
lv
es
s
elec
tin
g
an
d
d
ev
el
o
p
in
g
ML
m
o
d
els
to
p
er
f
o
r
m
i
n
tellig
en
t
en
er
g
y
o
p
tim
izatio
n
.
T
h
e
f
o
llo
win
g
m
o
d
els ar
e
co
n
s
id
er
e
d
:
−
Ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
(
ANN
)
: u
s
ed
f
o
r
lo
a
d
f
o
r
ec
asti
n
g
an
d
p
r
ed
ictin
g
p
ea
k
d
e
m
an
d
−
Dee
p
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
(
DR
L
)
:
e
m
p
lo
y
ed
f
o
r
d
y
n
a
m
ic
s
ch
ed
u
lin
g
o
f
ch
ar
g
in
g
b
ased
o
n
g
r
id
co
n
d
itio
n
s
−
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SVM
)
: u
tili
ze
d
f
o
r
class
if
icatio
n
t
ask
s
lik
e
d
em
an
d
ca
teg
o
r
izati
o
n
Mo
d
el
ar
ch
itectu
r
e
a
n
d
tr
ai
n
in
g
s
etu
p
:
−
ANN:
A
f
ee
d
f
o
r
war
d
n
etwo
r
k
with
3
h
i
d
d
en
la
y
er
s
(
6
4
,
3
2
,
an
d
1
6
n
eu
r
o
n
s
)
,
R
eL
U
ac
tiv
atio
n
,
tr
ain
ed
u
s
in
g
Ad
am
o
p
tim
izer
with
M
SE
lo
s
s
−
DR
L
:
ac
to
r
-
cr
itic
m
o
d
el
with
s
tate
in
p
u
ts
(
g
r
id
lo
ad
,
wea
th
e
r
,
tim
e)
,
r
ewa
r
d
b
ased
o
n
ef
f
icien
cy
an
d
co
s
t
s
av
in
g
s
−
SVM:
ra
d
ial
b
asis
f
u
n
ctio
n
k
e
r
n
el,
tr
ain
ed
u
s
in
g
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
Hy
p
er
p
ar
a
m
eter
tu
n
i
n
g
is
p
er
f
o
r
m
ed
u
s
in
g
g
r
id
s
ea
r
c
h
.
Fea
tu
r
e
s
elec
tio
n
is
ap
p
lied
u
s
in
g
p
r
in
cip
al
co
m
p
o
n
en
t
an
aly
s
is
(
PC
A)
t
o
im
p
r
o
v
e
m
o
d
el
ac
cu
r
ac
y
an
d
r
ed
u
ce
tr
ai
n
in
g
tim
e.
Fig
u
r
e
1
s
h
o
ws
th
e
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.
16
,
No
.
4
,
Dec
em
b
er
20
25
:
2860
-
2
8
6
7
2862
m
eth
o
d
o
l
o
g
ical
f
r
am
ew
o
r
k
f
o
r
o
p
tim
al
E
V
ch
ar
g
i
n
g
s
t
atio
n
p
lace
m
en
t
u
s
in
g
m
ac
h
in
e
lear
n
in
g
an
d
o
p
tim
izatio
n
tech
n
i
q
u
es
.
Fig
u
r
e
1
.
Me
th
o
d
o
lo
g
ical
f
r
a
m
ewo
r
k
f
o
r
o
p
tim
al
E
V
ch
ar
g
in
g
s
tatio
n
p
lace
m
en
t
u
s
in
g
m
ac
h
in
e
lear
n
in
g
an
d
o
p
tim
izatio
n
tech
n
i
q
u
es
2
.
3
.
Co
m
pa
ra
t
iv
e
a
na
ly
s
is
wit
h c
o
nv
ent
io
na
l m
et
ho
ds
T
o
ev
alu
ate
th
e
ef
f
icac
y
o
f
ML
-
b
ased
o
p
tim
izatio
n
,
tr
a
d
itio
n
al
h
eu
r
is
tic
alg
o
r
ith
m
s
ar
e
also
im
p
lem
en
ted
:
g
en
etic
alg
o
r
ith
m
s
(
GA)
,
p
a
r
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
,
an
d
r
u
le
-
b
ase
d
co
n
t
r
o
l
m
et
h
o
d
s
.
T
h
ese
ar
e
u
s
ed
t
o
s
o
lv
e
th
e
s
am
e
o
p
tim
izatio
n
p
r
o
b
lem
—
m
in
im
izin
g
en
e
r
g
y
co
s
t
a
n
d
ch
ar
g
in
g
tim
e
wh
ile
m
ain
tain
in
g
g
r
id
b
alan
c
e.
