Energy Conversion and Management 64 (2012) 499–508

Contents lists available at SciVerse ScienceDirect

Energy Conversion and Management

journal homepage: www.elsevier .com/ locate /enconman

Optimal control of building storage systems using both ice storage and thermalmass – Part I: Simulation environment

Ali Hajiah a, Moncef Krarti b,⇑a Building and Energy Technologies Department, Environmental & Urban Development Division, Kuwait Institute for Scientific Research, Kuwaitb Civil, Environmental and Architectural Engineering Department, University of Colorado, Boulder, CO 80309, United States

a r t i c l e i n f o a b s t r a c t

Article history:Received 16 July 2010Received in revised form 31 January 2012Accepted 2 February 2012Available online 6 April 2012

Keywords:Experimental testingIce storage tankOptimal controlPre-coolingThermal massThermal energy storage

0196-8904/$ - see front matter � 2012 Elsevier Ltd. Adoi:10.1016/j.enconman.2012.02.016

⇑ Corresponding author. Tel.: +1 303 492 3389; faxE-mail address: krarti@colorado.edu (M. Krarti).

This paper presents a simulation environment that can evaluate the benefits of using simultaneouslybuilding thermal capacitance and ice storage system to reduce total operating costs including energyand demand charges while maintaining adequate occupant comfort conditions within commercial build-ings. The building thermal storage is controlled through pre-cooling strategies by setting space indoor airtemperatures. The ice storage system is controlled by charging the ice tank and operating the chiller dur-ing low electrical charge periods and melting the ice during on-peak periods. Optimal controls for bothbuilding thermal storage and ice storage are developed to minimize energy charges, demand charges,or combined energy and demand charges. The results obtained from the simulation environment are val-idated using laboratory testing for an optimal controller.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

There are two common approaches to store cooling thermal en-ergy in buildings: active and passive systems. The active systemsconsist of ice or chilled water storage tanks, commonly known asthermal energy storage (TES) systems, which are charged at nightand discharged during the day. The passive systems utilize thethermal mass of the building materials to pre-cool the buildingat night when the electrical rates are low. Both active and passivesystems have been used to shift some of the cooling loads from on-peak to off-peak utility rate periods. Several benefits can be in-curred from reducing on-peak electrical demands. From a buildingowner’s point of view, the prime motivation for load shifting is toavoid high energy rates and to reduce the overall demand charges.From the power utility’s perspective, however, the benefit of activeor passive storage systems is their effectiveness in reducing peakelectricity demand [1].

Several simulation studies have shown that proper pre-coolingand discharge of building thermal storage inventory can attainconsiderable reductions of operating costs in buildings. Energy costsavings can result from both utility rate incentives and improve-ments in operating efficiency due to nighttime free cooling and im-proved chiller performance (lower ambient temperatures andmore even loading). Ranges of 10–50% in energy cost savings and

ll rights reserved.

: +1 303 492 7317.

10–35% in peak power reductions over night setup control weredocumented [2–8]. The savings were highest when cool ambienttemperatures allowed for free cooling. Other modeling studiesyielded similar results [9]. Common to these simulation studiesis that the level of savings and the superior control strategystrongly depend on the investigated HVAC system, the utility rate,the capacity of both passive storage available, and on the climate.

Several other studies have considered the use of active thermalenergy storage systems such as chilled water or ice storage tanks toreduce the operating costs while maintaining adequate occupantcomfort conditions in buildings. The basic operating strategy ofan active TES system is to charge the ice storage (i.e., to freezethe water) by operating the chiller during low electrical chargeperiods. During the on-peak periods, the ice storage is dischargedto meet the building cooling requirements. As a result, it is possibleto reduce or even eliminate the chiller operation during on-peakhours. Substantial cost savings have been reported by shiftingthe cooling load to off-peak periods, even with an increase in totalelectrical cooling energy use since more energy is used for the loadshifting process during off-peak hours [10–16]. Even though, cool-ing equipment usually operates more efficiently during nighttimeand early morning hours because of the low ambient temperatures.

Some investigations have considered the use of both passive andactive TES systems to reduce or even eliminating on-peak coolingdemands and thus reduce demand charges associated with operat-ing the cooling system [1,17,18]. However, very limited validationof the simulation results for control strategies suitable forcombined TES systems have been reported.

500 A. Hajiah, M. Krarti / Energy Conversion and Management 64 (2012) 499–508

In this paper, an environment simulation environment capableof evaluating various control strategies for both passive and activeTES systems is presented. The simulation environment determinesthe optimal control strategy to reduce energy, demand, or totalcharges for cooling commercial buildings. First, the simulationenvironment is described including various models used forestimating building thermal loads as well as energy consumingequipment such as chillers, fans, and pumps. The predictions ofthe simulation environment are validated against laboratory test-ing for selected optimal control. The laboratory experimentalset-up as well the control strategies tested are presented. Finally,the validation analysis of the simulation environment is discussed.

