Evaluation of energy renovation strategies for 12 historic building types using LCC optimization

Energy & Buildings 197 (2019) 156–170

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Evaluation of energy renovation strategies for 12 historic building types using LCC optimization Vlatko Milic´ a,∗, Klas Ekelöw a, Maria Andersson a, Bahram Moshfegh a,b a b

Division of Energy Systems, Department of Management and Engineering, Linköping University, 581 83 Linköping, Sweden Division of Building, Energy and Environment Technology, Department of Technology and Environment, University of Gävle, 801 76 Gävle, Sweden

a r t i c l e

i n f o

Article history: Received 24 September 2018 Revised 27 March 2019 Accepted 6 May 2019 Available online 7 May 2019 Keywords: OPERA-MILP LCC optimization Historic buildings Energy renovation Environmental performance

a b s t r a c t The life cycle cost (LCC) optimization is a vital method when performing building energy renovation. The present paper provides an evaluation of cost-optimal energy renovation strategies for historic buildings using LCC optimization software OPERA-MILP. The evaluation is performed based on preset targets depending on LCC (LCC optimum) and energy use (decrease by 50%), where the environmental performance is also addressed. Twelve building types, which are typical of the historic building stock in Visby, Sweden, are used as the study object. The results show possible decreases of 12–38% in LCC when targeting LCC optimum. When targeting a 50% decrease in energy use, the LCC is decreased in 21 of 26 cases compared to before energy renovation. Cost-efficient EEMs on the building envelope are characterized by low renovation costs and additional insulation of building components with poor thermal properties. Furthermore, the environmental performance from the energy renovations is highly dependent on the chosen energy system boundary. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Global energy use has doubled since the 1970s [1]. Generally, high energy use is closely linked to economic growth [2]. As a result of this, the energy use in industrialized countries is considerably higher compared to developing countries. In any event, with growing economies worldwide the global energy demand will most likely increase, with a higher strain on the environment as a side effect. Globally, buildings utilize 35% of the final energy use and contribute approximately one-third of CO2 emissions [3]; hence the building sector accounts for a significant proportion of energy use and environmental impact. Within the European Union (EU), buildings account for 40% of energy use [4]. Goals have been set in the EU whereby CO2 emissions are to be decreased by 40% by 2030 compared to 1990 and energy efficiency of a minimum of 27% is to be achieved compared to business as usual [5]. The sector with the greatest energy savings potential is the existing building stock [6] where more than 40% was constructed before 1960 and 90% before 1990 [7]. The existing building stock is also a key element in the process of achieving the aim of 80–95% reduction in greenhouse gas emissions in the EU by 2050 compared to 1990 [6]. Nationally, Sweden has set goals for the building sector whereby the energy use per heated area is to be decreased by 20%

∗

Corresponding author. ´ E-mail address: [email protected] (V. Milic).

https://doi.org/10.1016/j.enbuild.2019.05.017 0378-7788/© 2019 Elsevier B.V. All rights reserved.

and 50% by 2020 and 2050 respectively, compared to 1995 [8]. Approximately one-third of the buildings in Sweden were built before 1945 [9], hereafter referred to as historic buildings in this paper. With poorer thermal properties of the building envelope in older buildings compared to newer ones [10], this segment most likely accounts for a larger share of the total energy use. Many of the historic buildings, however, possess heritage values that must be considered during energy renovations. Due to difficulties that arise in the installation of energy efficiency measures (EEMs) while considering heritage values, these buildings are currently exempt from demands on energy efficiency [11]. However, with the set targets for the EU and Sweden, these buildings will most likely be affected when renovated [11]. Additionally, other energy renovation incentives exist beyond legal requirements, such as economic and environmental benefits [7]. At EU level, a methodology framework has been developed for calculating cost-optimal levels of minimum energy performance requirements in new and existing buildings [12]. A number of scientific investigations have been conducted within the field of energy efficiency and LCC optimization in buildings. Ekström et al. [13] investigated the cost-effectiveness of renovating single-family houses in Sweden from the 1960s and 1970s using life cycle cost (LCC) analysis and building energy simulation (BES). The results showed that the cost-effectiveness of energy renovation is highly dependent on the original type of heating system in the building and its running cost. The most profitable renovation measure was to install an exhaust air heat pump, and the least profitable was replacing old windows with new ones. Applying

V. Mili´c, K. Ekelöw and M. Andersson et al. / Energy & Buildings 197 (2019) 156–170

simulation and optimization procedures, energy efficiency and thermal comfort measures were analyzed for a representative residential construction in Salamanca, Mexico, based on five residential buildings [14]. In order to achieve the minimum annual energy cost in new residential constructions, roof and external wall insulation, as well as increased appliance and water heating system efficiency, are suggested. These measures correspond to annual energy savings of around 50%. Niemelä et al. [15] studied the impacts of different energy performance renovation measures in a brick apartment building with regard to cost-effectiveness and energy performance. The studied building represents a typical apartment building in Finland from the first half of the 1960s. A simulation-based multi-objective optimization analysis was performed where the primary energy use and LCC were minimized at the same time. The investigation showed that investments should focus on renewable energy systems for the highest profitability. It was also concluded that external financial support is needed to execute deep renovations targeting nearly zero-energy apartment buildings. Penna et al. [16] also applied a multi-objective optimization approach using various EEMs where the aim was to minimize LCC or energy use with the lowest achievable thermal discomfort. The case study was based on 12 residential reference buildings. The results showed that it is possible to approach zero energy targets with economic feasibility by implementing conventional EEMs such as insulation, but with a negative impact on thermal comfort. However, incentives are needed for the implementation of non-economic EEMs, which are more effective concerning energy savings and thermal comfort, e.g. mechanical ventilation. Broström et al. [17] presented a multidisciplinary method for assessing the potential of energy performance improvements, depending on predetermined targets in a stock of historic buildings and the associated consequences on heritage values of the energy efficiency interventions. The method was applied to a case study in the form of a common building type in Sweden, built in the 1920s. The method allows for LCC optimization of the building together with consideration of the heritage values. However, to achieve a 50% decrease in energy use, which was a set energy target, energy interventions that influence the aesthetics of the building significantly, e.g. external insulation, were needed. Hence, the building’s heritage values restricted the energy saving potentials. Liu et al. [18] also investigated the impact of heritage values on energy savings potential and LCC using LCC optimization. The case study consisted of a multi-family building from the 1890s. The aim was to find cost-optimal packages of EEMs for the building. Differences when considering the building as listed or non-listed, i.e. with or without consideration to heritage values, were investigated. The results show that the potential for decreasing energy use is greater when considering the building as non-listed. Depending on the energy target, the LCC might be higher for the listed building as some of the cost-optimal EEMs are not applicable due to the impact on heritage values. Alev et al. [19] verify the findings of Liu et al. [18] concerning limitations of potential for decreasing energy use because of building heritage values. The study was conducted by investigating renovation alternatives that enhance the energy performance of historic rural houses in the Baltic Sea region depending on different energy saving levels. This was investigated by field measurements and BES. Solutions in the form of improvements to the building service system and energy source had the greatest energy savings potential with a minimal effect on aesthetics. It was also stated that insulation of the external walls has a high energy savings potential, albeit with an effect on the building’s appearance. The preservation of heritage values during energy retrofits was also studied by Tadeu et al. [20] where a building from the beginning of the 20th century typical of the building stock in Coimbra, Portugal, was investigated with a focus on cost-optimality and environmental performance. The

