Energies,
Journal Year:
2024,
Volume and Issue:
17(23), P. 6087 - 6087
Published: Dec. 3, 2024
In
this
study,
firstly,
the
balance
between
exploration
and
exploitation
capabilities
of
weighted
mean
vectors
(INFO)
algorithm
was
developed
using
fitness–distance
(FDB)
method.
Then,
FDB-INFO
with
a
hyper-heuristic
method
to
create
beginning
optimal
population
by
Linear
Population
Reduction
Success
History-based
Adaptive
Differential
Evolution
(LSHADE)
novel
Hyper-FDB-INFO
presented.
Finally,
applied
solve
placement
sizing
FACTS
devices
for
power
flow
(OPF)
problem
incorporating
wind
energy
sources.
Moreover,
determining
is
an
additional
minimize
total
cost
generation
reducing
losses
system.
The
experimental
results
showed
that
more
effective
solver
than
SHADE-SF,
INFO,
Hyper-INFO
algorithms
integrating
OPF
problem.
Energy,
Journal Year:
2024,
Volume and Issue:
291, P. 130442 - 130442
Published: Jan. 23, 2024
This
paper
presents
a
strategy
based
on
the
hierarchical
rolling
horizon
control,
also
called
model
predictive
control
(MPC),
for
efficiently
managing
hydrogen-energy
storage
system
(HESS)
within
an
islanded
wind-solar
microgrid.
An
electrolyzer
uses
electricity
generated
from
renewable
sources
to
produce
clean
hydrogen,
which
is
then
re-electrified
by
fuel
cell
as
needed
meet
microgrid's
loads.
The
main
contribution
lies
in
incorporation
of
multiple
hydrogen
tanks
HESS,
distinguishing
it
existing
literature,
typically
focuses
single
tank.
HESS
enables
large
volumes
long-term
use,
allowing
microgrid
operate
autonomously
without
interaction
with
utility
grid.
In
order
ensure
optimal
performance,
selection
most
suitable
device
operation
at
each
time-step
crucial.
proposed
takes
into
account
economic
and
operational
costs,
degradation
aspects,
physical
constraints
while
simultaneously
ensuring
tracking
reference
demands
highest
priority
smoothing
out
variations
energy
sources.
Numerical
simulations
lab-scale
setup
demonstrate
that
controller
effectively
manages
thus
satisfying
optimizing
even
when
deviations
occur
between
predicted
real-time
scenarios.
Furthermore,
inclusion
allows
both
mitigate
fluctuations
power
load
demand.
Energy Reports,
Journal Year:
2024,
Volume and Issue:
11, P. 3509 - 3520
Published: March 16, 2024
The
electrification
of
transportation
through
the
widespread
adoption
electric
vehicles
(EVs)
has
raised
substantial
concerns
within
realm
power
grid
operations.
This
concern
predominantly
stems
from
elevated
electricity
demand
brought
about
by
surging
population
EVs,
consequently
exerting
strain
on
infrastructure
which
can
be
reduced
with
vehicle-to-grid
(V2G)
technology
integration.
To
address
this
issue,
paper
delves
further
into
integration
introducing
a
Virtual
Power
Plant
(VPP)
concept
to
enhance
synergy
between
EVs
and
grid.
study
aims
compare
different
realistic
objectives,
ranging
total
active
loss
voltage
drop
minimization
EV
profit
maximization
then
optimize
balance
distribution
quality
VPP
bi-level
modeling.
presented
model
is
devised
as
mixed-integer
quadratically
constrained
programming
(MIQCP)
incorporates
Temporal
Convolutional
Network
(TCN)
based
forecasting
handle
uncertain
behavior
residential
loads
using
historical
data.
experiments
are
conducted
in
IEEE
33-Bus
real-world
240-Bus
networks.
results
indicate
that
enabling
bidirectional
flow
yield
significant
profits
for
users
while
only
marginally
impacting
loss,
approximately
around
5%.
validation
underscores
how
V2G
not
presents
various
advantages
system
operators
but
also
benefits
simultaneously.
