development of a simulation model for combined pvt and …1583383/... · 2021. 8. 6. ·...
TRANSCRIPT
IN DEGREE PROJECT ENERGY AND ENVIRONMENT,SECOND CYCLE, 30 CREDITS
, STOCKHOLM SWEDEN 2021
Development of a Simulation Model for Combined PVT and Ground Source Heat Pump Systems
A TRNSYS Model Created for Commercial Use
HANNA OLAUSSON
EMMA WERNIUS
KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT
Master of Science Thesis
Department of Energy Technology
KTH 2020
Development of a Simulation Model for
Combined PVT and Ground Source Heat Pump
Systems
TRITA: ITM-EX 2021:423
Hanna Olausson
Emma Wernius
Approved
18/06/2021
Examiner
Hatef Madani
Supervisor
Nelson Sommerfeldt & Francisco
Beltran
Industrial Supervisor
José Acuña & Dag Rådeström
Contact person
José Acuña
Abstract The Swedish government has set a target of a 100% renewable electricity system by 2040. To
reach this goal, many actions have to be undertaken. Electrification of buildings is one action
to be undertaken as the residential sector accounts for a large share of greenhouse gas
emissions, where the most energy efficient method is to use heat pumps. Ground Source Heat
Pumps typically have the highest efficiency out of the different heat pumps. These types of
heat pumps are most commonly used in single family houses, as multi-family houses often are
located in highly densely areas. However, when adding hybrid PV/thermal collectors to ground
source heat pump systems, studies have shown that the borehole drilling area can be reduced,
which increases the potential for combined PV/thermal collectors and ground source heat pump
systems in multi-family areas.
In this project, a time-efficient, flexible and user- friendly model was developed to increase the
potential for designing combined PV/thermal collectors and ground source heat pump
systems. The model is based on the research model by Sommerfeldt and Madani (2019), where
the flexibility and time step of the model was investigated and adjusted. The finished model
was verified to the model by Sommerfelt and Madani (2019). Overall, the results show that the
new model gives similar results to the original model, despite all adjustments. The heaviest
adjustments were made in the heat pump where the quantitative results show a mean bias error
of -0.51 kWh and a total yearly difference of -5.18% for the compressor power. The
corresponding values for the condenser heating rate are -1.05 kWh and -2.86%. The user is
able to change boundary conditions such as location, PVT array, building size, and borehole
field size. The model takes approximately 2 minutes to run for a 20 years simulation on a
business grade desktop computer, which is a five-hour twenty minute reduction from the
original research model and assumed to be an acceptable time range for a commercial
applications.
Keywords: Electrify everything, Solar assisted heat pump, Solar Energy, TRNSYS
Sammanfattning Regeringen har satt ett mål om att nå ett 100% förnybart energisystem till 2040. För att nå
målet måste många insatser göras. Bostadssektorn står för höga halter av växthusgasutsläpp
och elektrifiering av sektorn är en lovande väg att gå. Värmepumpar tillhör de mer
energieffektiva tillvägagångssätten, av vilka bergvärmepumpar oftast erhåller den högsta
effektiviteten. Bergvärmepumpar är idag mestadels installerat i enfamiljshus där det finns
utrymme för att borra borrhål. Flerfamiljshus finns ofta i tätbebyggda områden där ytan
tillgänglig för borrhål är begränsad. Emellertid har studier har visat att kombinerade PVT och
bergvärmesystem kan minska borrhålsytorna, vilket ökar möjligheten för dessa typ av system
även till flerfamiljshus.
Detta projekt syftar till att utveckla en kommersiell modell för design av PVT och
bergvärmesystem för att underlätta design av dessa system. Att modellen är kommersiell
antyder på att den är användarvänlig, flexibel och tidseffektiv. Modellen är baserad på en
forskningsmodell av Sommerfeldt och Madani (2019) men justerades för att möta kraven för
en kommersiell modell. Den färdiga modellen jämfördes och verifierades med modellen av
Sommerfeldt och Madani (2019). Övergripande visades resultat som är jämförbara
med forskningsmodellen, trots alla justeringar. De största justeringarna gjordes i värmepumpen
där de kvantitativa resultaten visar ett mean bias error på -0.51 kWh och en årlig skillnad på
-5.18% för elbehovet i kompressorn. Motsvarande värden för värmeutbytet i kondensorn är
-1.05 kWh och -2.86%. Användaren har möjlighet att ändra gränsvillkor så som geografisk
plats, PVT system, byggnadsstorlek och storleken på borrhålsfältet. Den färdiga modellen tar
cirka två minuter att köra för en 20-års simulering på en typisk arbetsdator, vilket motsvarar en
minskning på 5 timmar och 20 minuter jämfört med originalmodellen. Detta kan anses vara
inom tidsramen för kommersiella appliceringar.
Nyckelord: Elektrifiera allt, solassisterad värmepump, Solenergi, TRNSYS
Acknowledgement This thesis is part of the SmartSol2 research project, funded by Mistra Innovation, which has
the goal of supporting the development of integrated heating, cooling and electricity system
solutions by using PVT and GSHP. This project is a collaboration between KTH Energy
Technology and Bengt Dahlgren AB. Thank you for the opportunity to be a part of this project.
It has been an honor and we hope our work will come to great value for you in the future.
During this thesis, many obstacles and unpredictable problems have been faced. To recreate a
research tool and make it a commercial grade model can sound like a simple task, however,
this was not trivial. Hours and hours have been spent in TRNSYS where we have
handled numerous of error messages. But we got to know TRNSYS and its behavior. In the
end we learned how to handle the program and all its features and flaws. Never have we known
a program as well as we now know TRNSYS.
This journey would never have been possible without our KTH supervisor and PVT + GSHP
guru, Nelson Sommerfeldt. To have a supervisor as involved as you have
been is invaluable. We are grateful for the ease of communication and that you always took
your time when we needed your help. We would never have known TRNSYS and the model
as we do today if it wasn’t for you. We appreciate your help, dedication and your confidence
in us and cannot thank you enough for always believing in us. If it wasn’t for you, our virtual
cat would probably have sacrificed all of its nine lives, instead of just seven, to this project.
Also, the greatest thank you to or other supervisors. Francisco, thank you for being with us
through the journey and listening to our talk about the model and TRNSYS problems. And José
and Dag, thank you so much for giving us the opportunity to work with you at Bengt Dahlgren
and believing in us throughout the project. To work with the four of you have been a pleasure.
Thank you for the opportunity and all the laughs during our meetings. Also a great thank you
to our examiner Hatef Madani for great support during this thesis.
Another thank you to our friends and family for your support during this spring and lastly, thank
you to our little school-crew who have kept us company during this pandemic.
Table of Content
1. Introduction ................................................................................................................................................................ 1
2. Background ................................................................................................................................................................. 2
2.1 PVT ........................................................................................................................................................................................................ 2
2.2 Heat Pumps ....................................................................................................................................................................................... 4
2.3 PVT and GSHP system ................................................................................................................................................................... 5
2.4 Borehole thermal energy storage ............................................................................................................................................ 7
2.5 Climate data ..................................................................................................................................................................................... 7
2.6 TRNSYS17 .......................................................................................................................................................................................... 8
3. Knowledge gap and objective .............................................................................................................................. 8
4. Methodology ............................................................................................................................................................... 9
4.1 Model development ........................................................................................................................................................................ 9
4.2 Model Verification ........................................................................................................................................................................ 10
4.3 Inputs and Outputs definition ................................................................................................................................................. 10
4.4 Study Case and Performance Characterization .............................................................................................................. 11
4.5 Scope and limitations ................................................................................................................................................................. 12
4.6 Key Performance Indicators .................................................................................................................................................... 12
5. Model modifications .............................................................................................................................................. 14
5.1 Summary of BM functions ......................................................................................................................................................... 14
5.2 Summary of Model Modifications .......................................................................................................................................... 16
5.3 Story of Model Development .................................................................................................................................................... 18
5.4 Model Description ........................................................................................................................................................................ 20 5.4.1 Space Heating and Domestic Hot Water.................................................................................................................21 5.4.2 Heat pump.............................................................................................................................................................................23 5.4.3 Borehole Field .....................................................................................................................................................................25 5.4.4 PVT ...........................................................................................................................................................................................26 5.4.5 Climate data .........................................................................................................................................................................28
5.5 Model Inputs ................................................................................................................................................................................... 31
6. Model Verifications ................................................................................................................................................ 32
6.1 One-year verification and KPI evaluation.......................................................................................................................... 32 6.1.1 Space Heating and Domestic Hot Water Verification ......................................................................................32 6.1.2 Heat Pump Verification ..................................................................................................................................................34 6.1.3 Borehole Field Verification ...........................................................................................................................................37 6.1.4 PVT Verification .................................................................................................................................................................40 6.1.5 Flexibility and Simulation Time .................................................................................................................................42
6.2 Lifetime Parametric Verification Study .............................................................................................................................. 43 6.2.1 PVT Parametrics ................................................................................................................................................................43
6.2.2 Borehole Field Parametrics ..........................................................................................................................................46
7. Study Case .................................................................................................................................................................. 50
7.1 System Behavior ............................................................................................................................................................................ 51
7.2 Parametric Study .......................................................................................................................................................................... 53
8. Discussion .................................................................................................................................................................. 57
8.1 Strengths of CGM .......................................................................................................................................................................... 57
8.2 Weaknesses of CGM...................................................................................................................................................................... 58
8.3 Future work .................................................................................................................................................................................... 58
9. Conclusion ................................................................................................................................................................. 59
List of Figures Figure 1. Unglazed and uninsulated PVT (left), Glazed PVT (right) ........................................................... 3
Figure 2. Series configuration with ground regeneration ............................................................................ 6
Figure 3. Parallel configuration without ground regeneration ..................................................................... 6
Figure 4: Methodology phases ................................................................................................................... 9
Figure 5: Study Case drawing .................................................................................................................. 11
Figure 6. Schematic overview of PVT+GSHP series/regenerative configuration ...................................... 14
Figure 7. BM TRNSYS model................................................................................................................. 15
Figure 8. Monthly heating load ................................................................................................................ 15
Figure 9: Graphic presentation of CGM ................................................................................................... 17
Figure 10: Space heating load used for model verification ....................................................................... 19
Figure 11. Thermal and electric output for Type 50 and Type 560 ........................................................... 20
Figure 12. System configuration for the CGM ......................................................................................... 21
Figure 13: Daily DHW profile ................................................................................................................. 22
Figure 14: Heat pump macro in CGM ...................................................................................................... 23
Figure 15: PVT macro in the CGM .......................................................................................................... 28
Figure 16. Weather macro in the CGM .................................................................................................... 28
Figure 17: Sensitivity analysis Stockholm sky temperature ...................................................................... 30
Figure 18: Sensitivity analysis Sundsvall sky temperature........................................................................ 30
Figure 19: SH and DHW demand in CGM and BM ................................................................................. 33
Figure 20: Monthly comparison of HP in CGM and BM .......................................................................... 35
Figure 21: Scatterplots of condenser heating rate (left) and compressor power (right) in CGM and BM ... 36
Figure 22: Extracted and injected heat in CGM and BM .......................................................................... 38
Figure 23: Scatterplots of heat injection (left) and heat extraction (right) in CGM and BM ....................... 38
Figure 24: Scatterplots of BH outlet temp. (left) and center temp. (right) in CGM and BM ...................... 39
Figure 25: Monthly verification PVT ....................................................................................................... 41
Figure 26: Scatterplots of thermal output (left) and electric output (right) in CGM and BM...................... 41
Figure 27: SPF for increased implementation of PVT in CGM and BM ................................................... 44
Figure 28: Borehole outlet temperature for increased implementation of PVT in CGM and BM ............... 45
Figure 29: Hourly comparison of borehole outlet temperature for coldest and hottest week ...................... 45
Figure 30: Injected heat hottest week of the year in CGM and BM ........................................................... 46
Figure 31: Extracted heat coldest week of the year in CGM and BM ........................................................ 46
Figure 32: SPF for different borehole spacings in CGM and BM ............................................................. 47
Figure 33: Borehole outlet temperature for different borehole spacings in CGM and BM ......................... 48
Figure 34: SPF for different borehole counts in BM and CGM................................................................. 49
Figure 35: Borehole outlet temperature for different borehole spacings in CGM and BM ......................... 49
Figure 36: PVT production east and west................................................................................................. 50
Figure 37: Heat demand and supplied heat in study case .......................................................................... 51
Figure 38: Thermal energy flows in study case ........................................................................................ 51
Figure 39: Extracted and injected heat in boreholes for the study case ...................................................... 52
Figure 40: Electric energy flows .............................................................................................................. 52
Figure 41: SPF for different borehole counts ........................................................................................... 53
Figure 42: Borehole outlet temperature with and without PVT - 8 boreholes ............................................ 54
Figure 43: SPF for different spacing ........................................................................................................ 54
Figure 44: borehole outlet temperature with and without PVT - 12 m spacing .......................................... 55
Figure 45: 2D contour plots for combined borehole count and spacing with PVT and without PVT ......... 56
Figure 46: TLCC for systems with and without PVT ............................................................................... 57
List of Tables Table 1. Variables tested with KPIs ......................................................................................................... 12
Table 2. Fluid properties in the system loops ........................................................................................... 21
Table 3: Fixed inputs in Type 586d and Type 742.................................................................................... 25
Table 4: Fixed parameters in Type 557 .................................................................................................... 26
Table 5: Fixed parameters in Type 560 .................................................................................................... 27
Table 6: Data in climate weather files ...................................................................................................... 29
Table 7: Variable model inputs ................................................................................................................ 31
Table 8. Quantitative KPIs for DHW ....................................................................................................... 33
Table 9: Quantitative KPIs for HP ........................................................................................................... 34
Table 10: Quantitative KPIs for HP temperatures..................................................................................... 36
Table 11: Quantitative KPIs for BH ......................................................................................................... 37
Table 12: Quantitative KPIs for BH temperatures .................................................................................... 39
Table 13: Quantitative KPIs for PVT ....................................................................................................... 40
Table 14: Quantitative KPIs for PVT temperatures .................................................................................. 42
Table 15: PVT array sizing for the verification parametric study .............................................................. 44
Table 16: Borehole field sizing for the parametric verification ................................................................. 46
Table 17: Borehole parametrics study case .............................................................................................. 53
Table 18: Corresponding borehole field area for parametric study in study case ....................................... 56
Nomenclature BM: Benchmark Model
BTES: Borehole Thermal Energy Storage
CGM: Commercial Grade Model
COP: Coefficient of Performance
DHW: Domestic Hot Water
GHG: Greenhouse Gas
GHE: Ground Heat Exchanger
GSHP: Ground Source Heat Pump
HP: Heat Pump
KPI: Key Performance Indicator
MAE: Mean Absolute Error
MBE: Mean Bias Error
MFH: Multifamily Houses
PV: Photovoltaic
PVT: Photovoltaic Thermal
SAHP: Solar Assisted Heat Pumps
SC: Self-Consumption
SF: Solar Fraction
SFH: Single Family Houses
SH: Space Heating
SPF: Seasonal Performance Factor
SS: Self-Sufficiency
TMY: Typical Meteorological Year
TLCC: Total Life Cycle Cost
VAT: Value Added Tax
1
1. Introduction
The environmental problems related to energy and electricity production are a widespread
concern today including greenhouse gas (GHG) emissions, air and water pollution. To
overcome these problems, the development of renewable energy sources is crucial. To support
the development, the Swedish government has set a target of a 100% renewable electricity
system by 2040 as well as obtaining 50% more efficient energy use by 2030 compared to 2005
(Regeringen, 2020).
The energy utilization in the residential sector accounts for a large share of GHG emissions
(Geng et al. 2017). To reduce the carbon footprint, electrification of buildings is a promising
way to go (Deason & Borgeson, 2019). The most energy efficient method to provide heat in
electrified buildings is heat pumps, in which ground source heat pumps (GSHP) typically have
the highest efficiency (Rees, 2016). Currently, Sweden has the highest penetration of heat
pumps in the world (IEA HPT, 2018), where the vast majority are installed in single-family
houses (SFH). One reason why the market is smaller for multi-family houses (MFH) is limited
land area available for drilling, especially for MFH located in densely populated areas. The
land area around the building available for drilling is an important parameter for the efficiency
of heat pumps. An undersized or overly dense borehole field reduces the efficiency of the
system by accelerating the ground temperature reduction (Sommerfeldt et. al, 2018).
In order to improve GSHP systems, solar assisted heat pumps (SAHP) have been investigated
for a long time (Andrews, 1981; Mowry, 1964; Terrell, 1979). SAHP systems are available in
multiple design and configuration options, both in terms of the heat pump, solar collectors and
hydronic connections (Sommerfeldt et. al, 2016). The majority of the research on SAHP has
been done with solar thermal collectors (Haller et al., 2014), however, recent research has
shown potential for systems with photovoltaic/thermal (PVT) hybrid collectors and GSHP
(Abu-Rumman et al., 2020; Sommerfeldt and Madani, 2019; Wang et al., 2021). The co-
generation of electricity, heating and cooling has several advantages but mainly that more
renewable energy is produced per roof area compared to other solar systems (Haller et al,
2015). Further, the system can reduce the required borehole length and land area for GSHP
while maintaining high efficiency, as well as improve the renewable fraction of buildings
(Sommerfeldt et al., 2018).
PVT+GSHP systems research is complex, relying on large, detailed simulation models that are
typically slow to run. To help increase adoption of the concept, a flexible and fast commercial
grade model is needed to support engineers looking to apply it in their projects.
