IOP Conference Series Earth and Environmental Science,
Journal Year:
2024,
Volume and Issue:
1415(1), P. 011001 - 011001
Published: Dec. 1, 2024
This
paper
presents
an
overview
of
the
5th
International
Conference
on
Sustainable
Futures:
Environmental,
Technological,
Social,
and
Economic
Matters
(ICSF
2024),
held
in
May
2024.
The
conference
brought
together
over
250
researchers,
practitioners,
educators
from
19
countries
to
share
cutting-edge
research
innovative
solutions
across
a
wide
range
sustainability-related
disciplines.
proceedings
cover
diverse
topics,
including
climate
change,
disaster
risk
reduction,
sustainable
infrastructure,
education
for
sustainability,
environmental
engineering,
business
practices.
Key
themes
that
emerged
include
integration
digital
technologies
sustainability
efforts,
impacts
global
crises
development,
importance
interdisciplinary
approaches.
showcased
both
theoretical
advancements
practical
applications,
with
particular
focus
addressing
United
Nations
Development
Goals.
highlights
conference’s
role
fostering
dialogue
collaboration
address
pressing
challenges
shape
more
future.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: May 13, 2024
Abstract
This
paper
explores
scenarios
for
powering
rural
areas
in
Gaita
Selassie
with
renewable
energy
plants,
aiming
to
reduce
system
costs
by
optimizing
component
numbers
meet
demands.
Various
scenarios,
such
as
combining
solar
photovoltaic
(PV)
pumped
hydro-energy
storage
(PHES),
utilizing
wind
PHES,
and
integrating
a
hybrid
of
PV,
wind,
have
been
evaluated
based
on
diverse
criteria,
encompassing
financial
aspects
reliability.
To
achieve
the
results,
meta-heuristics
Multiobjective
Gray
wolf
optimization
algorithm
(MOGWO)
Grasshopper
(MOGOA)
were
applied
using
MATLAB
software.
Moreover,
optimal
sizing
has
investigated
real-time
assessment
data
meteorological
from
Sillasie,
Ethiopia.
Metaheuristic
techniques
employed
pinpoint
most
favorable
loss
power
supply
probability
(LPSP)
least
cost
(COE)
total
life
cycle
(TLCC)
system,
all
while
meeting
operational
requirements
various
scenarios.
The
Multi-Objective
Grey
Wolf
Optimization
technique
outperformed
Algorithm
problem,
suggested
results.
Furthermore,
MOGWO
findings,
PV-Wind-PHES
demonstrated
lowest
COE
(0.126€/kWh)
TLCC
(€6,897,300),
along
satisfaction
village's
demand
LPSP
value.
In
PV-Wind-PHSS
scenario,
are
38%,
18%,
2%,
1.5%
lower
than
those
Wind-PHS
PV-PHSS
at
0%,
according
Overall,
this
research
contributes
valuable
insights
into
design
implementation
sustainable
solutions
remote
communities,
paving
way
enhanced
access
environmental
sustainability.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Jan. 3, 2025
The
maximum
power
delivered
by
a
photovoltaic
system
is
greatly
influenced
atmospheric
conditions
such
as
irradiation
and
temperature
surrounding
objects
like
trees,
raindrops,
tall
buildings,
animal
droppings,
clouds.
partial
shading
caused
these
the
rapidly
changing
parameters
make
point
tracking
(MPPT)
challenging.
This
paper
proposes
hybrid
MPPT
algorithm
that
combines
benefits
of
salp
swarm
(SSA)
hill
climbing
(HC)
techniques.
As
long
rate
change
irradiance
does
not
exceed
specific
limit,
HC
mode
applied
to
track
global
(GMPP).
Once
high
in
detected,
SSA
activated.
Moreover,
proposed
employs
concept
boundary
handle
fast
slow
fluctuating
patterns.
A
comprehensive
comparative
evaluation
SSA-HC
with
state-of-the-art
algorithms
has
been
undertaken.
Four
distinct
cases
have
examined,
including
varying
rates
conditions.
validated
tested
using
developed
hardware
setup,
simulated
MATLAB
for
solar
(PV)
systems,
compared
standard
HC.
performance
capability
this
technique
at
both
steady-state
dynamic
under
rapid
gradual
changes
demonstrates
its
superiority
over
recent
algorithms.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Aug. 7, 2024
Maximum
power
point
tracking
(MPPT)
is
a
technique
involved
in
photovoltaic
(PV)
systems
for
optimizing
the
output
of
solar
panels.
Traditional
solutions
like
perturb
and
observe
(P&O)
Incremental
Conductance
(IC)
are
commonly
utilized
to
follow
MPP
under
various
environmental
circumstances.
However,
these
algorithms
suffer
from
slow
speed
low
dynamics
fast-changing
environment
conditions.
To
cope
with
demerits,
data-driven
artificial
neural
network
(ANN)
algorithm
MPPT
proposed
this
paper.
By
leveraging
learning
capabilities
ANN,
PV
operating
can
be
adapted
dynamic
changes
irradiation
temperature.
Consequently,
it
offers
promising
environments
as
well
overcoming
limitations
traditional
techniques.
In
paper,
simulations
verification
experimental
validation
ANN-MPPT
presented.
Additionally,
analyzed
compared
methods.
The
numerical
findings
indicate
that,
examined
methods,
approach
achieves
highest
efficiency
at
98.16%
shortest
time
1.3
s.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: July 8, 2024
Abstract
The
use
of
plug-in
hybrid
electric
vehicles
(PHEVs)
provides
a
way
to
address
energy
and
environmental
issues.
Integrating
large
number
PHEVs
with
advanced
control
storage
capabilities
can
enhance
the
flexibility
distribution
grid.
