Life Cycle Cost Analysis of novel Heat Recovery Systems using renewable Heat Sources - A Case Study of a Multi-Family Building in Sweden

Abstract

Space heating in buildings accounts for a large share of the energy usage in Scandinavian countries. An efficient concept to recover heat from renewable sources can help to reduce this demand in order to approach nearly zero-energy buildings. The present thesis evaluated the economic performance of mechanic ventilation heating with heat recovery systems. MVHR units were retrofitted with an air-preheating technology employing wastewater or geothermal energy as their heat source. The designed systems were simulated for a multi-family house in central Sweden. By looking at the life cycle cost over a lifespan of 20 years, the observed systems were being evaluated economically and set into comparison to a standard MVHR system. Furthermore, statistical analyses were carried-out to counter the uncertainty that comes with the calculation. It was found that the studied wastewater systems have a high possibility of generating savings in this period, while the one fed by geothermal energy is less likely to compensate for its high initial cost. All air-preheating systems however, managed to reduce operational cost by 35-45% due to lower energy usage.

Full text

I
Life Cycle Cost Analysis of novel Heat
Recovery Systems using renewable Heat
Sources
A Case Study of a Multi-Family Building in Sweden
Bachelor’s Thesis
Submitted in Fulfillment
of the Requirements for the Degree
‘Bachelor of Engineering‘
First Assessor
Prof. Dr.-Ing. Stephan Pitsch
Second Assessor
Behrouz Nourozi, MSc
Author
Simon Härer
Bei den Kellerhäusern 12
74592 Kirchberg/Jagst-Eichenau, Germany
Study Program: International Project Engineering
Matriculation Number: 751471
8th Semester
Submission Date
31.01.2019

Declaration of Academic Honesty
II
Declaration of Academic Honesty
I affirm that I wrote this degree thesis by myself, that I did not use other than the
denoted sources and aids as well as having identified all textual and analogous
sites for the thesis. This thesis was still not presented to a commission for
examination and I have not committed plagiarism against others.
Reutlingen, den 31.01.2019 ____________________

Preface
III
Preface
I would like to first and foremost take this opportunity and thank Prof. Dr Stephan
Pitsch for initializing the contact with the team in Stockholm and making this
thesis project possible. Furthermore, his sincere guidance as my first supervisor
has helped me to stay on track and focus on the key elements of my research.
The present thesis is a result of the ongoing partnership between Reutlingen
University and the Division of Fluid and Climate Technology Division at KTH
Royal Institute of Technology in Stockholm.
I would also like to express my sincere gratitude to my co-supervisor and
colleague Behrouz Nourozi for the continuous support throughout my thesis
project, for his patience and engagement in the research project. His advice has
helped me during all the time of research and writing process, - and could not
have been better.
My sincere thanks also go to the Division of Fluid and Climate Technology at KTH
who provided me with the adequate resources to execute my work. This thesis
was a part of an ongoing research project at the mentioned division which was
funded by the Swedish Energy Agency and the Development Fund of the
Swedish Construction Industry (SBUF). A brief summary of the main findings from
this thesis will also be published n - February 27th 2019 in the regular issue of
REHVA (Federation of European Heating, Ventilation and Air Conditioning
Associations)journal.
Furthermore, I would like to thank my two colleagues Dr Adnan Ploskić and Dr
Qian Wang for sharing their knowledge with me. With their passionate
participation and inputs, they gave me the right impulses to steer my thesis in the
right direction.
Finally, I would like to thank my family: my parents and my sister for supporting
me spiritually throughout writing this thesis and my life in general.

Abstract (English)
IV
Abstract (English)
Space heating in buildings accounts for a large share of the energy usage in
Scandinavian countries. An efficient concept to recover heat from renewable
sources can help to reduce this demand in order to approach nearly zero-energy
buildings. The present thesis evaluated the economic performance of mechanic
ventilation heating with heat recovery systems. MVHR units were retrofitted with
an air-preheating technology employing wastewater or geothermal energy as
their heat source. The designed systems were simulated for a multi-family house
in central Sweden. By looking at the life cycle cost over a lifespan of 20 years,
the observed systems were being evaluated economically and set into
comparison to a standard MVHR system. Furthermore, statistical analyses were
carried-out to counter the uncertainty that comes with the calculation.
It was found that the studied wastewater systems have a high possibility of
generating savings in this period, while the one fed by geothermal energy is less
likely to compensate for its high initial cost. All air-preheating systems however,
managed to reduce operational cost by 35-45% due to lower energy usage.
Keywords: wastewater, geothermal energy, heat recovery, MVHR, life cycle
cost, multi-family house

Kurzzusammenfassung (Deutsch)
V
Kurzzusammenfassung (Deutsch)
Die Raumheizung macht einen bedeutenden Anteil der Energiekosten in
skandinavischen Haushalten aus. Ein effizientes Konzept zur
Wärmerückgewinnung aus erneuerbaren Quellen kann dabei helfen den
Energiebedarf zu reduzieren. Die vorliegende Thesis bewertet das wirtschaftliche
Potenzial von mechanischen Lüftungseizungen mit Wärmerückgewinnung. Die
Systeme wurden mit Luftvorwärmern ausgerüstet, welche Abwasser oder
Geothermie als Wärmequelle nutzen. Die hierfür entworfenen Konzepte wurden
unter den Bedingungen eines Mehrfamilienhauses in zentral Schweden simuliert.
Durch das Ermitteln der Lebenszykluskosten über einen Zeitraum von 20 Jahren,
wurden die Konzepte aus finanzieller Sicht bewertet und in Vergleich zu einer
konventionellen Lüftungsheizung gesetzt. Weiterhin wurden statistische Modelle
angewendet, um der Unsicherheit entgegenzuwirken, die mit der Berechnung der
Kosten kommt.
Es hat sich herausgestellt, dass die Abwassersysteme eine sehr hohe
Wahrscheinlichkeit aufweisen in diesem Zeitraum Einsparungen zu erzeugen.
Das Konzept, welches mit Geothermie versorgt wurde, erreicht im selben
Zeitraum noch keinen Kostenvorteil. Dies ist den hohen Anschaffungskosten zu
schulden. Jedoch ist es durch alle untersuchten Luftvorwärmersysteme möglich,
die Betriebskosten aufgrund niedriger Energiekosten um 35-45% zu senken.
Schlagwörter: Abwasser, Geothermie, Wärmerückgewinnung, Lüftung,
Lebenszykluskosten, Mehrfamilienhaus

VI
Table of Contents
Declaration of Academic Honesty ...................................................................... II
Preface .............................................................................................................. III
Abstract (English) .............................................................................................. IV
Kurzzusammenfassung (Deutsch) ..................................................................... V
Table of Contents .............................................................................................. VI
1 Introduction ................................................................................................. 1
1.1 Motivation ............................................................................................ 2
1.2 State of the Art ..................................................................................... 5
1.3 Objectives and Structure of this Thesis .............................................. 12
1.4 Theoretical Introductions and Foundations ........................................ 12
1.4.1 Definition of Life Cycle Cost Analysis ......................................... 12
1.4.2 Calculation of Life Cycle Costs ................................................... 14
1.4.3 Potential Limits of Life Cycle Costing .......................................... 16
1.4.4 Sensitivity Analysis ..................................................................... 18
1.4.5 Monte Carlo Simulation .............................................................. 18
2 LCCA Methodology ................................................................................... 20
2.1 Establishing Objectives and determining the Criteria ......................... 20
2.2 Simulation Setup ................................................................................ 21
2.2.1 Identifying and developing Design Alternatives .......................... 21
2.2.2 Building Setup and Boundary Conditions ................................... 24
2.3 Gathering Cost Information ................................................................ 25
2.3.1 Initial Investment Cost ................................................................ 26
2.3.2 Energy Cost ................................................................................ 26
2.3.3 Maintenance, Repair and Replacement Costs ........................... 27
2.3.4 Discount Rate and observed Life Span ...................................... 28

Table of Contents
VII
2.4 Parameters for Statistical Analyses ................................................... 30
3 Results and Discussion ............................................................................. 33
3.1 Results ............................................................................................... 33
3.2 Discussion ......................................................................................... 39
4 Conclusion ................................................................................................ 42
References ..................................................................................................... XLV
List of Figures ..................................................................................................... L
List of Tables ..................................................................................................... LI
Nomenclature ................................................................................................... LII
Appendix ......................................................................................................... LIII
Appendix A .................................................................................................. LIII
Appendix B ................................................................................................... LV

1
1 Introduction
Global warming is going to be one of the most critical issues for future
generations. An Increase in average global temperature and more frequent
occurrences of extreme weathers (e.g. hurricanes, heat waves or floods) and a
future rise of the sea level is to be expected in the near future.
The whole process is being accelerated due to a rapid incline in emission of
greenhouse gases (such as carbon dioxide, methane and nitrous dioxide) [1].
Even though these gases constantly emit in our atmosphere since the earth exist,
the clear majority of scientists agree that the recent developments in global
warming are being hastened since the industrialization.
The search for the origin of the greenhouse gases, leads to the energy sector
including its producers as well as the consumers. Looking at the European Union
for example, one can observe that the building sector accounts for around 40%
of the total energy usage of all 28 member states. This puts the sector on the
number one spot as the biggest energy end-user in the EU ahead of transport
(30%) and industry (25%) [2].
This is why the European Union has set themselves the goal to transition to
"nearly-zero-energy-buildings" by the end of 2020. It means that all new buildings
are required to present a net energy usage close to zero. One of the main
strategies in order to reach the target, is the expansion of the use of renewable
energy and foster the decentralization of the energy production [3].
To pin it down even more to the major energy users as well as energy cost driver
in residential buildings, a study of the European Environment Agency on the
dwelling market in all member states reveals that more than half of the energy is
used for space heating (see Figure 1-1) [4]. In colder climates, such as in the
present case study (Sweden), residents need about 1,1 Toe (≈13 MWh) of energy
to heat their living-area for a year.

