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Energy and Buildings 59 (2013) 111122

Contents lists available at SciVerse ScienceDirect

Energy and Buildings

j ourna l ho me p age: www.elsev ier .com/ locate /enbui ld

nergy analysis of chilled water system configurations usingimulation-based optimization

uzaffar Alia,b,, Vladimir Vukovica, Mukhtar Hussain Sahirb, Giuliano Fontanellaa

Energy Department, Austrian Institute of Technology, Giefinggasse 2, 1210 Vienna, AustriaMechanical Engineering Department, University of Engineering and Technology Taxila, Pakistan

r t i c l e i n f o

rticle history:eceived 26 March 2012eceived in revised form 24 July 2012ccepted 3 December 2012

eywords:hilled water systemsptimization

a b s t r a c t

The paper presents an incremental development of the methodology for chilled water system designoptimization. Initially, the system configuration parameters are varied with fixed design conditionsto confirm the established best practice design criteria, followed by a comprehensive system designoptimization. The implemented simulation-based optimization approach couples the Dymola/Modelicadynamic modeling and simulation program with GenOpt generic optimization program to find optimalsystem configuration. A dynamic system model is developed to vary and simulate different chilled watersystem configurations. Optimization of the chilled water system is achieved at both design and configura-odeling and simulationymola/ModelicaVAC system configurationsnergy performance

tion level using five design variables. Two discrete variables are related to system configuration: numberof chillers and number of cooling towers and three continuous variables are related to system design:building load demand, temperature difference across condenser, and cooling tower fan speed. The strat-egy of varying system design and configuration variables together for overall system optimization provedto be the most energy efficient. For a fixed building load demand, power consumption of the consideredsystem can be reduced 1743% by selecting an optimal system configuration.. Introduction

Energy performance of buildings strongly depends on selectionf heating, ventilation, and air-conditioning (HVAC) system con-guration as HVAC systems account for approximately 3060% ofhe total building energy consumption, depending on the build-ng type [13]. As buildings are responsible for 2040% of the totalnergy consumed [1], climatization systems consume 1020% ofotal energy in the developed countries. It has been observed thathilled water system consisting of primary HVAC system compo-ents, e.g. chillers, cooling towers, pumps accounts for most ofhe electricity use in the HVAC system [3]. Thus, selecting optimalhilled water system configuration can result in substantial poweravings. In practice, however, selection of optimal system configu-ation is a complex task in terms of effort and time as multifaceted

hilled water system configurations need to be analyzed. ThereforeVAC practitioners select the system configurations and design

Corresponding author at: Energy Department, Austrian Institute of Technology,iefinggasse 2, 1210 Vienna, Austria. Tel.: +43 050550 6484; fax: +43 050550 6613;obile: +923005316356.E-mail addresses: muzaffar.ali@uettaxila.edu.pk, ali.muzaffar@ait.ac.at (M. Ali),

ladimir.vukovic@ait.ac.at (V. Vukovic), mukhtar.sahir@uettaxila.edu.pkM.H. Sahir), giuliano.fontanella@ait.ac.at (G. Fontanella).

378-7788/$ see front matter 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.enbuild.2012.12.011 2012 Elsevier B.V. All rights reserved.

parameters at the initial design stage based on their experienceapractice that could result in sub-optimal system operation.

In the current paper, simulation-based optimization method-ology for optimal selection of chilled water system configurationat initial design stage is presented. The methodology is applied inthree different strategies: (1) use fixed system design conditions tovalidate the methodology in comparison to the real system, (2) inaddition, vary the number of cooling towers according to the cool-ing tower flow turndown ratio to validate the best practice designcriteria, and (3) overall system optimization approach varying thesystem design and configuration parameters.

2. Overview of modeling, simulation, and optimizationtools

Modeling and simulation can play a crucial role throughoutthe system lifetime: (1) initial design stages, when the controlstrategies are decided, (2) validation of system performance, (3)commissioning, and (4) operation support. Object-oriented mod-eling approaches are particularly suited to this task due to theirflexibility, inherent openness, and reusability features [4].Modelica [5] is an equation-based, object-oriented, acausalmodeling language developed for multi-domain modeling of hybridsystems. Component models are described by differential, alge-braic, and/or discrete equations. To enhance reusability, models

dx.doi.org/10.1016/j.enbuild.2012.12.011http://www.sciencedirect.com/science/journal/03787788http://www.elsevier.com/locate/enbuildmailto:muzaffar.ali@uettaxila.edu.pkmailto:ali.muzaffar@ait.ac.atmailto:vladimir.vukovic@ait.ac.atmailto:mukhtar.sahir@uettaxila.edu.pkmailto:giuliano.fontanella@ait.ac.atdx.doi.org/10.1016/j.enbuild.2012.12.011

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ditions are based on the typical operating conditions specifiedby the Air Conditioning and Refrigeration Institute (ARI) standard550/590-2003 [34] and Cooling Tower Institutes (CTI) test con-ditions [35]. Design wet bulb temperature of 17 C (63 F) and dry

Table 1Details of the investigated chilled water system.

