LCC assessments and environmental impacts on the energy renovation of a multi-family building from the 1890s

Energy and Buildings 133 (2016) 823–833

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LCC assessments and environmental impacts on the energy renovation of a multi-family building from the 1890s Linn Liu a,b,∗ , Patrik Rohdin a , Bahram Moshfegh a,b a b

Division of Energy Systems, Department of Management and Engineering, Linköping University, Sweden Division of Building, Energy and Environment Technology, Department of Technology and Environment, University of Gävle, Sweden

a r t i c l e

i n f o

Article history: Received 8 June 2016 Received in revised form 21 October 2016 Accepted 22 October 2016 Available online 24 October 2016 Keywords: LCC assessments Environmental impacts Energy efficiency measures package Listed/non-listed building Renovation Energy targets

a b s t r a c t The 2020 and 2050 energy targets increase requirements on energy performance in the building stock, thus affecting both listed and non-listed buildings. It is important to select appropriate and cost-optimal energy efficiency measures, using e.g. Life Cycle Cost (LCC) optimization. The aim of this paper is to find cost-optimal packages of energy efficiency measures (EEMs) as well as to explore the effects of specific predesigned energy target values for a listed Swedish multi-family building from the 1890s. The purpose is also to show the effects on energy use, LCC, primary energy use and CO2 emissions of different energy targets, discount rates, electricity prices and geographic locations. The results show that separate energy targets could be an effective way to simplify the implementation for listed buildings. Furthermore, a cost-optimal package of EEMs is more sensitive to changes in discount rate than in electricity price. The energy renovation has impact on the primary energy use and CO2 emissions. The lower the discount rate is, the more EEMs will be implemented and the easier the national energy targets may be achieved. A higher electricity price also leads to more EEMs being implemented but at the same time higher running costs. © 2016 Elsevier B.V. All rights reserved.

1. Introduction During the past 40 years, global primary energy demand has doubled. In Sweden, similar to many other European countries, the energy demand in the built environment accounts for about 40% of total energy use [1]. The European Commission has declared that one of its most important tasks is to break the mechanisms of global warming. This puts a special focus on the reduction of greenhouse gases. As a result, the European Commission has published a revision of the energy performance of buildings directive [2] which includes requirements for buildings being built or rebuilt. This also leads to changes for listed buildings. Efficient energy supply and energy use enable efficient use of buildings and their contents in the long run. For the managers of listed buildings, an efficient use of energy is also important so that the buildings meet the basic requirements for indoor environment, running costs and environmental performance, while the heritage value is preserved. The results, however, are reported as undesirable.

∗ Corresponding author at: Division of Energy Systems, Department of Management and Engineering, Linköping University, Sweden. E-mail address: [email protected] (L. Liu). http://dx.doi.org/10.1016/j.enbuild.2016.10.040 0378-7788/© 2016 Elsevier B.V. All rights reserved.

Many old buildings have lost part of their heritage values due to inappropriate measures such as fac¸ade insulation and window replacement [3]. There are examples however in which the building’s original architecture and character have been taken into consideration [4]. Life Cycle Cost (LCC) analysis has been strongly recommended as a useful tool during renovation of buildings from an economic point of view [5–7]. LCC analysis is a method which can be used to find comparable costs for different investment alternatives. From this perspective a cost-optimal solution can be determined for a building. In addition, a concept called ‘Total Measure Concept’ (TMC) has been recommended by the Swedish Association of Local Authorities and Regions (UFOS) in order to find the best energy solution for the building [8]. The idea is that the measures should be profitable in relation to the discount rate used and the measures should be energy related. Even though the built environment in Sweden has a large number of listed buildings, as 15% of all blocks of flats and 27% of all single-family houses were built before 1940 [9], research on listed buildings’ energy efficiency potential and indoor environment is scarce in Sweden: Rohdin et al. [10] have investigated indoor environment and energy use in a listed building in Sweden using field measurements and building energy simulation. Ståhl et al. compiled existing knowledge regarding sustainable and careful renovation and energy efficiency of listed buildings [4]. Liu et al.

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2. Life cycle cost optimization Nomenclature A Heated floor area, m2 ACH Air change rate, 1/h Awindow Window area, m2 C1 The inevitable cost, SEK/m2 C2 The direct insulation cost, SEK/m2 C3 The direct insulation cost, SEK/(m2 m) Constant, SEK/m2 C4 C5 Constant, SEK C6 Constant, SEK/kW C7 Cost of pipe system, SEK Cinsul. Insulation cost, SEK Cwindow Cost of window replacement, SEK Cheating unit Cost of heating units, SEK Dex.wall Thickness of the external wall, cm E Building’s energy use, kWh or MWh Annually recurring cost, SEK F G Future non-recurring cost, SEK Number of year n P Power of the heating unit, kW PV Present value, SEK r Real discount rate, % Indoor temperature, ◦ C Tindoor U Overall heat transfer coefficient, W/(m2 K) V Volume, m3

