A strategy for prioritising interactive measures for enhancing energy efficiency of air-conditioned buildings

Energy 28 (2003) 877–893 www.elsevier.com/locate/energy

A strategy for prioritising interactive measures for enhancing energy efficiency of air-conditioned buildings W.L. Lee a,∗, F.W.H. Yik a, P. Jones b a

Research Centre for Building Environmental Performance, Department of Building Services Engineering, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong SAR, China b Welsh School of Architecture, Cardiff University, Cardiff, UK Received 23 May 2001

Abstract Within a given budget, selection of the optimal set of measures for enhancing the energy efficiency of a building is often based on the relative order of the feasible measures, prioritised according to either the life cycle cost saving or the economic benefit–cost ratio of the measures. A sensitivity analysis shows that, compared to the life cycle cost analysis, the benefit–cost ratio analysis is less susceptible to the influence of uncertainties in the estimates of the present value of the life cycle energy saving and cost. Where interactive measures are involved, the effects of some are dependent on the co-existence of other measures. The prioritisation determined according to the benefit–cost ratios of individual measures, each taken in the absence of all the others, can lead to the choice of a range of measures that is below optimal. Selection of the optimal set of energy efficiency enhancement measures requires a multistep approach, which is exemplified by the case study described in the paper.  2003 Elsevier Science Ltd. All rights reserved.

1. Introduction With a total land area of about 1100 square kilometres, and a population of around 6.8 million, Hong Kong is one of the most densely populated cities worldwide. Although the number of permanent residents of Hong Kong only accounts for about 0.0014% of the world’s population, Hong Kong is responsible for roughly 0.2% of the world’s total energy consumption [1]. Energy statistics show that the use of coal and natural gas for electricity generation accounts for about

∗

Corresponding author: Tel.: +1-852-2766-5852; fax: +1-852-2774-6146. E-mail address: [email protected] (W.L. Lee).

0360-5442/03/$ - see front matter  2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0360-5442(03)00005-7

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Fig. 1. Electricity consumption of Hong Kong by type of user, 1990–2000.

two-thirds of the primary energy requirement of Hong Kong [2]. The associated CO2 emission amounted to 2.14x108 tons per annum [3–5]. The above energy statistics show that efficient use of electricity is a key measure for conservation of energy resources and reduction of global warming gas emissions in Hong Kong. Fig. 1 shows the electricity consumption of the commercial, industrial and domestic sectors in Hong Kong from 1990 to 2000 [6]. Over this period, while the overall electricity consumption of Hong Kong rose at an average rate of 6% per annum, the share of the commercial sector increased from 49 to 62%. Fig. 2 shows the relative amounts of electricity consumed for the major enduses in typical commercial buildings Hong Kong [7]. Air-conditioning is the dominant electricity end-use, responsible for 48% of the electricity consumption in commercial buildings, which

Fig. 2. Electricity consumption of commercial buildings by end-uses.

