SIMULATION MODEL AND ANALYSIS OF A SMALL SOLAR-ASSISTED REFRIGERATION SYSTEM: DYNAMIC SIMULATION AND OPTIMIZATION

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1 Proceedings of IMECE8 8 ASME International Mechanical Engineering Congress and Exposition October 3-November 6, 8, Boston, Massachusetts, USA IMECE SIMULATION MODEL AND ANALYSIS OF A SMALL SOLAR-ASSISTED REFRIGERATION SYSTEM: DYNAMIC SIMULATION AND OPTIMIZATION F. Calise, M. Dentice d Accadia, A. Palombo DETEC University of Naples Federico II P.le Tecchio 8, 85 Naples, Italy L. Vanoli DSA University of Naples Federico II Via Università, Portici (NA), Italy ABSTRACT In this paper, a complete zero-dimensional transient simulation model of a solar-assisted refrigeration plant is presented. In addition, a case study is discussed, aiming at determining the optimal configuration of the system, from the energetic point of view, in a specific application. The system under analysis consisted of several components: evacuated solar collectors, circulation pumps, variable speed pump, water storage tanks, auxiliary heater, single-stage H O-LiBr absorption chiller, cooling tower, feedback controller, on/off hysteresis controller, single lumped capacitance building and controllers. The simulation was performed using the TRNSYS environment which is provided by a large component library. This software also includes a detailed database with weather parameters for several European cities. The system and the building were simulated using TRNSYS built in models. The system was simulated using specially designed control strategies and varying the main design variables. In particular, a variable speed pump on the solar collector was implemented in order to maximize the tank temperature and minimizing the heat losses. Finally a sensitivity analysis was also performed in order to calculate the set of synthesis/design parameters that maximize the total system efficiency. INTRODUCTION Solar-assisted air conditioning is a very promising concept. Usually, the demand for cooling coincides with the availability of solar radiation, whereas conventional electric-driven systems have the problem of providing their minimum capacity in the hottest day hours. In addition, the use of solar energy in refrigeration can be very useful in limiting the growth of the electric energy demand in summer and in sustaining the development of technologies based on renewable energy sources. Many institutions are presently involved in R&D and demonstration activities on this field: for example, in 998 the International Energy Agency (IEA) launched a program ( Solar Heating and Cooling, SHC ) aimed at improving conditions for the market introduction of solar assisted cooling systems. This program promoted a reduction of primary energy consumption and electricity peak loads due to air conditioning and thereby developed an environmentally friendly way of air conditioning of buildings (IEA, Task 38, formerly Task 5). In the last few years, a lot of research work has been done in the field of the analysis and optimization of solar cooling systems, mainly aiming at developing simulation codes for the design analysis and optimization of such systems. This research activity was often developed using transient simulation environments, such as TRNSYS, MATLAB/SIMULINK, ENERGY PLUS, etc. In particular, Florides et al. developed a very interesting simulation model in TRNSYS for a Cypriot building [, ]. Such model was based on the use of several built-in components (thermostats, auxiliary boilers, tanks, pumps absorption refrigerator, house load and different types of solar collectors: flat plate collectors, evacuated tube collectors, CPC collectors). The system was simulated, varying the main design parameters storage tank volume, collector area, etc. analyzing the results about the energetic, economic and environmental points of views. The result of this work is a flexible tool for Life Cycle Analyses and optimization. Corresponding author: francesco.calise@unina.it; tel: ; fax: Copyright 8 by ASME

2 A similar work was also performed by Assilzadeh et al. [3], for a Malaysian building using the same procedure and system layouts shown in [, ]. The above described simulation tool allowed to establish the optimal collector area and slope for a Malaysian solar cooling system. A sensitivity analysis was also included, varying: collector slope, pump flow, boiler set point temperature, storage tank volume and collector area. The same analysis and optimization was performed by Gaddhar et al for a solar cooling prototype located in Beirut [4]. Atamaca and Yigit performed the analysis for the city of Antalya (Turkey), implementing a more complex mathematical model for the Li-Br Absorption Chiller [5]. A very detailed mathematical model was also implemented by Ardheali et al. [6] and Joudi et al. [7, 8], respectively for the Iranian and