2
2. Background
SAHPs have been investigated for a long time, but have grown in the market as a result of the
reduced cost for solar technologies in recent years. Until previously, studies of solar heat pumps
mainly considered solar thermal collectors. However, economic support, reduced
manufacturing costs, and improved technology have led to increased solar photovoltaic (PV)
installations, which in turn has increased the interest in research about PV based SAHP. Studies
have shown that economic gains can be made in PV based SHP systems. However, the system
efficiency and coefficient of performance (COP) decrease and tank losses increase. In order to
obtain a higher efficiency and optimize limited roof areas, PVT collectors can be used instead.
PVT + GSHP systems have received attention as an area with great development potential for
multifamily houses (MFH) with limited space for drilling and small roof areas (Sommerfeldt
and Madani, 2019).
The following chapters introduce PVT, GSHP and the combination of the two. It also
introduces the starting point for this work, a model used in detail research, and the requirements
for the commercial grade model to be developed.
2.1 PVT
PVT collectors are a hybrid solar PV and solar thermal technology which produces both
electricity and heat (Lamnatou & Chemisana, 2016). PVT was first developed by Martin Wolf
in 1970 and in the last years, the interest for the technology has increased (Cremers et al., 2015).
One major part that affects the efficiency of PV modules is the heat that cannot be converted
into electricity. This heat increases the cell temperature, which in turn decreases the efficiency
of the PV module. To avoid unnecessary high cell temperatures, different methods for cooling
the modules have been tested and developed where water heat extraction has been found to be
one of the most effective methods. When using this method, water circulation is connected on
the back of the PV module, as close to the PV surface possible, working as a heat exchanger.
The results from this are a cooler PV module with increased efficiency, and heated water. From
this PVT systems can be obtained, if the heated water is further utilized (Lamnatou &
Chemisana, 2016).
There are different design and configuration options of liquid PVT modules (Kim and Kim,
2012; Michael et al. 2015). The type of design to be used depends on the application. For
building applications, the most common PVT configuration is a PV module coupled together
with a flat plate collector (Kamel et al. 2015). There are different design options for this type
and it depends on the purpose of the system which design configuration is the most preferable.
The PV and flat plate collector configuration can be divided into the design options unglazed
or glazed. The difference between the two types is that a glass top surface is directly in contact
with the PV cells for unglazed PVT collectors, whereas glazed PVT collectors have an air gap
between the glass top surface and the PV cells (Sommerfeldt & Madani, 2018). The efficiency
differs between the two options, and the unglazed collector often has greater electrical
efficiency but lower thermal efficiency than the glazed collector (Kim & Kim, 2012). An
3
illustration of the unglazed and glazed PVT configurations is presented in Figure 1, where the
unglazed illustration is uninsulated. An unglazed and uninsulated collector is more affected by
the temperatures of the ambient air and the fluid temperature in the collector than an insulated
collector. Higher collector temperature than ambient results in greater losses, however, the
gains become higher when the fluid temperature is lower than ambient (Sommerfeldt and
Madani, 2018). In 2018, the market share of PVT collectors in buildings was 57% uncovered
water collectors, 41% air collectors and 2% covered water collectors. France, Korea, China and
Germany had the highest shares of PVT capacity in the same year (Lämmle et al., 2020).
Figure 1. Unglazed and uninsulated PVT (left), Glazed PVT (right) (Sommerfeldt and Madani, 2018)
IEA SHC, the largest solar cooling and heating research network in the world, has come up
with their Task 60, which focuses on PVT systems. The aim with the task is to provide new
solutions where the PVT technology can offer more than the PV and solar thermal technologies
alone (IEA, n.d.). PVT has great potential to grow on the market in the upcoming years. The
technology is especially promising in densely urban areas where the rooftop area is limited, to
maximize the solar potential on the available space (Lämmle et.al., 2020).
In 2018, the total installed PVT capacity globally was 525 MW thermal and178 MW PV (Weiss
and Spörk-Dür, 2019). The corresponding value for installed PV capacity for grid connected
systems was 450 GW in 2018 and is expected to grow to 2,840 GW in 2030 globally (IRENA,
2019). This implies that the installed PV capacity from PVT corresponded to 0.04% of the
global PV installation in 2018. If it is assumed that the same share will maintain, the installed
PV capacity from PVT would reach 1125 MW in 2030. However, to achieve this increase,
some actions have to be done. Overall, the awareness of PVT and PVT systems need to
increase. Information about the technology must reach governments, architects, planners,
installers, PV and heat pump companies as well as engineering students to increase the
penetration of the technology. Another challenge is that the price of both PVT collectors and
PVT systems need to decrease and the PVT systems must become simpler to install (IEA SHC
Task 60, 2020).
4
2.2 Heat Pumps
Heat pumps are an energy efficient alternative for cooling and heating buildings. The main idea
of the technology is to create a temperature difference, thereby pumping heat from a lower,
less useful temperature to a higher desired temperature. The main parts of a heat pump are the
compressor, an evaporator, a condenser and an expansion valve, which all have different
functions (Grassi, 2018). In dynamic simulation programs, quasi-steady-state performance map
models are the most common heat pump models used. This type of model is also called
empirical black box models. These models use a restricted number of sampling points from
measurements of a performance map to interpolate in-between the points or to fit a two-
dimensional polynomial plane. Furthermore, the heat pump hot-side heat source inlet and outlet
temperatures are used to calculate the electricity demand and the thermal output, where the
outlet temperature is the desired temperature for the heat to be provided (Haller et al., 2015).
The focus of this project is on electrically driven compressor ground source heat pumps
(GSHP). Most heat pumps in the GSHP category use a brine fluid in the heat pump circuit to
transfer heat from the ground into the heat pump and thereafter pump the heat to a higher level
(Haller et al., 2015). This type of heat pump is explained below.
Around 10-20 meters below the earth surface, there is a steady temperature which GSHPs can
utilize via pipes in the ground for space heating (SH) and domestic hot water (DHW) (Sanner,
2017). When using a brine or water source heat pumps linked to a ground heat exchanger
(GHE), the GSHP system is classified as an indirect system, or a closed loop system. This is
the most common type of GSHP system as it is simpler to install than a direct expansion system,
which has the refrigerant of the heat pump circulating directly through the GHE. The direct
expansion system is more efficient, but the simpler installation of the indirect systems
overcomes the efficiency (Haller et.al., 2015). Furthermore, the indirect systems have the
advantage of using less refrigerant compared to direct systems, which can be a source for direct
systems releasing more GHG emissions than indirect systems (Saba et al., 2009). GHEs have
the advantage that they do not have to be defrosted and due to the stable temperatures in the
ground, the heat pump compressor does not suffer from thermal and mechanical stress to the
same extent as for example an air source heat exchanger (Haller et.al., 2015). The lifetime of a
GSHP is 20-25 years and the average seasonal performance factor (SPF), for the total system
including all the auxiliary components, in Central Europe has been measured to 3.9 compared
to 2.9 for ASHPs and 3.7 for horizontal ground heat exchangers (Haller et.al., 2015). Boreholes
can have a lifetime up to 60 years (Björk et al., 2013).
When the GSHP mainly provides heat, the temperature of the soil/rock surrounding the GHE
reduces. Usually, this is accounted for when the system is designed to make sure that the
performance during the GSHPs lifetime will be satisfactory. However, when a GHE is
undersized, the efficiency of the heat pump will decrease which increases the electricity use.
Undersized GHEs come with potential problems as the installation of GSHP systems in
buildings are reaching two or even three decades since installations in countries mature on the
GSHP market, such as Sweden. Since the average life time of a GSHP is 20-25 years as
5
mentioned above, many systems in Sweden will face an update with newer more efficient heat
pumps. As the temperature in the ground degrades during its operational time, a higher
efficiency of the heat pump will lead to undersized GHE. This could be solved by increasing
the size of the borehole field, but this requires more land area and is not sustainable in the long
term. To solve this problem, PVT+GSHP systems could be a sustainable solution where PVT
can be used as either a secondary heat source to the heat pump, or to regenerate heat to the
ground when the heating demand in the building is low, for example during summer months
(Sommerfeldt et al., 2020).
2.3 PVT and GSHP system
The interest for PVT+GSHP systems has increased in recent years and there has been much
research in the area. Sommerfeldt and Madani (2019) performed a parametric study to analyze
the technical and economic performance of GSHP and PVT systems, connected in a
series/regenerative configuration. The study focuses on MFH in Sweden where the heat pump
market is limited by land restrictions for boreholes and noise restrictions for air heat
exchangers. The results show that the borehole length can be reduced by 18% or spacing by
50% when using PVT, without reducing the seasonal performance factor that is achieved in
systems without PVT. However, the result shows that the impact from PVT on the system
efficiency is relatively small when the borehole field is correctly sized for the desired building
load.
Abu-Rumman et al. (2020) studied a new PVT+GSHP system to reduce electricity
consumption and electricity shortages in buildings in Jordan. The performance of a
PVT+GSHP system was compared to a system where PV and GSHP were installed separately.
It was found that the efficiency of the electricity production increased by 9.5% due to reduced
PV panel temperatures in the PVT+GSHP system. The COP of the heat pump increased from
4.6 to 6.2 as the electricity consumption decreased by 25.7%. Further, the net present value
(NPV) increased by 13.2% and the life cycle cost was reduced by 3.9% compared to when the
GSHP and PV operated separately.
Including PVT in a GSHP system provides two main benefits. Firstly, it acts as a secondary
thermal energy source to the heat pump, which in turn reduces the heat demand from the
ground. Secondly, excess heat from the PVT collectors can be used to regenerate the ground in
the summer months. The regeneration stabilizes the ground temperature and solves the problem
with undersized GSHP systems (Sommerfeldt et al. 2020). There are many different system
configurations, including different components and design for PVT and GSHP systems
(Sommerfeldt and Madani, 2016). The system can either use a series or parallel configuration
depending on which side of the heat pump the solar heat is used. When the heat from a solar
collector is used as an energy source for the evaporator in the heat pump, the system is referred
to as a series configuration. On the contrary, a parallel configuration is used when the heat is
supplied on the hot side of the heat pump. A graphic presentation of the system configurations
mentioned are presented in Figure 2 and 3 respectively. Since the series configuration feeds the
heat pump on the cold side, the collector operates at low temperatures, at or below the ambient
6
air temperature. Consequently, the collector can gain heat from the ambient air instead of losing
heat to the ambient air. This entails that higher solar collector yields can be achieved when
operating at lower temperatures (Haller et al. 2015).
Figure 2. Series configuration with ground regeneration (Haller et al., 2015)
Figure 3. Parallel configuration without ground regeneration (Haller et al., 2015)
It is important to emphasize simplicity in configurations and components when finding a
techno-economic optimized system. A series/regenerative system with unglazed and
uninsulated PVT collectors could be motivated by its ease of integration, efficiency and low
cost. A series configuration is easier to implement to a standard GSHP system, compared to a
parallel configuration, which typically requires a larger hot water tank. Further, a series
configuration fits better with unglazed PVT collectors, which currently are the most common
type on the market. Unglazed collectors can also have a higher efficiency than glazed when
using low temperatures from the borehole circuit (Sommerfeldt and Madani, 2018).
The series/regenerative configuration has the primary benefit of reducing the required land area
for boreholes, rather than increasing the efficiency of the heat pump. This specific
7
configuration is working by feeding the evaporator in the heat pump directly during co-
operation and feeding the boreholes during times when the heat pump is off (Sommerfeldt and
Madani, 2019).
2.4 Borehole thermal energy storage
Borehole thermal energy storage (BTES) has received attention for its ability to store solar
energy from summer to winter and thereby increase the solar fraction. This applies to large
systems, in particular at community level (Sommerfeldt and Madani, 2019). The efficiency of
BTES is mainly dependent on the thermal properties of the ground, the store’s shape and size
as well as the temperature at the boundary of the store (Nordell and Hellström, 2000). The
optimal BTES volume is strongly dependent on the annual heat demand and supply temperature
of the heat. The most efficient conditions for BTES are low temperature heat distribution and
high loads (Pahud, 2000). The definition of BTES is non-uniform and it is debatable which
requirements that should be fulfilled to be called BTES (Gehlin 2016). For example, if the
temperature must be lifted above the undisturbed temperature or not. In the case of
PVT+GSHP, the ground is recharged with heat to prevent it from becoming colder rather than
lifting the ground temperature above the undisturbed temperature. However, Nordell (2000)
presented a broad and detailed definition of BTES and concluded that it comprises systems at
all temperature levels. Therefore, preventing the ground from becoming colder by recharging
is defined as BTES in this report.
Cimmino and Eslami-Nejad (2017) performed a simulation model to investigate how solar heat
injection can reduce the borehole length for shallow ground heat exchangers. The results
showed that BTES can reduce the required borehole length up to 29 % when using 10 m2 of
solar collector area. Further, it was shown that the reduction in length was greatest for borehole
fields, while single borehole arrangement had a relatively small reduction. Therefore, BTES is
more suitable for MFH with borehole fields compared to SFH with a single borehole.
Shah et al. (2020) investigated the performance of a borehole-coupled heat pump seasonal solar
thermal storage system in different cold climates. The result showed that the ground
temperature was stabilized and higher seasonal compressor heating coefficient of performance
and COP were achieved for all locations. Similar to Pahud (2000), it was found that the most
efficient conditions were in the locations with highest annual heat demand, thus the coldest
location.
2.5 Climate data
Climate data can be found from many different sources and in different classes of data. For
building energy simulations, the most widely used weather files represents a Typical
Meteorological Year (TMY). TMY files include data for every hour of the year, where the
monthly data comes from years when the climate was considered the most typical for the area.
TMY files can be found from many different sources and in different file formats. However, to
8
receive the most accurate simulation results, locally measured weather data is the best option,
but this is costly and therefore rarely used (Bhandari et al. 2012).
In Sweden, SMHI, commissioned by SVEBY, has conducted hourly climate weather for 310
sites in the country. The files represent a TMY based on weather data for 1981-2010 and were
published in 2015 (SMHI, 2016). These weather data files are free to download and include
most of the parameters needed for energy simulations. However, some required parameters for
specific simulations are missing, but these can be calculated with the data included in the file.
2.6 TRNSYS17
TRNSYS stands for Transient System and is a common research tool to use for designing
systems with transient behavior, for instance buildings thermal analysis, HVAC, electrical
systems or solar energy applications (Klein et al., 2009). TRNSYS can simulate the
performance of the before mentioned system designs, and offer a modular structure and can
with component routines handle time dependent functions and weather data, which makes the
software flexible. Among engineers and researchers in the area, TRNSYS is a reference
software to be used for these types of applications (University of Wisconsin, 2018).
As TRNSYS is a common research tool, there are some limitations when using the program
for commercial use. For example, it can be tricky to change boundary conditions in the models
and they can also become unstable when they are highly modified. For these kinds of changes,
a program like Polysun could be more convenient to use, as it is a more commercial focused
program for the same kinds of simulations. However, TRNSYS is a broad program with many
possibilities which other programs lack.
3. Knowledge gap and objective
The interest of PVT assisted heat pumps has increased in recent years for its potential to
increase the market for GSHP in MFH (Sommerfeldt and Madani, 2020) and research on the
subject is showing promising results (Abu-Rumman et al., 2020; Sommerfeldt and Madani,
2019). Until today, the models for these types of systems are complicated and made primarily
for research or in support of pilot projects. To enable the market to grow and increase the
awareness of the technical and economic benefits of the technology, there is a need for a simple,
user-friendly tool for designing PVT + GSHP systems, thus, a commercial grade model.
The aim with this project is to bring forward a Commercial Grade Model of Sommerfeldt and
Madani (2019) for design and simulation of PVT + GSHP systems in building applications.
The model will be made in TRNSYS17 in a flexible, time efficient and user-friendly manner,
and thereby meet the requirements of a commercial grade model.
9
The model will be verified by comparing it to the research model by Sommerfeldt and Madani
(2019) and evaluate the effect that simplifications of the model may have on credibility and
uncertainty of simulation results. This will be done by using qualitative and quantitative KPIs.
In addition, the performance of the model will be characterized by a parametric study and
technical metrics. Further, a study case is performed to test other boundary conditions in the
model.
4. Methodology
Figure 4 presents the phases followed to fulfill the objective of this thesis, however, it is
important to mention that the process is iterative to some extent.
Figure 4: Methodology phases
4.1 Model development
The model development is an extensive part of the approach. Initially, the model of
Sommerfeldt and Madani (2019) is analyzed to understand its function and design as well as
its strengths and weaknesses. Further on in the report, this model will be referred to as the
Benchmark Model (BM). Possible approaches to simplify and develop the model and
alternative components are analyzed and evaluated. This includes features that can make the
model more flexible, time efficient and user-friendly, such as enable the user to quickly change
input files for building loads and other site-specific data, adapt the model for longer time steps
and simplify or change components that require specific data.
The time step is the primary factor in making the model more time efficient. Therefore, the
time step is increased from three minutes in the BM to one hour. This in turn requires major
changes in how the heat pump and heat supply are modeled and controlled. Further, the BM is
built with a detailed building physics model and custom weather file format, which requires
modifications to enable rapid simulations for a broad range of buildings and locations.
10
Suggestions on development opportunities are tested and modeled in TRNSYS 17, where the
new model is created. In the remainder of the report, the new model is referred to as the
Commercial Grade Model (CGM). A detailed description of the CGM is provided in Chapter
5.4 Model description.
4.2 Model Verification
When the model development is finished, the CGM is verified. The CGM is verified by
comparing it quantitatively and qualitatively to the BM. The verification includes quantitative
terms such as accuracy of hourly and yearly values and simulation time. The qualitative terms
include flexibility and how user friendly the model is. The same boundary conditions are
applied in both models in order to be able to compare the outputs, this case is called the base
case.
The base case is a MFH with 30 apartments that is based on a typical Swedish construction,
located in Stockholm. The footprint area is 50 m by 10 m and the total heated area is 2000 m2.
