Category Archives: EuroSun2008-11

Modelling the Performance of a Solar Heated Swimming Pool

R. Kicsiny* and I. Farkas

Szent Istvan University, Department of Physics and Process Control, Pater K. u. 1., Godollo, H-2103 Hungary

Corresponding Author, Kicsiny. Richard@gek. szie. hu


This paper introduces the realization and application of a block-oriented type physically-based model of solar heated thermal systems which is able to work with inputs from either measurements or meteorological models. On the basis of measured data of a particular combined, with more consumers, solar heating system the validation of the model is shown in domestic hot water heating operation. Using meteorological models for the same system in swimming pool operation it is also shown that there are significant fluctuations in the pool temperature as the evaporation heat loss coefficient changes within the bounds of different recommendations in literature.

Keywords: combined solar heating system, physically-based modelling, measurements, meteorological models

1. Introduction

In view of the possibility of harnessing solar energy in swimming pool applications and the increasing amount of such installations, it is important to develop the efficiency of solar heated systems. In order to improve any simple or combined solar heating system, physically-based modelling is an exact, theoretically overseen tool. Former works on modelling of solar thermal systems can be fond in [4], [5] and [6].

The aim of this work is to realize a mathematical model corresponding to solar heating systems that takes into account all the substantial energy components as well as the physically-based specifications of them. Physical bases are well described in details in [2]. In addition one should be able to validate it using measured data. The model should be able to work up inputs from either measurements or meteorological models and should be flexible for easy adapting to any similar systems. A method of meteorological modelling can be found in [1].

GIS and Renewable Energies

The use of GIS in renewable energy that began in the 90s decade went through considerable progress and as a result various decision support tools were developed [1]. The GIS applications that were developed can be classified in three groups: GIS as a decision support tool for integration of renewable energies on a large scale and at a regional level, GIS for assessment of distributed energy generation and that connected to the electric network and GIS for decentralized production systems and autonomous production of electricity.

The GIS decision support tool for integration of renewable energies on a large scale and at a regional level (European Community) propose to analyze: evaluation of renewable energy potential (solar, aeolic, biomass and minihydraulic), its participation in relation to primary regional energy and the potential reduction of CO2 emissions [2]; evaluation of economic and potential renewable energies to identify regions of the European Community where the renewable sources were competitive at a determined risk [3]; evaluation of renewable energy potential, calculate the final use in the region and according to the scenario the insertion of a given renewable energy, makes its dimensioning and evaluates the economic impacts [4].

The GIS for evaluation of distributed generation, connected to the electric network considers the following renewable energy sources: aeolic, biomass and solar. The majority of GIS applications for aeolic energy deal with the optimum localization of the aeolic plants, consider the potential of the resource, the infrastructure for access and transmission lines and environmental restrictions and land use [5, 6, 7] , also Matthies [8]considers petroleum platforms, shipping routes and submarine lines for study of optimum localization of offshore aeolic farms. Marnay [9] developed a study of the use of photovoltaic solar energy installed on roofs of residences in the USA using GIS. The project included a municipality level resolution and considered the following variables: solar irradiation, average price of electricity and spatial distribution of the population.

A peculiar characteristic of biomass utilization is the necessity of transport from the local of cutting to a central transformer which makes the geographic proximity of the offer a determining factor for the final cost of the generated electric energy. Noon [10] developed a GIS for the Tennesy Valley, USA which considered the following variables: the characteristics of places of biomass offer, places of demand and transport costs, technical characteristics of the plants, forestry residues or cultivated forests, road networks and administrative limits. Voivontas [11] developed a work on the Island of Crete about the biomass potential for generation of electric energy. It deals with the combination of evaluation of the resources (theoric and/or available), the transport cost, the identification of optimum locals for biomass cultivation and the definition of the size of the plant for determining its economic competitiveness in the presence of conventional sources. Similarly in Spain one was developed IBERINCO [12].

