I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
pu
t
er
E
ng
ineering
(
I
J
E
CE
)
Vo
l.
1
6
,
No
.
2
,
A
p
r
il
20
2
6
,
p
p
.
598
~
607
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijece.
v
1
6
i
2
.
pp
5
9
8
-
6
0
7
598
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
M
ulti
-
o
bje
ctive o
ptimiza
tion o
f
dis
tribut
ed genera
t
i
o
n
pla
cement
a
nd
siz
ing
in
a
ctive dis
tr
ibution
ne
tworks
co
nsidering
harm
o
nic distortio
n
T
rieu
Ng
o
c
T
o
n,
P
ho
ng
M
in
h L
e,
T
a
n
M
inh
L
e
F
a
c
u
l
t
y
o
f
E
l
e
c
t
r
i
c
a
l
a
n
d
E
l
e
c
t
r
o
n
i
c
s
En
g
i
n
e
e
r
i
n
g
,
T
h
u
D
u
c
C
o
l
l
e
g
e
o
f
Te
c
h
n
o
l
o
g
y
,
H
o
C
h
i
M
i
n
h
C
i
t
y
,
V
i
e
t
n
a
m
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
n
1
0
,
2
0
2
5
R
ev
is
ed
Dec
2
,
2
0
2
5
Acc
ep
ted
J
an
1
5
,
2
0
2
6
Th
is
p
a
p
e
r
p
re
se
n
ts
a
m
u
lt
i
-
o
b
jec
ti
v
e
o
p
ti
m
iza
ti
o
n
m
o
d
e
l
fo
r
o
p
ti
m
a
l
p
lac
e
m
e
n
t
a
n
d
siz
in
g
o
f
i
n
v
e
rter
-
b
a
se
d
d
istr
ib
u
ted
g
e
n
e
ra
ti
o
n
(DG
)
u
n
it
s
i
n
a
c
ti
v
e
d
istri
b
u
ti
o
n
p
o
we
r
sy
ste
m
s
(DPS
),
c
o
n
sid
e
rin
g
th
e
ir
i
m
p
a
c
t
o
n
h
a
rm
o
n
ic d
isto
r
ti
o
n
.
Th
e
m
o
d
e
l
si
m
u
lt
a
n
e
o
u
sl
y
m
i
n
imiz
e
s
to
tal
p
o
we
r
lo
ss
e
s
a
n
d
to
tal
h
a
rm
o
n
ic
d
ist
o
rti
o
n
(T
HD
),
e
n
su
rin
g
c
o
m
p
l
ian
c
e
with
IEE
E
5
1
9
sta
n
d
a
rd
s.
T
o
so
lv
e
t
h
is
p
r
o
b
le
m
,
th
e
re
p
ti
le
se
a
rc
h
a
lg
o
rit
h
m
(RUN
)
is
a
p
p
li
e
d
a
n
d
c
o
m
p
a
re
d
with
t
h
re
e
m
e
tah
e
u
risti
c
a
lg
o
rit
h
m
s:
m
u
lt
i
-
o
b
jec
ti
v
e
p
a
rti
c
le
sw
a
rm
o
p
ti
m
iza
ti
o
n
(M
O
P
S
O)
,
m
u
lt
i
-
o
b
jec
ti
v
e
g
re
y
wo
lf
o
p
ti
m
ize
r
(M
OG
WO),
a
n
d
m
u
lt
i
-
o
b
jec
ti
v
e
wh
a
le
o
p
ti
m
iza
ti
o
n
a
lg
o
r
it
h
m
(M
OWOA
)
.
S
imu
latio
n
re
su
lt
s
o
n
IEE
E
3
3
-
b
u
s
a
n
d
6
9
-
b
u
s
sy
ste
m
s
sh
o
w
t
h
a
t
re
p
ti
le
se
a
rc
h
a
lg
o
rit
h
m
(RUN
)
re
d
u
c
e
s
p
o
we
r
l
o
ss
e
s
b
y
u
p
t
o
6
.
1
%
a
n
d
THD
b
y
2
1
.
7
%
c
o
m
p
a
re
d
to
M
OPS
O.
M
o
re
o
v
e
r,
th
e
re
su
lt
s
c
o
n
firm
a
stro
n
g
c
o
rre
latio
n
b
e
twe
e
n
DG
o
u
t
p
u
t
p
o
we
r
a
n
d
h
a
rm
o
n
ic
a
m
p
li
tu
d
e
s,
h
ig
h
li
g
h
ti
n
g
th
e
imp
o
rtan
c
e
o
f
p
o
we
r
q
u
a
li
ty
a
wa
re
DG
p
lan
n
in
g
.
K
ey
w
o
r
d
s
:
Dis
tr
ib
u
ted
g
en
er
atio
n
Dis
tr
ib
u
tio
n
n
etwo
r
k
s
Mu
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
R
ep
tile sear
ch
alg
o
r
ith
m
T
o
tal
h
ar
m
o
n
ic
d
is
to
r
tio
n
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
T
r
ieu
Ng
o
c
T
o
n
Facu
lty
o
f
E
lectr
ical
an
d
E
lectr
o
n
ics E
n
g
in
ee
r
in
g
,
T
h
u
D
u
c
C
o
lleg
e
o
f
T
ec
h
n
o
lo
g
y
5
3
Vo
Van
N
g
an
,
L
i
n
h
C
h
ieu
,
T
h
u
Du
c
city
,
Ho
C
h
i M
in
h
C
ity
7
0
0
0
0
0
,
Vietn
a
m
E
m
ail: to
n
n
g
o
ct
r
ieu
@
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
in
teg
r
atio
n
o
f
d
is
tr
ib
u
te
d
g
en
er
ato
r
s
(
DG)
,
esp
ec
ially
in
v
er
ter
b
ased
tech
n
o
l
o
g
ie
s
s
u
ch
as
p
h
o
to
v
o
ltaic
an
d
win
d
p
o
wer
,
h
as
b
ec
o
m
e
an
ess
en
tial
co
m
p
o
n
e
n
t
in
m
o
d
er
n
d
is
tr
ib
u
ti
o
n
p
o
wer
s
y
s
tem
s
(
DPS)
[
1
]
,
[
2
]
.
W
h
en
s
tr
ateg
ic
ally
p
lace
d
an
d
p
r
o
p
e
r
ly
s
ized
,
DG
u
n
its
ca
n
e
n
h
an
ce
s
y
s
tem
ef
f
icien
c
y
,
r
ed
u
ce
p
o
wer
lo
s
s
es,
im
p
r
o
v
e
v
o
ltag
e
p
r
o
f
iles
,
an
d
p
r
o
m
o
te
en
e
r
g
y
s
elf
-
s
u
f
f
icien
cy
[
3
]
,
[
4
]
.
Ho
wev
er
,
th
e
r
ap
id
d
ep
lo
y
m
e
n
t
o
f
in
v
e
r
ter
b
ased
DGs
al
s
o
in
tr
o
d
u
ce
s
tech
n
ic
al
ch
allen
g
es
ch
ief
am
o
n
g
th
em
is
p
o
wer
q
u
ality
d
eg
r
ad
atio
n
d
u
e
to
in
cr
ea
s
ed
h
ar
m
o
n
ic
d
is
to
r
tio
n
[
5
]
,
[
6
]
.
As
m
o
r
e
in
v
er
ter
b
ased
DG
s
ar
e
co
n
n
ec
ted
to
t
h
e
d
is
tr
ib
u
tio
n
n
etwo
r
k
s
,
th
eir
h
i
g
h
f
r
eq
u
e
n
cy
s
witch
in
g
ch
ar
ac
ter
is
tics
ca
n
ca
u
s
e
s
ig
n
if
ican
t
wav
e
f
o
r
m
d
is
to
r
tio
n
.
T
h
is
lea
d
s
to
s
ev
e
r
al
ad
v
er
s
e
ef
f
ec
ts
,
in
clu
d
in
g
eq
u
ip
m
en
t
o
v
er
h
ea
t
in
g
,
in
ac
cu
r
ate
m
ea
s
u
r
em
e
n
ts
,
m
alf
u
n
ctio
n
o
f
p
r
o
tectio
n
s
y
s
tem
s
,
an
d
r
ed
u
ce
d
eq
u
ip
m
en
t
life
s
p
an
[
7
]
,
[
8
]
.
T
o
m
ain
tain
ac
ce
p
tab
le
p
o
wer
q
u
ality
,
th
e
I
E
E
E
5
1
9
s
tan
d
a
r
d
m
an
d
ates
th
at
th
e
to
tal
h
ar
m
o
n
ic
d
is
to
r
tio
n
(
T
H
D)
at
ea
ch
b
u
s
m
u
s
t
n
o
t
ex
ce
e
d
5
%
[
9
]
,
[
1
0
]
.
Hen
ce
,
DG
p
la
n
n
in
g
m
u
s
t
ac
co
u
n
t
n
o
t
o
n
ly
f
o
r
p
o
wer
lo
s
s
m
in
im
izatio
n
b
u
t
also
f
o
r
h
ar
m
o
n
ic
m
itig
atio
n
,
f
o
r
m
in
g
a
m
u
lti
-
o
b
jectiv
e
an
d
n
o
n
lin
ea
r
o
p
tim
izatio
n
p
r
o
b
lem
[
1
1
]
,
[
1
2
]
.
Pre
v
io
u
s
s
tu
d
ies
h
a
v
e
p
r
o
p
o
s
ed
v
a
r
io
u
s
a
p
p
r
o
ac
h
es
to
tac
k
le
th
is
ch
allen
g
e,
i
n
clu
d
in
g
th
e
u
s
e
o
f
ad
v
an
ce
d
m
eta
h
eu
r
is
tic
alg
o
r
ith
m
s
f
o
r
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
[
1
3
]
,
[
1
4
]
.
Fo
r
in
s
tan
ce
,
ex
ten
s
io
n
s
o
f
well
-
k
n
o
wn
alg
o
r
ith
m
s
s
u
ch
a
s
ar
tific
ial
b
ee
co
lo
n
y
(
AB
C
)
[
1
5
]
,
[
1
6
]
,
g
r
e
y
wo
l
f
o
p
tim
izer
(
GW
O)
[
1
7
]
,
[
1
8
]
,
an
d
wh
ale
o
p
tim
izatio
n
alg
o
r
ith
m
(
W
OA)
[
1
9
]
,
[
2
0
]
h
a
v
e
s
h
o
wn
p
r
o
m
is
in
g
r
esu
lts
in
s
o
l
v
in
g
r
elate
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Mu
lti
-
o
b
jective
o
p
timiz
a
tio
n
o
f d
is
tr
ib
u
ted
g
en
era
tio
n
p
la
c
eme
n
t a
n
d
s
iz
in
g
in
a
ctive
…
(
Tr
ieu
N
g
o
c
To
n
)
599
p
r
o
b
lem
s
.
Ho
we
v
er
,
m
o
s
t
o
f
th
ese
wo
r
k
s
eith
er
n
e
g
lect
h
a
r
m
o
n
ic
d
is
to
r
tio
n
as
a
p
r
im
a
r
y
co
n
s
id
er
atio
n
o
r
tr
ea
t
it
as
a
s
ec
o
n
d
ar
y
c
o
n
s
tr
a
in
t,
with
o
u
t
ex
p
licitly
m
o
d
eli
n
g
th
e
c
o
r
r
elatio
n
b
etwe
en
D
G
o
u
tp
u
t
p
o
wer
an
d
h
ar
m
o
n
ic
a
m
p
litu
d
es
T
o
ad
d
r
ess
th
is
g
ap
,
th
is
s
t
u
d
y
p
r
o
p
o
s
es
a
m
u
lti
-
o
b
ject
iv
e
o
p
tim
izatio
n
m
o
d
el
t
h
at
ex
p
licitly
in
co
r
p
o
r
ates
h
ar
m
o
n
ic
d
is
to
r
tio
n
in
t
o
th
e
p
lan
n
in
g
p
r
o
ce
s
s
o
f
in
v
er
ter
b
ased
DGs.
T
h
e
m
o
d
el
is
d
esig
n
ed
t
o
m
in
im
ize
b
o
th
ac
tiv
e
p
o
wer
l
o
s
s
es
an
d
T
HD
lev
els,
en
s
u
r
i
n
g
co
m
p
lian
ce
with
I
E
E
E
5
1
9
s
tan
d
ar
d
s
[
2
1
]
.
T
o
s
o
lv
e
th
is
p
r
o
b
lem
,
th
e
r
e
p
tile
s
ea
r
ch
alg
o
r
ith
m
(
R
UN)
,
a
r
ec
en
t
m
etah
e
u
r
is
tic
in
s
p
ir
ed
b
y
t
h
e
p
r
e
d
ato
r
y
b
eh
av
io
r
o
f
r
ep
tiles
[
2
2
]
,
is
ad
o
p
ted
a
n
d
b
en
ch
m
a
r
k
ed
ag
ain
s
t
th
r
ee
well
-
k
n
o
wn
o
p
tim
izatio
n
alg
o
r
ith
m
s
:
m
u
lti
-
o
b
jectiv
e
p
a
r
ticle
s
war
m
o
p
tim
izatio
n
(
MO
PS
O)
[
1
4
]
,
m
u
lti
-
o
b
jectiv
e
g
r
ey
w
o
lf
o
p
tim
izer
(
MO
GW
O)
[
2
3
]
,
a
n
d
m
u
lti
-
o
b
jectiv
e
wh
al
e
o
p
tim
izatio
n
alg
o
r
ith
m
(
MO
W
OA)
[
2
4
]
.