Total cooling capacity kW [tons] 8175 [2325](Three identical sets of chillers, pumps, and cooling towers)Chillers

Compressor type CentrifugalNominal cooling capacity kW [tons] 2725 [775]Nominal compressor power kW [tons] 446.4 [127]Minimum cooling capacity kW [tons] 457 [130]Design COP 6.1Design chilled water supply/return temperature C [F] 6.7/12.2 [44/54]Design chilled water flow rate l/s [gpm] 57 [900]Design condenser water entering temperature C [F] 29.4 [85]Design condenser water flow rate l/s [gpm] 111[1760]

Cooling towersType Draw-throughWater flow rate l/s [gpm] 76 [1200]Fan motor power kW [hp] 18.65 [25]Design wet bulb temperature C [F] 17 [62.6]Design dry bulb temperature C [F] 26 [78.8]Design approach temperatures C [F] 8.3[15]12 M. Ali et al. / Energy and

an be symbolized graphically via icons [6]. Modelica features, suchs acausal, declarative modeling, code transparency, encapsulationnd modularity, inheritance, and reusability, are summarized in aumber of studies [79]. Modelica Standard Library (MSL) as a basisor more enhanced model development is freely distributed, whileumerous other libraries can be downloaded or purchased [1013].Models developed in the Modelica language cannot be executed

irectly. Rather, a simulation tool translates a Modelica model inton executable program [14]. The current study uses Dymola [15]hat executes symbolic manipulations to reduce the dimensionalityf the linear and non-linear systems of equations defined by theodelica model. The execution of a Modelica model also performsutomatic differentiation followed by generation and compilationf C/C++ code [6,16].Optimization tools are used in combination with energy per-

ormance simulation software to optimize a set of parameters for given cost function and achieve simulation-based optimizationf HVAC systems [7]. Coupling of TRNSYS-based simulation [17]nd evolutionary programming demonstrated effective potentialf HVAC system optimization for energy management [18]. IDA-CE [19] was coupled with GenOpt to show temporal advantages ofimulation-based optimization in finding the optimal values of theuilding assembly and HVAC system design variables [20]. A com-ination of EnergyPlus [21] and GenOpt was used for optimizationf energy consumption in a school building with hydronic heatingystem [22].

GenOpt is a generic optimization program for single or multiarameter optimization with efficient search techniques [23].enOpt is developed for optimization problems where the costunction is computationally expensive to evaluate and its deriva-ives are not available or may not even exist. GenOpt library consistsf local and global multi-dimensional optimization algorithms.n addition, new optimization algorithms can also be added byxtending the superclass Optimizer [24,25].

. Chilled water system optimization

Performance optimization of chilled water system equipmentnd control was achieved in different studies through a wide vari-ty of optimum strategies and operating sequences. A theoreticalptimal plant performance (TOPP) model was applied to select opti-al control of a chilled water system consisting of three chillersnd five cooling towers [26]. A simplified model was developed forvaluation of system configurations of a multiple-chiller systemonsisting of a maximum of ten equally sized chillers [27]. Withhis model, the designer could easily complete initial evaluation ofystem configurations. Similarly, an optimization model was devel-ped for a chilled water system with three chillers and four coolingowers for deciding on the best operating mode of the entire system28]. In another study, four design options were studied to decidehe optimal number and size of chillers operating with maximumystem performance. The study estimated that electricity savingsf 10.1% could be achieved with six chillers of three different sizesnstead of four equally sized chillers [29]. Selection of the optimalonfiguration after analyzing all the possible alternatives is a com-lex task in terms of efforts and time. However, such a task can befficiently accomplished through a simulation-based optimizationtrategy [30].

Design parameters of individual system components playrucial role for the selection of optimal chilled water system con-gurations. Therefore, it is essential to operate a system with

ptimal values of the design parameters during the evaluation ofifferent system configurations. Effect of the design parameter val-es and control on the performance of chilled water systems haseen extensively studied. Load-based speed control integrated withings 59 (2013) 111122

variable condenser water flow and optimal tower fan speed wasintroduced to achieve optimum chilled water system performanceand economical benefits [31,32]. It was analyzed that load-basedspeed control could reduce the annual system electricity use by5.3% and operating cost by 4.9% as compared to using constantspeed fans and pumps. Selection of the optimal condenser waterdesign temperatures is a crucial and complex task at the initialdesign stage due to the significant impact on the system energy con-sumption. Substantial first-cost savings can be achieved using highcondenser water temperature differences [33]. Therefore, the cur-rent study considers optimal selection of the design temperaturedifference across the condenser at various building load demands.

4. Methodology

The current study uses automated simulation-based opti-mization approach to select the optimal chilled water systemconfiguration. In this approach, the equation-based object-oriented (EOO) chilled water system model is developed inDymola/Modelica and coupled with GenOpt. The model uses hybridoptimization algorithm with total system power consumption asan objective function. Finally, the minimum power consumptionwith respect to the specific building load demand determines theoptimal system configuration.