SOLARGIS [13] is a GIS for rural electrification with renewable sources of energy. It is a paradigm of a GIS tool for this purpose as much for its completeness, relatively ample diffusion of its use as also for the periodic up-dating and improvements. The principal objectives of its development were: the demonstration of value of the GIS methodology for the development of the implantation of renewable sources for rural electrification, demonstration of its applicability in some target regions and proper construction of a GIS tool for analysis of the potential renewable sources [13]. The SOLARGIS methodology intends to obtain the best option for rural electrification, in isolated locals using renewable systems or not, being individual user or users connected to a local mini-network. The isolated residence could be electrified by PV systems, aeolic, gas generator, diesel group or interconnected to a network. A mini-network could be supplied by group diesel or hybrid aeolic-diesel system. The high potential regions are determined through comparison of equivalent costs of electrification of the energy systems considered. For this calculation, with a resolution of 1 km2, the following information is used: habitation density, mean annual global irradiation on the collector plane, mean annual wind velocity and distance of residence to electric energy network. SOLARGIS was applied in Tunis (Tunisia), Kairouan (Tunisia), Marrocos, Sicilia (Italia), Andaluzia (Spain), Murcia (Spain), Crete (Greece), Island of Santiago( Cape Green), Para (Brazil) and Karnataka (India).

NREL [14] is a GIS tool that is accessible via web that permits visualization of monthly mean solar irradiation with a spatial resolution of 40 km x 40 km (low) and in high resolution 10 km x 10 km for a collector plane facing the south, with the tilt in relation to the horizontal equal to latitude or the direct normal solar irradiation. In the same way it produces estimates of aeolic energy on an annual base, for low resolution (1/3 or 1/4 degrees) and high resolution cell size of 20-1000 m. Also the tool includes an estimation of the biomass potential derived from the different sources (agricultural residues, waste, forestry residues, among others).

RENEWABLE ENERGY ATLAS OF THE WEST [15] is a GIS tool that is accessible via web which synthesizes the data and maps on renewable energies for eleven American states that are situated in the West of America: Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington and Wyoming. At a regional level (West American) the atlas shows, solar, aeolic, biomass and geothermic resources, the present mix of electricity supply, the renewable energy systems installed and information on public policies for stimulating the use of renewable energies. Also the present capacity of transmission lines, an estimation of charge growth in the region, aspects of soil use and environmental impacts are presented. In other sections the Atlas makes an in detail account of this information at state level.

PV-GIS [16] is a GIS application that is available in real time in web for calculating the photovoltaic solar energy potential in Europe. The user can navigate over solar irradiation maps and obtain the solar irradiance incident on a photovoltaic module positioned at different inclinations. A second application permits the obtention of the monthly mean daily profile for a given month and different positions and inclinations of the module. Finally a third application calculates the photovoltaic electric energy for a given configuration of PV systems that are localized in a given place.

Irradiative heat loss

Even if many works [3] had analyzed the problem of the heat losses due to radiation and conduction, it is important to introduce a short summary. The first term to be evaluated is the net

inward radiative heat flux defined as:


The irradiation, G, in general case can be written as:

■ ■ ■: ■ (5)

J is the radiation heat loss from the total irradiation arriving on the solar tube, is the mutual irradiation, FoniiJ is the ambient view factor, a is the Stephan-Boltzmann constant.


Solving for the surface-to-ambient radiation, qr:


Assuming Gm= 0 and Famb= 1, the solution has given for a single tube. A computation of./takes count of the incident angle on the surface of the solar tube, of the reflectivity/transmittance of the first borosilicate glass shell, of the optical properties of the cermet layer. In the surface of a single evacuated tube together with the parabolic concentrator, G has a value of about 160W. Table 1 represents the radiation heat losses due to different causes. The radiation is converted into heat energy from the cermet layer and transferred by conduction phenomena to the borosilicate glass and to the different layers (aluminium, air and copper) until the vector fluid.

