Category Archives: SonSolar

Solar thermal systems: potential and policy proposals

The actual solar thermal exploitation in Sicily represents an instance of under utilization a resource widely available and, at the same time, with competitive costs.

The assessment of solar thermal potential carried out in Energy Master Plan has been referred to the sector and end uses listed in the following table.


End Uses


DHW production and back up of space heating


DHW production in the hotel under-sector


DHW production nearby collective users: swimming pools, camping, barracks, prisons, sporting plants, etc.


Summer and Winter air-conditioning for offices and commerce


Agroindustrial processes and desalination

Table 4. Sectors and end uses considered in the assessment of solar thermal potential

As an example, a brief description of the methodology for assessment of the potential of energy saving in the residential sector is now reported.

A reference building stock has been defined taking into consideration all the family house buildings and all the apartments located at the top floor of the buildings. Assuming the climate conditions and the building park characteristics, the possible energy production and saving have been assessed comparing different conventional sources.

All the one store buildings and all the apartments located at the top floor of the multi-storey buildings have been considered as suitable for solar thermal plants. Only apartments already equipped with an autonomous heating system have been considered "technical” suitable for a solar integration installation.

The solar fraction has been evaluated with the f-chart method. The economic analysis has been performed considering several fuel for the back-up system.

In addition, the typical user has been assumed to be a family of 4 people.

All these data have been utilised in order to set up a new type of "economic suitable graph”, in which are compared for different investment costs and different conventional sources (variable price of kWh), the annual costs saving and back-up costs using the optimised solar thermal system.

The use of this diagram is explained using the corresponding points indicated on it:

1. plant specific cost [€/m2];

2. cost of conventional fuel curve [€/kg];

3. optimal surface to be installed in this climatic areas for 4 persons [m2];

4. solar fraction;

5. annual costs saving curve [€];

6. annual back-up costs with conventional source [€];

7. pay back time curve;

8. pay back time [year].

Table 5 reports the potential of solar energy utilization in the residential sector together with the set of actions foreseen in the short and in the medium term in Action Plan.

Figure 4. Tool to assess the economic benefits of solar thermal systems

In the residential sector, a program funding 235.000 m2 in the short term and 400.000 m2 in the medium term of solar collectors have been proposed for DHW systems. The primary energy saving and the avoided CO2 are expected to be about 33 ktep and 77,4 ktCO2 (short t.) and 51 ktep and 114,2 ktCO2 (medium t.). The assumed share of capital cost financed is 30% in the short term (about 35 M€ ) and of 15% (about 21 M€) in the medium term when, in addition, a reduction of unitary costs has been hypothesized.

For the tertiary sector a program financing 25.000 m2 (short t.) and 50.000 m2 (medium t.) of solar collectors has been proposed for low temperature systems. The energy saving and the avoided CO2 are about 1,8 ktep and 3,7 ktCO2 (short t.) and 4,5 ktep and 9,0 ktCO2 (medium t.). The share of funding was assumed to be from the 30% (about 3,5 M€ ) to the 20% (about 4 M€). The sectors that have to be privileged are the ones in which the demand shows a summer peak load or alternatively the users characterized by steady and substantial consumptions during the course of years. The most interested users to be considered are: swimming pools, camping, barracks, prisons, sporting plants, hospitals and clinical medicines, bathing establishments.

A program for an extensive demonstration of solar cooling systems in tertiary and public buildings of about 15 M€ has been proposed. The expected energy and CO2 saving up are in the medium term about 5,2 ktep and 10,5 ktCO2 considering a share of financing of 50%. In the short term the funding intensity will be higher (70%) in order to evaluate the technical and economic performances of some configurations user/plant and to promote the diffusion of a local know-how.

Solar Thermal



Authority [k€]

Surface installed [m2]

Fossil fuel saving [GWh/year]

Electric energy saving [GWh/year]

% of final consumptions in the civil sector

%of gross internal

primary energy consumption



Technical and Economic potential DHW residential








Technical and Economic potential DHW+SH residential








Short term

Residential Action (contr. 30%)








Action: Hotels and big users (contr. 30%) e P. A.









demonstrative plants/Solar Cooling (contr. 70%)





Medium term

Action Residential (contr. 15%)








Action : Hotels and big users (contr. 20%) and P. A.








