Combination of Parameters

5.4.1 Combination scenarios with ASKO meteo

A combination of the parameters with the lowest RMSD values may be a good fit in order to reduce monthly deviation in a combined parameter scenario. Deriving a combination from the previous results based upon thermal loss according to module temperature and experimental soiling may be a good start. Albedo did not affect the simulation result noticeably and is not included. The reduced ohmic resistance loss is more accurate than the PVsyst estimation and is included in the combined scenario, named Comb-1. Other discrepancies can then be inves-tigated and evaluated.

Figure 5.13: Monthly Relative Error (RE) for S2 and Comb-1. The Comb-1 RE shows consistent lower estimations.

Comb-1 presents a notable overestimation for all summer months as presented in figure 5.13.

The result is a yearly energy estimate of 309858kW h, an overestimation from produced energy of 3,3 % compared to the underestimation of 0,4 % in S2. The combination of these parame-ters resulted in a RMSD of 1165,23kW h. That is a 29,26 % reduction from the deviation in S2.

The figure shows how combining the parameters create a more consistent error compared to S2.

The increase of yearly simulated energy corresponds with an increased performance ratio (PR) of 85,9 %. The Comb-1 scenario results in more accurate estimations on a monthly basis. The RMSD is reduced, resulting in a stronger positive correlation compared to S2. The correlation is presented in figure 5.14. In S2, the over and under estimations neutralized each other, creating a false impression of yearly accuracy. Monthly error in S2 was more significant and as figure 5.14 shows, the R-squared value for Comb-1 is stronger, indicating a better fit than the values estimated in S2.

Figure 5.14: Correlation plot for the Comb-1 scenario. The plot shows a strong positive correla-tion. The R-squared value is 99,987 %.

The results so far prove considerable monthly deviations in S2. Adjusting some of the parame-ters individually reduced deviation significantly. When combining all the parameparame-ters however, the simulation result is not as accurate as the individual results indicate that a combined sce-nario could become. It is hard to argue to what extent each parameter contribute to loss for the real system. Also, the influence of experimental parameters as LID and IAM must be discussed.

As figure 5.13 show, the combined simulation results in an overestimate. This may be because adjusting ohmic and thermal loss both contributed to increased simulated energy production.

Experimental soiling increased simulated energy during summer months although it resulted in a yearly underestimate.

Figure 5.15: Comb-2 scenario: Relative Error.

In the combined scenario (Comb-1), the most significant deviations are during summer. Analyz-ing the combined result, the soilAnalyz-ing values used for summer may have been too low. Perhaps the system at ASKO is prone to more dust and contaminant particles accumulating on the surface of the modules than first assumed, despite frequent rainfall. For the combined scenario, one may

also consider losses due to LID. As the Comb-1 result estimate too much, a proposed default LID loss of 2 % does not seem so far-fetched and will reduce simulated energy on a monthly basis. A Comb-2 scenario, combing LID loss with an increased summer soiling loss of 2 % (all 1% values replaced by 2 % values) results in a yearly simulated energy of 301329kW h. That is a 0,46 % overestimation of produced energy. The RE decreases significantly from the previous scenario, as presented in figure 5.15.

The Comb-2 RE is not as consistent for every month, as Comb-1. The RE is lower and results in a RMSD of 287kW h, which is a 82,58 % reduction from S2. That decrease in deviation from both S2 and Comb-1 is considerable. The PR decreased slightly to 83,6 % due to increased losses. The result is a stronger correlation between simulated and produced energy, as presented in figure 5.16. The corresponding R-squared of 99,990% indicates a very strong correlation.

Figure 5.16: Correlation plot for the Comb-2 scenario. The plot shows a strong positive correla-tion. The R-squared value is 99,990 %.

The comb-2 scenario includes LID. More parameters included in the simulation, results in fur-ther introduced error. Although the correlation of the data is strong, values of the parameters can be discussed. The soiling values of 2 % are lower than the default of 3 % in PVsyst. These

val-ues are experimental and include a lot of uncertainty. These valval-ues are based upon meteo data from 2015 and will most likely differ from other years. However, the soiling values used are an indication of how the issue of soiling should be treated in PVsyst. The RE for Comb-2 indicates that the experimental soiling values for months affected by snow are too high, as the 2 %LID loss is introduced. The LID loss is a researched and discussed loss that may be completely wrong for this system. To determine if this system is exposed to LID, a study of the actual module at ASKO should be conducted.

Section summary

A combination of parameters for ASKO meteo data results in an over estimate of yearly simu-lated energy. Performance ratios are increased from ASKO scenario 2. The root mean square deviation is decreased compared to scenario 2, and presents a stronger correlation between simulated and produced energy. The overestimate suggests possible losses due to LID and in-creased soiling levels for summer months, which (when included) results in better coherence between simulated and produced energy.