$1.2m for short-term solar forecasting
A $1.2 million project is underway to increase the accuracy of short-term weather forecasting to better predict solar and wind power generation. Over the next 18 months, UniSA Professor of Environmental Mathematics John Boland and his team will work on designing what is claimed to be the world’s first short-term statistical forecasting model that will predict weather conditions from five minutes up to 1–2 hours ahead.
Prof Boland said that inaccurate short-term forecasts of wind and solar generation have cost Australia’s renewable energy sector around $5 million over the past decade. Working alongside colleagues from CSIRO, UNSW and Genex Power, Prof Boland hopes to produce an industry best-practice forecast model to ultimately cut power prices and make renewable energy more reliable.
Cloud cover is one factor affecting accurate forecasts, causing scheduling errors and affecting electrical outputs, and the UniSA research aims to help rectify this.
“Accurately forecasting the output of grid-connected solar systems is critical to increasing the overall penetration of solar and renewables. This is important for the stability and management of the electrical system as a whole,” Prof Boland said.
“Clouds can move and form very quickly, creating complex atmospheric layers which often move in different directions. The existing forecasting systems for wind and solar are designed for longer-term time frames and have led to multiple issues over the years. This highlights the need for reliable short-term forecasts to provide confidence to both renewable generators and the entire industry,” he added.
The project will implement a short-term solar forecasting system on five operational solar farms spread from far north Queensland to Victoria, with all sites experiencing changeable weather conditions. A range of technologies will be implemented, including skyward-facing cameras with machine vision algorithms to track and predict cloud motion, satellite imaging, statistical models and power conversion models.
“It is the first time all these forecasting techniques have been combined to help create a single model to more accurately forecast weather. This will be a world first and will represent an international benchmark for short-term solar power forecasting,” Prof Boland explained.
Prof Boland said the industry gains will be substantial, reducing both grid instability and costs. “By producing forecasts that are more aligned to the actual solar output, renewable energy generators will no longer be unfairly penalised and the benefits will be passed on to consumers.”
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