Category Archives: EuroSun2008-3

Model and parameters

The model of the power outputs calculated in the performed simulations is described by the following equations and parameters [2].

P = ‘HobKtaGb +^odGd — a1((Tout+Tin)/2-Tamb)- a2((Tout+Tin)/2-Tamb)2 (1)

where Kta = 1-bo(1/cos0-1) (2)

Подпись: Monitored parameters: P Power from collector (W/m2 ) Gb Beam Irradiance (W/m2 ) Gd Diffuse Irradiance (W/m2 ) Tin Inlet temperature Tout Outlet temperature Tamb Ambient temperature Glazed areas: Aactive elect.= 3.5m2 Solar8 electric active glazed area Aactive tamp 3.7m2 Solar8 thermal active glazed area ASolarS= 4.6m2 Solar8 total glazed area Подпись: Parameters in the collector model: qob Beam efficiency a1 Heat loss factor [W/m2 K] a2 factor [W/m2 K2] Temperature dependence of heat loss a=a1+a2*AT (4) Kta Angle of incidence modifier for beam irradiance bo Angular coefficient Kdiffuse Diffuse incident angle modifier 0 Angle of incidence onto the collector [°]

qod=Kdiffuse*qob (3)

Simulation Parameters:

Table 2. Systems parameters introduced in the performed simulations with Winsun software.

Solar system

Поь (-)

Kdiffuse (-)

aj (W/m2 oC)

a2 (W/m2 oC2)

be (-)

Thermal Solar8 per active glazed area

0.64

0.1

3.09

0

0.1

Electrical Solar8 per active glazed area (50 oC)

0.076

0.1

0

0

0.1

Flat plate collector (50 oC)

0.8

0.9

3.5

0

0.1

PV module (25 oC)

0.16

0.9

0

0

0.1

A COOLING SYSTEM FOR A HYBRID PV/THERMAL LINEAR CONCENTRATOR

D. Chemisana1* , J. Cipriano, M. Ibanez, B. Abbdel Mesih, A. Mellor

1 University of Lleida, 25001 Lleida, Spain.

Daniel Chemisana, daniel. chemisana@macs. udl. cat

Abstract

This paper presents the thermal evaluation of an evacuated PVT collector designed to operate under concentrated radiation (15 suns). Finite volume 3D numerical computations have been carried out in order to study the thermal characteristics of different rectangular cross section aluminium pipes and to test the performance of the PVT collector with several laminar flow rates. Experiments with the same laminar flows show the same behavior than in the numerical results.

Keywords: linear concentration, active cooling, PVT.

1. Introduction

Within the field of solar collectors, there is a group of hybrid photovoltaic/thermal (PVT) generators, which simultaneously convert sunlight into electrical and thermal energy. Electrical energy is produced by photovoltaic cells. Thermal energy is produced by means of a circulation of fluid around the hottest parts of the system (namely the photovoltaic cells). Due the difference in temperatures, heat is transferred to the fluid providing an energy source whilst cooling the cells.

PVT systems can operate under concentrated or un-concentrated light. The system analysed in this research, will operate under linear concentrated radiation.

These PV/T systems have an inherent contradiction: from the point of view of the electricity generation, the temperature of the PV cells must be kept as low as possible, which leads to low outlet temperatures of the thermal fluid, by contrast, from the thermal energy point of view high outlet temperatures are needed. Hence, a balance between PV efficiency and thermal energy production must be chosen.

Control Algorithm

The software used for the PLC programming was the Siemens Simatic Step 7 [13], with the Simatic S7 Prodave V5.5 [14] needed for the communication between the Scada system and the PLC network.

The designed control algorithm was implemented using the Ladder Diagram language [15].

The developed control algorithm is illustrated in fig. 8.

A short description of the tasks performed by the tracker controller, regarding the above referred algorithm, is described beside the algorithm.

