{"id":145721,"date":"2026-07-01T08:59:20","date_gmt":"2026-07-01T00:59:20","guid":{"rendered":"https:\/\/www.curtin.edu.au\/research\/?post_type=hdr-r-projects&p=145721"},"modified":"2026-07-01T08:59:20","modified_gmt":"2026-07-01T00:59:20","slug":"moving-beyond-traditional-forecasting-and-modelling-for-electricity-networks","status":"publish","type":"hdr-r-projects","link":"https:\/\/www.curtin.edu.au\/research\/hdr-r-projects\/moving-beyond-traditional-forecasting-and-modelling-for-electricity-networks\/","title":{"rendered":"Moving beyond traditional forecasting and modelling for electricity networks"},"content":{"rendered":"\n
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The accelerating energy transition is fundamentally reshaping electricity networks. The rapid proliferation of large, complex loads \u2014 including data centres, electric vehicle fleets, and industrial facilities with embedded renewable generation \u2014 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.<\/p>\n\n\n\n

Aim  <\/p>\n\n\n\n

\u2022 Developing an advanced mathematical model that characterises the behaviour of large, bi-directional loads \u2014 including data centres with on-site renewable generation \u2014 within electricity distribution and transmission networks.
\u2022 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.
\u2022 Formulating and solving optimisation problems for the real-time and day-ahead management of large loads, integrating forecasting uncertainty directly into decision-making frameworks.<\/p>\n\n\n\n

Objectives <\/p>\n\n\n\n

The specific objectives will be refined during discussions over the course of the PhD, but these are some example directions
\u2022 Review and critically assess existing modelling, forecasting, and management approaches for large loads in electricity networks, identifying mathematical and algorithmic gaps.
\u2022 Develop stochastic and\/or robust optimisation models for large load scheduling and grid integration, accounting for uncertainty in renewable generation and demand.
\u2022 Design probabilistic forecasting methods tailored to the characteristics of large, bi-directional loads.
\u2022 Develop algorithms to solve the resulting optimisation problems at operational timescales.
\u2022 Validate the proposed frameworks using realistic network datasets, benchmarking against current methods.<\/p>\n\n\n\n

Significance <\/p>\n\n\n\n

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).<\/p>\n\n\n\n

Ideal Candidate <\/p>\n\n\n\n

Essential Characteristics
\u2022 Highly self-motivated PhD candidate with good organisational skills and an interest in solving complex problems
\u2022 Has a bachelor\u2019s degree or higher having undertaken a substantial number of units involving a mixture of applied mathematical and computational analysis
\u2022 Is interested in coding and developing mathematical algorithms
\u2022 Is willing to learn new concepts regarding the specific project applications to electricity networks.
\u2022 Must be eligible to enrol in PhD programs at Curtin.
Preferred Characteristics
\u2022 Has strong theoretical background in applied mathematics.
\u2022 Has undertaken research projects during their bachelor\u2019s degree
\u2022 Is keen to tackle complex problems and able to adapt quickly.
\u2022 Has strong verbal and written communication skills.<\/p>\n\n\n\n

This project is open to Domestic applicants only.\u00a0<\/p>\n\n\n\n

Scholarship  <\/p>\n\n\n\n

If you are identified as the preferred candidate for this project, you may be considered for an RTP scholarship<\/a>. <\/p>\n\n\n\n

Enquires and How to Apply <\/p>\n\n\n\n

For enquires about this opportunity contact Dr. Hoa Bui via Hoa.bui@curtin.edu.au<\/a><\/p>\n\n\n\n

To formally apply submit an Expression of Interest<\/a> to Dr. Hoa Bui during the Central Scholarship round (July 1st – July 31st 2026)\u00a0<\/p>\n\n\n\n

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