Action : Solar Cooling (contr. 50%)








Table 5. Potential of solar energy utilization in short and medium term

Another key issue is the involvement of the Energy Service Companies stimulating the market of "energy saving”. A significant contribution by the private market of "white certificates” is expected to be free of public funding. At the same time, there will be activated measures voted to stimulate the existing economic potential through enterprise support, information, training and cultural activities in order to promote a diffusion of this technology with lower costs.

The voluntary agreement among the system suppliers and a Regional control Body is one of crucial tool to successfully achieve the actions. The supplier has to grant the energy performances of the system installed according to standard contracts. In addition suppliers, installers and technicians must be affiliated in a Regional Register.


The new Energy Action Plan for the Sicily Region will apply measures for the support of PV and solar thermal on the basis of a more "user oriented” approach and a merit strategy for SMEs and public actors.

For PV systems, where the economic efficiency of the last funding policies, resulted very low, particular attention will be dedicated to the "quality” of the project and the system. This will be obtained through new assessment procedures for project funding and a shift from capital cost to produced energy funding. Private enterprises will be supported on the basis of voluntary agreements for the certification of the products quality.

Residential DHW system will be stimulated through direct incentives to the small user such as bonus distributed by sellers and/or installers. A positive factor to be supported is the involvement of energy utilities and Energy Saving Companies in the "energy saving” market created by the new national rules.

The results of these policies might be very relevant in the Regional energy contest: about 4,5% of saving of primary energy in the civil sector (of which 0,5% due to PV system), correspondent to the 1% of the gross internal demand, and about 290 ktCO2 of avoided emission per year, correspondent to 4-5% of the objective of Kyoto Protocol for Sicily.

Finance and Insurance Markets

Experience in the different branches became very important because the insurance market changed dramatically and hardened. Today the market is characterized by restrictions in capacities (with the decline in stocks there was a decrease of the reserves of the insurers). The increasing demand caused an increase in price — meaning premiums. Higher premiums are also reached with hardened conditions — meaning coverage of less risks for the same price.



Own risk







Own risk

Figure 2: The Insurance Markets worldwide increase the Importance of Risk Management

Figure 2 shows the change in the insurance markets. In the past it was considered which part of the risk was to bear self (this could be the deductible) and the remaining was transferred onto insurances. Today there is only few possibility to choose. The Insurer lays down what he is willing to insure and the remaining rest cannot be transferred.

This means: Risk management becomes more and more important. If the risk in the beginning is reduced as far as possible, there is only a minor part remaining.

In outside financing projects a loan is only granted if the repayment is assured. For this reason often the financing institute lays down which risks have to be insured and the institute itself is the recipient of the indemnification.

A comprehensive and conclusive Risk Management program gives confidence to the operator in the future and gives evidence to the financial institutes that the owner has examined the risks of the project. Furthermore it is possible that the financing conditions are improved because according to Basle II less risk mean better financing conditions. Finally the project cost will decrease because there will be less unexpected expenses. Taking these points into consideration it can be stated that the financing of photovoltaic projects is facilitated if Risk Management is included.

Current Activities and Future Prospects

There are several organizations and companies currently active in development of solar energy applications in Georgia:

A non-governmental group, International Energy Center ENECO, has been involved in renewable energy activities in Georgia since 1994. The most interesting of their projects are:

• Installation of NRG Systems (USA) wind data logger on Mount Sabueti in August 1996. The obtained data is to be used for assessing the feasibility of constructing a pilot wind station (3 wind turbines, rated at 110 kW each). Thus, ENECO possesses high accuracy data for one of the promising sites for future wind park development.

• Pilot Solar Station for a Children’s Orphanage. In 1996-97, with financial support from UNDP and Foundation Energies Pour le Monde, ENECO developed and installed a solar system consisting of Giordano (France) solar collectors and Isofoton (Spain) PV panels to supply hot water and electricity to one of the orphanage houses in Tbilisi.

“Mze, Ltd” is still manufacturing, at less than 10% of its soviet era production capacity, simple solar collectors for water heating. As Soviet Union broke up and central governmental orders are no longer forthcoming, demand for these solar collectors is low
due to insufficient advertisement/education, low income of the population and competition from other companies importing similar, but higher quality/efficiency collectors from abroad.

Renewable Energy Department at “Energogeneratsia” (state power generation company of Georgia) has been liaising renewable energy activities in the country. The department was directly involved in solar, geothermal, biogas and wind energy generation feasibility studies. One of the projects implemented, together with Tomen and Nichimen Corporations (Japan), ENECO and “Karenergo” (state wind energy company of Georgia), was a wind energy potential assessment measurements for several promising sites (including Mount Sabueti) in Georgia.

