Intelligent Decision Technologies,
Год журнала:
2024,
Номер
18(4), С. 3091 - 3104
Опубликована: Ноя. 1, 2024
This
article
aimed
to
use
the
proximal
policy
optimization
(PPO)
algorithm
address
limitations
of
power
system
startup
strategies,
enhance
adaptability,
coping
ability,
and
overall
robustness
variable
grid
demand
integrated
renewable
energy,
constraints
in
start-up
strategy
are
optimized.
Firstly,
this
constructed
a
dynamic
model
system,
including
key
components
such
as
generators,
transformers,
transmission
lines;
secondly,
it
PPO
designed
interfaces
that
allow
interact
with
model;
afterward,
state
variables
were
determined,
reward
function
was
evaluate
efficiency
stability
system.
Next,
adjusted
trained
iterated
multiple
times
simulation
environment
guide
learn
optimal
strategy.
Finally,
an
effective
evaluation
can
be
conducted.
The
research
results
showed
after
by
algorithm,
stable
frequency
only
took
about
23
seconds,
recovery
time
reduced
33.3%
under
sudden
load
increase.
used
significantly
optimize
intelligent
IEEE Access,
Год журнала:
2024,
Номер
12, С. 43155 - 43172
Опубликована: Янв. 1, 2024
In
light
of
the
growing
prevalence
distributed
energy
resources,
storage
systems
(ESs),
and
electric
vehicles
(EVs)
at
residential
scale,
home
management
(HEM)
have
become
instrumental
in
amplifying
economic
advantages
for
consumers.
These
traditionally
prioritize
curtailing
active
power
consumption,
often
an
expense
overlooking
reactive
power.
A
significant
imbalance
between
can
detrimentally
impact
factor
home-to-grid
interface.
This
research
presents
innovative
strategy
designed
to
optimize
performance
HEM
systems,
ensuring
they
not
only
meet
financial
operational
goals
but
also
enhance
factor.
The
approach
involves
strategic
operation
flexible
loads,
meticulous
control
thermostatic
load
line
with
user
preferences,
precise
determination
values
both
ES
EV.
optimizes
cost
savings
augments
Recognizing
uncertainties
behaviors,
renewable
generations,
external
temperature
fluctuations,
our
model
employs
a
Markov
decision
process
depiction.
Moreover,
advances
model-free
system
grounded
deep
reinforcement
learning,
thereby
offering
notable
proficiency
handling
multifaceted
nature
smart
settings
real-time
optimal
scheduling.
Comprehensive
assessments
using
real-world
datasets
validate
approach.
Notably,
proposed
methodology
elevate
from
0.44
0.9
achieve
31.5%
reduction
electricity
bills,
while
upholding
consumer
satisfaction.
IEEE Access,
Год журнала:
2024,
Номер
12, С. 109984 - 110001
Опубликована: Янв. 1, 2024
As
the
landscape
of
electric
power
systems
is
transforming
towards
decentralization,
small-scale
have
garnered
increased
attention.
Meanwhile,
proliferation
artificial
intelligence
(AI)
technologies
has
provided
new
opportunities
for
system
management.
Thus,
this
review
paper
examines
AI
technology
applications
and
their
range
uses
in
electrical
systems.
First,
a
brief
overview
evolution
importance
integration
given.
The
background
section
explains
principles
systems,
including
stand-alone
grid-interactive
microgrids,
hybrid
virtual
plants.
A
thorough
analysis
conducted
on
effects
aspects
such
as
energy
consumption,
demand
response,
grid
management,
operation,
generation,
storage.
Based
foundation,
Acceleration
Performance
Indicators
(AAPIs)
are
developed
to
establish
standardized
framework
evaluating
comparing
different
studies.
AAPI
considers
binary
scoring
five
quantitative
Key
(KPIs)
qualitative
KPIs
examined
through
three-tiered
scale
–
established,
evolved,
emerging.
