dynamicsimulation of a building central chilling system and evaluation of emcs on-line control...

8/12/2019 Dynamicsimulation of a Building Central Chilling System and Evaluation of EMCS on-line Control Strategies

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Pergamoni/din g ondEmironmenr, Vol. 33, No. 1, pp. I-20, 1998

0 1997 Published by Elsewer Science Ltd. All rights reservedPrinted in Great Britain

0360-1323198 %19.OO+O.Oil

PII: S03604323 97)0001~X

Dynamic Simulation of a Building CentralChilling System and Evaluation of EMCSOn-Line Control StrategiesSHENGWEI WANG* Receiv ed 20 Januar y 1997; accept ed 30 Ap ri l 1997)

Dy nami c models of centr if ugal chill ers, heat exchangers, seaw ater and chill ed-wat er netw orks,cooli ng coil , actuator , sensor, vari able-speed pump and DD C control ler of EM CS are developed tosimul ate the dynami cs of a seaw ater-cooled chil li ng system control led by EMCS on-l ine strat egies.The thermal, hydr auli c, energy and contr ol performances of the system are simul ated. The on-l inecontrol str ategies i.e. adapti ve and derivat iv e strat egies) developedfor the centr al chi ll ing systemare tested and eval uated by applyi ng them to contr ol the simul ated li vi ng chil li ng system underdif ferent AH U dynami c loads. Thi s paper presents the models, system dynami c simu lat ion of thechil l ing system, chil l i ng system performance monitori ng, vali dation of simul ation, EMCS on-li necontrol str ategies and eval uati on of the strat egies. 0 1991 Publi shed by Elsevi er Science Lt d.

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NOMENCLATUREimpeller exit area, area [m ]velocity [m/s]specific heat capacity [kJ/(kg K)]capacity flow rate, thermal capacity [kW]frequency [Hz]enthalpy [kJ/(kg K)]hydrodynamic losses [kW/kg]polytropical compression work [kW/kg]compressor theoretical head [kW/kg]mass flow rate [kg/s]number of transfer units [-_Ipressure [kPa]valve position [-_Iheat transfer rate [kW]heat transfer resistance [k/kW]sensible heat ratio [-_Itemperature [Climpeller tip peripheral speed [m/s]overall heat transfer coefficient [w/K]specific volume [m3/kg]power consumption [kW]

Greek lett ersB impeller vane angle6 heat exchanger efficiency0 prerotation vane anglet time [s]

Subscriptsa airC coil

cd condenser or condensationcamp compressorev evaporator or evaporation

*Department of Building Services Engineering, The HongKong Polytechnic University, Kowloon, Hong Kong.

1

ex exhaustexch heat exchanger

f fictitious flowimp impellerin inletmax maximummin minimum

pi pipePu pumpsea seawatertot total

W waterwt wet regime

INTRODUCTIONDynamic simulation of a building HVAC system hasbeen used in assisting the building control application inthree areas as summarised by Kelly and Dexter in acomprehensive literature review [1, 21: regulation, super-visory control and optim ised buil ding control. In practice,these topics will tend to overlap one another.

When simulation aims at studying or optimising theregulation of HVAC processes (local loop control stra-tegies), only the dynamics of certain processes or sub-systems is of concern. The studies on control performanceand adaptive controls of air handling unit (AHU) andvariable air volume (VAV) are examples of such an appli-cation [3-91.

The IEA research project Annex 17 gives an examplereflecting the advance of system simulation in testingand evaluating energy management and control systems(EMCS) supervisory control strategies for entire buildingsystems as well as control strategies implemented in real


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Dynamic simulation of a building central chilling systemi7 From Building W I

Power TransducerTemperature Sensor

ifferential Pressure SensorFlow Meter

Ihiller

Fig. 1. Central chilling system and chilled-water network with measurement points essential for EMCScontrols.

across the secondary pumps, and the seawater flowrate are monitored by EMCS.

CHILLING SYSTEM COMPONENT MODELSCentri@gal chiller

The chiller model simulates the dynamic performanceof single or multi-stage water-cooled centrifugal chillersusing a steady-state approach and a dynamic approach.

The model simulates the chiller performance undervarious working conditions on the basis of the impellertip speed (u,), impeller exhaust area (A), impeller bladeangle (8) and 13 other coefficients/constants, which mightbe available from chiller technical data or identified (par-tlally or fully) by an associated preprocessor using chillerperformance data under full load and partial load. Thechiller chilling capacity is assumed to be controlled byadjusting the inlet vane angle (0). Figure 2a shows therefrigeration cycle of the two-stage centrifugal chillermodel.

Compressor. The compressor is modelled on the basisof mass conservation, Euler turbomachine equation andenergy balance equation. Figure 2b shows the velocitytriangles of the impeller and prerotation vane. The Eulerequation is modified by considering the impeller exitradial velocity (c,J distribution, and derived as equation(1):

where h,, is the theoretical head, B is the ratio of impellerchannel depth at intake to that at exhaust D and v arespecific volume at impeller intake and exhaust, respec-tively.