Former military aviation factory “Tbilaviamsheni”, still manufacturing and servicing well- known Su-25 and MiG-21 fighters, due to lack of military orders, has started to manufacture various types of civil equipment, including hydro power plant turbines. Possessing military technology and assembly lines, the plant is capable of producing high efficiency solar installations so needed for the Georgian market.

Under the United States Agency for International Development (USAID) funding PA Government Services — Georgia through its local sub-contractors has recently implemented two small solar energy installations as parts of Energy Efficiency projects:

• 20 m2 solar collectors in Bolnisi (southern Georgia) — providing hot water to an elderly nursing house “Chagara”.

• 60 m2 solar collectors in Batumi (Ajara, south-western Georgia) — providing hot water to a small private hotel “L-Bakuri”.

Few entrepreneurs and small construction firms are also importing solar water heaters (mostly Turkish and Italian-made) and on occasional basis more expensive PV panels (island systems) for individual housing projects.

Although long a matter of discussions and various drafts by the State Chancellery and Ministry of Fuel and Energy, only in early 2002 a “Program of Application of Natural (Renewable) Energy Resources of Georgia” was submitted by the Ministry to the President for approval. The program is still in the air and not implemented.

Georgia is a mountainous country with numerous resorts and settlements scattered in remote locations, often cut off from the main electricity grid and in acute need of energy supply. These instances could be well suited for solar energy applications, similar to Aspindza Solar Settlement project, but driven by economic levers of small business and tourism industry.


The weakness of the power sector is one of the major obstacles to economic growth in Georgia. Long power outages are a daily occurrence in much of the country, and parts of Georgia do not receive any electricity for several days at all. Especially during the last few years, due to financial crisis in the energy sector, it is very difficult to pay for natural gas or oil imports to meet the country’s energy needs and the country is forced to rely heavily on hydropower resources and to look for alternatives to thermal power generation.

Although renewable sources other than hydro currently have no significant share in the electrical energy sector of Georgia, their importance is well acknowledged and there is a certain (but not sufficient) activity in this direction. Hopefully, in the near future we will see
much wider application of solar, wind, biogas and geothermal energy systems satisfying the population’s basic energy needs.

As to solar energy, in contrast to western countries where governments provide incentive programs and legal frameworks for development of solar (and other renewable) energy applications, the driving force in Georgia most likely will be population and small businesses (unsupported from the government) looking for viable, dependable and economically justified small island power installations providing alternative energy independent of unreliable and already very expensive national grid.

Surface Design of Sky Radiators. Using Diffractive Grating Structures

Hiroo YUGAMI and Keiko NISHI, Toshimitsu ITOKO and Yoshiaki KANAMORI

Department of Mechanical Systems and Design Engineering,
Graduate School of Engineering, Tohoku University
Aoba01, Aramaki, Aoba-ku, Sendai 980-8579, JAPAN
e-mail:h_yugami@energy. mech. tohoku. ac. jp

The spectrally selective optical properties of wavelength selective radiation based on periodically microstructured metal surface were investigated. The surface grating optics fabricated on several metallic substrates is applied to make high performance sly radiation cooling systems. The characteristic features of this technique are the flexibility of optical property control of substrates and the zenithal angular dependence of optical refractivity. The optimum surface structures are determined by numerical calculations by RCWA. The cooling power is estimated by a simple atmospheric model using the simulated emission properties.

1. Introduction

Figure 1 Schematic spectra of blackbody and atomospheric radiation.

Growing energy demands in the household sector is one of serious problem to the reduction of the carbon dioxide emission in Japan. Especially, the peak power in the summertime is caused by electric power demand for the cooling. The sky radiators have been considered as a useful passive cooling system for household, and have been investigated by many researchers [1-3].

The wavelength region between 8-13pm, which is called the atmospheric window, shows high transmittance in thermal emission spectrum of the atmosphere as shown in Fig.1. In other word, the thermal radiation from ground in this wavelength region can reach to outer space more efficiently than that in other wavelength regions. This atmospheric window plays an important role to the sky radiation cooling. Using this atmospheric window, if the more waste heats can be emitted to the space, the warming phenomenon will be defused.