T
h
e
co
m
p
a
r
is
o
n
f
o
c
u
s
es
o
n
c
o
m
p
u
tatio
n
al
c
o
m
p
lex
ity
,
c
o
n
v
er
g
en
ce
s
p
ee
d
,
ad
ap
tab
ilit
y
to
r
ea
l
-
tim
e
ch
an
g
es
,
an
d
e
n
er
g
y
ef
f
icien
cy
.
T
h
is
an
aly
s
is
p
r
o
v
id
es
in
s
ig
h
t
i
n
to
th
e
ad
v
an
tag
es
an
d
tr
ad
e
-
o
f
f
s
b
etwe
en
h
eu
r
is
tic
an
d
AI
-
b
ased
m
o
d
els.
2
.
4
.
Sim
ula
t
i
o
n e
nv
iro
nm
en
t
a
nd
M
L
-
ba
s
e
d o
ptim
iza
t
io
n f
ra
m
ewo
r
k
B
ef
o
r
e
d
ep
lo
y
m
e
n
t,
th
e
d
ev
el
o
p
ed
m
o
d
els
ar
e
test
ed
in
a
s
im
u
lated
s
m
ar
t
g
r
id
en
v
ir
o
n
m
en
t
u
s
in
g
p
latf
o
r
m
s
s
u
ch
as
MA
T
L
AB
/Si
m
u
lin
k
an
d
Op
en
DSS
.
2
.
4
.
1
.
I
ntr
o
du
ct
io
n t
o
M
L
-
ba
s
ed
o
ptim
iza
t
io
n
ML
-
b
ased
en
er
g
y
m
a
n
ag
em
en
t
s
y
s
tem
s
d
y
n
am
ically
allo
ca
te
ch
ar
g
in
g
lo
ad
s
b
ased
o
n
p
r
e
d
ictio
n
s
o
f
en
er
g
y
av
ailab
ilit
y
an
d
d
em
a
n
d
.
T
h
ese
m
o
d
els
o
f
f
er
th
e
ab
ilit
y
to
i)
Fo
r
ec
ast
d
em
an
d
s
u
r
g
es
an
d
r
en
ewa
b
le
en
er
g
y
o
u
tp
u
t
;
ii)
Sch
ed
u
le
E
V
ch
ar
g
in
g
d
u
r
in
g
o
f
f
-
p
ea
k
h
o
u
r
s
;
an
d
iii)
Ad
ap
tiv
ely
r
esp
o
n
d
to
g
r
id
i
n
s
tab
ilit
y
o
r
en
er
g
y
s
h
o
r
tag
es
.
T
h
is
p
r
e
d
ictiv
e
s
ch
ed
u
lin
g
en
s
u
r
es
th
at
th
e
en
er
g
y
d
e
m
an
d
f
r
o
m
E
V
s
is
d
is
tr
ib
u
ted
in
a
b
alan
ce
d
way
ac
r
o
s
s
d
if
f
er
en
t
tim
e
in
ter
v
als.
T
h
e
s
y
s
tem
in
tellig
en
tly
p
r
io
r
itizes
en
er
g
y
f
r
o
m
r
en
ewa
b
le
s
o
u
r
ce
s
wh
en
av
ailab
le.
Un
d
e
r
o
p
tim
al
wea
th
er
c
o
n
d
itio
n
s
,
r
en
ewa
b
le
en
er
g
y
u
tili
za
tio
n
r
ea
ch
es
u
p
to
8
5
%
,
s
ig
n
if
ican
tly
r
ed
u
cin
g
r
elian
ce
o
n
f
o
s
s
il
-
f
u
el
p
o
wer
.
T
h
is
o
p
tim
izatio
n
im
p
r
o
v
es
s
u
s
tain
ab
ilit
y
wh
ile
p
r
eser
v
in
g
e
n
er
g
y
av
ailab
ilit
y
d
u
r
in
g
p
ea
k
h
o
u
r
s
.
2
.
4
.
2
.
Sim
ula
t
io
n det
a
ils
T
o
e
f
f
e
c
t
i
v
el
y
e
v
a
l
u
a
te
t
h
e
p
r
o
p
o
s
e
d
o
p
t
i
m
i
za
t
i
o
n
f
r
a
m
ew
o
r
k
,
a
d
e
t
ai
l
e
d
s
i
m
u
l
at
i
o
n
s
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t
u
p
i
s
d
e
v
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l
o
p
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d
t
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p
l
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c
a
t
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t
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r
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tio
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tech
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r
o
p
tim
a
l E
V
ch
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p
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S
o
ma
s
u
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d
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r
a
m
)
2863
d
e
m
a
n
d
u
n
d
e
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v
e
r
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r
at
i
n
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c
o
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d
i
t
i
o
n
s
.