Iraqi climates. An experimental work was also performed by Hammad et al. [9], for a Jordan building. The authors described the performance of a.5-ton solar cooling prototype, paying special attention to the variation of the Coefficient of Performance (COP) of the Absorption Heat Pump (ACH), increasing the solar irradiance. The maximum value achieved for the COP was.85 at about 5 W/m incident solar irradiance. All the above mentioned works were developed implementing very similar system layouts. Further research projects are related to the development of innovative solar cooling systems based on emerging technologies, such as solar fiber-optic mini-dish systems and thermodynamic cascade systems []. In this scientific background, the authors aimed at developing a dynamic model of a prototype of solar assisted absorption refrigeration system, to be installed in the next months in Naples (Italy). The simulation tool presented in this paper, able to perform energy, exergy, environmental and lifecost analyses, will be used to optimize the design of such system, determining the best set of the most important synthesis/design parameters. In this paper, only the energy analysis model is presented, including some innovative control strategies based on variable-volume pumps and including a sensitivity analysis on a few design parameters. Future works will include the development of an economic model, in order to perform a complete exergy and thermoeconomic analysis of the system []. DESCRIPTION OF THE SYSTEM The solar cooling system analyzed in this paper was simulated using the TRNSYS environment, using a combination of the built-in components model, available in its library []. The system, shown in Figure, consists of: evacuated tubes solar collectors (SC), a variable speed pump on the solar loop (P), a feedback controller - including a solar control system - on the solar loop (FBC), a storage tank (TK) subject to thermal stratification, between the solar loop and the hot water (HW) loop, a gas-fired auxiliary boiler (AH) located at the exit of TK before entering the absorption chiller (ACH), a fixed volume pump for the ACH/TK loop (P), a Li-Br single stage absorption chiller (ACH), a closed circuit cooling tower (CT), a fixed volume pump (P3) for the cooling water loop (CW), a fixed volume pump for the chilled water (CHW) loop (P4), an inertial storage tank (TK), a single-lumped capacitance building (B), an hysteresis on/off controller for the CW loop (OFC). In addition, some additional TRNSYS components are required to perform the simulation: i) hourly weather data for the location selected, including temperature, humidity, radiation, wind velocity, etc.; ii) lighting and occupation profiles to be employed in the calculation of the building gains; iii) psychometric calculator, required to evaluate the wet bulb temperature to be employed in the CT model; iv) data readers including the values of parameters for the current simulation. The operating principle of the system can be summarized as follows. The solar loop is constituted by the variable volume pump P, the solar collectors SC, the feedback controller FBC and the solar controller SCS; the fluid evolving in the solar loop is a pressurized mixture water/glycol, with a boiling point of C. The SC is hydraulically connected with the top of the tank TK, whereas the pump draws the fluid from the bottom of the TK to the inlet of the SC. The variable speed pump P is controlled by FBC and SCS by reading the data of two temperature sensors: i) SC outlet temperature; ii) TK bottom temperature. The FBC shuts down the pump in case the SC outlet temperature is lower than the TK bottom temperature, in order to prevent heat dissipation by SC in case of scarce/zero solar irradiation; when the SC outlet temperature is higher than the TK bottom temperature, the FBC generates a control signal for P, so that the pump speed is varied in order to achieve the fixed temperature set point at the SC outlet. In fact, in case of scarce irradiation, the pump flow is reduced; on the other hand, in case of high solar irradiation, the pump flow is increased up to its maximum value. This control strategy is very useful since: i) prevents SC overheating, in case of high solar irradiation and low cooling load; ii) permits to achieve high SC outlet temperatures even in case of low solar irradiation; iii) prevents heat dissipation, since the SC only operates when its outlet temperature is higher than the TK bottom temperature. Therefore, the TK is connected on one side (in the following: side ) with the solar loop, and on the other side (in the following: side ) with the ACH hot water loop. In fact, the pump P draws the fluid from the top of the TK, to the auxiliary heater (AH). This component is controlled by an ON/OFF controller which shuts down the AH when the TK outlet temperature is higher than the set-point fixed for the AH. Thus, the fluid exiting the AH enters the primary side of the single stage Li-Br absorption chiller (ACH), heating up the generator; the ACH outlet fluid is finally brought the bottom of side () of the tank TK. Obviously, the ACH is also connected with the closed circuit cooling tower (CT) loop, equipped with a fixed volume pump (P3) and with the chilled water circuit. In particular, the fixed flow pump P4 draws chilled water from the ACH and drives it to the inertial storage tank TK; the water exiting the TK is then brought to the fan-coil units placed in the building (B), and finally returns to the ACH. The CHW, CW Copyright 8 by ASME

