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Dynamic modeling of seasonal thermal energy storage systems in
existing buildings
Carol Pascual*, Asier Martinez and Maider Epelde
Tecnalia, Energy and Environment Division
20730 Azpeitia
Spain
Roman Marx and Dan Bauer
University of Stuttgart
Institute of Thermodynamics and Thermal Engineering (ITW)
Research and Testing Centre for Thermal Solar Systems (TZS)
70550 Stuttgart
Germany
1. ABSTRACT
This paper presents a system simulation and parametric study of the most standard Seasonal
Thermal Energy Storage System (STES) configuration, hot water tank STES system for district
heating. This configuration is detailed is modeled in detail in TRNSYS, a dynamic simulation
program, in order to integrate the different sub-systems (storage, generation and consumption)
and optimize the overall performance of the whole system for four different climate zones
Southern (Madrid), Northern (Stockholm), Central (Amsterdam) and Eastern Europe (Warsaw).
These locations have been selected as reference locations in order to realize simulations and get
throughout results. The aim of the chosen configuration is to cover the required space heating
demand and domestic hot water preparation (DHW) of 50 retrofitted dwellings (district heating
network) for each location and analyze the different alternatives concerning economic and
technical aspects.
Keywords: Seasonal thermal energy storage system (STES); Dynamic modeling; TRNSYS;
Heat pumps (HP); Solar thermal.
2. NOMENCLATURE
A Collector area [m2]
ATES Aquifer Thermal Energy Storage*
BTES Borehole Thermal Energy Storage
C Thermal Capacity of the heat pump
[kWth]
CSHPSS Central Solar Heating Plants with
Seasonal Storage
* Corresponding author at: TECNALIA, Phone: +34 667119581,
e-mail: carol.pascual@tecnalia.com
DHW Domestic Hot Water
E Electricity consumption
FPC Flat Plate Collectors
F Primary energy savings / CO2 emissions
reduction [%]
f Primary energy or CO2 eq. emission factors
HP Heat Pump
Inv Investment cost [€]
PTES Pit Thermal Energy Storage systems
Q Energy delivered or consumed [kWh]
Proceedings from The 55th Conference on Simulation and Modelling (SIMS 55), 21-22 October, 2014. Aalborg, Denmark
250
STES Seasonal Thermal Energy Storage System
TTES Tank Thermal Energy Storage
V Storage volume [m3]
Z Annual cost [€]
η efficiency
Subscripts:
Aux Auxiliary
Col Collector
Eq Equivalent
Hp Heat Pump
PE Primary Energy
Ref Reference
Th Thermal
3. INTRODUCTION
Energy use in buildings accounts for approximately
40% of EU energy consumption. Energy efficiency
in new buildings is important, but existing building
stock is the main target. Existing buildings,
however, are characterized by particular
requirements and constraints that are not present in
new buildings and that require new developments
and adaptation of existing technologies. In order to
fulfil the most recent EU directives, solutions for a
drastic reduction in primary energy consumption are
required. Space heating and domestic hot water
preparation (DHW) represent the largest part of
energy use in buildings nowadays, thus solar thermal
energy seems to be one of the most promising heat
source.
The technology of large scale seasonal thermal
energy storage has been investigated in Europe (only
north of Europe) since the middle of the 1970´s. The
first demonstration plants were realized in Sweden
in 1978/79. Besides Sweden also Switzerland,
Denmark and Germany investigated STES and built
demonstration plants.
In Germany, eleven large scale Central Solar
Heating Plants with Seasonal Storage (CSHPSS)
demonstration plants have been built since 1996.
They are designed for solar fractions of between 35
and 60% of the total annual heat demand for DHW
and space heating of the connected consumers.
During the past fifteen years of research on thermal
seasonal storage technologies four different types of
different types of storages turned out as main focus
for the ongoing engineering research (Figure 1).
