Journal of Applied Data Sciences,
Journal Year:
2024,
Volume and Issue:
5(1), P. 122 - 132
Published: Jan. 29, 2024
This
research
examines
public
sentiment
regarding
electric
vehicle
incentives
through
analysis
of
online
comments.
These
include
tax
deductions
and
other
financial
rewards
offered
to
promote
the
adoption
vehicles.
In
this
study,
researchers
collected
analyzed
over
1,000
comments
from
various
platforms
understand
public's
perspective
on
these
incentives.
The
study
employs
Support
Vector
Machine
(SVM),
a
powerful
machine
learning
algorithm,
as
main
method
utilizes
Term
Frequency-Inverse
Document
Frequency
(TF-IDF)
analyze
comment
texts.
findings
depict
significant
variation
in
Approximately
57.3%
express
negative
towards
incentives,
while
33.2%
are
positive,
rest
neutral.
There
is
strong
support
for
particularly
standpoint.
However,
some
dissatisfaction
expressed,
especially
prices
charging
infrastructure
availability.
External
factors
such
government
policies
significantly
influence
sentiment.
Easy
access
also
plays
crucial
role
shaping
positive
Environmental
issues
contribute
view
Policy
recommendations
arising
emphasize
need
consider
when
designing
implementing
Improvement
efforts
pricing,
infrastructure,
environmental
education
can
help
enhance
society.
provides
valuable
insights
into
influencing
results
serve
foundation
better
decision-making
development
sustainable
environmentally
friendly
Journal of Renewable and Sustainable Energy,
Journal Year:
2025,
Volume and Issue:
17(1)
Published: Jan. 1, 2025
The
diverse
load
profile
formation
and
utility
preferences
of
multitype
electricity
users
challenge
real-time
pricing
(RTP)
welfare
equilibrium.
This
paper
designs
an
RTP
strategy
for
smart
grids.
On
the
demand
side,
it
constructs
functions
reflecting
user
characteristics
uses
multi-agents
different
interests.
Considering
industrial
users,
small-scale
microgrids,
distributed
generation,
battery
energy
storage
systems
are
included.
Based
on
supply
interest,
a
online
multi-agent
reinforcement
learning
(RL)
algorithm
is
proposed.
A
bi-level
stochastic
model
in
Markov
decision
process
framework
optimizes
strategy.
Through
information
exchange,
adaptive
scheme
balances
interest
achieves
optimal
strategies.
Simulation
results
confirm
effectiveness
proposed
method
peak
shaving
valley
filling.
Three
fluctuation
scenarios
compared,
showing
algorithm's
adaptability.
findings
reveal
potential
RL-based
resource
allocation
benefits
Innovations
modeling,
construction,
application
have
theoretical
practical
significance
market
research.
Renewable energy focus,
Journal Year:
2024,
Volume and Issue:
49, P. 100569 - 100569
Published: March 26, 2024
Indonesia
possesses
the
greatest
potential
in
world
for
bio-compressed
natural
gas
(bio-CNG).
This
bio-CNG
is
purified
from
biogas
that
generated
decomposing
organic
liquid
waste
at
palm
oil
mills
(POMs).
Unfortunately,
this
great
has
not
been
utilized
much
due
to
several
obstacles
such
as
remote
location
of
POMs,
transportation
mode,
and
utilization
purposes.
These
factors
have
a
significant
impact
on
feasibility
selling
prices.
However,
case
study
conducted
Riau
province
showed
by
clustering
selecting
appropriate
modes,
generating
additional
income
carbon
trading,
price
can
be
achieved.
The
resulted
obtaining
two
clusters
seven
POMs
were
spread
over
30km
radius
Pekanbaru
City.
Bio-CNG
substitute
97
%
gasoil
bus
fuel,
trucking
was
found
result
lowest
price,
which
10.7
USD/MMBTU.