Fourier amplitude sensitivity test

The Fourier amplitude sensitivity test (FAST) is a quantitative method [3]. It computes the contribution of each parameter to variations of the target function. It is called the “main effect” and defined as

S, = varx.|£ (y|x >’ (2)

j var(y)

Variations in numerator and denominator of (2) are multidimensional integrals over appropriate spaces. Their computation is very expensive. In the FAST, they are replaced by the one-dimensional integrals over the some curve exploring the space

x (s) = K (sin o,-s) (3)

In the next stage of the method, the target function y(s) = y(x(s),x2(s),…,xn(s)) is expanded in the


Fourier series y(s) = ^ {A. cos js + Bj sin js}, and then the spectrum Л2 = Aj + B^ of the Fourier


series expansion is used for calculation of the main effects.

The FAST algorithm can quantify influence of the parameters but for this it requires more calculations of the target function than the Morris method. If the system depends on very large number of parameters then it would be reasonable first to apply the Morris method and then to quantify the influence of only the most important parameters by the FAST algorithm.

Software to Visualize, Analysis and Identification of Solar Systems

L. Bujedo1*, J. Rodriguez2, P. J. Martfcnez3, and J. Vicente1

1 CARTIF, Parque Tecnologico de Boecillo Parc 205, 47151 Boecillo, Spain
2 Institute for Renewable Energy, EURAC Research. Viale Druso 1, 39100 Bozen/Bolzano. Italy
3 University Miguel Hernandez, Av. De la Universidad s/n, 03202 Elche Spain
* Corresponding Author, luibuj@cartif. es


The present work, describes the main features of a software developed by the research centre CARTIF under the programming environment LabVIEW ®, which allows to visualize all of the variables collected by means of the installation’s instrumentation to measure different values (temperatures, flows and pressures), and the meteorological station (direct and diffuse radiation, ambient temperature, relative humidity, etc.), and its subsequent exploitation.

The program apart from calculating the identification parameters for the different elements: performance curves for the collectors, efficiency of the heat exchanger, looses on tanks or hydraulic performance, allows the analysis of the system along a time period in a particular date doing a balance of the energetic variables during this period. With the previously recorded values is calculated the average performance for the different elements.

This program has been very useful for other works based on simulation using TRNSYS, as it shows directly the values to be introduced on the model in order to compare the real system and the simulated one.

Keywords: solar cooling, identification, performance, LabVIEW ®.

1. Introduction

As [1] establish one of the problems that solar cooling installations have to face is “the lack of practical experience and acquaintance among architects, builders and planners with the design, control and operation of these systems”.

Despite there are an important number of operative installations, there is no information related to their exploitation. Among the causes can be found: insufficient monitoring of the different heat flows, what prevent to close the power balances. Some of the errors usually committed, can be for example the lack of flow measures because there are constant flow pumps, and with the system working the flow must be constant as well. Nevertheless the experience becomes evident that it is not like this, since the more elemental change on the hydraulic structure of the installation modify the value of this flow, such as for example to feed the generator with the boiler or to do it with the solar installation.

Another problem is to use measuring systems that does not allow to store the values into files, and so sometimes, it must be done a manual collection of the data measured.

On the contrary to have measures of temperatures, flows and pressures and record they periodically
(0.5 minutes), make possible the analysis of the installation performance or the identification of the

different elements and posterior modelling. Allows as well, detection of operation or design failures. The authors would like to highlight the importance that monitoring has for installations as a way to know what is happening and a tool obtain conclusions to learn from the things well done as well as for those wrong.

