Category Archives: EuroSun2008-3

Measurement of the slope errors of a linear PV/T Fresnel reflector

B. Abdel Mesih 1* , J. I. Rosell 1 , J. Illa 2, D. Chemisana 1

1 Department of environmental and soil sciences, University of Lleida, Spain
2 Department of computer science and industrial engineering, University of Lleida, Spain
* Corresponding Author, bahy@macs. udl. cat


The objective of this work is to measure the surface slope errors of the mirrors of the 22-
suns photovoltaic-thermal concentrator that is installed in the University of Lleida in Spain.

A set of photos are taken with a digital camera placed at a distance perpendicular to the mirrors plane which is oriented towards the camera. The analysis of the reflected pattern of the edges of a target on the mirrors shows the irregularities of the mirrors surface. The deformation of the observed pattern could be due to installation errors, misalignments, or bending of mirrors under to their own weight. A geometrical algorithm based on the principles of projective geometry is used with the aid of numerical software to analyze and detect the edges of the absorber. The aim is to find the distribution of actual normal vectors of each mirror strip and to calculate the root mean square error (RMSE). This work gives insight into the loss of optical quality due to reflector errors which affects both the electrical and thermal output of the concentrator severely.

Keywords: Solar concentrators, linear PV/T Fresnel reflector, surface slope errors

1. Introduction

The optical quality of reflectors in photovoltaic thermal concentrator systems affects both the electrical and thermal production drastically. Misalignments of the mirrors during the installation phase, problems with the holding structure, or surface dents, all contribute to both non-uniform illumination and temperature on the PV modules. The current produced by the PV cells, which are usually connected in series, is directly proportional to the incident radiation. Consequently, the cell that receives the least illumination is then the one that determines the power output of the whole PV module. The negative effect of non-uniform illumination on the performance of a whole concentrator system has been well shown by Franklin and Coventry [3].

There are several methods to quantify the slope errors of the reflector components of solar concentrators. Scanning Hartman Optical Tester (SHOT) and video-SHOT (VSHOT) have been used since the 1970’s with great success. There are a number of publications that focus on the principles of VSHOT and applications [4, 11-13]. Photogrammetry is another reliable method that is popular in the field of assessing the slope errors of solar concentrators [7-9]. The curvature of specular reflecting surfaces has been also addressed in a number of publications concerning deflectometry and deflectometric measurements [5, 6]. In this paper, a new approach presented by Ulmer et. al [10] is used to measure the surface slope errors of the PV/T generator installed in Lleida, Spain. The method is called the absorber reflection method (ARM). The method relies on the fact that an observer can easily detect the deformation in the reflecting surface when observing the irregularities in the reflection pattern of a target which has defined and known uniform
contours. The results obtained by the ARM shows big resemblance to the results obtained by photogrammetric methods in assessing the surface errors of parabolic troughs.

Load Prognosis

For an optimal operation of grids and cogeneration plants good prognoses of the expected loads (electric and thermal) and feed-in of fluctuating generation are essential. For this we suggest a statistic method with empiric formulas to fit the load. In several projects we tested an algorithm based on the method of multiple linear regression [4],[7]. The scheme of this algorithm is shown in Fig. 4.

Basis for this method is a database with a time horizon of at least one year, which contains the profile of the value y[t] to be predicted together with other data {z1[t], .., Zn[t]} that might correlate with y. These are calendar data (e. g. day of year) and weather data (e. g. temperature, global radiation). To keep the database as up-to-date as possible it is continuously actualised with the newest measurements

before the actual prognosis calculation. The method of multiple linear regression [8] estimates the given depended variable y[t] by a linear function y[t] = co + ci Xi[t] + c2 X2[t] + … + cn Xn[t] + e[t]

Подпись: Fig. 4. Scheme of the prognosis algorithm.

of the given independent variables {x1 [t], .., xn[t]}, where the factors {c0, .,cn} are unknown and e[t] gives the error for each time step.