Energy balance equations are applied to two controlvolumes, i.e. compressor control volume (from com-pressor suction to compressor exhaust) and impeller con-trol volume (from compressor suction to impeller exit) asshown in equations (2) and (3):

h,, = h,,, ,mp -t h,, nnp + 2: (3)where h,,, is polytropic compression work, hhyd s hydro-dynamic losses, ci is vapour velocity at impeller exhaust.

Hydrodynamic losses in two control volumes are con-sidered to be composed of three elements, i.e. flow frictionlosses, inlet losses and incidence losses as shown in equa-tions (4) and (5). The flow friction losses are consideredproportional to the compressor volume flow rate squaredand thus proportional to the impeller exit radial velocitysquared (&). The inlet losses are considered pro-portional to the velocity through prerotation vanes chan-


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4 Shengwei Wang

Pcd

P,

(b)

impeller

--_ U_~- _ \\L-_L ; +-+=-=j\ pre-rotation vane / I Cd

Fig. 2. Schematic of refrigeration cycle and velocity triangles.

nel squared. Incidence losses are considered proportionalto the shock velocity component squared.

where [, $,, tj2 and x are the introduced constants.Given the evaporator pressure, condenser pressure and

position of inlet vanes (value of 8), the compressor modelcan calculate radial velocity and specific volume at impel-ler exhaust, and thus refrigerant mass flow rate andinternal power. The compressor capacity is controlled bythe inlet vanes angle (0) as shown in equations (4) and(5).For multi-stage chillers, single stage compressor equa-tions are used to calculate the first stage. The second

stage is assumed to have the same flow efficiency andcompression ratio as the first stage. Mass and energyconservations are applied to the economiser and the mix-ing process at the second stage suction. Only the firststage impeller geometric parameters are of concern.

Condenser and evaporator. The evaporator and con-denser are simulated using the classical heat exchangerefficiency method. By considering the effects of water flowrate (MW,c,,Mw,J and heat flux (Qe,, Qcd), the evaporatorand condenser overall heat transfer coefficients (UA,,,(/A_,) are represented empirically as shown by equations(6) and (7):

CIA,, = [C,M,~,8+C2Qevo-74s+C3]~,UA,* = [C f,::+ C,Qz+ CJ,

where C,-C, are constants.

(6)(7)


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Dynamic simulation of building central chilling system 5

The evaporation and condensation temperatures, andthus the evaporator and condenser pressures, are calcu-lated, given the chilling capacity (Q,), heat rejection(Qcd), chilled and cooling water flow rates and inlet tem-peratures.

Power consumption. Chiller power consumption (IV) iscalculated on the basis of the compressor internal power(IV,,). With the consideration of compressor mechanicaland leakage losses, motor electrical and mechanicallosses, the chiller power consumption is the sum of threecomponents, i.e. the internal compression power (W,,), avariable part of the losses which is proportional to theinternal compression power, and a constant part of thelosses (IV,) as shown in equation (8):

W= aW,,+ W,,where a is a coefficient.

(8)

Model parameter identification. Of the model par-ameters, impeller geometric parameters (zL~, A, /I) areeither given by the manufacturer or, together with theother parameters, identified according to the per-formance data given by manufacturers or from fieldmeasurements, Parameter identification is carried out bya preprocessor developed for the model.

Dynamics model of chiller. The dynamics of the chilleris simulated by assuming two thermal storages, one atthe cooling water inlet of the condenser and the other atthe chilled-water inlet of the evaporator, as shown inFig. 3 and represented mathematically by two first-orderdifferential equations (equations (9) and (10)). This sim-ple approach presents both the dynamic response of thechiller to the change of working conditions (inlet tem-peratures) and the dynamic effects of the working con-dition changes on the compressor load.

(9)

(IO)whereT,,,,,and Tcd,,nre the inlet temperatures of chilled-

Condenser

ExpansionDevice Compress0

Chilled Water Evaporator Chilled waterO k Inlet I

Fig, 3. Schematic of chiller dynamic model.

water at the evaporator and cooling water at thecondenser, T:,,,, and T:d,,n are the inlet temperatures ofchilled-water and condenser cooling water after intro-ducing dynamic effects of evaporator and condenser.Variable-speed pump and seawater network

The variable-speed seawater pump set is simulated bya steady-state pump, a steady-state frequency inverterand a dynamic actuator of the inverter. The frequency atthe outlet of the inverter is linear to the input signal fromthe actuator. The efficiency of the inverter is includedwithin the model of pump energy performance. Theenergy performance and pump characteristics at variousspeeds are simulated using fourth-order polynomial func-tions as shown in equations (11) and (12). The coefficientsof the equations are determined by regression using theperformance data from experiments or manufacturerscatalogues.