Simu
latio
n
s
ar
e
co
n
d
u
cted
o
n
two
s
tan
d
ar
d
test
s
y
s
tem
s
th
e
I
E
E
E
3
3
-
b
u
s
an
d
6
9
-
b
u
s
DPS.
R
esu
lts
s
h
o
w
th
at
R
UN
co
n
s
i
s
ten
tl
y
o
u
tp
er
f
o
r
m
s
th
e
co
m
p
ar
e
d
m
eth
o
d
s
in
ter
m
s
o
f
o
p
tim
izatio
n
q
u
ality
,
co
n
v
er
g
en
ce
s
p
ee
d
,
r
o
b
u
s
tn
e
s
s
,
an
d
T
HD
co
m
p
lian
ce
.
Mo
r
eo
v
e
r
,
th
e
r
esu
lts
r
ev
ea
l
a
s
tr
o
n
g
co
r
r
elatio
n
b
etwe
en
DG
o
u
tp
u
t
p
o
wer
an
d
h
ar
m
o
n
ic
d
is
to
r
tio
n
lev
els
at
in
d
iv
id
u
al
b
u
s
es,
em
p
h
asizin
g
th
e
im
p
o
r
ta
n
ce
o
f
p
o
wer
q
u
ality
awa
r
e
DG
p
lan
n
in
g
in
ac
tiv
e
d
is
tr
ib
u
tio
n
s
y
s
tem
s
.
T
h
e
k
e
y
co
n
tr
ib
u
tio
n
s
o
f
th
is
p
ap
er
ar
e
as
f
o
llo
ws:
−
A
n
ew
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
m
o
d
el
th
at
in
c
o
r
p
o
r
ates
h
ar
m
o
n
ic
d
is
to
r
tio
n
as
a
p
r
im
ar
y
o
b
jectiv
e
in
DG
p
lan
n
in
g
;
−
A
co
r
r
elatio
n
b
ased
h
ar
m
o
n
ic
m
o
d
el
th
at
lin
k
s
DG
o
u
tp
u
t p
o
wer
with
v
o
ltag
e
d
is
to
r
tio
n
lev
els;
−
Ap
p
licatio
n
o
f
t
h
e
R
UN
to
s
o
lv
e
th
e
co
n
s
tr
ain
ed
n
o
n
lin
ea
r
o
p
tim
izatio
n
p
r
o
b
lem
;
−
C
o
m
p
ar
ativ
e
s
im
u
latio
n
s
o
n
I
E
E
E
3
3
-
b
u
s
an
d
6
9
-
b
u
s
DPS
d
em
o
n
s
tr
atin
g
th
e
s
u
p
e
r
io
r
p
er
f
o
r
m
an
ce
o
f
R
UN
o
v
er
o
th
er
m
et
h
o
d
s
.
2.
P
RO
P
O
SE
D
M
A
T
H
E
M
A
T
I
CAL M
O
D
E
L
I
n
th
is
s
ec
tio
n
,
a
m
u
lti
o
b
j
ec
tiv
e
o
p
tim
izatio
n
m
o
d
el
is
d
ev
elo
p
ed
to
d
eter
m
in
e
t
h
e
o
p
tim
al
p
lace
m
en
t
a
n
d
s
izin
g
o
f
DGs
i
n
ac
tiv
e
DPS,
tak
in
g
in
to
ac
co
u
n
t
th
e
p
r
o
p
a
g
atio
n
o
f
T
HD.
T
h
e
m
o
d
el
c
o
n
s
is
ts
o
f
two
m
ain
o
b
jectiv
e
f
u
n
ct
io
n
s
:
i
)
m
in
im
izin
g
th
e
t
o
ta
l
tech
n
ical
p
o
wer
lo
s
s
es
in
th
e
n
etwo
r
k
,
an
d
ii
)
m
in
im
izin
g
th
e
T
HD
at
th
e
b
u
s
es wh
er
e
DGs a
r
e
in
s
talled
.
2
.
1
.
Dec
is
io
n v
a
ria
bles
I
n
th
e
p
r
o
p
o
s
ed
m
o
d
el,
th
e
d
e
cisi
o
n
v
ar
iab
les ar
e
d
ef
i
n
ed
as
f
o
llo
ws:
a.
x
i
∈
{
0
,
1
}
:
A
b
in
ar
y
v
a
r
iab
le
in
d
icatin
g
wh
eth
er
a
DG
u
n
it
is
in
s
talled
at
b
u
s
(
1
:
in
s
talled
,
0
:
n
o
t
in
s
talled
)
.
b.
dg
,
: T
h
e
ac
tiv
e
p
o
we
r
o
u
t
p
u
t o
f
th
e
DG
u
n
it a
t b
u
s
(
k
W
)
.
c.
ℎ
(
)
:
T
h
e
a
m
p
litu
d
e
o
f
th
e
h
-
o
r
d
er
h
ar
m
o
n
ic
co
m
p
o
n
e
n
t
at
b
u
s
,
wh
ich
is
d
eter
m
i
n
ed
b
ased
o
n
th
e
DG
o
u
tp
u
t
p
o
wer
.
T
ab
le
1
s
h
o
ws th
e
s
y
m
b
o
ls
an
d
n
o
tatio
n
s
u
s
ed
i
n
th
e
p
r
o
p
o
s
ed
o
p
tim
izatio
n
m
o
d
el
T
ab
le
1
.
Sy
m
b
o
ls
an
d
n
o
tatio
n
s
u
s
ed
in
th
e
p
r
o
p
o
s
ed
o
p
tim
iz
atio
n
m
o
d
el
S
y
mb
o
l
D
e
scri
p
t
i
o
n
U
n
i
t
B
i
n
a
r
y
v
a
r
i
a
b
l
e
i
n
d
i
c
a
t
i
n
g
d
g
p
l
a
c
e
m
e
n
t
a
t
b
u
s
i
–
,
O
u
t
p
u
t
p
o
w
e
r
o
f
d
g
a
t
b
u
s
i
K
W
V
o
l
t
a
g
e
m
a
g
n
i
t
u
d
e
a
t
b
u
s
i
PU
ℎ
H
a
r
mo
n
i
c
o
r
d
e
r
(
e
.
g
.
,
3
r
d
,
5
t
h
,
7
t
h
)
–
ℎ
,
A
mp
l
i
t
u
d
e
o
f
t
h
e
h
-
o
r
d
e
r
h
a
r
m
o
n
i
c
c
o
mp
o
n
e
n
t
a
t
b
u
s i
PU
To
t
a
l
h
a
r
mo
n
i
c
d
i
s
t
o
r
t
i
o
n
a
t
b
u
s i
%
R
e
si
st
a
n
c
e
o
f
b
r
a
n
c
h
c
o
n
n
e
c
t
i
n
g
b
u
s
i
a
n
d
j
O
HM
To
t
a
l
sy
s
t
e
m
p
o
w
e
r
l
o
ss
K
W
2
.
2
.
O
bje
c
t
iv
e
f
un
ct
io
n 1
:
min
im
iza
t
io
n o
f
po
wer
lo
s
s
es
T
h
e
f
ir
s
t
o
b
jectiv
e
aim
s
to
m
in
im
ize
th
e
to
tal
tech
n
ical
p
o
wer
lo
s
s
es
in
t
h
e
d
is
tr
ib
u
tio
n
p
o
wer
s
y
s
tem
s
(
DPS
)
an
d
is
f
o
r
m
u
lated
as
(
1
)
:
1
=
∑
(
,
)
∈
⋅
2
+
2
2
(
1
)
w
h
er
e,
L
: Set
o
f
all
b
r
an
c
h
es in
th
e
DPS;
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
2
,
Ap
r
il
20
2
6
:
5
9
8
-
607
600
P
ij
,
Q
ij
: Po
wer
f
lo
ws o
n
b
r
an
ch
(
i
,
j
)
;
R
ij
: Res
i
s
tan
ce
o
f
b
r
an
c
h
(
i
,
j
)
;
V
i
: V
o
ltag
e
m
ag
n
itu
d
e
at
b
u
s
i
,
co
n
s
id
er
in
g
o
n
ly
t
h
e
f
u
n
d
am
en
t
al
co
m
p
o
n
en
t (
5
0
Hz)
.
2
.
3
.
O
bje
c
t
iv
e
f
un
ct
io
n 2
:
min
im
iza
t
io
n o
f
ha
r
m
o
nic dis
t
o
rt
io
n
T
h
e
s
ec
o
n
d
o
b
jectiv
e
m
in
im
izes
th
e
T
HD
at
b
u
s
es
wh
er
e
DGs
ar
e
in
s
talled
.
T
HD
a
t
a
b
u
s
is
ca
lcu
lated
ac
co
r
d
i
n
g
to
t
h
e
I
E
E
E
5
1
9
s
tan
d
ar
d
as
(
2
)
:
T
HD
i
=
√
∑
(
V
h
(
i
)
V
1
(
i
)
)
2
H
h
=
3
,
5
,
7
,
…
(
2
)
T
h
e
o
v
e
r
all
h
ar
m
o
n
ic
o
b
jectiv
e
f
u
n
ctio
n
is
ex
p
r
ess
ed
as
(
3
)
:
f
2
=
∑
T
HD
i
i
∈
N
dg
(
3
)
T
h
e
T
HD
f
u
n
ctio
n
p
en
alize
s
h
ig
h
h
a
r
m
o
n
ic
co
n
te
n
t
at
cr
iti
ca
l
n
o
d
es,
th
u
s
p
r
o
m
o
tin
g
DG
co
n
f
ig
u
r
atio
n
s
th
at
r
ed
u
ce
d
is
to
r
tio
n
at
th
e
s
y
s
tem
lev
el.
W
h
er
e,
−
V
h
(
i
)
: A
m
p
litu
d
e
o
f
t
h
e
h
th
-
o
r
d
er
h
ar
m
o
n
ic
co
m
p
o
n
e
n
t a
t b
u
s
;
−
V
1
(
i
)
: A
m
p
litu
d
e
o
f
t
h
e
f
u
n
d
am
e
n
tal
v
o
ltag
e
co
m
p
o
n
e
n
t a
t b
u
s
;
−
N
dg
: Set
o
f
b
u
s
es wh
er
e
DGs a
r
e
i
n
s
talled
.
I
n
th
is
s
tu
d
y
,
o
n
ly
d
o
m
in
an
t
h
ar
m
o
n
ic
o
r
d
er
s
(
3
r
d
,
5
th
,
an
d
7
th
)
ar
e
c
o
n
s
id
er
ed
,
as
th
ese
ar
e
ty
p
ically
g
en
e
r
ated
b
y
in
v
e
r
te
r
-
b
ased
DG
u
n
its
an
d
h
a
v
e
th
e
m
o
s
t
s
ig
n
if
ican
t
im
p
ac
t
o
n
v
o
ltag
e
d
is
to
r
tio
n
in
lo
w
v
o
ltag
e
d
is
tr
ib
u
tio
n
s
y
s
tem
s
.
2
.
4
.
T
ec
hn
ica
l c
o
ns
t
ra
ints
T
h
e
o
p
tim
izatio
n
is
s
u
b
ject
to
th
e
f
o
llo
win
g
tec
h
n
ical
co
n
s
tr
ain
ts
:
−
Vo
ltag
e
lim
its
:
≤
≤
∀
∈
(
4
)
W
h
er
e
min
=
0
.
95
pu
,
max
=
1
.
05
pu
.
−
DG
ca
p
ac
ity
co
n
s
tr
ain
t
0
≤
P
dg
,
i
≤
x
i
⋅
P
dg
m
ax
∀
i
∈
N
(
W
ith
max
=
500
kW
)
(
5
)
−
M
ax
im
u
m
n
u
m
b
er
o
f
DG
u
n
its
∑
x
i
i
∈
N
≤
N
dg
m
ax
(
max
=
3
(
3
3
-
b
u
s
,
max
=
5
(
6
9
-
b
u
s
)
)
(
6
)
−
P
o
wer
q
u
ality
co
n
s
tr
ain
t (
h
ar
m
o
n
ic
lim
it)
THD
≤
5%
∀
∈
b
u
s
s
et
(
7
)
T
h
ese
co
n
s
tr
ain
ts
en
s
u
r
e
th
at
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
r
esp
ec
ts
b
o
th
tech
n
ical
an
d
r
eg
u
l
ato
r
y
r
eq
u
i
r
em
en
ts
,
p
av
in
g
t
h
e
way
f
o
r
an
ef
f
ec
tiv
e
s
o
lu
tio
n
ap
p
r
o
ac
h
p
r
esen
ted
in
th
e
f
o
llo
win
g
s
ec
tio
n
.
3.
RE
S
E
ARCH
M
E
T
H
O
D
I
n
th
is
s
tu
d
y
,
th
e
p
r
o
b
lem
o
f
jo
in
tly
o
p
tim
izin
g
th
e
p
lace
m
en
t
an
d
s
izin
g
o
f
DGs
in
ac
tiv
e
DPS
is
f
o
r
m
u
lated
as
a
m
u
lti
-
o
b
jecti
v
e
o
p
tim
izatio
n
m
o
d
el.
