1. 1 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, Lawrence Berkeley National Laboratory, Berkeley, CA Intelligent Building Operations Workshop 06/21/2013

2. 2 Outline Introduction Case 1: Modeling a Direct Expansion Coil Case 2: Optimization of Chiller Plant Control for Data Center Conclusion and Outlook

3. 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

4. 4 Case 1: Modeling of a DX Coli 4 North Wing of Building 101, Philadelphia, PA DX Coil with 2 Condensing Units

5. 5 Measured Data Measured Power for August 2012

6. 6 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.

7. 7 Calibration Power [W] Energy [J] ModelMeasured Data T out [degC] 0.3% difference Variable Speed DX coil, 8/1-8/7/2012

8. 8 Validation Power [W] Energy [J] ModelMeasured Data T out [degC] 4% difference Variable speed DX Coil, 8/15-8/21/2012

9. 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 t wai is used to prevent short cycling

10. 10 Discrete vs. Continuous Time Control 6-Stage DX Coil, t wai =120s (Discrete) Variable Speed DX Coil (Continuous) ModelMeasured Data T out 6-Stage DX Coil, t wai =1s (Discrete) Simulation of 8/1-8/7/2012

11. 11 Discrete vs. Continuous Time Control DX Coil ModelCPU TimeState Events Variable Speed10s1 6-Stage (t wai =120s) 46s3,912 6-Stage (t wai =1s) 1,850s64,330 Simulation of 8/1-8/7/2012 Comparison of Numerical Performance

12. 12 Outline Introduction Case 1: Modeling of a Direct Expansion Coil Case 2: Optimization of Chiller Plant Control for Data Center Conclusion and Outlook

13. Case 2: Chiller Plant for Data Center Cooling Objective: W (Pump) W (Fan) W (Chiller) ↑↑↑↓↓ ↓↓↓↑↑ Challenges in Optimization: Background: 1.5 percent of the nation’s electricity. half of the electricity in data centers is used for cooling.

14. 14 Configurations Cooling Load500 kW LocationSan Francisco Water Side Economizer (WSE)a. Without WSE; b. With WSE Supply Air Set Temperature (T air,set )From 18 C to 27 C Max Chiller Setpoint (T chi,max )From 6 C to 26 C WSE T air,set T chi,max Condenser Water Pump Chilled Water Pump Fan

15. 15 Modelica Models of Chiller Plant with WSE

16. Setpoint Reset Control 16 Modelica Implementation Chilled Water Loop Difference Pressure and Chiller Setpoint Temperature Reset

17. Water Side Economizer Control 17 Modelica Implementation Schematic of State Graph

18. 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

19. Results: With and Without WSE 19 Without WSEWith WSE

20. System With WSE: Hours of Chiller Operation 20 T air,set 18C 27C T chi,max 6C 22C

21. System with WSE: Control Actions in a Hot Day 21 June 30

22. Discrete vs. Continuous Time Control 22 Discrete Time Control (Trim and Response Logic) Continuous Time Control (PI Control)

23. Discrete vs. Continuous Time Control 23 Comparison of Numerical Performance DiscreteContinuous CPU time for simulation of 1 week 7.58 s0.26 s Number of steps10,274386 Number of (model) time events5,0400

24. 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. 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?

26. 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