Труды Всемирного конгресса Международного общества солнечной энергии - 2007. Том 2
Подождите немного. Документ загружается.
Proceedings of ISES Solar World Congress 2007: Solar Energy and Human Settlement
752
3. SIMULATION INPUT DATA
It is assumed that the PTC is situated in Hefei (lat. 31.85
o
).
The solar radiation data of 22 September is used in the
simulation (Fig. 2). The solar radiation data is obtained
from the clear-day solar radiation model [4]. The PTC
specifications are given in Table 1. The ambient
temperature is 25ć, and the wind velocity is 1.5m/s. The
working fluid is water.
8:00 10:00 12:00 14:00 16:00
0
100
200
300
400
500
600
700
800
900
Solar Radiation W/m
Time
Beam Radiation
Diffuse Radiation
Fig. 2: Solar radiation data of 22 September, Hefei.
TABLE 1: PTC SPECIFICATIONS
Concentration ratio 48
Width of collector aperture 2m
Focal distance 0.92m
Glass envelope
Diameter 0.066m/0.063m
Transmittance/absorptance 0.90/0.05
Absorber tube Copper
Diameter 0.042m/0.037m
Absorptance 0.85
Reflector reflectance 0.9
Tracking mode South facing
One-axis horizontal
4. RESULTS AND DISCUSSION
The results are obtained for 25ć inlet water temperature.
Fig. 3 and Fig. 4 show the variation of the water outlet
temperature and the energy efficiency as a function of mass
flow rate at tube length of 12m. Fig.3 shows that the water
outlet temperature is obviously influenced by the mass flow
rate. A lower mass flow rate results in a higher water outlet
temperature. For example, at 12:00 when the mass flow
rate decreases from 0.6kg/s to 0.05kg/s, the water outlet
temperature increases from 30.39ć to 89.49ć. Fig. 4
shows that with the increase of mass flow rate, energy
efficiency increases, but the increase is not obvious.
8:00 10:00 12:00 14:00 16:00
20
30
40
50
60
70
80
90
mv=0.05kg/s
mv=0.1kg/s
mv=0.2kg/s
mv=0.3kg/s
mv=0.6kg/s
Water Outlet Temperature C
Time
Fig. 3: Variation of water outlet temperature with mass flow
rate along a day.
8:00 10:00 12:00 14:00 16:00
0.2
0.3
0.4
0.5
0.6
0.7
Energy Efficiency
Time
mv=0.6
mv=0.01
mv=0.015
Fig. 4: Variation of energy efficiency with mass flow rate
along a day.
Fig. 5 and Fig. 6 show the variation of the water outlet
temperature and the energy efficiency as a function of the
tube length at mass flow rate of 0.1kg/s. It shows that both
the water outlet temperature and the energy efficiency
increase when the tube length is increasing.
Fig. 7 shows that along a day, the incidence angle of solar
beam to collector aperture decreases in the morning and
3 SOLAR COLLECTOR TECHNOLOGIES AND SYSTEMS
753
reaches the minimum 0
o
at 12:00 which means at that time
the solar beam is normal to the collector aperture. After
12:00, the incidence angle begins to increase till sunset.
8:00 10:00 12:00 14:00 16:00
20
30
40
50
60
70
80
L=1m
L=5m
L=10m
L=15m
L=20m
Water Outlet Temperature C
Time
Fig. 5: Variation of water outlet temperature with tube
length along a day.
8:00 10:00 12:00 14:00 16:00
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
L=1m L=5m L=10m
L=15m L=20m
Energy Efficiency
Time
Fig. 6: Variation of energy efficiency with tube length along
a day.
Fig. 8 shows the distribution of the concentrated solar
radiation along focal axis at different time. It shows that
when the solar beam is not normal to the collector aperture,
only could a part of the absorber tube receive the
concentrated solar radiation. This means that not all of the
incident solar beam radiation is focused on the absorber
tube besides optical loss. For example, at 7:00, from 0m to
3.42m, no incident solar beam radiation is focused on the
absorber tube; from 3.42m, the concentrated solar beam
radiation received by the absorber tube begins to increase
and reaches the maximum at 4.45m; After 4.45m, the
concentrated solar beam radiation received by the absorber
tube keeps the maximum. With the decrease of the
incidence angle, more incident solar beam radiation on
collector aperture is focused on the absorber tube. It
explains the variation of the energy efficiency along a day
shown in Fig. 4 and Fig. 6.
