building hvac control systems - role of controls and optimization

Building HVAC Control Systems - Role of Controls and OptimizationH. S. Sane, C. Haugstetter and S. A. Bortoff

United Technologies Research CenterEast Hartford, CT 06108, USA

[email protected]

Abstract As building systems become more integrated, oneinherently introduces coupling between previously independentdesigns. Model-based controls and analysis tools are key toidentify problems and solution-paths early on. A model-basedapproach facilitates the developments of monitoring equipment,fault-tolerance, diagnosis and controls algorithms, that arecritical in the operation of integrated system. This paperintroduces a rich set of problems in controls and optimizationrelated to commercial chilled water cooling plants and relatedsystems. The problems range from opportunities in multivari-able optimal control, dynamic resource allocation and controlover networks. As an example, dither-based extremum seekingbased controls is proposed as a online optimization algorithmfor chilled water plant control.

I. INTRODUCTION

Energy consumed in commercial buildings is a significantfraction of that consumed in all end-use sectors. Buildingsconsume approximately 36% of total US primary energyuse by sector (transportation and industry consuming theremaining). For commercial buildings, approximately 32% ofenergy is consumed by the HVAC system, while an additional8% is used to heat water (see Chart in Figure 1). Moreover,the commercial building segment continues to grow.

15%

13%

4%

8%

27%

3%

2%

6%

22%

Space Heating

Cooling

Ventilation

Water heating

Lighting

Refrigeration

Cooking

Office Equipment

Other

Fig. 1. Energy Consumption in U.S. Commercial Buildings

Heating, Ventilation and Air Conditioning (HAVC) Sys-tems and other building components are typically designedand specified independently of each other. A path to achiev-ing higher overall energy conversion efficiencies is to enableand create hybrid building energy systems that integrateemerging component and control system technologies intothe broader HVAC building system and optimize buildingsystem energy efficiency.

This work was supported by the National Institute of Standards andTechnology Advanced Technology Program under agreement number70NANB4H3024, and the United Technologies Corporate Research.

One common reason for sub-optimal HVAC performanceis that the selection of HVAC equipment and independentcomponents tend to be grossly oversized. This impropersizing results in excessive cycling and significant degradationin off-design operation. Integration can occur in several waysand balance and combination of these methods can lead tosignificant system gains. At the physical level functionalitycan be combined into integrated subsystems that symbioti-cally improve overall efficiency. For example, the wasteenergy from a solid oxide fuel cell exhaust stream can beharnessed by a coupled microturbine combustor to improvethe hybrid system efficiency. Additionally, waste heat froma microturbine can be used to drive the water-separation inan absorption chiller. This approach represents energy flowintegration at the physical product level. The HVAC industryis moving towards a more integrated solutions approachbased upon several emerging open networking protocol suchas BACnet or LONWorks. At the building system opera-tions level, information is collected and used to optimizeperformance using centralized building management systems(BMS). This approach represents information and real timedata flow integration.

We identify four thrust areas as enablers for future buildingintegration:

1) Physics-based, system level dynamic models of build-ing energy systems and the integration of these modelswith existing building envelope simulators;

2) Information systems used to integrate modelling &analysis tools and manage information in support ofconcurrent, collaborative design of integrated HVAC/Rand cooling, heat and power (CHP) solutions;

3) Algorithms for optimal, robust supervisory control ofintegrated building energy systems, along with theirrealization on industry-standard embedded platforms;

4) Systems-oriented sizing and specification tools thatenable optimization of turn-key integrated HVAC/Rand CHP designs at building construction time.

The objective of this paper is to bring forth a rich classof modelling, dynamics and controls problems that are en-countered in analysis of integrated HVAC/building systems.A survey of literature pertaining to controls for HVAC andbuilding systems shows the lack of systematic dynamicanalysis and control design approach. The algorithms usuallyare based on ad-hoc table based rules which are modifiedand improved based on field experience rather than rigorousmodeling and dynamical analysis. The relevance to the

Proceedings of the 2006 American Control ConferenceMinneapolis, Minnesota, USA, June 14-16, 2006

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controls community ranges in the realms of applying theclassical methodology of model-based and predictive con-trols, analysis of complex interconnected dynamical systems,multivariable control analysis and exploration of new ways tocontrol over large-scale networks. It is imporant to evaluatethese strategies against practical issues including unobserv-able dynamics, transport delays, and inherent nonlinearities.