4.1. Description of the investigated chilled water system

The considered chilled water system serves a large office build-ing in Southern California [26]. As Table 1 illustrates, the systemcontains three equally sized chillers rated at a cooling capacity of2725 kW (775 tons) each. The system has five draw-through cross-flow equally sized cooling tower cells, designed for approximately76 l/s (1200 gpm). Three equally sized condenser water pumps,designed for 111 l/s (1760 gpm) with 19 kW (25 hp) motors, arepiped together in a headered arrangement so that each pump canserve any of the chillers or tower cells. Similarly, three equally sizedchilled water pumps, designed for 57 l/s (900 gpm) with 30 kW(40 hp) are also headered. In the current study, the design con-Design range temperature C [F] 5.56 [10]

PumpsRated power of each chilled water pump kW [hp] 30 [40]Rated power of each condenser water pump kW [hp] 19 [25]

M. Ali et al. / Energy and Buildings 59 (2013) 111122 113

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ulb temperature of 26 C (78.8 F) are used for cooling tower modelccording to the ASHRAE standard 90.1-2004 climate data for Sanrancisco [36].

.2. Model development

Component models for the investigated chilled water systemere used from the Modelica standard and LBL Buildings libraries.raphical representation of the chilled water system, modeled inymola, is shown in Fig. 1. Centrifugal chiller component model,eveloped by AIT (Austrian Institute of Technology) is based onRNSYS centrifugal chiller model TYPE 68 [37]. The pump modelith prescribed mass flow rate was used to model three chilled

ater pumps and three condenser water pumps. Model of a steadytate cooling tower with variable speed fan using a York coolingower performance curve to compute the approach temperatureas employed for heat rejection from the chillers. The appliedlled water system model in Dymola.

pump and cooling tower models were part of the Buildings library.All component models were operating in parallel arrangementunder the design conditions specified in Table 1.

Centrifugal chillers were operated with fixed chilled water sup-ply and return temperatures at 6.7 C (44 F) and12.5 C (54 F),respectively. Chiller sequencing allowed the running chillers tooperate at the same part load conditions. The demand loads, Qload,were applied from an external text file with an increment of35.16 kW (10 tons) up to the peak load demand. Each applied loaddetermined the evaporator mass flow rate assuming fixed designtemperature difference of 5.55 C (10 F) across the evaporator.Thus, load-based mass flow rates were applied through the pre-scribed flow chilled water pumps on the evaporator side of the

chillers. Similarly, the condenser water flow rate was determinedto satisfy the required amount of heat rejection by the condenser.Such heat rejection was computed from the chiller model basedon the applied load, Qload. The condenser water flow rates were

114 M. Ali et al. / Energy and Buildings 59 (2013) 111122

Table 2Summary of estimated initial costs (costs in D are estimated based on the 1.23$/D exchange rate).

Description Cost/ton (D [$]) Total unit cost (D [$])

Water-cooled centrifugal chiller (nominal 775 tons each) 147 [180] 113,925 [139,500]Chiller installation cost 37 [45] 28,370 [34,875]Cooling tower (nominal 400 tons each) 106 [130] 42,400 [52,000]Cooling tower installation cost 4.1 [5] 1640 [2000]Piping/fitting/valvea chilled water side 13,015 [16,000]b condenser water side 19,523 [24,000]c adding another chiller in the system 4393 [5400]d adding another cooling tower in the system 2440 [3000]Estimated baseline cost for 1 chiller and 1 cooling tower 218,873 [268,375]Contractor markup (25%) 54,718 [67,094]Total baseline cost 273,591 [335,469]Estimated cost for adding each chiller 146,688 [179,775]Contractor markup (25%) 36,672 [44,944]Total cost of an additional chiller 183,360 [224,719]

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Estimated cost for adding each cooling towerContractor markup (25%) Total cost of an additional cooling tower

alculated keeping the fixed design temperature difference acrosshe condenser according to the calculated heat rejection rate forhe first and second strategies, while the third strategy addition-lly varied the temperature differences across the condenser. Theumber of cooling towers was decided based on the calculated con-enser water flow rate. The cooling towers operated with fixedesign conditions in terms of design range and approach temper-tures given in Table 1. In the first strategy, the number of coolingowers varied according to the real considered system but in theemaining strategies cooling towers were varied according to theow turndown ratio up to one-third of the design [38]. Accordingo such criterion, maximum number of cooling towers in the cur-ent study could be 25 provided with an appropriate temperatureifference across the condenser at higher loads. Such high numberf cooling towers is modeled as an array of parallel componentsith identical design parameters. Consequently, Dymola/Modelicaodel was capable of varying the number of chillers, numberf cooling towers, temperature difference across the condenser,ooling tower fan input signal, and load, from an external textle in order to find optimal chilled water system configurationetup.

.3. Objective function

The current study defines the objective function for systemesign optimization as the total system power consumption. Forhe optimal configuration design, the objective function is mini-al while satisfying the building load demand. Eq. (1) computes

he objective function within the Dymola/Modelica model for var-ous system configurations considering the power consumptionf specific number of chillers (chiller.PTot), chilled water pumpsCHPump.PElc), condenser water pumps (CTPump.PElc), and coolingowers (CoolingTower.PFan) against the fixed load demand.

total = CHPump.PElc + CTPump.PElc + CH Chiller.PTot + CT CoolingTower.PFan (1)

ere CH is the number of chillers and CT is the number of coolingowers.