8:00 10:00 12:00 14:00 16:00
0
30
60
90
Incidence Angle
Time
Fig. 7: Variation of incidence angle along a day.
024681012
0
5000
10000
15000
20000
25000
30000
35000
Concentrated Radiation W/m
X Axis (m)
6:30 7:00 8:00 9:00
10:00 11:00 12:00
Fig. 8: Distribution of concentrated solar radiation along
focal axis at different time.
5. CONCLUSIONS
In this study, a dynamic model of parabolic trough solar
collector for dynamic system simulation has been presented.
And a south facing, one-axis tracking collector is simulated.
The variation of the incidence angle of solar beam to
collector aperture and the distribution of concentrated solar
radiation along focal axis has been calculated.
The mass flow rate and the tube length influence the
Proceedings of ISES Solar World Congress 2007: Solar Energy and Human Settlement
754
thermal performance of the parabolic trough solar collector.
When the mass flow rate decreases, the water outlet
temperature becomes higher while the energy efficiency
becomes lower, but the variation of the energy efficiency is
not obvious. With the increase of the tube length, both the
water outlet temperature and the energy efficiency increase.
Incidence angle of solar beam to collector aperture
obviously influences the thermal performance of the
parabolic trough solar collector. When the incidence angle
decreases, more incident solar beam radiation on collector
aperture is focused on the absorber tube, and this leads to a
higher water outlet temperature and higher energy
efficiency.
6. NOMENCLATURE
T temperature, K D diameter, m
A section area, m
2
I solar radiation, W/m
2
F view factor, - W width of the reflector, m
H focal distance, m L length of the reflector, m
C
p
specific heat, J/kg
K (τα) effective absorptance, -
ρ density, kg/m
3
τ transmittance, -
r reflectivity, - θ incidence angle, deg
β tilt angle, deg γ azimuth, deg
α altitude, deg
k thermal conductivity, W/m K
h heat transfer coefficient, W/m
2
K
σ Stefan-Boltzmann constant, W/m
2
K
4
Subscripts
g glass envelope abs absorber tube
w water c collector aperture
b solar beam d solar diffuse
p reflector plate r reflect
t tube i inner
o outer s solar
a ambient
7. REFERENCES
(1) Dudley V, Kolb G, Sloan M, Kearney D, “SEGS LS2
solar collector, test results”, Report of Sandia National
Laboratories, SANDIA94-1884, USA; 1994.
(2) S. D. Odeh, G. L. Morrison, M. Behnia, “Modeling of
parabolic trough direct steam generation solar
collectors”, Solar Energy, 1998, 62(6):395-406.
(3) N. Eskin, “Transient performance analysis of
cylindrical parabolic concentrating collectors and
comparison with experimental results”, Energy
Conversion and Management, 1999, 40(2):175-191.
(4) Qiu Guoquan, Xia Yanjun, Yang Hongyi, “An
optimized clear-day solar radiation model”, Acta
Energiae Solaris Sinica, 2001, 22(4):456-460.
(5) T. T. Chow, “Performance analysis of photovoltaic-
thermal collector by explicit dynamic model”, Solar
Energy, 2003, 75(2):143-152.
(6) G. C. Bakos, I. Ioannidis, N. F. Tsagas, I. Seftelis,
“Design, optimisation and conversion-efficiency
determination of a line-focus parabolic-trough
solar-collector (PTC)”, Applied Energy, 2001,
68(1):43-50.
ANALYSIS OF SOLAR SPACE HEATING IN VARIOUS AREAS OF CHINA
He Tao
China Academy of Building Research
No.30,Beisanhuandonglu
Beijing 100013, China
iaccabr@yahoo.co
ABSTRACT
More than a half of China has the requirement for space
heating in winter. Energy consumption for space heating is
the main part of building energy consumption in north
China. At the same time, solar energy is very rich in China.