Wpu(.L pu)= z i GJMpu, (11)f-O,=0(12)

where WpUand PpUare the pump power consumption andpressure head, respectively,fis the frequency input to thepump, M,, is the water flow rate through a pump, and Gand E are coefficients.

To avoid the difficulty of convergence, which is facedin most cases when solving the equations in separatemodels using component-based simulation programs, thepressure flow characteristics of the entire seawater net-work are simulated in one single model (Fig. 4). Thepressure flow balance of the seawater network is obtainedby solving the pressure flow equations of the entire net-work within one single model by internal iteration. Thethermal dynamic performance of heat exchangers is simu-lated by another separate model which uses the waterflow rates through heat exchangers available from theoutputs of the seawater network model.

The model of the seawater network pressure flowcharacteristics consists of the pressure balance equation(equation (13)) flow balance equation (equation (14))and the equations presenting the pressure flow charac-teristics of exchangers, pipe and variable-speed seawaterpumps. The pressure flow characteristics of the pipes andheat exchangers are modelled by polynomial equationsand the coefficients of the equations are determined usingthe pressure flow performance data measured on site.These coefficients might be determined by the pipe andvalve pressure loss equations when details of the geo-graphic data of the network are available.

P&f; M,,) - Pp,(M,,,)- Pexch,XKxch.r) = 0, (13)

Secondary chilled-water networkThe pressure flow balance of the secondary chilled-water network is shown in Fig. 5. The building is dividedinto five zones. The pressure flow characteristics of theselected AHU from each zone are simulated and all the


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Water PipeFig. 4. Schematic of seawater network model.

Fig. 5. Schematic of secondary chilled-water network model.

other AHUs in the same zone are considered by mul-tiplying the flow rate of the simulated AHU by a ratio.The entire secondary chilled-water network flow pressurebalance is simulated and solved by one single model,similarly to avoid difficulties in convergence.

As inputs of the network model, the positions of thetwo-way moderating valves are given by the actuatormodel controlled by the AHU supply air temperaturecontroller, and the flow rate through the coil is computedby this network model and returned to the AHU coilthermal model for AHU temperature loop simulation.

The model calculates the flow resistance of the coilsaccording to positions of associated valves.

The pressure flow and energy characteristics of theconstant speed secondary chilled-water pumps are simu-lated by giving the pump curve and power curve. Theflow resistances of the main supply and return pipes andpipes between the zones are considered to be constant.Knowing the number of pumps in operation, the pos-ition of AHU moderating valves and the total chilled-water flow rate from chillers, the network model deter-mines the network balance and computes the water flow


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Dynamic simulation of a building central chilling system 7

t cding Water Inlet Cooling Water OutletFig. 6. Schematic of heat exchanger dynamic model

11

rates through individual AHUs, the pressures at variouspoints in the network, the flow through individual pumps,the pump power consumption and the water flow ratethrough the bypass.Pri mary seaw at er heat exchanger

The dynamic performance of the heat exchangers isrepresented by one classical steady-state heat transfermodel and one simple dynamic model. The heat transfermodel computes the number of transfer units (NTU)and heat transfer efficiency E using equation (15) for acounterflow heat exchanger and then computes thesteady-state seawater and condenser water outlet tem-peratures. Since the condenser water flow rate is constantin the system, the heat transfer coefficient varies accord-ing to the change of seawater flow rate.

e=l-e- ,,,(,-2)1-+ IndX

Two thermal storages are assumed

(15)

at the seawateroutlet and cooling water outlet, respectively, to simulatethe dynamic response of a heat exchanger (Fig. 6). Twodifferential equations represent the dynamic per-formances as shown by equations (16) and (17):

C d K,,,e,___ = C.cd dz P.Ww,cd(Tw.cd,ex- G,cd,exh 17)

where Sea,,, nd Tw,cd,exre the steady-state outlet tem-peratures of seawater and condenser cooling, TX,,,, andT ,cd,ex are the water outlet temperatures of seawater andcooling water after introducing dynamic effects of anexchanger, C,,, and Cw,cd are capacities of thermalstorages in the seawater and condenser cooling watersides, respectively.Cooli ng coil

The model is developed on the basis of the math-ematical model proposed by Lebrun et al . in Annex 17[19]. The model includes a steady-state approach and adynamic approach. A first-order differential equation isused to represent the dynamics of a coil with lumpedthermal mass. The dynamic equation on the basis ofenergy balance ensures that the energy is conserved.

(18)where t, is the mean temperature of the coil, fa,inand t,,,,

are the inlet air and water temperatures, C, is the overallthermal capacity of the coil, R, and R2 are the overallheat transfer resistances at the air and water sides.

The air and water temperatures at the outlet (t t,ex,w,excan therefore be computed by the heat balances of bothsides:

t = t _ SHR(ta.in- ca,ex a,m &Ca t t tc L,mw,ex = w t n &Cw

19)

where C, and C, are the capacity flow rates of air andwater, SHR is the sensible heat ratio. SHR takes the samevalue calculated in the same inlet condition in the steady-state case using the bypass factor method.