The error is minimised with a least square estimation, which has got the advantage that it can be solved analytical with a linear set of equations. From experience we know, that the thermal load will not depend linear on the input variables. To integrate also nonlinear relations the variables xi[t] are introduced. They are defined as a function of the input data in the database {z1[t], .., zn[t]}:

xi[t] = f (zi[t],…,zJt]) (4)

In a pre-analysis we tested, which combination of input variables fits the load best and got for each hour a model equation. The regression analysis results in a specific formula for each hour of the day, e. g. hour = 13:

Pioad. iih = Cl + C2 • Sunday + c3 ■ doy2 + c4 ■ doy4

r, ump ■ r,,-hmp — r — limp’ <ч limp’ (5)

+C9 • sunrise + C10 • sunset


For the prognosis the multiple linear regression routine calculates the coefficients {c1, …, cn} for the chosen equation by use of the updated database. The resulting model is used together with weather forecasts from a commercial meteorological service. Fig. 5 shows the curve of a thermal load of a real district heating system and the prognosis which was predicted one day ahead.

Impact on overproduced power level

From the grid point of view, the instantaneous level of the overproduced power is critical, since it affects voltage levels locally in the grid. An analysis involving mean load cannot give any detailed insights into grid issues, but an average response in power overproduction to load matching measures can be determined. As an example, Figure 3 shows a duration graph over the overproduced power for the base case (case 0) and the two DSM cases (2a and 2b).

For the ALR 2 setup, the overproduced energy is heavily reduced, for the more extensive DSM scheme (case 2b) almost entirely. For the ALR 8 setup the effect is smaller since the amount of shiftable energy is smaller compared to the overproduction. A comparison with the panel orientation cases (not shown here) suggests that the DSM option is more effective at ALR 2 while the orientation options are more effective at ALR 8, since they shift more of the heavy overproduction from midday. A more comprehensive analysis of the impact on the overproduced power will be covered in [15].




For large system setups, corresponding to high penetration levels of PV, energy storage has the greatest potential of obtaining a better match between load and production in terms of solar fraction, although both DSM and PV array orientation options have comparable impacts. At more moderate overproduction, orientation and DSM options seem slightly better, because of energy losses in the storage medium.


[1] N. I. Carlstedt, B. Karlsson, E. Kjellsson, L. Neij, O. Samuelsson (2007), Konkurrenskraft for natansluten solel i Sverige (Competitiveness of grid-connected solar electricity in Sweden), Elforsk Report 06:57.

[2] PV-UPSCALE (2007), Publications review on the impacts of PV distributed generation and electricity networks, http://www. pvupscale. org.

[3] J. V. Paatero, P. D. Lund, Renewable Energy 32 (2007), 216-234.

[4] M. Thomson, D. G. Infield, IET Renewable Power Generation 1 (2007) 33-40.

[5] J. Widen, E. Wackelgard, K. Ellegard, Modeling household electricity load from time-use data. International Scientific Conference on “Green Energy with energy management and IT” in connection with the Swedish National Energy Convention 2008, Alvsjo fair, Stockholm, 12-13 March 2008.

[6] J. Widen, M. Lundh, I. Vassileva, E. Dahlquist, K. Ellegard, E. Wackelgard, Constructing load profiles for household electricity and hot water from time-use data — modelling approach and validation. Manuscript submitted to Energy and Buildings.

[7] A. Capasso, W. Grattieri, R. Lamedica, A. Prudenzi, IEEE Transactions on Power Systems, 9 (1994) 957-964.

[8] K. Ellegard, M. Cooper, electronic International Journal of Time Use Research, 1 (2004), 37-59.

[9] Swedish Consumer Agency, http://www. konsumentverket. se.

[10] Satel-Light, The European Database of Daylight and Solar Radiation, http://www. satel-light. com.