II. OVERVIEW OF THE PAPERSection III provides an overview of building dynamics and

opportunities for applying controls and dynamical analysismethods are also discussed. In Section IV, we focus onchilled water dynamics and discuss a subset of problemsinvolved in control of chilled water plants. As an exampleof controls application, we consider a problem of optimalsupervisory control of chilled water plant temperature set-points in Section V. Specifically, we apply extremum seekingmethods for controlling tower supply water (condenser watersetpoint) for online optimization of total power.

III. BUILDING DYNAMICSA typical building and its interaction with the HVAC

system can be classified in three physically distinct albeitcoupled dynamical systems, namely, building air deliveryand thermal dynamics (heat-exchange with air and structure),cooling and heating water equipment, delivery and thermaldynamics, and exogenous dynamics including weather, solarradiation and internal thermal loads. Performance of a build-ing HVAC is measured in terms of energy consumed, comfortand life cycle cost and is closely dependent on interactionof these dynamical systems. Energy costs are a function ofthe local utility-rate structure.

A. Air-Side DynamicsThe building air dynamics involves variation in air prop-

erties (pressure, flow, temperature, density and composition)in the air delivery systems (fans, air-handlers, exhausts)and building zones (rooms, corridors, plenum). Thermaldynamics of the building directly relates to comfort and ismost pertinent to HVAC system design. The thermal capacityof the building air and structure determines the response ofthe thermal dynamics. A tall space, like a hotel atrium, canhave buoyancy driven flows creating large temperature andpressure gradients.

ASHRAE and government building guidelines haveevolved over time imposing specific requirements on temper-ature, humidity, ventilation and contaminants (CO2, smoke,etc.). Often it is desirable to maintain a positive pressurewithin buildings or certain zones to prevent ambient infil-tration or maintain interzonal flows in certain directions forcontaminant control purposes (such as from a dining areainto the kitchen in a restaurant).

It is essential to have good models of the air dynamicsin the building, both from the design point of view andfor the purpose of developing control systems. There isa need for systematic methods (e.g. graph-theory, POD,model-decomposition) for model reduction from full order

CFD (Computational Fluid Dynamics) simulations to coarsecontrol-oriented models. These models can then be used toperform fundamental limit of performance analysis, controland sensor placement and algorithm design.

Some other interesting problems in building air dynamicsappear in the design of fire-suppression and contaminantcontrol systems. Inert gas fire suppression systems are basedon the principle that these heavy gases descend in the room,causing a reduction in oxygen supply to the combustion zone.This topic would be discussed in one of the submitted papersin the invited session.

B. Chilled Water DynamicsIn commercial buildings, water (chilled or hot) is cir-

culated through a series of fluid-to-air heat exchangers toprovide cooling or heating energy the building zones. Chilledand hot water is usually supplied in separate water loopsby chillers and boilers. Chillers are refrigeration machineswhich extract energy from building-water to a refrigerantwhich in turn rejects the heat to ambient using coolingtowers. Specific control problems involving chilled waterdynamics are discussed in Section IV and will be skippedhere.

C. Loads and DisturbancesThe loads and disturbance dynamics refers to heat, humid-

ity or contaminant sources, which include outside weather,solar radiation, computers and lab equipment, kitchen equip-ment, people and other latent and sensible heat sources.Depending on the size of the building, these effect can causesignificant dynamic coupling with the HVAC equipment.The ability of HVAC equipment to reject disturbances alsodepend on ambient conditions. For example, the coolingcapcity of cooling water towers are limited by the ambientwet-bulb temperature.

D. Energy CostsThe energy costs are a strong function of the utility

provider and its rate structure. For example, the electricutility cost structure consists of energy cost and peak power(fixed-horizon maximum) costs and the rate strongly dependson the time of the day. This poses an interesting model-predictive control problem of minimizing a discontinuousenergy cost and satisfying comfort requirements within theconstraints of building dynamics, weather and occupancyprediction. For example, a night-cooling approach makesuse of the buildings structural heat-capacity as a means ofreducing the operating costs. During summer, the buildingis over-cooled during unoccupied hours when electric utilitycost is low. During occupied hours the cold building structureabsorbs heat thereby requiring reduced HVAC cooling duringpeak rate hours.