157

study shows that significant decreases in energy use and environmental impact can be achieved without affecting the heritage value. It was also stated that a minimization of economic and environmental costs is enabled with the implementation of the most suitable energy retrofit packages. Arumägi and Kalamees [21] suggest different primary energy limits for historic wooden buildings because of the problems with EEMs targeting the envelope since the heritage values are affected. Their investigation was based on historic wooden apartment buildings in Estonia, involving 29 buildings and 41 apartments, which were built before the Second World War. The results showed that decreases of 20–65% in primary energy were enabled by the implementation of different insulation measures, HVAC solutions and energy sources. By combining building service systems with insulation measures, higher energy savings can be achieved with economic feasibility. While there are a few scientific investigations on the energy renovation of historic buildings using LCC optimization, research on energy renovation strategies of historic buildings using LCC optimization in order to evaluate the most cost-efficient renovation strategy are scarce. Furthermore, considering the set targets regarding energy use and CO2 emissions of for example the EU and local governments, assessments of the environmental impacts of suggested energy renovation strategies must also be addressed when proposing EEMs, beside the economic aspects. As older buildings generally have less efficient thermal performance compared to newer buildings [10], the historic building stock built before 1945 in Sweden, representing about one-third of the buildings nationally [9], most likely accounts for a significant share of total energy use in the building sector. For this reason, there is a need for a better understanding of how to achieve cost-efficient energy renovation strategies in historic buildings, as well as the potential impacts of such measures on the environment. The objective of this paper is to evaluate cost-optimal energy renovation strategies for different historic residential building types in a Northern European climate using LCC optimization from preset targets regarding LCC and energy use. CO2 emissions and primary energy use are calculated in order to evaluate the environmental performance from the energy renovations. Furthermore, different building starting conditions concerning renovation needs are also investigated with regard to the above-mentioned parameters. This paper explores the window of opportunities in terms of minimizing life cycle cost together with associated effects on CO2 emissions and primary energy use during energy renovation of historic residential buildings built before 1945. The results of this paper will provide guidance regarding appropriate energy renovation measures, from an economic point of view, for the owners of different kinds of residential properties when undergoing energy renovation for various historic building types located in cold climates similar to Sweden. Twelve building types representing common types of historic buildings in Visby, a town situated in the southeast of Sweden on the island of Gotland, are used as the study object. The paper is structured as follows. Section 2 describes the methods used in this study. A description of the study object is given in Section 3 in the form of physical characteristics of the studied buildings, and economic and technical data for the EEMs. The results are presented and discussed in Section 4. Lastly, the conclusions of the study are given in Section 5. 2. Methods 2.1. Life cycle cost optimization in buildings Life cycle costing is defined as “a valuable technique that is used for predicting and assessing the cost performance of constructed assets” by the Buildings and Constructed Assets Standard ISO 15686

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[22]. From an economic perspective, LCC analysis is proposed as a suitable tool during energy renovations of buildings [23]. According to Gustafsson [24], the LCC of a building’s energy-related retrofits can be determined by the investment cost of the heating system and EEMs on the building envelope, energy costs and maintenance costs connected to building components. Costs that occur in the future can be managed using the net present value (NPV) method. The method is applicable when considering the value of money at different times by discounting the costs to a base year. As both non-recurring costs and annually recurring costs occur, it is important to differentiate between these. These can be calculated according to Eqs. (1) and (2) respectively.

NPV =

C

(1 + r )n

NPV = C ·

1 − (1 + r )−n r

(1)

(2)

where NPV = net present value, C = cost, r = discount rate and n = number of years between the expenditures in Eq. (1), and number of years in the studied time period in Eq. (2). In order to minimize LCC one needs to use optimization, given that the problem can be constructed as a mathematical formulation. Optimization is a set of techniques where the optimal solution is generated from a mathematical problem [25]. An optimization problem is described by the defined variables, the formulation of the objective function and the constraints of the model. The model is built on variables that can take on different values which in turn are optimized. The objective function states different solutions by accepting different values for the variables. A common optimization method is mixed integer linear programming (MILP). MILP originates from linear programming (LP), where the objective function and all constraints are linear [25]. The difference between MILP and LP is that some of the variables in MILP are restricted to take on values of integers. Binary variables can be used for solving integer problems where the variables adopt a value of either 1 or 0 depending on the selection and rejection of variables for the optimal solution. 2.2. Implementation of life cycle cost optimization To obtain the cost-optimal combination of renovation interventions, the in-house developed optimization software OPtimal Energy Retrofit Advisory-Mixed Integer Linear Program (OPERAMILP) is used. OPERA-MILP is the result of a further development of the program OPtimal Energy Retrofit Advisory (OPERA) developed at the Division of Energy Systems at Linköping University in the late 1980s [24]. In the current version of the software, a MILP problem is designed which is solved using the optimizer CPLEX [26]. The advantages of the software include the use of basic input data, such as U-values of building components and monthly climate data, time-efficient LCC analysis due to fast optimization procedure and the possibility of obtaining the cost-optimum level of a building depending on different energy targets. OPERA-MILP has previously been used in scientific studies with the objective of minimizing the LCC for various buildings, e.g. [17,18,27–30]. The performance of OPERA-MILP was investigated by Milic´ et al. [29] considering calculations of energy use and power demand together with the consequential impact on LCC. The study was performed by comparison with the established BES software IDA ICE. IDA ICE has previously been validated according to Envelope BESTEST in the scope of IEA Task 12 where it was stated that IDA ICE is a good example of software for performing detailed building physics and HVAC simulations [31]. This proves that the selection of IDA ICE for comparative testing is well-founded. The results presented by Milic´ et al. [29] showed a satisfactory

