Research

#saveARENA - There Has Never Been a More Critical Time for Renewables Research in Australia

I Have an Urgent & Important Message for You about The Australian Renewable Energy Agency

For those of you who follow my page, or have adventured here from beyond, it should be readily apparent to you that I’m an academic working in solar energy related research, more specifically in the modelling & forecasting of distributed PV power output.

To date, the majority of my work has been funded by ARENA, the Australian Renewable Energy Agency.  A funding body that has been absolutely crucial in supporting Australia as a global leader in renewable energy research over the past several years.

But ARENA is Under Imminent Threat

But right now, ARENA is under threat. The Coalition Government is planning legislation that will strip it of more than $1B of funding, and Labor are saying they won’t oppose it.

Let me explain to you why these cuts to ARENA research funding are a bad idea.

ARENA is currently funding nine university led research projects across Australia with more than $17M in funding, which are focused on partnering industry and researchers together to solve the major challenges we face integrating renewables into our electricity grid. What’s more, they intend to produce commercial outcomes valuable to the Australian economy.  (Isn't this exactly what the Australian government wants universities to accomplish? i.e. the National Innovation Science Agenda)

As an example, look no further than my own project, which received $1M from ARENA and raised $300k of industry cash to build distributed PV modelling and forecasting software for six distribution networks: ActewAGL, Ergon, Essential Energy, Western Power Horizon Power & Power and Water.

Perhaps this isn't quite enough industry partnership activity for the Coalition government?

Perhaps this isn't quite enough industry partnership activity for the Coalition government?

Don't believe the lies about a lack of value: ARENA has shifted toward driving commercially-focused outcomes

At the ANU, we're not just developing this distributed PV modelling technology to write some fancy publications.  We are delivering our software into the operations of our distribution network partners and creating a technology that will enable higher penetrations of solar to be added to the grid.  Going one step further, this software is being commercialised by an ANU start-up company, which we have already formed and is starting to complete this work now.  In the future, we hope to take this technology beyond Australia, and into other countries around the world.

Without ARENA funded research like mine, fewer solar PV systems will be permitted on our distribution networks.  Without research projects like the other eight industry-researcher collaborations in ARENA's R&D funding round, what other innovative technologies will Australia miss out on

Some Awesome Examples of Current ARENA R&D:

ARENA & Industry Funded: Drones that survey solar farms

ARENA & Industry Funded: Integrating solar thermal into alumina processing

ARENA & Industry Funded: Batteries & Solar PV into Apartment Buildings

If my project, and others like it, aren’t part of the “Innovation” agenda that the government wants, part of its “Ideas Boom”, part of the technology driven future of the Australian economy where it produces new ideas of global relevance, I don’t know what is.

If these projects aren't part of the "Ideas Boom"... I don't know what is!

We simply have a government that is demanding innovation on one hand, but clearly saying “just not for renewables” on the other.

What do we do about it?

Across Australia, our renewables powered people groups are springing into action, and our universities aren't letting this go down without a fight:

Solar Citizens has made it easy for you to tell your local parliamentarian to stop this madness

GetUp has put together a #saveARENA campaign where you can share your story about working on an ARENA funded project to your social media accounts.

I want YOU to join with me to #saveARENA

So I want YOU, to join with me, to tell our Parliament to #saveARENA. The legislation to cut $1B from ARENA is set to be introduced to Parliament early next week as part of an “omnibus bill”, so we must act now.

Let me be clear: There has never been a more critical moment for the future of renewables in Australia, or for our potential to be a leader in renewable energy technologies throughout the Asia-Pacific, and more broadly, across the globe.

Renewably Yours,

Dr. Nick Engerer


Be sure to retweet my twitter video:

And Follow #saveARENA on Twitter:

 

 

#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:


What's Happening at #MODSIM2015

Find Stuff!


PhD Thesis: What I did, What I found, Why it Matters

Several years ago, I decided I wanted to write a Masters Thesis that didn't just sit on a shelf, collecting dust.  Call me idealistic, but I wanted to adopt a philosophy of "science off the shelf" 

[April 2016 Update: My PhD Thesis is now finalised! Download it here: Part 1  |  Part 2 ]
 


[download my submitted thesis - part 1]

[download my submitted thesis - part 2]

Back at the University of Oklahoma School of Meteorology, there was a running joke amongst graduate students that they could stick a $20 bill in their thesis, check back in 5-10 years, and find it still nestled between the pages, safe and sound.  While it was humorous, and mostly in jest, it reflected the mentality of postgraduate education/research work - what we were doing was mostly just to tick boxes & get our degree so that we could one day do something that mattered. In reality, our 'research' would sit on a library shelf, acquiring dust.