TECHNO-ECONOMIC FEASIBILITY ANALYSIS OF PV-WIND. HYBRID SYSTEMS FOR SWEDEN

Подпись:F. Fiedler1*, V. Pazmino1, I. Berruezo1, V. Maison1 and E. Wackelgard

1 Solar Energy Research Center SERC, Hogskolan Dalama, S-78188 Borlange, Sweden

Corresponding Author, ffi@du. se

Abstract

PV-Wind-Hybrid systems for stand-alone applications have the potential to be more cost efficient compared to PV-alone systems. The two energy sources can, to some extent, compensate each others minima. The combination of solar and wind should be especially favorable for locations at high latitudes such as Sweden with a very uneven distribution of solar radiation during the year. In this article PV-Wind-Hybrid systems have been studied for 11 locations in Sweden. These systems supply the household electricity for single family houses. The aim was to evaluate the system costs, the cost of energy generated by the PV- Wind-Hybrid systems, the effect of the load size and to what extent the combination of these two energy sources can reduce the costs compared to a PV-alone system. The study has been performed with the simulation tool HOMER developed by the National Renewable Energy Laboratory (NREL) for techno-economical feasibility studies of hybrid systems. The results from HOMER show that the net present costs (NPC) for a hybrid system designed for an annual load of 6000 kWh with a capacity shortage of 10% will vary between $48,000 and $87,000. Sizing the system for a load of 1800 kWh/year will give a NPC of $17,000 for the best and $33,000 for the worst location. PV-Wind-Hybrid systems are for all locations more cost effective compared to PV-alone systems. Using a Hybrid system is reducing the NPC for Borlange by 36% and for Lund by 64%. The cost per kWh electricity varies between $1.4 for the worst location and $0.9 for the best location if a PV-Wind-Hybrid system is used.

Keywords: Techno-economic feasibility, PV-Wind-Hybrid systems

1. Introduction

In the Nordic countries stand-alone PV systems are mainly used to supply electricity to remote weather and telecommunication stations, traffic signals/lights and for other remote applications with a relatively low power demand. Due to the extensive developed electrical grid only a few remote residential buildings for all year round usage are supplied by stand-alone systems. An obstacle for the use of stand-alone PV systems is also the uneven distribution of the solar radiation causing high costs if the system needs to be sized for a constant load throughout the year. As the example for Gothenburg in Figure 1 shows has wind power the potential to compensate at least to some extent the low irradiation during the winter. The average wind power at the most locations in Sweden is higher during the seasons with low irradiation. Another reason why the combination of PV — and wind-alone systems can be economical interesting is that the costs for PV modules per Watt peak are still higher than the cost per Watt peak of wind turbines. If there will be a cost benefit or not depends of course also on other parameters, especially on the available local wind speed.

1st International Congress on Heating, Cooling, and Buildings " ‘ 7th to 10th October, Lisbon — Portugal *

image046

Fig. 1. Monthly average wind power at 10 m height and monthly average horizontal solar radiation for

Gothenburg (TMY weather data).

Studies have performed for several other locations worldwide showing often that PV-Wind-Hybrid systems can be more cost effective than PV-alone or wind-alone systems [1-5]. The results presented in this paper are based on two Master theses reports of students of the European Solar Engineering School in Borlange/Sweden [6, 7].

2. Aims

In this paper PV-Wind-Hybrid systems have been studied for 11 locations in Sweden. The aim was to evaluate the system costs, the cost of energy generated by the PV-Wind-Hybrid systems, the effect of the load size and to what extent the combination of these two energy systems can reduce the costs compared to a PV-alone system.

Optimisation results

Optimisation results for a day in May are shown in Fig. 3. The left diagrams illustrate the thermal balance and on the right the electric balance in the grid is plotted. Because of high irradiation for the selected day, around noon the PV plants provide more than the electrical power needed in the grid. In the KWK-G-scenario the CHP operates thermally driven always for short time periods. So only a small part of the storage is used.

In the scenarios with variable tariffs (VDE and LOCAL) the operation is shifted to high price times and the storage is maximally charged to use the heat in low price times. In the VDE-scenario the highest return can be achieved during noon. Consequently most CHP operation is shifted in this time. But at the same time the PV plant feeds in maximal and already exceeds the local grid load. The additional CHP power can not be used locally and must be transported to the medium voltage grid.