Tank thermal energy storage (TTES): consists of
underground reinforced concrete tank filled with
water, connected to charging and discharging
loops.
PIT thermal energy storage (PTES): is made of
an artificial pool filled with storage material
closed by a lid.
Borehole thermal energy storage (BTES): In this
kind of storage, the heat is directly stored in the
underground. Ducts are inserted into vertical
boreholes to build a huge heat exchanger.
Aquifer thermal energy storage (ATES):
Naturally occurring self-contained layers of
ground water are used for heat storage.
The overall objective of EINSTEIN (Effective
integration of seasonal thermal energy storage
systems in existing buildings) project is the
development, evaluation and demonstration of a low
energy heating system based on Seasonal Thermal
Energy Storage (STES) concept in combination with
heat pumps (HP) for space heating and DHW
requirements for existing buildings to drastically
reduce the energy consumption.
This paper presents the detailed modeling and
parametric study of STES system.
Figure 1: The four sensible thermal energy storage
technologies (source: Solites).
Proceedings from The 55th Conference on Simulation and Modelling (SIMS 55), 21-22 October, 2014. Aalborg, Denmark
251
4. SYSTEM DESCRIPTION
The model of the system will be divided into four
subsystems: Heat generation, heat storage,
distribution and heat consumption. Figure 2 shows
the system concept for the simulations of the STES
system.
Solar collectors deliver heat to the system either by a
direct heat supply to the short buffer tank or by
charging the STES. If there is not solar energy
available to cover the demand, heat can be
discharged from the STES either by an external heat
exchanger or by a heat pump. However, if there is
no solar energy available and the STES is
completely discharged, the energy demand of the
district net will be covered by the auxiliary backup
boiler.
Heat generation:
In this configuration there are three heat generation
sources, flat plate solar thermal collectors, water-to-
water heat pump and the auxiliary boiler.
The solar system consist of a flat plate solar thermal
collector field facing south with a slope of 40° and a
water-glycol circuit connected to an external heat
exchanger with a pump to control the temperature in
the loop.
The heat pump is connected directly to STES as heat
source and short term buffer tank as heat sink. The
heat pump is switched on when there is no solar
energy available to discharge the STES. The electric
driven compression heat pump design is based on a
modified performance map from the data of the heat
pump WRL400X from Airlan [1]. A Gas boiler is
integrated as auxiliary heating system.
Heat storage:
The choice for a certain type of seasonal storage
mainly depends on the local prerequisites like the
geological and hydro-geological situation in the
underground of the respective construction site.
Above all an economical rating of possible storages
according to the costs per GJ of thermal energy that
can be used from the storage allows the choice of the
best storage technology for every single project. In
this paper TTES is modelled as STES and simulated
because this kind of seasonal storage can be built at
early any place. TTES is connected to solar loop by
an external heat exchanger to be charged and to
another heat exchanger or heat pump to discharge
and provide the required energy to the system.
A short term water tank is also included in the
system in order to facilitate the operation of the heat
pump and decouple the energy production and
consumption.
Heat distribution and consumption:
Distribution system refers to water circuits to cover
DHW and space heating of the district heating
network.
A district of 50 retrofitted residential buildings is
defined for four locations, Amsterdam, Madrid,
Warsaw and Stockholm. The table below shows the
annual demand of each location.
Table 1: Yearly demand of four locations.
The base for the profile is the profile of the IEA
Task 26 building [2]. The load for space heating and
hot water preparation is temporarily distributed by
standardized normal distribution taking the
simultaneity factor for district heating networks [2]
into account. Figure 3 shows the load profile for one
Nº of buildings DH demand / MWh per year
Amsterdam 50 552.40
Stockholm 50 855.91
Madrid 50 370.29
Warszawa 50 692.01
Figure 2: Studied STES system configuration.
Proceedings from The 55th Conference on Simulation and Modelling (SIMS 55), 21-22 October, 2014. Aalborg, Denmark
252
family house in a small district heating network in
Amsterdam. The supply temperature of the district
heating is 70°C.