Yearly kWh production for a simulated 2kW Sanyo array in Oklahoma

Yearly kWh production for a simulated 2kW Sanyo array in Oklahoma

But I wasn't content to resign myself to this fate. I felt compelled to produce something that mattered, that would have real-world relevance.  So I brought on an industry partner, a local solar installer in Oklahoma, and put together a solar map for Oklahoma, not based on solar radiation - but on kWh hours of electricity generation, which immediately translates to payoff times and dollars. [download my M.S. Thesis]

It was a tremendously successful experience, and as I moved forward into my PhD project, I was very determined to not only maintain that approach, but to expand it.  Now, looking back, I can see that approach has paid off in great ways, which I'd like to share with you.  Because what I've discovered, what I've developed and what I am now positioned to do with the technology I created, is all highly relevant, commercially viable and ready to hit the ground running.

What I did and why

Australian PV installations by year, as provided by the Australian PV Institute

There is ~4GW of solar energy installed in Australia, which is dominated by ~1.4 million small-scale photovoltaic (PV) arrays.  These arrays are relatively small (average size 1.5kW in 2011, growing to 4.5kW by the end of 2014), and the vast majority of these installations are un-monitored.  This means that their minute-by-minute performance is not recorded.  In fact, the only information collected for most of these systems is the total quarterly production as reported on electricity bills.  Long story short, this lack of information limits the number of PV systems that can be installed in a given region (like here).

Measured PV power output, divided by that PV systems clear sky power output

Measured PV power output, divided by that PV systems clear sky power output

So what did I do about it? I developed a method for estimating the power output from many thousands of PV systems using a small selection of monitored PV systems (which report their minute-by-minute or hour-by-hour generation).  This method is called  “the clear-sky index for photovoltaics”, KPV. [read more]

Along the way, I had to do significant work in the field of solar radiation modelling, validating clear sky radiation models and developing a new type of solar radiation model fit to Australian radiation data (a "separation model").  I also had to show that my new method (using solar panels as a sensor network) was able to do the job just as well as professional grade radiation equipment.  

Pyranometer based methods versus KPV based methods.  The takeaway? Get the sites close enough (within 5km) and my new method is just as good as using a more sparse network of professional grade solar radiation sensors.  

It was also necessary to develop new quality control routines for the PV system power output data.  This type of data is messy, often provided by non-experts and hasn't been used in the way I've proposed before.  This was accomplished through the development of a new quality control routine called QCPV (now going through the review process in a major journal).

~200 solar PV systems installed in Canberra, which are reporting their data in real-time

Once I sorted out the quality control work, it became possible to work with data from many hundreds of solar PV systems, as well as scale my KPV method up to many thousands of systems.  So I forged ahead, using Canberra as a proof-of-concept, creating a city-wide distributed PV simulation of its 12,000+ embedded PV generators (based on December 2012 installation data).  

I then paired this simulation system with weather events that cause broad-scale, rapid changes in the power output of all of the PV systems at the same time [check it out].  It is these types of events which are the most likely to cause future grid stability problems.  The basic idea is that, when you have a wide-spread solar network, the negative effects of partly cloudy days are "smoothed out" by those systems being positioned over broad region, but during certain weather events, it is not possible to smooth out these impacts, because the cloud features are too widespread, sudden and thick.

"when you have a wide-spread solar network, the effects of partly cloudy days are "smoothed out" by those systems being positioned over large region, but during certain weather events, it is not possible to smooth out these impacts, because the cloud features are too widespread, sudden and thick"

What I did find?

Let me keep this as focused and brief as possible...

KPV estimates (color) versus measurements (black) under a positive ramp event

Firstly, I demonstrated that my newly proposed KPV method was much better than existing methods, showing that it was well-behaved under all cloud cover conditions, and performed well under positive and negative ramp events. [download publication][read blog post]

Next I found that for clear sky radiation models, operating in Australia, global clear sky simulations, are best computed by the Solis, Esra and REST2 approaches, while the Iqbal, Esra and REST2 methods are the most proficient clear sky beam models. [download publication][read blog post]

The Engerer 2 separation model at work (blue model estimates, grey observations)

 After that, I found that only the Perez separation model performed satisfactorily for high resolution (one minute) solar radiation data.  In response to this, I developed three new separation models, which gave slight improvements over the Perez model and greatly exceeded the performance of all other existing model techniques. [download publication]

Once that was handled, I compared radiation sensor based methods to my PV data based approaches, with a student project.  This study found that the approaches were equally as good for separation distances of 5km or less. Given that PV sensors are "cheap" (someone else pays for them) - this was a great finding. [download publication][read blog post]

