Inter
national
J
our
nal
of
Electrical
and
Computer
Engineering
(IJECE)
V
ol.
11,
No.
6,
December
2021,
pp.
4740
4750
ISSN:
2088-8708,
DOI:
10.11591/ijece.v11i6.pp4740-4750
r
4740
P
o
wer
system
operation
considering
detailed
modelling
of
the
natural
gas
supply
netw
ork
Ricardo
Mor
eno
1
,
Diego
Larrahondo
2
,
Oscar
Flor
ez
3
1,2
Uni
v
ersidad
Aut
´
onoma
de
Occidente,
Cali,
Colombia
3
Uni
v
ersidad
Distrital
Francisco
Jos
´
e
de
Caldas,
Colombia
Article
Inf
o
Article
history:
Recei
v
ed
May
11,
2020
Re
vised
May
25,
2021
Accepted
Jun
12,
2021
K
eyw
ords:
Natural
g
as
system
Optimal
po
wer
flo
w
Po
wer
systems
Rene
w
able
ener
gy
W
ind
po
wer
ABSTRA
CT
The
ener
gy
transition
from
fossil-fuel
generators
to
rene
w
able
ener
gies
re
presents
a
paramount
challenge.
This
is
mainly
due
to
the
uncertainty
and
unpredictability
asso-
ciated
with
rene
w
able
re
sources.
A
greater
fle
xibility
is
requested
for
po
wer
system
op-
eration
to
fulfill
demand
requirements
considering
security
and
economic
restrictions.
In
particular
,
the
use
of
g
as-fired
generators
has
increased
to
enhance
system
fle
xibility
in
response
to
the
inte
gration
of
rene
w
able
ener
gy
sources.
This
paper
pro
vides
a
com-
prehensi
v
e
formulation
for
modeling
a
natural
g
as
supply
netw
ork
to
pro
vide
g
as
for
thermal
generators,
considering
the
use
of
wind
po
wer
sources
for
the
operation
of
the
electrical
system
o
v
er
a
24-hour
period.
The
results
indicate
the
requirements
of
g
as
with
dif
ferent
wind
po
wer
le
v
el
of
inte
gration.
The
model
is
e
v
aluated
on
a
netw
ork
of
20
N
G
nodes
and
on
a
24-b
us
IEEE
R
TS
system
with
v
arious
operati
v
e
settings
during
a
24-hour
period.
This
is
an
open
access
article
under
the
CC
BY
-SA
license
.
Corresponding
A
uthor:
Ricardo
Moreno
Ener
gy
and
Mechanical
Department
Uni
v
ersidad
Aut
´
onoma
de
Occidente
Cali,
Colombia
Email:
rmoreno@uao.edu.co,
odflorez@udistrital.edu.co
1.
INTR
ODUCTION
T
w
o
crucial
sectors
for
life
no
w
adays
are
natural
g
as
(NG)
and
electricity
.
Although
these
sectors
follo
wed
dif
ferent
paths
throughout
the
majority
of
the
20th
century
,
in
the
last
25
years
the
y
ha
v
e
progressi
v
ely
con
v
er
ged.
The
usage
of
NG
for
electrici
ty
generation
through
g
as-fired
generation
plants
has
enhanced
the
interdependence
among
g
as
and
electric
po
wer
sources
[1].
Consequently
,
the
g
as
and
po
wer
systems
ha
v
e
become
intertwined,
leading
to
ne
w
challenges
due
to
the
comple
xity
in
v
olv
ed
in
the
issues
that
each
poses
to
the
other
[2].
In
terms
of
g
as-fired
po
wer
plants,
limitations
due
to
both
g
as
supply
contracts
and
access
to
the
g
as
netw
ork
are
unkno
wn,
generating
ef
fects
and
e
xternalities
in
their
operation.
From
the
g
as
system
perspecti
v
e,
the
demand
for
natural
g
as
from
residential
and
industrial
areas
is
more
predictable
and
less
v
olatile,
compared
to
the
natural
g
as
consumption
for
electricity
generation.
F
or
these
reasons,
the
g
as
system
interconnected
to
the
electrical
system
requires
greater
fle
xibility
.
Ne
v
ertheless,
fle
xibility
is
e
xpensi
v
e
because
it
requires
additional
e
xtraction
and
transmission
capacities
to
pro
vide
the
necessary
operational
mar
gin
[3].
Moreo
v
er
,
reducing
greenhouse
g
as
emissions
is
imperati
v
e
for
climate
change
mitig
ation,
leading
to
increased
in
v
estments
in
reducing
con
v
entional
fossil
fuel-based
po
wer
generation
[4]-[6].
Therefore,
a
massi
v
e
ef
fort
has
been
made
w
orldwide
to
inte
grate
rene
w
able
ener
gy
technologies
[7].
This
implementation
J
ournal
homepage:
http://ijece
.iaescor
e
.com
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
4741
has
lar
gely
been
done
through
photo
v
oltaic
and
wind
systems
[8].
Despite
this,
the
use
of
these
resources
can
influence
the
electric
po
wer
system
operation
due
to
certain
characteristics
of
these
resources
such
as
uncertainty
and
v
ariability
[9].
Generating
ne
w
requirements
for
dealing
with
disruptions,
i.e.
challenges
in
stability
[10].
Therefore,
ener
gy
systems
require
strate
gies
to
impro
v
e
their
fle
xibility
and
stability
,
and
thus
achie
v
e
the
inte
gration
of
these
intermittent
resources
and
meeting
the
requirements
of
the
demand
[11],
[12].
One
of
the
technologies
currently
used,
to
balance
intermittenc
y
and
increase
system
fle
xibility
,
is
g
as-
fired
thermal
generators
[13].