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t
p
r
o
v
i
d
e
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a
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is
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i
c
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d
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as
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s
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y
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te
m
s
t
a
b
il
i
t
y
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p
e
r
f
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r
m
a
n
c
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,
a
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d
a
d
a
p
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a
b
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l
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ty
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T
h
e
f
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i
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p
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o
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r
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c
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f
t
h
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s
i
m
u
l
a
ti
o
n
f
r
a
m
e
w
o
r
k
a
n
d
i
t
s
f
u
n
c
t
i
o
n
a
lit
y
i
n
v
a
l
i
d
a
t
i
n
g
t
h
e
o
p
t
i
m
i
z
a
ti
o
n
m
o
d
e
l
:
i
)
S
m
a
r
t
g
r
i
d
m
o
d
e
l
:
s
i
m
u
l
a
t
es
g
r
i
d
c
o
n
s
t
r
a
i
n
ts
,
p
o
we
r
f
l
o
w
,
a
n
d
r
e
a
l
-
t
i
m
e
d
a
t
a
i
n
p
u
ts
;
i
i
)
E
V
c
h
a
r
g
i
n
g
d
e
m
a
n
d
m
o
d
u
l
e
:
m
o
d
els
d
i
f
f
e
r
e
n
t
c
h
a
r
g
i
n
g
b
e
h
a
v
i
o
r
s
(
f
a
s
t
/s
l
o
w
c
h
a
r
g
i
n
g
,
r
a
n
d
o
m
a
r
r
i
v
a
l
s
)
;
a
n
d
i
ii
)
R
e
n
e
w
a
b
l
e
e
n
e
r
g
y
m
o
d
u
l
e
:
s
i
m
u
l
a
t
es
s
o
l
a
r
/
wi
n
d
g
e
n
e
r
a
t
i
o
n
u
s
i
n
g
h
i
s
t
o
r
i
ca
l
a
n
d
s
y
n
t
h
e
t
i
c
w
e
a
t
h
e
r
d
a
t
a
.
E
d
g
e
c
o
m
p
u
t
i
n
g
f
r
a
m
e
w
o
r
k
s
a
r
e
a
l
s
o
c
o
n
s
i
d
e
r
e
d
t
o
e
n
a
b
l
e
d
e
c
e
n
t
r
a
li
z
e
d
d
e
c
is
i
o
n
-
m
a
k
i
n
g
,
w
h
i
c
h
e
n
h
a
n
c
e
s
r
e
al
-
t
im
e
r
e
s
p
o
n
s
i
v
e
n
es
s
a
n
d
r
e
d
u
c
e
s
c
l
o
u
d
d
e
p
e
n
d
e
n
c
y
.
2
.
5
.
P
er
f
o
r
m
a
nce
ev
a
lua
t
io
n a
nd
v
a
lid
a
t
io
n
T
o
en
s
u
r
e
p
r
ac
ticality
an
d
s
c
alab
ilit
y
,
th
e
f
in
al
p
h
ase
in
v
o
lv
es
a
th
o
r
o
u
g
h
ev
alu
atio
n
o
f
th
e
ML
m
o
d
els b
ased
o
n
th
e
f
o
llo
win
g
m
etr
ics:
i)
E
n
er
g
y
ef
f
icien
cy
:
re
d
u
ctio
n
in
to
tal
en
e
r
g
y
c
o
n
s
u
m
ed
p
er
ch
a
r
g
in
g
cy
cle
;
ii)
C
h
ar
g
in
g
tim
e
r
ed
u
ctio
n
:
a
v
e
r
ag
e
tim
e
s
av
in
g
s
p
er
u
s
er
;
iii)
C
o
s
t
-
ef
f
ec
tiv
e
n
ess
:
d
ec
r
ea
s
e
in
elec
tr
icity
co
s
t
u
s
in
g
s
m
ar
t
s
ch
ed
u
lin
g
;
iv
)
Gr
id
st
ab
il
ity
:
f
r
eq
u
e
n
cy
o
f
lo
a
d
im
b
a
lan
ce
s
an
d
v
o
ltag
e
v
io
latio
n
s
;
an
d
v
)
R
en
ewa
b
le
u
tili
za
tio
n
:
p
er
ce
n
tag
e
o
f
to
tal
en
er
g
y
s
u
p
p
lied
f
r
o
m
r
e
n
ewa
b
les
.
A
s
en
s
i
tiv
ity
an
aly
s
is
is
co
n
d
u
cted
to
test
r
o
b
u
s
tn
ess
u
n
d
er
v
ar
y
in
g
co
n
d
itio
n
s
,
s
u
ch
as
s
u
d
d
en
d
em
an
d
s
u
r
g
es
,
r
en
ewa
b
le
g
en
er
atio
n
d
r
o
p
-
o
f
f
s
,
an
d
g
r
id
d
is
tu
r
b
an
ce
s
.
