Category Archives: EuroSun2008-11

The development of the simulation environment for the energy. management of the solar assisted district heating grid in Wels

G. Steinmaurer

ASiC — Austria Solar Innovation Center, A — 4600 Wels, Roseggerstrasse 12, Austria.

steinmaurer, gerald@asic. at

Abstract

In Wels, Austria, an existing district heating grid with a fossil main heat source and a thermal storage tank will be extended by a solar thermal feed-in. The combination of an existing heat source, a solar thermal plant and a thermal storage tank makes it necessary to develop a ‘load management’ as a fundamental component, which coordinates the energy distribution by using optimization methods and solar radiation forecast. To asses the possibilities and the capability of the load management it was necessary to design an appropriate simulation environment within the well known dynamical simulation software Matlab/Simulink and the Carnot-Blockset. The use of this simulation environment makes it necessary to built up new components, which has not been included so far in the Software-library. To evaluate the validity of new parts, system identification methods and parameter estimation procedures have been implemented.

Keywords: solar thermal power plant, energy management, energy coordination, optimal power flow, Matlab, Simulink, Carnot-Blockset.

1. Introduction

In Wels, Upper Austria, an existing district heating grid with a fossil main heat source and a thermal storage tank will be extended by a solar thermal feed-in. By considering the economic efficient operation of the overall system, the energy-efficient integration of the solar thermal plant (approx. 3.700m2) has to be investigated particularly [1]. The existence of different energy sources (the heat source and the solar thermal plant) and the possibility to store energy in a thermal storage tank produces the necessity of a so called “load management”. This essential component coordinates and distributes the thermal energy within the overall plant.

The load management system is developed using optimization methods and includes additionally solar radiation forecast to estimate the heat demand of the district heating grid as well as the expected solar thermal power.

2. Initial Situation

Validation of the model

For kindergarten operation there are available monitored data that we can expect to approximate by calculations the changing of the studied variables, for example solar storage temperature, within a few percentages accuracy. Comparison of the calculation by the physically-based model and the measurements is shown to this case thereinafter.

Both for the swimming pool and for the kindergarten models there are parameters of which the exact values are not known, they must be determined somehow. Such parameters are the optical efficiency (no) and the overall heat loss coefficient (UL) of the collector in the collector sub-model, the ke coefficient of the heat exchanger in the heat exchanger sub-model, the heat loss coefficient (ks) and the surface area (As) of the wall of the solar storage tank in the solar storage sub-model.

Given values for the calculations are as follows:

П0 = 0,74, catalog data for the SlU-system,

UL = 7 W/(m2K), recommendation from [9],

ke = 2461,5 W(m2K), determined from data given by manufacturer, ks = 0, assuming storage with no heat loss.

Since the storage has no heat loss the value of As is of no interest.

In accordance with Fig. 3 the temperature of the solar storage has been determined for two independent day (8 March, 2005, 8 April, 2005), by running the model with available measured data. Fig. 4 and Fig. 5 contain the comparison of the calculated and the measured values, as well.

image38

Fig. 4. Comparison of storage temperatures for 8 March, 2005.

As a result of the modelling the absolute difference of the calculated solar storage — and the measured solar storage outlet temperature has a mean value of 1,11°C, standard deviation of 0,6°C, maximum value of 2,32°C and residual value of 0,51°C for the day 8th of March, 2005.

image39

Fig. 5. Comparison of storage temperatures for 8 April, 2005.

As a result of the modelling the absolute difference of the calculated solar storage — and the measured solar storage outlet temperature has a mean value of 0,73°C, standard deviation of 0,49°C, maximum value of 1,78°C and residual value of 0,69°C for the day 8th of April, 2005.

GIS data bank

The following information was identified, collected and analyzed: potential of solar, aeolic and biomass resource, socio-economic information, geographic maps, infrastructure data, hydric resources (subterranean wells), soils and climatic aptitude, possible nuclei for becoming training and technical assistance centers (technical and agricultural schools, universities),and finally mapping of existent solar installations. The data used where possible, were automatically migrated, if not were digited and converted in ACESS data bank.

Acknowledgements — We thank the National Research Council (CNPq) , Brazilian Electric Centers SA (ELETROBRAS), The Sao Francisco Hydro Eletric Company (CHESF)and Coordination for Perfection of High Level Personnel (CAPES) for the aid in research projects on solar energy that provided material means and scientific ambient for carrying out this research.

