The Engerer2 Diffuse Fraction Model (Global Radiation Separation Model)


August 2016 Update: You can learn how to compute the Engerer2 model in my Rpackage here!

This blog post explores the success of the Engerer 2 model as laid out in: Engerer, Nicholas A.  Minute resolution estimates of the diffuse fraction of global irradiance for southeastern Australia. Solar Energy. 116, June 2015, pp. 215-237.

[download the paper here]


One of the key outcomes from my PhD thesis was the validation of two different types of radiation models: clear sky and separation, against one minute resolution data. For the clear sky validation, I found suitable performance from several models for use in Australia, but the available separation models, however, did not have acceptable performance

The main issue with the available separation models (models that take a global radiation measurement from a pyranometer and separate it into its direct and diffuse components), is that they are regression based, with the original data being hourly averages of radiation.  At minute-resolution timescales, the relationship with global and diffuse/direct radiation is very different.  For one, there are very rapid fluctuations in the incoming radiation budget across these timescales.  Another big difference is the influence of cloud enhancement which is where radiation arriving at the surface exceeds the clear sky value because of non-linear interactions with some types of cloud decks. 

The Engerer2 model, with the diffuse fraction (Kd), plotted against the clearness index (Kt)

Making big changes, in the name of science

Thus, when I formulated my model, I knew that I had to make some significant advancements upon the existing methods/literature.  The principal improvements made with this model are four-fold:

  1. Inclusion of a physical model (REST2 clear sky model), making the model 'quasi-physical', much like the DISC model written by Eugene Maxwell (Maxwell 1987)
  2. The model is the only one of its class (as of the time of publication) that has been fit to minute resolution data (most other models have been designed for hourly data)
  3. There are two new variables, which have not previously been utilised in a separation model.  These are delta_Ktc (deviation of observed clearness index from clear sky value of clearness index)
  4. and K_de (the portion of the diffuse fraction that is attributable to cloud enhancement events)
 The Engerer2 model formulation.  Please  read the paper  for more information.

The Engerer2 model formulation.  Please read the paper for more information.

independent assessment of the model: it works very, very well

The result is an impressive performance of the model against the current suite representing the state-of-the-art.  In a recent study, Gueymard and Ruiz-Arias 2015, radiation data from 54 sites around the globe were used to validate 140 separation models.  In this study, the Engerer2 model was the best!  Here it is, as described in the text:

“It is found that two models stand out over the arid, temperate and tropical climate zones: ENGERER2 and PEREZ2. These two models share two important features: (i) They include a variability predictor; and (iii) They leverage clear-sky irradiance estimates. The reason why ENGERER2 performs consistently better than PEREZ2 or other models is most likely because it was actually derived from 1-min data (compared to hourly data for PEREZ2 or most other models tested here). Based on the ensemble of statistical results obtained here, it is concluded that ENGERER2 has the best generalization skill, and can thus be considered a ‘‘quasi-universal” 1-min separation model, wherever and whenever low-albedo conditions prevail.”
 Figure 2 in Gueymard & Ruiz-Arias 2015. Displaying the stations at which radiation data was used for model evaluation.

Figure 2 in Gueymard & Ruiz-Arias 2015. Displaying the stations at which radiation data was used for model evaluation.

And well... was that ever quite the compliment (especially coming from a scientist whom I've looked up to for so long)!  I am very pleased with this result, because now, my Engerer2 model is the ‘'quasi-universal' 1-min separation model" and has been accepted to be of global standard.  That makes my inner nerd quite happy, I'll admit. "Chuffed" as my Aussie friends would say :-).  And now I've have been given a reason to write a long overdue blog post about this research work.  As well as deliver a little surprise...

NOw, the Engerer2 model is in demand

As a result of this excellent outcome, I have several researchers in the community who would like to use my model, and I am quite happy to oblige.  So with this post, I am also announcing that a beta version of my Rpackage "anusolar" is now available, on request.  You can read more about this software at nickengerer.org/rpackage and where you can find out how to use the Engerer 2 separation model!  This package will allow you to do more than that, including PV simulation, KPV calculation and creating output from clear-sky radiation models.  So go check it out!