This
is
because
the
y
ha
v
e
technical
requirements,
such
as
shorter
on/of
f
times,
greater
up
and
do
wn
ramps,
lo
wer
emissions
and
higher
financial
performance
in
comparison
with
traditional
coal-fired
po
wer
plants
[14].
Despite
this,
the
fle
xibility
the
y
can
b
r
ing
to
the
system
is
limited
a
n
d
requires
greater
fle
xibility
of
the
g
as
system
[3].
Consequently
,
this
paper
pro
vides
a
comprehensi
v
e
formulation
for
modeling
a
NG
supply
netw
ork
to
pro
vide
g
as
for
thermal
generators,
considering
the
use
of
wind
po
wer
sources
for
the
operation
of
the
electrical
system
o
v
er
a
24-hour
period.
Inte
grated
NG
and
electricity
system’
s
tar
get
functionality
is
to
minimize
the
o
v
erall
cost
of
the
system,
taking
into
account
po
wer
generation
costs,
g
as
costs
and
losses
in
both
systems.
The
resulting
formulation
pro
vides
optimum
results
re
g
arding
all
generators,
g
as
consumption
requirements
and
technical
requirements
of
the
g
as-fired
generators,
all
under
v
arious
operating
states
and
scenarios.
This
paper
is
structured
in
these
sections:
Section
2
presents
the
problem
description
and
formul
ation.
In
section
3,
the
electrical
and
g
as
systems
and
their
parameters
are
outlined.
Then,
using
the
described
systems,
the
formulation
is
tested.
The
results
are
analyzed
and
discussed
at
the
end
of
this
section.
Some
concluding
remarks
on
this
topic
are
gi
v
en
in
section
4.
Electric
po
wer
systems
and
g
as
systems
ha
v
e
been
e
xtensi
v
ely
studied
from
dif
ferent
points
of
vie
w
.
The
first
ha
v
e
been
analyzed,
through
the
optimal
ener
gy
flo
w
for
the
dispatch
of
the
generation
resources
[15]-
[17].
As
a
result,
the
formulation
of
the
multi-period
DC
optimal
po
wer
flo
w
(DCOPF)
has
been
enhanced
in
order
to
incorporate
rene
w
able
ener
gy
generation’
s
v
ariability
,
considering
f
actors
such
as
electricity
demand
and
wind
a
v
ailability
uncertainty
[18]-[21].
On
the
other
hand,
the
g
as
system
has
been
analyzed
through
dif
ferent
studies.
In
one
of
them,
a
detailed
study
of
g
as
transmission
w
as
made,
implementing
a
simple
x
algorithm
e
xpansion
[22].
Martin
et
al.
[23],
a
fully
intermix
ed
model
w
as
applied
for
the
g
as
netw
ork
optimization
steady-state
case.
Pfetsch
et
al.
[24]
in
v
estig
ate
se
v
eral
approaches
in
order
to
resolv
e
a
main
challenge
in
pipeline
g
as
transmission,
which
is
the
nomination
v
alidation
problem,
through
dif
ferent
algorithms
based
on
linear
and
nonlinear
mix
ed-inte
gral
methods.
The
interdependence
of
electricity
and
NG
systems
has
been
e
xtensi
v
ely
studied.
Zhang
et
al.
[25],
in
order
to
simulate
the
coordinated
stochastic
model
for
the
economic
response
of
the
hourly
po
wer
system
demand,
along
with
the
NG
transport
constraints,
a
Monte-Carlo
simulati
on
w
as
applied.
Operational
perfor
-
mance
of
h
ydrothermal
systems
and
the
NG
netw
ork
in
the
short
term
w
as
studied
at
[26].
Because
of
the
necessity
of
g
as-fired
po
wer
plants,
the
transport
of
NG
through
pipelines
af
fects
po
wer
generation
and
trans-
mission
with
respect
to
safety
and
economics,
a
situation
that
w
as
studied
in
[27].
And
the
reference
[28]
used
the
Ne
wton-Raphson
formulation
to
analyze
inte
grated
electricity
and
NG
systems.
In
addition,
considerable
research
has
been
done
on
the
coordination
of
natural
g
as
and
electr
icity
systems.
Qadrdan
et
al.
[29]-[31]
in
v
estig
ated
ho
w
the
increase
in
wind
po
wer
plants
impacts
on
the
British
g
as
netw
ork.
Sohrabi
et
al.
[32],
performed
ste
ady-state
inte
grated
NG
transmission
netw
ork
and
po
wer
system
formulation,
taking
into
account
the
mark
et
price
of
electricity
on
the
basis
of
the
information
g
ap
decision
theory
.
The
impact
of
NG
system
on
the
short-term
planning
of
the
electricity
system
considering
a
considerable
increase
of
rene
w
able
ener
gies
is
presented
in
[33].
An
operational
strate
gy
based
on
interv
al
optimization
for
inte
grated
g
as
and
electricity
systems
is
proposed
in
[34]
to
enhance
system
performance
considering
demand
response
and
rene
w
able
ener
gy
sources
uncertainty
.
P
ower
system
oper
ation
considering
detailed
modelling
of
the
natur
al
gas
supply
network
(Ricar
do
Mor
eno)
Evaluation Warning : The document was created with Spire.PDF for Python.
4742
r
ISSN:
2088-8708
2.
PR
OBLEM
FORMULA
TION
2.1.
Notation
2.1.1.
Indices
g
Inde
x
of
thermal
units
of
generation.
i;
j
Inde
x
of
netw
ork
b
uses
connected
by
transmission
branches.
m;
n
Inde
x
of
g
as
b
uses
connected
by
a
line
pipe.
t
Inde
x
of
time
periods
(hour).
2.1.2.