References

[1] Bravo, J. D. (2002) Los sistemas de information geografica em la planificacion e integration de enrgias renovables, ISBN 84-7834-434-9, Editorial CIEMAT, Madrid, Espana.

[2] Bourges, D. et al. (1996) A geographical information system for large scale integration of renewable energies into regional energy markets, Renewable Energy Development European Conference and APAS — RENA Contractors Meeting, EDIFIR, Florencia.

[3] Rialhe, A. (1996) Epure project: economical potential use of renewable energy, Renewable Energy Development European Conference and APAS-RENA Contractors Meeting, EDIFIR, Florencia.

[4] Diakoulaki, D. et al. (1996) Implementing large scale integration of renewable a pilot study for operational plans and policies (REPLAN), 107-115.

[5] Voivontas, D., Assimacopoulos, D. and Mourelatos, A. ( 1998) Evaluation of renewable energy potential using a GIS decision support system, Renewable energy, Vol. 13, No. 3, 333-344.

[6] Petit, C. (1995) Winds of change. GIS helps site wind farms in France. GIS Europe, Godmanchester (Huntington), GeoTec media, pags. XVII-XVIII.

[7] Baban, S. M. J. and Parry, T. (2001) Developing and applying a GIS-assisted approach to locating wind farms in the UK, Renewable Energy, Vol. 24, No. 1, 59-71.

[8] Matthies, H. et al. (1994) An assessment of the offshore wind potential in the EC, EWEC94, Greece,

111- 115.

[9] Marnay, C. H. et al. (1997) Estimating the environmental and economics effects of widespread residential PV adoption using GIS and NEMS, Ernest Lawrence Berkeley National Laboratory,

University of California, EEUU Report, LBNL-41030, UC-1321.

[10] Noon, C. H. and Daly, M. J. (1996) GIS-based biomass resource assessment with BRAVO, Biomass and Bioenergy, Vol. 10, No. 2-3, 101-109.

[11] Voivontas, D. et al. (2001) Assessment of biomass potential for power production: a GIS based method, Biomass and Bioenergy, Vol. 20, 101-112.

[12] IBERINCO(1998) Oportunidades para la production de energia a partir de biomasa en La Rioja (Espana) y La Toscana (Italia), ALTENER pilot projects, Contract No. XVII/4, 1030/Z/98-214.

[13] SOLARGIS TEAM (1996) Solargis handbook, Comision Europea, Direction General XII, Bruselas.

[14] NREL (2007), Dynamic maps, GIS data and analysis tools, www. nrel. gov/gis/maps

[15] RENEWABLE ENERGY ATLAS OF THE WEST (2007), www. energyatlas. org, 2007.

[16] Suri, M. Huld, T. A. and Dunlop, E. D. (2005) PV_GIS: a web-based solar radiation database for the calculation of PV potential in Europe, Int. Journal of Sustainable Energy, Vol. 24, No. 2, 55-67.

Optimization of Polymeric Solar Thermal Collectors by Fluid Dynamic Simulations

Steffen Jack1, Michael Kohl1, Axel Mkller2, Karl-Anders Weiss1*

1 Fraunhofer Institute for Solar Energy Systems ISE, Heidenhofstrasse 2, 79110 Freiburg, Germany
2 Dr. Axel Muller — HTCO, Postfach 700203, 79056 Freiburg, Germany
* Corresponding Author, Karl-Anders. Weiss@ise. fraunhofer. de

Abstract

In order to develop a preferably efficient and durable polymer collector the use of computational fluid dynamics (CFD) and FEM-Simulations opens up a fast and efficient way to determine key parameters and problems. The low intrinsic thermal conductivity of polymeric materials and their limited temperature stability can be partly compensated by the optimization of the design of the collectors. Here numerical simulation tools are utilised to analyse and compare different geometries of absorbers or absorber tubes.

Computational fluid dynamics is used to optimize the collector geometries by Dr. Axel Muller — HTCO. The aim is to develop a layout which assures a homogeneous flow, a maximized contact area between the absorber and the heat transfer fluid and an optimised heat transfer into the fluid. The simulation of different collector and absorber layer geometries discussed in this paper point out the quantifying parameters which allow an efficiency optimisation.