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amounts to approximately 30% of the electricity generated within and imported into Hong Kong each year. The environmental burden of the use of electricity for air-conditioning points to the need for greater investment in measures that will enhance the energy efficiency of buildings and air-conditioning systems. One approach is to integrate the building with an entire package of energyefficient measures, so that each measure achieves complementary benefits [8]. However, as there is always a budgetary constraint for every building development project, the designer has to select the optimal set of energy efficiency enhancement measures that would lead to the greatest benefit within the cost framework [9,10]. To help the developers and designers strike an optimal balance between the first costs and the energy benefits, it is necessary to develop a methodology for comparing the cost effectiveness of a range of optional energy efficiency enhancement measures. First, a choice needs to be made on the method for quantification of the cost effectiveness of various energy efficiency enhancement measures. The next step is to estimate the potential energy saving of adopting each measure. The electricity consumption of an air-conditioning system is affected by a combination of compounding factors [11]. However, in most of the cost-effectiveness studies, the energy saving caused by adopting each measure would be estimated in isolation, ignoring the influences of the adoption of a sequence of other different measures [9]. As a result, the energy saving may be over- or under-estimated and thus, distort the cost-effectiveness analysis result. For interactive measures, the energy benefit of one measure will be dependent on what other measures have already been taken on board. For instance, the use of a better glazing will help reduce the cooling load on the central air-conditioning plant. The saving that can be gained by adopting more efficient chillers, therefore, will be smaller when better glazing is also used than when it is not. The inclusion of more measures may often lead to diminishing returns but some measures can be complementary. An example of complementary measures is the concurrent use of dimming control over the artificial lighting system and variable speed fan in the variable air volume system for the same space in a building. The reduced heat gain from the lighting system will not only lead to a reduction in the lighting power demand, it will also allow the fan to run at a lower speed leading to further reduction in the fan power. These effects have to be taken into consideration in the selection of the optimal set of measures for adoption. In this paper, a sensitivity analysis is presented, which compares the two most widely used methods for the evaluation of the cost effectiveness of energy efficiency enhancement measures, to see which method would be the more appropriate for use in the selection of measures for adoption. The discrepancy in the calculated cost effectiveness caused by ignoring the interacting effects among the measures is also examined. A method that would allow more realistic determination of the cost effectiveness of energy efficiency enhancement measures is presented and exemplified by a case study. 2. Cost effectiveness evaluation methods The benefit–cost ratio (B/C) and the life cycle cost (LCC) analysis methods are, by far, the most widely used methods in cost-effectiveness analyses for comparing the viability of one option against other available options [13,14]; the greater the B/C ratio or the smaller the LCC value, the more worthwhile to take the option compared to the other options.

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2.1. Life cycle cost saving (LCCS) The life cycle cost (LCC) analysis method has traditionally been considered an appropriate means of combining initial and operating costs into a single economic factor for use in effective decision-making. LCC is a measure of the total cost of producing, operating and disposing a design option. The technique is based on discounting, or reducing, all costs to current values or representing them as an annual cost. In evaluating the feasibility of energy efficiency enhancement measures for a building, the life cycle cost saving (LCCS) of each measure is often evaluated with reference to a baseline building design, which represents a workable design without any of the energy enhancement measures [15]. The LCCS of the ith measure based on an analysis period of n years and a discount rate of d % per annum [16] can be represented mathematically as shown in Eq. (1) below. Amongst the range of energy efficiency enhancement measures, a measure with a higher LCCS would be more desirable than those with lower LCCS. LCCSio ⫽ Bio ⫺ Cio and Bio ⫽ AECio ⫻ EU ⫻

(1)

冉 冊冉

1 1 1⫺ d (1 ⫹ d)n

冊

(2)

where Cio, is the increase in cost incurred by incorporation of the ith measure compared to the cost of the baseline building (HK$); Bio, the benefit of having the ith measure compared to the baseline building (HK$); AECio, the annual saving in electricity consumption due to the adoption of the ith measure compared to the baseline building (kWh/year); EU, the electricity tariff (HK$/kWh); d, the discount rate; n, the analysis period (year). The maintenance and the disposal costs of an air-conditioning installation are determined predominantly by the size of the building and the types and number of equipment [17]. Replacing a less efficient equipment by a more efficient one will incur little differences in such costs. For simplicity, the assumption was made that the maintenance cost and the disposal cost are the same for the baseline building with and without the energy efficiency enhancement measures. In evaluating the LCCS (Eq. (1)), the energy benefits of the energy efficiency enhancement measures should first be determined, and then discounted to the present value based on a predetermined discount rate, electricity price and analysis period. Inevitably, the result is subject to uncertainty. Apart from the uncertainties associated with the energy benefit and cost estimates, the choice of the discount rate, electricity price and the analysis period will introduce further uncertainties to the estimated LCCS. The electricity price used is contentious, as it is not easy to forecast future energy prices accurately. The discount rate of 5% is often used [12], but this has received criticism from some economists and a conclusive value is still lacking [18]. Likewise, the analysis period is equally arguable. The LCCS is a good reference for making the decision of whether or not to adopt a particular measure. However, when applied to selecting which among the available options should be chosen, a low cost option that would incur a moderate benefit may not be distinguishable from a high cost, high benefit option.

