Risk assessment of climate change mitigation measures. This project will consider market based mechanisms for environmental protection policies and will have both a theoretical and a practical dimension. The main benefactors of the project will be environmental regulators and policy makers working in this area.
A cloud computing environment for managing foreign exchange risk. This project involves using parallel computing and machine learning to improve the real-time handling of a bank's foreign exchange risk. It will lead to improved techniques to manage exchange rate variations, enabling banks to better assess their risk and will ultimately lead to improved services for Australian companies.
Switching Dynamics Approach for Distributed Global Optimisation . This project aims to create a breakthrough switching dynamics approach and new technology to speed up finding optimal solutions. It will develop a distributed switching dynamics based optimisation scheme for global optimisation problems in industrial big-data environments where timely decision making is required. It will result in a practical technology for industry optimisation problems such as economic energy dispatch in smart g ....Switching Dynamics Approach for Distributed Global Optimisation . This project aims to create a breakthrough switching dynamics approach and new technology to speed up finding optimal solutions. It will develop a distributed switching dynamics based optimisation scheme for global optimisation problems in industrial big-data environments where timely decision making is required. It will result in a practical technology for industry optimisation problems such as economic energy dispatch in smart grids and optimal charging and discharging tasks in a large network of electric vehicles, helping Australian power industry improve efficiency and security, as well as training the next generation scientists and engineers for Australia in this emerging field.Read moreRead less
Emissions trading and the design and operation of Australia's energy markets. Past research has applied the methods of experimental economics to focus on capacity markets, futures markets, and demand side response management options for electricity markets in the United States of America. This project will examine research priorities for Australia's energy markets. These include market impacts on investment decision-making, trade in Renewable Energy Certificates, and the consequences of carbon e ....Emissions trading and the design and operation of Australia's energy markets. Past research has applied the methods of experimental economics to focus on capacity markets, futures markets, and demand side response management options for electricity markets in the United States of America. This project will examine research priorities for Australia's energy markets. These include market impacts on investment decision-making, trade in Renewable Energy Certificates, and the consequences of carbon emissions trading for energy market outcomes. Australia's electricity markets are primarily financial markets and new policy developments present both risks and opportunities. By designing and testing markets, experiments will be used to test market performance and for unforeseen consequences of new market policies.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100274
Funder
Australian Research Council
Funding Amount
$415,675.00
Summary
Graph Neural Networks for Efficient Decision-making towards Future Grids. This project aims to develop a breakthrough framework for decision-focused learning by integrating explainable graph neural networks and efficient computational methods. It expects to create new methodologies of graph representation learning for unlocking data insight with spatiotemporal knowledge while to build new accelerated optimisation theories for speeding up decision-focused learning model. The expected outcomes wil ....Graph Neural Networks for Efficient Decision-making towards Future Grids. This project aims to develop a breakthrough framework for decision-focused learning by integrating explainable graph neural networks and efficient computational methods. It expects to create new methodologies of graph representation learning for unlocking data insight with spatiotemporal knowledge while to build new accelerated optimisation theories for speeding up decision-focused learning model. The expected outcomes will advance big spatiotemporal data analytics and nonlinear optimisation theory for solving decision-making tasks towards a future energy system. This should promote the Australian power industry transition to a sustainable future grid based on a digitalisation approach to efficient energy management against climate changes.Read moreRead less