Small Central Solar Heating Systems Measurement - Simulation Model Validation - Evaluation

Jochen Dahrn Dept. of Building Services Engineering Chalmers University of Technology

ABSTRACT

A new solar heating plant for a small residential ameain Onsal~ south of Goteborg, Sweden, was put into operation in spring 1996. The system was designed with a newly developed collector roof of about 200m2 collector area and a diurnal water heat stclre of 18m3. Nine buildings with 36 residential units are connected to the heating plant. Detailed measurements, according to prelimina~ European Standards, were carried out to validate a simulation model (TRNSYS) for the systt m. A sensitivity analysis of the validated model for major system parameters applying a factorial desig ~ experiment was carried out and recommendations for an optimized system design are given. As an aid for the designer a simplified model (T’RNSED) based on the validated model was created. Measurements, model validation, thermal performance predictions and a sensitivity analysis of major model parameters are described.

Introduction

The design of this particular solar plant is based on lessons learned frc~m earlier plants complemented with detailed system simulations using TRNSYS. In these plants the heat store is a key component concerning system performance. Thus, the new system design is focused on the store configuration. A new store model, i.e. the MULTIPORT store model (Dri.ick & Hahne 1996), enabled a more detailed evaluation of the heat exchanger design, stratification, placing of control sensors, etc. Previous store designs (atmospheric pressure) all comprised several internal fin tube heat exchangers on the load side. The solar collector circuit has been connected either via an external heat exchanger with one storage inlet in the middle of the tank or, due to economic reasons, via several parallel connected internal fin tube heat exchangers. The system control is sir~ple with a few temperature sensors. The storage design applied in Onsala comprises a,collector circuit connected via an external heat exchanger with two storage inlets in the middle and on the top. This system is believed to be the most advantageous from a thermal as well as economic point of view (Dahm 1994, 1996, 1997).

Small Central Solar Heating Systems Measurement -5.23 Qloss rv+i++R+’-,

Figure 1: Solar Plant Onsala under construction in spring 1996, schematic drawing

To evaluate the performance of the plant and the design simulation tool, detailed measurements as proposed in a preliminary European standard were carried out. Using these measurements the simulation model components were validated and combined to a system simulation model. The pefiormance of the plant is predicted for a variety of load conditions by the system simulation model and applying controller settings. For the validated model, a sensitivity analysis was carried out on an economical basis. A so called factorial design experiment on two levels was carried out for major system j)arameters. This method in combination with economical criteria shows the effect of single parameters and parameter interactions. Based on the validated model, an easy to use clesign tool was created in order to spread the knowledge to a broader audience, i.e. consultant agencies.

Measurements

The measurements were carried out by the Monitoring Center at the Chalmers University of Technology. To cope with the requirements proposed in the preliminary European standard prEN 12977-2 (CEN 1997), additional monitoring equipment was installed to measure the energy balance for the store and collector field performance in more detail. The planning of the measurement system, e.g. placing and choice of the sensors was integrated in the early stages of the design process to assure that all devices were correctly installed. After commissioning and putting the measurement system into operation, all functions and components of the plant were checked and detected errors were corrected. The additional monitoring system comprises additional temperature sensors, more accurate flow meters in the solar loop and an additional pyranometer to measure the diffbse solar rac[iation. With this equipment, measurements were carried out from the 25 .April to 9 July 1996. The values were sampled in 15s (7. 5s for solar radiation) intervals and the average recorded every 10th minute. ,4fter 9 July some of the additional measurement equipment, i.e. the additional pyranometer, was dismounted and all values were recorded in hourly values.

5.24. Ilabm The overall perflorrnance of the solar heating system can be shown as energy balance of the store for two periods (see Figure 2). The more accurate and more in detail measured period (25 April until 9 July) is used later on for parameter identification and model validation.

15

10 z’ QAUX ~5 Q~O~ . % o a) z -5 QDwCl~ i Q~W -10 Q~H -15 17 19 21 23 25 27 29 31 33 35 37 39 41’ 43 45 47 4$) 51

Week No. (25.04. -:31.12.1996)

Figure 2: Measured overall performance of the Ons,ala plant for the second half 01’1996 in weekly values.

