Smart Energy,
Journal Year:
2024,
Volume and Issue:
14, P. 100138 - 100138
Published: March 21, 2024
Achieving
a
sustainable
energy
future
requires
clean,
affordable
supply
and
active
consumer
engagement
in
the
market.
This
study
proposes
to
evaluate
simulate
consumers'
willingness
participate
demand-side
management
programs
using
an
agent-based
modelling
approach
address
social
learning
effect
as
key
factor
influencing
behaviour.
The
proposed
model
simulates
households'
electricity
interactions
examining
how
shift
usage
is
encouraged
through
environment,
while
accounting
for
diversity
among
consumers.
Data
from
survey
conducted
Portugal,
including
questions
about
influence
of
recommendations
friends
or
family
members
on
individuals'
engage
demand
response
activities,
are
used
test
simulation.
findings
reveal
that
significantly
impacts
acceptance,
yet
extent
this
varies
depending
socio-economic
characteristics
confirms
effective
capturing
dynamics
supporting
market
decision
making,
providing
valuable
insights
devising
consumers
strategies.
Energy Conversion and Management,
Journal Year:
2024,
Volume and Issue:
315, P. 118795 - 118795
Published: July 16, 2024
Nowadays,
residential
households,
including
both
consumers
and
emerging
prosumers,
have
exhibited
a
growing
demand
for
active/reactive
power.
This
surge
arises
from
activities
such
as
charging
electrical
devices,
leveraging
flexible
resources,
integrating
renewable
energy
sources.
To
meet
this
escalating
effectively,
operators
must
ensure
the
provision
of
an
ample
supply
Achieving
necessitates
identification
influential
factors
generation
precise
forecasts
power
demand.
Hence,
work
proposes
efficient
hybrid
deep
learning
model
consisting
long
short-term
memory
self-Attention
(LSTM-Attention).
incorporates
explicit
time
encoding
to
forecast
one-hour-ahead
consumption
active
reactive
using
real-time
data
units.
The
integration
models
represents
strategic
development
robustness.
Leveraging
inherent
strengths
architectures
allows
synergistic
compensation
that
addresses
limitations
within
each,
contributing
overall
effective
forecasting
model.
Moreover,
Shapley
Additive
Explanations
(SHAP)
framework
was
employed
interpretability,
investigation
underscores
pivotal
role
incorporating
temporal
features
into
forecasting.
SHAP
findings
can
be
effectively
applied
in
management
strategies
optimally
enhance
response.
Finally,
evaluate
effectiveness
proposed
model,
comprehensive
array
performance
metrics
employed.
results
demonstrate
superior
accuracy
compared
alternative
models.
achieved
lowest
root
mean
square
error
(RMSE),
absolute
(MAE),
percentage
(MAPE)
with
value
0.0256,
0.0181,
14.255
%,
respectively.
formulated
method
also
significantly
contribute
industrial
sector
by
improving
forecasting,
thereby
enhancing
interpretability
identifying
most
critical
factors.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 34208 - 34221
Published: Jan. 1, 2024
Cloud
computing
platform
offers
numerous
applications
and
resources
such
as
data
storage,
databases,
network
building.
However,
efficient
task
scheduling
is
crucial
for
maximizing
the
overall
execution
time.
In
this
study,
workflows
are
used
datasets
to
compare
algorithms,
including
Shortest
Job
First,
First
Come,
Served,
(DVFS)
Energy
Management
Algorithms
(EMA).
To
facilitate
comparison,
number
of
virtual
machines
in
Visual
Studio
Net
framework
environment
increased.
The
experimental
findings
indicate
that
increasing
reduces
Makespan.
Moreover,
Algorithm
(EMA)
outperforms
by
2.79%
CyberShake
process
surpasses
Serve
algorithm
12.28%.
Additionally,
EMA
produces
21.88%
better
results
than
both
algorithms
combined.
For
Montage
process,
performs
4.50%
25.75%
superior
policy.
Finally,
we
ran
simulations
determine
performance
suggested
mechanism
contrasted
it
with
widely
energy-efficient
techniques.
simulation
demonstrate
structural
design
may
successfully
reduce
amount
give
suitable
cloud.
World Electric Vehicle Journal,
Journal Year:
2022,
Volume and Issue:
13(12), P. 222 - 222
Published: Nov. 22, 2022
Recently,
electric
vehicles
(EVs)
that
use
energy
storage
have
attracted
much
attention
due
to
their
many
advantages,
such
as
environmental
compatibility
and
lower
operating
costs
compared
conventional
(which
fossil
fuels).
In
a
microgrid,
an
EV
works
through
the
stored
in
its
battery
can
be
used
load
or
source;
therefore,
optimal
utilization
of
clusters
power
systems
has
been
intensively
studied.
This
paper
aims
present
application
intelligent
control
method
bidirectional
DC
fast
charging
station
with
new
structure
solve
problems
voltage
drops
rises.
this
switching
strategy,
converter
is
modeled
station,
which
controls
constant
current
reduced
considers
microgrid
stability.
The
proposed
not
complicated
because
simple
direct
realizes
reactive
compensation,
provide
sufficient
injected
network.
As
result,
test
presented
on
system
electrical
outlets
two-way
compensation
AC/DC
converters
are
exchange
maintain
link
well
network
bus
range
basis.
strategy
carried
out
simulation
charge
control,
adjust
modify
factor
MATLAB
software
environment
then
verified.
Finally,
results
indicate
high
safety
without
increasing
battery’s
maximum
voltage.
It
also
significantly
reduce
time
common
CV
mode.