accuracy in the calculations performed with OPERA-MILP. The total percentage difference in calculations of LCC between the two types of software was a maximum of 8%. It should be noted that the development of OPERA-MILP is performed continuously by the authors of this paper, and by other researchers at the Division of Energy Systems at Linköping University. Depending on the defined set target of the LCC analysis, three principally different optimization procedures occur with regard to LCC and energy use, see Fig. 1. Included in all procedures is an assessment of the suggested EEMs after optimization. The assessment addresses the suitability of the selected EEMs in terms of implementation in the building. In this study, the assessment is performed with regard to the building heritage values. If an EEM is suggested that is not appropriate with regard to heritage values, the LCC optimization is performed again where the inappropriate EEM is excluded as a possible measure. This enables an analysis of the potential energy efficiency gap when considering and when not considering building heritage values. It should be noted that the LCC is also affected by this gap. In this study, the heritage values are regarded as being affected when outside insulation of external walls or the replacement of existing windows with windows containing exterior aluminum cladding are selected. This assessment can also be performed concerning factors other than heritage values, e.g. impact on the hygrothermal performance of the building. An optimization of the LCC is made where the lowest LCC is obtained together with corresponding energy use, ELCC-opt , as shown on the left-hand side in Fig. 1. The cost-optimal heating system is selected along with suggested EEMs on the building envelope, provided that the EEMs are profitable from an economic perspective. However, note that the mathematical optimum is obtained by a simultaneous optimization of all measures and factors connected to them, such as fuel cost and set life length for a heating system. This means that the cost-optimal point for a building may only consist of the selection of a cost-optimal heating system and no EEMs targeting the building envelope. Lastly, if any of the selected EEMs are inappropriate due to impact on heritage values, a new LCC optimization is performed without these EEMs. Identically to the previously described optimization procedure, LCC optimization can also be carried out by setting the allowed energy use, E2 , to a lower value than the LCC optimum (ELCC-opt ). This means that it is possible to set the decrease in energy use to any minimum value; in this case a value lower than the energy use obtained at LCC optimum. Thereby, LCC optimization is executed with the given constraint on energy use. As in the previously described optimization procedure, a new LCC optimization is performed if any of the selected EEMs are inappropriate considering heritage values. This optimization procedure is illustrated in the center of Fig. 1. Shown to the right in Fig. 1 is the third option, where the energy use, E3 , is set at a higher value than the LCC optimum (ELCC-opt ). Hence, a comparison of the cost-efficiency is performed between the EEMs (based on cost per kWh saved) with the most expensive EEM removed first, etc., as long as the energy use is below the set target (E3 ). No assessment of EEMs is needed since any inappropriate EEMs are already excluded at LCC optimum (ELCC-opt ) according to the first described optimization procedure.

2.3. Economic and energy calculations in OPERA-MILP Costs that are considered for the optimization are investment costs for the heating system and EEMs on the building envelope, as well as energy costs and maintenance costs for building components. The maintenance cost, or inevitable cost, includes costs such as the demolition and construction of a new façade. The residual value at the end of the life cycle is subtracted from the total LCC.

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Fig. 1. Different optimization procedures in OPERA-MILP.

Hence, the total LCC of a building is calculated according to Eq. (3).

LCCtotal = LCCinvestment cost + LCCenergy

cost

+ LCCmaintenance cost

(3)

LCCtotal represents the total LCC of the building during the optimization period, LCCinvestment cost is the investment cost for EEMs on the building envelope and the heating system, LCCenergy cost is the energy cost during the optimization period and LCCmaintentance cost is the maintenance costs connected to building components. A number of EEMs targeting the building envelope and heating systems have been incorporated into OPERA-MILP, in order to obtain cost-optimal packages of renovation measures. The EEMs on the building envelope are replacing windows with three different types, weatherstripping, floor insulation, roof insulation and external wall insulation on the inside and outside of the wall. A step resolution of 2 cm between each insulation step is used in the current study, with the minimal insulation thickness set at 2 cm, and the maximum at 42 cm. Heating systems include district heating (DH), electric radiators (ER), groundwater heat pump (GHP) and wood boiler (WB). The incorporated EEMs on the building envelope and heating systems give more than three hundred thousand possible combinations of energy renovation solutions when considering all different steps in EEMs. The expenditures are calculated from cost functions formulated for both the EEMs targeting the building envelope and the heating systems. The cost functions for weatherstripping, replacing windows, insulation measures and heating systems are given by Eqs. (4)–(7), respectively.

Cws. = C1 · m

(4)

Cw. = C2 · Awindow

(5)

Ci.m. = C3 · Ab.c + C4 · Ab.c + C5 · Ab.c · t

(6)

Ch.s. = C6 + C7 · Ph.s. + C8 · Ph.s.

(7)

In Eq. (4), Cws. presents the total weatherstripping cost, C1 is the weatherstripping cost per window and m is the number of windows in the building. Eq. (5) presents the cost function for replacing windows where Cw. is the total window replacement cost, C2 is the window replacement cost per m2 , including frame, and Awindow is the total window area. In Eq. (6), which presents the cost function for the various insulation measures, Ci.m. is the total cost for the insulation measure, C3 is the maintenance or inevitable cost per m2 , Ab.c is the total area of the building component, C4 is the fixed part of the insulation cost per m2 , C5 is the variable insulation cost per m2 depending on insulation thickness and t is the insulation thickness. Eq. (7) presents the cost function describing the installation cost for the heating systems, which is differentiated depending on whether the cost is a base cost connected to the heating system or varies depending on the maximum power of the heating system. Ch.s . presents the total installation cost for the heating system, C6 is the base cost for the heating system not depending on power, C7 is the cost depending on the power of the heating system, Ph.s is the maximum power of the heating system and C8 is the cost of pipe work depending on the power of the heating system. Here it is worth noting that the electric power input is considered in the case of GHP. Furthermore, it is possible to consider an annual cost related to a heating system, such as the fee for electric fuses for ER and GHP and a fixed annual subscription fee for district heating. The calculation of the building’s energy balance for heating is performed using a quasi-steady-state approach based on a time resolution of 12 time steps, with each step corresponding to a month during a year. The energy balance includes heat losses in the form of transmission, ventilation and infiltration, as well as heat gains in the form of solar gains and heat from internal heat generation, i.e. heat from electrical appliances and building occupants. A utilization factor is considered for the heat gains in the energy balance. This means that the energy calculations are performed on a monthly basis in accordance with the calculation procedure in EN ISO 13790 [32]. The calculation of the energy balance

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Table 1 Different CO2 scenarios depending on assumptions concerning biomass use and electricity production. Electricity/biomass

Unlimited resource

Limited resource

Average Swedish electricity production Marginal electricity production

Scenario A Scenario C

Scenario B Scenario D

is presented in Eq. (8) for each time step.

Q (H )n = Q (h.l. )n −

η · Q (h.g. )n

(8)

Q(H)n represents the energy need for heating (J), Q(h.l.)n is the total heat transfer from transmission, ventilation and infiltration (J), η represents the dimensionless utilization factor for heat gains, Q(h.g.)n is the heat gain from solar gains and heat from internal heat generation (J) and the index ‘n’ stands for the month for which the calculation is performed. The domestic hot water use is added to the energy need for heating in order to calculate the total energy use for each time step. The maximum heat power demand P (W) is calculated according to Eq. (9).