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2.2. Benefit–cost ratio (B/C) The economic benefit–cost ratio (B/C) is another means of measuring the viability of an energy efficiency enhancement measure [19–21]; the higher this ratio, the more worthwhile to invest in the measure. Mathematically, the economic benefit–cost ratio of incorporating the ith measure into the baseline building (B/C)io can be determined using: (B / C)io ⫽

Bio Cio

(3)

The benefit of an energy efficiency enhancement measure is to be evaluated based on the present value of the life cycle energy saving as shown in Eq. (2). Similar to LCCS, evaluation of the benefit–cost ratio relies on the estimates of future performance. An advantage of this method is that the result is less susceptible to the uncertainties associated with the estimated benefit and cost, and is subject to less influence from the relative order of magnitude of the initial cost difference and the benefit. 2.3. Sensitivity analysis The following compares the errors incurred to the estimates of the LCCS and the (B/C) due to errors in the estimates of B and C. From Eqs. (1) and (3), ⌬LCCS ⫽

∂LCCS ∂LCCS ⌬B ⫺ ⌬C ∂B ∂C

(4)

⌬(B / C) ⫽

∂(B / C) ∂(B / C) ⌬C ⫹ ⌬B ∂C ∂B

(5)

where ⌬LCCS is the error in the estimate of the life cycle cost saving LCCS; ⌬(B/C), the error in the estimate of the benefit–cost ratio (B/C); ⌬B, the error in the estimate of the benefit B; ⌬C, the error in the estimate of the cost difference C. It follows that ⌬LCCS ⫽ ⌬B ⫺⌬C

(6)

B 1 ⌬(B / C) ⫽ ⌬B⫺ 2⌬C C C

(7)

After normalising Eqs. (6) and (7) respectively by eqs. (1) and (3), and after algebraic manipulations, we get:

冉

冊 冉

⌬LCCS ⌬B B / C ⌬C 1 ⫺ ⫽ LCCS B (B / C)⫺1 C (B / C)⫺1 ⌬(B / C) ⌬B ⌬C ⫽ ⫺ . B/C B C

冊

(8) (9)

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Since the error of estimation for both the benefit and the incremental cost can be either a positive or a negative value, the above equations are re-written as:

冉 冉 冊 冉 冊

冉

冊冊

⌬LCCS |⌬C| |⌬B| B / C 1 ⫹ ⫽ ⫾ LCCS B (B / C)⫺1 C (B / C)⫺1

(10)

⌬(B / C) |⌬B| |⌬C| ⫽ ⫾ ⫹ B/C B C

(11)

It can be seen from Eq. (10) that the relative error in the estimate of the life cycle cost saving (⌬LCCS/LCCS) is dependent not only on the relative errors in the estimates of the benefit (⌬B/B) and the incremental cost (⌬C/C); it is dependent also on the benefit–cost ratio (B/C) of the measure concerned. However, as shown in Eq. (11), the relative error in the estimate of the benefit–cost ratio (⌬(B/C)/(B/C)) is simply the sum of the relative errors in the estimates of the benefit and the incremental cost. Fig. 3a shows how ⌬LCCS/LCCS and ⌬(B/C)/(B/C) vary with B/C when both the relative errors

Fig. 3. Relative errors in estimates of LCCS and B/C.

