vcc wallace

7/28/2019 Vcc Wallace

1/23

Modeling And Control of Vapor Compression Cycle

Matt Wallace, Ryan McBride, Siam Aumi and Prashant Mhaskar

Department of Chemical Engineering

McMaster University

Hamilton, Ontario

John House and Tim Salsbury

Johnson Controls Inc.

June 24, 2011

M. Wallace (McMaster) Energy Efficient Temperature Control 1 / 21


2/23

Introduction

Introduction: Energy Consumption Government regulations and initiatives have placed a large emphasis ($$$) on

the reduction of energy consumption and increase in energy efficiency

Distribution of Secondary Energy Usagein Canada. (Natural Resources Canada)

Heating, ventilation, and air-conditioning (HVAC) systems responsible for40-50% of total building energy consumption

15 to 20% per annum of energy consumption can be reduced by efficient andoptimal operation of buildings (NRCan)



3/23

Introduction

Improvement Strategies

Design:

1 Building design (LEED certified buildings)

2 Retrofit replace existing equipment with more energy efficienttechnology (i.e. EnergyStar certified equipment)

Building operation and control (Controller Complexity):

Lowest - Local control of cooling device using classical control strategies

Middle - Advanced control of cooling devices

Highest - Integrated control accounting for startup/shut down ofcooling units in addition to emphasis on cost and energy efficientoperation



4/23

Background Theory

Background

Multiple PI control strategy regulating experimental Vapor CompressionCycle (VCC) (Keir and Alleyne [2007])

Nonlinear Model Predictive Control (MPC) designs for regulation ofexperimental VCC plant/various chiller system configurations (Leducqet al. [2006], Ma et al. [2010b])

Linear-state space based MPC to regulate a chiller network (Sandipanet al. [2010])

MPC of cooling unit subject to time-weather-dependent heat loads (Maet al. [2010a], May-Ostendorp et al. [2010])

Significant benefit from MPC strategies in providing desired cooling whileminimizing energy usage

Current Work :

MPC of a detailed model of a primary unit

Interfaced with detailed building model (EnergyPlus)

Compare with classical control strategyM. Wallace (McMaster) Energy Efficient Temperature Control 4 / 21

V C i C l


5/23

Vapor Compression Cycle

Modeling the plant-Vapor Compression Cycle (VCC)

Many air conditioning/refrigeration primary devices use a VaporCompression Cycle (VCC) to remove heat from a desired region anddissipate the heat to an alternate region

Air Cooled VCC

1 2 Evaporator- Refrigerant absorbs zone air heat

and is evaporated2 3 Compressor- Superheated vapor is compressed to

a higher P/T

3 4 Condenser

- Refrigerant rejects heat to theatmosphere and condenses to liquid

4 1 Expansion Valve- Expansion device reduces the

pressure of the refrigerant creating a

two-phase mixtureM. Wallace (McMaster) Energy Efficient Temperature Control 5 / 21

Vapor Compression C cle


6/23

Vapor Compression Cycle

VCC Model

VCC model adapted from the Air Conditioning & Refrigeration Center(ACRC) Thermosys simulator

Dynamic Model:

Condenser

Evaporator

Compressor

Static Model:

Expansion Valve

Piping

Highly nonlinear model (13 ODEs) including lookup tables

Parameters estimated experimentally (r-134a, air medium)

Is it a perfect model of a VCC- No-but captures the essential features

Necessary to evaluate controller designs


Interfaced System


7/23

Interfaced System

Modeling the plant-Interfaced System

VCC model interfaced with small office building model

Small office building model developed by EnergyPlus; United StatesDepartment of Energy (US DOE) software

Weather Databased on variousyearly recordings

Single story office(511m2)

5 thermal zones

Location: Chicago, IL

(July 25th)


Interfaced System


8/23

Interfaced System

MATLAB-EnergyPlus Interface

MATLAB VCC connected to EnergyPlus through the Building Controls

Virtual Test Bed (BCVTB)

VCC model acts as a rooftop AC unit supplying cooling to perimeter zone 2(67m2)

Cooling load across evaporator inputted to EnergyPlus model as negativesensible and latent heat loads