2. Description of the simulation environment

This section provides a description of the simulation environ-ment used to investigate the potential benefits of using both activeand passive storage systems to minimize cooling operating costsfor buildings. In particular, the simulation environment consistsof a building thermal model, cooling system models, and optimiza-tion modules. Fig. 1 presents the flowchart of the simulation envi-ronment and includes four main modules: Input Data, BuildingModels, Control Strategies, and Controlled Building. Through theInput Data module, the user has to provide the required data(building features such construction details, internal gains, andoperating schedules as well as weather data and utility rates), tofirst estimate thermal loads and energy end uses using the BuildingModels module and then to determine the optimal control strate-gies using the Control Strategies module. The actual implementa-tion of the control strategies and associated performance istypically evaluated and measured through the Controlled Buildingmodule. In the following sections, a brief description is providedfor the two main simulation environment modules: the BuildingModels and the Control Strategies. The simulation environmentpredictions are validated using measured data from a full-scalelaboratory testing. Since the simulation environment is model-based, it can be applied to a wide range of building types and oper-ating conditions.

2.1. Building models

The building thermal loads are calculated using the transferfunction method (TFM) outlined in the ASHRAE FundamentalsHandbook [19]. The TFM is a comprehensive analysis method that

Fig. 1. Flowchart of the sim

can be used to estimate the hourly cooling load for conditionedspaces. The TFM can also be used to predict the effects on indoorcomfort conditions for various HVAC system types, control strate-gies, and operating schedules.

A simulation algorithm is developed based on TFM to model dy-namic thermal performance of buildings [20]. The algorithm is de-signed to handle any building type using either a single-zonemodel or a multiple-zone model. The user needs to provide theprogram with specific input data needed for the simulation analy-sis including:

� building data, such R-values, capacitances of materials used inwalls and roofs,� wall and roof conduction transfer function coefficients,� room transfer functions coefficients based on the type of con-

struction used (light, medium, and heavy),� normalized coefficients of space air transfer functions,� hourly outside air temperatures,� hourly solar heat gain factors,� type of lights used (fluorescent or tungsten) and their density

expressed in watts per square foot,� number of people and occupancy schedule,� infiltration and ventilation rates.

The predictions of the program have been validated for thecase-study presented in ASHRAE Handbook [19] against state-of-the-art whole-building energy simulation model predictions [20].

2.2. HVAC system model

The simulation environment includes various models for HVACsystems including air handling units with supply fans, chillers, aswell as ice storage tanks. The electrical energy used by each ofthe HVAC equipment is then considered to estimate the total elec-trical energy consumption by the cooling central plant as outlinedin the following sections.

2.2.1. Chiller modelTypically, chillers are used to produce chilled water for direct

cooling of building spaces. The rate of refrigeration is expressedas tons of cooling, which is equivalent to 12,000 Btu/h (3.5 kW).Because chillers rarely operate at their design capacity, the modelused in this study incorporates the part-load performance ofthe chiller. The operation of the cooling equipment at part-load

ulation environment.

A. Hajiah, M. Krarti / Energy Conversion and Management 64 (2012) 499–508 501

conditions can be modeled using an energy penalty that can bedescribed by a quadratic function of the part-load ratio PLR [19]:

PLRðtÞ ¼ ERðtÞCCAPðtÞ ð1Þ

where PLR(t) is the part load ratio at every hour, ER(t) the coolingdemand or heat extraction, Btu/h (kW), and CCAP(t) is the full-loadcapacity of chiller, Btu/h (kW).

By using manufacturers’ data, a model that expresses the relation-ship between the chiller capacity CCAP(t) and the outdoor air dry-bulbtemperature TDB(t) can be obtained as a linear curve fit [12]:

CCAPðtÞ ¼ CCAPnomf1þ d½Tref � TDBðtÞ�g ð2Þ

where CCAPnom is the nominal capacity of the chiller, Btu/h (kW), d acorrelation coefficient (d = 0.005), Tref a reference temperature[Tref = 85 �F (29.4 �C)], and TDB(t) is the outdoor air dry-bulb tem-perature in �F (�C).

The nominal capacity of the chiller is based on the 45 �F (7.2 �C)chilled water leaving temperature. The chiller is sized to meet thedesign peak value, which is the maximum sensible heat extractioncapacity required to maintain the space interior temperature at aconstant 75 �F (23.9 �C), assuming continuous operation of coolingequipment (i.e., system is operating for the 24 h per day).

The chiller electrical power input Pel,chiller(t) is generally ex-pressed as a quadratic function of the part-load ratio, PLR, as fol-lows [20]:

Pel;chillerðtÞ ¼ EIRdesCCAPnomðaþ bPLRþ cPLR2Þ ð3Þ

where EIRdes is the electric input ratio at design conditions, and a, band c the chiller-specific part-load coefficients obtained by curvefitting from manufacturers data.

The ratio of the EIR at part-load to the design EIR is expressedas:

EIRðPLRÞEIRdes

¼ aþ bPLRþ cPLR2

PLR

In this study, a hermetic reciprocating liquid chiller is assumed.The coefficients are set as follow [20]:

a ¼ 0:088065; b ¼ 1:137742; c ¼ �0:225806:

It should be noted that Eq. (3) can be used for any chiller type byselecting the proper coefficients a, b, and c.

For the ice chiller, the part load ratio, PLRice, during chargingmode can be expressed as [12]:

PLRice ¼u � SCAPnom

CCAPnom ice � ð1þ d � ðTref � TDBðtÞÞÞ ð4Þ

where u is the ice tank charging rate, SCAPnom the nominal capacityof ice storage [ton h], and CCAPnom_ice is the nominal capacity of icechiller [ton].

