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u
r
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Sm
ar
t
g
r
id
s
ar
e
th
e
f
u
tu
r
e
o
f
e
lectr
ical
g
r
id
s
[
2
]
,
a
n
d
m
icr
o
g
r
id
s
(
MG
)
ar
e
th
e
cr
itical
co
n
s
titu
en
ts
o
f
a
s
m
ar
t
g
r
id
[
3
]
.
MG
s
ar
e
s
m
all
au
to
n
o
m
o
u
s
g
r
id
s
with
d
is
tr
ib
u
ted
en
er
g
y
r
eso
u
r
ce
s
s
u
p
p
ly
in
g
co
n
tr
o
llab
le
lo
ad
s
with
e
n
er
g
y
s
to
r
ag
e
s
y
s
tem
s
f
o
r
b
ac
k
u
p
[
4
]
.
Su
c
h
MG
s
ca
n
b
e
o
p
er
ated
in
two
m
o
d
es:
g
r
id
-
c
o
n
n
ec
te
d
(
GC
)
an
d
is
lan
d
ed
m
o
d
e
(
I
M)
[
5
]
.
W
h
en
a
MG
s
u
f
f
ices
its
n
ee
d
s
f
r
o
m
its
r
eso
u
r
ce
s
with
o
u
t
g
ettin
g
a
b
ac
k
u
p
f
r
o
m
th
e
u
tili
ty
g
r
i
d
,
it
o
p
er
ates
in
I
M
a
n
d
v
ice
v
er
s
a.
As
th
e
wo
r
k
f
o
cu
s
es
o
n
s
atis
f
y
in
g
th
e
ty
p
ica
l
co
m
m
u
n
ity
lo
ad
s
,
th
e
r
e
is
a
n
ee
d
to
co
n
s
id
er
a
h
y
b
r
id
MG
ar
ch
itectu
r
e,
i.e
.
,
a
MG
wi
th
b
o
th
alter
n
atin
g
cu
r
r
en
t
(
AC
)
an
d
d
i
r
ec
t
cu
r
r
en
t
(
DC
)
f
ee
d
er
s
[
6
]
.
Als
o
,
th
is
r
ed
u
ce
s
th
e
n
ee
d
f
o
r
m
o
r
e
p
o
wer
elec
tr
o
n
i
c
co
n
v
er
s
io
n
d
ev
ices
[
7
]
.
T
h
er
e
f
o
r
e,
h
y
b
r
id
MG
ar
c
h
itectu
r
e
[
8
]
is
th
e
ch
o
s
en
f
o
r
th
is
a
n
aly
s
is
.
I
n
d
ia
is
alr
ea
d
y
f
o
cu
s
in
g
to
in
cr
ea
s
e
its
r
en
ewa
b
le
ca
p
ac
ity
to
5
0
0
GW
b
y
th
e
en
d
o
f
2
0
3
0
[
9
]
.
T
h
e
m
ajo
r
f
o
cu
s
is
o
n
s
o
lar
an
d
win
d
r
eso
u
r
ce
s
.
T
h
er
ef
o
r
e,
th
ese
r
eso
u
r
ce
s
ar
e
co
n
s
id
e
r
ed
.
T
h
e
i
n
ter
m
itten
t
n
atu
r
e
o
f
r
en
ewa
b
le
e
n
er
g
y
s
o
u
r
ce
s
m
an
d
ates
a
p
o
we
r
b
a
ck
u
p
[
1
0
]
.
I
n
ca
s
e
o
f
I
M
o
p
er
atio
n
,
th
er
e
will
b
e
n
o
g
r
id
b
a
ck
u
p
an
d
a
r
e
m
o
te
co
n
v
en
tio
n
al
p
o
wer
g
en
er
ati
o
n
[
1
1
]
r
eso
u
r
ce
is
n
ee
d
ed
.
Diesel
g
en
er
ato
r
s
(
DiG)
ar
e
th
e
b
est
r
em
o
te
g
en
er
atin
g
s
y
s
tem
s
co
n
s
id
er
in
g
th
is
r
esear
ch
.
T
h
er
ef
o
r
e
,
th
e
g
en
er
al
h
y
b
r
id
m
icr
o
g
r
id
(
HM
)
s
y
s
tem
co
n
s
id
er
ed
f
o
r
o
p
tim
izatio
n
c
o
n
s
is
ts
s
o
l
ar
P
V
(
SP
V)
,
win
d
tu
r
b
in
es
(
W
T
)
,
b
atter
y
en
er
g
y
s
to
r
a
g
e
s
y
s
tem
s
(
B
E
SS
)
,
DiG
an
d
lo
ad
s
alo
n
g
with
n
ec
ess
ar
y
p
o
wer
elec
tr
o
n
ic
d
ev
ices.
T
h
e
p
lan
n
i
n
g
o
f
MG
s
in
clu
d
es
s
ev
er
al
co
s
t
f
ac
to
r
s
[
1
2
]
s
u
ch
as
ca
p
ital
co
s
t,
o
p
er
atio
n
an
d
m
ain
ten
an
ce
(
O&
M)
co
s
t
an
d
r
ep
lace
m
en
t
co
s
ts
as
th
e
p
la
n
n
in
g
is
d
o
n
e
f
o
r
a
p
er
io
d
o
f
2
5
y
ea
r
s
.
T
h
e
DiG
in
v
o
lv
es
ce
r
tain
p
ar
a
m
eter
s
a
n
d
co
n
s
tr
ain
ts
[
1
3
]
.
T
h
e
p
r
im
ar
y
o
b
jectiv
e
is
to
d
esig
n
a
s
y
s
tem
wh
ich
m
ee
ts
lo
ad
s
with
v
er
y
less
co
s
ts
a
n
d
h
ig
h
r
en
ewa
b
le
p
e
n
etr
atio
n
.
A
r
en
o
w
n
ed
o
p
tim
izatio
n
tech
n
iq
u
e
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
[
1
4
]
is
u
s
ed
f
o
r
o
p
tim
izatio
n
.
T
o
ev
alu
ate
th
e
o
p
tim
al
n
atu
r
e
an
d
f
ea
s
ib
ilit
y
,
th
e
p
ar
am
eter
s
s
u
ch
as
co
s
t
o
f
en
e
r
g
y
(
C
OE
)
,
lo
s
s
o
f
p
o
wer
s
u
p
p
ly
p
r
o
b
a
b
ilit
y
(
L
PS
P),
an
d
r
en
ewa
b
le
f
r
ac
tio
n
(
R
F)
ar
e
co
n
s
id
er
ed
.
L
iter
atu
r
e
r
ev
iew
is
s
h
o
wn
in
T
ab
le
2
.
I
n
[
1
5
]
th
e
a
u
t
h
o
r
s
p
r
o
p
o
s
ed
a
GC
MG
in
B
ei
jin
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
Op
timiz
in
g
micro
g
r
id
d
esig
n
s
to
w
a
r
d
s
n
et
-
z
ero
emis
s
io
n
s
fo
r
s
ma
r
t c
itie
s
:
…
(
A
lb
ert P
a
u
l A
r
u
n
ku
ma
r
)
129
to
o
p
tim
ize
NPC
an
d
C
OE
.