Some drawbacks of conventional sky radiators are low efficiency in summer season with high humidity, heavy weight and large body. These drawbacks prevent spreading of the practical use of sky radiators. To improve these problems, a sky radiator with surface grating structures is proposed in this study.

The spectrally selective optical properties of wavelength selective radiation based on periodically microstructured metal surface were investigated [4-7]. The surface grating optics fabricated on several metallic substrates is applied to make high performance sly radiation cooling systems. The characteristic features of this technique are the flexibility of optical property control of substrates and the zenithal angular dependence of optical refractivity. These are quite advantage features for the sky radiation cooling application.

In this study, we have developed the programming code based on RCWA (Rigorous coupled-wave analysis) to find the optimum structure for the sky radiator. The cooling power of the designed sky radiator is estimated by the simulated emission spectra. A microstructured sky radiator surface is fabricated by the Si micromachining technique, and the optical properties are measured on these samples.

Decision Support System

The so called decision support system unites the error detection routine that decides about the occurrence of a malfunction in a PV system, the footprint algorithm that detects causes for a system failure, data bases that store long-term surveillance data of a PV system, and the notification system that informs the operator. It is the central system that manages all information. Its central element is the footprint algorithm developed at the Fraunhofer Institute of Solar Energy Systems that will be described here in more detail.

Development of the footprint algorithm

For the development of the footprint algorithm monitored data of several PV systems have been analysed for the occurrence of system malfunction periods. The aim of this analysis is to identify typical error patterns.

From the database of the German 1000-roofs programme hourly mean values have been extracted. The analysis concentrated on two monitored signals, the irradiance and the produced AC power.

Displaying scatter diagrams (Figure 2) and using a normalised presentation of the

produced AC power allowed in a first try the detection of the following errors:

• String error (four of the eight strings of the PV system were disconnected for a few days). The number of strings disconnected could be derived successfully.

• MPP tracking error. Within a three-days period, an excursion of the MPP-tracking system of the inverter could be detected in one of the days. The analysis of the power production pattern on hourly base is a pre-condition for detecting this error source.

• Snow coverage. Difficult to detect within one day. A snow cover may disappear within days, causing the AC power production continuously approaching from nearly zero to the expected values over days.

Figure 2: Example: String error in a system from the D-1000 data base. The scatter diagram above indicates the occurrence of the failure; the figure below shows the decrease in power production (normalised) on the time axis. With the correct assumption of 50% of disconnected strings, the ‘virtual’ correction of the data (blue boxes) is more close to the average production profile than with the assumption of one string more or less disconnected (n+1, n-1).

This analysis has shown so far that for the allocation of different error sources hourly values of irradiation and AC power are necessary. Daily mean values are not sufficient. Furthermore, the uncertainties of satellite-derived irradiance values demand more effort in averaging processes in the footprint method to reduce the expected errors.

The footprint algorithm

The footprint method is divided into two steps. The first step contains a pre-sorting algorithm that prepares the calculated and the monitored yields to take the errors from the satellite data into account. The second step is the identification of the error source.

In general, normalised signals will be considered: P_sim / P_mon = simulated power / monitored power; P_mon / P_inst = monitored power / installed power.

Since the individual calculated yield values with hourly time resolution are expected to be provided with large errors, the approach in the pre-sorting of data is as follows:

The signals P_sim / P_mon will be sorted in intervals with an interval average value P*. The interval average P* shows in general a smaller variance than the variances of the individual signals. Thus, P* exhibits more stability and allows an improved detection of errors.

The intervals are defined in two nearly independent domains: The signals are sorted into a capacity domain and into a time domain. In the capacity domain, the intervals are fractions of P_mon / P_inst, and the time domain consists of hourly intervals. For both domains, the interval averages are determined. The different spatial distribution of the interval averages in both domains is a pre-condition to detect the error source in the subsequent footprint algorithm. Figure 3 illustrates the sorting into intervals in the capacity domain.

Three averaging periods are considered: One day (the past day), the last seven days and the last 30 days. Thus, for each interval in both domains, there will be three interval averages calculated according to the considered periods. An increase in accuracy of the signal pattern is expected with this approach.

Figure 3: Signal sorting in the capacity domain.

First tests of this approach were made using monitored data from:

a) A grid-connected PV system installed at Oldenburg University. For this system, monitored and simulated yield values including the standard deviations due to the irradiation uncertainties have been submitted.

b) A grid-connected PV system installed at a secondary school building in Freiburg. For this system, the simulated yields were determined with a simple model and standard deviations of the signals were estimated.