The long side is facing south and the inclination of the roof is 20°. 50 PVT collectors are placed
on the south side of the roof in a parallel configuration. It is assumed that it lives 60 people in
the building. Therefore, 60 people are used to calculate the DHW load. The heat pump that is
used has a nominal heating power of 52 kW at 3600 RPM with a rated seasonal COP of 5.3.
The compressor speed range is 1500 to 6000 RPM with a corresponding rated thermal power
range of 21-88 kW. The borehole field consists of 12 boreholes á 300 m with 20 meters spacing
in-between. The weather data for the simulation is based on a typical meteorological year and
is supplied by Meteonorm 7.2. The BM uses a weather file with three-minute time steps which
is converted to one-hour time steps to fit the CGM model. All critical parameters for the base
case are presented in Appendix I.
To make sure that the model is working correctly for parametric studies, three verification cases
are tested:
• Case 1: PVT array sizes (0, 48, 96 and 144)
• Case 3: Borehole spacing (5, 10, 15 and 20 m)
• Case 4: Borehole counts (6, 8, 10 and 12)
All cases are tested for the same building size, weather data and heat pump size as the Base
Case and there is only one parameter changed for each case. The parametric verification study
is done for a 20-year simulation to see the differences between the CGM and the BM over their
lifetimes.
4.3 Inputs and Outputs definition
In the finished model, both technical and economic inputs and outputs are defined. The inputs
in the model are defined to enable the user to change the boundary conditions as desired based
on their building and geographic location. Moreover, the outputs give the technical and
11
economic performance for the simulation. With these outputs, the user will be able to see the
results from the simulation directly, and it can also be compared to other technologies in the
field. To improve the visualization of the outputs, an Microsoft Excel dashboard is created to
present the economic and technical outputs from the CGM.
4.4 Study Case and Performance Characterization
A study case is performed to apply different boundary conditions to test the model for a typical
engineering workflow. The study case includes a project with two houses located in Nyköping,
approximately 100 km southwest of Stockholm. House 1 is facing north-west/south-east and
House 2 is facing west/east, as presented in Figure 5. House 1 has an available area for 59 PVT
collectors on the south side of the roof and House 2 has an available area for 58 collectors,
divided on the roofs facing east and west. The available area for the borehole field is 1200 m2.
Moreover, House 1 has four floors and House 2 has 5 (see Figure 5) and it is assumed that 80
people live in the houses, which is used to calculate the DHW demand. All critical input
parameters for the Case Study are presented in Appendix II.
A parametric study is performed on the Case Study to characterize the performance of the
implemented PVT+GSHP system. The parameters tested are:
• Borehole spacing (17, 12 and 7 m)
• Borehole count (8, 6 and 4)
Figure 5: Study Case drawing
12
4.5 Scope and limitations
This model should be able to simulate PVT + GSHP systems for a broad range of buildings. It
is limited to a series/regenerative configuration, but have the opportunity of implementing new
boundary conditions for new projects. The boundary conditions to be considered are climate,
building loads, borehole geometry and PVT size. Moreover, the focus is on residential
buildings with heating only systems, cooling demand is not considered.
4.6 Key Performance Indicators
A number of Key Performance Indicators (KPI) were chosen to be able to verify the model
compared to the BM. Both quantitative and qualitative KPIs are chosen to verify the accuracy
and the flexibility of the final model, compared to the BM. However, it is important to mention
that the BM is a research model and is not the same as empirical data. Therefore, the CGM is
only validated insomuch as it compares to the original research model. The KPIs are defined
below and are applied to variables of great importance in the model, presented in Table 1.
Table 1. Variables tested with KPIs
Parameters Unit
Compressor power kWh
Condenser heating rate kWh
Evaporator heating rate kWh
Electric output PVT kWh
Thermal output PVT kWh
Heat injected to borehole kWh
Heat extracted to heat pump kWh
DHW load kWh
Temperature outlet PVT °C
Temperature outlet condenser °C
Temperature outlet evaporator °C
Temperature outlet borehole °C
Temperature center borehole °C
Mean Bias Error (MBE) represents the systematic error based on the hourly error of the CGM
and is intended to estimate average model bias. MBE shows the average error in units of the
variable of interest. It is defined by Equation 1 and is calculated for every hour during one year.
𝑀𝐵𝐸 = 1
𝑁∑(𝑥𝑜𝑏𝑠,𝑖 − 𝑥𝑜𝑟𝑔,𝑖)
𝑁
𝑖=1
Eq.1
where 𝑥𝑜𝑟𝑔,𝑖 is the value of the BM, 𝑥𝑜𝑏𝑠,𝑖 is the observed value in the CGM and 𝑁 is the
number of time steps. One limitation with MBE is that negative and positive errors cancel out
each other. Therefore, Mean Absolute Error (MAE) is used to estimate the average magnitude
13
of errors without taking the direction into account. MAE is defined similar to MBE but with
the absolute value added, see Equation 2.
𝑀𝐴𝐸 = 1
𝑁∑|𝑥𝑜𝑏𝑠,𝑖 − 𝑥𝑜𝑟𝑔,𝑖|
𝑁
𝑖=1
Eq.2
To get an idea of scale, the normalized value for the MBE and MAE is also presented. The
normalized values are obtained by using the peak hourly value as a denominator to give the
percentage. Further, the total summed difference, including the difference in their respectively
unit and percent is compared to verify the model against the BM. This is done on a yearly and
monthly basis.
An important feature of a commercial grade model is the simulation time, which indicates the
importance of comparing this parameter. The simulation time is measured in seconds using the
same computer in order to have the same prerequisites. The computer used for the simulations
is a HP EliteDesk 800 G3 TWR running Windows 10 with a x64-based processor. The
processor is an Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz and 32 GB RAM. It should also
be mentioned that the simulations were performed on this computer via Remote Access.
The qualitative KPIs are a measure of the flexibility of the model. It is measured in the ease of
use compared to the BM, such as the ability to change boundary conditions, i.e. weather
conditions, building size and number of PVT arrays.
14
5. Model modifications To clarify the changes between the BM and the CGM, a short explanation of the functions of
the BM is provided as well as a summary of the modifications made in the CGM are presented
in the following chapters.
The system configuration simulated in the models is visualized in Figure 6. It is a
series/regenerative system where the PVT is connected to a HEX on the cold side of the heat
pump to boost the borehole loops when the heat pump is on. When the heat pump is off, the
PVT instead regenerate the ground.
Figure 6. Schematic overview of PVT+GSHP series/regenerative configuration
5.1 Summary of BM functions
This chapter aims to summarize the functions of the BM in short terms. A detailed description
of the BM is provided in Sommerfeldt and Madani (2019).
A simplified visual presentation of the BM in TRNSYS is presented in Figure 7, where the
green squares represent macros with several components inside. The BM is a research-based
model done for a specific case with limitations to easily change boundary conditions. However,
the boundary conditions can be changed but it requires modifications in the TRNSYS model.
In general terms, the model simulates the series/regenerative system and its transient behavior
based on input data and component design. All components in the BM run simultaneously to
receive the desired behavior and outputs of the model. Some data are imported as input files
while other are handled directly in the TRNSYS components. The main system components
are a climate reader and processor, building model, hot water storage tanks, heat pump,
borehole field and PVT collectors.
15
Figure 7. BM TRNSYS model
The model is made for a common Swedish construction, modeled in TRNSYS by using Type
56 as a multi-zone building. The heating demand is calculated simultaneously and the heat
deliver is controlled by thermostatic valves and radiators for each zone. The supplied SH load
is presented in Figure 8. For DHW, two 1,000-liter tanks modeled using Type 60 are connected
in series and the DHW profile is generated using a Markov chain load model. This profile is
provided to match the demand for this specific building where it is assumed that 60 people are
living.
Figure 8. Monthly heating load
16
The heat pump is based on a commercial variable speed compressor with a nominal heating
power of 52 kW at a compressor speed of 3600 RPM, with a compressor speed range from
1500 to 6000 RPM, which corresponds to a rated thermal range of 21-88 kW. A multi-
dimensional interpolated performance map created according to the EN14825 standard
(Madani et al., 2011) is used to simulate the performance of the heat pump. An interpolator
(Type 581) is used to read the map by accepting the compressor speed and inlet temperatures
from water and brine circuits and returns the condenser heating rate and the electricity demand
of the heat pump. The compressor speed is set by a PID-controller (Type 23) that uses a heating
curve, a function of ambient temperature, as the set point. Moreover, there are limitations for
maximum ramp up/down speed and on/off switching. The DHW and SH are provided
separately meaning that if there is need for SH and DHW at the same time, the DHW is
prioritized. When switching from SH to DHW, the compressor speed is remained at the same
properties as for the SH until the DHW has been charged to the desired temperature in the
tanks.
The borehole field is simulated using Type 557a. Type 557a simulates a vertical BHE and is
based on the Duct Ground Heat Storage Model by Hellström (1989). Furthermore, the PVT is
modeled using Type 560. It is unglazed and uninsulated, using monocrystalline Perlight 280
W as the PV module with a HEX fixed on the backside. Climate data is imported as an input
file for the BM and is represented by TMY weather data from Meteonorm 7.2 for Stockholm,
Sweden. Since the BM is simulating with a 3 minutes time step, the climate data is inserted
with the same interval.
5.2 Summary of Model Modifications
In this chapter, a summary of the major modifications in the transition from the BM to the
CGM are presented. This chapter is provided to help the reader understand the changes and
differences between the BM and CGM more easily. A detailed description of the final model
is presented in 5.4 Model Description.
In regards to the goal of making the model flexible, time efficient and user friendly, the ability
to change boundary conditions easily as well as decrease the simulation time are the main
targets for the model modifications. However, the same system configuration as in the BM is
used in the CGM (series/regenerative).
A visual presentation of the CGM model in TRNSYS is presented in Figure 9. All component
types used in the CGM in TRNSYS are summarized in Appendix III.
17
Figure 9: Graphic presentation of CGM
The simulation time is one of the main limitations in the BM. Therefore, changes to reduce the
simulation time were prioritized. The main reasons for high computational power requirement
and long simulation times are the 3-minute time step. Therefore, the time step was increased to
hourly time steps. Moreover, with the goal to make a flexible model, one requirement is to be
able to use public weather data sources which mainly provide data on an hourly basis. This is
another motivation for the increased time step. However, it is important to mention that the
change from 3 minutes time step to one-hour time step required many modifications in the
model since all control functions and input files were made to fit the 3 minutes time step in the
BM.
Another heavy component in the BM, in regards to the simulation time, is the building model
(Type 56). The building construction in the BM is fixed and limited to this specific building
type. However, with the goal to increase the flexibility of the model, one requirement is to be
able to change the heating demand for the desired building. Therefore, Type 56 is removed and
replaced by an input file to be changed by the user. With the opportunity to import space heating
demand as an input file, the CGM can be integrated in existing workflows where building loads
are already simulated.
In regards to the DHW, the two 1,000-liter tanks are removed. Moreover, the DHW profile is
changed in the CGM in order to be able to adapt the mass flow rate dependent on the number
of people living in the building. The mass flow rate is based on an hourly profile where the
number of people and mass flow/person/day is an input in the CGM. This is done in order to
adjust the mass flow based on these parameters. Hence, the DHW profile is not fixed for one
specific building type as in the BM, and does therefore not have to be changed when changing
building. Based on the DHW profile, the heating load required for the DHW is calculated
directly in TRNSYS.
18
The CGM is based on the same commercial heat pump as in the BM in regards to technical
specifications. However, heavy adjustments in the heat pump component are required to match
the time step of one hour and the fact that the CGM no longer include the building load
simulation and DHW tanks. Instead, the imported SH load together with the calculated DHW
load is sent to the heat pump as a total heating requirement. Therefore, the heat pump is
reconstructed in a way to force the heat pump to supply the condenser heating rate based on
the demand. In the BM, the compressor speed and water and brine temperatures are sent to the
performance map to receive the condenser heating rate and compressor power. In the CGM
however, the working principle is reversed. Thus, the condenser heating rate together with the
temperatures are sent to the performance map to receive the correct compressor speed. The
compressor speed is then regulated to match the compressor speed range of the heat pump. It
is then sent to a second performance map to receive the corresponding condenser heating rate
and compressor power for the given compressor speed.
In regards to the borehole field, the same component as in the BM is used, hence Type 557a.
No adjustments are performed on this component. The same applies for the PVT in regards to
Type 560 and the technical specifications. However, there is an ability to add an additional
PVT array in the CGM.
5.3 Story of Model Development
This chapter provides the story of model development that describes the pathway from the BM
to the final version of the CGM as well as the obstacles that were faced during the way.
When the building model (Type 56) was replaced with an input file to be changed by the user,
it was desirable to find the corresponding load profile for that specific building type to use for
the verification. This because the same boundary conditions have to be used in the CGM as in
the BM to be able to compare the outputs correctly. However, as the heating load is calculated
simultaneously in the BM, there is no prepared SH load profile for this building with the applied
boundary conditions. However, the BM gives the supplied heat for the building as an output.
This output is dependent on the heat pump signals and controls in the BM. As the BM supplies
heat separately to the SH or the DHW, the supplied heat for the SH becomes 0 kW when the
DHW is supplied, and vice versa. This creates big variations in the load profile, especially
during winter when the profile should be more even. This entails that the SH load used in the
CGM, hence the supplied SH heat from the BM, does not exactly correspond to the real heating
demand for the building. However, since the BM supplies what the building demands when
there is no DHW demand, this profile is used as the SH input file in the CGM for the
verification to be able to compare the models correctly. It should be mentioned, that when using
this profile as the space heating demand in the CGM, the heat pump receives no space heating
load for the hours when the BM is feeding DHW. This entails that the heat pump turns off since
the DHW load itself often is too low for the heat pump to turn on. The space heating load used
for the verification is presented in Figure 10.
19
Figure 10: Space heating load used for model verification
As mentioned, the BM has two DHW storage tanks with electric heating elements included in
the model. When the CGM was remade to run on one-hour time steps instead of three-minute
time steps, issues with the tanks occurred. When running on three-minute steps, the heating
elements in the tank turned on for one- or a few-time steps until the temperature in the tank
reached the desired DHW temperature. However, when running on one-hour time steps, the
heating element remained on until the next time step (for one hour) when, in reality, it would
turn off when the desired temperature was reached. This led to an overheated tank and
temperatures of the returning water to the heat pump reaching above 100 °C. Several methods
were investigated to solve this problem. However, the conclusion was that DHW storage tanks
could not be used with one-hour time step in the CGM. Because of this, the tanks were
eliminated from the model.
When removing the building model and the DHW tanks as well as increasing the time step, the
heat pump model did not respond as expected. One of the reasons for this is that the temperature
difference over the building and DHW tanks were not regulated as before. Therefore, a method
of forcing the heat pump to respond and supply the correct load was investigated. In the BM,
the compressor speed is calculated based on the inlet temperatures to the heat pump and the
desired temperatures for the SH and DHW. However, since the CGM already have the required
heating load for the SH and DHW, the idea of using that as an input to the heat pump was
investigated. To enable this idea, a solution with two performance maps were performed. This
method was successful and used in the CGM. However, major modifications had to be made
in the heat pump function and controls to be able to use the method.
In TRNSYS, there are several PVT module types. The most widely used PVT type is Type 50,
which is available in the default library. Pressiani (2016) made a review of all the available
PVT types in TRNSYS and concluded that Type 50 contains some constant parameters that
affects the simulation negatively. Pressiani also presents Type 560, the PVT type used in the
20
BM, as a more accurate PVT model in TRNSYS as it can control more inputs and the constant
parameters in Type 50 are instead iterative. However, it is not as widely used as Type 50, but
it is a promising tool for modeling of PVT systems in TRNSYS. Although, Type 50 is simpler
to use than Type 560, and therefore it was tested in the CGM to see how it affected the model.
Both Type 560 and 50 were tested for the same boundary conditions as in the base case and the
results are presented in Figure 11. The light orange and light blue bars represent Type 50 and
the dark orange and dark blue bars represent Type 560. The results showed that the thermal
output (graph to the left) in Type 50 was overestimated, while the electric output (graph to the
right) in Type 50 was underestimated compared to Type 560. As Type 560 contains many
inputs and Type 50 gave large errors, other methods were tested including creating a new PVT
collector for TRNSYS, where the accuracy would be as good as possible but the required inputs
would be as few as possible. However, this is not trivial and from the iterative process, the final
conclusion was to use Type 560 as the PVT model in the CGM. For commercial use, ideally a
coefficient-based PVT model and/or a library with different PVT types that can be used in Type
560 would make the model much easier to apply. However, this is outside the scope of this
project.
Figure 11. Thermal and electric output for Type 50 and Type 560
Moreover, Type 560 requires many climate input parameters. Many different data sources were
investigated to find the most suitable data source for the CGM. The final decision was to use
the TMY weather data files from SMHI. Although there are still some missing data necessary
for Type 560, it contains more input parameters than many other public weather data sources.
However, a motivation for using the SMHI weather files is that the user will be able to choose
from 310 sites in Sweden in a flexible way.
5.4 Model Description
A visual presentation of the final system configuration of the CGM is shown in Figure 12. The
electric load in the system consists of the electricity demand in the heating system, no electric
load for other appliances is included. Therefore, the PV generation from the PVT is exclusively
used for the heating system. When overproduction occurs, it is assumed that the electricity is
sold to the grid. There are no storage tanks included in the model, meaning that in this case, the
heat from the heat pump is supplied directly to the SH and DHW. A backup boiler is included
in the system to support the heat pump for the hours when the capacity of the heat pump is
exceeded. A detailed description of each component of the model is described in this chapter.
