Energy Conversion and Management 64 (2012) 509–515

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Energy Conversion and Management

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

Optimal controls of building storage systems using both ice storageand thermal mass – Part II: Parametric analysis

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:Ice storage tankOptimal controlPre-coolingThermal massThermal energy storage

0196-8904/$ - see front matter � 2012 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.enconman.2012.02.020

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

This paper presents the results of a series of parametric analysis to investigate the factors that affect theeffectiveness of using simultaneously building thermal capacitance and ice storage system to reduce totaloperating costs (including energy and demand costs) while maintaining adequate occupant comfort con-ditions in buildings. The analysis is based on a validated model-based simulation environment andincludes several parameters including the optimization cost function, base chiller size, and ice storagetank capacity, and weather conditions. It found that the combined use of building thermal mass andactive thermal energy storage system can save up to 40% of the total energy costs when integrated opti-mal control are considered to operate commercial buildings.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

There are two common approaches to store thermal energy inbuildings. The most common approach is to install ice or chilledwater storage tanks that are charged at night and discharged dur-ing the day [1–6]. An alternative approach is to utilize the thermalmass of the structural building materials to pre-cool the building atnight when the electrical rates are low. By pre-cooling the buildingduring the night and early morning hours, the thermal capacitanceof the building can be utilized to shift some of the cooling loadsfrom on-peak to off-peak utility rate periods [7–12]. Both ap-proaches for storing thermal energy attempt to reduce peak cool-ing demand during the day by operating the cooling systemduring the night. Generally, thermal energy storage (TES) systemsare designed to produce the necessary cooling storage during off-peak hours in order to take advantage of cheaper electric utilityrates.

This paper investigates the effects of various design and operat-ing factors on the optimal controls of using simultaneously build-ing thermal capacitance and ice storage system to reduce theoperating costs while maintaining adequate occupant comfort con-ditions in commercial buildings [13]. The building TES can be con-trolled by setting zone temperatures without affecting the thermalcomfort level of the occupants. The basic operating strategy of theactive TES system is to charge the ice storage (i.e. to freeze the

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: +1 303 492 7317.

water) by operating the chiller during low electrical charge peri-ods. During the on-peak periods, the ice storage is discharged tomeet the building cooling requirements. As a result, it is possibleto reduce or even eliminate the chiller operation during on-peakhours [14].

The parametric analysis presented in this paper is based on avalidated simulation environment described in a companion paper[15]. The simulation environment is model-based and can be usedto evaluate the effectiveness of conventional and optimal controlstrategies of using both building thermal mass and ice storage sys-tem in order to reduce the operating cost while meeting the build-ing cooling requirements. The analysis includes several parametersincluding the optimization cost function, base chiller size, and icestorage tank capacity, and weather conditions.

First, the simulation model analysis is described including theoffice building model, the cooling plant, the optimization cost func-tion, and the utility rate used in the analysis. Then, selected resultsof the parametric analysis are presented and discussed.

2. Simulation model description

2.1. Building model

An office space is considered throughout the parametric analy-ses presented in this paper. The selected office building model hasa rectangular shape with a dimension of 200 ft (61 m) by 100 ft(30.5 m). The 200 ft (61 m) walls have an east and west orientationwhile the 100 ft (30.5 m) walls have a north and south orientation.

North Zone

East Zone

South Zone Core Zone

N

100’

200’

West Zone

Fig. 1. The building model with five zones.

Table 1Construction details and R-values of the building envelope.

Structure description R-value h.ft2 �F/Btu(m2 �C/W)

Roof (50 0 insulation, with 20 0 h.w. concrete deck) 18 (3.2)Exterior walls (120 0 h.w. concrete, 20 0 insulation) 9 (1.6)Interior walls (40 0 h.w. concrete block with 3=4

0 0

plaster)2.4 (0.4)

Windows with light colored venetian blinds 1.2 (0.2)

Table 2Characteristics of the utility rate structure.

Rate feature Value

On-peak hours (10 am–10 pm)On-peak energy charges ($/kW h) 0.0389Off-peak energy charges ($/kW h) 0.0208On-peak demand charges ($/kW) 7.50Off-peak demand charges ($/kW) 0

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The height of the walls is 9 ft (2.75 m). The building model includesa core zone and four perimeter zones as illustrated in Fig. 1. Thebuilding is occupied from 8:00 am to 5:00 pm with a density of100 ft2 (9.3 m2) per person in the perimeter zones and 200 ft2 per(18.6 m2) person in the core zone. A ventilation rate of 20 cfm(9.4 L/s) per person was assumed. The construction details as wellas the R-values used for the walls, roof, and windows are listed inTable 1.