About 70% of the area of north China is belonged to rich or
very rich solar energy area. The annual solar irradiation is
more than 5400 MJ / m
2
. The rest small part also has the
annual solar irradiation between 4200 and 5400 MJ /
m
2
.Compare with north Europe where solar space heating
is more popular than China, China has more solar energy in
winter, China now is also promoting the building energy
efficiency, and the energy consumption for space heating
will be cut down more than 50%. These are very helpful for
the application of solar space heating. But the low ambient
temperature in winter is also a disadvantage for solar
collectors. Is there a possibility to popularize solar space
heating in China, and how to apply it? This paper selects
ten typical cities in north China, analyses the solar space
heating system in multiple story inhabited building using
building energy simulation software. The recommendatory
solar fraction of solar space heating of these cities is given,
the effect of various systems and solar collectors are also be
discussed.
1. SPACE HEATING ENERGY CONSUMPTION
The total floor area of buildings in China is about 400
billion m
2
. This figure will be up to 700 billion m
2
in 2020.
Building energy consumption including heating, cooling,
lighting, cooking etc. has account for 27.5% of the total
energy consumption of the country. This percentage will be
higher along with the rapid economic development.
There five climate zone for heating and cooling, that is
severe cold zone, cold zone, hot summer cold winter zone,
hot summer and warm winter zone and temperate zone, see
Fig. 1. Severe cold and cold zone mainly including the
north China is called heating zone. The number of days that
the average temperature is lower that 5 ć are more than
90. The heating zone’s area can account for 70% of the
territorial area.
Fig .1: Building thermal design zone of China.
Proceedings of ISES Solar World Congress 2007: Solar Energy and Human Settlement
756
In 1980s, building energy consumption for space heating
was very high, at 40 to 50 W/m
2
. now, China compels
to promote a energy efficiency standard in new buildings,
the new buildings energy consumption for space heating
should not be higher than the 50% of the old buildings,
that is 20 to 30 W/m
2
. Some Province and cities, such as
Shandong, Beijing, Hebei, have begun to promote the 65%
energy save standard. The remarkable cutting down of
energy consumption for space heating create good
condition for the application of solar space heating in
China.
2. SOLAR RESOURCE IN CHINA
China’s solar resource can be ranked as four areas, which is
very rich (I), rich (II), normal (III) and poor (IV) area.
Global irradiation of the very rich area is more than 6700
MJ/(m
2
ga), rich area has 5400 to 6700 MJ/(m
2
ga) global
irradiation, the normal area has 4200 to 5700 MJ/(m
2
ga)
global irradiation, global irradiation of poor area is less
than 4200 MJ/(m
2
ga), see Fig.2.
Fig. 2: Solar Resource area of China.
Compare Fig. 1 with Fig. 2, we can find that the same
building thermal design zone may be divided into different
solar resource areas, but nearly 70% of the heating zone’s
solar resource are rich or very rich, the rest part are
belonged to normal area. All the heating zone’s solar
resource in are much better than Austria, Germany,
Denmark and Sweden, which are the four countries where
solar space heating are more popular than in China, see
Fig.3. China has an advantageous solar resource condition
to promote solar space heating.
Fig. 3: Global irradiation in the world.
However, China also has a disadvantage in using solar
space heating, the very low ambient temperature in winter.
Solar space heating system’s thermal performance is not
depended only on solar resource or ambient temperature.
It’s depended on the reduced temperature difference, T*,
see equation 1.
T*= (t-t
a
)/G
Where,
tütemperature of solar collectors, ć;
t
a
üambient temperature, ć;
GüGlobal irradiance, W/m
2
.
For solar space heating system, the temperature of solar
collectors can be set to a lower value at 50 ć. Five cities
include Vienna, Geneva, Stuttgart, Copenhagen and
Beijing’s average temperature, average global irradiance
and sunshine duration in January are selected to calculate
reduced temperature difference.
