Predicting power generation from wind and solar
Machine-learning technology is being used to accurately predict energy generation from wind and solar for integration with the national grid, as part of a collaboration between Monash University’s Grid Innovation Hub, Worley and Palisade Energy.
The Australian Renewable Energy Agency (ARENA)-funded project aims to provide wind and solar power generators with more accurate and reliable five-minute-ahead self-forecasting tools.
By improving the accuracy of five-minute-ahead forecasts required by the National Electricity Market, the forecasting solutions developed by the Worley and Monash team can enable a more secure and reliable grid. This is made possible through better forecasts to reduce the frequency of poor dispatch, thereby supporting a higher share of renewables in the market without compromising on overall grid stability.
The development of the machine learning forecasting methodology was led by Dr Christoph Bergmeir from the Department of Data Science and AI at the Faculty of Information Technology (IT) at Monash University, in collaboration with Monash Business School’s Department of Econometrics and Business Statistics, and was initiated by the Monash Energy Institute’s Grid Innovation Hub.
“Predicting short-term renewable energy generation is not an easy task. Renewable energy cannot be produced on demand, as it is bound to natural resources such as the wind and sun. Therefore, in order to achieve a stable network and enough power generation, we need a reliable short-term prediction method,” Dr Bergmeir said.
“By introducing machine learning methodologies to this short-term forecasting process, we’re able to apply algorithms that are trained on historical time series data, resulting in the accurate forecasting of wind and solar energy.”
The key benefits of the project include increased renewable energy penetration in the grid due to improved dispatchability of renewable generation, and reduction in frequency control ancillary services (FCAS) payments by generators resulting from the failure to meet forecast targets.
“Our forecasting solution provides immediate value to our existing renewables customers as they target lower FCAS charges. And with PowerPredict officially launched, renewable generators in Australia and internationally can benefit from our power forecasting technology,” said Denis Marshment, Global Vice President of Data Science Customer Solutions at Worley.
The research and development of these models is expected to add to the overall body of knowledge around the application of machine learning and other AI technologies to wind and solar forecasting.
“Natural variations in weather make it difficult for renewable generators to accurately forecast their short-term power generation levels, and this impacts grid stability. In 2020 alone, inaccurate power predictions cost Australian generators $210 million, so using machine learning algorithms to see five minutes into the future is incredibly valuable. Our forecasting algorithms achieved a 45% improvement in our customers’ power output predictions,” Marshment said.
The technology has the potential to lower energy prices across the board, and potentially open up avenues for hydro and other forms of clean energy.
“If renewable generators can lower their causer pays factors, they can produce electricity cheaper, and eventually that saving could be passed on to the customers. It would also make renewables more competitive, which is also a desirable outcome,” Dr Bergmeir said.
Associate Professor Ariel Liebman, Director of the Monash Energy Institute, added, “This is an exciting and timely application of one of the Monash Energy Institute’s and Grid Innovation Hub’s star computer science and AI teams. This project shows how industry, represented by our visionary partners Worley, and academia can create real impact together both commercially and in contributing to the global effort to stop climate change.”
Predicting power generation from wind and solar was originally published by Sustainability Matters.