Ain Shams Engineering Journal,
Journal Year:
2024,
Volume and Issue:
unknown, P. 102663 - 102663
Published: Feb. 1, 2024
This
paper
presents
a
novel
approach
to
solve
the
Probabilistic
Optimal
Power
Flow
(POPF)
problem
using
Enhanced
Walrus
Optimization
(EWO)
Algorithm.
The
proposed
EWO
is
applied
30
and
118-bus
IEEE
systems,
demonstrating
its
effectiveness
in
handling
complexities
of
grid
with
renewable
energy
sources
(RESs).
algorithm
effectively
addresses
uncertainties
associated
RES
generation,
ensuring
system
reliability
minimizing
generation
costs.
optimization
method
performs
better
than
existing
algorithms,
achieving
smooth
speedy
convergence
high
solution
accuracy.
research
findings
demonstrate
that
an
efficient
tool
for
tackling
POPF
power
systems
RESs.
Moreover,
methodology
extensively
clarified
by
sensitivity
analyses.
work
demonstrates
potential
as
viable
integration-assisted
optimization,
providing
opportunities
more
study
into
cutting-edge
techniques.
Energies,
Journal Year:
2024,
Volume and Issue:
17(7), P. 1562 - 1562
Published: March 25, 2024
Challenges
in
the
operation
of
power
systems
arise
from
several
factors
such
as
interconnection
large
systems,
integration
new
energy
sources
and
increase
electrical
demand.
These
challenges
have
required
development
fast
reliable
tools
for
evaluating
systems.
The
load
margin
(LM)
is
an
important
index
stability
but
traditional
methods
determining
LM
consist
solving
a
set
differential-algebraic
equations
whose
information
may
not
always
be
available.
Data-Driven
techniques
Artificial
Neural
Networks
were
developed
to
calculate
monitor
LM,
present
unsatisfactory
performance
due
difficulty
generalization.
Therefore,
this
article
proposes
design
method
Physics-Informed
parameters
will
tuned
by
bio-inspired
algorithms
optimization
model.
Physical
knowledge
regarding
incorporated
into
PINN
training
process.
Case
studies
carried
out
discussed
IEEE
68-bus
system
considering
N-1
criterion
disconnection
transmission
lines.
results
obtained
proposed
showed
lower
error
values
Root
Mean
Square
Error
(RMSE),
(MSE)
Absolute
Percentage
(MAPE)
indices
than
Levenberg-Marquard
method.
Energies,
Journal Year:
2024,
Volume and Issue:
17(7), P. 1716 - 1716
Published: April 3, 2024
In
recent
times,
there
have
been
notable
advancements
in
solar
energy
and
other
renewable
sources,
underscoring
their
vital
contribution
to
environmental
conservation.
Solar
cells
play
a
crucial
role
converting
sunlight
into
electricity,
providing
sustainable
alternative.
Despite
significance,
effectively
optimizing
photovoltaic
system
parameters
remains
challenge.
To
tackle
this
issue,
study
introduces
new
optimization
approach
based
on
the
coati
algorithm
(COA),
which
integrates
opposition-based
learning
chaos
theory.
Unlike
existing
methods,
COA
aims
maximize
power
output
by
integrating
efficiently.
This
strategy
represents
significant
improvement
over
traditional
algorithms,
as
evidenced
experimental
findings
demonstrating
improved
parameter
setting
accuracy
substantial
increase
Friedman
rating.
As
global
demand
continues
rise
due
industrial
expansion
population
growth,
importance
of
sources
becomes
increasingly
evident.
energy,
characterized
its
nature,
presents
promising
solution
combat
pollution
lessen
dependence
fossil
fuels.
research
emphasizes
critical
COA-based
advancing
utilization
underscores
necessity
for
ongoing
development
field.