Introduction
2
Figure 1-1 Energy usage by dwelling per household in the EU, 2009 [4]
The potential for savings in this field is therefore a great one. Goetzler et al. [5]
identified the advancement of Heating, Ventilation and Air Conditioning
technologies (HVAC) and their steady improvements in terms of energy
efficiency.
However, the study from the European Environment Agency did not fully reflect
these technical developments. Even though the products run more efficient than
in the past, energy usage for space heating has increased steadily for years [4].
1.1 Motivation
There are multiple factors that need to be considered when trying to give
reasoning for these two trends that do not show the strong correlation that should
be expected. One approach is to look at it from a financial perspective: Innovative
or more efficient products in comparison to the predecessor are commonly
attributed as being more expensive in purchasing. The tradeoff even though is
that they require less energy and save money to the customer on a later stage of
the product life cycle. For the building sector, where product lives range from
about 15 years and upwards, an energy-saving product can be decisive when it
comes to the total costs of ownership [6].

Introduction
3
Figure 1-2: Life cycle cost factors: 30-year period for federal facilities in the USA [7]
Figure 1-2 shows that operating costs account for the biggest proportion of total
costs of ownership in the building sector [7]. When a decision is being made for
buying an HVAC system, the lowest bid usually wins. However, these offers very
often only include the initial costs (component price, taxes, markups and
installation) and do not give an outlook on the future costs that are to be expected
(energy costs, maintenance costs, replacement costs). Reasons for that are the
greater effort necessary to calculate future costs. It is required to make
assumptions on how some costs will evolve in the upcoming years, which comes
with a risk of uncertainty about the accuracy of these estimations. Yet there is
great potential behind this approach and is mostly worth the effort.
A practical methodology to capture a system’s cost along its complete lifetime is
the Life Cycle Cost Analysis (LCCA). It is a cost-accounting tool that helps to
evaluate the economic performance of a set of systems that are competing. The
result of an LCCA helps with the decision-making by identifying the most cost-
effective design amongst all alternatives. Companies use this methodology also
to gain financial transparency about their projects and foster a long-term and
more holistic view on their products. This approach is already used by the majority
of professional clients in the Swedish building sector to capture the total cost of
their projects and to evaluate the potential of upcoming technologies compared
to established solutions [8]. This thesis observes the financial potential of novel
heat-recovery ventilation systems for multi-family houses (MFH) in Sweden. To

Introduction
4
understand the purpose of this selection, it is advantageous to briefly observe the
development of the building sector in the country.
The Swedish Market for Heating Solutions
The multi-institutional project “Heating Market in Sweden” [9] assessed district
heating to be the most widely used system in the country. However, a growth in
more individualized heating, meaning that consumers take responsibility for their
own heat supply, will be expected. The strategy is to implement technology that
extracts heat from natural resources in proximity to the building and use it for
space heating.
Furthermore, with the improvement of house insulation technology, district
heating providers suffer lower demands [10] and struggle with covering the high
fixed costs of their products. The lower heat load in MFHs increases the potential
for replacing the conventional high-temperature [max. 90 °C] with low-
temperature [max 55 °C] heating systems [11] Representatives of this category
are renewable energy systems like the ground-source heat pumps, biomass and
heat recovery solutions. These concepts were found to be more energy-efficient
and sustainable in colder climates [12]. The main reasons are the greater use of
renewable energy as a source and the lower emissions of greenhouse gases.
This would all align with the policies for future buildings in the EU.
When it comes to heat recovery from ventilated air from buildings, mechanical
ventilation with heat recovery (MVHR) systems are gaining popularity, especially
in Swedish MFH [13]. A study from Tommerup and Svendsen [14] proofed that
up to 80% of the demand for ventilation energy could be saved by shifting the
ventilation system to a concept using heat recovery from return air
1
.
Furthermore, there is great potential in extracting the heat from wastewater and
use it for space heating or domestic hot water (DHW) [15].
Another heat source that is widely accessible, is the geothermal energy. When
digging deep in the ground of the earth, one can experience a constant
temperature relatively higher than the air temperature on the surface during
winter season. In the warmer months it is the opposite, which provides an
opportunity for cooling. Hence, it provides a year-round stable heat source. The
energy is already being exploited in several buildings with ground-source heat
1
The study was conducted for houses in Denmark. Yet the environmental factors are comparable.

Introduction
5
pumps. A new approach is to use passive borehole- or ground-coupled systems
[16]. These concepts basically consist of pipes running in the ground utilized as
heat exchanger to absorb heat from the soil.
Utilizing wastewater, exhaust air and boreholes for heat recovery provides great
potential that can contribute to the EU’s goal of nearly-zero-energy buildings. One
advantage of these systems is that they work without employing an expensive
heat pump and therefore, save the purchasing cost. However, single-handedly
preheating they can only contribute partially to the demanded heat load for MFHs.
The idea is to apply them in combination with an MVHR unit and use the
renewable heat sources to preheat the incoming outdoor air to create more
efficient systems. Yet, there is still a lack of information about the financial side
for these solutions. To raise awareness for this technology and establish it in the
market, potential customers also need to be confronted with the cost benefits; not
just the ones for the purchase, but the expenses over the complete life cycle
needs to be captured. The following chapter gives an overview on similar cases
which were found during the literature review. Relevant studies are listed that
help with the evaluation of the financial benefits of this technology and how the
LCCA approach has already been utilized in the building sector.
1.2 State of the Art
Given that the ventilation heating concept observed in the present thesis is a
rather new and unique concept, the studies in this field are limited. Further
constraints are being put on the literature research considering that the details in
the case studies observed matter a lot and make them difficult to compare. HVAC
systems are usually customized products and vary significantly in their setup.
Depending on their application, different components in different dimensions are
required. The type and size of the building also has a lot of influence on the
outcome. When it comes to space heating for example, a proper building’s façade
and continuous retrofitting of the building envelope contribute fundamentally to
the overall heating demand. Furthermore, the location of the building has a big
impact on the outcome. The same system can be cost-effective in a country with
a Scandinavian climate, but not in a Mediterranean climate. Moreover, other

Introduction
6
legal, local and economic factors, like the local electricity price and the supply
network for energy, can make a major difference. A great demonstration of this
issue can be seen in multiple articles by Gustafsson and Karlsson et al. [17], [18]
and [19]. They studied energy saving options for multi-family houses in Sweden
by alternating the building envelope and the HVAC system. In several LCCAs
and thermal analyses of different buildings, they came to different conclusions
depending on the size of the house and the retrofit strategy used for the building
envelope. Heating systems that were considered best option for one building,
were not favored in other cases.
In addition, when it comes to an LCCA, significant deviations between results can
also be caused by the assumptions made for the calculation. The methodologies’
strength is that it enables the decision-maker to compare systems that are all
analyzed under the exact same conditions. Transferring knowledge from one
study to another is therefore extremely difficult and requires a high level of
abstraction. Only few studies on this technology considered the financial side of
the products. Most of them focused on the technical analysis with the goal to
optimize the system with its components.
The approach taken for this literature review is to begin with a broader view by
looking at adjacent technologies and see how they perform compared to
conventional heating systems. Heat pump concepts that absorb the heat from
wastewater and the soil will be analyzed to primarily highlight the potential of
these two energy sources. Then the focus is put on the application of these
renewable heat sources for space heating and especially their utilization for air-
preheating. When looking at technical analyses, the performance of the
components is a great indicator for the occurring costs. One can expect the
observed technology to be higher in purchasing costs, but still to turn out to be
more economic on the long run due to a more energy efficient performance
compared to conventional heating systems. In the final paragraph, studies
utilizing the LCCA in the building sector are presented to capture the benefits and
challenges of the methodology.
Adjacent Technologies and the Potential of renewable Heat Sources
Heat pumps are an established technology in the Swedish market for the
exploitation of renewable heat sources [9]. Extracting heat from wastewater even
though, is still a rather new concept for the residential building sector. Practical