[11] P. Bennich, A. Persson, Methodology and first results from end-use metering in 400 Swedish households. In Proceedings of EEDAL 06 International energy efficiency in domestic appliances and lighting conference, Gloucester 21-23 June 2006.

[12] J. A. Duffie, W. A. Beckman (1991), Solar Engineering of Thermal Processes, John Wiley & Sons, Inc.

[13] D. L. King, J. A. Kratochvil, W. E. Boyson, W. I. Bower, Field experience with a new performance characterization procedure for photovoltaic arrays, 2nd World Conference and Exhibition on Photovoltaic Solar Energy Conversion, 1998.

[14] Meteotest, http://www. meteotest. ch.

[15] J. Widen, E. Wackelgard, P. Lund, Options for improving the load matching capability of distributed photovoltaics at high latitudes, manuscript to be submitted to Solar Energy.

Static surface vs. tracking concentrating surface

Another issue to take into account is that concentrating solar systems, with concentration ratio C=10, can make use only of the beam irradiation plus 10% of the diffuse one, roughly. On the contrary, non­concentrating systems make use of the global irradiation coming from the sun. Thus, the received global irradiation by a non-concentrating static surface was compared with the beam irradiation plus 10% of the diffuse onto a tracking concentrating surface (Table 4).

Table 4. Incident irradiation on a static non-concentrating surface and on a tracking concentrated surface. Static surface inclination from horizontal is 40° in Stockholm, 30° in Lisbon and 20° in Lusaka.

Static surface vs. tracking concentrating surface







Static non-concentrating surface G (kWh/m2,yr)




North-South tracking concentrating surface Gb + 10%*Gdiff (kWh/m2,yr)




Ratio Static/Tracking concentrating surfaces output




The global irradiation incident on a static surface is higher when compared with the beam irradiation plus 10% of the diffuse towards a tracking concentrating surface. This means that a non-concentrating fixed collector receives more usable irradiation than a tracking concentrating one like Solar8. Closer to the equator, the beam irrradiation values are higher and this result becomes less accentuated.

Summary and conclusions

PV-Wind-Hybrid systems are for all locations more cost effective compared to PV-alone systems. Adding a wind turbine halves the net present costs (NPC) for the coastal locations in the south of Sweden and cuts the NPC by one third for a location as Borlange with low wind speeds. The load that has to be supplied has of course a large impact on the system size and costs. The results from the simulations show that the NPC for a hybrid system designed for an annual load of 6000 kWh 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.

However, theses values are calculated for a capacity shortage allowance of 10%. The question is of course if such a shortage is acceptable in a single family house and if not what means could be applied to supply the remaining 10% and what would this cost. These questions have not been studied but as Figure 4 shows for most location it would increase the cost significantly if the last 10% should be supplied with the PV-Wind system. The cost per kWh electricity produced by a PV-Wind-Hybrid system varies between 1.4$ for the worst location and 0.9$ for the best location.


[1] Borowy, B. S., and Salameh, Z. M. (1994). "Optimum photovoltaic array size for a hybrid wind/PV system." Energy Conversion, IEEE Transaction on, 9(3), 482-488.

[2] Celik, A. N. (2002). "Optimisation and techno-economic analysis of autonomous photovoltaic-wind hybrid energy systems in comparison to single photovoltaic and wind systems." Energy Conversion and Management, 43(18), 2453-2468.

[3] Koutroulis, E., Kolokotsa, D., Potirakis, A., and Kalaitzakis, K. (2006). "Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms." Solar Energy, 80(9), 1072-1088.

[4] McGowan, J. G., Manwell, J. F., Avelar, C., and Warner, C. L. (1996). "Hybrid wind/PV/diesel hybrid power systems modeling and South American applications." Renewable Energy, 9(1-4), 836­847.

[5] Protogeropoulos, C., Brinkworth, B. J., and Marshall, R. H. (1997). "Sizing and techno-economical optimization for hybrid solar photovoltaic/wind power systems with battery storage." International Journal of Energy Research, 21(6), 465-479.

