Canberra

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. 


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

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

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

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

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

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

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

[download presentation]

Presentation Slides:

Presentation Videos: Simulations

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

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

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

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


Introducing a Community Powered Solar Energy Forecasting Project for Canberra

In Our Renewables-Powered Future - Rooftop Solar Energy Will Play a Dominant Role.

In January 2014, Australia had nearly 1.2 million solar energy installations, with nearly all of them on rooftops.  Their combined capacity now exceeds 3GW, and is forecast to grow to as much as 23GW  by 2030 - with most of the new installations showing up on Australian homes and businesses.  

Now, if those numbers don't mean much to you - let's just say that's an enormous amount of energy generation and has very significant consequences for the way the electricity market in Australia works.

As I want to keep this post short and straight to the point, I won't go into the specifics of how, why and when high penetrations of distributed PV systems will start causing problems - but we're getting very close.

This is precisely why ARENA has funded our distributed solar energy forecasting project

Starting now, we're deploying up to 300 data loggers to homes with solar PV systems in the ACT region.  Our aim is to cover all of Canberra with a 'sensor' network of rooftop PV systems reporting live data.  By joining this dataset with the deployment of 10-12 sky imagers - we're going to be able to predict how and when the total solar power output of a city changes in real-time.  Once we prove we can do it - we'll replicate the project across all major cities in Australia.

Not convinced?  Let me give you two concrete examples that show why this project is important:

Consider these two scenarios:

  1. Clearing fog results in a rapid increase in PV power production across the whole of Canberra in less than an hour.
  2. Cloud bands suddenly obscure an entire city's PV systems in under 30 minutes

Well lucky you, I have some videos showing that this actually happens:

Positive ramp event from clearing fog

Cloud bands cause negative and positive ramp events 

Currently, these 'ramp events' are manageable.  But what if we double or triple our total installed capacity?  At some point, very soon, it's going to start being a problem.


Here's the fun part:  with our new project, you get to be part of the solution!

We have a team of scientists already working on this problem, and we're ready for more data.  Our ANU-NICTA partnership needs YOU to sign-up for our project as a volunteer to have your home solar energy system report its energy generation.  It's FREE and it allows you to become part of our science project.  

If you're interested, all you have to do is fill in the information at this link:

http://www.nicta.com.au/solar-monitoring-portal/


Want to learn more?

For starters, I've done an informative interview which I've posted to YouTube - most of the important bits are in Part 2:

Interview Part 1 of 2

Interview Part 2 of 2

If you're interested in the more technical information behind our project, I suggest you check out my scientific presentations and stay tuned for future posts (RSS at top-right).   I've purposefully left out a lot of the technical information to keep this post simple.  

You can also read more at the NICTA project page 

I look forward to seeing volunteers fill-up our inbox!  Tell your family and friends all about this project - and help solar succeed in Australia!

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