Introduction
7
implementations of this kind can be observed mainly on the Chinese market
[20] but also in the European region [21]. In both papers, an outlook predicted
that the heat source was on the rise due to its great potential to recover and save
a significant amount of energy utilizable for space heating.
A study by Paiho et al. [22], performed an LCCA on different heat pump concepts
for residential buildings in Finland. GSHPs were slightly in favor among other heat
pumps using different sources, due to its high efficiency as well as its reliable and
constant heat source.
Ventilation Heat Recovery and Air Preheating
Davidsson et al. [23] designed a hybrid ventilation model that incorporated heat
recovery from wastewater to preheat the incoming outdoor air. They simulated
several solutions using the two technologies in a family house under the climate
conditions of Malmö in Sweden. Their results showed energy savings with the
employment of an air-preheater; however, they were limited. Yet, they recognized
the potential for this technology, so that after further improvements in this field, it
could generate greater savings.
A study by Wang and Ploskic [24] observed the heat performance of MVHR
systems in addition with an air preheater fed by wastewater as heat source. An
MFH in an extreme northern Swedish climate was simulated. They found that the
peak heat loads could be reduced by 27% to 40% when using such a setup
compared to not preheating.
In a 2005 study, Florides and Kalogirou [25] supported the idea of recovering
geothermal heat for space heating and identified important design criteria for such
a system. Most examples examined work with heat pumps. However, utilizing
only borehole heat exchangers (BHE) for preheating the air was considered an
option. These systems came with high installation costs for the drilling of the
holes, which they concluded as the main problem.
Another paper reviewing this technology in 2018 by Gao et al. [26] , came to a
similar conclusion. According to this study, GSHPs were still the favorable
solution due to their efficiency. Borehole drilling technology still needed to
advance in order to become a competitive alternative.
Lundh and Dalenbäck [27] evaluated the first two years in performance of a
project in a Swedish housing complex, which comprised 50 residential units. They
applied a heating system that coupled solar heating with BHE. They identified

Introduction
8
some issues with the concept, since few historic experiences were available.
Some of the problems included leakages in the borehole due to the inefficient
construction, which caused long downtimes and assumable high costs.
In another practical example from an office building in Germany, where BHE
assisted the ventilation system in preheating the air, problems with the design
were documented as well. [28] After two years of operating, the system was
labeled as undersized, since it was struggling to raise the air temperature to
sufficiently high levels. Besides that, the technology was recognized as viable
and competitive to conventional heating solutions with future research.
The Impact of an Air-Preheater on Frost-Avoidance
Another significant factor to consider when evaluating the performance of MVHR
systems, is the formation of frost inside the unit. This issue was studied by Fisk
et al. [29]. Due to the cold air flow and the moisture in the return air, which after
being cooled-down it can no longer retain, the development of ice in the
component is inevitable. This blockade led to an increased pressure drop,
reduced air flow and heat transfer rate. The component reacted by switching into
a defrosting cycle. During this period, the heat exchanger is working at lower
efficiency, since more electricity is used for eliminating the frost.
Nasr et al. [30] reviewed defrosting methods and evaluated their efficiency to
reduce the downtime of air-to-air cross-flow heat exchangers, which is the main
component as used in the MVHR. The experiments were conducted under the
climate conditions of three northern cities of North America. Air-preheating was
found to be the most effective method to reduce energy usage caused by
defrosting. Up to 44% could be saved per cycle.
Nourozi et al. observed the impact of defrosting on MVHR systems combined
with air-preheaters using wastewater as a source in a MFH in Sweden. In one
study [31], they observed a system very similar to the one used in the present
thesis. He discovered that air-preheaters do not significantly contribute to energy
savings. However, they reduced the need for defrosting in the heat exchanger
(plate and rotary heat exchanger were studied) by almost 50%. This led to less
downtime due to fewer defrosting-cycles and hence a higher efficiency of the
whole system.
In a subsequent paper, Nourozi et al. [32] have proofed these findings. They
furthermore considered a configuration that preheated the air by using borehole

Introduction
9
heat exchangers and set it in comparison to two concepts using wastewater for
heat recovery, which differed only in the manner the wastewater is being stored.
All three systems were very similar to the setups observed in the present thesis.
Figure 1-3 illustrates these findings. System 1 is the single one using geothermal
energy. System 2 works with a stratified wastewater tank, while system 3 is
similar but uses an unstratified one to store the heat source. Tout represents the
outdoor air without air-preheating. The figure also shows the duration curves for
the preheated air temperatures as well as the defrosting threshold for two types
of heat exchangers. When the temperature is below the frost threshold, the
system is spending more time in defrosting mode. During this time, it is unable to
contribute to the ventilation heat load of the building.
Figure 1-3: Reduction of defrosting need of MVHR using the studied outdoor air preheating systems;
1=2904h [32]
Although the geothermal heat recovery concept performed even better in terms
of reducing defrosting time, they also found out in this paper that the systems
supported by wastewater showed higher efficiency. This is caused by reduced
energy usage of the pumps, which are required for the circulation of the energy
carrier.
Another finding was that the air-preheater only needed to operate when the
outdoor air was below the threshold. Further preheating decreased the heat

Introduction
10
recovery efficiency of the MVHR. When implementing a temperature control,
which shuts down the air-preheater once outdoor temperatures climb above the
threshold, the MVHR units worked at a thermal efficiency of 80 % for more than
90% of the time. Moreover, the pumps, that circulate the medium between the
air-preheater and the heat source, only needed to operate for less than 37 % of
the studied time period.
Application of LCCA in the Building Sector
A generic life cycle approach to capture the total cost of a product, is a widely
used method in the Swedish building market. A survey by Sterner [8] asking
professional clients in the Swedish building sector about the consideration of
LCCA in their decision-making process, came to the result that 66% of those who
replied used the method. Most LCCAs performed do not cover the project building
as a whole. Instead the clients surveyed stated that the methodology is mainly
used to investigate on installation equipment like the HVAC system. Even though
the LCCA is a common method used in many fields for assessing the financial
performance of a product, no official standards every institution obeys to are
existing. In a study by Teshenizi et al. [33], an urgent appeal was induced to
create a general set of clear guidelines for LCCAs in the building sector. A defined
process would help the industry as well as the policy makers to properly interpret
the result, make them more reliable and comparable. Such a document would
need to include the proper application of the right calculation methods, assistance
for gathering data and tools to verify the result.
The European Commission published a “framework for calculating cost-optimal
levels of minimum energy performance requirements for buildings and
building elements” [34], which will be considered for the methodology in the
present thesis.
Schade [35] created a comparison of the most common calculation methods for
LCC by highlighting their advantages and disadvantages. It is concluded that the
net present value (NPV) technique was the most applicable for the building
sector. This calculation method came with a great balance between accuracy of
the result and a reasonable amount of effort. Furthermore, she addressed the
issue that there was a lack of data in the Scandinavian market to base the
calculations on. Most construction companies relied on the experience they
gained through previous projects to forecast future costs.

Introduction
11
To counter the issues with imprecise data causing vague results, further
developments in the methodology include the implementation of statistical
models.
Di Giuseppe et al. [36] proposed an alternative, probability-based LCC-
methodology for the building sector. It considered the uncertainty that comes with
the determination of certain economic factors. A process was provided that shall
increase the robustness of the result. They encouraged the implementation of
two mathematical-statistical methods, namely Monte Carlo simulation and
sensitivity analysis to capture the risk.
Both techniques have already been applied in few case studies in the building
sector to deal with uncertainty for economic as well as technical factors ([37] and
[38]).
To finalize the literature review, it is to conclude that the limited studies on this
field consent to the great potential of this heat source and technology for
retrofitting MVHR units. Their efficiency can be improved when modifying with an
air-preheater using heat recovery. The downtime and total energy usage of the
system can be reduced significantly. The lack of information in the research about
the financial side of this concept becomes evident. The thesis project shall fill this
gap by providing data about the economics of the technology.
LCCA has been proofed as an established method in the building sector. Even
though there are still flaws with this technique, there is a set of solutions available
to tackle and minimize the downsides.
With the findings from the literature review, it is now possible to model a suitable
LCCA-approach for the present case study.