The heat transfer calculation applies the classical num-ber of transfer unit (NTU) and heat transfer effectivenessmethods. The classical method to calculate the effect ofthe fin on the air side on thermal resistance is applied.Two different methods are used to calculate the heatconvection coefficient on the air side in dry and wetregions, respectively.

In the dry regime, the overall heat transfer resistanceR) is computed as follows:

NT&$= ACmi R, Rm R,) (21)Ill,> , (22)

R= Tam Tw,,, 1Q Gli where A is the total heat transfer surface area, R,, R,and R, are the heat transfer resistances of air side convec-tion, coil metal and water side convection, N,,, is thenumber of the row.

In a wet regime, a fictitious air flow is assumed, whichhas a specific heat equal to the average saturation specificheat (c,, specific heat of saturation moisture air at theaverage temperature of air inlet wet bulb temperatureand water inlet temperature). The air capacity flow rateand air convection coefficient of the fictitious air flow(C,, h,,,) are as follows:

G = m,c,,ha,wt ha:,

where cP, is the specific heat of moisture air.Then, the overall heat transfer resistance R) is com-

puted using the same approach:AUNTU,=C= A

mm f Cm f R,,, + Rm R,) (26)or = f (27)

(28)29)


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where subscript wt represents wet regime, f rep-resents fictitious air flow.

Having the overall heat transfer resistance, the overallheat transfer resistances in the water side (R,) and airside (R,) are calculated as below. When the coil is in awet regime, the corresponding values in the wet regimeare used.

R

I= R &f&P

R,+ R,+R,R

2= R fLtRnl2

R,+ R,fR; (32)

Actuator, sensor and pipeThe actuator model is used to represent the charac-

teristics of actuators [6]. The actuator is assumed to accel-erate very quickly and then turn at constant speed. Aminimum change (e.g. the sensitivity of the actuatordefined as a parameter of the model) in a demandedposition is required to restart the actuator. The modelincludes the hysteresis in the linkage between actuatorsand valves or dampers. If a valve stem is driven by arotary actuator, the speed of the valve stem varies withthe position of the crank.

The actuator model also counts the number of start/stop or reversals of an actuator and the value of trav-elled distance of the valve (counted as one unit when avalve moves from its minimum position to its maximumposition). These figures provide indications of wearpotential of an actuator/valve and hence the cost of fail-ure or maintenance.

The dynamics of temperature and pressure sensors aresimulated by using the time constant method. One first-order differential equation (equation (33)) represents thedynamic characteristics of a sensor:

dy y-y-=~dt T, (33)where y is the true value of the measured variable, 2: isthe measured value of the variable, and T, is the timeconstant.

The thermal dynamic characteristics of water pipes aresimulated by using the pipe model TYPE3 1 using variablesize segments of fluid available in the TRNSYS library.Entering fluid shifts the position of existing segments.The mass of the new segment is equal to the flow rate ofthe simulation time step. The new segments temperatureis that of the incoming fluid. The outlet of this pipe is acollection of the elements pushed out by the inlet flow.Direct digital control

Unlike so-called perfect models of a controller nor-mally used to test the energy performance of controlstrategies, the realistic models developed represent thefollowing functions of the BEMS: Direct Digital Control(DDC) functions, discrete time operation of digital con-trollers and supervisory control strategies.

The time scheduling of a sampling cycle is consideredto be in four steps: process variable sampling, control

output computation, control signal output, and waitingtime for the next sampling cycle.

The PID control function used in DDC loops uses theISA algorithm, which has the discrete form as shown byequation (34). The handling of the proportional term,integral term and derivative term follows the algorithmimplemented in commercial building energy managementand control systems.

KATUk = Kpek+ I,_, + e(e_, +e,) +KpTdw,I

where(34)

Zk_,+5 (ei_,+ek)=Zk,I

with ek, ek_, the error signals at current sampling andprevious sampling, K, the proportional gain, T, the inte-gral time, Td the derivative time, AT the sampling inter-val, Zk and Ik_, the integral time at the current and pre-vious time steps, U, the output of PID.

MONITORING OF CHILLING SYSTEMPERFORMANCE

The existing Building Management System is used tomonitor the central chilling system. It is important tocalibrate the sensors and transducers of every BMS moni-toring point before performance monitoring. Over 2Ctemperature error in some temperature sensors wasobserved when calibrating the sensors in this particularbuilding. The performance data monitored by BMS werecorrected according to the correlation between the BMSreadings and true values obtained by calibration.

Evaporator inlet water temperature, evaporator outletwater temperature, condenser inlet water temperature,condenser outlet water temperature, power consumption(kW) of the chillers are recorded every 2 min by the BMSduring the chiller monitoring stage. The chilled-waterflow rate to the building, supply and return temperaturesof chilled water, and bypass water flow rate are alsorecorded by BMS during monitoring. The water flowrates through evaporators and condensers were measuredonce and are assumed to be constant.