Mathematical Formulation

The problem of air circulation and heat transfer can be modeled through mass, momentum and energy conservation equations. Assuming incompressibility, the mathematical formulation for the general problem can be written as: Find u, p and в satisfying the following system, div (u) = 0, in Qx [0, T], (1)


p — + p (Vu)u — 2p div s (u) + Vp + р^Р(в-в) = 0, in Q x [0,T], (2)


pcp — + pc^u. (V в) — к div Ve = 0, in Qx[0,T ] , (3)

Vu. n = 0 in Г„ x [0,T], u(x, t) = u(x, t) in Tux [0,T],u(x,0)=u0 in Q x [0,T], kVe. n = 0 in Гd x[0,T], в(х, t) = в in Г x[0,T] and в(х,0) = во(x), in Q

where: u = u(x, t) is the velocity vector, p=(x, t) is the pressure, в = в(х, t) is the temperature, p is the viscosity, p is the density, к is the thermal condutivity, в is the reference temperature, n is the normal vector, c is the

specific heat, p is the coefficient of thermal expansion, g is the gravity vector, e(u) = ^(Vu + Vur), Q is the bounded domain with boundary Г = Гц иГ, = Гс иrd with Гц пГ, = Гс пГd =0 and the time t є [0,T].

The term pgp(e — воо ) allows the coupling of the air circulation and the heat transfer problems.

2. Methods

For the air circulation problem the numerical solutions are here obtained by a stabilized mixed finite element method that allows us to deal with the difficulties that come from the first equation system, Equations (1) and (2): the difficulty in constructing approximation spaces for problems with internal constraint; non-linearities of the convective type and numerical instabilities when advection effects are dominant. Here, a Petrov-Galerkin type method [6] was implemented and applied to analyze indoor air circulation cases, ensuring stability for dominant advection and for the internal constraint. In the case

of a heat transfer problem a stabilized finite element method was implemented — Streamline Up-wind Petrov-Galerkin (SUPG) [7].

Being L and H1 the usual Hilbert spaces and Rf the Lagrange polynomial space of the degree l and class C0. Then, defining the following approximation spaces

V={u„ є (H0Q)nRf))2,u„(x, t) = Uh(x, t) in Г, }c (HW, V0={v„ є (H0Q)nR(Q))2,v„(x, t) = 0 in f }c (Hf))2, Ph=[pf є (L2(Q) n Rf (Q)); I PfdQ = 0jc (L2(Q)), Sh = {% % (x, t) є(н1 (Q)n Rf (Q)) ,% (x, t) = % (x, t) in Гс }c (H*(Q)), Sf = {s„ sh (x, t) є (H1 (Q)n Rf (Q)), Sf (x, t) = 0 in Гс j c (H *(Q)) with the usual norm ||u||2 = | |u 110+lVull0 of Hl and N HIp0of L2.

The wind field can be determined by solving the following formulation:


Find {uh, ph } є Vh x Ph satisfying the following system B (uf, Ph; vh, q) = ° v (vh, q) єКx Ph, where:

((Vvh ) ah — 2^ div Фи ) + V4h ))h+H ph, 4h ), v vh є Vh e Чи є Ph.

with у << 1 and and Д stabilized parameters suggested by Franca and Frey [6].

Подпись: % -%,sh | + (kVf,VSf) + (u.V%,Sf) + J Подпись: % 51 image157 Подпись: = 0, v s, Подпись: S ,0

And find %(x, t) satisfying the following system:

With r= kuVs^,the SUPG stabilized parameters suggested by Brooks and Hughes [7]. The time discretization has been done by backward Euler finite differences.

Fin efficiency

Fin efficiency in Type1223new is calculated using an analytical solution for circular plain fins. This can, however, overestimate the fin efficiency for high performance heat transfer surfaces (fins with waves, slits and louvers) [16]. If the overall heat transfer coefficient is determined using the same fin efficiency calculation as in developing of a heat transfer correlation, no error will be generated. Therefore, the fin efficiency calculation according to Schmidt [15] (taken from [9]) was implemented in the model, which was used in developing of the implemented correlations.

1.2. Liquid-side correlation

For a liquid-side heat transfer correlation a simplified Gnieliski correlation Eq. (1) was implemented, which corresponds to the chosen airside heat transfer correlations [10-13].