IV. CONTROL PROBLEMS IN CHILLED WATER DYNAMICSThe use of cooling plants that employ multiple machines is

the most common method of providing cooling for mediumand large commercial and institutional buildings in theUnited States.

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Chiller 1

Chiller 2

Chiller n

Zone 1

Zone 2

Zone 3

Zone m

Tower 1

Tower 2

Leaving Chilled

Water Supply

Bypass

Air Handlers

Secondary Pumps

Cooling Towers

Primary Pumps

Condenser

Water Supply

Building Return

Water

Fig. 2. Schematic of the chilled-water-plant

Expansio

n

Va

lve Co

mp

ress

or

Evaporator (liq-to-gas)

Condenser (gas-to-liq)

Hot refrigerant(high pres. gas)

Cold refrigerant(low pres gas)

Hot refrigerant(high pres. liquid)

Cold refrigerant(low pres liquid)

Building waterReturn (18qC)

Chilled WaterSupply (6 qC)

ToCoolingTower (35qC)

FromCoolingTower (27qC)

Fig. 3. Schematic of a chiller

A schematic of a typical chilled water plant (CWP) isshown in Figure 2. A bank of chillers provide chilled waterto the building air handlers, which are essentially water toair heat exchangers. Supply fans distribute the cold air fromthe air handlers throughout the building. Electric chillers arebased of refrigerant vapor compression engines powered bycentrifugal, reciprocating or rotating compressors. Absorp-tion chillers are based on chemical absorption of water inLithium Bromide solution under vacuum. In electric chillers(Figure 3), the low-pressure refrigerant in the evaporatorextracts heat from the building return water and supplies it ata prescribed chilled water supply temperature. On the otherhand, high-pressure refrigerant in the condenser rejects heatto cold water supplied by the cooling tower. The coolingtower rejects the heat to the environment by using forcedair-cooling.

A. Supply Flow ControlTypically chilled water loops are decoupled in two loops

with the help of a chilled water bypass (Figure 2). Theprimary pump supplies water at a constant rate and thesecondary pump (in the secondary loop) circulates chilledwater to the building as required by the air-handlers reactingto the varying thermal loads in the building. The bypass com-

pensates for any excess flow that does not pass through thesecondary loop. This configuration makes the chilled watersystem robust and stable. However, at reduced thermal loads,the air handlers reduce the required secondary flow resultingin increased bypass flow, which introduces inefficiency in theoverall system performance. Many manufacturers of chillersnow make chillers with variable flow primary pumps [25],where the primary flow is reduced at part-load conditionsin order to reduce the bypass flow. This couples the chilleroperation with thermal loads and disturbances and requirescareful control to avoid equipment operating constraints. Aschilled water systems using variable speed drives becomemore prevalent [25], [26], controls would play an increas-ingly critical role in robust performance and optimization.

B. Supply Temperature ControlThe power consumption of a chiller is sensitive to the

condenser water supply temperature provided by the coolingtower control. Chillers typically are characterized by theircoefficient of performance (curves) which relate the powerconsumed to the cooling provided, COP = Cooling/Power.A large COP implies a more efficient chiller. Chiller COPstrongly depend on the operating conditions including cool-ing load, condenser water supply temperature and chilled wa-ter supply temperature and water flow through the condenserand evaporator. Optimizing the condenser water temperaturecan provide significant system energy savings [11], [12].Tower fan power varies approximately with the cube ofthe tower flow rate. Increasing the tower airflow providesa cooler condenser water temperature reducing the chillerpower requirement, however at the expense of an increase intower fan power consumption. For a given set of conditions,an optimal tower control exists that minimizes the sum ofthe chiller and cooling tower fan power. Methods in theHVAC literature [9], [11], [22], [38] present methods to doon-line optimization, but these usually ignore or disregard thesystem dynamics. The optimal control changes through timein response to uncontrolled variables including the ambientconditions and cooling loads.