3.
RE
SU
L
T
AND
DI
SCUS
SI
O
N
T
h
e
p
r
o
p
o
s
ed
m
ac
h
in
e
lear
n
i
n
g
-
b
ased
o
p
tim
izatio
n
m
o
d
el
f
o
r
E
V
ch
a
r
g
in
g
was
im
p
lem
en
ted
an
d
ev
alu
ated
.
T
h
is
s
ec
tio
n
p
r
esen
ts
k
ey
f
in
d
in
g
s
,
co
m
p
ar
is
o
n
s
with
co
n
v
en
tio
n
al
tech
n
iq
u
es,
an
d
d
is
cu
s
s
io
n
s
o
n
p
er
f
o
r
m
an
ce
,
c
o
s
t e
f
f
icien
cy
,
g
r
id
s
tab
ilit
y
,
an
d
f
u
tu
r
e
im
p
r
o
v
em
en
ts
.
3
.
1
.
Co
m
pa
ra
t
iv
e
perf
o
r
m
a
nce
a
na
ly
s
is
T
h
e
ML
-
b
a
s
e
d
a
p
p
r
o
a
c
h
w
a
s
c
o
m
p
a
r
e
d
w
i
t
h
t
r
a
d
i
t
i
o
n
a
l
o
p
t
i
m
i
z
a
t
i
o
n
m
e
t
h
o
d
s
s
u
c
h
a
s
g
e
n
e
t
i
c
a
l
g
o
r
i
t
h
m
(
G
A
)
,
p
a
r
t
i
c
l
e
s
w
a
r
m
o
p
t
i
m
i
z
a
t
i
o
n
(
P
S
O
)
,
a
n
d
r
u
l
e
-
b
a
s
e
d
c
o
n
t
r
o
l
.
T
h
e
r
e
s
u
l
t
s
i
n
d
i
c
a
t
e
t
h
a
t
t
h
e
M
L
m
o
d
e
l
o
u
t
p
e
r
f
o
r
m
s
c
o
n
v
e
n
t
i
o
n
a
l
t
e
c
h
n
i
q
u
e
s
i
n
t
e
r
m
s
o
f
c
o
s
t
e
f
f
i
c
i
e
n
c
y
,
a
s
d
e
t
a
i
l
e
d
i
n
T
a
b
l
e
1
.
F
i
g
u
r
e
2
s
h
o
w
s
t
h
e
c
o
m
p
a
r
i
s
o
n
o
f
t
h
e
c
h
a
r
g
i
n
g
o
p
t
i
m
i
z
a
t
i
o
n
t
e
c
h
n
i
q
u
e
.
T
h
e
ML
-
b
a
s
e
d
o
p
t
i
m
i
z
a
t
i
o
n
m
o
d
e
l
a
c
h
i
e
v
e
d
a
15
-
2
0
%
i
m
p
r
o
v
e
m
e
n
t
i
n
c
o
s
t
e
f
f
i
c
i
e
n
c
y
c
o
m
p
a
r
e
d
t
o
r
u
l
e
-
b
a
s
e
d
a
p
p
r
o
a
c
h
e
s
a
n
d
8
-
1
2
%
h
i
g
h
e
r
e
f
f
i
c
i
e
n
c
y
t
h
a
n
G
A
a
n
d
PSO
.
T
h
i
s
d
e
m
o
n
s
t
r
a
t
e
s
t
h
e
s
u
p
e
r
i
o
r
a
d
a
p
t
a
b
i
l
i
t
y
o
f
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
i
n
o
p
t
i
m
i
z
i
n
g
c
h
a
r
g
i
n
g
s
c
h
e
d
u
l
e
s
d
y
n
a
m
i
c
a
l
l
y
.
3
.
2
.
M
o
del per
f
o
rma
nce
ev
a
lua
t
io
n
T
h
e
m
ea
n
s
q
u
ar
ed
e
r
r
o
r
(
MSE
)
an
d
R
-
s
q
u
ar
ed
(
R
²)
s
co
r
es
wer
e
u
s
ed
to
ev
alu
ate
th
e
ac
cu
r
ac
y
o
f
th
e
ML
-
b
ased
o
p
tim
izatio
n
.
T
h
e
r
esu
lts
ar
e
s
u
m
m
ar
ized
in
T
ab
le
2
.
T
h
e
lo
w
MSE
v
alu
e
in
d
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I
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8
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I
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Dr
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tili
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h
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ap
p
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g
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Fig
u
r
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4
s
h
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les b
ased
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3
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4
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ased
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ased
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ased
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
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Sy
s
t
I
SS
N:
2088
-
8
6
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ased
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Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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t J Po
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Vo
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16
,
No
.
4
,
Dec
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b
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20
25
:
2860
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2866
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.
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