TABLE 1: FIVE CITIES REDUCED TEMPERATURE
DIFFERENCE
Ambient
temperature
Solar
irradiance
T*
Beijing -0.62 349 0.15
Vienna 0.46 73 0.68
Geneva 2.94 81 0.58
Stuttgart 1.80 75 0.64
Copenhagen 1.74 85 0.57
3 SOLAR COLLECTOR TECHNOLOGIES AND SYSTEMS
757
From Table 1 we can see that although the Ambient
temperature is much lower than other cities, Beijing has
more sunshine in winter, its T* is the smallest one and its
thermal efficiency is the highest. Beijing is belonged to
cold building thermal design zone and rich area, it’s a
typical city in China. China has the natural advantages to
apply solar space heating.
3. SELECT OF REFERENCE BUILDING AND SYSTEM
In order to know the detailed performance of solar space
heating system, a reference building which is similar with
the common Chinese residential building and its typical
system should be determined. The reference building has
five floors. The floor area is 3750 m
2
, facing south, and 30
families. This building will be insulated according to the
national standard. The solar space heating system is an
indirect system, with floor radiation heating terminal
system, see Fig. 4.
Fig. 4: Indirect solar space heating system.
4. RESULAT OF SIMULATION
This paper select ten cites to simulate the solar space
heating system. These ten cites can basically cover with the
heating area with different solar resource. To make the
system is more practical, the initial cost for the solar
heating system are limited under 200 Yuan/m
2
(roughly 20
Euro), which is about 10% of the total cost of the building.
The simulation software are EnergyPlus 1.2.0, the weather
data are get from www.energyplus.gov. The simulation
results are list in Table 2.
TABLE 2: TEN CITIES SOLAR SPACE HEATING
SIMULATION RESULTS
Ambi-
ent
Temp.
Solar
Fraction
Thermal
Design
Zone
Solar
Resource
Area
ć
Beijing 0.65 37.72% Cold II
Jinan 1.95 36.98% Cold II
Lhasa 1.45 93.63% Cold I
Xian 1.41 23.84% Cold II
Urumqi -6.33 16.76%
Very
Cold
II
Chang
-chun
-6.36 21.91%
Very
Cold
III
Xining -1.88 37.81%
Very
Cold
I
Shenyang -4.30 25.08%
Very
Cold
II
Shanghai 5.12 36.93%
hot
summer
cold
winter
III
Nanjing 3.68 24.41%
hot
summer
cold
winter
III
Under the limitation of the initial cost of the solar space
heating system, some cities’ solar fraction are less than
30%, the payback time will be longer than 10years.To get
more valuable economical result, the energy consumption
should be father cut down.
5. CONCLUSION
The remarkable cutting down of energy consumption for
space heating create good condition for the application of
solar space heating in China.
In order to get more energy save effect, great effort should
be taken to father cut down energy consumption for solar
space heating.
Compare with some Europe countries, China has better
Proceedings of ISES Solar World Congress 2007: Solar Energy and Human Settlement
758
solar resources to apply solar space heating.
Using solar space heating can raise the building initial
construction cost. To promote this renewable energy‘s
application in the building, local government should give
some subsidies to the owner.
6. REFERENCES
(1) Werner Weiss. Solar Heating System for Houses A Design
Handbook for Solar Combisystems. London: James &
James (Science Publisher) Ltd, 2003
(2) Zheng Ruicheng, “Technical Guidebook for Solar
Water Heating System of Civil Buildings”, Beijing:
Chemical Industry Press,2006
(3) China Academy of Building Research, JGJ 26-95.