Initial costs of each system configuration are also importantspect for system selection. The initial costs involve the cost ofhillers, cooling towers and piping/fittings/valves required for effi-

ient hydronic design. However, the costs of such equipmentary country-to-country and region-to-region. Therefore the costsre estimated based on the data published by the selected man-facturers [39,40] and cost studies related to such equipment46,480 [57,000]11,620 [14,250]58,100 [71,250]

[33,38,41,42]. The initial and installation costs of water-cooled cen-trifugal chillers and cooling towers are estimated based on the unitcapacity, i.e. cost per ton. The piping costs, including fittings andvalves, are estimated using Pipe Size Optimization tool [33,43]. Thetool calculates the initial piping costs based on the flow rate forspecified piping segments. In addition, the tool also accounts forthe number and types of various valves and fittings typically usedin chilled water systems. The costs of different valves and fittingsare also provided based on the respective pipe sizes. In the cur-rent study, the number and type of valves and fittings are decidedfrom the piping schematic of the reference system [26]. The ini-tial and installation costs of water-cooled centrifugal chillers andcooling towers along with the piping/fitting/valve costs are sum-marized in Table 2. The estimated value of 25% contractor markupis considered in the calculations [38].

As the current study involves different configurations of thechilled water system varying the number of chillers and coolingtowers, the final estimated initial cost of each configuration iscalculated from Eq. (2).

Ctotal = CH CPchiller (Cchiller + Cinst.CH) + CT CPtower (Ctower + Cinst.CT ) + Cpiping/fittings/valves (2)

The piping/fitting/valve cost is calculated from Eq. (3).

Cpiping/fittings/valves = CCHWside + CCWside + CH Cadd.chiller + CT Cadd.tower (3)

Here Ctotal is the total initial cost, CPchiller, CPtower are the chillerand cooling tower nominal capacities (tons), Cchiller, Ctower are theunit initial the chiller and cooling tower costs (cost/ton), Cinst.CH,Cinst.CT are the unit installation costs of the chiller and coolingtower (cost/ton), respectively, Cpiping/fittings/valves is the total cost ofpiping/fittings/valves, CCHWside, CCWside are the baseline costs of pip-ing/fittings/valves for the chilled water and condenser water sides,and Cadd.chiller, Cadd.tower are the costs of piping/fittings/valves foradding each chiller and cooling tower in the system, respectively.

The estimated initial costs of each configuration are used foreconomic considerations related to the automatically chosen sys-tem configurations based upon the defined optimization objectivefunction.4.4. Optimization algorithm

As previously mentioned, overall optimization of the chilledwater system involves five design variables with the total system

M. Ali et al. / Energy and Buildings 59 (2013) 111122 115

Table 3Design variables and bounds.

Tower fanspeed, F (%)

Temp. differencecondenser side, T (C) [F]

No. ofchillers, CH

No. of coolingtowers, CT

Building load Qload(kW) [Tons]

Minimum 0.3 3 [5.4] 1 3 1055 [300]

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Maximum 1 15 [27] Step 0.01 0.01 Initial 1 3

ower consumption as an objective function. Two discrete designariables are the number of cooling towers CT, and the numberf chillers CH. Three continuous design variables are building loademand Qload, temperature difference across the condenser T, andooling tower fan speed F. Table 3 shows bounds of the consideredariables.GenOpt optimization algorithms are categorized with respect

o the problems they are meant to solve. Thus, various algorithmsre available for continues, discrete, and mix of continuous andiscrete independent variables. For problems including both con-inuous and discrete independent variables, such as the currentptimization, GenOpt contains Particle Swarm Optimization (PSO)lgorithms, and a hybrid Generalized Pattern Search Particle Swarmptimization with Constriction Coefficient HookeJeeves (GPSP-OCCHJ) algorithm.The hybrid global optimization algorithm GPSPSOCCHJ consists

f a stochastic population-based constriction coefficient PSOCClgorithm and a direct search HookeJeeves (HJ) algorithm. The keydvantage of this algorithm is the global PSO search which increaseshe possibility of getting close to the global minimum rather thanchieving only a local minimum, while the HJ algorithm refines theearch locally [44]. The GPSPSOCCHJ algorithm parameters chosenn the current study are summarized in Table 4 [25].

.5. Simulation-based optimization

After the development of the chilled water system model inymola/Modelica the simulation model is coupled with GenOpt tond optimal values of design parameters, which ultimately decidehe optimal system configuration. Fig. 2 shows the implementationf the simulation-based optimization approach. The optimizationrocess repeats iteratively until a minimum of the objective func-ion is found [25,30].

. Results and discussionA wide range of design and configuration factors influences thehilled water system performance. At a system design level, theelection of suitable design conditions plays a crucial role, e.g. nom-nal water flow and temperature difference across the evaporator

able 4ptimization algorithm input parameters.

Parameters Value

Neighborhood topology von-NeumannNeighborhood size 5Number of particles 20Number of generations 5Seed 1Cognitive acceleration 2.8Social acceleration 1.3Max velocity gain continuous 0.5Max velocity discrete 4Constriction gain 0.5Mesh size divider 2Initial mesh size exponent 0Mesh size exponent increment 1Number of step reductions 43 18 7032 [2000]1 11 3

and condenser, cooling tower approach and wet bulb temperature.At a system configuration level, decision about the number, type,and size of chillers, pumps, and cooling towers extensively affectthe energy performance of chilled water systems.