Fluorescent lighting fixtures are used in the building modelwith a power density of 1.0 W/ft2 (10.7 W/m2) operating from8:00 am to 5:00 pm daily. Moreover, equipment and appliancesare modeled with a power density of 1.5 W/ft2 (16.1 W/m2) oper-ating from 8:00 am to 5:00 pm along 0.5 W/ft2 (5.4 W/m2) operat-ing continuously 24 h per day.

2.2. Utility rate structure

Time of use rate structure is used for the analysis of the one-zone building model [9,16]. Specifically, the utility rate assumesthe on-peak period to be from 10:00 am to 10:00 pm with off-peakperiod covering the rest of the hours. On-peak energy charges areassumed to be $0.0389/kW h while the off-peak energy chargesare $0.0208/kW h. For the on-peak demand charges, a rate of$7.50/kW is used and no charges are applicable for the off-peakhours. This first rate is considered to have strong incentives forcooling load shifting from on-peak to off-peak hours. Table 2

outlines the characteristics of the utility price structure used inthe parametric analysis presented in this paper.

2.3. Cost function for optimal controller

Using the simulation environment described in a companionpaper [15], several parametric analyses are carried out to evaluatethe effectiveness of using both building thermal mass and ice stor-age system to reduce the operating cost while meeting the buildingcooling requirements. For this study, three separate optimal con-trol strategies are considered to minimize one of three costfunctions:

� Energy charges only.� Demand charges only.� Total charges including both energy and demand charges.

These three optimization strategies are evaluated against base-case controls:

� Conventional control of cooling system using a fixed temper-ature set point of 76 �F from 8:00 am to 5:00 pm This basecase is selected to investigate the effectiveness of using build-ing thermal mass [15].

� Chiller-priority control when an ice storage system is utilized[3,15].

Several parametric analyses are carried using the office buildingmodel to determine the effectiveness of thermal mass/ice storageoptimal controller under various design and operating conditions.Selected results are presented in this paper. In this paper, selectedresults obtained when the office building is located in Chicago, ILare discussed. Typically, results for both design day (July 21) aswell as typical day (September 28) are discussed. It should be thatthe percent cost savings obtained for typical day are indicative ofthe annual percent cost savings associated with various controlstrategies.

3. Discussion of selected results

The optimal controls for building thermal mass/ice storage pro-vide specific guidelines on how to operate the cooling equipmentfor 24 h period. Indeed, the optimization algorithm investigatesall the pre-cooling options and determines the specific hour atwhich pre-cooling starts, the length of pre-cooling, and specifictimes when the base and ice chiller need to be turned on and off.The 24 h optimization program uses initial values for the thermo-stat temperature settings and the ice storage state of charges. Theoptimization algorithm starts its search at the specified initial con-ditions to find the optimized settings of the thermostat and statesof charge of the ice storage system that minimize the specific costfunction, i.e. energy charges, demand charges, or total energy anddemand charges. The thermal mass/ice storage optimization thusproduces a set of 24 temperature thermostat settings and a set of24 state-of-charge levels.

The medium mass office building model described above is usedin the analysis. Specifically, the simulation analysis presented inthis paper uses a 30 ton base chiller, a 15 ton ice chiller, and a180 ton/h ice storage tank. The optimization analysis is performedusing a time-of-use utility rate structure with strong incentives forpeak load shifting as outlined in Table 2.

The results of the optimization analysis show that additionalsavings occur when building thermal mass and ice storage systemare used simultaneously. A brief discussion of the results obtainedfrom the optimization analysis when both building thermal mass

Fig. 2. Indoor temperature settings obtained for the optimization of energy,demand, and total daily operating cost and for chiller priority control.

A. Hajiah, M. Krarti / Energy Conversion and Management 64 (2012) 509–515 511

and ice storage system are utilized is provided in the following sec-tions for various operating and design conditions.

3.1. Impact of cost function

In this analysis, the impact of the optimization cost function onthe control strategy to operate both TES systems is investigated.The indoor temperature settings for the cases when the optimiza-tion is based on minimizing energy cost only, demand cost only,and total cost (including both energy and demand charges) as wellas for the baseline of using chiller priority control without any pre-cooling are indicated in Fig. 2 [15].