Mechanical stresses in the collectors and channels which are caused by temperature gradients or external loads are calculated with FEM-tools. This topic is important in particular if low-cost polymers like polyolefins shall be used. The material stresses are in acceptable range for the analyzed steady state situations. But the combination of different polymers in an integrated collector with bonded absorber and frame can lead to high deformations due to thermal expansion.

Keywords: Solar Thermal Collectors, Polymers, Fluid Dynamics, FEM-Simulation

1. Introduction

The scarcity of fossil fuels is beyond question — one way to save these resources is to make use of solar thermal energy for domestic hot water. So far, solar thermal collectors mainly consist of glass and metal parts. Not simply substituting materials in existing systems but developing a funda­mentally new design for a polymeric collector is the objective of the research at the Fraunhofer ISE and Dr. Axel Muller — HTCO in the framework of Task 39 of the Solar Heating and Cooling Programme of the IEA.

Since the economic viability of solar collectors is strongly linked to the costs of the system, a decrease of the system costs would lead to a higher market penetration. Key advantages of polymers are abundance, weight reduction and more freedom in design, along with the benefits and cost savings associated with well established manufacturing processes and improved fastening, reduced number of parts, and overall assembly refinements. However, also the probably changed system performance is an important element and may not be forgotten.

The Fraunhofer ISE is currently working on the concept of a fully polymeric collector, in order to consider these elements in an integrated way, as only then the full potential of polymeric materials can be used. Important parameters are of course the absorption of solar radiation and — in comparison to metals — the usually lower thermal conductivity and heat capacity. On the other hand one has to consider the intrinsic stress factors like UV-radiation, high temperatures and mechanical loads because the systems have to reach a service life-time of more than 20 years.

In this early development phase numerical simulation tools are used to analyse these topics. The aim is to develop a collector layout considering production and material boundary conditions with a best possible degree of efficiency. With the help of fluid dynamic simulations, one can calculate e. g. the heat transfer from the absorber layer into the heat transfer fluid and optimize it. So one can realise cost and time savings for engineering and prototype production. Parameter sensitivity studies for absorber channels designs are presented as well as efficiency calculations of entire collectors. Another important topic is the simulation of the behaviour of collectors or parts of them during stagnation conditions. Here the temperature distribution and the maximum temperatures are most interesting in reference to thermo-mechanical stresses which occur due to thermal expansion.

Characterisation of the Optical Properties of Solar Collectors by Ray-tracing Simulations

P. Di Lauro*, S. HeB, S. Rose, M. Rommel

Fraunhofer Institute for Solar Energy Systems (ISE), Department Thermal Systems and Buildings,
Heidenhofstrasse 2, 79110 Freiburg, Germany

* Corresponding Author, paolo. di. lauro@ise. fraunhofer. de
Abstract

Purpose

The optical efficiency n0 and the Incidence Angle Modifier (IAM) are very important parameters in terms of the characterisation of the thermal performance of solar collectors. These two parameters give the significant distinguishing information about the optical performance of a solar collector.

Methods

The research group “Thermal Collectors and Applications” at the Fraunhofer ISE uses the simulation program OptiCAD for investigations on the optical properties of collectors. OptiCAD is a so called “forward ray-tracer”. Flat-plate collectors, evacuated tubular collectors and also collectors with reflectors may be modelled in OptiCAD. After parameterisation of different components and materials of the collector (cover glass, absorber, reflector etc.) the optical efficiency and the 3D-IAM (Incidence Angle Modifier in three dimensions) can be calculated by ray-tracing. The principle followed is that every simulated ray starting from the light source is calculated, followed during all processes (transmission, reflection, absorption) until it is finally absorbed.

The properties of the collector with respect to diffuse radiation can be calculated from the simulated 3D-IAM assuming an isotropic distribution of diffuse radiation.

Results and Conclusion

Different collectors which were tested at the testing laboratory of the Fraunhofer ISE have been modelled in OptiCAD and their optical parameters have been determined by simulation. In the poster presentation it is shown that the measured and the calculated parameters fit very well. It is discussed how the ray-tracing investigations are a valuable and helpful tool for the development and improvement of solar thermal collectors.

