Energies,
Journal Year:
2024,
Volume and Issue:
17(12), P. 2968 - 2968
Published: June 17, 2024
The
prospect
of
the
energy
transition
is
exciting
and
sure
to
benefit
multiple
aspects
daily
life.
However,
various
challenges,
such
as
planning,
business
models,
access
are
still
being
tackled.
Energy
Communities
have
been
gaining
traction
in
transition,
they
promote
increased
integration
Renewable
Sources
(RESs)
more
active
participation
from
consumers.
optimization
becomes
crucial
support
decision
making
quality
service
for
effective
functioning
Communities.
Optimization
context
has
explored
literature,
with
increasing
attention
metaheuristic
approaches.
This
paper
contributes
ongoing
body
work
by
presenting
results
a
benchmark
between
three
classical
methods—Differential
Evolution
(DE),
Genetic
Algorithm
(GA),
Particle
Swarm
(PSO)—and
recent
approaches—the
Mountain
Gazelle
Optimizer
(MGO),
Dandelion
(DO),
Hybrid
Adaptive
Differential
Decay
Function
(HyDE-DF).
Our
show
that
newer
methods,
especially
(DO)
(HyDE-DF),
tend
be
competitive
terms
minimizing
objective
function.
In
particular,
(HyDE-DF)
demonstrated
capacity
obtain
extremely
results,
on
average
3%
better
than
second-best
method
while
boasting
around
2×
10×
speed
other
methods.
These
insights
become
highly
valuable
time-sensitive
areas,
where
obtaining
shorter
amount
time
maintaining
system
operational
capabilities.
Energy Strategy Reviews,
Journal Year:
2024,
Volume and Issue:
54, P. 101461 - 101461
Published: July 1, 2024
Increasing
electrical
energy
consumption
during
peak
hours
leads
to
increased
losses
and
the
spread
of
environmental
pollution.
For
this
reason,
demand-side
management
programs
have
been
introduced
reduce
hours.
This
study
proposes
an
efficient
optimization
in
Smart
Urban
Buildings
(SUBs)
based
on
Improved
Sine
Cosine
Algorithm
(ISCA)
that
uses
load-shifting
technique
for
as
a
way
improve
patterns
SUBs.
The
proposed
system's
goal
is
optimize
SUBs
appliances
order
effectively
regulate
load
demand,
with
end
result
being
reduction
average
ratio
(PAR)
consequent
minimization
electricity
costs.
accomplished
while
also
keeping
user
comfort
priority.
system
evaluated
by
comparing
it
Grasshopper
Optimization
(GOA)
unscheduled
cases.
Without
applying
algorithm,
total
cost,
carbon
emission,
PAR
waiting
time
are
equal
1703.576
ID,
34.16664
(kW),
413.5864s
respectively
RTP.
While,
after
GOA,
improved
1469.72
21.17
355.772s
ISCA
Improves
PAR,
1206.748
16.5648
268.525384s
respectively.
Where
13.72
%,
38.00
13.97
%
And
method,
29.16
51.51
35.07
According
results,
created
algorithm
performed
better
than
case
GOA
scheduling
situations
terms
stated
objectives
was
advantageous
both
utilities
consumers.
Furthermore,
has
presented
novel
two-stage
stochastic
model
Moth-Flame
(MFOA)
co-optimization
capacity
planning
systems
storage
would
be
incorporated
grid
connected
smart
urban
buildings.
Sustainable Cities and Society,
Journal Year:
2024,
Volume and Issue:
114, P. 105721 - 105721
Published: Aug. 3, 2024
Microgrid
cost
management
is
a
significant
difficulty
because
the
energy
generated
by
microgrids
typically
derived
from
variety
of
renewable
and
non-renewable
sources.
Furthermore,
in
order
to
meet
requirements
freed
markets
secure
load
demand,
link
between
microgrid
national
grid
always
preferred.
For
all
these
reasons,
minimize
operating
expenses,
it
imperative
design
smart
unit
regulate
various
resources
inside
microgrid.
In
this
study,
idea
for
multi-source
operation
presented.
The
proposed
utilizes
Improved
Artificial
Rabbits
Optimization
Algorithm
(IAROA)
which
used
optimize
based
on
current
prices
generation
capacities.
Also,
comparison
optimization
outcomes
obtained
results
implemented
using
Honey
Badger
(HBA),
Whale
(WOA).
prove
applicability
feasibility
method
demand
system
SMG.
price
after
applying
HBA
6244.5783
(ID).
But
Algorithm,
found
4283.9755
(ID),
1227.4482
By
comparing
with
conventional
method,
whale
algorithm
saved
31.396
%
per
day,
artificial
rabbit's
80.3437
day.
From
gives
superior
performance.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Feb. 11, 2025
Parameter
identification
in
a
Proton
Exchange
Membrane
Fuel
Cell
(PEMFC)
entails
the
application
of
optimization
algorithms
to
ascertain
optimal
unknown
variables
essential
for
crafting
an
accurate
model
that
predicts
fuel-cell
performance.
These
parameters
are
typically
not
included
manufacturer's
datasheet
and
must
be
identified
ensure
precise
modeling
forecasting
fuel
cell
behavior.
This
paper
introduces
recently
developed
hybrid
algorithm
(Aquila
Optimizer
Arithmetic
Algorithm
Optimization
(AOAAO))
enhances
AO
AAO
algorithm's
efficiency
through
novel
mutation
strategy,
aimed
at
determining
seven
PEMFC
during
process.
function
as
decision
variables,
objective
minimization
is
sum
square
error
(SSE)
between
predicted
actual
measured
voltages.
