The Australian Renewable Energy Agency (ARENA) is funding a high-tech method that could bring down the price of solar energy.

An Australian-first solar energy project using Cloud Predictive Technology (CPT) to anticipate solar energy output has been launched with $2.3 million in support from ARENA.

The project to supply 1MW of solar energy to the Karratha Airport promises to answer questions about how CPT can make solar generation cheaper and more efficient by reducing or eliminating storage requirements.

ARENA CEO Ivor Frischknecht said the project could also lead to a rise in the number of renewable energy projects in the North West of Australia and beyond.

“It will be the first time cloud predictive technology has been used on a solar PV installation of this size connected to a network,” Mr Frischknecht said.

“Because clouds can lead to a sudden drop in solar output, commercial solar power generation on a smaller network usually has costly storage requirements to ‘smooth out’ supply into the grid. Employing CPT reduces the need for this buffer, meaning solar generation can be installed and operated more cheaply.”

The project will be connected to the North West Interconnected System (NWIS), Horizon Power’s network servicing Western Australia’s Pilbara mining region.

Mr Frischknecht said that while customers on the NWIS experience high electricity prices and the Pilbara region had excellent solar resources, development of renewable projects had been affected by high storage requirements stipulated by the network operator.

“Battery storage can help smooth out energy output and is becoming cheaper as technology advances. However, it is currently a major expense for new projects in the region,” Mr Frischknecht said.

“This project is aiming to satisfy network requirements with fewer batteries by enhancing storage effectiveness with cloud prediction, potentially opening the door for more renewable energy projects in the region.”

The Australian project is part of a growing tide of international efforts to use advanced data analysis to improve renewable energy outcomes.