Introduction
12
1.3 Objectives and Structure of this Thesis
The aim of this work is to compare novel heat recovery systems implemented in
an MFH in Sweden on a financial perspective. The central method used is the
LCCA. Key questions that will be answered are:
• Which of the systems is the most economical in terms of life cycle costs?
• How can the risk of imprecise estimations be captured and eventually
facilitate the purchasing choice for the decision-maker?
In the following step, the fundamentals for carrying-out an LCCA will be
introduced. Furthermore, the advantages and limits of this methodology will be
covered, which indicates how the result is to be interpreted in a broader view.
Since a few assumptions are required to predict some of the costs, special focus
will be placed on the validation of the result by applying common mathematical
and statistical models. The methods used will be introduced in this chapter.
In chapter 2, the methodology will be applied. First the scenario will be defined,
which includes the environmental factors (building dimensions and climate) as
well as the systems observed will be introduced. Next all relevant cost factors will
be identified along with the parameters.
Chapter 3 delivers the results and their evaluation. Finally, in Chapter 4
Conclusions are drawn and potential improvements for further research are being
proposed.
1.4 Theoretical Introductions and Foundations
1.4.1 Definition of Life Cycle Cost Analysis
To understand the term, it is best practice to differentiate it from similar methods
that are often used interchangeably. Even though these techniques are often

Introduction
13
implemented combined, they deliver different outcomes that need to be evaluated
accordingly.
Life cycle cost or total cost of ownership captures all types of financial costs to a
product or process along its life span. The process that outputs this value is called
life cycle costing (LCC). The types of costs considered for the calculation differ
depending on what kind of product is being observed. The components of a LCC
in the building sector typically include purchasing costs, installation costs, energy
costs, maintenance and repair costs, down time costs, environmental costs and
disposal costs.
When an LCC is being performed on multiple alternatives under the same
conditions, the life cycle cost analysis (LCCA) is a tool that helps to compare the
options with each other on different levels and eventually find the one solution
that, with a high certainty, will be the most economical. A proper LCCA therefore
also comprises multiple techniques that can be used to minimize the risk of
imprecise assumptions made in the LCC model. The systems observed include
a base case, which represents the current state (no initial investment costs but
with higher operating costs), and one or more alternative systems (high initial
investment costs and lower operating costs) that are being evaluated against the
base case.
Another technique, that is considered as the complementary part to an LCCA, is
known under the term of life cycle assessment (LCA). Both methods together are
used for decision-making when it comes to purchasing or developing a new
product. LCAs measure the environmental performance of a system and
evaluates the impacts of its use for the future (e.g. emissions, toxification, land
occupation). An LCA captures impacting factors of a product that cannot be
expressed fully in monetary terms and gives these aspects a weight in the
decision making process [39]. An LCCA instead, only looks at direct economic
costs or in some cases delivers approaches to quantify environmental or social
costs.

Introduction
14
1.4.2 Calculation of Life Cycle Costs
Net Present Value Method
The NPV method is commonly used for products with a long lifespan, where most
of the costs occur in the years after the initial investment [35]. The time value of
money is being considered, because for example 1000 SEK today will not have
the same value as 1000 SEK in ten years from now. This is caused by several
economic factors such as inflation, opportunity costs and other changes in the
market. To make costs occurring at different time periods comparable, it is
necessary to convert future costs into their equivalent in present time. The
conversion rate for calculating the present value is the interest rate or discount
rate. It comprises the important economic factors and represents the future
development of prices on the market.
  
󰇛 󰇜
PV: present value [SEK]
FV: future value [SEK]
r: discount rate [%]
n: time period [year]
(1-1)
The net present value furthermore includes the initial investment costs in period
0 as well as the difference in incomes and costs, also known as cash flow, for
each time period separately. The sum of the present values of all periods leads
to the final formula for NPV [40]:
    
󰇛 󰇜


NPV: net present value [SEK]
C0: initial investment costs [SEK]
N: total amount of periods [year]
Cn: cash flow in period [SEK]
r: discount rate [%]
n: time period [year]
1-2

Introduction
15
The most cost-effective option is the one with the highest NPV. Since we are
dealing exclusively with costs and no incomes in our cases, we can expect
negative results only. It means that the NPV closest to zero will be our best option.
Initial Investment costs and cash-in and cash-out factors can be numerous and
vary depending on the project. For the building sector it can however always be
broken down into following aspects:
      

Cn: cash flow in period [SEK]
1-3
    

C0: initial investment costs [SEK]
1-4
The single cost factors listed in equation 1-3 and 1-4 can be split up even more
into their origins and adapted further depending on the product.
Discounted Payback Period
Another important Key Performance Indicator that goes along very well with the
calculation of the NPV is the discounted payback period. „The payback period is
the amount of time it takes the consumer to recover the assumed higher purchase
expense of more energy-efficient equipment as a result of lower operating costs“
[37]. Discounted payback furthermore considers the time value of money. The
input parameters (costs) are the same as for the NPV method. The most efficient
way to analyze it, is visualizing the cumulative costs of each alternative in a graph.

Introduction
16
Figure 1-4: Basic elements of discounted payback period analysis
The break-even point is the intersection of the two lines and defines the point in
time, when cumulative costs for these two systems are equal. From this moment
on, the system with higher initial investment costs has paid off and generates
savings compared to the other system.
1.4.3 Potential Limits of Life Cycle Costing
To fully comprehend the results returned by this methodology, it is necessary to
have a look at what constraints the LCC is attributed with. It helps to draw the
right conclusions from our findings and to see to what extend the theory
represents the reality. Furthermore, ideas and concepts are being explained that
aim at countering the weak points of an LCC.
Focused exclusively on the financial Side
In times when sustainability is put more into focus for business decisions, an LCA
seems to have the advantage over the LCCA. Only costs do not fully reflect a
product and the impact on the environment needs to be evaluated as well.
However, LCC-oriented tools are developed for pure financial analysis and the
focus is therefore different from that of an environmental analysis. LCA results

Introduction
17
are a comparison of several factors individually, which causes one system to be
better in one category but maybe not in another. LCCA is clearer about the
outcome. In the present case, the sustainability factor is already being
considered, as all studied concepts contribute to a more ecological way of heating
buildings than conventional systems. Furthermore, all designed systems aim at
being more cost-effective by reducing mostly energy costs, which results in lower
energy usage and less emissions. However, the present thesis does not intend
to make profound claims regarding sustainability.
Uncertainty of the Result
The major critique point for LCC is that its calculations are partly built on
estimations and assumptions. This adds a factor of uncertainty to the result. A
reason for making assumptions is the lack of available data in certain industries.
There is sometimes few or even no reliable data to base calculations on, which
often forces the decision maker to make assumptions that are biased and
subjective in nature [41].
The missing data is also the biggest challenge for this thesis work. Even though
there are some reliable databases like the collection created by RSMeans® [42],
which is available to public and regularly updated, just a fraction of it is applicable
for the Swedish market. In reality labor costs and taxes are too far apart in the
two countries.
Despite this fact, a LCCA can still be performed and deliver a reliable result if
done properly. When carrying out an LCCA, it is important to understand, that the
absolute costs are not as relevant as their relation to the absolute costs of other
systems. This means that during the calculation, every system needs to be
treated with the exact same assumptions. In this case the deviation of the results
from the real costs is being canceled out when only looking at the difference in
costs between the systems [43]. This idea goes along with the purpose of an
LCCA, which is the financial comparison of alternatives.
Another method to reduce uncertainty, is the implementation of statistical tools in
the LCC. They capture assumptions, that are being expressed in values, and
consider their variability for the calculation. The outcome is not just a final value
for costs or savings; it also returns the likelihood of the result. Basically, the
statistical tools only capture the uncertainty and assign a numerical value to it.
However, by doing that, the decision maker is being provided with a more

Introduction
18
transparent view on the problem and eases his subjective impression of the
uncertain, which eventually facilitates the final decision under uncertainty for him.
The techniques applied in the present thesis are the sensitivity analysis and the
Monte Carlo simulation, which are explained in the following
1.4.4 Sensitivity Analysis
The sensitivity analysis is a useful tool to analyze single assumptions made in
the LCC and to identify how much they contribute to the total costs. This is
achieved by assigning different values to them and monitor how the total NPV
changes. From a mathematical perspective, a function with the output NPV and
a cost input factor as the variable of this function will be created. Other inputs
remain constant. In the final result, you can see how sensitive a product is to a
certain input factor. The higher the sensitivity the more risk can be attributed to
the assumptions on which the input is based. For the deviation of the assumed
value, a certain range will be defined in which the input variable can vary. To keep
it realistic, the range is based on historical experiences.
Sensitivity analysis has its strength in measuring how responsive the system is
to a single input factor and evaluate its assumptions. The downside of this
technique is that by doing so we create another assumption: All other inputs
remain constant. This approach is not very practically, as all inputs will vary over
time. For a broader view on the uncertainty that comes with the LCCA, one more
method is applied that complements the sensitivity analysis and covers its
weakness.
1.4.5 Monte Carlo Simulation
A brief explanation of what a Monte Carlo algorithm does in a financial
perspective, is given by Parnell et al.: „Monte Carlo analysis is a useful tool for
quantifying the uncertainty in a cost estimate. The Monte Carlo process creates
a probability distribution for the cost estimate by rolling up all forms of uncertainty
into a single distribution that represents the potential system costs. Once this

Introduction
19
distribution has been constructed, the analyst can provide management with
meaningful insight about the probability that the cost exceeds a certain threshold
[…].“ [44].
The Monte Carlo simulation is a numerical analysis. Therefore, it can be realized
with an algorithm as visualized in Figure 1-4.
Figure 1-5: Activity diagram of the Monte Carlo algorithm
The simulation tool attributes a certain statistical distribution of values to the
inputs instead of a fixed number. Usually, inputs are being simulated that are
attributed with a level of uncertainty caused by assumptions. Based on historical
data or the results of an expert survey, a range within the distribution, that holds
realistic values, is defined. From this range a random value is assigned to each
input considered in the simulation. Once a randomized value was assigned, the
calculation process is being executed. The inputs obtained are being stored for
later use. The process described until here, is being looped as many times as
desired. Eventually, a set of possible outputs is being achieved. They now can
be displayed in a distribution chart to visualize the frequency of the results. With
a high number of loops executed, the statistical law of large numbers is applied
and as the sample number grows, the histogram averages around a value which
most likely represent the expected output. By implication, the probability of an
outcome to be found in any other defined range can be seen. In the case of an
LCCA, a significant range would be the one that comprises all outputs which
generate savings compared to the base system.