have studied energy saving potential and LCC of eleven residential buildings including one listed building [11]. Broström et al. have described methods to assess the potential for energy retrofits in Swedish listed buildings [12]. Other studies from other countries in this field include Alev et al. [13] who have analysed renovation alternatives to improve energy performance of three listed buildings in the Baltic Sea region by using field measurements and simulations. Moran et al. [14] have studied how to reduce energy use and CO2 -emissions in listed dwellings using a simulation tool called Passive House Planning Package. Fabbri et al. [15] have studied parameters which will affect the microclimate of the famous Maletestiana library in Italy and how it will be changed using field measurements and monitoring. Judson et al. [16] have studied how owners of listed buildings balance emerging needs for environmental sustainability while retaining heritage value using qualitative interviews. The overall conclusion is that listed buildings require an individual approach due to their heterogeneity, as the type of construction, materials, heritage value and use can vary greatly. Thus there is no single, universal solution for how to make them more energyefficient while at the same time preserving their cultural value. The aim of this paper is to find a cost-optimal package of energy efficiency measures as well as to explore the effects of specific predesigned energy target values for a listed Swedish multi-family building from the 1890s. The targets explored are (B) the national energy target of the building sector’s energy use, to reduce energy use in buildings by 20% by 2020 compared to 1995 [17], (D) the national energy target of the building sector’s energy use, to reduce energy use in buildings by 50% by 2050 compared to 1995 [17]. The purpose is also to show the effects of different energy targets, real discount rate, electricity price and geographic location on energy use, LCC, primary energy and CO2 emissions. The studied building is considered both as a listed and a non-listed building.

The early stages of planning have significant influence for the future performance of a building. The same statement is also true when it comes to major renovation of an existing building and the optimization potential is great at a low cost. Furthermore, it can be shown that early design stages determine up to 80% of building operational costs as well as environmental impacts for new buildings. The operational costs typically greatly exceed the construction costs over a life cycle. However, exactly how much more this will be as well as the ratio of initial to following costs depends on the quality of construction, intensity of use and building type, as well as on considered life time of the building [18]. The same type of conceptual problem is presented when major renovation projects are carried out where one perspective is to find a cost-optimal package of measures to include in the renovation process. LCC optimization software called OPERA-MILP (Optimal Energy Retrofits Advisory-Mixed Integer Linear Program) [19] has been used in order to find the cost-optimal energy renovation strategy for a building during its whole life cycle. OPERA-MILP is based on mixed integer linear programming (MILP) and was first developed at the Division of Energy Systems in Linköping University during the 1980s. The software has been used for studying multi-family buildings [20–23], and also one heritage single-family building [12]. The input data to the OPERA-MILP requires building information, e.g. U-values of the building, indoor air change rate, indoor and outdoor temperatures, costs and performance for different measures, energy prices and the real discount rate. Then an iterative procedure determines the cost-optimal combination of measures by minimizing LCC. The outputs include (1) dimensioned building’s power (W); (2) annual energy demand (Wh) of the building; (3) an optimal combination of different energy efficiency measures (EEMs); (4) energy savings specified for different energy carriers (Wh); (5) the total costs of each measure (SEK); and (6) the total LCC for the building (SEK). This LCC represents the sum of the investment, running cost and residual value. The investment cost includes the construction cost of the suggested heating system, as well as costs for each suggested measure and the inevitable retrofit cost. The inevitable retrofit cost includes costs of e.g. erecting scaffolding, dust cover, painting and washing the fac¸ade, and is thus independent of the building itself and does not influence the optimal thickness of e.g. insulation. The running cost is the life cycle cost of running the installed heating system, which is labeled LCCEnergy from here on. The expression total LCC can thus be stated as in Eq. (1) [12,19]. Post-processing may also include CO2 emission calculation. The EEMs which are included in OPERA-MILP are (1) attic floor insulation; (2) ground floor insulation; (3) external wall insulation from outside; (4) external wall insulation from inside; (5) window replacement.1 ; (6) weather-stripping; and (7) heating system installation2 LCCtot = Investment + LCCEnergy − Residual value

(1)

In order to make the costs comparable the present value method is used, which transfers all the cost during the project time to a base year. For a future non-recurring cost, G, the present value (PV) can be calculated using Eq. (2). For annually recurring costs, F, the present value can be calculated as in Eq. (3). PV = G·(1 + r)−n

(2)

1 Window type I: 2+1 glazed. Window type II: 3 glazed with LE glass. Window type III: 3 glazed+ LE + Gas. 2 Three heating systems are included, a wood boiler (WB), a ground water heat pump (HP) and a district heating (DH).

L. Liu et al. / Energy and Buildings 133 (2016) 823–833

825

Fig. 1. The three types of LCC optimization with or without additional energy constraints.

PV = F ·

1 − (1 + r)−n r

(3)

In Eqs. (2) and (3), r is the real discount rate, n is the number of years until event G or F will occur. Since the parameters have strong influence on the results as well as being hard to predict, it is of interest to include them in a sensitivity analysis. In addition, the prognosis of energy price development is also difficult and highly uncertain, and will thus also be included [18]. The cost functions (C) of the EEMs are described differently depending on whether it is an insulation measure (Eq. (4)), window replacement (Eq. (5)) or heating system (Eq. (6)) [19].

that is lower than the optimum ELCC a new solution is found. The third option is that a target value E2 higher than ELCC is sought, then a process of removing the least cost-efficient EEMs is used. In order to include specific aspects of preserving the heritage value of a building, unsuitable measures are removed from the optimization process. These considerations are decided before optimization. The impacts of the implemented EEMs on the building’s heritage value are shown in Broström et al. [12]. The three types of LCC optimization with or without different additional energy constraints are illustrated in Fig. 1. 3. Description of the studied building

C insul. = C 1 + C 2 + C 3 ·t

(4)