In the LOCAL-scenario the highest return can be achieved, if the grid load is high and additional the PV input is small. These times are in the early morning and evening. Because at these times no PV power is available almost all CHP power can be used in the grid and as a result a bigger part of the electric load profile is covered by local generation.

i

electrical balance

image114

 

VDE: EEX ♦ time dependent bonus

cel(t)

— І

ШШш

1

 

image115

time [hour]

chp generation и storage

 

ISL ‘{j

— О ¥,

 

time [hour]

ra chp generation a PV generation u transformer

 

image116image117

image118

Fig. 3. Optimisation result for a day in May: Electric and thermal balance of the local grid.

In all three scenarios the CHP produced 225 kWh of electricity at the chosen day. With a gas price cfuel = 0.05 €/kWh and an electric efficiency of nel = 30 % this results in fuel costs of 37.50 €. The return for the sold electricity was lowest in the KWK-G scenario with 19 €. In the scenarios the operation was shifted to high price times and results in significant higher returns: VDE: 31.85 € and LOCAL: 32.56 €.

Thermal efficiency

The average thermal efficiency as modelled in TRNSYS was 22%. This value for thermal efficiency agrees with values for thermal efficiency as illustrated in graphical results of Tonui and Tripanagnostopoulos [10] for a PVT system with a channel depth of 0.15m. The convective top heat loss coefficient of the PVT system varies with wind speed according to the equation used by Cox and Raghuraman [11],

hg = 1.247 x[ — Ta)x cos#] + 2.658 x Vw (5)

where hg is the convective top heat loss coefficient from the glass cover, Tg is the temperature of the glass cover, Ta is the ambient temperature, 0 is the tilt angle and Vw is the wind velocity. The simulated values for the convective heat loss coefficient were reasonably high as inspection of the Sydney TMY2 weather data revealed an annual average wind speed of 5 m/s.

2. Conclusion

The PVT and building energy modelling and simulation shows promising potential for PVT systems to be integrated into well insulated residential houses in the Sydney climate. The results presented in this paper illustrate that a covered PVT system could feasibly provide adequate thermal comfort for occupants while also achieving the aims of eliminating the need for heaters, increasing the electrical output from the photovoltaic system and further reducing the energy requirement of the household. Further investigations into the pressure drops and required fan power to operate the PVT system and also the use of the building integrated PVT system with an air/earth heat exchanger for both winter heating and summer cooling will continue on from this work.

References

[1] EMET Consultants, (2004), Energy Efficiency Improvement in the Residential Sector. Report prepared by EMET Consultants Pty Ltd for the Sustainable Energy Authority of Victoria.

[2] P. G. Charalambous, G. G. Maidment, S. A. Kalgirou, K. Yiakoumetti, (2007), Photovoltaic thermal (PV/T) collectors: A review. Applied Thermal Engineering, 27, 275-286.

[3] M. Posnansky, S. Gnos, S. Coonen, (1994), The Importance of Hybrid PV-Building Integration. Conference paper: WCPEC 1994 Hawaii.

[4] A. Lloret, J. Andreu, J. Merten, J. Puigdollers, O. Aceves, L. Sabata, M. Chantant, U. Eicker, (1998), Large Grid-connected Hybrid PV System Integrated in a Public Building.

[5] B. P. Cartmell, N. J. Shankland, D. Fiala, V. Hanby, (2004), A multi-operational ventilated photovoltaic and solar air collector: application, simulation and initial monitoring feedback. Solar Energy, 76 (2004) 45-53.

[6] M. Bakker, H. A. Zondag, M. J. Elswijk, K. J. Strootman, M. J.M. Jong, (2005), Performance and costs of a roof sized PV/thermal array with a ground coupled heat pump. Solar Energy, 78, 331-339.

[7] U. Eicker, (2003), Solar technologies for buildings. Wiley, Hoboken, NJ.