Chiller power decreases with an increase in chilled watersupply temperature. However, constraints on the water-to-air heat exchange on the building air-handlers restrict themaximum chilled water supply temperature as a function ofbuilding thermal load. Chillers usually consume about 40-60% of the total energy consumed in a chilled water system,hence the chilled water supply temperature reset methodshave significant impact on system energy consumption.

As an example, we consider a chilled water plant with(similar to one in Figure 2) with four chillers and two towersand constant flow pumps for each unit. Based on empiricalmodels for chiller and tower performance, we perform agradient based extremum search for optimal chilled waterand condenser water supply temperature in order to obtainminimum total power (chillers and towers) for varying ther-mal loads. As baseline, we consider a chilled water set-pointof 70C and a commonly adopted strategy that maintainscondenser water set-point at a constant 4o difference above

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ambient wet-bulb temperature. Based of a simulation for atypical summer day, that we obtain about 10-20% reductionin energy consumption (Figure 4), of which 7-14% improve-ment is due to optimizing tower return temperature, and 3-6% improvement due to optimizing chilled water temperature(optimal temperatures are shown in Figure 5).

0 2 4 6 8 10 12 14 16 18 20 22 240

500

1000

1500

2000

2500

3000

3500

4000

4500

time [hours]

TotalPower[W]

baseline at TCoWSP = 4odegC + T

w etbulbOptimized

Tota

l Pow

er

[kW]

Time [hours]0 2 4 6 8 10 12 14 16 18 20 22 24

0

500

1000

1500

2000

2500

3000

3500

4000

4500

time [hours]

TotalPower[W]

baseline at TCoWSP = 4odegC + T

w etbulbOptimized

Tota

l Pow

er

[kW]

Time [hours]

Fig. 4. Total power improvement over baseline operation: Limit ofPerformance

0 5 10 15 20 2528

30

32

34

36

38

40

42

44

time [hours]

Condenserwatersetpoint[C]

0 5 10 15 206

6.5

7

7.5

8

8.5

9

9.5

10

10.5

11

time [hours]

chilledwatersetpoint[C]

Cond

ense

r W

ater

Set P

oint

Leav

ing

Chille

d W

ater

Set P

oint

Wet-bulb

3-chillers on

Fig. 5. Condenser water and chilled water supply setpoints correspondingto the simulation in Figure 4.

C. Resource Allocation: Chiller Sequencing ControlChilled water plants typically include multiple chillers

and towers. This permits staging equipment to meet thechanging loads. The term sequencing refers to activating ordeactivating chiller or tower units in a chiller water system.Hence, it is beneficial to select the optimal combination ofavailable chillers, fans, towers and pumps that maximize theoperating system efficiency. The main requirement from apractical standpoint is avoid excessive number of switches(activation/deactivation) in order to eliminate chiller start-up,shutdown times and increase equipment life.

A common strategy for sequencing chillers is typicallyaccomplished by the capacity method in which additionalchillers are activated when the operating units have insuf-ficient capacity to meet the current load, and chillers aredeactivated when the current load can be met with one fewermachines operating [41], [6].

Given a bank of chillers, choosing the optimal combi-nation of chillers and optimal distribution of cooling load

among those chillers can be seen as a dynamic resourceallocation problem. Consider a large commercial facility with15 chillers with different capacities and efficiency curves,leading to 215 combinations. For each discrete combination,there is an optimal selection of its continuous operationalvariables, including chilled water flow and temperature set-points, that distribute a given thermal load optimally amongthe participating chillers. This becomes a nonlinear mixedinteger optimization problem with potentially multiple so-lutions. Since, it is desirable to minimize the number ofswitches, it is important to identify solution clusters whichremain close as the building loads vary.