C window = C 4 ·Awindow

(5)

3.1. The physical mode

C heatingunit = C 5 +C 6 ·P+C 7 ·P

(6)

A multi-family building from the 1890s which is located in Stockholm, Sweden, has been chosen as the case study building. The average annual outdoor temperature of Stockholm is 7.4 ◦ C during a normal year [25]. The building is north-south oriented and consists of five stories (one heated basement and four stories of apartments) and one unheated attic floor at the top, see Fig. 2. The total heated floor area is 1000 m2 (20 m × 10 m on each floor). The height of basement and attic floor is 2.2 m and 1.2 m. The height of Floor 1, 2, 3 and 4 (F1, F2, F3 and F4) is 3 m. The exterior walls are constructed of brick. The thicknesses of the exterior walls are heterogenic at different floors, which means that the properties and thicknesses of the exterior walls are different on different floors. The building includes 56 two-pane windows with U-value of 2.7 W/(m2 K). The technical information about the building has been summarized in Table 1. The building uses a natural ventilation system and the existing heating system is a hydronic system connected to the district heating grid. The construction of the study building represents a typical multi-family building built during the 1880s and 1890s [26].

In Eq. (4), Cinsul. is the total insulation cost, C1 presents the maintenance (inevitable) cost of erecting scaffolding, demolition and building a new external fac¸ade. C2 and C3 are connected to the direct insulation cost, including material and labour cost etc. and t is the added insulation thickness. Cwindow is the cost of window replacement, C4 represents the specific cost and Awindow is window area. The same structure is used for heating systems where C5 and C6 are specific economic constants and, P is the power of the unit. C7 is the cost of pipe works and other associate construction costs that do not scale with size. C5 is the investment cost of the heating system. This structure of cost functions when combine with cost of inevitable retrofit work and costs of energy etc. allows us to minimize the LCC and thus obtain a mathematical optimum. The Swedish database called ‘Wikell Section database’ (Wikells Sektionsfakta in Swedish) [24] was used to compile these cost functions. The database includes up-to date prices, labour costs and costs for all parts of a construction project. 2.1. Adding additional constraints

3.2. The properties and cost functions for the EEMs Adding the possibility to explore pre-set energy targets or to remove unsuitable EEMs, for example from a preservation point of view, exists in the form of adding additional constraints in the optimization. The methodology for adding additional energy constraints is illustrated in Fig. 1. The left (a) represents the total process. If no additional constraints are added the optimized solution is obtained (ELCC ). If, however, (b), an energy target (E1 ) is added

The added insulation has the thermal conductivity (-value) of 0.038 W/(m K) including joists and studs, etc. The maximum insulation thickness is set to 42 cm. Three different window types with U-values of 1.5, 1.1 and 0.8 W/(m2 K) could be chosen instead of the existing window type [28,29]. The remaining life times of attic floor, basement, external wall, windows and weather-stripping are set to

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Table 1 Basic information of each floor of the building. Tindoor (◦ C) Basement F1 F2 & F3 F4 a

a

17 22 22 22

˙(UA) (W/K)

Uex.wall W/(m2 K)

Ufloor W/(m2 K)

Uattic W/(m2 K)

Dex.wall (cm)

ACH (1/h)

150.8 161.5 181.7 373.7

0.86 0.86 1.09 1.49

0.38 – – –

– – – 0.78

60 60 45 30

– 0.68 0.68 0.68

[27].

be zero, while the new life times for these building parts are set to 50 years. The life time of a new window and weather-stripping are 30 years and 10 years respectively. In Table 2 the coefficients for the cost functions presented above are specified. The price of weather-stripping of a window is set to be 197 SEK each. The same database (Wikell section data) has also been used to find the costs of heating systems [24]. During the optimization, the study building is separated into five parts therefore C5 should also be divided by 5 in order for the costs to be correctly allocated. In this study, the remaining life time of the building’s current heating system is set to zero, to represent a major renovation. The new life cycle times, efficiencies, Coefficient of Performance (COP) and costs of all three heating units are presented in Table 3. 3.3. Energy and electricity prices In Sweden, electricity prices vary depending on a number of factors, e.g. supply and demand, rainfall, etc. The energy companies base their prices on these variations. The electricity price model used in this study is adapted from an energy company. In this model the annual electricity use is divided into 17 segments where the months from November to March have two segments each. The first segment is a daily price period and the second is a fixed price period. For the remaining months, April to October, only one

segment is used. The daily electricity price period is from Monday to Friday from 6a.m. to 10p.m. during November and March and the rest of the time is specified as a fixed price period. In addition to this price model there are also additional fees for the grid (‘grid fee’) and the market (‘market fee’). In this study a fixed market fee of 0.95 SEK/kWh together with a flexible grid fee of 0.52 SEK/kWh during the daily price period and 0.12 SEK/kWh during the fixed electricity price periods have been used. During April, May, June, July, August and September a fixed electricity price of 1.07 SEK/kWh has been used. On the contrary, January, February, March, November and December include also a daily price which is 37% higher (1.47 SEK/kWh) than the fixed price. The district heating price model is based on a fixed tariff, which is 0.68 SEK/kWh, and an annual subscription fee, which is 6000 SEK [33]. The price of biomass (pellets) is also implemented as a fixed price, which is 0.55 SEK/kWh [34].

