PhD

Announcing My ARENA Project: Real-time Operational PV Simulations for Distribution Network Service Providers

ARENA Project Launch - 13 April 2016

Members of the successful ARENA projects - who is that handsome guy front right? ;-)

Members of the successful ARENA projects - who is that handsome guy front right? ;-)

This has been a truly incredible week.  I have just returned from Perth, Australia where the Australian Renewable Energy Agency (ARENA) announced the outcomes of their Industry-Researcher development funding round.  All up, 9 projects were funded, totaling over $17M in funding, comprising a very impressive array of projects.  Proudly, the ANU is leading 3 projects and pulled in $4.7M from the scheme!  And looking across the remaining 6 projects, I can't help but feel incredibly proud of the innovative work Australian researchers are doing, as well as be excited about the future of renewable energy Down Under!

What's great about this funding round, is that it required industry and researchers to work together, and put up projects which have clear commercialisation potential.  That means projects which are "real-world relevant" were required, which, if you know me, meant that I was pretty darn excited!  This funding round is exactly where my passions and interest lie, and it lines up very well with the government's desired outcomes from universities in Australia, and is well aligned with the big push for innovation across the higher education sector

So, Why was I there?

Why do this? Lemme explain..

Why do this? Lemme explain..

To answer that question, we need some background.

I have spent the last few years working on another ARENA project which has focused on distributed small scale solar forecasting via machine learning and computer vision techniques.  This was an ANU-NICTA collaboration, which will wrap up mid-2016, (just before NICTA is subsumed into Data61/CSIRO - cue the doomsday music!).  One of the outcomes of this project, was my PhD work on the Regional PV Simulations System (RPSS).

RPSS v1 during a positive collective ramp event

RPSS v1 during a positive collective ramp event

Version 1 of the RPSS was developed within my thesis, and produced a modelling environment which simulated 12,000+ small-scale solar PV systems in Canberra (based on December 2012 installation data from ActewAGL).  This used data from PV systems that reported their power output on PVOutput.org, which through the magic of KPV, I was able to upscale into city-wide simulations

In my thesis, I used several critical collective ramping events to demonstrate how quickly the power output from 12,000+ PV systems can change across an entire region.  This was a fun tool, but only worked with historical data and at surburb-level.  Read between the lines here: it was cool, but ultimately not directly useful. This is the type of science that would stay 'on-the-shelf'.

Enter RPSS Version 2

After I submitted my PhD, I continued to work on the RPSS, developing it up to version 2, which I launched at the Solar World Congress in Daegu, Korea in November 2015. This version featured two major upgrades:

  1. It worked with near real-time data from the PVOutput.org live solar API, updating every 5 minutes (check out the beta at http://rpss.info).
  2. It mapped the simulated PV systems to the transformer nodes on ActewAGL's distribution network (now it's actually useful!)
RPSS v2, live data and mapped to ActewAGL's network

RPSS v2, live data and mapped to ActewAGL's network

These significant advancements demonstrated my ability to take the science off the shelf, get it out of the lab, and into relevance.  Working with live data is not easy (which is why the current version is merely a clunky beta!), nor is convincing an electrical utility to give you detailed data about their distribution network, but it teaches you about the many things required to advance a science to a technology.  And if you're like me, you'll fall in love with the challenge.

It was this second version of the RPSS that I took forward to ARENA, requesting funding to deploy it to distribution networks around Australia in order to address the challenges with integrating high-penetrations of solar PV into Australian electricity networks.  Does that sound like fun to you? Because it sure as hell does to me!

The Challenge: High penetration solar PV

High penetration solar in a neighbourhood in Canberra - solar PV on every roof!

High penetration solar in a neighbourhood in Canberra - solar PV on every roof!

With more than 1.5 million solar PV systems installed to date, totaling more than 4.5GW of capacity, the maximum penetration levels of solar PV are being reached in some areas of Australia. (e.g. Ergon 3.5 kW per system limits, Horizon Power PV limits on radial style networks). 

This 'maximum penetration' level refers to the maximum allowable amount of solar PV a utility will allow on a given part of their network.  The key word here is 'allow'.  For the most part, Distribution Network Service Providers (DNSPs) are taking preventative approaches to distributed PV intermittency, imposing maximum penetration levels that are significantly lower than are technically achievable.