#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!


#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 PVOutput.org, 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 http://rpss.info.  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)


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 Thesis Submitted!: An Extraordinary Adventure

4 years, and several lifetimes later, I'm submitting my PhD Thesis

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


In October 2010, I got an email, advertising for a PhD scholarship in Solar Radiation & Energy with an organisation called CSIRO, at a place called The Australian National University, in a city called Canberra.  To be honest - I'd never heard of any of these things before.  But, having just made big progress on my M.S. thesis, focusing on solar energy, and not seeing any options in the U.S. in my field - I decided to a take a big chance, and apply for the position.

Several months, countless drafts of applications, and multiple scholarship awards later, I found myself with all my belongings narrowed down to 8 suitcases, and my (now fiancé), Teddi at my side, boarding a Qantas flight for Sydney.  It was June 2011, and I had no idea what I was getting myself into.

 The Intellectual Property Bomb Goes Ka-Boom!

The Intellectual Property Bomb Goes Ka-Boom!

 

I dropped like a bomb into an Intellectual Property (IP) argument, where CSIRO wanted me to sign over all my rights, IP & future profits, with no research license, and The ANU was telling me and them, that was a rotten deal.  So by September, after plenty debate and conversations with lawyers, I found myself saying 'see ya' to CSIRO - and all those hard earned scholarship dollars, plunging myself into a black hole, devoid of financing, supervisors and a PhD project to undertake. But looking back, man, that was a very good decision

"I dropped like a bomb into an Intellectual Property (IP) argument..."

But, at the time, I sure did I feel like I was in big, BIG trouble. I had no scholarship money, $30k of tuition per year to pay, and I was thousands of miles from friends and family - not to mention I'd just dropped $4k to get my dog here!

But The ANU looked out for me.  I was awarded an APA and IPRS scholarship in October 2011, and I was back on my feet.  My supervisor, Frank Mills, was able to help me patch my first semester of fees together, and get me a living allowance to hold me over (thanks Steve Dovers!).  From these new scholarships, I was set for the next 3-4 years, to figure this PhD thing out.

So yeah, about this PhD thing - now I have no project, no data, no supervisory panel and no clue what the hell I'm doing in Australia.
 Solar PV arrays as our sensor network? Our innovative idea.

Solar PV arrays as our sensor network? Our innovative idea.

But shortly after these scholarships appeared, so did new opportunities.  I met two inspiring senior scientists - who recognised my potential and respected my ideas from the very beginning: Professor Andrew Blakers (ANU) and Professor Bob Williamson (NICTA).  Together, we pioneered a new project, successfully obtaining funding from the Australian Solar Institute (now ARENA), for a two year project developing machine learning based distributed solar forecasting.  So from the ground up, I built my PhD project research team, its data, and all the analysis tools I needed. It was a heck of a lot of work and it challenged me in many ways, but it quickly became clear that the falling out with CSIRO, allowed something even better to evolve.  I was also empowered by people like Andrew, Bob and Frank - who gave me their respect and believed in my ideas, as we moved our new project forward. Upon reflection, I think that those two things are the best a young scientist could ask for.

So things were looking up! But I just couldn't help myself....

So, as if being a PI on a $850k grant project and doing my PhD wasn't enough, I decided to apply for a job opening for a lecturer at The ANU Fenner School of Environment & Society (my host institution).  It was early 2013, and I was bold & naive, with no idea what I was getting myself into.  I went up against people with CVs many pages long, boasting of 10 years more experience in the field.  But I fought hard, sold myself well, and played the youthful energetic kid with big ideas and a promising future angle.  And, well, somehow, I pulled it off, landed the job, and found myself lecturing & convening for two courses with 70-100 students each.  