5.1. Baseline system configurations: 1st strategy

In the first strategy, the number of chiller(s) varied between 1and 3, while the number of cooling tower varied between 3 and 5 tofind optimal system configuration of the considered chilled watersystem. The temperature difference across the condenser was fixedat 4.45 C (8 F) according to ARI standard design conditions withfixed design chilled water supply and return temperature and fulltower fan speed.

The chiller component model was operational in three modes:(1) shutdown mode, if the applied Qload is less than the minimumload, Qmin, specified in the model, (2) normal mode, if applied loadis more than the specified minimum and less than the specifiedmaximum load, and (3) overload mode, if Qload is more than thespecified maximum load, Qmax. The modes enable the chiller modelto properly react during the simulation subject to the applied load.In the shutdown mode, the leaving condenser and evaporator watertemperatures become equal to the corresponding entering temper-atures and total power consumption, condenser heat transfer rate,and COP are set to zero. In the overload mode, Qload is set to Qmaxand new leaving evaporator water temperature and COP are deter-mined. As such, the modes are needed for the simulation control,rather than a real system, in order to prevent the chiller model fromcoming up with unrealistic chilled water supply temperatures tomeet the cooling demand.

In the current study, the chiller is always operated in the normalmode. Therefore, the number of chillers was varied satisfying thenormal operating mode requirements. Fig. 3 shows the total systempower consumption and initial cost of different configurations atcritical loads where system configuration changes to yield energybenefit. The power consumption of each configuration shown inFig. 3 is based on fixed design conditions throughout the load vari-ation. In such cases the variable loads correspond to the part loadoperation of the system due to variable building occupancy.

In Fig. 3(A), the optimal configuration requiring minimum totalpower consumption at Qload of 1054.8 kW (300 tons) has 1 chiller(CH) and 3 cooling towers (CT). Similarly, optimal configurationat 1582.2 kW (450 tons) is 1CH4CT, at 2461.2 kW (700 tons) is1CH5CT, at 3516 kW (1000 tons) is 2CH5CT, and at 5274 kW(1500 tons) is 3CH5CT as shown in Fig. 3(B)(E), respectively. Theresults show that adding cooling towers is beneficial for two rea-sons: (1) reduction of the entering condenser water temperaturereduces chiller power consumption due to decrease in chiller lift,and (2) decrease in pressure drop across the condenser water pumpreduces the pump power consumption.

The power consumption is also affected by the number ofchillers, depending upon the chiller coefficient of performance

(COP) at part load conditions. For example, consider the case when2461.2 kW (700 tons) Qload is applied on chiller(s) with nominalload of 2725 kW (775 tons) and five cooling towers. If the systemoperates with one chiller, the applied load is approximately 90% of

116 M. Ali et al. / Energy and Buildings 59 (2013) 111122

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Fig. 2. Flow chart of simulat

he nominal load, yielding a chiller COP of 7.68. Chiller COP reduceso 7.57 and 6.83 when system operates with two and three chillerst 45% and 30% of the nominal load, respectively.Fig. 4 summarizes the optimal configuration options minimizing

he total system power consumption with respect to the numberf chillers and cooling towers at full range of load demand. Theptimal configurations shown in Figs. 3 and 4 are in agreementith the real system design [26] and thus validate the proposedethodology.Moreover, due to the initial configuration costs shown in Fig. 3,

avings in total power consumption are more significant at higherhan at lower demand loads per unit initial cost increment.

.2. Modified system configurations: 2nd strategy

More cooling towers within the flow limit, i.e. flow turndownatio of one-third of the design flow [38], are generally beneficial asower fan speed and power are required. The original chilled waterystem design used five cooling towers with 76 kg/s design flow.n the second strategy, the number of cooling towers within theow constraint is based on the temperature difference across theondenser. Such temperature difference determines the required

ater flow rate through the condenser and cooling towers to satisfy

he condenser heat rejection for specific Qload. Eqs. (4) and (5) weresed to find the lower and upper limits for the number of coolingowers, respectively, satisfying the flow constraints.sed optimization approach.

Upper limit:

CT C1C2

QloadT

1ml

(4)

Lower limit:

CT C1C2

QloadT

1md

(5)

where CT is the number of cooling towers, C1 is the coefficientbetween the applied load and condenser heat rejection, C2 is thespecific heat of water, Qload is the applied load, T is the tem-perature difference across the condenser, md is the cooling towerdesign water flow rate, i.e. 76 kg/s, and ml is limiting water flowrate, i.e. 25.3 kg/s. Integer output of the above equations was usedto determine the number of cooling towers. For example, if the Qloadis 2637 kW (750 tons), the temperature difference across the con-denser could vary between 3 C (5.4 F) and 9.5 C (17.1 F) withinthe cooling tower flow design limits. At 3 C (5.4 F), mass flow rateto satisfy the condenser heat rejection was 236 kg/s (3741 gpm),resulting in the chilled water system operation with a maximum of9 cooling towers within flow limit. Thus, 39 cooling towers wereevaluated to find the impact on system performance at the specificload. Fig. 5 shows possible numbers of cooling towers for different

values of temperature difference across the condenser along withthe corresponding water mass flow rates at 2637 kW (750 tons).Similarly, the feasible number of cooling towers is determined forall load values.