Keywords: solar thermal collector, 3D-IAM, diffuse radiation, ray-tracing

1. Introduction

The optical efficiency no[11] and the Incidence Angle Modifier (IAM) play an important role for the efficient utilisation of solar radiation by means of thermal collectors. Those parameters often are determined at an outdoor test facility, in most cases by using a tracker, or at an indoor solar simulator. But these characteristics now can also be calculated by modelling the collector in a simulation software. For this process the optical properties of the different component’s materials that the collector is composed of are to be displayed. The correct description of the actual optical and geometrical parameters of all of the collector components is important for the quality of the results of such a ray-tracing simulation. Therefore, it makes sense to measure the optical properties like transmission, reflection and absorption of each individual element and accordingly implement them in the ray-tracing program.

The result of a simulation of an emulated collector is the effective transmittance-absorptance products (та)е^ under a particular direction and incidence angle of sun rays. This characteristic

can be simulated for all directions (3D) of incident rays. However, it has to be considered that this factor is not equal to the mathematical product of the two optical properties (transmission and absorption). It additionally takes into account general geometrical effects of the complete collector and the interaction of the components like multiple reflections between them.

The IAM then presents the relation of (та)during solar radiation of a certain direction to the one

under perpendicular solar incidence. In order to finally validate those simulated data by comparing with the measured “optical efficiencies” an assumption for the collector efficiency factor F’ has to be made which is assumed to be constant.

Moreover, it is possible to give a proposition about the acceptance for diffuse radiation of the collector by using the simulated IAM-values for direct radiation and supposition of an isotropic distribution of diffuse irradiation on the collector from the virtual hemisphere.

Ray-tracing simulations of the optical parameters of solar thermal collectors offer the following advantages:

• Determination of the optical properties of a collector that is not yet build

• Variation and improvement of the design of a collector during the construction phase

• Predictions of solar gains by simulation of the IAM for direct solar incidence under any incidence angle

• Identification of the acceptance for diffuse radiation of the modelled collector by deriving a diffuse IAM from the IAM-values for direct radiation (assumption of isotropic distributed diffuse radiation)

The Mathematical Modeling of a Solar Collector’s Absorber

P. Shipkovs1*, M. Vanags1, KXebedeva1, G. Kashkarova1, J. Shipkovs1, V. Barkans2

institute of Physical Energetics, Aizkraukles Street 21, Riga LV-1006, Latvia
2 Latvian Maritime Academy, Kronvalda Boulevard 6, Riga, LV-1206, Latvia
* Corresponding Author, shipkovs@edi. lv

Abstract

The paper gives the stationary heat conduction process is studied theoretically for flat plate surfaces of the absorber of a solar collector. Based on the mathematical model of a stationary heat conduction process in flat surfaces of a solar collector’s absorber described by the authors in previous works, the geometry of such an absorber is analysed from the viewpoint of the most optimal temperature field in it. Special attention is given to simplification of the mathematical model and to creation of a computer model that would reflect the two-dimensional temperature field in the collector’s absorber. Introducing into the computer model the solar radiation density received from an external data source/storage medium the authors obtain the temperature field in the absorber and show that it is possible to define its optimal geometry if the maximum heat power is found from the cross-sections area, since the main parameters in the mathematically described heat conduction process are geometrical sizes of the absorber. The developed computer model will form a basis for creation of new software intended for this particular new innovative idea relating to the design and making technology of flat plate solar collectors without application of expensive experimental materials. The main conclusion is that the carried out mathematical description can help to find the optimal sizes for the absorber, which, taken for the whole collector, would provide its maximum efficiency.

Keywords: heat flow, solar collector’s absorber, mathematical modeling

1. Introduction

The heat flow in the absorber of a solar collector, which occupies there a definite space and in which this heat spreads by conduction, is now being studied intensively [1-3]. The process can be considered mathematically if we know the temperature at any time and any point of this space. There are cases when it is sufficient to measure the temperature at separate points and to tabulate the data obtained. However for a solar collector it is advantageous to preliminarily analyze the heat conduction thus considering the problem theoretically. Besides, the temperature not always can be measured at all points. The challenge is therefore to obtain the temperature function theoretically, depending on the time and spatial coordinates. Having obtained such a function we can use it further for mathematical modeling. This function will definitely contain some solar collector’s parameters, such as, e. g., the thickness of the absorber’s plate and the distance between the tubes (which are ideally connected by soldering to the absorber), the tube diameter, etc.; by varying these parameters in our mathematical model we can calculate the most optimal temperature condition in the absorber, making it possible to abandon a huge time-consuming and expensive experimental work for determination of the heat transfer from the absorber’s plate to the heat carrier flowing through the tube [1].