[6] Berruezo, I., and Maison, V. (2006). "Electricity Supply with PV-Wind Systems for Houses Without Grid Connection," Master thesis, Hogskolan Dalarna, Borlange.

[7] Pazmino, V. (2007). "PV-Wind Energy Hybrid Systems Techno-Economic Feasibility Analysis for Different Swedish Locations," Master thesis, Hogskolan Dalarna, Borlange.

Numerical analysis

2.1. General specifications

The configuration of the PVT module consists of a row of photovoltaic cells with a rectangular surface area of 1cm x 1m, placed at the top of the aluminium heat sink. The encapsulation can be divided into various elements.

1. — In the surface at which concentrated radiation is received, an EVA film is applied to the cells, and high absorption glass with low iron content is used as an outer skin. This reduces deterioration of the cells and minimises the thermal losses through the top of the module.

2. — Between the cell and the heat sink a strip of electrical insulation is inserted using a double sided adhesion (Chomerics Thermattach T404). This method considerably-simplifies the adhesion process, as it simultaneously serves to insulate the cell and to fix it in right position.

3. — Finally, the lateral and underneath faces of the heat sink are thermally insulated-with a plate of temperature resistant polypropylene.

image083 Подпись: Fig.2. Simulation Scheme.

The figure 2 shows a scheme of the proposed cooling system. Concerning to the boundary conditions they must be fixed in the numerical study. In the outer upper face of the cooling device a, Neumann boundary condition of 1000W/ m2 is applied. This represents the heat flow per unit surface area that the cells transmit to their back contact. The remaining lateral boundaries are considered to be adiabatic surfaces, assuming that the insulation is sufficiently wide to achieve this requirement. Finally, at the surfaces that represent the entry and exit of the fluid flow, the boundary conditions are fixed flow at the entrance and free flow at the exit.

As a line of symmetry exists in the geometry (Fig.1) only half of the system will be modelled.

The tube is made of aluminium (thermal conductivity k =202W/mK). The analysed cross sections are those of the usual commercial tubes with rectangular cross section. The only requirement is a fixed section at the upper face where to accommodate the row of PV cells (1cm width). In the table 1, a summary of the analysed sections is shown. The difference among these three sections relates to the height of the section, it varies from 7 to 27 and hence the aspect ratio increases.

Table 1. Dimensions of the commercial aluminium cross sections analysed in this research































The SDS process for silicon ribbon growth

Joao M. Serra*, C. Pinto, Miguel C. Brito, Jorge Maia Alves, Killian Lobato, Antonio Vallera

DEGGE/SESUL University of Lisbon
Campo Grande ED-C8, 1749-016 Lisboa, Portugal
Corresponding Author, imserra@fc. ul. pt


A lot of research has been done to try to reduce the costs of solar cells by developing ribbon growth techniques that bypass the ingot/wafering step. The SDS-Silicon on Dust Substrate process, here described, is a technique to produce ribbons directly from the gas phase. Test solar cells were fabricated on SDS ribbons as a demonstration of concept of this new technique. Keywords: Photovoltaics; Solar Cells; Ribbons

1. Introduction

A lot of effort has gone into reducing the costs of solar cells by bypassing the ingot/wafering process, which is currently the dominant industrial process. Early on it was realised that such a wasteful process could be avoided by the direct preparation of silicon sheet by ribbon growth techniques [1][2]. The SDS-Silicon on Dust Substrate process, described here, is a new method for the growth of silicon ribbons for photovoltaic applications.

Description of the PV/T concentrator

The system developed at the University of Lleida (Figure 1) is a two-axis sun tracking system using water as a working fluid. The absorber has 72 mono-crystalline Si solar cells which are adhered on top of two circular tubes. Water runs in these tubes to cool down the cells thus enhancing their efficiency and the outlet water is guided to a storage tank where it can be further utilized for domestic hot water production or running a solar-driven chiller. The 12 m2 reflecting system (receiver) is composed of 30 white mirrors that reflect light onto the PV focus band of 0.10 m width and 3.90 m long. The mirrors are of equal length but have unequal widths and are installed at various tilt angles. The solar cells are illuminated by approximately 22 times the solar beam irradiance. The generator’s rated power is 1 KWectrcial and 4 KWthermal.