P
arameters
A
i
W
ind
turbine
po
wer
connected
to
b
us
i
(MW).
w
i;t
A
v
ailable
wind
for
the
turbine
connected
to
b
us
i
at
time
t
C
m;n
Constant
of
the
g
as
line
pipe
connecting
node
m
to
n
.
L
i;t
Electric
load
in
b
us
i
at
time
t
.
b
g
Thermal
unit
fuel
cost
ratio.
P
max
g
,
P
min
g
Maximum/Minimum
po
wer
generation
limits
for
thermal
units.
P
max
ij
Maximum
po
wer
flo
w
limits
of
branch
connecting
b
us
i
to
j
.
x
ij
Reactance
of
branch
connecting
b
us
i
to
j
.
r
ij
Resistance
of
branch
connecting
b
us
i
to
j
.
Ef
ficienc
y
of
thermal
units.
k
t
Gas
demand
percentage
at
time
t
.
R
up
g
,
R
dow
n
g
Ramp-up/do
wn
limits
of
thermal
generation
unit
g
(MW/h).
S
g
max
n
,
S
g
min
n
Maximum/Minimum
g
as
supply
limits
at
node
n
.
S
d
n
Gas
demand
in
node
n
(Scm).
P
r
max
n
,
P
r
min
n
Maximum/Minimum
g
as
pressure
limits
at
node
n
.
V
O
O
L
Load
loss
v
alue
($/MWh).
V
W
C
W
ind
loss
v
alue
($/MWh).
H
Con
v
ersion
v
alue
(MBtu/10
6
S
cm
)
:
2.1.3.
V
ariables
P
ij
;t
Acti
v
e
po
wer
flo
w
of
branch
connecting
b
us
i
to
j
at
time
t
(MW).
P
g
;t
Acti
v
e
po
wer
produced
by
thermal
unit
g
at
time
t
(MW).
P
w
i;t
Acti
v
e
po
wer
generated
by
wind
turbine
connected
to
b
us
i
at
time
t
(MW).
P
w
c
i;t
Constrained
wind
turbine
po
wer
connected
to
b
us
i
at
time
t
(MW).
f
m;n;t
Gas
flo
w
from
node
m
to
node
n
at
time
t
(Scm).
LS
i;t
Load
shedding
in
b
us
i
at
time
t
(MW).
P
r
n;t
Gas
pressure
at
node
n
time
t
(bar).
S
g
n;t
Gas
supply
in
node
n
time
t
(Scm).
S
e
n;t
Gas
demand
in
node
n
time
t
for
generation
of
thermal
units
(Scm).
E
C
24-hour
electrical
operating
costs
($).
GC
24-hour
g
as
operating
costs
($).
O
F
24-hour
T
otal
operating
costs
($).
i;t
V
oltage
angle
of
b
us
i
at
time
t
(rad).
2.2.
F
ormulation
This
formulation
is
based
on
an
optimization
problem,
designed
to
determine
the
lo
west
o
v
erall
system
operating
cost,
deri
v
ed
from
the
production
of
electricity
to
meet
demand
o
v
er
a
24-hour
period,
including
all
g
as
system
costs
as
specified
in
(1).
O
F
=
E
C
+
GC
(1)
The
(2)
sho
ws
the
total
cost
of
electricity
generation
for
a
24-hour
period.
Through
the
sum
of
the
cost
of
producing
ener
gy
with
g
thermal
units
during
a
t
time
interv
al,
plus
the
costs
associated
with
load
loss,
plus
the
costs
associated
with
non-utilization
of
the
maximum
wind
generation
source
a
v
ailable.
Int
J
Elec
&
Comp
Eng,
V
ol.
11,
No.
6,
December
2021
:
4740
–
4750
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
4743
E
C
=
X
g
;t
b
g
P
g
+
X
i;t
(
V
O
LL
LS
i;t
+
V
W
C
P
w
c
i;t
)
(2)
Dispatch
model
constraints
are
determined
by
the
optimal
po
wer
flo
w
equations.
The
DC
optima
l
po
wer
flo
w
equilibrium
is
defined
by
(3).
The
amount
of
po
wer
flo
wing
in
each
line
is
determined
by
(4).
The
constraints
on
the
maximum
amount
of
po
wer
that
can
flo
w
through
each
line
are
gi
v
en
by
the
(5).
X
g
P
g
;t
+
LS
i;t
+
P
w
i;t
L
i;t
=
X
j
P
ij
;t
(3)
P
ij
;t
=
1
X
ij
(
i;t
j
;t
)
(4)
P
max
ij
;t
P
ij
;t
P
max
ij
;t
(5)
On
the
other
hand,
the
restrictions
for
thermal
generation
units
are
defined
in
(6),
(7),
and
(8),
where
(6)
corresponds
to
the
operational
range
of
thermal
generators.
The
(7)
and
(8)
specify
the
maximum
limits
of
the
ramp-up
and
ramp-do
wn
of
thermal
generators,
i.e.
the
maximum
v
alue
that
can
v
ary
the
po
wer
of
a
generator
in
a
one-hour
interv
al.
P
min
g
;t
P
g
;t
P
max
g
;t
(6)
P
g
;t
P
g
;t
1
R
up
g
(7)
P
g
;t
1
P
g
;t
R
dow
n
g
(8)
The
load
shedding
of
b
us
i
is
restricted
to
the
e
xisting
demand
at
time
t
as
indicated
in
(9).
0
LS
i;t
L
i;t
(9)
The
constraints
for
wind
ge
n
e
ration
are
defined
in
(10)
and
(11),
where
(10)
corresponds
to
to
the
amount
of
a
v
ailable
wind
po
wer
not
utilized.
In
(11)
indicates
the
range
of
minimum
and
maximum
po
wer
that
a
wind
turbine
can
produce,
taking
into
account
the
capacity
v
ariables
and
wind
a
v
ailability
.
