Advancements in data-driven voltage control in active distribution networks: A Comprehensive review
Results in Engineering,
Год журнала:
2024,
Номер
23, С. 102741 - 102741
Опубликована: Авг. 19, 2024
Distribution
systems
are
integrating
a
growing
number
of
distributed
energy
resources
and
converter-interfaced
generators
to
form
active
distribution
networks
(ADNs).
Numerous
studies
have
been
undertaken
mitigate
various
challenges
in
ADNs.
However,
voltage
deviation
reactive
power
control
still
requires
more
attention
from
researchers
system
engineers.
The
Volt/VAr
(VVC)
concept
has
developed
improve
the
quality,
minimize
losses,
maintain
profile
deployed
utility-owned
legacy
mechanisms
such
as
on-load
tap
changers,
capacitor
banks,
automatic
regulators
operate
discrete,
slow
timescales
unidirectionally,
rendering
them
insufficient
for
optimal
regulation
Owing
increasing
use
smart
meters,
inverters
(SIs),
sensors,
data
analytics
tools,
improved
communication
networks,
become
an
important
resource.
Data-driven
approaches,
particularly
reinforcement
learning
(RL)-based,
therefore
gained
recent
years
effectively
solving
VVC
decision-making
problem.
This
comprehensive
review
presents
detailed
analysis
advanced
approaches
used
address
It
includes
general
overview
problem
formulation,
frameworks,
basic
notations,
well
comparisons
existing
recently
proposed
methods.
study
focuses
on
data-driven
especially
RL-based
algorithms.
Some
open
research
experienced
application
these
algorithms
safety,
data,
scalability,
problems,
interpretability
cybersecurity
threats
presented
alongside
future
perspectives
Internet
Things
(IoT),
Transfer
Learning
(TL),
hybrid
human-in-the-loop
AI
approaches.
Язык: Английский
A Hierarchical Voltage Control Strategy for Distribution Networks Using Distributed Energy Storage
Electronics,
Год журнала:
2025,
Номер
14(9), С. 1888 - 1888
Опубликована: Май 6, 2025
This
paper
presents
a
novel
hierarchical
voltage
control
framework
for
distribution
networks
to
mitigate
violations
by
coordinating
distributed
energy
storage
systems
(DESSs).
The
establishes
two-layer
architecture
that
integrates
centralized
optimization
with
execution.
In
the
upper
layer,
model
predictive
(MPC)-based
controller
computes
optimal
power
dispatch
trajectories
critical
buses,
effectively
decoupling
slow-timescale
from
real-time
adjustments.
lower
broadcast-based
dispatches
parameterized
regulation
signals,
enabling
autonomous
active
tracking
DESS
units.
design
explicitly
addresses
scalability
limitations
of
conventional
and
cyber
vulnerabilities
peer-to-peer
strategies.
effectiveness
proposed
is
verified
on
modified
IEEE
34-bus
123-bus
test
feeder.
results
show
method
can
average
violation
93.7%
robustness
even
under
60%
communication
loss
condition.
Язык: Английский
Intelligent Control Strategy for Coal to Ethylene Glycol Wastewater Emission Reduction based on Dynamic Simulation and Reinforcement Learning
Process Safety and Environmental Protection,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 1, 2024
Язык: Английский
Distribution Network Anomaly Detection Based on Graph Contrastive Learning
Journal of Signal Processing Systems,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 11, 2024
Язык: Английский
Day-Ahead Economic Dispatch Strategy for Distribution Networks with Multi-Class Distributed Resources Based on Improved MAPPO Algorithm
Mathematics,
Год журнала:
2024,
Номер
12(24), С. 3993 - 3993
Опубликована: Дек. 19, 2024
In
the
context
of
global
response
to
climate
change
and
promotion
an
energy
transition,
Internet
Things
(IoT),
sensor
technologies,
big
data
analytics
have
been
increasingly
used
in
power
systems,
contributing
rapid
development
distributed
resources.
The
integration
a
large
number
resources
has
led
issues,
such
as
increased
volatility
uncertainty
distribution
networks,
large-scale
data,
complexity
challenges
optimizing
security
economic
dispatch
strategies.
To
address
these
problems,
this
paper
proposes
day-ahead
scheduling
method
for
networks
based
on
improved
multi-agent
proximal
policy
optimization
(MAPPO)
reinforcement
learning
algorithm.
This
achieves
coordinated
multiple
types
within
network
environment,
promoting
effective
interactions
between
grid
coordination
among
Firstly,
operational
framework
principles
proposed
algorithm
are
described.
avoid
blind
trial-and-error
instability
convergence
process
during
learning,
generalized
advantage
estimation
(GAE)
function
is
introduced
improve
algorithm,
enhancing
stability
updates
speed
training.
Secondly,
model
containing
constructed,
model,
actions,
states,
reward
designed.
Finally,
effectiveness
solving
problem
grids
verified
using
IEEE
30-bus
system
example.
Язык: Английский