P = q(s.h.l. ) · (Tindoor − TODT. ) + P (h.w. )

(9)

q(s.h.l.) is the specific heat loss coefficient from transmission, ventilation and infiltration (W/°C), Tindoor presents the set-point indoor temperature (°C), TODT. is the outdoor design temperature (°C) and P(h.w.) is the heat power demand for domestic hot water (W). 2.4. Primary energy use and CO2 emissions Buildings undergoing energy renovation will contribute to a change in environmental performance. Because of major global environmental challenges due to factors such as growing economies in many parts of the world, with larger emissions as a result, the environmental performance of buildings is a strong incentive for carrying out energy renovations. The environmental performance in this study is investigated by calculating primary energy use and CO2 emissions. The primary energy factors for electricity and wood pellets are set at 2.5 [33] and 0.11 [34] respectively. The factor for electricity is motivated by the fact that in European electricity production approximately 2.5 units of energy are needed in order to deliver 1 unit of electricity to the end user. In the calculations of the primary energy factor for wood pellets, extraction, processing and transportation have been considered [33]. 0.31 is set as the primary energy factor for district heating as this is the local value for Visby [35]. A constant value for CO2 emissions of 57 kg/MWh is used for district heating, which corresponds to the actual value for Visby [35]. The reason for the relatively low emission factor of district heating is explained by the fact that fossil fuels only constitute 3% of the total fuel input. In order to investigate the impact of different energy system boundaries, two different CO2 emission factors are considered for electricity production and biomass. For emissions related to electricity production, average Swedish electricity production with emissions of 11 kg CO2 /MWh [36] and marginal electricity production with emissions of 714 kg CO2 /MWh [37] are used. The marginal production of electricity is here assumed to occur during peak hours, produced by a condensing coal power plant in a fully deregulated European market. Biomass is regarded as both an unlimited resource and a limited resource, where condensing coal power plants are assumed to be the marginal user of biomass giving emissions of 8 [37] and 356 [37] kg CO2 /MWh respectively. The assumed emission factors related to electricity production and biomass use result in four different scenarios presented in Table 1. The local value for Visby concerning CO2 emissions for district heating is considered in all scenarios. Note

that this paper does not aim to calculate specific emissions from different energy sources, but to show the environmental impact from cost-optimal energy renovation and different energy system boundaries. 3. Study object 3.1. Physical characteristics of the buildings This study object consists of 12 historic residential building types, which are typical historic buildings in the town of Visby. The building types represent 920 residential buildings in the historic building stock built before 1945 in Visby. Visby is situated in the southeast of Sweden on the island of Gotland, and has been inscribed by UNESCO on the World Heritage List since 1995 [38]. Two different starting conditions of the buildings concerning renovation needs are investigated for a time period of 50 years. In starting condition 1, SC1, the remaining lifetime is set at 50 years for the building components, i.e. solely simulating energy renovation where EEMs on the building envelope are only selected if profitable. In starting condition 2, SC2, the remaining lifetime of the building components is set at 0 years, representing a renovation need from the start of the analysis period. For example, this means that window replacement is necessary at the beginning of the optimization period. In order to study the economic effects of selecting of a cost-optimal heating system, the remaining lifetime is set at 0 years for the heating system in both starting conditions. Also, there is currently no data about the original heating systems in the studied building types. Three different cases are investigated for each starting condition. The cases are developed with the aim of investigating the effects of optimizing the LCC with no constraints on energy use, and LCC optimization depending on preset energy targets in accordance with the Swedish energy targets for 2050 [8], compared to a reference case. More specifically, the investigated cases are: •

•

•

Case 1 - Reference case. District heating is set as the default heating system as this is the most common heating form in Sweden. It should be noted that district heating is available in Visby. No EEMs on the building envelope are introduced during the analysis time. Case 2 - LCC optimum is obtained by allowing cost-optimal selection of EEMs on the building envelope and a heating system. Case 3 - 50% decrease in energy use in accordance with the national set energy targets for 2050, also obtained by allowing the selection of EEMs on the building envelope and a cost-optimal heating system.

The 12 building types in this study are obtained based on a categorization study of the historic district of the city of Visby, Sweden [39,40]. The categorization method includes three major steps: inventory of the building stock (gathering and compilation of building data), categorization (allocating buildings in groups depending on building characteristics) and selection of building types that are representative of the building stock (each building type selected based on average values of various building characteristics). The case study included 1048 buildings built before 1945. After eliminating diverging buildings from each category, in terms of building volumes outside the standard deviation, the number of

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161

Fig. 2. Photos representing the six building categories. Building category 1 = top left corner, building category 2 = top center etc.

Fig. 3. The twelve building types.

buildings was reduced to 920. The categorization method resulted in six building categories, which represent common historic building types in the town of Visby. The building categories are shown in Fig. 2. Building category 1 is shown in the top left corner, building category 2 in the top center, and so forth. Categories 1–3 represent single-family houses with one story and a heated attic floor. Categories 4–6 represent apartment buildings with two stories and a heated attic floor. Categories 1 and 4 have no adjoining walls to the building. Categories 2 and 5 have one adjoining wall and categories 3 and 6 have two adjoining walls. Each category consists of both wood and stone buildings. This resulted in a total of 12 building types: 1W-6W and 1S-6S (“W” indicating a building structure of wood and “S” indicating a building structure of stone). Thereby, the differences between the building types can be summarized by these parameters: building structure, building envelope thermal performance, basement type, number of stories and adjoining walls. See Fig. 3 for an illustration of the building types. Table 2 presents the corresponding data about

the building construction, including U-values with consideration to thermal bridges and air change rate, as well as the number of buildings each building type represents. The buildings are naturally ventilated. A ventilation rate of 0.35 l/s per heated m2 is assumed in all buildings. The infiltration rate is calculated by assuming the air leakage to be 5% of the air leakage that occurs at a pressure difference of 50 Pa as suggested by Awbi [41]. The infiltration losses at 50 Pa are estimated at 1 l/s per enclosed m2 . In all building types, the majority of the window area faces east and west from the building with double-glazed windows. The indoor temperature is set at 21 °C for all buildings [42]. Domestic hot water use, household electricity and occupancy heat are estimated using data from the construction and real estate industry [43]. Solar gains are estimated with a window model using location-based climate data from ASHRAE IWEC2. The window model is implemented in the BES software IDA ICE [44], version 4.7.1 used in this study, and the results are thereafter used in OPERA-MILP.

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Table 2 Building construction data and number of buildings each building type represents. Construction data/building type

1W

1S

2W

2S

3W

3S

4W

4S

5W

5S

6W

6S

No. or buildings

309

55

166

46

25

16

33

75

30

83

18

64

Building structure Basement type

Wood Stone

x

Crawl space Unheated basement

x

x

x

x

No. of adjoining walls

x

x x

x

x x

x

x x

x x

x

x x

x

x

x

x

x

0

0

1

1

2

2

0

0

1

1

2

2

External wall

Area (m2 ) U-value (W/m2 °C)

86 0.65

80 1.8

61 0.65

57 1.8

45 0.65

43 1.8

245 0.67

235 1.97

180 0.67

173 1.97

116 0.67

112 1.97

Windows

Area (m2 ) U-value (W/m2 °C)

12 2.9

12 2.9

12 2.9

12 2.9

12 2.9

12 2.9

44 2.9

44 2.9

37 2.9

37 2.9

30 2.9

30 2.9

Roof

Area (m2 ) U-value (W/m2 °C)

