Evolutionary game-theoretical approaches for long-term strategic bidding among diverse stakeholders in large-scale and local power markets: Basic concept, modelling review, and future vision
International Journal of Electrical Power & Energy Systems,
Год журнала:
2025,
Номер
166, С. 110589 - 110589
Опубликована: Март 9, 2025
Язык: Английский
Carbon emissions reduction in shipping based on four-party evolutionary game
Frontiers in Marine Science,
Год журнала:
2025,
Номер
12
Опубликована: Фев. 4, 2025
In
order
to
realize
a
win-win
situation
between
economic
development
and
environmental
benefits,
this
paper
constructs
four-party
evolutionary
game
model
including
the
government,
two
homogeneous
ports
shipping
companies
based
on
theory.
By
calculating
payoff
matrices
of
four
parties
replicating
dynamic
equations,
according
Jacobi
matrix,
we
study
discuss
possible
stabilization
points
under
five
different
scenarios.
The
is
simulated
using
MATLAB
relevant
parameters
are
selected
for
sensitivity
analysis.
results
show
that
benefits
maximized
when
government
does
not
implement
policy
port
use
shore
electricty
system
(i.e.,
stability
point
E12
(0,1,1,1)).
Meanwhile,
by
analyzing
size
sensitivity,
t=1.116,
large-scale
evolution
tends
0,
while
small-scale
fluctuates
up
down,
which
leads
conclusion
have
more
potential
able
gain
faster.
This
provides
theoretical
support
implementation
systems,
pointing
out
key
role
in
promoting
electricty.
It
reference
effectively
context
carbon
emission
reduction,
especially
important
helps
maximize
operations.
Язык: Английский
Generation of typical scenarios for distribution networks in planning stage considering photovoltaic and load growth characteristics
Frontiers in Energy Research,
Год журнала:
2025,
Номер
13
Опубликована: Март 25, 2025
With
the
increasing
integration
of
distributed
rooftop
photovoltaic
(PV)
systems
into
distribution
networks,
traditional
scenario
generation
methods
based
solely
on
historical
PV
data
have
become
inadequate.
This
paper
proposes
a
planning-stage
method
to
address
challenges
high-penetration
integration.
The
combines
Conditional
Generative
Adversarial
Networks
(CGAN)
with
an
improved
Bass
model
estimate
new
capacity.
Load
scenarios
are
constructed
by
analyzing
regional
load
growth
patterns.
Typical
weather
days
classified
using
Spearman’s
rank
correlation
coefficient
form
joint
PV-load
scenarios,
which
then
reduced
k-means
clustering.
study
compares
multi-scenario
energy
storage
configuration
schemes
considering
those
only
predictions.
Results
demonstrate
that
generated
align
well
future
actual
operating
scenarios.
Furthermore,
scheme
outperforms
predictions,
indicating
proposed
method’s
effectiveness
in
addressing
network
planning.
Язык: Английский
Evolutionary Game Theory-Based Analysis of Power Producers’ Carbon Emission Reduction Strategies and Multi-Group Bidding Dynamics in the Low-Carbon Electricity Market
Processes,
Год журнала:
2025,
Номер
13(4), С. 952 - 952
Опубликована: Март 23, 2025
China’s
power
generation
system
has
undergone
reforms,
leading
to
a
competitive
electricity
market
where
independent
producers
participate
through
bidding.
With
the
rise
of
low-carbon
policies,
must
optimize
bidding
strategies
while
reducing
carbon
emissions,
creating
complex
interactions
with
local
governments.
Evolutionary
game
theory
(EGT)
is
well-suited
analyze
these
dynamics.
This
study
begins
by
summarizing
fundamental
concepts
trading
markets,
including
transaction
models,
mechanisms,
and
reduction
strategies.
Existing
research
on
application
evolutionary
in
markets
reviewed,
focus
theoretical
constructs
such
as
stable
replicator
Based
this
foundation,
conducts
detailed
mathematical
analysis
symmetric
asymmetric
two-group
models
general
scenarios.
Building
upon
three-group
framework
developed
within
producer
groups
between
regulators
under
mechanisms.
A
core
innovation
incorporation
case
based
market,
which
examines
dynamics
governments
regarding
includes
analyzing
how
regulatory
incentives,
market-clearing
prices,
demand-side
factors
influence
producers’
emission
behaviors.
The
also
provides
for
small,
medium,
large
producers,
revealing
significant
impact
pricing
prices
strategic
decision-making.
Specifically,
finds
that
small
tend
adopt
more
conservative
strategies,
aligning
closely
take
advantage
economies
scale,
adjusting
their
at
higher
capacities.
explores
conditions
achieve
equilibrium,
well
implications
equilibria
both
efficiency
environmental
sustainability.
reveals
integrating
into
significantly
impacts
behaviors
long-term
stability,
especially
governmental
penalties
incentives.
findings
provide
actionable
insights
policymakers,
contributing
advancement
theories
supporting
global
transition
sustainable
energy
systems.
Язык: Английский
Integrating Evolutionary Game-Theoretical Methods and Deep Reinforcement Learning for Adaptive Strategy Optimization in User-Side Electricity Markets: A Comprehensive Review
Mathematics,
Год журнала:
2024,
Номер
12(20), С. 3241 - 3241
Опубликована: Окт. 16, 2024
With
the
rapid
development
of
smart
grids,
strategic
behavior
evolution
in
user-side
electricity
market
transactions
has
become
increasingly
complex.
To
explore
dynamic
mechanisms
this
area,
paper
systematically
reviews
application
evolutionary
game
theory
markets,
focusing
on
its
unique
advantages
modeling
multi-agent
interactions
and
strategy
optimization.
While
excels
explaining
formation
long-term
stable
strategies,
it
faces
limitations
when
dealing
with
real-time
changes
high-dimensional
state
spaces.
Thus,
further
investigates
integration
deep
reinforcement
learning,
particularly
Q-learning
network
(DQN),
theory,
aiming
to
enhance
adaptability
applications.
The
introduction
DQN
enables
participants
perform
adaptive
optimization
rapidly
changing
environments,
thereby
more
effectively
responding
supply–demand
fluctuations
markets.
Through
simulations
based
a
model,
study
reveals
characteristics
under
different
conditions,
highlighting
interaction
patterns
among
complex
environments.
In
summary,
comprehensive
review
not
only
demonstrates
broad
applicability
markets
but
also
extends
potential
decision
making
through
modern
algorithms,
providing
new
theoretical
foundations
practical
insights
for
future
policy
formulation.
Язык: Английский