3 and HW loops are controlled by the hysteresis on/off controller OFC: P, AH, ACH, P3, P4 and CT are shut down when the indoor average temperature in the building is lower than the fixed set-point, and are switched on when this temperature overcomes the set-point. This strategy maximizes the system efficiency. Finally, the building internal loads are calculated on the basis of the fixed profiles of internal gains and using the irradiation data included in the TRNSYS weather component. Figure - System layout SIMULATION MODEL The components of the solar assisted refrigeration system were simulated on the basis of the TRNSYS built-in library []. In the following, the simulation models will be briefly described, paying special attention to the fixed synthesis/design parameters. Evacuated Tube Solar Collectors (SC) In the SC simulation model, included in the TRNSYS library, the thermal efficiency of the collector is calculated using the Hottel-Whiller-Bliss equation: Q L in, SC amb u F U t t R SC FR () n ASC IT IT U L/ T tin, SC tamb Eq. () can be also written as: SC t in, SC tamb tin, SC tamb a a a () I IT The coefficients a, a and a are available for any collector tested according to ASHRAE or European Standards. The SC model also takes into account that standard efficiency curves are calculated for a single collector in clear days at normal incidence and nominal flow rate, whereas the inlet flow rate may vary during the operation, the collectors may be connected in series and the solar irradiation is usually non-normal. These effects are accounted for by introducing appropriate correction factors []; details regarding the correction algorithms are given in the TRNSYS documentation. Finally, the SC model also considers that evacuated tube collectors are optically nonsymmetric, introducing biaxial Incidence Angle Modifiers (IAM) coefficients both for beam and diffuse radiation. [3, 4]. The set of parameters used for simulating the solar collectors in the case study presented in the paper is summarized in Table. Table - Parameters for the Solar Collectors, SC Number in series - Collector Area 3 m Nominal flow rate kg/(h m ) a.8 - a kj/(h m K) a.3 kj/(h m K ) Collector slope 3 degree Collector azimuth (sud) degree Pumps Four circulation pumps are included in the simulation model: a variable speed pump (P) and three fixed speed pumps (P, P3 and P4). Their simulation is based on simple energy and mass balances. In addition, the model also takes into account the energy transferred from the pump motor to the fluid and to the environment []: the heat transferred from the pump to the fluid, taking into account that the pump motor is housed outside the fluid stream, is: Q P (3) fluid motor pumping while the heat transferred to the environment and the pump outlet temperature are: Q ambient P motor (4) Q fluid TP, out TP, in m (5) Finally, the power drawn by the pump at any current time is calculated as: n i i P i P P (6) In the simulation presented in this paper, eq. (6) was assumed to be linear. In the case of pump P, the actual flow T 3 Copyright 8 by ASME

4 rate is the product between the input control function and the flow rate. The set of parameters used for simulating the pumps in the case study is summarized in Table. Table Parameters for pumps and electric motors P mass flow rate 86 kg/h P mass flow rate 667 kg/h P3 mass flow rate 585 kg/h P4 mass flow rate kg/h (P, P, P3, P4).9 - motor (P, P, P3, P4).9 - pumping Power coefficient,.5 - Solar feedback controller (FBC) and control system (SCS) The FBC calculates the control signal to be sent to P in order to maintain the controlled variable (SC outlet temperature) at the fixed set-point. In addition, it stops P when the TK bottom temperature is higher than the SC outlet temperature. The FBC models a real PID feedback controller and continuously adapts its output signal, using the secant method to calculate the control signal that minimizes the tracking error. It is also provided with a threshold value for nonzero output, that permits to simulate the pump minimum flow rate, avoiding the use of unrealistic values (i.e., P flow rates lower than the minimum value). The set of parameters for the feedback controller in the case study is