T
o
e
f
f
ec
tiv
ely
s
o
lv
e
th
is
co
m
p
lex
p
r
o
b
lem
,
a
r
ec
en
t
n
atu
r
e
in
s
p
ir
e
d
m
eta
h
eu
r
is
tic,
th
e
R
UN
is
em
p
lo
y
ed
[
2
2
]
.
R
UN
is
s
p
ec
if
ically
well
s
u
ited
f
o
r
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
task
s
th
at
in
v
o
lv
e
h
ar
m
o
n
ic
d
is
to
r
tio
n
co
n
s
tr
ai
n
ts
.
Dr
awin
g
in
s
p
ir
atio
n
f
r
o
m
th
e
d
y
n
am
ic
s
tatic
p
r
ed
ato
r
y
b
e
h
av
io
r
o
f
r
ep
til
es,
R
UN
m
ain
tain
s
a
s
tr
o
n
g
b
alan
ce
b
etwe
en
g
lo
b
al
ex
p
lo
r
atio
n
a
n
d
l
o
ca
l
ex
p
lo
itatio
n
,
th
e
r
eb
y
en
h
a
n
cin
g
its
ab
ilit
y
to
id
e
n
tify
h
ig
h
q
u
ality
s
o
lu
tio
n
s
.
Ad
d
itio
n
ally
,
th
e
alg
o
r
ith
m
ex
h
ib
its
r
ap
id
co
n
v
er
g
e
n
ce
a
n
d
co
n
s
is
ten
t
p
e
r
f
o
r
m
an
ce
ac
r
o
s
s
m
u
ltip
le
in
d
ep
e
n
d
en
t
r
u
n
s
,
m
ak
in
g
it
a
r
o
b
u
s
t
ch
o
ice
f
o
r
s
o
lv
in
g
n
o
n
lin
ea
r
an
d
co
n
s
tr
ain
t
in
ten
s
iv
e
p
r
o
b
lem
s
s
u
ch
as
DG
p
lan
n
in
g
with
p
o
wer
q
u
ality
co
n
s
id
er
atio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Mu
lti
-
o
b
jective
o
p
timiz
a
tio
n
o
f d
is
tr
ib
u
ted
g
en
era
tio
n
p
la
c
eme
n
t a
n
d
s
iz
in
g
in
a
ctive
…
(
Tr
ieu
N
g
o
c
To
n
)
601
3
.
1
.
So
lutio
n r
epre
s
ent
a
t
io
n
a
nd
o
bje
ct
iv
e
f
un
ct
io
n
E
ac
h
ca
n
d
id
ate
s
o
l
u
tio
n
in
t
h
e
o
p
tim
izatio
n
p
r
o
ce
s
s
co
n
s
is
ts
o
f
:
−
DG
pl
a
c
e
me
n
t
: Rep
r
esen
ted
b
y
a
b
i
n
ar
y
v
ec
to
r
∈
{
0
,
1
}
,
in
d
icatin
g
wh
eth
er
a
DG
is
in
s
talled
at
b
u
s
i
.
−
DG
output
p
o
wer
: Rep
r
esen
t
ed
b
y
a
co
n
tin
u
o
u
s
v
ec
to
r
dg
,
∈
[
0
,
dg
]
.
T
h
e
two
o
b
jectiv
e
f
u
n
ctio
n
s
ar
e
d
ef
in
ed
as:
−
1
:
To
ta
l
p
o
wer
lo
s
s
es in
th
e
n
et
wo
r
k
(
k
W
)
.
−
2
:
T
HD
at
DG
in
s
talled
b
u
s
es.
T
h
e
co
m
b
i
n
ed
o
b
jectiv
e
f
u
n
ctio
n
is
ex
p
r
ess
ed
as
(
8
)
:
=
1
⋅
1
+
2
⋅
2
+
P
en
a
lty
T
HD
(
8
)
I
n
th
is
s
tu
d
y
,
th
e
weig
h
tin
g
co
ef
f
icien
ts
ar
e
s
et
to
w
₁
=
0
.
6
(
p
o
wer
lo
s
s
)
an
d
w
₂
=
0
.
4
(
T
HD)
b
ased
o
n
p
r
elim
in
ar
y
s
en
s
itiv
ity
an
al
y
s
is
.
W
h
er
e
1
+
2
=
1
ar
e
th
e
weig
h
tin
g
co
ef
f
icien
ts
,
an
d
P
en
a
lty
T
HD
is
a
p
en
alty
ter
m
a
p
p
lied
if
t
h
e
T
H
D
at
an
y
b
u
s
v
io
lates th
e
I
E
E
E
5
1
9
lim
it.
P
en
a
lty
T
HD
=
{
0
,
if
THD
≤
THD
max
∀
⋅
∑
(
0
,
THD
−
THD
max
)
,
o
th
erw
is
e
(
9
)
T
o
b
alan
ce
th
e
two
co
n
f
lictin
g
o
b
jectiv
es,
p
o
wer
lo
s
s
m
in
im
izatio
n
an
d
h
ar
m
o
n
ic
d
i
s
to
r
tio
n
r
ed
u
ctio
n
,
weig
h
tin
g
co
ef
f
icien
ts
1
an
d
2
ar
e
in
tr
o
d
u
ce
d
in
th
e
ag
g
r
eg
ated
f
itn
ess
f
u
n
ctio
n
.
I
n
th
is
s
tu
d
y
,
1
=
0
.
6
a
n
d
2
=
0
.
4
to
s
lig
h
tly
p
r
i
o
r
itize
th
e
r
ed
u
ctio
n
o
f
p
o
wer
lo
s
s
es
w
h
ile
s
till
g
iv
in
g
s
u
f
f
icien
t
im
p
o
r
tan
ce
to
T
HD
m
in
im
izatio
n
.
T
h
is
s
elec
tio
n
is
b
ased
o
n
th
e
o
p
e
r
atio
n
al
p
r
io
r
ity
o
f
m
in
im
izin
g
en
er
g
y
lo
s
s
in
DPS,
esp
ec
ially
u
n
d
er
in
cr
ea
s
in
g
lo
ad
co
n
d
itio
n
s
,
wh
ile
m
ain
tain
i
n
g
ac
ce
p
tab
le
p
o
we
r
q
u
ality
.
Sen
s
itiv
ity
an
aly
s
is
was
co
n
d
u
cte
d
an
d
s
h
o
wed
th
at
s
m
all
v
ar
iatio
n
s
in
th
ese
weig
h
ts
d
o
n
o
t
s
ig
n
if
ican
tly
af
f
ec
t
t
h
e
o
p
tim
al
lo
ca
tio
n
s
b
u
t m
ay
s
h
if
t t
h
e
s
izin
g
m
ar
g
in
ally
.
3
.
2
.
P
r
o
po
s
ed
a
lg
o
rit
hm
:
R
UN
T
h
e
R
UN
,
p
r
o
p
o
s
ed
b
y
Ab
u
alig
ah
et
a
l.
[
2
2
]
,
is
a
n
o
v
el
m
etah
eu
r
is
tic
in
s
p
ir
ed
b
y
t
h
e
p
r
ed
ato
r
y
b
eh
av
io
r
o
f
r
ep
tiles
s
u
ch
as
cr
o
co
d
iles
.
R
UN
in
co
r
p
o
r
ates
two
m
ain
s
ea
r
ch
p
h
ases
:
Glo
b
al
s
ea
r
ch
to
ex
p
lo
r
e
th
e
s
o
lu
tio
n
s
p
ac
e,
lo
ca
l
s
ea
r
c
h
to
r
ef
in
e
s
o
lu
tio
n
s
n
ea
r
th
e
cu
r
r
en
t
b
est.
T
h
e
alg
o
r
ith
m
m
ain
tain
s
p
o
p
u
latio
n
d
iv
er
s
ity
an
d
av
o
id
s
p
r
e
m
atu
r
e
co
n
v
er
g
en
ce
,
m
ak
i
n
g
it
h
ig
h
ly
ef
f
ec
tiv
e
f
o
r
s
o
lv
in
g
n
o
n
lin
ea
r
co
n
s
tr
ain
ed
p
r
o
b
lem
s
.
3
.
3
.
So
lutio
n upd
a
t
e
m
ec
ha
nis
m
R
UN
u
p
d
ates
th
e
s
o
lu
tio
n
p
o
s
i
tio
n
th
r
o
u
g
h
two
m
ain
s
tag
es:
E
x
p
lo
r
atio
n
p
h
ase:
+
1
=
+
1
⋅
(
2
)
⋅
|
3
⋅
be
s
t
−
|
(
10
)
E
x
p
lo
itatio
n
p
h
ase:
+
1
=
be
s
t
+
4
⋅
(
−
)
(
11
)
W
h
er
e
:
s
o
lu
tio
n
o
f
in
d
iv
id
u
al
at
iter
atio
n
;
be
s
t
:
c
u
r
r
en
t
g
lo
b
al
b
est
s
o
lu
tio
n
;
,
:
r
an
d
o
m
l
y
s
elec
ted
in
d
iv
id
u
als;
1
,
2
,
3
,
4
:
r
an
d
o
m
c
o
n
tr
o
l
p
ar
am
eter
s
.
3
.
4
.
B
ina
ry
v
a
ria
ble ha
nd
lin
g
a
nd
co
ns
t
ra
int
pro
ce
s
s
ing
T
h
e
b
in
a
r
y
d
ec
is
io
n
v
ar
iab
le
(
DG
p
lace
m
en
t)
is
d
er
iv
e
d
f
r
o
m
th
e
co
n
tin
u
o
u
s
d
o
m
ain
u
s
in
g
a
s
ig
m
o
id
f
u
n
ctio
n
:
x
i
=
{
1
,
if
σ
(
X
i
)
>
r
an
d
(
0
,
1
)
0
,
o
th
er
wis
e
wh
er
e
σ
(
z
)
=
1
1
+
e
−
z
(
1
2
)
C
o
n
s
tr
ain
ts
o
n
p
o
wer
o
u
tp
u
t,
v
o
ltag
e
lev
els,
an
d
h
ar
m
o
n
i
c
lim
its
ar
e
h
an
d
led
b
y
ad
d
i
n
g
a
p
en
alty
to
th
e
o
b
jectiv
e
f
u
n
ctio
n
wh
e
n
v
io
lat
ed
.
3
.
5
.
Ste
ps
o
f
t
he
run a
lg
o
rit
hm
T
h
e
s
tep
s
o
f
th
e
r
u
n
alg
o
r
ith
m
ar
e
as f
o
llo
ws
:
Step
1:
I
n
itialize
a
p
o
p
u
latio
n
o
f
in
d
iv
i
d
u
als.
E
ac
h
in
d
i
v
id
u
al
r
e
p
r
esen
ts
a
ca
n
d
id
ate
s
o
lu
tio
n
,
i
n
clu
d
in
g
DG
p
lace
m
en
t v
ec
to
r
an
d
c
o
r
r
esp
o
n
d
in
g
o
u
t
p
u
t p
o
wer
s
DG
,
.
Step
2
: E
v
alu
ate
th
e
o
b
jectiv
e
f
u
n
ctio
n
f
o
r
ea
ch
in
d
iv
id
u
al,
wh
ich
in
clu
d
es:
−
1
: to
tal
p
o
wer
lo
s
s
in
th
e
n
etwo
r
k
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
2
,
Ap
r
il
20
2
6
:
5
9
8
-
607
602
−
2
:
T
HD
at
DG
b
u
s
es,
−
A
p
en
alty
ter
m
a
p
p
lied
if
T
HD
ex
ce
ed
s
th
e
I
E
E
E
5
1
9
lim
it.
Step
3
: I
d
en
tif
y
th
e
in
itial g
lo
b
al
b
est s
o
lu
tio
n
be
s
t
f
r
o
m
th
e
ev
a
lu
ated
p
o
p
u
latio
n
.
Step
4
: Fo
r
ea
ch
iter
atio
n
=
1
to
,
p
er
f
o
r
m
th
e
f
o
llo
win
g
s
tep
s
:
−
Dete
r
m
in
e
th
e
s
ea
r
ch
p
h
ase:
+
I
f
<
/
2
,
ap
p
ly
th
e
ex
p
lo
r
atio
n
p
h
ase
to
en
h
an
ce
g
lo
b
al
s
ea
r
ch
;
+
I
f
≥
/
2
,
ap
p
ly
th
e
ex
p
lo
itatio
n
p
h
ase
to
r
ef
in
e
lo
ca
l so
lu
tio
n
s
.
−
Up
d
ate
th
e
p
o
s
itio
n
o
f
ea
c
h
in
d
iv
id
u
al
u
s
in
g
th
e
co
r
r
esp
o
n
d
in
g
u
p
d
ate
f
o
r
m
u
la
b
ased
o
n
th
e
s
elec
ted
p
h
ase.
−
Ap
p
ly
a
s
ig
m
o
id
tr
an
s
f
er
f
u
n
ctio
n
to
co
n
v
er
t
c
o
n
tin
u
o
u
s
d
ec
is
io
n
v
ar
iab
les
to
b
in
ar
y
f
o
r
m
f
o
r
DG
p
lace
m
en
t d
ec
is
io
n
s
.