P
w
c
i;t
=
w
t
A
i
P
w
i;t
(10)
0
P
w
i;t
w
t
A
i
(11)
The
(12)
indicates
the
total
cost
to
supply
g
as
considering
the
purchase
price
of
g
as
deli
v
ered
at
node
n
during
an
interv
al
of
time
t
.
GC
=
X
n;t
c
n
S
g
n;t
H
(12)
The
flo
w
conserv
ation
equation
at
node
n
,
insures
the
g
as
balance
at
node
n
and
is
gi
v
en
by
the
(13).
X
m
f
n;m;t
=
X
m
f
m;n;t
+
S
g
n;t
k
t
S
d
n
S
e
n;t
(13)
No
w
,
is
up
to
consider
the
constraints
on
the
g
as
transportation
tubes.
In
this
case,
there’
s
no
dis
tinction
between
passi
v
e
and
acti
v
e
g
as
transportation
tubes.
F
or
the
g
as
transportation
tube,
the
relation
between
the
flo
w
f
m;n;t
in
the
g
as
transport
ation
tube
(
m;
n
)
and
the
pressures
P
r
m;t
and
P
r
n;t
is
gi
v
en
by
the
(14).
P
ower
system
oper
ation
considering
detailed
modelling
of
the
natur
al
gas
supply
network
(Ricar
do
Mor
eno)
Evaluation Warning : The document was created with Spire.PDF for Python.
4744
r
ISSN:
2088-8708
Where
C
m;n
is
a
v
alue
that
v
aries
according
to
length,
diameter
and
absolute
roughness
of
the
pipeline
and
g
as
composition.
f
2
m;n;t
=
C
2
m;n
(
P
r
2
m;t
P
r
2
n;t
)
(14)
Alternately
,
the
restrictions
for
g
as
units
are
defined
in
(15),
and
(16),
where
(15)
corresponds
to
the
limitations
of
g
as
inflo
w
at
a
supply
node
n
.
In
the
case
of
g
as
pressure
restrictions,
natural
g
as
transportation
companies
must
not
recei
v
e
natural
g
as
at
a
higher
pressure
than
that
guaranteed
by
the
supplier
at
the
input
point.
On
the
contrary
,
the
demand
at
each
e
xit
point
must
be
satisfied
at
a
minimum
pressure
secured
to
the
industrial
user
or
the
local
distrib
ution
compan
y
,
this
is
e
xpressed
in
(16).
S
g
min
n
S
g
n;t
S
g
max
n
(15)
P
r
min
n
P
r
n;t
P
r
max
n
(16)
Finally
,
g
as-fired
po
wer
plants
connect
the
electricity
and
natural
g
as
systems.
Since
g
as
fired
po
wer
plants
produce
ener
gy
for
the
electric
system
as
a
supplier
,
and
at
the
same
time,
the
y
consume
natural
g
as,
making
it
a
requirement
for
the
natural
g
as
system.
This
is
e
xpressed
in
the
(17).
b
g
P
g
=
S
e
n;t
H
2
N
(17)
3.
RESUL
TS
AND
DISCUSSION
W
ith
the
objecti
v
e
of
quantify
the
requirements
of
g
as
according
to
wind
inte
gration,
a
po
wer
system
case
is
used
to
simulate
operational
conditions
with
a
detailed
modeli
ng
of
the
g
as
netw
ork.
In
this
section
dif
ferent
scenarios
are
e
v
aluated,
with
a
24
hour
period
to
consider
the
dispatching
of
generators.
All
simula-
tions
were
completed
by
a
personal
computer
(PC)
using
Ipopt
R
Solv
er
(3.12.10)
under
the
JuMP
0.1
9.2
Julia
platform
[35].
3.1.
Case
description:
IEEE
24-b
us
and
gas
netw
ork
20-stations
This
section
presents
the
results
and
simulations
on
the
g
as
needs
in
the
ener
gy
transition
considering
the
inte
gration
of
wind
ener
gy
.
This
is
based
on
the
g
as
netw
ork
linkage
with
electricity
netw
ork,
sho
wn
in
Figure
1.
Also,
considering
dif
ferent
demand
cases,
wind
a
v
ailability
profiles,
and
installed
wind
capacities.
The
g
as
netw
ork
linkage
with
electricity
netw
ork,
is
composed
for
a
modified
IEEE
24-b
us
po
wer
system
and
the
g
as
netw
ork
of
Belgium,
modified
from
[15],
[22].
The
data
for
thermal
units
are
listed
in
T
able
1,
modify
from
[15].
In
T
able
2
the
lines
reactance,
po
wer
line
constraints
and
interconnections
are
sho
wn,
modify
from
[15].
NG
system
nodes’
technical
and
economic
features
are
sho
wn
in
T
able
3,
and
the
operational
specifications
of
g
as
netw
ork
are
pro
vided
in
T
able
4,
modifying
the
information
from
[15],
[22].
3.2.
Results
The
system
performance
w
as
e
v
aluated
on
the
basis
of
an
increase
in
wind
po
wer
generation
capac-
ity
,
starting
with
a
case
without
wind
po
wer
installed
(i.e.
0
MW),
then
a
case
with
an
installed
capacity
of
500
MW
and
a
case
of
1000
MW
.
The
analysis
highlights
the
changes
in
g
as
requirements
for
po
wer
generation
during
a
24-hour
period
due
to
wind
a
v
ailability
.
The
g
as
requirements
according
to
wind
po
wer
generation
o
v
er
a
24-hour
period
are
sho
wn
in
Figure
2.
It
can
be
seen
that
t
he
g
as
requirements
in
the
case
without
wind
po
wer
a
v
ailable
ha
v
e
a
similar
performance
in
all
the
simulations.
Con
v
ersely
,
when
ha
ving
an
installed
wind
po
wer
(i.e.