summarized in Table 3. Table 3 Parameters for the Feedback Controller, FBC SC outlet temperature set-point 9 C Threshold value for non-zero output 5 % Storage tanks The system layout includes two storage tanks, subject to thermal stratification: the first one for the solar loop (TK) and the second one for the CHW loop (TK). Their modelling is based on the assumption that the tank consists of N fully-mixed segments, of equal volume. The tanks are also provided by a pressure relief valve to account for boiling effects; the valve model takes into account the energy released but neglects the corresponding loss of mass. Therefore, the temperatures of the N nodes are calculated on the basis of simple unsteady energy and mass balances [, 5]. The set of parameters used for simulating the storage tanks in the case study is summarized in Table 4. Auxiliary Heater (AH) The heater only operates when its inlet temperature is lower than the fixed set-point. In this case, the heater burns natural gas to heat the fluid up to the fixed set-point. The model of the AH considers: i) the overall efficiency of the boiler; ii) the losses between the heater and its surroundings during the operation. Therefore, the outlet AH temperature at a given time is: Q AH, AH m AHcp, fluidt in, AH UA T AH in, AH UA T AH amb T out, AH (7) UAAH m AHcp, fluid The set of parameters regarding the auxiliary heater in the case study is summarized in Table 5. Table 4 Parameters for the tanks, TK and TK TK Volume/ SC area 5 L/m TK boiling point C TK overall loss coefficient 3. kj/(h m K) TK nodes 5 - TK height. m TK Volume. m 3 TK boiling point C TK overall loss coefficient 3. kj/(h m K) TK nodes 5 - TK height.8 m Table 5 Parameters for the Auxiliary Heater, AH Rated thermal capacity 55 kj/h AH efficiency.9 - AH overall loss coefficient 3 kj/(h K) AH outlet temperature set-point 85 C Absorption Chiller (ACH) A single-effect hot water LiBr-HO absorption chiller was considered. In this work, the ACH is simulated using a normalized catalog data lookup approach. The electric power consumption is neglected [6]. The performance data are numerically express by the cooling ratio factor and the input heat ratio factor, respectively shown in the following eqs. (8) and (9): Q f f, T, T, T, T c, AHP QC DL in, CHW in, HW in, CW SETout, CHW Q c, AHP, Q f f, T, T, T, T H, AHP QH DL in, CHW in, HW in, CW SETout, CHW Q H, AHP, () () Note that the input HW energy flow is determined on the basis of a fixed value of the ACH Coefficient Of Performance, that is: Q c, AHP, H, AHP, () COPAHP, Q The thermal energy required to cool the ACH is: Q m c ( T T ) (3) c, req CHW p, CHW in, CHW setout ; CHW Therefore, the design load ratio is: 4 Copyright 8 by ASME

5 f DL Q Q c, req c, (4) Then, from the factors in eqs. () and (), the cooling and heating rate at any given time can be evaluated. Such values are subsequently employed in the energy balances, in order to calculate the HW, CHW and CW outlet temperatures. The set of parameters used for the absorption chiller in the case study is summarized in Table 6. Table 6 Parameters for the Absorption Chiller, ACH Rated Cooling Power 45 kj/h Rated COP.8 - CHW outlet temperature set-point 7 C Auxiliary electrical power kj/h Cooling Tower (CT) A closed circuit cooling tower was considered, i.e. the CT working fluid (CW) is completely isolated from air and condensation water. The simulation model included in TRNSYS is based on the algorithms presented by Zweifel [7]; it shows great accuracy and reliability, since is capable to match the manufacturers catalog data values over a wide range of operating conditions []. The model requires to set conditions at design point (ambient dry bulb and wet bulb temperatures, ambient pressure, inlet and outlet CW temperatures, CW fluid flow rate, air flow rate, fan power). The air wet bulb temperature is calculated using the psychrometric calculator included in the TRNSYS library. The model relies on the basic assumption that the satu air temperature is the temperature at the air-water interface and is also the temperature of the outlet fluid. The electric power required by the fan is calculated using the same approach employed for pumps, by a fourth order polynomial (eq. (6)). The set of parameters used in the case study for the cooling tower is summarized in Table 7. Table 7 Parameters for the Cooling Tower, CT Rated amb. dry bulb temperature 35 C Rated amb. wet bulb temperature 5.6 C Rated inlet CW temperature 33 C Rated outlet CW temperature 8 C Rated CW flow rate 585 kg/h Rated air flow rate 465 kg/h Rated Fan power kj/h Rated amb. pressure.3 bar Building A simple building model was included, in order to estimate the cooling load and the outlet CHW temperature. The model is based on a single lumped capacitance building developed by ASHRAE [6]. Here, the building average temperature and the CHW outlet temperature are calculated