AI-integrated predictive control for flexible green power system

High penetration of renewable energy, particularly photovoltaic (PV) and battery storage systems, is fundamentally transforming the structure and dynamic characteristics of the modern Australia grid.

During 2025 and 2026, renewables provided roughly 42.7% to 43% of Australia's total electricity generation. These integrated energy devices, along with the connected converters, face big challenges over large-scale data and multi-level responses.

In this project, you will aim to develop next-generation coordinated predictive frameworks by artificial intelligence tools for multi-source power systems. Rapid and coordinated dynamic control is expected to be achieved in highly uncertain and rapidly changing energy networks.

UQ's School of Electrical Engineering and Computer Science has world-class research facilities in renewable energy integrations, which include access to several renewable energy generators, a Renewable Energy laboratory, and an industry 4.0 UQ Energy TestLab with a hardware-in-loop experimental set-up in both RTDS and Opal-RT platforms, as well as state-of-the-art industry-based software tools.

This project is supported by the Research project scholarship.

Learn more about the Research project scholarship.

Your application will be assessed on a competitive basis.

We take into account your:

• previous academic record
• publication record
• honours and awards
• employment history.

A working knowledge of converter-based hardware experiments would be of benefit to someone working on this project.

You will demonstrate academic achievement in the field of converter control with solid knowledge of power electronics and the potential for scholastic success.

A background or knowledge of power electronics control, converter-based modelling and control in MATLAB is highly desirable.

You must submit an expression of interest (EOI) by 9 November, 2026 9 November, 2026.

Before you apply

1. Check your eligibility for the Doctor of Philosophy (PhD).
2. Prepare your documentation.
3. If you have any questions about whether the project is suitable for your research interests, contact Dr Tingting He (t.he2@uq.edu.au).

When you apply

To apply, submit an expression of interest (EOI) for the program. You don't need to apply separately for the project or scholarship. How to submit an EOI

In your EOI, complete the ‘Scholarship/Sponsorship’ section with the following details:

1. Are you applying for an advertised project: 'Yes'
2. Project: 'Research project scholarship'
3. Scholarship Code Listed in the Advertisement: PREDICTIVE-YAN
4. Link to Scholarship Advertisement: https://study.uq.edu.au/study-options/phd-mphil-professional-doctorate/projects/ai-integrated-predictive-control-flexible-green-power-system

Submit an EOI