Dynamic modeling of seasonal thermal energy storage systems … · 2014. 12. 9. · Thermal Energy Storage System (STES) configuration, hot water tank STES system for district heating

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: [email protected]

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

mailto:[email protected]

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).


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.


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


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


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 / %


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 / %


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 / %


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


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


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


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


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.


area, STES volume and HP capacity, Stokholm.


area, STES volume and HP capacity, Madrid.


area, STES volume and HP capacity, Warsaw.

30%

40%

50%

60%

70%

80%

50% 75% 100% 125% 150%

Fsaving depending on Coll. area / %


40%

50%

60%

70%

80%

50% 75% 100% 125% 150% 175%

Fsaving depending on STES volume/ %


40%

45%

50%

55%

60%

65%

70%

40% 60% 80% 100% 120% 140% 160% 180%

Fsaving depending on HP capacity/ %


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


120

130

140

150

160

170

180

40% 60% 80% 100% 120% 140% 160% 180%

System Heat cost, Madrid €/MWh


180

200

220

240

260

280

40% 60% 80% 100% 120% 140% 160% 180%

System Heat cost, Warsaw €/MWh



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.


257

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illustrated edition, 2003.

[3] A: Klein, S.A. et al, 2010, TRNSYS 17: A

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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,

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[5] Winter, W.; Haslauer, T.; Obernberger, I.:

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[6] Mateo Guadalfajara, Miguel A. Lozano,

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www.iea.org


258

http://sel.me.wisc.edu/trnsys

http://www.iea.org/

Documents

Dynamic modeling of seasonal thermal energy storage systems … · 2014. 12. 9. · Thermal Energy Storage System (STES) configuration, hot water tank STES system for district heating