using unsteady energy balance on the building control volume including walls, air and cooling coil. The sum of all thermal flows entering this control volume (transmission, ventilation, solar and gains) represent the cooling load, to be matched by the CHW flowing through the cooling coils. The gains are calculated on the basis of fixed profiles for lighting (%, 4 pm to 6 pm) and occupation (%, 8 am to 6 pm), referred to a typical office building, and taking into account the solar irradiation on the transparent surfaces. Note that the transmission load maybe negative, in case of indoor average temperature lower than the environmental one. The building average temperature is calculated on the basis of the following balance: dt d (5) B cv QCoil Qgain UB AB TB Tamb The CHW coil outlet temperature and the coil cooling energy is calculated using the well-known ε-ntu method [8]. The building average temperature is controlled by the OFC controller (with hysteresis), which is set at 6 C ±.5 C. It is connected to the P, AH, ACH, P3, P4 and CT, and is capable to shut them down when the indoor temperature is lower than the corresponding set-point or when no person is occupying the building. The set of parameters used for simulating the building in the case study is summarized in Table 8. Table 8 Parameters for the Building, B Building Length 4 m Building Width 4 m Building Height 4. m Wall Transmittance. W/m K Window Transmittance 3. W/m K Wall surface area 9 % Window surface area % Window shading factor. - Wall weight kg/m Wall specific heat. kj/kg K Occupancy. person/m Lighting load 5 W/m Equipment load 3 W/m 3 People load 3 W/person A CASE STUDY: RESULTS AND DISCUSSION The solar refrigeration system, described in the previous sections, was simulated on a yearly base, using the data shown in Tables from to 8. The weather data were referred to Naples, Italy, assuming that the system had to operate from hour 36 to hour 796, corresponding to the typical cooling period for Naples. The overall results provided by the simulation on a yearly base are shown in Table 9. Figure and Figure 3 show the results on a daily base. From Table 9, it can be observed that the system achieved a significant value of the solar fraction (F sol ): more than 7% of the overall building cooling load was supplied by the solar system. On the other hand, the overall solar collector efficiency (Q usc /A*I T ) was remarkably low, mainly due to the high temperature differences between the fluid evolving in the solar collector and the environment. Note that this parameter is calculated also considering the time steps 5 Copyright 8 by ASME

6 Thermal Flow Rate (kj/h) Effciency/Fractor in which the cooling provided from the solar system is zero, whereas the solar radiation incident is positive. This may occur when the TK temperature approaches the boiling limit. Table 9 also shows that the tank losses, although low, are not negligible since they are in the same order of magnitude of the heat required by the AH. The average ACH Coefficient of Performance is very high, about.8, also due to the AH control strategy, that operates in order to avoid significant decreases in the ACH HW inlet temperature, preventing ACH capacity drops. The yearly-based results are detailed in Figure and Figure 3, respectively showing the daily thermal flows and the daily solar fraction and collector efficiency. Table 9 Results of the simulation for the case study Parameter Unit Office Office opt. Residential A*I T kj 9.9E+8 9.9E+8 9.9E+8 Q usc kj.3e+8.5e+8.e+8 Q losstk kj.78e E+7.99E+7 Q AH kj 7.66E+7.95E+7 6.9E+7 η SC -.34E-.5E-.E- F Sol - 7.3E- 8.65E- 6.8E- Q losstk kj -8.76E E E+5 Q tach kj.58e+8.58e+8.8e+8 Q CT kj 4.8E+8 4.8E+8 4.6E+8 Q cach kj.e+8.e+8.8e+8 In particular, such figures show that the net solar gain is much lower than the incident radiation, for whatever day. In fact, the solar collector efficiency rarely overcomes 4%. 8 x Q t,ach Q loss,tk Q CT Q C,ACH DAY Figure - Daily thermal flow rates This is due to the set point temperature at the collector outlet, that determines significant thermal losses also in case of high ambient temperatures. It is also noteworthy that the contribution from the AH is mainly required when the ambient temperature is high and/or the incident radiation is low. The thermal energy provided by the AH, depending on TK, ambient temperature and solar radiation, is much more discontinuous than the thermal losses in the storages, which mainly depend on the ambient temperature. As a result, the AH peak is much higher than the TK losses, but the overall energy values are in the same order of magnitude DAY Figure 3 - Daily solar fraction and collector efficiency Figures from 4 to 7 show temperatures and heat flows for typical summer days. In particular, Figure 4 shows the ambient air profile, and the temperature of the building, that is always equal to 6 C ±.5 