Technologies,
Journal Year:
2024,
Volume and Issue:
12(11), P. 226 - 226
Published: Nov. 11, 2024
This
study
focuses
on
microgrid
systems
incorporating
hybrid
renewable
energy
sources
(HRESs)
with
battery
storage
(BES),
both
essential
for
ensuring
reliable
and
consistent
operation
in
off-grid
standalone
systems.
The
proposed
system
includes
solar
energy,
a
wind
source
synchronous
turbine,
BES.
Hybrid
particle
swarm
optimizer
(PSO)
genetic
algorithm
(GA)
combined
active
disturbance
rejection
control
(ADRC)
(PSO-GA-ADRC)
are
developed
to
regulate
the
frequency
amplitude
of
AC
bus
voltage
via
load-side
converter
(LSC)
under
various
operating
conditions.
approach
further
enables
efficient
management
accessible
generation
general
consumption
through
bidirectional
battery-side
(BSC).
Additionally,
method
also
enhances
power
quality
across
link
mentoring
photovoltaic
(PV)
inverter
function
as
shunt
filter
(SAPF),
providing
desired
harmonic-current
element
nonlinear
local
loads
well.
Equipped
an
extended
state
observer
(ESO),
PSO-GA-ADRC
provides
estimation
compensation
disturbances
such
modeling
errors
parameter
fluctuations,
stable
solution
interior
current
loops.
positive
results
from
hardware-in-the-loop
(HIL)
experimental
confirm
effectiveness
robustness
this
strategy
maintaining
real-world
scenarios.
Energy Reports,
Journal Year:
2024,
Volume and Issue:
11, P. 4192 - 4212
Published: April 11, 2024
To
address
the
intermittency
of
renewable
energy
sources,
global
warming,
and
increasing
load
demands,
this
paper
proposes
optimal
sizing
a
multi-energy
microgrid
(MEMG)
consisting
electrical,
thermal,
cooling,
hydrogen
networks.
The
system
integrates
storage
EVs
along
with
resilience
to
facilitate
robust
operation
scheme.
introduces
an
improved
backup
mechanism
for
using
hybrid
storage.
Then,
random
samples
stochastic
parameters
are
generated
Monte
Carlo
Simulations
(MCS).
end,
mixed-integer
linear
programming-based
model
is
developed
minimize
cost,
emissions,
shed.
CPLEX
solver
applied
solve
it
efficiently.
MEMG
network
reduces
cost
by
4%
emissions
40%.
case
study
validates
that
(BES-HST-TST)
can
effectively
reduce
electricity
grid
purchase
zero,
making
self-sufficient
while
yielding
least
annual
(ASC)
3855562$
decreasing
8385
kg,
resulting
in
economic
savings,
environmental
sustainability,
increased
utilization
renewables.
Notably,
V2G
save
0.7546%
extra
incurred
due
integration
significantly
CO2
4%.
A
novel
finding
proposed
keeping
retaining
31.46%
form.
It
mitigates
risk
associated
power
outages
achieving
ASC
3858933$
8043
kg.
These
results
help
realize
green,
cost-effective,
efficient
system.
Applied Sciences,
Journal Year:
2025,
Volume and Issue:
15(4), P. 1744 - 1744
Published: Feb. 8, 2025
The
growing
need
for
sustainable
energy
solutions
has
propelled
the
development
of
Hybrid
Renewable
Energy
Systems
(HRESs),
which
integrate
diverse
renewable
sources
like
solar,
wind,
biomass,
geothermal,
hydropower
and
tidal.
This
review
paper
focuses
on
balancing
economic,
environmental,
social
technical
criteria
to
enhance
system
performance
resilience.
Using
comprehensive
methodologies,
examines
state-of-the-art
algorithms
such
as
Multi-Objective
Particle
Swarm
Optimization
(MOPSO)
Non-Dominated
Sorting
Genetic
Algorithm
II
(NSGA-II),
alongside
Crow
Search
(CSA),
Grey
Wolf
Optimizer
(GWO),
Levy
Flight-Salp
(LF-SSA),
Mixed-Integer
Linear
Programming
(MILP)
tools
HOMER
Pro
3.12–3.16
MATLAB
9.1–9.13,
have
been
instrumental
in
optimizing
HRESs.
Key
findings
highlight
role
advanced,
multi-energy
storage
technologies
stabilizing
HRESs
addressing
intermittency
sources.
Moreover,
integration
metaheuristic
with
machine
learning
enabled
dynamic
adaptability
predictive
optimization,
paving
way
real-time
management.
HRES
configurations
cost-effectiveness,
environmental
sustainability,
operational
reliability
while
also
emphasizing
transformative
potential
emerging
quantum
computing
are
underscored.
provides
critical
insights
into
evolving
landscape
offering
actionable
recommendations
future
research
practical
applications
achieving
global
sustainability
goals.
