Mapping current- and future- weather and climate-related hazards for electricity sector across Australia

Human-induced global warming is already affecting weather and climate extreme events in many regions world-wide at a rate that is unprecedented in at least the last 2000 years. In Australia, we are exposed to just about every weather- and climate-related hazards annually, ranging from severe thunderstorms, tropical cyclones and devastating floods to extreme bushfire and heatwave events (see a report on Climate Change in a Land of Extremes).

Over recent years, these extreme events are observed to occur in multiple combinations – occurrence of such multiple events simultaneously are often referred to as compound extreme events – causing widespread risks to various sectors including, but not limited to, socio-economic, environment and infrastructure. With future projections of increased level of greenhouse global warming, risks associated with compound weather and climate events in Australia are very likely to exacerbate.

The energy sector is no doubt highly vulnerable to climate change impacts, particularly through changes in frequency and intensity of extreme weather and climate patterns. A number of extreme weather and climate events have unfolded in recent past – e.g., the severe Black Summer bushfires in 2019-20 season over many parts of Australia – causing substantial disruption to electricity network. In addition, the energy sector also plays a seminal role in addressing transformational changes in reducing current and future carbon footprint for climate change mitigation purposes.

Given the increasing coverage of the electricity network across Australia through existing and new infrastructure development, providing the much-needed information on present and future state of extreme weather- and climate-related risks at regular spatial resolution to inform decision-making processes is more important now than ever before. Therefore, this project seeks to:

• objectively identify key weather and climate factors that affect electricity network and create a historical database of key weather and climate hazards across Australia.
• use state-of-the-art climate model projections of weather- and climate-related extreme events to determine how the associated risks are likely change due to human-induced global warming.

Supervisors

Dr Savin Chand: My accomplishments in Research have always been meritorious and award-winning throughout my academic career at FedUni. In 2017, I was awarded the Vice Chancellor’s Award for Research Excellence for leading world-class research in the research discipline area of “weather and climate extremes”. I continued to excel in these areas through my cutting-edge research with leading scientists from around the world. Very recently, I published a paper entitled ‘declining tropical cyclone frequency under global warming’ as a lead author in the prestigious Nature Climate Change journal. This work received a huge media coverage and scientific recognition globally. I have also received around $570k of external research grants as a Lead Investigator in the last five years, including over $180k during 2022 alone and have also published extensively in top-tier journal in the field. Currently, I am supervising five PhD candidates (three as Principal Supervisor).

Enhanced System Planning through Transmission - Distribution Interface with Aggregated DER and Load Flexing

Over the following decades, a significant transformation is expected in the automotive industry, with a larger number of electric vehicle (EV) uptake in the Australian vehicle fleet. Australian Energy Market Operator (AEMO) has forecasted that around 5.5 million EVs will join the Australian vehicle fleet in the next 20 years. As a result, electricity networks, market operators, and distribution network service providers (DNSPs) foresee an urgent need to develop a system-wide model and assessment to ensure that the grid is ready for this paradigm shift (e.g., hot water storage, behind-the-meter DERs, large batteries, EVs). Several excellent research works have already been conducted on the electrification of transportation and its potential impact on the distribution system to address multifaceted issues, such as customer acceptance and expectations, impacts from unmanaged EVs on distribution system voltage profile and utilisation, distribution network integration of EVs using active management, and techno-economic network and system integration of EVs.

However, the following issues would pose challenges to the electrification of the transportation and planning of the systems to make the grid transition from 2025 to 2040:

• What are the modelling requirements, parameters, and information to achieve the distribution system model with different DERs, EVs, and other new loads?
• To what extent the distribution system modelling is required to assess the contribution of the distribution system to the overall system strength and performance?
• What would be the control mechanism among solar PV, behind-the-meter and in-front-of-the-meter batteries, and EVs to ensure the distribution grid is ready for the system's slow, progressive, and step-change defined in the integrated system planning by AEMO?
• What are the frameworks, platforms, and relevant grid codes and standards for support services from downstream to upstream?

This research proposes to develop frameworks for seamless electromobility in the network through comprehensive modelling, parameterisation, intelligent control, testing, and real-time validation. The proposed tasks of this research project aim to address the following research gaps:

• Developing a detailed distribution system model with different DERs, EVs, and other new loads with the capability of extending the distribution grid capacity and ensuring that the grid is ready for the electrification of transport, and new loads, contributing towards overall system-wide support development;
• Proposed approaches for distribution network issues at the equivalent representation for the transmission system planning (interactive TSO-DSO planning and operation and informed potential integrated system planning developments);
• Fixability characterisation of DER as a function of inverter headroom, IEEE, and Australian standards;
• An equivalent acceptable representation of distribution networks for TSO-DSO interface steady state, and quasi-dynamic studies and assessment;

Coordinated control of various technologies such as EVs, hot water storage, behind-the-meter batteries, in front of meter batteries to ensure grid flexibility.