−
Re
-
ev
alu
ate
th
e
o
b
jectiv
e
f
u
n
c
tio
n
f
o
r
ea
ch
u
p
d
ated
in
d
iv
id
u
al.
−
Up
d
ate
be
s
t
if
a
n
ew
in
d
iv
id
u
al
y
i
eld
s
a
b
etter
s
o
lu
tio
n
.
Step
5
: Rep
ea
t
s
tep
4
u
n
til th
e
m
ax
im
u
m
n
u
m
b
e
r
o
f
iter
atio
n
s
is
r
ea
ch
ed
.
Step
6:
Ou
tp
u
t
th
e
f
i
n
al
o
p
ti
m
al
s
o
lu
tio
n
be
s
t
,
wh
ich
in
clu
d
es:
o
p
tim
al
DG
p
lace
m
en
t
(
b
u
s
lo
ca
tio
n
s
)
,
o
p
tim
al
DG
s
izin
g
at
ea
ch
s
elec
ted
n
o
d
e,
t
o
tal
s
y
s
tem
p
o
wer
lo
s
s
,
an
d
th
e
f
in
al
T
HD
lev
e
ls
ac
r
o
s
s
th
e
s
y
s
tem
.
T
o
f
u
r
t
h
er
clar
if
y
th
e
R
UN
im
p
lem
en
tatio
n
,
th
e
alg
o
r
ith
m
ic
s
tep
s
ar
e
s
u
m
m
ar
ized
as
in
Fig
u
r
e
1
.
S
t
a
rt
En
d
l
oop
i
f t =
T
O
ut
put
fi
na
l
s
ol
ut
i
on
Re
c
a
l
c
ul
a
t
e
F
, U
pda
t
e
be
s
t
s
ol
ut
i
on
Ini
t
i
a
l
i
z
e
pop
ul
a
t
i
on
E
va
l
ua
t
e
obj
e
c
t
i
ve
func
t
i
on F
If
t<
T/2
Yes
:
Exp
lora
tio
n
No:
Exploitation
No
Y
e
s
U
pda
t
e
pos
i
t
i
ons
of i
ndi
vi
dua
l
s
Ide
nt
i
fy i
ni
t
i
a
l
be
s
t
s
ol
ut
i
on
Fig
u
r
e
1
.
R
UN
f
o
r
DG
p
lan
n
i
n
g
4.
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
an
d
d
is
cu
s
s
es
th
e
s
im
u
latio
n
r
esu
lts
o
b
tain
ed
b
y
a
p
p
ly
in
g
th
e
p
r
o
p
o
s
ed
m
o
d
el
o
n
3
3
-
b
u
s
a
n
d
6
9
-
b
u
s
DPS.
P
er
f
o
r
m
a
n
ce
is
ev
alu
ated
b
ased
o
n
th
r
ee
m
etr
ics:
p
o
wer
l
o
s
s
,
T
HD
co
m
p
lian
ce
,
an
d
co
n
v
er
g
e
n
ce
b
e
h
av
io
r
.
C
o
m
p
ar
ativ
e
r
esu
lts
with
MO
PS
O,
MO
G
W
O,
an
d
MO
W
OA
ar
e
in
clu
d
ed
to
v
alid
ate
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
R
UN
alg
o
r
ith
m
.
I
n
th
is
s
im
u
latio
n
,
DGs
ar
e
m
o
d
eled
as
in
v
er
ter
-
b
ased
s
o
u
r
ce
s
,
wh
ich
in
h
e
r
en
tly
i
n
tr
o
d
u
ce
h
ar
m
o
n
ic
d
is
to
r
tio
n
p
ar
ticu
lar
ly
at
th
e
3
rd
,
5
th
,
a
n
d
7
th
o
r
d
er
s
.
T
h
e
am
p
litu
d
e
o
f
ea
ch
h
ar
m
o
n
ic
co
m
p
o
n
en
t
is
d
ef
in
ed
as
a
p
er
ce
n
ta
g
e
o
f
th
e
DG’
s
o
u
tp
u
t
p
o
wer
at
th
e
co
r
r
esp
o
n
d
in
g
b
u
s
,
f
o
llo
win
g
t
h
e
m
o
d
el:
V
h
(
i
)
=
k
h
⋅
P
dg
,
i
,
h
∈
{
3
,
5
,
7
}
(
13
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Mu
lti
-
o
b
jective
o
p
timiz
a
tio
n
o
f d
is
tr
ib
u
ted
g
en
era
tio
n
p
la
c
eme
n
t a
n
d
s
iz
in
g
in
a
ctive
…
(
Tr
ieu
N
g
o
c
To
n
)
603
T
h
e
v
o
ltag
e
q
u
ality
at
ea
c
h
b
u
s
is
ev
alu
ated
u
s
in
g
th
e
T
HD,
ca
lcu
lated
as
(1
4
)
:
T
HD
i
=
√
∑
(
V
h
(
i
)
V
1
(
i
)
)
2
h
(
14
)
T
h
e
m
ax
im
u
m
allo
wab
le
T
H
D
th
r
esh
o
ld
is
s
et
to
5
%,
a
s
r
ec
o
m
m
en
d
e
d
b
y
th
e
I
E
E
E
5
1
9
s
tan
d
ar
d
.
T
h
e
m
ax
im
u
m
n
u
m
b
er
o
f
DGs
in
s
talled
is
s
et
to
3
f
o
r
th
e
3
3
-
b
u
s
s
y
s
tem
an
d
5
f
o
r
t
h
e
6
9
-
b
u
s
s
y
s
tem
.
T
h
e
o
u
tp
u
t
p
o
wer
o
f
ea
ch
DG
is
co
n
s
tr
ai
n
ed
b
etwe
en
0
an
d
5
0
0
k
W
.
T
h
e
p
o
ten
tial
in
s
tallatio
n
s
ites
ar
e
s
elec
ted
f
r
o
m
lo
ad
b
u
s
es
o
n
ly
(
e
x
clu
d
in
g
t
h
e
m
ain
s
o
u
r
ce
b
u
s
)
.
All
o
p
ti
m
izatio
n
alg
o
r
ith
m
s
(
R
UN,
MO
PS
O,
MO
GW
O,
an
d
MO
W
OA)
ar
e
c
o
n
f
i
g
u
r
e
d
with
th
e
s
am
e
p
ar
am
eter
s
t
o
en
s
u
r
e
f
air
co
m
p
ar
is
o
n
(
m
a
x
im
u
m
n
u
m
b
er
o
f
iter
atio
n
s
:
1
0
0
,
p
o
p
u
latio
n
s
ize:
5
0
,
n
u
m
b
e
r
o
f
in
d
ep
e
n
d
e
n
t
r
u
n
s
:
2
0
)
.
E
ac
h
alg
o
r
ith
m
u
tili
ze
s
a
we
ig
h
ted
o
b
jectiv
e
f
u
n
ctio
n
with
1
=
0
.
5
an
d
2
=
0
.
5
to
s
im
u
ltan
eo
u
s
ly
m
in
im
ize
p
o
wer
lo
s
s
es
an
d
h
ar
m
o
n
i
c
d
is
to
r
tio
n
.
A
p
en
alty
f
u
n
ctio
n
b
ased
o
n
T
HD
v
i
o
latio
n
s
is
also
in
co
r
p
o
r
ated
ac
r
o
s
s
all
ap
p
r
o
ac
h
es.
4
.
1
.
33
-
B
US D
P
S
Fig
u
r
e
2
p
r
esen
ts
th
e
s
in
g
le
lin
e
d
iag
r
am
o
f
th
e
3
3
-
b
u
s
DPS,
co
m
p
r
is
in
g
3
7
b
r
a
n
ch
es,
an
d
clea
r
ly
d
ep
ictin
g
th
e
co
n
n
ec
tiv
ity
b
et
wee
n
th
e
m
ain
s
u
b
s
tatio
n
,
lo
a
d
b
u
s
es,
f
ee
d
er
lin
es,
an
d
ca
n
d
id
ate
n
o
d
es
f
o
r
DG
in
teg
r
atio
n
.
T
h
e
s
y
s
tem
to
p
o
l
o
g
y
an
d
elec
tr
ical
p
ar
a
m
eter
s
ar
e
ad
o
p
ted
f
r
o
m
r
ep
u
tab
le
s
o
u
r
ce
s
[
2
5
]
,
[
2
6
]
to
en
s
u
r
e
th
e
cr
e
d
ib
ilit
y
an
d
r
ep
r
o
d
u
cib
ilit
y
o
f
th
e
s
im
u
latio
n
r
esu
lts
.
5
4
6
8
2
3
7
19
9
12
11
14
13
16
15
18
17
26
27
28
29
30
31
32
33
23
24
25
20
21
22
10
2
3
5
4
6
7
18
19
20
33
1
9
10
11
12
13
14
34
8
21
35
15
16
17
25
26
27
28
29
30
31
32
36
37
22
23
24
1
Fig
u
r
e
2
.
Sin
g
le
lin
e
d
iag
r
a
m
o
f
th
e
3
3
-
b
u
s
DPS
T
h
e
s
im
u
latio
n
r
esu
lts
p
r
ese
n
ted
in
T
ab
le
2
clea
r
ly
in
d
i
ca
te
th
at
th
e
p
r
o
p
o
s
ed
R
UN
alg
o
r
ith
m
o
u
tp
er
f
o
r
m
s
th
e
b
e
n
ch
m
ar
k
m
eth
o
d
s
(
MO
PS
O,
MO
G
W
O,
an
d
MO
W
OA)
in
ter
m
s
o
f
th
r
ee
cr
itical
p
er
f
o
r
m
an
ce
i
n
d
icato
r
s
:
to
tal
p
o
wer
lo
s
s
,
T
HD,
a
n
d
DG
all
o
ca
tio
n
ef
f
ec
tiv
en
ess
.
Sp
ec
if
ic
ally
,
R
UN
ac
h
iev
es
th
e
lo
west
to
tal
p
o
wer
lo
s
s
o
f
1
3
9
.
2
k
W
,
r
ep
r
esen
tin
g
a
r
ed
u
ctio
n
o
f
4
.
2
%,
2
.
0
%,
an
d
3
.
7
%
co
m
p
ar
ed
t
o
MO
PS
O
(
1
4
5
.
3
k
W
)
,
MO
G
W
O
(
1
4
2
.
1
k
W
)
,
an
d
MO
W
OA
(
1
4
4
.
6
k
W
)
,
r
esp
ec
tiv
ely
.
I
n
ter
m
s
o
f
p
o
wer
q
u
ality
,
R
UN
m
ain
tain
s
th
e
to
tal
h
ar
m
o
n
ic
d
is
to
r
tio
n
at
2
.
7
8
%,
s
ig
n
if
ican
tly
b
elo
w
th
e
I
E
E
E
5
1
9
th
r
esh
o
ld
(
5
%)
an
d
lo
wer
th
an
th
e
c
o
r
r
esp
o
n
d
i
n
g
r
esu
lts
o
f
MO
P
SO
(
3
.
4
6
%),
MO
GW
O
(
3
.
0
2
%),
an
d
MO
W
OA
(
3
.
1
1
%).
T
ab
le
2
.
Op
tim
izatio
n
r
esu
lts
o
n
th
e
3
3
-
b
u
s
DPS
M
e
t
h
o
d
(
n
o
d
e
)
(
M
W
)
(
k
W
)
TH
D
(
%)
Ti
me
(
s)
R
U
N
6
,
1
4
,
3
0
0
.
4
,
0
.
2
5
,
0
.
3
3
1
3
9
.
2
2
.
7
8
1
6
.
2
MO
-
PSO
6
,
1
8
,
2
9
0
.
3
8
,
0
.
2
,
0
.
3
4
1
4
5
.
3
3
.
4
6
1
2
.
7
MO
-
G
W
O
7
,
1
3
,
2
8
0
.
3
7
,
0
.
2
1
,
0
.
3
2
1
4
2
.
1
3
.
0
2
1
4
.
9
MO
-
W
O
A
5
,
1
5
,
2
7
0
.
3
6
,
0
.
2
3
,
0
.
3
1
1
4
4
.
6
3
.
1
1
1
3
.
5
T
h
is
im
p
r
o
v
em
en
t
ca
n
b
e
attr
ib
u
ted
to
R
UN’
s
ef
f
ec
ti
v
e
b
alan
ce
b
etwe
en
ex
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
,
en
ab
lin
g
it
to
i
d
en
tify
well
d
is
tr
ib
u
ted
DG
p
lace
m
en
ts
.
I
n
p
ar
ticu
lar
,
R
UN
s
elec
ts
b
u
s
es
[
6
,
1
4
,
3
0
]
with
o
u
tp
u
t
p
o
we
r
s
[
0
.
4
,
0
.
2
5
,
0
.
3
3
]
MW,
clo
s
ely
alig
n
ed
with
th
e
s
y
s
tem
’
s
lo
ad
p
r
o
f
ile.
T
h
is
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
2
,
Ap
r
il
20
2
6
:
5
9
8
-
607
604
co
n
f
ig
u
r
atio
n
e
n
s
u
r
es
b
o
th
v
o
ltag
e
s
u
p
p
o
r
t
an
d
h
ar
m
o
n
ic
m
itig
atio
n
.