3.4. Limitations In this study interior insulation of the external walls is not considered to be a viable choice and is thus not included. One reason is to avoid condensation in the wall which in turn will lead to moisture and mould problems, while another reason is to avoid reduction of the living area. In order to combine the evaluation of heritage value with energy and cost effectiveness of the building, the focus in this study is on the exterior appearance of the studied building, which means that no changes to exterior appearance (external wall insulation from outside and window replacement) and only minor material changes are allowed. The primary energy factors for district heating and electricity used are 1.2 and 2.5, respectively [35,36]. To calculate the CO2 emissions of district heating and electricity, different CO2 emission factors are presented in Table 4. The CO2 emission factors used for district heating are for four cities (Luleå, Falun, Stockholm and Malmö) located in Sweden from north to south with different average outdoor temperatures (3.6 ◦ C, 5.6 ◦ C, 7.4 ◦ C and 8.2 ◦ C) [37,42], Swedish average production value (SADHP) [38]. Luleå and Falun have a relatively low proportion of fossil fuel usage of 5% and 3% respectively in their district heating production [37]. In comparison, Malmö has a relatively high proportion of fossil fuel of 38% [37]. The energy company Fortum AB in Stockholm aims for heat supply systems from a global systems perspective. In addition the net balance to the power grid is

Table 2 Input data of the cost functions of EEMs [24]. C1 (SEK/m2 ) C2 (SEK/m2 ) C3 (SEK/m2 m) C4 (SEK/m2 )

Fig. 2. Example of the type of building used in the case study [26].

Attic floor Underground External wall inside External wall outside Existing window Window type I Window type II Window type III

0 0 1 717 580 – – – –

41 582 365 1 056 – – – –

556 842 1 400 5 988 – – – –

– – – – 7 156 9 726 10 316 11 880

L. Liu et al. / Energy and Buildings 133 (2016) 823–833

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Table 3 Life time of different heating system units, efficiency/COP and the installation costs [24,30–32].

Wood boiler (WB) Heat pump (HP) District heating (DH)

Life time of the unit (yr)

Life time of the pipe network (yr)

Efficiency/COP of the HP unit

C5 /5 (SEK)

C6 (SEK/kW)

C7 (SEK/kW)

15 25 25

50 50 50

␩ = 0.75 COP = 2.5 ␩ = 0.95

13600 14200 25170

2119 3882 415

160 35015 160

also included in the CO2 emission factor calculation. This results in Fortum AB having relatively high CO2 emissions. The CO2 emission factors for electricity used are Swedish average electricity mix production value (SAEP), Nordic electricity mix production (NEMP) [42], Nordic marginal electricity produced by coal condensing power plant (NMEP); see Table 4. The SAEP has the lowest CO2 emission factor. However, in recent years, this approach has been replaced by the Nordic electricity mix that considers the import and export of electricity to neighbouring countries. The NEMP will reflect the electricity system’s physical appearance where the Nordic countries (excluding Iceland) have basically unlimited transmission capacity in comparison with the rest of European countries, where the transport is limited. The third method is called NMEP which means the last produced unit of electricity by using the electricity system. Deciding which electricity is marginal production depends on the total electricity consumption at each moment. During peak load hours, the marginal electricity will be produced by a coal power plant, based on the idea that this is the most expensive production unit in the system, and thus the one that is first removed when the peak load is reduced. At other times, during low load hours, the marginal electricity production will have low carbon intensity and be based on sources such as wind-power or hydropower. 4. Results 4.1. Energy use, LCC and energy efficiency measures in different cases The energy use, total LCC and energy efficiency measures in different cases for the non-listed and listed buildings are shown in Fig. 3. Four cases have been investigated for each building: (1) the reference case (A, A1 ); (2) 20% energy use reduction (case B, B1 ); (3) optimum (case C, C1 ); and (4) 50% energy use reduction (case D, D1 ). The results from case A, B, C and D are for the non-listed building, and A1 , B1 , C1 and D1 are for the listed building. The sub-script indicates that the building is considered a listed building. The building’s energy use in the reference case (A and A1 ) is 165.5 kWh/m2 before any energy efficient measures are introduced and the total LCC is 5159 kSEK. In the reference case the total LCC includes the installation cost of district heating system, inevitable cost and energy cost of running the district heating. As shown in Fig. 3 and Table 5, at the optimum point (in case C), the nonlisted building’s total LCC is 3926 kSEK and the energy use will

be 119 kWh/m2 which indicates a 28% energy use reduction. For the listed building the external wall insulation from outside has to be removed as an option since it will cause visual and material changes [12]. The total LCC and energy use for the listed building at the optimum point will then become 4009 kSEK and 130.7 kWh/m2 respectively, which represents a 21% energy use reduction. When the additional 20% constraint is added, the total LCC and energy use are the same for both the listed and the non-listed building. This is since the suggested EEMs are suitable for the building whether it is considered a non-listed or a listed building. In comparison, when a 50% energy use reduction is required in case D and D1 , the total LCC and energy use are different for the non-listed and listed building, since the external wall insulation and window replacement are not allowed when a building’s heritage value is considered. Additionally, it is also shown that both the non-listed and listed building can achieve a 20% reduction in energy use without major renovation. But reaching the long-term national target of energy saving up to 50% requires major renovation, which is in conflict with the preservation requirements (compare point D and D1 ). Fig. 3 also shows that the listed building is not capable of reaching the 50% energy use reduction without allowing changes to the wall structure. The maximum energy use reduction for the listed building is 35%. The optimal point for the listed building appears earlier, which means higher energy use and total LCC compared to the non-listed one (compare point C with C1 or D with D1 ). In Fig. 4 the total LCC of each case is presented in detail. The yellow bar presents the running costs of the installed heating systems and the other bar presents the investment cost which is broken down in different costs of EEMs, inevitable cost and installation cost of the heating system. As shown in the figure, for the investment cost, the installation cost of the heating system is reduced from case A to D while the cost of EEM implementation increases. The percentage of EEM implementation cost increases from 35% to 59% for the non-listed building and 35% to 48% for the listed building. The LCCEnergy is reduced from case A to D since the required power of the district heating and heat pump has been reduced. This indicates that the more EEMs are implemented the more restrictive the energy requirement becomes, which lead to less utilization of the heating system. This trend is shown more clearly in the non-listed building than in the listed building, since there are more EEMs taken by the non-listed building than by the listed building. 5500 Non-listed building Listed building