Ergon is limiting the size of individual solar PV systems on parts of their distribution network

Ergon is limiting the size of individual solar PV systems on parts of their distribution network

The Key issue: solar PV = ?

There is one key item that is holding DNSPs back from being more liberal with their maximum PV penetration levels: most Australian DNSPs have no active feedback quantifying how much electricity their embedded PV generators are currently producing at any given time.  On that note, they also don't know how much PV variability will occur in the near future, nor what has occurred in the past.  So how could they possibly manage the inevitable solar future where everyone has solar PV on their roof if they have no idea how much power they are generating at any given time?

This is the key knowledge gap my project will address:  quantifying the current and expected distributed PV power production across distribution networks in near real-time.  That means quantifying distributed PV power output with enough lead-time to do something about PV-induced voltage fluctuations.  In other words, using technology to enable proactive, rather than reactive, grid management. 

What We'll do in response to the Challenge

The solar knowledge gap: how much solar & when/where?

The solar knowledge gap: how much solar & when/where?

This project will take the Regional PV Simulation System (RPSS v2) and develop and deploy it as an operational software that provides distribution network service providers (DNSPs) with real-time distributed photovoltaic (PV) simulations that are mapped to their distribution network.  The output of these simulations will be directly aimed at the knowledge gap that exists between distributed PV integration challenges and their solutions (e.g. energy storage technologies and/or remote demand/supply management). 

HOW we'll do it: Key PArtnerships

Distributed solar PV simulations are not totally unique to my research, and are used by many other researchers in Australia and the rest of the world.  They are integrated into the APVI Solar Map, they are used in Clean Power Research's PV FleetView product, as well as in many related projects around the world.  Where this project sets itself apart from all others is in the consortium I pulled together to accomplish the task and the unique data that they will provide.

This project will be led by myself (Chief Investigator), with the ANU being the sole research partner. Everyone else is an industry partner, and I'm excited to tell you about them.  They fall into a few key categories:

1. Distribution Network Service Providers (DNSP)

DNSP partners ARENA

This project includes active participation from 6 DNSPs, who will provide information about the solar PV systems installed on their networks, so that we can deploy the RPSS to their service region. I am hopeful that more DNSPs will join this project in the near future.

Photo taken in lobby of Western Power's Head Office in Perth CBD

Photo taken in lobby of Western Power's Head Office in Perth CBD

Photo taken in Horizon Power's Head Office in Bentley, WA

Photo taken in Horizon Power's Head Office in Bentley, WA

2. TWo Inverter companies, an inverter wholesaler & a solar installer

One of the primary inputs to the RPSS is monitored solar PV data, which we currently get from PVOutput.org in the online beta version.  This isn't quite good enough, as this data is slow and unreliable, mostly because it comes from non-professional sources. 

This project will work with two inverter companies, SMA Australia and Fronius, to develop real-time, rapid update monitored PV data inputs to the RPSS.  The number of internet connected PV inverters is sky-rocketing, with Fronius installing 50/week at present.  This is an excellent and rapidly growing source of data.

Key project supporter & CTO/Owner of Si Clean Energy, Peter Bulanyi

Key project supporter & CTO/Owner of Si Clean Energy, Peter Bulanyi

This project is receiving significant support from inverter wholesaler, Si Clean Energy, with its involvement led by Owner/CTO Peter Bulanyi.  Peter has been the project's number one supporter from day one, and I would like publicly thank him for his adamant, steadfast support!  Si Clean Energy will also grant access to the AllSolus monitoring network of 1000+ irradiance sensors and PV sites across Australia. 

 

Benn Masters, Director of SolarHub & key project supporter

Benn Masters, Director of SolarHub & key project supporter

We will also be working with solar & battery installer SolarHub, who are a progressive, high-quality company based in Canberra.  SolarHub has been connecting a large number of monitoring systems to PV installations across Canberra, and will grant access to these data over the course of the project.  They have also been a key supporter of the project from its very beginnings, and I would like to also publicly thank Benn Masters, Director at SolarHub for his significant & strategic contributions to the project.