Beijing: China Planning Press,1996
(4) World map of yearly average global irradiation (on
horizontal surface), METEOTEST, Berne, Switzerland,
http://www.meteonorm.com
AUTOMATIC FAULT DETECTION FOR BIG SOLAR HEATING SYSTEMS
Frank Wiese, *Klaus Vajen, Michael Krause
Universität Kassel
Institut für Thermische Energietechnik,
34109 Kassel (Germany)
* solar@uni-kassel.de, www.solar.uni-kassel.de
Andreas Knoch
Wagner&Co Solartechnik
Zimmermannstr. 12
35019 Cölbe (Germany)
ABSTRACT
Solar heating systems require a permanent function control
to ensure a proper operation. A new method with automatic
fault detection for large solar heating systems has been
developed. In a first and second step faults are identified by
checking the reliability of individual measured values and
via logical expressions of linked measured values. Since
not all faults are identified by these simple procedures, in a
third step measured energy gains are compared to simulated
energy ones. The method has been tested and several
failures have been identified. The method is characterized
by a high level of automation and only few additional
sensors are needed.
1. INTRODUCTION
Solar heating systems are more complex than for instance
PV systems and require a permanent function control to
ensure a proper operation (Altgeld and Mahler 99, Keilholz
05, Vogelsanger and Haller 2005), especially if A
col
>100 m².
A few approaches to overcome this problem have been
developed so far: Guaranteed Solar Gains (Luboschik et al
97), In-Situ Short-Term Testing (Beikircher et al 99,
Schwenk et al 2001) and Input-Output Controller (Vanoli
01). All these methods have the disadvantage that they
either do not cover the whole system including back-up
heating or do not work on a long-term basis. Therefore,
systems faults can only be detected so far by costly
measurements and investigations carried out by
experienced engineers, as done in the German Solarthermie
2000 program (Peuser 00). A new method has been
investigated at Kassel University. The approach described
in this paper aims to (1) be operated during the whole life
time of a large solar heating system, (2) to be able to detect
and furthermore identify faults and (3) to be inexpensive by
using mainly sensors which are already installed for the
system control. Details are described in (Wiese 06).
As shown in Fig. 1, the system control unit has been
integrated in the monitoring concept. It collects the data
necessary to monitor the system and, so far, transfers them
to a PC mounted on site. A logical structure of the
supervising system is shown in Fig. 2. At night, the
measured data, which are stored as one-minute mean
values, are automatically transferred to the monitoring PC
at Kassel University, imported into a database and
evaluated.
In a first control step, the measured values are checked for
missing data. If 95% of the daily data are available in the
database, a “stationary” plausibility check is carried out in a
second step using different data measured at the same time.
In a third step, the solar gains are checked with dynamic
system simulations. At the end, the results are stored and, if
necessary, warnings and alerts are generated. Plausibility
checks and simulations are described more thoroughly in
the next chapters.
Proceedings of ISES Solar World Congress 2007: Solar Energy and Human Settlement
760
Fig. 1: Schematic of the data handling during the field tests
carried out at a domestic hot water system (DHW).
Start of program
File transfer
Saving in
database
Fault detection
Data missing?
pausibility
check
Simulation
base check
comparision of
measured and
simulated energy
correct?
Alert if
necessery
End of program
Faults possible to
identify?
Control level 1
Control level 2
Control level 3
Fig. 2: Sequencing of the function control for big solar
heating systems on the monitoring PC.
2. “STATIONARY” PLAUSIBILITY CHECKS
For the plausibility checks, the system behaviour has been
reproduced through stationary mathematical algorithms and
the data are scanned for faults step by step. Depending on
the faults looked for, algorithms of different complexity are
necessary. A cable defect at a temperature sensor is
relatively easily detectable due to unreasonable measured
values, whereas different data need to be combined to
detect for instance the failure of a pump. Additionally, in
order to test the flow rate in the respective hydraulic circuit
it needs to be investigated whether the on-condition is met
at that time. Fig. 3 shows the test of the UA-value of the
heat exchanger in a solar heating system in a hospital in
Frankfurt/M.(Germany). The UA-value has been calculated
from measured values of four temperatures and the flow
rate in the collector circle. The upper and the lower curves
show the confidence interval for the UA-value, taking into
account the accuracy of the measurements. Hereby,
nonconforming components can be identified, but also
slowly decreasing heat transfer coefficients. In this example,
the manufacturers specification is permanently far above
the confidence limit of the measurements. Thus, a decision
can be made to change or to clean the heat exchanger. If the
ascertained value seems to be still tolerable, the boundaries
to give an alarm might be adopted as well.