21
The model provides both technical and economic outputs, which can be used to characterize
the performance of the model and to more easily be able to compare the model to other methods
used for similar applications. The TRNSYS outputs files are treated in Microsoft Excel to
increase the visualization. The output definitions are presented in Appendix IV.
Figure 12. System configuration for the CGM
The fluid properties for the working fluids used in the circuits are presented in Table 2. Water
is used in the heating circuit, a glycol/water mixture is used in the PVT circuit and an
ethanol/water mixture is used in the borehole circuit.
Table 2. Fluid properties in the system loops
Loop Fluid Density (kg/m3) Heat capacity (kJ/kgK)
Heating Water 1000 4.19
PVT 40% Glycol/60% Water 1035 3.68
Borehole 30% Ethanol/70% Water 1033 3.75
5.4.1 Space Heating and Domestic Hot Water
A text file with temperature and mass flow rate for DHW is used as an input file to the CGM.
The temperature is based on the returning temperature from ground/mains and the mass flow
rate is based on an hourly DHW profile by Ahmed et al. (2016). The profiles in l/person/h used
are for weekdays and weekends in August and November in a building where it is assumed to
live more than 50 people. The profiles were converted into percentages per hour based on the
daily water consumption. These were then put together into a yearly profile where August
represents the months April-September, and November represents October-March. The
finished profile for 24 hours on both weekdays and weekends in August and November is
22
presented in Figure 13. In the TRNSYS model, the daily water consumption per person and the
number of people living in the building can be changed to receive the desired hourly mass flow
rate for the building.
Mass flow (l/h) = Hourly hot water share (%) ∗ Number of people ∗ Daily hot water Eq.3
consumption (l/person/day)
where Hourly hot water share (%) is the share of the total daily hot water consumption
consumed for one specific hour.
From the DHW hourly profile, the DHW load is calculated with Equation 4.
𝑄𝐷𝐻𝑊 = �̇�𝐷𝐻𝑊 ∗ 𝑐𝑝 ∗ (𝑇𝐷𝐻𝑊 − 𝑇𝑖𝑛,𝐻𝑃) Eq.4
where 𝑇𝐷𝐻𝑊 is the temperature of the DHW and set at constant 50°C and 𝑇𝑖𝑛,𝐻𝑃 is the
temperature from the ground/mains provided in the DHW input file, �̇�𝐷𝐻𝑊 is the mass flow
rate from the DHW hourly profile and 𝑐𝑝 is the specific heat capacity of water.
Figure 13: Daily DHW profile
The SH is an hourly input file for one year read by Type 9. The user can use their own file
based on the desired building, inputted in kWh/h. The SH and DHW load profiles are added
together and sent to the heat pump as the total building load.
To make sure that the heat pump can provide all the load to the DHW and SH, a memory
function using Type 93 is used. In this component, the load that cannot be provided from the
heat pump one hour is added to the next hour. Hence, if the load provided to the heat pump
makes it run on a compressor speed under the compressor speed range, the heat pump turns off
and the load for that hour cannot be provided. If this happens, Type 93 stores the non-supplied
load and adds it to the next hour. This continues until the load is enough for the heat pump to
turn on, and all the load that was not supplied the hours before is instead supplied during that
hour.
23
The temperature drop over the building is calculated with Equation 5 where 𝑄𝑡𝑜𝑡 is the total
load, �̇�𝑙𝑜𝑎𝑑 is the mass flow from the heat pump and 𝑐𝑝,𝐻2𝑂 is the specific heat capacity of
water. When the supplied load is zero, the temperature entering the building is equal to the
temperature leaving the building.
𝑑𝑇 =𝑄𝑡𝑜𝑡
�̇�𝑙𝑜𝑎𝑑 ∗ 𝑐𝑝,𝐻2𝑂
Eq.5
5.4.2 Heat pump
The model for the heat pump macro is shown in Figure 14.
Figure 14: Heat pump macro in CGM
The working principle of the heat pump is that the desired heating demand for SH and DHW
and the inlet water and brine temperatures are entering the first Performance Map (Type 581b).
It interpolates the values and returns the desired speed of the compressor. The speed of the
compressor is regulated to make sure that the compressor speed never exceeds the range of
1500 to 6000 RPM. Thus, the heat pump turns off if the compressor speed is less than 1500
RPM, and stays at 6000 RPM if the compressor speed exceeds 6000 RPM. The regulated
compressor speed and the water and brine inlet temperatures are entering Performance Map 2
(Type 581b) and returns the heat rate delivered to the condenser (Q1) and the compressor power
(E). Using these, the evaporator heat rate extraction (Q2) is calculated with Equation 6.
�̇�2 = �̇�1 − �̇�
Eq.6
24
The condenser and evaporator heat rates are applied to the water and brine fluids respectively
using Equations 7 and 8. Where Tin is the inlet temperature Tout is the outlet temperature, �̇� is
the heat rate, �̇� is the mass flow and cp is the specific heat capacity of the fluid.
𝑇𝑜𝑢𝑡,𝑒𝑣𝑎𝑝 = 𝑇𝑖𝑛,𝑒𝑣𝑎𝑝 +�̇�2
�̇�𝑏𝑟𝑖𝑛𝑒 ∗ 𝑐𝑝,𝑏𝑟𝑖𝑛𝑒
Eq.7
𝑇𝑜𝑢𝑡,𝑐𝑜𝑛𝑑 = 𝑇𝑖𝑛,𝑐𝑜𝑛𝑑 +�̇�1
�̇�𝐻2𝑂 ∗ 𝑐𝑝,𝐻2𝑂
Eq.8
A multi-dimensional interpolated performance map is created according to the EN14825
standard (Madani et al., 2011). The performance map is extended with an 3D interpolation
using an Ordinary Kriging method, since the complete operating range of the heat pump is not
covered by the map. Ordinary Kriging method is a method to interpolate values within a range
and extrapolates the values to the entire range of interest (Fanchi, 2002). The original
performance map has a range from -5 to 5 for the source temperature and 28 to 55 for the supply
temperature. The expanded performance map has a range from -10 to 25 for the source
temperature and from 20 to 65 for the supply temperature and the corresponding values for
condenser heating rate and compressor power. The frequency has the same range as in the
original performance map at 1500 to 6000 RPM. Two versions of the performance map are
created and is interpolated using Type 581b in TRNSYS.
Performance Map 1: uses the inlet temperatures from the borehole brine circuit and
demand water circuit and the desired heat rate delivered to the
condenser. It returns the desired compressor speed and the
electricity demand.
Performance Map 2: uses the inlet temperatures from the borehole brine circuit and
demand water circuit and the desired compressor speed. It
returns the heat delivered to the condenser and the electricity
demand.
The heat pump is modeled with a scaling function to simulate different sizes of the heat pump.
The scaling factor is decided based on the rated peak power, where 78 kW at 6000 RPM and a
source temperature of 0 and a supply temperature of 55 corresponds to scale 1. The scale
increases and decreases proportionally where 156 kW corresponds to a scale of 2 and 39 kW
to a scale of 0.5 etc. The user can specify the rated peak power of the heat pump that they are
using and TRNSYS calculates the scale factor. When the total heating load is sent to the het
pump, it is scaled to fit to the heat pump size when the scale is 1. The converted load is sent to
the first performance map. Thereafter, the outputs from the second performance map are scaled
up with the scaling factor to provide the correctly scaled values for the condenser and the
evaporator. This approach makes the RPM stay in the range of 1500 and 6000 but in theory
changes the size of the heat pump.
25
The circulation flow rates are varied with the compressor speed in order to keep a temperature
increase of 10 K over the condenser and temperature decrease of 3 K over the evaporator. The
supply temperature for the SH is set by using the heating curve presented in Equation 9, where
𝑇𝑎𝑚𝑏 is the ambient temperature. The supply temperature for the DHW is a fixed temperature
at 55°C. Further, the circulation flow rates are changed proportionally to the scale.
𝑇ℎ𝑐 = −0.0132 ∗ 𝑇𝑎𝑚𝑏2 − 0.9321 ∗ 𝑇𝑎𝑚𝑏 + 45.857
Eq.9
The pumping power for the circuits are assumed to be the same as in the BM. The pumping
power for the water circuit is directly proportional to the compressor frequency in a range of
60-250W. In addition, a constant electricity load is added for the circulation of the radiator
fluid of 250W, which is multiplied with the scale to match the scaled heat pump. The pumping
power for the borehole circuit is calculated with a pump (Type 742) with inputs from a pipe
pressure drop calculator (Type 586d). The inputs used in the pump and pipe pressure drop
controller are presented in Table 3. The length of section 2 is calculated by multiplying the
borehole length by two, since the boreholes are plumbed in a parallel configuration. An
additional 40 m is added to the length for the pressure drop in the distribution manifold.
Table 3: Fixed inputs in Type 586d and Type 742
Pump (Type 586d) Section 1 Section 2 Section 3
Length (m) 10 Borehole length*2 +40 10
Diameter (m) 0.05 U-tube inner diameter 0.05
Pipe pressure drop calc. (Type
742) Value
Overall Pump Efficiency (%) 60
Motor Efficiency (%) 90
The backup boiler is controlled to turn on when the condenser heating rate is lower than the
required heating demand and the compressor speed is at its maximum. When it is on, it provides
the heat that is missing, thus the difference between the condenser heating rate and the heating
demand. It turns off when the condenser heating rate is equal to the heating demand and the
compressor speed decreases to below its maximum. The backup boiler heat rate is applied to
the water fluid using Equation 10.
𝑇𝑜𝑢𝑡,𝐵𝐵 = 𝑇𝑖𝑛,𝐵𝐵 +�̇�𝐵𝐵
�̇�𝐻2𝑂 ∗ 𝑐𝑝𝐻2𝑂
Eq.10
5.4.3 Borehole Field
Type 557a is used to simulate the borehole field in the CGM, as in the BM. Type 557a simulates
a vertical BHE and is based on the Duct Ground Heat Storage Model by Hellström (1989). The
storage region around the BHE is assumed to be a cylindrical volume and is represented using
a two-dimensional mesh and radial and vertical coordinates (De Rosa et. al. 2014). This type
entails a good estimation of the ground temperature over a longer simulation time. However,
26
the main limitation of Type 557 is the neglection of advection effect in the temperature leaving
the borehole circuit and the steady-state assumption (De Rosa et. al. 2015). To overcome this
problem, a solution has been developed and validated by Pärisch et al. (2015). A pipe model
with wall capacity is added to enable circulation when the heat pump is off and, in this way,
continuously consider the heat transfer. This solution is adapted and used in this model by
adding a pipe (Type 21) after the borehole field and expose it to a fixed heat transfer rate and
connecting the average soil temperature near the boreholes from Type 557 during residence
time. The average temperature of the pipe is applied when there is a mass flow. Thermal
characteristics of a Swedish granite with ground water filled boreholes are used to represent
the ground (Acuña, 2013). The fixed baseline parameters for the borehole field used in Type
557 are presented in Table 4.
Table 4: Fixed parameters in Type 557
Parameter Value
Header Depth (m) 5
Number of Vertical Regions (-) 10
Number of Boreholes in Series (-) 1
Center-to-center half distance (m) 0.04
Pipe Thermal Conductivity (kJ/h*m*K) 0.040
Gap thickness (m) 0
Pipe-to-pipe Heat Transfer (-) -1 (accounts for the heat transfer
between the U-tube pipes)
Insulation indicator (-) 0
Maximum Storage Temperature (°C) 50
Number of Ground Layers (-) 1
Thickness of Layer (m) 1000
Number of Preheat Years (-) 0
5.4.4 PVT
As mentioned previously, the PVT collector component in CGM is the same as in BM, modeled
using Type 560. It is unglazed and uninsulated, using monocrystalline Perlight 280 W as the
PV module with a HEX fixed on the backside. The critical thermal input values for Type 560
was derived from dynamic testing by Sommerfeldt and Ollas (2017). The thermal input values
and other baseline PVT parameters for Type 560 are presented in Table 5. As a complement to
Type 560, a convection calculator (Type 1232) is used to calculate the convection coefficient
from a flat surface to the ambient air. To simulate the fluid capacity of plumbing and the
collectors, a pipe (Type 31) is included. Further, the user can choose to have PVT arrays in one
or two directions, thus, on one or two different roof areas.
27
Table 5: Fixed parameters in Type 560
Parameter Value
Collector length (m) 1.658
Collector width (m) 0.992*Ncollector
Absorber Plate Thickness (m) 0.003
Thermal Conductivity (kJ/hmK) 720
Number of tubes (m) 6*Ncollector
Tube Diameter (m) 0.012
Bond Width (m) 0.012
Bond Thickness (m) 0.003
Bond Thermal Conductivity (W/mK) 45
Resistance of Substrate Material (m2K/W) 0.015
Resistance of Back Material (m2K/W) 0.00001
Reflectance (-) 0.15
Emissivity (-) 0.9
PV Efficiency at Reference Condition (%) 17.02
Efficiency Modifier – Temperature (%/K) -0.41
Type 740 is used in the circuit to model a pump to circulate the working fluid, a mixture of
60% water and 40% propylene glycol. The electricity demand is based on pressure drop, motor
efficiency and flow rate and is calculated in Type 740. The motor efficiency is fixed at 90%
and the pressure drop is assumed to be fixed 50 kPa. This is assumed fixed as the plumbing
configurations most likely would change for different collector areas and flow rates
(Sommerfeldt and Madani, 2019). Type 740 is controlled with a typical deadband approach
using Type 2b, where the temperature difference is calculated based on the collector outlet
temperature and borehole outlet temperature. Sommerfeldt and Madani (2019) performed a
parametric study of cut-in and cut-out temperatures for different mass flows and concluded that
the maximum net savings occur at a mass flow of 20 L/h/m2 with cut-in and cut-out temperature
at 6 K and 1 K respectively. Therefore, this is used in the CGM, however this can be changed
by the user if desired. This approach means that the temperature difference between the
collector outlet temperature and the borehole outlet temperature must be over 6 K for the
control to turn on. If the signal is on for the previous timestep, a temperature difference of 1 K
is enough for the signal to remain on. When using two arrays in the model, the arrays are
controlled individually with the deadband approach. A controlled flow diverter (Type 11f) is
used to distribute the flow to each array, based on the control signal and required mass flow
rate. Type 11h are then used as a tee piece to reconnect the mass flows from the arrays, before
entering the HEX. The HEX connecting the PVT and borehole circuits together is modeled
using Type 650. It has a fixed efficiency of 80% and regulates the temperature to assure that
the heat pump is provided with maximum 25°C. A visual presentation of the PVT macro is
presented in Figure 15.
28
Figure 15: PVT macro in the CGM
5.4.5 Climate data
The weather macro is presented in Figure 16. The weather file is read by a data reader, Type 9,
with an hourly time step. The global horizontal irradiance is then processed in a solar radiation
processor (Type 16a) to receive the irradiance on the surface(s), thus, adapted to the azimuth
angle(s) and slope(s).
Figure 16. Weather macro in the CGM
As mentioned, the TMY files from SMHI are the climate data used in the CGM. The data
included in the SMHI weather files are presented in Table 6.
29
Table 6: Data in climate weather files
Data included in the climate weather files
Wind direction (Degrees)
Wind speed (m/s)
Dry-bulb temperature (°C)
Relative Humidity (%)
Total cloud cover (octas)
Global horizontal irradiance (W/m2)
Direct normal irradiance (W/m2)
Diffuse horizontal irradiance (W/m2)
The missing parameters in the SMHI weather files that is required for the PVT collector (Type
560) and solar radiation processor are air pressure, sky temperature and ground reflectance.
The ground reflectance is assumed to be fixed at 0.2 which is an estimate of average ground
reflectance used in the absence of snow (up to 2.5 cm of snow on the ground) (Liu & Jordan,
1963). The air pressure is assumed to be constant at 100 kPa which corresponds to normal
pressure at sea level.
The sky temperature on the other hand, is calculated since it is heavily dependent on weather
conditions. For calculation of sky temperature, researchers have presented different formulas
since the early 1900’s (Algarni and Nutter, 2015). The sky temperature is different from the
ambient air temperature and due to elevation, the sky temperature is almost always lower than
the ambient temperature (Berdahl and Fromberg, 1982). There are many factors affecting the
sky temperature, such as cloud cover, relative humidity, dew point and other site conditions
(Algarni and Nutter, 2015). Researchers have presented both simple and more complex
formulas for the parameter. However, the most commonly used sky temperature formulas used
are the simple ones, as many require the ambient air temperature as the only input. These
formulas are derived from approximated data, due to lack of correctly measured data (Algarni
and Nutter, 2015). Swinbank (1963) used average values for relative humidity and elevation to
come up with a direct model based on the air temperature. This is one of the most commonly
used sky temperature models due to its simplicity (Algarni and Nutter, 2015). Since this model
is simplified, errors can be expected. However, some of the parameters for the more complex
sky temperature models are missing in the SMHI weather files which is why Swinbank’s
formula is the sky temperature model used in the CGM. This formula is presented in Equation
11.
Nowak (1988) conducted a comparison of sky temperatures calculated with Equation 3 and
measured sky temperatures and concluded that the sky temperature may be up to 10 °C higher
in large city areas with atmospheric pollution, compared to the one that is calculated with
Swinbank’s formula. To consider this uncertainty, a sensitivity analysis was done on this
𝑇𝑠𝑘𝑦 = 0.0553 ∗ (𝑇𝑎𝑚𝑏)1.5
Eq.11
30
parameter where Swinbank’s formula was used to calculate the sky temperature with the
weather data used for the BM, where sky temperature is included. Also, an addition of 10, 8,
6, 4 and 2 °C was added to the Swinbank formula to take Nowak’s findings in consideration.