A
h
y
b
r
id
MG
was d
esig
n
ed
b
y
Ab
d
in
et
a
l
.
[
1
6
]
f
o
r
I
M
o
p
er
a
tio
n
in
B
eijin
g
an
d
a
GC
P
V
MG
is
en
u
n
ciate
d
in
[
1
7
]
f
o
r
r
esid
en
tial
co
m
m
u
n
ity
.
A
HM
m
o
d
el
wa
s
an
aly
ze
d
f
o
r
its
f
ea
s
ib
ilit
y
in
GC
an
d
I
M
o
p
er
atio
n
b
y
Da
s
et
a
l
.
[
1
8
]
.
A
PV
an
d
b
io
g
as
MG
m
o
d
el
is
an
aly
ze
d
f
o
r
v
ar
io
u
s
ec
o
n
o
m
i
c
co
n
d
itio
n
s
b
y
Kasaeia
n
et
a
l.
[
1
9
]
f
o
r
Go
ls
h
an
city
.
An
o
th
er
PV
-
b
ased
GC
MG
is
p
r
o
p
o
s
ed
in
[
2
0
]
f
o
r
C
h
in
a.
Fo
r
r
u
r
al
elec
tr
if
icatio
n
,
au
th
o
r
s
in
[
2
1
]
p
r
o
p
o
s
ed
a
GC
m
o
d
el
f
o
r
Do
d
d
ap
alli,
An
d
h
r
a
Pra
d
esh
.
All
th
ese
liter
atu
r
es
u
s
ed
HOM
E
R
f
o
r
o
p
tim
izatio
n
.
A
ca
s
e
s
tu
d
y
was
ca
r
r
ied
o
u
t
in
Per
th
city
b
y
E
s
s
ay
eh
et
a
l
.
[
2
2
]
u
s
in
g
m
ix
ed
in
te
g
er
n
o
n
-
lin
ea
r
p
r
o
g
r
am
m
in
g
.
T
ak
in
g
o
n
l
y
t
h
e
NPC
as
o
b
jectiv
e,
th
e
au
th
o
r
p
r
o
p
o
s
ed
a
HM
f
o
r
Sain
t
Ma
r
tin
I
s
lan
d
[
2
3
]
.
Usi
n
g
SMO
alg
o
r
ith
m
,
a
HM
is
p
r
o
p
o
s
ed
f
o
r
SC
M
in
T
am
il
Nad
u
b
y
au
th
o
r
s
in
[
2
4
]
.
T
h
e
m
ajo
r
r
esear
ch
co
n
tr
ib
u
ti
o
n
s
o
f
th
e
a
r
ticle
ar
e
lis
ted
b
el
o
w:
-
Op
tim
al
HM
m
o
d
els
f
o
r
all
1
6
cities
in
th
e
ea
s
ter
n
an
d
n
o
r
th
-
ea
s
ter
n
elec
tr
ica
l
zo
n
es
ar
e
p
r
o
p
o
s
ed
u
s
in
g
th
e
o
p
tim
ized
r
esu
lts
o
f
PS
O.
-
T
h
e
tech
n
ical
an
d
ec
o
n
o
m
ic
f
ea
s
ib
ilit
y
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
els
ar
e
an
aly
ze
d
a
n
d
th
e
b
est
m
o
d
el
with
less
C
OE
,
L
PS
P,
an
d
h
ig
h
R
F a
r
e
ch
o
s
en
to
b
e
th
e
b
est o
p
tim
al
d
esig
n
f
o
r
ea
ch
city
.
-
T
h
e
m
o
d
ellin
g
in
clu
d
es a
ll th
e
co
s
t e
lem
en
ts
to
en
s
u
r
e
th
e
p
r
o
p
er
ec
o
n
o
m
ic
f
ea
s
ib
ilit
y
an
aly
s
is
.
-
As
th
e
p
r
o
p
o
s
ed
MG
s
y
s
tem
m
ajo
r
ly
u
s
es
r
en
ewa
b
le
e
n
er
g
y
to
g
e
n
er
ate
p
o
wer
an
d
th
e
r
e
n
ewa
b
le
f
r
ac
tio
n
is
tak
en
in
to
ac
co
u
n
t,
th
er
e
will b
e
a
s
ig
n
if
ican
t r
e
d
u
ctio
n
in
t
h
e
to
tal
ca
r
b
o
n
f
o
o
tp
r
in
t.
T
h
e
r
est
o
f
t
h
e
ar
ticl
e
is
o
r
g
a
n
iz
e
d
as
f
o
l
lo
ws:
Se
cti
o
n
2
e
x
p
la
in
s
th
e
m
at
h
e
m
at
ica
l
m
o
d
e
lin
g
o
f
t
h
e
co
m
p
o
n
e
n
ts
o
f
t
h
e
h
y
b
r
i
d
m
ic
r
o
g
r
i
d
s
y
s
te
m
(
HM
GS)
.
T
h
e
a
d
a
p
t
ed
e
n
e
r
g
y
m
an
ag
em
e
n
t
s
t
r
ate
g
y
is
ela
b
o
r
at
ed
in
s
ec
ti
o
n
3
.
Se
cti
o
n
4
e
n
u
n
ci
a
tes
th
e
o
p
ti
m
i
za
t
io
n
te
ch
n
i
q
u
e
u
s
e
d
,
w
h
e
r
ea
s
c
o
n
s
tr
ai
n
ts
a
n
d
p
a
r
a
m
e
te
r
s
r
e
lat
ed
to
t
h
e
o
p
er
ati
o
n
ar
e
p
r
es
en
te
d
i
n
s
ec
t
io
n
5
.
Se
cti
o
n
6
c
o
n
cl
u
d
es t
h
e
a
r
t
icl
e
wit
h
th
e
r
esu
lts
a
n
d
d
is
c
u
s
s
i
o
n
.
T
ab
le
2
.