The paper presents the mathematical description for the heat conduction on the plane surface of a solar collector’s absorber (further in the text absorber). The collector plate’s cross-section is considered that is perpendicular to the axis of a tube with liquid. The cross-section is conditionally divided into three parts. Having chosen one of them, we will attach to it a corresponding coordinate system.

The temperature [K] on the plate (the OiABC section) is designated with T(x, z), the
temperature in the cylindrical coating — with Т2(г, ф), and the temperature of the liquid — with T3(r,

,ф).

Measurements of hot water in two multi-family houses

Energy use is since 2005 measured in the apartments in two multi-family houses in Vasteras, Sweden, by the housing company. The momentary power required for hot water is continuously registered in every apartment and the hourly data, which the analysis is based on, are averages of a number of such momentary measurements. Measured data from two years, 2005 and 2006, has been processed and evaluated so far and is presented in this paper, but has also been reported in [7]. Both hot water and electricity demand are measured on an hourly basis.

The two buildings comprise 24 apartments of a total area of 1894 m2, occupied by totally 40 persons (19 and 21 residents respectively). The buildings were constructed in 2001 and are provided with energy efficient facilities. Energy costs for utilities, such as heating, electricity, cold and hot water, are included in the total rent per apartment, which is the most common solution in Sweden. Therefore, the tenants are not encouraged to save energy to the same extent as households in single-family houses or apartments with individual billing. There are further plans to install individual measuring equipment for hot water and electricity in 1 300 apartments. Another 300 apartments have already been measured by the residential company, but this data has not yet been processed.

3. Results and analysis

The results have been analysed in different ways; the modelled and measured hot water demand have been compared on an annual and hourly basis to investigate the agreement and the validity of the model. Furthermore, the variation in measured hot water use on a monthly, weekly and daily basis has been investigated to enable further improvements of the model.

Comparison with a producer design software

The model performance with 2 different airside correlations (Wang et al. [12] for wavy fins and Wang et al. [10] for plain fins) was compared with Type1223new and the producer design software Guntner Product Calculator (GPC) of the company Guntner GmbH. Two staggered tube lay outs available in GPC (HX 1 and HX 2, heat exchanger length of 1.25 m and height 1 m, 10 passes) with wavy fins (corrugation angle = 15°) were considered. For both geometries the heat transfer rate, calculated by the model with the correlation for wavy fins, is about 10% for HX 1 and less than 5% for HX 2 lower than that of the GPC, (Fig. 4). It has to be noted, that transverse tube pitch Pt in HX 1 is out of the validity range of the correlation for wavy fins, extrapolation of the correlation is in general not recommended. The heat transfer rate, calculated by the model for plain fins, is lower than the GPC heat transfer rate too (Fig. 4), which is plausible. However, HX 1 is also out of the validity range of the plain fin correlation, the deviation of this correlation to the GPC for HX 1 is even smaller than that of the wavy fin correlation, probably because of the less complex structure. Unlike these correlations, Type1223new with the Elmahdy and Biggs [5] correlation for plain fins gives higher heat transfer rate values than the GPC. It is, however, unfeasible that plain fins have higher heat transfer coefficient than wavy fins (compare with [20]).

Подпись: Fig. 4 Heat transfer rate calculated by the model (as plain and wavy fins), Type1223new and GPC for different air flow rates and two geometries (left for HX 1, right for HX 2).

The airside pressure drop, determined with the correlations for plain and wavy fins, is significantly lower than that, calculated by the GPC, Fig. 5. Whereas liquid-side pressure drop calculation slightly overestimate the pressure drop calculated by the GPC and is in agreement with the GPC when calculated as smooth tubes.

image274,image275
image276

Fig. 5 Airside pressure drop calculated by the model (as plain and wavy fins) and GPC for different air flow
rates and two geometries (left for HX 1, right for HX 2).

2. Conclusion

A model for fin-and-tube heat exchangers, which is based on empirical heat transfer and flow friction correlations, is presented here. The selected correlations are developed with larger data base and have complete description of the reduction method than the one used in Type1223new. In general one needs to be careful with empirical correlations, especially with complex ones, and one needs to prove simulation results. It is not recommended to extrapolate the correlations for configuration outside of the validity range. If configuration outside of the validity shall be simulated (e. g. for optimization of heat exchanger configuration) it appears to be sensible to use less complex correlations, e. g. Wang et al. [10] instead of Wang et al. [12].