I
n
co
n
tr
ast,
th
e
alter
n
ativ
e
alg
o
r
ith
m
s
ten
d
to
allo
ca
te
DGs
with
lo
wer
ca
p
ac
ities
o
r
less
s
tr
ateg
i
c
p
lace
m
en
t,
lead
in
g
t
o
s
u
b
o
p
tim
al
co
m
p
en
s
atio
n
f
o
r
p
o
wer
lo
s
s
es a
n
d
h
ar
m
o
n
ic
s
u
p
p
r
ess
io
n
.
Alth
o
u
g
h
t
h
e
ex
ec
u
tio
n
tim
e
o
f
R
UN
(
1
6
.
2
s
ec
o
n
d
s
)
is
m
ar
g
in
ally
h
i
g
h
er
t
h
an
th
at
o
f
th
e
o
th
e
r
m
eth
o
d
s
,
th
e
a
d
d
ed
c
o
m
p
u
tatio
n
al
ef
f
o
r
t
is
ac
ce
p
tab
le
co
n
s
id
er
in
g
th
e
s
u
b
s
tan
tial
g
ain
s
in
s
o
lu
tio
n
q
u
ality
an
d
th
e
f
ac
t
th
at
DG
p
lan
n
in
g
is
an
o
f
f
lin
e,
non
-
r
ea
l
tim
e
task
.
T
h
e
co
n
v
er
g
e
n
ce
b
eh
av
io
r
illu
s
tr
ated
in
Fig
u
r
e
3
f
u
r
th
er
v
alid
ates
R
UN’
s
s
u
p
er
io
r
ity
:
it
ac
h
iev
es
th
e
m
o
s
t
s
tab
le
an
d
r
ap
id
c
o
n
v
er
g
e
n
ce
,
r
ea
ch
i
n
g
n
ea
r
o
p
tim
al
f
itn
ess
with
in
a
p
p
r
o
x
im
ately
4
0
iter
atio
n
s
.
W
h
ile
MO
PS
O
co
n
v
er
g
es
f
aster
in
itially
,
it
s
u
f
f
er
s
f
r
o
m
s
lig
h
t
o
s
cillatio
n
s
,
wh
e
r
ea
s
MO
GW
O
an
d
MO
W
OA
co
n
v
e
r
g
e
m
o
r
e
s
lo
wly
wit
h
less
co
n
s
is
ten
cy
.
Ov
er
all,
th
ese
f
in
d
i
n
g
s
co
n
f
ir
m
th
e
r
o
b
u
s
tn
ess
,
ef
f
icien
c
y
,
an
d
p
r
ac
tical
ap
p
licab
ilit
y
o
f
th
e
R
UN
alg
o
r
ith
m
f
o
r
h
ig
h
q
u
ality
DG
p
lan
n
i
n
g
u
n
d
er
h
ar
m
o
n
ic
co
n
s
tr
ain
ts
.
Fig
u
r
e
3
.
C
o
n
v
er
g
e
n
ce
cu
r
v
es o
f
th
e
o
p
tim
izatio
n
alg
o
r
ith
m
s
o
n
th
e
3
3
-
b
u
s
DPS
4
.
2
.
69
-
B
US D
P
S
Fig
u
r
e
4
s
h
o
ws
th
e
s
in
g
le
-
lin
e
d
iag
r
am
o
f
th
e
6
9
-
b
u
s
DP
S,
co
m
p
r
is
in
g
7
3
b
r
an
ch
es
a
n
d
a
m
o
r
e
co
m
p
lex
to
p
o
lo
g
y
th
a
n
th
e
3
3
-
b
u
s
s
y
s
tem
.
I
t
d
ep
icts
th
e
co
n
n
ec
tio
n
s
am
o
n
g
th
e
m
ain
s
u
b
s
tatio
n
,
lo
ad
b
u
s
es,
f
ee
d
er
s
,
an
d
p
o
ten
tial
s
ites
f
o
r
r
en
ewa
b
le
in
teg
r
atio
n
.
T
h
e
n
etwo
r
k
s
tr
u
ctu
r
e
a
n
d
d
ata
ar
e
b
ased
o
n
tr
u
s
ted
s
o
u
r
ce
s
[
2
5
]
,
[
2
6
]
en
s
u
r
in
g
co
n
s
is
ten
cy
in
s
im
u
latio
n
.
T
h
is
co
n
f
ig
u
r
atio
n
p
r
o
v
i
d
es
a
r
o
b
u
s
t
test
b
ed
f
o
r
ev
alu
atin
g
o
p
tim
izatio
n
alg
o
r
ith
m
s
in
r
e
d
u
cin
g
p
o
wer
lo
s
s
es
an
d
i
m
p
r
o
v
in
g
v
o
ltag
e
p
r
o
f
iles
in
lar
g
e
s
ca
le
DPS.
1
5
4
6
8
2
3
7
19
9
12
11
14
13
16
15
18
17
27
66
67
23
24
25
20
21
22
26
68
69
10
36
37
39
38
40
41
43
42
44
45
46
58
53
55
54
56
59
65
60
62
61
64
63
47
49
48
50
33
28
30
29
32
31
34
35
1
2
5
4
6
7
8
9
12
11
14
13
10
19
16
15
18
17
27
23
24
25
20
21
22
26
33
28
30
29
32
31
34
35
37
39
38
40
41
43
42
44
45
46
36
47
49
48
51
52
50
51
52
57
58
53
55
54
56
59
65
60
62
61
64
63
57
66
67
68
69
70
71
72
73
Fig
u
r
e
4
.
Sin
g
le
-
li
n
e
d
iag
r
a
m
o
f
th
e
6
9
-
b
u
s
DPS
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Mu
lti
-
o
b
jective
o
p
timiz
a
tio
n
o
f d
is
tr
ib
u
ted
g
en
era
tio
n
p
la
c
eme
n
t a
n
d
s
iz
in
g
in
a
ctive
…
(
Tr
ieu
N
g
o
c
To
n
)
605
T
h
e
s
im
u
latio
n
o
u
tco
m
es
o
n
th
e
I
E
E
E
6
9
-
b
u
s
DPS,
as
s
u
m
m
ar
ized
in
T
ab
le
3
a
n
d
ill
u
s
tr
ated
in
Fig
u
r
e
5
,
f
u
r
th
er
r
ein
f
o
r
ce
th
e
s
u
p
er
io
r
p
e
r
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
R
UN
alg
o
r
ith
m
o
v
er
th
e
b
e
n
ch
m
ar
k
m
eth
o
d
s
.
I
n
ter
m
s
o
f
p
o
wer
l
o
s
s
m
in
im
izatio
n
,
R
UN
ac
h
ie
v
ed
th
e
b
est
r
esu
lt
with
a
to
ta
l
lo
s
s
o
f
2
1
2
.
7
k
W
,
im
p
r
o
v
in
g
u
p
o
n
MO
PS
O
(
2
2
3
.
5
k
W
)
,
MO
GW
O
(
2
1
8
.
3
k
W
)
,
an
d
MO
W
O
A
(
2
2
1
.
6
k
W
)
b
y
4
.
8
%,
2
.
6
%,
an
d
4
.
0
%,
r
esp
ec
tiv
el
y
.
R
eg
ar
d
in
g
p
o
wer
q
u
ality
,
R
UN
also
r
ec
o
r
d
ed
th
e
lo
west
T
HD
o
f
3
.
1
5
%,
well
with
in
th
e
I
E
E
E
5
1
9
s
tan
d
ar
d
an
d
lo
wer
th
an
MO
PS
O
(
3
.
9
1
%),
MO
G
W
O
(
3
.
4
1
%),
an
d
MO
W
OA
(
3
.
5
7
%).
T
ab
le
3
.
Op
tim
izatio
n
r
esu
lts
o
n
th
e
6
9
-
b
u
s
DPS
M
e
t
h
o
d
(
n
o
d
e
)
(
M
W
)
(
k
W
)
TH
D
(
%)
Ti
me
(
s)
R
U
N
1
7
,
2
4
,
6
1
,
6
5
,
6
6
0
.
4
5
,
0
.
3
9
,
0
.
3
,
0
.
2
6
,
0
.
3
1
2
1
2
.
7
3
.
1
5
2
8
.
5
M
O
P
S
O
1
6
,
2
6
,
6
0
,
6
7
,
6
8
0
.
4
4
,
0
.
3
5
,
0
.
2
9
,
0
.
2
5
,
0
.
3
2
2
3
.
5
3
.
9
1
2
2
.
2
M
O
G
W
O
1
8
,
2
5
,
5
9
,
6
4
,
6
9
0
.
4
3
,
0
.
3
6
,
0
.
2
8
,
0
.
2
4
,
0
.
3
1
2
1
8
.
3
3
.
4
1
2
5
.
6
M
O
W
O
A
2
0
,
2
7
,
6
2
,
6
3
,
6
6
0
.
4
2
,
0
.
3
7
,
0
.
2
7
0
.
2
3
,
0
.
3
2
2
2
1
.
6
3
.
5
7
2
4
.
0
Fig
u
r
e
5
.
C
o
n
v
er
g
e
n
ce
cu
r
v
es o
f
th
e
o
p
tim
izatio
n
alg
o
r
ith
m
s
o
n
th
e
6
9
-
b
u
s
DPS
T
h
e
en
h
a
n
ce
d
p
er
f
o
r
m
an
ce
o
f
R
UN
ca
n
b
e
attr
ib
u
ted
t
o
its
b
alan
ce
d
ex
p
lo
r
atio
n
-
e
x
p
lo
itatio
n
m
ec
h
an
is
m
,
wh
ic
h
lead
s
to
m
o
r
e
e
f
f
ec
tiv
e
DG
allo
ca
tio
n
s
.
I
n
p
a
r
ticu
lar
,
R
UN
s
elec
ted
b
u
s
es
[
1
7
,
2
4
,
6
1
,
6
5
,
6
6
]
with
p
o
wer
o
u
tp
u
ts
[
0
.
4
5
,
0
.
3
9
,
0
.
3
0
,
0
.
2
6
,
0
.
3
1
]
MW,
r
ef
lectin
g
a
well
d
is
tr
ib
u
ted
c
o
n
f
ig
u
r
atio
n
alig
n
e
d
with
k
ey
lo
ad
n
o
d
es.
T
h
is
co
n
f
ig
u
r
atio
n
r
esu
lts
in
b
etter
s
u
p
p
o
r
t
f
o
r
v
o
ltag
e
p
r
o
f
i
les
an
d
h
ar
m
o
n
ic
s
u
p
p
r
ess
io
n
.
I
n
c
o
n
tr
ast,
th
e
alter
n
ativ
e
alg
o
r
ith
m
s
s
h
o
we
d
less
ef
f
ec
tiv
e
s
izin
g
an
d
p
o
s
itio
n
in
g
s
tr
ateg
ies,
co
n
tr
ib
u
tin
g
to
h
ig
h
e
r
lo
s
s
es
an
d
g
r
ea
ter
h
ar
m
o
n
ic
d
is
to
r
tio
n
.
Alth
o
u
g
h
R
UN
r
eq
u
ir
e
d
a
s
lig
h
tly
lo
n
g
er
ex
ec
u
tio
n
tim
e
(
2
8
.
5
s
ec
o
n
d
s
)
co
m
p
ar
e
d
to
MO
PS
O
(
2
2
.
2
s
ec
o
n
d
s
)
,
MO
GW
O
(
2
5
.
6
s
ec
o
n
d
s
)
,
an
d
MO
W
OA
(
2
4
.
0
s
ec
o
n
d
s
)
,
th
is
o
v
er
h
ea
d
is
r
ea
s
o
n
ab
le
g
iv
en
th
e
im
p
r
o
v
ed
s
o
lu
tio
n
q
u
ality
an
d
th
e
o
f
f
lin
e
n
atu
r
e
o
f
DG
p
lan
n
in
g
.
Fig
u
r
e
5
s
h
o
ws th
e
co
n
v
er
g
en
ce
p
r
o
f
iles
o
f
th
e
f
o
u
r
alg
o
r
ith
m
s
.
R
UN
co
n
s
is
ten
t
ly
ex
h
ib
its
th
e
m
o
s
t
s
tab
le
an
d
r
ap
id
co
n
v
er
g
e
n
c
e,
attain
in
g
th
e
lo
west
a
g
g
r
eg
ated
f
itn
ess
v
alu
e
with
in
ap
p
r
o
x
im
ately
4
0
iter
atio
n
s
.
MO
PS
O
also
co
n
v
er
g
es
q
u
ick
ly
b
u
t
d
is
p
lay
s
n
o
ticea
b
le
o
s
cillatio
n
s
in
later
g
en
e
r
atio
n
s
.
I
n
co
n
tr
ast,
MO
GW
O
an
d
MO
W
OA
co
n
v
er
g
e
m
o
r
e
s
lo
wly
an
d
p
r
esen
t
g
r
ea
ter
f
lu
ctu
ati
o
n
s
th
r
o
u
g
h
o
u
t
t
h
e
o
p
tim
izatio
n
p
r
o
ce
s
s
,
r
ef
lecti
n
g
wea
k
er
s
tab
ilit
y
an
d
r
elia
b
ilit
y
.