L
iter
atu
r
e
r
ev
iew
Li
t
Lo
c
a
t
i
o
n
M
i
c
r
o
g
r
i
d
m
o
d
e
l
O
p
t
i
mi
z
a
t
i
o
n
P
a
r
a
me
t
e
r
s/
g
o
a
l
s
[
1
5
]
B
e
i
j
i
n
g
,
C
h
i
n
a
W
E+
B
ESS
+
G
r
i
d
H
O
M
ER
LC
O
E
&
N
P
C
[
1
6
]
B
a
n
d
a
r
A
b
b
a
s
P
V
+
W
E+
G
r
i
d
H
O
M
ER
LC
O
E,
N
P
C
,
& H
2
[
1
7
]
B
e
i
j
i
n
g
,
C
h
i
n
a
P
V
+
G
r
i
d
H
O
M
ER
LC
O
E
&
N
P
C
[
1
8
]
R
a
j
s
h
a
h
i
P
V
+
D
i
G
+
B
ESS
+
G
r
i
d
H
O
M
ER
LC
O
E,
N
P
C
,
&
R
F
[
1
9
]
G
o
l
s
h
a
n
P
V
+
D
i
G
+
B
i
o
g
a
s+G
r
i
d
H
O
M
ER
LC
O
E,
N
P
C
,
&
R
F
[
2
0
]
C
h
i
n
a
P
V
+
D
i
G
+
G
r
i
d
H
O
M
ER
LC
O
E
&
N
P
C
[
2
1
]
D
o
d
d
a
p
a
l
l
i
P
V
+
W
E+
D
i
G
+
B
ESS
+
G
r
i
d
H
O
M
ER
LC
O
E,
N
P
C
,
&
R
F
[
2
2
]
P
e
r
t
h
c
i
t
y
P
V
+
G
r
i
d
M
I
N
LP
LC
O
E
&
N
P
C
[
2
3
]
B
a
n
g
l
a
d
e
s
h
P
V
+
W
E+
B
i
o
+
E
c
o
w
a
v
e
+
B
ESS
H
O
M
ER
N
P
C
[
2
4
]
Ta
mi
l
n
a
d
u
P
V
+
W
E+
D
i
G
+
B
ESS
S
p
i
d
e
r
m
o
n
k
e
y
o
p
t
i
m
i
z
a
t
i
o
n
(
S
M
O
)
LC
O
E,
N
P
C
,
&
R
F
2.
M
AT
H
E
M
AT
I
CA
L
M
O
D
E
L
I
NG
O
F
T
H
E
H
M
G
S CO
M
P
O
NE
N
T
S
T
h
is
s
ec
tio
n
p
r
esen
ts
th
e
m
at
h
em
atica
l
m
o
d
elin
g
o
f
th
e
H
MG
S
co
m
p
o
n
en
ts
.
T
h
e
h
y
b
r
id
m
icr
o
g
r
id
s
y
s
tem
co
n
s
is
ts
o
f
s
o
lar
PV
s
,
win
d
en
e
r
g
y
(
W
E
)
s
y
s
te
m
,
B
E
SS
,
in
v
er
ter
,
an
d
d
ie
s
el
g
en
er
ato
r
.
T
h
e
m
ath
em
atica
l m
o
d
ellin
g
o
f
ea
ch
co
m
p
o
n
en
t is ela
b
o
r
ated
wi
th
its
ass
o
ciate
d
p
ar
am
eter
s
.
2
.
1
.
M
o
dellin
g
o
f
s
o
la
r
P
V
(
SPV)
T
h
e
p
o
we
r
o
u
tp
u
t
o
f
th
e
SP
V
s
y
s
tem
is
ca
lcu
lated
u
s
in
g
th
e
f
o
ll
o
win
g
m
at
h
em
atica
l
ex
p
r
ess
io
n
as in
(
1
)
.
=
[
1
+
(
+
(
0
.
0256
)
−
)
]
(
1
)
W
h
er
e
is
SP
V
o
u
tp
u
t
p
o
wer
,
is
th
e
SP
V
'
s
r
ated
p
o
wer
u
n
d
er
r
ef
e
r
en
ce
co
n
d
itio
n
s
,
is
th
e
ir
r
ad
iatio
n
f
r
o
m
th
e
s
u
n
(
W
/m
2
)
.
T
h
e
ir
r
ad
iatio
n
at
s
tan
d
a
r
d
test
co
n
d
itio
n
s
(
STC)
is
r
e
p
r
esen
ted
b
y
[
=1
0
0
0
W
/m
2
]
an
d
r
ep
r
esen
ts
th
e
ce
ll
tem
p
er
atu
r
e
(
º
C
)
o
f
SP
V
at
STC.
T
h
e
am
b
ien
t
tem
p
er
atu
r
e
o
f
th
e
SP
V
ce
ll
is
r
ep
r
esen
t
ed
b
y
[
=2
5
0
º
C
]
an
d
th
e
is
(
-
3
.
7
×
10
-
3
(
1
/
°
C
)
)
wh
ich
is
th
e
tem
p
er
atu
r
e
c
o
ef
f
icien
t.
2
.
1
.
1
.
M
o
dellin
g
o
f
wind
ene
rg
y
(
WE
)
s
y
s
t
em
T
h
e
r
ec
o
r
d
ed
win
d
s
p
ee
d
s
a
r
e
co
n
v
er
ted
in
te
r
m
s
o
f
eq
u
iv
alen
t
win
d
tu
r
b
in
e
h
eig
h
t
v
alu
es
to
ca
lcu
late
th
e
p
o
wer
g
en
er
ated
.
ℎℎ
=
(
ℎ
ℎ
ℎ
)
(
2
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J
Ap
p
l Po
wer
E
n
g
,
Vo
l.
14
,
No
.
1
,
Ma
r
ch
20
25
:
1
27
-
1
37
130
ℎℎ
(
m
/s
)
an
d
(
m
/s
)
is
th
e
win
d
v
elo
city
at
W
T
h
u
b
(
ℎ
ℎ
)
an
d
r
ef
er
en
ce
h
eig
h
t
(
ℎ
)
r
esp
ec
tiv
ely
.
T
h
e
h
u
b
a
n
d
r
e
f
er
en
ce
h
eig
h
t
o
f
a
W
T
ar
e
r
ep
r
ese
n
ted
b
y
ℎ
ℎ
(m)
an
d
ℎ
(
m
)
,
r
esp
ec
tiv
el
y
.
α
is
th
e
Hellm
an
n
ex
p
o
n
en
t.
T
h
e
W
T
r
ated
v
elo
city
is
Vr
(
m
/s
)
,
Vcu
t
-
o
u
t
(
m
/s
)
an
d
Vcu
t
-
in
(
m
/s
)
d
en
o
te
th
e
W
T
cu
t
-
o
u
t
an
d
cu
t
-
i
n
v
el
o
city
r
esp
ec
tiv
ely
.
T
h
e
W
T
p
o
wer
o
u
tp
u
t
ca
n
b
e
d
e
f
in
ed
as
(
3
)
.
W
h
er
e
,
is
th
e
ac
tu
al
p
o
wer
g
e
n
er
ated
b
y
W
T
,
(
k
W
)
is
th
e
W
T
r
ated
p
o
wer
a
n
d
V
W
(
m
/s
)
is
th
e
win
d
v
elo
city
.
=
{
0
,
3
−
−
3
[
3
−
−
3
]
if
V
W
<V
cut
-
in
, V
W
>V
cut
-
out
(
3
)
if
V
cut
-
in
≤V
W
≤V
r
if
V
r
≤
V
W
< V
cut
-
out
2.
3
.
M
o
dellin
g
o
f
B
E
SS
a
nd
i
nv
er
t
er
I
n
s
tead
o
f
tr
a
d
itio
n
al
B
E
SS
m
o
d
elin
g
,
th
e
Au
D
a
n
d
th
e
l
o
ad
r
e
q
u
ir
ed
ar
e
in
co
r
p
o
r
ate
d
t
o
ca
lcu
late
th
e
ca
p
ac
ity
o
f
th
e
b
atter
y
.
