Complex energy system
Discover our projects on the future of grids
Enhancing Transactive Demand Response and Hosting Capacity
Evaluation for Community Microgrid
This project aims to advance transactive demand response (TDR) systems for electrified thermally adaptive buildings integrated in community microgrids for dynamic operating envelopes (DOE) and hosting capacity (HC) assessments. Leveraging previous collaborations with Swinburne University, Victoria University, CSIRO, and the CEA Smart Grid Laboratory, the project will develop a robust energy market optimization system and adaptive building management systems that responds dynamically to grid conditions. Through the implementation of cutting-edge technologies such as micro- Phasor Measurement Units (micro-PMUs), the project will enhance power quality monitoring and improve BMSs performance, buildings thermal performance and grid resilience. The goal of this project is to develop a smart energy management system that optimizes energy distribution and supports a more sustainable, efficient building energy systems, and resilient grid infrastructure. This work will have far- reaching implications for both Australian and European energy markets, contributing significantly to the global energy transition and buildings electrifications.
Human-Centred AI for Complex Energy Systems
As the world moves away from fossil fuels for power supply, conventional grids should evolve to facilitate the integration of multiple sources of renewable energy. One of the main challenges in this transition is to find ways of coping with the increasing variability of renewable-based production. To ensure the stability of the grid, it will be necessary to have enough "controllability" of consumption so that it can better "follow" the production (assuming part of the power flow can be stored).
A key part of this challenge is to enhance the flexibility and engagement of end-users. To that end, a consortium of researchers will work in partnership with industry stakeholders to build effective and resilient interactions of AI/ML models of control and forecast with humans for the efficient management of complex energy networks. Our approach combines human factors, AI and ML techniques to better model consumption. Sociotechnical frameworks that support the development of trust in this teaming will play a key role in its success. The outcomes of this research should advance the instantiation of management systems to support more resilient grids through data-driven modelling and control.
The University of Adelaide’s Professor Anton Middelberg, Deputy Vice-Chancellor and Vice-President (Research), welcomed the FACET funding. “This collaboration demonstrates the University of Adelaide’s ongoing commitment to deliver world-class research into artificial intelligence that aims to support a more sustainable future,” said Professor Middelberg. “I congratulate our team on the start of their collaboration with Université Grenoble-Alpes. The links formed through this agreement will have a global impact.”
Partners: University of Adelaide (including the Andy Thomas Centre for Space Resources and the Australian Institute of Machine Learning), Université Grenoble-Alpes, CEA Smart Grids Laboratory, French National Centre for Scientific Research (CNRS’ CROSSING lab)