For the energy cost minimization problem, the optimal controltends to set the building temperature at its upper bound of 76 �F(24.4 �C) during the beginning of the on-peak period (i.e. 9:00am). For the demand minimization problem, the occupied on-peakperiod begins with the indoor temperature set at the lower boundof the acceptable comfort range [i.e. 68 �F (20 �C)].

When the optimization is based on minimizing both energy anddemand charges, the optimization provides settings similar tothose obtained for the case of minimization of the energy charges.The on-peak period starts with the indoor temperature set at theupper bound of the acceptable thermal comfort range [i.e. 76 �F(24.4 �C)].

When the optimization is based minimizing the demand costonly, the optimization tends to have the thermostat setting at itslowest limits [i.e. 68 �F (20 �C)] during the first hours of occupancy.Then, the indoor temperature rises slowly during the warming-upphase so that the temperature upper bound [i.e. 76 �F (24.4 �C)] isnot reached until the last hours of occupancy (3:00 pm–5:00 pm).In order to ensure the slow temperature rise, cooling is providedthroughout the occupancy period. To prevent a high peak demandin the cooling load, the indoor temperature rise is delayed so it

Table 3Performance of three optimal controls for minimizing energy, demand, and total daily ope

Energy cost ($) Demand

Base case 106.1 34.4Chiller priority 96.5 29.8Minimizing energy cost 79.1 21.3Minimizing demand cost 97.5 20.5Minimizing total cost 79.1 21.3

does not reach its upper bound too early in the day. In the after-noon, when the heat gain is at its highest level, the indoor temper-ature set point is allowed to increase and the building thermalmass can still absorb heat, resulting in a reduction in the coolingload. Thus, a high demand charge forces the optimal control tomake the electric demand curve more uniform.

When optimization is based on energy charges, savings of 25.4%in the daily energy cost relative to the base case conventional con-trol strategy can be achieved. For demand charges optimization,savings of 40.4% in the peak electrical demand are obtained. Final-ly, the savings obtained when minimizing both energy and de-mand charges are 28.5%. The results of the cooling plant dailyoperating cost for the three optimized cases: optimization of en-ergy cost only, optimization of demand cost only, and the optimi-zation of total cost (energy and demand charges) are summarizedin Table 3. The base case consists of direct cooling from 8:00 am to5:00 pm with no pre-cooling and no utilization of the ice storagesystem. The results for the chiller priority control without pre-cooling have been included in Table 3 for comparison purposesto assess the benefits of using both types of thermal energy storagesystems (i.e. building thermal mass and ice storage) relative to aconventional control strategy for ice storage systems. More de-tailed comparative analysis of the performance of optimal controlsrelative to chiller priority controls is provided in the following sec-tion. It should be noted that the chiller (50 ton) of the base case issized to meet the building peak design load through direct cooling.However, a 30 ton base chiller, a 15 ton ice chiller and 180 ton/hice storage tank are used in the chiller priority control strategy.

3.2. Comparative analysis of optimal controls and conventionalcontrols

In this section, the performance of the optimal controller tominimize the total daily operating cost (including energy and de-mand charges) is evaluated against the performance incurredwhen no pre-cooling is considered but using a chiller priority con-trol for the ice storage system. The indoor temperature profileswithin the office space are illustrated in Fig. 2 for both optimal con-trol and chiller priority control. The hourly variation of the totalsensible cooling load (heat extraction) for both the optimal controland the chiller priority control are provided in Fig. 3.

It can be noticed from Fig. 3 that no heat needs to be extractedfrom 5:00 pm to 9:00 pm (which marks the beginning of the unoc-cupied on-peak period) when optimal control is used. Thus, thecooling system is not operated during these unoccupied on-peakhours. Indeed, the extraction of heat starts at the first hour of theoff-peak period (9:00 pm) using the base chiller in order to pre-cool the thermal mass of the building. The pre-cooling continuesthroughout the night until the beginning of the on-peak period(9:00 am). From 9:00 am to 5:00 pm cooling is supplied to thebuilding (i.e. heat is extracted) using both the base chiller (directcooling) and the ice storage system (operated in a dischargingmode).

The time variation of the ice storage state of charge for bothoptimal control and chiller priority control is displayed in Fig. 4.For the optimal control, the ice storage charging process (using

rating cost relative to conventional and chiller-priority controls.

cost ($) Total cost ($) Savings in total cost (%)

140.4 –126.3 10.0100.4 28.5117.9 16.0100.4 28.5

Fig. 3. Heat extraction (ER) obtained for optimal control and for chiller prioritycontrol.