Acknowledgements

The authors would like to express their gratitude to the Volkswagen Foundation, Germany for the financial support.

Nomenclature

Af

m2

minimum flow area

Q

W

heat transfer rate

Aface

m2

frontal area

Re

Reynolds number

Ai

m2

tube inside surface area

T

°C

temperature

Ao

m2

total airside surface area

U

W/m2K

heat transfer coefficient

C

W/K

heat capacity rate

Sf

m

fin thickness

Dc

m

collar diameter

є

heat exchanger effectiveness

Di

m

tube inner diameter

n

fin efficiency

Do

m

tube outer diameter

0

0

fin corrugation angle

f

friction factor

P

kg/m3

density

G

kg/m2s

air mass flux based on

z

ratio of minimum flow area

minimum flow area

to face area

j

=Nu/(RePr1/3)

Subscripts

Colburn factor

k

W/mK

thermal conductivity

1

airside inlet

N

number of tube rows

2

airside outlet

Pi

m

longitudinal tube pitch

i

tube inner side

Pt

m

transverse tube pitch

m

mean

Pr

Prandtl number

min

minimum value

References

[1] E. Frank, K. Vajen, A. Obozov, V. Borodin (2006): Preheating for a District Heating Net with a Multicomponent Solar Thermal System, Proc. EuroSun 2006, Glasgow

[2] Guntner GmbH (2007), personal communications.

[3] Brandemuehl, M. J., HVAC 2 Toolkit: A Toolkit for Secondary HVAC System Energy Calculations, ASHRAE 629-RP, Joint Center for Energy Management, University of Colorado, 1993

[4] Chillar, R. J, Liesen, R. J., Improvement of the ASHRAE secondary HVAC toolkit simple cooling coil model for simulation, Proceedings of the 1st SimBuild Conference, International Building Performance Simulation Association, 2004

[5] Elmahdy, A. H and Biggs, R. C., Finned tube heat exchanger: correlation of dry surface heat transfer, ASHRAE Transactions Vol. 85, Part 2, 1979

[6] Haaf, S., Warmeubergang in Luftkuhlern, pp. 435-491, in Plank, R., Handbuch der Kaltetechnik,

Springer Verlag, Berlin, 1988

[7] Wang, C. C., Chang, C. T. (1998), Heat and Mass Transfer for Plate Fin-and-Tube Heat Exchangers, with and without Hydrophilic Coating, Int. J. of Heat and Mass Transfer 41, 3109-3120

[8] Wang, C. C., Hsich, Y. C., Chang, C. T and Lin, Y. T. (1997), Performance of Finned Tube Heat Exchangers under Dehumidifying Conditions, J. Heat Transfer 119, 109-117

[9] Wang, C. C., Chi, K.-Y. (2000), Heat transfer and friction characteristics of plain fin-and-tube heat exchangers, Part I: new experimantal data, Int. J. of Heat and Mass Transfer 43 (2000), 2681-2691

[10] Wang, C. C., Chi, K.-Y., Chang, C.-J. (2000), Heat transfer and friction characteristics of plain fin-and — tube heat exchangers, Part II: Correlations, Int. J. of Heat and Mass Transfer 43 (2000), 2693-2700

[11] Wang, C. C., Lin, Y.-T., Lee, C. J. (2000), An airside correlation for plain fin-and-tube heat exchangers in wet conditions, Int. J. of Heat and Mass Transfer 43 (2000), 1869-1872

[12] Wang, C. C., Hwang, Y.-M., Lin, Y.-T. (2002), Empirical correlations for heat transfer and flow friction characteristics of herringbone wavy fin-and-tube heat exchangers, Int. J. of Refrigeration 25, 673-680

[13] Pirompugd, W., Wongwises, S., Wang, C. C. (2006), Simultaneous heat and mass transfer characteristics for wavy fin-and-tube heat exchangers under dehumidifying conditions, Int. J. of Heat and Mass Transfer 49, 132-143

[14] Jacobi, A. M., Park, Y., Tafti, D., Zhang, X. (2001), As assessment of the state of the art and potential design improvements for flat-tube heat exchangers in air conditioning and refrigeration applications — Phase I, Final Report. ARTI 21-CR Program Contract No. 605-20020

[15] Schmidt, Th. E. (1949), Heat transfer calculations for extended surfaces, Refrigerating Engineering, April 1949, 351-357

[16] Perrotin, T. and Clodic, D. (2003), Fin efficiency calculation in enhanced fin-and-tube heat exchangers in dry conditions, Proc. Int. Congress of Refrigeration 2003, Washington, D. C.