During discharge mode, where the base chiller may be used butthe ice chiller is kept idle, the base chiller part load ratio, PLRchw, isexpressed as [12]:

PLRchwðtÞ ¼ERðtÞ þ ðu � SCAPnomÞ

CCAPnom � ð1þ d � ðTref � TDBðtÞÞÞ ð5Þ

with u is the discharging rate (u < 0).The ice chiller PLR is set to:

PLRice ¼ 0

2.2.1.1. Fan model. The hourly electrical power of the fan, Pel,fan(t) inkW, is expressed as [1]:

Pel;fanðtÞ ¼DPa � _ma

pa � gfan � cð6Þ

where DPa is the pressure drop of air [in WG (kPa)], _ma the air mass-flow rate [lbm/h (kg/s)], pa the air density [lbm/ft3 (kg/m3)], gfan thefan efficiency = 0.70, and c is the conversion factor.

The hourly air mass-flow rate, _ma, is calculated from the follow-ing equation [20]:

_ma ¼ER

cp;airðTzone � TsÞð7Þ

where ER is the heat extraction rate [Btu/h (W)], i.e., the rate atwhich heat is removed from the conditioned space. Cp,air the specificheat of air [Btu/lbm �F (J/kg �C)], Tzone the temperature of the zone[�F (�C)], and Ts is the supply air temperature (�F).

The pressure drop of air, DPa, across fan depends on several fac-tors including the size of the air-handling unit, the length of theduct system, and the type of heating/cooling coils. Typically, thepressure drop for supply fans ranges from 1 to 5 in of water(0.25–1.25 kPa).

2.2.2. TES system and pump modelsTwo active TES systems can be considered including chilled

water or ice-storage tanks [21,22]. In this study, ice storage systemwith its dedicated charging chiller is considered as part of the cool-ing plant. Two pumps are modeled to be part of the cooling plant.The first pump is associated with the base chiller to provide directcooling. A second pump has been added for charging and discharg-ing the ice tank.

The hourly electrical power of pump Pel,pump(t) in kW isexpressed as [19]:

Pel;pumpsðtÞ ¼DPw � _mw

pw � gpump � cð8Þ

where DPw is the pressure drop of water [in WG (kPa)], _mw thewater mass-flow rate [lbm/h (J/kg �C)], pw the water density [lbm/ft3 (kg/m3)], gpump the pump efficiency = 0.80, and c is the conver-sion factor.

The water mass-flow rate, _mw, is calculated from the followingequation [19]:

_mw ¼_macp;airðTma � TsÞ

cp;waterðTcoil;out � Tcoil;inÞð9Þ

where Tma is the mixed air temperature [�F (�C)], Cp,water the specificheat of water [Btu/lbm �F (J/kg �C)], Tcoil,out the supply water tem-perature [�F (�C)], and Tcoil,in is the chilled water temperature [�F(�C)]

The pressure drop of water (DPw) across the pump depends onsuch factors as the length of the piping system and the water flowrate.

It should be noted here that during discharging mode (u 6 0),the hourly water mass-flow rate, _mw, for the first pump is calcu-lated from the following equation [20]:

_mw ¼ERþ ðu � SCAPnomÞ

cp;waterðTcoil;out � Tcoil;inÞð10Þ

The hourly water mass-flow rate for the second pump duringdischarging mode ð _mdischargeÞ is calculated from [19]:

_mdischarge ¼�u � SCAPnom

Cp;water � DTdischargeð11Þ

where DTdischarge is the temperature difference during dischargingmode [assumed DTdischarge = 10 �F (5.6 �C)].

During charging mode (u > 0), the hourly water mass-flow ratefor the second pump ð _mchargeÞ is calculated from:

_mcharge ¼u � SCAPnom

Cp;water � DTchargeð12Þ

502 A. Hajiah, M. Krarti / Energy Conversion and Management 64 (2012) 499–508

where DTcharge is the temperature difference during charging mode[assumed DTcharge = 5 �F (2.8 �C)].

The total electrical power used during a time step by the secondpump is equal to the sum of the power consumed by the samepump during charging and discharging modes:

Pel;pump2ðtÞ ¼ Pel;pump2 chargingðtÞ þ Pel;pump2 dischargingðtÞ ð13Þ

2.2.3. Total cooling electrical powerThe total cooling electrical power use, Pel(t), for the cooling

plant is the sum of the electric power used by the two chillers,the air-handling unit fan, and the mechanical pumps:

PelðtÞ ¼ Pel;base-chillerðtÞ þ Pel;ice-chillerðtÞ þ Pel;fanðtÞ þ Pel;pump1ðtÞþ Pel;pump2ðtÞ ð14Þ

2.3. Control strategies

The simulation environment can determine the impact of vari-ous control strategies on the building performance by estimatingits energy use, peak demand, and thermal comfort. The controlstrategies that can be modeled include:

� Conventional controls without load shifting (i.e., no pre-coolingof building thermal mass and no utilization of ice storagesystem).� Pre-cooling of building thermal mass (typically unoccupied

periods) to reduce peak cooling loads [3].� Chiller-priority control strategy through the operation during

on-peak periods of first the chiller (with a capacity smaller thanthe peak cooling load) and then the ice storage tank to meet anythermal load above the chiller capacity [10,13].� Storage-priority control strategy through the operation during

on-peak periods of first the ice storage and then the chiller tomeet cooling thermal loads. The storage-priority control is setso the ice tank is not discharged prematurely [10,13].