20
2 LCCA Methodology
As stated in the state-of-the-art review, an official set of guidelines for conducting
an LCCA in the building sector has not been established yet. Current state is that
there is a variety of guidelines on the market and it is common practice to pick
the one suiting best to the case at hand. For the specific process steps, a
guideline from Megan et al. [45], which is used to evaluate construction projects
at Stanford University, will be followed. The steps are as following:
1. Establishing objectives for the analysis
2. Determining the criteria for evaluating alternatives
3. Identifying and developing design alternatives
4. Gathering cost information
5. Developing an LCCA for each alternative
The steps will be executed along chapter 2 and described more detailed in the
individual subchapters.
2.1 Establishing Objectives and determining the Criteria
For Step one, some complementary criteria will be added to the objectives
defined in chapter 1.2. The aim is not just to identify the most economical
alternative, but also to see if the other systems can compete with the base case.
The result shall give answer to the financial advantages or disadvantages of
renewable heat sources used for air-preheating in a MFH in Sweden.
Regarding Step two, the metrics, that will define the economical superiority of one
system over another, are the NPV and the discounted payback period. Sensitivity
analysis and Monte Carlo simulation will also observe these two criteria to detail
the result.

LCCA Methodology
21
2.2 Simulation Setup
2.2.1 Identifying and developing Design Alternatives
For Step three, the observed systems are being introduced and explained in this
subchapter.
They all build up on the base system which is a regular MVHR unit. The basic
principle of the technology is illustrated in Figure 2-1.
Figure 2-1: Working principle of MVHR units [46]
The machine performs a constant air circulation between stale and polluted air
on the inside and the fresh air on the outside of the building. It does so by
employing a duct network covering all heated rooms. Two fans, one for incoming
and outgoing air flow each, are employed to transport the air.
The goal is to reduce contamination and maintain a comfortable indoor
environment at a temperature of about 18°C. In case of frost-formation in the heat
exchanger, the unit bypasses the air to an electrical heater. With this concept,
the heat exchanger gets defrosted while the supply air can still be maintained at
the desired indoor temperature. Especially in colder months, the challenge is to
recover the heat captured in the return air. The solution is to let both air flows

LCCA Methodology
22
pass through a heat exchanger. The cold inlet outdoor air absorbs the heat from
the outgoing air and redirects it back into the building. The benefits of this
application are a reduced heating demand for the building and lower energy
costs. For the observed case studies, a plate heat exchanger was picked. The
only downside would be that rotary heat exchangers are more robust to defrosting
[32]. However, the idea of adding a preheating step is to prevent this issue from
coming up in the first place.
The modifications made in system 1,2 and 3 include the air-preheater, which is
yet another heat exchanger. The component is installed prior to the MVHR unit.
It is used to transfer energy from the individual heat source to the inlet air. The
medium carrying the renewable heat is being transported from the heat storage
to the air-preheater by a pump through a piping system. When the air exits the
preheater, it has a temperature slightly above the frost threshold.
Figure 2-2 Schematic of the studied systems [32]
System 1 works with a geothermal heat source. A brine circulates inside the
pipes, passing through a hole in the ground and all the way up to the preheater.
The pipes in the ground have a high conductivity rate and enable heat exchange
between the soil and the brine.
System 2 uses wastewater for heat recovery. First the contaminated water
coming from the apartments is being filtered in a black water cleaner. For this

LCCA Methodology
23
system, the water is being collected in a stratified tank.
Stratification is generally known as the characteristic of a fluid to build different
layers of temperature depending on the height. A good example is the higher
temperature of surface water in a lake compared to the water deeper down. The
lower density of warmer water is one reason for the different levels. Stratified
tanks use this physical effect to their advantage. The component observed in this
study has flat plates mounted horizontally inside the storage room. New and
relatively warm water is coming into the tank from the top and cools down over
the time by mixing with the colder water on the lower part of the tank. The plates
can slow down this process and therefore increase the stratification [47].
Relatively warm water can be found at the surface and only this portion will be
used for preheating the air. As a result, more heat can be absorbed by the supply
air from the water and the system operates more effective. The cooled water is
then being transported back into the tank, but of course enters it at the lower
section now to maintain stratification. Water is being redirected to the tank,
because the water level needs to remain above a minimum, so that the outlet on
top of the tank can always suck in supply water for the preheater. When there is
enough water in the storage room, some of it will be released into the sewage
system. The system is consequently working the most efficient, when new warm
water is continuously being added to the tank. When the supply drops, the
average surface temperature decreases as well which results in lower efficiency.
Another issue with the stratified tank is that it is relatively costly due to its
complexity. Therefore system 3 is added to the study to see if an unstratified tank
can perform just as well. The water intake of the tank for the supply to the air-
preheater is situated at the lower section. This solution therefore does not benefit
of the effect of stratification, which decreases the efficiency of preheating.
However, a constant supply of water is secured without the need of a control
system, that occasionally releases the water.
Table 2-1 summarizes the central components installed in the systems along with
the required dimensions.

LCCA Methodology
24
Table 2-1: Central components of all four systems observed
Component
Dimensions
MVHR with Plate Heat Exchanger
Air Flow Rate: 470 l/s
Water-to-Air Heat Exchanger (Cross-flow)
Power: 6,1 kW ≈ 21000 BTU/hr
Pump
System Nr.
1
2
3
Flow Rate [kg/s]
0,66
0,2
0,065
Water Tank
Volume: 4 m³ (for System 2 and 3)
Borehole
Depth: 250 m
2.2.2 Building Setup and Boundary Conditions
Before getting into the following steps of the actual calculation, the scene needs
to be set that furthermore defines the environmental conditions the system will
operate in. As derived from the literature research, it is evident how significantly
the surroundings impact our result. Moreover, some boundary conditions for the
simulation are set in the following.
The MFH is based on a case study by Simanic [48] reporting on a building located
in Örebro, Sweden. The building underwent a renovation in 2014 including
improvements in the insulation and implementation of a MVHR system. The
housing complex holds a total heated floor area of 876m². The wastewater
generation was set to 160 l/person/day simulated with a varying inlet temperature
between 19°C and 25°C. The soil temperature at the maximum depth of the
borehole at 250m is about 10°C [48].
Figure 2-3: Case study building located in Örebro, Sweden [48]

LCCA Methodology
25
Some of the boundary conditions that were considered for the simulation are as
following:
• The systems will exclusively cover ventilation heating demand to reach
thermal comfort. This is provided by maintaining a room temperature of
18°C and an air flow rate of 470 l/s. Heat demand to surpass this
temperature level is supplied by further equipment of the apartments like
radiators, which are not considered in this thesis.
• Since heating is predominantly active in the colder months only, the
observed period is set from December to March, where average outdoor
temperatures between -22°C and +6°C are to be expected. For ventilation
heating in the remaining months, temperatures do not drop low enough so
that preheating is not required to prevent defrosting. One period in the LCC
is still defined as one full calendar year. Air-preheaters are always
performing on a level, where they heat up the supply air just above the
threshold when frosting occurs. Under this condition, the advantages of
air-preheating and the MVHR are employed at best in terms of heat
recovery efficiency [32].
2.3 Gathering Cost Information
In the fourth step, the general equations 1-3 and 1-4, which define the important
cost factors for LCC in the building sector, are being adapted to the specific case
at hand.
Figure 2-4: Overview on cost factors considered in LCC

