Project description
Microgrids aggregate distributed energy resources, bringing energy security to customers, from individual residences to businesses. Microgrids can contribute to net-zero transition to mitigate climate change in the energy sector by integrating renewables, storage, and consumer energy resources, such as demand-responsive loads.
AI is becoming a game-changing enabler to speed up the race against climate change in various sectors, largely reshaping the energy landscape. The digitalisation of the grid enables real-time interactions between consumers and the grid, and generates a large amount of data that can help system operations. AI techniques provide promising tools for managing digital grids. However, it is commonly known that the effectiveness of AI enabled systems is limited by the inability to explain the decisions to human users, becoming a barrier to AI applications in energy systems.
This PhD project aims to design and implement Explainable AI-based human-in-the-loop algorithms for microgrid management. The decisions of the developed algorithms are expected to be explainable to humans enabling stakeholders and consumers to understand and appropriately trust the models. The developed algorithms can be incorporated in different digital platforms and ecosystems, such as the Smart Institution platform.
Role in the project
The successful candidate will carry out project activities include but are not limited to:
- Collect data from the Monash university microgrid;
- Develop AI-based human-in-the-loop theory and algorithm for autonomous decision-making;
- Analyse energy user behaviours, and identify applications and use cases for microgrids;
- Test and verify the developed methods, and provide recommendations to manage microgrids;
- Publish findings in high-quality conferences and journals;
- Collaborate with supervisors and other team members.
Scholarship Information
A full PhD scholarship is available, including Monash tuition, stipend (of AU$34,938 plus Industry Top-up $6000, per annum, tax exempt), and standard student health insurance for the entire PhD duration (of up to 3.5 years). This project is funded by ENGIE through OPTIMA (ARC Industrial Transformation Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications).
How to apply
There are two ways (either is fine) you can apply to this position.
1) You can contact the supervisors directly by submitting your applications with necessary documents via supervisorconnect (this website).
2) You can also fill in this Google Form - https://docs.google.com/forms/d/e/1FAIpQLSfSruz2IrUQn_IK5AqqmPEx3tQ8sq2AA9ChCaFvvts_pDxp7w/viewform
Required knowledge
Candidate eligibility and required knowledge
Both domestic and international candidates are welcome. In particular, we encourage female identifying or Indigenous applications. Applicants should have a first-class honours or masters degree or equivalent in a related discipline, or a combination of an upper second-class honours or masters degree in a related discipline together with a strong research track record.
In addition to the eligibility criteria, candidates should also have the following skills and/or experience:
- Degree in Computer Science, IT, Electrical Engineering, or equivalent
- Excellent mathematical and analytical skills
- Excellent skills in AI and machine learning techniques
- Excellent programming skills in Python, Julia, etc.
- Excellent written and verbal communication skills
- Knowledge of power and energy network analysis and simulation highly desirable
During the selection process, candidates will also be assessed upon their ability to:
- Independently pursue their work
- Collaborate with others
- Have a professional approach
- Analyse and work with complex issues and
- Formulate scientific texts.