M. Ali et al. / Energy and Buildings 59 (2013) 111122 117

Fig. 3. Total power consumption and costs of the Baseline system configurations at various demand loads (A) Qload = 1055 kW, (B) Qload = 1582 kW, (C) Qload = 2637 kW, (D)Qload = 3516 kW, (E) Qload = 5274 kW, and (F) Qload = 7032 kW.Fig. 4. Summary of optimal system configurations. Fig. 5. Number of cooling towers within flow limit at Qload of 2637 kW (750 tons).

118 M. Ali et al. / Energy and Build

Table 5Range of configuration parameters used for optimization at various loads with con-stant condenser side temperature difference (4.45 C [8 F]) at full fan speed (100%).

Load, Qload (kW [tons]) No. of chillers, CH No. of cooling towers, CT

7032 [2000] 3 6175274 [1500] 23 4123516 [1000] 23 382461 [700] 13 26

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performance of the whole system. For the overall system optimiza-

FQ

1582 [450] 12 241055 [300] 12 23

Compared to the real system design operating with maximumve cooling towers, the investigated chilled water system wasperated with more cooling towers in accordance to the aforemen-ioned relationships (4) and (5). The upper and lower limits of theumber of cooling towers depend on the temperature difference,T across the condenser for each load value. In the second strategy,

he feasible number of cooling towers operating at full fan speeds presented in Table 5 with fixed T of 4.45 C (8 F) at variousload values. Number of cooling towers with varying T acrosshe condenser is considered in the third strategy. Results of theptimization show that operating the chilled water system with

ore cooling towers is beneficial compared to the referent designystem. Even with full fan speed, increasing the number of coolingowers will decrease the total system power consumption. Fig. 6

ig. 6. Total power consumption and costs of the modified system configurations at variload = 3516 kW, (E) Qload = 5274 kW, and (F) Qload = 7032 kW.ings 59 (2013) 111122

shows significant decrease in the total system power consumptiondue to the introduction of additional cooling towers at higherloads. Although such effect diminishes as the number of coolingtowers rises, the second strategy confirms the best design practice:maximize the efficiency by running as many cooling towers aspossible within the flow limit [26,38]. At the same time, the costslinearly depend upon the number of used equipment componentsand a break-even point can be determined based on the economicanalyses evaluating the cost-benefit of various design configura-tions based on yearly operating profile. However, such analysesfall outside of scope of the current study.

5.3. Overall chilled water system optimization: 3rd strategy

Optimal chilled water systems can be selected at the designstage with respect to system design and configuration parame-ters. For the design optimization, it is significant to vary condenserwater flow rate, temperature difference across the condenser,and cooling tower fan speed for specific load demand. Opti-mization of the system configuration in terms of the number ofchillers and cooling towers also extensively affects the energytion, simulation-based optimization strategy was implemented bycoupling Dymola/Modelica with GenOpt and using a hybrid PSOG-PSCCHJ optimization algorithm.

ous demand loads (A) Qload = 1055 kW, (B) Qload = 1582 kW, (C) Qload = 2637 kW, (D)

M. Ali et al. / Energy and Buildings 59 (2013) 111122 119

tion o

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TR

Fig. 7. Iteration runs for the minimiza

Fig. 7 shows a sample optimization search made for one specificoad demand. Difference between the two algorithms, PSO stochas-ic population-based and HJ direct search, forming the hybridSOGPSCCHJ algorithm can be identified from the iterations shownn Fig. 7. The iterations involve computation of objective functionith respect to varying parameters related to system design andonfiguration. The execution time for an optimization using a sin-le generation as PSO input parameter at a specific load demandas about 1.5 h on a 3.2 GHz PC with 1 GB RAM, as compared tobout 10 min on a 3.16 GHz Core 2 PC with 4 GB RAM. Increasinghe number of PSO generations from 1 to 5 increased the executionime threefold.

Peaks of Ptotal in both, PSO and GPS HookeJeeves regions of theybrid algorithm depict the penalty due to the unfeasible numberf cooling towers during the variation of T across the condenser.hus, for the optimal chilled water system design, the key con-ern is selection of the design temperature difference across theondenser.

One way to ensure optimal design could be through the investi-ation of chiller lift, i.e. the difference between chilled water supplynd condenser water return temperatures, as a proxy for the dif-erence between condenser and evaporator refrigerant pressures.ower lift would imply lower chiller power consumption butncreased consumption of other components. However, the currenttrategy finds the optimal condenser supply-return temperatureifference which can be considered as a proxy for the chiller lift inhe case of fixed chilled water supply temperature.

Feasible range of the possible system optimization parametersgainst various load demands is shown in Table 6. Fig. 8 shows aubset of the possible alternatives evaluated by the optimizationlgorithm deciding the selection of parameters that cause mini-ization of the objective function. The presented ranges of Ptotalre based on the variation of T across the condenser and varia-ion of cooling tower fan speeds. The estimated initial costs of eachvaluated configuration are also presented. As the overall optimiza-

ion resulted in the selection of maximum five cooling towers, theesults indicate the benefit of varying the water temperature dif-erence across the condenser and cooling tower fan speeds insteadf using constant design values. In addition, such variation resulted

able 6ange of system parameters used for optimization at various loads.