For both systems, trouble-free operation periods were used for the first test of the approach. Figure 4 shows the interval averages in both domains for the 30 days period from data of the system at Oldenburg University.

Figure 4: Interval averages from the 30-days period of the test system at Oldenburg University. For this period, a slight systematic lower production than expected in the upper power range, mainly in the afternoon hours, can be detected.

Figure 5: Error pattern for the 30-days test period of the Oldenburg University PV system (extracted from interval averages as shown in figure 3).

With the described preparation of the signals it is possible to reduce the system behaviour to more simple error pattern, as shown in Figure 5. These error pattern may then be compared with pre-defined error pattern for specific system malfunctions.

An example for a pre-defined error pattern for shading is given in Figure 6. Probability weights are distributed according to the expected appearance of the error. The probability for this error increases as the real system behaviour follows this specific error pattern.. The method may use additional input values (e. g. clear sky index, sun position).

A promising approach was developed to detect system errors on the one hand and to distinguish between system errors on base of pre-defined footprint tables on the other hand. An advantage of the method is that decisions will be made preferably on base of interval averages instead of unstable individual signals. In addition, a lack of individual signals for short periods due to server, network or other problems will not affect the procedure seriously (Wiemken and Heydenreich, 2004).

Figure 6: Example: Pre-defined error profile (footprint-table) for shading. Probabilities are distributed to the yellow marked circles; the blank circles contain probabilities of zero.


In the different parts of the PVSAT-2 project good progress has been made in developing a comfortable PV system surveillance.

The development of the footprint method so far has been a successful effort in error detection. The part of the method described in this paper gives a first view on the functionality. The entire error detection routine and also the footprint algorithm will be able to consider more errors than described here.

The PVSAT-2 procedure will be validated in a one year field test in Germany, the Netherlands, and Switzerland.

e PVSAT-2 project is supported by the 5th framework programme of the European Community under the contract number NNE5-2001-00571.

Models for PV/Wind Hybrid Power Generating System

Mustafa Engin, Dr., Solar Energy Institute, Ege Mes. Yuk. Okulu, Ege University, Bornova,

35100, Izmir, Turkey

Metin Colak, Dr., Department of Electrical &Electronics Engineering, Faculty of
Engineering, Ege University, Bornova, 35100, Izmir, Turkey

Configuration of the PV/wind hybrid energy system depends on the wind speed and solar irradiation at the considered site. The cost-effective, reliable design and appropriate operation of the hybrid systems is important. The design problem of the hybrid system is non-linearity due to the non-linear component characteristics. This non-linearity problem was solved using genetic algorithm by Seeling-Hochmuth and Marrison (1997. Beyer and Langer, (1996), determine the size of the hybrid system using limited meteorological parameters. Kellogg et al. (1998) developed a simple iterative technique that is based on energy balance, for sizing PV-wind hybrid energy system. Green and Manwell, (1995), Morgan et al. (1997) presented software tools that assess hybrid system performance for pre-determined system configuration. Habib et al. (1999) defines cost ratios between the PV system, wind power systems, and storage for cost-effective hybrid system design.

In this paper a hybrid system mathematical model was proposed. The hybrid system model contains simple mathematical models for each individual element of PV/wind hybrid power generating system. The models for PV cell, wind turbine and battery are based on model description found in the literature. The other components models for PV/wind hybrid system, namely charger, inverter, converter, load and controller are based on electrical and electronics knowledge. Proposed hybrid system model can be used to size cost-effective hybrid power generating system configuration with highest reliability. The implementation is done using MATLAB — SIMULINK, a simulating program. Simulating hybrid system model can also be used for predicting the performance of hybrid power generating systems. A comparison is made between model simulations and measurements that taken from PV/wind hybrid power generating system settled at Solar Energy Institute for lighting.

Selecting the number of production factor levels

Choosing the appropriate number of levels for a continuously measurable factor is a difficult task and requires additional considerations. If the purpose of the factor is exploratory, two levels set at the extreme boundaries of the feasible operating range may be sufficient in determining if it is of any importance. If the basic concern is what needs to be monitored and controlled, two levels are also usually sufficient. If the purpose of the factor is for fine tuning optimum conditions, more than two levels will provide greater insight into selecting better levels for achieving the experiment objectives (Peace, 1993).