W
h
er
e
is
th
e
b
atter
y
ca
p
ac
ity
,
is
r
eq
u
ir
ed
lo
ad
,
is
th
e
n
u
m
b
er
o
f
Au
D,
is
th
e
b
atter
y
'
s
d
ep
th
o
f
d
is
ch
ar
g
e
(
8
0
%),
an
d
r
ep
r
esen
ts
th
e
ef
f
icien
cies
o
f
th
e
in
v
e
r
ter
(
9
5
%)
an
d
b
atter
y
(
8
5
%).
T
h
e
h
i
g
h
est SO
C
(
)
an
d
lo
west SOC
(
)
o
f
th
e
b
atter
y
ar
e
u
s
ed
to
d
eter
m
in
e
its
p
r
esen
t
.
T
h
e
en
er
g
y
ex
ce
s
s
o
r
d
ef
icit o
f
th
e
b
atter
y
s
to
r
a
g
e
s
y
s
tem
ca
n
b
e
ex
p
lain
ed
as
(
4
)
an
d
(
5
)
.
=
.
(
4
)
=
(
+
)
−
(
5
)
<
0
an
d
>
0
r
ep
r
esen
ts
th
e
s
h
o
r
tf
all
a
n
d
ex
ce
s
s
o
f
b
atter
y
p
o
wer
n
e
ed
ed
to
m
atc
h
th
e
lo
a
d
d
em
a
n
d
f
o
r
h
o
u
r
t,
r
esp
ec
tiv
ely
.
r
ep
r
esen
ts
th
e
SP
V
p
o
wer
p
r
o
d
u
ce
d
in
h
o
u
r
t.
r
ep
r
esen
ts
th
e
win
d
en
e
r
g
y
p
r
o
d
u
ce
d
i
n
h
o
u
r
t.
T
h
e
cu
s
to
m
er
lo
a
d
p
atter
n
f
o
r
h
o
u
r
t
is
wh
er
e,
d
en
o
tes
in
v
er
ter
ef
f
icien
cy
.
an
d
(
−
1
)
r
ep
r
esen
ts
th
e
SOC
o
f
th
e
b
atter
y
d
u
r
in
g
h
o
u
r
t
a
n
d
th
e
p
r
ev
io
u
s
h
o
u
r
(
t
-
1
)
r
esp
ec
ti
v
ely
wh
er
e
th
e
ch
a
r
g
in
g
an
d
d
is
ch
ar
g
in
g
p
o
wer
o
f
th
e
b
atter
y
at
th
e
p
r
esen
t
h
o
u
r
t
is
r
e
p
r
esen
t
ed
b
y
a
n
d
−
r
esp
ec
tiv
ely
.
=
(
−
1
)
(
1
−
)
+
(
−
+
)
(
6
)
=
(
)
(
)
+
(
1
−
99
(
1
−
1
−
9
)
2
)
+
(
1
−
1
−
99
(
1
−
1
−
9
)
2
−
1
)
(
)
2
(
7
)
T
h
e
in
v
e
r
ter
ef
f
icie
n
cy
is
ca
lc
u
lated
b
y
(
7
)
.
an
d
d
en
o
tes
th
e
s
elf
-
d
is
ch
ar
g
e
r
ate
an
d
b
atte
r
y
ef
f
icien
c
y
r
esp
ec
tiv
ely
.
W
h
er
e
Ƞ
an
d
Ƞ
d
en
o
tes
th
e
in
v
er
ter
'
s
ef
f
icien
cy
at
1
0
%
an
d
1
0
0
%
o
f
its
n
o
m
in
al
p
o
wer
,
r
esp
ec
tiv
ely
.
2
.
4
.
M
o
dellin
g
o
f
dies
e
l g
ener
a
t
o
r
T
h
e
ef
f
icien
cy
o
f
th
e
d
iesel
g
en
er
ato
r
is
ca
lcu
lated
b
y
ev
al
u
atin
g
th
e
p
o
wer
p
r
o
d
u
ce
d
to
th
e
r
ate
o
f
f
u
el
co
n
s
u
m
p
tio
n
.
=
+
(
8
)
r
ep
r
esen
ts
th
e
d
iesel
p
o
wer
g
en
er
ated
in
(
k
W
)
b
y
co
n
s
u
m
in
g
f
u
el
p
e
r
h
o
u
r
(
L
/h
o
u
r
)
d
en
o
ted
b
y
at
tim
e
t.
is
r
ated
n
o
m
in
al
p
o
w
er
(
k
W
)
o
f
th
e
d
iesel
p
lan
t.
T
h
e
f
u
el
u
s
ag
e
co
ef
f
icien
ts
(
c
o
n
s
tan
ts
)
ar
e
r
ep
r
esen
ted
b
y
th
e
v
alu
es a
an
d
b
,
wh
ich
ar
e
n
ea
r
ly
eq
u
iv
ale
n
t to
0
.
2
4
6
a
n
d
0
.
0
8
4
1
5
,
r
esp
e
ctiv
ely
.
3.
SM
AR
T
E
NE
RG
Y
M
ANAG
E
M
E
N
T
ST
RAT
E
G
Y
AND
P
ARAM
E
T
E
RS F
O
R
AN
A
L
YS
I
S
T
o
f
ac
ilit
ate
th
e
o
p
tim
al
u
s
e
o
f
r
eso
u
r
ce
s
an
d
t
o
en
s
u
r
e
th
e
en
er
g
y
d
e
m
an
d
b
alan
ce
th
e
f
o
llo
win
g
s
m
ar
t e
n
er
g
y
m
an
ag
em
e
n
t
s
tr
ateg
y
is
ap
p
lied
.
T
h
e
s
tr
ateg
y
is
m
ap
p
ed
as f
o
llo
ws:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
Op
timiz
in
g
micro
g
r
id
d
esig
n
s
to
w
a
r
d
s
n
et
-
z
ero
emis
s
io
n
s
fo
r
s
ma
r
t c
itie
s
:
…
(
A
lb
ert P
a
u
l A
r
u
n
ku
ma
r
)
131
-
T
h
e
r
en
ewa
b
le
en
e
r
g
y
r
eso
u
r
ce
s
(
R
E
R
s
)
(
PV+
W
T
)
g
en
er
ate
s
u
f
f
icien
t
en
er
g
y
an
d
m
ee
ts
th
e
lo
ad
if
>
.
T
h
en
,
t
h
e
ex
ce
s
s
en
er
g
y
is
u
s
ed
f
o
r
ch
ar
g
i
n
g
th
e
b
atter
y
u
n
t
il
=
.
-
T
h
e
(
R
E
R
s
)
ar
e
u
n
ab
le
to
g
en
er
ate
en
o
u
g
h
en
e
r
g
y
to
m
ee
t
th
e
lo
ad
,
an
d
>
.
T
h
er
ef
o
r
e,
th
e
b
atter
y
b
an
k
'
s
p
o
wer
will ser
v
e
as th
e
m
ain
b
ac
k
u
p
.
-
T
h
e
R
E
R
s
ca
n
m
ee
t
cu
s
to
m
er
d
em
an
d
d
u
r
in
g
h
o
u
r
t
if
=
;
b
u
t,
if
≥
at
th
at
tim
e,
PV a
n
d
W
T
ar
e
u
n
ab
le
to
r
ep
l
en
is
h
th
e
b
atter
y
b
an
k
.