[17] Engineering Science Data Unit 86018 with Amendment A, ESDU International plc, London, 1991, pp. 92-107

[18] Shah, R. K. and Seculic’, D. P. (2003), Fundamentals of heat exchanger design, John Wiley & Sons Inc.

[19] Wagner, W. (2001), Stromung und Druckverlust, 5. Auflage, Vogel Buchverlag

[20] Gomini, G, Nonino, C. and Savino, S. (2003), Effect of space ratio and corrugation angle on convection enhancement in wavy channels, Int. J. of Num. Methods for Heat&Fluid Flow Vol. 13, No. 4, 500-519

Simulations

The simulation tool allows the user to choose different parameters related with the simulation process: time-step, accuracy, etc. The smaller the time-step, the longer the simulation time but the more precise are the results concerning the PCM interaction. This statement has to be taken into account when a PCM simulation is carried out. It was checked in previous simulations that bigger time-steps (to perform faster simulations) hid the operation of the PCM.

The length of the simulation can be chosen by the user going from daily simulations to two years simulation. Four different climates can be selected representing the four most common climates in Europe: Stockholm (Sweden) for a moderate northern climate, Zurich (Switzerland) for a moderate central climate, Barcelona (Spain) for a coast Mediterranean climate with high humidity and temperature in summer but a mild winter, and Madrid (Spain) with a continental Mediterranean climate with high temperature but low humidity in summer. The climate chosen for the simulations carried out was Madrid.

The building simulated is a two 70 m2 storey single family house facing south. Four different buildings can be selected and their differences depend on the energy demand of the building, existing an energy demand of 15, 30, 60 o 100 kWh/m2. Even a 100 kWh/m2 without shading can be chosen in order to simulate cooling demand. The 60 kWh/m2 was the selected house for the simulations performed.

The auxiliary system had a nominal power of 10 kW and the water introduced into the store was heated up to 63 °C (set point). The collectors field is 10 m2 of flat plate collectors facing south with a slope of 45 °C. The storage tank volume is 800 L following the recommendations of W. Weiss [6].

Two different types of simulations were carried out. In the first one, the aim was to observe the influence of some parameters in the result of the simulations. The parameters changed were the different inlets and outlets of the store and the position of the temperature sensors that operates the auxiliary system. Annual simulations were carried out and the results were evaluated for every month
and every week. Temperatures into the store and in every inlet and outlet of the tank as well as mass flow rates were the values checked to observe the influence of the PCM in the system behaviour. Fig. 2 shows the relative position of each sensor into the store.

image010 image011 image012 Подпись: Variable Description Tfluid4 Fluid temperature at sensor 4 Tfluid3 Fluid temperature at sensor 3 Tfluid2 Fluid temperature at sensor 2 Tfluid1 Fluid temperature at sensor 1 Tpcm PCM temperature Tpcm PCM temperature TSA Water temperature from store to auxiliaiy TAS Water temperature from auxiliary to store TSB Water temperature from store to space heating TBS Water temperature from space heating to store TSD Water temperature from store to DHW TXdS Water temperature from DHW heat exchanger to store TSC Water temperature from store to solar collectors TXsS Water temperature from solar collectors heat exchanger to store Tssa1 First on/off auxiliary temperature sensor Tssa Second on/off auxiliary temperature sensor

The second set of simulations was mainly focused on the influence of the PCM. Three different values of the Parea ratio were simulated: 0.25, 0.5 and 0.75. Also the geometry of the module was changed, different lengths of the PCM modules and different diameters were simulated. For a given PCM mass, the thinner the module was, the higher the number of modules into the storage. Also two different kind of PCM were simulated into the store focused in the two different demands. The sodium acetate — graphite compound for the DHW demand and placed at the top of the tank and the RT48 paraffin for the space heating demand and placed in the middle of the tank.