These six parameters were then compared to the original sky temperature from Meteonorm. To
increase the accuracy of the sensitivity analysis, the same was done for a Meteonorm weather
file for Sundsvall in Sweden. Figure 17 and 18 shows the difference in the sky temperature
calculations compared to the original sky temperature in Stockholm and Sundsvall
respectively. As can be seen, the calculated sky temperature has a slightly different pattern than
the original sky temperature. However, during the summer months (May-Aug), the original sky
temperature and the calculated sky temperature with an addition of 4 °C to Swinbank’s formula
is almost the same for both Stockholm and Sundsvall. Since these are the months in Sweden
with most sunlight and thereby, the period when the big majority of thermal and electric
production occur, it is assumed that this is the best formula to choose for the sky temperature
in the CGM.
Figure 17: Sensitivity analysis Stockholm sky temperature
Figure 18: Sensitivity analysis Sundsvall sky temperature
31
5.5 Model Inputs
All TRNSYS components contain input parameters which can be manually changed directly
in the components. However, to increase the ease of use in the CGM, an extra component where
all necessary boundary conditions easily can be changed in the same place were created. In that
way, the user does not have to go in to each component and change the boundary conditions
manually. The user changes the inputs in this component and the components dependent on
these inputs reads the values. However, this does not include the weather and SH input files.
These have to be changed manually in Type 9 in the weather macro and in the SH data reader
respectively. A summary of the inputs in the added component is defined in Table 7.
Table 7: Variable model inputs
Part in model Model inputs
Weather Location (310 sites in Sweden)
Latitude (°)
Space Heating Load file in kWh/h for one year (.csv)
Domestic Hot Water Number of people living in the building
Liters per person and day (l/person/day)
PVT (Array 1)
Number of collectors
Slope (°)
Roof direction (azimuth angle) (°)
PVT (Array 2)
Number of collectors
Slope (°)
Roof direction (azimuth angle) (°)
PVT Loop
Mass flow (liter/m2/h)
Cut in temperature (°C)
Cut out temperature (°C)
Borehole field
Mass flow (l/s/borehole)
Borehole depth (m)
Borehole diameter (m)
Borehole number
Borehole spacing
U-pipe inner diameter (m)
U-pipe outer diameter (m)
Storage thermal conductivity (W/m*K)
Storage heat capacity (kJ/m3/K)
Fill thermal conductivity (W/m*K)
Fluid specific heat (kJ/kg*K)
Initial surface temperature of storage volume (°C)
Initial thermal gradient of storage volume (-)
Thermal conductivity of layer (W/m*K)
Heat capacity of layer (W/m3/K)
Heat pump Rated Peak power (kW)
32
6. Model Verifications
This chapter provides the verification between the BM and CGM. The verification includes
KPI evaluation on a yearly basis as well as a lifetime parametric verification. Preferably, the
model verification would have been performed when each component in the report had been
changed individually. However, due to the transient behavior and the dependency of each
component in the model, the modifications were performed in an iterative way where changes
in each independent part affected the other components in the model. Therefore, it is impossible
to show the impact of each component without including the other changes in the model since
the model does not work when changing one component only. This mainly applies for the
dependency of 3 minutes time step in the BM and all the changes that were required for the
model to work for hourly time steps. Because of this, the model verification is performed for
the final version of the model when all components are updated as described in the model
description. This is important to have in mind when analyzing the output of the CGM since the
components affect the performance of each other.
The verification is done for the base case explained in 4.2 Model Verification, thus the same
boundary conditions are applied in both the CGM and BM for the verification. The same
climate data from Meteonorm is used for Stockholm. However, it should be mentioned that the
data is converted from 3 minutes time step to one-hour time step to be used in the CGM. The
SMHI file for Stockholm could not be used for the verification since the climate data from the
two sources differ. The SMHI could not be used in the BM since the BM is built for the
Meteronorm file. However, since the SMHI files do not include the sky temperature, ground
reflectance and air pressure, these parameters were calculated and set constant according to the
explanation in 5.4.5 Climate data in the CGM. This was done in order to analyze the impact of
the modifications for these parameters. The impact of the modifications of these parameters
will show in the PVT verification, as the PVT collector (Type 560) is the component dependent
on these parameters. Furthermore, all parameters for the boundary conditions in the base case
are presented in Appendix I.
6.1 One-year verification and KPI evaluation
This chapter presents the verification on a yearly basis. This includes KPI evaluation, monthly
comparisons and scatter plots of critical parameters in the models.
6.1.1 Space Heating and Domestic Hot Water Verification
The total heat demand and the share of SH and DHW for the CGM and BM are presented in
Figure 19. The monthly distribution as well as the share between the SH and DHW are similar
in both models. There are small differences in the DHW load, especially during the summer
months, since the load is simulated in different ways in the models.
33
Figure 19: SH and DHW demand in CGM and BM
Table 8 shows the quantitative KPIs for the DHW load. The SH load is excluded from these
results as the SH load in the CGM is the supplied heat from the BM. The MAE is 4.29 kWh
which corresponds to a normalized value of 15.1%. The MBE showing lower errors of -0.38
kWh corresponding to -1.32%, which indicates that the positive and negative errors cancel out
each other to a large extent. However, on a yearly basis, the difference only reaches -4.38%. It
is important to remember, for this verification, how the models supply heat in different ways.
The BM feed the SH and DHW separately at different time steps while the CGM feed the SH
and DHW simultaneously. This entails differences in how the heat pumps operate, especially
on an hourly basis. This is the reason for the errors being larger on an hourly basis compared
to the summed values. Moreover, the DHW demand in the CGM is calculated based on a fixed
supply temperature which is a source of differences in the models. However, the differences
are within the range of uncertainty and therefore, the DHW profile can be considered
reasonable, as well as the assumption of a fixed temperature of the DHW.
Table 8. Quantitative KPIs for DHW
DHW demand
CGM (kWh/year) 71,764
BM (kWh/year) 75,052
Yearly difference (kWh) -3,287
Yearly difference (%) -4.38
MAE (kWh) 4.29
MAE normalized (%) 15.1
MBE (kWh) -0.38
MBE normalized (%) -1.32
34
6.1.2 Heat Pump Verification
The results from the verification of the heat pump can be found in Table 9 and Figure 19 below.
Firstly, Table 9 shows the summed compressor power and condenser and evaporator heating
rate for the CGM and the BM for one year and the difference between the outputs. The yearly
heat pump components show reasonable results compared to the BM, with the biggest total
difference in compressor power, where the CGM has 4.23% lower power than the BM.
Moreover, the MAE and MBE are presented for the compressor power, condenser and
evaporator heating rates as well as the normalized values for these. The condenser heating rate
has the biggest MAE at 10.42 kW followed by the evaporator heating rate at 7.53 kW.
However, when looking at the normalized values the largest error occurs in the evaporator. The
MBE shows smaller error which indicates that the directions of the error cancel out each other
and results in overall reasonable outputs. Although, when comparing the CGM and BM, it is
important to emphasize how different the heat pumps work in the models, which will always
cause differences in the results. Moreover, the domestic hot water loads are slightly different
in the models which is another reason for differences in the heat pump outputs between the BM
and CGM.
Table 9: Quantitative KPIs for HP
Compressor power Condenser heating
rate
Evaporator heating
rate
CGM (kWh/year) 81,887 316,296 234,409
BM (kWh/year) 85,512 322,909 237,397
Yearly difference (kWh) -3,620 -6,611 -2,990
Yearly difference (%) -4.23 -2.05 -1.26
MAE (kWh) 2.93 10.42 7.53
MAE normalized (%) 10.76 12.01 12.06
MBE (kWh) -0.41 -0.75 -0.34
MBE normalized (%) -1.52 -0.87 -0.55
In Figure 20, the monthly comparisons for the three heat pump parameters can be seen. The
values slightly differ between the two cases for every month, but overall, the pattern look very
similar for all three parameters.
35
Figure 20: Monthly comparison of HP in CGM and BM
In addition to the monthly and yearly values, Figure 21 shows scatterplots of the compressor
power and condenser heating rate with the CGM and BM on the y-axis and x-axis respectively.
Each point in the plots represents the compressor power and condenser heating rate from each
model for a given time step (every hour in one year). Moreover, guide lines have been added
to identify potential error patterns or/and outliners, where the dark line represents equal value
and the light lines represent error boundaries at ± 10%. As can be seen in the graph, the points
are widely distributed and have many outliners. The reason for this is the differences in how
the heat pumps work in the models. Primarily, the difference in time step has a large impact.
In the BM, the compressor speed is regulated with 3 minutes interval, the corresponding time
interval for the CGM is one hour. Since the points represents the compressor power and
condenser heating rate for one hour, the output for each 3 minutes interval is summed to one
hour in the BM. For example, if the heat pump is on for 15 minutes and then shuts off for the
remaining 45 minutes, the total condenser heating rate and compressor power for that hour
becomes the output for the first 15 minutes. In the CGM however, the compressor speed is
regulated with one-hour interval, which means that the compressor speed is constant for one
hour at the time, which also applies to the compressor power and condenser heating rate. This
difference causes large differences on an hourly basis and is also the reason for the large empty
area in the bottom in both Figures. Since the heat pumps are controlled to shut off for
compressor speeds below 1500, the lowest corresponding condenser heating rate is around 20
kW in the CGM. The same principle applies for the BM, but since it can shut off after shorter
time periods, the lowest hourly value can be in the range between 0 and 20.
Another reason for the outliners and error patterns are the differences in how the DHW and SH
are supplied. In the BM, the SH and DHW are supplied separately, meaning that the heat pump
never provides heat to both SH and DHW at the same time step, which is possible in the CGM.
36
This causes differences in the load profile on an hourly basis, where higher loads are obtained
for a given time step in the CGM while the same load are distributed over several timesteps in
the BM.
However, on a daily, monthly and yearly basis, the differences between the CGM and BM are
relatively small which indicates that the heat pump in the CGM is working properly. The
differences on an hourly basis depend on how differently they are modeled and controlled.
Figure 21: Scatterplots of condenser heating rate (left) and compressor power (right) in CGM and BM
The yearly average, MAE and MBE of the outlet temperature in the condenser and evaporator
are presented in Table 9. The average condenser outlet temperature in the CGM is slightly
lower than in the BM, which results in a negative MBE. The condenser outlet temperature is
highly dependent on the inlet temperature to the condenser from the building. In the CGM, this
temperature is calculated while it is dependent on the DHW tanks and radiators in the BM.
Therefore, differences in this temperature can be expected. Regarding the evaporator outlet
temperature, the yearly average is slightly higher for the CGM.
Table 10: Quantitative KPIs for HP temperatures
Outlet temp. condenser Outlet temp. evaporator
CGM yearly average (°C) 44.94 4.64
BM yearly average (°C) 47.50 4.15
MAE (°C) 5.06 1.48
MBE (°C) -2.56 0.49
37
6.1.3 Borehole Field Verification
When it comes to the borehole verification, some deviations must be mentioned. As mentioned
in 5.3 Story of Model Development, the CGM uses the supplied heat from the BM as the space
heating load input. This causes the heat pump in the CGM to turn off when the BM is feeding
the DHW. When the heat pump is off and there is a thermal output from the PVT, the ground
is regenerated. For this case and this specific SH input file, this means that the ground is
regenerated during hours when the heat pump should be on. Another reason for the variation
in the results for the borehole verification is the different time steps. The mentioned differences
entail that the heat pump in the CGM is off more than the heat pump in the BM, and more heat
is injected to the boreholes. The yearly difference, MAE and MBE are presented in Table 11
where it can be seen that the yearly difference of the heat injection reaches 39%. The
corresponding value for heat extraction is 1.73%. However, when looking at the normalized
MAE and MBE for the heat injection, the percentages are much lower. This can be explained
by the fact that the injected heat is zero for the majority of the time in both models, which
equals an error of zero for all those hours. This implicates that the MAE and MBE are relatively
low compared to the yearly difference.
Table 11: Quantitative KPIs for BH
Figure 22 shows the extracted and injected heat as well as the net heat in the boreholes for both
the CGM and the BM. As described above, it can be seen in this figure that the CGM injects
more heat. However, it also extracts slightly more. Because of this, the net heat lines for both
the CGM and the BM looks similar. The yearly difference in the net heat is -1.13%, which can
be considered acceptable. To investigate the impact of the interrupted SH supply, a load profile
for the building demand used in the BM, not dependent on the heat pump signals in the BM,
could be tested, however, such profile was not available due to unresolvable discrepancies in
the building model (Type 56). Furthermore, due to the CGM being verified to only one model,
this part of the model needs more verification in future work to justify the function and
credibility of the CGM.
Heat extracted from BH Heat injected to BH
CGM (kWh/year) 221,643 21,598
BM (kWh/year) 217,877 15,538
Yearly difference (kWh) -3,766 6,060
Yearly difference (%) 1.73 39.00
MAE (kWh) 5.92 1.73
MAE normalized (%) 9.48 6.56
MBE (kWh) -0.43 0.69
MBE normalized (%) 0.69 2.62
38
Figure 22: Extracted and injected heat in CGM and BM
In addition to the monthly and yearly values, scatterplots are presented for the injected and
extracted heat, showed in Figure 23, with the BM and CGM on the x-axis and y-axis
respectively. Each dot represents the heat injected (left) and heat extracted (right) from each
model for a given time step. Moreover, guide lines have been added to identify potential error
patterns and/or outliners, where the dark line represents equal value and the light lines represent
error boundaries at ± 10%. As can be seen in the figures, there are big differences on an hourly
basis between the models. Regarding the heat injection, the CGM is injecting more than the
BM for a big majority of the time while the distribution is more outspread for the heat
extraction. However, the differences can partly be explained by the load used for the
verification and the differences between the CGM and BM as previously mentioned.
Figure 23: Scatterplots of heat injection (left) and heat extraction (right) in CGM and BM
39
The MAE and MBE for the borehole outlet temperatures are presented in Table 12. When
looking at the outlet and center temperatures in the boreholes, the MAE and MBE are showing
good results, which indicates that the differences in the injected and extracted heat do not have
that big impact on the temperature when simulating for one year. As expected, MAE is higher
for both the center and outlet temperature compared to the MBE. However, when looking at
the temperature difference in the boreholes, it is important to analyze the behavior over a longer
time period since the injected and extracted heat have a great impact of the temperature over
time. This comparison is presented in 6.2 Lifetime Parametric Verification Study.
Table 12: Quantitative KPIs for BH temperatures
In addition to the yearly average, MAE and MBE, scatterplots of the borehole outlet
temperature and borehole center temperatures visualizes the hourly differences between the
CGM and BM. The scatterplots of the temperatures are presented in Figure 24 where each dot
in the figures represent the borehole outlet temperature and borehole center temperature
respectively for every hour. Moreover, guide lines have been added to identify potential error
patterns or/and outliners, where the dark line represents equal value and the light lines represent
error boundaries at ± 10%. As can be seen in the graph, the majority of the dots are within the
± 10% boundaries with some exceptions.
Figure 24: Scatterplots of BH outlet temp. (left) and center temp. (right) in CGM and BM
BH outlet temperature BH center temperature
CGM yearly average (°C) 5.10 5.52
BM yearly average (°C) 5.09 5.50
MAE (°C) 0.13 0.05
MBE (°C) 0.01 0.02
40
6.1.4 PVT Verification
For the PVT verification, only one PVT array is used is used in the CGM. It is sized and
oriented the same as in the BM.
Table 13 presents the thermal and electric output of the PVT collector for the CGM and the
BM as well as the difference between these. As can be seen in the table, the annual thermal and
electric outputs are slightly decreased in the CGM compared to the BM. However, the relative
difference is under 3% and 1%, respectively, for the thermal and electric output. MAE for the
thermal output is 0.37 kWh and the MBE is -0.12 kWh which corresponds to normalized values
of 1.27% and -0.42% respectively. The corresponding values for the electric outputs are 0.03
kW and -0.01 kWh and normalized values reaching 0.26% and -0.12%. As expected, the MAE
is higher than the MBE which depends on that the error cancel out each other in the MBE. Most
likely, the differences in thermal and electric output depends on the differences in sky
temperature, ground reflectance and air pressure. These factors create different prerequisites
for the models and entails small variations in the weather data which is a crucial parameter for
the thermal and electric output. The thermal output is also highly dependent on the control
signal for the PVT pump. The hourly time step in the CGM entails that the pump signal
(ON/OFF) remains the same for one hour, compared to the BM that can have variations with
three minutes intervals. This entails small differences in thermal output, especially when the
signal goes from off to on or on to off. However, the yearly difference, MAE and MBE for the
thermal and electric output are relatively small and can be considered to be within the range of
uncertainty. Table 13: Quantitative KPIs for PVT
Thermal output Electric output
CGM (kWh/year) 34,646 12,776
BM (kWh/year) 35,715 12,902
Yearly difference (kWh) -1,070 -127
Yearly difference (%) -3.00 -0.98
MAE (kWh) 0.37 0.03
MAE normalized (%) 1.27 0.26
MBE (kWh) -0.12 -0.01
MBE normalized (%) -0.42 -0.12
The monthly comparison of the thermal and electric output is presented in Figure 25 which
indicate that the monthly distribution is similar in the models. However, the difference is larger
during the fall, especially for the thermal output during September-November. One reason for
this could be the fact that the sky temperature is modified and adapted to match the summer
months and therefore, bigger variations can occur during the rest of the year. Figure 16 in 5.4.5
Climate data shows the differences between the calculated sky temperature and the sky
temperature from the Meteonorm file used in the BM. In this figure, it can be seen that the
biggest variations occur during the fall which is a source for the variations in thermal output
during this specific period.