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in the estimates of the benefit (⌬B/B) and the incremental cost (⌬C/C) equal to 0.15 (only the positive values are shown). The effect of varying ⌬B/B and ⌬C/C are shown in Fig. 3b–d for three cases: ⌬B/B reduced to 0.1 and ⌬C/C increased to 0.2 (Fig. 3b); ⌬B/B increased to 0.2 and ⌬C/C reduced to 0.1 (Fig. 3c); and ⌬B/B increased to 0.2 and ⌬C/C reduced to 0. It can be seen from these graphs that the relative error in the estimate of the life cycle cost saving can become very large at the lower range of the benefit–cost ratio value. In the region where the benefit–cost ratio value is high, the relative error will be slightly smaller than that of the benefit–cost ratio when there is a significant relative error in the incremental cost estimate (⌬C/C) but will become greater if ⌬C/C tends to zero. The above analysis shows that compared to the life cycle cost saving, the benefit–cost ratio is more robust, in that the relative error in its estimate is dependent only on the relative errors in the estimates of the benefit and the incremental cost. The relative error in the estimate of the life cycle cost saving, however, is subject further to a scaling effect that is dependent on the relative magnitude of the benefit and the incremental cost. 3. Prioritising strategy 3.1. Baseline building To provide a basis for the present study, a 40-storey model building, which is representative of the typical configuration of commercial office buildings in Hong Kong, was used as the baseline building. The building and the system design characteristics of the baseline building were established from data obtained in an extensive building energy survey [22,23]. The floor layout and details of the building are summarised in Fig. 4 and Table 1 respectively.

Fig. 4. Layout of the model office building.

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Table 1 Summary of characteristics of the baseline building Case 1: a marginally acceptable building

Case 2: an energy efficient building

Building data No. of floors (N) Floor to floor height Perimeter zone area (P) Interior zone area (I) Core – non air-conditioned (C) Floor area, air-conditioned (P+I) Total air-conditioned area ((P+I)×N)

40 3.2 m 455 m2 616 m2 225 m2 1071 m2 42840 m2

40 3.2 m 455 m2 616 m2 225 m2 1071 m2 42840 m2

Building envelope Window/wall area ratio Shading coefficient Overall thermal transfer value Installed lighting load Appliances load

0.5 0.45 34.7 W/m2 25 W/m2 25 W/m2

0.333 0.27 15.0 W/m2 14 W/m2 25 W/m2

6651 kW (1891 ton) Air-cooled 6 × 320 ton chillers 1.25 kW / ton Singe-loop, constant speed with differential pressure by-pass control

6120 kW (1740 ton) Direct seawater cooled 3× 600 ton chillers 0.65 kW / ton Two-loop pumping with variable flow, variable speed secondary pumps 0.8 VAV system with variable speed supply fan 0.65 59 kWh/m2 94 W/m2 110 VA/m2

Air-conditioning system Design chiller plant capacity Method of heat rejection Chiller combination Chiller design performance Chilled water pumping system

Pump efficiency Air-side system Fan efficiency Annual electricity consumption Peak power consumptiona Maximum electricity demanda a

0.6 VAV system with inlet guide vane control 0.55 138 kWh/m2 143 W/m2 169 VA/m2

Including power consumption of lighting, appliances and miscellaneous loads in the office block.

3.2. Energy prediction model The energy saving that various energy efficiency enhancement measures would bring, individually or collectively, can be estimated from the difference in the energy use of the baseline building with and without the measures. Instead of using a detailed computer building energy prediction program, a simplified regression model, as described below, was used in this study. This not only simplified the calculations, it allowed a clearer picture of the interactions among the measures to be seen. Being a multivariate model, the model can predict energy saving, and hence the benefit, that adopting an independent measure, or a combination of measures, would yield.

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The energy prediction model used in this study, as shown in Eq. (12), is a regression model that relates the annual electricity consumption of the air-conditioning system to the key parameters characterising the energy performance of the building and its air-conditioning system [24]. The model was formulated with reference to the fundamental formulae for design cooling load estimation according to ASHRAE’s CLTD/SHGF/CLF method [25]. The model coefficients were derived using a multiple regression method, based on the building and system characteristics of 14 actual buildings in Hong Kong and the annual air-conditioning electricity consumption predicted for these 14 buildings using the detailed simulation programs HTB2 and BECON [26,27]. The coefficient of determination (r2) of the model was 0.952. Details about the development of the regression model have been reported in an earlier paper [24]. AEC ⫽ 4.763⫺13.84(AG ⫻ UG ⫻ Ta / COP) ⫹ 364.709(AG ⫻ UG / COP) ⫹ 75.683(VR / COP) ⫹ 2.359(QLGT / COP) ⫹ 1.484(QSPW / COP) ⫹ 0.688(CPP ⫻ PP)