AOAAO
demonstrated
superior
performance
across
various
metrics,
achieving
SSE
minimum
comparison
other
compared
algorithm.
AOAAO's
robustness
was
validated
extensive
testing
with
six
commercially
available
PEMFCs,
including
BCS
500
W-PEM,
W
SR-12PEM,
Nedstack
PS6
PEM,
H-12
HORIZON
250
W-stack,
twelve
case
studies
derived
from
operational
conditions
detailed
manufacturers'
datasheets.
For
each
datasheet,
both
Current–Voltage
(I/V)
Power–Voltage
(P/V)
characteristics
PEMFCs
scenarios
closely
aligned
those
observed
experimental
data,
affirming
accuracy,
robustness,
time
real-time
modeling.
In
terms
computational
efficiency,
runtime
significantly
faster
than
all
algorithms,
demonstrating
improvement
approximately
98%.
Mathematics,
Journal Year:
2023,
Volume and Issue:
11(22), P. 4565 - 4565
Published: Nov. 7, 2023
Accurate
parameter
estimation
is
crucial
and
challenging
for
the
design
modeling
of
PV
cells/modules.
However,
high
degree
non-linearity
typical
I–V
characteristic
further
complicates
this
task.
Consequently,
significant
research
interest
has
been
generated
in
recent
years.
Currently,
trend
marked
by
a
noteworthy
acceleration,
mainly
due
to
rise
swarm
intelligence
rapid
progress
computer
technology.
This
paper
proposes
developed
Mountain
Gazelle
Optimizer
(MGO)
generate
best
values
unknown
parameters
generation
units.
The
MGO
mimics
social
life
hierarchy
mountain
gazelles
wild.
was
compared
with
well-recognized
algorithms,
which
were
Grey
Wolf
(GWO),
Squirrel
Search
Algorithm
(SSA),
Differential
Evolution
(DE)
algorithm,
Bat–Artificial
Bee
Colony
(BABCO),
Bat
(BA),
Multiswarm
Spiral
Leader
Particle
Swarm
Optimization
(M-SLPSO),
Guaranteed
Convergence
algorithm
(GCPSO),
Triple-Phase
Teaching–Learning-Based
(TPTLBO),
Criss-Cross-based
Nelder–Mead
simplex
Gradient-Based
(CCNMGBO),
quasi-Opposition-Based
Learning
Whale
(OBLWOA),
Fractional
Chaotic
Ensemble
(FC-EPSO).
experimental
findings
statistical
studies
proved
that
outperformed
competing
techniques
identifying
Single-Diode
Model
(SDM)
Double-Diode
(DDM)
models
Photowatt-PWP201
(polycrystalline)
STM6-40/36
(monocrystalline).
RMSEs
on
SDM
DDM
2.042717
×10−3,
1.387641
1.719946
1.686104
respectively.
Overall,
identified
results
highlighted
MGO-based
approach
featured
fast
processing
time
steady
convergence
while
retaining
level
accuracy
achieved
solution.
Biomimetics,
Journal Year:
2024,
Volume and Issue:
9(5), P. 298 - 298
Published: May 17, 2024
In
recent
years,
swarm
intelligence
optimization
methods
have
been
increasingly
applied
in
many
fields
such
as
mechanical
design,
microgrid
scheduling,
drone
technology,
neural
network
training,
and
multi-objective
optimization.
this
paper,
a
multi-strategy
particle
hybrid
dandelion
algorithm
(PSODO)
is
proposed,
which
based
on
the
problems
of
slow
speed
being
easily
susceptible
to
falling
into
local
extremum
ability
algorithm.
This
makes
whole
more
diverse
by
introducing
strong
global
search
unique
individual
update
rules
(i.e.,
rising,
landing).
The
ascending
descending
stages
also
help
introduce
changes
explorations
space,
thus
better
balancing
search.
experimental
results
show
that
compared
with
other
algorithms,
proposed
PSODO
greatly
improves
optimal
value
ability,
convergence
speed.
effectiveness
feasibility
are
verified
solving
22
benchmark
functions
three
engineering
design
different
complexities
CEC
2005
comparing
it
algorithms.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 74979 - 74992
Published: Jan. 1, 2024
Proton
Exchange
Membrane
Fuel
Cells
(PEMFCs)
play
a
crucial
role
in
the
advancement
of
clean
hydrogen
vehicles.
Their
ability
to
convert
into
electricity
makes
them
promising
candidates
replace
conventional
engines.
However,
optimizing
their
performance
and
efficiency
necessitates
accurate
modeling
techniques
capable
simulating
behavior.
In
this
context,
paper
proposes
an
advanced
approach
for
precise
parameter
estimation
PEMFC
models.
Employing
Enhanced
Walrus
Optimization
(EWO)
algorithm
integrated
with
Lévy
flight
exploration,
tackles
inherent
nonlinearity
systems.
The
technique
aims
minimize
squared
error
between
measured
simulated
terminal
voltage,
thereby
ensuring
superior
accuracy
robustness
compared
established
algorithms.
effectiveness
proposed
model
is
validated
through
comparisons
theoretical
simulations
experimental
measurements.
findings
demonstrate
efficacy
EWO
algorithm,
consistently
outperforming
previously
published
algorithms
achieving
notably
lower
errors.
Moreover,
incorporation
flights
enhances
algorithm's
capabilities,
leading
expedited
convergence
more
estimations.
Beyond
facilitating
estimation,
enhanced
strategy
opens
avenues
refining
design
optimization
strategies
fuel
cell
research
development.
major
contributions
include
enhancement
WO
evaluation
accuracy,
assessment
model.
By
furnishing
models
evidence,
paves
way