Fig. 1. 22-sun PV/T concentrator

Operation of decentralised generation in the distribution grid

The different operation strategies for the cogeneration plants influence the power quality in the grid. To evaluate the power quality we used a load flow analysis [9]. For that analysis it is assumed that all load and generation are 3 phase symmetric. So the result will be the same for all phases. The grid is represented by the admittance matrix, which contains all elements of the used equivalent circuits. The algorithm for solving the load flow equations is implemented in the open source language R (www. r — project. org).

To evaluate the power quality a load flow analysis for the low voltage grid described above was made. For the analysis it was assumed that the electricity demand is distributed equally to the 4 houses. Also the 4 PV plants have the same generation schedule. The feed-in of the CHP was defined by the optimisation results described above. As slack node the node GRID (Fig. 1) was set. At this node the voltage is fixed the reference voltage of 395 V. This is less than the nominal voltage of 400 V to increase the input of decentralised generation in the grid. The slack node also must absorb/provide the needed/surplus power. The load flow analysis was repeated for each time step.


Fig. 6 shows the simulated voltage profile at the regarded day in May at the connection point of the CHP system for five scenarios. In the “BASE” scenario no decentralised generation is available. In this traditional scenario the lowest voltages can be seen. Beginning from the slack node the voltage decreases depending on the height of load until the end of the line. In the scenario “PV only” the four PV plants feed in and change the load in the grid. This results in an increasing voltage during PV input to about 424 V, which almost reaches the upper limit of the allowed voltage range of (+6 %/-10 % of the nominal voltage). In these times additional CHP generation will violate the voltage criterion, which can be seen in the three CHP scenarios. In the scenario “KWK-G” the cogeneration plant is thermal driven and feed in according to the schedule shown in Fig. 3. It can be seen clearly that some of the operation blocks during high PV input times increase the voltage up to 435 V which violates the voltage criterion. A similar behaviour can be seen in the scenario “VDE” The highest payment for

produced energy is at 10 am where the transport capacity is restricted due to high PV input. In “LOCAL” scenario the high price times are in the early morning and evening. So the CHP operation blocks are shifted to these times. Because of this shifting all the produced energy of the CHP can be used in the local grid and no further violations are caused. The maximum voltage does not exceed the maximal voltage in the “PV only” scenario.

As it can be seen in the results of Fig. 6 the locally optimised CHP operation with a local price curve can help to control decentralised generation with respect to restrictions from the grid. In the scheduling process of generation it must be avoided, that all generators feed in simultaneously if there is little load and the grid is near its transport capacity. Because almost all plants do have local conditions to be fulfilled (e. g. heat restriction for CHP plants) it seems not to be possible to control all of them by a central device. These restrictions only can be known by the local operator. With the local price curve he also will automatically act according to the grid operator’s interests as much as he can.

2. Conclusion

Within this paper we demonstrate the potential of local management of decentralised generation to increase grid capacity towards RES and DER, taking cogeneration as example. For times of high grid utilization due to local excess energy production, lower pay-back prices for controllable electricity feed-in are defined by the local grid operator, while during times of low grid load prices are increased accordingly. This local component to the price curve for produced electricity stimulates local operators to shift their generation to high-price times. With this control method the grid operator can influence operation without direct access to cogeneration devices and the amount of RES can be increased without upsizing the grid facilities. This method can easily be extended to loads, which will be influenced by flexible tariffs. With the propagation of “Smart Metering” systems, the option of tariff driven loads and generation will be available in the near future.


