Processes,
Journal Year:
2025,
Volume and Issue:
13(3), P. 808 - 808
Published: March 10, 2025
Offshore
wind
turbines
have
garnered
significant
attention
recently
due
to
their
substantial
energy
harvesting
capabilities.
Pitch
control
plays
a
crucial
role
in
maintaining
the
rated
generator
speed,
particularly
offshore
environments
characterized
by
highly
turbulent
winds,
which
pose
huge
challenge.
Moreover,
hydraulic
pitch
systems
are
favored
large-scale
superior
power-to-weight
ratio
compared
electrical
systems.
In
this
study,
proportional
valve-controlled
system
is
developed
along
with
an
intelligent
strategy
aimed
at
developing
power
turbines.
The
proposed
utilizes
cascade
configuration
of
improved
recurrent
Elman
neural
network,
its
parameters
optimized
using
customized
particle
swarm
optimization
algorithm.
To
assess
effectiveness,
two
other
strategies,
network
and
tested
benchmark
turbine
simulator.
Results
demonstrate
effective
generation,
yielding
78.14%
87.10%
enhancement
mean
standard
deviation
error
respectively.
These
findings
underscore
efficacy
approach
generating
power.
Applied Sciences,
Journal Year:
2025,
Volume and Issue:
15(3), P. 1407 - 1407
Published: Jan. 29, 2025
Recently,
electric
distribution
grids
supply
not
only
loads
but
also
heating
and
cooling
simultaneously
to
increase
the
efficiency
of
system
reduce
emission
greenhouse
gases.
An
energy
management
(EMS)
combined
total
expense
including
environmental
damage
cost
cooling,
heating,
power
(CCHP)
smart
in
a
cooperative
framework
is
proposed
this
paper.
The
entire
problem
modelled
as
unit
commitment
interval
mixed
integer
quadratic
program
(UCIMIQP).
UC
developed
respond
operation
electric,
systems
takes
into
consideration
exchange
between
these
systems.
In
addition,
demand
response
(DR)
incorporated
with
optimization
decision
variable
shave
peak
load
cost.
converted
expense,
unified
function
that
possible
solve
one
step,
where
suitable
for
online
operation.
Furthermore,
set
realistic
constraints
considered
make
approach
close
real
scenario.
To
verify
effectiveness
robustness
model,
analysis
applied
grids,
which
include
electrical,
systems,
operated
cooperatively.
interaction
makes
more
flexible
economical.
results
show
reduced
through
an
Additionally,
reduces
maximum
decreases
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: March 23, 2025
This
paper
investigates
the
economic
energy
management
of
a
wireless
electric
vehicle
charging
stations
(EVCS)
connected
to
hybrid
renewable
system
comprising
photovoltaic
(PV),
wind,
battery
storage,
and
main
grid.
The
study
adopts
an
Improved
Harris
Hawk
Optimization
(IHHO)
algorithm
optimize
minimize
operational
costs
under
varying
scenarios.
Three
distinct
EV
load
profiles
are
considered
evaluate
performance
proposed
optimization
technique.
Simulation
results
demonstrate
that
IHHO
achieves
significant
cost
reductions
improves
utilization
efficiency
compared
other
state-of-the-art
algorithms
such
as
Quantum
Particle
Swarm
(IQPSO),
Honeybee
Mating
(HBMO),
Enhanced
Exploratory
Whale
Algorithm
(EEWOA).
For
scenarios
with
energies,
reduced
electricity
by
up
36.41%,
achieving
per-unit
low
3.17
INR
for
most
demanding
profile.
Under
generation
disconnection,
maintained
its
superiority,
reducing
37.89%
unoptimized
dispatch
strategies.
integration
storage
further
enhanced
system's
resilience
cost-effectiveness,
particularly
during
periods
unavailability.
algorithm's
robust
performance,
reflected
in
ability
handle
dynamic
challenging
conditions,
demonstrates
potential
practical
deployment
real-world
EVCS
powered
systems.
findings
highlight
reliable
efficient
tool
optimizing
dispatch,
promoting
energy,
supporting
sustainable
infrastructure
development.
outperforms
all
benchmark
algorithms,
35.82%
Profile
3,
minimum
3.11
INR/kWh
across
Specifically,
achieved
lowest
6479.72
INR/day
1,
10,893.23
2,
20,821.63
consistently
outperforming
IQPSO,
HBMO,
EEWOA.