Figure 3: Hourly demand for one family house in
Amsterdam.
5. IMPLEMENTATION IN TRNSYS
The system described before was modeled using
TRNSYS Version 17, transient thermal energy
modeling software developed at the University of
Wisconsin-Madison [3].
Heat generation:
The flat plate collectors (FPC) are modelled by the
standard type 1a. The FPC is integrated into a solar
primary loop consisting of a pump (type 114) and a
heat exchanger (type 91). And the solar heat is
transferred to other modules at the heat exchanger
(HX solar loop).
The heat pump is based on a modified performance
map from the data of the heat pump WRL400X from
Airlan [1]. To use the data in TRNSYS, type 42b is
selected. The type can interpolate the performance
figures for given outlet respectively input
temperatures of the heat pump on the brine/water
side of evaporator and condenser. In this case the
inlet temperature of evaporator and condenser are
the independent variables and the thermal power of
the condenser and the electrical power of the
compressor are the depending variables. The
modifications of the data have been made in order to
suit the requirements to a heat pump used in a STES
system.
As an auxiliary heating system a boiler is integrated.
Therefore the auxiliary heater model (type 6) is
used. If the temperature is lower than the set point
temperature the heater modulates from 0-100 % of
the maximum heating power to reach the set point
temperature. The efficiency is set to 100 % so that
the energy consumption of the boiler equals the
heating power.
Heat storage:
The multi-port store model (non-standard type 340
[4]) is used for modelling the TTES. For the
simulation direct charging and discharging units are
used called double-ports. The solar yield is charged
into the top of the store using a stratification device.
The return flow for the solar collectors is taken from
the bottom of the store. A temperature sensor at ¼ of
height from the bottom is used as lower input value
for the hysteresis controller of the secondary
collector loop pump. Hence a buffer volume for
solar heat of ¾ of the buffer store is generated
leaving ¼ of the volume for the return flow of the
district heating net at the bottom which is also
connected to a stratification device. The supply flow
for the district heating net respectively the heat
pump (evaporator side) is taken from the top of the
store.
The short term buffer storage is also simulated by
the type 340 multiport store model. The system is
charged on top of the store by the heat pump or
TTES and the return will take from the bottom. The
buffer provides the energy to the distribution loop
from the top of the buffer and the return to the
bottom. The buffer storage is modelled as stratified
and insulated water tank.
Heat distribution and consumption:
The district heating network is modelled by the type
31 either for the supply and return pipe.
A load module is developed to simulate the load side
of the district heating net on building level. The type
9e is used as data reader. The data files used have an
hourly resolution of the thermal heat consumption
(in kWh).
6. RESULTS
For the comparison of the efficiency of different
concepts and parameter sets different characteristic
numbers have been calculated. A definition is
presented in the following.
6.1 Environmental analysis
Described system is environmentally evaluated.
Primary energy saving and CO2 equivalent emission
reduction are taken into account for analyzing
system and calculated using equations (1-2).
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
0 800 1600 2400 3200 4000 4800 5600 6400 7200 8000
De
man
d k
W
Time hours
Proceedings from The 55th Conference on Simulation and Modelling (SIMS 55), 21-22 October, 2014. Aalborg, Denmark
253
∑
∑
∑
∑
(1)
∑
∑
(2)
Reference system consists on a gas boiler with an
efficiency of 0.9. Primary energy factors are 1.3 and
2.3 for gas and electricity respectively and CO2
equivalent emissions 294 and 415 g∙kWh-1
.
6.2 Economic analysis
Main equipment’s investment costs are considered
for calculating the initial system installation
investment cost. Collector field and STES costs
were estimated as [5]. It has been estimated that
average investment costs for a heat pump were on
average 500 € per kWth [6].