-
T
h
e
b
atter
y
b
an
k
is
em
p
ty
(
=
m
ax
)
a
n
d
th
e
R
E
R
s
ar
e
n
o
t
ab
le
to
m
ee
t
th
e
lo
a
d
(
<
).
T
h
e
DG
ac
ts
as
a
s
ec
o
n
d
ar
y
b
ac
k
u
p
s
o
u
r
ce
to
m
ee
t l
o
ad
an
d
r
ep
len
is
h
th
e
b
atter
y
b
a
n
k
.
T
h
e
p
ar
a
m
eter
s
an
d
o
p
er
atio
n
al
co
n
s
tr
ain
ts
co
n
s
id
er
e
d
ar
e
e
x
p
lain
ed
f
u
r
th
er
in
th
e
f
ea
s
ib
ilit
y
an
aly
s
is
o
f
t
h
e
s
y
s
tem
.
T
h
e
th
r
ee
m
ajo
r
p
ar
a
m
eter
s
o
f
co
n
s
id
er
atio
n
ar
e
C
OE
,
L
PS
P
,
an
d
R
F.
3
.
1
.
Co
s
t
o
f
ener
g
y
C
OE
is
o
n
e
o
f
th
e
m
o
s
t
p
o
p
u
l
ar
an
d
ex
te
n
s
iv
ely
u
s
ed
in
d
ica
to
r
s
o
f
th
e
f
in
a
n
cial
f
ea
s
ib
ilit
y
o
f
h
y
b
r
id
en
er
g
y
s
y
s
tem
s
.
I
t
is
th
e
r
atio
o
f
th
e
to
tal
NPC
(
$
)
to
th
e
an
n
u
al
lo
ad
(
k
W
h
)
.
T
h
e
to
ta
l
NPC
in
clu
d
es
all
ca
p
ital c
o
s
ts
,
O&
M
ex
p
en
s
es
,
an
d
r
ep
lace
m
e
n
t p
r
ices.
T
h
e
h
o
u
r
ly
c
u
s
to
m
er
lo
ad
u
s
ag
e
is
d
en
o
ted
b
y
P
load
(
h
)
.
3
.
1
.
1
.
Ca
pita
l c
o
s
t
T
h
e
p
r
ice
o
f
b
u
y
i
n
g
th
e
co
m
p
o
n
en
ts
o
f
an
HM
s
h
o
wn
in
Fig
u
r
e
1
,
s
u
ch
as
th
e
DiG,
SP
V
p
an
els,
W
T
,
an
d
b
atter
y
,
co
n
s
titu
tes
t
h
e
ca
p
ital
c
o
s
t.
T
h
e
to
tal
an
ti
cip
ated
co
s
t
o
f
all
th
e
MG
s
y
s
tem
's
co
m
p
o
n
e
n
ts
m
ak
es u
p
th
e
s
y
s
tem
'
s
ca
p
ital c
o
s
t.
T
h
e
ca
p
ital c
o
s
t is d
ef
in
e
d
as
(
9
)
.
=
{
+
+
+
+
}
(
9
)
W
h
er
e
,
,
,
,
ar
e
th
e
r
ati
n
g
s
o
f
d
iesel
g
en
er
ato
r
,
SP
V,
W
T
,
b
atter
y
,
an
d
in
v
er
te
r
r
esp
ec
tiv
ely
an
d
,
,
an
d
ar
e
th
e
co
s
ts
p
er
k
W
f
o
r
DiGs,
b
atte
r
y
,
a
n
d
i
n
v
er
ter
r
esp
ec
tiv
ely
.
an
d
ar
e
p
lan
t
in
s
tallatio
n
co
s
ts
p
er
k
W
f
o
r
SP
V
an
d
W
T
r
esp
ec
tiv
ely
.
T
h
e
ON/OFF
s
tatu
s
o
f
ea
c
h
u
n
it
is
d
en
o
ted
b
y
th
e
d
ec
is
io
n
v
a
r
iab
les in
HM
s
y
s
tem
.
R
D
i
G
=
{
1
Die
s
e
l
G
e
n
e
r
a
tor
is
Up
0
Die
s
e
l
G
e
n
e
r
a
tor
is
Dow
n
,
R
S
PV
=
{
1
Sol
a
r
PV
Sys
te
m
is
Up
0
Sol
a
r
PV
Sys
te
m
is
Dow
n
R
WT
=
{
1
W
in
d
Pow
e
r
is
Up
.
0
W
in
d
Pow
er
is
Dow
n
.
,
R
b
=
{
1
B
a
tte
r
y
is
Up
0
B
a
tte
r
y
is
Dow
n
,
R
i
=
{
1
Inve
r
t
e
r
is
Up
0
Inve
r
t
e
r
is
Dow
n
(
1
0
)
W
h
er
e
R
Di
G
,
R
SPV
,
R
WT
,
R
b
,
an
d
R
i
ar
e
d
ec
is
io
n
v
ar
iab
les
ass
o
ciate
d
with
D
iGs
,
SP
V,
W
T
,
b
atter
ies
,
an
d
in
v
er
ter
s
r
esp
ec
tiv
ely
.
Fig
u
r
e
1
.
T
h
e
g
e
n
er
al
ar
ch
itec
tu
r
e
o
f
t
h
e
co
n
s
id
er
e
d
HM
GS
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J
Ap
p
l Po
wer
E
n
g
,
Vo
l.
14
,
No
.
1
,
Ma
r
ch
20
25
:
1
27
-
1
37
132
3
.
1
.
2
.
O
pera
t
io
n a
nd
m
a
inte
na
nce
co
s
t
s
O&
M
co
s
ts
v
ar
y
an
d
ar
e
in
f
lu
en
ce
d
b
y
in
ter
est
r
ates
an
d
i
n
f
latio
n
.
Fo
r
th
e
s
ak
e
o
f
th
is
s
tu
d
y
,
th
e
in
f
latio
n
an
d
in
ter
est r
ates o
f
t
h
e
MG
s
y
s
tem
co
m
p
o
n
en
ts
ar
e
s
et
at
5
% a
n
d
1
3
%,
r
esp
ec
tiv
ely
.
=
{
[
(
1
+
)
(
−
)
]
[
1
−
(
1
+
1
+
)
]
{
+
+
+
+
}
,
(
≠
)
{
+
+
+
+
}
,
(
=
)
(
1
1
)
d
is
in
f
latio
n
r
ate
f
o
r
co
m
p
o
n
en
t
r
ep
lace
m
en
t,
a
n
d
s
is
r
ea
l
in
ter
est
r
ate.
T
h
e
co
m
p
o
n
e
n
t
life
tim
e
in
a
y
ea
r
is
d
en
o
ted
b
y
C
L
T
.
&
,
&
,
&
,
&
,
&
r
ep
r
esen
ts
O&
M
co
s
ts
f
o
r
d
iesel
g
en
er
at
o
r
,
SP
V,
W
T
,
b
atter
y
,
an
d
in
v
e
r
ter
,
r
esp
ec
tiv
ely
.