After the 9* of July measurements were recorded in hourly intervals, as some of the additional measurement devices were taken down. A summary of al] energies transferred during :hese two periods is given in Table 1.

Table 1: Measured enerw balance. The store heat loss 0,....s,,,.. is calculated from energv–– —-—-J balance. Date \ Energy (MWh) G;, ,., Q~O, QAUX Qmnv Q.H~circ QsH QIoss_ 25’hof April - 9ti of July 74.0 17.7 36.2 -8.4 -4.6 -36.4 -4.6 Total Jul. - Dec. 1996 113.5 26.6 115.0 -26.2 -16.9 -99.8 +1..z”-’-

Although the measurements are within 1-2% accuracy for each port, it is not possible to determine the heat losses of the store from the measured energy balance. The domestic hot water demand (DHW) in Onsala is much lower than projected. Extrapolating the measured value the demand would be 1.1 MWh per year per residential unit. This is about 40% of the designed demand. The DHW circulation losses amount to about 50’%0of the D13W demand. The space heating demand (incl. heat distribution losses) is as designed.

Parameter Identification and Model Validation

The evaluation of the solar heating plant is carried out based on a short-term system test and long-term performance predictions using a validated simulation model according to the planned European standard prEN 12977-2.

Small Central Solar Heating Systems Measurement -5.25 updateparameterset The short-tern] system test for w long-term performance prediction requires measurements, accurate enough to be able to identifi parameters and validate a detailed simulation model, e.g. TRNSYS. Using the valid model together with yearly weather data :~or one location, e.g. a test reference year, the performance of the system can then be predicted. The general parameter identification procedure is shown in Figure 3. In Onsala, measurements were carried out according to the specified standard. Additionally a specially designed test sequence was carried out, Figure 3: Parameter identification procedure, to identi~ some of the heat store Example: solar collector parameters. With these measurements the collector circuit and store parameters were identified. The system simulation model was then validated by combining the current simulation model components, i.e. solar collector, solar loop heat exchanger and heat store.

Solar Collector Field Parameters

The solar collector circuit is connected via a plate heat exchanger to the secor dary store circuit (Dalenback & Ivarsson 1996). The collector model used for simulation and parameter identification is the MFC- collector model (Isakson 1995). The collector aperture area was meawred and set to 204.9m2. The effective heat capacity, C.fl, the collector zero loss efficiency q~, the first and second order coefficient kl, k2 in the efficiency curve as well as the coefilcient r of the inciden;e angle identifier were free for identification. Indicator for the best set of parameters is the lowest objective finction (average deviation between measured - calculated power each time step). The identified parameters for the collector field are shown in Table 2.

Table 2: Identified collector field parameters. r [-] objective [W] ...C~fi.. [kJ/(K,m2)]—.——.._— Tlo [-1 __, k, [W/(K,m2) ] -.....———k2 [W/(K2,m2). .] ...... 3.85 +0.__i9 0.79 +0.00 5.28 +0.07 0.00 +0.2 0.145 +0.001 1287.0

The model parameters include the heat losses of heat exchanger and distribution pipes, since the measured collector output (power measured in secondary loop) is reduced as a result c f those losses.

5.26- Dabm To validate the collector parameters, the energy output of the model was compared to the measured output and the relative error for the 21/2montlh period was +1. 7°/0. To simulate the collector circuit as a whole the solar heat exchangers UA-value must be known. Here the TRNSYS standard TYPE 5 heat exchanger model was used in counter flow mode and the identified UA-value was 15400 W/K (objective: 384 W). When comparing the measured and calculated energy o ~tput for the 2% month period the relative error is +0.2°/0. However, the heat exchanger does not pctiorm as well as designed: at a power of 100 kW (design power) the lc~garhhmic mean temperature difference is 6.5K instead of 5K as specified.