LCCA Methodology
26
The adjustments made are being explained in the specific subchapters.
Recycling cost or end-of-life management cost are not considered, as their impact
is assumed to not influence the result significantly.
2.3.1 Initial Investment Cost
Initial investment cost comprises all occurring costs prior to the first
commissioning of the system. This includes the expenses to acquire the
components as well as the services up to this point.
Purchasing costs hold the cost for the manufacturing, the taxes and retail
markups. Installation costs are defined by the labor cost including delivery and
equipment required.
Cost for the ductwork were excluded in this work, since they are the same for all
four systems. They eventually do not contribute to the cost differences between
the systems and therefore have no impact on answering the research question.
The costs are based on the local market prices given by the retailers and are
researched to the best of the author’s knowledge.
2.3.2 Energy Cost
The gathering of data for energy cost is split up into two fields: Information about
the energy usage of the systems and the cost per unit.
For identifying the annual energy demand, a simulation with TRNSYS® (short for:
Transient Systems Simulation) has been carried out. TRNSYS is a graphical
transient simulation software package commonly used for simulating thermal
behavior in buildings. It can be utilized to simulate the whole system or to just
monitor the performance of the single components in it [49]. The models were fed
with input data regarding the temperatures from all heat sources, the dimensions
of the components as well as the layout of the building itself. The model outputs
the heating demand of the building as well as the energy, in form of electricity,

LCCA Methodology
27
required by the components. Pumps and the MVHR unit are the most significant
electricity users that were monitored during the simulation.
For the cost side, information about the current electricity price and a prediction
for the future development of it needs to be generated.
The cost per kWh for period 1 of the LCC are based on Swedish market prices in
2018 [50].
In order to identify a future trend for the tariffs, historic data on the development
of the electricity price in Sweden are considered. The household price has
increased over the last 25 years with high fluctuations. Annual changes going
from as low as -3,5 % and peaking at +10% can be observed. The highest
deviations from a rather steady increase can be seen in the period from 2008 to
2014. The main reasons for that are the changes in the energy mix towards a
higher share of electricity generated by renewable technology [51] . After this
transition period, the price drops again a little and continues its steady incline.
Such an incident is not to be expected for the near future again. To sum it up, it
is reasonable to calculate using an increase in total electricity price of 3% per
year.
2.3.3 Maintenance, Repair and Replacement Costs
In order to support the energy cost calculation, it is necessary to ensure that the
systems are continuously running with a minimum of downtime and high
efficiency throughout its entire lifetime. The idea is to establish a solid
maintenance plan that counters the common failures of the components before
they occur.
Most companies base their estimations for these types of costs on experience
from preceding projects [8]. However, all the data collected through the years
only helps little when new technologies are implemented for the first time.
Maintenance and reparation are not to be confused with each other. The first term
describes measures to ensure functionality of a system by regularly performing
routine tasks. The latter one defines the process of dealing with unexpected
failure of the system and getting it up and running again. It does not include the

LCCA Methodology
28
cost, when the component is unrepairable. This factor is considered in
replacement cost. Qualitatively good maintenance is supposed to reduce
occasional repair jobs.
Even though the principles are different, it is reasonable to comprise both cost
factors into one, as they both emerge on a regular basis throughout the operating
period of the product. One approach is to derive maintenance and repair cost
from the initial investment cost. Wu and Clements-Croome [52] investigated the
ratio of these two cost factors for 20 different types of HVAC solutions. They found
this ratio to be stable between a range of 1,8% to 4,0% for all products
independent of purchasing price. As a conclusion from the paper, one can
account an average of 2,4% of the initial investment cost for annual maintenance
cost. This value will be the baseline for the investigation in the present thesis and
the input factor for the base case. The ratio for both wastewater system was
assumed to be 4%. One can expect the expenses in this field to be higher, since
maintenance includes a few more routine tasks in order to maintain the efficiency
(unclogging, replacing filters, cleaning the tanks) [53]. System 1 is a bit more
practical in this case. The ratio was estimated as 2%
Replacements are a special form of planned maintenance. Certain components
are being replaced at the end of their service life with a new one. The
expenditures are approached by adding purchasing cost and installation cost, as
these are the two cost factors that reoccur when exchanging a part. They will be
added to the cash-out side in the exact year when the official service life of that
component ends. For example, when a part needs to be replaced after 6 years,
the costs will be computed in year 6, year 12, year 18 and so on. The lifespan of
each component is based on information provided by the ASHRAE Database for
service life and maintenance cost of HVAC components [54].
2.3.4 Discount Rate and observed Life Span
The discount rate is a critical factor for the outcome of the study. Its value defines
the weight of costs occurring in the future to the present value. Going with a
discount rate that is too high will favor low investment cost alternatives, while for
a low or even negative discount rate the result will be biased towards systems

LCCA Methodology
29
with high operating cost.
For the analysis the real discount rate is used, as it considers economic
development in the sector. It comprises out of the nominal discount rate and the
inflation rate. The nominal rate was found by analyzing the interest rate for five-
year Swedish government bonds [13]. The inflation rate has been averaged by
using the data of Sweden's consumer price index (CPI) over the past 30 years
[55]. Using the Fisher Equation as presented in Formula 2-1, the real discount
rate is calculated.
 󰆒 
 
r: discount rate [%]
r’: nominal discount rate [%]
f: inflation rate [%]
(2-1)
Based on our inputs, a real discount rate of 2,4% is reached for the LCC-
calculation.
The study life defines the period over which the LCCA will be carried out and
costs will be examined. In the building sector, a rather long lifespan covering
several decades is to be expected. However, a longer perspective also increases
the uncertainty in the result, because assumptions made at present time attempt
to model the future to a larger extend [10]. Furthermore, it is to consider that in
the field of HVAC, disruption can be seen as relatively high. A heating system
nowadays will be outdated and potentially replaced years before the end-of-life
of the building it is implemented in.
For the present case, the life span is chosen based on the expected service life
of the MVHR, which is the central component of all observed systems. The unit
needs to be replaced after 11 years. Utilizing this period as the total life cycle for
the systems is a bit too short to get representative results. The studied lifetime is
therefore extended to the point right before the unit needs to be replaced a
second time. The system will be observed for 20 years of operation. The point in
time for the purchase of the system is picked as year 0 and is therefore not
discounted.

LCCA Methodology
30
2.4 Parameters for Statistical Analyses
The statistical tools will be applied to monitor possible deviations of the input
values. With this approach, the risk coming with the calculation of the LCC
becomes transparent. The two tools, introduced in chapter 1.4 are both applied
here, as each of them has their weaknesses, which can be compensated by the
other one. The sensitivity analysis only observes one input factor and is therefore
beneficial when analyzing the responsiveness of the system to changes to one
specific value. The Monte Carlo simulation on the other hand, has the ability to
vary all factors at the same time and hence simulate a large number of scenarios
with different outcomes. However, it makes it impossible to analyze how a single
input factor has influenced the outcome of the scenario.
Only the factors that come with a high degree of uncertainty caused by the
assumptions they are based on will be observed. Maintenance and repair cost,
electricity price and discount rate are identified as the critical economic factors
that require closer investigation. The range, in which they are going to be varied,
is based on historical data and reference cases.
Variations in maintenance and repair cost can be explained by unexpected
failures in the system, which demand an extra repair job or closer supervision.
The cost in this field tend to rather turn out higher as initially forecasted.
Gustafsson et al. [56] analyzed HVAC systems in combinations with further
energy renovation measures for MFH in Sweden. In his sensitivity analysis, they
looked at increasing costs for initial investment and maintenance varying
between ±20% to cover the prices for components on the lower and upper limit
of the price range. Since in the present thesis we will only observe the
responsiveness of maintenance and repair cost, a range up to 15% is reasonable
and negative values are practically not relevant. That covers the input value for
the base system. Since the ratios for system 1-3 were estimated to be a bit lower/
higher, they will also be adapted for every iteration to maintain this relation of the
maintenance cost.
The span for the development of the electricity price is derived from historical
data of the Swedish energy market. The cost for electrical energy in the country

LCCA Methodology
31
has fluctuated over the last two decades annually between -4% to +10% [57].
This sets the range that will be observed in the analysis.
The discount rate as it was calculated in chapter 2.2.4 also underwent
inconstancies over the last decades. The variation for this factor extends from
+7% down to more recent developments around -2%, which is acknowledged in
the sensitivity analysis.
The ranges are the same for the performance of the Monte Carlo simulation.
However, it is more realistic that the average of the randomized values should be
close to the ones used in the initial LCC. Values on the upper and lower limit
should also appear, but with a lower likelihood. Therefore, the inputs are
randomized using a normal distribution. The standard deviation is approximated
with a common rule of thumb [58]:
   

s: standard deviation
max: upper limit
min: lower limit
(2-2)
Figure 2-5: Normal distribution of input parameters for Monte Carlo simulation
The simulation was run for 1000 iterations, meaning that this also equals the
amount of combinations of randomized inputs. As the outputs to be monitored,
the NPV and the payback period were chosen again.