In the absence of exact nature of relationship between the independent variable and the performance parameter, one could choose 2 level settings. After analysing the experimental data, one can decide whether the assumption of level setting is right or not based on the percent contribution and the error calculations. TeVA, the Solar EVA Molten time is the time taken to ensure that the EVA is completely molten inside the laminator. If this time is too short the EVA will capture air pockets and therefore create voids at the interface, too long a time will cause the EVA to prematurely solidify and post cure which can lead to insufficient adhesion to the backing cover. Tpc, Post Curing time is the time taken to allow the EVA to solidify while a pressure is applied to the laminate. If this time is too long the EVA will start to crystallise, too short a time will jeopardise the gel content, which in turn affects the integrity of the laminate. Published reports indicate that of all the factors that affect Thermal Interface Resistances, the laminate Teva is the probably the most crucial. Therefore Teva was set at three levels and Tpc at two. The operating temperature is also set at three levels.

Matching Implementation of the PV cells with the product functionality

In comparing how an idea is implemented one can determine whether it could be called a mature design. For example the batteries of the consumer products like Cellular Phones,

CD player and PDAs can be recharged directly during the time they lie idle by PV modules.

In Figure 5 on the left, the PV module is placed directly on the battery. This PV battery replaces the normal battery at the bottom or backside of a cellular phone. The idea is innovative but the implementation is not quite mature. Although the PV batteries have a cool appearance and can be delivered in a variety of colours, the utilisation as battery is not convenient and not optimal from light energy conversion efficiency point of view. For instance the battery is placed at the bottom of the cellular phone which means that in normal use the buttons are facing upwards and the PV module downwards not facing the light. In other words attached to the cellular phone the PV module simply does not function. Another disadvantage of this implementation at the bottom location is that the surface is easily scratched, reducing the transparency of the sensitive surface and reducing therefore the PV functionality.

Figure 5: PV batteries on the back or bottom side of cellular phones and on the cover of

In Figure 5 on the right more mature designs are presented. Here the PV module is placed on the top-cover. These top-covers might even be designed with a curved surface [Luther, 2003]


2.4 Applications and Design Cases Figure 6: Solar

Recharge point

2.4.1 The Solar Powered Recharge Point A novel recharge station for electrical vehicles was designed in the framework of a master thesis project [van Beers, 2002]. The storage capacity of various storage media and PV cells have
been analysed including the environmental impact of this recharge station. The synergy of wavelike roof which functions both as sunshade or shelter and as holder of PV panels has been demonstrated, see figure 6 and section 3.1. By introducing curved solar panels, the aesthetics is enhanced and the movement of mobility is stressed. The movement is even more emphasized by the ‘random arrangement’ of the PV cells in the panels.

Application of the standards EN13363

The standard EN13363-2 gives a methodology to calculate the optical properties of slat devices, however with two deficiencies: it is assumed that no direct sun may penetrate the blinds. Therefore the range of incidence angles where the method can be applied, is limited and dependent on the tilt angle. Therefore we did not use that model but our own extended model. The heat transport in EN13363-2 is similar to ISO15099 and thus should produce results very close to the WIS program. We tested therefore only the simplified approach of the part 1, where the g-value of solar shading plus glazing is calculated from optical properties (transmittance and reflectance) of the shading device and the g — and U — value of the glazing.

profile angle

tilt angle









































Table 2: Results for three different models (ISE, WIS, EN) for glazing Silverstar and

internal white blinds compared with experimental g-value (exp)

Using this simplified method of the standard EN13363-1 ("model EN”) a third set of results can be produced and be compared to the data. The optical data for the blinds were the ones used in the ISE model. The EN-model always produced the most conservative data which is reasonable for a simplified standard method.

profile angle

tilt angle



































Table 3: Results for three different models (ISE, WIS, EN) for glazing Silverstar and

external white blinds compared with experimental g-value (exp)






a 0.30 0.20 0.10


-90 -60 -30 0 30 60 90

profile angle [deg]

Figure 9: Experimental data in comparison with WIS-model using flat slats and the ISE extended view factor model using curved slats; white integrated blinds with solar control glazing g=47%, tilt angles 0°, 45° and 70°

Integrated solar shading

For integrated blinds no resistance model had been developed. Therefore for comparison we used the very simple approach of EN13363-1 for integrated blinds. It can be seen from Figure 9 that the maximum g-value is overpredicted although curved slats have been modelled. The problem is not the optical part but the thermal part of the model! The WIS calculation based on ISO15099 gives a reasonable approximation of the experimental data.


