Modeling of HVAC System for
Controls Optimization Using Modelica
Wangda Zuo1, Michael Wetter2
1 Department of Civil, Architectural and Environmental Engineering,
University of Miami, Coral Gables, FL
2 Building Technology and Urban Systems Department,
Lawrence Berkeley National Laboratory, Berkeley, CA
Intelligent Building Operations Workshop
06/21/2013
1
Outline
Introduction
Case 1: Modeling a Direct Expansion Coil
Case 2: Optimization of Chiller Plant Control
for Data Center
Conclusion and Outlook
2
Introduction
Motivation
• Energy saving potential from better building control is about 30%
• Computer tools can be used for the design, evaluation and
optimization of HVAC control
Limitation of Current Tools
• Idealized control
• Time step too large
• Fixed time step
Opportunity with Modelica
• Equation-based object-oriented modular modeling
• Fixed and variable time step solvers
3
Case 1: Modeling of a DX Coli
North Wing of Building 101, Philadelphia, PA
Condenser
Unit B
DX Coil with
2 Condensing Units
Refrigerant
Hot Gas
Condenser
Unit A
Refrigerant
Hot Gas
Return Air
Cooling
Coil A
Zone1
Zone2
Zone3
Zone4
Zone5
Zone6
Zone7
Zone8
Zone9
VAV1
VAV2
VAV3
VAV4
VAV5
VAV6
VAV7
VAV8
VAV9
Damper
Cooling
Coil B
Outdoor
Air
Mixing
Box
Fan
Supply Air
Heating
Coil
4
Boiler
Pump
Measured Data
Measured Power for August 2012
5
Model Calibration Design
Using measured data to calibrate the nominal COPs for performance
curves of 6 stages so that calculated energy consumption is close to
measured data.
𝑚𝑖𝑛
𝑇𝑖𝑛
𝑋𝑖𝑛
𝑇𝑜𝑢𝑡
6
Calibration
Model
Measured Data
Tout [degC]
Power [W]
Energy [J]
0.3% difference
Variable Speed DX coil, 8/1-8/7/2012
7
Validation
Tout [degC]
Model
Measured Data
Power [W]
Energy [J]
4% difference
Variable speed DX Coil, 8/15-8/21/2012
8
Discrete vs. Continuous Time Control
Option 1: Variable Speed DX Coil
• Control input: Real from 0 to 1
• Coil runs smoothly using performance curves for 6 speeds
Option 2: 6 Stage DX Coil
• Control input: Integer from 0 to 6
• A time delay twai is used to prevent short cycling
9
Discrete vs. Continuous Time Control
Tout
Model
Measured Data
Variable Speed DX Coil (Continuous)
6-Stage DX Coil, twai=120s (Discrete)
6-Stage DX Coil, twai=1s (Discrete)
Simulation of 8/1-8/7/2012
10
Discrete vs. Continuous Time Control
Comparison of Numerical Performance
Simulation of 8/1-8/7/2012
DX Coil Model
CPU Time
State Events
Variable Speed
10s
1
6-Stage
(twai=120s)
6-Stage
(twai=1s)
46s
3,912
1,850s
64,330
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Outline
Introduction
Case 1: Modeling of a Direct Expansion Coil
Case 2: Optimization of Chiller Plant Control
for Data Center
Conclusion and Outlook
12
Case 2: Chiller Plant for Data Center Cooling
Background:
• 1.5 percent of the nation’s electricity.
• half of the electricity in data centers is used for cooling.
Objective:
Decrease Power Usage Effectiveness (PUE):
PUE=
Total Facility Power
IT Equipment Power
Challenges in Optimization:
𝑄 𝑡 = 𝑐 𝑚(𝑡) ∆𝑇(𝑡)
𝑚(𝑡)
W
(Pump)
W
(Fan)
∆𝑇(𝑡)
W
(Chiller)
↑
↑
↑
↓
↓
↓
↓
↓
↑
↑
Configurations
Cooling Load
500 kW
Location
San Francisco
Water Side Economizer (WSE)
a. Without WSE; b. With WSE
Supply Air Set Temperature (Tair,set)
From 18 C to 27 C
Max Chiller Setpoint (Tchi,max)
From 6 C to 26 C
Condenser
Water Pump
WSE
Tchi,max
Chilled Water Pump
Fan
Tair,set
14
Modelica Models of Chiller Plant with WSE
15
Setpoint Reset Control
Chilled Water Loop Difference Pressure and Chiller Setpoint Temperature Reset
Modelica Implementation
16
Water Side Economizer Control
Schematic of State Graph
Modelica Implementation
17
Results: With and Without WSE
How much does the 0.13 in PUE for a 500 kW data
center mean?
- 438,000 kWh / year
- $87,600 if $0.2 / kWh
18
Results: With and Without WSE
Without WSE
With WSE
19
System With WSE: Hours of Chiller Operation
Tair,set
18C
27C
Tchi,max
6C
22C
20
System with WSE: Control Actions in a Hot Day
June 30
21
Discrete vs. Continuous Time Control
Discrete Time Control (Trim and Response Logic)
Continuous Time Control (PI Control)
22
Discrete vs. Continuous Time Control
Comparison of Numerical Performance
Discrete
CPU time
for simulation of 1 week
Number of steps
Number of (model) time events
Continuous
7.58 s
0.26 s
10,274
386
5,040
0
23
Outline
Introduction
Case 1: Modeling of a Direct Expansion Coil
Case 2: Optimization of Chiller Plant Control
for Data Center
Conclusion and Outlook
24
Conclusion and Challenges
Conclusion
• The case studies demonstrate the potential of Modelica for the
modeling and optimization of HVAC system control
• Model performance varies depending on how it is constructed
Challenges
• How to ensure that the models can be stably and efficiently
solved?
• How to handle the fast transient in control system and slow
response in building thermal system at the same time?
25
Acknowledgements
Collaborators:
Purdue University: Donghum Kim, James Braun
EEB Hub: Ke Xu, Richard Sweetser, Tim Wagner
Funding Agencies:
• Department of Energy
• Energy Efficient Buildings Hub
26