E3S Web of Conferences,
Год журнала:
2025,
Номер
616, С. 03011 - 03011
Опубликована: Янв. 1, 2025
The
need
to
physically
upgrade
and
expand
India’s
inadequate
overburdened
electric
power
structure
has
emerged
as
a
national
imperative
given
contemporary
societal,
ecological,
legal
conditions
well
novelty
risks.
It
targets
the
development
of
safer,
more
flexible
reliable
systems,
in
view
increasing
customers’
demand
for
enhanced
quality.
This
article
focuses
characteristics
new
generation
Smart
Grids
(SG)
with
focus
on
advanced
communication
control
creating
self-healing
systems.
paper
examines
capabilities
like
fault
detection,
isolation
restoration
along
sophisticated
QoS
both
bulk
transmission
distribution.
reasoning
provided
here
lends
significant
support
adoption
Dynamic
Probabilistic
Optimal
Power
Flow
(DSOPF)
an
important
enabler
smart
grid.
expands
how
adding
DSOPF
DMS
capability
can
facilitate
these
design
objectives
provide
foundation
progressive
integrated
grids.
Journal of Electrical Engineering and Technology,
Год журнала:
2023,
Номер
18(2), С. 719 - 733
Опубликована: Янв. 12, 2023
With
increasing
demand
for
energy,
the
penetration
of
alternative
sources
such
as
renewable
energy
in
power
grids
has
increased.
Solar
is
one
most
common
and
well-known
existing
networks.
But
because
its
non-stationary
non-linear
characteristics,
it
needs
to
predict
solar
irradiance
provide
more
reliable
Photovoltaic
(PV)
plants
manage
supply
demand.
Although
there
are
various
methods
irradiance.
This
paper
gives
overview
recent
studies
with
focus
on
forecasting
ensemble
which
divided
into
two
main
categories:
competitive
cooperative
forecasting.
In
addition,
parameter
diversity
data
considered
also
preprocessing
post-processing
All
these
investigated
this
study.
end,
conclusion
been
drawn
recommendations
future
have
discussed.
Energy Reports,
Год журнала:
2024,
Номер
11, С. 1376 - 1398
Опубликована: Янв. 13, 2024
As
the
world
continues
to
seek
sustainable
and
efficient
energy
solutions,
integration
of
advanced
technologies
into
smart
grid
management
(SEGM)
becomes
a
paramount
focus.
The
advent
Sixth
Generation
(6G)
wireless
networks
promises
revolutionize
way
grids
are
monitored,
controlled,
optimized.
This
review
paper
explores
potential
6G
in
context
SEGM.
It
discusses
vision
techniques
that
can
be
harnessed
unlock
full
capabilities
networks.
delves
challenges
opportunities
presented
by
technology,
addressing
issues
such
as
scalability,
security,
real-time
monitoring,
dynamic
spectrum
access.
Moreover,
it
how
enable
seamless
with
other
technologies,
blockchain
cybertwin,
enhance
resilience
reliability
grids.
comprehensive
aims
shed
light
on
transformative
role
networks,
paving
for
intelligent
future
management.
IEEE Access,
Год журнала:
2024,
Номер
12, С. 90461 - 90485
Опубликована: Янв. 1, 2024
Solar
energy
is
largely
dependent
on
weather
conditions,
resulting
in
unpredictable,
fluctuating,
and
unstable
photovoltaic
(PV)
power
outputs.
Thus,
accurate
PV
forecasts
are
increasingly
crucial
for
managing
controlling
integrated
systems.
Over
the
years,
advanced
artificial
neural
network
(ANN)
models
have
been
proposed
to
increase
accuracy
of
various
geographical
regions.
Hence,
this
paper
provides
a
state-of-the-art
review
five
most
popular
ANN
forecasting.
These
include
multilayer
perceptron
(MLP),
recurrent
(RNN),
long
short-term
memory
(LSTM),
gated
unit
(GRU),
convolutional
(CNN).
First,
internal
structure
operation
these
studied.
It
then
followed
by
brief
discussion
main
factors
affecting
their
forecasting
accuracy,
including
horizons,
meteorological
evaluation
metrics.
Next,
an
in-depth
separate
analysis
standalone
hybrid
provided.
has
determined
that
bidirectional
GRU
LSTM
offer
greater
whether
used
as
model
or
configuration.
Furthermore,
upgraded
metaheuristic
algorithms
demonstrated
exceptional
performance
when
applied
models.
Finally,
study
discusses
limitations
shortcomings
may
influence
practical
implementation
Renewable energy focus,
Год журнала:
2023,
Номер
48, С. 100529 - 100529
Опубликована: Дек. 20, 2023
The
efforts
to
revolutionize
electric
power
generation
and
produce
clean
sustainable
electricity
have
led
the
exploration
of
renewable
energy
systems
(RES).
This
form
is
replenished
cost-effective
in
terms
production
maintenance.
However,
RES,
such
as
solar
wind
energies,
intermittent;
this
one
drawbacks
its
usage.
In
order
overcome
limitation,
studies
been
undertaken
forecast
availability
output.
current
trending
method
forecasting
generated
by
RES
artificial
intelligence
(AI)
method.
with
all
potential,
traditional
AI,
Artificial
Neural
Network
(ANN),
Support
Vector
Machine
(SVM)
many
more,
does
not
it
all.
Because
this,
metaheuristic
algorithms
are
being
explored
optimization
techniques
increase
performance
accuracy
these
AI
methods
some
challenges
models.
study
presents
an
insightful
survey
(traditional
metaheuristic)
systems.
A
existing
surveyed
literature
was
presented.
taxonomy
formulated,
theoretical
backgrounds
were
Also,
various
forms
improved
versions
applied
optimize
classical
systems'
output
surveyed.
conceptual
framework
hybrid
application
formulated.