41
Figure 25: Monthly verification PVT
In addition to the monthly and yearly values, Figure 26 shows scatterplots of the thermal (left)
and electric (right) output with the CGM and BM on the y-axis and x-axis respectively. Each
point in the plots represents the thermal and electric output from each model for a given time
step. Moreover, guide lines have been added to identify potential error patterns and/or outliners,
where the dark line represents equal value and the light lines represent error boundaries at ±
10%. The general trend is that the points are within the guide lines of ± 10% for both the thermal
and electrical output. However, there are some outliers in the thermal output around zero, which
can be explained by the signal variations due to the time step difference mentioned above.
Figure 26: Scatterplots of thermal output (left) and electric output (right) in CGM and BM
The outlet temperature from the PVT in the CGM and BM as well as the MAE and MBE for
this parameter is presented in Table 14. The temperatures are similar to each other and the
MAE and MBE is low which indicates that the PVT in the CGM is working correctly in regards
to temperature. However, the difference between the PVT outlet temperatures in the BM and
CGM is most likely because of the modified weather parameters.
42
Table 14: Quantitative KPIs for PVT temperatures
Outlet temp. PVT
CGM yearly average (°C) 5.77
BM yearly average (°C) 6.23
MAE (°C) 0.03
MBE (°C) -0.45
6.1.5 Flexibility and Simulation Time
In this chapter the flexibility of the CGM is summarized and the simulation time is presented,
compared to the BM.
To compare the CGM with the BM, the flexibility and ease of use has been increased. A
summary of the flexibility improvements is presented here.
• The model is adapted for SMHI weather files and the user has 310 sites in Sweden to
choose from.
• The user has the option to simulate the system for their desired building with an input
file with the SH load and the number of people living in the building.
• There is an option to use either one or two different PVT arrays in different directions
for the PVT. However, the flexibility of the PVT part in the CGM is limited, as the
PVT type is fixed to only one collector type.
• The heat pump can be scaled to fit the heat demand of the desired building.
With the above-mentioned improvements for the flexibility, the goal of making the CGM
flexible, time efficient and user friendly has partly been fulfilled. However, some parts in the
model have limited the capability of fully reaching the goal. This applies primarily to the PVT
collector and DHW tank. The PVT collector (Type 560) used in the CGM has a high accuracy
but require many inputs that is not available on a regular specification sheets for PVT modules.
Therefore, the choice of having a fixed PVT collector type was taken to simplify for the user,
since no other accurate PVT collector types are available in TRNSYS that require less inputs.
The results from this emphasizes the importance of developing a new accurate PVT collector
type in TRNSYS that only requires inputs that is available on regular specification sheets for
PVT collectors. However, this is outside the scope of this project and therefore, assigned for
future work. Furthermore, the limitations with the DHW is another barrier to reach the goal of
flexibility. The original ambition was to have a DHW tank and enable the user to size the tank
depending on the desired building. Although, this was only possible if the time step was
decreased to 12 minutes which increases the simulation time considerably and thereby opposes
the goal of a time efficient model. Therefore, it was decided to keep the 1h time step and remove
the DHW tank. However, the verification has shown that the method for simulating the DHW
load without a DHW tank is successful. When comparing the flexibility of the CGM and BM,
several improvements have been made to enable simulations for different buildings and
geographical locations with the option of having one or two arrays and a scalable heat pump.
43
Moreover, it can also be concluded that the CGM has fulfilled the goal of a time efficient
model. The simulation time for the CGM is approximately 5 seconds per year and 120 seconds
for a simulation time of 20 years. This implicates that the simulation time has decreased with
approximately 5 hours and 20 minutes for a 20-year simulation. In addition to the long
simulation time in the BM, it requires to run in two separate 10 years simulations in order to
obtain a full simulation of 20 years. In the CGM, one simulation of 20 years can be obtained
directly. The changes that decreased the simulation time was mainly adapting the CGM to a
time step of 1h instead of 3 minutes, as well as removing the building load simulation from the
BM. However, these changes required major changes in how the CGM was structured and
controlled.
In addition to make the model flexible and time efficient, the goal was to make the model user
friendly. The best solution would be to package the model to enable use of the model even if
the user does not have a TRNSYS license. However, it was concluded that the model requires
some further flexibility improvements, assigned for future work, before the model will be
packaged. This mainly applies to development of a new PVT collector type in TRNSYS.
Although, the CGM is designed in a user-friendly manner to simplify for the user and therefore,
the goal of making the model user friendly can be considered as partly fulfilled.
6.2 Lifetime Parametric Verification Study
In addition to the verification performed for the first simulation year, the CGM is verified over
its lifetime with a parametric verification study. The parametric study mainly includes different
parameters in regards to the implementation of PVT and borehole field. The same parametric
study is performed in the CGM and the BM in order to see how the CGM perform for different
conditions and over time. Beyond the parameters that are changed in the parametric study, the
same boundary conditions as in the one-year verification are applied.
For the Lifetime Parametric Verification Study, the borehole outlet temperature and SPF4 are
analyzed for the systems. SPF4 expresses the total heat demanded by the building to the total
electricity consumption of the heating system over a year. The total heat demand includes SH
and DHW. The equation for SPF4 is presented in Appendix IV under Seasonal Performance
Factor. The borehole outlet temperature is presented since it is often used to dimension ground
source heat pump systems and therefore it is seen as a critical value.
6.2.1 PVT Parametrics
The PVT array sizing for the verification parametric study is presented in Table 15 The
collector count ranges from 0 to 144 where 144 implicates that the entire roof is maximized
with PVT collectors.
44
Table 15: PVT array sizing for the verification parametric study
Number of collectors Area (m2) Rated PV power (kWp)
0 - -
48 79 13.2
96 157 26.4
144 236 40.3
Figure 27 presents the SPF4 for increased implementation of PVT collectors in the CGM and
the BM when having 12 boreholes and a borehole spacing of 20 m. The solid lines represent
the CGM and the dashed lines represent the BM for different collector counts. As can be seen,
the SPF4 is slightly higher for the CGM compared to the BM for all collector counts. One
reason for the differences is that the BM has a backup heater for the DHW that is included in
the calculations for the SPF4. However, the biggest difference reaches 1.9%, which can be
considered acceptable. The similarities in SPF4 indicates that the system behavior in the models
are similar when increasing the implementation of PVT collectors as well as having similar
system behavior over its lifetime.
Figure 27: SPF for increased implementation of PVT in CGM and BM
Figure 28 presents the borehole outlet temperature for increased implementation of PVT
collectors in the CGM and BM when having 12 boreholes and a borehole spacing of 20 m. The
solid lines represent the CGM and the dotted lines represents the BM. As can be seen, the
borehole outlet temperatures are almost identical for both models for all collector counts, which
indicates that the differences in injected and extracted heat discussed in chapter 6.1.3 Borehole
Field Verification does not have a significant effect on the borehole outlet temperature over the
lifetime.
45
Figure 28: Borehole outlet temperature for increased implementation of PVT in CGM and BM
In order to see the difference on an hourly basis, the borehole outlet temperatures are compared
for the coldest and hottest week in year 10 presented in Figure 29. The comparison is performed
for a system with 144 collectors and 12 boreholes with 20 m spacing. As can be seen, the
borehole outlet temperatures follow the same pattern in the models for both the hottest and
coldest week of the year. However, there are small variations between the models which can
be expected because of the differences in time step and control signals.
Figure 29: Hourly comparison of borehole outlet temperature for coldest and hottest week in CGM and BM
In addition to the borehole outlet temperatures, the injected and extracted heat are presented
for the same weeks in Figure 30 and 31 respectively. The injected heat is presented for the
hottest week and the extracted heat is presented for the coldest week. As can be seen, there are
variations on an hourly basis both in injected and extracted heat. The general trend for the
injected and extracted heat is that there are more distinct variations between each hour in the
46
CGM, while the curves are smoother in the BM. The main reason for this is that the CGM is
simulating with hourly timesteps while the BM varies with three minutes intervals. It is also
visible that the CGM injects and extracts slightly more, which agrees with the results presented
in 6.1.3 Borehole Field.
Figure 30: Injected heat hottest week of the year in CGM and BM
Figure 31: Extracted heat coldest week of the year in CGM and BM
6.2.2 Borehole Field Parametrics
The different combinations tested for the borehole field verification parametrics are presented
in Table 16. The borehole count and spacing are varied individually of each other in a range of
6-12 and 5-10 respectively. The depth for each borehole is kept constant for all cases. However,
the total length varies with the borehole count. In all cases, 144 PVT collectors are used in the
simulations.
Table 16: Borehole field sizing for the parametric verification
Count Spacing Depth Total length
6 20 300 1800
8 20 300 2400
10 20 300 3000
12 20 300 3600
12 5 300 3600
12 10 300 3600
12 15 300 3600
12 20 300 3600
47
The SPF4 with different borehole spacings for the CGM and BM are presented in Figure 32.
As for the PVT Parametrics, the solid lines represent the CGM and the dashed lines represent
the BM for different borehole spacings. In general terms, the SPFs follows the same behavior
during the whole lifetime. Although, small variations can be identified for all cases where the
SPF is slightly higher for the CGM throughout the whole simulation period. However, the
largest difference reaches 1.7%, which can be considered acceptable.
Figure 32: SPF for different borehole spacings in CGM and BM
The borehole outlet temperatures for different borehole spacings are presented in Figure 33. As
in the PVT parametrics, the solid lines represent the CGM and the dotted lines represents the
BM for different borehole spacings. As in the PVT parametrics, the borehole outlet
temperatures in the CGM and BM are almost identical for the different borehole spacings.
48
Figure 33: Borehole outlet temperature for different borehole spacings in CGM and BM
The SPF4 for different borehole counts are presented in Figure 34. As in the previous
parametrics, the solid lines represent the CGM and the dotted lines represents the BM for
different borehole spacings. Also for this case, the difference between the CGM and the BM
are within reasonable uncertainty, with a difference of maximum 1.6% during the whole
simulation period. As previously mentioned, the differences between the models mainly
depends on the fact that the BM has a backup boiler for DHW that is removed in the CGM.
However, in this figure, it can be seen that the difference decreases between the models for
reduced borehole counts. This is because the heat pump backup-heater in the CGM must
provide more heat when the borehole count reduces. The increase in heat from the back-up
heater is larger in the CGM compared to the BM since the DHW and SH are supplied
simultaneously, which is not the case in the BM. As the SH and DHW are supplied at different
time steps in the BM, the load never reaches the same level as in the CGM, which is why more
heat from the back-up heater in the CGM is required compared to the BM. This entails that the
difference between the CGM and BM decreases, as the electricity required for the DHW
backup-heater together with the electricity need for the heat pump back-up heater in the BM
approaches the same electricity need as for the heat pump backup-heater in the CGM alone.
Since the differences increase for larger systems, some further verification for larger systems
would be valuable.
49
Figure 34: SPF for different borehole counts in BM and CGM
In Figure 35, the borehole outlet temperatures for different borehole counts in the CGM and
BM are presented. Similar to the previous parametrics, the solid lines represent the CGM and
the dotted lines represents the BM for different borehole counts. In this case, the borehole outlet
temperatures are basically identical for all borehole counts during the whole simulation period.
Figure 35: Borehole outlet temperature for different borehole spacings in CGM and BM
50
When looking at the lifetime parametric verification of the CGM, it can be concluded that the
CGM is working almost identical as the BM for the given parameters. There are small
differences identified, especially in the verification on an hourly basis. This can be expected
because of how different the models work in regards to time step, flexibility and function.
However, on a systems level and with a lifetime perspective, the differences between the model
outputs are proportionally small.
7. Study Case
The study case has two buildings and space for PVT on three different roofs, explained in
chapter 4.4 Study Case and Performance Characterization. Since the CGM only has the option
to simulate two different PVT arrays, the PVT arrays on House 2 was simulated as only one
PVT array facing west. This assumption was based on testing the CGM for one PVT array due
east and one due west to see the differences in electric and
thermal production. 29 collectors due east and the same number due west were tested, as these
are the maximum number of collectors for each roof on House 2. The differences in production
for the two directions can be seen in Figure 36. The production for the array due west is slightly
higher, however the difference is not critical. Therefore, it was assumed that having the total
amount of PVT collectors for House 2 due one direction would give reasonable results for the
study case. Moreover, the study case was tested for different borehole spacing and borehole
lengths with a full roof of PVT collectors (House 1 + House 2). The system behavior for one
year and the results for the different parametrics over 20 years are presented later on in this
chapter.
Figure 36: PVT production east and west
51
7.1 System Behavior
In this chapter, the system behavior for the Study Case is presented. The system behavior is
shown for one year with PVT collectors on both House 1 and House 2 (a total of 117 collectors),
8 boreholes with 17 m spacing and 80 people living in the building. All the input parameters
can be found in Appendix II. Figure 37 shows the heat demanded and heat supplied in the
system, and as can be seen, the heat pump delivers what the building demands every month.
Figure 37: Heat demand and supplied heat in study case
Figure 38 shows the PVT thermal energy distribution to the left. This graph shows the how
much of the thermal production from the PVT that goes to the HP and how much that goes to
the borehole field. In the right graph, the evaporator demand is added to the Figure. The
evaporator demand (light orange bars) represent the thermal energy demand for the HP which
in systems without PVT is completely covered by the borehole field. In total, the PVT thermal
production covers 35.5% of the evaporator demand, which is 167 MWth per year, and during
June, July and August, the PVT thermal energy can cover all of the evaporator demand.
Figure 38: Thermal energy flows in study case
52
Figure 39 shows the injected and extracted heat in the boreholes as well as the net value. The
negative bars represent the extracted heat, the positive bars the injected heat and the line the
net value. The system extracts the most during the winter months and injects most during
summer months, which can be explained by the low thermal production from the PVT during
winter while the heat demand is highest. Respectively, the highest PVT thermal production
occur during the summer months when the heat demand is low and the HP turns off, which
leads to regeneration in the borehole field. In total, the extracted heat over a year is 150 MWth
and the injected heat is 36 MWth.
Figure 39: Extracted and injected heat in boreholes for the study case
When instead looking at the electricity in the system, Figure 40 shows the electric demand
covered by the PV electricity production for each month during one year. During May-Sep, the
PV is overproducing electric energy. From this it can be assumed that the primary electricity
source for the HP during these months are the own-produced PV electricity, while the
overproduction goes to other electricity needs in the building or is sold to the grid.
Figure 40: Electric energy flows
53
7.2 Parametric Study
For the study case borehole parametrics, the same structure as in the Lifetime Parametric Study
was used. However, in this study, other borehole counts and spacings were investigated since
less boreholes are required in the system and the available drilling area is different from the
base case. In addition, the borehole counts and spacing parametrics were combined in different
combinations. The parametrics for this parametric study are presented in Table 17. The
borehole parametrics are presented with 0 and 144 PVT collectors in order to compare the
impact of adding PVT to the system.
Table 17: Borehole parametrics study case
Count Spacing (m) Depth (m) Total length (m)
4 17-12 (5 m step) 250 1000
6 17-12 (5 m step) 250 1500
8 17-12 (5 m step) 250 2000
In Figure 41, the SPF4 for the borehole count parametrics with and without PVT are presented.
As expected, the SPF4 decreases for reduced borehole counts. However, the reduction is smaller
when adding PVT to the system. For example, the same SPF is obtained after 20 years for 8
BH – 0 PVT system as for the 6 BH – PVT system which demonstrates the advantage of
implementing PVT and the opportunity of reducing the borehole count from 8 to 6 boreholes.
The same pattern is visible for the 6 BH – 0 PVT and the 4 BH – PVT system as they approach
the same SPF in year 20. In addition, the SPF4 is kept more constant during the whole
simulation period for all systems with PVT compared to the system without.
Figure 41: SPF for different borehole counts
Figure 42 shows the maximum, minimum and average outlet temperatures in the boreholes for
a system with 8 boreholes and 17 m spacing, both with and without PVT. When adding PVT
to the system, the maximum outlet temperature increases significantly compared to the system
without PVT. The average and minimum temperatures follow the same pattern. In year 20, the
54
average temperature during peak heating season is approximately 2 °C higher for the system
with PVT.
Figure 42: Borehole outlet temperature with and without PVT - 8 boreholes
Figure 43 shows the SPF4 for a system with 8 boreholes and different spacings, with and
without PVT. Compared to the borehole count, the difference is not as significant. However, it
is clear that the implementation of PVT has a positive impact on the SPF4. Moreover, it can be
concluded that the system is not as negatively affected by reduced spacing when having PVT
since the reduction in SPF is more distinct with reduced spacing for the systems without PVT.
For all spacings, the system with PVT maintains a higher SPF4 compared to the system without.
For the system with 7 m spacing and implemented PVT, the SPF4 reaches 3.33 in year 20. The
SPF4 for the corresponding system without PVT reaches 3.07, which implicates an increase of
8.5% for the system with implemented PVT.
Figure 43: SPF for different spacing
55
The borehole outlet temperature for a system with 8 boreholes and 12 m spacing are shown in
Figure 44. As for the SPF, it is clear that the implemented PVT has a positive impact on the
temperature. As for the borehole count parametrics, the difference between the average
temperature during peak heating season is about 2 °C when comparing the systems with and
without PVT. However, the difference during summer reaches about 4°C for the same year.
Figure 44: borehole outlet temperature with and without PVT - 12 m spacing
Figure 45 present the SPF4 when the borehole count and spacing are reduced simultaneously,
which means that the total drilling area is reduced. To see the impact of the PVT, these figures
show the SPF4 for combination of decreased spacing and length of the borehole field, both with
PVT, which is the graph on the left-hand side, and without PVT on the right-hand side. The x-
axis represents the number of boreholes and the y-axis represents the spacing between the
boreholes. The colors represent ranges of SPF4, and both graphs are colored equally for the
same SPF4 ranges. As can be seen, the systems without PVT have an increased distribution of
SPF4 which shows that they are more sensitive for decreased spacing and length. The highest
SPF4 is reached in all system combinations with PVT. Having 8 boreholes and 17 m spacing
without PVT is in the same SPF4 range as having 6 boreholes and 12 m spacing when PVT is
added to the system. This again shows the great impact of adding PVT to ground source heat
pump systems.