(L) DHW System Frankfurt (m)
Fig. 3: UA-value of the heat exchanger in the collector
circuit, derived as 10-minute mean values from
measured data (dashed black line) at a large solar
heating system for a typical day. The dotted lines
show the upper and lower confidence limit, taking
into account uncertainties of the temperature
measurements of and of the flow rate of. The grey
dots show the specification of the manufacturer.
It could be shown that long term monitoring using such
control algorithm is possible with some constraints . Wrong
operating and/or implemented controllers can be detected
and partly even identified as well as poorly working pumps,
heat exchangers and several hydraulic faults.
3 SOLAR COLLECTOR TECHNOLOGIES AND SYSTEMS
761
Advantages of the method are that only very few sensors
are required additionally to those which are anyway
necessary to control the system and that failures can easily
be reported due to the connection of the monitoring PC to
internet and email. A disadvantage is that reduced yields of
the system cannot be quantified so far. Thus, it is difficult
to decide whether measures to improve the function of the
system are worthwhile. Also, identification algorithm could
not be identified for all conceivable system failures.
Furthermore, most of the large solar heating systems are
individually planned. Even after very careful adaptations of
the algorithms to the respective solar heating system, some
probability remains that a failure with considerably reduced
solar yields remains undetected. To avoid this, a further
control procedure is recommended which is based on
dynamic system simulations.
3. SYSTEM MONITORING BASED ON DYNAMIC
SIMULATIONS
The aim of the proposed procedure using system
simulations is to determine numerically the designed solar
gain in order to compare these values automatically with
the actual gain derived from measured values. This
comparison indicates whether the system is operating in the
respective period without important solar gain reductions,
cf. fig. 4. For the comparison, enthalpy values for charging
and discharging of the storage tanks are selected.
Actual system gain: The actual system gain can be
determined using the enthalpy flow through the heat
exchanger using the following equation:
QcVTρ=⋅ ⋅⋅Δ
As measured values, the flow rate as well as inlet and outlet
temperatures on one side of the heat exchanger (either
primary or secondary) are required. To consider sensor
uncertainties, maximum errors D and DT can be
estimated and considered. With an integration of the
measured values over time an estimation of maximum Q
max
and minimum Q
min
gains can be made. Q
max
and Q
min
are
forming a confidential range, in which the real values
considering the sensor uncertainties can be expected.
()
()()
Q c VV T TT T tρ
⎡⎤
=⋅⋅ +Δ ⋅ +Δ − −Δ ⋅Δ
⎣⎦
()
()()
QcVVTTTTtρ
⎡⎤
=⋅⋅ −Δ ⋅ −Δ − +Δ ⋅Δ
⎣⎦
System design yield: In order to determine the design yield
of a solar heating system for a desired period (e.g. one day),
the installed solar heating system including the complete
control strategy has been implemented in a powerful
simulation tool (TRNSYS). To reduce the effort of
implementing new solar heating systems in the simulation
environment, only standardized system designs have been
investigated so far. The simulations are carried out with
measured irradiation, ambient temperature, cold water inlet
temperature and flow rate as boundary conditions. The
simulations supply the cumulated enthalpy transfer through
the heat exchangers.
For a validated system implementation, the determined
solar gain can be considered as the design yield of a well
functioning solar heating system. However, even for the
determination of the modelled solar gain, uncertainties of
the measurement of the boundary conditions as well as
uncertainties of the numerical models need to be considered.
For instance, the reliability of the simulation result depends
on simplifications in the reproduction of the system,
uncertainties of parameters, and the
time step of the simulation and the
input data. In (Wiese 06),
comprehensive investigations have
been carried out to determine the
influence of various factors. Very
influential are the simulation input
data in the order irradiance, ambient
temperature, water demand profile
and cold water inlet temperature as
monitoring of
system and
climate
simulation of
system behaviour
boundary
conditions
comparison daily
energy gain
evaluation
storage
alerting if
necessery
calculated gain
measured gain
Fig. 4: Rough scheme of the simulation based system monitoring.