5300 5100

District heating

CO2 emission factor (kg/MWh)

City of Luleå, Sweden City of Falun, Sweden City of Stockholm, Sweden (Fortum AB) City of Malmö, Sweden Swedish average district heating production (SADHP)

18 15 133 124 74

Electricity Swedish average electricity production (SAEP) Nordic electricity mix (NEM) Nordic marginal electricity production (NMEP)

11 74 933

4900

LCC (kSEK)

Table 4 Used CO2 emission factors of district heating and electricity [37–42].

A, A1

4700 4500 4300

B, B1

D1

4100

D

3900

C1 C

3700 3500 80

90

100

110

120

130

140

150

160

170

E (kWh/m2) Fig. 3. Energy use and total LCC after optimization for the cases A-D and A1 -D1 .

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Table 5 Energy efficient measures after optimization for the cased A-D and A1 -D1. Items

Case A & A1

B & B1

C

C1

D

D1

Heating system: Power Weather stripping Attic floor insulation External wall insulation

DH: 62 kW – – –

HP: 19 kW F1–F4 16 cm –

HP: 17 kW F1–F4 32 cm 8 cm @ F4

HP: 19 kW F1–F4 34 cm –

HP: 16 kW F1–F4 42 cm –

Window Ground floor insulation

– –

– –

– –

– –

HP: 12 kW F1–F4 42 cm 16 cm @ F2–F3 36 cm @ F4 Type II, F2–F4 –

Type I, F2–F4 42 cm

Fig. 4. Investment cost and LCCEnergy for non-listed and listed buildings in different cases (blue: installation cost of the heating system; red: inevitable cost; green: insulation cost; black: weather-stripping cost; purple: window replacement cost; yellow: running cost).

The primary energy use of the listed and non-listed building in (kWh/m2 yr) is presented in Table 6. The CO2 emissions in (ton/yr) by the building in different cases are illustrated in Fig. 5. The primary energy use of the non-listed building is lower than the listed building since the energy use of the non-listed building is lower. The primary energy use has been reduced by 37% in case B, 43% for non-listed building and 37% for listed building in case C, 60% for non-listed building and 49% for listed building in case D. The difference between the primary energy use in kWh/(m2 yr) and the building’s specific energy use is small, because the primary energy factor of electricity is equal to the used COP of the heat pump in the current study. The CO2 emissions vary depending on which type of heating system the building is connected to. In case A, when the building is connected to district heating, five different CO2 emission factors have been used to evaluate the building’s CO2 release: Luleå, Falun, Stockholm, Malmö and Swedish average production. As shown in Fig. 5, when the building is connected to the district heating system, the CO2 emissions by the non-listed and listed building are the same, which are 23.2 ton/year, 21.6 ton/year, 12.9 ton/year, 3.1 ton/year and 2.6 ton/year by using the factors from Stockholm, Malmö, Swedish average, Luleå and Falun. In cases B, C and D, when the building is heated by heat pump, three different CO2 emission factors of electricity have been investigated, which are: Swedish average electricity production (SAEP), Nordic electricity mix production (NEMP) and Nordic marginal electricity production (NMEP). The CO2 emissions by the non-listed and listed building are the same in case B but differ in case C and D. The listed building releases more CO2 than the non-listed building since it has higher

energy use than the non-listed building in case C and D. When the building uses a heat pump, the CO2 emissions are highest when the NMEP method is used, 49 ton/year in case B, and lowest when the SAEP method is used, 0.4 ton/year in case D. 4.2. Sensitivity analysis In the sensitivity analysis, only heat pump is selected as the heating system since it is shown to be the optimal choice in all the above presented studies. Parameters such as discount rate and energy price will vary in the sensitivity analysis since they are the parameters which have great impact on the present value (PV) and LCC will thus affect the optimal solution. In addition, different geographic locations will also be included in the sensitivity analysis. 4.2.1. Discount rate The default real discount rate used in this study is 5%. The choice was based on the fact that it is commercially used in real projects

Table 6 The primary energy use by listed and non-listed building in different cases. Primary energy use (kWh/m2 yr)

Non-listed building Listed building

Case A & A1

B & B1

C & C1

D & D1

209 209

133 133

119 131

84 107

Fig. 5. CO2 emssions by the non-listed and listed buildings in different cases.

L. Liu et al. / Energy and Buildings 133 (2016) 823–833

Fig. 6. Energy use and the total LCC of the non-listed building with discount rate 1%, 5% and 9%.