"And, well, somehow, I pulled it off, landed the job, and found myself lecturing & convening for two courses with 70-100 students each." 
 Accepting a Citation for Outstanding Contribution to Student Learning by an Early Career Academic in December 2014.

Accepting a Citation for Outstanding Contribution to Student Learning by an Early Career Academic in December 2014.

Surely I was mad.  A full-time teaching position & a big time grant project - when's the PhD going to be the focal point? Could I really pull this off? I was courageous enough to think that I could, and I strategised my way forward, quite successfully. Things were actually shaping up nicely!

But just as I was starting to piece it all together, and get my first PhD related publications sorted, and the first round of experiments for our project underway, life threw me the biggest freaking curveball it could think of.  In a tragic turn of events, along with the birth of our son, came a scary and rare cancer diagnosis for my beautiful wife.  

We were gutted. Overwhelmed. And the PhD quickly became the least of my worries.

Over the next 6 months, while teaching, PhDing and grant-doing - I had to fight, scream and campaign to get her to the top of the waitlist for a peritonectomy surgery.   From there, in March 2014, she underwent the 12 hour operation, and then spent 5 weeks in the hospital, recovering from the brutal battering her body had taken.  Once we came back home, it was a few months before she was back to 90% (do you ever make it to 100% after something like that?).

Wow, what ride! But both happy to be alive

So, cancer beaten!  Then, and maybe then, it was time to focus on the PhD?  But alas, I'd picked up another course to convene, in first semester, called The Blue Planet.  Now I was without a semester off from teaching, was seriously behind on everything, and I was feeling pretty wiped out from it all!

What had I gotten myself into? Teacher/Researcher/PhD-er/Family-Man - what a load to carry! 

 Thinking big - my PhD developed a city-wide simulation system for distributed solar PV systems.

Thinking big - my PhD developed a city-wide simulation system for distributed solar PV systems.

But you know what? After you battle your way through something as crazy as cancer, where you face your biggest fears - you actually come out on the other side with a different perspective.  I realised, that after that ordeal - I wasn't actually afraid of failure like I had been before.  A PhD thesis, convening two courses and running a research project, it all was much less intimidating.  There was no more need to fear some distant failure, some imagined rejection, or any manufactured worries.  I'd been through the darkest period imagineable - and that perspective, really put me in a position of strength:  I wasn't afraid to dream big, take on large responsibility and slug it out one day at a time.  In fact, I began to see it as kinda fun.  A new freedom emerged - one where I wasn't held back by doubt, but was emboldened by believing in myself and a recognition of my own mortality. The time to go big, or go home, was now or never! 

So abandoning my philosophical promulgation, we can fast forward through 2014 and early 2015, and somehow, by April 2015, I was giving my final seminar. I had multiple publications under my belt, and it was time to do the dirty work - writing up my intro, conclusion and linking text.  My "PhD by compilation" was taking shape, and all I needed to do was put in the hours, and keep up that confident approach in the process.  

 The death of my laptop, one month prior to submission...

The death of my laptop, one month prior to submission...

But, of course, life wasn't content to let me sail through though! I was constantly distracted by my efforts to get permanent residency (skills verification, english tests, medical exams, etc), my computer died in the final month of writing, and then my son decided to give our family the worst week-long gastro episode in existence.  And on top of that a new funding round emerged from ARENA which was far too good to pass up.  So instead of happily writing my PhD thesis in May-July 2015, I instead found myself preparing a grant application with multiple industry partners and a bit more of that bold, crazy thinking.  But nevertheless, I pulled it off, and here I am, just a few hours from submitting.

Upon reflection, I think that I've learned to tread carefully on the lines between (in)sanity!

So, in conclusion, when I submit that 200 page beast of epic proportions this afternoon at 3PM - I'll do it  with my cancer-punching wife by my side, ready to dream bigger than ever, with a resounding "Hell yeah! I did it!" in my heart. And if I can, amidst all that has happened.  You can too!

Thanks PhD, for the adventure of a lifetime! 

Oh, and good riddance too! :-)

 

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