3
.
1
.
3
.
Repla
ce
m
ent
c
o
s
t
s
T
h
e
co
s
t
o
f
r
e
p
lacin
g
t
h
e
MG
s
y
s
tem
's
co
m
p
o
n
e
n
t
p
a
r
ts
is
co
n
s
id
er
ed
at
ea
ch
s
tep
o
f
th
e
s
y
s
tem
's
life
s
p
an
.
C
o
n
s
eq
u
en
tly
,
t
h
e
p
r
o
ject'
s
cu
r
r
en
t
ap
p
r
o
x
im
atio
n
i
s
ev
alu
ated
f
o
r
all
co
m
p
o
n
en
t
r
ep
lace
m
en
t
co
s
ts
.
T
h
e
p
r
esen
t c
o
s
t o
f
r
ep
lacin
g
a
MG
s
y
s
tem
ca
n
b
e
ex
p
r
ess
ed
as
(
1
2
)
.
=
(
1
+
1
+
)
{
+
+
+
+
}
(
1
2
)
is
th
e
co
m
p
o
n
en
t
co
s
t
o
f
th
e
d
iesel
g
en
er
ato
r
u
n
it.
Similar
l
y
,
,
,
,
d
en
o
tes
t
h
e
u
n
i
t
co
m
p
o
n
en
t
co
s
ts
o
f
th
e
SP
V
g
en
er
atio
n
,
W
T
,
b
atter
y
,
an
d
th
e
i
n
v
er
ter
r
esp
ec
tiv
ely
.
,
,
,
,
an
d
r
ep
r
esen
ts
th
e
r
ep
lace
m
en
t c
o
s
t o
f
d
iesel g
en
er
at
o
r
,
SP
V,
W
T
,
b
atter
y
,
an
d
th
e
in
v
er
ter
r
esp
ec
tiv
ely
.
3
.
2
.
L
o
s
s
o
f
po
wer
s
up
ply
pro
ba
bil
it
y
(
L
P
SP)
T
h
e
HM
s
y
s
tem
'
s
r
eliab
ilit
y
is
ass
ess
ed
u
s
in
g
th
e
L
PS
P.
T
h
e
ch
an
ce
th
at
th
e
p
o
wer
s
u
p
p
ly
is
ab
le
to
m
ee
t
d
em
an
d
d
u
e
t
o
a
lack
o
f
en
er
g
y
f
r
o
m
r
e
n
ewa
b
le
s
o
u
r
ce
s
o
r
tech
n
ical
is
s
u
es
is
in
d
icate
d
b
y
th
e
L
PS
P
wh
ich
is
ex
p
r
ess
ed
b
y
(
1
3
)
.
=
∑
(
−
−
+
)
8760
∑
=
1
∑
8760
=
1
(
1
3
)
At
tim
e
t,
th
e
p
o
wer
g
en
er
ate
d
b
y
SP
V,
W
T
,
an
d
d
iesel
g
en
er
ato
r
ca
n
b
e
d
e
n
o
ted
b
y
,
,
an
d
r
esp
ec
tiv
ely
.
W
h
er
e
th
e
lo
a
d
at
t
is
r
ep
r
esen
ted
b
y
.
d
en
o
tes
th
e
b
atter
y
m
in
im
u
m
s
to
r
ag
e
ca
p
ac
ity
.
T
h
e
liter
atu
r
e
in
d
ic
ates
th
at
th
e
L
PS
P
v
alu
e
n
ee
d
s
to
b
e
less
th
an
5
%.
A
ch
a
llen
g
in
g
s
ce
n
ar
io
is
u
s
ed
to
p
er
f
o
r
m
th
e
r
eliab
ilit
y
ass
ess
m
en
t
in
th
is
s
tu
d
y
,
w
h
en
>
,
wh
er
e
r
ep
r
esen
ts
th
e
t
o
tal
p
o
wer
g
e
n
er
ated
b
y
th
e
w
h
o
le
s
y
s
tem
d
u
r
in
g
tim
e
t.
3
.
3
.
Renew
a
ble
f
ra
ct
i
o
n (
RF
)
I
n
o
p
tim
izatio
n
p
r
o
g
r
am
m
in
g
,
R
F
is
a
th
r
esh
o
ld
u
s
ed
to
co
m
p
ar
e
th
e
en
e
r
g
y
p
r
o
d
u
ce
d
b
y
a
d
iesel
p
o
wer
g
en
er
ato
r
with
a
r
en
ew
ab
le
p
o
we
r
g
en
er
ato
r
.
W
h
en
t
h
e
r
e
n
ewa
b
le
c
o
m
p
o
n
en
t
is
ze
r
o
,
t
h
e
h
y
b
r
i
d
MG
s
y
s
tem
f
u
lly
r
elies
o
n
a
d
iesel
g
en
er
ato
r
t
o
p
r
o
v
id
e
its
elec
tr
ical
d
em
an
d
s
.
T
h
e
(
1
4
)
ca
n
b
e
u
s
ed
to
d
escr
ib
e
it.
T
h
e
r
an
g
e
o
f
th
e
R
F
is
0
to
1
.
W
h
er
ea
s
1
d
en
o
tes
co
m
p
l
ete
r
elian
ce
o
n
r
en
ewa
b
le
e
n
er
g
y
s
o
u
r
ce
s
an
d
0
d
en
o
tes n
o
u
s
e
o
f
r
e
n
ewa
b
le
e
n
er
g
y
.
(
%
)
=
(
1
−
∑
8760
=
1
∑
(
+
)
8760
=
1
)
×
100
(
1
4
)
4.
O
P
T
I
M
I
Z
AT
I
O
N
T
E
CH
NI
Q
UE
AND
M
E
T
H
O
DO
L
O
G
Y
T
h
r
ee
c
r
iter
ia
(
R
F,
C
OE
,
an
d
L
PS
P)
ar
e
tak
en
in
to
ac
co
u
n
t f
o
r
c
h
o
o
s
in
g
an
ec
o
n
o
m
ically
v
iab
le
MG
d
esig
n
with
th
e
id
ea
l
s
ize.
T
h
e
ec
o
n
o
m
ics
an
d
p
o
wer
s
u
p
p
l
y
d
ep
en
d
ab
ilit
y
ar
e
o
f
m
ajo
r
e
m
p
h
asis
.
T
h
er
ef
o
r
e
,
th
e
two
v
ar
ia
b
les C
OE
an
d
L
P
SP
ar
e
s
e
lecte
d
as th
e
g
o
als.
T
h
e
m
u
lti
-
o
b
jectiv
e
p
r
o
b
lem
is
r
ed
u
ce
d
to
a
s
in
g
le
o
b
jectiv
e
f
o
r
co
m
p
u
tatio
n
al
s
im
p
licity
.
Sin
ce
b
o
th
g
o
als
a
r
e
eq
u
ally
im
p
o
r
tan
t,
th
ey
ar
e
ea
ch
g
iv
en
eq
u
al
weig
h
ts
o
f
0
.