71 0.18

65 0.18

79 0.18

73 0.18

92 0.18

86 0.18

170 0.25

161 0.25

159 0.25

150 0.25

159 0.25

150 0.25

Floor

Area (m2 ) U-value (W/m2 °C)

49 1.10

44 1.10

50 1.10

44 1.10

58 1.10

52 1.10

133 0.23

123 0.23

124 0.23

115 0.23

129 0.23

120 0.23

98 216 0.76

87 192 0.77

100 219 0.74

88 194 0.75

116 256 0.72

104 228 0.73

398 942 0.65

369 874 0.65

372 881 0.64

345 817 0.64

387 917 0.62

360 852 0.62

Heated area (m2 ) Heated volume (m3 ) Air change rate (ACH)

Table 3 Costs and heat transfer coefficients for window replacement in SC1 and SC2. Window type

C2 (SEK/m2 window)

Heat transfer coefficient (W/m2 °C)

Starting condition

Secondary glazing/double-glazed Triple-glazed Triple-glazed + low emission glassa

1856/6738 8492 12 169

1.6 / 1.5 1.2 0.8

SC1/SC2 SC1 and SC2 SC1 and SC2

a

Considered as inappropriate with regards to the building heritage values, see Section 2.2. Table 4 Investment costs for the insulation measures [49]. Building component

C3, wood/stone (SEK/m2 )

C4, wood/stone (SEK/m2 )

C5, wood/stone (SEK/m2 ·m)

Roof Floor External wall inside External wall outside

0/0 0/0 153/153 407/407

0/0 242/242 908/1 335 2 411/2 571

679/679 799/799 1 267/1 267 1 267/1 267

3.2. Properties and costs for the energy efficiency measures and heating systems This section presents properties and installation costs for the EEMs targeting the building envelope and heating systems implemented in OPERA-MILP following the cost functions presented in Section 2.3. All costs include VAT. The lifetime is set at 50 years for all insulation measures and 30 years for windows as estimated by Adalberth and Wahlström [45]. The lifetime for weatherstripping is assumed to be 10 years. The thermal conductivity of additional insulation, including joists, is set at 0.037 W/m °C. For pipe works in the heating systems, a lifetime of 50 years is set. The exchange rate is set at 10.58 SEK ≈ 1 Euro [46]. Costs that occur in the future are discounted to the base year with a discount rate of 5% [47]. This is in the same value range that is used commercially in construction projects. Table 3 presents the costs for replacing windows together with corresponding U-values, as well as which starting condition each change of window is associated with. Two of the changes of windows are the same, and one differs. In SC1, with the remaining lifetime set at 50 years for the original windows, an energy-efficient glass pane is added on the inside of the original window (“secondary glazing” in Table 3). The cost for the secondary glazing window type is obtained using manufacturer data from Grundels Fönstersystem AB with Uvalues from a study performed by the Swedish Energy Agency

[48]. In SC2, the remaining lifetime is set at 0 years for the original windows, and a new double-glazed window is selected with costs and U-values based on manufacturer data from Elitfönster AB. Also using data from Elitfönster AB, costs and U-values for the two other window types have been set. The cost for weatherstripping, C1 , is estimated at 441 SEK/window for buildings 1W3S, and 617 SEK/window for buildings 4W-6S (the higher cost for buildings 4W-6S is because of the larger windows), based on data from the Swedish database Wikells, with up-to-date market costs [49]. It should be noted that due to variations in building conditions and different costs at various construction companies, costs for specific buildings can obviously differ from those estimated in the present study. However, it is worth emphasizing that the cost database used is based on actual costs that are up to date. As building structures of both wood and stone are dealt with in this study, a differentiation is made between these costs concerning the external wall. This is shown in Table 4 by wood and stone indices, indicating a building structure of wood or stone respectively. C5 , the specific variable insulation cost depending on insulation thickness, is determined by forming linear functions dependent on insulation thickness and cost. Table 5 presents the estimated installation costs for the heating systems. The installation costs are calculated using data from Adalberth and Wahlström [45] and Wikells [49] by forming linear functions. Also presented in Table 5 are efficiencies (COP for

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Table 5 Installation costs for the heating systems, η or COP, lifetime, fuel price and annual cost. Heating system

C6 (SEK)

C7 (SEK/kW)

C8 (SEK/kW)

η/COP

Lifetime in years

Fuel price (SEK/MWh)

Annual cost (SEK)

DH ER GHP WB

22,611 2336 72,395 68,604

415 624 4778 1153

255 0 34,956 160

0.95 [50] 1 [52] 3 [50] 0.85 [50]

25 15 25 15

959a 1144a 1144a 540 [56]

315a 2910a 2910a 0

a

[51] [53] [54] [55]

Fuel prices and annual costs for district heating and electricity obtained from Gotlands Energi AB using data from 2016.

the GHP) for the heating systems together with the corresponding lifetime, fuel prices and annual cost. Since the development of fuel prices is very hard to predict, this is not taken into account in the current study. The electricity price for ER and GHP consists of two different fees, one connected to the electricity market and the other connected to the electricity grid. In addition, an annual cost for the electrical fuse has been taken into consideration. In the case of DH, an annual subscription fee is taken into account in order to follow the actual price model for district heating in Visby. 4. Results & discussion 4.1. Calculated energy use in the reference case In order to enable a comparison to be made of the energy usage after energy renovation in Cases 2 and 3 compared to the reference case, Case 1, the original energy usage of the building types is presented in Fig. 4. As two different building starting conditions concerning renovation needs are investigated, the energy usage is presented for both SC1 and SC2. As shown in Fig. 4, the calculated energy use varies between 134.5 and 200.1 kWh/m2 for single-family houses 1W–3W, and between 80.6 and 128.1 kWh/m2 for apartment buildings 4W– 6W. The corresponding figures for the building types with a building structure of stone are 187.4–324.0 kWh/m2 and 123.3– 219.8 kWh/m2 , respectively. The energy use for the building types is closely connected to the number of adjoining walls and the building structure type. Adjoining walls result in a smaller external wall area facing outdoor air, hence lower transmission losses in buildings with adjoining walls. The stone buildings have a higher specific energy use compared to the wooden buildings in the same building category. This is mainly explained by the higher transmission losses through the external walls. The impact on heat losses due to the type of basement is also noteworthy. Building categories 1–3 stand on a crawl space (U-value 1.1 W/m2 °C) and building categories 4–6 on an unheated basement (U-value 0.23 W/m2 °C). When comparing SC1 and SC2, the calculated energy usage is lower in SC2 in all buildings. This is explained by the fact that the remaining lifetime of the building components is 0 years in SC2, which results in the selection of new double-glazed windows (U-value of 1.5) compared to the original windows in SC1 with a

Fig. 4. Specific energy use in Case 1 for the two starting conditions. SC1 = circles and SC2 = triangles.