(12)

⫹ 4.966(CFP ⫻ FP) where AEC is the annual electricity consumption of a chiller plant per m2 of air-conditioned area (kWh/m2); AG, total window area per m2 floor area; CFP, coefficient of fan flow rate control method; COP, coefficient of performance of the chillers; CPP, coefficient of pump flow rate control method; FP, total installed fan power intensity (W/m2); PP, total installed pump power intensity (W/m2); QLGT, intensity of lighting load (W/m2); QSPW, intensity of equipment load (W/m2); SC, area weighted shading coefficient of window glasses; Ta, indoor design temperature (°C); UG, area weighted average heat transfer coefficient of window glasses (W/m2 K); VR, fresh air supply rate per m2 floor area (m/s) Since Eq. (12) is a regression model developed on the basis of a set of building models in Hong Kong, it should be applicable only to a limited range of characteristics of buildings as covered by those building models used in its development. Given that Hong Kong has a relatively homogeneous building stock and climate, this limitation of the model has not seriously restricted its application but for more heterogeneous locales, detailed simulation is needed for prediction of energy saving of various energy efficiency enhancing measures. A review of the building, system and operation characteristics of the baseline building used in the present study showed that it fell within the applicable range of the regression model. The predictions of the above regression model over a specific range of building and system characteristics were also found to be in agreement with the predictions of the detailed simulation package (HTB2 + BECON) to a maximum difference of ±10%. Hence, the model was considered sufficiently accurate for use in the present study. It can be seen that there are product terms in the energy prediction model, implying that the energy saving of one energy efficiency enhancement measure can be dependent on whether or not the other measures are already taken on board. Such energy efficiency enhancement measures are regarded as interactive options. 3.3. Cost data The costs of the energy efficiency enhancement measures needed for the benefit–cost ratio evaluation are dominated by the supply and installation costs of the additional equipment and/or

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material. The cost data used in this study were determined from the unit price data in priced bills of quantities or published cost data books [28] updated to the year 1998 before the occurrence of the financial turmoil in Hong Kong. The bills of quantities are a part of the tender document for a building contract, which provide a uniform basis for contractors to prepare their tenders for the contract work. The bills of quantities of an accepted tender contain the detailed quantities take-off, and the corresponding market prices of equipment and materials used in a particular building project. 3.4. Case studies In this study, consideration was given to the set of energy efficiency enhancement measures identified to be cost-effective considered in an earlier study [29], which may be divided into two categories: those that would lead to a more efficient air-conditioning system, and those that would help reduce the air-conditioning load. The air-conditioning system design measures include the use of more energy efficient equipment or system designs, for instance, chillers with high coefficient of performance (COP), and the use of VAV system designs that would allow better controls over the part-load performance of the equipment. The effectiveness of these measures have also been studied by others [30]. The other category of energy efficiency enhancement measures include the reduction of total glazed area, the use of electronic ballast and compact fluorescent lamps leading to a smaller installed lighting power density, as well as the adoption of a higher indoor set-point temperature. The characteristics of the baseline building were set with reference to the average design values of office buildings in Hong Kong or, where applicable, in marginal compliance with the requirements stipulated in the building energy codes [31–35]. Each of the energy efficiency enhancement measure incorporated into the building corresponded to the best achievable/practicable limits [36]. The ranges of design variations made are as summarised in Table 2. It can be seen that some of the energy efficiency enhancement measures have been excluded, such as the reduction in ventilation rate (VR) and the increase in the average heat transfer coefficient of window glasses (UG). Table 2 Range of design variations Energy efficiency enhancement measures

Varying range

AG COP CFP FP T CPP PP Qlgt

Window to wall ratio: 0.5 to 0.3, i.e. 0.143 m2 to 0.095 m2 Air-cooled chiller to water-cooled chiller, i.e. COP 2.7 to 5.4 VAV system with inlet guide vane to VAV system with variable speed fan Installed fan power 15.4 kW/m2 to 13 kW/m2 by improvement of fan motor efficiency 23 °C to 25.5 °C Single-loop constant speed pump to 2-loop variable speed pump Installed pump power 4.5 kW/m2 to 3.4 kW/m2 by reduction of piping losses Lighting intensity 25 W/m2 to 14 W/m2 by the adoption of electronic ballasts and compact fluorescent lamps Small power intensity 25 W/m2 to 20 W/m2 by better operating practice