From the results one can conclude that the optical calculation model based on the standard are not adequate for the systems investigated as they all are restricted to flat slats, and do not allow situations with direct transmittance to be evaluated. This will be important however, when the transmittance from the ground reflection will be evaluated. To approximate a complex shape of a diffuse lamella with an equivalent curved lamella of same height seems to reproduce the experimental data quite well. Also the heat transport based on a surface coefficient resistance model seems to be adequate whereas the simplified approach of EN13363-1 overpredicts the total solar energy transmittance.

Additional experimental details can be found in [14]

1.2 Solar simulator and spectral response results

The table 1 summarises the results gained from the I-V curve measurements (AM1.5g), as well as the results obtained by integrating the spectral response (SR) multiplied by the solar spectrum density. The initial current density of the cells was in the range of 30-31 mA/cm2. Both groups of cells show an increase in the short circuit current after encapsulation. The group with the AR layer outperforms nevertheless the group prepared without the AR layer in average by 2.65%. The results obtained by measuring the spectral responses are similar and the calculated short circuit current is around 2.73% higher with the AR layer.

The small difference of (2.73-2.65)%=0.11% between the results obtained with the direct Isc measurements (solar simulator) and the results obtained from the SR measurements indicate that spectral mismatch under the solar simulator did not influence the direct Isc measurements significantly. By considering the statistical nature of our measurement error [9,14], we estimate that the global improvement of the short circuit — current is 2.65%±0.25 %, which will be the value taken for the simulation of the yearly energy yield. The higher standard deviation for the results of group AR is due to a single cell which showed a lower improvement of Isc after embedding.

Table 1: Summary of the results obtained on the 22 mini-modules. NG (Normal Glass) indicates the group of the mini-modules without AR layer.



improvement in Isc solar simulator[%]

Improvement in Isc spectral response [%]

Modules NG



Standard deviation



Modules with AR layer



Standard deviation



Gain AR Layer



Figure 2a shows the spectral responses before and after encapsulation, averaged for the two groups of cells. For all mini-modules, a degradation of the SR below 400 nm is observed, which is linked to glass and EVA absorption. The SR is significantly improved for both groups in the range of 400 to 500 nm. This is mainly linked to a good match between the glass refractive index and the SiN/Si system. The AR layer brings an enhanced contribution in the range of 600-1000 nm. For the curves of the modules with the AR layer (Fig.2a), the apparent improvement of the SR has a minimum around 600­700 nm, which results from the fact that the reflection minimum before encapsulation (i. e., the reflection minimum given by the SiN layer) was already in that range, and could hence not be further improved. Although the effect is difficult to quantify, we estimate that diffuse light reflection on the screen-printed fingers followed by total internal reflection in the glass and subsequent collection by the cells, has a positive effect on the current collection in for both the aR and NG glasses cases, probably around 1%. Fig. 2b displays the results differently by showing the increase G in percent of the SR given by the Ar layer compared to the normal glass, according to

G = Gain [SRar (A)]-Gain [SRvg(^)],

where Gain [SR] = 1-SRafter/SRbefore-

It shows a broad maximum around 900 nm where it reaches around 3.5%. Note that the results are noisier below 400 nm and above 1000 nm because of the reduced system sensitivity at these wavelengths (lower cell response and weaker lamp intensities).

Wavelength [nm] a)












400 600 800 1000 1200

Wavelength [nm] b)

5 4 3 2 1 0

Fig. 2. a) Spectral response as a function of the wavelength before and after encapsulation for the two types of glasses. b) Gain in current in percent given by the the AR layer when compared to the case without layer.

Simulation results

In order to evaluate the performance of the deigned system, it is analysed on the basis of solar radiation conditions at the relevant site and the distribution of the expected consumption. Evaluation of parameters can be calculated from the system energy balance and used to identify losses and estimate the efficiency of the application. These parameters make it possible to compare the behaviour of the operation management modes or the system parameters at different locations. The basic quantity for calculating the evaluation parameters is the solar energy used, which is the amount of photovoltaically generated electrical energy actually used by the consumer. For the evaluation of the system another parameter are used like: solar fraction, performance ratio and the energy final yield. In case of the stand-alone system the battery parameter are important roll like, State of charge, battery capacity etc. For the system parameter evaluation we can use computer simulations software. The market today is divided in different category; this includes dimensioning tools, for example utility programs from the equipment manufacturers like Trace tools for SW series inverters. Programs for simulations that reproduced the PV system parameters and behaviour with the help of a computer. Dates and utility programs in this category the climatologic database play an important roll in the system evaluation. The system parameter evaluation in the present paper were used the N Sol and PV design Pro 4. legally used software packages and for de dates analyses the Homer and the Enersoft utility computer program. Unfortunately we don’t simulate the PV system with software from EU; all that we used in present paper come from USA.

