Predicting clouds will make solar cheaper

An Australian-first solar energy project that uses Cloud Predictive Technology (CPT) to anticipate solar energy output has been launched in Karratha, Western Australia, with $2.3 million in support from the Australian Renewable Energy Agency (ARENA).

The project to supply 1MW of solar energy to the Karratha Airport is being led by renewable energy development company SunEdison Australia and hopes 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.”

Mr Frischknecht said the airport project received funding because of its alignment with ARENA’s twin objectives of working to reduce the cost and increase the use of renewable energy in Australia.

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 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,” he 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.”