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!


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