_k_](Rep -1000)Pr(f,/2) D. J1 + 12.7f2 (Pr2/3 -1)





u. =



This simplified correlation is only valid for turbulent and transitional flow. At small Re numbers (< 4000) heat transfer can be considerably over predicted with this correlation.

1.3. Heat transfer rate calculation

The overall heat transfer resistance is defined from the following relationship










The authors of the airside heat transfer correlations applied s-NTU relationships for cross-counter flow heat exchangers from ESDU [17]. These relationships are used in the model instead of those in Type1223new for counter-flow heat exchanger. The number of heat transfer units, the capacity flow rate ratio C* and the efficiency s are defined as:

* C

C* = (5),






NTU = UA/Cmn (4),


s =




Knowing NTU and C*, the efficiency can be calculated using a corresponding s-NTU relationship. The total heat transfer rate is determined as:

Q =sQ =sC ■ (t — T ) (7)

max min in, air in, liq

Simulation parameters

The simulation period is one year with a time step of three minutes; weather data from Zurich is used. A single family house with a heating demand of 60 kWh/m2a is simulated. The maximum power of the burner is set to 10 kW and the set temperature of the auxiliary volume of the store is 63°C. A collector area of 20 m2 and a 1 m3 store is chosen.

3.2. Compared values and comparison of results of the different modelling methods

Подпись: sav,therm Подпись: 1 image305 Подпись: (1)

The comparison of the results is done by comparing yearly values of energy delivered to and taken from the store by the several loops (collector, domestic hot water, space heating, auxiliary heating), the resulting energy balance as well as the fractional thermal energy savings (fsav, therm) of the system defined as the auxiliary energy consumption of the solar thermal system (Esoi) compared to the final energy consumption of a reference system (Eref):

The results show that the deviation of the compared values caused by the interfaces and different component order is negligible. The difference between the above named yearly values of the two systems is less than 1 %. This is also according to a simulation test made with only one controller component of the collector loop connected directly to the tank instead of the interfaces in the subsystem model in SIMULATION STUDIO. This also causes little effect compared to simulations where this component is connected via the interfaces only. Because it is only one component, in this case the difference of the compared values is less than 0.03 %.

2. Conclusion

The use of the new implemented subsystem based structure level in SIMULATION STUDIO — the graphical user interface of TRNSYS 16 — helps to improve the graphical representation of complex models as well as achieving a possibility to replace subsystems in a simple way. Regarding the educational field, the new feature leads to a shorter training time and simplified construction of models and permits students to work on projects using and maintaining complex systems, because they are not forced to get familiar with the ASCII input file. In recent simulation projects the new modular

approach was already applied. A comparison with a standard model representation via manually implemented text file showed negligible deviation, namely less than 1 %, comparing yearly values of the energy balance of the store and the fractional thermal energy savings of a solar thermal system for hot water preparation and space heating support used in the IEA-SHC Task 32. This is most likely caused by the additional EQUATIONS used as interfaces of the subsystems. Nevertheless, the new implemented feature should only be a first approach to get a new structure level to the graphical user interface of TRNSYS. It is worth striving for an integration of it in some way in newer versions of SIMULATION STUDIO without the use of extra equations as interfaces. In the future a database of subsystems could be generated like the TRNSYS component database and the subsystems could then also be linked to each other in the common TRNSYS way.


[1] S. A. Klein et al, (2005). TRNSYS 16 — A Transient Simulation System. Solar Energy Laboratory, Wisconsin-Madison University.

[2] W. Weiss et al, (2003). Solar Heating Systems for Houses — A Design Handbook for Solar Combisystems, International Energy Agency, Solar Heating & Cooling Programme — Task 26.

[3] R. Heimrath, M. Haller, (2007). IEA-SHC-Task 32 — Project Report of Subtask A2 of Subtask A: The Reference Building, the Reference Heating System. Institute of Thermal Engineering, Graz University.

[1] Each with ideal pump dimensioning and operating at design point.

