Candidate:
Dario
Peralta
Moarry
Title:
Modeling
and
Operation
of
Ground
Source
Heat
Pumps
in
Electricity
Markets
Considering
Uncertainty
Date:
October
28,
2022
Time:
10:00
AM
Place:
REMOTE
ATTENDANCE
Supervisor(s):
Bhattacharya,
Kankar
-
Canizares,
Claudio
Abstract:
Ground
Source
Heat
Pump
(GSHP)
systems
have
grown
in
popularity
and
acceptance
worldwide
as
an
attractive
option
to
replace
conventional
Heating
Ventilation
and
Air
Conditioning
(HVAC)
technologies
due
to
their
capacity
to
provide
space
heating
and
cooling
in
buildings
and
houses.
Such
GSHP
systems
may
participate
as
a
price-taker
in
electricity
markets
through
a
load
aggregator
to
optimize
their
load
demand,
being
able
to
provide
grid
services,
such
as
load
shifting.
Therefore,
aggregated
GSHP
systems
have
the
potential,
if
properly
designed,
integrated,
and
applied,
to
yield
energy
and
carbon
savings
in
the
energy
market.
However,
the
integration
of
such
aggregated
GSHP
systems
brings
new
challenges
to
operators,
as
it
involves
uncertainties
on
ambient
temperature
and
electricity
price
forecasts,
which
can
be
highly
volatile
and
thus
impact
the
GSHP
system
operation
and
its
participation
in
electricity
markets.
From
a
detailed
literature
review
of
GSHP
applications
for
load
management
for
residential
users,
it
can
be
concluded
that
there
are
no
works
that
discuss
the
operational
performance
of
large-scale
GSHP
systems,
modeled
in
detail,
and
their
integration
in
electricity
markets;
additionally,
none
of
the
existing
works
have
considered
uncertainties
in
terms
of
ambient
temperature
and
electricity
price
forecasts
for
the
optimal
operation
of
aggregated
GSHP
systems.
After
a
comprehensive
review
of
the
relevant
background
related
to
GSHP
systems,
aggregator
strategies
in
the
electricity
market,
and
optimization
in
the
presence
of
uncertainties,
in
this
thesis,
a
detailed
mathematical
model
is
presented
of
a
GSHP
with
a
vertical
U-pipe
Ground
Heat
eXchanger
(GHX)
configuration
to
provide
residential
space
heating/cooling,
integrating
them
into
a
load
aggregator
model.
Based
on
this
model,
a
two-stage
operational
strategy
for
the
GSHP
price-taker
aggregator
participating
in
Day-Ahead
Market
(DAM)
and
Real-Time
Market
(RTM)
is
proposed,
to
determine
the
optimal
annual
heating/cooling
load
dispatch
to
control
the
temperatures
for
a
community
of
houses
that
minimizes
the
aggregator’s
cost.
Simulations
are
presented
then
of
an
aggregator’s
optimal
load
dispatch
with
a
conventional
HVAC
and
the
proposed
GSHP
alternative,
considering
comfort
maximization
vis-a-vis
minimization
of
electricity
costs,
and
showing
the
impact
of
each
objective
with
respect
to
the
dispatch
of
controllable
loads,
in-house
temperature,
and
total
procurement
costs.
Finally,
a
novel
model
based
on
Robust
Optimization
(RO)
is
proposed
and
developed,
considering
uncertainties
in
terms
of
the
DAM
and
RTM
electricity
prices
and
hourly
ambient
temperature
forecasts,
which
yields
an
optimum
schedule
that
protects
against
the
worst-case
scenario
for
a
given
level
of
conservatism.
The
RO
model
is
compared
and
validated
in
a
realistic
test
system
with
respect
to
Model
Predictive
Control
(MPC)
and
Monte
Carlo
Simulations
(MCS)
approaches
that
are
traditionally
used
to
manage
uncertainty.
It
is
shown
that
the
proposed
RO
approach
is
computationally
efficient
compared
to
the
MPC
and
MCS
approaches,
and
properly
accounts
for
the
considered
uncertainties,
demonstrating
the
advantage
of
the
presented
RO
technique
for
GSHP
dispatch
by
aggregators.
Friday, October 28, 2022 10:00 am
-
10:00 am
EDT (GMT -04:00)