Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Янв. 25, 2024
Abstract
As
is
known,
having
a
reliable
analysis
of
energy
sources
an
important
task
toward
sustainable
development.
Solar
one
the
most
advantageous
types
renewable
energy.
Compared
to
fossil
fuels,
it
cleaner,
freely
available,
and
can
be
directly
exploited
for
electricity.
Therefore,
this
study
concerned
with
suggesting
novel
hybrid
models
improving
forecast
Irradiance
(I
S
).
First,
predictive
model,
namely
Feed-Forward
Artificial
Neural
Network
(FFANN)
forms
non-linear
contribution
between
I
dominant
meteorological
temporal
parameters
(including
humidity,
temperature,
pressure,
cloud
coverage,
speed
direction
wind,
month,
day,
hour).
Then,
framework
optimized
using
several
metaheuristic
algorithms
create
predicting
.
According
accuracy
assessments,
attained
satisfying
training
FFANN
by
80%
data.
Moreover,
applying
trained
remaining
20%
proved
their
high
proficiency
in
forecasting
unseen
environmental
circumstances.
A
comparison
among
optimizers
revealed
that
Equilibrium
Optimization
(EO)
could
achieve
higher
than
Wind-Driven
(WDO),
Optics
Inspired
(OIO),
Social
Spider
Algorithm
(SOSA).
In
another
phase
study,
Principal
Component
Analysis
(PCA)
applied
identify
contributive
factors.
The
PCA
results
used
optimize
problem
dimension,
as
well
suggest
effective
real-world
measures
solar
production.
Lastly,
EO-based
solution
yielded
form
explicit
formula
more
convenient
estimation
IET Generation Transmission & Distribution,
Год журнала:
2023,
Номер
17(22), С. 4958 - 4974
Опубликована: Окт. 9, 2023
Abstract
Energy
management
of
a
virtual
power
plant
(VPP)
that
consists
wind
farm
(WF),
energy
storage
systems
and
demand
response
program
is
discussed
in
the
present
study.
The
introduced
strategy
realized
at
electrical
transmission
level
takes
into
account
collaboration
between
VPPs
day‐ahead
reserve
markets.
One
notable
feature
proposed
attempting
to
make
revenue
close
operating
cost
generating
units
as
much
possible.
objective
function
subjected
network‐constrained
unit
commitment
model,
up
down
requirements
VPP
constraints.
This
method
taking
uncertainty
system
loads,
market
price
WF
generation.
applied
hybrid
stochastic‐robust
scheduling
level,
where
scenario‐based
stochastic
programming
models
prices,
bounded
uncertainty‐based
robust
optimization
has
been
adopted
model
uncertainties
related
load
power.
Scheme
tested
on
IEEE
systems.
According
obtained
results,
coordination
mentioned
markets
demonstrates
capability
suggested
strategy.
Energy Reports,
Год журнала:
2023,
Номер
10, С. 2228 - 2250
Опубликована: Сен. 14, 2023
The
traditional
power
system
is
facing
significant
transformations
due
to
the
integration
of
emerging
technologies,
renewable
energy
sources
(RES),
and
storage
devices.
This
review
focuses
on
shift
from
centralized
decentralized
control,
enhancing
flexibility
for
stakeholders,
challenges
it
entails.
paper
identifies
problem
limited
adaptability
in
systems,
which
restricts
stakeholder
source
integration.
To
address
this,
proposes
a
transition
system.
It
explores
effects
privatization
restructuring,
fostering
competitive
market
across
generation,
transmission,
distribution
levels.
discusses
how
integrating
distributed
generations
(DGs)
demand-side
management
(DSM)
with
ICT
protocols
can
enhance
control
efficiency
reliability.
delves
into
deregulated
electricity
(DEM),
especially
new
generation
promoting
prosumer
participation.
leveraging
DSM
manage
supply–demand
variability
support
sectors.
also
necessity
producers
develop
effective
bidding
strategies.
concludes
key
findings
future
research
directions,
providing
an
overview
evolving
market's
trajectory.
aims
inform
sustainable
efficient
discourse
policy
decision-making.
Computer Science Review,
Год журнала:
2024,
Номер
51, С. 100617 - 100617
Опубликована: Фев. 1, 2024
Electricity
is
one
of
the
mandatory
commodities
for
mankind
today.
To
address
challenges
and
issues
in
transmission
electricity
through
traditional
grid,
concepts
smart
grids
demand
response
have
been
developed.
In
such
systems,
a
large
amount
data
generated
daily
from
various
sources
as
power
generation
(e.g.,
wind
turbines),
distribution
(microgrids
fault
detectors),
load
management
(smart
meters
electric
appliances).
Thanks
to
recent
advancements
big
computing
technologies,
Deep
Learning
(DL)
can
be
leveraged
learn
patterns
predict
peak
hours.
Motivated
by
advantages
deep
learning
grids,
this
paper
sets
provide
comprehensive
survey
on
application
DL
intelligent
response.
Firstly,
we
present
fundamental
DL,
response,
motivation
behind
use
DL.
Secondly,
review
state-of-the-art
applications
including
forecasting,
state
estimation,
energy
theft
detection,
sharing
trading.
Furthermore,
illustrate
practicality
via
cases
projects.
Finally,
highlight
presented
existing
research
works
important
potential
directions