Supervisors

Dr Rakibuzzaman Shah has experience working at and consulting with DNOs and TSOs on individual projects and collaborative work on a large number of projects (EPSRC project on Multi-terminal HVDC, Scottish and Southern Energy Multi-infeed HVDC) - primarily on the dynamic impacts of integrating new technologies and control application into large systems. He has been successfully involved in several funded projects totalling around $3.8 million from the government, industry partners, and universities. He has successfully supervised and co-supervised five PhD students to successful completion and two post-doctoral research associates in the last four years. Dr Shah is currently supervising four HDRs (two outside Federation University). Four post-doctoral research associates are now working on his projects (two outside Federation University). His research interest includes Future power grids (i.e., Renewable energy integration, wide-area control), asynchronous grid connection through VSC-HVDC, power system stability and dynamics, application of data mining in power system, application of control theory in power system, distribution system energy management and low carbon energy system.

Professor Nima Amjady is currently working as Professor, Renewable Energy Technologies and Director Centre for New Energy Transition Research at Federation University. His research interests include power system operation and planning, microgrid operation and planning, forecast processes of power systems and microgrids, renewable energies, storage systems, and applying artificial intelligence techniques to power systems. He has published 206 articles in the top-ranked journals in power and energy (e.g, IEEE Transactions, IET Journals). He is a Senior member of IEEE, PES liaison editor for IEEE Press, Editor of IEEE Transactions on Power Systems, Editor of IEEE Transactions on Sustainable Energy, and Editor of IEEE Power Engineering Letters. He has successfully supervised one post-doc, 24 PhD students, and 71 MSc students in the last 25 years.

Professor Syed Islam is currently the Associate Deputy Vice-Chancellor of Research and Innovation (Strategic Research) at Federation University. He has published over 350 technical papers in his area of expertise. His research interests are in power system condition monitoring, wind energy conversion, and smart power systems. He has received over $25 million in funding from industry, government, and other organizations for his research. He has successfully supervised over 30 PhD students to completion. More than ten post-docs have worked with him in several ARC, CRC, CSIRO, and power industry-funded projects. Currently, he is supervising five HDR students at Federation University. He is a Fellow of Engineers Australia, a Fellow of the IEEE IAS, PES, and DEIS, a Fellow of the IET and a chartered engineer in the United Kingdom, and a chartered professional Engineer in Australia. He is also an IEEE Distinguished lecturer.

Renewable Energy Probabilistic Forecasting Using Data Mining and Machine Learning Methods

Penetration of renewable energy sources, such as wind and solar energies, into power systems, is rapidly increasing to address today's serious concerns about the global warming, excessive fossil fuel depletion, and energy crisis. However, the weather-driven volatility and intermittency of wind/solar power pose several challenges for the operation and scheduling of wind/solar farms as well as power systems. Effective renewable energy forecasting is a crucial solution method to alleviate the adverse effects of renewable energy volatility. However, the time series of renewable energies, such as the time series of wind and solar powers, typically exhibit nonlinear behaviours, outliers, and irregular patterns. To appropriately model these complex behaviours, a renewable energy forecasting method should be able to effectively extract the informative features of the forecast process and construct the input/output mapping function of the renewable energy. In addition, the single-point forecast, which has been traditionally used in power systems (such as in load forecasting), may not be sufficient for the prediction of renewable energies as diverse realizations of a renewable energy, which are different from the expected value, can occur in practice.

To address the aforementioned issues, a renewable energy probabilistic forecasting method, based on data mining and machine learning methods, will be developed in this project. Data mining methods, such as those based on mutual information, will be used for selecting/extracting informative features of the forecast process. These input features are given to a forecast method (such as a deep neural network), which is trained by machine learning methods. In addition to point forecast, probability distribution of renewable energy will be also predicted in this project.

