The impact of soiling

Simulation in PVsyst with monthly soiling values as shown in table 4.8 reduced the monthly mean deviation considerably. Yearly estimate of 298006kW h is 0,65 % lower than the actual value. The soiling scenario according to calculated snow fractions resulted in a RMSD of 887,17 kW h. That is 46,14 % reduction in monthly RMSD compared to S2.

Figure 5.10 shows considerably less deviation between simulated and produced energy when monthly soiling values are adjusted. The results is increased production during the summer and decreased during the winter due to snow. There are however still significant deviations from produced energy during winter months. During summer, there is a small underestimate compared to produced energy. That is even with a soiling level at 1 %. This may indicate that soiling alone is not the only reason PVsyst underestimates simulated energy production during summer. The snow fractions were derived by counting number of days in a month with more than 1 cm of snow. Less than 1 cm can still completely cover the modules and therefore dimin-ish power production. Another important impact on power production is shading due to snow or frost. Light frost, snowfall or non-uniform snow melt may result in a string containing mod-ules that are completely shaded, partially shaded and completely clear. Shading of cells can, as

Figure 5.10: A comparison between produced and simulated energy with soiling adjustment.

The blue values are according to experimental soiling values as described.

described in section 2.4.3, lower the current and diminish the power production in a string to a minimum. The loss due to shading (figure 5.8) was very low compared to the loss due to soiling.

However, the shading scene in PVsyst does not account for partial shading due to snow or frost.

The system at ASKO consists of string inverters, thus partial shading of one module can signifi-cantly decrease the production for a string with up to 24 modules.

To account for the considerable deviations in the winter months, a new, experimental soiling scenario was derived with the intention to reduce deviation during the winter months. Such a scenario is possible due to the problem of quantifying snow and frost losses, as described above.

The experimental soiling values are presented in table 5.3 and the results of the simulation is presented in the first figure.

Table 5.3: Experimental soiling values derived from snow data and the results presented.

Date Jan Feb Mar Apr May Jun Jul Aug Sep Okt Nov Dec

Soiling: snow fraction (%) 45 50 10 3 1 1 1 1 1 1 7 13

Experimental soiling scenario (%) 55 34 17 3 1 1 1 1 1 6 22 21

The experimental soiling scenario resulted in a RMSD of 615,6 kW h, almost 16,5 percentage

points further reduction in mean monthly deviation compared to the first soiling scenario. That is a 62,63 % reduction in mean deviation per month compared to that of S2. The experimental scenario is a better estimation with an obvious decrease in RE for the winter months compared to the snow fraction scenario. Yearly energy estimate is however further reduced to 295824kW h, which is about a 1,34 % underestimate.

The soiling values used in the experimental soiling scenario simulate produced energy in the winter months more accurately than in the snow fraction scenario. Total losses due to snow and frost may therefore be greater than the losses estimated by the snow fraction scenario. Shading due to snow can be prevented by the use of micro-inverters instead of string inverters. The over-estimation in S2 for some of the winter months prone to snowfall and frost (January, February, March, November and December) is 7557kW hin total, which is 2,5 % of the produced energy at ASKO. If micro inverters are better suited for systems as ASKO is an economic decision.

Adjusting for soiling with the aim to improve the accuracy of the simulation in the winter can give an indication of how much energy is lost due to snow covering the modules. For small sys-tems and even for an industrial system as this, the lost energy does not constitute a significant part of the total production. Estimation of energy loss can be important to deduct if measures have to be made in order to reduce loss. To pay for cleaning snow of the modules is both ex-pensive and time consuming, and most likely not worth the cost in terms of the extra energy received. However, other design options that reduce snow covering may be explored, such as adjusting the tilt and orientation of the system. Frame-less modules for systems with low tilt can decrease the effect of soiling accumulation and increase the cleaning effect of rainfall . Soil-ing values will change from year to year, dependSoil-ing on the weather conditions.

During simulations in PVsyst, soiling loss is accounted for as an irradiation loss. Although the impact of soiling on system performance is uncertain, some questions are evident from the results. Increasing the soiling levels, results in an increased soiling loss, from the default 3 % to 4

% in the experimental soiling scenario, presented in appendix B.2. At the same time, irradiation loss and electrical loss according to detailed module calculation due to near shadings decreased.

The decrease in irradiation loss is very small, however the electrical loss decreases from 0,4 % to 0 %. Based upon theory about shading, increased soiling (especially snow) should results in higher shading losses, and then in particular electrical loss. However, this aspect is not included in the estimation of soiling impact in the PVsyst software. A result of this may be that PVsyst

underestimate predicted energy more during winter than assumed, or based upon snow depths.

The underestimate must therefore be corrected by even higher soiling levels for winter months.

PV loss due to temperature increased slightly. The only adjustments made were soiling levels.

Assuming that is the cause, it can seem as increased soiling on modules contains more of the heat inside the modules. That is not far from the the case of snow, which is a good insulator.