This
study
proposes
an
innovative
management
strategy
(EMS)
using
Iterative
map-based
self-adaptive
crystal
structure
algorithm
(SaCryStAl)
specifically
designed
for
microgrids
renewable
sources
(RESs)
PHEVs.
goal
is
optimize
multi-objective
scheduling
microgrid
wind
turbines,
micro-turbines,
fuel
cells,
solar
photovoltaic
systems,
batteries
balance
power
store
excess
energy.
aim
minimize
operating
costs
while
considering
impacts.
optimization
problem
framed
as
nonlinear
constraints,
fuzzy
logic
aid
decision-making.
In
first
scenario,
optimized
all
RESs
installed
within
predetermined
boundaries,
in
addition
grid
connection.
second
operates
turbine
at
rated
power.
third
case
involves
integrating
into
three
charging
modes:
coordinated,
smart,
uncoordinated,
utilizing
standard
RES
SaCryStAl
showed
superior
performance
operation
cost,
emissions,
execution
time
compared
traditional
CryStAl
other
recent
methods.
proposed
achieved
optimal
solutions
scenario
cost
emissions
177.29
€ct
469.92
kg,
respectively,
reasonable
frame.
it
yielded
values
112.02
196.15
respectively.
Lastly,
achieves
319.9301
€ct,
160.9827
128.2815
uncoordinated
charging,
coordinated
smart
modes
Optimization
results
reveal
that
outperformed
evolutionary
algorithms,
such
differential
evolution,
CryStAl,
Grey
Wolf
Optimizer,
particle
swarm
optimization,
genetic
algorithm,
confirmed
through
test
cases.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Jan. 25, 2025
This
study
first
proposes
an
innovative
method
for
optimizing
the
maximum
power
extraction
from
photovoltaic
(PV)
systems
during
dynamic
and
static
environmental
conditions
(DSEC)
by
applying
horse
herd
optimization
algorithm
(HHOA).
The
HHOA
is
a
bio-inspired
technique
that
mimics
motion
cycles
of
entire
horses.
Next,
linear
active
disturbance
rejection
control
(LADRC)
was
applied
to
monitor
HHOA's
reference
voltage
output.
LADRC,
known
managing
uncertainties
disturbances,
improves
anti-interference
capacity
point
tracking
(MPPT)
speeds
up
system's
response
rate.
Then,
in
comparison
traditional
(perturb
&
observe;
P&O)
metaheuristic
algorithms
(conventional
particle
swarm
optimization;
CPSO,
grasshopper
GHO,
deterministic
PSO;
DPSO)
through
DSEC,
simulations
results
demonstrate
combination
HHOA-LADRC
can
successfully
track
global
peak
(GMP)
with
less
fluctuations
quicker
convergence
time.
Finally,
experimental
investigation
proposed
accomplished
NI
PXIE-1071
Hardware-In-Loop
(HIL)
prototype.
output
findings
show
effectiveness
provided
may
approach
value
higher
than
99%,
showed
rate
converging
oscillations
detection
mechanism.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: June 17, 2024
Abstract
This
study
looks
into
how
to
make
proton
exchange
membrane
(PEM)
fuel
cells
work
more
efficiently
in
environments
that
change
over
time
using
new
Maximum
Power
Point
Tracking
(MPPT)
methods.
We
evaluate
the
efficacy
of
Flying
Squirrel
Search
Optimization
(FSSO)
and
Cuckoo
(CS)
algorithms
adapting
varying
conditions,
including
fluctuations
pressure
temperature.
Through
meticulous
simulations
analyses,
explores
collaborative
integration
these
techniques
with
boost
converters
enhance
reliability
productivity.
It
was
found
FSSO
consistently
works
better
than
CS,
achieving
an
average
increase
12.5%
power
extraction
from
PEM
a
variety
operational
situations.
Additionally,
exhibits
superior
adaptability
convergence
speed,
maximum
point
(MPP)
25%
faster
CS.
These
findings
underscore
substantial
potential
as
robust
efficient
MPPT
method
for
optimizing
cell
systems.
The
contributes
quantitative
insights
advancing
green
energy
solutions
suggests
avenues
future
exploration
hybrid
optimization
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: July 5, 2024
Abstract
This
study
examined
the
optimal
size
of
an
autonomous
hybrid
renewable
energy
system
(HRES)
for
a
residential
application
in
Buea,
located
southwest
region
Cameroon.
Two
systems,
PV-Battery
and
PV-Battery-Diesel,
have
been
evaluated
order
to
determine
which
was
better
option.
The
goal
this
research
propose
dependable,
low-cost
power
source
as
alternative
unreliable
highly
unstable
electricity
grid
Buea.
decision
criterion
proposed
HRES
cost
(COE),
while
system’s
dependability
constraint
loss
supply
probability
(LPSP).
crayfish
optimization
algorithm
(COA)
used
optimize
component
sizes
HRES,
results
were
contrasted
those
obtained
from
whale
(WOA),
sine
cosine
(SCA),
grasshopper
(GOA).
MATLAB
software
model
components,
criteria,
constraints
single-objective
problem.
after
simulation
LPSP
less
than
1%
showed
that
COA
outperformed
other
three
techniques,
regardless
configuration.
Indeed,
COE
using
0.06%,
0.12%,
lower
provided
by
WOA,
SCA,
GOA
algorithms,
respectively,
Likewise,
PV-Battery-Diesel
configuration,
0.065%,
0.13%,
0.39%
respectively.
A
comparative
analysis
outcomes
two
configurations
indicated
configuration
exhibited
4.32%
comparison
Finally,
impact
reduction
on
assessed
decrease
resulted
increase
owing
nominal
capacity
diesel
generator.