56
Figure 45: 2D contour plots for combined borehole count and spacing with PVT (left) without PVT (right)
Table 18 shows the total drilling area for the different borehole field combinations. Going back
to the same example as just mentioned with the reduction from 8 boreholes with 17 meters
spacing without PVT to 6 boreholes with 12 meter spacing with PVT, these systems gives the
exact same SPF4 after 20 years. This means that the total drilling area can be reduced with
approximately 580 m2 for this building case when PVT is added to the system. Again, that
shows the potential for these combined PVT and ground source heat pump systems, especially
in densely populated areas where many multifamily houses with limited area for drilling are.
Table 18: Corresponding borehole field area for parametric study in study case
867 m2 578 m2 289 m2 17
432 m2 288 m2 144 m2 12
147 m2 98 m2 49 m2 7
8 6 4 Count/
Space
In the Microsoft Excel dashboard created for data processing, the total life cycle cost for the
system is one of the treated outputs (see Appendix IV for the definition of TLCC). For this
reason, Figure 46 presents the TLCC for the systems with PVT as well as for the systems
without PVT for 4, 6 and 8 boreholes, all with 12 m spacing. In the calculations, no subsidies
for PVT are considered. Moreover, the TLCC for district heating since that is the dominating
heating source in MFH today. As can be seen, the TLCC increases when PVT is added to the
system for all borehole field combinations. This has to do with the high price of the PVT
collectors. However, it should be mentioned that when adding PVT to the system, the cost for
the electricity demand in the system decreases as the electric output from the PVT is used
directly in the system. Moreover, the efficiency of the system is also increased when PVT is
included. Although, as can be seen in Figure 46, the added cost for the PVT collectors are not
returned by the electricity savings when comparing the systems with corresponding system
without PVT. However, it should be mentioned that adding PVT allows buildings with area
constraints to install heat pump systems, which is cheaper than other alternatives. For example,
57
as can be seen in the figure, all systems with and without PVT have lower TLCC than district
heating. This highlight the advantage of heat pump-based systems.
Also, it should be mentioned that the price for the land area of the borehole field is not included
in these calculations. If it was included, the price differences with and without PVT would
decrease. However, it can be concluded that a reduction in price of the PVT is necessary for
these systems to become economically favorable compared to system with heat pumps only.
Figure 46: TLCC for systems with and without PVT
8. Discussion
The goal with this study is to develop a commercial model that is flexible, user-friendly and
time efficient. Although, it is important to mention the complexity of the model, even though
it is simplified in many ways. The CGM requires very many inputs that most likely differ more
or less from real world cases, which proves the need to test the variables used. However, there
are too many potential variables to test and outside the scope of this study.
The advantage of using TRNSYS is the high level of precision and the ability to capture
transient behavior. However, it is a demanding challenge to create a flexible, user-friendly and
time efficient model while keeping the high level of precision and transient behavior. During
the model development, many options have been studied to succeed in maintaining a high level
of accuracy but at the same time achieve the goals of time efficiency, ease of use and flexibility.
However, there are already very accurate models that are very time consuming and relatively
inflexible. Therefore, flexibility and time efficiency have been prioritized in the first place, but
without significantly reducing precision.
8.1 Strengths of CGM
The main strength with the CGM is the ability to simulate PVT + GSHP systems in a time
efficient and flexible way. A simulation of 20 years with model outputs and associated
58
technical and economic parameters can be achieved in 120 seconds, which is a time reduction
of 5 hours and 20 minutes compared to the BM. This makes it easier to simulate PVT + GSHP
systems in a more commercial and time-efficient way, and thereby contribute to the PVT +
GSHP field and increase the awareness of the technical and economic benefits of the
technology. A simple way to simulate PVT + GSHP system will hopefully simplifies
establishment in the commercial sector and not only in research.
Further, several features for flexibility has been implemented. The user can choose between
310 geographical locations in Sweden, with data from SMHI. Further, the heating load for the
desired building is arranged as an input, which implicates great flexibility in building boundary
conditions. This opportunity can also integrate the CGM in existing workflows where other
tools are used to simulate building loads. Moreover, the ability of using several arrays in
different directions are a feature that improves the flexibility of the PVT. This also applies to
the possibility to scale the heat pump in an easy way, by changing only one parameter.
8.2 Weaknesses of CGM
In order to enable a time efficient model, the time step was increased from three minutes to one
hour. However, it is important to mention that the transient behavior of the system is lost to
some extent as a result of this change. Which in turn leads to limitations with the CGM,
especially in regards to the DHW tank. In order to keep the DHW tanks and partly capture the
transient behavior, the time step need to be reduced to 12 minutes, which in turn increases the
simulation time considerably. Therefore, it was concluded to keep the time step of one hour
and simplify the DHW by removing the tank.
Further, the PVT collector causes obstacles in achieving flexibility. The goal was to use a PVT
collector in TRNSYS with high accuracy that requires input data that is available on
specification sheets. However, such a type is not available in TRNSYS today. Therefore, the
CGM is limited to one known PVT collector type with fixed inputs.
Moreover, the sky temperature, air pressure and ground reflectance are not included in the
weather files from SMHI which entails a weakness of the CGM since these parameters are
calculated based on assumptions and equations.
8.3 Future work
It is important to mention that the CGM is verified using a single research model. It would be
desirable to verify the CGM to several research models in order to capture possible deviations.
However, such an additional verification model could not be accessed in this study. Even better
would be to verify the model using a real case. Although, such a case is not available. However,
an additional verification is necessary to fully confirm that the CGM is working as expected.
Especially in regards to the injected and extracted heat to/from the boreholes and the
differences in SPF between the CGM and BM for increased borehole fields.
59
From this work, it was found that there is need for a more detailed investigation of the PVT
modelling in TRNSYS. To be able to change the type of PVT collector, a library of different
types of collectors with the specific parameters for Type 560 has to be conducted. Another
option is to make a new TRNSYS type for PVT which requires less inputs but that has greater
potential than for example Type 50. This is outside the scope of this thesis and is assigned for
future work. However, it is an important part of fully achieving the goal of this study.
Moreover, this study only considers unshaded roofs, which is a development potential in order
to achieve a flexible model.
For this type of simulations, it would be desirable to have a weather database with all necessary
weather data to minimize the possible source of errors from assumptions and models. However,
if a PVT type that does not require that many inputs is used, the need of such a weather database
would decrease. Although, for future model simulations, it would be desirable to have all data
from the same source in order to be consistent in comparisons and analyzes.
In order to fully achieve the goal of this study, it would be desirable to package the model into
a stand-alone tool. It would be possible to perform a packaging at this stage. However, the
CGM include some parts that require further work at this stage and therefore, it is desirable to
complete these parts before continuing with the packaging.
9. Conclusion
From this study, a model for commercial use was developed for design and simulation of PVT
+ GSHP systems in building applications. The goal of making a flexible, time efficient and
user-friendly model was achieved with overall good results for the investigated parameters
compared to the BM. The CGM can be considered time efficient, flexible and easy to use when
comparing it to the BM. However, there are opportunities for increasing the flexibility and
user-friendliness further, which is assigned for future work.
60
References Abu-Rumman, M., Hamdan, M., Ayadi, O., 2020. Performance enhancement of a
photovoltaic thermal (PVT) and ground-source heat pump system. Geothermics, May,
Vol 85. doi: https://doi.org/10.1016/j.geothermics.2020.101809
Acuña, J. 2013. Distributed Thermal Response Tests – New Insights on U-pipe and Coaxial
Heat Exchangers in Groundwater-Filled Boreholes. KTH Royal Institute of
Technology
Algarni, S. and Nutter, D. 2015. Survey of Sky Effective Temperature Models Applicable to
Building Envelope Radiant Heat Transfer.
https://www.researchgate.net/publication/282972927_Survey_of_Sky_EffectiveTemp
erature_Models_Applicable_to_Building_Envelope_Radiant_Heat_Transfer
Andrews, J.W., 1981. Ground coupled solar heat pumps: analysis of four options. 4th ASME
Solar Energy Division Conference. American Society of Mechanical Engineers, Reno,
Nevada
Berdahl, P. and Fromberg, R. 1982. The thermal radiance of clear skies. Solar Energy. Vol.
29(4). pp. 299-314.
Bhandari, M., Shrestha, S., New, J. 2012. Evaluation of weather datasets for building energy
simulation. Energy and Building. Vol 49, pp. 109-118.
Björk, E., Acuña, J., Granryd, E., Mogensen, P., Nowacki, J-E., Palm, B. and Weber, K.
2013. Bergvärme på djupet. US-AB. Stockholm, Sweden. ISBN: 978-91-7501-754-9
Bunea, M., Eicher, S., Hildbrand, C., Bony, J., Perers, B., & Citherlet, S., 2012. Performance
of solar collectors under low temperature conditions: Measurements and simulations
results, in: Eurosun 2012 - ISES-Europe Solar Conference. Rijeka, Croatia.
Cimmino, M., Eslami-Nejad, P. 2017. A simulation model for solar assisted shallow ground
heat exchangers in series arrangement. Energy and Buildings, Vol 157, pp. 227-246.
doi: https://doi-org.focus.lib.kth.se/10.1016/j.enbuild.2016.03.019
Cremers, Jan. et.al. 2015. Experimental Analyses of Different PVT Collector Designs for
Heating and Cooling Applications in Buildings. Energy Procedia, November,
Vol 78, pp. 1889-1894. doi: https://doi.org/10.1016/j.egypro.2015.11.356
Deason, J., Borgeson, M. 2019. Electrifiction of buildings: Potential, Challenges and
Outlook. Current sustainable/renewable energy reports. Vol 6(4), pp. 131-139
61
De Rosa, M., Ruiz-Calvo, F., Corberán, J.M., Montagud, C., Tagliafica, L.A., 2014.
Borehole. modelling: a comparison between steady-state model and a novel dynamic
model in a real ON/OFF GSHP operation. Journal of Physics: Conference Series 547
012008
De Rosa, M., Ruiz-Calvo, F., Corberán, J.M., Montagud, C., Tagliafica, L.A., 2015. A novel
TRNSYS type for short-term borehole heat exchanger simulation: B2G model.
Energy Conversion and Management. Vol 100, pp. 346-357. doi:
https://doi.org/10.1016/j.enconman.2015.05.021
Fanchi, J.R. 2002. Chapter 11 – Modern Reservoir Characterization Techniques. Shared
Earth Modeling, pp. 182-198. Doi: 10.1016/B978-075067522-2/50011-2
Gehlin, S. 2016. 11 – Borehole thermal energy storage. Advances in Ground-Sourve Heat
pump Systems. pp. 295-327.
Geng, Y., Chen, W., Liu, Z., Chiu, A.S.F, Han, W., Liu, Z., Zhong, S., Qian, Y., You, W.,
Cui, X. 2017. A bibliometric review: Energy consumption and greenhouse gas
emissions in the residential sector. Journal of Cleaner Production: Vol. 159, pp. 301-
316. doi: https://doi.org/10.1016/j.jclepro.2017.05.091
Grassi W. 2018. Types of Compression Heat Pumps and Their Main Components. Heat
Pumps, pp.15-71. Green Energy and Technology. Springer, Cham. doi:
10.1007/978-3-319-62199-9_2
Haller, M.Y., Carbonell, D., Mojic, I., Winteler, C., Bertram, E., Bunea, M., Lerch, W.,
Ochs, F., 2014. Solar and heat pump systems - summary of simulation results of the
IEA SHC task 44/HPP annex 38, in: 11th IEA Heat Pump Conference. Montreal, pp.
1–12
Haller, M.Y., Bertram, E., Dott, R., Afjei, T., Carbonell, D., Ochs, F., Heinz, A., Sunliang,
C., Siren, K., 2015. Components and thermodynamic aspects, in: Solar and Heat
Pump Systems for Residential Buildings. wiley, pp. 23–62. doi:
10.1002/9783433604830.ch03
Hellström, G. 1989. Duct ground heat storage model - Manual for computer code.
Department of Mathematical Physics, University of Lund, Sweden.
IEA. n.d. Task 60 - Application of PVT collectors. [online] Available at:
https://task60.iea-shc.org/ [Accessed 2021-01-29]
IEA HPT. The heat pump - a Swedish success story! [online] Available at:
https://heatpumpingtechnologies.org/news/1/53749/ [Accessed 2021-03-12]
62
IEA SHC Task 60. 2020. Technology Position Paper: PVT Collectors and Systems. [pdf]
Available at: https://task60.iea-shc.org/Data/Sites/1/publications/IEA-SHC-Task60-
PVT-Technology-Position-Paper.pdf [Accessed 2021-02-01]
IRENA, 2019. Future of Solar Photovoltaic: Deployment, investment, technology, grid
integration and socio-economic aspects (A Global Energy Transformation: paper).
International Renewable Energy Agency. Abu Dhabi.
Kamel, R.S., Fung, A.S., Dash, P.R.H., 2015. Solar systems and their integration with heat
pumps: A review. Energy Build. 87, pp. 395–412. doi:10.1016/j.enbuild.2014.11.030
Kim, Jin-Hee and Kim, Jun-Tae. 2012. Comparison of Electrical and Thermal Performances
of Glazed and Unglazed PVT Collectors. International Journal of Photoenergy, vol.
2012, Article ID 957847, 7 pages, 2012. https://doi.org/10.1155/2012/957847
Klein, S., Beckman, WA., Mitchell, J., Duffie, JA., Freeman, T. 2009. TRNSYS 17, A
Transiet System Simulation Program 2009.
Konsumenternas Energimarknadsbyrå. 2020. Vad är fjärrvärme? [online] Available at:
https://www.energimarknadsbyran.se/fjarrvarme/vad-ar-fjarrvarme/ [Accessed 2020-
05-13]
Lamnatou, Chr. & Chemisana, D. 2017. Photovoltaic/thermal (PVT) systems: A review with
emphasis on environmental issues. Renewable Energy, May, Vol 105, pp. 270-287.
doi: https://doi.org/10.1016/j.renene.2016.12.009
Liu, B.Y.H, Jordan, R.C. 1963 The Long-Term Averge Performance Of Flat-Plate Solar-
Energy Collectors. Solar Energy Vol. 7 (2), pp.53-74.
Lämmle, M., Herrando, M and Ryan, G. 2020. Basic concepts of PVT collector technologies,
applications and markets. SHC Task 60/Report D5.
Madani, H. Claesson, J., Lundqvist, P. 2011. Capacity control in ground source heat pump
systems. Part I: modelling and simulation. International journal of refrigeration. Vol
34 (6), pp. 1338-1347
Mazzoti, W., Acuña, J., Lazzarotto, A., Palm, B., 2018. Deep Boreholes for Ground-Source
Heat Pump – Effsys Expand Final Report.
Michael, J.J., Iniyan, S., Goic, R. 2015. Flat plate solar photovoltaic-thermal (PV-T) systems:
A reference guide. Renewable and Sustainable Energy Reviews. Vol 51, pp. 62-88.
doi: https://doi.org/10.1016/j.rser.2015.06.022
63
Mowry, G.R. 1964. Solar energy supplemented rural-home heat pump. Solar Energy, Vol
8(1), pp. 12-16.
Nord Pool Spot, 2020. Elspot Day-Ahead Price. Available at:
https://www.nordpoolgroup.com/ [Accessed 07-05-2021]
Nordell, B.O., Hellström, G. 2000. High temperature solar heated seasonal storage system
for low temperature heating of buildings. Solar Energy, Vol 69(6), pp 511-523
Nowak, H. 1988. The sky temperature in net radiant heat loss calculations from low-sloped
roofs. Infrared Physics, Vol 29(2-4), pp. 231-232. doi: https://doi.org/10.1016/0020-
0891(89)90055-9
Pahud, D. 2000. Central solar heating plants with seasonal duct storage and short-term water
storage: design guidelines obtained by dynamic system simulations. Solar Energy,Vol
69(6), pp. 495-509. doi: https://doi.org/10.1016/S0038-092X(00)00119-5
Pressiani, M. 2016. Photovoltaic and thermal hybrid solar collectors: TRNSYS analysis and
possible improvements. Politecnico Milano.
Pärisch, P., Mercker, O., Oberdorfer, P., Bertram, E., Tape, R., Rockendorf, G., 2015. Short-
term experiments with borehole heat exchangers and model validation in TRNSYS.
Renewable Energy. Vol 74, pp. 471-477. Doi:
https://doi.org/10.1016/j.renene.2014.07.052
Rees, S.J. 2016. An introduction to ground source heat pumping technology. Advances in
ground source heat pump systems. pp 1-25. Available at:
https://www.sciencedirect.com/science/article/pii/B9780081003114000017 [Accessed
2020-11-13]
Regeringen. 2020. Sveriges integrerade energi- och klimatplan. [pdf] Available at:
https://ec.europa.eu/energy/sites/ener/files/documents/se_final_necp_main_se.pdf
Saba, S., Slim, R., Palandre, L., Clodic, D. 2009. Inventory of Direct and Indirect GHG
Emissions from Stationary Air Conditioning and Refrigeration Sources, with Special
Emphasis on Retail Food Refrigeration and Unitary Air Conditioning. State of
California Air Resources Board. California, USA.
Sanner, B., 2017. Ground Source Heat Pumps – history , development , current status , and
future prospects. 12th IEA Heat Pump Conference 2017 1–14.
