Renewable Energy and Microgrids
Making renewable energy reliable
The Renewable Energy and Microgrids research cluster looks at the future of energy generation and how to improve renewable energy distribution so it is safer and more effective.
This group’s research is focused on methods to safely integrate renewable energy and electric vehicles, while delivering power more efficiently and reliably.
The discipline is uncovering ways to implement automatic grid reconfiguration to prevent and restore outages, creating a process for the system to independently get back online when the power goes out.
The Renewable Energy and Microgrids group is investigating methods to enable consumers to have greater control over their electricity consumption, giving customers the opportunity to actively participate in the electricity market.
The overall goal of this research area is to examine the interconnected challenges involved in the transition from the current central generated energy model to a renewable energy network micro-grids system.
This cluster brings together a wealth of interdisciplinary academics, from Electrical, Electronic and Computing Engineering, Mathematics and Statistics, Computer Science, Chemical Engineering and the Centre for Offshore Foundation Systems.
Research opportunities are available for prospective students in this cluster. You can learn more by emailing the Pre-candidature team at the Graduate Research School.
- Power generation forecasts of renewable energy sources
- Load forecasts, economic dispatches (spinning reserve optimisation), demand response and demand planning
- Phase identification and phase balancing
- Micro-grid identification and design
- Voltage and frequency regulation of micro-grids
- Integration of renewable energy sources to existing power grid and stability analysis
- Network line loss analysis
- Impact of electric vehicles on power grid
- Efficiency improvement using micro-grid smart meter data
- Optimal placement and sizing of distributed generation sources in distribution networks
2017 - 2019
Australian Research Council Discovery Projects
- ‘Navigating tipping points in complex dynamical systems’
- Michael Small, Willem Lesterhuis, Anthony Bosco, Ayham Zaitouny
Industrial Transformation Training Centres (submitted)
- ARC Training Centre for Transforming Maintenance through Data Science
- A.Rohl, M.Hodkiewicz, M. Small, R.Loxton, K.O'Halloran, T.Tan, V.Celo, M.Reynolds, I.Howard, W.Liu, C.Aldrich, R.L.While, T.French, E.Cripps, C.Mudge, R.Cardell- Oliver, M.Griffin, R.Fraser, J.Klump, G.Brown, M.Lomman, C.Paris, G.Singh, U.Engelke
WA Main Roads
- Main Roads Video Analytics
- M. Reynolds, D. Huynh, Y. Sun, C. Liu
iMOVE CRC 2-001
- Intermodal Logistics for Perth
- S. Bierman, P. Bergey, R. Cardell-Oliver, M. Reynolds, C. Standing
iMOVE CRC 1-003
- Enhanced Network Performance Prediction through Analytics
- S. Bierman, Y. Sun, W. Liu, M. Reynolds, D. Olaru
Complex Data Analysis
Using innovative techniques, the Complex Data Modelling research group develop mathematical, statistical and computational methodology to support engineering projects.Read more
Relevant study areas
Contact Professor Tyrone Fernando
Get in touch+6 8 6488 3954
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