Qspw

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VR was not varied because most buildings would be maintaining the minimum ventilation rate that would satisfy the indoor air quality requirement, and this should not be reduced for the sake of energy saving, to protect the health of the occupants. UG was not considered because its value will only vary by a small extent among the typical types of glazing (single glazing) adopted in buildings in Hong Kong. The results for four scenarios are summarised in Table 3. Each of the energy enhancement measures considered corresponds to a change in one of the building or system parameters by an extent as summarised in Table 2. The first three scenarios in Table 3 correspond to a sequence of adoption of these measures that followed the prioritisation of the measures according to their individual benefit–cost ratios (scenario 1), the annual energy saving of each when applied in isolation (scenario 2), and a random order (scenario 3). The AECij values are the incremental annual energy saving when the ith measure was incorporated whilst the baseline building had already been incorporated with up to the (i⫺1)th measure. It can be seen that the same total energy saving, 78.5 kWh/m2, would be achievable when all the measures were ultimately adopted irrespective of the different sequences of selection in the three scenarios. The overall saving shown under scenario 4, which amounts to 106.6 kWh/m2, was the sum of the estimated saving achievable from each measure in isolation. These results show that ignoring the mutual influences of the measures will overestimate the energy saving by 28.1 kWh/m2. Furthermore, it can be seen that the predicted energy saving for an individual measure could largely vary with the order it was selected, and with the measures already selected. For instance, the energy saving by improving the COP of chillers would be 39.8 kWh/m2 for scenarios 1 and 2, but would become 28.1 kWh/m2 for scenario 3. Different energy saving values was found Table 3 Energy impact varies with preference order Scenario Multiple measures 1

Single measure 2

3

4

Design option

Preference order

AECij

Preference order

AECij

Preference order

AECij

Preference order

AECij

COP AG Qspw CFP Ta FP CPP PP Qlgt ΣAECij

1 2 3 4 5 6 7 8 9

39.8 2.2 1.4 20.6 3.3 4.7 1.2 0.5 4.8 78.5

1 7 3 2 4 6 8 9 5

39.8 0.6 1.4 20.6 4.9 4.7 1.2 0.5 4.8 78.5

9 8 6 5 7 4 3 2 1

28.1 1.1 2.7 17.4 9.9 7.9 0.9 0.8 9.6 78.5

1 1 1 1 1 1 1 1 1

39.8 4.5 9.9 20.6 7.9 1.2 0.8 9.6 12.4 106.6

Scenario 1, prioritise according to benefit/cost ratio; scenario 2, prioritise according to AEC; scenario 3, random order; scenario 4, single option; AECij, annual electricity consumption saving ‘with’ and ‘without’ the ith design option (kWh/m2); ΣAECij, total annual electricity consumption saving for incorporation of all the design options (kWh/m2).

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among the three scenarios for the same reduction in the glass area per unit floor area, i.e. a reduction in AG. This implies that if just a subset of the measures were to be adopted under the constraint of the available budget, the sequence of adoption of different measures will have a significant impact on the final energy saving. This case study shows that the energy saving cannot be correctly evaluated for the determination of the benefit–cost ratios before the priority order is determined. However, the setting of the priority order is, in turn, dependent on the benefit–cost ratios. Therefore, the one-step prioritising strategy that is based on the benefit–cost ratios determined with reference to the adoption or otherwise of each energy enhancement measure individually should not be used when a range of interactive energy efficiency enhancement measures is involved. This situation would need to be dealt with by using a ‘multi-step’ selection method. Details of the proposed approach are explained in the following section.

