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.

Month	Sys Losses	PV Ah	Load Ah	Night %	Batt Days	ALR
Jan.	0.1	30.03	51.09	47.6	131.81	0.59
Feb.	0.1	40	54.17	48	124.3	0.74
Mar.	0.1	55.76	51.56	48.2	130.59	1.08
Apr.	0.1	63.2	48.54	48.4	138.71	1.3
May.	0.1	72.93	45.7	48.3	147.35	1.6
Jun.	0.1	75.41	46.75	49	144.02	1.61
Jul.	0.1	75	47.09	48.7	143	1.59
Aug.	0.1	72.84	47.36	48.4	142.17	1.54
Sep.	0.1	63.44	50.82	48.2	132.51	1.25
Oct.	0.1	53.28	57.76	48.2	116.57	0.92
Nov.	0.1	29.93	56.48	48.1	119.23	0.53
Dec.	0.1	23.18	59.22	47.9	113.71	0.39

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

100 80-

20

0

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.

0

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

40

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.

1600 1400 .n 5 1200 1000 CD 800 600 03

□ PV array energy[ Wh] Homer pro

□ PV array energy[ Wh] Enersoft

400 200

Sep

Nov

0

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 —		Year
		kW h Saved USD	Г
		kW h Saved Euro
		і
	n J
	j лґІІІІ 1
0	——— ^ІНППЛПЛЇ J J J 11 ■

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

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).