C, due to the OFC control strategy. Here it also possible to detect the building capacitance effect, in the temperature difference between the building and the environment. In Figure 4 the inlet and outlet CT temperatures are also reported: these temperatures are significantly higher than the ambient wet bulb temperature, and their profiles are mainly affected by the ACH cooling load. Finally, it can be noted that in the CHW loop the temperature of the water entering the building is controlled by the ACH set-point, while the exiting water temperature depends on the building cooling load. However, the inertial tank TK allows to achieve stable exiting temperatures, always close to the design value of C. The stratification in the TK is poor, and the insulation is very efficient, since, during the night, the local temperature increase is lower than 3. C. Figure 5 shows the temperature profiles in the SC and HW loops. The solar control is capable to keep the set-point value until 9: am. Later, the increase in the solar radiation induces an increase of the SC outlet temperature, over the set-point. When this happens, the controller sets the pump flow rate to the minimum value, in order to limit this increase. Even if the SC temperature is so high, the TK temperature is significantly lower, specially before 9: am. Finally, after 5: pm, when the solar radiation decreases, the TK outlet temperature decreases under the AH set-point, causing its activation, as also shown in Figure 6. SC F sol 6 Copyright 8 by ASME

7 Efficiency/factor Temperature ( C) Thermal Flow Rate (kj/h) Temperature ( C) t out,ct t B t wb t amb t out,chw t in,ct t in,chw lower, due to the dramatic oscillations shown in Figure 7. Here, it is clearly shown that the SC efficiency does not depend only on incident ration (Figure 6) and ambient temperature (Figure 4): in fact, it is low even when the radiation and the ambient temperatures are high (e.g. : am). Therefore, this effect is mainly exogenous, and is due to the tank, that is not capable to store the energy coming from the SC loop, inducing the SCS to limit the P3, which determines the efficiency drop shown in Figure 7. x 5 8 I T *A SC Q B hour Figure 4 - CHW, CW and ambient temperatures 4th July t in,sc 6 4 Q usc Q AH Q C,AHP 95 t in,h,ahp t out,h,ahp 9 85 t TKavg t out,ah hour Figure 6 - Thermal Flow rates 4th July.4 8. COP AHP hour Figure 5 - SC and HW temperatures 4 July In this latter figure, it also shown the profile of incident radiation and the oscillating contribution provided from the solar source. Such oscillations mainly occur when thetk temperature is significantly higher than the SC set-point: in this case, the SCS limits the solar energy input in order to avoid fluid boiling. Obviously, such circumstance could be avoided using a larger storage tank. In Figure 6, the building cooling load is also shown: here, it is clear that this load mainly consists of internal gains. Therefore, the slowly variable building load leads to the ACH cooling and heating profiles displayed in the same figure. Finally, note that the ACH is activated at about :, since, before this time, the building temperature is below the fixed set-point. Therefore, the TK must be designed to store the solar energy gain for several hours, before the corresponding the cooling demand. The selection of the optimal volume of the storage tank is a very complex task and will be discussed in the next section. Figure 7 shows that the actual efficiency of the solar collector may reach values up to 55%; however, the corresponding daily or yearly average value is significantly SC f QC f QH hour Figure 7 - Efficiencies 4th July However, the trends shown in the previous figures may significant vary with the day selected for the analysis. The results of the simulation are also investigated on a weekly base. During weeks specially hot, CHW returning water may overcome the C design point, the TK temperature rarely overcomes the SC set-point and the AH contribution dramatically increases. The contrary occurs in a relatively cooler week, where very often the solar contribution is much higher than the ACH demand, causing the opening of the TK pressure relief valve, which discharges mass and energy when the TK temperature approaches the boiling point. In this week, 7 Copyright 8 by ASME