Load, Qload (kW [tons]) No. of chillers, CH No. of cooling towers, CT

7032 [2000] 3 325 5274 [1500] 23 318 3516 [1000] 23 312 2461 [700] 13 38 1582 [450] 13 36 1055 [300] 12 24 f Ptotal at Qload of 3516 kW [1000 tons].

in lower Ptotal and smaller number of cooling towers than the othertwo previously considered strategies. Therefore, such design is alsomore appropriate from economic aspects as it would decrease theinitial cost of the overall system.

Table 7 shows the optimal combination of system parametersto achieve the minimum total power consumption. For example, atthe low load demand of 1054.8 kW (300 tons), the chilled water sys-tem optimally operates with one chiller and three cooling towersmaintaining the optimal design T of 4 C (7.2 F) across the con-denser, while the optimal cooling tower fan speed is 47.94%. Thesystem has a minimum total power consumption of 171.56 kW. Allother feasible combinations in terms of the number of chillers, cool-ing towers, temperature differences across the condenser, and CTfan speeds will result in higher total system power consumptions.Similarly, at the peak design load of 7032 kW (2000 tons), the opti-mal system design configuration yields a total power consumptionof 1557.3 kW comprising of three chillers and five cooling towersoperating with optimal design T of 16.94 C (30.5 F) at fan speedof 85.3%. The optimal configurations comprise of maximum fivecooling towers despite of the fact that the number of cooling tow-ers within the flow limit can be up to 25. Consequently, in the case ofvarying condenser water temperature difference, water mass flowrate and cooling tower fan speed, may not be beneficial to run asmany cooling towers as possible within the flow constraints. Theresults shown in Table 7 are in agreement with the real system con-figurations. The optimal fan speeds below 90% also confirm the bestdesign practice, as suggested by Mark Hydeman, P.E., Taylor Engi-neering: running the cooling tower fans below 90% speed wouldachieve higher energy efficiency. Due to the cubic relation betweenthe fan power and airflow rate, the cooling tower energy consump-tion greatly increases with the top 10% fan speed, achieving a verysmall drop in condenser water supply temperature.

5.4. Summary of the chilled water system analysesA methodology is proposed for design optimization of the chilledwater system by applying three strategies: (1) baseline systemcomprising of a maximum of five cooling towers with fixed designtemperature difference across the condenser at full fan speed,

Temperature difference across condenser, T (C [F]) Fan speed, F (%)

325 [5.445] 30100315 [5.427] 30100312 [5.421.6] 3010038 [5.414.4] 3010035 [5.49] 3010034 [5.47.2] 30100

120 M. Ali et al. / Energy and Buildings 59 (2013) 111122

Fig. 8. Total power consumption and costs of the overall optimized system configurations at various demand loads (bars represent power range due to varying optimizationi Qload =a

(adsedd

TO

nput parameters) (A) Qload = 1055 kW, (B) Qload = 1582 kW, (C) Qload = 2637 kW, (D) nd (F) Qload = 7032 kW.

2) modified system with increased number of cooling towersccording to the flow turndown limit and fixed design temperatureifference across the condenser at full fan speed, and (3) modified

ystem varying both, the system design and configuration param-ters. While the first two strategies confirm the established bestesign practice and verify the simulation models, the third strategyefines a systematic approach for the overall design optimization

able 7ptimal values of system parameters and objective function at various loads.

Load, Qload(kW [tons])

No. of chillers,CH

No. of coolingtowers, CT

Teco

7032 [2000] 3 5 165274 [1500] 3 5 123516 [1000] 2 5 82461 [700] 2 4 71582 [450] 1 4 41055 [300] 1 3 4 3516 kW, (E1) Qload = 5274 kW with 2 chillers, (E2) Qload = 5274 kW with 3 chillers

of chilled water systems. In addition, the strategies consider ini-tial costs of equipment, including chillers, cooling towers andpiping/fittings/valves, having in mind that additional chillers and

cooling towers typically decrease the annual energy costs and pay-back period [33,41].

Optimal total system power consumptions and system energyuse in kW/ton from the considered approaches are shown in

mperature difference acrossndenser, T (C [F])

Fan speed,F (%)

OptimalPtotal (kW)

.94 [30.5] 85.31 1557.3

.68 [22.8] 75.44 993.1

.45 [15.2] 62.8 582.7

.63 [13.7] 59.75 394.9

.88 [8.8] 49.3 240.7 [7.2] 47.9 171.6

M. Ali et al. / Energy and Buildings 59 (2013) 111122 121

Table 8Optimal values of Ptotal and system energy use of all strategies (minimum values highlighted).