Finally,
discussion,
insight,
models
future
directions
Heliyon,
Год журнала:
2024,
Номер
10(4), С. e26088 - e26088
Опубликована: Фев. 1, 2024
The
use
of
renewable
energy
sources
(RESs)
at
the
distribution
level
has
become
increasingly
appealing
in
terms
costs
and
technology,
expecting
a
massive
diffusion
near
future
placing
several
challenges
to
power
grid.
Since
RESs
depend
on
stochastic
—solar
radiation,
temperature
wind
speed,
among
others—
they
introduce
high
uncertainty
grid,
leading
imbalance
deteriorating
network
stability.
In
this
scenario,
managing
forecasting
RES
is
vital
successfully
integrate
them
into
grids.
Traditionally,
physical-
statistical-based
models
have
been
used
predict
outputs.
Nevertheless,
former
are
computationally
expensive
since
rely
solving
complex
mathematical
atmospheric
dynamics,
whereas
latter
usually
consider
linear
models,
preventing
from
addressing
challenging
scenarios.
recent
years,
advances
machine
learning
techniques,
which
can
learn
historical
data,
allowing
analysis
large-scale
datasets
either
under
non-uniform
characteristics
or
noisy
provided
researchers
with
powerful
data-driven
tools
that
outperform
traditional
methods.
paper,
systematic
literature
review
conducted
identify
most
widely
learning-based
approaches
forecast
results
show
deep
artificial
neural
networks,
especially
long-short
term
memory
accurately
model
autoregressive
nature
output,
ensemble
strategies,
allow
handling
large
amounts
highly
fluctuating
best
suited
ones.
addition,
promising
integrating
forecasted
output
decision-making
problems,
such
as
unit
commitment,
address
economic,
operational
managerial
grid
discussed,
solid
directions
for
research
provided.
Designs,
Год журнала:
2024,
Номер
8(1), С. 10 - 10
Опубликована: Янв. 18, 2024
A
comprehensive
review
of
uncertainties
in
power
systems,
covering
modeling,
impact,
and
mitigation,
is
essential
to
understand
manage
the
challenges
faced
by
electric
grid.
Uncertainties
systems
can
arise
from
various
sources
have
significant
implications
for
grid
reliability,
stability,
economic
efficiency.
Australia,
susceptible
extreme
weather
such
as
wildfires
heavy
rainfall,
faces
vulnerabilities
its
network
assets.
The
decentralized
distribution
population
centers
poses
supplying
remote
areas,
which
a
crucial
consideration
emerging
technologies
emphasized
this
paper.
In
addition,
evolution
modern
grids,
facilitated
deploying
advanced
metering
infrastructure
(AMI),
has
also
brought
new
system
due
risk
cyber-attacks
via
communication
links.
However,
existing
literature
lacks
analysis
encompassing
related
events,
cyber-attacks,
asset
management,
well
advantages
limitations
mitigation
approaches.
To
fill
void,
covers
broad
spectrum
considering
their
impacts
on
explores
conventional
robust
control
probabilistic
data-driven
approaches
modeling
correlating
uncertainty
events
state
optimal
decision
making.
This
article
investigates
development
scenario-based
operations,
microgrids
(MGs)
energy
storage
(ESSs),
demand-side
frequency
ancillary
service
(D-FCAS)
reserve
provision
regulation
ensure
design
uncertainty-tolerance
system.
delves
into
trade-offs
linked
with
implementation
strategies,
computational
speed,
It
how
these
strategies
may
influence
planning
operation
future
grids.
E3S Web of Conferences,
Год журнала:
2025,
Номер
619, С. 03002 - 03002
Опубликована: Янв. 1, 2025
This
‘Smart
Urban
Waste
Management
System’
outlines
an
innovative
architecture
for
the
current
challenges
in
urban
waste
management
through
application
of
IoT,
AI
and
Blockchain
technologies
to
increase
efficiency,
transparency,
sustainability.
IoT
enabled
smart
bins
with
sensors
are
used
system
monitoring
levels
tracking
generating
real
time
data
platforms.
Using
computer
vision,
powered
algorithms
leveraged
predict
generation
patterns
planning,
optimize
collection
routes
optimisation
automation
segregation.
Additionally,
blockchain
technology
enables
secure
transparent
collection,
segregation
disposal
systems,
accountability.
communication
protocols
such
as
LoRaWAN
NB-IoT
implemented
guarantee
low
cost
high
scalability,
using
minimal
power,
fitting
very
well
any
large
city.
In
this
dissertation,
we
investigate
how
these
can
be
joined
seamlessly
form
a
circular,
data-driven
ecosystem
that
helps
achieve
principles
circular
economy
by
encouraging
resource
repurposing
energy
recovery.
Energies,
Год журнала:
2023,
Номер
16(24), С. 8057 - 8057
Опубликована: Дек. 14, 2023
This
review
paper
provides
a
summary
of
methods
in
which
artificial
intelligence
(AI)
techniques
have
been
applied
the
management
variable
renewable
energy
(VRE)
systems,
and
an
outlook
to
future
directions
research
field.
The
VRE
types
included
are
namely
solar,
wind
marine
varieties.
AI
techniques,
particularly
machine
learning
(ML),
gained
traction
as
result
data
explosion,
offer
method
for
integration
multimodal
more
accurate
forecasting
applications.
aspects
include
optimized
power
generation
into
grids,
including
demand
forecasting,
storage,
system
optimization,
performance
monitoring,
cost
management.
Future
applications
proposed
discussed,
issue
availability,
quality,
addition
explainable
(XAI),
quantum
(QAI),
coupling
with
emerging
digital
twins
technology,
natural
language
processing.