[8] ASHRAE, (2008). ASHRAE Publishes Energy Performance Comparison Standard. Available online: http://www. ashrae. org/pressroom/detail/16438 (Accessed 22/07/2008).

[9] M. Hart, R. de Dear, (2004), Weather sensitivity in household appliance energy end-use. Energy and Buildings, 36, 161-174.

[10] J. K. Tonui, Y. Tripanagnostopoulos, (2007), Air-cooled PV/T solar collectors with low cost performance improvements. Solar Energy, 81, 498-511.

[11] C. H. Cox, P. Raghuraman, (1985), Design considerations for flat-plate photovoltaic/thermal collectors. Solar Energy, 35, 227-241.

Impact on solar fraction

Table 1 summarizes the results of simulations with the five load matching cases described above together with a base case (case 0) with the default load and production shown in Figure 2 and theoretical limits to the solar fraction (daily and annual optimum).

Table 1. Annual and summer solar fraction for different load matching cases and relative system sizes for detached houses. Daily optimum refers to the theoretical solar fraction that would occur if each daily load and demand were optimally matched. Annual optimum refers to the solar fraction that would result from an optimal match of the total annual load and demand. The summer solar fraction is calculated over the

months of May, June and July.

Summer

Summer

Annual

Annual

Case

ALR 2

ALR 8

ALR 2

ALR 8

0

0.36

0.58

0.21

0.35

1a

0.36

0.65

0.21

0.38

1b

0.39

0.66

0.19

0.37

2a

0.39

0.64

0.23

0.41

2b

0.41

0.73

0.24

0.48

3

0.37

0.80

0.21

0.43

Daily opt.

0.41

0.98

0.24

0.65

Annual opt.

0.41

1.0

0.24

0.94

It is seen from the table that the summer and annual solar fractions for the smaller ALR system are rather close to optimum already in the base case, since the overproduction is not very substantial. For ALR 8 the difference between the orientation cases and the optimum limits is much greater. The re-orientation cases (1a and 1b) yield somewhat increased solar fractions, although the effect is relatively small. The DSM options (cases 2a and 2b) yield higher solar fractions throughout, although in case 2a for the summer the figures are comparable and somewhat higher for the re­orientation case. Although storage (case 3) is the most flexible of the options, the loss of energy that depends on the efficiency of the storage medium makes it in some cases slightly worse than both DSM and panel orientation options.

Sun tracking orientation

The Solar8 system is mounted on our laboratorial facilities with its tracking axis oriented in the East — West position. It is possible to simulate the received irradiation by a tracking surface both with the axis in East-West and North-South direction for several climates at different latitudes. The results are given in Table 3.

By the analysis of the results, one can conclude that it is always better to track the sun around an axis with North-South direction. This effect is even more relevant when the system is moved closer to the equator where the sun reaches higher altitudes and moves around the sky from East to West direction, mostly.

Table 3. Incoming beam and global irradiation onto a tracking surface with axis in East-West and North-South

direction for Stockholm, Lisbon and Lusaka.

Sun tracking orientation of the surface

Stockholm (lat=59.2°N)

Lisbon (lat=38.7°N)

Lusaka (lat= 15.4°S)

G

(kWh/m2,yr)

Gb

(kWh/m2,yr)

G

(kWh/m2,yr)

Gb

(kWh/m2,yr)

G

(kWh/m2,yr)

Gb

(kWh/m2,yr)

Tracking surface around North-South axis

1343.0

787.3

2187.0

1445.0

2594.0

1754.0

Tracking surface around East-West axis

1262.0

717.6

1973.0

1263.0

2289.0

1474.0

Ratio N-S/E-W tracking

1.06

1.10

1.11

1.14

1.13

1.19

Linear concentrators with active dissipation systems which generate thermal energy

In 1981 Florschuetz [4] remarked that the use of air as an active refrigeration system is not a viable alternative because of its low thermal capacity and diffusivity. He found that water is a fluid whose properties allow for a better thermal interchange and consequentially the achievement of higher concentrations without the negative effects of the temperature over the PV cell efficiency.

