Applied Sciences,
Год журнала:
2024,
Номер
14(23), С. 11001 - 11001
Опубликована: Ноя. 26, 2024
Existing
short-circuit
calculation
methods
for
distribution
networks
with
renewable
energy
sources
ignore
the
fluctuation
of
and
cannot
reflect
impact
load
changes
on
current
in
real
time
at
all
times
day
extreme
scenarios.
A
real-time
method
is
proposed
to
take
into
account
stochastic
nature
distributed
generators
(DGs)
electricity
loads.
Firstly,
continuous
power
flow
calculated
based
output
And
then,
equivalent
DG
models
low-voltage
ride
through
(LVRT)
strategies
are
substituted
iterative
obtain
currents
main
branches
time.
The
effects
different
curves
network
quantitatively
analyzed
during
output,
which
can
provide
an
important
basis
setting
relay
protection
study
new
principles
protection.
Buildings,
Год журнала:
2025,
Номер
15(7), С. 1118 - 1118
Опубликована: Март 29, 2025
With
global
carbon
emissions
continuing
to
rise
and
urban
energy
demands
growing
steadily,
understanding
how
block
morphology
impacts
building
photovoltaic
(PV)
efficiency
consumption
has
become
crucial
for
sustainable
development
climate
change
mitigation.
Current
research
primarily
focuses
on
individual
optimization,
while
block-scale
coupling
relationships
between
PV
utilization
remain
underexplored.
This
study
developed
an
integrated
prediction
optimization
tool
using
deep
learning
physical
simulation
assess
design
parameters
(building
morphology,
orientation,
layout)
affect
performance.
Through
a
methodology
combining
modeling,
potential
assessment,
simulation,
the
quantified
parameters,
utilization,
consumption.
Results
demonstrate
that
appropriate
forms
layouts
reduce
shadow
obstruction,
enhance
system
capability,
simultaneously
improve
reducing
The
provides
improved
accuracy,
enabling
planners
scientifically
maximize
generation
minimize
use.
Extensive
experimental
validation
demonstrates
model
analytical
methods
proposed
in
this
will
help
break
through
limitations
of
research,
making
PV-energy
analysis
at
scale
possible,
providing
scientific
basis
achieving
low-carbon
transformation
sector.
Energies,
Год журнала:
2025,
Номер
18(9), С. 2225 - 2225
Опубликована: Апрель 27, 2025
To
face
the
global
energy
crisis,
requirement
of
transition
and
sustainable
development
has
emphasized
importance
controlling
building
management
systems.
Reinforcement
learning
(RL)
shown
notable
energy-saving
potential
in
optimal
control
heating,
ventilation,
air-conditioning
(HVAC)
However,
coupling
algorithms
environments
limits
cross-scenario
application.
This
paper
develops
chiller
plant
models
OpenAI
Gym
to
evaluate
different
RL
for
optimizing
condenser
water
loop
control.
A
shopping
mall
Changsha,
China,
was
selected
as
case
study
building.
First,
an
simulation
model
EnergyPlus
generated
using
AutoBPS.
Then,
system
developed
validated
by
comparing
it
with
results.
Moreover,
two
algorithms,
Deep-Q-Network
(DQN)
Double
(DDQN),
were
deployed
flow
rate
approach
temperature
cooling
towers
environment.
Finally,
optimization
performance
DQN
across
three
climate
zones
evaluated
AutoBPS-Gym
toolkit.
The
findings
indicated
that
during
season
a
method
resulted
savings
14.16%
system,
whereas
DDQN
achieved
14.01%.
Using
average
values
from
DQN,
recorded
10.42%
compared
baseline.
Furthermore,
implementing
algorithm
climatic
led
4.0%,
highlighting
toolkit’s
ability
effectively
utilize
various
environmental
contexts.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Июнь 2, 2025
The
most
popular
concept,
demand-side
management
(DSM)
aims
to
economize
the
operation
and
performance
of
a
distribution
model.
major
policies
DSM
are
Load-altering
load-curtailing
policies,
both
work
lower
system's
peak
demand.
load
altering
or
shifting
employs
an
optimization-enabled
strategy
that
reallocates
loads
elastic
types
periods
pricing,
therefore
mitigating
peaks
by
filling
in
troughs.
latter
provides
incentives
customers
for
participating
curtailing
during
hours.
This
research
implement
integrated
curtailment
algorithms
inside
microgrid
with
low
voltage
category
system
distribute
energy
resources
optimally
scheduled,
hence
minimizing
overall
operating
costs.
To
amplify
complexity
work,
subject
MG
also
facilitates
charging
vehicles
plug-in
hybrid
electric
(PHEV)
types,
hence,
smart
uses
real-time
tariff
(RTT)
established
aggregators
is
suggested
PHEV
coordination
reduce
daily
cost.
utilizes
technologies
connect
grid
vice
versa.
differential
evolution
(DE)
algorithm
was
used
as
optimization
tool
study.
Numerical
results
validate
combined
cum
policy
proved
be
more
economical
efficient
reducing
TOC
from
889
716¥
872
702¥
type
I
II
respectively
compared
scenarios
when
those
were
individually.
Additionally,
total
cost
(TOC)
further
reduced
considered
changing
its
arrival-departure
time
hours
day
demand
RTT
lesser.
Vehicles,
Год журнала:
2024,
Номер
6(4), С. 2075 - 2105
Опубликована: Дек. 3, 2024
The
increasing
demand
for
more
efficient
and
sustainable
power
systems,
driven
by
the
integration
of
renewable
energy,
underscores
critical
role
energy
storage
systems
(ESS)
electric
vehicles
(EVs)
in
optimizing
microgrid
operations.
This
paper
provides
a
systematic
literature
review,
conducted
accordance
with
PRISMA
2020
Statement,
focusing
on
studies
published
between
2014
2024
sourced
from
Web
Science
Scopus,
resulting
97
selected
works.
review
highlights
potential
EVs,
not
only
as
transport
solutions
but
also
mobile
resources,
enhancing
flexibility
stability
through
vehicle-to-grid
(V2G)
systems.
It
importance
advanced
control
strategies,
such
Model
Predictive
Control
(MPC)
hybrid
AC/DC
microgrids,
improving
flow
management
operational
resilience.
Despite
these
advancements,
gaps
remain
comprehensive
ESS
particularly
regarding
interoperability
components
lack
optimization
frameworks
that
holistically
address
dynamic
pricing,
grid
stability,
integration.
synthesizes
existing
technologies
offers
insights
future
research
aimed
at
advancing
sustainability,
efficiency,
economic
viability
microgrids.