V. OPTIMAL CONTROL EXAMPLE: DITHER-BASEDEXTREMUM SEEKING CONTROL

In this section, we consider the problem of optimizingcondenser water temperature (tower-supply temperature) us-ing dither-based extremum-seeking control [3]. As discussedin Section IV-B, for a given selection of towers and chillersthere is an optimum condenser supply temperature thatminimizes the sum total of chiller and cooling tower powerconsumption. The total power consumption as a function ofthermal load and condenser water is shown as a surface plotin Figure 9. The analysis presented at the end of Section IV-B(Figures 4 and 5) relies on empirical knowledge of chiller andtower performance curves. In this section, we do not assumethe knowledge of these empirical models and consider in-stantaneous total power measurement and condenser supplytemperature as the only measured variables. In absence ofmodels of towers and chiller, the approximate convexity ofthe problem (Figure 9) can used to apply extremum seekingmethods.

The structure of an extremum seeking control is shownin Figure 6. The output signal y, which in our case isthe total power, is minimized when the set-point parameter = , being the condenser water supply temperature.The condenser water set-point is superimposed with asmall dither signal a sin(t) of chosen frequency andamplitude a. The output y is then passed through an highpass filter s/(s+h) that passes the dither frequency content.The output of the high pass filter is multiplied with phase-shifted by sin(t ) to generate the gradient directionfor condenser water setpoint update. Integrating along thisdirection drives towards . It is important to note that thedither frequency has to lie in the pass-band of the filter,that is > h. In addition, the parameter has to be chosenaccording to the phase-lag of the system at and other timedelays in system dynamics. For more details about extremumseeking theory, please refer to [3].

For simulation purposes, consider midday ambient con-ditions shown in Figure 7. Figure 8 shows the block dia-gram representation of the closed-loop. The parameters forextremum seeking control are chosen to be: dither amplitudea = 0.5K, frequency = 0.0024Hz (7 minutes), h =0.001Hz (15 minutes) and = 120o.

Figure 9 shows the evolution of the total power (red solidtrajectory) for varying ambient conditions with respect to

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the total power as a function of thermal load and condenserwater temperature set-point (surface plot). It is seen thatthe extremum seeking controller updates the tower set-point(Figure 10) in order to maintain minimum power consump-tion. Figure 11 compares the total power consumption usingthe extremum seeking controller with a commonly adoptedcontrol strategy that maintains condenser water at a constant4o difference above ambient wet-bulb temperature, showing10% improvement on an average.

)(sFi )(sFo

uhs

s

)sin( ta Z

T y

Input Dynamics Output Dynamics

Convex

nonlinearity

Plant

Controller

Ije

POWER

Tower

Return

Temperature

Set Point

Fig. 6. Extremum seeking control algorithm

2 4 6 8 10 12 14 16 18 20301

302

303

304

305

306

307

time [h]

Mid

Day

Con

ditio

ns [K

]

Wetbulb Temp

Ambient Temp

Fig. 7. Mid-day conditions: three chillers running

Cooling

Towers

Chillers

Fan

speed

Tower

power

T

Tower

Control

T

Bu

ild

ing

Load

Condenser

heatTotal

Power

Extremum

Seeking

Controller

T: condenser water set-point, T: Tower supply temperature

T

Chiller

power

Fig. 8. ESC algorithm with chilled water plant

90

95

100

302304306308310312

314316

2500

3000

3500

4000

4500

5000

Therm

al load

[%]

Wetbulb Temp 300K, number of chillers = 3

Tower Set Point [K]

Tota

l Pow

er [K

W]

Fig. 9. Extremum seeking control: trajectory evolution with varying thermalload.

2 4 6 8 10 12 14 16 18 204.5

5

5.5

6

6.5

7

7.5

8

time [h]

Cond

ense

r Wat

er S

etpo

int [K

]temperature above

wetbulb temperature

Fig. 10. Extremum seeking control output: condenser water set-point

2 4 6 8 10 12 14 16 18 202500

3000

3500

4000

4500

time [h]

Tota

l Pow

er [k

W]

Constant TowerSetpoint

With extremumseeking control

Fig. 11. Total Power Consumption: comparison of extremum seekingcontrol set-point and a strategy that maintains condenser water at a constant4o difference above ambient wet-bulb

VI. ACKNOWLEDGMENTSThe authors gratefully acknowledge the support of Na-

tional Institute of Standards and Technologys Advanced

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Technology Program and United Technologies Corporation.

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building hvac control systems - role of controls and optimization

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