500
MW),
the
g
as
requirements
present
a
greater
v
ariability
,
which
gro
ws
when
f
acing
dif
ferent
wind
patterns
as
the
installed
wind
po
wer
increases
(i.e.
1000
MW).
As
instal
led
wind
po
wer
increases,
in
some
cases,
peak
g
as
requirements
also
rise.
These
peaks
may
be
more
noticeable
in
cases
with
high
v
ariability
in
the
wind
profile,
lik
e
the
one
presented
in
Figure
2(b).
At
hour
11
in
the
case
with
0
MW
of
installed
wind
capacity
,
there
is
a
requirement
of
g
as
41130
Scm,
in
the
cases
of
500
MW
and
1000
MW
of
installed
wind
capacity
,
the
g
as
requirements
increase
by
100%
and
315%
respecti
v
ely
.
Also
in
Figure
2(b),
it
can
be
seen
that
when
there
is
a
decrease
in
the
g
as
requirement,
the
decrease
is
more
considerable,
as
the
installed
wind
capacity
increases,
as
can
be
seen
in
the
16th
hour
.
Int
J
Elec
&
Comp
Eng,
V
ol.
11,
No.
6,
December
2021
:
4740
–
4750
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
4745
Figure
1.
Gas
netw
ork
linkage
with
electricity
netw
ork
T
able
1.
Thermal
generation
data
for
the
24-b
us
test
system
Gen
Bus
P
min
g
(MW)
P
max
g
(MW)
Mar
ginal
Cost
(MW)
R
up
g
(MW/h)
R
dow
n
g
(MW/h)
1
18
100
400
5.47
17
17
2
21
100
400
5.49
17
17
3
1
30.4
152
23.32
84
84
4
2
30.4
152
23.32
84
84
5
15
54.25
155
16
21
21
6
16
54.25
155
10.52
21
21
7
23
108.5
310
10.52
21
21
8
23
140
350
10.89
28
28
9
7
75
350
20.7
49
49
10
13
206.85
591
20.93
21
21
11
15
12
60
26.11
7
7
12
22
0
300
30
100
100
T
able
2.
Branch
data
for
the
24-b
us
test
system
From
T
o
X
ij
(p.u)
Rating
(MV
A)
From
T
o
X
ij
(p.u)
Rating
(MV
A)
1
2
0.0139
175
11
13
0.0476
500
1
3
0.2112
175
11
14
0.0418
500
1
5
0.0845
175
12
13
0.0476
500
2
4
0.1267
175
12
23
0.0966
500
2
6
0.192
175
13
23
0.0865
500
3
9
0.119
175
14
16
0.0389
500
3
24
0.0839
400
15
16
0.0173
500
4
9
0.1037
175
15
21
0.0245
1000
5
10
0.0883
175
15
24
0.0519
500
6
10
0.0605
175
16
17
0.0259
500
7
8
0.0614
175
16
19
0.0231
500
8
9
0.1651
175
17
18
0.0144
500
8
10
0.1651
175
17
22
0.1053
500
9
11
0.0839
400
18
21
0.013
1000
9
12
0.0839
400
19
20
0.0198
1000
10
11
0.0839
400
20
23
0.0108
1000
10
12
0.0839
400
21
22
0.0678
500
P
ower
system
oper
ation
considering
detailed
modelling
of
the
natur
al
gas
supply
network
(Ricar
do
Mor
eno)
Evaluation Warning : The document was created with Spire.PDF for Python.
4746
r
ISSN:
2088-8708
T
able
3.
T
echnical
and
economical
characteristics
of
g
as
nodes
Gas
Node
S
g
min
n
(Scm)
S
g
max
n
(Scm)
S
d
n
(Scm)
P
r
min
n
(Scm)
P
r
max
n
(Scm)
C
n
(USD/MBTU)
Anderlues
0
1.20
0.00
0.00
66.2
0.00
Antwerpen
0
0.00
4.03
1.25
80.0
0.00
Arlon
0
0.00
0.22
0.00
66.2
0.00
Berneau
0
0.00
0.00
0.00
66.2
0.00
Blare
gnies
0
0.00
15.62
2.08
66.2
0.00
Brugge
0
0.00
3.92
1.25
80.0
0.00
Dudzele
0
8.40
0.00
0.00
77.0
2.28
Gent
0
0.00
5.26
1.25
80.0
0.00
Lie
ge
0
0.00
6.39
1.25
66.2
0.00
Loenhout
0
4.80
0.00
0.00
77.0
2.28
Mons
0
0.00
6.85
0.00
66.2
0.00
Namur
0
0.00
2.12
0.00
66.2
0.00
Petange
0
0.00
1.92
1.04
66.2
0.00
Peronnes
0
0.96
0.00
0.00
66.2
1.68
Sinsin
0
0.00
0.00
0.00
63.0
0.00
V
oeren
0
22.01
0.00
2.08
66.2
1.68
W
anze
0
0.00
0.00
0.00
66.2
0.00
W
arland
0
0.00
0.00
0.00
66.2
0.00
Zeebrugge
0
11.59
0.00
0.00
77.0
2.28
Arlon
0
0.00
0.00
0.00
80.0
0.00
T
able
4.