Management of Environmental Quality An International Journal,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 14, 2025
Purpose
This
study
evaluates
the
performance
of
genetic
algorithms
(GAs)
in
optimizing
sizing
wind
photovoltaic
systems
with
battery
energy
storage
(BESS).
The
objective
is
to
determine
whether
GAs
can
balance
generation
and
storage,
improve
system
autonomy,
reduce
operating
costs
meet
residential
demand
real-world
environments.
Design/methodology/approach
A
algorithm
was
used
as
a
multi-objective
optimization
tool
optimal
solar
panels,
turbines
BESS.
model
considers
demand,
climate
variability
resource
intermittency.
Metrics
such
total
deficit
(TED)
were
evaluated.
results
compared
exhaustive
search
validate
effectiveness
GA.
Findings
GA
identified
an
configuration
TED
3.76%,
autonomy
96.24%
efficiency
95%
cost
USD
7,500.
In
contrast,
achieved
4.3%,
range
95.7%
90%
at
8,000.
Although
both
methods
ensure
performance,
stands
out
for
its
computational
ability
multiple
targets.
Originality/value
not
only
highlights
usefulness
designing
hybrid
microgrids
that
address
renewable
intermittency
economic
viability
but
also
contributes
sustainable
development
goal
by
promoting
affordable
solutions
communities.
Advances in business information systems and analytics book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 49 - 76
Published: April 15, 2024
This
study
envisions
the
future
trajectory
of
intelligent
optimization
and
machine
learning
(ML)
in
realm
business
analytics,
introducing
novel
perspectives.
It
investigates
synergy
between
big
data
analytics
ML,
underscoring
effectiveness
deep
architectures
unravelling
complex
patterns.
Emphasizing
interpretability,
explores
development
ML
models
tailored
for
contexts
delves
into
decentralized
model
training
privacy
through
edge
computing
federated
learning.
In
domain,
it
addresses
ascendancy
customized
meta-heuristic
algorithms
convergence
heightened
operational
efficiency.
research
contributes
to
a
nuanced
understanding,
fostering
innovative
applications
dynamic
landscape
analytics.
has
been
observed
that
techniques
are
very
useful
Journal Of Big Data,
Journal Year:
2025,
Volume and Issue:
12(1)
Published: Jan. 28, 2025
Abstract
This
paper
presents
a
binary
variant
of
the
recently
proposed
spider
wasp
optimizer
(SWO),
namely
BSWO,
for
accurately
tackling
multidimensional
knapsack
problem
(MKP),
which
is
classified
as
an
NP-hard
optimization
problem.
The
classical
methods
could
not
achieve
acceptable
results
this
in
reasonable
amount
time.
Therefore,
researchers
have
turned
their
focus
to
metaheuristic
algorithms
address
more
and
However,
majority
MKP
suffer
from
slow
convergence
speed
low
quality
final
results,
especially
number
dimensions
increases.
motivates
us
present
BSWO
discretized
using
nine
well-known
transfer
functions
belonging
three
categories—X-shaped,
S-shaped,
V-shaped
families—for
effectively
efficiently
In
addition,
it
integrated
with
improved
repair
operator
4
(RO4)
hybrid
variant,
BSWO-RO4,
improve
infeasible
solutions
achieving
better
performance.
Several
small,
medium,
large-scale
instances
are
used
assess
both
BSWO-RO4.
usefulness
efficiency
also
demonstrated
by
comparing
them
several
optimizers
terms
some
performance
criteria.
experimental
findings
demonstrate
that
BSWO-RO4
can
exceptional
small
medium-scale
instances,
while
genetic
algorithm
RO4
be
superior
instances.
Additionally,
experiments
efficient
than
RO2.