[11,43]. In the sensitivity study six more different real discount rates will be tested to visualize the effect. The trend of how the energy use and total LCC of e.g. the nonlisted building will vary when the discount rate varies between 1%, 5% and 9% is shown in Fig. 6. The overall result is as expected, i.e., the lower the discount rate, the higher the total LCC becomes. Fig. 6 also shows that the optimum point moves from right to left of the x- axes, which means the lower discount rate is, the lower the optimum energy use becomes. Fig. 7 shows how the energy use and the LCC will change by using real discount rate of 1%, 3%, 5%, 5.5%, 6%, 7% and 9%. The difference in energy use between the non-listed and listed building is largest when the discount rate is 1% and this difference besomes less with a higher discount rate, as illustrated in Fig. 7(a). When the discount rate is 1%, the difference of energy use between the non-listed and listed building is 27%, but the difference of LCC between those buildings is only 6%. For the non-listed building, the energy use becomes almost stable when the discount rate is higher than 5%, since the external wall insulation is not suggested to be implemented when a higher discount rate has been selected. For the listed building, the energy use becomes stable after the discount rate becomes higher than 5%. The floor insulation, which is 42 cm with 1% discount rate, has decreased to 34 cm with a discount rate of 5%. Furthermore, it is shown in Fig. 7(b) that discount rate has large impact on the total LCC of both buildings.

4.2.2. Electricity price In the sensitivity analysis nine different electricity prices will be tested in order to investigate how the energy use and LCC vary.

Fig. 8. Energy use and LCC of the non-listed building when choosing 400% (4.26 SEK/kWh), current electricity price (i.e., 1.06 SEK/kWh) and 25% (0.27 SEK/kWh) of the current electricity price.

As expected the result shows that the higher the electricity price is, the higher the LCC becomes. This is illustrated in Fig. 8. However, what is more interesting is how the optimum (point C) is moving with changing electricity price. With an electricity price which is 4 times higher (4.26 SEK/kWh) than the current electricity price, point C (the optimum point) has exceeded point D (the 50% energy use reduction point). This means that under those circumstances the long-term national target to reduce the energy use by 50% would be less strict than the cost-optimum. With that energy price the optimal specific energy use is 65 kWh/m2 . The electricity prices varied between 25% (0.27 SEK/kWh), 50% (0.53 SEK/kWh), 150% (1.59 SEK/kWh), 160% (1.70 SEK/kWh), 175% (1.86 SEK/kWh), 200% (2.13 SEK/kWh), 250% (2.66 SEK/kWh), 300% (3.12 SEK/kWh) and 400% (4.25 SEK/kWh) of the current electricity price (1.06 SEK/kWh) in the following sensitivity study. In addition a daily price which is 37% higher than the fixed electricity price has also been used. The cost-optimal energy use difference between the non-listed and listed building becomes considerably larger after the electricity price becomes 1.59 SEK/kWh (1.5 times higher than the current electricity price). This is mainly because window replacement starts to come into the solution at this point. It is also shown in the figure that the listed building’s energy use flattens out before the non-listed building’s after this point. This is because it becomes more difficult to reduce the listed building’s energy use when the electricity price becomes 1.59 SEK/kWh since several different energy efficiency measures are not available for this type of building. The total LCC changes almost linearly with electricity price increases as shown in Fig. 9(b). The difference in

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energy use and total LCC becomes larger when the electricity price increases when comparing the listed and non-listed building. 4.2.3. The combination of varied discount rate and electricity price In the sensitivity analysis the combination of varied discount rate and electricity price has been tested in order to investigate how the energy use and LCC vary. The chosen discount rates (r) are 1%, 3% and 5% and the chosen electricity prices are 400% of the current price, the current electricity price and 25% of the current electricity price. The reason of why the maximum discount rate is limited to 5% for the analysis is because the variation of specific energy saving, i.e. E, for both the non-listed and listed building is insignificant when the discount rate is larger than 5%. As it is observed, the combination of lowest discount rate which is 1% and highest electricity price which is 400% of the current electricity price will lead to the highest LCC (8880 kSEK for the non-listed building, 11540 kSEK for the listed building) but the lowest energy use (58.2 kWh/m2 for nonlisted building, 107.4 kWh/m2 for listed building). In the contrary, the combination of highest discount rate which is 5% and lowest electricity price which is 25% of the current electricity price will lead to lowest LCC (3179 kSEK for the non-listed building, 3191 kSEK for the listed building) but the highest energy use (119.7 kWh/m2 for the non-listed building, 131.2 kWh/m2 for the listed building). This trend is the same for both non-listed building and listed building. 4.2.4. Geographic locations The geographic location is also expected to have influence on the studied parameters, for which reason four locations with different climate are shown to illustrate this difference. The chosen places are: Luleå (latitude 65.58 and longitude 22.15), Falun (latitude 60.6 and longitude 15.6), Stockholm (latitude 59.3 and longitude 18.05) and Malmö (latitude 55.6 and longitude 13). The average annual outdoor temperatures of these cities are 3.6 ◦ C, 5.6 ◦ C, 7.4 ◦ C and 8.2 ◦ C, respectively. When comparing LCC and saved energy (difference between before refurbishment and after) illustrated in Fig. 10, the expected trend is shown that the lower the outdoor temperature and the less solar energy there is, the more energy it is cost-optimal to save. By placing the non-listed building in the climate zone with average temperature of 3.6 ◦ C, 5.6 ◦ C 7.4 ◦ C and 8.2 ◦ C, the energy use reduction at the optimum will be 50% (111 kWh/m2 ), 38% (73 kWh/m2 ), 28% (46 kWh/m2 ) and 27% (41 kWh/m2 ). For the listed building, the energy use reduction will be 24% (52 kWh/m2 ), 22% (42 kWh/m2 ), 21% (35 kWh/m2 ) and 20% (32 kWh/m2 ). This means that it is costoptimal to reduce both the non-listed and listed building’s energy use by 20% wherever it is located in Sweden. But it is only optimal to reduce the non-listed building’s energy use by 50% when the average outdoor temperature is 3.6 ◦ C. However, it is not costoptimal either for the non-listed buildings or the listed buildings

