Energy Frontiers
Combining interdisciplinary research, industry collaboration and scalable technology to build a resilient, sustainable future.
About the program
Aligning with Swinburne University of Technology’s sustainability goals, Energy Frontiers is paving the way for a cleaner, more sustainable energy future. The program is an integral part of the Innovative Planet Research Institute, focusing on transformative research to provide sustainable, innovative solutions for today’s critical energy challenges.
Guided by a vision of achieving sustainable energy solutions, the program is designed to increase renewable energy adoption, enhance energy efficiency, and promote sustainable practices through advanced research and innovation – supporting a future where clean, efficient and equitable energy is accessible to all.
Our philosophy
Our philosophy focuses on innovation, sustainability, community empowerment and policy integration to build a resilient energy future.
A key priority is accelerating the adoption of renewable energy sources such as solar, wind and hydrogen to reduce reliance on fossil fuels.
Bridging scientific advancements with real-world applications, we advocate for forward-thinking policies that enable seamless technology integration – ensuring lasting benefits for society and the energy sector.
Our research
Our research is centred on advancing energy systems through the integration of cutting-edge technologies like AI, machine learning and IoT to improve reliability, efficiency and performance. We aim to develop community-driven solutions that encourage renewable energy adoption and microgrid development – fostering energy independence.
By combining strategies for energy generation, storage and distribution with data-driven insights, we strive to reduce carbon footprints. Additionally, we work on aligning innovation with policy and market frameworks to seamlessly integrate renewable energy into existing systems. The Energy Frontiers program spans several interconnected research domains.
Our research domains
Energy data and analytics
Big data and AI applications for forecasting energy demand, optimising energy distribution, and fault detection
Energy storage solutions
Innovations in scalable battery storage, hydrogen fuel cells and other long-term energy storage technologies
Renewable energy integration
Solar PV optimisation, wind energy systems, and hydrogen-based electricity generation
Development of alternative energy storage solutions to support renewable adoption
Smart grids and energy management
Advanced IoT-enabled systems for grid optimisation and predictive maintenance
Demand-response mechanisms and energy management systems for increased efficiency
Sustainability and policy advocacy
Development of frameworks to reduce carbon footprints and promote sustainable energy use
Community education and engagement to increase participation in energy-efficient practices
Our projects
Project team
This project explores the development of dynamic wireless charging systems for electric vehicles (EVs) to enhance their accessibility and convenience. The primary focus is on creating in-motion EV charging technology suitable for both urban and highway environments, while also reducing the dependence on conventional stationary charging infrastructure.
Our project is supported by industry collaborators including Ace Infrastructure Pty Ltd, Sea Electric Pty Ltd, Fleet Plant Hire Limited, Royal Melbourne Institute of Technology, Swinburne University of Technology, Siemens Ltd, ARRB Group Ltd, and Net Zero Stack Pty Ltd.
Reports and publications
- A new cross-overlapped decoupling coil structure for EV dynamic inductive wireless charging system
- Enhancing misalignment tolerance using naturally decoupled identical dual-transmitter-dual-receiver coils for wireless EV charging system
- Enabling quadruple-D compensation coil integration for efficient power transfer to receiver in wireless power transfer systems for EV charging
- A new coil structure of dual transmitters and dual receivers with integrated decoupling coils for increasing power transfer and misalignment tolerance of wireless EV charging system
- High-efficiency long-distance wireless power transfer using BaO and GaN magnetron's cathode
- A design method for developing a high misalignment tolerant wireless charging system for electric vehicles
- Critical analysis of simulation of misalignment in wireless charging of electric vehicles batteries
Project team
This project explores how artificial intelligence and machine learning can enhance energy management by predicting both energy generation and consumption patterns. The research focuses on improving grid efficiency through real-time demand prediction – allowing for optimised energy distribution. Additionally, AI-driven analytics play a crucial role in minimising energy waste – ensuring smarter and more sustainable energy systems.
Reports and publications
- Optimized support vector regression-based model for solar power generation forecasting on the basis of online weather reports
- Multi-agent based operational cost and inconvenience optimization of PV-based microgrid
- Robust-mv-M-LSTM-CI: Robust energy consumption forecasting in commercial buildings during the COVID-19 pandemic
- Predicting commercial building energy consumption using a multivariate multilayered long-short term memory time-series model
- Adaptive control systems for dual axis tracker using clear sky index and output power forecasting based on ML in overcast weather conditions
- Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting
Project team
This project – funded by the Australian Renewable Energy Agency (ARENA) – focuses on developing and implementing intelligent demand-response systems to create dynamic energy markets within community microgrids.
The research explores energy trading between households and businesses while optimising energy usage, particularly in HVAC systems, by responding to real-time demand and supply fluctuations.
Conducted in collaboration with CSIRO, Bramec, KIG and NI, the project aims to enhance energy efficiency and sustainability through advanced market-driven solutions.
Project team
- Professor Mehdi Seyedmahmoudian
- Professor Alex Stojcevski
- Professor Saad Mekhilef
- Professor Prem Prakash Jayaraman
- Professor Anthony McCosker
With a view to harnessing artificial intelligence to optimise renewable energy systems and accelerate decarbonisation efforts, this project – funded by the Department of Foreign Affairs and Trade (DFAT) – focuses on AI-based forecasting of energy generation and consumption, and enhancing grid reliability and renewable energy adoption.
This project studies mitigating power fluctuations in renewable energy systems. It explores advanced non-linear control methods to enhance energy sharing between distributed energy resources, while conducting power quality analysis in the presence of non-linear loads. The research aims to improve the stability and reliability of microgrids by addressing key challenges associated with renewable energy integration.
Reports and publications
- Implementation of control strategies for energy storage systems and interlinking converters in an interconnected hybrid microgrid system for optimal power management using OPAL-RT
- Direct power control based on point of common coupling voltage modulation for grid-tied AC microgrid PV inverter
- A relaxed constrained decentralised demand side management system of a community-based residential microgrid with realistic appliance models
- Dynamic load reference based DQ-axis synchronous frame control method of grid connected PV hybrid microgrid
- Fixed frequency sliding mode control of distributed generation in microgrids for the balanced and nonlinear loads
- Voltage stability and power sharing control of distributed generation units in DC microgrids
The FACET project aims to advance energy management within community microgrids by developing an enhanced transactive demand response (TDR) system and integrating innovative dynamic pricing models based on dynamic operating envelopes (DOE) and hosting capacity (HC). Building on the foundation of the iHub-DCH5 initiative, this project addresses the challenges of grid stability, increasing renewable energy integration and optimising energy market transactions.
A collaborative effort between Swinburne University of Technology and its partners in Australia and the France, the project uses advanced simulations, real-time data and micro-phasor measurement units (micro-PMUs) for granular data analysis – ensuring a robust framework for energy market optimisation across Australian and European contexts for flexible loads participating in the estimation of DOE.
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Have a question?
For more information, please contact our research program leader Professor Mehdi Seyedmahmoudian at mseyedmahmoudian@swinburne.edu.au.