Bifacial PV modeling

[Ali Sohani]

Dear All,

There is pvlib.bifacial.pvfactors.pvfactors_timeseries() and some other similar functions in PVlib in python, which work based on determination of the TOTAL received solar radiation on a bifacial module (Summation of front and back). Nonetheless, the used ModelChain is similar to the monofacial modules. Taking this point into the consideration, my question is "Is there any specific function like ModelChain that is written specifically for a bifacial PV and not in common with mono facial PV?

Thanks 

Ali

[kevina...@gmail.com]

Ali,

It's true that ModelChain (currently) has no concept of rear-side irradiance, but it's still possible to simulate bifacial systems using ModelChain, you just need to do the irradiance simulation separately.  This example in the documentation (contributed by a community member!) shows a way to do this: https://pvlib-python.readthedocs.io/en/stable/gallery/bifacial/plot_bifi_model_mc.html

Does that answer your question, or are you asking about whether there are other modeling differences between monofacial and bifacial that ModelChain fails to take into account?

Kevin

[Ali Sohani]

Dear Kevin,

It is my deep pleasure that you take your time to share your exercises with me. This is so valuable to me and I would like to initially thank you very much for that!

I employed the approach you referred me, with the real temperature and POA data instead of modeling approach (As a suggestion for the future versions I would like to say the memory consuming of PVfactros could be modified, e.g., after calculation for a time span, the unnecessary data for those period could be removed). However, there is an offset between the measured and calculated data. Therefore, I am wondering where the sources are (I am sure the source is more than one). One of them, I guess, is some points which are not considered in modeling of bifacial compared to the monofacial. Another is I guess bifaciality would not be constant.

I hope my explanations are not confusing :). However, I appreciate if you let me know your feedback on them. In addition, how could I find the working principle of the default ModelChain approach to calculate dc power? Maybe, reading it would be a good point to start.

Thanks a lot again.

Kindest regards,

Ali

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[kevina...@gmail.com]

Hi Ali,

You're right that there are many possible reasons for the model's predictions to be offset from measurements.  Of course every step of the modeling process is an approximation of reality, so there will be at least as many sources of error as there are modeling steps :).  For bifacial systems, the rear-side irradiance models are often not as accurate as we would like them to be.  You might try using the infinite_sheds model instead of pvfactors and see if that improves your results: https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.bifacial.infinite_sheds.get_irradiance.html

More generally, it's difficult for us to guess where the error is coming from without knowing more about your simulation.  If you are able to share your pvlib code and data, maybe someone here will take a look.

Regarding the DC calculation, it depends which DC model you're using.  If you are using CEC PV module parameters, you can read the references listed here: https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.pvsystem.calcparams_cec.html.  Although it will be a lot to read if you are not already familiar with PV power modeling...

Kevin