[2] Simulation Environment

In order to evaluate and optimise thermosiphon solar hot water systems, a simulation model in the Matlab/Simulink environment [3] was developed. In addition, the Simulink-based CARNOT

# Current address: Institut fur Solartechnik SPF, Rapperswil, Switzerland

[4] For example, air flow velocity > 2 m/s for even distribution and < 3.5 m/s (otherwise pressure drop too

high) [2]

[6] Fortran source code of the model can be downloaded from the TRNSYS website. http://sel. me. wisc. edu/trnsys/tmlib/ASHRAE_secondary_toolkit/heat_and_mass_trnsfr/1223NEW. for

[7] American Society of Heating, Refrigerating and Air-Conditioning Engineers

[8] Considering of condensation is of importance for the mentioned application in Bishkek, Kyrgyzstan as the dew point temperature often exceeds the water inlet temperature of 12°C.

[9] For example, the heat transfer coefficient calculated from (overall heat transfer) measurement data is lower if the heat exchanger is considered as a counter flow heat exchanger (the assumption valid for high number of tubes in air flow direction, e. g. [6]) than that for a cross-counter flow heat exchanger (e. g. [7-13])

’ Fanning factor is defined as the ratio of wall shear stress to the flow kinetic energy per unit volume [18]

[11] Thermal efficiency of a collector where ambient temperature equals the collector working temperature (i. e. no heat losses)

[12] This is necessary to be able to use time dependent values, e. g. temperatures, within equations.

Experimental evaluation of model

Mathematical model has been experimentally validated in the frame of solar collectors testing according to European standard [5] in the Solar Laboratory operated under Department of Environmental Engineering at Faculty of Mechanical Engineering, Czech Technical University in Prague. Different construction of tested solar collectors has been chosen to validate the results from mathematical model with instantaneous efficiency data obtained experimentally under steady — state conditions. Experimental data and efficiency curves calculated from model are graphically compared.

Experimental data points of solar collector efficiency are coupled with uniform uncertainty bars in the graphs. Expanded efficiency uncertainty has been assessed for experimental data from both type A (statistical) and B (instrumental) uncertainties considering the coverage factor 2 with 95% level of confidence [5, 6] and for usual steady state conditions of measurements is between 3 and 4 %.

image12 image13

The theoretical calculation of efficiency curve by model is subjected to uncertainty of input parameters. While geometrical parameters are easily available with high degree of confidence, number of parameters defining the properties of collector parts is found uncertain within narrow range (e. g. absorber and glazing properties parameters, mostly ± 1 %), middle range (e. g. conductivity of insulation layer dependent on its temperature and density, ± 10 %) and quite broad range (e. g. emissivity of absorber back side, insulation or collector frame, > 50 %). Each of varying parameter has a different impact (sensitivity) to resulting efficiency value from high effect of absorber and glazing optical properties to negligible effect of frame external surface emissivity. Uncertainty of input parameters and its influence to calculated efficiency has been expressed by two borderlines where the collector efficiency values can be found in reality.

Fig. 4. Experimental evaluation of the mathematical model by collector testing (different absorber quality)

Fig. 4 shows validation of the model for two examples of different atmospheric flat-plate collectors. Collector on the left consists of nonselective absorber without conductive bond to register pipes (steel absorber is bond to copper pipe only by spot grip-contact). Standard safety glazing and mineral wool insulation are used in its construction. Determination of absorber-pipes bond conductance is a main source of uncertainty in the calculation.

Collector on the right is a representative of high-quality solar collectors with state-of-art copper laser welded absorber. High performance selective coating and solar antireflective glazing properties from optical testing reports were provided thus reducing the uncertainty of calculation to very low values. Due to sufficient back side insulation the influence of uncertain internal and external surfaces emissivity has decreased to minimum.

