29-12-2012, 05:50 PM
Model Predictive Control for the Operation of Building Cooling Systems
1Model Predictive Control.pdf (Size: 887.96 KB / Downloads: 36)
Abstract
This brief presents a model-based predictive control
(MPC) approach to building cooling systems with thermal energy
storage. We focus on buildings equipped with a water tank used
for actively storing cold water produced by a series of chillers.
First, simplified models of chillers, cooling towers, thermal storage
tanks, and buildings are developed and validated for the purpose of
model-based control design. Then an MPC for the chilling system
operation is proposed to optimally store the thermal energy in the
tank by using predictive knowledge of building loads and weather
conditions. This brief addresses real-time implementation and
feasibility issues of the MPC scheme by using a simplified hybrid
model of the system, a periodic robust invariant set as terminal
constraints, and a moving window blocking strategy. The controller
is experimentally validated at the University of California,
Merced. The experiments show a reduction in the central plant
electricity cost and an improvement of its efficiency.
INTRODUCTION
THE building sector consumes about 40% of the energy
used in the United States and is responsible for nearly 40%
of greenhouse gas emissions. It is therefore economically, socially
and environmentally significant to reduce the energy consumption
of buildings. For a wide range of innovative heating
and cooling systems, their enhanced efficiency depends on the
active storage of thermal energy.
This brief focuses on the modeling, control design and real
time implementation of the thermal energy storage on the
campus of the University of California, Merced. The campus
cooling system consists of a chiller plant (three chillers redundantly
configured as two in series, one backup in parallel), an
array of cooling towers, a 7000 m thermal energy storage tank,
a primary distribution system and secondary distribution loops
serving each building of the campus. The two series chillers
are operated each night to recharge the storage tank which
meets campus cooling demand the following day.
SYSTEM MODEL
Fig. 1 shows the main components of the UC Merced
Campus used to generate, store and distribute thermal energy.
The system consists of a condenser loop, a primary loop, a
secondary (campus) loop, and several tertiary (building) loops.
The chilled water is generated via chillers and cooling towers
within the primary and condenser loops. The chilled water is
stored in a stratified thermal energy storage tank, and distributed
to the buildings throughout the campus via the secondary loop.
Internal building loops use pumps and valves to distribute the
chilled water to the fan coils and air handling units (AHUs)
that deliver cold air to the thermal zones. The chilled water is
warmed by the air-side load of the buildings and returned to
the secondary loop.
Terminal Constraints
It is well known that stability and feasibility are not ensured
by the MPC law without terminal cost and terminal constraints
[16]. Usually the problem is augmented with a terminal cost and
a terminal constraint set . Typically is a robust control
invariant set [16]. A robust control invariant set enjoys the
following property: if the system initial state belongs to the set
, then the system can be controlled to be in at all future time
instants and for all admissible disturbances. It is well known that
by using a robust control invariant terminal set , the persistent
feasibility of the MPC strategy is guaranteed (i.e., if Problem
(10) is feasible for a given , then it is feasible for all
). Definitions and properties of invariant set can be found in
[1], [16]. A treatment of sufficient conditions which guarantees
persistent feasibility of MPC problems goes beyond the scope
of this work and can be found in the survey [16].
EXPERIMENTAL SETUP
The MPC controller outlined in Section III has been implemented
at UC Merced. The detailed experimental setup is described
below. The MPC controller computes the set points for
cooling towers, chillers and the thermal storage tank at the central
plant. Because of lower level control loops, the closed loop
system indirectly affects all the components of the campus including
the pumps and fan coils of the distribution system.
The MPC algorithm is implemented in MATLAB and running
in real-time on a Pentium 4 Intel processor. The average computational
time for solving an optimization problem was 20 min
which ensured real-time implementation with the chosen 1-hr
sampling time. The MPC algorithm receives and sends data to
the campus through the building automation system “Automated
Logics Web Control” (ALC) system.
CONCLUSION
We presented the development of a model-based multi-variable
controller for building cooling systems equipped with
thermal energy storage by using prediction of weather conditions
and buildings loads. We have been focusing on the
architecture of the UC, Merced campus and shown that a simplified
hybrid model can be used to predict the main behavior of
the overall system. An MPC has been designed to optimize the
scheduling and operation of the central plant to achieve lower
electricity cost and better performance. Two main conclusions
can be drawn from the experimental results. First, our study
has enabled a 19.1% improvement of the plant COP compared
to the original baseline logic. Second, the scheme has been
used to confirm that some of the control profiles chosen by
the operators and plant managers are very close to the control
profiles suggested by MPC.