The power consumption of one selected chiller wasmonitored under various condenser inlet water tem-peratures (around 27, 30, 31, 32C), chilled-water dis-charge temperatures (around 7, 8, 9C) and variouscooling loads. The performance of other chillers wasmonitored in certain conditions to check the difference.The performance data in full load were obtained whenthe chilled-water outlet temperature was obviously higherthan its set-point.

The performance data in full load and partial loadwere used to identify the parameters of the chiller usingthe preprocessor of the chiller model. Figure 7a and bshow a comparison between predicted and measuredpower consumptions and chiller capacities in full load.The difference between the predicted and measuredpower consumptions is less than 10%. The differencebetween the predicted and measured capacities in fullload is less than 5%. These differences can be explained


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0 200 400 600 800Measulod power consumption kw)

(b)+ 5%.

., 0 _I , 00 Y0 ,/ , -5%/ 9 o,*/ 9 09,I I ,.

, ,I,/ ,, *,

3000Measwcd full load capacity kw)

3500

Fig. 7. Comparison between predicted and measured powerconsumption and capacity.

by the accuracy of the chiller model and the measure-ment.

The heat transfer coefficient of the seawater heatexchanger under various seawater flow rates (Fig. 8a)was obtained by measuring the inlet/outlet temperaturesin both sides and the seawater flow rate. The seawaterpressure loss across the heat exchangers under variousflow rates was measured (Fig. 8b).

The pressure loss of the water across the seawatersupply and return pipelines under various flow rates wasmeasured (Fig. 8~). The pressure head and power con-sumption of one selected seawater pump under differentflow rates and different input frequencies (35, 40, 45,49Hz) were monitored. The other pumps were testedin certain selected conditions. No significant differencesbetween different pumps were observed.

The secondary chilled-water distribution pump per-formances and pressure losses on the AHLJ includingvalves were also tested in a few conditions. The powerconsumptions of the constant speed condenser and evap-orator water circulation pumps were measured manuallyand are assumed to be constant.

SYSTEM SIMULATION AND EVALUATIONA Transient System Simulation Program (TRNSYS)

is used as the platform for the dynamic simulation of a

HVAC system. Figure 9 shows the schematics of theinterconnection of the component models (informationdiagram) in simulation.

Five AHUs are simulated, each of which representsthe dynamic load of AHUs in one of the five zones of theentire building. Only one zone is occupied 24 h a day.The AHU outlet temperature is controlled by a digitalPID controller at a given set-point which is different fromseason to season. A two-way moderating valve is used tocontrol the water flow rate through a coil, which is drivenby an actuator according to the control signal given fromthe temperature controller. The flow rate through eachsimulated AHU coil is multiplied by a factor to representthe total load of all AHUs in the zone.

To avoid the difficulty in convergence when solvingthe pressure flow balances of a chilled-water networkand seawater network, the pressure flow balances ofthe chilled-water network and seawater network aresimulated by separate models. The water flow ratesthrough cooling coils and heat exchangers as the outputsof the water network models are given to the performancemodels as inputs. The temperature controller will adjustthe valve positions and then the flow rate to control thetemperatures at the set-points.

The air flow rate and AHU inlet temperatures fordifferent seasons are given by data files during simulationas a test condition, which has slightly different patternsfor the AHUs in different zones to ensure that controlactions of AHUs in different zones are not simultaneous.The overall cooling load of the AHU in the entire build-ing in four different seasons used in the simulation exer-cises is shown in Fig. 10, which is selected by referring tothe actual building load in different seasons from fieldmonitoring.

Tests were performed to check the accuracy of thesystem performance obtained by simulation. Tests showthat the performance data given by simulation generallymatch the measured values in the same condition. Thecomparison of some important variables of a 1 day testare shown in Fig. 11. The simulation was conducted bygiving the monitored data of numbers of chillers andpumps in operation, chilled-water supply and returntemperatures, seawater pump frequency and seawatertemperature as test conditions.

Figure lla shows the total consumption of chillers.One chiller was used overnight and two chillers were usedduring the day. The difference between the simulatedand measured chiller total power consumption is within50 kW and not more than 10% in the test, with exceptionfor a very short period. Figure 11 b shows the comparisonbetween simulated and measured condenser inlet andoutlet temperatures in the same test case. The simulatedcondenser inlet temperature was slightly higher than themeasured values (about 0.4 to 0.8C) at most times in theday. The simulated condenser outlet temperature wasabout 0.5 to 1.2C higher than the measured values atmost times of the day.