TSC

Подпись: TXdS

Fig. 2. Position of the monitored sensors inside the storage tank

More results about case 2

Cases 2A and 2B deal with photovoltaic panels, table 4 shows field production on the whole year. PV field feeds grid network, so electricity generation can be done when vapour compression chiller is not operating. Conservative shading assumptions is taken for all other results. For case 2A, PV field produces more than the whole vapour compression chiller electricity consumption, thus the auxiliaries consumption will be decreased respectively by 2.04, 3.68 and 6.31 kWh/(m2 year) for Paris, Stockholm and Lisbon.

energy

(linear shading)

energy

(conservative

shading)

Paris

81.43

71.76

Stockholm

74.18

60.72

Lisbon

151.70

128.93

Table 4. PV energy production [kWh/(net_panel_area year)]

2.3. More results about case 3.

Simulations run for case 3 have been optimised to minimise boiler gas consumption. For each location it results a panel slope, a storage tank volume. A summary of results dedicated to case 3 is presented in table 5. Figures given are only about heating and cooling production consumption.

Gas cons

Electricity

cons

Solar

fraction

Solar

Energy

Solar energy

Electrical

COP

m2 coll by kWcold

Pannel

slope

storage

tank

case 3A

kWh/(m2

year)

kWh/(m2

year)

%

kWh/(m2

year)

kWh/(coll_net_area

year)

coll_net_area/ kWc nominal

degree

m3

Paris

60.67

2.92

45

43.25

380.73

13.35

1.35

15.00

7.00

Stockholm

99.79

2.11

31

38.70

340.64

13.99

1.35

15.00

8.00

Lisbon

9.08

4.85

93

82.21

723.72

10.94

0.95

25.00

11.00

case 3B

Paris

92.08

2.23

13

12.48

439.29

15.89

0.34

15.00

3.00

Stockholm

126.15

1.53

10

12.14

427.44

16.08

0.34

15.00

3.00

Lisbon

63.41

3.58

32

27.19

957.34

14.53

0.24

25.00

3.00

Table 5. Case 3 summary

Some details should be given on this table: solar energy is the heat collected by the solar field and feeding storage tank; solar fraction is computed following equation 1. Table 6 proposes primary energy savings by net collector area for case 3A. It can be put in relation with table 4 given for case 2 (table 4 values must be multiplied by 2.5 to have primary energy).

Подпись: (equ. 1)Solar energy

Solar fraction =

(Heating load + Chiller hot water consumption)

Location

CASE 3A

Primary energy savings by area of collector

Paris

51.67

Stockholm

107.60

[kWh/(net_coll_area year)]

Lisbon

225.02

Table 6. Primary energy savings by collector area

3. Conclusion

In this work, design and energy performance of different kinds of air conditioning system were analysed. A complete building simulation model was developed with parameters found in IEA ECBCS Annex 48 research project. Heating and cooling emission and distribution systems were also defined as well as heat/cold production devices. This simulation has been run in three different locations. The comparison between three cases gives the potential energy savings of two solar technologies in relation to classical air-conditioning. A first analysis of auxiliaries showed that they have a huge weight in the primary energy balance. For case 1, it varies from 60% in Stockholm to 80% in Lisbon. Therefore it shows an important energy savings potential. When using classical vapour compression chiller and no solar energy, cooling cost less primary energy than heating due to COP higher than 2.5 (converting net energy to primary energy for Belgium). If solar energy conversion technology is implemented, a key point is available area for installing panels. In each case (A or B), primary energy savings are higher using PV panels instead of thermally driven chiller (assisted by solar thermal panels). It is another important result of this study. PV panels are directly connected to grid, then solar energy is use even if the building has low needs (e. g. during the weekend). For case 3.B (12 floors), solar fraction is very low, operating this system consumes more energy than classical air-conditioning. A better control can reduce this trend but a much higher solar fraction is required to save primary energy.

Systems costs have not been approached in this study. If this analysis is performed, the comparison should be done between solar and classical air-conditioning. An important point for PV is that, nowadays, electrical energy can be sold by the producer at a very interesting price. Moreover in some countries, such Belgium, kWh photovoltaic are paid directly at the production, so money can be saved even if electricity is not used in the building. Solar thermal energy has no such high financial incentive.

References

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Thesis, Royal Institute of Technology, KTH, Sweden

[2] TRNSYS simulation studio, Version 16.00.0038 Licensed to Universite of Liege

[3] Stabat P. (2007). IEA48 — Description of Type 1c air-conditioned office buildings for simulation test,.IEA — ECBCS Annex 48 working document

[4] ALESSANDRINI J. M. et al. (2006) Impact de la gestion de l’eclairage et des protections solaires sur la consommation d’energie de batiments de bureaux climatises, Climamed, Lyon, France, 2006

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