T
echnical
characteristics
of
g
as
netw
ork
From
T
o
Acti
v
e
C
2
m;n
From
T
o
Acti
v
e
C
2
m;n
Zeebrugge
Dudzele
0
9.07027
Berneau
Lie
ge
0
0.02701
Zeebrugge
Dudzele
0
9.07027
Lie
ge
W
arnand
0
1.45124
Dudzele
Brugge
0
6.04685
Lie
ge
W
arnand
0
0.02161
Dudzele
Brugge
0
6.04685
W
arnand
Namur
0
0.86384
Brugge
Zomer
gem
0
1.39543
Namur
Anderlues
0
0.90703
Loenhout
Antperwen
0
0.10025
Anderlues
Peronnes
0
7.25622
Antperwen
Gent
0
0.14865
Peronnes
Mons
0
3.62811
Gent
Zomer
gem
0
0.22689
Mons
Blare
gnies
0
1.45124
Zomer
gem
Peronnes
0
0.65965
W
arnand
W
anze
0
0.05144
V
oeren
Berneau
1
7.25622
W
anze
Sinsin
1
0.00642
V
oeren
Berneau
1
0.10803
Sinsin
Arlon
0
0.00170
Berneau
Lie
ge
0
1.81405
Arlon
Petange
0
0.02782
Similar
to
peak
g
as
requirements,
as
wind
capacity
increases,
so
can
o
v
erall
g
as
consumption
in
a
24-hour
period.
This
can
be
seen
in
Figure
2(d),
in
which
g
as
consumption
for
the
case
of
500
MW
of
installed
wind
capacity
is
higher
in
almost
e
v
ery
hour
o
v
er
the
case
without
wind
capacity
.
As
well
as
the
g
as
require-
ments
are
greater
according
with
the
increase
in
wind
po
wer
capacity
.
Ne
v
ertheless,
there
may
be
cases
in
which
increasing
the
wind
capacity
generates
a
decrease
in
g
as
consumption
in
a
fe
w
hours,
as
sho
wn
in
Figure
2(c).
This
is
wh
y
g
as
consumption
w
as
e
v
aluated
for
a
period
of
192
hours,
equi
v
alent
to
8
days,
for
dif
ferent
installed
wind
capacity
,
starting
from
0
MW
to
1100
MW
,
in
periods
of
100
MW
,
as
sho
wn
in
Figure
3.
These
results
depend
directly
on
the
technical
and
economic
requirements
of
the
thermal
generators,
present
in
T
able
1,
and
the
economic
requirements
of
the
g
as,
present
in
T
able
3.
This
is
because,
in
this
case,
the
cost
of
natural
g
as
used
for
po
wer
generation
is
higher
than
the
a
v
erage
cost
of
thermal
generators
that
are
not
g
as-fired.
This
generates
that
when
solving
the
optimal
flo
w
of
po
wer
and
the
economic
dispatch,
it
looks
for
to
use
in
a
minimum
w
ay
the
g
as,
and
t
herefore
the
g
as-fired
generators.
Ho
we
v
er
,
due
to
the
technical
limitations
of
some
thermal
generators,
in
particular
their
reduced
rate
of
po
wer
change
per
hour
,
it
is
necessary
to
use
g
as-fired
thermals,
to
supply
the
v
ariations
present
in
the
po
wer
system.
V
ariations
in
the
po
wer
system
as
mentioned
abo
v
e
increase
when
implementing
wind
systems.
This
is
due
to
the
f
act
that
in
the
search
to
use
wind
ener
gy
in
totality
,
there
are
some
fluctuations
in
the
po
wer
generation
of
the
el
ectrical
system,
which
must
be
controlled
by
the
other
generation
plants,
in
order
to
ha
v
e
an
optimal
po
wer
flo
w
.
These
fluctuations
in
po
wer
generation
gro
w
proportionally
as
the
installed
wind
capacity
increases.
Gi
v
en
the
limited
ramps
of
coal-fired
po
wer
plants,
v
ariations
must
be
controlled
by
g
as-fired
po
wer
Int
J
Elec
&
Comp
Eng,
V
ol.
11,
No.
6,
December
2021
:
4740
–
4750
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
4747
plants,
which
leads
to
increased
g
as
consumption.
And
a
s
the
fluctuati
o
ns
gro
w
,
due
to
the
increase
in
installed
wind
capacity
,
so
does
g
as
consumption,
as
sho
wn
in
Figure
3.
Figure
2.
Hourly
g
as
requirements
for
installed
wind
po
wer
capacities
within
a
24-hour
period
Figure
3.
T
otal
g
as
requirements
for
installed
wind
po
wer
capacities
P
ower
system
oper
ation
considering
detailed
modelling
of
the
natur
al
gas
supply
network
(Ricar
do
Mor
eno)
Evaluation Warning : The document was created with Spire.PDF for Python.
4748
r
ISSN:
2088-8708
As
mentioned
abo
v
e,
one
of
the
points
to
consider
were
the
technical
requirements
of
the
g
as
fired
thermal
generators,
specifically
in
this
case
the
up
and
do
wn
ramps.
These
ramps
become
critical
to
an
inter
-
mittent
rene
w
able
ener
gy
source,
such
as
wind
po
wer
.
In
cases
s
uch
as
t
his
one,
the
ramps
of
the
coal-fired
generators
are
v
ery
limited,
lea
ving
the
task
of
controlling
the
intermittent
to
the
g
as-fired
generators.
Ho
we
v
er
,
the
ramps
of
the
g
as
generators,
ha
v
e
their
restrictions,
and
the
v
alue
of
these
is
intrinsic
to
the
generator
,
which
means
that
the
limits
designed
for
the
generator
cannot
be
e
xceeded
or
changed
[13].
Therefore,
when
including
wind
po
wer
generation,
the
limits
of
up
and
do
wn
ramps
must
be
kno
wn,
especially
those
of
the
g
as
fired
po
wer
plants.
That
is
wh
y
the
ramps
were
determined
both
up
and
do
wn,
for
a
period
of
192
hours,
for
three
cases
of
installed
wind
capacity
(0
MW
,
500
MW
and
1000
MW),
as
sho
wn
in
Figure
4.