4. Prioritising strategy As discussed above, benefit–cost ratio is an adequate reference for prioritisation of energy efficiency enhancement measures. If the one-step approach were adopted, the benefit–cost ratio of each measure would first be evaluated by assuming a discrete choice of measures, and the priority order would be set simply based on the relative magnitudes of the benefit–cost ratios (B/Ci,0) in descending order as shown below: B / C1,0,…, B / CN-1,0, B / CN,0 MAX

→

MIN

This, however, is inadequate for selecting a sequence of interactive measures. Instead, the priority order should be established by a ‘multi-step’ selection approach, which would involve N⫺1 rounds of selection for N number of energy efficiency enhancement measures. However, there are energy efficiency enhancement measures that can be incorporated with minimal cost, or even no additional cost, and at anytime during the life-cycle of the building, such as the use of variable fan speed controls to replace inlet guide vane controls; the use of more efficient fan motors; the adoption of good practice to reduce unnecessary use of electrical appliances; and the increase in indoor set-point temperature. Because of the insignificant cost for implementing these measures, the cost term Cio in Eq. (3) was considered to be zero. For those no cost measures, it is logical to assume that they should be given first priority. The baseline building should therefore include the incorporation of all the no cost measures. With the no cost measures excluded from the prioritisation process, say there are X measures, there left N⫺X⫺1 rounds of selection. The first measure to be selected should be the one with the highest benefit–cost ratio, for instance B/Ci,0, among which with each of the measures taken in the absence of the others. For the second selection round, with the ith measure already selected in the first round, the benefits should be evaluated based on the baseline building ‘with’ the ith measure incorporated. Out of the N⫺X⫺1 measures, the measure with the highest benefit–cost ratio should again be selected. The selection process should continue until only the last measure is left. The benefit–cost ratios and the LCCS for various energy efficiency enhancement measures,

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evaluated according to the above multi-step selection approach are summarised in Table 4. It can be seen that there are large variations in the benefit–cost ratios among the measures, which range from a positive value to a negative value. Since all the measures can lead to a positive benefit, a measure that yields a negative benefit–cost ratio is one that leads also to a reduction in the initial cost. For this reason, it will stay at the top of the priority list. The conversion from aircooled chillers to water-cooled chillers and the reduction in window to wall ratio from 0.5 to 0.33 are two examples. The former was mainly due to the reduction in the number of chillers and the associated circulating pumps from 6 to 3, on the basis that the unit capacity of watercooled chillers is normally larger than that of air-cooled chillers. However, the reduction in the operational flexibility due to the use of fewer chillers was not considered in the analysis. Likewise for reducing the window to wall ratio, the initial cost saving was mainly due to the reduced area of the glazing, which is more costly than the external walls (HK$3500/m2 vs. HK$1250/m2) [22]. However, impacts on the aesthetic and property values due to the reduced glazing areas are too difficult to quantify for inclusion into the cost analysis. The priority order and the benefit–cost ratios evaluated by the one-step and multi-step selection approaches are summarised in Table 4. It can be seen that benefit–cost ratios and LCCS can be correctly determined only if the multi-step approach was used in the prioritisation. Amongst various measures, the conversion from water-cooled to air-cooled chillers (COP) stays at the top of the priority list. The reduction in the installed lighting intensity due to the use of electronic ballast and compact fluorescent lamps yielded the lowest benefit–cost ratio. This is somewhat expected because of the significant difference in the unit price between an electronic ballast and an electromagnetic ballast, which are HK$71 and HK$4, respectively [37]. 5. Energy conservation supply curve Besides the priority issue, the designers and developers also need to observe their budget constraints in selecting a range of improvement measures for adoption. Fig. 5 shows the energy Table 4 Prioritising results for one-step and multi-step selection approaches Measure

CFP FP Ta Qspw COP AG PP CPP Qlgt

One-step Initial B/Ci,o ratio N/A

⫺3.4 ⫺2.4 106.3 105.0 3.7

N/A, not applicable.