Figure 1 lc and d show the comparison between thesimulated and measured seawater flow rates and pumpconsumptions. The flow rate was monitored by the BMSand the pump power consumption was recorded manu-ally. The difference between the simulated and measuredseawater flow rates was not more than 4% in the test. The


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250

200

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100

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6040200

80 100 120 140 160 180 200 220Water Flow RateL/s)

100 125 150 175 200 225 250Water F low Rate (L/s)

300 350 400 450 500 550Water F low Rate (L/s)

Fig. 8. Performance of heat exchanger and seawater pipes,


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8000

E= 6000d4

4000

WinterMiddle l

1 Middle II ~ k

3:oo 6:00 9:oo 12:oo 1500 18:OO 21:oo 0:oo

Time (hour)Fig. 10. Total cooling load of AHUs in the building.

difference between the measured and simulated seawaterpump power consumption was less than 3% in the test.

CENTRAL CHILLING SYSTEM CONTROLSTRATEGIES

Sequence and local oop control sThree local loop control strategies and one supervisory

control strategy are used to control the chilling system.Five PID controllers are used to control the AHU supplyair temperatures.

The chiller sequence controller controls the number ofchillers in operation according to a few criteria, increasingthe number of chillers and reducing the number of chil-lers. If a negative bypass Aows over a certain value forsignificant time, one more chiller will be switched on. Ifthe measured total chilling load of the building is, forsignificant time, less than the total cooling capacity ofthe running chillers when one of the currently runningchillers is switched off, one chiller will be switched off.The number of seawater pumps in operation depends onthe number of chillers in use, i.e. one pump is used if oneor two chillers are in operation, two pumps are used ifthree or four chillers are used.

The sequence controller of the secondary pumps deter-mines the number of pumps in use according to the mea-sured pressure head at the secondary pump supply.Switch set-points for increasing the number of pumps(from one to two and from two to three) and reducingthe number of pumps (from three to two and from twoto one) are set as the parameters of the controller.

One PID pressure controller is used to maintain theseawater pressure head at the inlet of the heat exchangersat its set-point, which is reset by the seawater pressuresupervisory control strategy. The pressure controllersenses the pressure at the inlet of heat exchangers andadjusts the frequency output of the frequency invertersusing the PID algorithm.

Opt im al pressure set- poi nt resetOn-line optimal reset of the seawater pressure set-point

is achieved by a derivative control strategy and an adapt-ive control strategy (Fig. 12). The derivative control strat-egy resets the pressure set-point by adjusting the pressureset-point according to the estimated derivative of the totalpower with respect to seawater pressure. The adaptivestrategy is used to identify the system parameters essentialfor the derivative control strategy.

The derivative control adapts the knowledge of oper-ators in supervising and optimising a controlled variable.The controller estimates the derivative of total powerW,,, with respect to the seawater pressure (P) across theheat exchanger. If the value of the derivative is positive,the pressure should be reduced. In contrast, the pressureshould be increased if the derivative has a negative value.The pressure will approach the optimal point when thederivative approaches zero at a certain building load orcooling load. In the actual operation of a building chillingsystem, the adjustment will be a regular task for a con-troller to maintain a zero derivative value, since the loadis changing.

The BMS estimates the derivative of the pump poweron the basis of equation (35) using the system informationavailable from conventional BMS monitoring systems.To properly adjust the pressure, the controller shouldidentify the derivatives of pump power and the heat trans-fer coefficient with respect to the pressure, and shouldobtain the chiller energy power, mean temperaturedifference and heat transfer coefficient.

(35)

where W,, s seawater pump power, WC s chiller power,C, is a constant coefficient representing the effect of con-denser cooling water temperature on chiller power, T,is the mean temperature difference between condensercooling water and seawater in an exchanger.


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1400

1200

g 10000g 800Ezs 600vBg 400a

200

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4035

3052 25eEg 20EF

1.5

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a-+-Power (Simulated)-Power (Measured)

Time (hour)

@I

Temp. at Condensor Outlet Simulated)-+ Temp. at Condensor Inlet Measured)-+Temp. at Condensor Outlet Measured) _

Time hour)


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500450400350z2 300

Q)3 250ti4 200IA 150

10050

0

100

1

( cc>

-Total Sea Water Flow Rate (Simulated) 1

Time hour)

- Seawater Pump Consumption (Simulated) IL__t Seawater Pump Consumption (Measured)


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Dynamic simulation of a building central chilling system 15Total Pump Power Power of Chillers P. Tseal. Hz)

~...4

TW.CdlTW.CdZTsea2on/off)

Fig. 12. Schematic of pressure set-point reset strategies

Having the derivative of the total power with respectto the pressure, the controller adjusts the pressure set-point with a change rate determined by the derivativefunction shown in equation (36):

(36)where dP,,,/dt is the change rate for pressure set-pointadjustment and K is the proportional gain (a positiveconstant).

