#MODSIM2015 - Extracting Solar Radiation Measurements from PV System Power Output

 A bit of background info on my 21st International Congress on Modelling and Simulation (MODSIM2015) paper

[download the paper]

#MOSIM2015 is underway! And lucky me, I’m here at our wonderful sunny Gold Coast location awaiting my opportunity to present an innovative modeling approach which estimates solar radiation using PV system power output.

Without further delay, let’s dig right in...

The Premise

A pyranometer!

A pyranometer!

Here’s the premise:  photovoltaic solar panels are not altogether different from pyranometers.  In most cases, pyranometers – the global ‘go-to’ solar radiation sensor – use a silicon wafer to measure the global horizontal irradiance at a given location.  Interestingly, this silicon wafer is based on the same technology as most PV modules.  The result is that the power output from a PV system has a first order relationship with the global solar radiation arriving in the plane-of-array.  As a scientist, this creates a sense of curiousity within me!  I wonder, can we use the power output from a PV system to then work our way back to a standard radiation measurement?

The Hypothesis

Let’s put that pondering within the framework of the scientific method, forming a hypothesis:

“Solar panels are not altogether different from pyranometers, thus one should be able to use their power output as an input to a ‘separation’ model to estimate the diffuse and beam components of solar radiation”

What is a separation model?

Over the past several decades, there have been many (as in hundreds) of models developed for the purpose of separating the diffuse and direct (beam) components from a global horizontal irradiance measurement.  In my recent paper (link), I describe this more thoroughly, so dig into that if you’re interested in learning more (or if you are a bit lost on the above bold terms)

The Engerer 2 model

The Engerer 2 model

What I’ve done with my MODSIM2015 paper, is use my own separation model format – the “Engerer 2” model - and modified it to accept PV power output measurements as the primary input, in place of pyranometer measurements.  The overall goal here, is to estimate the diffuse component of radiation using only the measured PV power output through this model and then test how well it performed.

In this paper, we use a scientific approach to test this.  Two different model formats were tested.  One with a single input, and another with four inputs.  We compare a standard pyranometer based model (Kt), fit to the same data, to the PV power output based model (KPV).  This allows us to directly compare the two approaches in a controlled, scientific fashion. 

Now, in order to do this, we need data.  So, I turned to my usual sources of data, PVOutput.org and the Bureau of Meteorology solar radiation monitoring sites.  Fortunately, I was able to identify 18 PV sites in Adelaide and Melbourne which were of very good quality and located within 10km of a solar radiation monitoring site. 

Next, I processed these data through quality control algorithms (read more in my PhD thesis) and then averaged them to hourly values.  With this QC complete, we’re able to start fitting and testing the models. 

There are a lot of details here, which I’ll glaze over in this blog post, but you can review in our paper.  Such as what QC methods I used, how I chose the PV sites, etc, etc.  I’ll leave it to you to investigate this further!  I’d rather spend my time here talking about what we found, because it is pretty cool.

The Results

First, in fitting and testing the single parameter model, the general approach undertaken was validatedThe idea works.  The relationship used in my diffuse fraction model (Engerer 2) relies on a generalized logistic function based on the relationship between the Diffuse Fraction (Kd) and the Clearness Index (Kt).  However, in the PV power output based model, we use KPV in place of Kt,  KPV is the clear-sky index for photovoltaics, and allows for the removal of all the individual nuances from the PV power output time series (use the link above to learn more)

This is a great start, but the results for a single input model aren’t very good.  This is because the model is too simple for the complex relationship between radiation and cloud cover.  But never fear, that’s where the Engerer 2 model comes in.

For those of you who are not familiar with this field, it's worth mentioning that the Engerer 2 model was recently declared the "quasi-universal" separation model in a global study using 54 sites to test 140 different models in a study by Chris Gueymard, a leader in the solar radiation modelling field.  So we're taking a modern approach to this problem.

Herein, we use the Engerer 2 model format to further test our hypothesis.  Except for the solar PV power output based model, we change another one of the input variables so that it is based on PV power output instead of a pyranometer measurement. This is the deviation from clear-sky variable (read more in the paper).

Kt versus KPV based modelling within the Engerer 2 model framework. 

Kt versus KPV based modelling within the Engerer 2 model framework. 

The results were very encouraging! The figure above demonstrates that by using only the PV power output, we are able to use our generalised logistic function based model to estimate the diffuse fraction of solar radiation with only a 5% increase in rRMSE and 0.7% increase in rMBE over a pyranometer based model.  Moreover, the observed relative errors are within the ‘good’ modeling requirements established by Gueymard and Meyers 2008.

Great, now what?

Moving forward with this result, there is now more work to do! For example, with a pyranometer measurement, once we have the diffuse component, we get the direct component through the closure equation. With PV system power output, this isn't possible.   So we'll need to test our ability to do extract the beam component further.  Possibly, this might require the joint development of another model to extract it

Despite the further work required, the results herein remain exciting. The approach used in our paper show that extracting solar radiation measurements from PV system power output can be done. This is a world first for such a result, and it is exciting to know that further work in this space is realistic. 

I am very hopeful that this result will encourage others in the field to further explore this conceptual work.  If we can extract solar radiation measurements from PV power output, we suddenly can create a very rich dataset of radiation measurements globally, greatly augmenting our ability to anlayse the spatio-temporal evolution of incoming solar radiation.  Such a dataset can assist in the validation of satellite measurements or global climate/meteorological modeling tools. 

So, with this very awesome scientific finding, I’ll leave you with the above thoughts, and hope that you’ll go on to read the paper, cite the results and further develop the prospect of having millions of additional solar radiation sensors globally.

[download the paper]

[download the presentation]

View the presentation below:


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