to reach the 2050 energy target regardless of location. It may also be stated that the energy use difference between the nonlisted and listed building becomes smaller the higher the outdoor temperature becomes. This difference also becomes smaller when the building will get more solar energy. 5. Discussion 5.1. Will the studied building reach the swedish energy targets? The result shows that it is cost optimal for both the non-listed and listed building to achieve 28% and 21% energy use reduction respectively. This means that the 2020 energy target of 20% is below the cost-optimal point with the given parameters. The 2050 target of 50% may be achieved by the non-listed building, but when the constraints relevant to listed buildings are added the cost-optimality changes as some EEMs which are in direct conflict with the building’s heritage value may not be implemented. This has some policy implications, as the current national target does not include specific targets for different building types, e.g. listed buildings and buildings without conservation requirements. Furthermore, from a cost-optimality perspective it would be beneficial to break down the long-term targets into measureable milestones, so that those buildings which cannot achieve the 2050 target directly are still on the right path to reduce their energy use [8]. In addition, the result has also shown that the total LCC has only increased by 2% for the non-listed building or 4% for the listed building when the energy use reduction increases from 20% to 50% (for the non-listed building) or 35% (for the listed building). This

Fig. 10. The saved energy and the total LCC of the non-listed and the listed building in different locations with different outdoor temperatures.

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indicates that the suggested package of EEMs is close to the costoptimum, thus that the total LCC does not increase much when the target is raised from 20% to 50% for the non-listed building or 35% for the listed building. However, it is important to note that the change in initial investment is larger. This may be considered by housing companies and private owners before renovating their buildings. Fig. 4 shows that the energy efficiency costs are in the range of 1413 SEK/m2 –2421 SEK/m2 for the current project. This is shown to be within the price range when compared with other energy efficiency renovation projects in Sweden. In those projects the costs range from 545 SEK/m2 to 3490 SEK/m2 [11,43]. In order to achieve the 2050 energy target, it is important to consider the energy efficiency measures as a package rather than as individual measures. A similar concept has also been recommended by the Swedish Association of Local Authorities and Regions (UFOS). When using OPERA-MILP, an energy efficiency measure (EEM) package can be compiled. The EEMs which are included in the package are sorted from most cost-efficient to least cost-efficient. With the EEM package, the cost-optimal solution can be found for the building. In an EEM package, the profitable measures finance the less profitable measures, thereby making it possible to achieve a well-balanced energy use reduction from both economic as well as efficiency point of view. 5.2. How will the discount rate affect the building in meeting the swedish energy targets? One of the most important factors in the LCC calculation is the discount rate, which is based on the financial situation and strategy of the investing company or experience of the investor and is therefore always to some degree subjective, such as inflation and the country’s economy, etc. [18]. The choice of discount rate has large impact on the total LCC and the optimal solution as well as on what package of EEMs will be included. The discount rate (r) which has been used in the current study, takes into account not only the present value of money but also the risk or uncertainty of the future cash flow. A real discount rate of 5% has been used in this study. The influence of different discount rates shows that the lower the discount rate, the higher the total LCC becomes, and vice versa. Low discount rate (r) means low required rate of return which in turn means e.g. a low risk for investment or a low required rate of return in the company. This in turn will lead to that a low discount rate will result in more EEMs implemented as a lower rate of return on capital investments is used. As a consequence the LCC may increase. Furthermore, a higher discount rate results in lower cost of EEMs and also lower running and investment costs of heating systems, which in turn leads to lower LCC. On the other hand, a lower discount rate leads to higher present value of EEM implementation and higher running and investment costs of the heating system, which in turn leads to higher LCC. Fig. 6 also shows that with current discount rate which is 5% the non-listed building may reach both the national energy targets. However, the listed building will only reach the 2020 target if cost optimality is a criterion. The energy saving for both the non-listed and listed building starts to be stabilized when the discount rate is larger than 5%. In this study it is also shown that a discount rate higher than 5%, for the studied non-listed and listed building will not lead to implementation of less EEMs. The lower the discount rate is, the lower required rate of return is for investment, the more EEMs will be implemented and the easier the national energy targets may be achieved. A low discount-rate will thus provide a strong incentive of aiding buildings to reach long-term energy targets. Though the discount rate has great impact on buildings in meeting the national energy targets, it is difficult for an individual person or organization to choose which discount rate should be used since the above mentioned factors can affect the discount rate.