In addition, the simulation environment can identify throughoptimization search the best control for using both the buildingthermal mass and ice storage tank to minimize a specific cost func-tion such as energy costs, demand costs, or total costs. As demon-strated by several studies [1–5], optimal control takes advantage ofthe building thermal capacitance to minimize the operating costover a specified period of time such as a day while meeting somerequired constraints. Building loads are shifted to off-peak hoursby proper adjustment of space temperature set points (thermostatsettings) throughout the day.

The operating cost is typically equal to the product of the powerconsumption of cooling plant equipment and the electricity rate.The constraints include the following:

� The required comfort conditions for the building occupantssuch as the indoor temperature.� The limits on the operation of the cooling equipment and its

capacity.

The optimal solution is a trajectory of controls throughout thespecified optimization period. The control variables include zoneset points, coil discharge air temperatures, and all plant controlssuch as chilled-water temperature, pump and fan speeds.

The optimal control depends on several factors including thebuilding’s characteristics, the cooling system’s performance, andthe ambient conditions. In order to have a complete specificationof the optimization problem it is necessary to set the followingparameters [3,13,18]:

� an optimization period (i.e., length for summation of costs),� a stage interval (i.e., a time-step),� a set of initial and final thermal conditions for the building.

An optimization period of 24 h was selected and implementedin the simulation environment for two main reasons. First, a dayis the natural cycle for the internal gains due to people, lights,and office equipment, and also for the ambient weather conditions(such as the outdoor air temperature and solar radiation). Second,it was found that an optimization period of less than 24 h is typi-cally sufficient to realize almost all of the cost savings that canbe achieved by a true optimal control [13,18].

A time interval of 1 h has been used in the simulation analysisof the building model. For a 24-h plant operation, there will be aset of 24 temperature set points for each zone. These optimizedset-point temperatures of the office building model minimize thetotal daily operating cost taking into account the constraint of hav-ing the indoor temperatures within the comfort range defined bythe upper and lower temperatures limits.

The optimal control strategies of this research work were devel-oped for three different cost functions: minimum daily energycosts, minimum daily peak electrical demand cost, and minimumdaily total cost that include both energy and demand charges.

In a real application of dynamic building control, the controllerwould try to minimize total energy costs during the year. For de-sign days or days close to design conditions, the reduction of peakdemand is more important than the minimization of energy costs.Generally, peak demands can often be reduced at the expense ofhigher total energy costs. The ideal trade-off depends on the utilityrate structure. The goal of this part of the research work is to esti-mate the maximum achievable potential for both energy and peakdemand reductions.

2.4. Optimization cost function

Optimal control is that control trajectory that minimizes thecombined energy and demand charges over the simulation period.The objective function to be minimized is the total cost (i.e., includ-ing both energy and demand) of the cooling system. The total costof the cooling system is represented mathematically as [12,13]:

C ¼ rd;0Pmax;0 þ rd;1Pmax;1 þXK

k¼0

re;cðkÞPðkÞDt ð15Þ

where C is the combined cost including both energy and demandcharges, rd,0 the off-peak demand charge $/kW, rd,1 the on-peak de-mand charge $/kW, Pmax,v the peak demand incurred in rate period v(v = 0 for off-peak, and v = 1 for on-peak), re the energy charge $/kW h, K the total number of hours in the simulation period, P(k)the total cooling and non-cooling plant power consumption at k,c(k) = 0 if k is during off-peak period, and 1 if k falls during on-peakperiod, and Dt is the time interval of 1 h.

For the case where only energy charges are minimized, the costfunction is reduced to [13]:

Cenergy ¼XK

k¼0

re;cðkÞPðkÞDt ð16Þ

On the other hand, if only the demand charges are minimized,the cost function becomes [13]:

Cdemand ¼ rd;0Pmax;0 þ rd;1Pmax;1 ð17ÞThus, the optimal control that seeks to minimize the combined

energy and demand charges has the following cost function:

C ¼ Cenergy þ Cdemand ð18Þ

The electrical energy cost represents the total cooling plantpower consumption multiplied by the energy charge $/kW h. Theelectric demand cost on the other hand, is generally based on the

A. Hajiah, M. Krarti / Energy Conversion and Management 64 (2012) 499–508 503

customer’s maximum electrical power over 1 month. Themaximum electrical power Pmax is defined as the maximum valueof electrical power consumed in the building during on-peak peri-od of the day. Because the simulation period of this research studyis assumed to be a day (i.e., 24-h period), the electrical demandcost (i.e., demand charge multiplied by Pmax) is discounted by afactor of 30 to reflect the charges associated to one day in a typicalmonth (i.e., average demand daily cost).