T
h
ese
o
b
s
er
v
atio
n
s
v
alid
ate
th
e
s
ca
lab
ilit
y
,
r
o
b
u
s
tn
ess
,
an
d
p
r
ac
tical
a
p
p
l
icab
ilit
y
o
f
th
e
R
UN
alg
o
r
ith
m
in
s
o
lv
in
g
lar
g
e
s
ca
le
DG
p
lan
n
in
g
p
r
o
b
lem
s
u
n
d
er
p
o
wer
q
u
ality
co
n
s
tr
ain
ts
.
5.
CO
NCLU
SI
O
N
T
h
is
p
ap
er
p
r
esen
te
d
a
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
m
o
d
el
f
o
r
d
eter
m
in
in
g
th
e
o
p
tim
al
p
lace
m
en
t
an
d
s
izin
g
o
f
in
v
er
te
r
-
b
ased
DG
u
n
its
in
ac
tiv
e
DPS,
ex
p
lici
tly
ad
d
r
ess
in
g
th
e
im
p
ac
t
o
f
h
ar
m
o
n
ic
d
is
to
r
tio
n
,
an
is
s
u
e
o
f
ten
u
n
d
e
r
esti
m
ated
in
p
r
ev
io
u
s
s
tu
d
ies.
T
h
e
m
o
d
e
l
was
d
esig
n
ed
to
s
im
u
ltan
eo
u
s
ly
m
in
im
ize
to
tal
p
o
wer
lo
s
s
es
an
d
T
HD,
in
co
m
p
lian
ce
with
I
E
E
E
5
1
9
s
tan
d
ar
d
s
,
th
e
r
eb
y
en
s
u
r
in
g
b
o
th
tech
n
ical
ef
f
icien
c
y
an
d
p
o
wer
q
u
ality
.
T
o
s
o
lv
e
t
h
is
co
m
p
lex
o
p
tim
izatio
n
p
r
o
b
lem
,
th
e
R
UN,
a
r
ec
en
t
m
etah
eu
r
is
tic
in
s
p
ir
ed
b
y
r
ep
tili
an
h
u
n
tin
g
s
tr
ateg
ies,
was
ad
o
p
ted
an
d
b
e
n
ch
m
a
r
k
e
d
ag
ain
s
t
th
r
ee
estab
lis
h
ed
alg
o
r
ith
m
s
:
MO
PS
O,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
2
,
Ap
r
il
20
2
6
:
5
9
8
-
607
606
MO
GW
O,
an
d
MO
W
OA.
Si
m
u
latio
n
e
x
p
er
im
e
n
ts
o
n
th
e
I
E
E
E
3
3
-
b
u
s
an
d
6
9
-
b
u
s
test
s
y
s
tem
s
d
em
o
n
s
tr
ated
th
at
R
UN
co
n
s
is
ten
tly
o
u
tp
er
f
o
r
m
ed
th
e
alter
n
ativ
es
in
ter
m
s
o
f
lo
s
s
r
ed
u
ctio
n
,
T
HD
m
in
im
izatio
n
,
an
d
co
n
v
er
g
en
ce
s
tab
ilit
y
.
T
h
ese
r
esu
lts
v
alid
ate
th
e
al
g
o
r
it
h
m
’
s
r
o
b
u
s
tn
ess
an
d
e
f
f
ec
ti
v
en
ess
in
s
o
lv
in
g
n
o
n
lin
ea
r
,
c
o
n
s
tr
ain
t
in
ten
s
iv
e
p
lan
n
in
g
p
r
o
b
lem
s
in
d
is
tr
ib
u
tio
n
n
etwo
r
k
s
.
T
h
e
f
in
d
in
g
s
also
r
ev
ea
led
a
clea
r
co
r
r
elatio
n
b
etwe
en
DG
o
u
tp
u
t
p
o
wer
an
d
h
a
r
m
o
n
ic
am
p
litu
d
es,
u
n
d
er
s
co
r
i
n
g
th
e
im
p
o
r
ta
n
ce
o
f
in
co
r
p
o
r
atin
g
h
ar
m
o
n
ic
d
is
to
r
tio
n
ex
p
licitly
in
to
DG
p
lan
n
i
n
g
f
r
a
m
ewo
r
k
s
.
Fr
o
m
a
p
r
ac
ti
ca
l
p
er
s
p
ec
tiv
e,
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
o
f
f
er
s
a
m
o
r
e
co
m
p
r
eh
e
n
s
iv
e
to
o
l
f
o
r
n
e
two
r
k
o
p
er
ato
r
s
t
o
im
p
r
o
v
e
s
y
s
tem
r
eliab
ilit
y
an
d
p
o
wer
q
u
ality
wh
ile
i
n
teg
r
atin
g
r
en
ewa
b
le
en
er
g
y
s
o
u
r
ce
s
.
Fu
tu
r
e
r
esear
ch
d
ir
ec
tio
n
s
m
a
y
in
clu
d
e
ex
ten
d
in
g
th
e
m
o
d
el
t
o
u
n
b
alan
ce
d
th
r
ee
p
h
ase
s
y
s
tem
s
,
in
teg
r
atin
g
d
y
n
am
ic
lo
ad
p
r
o
f
iles
,
an
d
ex
p
lo
r
in
g
h
y
b
r
id
f
r
am
ewo
r
k
s
th
at
c
o
m
b
in
e
m
et
ah
eu
r
is
tics
with
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
f
o
r
f
aster
co
n
v
er
g
en
ce
an
d
b
etter
g
en
er
aliza
tio
n
in
r
ea
l
-
tim
e
s
m
ar
t g
r
id
a
p
p
licatio
n
s
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
Au
th
o
r
s
s
tate
n
o
f
u
n
d
in
g
in
v
o
lv
ed
.
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
Au
th
o
r
s
s
tate
n
o
co
n
f
lict o
f
in
t
er
est.
DATA AV
AI
L
AB
I
L
I
T
Y
Data
a
v
aila
b
i
lit
y
is
n
o
t
a
p
p
li
ca
b
le
t
o
t
h
is
p
ap
er
as
n
o
n
e
w
d
a
t
a
w
er
e
cr
ea
te
d
o
r
an
al
y
z
e
d
i
n
t
h
is
s
t
u
d
y
.
RE
F
E
R
E
NC
E
S
[
1
]
N
.
D
h
a
r
a
v
a
t
,
S
.
K
.
S
u
d
a
b
a
t
t
u
l
a
,
a
n
d
S
.
V
e
l
a
mu
r
i
,
“
R
e
v
i
e
w
o
n
t
h
e
i
n
t
e
g
r
a
t
i
o
n
o
f
d
i
st
r
i
b
u
t
e
d
g
e
n
e
r
a
t
i
o
n
s
(
so
l
a
r
,
w
i
n
d
)
a
n
d
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
s
c
o
n
n
e
c
t
e
d
t
o
t
h
e
d
i
s
t
r
i
b
u
t
i
o
n
s
y
st
e
m
t
o
m
i
n
i
mi
z
e
p
o
w
e
r
l
o
ss
a
n
d
v
o
l
t
a
g
e
p
r
o
f
i
l
e
e
n
h
a
n
c
e
me
n
t
,
”
AI
P
C
o
n
f
e
r
e
n
c
e
Pro
c
e
e
d
i
n
g
s
,
v
o
l
.
2
4
5
5
,
n
o
.
O
c
t
o
b
e
r
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
6
3
/
5
.
0
1
0
0
9
5
7
.
[
2
]
R
.
V
i
r
a
l
a
n
d
D
.
K
.
K
h
a
t
o
d
,
“
O
p
t
i
mal
p
l
a
n
n
i
n
g
o
f
d
i
st
r
i
b
u
t
e
d
g
e
n
e
r
a
t
i
o
n
sy
s
t
e
ms
i
n
d
i
st
r
i
b
u
t
i
o
n
sy
s
t
e
m
:
A
r
e
v
i
e
w
,
”
Re
n
e
w
a
b
l
e
a
n
d
S
u
s
t
a
i
n
a
b
l
e
E
n
e
rg
y
R
e
v
i
e
w
s
,
v
o
l
.
1
6
,
n
o
.
7
,
p
p
.
5
1
4
6
–
5
1
6
5
,
2
0
1
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
r
s
e
r
.
2
0
1
2
.
0
5
.
0
2
0
.
[
3
]
C
.
K
.
D
a
s
,
O
.
B
a
ss,
G
.
K
o
t
h
a
p
a
l
l
i
,
T.
S
.
M
a
h
mo
u
d
,
a
n
d
D
.
H
a
b
i
b
i
,
“
O
v
e
r
v
i
e
w
o
f
e
n
e
r
g
y
st
o
r
a
g
e
sy
s
t
e
ms
i
n
d
i
st
r
i
b
u
t
i
o
n
n
e
t
w
o
r
k
s
:
P
l
a
c
e
me
n
t
,
si
z
i
n
g
,
o
p
e
r
a
t
i
o
n
,
a
n
d
p
o
w
e
r
q
u
a
l
i
t
y
,
”
Re
n
e
w
a
b
l
e
a
n
d
S
u
s
t
a
i
n
a
b
l
e
E
n
e
r
g
y
Re
v
i
e
w
s
,
v
o
l
.
9
1
,
n
o
.
N
o
v
e
mb
e
r
2
0
1
6
,
p
p
.
1
2
0
5
–
1
2
3
0
,
2
0
1
8
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
r
ser.2
0
1
8
.
0
3
.
0
6
8
.
[
4
]
M
.
F
.
S
h
a
a
b
a
n
,
Y
.
M
.
A
t
w
a
,
a
n
d
E.
F
.
El
-
S
a
a
d
a
n
y
,
“
D
G
a
l
l
o
c
a
t
i
o
n
f
o
r
b
e
n
e
f
i
t
m
a
x
i
mi
z
a
t
i
o
n
i
n
d
i
st
r
i
b
u
t
i
o
n
n
e
t
w
o
r
k
s,”
I
E
E
E
T
ra
n
s
a
c
t
i
o
n
s
o
n
P
o
w
e
r
S
y
s
t
e
m
s
,
v
o
l
.
2
8
,
n
o
.
2
,
p
p
.
6
3
9
–
6
4
9
,
2
0
1
3
,
d
o
i
:
1
0
.
1
1
0
9
/
TPW
R
S
.
2
0
1
2
.
2
2
1
3
3
0
9
.
[
5
]
H
.
R
.
Esm
a
e
i
l
i
a
n
a
n
d
R
.
F
a
d
a
e
i
n
e
d
j
a
d
,
“
D
i
s
t
r
i
b
u
t
i
o
n
s
y
st
e
m
e
f
f
i
c
i
e
n
c
y
i
m
p
r
o
v
e
m
e
n
t
u
s
i
n
g
n
e
t
w
o
r
k
r
e
c
o
n
f
i
g
u
r
a
t
i
o
n
a
n
d
c
a
p
a
c
i
t
o
r
a
l
l
o
c
a
t
i
o
n
,
”
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
E
l
e
c
t
ri
c
a
l
Po
w
e
r
a
n
d
E
n
e
r
g
y
S
y
st
e
m
s
,
v
o
l
.
6
4
,
p
p
.
4
5
7
–
4
6
8
,
2
0
1
5
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
j
e
p
e
s.
2
0
1
4
.
0
6
.
0
5
1
.
[
6
]
M
.
A
b
d
e
l
b
a
d
e
a
,
T.
A
.
B
o
g
h
d
a
d
y
,
a
n
d
D
.
K
.
I
b
r
a
h
i
m,
“
E
n
h
a
n
c
i
n
g
a
c
t
i
v
e
r
a
d
i
a
l
d
i
st
r
i
b
u
t
i
o
n
n
e
t
w
o
r
k
s
b
y
o
p
t
i
m
a
l
s
i
z
i
n
g
a
n
d
p
l
a
c
e
me
n
t
o
f
D
G
s
u
s
i
n
g
m
o
d
i
f
i
e
d
c
r
o
w
sea
r
c
h
a
l
g
o
r
i
t
h
m,
”
I
n
d
o
n
e
s
i
a
n
J
o
u
r
n
a
l
o
f
El
e
c
t
ri
c
a
l
En
g
i
n
e
e
ri
n
g
a
n
d
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
,
v
o
l
.
1
6
,
n
o
.
3
,
p
p
.
1
1
7
9
–
1
1
8
8
,
2
0
1
9
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
e
e
c
s.
v
1
6
.
i
3
.
p
p
1
1
7
9
-
1
1
8
8
.
[
7
]
Q
.
T.
Tr
a
n
,
A
.
V
.
Tr
u
o
n
g
,
a
n
d
P
.
M
.
Le
,
“
R
e
d
u
c
t
i
o
n
o
f
h
a
r
mo
n
i
c
s
i
n
g
r
i
d
-
c
o
n
n
e
c
t
e
d
i
n
v
e
r
t
e
r
s
u
si
n
g
v
a
r
i
a
b
l
e
sw
i
t
c
h
i
n
g
f
r
e
q
u
e
n
c
y
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
t
ri
c
a
l
P
o
w
e
r
a
n
d
En
e
r
g
y
S
y
st
e
m
s
,
v
o
l
.
8
2
,
p
p
.
2
4
2
–
2
5
1
,
2
0
1
6
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
j
e
p
e
s.