Fig. 4. Ice storage state of charge obtained by optimization of the total dailyoperating cost compared to that incurred using chiller priority control.

Fig. 5. Total cooling electrical power (kW) obtained by optimization of the totaldaily operating cost compared to that incurred using chiller priority control.

Fig. 6. Base chiller electrical power (kW) obtained by optimization of the total(both energy and demand) operating charges compared to that incurred usingchiller priority control.

Fig. 7. Ice chiller electrical power obtained by optimization of the total operatingcharges compared to that incurred using chiller priority control.

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the ice chiller) starts immediately at the first off-peak hour (9:00pm). The ice chiller continues to charge the ice tank until the endof the off-peak period (9:00 am) when 88% of the tank capacityis charged (x = 0.88). The ice storage discharge process starts at9:00 am and continues until the end of occupancy (5:00 pm). In-deed, the ice tank is progressively depleted during the period span-ning from 9:00 am to 5:00 pm to assist the base chiller.

The total cooling electrical power (kW) for the optimal controland the chiller priority control is presented in Fig. 5. The electricalpower for base chiller and ice chiller are shown in Figs. 6 and 7,respectively.

3.3. Sequential optimal controls

In this section, the benefits of using integrated optimization todevelop optimal controls that are capable of operating simulta-neously passive and active thermal energy storage (TES) systemsare assessed. In the integrated optimization, 48 variables (24 tem-perature settings and 24 charging/discharging rates) are optimizedfor each day as discussed in [15]. First the benefits of the integratedoptimization of both TES systems are evaluated compared to using

Table 4Cost savings obtained for optimal TES control, optimized pre-cooling, and optimalcontrol using both building thermal mass and ice storage system.

Control type Energycost ($)

Demandcost ($)

Totalcost ($)

Savings intotal cost (%)

Conventional (no TESsystem)

106.1 34.4 140.4 –

Optimized pre-cooling 91.3 28.2 119.5 14.9Optimized ice storage

system96.0 29.3 125.3 10.8

Optimized pre-cooling andice storage system

81.0 20.0 101.0 28.1

Table 5Comparison of the performance of sequential optimization, integrated optimization,and optimized pre-cooling in minimizing the daily total cost.

Control type Energycost ($)

Demandcost ($)

Totalcost ($)

Savings intotal cost (%)

Conventional (no TESsystem)

106.1 34.4 140.4 –

Optimized pre-cooling 91.3 28.2 119.5 14.9Optimized ice storage

system81.8 20.9 102.7 26.9

Optimized pre-cooling andice storage system

81.0 20.0 101.0 28.1

Table 6Effect of the base chiller size on the total daily cost for a design day.

Base chiller (ton) Total daily cost ($) % Savings(relative to base case)

50 (base case) 140.40 –20 108.40 22.8025 103.0 26.6030 100.40 28.5035 100.50 28.4040 101.0 28.1045 101.0 28.1050 101.0 28.10

Table 7Effect of the base chiller size on the total daily cost for a design day.

Base chiller (ton) Total dailycost ($)

% Savings(relative to base case)

50 (base case) 121.0 –15 93.3 22.925 93.7 22.650 94.4 22.0

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only one TES system at a time. Table 4 summarizes the results ofusing optimal ice storage system control, optimized pre-cooling,and optimal control using both building thermal mass and ice stor-age system for the office building (medium mass) located in Chi-cago, IL.

The ice storage system optimal controller achieved total costsavings of 10.8% relative to the conventional control (system oper-ating during occupancy hours only with no pre-cooling). However,higher savings of 28.1% in the total daily operating cost are ob-tained with the use of the combined optimal controller (i.e. useof both pre-cooling and ice storage) relative to the conventionalcontrol. This result illustrates clearly the advantage of using bothpassive thermal energy storage system (i.e. building thermal mass)and an active thermal energy storage system (i.e. ice storage tank)to minimize total building energy costs.

Instead of the integrated optimization (with 48 variables/day toidentify), sequential optimization is considered to reduce the com-putation efforts. The sequential optimization is based on usingoptimized pre-cooling first followed by an optimized use of theice storage system to minimize the cost function (i.e. energy cost,demand cost, or total cost). Thus, the sequential optimization iden-tifies only 24 variables per day at a time. Specifically, the hourlythermostat settings for the space temperatures resulted from theoptimized pre-cooling of building thermal mass are used as inputsin the TES optimization (i.e. the charging/discharging of ice storagetank). The results of a comparative analysis for minimizing the to-tal cost using both sequential and integrated optimization schemesare shown in Table 5.