The optimal control actions depend strongly on the electric ratestructure. The demand charges penalize high electric power peaks.Therefore, the optimal control attempts to make the daily electricalenergy use profile more uniform. For building thermal mass pre-cooling options, the optimization problem is to determine thehourly zone temperature set points that minimize a specific costfunction. The optimization is constrained by the condition thatthermal comfort be maintained during all occupied hours such thatthe zones indoor temperature is within the occupied set-point lim-its. A low limit of Tmin = 68 �F (20 �C) and an upper limit ofTmax = 76 �F (24.4 �C) were selected as a range of the space thermo-stat temperature settings during the occupancy schedule.

To solve these optimization problems of the energy cost, de-mand cost, and the combined cost, the direct search complexmethod was employed. A brief description of this search methodis presented in the next section.

2.5. Optimization method

The direct search complex method can be used to solve nonlin-ear constrained minimization problems. This method only employsfunctional comparisons and does not require derivative informa-tion to find the minimum point. The method is shown to be effec-tive and computationally compact. It is simple to program anddoes not require high computer storage [23]. A complete descrip-tion of this method can be found in [24,25].

For this study, three separate control tests were performed:

� optimized pre-cooling strategy for energy cost minimization,� optimized pre-cooling strategy for peak demand cost

minimization,� optimized pre-cooling strategy for both energy and peak

demand cost minimization.

The optimization problem in this study is non-linear since thecost function and the constraints are dependent on the non-linearperformance of the cooling system equipment.

3. Experimental validation

3.1. Testing set-up

The developed simulation environment program has beentested against the experimental results performed using a realscale HVAC laboratory located in Boulder, CO. The laboratory issized to provide 75 kW (about 20 tons) of cooling load which isrepresentative of a typical floor [12,500 ft2 (1250 m2)] for a com-mercial building. The Laboratory incorporates a central heatingand cooling plant, an air-handling unit with variable speed drives,an outside air conditioning station, two load simulator zones, and afull scale office zones. The lab is also equipped with an indirect ice-on coil storage tank of 190-tons/h (665 kW h) of nominal capacity.

Outside air enters through an Outside Air Central Station(OACS), where it can be conditioned to the desired environmentalconditions. The OACS consists of a preheating coil, heating coil,cooling coil, humidifier, and an outside air fan of 3 kW of nominalpower. It also has a freezing protection control, which closes adamper installed upstream of the preheating coil in case of

extreme outdoor weather conditions. If the outside air temperaturedrops below the freezing point, the damper blocks the airflowthrough the coils to prevent freezing in case of no-fluid circulation.

The outside air is allowed to mix with the return air by modu-lation of the return, outside, and exhaust air dampers for energyconservation and indoor air quality purposes. The mixed air flowsacross the main air handling, which consists of a heating coil, cool-ing coil, humidifier and supply air fan of 3 kW of nominal capacity,where it is conditioned to the desired temperature and humidityset points. Then, the air is supplied to the zone, where variableair volume boxes, called fan powered mixing boxes, are used forindividual zone temperature control [26].

Heating and cooling loads can be imposed in each zone usingfan coil unit load generators, electrical baseboard, and cooling coils.The Full Size Zone-West (FSZ-W) is used for the experiments per-formed in this study. This zone represents a typical office spacewith 20-ft (6.1-m) by 20-ft (6.1-m) dimensions. It has a suspendedceiling and raised floor, a floor to ceiling height of 8 ft, 2-ft high re-turn plenum located above the ceiling, and a 1.5 ft (0.45 m) raisedfloor with a mass of 40 lbm/ft2 (186 kg/m2) The thermal resistancevalues are respectively, R-54 for the walls, R-49 for the ceiling, andR-30 for the floor. A maximum of about 1100 cfm (520 L/s) of con-ditioned air enters the zone through a fan-powered mixing box.

Heating and cooling loads can be imposed by a fan-coil unitgenerator, which can provide a maximum heating load of36 MBtu/h (10.5 kW) and a maximum cooling load of 30 MBtu/h(8.8 kW). Hot water at 140 �F (60 �C), provided by the boiler, isused to generate the cooling load, while a brine-water solution ofabout 40 �F (4.4 �C), supplied by the chiller, is used to impose theheating load. Baseboards installed in the perimeter of the zonecan generate supplemental cooling loads.

As depicted in Fig. 2, the cooling system for the Larson Labora-tory consists of the following equipment:

� A screw-type chiller with a nominal capacity of 67.6 tons.� An internal ice-on-coil storage tank with a 190 tons/h

(665 kW h) of nominal capacity.� A centrifugal pump of 75 gallon per minute or gpm (17 m3/h) of

water, 35 ft (10.7 m) of head, 1750 rpm, and 1 ½ HP is installedin the primary chilled water loop. A second centrifugal pump ismounted in series with the first pump. The characteristics of thesecond pump are: a flow rate of 95 gpm (21.5 m3/h), head of50 ft (15.2 m), 1759 rpm, and 3 HP of nominal power at ratingconditions. A centrifugal pump of 100 gpm (22.7 m3/h) of water,55 ft (16.8 m) of head, 1750 rpm and 3 HP of nominal power isinstalled in the secondary chilled water loop.� A three-way valve, is installed in each circuit to control the tem-

perature of the brine entering/leaving the chiller and the icetank.� An air-cooled condenser with six independent air fans of 480 V

located outside the Larson Laboratory. It uses R-22 refrigerantand has a 50-tons rated capacity.

