E3S Web of Conferences,
Journal Year:
2025,
Volume and Issue:
619, P. 01004 - 01004
Published: Jan. 1, 2025
The
growing
demand
for
electric
vehicles
(EVs)
presents
significant
challenges
urban
charging
infrastructure,
particularly
in
balancing
user
demand,
operational
efficiency,
and
grid
stability.
This
study
applies
non-cooperative
cooperative
game
theory
models
to
analyze
the
interactions
between
EV
users,
station
operators,
managers.
model
shows
that
self-interested
behavior
leads
congestion
at
high-demand
stations,
inefficient
pricing
dynamics,
overloads
during
peak
hours.
Stakeholders
reach
a
Nash
Equilibrium,
but
resulting
system
inefficiencies—uneven
utilization
high
loads—highlight
need
coordinated
strategies.
In
contrast,
fosters
collaboration
among
stakeholders,
leading
improvements
performance.
Through
dynamic
strategies
off-peak
incentives,
achieves
more
balanced
across
stations
ensures
stability
by
preventing
peak-hour
overloads.
Simulations
demonstrate
this
approach
reduces
stabilizes
while
maintaining
loads
well
below
maximum
capacity.
research
underscores
value
of
creating
sustainable
scalable
network.
Key
include
data
sharing,
stakeholder
alignment,
adjustment.
Addressing
these
issues
will
be
essential
widespread
implementation
systems.
Future
should
focus
on
real-world
trials
policy
development
support
large-scale
adoption
solutions.
Energy Strategy Reviews,
Journal Year:
2024,
Volume and Issue:
54, P. 101471 - 101471
Published: June 29, 2024
Electrification
of
transport
industry
in
China
presents
several
new
prospects
to
fulfil
requirements,
which
are
necessary
encounter
increasing
issues
energy
security,
air
quality,
and
lower
the
dependence
on
fossil
fuels.
The
Chinese
government
is
paying
significant
attention
EV
market
penetration
consumer
adoption
through
numerous
demonstration
programs/plans
with
attractive
transportation
policies.
In
this
study,
key
factors
included
barriers
policies
comprehensively
reviewed,
can
enhance
intention
adopt
EVs.
This
research
study
extensively
demonstrates
positive
impact
two
distinguished
types
including
financial
preferential
consumer's
EVs
by
implementing
an
extended
improved
version
Theory
Planned
behavior
(TPB).
A
case
Shanghai
performed
survey
314
respondents,
further
evaluated
structural
equation
modelling
(SEM)
assess
aspects
adoption.
particular,
construct
items
TPB
attitude,
subjective
norm
(SN),
perceived
behavioral
control
(PBC)
investigated
detail
present
their
joint
purchasing
consumers.
confirmatory
factor
analysis
(CFA)
AMOS
for
assessment
findings.
findings
from
reveal
that
have
a
considerable
towards
shaping
attitude
consumers
significantly
related
However,
Shanghainese,
more
positively
associated
purchase
comparison
Consequently,
playing
crucial
role
controlling
China.
principal
policy
suggestions
various
provide
multifaceted
perceptions
stakeholders
envision
electrified
transportation.
World Electric Vehicle Journal,
Journal Year:
2024,
Volume and Issue:
15(7), P. 324 - 324
Published: July 21, 2024
The
rapid
proliferation
of
electric
vehicles
(EVs)
presents
both
opportunities
and
challenges
for
the
electrical
grid.
While
EVs
offer
a
promising
avenue
reducing
greenhouse
gas
emissions
dependence
on
fossil
fuels,
their
uncoordinated
charging
behavior
can
strain
grid
infrastructure,
thus
creating
new
operators
EV
owners
equally.
nature
vehicle
may
lead
to
emergence
peak
loads.
Grid
typically
plan
demand
periods
deploy
resources
accordingly
ensure
stability.
Uncoordinated
introduce
unpredictability
variability
into
load
patterns,
making
it
more
challenging
manage
loads
effectively.
This
paper
examines
implications
address
this
challenge
proposes
novel
dynamic
optimization
algorithm
tailored
schedules
efficiently,
mitigating
power
peaks
while
ensuring
user
satisfaction
requirements.
proposed
“Proof
Need”
(PoN)
aims
schedule
based
collected
data
such
as
state
charge
(SoC)
EV’s
battery,
charger
power,
number
connected
per
household,
end-user’s
preferences,
local
distribution
substation’s
capacity.
PoN
calculates
priority
index
each
coordinates
all
at
times
in
way
that
does
not
exceed
maximum
allocated
was
tested
under
different
scenarios,
results
comparison
between
an
baseline
scenario
coordinated
model,
proving
efficiency
our
algorithm,
by
40.8%
with
no
impact
overall
total
time.