Post QC KPV estimates, very tight correlation, great results

Then I dug into the development of the QCPV algorithm (quality control), demonstrating that the method I created can result in a 43% reduction in Mean Absolute Percent Error (MAPE) over the raw data. [pre-print coming soon]

Second to last, with another student project, we categorised the weather events that cause those large scale, collective changes in PV power output discussed earlier.  Positive collective ramp events (sudden clearing) were caused by Australian northwest cloud bands and radiation fog dissipation. Negative collective ramp (sudden cloud cover arrival) events were caused most frequently by the passage of cold fronts and thunderstorms.[download manuscript][read blog post]

Finally, I put it all together, with the city-wide PV simulation system, using it to simulate the changes in total power and energy output from these collective ramp events.  I was able, for the first time, to quantify (aka determine a representative number) the amount of power that (dis)appears on the electrical grid during these events.  For example, a thunderstorm event on 19 February 2014 removed 20.78 MW of power generation from the local grid over an 85 minute period, which equates to approximately 14.54 MWh of energy generation forgone over that period.  That's probably enough to change prices on the energy market - not very much, but as the solar installation numbers continue to grow, that influence will grow significantly.  

Here's the thunderstorm ramp event from 19 February 2014, along with some satellite imagery.

What is my overall conclusion?

The overall conclusion, is that the developed regional simulation system for distributed solar PV, made possible by an upscaling of my KPV methodology, represents a significant, unique and promising tool for scientific, engineering and operational purposes.  

In the simplest of terms: I built a very handy tool, with cheap inputs that can be run anywhere that solar PV systems are reporting their power output data.

Where next?

I have a full-time lecturer position ("professor" in the American use of the word) at The Australian National University, where I work in the Fenner School of Environment and Society (employed since July 2013).  I am using the freedom and security this position provides me with, to apply for funding to scale this simulation system up, Australia-wide.  I'll join it with the new Himawari 8/9 satellite data, and pair up with the energy market/utilities in Australia, in order to help large amounts of distributed solar to be added to the grid.  

You could say the future is sunny and bright (#punny).  And with HUGE amounts of solar being installed globally, the solar century is before us.  There are plenty of opportunities for this science to stay off of that dust shelf.  So I'd say, overall, this whole PhD thing has been a smashing success! Even if it was a bit of a wild ride.  I hope to have more exciting news soon - for now, I'll get back to working getting this simulator to run real-time in Canberra...

PhD Final Seminar - City-wide Simulations of Distributed Photovoltaic Power Production

I've just successfully completed my PhD defence seminar, and am excited to share with you the results of my thesis project.

It sure is tough to fit a description of everything that I've accomplished over the past four years into an hour long seminar.  Well, in fact, it's impossible!  So much goes into a PhD thesis (dissertation), and only the author themselves will truly understand all the work that went into it.

But I think I've managed to do a good job getting the best of the best information into a manageable format - and I'll be able to share a video of the final seminar with you in a few weeks.  But for now, I'd like to get the slides up on the web, and post some of the simulation videos.

In the shortest summary possible: I've created a simulation system for Canberra PV installations, which uses a subset of monitored PV systems (approximately 80-160 systems, depending on the site availability/date) to simulate 12,500+ PV arrays.  The installed PV arrays are based on data provided by ActewAGL (local utility/distributor) about the rated capacity and suburb-level location of each install through December 2012. 

The heart of the simulation system is an application of the KPV methodology, which uses the power output from one PV system to simulate the performance of another nearby PV system. I've published a paper on the topic in Solar Energy  for those of you who are interested in the more technical side of things.

For now, I'm going to stop writing, post the slides and simulation videos, and let them speak for themselves.  I hope you enjoy them:

[download presentation]

Presentation Slides:

Presentation Videos: Simulations

Video of a a clear sky day for all PV systems installed in Canberra. The images you see are the result of using ~70 PV systems to simulate the remaining 12,500. 

Video of a high variability day where broad convective clouds moved through Canberra. 

Video of a positive ramp event resulting from fog dissipation in Canberra. The images you see are the result of using ~80 PV systems to simulate the remaining 12,500. 

Video of a negative ramp event resulting from convection moving into Canberra. The images you see are the result of using ~140 PV systems to simulate the remaining 12,500. 


Workshop with the Japanese Meteorological Agency: a Research Update

This week, I had the pleasure to present my current research to Mr. Toshi Kurino of the JMA

And I thought, why not use this change to provide an update on my current  research efforts? I've already put together a powerpoint on the topic - which did all the work for me on organising my thoughts.  So here goes.  What am I up to?

I've tried to make this easy. There's a slideshow below with accompanying audio hosted on soundcloud.  If you'd prefer, you can grab the presentation here:

[download presentation]


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