(3)
(4)
(5)
The rest of the existing equipment’s costs (pumps,
heat exchangers, valves, etc.) are considered 25% of
total investment and engineering indirect cost
(engineering project, project management,
assurances, etc.) are estimated as 12%.
The annual operation and maintenance cost are
estimated in 1.5% of the investment cost according
to criteria proposed by IEA [7]. Annual costs are
calculated with the next equation for each element:
( ( ) (( ) )) (6)
Where:
i: annual interest rate (3.0%)
ni: equipment lifetime (25, 50 and 20 years for
collectors, STES and Heat pump respectively.)
fope: annual operation and maintenance cost
(0.015 y-1
)
System heat costs are the ratio between annual cost
and system load.
These indicators will be used for considering best
system configuration depending on collector field
area, STES volume and heat pump capacity.
6.3 Simulation Results
For each location one reference system has been
defined. The objective of the reference systems is to
Table 2: Reference cases TRNSYS results and indicator values for 4 locations.
A) Total Generation / MWh 613.90 912.11 431.86 751.68
a.1) System Generation / MWh 359.03 567.48 268.47 427.54
a.1.1) Solar direct / MWh 10.80 1.76% 11.02 1.21% 12.05 2.79% 10.28 1.37%
a.1.2) STES + HP / MWh 348.24 556.46 256.41 417.27
a.1.2.1) Directly STES / MWh 230.04 37.47% 371.66 40.75% 181.33 41.99% 287.30 38.22%
a.1.2.2) HP / MWh 118.20 19.25% 184.80 20.26% 75.08 17.39% 129.96 17.29%
a.2) Auxiliary Boiler / MWh 254.86 41.52% 344.63 37.78% 163.40 37.84% 324.14 43.12%
B) District demand / MWh 552.40 855.91 370.29 692.01
C) System looses / MWh 61.50 10.02% 56.20 6.16% 61.57 14.26% 59.67 7.94%
D) Environmental factors
d.1) Primary Energy Saving / % 55.53 59.05 59.63 54.19
d.2) CO2-equivalent Savings / tons/ y 108.74 171.53 82.25 139.02
E) Economic factors
e.1) Initial Investment / k€ 1093.15 1670.75 493.17 1414.06
e.2) Equipment Anual Costs / k€ 76.77 104.23 39.97 94.69
e.3) System Heat Costs / €∙MWh-1 213.83 183.76 149.24 221.60
Amsterdam Stockholm Madrid Warszawa
Proceedings from The 55th Conference on Simulation and Modelling (SIMS 55), 21-22 October, 2014. Aalborg, Denmark
254
30,3 36,8 44,8 49,2 52,8 42,9 43,6 44,8 45,8 46,7 50,1 48,9 44,8 40,1 39,0
17,3
18,8
17,4
18,820,5
17,8 17,7 17,4 17,2 17,2 8,0 10,1 17,4 26,1 28,2
52,4 44,4 37,8 30,0 26,8 39,3 38,7 37,8 37,0 36,1 41,9 41,0 37,8 33,8 32,8
0%
20%
40%
60%
80%
100%
1 2 Ref. 3 4 1' 2' Ref. 3' 4' 1'' 2'' Ref. 3'' 4''
Madrid System Parametric Study / %
STES + Solar Heat Pump Auxiliary System
24,9 33,6 39,6 43,9 50,2 37,0 37,9 39,6 40,3 42,9 41,0 39,6 37,4 36,5 34,5
17,4
16,2
17,3
21,218,7
13,5 16,3 17,325,8 23,3
13,4 17,3 26,3 28,9 30,6
57,7 50,3 43,1 34,9 31,1 49,5 45,8 43,1 33,9 33,8 45,6 43,1 36,4 34,6 34,9