The cooling plant consists of a primary and secondary circuitwith a de-coupling configuration that allows the mixing of thebrine through the two loops, while keeping the flow rate constantin both circuits. The primary circuit consists of the chiller and icestorage tank, which is installed downstream of the chiller. Athree-way valve downstream of the chiller controls the brine blendtemperature leaving the ice tank. Another three-way valve allowscontrolling the brine temperature entering or leaving the chiller.The secondary circuit consists of the brine supply to the coolingcoils and return to the chiller. A brine solution consisting of an eth-ylene glycol–water solution (29% weight of ethylene glycol), with afreezing point of 8 �F flows through the chiller and the coolingcoils.

Fig. 2. Isometric view of Larson HVAC Laboratory.

504 A. Hajiah, M. Krarti / Energy Conversion and Management 64 (2012) 499–508

3.2. Simulation modeling

The simulation analysis performed for the lab model has beenconducted to optimally pre-cool thermal mass to:

� minimize energy costs only,� minimize demand costs only, and� minimize total costs (i.e., minimize the sum of energy and

demand costs).

For all the cases, the following utility rate is assumed:

� Energy charges are $0.10/kW h for on-peak hours and $0.02/kW h for off-peak hours.� Demand charges are $8.00/kW and $0.40/kW for on-peak and

off-peak periods, respectively.� On-peak hours are from 11:00 a.m. to 5:00 p.m. The other hours

are considered to be off-peak hours.

Fig. 3 shows the space indoor temperature profiles for the threeoptimized cases described above compared to conventional cooling(referred to as the base case). Table 1 summarizes the cost savingsobtained from the simulation environment described in this paper.

The simulation results indicate that even with little mass (as inthe case for the full zone in the Larson Laboratory), significantsavings can be achieved using the optimal controls especially when

the demand charges are optimized (in this case 16.3% savings areobtained).

For the optimal controls that minimize total charges, more than3.9% savings can be achieved. However, only 0.6% savings areachieved for the optimal controls that minimize energy charges.

Based on these results, three control options are implementedin the Larson Laboratory:

� Base case or conventional controls (i.e., condition the space dur-ing occupied period ranging from 8:00 a.m. to 5:00 p.m.).� Optimal controls to minimize demand charges.� Optimal controls to minimize total charges (i.e., both energy

and demand charges).

The results of the Laboratory testing are summarized in Table 1and are described in more details in the following sections.

3.3. Experimental testing

Throughout the experimental testing, a typical cooling load pro-file for an office space is imposed in the full zone as shown in Fig. 4.The cooling load was generated in the zone by operating baseboardheaters for 9 h of occupancy (from 8:00 a.m. until 5:00 p.m.). Forall tests conducted in this study, the outside air was heated to95 �F (35 �C) with the help of the preheating coils located withinthe Outside Air Conditioning Station by operating the preheating

Fig. 3. Indoor air temperatures profiles for base case and three optimized controls.

Table 1Simulation results for savings in the daily costs associated with three optimizedcontrols.

Energycost ($)

Demandcost ($)

Totalcost($)

Savingsin energycost (%)

Savings indemandcost (%)

Savingsin totalcost (%)

Basecase

18 8 26 – – –

Energycost

17.9 7.1 25 0.6 11.25 3.9

Demandcost

22 6.7 28.7 �22.2 16.3 �10.4

Totalcost

18 7 25 0 12.5 3.9

Fig. 4. Measured cooling load profile imposed by baseboard heaters in the FSZ-W atLarson Laboratory.

(a) Indoor Temperature Profile

(b) Air Flow Rate and Damper Fan-Powered Mixed Box Position

Fig. 5. Indoor temperature and air-flow rate and fan power mixed box damperposition for conventional control strategy.

A. Hajiah, M. Krarti / Energy Conversion and Management 64 (2012) 499–508 505

coils. Moreover, the supply air temperature is set to be 55 �F(12.7 �C) for the base case and 50 �F (10 �C) when pre-cooling isconsidered.

Using the data acquisition system of the laboratory, severalparameters are continuously monitored including zone tempera-tures, AHU supply air temperatures, supply and return air flowrates, damper positions, and the energy consumption of the cooling

system components such as fans, pumps, air cooled condenser andchiller.

3.3.1. Base case controlUsing conventional control with no pre-cooling, the cooling sys-

tem is operated to maintain a desired constant temperature of75 �F (23.9 �C) as shown in Fig. 5a. Depending on the cooling load,the supplied air through the fan-powered mixing box is varied tomaintain thermal comfort within the zone as shown in Fig. 5b.

3.3.2. Demand cost optimal controlIn this strategy, the cooling system is controlled to minimize the

demand cost while maintaining indoor thermal comfort within thefull zone during occupancy period (i.e., 8:00 a.m. through5:00 p.m.). In this test, thermal comfort is considered acceptablewhen the indoor temperature is maintained between 68 �F(20 �C) and 76 �F (24.4 �C). Pre-cooling of the indoor thermal massduring unoccupied hours is used to minimize the demand charges.The optimal settings for the operation of the cooling system are de-fined from the simulation environment.

(a) Indoor Temperature Profile

(b) Air Flow Rate and Damper Fan-Powered Mixed Box Position

Fig. 6. Indoor temperature and air-flow rate and fan power mixed box damperposition for optimal control strategy to minimize demand charges.