Подпись: (tm - ta )IG [m2.K/W] Подпись: (tm - ta)IG [m2.KIW]

Mathematical model has been also tested in the field of solar flat-plate evacuated collectors. Validation has been performed on commercial evacuated collector with selective absorber and no insulation applied at the back of absorber (only air layers at given pressure). The collector envelope consists of moulded metal frame and low iron glazing. Support pillars to bear the underpressure stress are placed between glazing and back side of the collector and penetrating the absorber through holes (elimination of thermal bridges, not considered in modelling). The atmospheric variant of the collector (interior pressure 100 kPa) has been evaluated as a reference case (see Fig. 5, graph on the left). The evacuated variant has been tested with interior pressure reduced to 9 kPa (see Fig. 5, graph on the right.

Fig. 5. Experimental evaluation of the mathematical model by collector testing (different interior pressure) 6. Conclusion and outlook

The principles of mathematical model and design tool KOLEKTOR 2.2 for design and virtual prototyping of solar flat-plate collectors have been described. Design tool allows the determination of solar collector efficiency curve, parametric analysis to obtain information on different parameters influence on collector performance and especially for investigation of thermal performance of advanced solar collectors (building integrated, evacuated collectors, etc.). The model has been validated by experimental data from testing of solar collectors with different construction concepts (atmospheric collector with spectrally non-selective and selective absorber; evacuated collector with selective absorber under different interior pressures).

The model and design tool is under continuous development. Validation of the model for unglazed solar thermal collector type is planned and huge experimental investigations are expected due to high uncertainty in modelling of wind convection heat transfer coefficients known from literature. Mathematical model of solar thermal flat-plate collector (glazed, unglazed) and design tool KOLEKTOR will stand as a basis for development of universal solar photovoltaic-thermal liquid collector model. Advanced PV/T model will allow PV collector modelling (fluid mass flow equal zero, considering influence of temperature on electric efficiency), PT collector modelling (PV reference efficiency equal zero) or PV/T collector modelling.


The development of mathematical model and design tool KOLEKTOR has been supported by research project MSM 684077011 “Environmental Engineering” granted by Ministry of Education, Youth and Sports. The experimental validation of the model has been supported by research project CTU 880590 „Experimental validation of mathematical model for flat-plate evacuated solar thermal collector“.


[1] J. A. Duffie, W. A. Beckman, Solar Engineering of Thermal Processes. 3rd edition, Wiley & Sons, Inc., 2006.

[2] D. Y. Goswami, F. Kreith, J. F. Kreider, Principles of Solar Engineering, 2nd edition, Taylor & Francis, 1999.

[3] Solar Energy — The State of Art, ed. by J. Gordon (ISES), James & James, 2001.

[4] TRNSYS 16 release, Mathematical reference, Wisconsin University, 2004.

[5] G. Fraisse, Ch. Plantier, Development and experimental validation of a detailed flat-plate solar collector model, 5th European TRNSYS user meeting, 2003.

[6] J. Koo, Development of a Flat-Plate Solar Collector Design Program, Master Thesis. University of Wisconsin-Madison, 1999.

[7] E. Mathioulakis, K. Vorostopoulos, V. Belessiotis, Assessment of Uncertainty in Solar Collector Modeling and Testing. Solar Energy 66, 337-347, 1999.

[8] Ch. Muller-Scholl, U. Frei, Uncertainty Analyses in Solar Collector Measurement. Proc. of Eurosun 2000, Copenhagen, 2000.

[9] T. Matuska, V. Zmrhal, Software tool KOLEKTOR 2.2, available from http://www. fsid. cvut. cz/~matuskat/kolektor. htm

Heating and cooling systems implementation

1.2. Introduction

Sizing of component is done for one floor. Extrapolation can be done to the whole building. For the different locations, the device power is equal to the maximum heating and cooling load computed before. Nevertheless the same performances are chosen whatever the nominal power. The three cases differ only by the heating and cooling production plant. Emission and distribution of heat/cold is kept as described above. In this way decoupling can be done about two parts of the simulation: building consumption; heating/cooling system consumption. For cases where solar energy is used, the collectors field is assumed to be located on the roof. This implies a limitation of collectors area. To go through this limitation, two versions are presented for case 2 and 3: one roof for 12 floors (case B.), one roof for 3 floors (case A).

