Sustainability,
Год журнала:
2023,
Номер
15(11), С. 9107 - 9107
Опубликована: Июнь 5, 2023
Accurate
wind
power
prediction
is
vital
for
improving
grid
stability.
In
order
to
improve
the
accuracy
of
prediction,
in
this
study,
a
hybrid
model
combining
time-varying
filtered
empirical
modal
decomposition
(TVFEMD),
improved
adaptive
sparrow
search
algorithm
(IASSA)-optimized
phase
space
reconstruction
(PSR)
and
echo
state
network
(ESN)
methods
was
proposed.
First,
data
were
decomposed
into
set
subsequences
by
using
TVFEMD.
Next,
PSR
used
construct
corresponding
matrix
sequences,
which
then
divided
training
sets,
validation
testing
sets.
Then,
ESN
subsequence
prediction.
Finally,
predicted
values
all
subseries
determine
final
power.
To
enhance
performance,
terms
discoverer
position
update
strategy,
follower
population
structure.
IASSA
employed
synchronously
optimize
multiple
parameters
PSR-ESN.
The
results
revealed
that
proposed
has
higher
applicability
than
existing
models.
Sustainability,
Год журнала:
2025,
Номер
17(10), С. 4621 - 4621
Опубликована: Май 18, 2025
Power-generating
ships
(PGSs)are
considered
some
of
the
largest
mobile
energy
resources.
A
novel
model
is
proposed
in
this
work
to
evaluate
integration
PGSs
into
power
grid
operations.
The
optimally
coordinates
enhance
objectives,
providing
optimal
variables
for
generation
resource
scheduling
and
routing
ships.
Two
case
studies
were
used
simulate
system
validate
effectiveness
model.
significantly
contributes
field
applied
mathematical
modeling
by
developing
complex
algorithms
addressing
logistical
challenges
sources.
This
dual
aspect
emphasizes
model’s
robustness
handling
multidimensional
optimization
problems
inherent
integrating
resources
with
static
systems.
Integrating
operations
represents
a
practical
implementation
engineering
solutions
designed
flexibility
reliability
networks.
not
only
improves
operational
efficiency
but
also
resilience
infrastructure
adaptable
resource.
approach
exemplifies
potential
innovative
address
contemporary
distribution,
ultimately
leading
more
sustainable
resilient
International series in management science/operations research/International series in operations research & management science,
Год журнала:
2025,
Номер
unknown, С. 193 - 236
Sustainability,
Год журнала:
2022,
Номер
14(17), С. 11002 - 11002
Опубликована: Сен. 2, 2022
A
large-scale
renewable-based
sustainable
power
system
requires
multifaced
techno-economic
optimization
and
energy
penetration.
Due
to
the
volatile
non-periodic
nature
of
renewable
energy,
uncertainty
renewables
combined
with
load
uncertainties
significantly
impacts
operational
efficiency
integration.
The
complexities
in
balancing
demand,
generation,
maintaining
reliability
have
introduced
new
challenges
current
distribution
system.
Most
associated
can
be
effectively
reduced
by
using
a
battery
storage
(BESS)
right
techniques
for
handling
uncertainties.
In
this
paper,
distributionally
robust
(DRO)
technique
linear
decision
rule
is
formulated
unit
commitment
(UC)
framework
optimal
scheduling
network
that
consists
wind
farm,
solar
PV,
distributed
generator
(DG),
BESS.
To
cut
cost
per
unit,
BESS
plays
an
important
role
storing
at
off-peak
time
on-peak-time
use
relatively
lower
prices.
For
all-time
minimum
overall
systems
cost,
size
connected
provide
DGs.
Three
case
studies
are
IEEE
14
bus
(converted
from
MW
kW
match
available
market)
solved
proposed
achieve
maximum
operating
point
capacity
BESS,
i.e.,
wind,
hybrid.
Each
study
has
its
own
30-min
interval
schedule
DGs
along
comparison
without
impact
on
start-up
shut
down
reported
all
dynamic
economic
dispatch
results,
including
battery’s
state-of-charge
profile.
handle
allows
economical
sizing
comparatively
computational
processing
complexities.
Sustainability,
Год журнала:
2023,
Номер
15(7), С. 6208 - 6208
Опубликована: Апрель 4, 2023
In
the
proposed
wind-storage
combined
operation
technology,
storage
side
is
foreseen
to
play
a
significant
role
in
power
system
day-ahead
generation
scheduling.
Based
on
operational
characteristics
of
pumped
stations,
dispatching
method
with
wind
farms
and
stations
studied.
The
mode
that
aims
at
lowest
operating
cost
proposed,
taking
into
consideration
coordination
relationship
between
scheduling
benefit
total
peak-shaving
economy
fluctuation
new
energy
output.
First,
constraint
reservoir
capacity,
output
power,
daily
pumping
station
account,
model
constructed,
objective
minimizing
costs.
Then,
imperial
competition
algorithm
applied
model.
Finally,
compared
standard
particle
swarm
optimization
algorithm.
simulation
results
based
4-unit
10-unit
systems
indicate
effective
robust
for
stations.
Sustainability,
Год журнала:
2023,
Номер
15(11), С. 9107 - 9107
Опубликована: Июнь 5, 2023
Accurate
wind
power
prediction
is
vital
for
improving
grid
stability.
In
order
to
improve
the
accuracy
of
prediction,
in
this
study,
a
hybrid
model
combining
time-varying
filtered
empirical
modal
decomposition
(TVFEMD),
improved
adaptive
sparrow
search
algorithm
(IASSA)-optimized
phase
space
reconstruction
(PSR)
and
echo
state
network
(ESN)
methods
was
proposed.
First,
data
were
decomposed
into
set
subsequences
by
using
TVFEMD.
Next,
PSR
used
construct
corresponding
matrix
sequences,
which
then
divided
training
sets,
validation
testing
sets.
Then,
ESN
subsequence
prediction.
Finally,
predicted
values
all
subseries
determine
final
power.
To
enhance
performance,
terms
discoverer
position
update
strategy,
follower
population
structure.
IASSA
employed
synchronously
optimize
multiple
parameters
PSR-ESN.
The
results
revealed
that
proposed
has
higher
applicability
than
existing
models.