Heat Store Parameters

The preliminary European standard prEN 12977-3 provides several specially, designed test sequences to identi~ store model parameters under laboratory conditions for stores up to 3 m3. Since the investigated store is built on site and has a volume of about 18 m3 it is not possible to apply test sequences as specified in this standard. To determine the heat loss of this store, the effective store volume and the UA-value of the domestic hot water heat exchanger, a special test was designed (Dahm 1997). In this special test sequence the effective storage volume was determined to be 18.5m3 (designed volume: 18.0m3). The heat loss of the storage vessel was identified to be 53 W/K (design case: 55 W/K). The relative error in transferred power is 2.9°/0 for this test sequence. The UA- value of the DHW heat exchanger was determined depending on the average mean temperature of the heat transfer. To identi~ the effective inlet heights of the solar loop, the UA-value of the :~ace heating heat exchanger, the effective vertical heat conductivity and the effective outlet heights of th a auxiliary boilers (Figure 4), a sequence of 14 days was taken from the measured 2 % month period (du e to computation time). Here the parameters identified in the special designed test were given as input.

.’> /’,’, ,’ , I DHW .—

rel. heightoutlet

Ispaceheating:HSPj ,’ , ,’‘;”, SOL / ~

Figure 4: System schematic: Parameters free for identification using a 14-day sequence. Table 3: Identified parameters values, 14-day sequence. C~fi [W/(K,m)] UAsh [W/K] HSi [-] HB 10 [-] HSPi [-] objective [W]

Small Central Solar Heating Systems Measurement -5.27 10.3 +1.1 5280+297 0.78 +0.02 0.43 +0.00 0.46 +0.01 3029

To validate the store model the 2% month measurement was restimulated using the identified store parameters. The relative error for each port (port ==inlet - outlet connection pair of one circuit) of the store was recorded and the highest value was 12°/0 fen-the auxiliary port. Two rea~lons may account for the high relative error: 1) Erroneous measurement data. On some occasions tho flow rate in the solar loop could not be determined due to overheating. :2) Some parameters could not be identified, i.e, the effective inlet height of the lower solar inlet.

System Model Validation

To obtain a valid system simulation model all validated model components were combined. The controller iimctions were identified, applied and an auxiliary boiler component was ;onnected to the model. With the system model as a whole, the 21A month period was simulated usirg measured load and weather data as an input for the simulation. The energy balance for the store is shown Table 4.

Table 4: Measured - calculated solar gain. Sequence for parameter identification; 22ndof May until the 9* of July. Input: solar radiation on the collector plane, control functions. ..""."._..__...... -_____-_- ...... -._Q,.I~whl -%[km]—-Q~~~ [k~l ,_Qcirc[k~l ,Qs. [k~l “...—= measured 12,990 9,070 -6,680 -3,310 -10,600 ———calculated——————————————-—--—13,620 ———9,830————.———-6,680——-———————-3,310—————————-——-10,600 relative error [0/0] 4.8 8.5 (input) (input) (input)

The total relative error is below 10% and thus, the model is taken as being validated. However, the requirements of the proposed European standard, i.e., a maximum relative error for each connection port of 10’%0on a daily basis, could not be fulfilled. Thus, the prelimina~ European standard test procedures have to be examined for this type of plants.

Performance Prediction and System Design

The yearly performance of the plant can be predicted by applying me.asure:d weather data, preferably a test reference year for one location, and a typical domestic hot water and space heating load profile. Of major interest is the plants performance for different load conditions. Therefore a daily domestic hot water load profile and a heating load typical for Swedish houses was connected to the valid system model. The DHW load equals an average consumption of 7. 5kWh per day and residential unit (Aronsson 1996). The measured DHW load in Onsala is about 50°/0 of this value. Simulating the plant using weather data for Goteborg 1984, the specific heat demand amounts to 63 kWh/m2,a (total

5.28- Dahm heated floor area: 2780m2). The measured value in Onsala is in the same order of magnitude with 34 kWh/m2,%a (second half of 1996),

~ 35 ~ ‘T 350 1 ~--- { 30+-- .~ ~. 300 --- g .- ~ ,.- .---’ 0 25 -- --/-.--~/ ~.-”-.. 250 m .>,- -.-’ .<:’’:*:””-”””’--’, ~n~a,a 20 :?*”- 200 ‘-->:>.= .-...... solarfraction,DHW = 7.5 15 —- -—----” 150 solar gain, DHW = 7.5 kWh day,ResidentialU~f F“3 ... . solarfraction,DHW = 4.0 lo~ -+~A 100 solargain,DHW = 4.0 }- 18 22 26 30 34 38 42 46 50 54 Numberof ResidentialUnite

Figure 5: Solar fraction and energy gain depending m the number of residential units for the reference simulation model and the reference model with a 50°/0 reduced DHW load (as measured in Onsala).