[1] A European Strategic Energy Technology Plan: Technology Map, Commission of the European Communities, SEC(2007)1510, Brussels 2007

[2] Leprich, U.; Bauknecht, D.: Dezentrale Energiesysteme und aktive Netzbetreiber, BMU-Fachtagung Perspektiven Dezentraler Energiesysteme, Berlin 2006

[3] www. netmod. org

[4] Wille-Haussmann, B., Erge, T., Wittwer, C.: Decentralised Optimisation of Cogeneration in Virtual Power Plants, CISBAT 2007 — Renewables in a changing climate Innovation in the built environment, Lausanne, Switzerland, 4.-5.9.2007

[5] Bronstein, I.: Taschenbuch der Mathematik, Harri Deutsch 1999

[6] VDE-Studie: Smart Distribution 2020: Virtuelle Kraftwerke in Verteilungsnetzen, 2008

[7] Backhaus, K., Erichson, B., Plinke, W., Weiber, R.: Multivariate Analysemethoden, Springer 2005

[8] Faraway, J.: Practical Regression and Anova using R, 2002

[9] Spring, E.: Elektrische Energieverteilnetze, VDE-Verlag 2003

Coupling solar collectors and co-generation units in solar assisted heating and cooling systems

A. Napolitano 12*, G. Franchini1, G. Nurzia2, W. Sparber2

1 University of Bergamo, Department of Industrial Engineering, Viale Marconi n. 5, 24044 Dalmine (BG), Italy
2 EURAC, Institute for Renewable Energy, Viale Druso, n. 1, 39100 Bolzano, Italy
* Assunta Napolitano, assunta. napolitano@unibg. it


The present work reports the main issues of coupling solar collectors and cogeneration units for heating and cooling purposes which have been derived from the detailed analysis of a study case. The work suggests a procedure for planning such systems so that solar collectors and a cogenerator do not interfere in their respective operation. The procedure includes the selection of a layout, the definition of a control strategy and the sizing of each component of such a plant. The outputs of the procedure are used in TRNSYS dynamic simulations to assess the performance of the planned plant.

Keywords: solar heating and cooling, co-generation, planning, simulation

1. Introduction

In Solar Heating and Cooling (SHC) plants, solar collectors are typically assisted by auxiliary technologies to match the entire user’s heat demand. Gas boilers are commonly employed as heat back up systems, but further machines fit to the same use. Among the possible supplementary technologies, Combined Heat and Power (CHP) generators, also known as Cogeneration systems, offer some advantages.

Many technologies are available for cogeneration but in this work only gas engine based CHP units are considered in combination with solar collectors, both for heating and cooling purposes. The latter is supposed to be achieved by means of an absorption chiller. When heat recovered from a CHP system is used to power an absorption machine, this not only meets the cooling load, but also reduces the peak electric demand caused by the cooling request [1]. Moreover, a balanced heat demand over the year, due to the presence of an absorption chiller, improves cogeneration application project economics by increasing its operating hours per year [2]. When CHP units are selected to assist solar collectors for heating and cooling purposes, further advantages are added to the above mentioned ones. In fact, CHP systems support solar collectors by providing a heat source which derives from thermal recovery. Hence, such a back up results in a more efficient energy supply and fuel saving compared to conventional systems (e. g. boilers), especially in cooling season as heat driven chillers require heat amounts larger then the electric demand of conventional chillers.

Despite of these advantages, coupling solar collectors and a CHP unit (SHC-CHP systems) for heating and cooling purposes present certain critical issues, as shown by the study case below reported. The main issue is to fit to each other a typically unsteady heat source (the solar radiation) and a system which needs steady working conditions (N. B. the primary function of an engine heat recovery
equipment is to cool the engine [1]), with the aim of meeting the heat demand, both in winter and summer. As planning such systems can be rather complex, a research work is being carried out to identify the main criteria for optimal designing and sizing. This work is based on dynamic simulations on TRNSYS platform to test on one hand first selected layout and control strategy, on the other hand the influence of various choices of sizes.