3. ADvisory support

Patrick Dale, Director, Aeris Capital

Patrick Dale, Director, Aeris Capital

Jesse Warburg, Associate Director Aeris Capital

Jesse Warburg, Associate Director Aeris Capital

We have two companies providing in-kind support to the project.  The first is Aeris Capital, whose Director Pat Dale and Associate Director Jesse Warburg are both very excited about the consortium in place and the technology we plan to develop.  Their role in the project is to help drive it toward valuable outcomes for industry and guide us through the commercialisation of the technology. 

Second, we'll have some input from local solar installer and owner of a solar PV monitoring device called the 'esquid', Soly Ltd., whom will provide technical advice into the project.

4. outreach support: Australian Photovoltaic Institute

Finally, we have negotiated a partnership with the Australian Photovoltaic Institute (APVI) to work simulations from the RPSS into the APVI Live Solar Map, which will expand upon the incredible work being done by this progressive and very important voice for solar in Australia.  This is a great place to insert kudos to Dr. Anna Bruce at UNSW for her excellent work on this APVI Live Solar Map, which is an invaluable contribution to solar PV science outreach.  I am very excited to help build upon their Live Solar Map tool, and contribute to improving on this impressive work out of UNSW. 

What we'll do: merging datasets, Deploying RPSS

RPSS modeled ramp events across 3 transformer nodes in ActewAGL's network.

RPSS modeled ramp events across 3 transformer nodes in ActewAGL's network.

Using our unique consortium of partners, we'll be deploying increasingly advanced versions of the RPSS to the distribution networks of each DNSP.  In the first year of the project (2016-2017), we'll deploy the RPSS v2, based on currently available PV monitoring data-streams like PVOutput.org or the AllSolus network.  This version will be about 10 minutes behind 'real-time' and will be based heavily off the existing work that I've done during my PhD.

In 2017-2018, we will advanced the RPSS to include real-time input data from the Himawari 8 satellite as well as rapid-update PV monitoring input from SMA Australia and Fronius, closing the gap between 'near real-time' and 'real-time' operations.  This version will include updates from the latest in PV simulation and solar radiation radiation research.

A selfie from my April 2016 visit with Western Power

A selfie from my April 2016 visit with Western Power

Then finally, in 2018-2019, we'll have all the kinks worked out of the simulation system, having advanced it to true real-time status across all participating DNSP networks.  This version will then be used in the control rooms of these DNSPs to appropriately manage intermittent, distributed PV power production. 

It is highly likely that they will be pairing distributed PV modelling data with technologies like energy storage, to raise the maximum penetration levels of PV across their electricity network.  After all, that is the entire goal!  I look forward to updating this blog throughout the course of the project.

Up Next: Putting together an enthusiastic talented Team!

This project will kick off from July 2016, so I am now looking to put together a team of creative and inspired Originals, who are ready to dedicate their energy to pulling off a fantastic, influential and real-world driven project.  At this time the team I envision looks something like this:

Are you a unique, passionate person with awesome ideas? Then I want YOU participating on this project!

Are you a unique, passionate person with awesome ideas? Then I want YOU participating on this project!

  • A project manager/power systems engineer
  • 1-2 software engineers
  • 2 postdoctoral researchers
  • 6-8 ANU PhD candidates
  • 6-8 International PhD candidate visiting students
  • 8-10 ANU Masters research students
  • 20-30 ANU undergraduate research students

The core team consists of the engineer, programmers & postdoctoral researchers. Each of this persons will be fully funded by the project. Each of these positions will be advertised via the ANU over the next few months.

The secondary team will consist of promising PhD candidates, masters students and undergraduates.  The project has limited funds for scholarships, so I am searching for students who can self-fund themselves.  This would mean an APA scholarship for domestic PhD students, or country of origin funding sources for international PhD students.  Masters and undergraduate students who are enrolled at The ANU can participate in the project through coursework credit. 

I will be putting together a "How to Work with Me" webpage for all ranges of students in the next few days! Stay tuned!

I am very excited to see what our team will look like in the near future and sincerely hope that anyone who wants to participate will get in contact with me.

That's enough for now!

Thanks for reading about this exciting project, and looking into my latest research update.  I am excited to share more information with you, as this project moves forward, and plan to ensure our experiences from this project are shared with research scientists Australia-wide.  Stay tuned!

See ya next time!

See ya next time!



Find More Stuff:


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!

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! :-)

 

/*