In Figure 5 the solar fraction and the solar gain are shown as a fimction of the load factor (No. of residential units) for 50°/0 DHW load (as actually measured in Onsala) and for average domestic hot water consumption. By varying the DHW load *50V0 with regard tct the average demand (7.5 kWh/day,res.unit = 100Yo) the solar gain varies about +12V0. Varying the number of residential units (the load factor) by +500/0 (onsala = 36 residential units = 1000/0), the solar gain varies by+ 15°/0. Since TRNSYS is a time consuming research tool , consultants find it of little interest. A design tool has to be understandable and easy to use. This study provided a foundation to create a simulation tool of this type. Based on the valid system simulation model, a simplified version was developed. Literally the valid TRNSYS system model was taken and transferred to a TRNSED simulation model. This means, that the same valid simulation model is used for calculations, but a restrictecl number of typical inputs are free to be edited by the user. The output of each simulation consists of cme control file containing errcm and simulation status messages, a file containing the energy balance of the store on a monthly basis and of most interest, a file containing the yearly solar fraction, the net solar energy gain, the tctal load and the required auxiliary heat. The model is now available for the region arounci Goteborg and for the space heating load of a typical well-insulated Swedish house with radiant floor heating. In fiture versions it is planned to offer the user a different climate and different house types as slpace heating load. The design tool should only be used to design solar plants with a similar system configuration, Sensitivity Analysis of Model Parameters

Small Central Solar Heating Systems Measurement -5.29 The parameter identification process as described enables the validation of a simulation model by identi~ing values for arbitrarily chosen model parameters. Despite the fact, that we derive a more or less valid simulation model, it is at this point not possible to determine the influence of single model parameters on the petiormance of one component or on the performance of the system as one unit. In many cases it is of interest, not only to know the exact value of one parameter, e.g. the tank volume or the collector area, but also its impact on the system’s performance by varying its value. For example, a designer of a similar system is interested in the change of system performance if he increases the collector area or the tank volume. In fact, to optimize the system’s periiormance it would be interesting to know, whether the collector area must be increased together with the tank volume or if an increase in collector area alone would be sufficient to increase the systems performance. The traditional method to perform a model sensitivity analysis is to vary the value of one parameter at a time. This method requires many variations especially when information about model parameter interaction is desired. To accelerate the analysis and to receive additional information about parameter interaction, so-called factorial design for experiments (Hunter & Box & Hunter 1978) offers a more sophisticated method. In general the investiga~tor selects a fixed number of levels for each parameter and runs simulations with all possible combinations. Here a factorial design at two levels (25 design = two levels, five parameter) is applied to perilorrn a sensitivity analysis of major system parameters. To prepare the factorial design experiment, the response variable, i.e. the variab Ie to optimize, as well as parameters of interest at two levels each must be chosen carefi-dly, since the result is greatly dependent on this choice. Here, the response variable may be solar fraction or auxiliary energy, since solar energy replaces a fraction of auxiliaty energy needed to cover the heat demand. For this experiment auxiliary energy was chosen as response variable. Dependent on its definition, a maximum in solar ii-action does not always inherit a minimum of auxiliary energy usage, which is of main interest for the clwner/user of the plant. Five parameters, i.e. solar collector area, the solar collector coefficient K1 characterizing the convective loss of the collector, the UA-value of the solar heat exchanger, the tank volume and insulation thickness on its sides (UA-value) were chosen. In general, any model parameter might be investigated, as long as it makes sense to interpret its value. For each parameter two 1evels (e.g. one + and - level) have to be fixed. It is important, that changing the level has the same relative value for all parameters, since this method evolves the effect on the response variable for each parameter (and parameter interaction) by changing the level. Table 5 lists the parameters, chosen levels and as a reference the validated value (bold).