With
minimum
reach
4.84
contributes
reducing
greenhouse
emissions,
which,
turn,
increase
economic
value
bio-CNG.
pattern
usage
selection
mentioned
above
should
considered
when
utilizing
resulting
biogas-POME
purification.
It
still
necessary
provide
supportive
policies
government.
Processes,
Journal Year:
2025,
Volume and Issue:
13(1), P. 109 - 109
Published: Jan. 3, 2025
While
tradable
green
certificates
(TGCs)
and
carbon
emission
trading
(CET)
play
key
roles
in
achieving
peak
neutrality,
the
coupling
effects
between
these
two
policies
on
medium-
long-term
electricity
market
spot
are
still
uncertain.
In
this
study,
we
firstly
construct
a
multi-scale
framework
to
sort
out
information
transfer
of
four
markets.
Secondly,
establish
system
dynamics-coupled
model
with
five
sub-modules,
including
power
markets,
market,
market.
Subsequently,
adjust
policy
parameters
(carbon
quota
benchmark
price,
auction
ratio,
renewable
energy
ratio)
set
up
scenarios
compare
analyze
impacts
CET
TGC
mechanisms
reduction
when
they
act
alone
or
synergy,
order
provide
theoretical
basis
for
adjustment
strategies
entities
setting
parameters.
The
results
show
that
can
increase
prices
promote
enter
while
TGCs
high
proportion
consumption
but
lower
long
time.
coordinated
implementation
improve
market’s
adaptability
penetration,
it
may
also
result
redundancy.
Energy Strategy Reviews,
Journal Year:
2024,
Volume and Issue:
54, P. 101426 - 101426
Published: May 30, 2024
The
collaborative
development
of
the
electricity
and
carbon
markets
can
reduce
transaction
costs,
stimulate
energy
conservation
emission
reductions,
accelerate
social
transition
to
low
carbon.
In
this
paper,
we
present
a
comprehensive
review
current
state
policy
challenges
electricity-carbon
trading
an
in-depth
analysis
key
research
directions
technical
details
collaboration
in
context
large-scale
access
renewable
energy.
Then,
by
constructing
multi-agent
behavioral
decision-making
model,
refine
mechanism
synergy,
simulated
market
clearing
outcomes
under
different
mechanisms,
proposed
policies
incentive
mechanisms
promote
trading.
Furthermore,
leveraging
massive
data
systems,
design
measurement
method
flow
tracking
for
low-carbon
companies,
achieving
real-time
accounting
power
system.
We
establish
simulation
model
markets,
enabling
accurate
prediction
day-ahead
intensity.
This
study
provides
new
framework
complete
route
coordination,
which
further
transformation
PLoS ONE,
Journal Year:
2024,
Volume and Issue:
19(6), P. e0304478 - e0304478
Published: June 13, 2024
In
the
context
of
evolving
landscape
reduction
in
carbon
emissions
and
integration
renewable
energy,
this
study
uses
system
dynamics
(SD)
modeling
to
explore
interconnected
trading
(CT),
tradable
green
certificate
(TGC)
trading,
electricity
markets.
Using
differential
equations
with
time
delays,
provides
a
comprehensive
analysis
structural
relationships
feedback
mechanisms
within
between
these
Key
findings
reveal
intricate
interplay
prices,
prices
under
various
coupling
mechanisms.
For
example,
three-market
mechanism,
stabilize
around
150
Yuan/ton,
while
reach
peak
0.45
Yuan/KWH,
impacting
which
fluctuate
0.33
1.09
Yuan
/
KWH
during
simulation
period.
These
quantitative
results
shed
light
on
nuanced
fluctuations
market
anticipated
purchases
sales
volumes
each
market.
The
insights
gleaned
from
offer
valuable
implications
for
policy
makers
stakeholders
navigating
complexities
emission
strategies,
energy
equilibrium.
By
understanding
multi-market
coupling,
can
better
formulate
policies
strategies
achieve
sustainable
transitions
mitigate
impacts
climate
change.