The planed system was simulated for a two different area used the N Sol and PV design Pro 4 software. The result is presented in Table 2.


Sys Losses


Load Ah

Night %

Batt Days






















































































Table 2. Simulation result of the PV system

Where the ALR means the array to load ratio. That is a measure of the oversizing of the system and its ability to recharge a battery quickly after a cloudy period the ALR should be grater then 1 or the array will not produced sufficient energy to supply the loud for that month. If ALR is less then 1 the system will operate for long period but the SOC of battery is low.

□ Variability

□ Correlation □Avg LOLP □Avg SEP □Avg SOC





In Figure 5. the state of charge of the battery is presented, like the ALR value this indicated that in winter period the DOD is very low. In this case for the system protection the user need to reduce the energy consumption or to extend the configuration with one diesel generator.

Figure 5. Monthly values of the battery state of charge

In the Figures 6 and 7 shows the monthly energy balance of the system. That compares the simulation result using different simulations software’s with the measured data from the Enerpac monitoring equipment.


PV array aot[ Ah/day] NSol

□ PV array aot[ Ah/day] Enersoft

Jan. Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 6. The energy performance comparation of the PV system


The practically experience shows that, during the summer months a large storage capacity does not offer any benefits an increase in the solar fraction is achieved primarily by increasing the solar generator output. In contrast in winter a larger storage capacity leads to a marked increase in the solar fraction.




5 1200




600 03

□ PV array energy[ Wh] Homer pro

□ PV array energy[ Wh] Enersoft






Jan. Mar May Jul Month

The differences in both cases are caused by de different radiation conditions between the measured, real dates and the simulated value.

Figure 7. The energy performance comparation of the PV system

Because the used solar radiation data come from statistical date base the simulations results in case of mountain application are not representative different from the values obtained in the case study. In this paper the financial analysis was elaborated for the mountain application using the PV Design Pro 4 software, the following data were applied: the electrical energy price is 0.38 USD/kWh, the applied energy price inflation was assumed to 15%. For the detailed life cycle analysis more parameters are necessary, as investment cost, operation and maintenance cost, debit rate, VAT and etc. which also suggested by the software. The figure shows the financial save for a 25 year period in USD and Euro.

5000 —


kW h Saved USD


kW h Saved Euro


n J

j лґІІІІ 1


——— ^ІНППЛПЛЇ J J J 11 ■

1 2 3 4 5 6 7 8 9 1011 12131415161718192021 2223242526


Figure 8. The saved expense lasting 25 years

The system payback time is 24 year that means a long-term investment, the main problem of the renewable energy technology application especially PV is, that without any subsidy from government and EU such amount of investment it is rather high for the people who living in this remote area.

2. Conclusion

The use of renewable energy sources to develop remote place have significant advantages: as reducing the pollution of environment, creating new jobs, making possible to include isolated areas or place difficult to reach otherwise.

The developed model described in this paper can be used successfully in different remote application sited in mountain and seaside area. Checking the validity of the simulation results for island, stand alone system is however, more difficult, the system planners can check the results using rules of thumb or by comparing them to values gained from experience from PV system which is already exist. Using different simulations software we can solve the problem, the main experience in this case study indicated that for technical and financial analysis of the system is useful the PV design Pro 5 and for study of the battery capacity and sizing them is the N Sol software. For detailed sensitivity analysis is indicated to us the Homer Pro software.

It has been concluded that the estimated payback time is about 20 years for the Romanian condition

3. Acknowledgments

This paper was carried out within the framework of the projects, Romanian-Hungarian TET RO-11/2002, OTKA T-42520 and Co-Funded by European Commission DG RTD under the Contract No: ENK 5-CT-2002-80667 “Solar and wind technology excellence, knowledge exchange and twinning actions Romania centre“ (RO-SWEET).