The
ramps
were
calculated
by
the
hourly
dif
ference
of
the
po
wer
generated
by
all
the
g
as
fired
thermal
generators,
i.e.
the
v
alues
of
the
ramps
are
not
from
a
single
generator
,
on
the
contrary
the
y
are
the
sum
of
the
ramps
required
from
all
the
g
as
fired
generators,
at
the
same
instant
of
time.
In
Figure
4,
it
can
be
seen
ho
w
the
gro
wth
in
installed
wind
capacity
leads
to
an
increase
in
the
technical
requirements
of
g
as
fired
boilers,
in
this
case
the
ramps.
Note
that
the
gro
wth
is
in
the
absolute
v
alue
of
the
ramps,
ie
is
gi
v
en
both
in
the
up
and
do
wn
ramps.
In
the
case
where
there
is
no
wind
po
wer
generation,
the
critical
v
alues
of
the
up
and
do
wn
ramps
are
332
MW/h
and
159
MW/h
respecti
v
ely
.
F
or
t
h
e
case
of
500
MW
of
installed
wind
capacity
,
the
critical
v
alues
of
the
up
and
do
wn
ramps
are
1.3
and
2
times
higher
than
the
cas
e
of
0
MW
res
pecti
v
ely
.
And
for
the
case
of
1000
MW
of
installed
wind
capacity
,
the
critical
v
alues
of
the
up
and
do
wn
ramps
are
2.1
and
3.3
times
higher
than
the
initial
case
respecti
v
ely
.
Whereas
the
v
alues
of
the
up
and
do
wn
ramps
of
a
g
as
fired
thermal
generator
tend
to
be
the
same,
only
the
peak
v
alues
of
the
ramps
can
be
tak
en.
Hence,
for
the
implementation
of
500
MW
of
wind
po
wer
generation,
the
critical
v
alues
of
the
ramps
are
increased
by
26%.
Under
normal
conditions,
the
ramps
required
by
thermal
generators
do
not
reach
their
limits,
thus
it
is
not
necessary
to
mak
e
major
changes
to
the
system
to
achie
v
e
these
tar
get
ramps.
Nonetheless,
in
the
case
of
implementing
1000
MW
of
wind
po
wer
generation,
the
critical
v
alues
of
the
ramps
are
increased
by
108%
with
respect
to
the
initial
case.
Therefore,
it
is
necessary
to
double
the
number
of
ram
p
s
that
are
currently
a
v
ailable,
which
generates
the
need
for
the
implementation
of
another
thermal
g
as
generator
,
with
certain
technical
requirements,
to
achie
v
e
these
tar
get
ramps.
Figure
4.
Gas
netw
ork
linkage
with
electricity
netw
ork
4.
CONCLUSION
Global
ener
gy
sources
are
being
transitioned
by
reducing
the
consumption
of
fossil
fuels
and
replacing
them
with
rene
w
able
resources,
in
an
ef
fort
to
reduce
greenhouse
g
as
emissions.
In
contrast,
it
is
necessary
to
incorporate
natural
g
as
into
the
ener
gy
tra
nsition
discourse
in
order
to
meet
the
operational,
economic,
political
and
social
needs
of
the
countries.
In
this
paper
,
a
detailed
model
of
an
economic
dispatch
for
a
24-b
us
electric
system
interconnected
with
a
20
node
natural
g
as
netw
ork,
that
in
v
olv
es
wind
ener
gy
w
as
presented
under
se
v
eral
operational
settings
for
a
24-hour
time
frame.
It
w
as
demonstrated
that
the
planning
process
Int
J
Elec
&
Comp
Eng,
V
ol.
11,
No.
6,
December
2021
:
4740
–
4750
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
4749
required
between
the
NG
infrastructure
and
the
po
wer
system
can
be
achie
v
ed
through
the
mathematical
model
presented.
Due
to
the
uncertainty
and
unpredictability
as
sociated
with
rene
w
able
resources
and
the
limited
te
ch-
nical
characteristics
of
coal-fired
generators,
then
the
g
as
will
play
a
role
in
the
ener
gy
transition.
This
leads
to
a
greater
need
for
g
as
and
g
as-fired
generators,
re
g
ardless
of
the
f
act
that
the
cost
of
generating
ener
gy
from
these
is
higher
t
h
a
n
for
coal-fired
ones.
The
fle
xibility
of
both
the
po
wer
and
g
as
systems
is
vital
for
the
imple-
mentation
of
rene
w
able
ener
gy
sources.
Furthermore,
this
fle
xibility
is
limited
and
costly
.
Consequently
,
the
e
xcessi
v
e
increase
in
the
use
of
rene
w
able
ener
gies
can
cause
the
system
to
reach
its
operational
limits,
with-
out
achie
ving
optimal
resul
ts,
and
it
is
necessary
to
mak
e
technical
changes
in
the
system.
Examples
of
these
changes
include
the
implementation
of
ne
w
g
as-fired
po
wer
plants,
ne
w
transmission
lines,
greater
a
v
ailability
of
g
as,
and
ne
w
g
as
transportation
tubes.
A
CKNO
WLEDGEMENT
The
authors
gratefully
ackno
wledge
the
support
of
the
Uni
v
ersidad
Aut
´
onoma
de
Occidente
in
Cal
i,
Colombia.
REFERENCES
[1]
H.
Chuan,
L.
T
ianqi,
W
.
Lei,
and
M.
Shahidehpour
,
“Rob
ust
coordination
of
interdependent
electricity
and
natural
g
as
systems
in
day-ahead
scheduling
for
f
acilit
ating
v
olatile
rene
w
able
generations
via
po
wer
-to-g
as
technology
,
”
Journal
of
Modern
Po
wer
Systems
and
Clean
Ener
gy
,
v
ol.
5,
no.
3,
pp.
375–388,
2017,
doi:
10.1007/s40565-017-0278-z.