(a) Indoor Temperature Profile

(b) Air Flow Rate and Damper Fan-Powered Mixed Box Position

Fig. 7. Indoor temperature and air-flow rate and fan power mixed box damperposition for optimized pre-cooling control strategy to minimize total charges.

506 A. Hajiah, M. Krarti / Energy Conversion and Management 64 (2012) 499–508

The variations of the measured indoor air temperature as well asof the air flow rate supplied to the full zone are provided in Fig. 6when the demand charges are minimized. As shown in Fig. 6a,the optimal control maintains the indoor temperature at the lowerlimit temperature set point of 60 �F (15.5 �C) during most of theunoccupied hours. Pre-cooling process started as soon as5:00 p.m. (i.e. end of occupied period) and continued throughoutthe night until 8:00 a.m. (i.e., beginning of occupied period). The in-door temperature was maintained at the lower limit of 68 �F (20 �C)during off-peak occupied hours from 8:00 a.m. to 11:00 a.m. andgradually increased to the upper limit of 76 �F (24.4 �C) duringthe on-peak period (i.e. from 11:00 a.m. to 5:00 p.m.). Fig. 6b indi-cates that the damper for the fan-powered mixing box is set to becompletely closed for 1 h from 8:00 to 9:00 a.m. so no conditionedair was supplied to the full zone in order to allow the indoor airtemperature to reach the lower comfort limit of the temperaturesetting [i.e., 68 �F (20 �C)].

3.3.3. Total cost optimal controlThe testing protocol for this control strategy is similar to that

carried for the demand cost optimal control except that the

settings are defined by the simulation environment to minimizethe total costs (i.e., the sum of the energy and demand charges)as defined by the cost function of Eq. (17).

Measured indoor air temperature variation obtained for the to-tal const optimal control test is shown in Fig. 7a. The optimal con-troller maintains the indoor temperature at the lower limittemperature set point of 60 �F (15.5 �C) by pre-cooling the full zonefrom 3:00 a.m. until 8:00 a.m. (i.e., at the start of the occupied per-iod). As noted for the demand cost optimal control testing, the in-door temperature is maintained at the lower limit of 68 �F (20 �C)during off-peak occupied hours from 8:00 a.m. to 11:00 a.m. Theindoor temperature remained at the upper limit of 76 �F (24.4 �C)during the on-peak period which starts at 11:00 a.m. until5:00 p.m.

Fig. 7b presents the variation of the airflow rate supplied to thefull zone as well as of the fan power mixed box damper positionobtained for testing the total cost optimal control strategy. As indi-cated in Fig. 7b, the damper closed twice during occupied hours:for about 1 h from 8:00 to 9:00 a.m. and again at 11:00 a.m. whichmarks the beginning of on-peak hours. During both times, no airwas supplied to the full zone. The controller allows the indoor air

Table 2Measured energy, demand, and total costs for the tested conventional and optimal controls.

Energy cost ($) Demand cost ($) Total cost ($) Savings in energy cost (%) Savings in demand cost (%) Savings in total cost (%)

Base case 20.5 9.1 29.6 – – –Demand cost 19 7.9 27.3 7.3 13.2 7.8Total cost 18.2 8.2 26.4 11.2 9.9 10.8

(a) Conventional Control

(b) Demand Cost Optimal Control

(c) Total Cost Optimal Control

Fig. 8. Comparison between experimental results and simulation predictions forthree control strategies.

A. Hajiah, M. Krarti / Energy Conversion and Management 64 (2012) 499–508 507

temperature to reach to the lower comfort limit of 68 �F (20 �C) atthe beginning of occupancy and then to increase to the upper com-fort limit [i.e., 76 �F (24.4 �C)] at the start of on-peak hours and toremain stable at that same temperature of 76 �F (24.4 �C) until theend of on-peak period at 5:00 p.m.

3.4. Summary of testing results

The results of the experimental analysis for the three controlstrategies carried out under controlled laboratory conditions aresummarized in Table 2. The results indicate significant cost savingscan be achieved using the optimal controls compared to the con-ventional control strategy especially for the demand cost optimalcontrol. Indeed, when charges are optimized, 13.2% savings in de-mand charges are obtained with 9.9% savings in total charges.When total charges are minimized, more 10.8% cost savings canbe achieved.

Fig. 8 compare simulation and experimental results for the threetested control strategies tested. In general, fair agreement isobtained between the simulation predictions and experimental re-sults for the three control strategies. For all the three tested control

strategies, the measured savings are within 10% of the predictedcost savings. The measured energy, demand, and total costs forcontrol strategies are generally slightly higher that the results ob-tained from the simulation analysis. The lower predicted valuesfor energy and demand costs are most likely due to the fact thatthe actual energy efficiencies of various energy consuming devicessuch as fans, pumps, and chiller may be lower than the assumedvalues in the simulation models.

4. Summary and conclusions

A simulation environment has been developed to investigatethe potential of using simultaneously building thermal mass andice storage system to reduce total operating costs while maintain-ing adequate occupant comfort level conditions in commercialbuildings. The simulation environment can determine optimal set-tings for both indoor temperature and charging/discharging ratefor the ice storage system in order to minimize energy, demand,or total costs. The results of the simulation environment have beenvalidated against experimental tests for conducted in a controlledlaboratory conditions. The results of the validation analysis indi-cated that the simulation environment predict cost savings foroptimal controls with 10% agreement when compared to theexperimental measurements.