2
0
1
6
.
0
3
.
0
2
7
.
[
8
]
R
.
M
a
h
a
n
t
y
a
n
d
P
.
G
u
p
t
a
,
“
V
o
l
t
a
g
e
st
a
b
i
l
i
t
y
a
n
a
l
y
si
s
i
n
u
n
b
a
l
a
n
c
e
d
p
o
w
e
r
sy
st
e
ms
b
y
o
p
t
i
ma
l
p
o
w
e
r
f
l
o
w
,
”
I
EE
Pr
o
c
e
e
d
i
n
g
s
-
G
e
n
e
r
a
t
i
o
n
,
T
r
a
n
sm
i
ssi
o
n
a
n
d
…
,
v
o
l
.
1
5
1
,
n
o
.
3
,
p
p
.
2
0
1
–
2
1
2
,
2
0
0
4
,
d
o
i
:
1
0
.
1
0
4
9
/
i
p
-
g
t
d
.
[
9
]
M
.
R
.
Ja
n
n
e
sar,
A
.
S
e
d
i
g
h
i
,
M
.
S
a
v
a
g
h
e
b
i
,
a
n
d
J.
M
.
G
u
e
r
r
e
r
o
,
“
O
p
t
i
ma
l
p
l
a
c
e
m
e
n
t
,
si
z
i
n
g
,
a
n
d
d
a
i
l
y
c
h
a
r
g
e
/
d
i
sc
h
a
r
g
e
o
f
b
a
t
t
e
r
y
e
n
e
r
g
y
st
o
r
a
g
e
i
n
l
o
w
v
o
l
t
a
g
e
d
i
st
r
i
b
u
t
i
o
n
n
e
t
w
o
r
k
w
i
t
h
h
i
g
h
p
h
o
t
o
v
o
l
t
a
i
c
p
e
n
e
t
r
a
t
i
o
n
,
”
Ap
p
l
i
e
d
E
n
e
r
g
y
,
v
o
l
.
2
2
6
,
n
o
.
M
a
y
,
p
p
.
9
5
7
–
9
6
6
,
2
0
1
8
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
p
e
n
e
r
g
y
.
2
0
1
8
.
0
6
.
0
3
6
.
[
1
0
]
D
.
C
o
mm
i
t
t
e
e
,
I
.
P
o
w
e
r
,
a
n
d
E.
S
o
c
i
e
t
y
,
“
I
EEE
S
t
d
5
1
9
-
2
0
1
4
,
”
I
EE
E
S
t
d
5
1
9
-
2
0
1
4
,
v
o
l
.
2
0
1
4
,
2
0
1
4
.
[
1
1
]
L.
M
a
l
l
a
i
a
h
,
A
.
D
.
K
u
l
k
a
r
n
i
,
B
.
R
.
P
r
a
sa
d
,
a
n
d
A
.
Ta
m
ma
i
a
h
,
“
T
h
e
o
p
t
i
m
a
l
i
n
t
e
g
r
a
t
i
o
n
o
f
m
u
l
t
i
p
l
e
D
G
s
u
n
d
e
r
d
i
f
f
e
r
e
n
t
l
o
a
d
m
o
d
e
l
s
u
si
n
g
a
r
t
i
f
i
c
i
a
l
b
e
e
c
o
l
o
n
y
-
h
i
l
l
c
l
i
m
b
i
n
g
a
l
g
o
r
i
t
h
m
,
”
T
r
e
n
d
s
i
n
S
c
i
e
n
c
e
s
,
v
o
l
.
1
9
,
n
o
.
1
3
,
2
0
2
2
,
d
o
i
:
1
0
.
4
8
0
4
8
/
t
i
s
.
2
0
2
2
.
4
6
3
3
.
[
1
2
]
P
.
P
r
a
k
a
s
h
a
n
d
D
.
K
.
K
h
a
t
o
d
,
“
O
p
t
i
m
a
l
s
i
z
i
n
g
a
n
d
si
t
i
n
g
t
e
c
h
n
i
q
u
e
s
f
o
r
d
i
st
r
i
b
u
t
e
d
g
e
n
e
r
a
t
i
o
n
i
n
d
i
s
t
r
i
b
u
t
i
o
n
s
y
st
e
ms:
A
r
e
v
i
e
w
,
”
Re
n
e
w
a
b
l
e
a
n
d
S
u
st
a
i
n
a
b
l
e
En
e
rg
y
R
e
v
i
e
w
s
,
v
o
l
.
5
7
,
p
p
.
1
1
1
–
1
3
0
,
2
0
1
6
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
r
ser.
2
0
1
5
.
1
2
.
0
9
9
.
[
1
3
]
H
.
B
.
Y
a
m
c
h
i
,
H
.
S
h
a
h
s
a
v
a
r
i
,
N
.
T
.
K
a
l
a
n
t
a
r
i
,
A
.
S
a
f
a
r
i
,
a
n
d
M
.
F
a
r
r
o
k
h
i
f
a
r
,
“
A
c
o
s
t
-
e
f
f
i
c
i
e
n
t
a
p
p
l
i
c
a
t
i
o
n
o
f
d
i
f
f
e
r
e
n
t
b
a
t
t
e
r
y
e
n
e
r
g
y
st
o
r
a
g
e
t
e
c
h
n
o
l
o
g
i
e
s
i
n
m
i
c
r
o
g
r
i
d
s
c
o
n
s
i
d
e
r
i
n
g
l
o
a
d
u
n
c
e
r
t
a
i
n
t
y
,
”
J
o
u
r
n
a
l
o
f
E
n
e
r
g
y
S
t
o
r
a
g
e
,
v
o
l
.
2
2
,
n
o
.
Ja
n
u
a
r
y
,
p
p
.
1
7
–
2
6
,
2
0
1
9
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
st
.
2
0
1
9
.
0
1
.
0
2
3
.
[
1
4
]
A
.
Z
e
i
n
a
l
z
a
d
e
h
,
Y
.
M
o
h
a
mm
a
d
i
,
a
n
d
M
.
H
.
M
o
r
a
d
i
,
“
O
p
t
i
ma
l
mu
l
t
i
o
b
j
e
c
t
i
v
e
p
l
a
c
e
m
e
n
t
a
n
d
si
z
i
n
g
o
f
mu
l
t
i
p
l
e
D
G
s
a
n
d
sh
u
n
t
c
a
p
a
c
i
t
o
r
b
a
n
k
s
s
i
m
u
l
t
a
n
e
o
u
s
l
y
c
o
n
si
d
e
r
i
n
g
l
o
a
d
u
n
c
e
r
t
a
i
n
t
y
v
i
a
M
O
P
S
O
a
p
p
r
o
a
c
h
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
t
r
i
c
a
l
P
o
w
e
r
a
n
d
En
e
rg
y
S
y
s
t
e
m
s
,
v
o
l
.
6
7
,
p
p
.
3
3
6
–
3
4
9
,
2
0
1
5
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
j
e
p
e
s.
2
0
1
4
.
1
2
.
0
1
0
.
[
1
5
]
A
.
K
u
m
a
r
,
R
.
V
e
r
m
a
,
N
.
K
.
C
h
o
u
d
h
a
r
y
,
a
n
d
N
.
S
i
n
g
h
,
“
O
p
t
i
ma
l
p
l
a
c
e
men
t
a
n
d
si
z
i
n
g
o
f
d
i
s
t
r
i
b
u
t
e
d
g
e
n
e
r
a
t
i
o
n
i
n
p
o
w
e
r
d
i
s
t
r
i
b
u
t
i
o
n
s
y
st
e
m:
a
c
o
m
p
r
e
h
e
n
si
v
e
r
e
v
i
e
w
,
”
E
n
e
r
g
y
S
o
u
r
c
e
s
,
P
a
rt
A:
R
e
c
o
v
e
ry,
U
t
i
l
i
z
a
t
i
o
n
a
n
d
E
n
v
i
ro
n
m
e
n
t
a
l
Ef
f
e
c
t
s
,
v
o
l
.
4
5
,
n
o
.
3
,
p
p
.
7
1
6
0
–
7
1
8
5
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
8
0
/
1
5
5
6
7
0
3
6
.
2
0
2
3
.
2
2
1
6
1
6
7
.
[
1
6
]
M
.
K
e
f
a
y
a
t
,
A
.
L
.
A
r
a
,
a
n
d
S
.
A
.
N
.
N
i
a
k
i
,
“
A
h
y
b
r
i
d
o
f
a
n
t
c
o
l
o
n
y
o
p
t
i
m
i
z
a
t
i
o
n
a
n
d
a
r
t
i
f
i
c
i
a
l
b
e
e
c
o
l
o
n
y
a
l
g
o
r
i
t
h
m
f
o
r
p
r
o
b
a
b
i
l
i
s
t
i
c
o
p
t
i
m
a
l
p
l
a
c
e
me
n
t
a
n
d
si
z
i
n
g
o
f
d
i
s
t
r
i
b
u
t
e
d
e
n
e
r
g
y
r
e
s
o
u
r
c
e
s,
”
E
n
e
r
g
y
C
o
n
v
e
rsi
o
n
a
n
d
M
a
n
a
g
e
m
e
n
t
,
v
o
l
.
9
2
,
p
p
.
1
4
9
–
1
6
1
,
2
0
1
5
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
c
o
n
m
a
n
.
2
0
1
4
.
1
2
.
0
3
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Mu
lti
-
o
b
jective
o
p
timiz
a
tio
n
o
f d
is
tr
ib
u
ted
g
en
era
tio
n
p
la
c
eme
n
t a
n
d
s
iz
in
g
in
a
ctive
…
(
Tr
ieu
N
g
o
c
To
n
)
607
[
1
7
]
Z.
H
.
Y
u
e
,
S
.
Z
h
a
n
g
,
a
n
d
W
.
D
.
X
i
a
o
,
“
A
n
o
v
e
l
h
y
b
r
i
d
a
l
g
o
r
i
t
h
m b
a
s
e
d
o
n
g
r
e
y
w
o
l
f
o
p
t
i
mi
z
e
r
a
n
d
f
i
r
e
w
o
r
k
s a
l
g
o
r
i
t
h
m
,
”
S
e
n
s
o
rs
(
S
w
i
t
zer
l
a
n
d
)
,
v
o
l
.
2
0
,
n
o
.
7
,
p
p
.
1
–
1
7
,
2
0
2
0
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
0
0
7
2
1
4
7
.
[
1
8
]
A
.
F
a
t
h
y
a
n
d
A
.
Y
.
A
b
d
e
l
a
z
i
z
,
“
G
r
e
y
w
o
l
f
o
p
t
i
m
i
z
e
r
f
o
r
o
p
t
i
m
a
l
s
i
z
i
n
g
a
n
d
si
t
i
n
g
o
f
e
n
e
r
g
y
s
t
o
r
a
g
e
sy
s
t
e
m i
n
e
l
e
c
t
r
i
c
d
i
st
r
i
b
u
t
i
o
n
n
e
t
w
o
r
k
,
”
El
e
c
t
ri
c
P
o
w
e
r
C
o
m
p
o
n
e
n
t
s a
n
d
S
y
st
e
m
s
,
v
o
l
.
4
5
,
n
o
.
6
,
p
p
.
6
0
1
–
6
1
4
,
2
0
1
7
,
d
o
i
:
1
0
.
1
0
8
0
/
1
5
3
2
5
0
0
8
.
2
0
1
7
.
1
2
9
2
5
6
7
.
[
1
9
]
Z.
Y
u
a
n
,
W
.
W
a
n
g
,
H
.
W
a
n
g
,
a
n
d
A
.
Y
i
l
d
i
z
b
a
si
,
“
A
n
e
w
m
e
t
h
o
d
o
l
o
g
y
f
o
r
o
p
t
i
ma
l
l
o
c
a
t
i
o
n
a
n
d
s
i
z
i
n
g
o
f
b
a
t
t
e
r
y
e
n
e
r
g
y
s
t
o
r
a
g
e
sy
st
e
m
i
n
d
i
st
r
i
b
u
t
i
o
n
n
e
t
w
o
r
k
s
f
o
r
l
o
ss
r
e
d
u
c
t
i
o
n
,
”
J
o
u
rn
a
l
o
f
En
e
rg
y
S
t
o
r
a
g
e
,
v
o
l
.
2
9
,
n
o
.
M
a
r
c
h
,
p
.
1
0
1
3
6
8
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
s
t
.
2
0
2
0
.
1
0
1
3
6
8
.
[
2
0
]
D
.
B
.
P
r
a
k
a
s
h
a
n
d
C
.
La
k
sh
mi
n
a
r
a
y
a
n
a
,
“
M
u
l
t
i
p
l
e
D
G
p
l
a
c
e
m
e
n
t
s
i
n
r
a
d
i
a
l
d
i
st
r
i
b
u
t
i
o
n
s
y
st
e
m
f
o
r
m
u
l
t
i
o
b
j
e
c
t
i
v
e
s
u
si
n
g
W
h
a
l
e
O
p
t
i
mi
z
a
t
i
o
n
A
l
g
o
r
i
t
h
m,”
A
l
e
x
a
n
d
r
i
a
En
g
i
n
e
e
ri
n
g
J
o
u
rn
a
l
,
v
o
l
.
5
7
,
n
o
.