Besides the chiller power, heat transfer coefficient andmean temperature difference, two derivatives (derivativesof heat transfer coefficient and pump power with respectto pressure) are essential in order to identify the derivativeof total power. Recursive Least-Squares (RLS) esti-mation with exponential forgetting is implemented toidentify the derivatives of the heat transfer coefficient andpump power with respect to the pressure. It is also used asa filter in identifying the heat transfer coefficient. Second-order models for the pump power and heat transfercoefficient are used, which are shown by equations (37)and (38):

UA(t) = MfMl(or~2(t)l *Lp(o>m21~ (37)Wp(O = Mf),c,(o,G(~)l* LpmYo21T, (38)

where b,(t), b,(t), b,(t), co(t), c,(t), c2(t)are time-dependentparameters to be identified.

Four estimators for heat transfer coefficient are used,one for each chiller exchanger. Each estimator requeststhe water temperatures at the inlet and outlet of therelevant exchanger, and the pressure. One estimator isused for pump power derivative. At each sampling step,the derivatives of heat transfer coefficient and pump

TW,CXIITw.cd2Tseaz(on/off)

Tw,cdl1 Tw,cd2- Tsea2- on/off)

power, and the heat transfer coefficient, are estimatedusing the coefficients identified.

PERFORMANCE TEST AND EVALUATIONOF CONTROL STRATEGIES

Two simulation exercises were conducted to test thedynamic response and energy performance of the systemand control strategies under various loads. In the firstexercise, three 1 day tests were performed in each of thefour cases. In the first test, the pressure is reset by theoptimal reset strategy. In the other two tests, the pumpwas set to be minimum or maximum speed. In the secondexercise, the control strategies were tested over 4dayswith the same building cooling load of the summercase for all four days. The heat transfer coefficient of theheat exchangers reduced gradually for 30% over 4 days.

Figure 13 presents examples of pressure control loopstability during tuning the parameters of the controllers.Figure 13a shows the performance of seawater pressurecontrol loop when an unsuitable (too high) proportionalgain of the PID pressure controller was used. The fre-quency of the pump was not controlled stably and thepressure across the heat exchangers and pumps was oscil-lating. The pressure set-point oscillated slightly. Figure13b shows the performance of the same control loopwhen an unsuitable proportional gain of pressure set-point reset controller was used. The pressure set-pointoscillated obviously and caused the oscillation of thepump frequency and pressures across the heat exchangersand pumps. The pressure was significantly higher thanits set-point during the night since the pump frequencywas at a minimum and cannot be reduced further.


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16 Shengwei Wang280

240

200

80

40

0

280

240

Pressure Head of Pump n d

Pressure Set-Point

0 2 4 6 8 10 12 14 16 18 20 22 24Time hour)

l Pressure across ExchangerPressure Set-Point

0 _

20

-7

60

55

50

45 Qz

40 z?9%

35 %

30

25

200 2 4 6 8 10 12 14 16 18 20 22 24

Time hour)Fig. 13. Examples of seawater pressure control loop stability during tuning.

Figure 14 shows the seawater loop and chiller oper- and frequency had a peak at the beginning of the dayation during tests on optimal reset strategies in different operation mode, which was the result of high start chillercases. Figure 14a shows the pressures and seawater pump load and the response of the strategy to the sudden loadfrequency of optimal reset strategy in summer case. change. It disappeared quickly, which shows that theFigure 14c shows the number of chillers in operation in stability of the strategy is acceptable. During the mainthe test of summer and middle-II cases. In night part of the day, the reset controller regulated the pressureoperation mode, the pump was set to a minimum speed, according to the change of the building cooling load,whilst only one chiller was in operation. The pressure while four chillers were in operation. It can be observed


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Dy namic simul ati on of a building central chi l l i ng system 17300 60

250

200

, , ,&Ik

100

50

0

40 uhgs

35%

30

25

-t 20

280

240

200

32 160tEI 120k

80

40

0

0 2 4 6 8 10 12 14 16 18 20 22 24Time (hour)

(b)Pressure Head of pump

Pressure across Exchanger

60

55

50

25

200 2 4 6 8 10 12 14 16 18 20 22 24

Time (hour)

4-

3-; I&-z2 2i II/

-_l- I

0 -1~ ~__ + _-_t+.__ -7~-.....-~- ,___~---i--___~__. ~~_ 10 2 4 6 8 10 12 14 16 18 20 22 24

Time (hour)Fig. 14. Seawater pressures, pump frequency and chiller operation (exercise 1)


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18 Shengwei WangTable 1, Electricity consumption at various loads

Test case Control strategySeawater

pump (lo6 kJ) Chiller ( 10h kJ)Chiller + seawater

pump ( 10h kJ)Comparison withadaptive reset (%)

Winter (1 day) Minimum 6.31 41.90 48.21 0.00Maximum 16.39 38.72 55.11 + 14.31

Adaptive reset 6.44 41.77 48.21Middle-l (1 day) Minimum 6.30 80.71 87.0 1 f5.15

Maximum 16.20 71.59 87.79 +6.10Adaptive reset 8.60 74.14 82.75 _Middle-II (1 day) Minimum 9.00 109.74 118.74 fl.70

Maximum 23.42 100.36 123.78 + 6.02Adaptive reset 11.16 105.59 116.75

Summer (1 day) Minimum 9.29 146.00 155.29 f3.83Maximum 24.19 130.88 155.06 +3.68Adaptive reset 13.34 136.36 149.70

that the pressure changed following the change of chillerload. A peak appeared around 16:OOpm with the chillerload. Near the end of the day, the controller set thepressure at a low level, since two chillers worked at partialload.

