Robust and interpretable data-driven algorithms for power networks
Funded by the Faculty of Information Technology for the Early Career Research Seed Grant.
About the project
Advanced economies are addressing the threats of climate change by transitioning fossil fuel energy sources (gas heating, petrol vehicles, coal power generation) to renewable sources.
However, blackouts can arise during this transition because existing power grids were engineered decades ago for large centralised coal power generation and cannot accommodate small distributed power generation, such as home solar systems.
Future power networks mitigate blackouts using algorithms that autonomously manage power supply and demand based on various parameters such as weather forecasts. This project will develop robust data-driven algorithms for optimally controlling power grids in risky and uncertain situations where current algorithms fail.
Researchers involved
- Dr Buser Say
- Dr Frits de Nijs
- Dr Hao Wang
- Dr Edward Lam
- William Maclean
- Ryan Hartshorne
- Lachlan Chumbley
- Ravindu Nanayakkara
- Daniel Ong
Project partners
- Monash DeepNeuron