After this study, a group of authors developed a series of active cooling systems using water (Edenburn, O’Leary and Clements, Chenlo and Cid, Russell). Although each system used the water cooling device to optimise conditions for electricity production, none of them analyse the possibility of taking advantage of the thermal energy produced by the warming up the water.

At present, there are two principal systems which optimise both the electricity production and the thermal energy production:

• CHAPS (Combined Heat And Power Solar), developed at the Australian National University. It consists of a parabolic concentrator with a ratio of 37X which focuses radiation onto a PVT module. The module converts the radiation into thermal and electrical energy with efficiencies of 57% and 11% respectively. The prototype was initially designed as a photovoltaic system with active cooling, the idea later evolved to use the water to capture the thermal energy. Reference data of the thermal gain achieved by the collector is not mentioned in any of the reference publications for the system [5].

• BIFRES, developed at the University of Lleida, is a system which concentrates radiation by Fresnel reflection to a concentration factor of 22X. The hybrid module operates with a nominal thermal efficiency of 59%, permitting the c-Si photovoltaic cells to operate at an optimum efficiency of 11.9% [2].

Both systems positively satisfy the requirements of actively cooling the cells whilst acting as a thermal collector with acceptable efficiencies, above 50%. However, in both cases the PVT module design is not straightforward. Both groups have opted for a tube of circular cross-section appended to an absorber on which the photovoltaic cells are placed. The two systems have significant differences: the heat sink is made of aluminium in the CHAPS system and of copper in the BIFRES system, also the tube developed at the ANU is furrowed with the goal of improving convection into the fluid.

After analyzing these two systems, some improvements may arise: It is well known that rectangular sections have higher Nusselt numbers than circular or square sections. A section with a higher aspect ratio (a), permits a greater thermal interchange into the fluid, where (a) is defined as the quotient between the long and short side of the rectangle [6]. Besides, an attractive concept such as the architectural integration is not well solved in the majority of PVT systems. As a consequence of their dimensions, PVT systems are only suitable for installation on flat roofs.

In this research is proposed, with the same concentration ratio than in the other systems explained before, to reduce the dimensions of the concentrator and the absorber to facilitate the integration in buildings.

Supervisory System

The Scada system was developed over the platform Axeda Supervisor Wizcon for Windows & Internet V8.2 [16]. The SCADA system used to implement this monitoring and control strategy permits the selective access to the application, depending on the user’s responsibility degree. In this paper we developed three user levels: Operators, Supervisors and Administrators. Several SCADA menus were built. The main characteristic of a SCADA Menu is to be simple, explicit and quick on transmitting the information to the operator or to the System administrator.

One of the developed Graphical User Interfaces (GUI) is shown in fig 9. As this SCADA platform is web enabled, all the GUI displayed data is also on-line accessible through the internet.

In fig. 9 it is shown the developed main menu for the sun-tracker system. The on-line available information, referring actual data from the tracker unit is: actual position for both axis, actual PV — power generated, max. daily PV-power generated, actual efficiency ratio.

4. Conclusion

This paper proposed the optimization of the electric energy production by photovoltaic cells through the development of an intelligent sun-tracking system. The developed solution has many advantages in relation to similar existing devices, as this system is autonomous regarding the information needed to process the optimal orientation and is intelligent in a way that it performs on-line monitoring of the photovoltaic energy production.

An experimental prototype was built and field results have proven the good performance of the developed tracking system.

The observed increase in power generation, in relation to other PV-systems, without tracking devices, is of similar magnitude (ca. 25%) as for other usual tracking solutions. However, this system has a relative advantage, as it measures exactly the controlled variable: the actual PV — power generation.

Подпись: Fig. 8. Control Algorithm for the Tracking system Box0: After reset is activated, the system stores the PV — power generated in the actual position, Pactual, in the variable Pin. The system searchs its reference — null position. It moves until it finds the hardhome position (both external proximity sensors on). In this position the system assumes the absolute orientation angles for both axis equal zero (a1 = a2 = 0). The maximal Power, Pmax is set to zero. Both counters, C1, C2, are loaded;

Box1: After start is activated, the system iniciates the search for the maximal power generated in axis 1, with an angle increment a10. The system stores the power generated in variable P1.