Priority order

Multi-step Priority order

1 1 1 1 2 3 4 5 6

1 1 1 1 2 3 5 4 6

Genuine B/Ci,j ratio LCCS (HK$x105) N/A

92.7

⫺3.4 ⫺0.1 1.3 3.6 0.4

89.0 2.3 1.2 3.2 12.4

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Fig. 5. Energy conservation supply curve.

conservation supply curve constructed for the baseline building [38]. The method used to derive the curve is similar to the one described by Meier and others [39–42]. The incremental cost per unit energy saved of an individual energy efficiency enhancement measure is plotted in the yaxis, whilst the cumulative energy saved is plotted in the x-axis. The measure with the highest priority order will be placed on the left, based on the assumption that such measure would be adopted before those with lower priority orders. This curve resembles the supply curve of an economic good where the y-axis denotes the marginal cost and the x-axis denotes the quantity supplied. Hence, the curve illustrates that the incremental cost per unit energy saved would increase with the total amount saved, which indicates that there would be a diminishing rate of economic return. This is consistent with findings of other studies [43,44]. According to Fig. 5, although reducing the window area appears to be the most economical option, their contribution to the total energy saved is limited in comparison with all the other measures investigated. Under a constrained budget, Fig. 5 can help identify the range of measures that would be affordable and the potential energy saving.

6. Impact of prioritising strategy on energy use To quantify the impact of the prioritising strategy on energy use, the cumulative LCCS and the cumulative annual energy consumption saved (AEC) due to the adoption of the measures selected

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according to the one-step and the multi-step selection approaches are compared in Table 5. It can be seen that the cumulative LCCS and AEC values for the implementation of measures 1 to 7 are HK$2 × 105 and 0.8 kWh/m2 higher if multi-step selection approach has been adopted. As there was no difference in the additional cost required for introducing measures 1 to 7 selected according to the two approaches, the different AEC values indicates that under the same budgetary ceiling, the adoption of the measures selected based on the multi-step approach can actually lead to energy and life cycle cost savings. 7. Conclusion In this study, the benefit–cost ratio has been shown to be an appropriate economic parameter for cost effectiveness analysis of various energy efficiency enhancement measures that are being considered simultaneously. It has the advantage over life cycle cost saving analysis that it is less susceptible to the uncertainties and limitations in the estimates of cost and benefit. To account for the interactive relationship between the measures, the use of multi-step selection approach is recommended so that the cost effectiveness can be correctly determined to enable the developers and the designers to prioritise various interactive energy efficiency enhancement measures and make the right selection within the available budget. Based on typical characteristics of buildings and systems in Hong Kong, and the local price data, a set of benefit–cost ratios is established, which can provide some insight to the policy-makers in Hong Kong to revisit their energy efficiency policy, such as rebate schemes for adoption of energy efficiency enhancement measures in buildings. Amongst various energy efficiency enhancement measures, the conversion from water-cooled to air-cooled chillers stays at the top of the priority list. This result is consistent Table 5 Cumulative savings for one-step and multi-step approaches Priority order

1 2 3 4 5 6 7 8 9

Measure

Cumulative costs (HK$x104)

Incremental cost (HK$x104)/kWh saved

Cumulative AEC savings (kWh/m2)

Cumulative LCCS (HK$x105)

One-step Multistep

One-step Multistep

One-step Multistep

One-step Multistep

One-step Multistep

CFP FP Ta SPW COP AG PP CPP Qlgt

0 0 0 0 ⫺264 ⫺649 ⫺640 ⫺631 ⫺335

0 0 0 0 ⫺7.6 ⫺427.5 22.5 7.5 60.3

35.9

35.9

92.7

92.7

70.5 71.4 71.8 73.0 77.9

70.5 71.4 72.6 73.0 77.9

181.6 183.9 185.1 188.3 200.7

181.6 183.9 187.1 188.3 200.7

CFP FP Ta SPW COP AG CPP PP Qlgt

0 0 0 0 ⫺264 ⫺649 ⫺640 ⫺631 ⫺335

0.0 0.0 0.0 0.0 ⫺7.6 ⫺427.5 7.5 22.5 60.3

The annual electricity consumption for air-conditioning of the baseline building is 138.6 kWh/m2. Measures 7 and 8 are of the same initial cost [22].

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