U-value of 2.9. A side effect of the window replacement is weatherstripping, as the new windows are assumed to be airtight, also resulting in decreased heat losses and energy use in SC2. It is important to note the use of a coarse dynamic calculation procedure in OPERA-MILP. The use of refined dynamic simulation software is for example needed in buildings with a high time constant and buildings with modern energy systems including both comfort cooling and heating. When working with buildings with a high time constant the solar gains and internal heat generation are sufficient to supply the building with energy for much of the year. In these situations, refined simulation software is required to accurately calculate energy usage. However, the buildings in this study have rather low time constants because of poor thermal performances, see Table 2. In addition, there is no comfort cooling in the buildings. As mentioned in Section 2.2, the performance of OPERA-MILP has previously been investigated by Milic´ et al. [29] through comparison with BES software IDA ICE. The study objects in the investigation by Milic´ et al. [29] consisted of buildings with poor thermal performance and rather low time constants, similar to the characteristics of the buildings in the current study. A part of the investigation addressed the impact of varying internal heat generation and multi-zone modeling in IDA ICE compared to constant heat generation and single-zone modeling. The results showed a maximum annual difference of 4% in calculations of energy usage between the two simulation cases. Overall, good agreement between the two types of software was noted, including when considering varying internal heat generation in IDA ICE. 4.2. Cost-optimal energy renovation strategies Fig. 5 presents the cost-optimal heating system together with the maximum building power demand for each case. An iteration of the LCC optimization is performed for building 6W in Case 3 for both starting conditions according to Fig. 1, indicated with “Historic” in Fig. 5, because of the selection of an inappropriate EEM concerning the building’s heritage values. This only occurs in building 6W and none of the other building types. Triple-glazed + low emission glass windows are selected to achieve a 50% decrease in energy use that is inappropriate with regards to heritage values, see Section 2.2. As shown in Fig. 5, the calculated power demand is always higher in SC1 compared to SC2 in Case 1. This is because of the replacement of windows in SC2, resulting in decreased heat losses. The selection of a cost-optimal heating system is dependent on the building starting condition only in building type 1S in Case 3. This is due to a higher power demand in SC1 compared to SC2, at 5.2 kW and 4.7 kW respectively. Overall, either WB or DH is the cost-optimal heating system in Cases 2 and 3 (excluding Case 1 because DH is selected by default, as previously mentioned in Section 3.1). DH is cost optimal in 23 of 50 energy renovation strategies. This is when building 6W is included in Case 3 when considering the building’s heritage values, which gives two additional outcomes. The corresponding figure for WB is 27 of 50 strategies. Generally, DH is cost-optimal in single-family houses 1W-3S and WB in apartment buildings 4W-6S. This indicates that DH is suitable for implementation in single-family houses and WB

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Fig. 5. Selected heating systems and maximum building power demand. WB = triangles and DH = circles, SC1 = filled symbols and SC2=unfilled symbols. An inappropriate EEM is selected in building type 6W in Case 3, for both SC1 and SC2. The iterated optimization solution without the inappropriate EEM is indicated with “Historic”.

in apartment buildings in situations where the buildings have undergone energy renovation. The selection of a cost-optimal heating system in each optimization is closely connected to installation and running costs, but also to factors such as assumed lifetime and efficiency of the various heating systems. DH is benefitted by a low energy use in the buildings due to a high running cost and a low installation cost. WB, with a rather high installation cost and a low running cost, is cost-optimal in many of the cases in this study where the buildings have been energy renovated and have a moderate energy use. A very high installation cost for the GHP but a low running cost is cost-optimal for buildings with a high energy use, and is therefore not suggested in any of the buildings in Cases 2 and 3. In fact, deeper investigation shows that GHP is cost-optimal for some of the studied buildings in the reference case when DH is not set as the default heating system. ER is never selected as a heating system due to its high running cost. As previously mentioned in Section 3.2, it is necessary to note that the installation costs are developed in a general sense, and also that many assumptions are made connected to the various heating systems. The true installation costs for a heating system can vary depending on e.g. manufacturer and the local conditions connected to a building. Nevertheless, the results show that with the use of LCC optimization procedures, it is possible to obtain the cost-optimal heating system for a specific building type with regard to different targets concerning LCC and energy use. Lastly, it should be noted that in building type 6W in Case 3, triple-glazed + low emission glass windows are selected in both starting conditions in order to achieve a 50% decrease in energy use. Replacing these windows is not allowed in the current study; therefore, an iteration of the LCC optimization is performed according to Fig. 1 without the inappropriate window type. In any

event, the impact on building power demand is insignificant when considering heritage values. This is seen in Fig. 5, where the power demand is approximately the same when considering and not considering heritage values, only differing by 0.3 kW. By selecting cost-efficient EEMs targeting the building envelope together with a cost-optimal heating system, the lowest possible LCC will be achieved for each building type. It should be noted that the effect of fuel price variations is not considered, see Section 3.2. The profitability of EEMs on the building envelope is closely connected to current fuel prices. This is shown, for example, in a study by Liu et al. [18] when also using LCC optimization. Fewer EEMs are selected with low fuel prices and more EEMs are selected with high fuel prices. Moreover, no cost sensitivity analysis for the EEMs is included in this study because of the extensive assessment of 12 various historic building types in terms of cost-optimal energy renovation strategies. In addition, to investigate the profitability of only one EEM requires a very large number of optimizations because the study object consists of 12 buildings. The selection of window types for the studied cases is presented in Table 6. As mentioned in Section 4.1, it is assumed that weatherstripping is a side effect when window replacement is selected. Weatherstripping is selected in all cost-optimal energy renovation strategies when window replacement is not cost-efficient. This is because of low renovation costs for weatherstripping. Moreover, it is worth noting that the change in solar gains as a result of window replacement is considered in the energy calculations. That is to say, the g-value of the windows is taken into account in the energy calculations. From a cost-efficiency point of view, window replacement is not a common EEM to install in the building types in SC1 with a remaining lifetime of 50 years for the original windows. However, adding an extra glass pane to the original window, i.e.

Table 6 Selected window type in Cases 1–3 for SC1 and SC2.

SC1

SC2

a b

Case

1W

1S

2W

2S

3W

3S

4W

4S

5W

5S

6W

1

–

–

–

–

–

–

–

–

–

–

–

–

2

Secondary glazing

–

Secondary glazing

–

Secondary glazing

–

–

–

–

–

–

–

3

Secondary glazing

–

Secondary glazing

–

Secondary glazing

–

Secondary glazing

–

Secondary glazing

–

Tripleglazed + l.e. glassa / tripleglazedb

1

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

2

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

3

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Doubleglazed

Tripleglazed + l.e. glassa / tripleglazedb

l.e. = low emission glass windows. Iteration of LCC optimization with consideration to heritage values (triple-glazed windows selected).

