modeling of hvac system for controls optimization using modelica
DESCRIPTION
Modeling of HVAC System for Controls Optimization Using Modelica. Wangda Zuo 1 , Michael Wetter 2 1 Department of Civil, Architectural and Environmental Engineering, University of Miami, Coral Gables, FL 2 Building Technology and Urban Systems Department, - PowerPoint PPT PresentationTRANSCRIPT
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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
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Outline
Introduction
Case 1: Modeling a Direct Expansion Coil
Case 2: Optimization of Chiller Plant Control for Data Center
Conclusion and Outlook
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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
Zone5
VAV5
Zone6
VAV6
Zone7
VAV7
Zone8
VAV8
Zone1
VAV1
Zone2
VAV2
Zone3
VAV3
Zone4
VAV4
CoolingCoil A
CoolingCoil B
HeatingCoil
MixingBox
OutdoorAir
Damper
Return Air
Supply Air
Boiler
Fan
Pump
CondenserUnit A
Condenser Unit B
Hot Gas
Hot Gas
Refrigerant
Refrigerant
Zone9
VAV9
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Case 1: Modeling of a DX Coli
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North Wing of Building 101, Philadelphia, PA DX Coil with 2 Condensing Units
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Measured Data
Measured Power for August 2012
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Model Calibration Design
�̇�𝑖𝑛
𝑇 𝑖𝑛𝑋 𝑖𝑛
𝑇 𝑜𝑢𝑡
Using measured data to calibrate the nominal COPs for performance curves of 6 stages so that calculated energy consumption is close to measured data.
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Calibration
Power [W]
Energy [J]
Model Measured DataTout [degC]
0.3% difference
Variable Speed DX coil, 8/1-8/7/2012
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Validation
Power [W]
Energy [J]
Model Measured DataTout [degC]
4% difference
Variable speed DX Coil, 8/15-8/21/2012
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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
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Discrete vs. Continuous Time Control
6-Stage DX Coil, twai=120s (Discrete)
Variable Speed DX Coil (Continuous)
Model Measured DataTout
6-Stage DX Coil, twai=1s (Discrete)
Simulation of 8/1-8/7/2012
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Discrete vs. Continuous Time Control
DX Coil Model CPU Time State Events
Variable Speed 10s 1
6-Stage (twai=120s)
46s 3,912
6-Stage (twai=1s)
1,850s 64,330
Simulation of 8/1-8/7/2012
Comparison of Numerical Performance
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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
Case 2: Chiller Plant for Data Center Cooling
Objective:
W (Pump)
W (Fan)
W (Chiller)
↑ ↑ ↑ ↓ ↓
↓ ↓ ↓ ↑ ↑
Challenges in Optimization:
Decrease Power Usage Effectiveness (PUE):
PUE=
𝑄 (𝑡 )=𝑐�̇� (𝑡)∆𝑇 (𝑡)
Background:• 1.5 percent of the nation’s electricity.• half of the electricity in data centers is used for cooling.
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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
WSE
Tair,set
Tchi,max
Condenser Water Pump
Chilled Water Pump
Fan
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Modelica Models of Chiller Plant with WSE
Setpoint Reset Control
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Modelica Implementation
Chilled Water Loop Difference Pressure and Chiller Setpoint Temperature Reset
Water Side Economizer Control
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Modelica Implementation
Schematic of State Graph
Results: With and Without WSE
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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
Results: With and Without WSE
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Without WSE With WSE
System With WSE: Hours of Chiller Operation
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Tair,set 18C 27C
Tchi,max 6C
22C
System with WSE: Control Actions in a Hot Day
21June 30
Discrete vs. Continuous Time Control
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Discrete Time Control (Trim and Response Logic)
Continuous Time Control (PI Control)
Discrete vs. Continuous Time Control
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Comparison of Numerical Performance
Discrete Continuous
CPU timefor simulation of 1 week
7.58 s 0.26 s
Number of steps 10,274 386
Number of (model) time events 5,040 0
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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
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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?
Acknowledgements
Collaborators:Purdue University: Donghum Kim, James BraunEEB Hub: Ke Xu, Richard Sweetser, Tim Wagner
Funding Agencies: • Department of Energy• Energy Efficient Buildings Hub
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