As indicated in Table 5, the sequential optimization provides al-most similar cost savings than the integrated optimization at leastfor the building, HVAC system, and utility rate structure consideredin the analysis. Thus, sequential optimization (i.e. optimization ofpre-cooling strategy that utilizes the building thermal mass fol-lowed by optimization of the charging/discharging of ice storagetank) can be considered instead of a more complex integrated opti-mization that attempts to simultaneously determine the optimalsettings for both pre-cooling and ice storage system operation.However, further investigation is needed to determine if this con-

clusion is valid for other building types, climate conditions, andutility structures.

3.4. Impact of design parameters

This section investigates the effects of cooling system capacitieson the total operating cost of the cooling system using the devel-oped integrated TES optimal controller. Specifically, the impactsof the base chiller size, ice chiller size, and ice tank size are evalu-ated under design day conditions and typical day conditions.

3.5. Effect of the base chiller size

3.5.1. Base chiller size effect under design conditionsTable 6 summarizes the results of an analysis that investigates

the effect of varying the size of the base chiller on the total (energyand demand) operating cost of the cooling system for the designday. The results provided in Table 6 indicate that optimal controlscan achieve cost savings in the range of 22–28% relative to the con-ventional operating strategy. The cost savings are the highest forthe design day conditions when the base chiller size is 30 ton.

For base chiller capacities lower than 30 ton, the cost savingsdecrease significantly as the base chiller capacity is reduced. Thisbehavior is due to the fact that a small base chiller does not provideenough cooling capacity to pre-cool the building thermal mass todesirable low temperatures. Meanwhile, when the base chiller ishigher than 30 ton, the cost savings is slightly reduced (relativeto the savings of 28.5% achieved for a 30 ton base chiller) but re-mains at the same level of 28.1% for any large base chiller. This re-sult is due to the part-load performance of the chiller. Indeed,when the base chiller is large, the cooling loads for both pre-cool-ing and direct cooling represent smaller fractions (relative to thecase of 30 ton base chiller) of the chiller capacity. Thus, the largerbase chillers have to be operated at lower part load ratios and thusoperate less efficiently.

3.5.2. Base chiller size effect under typical conditionsA similar analysis that investigates the effect of varying the size

of the base chiller on the total (energy and demand) operating costof the cooling system has been performed for the typical day (i.e.the 28th day of September in Chicago, IL). The savings in the totalcost were identical for the three base chiller capacities (50, 25, and

Table 8Effects of the ice chiller size on the total daily cost for design day.

Ice chiller (ton) Total daily cost ($) % Savings (relative to base case)

Base case 140.40 –5 117.40 16.4010 110.70 21.2015 103.0 26.6420 102.20 27.2125 101.90 27.42

Table 10Effect of the ice tank capacity on the total daily cost for design day.

Ice tank (ton h) Total daily cost ($) % Savings (relative to base case)

– 140.40 –75 114.10 18.73100 112.10 20.20125 109.90 21.72150 107.90 23.15175 104.30 25.7200 101.70 27.60

Table 11Effect of the ice tank capacity on the total daily cost for design day.

Ice tank (ton h) Total daily cost ($) % Savings (relative to base case)

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15 tons) selected in the analysis. Table 7 summarizes the results ofthis analysis that investigates the effect of varying the size of thebase chiller on the total (energy and demand) operating cost ofthe cooling system under typical day conditions.

– 121.0 –60 99.9 17.4100 93.9 22.4180 93.3 22.9

3.6. Effect of the ice chiller size

3.6.1. Ice chiller size effect under design conditionsTable 8 summarizes the results of an analysis that investigates

the effect of varying the size of the ice chiller on the total operatingcost of the cooling system for design day conditions. The ice chillerof the cooling plant is designed only to charge the ice tank duringoff-peak hours. It cannot be used for direct cooling during occupiedhours or for pre-cooling during unoccupied hours and off-peakhours.

As indicated in Table 8, the cost savings achieved by optimalcontrol increase with the size of the ice chiller. The rate of increasebecomes significantly small when the ice chiller capacity is above15 ton. These results stem from the fact that for small ice chillercapacities, the load-shifting benefits of ice storage systems arenot fully realized. These benefits are almost completely achievedfor an ice chiller capacity of 15 ton. Any larger ice chiller capacitydoes not provide significant additional load shifting potential.