8 Efficiency/Factor Thermal Flow Rate (kj/h) Efficiency/Factor the ACH operates for very short periods and the AH energy demand is significantly low. On the other hand, the operation of the SC is much more discontinuous, due to the dramatic decrease in the building cooling load. SENSITIVITY ANALYSIS As also shown in the case study, a large number of parameters influence the performance of a solar-assisted airconditioning system, in particular: set point temperatures, managing of boiling point, optimal volume of the storage tanks, optimal area of the solar collectors, etc. Therefore, a sensitivity analysis was performed, in order to determine the set of design and operational parameters that maximize the performance of the system. However, a simplified approach was here adopted, consisting in varying the parameters one at the time, while the others are fixed at their nominal values. Future works will include a complete energy and thermoeconomic optimization, considering the simultaneous variation of all the most important synthesis/design parameters. Area of the Solar Collectors, A SC. The SC area was varied in a wide range, from 5 up to 4 m. The results are show in Figure 8 and Figure 9. In particular, Figure 8 displays the increasing function F sol, also showing that the SC efficiency slightly decreases with A SC. In fact, the higher A SC, the higher the amount of incident solar radiation (Figure 9), while the building cooling load is the same. Therefore, higher SC areas would determine a lower use of the solar energy available. On the other hand, larger A SC would also cause a dramatic reduction of the AH energy demand, leading to high values of F sol. The determination of the optimal value of the SC area is a typical thermoeconomic problem, since the optimum trade-off between higher capital cost (larger Area) and lower operating costs (lower energy request for the AH). This analysis will be performed in forthcoming works. Volume of the storage tank TK(ratio TK/ A SC) The volume of TK is very important in order to maximize the overall system performance. Small tanks would determine a fast system response but also larger waste of solar energy, not stored in the tank, in case of low cooling loads; on the other hand, large tanks would determine slow system response, higher thermal losses and higher capital costs. This trade-off is clearly shown in Figure, where the solar fraction reaches a maximum close to L/m x Area SC (m ) Figure 8 η SC and F sol, varying A SC SC F sol Area SC (m ) I T *A Q U,SC Q loss,tk Q AH Figure 9 - Thermal flows, varying A SC SC F sol V TK /A SC (L/m 3 ) Figure η SC and F sol, varying V TK /A SC 8 Copyright 8 by ASME

9 Thermal Flow Rate (kj/h) Efficiency/Factor Thermal Flow Rate (kj/h) x 8 Q U,SC Q loss,tk Q AH V TK /A SC (L/m 3 ) Figure - Thermal flows, varying V TK /A SC In addition, the SC efficiency is an increasing function of this parameter, since the larger the tank, the larger the amount of energy storable. Note also that large tanks prevent the possibility of fluid boiling and reduce losses through the pressure relief valve. Figure also shows that larger tanks would determine a lower AH energy demand, but also higher thermal losses. Set-point temperatures (T set,ah, T set,sc ) and mass flow rate m P4 A sensitivity analysis was also performed for the AH and SC outlet set-point temperatures. These temperatures were varied in the ranges from 8 C to 9 C and from 85 C to 95 C, respectively. In both cases, the results showed that system performance is slightly affected by these parameters. However, the best performance (higher Fsol and ηsc) could be achieved for the lowest temperature values (8 C and 85 C, respectively). In fact, the lower the set-point temperatures, the lower the thermal losses, and the higher the SC efficiency. The same analysis was also performed for the P4 flow rate. The system is slightly affected by this parameter, with an optimal value close to 5 kg/h. Finally, the building set-point temperature was varied, using both UE and US standards. The results are displayed in Figure and Figure 3.The solar fraction significantly depends on this set point, since an increase of such parameter would determine a remarkable decrease in the cooling load to be covered by the ACH. In addition, for higher building setpoint, a larger amount of solar energy has to be stored, determining higher average temperatures in the solar loops, with higher thermal losses in the TK and lower efficiency of the SC. The analysis performed for the location at Naples was performed for two more Italian cities, Milan (MI) and Trapani (TP), featured by very different values of the Heating Degree- Day (DD) values. A comparison is synthetically shown in Figure 4 and Figure 5. In Milan, the maximum solar fraction is achieved: even with low solar irradiation, the cooling load is much lower than that required for Naples or Trapani. On the other hand, in Trapani the maximum SC efficiency is achieved, since the temperature difference between SC and the environment is minimum. In addition, the losses in the storage increase with DD, and the energy required to the AH dramatically decreases with DD, due to the significant reduction of the cooling load. On the basis of the results provided by the sensitivity analysis, the simulation was re-started, using the approximate optimal values estimated for the TK volume, the P4 mass flow rate and the set point temperatures Tset,AH, and Tset,SC. The results are displayed in Table 9, and show a significant increase of the overall system performance. Finally, using the same parameters of the previous cases, the simulation was also performed with typical residential profiles for lighting and occupation. The results are displayed in Table 9. The residential building has a lower but very discontinuous cooling load, so that the system efficiency is significantly lower with respect to the other cases SC F sol t set,b 8 x Figure η SC and F sol, varying tset,b Q U,SC Q AH Q t,ahp Q t,ct Q c,ahp t set,b Figure 3- Thermal flows, varying tset,b 9 Copyright 8 by ASME