1.3. Case 1: Gas boiler and vapour compression chiller

Heating and cooling production for this case has also been defined in the frame of the IEA Annex 48 report. Gas boiler performance is the same for each case: yield at 100 % load is 89.2 %; yield at 30% load is 88.2 %; losses at 0% load are 1.3 kW. Interpolation is done between these points. Heating curve has set point between 45°C and 90°C depending on external temperature.

The cold production is provided by an air-chiller with COP = 3.5. The set point of cold water is always 7°C whatever the case and external conditions. Due to lack of data, part load performance decrease is not taken into account.

1.4. Case 2: Gas boiler, vapour compression chiller, PV field

Gas boiler and chiller have similar behaviour as for case 1. The only difference is that Photovoltaic panels are placed on the roof to feed electrical grid (Panel chosen: SHARP NDL3E62). Given the roof surface, optimisation is done to produce maximum electricity on a yearly basis. A special TRNSYS shading model dedicated to PV is implemented (Type 551). A location has an optimal inclination angle

[5] , the optimal number of rows is found (10 rows). Inverter efficiency is set to 0.78. It is assumed that PV panels work at their optimal point (current-voltage) each time. For case 2A building has 3 floors for case 2B, 12 floors. Roof panel field contains 700 elements and has a total net area of 610 m2.

1.5. Case 3: Gas boiler, solar thermal field, absorption chiller

For this case, gas boiler power is designed to feed the absorption chiller when the load is maximum. It implies a higher power value than for previous cases. The heating/cooling system of case 3 requires additional equipment:

• Solar thermal panels : evacuated tube collector SCHOTT ETC 16

• Absorption chiller : YAZAKI WFC-SH 30 (Nominal point : 105 kWcold; thermal COP = 0.695)

• Storage tank

• Cooling tower : AEC Cooling Tower Systems FG 2004

• Heat exchanger

The central element of the simulation scheme is the storage tank (TRNSYS type 534); four circuits are linked to it: gas boiler, building heating network, absorption chiller hot water circuit and solar panel circuit. 20 cm width rockwool insulation has been modelled in order to decrease storage losses.

Storage tank volume is optimized: this value ranges from 3 to 11 m3 depending on the simulation case and location.

Absorption chiller behaviour has been implemented in a new TRNSYS type 255 (nearly the same as existing type 107) based on manufacturer curves [6] . The model takes into account the energy balance, but not the chiller inertia nor other dynamic effects. Cooling tower fan speed is controlled by rejection circuit temperature. Solar energy passes trough a heat exchanger (95% efficiency) and heats the bottom of the storage tank. Its upper part is heated at 89°C by gas boiler in order to feed the absorption chiller at nominal point. No temperature control neither flow variation is provided on the hot water. Nominal power for absorption chiller is 105 kW for Paris and Stockholm, 150 kW for Lisbon.

The whole solar field has a net collector area of 427 m2 and the slope is optimized for each location. There are four rows (less than case 2 due to size of panels). Mass flow of the fluid has been chosen to 30 litres/(m2_coll_net_area hour) [7] . New auxiliaries’ consumptions have to be taken into account in this case. Common values are given by H.-M. Henning [8] : 0.02 kWhelec/kWhth for solar system, 0.03 kWhelec/kWhth for heat rejection, 0.01 kWhelec/kWhth for absorption chiller.

2. Results

2.1. Introduction

Hereafter are presented simulations results for each case and each location. They emphasize the energy consumption of building. Variable selection for presentation is based on reference book [9]; for example, primary energy savings are related to collector area. When converting net energy consumption in primary energy consumption, the selected coefficient is 2.5 for electricity and 1 for fossil fuels. These are legal values for Belgium. All values are given in kWh by building squared meter per year (building area is 15000 m2 for twelve floor, internal zones area is used), or other units if necessary.