modeling of hvac system for controls optimization using modelica

1

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

2

Outline

Introduction

Case 1: Modeling a Direct Expansion Coil

Case 2: Optimization of Chiller Plant Control for Data Center

Conclusion and Outlook

3

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

4

Case 1: Modeling of a DX Coli

4

North Wing of Building 101, Philadelphia, PA DX Coil with 2 Condensing Units

5

Measured Data

Measured Power for August 2012

6

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.

7

Calibration

Power [W]

Energy [J]

Model Measured DataTout [degC]

0.3% difference

Variable Speed DX coil, 8/1-8/7/2012

8

Validation

Power [W]

Energy [J]

Model Measured DataTout [degC]

4% difference

Variable speed DX Coil, 8/15-8/21/2012

9

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

10

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

11

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

12

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.

14

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

15

Modelica Models of Chiller Plant with WSE

Setpoint Reset Control

16

Modelica Implementation

Chilled Water Loop Difference Pressure and Chiller Setpoint Temperature Reset

Water Side Economizer Control

17

Modelica Implementation

Schematic of State Graph

Results: With and Without WSE

18

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

19

Without WSE With WSE

System With WSE: Hours of Chiller Operation

20

Tair,set 18C 27C

Tchi,max 6C

22C

System with WSE: Control Actions in a Hot Day

21June 30

Discrete vs. Continuous Time Control

22

Discrete Time Control (Trim and Response Logic)

Continuous Time Control (PI Control)

Discrete vs. Continuous Time Control

23

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

24

Outline

Introduction

Case 1: Modeling of a Direct Expansion Coil

Case 2: Optimization of Chiller Plant Control for Data Center

Conclusion and Outlook

25

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

26