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Research Papers

Optimal Setpoints for HVAC Systems via Iterative Cooperative Neighbor Communication

[+] Author and Article Information
Matthew Elliott

Stingray Offshore Solutions,
8901 Jameel Rd., Ste 100,
Houston, TX 77040
e-mail: matt.elliott@gmail.com

Bryan P. Rasmussen

Associate Professor
Mechanical Engineering,
Texas A&M University,
3123 TAMU,
College Station, TX 77843-3123
e-mail: brasmussen@tamu.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received July 31, 2013; final manuscript received June 7, 2014; published online August 28, 2014. Assoc. Editor: Prashant Mehta.

J. Dyn. Sys., Meas., Control 137(1), 011006 (Aug 28, 2014) (15 pages) Paper No: DS-13-1295; doi: 10.1115/1.4027887 History: Received July 31, 2013; Revised June 07, 2014

Heating, ventilation, and air conditioning systems in large buildings frequently feature a network topology wherein the outputs of each dynamic subsystem act as disturbances to other subsystems. The distributed optimization technique presented in this paper leverages this topology without requiring a centralized controller or widespread knowledge of the interaction dynamics between subsystems. Each subsystem's controller calculates an optimal steady state condition. The output corresponding to this condition is then communicated to downstream neighbors only. Similarly, each subsystem communicates to its upstream neighbors the predicted costs imposed by the neighbors' own calculated outputs. By judicious construction of the cost functions, all of the cost information is propagated through the network, allowing a Pareto optimal solution to be reached. The novelty of this approach is that communication between all plants is not necessary to achieve a global optimum. Since each optimizer does not require knowledge of its neighbors' dynamics, changes in one controller do not require changes to all controllers in the network. Proofs of convergence to Pareto optimality under certain conditions are presented, and convergence under the approach is demonstrated with a simulation example. The approach is also applied to a laboratory-based water chiller system; several experiments demonstrate the features of the approach and potential for energy savings.

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References

Figures

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Fig. 1

Simplified schematic of a typical building HVAC system

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Fig. 2

Schematic of a NC-OPT subsystem. The controller only communicates with subsystems that affect or are affected by the controlled plant.

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Fig. 3

Example of a system that can be setup as an NC-OPT network. (a) Physical collection of neighboring rooms with controllers, and uncontrolled exogenous disturbance coming from outside temperature. (b) Subsystems displayed as NC-OPT network.

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Fig. 4

Experimental system schematic

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Fig. 5

Network topology schematic, showing the physical signals passing between subsystems. Signal names are the same as in Fig. 4 and Table 1.

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Fig. 6

Subsystem 1 block diagram. Compressor and condenser use compressor rpm to control suction pressure and power consumption.

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Fig. 7

Typical block diagram, evaporators (subsystems 2–4). The EEV is used to track a superheat setpoint.

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Fig. 8

Typical block diagram, zones (subsystems 5–7). The water pump is used to control temperature drop and zone temperature.

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Fig. 9

Simulation convergence at t = 0: Inputs converging to centralized optimal values over cmax iterations

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Fig. 13

Estimated disturbance

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Fig. 14

Changing input setpoints for (a) superheat and (b) temperature drop, in the presence of an increase in heat load disturbance. User-desired setpoints are also shown.

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Fig. 15

(a) Compressor speed and (b) power consumption in the presence of an increase in heat load disturbance

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Fig. 16

Tradeoff between setpoint tracking and power consumption during an increase in heat load disturbance, The actual temperature and the setpoint are shown. Additionally, the actual NC-OPT prediction and the predicted results if the control inputs remained unchanged are shown.

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Fig. 17

(a) Pressure, (b) compressor speed, and (c) power comparisons for two different power setpoints

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Fig. 18

Zone temperature tracking comparisons

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Fig. 10

Centralized cost t = 0 converging to minimum over cmax iterations

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Fig. 11

Zone 1 temperature change and zone temperature inputs for two tests with the same setpoint step change but with two different numbers of iterations per sample, compared to centralized optimal values compared

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Fig. 12

Zone 1 actual temperatures for two tests with the same setpoint step change but with two different numbers of iterations per sampling period

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