Solar Energy

PVSC44: Himawari 8 enabled real-time distributed PV simulations for distribution networks

Just over a year ago, I announced an exciting new project focused on delivering real-time distributed PV simulations to distribution network service providers (DNSPs). This is an Australian Renewable Energy Agency (ARENA) funded research project, led by the Australian National University. As the Chief Investigator on this research project, I have spent the last year regularly meeting with and networking with DNSPs across the country. As a result, we now have 12 of the 15 DNSPs on board, which is a tremendous outcome.

Each of theses DNSPs is providing us with the metadata for the installed PV system within their networks, which we are using to produce simulations of their power output.  This work extends the efforts of my PhD, where I built the Regional PV Simulation System (RPSS) through collaboration with locality utility ActewAGL, but adds new capability through real-time PV monitoring and derived irradiance estimates from the Himawari 8 satellite.

This week, I am at the PVSC44 conference, co-chairing the session on solar forecasting for grid integration of PV, as well as presenting a paper which outlines our modelling methodology for this new tool. I wanted to take the chance to put up an accompanying blog post, to help make it easily accessible to my fellow researchers. Of course if you want the real details, you should check out the paper :-).  It is important to note that the following methods are the result of a close industry collaboration with industry partner Solcast, and represented the state-of-the-art of its satellite nowcasting technologies from early 2017

[Download the Paper]

Here's how it all works..

Flow diagram of our methodology

Flow diagram of our methodology

We take images from the Himawari-8 satellite, in particular the visible and infrared channels, and apply the general principles of the Heliosat-2 approach for turning these into cloud opacity estimates. What's very interesting about this process, is that the spatial and temporal scales of this data are quite advanced, as compared to other geostationary weather satellites (e.g. GOES 13/15 over the US). At 1-2km^2 resolution with 10 minute update cycles, the radiation modelling enabled by this satellite is quite an exciting advancement for our field.

We use the Himawari 8 data in combination with the GFS NWP model to decompose clouds into lower, middle and upper troposphere layers, and then aggregate these layers to estimate total cloudiness. Total cloud opacity (represented as an index between 0 and 1) is then derived using differences between the lowest visible return value of albedo. With this value in hand, we apply a linear reduction to the Esra clear sky radiation model to produce an estimate of global horizontal irradiance (Gh) for each pixel. Estimates of Gh are afterwards decomposed into direct normal irradiance (Bn) and diffuse horizontal irradiance (Dh) through the Engerer2 separation model.

Satellite imagery decomposition

Satellite imagery decomposition

Radiation Validation on "Estimated Actuals"

estimated actuals

For this conference paper, we have validated the Gh values produced by this system. These "Estimated Actuals" are produced using the satellite scan for each time stamp (no forecasting involved). It is necessary to compare these outputs to the observed radiation conditions at the surface, which we accomplish using data from the Australian Bureau of Meteorology.  Using 7 sites and 2 months of data from the BoM, we were able to determine these estimated actuals to have a Mean Bias Error (MBE) of -7 W m-2 and Root Mean Squared Error (RMSE) of 55 W m-2.  This accomplishes the industry standard of a 'good' radiation model, which is defined in the literature as having an relative MBE less than 5% (1.8% reported) and relative RMSE less than 15% (10.7% reported).

PV Power Estimated Actuals + Validation

The next step in the methodology is to compute PV power output calculations, based on the installed characteristics of the PV systems considered.  This requires information such as the azimuth & tilt of the system, as well as the total installed capacity. We apply the Reindl transposition model to estimate the available plane of array irradiance, followed by a quadratic PV power model to compute the estimated power output. By combining the Estimated Actuals from the satellite system with this model, we also can compute Estimated Actuals for PV system power output.