4
,
p
p
.
2
7
9
7
–
2
8
0
6
,
2
0
1
8
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
e
j
.
2
0
1
7
.
1
1
.
0
0
3
.
[
2
1
]
O
.
H
.
A
b
d
a
l
l
a
,
S
.
El
masr
y
,
M
.
I
.
El
K
o
r
f
o
l
l
y
,
a
n
d
I
.
H
t
i
t
a
,
“
H
a
r
m
o
n
i
c
a
n
a
l
y
si
s o
f
a
n
a
r
c
f
u
r
n
a
c
e
l
o
a
d
b
a
se
d
o
n
t
h
e
I
EEE
5
1
9
-
2
0
1
4
st
a
n
d
a
r
d
,
”
2
0
2
2
2
3
rd
I
n
t
e
rn
a
t
i
o
n
a
l
M
i
d
d
l
e
Ea
st
P
o
w
e
r S
y
st
e
m
s C
o
n
f
e
r
e
n
c
e
,
ME
P
C
O
N
2
0
2
2
.
2
0
2
2
2
3
r
d
I
n
t
e
r
n
a
t
i
o
n
a
l
M
i
d
d
l
e
Ea
s
t
P
o
w
e
r
S
y
st
e
ms
C
o
n
f
e
r
e
n
c
e
(
M
EPCO
N
)
.
I
EEE,
p
p
.
1
–
7
,
2
0
2
2
.
d
o
i
:
1
0
.
1
1
0
9
/
M
EP
C
O
N
5
5
4
4
1
.
2
0
2
2
.
1
0
0
2
1
7
2
5
.
[
2
2
]
L.
A
b
u
a
l
i
g
a
h
,
M
.
A
.
El
a
z
i
z
,
P
.
S
u
m
a
r
i
,
Z.
W
.
G
e
e
m
,
a
n
d
A
.
H
.
G
a
n
d
o
mi
,
“
R
e
p
t
i
l
e
se
a
r
c
h
a
l
g
o
r
i
t
h
m
(
R
S
A
)
:
a
n
a
t
u
r
e
-
i
n
s
p
i
r
e
d
met
a
-
h
e
u
r
i
s
t
i
c
o
p
t
i
m
i
z
e
r
,
”
Ex
p
e
r
t
S
y
s
t
e
m
s w
i
t
h
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
1
9
1
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
sw
a
.
2
0
2
1
.
1
1
6
1
5
8
.
[
2
3
]
T.
N
.
T
o
n
,
H
.
H
.
L
a
i
,
L.
V
a
n
P
h
a
m,
a
n
d
T
.
N
.
H
o
a
n
g
,
“
O
p
t
i
mi
z
a
t
i
o
n
o
f
d
i
st
r
i
b
u
t
e
d
g
e
n
e
r
a
t
i
o
n
p
l
a
n
n
i
n
g
t
o
m
a
x
i
mi
z
e
t
h
e
a
b
s
o
r
p
t
i
o
n
r
a
t
e
o
f
r
e
n
e
w
a
b
l
e
e
n
e
r
g
y
i
n
d
i
s
t
r
i
b
u
t
i
o
n
n
e
t
w
o
r
k
s,”
E
n
g
i
n
e
e
r
i
n
g
,
T
e
c
h
n
o
l
o
g
y
a
n
d
A
p
p
l
i
e
d
S
c
i
e
n
c
e
R
e
se
a
rc
h
,
v
o
l
.
1
5
,
n
o
.
3
,
p
p
.
2
3
0
0
8
–
2
3
0
1
3
,
2
0
2
5
,
d
o
i
:
1
0
.
4
8
0
8
4
/
e
t
a
sr
.
1
0
9
2
1
.
[
2
4
]
L.
A
.
W
o
n
g
,
V
.
K
.
R
a
m
a
c
h
a
n
d
a
r
a
m
u
r
t
h
y
,
S
.
L.
W
a
l
k
e
r
,
P
.
T
a
y
l
o
r
,
a
n
d
M
.
J.
S
a
n
j
a
r
i
,
“
O
p
t
i
m
a
l
p
l
a
c
e
me
n
t
a
n
d
s
i
z
i
n
g
o
f
b
a
t
t
e
r
y
e
n
e
r
g
y
st
o
r
a
g
e
s
y
st
e
m
f
o
r
l
o
sses
r
e
d
u
c
t
i
o
n
u
s
i
n
g
w
h
a
l
e
o
p
t
i
m
i
z
a
t
i
o
n
a
l
g
o
r
i
t
h
m,”
J
o
u
r
n
a
l
o
f
E
n
e
rg
y
S
t
o
ra
g
e
,
v
o
l
.
2
6
,
n
o
.
M
a
y
,
p
.
1
0
0
8
9
2
,
2
0
1
9
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
st
.
2
0
1
9
.
1
0
0
8
9
2
.
[
2
5
]
M
.
E.
B
a
r
a
n
a
n
d
F
.
F
.
W
u
,
“
N
e
t
w
o
r
k
r
e
c
o
n
f
i
g
u
r
a
t
i
o
n
i
n
d
i
s
t
r
i
b
u
t
i
o
n
s
y
st
e
ms
f
o
r
l
o
ss
r
e
d
u
c
t
i
o
n
a
n
d
l
o
a
d
b
a
l
a
n
c
i
n
g
,
”
I
EEE
T
ra
n
s
a
c
t
i
o
n
s
o
n
P
o
w
e
r De
l
i
v
e
r
y
,
v
o
l
.
4
,
n
o
.
2
.
p
p
.
1
4
0
1
–
1
4
0
7
,
1
9
8
9
.
d
o
i
:
1
0
.
1
1
0
9
/
6
1
.
2
5
6
2
7
.
[
2
6
]
T.
T.
N
g
u
y
e
n
,
T.
T.
N
g
u
y
e
n
,
L.
T
.
D
u
o
n
g
,
a
n
d
V
.
A
.
Tr
u
o
n
g
,
“
A
n
e
f
f
e
c
t
i
v
e
me
t
h
o
d
t
o
s
o
l
v
e
t
h
e
p
r
o
b
l
e
m
o
f
e
l
e
c
t
r
i
c
d
i
st
r
i
b
u
t
i
o
n
n
e
t
w
o
r
k
r
e
c
o
n
f
i
g
u
r
a
t
i
o
n
c
o
n
s
i
d
e
r
i
n
g
d
i
st
r
i
b
u
t
e
d
g
e
n
e
r
a
t
i
o
n
s
f
o
r
e
n
e
r
g
y
l
o
ss
r
e
d
u
c
t
i
o
n
,
”
N
e
u
r
a
l
C
o
m
p
u
t
i
n
g
a
n
d
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
3
3
,
n
o
.
5
,
p
p
.
1
6
2
5
–
1
6
4
1
,
2
0
2
1
,
d
o
i
:
1
0
.
1
0
0
7
/
s0
0
5
2
1
-
0
2
0
-
0
5
0
9
2
-
2.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Tr
ieu
Ng
o
c
T
o
n
wa
s
b
o
rn
i
n
Qu
a
n
g
Ng
a
i,
Vie
tn
a
m
,
i
n
1
9
8
1
.
He
re
c
e
iv
e
d
h
is
B.
En
g
.
(2
0
0
5
),
M
.
E
n
g
.
(
2
0
0
9
),
a
n
d
P
h
.
D.
(2
0
2
3
)
d
e
g
re
e
s
i
n
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
fr
o
m
Ho
Ch
i
M
i
n
h
Ci
ty
Un
iv
e
rsity
o
f
Tec
h
n
o
lo
g
y
a
n
d
E
d
u
c
a
ti
o
n
,
Vie
t
n
a
m
.
He
is
c
u
rre
n
tl
y
a
lec
tu
re
r
a
t
th
e
F
a
c
u
lt
y
o
f
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
E
n
g
i
n
e
e
rin
g
,
T
h
u
Du
c
Co
ll
e
g
e
o
f
Tec
h
n
o
lo
g
y
,
Ho
Ch
i
M
i
n
h
Cit
y
,
Vie
tn
a
m
.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
p
o
we
r
d
ist
rib
u
ti
o
n
s
y
ste
m
s,
re
n
e
wa
b
le
e
n
e
rg
y
in
te
g
ra
ti
o
n
,
a
n
d
o
p
ti
m
iz
a
ti
o
n
o
f
d
istr
ib
u
ted
g
e
n
e
ra
ti
o
n
.
He
h
a
s
p
u
b
li
sh
e
d
se
v
e
ra
l
sc
ien
ti
fic p
a
p
e
rs an
d
is ac
ti
v
e
in
n
a
ti
o
n
a
l
a
c
a
d
e
m
ic n
e
two
rk
s o
n
re
n
e
wa
b
le en
e
rg
y
.
He
h
a
s a
lso
re
c
e
iv
e
d
m
u
lt
i
p
le
in
stit
u
ti
o
n
a
l
a
wa
rd
s
fo
r
h
i
s
c
o
n
tri
b
u
t
io
n
s
t
o
e
d
u
c
a
ti
o
n
a
n
d
a
p
p
li
e
d
re
se
a
rc
h
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
t
o
n
n
g
o
c
tr
ieu
@m
a
il
.
c
o
m
.
Ph
o
n
g
Mi
n
h
Le
wa
s
b
o
rn
i
n
Ti
e
n
G
ian
g
,
Vie
tn
a
m
,
in
1
9
8
2
.
He
re
c
e
iv
e
d
h
is
B.
En
g
.
(2
0
0
5
)
a
n
d
M
.
E
n
g
.
(2
0
0
8
)
d
e
g
re
e
s
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
fro
m
Ho
Ch
i
M
in
h
Cit
y
Un
iv
e
rsity
o
f
Tec
h
n
o
l
o
g
y
a
n
d
Ed
u
c
a
ti
o
n
,
Vie
t
n
a
m
.
He
is
c
u
rre
n
tl
y
a
lec
tu
re
r
a
t
th
e
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
a
n
d
E
lec
tro
n
ics
E
n
g
i
n
e
e
rin
g
,
T
h
u
D
u
c
Co
ll
e
g
e
o
f
Tec
h
n
o
l
o
g
y
,
H
o
Ch
i
M
in
h
Cit
y
,
Vie
tn
a
m
.
His
tea
c
h
in
g
a
n
d
re
se
a
rc
h
in
tere
sts
fo
c
u
s
o
n
p
o
we
r
sy
ste
m
s,
e
lec
tri
c
a
l
m
a
c
h
in
e
s,
a
n
d
th
e
a
p
p
l
ica
ti
o
n
o
f
o
p
t
imiz
a
ti
o
n
tec
h
n
iq
u
e
s
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
.
He
h
a
s
p
a
rti
c
ip
a
ted
in
se
v
e
ra
l
a
p
p
li
e
d
re
se
a
rc
h
p
ro
jec
ts
a
n
d
re
g
u
l
a
rly
c
o
n
tri
b
u
tes
to
tec
h
n
ica
l
train
in
g
p
r
o
g
ra
m
s i
n
th
e
f
ield
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
p
h
o
n
g
l
e
m
in
h
@td
c
.
e
d
u
.
v
n
.
Ta
n
Mi
n
h
Le
wa
s
b
o
rn
i
n
Ti
e
n
G
ian
g
,
Vie
tn
a
m
,
in
1
9
9
0
.
He
re
c
e
iv
e
d
h
is
B.
E
n
g
.
(2
0
1
4
)
a
n
d
M
.
En
g
.
(
2
0
1
6
)
d
e
g
re
e
s in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
fr
o
m
Ho
Ch
i
M
in
h
Cit
y
Un
i
v
e
rsit
y
o
f
Tec
h
n
o
l
o
g
y
a
n
d
Ed
u
c
a
ti
o
n
,
Vie
tn
a
m
.
He
is
c
u
rre
n
tl
y
a
lec
t
u
re
r
a
t
t
h
e
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
a
n
d
E
lec
tro
n
ic
En
g
in
e
e
rin
g
,
Th
u
Du
c
Co
ll
e
g
e
o
f
Tec
h
n
o
l
o
g
y
,
H
o
C
h
i
M
in
h
Cit
y
,
Vie
tn
a
m
.
His
re
se
a
rc
h
in
tere
sts
in
c
l
u
d
e
p
o
we
r
g
ri
d
s,
re
n
e
wa
b
le
e
n
e
rg
y
,
p
o
we
r
s
y
ste
m
sta
b
il
it
y
,
a
n
d
o
p
ti
m
iza
ti
o
n
tec
h
n
i
q
u
e
s
fo
r
p
o
we
r
d
istri
b
u
ti
o
n
sy
ste
m
s.
He
h
a
s
c
o
n
tri
b
u
ted
to
se
v
e
ra
l
a
c
a
d
e
m
ic
p
u
b
li
c
a
ti
o
n
s
a
n
d
a
c
ti
v
e
ly
e
n
g
a
g
e
s
i
n
c
o
ll
a
b
o
ra
ti
v
e
re
se
a
rc
h
o
n
sm
a
rt
g
rid
tec
h
n
o
l
o
g
ies
a
n
d
su
sta
i
n
a
b
le en
e
r
g
y
s
o
lu
t
io
n
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
:
lem
in
h
ta
n
@td
c
.
e
d
u
.
v
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.