LCCA Methodology
32
All input factors for the following LCC are now set. To summarize this chapter,
table 2-2 gives an overview over some of the relevant parameters that were
generated for the calculation.
Table 2-2: Input parameters for the LCC
Cost factor
Value
Range
Electricity price in year 1
1,34 SEK/kWh
Electricity price trend
3,0%
-4% - +10%
Maintenance and repair
2,4%
+1% - +15%
Discount rate
2.4%
-2% - +7%
Life span
20 years

Results and Discussion
33
3 Results and Discussion
In the following chapter, the obtained results of the case study are presented. It
holds the execution of the fifth and final step of the LCCA guideline. The data
displayed in the following were obtained and visualized through a cost-simulation
model realized in Microsoft Excel®. An excerpt of the cost-model can be found in
appendix A. Appendix B holds the code for the Monte Carlo Simulation and an
excerpt of the raw data that was generated with it. The subchapters are structured
according to the different methods that were used in the thesis. All monetary
values are displayed in Swedish krona (SEK). Finally, the outcomes are
interpreted in the discussion section. Results for the systems 1- 3 are mainly set
into comparison to the base system as the goal is to find out, if the designed
systems can already compete with the established technology.
3.1 Results
Life Cycle Cost Analysis
Figure 3-1 displays how the cost for each system accumulate over the observed
lifespan. The base system starts with the lowest initial cost in year 0. However,
out of all four systems it has the highest increase in cost over the following years.
The base systems cost increase by around 90000 SEK every three years, while
System 1-3 only change by about 40000 SEK in the same time period. It
eventually turns out to have the third highest NPV at the end of year 20. A break-
even point is reached after about 7,4 years for system 3 and after about 16,6
years, system 2 will pay off. At the end of the observed period, system 2 manages
to save around 50000 SEK, while system 3 can economize 180000 SEK. System
1 has a steady increase in cost similar to 2 and 3. However, the initial costs are
more than twice as high compared to the base case. Within the 20 years, it is not
breaking-even with the system using exclusively MVHR, even though the cost
gap is closed down to about 25000 SEK.

Results and Discussion
34
Figure 3-1: Accumulated cost and discounted payback period of the studied systems
Figure 3-2 highlights the total NPV at the end of year 20 and shows the costs split
up into initial (year 0) and operating cost (year 1-20). The graph mainly serves to
visualize the relation of these two cost sides compared to each other. The base
case comes with operating cost exceeding the initial cost by around 300%. For
system 1, the initial cost dominates over operating cost with a ratio of about 1,4:1.
For the wastewater systems, both cost sides are about equally balanced.
Compared to the base system, the operating cost for system 1-3 decrease by 35
– 45%.
Figure 3-2: Total NPV of all studied systems

Results and Discussion
35
Figure 3-3 holds the total energy cost of all four systems. The cost in the graph
are discounted to NPV and intend to highlight the relationship of the four systems
to each other. While the cost for the three systems using air-preheating are on a
similar level, the MVHR-only concept generates cost more than three times
higher in this field.
Figure 3-3: Energy Cost per System
Sensitivity Analysis
The graphs for the results on the sensitivity analysis visualize the development
of the total NPV at the end of year 20 when one single input-factor is varied in a
plausible range. It is therefore not to be confused with the results for accumulated
cost (Figure 3-1). The goal of this analysis was mainly to monitor the
responsiveness of the total cost of each system when adding variation to one
input factor.

Results and Discussion
36
Figure 3-4: Sensitivity analysis – maintenance and repair cost
Maintenance and repair cost is the most sensitive economic factor for system 1
(see figure 3-4). With an increase of maintenance expenses from the initial 2,4%
to about 11,5% relatively to initial cost, the total NPV already doubles. The base
case shows the lowest changes in NPV in reaction to rising maintenance and
repair cost.
Figure 3-5: Sensitivity analysis - electricity price trend
This observation however does not reflect in the findings on the sensitivity
analysis for the electricity price trend (see figure 3-5). An annual increase of 6%
lifts the total NPV of the base case up by already 20% compared to the initial
0
200
400
600
800
1000
1200
1400
1600
1800
2000
12345678910 11 12 13 14 15
Total NPV [SEK x 10³]
Maintenance Costs relatively to Initial Investment Costs [%]
Maintenance Costs
Base System System 1 System 2 System 3
0
200
400
600
800
1000
1200
1400
-4 -3 -2 -1 012345678910
Total NPV [SEK x 10³]
Electricity Price Trend [%]
Electricity Price Trend
Base System System 1 System 2 System 3

Results and Discussion
37
trend of 3%. At the same time however, the base system benefits the most from
stagnating electricity prices as well. Systems 1-3 increase all by just around
180000 SEK over the whole spectrum.
Figure 3-6: Sensitivity analysis - discount rate
A similar observation can be made for the discount rate (see figure 3-6). The base
system shows the highest responsiveness to changes in this value. Varying the
input from -2% to +8% causes the total NPV to change by about 600000 SEK.
Meanwhile, systems 1-3 decrease in the same range by a value of just around
300000 SEK. For the base system, total cost shoots up to 1200000 SEK in case
of deflation (negative discount rate).
Monte Carlo Simulation
For the Monte Carlo Simulation, the total NPV is displayed in terms of savings
compared to the base system. Payback periods greater than the study time of 20
years were summarized in one column. The charts in Figure 3-7 a)-c) display the
frequency charts along with the accumulated probability. The figures on the
horizontal axis define the limits of the intervals for the respective bar right above.
All bars highlighted in red comprise the scenarios, in which the individual system
turned out to not save cost over the base system. All 1000 iterations are displayed
here.
0
200
400
600
800
1000
1200
1400
-2 -1 012345678
Total NPV [SEK x10³]
Discount Rate [%]
Discount Rate
Base System System 1 System 2 System 3

Results and Discussion
38
Figure 3-7: Monte Carlo simulation: a) system 1, b) system 2, c) system 3
System 1 has a 36% probability of still turning profitable within the observed
period. However, on average this solution does not generate savings over the
base system. When it comes to the extremes, some scenarios were identified

Results and Discussion
39
where it could break-even after 11 years.
The second systems’ probability to break-even in 20 years is at 52% with the
lowest payback time in year 8.
System 3 comes with a possibility of being more economic at 85% and out of
1000 scenarios, 16 were found where it reaches a break-even after 5 years.
Table 3-1 summarizes the findings from the simulation by showing the arithmetic
mean, median, minimum and maximum value of all total NPVs.
Table 3-1: Mean, median, minimum and maximum of all values for savings
System 1
System 2
System 3
Arithmetic Mean
-22247 SEK
31495 SEK
182924 SEK
Median
-54585 SEK
12896 SEK
149343 SEK
Minimum
-454111 SEK
-632067 SEK
-283364 SEK
Maximum
1064265 SEK
1097397 SEK
1272748 SEK
3.2 Discussion
As anticipated by the author and already proofed in the literature, the base system
has the highest expenses during the years of operating, which becomes its major
disadvantage in the analysis. The chart in 3-2 is an information of high interest,
especially for the decision-maker in a business case. It shows that for the solution
using MVHR-only the purchasing cost does not reflect the total cost of the system
and that most of the money will be spent in the later stages of the life cycle.
Furthermore, the results confirm the potential and importance of the LCCA-
approach for such purchasing decisions. The methodology provides an option to
generate a more transparent view and helps to capture all relevant cost occurring
during the products life cycle.
With the highest electricity usage out of all systems, the total cost accumulates
much faster for the base system. Considering that, the base case is the only one
not including a pump and the energy usage for the fans are similar for all four
systems, one can identify the impact of defrosting as a critical factor for the high
energy cost.
Both wastewater systems manage to generate savings within the 20 year-period.
The more advanced technology incorporated in system 2 does not provide the

Results and Discussion
40
anticipated savings during the operating and is therefore still more expensive than
the solution with the regular non-stratified tank. That is why system 3 appears to
be the most economical alternative out of all.
System 1 comes with an initial investment cost that is too high in order to be
competitive. The main reason is the high cost for the borehole drilling according
to current market prices. Even though operating cost are much lower compared
to the base system, it cannot compensate for its high capital cost in the beginning.
However, looking at the graph in Figure 3-1, one can assume the base system to
overtake the borehole system after about 22 years, which is just short behind the
observed life time. Yet, as mentioned in chapter 2.2.4, the lifetime was purposely
not extended any further, because this action would come with an increased
uncertainty as it is an attempt to model costs far in the future.
The air-preheating solutions come with another advantage in regard to the
practical implantation into a building. With a lower energy usage, the real estate
value rises as well. On a broader view, this has further advantages on the
economics of these systems. Such an observation has however not been
considered for this study and is therefore not quantified here.
In the sensitivity analysis, one can identify the greatest risks linked to some of
the systems. Especially the base case system can turn out to be even more costly
with unexpected changes in the market. The discount rate for example is
predicted to decrease even more in Sweden and get closer to zero [55], which
would make future cost even more valuable.
On the other side, maintenance and repair cost for system 1 can shoot up rapidly
once there are issues with the piping in the ground. The course of the curves
shown in figure 3-4 however, are explainable by the nature of the assumption.
Maintenance cost increase gradually based on the initial cost, which are fixed. In
a practical case, the maintenance and repair cost would probably not increase in
the same relation as they do in the sensitivity analysis. Yet, system 1 has potential
to still have the highest growth in cost, because repair jobs on the underground
components of it will turn out relatively expensive.
Both wastewater systems show the lowest responsiveness in all cases. Surely
there can be variations in other cost factors that were not considered in the
sensitivity analysis, however the data available for them is more precise and
therefore comes with a lower uncertainty.