Control Structure Design


9/23


Classical Control Strategy

PI controller variable pairings obtained on stand-alone VCC

Expansion valve opening-evaporator superheat temperature (TSH

) andcompressor RPM-supply air temperature (TSA)

PI controllers initialized with IMC (internal model control) values andtuned by minimizing IAE (integral of absolute error) in response todisturbances rejection




10/23

g


Zone dynamics slower than VCC dynamics motivating a cascade controlstructure to regulate zone conditions

Outer loop: Zone air temperature (TZone) - supply air temperature(TSA) SP

Inner loop: Either the individual PI controllers or a linear ModelPredictive Control (MPC) strategy regulating TSA and evaporatorsuperheat temperature (TSH)

PI Control




11/23

g


Zone dynamics slower than VCC dynamics motivating a cascade controlstructure to regulate zone conditions

Outer loop: Zone air temperature (TZone) - supply air temperature(TSA) SP

Inner loop: Either the individual PI controllers or a linear ModelPredictive Control (MPC) strategy regulating TSA and evaporatorsuperheat temperature (TSH)

MPC Control


MPC Design


12/23

Autoregressive Exogenous (ARX) Model IdentificationARX Model Formulation:

Input/Disturbance variable pseudo random binary sequences (PRBS) used for

MPC model identification and fitting an equation of the following form :

Yi(k) = a1Y1(k 1) + . . . aNa Y1(k Na) + b1Y2(k 1) + . . . bNbY2(kNb)

+ c1U1(k 1) + . . . cNcU1(kNc) + d1U2(k 1) + . . . dNdU2(k Nd)

+ e1Z1(k 1) + . . . eNeZ1(k Ne + f1Z2(k 1) + . . . fNfZ2(k Nf)

Figure: PRBS I/O data used to form ARX models

Note : Model not obtained via linearization, but instead through

identification experimentM. Wallace (McMaster) Energy Efficient Temperature Control 11 / 21

MPC Design


13/23

Model Predictive Control Formulation

MPC Formulation:

Minimize - Setpoint deviation for Supply Air Temperature, compressorrpm (not deviation), compressor, valve movement

Subject to: Constraints on the inputs and Superheat, disturbance model,plant-model mismatch correction

Model: Increasing RPM increases Superheat and increases cooling

Increasing Valve opening decreases Superheat and increases cooling

Higher RPM implies higher energy usage

Lower SH results in increased energy efficiency (just a consequence ofthe above)


MPC Design


14/23

Model Predictive Control FormulationMPC Formulation:

minui

Nci=1

(y2,i ySP2 )TQ2(y2,i ySP2 ) + uT1 R1u1+

uT2 Rd2u2 + uT

1 Rd1u1subject to:

3.5C 1,low + 1,low y1,i 20C 1,up + 1,up

umin ui umax

umin ui umax

where yi(k) = a1y1(k 1) + . . . fNfz2(k Nf),

ySP2 = ySP

2 2 + 2,

j =yj(0)yj(0)

j j = 1, 2

(1)


MPC Design


15/23

Model Predictive Control Formulation (Continued)MPC Formulation (Continued):

2(k) = (y

SP

2

y2(0))2+ 2(k 1),

1,low(k) =

(3.5y1(0))

1,low+ 1,low(k 1) : y1(0) < 3.5

0 : y1(0) 3.5,

1,up(k) = (20y1(0))

1,up + 1,up(k 1) : y1(0) > 200 : y1(0) 20

Parameters Nc Q2 R1 Rd2 Rd1 2 1,low

Value 4 950 35017002 0.5 0.004 80 10

1,up 2 1,low 1,up umin umax umin umax

200 2 50 10

678.8 6

1700 15

200 1

200 1

ui =

RPMi Valvei

T

, ui = ui - ui1 y

1y

2i

= TSH TSAi, z

1z

2i

= TAmbient TReturni


Closed-loop Preliminary Results


16/23

Closed-loop VCC Simulation Conditions

Stand-alone VCC was regulated initially using both classical and MPC

schemes Constant air temperature and humidity values chosen corresponding to

condensation conditions

Air Parameter ValueTamb (

oC) 28

Treturn (o

C) 26RH (%) 87

TSA SP trajectory values corresponded to a range of possible operatingconditions for interfaced VCC