5
.
T
h
e
f
itn
e
s
s
f
u
n
ctio
n
[
2
5
]
ca
n
b
e
wr
itt
en
as
(
1
5
)
.
T
h
e
co
n
s
tr
ain
ts
d
ef
in
e
as
(
)
≥
0
∈
{
1
,
…
}
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
Op
timiz
in
g
micro
g
r
id
d
esig
n
s
to
w
a
r
d
s
n
et
-
z
ero
emis
s
io
n
s
fo
r
s
ma
r
t c
itie
s
:
…
(
A
lb
ert P
a
u
l A
r
u
n
ku
ma
r
)
133
=
min
{
∑
(
)
=
1
}
ℎ
≥
0
∑
=
1
=
1
(
1
5
)
r
ep
r
esen
ts
th
e
v
ec
to
r
o
f
d
ec
is
io
n
v
ar
iab
les
an
d
W
i
s
tan
d
s
f
o
r
t
h
e
weig
h
t
e
q
u
iv
alen
t
s
ig
n
if
ican
ce
o
f
ea
c
h
o
b
jectiv
e,
d
en
o
tes th
e
weig
h
t
o
f
th
e
o
b
jectiv
e
an
d
is
th
e
o
b
jectiv
e
f
u
n
ctio
n
wh
o
s
e
u
p
p
er
b
o
u
n
d
is
d
e
n
o
ted
by
.
Du
e
to
th
e
h
ig
h
c
o
n
v
e
r
g
in
g
n
at
u
r
e
o
f
PS
O,
it
is
s
elec
ted
f
o
r
o
p
tim
izatio
n
.
Fo
r
a
d
ef
in
ed
f
itn
ess
f
u
n
ctio
n
,
th
e
f
itn
ess
o
f
ea
ch
p
ar
ticle
is
ev
alu
ated
f
ir
s
t.
Fo
llo
wed
b
y
th
e
v
elo
city
an
d
p
o
s
iti
o
n
o
f
ea
ch
p
ar
ticle
is
u
p
d
ated
to
d
eter
m
in
e
th
e
in
d
iv
id
u
al
an
d
g
lo
b
al
b
est f
itn
ess
p
o
s
it
io
n
.
T
h
e
s
war
m
p
o
s
itio
n
o
f
ea
ch
p
ar
ticle
is
u
p
d
ated
u
s
in
g
(
1
6
)
.
+
1
=
+
+
1
(
1
6
)
+
1
=
×
[
+
1
1
(
−
)
+
2
2
(
−
)
]
(
1
7
)
=
2
2
−
∅
−
√
∅
2
−
√
4∅
(
1
8
)
W
h
er
e,
an
d
r
ep
r
esen
ts
th
e
p
o
s
itio
n
an
d
v
elo
city
o
f
p
ar
ticle
in
t
h
e
iter
atio
n
,
1
1
(
−
)
−
in
dividua
l
c
omp
on
e
n
t
,
an
d
2
2
(
−
)
−
Socia
l
C
omp
on
e
n
t
.
T
h
e
p
lan
n
in
g
co
n
ce
r
n
s
d
is
cu
s
s
ed
in
th
is
ar
ticle
ar
e
ad
d
r
ess
ed
b
y
th
e
f
o
u
r
ch
o
ice
v
ar
iab
les
-
th
e
n
o
.
o
f
SP
V
p
an
els,
W
T
,
d
iesel g
e
n
er
ato
r
s
,
an
d
Au
D.
C
o
n
s
id
er
in
g
th
e
r
eg
io
n
wh
e
r
e
th
e
MG
is
b
ein
g
b
u
ilt,
th
e
f
o
l
lo
win
g
r
estrictio
n
s
s
h
o
u
ld
b
e
ap
p
lied
to
th
e
d
ec
is
io
n
v
ar
ia
b
les.
0
≤
≤
,
0
≤
≤
,
0
≤
≤
,
0
≤
≤
(
1
9
)
N
SPV
an
d
N
WE
ar
e
n
o
.
o
f
SP
V
p
an
els
an
d
W
T
r
esp
ec
tiv
ely
,
N
AuD
an
d
N
DiG
ar
e
n
u
m
b
e
r
o
f
d
iesel
g
en
e
r
ato
r
s
,
Au
D,
,
,
,
an
d
ar
e
m
ax
im
u
m
n
o
.
o
f
SP
V
p
an
els
an
d
W
T
(
A
u
D)
an
d
d
iesel
g
en
e
r
ato
r
r
esp
ec
tiv
ely
.
5.
SI
M
UL
A
T
I
O
N
A
ND
DI
SC
USSI
O
N
O
F
T
H
E
RE
SUL
T
S
T
h
e
ty
p
ical
2
4
-
h
o
u
r
lo
a
d
p
r
o
f
ile
s
h
o
wn
in
Fig
u
r
e
2
is
co
n
s
id
er
ed
f
o
r
th
e
s
im
u
latio
n
.
T
h
e
win
d
s
p
ee
d
,
ir
r
ad
ian
ce
,
a
n
d
ce
ll
tem
p
er
at
u
r
e
a
r
e
o
b
tain
e
d
f
r
o
m
th
e
N
ASA
p
o
wer
d
ata
ac
ce
s
s
v
ie
wer
web
s
ite
f
o
r
t
h
e
s
elec
ted
lo
ca
tio
n
s
.
A
s
am
p
le
o
f
d
ata
o
b
tain
ed
is
s
h
o
wn
f
o
r
two
cities
in
Fig
u
r
e
3
alo
n
g
w
ith
a
s
am
p
le
p
ar
eto
cu
r
v
e.
T
o
attain
c
o
n
v
e
r
g
en
c
e,
1
0
0
iter
atio
n
s
u
s
in
g
1
0
0
p
ar
ticles
ea
ch
tim
e
wer
e
ca
r
r
ied
o
u
t
f
o
r
ev
er
y
s
im
u
latio
n
.
Fig
u
r
e
2
.
T
y
p
ical
2
4
-
h
o
u
r
lo
a
d
p
r
o
f
ile
co
n
s
id
er
e
d
f
o
r
o
p
tim
i
za
tio
n
5
.
1
.
E
a
s
t
er
n r
eg
io
n
T
h
e
s
im
u
latio
n
r
esu
lts
o
b
tain
ed
f
o
r
th
e
n
in
e
cities
in
th
e
ea
s
ter
n
elec
tr
ical
zo
n
e
is
d
is
cu
s
s
ed
in
th
is
s
ec
tio
n
.
Fig
u
r
e
4
(
a)
s
h
o
ws
th
e
o
p
tim
al
s
ize
o
f
th
e
HM
GS
o
b
tain
ed
wh
er
ea
s
,
th
e
L
PS
P,
C
OE
,
R
F,
an
d
Au
D
ar
e
s
h
o
wn
i
n
Fig
u
r
e
4
(
b
)
.
A
m
o
n
g
th
e
9
cities
o
f
th
e
ea
s
ter
n
r
e
g
io
n
,
B
ih
ar
s
h
ar
if
ac
h
ie
v
es
v
er
y
less
C
OE
(
2
.