1 2 Ref. 3 4 1' 2' Ref. 3' 4' 1'' Ref. 2'' 3'' 4''
Warsaw System Parametric Study / %
STES + Solar Heat Pump Auxiliary System
27,7 33,7 39,2 45,7 49,5 36,7 38,8 39,2 40,9 41,6 42,2 39,2 36,6 36,8 37,1
16,215,7
19,317,1
18,4
17,0 15,3 19,3 20,5 21,913,0 19,3 26,7 25,4 28,2
58,4 50,6 41,5 37,2 32,1 46,3 44,2 41,5 38,7 36,5 44,7 41,5 36,7 35,2 34,6
0%
20%
40%
60%
80%
100%
1 2 Ref. 3 4 1' 2' Ref. 3' 4' 1'' Ref. 2'' 3'' 4''
Amsterdam System Parametric Study / %
STES + Solar Heat Pump Auxiliary System
28,5 36,1 41,9 46,7 49,1 37,8 39,7 41,9 43,1 46,7 44,6 41,9 39,3 38,5 38,6
19,4
20,120,3
18,319,5
11,417,2
20,3 23,022,3
12,0 20,3 28,4 29,1 28,8
52,1 43,8 37,8 35,1 31,4 50,8 43,1 37,8 33,9 31,0 43,4 37,8 32,3 32,4 32,7
1 2 Ref. 3 4 1' 2' Ref. 3' 4' 1'' Ref. 2'' 3'' 4''
Stockholm System Parametric Study / %
STES + Solar Heat Pump Auxiliary System
cover 40% of the demand by solar thermal energy,
20% by the heat pump and the rest by the auxiliary
heater.
Table 2 shows the results of simulations and the
values of described indicators for the reference cases
in Amsterdam, Madrid, Warsaw and Stockholm.
The simulations have been run for two years of
operation but the values given represent results for
the 2nd
year.
Figure 4: Collector area, STES volume and HP capacity sensitivity analysis. Covered demand
distribution depending on location and studied combinations.
Table 3: Values for reference systems and studied parameters (Collector area, STES volume and HP capacity
scaled by a linear scaling factor- HP Power Factor).
Amsterdam Solar Col. Area / m2
STES Volume / m3
HP Power Factor
Reference Case 1000 2000 2.00
Case 1, 2, 3, 4 600 - 800 - 1200 - 1400 2000 2.00
Case 1', 2', 3', 4' 1000 1200 - 1600 - 2400 - 2800 2.00
Case 1'', 2'', 3'', 4'' 1000 2000 1.5-2.5-3.0-3.5
Stockholm Solar Col. Area / m2
STES Volume / m3
HP Power Factor
Reference Case 1500 3500 2.00
Case 1, 2, 3, 4 900 - 1200 - 1800 - 2100 3500 2.00
Case 1', 2', 3', 4' 1500 2100 - 2800 - 4200 - 5600 2.00
Case 1'', 2'', 3'', 4'' 1500 3500 1.5-2.5-3.0-3.5
Madrid Solar Col. Area / m2
STES Volume / m3
HP Power Factor
Reference Case 350 600 2.00
Case 1, 2, 3, 4 210 - 280 - 420 - 490 600 2.00
Case 1', 2', 3', 4' 350 360 - 480 - 720 - 840 2.00
Case 1'', 2'', 3'', 4'' 350 600 1.0-1.5-2.5-3.0
Warszawa Solar Col. Area / m2
STES Volume / m3
HP Power Factor
Reference Case 1100 3500 2.00
Case 1, 2, 3, 4 660 - 880 - 1320 - 1540 3500 2.00
Case 1', 2', 3', 4' 1100 2100 - 2800 - 4200 - 5600 2.00
Case 1'', 2'', 3'', 4'' 1100 3500 1.5-2.5-3.0-3.5
Proceedings from The 55th Conference on Simulation and Modelling (SIMS 55), 21-22 October, 2014. Aalborg, Denmark
255
All results have been achieved by running
simulations in 6 minutes time steps.
Figure 5: Collector area influence on primary energy
savings, %.