Supervisors:

Nima Amjady: has published 206 articles in peer-reviewed ISI-cited journals. Also, he has 52 conference presentations and chairmanships. His publications have attracted more than 13000 citations (Google Scholar) with H-index of 61. He is a Senior member of IEEE, PES liaison editor for IEEE Press, Editor of IEEE Transactions on Power Systems, Editor of IEEE Transactions on Sustainable Energy and Editor of IEEE Power Engineering Letters. He has successfully supervised one post-doc, 24 PhD students and 71 MSc students in the last 25 years. In July 2022, he joined Federation University, where he is currently working as a Professor (Academic Level E) and Director of Centre for New Energy Transition Research.

Syed Islam: is currently the Associate Deputy Vice Chancellor (Research and Innovation) and previously the Executive Dean for the Institute of Innovation Science and Sustainability at Federation University Australia. He was the John Curtin Distinguished Professor in Electrical Power Engineering and the Director of Centre for Smart Grid and Sustainable Power Systems at Curtin University, Perth, Australia. He is a Fellow of the Engineers Australia, a Fellow of the IEEE and a Fellow of the IET. He has supervised more than 20 research staff/students to completion.

Energy management for smart homes with solar PVs and electric vehicles

With the increasing penetration of distributed energy sources (e.g., renewable source, energy storage system, distributed generation unit) associated with power electronics and information communication technologies, modern buildings are becoming autonomous energy systems. In these scenarios, advanced energy management systems are highly desired to enhance the energy efficiency of buildings. In particular, home energy management systems (HEMSs) have attracted in recent years significant attention in both academia and industry. This project will develop effective energy management systems for future smart homes with solar photovoltaics and electric vehicles. The operations of the generation and electric appliances are controlled in response to dynamic price to reduce the consumers’ electricity bill while minimising power loss. This project requires PhD candidates to have fundamental knowledge of renewable energies, energy systems, and control.

Supervisors

Jiefeng Hu is currently an Associate Professor and Program Coordinator of Electrical Engineering and Renewable Energy at Federation University Australia. He is also the stream leader of Centre for New Energy Transition Research (CfNETR). His research interests include power electronics, renewable energy, and smart microgrids. He is an IEEE Senior Member, and Editor of IEEE Transactions on Energy Conversion. He has led several research and industry projects to develop advanced renewable energy technologies for smart grids and smart cities. He has extensive supervisory experience. Up to now, he has supervised 5 PhD students, one postdoctoral fellow and 7 research associates to completion.

Nima Amjady is currently working as Professor, Renewable Energy Technologies and Director Centre for New Energy Transition Research. His research interests include power system operation and planning, microgrid operation and planning, forecast processes of power systems and microgrids, renewable energies. He is a Senior member of IEEE, PES liaison editor for IEEE Press, Editor of IEEE Transactions on Power Systems, Editor of IEEE Transactions on Sustainable Energy and Editor of IEEE Power Engineering Letters. He has extensive supervisory experience of more than 20 research staff/students.

Syed Islam is currently the Associate Deputy Vice Chancellor (Research and Innovation) and previously the Executive Dean for the Institute of Innovation Science and Sustainability at Federation University Australia. He was the John Curtin Distinguished Professor in Electrical Power Engineering and the Director of Centre for Smart Grid and Sustainable Power Systems at Curtin University, Perth, Australia. He is a Fellow of the Engineers Australia, a Fellow of the IEEE and IEEE PES and a Fellow of the IET. He has supervised more than 20 research staff/students to completion.

Forecasting impacts of weather and climate extremes on renewable energy sector using data mining and machine learning approaches

Human-induced global warming is already affecting weather and climate extreme events in many regions world-wide at a rate that is unprecedented in at least the last 2000 years. In Australia, we are exposed to just about every weather- and climate-related hazards annually, ranging from severe thunderstorms, tropical cyclones and devastating floods to extreme bushfire and heatwave events. Over recent years, these extreme events are observed to occur in multiple combinations – occurrence of such multiple events simultaneously are often referred to as compound extreme events – causing widespread risks to various sectors including, but not limited to, socio-economic, environment and infrastructure. With future projections of increased level of greenhouse global warming, risks associated with compound weather and climate events in Australia are very likely to exacerbate.

Energy sector is no doubt highly vulnerable to climate change impacts, particularly through changes in frequency and intensity of extreme weather and climate patterns. Weather-driven volatility and intermittency of wind/solar power pose several challenges for the operation and scheduling of wind/solar farms as well as power systems. Effective renewable energy forecasting is a crucial solution method to alleviate the adverse effects of renewable energy volatility. However, the time series of renewable energies, such as the time series of wind and solar powers, typically exhibit nonlinear behaviours, outliers, and irregular patterns. To appropriately model these complex behaviours, as well as incorporate intermittent behaviours of weather and climate extremes, a renewable energy forecasting method should be able to effectively extract the informative features of the forecast process and construct the input/output mapping function of the renewable energy. In addition, the single-point forecast, which has been traditionally used in power systems (such as in load forecasting), may not be sufficient for the prediction of renewable energies as diverse realizations of a renewable energy, which are different from the expected value, can occur in practice.