2
0
9
4
$
/k
W
h
)
,
m
ak
i
n
g
it
t
h
e
m
o
s
t
e
co
n
o
m
ic
m
o
d
el,
wh
er
ea
s
,
Mu
za
f
f
ar
p
u
r
e
x
h
ib
its
a
v
er
y
less
L
PS
P
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J
Ap
p
l Po
wer
E
n
g
,
Vo
l.
14
,
No
.
1
,
Ma
r
ch
20
25
:
1
27
-
1
37
134
(
0
.
0
1
4
3
5
)
an
d
is
th
e
m
o
s
t
r
el
iab
le
m
o
d
el
i
n
th
e
ea
s
ter
n
zo
n
e.
Als
o
,
Mu
za
f
f
ar
p
u
r
ex
h
ib
i
ts
a
h
ig
h
er
R
F
o
f
57
.
8
7
5
%.
Als
o
,
th
r
ee
cities:
B
h
ag
alp
u
r
,
B
ih
ar
s
h
ar
if
,
an
d
Mu
za
f
f
ar
p
u
r
ex
h
i
b
it
a
g
o
o
d
p
er
f
o
r
m
an
ce
f
o
r
n
u
m
b
er
o
f
Au
D
with
a
to
tal
o
f
8
d
ay
s
.
T
ak
in
g
all
p
ar
am
eter
s
in
to
ac
co
u
n
t,
th
e
m
o
d
el
d
e
v
elo
p
e
d
f
o
r
Mu
za
f
f
ar
p
u
r
,
ex
h
ib
its
b
est o
p
tim
al
p
er
f
o
r
m
a
n
ce
.
5
.
2
.
No
r
t
h
-
ea
s
t
er
n r
eg
io
n
T
h
e
o
p
tim
al
s
ize
an
d
v
alu
e
o
f
p
ar
am
eter
s
ev
alu
ated
f
o
r
th
e
HM
GS
f
o
r
th
e
No
r
th
-
E
aste
r
n
zo
n
e
is
s
h
o
wn
in
Fig
u
r
es
5
(
a)
an
d
5
(
b
)
r
esp
ec
tiv
ely
(
s
ee
in
Ap
p
en
d
ix
)
.
Ko
h
im
a
h
as
a
v
e
r
y
g
o
o
d
ec
o
n
o
m
ic
m
o
d
el
with
a
C
OE
o
f
2
.
2
3
6
4
$
/k
W
h
an
d
Pas
ig
h
at
ex
h
ib
its
a
v
er
y
less
L
PS
P
o
f
0
.
2
5
7
5
wh
ich
m
ak
es
it
th
e
r
eliab
le
m
o
d
el
in
th
e
n
o
r
t
h
-
ea
s
ter
n
r
e
g
io
n
.
On
ac
c
o
u
n
t o
f
R
F,
Ag
ar
ta
la
m
o
d
el
f
o
u
n
d
s
to
u
tili
ze
r
en
e
wab
le
en
er
g
y
a
lo
t
f
o
r
th
e
en
e
r
g
y
p
r
o
d
u
ctio
n
wi
th
a
v
al
u
e
o
f
6
5
.
8
5
%
R
F.
Als
o
,
Ag
ar
tala
&
I
m
p
h
al
is
f
o
u
n
d
to
h
av
e
a
h
ig
h
n
u
m
b
er
o
f
Au
D
(
9
d
ay
s
)
.
(
a)
(
b
)
(
c)
(
d
)
Fig
u
r
e
3
.
W
ea
th
er
c
h
ar
ac
ter
is
tics
(
h
o
u
r
ly
i
r
r
ad
ian
ce
a
n
d
ce
ll
tem
p
er
atu
r
e
d
ata)
o
b
tai
n
ed
f
o
r
(
a)
Ag
ar
tala,
(
b
)
I
m
p
h
al,
(
c)
h
o
u
r
ly
win
d
s
p
ee
d
d
ata,
an
d
(
d
)
Par
eto
cu
r
v
e
an
d
th
e
s
im
u
latio
n
r
esu
lt f
o
r
R
an
ch
i u
s
in
g
PS
O
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
Op
timiz
in
g
micro
g
r
id
d
esig
n
s
to
w
a
r
d
s
n
et
-
z
ero
emis
s
io
n
s
fo
r
s
ma
r
t c
itie
s
:
…
(
A
lb
ert P
a
u
l A
r
u
n
ku
ma
r
)
135
(
a)
(
b
)
Fig
u
r
e
4
.
T
h
e
s
im
u
latio
n
r
esu
l
ts
f
o
r
E
aster
n
zo
n
e
cities
:
(
a)
o
p
tim
al
s
ize
an
d
(
b
)
o
p
tim
ized
p
ar
am
eter
s
(
a)
(
b
)
Fig
u
r
e
5
.
T
h
e
s
im
u
latio
n
r
esu
l
ts
f
o
r
No
r
th
-
E
aster
n
zo
n
e
cities
:
(
a)
o
p
tim
al
s
ize
an
d
(
b
)
o
p
ti
m
ized
p
ar
am
eter
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J
Ap
p
l Po
wer
E
n
g
,
Vo
l.
14
,
No
.
1
,
Ma
r
ch
20
25
:
1
27
-
1
37
136
6.
CO
NCLU
SI
O
N
AND
F
U
T
U
RE
SCO
P
E
T
h
e
p
r
o
p
o
s
ed
h
y
b
r
i
d
m
icr
o
g
r
i
d
s
y
s
tem
(
HM
GS)
m
o
d
el,
en
h
an
ce
d
with
a
s
m
ar
t
en
e
r
g
y
m
a
n
ag
em
en
t
s
y
s
tem
(
SEM
S),
h
as
b
ee
n
o
p
tim
ized
to
ad
d
r
ess
en
er
g
y
ac
ce
s
s
is
s
u
es
an
d
d
is
p
ar
ities
in
t
h
e
s
m
ar
t
cities
d
esig
n
ated
in
I
n
d
ia’
s
ea
s
ter
n
an
d
n
o
r
th
ea
s
ter
n
z
o
n
es.
T
h
e
o
p
tim
izatio
n
p
r
o
ce
s
s
d
eter
m
in
ed
th
e
id
ea
l
s
y
s
tem
s
izes,
en
s
u
r
in
g
a
co
m
p
r
eh
e
n
s
iv
e
ec
o
n
o
m
ic
m
o
d
el
th
at
ac
c
o
u
n
ts
f
o
r
all
co
s
t
elem
e
n
ts
,
in
clu
d
in
g
in
f
latio
n
.
T
h
is
m
o
d
el
aim
s
to
m
itig
ate
r
eg
io
n
al
en
er
g
y
d
is
p
ar
ities
b
y
im
p
r
o
v
in
g
en
e
r
g
y
r
eliab
ilit
y
an
d
ac
ce
s
s
ib
ilit
y
.
I
n
th
e
ea
s
ter
n
zo
n
e,
th
e
MG
m
o
d
el
f
o
r
Mu
za
f
f
ar
p
u
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RE
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NC
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[
1
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