Figure 14b shows the pressures and seawater pumpfrequency of optimal reset strategy for middle-II case.In night operation mode, the pump was at minimumspeed. During the main part of the day, the reset con-troller regulated the pressure according to the change ofthe building cooling load, while three chillers were inoperation. Around 16:00pm, there was an obviousincrease of pump frequency due to the peak of the chillingload. The two shocks occurring were the response of thecontrol to the changes of chiller number between threeand four. Before the end of the day, two chillers workedat high load, the controller set a high frequency for thesingle pump in operation.

Energy statistics are shown in Table 1. In the sum-mer case, the optimal pressure set-point reset strategysaved 3.83% of the total electricity in comparison withthe electricity consumption when setting the pump atminimum speed, and saved 3.68% of the total electricityin comparison with the electricity consumption when set-ting the pump at maximum speed.

In the middle-II case, it saved 1.7% of the totalelectricity in comparison with the electricity consumptionwhen setting the pump at minimum speed, and saved6.02% of the total electricity in comparison with theelectricity consumption when setting the pump atmaximum speed. In the middle-I case, it saved 5.15%of the total electricity in comparison with the electricityconsumption when setting the pump at minimum speed,and saved 6.1% of the total electricity in comparisonwith the electricity consumption when setting the pumpat maximum speed. In the winter case, it had no saving

on the total electricity in comparison with the electricityconsumption when setting the pump at minimum speedsince the optimal control was very close to the minimumsetting. But it saved 14.31% of the total electricity incomparison with the electricity consumption when settingthe pump at maximum speed.

Figure 15 shows the pressure across the heatexchangers and pump, and the frequency for the seawaterpumps in the second exercise. Similar patterns of thecontrolled pressure and frequency curves during 4daysshow that the control strategies have consistent per-formance. The controlled pressure and frequencyincreased gradually over 4 days. It shows that the optimalcontrol set-point increased as the heat transfer coefficientreduced.

The energy performance of the control strategies in theexercise is shown in Table 2. The adaptive pressure set-point control strategies saved 5.31% of the total elec-tricity consumption in comparison with the electricityconsumption when setting the pump at minimum speed,and saved 3.11% of the total electricity in comparisonwith the electricity consumption when setting the pumpat maximum speed. When comparing the data with thatin the summer case test in the first exercise, the optimalpoint of the pressure setting was closer to the maximumpump speed and the daily total electricity consumptionwas 2.4% higher, due to the reduction of the heat transfercoefficient of the heat exchangers.

SUMMARY AND CONCLUSIONSSimulation exercises show that the dynamic system

simulation is a convenient and suitable tool in testingand evaluating the control performance of large centralchilling systems and their on-line control strategies. How-ever, simulation tests show that there are difficulties in

Table 2. Electricity consumption with variable system characteristics

Test caseControlstrategy

Seawaterpump ( lo6 kJ)

Chiller(lOh kJ)

Chiller + seawater Comparison withpump (IO6 kJ) adaptive reset (%)

Summer (4days with variable (/A) Minimum 37.15 608.57 645.72 +5.31Maximum 96.79 535.44 632.23 f3.11Adaptive reset 57.68 555.47 613.15


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20 Shengwei Wang15. Wang, S. W., Haves, P. and Nusgens, P., Design, construction and commissioning of building

emulators for EMCS applications. ASHRAE Transacrions, 1994, 100(l), 146551473.16. Kelly, G., Park, K. and Barnett, J. P., Using emulator/testers for commissioning EMCS software,

operator training, algorithm development, and tuning local loops. ASHRAE Transactions, 1991,97(l), 6699678.

17. Haves, P. and Dexter, A. L., Use of a building emulator to emulate control strategies implemented.In Commercial BEMS, Proceedings of Building Environmental Performance 91, Canterbury, 1991.18. Peitsman, H., Wang, S. W., Haves, P., Karki, S. and Park, C., The reproductivity of tests on energymanagement and control systems using building emulators. ASHRAE Transactions, 1994, 100(l),1455-1464.

19. Lebrun, J., Ding, X., Eppe, J. P. and Wasacz, T., Cooling coil models to be used in transient and/orwet regimes - theoretical analysis and experimental validation. In Proceedings of System Simulationin Buildings 90, Liege, Belgium, 1990.

dynamicsimulation of a building central chilling system and evaluation of emcs on-line control...

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