10 Thermal Flow Rate (kj/h) Efficiency/Factor TP NA SC F sol MI x Degree-Day ( C) Figure 4 η SC and F sol, varying DD TP NA Degree-Day ( C) Figure 5- Thermal flows, varying DD I T *A Q U,SC Q AH Q t,ahp Q t,ct Q c,ahp CONCLUSIONS In the paper, a complete dynamic model of a solar assisted refrigeration plant has been presented. The tool was used in order to analyze the performance of the system as a whole and the behavior of the main plant component. A sensitivity analysis was also presented, determining the approximate optimum values of a few design and operational parameters. Results showed that it is mandatory to select appropriately the volume of the storage tank and to lower the SC and AH set-point temperatures. Future developments of this work will include the development of a complete cost model, to be employed in a thermoeconomic analysis and optimization: the result of this work will be the design of a pre-commercial, optimized prototype to be installed in Naples, for experimental analyses and model validation. NOMENCLATURE Efficiency MI Q u tach Useful solar collector energy gain (kj/h) Q ACH input thermal energy (kj/h) Q cach ACH cooling energy (kj/h) A Area (m ) I Total radiation on SC surface (kj/h m ) T F R n Heat removal factor trasmittance-absorptance at normal incidence angle U trasmittance (first term, kj/h m K) L U LT / trasmittance (second term, kj/h m K ) Q heat flow (kj/h) P Mechanical Power (kj/h) m mass flow rate (kg/h) UA overall trasmittance (kj/h K) COP coefficient of performance c Specific heat (kj/ kg K) p F sol Solar Fraction time (h) density (kg/m 3 ) Control Function [,] subscripts in out amb req set REFERENCES inlet outlet ambient at nominal conditions required set by the controller. Folrides, G.A., Kalogirou, S.A., Tassou, S.A., Wrobel, L.C., Modelling and simulation of an absorption solar cooling system for Cyprus. Solar Energy,. 7(): p Folrides, G.A., Kalogirou, S.A., Tassou, S.A., Wrobel, L.C., Modelling, simulation and warming impact assessment of a domestic-size absorption solar cooling system. Applied Thermal Engineering,. : p Assilzadeh, F., Kaligirou, S.A., Ali, Y., Sopian, K., Simulation and optimization of a LiBr solar absorption cooling system with evacuated tube collectors. Renewable Energy, 5. 3: p Ghaddar, N.K., Shihab, M., Bdeir, F., Modeling and simulation of solar absorption system performance in Beirut. Renewable Energy, 996. (4): p Copyright 8 by ASME

11 5. Atamaca, I., Abdulvahap, Y., Simulation of solarpowered absorption cooling system. Renewable Energy, 3. 8: p Ardehali, M.M., Shaharestani, M., Adams C.C., Energy simulation of solar assisted absorption system and examination of clearness index effects on auxiliary heating. Energy Conversion and Management, 7. 48: p Khalid A.Joudi, Q.J.A.-G., Development of design charts for solar cooling systems. Part I: computer simulation for a solar cooling system and development of solar cooling charts. Energy Conversion and Management, 3. 44: p Khalid A.Joudi, Q.J.A.-G., Development of design charts for solar cooling systems. Part II: Application of the cooling f-chart. Energy Conversion and Management, 3. 44: p Hammad, M., Zurigat, Y., Performance of a second generation solar cooling unit. Solar Energy, (): p Gordon, J.M., Choon Ng, K., Highe Efficiency Solar Cooling. Solar Energy, (): p Kotas, T.J., The exergy method of thermal plant analysis, ed. K.P. Co Solar Energy Laboratory, U.o.W., Madison, TRNSYS. A transient system simulation program. 3. McIntire, W.R., Factored approximation for biaxial incidence angle modifier. Solar Energy, 98. 4(4): p Theunissen, P.H., Beckman, W.A., Solar trasmittance characteristics of evacuated tubular collectors with diffuse back reflectors. Solar Energy, (4): p Klein, S.A., A design procedure for solar heating systems, in Department of Chemical Engineering. 976, University of Wisconsin-Madison. 6. ASHRAE, Handbook of Fundamentals, ed. R.a.A.- C.E. American Society of Heating., Atlanta. 7. Zweifel, G., Dorer, V., Koshenz, M., Weber, A., Building energy and system simulation programs: model development, coupling and integration, ed. EMPA. 8. Kakac, S., Liu, H., Heat Exchanger Selection, Rating, And Thermal Design., ed. C. Press Copyright 8 by ASME

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