Feasible and infeasible TSA SP values used; infeasible values possible assudden fluctuations in zone air conditions could make a feasible SPinfeasible

Once stand-alone VCC was regulated, both control structures were

implemented on the interfaced systemM. Wallace (McMaster) Energy Efficient Temperature Control 15 / 21



17/23

Closed-loop performance criteria

TSA tracking of prescribed SP value; quantified using IAE

VCC energy demand quantified as the sum of instantaneous compressorpower (inst) over the test period

instant = mk(hout

hin)k(2)

Total Energy = t

tendi=1

instant,i

(3)

Maintain TZone at a SP of 24oC; ensure comfort standards (loosely

based on ASHRAE) satisfied

Results in a meaningful TSA SP sent to the primary unit controller




18/23

Simulation Results: Stand-alone VCC Input/OutputProfiles

MPC exploits trade-off between TSA SP tracking and high RPM values; High

cooling loads correspond to operating at high VO values, resulting in low TSHM. Wallace (McMaster) Energy Efficient Temperature Control 17 / 21



19/23

Simulation Results: Stand-alone VCC Measures

Control Structure PI(TSH SP= 10oC) MPC

TSA IAE ( so

C) 35.32 9.41TSA Cumulative Settling Time (seconds) 6120 8400

Cumulative Energy (kJ) 10017 9217

Large discrepancy in TSA IAE caused by tracking ability of infeasible TSA SP

PI strategy maintained TSH at its SP reducing ability of PI strategy tominimize TSA SP deviation

PI strategy using a TSH SP= 20oC was examined and had similar SP

tracking performance as MPC, but used more energy

Control Structure PI(TSH SP= 10oC) PI(TSH SP= 20

oC)TSA IAE ( s

oC) 35.32 10.40TSA Cumulative Settling Time (seconds) 6120 5520

Cumulative Energy (kJ) 10017 11915




20/23

Simulation Results: Interfaced VCC Input/Output Profiles

MPC again exploits trade-off existing in the system in addition to achieving

off-set free trackingM. Wallace (McMaster) Energy Efficient Temperature Control 19 / 21



21/23

Simulation Results: Interfaced Measures

Control Structure PI(TSH SP= 10oC) MPC

Cumulative Energy (kJ) 5080 4284TSA IAE ( soC) 2907 881

Zone Temperature Responses

TZone maintained within comfort standards for entire test period using

MPCM. Wallace (McMaster) Energy Efficient Temperature Control 20 / 21

Conclusions


22/23

Conclusions/Future Work MPC uses less energy (reduction by 16 % relative to PI) to provide

better supply air tracking (a 70 % improvement compared with PI)

Achieved through using the multi-variable nature of the problem andconstraint handling abilities of MPC

MPC allows the SH to drop low (when possible), resulting in betterenergy efficiency, and higher when necessary to provide the desiredcooling

Evaluate using MPC as a RTO layer, with adaptive PIs with decouplersworking at the lower level

Data-based model for use within MPC

Acknowledgment

Financial support from NSERC (CRD) and JCI is gratefullyacknowledged


Conclusions

M Keir and A Alleyne Feedback structures for vapor compression cycle


23/23

M. Keir and A. Alleyne. Feedback structures for vapor compression cyclesystems. American Control Conference, 2007.

D. Leducq, J. Guilpart, and G. Trystram. Non-linear predictive control of avapour compression cycle. International Journal of Refrigeration, 29:

761772, 2006.J. Ma, J. S. Qin, and T. Salsbury. Real-time model predictive control for

energy and demand optimization of multi-zone buildings. 2010 AIChEConference, 2010a.

Y. Ma, F. Borrelli, B. Hencey, B. Coffey, S. Bengea, and P. Haves. Model

predictive control for the operation of building cooling systems. 2010American Control Conference, 2010b.

P. May-Ostendorp, G. P. Henze, C. D. Corbin, B. Rajagopalan, andC. Felsmann. Model-predictive control of mixed-mode buildings with ruleextraction. Building and Environment, pages 110, 2010.

M. Sandipan, A. Alleyne, and V. Chandan. Predictive control of complexhydronic systems. 2010 American Control Conference, 2010.


vcc wallace

Documents