Results and Discussion
41
The Monte Carlo simulation confirms the findings from the previous results and
gives a holistic insight into the risks attributed to the calculations. Again system 3
has the highest likelihood of being profitable over the base system. Also, for the
borehole system a surprisingly high share of cases was found, where it reaches
a break-even point within 20 years.
It is to mention that the limits of the ranges selected for the input factors were
very well-spaced. Therefore, some combinations occurred that are extremely
unlikely and can be found on the upper and lower of the horizontal axes. These
ones can be considered as outliers. When comparing the calculated means to
the results of the initial LCCA with the fixed values, one can see that the
estimations are reasonably accurate and represent the simulated scenarios in a
qualitatively good manner.
It is hereby confirmed what was found in the reviewed literature: Frost-avoidance
solutions have a great potential for optimizing space heating with MVHR-units;
not only from a technical- but also from a financial perspective. Energy cost can
be cut significantly with these setups and eventually compensate for higher initial
investment cost. The high capital costs for borehole drilling are still the greatest
disadvantage for that system and caused it to be the most expensive alternative
out of all four. However, the statistical analyses show that there is still some risk
left with the results obtained and that different outcomes are likely under different
economic circumstances.

Conclusion
42
4 Conclusion
In this case study, an MFH in Sweden was equipped with an MVHR and an air-
preheating system employing heat recovery from renewable heat sources. All
alternatives were observed under a financial perspective using the LCCA-
methodology. The systems were compared to the results for a base system,
which was a single MVHR-unit.
The main questions the study aimed to find solutions for can now be answered
as following:
• Which of the systems is the most economical in terms of life cycle costs?
The system with the lowest life cycle costs out of all is the third one with the
unstratified tank. According to the calculations, it saves an average of 180000
SEK after 20 years of operating compared to the MVHR-only solution.
• How can the risk of imprecise estimations be captured and eventually
facilitate the purchasing choice for the decision-maker?
The sensitivity analysis and the Monte Carlo simulation serve as practical tools
to cover all uncertainties coming with the calculation of LCC. These two
techniques provide a transparent view on the possible outcomes of the LCCA.
One can see that certain economic developments could potentially change the
order in the result and eventually put other systems in favor. By applying the two
statistical methods, the decision-maker gets a holistic picture of the business
case. This facilitates his purchasing-decision since he is completely aware of the
risk coming with it.
Additional Discoveries
Besides the answers for the research questions, further takeaways can be
deduced from the thesis.
The LCCA methodology proofed itself as a useful tool to capture all relevant costs
that are assigned to the systems. Furthermore, the statistical tools helped to
evaluate the outcomes and proof its validity. Results can even though be made
more reliable by creating a solid database to base the calculations on. Such a
solution for the building industry would help making the LCCA approach become

Conclusion
43
more reliable. The development of guidelines such as the one provided by the
EU [34] is also beneficial, however it will be a huge challenges to find a
standardized process that suits for every building and building equipment project.
The technical characteristics of the concepts, that should be more looked into in
order to increase profitability, are the counteractions taken for defrosting and the
technology for borehole drilling. The avoidance of frost has proved itself as a
significant factor for cutting energy cost. Air-preheating is a practical solution to
prevent it from occurring and maintain a high efficiency of the MVHR-unit. The
technology can help to design buildings with the by the EU anticipated “nearly-
zero” energy balance.
While wastewater system can already generate savings with state-of-the-art
technology, there is still need for improvement in the exploitation of geothermal
energy. The drilling process holds a high potential for cost cuts in the future.
Proposals for further Research
The setup and outcome of this thesis also creates a new set of questions that can
serve as initial impulses for further research in this field. When looking beyond
the boundary conditions established in the case study, the full potential of the
systems can be captured more accurately.
Since the main operating expenses were saved by reducing defrosting time, one
can expect that in colder climates than the one observed here, the air-preheating
solutions gain an even bigger economic advantage over the MVHR-only concept.
Also, warmer climates can be simulated in order to find out where the limit
currently is. When crossing the border of Sweden, one should however put the
local economic factors under consideration. Across the European continent, the
electricity and gas prices vary significantly [50] so that fuel-based systems might
be in favor in some countries.
A more integrated view on the technology is created when the observation period
is extended beyond the coldest months of the year. Previous studies on the
observed systems show that preheating is only contributing to better efficiency,
when it is used exclusively during outdoor temperature below the frost threshold
[32]. Going to the other extreme, it would be interesting to find out if the setups
can assist for cooling on extremely hot days. In summer, when the temperature
of wastewater and soil is below the outdoor temperatures, air-precooling could

Conclusion
44
be employed to contribute to the cooling demand of the building. In this case, the
air-preheater would lose its major benefits of frost-avoidance. Therefore, further
retrofit options should be studied to create a year-round solution. Such an
observation would define the universality of the technology and identify its full
market potential.

45
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List of Figures
L
List of Figures
Figure 1-1 Energy usage by dwelling per household in the EU, 2009 [4] ........... 2
Figure 1-2: Life cycle cost factors: 30-year period for federal facilities in the USA
[7] ....................................................................................................................... 3
Figure 1-3: Reduction of defrosting need of MVHR using the studied outdoor air
preheating systems; 1=2904h [32] ..................................................... 9
Figure 1-4: Basic elements of discounted payback period analysis ................. 16
Figure 1-5: Activity diagram of the Monte Carlo algorithm ............................... 19
Figure 2-1: Working principle of MVHR units [46] ............................................ 21
Figure 2-2 Schematic of the studied systems [32] ............................................ 22
Figure 2-3: Case study building located in Örebro, Sweden [48] ..................... 24
Figure 2-4: Overview on cost factors considered in LCC ................................. 25
Figure 2-5: Normal distribution of input parameters for Monte Carlo simulation31
Figure 3-1: Accumulated cost and discounted payback period of the studied
systems ............................................................................................................ 34
Figure 3-2: Total NPV of all studied systems ................................................... 34
Figure 3-3: Energy Cost per System ................................................................ 35
Figure 3-4: Sensitivity analysis - maintenance cost .......................................... 36
Figure 3-5: Sensitivity analysis - electricity price trend ..................................... 36
Figure 3-6: Sensitivity analysis - discount rate ................................................. 37
Figure 3-7: Monte Carlo simulation: a) system 1, b) system 2, c) system 3 ..... 38
Figure 0-1: Excerpt from the cost simulation for the base system ................... LIV
Figure 0-2: Code for the Monte Carlo simulation .............................................. LV
Figure 0-3: Excerpt from the raw data of the Monte Carlo simulation ............. LVI

List of Tables
LI
List of Tables
Table 2-1: Central components of all four systems observed .......................... 24
Table 2-2: Input parameters for the LCC .......................................................... 32
Table 3-1: Mean, median, minimum and maximum of all values for savings ... 39

Nomenclature
LII
Nomenclature
A
ASHRAE ........ American Society of Heating,
Refrigerating and Air-Conditioning
Engineers
B
BHE ................... Borehole heat exchangers
D
DHW ............................. Domestic hot Water
H
HVAC ............... Heating, Ventilation and Air
Conditioning
L
LCAA ..................... Life Cycle Cost Analysis
LCC ................................. Life Cycle Costing
M
MFH .............................. Multi-Family House
MVHR ...... Mechanical Ventilation with Heat
Recovery
N
NPV ................................. Net Present Value
S
SEK .. Swedish Krona (Currency in Sweden)

Appendix
LIII
Appendix
Appendix A
The screenshot in figure A-1 is an excerpt from the simulation that has been
created in Microsoft Excel® to model the life cycle cost. It shows the calculation
for the base system. The sheets for the other three systems are setup
accordingly.

Appendix
LIV
Figure 0-1: Excerpt from the cost simulation for the base system

Appendix
LV
Appendix B
The screenshot in figure B-1 displays the algorithm that has been coded in
Microsoft Excel® VBA to realize the Monte Carlo simulation. Figure B-2 is an
excerpt from the raw data that has been generated by this simulation.
Figure 0-2: Code for the Monte Carlo simulation

Appendix
LVI
Figure 0-3: Excerpt from the raw data of the Monte Carlo simulation