Figure 6: Influence of STES volume on primary
energy savings, %.
Figure 7: Influence of HP capacity on primary
energy savings, %.
6.4 Parameter sensitivity analysis
For the main parameters - collectors area, STES
volume and heat pump capacity - a sensitivity
analysis was carry out to find the dominating
parameter for the economy and efficiency of the
system. Table 3 shows both, reference and studied
combination of parameters for each location.
Reference systems where designed in order to cover
demand by 40% by solar thermal energy, 20% by
heat pump and the rest 40% by auxiliary heater.
Figure 4 shows the distribution of covering the
demand by the influence of varying the dimension of
Figure 8: System heat cost depending on collector
area, STES volume and HP capacity, Amsterdam.
Figure 9: System heat cost depending on collector
area, STES volume and HP capacity, Stokholm.
Figure 10: System heat cost depending on collector
area, STES volume and HP capacity, Madrid.
Figure 11: System heat cost depending on collector
area, STES volume and HP capacity, Warsaw.
30%
40%
50%
60%
70%
80%
50% 75% 100% 125% 150%
Fsaving depending on Coll. area / %
Amsterdam Stockholm Madrid Warszawa
40%
50%
60%
70%
80%
50% 75% 100% 125% 150% 175%
Fsaving depending on STES volume/ %
Amsterdam Stockholm Madrid Warszawa
40%
45%
50%
55%
60%
65%
70%
40% 60% 80% 100% 120% 140% 160% 180%
Fsaving depending on HP capacity/ %
Amsterdam Stockholm Madrid Warszawa
180
200
220
240
260
280
40% 60% 80% 100% 120% 140% 160% 180%
System Heat cost, Amsterdam €/MWh
Collector area STES Volume HP Capacity
160
170
180
190
200
210
220
40% 60% 80% 100% 120% 140% 160% 180%
System Heat cost, Stokholm €/MWh
Collector area STES Volume HP Capacity
120
130
140
150
160
170
180
40% 60% 80% 100% 120% 140% 160% 180%
System Heat cost, Madrid €/MWh
Collector area STES Volume HP Capacity
180
200
220
240
260
280
40% 60% 80% 100% 120% 140% 160% 180%
System Heat cost, Warsaw €/MWh
Collector area STES Volume HP Capacity
Proceedings from The 55th Conference on Simulation and Modelling (SIMS 55), 21-22 October, 2014. Aalborg, Denmark
256
the main parameters on demand covering
distribution.
The results show that if larger collector areas are
installed less auxiliary energy is needed and primary
energy is saved. By analyzing the STES volume , it
can be obserbed that larger storage volumes lead to
lower auxiliary energy consumption, but the
influence is as pronounced as increasing collector’s
area. Finally, higher heat pump capacity implies the
coverage of a larger percentage of the demand by the
heat pump, reducing both, energy covered by solar
thermal and auxiliary energy. At a certain heat pump
capacity practically no more changes are observed.
Figure 5, Figure 6 and Figure 7 show the influence
of different parameters on the fractional primary
energy saving. The 100% represents the reference
system defined above.
The influence of the collector area on the primary
energy saving is similar for all locations, in Madrid,
Amsterdam and Warsaw the maximum increase
respect the reference system is about 25% whereas
in the case of Stockholm is about 20% (Figure 5).
The influence of the STES volume is smaller than
the influence of the collector area. In the case of
Madrid there is only minor improvement when the
volume increases whereas in the cases of Stockholm,
Amsterdam and Warsaw the primary energy saving
increases about 15% (Figure 6).
The heat pump has a maximum influence on primary
energy of 9% for all the locations (Figure 7).
Increasing values of the assessed parameters would
suppose a reduction of auxiliary energy but
simultaniuosly the initial (and operational) costs
increase as well. Heat cost is used in order to
evaluate increasing collector area, STES volume and
HP capacity and at the optimsed dimension
economic advantage can be identified.
