CSIRO (Australia's National Science Agency) 
Clayton, Australia Resumen de la oferta
CSIRO offers a four-year Industry PhD Scholarship focused on decentralised optimisation and coordination of distributed Customer Energy Resources (CER) within the Australian electricity system. The project addresses challenges arising from the transition to a bidirectional grid dominated by rooftop solar, batteries, and electric vehicles. Students will develop frameworks allowing behind-the-meter devices to collectively shape net demand while respecting network constraints. Responsibilities include building high-fidelity simulation environments and designing hierarchical coordination architectures using machine learning and agentic AI. The role involves comparing decentralised approaches with centralised virtual power plant control to enhance grid reliability and reduce emissions. Supervision is provided by Monash University and CSIRO, with industry engagement through AGL Energy. Ideal candidates possess strong quantitative skills in mathematics and programming, particularly Python, along with an interest in optimisation and artificial intelligence. The scholarship provides approximately $48,000 per annum tax-exempt.
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Descripción del proyecto
Funding and duration
four year scholarship package totalling approximately $48,000 per annum tax exempt (2026 rate); four-year scholarship and research training program.
Project overview
Project title
Decentralised optimisation and coordination of distributed CER in the Australian electricity system.
Project description
Australia’s electricity system is rapidly evolving from a centralised, one‑way supply model to a highly distributed, bidirectional grid dominated by rooftop solar, batteries, electric vehicles and other customer energy resources (CER). This transition creates opportunities for customers to reduce bills and emissions but also introduces operational challenges, including volatile net loads, and emerging risks to local network security.
This project will develop new decentralised optimisation and coordination frameworks that allow large populations of behind‑the‑meter devices to collectively shape net demand while respecting distribution‑network constraints and market incentives. The student will build high‑fidelity, network‑constrained simulation environments calibrated to Australian feeders and realistic household portfolios that include PV, batteries, EVs and flexible loads. On top of this, they will design and test hierarchical coordination architectures that combine distributed optimisation, machine learning and agentic AI /multi‑agent controllers. The work will compare decentralised approaches with centralised virtual power plant (VPP) control in terms of peak reduction, ramping, renewable curtailment, customer cost and compliance with network limits. In partnership with CSIRO and the industry partner, the project will generate practical pathways for industry deployment, supporting a flexible, low‑carbon and reliable electricity system in the Australian National Electricity Market.
Supervisory team
University
Name of university supervisor | Mohammed Eunus Ali
Name of university | Monash University
Email address | eunus.ali@monash.edu
Faculty | Faculty of Information Technology
CSIRO
Name of CSIRO supervisor | Ashfaqur Rahman
Email address | Ashfaqur.Rahman@csiro.au
CSIRO Research Unit | Technology
Industry
Name of industry supervisor | Mukaddim Pathan
Name of organisation | AGL Energy Limited
Email address | m.pathan@agl.com.au
Further details
Primary location of student | Monash University, Clayton Campus, Wellington Road, Clayton VIC 3800, Australia
Industry engagement component location | AGL Energy Limited, 699 Bourke Street, Docklands VIC 3008, Australia
Other locations | CSIRO Clayton, Research Way, Clayton, VIC 3168, Australia
Ideal student skillset | A strong quantitative background and an interest in optimisation and artificial intelligence. A degree in computer science, data science, electrical engineering, or a related discipline, with solid foundations in mathematics (linear algebra, optimisation, probability) and programming experience in Python or similar languages. Knowledge of distributed energy resources, electricity markets, or power‑flow modelling is desirable but not essential; these can be developed during the PhD. Strong analytical and problem‑solving skills, the ability to work with real‑world data and models, and an interest in collaborating with industry and government partners. Good written and oral communication skills are important for publishing results and engaging with stakeholders.
Application close date | Open until position filled
Apply | Contact Mohammed Eunus Ali