Throughout the Australian electricity grid, the flow of data is helping to advance transition from fossil fuels to renewable energy. The latest issue of pv magazine in print hits newsstands today (November 2) with a feature on how innovators such as Greensync, Redback Technologies and SwitchDin are using data and software to successfully bring more renewables into a grid designed around centralised coal-fired power stations.
Amid a flurry of digital activity in the energy industry, Australia’s Clean Energy Regulator is also systematically and constantly upgrading its validation systems for Small-scale Technology Certificates (STCs) and Large-scale Generation Certificates (LGCs), to build confidence in the certification schemes that have incentivised Australian’s renewable revolution at all levels.
A CER spokesperson told pv magazine, “We expect to validate more than 30 million large-scale generation certificates this year, which is almost double the amount validated in 2016.” Each LGC represents 1 MWh of clean energy generated in the large-scale renewables sector, and can be sold to entities — mostly energy retailers — seeking to meet their obligations to provide a proportion of their energy from renewables.
The CER will this year also register double the number of STCs — “we expect more than 400,000 applications”, says the spokesperson — compared to 2016. Each STC also represents 1MWh of clean energy generated, overwhelmingly in the rooftop solar sector, and can be sold to recoup part of the cost of purchasing and installing a system, or transferred to other entities at a negotiated price.
The validity of claims for renewable energy certificates is critical to upholding the market for their sale and trade, and for guarding against fraudulent applications for reimbursement.
From ponderous regulation to self-serve and a blockchain future
“We use sophisticated data matching and analytics, and geospatial capabilities to support compliance monitoring and enforcement campaigns,” says the CER spokesperson
Adhering to open data and national Application Programming Interface (API) standards, the CER shares data with the Australian Energy Market Operator (AEMO), correlating residential and commercial electricity meter readings, which are automatically updated with applications for certificates, to determine the probability that a small-scale solar system has been installed.
More than 60% (up from 50% in July) of STC applications now also make use of the Solar Panel Validation (SPV) smartphone app, introduced in 2018, which scans panel serial number barcodes to verify that panels meet Australian Standards and carry a manufacturer’s warranty. This secure package of data is collected by the system installer and sent separately to the solar retailer who provides it back to the customer as proof of quality and warranty; and to the Australian Government’s system for managing certificate claims under the Small Scale Renewable Energy Scheme (SRES).
The value of certificate claims over the past 12 months adds up to more than $1 billion. To protect the integrity of those claims from ineligible solar panel imports, to increase consumer confidence, and to protect the solar supply chain, the CER developed a validation solution that would not increase regulation nor require “an army of inspectors”, says the CER spokes person.
The resulting SPV digital application, it says, is low cost, reduces red tape, enables approval of most claims within 24 hours, and “allows industry to self-regulate and ensure sustainable business growth and consumer confidence beyond the life of the SRES scheme”, which will decrease towards its end in 2030.
The CER is now exploring a further enhancement in which distributed-ledger blockchain technology would, it says, “provide real-time multi-point verification of serial numbers at the site of installation/pre-certificate claim and during our claim assessment processing.”
Reducing the regulatory burden on mid-scale solar generators
For renewable generators under 1 MW capacity, the Regulator uses solar irradiance data from the Bureau of Meteorology to measure LGC eligibility, because AEMO data only captures generation exported to the grid, and not generation that is consumed on site, which also counts towards the certificate entitlement.
The agency is expanding its project to hone precision in this < 1 MW capacity with “a pilot program to trial the use of solar irradiance and other data to automatically determine the monthly generation and LGC entitlement of accredited solar photovoltaic power stations”.
The solar irradiance method calculator as the prototype developed by the CER is known, uses a range of data input — including localised satellite solar-irradiation data, ground-based temperature measurements and system characteristics — to determine the amount of electricity generated by a solar PV plant. Data from both the Bureau of Meteorology and from Solcast’s, sophisticated solar-forecasting technology is used to support the CER’s calculations.
This new irradiance method has been designed to significantly reduce the regulatory burden on mid-scale solar, and incentivise ongoing participation of the sector in the Large-Scale Renewable Energy Target.
“The administration associated with the measurement, calculation, submission, assessment and validation of these claims for mid-scale solar is disproportionate to the LGCs produced,” says the spokesperson, who added that the new functionality would help to ease that burden.
Machine learning supported in the cloud
The CER has recently transitioned its systems to the cloud, which it anticipates will provide an adaptable and scalable environment, enabling greater efficiencies than traditional data centres.
In partnership with Microsoft and Esri geographic information systems (GIS), the Regulator is exploring the potential to use machine learning to assist human decision making in its analysis of large data sets.
As part of its broad remit to administer schemes legislated by the Australian Government for measuring, managing, reducing or offsetting Australia’s carbon emissions, it plans to design pilots that use machine learning to streamline and improve the process of applying for credits through the Emissions Reduction Fund (ERF).
The ERF incentivises businesses to adopt practices and technologies — such as energy efficiency, capture of fugitive emissions and reductions in energy-intensive transport — that reduce their greenhouse-gas emissions.
Recent co-design workshops have pinpointed the pain points in submitting ERF project applications, and the CER spokesperson says, “Simplifying the processes through smarter forms and online tools for uploading GIS files will make it easier to identify eligible project areas and provide real-time feedback to the participant.”
Overall, in gathering, combining and analysing data from a variety of sources, the CER aims to reduce the regulatory burden for participants in the schemes it administers, manage compliance in a way that benefits consumers and the market, and provide visibility over the impact its work is having.
Recent upgrades in its digital capabilities have already helped to “automate processes and protect scheme integrity while maintaining the same or better processing timeframes, with the same number of resources”, the Regulator spokesperson says.
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