In a companion paper, the simulation environment is utilized toperform a series of parametric analyses to assess the potential en-ergy cost savings associated with using both thermal mass and icestorage system in commercial buildings [27]. The parametric anal-yses can help identify the most important parameters that affectthe performance of various control strategies for the combinedthermal energy storage system.

References

[1] Kintner-Meyer M, Emery AF. Optimal control of an HVAC system using coldstorage and building thermal capacitance. Energy Build 1995;23(1):19–31.

[2] Ruud MD, Mitchell JW, Klein SA. Use of building thermal mass to offset coolingloads. ASHRAE Trans 1990;96(2):820–8.

[3] Braun JE. Reducing energy costs and peak electrical demand through optimalcontrol of building thermal storage. ASHRAE Trans 1990;96(2):876–88.

[4] Rabl A, Norford LK. Peak load reduction by preconditioning buildings at night.Int J Energy Res 1991;15:781–98.

[5] Keeney KR, Braun JE. A simplified method for determining optimal coolingcontrol strategies for thermal storage in building mass. Int J HVAC&R Res1996;2(1):59–78.

[6] Morgan S, Krarti M. Impact of electricity rate structures on energy cost savingsof pre-cooling controls for office buildings. Build Environ 2006;42(8):2810–8.

[7] Henze GP, Felsmann C, Florita AR, Brandemuehl MJ, Cheng H, Waters CE.Optimization of building thermal mass control in the presence of energy anddemand charges (1313-RP). ASHRAE Trans 2008;114(2):75–84.

[8] Henze GP, Florita AR, Brandemuehl MJ, Felsmann C, Cheng H. Advances innear-optimal control of passive building thermal storage. In: ASME 3rdinternational conference on energy sustainability. San Francisco, CA; 2009.

[9] Morris FB, Braun JE, Treado SJ. Experimental and simulated performance ofoptimal control of building thermal storage. ASHRAE Trans1994;100(1):402–14.

[10] Braun JE. A comparison of chiller-priority, storage-priority, and optimalcontrol of an ice storage system. ASHRAE Trans 1992;98(1):893–902.

[11] Drees KH, Braun JE. Development and evaluation of a rule-based controlstrategy for ice storage systems. Int J HVAC&R Res 1996;2(4):312–36.

[12] Henze GP, Krarti M. Ice storage system controls for the reduction of operatingcost and energy use, ASME solar energy. Eng J 1998;120(4):275–82.

[13] Henze GP, Krarti M, Brandemuehl MJ. A simulation environment for theanalysis of ice storage controls. Int J HVAC&R Res 1997;3(2):128–48.

508 A. Hajiah, M. Krarti / Energy Conversion and Management 64 (2012) 499–508

[14] Massie DD. Optimal neural-network-based controller for ice storage systems.PhD Dissertation, University of Colorado, CO; 2008.

[15] Henze GP, Krarti M. The impact of forecasting uncertainty on the performanceof a predictive optimal controller for thermal energy storage systems. ASHRAETrans 1999;105(2):553–61.

[16] Braun JE. A near-optimal control strategy for cool storage systems withdynamic electric rates. Int J HVAC&R Res 2007;13(4):557–80.

[17] Liu S, Henze GP. Experimental analysis of simulated reinforcement learningcontrol for active and passive building thermal storage inventory –Part 1:theoretical foundation. Energy Build 2006;38(2):142–7.

[18] Henze GP, Krarti M, Brandemuehl MJ. A simulation environment for theanalysis of ice storage controls. Int J HVAC&R Res 1997;3(2):128–48.

[19] ASHRAE. ASHRAE fundamentals handbook. Atlanta, GA: American Society ofHeating, Refrigerating and Air-Conditioning Engineers, Inc.; 2009.

[20] LBL. DOE-2 engineering manual, version 2.1A, LBL-Report-11353. Berkeley, CA:Lawrence Berkeley National Laboratory and Los Alamos National Laboratory;1991.

[21] Lee AHE, Jones JE. Modeling of an ice-on-coil thermal energy storage system.Energy Convers Manage J 1996;37(10):1493–507.

[22] Babak KS, Ardehali NM. Numerical simulation of water solidificationphenomenon for ice-on-coil thermal energy storage application. EnergyConvers Manage J 2003;44(2):85–92.

[23] Rao SS. Optimization theory and applications. 2nd ed. New York: Halsted PressBook, John Wiley & Sons; 1984.

[24] Nelder JA, Mead R. A simplex method for function minimization. Comput J1965;7:308–12.

[25] Gill PE, Murray W, Wright M. Practical optimization. New York: AP AcademicPress; 1981.

[26] Kreider JF, Curtiss PS, Massie D, Jeannette E. A commercial-scale universityHVAC laboratory. ASHRAE Trans 1999;13(4):919–27.

[27] Hajiah A, Krarti M. Optimal control of building storage systems using both icestorage and thermal mass, Part II: parametric analysis. J Energy ManageConvers 2012.