The accelerating energy transition is fundamentally reshaping electricity networks. The rapid proliferation of large, complex loads — including data centres, electric vehicle fleets, and industrial facilities with embedded renewable generation — is pushing traditional grid modelling and forecasting approaches to their limits. These loads are not merely large in scale; they are increasingly bi-directional, capable of both consuming and injecting power into the network and deeply coupled with stochastic renewable sources such as solar and wind. Conventional deterministic planning tools and statistical forecasting methods were not designed for this level of complexity, heterogeneity, and uncertainty. This project will develop a new generation of mathematical modelling and optimisation frameworks that go beyond traditional approaches, enabling robust, adaptive management of large loads within modern electricity networks under real-world uncertainty.
Aim
• Developing an advanced mathematical model that characterises the behaviour of large, bi-directional loads — including data centres with on-site renewable generation — within electricity distribution and transmission networks.
• Designing a forecasting algorithm that quantifies and propagates uncertainty in demand, renewable output, and grid state, moving beyond point forecasts to probabilistic and scenario-based representations.
• Formulating and solving optimisation problems for the real-time and day-ahead management of large loads, integrating forecasting uncertainty directly into decision-making frameworks.
Objectives
The specific objectives will be refined during discussions over the course of the PhD, but these are some example directions
• Review and critically assess existing modelling, forecasting, and management approaches for large loads in electricity networks, identifying mathematical and algorithmic gaps.
• Develop stochastic and/or robust optimisation models for large load scheduling and grid integration, accounting for uncertainty in renewable generation and demand.
• Design probabilistic forecasting methods tailored to the characteristics of large, bi-directional loads.
• Develop algorithms to solve the resulting optimisation problems at operational timescales.
• Validate the proposed frameworks using realistic network datasets, benchmarking against current methods.
Significance
Data centres alone account for a rapidly growing share of global electricity consumption, and the integration of AI infrastructure is accelerating this trend. When co-located with renewable generation and operating as bi-directional grid participants, they represent one of the most technically challenging and consequential problems in modern power systems. Yet the mathematical tools available to network operators and planners remain largely rooted in deterministic, single-asset paradigms. This project will directly address this gap, producing algorithms and frameworks with immediate relevance. Thus, this project aligns directly with the school’s sustainability goals, specifically SDG 9 (Industry, Innovation & Infrastructure) and SDG 7 (Affordable &Clean Energy).
Ideal Candidate
Essential Characteristics
• Highly self-motivated PhD candidate with good organisational skills and an interest in solving complex problems
• Has a bachelor’s degree or higher having undertaken a substantial number of units involving a mixture of applied mathematical and computational analysis
• Is interested in coding and developing mathematical algorithms
• Is willing to learn new concepts regarding the specific project applications to electricity networks.
• Must be eligible to enrol in PhD programs at Curtin.
Preferred Characteristics
• Has strong theoretical background in applied mathematics.
• Has undertaken research projects during their bachelor’s degree
• Is keen to tackle complex problems and able to adapt quickly.
• Has strong verbal and written communication skills.
This project is open to Domestic applicants only.
Scholarship
If you are identified as the preferred candidate for this project, you may be considered for an RTP scholarship.
Enquires and How to Apply
For enquires about this opportunity contact Dr. Hoa Bui via Hoa.bui@curtin.edu.au
To formally apply submit an Expression of Interest to Dr. Hoa Bui during the Central Scholarship round (July 1st – July 31st 2026)