6S

–

Doubleglazed

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Table 7 Energy use for Cases 2 and 3 in SC1, and Cases 1–3 in SC2 with respective percentage decrease compared to Case 1 in SC1. Case

1W

1S

2W

2S

3W

3S

4W

4S

5W

5S

6W

6S

SC1

Case 2 (kWh/m2 ) % decreasea Case 3 (kWh/m2 ) % decreasea

113.1 43.5 99.1 50.9

97.7 72.5 165.6 49.4

94.7 47.0 91.1 49.0

89.1 68.5 137.9 48.5

81.2 49.6 81.2 49.6

81.9 63.9 102.0 53.6

113.1 11.7 70.2 49.1

91.0 60.5 108.8 51.6

101.3 12.2 62.2 49.0

84.6 56.5 95.1 50.6

86.8 12.4 51.4/53.7b 50.0/49.0b

76.8 47.6 74.1 50.0

SC2

Case 1 (kWh/m2 ) % decreasea Case 2 (kWh/m2 ) % decreasea Case 3 (kWh/m2 ) % decreasea

167.9 16.0 111.5 44.2 97.7 51.6

288.8 10.9 79.3 77.7 147.0 55.0

150.2 15.9 93.5 47.6 91.1 49.0

234.0 12.1 72.3 74.5 117.4 56.1

136.4 15.4 80.2 50.2 80.2 50.2

190.2 12.8 67.8 70.1 99.7 54.4

103.8 18.9 98.8 22.9 69.7 49.2

193.7 11.8 76.1 67.0 99.2 55.4

93.4 19.0 89.2 22.7 61.9 49.0

163.6 12.7 71.4 63.3 89.4 52.7

81.6 17.7 77.6 21.7 51.4/53.7b 50.0/49.0b

124.3 13.2 66.6 54.6 73.1 49.4

a b

Percentage decrease in total energy use compared to Case 1 in SC1. Iteration of LCC optimization with consideration to heritage values.

corresponding to energy-efficient windows, is cost-efficient in some cases. When window replacement is necessary because the remaining lifetime is set at 0 years in SC2, the suggested window type for most cases is ordinary double-glazed windows. In any event, in both starting conditions for building type 6W, the window type with the best thermal performance is suggested in Case 3 in order to achieve a 50% decrease in energy use. One consequence of changing to triple-glazed + low emission glass windows is that the building’s heritage values are affected, according to the assumptions made in this study. Therefore, an iteration of the LCC optimization is made according to Fig. 1 without the inappropriate window. This results in the suggestion of triple-glazed windows. Of the insulation measures incorporated into OPERA-MILP, inside insulation of the external walls, roof insulation and floor insulation are cost-efficient to select in at least some of the optimizations in this study. Fig. 6 presents the size of each EEM, given in cm, in the studied cases. Inside insulation of the external walls is cost-efficient in building types with a building structure of stone, due to a high U-value. This is also a common cost-efficient EEM among the wooden buildings to achieve a 50% decrease in energy use in Case 3. Generally, roof insulation is cost-efficient to select in both Case 2 and Case 3 despite a low original U-value in all building types. The reason for this is that the retrofit cost is low. Floor insulation is cost-efficient in all building types standing on a crawl space because of the high transmission losses. Also, to achieve a 50% decrease in energy use the floor needs to be insulated in some of the buildings standing on unheated basements. Nevertheless, the rather thick floor insulation selected for the buildings standing on a crawl space needs to be mentioned. In the categorization of the building types [39,40], there is no additional data about the properties of the crawl space, e.g. the crawl space height. Therefore, the practical feasibility of the selected floor insulation thickness is noteworthy. It is likely that it is not feasible to insulate with the proposed thickness in many situations where buildings are standing on a crawl space. When considering heritage values for building type 6W in Case 3 for both building starting conditions, the maximum insulation steps are suggested. The reason is that the building envelope is fairly good in the reference case, and as the windows with the best energy performance are not applicable when considering heritage values, the maximum insulation steps are suggested instead. As Fig. 6 shows, the difference in the size of the insulation measures between the starting conditions varies more in Case 3 compared to Case 2. This is mainly explained by the cost-effective comparison between EEMs, see Fig. 1, which results in the percentage decrease in energy use not being exactly the same in all cases. However, the same tendency in suggested EEMs in Case 3 is noted between the starting conditions. Table 7 presents how the selected EEMs on the building envelope affect specific energy use for the building types, and also the

percentage decrease in total energy use before the implementation of any EEMs targeting the building envelope, i.e. Case 1 in SC1. The selection of inside insulation of external walls leads to a reduced heated area in the buildings. The largest percentage decrease in heated area, 9%, occurs in building type 1S in Case 2 for both starting conditions corresponding to 8 m2 . The decrease in heated area is considered when calculating the specific energy use. The cost-optimal point, which corresponds to Case 2, has a percentage decrease in energy use between 11.7% and 77.7%. The buildings with the highest decrease in energy use at LCC optimum are the stone buildings and wooden buildings 1W-3W. Why the cost-optimal point corresponds to such a high decrease in energy use is explained by the generally high transmission losses from the floor in the single-family houses, 1W-3S, and the external walls in the stone buildings, before energy renovation. The opposite behavior is obtained for building type 4W-6W where the building envelope in the original case is considerably better, resulting in decreases in energy use between 11.7% and 23.1% at LCC optimum. For all building types, a 50% decrease in total energy compared to Case 1 in SC1 is possible, except for 6W when the building’s heritage values are considered. However, the decrease in total energy use is 49.0% with the maximum insulation steps and the threepane windows selected, which is a considerable decrease compared to the reference case, corresponding to a specific energy use of 53.7 kWh/year after energy renovation. This indicates that the potential decrease in energy use is affected by the preservation of building heritage values. The fact that heritage values can affect energy efficiency potential is confirmed by the findings of Arumägi and Kalamees [21], Liu et al. [18] and Alev et al. [19].

4.3. Profitability The profitability of energy renovation for the different building types during an analysis period of 50 years is given in Fig. 7. This is shown by presenting the total LCC before energy renovation (solid blue bars) and the difference in LCC in Cases 2 and 3 compared to Case 1 (black striped bars and red spotted bars, respectively), i.e., a decrease in LCC in Cases 2 and 3 is illustrated by bars on the negative side of the axis, and an increase on the positive side. As seen in Fig. 7, the LCC is decreased for all buildings at LCC optimum compared to Case 1. The percentage decrease varies between 23% and 38% in SC1 and between 12% and 31% in SC2. In total the decrease in LCC varies between 101 and 584 kSEK in SC1 and between 75 and 541 kSEK in SC2. Hence, by selecting a cost-optimal heating system and cost-effective EEMs on the building envelope, financial gains are possible during energy renovation with the use of optimization. The LCC in the reference case is higher for all buildings in SC2 compared to SC1. This is because the remaining lifetime is set at 0 years for the building

166

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Fig. 6. The size of the selected insulation measures in SC1 and SC2 for Cases 2 and 3. SC1 = filled and SC2 = unfilled.