Box2: If P1 < Pmax, the system goes to Box 4, and follows for a new position;

Box3: If P1 > Pmax, this position is stored in the variables: a 1max, a2max. The max. Power value, Pmax is actualized with the new Power value P1;

Box4: Counter for axis 1 is updated;

Box5: After all orientations for axis 1 are evaluated, regarding a fixed orientation for axis 2, axis 2 is positioned in a new position, with an angle increment a20, and axis 1 returns to its initial position a1=0. The system re-initiates the search for the optimal orientation of axis 1, regarding the new position of axis 2. The information flux returns to box 1.

Box6: After all orientations for axis 1 are evaluated, regarding all different positions of axis 2, the system compares the maximal power found (Pmax) with the initial Power generated, before the search process had begun (Pin). If the new Power value is greater than a pre-defined gain, this new correspondent orientation (a1max, a2max) is sent to all park panels. If the power gain is not enough, the new found position is not to follow by the other PV-panels.

Подпись: Fig. 9. Sun Tracker System: SCADA main Menu

Box7: After a pre-defined time interval (K) the tracker system initiates a new complete search process in both axis. The information flux returns to box 0.

Acknowledgment

This work was partially funded by the FCT through program POCTI-SFA-10-46-IDMEC, subsidized by FEDER and by the Project PETER — PIC Interreg IIIA SP6.E53/03.

References

[1] Buresch, M. Photovoltaic energy systems design and installation. New York. McGraw-Hill, 1983.

[2] German Advisory Council on Global Change, 2003 (http://www. wbgu. de)

[3] Atlas I, Sharaf A.; “A Fuzzy Logic Power Tracking Controller for a Photovoltaic Energy Conversion Scheme”; Electr. Power Syst. Res. J., 1992; 25 (3); pp. 227-238

[4] Atlas I, Sharaf A.; “A Novel on-line MPP search algorithm for PV arrays”; IEEE Trans. Energy Convers., 1996; 11 (4); pp. 748-754

[5] Hua, C., Lin, J.; “An on-line MPPT algorithm for rapidly changing illuminations of Solar arrays”; Renew Energy 2003; 28; pp. 1129-1142.

[6] Benlarbi K., Mokrani L., Nait-Said M., “A Fuzzy Global Efficiency Optimization of a Photovoltaic Water Pumping System”; Sol Energy 2004; 77; pp. 203-216.

[7] Hua C., Lin J.; “A modified tracking algorithm for maximum power tracking of solar array”; Energy Conversion and Management 2004, Vol. 45, pp. 911-925.

[8] Chen Y., Liu Y., Wu F.; “Multi-Input Converter with Power Factor Correction, Maximum Power Point Tracking, and Ripple-free Input Currents”; IEEE Trans. Power Electron. 2004, 19 (3), pp. 631-639

[9] http://solardat. uoregon. edu/ SolarPositionCalculatorhtml

[10] Hoppe D.; “Solar-Tracking Mirror with Radiation Sensor”; Publ. Nr. DE4425125; European Patent Office, esp@cenet database.

[11] F. R. Rubio, M. G. Ortega, F. Gordillo and M. Lopez-Martinez; “Application of new control strategy for sun tracking”; Energy Conversion and Management, Vol. 48, Issue 7, July 2007, Pages 2174­2184.

[12] Simatic Net — NCM S7 for Profibus/ FMS. SIEMENS 12/2001.

[13] System Software for S7-300 and S7-400 — Reference Manual, SIEMENS 08/2000; A5E00069892- 02

[14] Simatic S7 Prodave S7 — Toolbox for PGs and PCs, SIEMENS, 2001

[15] Simatic S7-300 — Ladder Logic (LAD) for S7-300, SIEMENS, 2001.

[16] Wizcom for Windows and Internet 8.2 User Guide, AXEDA Systems 2002