Screen Shot 2017-06-27 at 14.47.55 pm.png

In order to accomplish this step & validate it, we collected data from 78 PV systems across the Canberra, Australia region for a period of 6 months.  Using these time-series data, we then applied our QCPV methodology to both quality control the reported power outputs, but also to determine the actual azimuths & tilts for the POA radiation modelling step.  For this validation, we report an MBE of 0.04 W/Wp and RMSE of 0.15 W/Wp

Overall scatter in the validation data shows well behaved results, but with accuracy losses nearly double that of the radiation validation. We note that this validation reports uneven bias across the distribution of PV system measurement values, suggesting further refinement of the quadratic PV model coefficients could be required.

Scaling it up for DNSPs

Given the relatively good performance of the modelling system (much improvement needed still for operational purposes) we apply this system to a network wide simulation.  By utilising the metadata for 15,500+ embedded solar PV systems in the ActewAGL distribution network, we produce Estimated Actuals for the PV power systems in this network via this methodology. Additionally, we implement a simple cross-validation using 6 PV systems in a real-time scenario and provide the demo in video format:

In the provided image, we see the Estimated Actuals for each individual PV system in the ActewAGL network, as provided for the 23rd of June 2016. This day was selected based upon its complicated, multi-layered cloud conditions to best demonstrate the capability of this system in a challenging circumstance. A heat map colour profile shows the relative power output from each PV system based on its installed capacity.  This is accompanied by the data from the 6 PV cross-validation sites in the circles which are colour filled according to their observed power output.

At bottom right of this image, the Estimated Actuals for these systems are displayed in grey, with the red line showing the observed power output values. Overall, we observe a slightly positive bias in the Estimated Actuals of 0.038 W/Wp and a relatively lower RMSE value of 0.12 W/Wp.

A Great Start: Where Next?

Overall, our team is quite happy with this first set of results, but we've many improvements to make to this system moving forward. As a part of this validation, we've already found & corrected issues with positive cloud opacity bias during overcast conditions, and we have implemented a dynamic kernel which corrects the cloud shadow displacement at low sun angles. Up next for us, is improving the radiation modelling routines. For one, Engerer2 was not designed for this purpose and our linear reduction on the Esra model needs to be updated.

The good news is, we've filled up our team with postdocs, PhDs, honours students and software developers, and are working hard to make these updates.  Soon, we'll even highlight the forecasting capability we've added to the system in an upcoming issue of Progress in Solar Energy.  In the meantime, we're deploying this system for our participating DNSP partners right now, and will iterate forward based on their feedback. So check back soon and connect with us on Research Gate to stay up to date & to collaborate.

It's all a part of teaming up for that solar-powered future! And in that spirit be sure to check out our data sharing campaign, where we are making the output of this system available for your research purposes. Read more at this link!

Lastly, the ANU team would like thank & acknowledge our industry partner Solcast's contributions to this project. 


A Data Sharing Compaign to Enable Researchers

A Call to University Researchers to "Get Applied"

It's no big secret that Australia's electricity sector is undergoing a rapid transformation, one that continues to surprise even the most aggressive growth targets for solar and storage, and has our regulators and market operators in a very demanding position. High electricity wholesale and spot prices, mean the costs of electricity are going up. Meanwhile, the cost of renewables and storage are falling quickly.  All across Australia, innovative companies are popping up which are providing solutions in the space of demand management, energy storage and energy trading and driving this transformation from the ground up.

in this electricity sector transformation, what role can universities play?

In this mix, I wonder, what role can Universities play? That's what my Academic mindset wants to know: how can we as researchers contribute to this challenging transformation?  I've had the great privilege of traveling much of Australia lately to visit with many segments of the electricity industry, and in each of those instances, I am filled with ideas on where Universities could contribute to the challenges that this sector is facing. 

But in wondering how best to make these relationships, these BIG ideas happen, I find myself exceptionally time-constrained. I simply can't work with everyone directly! Yet I feel a strong drive to help my fellow University researchers reach the real-world - how best to reconcile these?