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5.3. How will the electricity price affect the building in meeting the swedish energy targets? The second factor which has large significance on LCC is energy price. Since the future energy price development is hard, if not impossible, to predict a price range has been investigated to study the stability of the chosen EEMs. In general the results show the expected correlation that when the electricity price increases, the energy use will be reduced and more EEMs will be implemented at the cost-optimal point. The same is also shown that a low electricity price will result in low running cost and therefore fewer EEMs will be implemented at the cost-optimal point. In Fig. 8 the correlation is illustrated that the optimum point moves to the left (lower specific energy use) when the electricity price increases. When the electricity price increases by 400%, the optimum point will move past point D, which indicates the 50% energy use reduction. Fig. 9 shows that an electricity price of 25–100% of the current price will only lead to large energy savings if the electricity price is increased by 50% since implementation of window replacement starts to be suggested. A higher electricity price will lead to more suggested EEMs and thus lower total energy use, but from the housing company’s or a private homeowner’s perspective, lower energy price will of course lead to lower running costs. Anyway, since there are several factors such as water levels in the Nordic water reservoirs, cyclical fluctuations, energy-related taxes, government fees, demand and supply, ect. that can affect the electricity price, the effects on building’s LCC and energy use by electricity price are difficult to predict for a single person or country. The sensitivity analysis has only been done when heat pump is selected as the heating system since it is shown to be the optimal choice in all the presented studies. 5.4. How will combined discount rate and electricity cost variation affect the building in meeting the swedish energy targets? The combinition impact of varied discount rate and electricity price has confirmed the statements which have been mentioned above. The combination of low discount rate and high electricity price will lead to high energy use reduction and also high LCC. In the contary, the combination of high discount rate and low electricity price will lead to low energy use reduction and low LCC. From the energy efficiency point of view, low discount rate combines with high electricity cost will thus benefit buildings to reach long-term energy targets. 5.5. How will energy renovation impact the primary energy use and CO2 emissions? Decisions related to the building’s process have long-term consequences, particularly for the environment and the use of energy [44]. One aspect of this is shown in Table 6 where the primary energy use values of both listed and non-listed building are presented. The non-listed building is lower than the listed building since the energy use of the non-listed building is lower. The other aspect of this is shown in Fig. 5 where different levels of CO2 emissions are released by the studied building when it is connected to different heating systems. Five CO2 emission factors of district heating system and three CO2 emission factors of electricity have been used to investigate how building’s CO2 emission will be affected by different perspectives. The chosen CO2 -emission factors for district heating systems are locally based, while the chosen CO2 emission factors for electricity take into account the Nordic electricity production system. This is because district heating is local, although there are linked networks of district heating in Sweden, and in most cases the local district heating plant has the largest impact on the environment. The main difference between these factors is caused

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by the proportion of fossil fuels used in the district heating production process. On the other hand, the Swedish electricity suppliers buy electricity on the power market Nord Pool which sells electricity from all the Nordic countries. Therefore the CO2 emission factors related to NPMP and NMEP are chosen. In addition, the CO2 emission factor for Swedish average production of district heating and electricity is also chosen in order to compare with the other methods. Comparing the CO2 emissions by listed and non-listed buildings using the SAEP method; will result in the lowest CO2 emissions since this method does not take into account the import or export of electricity to other countries. The CO2 emissions will be higher by using the other two methods. The advantage of evaluating CO2 emission by using the NEMP method is that this method reflects the actual emissions from the production of electricity. In addition, the emissions from all electricity use within the system boundary will be equal to the total emissions. The NMEP has the highest CO2 emissions since the electricity is assumed to be produced by a coal condensing plant in this project. Thus by using the abovementioned methods, the most CO2 -emissions are in case B, which is 49.4 ton/year by using the NMEP method. The least CO2 emissions are in case D, which is 0.4 ton/year by using the Swedish average mix method. In order to achieve the long-term energy targets, a sound financial structure and incentives are needed. More knowledge is needed to understand how this affects different building types as well as understanding how this affects other values in our building environment. Using e.g. an LCC optimization method may help decisionmakers with one dimension when choosing packages of EEMs. It is also important to highlight conflicting objectives in this area. Different building types have different capacities/conditions to reach the energy targets. Separate energy targets could be an effective way to simplify the interpretation of the targets for listed buildings. Expertise in both energy and environment are needed in cooperation in order to achieve the energy targets cost-optimally, energy efficiently and in an environmentally sound way.

6. Conclusions In Sweden, increased requirements in terms of energy efficiency within the building sector also increases interest in finding methods for how to both reduce energy use and preserve the heritage values in the built environment. This paper presents a method to find costoptimal solutions for a non-listed and listed building using the life cycle cost optimization method. The results show that the building is capable of reaching the 2020 energy target whether or not the building’s heritage value is considered. On the other hand, when the building’s heritage value is not considered, the building can also reach the 2050 energy target. However, it will be difficult when the building is considered a listed building and the cost-optimal constraint is included. Separate energy targets could be made for listed and non-listed buildings as they have different capacities and conditions in energy saving. The impact of changing the discount rate is important but not as sensitive as changes in electricity price. The lower the discount rate is, the more EEMs will be implemented and the easier the national energy targets will be achieved. A higher electricity price leads to more energy savings but higher running cost. It is also worth mentioning that the choice of discount rate and electricity price by political decision-makers, housing companies or private homeowners will be different depending on perspective, e.g. national or local. This will in turn impact whether the building stock can achieve the national energy targets or higher energy requirements. The primary energy use reduction of non-listed building is higher than of the listed building. The difference in CO2 emissions by

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