To address the aforementioned issues, a renewable energy probabilistic forecasting method, based on data mining and machine learning methods, will be developed in this project. Data mining methods, such as those based on mutual information, will be used for selecting/extracting informative features of the forecast process. These input features are given to a forecast method (such as a deep neural network), which is trained by machine learning methods. In addition to point forecast, probability distribution of renewable energy will be also predicted in this project. The forecasted probability density function can be used both for defining renewable energy scenarios (which can subsequently be used for the operation of wind/solar farms and renewable energy-integrated power systems) and for predicting the impacts of extreme weather conditions (such as the tails of wind speed/power density function that can be used to predict the impacts of thunderstorms and hurricanes on wind farms and wind power-intagrated power systems).

Dr Savin Chand: My accomplishments in Research have always been meritorious and award-winning throughout my academic career at FedUni. In 2017, I was awarded the Vice Chancellor’s Award for Research Excellence for leading world-class research in the research discipline area of “weather and climate extremes”. I continued to excel in these areas through my cutting-edge research with leading scientists from around the world. Very recently, I published a paper entitled ‘declining tropical cyclone frequency under global warming’ as a lead author in the prestigious Nature Climate Change journal. This work received a huge media coverage and scientific recognition globally. I have also received around $570k of external research grants as a Lead Investigator in the last five years, including over $180k during 2022 alone and have also published extensively in top-tier journal in the field. Currently, I am supervising five PhD candidates (three as Principal Supervisor).

Nima Amjady is currently working as Professor, Renewable Energy Technologies and Director Centre for New Energy Transition Research. His research interests include power system operation and planning, microgrid operation and planning, forecast processes of power systems and microgrids, renewable energies. He is a Senior member of IEEE, PES liaison editor for IEEE Press, Editor of IEEE Transactions on Power Systems, Editor of IEEE Transactions on Sustainable Energy and Editor of IEEE Power Engineering Letters. He has extensive supervisory experience of more than 20 research staff/students.

Syed Islam is currently the Associate Deputy Vice Chancellor (Research and Innovation) and previously the Executive Dean for the Institute of Innovation Science and Sustainability at Federation University Australia. He was the John Curtin Distinguished Professor in Electrical Power Engineering and the Director of Centre for Smart Grid and Sustainable Power Systems at Curtin University, Perth, Australia. He is a Fellow of the Engineers Australia, a Fellow of the IEEE and IEEE PES and a Fellow of the IET. He has supervised more than 20 research staff/students to completion.

Scholarship applicants must be eligible to undertake a PhD. Applicants must verify that they meet the eligibility requirements outlined on the Graduate Research School website before they apply.

If you are making a case for Honours equivalence, in order to demonstrate your eligibility, please ensure that you provide detailed information to support your case.

Applications are open to Australian residents, Australian permanent residents and international applicants.

• Students are expected to commit to full-time study for the duration of their candidature.
• Scholarships are for a period of three years and extension to scholarships will not be granted.
• The successful scholarship applicant must study on campus in Ballarat.
• The successful scholarship applicant must participate in an internship period.
• The successful scholarship applicant must formally assign, in advance, all rights, title and interest they may have in any IP developed to the University prior to commencing a project.

View the general conditions for Federation University HDR scholarships on the Graduate Research School website. Where these conditions differ to those on this form, the conditions outlined for this specific scholarship take precedence.

Applicants who do not complete all steps will not be considered.

Step 1: Follow the application process outlined at: How to apply

Step 2: Provide a copy of your CV and a 1000-word statement covering the following areas:

• Discuss your motivations for applying for this PhD scholarship, and your intended research outcomes (both for yourself and for the center)
• Discuss some of the key existing research literature which impacts this topic area
• Discuss your relevant research background, experience and outcomes as they are relevant to the project
• Discuss your plan/proposal to run the project along with the required facilities (hardware, software, etc.)
• Discuss potential challenges and how you might overcome them

Applicants are not required to provide a 250 Project Summary.

Step 3: Complete the Scholarship Expression of Interest Form