Table 5: Fixed parameter levels for the 2S factorial design; bold values are validated (ref chapter 4).

5.30- l)abm IPARAMETER ■I - level I + level

AcOll ! 201 K, 5.28 W/K,m’ 4Q R rn3‘2 ‘i I := v tank ,“. ” ,,, 11A ‘~ank, side 1 9.8 UA.., 15500 “KW/K z%+

Many criteria to establish levels can be employed, e.g. choosing levels by technically possible boundaries or applying economical criteria. For this study changing the level of any parameter produces similar costs of about 470 ECU ( lECU- 1$). Increasing the collector area by 4 m2 or increasing the store volume by 1 m3 for example is equivalent to this cost. Here the additional cost for increased building area is neglected, but should be taken into account for larger changes in the storage volume. A lower K1- value of the solar collector can be achieved by increasing the collector backside insulation thickness by about 2 cm. A lower UA-value of the tank can be achieved by increasing the tank insulation thickness at the sides from 0.2 m to 0.3 m. A higher UA-value of the solar 3eat exchanger is achieved by enlarging the heat exchanger area. Given these assumptions a complete factorial experiment design at two levels (25 = 32 simulation runs) was carried out to investigate the effect of each parameter or parameter interaction on the response variable (auxiliary energy) by investing a fixed amount of money.

3.0 T

2.5 response variable: auxilieryenergy t A = collector area 1 — B = collector loss coetTcient K, C = tank volume D = tank heat loss (UAtank,de) E = solar heat exchanger UA-value L

parameter and parameter interactions

Figure 6: Pareto- chart, complete 25 factorial experiment design.

The calculated auxiliary energy for all 32 simulation runs is on average 259 MWh/a. The absolute of the effect of major parameters and parameter interactions on the auxiliary energy is shown in Figure 6, It is obvious, that single parameters affect the use of auxiliary energy consid wably. The main

Small Central Solar Heating Systems Me,zsurement -5.31 effects B, D, E and A appear to be significant. Two-factor interaction effects and interactions of higher order are small and might not be significant. To determine the error of an effect (related to a t- distribution), i.e. to determine which effect is significant within a certain confidence interval, the experiment should be replicated, e.g. using different weather data. Additionally the significance of effects was checked by plotting effects in a normal probability chart (Figure 7). If all effects are roughly normal- distributed they would plot a straight line. Significant effects, i.e. effects that do not occur by chance, diverge from this line. -

99.99 – ‘-— .. ...

99.9 L

99 ! 0 95 : d 41 ~ 90 t 4 0 4 s 70 i = n 50 5 I ~ solar heat ~ x 3(I : p exchanger tank UA- value Q ,0 : ● ● 5 :0 tank volume collector loss ● 1 . coefficient KI collector area

0.1 k {

,,,~1. I I -3 -2 -1 (1 1 2 3 effect [lMWh/a]

Figure 7: Normal plot of effects.

The pareto- chart and the normal plot of effects show that the collector loss coefficient Kl, the tank UA-value, solar heat exchanger size and the collector area are significant under given circumstances, but not the tank volume. To confirm the result and to test the valid range of the parameter effect, the heat loss coefficient was varied over a wider range. Figure 8 shows a variation of the collector loss coefficient over the range of 2.5 to 7.5 WiK,m2 together with the effect for each variation step. The same variation step as applied in the two-level factorial design experiment was used (characterizing 2 cm insulation material in the collector).

