#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, 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:

What's Happening at #MODSIM2015

Find Stuff!

#SWC2015 - Launching the Regional PV Simulation System in Daegu, Korea at the Solar World Congress

Our conference paper is entitled: Real-time Simulations of 15,000+ Distributed PV Arrays at Sub-Grid Level using the Regional PV Simulation System (RPSS)

[download it here]

It's an absolute pleasure to be writing to you from Daegu, Korea, where a few hundred solar energy scientists are gathered for the International Solar Energy Society's 2015 Solar World Congress.  I've been inspired by the work that I've seen here, and I'm quite proud to be a part of this community.  

At this conference, it is my great pleasure to officially launch my latest research outcome, which progresses my PhD thesis out of the laboratory, and into the real-world.  It's been a fun challenge to make this leap from science to technology, and I'm honestly quite enthused by the outcome.

From Science to Tech, How We Did It

RPSS stands for Regional PV Simulation System, which was developed by Nicholas Engerer during his PhD thesis, and is now undergoing further development to achieve operational deployment in Australia.

Over the past several months, I have been working with ANU Engineering student James Hansard to get the Regional PV Simulation System up and running, live, in the Canberra, Australia region.  This has been made possible through several mechanisms, the foremost of which was excellent ingenuity and determination from James (you can download his thesis here). 


There are 100+ sites reporting their power output into the RPSS as of November 2015

There are 100+ sites reporting their power output into the RPSS as of November 2015

Using an allocation on the National Computational Infrastructure, the RPSS is now running in near real-time via a series of three virtual machines.  The first machine is gathering PV power output data in near real-time (about 10-15 minute delay) from 100+ sites via, a webpage where users publicly report their PV system's power output.  The second machine runs the RPSS algorithms and produce the output files/graphics.  The latter of these three VMs, is a web server, which hosts the latest simulations at  At this webpage, you can see the RPSS output for the day, mapped to ActewAGL's distribution network.  

RPSS version 2: 15,000+ PV Systems Simulated by Transformer Node

In the latest version of the RPSS, I am now simulating 15,000+ PV systems, grouped by transformer.  This is a significant advancement from the 12,000+ PV systems mapped by suburb in my PhD thesis, which progresses the RPSS towards industry relevant outcomes.  If we can quantify the contributions of PV to the grid at a high resolution, and forecast any significant changes ahead of time, then the intermittency of distributed PV can be mitigated.  Getting the RPSS running in near real-time is the first step in this process.

Quantifying PV Power Output by Distribution Node (Transformer)

When we break down our simulations by transformer, we start to observe very interesting behaviours from the collectives of distributed PV installed behind them that have not previously been observed in Australia.  Let me show you what I mean!  First, let's watch a simulation (RPSS output) from 5 March 2014:

Now, if you don't think that video is pretty cool, then you must be much less nerdy than I am.  I think its fantastic how, using only input PV data from a 100+ sites, we can observe cloud-shadow like features in the simulation! (That's the power of KPV!)

Now, let's look in detail, at the simulated power output grouped by transformer node (below)..

The three different coloured lines above, each correspond to the simulated power output at a given transformer (node).  These are shown on the right side of the simulation video above.  What is very interesting, is the observed differences in ramp event timing that occur between the three nodes during the strong negative collective ramp event that occurs around 11AM.  From this, we can see that the timing of these events are very important to predict precisely, in both space and time, as the strong ramping can occur at different times for each of them.  In the strong positive ramp that follows, there is a similar mismatch in the timing of the ramp peaks.  For a scientist, this is fascinating.  For a distribution company, its a clear example of the challenge of PV intermittency.  

And all of it motivates further development and deployment of this tool

If you found this post interesting, you can dig into the science a bit deeper by downloading our paper below, or by viewing the slides from the presentation.  There is also a tag cloud below, to help you navigate through other content in my webpage.

[You can download our ISES SWC 2015 paper here]

[Or you can download a PDF of the talk]

Or view it in the slideshow below:

SWC2015 Action:

Tag Cloud (Find More Stuff)

Solar 2014: Which Clear-Sky Radiation Model is Best for Use in Australia?

Update 2015: This research is now available as a paper in Solar Energy journal!

[download paper]

Citation: Engerer, Nicholas A and Franklin P. Mills. Validating Nine Clear Sky Radiation Models in Australia. Solar Energy. 120, October 2015, pp. 9-24.

Have you ever wondered which clear-sky solar radiation model you should use in your research project or solar energy simulation?  When I was designing my KPV method for estimating PV system power output I needed to figure out which clear-sky model would be the best one to use.  But there was a problem - I couldn't find a single validation study for clear-sky radiation modelling for Australia!

So in the paper, I had to do a quick model validation using one year of radiation data from Wagga Wagga, from which I decided on the Esra model.  But that simple validation left me wondering which model REALLY was the best?  

Thus I embarked on a scientific journey to discover which model was the best for use in Australia using the solar radiation data from 14 sites in Australia:


First, I set a few ground rules.  I wasn't going to use any radiation model that was overly complicated, nor was I going to use atmospheric variables that were difficult to obtain.

This meant using climatological values for input values such as the Linke Turbidity coefficient or ozone content - rather than using direct measurements from a photometer (because who honestly has spectral data?).  I think this is important, because a validation study should focus on models that are widely applicable so that it is widely useful.

In the end, I chose nine models from the options from both beam (direct) and global radiation: 

In the many Australian presentations and publications I've read and attended over the past few years, the most common clear-sky radiation model used is the Ineichen-Perez model.  

However, my research shows that the Ineichen-Perez model is not even in the top-three best choices.  So we really shouldn't be using it in our research as it is introducing unnecessary errors into our collective research knowledge.

What models are the best to use?  For the beam models, the top three choices are the Iqbal-C, Esra and REST2 models.  And for the global models, they are the Solis, Esra and REST2 models.

If we were to chose the best overall model, that would be the Esra model.  Which edges out the REST2 model, due to is large errors at high zenith angles.

You can read more about this in my Solar 2014 Poster Presentation [direct download] and the hopefully an upcoming article in Solar Energy journal (fingers crossed).

Until then, the quick answer is...

The best clear-sky radiation model for use in Australia is the Esra model!