64
Shah, S.K., Aye, L., Rismanchi, B., 2020. Simulated performance of a borehole-coupled heat
pump seasonal solar thermal storage system for space heating in cold climate. Solar
Energy, Vol 202, pp. 365-385. doi:
https://doi-org.focus.lib.kth.se/10.1016/j.solener.2020.03.111
SMHI. 2016. Nya klimatdatafiler för beräkning av byggnaders energiprestanda. Available
at: https://www.smhi.se/professionella-tjanster/professionella-tjanster/fastighet/nya-
klimatdatafiler-for-berakning-av-byggnaders-energiprestanda-1.85028 [Accessed
2021-03-27]
Sommerfeldt, N., Ollas, P., 2017. Reverse engineering prototype solar PV/thermal collector
properties from empirical data for use in TRNSYS type 560. In: Proc. of the ISES
Solar World Congress 2017. ISES, AbuDhabi.
https://doi.org/10.18086/swc.2017.18.11
Sommerfeldt, N. & Madani, H. 2016. Review of solar PV/Thermal plus ground source heat
pump system for european multi-family houses. International Solar Energy Society -
Conference Proceedings, Eurosun, Palma de Mallorca
Sommerfeldt, N. & Madani, H. 2018. Ground Source Heat Pumps for Swedish Multi-Family
houses. effsys expand (Energimyndigheten)
Sommerfeldt, N. & Madani, H. 2019. In-depth techno-economic analysis of PV/Thermal plus
ground source heat pump systems for multi-family houses in a heating dominated
climate. Solar Energy, Vol 190, pp. 44-62. doi:
https://doi-org.focus.lib.kth.se/10.1016/j.solener.2019.07.080
Sommerfeldt, N; Madani, H; Beltran, F. 2020. Solar PVT for heat pumps: Collector
development, systems integration, and market potential, in: International Conference
by IBPSA-Nordic. OsloMet.
Stockholm Exergi. 2021. Pris och avtal bostadsrättsföreningar [online]. Available at:
https://www.stockholmexergi.se/fjarrvarme/bostadsrattsforening/vadkostardetbostadsr
attsforening/ [Accessed 2020-05-13]
Swedish Energy Markets Inspectorate. 2020. The Swedish Electricity and Natural gas market
2019. (R2020:07) Eskilstuna, Sweden
Swinbank, W.C. 1963. Long-wave radiation from clear skies. Royal Meterological Society.
Vol.89 (381) p.339-348
Terrell, R.E. 1979. Performance and analysis of a “series” heat pump-assisted solar heated
residence in Madison, Wisconsin. Solar Energy. Vol.23(5), pp. 451-453
65
Wang, Y., Zhang, Y., Hao, J., Pan, H., Ni, Y., Di, J., Ge, Z., Chen, Q., Guo, M. 2021.
Modeling and operation optimization of an integrated ground source heat pump and
solar PVT system based on heat current method. Solar Energy. Vol.218, pp. 492-502.
Weiss, W., Spörk-Dür, M. 2019. Solar Heat Worldwide. IEA Solar Heating and Cooling
Programme. Leobersdorf, Austria.
66
Appendix I – Base Case parameters
Part in model Model inputs Value
Weather Location (310 sites in Sweden) Stockholm
Space Heating Load file in kWh/h for one year (csv) -*
Domestic Hot Water Number of people living in the building
Liters per person and day (l/person/day)
60
85
PVT (Array 1)
Number of collectors 50
Slope (°) 20
Roof direction (azimuth angle) (°) 0
PVT (Array 2)
Number of collectors
Slope (°)
Roof direction (azimuth angle) (°)
0
-
-
PVT Loop
Mass flow (liter/m2/h)
Cut in temperature (°C)
Cut out temperature (°C)
20
6
1
Borehole field
Borehole depth (m)
Borehole diameter (m)
Borehole number
300
0.15
12
Borehole spacing
U-pipe inner diameter (m)
U-pipe outer diameter (m)
Storage thermal conductivity (W/m*K)
Storage heat capacity (W/m3/K)
Fill thermal conductivity (W/m*K)
Fluid specific heat (kJ/kg*K)
Fluid density kg/m3
Initial surface temperature of storage volume (°C)
Initial thermal gradient of storage volume (-)
Thermal conductivity of layer (W/m*K)
Heat capacity of layer (W/m3/K)
20
0.036
0.04
2.8
560
1
3.75
1055
7
0
2.8
560
Heat pump Rated Peak power 78
*The supplied heat to the space heating in the BM is used as the SH load in the CGM. This is
used in order to simulate exactly the same heating load as in the BM in order to make a fair
comparison and verification.
67
Appendix II – Study Case parameters
Part in model Model inputs Value
Weather Location (310 sites in Sweden) Nyköping
Space Heating Load file in kWh/h for one year (csv) -
Domestic Hot Water Number of people living in the building
Liters per person and day (l/person/day)
80
55
PVT (Array 1)
Number of collectors 59
Slope (°) 20
Roof direction (azimuth angle) (°) -12
PVT (Array 2)
Number of collectors
Slope (°)
Roof direction (azimuth angle) (°)
58
20
-90
PVT Loop
Mass flow (liter/m2/h)
Cut in temperature (°C)
Cut out temperature (°C)
20
6
1
Borehole field
Mass flow (l/s/borehole)
Borehole depth (m)
Borehole diameter (m)
Borehole number
0.7
250
0.115
8
Borehole spacing (m)
U-pipe inner diameter (m)
U-pipe outer diameter (m)
Storage thermal conductivity (W/m*K)
Storage heat capacity (W/m3/K)
Fill thermal conductivity (W/m*K)
Fluid specific heat (kJ/kg*K)
Fluid density kg/m3
Initial surface temperature of storage volume (°C)
Initial thermal gradient of storage volume (-)
Thermal conductivity of layer (W/m*K)
Heat capacity of layer (W/m3/K)
17
0.0423
0.0450
2.8
560
1
3.75
950
7
0
2.8
560
Heat pump Rated Peak power 60
68
Appendix III – TRNSYS types
Components Type
PVT/BH loop
Borehole Field 557a
Heat exchanger PVT/BH 650
PVT collector 560
On/Off differential controller PVT 2b
Controlled flow diverter 11f
Tee piece 11h
Circulation pump PVT loop 740
Pipe/Duct 31
DHW + SH
Input value recall (load memory) 93
Pipe/Duct 31
Data reader SH (.csv) 9a
Data reader DHW (.txt) 9c
Heat Pump
Circulation pump BH to Evaporator 742
Pipe Pressure Drop Calculator 586
Multi-Dimensional Data Interpolation (Performance Maps) 581b
Weather
Data Reader 9e
Solar Radiation Processor 16a
69
Appendix IV – Model outputs For both the technical and economic inputs, there are default values presented for the user to
choose.
The model provides both technical and economic outputs, which can be used to characterize
the performance of the model and to more easily be able to compare the model to other methods
used for similar applications. The outputs are presented in this chapter and the table below
presents the parameters used in the definitions of equations. The Figure below presents a
visualization of where the technical parameters can be found in the system configuration.
Symbol Description Symbol Description
𝑬𝑷𝑽,𝒔𝒄 PV generation used by the HVAC 𝑰𝑷𝑽𝑻 Investment PVT
𝑬𝑯𝑷 Electric energy to the compressor in the
HP 𝑶𝑴𝑬𝑳 Operation and maintenance electricity
purchases
𝑬𝑩𝑩 Electric energy to the backup boiler 𝑬𝑺𝑯𝑷 Electricity for the operation of PVT and
HP
𝑬𝒑,𝒔𝒓𝒄 Electric energy to the borehole circuit
pump 𝑷𝒆𝒍,𝒓 Retail price
𝑬𝒑,𝒔𝒏𝒌 Electric energy to heat delivery pump 𝑬𝒑𝒗,𝒐𝒑 Overproduction of electricity sold to the
grid
𝑷𝒆𝒍,𝒔 Wholesale price
𝑬𝒑,𝑷𝑽𝑻 Electric energy to PVT circuit pump d Discount rate
𝑬𝑷𝑽,𝒕𝒐𝒕 Total PV electricity production 𝑶𝑴𝑬𝑸 System maintenance for the heat pump
and solar equipment
𝑸𝒅𝒉𝒘 Supplied heat to DHW 𝑹𝑽𝑷𝑽𝑻 Residual value for PVT
𝑸𝒔𝒉 Supplied heat to space heating 𝑹𝑽𝑩𝑯 Residual value for BH
𝑰𝑯𝑷 Investment for HP
𝑰𝑩𝑯 Investment for BH
70
Total Electricity Demand
Total electricity demand is the total electricity required to operate the PVT+GSHP system. It
includes the electricity used to drive the compressor in the heat pump and other necessary
devices in the heating system, such as pumps and heaters. The electricity demand is defined by
Equation 12.
𝐸𝑡𝑜𝑡 = 𝐸𝐻𝑃 + 𝐸𝐵𝐵 + 𝐸𝑝,𝑠𝑟𝑐 + 𝐸𝑝,𝑠𝑛𝑘 + 𝐸𝑝,𝑃𝑉𝑇 Eq.12
Self-consumption
Self-consumption (SC) gives an indication of how much of the PV production from the PVT
system that is self-consumed by the system, in this case the heat pump. SC is useful together
with SF as they can support each other to give an indication of the most optimal system, where
as much electricity as possible from the PVT system is sent, in this case, to the heat pump. SC
is defined by Equation 13.
𝑆𝐶 = ∑𝐸𝑖𝑃𝑉,𝑠𝑐
𝐸𝑖𝑃𝑉,𝑡𝑜𝑡
8760
𝑖=1
Eq.13
Self-sufficiency
Self-sufficiency (SS) indicates how much of the demand that is covered by the solar
technology, in this case the PVT system. The SS is based on Sommerfeldt (2018), where the
same system configuration as in this project is handled. In this system configuration, both the
SH and the DHW are provided by the HP and backup boilers. These components use electricity
as its energy source, which means that the SS can be calculated with electric energy only. The
importance in this calculation of SS however, is that the heat pump is the first prioritization for
the PV electricity which means that only the self-consumed PV electricity in the heat pump is
to be considered, not the PV production that is further utilized in buildings or sold to the grid.
SS is defined in Equation 14.
𝑆𝑆 = 𝐸𝑃𝑉,𝑠𝑐
𝐸𝑡𝑜𝑡
Eq.14
Solar Fraction
Solar Fraction (SF) shows the fraction of the PV production compared to the total electricity
demand in the system. Compared to SS, this shows how much electricity the PVT system
produces, and not how much of that electricity that the system uses. In other words, the SF
takes both the used and sold PVT electricity into account while SS only considers the self-
consumed PV production. The Solar Fraction is expressed in Equation 15.
𝑆𝐹 = 𝐸𝑃𝑉,𝑡𝑜𝑡
𝐸𝑡𝑜𝑡
Eq.15
71
Seasonal Performance Factor
Seasonal Performance Factor (SPF) is used to evaluate the efficiency of system. Equation 16
(SPF1) presents the Heat Pump SPF and Equation 17 (SPF4) presents the heating system SPF.
SPF1 expresses the ratio of the condenser heat and the compressor power over a year, and SPF4
expresses the total heat demanded by the building to the total electricity consumption of the
heating system over a year. The total heat demand includes SH and DHW.
𝑆𝑃𝐹1 =𝑄𝑐𝑜𝑛𝑑
𝐸𝑐𝑜𝑚𝑝
Eq.16
𝑆𝑃𝐹4 =𝑄𝑠ℎ + 𝑄𝑑ℎ𝑤
𝐸𝑡𝑜𝑡
Eq.17
Total Life Cycle Cost
Total life cycle cost (TLCC) is used to compare the cost of the PVT + GSHP system to other
systems and is described by Equation 18. The main components of the TLCC are investment
(IX), operation and maintenance (OMX) and residual value (RVX) and these are applied to the
different components in the systems. IHP, IBH, and IPVT, are the investment cost for the heat
pump, borehole and PVT. 𝑂𝑀𝐸𝑄 is the system maintenance for the heat pump and solar
equipment.
𝑇𝐿𝐶𝐶𝑃𝑉𝑇+𝐺𝑆𝐻𝑃 = 𝐼𝐻𝑃 + 𝐼𝐵𝐻 + 𝐼𝑃𝑉𝑇 + 𝑂𝑀𝐸𝑄 + 𝑂𝑀𝐸𝐿 − 𝑅𝑉𝑃𝑉𝑇 − 𝑅𝑉𝐵𝐻 Eq.18
For the simulations in this report, the heat pump cost is assumed to be 5000 sek/kW The
investment cost for the BH is calculated using a regression model by Mazzoti et al. (2018) that
calculates the borehole field cost based on total length and number of boreholes. The equation
is presented in Equation 19, where H is the borehole depth, and Nb is the number of boreholes.
The regression model does not include value added tax (VAT), therefore, a 25% VAT is added
to the cost.
𝐼𝐵𝐻 = (158 + 3.38 ∗ 10−4𝐻2 + 100)𝑁𝑏𝐻 + 93,000 Eq.19
The investment cost for the PVT is calculated using Equation 20, where CPVT,fix is a fixed
project cost, CPVT,col is the cost for the collector and CPVT,int is the cost of supporting equipment
and installation. For the simulations in this report, the following prices are used; it is assumed
that the fixed cost is 130,000 sek, further the collector price is 4000 sek each and the installation
for each collector is 2400 sek. The prices are given without VAT, therefore, a 25% VAT is
added. However, these parameters are inputs that can be changed by the used in future
simulation if desired.
𝐼𝑃𝑉𝑇 = 𝐶𝑃𝑉𝑇,𝑓𝑖𝑥 + (𝐶𝑃𝑉𝑇,𝑐𝑜𝑙 + 𝐶𝑃𝑉𝑇,𝑖𝑛𝑡) ∗ 𝑁𝑃𝑉𝑇
Eq.20
72
The operation and maintenance cost for the electricity purchases, OMEL, is defined in Equation
21. The electricity purchase includes the required electricity for the heat pump and PVT
operation, ESHP, at the retail price, Pel,r. The overproduction of PV, Epv,op that is sold at the
wholesale price Pel,s is subtracted from the electricity purchase. The operation and maintenance
is calculated with a discount rate, d, of 3%. For the simulations in this report, a retail price of
1 SEK/kWh is used, which is an average contract for the period between 2010-2018 (Swedish
Energy Markets Inspectorate, 2019) with an assumed extrapolated growth rate of 1%. The
electricity sale price is set according to the market price at 0.5 SEK/kWh (Nord Pool Spot,
2021). No PV/PVT subsidies for prosumers, such as green certificates and feed-in bonuses are
accounted for in the sale price.
𝑂𝑀𝐸𝐿 = ∑(𝐸𝑆𝐻𝑃 ∗ 𝑃𝑒𝑙,𝑟) − (𝐸𝑝𝑣,𝑜𝑝 ∗ 𝑃𝑒𝑙,𝑠)
(1 + 𝑑)𝑦
20
𝑦=1
Eq.21
Of all parts in the system, the heat pump has the shortest expected lifetime. To consider that
the PVT and the boreholes have longer lifetimes, and thereby additional values, their
discounted residual values (RV) are subtracted from the TLCC. The RV for the PVT is
calculated from year y to Lpvt, where y is first year after the expected lifetime of the heat pump
and LPVT, is the expected lifetime of the PVT. The equation for the RVPVT is presented in
Equation 22. Likewise, the RV for the boreholes is calculated from year, y, to LHP, where where
y is first year after the expected lifetime of the heat pump and LHP, is the expected lifetime of
the heat pump. The RVs are calculated with a discount rate of 3%. The corresponding equation
is presented in Equation 23.
𝑅𝑉𝑃𝑉𝑇 = ∑(𝐸𝑝𝑣,𝑠𝑐 ∗ 𝑃𝑒𝑙,𝑟) + (𝐸𝑝𝑣,𝑜𝑝 ∗ 𝑃𝑒𝑙,𝑠) − 𝑂𝑀𝐸𝑄,𝑃𝑉𝑇
(1 + 𝑑)𝑦
𝐿𝑃𝑉𝑇
𝑦=𝐿𝐻𝑃+1
Eq.22
𝑅𝑉𝐵𝐻 = ∑𝐼𝐵𝐻
1 − (1 + 𝑑)60/𝑑∗
1
(1 + 𝑑)𝑦
𝐿𝐵𝐻
𝑦=𝐿𝐻𝑃+1
Eq.23
The TLCC of the PVT+GSHP system is compared to the TLCC of the district heating when
delivering the same amount of heat as the PVT+GSHP system. District heating is used since it
is the most common heat supply in MFH in Sweden (Konsumenternas Energimarknadsbyrå,
2020). The TLCC is calculated with Equation 24, where QSH and QDHW are the heat demands
for SH and DHW. The prices are taken from the recent price structure from Stockholm Exergi
(2021) consisting of a fixed annual fee (Cfixed) at 850 sek/kW for peak demand and variable
73
pricing for summer and winter (PDH) at 250 sek/MWh from April-October and 656 sek/MWh
from November-March. Further, the TLCCDH is discounted with a discount rate of 3%.
𝑇𝐿𝐶𝐶𝐷𝐻 = ∑(𝑄𝑆𝐻 + 𝑄𝐷𝐻𝑊) ∗ 𝑃𝐷𝐻 + 𝐶𝑓𝑖𝑥𝑒𝑑
(1 + 𝑑)𝑦
20
𝑦=1
Eq.24
For the economic outputs, the fixed inputs that the user need to change in order to receive the
economic outputs are presented in the Table below.
Parameters Economic inputs
System maintenance OM_eq
Investment cost heat pump I_hp
Investment cost borehole I_bh
Investment cost PVT I_pvt
Lifetime heat pump L_hp
Lifetime borehole L_bh
Lifetime PVT L_pvt
Retail price electricity P_el,r
Wholesale price electricity P_el,s
Discount rate d
74
TRITA -ITM-EX 2021:423
www.kth.se