components, which results in e.g. window replacement since the old ones need to be replaced. Generally, the financial gains are lower when targeting a 50% decrease in energy use (i.e. Case 3). In fact, financial losses occur in building type 5W in SC2 and building type 6W in both building starting conditions. Nevertheless, Case 3 gives a lower LCC in 21 of 26 optimizations (including the optimization when considering building heritage values for building type 6W) compared to Case 1. The change varies between an increase of 45% (building type 6W in SC1) and a decrease of 36% (building type 4S in SC1). The corresponding overall figures are financial losses of 342 kSEK (building type 6W in SC1) and gains of 553 kSEK (building type 4S in SC1). Overall, it is less economical to achieve a 50% decrease in energy

use for the apartment buildings in wood, 4W-6W. The reason is that the original thermal performance of these building types is comparatively good, see Table 2. It should be noted that in building type 6W, the LCC is lower in Case 3 when considering heritage values compared to the optimization procedure when heritage values are not considered. Triple-glazed + low emission glass windows are suggested in the original optimization in order to achieve the set energy target. When considering heritage values, the considerably cheaper tripleglazed windows are suggested, hence the lower LCC. Regarding Case 1, it is important to note that district heating is set as the heating system by default. In practice, this may not be the case for all building types. Therefore, this assumption is

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Fig. 7. Case 1 = solid blue bars, difference in LCC for Cases 2 and 3 compared to Case 1; black striped bars, and red spotted bars, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

likely to result in lower financial savings for some building types, or even financial losses, when undergoing energy renovation. The reason for this is that DH is generally benefitted by a low energy use, as mentioned in Section 4.2. However, DH is the most common heating system in Sweden and therefore it is considered a reasonable assumption since no data is currently available for the original heating systems in the various building types. 4.4. Primary energy use and CO2 emissions The environmental performance of energy renovation is investigated by calculating primary energy use and CO2 emissions on an annual basis. The calculated primary energy use per m2 and year is presented in Fig. 8. The solid blue bars indicate primary energy use in Case 1. The differences in Cases 2 and 3 compared to Case 1 are

illustrated by black striped and red spotted bars, respectively. This means that a decrease in primary energy use in Cases 2 and 3 is illustrated by bars on the negative side of the axis, and an increase on the positive side. The primary energy use varies annually between 6.7 (building type 6W, Case 3 in both starting conditions) and 105.7 kWh/m2 (building type 1S, Case 1 and SC1). When comparing the wooden and stone buildings before energy renovation in the same building category, for example 1S and 1W, it is noted that the primary energy use is higher in the stone buildings. This is because of the poorer thermal performance of the stone buildings. Another tendency in Case 1 is lower primary energy use in SC2 when comparing the same building between the two starting conditions, which is explained by replacing windows as mentioned in Section 4.1.

168

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Fig. 8. Primary energy use for each building type. Case 1=solid blue bars, difference in primary energy use for Cases 2 and 3 compared to Case 1; black striped bars and red spotted bars, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 9. Specific CO2 emissions for each building starting condition, case and building type. Case 1 = solid blue bars, difference in CO2 emissions for Case 2 and 3 compared to Case 1; black striped bars and red spotted bars, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

For both single-family houses 1W-3S and apartment buildings 4W-6S, the primary energy use is decreased in Cases 2 and 3 compared to Case 1. This is explained simply by the lower energy use because of the selection of EEMs on the building envelope. When WB is the selected heating system with biomass (primary energy factor 0.11) as fuel instead of district heating (primary energy factor 0.31), the decrease in primary energy use is even larger compared to the decrease in energy use. This occurs in building 1S (Case 3 and SC1) and apartment buildings 4W-6S (Cases 2 and 3 in both starting conditions). Calculated CO2 emissions per m2 and year are presented in Fig. 9. The solid blue bars indicate the CO2 emissions in Case 1. The differences in Cases 2 and 3 compared to Case 1 are illustrated by black striped and red spotted bars, respectively. Because neither GHP nor ER is suggested in any of the optimizations, only scenarios

A and B are shown with different assumptions concerning biomass use. The highest CO2 emissions occur in building type 1S in Case 3, SC1 and scenario B, with annual emissions of 69.4 kg CO2 /m2 . The lowest emissions occur in 6W in Case 3, Scenario A in both starting conditions, with 0.5 kg CO2 /m2 . As seen in Fig. 9, large variations in CO2 emissions are obtained depending on which scenario is studied. In scenario A, the emissions are reduced in all buildings when comparing Cases 2 and 3 with Case 1 due to the selection of EEMs. Another reason for this is that biomass is regarded as an unlimited resource. However, when considering biomass as a limited resource according to the assumptions made in scenario B, the emissions are increased in buildings 4W-6S, and also in building 1S for SC1 in Case 3. This is explained by the selection of WB in these buildings instead of DH. The results state that the CO2

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emissions from the various building types are strongly dependent on the selected energy system boundary. Although a building has been subject to cost-optimal energy renovation, which results in better thermal performance, it is possible that the CO2 emissions increase.

their heritage values. In addition, the authors want to thank the Swedish Energy Agency for their financial support.

5. Conclusions

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This paper presents an evaluation of cost-optimal energy renovation strategies for various historic building types in a Northern European climate. The study was performed using LCC optimization software OPERA-MILP. Moreover, how economics and environmental performance are affected depending on different targets of LCC (LCC optimum) and energy use (decrease by 50%) were also studied, as well as the impact from different building starting conditions concerning renovation needs. Twelve building types representing 920 historic buildings in Visby, Sweden, were used as the study object. The results show that by using LCC optimization, economically viable solutions can be obtained in historic buildings during energy renovation. When targeting LCC optimum during optimization, the decrease in LCC varies between 12% and 38%. However, when targeting a 50% decrease in energy use the LCC savings are decreased, in some cases resulting in a higher LCC compared to before energy renovation. However, the results show that it is profitable to decrease energy use by 50% in 21 of 26 cases. The tendencies are similar concerning economics whether or not the building has a renovation need at the start of the optimization period. This is also valid for the selection of cost-effective EEMs on the building envelope in the various building types. Profitable EEMs include measures that target the building envelope with high transmission losses originally, such as additional insulation of the external walls in stone buildings (applied to six building types representing 339 buildings) between 16 and 20 cm at LCC optimum, and measures with low renovation costs. In the case with additional insulation of the external walls it is important to be aware of the decrease in heated area that is a maximum of 9% in the current study. Weatherstripping (applied to all building types), roof insulation in the range from 10 to 12 cm (applied to all building types) and floor insulation from 24 to 26 cm for building types standing on a crawl space (applied to six building types representing 617 buildings) are other common cost-effective EEMs on the building envelope when targeting LCC optimum. To achieve a 50% decrease in energy use secondary glazing is cost-effective to a higher extent compared to at LCC optimum, as well as additional insulation of the external walls in wooden buildings. The results of this paper also show that the choice of a cost-optimal heating system varies depending on factors such as building construction properties and running costs. Generally, DH is suitable for implementation in singlefamily houses (applied to six building types representing 617 buildings) and WB in apartment buildings (applied to six building types representing 303 buildings) when energy renovation has been undertaken for the buildings. Furthermore, it is concluded that environmental impacts from energy renovations are closely connected to the assumptions made about the energy system boundary. Conflicts of interest None Acknowledgments The authors want to thank Professor Tor Broström, PhD candidate Petra Eriksson and research assistant Anna Donarelli from Uppsala University, Sweden, for their input on historic buildings and

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