3.6.2. Ice chiller size effect under typical conditionsTable 9 summarizes the results of an analysis that investigates

the effect of varying the size of the ice chiller on the total operatingcost of the cooling system for typical day conditions. The resultsprovided in Table 9 indicate that optimal controls can achieve costsavings in the range of 17–23% relative to the conventional operat-ing strategy (i.e. system operating from 8:00 am to 5:00 pm to pro-vide direct cooling). The cost savings are the highest for the typicalday conditions when the ice chiller size is about 10–15 tons.

Similar to the findings for design day conditions, the cost sav-ings achieved by optimal control increase with the size of the icechiller. The rate of increase becomes significantly small when theice chiller capacity is above 10 ton. These results stem from the factthat for small ice chiller capacities, the load-shifting benefits of icestorage systems are not fully realized. With the 5 ton ice chiller,even though the charging period of the ice tank is longer thanthe cases with larger capacities, the maximum state of charge ofthe ice tank is 50%. Moreover, the base chiller is needed to meetthe building load during the occupancy period and is operatingat a low part load ratio. The load-shifting benefits of ice storagesystems are almost completely achieved for an ice chiller capacity

Table 9Effect of the ice chiller size on the total daily cost for typical day.

Ice chiller (ton) Total daily cost ($) % Savings (relative to base case)

Base case 121.0 –5 100.0 17.410 93.4 22.815 93.3 22.9

of 10 ton. Any larger ice chiller capacity does not provide signifi-cant additional load shifting potential.

3.7. Effect of the ice tank size

3.7.1. Ice tank size effect under design conditionsTable 10 summarizes the results of an analysis that determines

the effect of varying the size of the ice tank on the total operatingcost of the cooling system under design day conditions. Table 10also shows the percent cost savings in total electrical energy costobtained by using optimal controls relative to the conventionaloperating strategy (i.e. direct cooling without pre-cooling or useof TES system).

As expected, the results of Table 10 indicate the cost savings po-tential incurred from optimal control increases with the size of theice storage tank. Indeed, the higher the ice storage system, moreload-shifting can be achieved from on-peak to off-peak hours.

3.7.2. Ice tank size effect under typical conditionsTable 11 summarizes the results of an analysis that determines

the effect of varying the size of the ice tank on the total operatingcost of the cooling system under typical day conditions.

Similar to the findings with design day conditions, the results ofTable 11 indicate the cost savings potential incurred from optimalcontrol increases with the size of the ice storage tank. The resultsindicate that optimal controls can achieve cost savings in the rangeof 17–23% relative to the conventional operating strategy (i.e. base-case). The cost savings are the highest for the typical day condi-tions when the ice chiller size is about 180 ton h. Thus, large icetanks provide more total cost savings for both design and typicaldays. Economical analysis should be carried out to estimate thecost-effective ice tank size.

4. Summary and conclusions

This paper has investigated some of the important factors thataffect the performance of optimal controls of using simultaneouslybuilding thermal capacitance and ice storage system to reduce thecooling system total operating costs (including energy and demandcosts) while maintaining adequate occupant comfort level condi-tions in office buildings. The building thermal storage is utilizedthrough pre-cooling strategies by setting space temperatures tospecific values during unoccupied and occupied hours. Moreover,an ice storage tank is charged by operating the chiller during lowelectrical charge periods. During on-peak periods, the ice storage

A. Hajiah, M. Krarti / Energy Conversion and Management 64 (2012) 509–515 515

is discharged to meet the building cooling requirements. As a re-sult, it is possible to reduce or even eliminate the chiller operationduring on-peak hours.

The optimization results discussed in this paper for a typical of-fice building model under different weather conditions and variousdesign options indicate that significant cost savings (up to 40%) canbe achieved in the cooling system total operating cost. Generally,the results indicate that optimal control for both building thermalmass pre-cooling and ice storage operation outperforms all ofother conventional controls and sequential optimal controls underall climate conditions, utility rate structures, and system designs.However, the analysis presented in this paper showed that sequen-tial optimal control can achieve the majority of the cost savings po-tential of the developed optimal controls for both pre-cooling andice storage charging/discharging.

While laboratory testing have been carried to validate the costsavings benefits of the optimal controls using both building massand ice storage, additional fields testing of the developed optimalcontrols would be the following natural step in further investigat-ing the benefits of optimal controls for using simultaneously build-ing thermal mass and thermal energy storage system.

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