Solar Data Enables

In contemplating this, I came to an interesting conclusion: high-resolution solar data is actually an enabling resource.  Let me explain what I mean - want to decide how to charge/discharge a battery? You need to know when the sun will or will not shine. Want to build a demand management algorithm for a commercial building? You need to understand how the rooftop solar array's power output will fluctuate during peak demand periods.  Need to orchestrate the dispatch of hundreds of MW of storage technologies? When will the next solar ramp event arrive and drive 100+MW shortfall of supply over the energy market zone you're trading in?

In the future grid, solar data is everything.  In fact, in my recent talk to the Energy Networks Australia "Grid Edge" Event [watch it here], I proposed that solar intermittency is the next frontier of opportunity for future grid technologies.

If we, as Universities, want to contribute to the future grid, this incredible transformation, then the research we complete will inherently require access to good solar data. And by 'data', I more specifically mean solar radiation or solar power generation, at high spatial and temporal resolutions.

Data Access Can Be Hard

However, solar data access is not easy. Most solar data in Australia is based on Typical Meteorological Years (TMY) climatologies, comes from only a handful of solar radiation sites scattered about the continent, or comes bundled on a harddrive at hourly resolutions and full of bias error. Other options include paying significant cash to commercial companies, which are happy to make you pay thousands of dollars for small datasets - no thanks! Shouldn't there be a better solution?

Building a Solar Forecasting System is Not Easy!

Example forecasts for small-scale solar in South Australia from 22 March 2017. Probability bounds are provided in the shaded regions of each forecast.

Example forecasts for small-scale solar in South Australia from 22 March 2017. Probability bounds are provided in the shaded regions of each forecast.

In addition to historical solar radiation data, researchers equally need to be able to access solar forecasting information. If you want to build any of the future grid enabling technologies which seize the opportunity of intermittency, they must be built in a fashion which captures the uncertainty of solar forecasting. 

At ANU/Solcast, we are pretty good at predicting the near-term availability of solar radiation, but we're not perfect either.  Given the stochastic nature of cloud formation & dissipation, their fast-moving, fast-changing characteristics and their complex optical properties, we are never going to get a cloud/solar radiation forecast 100% correct, 100% of the time.  But what we can do, is provide researchers with access to a state-of-the-art solar forecasting technology with world-class accuracy, that includes probabilistic forecast information. This way, they can build tech with these uncertainties built in - a key part of orchestrating the future grid!

Solar Data + Solar Forecasts = Easy to Share

Our Himawari 8 derived solar irradiance datasets at right, compared with the Bureau's old satellite data at left.

Our Himawari 8 derived solar irradiance datasets at right, compared with the Bureau's old satellite data at left.

In my personal quest to enable other researchers, I've realised, that in my position, with my team at the ANU, and our work with our start-up company Solcast, we can fix this problem.  We can openly and freely share solar radiation data, PV system power output datasets and solar forecasts through our joint ARENA project.   As project Chief Investigator, I have decided to inject some ARENA project funds into a data sharing campaign as part of our knowledge sharing efforts, to support the computational requirements of servicing this data to external parties. 

To be clear, that means that YOU, the researcher, get to extract the data for free for R&D purposes

And with the #SolcastAPI it is actually that easy. Within minutes of registering, you can access historical "Estimated Actuals" derived from the Himawari 8 satellite (right now, reaching back 7 days, in the near future - much deeper!) and solar radiation forecasts for GHI, DNI and DHI (powered by my Engerer2 model).  You can also use the API to simulate solar PV system power output, either via the Estimated Actuals or through the forecasts (0-7 days our, 30 minute increments).

To get started, visit the data sharing page I placed on my webpage, to provide more information about this service.  There you'll find some links for getting started. In the near future, I'll add some demos which show how to access this data, and will provide some research ideas for using high-resolution solar data for applied university research.

It's all part of teaming-up for the solar powered future! I look forward to seeing what you come up with!


Find More Stuff:

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:


#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

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#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)


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