5.32- Dahm 300 ‘ -5 T z S 250- ~ --4 ~ determined effectK,: ~ 200 L& --3s al c +::” ~ o 150 -–---—---“-2 ~ L> ,-m s = x 100“ --1 a : + autiliafyenergy [MVVh] + effect [MWh/a] 50 0 0 2 4 6 8 10 collector loss coefficient K, ~/K,m2]

Figure 8: Variation in auxiliary energy and resulting effect by varying the collector loss coefficient K1

A higher loss coefllcient of the collector, K1, leads as expected to an increased use of auxiliary energy. Thus, increasing the collector insulation stepwise by 2 cm increases auxiliary energy from about 2 to 2.7 MWhla. It can be concluded that the result obtained by a two-level factorial design experiment for K1 is valid for the tested range. The cost for one saved MWh, taking an investment of 470 ECU, a discount rate of 0.08 for a period of 15 years is about 25 ECU/MWh. Relating these costs to conventional fhels i:heating oil: -45 ECU/MWh, wood pellets: -20 ECU/MWh) or the heat cost of 60 ECU/MWh for this specific plant an additional investment to increase the thermal insulation of the collector would become a paying proposition.

Summary

To establish thermal solar energy among conventional energy sources, a European standard comprising performance tests and inspection procedures for thermal solar plants was proposed. One goal of the standard is to predict the yearly performance of a system with a validated simulation model. An advantage with using a validated simulation tool is the possibility to evaluate al in the contract agreed upon guaranteed solar result. The solar plant in Onsala was evaluated based on a short-term system test of preliminary European standard prEN 12977-2, using a validated simulation model. The plant was measured during the second half of 1996. The first period of 2!4 months was measured in greater detail. Together with a specially designed test for the heat store of the system,, simulation model componems were validated and combined to a system simulation model. Control fi nctions and an auxiliary boiler component were added. This system simulation model was validated by comparing both calculated and measured

Small Central Solar Heating Systems Mt’asurement -5.33 energies transferred to the store for a period of 21%month. The maximum relative error was 8. 5°/0 for the auxiliary connection port of the store. To provide an easy to use simulation tool for a broader audience, i.e. consultant agencies, a TRNSED model based on the elaborated simulation model was developed and typical domestic hot water and space heating loads were connected. Using this TRNSED model, together with weather data for Goteborg 1984, the petiormance was predicted as a function of the load factor (residential units). In Onsala the measured domestic hot water consumption is 50% of an average DHW load, which decreases the solar gain by about 12%. A Sensitivity analysis using a factorial design experiment at two levels was carried out for five major system parameters. Under given circumstances a reduced collector heat loss coefficient is the most cost effective measure in order to improve system performance.

Acknowledgments

Thanks are due to the Swedish Council for Building Research and the Monitoring Center (MCTH) at the Chalmers University of Technology.

References

Aronsson,S. 1996.Fj&rv&-mekunders vdrme- och effektbehov, Ph.D. thesis, DocumentD35, Dept. of Building Services Engineering, ChalmersUniversityof Technology,Sweden. CEN StandardprEN 12977-2,-31997. “Thermalsolar systemsand components- Custombuilt systems- Part 2: Test Methods,Part 3: Performancecharacterizationof storesfor solawheating systems”,CEN TC312, Nwember 1997. Dahm, J. 1994. Design of a Solar Heating System for a Small Residential Building Area, Diploma Work,Dept. of Building ServicesEngineering, ChalmersUniversityof Technology,Sweden. Dahm, J. 1996.“Evaluationof the StorageConjuration for a SoklrHeatingPlant”, Proceedings Eurosun ’96, Freiburg. Dahm, J. 1997. Evaluation of a Solar Heating System for a Stnall Residential Building Area, LicentiateThesis, Dept. of Building ServicesEngineering, ChalmersUniversityof Technology,Sweden. Dalenback,J.-O.; Ivarsson,B. 1996.“RoofModuleCollector”,Proceedings Eurosun ’96, Freiburg. Driick,H., Hahne,E. 1996. “Multiport StoreModelfor TRNSYS, Proceedings of the Eurotherm Seminar No. 49, Eindhoven. Hunter, W.G., Box, G.E.P., Hunter, J.S. 1978. Statistics for Experimenters -An Introduction to Design, Data Ana@ys and Model Building, John Wiley& Sons,NewYork Isakson,P. 1995. Solar Collector Model for Testing and Simulation, PhD thesis, Building ServicesEngineering, Royal Institute of Technology,Sweden.

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