Figure 8, Figure 9, Figure 10 and Figure 11 show the
influence of the above assesed parameters on the
system heat cost of STES system at the four
different locations.
In general, increasing collector area would suppose a
reduction in the system heat cost. For larger
collector areas less auxiliary energy is used, but the
investment is not entirely justified increasing the
collector area. In Amsterdam and Stockholm no
improvement can be observed for cases 3 and 4 (see
Table 3) in heat cost. In case of Warsaw increasing
the area of 20% (comparing to reference system)
would leads to a reduction of heat cost.
Increasing the STES volume results in heat cost
increase in cases of Amsterdam and Madrid. The
results show that the volume of the reference system
is the optimum for Stockholm, and in case of
Warsaw, increasing the volume by 20% from the
reference one results in a reduction of almost 10% in
auxiliary energy use (Table 3) improving the
system’s heat cost and performance.
In case of the HP capacity an increasing of 25%
from reference system is well justified, reducing (or
maintaining in case of Madrid) heat cost of the
system reducing and also primary energy
consumption.
7. CONCLUSIONS
In the presented work, a dynamic simulation of
seasonal thermal energy storage system for a small
district heating network was conducted under
different climate conditions by using TRNSYS. The
results of a sensitivity analysis carried out were
shown.
The most sensitive parameter for the fractional
primary energy saving for all locations is the solar
thermal collector area (Figure 5). The heat delivery
of the solar thermal collectors dominates the
possibility of using the heat pump by utilizing the
solar thermal energy.
Not only primary energy savings have to be taken
into account, being necessary to also assess system
heat cost. The highest primary energy saving with
smaller system heat cost for Amsterdam, Madrid and
Warsaw is the configuration with largest solar
collector area. System with HP pump capacity of
125% from reference is most suitable in Stockholm.
8. ACKNOWLEDGMENT
The research leading to these results has received
funding from the European Commission within
Seventh Framework Programme (FP/2007-2013)
under grant agreement No ENER/FP7/295983
(EINSTEIN). The authors gratefully acknowledge
this support and carry the full responsibility for the
content of this paper.
Proceedings from The 55th Conference on Simulation and Modelling (SIMS 55), 21-22 October, 2014. Aalborg, Denmark
257
9. REFERENCES
[1] AERMEC air conditioning: REVERSIBLE
CHILLER - Technical manual, IWRL2TI.
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[2] Weiss, W.: Solar Heating Systems for
Houses: A Design Handbook for Solar
Combisystems, Earthscan Verlag, Auflage:
illustrated edition, 2003.
[3] A: Klein, S.A. et al, 2010, TRNSYS 17: A
Transient System Simulation Program, Solar
Energy Laboratory, University of
Wisconsin, Madison, USA,
http://sel.me.wisc.edu/trnsys.
[4] Drück, H.; Pauschinger, T.: MULTPORT
Store Model for TRNSYS. User’s manual,
Institute of Thermodynamics and Thermal
Engineering, University of Stuttgart,
Stuttgart, 1997.
[5] Winter, W.; Haslauer, T.; Obernberger, I.:
Untersuchung der Gleichzeitigkeit in kleinen
und mittleren Nahwärmenetzen. Euroheat &
Power, Issue 09 & 10/2001, ISSN 0949-
166X, 2001.
[6] Mateo Guadalfajara, Miguel A. Lozano,
Luis M. Serra. A simple method to calculate
Central Solar Heating Plants with Seasonal
Storage.SHC 2013, International Conference
on Solar Heating and Cooling for Buildings
and Industry, September 23-25, 2013,
Freiburg, Germany
[7] International Energy Agency.OECD/IEA.
Renewables for Heating and Cooling –
Untapped Potential. IEA, 2007, Paris.
www.iea.org
Proceedings from The 55th Conference on Simulation and Modelling (SIMS 55), 21-22 October, 2014. Aalborg, Denmark
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