[2]
T
.
Li,
M.
Eremia,
and
M.
Shahidehpour
,
“Interdependenc
y
of
natural
g
as
netw
ork
and
po
wer
system
sec
urity
,
”
IEEE
T
ransactions
on
Po
wer
Systems,
v
ol.
23,
no.
4,
pp.
1817–1824,
2008,
doi:
10.1109/TPWRS.2008.2004739.
[3]
A.
Street,
L.
A.
Barroso,
R.
Chabar
,
A.
T
.
M
endes,
and
M.
V
.
Pereira,
“Pricing
fle
xible
natural
g
as
supply
contracts
under
uncertainty
in
h
ydrothermal
mark
ets,
”
IEEE
T
ransactions
on
Po
wer
Syst
ems,
v
ol.
23,
no.
3,
pp.
1009–1017,
2008,
doi:
10.1109/TPWRS.2008.926442.
[4]
A.
Zubair
,
A.
A.
T
an
vir
,
and
M.
M.
Hasan,
“Optimal
planning
of
standalone
solar
-wind-diesel
h
ybrid
ener
gy
system
for
a
coastal
area
of
bangla
desh,
”
International
Journal
of
Electrical
and
Computer
Engineering
(IJECE),
v
ol.
2,
no.
6,
pp.
731-738,
2012.
[5]
A.
F
ole
y
and
A.
G.
Olabi,
“Rene
w
able
ener
gy
technology
de
v
elopments,
trends
and
polic
y
implications
that
can
underpin
the
dri
v
e
for
global
climate
change,
”
Rene
w
able
and
Sustainable
Ener
gy
Re
vie
ws,
v
ol.
68,
Feb,
2017,
doi:
10.1016/j.rser
.2016.12.065.
[6]
J.
Restrepo-T
rujillo,
R.
Moreno-
Chuquen,
and
F
.
N.
Jim
´
enez-Garc
ıa,
“Strate
gies
of
e
xpansion
for
electric
po
wer
systems
based
on
h
ydroelectric
plants
in
the
conte
xt
of
climate
change:
Case
of
analysis
of
colombia,
”
International
Journal
of
Ener
gy
Economics
and
Polic
y
(IJEEP),
v
ol.
10,
no.
6,
pp.
66–74,
2020,
doi:
10.32479/ijeep.9813.
[7]
O.
Ellabban,
H.
Ab
u-Rub,
and
F
.
Blaabjer
g,
“Rene
w
able
ener
gy
resources:
Current
status,
future
prospects
and
their
enabling
technology
,
”
Rene
w
able
and
Sustainable
Ener
gy
Re
vie
ws,
v
ol.
39,
pp.
748–764,
2014,
doi:
10.1016/j.rser
.2014.07.113.
[8]
I.
E.
Agenc
y
,
Rene
w
ables
Information
2019.
OECD
Publishing,
2019.
[Online].
A
v
ailable:
https://www
.oecd-
ilibrary
.or
g/content/publication/f
a89fd56-en.
[9]
S.
S.
Sakthi,
R.
Santhi,
N.
M.
Krishnan,
S.
Ganes
an,
and
S.
Subramanian,
“W
ind
inte
gra
ted
thermal
unit
commitment
solution
using
gre
y
w
olf
optimizer
,
”
International
Journal
of
Electrical
and
Computer
Engineering
(IJECE),
v
ol.
7,
no.
5,
pp.
2309-2320,
2017,
doi:
10.11591/ijece.v7i5.pp2309-2320.
[10]
R.
Moreno
and
O.
Florez,
“Online
dynamic
assessment
of
system
stability
in
po
wer
systems
using
the
unscented
kalman
filter
,
”
International
Re
vie
w
of
Electrical
Engineering
(IREE),
v
ol.
14,
no.
6,
2019.
[11]
L.
E.
Jones,
Rene
w
able
ener
gy
inte
gration:
practical
management
of
v
ariability
,
uncertainty
,
and
fle
xibility
in
po
wer
grids.
Academic
Press,
2017.
[12]
S.
Cantillo
and
R.
Moreno,
“Po
wer
system
operation
considering
deta
iled
modelling
of
ener
gy
storage
systems,
”
International
Journal
of
Electrical
and
Computer
Engineering
(IJECE),
v
ol.
11,
no.
1,
pp.
182-200.
2021.
[13]
J.
Hentschel,
U.
Babi
c,
and
H.
Spliethof
f,
“
A
parametr
ic
approach
for
the
v
aluation
of
po
wer
plant
fle
xibility
options,
”
Ener
gy
Reports,
v
ol.
2,
pp.
40-47,
2016,
doi:
10.1016/j.e
gyr
.2016.03.002.
[14]
M.
P
anto
s,
“Mark
et-based
congestion
management
in
electric
po
wer
systems
with
increased
share
of
natural
g
as
dependent
po
wer
plants,
”
Ener
gy
,
v
ol.
36,
no.
7,
pp.
4244–4255,
2011,
doi:
10.1016/j.ener
gy
.2011.04.019.
[15]
A.
Soroudi,
Po
wer
System
Optimizati
on
Modeling
in
GAMS.
Springer
,
Aug
2017.
[16]
S.-C.
Ki
m
and
S.
R.
Salkut,
“Optimal
po
wer
flo
w
based
congestion
management
using
enhanced
genetic
algo-
rithms,
”
International
Journal
of
Electrical
and
Computer
Engineering
(IJECE),
v
ol.
9,
no.
2,
pp.
875-883,
2019,
doi:
10.11591/ijece.v9i2.pp875-883.
P
ower
system
oper
ation
considering
detailed
modelling
of
the
natur
al
gas
supply
network
(Ricar
do
Mor
eno)
Evaluation Warning : The document was created with Spire.PDF for Python.