Qload (kW [tons]) 1st strategy Ptotal (kW) kW/ton 2nd strategy Ptotal (kW) kW/ton 3rd strategy Ptotal (kW) kW/ton Percentage of power saving (%)

7032 [2000] 2757.3 1.37 1808.4 0.9 1557.3 0.78 43.55274 [1500] 1498.8 0.99 1178.8 0.78 993.1 0.66 33.83516 [1000] 752.7 0.75 706.9 0.71 582.7 0.58 22.62461 [700] 479.7 0.68 479.7 0.68 394.9 0.56 17.61582 [450] 297.8 0.66 297.8 0.66 240.7 0.53 19.21055 [300] 207.4 0.69 207.4 0.69 171.6 0.57 17.3

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able 8. The values of energy use (kW/ton) are in agreement withhe typical values for the chilled water systems [41]. It can bebserved that the lowest total power consumption, Ptotal, and sys-em energy use is achieved for the modified system with varyingemperature difference across the condenser and varying fan speed.lso, the performance of chilled water systems strongly depends onhe appropriate selection of mass flow rates and temperature dif-erences across the condenser. The simulation-based optimizationpproach enhances the decisioning about optimal variation in thesearameters resulting in the minimum total system power con-umption. Significant power savings between 17% and 43.5% coulde achieved operating chilled water systems at the optimal designnd configuration parameters compared to the baseline case.

.5. Potential benefits and limitations

The current study developed a model of the chilled water systemased on EOO approach and using open source component libraries.ptimization of HVAC systems involved variation of componentesign parameters as well as configurations. Dymola/Modelicanabled control of all component parameter values from externalext files for the purpose of design optimization. Serial and/or par-llel arrangements, as well as the number of component modelsere also decided from external files. Multiple instances of sin-le component models were used within the component arrays toary the number of cooling towers. The aforementioned featureselped to develop efficient and effective multi-domain systemodels for simulation and optimization. Automated optimizationf the systems involving many design and configuration param-ters offered the possibility to identify the most energy efficientesign. The applied optimization algorithm used fewer genera-ions as compared to the literature recommendations [25], as noariation in system optimization parameters was observed afterhe initial two generations. Such a selection of optimization inputarameters saved simulation time, while possibly hindering thechievement of globally optimal results. The methodology of cou-ling Dymola/Modelica and GenOpt proved as efficient in terms offforts, time, and ease to vary the design and configuration param-ters for the selection of optimal system configurations. The maineakness of Dymola/Modelica application to building simulations

s the lack of sufficient HVAC component models within the exist-ng modeling libraries. However, research activities are underwayo enhance the available libraries by developing additional compo-ent models [45,46].The current study assumes that the chilled water system compo-

ent sizes are predefined and optimizes the number of componentsnd design operating conditions using fixed parameters: chilledater supply and return temperatures, dry bulb and wet bulb

emperatures. Measurement uncertainties associated with the

quipment selection are neglected as well as their potential impactsn the optimization results of the total power consumption.As such the proposed approach could be considered as part of

he iterative design process, rather than an all-inclusive procedure.6. Conclusions and future work

In the current study, an incremental development of themethodology for chilled water system optimization is proposed.The equation-based object oriented modeling approach was usedto model a real chilled water system located at a SymantecCorporation building in South California, USA. The chilled watersystem model developed in Dymola/Modelica was capable ofvarying system design and configuration parameters at differentload demands. Dymola/Modelica model was coupled with GenOptoptimization software. The simulation-based optimization used ahybrid PSOGPSCCHJ optimization algorithm to find the optimalsystem configuration. The current study analyzes several designparameters having significant impact on the system performance.In addition, the estimated initial cost of each configuration is pro-vided for economic considerations. The chilled water system wasanalyzed considering three strategies, two of which involved con-stant temperature difference across the condenser at full fan speedsapplied to the baseline and modified system configurations. Thethird strategy varied the temperature difference across the con-denser as well as the cooling tower fan speeds in addition tothe system configuration parameters and proved to be the mostenergy efficient. Consequently, the optimal values of the consideredparameters were provided together with the calculated power con-sumption. Operating chilled water systems at the optimal condi-tions could result in significant total system power savings amount-ing up to 43.5% for the considered cases. The implemented auto-mated simulation-based optimization approach proved efficient interms of model development and computational time needed tofind the optimal configurations. The methodology represents a steptoward the design of software systems able to synthesize new andoptimal HVAC system configurations. Such development shouldhelp the design practitioners to select the optimal system configu-ration parameters at the initial stage. In the future, the developedsimulation-based methodology will be applied in a more compre-hensive analysis using hourly load profiles and climatic data.

Acknowledgements

The authors express profound gratitude to Mark Hydeman, P.E.,Fellow ASHRAE, Principal at Taylor Engineering LLC, Alameda, CA,USA, and Anton Haumer, Senior Engineer, AIT Austrian Instituteof Technology, Vienna, Austria, for their insightful comments andexpertise.

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Energy analysis of chilled water system configurations using simulation-based optimization1 Introduction2 Overview of modeling, simulation, and optimization tools3 Chilled water system optimization4 Methodology4.1 Description of the investigated chilled water system4.2 Model development4.3 Objective function4.4 Optimization algorithm4.5 Simulation-based optimization

5 Results and discussion5.1 Baseline system configurations: 1st strategy5.2 Modified system configurations: 2nd strategy5.3 Overall chilled water system optimization: 3rd strategy5.4 Summary of the chilled water system analyses5.5 Potential benefits and limitations

6 Conclusions and future workAcknowledgementsReferences