Candidate:
Carlos
Ceja
Espinosa
Title:
Energy
Management
Systems
for
Multi-Microgrid
Networks
Under
Uncertainties
Date:
June
8,
2023
Time:
1:00
PM
Place:
EIT
3142
Supervisor(s):
Canizares,
Claudio
-
Pirnia,
Mehrdad
Abstract:
Environmental
concerns
have
motivated
a
gradual
transformation
of
power
systems
in
recent
years,
mainly
focused
on
replacing
fossil
fuel-based
energy
sources
with
Renewable
Energy
Sources
(RESs)
such
as
solar
and
wind
energy.
However,
due
to
their
variable
nature,
the
large-scale
integration
of
RESs
poses
several
technical
challenges
for
the
safe
and
efficient
operation
of
evolving
power
systems.
The
adoption
of
microgrids
(MGs)
has
increased
as
a
viable
option
to
effectively
integrate
RESs
into
existing
grids
and
reduce
the
dependency
on
conventional,
centralized
power
stations,
as
well
as
enhancing
the
electrical
supply
resiliency.
Furthermore,
MGs
can
provide
sustainable
energy
to
remote
areas
in
which
a
connection
to
the
main
power
grid
is
not
possible.
In
this
context,
the
Energy
Management
System
(EMS)
of
the
MG,
which
is
responsible
for
determining
its
optimal
operation,
is
an
important
part
of
MG
control.
However,
the
variability
of
electricity
demand
and
RESs
within
an
MG
complicates
the
adequate
dispatch
of
the
MG
resources
to
maintain
supply-demand
balance.
Hence,
uncertainties
inherent
to
an
MG
must
be
taken
into
account,
which
is
one
of
the
main
topics
of
this
thesis.
The
coordinated
operation
of
multiple
MGs
as
a
multi-microgrid
(MMG)
system
has
recently
attracted
attention
due
to
the
potential
benefits
that
originate
from
a
coordinated
operation,
as
opposed
to
the
individual
and
independent
operation
of
each
MG.
The
collective
operation
enables
the
possibility
of
power
exchanges
among
MGs
and
the
main
grid,
which
can
mitigate
the
unpredictability
of
RESs,
as
well
as
reduce
the
operational
costs
by
taking
advantage
of
the
heterogeneity
of
load
and
generation
profiles
in
each
MG.
Furthermore,
differences
in
generation
costs
and
grid
buying/
selling
prices
can
incentivize
power
exchanges
and
ensure
the
maximum
utilization
of
RESs.
Therefore,
it
is
important
to
design
EMSs
that
adequately
consider
the
collective
operation
of
a
set
of
MGs
while
taking
uncertainties
into
account,
which
is
the
primary
focus
of
this
thesis.
In
the
first
part
of
this
thesis,
a
centralized
MMG
EMS
model
is
proposed,
which
is
formulated
as
a
cost
minimization
problem
that
considers
the
operation
of
all
MGs
and
their
interactions
among
each
other
and
the
main
grid
as
a
single
system.
The
model
includes
detailed
operational
constraints
of
thermal
generation
units
and
Energy
Storage
Systems
(ESSs),
as
well
as
power
capacity
limits
at
the
Point
of
Common
Coupling
(PCC)
of
each
MG.
A
decomposition
procedure
based
on
Lagrangian
relaxation
is
then
applied,
with
the
goal
of
separating
the
complete
problem
into
subproblems
corresponding
to
each
MG,
which
can
be
solved
independently
with
minimal
information
exchange
through
a
subgradient-based
distributed
optimization
algorithm.
Demand
and
solar
irradiance
data
from
a
realistic
Active
Distribution
Network
(ADN)
in
Sao
Paulo,
Brazil,
are
then
used
to
design
a
system
to
test
and
validate
the
proposed
models.
The
simulation
results
show
that
the
distributed
algorithm
converges
to
the
optimal
or
a
near-optimal
solution
of
the
centralized
model,
making
the
proposed
approach
a
viable
alternative
for
the
implementation
of
a
distributed
MMG
EMS.
Furthermore,
the
advantages
of
an
MMG
system
are
demonstrated
by
showing
that
the
operational
costs
of
the
system
are
significantly
reduced
when
MGs
are
able
to
exchange
power
among
each
other
and
with
the
main
grid,
compared
to
their
costs
in
individual
operation.
In
the
second
part
of
this
thesis,
the
proposed
centralized
MMG
EMS
model
is
reformulated
using
an
Affine
Arithmetic
(AA)
optimization
framework
to
consider
uncertainties
associated
with
electricity
demand
and
renewable
generation.
First,
the
uncertainties
are
characterized
by
their
affine
forms,
which
are
then
used
to
redefine
the
variables,
objective
function,
and
constraints
of
the
original
model
in
the
AA
domain.
Then,
the
linearization
procedure
of
the
absolute
values
introduced
by
the
AA
operators
is
explained
in
detail.
The
proposed
AA
model
is
validated
through
comparisons
with
the
deterministic
and
Monte
Carlo
Simulation
(MCS)
solutions.
The
test
system
used
in
the
aforementioned
MMG
distributed
dispatch
approach
is
utilized
to
show
that
the
AA
model
is
robust
under
a
range
of
possible
realizations
of
the
uncertain
parameters,
and
can
be
solved
with
lower
computational
burden
and
in
shorter
execution
times
with
respect
to
a
MCS
approach,
while
considering
the
same
range
of
uncertainties,
which
is
one
the
main
advantages
of
the
proposed
AA
model.
Furthermore,
it
is
demonstrated
that
the
affine
forms
of
the
solution
variables
can
be
used
to
find
a
dispatch
for
different
realizations
of
demand
and
renewable
generation,
with
no
need
to
repeatedly
solve
the
optimization
problem.
Thursday, June 8, 2023 1:00 pm
-
1:00 pm
EDT (GMT -04:00)